{"Deprecated":true,"DeprecatedNotice":"Amazon is no longer hosting this Data Lakehouse Ready dataset","Name":"1000 Genomes Phase 3 Reanalysis with DRAGEN 3.5 - Data Lakehouse Ready","Description":"The 1000 Genomes Project is an international collaboration which has established the most detailed catalogue of human genetic variation, including SNPs, structural variants, and their haplotype context. There were a total of 3202 individuals sequenced as part of Phase 3 of this project. The high coverage samples were processed using the Illumina DRAGEN v3.5.7b pipeline and are available at s3://1000genomes-dragen/. This dataset contains the VCFs transformed to Parquet/ORC in 3 different schemas - partitioned by samples, partitioned by chromosome and a nested data format. These representations of the 1000 Genomes DRAGEN data are stored in Parquet/ORC format and can be queried through [Amazon Athena](https://aws.amazon.com/athena/?whats-new-cards.sort-by=item.additionalFields.postDateTime&whats-new-cards.sort-order=desc). To add these tables to your Glue Data Catalog and for sample queries on this dataset, please refer to the link in our Documentation.","Documentation":"https://github.com/aws-samples/data-lake-as-code/tree/roda#readme","Contact":"https://github.com/aws-samples/data-lake-as-code/issues","ManagedBy":"[Amazon Web Services](https://aws.amazon.com/)","UpdateFrequency":"Not updated","Tags":["biology","bioinformatics","genetic","genomic","Homo sapiens","life sciences","parquet","population genetics","vcf"],"License":"Data from the 1000 Genomes Project is now available without embargo, following the final publication from the project. Use of the data should be cited in the usual way, with current details available at http://www.internationalgenome.org/faq/how-do-i-cite-1000-genomes-project.","Resources":[{"Description":"Parquet representations of 1000 Genomes VCF outputs from DRAGEN, ready for enrollment into Data Lake as Code.","ARN":"arn:aws:s3:::aws-roda-hcls-datalake/thousandgenomes_dragen","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Sample Queries on the 1000 Genomes, gnomAD and ClinVar data Lake","URL":"https://github.com/aws-samples/aws-genomics-datalake/blob/main/1000Genomes.ipynb","AuthorName":"Sujaya Srinivasan","Services":["Amazon Athena","AWS Glue"]}],"Tools & Applications":null,"Publications":null},"RegistryEntryAdded":"2021-09-13","RegistryEntryLastModified":"2025-02-04","Slug":"1000-genomes-data-lakehouse-ready","Sources":["awslabs-open-data-registry"]}
{"Name":"1000 Genomes","Description":"The 1000 Genomes Project is an international collaboration which has established the most detailed catalogue of human genetic variation, including SNPs, structural variants, and their haplotype context. The final phase of the project sequenced more than 2500 individuals from 26 different populations around the world and produced an integrated set of phased haplotypes with more than 80 million variants for these individuals.","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/1000genomes","Contact":"http://www.internationalgenome.org/contact","ManagedBy":"National Institutes of Health","UpdateFrequency":"Not updated","Tags":["aws-pds","fastq","genetic","genomic","life sciences","whole genome sequencing"],"License":"Data from the 1000 Genomes Project is now available without embargo, following the final publication from the project. Use of the data should be cited in the usual way, with current details available at http://www.internationalgenome.org/faq/how-do-i-cite-1000-genomes-project.","Resources":[{"Description":"http://www.internationalgenome.org/formats","ARN":"arn:aws:s3:::1000genomes","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Exploring Human Genetic Variation with 1000 Genomes in a Browser-Based Notebook on Scigantic","URL":"https://scigantic.com/archive/7cca00fd-c6d6-5658-8bae-9c2814c5bab8/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"}],"Tools & Applications":[{"Title":"Examine genomic variation across populations with AWS","URL":"https://aws.amazon.com/blogs/industries/examine-genomic-variation-across-populations-with-aws/","AuthorName":"Konstantinos Tzouvanas"}],"Publications":[{"Title":"Exploratory data analysis of genomic datasets using ADAM and Mango with Apache Spark on Amazon EMR","URL":"https://aws.amazon.com/blogs/big-data/exploratory-data-analysis-of-genomic-datasets-using-adam-and-mango-with-apache-spark-on-amazon-emr/","AuthorName":"Alyssa Marrow","AuthorURL":"https://research.eecs.berkeley.edu/~akmorrow/"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2026-04-19","Slug":"1000-genomes","Sources":["awslabs-open-data-registry"]}
{"Name":"1KG-ONT-VIENNA panel","Description":"The 1KG-ONT-VIENNA panel comprises medium coverage ONT sequencing data for 1.019 samples from the 1000 Genomes Project collection, structural variants, and their haplotype context.","Documentation":"https://github.com/1kg-ont-vienna/sv-analysis","Contact":"1kg.ont@imp.ac.at","ManagedBy":"Institute of Molecular Pathology","UpdateFrequency":"Irregular","Tags":["fast5","fastq","genetic","genomic","life sciences","whole genome sequencing"],"License":"There are no restrictions on the use of this data. Use of the data should be cited in the usual way, with current details available at https://github.com/1kg-ont-vienna/sv-analysis/","Resources":[{"Description":"Primary and derived data","ARN":"arn:aws:s3:::1kg-ont-vienna","Region":"eu-west-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Long-read sequencing and structural variant characterization in 1,019 samples from the 1000 Genomes Project","URL":"https://doi.org/10.1101/2024.04.18.590093","AuthorName":"Siegfried Schloissnig, Samarendra Pani, Bernardo Rodriguez-Martin, Jana Ebler, Carsten Hain, Vasiliki Tsapalou, Arda Söylev, Patrick Hüther, Hufsah Ashraf, Timofey Prodanov, Mila Asparuhova, Sarah Hunt, Tobias Rausch, Tobias Marschall, Jan O Korbel"}]},"RegistryEntryAdded":"2024-07-31","RegistryEntryLastModified":"2024-07-31","Slug":"1kg-ont-vienna","Sources":["awslabs-open-data-registry"]}
{"Name":"3DCoMPaT: Composition of Materials on Parts of 3D Things","Description":"3D CoMPaT is a richly annotated large-scale dataset of rendered compositions of Materials on Parts of thousands of unique 3D Models.\nThis dataset primarily focuses on stylizing 3D shapes at part-level with compatible materials.\nEach object with the applied part-material compositions is rendered from four equally spaced views as well as four randomized views.\nWe introduce a new task, called Grounded CoMPaT Recognition (GCR), to collectively recognize and ground compositions of materials on parts of 3D objects.\nWe present two variations of this task and adapt state-of-art 2D/3D deep learning methods to solve the problem as baselines for future research.\nWe hope our work will help ease future research on compositional 3D Vision.\n","Documentation":"https://3dcompat-dataset.org/","Contact":"mohamed.elhoseiny@kaust.edu.sa, yuchen.li@kaust.edu.sa","ManagedBy":"[Vision-CAIR, CEMSE, KAUST](https://cemse.kaust.edu.sa/vision-cair)","UpdateFrequency":"Continually improving 3D annotations and renderings","Tags":["aws-pds","computer vision","machine learning"],"License":"https://3dcompat-dataset.org/LICENSE\n","Resources":[{"Description":"3DCoMPaT Dataset","ARN":"arn:aws:s3:::3dcompat-dataset","Region":"us-west-1","Type":"S3 Bucket","Explore":["[Website](https://3dcompat-dataset.org/)"]}],"DataAtWork":{"Publications":[{"Title":"3DCoMPaT: Composition of Materials on Parts of 3D Things","URL":"https://3dcompat-dataset.org/pdf/paper.pdf","AuthorName":"Yuchen Li, Ujjwal Upadhyay, Habib Slim, Ahmed Abdelreheem, Arpit Prajapati, Suhail Pothigara, Peter Wonka & Mohamed Elhoseiny"}]},"RegistryEntryAdded":"2022-07-26","RegistryEntryLastModified":"2022-07-26","Slug":"3dcompat","Sources":["awslabs-open-data-registry"]}
{"Name":"3000 Rice Genomes Project","Description":"The 3000 Rice Genome Project is an international effort to sequence the genomes of 3,024 rice varieties from 89 countries.","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/3kricegenome","Contact":"http://iric.irri.org/contact-us","ManagedBy":"[International Rice Research Institute](https://www.irri.org/)","UpdateFrequency":"Not updated","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["agriculture","aws-pds","food security","genetic","genomic","life sciences"],"License":"This data is available for anyone to use under the terms of the Toronto Statement, which is available [here](http://www.nature.com/nature/journal/v461/n7261/box/461168a_BX1.html)","Resources":[{"Description":"http://s3.amazonaws.com/3kricegenome/README-snp_pipeline.txt","ARN":"arn:aws:s3:::3kricegenome","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"RiceGalaxy","URL":"https://github.com/InternationalRiceResearchInstitute/RiceGalaxy","AuthorName":"International Rice Research Institute","AuthorURL":"https://www.irri.org/"}],"Publications":[{"Title":"Identification and Allele Combination Analysis of Rice Grain Shape-Related Genes by Genome-Wide Association Study","URL":"https://www.mdpi.com/1422-0067/23/3/1065","AuthorName":"Meng B et al (2022)"},{"Title":"Rice Galaxy: an open resource for plant science","URL":"https://academic.oup.com/gigascience/article/8/5/giz028/5492234","AuthorName":"Juanillas V et al (2019)"},{"Title":"Structural variants in 3000 rice genomes","URL":"https://genome.cshlp.org/content/29/5/870.short","AuthorName":"Fuentes RR et al (2019)"},{"Title":"Tracking the origin of two genetic components associated with transposable element bursts in domesticated rice","URL":"https://www.nature.com/articles/s41467-019-08451-3","AuthorName":"Chen J et al (2019)"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2025-07-09","Slug":"3kricegenome","Sources":["awslabs-open-data-registry"]}
{"Name":"4D Nucleome (4DN)","Description":"The goal of the National Institutes of Health (NIH) Common Fund’s 4D Nucleome (4DN) program\nis to study the three-dimensional organization of the nucleus in space and time (the 4th dimension).\nThe nucleus of a cell contains DNA, the genetic “blueprint” that encodes all of the genes a living\norganism uses to produce proteins needed to carry out life-sustaining cellular functions. Understanding\nthe conformation of the nuclear DNA and how it is maintained or changes in response to environmental\nand cellular cues over time will provide insights into basic biology as well as aspects of human\nhealth and disease. The 4DN is an international consortium of researchers who generate data that\ninclude results from a variety of genomics and imaging assays with a focus on, but not exclusive to,\nthose that demonstrate close contact between chromatin loci that are non-adjacent on the linear DNA\nsequence of chromosomes. Additional assays probe the nuclear landscape in the context of interactions\nof chromatin with specific proteins, RNAs and epigenetic changes.\n","Contact":"support@4dnucleome.org","ManagedBy":"4DN Data Coordination and Integration Center (4DN-DCIC)","Documentation":"https://data.4dnucleome.org","UpdateFrequency":"Daily","Tags":["aws-pds","bioinformatics","biology","genetic","genomic","imaging","life sciences"],"License":"External data users may freely download, analyze, and publish results based on any 4DN data provided here without restrictions.","Resources":[{"Description":"Released and archived 4DNucleome data","ARN":"arn:aws:s3:::4dn-open-data-public","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Explore 4D Nucleome Data in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/1d72b85b-2498-5f39-92cb-afc36ea5aefb/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Finding and Downloading 4DN Data files","URL":"https://data.4dnucleome.org/help/user-guide/downloading-files","AuthorName":"4DN-DCIC","AuthorURL":"data.4dnucleome.org"},{"Title":"Using jupyterhub on the 4DN data portal","URL":"https://data.4dnucleome.org/tools/jupyterhub","AuthorName":"4DN-DCIC","AuthorURL":"data.4dnucleome.org"}]},"RegistryEntryAdded":"2021-03-22","RegistryEntryLastModified":"2026-11-03","Slug":"4dnucleome","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"The provider of this dataset will no longer maintain this dataset.  We are open to talking with anyone else who might be willing to provide this dataset to the community.  Contact <a href=\"mailto:opendata@amazon.com\">opendata@amazon.com</a>.","Name":"IRS 990 Filings (Spreadsheets)","Description":"Excerpts of electronic Form 990 and 990-EZ filings, converted to spreadsheet form. Additional fields being added regularly.","Documentation":"https://appliednonprofitresearch.com/documentation/irs-990-spreadsheets","Contact":"[info@appliednonprofitresearch.com](info@appliednonprofitresearch.com)","ManagedBy":"[Applied Nonprofit Research, LLC](https://www.appliednonprofitresearch.com/)","UpdateFrequency":"Quarterly","Tags":["aws-pds","regulatory","statistics","us","economics"],"License":"Attribution 4.0 International [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Excerpts of electronic Form 990 and 990-EZ filings, converted to spreadsheet form","ARN":"arn:aws:s3:::irs-990-spreadsheets","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":null,"Tools & Applications":null,"Publications":null},"RegistryEntryAdded":"2019-04-09","RegistryEntryLastModified":"2022-05-11","Slug":"990-spreadsheets","Sources":["awslabs-open-data-registry"]}
{"Name":"ANSI/AAMI EC13 Test Waveforms","Description":"The files in this set can be used for testing a variety of devices that monitor the electrocardiogram. The recordings include both synthetic and real waveforms.","Documentation":"https://doi.org/10.13026/C2WC7M","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2WC7M","ARN":"arn:aws:s3:::physionet-open/aami-ec13/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"aami-ec13","Sources":["awslabs-open-data-registry"]}
{"Name":"ABEJA CC JA","Description":"A large Japanese language corpus created through preprocessing Common Crawl data","Documentation":"https://github.com/abeja-inc/Megatron-LM/blob/main/docs/dataset/about_data.md","Contact":"abeja-datascience@abejainc.com","ManagedBy":"[ABEJA inc.](https://www.abejainc.com/)","UpdateFrequency":"None","Tags":["internet","japanese","natural language processing","web archive"],"License":"This data is available for anyone to use under the [Common Crawl Terms of Use](https://commoncrawl.org/terms-of-use/)","Resources":[{"Description":"Text corpus","ARN":"arn:aws:s3:::abeja-cc-ja","Region":"ap-northeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Tutorial of ABEJA CC JA dataset","URL":"https://github.com/abeja-inc/Megatron-LM/blob/main/docs/dataset/tutorials.md","AuthorName":"Kyo Hattori"}],"Publications":[{"Title":"Building a Large-Scale Japanese Corpus from Common Crawl and Its Preprocessing","URL":"https://tech-blog.abeja.asia/entry/abeja-nedo-project-part2-202405","AuthorName":"Kyo Hattori"}]},"RegistryEntryAdded":"2024-07-08","RegistryEntryLastModified":"2024-09-16","Slug":"abeja-cc-ja","Sources":["awslabs-open-data-registry"]}
{"Name":"Labeled raw accelerometry data captured during walking, stair climbing and driving","Description":"The database contains raw accelerometry data collected during outdoor walking, stair climbing, and driving for 32 healthy adults. Accelerometry data were collected simultaneously at four body locations: left wrist, left hip, left ankle, and right ankle, at a sampling frequency of 100 Hz. The 3-axial ActiGraph GT3X+ devices were used to collect the data. The data include labels of activity type performed (walking, descending stairs, ascending stairs, driving, clapping) for each time point of data collection. Basic demographic information of participants is also provided. All data are anonymized.","Documentation":"https://doi.org/10.13026/51h0-a262","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Open Database License v1.0","Resources":[{"Description":"https://doi.org/10.13026/51h0-a262","ARN":"arn:aws:s3:::physionet-open/accelerometry-walk-climb-drive/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"accelerometry-walk-climb-drive","Sources":["awslabs-open-data-registry"]}
{"Name":"Cardiorespiratory measurement from graded cycloergometer exercise testing","Description":"The present database is an ensemble of cardiorespiratory measurements acquired during 18 cycloergometer maximal graded exercise tests performed in the \"Adaptation, Climat Tropical, Exercice et Santé\" (ACTES) laboratory of the French West Indies University. Data correspond to heart R-wave to R-wave intervals, Oxygen consumption, and mechanical power output on a beat-to-beat basis recorded all along the graded exercise test sessions. Participants are 18 teenagers athletes (15.2±2 years) from the Regional Physical and Sports Education Centre (CREPS) of French West Indies (Guadeloupe, France), belonging to a regional division of sprint kayak and triathlon or a national division of fencing. Clinical data of the participants (i.e. age, Weight, Height, Power at ventilatory threshold 1 and 2, and sport discipline) are provided in a separate file.","Documentation":"https://doi.org/10.13026/2qs3-kh43","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/2qs3-kh43","ARN":"arn:aws:s3:::physionet-open/actes-cycloergometer-exercise/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"actes-cycloergometer-exercise","Sources":["awslabs-open-data-registry"]}
{"Name":"Estimating Activity from Instantaneous Heart Rate","Description":"Using only a heart rate time series, it is possible to measure a number of features that reflect the level of physical activity. Here we provide software for deriving an \"activity index\" based on measurements of mean heart rate, total power of the instantaneous heart rate time series over a given interval, and stationarity.","Documentation":"https://physionet.org/content/activity/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/activity/","ARN":"arn:aws:s3:::physionet-open/activity/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"activity","Sources":["awslabs-open-data-registry"]}
{"Name":"Abdominal and Direct Fetal ECG Database","Description":"The research material included in the Abdominal and Direct Fetal Electrocardiogram Database contains multichannel fetal electrocardiogram (FECG) recordings obtained from 5 different women in labor, between 38 and 41 weeks of gestation. The recordings were acquired in the Department of Obstetrics at the Medical University of Silesia, by means of the KOMPOREL system for acquisition and analysis of fetal electrocardiogram (ITAM Institute, Zabrze, Poland). Each recording comprises four differential signals acquired from maternal abdomen and the reference direct fetal electrocardiogram registered from the fetal head.","Documentation":"https://doi.org/10.13026/C2RP4B","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2RP4B","ARN":"arn:aws:s3:::physionet-open/adfecgdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"adfecgdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Google Satellite Embedding V1","Description":"COG (Cloud-Optimized GeoTIFF) files that together contain the AlphaEarth Foundations annual Satellite Embedding dataset. It contains the annual embeddings for the years from 2018 to 2024, inclusive.","Documentation":"https://source.coop/tge-labs/aef","Contact":"https://cloudnativegeo.org/join","ManagedBy":"[Source Cooperative](https://source.coop/)","UpdateFrequency":"As new data versions become available","Tags":["aerial imagery","aws-pds","earth observation","imaging","machine learning","satellite imagery"],"License":"CC-BY 4.0","Citation":"The AlphaEarth Foundations Satellite Embedding dataset is produced by Google and Google DeepMind.","Resources":[{"Description":"Google Satellite Embedding V1","ARN":"arn:aws:s3:::us-west-2.opendata.source.coop/tge-labs/aef","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://source.coop/tge-labs/aef/)"]}],"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-11-17","RegistryEntryLastModified":"2025-11-17","Slug":"aef-source","Sources":["awslabs-open-data-registry"]}
{"Name":"Aerial Imagery of Canada","Description":"Recent and historical high-resolution aerial imagery of Canada in Cloud Optimized Geotiff (COG) format. This dataset groups aerial imagery from the Government of Canada from different sources, including time series from the National Air Photo Library (NAPL) in the \"napl\" sub-bucket and prefix, and aerial orthophoto mosaics from Parks Canada in the \"parks-canada\" sub-bucket with park name as the prefix.\n<br/>\nThe NAPL of Natural Resources Canada archives over 6 million aerial photographs covering all of Canada, some of which date back to the 1920s. The NAPL mosaics are created using the best spatial resolution available for each time period, which means that the orthophotos composing a NAPL time series are not necessarily co-registered.\nUse cases include vegetation analysis, change detection using time series, land-use monitoring, and more. This dataset can be used to help communities understand climate change, enhance emergency management and response, and support sustainable resource development.\n<br/>\n<br/>\nImageries aériennes haute résolution récentes et historiques du Canada en format Cloud Optimized Geotiff (COG). Cet ensemble de données regroupe des images aériennes du Canada provenant de différentes sources, notamment les séries temporelles de la Photothèque nationale de l'air (PNA) dans le sous-répertoire et le préfixe « napl », et les mosaïques d'orthophotographies aériennes de Parcs Canada dans le sous-répertoire « parks-canada », ayant pour préfixe le nom du parc.\n<br/>\nLa PNA de Ressources naturelles Canada conserve plus de 6 millions de photographies aériennes couvrant l’ensemble du Canada, dont certaines remontent aux années 1920. Les mosaïques de la PNA sont réalisées à partir de la meilleure résolution spatiale disponible pour chaque période, ce qui signifie que les orthophotos composant une série temporelle de la PNA ne sont pas nécessairement corégistrées.\nLes cas d'utilisation comprennent l'analyse de la végétation, la détection des changements à partir de séries temporelles, le suivi de l'utilisation des terres, etc. Cet ensemble de données peut aider les communautés locales à évaluer les impacts des changements climatiques, à améliorer la gestion et la réponse aux situations d'urgence, et à soutenir le développement durable des ressources.\n","Documentation":"[National Air Photo Library / Photothèque nationale de l'air](https://ressources-naturelles.canada.ca/carte-outils-publications/imagerie-satellitaire-donnees-elevation-photos-aeriennes/photographies-aeriennes/phototheque-nationale-lair)","Contact":"geoinfo@nrcan-rncan.gc.ca","ManagedBy":"[Natural Resources Canada](https://nrcan.gc.ca/)","UpdateFrequency":"The dataset is updated as new aerial imagery gets processed.\n<br/>\nL'ensemble de données est mis à jour à mesure du traitement de nouvelles images aériennes.\n","Tags":["aerial imagery","aws-pds","canada","cog","stac"],"License":"[Open Government License (OGL)](https://open.canada.ca/en/open-government-licence-canada)","Citation":null,"Resources":[{"Description":"RGB-NIR aerial orthophoto imagery mosaic covering the full extent of Banff National Park, Ya Ha Tinda Ranch, and a 1 km buffer at 15 cm, collected on August 21st to 23rd, 2025 / Mosaique d'orthophoto aérienne RVB-PIR couvrant l'ensemble du parc national Banff, le ranch Ya Ha Tinda ainsi qu'une zone tampon de 1 km à une résolution de 15cm, aquise du 21 au 23 août 2025","ARN":"arn:aws:s3:::aerial-imagery-canada/national-parks/banff-national-park/","Region":"ca-central-1","Type":"S3 Bucket","Explore":["[STAC catalog](https://datacube.services.geo.ca/stac/api/search?collections=banff-national-park)","[STAC Browser by Radiant Earth](https://browser.moregeo.it/external/datacube.services.geo.ca/stac/api/collections/banff-national-park)","[Browse Bucket](https://aerial-imagery-canada.s3.ca-central-1.amazonaws.com/index.html)"]},{"Description":"Notifications for RGB-NIR aerial orthophoto imagery mosaic of Banff National Park, Ya Ha Tinda Ranch.","ARN":"arn:aws:sns:ca-central-1:675987781521:aerial-imagery-canada-object_created","Region":"ca-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Cloud-Optimized Geospatial Data Access","URL":"https://nrcan.github.io/cloud-optimized-geospatial/","AuthorName":"NRCan"},{"Title":"Get To Know a Dataset: Aerial Imagery of Canada / Apprendre à connaitre un jeu de données : Imagerie Aerienne du Canada (Bilingual)","URL":"https://github.com/NRCan/cloud-optimized-geospatial/blob/e4d4b9aff18466eded26facc3a87b5317dacdd12/how-to-guides/notebook/open-data-examples/get-to-know-a-dataset.ipynb","AuthorName":"NRCan"}]},"RegistryEntryAdded":"2026-06-16","RegistryEntryLastModified":"2026-09-28","Slug":"aerial-imagery-canada","Sources":["awslabs-open-data-registry"]}
{"Name":"A2D2: Audi Autonomous Driving Dataset","Description":"An open multi-sensor dataset for autonomous driving research. This dataset comprises semantically segmented images, semantic point clouds, and 3D bounding boxes. In addition, it contains unlabelled 360 degree camera images, lidar, and bus data for three sequences. The dataset supports learning tasks such as semantic segmentation, 3D object detection, SLAM, and depth estimation. We hope this dataset will further facilitate active research and development in AI, computer vision, and robotics for autonomous driving.","Contact":"a2d2-dataset@googlegroups.com","Documentation":"https://a2d2-dataset.github.io","ManagedBy":"Mentar Mahmudi, Jakob Geyer, Lorenz Hauswald","UpdateFrequency":"The dataset may be updated with additional or corrected data on a need-to-update basis.","Tags":["autonomous vehicles","aws-pds","computer vision","deep learning","lidar","machine learning","mapping","robotics"],"License":"https://creativecommons.org/licenses/by-nd/4.0/","Resources":[{"Description":"Original distribution as tar archives","ARN":"arn:aws:s3:::aev-autonomous-driving-dataset","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"Extracted per-file mirror of the dataset","ARN":"arn:aws:s3:::audi-autonomous-driving-dataset","Region":"eu-central-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"A2D2 Python tutorial notebook","URL":"https://nbviewer.org/urls/aev-autonomous-driving-dataset.s3.eu-central-1.amazonaws.com/tutorial.ipynb","AuthorName":"A2D2 Team, Audi Electronics Venture GmbH"},{"Title":"Autonomous Driving Data Service (ADDS)","URL":"https://github.com/aws-samples/amazon-eks-autonomous-driving-data-service","AuthorName":"Ajay Vohra, Amazon","Services":["Amazon EKS","Amazon Redshift","Amazon S3","Amazon FSx"]}]},"RegistryEntryAdded":"2019-08-12","RegistryEntryLastModified":"2026-07-11","Slug":"aev-a2d2","Sources":["awslabs-open-data-registry"]}
{"Name":"MIT-BIH Atrial Fibrillation Database","Description":"This database includes 25 long-term ECG recordings of human subjects with atrial fibrillation (mostly paroxysmal).","Documentation":"https://doi.org/10.13026/C2MW2D","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2MW2D","ARN":"arn:aws:s3:::physionet-open/afdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"afdb","Sources":["awslabs-open-data-registry"]}
{"Name":"PAF Prediction Challenge Database","Description":"This database of two-channel ECG recordings has been created for use in the Computers in Cardiology Challenge 2001, an open competition with the goal of developing automated methods for predicting paroxysmal atrial fibrillation (PAF). See the challenge announcement for information about the competition, and see Predicting Onset of Atrial Fibrillation for a brief overview of the clinical problem, its significance, and suggestions for further reading on the subject.","Documentation":"https://doi.org/10.13026/C2H59W","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2H59W","ARN":"arn:aws:s3:::physionet-open/afpdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"afpdb","Sources":["awslabs-open-data-registry"]}
{"Name":"A region-wide, multi-year set of crop field boundary labels for Africa","Description":"Crop field boundaries digitized in Planet imagery collected across Africa between 2017 and 2023, developed by [Farmerline](https://farmerline.co/), [Spatial Collective](https://spatialcollective.com/),  and the [Agricultural Impacts Research Group](https://agroimpacts.info/) at [Clark University](https://www.clarku.edu/), with support from the [Lacuna Fund](https://lacunafund.org/) ([Estes et al, 2024](https://arxiv.org/abs/2412.18483); [Wussah et al. (2023)](https://zenodo.org/records/11060871)). This dataset has been  further supplemented by additional labels collected primarily for  for 2018 over a subset of countries, which provide an example of their application in training and validating a CNN-based cropland mapping model  [(Khallaghi et al. 2025)](https://www.mdpi.com/2072-4292/17/3/474).\n","Documentation":"Information on the primary dataset can be found [here](https://github.com/agroimpacts/lacunalabels/). Documentation for added labels is available [here](https://github.com/agroimpacts/cnn-generalization-enhancement).","Contact":"airg@clarku.edu","ManagedBy":"[The Agricultural Impacts Research Group](https://agroimpacts.info/)","UpdateFrequency":"Updated versions of the dataset are added as they are developed","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["agriculture","cog","labeled","land cover","machine learning","satellite imagery"],"License":"[Planet NICFI participant license agreement](https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf)","Resources":[{"Description":"Field boundaries and corresponding Planet images","ARN":"arn:aws:s3:::africa-field-boundary-labels","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"[Additional rasterized field labels and corresponding Planet images](https://www.mdpi.com/2072-4292/17/3/474)","ARN":"arn:aws:s3:::africa-field-boundary-labels/extra","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Instructions on data access and label-making demonstration notebook","URL":"https://github.com/agroimpacts/lacunalabels","NotebookURL":"https://github.com/agroimpacts/lacunalabels/blob/main/notebooks/makelabels/label-chips.ipynb","AuthorName":"Lyndon Estes","AuthorURL":"https://github.com/ldemaz"}],"Publications":[{"Title":"A platform for crowdsourcing the creation of representative, accurate landcover maps","URL":"http://www.sciencedirect.com/science/article/pii/S136481521630010X","AuthorName":"Estes et al. (2016)"},{"Title":"A region-wide, multi-year set of crop field boundary labels for Africa","URL":"https://arxiv.org/abs/2412.18483","AuthorName":"Estes et al. (2024)"},{"Title":"A region-wide, multi-year set of crop field boundary labels for Africa","URL":"https://zenodo.org/records/11060871","AuthorName":"Wussah et al. (2023)"},{"Title":"Generalization enhancement strategies to enable cross-year cropland mapping with convolutional neural networks trained using historical samples","URL":"https://www.mdpi.com/2072-4292/17/3/474","AuthorName":"Khallaghi et al. (2025)"},{"Title":"High resolution, annual maps of field boundaries for smallholder-dominated croplands at national scales","URL":"https://www.frontiersin.org/article/10.3389/frai.2021.744863","AuthorName":"Estes et al. (2022)"},{"Title":"Technical report on label develop and processing","URL":"https://github.com/agroimpacts/lacunalabels/blob/main/docs/report/technical-report.pdf","AuthorName":"Wussah et al. (2023)"}]},"Citation":"Primary dataset: Estes et al. (2024). A region-wide, multi-year set of crop field boundary labels for Africa. arXiv:2412.18483. Additional labels: Khallaghi et al. (2025). Generalization enhancement strategies to enable cross-year cropland mapping with convolutional neural networks trained using historical samples. Remote Sensing, 17(3), 474.\n","RegistryEntryAdded":"2024-07-18","RegistryEntryLastModified":"2025-07-09","Slug":"africa-field-boundary-labels","Sources":["awslabs-open-data-registry"]}
{"Name":"Africa Soil Information Service (AfSIS) Soil Chemistry","Description":"This dataset contains soil infrared spectral data and paired soil property\nreference measurements for georeferenced soil samples that were collected\nthrough the Africa Soil Information Service (AfSIS) project, which lasted\nfrom 2009 through 2018. In this release, we include data collected during\nPhase I (2009-2013.) Georeferenced samples were collected from 19 countries\nin Sub-Saharan African using a statistically sound sampling scheme,\nand their soil properties were analyzed using *both* conventional soil\ntesting methods and spectral methods (infrared diffuse reflectance\nspectroscopy). The two types of data can be paired to form a training\ndataset for machine learning, such that certain soil properties can be\nwell-predicted through less expensive spectral techniques.\n","Documentation":"https://github.com/qedsoftware/afsis-soil-chem-tutorial","Contact":"afsis@qed.ai","ManagedBy":"[QED](https://qed.ai/)","UpdateFrequency":"As required","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["agriculture","aws-pds","environmental","food security","machine learning"],"License":"ODC Open Database License (\"[ODbL](https://opendatacommons.org/licenses/odbl/summary/index.html)\") version 1.0, with attribution to AfSIS","Resources":[{"Description":"Paired wet and dry chemistry measurements for georeferenced soils\ncollected by the Africa Soil Information Service (AfSIS), stored\nas CSV and OPUS files.\n","ARN":"arn:aws:s3:::afsis","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"AfSIS Soil Chemistry - Usage Tutorial","URL":"https://github.com/qedsoftware/afsis-soil-chem-tutorial/","AuthorName":"QED","AuthorURL":"https://qed.ai"}],"Publications":[{"Title":"Goalkeepers 2018, Soil - The Big Data Beneath Your Feet","URL":"https://www.youtube.com/watch?v=Fb9R0CnPMkc","AuthorName":"QED","AuthorURL":"https://qed.ai"}]},"RegistryEntryAdded":"2018-12-14","RegistryEntryLastModified":"2026-06-05","Slug":"afsis","Sources":["awslabs-open-data-registry"]}
{"Name":"AF Termination Challenge Database","Description":"This database of two-channel ECG recordings has been created for use in the Computers in Cardiology Challenge 2004, an open competition with the goal of developing automated methods for predicting spontaneous termination of atrial fibrillation (AF).","Documentation":"https://doi.org/10.13026/C2CC7Z","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2CC7Z","ARN":"arn:aws:s3:::physionet-open/aftdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"aftdb","Sources":["awslabs-open-data-registry"]}
{"Name":"AFVP - A Realistic Ventricular Rhythm Model During AF","Description":"AFVP generates a synthesized beat-to-beat interval sequence of ventricular excitations with a realistic structure observed during AF.","Documentation":"https://physionet.org/content/afvp/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/afvp/","ARN":"arn:aws:s3:::physionet-open/afvp/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"afvp","Sources":["awslabs-open-data-registry"]}
{"Name":"AG-LOAM Dataset","Description":"AG-LOAM dataset has been released to facilitate the evaluation of LiDAR-based odometry algorithms in agricultural environments.\n1) It was collected by a wheeled mobile robot at the Agricultural Experimental Station of the University of California, Riverside, during Winter 2022 and Winter 2023.\n2) It provides LiDAR point cloud data captured using a Velodyne VLP-16 sensor, along with ground-truth trajectories obtained from an RTK-GPS system.\n3) It consists of 18 sequences collected over three phases, covering diverse planting environments, terrain conditions, path patterns, and robot motion profiles.\n4) It spans a total operation time of 3 hours, covers a total distance of 7.5 km, and constitutes 150 GB of data.\n","Documentation":"https://github.com/UCR-Robotics/AG-LOAM","Contact":"Hanzhe Teng (hteng007@ucr.edu), Konstantinos Karydis (kkarydis@ece.ucr.edu)","ManagedBy":"[Autonomous Robots and Control Systems Lab](https://sites.google.com/view/arcs-lab)","UpdateFrequency":"NA","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["agriculture","aws-pds","lidar","localization","mapping","robotics"],"License":"Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).","Resources":[{"Description":"AG-LOAM Dataset sequences","ARN":"arn:aws:s3:::ucr-robotics/ag-loam-dataset","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Source code of the LiDAR-only odometry and mapping system","URL":"https://github.com/UCR-Robotics/AG-LOAM","AuthorName":"Hanzhe Teng et al."}],"Publications":[{"Title":"Adaptive LiDAR Odometry and Mapping for Autonomous Agricultural Mobile Robots in Unmanned Farms","URL":"https://www.sciencedirect.com/science/article/pii/S0168169925001292","AuthorName":"Hanzhe Teng, Yipeng Wang, Dimitrios Chatziparaschis, Konstantinos Karydis"},{"Title":"Adaptive LiDAR Odometry and Mapping for Autonomous Agricultural Mobile Robots in Unmanned Farms","URL":"https://arxiv.org/abs/2412.02899","AuthorName":"Hanzhe Teng, Yipeng Wang, Dimitrios Chatziparaschis, Konstantinos Karydis"}]},"RegistryEntryAdded":"2025-03-04","RegistryEntryLastModified":"2025-07-09","Slug":"ag-loam","Sources":["awslabs-open-data-registry"]}
{"Name":"AHA Database Sample Excluded Record","Description":"The files available here are not part of the AHA Database. The 0001.* and 0201.* files have been derived from a sample given to us in 1980 by the creators of the AHA Database. The original recording had been a candidate for inclusion in the database and was digitized and annotated using the same methods used for the AHA Database records. After the annotation process was complete, the recording was excluded from the AHA Database since it was found to contain ectopy of a higher grade than the class for which it had been chosen as a candidate.","Documentation":"https://doi.org/10.13026/C27P4P","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C27P4P","ARN":"arn:aws:s3:::physionet-open/ahadb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ahadb","Sources":["awslabs-open-data-registry"]}
{"Name":"AI3 Protein-Ligand Binding Affinity Dataset","Description":"The rapid advancement of computing technologies, particularly artificial intelligence (AI), has revolutionized various domains, including drug discovery. Curated datasets are crucial for developing reliable, generalizable, and accurate models for practical applications. Generating experimental data on a large scale is an expensive and arduous process. In domains such as medical diagnostics where real-life data is hard to obtain, synthetic data has been shown to be extremely valuable. We, teams from IIIT Hyderabad, Intel, AWS, and Insilico Medicine, have performed physics-based calculations (molecular dynamics simulations) on about 20,000 protein-ligand complexes. The dataset comprises molecular dynamics snapshots, binding affinities calculated using the MM-PBSA method, and individual energy components, including electrostatic and van der Waals interactions. DatasetFileFormats essentially incorporate i. 3D coordinates of the protein-ligand complexes (pdb) in tar.gz files, and ii. CSV files containing the energy data. DatasetUsages are on i. ML scoring function for predicting binding affinities of given protein-ligand complexes, ii. Classification models for predicting correct binding poses of ligands, iii. identification of cryptic binding pockets, and iv. optimization of binding features by exploiting the individual components of the energy (experimental data has only the total binding affinity). Further, the novelty of the dataset highlights the fact that existing AI/ML training datasets lack dynamic data and are inherently biased. Further, binding affinity data existing in the literature are obtained from different experimental protocols. Therefore, this dataset has been uniquely created (from the same computational protocols) followed by free energy calculations with molecular dynamics (MD) simulations. The dynamic data-enriched protein-ligand coordinates can be used to effectively train convolutional neural network-based regression models for more accurate binding affinity prediction.\n","Documentation":"https://github.com/devalab/AI3","Contact":"devalab@iiit.ac.in","ManagedBy":"International Institute of Information Technology Hyderabad","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","machine learning","molecular dynamics","pharmaceutical","protein","simulations"],"License":"https://devalab.in/AI3.html","Resources":[{"Description":"ai3data bucket includes coordinates and the energetics of ~20,000 protein-ligand binding affinity datasets. The subfolders of ai3data bucket consist of Version 1, Version2 and Version 3. Version1 contains the total Size of 10.4 GiB (Initial structure of the protein-ligand complex and the average binding affinities along with average energy components). Version2 contains the total Size of 1.2 TiB (Five trajectories of protein-ligand complex (200 snapshots in all) and the closest two water molecules for each of the protein-ligand complex, and the time series of the binding affinities along with average energy components). Version3 contains the total Size of 10.7 TiB (Five trajectories of completely solvated protein-ligand complex (200 snapshots in all), and the time series of binding affinities along with average energy components).","ARN":"arn:aws:s3:::ai3data","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"AI3: Protein-Ligand Binding Affinity Dataset","URL":"https://github.com/devalab/AI3","AuthorName":"Deva Priyakumar Lab","AuthorURL":"https://github.com/devalab"}],"Publications":[{"Title":"PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications","URL":"https://www.nature.com/articles/s41597-023-02872-y","AuthorName":"U. Deva Priyakumar","AuthorURL":"https://devalab.in"},{"Title":"PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications","URL":"https://www.nature.com/articles/s41597-022-01631-9","AuthorName":"U. Deva Priyakumar","AuthorURL":"https://devalab.in/"}]},"RegistryEntryAdded":"2025-07-26","RegistryEntryLastModified":"2025-09-17","Slug":"ai3","Sources":["awslabs-open-data-registry"]}
{"Name":"Airborne Object Tracking Dataset","Description":"Airborne Object Tracking (AOT) is a collection of 4,943 flight sequences of around 120 seconds each, collected at 10 Hz in diverse conditions. There are 5.9M+ images and 3.3M+ 2D annotations of airborne objects in the sequences. There are 3,306,350 frames without labels as they contain no airborne objects. For images with labels, there are on average 1.3 labels per image. All airborne objects in the dataset are labelled.","Documentation":"https://www.aicrowd.com/challenges/airborne-object-tracking-challenge","Contact":"airborne-object-tracking-challenge@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not updated","Tags":["amazon.science","computer vision","deep learning","machine learning"],"License":"Community Data License Agreement – Permissive, Version 1.0 https://cdla.dev/permissive-1-0/","Resources":[{"Description":"The training dataset is further split into smaller directories. Each subdirectory contains ImageSets and Images folders. Metadata and ground truth information about image sequences are saved as groundtruth.json (and its tabular representation  groundtruth.csv) in ImageSets folders. Information about airborne encounters are saved in  valid_encounters_maxRange700_maxGap3_minEncLen30.json and valid_encounters_maxRange700_maxGap3_minEncLen30.csv. The Images folder holds images sampled from one sequence per directory. Images are 2448 pixels wide by 2048 pixels high, encoded as 8-bit grayscale images and saved as PNG files.","ARN":"arn:aws:s3:::airborne-obj-detection-challenge-training","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore dataset](https://github.com/amazon-research/siam-mot/blob/main/readme/model_zoo.md)","[README](https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/blob/master/docs/DATASET.md)"]}],"RegistryEntryAdded":"2021-06-22","RegistryEntryLastModified":"2022-06-06","Slug":"airborne-object-tracking","Sources":["awslabs-open-data-registry"]}
{"Name":"AI Weather Prediction (AIWP) Model Reforecasts","Description":"<br/>\n<br/>\n\nThis is an archive of pure AI-based weather prediction reforecasts produced collaboratively between the Cooperative Institute for Research in the Atmosphere [(CIRA)](https://www.cira.colostate.edu/) and the NOAA Global Systems Laboratory [(NOAA-GSL)](https://gsl.noaa.gov/).\n\n<br/>\n<br/>\n\nCurrently, [FourCastNetv2-small](https://arxiv.org/abs/2306.03838), [Pangu-Weather](https://www.nature.com/articles/s41586-023-06185-3), and [GraphCast](https://www.science.org/doi/10.1126/science.adi2336) are included, with more models to come. Each of these models has been initialized with both NOAA [GFS](https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php) (directories with no extension) and ECMWF [IFS](https://www.ecmwf.int/en/forecasts/datasets/set-i) initial conditions (directories ending in \"_IFS\"). The datasets are updated with near-real-time data twice per day (00Z and 12Z initializations).\n\n<br/>\n<br/>\n\nFourCastNetv2-small and Pangu-Weather are available from 10/2020 to present, while GraphCast is available from 01/2022 to present. These periods are selected to avoid overlap with training and fine-tuning periods for each model. Data may be missing and is not guaranteed to be available at any given time. If there is data missing that you require, please email jacob.radford@noaa.gov.\n\n<br/>\n<br/>\n\nFor real-time visualizations, visit our [visualizations page](https://aiweather.cira.colostate.edu).\n\n<br/>\n<br/>\n\nThe directory structure is as follows\\: <br/>\ns3://noaa-oar-mlwp-data/MMMM_vNNN_III/YYYY/mmdd/MMMM_vNNN_III_YYYYmmddhh_fXXX_fYYY_ZZ.nc; where <br/>\nMMMM = Four character model code (FOUR, PANG, or GRAP) <br/>\nNNN = Three digit version number (currently 100 or 200) <br/>\nYYYY = Four digit year <br/>\nmm = Two digit month <br/>\ndd = Two digit day <br/>\nIII = Three character initial condition system (GFS) <br/>\nhh = Two digit initialization time (00, 06, 12, or 18) <br/>\nXXX = Three digit first forecast hour of file (currently 000 for all files) <br/>\nYYY = Three digit last forecast hour of file (currently 240 for all files) <br/>\nZZ = Two digit forecast step (currently 06 for all files) \n\n<br/>\n<br/>\nVariables available by model\\: <br/>\n\n- FourCastNet v2-small:\n    - 10-m u wind component\n    - 10-m v wind component\n    - 100-m u wind component\n    - 100-m v wind component\n    - 2-m temperature\n    - Surface pressure\n    - Mean sea level pressure\n    - Total column integrated water vapor\n    - Geopotential at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n    - Temperature at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n    - U wind component at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n    - V wind component at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n    - Relative humidity at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n- Pangu-Weather:\n    - 10-m u wind component\n    - 10-m v wind component\n    - 2-m temperature\n    - Mean sea level pressure\n    - Geopotential at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n    - Temperature at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n    - U wind component at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n    - V wind component at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n    - Specific humidity at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n- GraphCast Operational:\n    - 10-m u wind component\n    - 10-m v wind component\n    - 2-m temperature\n    - Mean sea level pressure\n    - Geopotential at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n    - Temperature at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n    - U wind component at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n    - V wind component at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n    - Specific humidity at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n    - Vertical velocity at: [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50] hPa\n    - 6-hr accumulated precipitation\n","Documentation":"https://noaa-oar-mlwp-data.s3.amazonaws.com/README.txt","Contact":"For any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. <br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov","ManagedBy":"Dr. Jacob Radford (jacob.radford@noaa.gov)","UpdateFrequency":"2 times a day, every 12 hours starting at midnight UTC","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["environmental","meteorological","weather"],"License":"Open Data. There are no restrictions on the use of this data.","Resources":[{"Description":"AIWP data","ARN":"arn:aws:s3:::noaa-oar-mlwp-data","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-oar-mlwp-data.s3.amazonaws.com/index.html)"]}],"Citation":"Please cite our [BAMS paper](https://doi.org/10.1175/BAMS-D-24-0057.1)","RegistryEntryAdded":"2024-12-10","RegistryEntryLastModified":"2026-05-07","Slug":"aiwp","Sources":["awslabs-open-data-registry"]}
{"Name":"Allen Brain Observatory - Visual Coding AWS Public Data Set","Description":"The Allen Brain Observatory – Visual Coding is a large-scale, standardized survey of physiological activity across the mouse visual cortex, hippocampus, and thalamus. It includes datasets collected with both two-photon imaging and Neuropixels probes, two complementary techniques for measuring the activity of neurons in vivo. The two-photon imaging dataset features visually evoked calcium responses from GCaMP6-expressing neurons in a range of cortical layers, visual areas, and Cre lines. The Neuropixels dataset features spiking activity from distributed cortical and subcortical brain regions, collected under analogous conditions to the two-photon imaging experiments. We hope that experimentalists and modelers will use these comprehensive, open datasets as a testbed for theories of visual information processing.\n","Documentation":"https://github.com/AllenInstitute/AllenSDK/wiki/Use-the-Allen-Brain-Observatory-%E2%80%93-Visual-Coding-on-AWS","Contact":"awspublicdataset@alleninstitute.org","ManagedBy":"[Allen Institute](http://www.alleninstitute.org/)","UpdateFrequency":"Annually","Tags":["aws-pds","electrophysiology","image processing","imaging","life sciences","Mus musculus","neurobiology","neuroimaging","signal processing"],"License":"http://www.alleninstitute.org/legal/terms-use/","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::allen-brain-observatory","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"SageMaker launch template with s3fs bucket mounts","ARN":"arn:aws:s3:::allen-brain-observatory-templates","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Use the Allen Brain Observatory – Visual Coding on AWS","URL":"https://github.com/AllenInstitute/AllenSDK/wiki/Use-the-Allen-Brain-Observatory-%E2%80%93-Visual-Coding-on-AWS","AuthorName":"Nika Keller, David Feng","AuthorURL":"https://twitter.com/AllenInstitute"}]},"RegistryEntryAdded":"2018-06-18","RegistryEntryLastModified":"2022-05-19","Slug":"allen-brain-observatory","Sources":["awslabs-open-data-registry"]}
{"Name":"Allen Cell Imaging Collections","Description":"This bucket contains multiple datasets (as Quilt packages) created by the Allen Institute for Cell Science. The types of data included in this bucket are listed below:\n\n1) Field of view or cropped images of cells\n2) Segmentations of structures in the images (e.g., boundaries of cells, DNA, other intracellular structures, etc.)\n3) Processed versions of the above images and segmentations\n4) Machine learning predictions and labels of the data listed above\n5) Models trained on the previously listed data\n6) Additional supporting non-image data related to the above listed data types (e.g., gene expression data, whole genome sequencing data, features derived from the images or model predictions, metadata)\n7) Simulation, analysis, and visualization data of in silico cell structures, cells, and cell populations\n\nExternal funding:\n\nThe generation of some datasets was supported by the National Human Genome Research Institute of the National Institutes under Award Number UM1HG011593. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\n","Documentation":"https://open.quiltdata.com/b/allencell","Contact":"cells@alleninstitute.org","ManagedBy":"[Allen Institute for Cell Science](https://www.allencell.org)","UpdateFrequency":"Biweekly","Tags":["aws-pds","biology","cell biology","cell imaging","Homo sapiens","image processing","life sciences","machine learning","microscopy"],"License":"https://www.allencell.org/terms-of-use.html","Resources":[{"Description":"Data files in a public bucket","ARN":"arn:aws:s3:::allencell","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"AICS Volume Viewer","URL":"https://github.com/AllenInstitute/volume-viewer","AuthorName":"Dan Toloudis","AuthorURL":"https://alleninstitute.org/what-we-do/cell-science/about/team/staff-profiles/daniel-toloudis/"},{"Title":"AICSImageIO","URL":"https://github.com/AllenCellModeling/aicsimageio","AuthorName":"Matthew Bowden, Jackson Brown, Jamie Sherman, Dan Toloudis","AuthorURL":"https://www.alleninstitute.org/what-we-do/cell-science/about/team/"},{"Title":"Allen Cell Feature Explorer","URL":"https://cfe.allencell.org/","AuthorName":"Allen Institute for Cell Science","AuthorURL":"https://www.alleninstitute.org/what-we-do/cell-science/about/team/"},{"Title":"Allen Cell Structure Segmenter","URL":"https://www.allencell.org/segmenter.html","AuthorName":"Jianxu Chen, Liya Ding, Matheus P. Viana, Melissa C. Hendershott, Ruian Yang, Irina A. Mueller, Susanne M. Rafelski","AuthorURL":"https://www.alleninstitute.org/what-we-do/cell-science/about/team/"},{"Title":"Integrated Mitotic Stem Cell","URL":"https://imsc.allencell.org","AuthorName":"Allen Institute for Cell Science","AuthorURL":"https://www.alleninstitute.org/what-we-do/cell-science/about/team/"},{"Title":"Pytorch 3D Integrated Cell","URL":"github.com/AllenCellModeling/pytorch_integrated_cell","AuthorName":"Gregory R. Johnson, Rory M. Donovan-Maiye, Mary M. Maleckar","AuthorURL":"https://www.alleninstitute.org/what-we-do/cell-science/about/team/"},{"Title":"Visual Guide to Human Cells","URL":"https://www.allencell.org/visual-guide-to-human-cells.html","AuthorName":"Allen Institute for Cell Science","AuthorURL":"https://www.alleninstitute.org/what-we-do/cell-science/about/team/"}],"Tutorials":[{"Title":"Download and train label-free models","URL":"https://github.com/AllenCellModeling/pytorch_fnet/blob/master/examples/download_and_train.py","AuthorName":"Greg Johnson","AuthorURL":"https://www.alleninstitute.org/what-we-do/cell-science/about/team/staff-profiles/greg-johnson/"},{"Title":"Integrating single-cell sequencing and nuclear imaging data","URL":"https://drive.google.com/file/d/1nuiYOurmbic7rZeHPen_M3fDbsN7n5Ws/view?usp=sharing","AuthorName":"Chengxiang Qiu and William Noble","AuthorURL":"https://noble.gs.washington.edu/"}],"Publications":[{"Title":"4D hybrid model interrogates agent-level rules and parameters driving hiPS cell colony dynamics","URL":"https://doi.org/10.1101/2024.08.12.607546","AuthorName":"Jessica S. Yu, Blair Lyons, Susanne Rafelski, Julie A. Theriot, Neda Bagheri & Graham T. Johnson","AuthorURL":"https://alleninstitute.org/about/team/"},{"Title":"A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes","URL":"https://doi.org/10.1038/s41598-021-94732-1","AuthorName":"Tanya Grancharova*, Kaytlyn A. Gerbin*, Alexander B. Rosenberg, Charles M. Roco, Joy E.Arakaki , Colette M. DeLizo, Stephanie Q. Dinh, Rory M. Donovan-Maiye, Matthew Hirano, Angelique M. Nelson, Joyce Tang, Julie A. Theriot, Calysta Yan, Vilas Menon, Sean P. Palecek, Georg Seelig & Ruwanthi N. Gunawardane","AuthorURL":"https://www.alleninstitute.org/what-we-do/cell-science/about/team/"},{"Title":"A deep generative model of 3D single-cell organization","URL":"https://doi.org/10.1371/journal.pcbi.1009155","AuthorName":"Rory M. Donovan-Maiye, Jackson M. Brown, Caleb K. Chan, Liya Ding, Calysta Yan, Nathalie Gaudreault, Julie A. Theriot, Mary M. Maleckar, Theo A. Knijnenburg & Gregory R. Johnson","AuthorURL":"https://www.alleninstitute.org/what-we-do/cell-science/about/team/"},{"Title":"A human induced pluripotent stem (hiPS) cell model for the holistic study of epithelial to mesenchymal transitions (EMTs)","URL":"https://doi.org/10.1101/2024.08.16.608353","AuthorName":"Caroline Hookway*, Antoine Borensztejn*, Leigh K. Harris*, Sara Carlson, Gokhan Dalgin, Suraj Mishra, Nivedita Nivedita, Ellen M. Adams, Tiffany Barszczewski, Julie C. Dixon, Jacqueline H. Edmonds, Erik A. Ehlers, Alexandra J. Ferrante, Margaret A. Fuqua, Philip Garrison, Janani Gopalan, Benjamin W. Gregor, Maxwell J. Hedayati, Kyle N. Klein, Chantelle L. Leveille, Sean L. Meharry, Haley S. Morris, Gouthamrajan Nadarajan, Sandra A. Oluoch, Serge E. Parent, Amber Phan, Brock Roberts, Emmanuel E. Sanchez, M. Filip Sluzewski, Lev S. Snyder, Derek J. Thirstrup, John Paul Thottam, Julia R. Torvi, Gaea Turman, Matheus P. Viana, Lyndsay Wilhelm, Chamari S. Wijesooriya, Jie Yao, Julie A. Theriot, Susanne M. Rafelski & Ruwanthi N. Gunawardane","AuthorURL":"https://alleninstitute.org/about/team/"},{"Title":"Automated human induced pluripotent stem cell culture and sample preparation for 3D live-cell microscopy","URL":"https://doi.org/10.1038/s41596-023-00912-w","AuthorName":"Benjamin W. Gregor*, Mackenzie E. Coston*, Ellen M. Adams, Joy Arakaki, Antoine Borensztejn, Thao P. Do, Margaret A. Fuqua, Amanda Haupt, Melissa C. Hendershott, Winnie Leung, Irina A. Mueller, Aditya Nath, Angelique M. Nelson, Susanne M. Rafelski, Emmanuel E. Sanchez, Madison J. Swain-Bowden, W. Joyce Tang, Derek J. Thirstrup, Winfried Wiegraebe, Brian P. Whitney, Calysta Yan, Ruwanthi N. Gunawardane & Nathalie Gaudreault","AuthorURL":"https://www.alleninstitute.org/what-we-do/cell-science/about/team/"},{"Title":"Cell states beyond transcriptomics: Integrating structural organization and gene expression in hiPSC-derived cardiomyocytes","URL":"https://doi.org/10.1016/j.cels.2021.05.001","AuthorName":"Kaytlyn A. Gerbin*, Tanya Grancharova*, Rory M. Donovan-Maiye*, Melissa C. Hendershott*, Helen G. Anderson, Jackson M. Brown, Jianxu Chen, Stephanie Q. Dinh, Jamie L. Gehring, Gregory R. Johnson, HyeonWoo Lee, Aditya Nath, Angelique M. Nelson, M. Filip Sluzewski, Matheus P. Viana, Calysta Yan, Rebecca J. Zaunbrecher, Kimberly R. Cordes Metzler, Nathalie Gaudreault, Theo A. Knijnenburg, Susanne M. Rafelski, Julie A. Theriot & Ruwanthi N. Gunawardane","AuthorURL":"https://www.alleninstitute.org/what-we-do/cell-science/about/team/"},{"Title":"Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories","URL":"https://doi.org/10.1101/2024.06.28.601071","AuthorName":"Julie C. Dixon*, Christopher L. Frick*, Chantelle L. Leveille*, Philip Garrison, Peyton A. Lee, Saurabh S. Mogre, Benjamin Morris, Nivedita Nivedita, Ritvik Vasan, Jianxu Chen, Cameron L. Fraser, Clare R. Gamlin, Leigh K. Harris, Melissa C. Hendershott, Graham T. Johnson, Kyle N. Klein, Sandra A. Oluoch, Derek J. Thirstrup, M. Filip Sluzewski, Lyndsay Wilhelm, Ruian Yang, Daniel M. Toloudis, Matheus P. Viana, Julie A. Theriot & Susanne M. Rafelski","AuthorURL":"https://alleninstitute.org/about/team/"},{"Title":"Integrated intracellular organization and its variations in human iPS cells","URL":"https://doi.org/10.1038/s41586-022-05563-7","AuthorName":"Matheus P. Viana, Jianxu Chen*, Theo A. Knijnenburg*, Ritvik Vasan*, Calysta Yan*... Allen Institute for Cell Science... Graham T. Johnson, Ruwanthi N. Gunawardane, Nathalie Gaudreault, Julie A. Theriot & Susanne M. Rafelski","AuthorURL":"https://www.alleninstitute.org/what-we-do/cell-science/about/team/"},{"Title":"Interpretable representation learning for 3D multi-piece intracellular structures using point clouds","URL":"https://doi.org/10.1101/2024.07.25.605164","AuthorName":"Ritvik Vasan, Alexandra J. Ferrante, Antoine Borensztejn, Christopher L. Frick, Nathalie Gaudreault, Saurabh S. Mogre, Benjamin Morris, Guilherme G. Pires, Susanne M. Rafelski, Julie A. Theriot & Matheus P. Viana","AuthorURL":"https://alleninstitute.org/about/team/"},{"Title":"Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy","URL":"https://doi.org/10.1038/s41592-018-0111-2","AuthorName":"Chawin Ounkomol, Sharmishtaa Seshamani, Mary M. Maleckar, Forrest Collman & Gregory R. Johnson","AuthorURL":"https://alleninstitute.org/about/team/"},{"Title":"Systematic gene tagging using CRISPR/Cas9 in human stem cells to illuminate cell organization","URL":"https://doi.org/10.1091/mbc.e17-03-0209","AuthorName":"Brock Roberts*, Amanda Haupt*, Andrew Tucker, Tanya Grancharova, Joy Arakaki, Margaret A. Fuqua, Angelique Nelson, Caroline Hookway, Susan A. Ludmann, Irina A. Mueller, Ruian Yang, Rick Horwitz, Susanne M. Rafelski & Ruwanthi N. Gunawardane","AuthorURL":"https://www.alleninstitute.org/what-we-do/cell-science/about/team/"}]},"RegistryEntryAdded":"2019-08-30","RegistryEntryLastModified":"2024-08-30","Slug":"allen-cell-imaging-collections","Sources":["awslabs-open-data-registry"]}
{"Name":"Human and Mammalian Brain Atlas","Description":"Human and Mammalian Brain Atlas (HMBA) is a major atlas of the BRAIN Initiative Cell Atlas Network (BICAN) that proposes to establish a comprehensive, highly granular cell atlas in complete adult human, macaque, and marmoset brains that links brain structure, function and cellular architecture. Release artifacts have been made available in this OpenData bucket to enable utilization along with their paper publications by the neuroscience community.","Documentation":"https://portal.brain-map.org/explore/hmba","Contact":"awspds@alleninstitute.org","ManagedBy":"[Allen Institute](http://www.alleninstitute.org/)","UpdateFrequency":"Never","Tags":["aws-pds","biology","gene expression","Homo sapiens","life sciences","Mus musculus","neurobiology","non-human primate","single-cell transcriptomics"],"License":"http://www.alleninstitute.org/legal/terms-use/","Citation":null,"Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::allen-hmba-releases","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Human-Mammalian Brain - Basal Ganglia - Data","URL":"https://alleninstitute.github.io/abc_atlas_access/descriptions/HMBA-BG_dataset.html","AuthorName":"Allen Institute for Brain Science","AuthorURL":"www.alleninstitute.org"},{"Title":"Human-Mammalian Brain - CCF Book","URL":"https://alleninstitute.github.io/CCF-MAP/","AuthorName":"Allen Institute for Brain Science","AuthorURL":"www.alleninstitute.org"}],"Tools & Applications":[{"Title":"HMBA Basal Ganglia resources in Brain Knowledge Platform's Data Catalog","URL":"https://knowledge.brain-map.org/data/POZ2HCPBT60DSDJ8UA7","AuthorName":"Allen Institute for Brain Science","AuthorURL":"www.alleninstitute.org"}]},"RegistryEntryAdded":"2025-08-11","RegistryEntryLastModified":"2025-10-30","Slug":"allen-hmba-releases","Sources":["awslabs-open-data-registry"]}
{"Name":"Allen institute intratelencephalic neuron connectivity paper supplemental data","Description":"organized and data files for plotting figures in the manuscript of VISp intratelencephalic (IT) neuron connectivity using MICrONS EM dataset","Documentation":"https://github.com/AllenInstitute/IT_connectivity/tree/main","Contact":"chi.zhang@alleninstitute.org","ManagedBy":"[Allen Institute](http://www.alleninstitute.org/)","UpdateFrequency":"Newly generated data will be uploaded upon revision request","Tags":["aws-pds","connectomics","electron microscopy","imaging","life sciences","neuroscience"],"License":"CC-BY 4.0","Resources":[{"Description":"csv and parquet data files for plotting manuscript figures","ARN":"arn:aws:s3:::allen-paper-supplements/czhang_preprint_2026/","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"IT-circuit-Figures-clean","URL":"https://github.com/AllenInstitute/IT_connectivity/tree/main","AuthorName":"valering_z"}],"Publications":[{"Title":"Cell-type-specific parallel pathways in the canonical cortical microcircuit","URL":"https://www.biorxiv.org/content/10.64898/2026.04.23.720412v2","AuthorName":"Chi Zhang, Casey M Schneider-Mizell, Bethanny P Danskin, Rachael Swanstrom, Erika Neace, Emily Joyce, Benjamin D Pedigo, Forrest C Collman, and Nuno Maçarico da Costa"}]},"RegistryEntryAdded":"2026-05-22","RegistryEntryLastModified":"2026-06-03","Slug":"allen-it-connectivity","Sources":["awslabs-open-data-registry"]}
{"Name":"Allen Ivy Glioblastoma Atlas","Description":"This dataset consists of images of glioblastoma human brain tumor tissue sections that have been probed for expression of particular genes believed to play a role in development of the cancer.  Each tissue section is adjacent to another section that was stained with a reagent useful for identifying histological features of the tumor.  Each of these types of images has been completely annotated for tumor features by a machine learning process trained by expert medical doctors.\n","Contact":"awspds@alleninstitute.org","Documentation":"https://glioblastoma.alleninstitute.org/static/docs","ManagedBy":"[Allen Institute](http://www.alleninstitute.org/)","UpdateFrequency":"Never","Tags":["aws-pds","biology","cancer","computer vision","gene expression","genetic","glioblastoma","Homo sapiens","image processing","imaging","life sciences","machine learning","neurobiology"],"License":"http://www.alleninstitute.org/legal/terms-use/","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::allen-ivy-glioblastoma-atlas","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing Ivy Glioblastoma Atlas Project data","URL":"https://github.com/AllenInstitute/open_dataset_tools/blob/main/example_notebooks/Accessing_Ivy_Glioblastoma_Atlas_Project_Data.ipynb","AuthorName":"Allen Institute for Brain Science","AuthorURL":"www.alleninstitute.org"}],"Tools & Applications":[{"Title":"Ivy Glioblastoma Atlas Project","URL":"https://glioblastoma.alleninstitute.org","AuthorName":"Allen Institute for Brain Science","AuthorURL":"www.alleninstitute.org"}],"Publications":[{"Title":"An anatomic transcriptional atlas of human glioblastoma","URL":"http://science.sciencemag.org/content/360/6389/660","AuthorName":"Ralph Puchalski, et al.","AuthorURL":"www.alleninstitute.org"}]},"RegistryEntryAdded":"2021-07-02","RegistryEntryLastModified":"2021-07-02","Slug":"allen-ivy-glioblastoma-atlas","Sources":["awslabs-open-data-registry"]}
{"Name":"Allen Mouse Brain Atlas","Description":"The Allen Mouse Brain Atlas is a genome-scale collection of cellular resolution gene expression profiles using in situ hybridization (ISH). Highly methodical data production methods and comprehensive anatomical coverage via dense, uniformly spaced sampling facilitate data consistency and comparability across >20,000 genes. The use of an inbred mouse strain with minimal animal-to-animal variance allows one to treat the brain essentially as a complex but highly reproducible three-dimensional tissue array. The entire Allen Mouse Brain Atlas dataset and associated tools are available through an unrestricted web-based viewing application (http://mouse.brain-map.org). The collection of > 650,000 images have been made available in this Open Data bucket to enable efficient access and analysis of the this dataset.\n","Contact":"awspds@alleninstitute.org","Documentation":"http://help.brain-map.org/display/mousebrain/Documentation","ManagedBy":"[Allen Institute](http://www.alleninstitute.org/)","UpdateFrequency":"Never","Tags":["aws-pds","biology","gene expression","genetic","image processing","imaging","life sciences","Mus musculus","neurobiology","transcriptomics"],"License":"http://www.alleninstitute.org/legal/terms-use/","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::allen-mouse-brain-atlas","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing Allen Mouse Brain Atlas data","URL":"https://github.com/AllenInstitute/open_dataset_tools/blob/main/example_notebooks/Accessing_Allen_Mouse_Brain_Atlas_Data.ipynb","AuthorName":"Allen Institute for Brain Science","AuthorURL":"www.alleninstitute.org"}],"Tools & Applications":[{"Title":"Allen Mouse Brain Atlas","URL":"https://mouse.brain-map.org","AuthorName":"Allen Institute for Brain Science","AuthorURL":"www.alleninstitute.org"}],"Publications":[{"Title":"Genome-wide atlas of gene expression in the adult mouse brain","URL":"http://www.nature.com/articles/nature05453","AuthorName":"Ed Lein, et al.","AuthorURL":"www.alleninstitute.org"}]},"RegistryEntryAdded":"2020-04-10","RegistryEntryLastModified":"2022-05-19","Slug":"allen-mouse-brain-atlas","Sources":["awslabs-open-data-registry"]}
{"Name":"Allen Institute for Neural Dynamics - Extracellular Electrophysiology Compression Benchmark","Description":"Extracellular electrophysiology data is growing at a remarkable pace. This data, collected neuropixels probes by the Allen Institute and the International Brain Lab can be used to benchmark throughput rates and storage ratios of various data compression algorithms.","Documentation":"https://allenneuraldynamics.github.io/data.html#aind-benchmark-dataephys-compression","Contact":"aindopendata@alleninstitute.org","ManagedBy":"[Allen Institute](http://neuraldynamics.alleninstitute.org/)","UpdateFrequency":"Weekly","Tags":["aws-pds","electrophysiology","life sciences","Mus musculus","neurobiology","signal processing"],"License":"CC-BY-4.0","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::aind-benchmark-data/ephys-compression","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"AIND ephys compression benchmark data","URL":"https://allenneuraldynamics.github.io/data.html#aind-benchmark-dataephys-compression","AuthorName":"David Feng, Alessio Buccino","AuthorURL":"https://twitter.com/AllenInstitute"}],"Publications":[{"Title":"Compression strategies for large-scale electrophysiology data","URL":"https://www.biorxiv.org/content/10.1101/2023.05.22.541700v2","AuthorName":"Alessio P. Buccino, Olivier Winter, David Bryant, David Feng, Karel Svoboda, Joshua H. Siegle.","AuthorURL":"http://neuraldynamics.alleninstitute.org/"}]},"RegistryEntryAdded":"2023-05-26","RegistryEntryLastModified":"2023-05-26","Slug":"allen-nd-ephys-compression","Sources":["awslabs-open-data-registry"]}
{"Name":"Allen Institute for Neural Dynamics - Extracellular Electrophysiology Hybrid Evaluation Benchmark","Description":"Evaluation of spike sorting methods is a challenging task, as it requires both ground-truth data and a variety of sorting algorithms to compare against. This dataset contains a set of hybrid data specifically designed for benchmarking spike sorting methods.","Documentation":"https://allenneuraldynamics.github.io/data.html#aind-benchmark-dataephys-hybrid-evaluation","Contact":"aindopendata@alleninstitute.org","ManagedBy":"[Allen Institute](http://neuraldynamics.alleninstitute.org/)","UpdateFrequency":"Weekly","Tags":["aws-pds","electrophysiology","life sciences","Mus musculus","neurobiology","signal processing"],"License":"CC-BY-4.0","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::aind-benchmark-data/ephys-hybrid-evaluation","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"AIND ephys hybrid evaluation benchmark data","URL":"https://allenneuraldynamics.github.io/data.html#aind-benchmark-dataephys-hybrid-evaluation","AuthorName":"David Feng, Alessio Buccino","AuthorURL":"https://twitter.com/AllenInstitute"}],"Publications":[{"Title":"Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data","URL":"https://elifesciences.org/reviewed-preprints/110170","AuthorName":"Alessio P. Buccino, Arjun Sridhar, David Feng, Karel Svoboda, Joshua H. Siegle.","AuthorURL":"http://neuraldynamics.alleninstitute.org/"}]},"RegistryEntryAdded":"2026-05-27","RegistryEntryLastModified":"2026-05-27","Slug":"allen-nd-ephys-hybrid-evaluation","Sources":["awslabs-open-data-registry"]}
{"Name":"Allen Institute for Neural Dynamics - Mouse Neuroanatomy and Physiology Data","Description":"The Allen Institute for Neural Dynamics (AIND) is committed to FAIR, Open, and Reproducible science. We therefore share all of the raw and derived data we collect publicly with rich metadata, including preliminary data collected during methods development, as near to the time of collection as possible.","Documentation":"https://docs.allenneuraldynamics.org/en/latest/policies_practices/data_organization.html","Contact":"aindopendata@alleninstitute.org","ManagedBy":"[Allen Institute](http://neuraldynamics.alleninstitute.org/)","UpdateFrequency":"Weekly","Tags":["aws-pds","electrophysiology","image processing","imaging","life sciences","Mus musculus","neurobiology","neuroimaging","signal processing"],"License":"CC-BY-4.0","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::aind-open-data","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"AIND Open Data Access","URL":"https://docs.allenneuraldynamics.org/en/latest/policies_practices/data_organization.html","AuthorName":"David Feng, Saskia de Vries","AuthorURL":"https://twitter.com/AllenInstitute"},{"Title":"Explore Allen Institute for Neural Dynamics ExA-SPIM Data in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/e8df638d-9374-527d-9ad5-8689c24537aa/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"}]},"RegistryEntryAdded":"2023-01-31","RegistryEntryLastModified":"2026-11-11","Slug":"allen-nd-open-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD)","Description":"The Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) consortium strives to gain a deep molecular and cellular understanding of the early pathogenesis of Alzheimer's disease and is funded by the National Institutes on Aging (NIA U19AG060909). The SEA-AD datasets available here comprise single cell profiling (transcriptomics and epigenomics) and quantitative neuropathology. To explore gene expression and chromatin accessibility information, the single-cell profiling data includes: snRNAseq and snATAC-seq data from the SEA-AD donor cohort (aged brains which span the spectrum of Alzheimer's Disease pathology) and neurotypical reference brains. To explore key pathological proteins and cell types of interest to Alzheimer's disease, the neuropathology data includes: full resolution brightfield images, images processed and segmented in HALO image analysis software, image annotations, and quantification summary files for the relevant stains including Abeta (6E10), IBA1, a-Synuclein, GFAP, H&E-LFB, NeuN, pTau(AT8), and pTDP43.\n","Documentation":"https://portal.brain-map.org/explore/seattle-alzheimers-disease/seattle-alzheimers-disease-brain-cell-atlas-download?edit&language=en","Contact":"awspublicdataset@alleninstitute.org","ManagedBy":"[SEA-AD](https://brain-map.org/consortia/sea-ad)","UpdateFrequency":"Annually","Tags":["aws-pds","biology","cell biology","cell imaging","epigenomics","gene expression","histopathology","Homo sapiens","imaging","life sciences","medicine","microscopy","neurobiology","neuroscience","single-cell transcriptomics","transcriptomics"],"License":"https://alleninstitute.org/legal/terms-use/","Resources":[{"Description":"Single cell profiling (transcriptomics and epigenomics) data files in a public bucket","ARN":"arn:aws:s3:::sea-ad-single-cell-profiling","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://sea-ad-single-cell-profiling.s3.amazonaws.com/index.html)"]},{"Description":"Update notifications for s3://sea-ad-single-cell-profiling. Users can subscribe to this SNS topic with [AWS Lambda](https://aws.amazon.com/lambda/) or [AWS Simple Queue Service](https://aws.amazon.com/sqs/).","ARN":"arn:aws:sns:us-west-2:208217671510:sea-ad-single-cell-profiling-object_created","Region":"us-west-2","Type":"SNS Topic"},{"Description":"Quantitative neuropathology (full resolution images, processed images, and quantifications) in a public bucket","ARN":"arn:aws:s3:::sea-ad-quantitative-neuropathology","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://sea-ad-quantitative-neuropathology.s3.amazonaws.com/index.html)"]},{"Description":"Update notifications for s3://sea-ad-quantitative-neuropathology. Users can subscribe to this SNS topic with [AWS Lambda](https://aws.amazon.com/lambda/) or [AWS Simple Queue Service](https://aws.amazon.com/sqs/).","ARN":"arn:aws:sns:us-west-2:208217671510:sea-ad-quantitative-neuropathology-object_created","Region":"us-west-2","Type":"SNS Topic"},{"Description":"Spatial transcriptomics data files in a public bucket","ARN":"arn:aws:s3:::sea-ad-spatial-transcriptomics","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://sea-ad-spatial-transcriptomics.s3.amazonaws.com/index.html)"]},{"Description":"Update notifications for s3://sea-ad-spatial-transcriptomics. Users can subscribe to this SNS topic with [AWS Lambda](https://aws.amazon.com/lambda/) or [AWS Simple Queue Service](https://aws.amazon.com/sqs/).","ARN":"arn:aws:sns:us-west-2:208217671510:sea-ad-spatial-transcriptomics-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"Seattle Alzheimer’s Disease Brain Cell Atlas","URL":"http://sea-ad.org","AuthorName":"Lein, E et al.","AuthorURL":"www.alleninstitute.org"}]},"RegistryEntryAdded":"2022-09-22","RegistryEntryLastModified":"2026-06-11","Slug":"allen-sea-ad-atlas","Sources":["awslabs-open-data-registry"]}
{"Name":"Allen Institute for Brain Science - Synaptic Physiology Public Data Set","Description":"This is a large-scale survey that describes the physiology (strength, kinetics, and short term plasticity) of thousands of synapses from patch clamp experiments in mouse visual cortex and human middle temporal gyrus.  \n","Documentation":"https://portal.brain-map.org/explore/connectivity/synaptic-physiology","Contact":"synapticphysiology@alleninstitute.org","ManagedBy":"[Allen Institute](http://www.alleninstitute.org/)","UpdateFrequency":"Finalized","Tags":["aws-pds","electrophysiology","Homo sapiens","life sciences","Mus musculus","neurobiology","signal processing"],"License":"http://www.alleninstitute.org/legal/terms-use/","Resources":[{"Description":"Database files used by aisynphys package","ARN":"arn:aws:s3:::allen-synphys","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"aisynphys python package for accessing synaptic physiology data","URL":"https://github.com/AllenInstitute/aisynphys","AuthorName":"Campagnola L., Seeman S., et al."}],"Publications":[{"Title":"Local connectivity and synaptic dynamics in mouse and human neocortex","URL":"https://pubmed.ncbi.nlm.nih.gov/35271334/","AuthorName":"Campagnola L., Seeman S., et al."}]},"RegistryEntryAdded":"2023-01-30","RegistryEntryLastModified":"2023-01-30","Slug":"allen-synphys","Sources":["awslabs-open-data-registry"]}
{"Name":"AI2 Reasoning Challenge (ARC) 2018","Description":"7,787 multiple choice science questions and associated corpora","Documentation":"https://allenai.org/data/arc","Contact":"info@allenai.org","ManagedBy":"[Allen Institute for AI](https://allenai.org)","UpdateFrequency":"Not updated","Tags":["aws-pds","csv","json","machine learning"],"License":"[CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/)","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::ai2-public-datasets","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengg","URL":"https://www.semanticscholar.org/paper/Think-you-have-Solved-Question-Answering-Try-ARC%2C-Clark-Cowhey/88bb0a28bb58d847183ec505dda89b63771bb495","AuthorName":"Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord"}]},"RegistryEntryAdded":"2021-04-20","RegistryEntryLastModified":"2021-04-20","Slug":"allenai-arc","Sources":["awslabs-open-data-registry"]}
{"Name":"Aristo Mini Corpus","Description":"1,197,377 science-relevant sentences","Documentation":"https://allenai.org/data/aristo-mini","Contact":"info@allenai.org","ManagedBy":"[Allen Institute for AI](https://allenai.org)","UpdateFrequency":"Not updated","Tags":["aws-pds","csv","json","machine learning"],"License":"[CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/)","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::ai2-public-datasets","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2021-04-20","RegistryEntryLastModified":"2021-04-20","Slug":"allenai-aristo-mini","Sources":["awslabs-open-data-registry"]}
{"Name":"AI2 Diagram Dataset (AI2D)","Description":"4,817 illustrative diagrams for research on diagram understanding and associated question answering.","Documentation":"https://allenai.org/data/diagrams","Contact":"info@allenai.org","ManagedBy":"[Allen Institute for AI](https://allenai.org)","UpdateFrequency":"Not updated","Tags":["aws-pds","machine learning"],"License":"[CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/)","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::ai2-public-datasets","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"A Diagram is Worth a Dozen Images","URL":"https://www.semanticscholar.org/paper/A-Diagram-is-Worth-a-Dozen-Images-Kembhavi-Salvato/e18ec2c9f0b4a817b8cf0435822bbc879d7db698","AuthorName":"Aniruddha Kembhavi, Michael Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, Ali Farhadi"}]},"RegistryEntryAdded":"2021-04-20","RegistryEntryLastModified":"2021-04-20","Slug":"allenai-diagrams","Sources":["awslabs-open-data-registry"]}
{"Name":"Discrete Reasoning Over the content of Paragraphs (DROP)","Description":"The DROP dataset contains 96k Question and Answer pairs (QAs) over 6.7K paragraphs, split between train (77k QAs), development (9.5k QAs) and a hidden test partition (9.5k QAs).","Documentation":"https://allenai.org/data/drop","Contact":"info@allenai.org","ManagedBy":"[Allen Institute for AI](https://allenai.org)","UpdateFrequency":"Not updated","Tags":["aws-pds","machine learning","natural language processing"],"License":"[CC BY](https://creativecommons.org/licenses/by/4.0)","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::ai2-public-datasets","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs","URL":"https://www.semanticscholar.org/paper/DROP%3A-A-Reading-Comprehension-Benchmark-Requiring-Dua-Wang/dda6fb309f62e2557a071522354d8c2c897a2805","AuthorName":"Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner"}]},"RegistryEntryAdded":"2021-04-20","RegistryEntryLastModified":"2021-04-20","Slug":"allenai-drop","Sources":["awslabs-open-data-registry"]}
{"Name":"AI2 Meaningful Citations Data Set","Description":"630 paper annotations","Documentation":"https://allenai.org/data/meaningful-citations","Contact":"info@allenai.org","ManagedBy":"[Allen Institute for AI](https://allenai.org)","UpdateFrequency":"Not updated","Tags":["aws-pds","csv","machine learning"],"License":"[CC BY](https://creativecommons.org/licenses/by/4.0)","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::ai2-public-datasets","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Identifying Meaningful Citations","URL":"https://www.semanticscholar.org/paper/Identifying-Meaningful-Citations-Valenzuela-Ha/1c7be3fc28296a97607d426f9168ad4836407e4b#citing-papers","AuthorName":"Marco Valenzuela, Vu A. Ha, Oren Etzioni"}]},"RegistryEntryAdded":"2021-04-20","RegistryEntryLastModified":"2021-04-20","Slug":"allenai-meaningful-citations","Sources":["awslabs-open-data-registry"]}
{"Name":"Quoref","Description":"24K Question/Answer (QA) pairs over 4.7K paragraphs, split between train (19K QAs), development (2.4K QAs) and a hidden test partition (2.5K QAs).","Documentation":"https://allenai.org/data/quoref","Contact":"info@allenai.org","ManagedBy":"[Allen Institute for AI](https://allenai.org)","UpdateFrequency":"Not updated","Tags":["aws-pds","machine learning","natural language processing"],"License":"[CC BY](https://creativecommons.org/licenses/by/4.0)","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::ai2-public-datasets","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning","URL":"https://www.semanticscholar.org/paper/Quoref%3A-A-Reading-Comprehension-Dataset-with-Dasigi-Liu/3838387ea8dd1bb8c2306be5a63c1c120075c5a2","AuthorName":"Pradeep Dasigi, Nelson F. Liu, Ana Marasović, Noah A. Smith, Matt Gardner"}]},"RegistryEntryAdded":"2021-04-20","RegistryEntryLastModified":"2021-04-20","Slug":"allenai-quoref","Sources":["awslabs-open-data-registry"]}
{"Name":"Reasoning Over Paragraph Effects in Situations (ROPES)","Description":"14k QA pairs over 1.7K paragraphs, split between train (10k QAs), development (1.6k QAs) and a hidden test partition (1.7k QAs).","Documentation":"https://allenai.org/data/ropes","Contact":"info@allenai.org","ManagedBy":"[Allen Institute for AI](https://allenai.org)","UpdateFrequency":"Not updated","Tags":["aws-pds","json","machine learning","natural language processing"],"License":"[CC BY](https://creativecommons.org/licenses/by/4.0)","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::ai2-public-datasets","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Reasoning Over Paragraph Effects in Situations","URL":"https://www.semanticscholar.org/paper/Reasoning-Over-Paragraph-Effects-in-Situations-Lin-Tafjord/2ebb01d08022a52c1025302379873dedb5100035","AuthorName":"Kevin Lin, Oyvind Tafjord, Peter Clark, Matt Gardner"}]},"RegistryEntryAdded":"2021-04-20","RegistryEntryLastModified":"2021-04-20","Slug":"allenai-ropes","Sources":["awslabs-open-data-registry"]}
{"Name":"AI2 TabMCQ: Multiple Choice Questions aligned with the Aristo Tablestore","Description":"9092 crowd-sourced science questions and 68 tables of curated facts","Documentation":"https://allenai.org/data/tablestore-questions","Contact":"info@allenai.org","ManagedBy":"[Allen Institute for AI](https://allenai.org)","UpdateFrequency":"Not updated","Tags":["aws-pds","machine learning","natural language processing"],"License":"[CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/)","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::ai2-public-datasets/tablestore-questions/","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2021-04-20","RegistryEntryLastModified":"2021-04-20","Slug":"allenai-tablestore-questions","Sources":["awslabs-open-data-registry"]}
{"Name":"AI2 Tablestore (November 2015 Snapshot)","Description":"68 tables of curated facts","Documentation":"https://allenai.org/data/tablestore","Contact":"info@allenai.org","ManagedBy":"[Allen Institute for AI](https://allenai.org)","UpdateFrequency":"Not updated","Tags":["aws-pds","machine learning","natural language processing"],"License":"[CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/)","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::ai2-public-datasets/tablestore/","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2021-04-20","RegistryEntryLastModified":"2021-04-20","Slug":"allenai-tablestore","Sources":["awslabs-open-data-registry"]}
{"Name":"Textbook Question Answering (TQA)","Description":"1,076 textbook lessons, 26,260 questions, 6229 images","Documentation":"https://allenai.org/data/tqa","Contact":"info@allenai.org","ManagedBy":"[Allen Institute for AI](https://allenai.org)","UpdateFrequency":"Not updated","Tags":["aws-pds","machine learning"],"License":"[CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/)","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::ai2-public-datasets","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2021-04-20","RegistryEntryLastModified":"2022-06-15","Slug":"allenai-tqa","Sources":["awslabs-open-data-registry"]}
{"Name":"Aristo Tuple KB","Description":"294,000 science-relevant tuples","Documentation":"https://allenai.org/data/tuple-kb","Contact":"info@allenai.org","ManagedBy":"[Allen Institute for AI](https://allenai.org)","UpdateFrequency":"Not updated","Tags":["aws-pds","machine learning","natural language processing"],"License":"[CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/)","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::ai2-public-datasets/tuple-kb/","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2021-04-20","RegistryEntryLastModified":"2021-04-20","Slug":"allenai-tuple-kb","Sources":["awslabs-open-data-registry"]}
{"Name":"ZEST: ZEroShot  learning  from Task descriptions","Description":"ZEST is a benchmark for zero-shot generalization to unseen NLP tasks, with 25K labeled instances across 1,251 different tasks.","Documentation":"https://allenai.org/data/zest","Contact":"info@allenai.org","ManagedBy":"[Allen Institute for AI](https://allenai.org)","UpdateFrequency":"Not updated","Tags":["aws-pds","machine learning","natural language processing"],"License":"[CC BY](https://creativecommons.org/licenses/by/4.0)","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::ai2-public-datasets/zest/","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2021-04-20","RegistryEntryLastModified":"2021-04-20","Slug":"allenai-zest","Sources":["awslabs-open-data-registry"]}
{"Name":"Alliance of Genome Resources","Description":"The Alliance of Genome Resources is a consortium that integrates genomic, genetic, and molecular data from leading model organism databases including Drosophila melanogaster, Caenorhabditis elegans, Danio rerio (zebrafish), Mus musculus (mouse), Rattus norvegicus (rat), Saccharomyces cerevisiae (yeast), Xenopus laevis and Xenopus tropicalis (frogs), and human reference data. The Alliance provides comprehensive datasets including gene annotations, disease associations, expression data (bulk and single-cell RNA-Seq), protein and genetic interactions, orthology relationships, variants and alleles, and complete genome sequences with annotations. Data is organized into Alliance-wide integrated datasets and organism-specific collections, supporting comparative genomics, disease modeling, and functional genomics research.","Documentation":"https://github.com/alliance-genome/agr_open_data","Contact":"help@alliancegenome.org","ManagedBy":"Alliance of Genome Resources Consortium","UpdateFrequency":"Quarterly releases (every ~3 months)","Tags":["aws-pds","bioinformatics","biology","Caenorhabditis elegans","Danio rerio","Drosophila melanogaster","fasta","gene expression","genetic","genome","genomic","Homo sapiens","life sciences","Mus musculus","protein","Rattus norvegicus","transcriptomics","vcf"],"License":"Most Alliance data is available under CC0 1.0 Universal (Public Domain Dedication). Some datasets may use CC-BY 4.0 (attribution required). Full details at https://www.alliancegenome.org/terms-of-use","Citation":"Alliance of Genome Resources Consortium. Alliance of Genome Resources Portal - unified model organism research platform. Nucleic Acids Research (2023). https://doi.org/10.1093/nar/gkac1003","Resources":[{"Description":"Alliance-wide integrated datasets including disease associations, gene expression, molecular and genetic interactions, orthology relationships, gene descriptions, and variants across all Alliance organisms. Data is organized by release version (8.3.0/, 8.2.0/, etc.), then by data type, with organism-specific collections for FB (FlyBase/Drosophila), MGI (Mouse), RGD (Rat), SGD (Yeast), WB (Worm), XBXL/XBXT (Xenopus), ZFIN (Zebrafish), and HUMAN reference data. Available in TSV, JSON, and VCF formats.","ARN":"arn:aws:s3:::alliance-genome-downloads","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://alliance-genome-downloads.s3.amazonaws.com/)"]},{"Description":"FlyBase-specific data for Drosophila melanogaster and related species, including gene annotations, GO annotations, expression data (bulk RNA-Seq, single-cell RNA-Seq), disease associations, phenotypes, interactions, orthologs, genome sequences (FASTA), and genome annotations (GFF3/GTF). Data organized by release (current/, FB2025_04/, etc.) with precomputed analysis files and complete Chado XML database dumps. Publicly accessible via HTTPS for direct download without AWS credentials.","ARN":"arn:aws:s3:::s3ftp.flybase.org","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse via HTTPS](https://s3ftp.flybase.org/releases/current/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Alliance of Genome Resources AWS Data Access Tutorials","URL":"https://github.com/alliance-genome/agr_open_data/blob/main/TUTORIAL.md","AuthorName":"Alliance of Genome Resources Consortium","AuthorURL":"https://www.alliancegenome.org"}],"Tools & Applications":[{"Title":"Alliance of Genome Resources Portal","URL":"https://www.alliancegenome.org","AuthorName":"Alliance of Genome Resources Consortium","AuthorURL":"https://www.alliancegenome.org"},{"Title":"FlyBase - Drosophila Database","URL":"https://flybase.org","AuthorName":"FlyBase Consortium","AuthorURL":"https://flybase.org"},{"Title":"MGI - Mouse Genome Database","URL":"http://www.informatics.jax.org","AuthorName":"MGI","AuthorURL":"http://www.informatics.jax.org"},{"Title":"RGD - Rat Genome Database","URL":"https://rgd.mcw.edu","AuthorName":"RGD","AuthorURL":"https://rgd.mcw.edu"},{"Title":"SGD - Saccharomyces Genome Database","URL":"https://www.yeastgenome.org","AuthorName":"SGD","AuthorURL":"https://www.yeastgenome.org"},{"Title":"WormBase - C. elegans Database","URL":"https://www.wormbase.org","AuthorName":"WormBase Consortium","AuthorURL":"https://www.wormbase.org"},{"Title":"Xenbase - Xenopus Database","URL":"http://www.xenbase.org","AuthorName":"Xenbase","AuthorURL":"http://www.xenbase.org"},{"Title":"ZFIN - Zebrafish Database","URL":"https://zfin.org","AuthorName":"ZFIN","AuthorURL":"https://zfin.org"}],"Publications":[{"Title":"Alliance of Genome Resources Portal - unified model organism research platform","URL":"https://doi.org/10.1093/nar/gkac1003","AuthorName":"Alliance of Genome Resources Consortium"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-10-17","RegistryEntryLastModified":"2025-12-09","Slug":"alliance-genome-resources","Sources":["awslabs-open-data-registry"]}
{"Name":"AllTheBacteria","Description":"All bacterial isolate whole-genome sequencing data from INSDC, uniformly assembled, quality-controlled, annotated, and searchable.","Documentation":"https://allthebacteria.org","Contact":"https://github.com/AllTheBacteria/AllTheBacteria/issues","ManagedBy":"[European Bioinformatics Institute](https://www.ebi.ac.uk/)","UpdateFrequency":"The current release is for all SRA bacterial isolate data up to August 2024. The \ncolllection will be updated occasionally, with no fixed schedule.\n","Tags":["assembly","bacteria","bioinformatics","fasta","genomic","life sciences","microbial genomics","short read sequencing","whole genome sequencing"],"License":"[MIT License](https://opensource.org/license/mit)","Resources":[{"Description":"Individual, compressed genome assemblies in .fasta format in a public S3 bucket.","ARN":"arn:aws:s3:::allthebacteria-assemblies","Region":"eu-west-2","Type":"S3 Bucket","Explore":null},{"Description":"Phylogenetically-compressed, batched xz archives of all genome assemblies in .fasta format in a public S3 bucket.","ARN":"arn:aws:s3:::allthebacteria-phylogeneticbatches","Region":"eu-west-2","Type":"S3 Bucket","Explore":null},{"Description":"Metadata for each genome assembly, including taxonomic information, in a public S3 bucket.","ARN":"arn:aws:s3:::allthebacteria-metadata","Region":"eu-west-2","Type":"S3 Bucket","Explore":null},{"Description":"A [LexicMap](https://github.com/shenwei356/LexicMap) index of all genome assemblies. This can be used for efficient sequence alignment against all genomes.","ARN":"arn:aws:s3:::allthebacteria-lexicmap","Region":"eu-west-2","Type":"S3 Bucket","Explore":null}],"DataAtWork":{"Publications":[{"Title":"AllTheBacteria - all bacterial genomes assembled, available and searchable","URL":"https://doi.org/10.1101/2024.03.08.584059","AuthorName":"Hunt M, Lima L, Anderson D, Hawkey J, Shen W, Lees J, Iqbal I","AuthorURL":"https://researchportal.bath.ac.uk/en/persons/zamin-iqbal"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2024-12-04","RegistryEntryLastModified":"2025-04-30","Slug":"allthebacteria","Sources":["awslabs-open-data-registry"]}
{"Name":"Amazon Berkeley Objects Dataset","Description":"Amazon Berkeley Objects (ABO) is a collection of 147,702 product listings with multilingual metadata and 398,212 unique catalog images. 8,222 listings come with turntable photography (also referred as \"spin\" or \"360º-View\" images), as sequences of 24 or 72 images, for a total of 586,584 images in 8,209 unique sequences. For 7,953 products, the collection also provides high-quality 3d models, as glTF 2.0 files. (NB - The license for all assets was updated from CC BY-NC to the more permissive CC BY in 2023, older announcements, artefacts or publications might not reflect this change)","Documentation":"https://amazon-berkeley-objects.s3.us-east-1.amazonaws.com/README.md","Contact":"amazon-berkeley-objects@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not updated","Tags":["amazon.science","computer vision","deep learning","information retrieval","machine learning","machine translation"],"License":"Creative Commons Attribution 4.0 (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Product listings and metadata in gzip-compressed CSV and JSON, catalog images and 360º-view images in JPEG and PNG and 3d models in glTF 2.0 (.glb) format.","ARN":"arn:aws:s3:::amazon-berkeley-objects","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore dataset](https://amazon-berkeley-objects.s3.us-east-1.amazonaws.com/index.html)","[README](https://amazon-berkeley-objects.s3.us-east-1.amazonaws.com/README.md)"]}],"RegistryEntryAdded":"2021-06-17","RegistryEntryLastModified":"2026-09-22","Slug":"amazon-berkeley-objects","Sources":["awslabs-open-data-registry"]}
{"Name":"Amazon Bin Image Dataset","Description":"The Amazon Bin Image Dataset contains over 500,000 images and metadata from bins of a pod in an operating Amazon Fulfillment Center. The bin images in this dataset are captured as robot units carry pods as part of normal Amazon Fulfillment Center operations.","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/aft-vbi-pds","Contact":"amazon-bin-images@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not updated","Tags":["amazon.science","computer vision","machine learning"],"License":"Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States (CC BY-NC-SA 3.0 US) https://creativecommons.org/licenses/by-nc-sa/3.0/us/","Resources":[{"Description":"Over 500,000 bin JPEG images and corresponding JSON metadata files describing items in bins in Amazon Fulfillment Centers.","ARN":"arn:aws:s3:::aft-vbi-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://aft-vbi-pds.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"Amazon Bin Image Dataset Challenge","URL":"https://github.com/silverbottlep/abid_challenge","AuthorName":"silverbottlep","AuthorURL":"https://github.com/silverbottlep"},{"Title":"Amazon Inventory Reconciliation using AI","URL":"https://github.com/OneNow/AI-Inventory-Reconciliation","AuthorName":"Pablo Rodriguez Bertorello, Sravan Sripada, Nutchapol Dendumrongsup","AuthorURL":"https://github.com/pablo-tech"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2022-06-06","Slug":"amazon-bin-imagery","Sources":["awslabs-open-data-registry"]}
{"Name":"VoiSeR","Description":"Voice-based refinements of product search","Documentation":"https://amazon-conversational-product-search.s3.amazonaws.com/README.md","Contact":"filicesf@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not currently being updated","Tags":["amazon.science","information retrieval","machine learning","natural language processing"],"License":"[cc-by-sa 4.0](https://creativecommons.org/licenses/by-sa/4.0/)","Resources":[{"Description":"VoiSeR Dataset","ARN":"arn:aws:s3:::amazon-conversational-product-search","Region":"us-east-1","Type":"S3 Bucket","Explore":["[voiser_products_v1.0.json](https://amazon-conversational-product-search.s3.amazonaws.com/voiser_products_v1.0.json)","[voiser_utterances_v1.0.json](https://amazon-conversational-product-search.s3.amazonaws.com/voiser_utterances_v1.0.json)","[voiser_images_v1.0.zip](https://amazon-conversational-product-search.s3.amazonaws.com/voiser_images_v1.0.zip)"]}],"DataAtWork":{"Publications":[{"Title":"VoiSeR: A New Benchmark for Voice-Based Search Refinement","URL":"https://www.aclweb.org/anthology/venues/eacl/","AuthorName":"Simone Filice, Giuseppe Castellucci, Marcus Collins, Eugene Agichtein & Oleg Rokhlenko"}]},"RegistryEntryAdded":"2021-04-12","RegistryEntryLastModified":"2022-06-06","Slug":"amazon-conversational-product-search","Sources":["awslabs-open-data-registry"]}
{"Name":"2021 Amazon Last Mile Routing Research Challenge Dataset","Description":"The 2021 Amazon Last Mile Routing Research Challenge was an innovative research initiative led by Amazon.com and supported by the Massachusetts Institute of Technology’s Center for Transportation and Logistics. Over a period of 4 months, participants were challenged to develop innovative machine learning-based methods to enhance classic optimization-based approaches to solve the travelling salesperson problem, by learning from historical routes executed by Amazon delivery drivers. The primary goal of the Amazon Last Mile Routing Research Challenge was to foster innovative applied research in route planning, building on recent advances in predictive modeling, and using a real-world problem and data. The dataset released for the research challenge includes route-, stop-, and package-level features for 9,184 historical routes performed by Amazon drivers in 2018 in five metropolitan areas in the United States. This real-world dataset excludes any personally identifiable information (all route and package identifiers have been randomly regenerated and related location data have been obfuscated to ensure anonymity). Although multiple synthetic benchmark datasets are available in the literature, the dataset of the 2021 Amazon Last Mile Routing Research Challenge is the first large and publicly available dataset to include instances based on real-world operational routing data. The dataset is fully described and formally introduced in the following Transportation Science article: https://pubsonline.informs.org/doi/10.1287/trsc.2022.1173","Documentation":"https://github.com/MIT-CAVE/rc-cli/blob/main/templates/data_structures.md","Contact":"lastmile-research-challenge@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"None","Collabs":{"ASDI":{"Tags":["infrastructure"]}},"Tags":["amazon.science","analytics","deep learning","geospatial","last mile","logistics","machine learning","optimization","routing","transportation","urban"],"License":"Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. The material for the Amazon Last Mile Routing Research Challenge is provided under a Creative Commons Attribution-NonCommercial 4.0 International Public License (the “License”). You may not use this material except in compliance with the License. You may obtain a copy of the License at: https://creativecommons.org/licenses/by-nc/4.0/legalcode.txt","Citation":"Merchan, Daniel; Arora, Jatin; Pachon, Julian; Konduri, Karthik; Winkenbach, Matthias; Parks, Steven; Noszek, Joseph (2022). Transportation Science.","Resources":[{"Description":"Dataset including training and testing data. Folder almrrc2021_data_training includes 6,112 historical routes used for model traning; folder almrrc2021_data_evaluation includes 3072 historical routes used for model evaluation. All routes correspond to year 2018. Please refer to the Tranportation Science article  (https://pubsonline.informs.org/doi/10.1287/trsc.2022.1173) for a comprehensive description of the dataset, and to this documentation page (https://github.com/MIT-CAVE/rc-cli/blob/main/templates/data_structures.md) for details on data structure and format.","ARN":"arn:aws:s3:::amazon-last-mile-challenges","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"AWS Last Mile Route Sequence Optimization","URL":"https://github.com/aws-samples/amazon-sagemaker-amazon-routing-challenge-sol","AuthorName":"Chen Wu, Yin Song, Verdi March, Eden Duthi"},{"Title":"Code repository used for the 2021 Amazon Routing Research Challenge (this repository is included for reference and documentation purposes only, you do not need to install it to access the data)","URL":"https://github.com/MIT-CAVE/rc-cli","AuthorName":"CAVE Lab, MIT Center for Transportation and Logistics","AuthorURL":"https://cave.mit.edu/"}],"Publications":[{"Title":"2021 Amazon Last Mile Routing Research Challenge: Data Set","URL":"https://pubsonline.informs.org/doi/pdf/10.1287/trsc.2022.1173","AuthorName":"Daniel Merchán, Jatin Arora, Julian Pachon, Karthik Konduri, Matthias Winkenbach, Steven Parks, Joseph Noszek"},{"Title":"Can language models be used for real-world urban-delivery route optimization?","URL":"https://www.cell.com/the-innovation/pdf/S2666-6758(23)00148-0.pdf","AuthorName":"Yang Liu, Fanyou Wu, Zhiyuan Liu, Kai Wang, Feiyue Wang, Xiaobo Qu"},{"Title":"Constrained Local Search for Last-Mile Routing","URL":"https://doi.org/10.1287/trsc.2022.1185","AuthorName":"William Cook, Stephan Held, Keld Helsgaun"},{"Title":"Crowdkeeping in Last-Mile Delivery","URL":"https://doi.org/10.1287/trsc.2022.0323","AuthorName":"Xin Wang , Okan Arslan , Erick Delage"},{"Title":"Does parking matter? The impact of parking time on last-mile delivery optimization","URL":"https://doi.org/10.1016/j.tre.2023.103391","AuthorName":"Sara Reed, Ann Melissa Campbell, Barrett W. Thomas"},{"Title":"Human-Centric Parcel Delivery at Deutsche Post with Operations Research and Machine Learning","URL":"https://doi.org/10.1287/inte.2023.0031","AuthorName":"Uğur Arikan , Thorsten Kranz, Baris Cem Sal, Severin Schmitt, Jonas Witt"},{"Title":"Integrating driver behavior into last-mile delivery routing - Combining machine learning and optimization in a hybrid decision support framework","URL":"https://doi.org/10.1016/j.ejor.2023.04.043","AuthorName":"Peter Dieter, Matthew Caron, Guido Schryen"},{"Title":"Inverse Optimization for Routing Problems","URL":"https://arxiv.org/abs/2307.07357","AuthorName":"Pedro Zattoni Scroccaro, Piet van Beek, Peyman Mohajerin Esfahani, Bilge Atasoy"},{"Title":"Learn global and optimize local A data-driven methodology for last-mile routing","URL":"https://doi.org/10.1016/j.cor.2023.106312","AuthorName":"Mayukh Ghosh, Alex Kuiper, Roshan Mahes, Donato Maragno"},{"Title":"Machine Learning for Data-Driven Last-Mile Delivery Optimization","URL":"https://doi.org/10.1287/trsc.2022.0029","AuthorName":"Sami Serkan Özarik, Paulo da Costa, Alexandre M. Florio"},{"Title":"Making opportunity sales in attended home delivery","URL":"https://doi.org/10.1016/j.cor.2023.106362","AuthorName":"Çelen Naz Ötken, Bariş Yildiz, Okan Arslan, Gilbert Laporte"},{"Title":"Planning robust drone-truck delivery routes under road traffic uncertainty","URL":"https://doi.org/10.1016/j.ejor.2023.02.031","AuthorName":"Yu Yang, Chiwei Yan, Yufeng Cao, Roberto Roberti"},{"Title":"Predicting drivers route trajectories in last-mile delivery using a pair-wise attention-based pointer neural network","URL":"https://doi.org/10.1016/j.tre.2023.103168","AuthorName":"Baichuan Mo, Qingyi Wang, Xiaotong Guo, Matthias Winkenbach, Jinhua Zhao"},{"Title":"Probability estimation and structured output prediction for learning preferences in last mile delivery","URL":"https://doi.org/10.1016/j.cie.2024.109932","AuthorName":"Rocsildes Canoy, Victor Bucarey, Jayanta Mandi, Maxime Mulamba, Yves Molenbruch, Tias Guns"},{"Title":"The Driver-Aide Problem Coordinated Logistics for Last-Mile Delivery","URL":"https://doi.org/10.1287/msom.2022.0211","AuthorName":"S. Raghavan , Rui Zhang"}]},"RegistryEntryAdded":"2022-05-27","RegistryEntryLastModified":"2025-07-09","Slug":"amazon-last-mile-challenges","Sources":["awslabs-open-data-registry"]}
{"Name":"Amazon-PQA","Description":"Amazon product questions and their answers, along with the public product information.","Documentation":"https://amazon-pqa.s3.amazonaws.com/readme.txt","Contact":"vitamin@amazon.com","ManagedBy":"Amazon","UpdateFrequency":"None","Tags":["amazon.science","machine learning","natural language processing"],"License":"https://cdla.dev/permissive-1-0/","Resources":[{"Description":"Amazon product questions and their answers, along with the public product information.","ARN":"arn:aws:s3:::amazon-pqa","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products","AuthorName":"Ohad Rozen, David Carmel, Avihai Mejer, Vitaly Mirkis, and Yftah Ziser","URL":"https://www.amazon.science/publications/answering-product-questions-by-utilizing-questions-from-other-contextually-similar-products"}]},"RegistryEntryAdded":"2021-05-14","RegistryEntryLastModified":"2022-06-17","Slug":"amazon-pqa","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"Amazon is no longer hosting the Multilingual Amazon Reviews Corpus and will continue to focus on serving customers and finding new ways to further scientific inquiry.","Name":"The Multilingual Amazon Reviews Corpus","Description":"We present a collection of Amazon reviews specifically designed to aid research in multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. 'books', 'appliances', etc.)","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/amazon-reviews-ml","Contact":"multilingual-reviews-dataset@amazon.com","ManagedBy":"Amazon","UpdateFrequency":"None specified.","Tags":["amazon.science","natural language processing","machine learning"],"License":"https://github.com/awslabs/open-data-docs/blob/main/docs/amazon-reviews-ml/license.txt","Resources":[{"Description":"A collection of Amazon reviews in English, Japanese, German, French, Spanish and Chinese.","ARN":"arn:aws:s3:::amazon-reviews-ml","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":null,"Tools & Applications":null,"Publications":[{"Title":"The Multilingual Amazon Reviews Corpus","URL":"https://arxiv.org/abs/2010.02573","AuthorName":"Phillip Keung, Yichao Lu, György Szarvas, Noah A. Smith"}]},"RegistryEntryAdded":"2020-05-27","RegistryEntryLastModified":"2024-03-27","Slug":"amazon-reviews-ml","Sources":["awslabs-open-data-registry"]}
{"Name":"Amazon Seller Contact Intent Sequence","Description":"When sellers need help from Amazon, such as how to create a listing, they often reach out to Amazon seller support through email, chat or phone. For each contact, we assign an intent so that we can manage the request more easily. The data we present in this release includes 548k contacts with 118 intents from 70k sellers sampled from recent years. There are 3 columns. 1. De-identified seller id - seller_id_anon; 2. Noisy inter-arrival time in the unit of hour between contacts - interarrival_time_hr_noisy; 3. An integer that represents the contact intent - contact_intent. Note that, to balance the need between data anonymization and usefulness, we randomly perturbed the interarrival time in an intricate way such that the temporal pattern are preserved and seller identity are anonymized to the largest extent. We also note that for each seller_id_anon, the interarrival_time_hr_noisy are already arranged in chonological order, the first contact_intent_id_anon is always the origin when sellers begin to sell with us and the interarrival_time_hr_noisy for each seller_id_anon are all relative with respect to the previous contact. A straightforward use case of the data is to predict the next timestamp and intent of a user given the user's history.","Documentation":"https://ascis.s3.us-west-2.amazonaws.com/README.md","Contact":"Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6riTWmh-cGw/aws-open-data) and use the `AWS Open Data` tag.","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"None","Tags":["amazon.science","Hawkes Process","machine learning","temporal point process"],"License":"https://cdla.dev/permissive-1-0/","Citation":"In addition, please cite the authors Li Dong, Matthew C. Spencer (2023).","Resources":[{"Description":"Amazon customer support contact intent sequence from sellers in an S3 bucket.","ARN":"arn:aws:s3:::ascis","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2023-06-21","RegistryEntryLastModified":"2023-06-21","Slug":"amazon-seller-contact-intent-sequence","Sources":["awslabs-open-data-registry"]}
{"Name":"Amazonia EO satellite on AWS","Description":"Imagery acquired\nby Amazonia-1 satellite.\nThe\nimage files are recorded and processed by Instituto Nacional de Pesquisas\nEspaciais (INPE) and are converted to Cloud Optimized Geotiff\nformat in order to optimize its use for cloud based applications.\nWFI Level 4 (Orthorectified) scenes are being\ningested daily starting from 08-29-2022, the complete\nLevel 4 archive will be ingested by the end of October 2022.\n","Documentation":"http://www.inpe.br/amazonia1","Contact":"https://lists.osgeo.org/mailman/listinfo/cbers-pds","ManagedBy":"[Frederico Liporace](https://github.com/fredliporace)","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","disaster response","earth observation","geospatial","imaging","satellite imagery","stac","sustainability"],"License":"https://creativecommons.org/licenses/by-sa/3.0/","Resources":[{"Description":"Amazonia 1 imagery (COG files, quicklooks, metadata)","ARN":"arn:aws:s3:::brazil-eosats","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://stac.scitekno.com.br/v100)","[stacindex](https://stacindex.org/catalogs/cbers)"]},{"Description":"STAC static catalog","ARN":"arn:aws:s3:::br-eo-stac-1-0-0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false},{"Description":"Notifications for new quicklooks","ARN":"arn:aws:sns:us-west-2:599544552497:NewAM1Quicklook","Region":"us-west-2","Type":"SNS Topic"},{"Description":"Topic that receives STAC V1.0.0 items as new scenes are ingested","ARN":"arn:aws:sns:us-west-2:769537946825:br-eo-stac-prod-stacitemtopic4BCE3141-Z8he7LYjqXFe","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Keeping a SpatioTemporal Asset Catalog (STAC) Up To Date with SNS/SQS","URL":"https://aws.amazon.com/blogs/publicsector/keeping-a-spatiotemporal-asset-catalog-stac-up-to-date-with-sns-sqs/","AuthorName":"Frederico Liporace","Services":["Amazon SNS","AWS Lambda","Amazon DynamoDB"]},{"Title":"The Evolution of ASDI's Data Infrastructure","URL":"https://developmentseed.org/blog/2023-10-20-asdi","AuthorName":"Sean Harkins","Services":["Amazon SNS","AWS Lambda","Amazon ECR","Amazon S3","Amazon Athena"]}],"Tools & Applications":[{"Title":"Amazonia 1 stactools package","URL":"https://github.com/stactools-packages/amazonia-1","AuthorName":"Frederico Liporace","AuthorURL":"https://github.com/fredliporace"},{"Title":"Amazonia 1 stactools-pipeline","URL":"https://github.com/developmentseed/stactools-pipelines/pull/33","AuthorName":"Frederico Liporace","AuthorURL":"https://github.com/fredliporace"},{"Title":"STAC V1.0.0 endpoint","URL":"https://stac.scitekno.com.br/v100","AuthorName":"Frederico Liporace","AuthorURL":"https://github.com/fredliporace"}]},"RegistryEntryAdded":"2022-08-29","RegistryEntryLastModified":"2025-02-27","Slug":"amazonia","Sources":["awslabs-open-data-registry"]}
{"Name":"Accelerating Medicines Partnership Alzheimer's Disease (AMP-AD) Diverse Cohorts Study","Description":"The Accelerating Medicines Partnership Alzheimer's Disease (AMP-AD) Diverse\nCohorts Study is a cross-consortium project generating harmonized datasets\nfrom diverse human cohorts. It provides post-mortem multi-omic data from more\nthan 850 individuals representing the spectrum of Alzheimer's disease\nphenotypes, with substantial representation of Black or African American and\nHispanic or Latino individuals. Data were generated by research teams at Rush\nUniversity, Emory University, Mayo Clinic, Columbia University, and the Icahn\nSchool of Medicine at Mount Sinai.\n","Documentation":"https://doi.org/10.7303/9618093","Contact":"https://www.synapse.org/Synapse:syn2580853/discussion/default","ManagedBy":"Sage Bionetworks","UpdateFrequency":"AD Knowledge Portal Data Releases are published monthly (https://news.adknowledgeportal.org/). Updates to specific studies are dependent on data contributor and ingress timelines.","Tags":["aws-pds","bioinformatics","biology","demographics","epigenomics","ethnicity","gene expression","genomic","genotyping","Homo sapiens","life sciences","medicine","neurobiology","neuroscience","protein","RNA-seq","single-cell transcriptomics","transcriptomics","whole genome sequencing"],"License":"https://adknowledgeportal.synapse.org/Explore/Studies/DetailsPage/StudyDetails?Study=syn51732482#Acknowledgement","Citation":"The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (NIH-NIA, U24AG061340).","Resources":[{"Description":"This study provides controlled-access post-mortem brain multi-omic data, including whole-genome sequencing, bulk RNA sequencing, proteomics, single-nucleus gene expression and chromatin accessibility data, and harmonized clinical and neuropathology metadata from diverse cohorts.","ARN":"arn:aws:s3:::amp-ad-diverse-cohorts.opendata.sagebase.org","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://adknowledgeportal.synapse.org/Data%20Access"}],"DataAtWork":{"Publications":[{"Title":"Bridging the gap: Multi-omics profiling of brain tissue in Alzheimer's disease and older controls in multi-ethnic populations","URL":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11485084/","AuthorName":"Reddy JS, Heath L, Vander Linden A, Allen M, Lopes KDP, Seifar F, et al."}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-09-25","RegistryEntryLastModified":"2026-09-25","Slug":"amp-ad-diverse-cohorts","Sources":["awslabs-open-data-registry"]}
{"Name":"Answer Reformulation","Description":"Original StackExchange answers and their voice-friendly Reformulation.","Documentation":"https://answer-reformulation-pds.s3-us-west-2.amazonaws.com/README.txt","Contact":"filicesf@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not currently being updated","Tags":["amazon.science","machine learning","natural language processing"],"License":"[cc-by-sa 4.0](https://creativecommons.org/licenses/by-sa/4.0/)","Resources":[{"Description":"Answer Reformulation Dataset","ARN":"arn:aws:s3:::answer-reformulation-pds","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Voice-based Reformulation of Community Answers","URL":"https://www2020.thewebconf.org/","AuthorName":"Simone Filice, Nachshon Cohen & David Carmel"}]},"RegistryEntryAdded":"2020-04-10","RegistryEntryLastModified":"2022-06-06","Slug":"answer-reformulation","Sources":["awslabs-open-data-registry"]}
{"Name":"NHGRI AnVIL Project","Description":"The NHGRI Analysis, Visualization, and Informatics Lab-space (AnVIL) Project (https://anvilproject.org/) is the National Human Genome Research Institute's cloud-based platform for genomic data sharing and analysis. AnVIL hosts widely used human genome reference datasets generated through NHGRI-funded research. AnVIL on Open Data on AWS provides public access to open-access datasets available through AnVIL. The project is a collaborative effort involving NHGRI, the Broad Institute, Johns Hopkins University, the University of California Santa Cruz, Vanderbilt University Medical Center, Brigham and Women's Hospital, the Carnegie Institution for Science, the City University of New York, the Fred Hutchinson Cancer Research Center, Harvard University, Oregon Health & Science University, Massachusetts General Hospital, Moffitt Cancer Center, Penn State University, and Washington University.","Documentation":"https://explore.anvilproject.org/datasets","Contact":"https://anvilproject.org/help","ManagedBy":"The AnVIL Project, and UC Santa Cruz Genomics Institute, University of California, Santa Cruz (UCSC)","UpdateFrequency":"Quarterly","Tags":["biology","gene expression","genome","genomic","Homo sapiens","life sciences"],"License":"https://anvilproject.org/faq/data-security","Citation":"Schatz MC, Philippakis AA, Afgan E, Banks E, Carey VJ, Carroll RJ, et al. [Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)](https://www.cell.com/cell-genomics/fulltext/S2666-979X(21)00106-3). Cell Genomics. 2022;2. doi:10.1016/j.xgen.2021.100085","Resources":[{"Description":"An S3 bucket containing all publicly accessible data files in the AnVIL Project. The bucket layout and access procedures are documented at https://github.com/DataBiosphere/azul/blob/develop/docs/mirror.rst and metadata can be viewed at https://explore.anvilproject.org/datasets or accessed programmatically at https://service.explore.anvilproject.org/","ARN":"arn:aws:s3:::anvilproject","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Data Browser UI](https://explore.anvilproject.org/datasets)","[Azul REST Web Service](https://service.explore.anvilproject.org/)"]},{"Description":"Notifications for new NHGRI AnVIL data","ARN":"arn:aws:sns:us-east-1:160936121715:anvilproject-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"A complete reference genome improves analysis of human genetic variation","URL":"https://doi.org/10.1126/science.abl3533","AuthorName":"Sergey Aganezov, Stephanie M. Yan, Daniela C. Soto, Melanie Kirsche, Samantha Zarate, Pavel Avdeyev, Dylan J. Taylor, Kishwar Shafin, Alaina Shumate, Chunlin Xiao, Justin Wagner, Jennifer McDaniel, Nathan D. Olson, Michael E. G. Sauria, Mitchell R. Vollger, Arang Rhie, Melissa Meredith, Skylar Martin, Joyce Lee, Sergey Koren, Jeffrey A. Rosenfeld, Benedict Paten, Ryan Layer, Chen-Shan Chin, Fritz J. Sedlazeck, Nancy F. Hansen, Danny E. Miller, Adam M. Phillippy, Karen H. Miga, Rajiv C. McCoy, Megan Y. Dennis, Justin M. Zook, Michael C. Schatz"},{"Title":"A draft human pangenome reference","URL":"https://www.nature.com/articles/s41586-023-05896-x","AuthorName":"Wen-Wei Liao, Mobin Asri, Jana Ebler, Daniel Doerr, Marina Haukness, Glenn Hickey, Shuangjia Lu, Julian K. Lucas, Jean Monlong, Haley J. Abel, Silvia Buonaiuto, Xian H. Chang, Haoyu Cheng, Justin Chu, Vincenza Colonna, Jordan M. Eizenga, Xiaowen Feng, Christian Fischer, Robert S. Fulton, Shilpa Garg, Cristian Groza, Andrea Guarracino, William T. Harvey, Simon Heumos, Kerstin Howe, Miten Jain, Tsung-Yu Lu, Charles Markello, Fergal J. Martin, Matthew W. Mitchell, Katherine M. Munson, Moses Njagi Mwaniki, Adam M. Novak, Hugh E. Olsen, Trevor Pesout, David Porubsky, Pjotr Prins, Jonas A. Sibbesen, Jouni Sirén, Chad Tomlinson, Flavia Villani, Mitchell R. Vollger, Lucinda L. Antonacci-Fulton, Gunjan Baid, Carl A. Baker, Anastasiya Belyaeva, Konstantinos Billis, Andrew Carroll, Pi-Chuan Chang, Sarah Cody, Daniel E. Cook, Robert M. Cook-Deegan, Omar E. Cornejo, Mark Diekhans, Peter Ebert, Susan Fairley, Olivier Fedrigo, Adam L. Felsenfeld, Giulio Formenti, Adam Frankish, Yan Gao, Nanibaa’ A. Garrison, Carlos Garcia Giron, Richard E. Green, Leanne Haggerty, Kendra Hoekzema, Thibaut Hourlier, Hanlee P. Ji, Eimear E. Kenny, Barbara A. Koenig, Alexey Kolesnikov, Jan O. Korbel, Jennifer Kordosky, Sergey Koren, HoJoon Lee, Alexandra P. Lewis, Hugo Magalhães, Santiago Marco-Sola, Pierre Marijon, Ann McCartney, Jennifer McDaniel, Jacquelyn Mountcastle, Maria Nattestad, Sergey Nurk, Nathan D. Olson, Alice B. Popejoy, Daniela Puiu, Mikko Rautiainen, Allison A. Regier, Arang Rhie, Samuel Sacco, Ashley D. Sanders, Valerie A. Schneider, Baergen I. Schultz, Kishwar Shafin, Michael W. Smith, Heidi J. Sofia, Ahmad N. Abou Tayoun, Françoise Thibaud-Nissen, Francesca Floriana Tricomi, Justin Wagner, Brian Walenz, Jonathan M. D. Wood, Aleksey V. Zimin, Guillaume Bourque, Mark J. P. Chaisson, Paul Flicek, Adam M. Phillippy, Justin M. Zook, Evan E. Eichler, David Haussler, Ting Wang, Erich D. Jarvis, Karen H. Miga, Erik Garrison, Tobias Marschall, Ira M. Hall, Heng Li, Benedict Paten"},{"Title":"Approaching complete genomes, transcriptomes and epi-omes with accurate long-read sequencing","URL":"https://doi.org/10.1038/s41592-022-01716-8","AuthorName":"Sam Kovaka, Shujun Ou, Katharine M. Jenike, Michael C. Schatz"},{"Title":"Beyond the Human Genome Project: The Age of Complete Human Genome Sequences and Pangenome References","URL":"https://doi.org/10.1146/annurev-genom-021623-081639","AuthorName":"Dylan J. Taylor, Jordan M. Eizenga, Qiuhui Li, Arun Das, Katharine M. Jenike, Eimear E. Kenny, Karen H. Miga, Jean Monlong, Rajiv C. McCoy, Benedict Paten, and Michael C. Schatz"},{"Title":"CNPI: Rapid Analyses of Human Copy Number Data","URL":"https://doi.org/10.1016/j.jmb.2025.169313","AuthorName":"Jack Ustanik, Tychele N. Turner"},{"Title":"Deciphering the impact of genomic variation on function","URL":"https://doi.org/10.1038/s41586-024-07510-0","AuthorName":"IGVF Consortium"},{"Title":"Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)","URL":"https://doi.org/10.1016/j.xgen.2021.100085","AuthorName":"Michael C. Schatz, Anthony A. Philippakis, Enis Afgan, Eric Banks, Vincent J. Carey, Robert J. Carroll, Alessandro Culotti, Kyle Ellrott, Jeremy Goecks, Robert L. Grossman, Ira M. Hall, Kasper D. Hansen, Jonathan Lawson, Jeffrey T. Leek, Anne O’Donnell Luria, Stephen Mosher, Martin Morgan, Anton Nekrutenko, Brian D. O’Connor, Kevin Osborn, Benedict Paten, Candace Patterson, Frederick J. Tan, Casey Overby Taylor, Jennifer Vessio, Levi Waldron, Ting Wang, Kristin Wuichet, AnVIL Team"},{"Title":"Jasmine and Iris: population-scale structural variant comparison and analysis","URL":"https://doi.org/10.1038/s41592-022-01753-3","AuthorName":"Melanie Kirsche, Gautam Prabhu, Rachel Sherman, Bohan Ni, Alexis Battle, Sergey Aganezov, Michael C. Schatz"},{"Title":"Scalable Nanopore sequencing of human genomes provides a comprehensive view of haplotype-resolved variation and methylation","URL":"https://doi.org/10.1038/s41592-023-01993-x","AuthorName":"Mikhail Kolmogorov, Kimberley J. Billingsley, Mira Mastoras, Melissa Meredith, Jean Monlong, Ryan Lorig-Roach, Mobin Asri, Pilar Alvarez Jerez, Laksh Malik, Ramita Dewan, Xylena Reed, Rylee M. Genner, Kensuke Daida, Sairam Behera, Kishwar Shafin, Trevor Pesout, Jeshuwin Prabakaran, Paolo Carnevali, Jianzhi Yang, Arang Rhie, Sonja W. Scholz, Bryan J. Traynor, Karen H. Miga, Miten Jain, Winston Timp, Adam M. Phillippy, Mark Chaisson, Fritz J. Sedlazeck, Cornelis Blauwendraat, Benedict Paten"},{"Title":"The complete sequence and comparative analysis of ape sex chromosomes","URL":"https://doi.org/10.1038/s41586-024-07473-2","AuthorName":"Kateryna D. Makova, Brandon D. Pickett, Robert S. Harris, Gabrielle A. Hartley, Monika Cechova, Karol Pal, Sergey Nurk, DongAhn Yoo, Qiuhui Li, Prajna Hebbar, Barbara C. McGrath, Francesca Antonacci, Margaux Aubel, Arjun Biddanda, Matthew Borchers, Erich Bornberg-Bauer, Gerard G. Bouffard, Shelise Y. Brooks, Lucia Carbone, Laura Carrel, Andrew Carroll, Pi-Chuan Chang, Chen-Shan Chin, Daniel E. Cook, Sarah J. C. Craig, Luciana de Gennaro, Mark Diekhans, Amalia Dutra, Gage H. Garcia, Patrick G. S. Grady, Richard E. Green, Diana Haddad, Pille Hallast, William T. Harvey, Glenn Hickey, David A. Hillis, Savannah J. Hoyt, Hyeonsoo Jeong, Kaivan Kamali, Sergei L. Kosakovsky Pond, Troy M. LaPolice, Charles Lee, Alexandra P. Lewis, Yong-Hwee E. Loh, Patrick Masterson, Kelly M. McGarvey, Rajiv C. McCoy, Paul Medvedev, Karen H. Miga, Katherine M. Munson, Evgenia Pak, Benedict Paten, Brendan J. Pinto, Tamara Potapova, Arang Rhie, Joana L. Rocha, Fedor Ryabov, Oliver A. Ryder, Samuel Sacco, Kishwar Shafin, Valery A. Shepelev, Viviane Slon, Steven J. Solar, Jessica M. Storer, Peter H. Sudmant, Sweetalana, Alex Sweeten, Michael G. Tassia, Françoise Thibaud-Nissen, Mario Ventura, Melissa A. Wilson, Alice C. Young, Huiqing Zeng, Xinru Zhang, Zachary A. Szpiech, Christian D. Huber, Jennifer L. Gerton, Soojin V. Yi, Michael C. Schatz, Ivan A. Alexandrov, Sergey Koren, Rachel J. O’Neill, Evan E. Eichler, Adam M. Phillippy"},{"Title":"The complete sequence of a human Y chromosome","URL":"https://doi.org/10.1038/s41586-023-06457-y","AuthorName":"Arang Rhie, Sergey Nurk, Monika Cechova, Savannah J. Hoyt, Dylan J. Taylor, Nicolas Altemose, Paul W. Hook, Sergey Koren, Mikko Rautiainen, Ivan A. Alexandrov, Jamie Allen, Mobin Asri, Andrey V. Bzikadze, Nae-Chyun Chen, Chen-Shan Chin, Mark Diekhans, Paul Flicek, Giulio Formenti, Arkarachai Fungtammasan, Carlos Garcia Giron, Erik Garrison, Ariel Gershman, Jennifer L. Gerton, Patrick G. S. Grady, Andrea Guarracino, Leanne Haggerty, Reza Halabian, Nancy F. Hansen, Robert Harris, Gabrielle A. Hartley, William T. Harvey, Marina Haukness, Jakob Heinz, Thibaut Hourlier, Robert M. Hubley, Sarah E. Hunt, Stephen Hwang, Miten Jain, Rupesh K. Kesharwani, Alexandra P. Lewis, Heng Li, Glennis A. Logsdon, Julian K. Lucas, Wojciech Makalowski, Christopher Markovic, Fergal J. Martin, Ann M. Mc Cartney, Rajiv C. McCoy, Jennifer McDaniel, Brandy M. McNulty, Paul Medvedev, Alla Mikheenko, Katherine M. Munson, Terence D. Murphy, Hugh E. Olsen, Nathan D. Olson, Luis F. Paulin, David Porubsky, Tamara Potapova, Fedor Ryabov, Steven L. Salzberg, Michael E. G. Sauria, Fritz J. Sedlazeck, Kishwar Shafin, Valery A. Shepelev, Alaina Shumate, Jessica M. Storer, Likhitha Surapaneni, Angela M. Taravella Oill, Françoise Thibaud-Nissen, Winston Timp, Marta Tomaszkiewicz, Mitchell R. Vollger, Brian P. Walenz, Allison C. Watwood, Matthias H. Weissensteiner, Aaron M. Wenger, Melissa A. Wilson, Samantha Zarate, Yiming Zhu, Justin M. Zook, Evan E. Eichler, Rachel J. O’Neill, Michael C. Schatz, Karen H. Miga, Kateryna D. Makova, Adam M. Phillippy"},{"Title":"The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update","URL":"https://doi.org/10.1093/nar/gkae410","AuthorName":"The Galaxy Community"},{"Title":"The Human Pangenome Project: a global resource to map genomic diversity","URL":"https://doi.org/10.1038/s41586-022-04601-8","AuthorName":"Ting Wang, Lucinda Antonacci-Fulton, Kerstin Howe, Heather A. Lawson, Julian K. Lucas, Adam M. Phillippy, Alice B. Popejoy, Mobin Asri, Caryn Carson, Mark J. P. Chaisson, Xian Chang, Robert Cook-Deegan, Adam L. Felsenfeld, Robert S. Fulton, Erik P. Garrison, Nanibaa’ A. Garrison, Tina A. Graves-Lindsay, Hanlee Ji, Eimear E. Kenny, Barbara A. Koenig, Daofeng Li, Tobias Marschall, Joshua F. McMichael, Adam M. Novak, Deepak Purushotham, Valerie A. Schneider, Baergen I. Schultz, Michael W. Smith, Heidi J. Sofia, Tsachy Weissman, Paul Flicek, Heng Li, Karen H. Miga, Benedict Paten, Erich D. Jarvis, Ira M. Hall, Evan E. Eichler, David Haussler, the Human Pangenome Reference Consortium"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-01-08","RegistryEntryLastModified":"2026-01-08","Slug":"anvilproject","Sources":["awslabs-open-data-registry"]}
{"Name":"Animal Tracking - Acoustic Telemetry - Quality controlled detections","Description":"Since 2007, the Integrated Marine Observing System’s Animal Tracking Facility (formerly known as the Australian Animal Tracking And Monitoring System (AATAMS)) has established a permanent array of acoustic receivers around Australia to detect the movements of tagged marine animals in coastal waters. Simultaneously, the Animal Tracking Facility developed a centralised national database (https://animaltracking.aodn.org.au/) to encourage collaborative research across the Australian research community and provide unprecedented opportunities to monitor broad-scale animal movements. The resulting dataset comprises observations of tagged animals in Australian waters collected by IMOS infrastructure as well as receivers operated by independent research projects and  organisations. This dataset constitutes a valuable resource facilitating meta-analysis of animal movement, distributions, and habitat use, and is important for relating species distribution shifts with environmental covariates.\n\nThis dataset comprises all available (2007 – ongoing) quality-controlled animal detections collected across the collaborative, continental IMOS network for a range of aquatic species (fish, sharks, rays, reptiles, and mammals). Here, raw animal detections collated via the IMOS Australian Animal Acoustic Telemetry Database have been quality-controlled as per Hoenner et al. (2018). This dataset is updated on a six-monthly basis.\n\nNote - There is a static snapshot of the database (up until 2017) (http://dx.doi.org/10.4225/69/5979810a7dd6f), and this has been documented in a Scientific Data Publication (Hoenner et al. 2018).","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/541d4f15-122a-443d-ab4e-2b5feb08d6a0","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["aws-pds","biology","life sciences","marine mammals","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - Animal Tracking Facility - Acoustic Tracking - Quality Controlled Detections (2007 - ongoing)","ARN":"arn:aws:s3:::aodn-cloud-optimised/animal_acoustic_tracking_delayed_qc.parquet","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - Animal Tracking Facility - Acoustic Tracking - Quality Controlled Detections (2007 - ongoing)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/animal_acoustic_tracking_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/animal_acoustic_tracking_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-06","RegistryEntryLastModified":"2026-06-05","Slug":"aodn_animal_acoustic_tracking_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Marine Animal - Satellite Relay Tagging -  Quality controlled profiles","Description":"CTD (Conductivity-Temperature_Depth)-Satellite Relay Data Loggers (CTD-SRDLs) are used to explore how marine animal behaviour relates to their oceanic environment. Loggers developed at the University of St Andrews Sea Mammal Research Unit transmit data in near real-time via the Argo satellite system. Data represented here was collected in the Southern Ocean, from elephant, fur and Weddell Seals. In 2024 data was added from flatback and olive ridley turtles, from a pilot study co-funded by the Royal Australian Navy in collaboration with the Australian Institute of Marine Science and Indigenous Ranger groups.\n\nData parameters measured by the instruments include time, conductivity (salinity), temperature, pressure and depth. The data represented by this record have been Qc'd and are the Australian subset of the MEOP-CTD database (MEOP: Marine Mammals Exploring the Oceans Pole to Pole), complemented with the most recent Southern Ocean deployment data. This Australian subset of the Southern Ocean database represents about one quarter of the entire MEOP-CTD database, which currently is about 52,000 profiles obtained from 275 CTD-SRDL tag deployments. The Australian dataset originated in 2004, and was initially collected by Mark Hindell's team based at the University of Tasmania, and in later years his data has formed part of the Animal Tracking Facility of Integrated Marine Observing System (IMOS).","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/95d6314c-cfc7-40ae-b439-85f14541db71","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["biology","chemical biology","chemistry","marine mammals","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of Satellite Relay Tagging Program - Southern Ocean - MEOP Quality Controlled CTD Profiles","ARN":"arn:aws:s3:::aodn-cloud-optimised/animal_ctd_satellite_relay_tagging_delayed_qc.parquet","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing Satellite Relay Tagging Program - Southern Ocean - MEOP Quality Controlled CTD Profiles","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/animal_ctd_satellite_relay_tagging_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/animal_ctd_satellite_relay_tagging_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"Integrated Marine Observing System (IMOS); Hindell, Mark [year-of-data-download], [Title], Antarctic Climate and Ecosystem Cooperative Research Centre (ACE CRC); Institute for Marine and Antarctic Studies (IMAS), University of Tasmania (UTAS), [data-access-URL], accessed [date-of-access].\" If data includes turtle data between January 2022 - June 2024, please cite as follows:\n\"Integrated Marine Observing System (IMOS); Hindell, Mark; Department of Defence [year-of-data-download],  [Title], Antarctic Climate and Ecosystem Cooperative Research Centre (ACE CRC); Institute for Marine and Antarctic Studies (IMAS), University of Tasmania (UTAS), [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-06","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_animal_ctd_satellite_relay_tagging_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","Contact":"info@aodn.org.au","DataAtWork":{"Tutorials":[{"AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb"},{"AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/model_sea_level_anomaly_gridded_realtime.ipynb","Title":"Accessing IMOS - OceanCurrent - Gridded sea level anomaly - Near real time","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/model_sea_level_anomaly_gridded_realtime.ipynb"}]},"Description":"Gridded (adjusted) sea level anomaly (GSLA), gridded sea level (GSL) and surface geostrophic velocity (UCUR,VCUR) for the Australasian region. GSLA is mapped using optimal interpolation of detided, de-meaned, inverse-barometer-adjusted altimeter and tidegauge estimates of sea level. GSL is GSLA plus an estimate of the departure of mean sea level from the geoid – mean sea level (over 18 years of model time) of Ocean Forecasting Australia Model version 3 (OFAM3). The geostrophic velocities are derived from GSLA and the mean surface velocity from OFAM3. \n\n\nThe altimeter data window for input to the Delayed Mode (DM) maps is symmetrical about the map date. The width of the window is dependent on the number of altimeters flying at the time and ranges from 30 days to 14 days. The altimeter data window for Near Real Time (NRT) maps is asymmetrical about the analysis date (-21 to 4 days). For both NRT and DM, altimeter data is weighted by the difference between the analysis date of the map and the time of each altimeter observation.\n\nReferences: http://imos.aodn.org.au/oceancurrent","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/0c9eb39c-9cbe-4c6a-8a10-5867087e703a","License":"http://creativecommons.org/licenses/by/4.0/","ManagedBy":"AODN","Name":"OceanCurrent - Gridded sea level anomaly - Near real time","Resources":[{"ARN":"arn:aws:s3:::aodn-cloud-optimised/model_sea_level_anomaly_gridded_realtime.zarr","Description":"Cloud Optimised AODN dataset of IMOS - OceanCurrent - Gridded sea level anomaly - Near real time","Region":"ap-southeast-2","Type":"S3 Bucket"}],"Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["ocean sea surface height","ocean velocity","oceans"],"UpdateFrequency":"As Needed","RegistryEntryAdded":"2024-08-07","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_model_sea_level_anomaly_gridded_realtime","Sources":["awslabs-open-data-registry"]}
{"Name":"National Mooring Network - CTD profiles","Description":"This collection includes conductivity-temperature-depth (CTD) profiles obtained at the National Reference Stations (NRS) as part of the water sampling program. The instruments used also measure dissolved oxygen, fluorescence, and turbidity. The collection also includes practical salinity, water density and artificial chlorophyll concentration, as computed from the measured properties. The data are processed in delayed mode, with automated quality control applied. The National Reference Station network is designed to provide baseline information, at timescales relevant to human response, that is required to understand how large-scale, long-term change and variability in the global ocean are affecting the ecosystems of Australia's coastal seas. The stations are operated by the Australian National Moorings Network (ANMN), a facility of the Integrated Marine Observing System (IMOS).","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/7b901002-b1dc-46c3-89f2-b4951cedca48","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["chemistry","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - Australian National Mooring Network (ANMN) - CTD Profiles","ARN":"arn:aws:s3:::aodn-cloud-optimised/mooring_ctd_delayed_qc.parquet","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - Australian National Mooring Network (ANMN) - CTD Profiles","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_ctd_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_ctd_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\" If using data from the Ningaloo (TAN100) mooring, please add to the citation - \"Department of Jobs, Tourism, Science and Innovation (DJTSI), Western Australian Government\". ","RegistryEntryAdded":"2024-08-07","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_mooring_ctd_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Moorings - Hourly time-series product","Description":"Integrated Marine Observing System (IMOS) have moorings across both it's National Mooring Network and Deep Water Moorings facilities. The National Mooring Network facility comprises a series of national reference stations and regional moorings designed to monitor particular oceanographic phenomena in Australian coastal ocean waters. The Deep Water Moorings facility (formerly known as the Australian Bluewater Observing System) provides the coordination of national efforts in the sustained observation of open ocean properties with particular emphasis on observations important to climate and carbon cycle studies, with selected moorings from its Deep Water Arrays sub-facility providing data to this collection.\nThis collection represents the hourly aggregated time-series product, which combines selected variables measured by all the instruments deployed at a site, binning the values into a fixed one-hour interval. Only good-quality measurements (according to the automated quality-control procedures applied by the National Mooring Network) are included. \nThe parameters are: temperature, salinity, dissolved oxygen, chlorophyll estimates, turbidity, down-welling photosynthetic photon flux (PAR), and current velocity, accompanied by depth and pressure when available. The observations were made using a range of temperature loggers, conductivity-temperature-depth (CTD) instruments, water-quality monitors (WQM), acoustic Doppler current profilers (ADCPs), and single-point current meters.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/efd8201c-1eca-412e-9ad2-0534e96cea14","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["chemistry","ocean velocity","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - Moorings - Hourly time-series product","ARN":"arn:aws:s3:::aodn-cloud-optimised/mooring_hourly_timeseries_delayed_qc.parquet","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - Moorings - Hourly time-series product","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_hourly_timeseries_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_hourly_timeseries_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\" If using data from the Ningaloo (TAN100) mooring, please add to the citation - \"Department of Jobs, Tourism, Science and Innovation (DJTSI), Western Australian Government\". ","RegistryEntryAdded":"2024-08-07","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_mooring_hourly_timeseries_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Altimetry calibration and validation","Description":"High precision satellite altimeter missions including TOPEX/Poseidon (T/P), Jason-1 and now OSTM/Jason-2, have contributed fundamental advances in our understanding of regional and global ocean circulation and its role in the Earth's climate and regional applications. These altimeter satellites essentially observe the height of the global oceans – as such, they have become the tool of choice for scientists to measure sea level rise – both at regional and global scales as well as giving information about ocean currents and large- and small-scale variability. The determination of changes in global mean sea level is of fundamental importance in understanding the response of the ocean to a continuing warming climate – both through thermal expansion of the ocean, melting of the major ice sheets of Greenland and Antarctica, and mountain glaciers, and redistribution of water over the continents and atmosphere. As with all scientific observations, it is vital that the measurement tool is operating within its specifications – calibration and validation is therefore an important component in this regard. \n\nThis IMOS sub-facility provides the sole southern hemisphere in situ calibration site to provide an ongoing calibration and validation data stream directly to the international (NASA and CNES sponsored) Ocean Surface Topography Science Team (OSTST). This contribution, importantly, spans multiple altimeter missions, thus enabling the assimilation of multi-mission satellite data to determine ongoing changes in sea level with flow-on effects to other uses of this data. The OSTST collectively oversees the operation and calibration of the precision altimetry missions, and ensures each of these missions is performing as accurately as possible in order to meet mission objectives. The IMOS supported calibration site in Australia is one of four primary in situ calibration/validation sites that contribute to the OSTST. The remaining sites include Harvest (USA), Corsica (France), and Gavdos (Greece). The Australian calibration site includes two comparison points where in situ data is compared against the altimeter – Bass Strait and Storm Bay. These two locations both lie on descending (N -> S) pass 088 of the satellite altimeter, and thus share similar satellite orbit characteristics. The use of these two sites allows detailed investigation into the accuracy of the altimeter over two different wave climates. The average significant wave height at Storm Bay is approximately double that observed at the comparatively sheltered Bass Strait location. One of the ongoing issues with satellite altimeter missions is the “Sea-state bias” correction which is related to wave shape and height. We plan to use the different wave climates at the two locations, coupled with the fact that some other things (such as orbit errors) are the same at both locations to improve the quality of this correction.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/78d588ed-79dd-47e2-b806-d39025194e7e","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["chemistry","ocean currents","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS Satellite Altimetry Calibration and Validation Sub-Facility","ARN":"arn:aws:s3:::aodn-cloud-optimised/mooring_satellite_altimetry_calibration_validation.parquet","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS Satellite Altimetry Calibration and Validation Sub-Facility","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_satellite_altimetry_calibration_validation.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_satellite_altimetry_calibration_validation.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-07","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_mooring_satellite_altimetry_calibration_validation","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - Bonney coast site - Sea water velocity - Delayed mode","Description":"The Bonney Coast (BONC) HF ocean radar system covers an area of the Bonney Coast, South Australia, which has a recurring annual upwelling feature near to the coast that significantly changes the ecosystem from one of warm water originating in Western Australia, to one dominated by cold upwelling water from off the continental shelf.  The dynamics of this area and the relationship between ocean circulation, chemistry and sediments control the larval species and the higher marine species and ecosystems in which they forage.  \n\nThe data from this site provide linking observations between the Southern Ocean and NSW through processes that occur on weekly to El Nino time scales.  \n\nThe BONC HF ocean radar system consists of two SeaSonde crossed loop direction finding stations located at Nora Creina (37.329 S 139.850 E) and Blackfellows Cave (37.940 S 140.457 E).  These radars operate at a frequency of 5.211 MHz, with a bandwidth of 50 KHz, a maximum range of 200 Km and a range resolution of 3 Km.  Within the HF radar coverage area surface currents are measured.\n\nThis site was decommissioned in March 2017.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/38dd003d-2f71-4715-bd3f-4b1cfdce391d","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["ocean currents","ocean velocity","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - Bonney Coast HF ocean radar site (South Australia, Australia) - Delayed mode sea water velocity","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_BonneyCoast_velocity_hourly_averaged_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - Bonney Coast HF ocean radar site (South Australia, Australia) - Delayed mode sea water velocity","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_BonneyCoast_velocity_hourly_averaged_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_BonneyCoast_velocity_hourly_averaged_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-06","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_bonneycoast_velocity_hourly_averaged_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - Capricorn bunker group site - Sea water velocity - Delayed mode","Description":"The Capricorn Bunker Group site is in the southern region  of the Great Barrier Reef Marine Park World Heritage Area (GBR).  The HF ocean radar coverage is from the coast to beyond the edge of the continental shelf.  This is an area where the East Australian Current (EAC) meanders as it moves south from the Swain Reefs and loses touch with the western land boundary.  The area is dynamic with warm EAC water recirculating and being wind-driven northwards along the coast inside the GBR lagoon.  The recirculating warm water contrasts with the upwelling tendency of the parts of the EAC which continue to move southwards.  The CBG radar is ideally placed to follow filaments of different water types and to investigate their impacts on coral reefs and fisheries.  \n\nApplications of the CBG radar data are in the frequency and location of regional upwelling that fuels productivity, and in the connectivity between reefs and islands.  At the central point of the CBG area of coverage is the  Heron Island Research Station which hosts many coral reef and ecology research programs.  The data will also be useful for validation of the operational Bluelink model, and the embedded higher resolution Whole-of-Reef model.   \n\nThe CBG HF ocean radar is a WERA phased array system with 12-element receive arrays located at Tannum Sands (-23.942 S, 151.371 E) and Lady Elliot Island (-24.111 S, 152.713 E).  These radars operate at a frequency of 8.348 MHz, with a bandwidth of 33 KHz and a maximum range of 200 Km and a range resolution of 4.5 Km.  Within the HF radar coverage area surface currents are measured.  Data are also collected from which wind directions and significant wave height can be calculated.  \n\nThe capital funding for this system was provided originally as an ARC LEIF Grant, assisted by contributions from James Cook University, The University of Queensland, the National Oceanic and Atmospheric Administration and the Australian Institute of Marine Science.\n\nThis site was decommissioned in April 2017.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/8a2d2824-0557-4110-a561-01ec35a9583d","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["ocean currents","ocean velocity","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - Capricorn Bunker Group HF ocean radar site (Great Barrier Reef, Queensland, Australia) - Delayed mode sea water velocity","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_CapricornBunkerGroup_velocity_hourly_averaged_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - Capricorn Bunker Group HF ocean radar site (Great Barrier Reef, Queensland, Australia) - Delayed mode sea water velocity","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_velocity_hourly_averaged_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_velocity_hourly_averaged_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-06","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_capricornbunkergroup_velocity_hourly_averaged_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - Capricorn bunker group site - Wave - Delayed mode","Description":"The Capricorn Bunker Group site is in the southern region  of the Great Barrier Reef Marine Park World Heritage Area (GBR).  The HF ocean radar coverage is from the coast to beyond the edge of the continental shelf.  This is an area where the East Australian Current (EAC) meanders as it moves south from the Swain Reefs and loses touch with the western land boundary.  The area is dynamic with warm EAC water recirculating and being wind-driven northwards along the coast inside the GBR lagoon.  The recirculating warm water contrasts with the upwelling tendency of the parts of the EAC which continue to move southwards.  The CBG radar is ideally placed to follow filaments of different water types and to investigate their impacts on coral reefs and fisheries.  \n\nApplications of the CBG radar data are in the frequency and location of regional upwelling that fuels productivity, and in the connectivity between reefs and islands.  At the central point of the CBG area of coverage is the  Heron Island Research Station which hosts many coral reef and ecology research programs.  The data will also be useful for validation of the operational Bluelink model, and the embedded higher resolution Whole-of-Reef model.   \n\nThe CBG HF ocean radar is a WERA phased array system with 12-element receive arrays located at Tannum Sands (-23.942 S, 151.371 E) and Lady Elliot Island (-24.111 S, 152.713 E).  These radars operate at a frequency of 8.348 MHz, with a bandwidth of 33 KHz and a maximum range of 200 Km and a range resolution of 4.5 Km.  Within the HF radar coverage area surface currents are measured.  Data are also collected from which wind directions and significant wave height can be calculated.  \n\nThe capital funding for this system was provided originally as an ARC LEIF Grant, assisted by contributions from James Cook University, The University of Queensland, the National Oceanic and Atmospheric Administration and the Australian Institute of Marine Science.\n\nThis site was decommissioned in April 2017.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/400a1237-af4d-45c6-a292-788cf0212522","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["ocean currents","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - Capricorn Bunker Group HF ocean radar site (Great Barrier Reef, Queensland, Australia) - Delayed mode wave","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_CapricornBunkerGroup_wave_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - Capricorn Bunker Group HF ocean radar site (Great Barrier Reef, Queensland, Australia) - Delayed mode wave","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_wave_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_wave_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_capricornbunkergroup_wave_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - Capricorn bunker group site - Wind - Delayed mode","Description":"The Capricorn Bunker Group site is in the southern region  of the Great Barrier Reef Marine Park World Heritage Area (GBR).  The HF ocean radar coverage is from the coast to beyond the edge of the continental shelf.  This is an area where the East Australian Current (EAC) meanders as it moves south from the Swain Reefs and loses touch with the western land boundary.  The area is dynamic with warm EAC water recirculating and being wind-driven northwards along the coast inside the GBR lagoon.  The recirculating warm water contrasts with the upwelling tendency of the parts of the EAC which continue to move southwards.  The CBG radar is ideally placed to follow filaments of different water types and to investigate their impacts on coral reefs and fisheries.  \n\nApplications of the CBG radar data are in the frequency and location of regional upwelling that fuels productivity, and in the connectivity between reefs and islands.  At the central point of the CBG area of coverage is the  Heron Island Research Station which hosts many coral reef and ecology research programs.  The data will also be useful for validation of the operational Bluelink model, and the embedded higher resolution Whole-of-Reef model.   \n\nThe CBG HF ocean radar is a WERA phased array system with 12-element receive arrays located at Tannum Sands (-23.942 S, 151.371 E) and Lady Elliot Island (-24.111 S, 152.713 E).  These radars operate at a frequency of 8.348 MHz, with a bandwidth of 33 KHz and a maximum range of 200 Km and a range resolution of 4.5 Km.  Within the HF radar coverage area surface currents are measured.  Data are also collected from which wind directions and significant wave height can be calculated.  \n\nThe capital funding for this system was provided originally as an ARC LEIF Grant, assisted by contributions from James Cook University, The University of Queensland, the National Oceanic and Atmospheric Administration and the Australian Institute of Marine Science.\n\nThis site was decommissioned in April 2017.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/742dc902-b300-4e3e-839f-04d03671aa09","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["meteorological","ocean currents","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - Capricorn Bunker Group HF ocean radar site (Great Barrier Reef, Queensland, Australia) - Delayed mode wind","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_CapricornBunkerGroup_wind_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - Capricorn Bunker Group HF ocean radar site (Great Barrier Reef, Queensland, Australia) - Delayed mode wind","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_wind_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_wind_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_capricornbunkergroup_wind_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - Coffs Harbour site - Sea water velocity - Delayed mode","Description":"The Coffs Harbour (COF) HF ocean radar site is located near the point at which the East Australian Current (EAC) begins to separate from the coast.  Here the EAC is at its narrowest and swiftest: to the north it is forming from the westwards subtropical jet, and to the south it forms eddies and eventually the warm water moves eastwards across the Tasman Sea, forming a front with the cold water of the Southern Ocean.  \n\nThe connection between coastal and continental shelf waters is fundamental to the understanding of the anthropogenic impact on the coastal ocean and the role of the ocean in mitigating urban marine water quality. The oceans off Eastern Australia and in particular in the region of New South Wales have a complex circulation structure, and are strongly connected with other regional seas and currents. \n\nThe East Australia Current (EAC) flows from tropical regions southward along the NSW continental slope, bringing tropical biota southward. These current structures provide strong physical and biological connectivity, allowing substantial transport and dispersion of biota between regions, and influencing the transport and upwelling of nutrients, which are fundamental to biological productivity.  \n\nThe COF HF ocean radar is a WERA phased array system with 16-element receive arrays located at Red Rock (-23.942 S, 151.371 E) to the north of Coffs Harbour and North Nambucca (-24.111 S, 152.713 E) to the south.  The area of focus is off Coffs Harbour where IMOS and the NSW government have mooring sites.  These radars operate at a frequency of 13.920 MHz, with a bandwidth of 100 KHz and a maximum range of 100 Km.  Within the HF radar coverage area surface currents are measured.  Data are also collected from which wind directions and significant wave height can be calculated.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/85da1645-2c63-45fa-97b5-4125165b999d","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["ocean currents","ocean velocity","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - Coffs Harbour HF ocean radar site (New South Wales, Australia) - Delayed mode sea water velocity","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_CoffsHarbour_velocity_hourly_averaged_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - Coffs Harbour HF ocean radar site (New South Wales, Australia) - Delayed mode sea water velocity","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_velocity_hourly_averaged_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_velocity_hourly_averaged_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-06","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_coffsharbour_velocity_hourly_averaged_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - Coffs Harbour site - Wave - Delayed mode","Description":"The Coffs Harbour (COF) HF ocean radar site is located near the point at which the East Australian Current (EAC) begins to separate from the coast.  Here the EAC is at its narrowest and swiftest: to the north it is forming from the westwards subtropical jet, and to the south it forms eddies and eventually the warm water moves eastwards across the Tasman Sea, forming a front with the cold water of the Southern Ocean.  \n\nThe connection between coastal and continental shelf waters is fundamental to the understanding of the anthropogenic impact on the coastal ocean and the role of the ocean in mitigating urban marine water quality. The oceans off Eastern Australia and in particular in the region of New South Wales have a complex circulation structure, and are strongly connected with other regional seas and currents. \n\nThe East Australia Current (EAC) flows from tropical regions southward along the NSW continental slope, bringing tropical biota southward. These current structures provide strong physical and biological connectivity, allowing substantial transport and dispersion of biota between regions, and influencing the transport and upwelling of nutrients, which are fundamental to biological productivity.  \n\nThe COF HF ocean radar is a WERA phased array system with 16-element receive arrays located at Red Rock (-23.942 S, 151.371 E) to the north of Coffs Harbour and North Nambucca (-24.111 S, 152.713 E) to the south.  The area of focus is off Coffs Harbour where IMOS and the NSW government have mooring sites.  These radars operate at a frequency of 13.920 MHz, with a bandwidth of 100 KHz and a maximum range of 100 Km.  Within the HF radar coverage area surface currents are measured.  Data are also collected from which wind directions and significant wave height can be calculated.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/e32e51d9-b0a5-4b95-9906-44e0c6c8d516","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["ocean currents","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - Coffs Harbour HF ocean radar site (New South Wales, Australia) - Delayed mode wave","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_CoffsHarbour_wave_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - Coffs Harbour HF ocean radar site (New South Wales, Australia) - Delayed mode wave","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_wave_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_wave_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_coffsharbour_wave_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - Coffs Harbour site - Wind - Delayed mode","Description":"The Coffs Harbour (COF) HF ocean radar site is located near the point at which the East Australian Current (EAC) begins to separate from the coast.  Here the EAC is at its narrowest and swiftest: to the north it is forming from the westwards subtropical jet, and to the south it forms eddies and eventually the warm water moves eastwards across the Tasman Sea, forming a front with the cold water of the Southern Ocean.  \n\nThe connection between coastal and continental shelf waters is fundamental to the understanding of the anthropogenic impact on the coastal ocean and the role of the ocean in mitigating urban marine water quality. The oceans off Eastern Australia and in particular in the region of New South Wales have a complex circulation structure, and are strongly connected with other regional seas and currents. \n\nThe East Australia Current (EAC) flows from tropical regions southward along the NSW continental slope, bringing tropical biota southward. These current structures provide strong physical and biological connectivity, allowing substantial transport and dispersion of biota between regions, and influencing the transport and upwelling of nutrients, which are fundamental to biological productivity.  \n\nThe COF HF ocean radar is a WERA phased array system with 16-element receive arrays located at Red Rock (-23.942 S, 151.371 E) to the north of Coffs Harbour and North Nambucca (-24.111 S, 152.713 E) to the south.  The area of focus is off Coffs Harbour where IMOS and the NSW government have mooring sites.  These radars operate at a frequency of 13.920 MHz, with a bandwidth of 100 KHz and a maximum range of 100 Km.  Within the HF radar coverage area surface currents are measured.  Data are also collected from which wind directions and significant wave height can be calculated.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/ffe8f19c-de4a-4362-89be-7605b2dd6b8c","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Tags":["meteorological","ocean currents","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - Coffs Harbour HF ocean radar site (New South Wales, Australia) - Delayed mode wind","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_CoffsHarbour_wind_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - Coffs Harbour HF ocean radar site (New South Wales, Australia) - Delayed mode wind","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_wind_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_wind_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_coffsharbour_wind_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - Coral coast site - Sea water velocity - Delayed mode","Description":"The Coral Coast (CORL) HF ocean radar system covers an area of the Western Australia Coast, Western Australia, an area subject to the variability of the Leeuwin Current (LC) and its coupling with coastal winds, tides, and waves. In this area the LC generates several eddies which control the larval species and the higher marine species and ecosystems in which they forage.\n\nThe CORL HF ocean radar system consists of two SeaSonde crossed loop direction finding stations located at Dongara (29.283 S 114.920E) and Green Head (114.967 E 30.073 S).  These radars operate at a frequency of 4.463 MHz, with a bandwidth of 25 KHz, a maximum range of 200 Km and a range resolution of 6 Km.  Within the HF radar coverage area surface currents are measured.\n\nThe CORL area of coverage has a small overlap of commonly observed ocean with the Turqoise Coast (TURQ) SeaSonde HF ocean radar system on its southern side, which in turns early overlaps with the Rottnest HF radar node.  Together, the CORL, TURQ and ROT systems provide continuous monitoring of the shelf from Fremantle to Geraldton.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/f7b36a1c-0936-4da6-b47f-94ed538b367e","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["ocean currents","ocean velocity","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - Coral Coast HF ocean radar site (Western Australia, Australia) - Delayed mode sea water velocity","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_CoralCoast_velocity_hourly_averaged_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - Coral Coast HF ocean radar site (Western Australia, Australia) - Delayed mode sea water velocity","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoralCoast_velocity_hourly_averaged_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoralCoast_velocity_hourly_averaged_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS, FRDC [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-06","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_coralcoast_velocity_hourly_averaged_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - Newcastle site - Sea water velocity - Delayed mode","Description":"The Newcastle (NEWC) HF ocean radar system covers an area of the Central Coast, New South Wales, an area subject to the variability of the East Australian Current (EAC) and its coupling with coastal winds, tides, and waves. In this area the EAC separates from the coast and generates several eddies which control the larval species and the higher marine species and ecosystems in which they forage.\n\nThe NEWC HF ocean radar system consists of two SeaSonde crossed loop direction finding stations located at Sea Rocks (32.441575 S 152.539022 E) and Red Head (33.010245 S 151.727059 E).  These radars operate at a frequency of 5.2625 MHz, with a bandwidth of 25 KHz, a maximum range of 200 Km and a range resolution of 6 Km.  Within the HF radar coverage area surface currents are measured.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/6dca1f8a-8337-4551-ac4b-a2d35ec6f333","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","ocean currents","ocean velocity","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - Newcastle HF ocean radar site (New South Wales, Australia) - Delayed mode sea water velocity","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_Newcastle_velocity_hourly_averaged_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - Newcastle HF ocean radar site (New South Wales, Australia) - Delayed mode sea water velocity","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_Newcastle_velocity_hourly_averaged_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_Newcastle_velocity_hourly_averaged_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS, SIMS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-06","RegistryEntryLastModified":"2025-09-04","Slug":"aodn_radar_newcastle_velocity_hourly_averaged_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - Northwest shelf site - Sea water velocity - Delayed mode","Description":"The Northwest Shelf (NWA) HF ocean radar system covers an area which includes the Ningaloo Peninsula and the Ningaloo Reef to the west.  The Ningaloo Reef is one of the longest and most pristine reefs in the world. The reef is rich in marine biodiversity, with shark whales, turtles and fish aggregations, and high primary and secondary productions which are controlled by the physical oceanographic processes.  \n\nThe NWA HF ocean radar is a WERA phased array system with 12-element receive arrays located at the Jurabi Turtle Centre (21.8068 S, 114.1015 E) and Point Billie (22.5432 S, 113.690 E).  These radars operate at a frequency of 5.2625 MHz, with a bandwidth of 25 KHz and a maximum range of 200 Km.  Within the HF radar coverage area surface currents are measured.  Data are also collected from which wind directions and significant wave height can be calculated.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/23c27e4f-c982-44e9-9ab7-71094d297549","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["ocean currents","ocean velocity","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - Northwest Shelf HF ocean radar site (Western Australia, Australia) - Delayed mode sea water velocity","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_NorthWestShelf_velocity_hourly_averaged_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - Northwest Shelf HF ocean radar site (Western Australia, Australia) - Delayed mode sea water velocity","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_NorthWestShelf_velocity_hourly_averaged_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_NorthWestShelf_velocity_hourly_averaged_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-06","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_northwestshelf_velocity_hourly_averaged_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - Rottnest shelf site - Sea water velocity - Delayed mode","Description":"The Rottnest Shelf (ROT) HF ocean radar system covers an area which includes Rottnest Island and the Perth Canyon to the north-west.  The Perth Canyon has the highest marine biodiversity in the region with whale and fish aggregations, and high primary and secondary productions which are controlled by the physical oceanographic processes.  \n\nCombined with the dynamics of the Perth Canyon is the dominant Leeuwin Current which produces a wake on the leeward side of Rottnest Island.   This is a topographically induced up-welling and associated primary and secondary productivity.  The region is influenced by Swan River outflow, submarine groundwater discharges , and waste-water outfalls.  \n\nThe ROT HF ocean radar is a WERA phased array system with 16-element receive arrays located at Fremantle (32.031 S, 115.746 E) and Guilderton (31.342 S, 115.489 E).  These radars operate at a frequency of 8.512 MHz, with a bandwidth of 33 KHz and a maximum range of 200 Km.  Within the HF radar coverage area surface currents are measured.  Data are also collected from which wind directions and significant wave height can be calculated.  \n\nThe ROT area of coverage has a small overlap of commonly observed ocean with the Turqoise Coast (TURQ) SeaSonde HF ocean radar system on its north side.  Together, the ROT and TURQ systems provide continuous monitoring of the shelf from Fremantle to Jurien Bay.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/028b9801-279f-427c-964b-0ffcdf310b59","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["ocean currents","ocean velocity","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - Rottnest Shelf HF ocean radar site (Western Australia, Australia) - Delayed mode sea water velocity","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_RottnestShelf_velocity_hourly_averaged_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - Rottnest Shelf HF ocean radar site (Western Australia, Australia) - Delayed mode sea water velocity","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_velocity_hourly_averaged_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_velocity_hourly_averaged_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-06","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_rottnestshelf_velocity_hourly_averaged_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - Rottnest shelf site - Wave - Delayed mode","Description":"The Rottnest Shelf (ROT) HF ocean radar system covers an area which includes Rottnest Island and the Perth Canyon to the north-west.  The Perth Canyon has the highest marine biodiversity in the region with whale and fish aggregations, and high primary and secondary productions which are controlled by the physical oceanographic processes.  \n\nCombined with the dynamics of the Perth Canyon is the dominant Leeuwin Current which produces a wake on the leeward side of Rottnest Island.   This is a topographically induced up-welling and associated primary and secondary productivity.  The region is influenced by Swan River outflow, submarine groundwater discharges , and waste-water outfalls.  \n\nThe ROT HF ocean radar is a WERA phased array system with 16-element receive arrays located at Fremantle (32.031 S, 115.746 E) and Guilderton (31.342 S, 115.489 E).  These radars operate at a frequency of 8.512 MHz, with a bandwidth of 33 KHz and a maximum range of 200 Km.  Within the HF radar coverage area surface currents are measured.  Data are also collected from which wind directions and significant wave height can be calculated.  \n\nThe ROT area of coverage has a small overlap of commonly observed ocean with the Turqoise Coast (TURQ) SeaSonde HF ocean radar system on its north side.  Together, the ROT and TURQ systems provide continuous monitoring of the shelf from Fremantle to Jurien Bay.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/9c6d6a0c-4983-4cb5-b119-02c11ce6af4e","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["ocean currents","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - Rottnest Shelf HF ocean radar site (Western Australia, Australia) - Delayed mode wave","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_RottnestShelf_wave_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - Rottnest Shelf HF ocean radar site (Western Australia, Australia) - Delayed mode wave","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_wave_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_wave_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_rottnestshelf_wave_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - Rottnest shelf site - Wind - Delayed mode","Description":"The Rottnest Shelf (ROT) HF ocean radar system covers an area which includes Rottnest Island and the Perth Canyon to the north-west.  The Perth Canyon has the highest marine biodiversity in the region with whale and fish aggregations, and high primary and secondary productions which are controlled by the physical oceanographic processes.  \n\nCombined with the dynamics of the Perth Canyon is the dominant Leeuwin Current which produces a wake on the leeward side of Rottnest Island.   This is a topographically induced up-welling and associated primary and secondary productivity.  The region is influenced by Swan River outflow, submarine groundwater discharges , and waste-water outfalls.  \n\nThe ROT HF ocean radar is a WERA phased array system with 16-element receive arrays located at Fremantle (32.031 S, 115.746 E) and Guilderton (31.342 S, 115.489 E).  These radars operate at a frequency of 8.512 MHz, with a bandwidth of 33 KHz and a maximum range of 200 Km.  Within the HF radar coverage area surface currents are measured.  Data are also collected from which wind directions and significant wave height can be calculated.  \n\nThe ROT area of coverage has a small overlap of commonly observed ocean with the Turqoise Coast (TURQ) SeaSonde HF ocean radar system on its north side.  Together, the ROT and TURQ systems provide continuous monitoring of the shelf from Fremantle to Jurien Bay.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/5ad9a6da-3f0b-4b2f-8212-48b386e1b98c","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["meteorological","ocean currents","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - Rottnest Shelf HF ocean radar site (Western Australia, Australia) - Delayed mode wind","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_RottnestShelf_wind_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - Rottnest Shelf HF ocean radar site (Western Australia, Australia) - Delayed mode wind","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_wind_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_wind_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_rottnestshelf_wind_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - South Australian gulfs site - Sea water velocity - Delayed mode","Description":"The South Australia Gulfs (SAG) HF ocean radar system covers the area of about 40,000 square kilometres bounded by Kangaroo Island to the east and the Eyre Peninsula to the north.  This is a dynamic region where warm water from the remnants of the Leeuwin current is moving from the west, and water with varying density is exchanging with Spencer Gulf and the Gulf of St Vincent.  Upwelling events occur from the deep ocean on the south side of the observation area.  This is a key ocean area for aquaculture and fishing, and is a major shipping thoroughfare.  The data from this HF ocean radar system link the dynamics of the Great Australian Bight to the Bonney Coast and on to Tasmania. \n\nSpecific research questions identified by the SA Node of IMOS are: What is the detailed connection between shelf currents and the Flinders Current to non-regional influences (e.g. Southern Ocean, WA)?  What is the nature of oceanic currents and processes (e.g. wave drift, mixing, eddies) that drive both winter and summer cross and along shelf exchange including the summertime upwelling systems and flushing of the gulfs?  How does the ocean circulation, wave drift, eddies and environmental variability affect the distribution of sediments, nutrients, the production of lower trophic levels\n(phytoplankton and zooplankton), the dispersal of larvae that underpin productive fisheries in the region and the effects of environment on aquaculture\n\nThe SAG HF ocean radar is a WERA phased array system with 16-element receive arrays located at Cape Wiles (34.943 S, 135.681 E) and Cape Spencer (35.294 S, 136.879 E).  These radars operate at a frequency of 8.512 MHz, with a bandwidth of 33 KHz and a maximum range of 200 Km.  Within the HF radar coverage area surface currents are measured.  Data are also collected from which wind directions and significant wave height can be calculated.  \n\nMost of the capital was contributed by the South Australia Research and Development Institute.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/cb2e22b5-ebb9-460b-8cff-b446fe14ea2f","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["ocean currents","ocean velocity","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - South Australia Gulfs HF ocean radar site (South Australia, Australia) - Delayed mode sea water velocity","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - South Australia Gulfs HF ocean radar site (South Australia, Australia) - Delayed mode sea water velocity","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_southaustraliagulfs_velocity_hourly_averaged_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - South Australian gulfs site - Wave - Delayed mode","Description":"The South Australia Gulfs (SAG) HF ocean radar system covers the area of about 40,000 square kilometres bounded by Kangaroo Island to the east and the Eyre Peninsula to the north.  This is a dynamic region where warm water from the remnants of the Leeuwin current is moving from the west, and water with varying density is exchanging with Spencer Gulf and the Gulf of St Vincent.  Upwelling events occur from the deep ocean on the south side of the observation area.  This is a key ocean area for aquaculture and fishing, and is a major shipping thoroughfare.  The data from this HF ocean radar system link the dynamics of the Great Australian Bight to the Bonney Coast and on to Tasmania. \n\nSpecific research questions identified by the SA Node of IMOS are: What is the detailed connection between shelf currents and the Flinders Current to non-regional influences (e.g. Southern Ocean, WA)?  What is the nature of oceanic currents and processes (e.g. wave drift, mixing, eddies) that drive both winter and summer cross and along shelf exchange including the summertime upwelling systems and flushing of the gulfs?  How does the ocean circulation, wave drift, eddies and environmental variability affect the distribution of sediments, nutrients, the production of lower trophic levels\n(phytoplankton and zooplankton), the dispersal of larvae that underpin productive fisheries in the region and the effects of environment on aquaculture\n\nThe SAG HF ocean radar is a WERA phased array system with 16-element receive arrays located at Cape Wiles (34.943 S, 135.681 E) and Cape Spencer (35.294 S, 136.879 E).  These radars operate at a frequency of 8.512 MHz, with a bandwidth of 33 KHz and a maximum range of 200 Km.  Within the HF radar coverage area surface currents are measured.  Data are also collected from which wind directions and significant wave height can be calculated.  \n\nMost of the capital was contributed by the South Australia Research and Development Institute.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/19da2ce7-138f-4427-89de-a50c724f5f54","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["ocean currents","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - South Australia Gulfs HF ocean radar site (South Australia, Australia) - Delayed mode wave","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_SouthAustraliaGulfs_wave_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - South Australia Gulfs HF ocean radar site (South Australia, Australia) - Delayed mode wave","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_wave_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_wave_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_southaustraliagulfs_wave_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - South Australian gulfs site - Wind - Delayed mode","Description":"The South Australia Gulfs (SAG) HF ocean radar system covers the area of about 40,000 square kilometres bounded by Kangaroo Island to the east and the Eyre Peninsula to the north.  This is a dynamic region where warm water from the remnants of the Leeuwin current is moving from the west, and water with varying density is exchanging with Spencer Gulf and the Gulf of St Vincent.  Upwelling events occur from the deep ocean on the south side of the observation area.  This is a key ocean area for aquaculture and fishing, and is a major shipping thoroughfare.  The data from this HF ocean radar system link the dynamics of the Great Australian Bight to the Bonney Coast and on to Tasmania. \n\nSpecific research questions identified by the SA Node of IMOS are: What is the detailed connection between shelf currents and the Flinders Current to non-regional influences (e.g. Southern Ocean, WA)?  What is the nature of oceanic currents and processes (e.g. wave drift, mixing, eddies) that drive both winter and summer cross and along shelf exchange including the summertime upwelling systems and flushing of the gulfs?  How does the ocean circulation, wave drift, eddies and environmental variability affect the distribution of sediments, nutrients, the production of lower trophic levels\n(phytoplankton and zooplankton), the dispersal of larvae that underpin productive fisheries in the region and the effects of environment on aquaculture\n\nThe SAG HF ocean radar is a WERA phased array system with 16-element receive arrays located at Cape Wiles (34.943 S, 135.681 E) and Cape Spencer (35.294 S, 136.879 E).  These radars operate at a frequency of 8.512 MHz, with a bandwidth of 33 KHz and a maximum range of 200 Km.  Within the HF radar coverage area surface currents are measured.  Data are also collected from which wind directions and significant wave height can be calculated.  \n\nMost of the capital was contributed by the South Australia Research and Development Institute.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/db049981-3d4e-4cb2-9c4b-e697650845b9","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Tags":["meteorological","ocean currents","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - South Australia Gulfs HF ocean radar site (South Australia, Australia) - Delayed mode wind","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_SouthAustraliaGulfs_wind_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - South Australia Gulfs HF ocean radar site (South Australia, Australia) - Delayed mode wind","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_wind_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_wind_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_southaustraliagulfs_wind_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Radar - Turquoise coast site - Sea water velocity - Delayed mode","Description":"The Turquoise Coast (TURQ) HF ocean radar system covers the area of shelf between Seabird and Jurien Bay and is the logical continuation of major research efforts to understand the role of the Leeuwin Current System (Leeuwin Current, the Leeuwin Undercurrent and Capes Current) in controlling not only the physical system but also its links to both pelagic and benthic ecosystems.  In contrast to eastern ocean basins, which are highly productive, Western Australia experiences an oligotrophic environment. The Leeuwin Current is a shallow (<300 m deep), narrow band (< 100 km wide) of warm, lower salinity, nutrient depleted water of tropical origin that flows poleward from Exmouth to Cape Leeuwin and into the Great Australian Bight.  The Current plays a dominant role in controlling the marine life and climate of the region.  \n\nQuestions which may be addressed by using the HF ocean radar data from TURQ (and ROT) include the variability of the Leeuwin current and its response to the ENSO cycle;  Leeuwin Current eddies and their interaction with the shelf waters; and the interaction between the Leeuwin Current, the Capes Current and coastal current during the summer.  This is an important region for Western Rock lobster recruitment, and the meanders of the warm Leeuwin Current influence the ecology.  This is a region with low tidal range and with a coastline subject to strong sea breezes and intense winter storms.  Coastally trapped waves may be generated by the winter weather systems and by tropical cyclones in the summer.  \n\nThe TURQ HF ocean radar system consists of two SeaSonde crossed loop direction finding stations located at Seabird (31.281 S 115.444 E) and Cervantes (30.506 S  115.060E). From 2012-12-15T11:00:00 the Cervantes station has been replaced by the Green Head station (30.073 S 114.967E) and from 2013-03-19T00:00:00 the Seabird station has been replaced by the Lancelin station (31.027 S 115.328 E). These radars operate at a frequency of 5.211 MHz, with a bandwidth of 50 KHz, a maximum range of 200 Km\nand a range resolution of 3 Km.  Within the HF radar coverage area surface currents are measured.\n\nThe TURQ area of coverage has a small overlap of commonly observed ocean with the Rottnest Shelf (ROT) WERA HF ocean radar system on its south side.  Together, the TURQ and ROT systems provide continuous monitoring of the shelf from Fremantle to Jurien Bay.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/055342fc-f970-4be7-a764-8903220d42fb","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["ocean currents","ocean velocity","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - ACORN - Turquoise Coast HF ocean radar site (Western Australia, Australia) - Delayed mode sea water velocity","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_TurquoiseCoast_velocity_hourly_averaged_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_TurquoiseCoast_velocity_hourly_averaged_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - ACORN - Turquoise Coast HF ocean radar site (Western Australia, Australia) - Delayed mode sea water velocity","ARN":"arn:aws:s3:::aodn-cloud-optimised/radar_TurquoiseCoast_velocity_hourly_averaged_delayed_qc.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2025-02-06","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_radar_turquoisecoast_velocity_hourly_averaged_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour -  MODIS - 1 day - Chlorophyll-a concentration (Carder model)","Description":"The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water.\n\nThere are multiple retrieval algorithms for estimating Chl-a. These data use the Carder method implemented in the SeaDAS processing software l2gen and described in Carder K. L., Chen F. R., Lee Z. P., Hawes S. K. and Cannizzaro J. P. (2003), MODIS Ocean Science Team Algorithm Theoretical Basis Document, ATBD 19, Case 2 Chlorophyll a, Version 7, URL: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod19.pdf (accessed 17 March 2022).","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/43ac4663-c8de-4eb0-9711-3da65cbecdd3","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - MODIS - 01 day - Chlorophyll-a concentration (Carder model)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_chlorophylla_carder_1day_aqua.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - MODIS - 01 day - Chlorophyll-a concentration (Carder model)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_carder_1day_aqua.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_carder_1day_aqua.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-12","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_chlorophylla_carder_1day_aqua","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour -  MODIS - 1 day - Chlorophyll-a concentration (GSM model)","Description":"The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. \n\nThere are multiple retrieval algorithms for estimating Chl-a. These data use the Garver-Siegel-Maritorena (GSM) method   implemented in the SeaDAS processing software l2gen and described in “Chapter 11, and references therein, of IOCCG Report 5, 2006, (http://ioccg.org/wp-content/uploads/2015/10/ioccg-report-05.pdf).","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/f73daf07-eb81-4995-a72a-ca903834509f","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - MODIS - 01 day - Chlorophyll-a concentration (GSM model)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_chlorophylla_gsm_1day_aqua.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - MODIS - 01 day - Chlorophyll-a concentration (GSM model)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_aqua.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_aqua.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-12","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_chlorophylla_gsm_1day_aqua","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour -  NOAA20 - 1 day - Chlorophyll-a concentration (GSM model)","Description":"The NOAA20 satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. \n\nThere are multiple retrieval algorithms for estimating Chl-a. These data use the Garver-Siegel-Maritorena (GSM) method implemented in the SeaDAS processing software l2gen and described in “Chapter 11, and references therein, of IOCCG Report 5, 2006, (http://ioccg.org/wp-content/uploads/2015/10/ioccg-report-05.pdf).","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/ad4d6908-3a36-4991-996a-5b00ac162794","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - Satellite Remote Sensing - NOAA20 - 01 day - Chlorophyll-a concentration (GSM model)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_chlorophylla_gsm_1day_noaa20.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - Satellite Remote Sensing - NOAA20 - 01 day - Chlorophyll-a concentration (GSM model)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_noaa20.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_noaa20.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_chlorophylla_gsm_1day_noaa20","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour -  SNPP - 1 day - Chlorophyll-a concentration (GSM model)","Description":"The SNPP satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. \n\nThere are multiple retrieval algorithms for estimating Chl-a. These data use the Garver-Siegel-Maritorena (GSM) method implemented in the SeaDAS processing software l2gen and described in “Chapter 11, and references therein, of IOCCG Report 5, 2006, (http://ioccg.org/wp-content/uploads/2015/10/ioccg-report-05.pdf).","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/6447f562-9063-4879-8598-ca0b5c4e0175","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - Satellite Remote Sensing - SNPP - 01 day - Chlorophyll-a concentration (GSM model)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_chlorophylla_gsm_1day_snpp.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - Satellite Remote Sensing - SNPP - 01 day - Chlorophyll-a concentration (GSM model)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_snpp.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_snpp.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_chlorophylla_gsm_1day_snpp","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour -  MODIS - 1 day - Chlorophyll-a concentration (OC3 model)","Description":"The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. \n\nThere are multiple retrieval algorithms for estimating Chl-a. These data use the OC3 method recommended by the NASA Ocean Biology Processing Group and implemented in the SeaDAS processing software l2gen. The OC3 algorithm is described at http://oceancolor.gsfc.nasa.gov/cms/atbd/chlor_a (and links therein).","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/d7a14921-8f3f-4522-9a54-e7d1df969c8a","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - MODIS - 01 day - Chlorophyll-a concentration (OC3 model)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_chlorophylla_oc3_1day_aqua.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - MODIS - 01 day - Chlorophyll-a concentration (OC3 model)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_aqua.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_aqua.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-12","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_chlorophylla_oc3_1day_aqua","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour -  NOAA20 - 1 day - Chlorophyll-a concentration (OC3 model)","Description":"The NOAA20 satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. \n\nThere are multiple retrieval algorithms for estimating Chl-a. These data use the OC3 method recommended by the NASA Ocean Biology Processing Group and implemented in the SeaDAS processing software l2gen. The OC3 algorithm is described at http://oceancolor.gsfc.nasa.gov/cms/atbd/chlor_a (and links therein).","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/77f46a04-f07d-4968-a5f9-1da8df9706a7","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - Satellite Remote Sensing - NOAA20 - 01 day - Chlorophyll-a concentration (OC3 model)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_chlorophylla_oc3_1day_noaa20.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - Satellite Remote Sensing - NOAA20 - 01 day - Chlorophyll-a concentration (OC3 model)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_noaa20.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_noaa20.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_chlorophylla_oc3_1day_noaa20","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour -  SNPP - 1 day - Chlorophyll-a concentration (OC3 model)","Description":"The SNPP satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. \n\nThere are multiple retrieval algorithms for estimating Chl-a. These data use the OC3 method recommended by the NASA Ocean Biology Processing Group and implemented in the SeaDAS processing software l2gen. The OC3 algorithm is described at http://oceancolor.gsfc.nasa.gov/cms/atbd/chlor_a (and links therein).","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/d222bd3f-b1a8-46f1-b462-90e44c5d9b44","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - Satellite Remote Sensing - SNPP - 01 day - Chlorophyll-a concentration (OC3 model)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_chlorophylla_oc3_1day_snpp.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - Satellite Remote Sensing - SNPP - 01 day - Chlorophyll-a concentration (OC3 model)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_snpp.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_snpp.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_chlorophylla_oc3_1day_snpp","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour -  MODIS - 1 day - Chlorophyll-a concentration (OCI model)","Description":"The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. \n\nThere are multiple retrieval algorithms for estimating Chl-a. These data use the OCI method (Hu et al 2012, doi: 10.1029/2011jc007395) recommended by the NASA Ocean Biology Processing Group and implemented in the SeaDAS processing software l2gen. The OCI algorithm is described at https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/  (and links therein).","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/24055e3a-94e5-40bb-b97f-7519f0482d6a","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - MODIS - 01 day - Chlorophyll-a concentration (OCI model)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_chlorophylla_oci_1day_aqua.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - MODIS - 01 day - Chlorophyll-a concentration (OCI model)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_aqua.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_aqua.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-12","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_chlorophylla_oci_1day_aqua","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour -  NOAA20 - 1 day - Chlorophyll-a concentration (OCI model)","Description":"The NOAA20 satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. \n\nThere are multiple retrieval algorithms for estimating Chl-a. These data use the OCI method (Hu et al 2012, doi: 10.1029/2011jc007395) recommended by the NASA Ocean Biology Processing Group and implemented in the SeaDAS processing software l2gen. The OCI algorithm is described at https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/ (and links therein).","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/a9e03db8-f277-45d1-821d-cfcbfe8ae6be","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - Satellite Remote Sensing - NOAA20 - 01 day - Chlorophyll-a concentration (OCI model)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_chlorophylla_oci_1day_noaa20.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - Satellite Remote Sensing - NOAA20 - 01 day - Chlorophyll-a concentration (OCI model)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_noaa20.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_noaa20.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_chlorophylla_oci_1day_noaa20","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour -  SNPP - 1 day - Chlorophyll-a concentration (OCI model)","Description":"The SNPP satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. \n\nThere are multiple retrieval algorithms for estimating Chl-a. These data use the OCI method (Hu et al 2012, doi: 10.1029/2011jc007395) recommended by the NASA Ocean Biology Processing Group and implemented in the SeaDAS processing software l2gen. The OCI algorithm is described at https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/ (and links therein).","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/d7f9eb3f-b81e-4421-8c75-761ffb33db5d","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - Satellite Remote Sensing - SNPP - 01 day - Chlorophyll-a concentration (OCI model)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_chlorophylla_oci_1day_snpp.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - Satellite Remote Sensing - SNPP - 01 day - Chlorophyll-a concentration (OCI model)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_snpp.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_snpp.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_chlorophylla_oci_1day_snpp","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour - MODIS - 1 day - Diffuse attenuation coefficient (k490)","Description":"The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the diffuse attenuation coefficient (Kd) at 490nm wavelength which provides information on how light is attenuated in the water column. It is defined as the scaling length of the exponential decrease of the downwelling irradiance and has units (m^-1). The MODIS K490 product estimates Kd at 490nm wavelength, using a semi-empirical model based on the ratio of water leaving radiances at 490nm and 555nm. The algorithm is described at https://oceancolor.gsfc.nasa.gov/atbd/kd/.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/a8632154-b8e5-493d-acd4-e458fae3ae26","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - MODIS - 01 day - Diffuse attenuation coefficient (k490 )","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_diffuse_attenuation_coefficent_1day_aqua.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - MODIS - 01 day - Diffuse attenuation coefficient (k490 )","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_aqua.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_aqua.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-12","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_diffuse_attenuation_coefficent_1day_aqua","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour - NOAA20 - 1 day - Diffuse attenuation coefficient (k490)","Description":"The NOAA20 satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the diffuse attenuation coefficient (Kd) at 490nm wavelength which provides information on how light is attenuated in the water column. It is defined as the scaling length of the exponential decrease of the downwelling irradiance, and has units (m^-1). The VIIRS K490 product estimates Kd at 490nm wavelength, using a semi-empirical model based on the ratio of water leaving radiances at 490nm and 555nm. The algorithm is described at https://oceancolor.gsfc.nasa.gov/atbd/kd/.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/37751f5b-3c93-40df-9913-951df310cbc8","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - Satellite Remote Sensing - NOAA20 - 01 day - Diffuse attenuation coefficient (k490)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_diffuse_attenuation_coefficent_1day_noaa20.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - Satellite Remote Sensing - NOAA20 - 01 day - Diffuse attenuation coefficient (k490)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_noaa20.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_noaa20.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_diffuse_attenuation_coefficent_1day_noaa20","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour - SNPP - 1 day - Diffuse attenuation coefficient (k490)","Description":"The SNPP satellite platform carries a VIIRS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the diffuse attenuation coefficient (Kd) at 490nm wavelength which provides information on how light is attenuated in the water column. It is defined as the scaling length of the exponential decrease of the downwelling irradiance, and has units (m^-1). The VIIRS K490 product estimates Kd at 490nm wavelength, using a semi-empirical model based on the ratio of water leaving radiances at 490nm and 555nm. The algorithm is described at https://oceancolor.gsfc.nasa.gov/atbd/kd/.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/697aa9e7-bf24-4f9e-9950-d5cfd78f1b35","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - Satellite Remote Sensing - SNPP - 01 day - Diffuse attenuation coefficient (k490)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_diffuse_attenuation_coefficent_1day_snpp.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - Satellite Remote Sensing - SNPP - 01 day - Diffuse attenuation coefficient (k490)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_snpp.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_snpp.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-04-29","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_diffuse_attenuation_coefficent_1day_snpp","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Sea surface temperature - Level 3 - Single sensor - Himawari-8 - 1 day - Night time","Description":"This is a regional GHRSST level 3 collated (L3C) dataset on 0.02-degree rectangular grid over the Australasian domain (70E to 190E, 70S to 20N) based on retrievals from the AHI imager on board Himawari-8 satellite. The Bureau of Meteorology (Bureau) produces Integrated Marine Observing System (IMOS) satellite SST products in the International Group for High Resolution SST (GHRSST) GDS2 file formats for Himawari-8 in real time and delayed mode. This product is composed of reprocessed multi-swath SSTskin retrievals obtained from compositing IMOS Himawari-8 hourly L3C files over the night (before dawn).  \nEvery 10 minutes, the Himawari-8 full disk is processed to retrieve SSTs by using the Radiative Transfer Model (RTTOV12.3) and Bayesian cloud clearing method based on the ESA CCI SST code developed at the University of Reading. The hourly product on native grid is then produced by compositing over multiple swaths for the previous 1 hour. Further, L3C-01day SST fields on native grid are obtained by selecting the highest quality data from the hourly SST records with priority being given to the value the closest in time to the product nominal hour.  The L3C-1day night product on native grid is then remapped over the 0.02-degree IMOS grid to compose IMOS L3C-1day night product (Govekar et al., 2021,  https://www.foo.org.au/wp-content/uploads/2021/11/Govekar_FOO_2021.pdf).   The product format is compliant with the GHRSST Data Specification (GDS) version 2.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/72b65fb8-84e1-4a56-b32c-7f15970903d2","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - SST - L3C - Himawari-8 - 1 day - night time - Australia","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_ghrsst_l3c_1day_nighttime_himawari8.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - SST - L3C - Himawari-8 - 1 day - night time - Australia","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3c_1day_nighttime_himawari8.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3c_1day_nighttime_himawari8.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-12","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_ghrsst_l3c_1day_nighttime_himawari8","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Sea surface temperature - Level 3 - Multi sensor - 1 day - Day and night time","Description":"This is a multi-sensor SSTfnd L3S product for a single 24 hour period, derived using sea surface temperature retrievals from the VIIRS sensor on the Suomi-NPP satellite and JPSS series of satellites, and AVHRR sensor on the NOAA and Metop series of Polar-orbiting satellites. The sensors and satellite platforms contributing to each file are listed in the sensor and platform global attributes in the file header. The SSTfnd is derived by adding a constant 0.17 degC to the NOAA AVHRR SSTskin observations, and 0 degC to the Metop and VIIRS SSTsubskin observations, after rejecting observations with low surface wind speeds (<6 m/s by day and <2 m/s at night). The Multi-sensor L3S product is provided as a 0.02deg x 0.02deg cylindrical equidistant projected map over the region 70E to 170W, 20N to 70S. The quality level for each pixel was remapped using the original ACSPO VIIRS and AVHRR L3U quality levels and Sensor Specific Error Statistics (SSES), as described in Griffin et al. (2017) Appendix at http://imos.org.au/facilities/srs/sstproducts/sstdata0/sstdata-references/, before compositing single swaths from the sensors. Each grid cell contains the 24 hour average of all the highest available quality SSTs that overlap with that cell, weighted by the area of overlap. Refer to the IMOS SST products web page at http://imos.org.au/sstproducts.html for further information.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/d7f3178d-869a-4eac-959d-71d1f5e24888","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - SST - L3S - Multi Sensor - 1 day - day and night time - Australia","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_ghrsst_l3s_1day_daynighttime_multi_sensor_australia.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - SST - L3S - Multi Sensor - 1 day - day and night time - Australia","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_multi_sensor_australia.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_multi_sensor_australia.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-26","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_ghrsst_l3s_1day_daynighttime_multi_sensor_australia","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Sea surface temperature - Level 3 - Single sensor - 1 day - Day and night time","Description":"This is a single-sensor multi-satellite  SSTfnd product for a single 24 hour period, derived using observations from AVHRR instruments on all available NOAA polar-orbiting satellites.  It is provided as a 0.02deg x 0.02deg cylindrical equidistant projected map over the region 70°E to 170°W, 20°N to 70°S.  Each grid cell contains the 24 hour average of all the highest available quality SSTs that overlap with that cell, weighted by the area of overlap.  The diagram at https://help.aodn.org.au/satellite-data-product-information/ indicates where this product fits within the GHRSST suite of NOAA/AVHRR products. The SSTfnd is derived by adding a constant 0.17 degC to the SSTskin observations after rejecting observations with low surface wind speeds (<6m/s by day and <2m/s at night) (see http://www.bom.gov.au/amoj/docs/2011/beggs.pdf). Matchups with buoy SSTfnd observations indicate typical 2014 biases of < 0.01 degC and standard deviations of 0.6 degC. Refer to the IMOS SST products web page at http://imos.org.au/sstproducts.html and Beggs et al. (2013) at http://imos.org.au/sstdata_references.html for further information.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/a136eee7-a990-4c06-a4f6-915657a2464e","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Tags":["oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - SST - L3S - Single Sensor - 1 day - day and night time - Australia","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_australia.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - SST - L3S - Single Sensor - 1 day - day and night time - Australia","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_australia.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_australia.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2024-08-07","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_ghrsst_l3s_1day_daynighttime_single_sensor_australia","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Sea surface temperature - Level 3 - Single sensor - 1 day - Day and night time - Southern Ocean","Description":"This is a single-sensor SSTfnd product for a single 24 hour period, derived using observations from AVHRR instruments on all available NOAA polar-orbiting satellites.  It is provided as a 0.02deg x 0.02deg cylindrical equidistant projected map over the Southern Ocean region 3°E to 158°W, 27°S to 78°S.  Each grid cell contains the 24 hour average of all the highest available quality SSTs that overlap with that cell, weighted by the area of overlap.  The diagram at https://help.aodn.org.au/satellite-data-product-information/ indicates where this product fits within the GHRSST suite of NOAA/AVHRR products. The SSTfnd is derived by adding a constant 0.17 degC to the SSTskin observations after rejecting observations with low surface wind speeds (<6m/s by day and <2m/s at night) (see http://www.bom.gov.au/amoj/docs/2011/beggs.pdf). Matchups with buoy SSTfnd observations indicate typical 2014 biases of < 0.01 degC and standard deviations of 0.6 degC. Refer to the IMOS SST products web page at http://imos.org.au/sstproducts.html and Beggs et al. (2013) at http://imos.org.au/sstdata_references.html for further information.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/165a23d7-5ef3-4cab-9e02-90c8adb941dd","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - SST - L3S - Single Sensor - 1 day - day and night time - Southern Ocean","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_southernocean.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - SST - L3S - Single Sensor - 1 day - day and night time - Southern Ocean","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_southernocean.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_southernocean.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-07","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_ghrsst_l3s_1day_daynighttime_single_sensor_southernocean","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Sea surface temperature - Level 3 - Single sensor - 1 month - Day time","Description":"This is a single-sensor multi-satellite SSTskin product for 1 month of consecutive day-time periods, derived using observations from AVHRR instruments on all available NOAA polar-orbiting satellites.  It is provided as a 0.02deg x 0.02deg cylindrical equidistant projected map over the region 70°E to 170°W, 20°N to 70°S.  Each grid cell contains the 1 month average of all the highest available quality SSTs that overlap with that cell, weighted by the area of overlap. The diagram at https://help.aodn.org.au/satellite-data-product-information/ indicates where this product fits within the GHRSST suite of NOAA/AVHRR products.  Matchups with buoy SST observations for the central date (adjusted to skin depths) indicate typical 2014 biases of < 0.3 degC and standard deviations of 0.7 degC.  Refer to the IMOS SST products web page at http://imos.org.au/sstproducts.html and Beggs et al. (2013) at http://imos.org.au/sstdata_references.html for further information.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/b8e9f613-9611-4692-a958-07f100d2c763","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - SST - L3S - Single Sensor - 1 month - day time - Australia","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_ghrsst_l3s_1month_daytime_single_sensor_australia.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - SST - L3S - Single Sensor - 1 month - day time - Australia","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1month_daytime_single_sensor_australia.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1month_daytime_single_sensor_australia.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-26","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_ghrsst_l3s_1month_daytime_single_sensor_australia","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Sea surface temperature - Level 3 - Multi sensor - 3 day - Day and night time","Description":"This is a multi-sensor SSTfnd L3S product for a single 72 hour period, derived using sea surface temperature retrievals from the VIIRS sensor on the Suomi-NPP satellite and JPSS series of satellites, and AVHRR sensor on the NOAA and Metop series of Polar-orbiting satellites. The sensors and satellite platforms contributing to each file are listed in the sensor and platform global attributes in the file header. The SSTfnd is derived by adding a constant 0.17 degC to the NOAA AVHRR SSTskin observations, and 0 degC to the Metop and VIIRS SSTsubskin observations, after rejecting observations with low surface wind speeds (<6 m/s by day and <2 m/s at night). The Multi-sensor L3S product is provided as a 0.02deg x 0.02deg cylindrical equidistant projected map over the region 70E to 170W, 20N to 70S. The quality level for each pixel was remapped using the original ACSPO VIIRS and AVHRR L3U quality levels and Sensor Specific Error Statistics (SSES), as described in Griffin et al. (2017) Appendix at http://imos.org.au/facilities/srs/sstproducts/sstdata0/sstdata-references/, before compositing single swaths from the sensors. Each grid cell contains the 72 hour average of all the highest available quality SSTs that overlap with that cell, weighted by the area of overlap. Refer to the IMOS SST products web page at http://imos.org.au/sstproducts.html for further information.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/4cbb9eeb-9ed3-4079-a180-2293a2c219b9","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - SST - L3S - Multi Sensor - 3 day - day and night time - Australia","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_ghrsst_l3s_3day_daynighttime_multi_sensor_australia.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - SST - L3S - Multi Sensor - 3 day - day and night time - Australia","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_3day_daynighttime_multi_sensor_australia.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_3day_daynighttime_multi_sensor_australia.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-26","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_ghrsst_l3s_3day_daynighttime_multi_sensor_australia","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Sea surface temperature - Level 3 - Single sensor - 6 day - Day and night time","Description":"This is a single-sensor multi-satellite SSTfnd product for a 144 hour period, derived using observations from AVHRR instruments on all available NOAA polar-orbiting satellites.  It is provided as a 0.02deg x 0.02deg cylindrical equidistant projected map over the region 70°E to 170°W, 20°N to 70°S. Each grid cell contains the 144 hour average of all the highest available quality SSTs that overlap with that cell, weighted by the area of overlap.  The diagram at https://help.aodn.org.au/satellite-data-product-information/ indicates where this product fits within the GHRSST suite of NOAA/AVHRR products. The SSTfnd is derived by adding a constant 0.17 degC to the SSTskin observations after rejecting observations with low surface wind speeds (<6m/s by day and <2m/s at night) (see http://www.bom.gov.au/amoj/docs/2011/beggs.pdf). Matchups with buoy SSTfnd observations for the central date indicate typical 2014 biases of < 0.03 degC and standard deviations of 0.6 degC.  Refer to the IMOS SST products web page at http://imos.org.au/sstproducts.html and Beggs et al. (2013) at http://imos.org.au/sstdata_references.html for further information.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/023ae12a-8c0c-4abc-997a-7884f9fec9cd","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - SST - L3S - Single Sensor - 6 day - day and night time - Australia","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_ghrsst_l3s_6day_daynighttime_single_sensor_australia.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - SST - L3S - Single Sensor - 6 day - day and night time - Australia","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_6day_daynighttime_single_sensor_australia.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_6day_daynighttime_single_sensor_australia.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-26","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_ghrsst_l3s_6day_daynighttime_single_sensor_australia","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Sea surface temperature - Level 4 - Multi sensor - Global Australian","Description":"An International Group for High-Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology using optimal interpolation (OI) on a global 0.25 degree grid.  This Global Australian Multi-Sensor SST Analysis (GAMSSA) v1.0 system blends infra-red SST observations from the Advanced Very High Resolution Radiometer (AVHRR) on NOAA and METOP polar-orbiting satellites, microwave SST observations from the Advanced Microwave Scanning Radiometer-2 (AMSR-2) on GCOM-W, and in situ data from ships, and drifting and moored buoys from the Global Telecommunications System (GTS).  \n\nAll SST observations are filtered for those values suspected to be affected by diurnal warming by excluding cases which have experienced recent surface wind speeds of below 6 m/s during the day and less than 2 m/s during the night, thereby resulting in daily foundation SST estimates that are largely free of diurnal warming effects.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/2d496463-600c-465a-84a1-8a4ab76bd505","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - SST - L4 - GAMSSA - World","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_ghrsst_l4_gamssa_1day_multi_sensor_world.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - SST - L4 - GAMSSA - World","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l4_gamssa_1day_multi_sensor_world.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l4_gamssa_1day_multi_sensor_world.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-06","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_ghrsst_l4_gamssa_1day_multi_sensor_world","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Sea surface temperature - Level 4 - Multi sensor - Regional Australian","Description":"An International Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology using optimal interpolation (OI) on a regional 1/12 degree grid over the Australian region (20N - 70S, 60E - 170W). This Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system blends infra-red SST observations from the Advanced Very High Resolution Radiometer (AVHRR) on NOAA and METOP polar-orbiting satellites, microwave SST observations from the Advanced Microwave Scanning Radiometer-2 (AMSR-2) on GCOM-W, and in situ data from ships, and drifting and moored buoys from the Global Telecommunications System (GTS).  \n\nAll SST observations are filtered for those values suspected to be affected by diurnal warming by excluding cases which have experienced recent surface wind speeds of below 6 m/s during the day and less than 2 m/s during the night, thereby resulting in daily foundation SST estimates that are largely free of diurnal warming effects.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/a4170ca8-0942-4d13-bdb8-ad4718ce14bb","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - SST - L4 - RAMSSA - Australia","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_ghrsst_l4_ramssa_1day_multi_sensor_australia.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - SST - L4 - RAMSSA - Australia","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l4_ramssa_1day_multi_sensor_australia.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l4_ramssa_1day_multi_sensor_australia.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-06","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_ghrsst_l4_ramssa_1day_multi_sensor_australia","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour - MODIS - 1 day - Nanoplankton fraction (OC3 model and Brewin et al 2012 algorithm)","Description":"The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. An empirical relationship is then used to compute an estimate of the relative abundance of three phytoplankton size classes (micro, nano and picoplankton). \n\nThe methods used to decompose chl_oc3 are described by Brewin et al in two papers in 2010 and 2012. The two methods, denoted Brewin2010at and Brewin2012in, used calibration data from the Atlantic and Indian Oceans respectively.  Users should note that these are unvalidated experimental products and they should familiarise themselves with the underlying algorithms and methodologies described in the published literature before making use of them.\n\nThe data are produced from the same data stream as the MODIS Chla_oc3 data set (https://catalogue-imos.aodn.org.au:443/geonetwork/srv/api/records/d7a14921-8f3f-4522-9a54-e7d1df969c8a). \n\nData are provided as two files per day, one with the percentage of nanoplankton, and one for picoplankton. The percentage of microplankton is computed as the balance to 100%, that is %micro = 100 - %nano - %pico.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/9fde091c-9d38-4b2f-ba23-c262c21a41a2","Contact":"FILL UP MANUALLY - CHECK DOCUMENTATION","ManagedBy":"FILL UP MANUALLY - CHECK DOCUMENTATION","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - MODIS - 01 day - Nanoplankton fraction (OC3 model and Brewin et al 2012 algorithm)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_nanoplankton_fraction_oc3_1day_aqua.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - MODIS - 01 day - Nanoplankton fraction (OC3 model and Brewin et al 2012 algorithm)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_nanoplankton_fraction_oc3_1day_aqua.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_nanoplankton_fraction_oc3_1day_aqua.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-12","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_nanoplankton_fraction_oc3_1day_aqua","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour - MODIS - 1 day - Net Primary Productivity (GSM model and Eppley-VGPM algorithm)","Description":"The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. Modelling is then used to compute an estimate of the Net Primary Productivity (NPP).\n\nThe model used is based on the standard vertically generalised production model (VGPM).  The VGPM is a \"chlorophyll-based\" model that estimates net primary production from chlorophyll using a temperature-dependent description of chlorophyll-specific        photosynthetic efficiency. For the VGPM, net primary production is a function of chlorophyll, available light, and the photosynthetic efficiency. The only difference between the Standard VGPM and the Eppley-VGPM is the temperature-dependent description of photosynthetic efficiencies, with the Eppley approach using an exponential function to account for variation in photosynthetic efficiencies due to photoacclimation. The similarity between these models (VGPM vs E_VGPM) is described more extensively in a paper by Elena-Carr et al (2006.). VGPM is a Depth Integrated Model(DIM) described by Behrenfeld and Falkowski (1997), with modification due to Eppley (1972) as implemented by Antoine and Morel (1996).\n\nThere are multiple retrieval algorithms for estimating Chl-a. These data (eppley_npp_chl_gsm) use the Garver-Siegel-Maritorena (GSM) method implemented in the SeaDAS processing software l2gen and described in “Chapter 11, and references therein, of IOCCG Report 5, 2006, (http://ioccg.org/wp-content/uploads/2015/10/ioccg-report-05.pdf). This product is recommended for use in the VGPM model. The product Eppley_NPP_chl_gsm is experimental, in the sense that it is challenging to validate due to the scarcity of insitu measurments. It should only be used with caution informed by understanding of the algorithms.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/27cc65c0-d453-4ba3-a0d6-55e4449fee8c","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - MODIS - 01 day - Net Primary Productivity (GSM model and Eppley-VGPM algorithm)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_net_primary_productivity_gsm_1day_aqua.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - MODIS - 01 day - Net Primary Productivity (GSM model and Eppley-VGPM algorithm)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_net_primary_productivity_gsm_1day_aqua.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_net_primary_productivity_gsm_1day_aqua.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-12","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_net_primary_productivity_gsm_1day_aqua","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour - MODIS - 1 day - Net Primary Productivity (OC3 model and Eppley-VGPM algorithm)","Description":"The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. Modelling is then used to compute an estimate of the Net Primary Productivity (NPP).\n\nThe model used is based on the standard vertically generalised production model (VGPM).  The VGPM is a \"chlorophyll-based\" model that estimates net primary production from chlorophyll using a temperature-dependent description of chlorophyll-specific        photosynthetic efficiency. For the VGPM, net primary production is a function of chlorophyll, available light, and the photosynthetic efficiency. The only difference between the Standard VGPM and the Eppley-VGPM is the temperature-dependent description of photosynthetic efficiencies, with the Eppley approach using an exponential function to account for variation in photosynthetic efficiencies due to photoacclimation. The similarity between these models (VGPM vs E_VGPM) is described more extensively in a paper by Elena-Carr et al (2006.). VGPM is a Depth Integrated Model(DIM) described by Behrenfeld and Falkowski (1997), with modification due to Eppley (1972) as implemented by Antoine and Morel (1996).\n\nThere are multiple retrieval algorithms for estimating Chl-a. These data (eppley_npp_chl_oc3) use the OC3 method implemented by the NASA Ocean Biology Processing Group in the SeaDAS processing software l2gen. The OC3 algorithm is described at https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/ (and links therein). The  Eppley_NPP_chl_oc3 product (based on the IMOS chl_oc3 product) is experimental and was created for comparison purposes with Eppley_NPP_chl_gsm, which is the recommended NPP product. It should only be used with full understanding of the algorithms. If you do not know which product to use, then go with Eppley_NPP_chl_gsm.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/b35b829c-9149-46c6-9e25-d0fd03463280","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - MODIS - 01 day - Net Primary Productivity (OC3 model and Eppley-VGPM algorithm)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_net_primary_productivity_oc3_1day_aqua.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - MODIS - 01 day - Net Primary Productivity (OC3 model and Eppley-VGPM algorithm)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_net_primary_productivity_oc3_1day_aqua.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_net_primary_productivity_oc3_1day_aqua.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-12","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_net_primary_productivity_oc3_1day_aqua","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour - MODIS - 1 day - Optical Water Type (Moore et al 2009 algorithm)","Description":"The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These measurements at discrete wavelengths represent the spectrum of light leaving the water surface, and the shape of the spectrum is characteristic of the water optical properties.\n\nMoore et al. (2009) applied a clustering technique to spectra to identify 8 sets of discrete optical water types. This product \"owt_csiro\" is produced using a CSIRO implementation of the Moore et al. algorithm, and testing shows that it closely reproduces the results of the original Moore et al implementation in the NASA SeaDAS package.  The CSIRO implementation was created because the implementation in SeaDAS has not been kept up to date, and to extend the product to the VIIRS sensor.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/60e57a2e-f88c-4b18-a3a1-1164c6259a10","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - MODIS - 01 day - Optical Water Type (Moore et al 2009 algorithm)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_optical_water_type_1day_aqua.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - MODIS - 01 day - Optical Water Type (Moore et al 2009 algorithm)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_optical_water_type_1day_aqua.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_optical_water_type_1day_aqua.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-12","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_optical_water_type_1day_aqua","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellite - Ocean Colour - MODIS - 1 day - Picoplankton fraction (OC3 model and Brewin et al 2012 algorithm)","Description":"The Aqua satellite platform carries a MODIS sensor that observes sunlight reflected from within the ocean surface layer at multiple wavelengths. These multi-spectral measurements are used to infer the concentration of chlorophyll-a (Chl-a), most typically due to phytoplankton, present in the water. An empirical relationship is then used to compute an estimate of the relative abundance of three phytoplankton size classes (micro, nano and picoplankton). \n\nThe methods used to decompose chl_oc3 are described by Brewin et al in two papers in 2010 and 2012. The two methods, denoted Brewin2010at and Brewin2012in, used calibration data from the Atlantic and Indian Oceans respectively.  Users should note that these are unvalidated experimental products and they should familiarise themselves with the underlying algorithms and methodologies described in the published literature before making use of them.\n\nThe data are produced from the same data stream as the MODIS Chla_oci data set (https://catalogue-imos.aodn.org.au:443/geonetwork/srv/api/records/d7a14921-8f3f-4522-9a54-e7d1df969c8a). \n\nData are provided as two files per day, one with the percentage of nanoplankton, and one for picoplankton. The percentage of microplankton is computed as the balance to 100%, that is %micro = 100 - %nano - %pico.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/bc428d0b-eff7-41b9-8d4c-10e666ee1312","Contact":"FILL UP MANUALLY - CHECK DOCUMENTATION","ManagedBy":"FILL UP MANUALLY - CHECK DOCUMENTATION","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["biology","oceans","satellite imagery"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SRS - MODIS - 01 day - Picoplankton fraction (OC3 model and Brewin et al 2012 algorithm)","ARN":"arn:aws:s3:::aodn-cloud-optimised/satellite_picoplankton_fraction_oc3_1day_aqua.zarr","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SRS - MODIS - 01 day - Picoplankton fraction (OC3 model and Brewin et al 2012 algorithm)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_picoplankton_fraction_oc3_1day_aqua.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_picoplankton_fraction_oc3_1day_aqua.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-12","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_satellite_picoplankton_fraction_oc3_1day_aqua","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Gliders - Delayed mode","Description":"The Australian National Facility for Ocean Gliders (ANFOG), with IMOS/NCRIS funding, deploys a fleet of eight gliders around Australia. The data represented by this record, are presented in delayed mode. The underwater ocean glider represents a technological revolution for oceanography. Autonomous ocean gliders can be built relatively cheaply, are controlled remotely and are reusable allowing them to make repeated subsurface ocean observations at a fraction of the cost of conventional methods. The data retrieved from the glider fleet will contribute to the study of the major boundary current systems surrounding Australia and their links to coastal ecosystems. The ANFOG glider fleet consists of two types; Slocum gliders and Seagliders. Slocum gliders (named for Joshua Slocum, the first solo global circumnavigator), manufactured by Webb Research Corp are optimised for shallow coastal waters (<200m) where high manoeuvrability is needed. ANFOG will have three Slocum gliders for deployment on the continental shelf. Seagliders, built at the University of Washington, are designed to operate more efficiently in the open ocean up to 1000m water depth. ANFOG uses their five Seagliders to monitor the boundary currents and continental shelves, which is valuable for gathering long-term environmental records of physical, chemical and biological data not widely measured to date. Whilst the Slocum gliders, due to their low cost and operational flexibility, will be of great use in intensive coastal monitoring, both types of gliders weigh only 52kg, enabling them to be launched from small boats. They have a suite of sensors able to record temperature, salinity, dissolved oxygen, turbidity, dissolved organic matter and chlorophyll against position and depth Sustained ocean observations will allow researchers to document the natural variability of the ocean, and better understand the effect of climate change on coastal ecosystems. The IMOS gliders will focus particularly on the major boundary currents that run down the Australian coast, the Leeuwin in the west and the East Australian Current (EAC). The study of these currents is critical for understanding the north-south transport of freshwater, heat and biogeochemical properties. The currents exert a large influence on coastal ecosystems, shipping lanes and fisheries.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/c317b0fe-02e8-4ff9-96c9-563fd58e82ac","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["chemistry","ocean currents","ocean velocity","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\" If using data from gliders deployed from Onslow, Western Australia, please add to the citation - \"Department of Jobs, Tourism, Science and Innovation (DJTSI), Western Australian Government\".","Resources":[{"Description":"Cloud Optimised AODN dataset of Ocean Gliders - delayed mode glider deployments","ARN":"arn:aws:s3:::aodn-cloud-optimised/slocum_glider_delayed_qc.parquet","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing Ocean Gliders - delayed mode glider deployments","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/slocum_glider_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/slocum_glider_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"RegistryEntryAdded":"2024-08-07","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_slocum_glider_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ships of Opportunity - Air-sea fluxes - Meteorological and flux - Delayed mode","Description":"Enhancement of Measurements on Ships of Opportunity (SOOP)-Air Sea Flux sub-facility collects underway meteorological and oceanographic observations during scientific and Antarctic resupply voyages in the oceans adjacent to Australia. Data product is quality controlled bulk air-sea fluxes and input observations. Research Vessel Real Time Air-Sea Fluxes, equips the Marine National Facility (MNF) (Research Vessels Southern Surveyor and Investigator), the Australian Antarctic Division (Research and Supply Vessels Aurora Australis and Nuyina), and Research Vessel Tangaroa with \"climate quality\" meteorological measurement systems, capable of providing high quality air-sea flux measurements and delivered to researchers on a near real-time basis. Obtaining the full set of air-sea fluxes essential for climate studies requires observations of: wind, air and sea temperature, humidity, pressure, precipitation, long- and short-wave radiation. The existing ship meteorological sensor sets are completed with IMOS instruments and a comprehensive annual calibration program implemented. Data streams are fed into existing ship data management systems, and broadcast via satellite back to Australia routinely. The observations are quality controlled at the Bureau of Meteorology and air-sea fluxes calculated using the COARE Bulk Flux algorithm. A daily file of calculated bulk fluxes and the observations used in the calculation are generated shortly after 0000UTC and provided to IMOS.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/07818819-2e5c-4a12-9395-0082b57b2fe8","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["air temperature","atmosphere","meteorological","oceans","radiation"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS-SOOP-Air Sea Flux","ARN":"arn:aws:s3:::aodn-cloud-optimised/vessel_air_sea_flux_product_delayed.parquet","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS-SOOP-Air Sea Flux: Flux product","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_air_sea_flux_product_delayed.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_air_sea_flux_product_delayed.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-06","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_vessel_air_sea_flux_product_delayed","Sources":["awslabs-open-data-registry"]}
{"Name":"Ships of Opportunity - Air-sea fluxes - Meteorological and sea surface temperature - Real time","Description":"Enhancement of Measurements on Ships of Opportunity (SOOP)-Air Sea Flux sub-facility collects underway meteorological and oceanographic observations during scientific and Antarctic resupply voyages in the oceans adjacent to Australia. Data product is quality controlled observations. Research Vessel Real Time Air-Sea Fluxes, equips the Marine National Facility (MNF) (Research Vessels Southern Surveyor and Investigator), the Australian Antarctic Division (Research and Supply Vessels Aurora Australis and Nuyina), and Research Vessel Tangaroa with \"climate quality\" meteorological measurement systems, capable of providing high quality air-sea flux measurements and delivered to researchers on a near real-time basis. Obtaining the full set of air-sea fluxes essential for climate studies requires observations of: wind, air and sea temperature, humidity, pressure, precipitation, long- and short-wave radiation. The existing ship meteorological sensor sets are completed with IMOS instruments and a comprehensive annual calibration program implemented. Data streams are fed into existing ship data management systems, and broadcast via satellite back to Australia routinely. The observations are quality controlled at the Bureau of Meteorology. A daily file of 1-minute averages of the observations are generated shortly after 0000UTC and provided to IMOS.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/9436802f-ed93-4660-be50-e77c3c07bd09","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["air temperature","atmosphere","meteorological","oceans","precipitation","radiation"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SOOP-Air Sea Flux (ASF) sub-facility - Meteorological and SST Observations","ARN":"arn:aws:s3:::aodn-cloud-optimised/vessel_air_sea_flux_sst_meteo_realtime.parquet","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SOOP-Air Sea Flux (ASF) sub-facility - Meteorological and SST Observations","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_air_sea_flux_sst_meteo_realtime.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_air_sea_flux_sst_meteo_realtime.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-06","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_vessel_air_sea_flux_sst_meteo_realtime","Sources":["awslabs-open-data-registry"]}
{"Name":"Ships of Opportunity - Biogeochemical sensors - Delayed mode","Description":"The IMOS Ship of Opportunity Underway CO2 Measurements group is a research and data collection project working within the IMOS Ship of Opportunity Multi-Disciplinary Underway Network sub-facility. The CO2 group sample critical regions of the Southern Ocean and the Australian shelf waters have a major impact on CO2 uptake by the ocean. These are regions where biogeochemical cycling is predicted to be particularly sensitive to a changing climate. The pCO2 Underway System measures the fugacity of carbon dioxide (fCO2) along with other variables such as sea surface salinity (SSS) and sea surface temperature (SST) using an automated system. The data represented by this record are presented in delayed mode. \n\nThe RV Aurora Australis provides data on surface ocean CO2 for the Southern Ocean, while the Southern Surveyor and its replacement in 2014, RV Investigator, covers shelf and offshore waters around Australia from the tropics to the sea-ice edge. The  RV L'Astrolabe is also used to collect data on route from Tasmania to Dumont d'Urville station, Antarctica. The New Zealand National Institute of Water & Atmospheric Research Ltd. (NIWA) has supported the setup of underway CO2 measurements on the RV Tangaroa. The Tangaroa covers oceans adjacent to New Zealand.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/63db5801-cc19-40ef-83b3-85ccba884cf7","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","chemistry","meteorological","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SOOP Underway CO2 Measurements Research Group - delayed mode data","ARN":"arn:aws:s3:::aodn-cloud-optimised/vessel_co2_delayed_qc.parquet","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SOOP Underway CO2 Measurements Research Group - delayed mode data","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_co2_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_co2_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2024-08-07","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_vessel_co2_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Citation":"For data collected prior to 1 July 2024: \" UNSW Sydney [year-of-data-download], IMOS SOOP - Vessels as Ships of Opportunity Sub-Facility - Real-time data, Downloaded from [url] on [date-of-download]\". For data collected after 1 July 2024: \" UNSW Sydney; IMOS [year-of-data-download], IMOS SOOP - Vessels as Ships of Opportunity Sub-Facility - Real-time data, Downloaded from [url] on [date-of-download]\".","Contact":"info@aodn.org.au","DataAtWork":{"Tutorials":[{"AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb"},{"AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_fishsoop_realtime_qc.ipynb","Title":"Accessing IMOS SOOP - Fisheries Vessels as Ships of Opportunity Sub-Facility - Real-time data","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_fishsoop_realtime_qc.ipynb"}]},"Description":"Fisheries Vessels as Ships of Opportunities (FishSOOP) is an IMOS Sub-Facility working with fishers to collect real-time temperature and depth data by installing equipment on a network of commercial fishing vessels using a range of common fishing gear.\n\nEvery day, fishing vessels operate broadly across the productive areas of Australia’s Exclusive Economic Zone where we have few subsurface ocean measurements. The FishSOOP Sub-Facility is utilising this observing opportunity to cost-effectively increase the spatial and temporal resolution of subsurface temperature data in Australia’s inshore, shelf, upper-slope, and offshore waters. The data is currently returned to each fishing boat in near-real time, so skippers can relate their catches to temperature-at-depth information.  The same data will also be collated to provide oceanographers with quality-controlled data for ground-truthing coastal models and to improve analysis and forecasts of oceanic conditions.\n\nThe IMOS funded data collection, follows on from a Fisheries Research and Development Corporation (FRDC) funded pilot project (2022-007) with the University of New South Wales, Fishwell Consulting and IMOS. In the first year of the project 32 commercial fishing vessels had been equipped with sensors. They covered a range of fishing vessels, including scallop dredges, tuna longlines, shark gillnets, otter board trawlers, lobster pots, fish traps, prawn trawlers, squid jigs, and danish seines. We also had a pre-trial test with one boat the year prior, with the sensor installed on a trawler.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/bdb84466-dc53-49ad-a60f-83d9fa0baed5","License":"http://creativecommons.org/licenses/by/4.0/","ManagedBy":"AODN","Name":"Ships of Opportunity - Fisheries vessels - Real time","Resources":[{"ARN":"arn:aws:s3:::aodn-cloud-optimised/vessel_fishsoop_realtime_qc.parquet","Description":"Cloud Optimised AODN dataset of IMOS SOOP - Fisheries Vessels as Ships of Opportunity Sub-Facility - Real-time data","Region":"ap-southeast-2","Type":"S3 Bucket"}],"Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["oceans"],"UpdateFrequency":"As Needed","RegistryEntryAdded":"2024-08-07","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_vessel_fishsoop_realtime_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ships of Opportunity - Sea surface temperature - 1-minute average data products","Description":"The Sea Surface Temperature (SST) sub-facility produces 1-minute average data products. Observed data are 1-minute median SST values and are retrieved from the vessel once an hour. High-resolution 1-minute median data are available in the delayed mode approximately every 3 months, these were produced in delayed mode using visual (manual) inspection of the quality flags after the data had been run through the automated quality control software at the Bureau of Meteorology The data products are produced from data from 3 ships: L'Astrolabe, Xutra Buhm and Wana Buhm, within this sub-facility working in the Australian region, Southern Ocean and South-East Asia. \n\nL'Astrolabe is a research vessel operating in Antarctic and sub-Antarctic waters during the summer period (usually October through March). 1-minute averaged SST (from the SBE 38 temperature sensor) and thermosalinograph salinity data are logged on the vessel and manually retrieved by CMAR staff after the voyage is completed. The data are run through the Bureau of Meteorology's automated quality control system followed by visual inspection and adjustment, if necessary, of the quality flags. This data stream is temporarily discontinued (2013). An SBE 48 hull-temperature sensor installed on both the MV Xutra Bhum and the MV Wana Buhm, produces observations of SST every 10 seconds. These data are recorded into 1 minute median SST values on the vessel and manually retrieved by Bureau of Meteorology staff approximately monthly. The data are run through the Bureau of Meteorology's automated quality control system followed by visual inspection and adjustment, if necessary, of the quality flags. Both of these data streams have now been discontinued (April 2013).","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/67f0b6ea-518f-4af5-9d21-dd3c08c7cb5c","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["air temperature","atmosphere","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud optimised AODN dataset of IMOS - SOOP-Sea Surface Temperature (SST) Sub-facility - 1-minute average data products","ARN":"arn:aws:s3:::aodn-cloud-optimised/vessel_sst_delayed_qc.parquet","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SOOP Sea Surface Temperature - delayed mode data","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_sst_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_sst_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2024-08-07","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_vessel_sst_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ships of Opportunity - Tropical research vessels - Real time","Description":"The research vessels (RV Cape Ferguson and RV Solander) of the Australian Institute of Marine Science (AIMS) routinely record along-track (underway) measurements of near-surface water temperature, salinity, chlorophyll (fluorescence) and turbidity (NTU) during scientific operations in the tropical waters of northern Australia, particularly the Great Barrier Reef (GBR). All data records include sampling time (UTC), position (Latitude, Longitude) and water depth (under keel). \n\nData are recorded at 10 second intervals. Data are measured with a Seabird SBE38 thermometer, Seabird SBE21 thermosalinograph and Wetlabs ECO-FLNTU-RT (optical sensor for chlorophyll fluorescence and turbidity). The turbidity data (NTU) are currently regarded as provisional in the absence of local validation due to intermittent bubble contamination. The sampling intakes are located at depths of 1.9m (RV Cape Ferguson) and 2.5m (RV Solander).\n\nThe data is used to inform spatial patchiness of these variables and for ongoing validation of satellite ocean temperature and ocean colour products.\n\nThe IMOS Ships of Opportunity, Sensors on Tropical Research Vessels sub-Facility was discontinued in June 2022. Sea surface temperature data collected by Australian Institute of Marine Science research vessels continues to be provided via the IMOS Ships of Opportunity, Sea Surface Temperature Sensors for Australian Vessels sub-Facility records on the AODN Portal. The near real time sea surface temperature data are quality controlled by the Australian Bureau of Meteorology and uploaded onto the Global Telecommunication System (GTS) for global dissemination to be used in real time sea surface temperature analyses and climate data records. Access to the additional data collected by the AIMS research vessels, is provided on the AIMS data repository here: https://apps.aims.gov.au/metadata/view/8af21108-c535-43bf-8dab-c1f45a26088c.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/8af21108-c535-43bf-8dab-c1f45a26088c","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["chemistry","oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of Sensors on Tropical Research Vessels: Enhanced Measurements from Ships of Opportunity (SOOP)","ARN":"arn:aws:s3:::aodn-cloud-optimised/vessel_trv_realtime_qc.parquet","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing Sensors on Tropical Research Vessels: Enhanced Measurements from Ships of Opportunity (SOOP)","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_trv_realtime_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_trv_realtime_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: For data collected prior to June 2022:  \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\" For data collected between June - October 2022:  \"AIMS, IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2024-08-07","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_vessel_trv_realtime_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ships of Opportunity - Expendable bathythermographs - Delayed mode","Description":"IMOS Ship of Opportunity Underway Expendable Bathythermographs (XBT) group is a research and data collection project working within the IMOS Ship of Opportunity Multi-Disciplinary Underway Network sub-facility. Five major (HRX) high-resolution XBT lines provide boundary to boundary profiling and closely spaced sampling to resolve mesoscale eddies, fronts and boundary currents. The lines are repeated 4 times per year by an on-board technician. The routes sample each major boundary current system using available commercial vessel traffic. All of the transects transmit data in real-time. The data represented by this record are presented in delayed mode. This SOOP dataset covers the Indian Ocean, Southern Ocean and Pacific Oceans.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/911f6b96-fa33-4621-9d8c-4d1cc14d94d0","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SOOP Expendable Bathythermographs (XBT) Research Group - XBT delayed mode data","ARN":"arn:aws:s3:::aodn-cloud-optimised/vessel_xbt_delayed_qc.parquet","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SOOP Expendable Bathythermographs (XBT) Research Group - XBT delayed mode data","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_xbt_delayed_qc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_xbt_delayed_qc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2025-02-26","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_vessel_xbt_delayed_qc","Sources":["awslabs-open-data-registry"]}
{"Name":"Ships of Opportunity - Expendable bathythermographs - Real time","Description":"XBT real-time data is available through the IMOS portal. Data is acquired by technicians who ride the ships of opportunity in order to perform high density sampling along well established transit lines. The data acquisition system used is the Quoll developed by Turo Technology. Data collected and is stored in netcdf files, with real-time data messages (JJVV bathy messages) created on the ship and sent to shore by iridium sbd. This is inserted onto the GTS by our colleagues at the Australian Bureau of Meteorology. The full resolution data is collected from the ship and returned for processing to scientists at CSIRO in Hobart. It undergoes a full scientific QC process which identifies both bad data and oceanic features such as eddies, fronts and temperature inversions. Finally, the data is distributed to the GTSPP global data acquisition centre in Washington DC where it is made freely available to all. This SOOP dataset covers waters around Australia and across to New Zealand.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/35234913-aa3c-48ec-b9a4-77f822f66ef8","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of IMOS - SOOP Expendable Bathythermographs (XBT) Research Group - XBT real-time data","ARN":"arn:aws:s3:::aodn-cloud-optimised/vessel_xbt_realtime_nonqc.parquet","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing IMOS - SOOP Expendable Bathythermographs (XBT) Research Group - XBT real-time data","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_xbt_realtime_nonqc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_xbt_realtime_nonqc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"IMOS [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2024-08-07","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_vessel_xbt_realtime_nonqc","Sources":["awslabs-open-data-registry"]}
{"Name":"Wave buoys observations - Real time","Description":"Buoys provide integral wave parameters. Buoy data from the following organisations contribute to the National Wave Archive: Manly Hydraulics Laboratory (part of the NSW Department of Planning and Environment (DPE), which has assumed function of the former NSW Office of Environment and Heritage (OEH)); Bureau of Meteorology; Western Australia Department of Transport (DOT); the Queensland Department of Environment and Science (DES); the Integrated Marine Observing System (IMOS); Gippsland Ports; the NSW Nearshore Wave Data Program from the NSW Department of Planning and Environment (DPE); the University of Western Australia (UWA); Deakin University, Pilbara Ports Authority and Flinders University and South Australian Research and Development Institute (SARDI). \n\nThe data from MHL, BOM, DOT, DES, Gippsland Ports and Pilbara Ports Authority, is gathered by the Waverider system developed by the Dutch company, Datawell. Some older wave data were collected using non-directional Waverider buoys. As technology advanced and directional measuring capabilities were developed in wave buoys, wave buoy networks were gradually upgraded to directional Waverider buoys. Therefore, some older datasets do not have directional information whereas newer datasets have directional information.\n\nThe data from IMOS, the NSW Nearshore Wave Data Program, DPE, UWA, Deakin, Flinders University and SARDI comes from Spotter Wave Buoys, developed by Sofar Ocean Technologies, which collect data similarly to the Waverider system.\n\nBOM, DES, DOT, MHL, IMOS (some) and Gippsland Ports - The wave buoy data is issued in GTS bulletins (which are only available for 24 hours) : IOWK01 (in BUFR format) and SWWE01 (CREX format). The data is collated by the Bureau into a CSV file (containing the last 24 hours of observations) which is updated every hour, at 5 minutes past the hour. This data is provided via WFS to the AODN.\nNote - The Maria Island buoy is currently undergoing repairs, so data is unavailable, we will update when resolved (May 2024).\n\nIMOS (some), DPE, UWA, Deakin, Flinders University + SARDI - The data is updated every hour, at 40 minutes past the hour.\n\nPilbara Ports Authority - The data is uploaded every hour, at 30 minutes past the hour.","Documentation":"https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/b299cdcd-3dee-48aa-abdd-e0fcdbb9cadc","Contact":"info@aodn.org.au","ManagedBy":"AODN","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["oceans"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Cloud Optimised AODN dataset of Wave buoys Observations - Australia - near real-time","ARN":"arn:aws:s3:::aodn-cloud-optimised/wave_buoy_realtime_nonqc.parquet","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and search for any AODN dataset","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"},{"Title":"Accessing Wave buoys Observations - Australia - near real-time","URL":"https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/wave_buoy_realtime_nonqc.ipynb","NotebookURL":"https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/wave_buoy_realtime_nonqc.ipynb","AuthorName":"Laurent Besnard","AuthorURL":"https://github.com/aodn/aodn_cloud_optimised"}]},"Citation":"The citation in a list of references is: \"Department of Transport [W.A]; State of Queensland, Department of Environment and Science; Australian Bureau of Meteorology; Department of Planning and Environment (DPE), New South Wales Government; Gippsland Ports; Integrated Marine Observing System; University of Western Australia (UWA); Deakin University, Pilbara Ports Authority, Flinders University and South Australian Research and Development Institute (SARDI) [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access].\"","RegistryEntryAdded":"2024-08-07","RegistryEntryLastModified":"2025-08-12","Slug":"aodn_wave_buoy_realtime_nonqc","Sources":["awslabs-open-data-registry"]}
{"Name":"Astrophysics Division Galaxy Morphology Benchmark Dataset","Description":"Hubble Space Telescope imaging data and associated identification labels for galaxy morphology derived from citizen scientist labels from the Galaxy Zoo: Hubble project.\n","Documentation":"https://github.com/erinleeryan/nasa_astro_aiml/tree/main/galaxymorphology","Contact":"Roopesh.Ojha@nasa.gov","ManagedBy":"[NASA](https://osdr.nasa.gov/)","UpdateFrequency":"No updates","Tags":["astronomy","aws-pds","machine learning","NASA SMD AI","satellite imagery"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"NASA APD Galaxy Morphology dataset","ARN":"arn:aws:s3:::nasa-apd-galaxymorph","Region":"us-west-2","Type":"S3 Bucket","Explore":null}],"DataAtWork":{"Publications":[{"Title":"Galaxy Zoo: morphological classifications for 120 000 galaxies in HST legacy imaging","URL":"https://academic.oup.com/mnras/article/464/4/4176/2527878","AuthorName":"Kyle W. Willett, Melanie A. Galloway, Steven P. Bamford, Chris J. Lintott, Karen L. Masters, Claudia Scarlata, B. D. Simmons, Melanie Beck, Carolin N. Cardamone, Edmond Cheung, Edward M. Edmondson, Lucy F. Fortson, Roger L. Griffith, Boris Häußler, Anna Han, Ross Hart, Thomas Melvin, Michael Parrish, Kevin Schawinski, R. J. Smethurst, Arfon M. Smith"}]},"RegistryEntryAdded":"2023-05-05","RegistryEntryLastModified":"2023-05-05","Slug":"apd_galaxymorph","Sources":["awslabs-open-data-registry"]}
{"Name":"Astrophysics Division Galaxy Segmentation Benchmark Dataset","Description":"Pan-STARSS imaging data and associated labels for galaxy segmentation into galactic centers, galactic bars, spiral arms and foreground stars derived from citizen scientist labels from the Galaxy Zoo: 3D project.\n","Documentation":"https://github.com/erinleeryan/nasa_astro_aiml/tree/main/galaxysegmentation","Contact":"Roopesh.Ojha@nasa.gov","ManagedBy":"[NASA](https://osdr.nasa.gov/)","UpdateFrequency":"No updates","Tags":["astronomy","aws-pds","machine learning","NASA SMD AI","segmentation"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"NASA APD Galaxy Sementation dataset consisting of astronomical FITS data format images","ARN":"arn:aws:s3:::nasa-apd-galaxysegment","Region":"us-west-2","Type":"S3 Bucket","Explore":null}],"DataAtWork":{"Publications":[{"Title":"Galaxy Zoo: 3D – crowdsourced bar, spiral, and foreground star masks for MaNGA target galaxies","URL":"https://academic.oup.com/mnras/article/507/3/3923/6347355","AuthorName":"Karen L Masters, Coleman Krawczyk, Shoaib Shamsi, Alexander Todd, Daniel Finnegan, Matthew Bershady, Kevin Bundy, Brian Cherinka, Amelia Fraser-McKelvie, Dhanesh Krishnarao, Sandor Kruk, Richard R Lane, David Law, Chris Lintott, Michael Merrifield, Brooke Simmons, Anne-Marie Weijmans, Renbin Yan"},{"Title":"Pan-STARRS Pixel Processing: Detrending, Warping, Stacking","URL":"https://iopscience.iop.org/article/10.3847/1538-4365/abb82b","AuthorName":"C. Z. Waters, E. A. Magnier, P. A. Price, K. C. Chambers, W. S. Burgett, P. W. Draper, H. A. Flewelling, K. W. Hodapp, M. E. Huber, R. Jedicke, N. Kaiser, R.-P. Kudritzki, R. H. Lupton, N. Metcalfe, A. Rest, W. E. Sweeney, J. L. Tonry, R. J. Wainscoat, and W. M. Wood-Vase"}]},"RegistryEntryAdded":"2023-05-05","RegistryEntryLastModified":"2023-05-05","Slug":"apd_galaxysegmentation","Sources":["awslabs-open-data-registry"]}
{"Name":"Apnea Detection from the ECG","Description":"Hilbert Transform based Sleep Apnea Detection using a Single Lead Electrocardiogram.","Documentation":"https://physionet.org/content/apdet/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/apdet/","ARN":"arn:aws:s3:::physionet-open/apdet/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"apdet","Sources":["awslabs-open-data-registry"]}
{"Name":"APEX-CONNECTS","Description":"The BRAIN Initiative Connectivity Across Scales (CONNECTS) program is working to create detailed maps of brain  wiring across different species and scales, using advanced imaging technologies.  APEX supports this effort by serving as a central hub that brings together and coordinates data and tools  from research focused on brain connectivity in humans and animals. Together, these efforts aim to improve our  understanding of how the brain is structured and functions.\n","Documentation":"https://brainlife.io","Contact":"brainlife.io@gmail.com","ManagedBy":"[Brainlife Team](https://brainlife.io/team/)","UpdateFrequency":"New datasets are added monthly","Tags":["analysis ready data","aws-pds","brain images","brain models","imaging","infrastructure","json","life sciences","machine learning","metadata","microscopy","neuroimaging","neuroscience","nifti","zarr"],"License":"[CC BY](https://creativecommons.org/licenses/by/4.0)","Citation":null,"Resources":[{"Description":"All APEX datasets are available for download","ARN":"arn:aws:s3:::apex-connects","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Brainlife AWS Tutorials","URL":"https://brainlife.io/docs/tutorial/aws-brainlife","AuthorName":"Brainlife","AuthorURL":"https://brainlife.io"}],"Tools & Applications":[{"Title":"Brainlife CLI (Command Line Interface)","URL":"https://github.com/brainlife/cli","AuthorName":"Brainlife","AuthorURL":"https://github.com/brainlife/cli"},{"Title":"Brainlife Web App","URL":"https://brainlife.io","AuthorName":"Brainlife","AuthorURL":"https://brainlife.io"}],"Publications":[{"Title":"Brainlife publications","URL":"https://brainlife.io/pubs","AuthorName":"Brainlife","AuthorURL":"https://brainlife.io"}]},"RegistryEntryAdded":"2025-04-15","RegistryEntryLastModified":"2026-04-22","Slug":"apex","Sources":["awslabs-open-data-registry"]}
{"Name":"Apnea-ECG Database","Description":"The data consist of 70 records, divided into a learning set of 35 records (a01 through a20, b01 through b05, and c01 through c10), and a test set of 35 records (x01 through x35), all of which may be downloaded from this page. Recordings vary in length from slightly less than 7 hours to nearly 10 hours each. Each recording includes a continuous digitized ECG signal, a set of apnea annotations (derived by human experts on the basis of simultaneously recorded respiration and related signals), and a set of machine-generated QRS annotations (in which all beats regardless of type have been labeled normal). In addition, eight recordings (a01 through a04, b01, and c01 through c03) are accompanied by four additional signals (Resp C and Resp A, chest and abdominal respiratory effort signals obtained using inductance plethysmography; Resp N, oronasal airflow measured using nasal thermistors; and SpO2, oxygen saturation).","Documentation":"https://doi.org/10.13026/C23W2R","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C23W2R","ARN":"arn:aws:s3:::physionet-open/apnea-ecg/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"apnea-ecg","Sources":["awslabs-open-data-registry"]}
{"Name":"Argo marine floats data and metadata from Global Data Assembly Centre (Argo GDAC)","Description":"Argo is an international program to observe the interior of the ocean with a fleet of profiling floats drifting in the deep ocean currents (https://argo.ucsd.edu). Argo GDAC is a dataset of 5 billion in situ ocean observations from 18.000 profiling floats (4.000 active) which started 20 years ago. Argo GDAC dataset is a collection of 18.000 NetCDF files.   It is a major asset for ocean and climate science, a contributor to IOCCP reports.\n","Documentation":"http://www.argodatamgt.org/Documentation","Contact":"codac@ifremer.fr","ManagedBy":"[Euro-Argo](https://www.euro-argo.eu/)","UpdateFrequency":"Data is updated daily.","Tags":["aws-pds","chemical biology","chemistry","climate","datacenter","digital assets","geochemistry","geophysics","geoscience","marine","netcdf","oceans"],"License":"Open data, there are no restrictions on the use of this data. https://creativecommons.org/licenses/by/4.0/","Citation":"Argo (2023). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE. https://doi.org/10.17882/42182","Resources":[{"Description":"Argo GDAC data and metadata","ARN":"arn:aws:s3:::argo-gdac-sandbox","Region":"eu-west-3","Type":"S3 Bucket","Explore":["[Browse Bucket](https://argo-gdac-sandbox.s3.eu-west-3.amazonaws.com/pub/index.html#pub/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Outreach activities","URL":"https://argo.ucsd.edu/outreach/","AuthorName":"Argo program","AuthorURL":"https://argo.ucsd.edu"},{"Title":"Outreach ressources for Argo floats","URL":"https://www.euro-argo.eu/Outreach/Outreach-material/Outreach-resources-about-Argo-floats","AuthorName":"Euro-Argo ERIC","AuthorURL":"https://www.euro-argo.eu"}],"Tools & Applications":[{"Title":"Argo data selection","URL":"https://dataselection.euro-argo.eu","AuthorName":"Euro-Argo ERIC","AuthorURL":"https://www.euro-argo.eu"},{"Title":"Argo floats dashboard","URL":"https://fleetmonitoring.euro-argo.eu","AuthorName":"Euro-Argo ERIC","AuthorURL":"https://www.euro-argo.eu"}],"Publications":[{"Title":"Argo data bibliography","URL":"https://argo.ucsd.edu/outreach/publications/bibliography/","AuthorName":"Argo program","AuthorURL":"https://argo.ucsd.edu"}]},"Collabs":{"ASDI":{"Tags":["oceans","climate","chemical biology","chemistry","climate","datacenter","digital assets","geochemistry","geophysics","geoscience","marine","netcdf"]}},"RegistryEntryAdded":"2023-08-16","RegistryEntryLastModified":"2023-08-17","Slug":"argo-gdac-marinedata","Sources":["awslabs-open-data-registry"]}
{"Name":"Annotated dataset of post-ischemic ventricular tachycardia electrograms (ARGO)","Description":"The analysis and identification of abnormal ventricular potentials (AVPs) in intracardiac electrograms (EGMs) are paramount in radiofrequency catheter ablation (RFCA) of post-ischemic ventricular tachycardia (VT). The ARGO dataset represents the first open-access dataset of post-ischemic VT EGMs featuring invasive and noninvasive electrophysiological recordings, as well as electroanatomical (EA) map data, including data annotation by three expert electrophysiologists and their final consensus. The dataset includes unipolar and bipolar EGMs, 12-lead surface ECGs, and EA maps, for a total of 1962 entries collected from nine post-ischemic VT patients using the CARTO® 3 mapping system. Every entry encompasses annotations of the bipolar EGMs in three different classes, i.e., Physiological, AVP, or Unknown, as well as the delineation of the onset and end of the pathological activation in the AVPs. The dataset also incorporates detailed information about the spatial localization of ablations performed during the RFCA procedures.","Documentation":"https://doi.org/10.13026/8gh2-e660","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/8gh2-e660","ARN":"arn:aws:s3:::physionet-open/argo/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"argo","Sources":["awslabs-open-data-registry"]}
{"Name":"Argoverse","Description":"Home of the Argoverse datasets.\n\nPublic datasets supported by detailed maps to test, experiment, and teach self-driving vehicles how to understand the world around them.\n\nThis bucket includes the following datasets:\n\n1. Argoverse 1 (AV1)\n  - Motion Forecasting\n  - Tracking\n2. Argoverse 2 (AV2)\n  - Motion Forecasting\n  - Lidar\n  - Sensor\n3. Trust, but Verify (TbV)\n  - Map Change Detection\n","Documentation":"https://argoverse.github.io/user-guide/","Contact":"https://github.com/argoverse/av2-api/issues","ManagedBy":"[Argoverse](https://argoverse.org)","UpdateFrequency":"Infrequently","Collabs":{"ASDI":{"Tags":["infrastructure"]}},"Tags":["autonomous vehicles","aws-pds","computer vision","geospatial","lidar","robotics"],"License":"[CC-BY-NC-SA-4.0](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html)","Resources":[{"Description":"Argoverse","ARN":"arn:aws:s3:::argoverse","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Argoverse 2 API","URL":"https://github.com/argoverse/av2-api","AuthorName":"Argoverse Authors","AuthorURL":"https://github.com/argoverse"}],"Tools & Applications":[{"Title":"conda-forge package for `av2`","URL":"https://anaconda.org/conda-forge/av2","AuthorName":"Argoverse Authors","AuthorURL":"https://github.com/argoverse"},{"Title":"PyPi package for `av2`","URL":"https://pypi.org/project/av2/","AuthorName":"Argoverse Authors","AuthorURL":"https://github.com/argoverse"}],"Publications":[{"Title":"Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting","URL":"https://arxiv.org/abs/2301.00493","AuthorName":"Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, Deva Ramanan, Peter Carr, James Hays"},{"Title":"Argoverse: 3D Tracking and Forecasting With Rich Maps","URL":"https://arxiv.org/abs/2301.00493","AuthorName":"Ming-Fang Chang, John Lambert, Patsorn Sangkloy, Jagjeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ramanan, James Hays"},{"Title":"Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection","URL":"https://arxiv.org/abs/2212.07312","AuthorName":"John Lambert, James Hays"}]},"RegistryEntryAdded":"2023-03-31","RegistryEntryLastModified":"2025-07-09","Slug":"argoverse","Sources":["awslabs-open-data-registry"]}
{"Name":"ARPA-E PERFORM Forecast data","Description":"The ARPA-E PERFORM Program is an ARPA-E funded program that aim to use\ntime-coincident power and load seeks to develop innovative management systems\nthat represent the relative delivery risk of each asset and balance the\ncollective risk of all assets across the grid. A risk-driven paradigm allows\noperators to: (i) fully understand the true likelihood of maintaining a\nsupply-demand balance and system reliability, (ii) optimally manage the system,\nand (iii) assess the true value of essential reliability services. This\nparadigm shift is critical for all power systems and is essential for grids\nwith high levels of stochastic resources. Projects will propose methods to\nquantify and manage risk at the asset level and at the system level.\n\nIn support of the ARPA-E PERFORM project, NREL has produced a set of\ntime-coincident load, wind, and solar generation profiles, including actual and\nforecasting time series. Both actuals and forecasts are provided in form of\ntime-series with high temporal and spatial fidelity. Both deterministic and\nprobabilistic forecasts are contained in the dataset.\n","Documentation":"https://github.com/PERFORM-Forecasts/documentation","Contact":"https://github.com/openEDI/PERFORM-Forecasts/documentation/issues","ManagedBy":"[National Renewable Energy Laboratory](https://www.nrel.gov/)","UpdateFrequency":"As needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["aws-pds","energy","environmental","geospatial","model","solar"],"License":"Creative Commons Attribution 3.0 United States License","Resources":[{"Description":"ARPA-E PERFORM Forecast data","ARN":"arn:aws:s3:::arpa-e-perform/","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"[Forecasts and Actuals for The Electric Reliability Council of Texas (ERCOT)](https://github.com/PERFORM-Forecasts/documentation)","ARN":"arn:aws:s3:::arpa-e-perform/ERCOT/","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"[Forecasts and Actuals for The New York Independent System Operator (NYISO)](https://github.com/PERFORM-Forecasts/documentation)","ARN":"arn:aws:s3:::arpa-e-perform/NYISO/","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"[Forecasts and Actuals for The Midcontinent Independent System Operator (MISO)](https://github.com/PERFORM-Forecasts/documentation)","ARN":"arn:aws:s3:::arpa-e-perform/MISO/","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"[Forecasts and Actuals for The Southwest Power Pool (SPP)](https://github.com/PERFORM-Forecasts/documentation)","ARN":"arn:aws:s3:::arpa-e-perform/SPP/","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"ARPA-E PERFORM","URL":"https://arpa-e.energy.gov/technologies/programs/perform","AuthorName":"ARPA-E","AuthorURL":"https://arpa-e.energy.gov"}]},"RegistryEntryAdded":"2021-03-19","RegistryEntryLastModified":"2023-02-28","Slug":"arpa-e-perform","Sources":["awslabs-open-data-registry"]}
{"Name":"Multimodal Imaging Dataset for Cerebrovascular Disease","Description":"As part of the ARPA-H AIR program, we are collecting prospective and retrospective\nComputed Tomography Angiography (CTA), Magnetic Resonance Angiography (MRA), and video fluoroscopy\nrecordings. In total, we will collect 2,500 CTA/MRA and 1,500 video fluoroscopy recordings. The 1,500 video\nfluoroscopy recordings will be paired with the corresponding CTA/MRA data. We will also upload annotations for\n150 of the recordings and images and 100 anatomic segmentations. These data will be collected in a\nmulti-institutional effort under the ARPA-H AIR program. The immediate goal of the program is to\ndevelop and train autonomous robots for stroke treatment. In the future, this dataset could support\nautonomous segmentation, labeling, and robotics for a range of cerebrovascular diseases. The data\nwill be uploaded in 4 tranches starting in October 2026 and ending in December 2027.\n","Documentation":"https://kitware.github.io/TAIR","Contact":"rachel.clipp@kitware.com","ManagedBy":"Kitware, Inc.","UpdateFrequency":"Four tranches between October 2026 and December 2027.","Tags":["3d-mesh","aws-pds","cardiovascular","dicom","health","life sciences","magnetic resonance imaging","medical imaging","medicine","neuroimaging","radiology","robotics","segmentation","simulations","stroke","stroke","x-ray"],"License":"Creative Commons Attribution 4.0 International License (CC BY 4.0)","Resources":[{"Description":"Multimodal clinical data such as images (DICOM) and video (MP4)","ARN":"arn:aws:s3:::multimodal-imaging-for-cerebrovascular-disease","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Notifications of new data","ARN":"arn:aws:sns:us-east-1:471112506215:multimodal-imaging-for-cerebrovascular-disease-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"TAIR Data Tutorial","URL":"https://github.com/Kitware/TAIR/blob/main/_notebooks/tutorial.ipynb","NotebookURL":"https://github.com/Kitware/TAIR/blob/main/_notebooks/tutorial.ipynb","AuthorName":"Kitware, Inc."}],"Tools & Applications":[{"Title":"TAIR: Toolkit for Autonomous Interventions and Robots","URL":"https://kitware.github.io/TAIR","AuthorName":"Kitware"}]},"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-04","Slug":"arpa-h-air","Sources":["awslabs-open-data-registry"]}
{"Name":"ARPA-I INSIGHTS Dataset","Description":"The ARPA-I INSIGHTS Dataset is a large-scale, high-density (~85 pts/m²) geiger-mode aerial LiDAR dataset covering over 1,600 km² across the Salt Lake City, UT and Denver, CO metropolitan regions, including major freeway corridors. Collected in June 2025, the dataset provides dense three-dimensional measurements with approximately 30 cm vertical and horizontal point spacing, enabling detailed representation of transportation infrastructure and surrounding urban environments. The data was acquired as part of a U.S. Department of Transportation ARPA-I-funded project, executed by MIT Lincoln Laboratory, to support state and regional transportation agencies. The dataset is designed to enable transportation digital twins, supporting virtual inspection, measurement, and inventory of infrastructure assets at corridor and regional scales. In addition to raw point clouds, the dataset includes annotated subsets for machine learning, with labeled classes for surface types (e.g., road, sidewalk, trail, driveway) and infrastructure elements (e.g., power lines, traffic signals, light poles). The data is optimized for cloud-based access through a STAC catalog with GeoParquet index, and tiled storage of cloud-optimized point clouds, supporting scalable workflows for geospatial analysis, infrastructure monitoring, and multimodal AI research.","Documentation":"https://github.com/VolpeUSDOT/ARPA-I_INSIGHTS_Documentation","Contact":"arpa-i-insights-admin@ll.mit.edu","ManagedBy":"MIT Lincoln Laboratory","UpdateFrequency":"Ad Hoc","Tags":["geospatial","lidar","machine learning","segmentation","transportation","urban"],"License":"CC-BY-4.0","Citation":null,"Resources":[{"Description":"S3 bucket containing STAC geoparquet indices, tiled COPC LAZ and LAS point clouds, and metadata for the INSIGHTS LiDAR, GIS-Surface-Labels, and Manual-Semantic-Labels datasets.","ARN":"arn:aws:s3:::arpa-i-insights","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://arpa-i-insights.s3.us-west-2.amazonaws.com/index.html)","[INSIGHTS-LiDAR GeoParquet Index](https://geoparquet.info/?url=https%3A%2F%2Farpa-i-insights.s3.us-west-2.amazonaws.com%2Flidar%2Fv1%2Fstac%2Findex%2Fitems.parquet)","[INSIGHTS-GIS-Labels GeoParquet Index](https://geoparquet.info/?url=https%3A%2F%2Farpa-i-insights.s3.us-west-2.amazonaws.com%2Flabels%2Fgis-surface%2Fv1%2Findex%2Fgis-surface-labels-index.geoparquet)","[INSIGHTS-Manual-Semantic-Labels GeoParquet Index](https://geoparquet.info/?url=https%3A%2F%2Farpa-i-insights.s3.us-west-2.amazonaws.com%2Flabels%2Fmanual-semantic%2Fv1%2Findex%2Fmanual-semantic-labels-index.geoparquet)"]},{"Description":"Notifications for INSIGHTS bucket updates","ARN":"arn:aws:sns:us-west-2:218254303756:arpa-i-insights-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get to know the ARPA-I INSIGHTS Dataset","URL":"https://github.com/VolpeUSDOT/ARPA-I_INSIGHTS_Documentation","NotebookURL":"https://github.com/VolpeUSDOT/ARPA-I_INSIGHTS_Documentation/blob/main/examples/get-to-know-ARPA-I-INSIGHTS.ipynb","AuthorName":"Jeffrey Liu","AuthorURL":"https://www.linkedin.com/in/jeffreyliu0/","Services":null}]},"DeprecatedNotice":null,"ADXCategories":["Environmental Data","Public Sector Data"],"RegistryEntryAdded":"2026-09-17","RegistryEntryLastModified":"2026-09-17","Slug":"arpa-i-insights","Sources":["awslabs-open-data-registry"]}
{"Name":"Artemis and Image Safari - Crop Imagery Datasets Spanning Globally Critical Crop Species","Description":"Artemis and Image Safari are complementary large-scale agricultural imaging datasets containing approximately 7.5 million field images (7,469,218) from smallholder and phenotyping farming systems across 18 crop species in 11 countries spanning sub-Saharan Africa, Latin America, and the United States. Artemis (1,387,663 images) pairs imagery with linked agronomic, genotypic, and environmental metadata across four crops and four countries. Image Safari (6,081,555 images) captures diversity across 18 crops and nine countries. A subset of 40,558 images includes organ-level annotations (10,887 Artemis; 29,671 Image Safari). Artemis annotations cover instance segmentation and object detection for common bean, cowpea, and sorghum (soybean has imagery only). Image Safari annotations cover point, semantic segmentation, scribble, object detection, and instance segmentation for 16 of 18 crops (finger millet and lentil have imagery only). The datasets combine crop imagery, structured metadata, and annotations to support agricultural foundation models, plant disease detection, growth stage recognition, and cross-environment generalisation research.\n","Documentation":"https://github.com/CIAT-Artemis/imagesafari-artemis-crop-images/blob/main/README.md","Contact":"alliance-data-helpdesk@cgiar.org","ManagedBy":"[Alliance of Bioversity International and CIAT](https://alliancebioversityciat.org/), [Quantitative Engineering Design (QED)](https://qed.ai), [CIMMYT](https://www.cimmyt.org), [International Potato Center (CIP)](https://cipotato.org), [AfricaRice](https://africarice.org), [Google Research Africa](https://research.google), [IITA](https://www.iita.org), and [Gates Foundation](https://www.gatesfoundation.org)","UpdateFrequency":"New imagery collected during annual field campaigns is reviewed, annotated, and added to the dataset approximately once per year. Annotation updates and quality corrections are versioned and documented in CHANGELOG.md.\n","Tags":["agriculture","aws-pds","computer vision","deep learning","food security","geospatial","image processing","imaging","labeled","life sciences","machine learning","sustainability"],"License":"Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA 4.0). https://creativecommons.org/licenses/by-sa/4.0/\n","Citation":"Artemis and Image Safari - Crop Imagery Datasets Spanning Globally Critical Crop Species was accessed on [DATE] at registry.opendata.aws/artemis-image-safari. Please also cite the associated data descriptor (Mutuvi S. et al.), https://doi.org/10.31220/agriRxiv.2026.00491.\n","Resources":[{"Description":"Artemis structured phenotyping images (1,387,663 images, four crops: common bean, cowpea, soybean, sorghum) with linked agronomic, genotypic, and environmental metadata. Annotations are published for common bean, cowpea, and sorghum; soybean has imagery only. Field images (JPEG, original acquisition resolution) and annotations organised by crop under annotations/standard/instance_segmentation/<set>/ and annotations/standard/object_detection/<set>/ (COCO JSON and related formats). Parquet and CSV metadata files under each crop directory's metadata/ subtree.\n","ARN":"arn:aws:s3:::alliance-artemis-imagesafari/Artemis","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://alliance-artemis-imagesafari.s3.us-west-2.amazonaws.com/?prefix=Artemis/)"]},{"Description":"Image Safari diversity-driven crop images (6,081,555 images, 18 crops) across nine countries in sub-Saharan Africa. Annotations are published for 16 of 18 crops; finger millet and lentil have imagery only. Field images (JPEG, original acquisition resolution) and annotations organised by crop under annotations for point, semantic_segmentation, scribble, object_detection, and instance_segmentation under annotations/standard/<modality>/<set>/ (COCO JSON, PNG masks, and related formats). Per-image metadata is provided as JSON sidecar files.\n","ARN":"arn:aws:s3:::alliance-artemis-imagesafari/ImageSafari","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://alliance-artemis-imagesafari.s3.us-west-2.amazonaws.com/?prefix=ImageSafari/)"]},{"Description":"Notifications for new Artemis and Image Safari data","ARN":"arn:aws:sns:us-west-2:066107820359:alliance-artemis-imagesafari-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: Artemis and Image Safari - Crop Imagery Datasets Spanning Globally Critical Crop Species","URL":"https://github.com/CIAT-Artemis/imagesafari-artemis-crop-images/blob/main/get-to-know-a-dataset-imagesafari_artemis.ipynb","NotebookURL":"https://github.com/CIAT-Artemis/imagesafari-artemis-crop-images/blob/main/get-to-know-a-dataset-imagesafari_artemis.ipynb","AuthorName":"Alliance of Bioversity International and CIAT","AuthorURL":"https://alliancebioversityciat.org/","Services":["Amazon S3","Amazon SageMaker","AWS Glue","Amazon Athena"]}],"Publications":[{"Title":"Artemis and Image Safari - Crop Imagery Datasets Spanning Globally Critical Crop Species\n","URL":"https://doi.org/10.31220/agriRxiv.2026.00491","AuthorName":"Mutuvi S., Guerena D., Zych M., Henday S., Girma E., Mungubariki T., Agesa B., Zochowski M., Ciolek D., Lazowik M., Chen J., Goeke L., Omwandho J., del Palma G., Malabi J., Phomebeya S., Sanena M., Marcos J. T. C., Ghandi H., Nas M., Rathore A., Mendes T., Yadav S., Adjah K. L., Woltering L., Lekasio E., Mushi B., Abraham L., Katunzi G., Remy S. L., Siyavora T., Selvaraj M., Casas J., Boukar O., Ongom P., Nakato G. V., Mwanje G., Agre P., Laporte M.-A., Asefa T., Odama R., Mukankusi C., and Wu W."}]},"RegistryEntryAdded":"2026-07-07","RegistryEntryLastModified":"2026-11-06","Slug":"artemis-image-safari","Sources":["awslabs-open-data-registry"]}
{"Name":"Automated Segmentation of Intracellular Substructures in Electron Microscopy (ASEM) on AWS","Description":"The Automated Segmentation of intracellular substructures in Electron Microscopy (ASEM) project provides deep learning models trained to segment structures in 3D images of cells acquired by Focused Ion Beam Scanning Electron Microscopy (FIB-SEM). Each model is trained to detect a single type of structure (mitochondria, endoplasmic reticulum, golgi apparatus, nuclear pores, clathrin-coated pits) in cells prepared via chemically-fixation (CF) or high-pressure freezing and freeze substitution (HPFS). You can use our open source pipeline to load a model and predict a class of sub-cellular structures in naive FIB-SEM cells images. If required, a fine-tuning procedure allows a model to be trained on a small amount of additional ground truth annotations to improve a prediction on a naive dataset. Together with the trained models, we also provide the training, validation and test datasets.","Documentation":"https://open.quiltdata.com/b/asem-project","Contact":"tklab@tklab.hms.harvard.edu","ManagedBy":"Kirchhausen Lab at Harvard Medical School","UpdateFrequency":"Data is added as it becomes available","Tags":["aws-pds","biology","cell biology","computer vision","electron microscopy","imaging","life sciences","microscopy","segmentation"],"License":"All available datasets and models are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License","Resources":[{"Description":"High resolution 3D cell image datasets","ARN":"arn:aws:s3:::asem-project/datasets/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Trained ML segmentation models for use in ASEM pipeline","ARN":"arn:aws:s3:::asem-project/models/","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Data layout and how to view","URL":"https://open.quiltdata.com/b/asem-project","AuthorName":"kirchhausenlab","AuthorURL":"https://github.com/kirchhausenlab"},{"Title":"How to use models","URL":"https://github.com/kirchhausenlab/incasem/blob/main/README.md","AuthorName":"kirchhausenlab","AuthorURL":"https://github.com/kirchhausenlab/incasem/blob/main/README.md"}],"Tools & Applications":[{"Title":"ASEM Colab Notebook (Interactive Demo)","URL":"https://colab.research.google.com/drive/1FgRKN2mM6x18S02Qe4XMyxFeFubZxaMw","AuthorName":"Patrick Stock","AuthorURL":"https://github.com/patrickstock"},{"Title":"TK Lab Data Explorer","URL":"http://asem-viewer-env.eba-rrnvmfwa.us-east-1.elasticbeanstalk.com/","AuthorName":"Patrick Stock","AuthorURL":"https://github.com/patrickstock"}],"Publications":[{"Title":"Deep neural network automated segmentation of cellular structures in volume electron microscopy","URL":"https://rupress.org/jcb/article-pdf/222/2/e202208005/1444783/jcb_202208005.pdf","AuthorName":"Benjamin Gallusser, Giorgio Maltese, Giuseppe Di Caprio, Tegy John Vadakkan, Anwesha Sanyal, Elliott Somerville, Mihir Sahasrabudhe, Justin O’Connor, Martin Weigert, Tom Kirchhausen"}]},"RegistryEntryAdded":"2023-12-21","RegistryEntryLastModified":"2024-12-20","Slug":"asem-project","Sources":["awslabs-open-data-registry"]}
{"Name":"ASF SAR Data Products for Disaster Events","Description":"synthetic Aperture Radar (SAR) data is a powerful tool for monitoring and assessing disaster events and can provide valuable insights for researchers, scientists, and emergency response teams. \nThe Alaska Satellite Facility (ASF) curates this collection of (primarily) SAR and SAR-derived satellite data products from a variety of data sources for disaster events.\n","Documentation":"https://asf-event-data.s3.us-west-2.amazonaws.com/README.md","Contact":"https://asf.alaska.edu/asf/contact-us/","ManagedBy":"[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)","UpdateFrequency":"Irregular, in response to disaster events\n","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aws-pds","cog","disaster response","geospatial","satellite imagery","stac"],"License":"This data falls under the terms and conditions of the [Creative Commons Zero (CC0) 1.0 Universal License](https://creativecommons.org/publicdomain/zero/1.0/) unless otherwise noted.","Citation":null,"Resources":[{"Description":"ASF Event data S3 bucket","ARN":"arn:aws:s3:::asf-event-data","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://asf-event-data.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new event data","ARN":"arn:aws:sns:us-west-2:654654592981:asf-event-data-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"ASF Event Search","URL":"https://search.asf.alaska.edu/#/?searchType=Event%20Search","AuthorName":"Alaska Satellite Facility","AuthorURL":"https://asf.alaska.edu/"}],"Publications":[{"Title":"Event Search Manual","URL":"https://docs.asf.alaska.edu/vertex/events/","AuthorName":"Alaska Satellite Facility","AuthorURL":"https://asf.alaska.edu/"},{"Title":"SARVIEWS Events Collection","URL":"https://docs.asf.alaska.edu/datasets/events_about/","AuthorName":"Alaska Satellite Facility","AuthorURL":"https://asf.alaska.edu/"}]},"DeprecatedNotice":null,"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2024-01-11","RegistryEntryLastModified":"2025-07-09","Slug":"asf-event-data","Sources":["awslabs-open-data-registry"]}
{"Name":"ASKAP Radio Telescope","Description":"\nASKAP is the CSIRO’s newest radio telescope. It is situated at the Inyarrimanha Ilgari Bundara, the CSIRO Murchison Radio-astronomy Observatory on Wajarri Yamaji Country in the Murchison region of Western Australia, about 800 km north of Perth. \n\nASKAP consists of 36 12m dishes, spread-out as far as 6km apart. It uses a new technology called Phased Array Feeds (PAFs), which allows it to see more of the sky at once. This novel technology allows ASKAP to achieve extremely high survey speed, making it one of the best instruments in the world for mapping the sky at radio wavelengths. \n\nInitial dataset available - The Rapid ASKAP Continuum Survey (RACS)\n\nRACS is the first large-area survey completed with ASKAP. This survey is revolutionary as the entire sky was observed in a matter of weeks, doing what previously took telescopes years to do. RACS initially covered the whole sky at 890 MHz (RACS-Low), and has since expanded to ASKAP’s other bands (1.4 and 1.7 GHz). RACS also covers the sky in multiple epochs, with a second epoch of RACS-Low and RACS-Mid obtained and processed.  \n\nRACS provides astronomers with a unique opportunity to study the radio sky and radio populations, in particular supermassive blackholes (active galactic nuclei) and their role in galaxy evolution. The multi-epoch approach also allows a study of the transient sky and testing and verification of calibration methods. The large area allows for cosmological studies, such as a search for anisotropy in the galaxy population, or cosmic dipole. \n","Documentation":"https://www.atnf.csiro.au/facilities/askap-radio-telescope/","Contact":"atnf-datasup@csiro.au","ManagedBy":"[Australia Telescope National Facility, CSIRO](http://www.atnf.csiro.au/)","Citation":"Please see the [ATNF acknowledgement page](https://www.atnf.csiro.au/resources/publications/atnf-publication-acknowledgement-statements/) for full citation instructions.","UpdateFrequency":"Roughly quarterly","Tags":["archives","astronomy","aws-pds"],"License":"CC-BY-4.0. Attribution required for refereed scientific papers.","Resources":[{"Description":"The Rapid ASKAP Continuum Survey (RACS) Public Data Releases","ARN":"arn:aws:s3:::askap-odp/racs-low1/","Region":"ap-southeast-2","Type":"S3 Bucket","RequesterPays":false},{"Description":"Notifications for new ASKAP data","ARN":"arn:aws:sns:ap-southeast-2:336305517014:askap-odp-object_created","Region":"ap-southeast-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"CSIRO ASKAP Science Data Archive User Guide","URL":"https://research.csiro.au/casda/casda-user-guide/","AuthorName":"CSIRO, ATNF"},{"Title":"Rapid Askap Continuum Survey (RACS) Home Page","URL":"https://research.csiro.au/racs/","AuthorName":"CSIRO, ATNF"}],"Publications":[{"Title":"ASKAP Publication List","URL":"https://www.atnf.csiro.au/facilities/askap-radio-telescope/publications/","AuthorName":"various, list maintained by CSIRO, ATNF"},{"Title":"ASKAP System Description paper","URL":"https://doi.org/10.1017/pasa.2021.1","AuthorName":"Hotan, A. et al."}]},"RegistryEntryAdded":"2025-09-09","RegistryEntryLastModified":"2025-10-17","Slug":"askap","Sources":["awslabs-open-data-registry"]}
{"Name":"ASL 1000","Description":"This dataset provides a high-fidelity collection of American Sign Language (ASL) videos annotated with 2D landmarks for hands, pose, and face. The data is designed to train advanced research and development in ASL recognition, translation, gesture analysis, and computer animation. The annotations for this dataset were generated using an automated data pipeline to pre-annotate keyframes from the source videos. As a final, critical step, all automated annotations were subsequently reviewed and meticulously corrected by human labellers to ensure the highest level of accuracy and reliability, making it suitable for training production-grade machine learning models.","Documentation":"https://github.com/NVIDIA/Trustworthy-AI/tree/main/ASL%20Developer%20Community","Contact":"trustworthyaiprojects@nvidia.com","ManagedBy":"[NVIDIA Corporation](https://www.nvidia.com/en-us/)","UpdateFrequency":"New data added as soon as it is available.","Tags":["aws-pds","machine learning","video"],"License":"Please see the [NVIDIA Dataset License](https://github.com/NVIDIA/Trustworthy-AI/blob/main/ASL%20Developer%20Community/NVIDIA%20Data%20License%20for%20ASL%20Project.pdf)","DataAtWork":{"Tutorials":[{"Title":"ASL Data Pipeline","URL":"https://github.com/NVIDIA/Trustworthy-AI/blob/main/ASL%20Developer%20Community/notebooks/asl_data_pipeline.ipynb","AuthorName":"NVIDIA"}],"Publications":[{"Title":"A Scalable Data Pipeline for ASL Media Processing and Annotation","URL":"https://www.researchgate.net/profile/Arun-Zachariah-2/publication/400638497_A_Scalable_Data_Pipeline_for_ASL_Media_Processing_and_Annotation/links/698ba09812f837212a195f1c/A-Scalable-Data-Pipeline-for-ASL-Media-Processing-and-Annotation.pdf","AuthorName":"Khanh Nguyen, Radha Sri-Tharan, Arun George Zachariah, Pratyusha Maiti, Latasha Grainger, Amariah Arias, Dnaijsha Minor, Jesse Oliver, Jenna Diamond, Dina Yared, Darragh Hanley, Christof Henkel, Alex Starnes, Kevin Schlichter, Suseella Panguluri, Michael Boone, Jeff Fisher, Nikki Pope, Anders Ahrensbach Mikkelsen, Nicole Maisonville, Oscar Örnberg, Jason Liang, Julianne Fong"}]},"Resources":[{"Description":"ASL 1000 Data","ARN":"arn:aws:s3:::trustworthyaiproduct","Region":"us-east-2","Type":"S3 Bucket","ControlledAccess":"https://www.nvidia.com/en-us/gated-resources/trustworthy-ai-american-sign-language/dataset/"}],"Citation":"Nguyen, Khanh, Radha Sri-Tharan, Arun George Zachariah, Pratyusha Maiti, Latasha Grainger, Amariah Arias, Dnaijsha Minor et al. \"A Scalable Data Pipeline for ASL Media Processing and Annotation.\" (2026).","RegistryEntryAdded":"2025-12-01","RegistryEntryLastModified":"2026-02-15","Slug":"asl_1000","Sources":["awslabs-open-data-registry"]}
{"Name":"Automatic Speech Recognition (ASR) Error Robustness","Description":"Sentence classification datatasets with ASR Errors.","Documentation":"https://github.com/anjiefang/asr-error-robustness","Contact":"njfn@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"N/A","Tags":["amazon.science","deep learning","machine learning","natural language processing","speech recognition"],"License":"See https://github.com/anjiefang/asr-error-robustness","Resources":[{"Description":"Datatasets with ASR Errors.","ARN":"arn:aws:s3:::asr-error-robustness","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Using Phoneme Representations to Build Predictive Models Robust to ASR Errors","URL":"https://assets.amazon.science/64/94/639ae0c44890837b0f1fbf11ef77/using-phoneme-representations-to-build-predictive-models-robust-to-asr-errors.pdf","AuthorName":"Anjie Fang, Simone Filice, Nut Limsopatham and Oleg Rokhlenko","AuthorURL":"https://anjiefang.github.io"}]},"RegistryEntryAdded":"2020-07-22","RegistryEntryLastModified":"2022-06-06","Slug":"asr-error-robustness","Sources":["awslabs-open-data-registry"]}
{"Name":"Africa Brick Kilns","Description":"This dataset includes detailed information about brick kilns, their locations, capacities, emissions, and other relevant attributes across Africa.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Brick%20Kilns/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["air quality","aws-pds","energy","environmental","infrastructure"],"License":"https://opendatacommons.org/licenses/by/1.0/","Citation":"APAD (Air Pollution Asset Data). Africa Brick Kilns. Available from the Registry of Open Data on AWS: https://registry.opendata.aws/","ADXCategories":["Environmental Data"],"Resources":[{"Description":"Asset level data - Brick_Kilns_Africa","ARN":"arn:aws:s3:::assetdata-igp/africa_assets_csvs/brick_kilns_main.csv","Region":"ap-southeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Africa Brick Kilns - raw CSV data","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Brick%20Kilns/brick_kilns_main.csv","AuthorName":"APAD","AuthorURL":"https://github.com/APAD2024"},{"Title":"Get To Know A Dataset: Africa Brick Kilns","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Brick%20Kilns/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Brick%20Kilns/get-to-know-a-dataset.ipynb","AuthorName":"APAD","AuthorURL":"https://github.com/APAD2024"}]},"RegistryEntryAdded":"2026-08-06","RegistryEntryLastModified":"2026-08-06","Slug":"asset-data-africa-brick-kilns","Sources":["awslabs-open-data-registry"]}
{"Name":"Africa Cement Plants","Description":"This dataset includes detailed information about cement plants, their locations, capacities, emissions, and other relevant attributes across Africa.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Cement/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["industrial"]}},"Tags":["air quality","aws-pds","environmental","industrial","infrastructure"],"License":"https://opendatacommons.org/licenses/by/1.0/","Citation":"APAD (Air Pollution Asset Data). Africa Cement Plants. Available from the Registry of Open Data on AWS: https://registry.opendata.aws/","ADXCategories":["Environmental Data"],"Resources":[{"Description":"Asset level data - Cement_Plants_Africa","ARN":"arn:aws:s3:::assetdata-igp/africa_assets_csvs/cement_africa_main.csv","Region":"ap-southeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Africa Cement Plants - raw CSV data","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Cement/cement_africa_main.csv","AuthorName":"APAD","AuthorURL":"https://github.com/APAD2024"},{"Title":"Get To Know A Dataset: Africa Cement Plants","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Cement/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Cement/get-to-know-a-dataset.ipynb","AuthorName":"APAD","AuthorURL":"https://github.com/APAD2024"}]},"RegistryEntryAdded":"2026-08-06","RegistryEntryLastModified":"2026-08-06","Slug":"asset-data-africa-cement","Sources":["awslabs-open-data-registry"]}
{"Name":"Africa Coal Plants","Description":"This dataset includes detailed information about coal power plants, their locations, capacities, emissions, and other relevant attributes across Africa.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Coal%20Plants/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["air quality","aws-pds","energy","environmental","infrastructure"],"License":"https://opendatacommons.org/licenses/by/1.0/","Citation":"APAD (Air Pollution Asset Data). Africa Coal Plants. Available from the Registry of Open Data on AWS: https://registry.opendata.aws/","ADXCategories":["Environmental Data"],"Resources":[{"Description":"Asset level data - Coal_Plants_Africa","ARN":"arn:aws:s3:::assetdata-igp/africa_assets_csvs/africa_coal_plants_main.csv","Region":"ap-southeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Africa Coal Plants - raw CSV data","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Coal%20Plants/africa_coal_plants_main.csv","AuthorName":"APAD","AuthorURL":"https://github.com/APAD2024"},{"Title":"Get To Know A Dataset: Africa Coal Plants","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Coal%20Plants/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Coal%20Plants/get-to-know-a-dataset.ipynb","AuthorName":"APAD","AuthorURL":"https://github.com/APAD2024"}]},"RegistryEntryAdded":"2026-08-06","RegistryEntryLastModified":"2026-08-06","Slug":"asset-data-africa-coal-plant","Sources":["awslabs-open-data-registry"]}
{"Name":"Africa Paper and Pulp Plants","Description":"This dataset includes detailed information about paper and pulp plants, their locations, capacities, emissions, and other relevant attributes across Africa.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Paper%20and%20Pulp/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["industrial"]}},"Tags":["air quality","aws-pds","environmental","industrial","infrastructure"],"License":"https://opendatacommons.org/licenses/by/1.0/","Citation":"APAD (Air Pollution Asset Data). Africa Paper and Pulp Plants. Available from the Registry of Open Data on AWS: https://registry.opendata.aws/","ADXCategories":["Environmental Data"],"Resources":[{"Description":"Asset level data - Paper_and_Pulp_Plants_Africa","ARN":"arn:aws:s3:::assetdata-igp/africa_assets_csvs/paper_pulp_Africa.csv","Region":"ap-southeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Africa Paper and Pulp Plants - raw CSV data","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Paper%20and%20Pulp/paper_pulp_Africa.csv","AuthorName":"APAD","AuthorURL":"https://github.com/APAD2024"},{"Title":"Get To Know A Dataset: Africa Paper and Pulp Plants","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Paper%20and%20Pulp/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Paper%20and%20Pulp/get-to-know-a-dataset.ipynb","AuthorName":"APAD","AuthorURL":"https://github.com/APAD2024"}]},"RegistryEntryAdded":"2026-08-06","RegistryEntryLastModified":"2026-08-06","Slug":"asset-data-africa-paper-and-pulp","Sources":["awslabs-open-data-registry"]}
{"Name":"Africa Power Generation Plants","Description":"This dataset includes detailed information about power generation plants, their locations, capacities, emissions, and other relevant attributes across Africa.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Power%20Generation/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["air quality","aws-pds","energy","environmental","infrastructure"],"License":"https://opendatacommons.org/licenses/by/1.0/","Citation":"APAD (Air Pollution Asset Data). Africa Power Generation Plants. Available from the Registry of Open Data on AWS: https://registry.opendata.aws/","ADXCategories":["Environmental Data"],"Resources":[{"Description":"Asset level data - Power_Generation_Plants_Africa","ARN":"arn:aws:s3:::assetdata-igp/africa_assets_csvs/Power_Generation.csv","Region":"ap-southeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Africa Power Generation Plants - raw CSV data","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Power%20Generation/Power_Generation.csv","AuthorName":"APAD","AuthorURL":"https://github.com/APAD2024"},{"Title":"Get To Know A Dataset: Africa Power Generation Plants","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Power%20Generation/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Power%20Generation/get-to-know-a-dataset.ipynb","AuthorName":"APAD","AuthorURL":"https://github.com/APAD2024"}]},"RegistryEntryAdded":"2026-08-06","RegistryEntryLastModified":"2026-08-06","Slug":"asset-data-africa-power-generation","Sources":["awslabs-open-data-registry"]}
{"Name":"Africa Steel Plants","Description":"This dataset includes detailed information about steel plants, their locations, capacities, emissions, and other relevant attributes across Africa.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Steel/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["industrial"]}},"Tags":["air quality","aws-pds","environmental","industrial","infrastructure"],"License":"https://opendatacommons.org/licenses/by/1.0/","Citation":"APAD (Air Pollution Asset Data). Africa Steel Plants. Available from the Registry of Open Data on AWS: https://registry.opendata.aws/","ADXCategories":["Environmental Data"],"Resources":[{"Description":"Asset level data - Steel_Plants_Africa","ARN":"arn:aws:s3:::assetdata-igp/africa_assets_csvs/Steel_plants_Africa_main.csv","Region":"ap-southeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Africa Steel Plants - raw CSV data","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Steel/Steel_plants_Africa_main.csv","AuthorName":"APAD","AuthorURL":"https://github.com/APAD2024"},{"Title":"Get To Know A Dataset: Africa Steel Plants","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Steel/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Steel/get-to-know-a-dataset.ipynb","AuthorName":"APAD","AuthorURL":"https://github.com/APAD2024"}]},"RegistryEntryAdded":"2026-08-06","RegistryEntryLastModified":"2026-08-06","Slug":"asset-data-africa-steel","Sources":["awslabs-open-data-registry"]}
{"Name":"Africa Waste Management Data","Description":"This dataset includes detailed information about waste management sites, their locations, capacities, emissions, and other relevant attributes across Africa.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Waste/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["sustainability"]}},"Tags":["air quality","aws-pds","environmental","infrastructure","sustainability"],"License":"https://opendatacommons.org/licenses/by/1.0/","Citation":"APAD (Air Pollution Asset Data). Africa Waste Management Data. Available from the Registry of Open Data on AWS: https://registry.opendata.aws/","ADXCategories":["Environmental Data"],"Resources":[{"Description":"Asset level data - Waste_Management_Sites_Africa","ARN":"arn:aws:s3:::assetdata-igp/africa_assets_csvs/waste_Africa_main.csv","Region":"ap-southeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Africa Waste Management Data - raw CSV data","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Waste/waste_Africa_main.csv","AuthorName":"APAD","AuthorURL":"https://github.com/APAD2024"},{"Title":"Get To Know A Dataset: Africa Waste Management Data","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Waste/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Africa/Asset_Data_Africa/Waste/get-to-know-a-dataset.ipynb","AuthorName":"APAD","AuthorURL":"https://github.com/APAD2024"}]},"RegistryEntryAdded":"2026-08-06","RegistryEntryLastModified":"2026-08-06","Slug":"asset-data-africa-waste","Sources":["awslabs-open-data-registry"]}
{"Name":"IGP Brick Kilns Bangladesh","Description":"This dataset includes detailed information about brick kilns, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Brick%20Kiln/with_FPs/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"as needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["air quality","aws-pds","energy","environmental","infrastructure"],"License":"https://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Asset level data - Brick_Kilns_IGP for Bangladesh","ARN":"arn:aws:s3:::assetdata-igp/asset_igp_csvs/Brick_kilns_BAN_coal.csv","Region":"ap-south-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Analyzing Brick Kiln Data and Emission Calculation","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Brick%20Kiln/with_FPs/Brick_kilns_BAN_coal.csv","AuthorName":"APAD"}]},"RegistryEntryAdded":"2026-02-22","RegistryEntryLastModified":"2026-04-19","Slug":"asset-data-igp-brick-kilns-ban","Sources":["awslabs-open-data-registry"]}
{"Name":"IGP Brick Kilns India","Description":"This dataset includes detailed information about brick kilns, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Brick%20Kiln/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"as needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["air quality","aws-pds","energy","environmental","infrastructure"],"License":"https://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Asset level data - Brick_Kilns_IGP for India","ARN":"arn:aws:s3:::assetdata-igp/asset_igp_csvs/Brick_Kilns_IND_coal.csv","Region":"ap-south-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Analyzing Brick Kiln Data and Emission Calculation","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Brick%20Kiln/corrected/Brick_Kilns_IND_coal.csv","AuthorName":"APAD"}]},"RegistryEntryAdded":"2026-02-22","RegistryEntryLastModified":"2026-04-19","Slug":"asset-data-igp-brick-kilns-ind","Sources":["awslabs-open-data-registry"]}
{"Name":"IGP Brick Kilns Pakistan","Description":"This dataset includes detailed information about brick kilns, their locations, capacities, emissions, and other relevant attributes around the Pakistann Gangetic Plain.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Brick%20Kiln/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"as needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["air quality","aws-pds","energy","environmental","infrastructure"],"License":"https://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Asset level data - Brick_Kilns_IGP for Pakistan","ARN":"arn:aws:s3:::assetdata-igp/asset_igp_csvs/Brick_Kilns_PK_coal.csv","Region":"ap-south-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Analyzing Brick Kiln Data and Emission Calculation","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Brick%20Kiln/corrected/Brick_Kilns_PK_coal.csv","AuthorName":"APAD"}]},"RegistryEntryAdded":"2026-02-22","RegistryEntryLastModified":"2026-04-19","Slug":"asset-data-igp-brick-kilns-pak","Sources":["awslabs-open-data-registry"]}
{"Name":"IGP Cement Plants","Description":"This dataset includes detailed information about cement plants, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Cement/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"as needed","Collabs":{"ASDI":{"Tags":["industrial"]}},"Tags":["air quality","aws-pds","environmental","industrial","infrastructure"],"License":"http://opendatacommons.org/licenses/by/1.0/","Resources":[{"Description":"Asset level data - Cement_Plants_IGP","ARN":"arn:aws:s3:::assetdata-igp/asset_igp_csvs/cement_plants.csv","Region":"ap-southeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Analyzing Cement Plant Data","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Cement/cement_plants.csv","AuthorName":"APAD"}]},"RegistryEntryAdded":"2026-02-22","RegistryEntryLastModified":"2026-04-20","Slug":"asset-data-igp-cement","Sources":["awslabs-open-data-registry"]}
{"Name":"IGP Coal Plant","Description":"This dataset includes detailed information about coal power plants, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Coal%20Plants/coal-plants.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"as needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["air quality","energy","environmental","infrastructure"],"License":"https://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Asset level data - Coal_Plants_IGP","ARN":"arn:aws:s3:::assetdata-igp/Coal Plants/","Region":"ap-south-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Analyzing Coal Plant Data and Emission Calculation","URL":"https://github.com/APAD2024/APAD-Asset-Data/tree/main/Asset%20Data/Coal%20Plants","AuthorName":"APAD"}]},"RegistryEntryAdded":"2024-12-30","RegistryEntryLastModified":"2026-04-19","Slug":"asset-data-igp-coal-plant","Sources":["awslabs-open-data-registry"]}
{"Name":"IGP Paper and Pulp Plant","Description":"This dataset includes detailed information about paper and pulp plants, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Paper%20and%20Pulp/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"as needed","Collabs":{"ASDI":{"Tags":["industrial"]}},"Tags":["air quality","aws-pds","environmental","industrial","infrastructure"],"License":"http://opendatacommons.org/licenses/by/1.0/","Resources":[{"Description":"Asset level data - Paper_and_Pulp_Plants_IGP","ARN":"arn:aws:s3:::assetdata-igp/Pulp and Paper Plants/paper_pulp_main.csv","Region":"ap-southeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Analyzing Paper and Pulp Plant Data","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Paper%20and%20Pulp/paper_and_pulp.csv","AuthorName":"APAD"}]},"RegistryEntryAdded":"2026-02-22","RegistryEntryLastModified":"2026-04-19","Slug":"asset-data-igp-paper-and-pulp","Sources":["awslabs-open-data-registry"]}
{"Name":"IGP Power Generation Plant","Description":"This dataset includes detailed information about power generation plants, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Power%20Generation/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"as needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["air quality","aws-pds","energy","environmental","infrastructure"],"License":"https://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Asset level data - Power_Generation_Plants_IGP","ARN":"arn:aws:s3:::assetdata-igp/asset_igp_csvs/Power_Generation.csv","Region":"ap-south-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Analyzing Power Generation Plant Data and Emission Calculation","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Power%20Generation/power_generation.csv","AuthorName":"APAD"}]},"RegistryEntryAdded":"2026-02-22","RegistryEntryLastModified":"2026-04-19","Slug":"asset-data-igp-power-generation","Sources":["awslabs-open-data-registry"]}
{"Name":"IGP Steel Plants","Description":"This dataset includes detailed information about steel plants, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Steel/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"as needed","Collabs":{"ASDI":{"Tags":["industrial"]}},"Tags":["air quality","aws-pds","environmental","industrial","infrastructure"],"License":"http://opendatacommons.org/licenses/by/1.0/","Resources":[{"Description":"Asset level data - Steel_Plants_IGP","ARN":"arn:aws:s3:::assetdata-igp/asset_igp_csvs/steel_plants.csv","Region":"ap-southeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Analyzing Steel Plant Data","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Steel/steel_plants.csv","AuthorName":"APAD"}]},"RegistryEntryAdded":"2026-02-22","RegistryEntryLastModified":"2026-04-19","Slug":"asset-data-igp-steel","Sources":["awslabs-open-data-registry"]}
{"Name":"IGP Waste Management Data","Description":"This dataset includes detailed information about waste management sites, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.","Documentation":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Waste/Readme.md","Contact":"https://github.com/APAD2024/APAD-Asset-Data/issues","ManagedBy":"APAD","UpdateFrequency":"as needed","Collabs":{"ASDI":{"Tags":["sustainability"]}},"Tags":["air quality","aws-pds","environmental","infrastructure","sustainability"],"License":"http://opendatacommons.org/licenses/by/1.0/","Resources":[{"Description":"Asset level data - Waste_Management_Sites_IGP","ARN":"arn:aws:s3:::assetdata-igp/asset_igp_csvs/waste.csv","Region":"ap-southeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Analyzing Waste Management Data","URL":"https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/IGP/Asset_Data_IGP/Waste/waste.csv","AuthorName":"APAD"}]},"RegistryEntryAdded":"2026-02-22","RegistryEntryLastModified":"2026-04-19","Slug":"asset-data-igp-waste","Sources":["awslabs-open-data-registry"]}
{"Name":"ASTER L1T Cloud-Optimized GeoTIFFs","Description":"The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1\nPrecision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains\ncalibrated at-sensor radiance, which corresponds with the ASTER Level 1B (AST_L1B),\nthat has been geometrically corrected, and rotated to a north-up UTM projection.\nThe AST_L1T is created from a single resampling of the corresponding ASTER L1A (AST_L1A) product.\n\nThe precision terrain correction process incorporates GLS2000 digital elevation data with\nderived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes\nwhere correlation statistics reach a minimum threshold. Alternate levels of correction are\npossible (systematic terrain, systematic, or precision) for scenes acquired at night or that\notherwise represent a reduced quality ground image (e.g., cloud cover).\n\nEach AST_L1T granule is converted into three different COG files based on the sensor and spatial resolution,\nVNIR at 15m, SWIR at 30m and TIR at 90m. The metadata required to transform the digital numbers (DN) to \nradiance and reflectance values are also incorporated as metadata in the TIFF files.\nThe filenaming convention and the organization of bands are described\n[here](https://github.com/awslabs/open-data-docs/tree/main/docs/aster-l1t).\n","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/aster-l1t","Contact":"support@earthdaily.com","ManagedBy":"[EarthDaily](https://earthdaily.com/)","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","cog","earth observation","geospatial","mining","natural resource","satellite imagery","sustainability"],"License":"There are no restrictions on the use of data, unless expressly identified prior to or at the time of receipt.\n","Citation":null,"Resources":[{"Description":"Imagery and metadata","ARN":"arn:aws:s3:::aster-l1t","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false},{"Description":"[New image notifications](https://github.com/awslabs/open-data-docs/tree/main/docs/aster-l1t#receive-data-notifications-with-amazon-sns)","ARN":"arn:aws:sns:us-west-2:526859492376:aster-l1t-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Working with ASTER L1T Visible and Near Infrared (VNIR) Data in R","URL":"https://git.earthdata.nasa.gov/projects/LPDUR/repos/aster-l1t/browse","AuthorName":"Cole Krehbiel","AuthorURL":"https://www.earthdata.nasa.gov/centers/lp-daac"}],"Tools & Applications":[{"Title":"EarthDaily EarthOne Platform","URL":"https://docs.earthone.earthdaily.com/","AuthorName":"EarthDaily","AuthorURL":"https://earthdaily.com"},{"Title":"Latitude-Longitude to Path-Row conversion","URL":"https://landsat.usgs.gov/landsat_acq#convertPathRow","AuthorName":"USGS","AuthorURL":"https://landsat.usgs.gov"},{"Title":"LatLongToWRS","URL":"https://github.com/robintw/LatLongToWRS","AuthorName":"Robin Wilson","AuthorURL":"https://blog.rtwilson.com/converting-latitudelongitude-co-ordinates-to-landsat-wrs-2-pathsrows/"}],"Publications":[{"Title":"ASTER L1T Algorithm Theoretical Basis","URL":"https://lpdaac.usgs.gov/documents/1400/AST_L1T_ATBD_V1.pdf","AuthorName":"David Meyer, Dawn Siemonsma, Barbara Brooks, and Lowell Johnson","AuthorURL":null},{"Title":"ASTER L1T Product Specification","URL":"https://lpdaac.usgs.gov/documents/1401/ASTER_L1T_Product_Specification_v1.pdf","AuthorName":"USGS EROS Data Center"},{"Title":"ASTER L1T Product User Guide","URL":"https://lpdaac.usgs.gov/documents/647/AST__L1T_User_Guide_V3.pdf","AuthorName":"USGS EROS Data Center","AuthorURL":"https://lpdaac.usgs.gov/products/ast_l1tv003/"}]},"RegistryEntryAdded":"2022-11-11","RegistryEntryLastModified":"2025-11-13","Slug":"aster-l1t","Sources":["awslabs-open-data-registry"]}
{"Name":"Auditory evoked potential EEG-Biometric dataset","Description":"This data set consists of over 240 two-minute EEG recordings obtained from 20 volunteers. Resting-state and auditory stimuli experiments are included in the data. The goal is to develop an EEG-based Biometric system. The data includes resting-state EEG signals in both cases: eyes open and eyes closed. The auditory stimuli part consists of six experiments; Three with in-ear auditory stimuli and another three with bone-conducting auditory stimuli. The three stimuli for each case are a native song, a non-native song, and neutral music.","Documentation":"https://doi.org/10.13026/ps31-fc50","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"PhysioNet Contributor Review Health Data License 1.5.0","Resources":[{"Description":"https://doi.org/10.13026/ps31-fc50","ARN":"arn:aws:s3:::physionet-open/auditory-eeg/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"auditory-eeg","Sources":["awslabs-open-data-registry"]}
{"Name":"Aurora Multi-Sensor Dataset","Description":"The Aurora Multi-Sensor Dataset is an open, large-scale multi-sensor dataset with highly accurate localization ground truth, captured between January 2017 and February 2018 in the metropolitan area of Pittsburgh, PA, USA by Aurora (via Uber ATG) in collaboration with the University of Toronto. The de-identified dataset contains rich metadata, such as weather and semantic segmentation, and spans all four seasons, rain, snow, overcast and sunny days, different times of day, and a variety of traffic conditions.\n<br/>\nThe Aurora Multi-Sensor Dataset contains data from a 64-beam Velodyne HDL-64E LiDAR sensor and seven 1920x1200-pixel resolution cameras including a forward-facing stereo pair and five wide-angle lenses covering a 360-degree view around the vehicle.\n<br/>\nThis data can be used to develop and evaluate large-scale long-term approaches to autonomous vehicle localization. Its size and diversity make it suitable for a wide range of research areas such as 3D reconstruction, virtual tourism, HD map construction, and map compression, among others.\n<br/>\nThe data was first presented at the International Conference on Intelligent Robots and Systems (IROS) in 2020, where it was nominated as a Finalist for Best Application Paper at the conference.\n","Documentation":"A third-party development kit authored by Andrei Bârsan of the University of Toronto, made available under the MIT License, can be found here: https://github.com/pit30m/pit30m. Aurora makes no representations as to the functionality or performance of the dev-kit.\n","Contact":"ams-dataset@aurora.tech","ManagedBy":"Aurora Operations, Inc.","UpdateFrequency":"This dataset is complete.","Collabs":{"ASDI":{"Tags":["infrastructure"]}},"Tags":["autonomous vehicles","aws-pds","computer vision","deep learning","image processing","lidar","machine learning","mapping","robotics","traffic","transportation","urban","weather"],"License":"This data is intended for non-commercial academic use only. It is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.","Resources":[{"Description":"Aurora Multi-Sensor Dataset","ARN":"arn:aws:s3:::pit30m","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Introduction to Visualizing Sensor Types (Jupyter notebook)","URL":"https://studiolab.sagemaker.aws/import/github/pit30m/pit30m/blob/main/examples/tutorial_00_introduction.ipynb","AuthorName":"Andrei Bârsan (note: Aurora makes no representations as to the accuracy or functionality of the tutorial)","Services":["Amazon SageMaker Studio Lab"]}],"Publications":[{"Title":"\"Pit30M: A benchmark for global localization in the age of self-driving cars\", in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4477-4484)","URL":"https://ras.papercept.net/images/temp/IROS/files/0132.pdf","AuthorName":"Martinez, J., Doubov, S., Fan, J., Bârsan, I. A., Wang, S., Máttyus, G., Urtasun, R."}]},"RegistryEntryAdded":"2023-05-16","RegistryEntryLastModified":"2025-07-09","Slug":"aurora_msds","Sources":["awslabs-open-data-registry"]}
{"Name":"Australasian Genomes","Description":"Australasian Genomes is the genomic data repository for the Threatened Species Initiative (TSI) and the ARC Centre for Innovations in Peptide and Protein Science (CIPPS). This repository contains reference genomes, transcriptomes, resequenced genomes and reduced representation sequencing data from Australasian species. Australasian Genomes is managed by the Australasian Wildlife Genomics Group (AWGG) at the University of Sydney on behalf of our collaborators within TSI and CIPPS.","Documentation":"https://awgg-lab.github.io/australasiangenomes/","Contact":"soles.wildlife-genomics@sydney.edu.au","ManagedBy":"[Australasian Wildlife Genomics Group at The University of Sydney](https://www.sydney.edu.au/science/our-research/research-areas/life-and-environmental-sciences/wildlife-genomics-group.html)","UpdateFrequency":"As new genomes are produced","Tags":["aws-pds","biodiversity","biology","conservation","genetic","genomic","life sciences","transcriptomics","wildlife"],"License":"https://threatenedspeciesinitiative.com/threatened-species-initiative-data-policy-v1-0-june-2020/","Resources":[{"Description":"Data files","ARN":"arn:aws:s3:::threatenedspecies","Region":"ap-southeast-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://threatenedspecies.s3.ap-southeast-2.amazonaws.com/index.html)"]},{"Description":"Data files","ARN":"arn:aws:s3:::koalagenomes","Region":"ap-southeast-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://koalagenomes.s3.ap-southeast-2.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2021-09-20","RegistryEntryLastModified":"2022-11-08","Slug":"australasian-genomics","Sources":["awslabs-open-data-registry"]}
{"Name":"Bureau of meteorology Atmospheric Regional Projections for Australia (BARPA)","Description":"BARPA Projections contains a moderate-resolution (BARPA-R) and a kilometre-scale (BARPA-C) regional climate projections over Australia and surrounding regions. \n\nThe current collections are developed based on CMIP6 global climate projections. It consists of an ensemble of historical and future climate model fields. It can be used for climate research, impact modelling, developing climatology products, and future likelihood products, driving downstream impact modelling (e.g., hydrological, coastal, and fire), for case studies and stress testing of environments, infrastructure and assets under different future scenarios. BARPA includes:\n- **BARPA-R (Regional) Product**: BARPA-R is a moderate-resolution (17 km) regional climate projections over Australia and the surrounding regions including parts of southeast Asia and New Zealand. The dataset includes hourly, 3-hourly, daily and monthly data for temperature, moisture, wind and flux variables at sub-surface, surface, and pressure levels, and heights above surface. The vertical levels include 1000, 950, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10 hPa pressure levels and 10, 50, 100, 150, 200, 250 and 1500 metre above surface.\n- **BARPA-R Convective Parameters Product**: This BARPA-R based dataset includes 3-hourly data for a collection of convective parameters over Australia. These are diagnostics used to characterise environments favourable for thunderstorms and their associated hazards (heavy rainfall, large hail, damaging wind gusts, and tornadoes). These include parcel-based indices such as convective available potential energy (CAPE) and convective inhibition (CIN) for surface-based, mixed-layer (ML), and most-unstable (MU) parcels; thermodynamic parameters such as bulk wind difference (BWD); and composite parameters such as derecho composite (DCP), significant tornado parameter (STP). Daily and monthly mean and maximum values are also available for surface-based CAPE and CIN.\n- **BARPA-C (Convective-Scale) Product**: BARPA-C is a kilometre-scale (4.4 km) regional climate projection, over Australia. The dataset includes 20-minute, hourly, 3-hourly, daily and monthly data for temperature, moisture, wind and flux variables at sub-surface, surface, and pressure levels , and heights above surface. There are additional variables unique to convection-permitting models, namely number of lightning flash, updraft helicity, column-maximum updraft, and derived radar reflectivity. The vertical levels include 1000, 975, 950, 925, 900, 850, 800, 750, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10 hPa pressure levels (22) and 50, 100, 150, 200, 250 and 1500 metre above surface.\n- **CORDEX-CMIP6-BARPA-R Product**: This dataset is a subset of BARPA-R modelling variables are regridded for CMIP6-CORDEX standard grid AUS-20i. These variables belong to the CMIP6-CORDEX ATMOS CORE variables. They include hourly, daily and monthly data for surface and near-surface variables for pressure, temperature, moisture, wind, precipitation and radiation. The BARPA-R model is registered with source_id BARPA-R1-NN for CORDEX-CMIP6.\n","Documentation":"[BARPA](https://opus.nci.org.au/spaces/NDP/pages/264241161/BOM+BARPA+py18)","Contact":"data_governance_office@bom.gov.au","ManagedBy":"[Bureau of Meteorology](https://www.bom.gov.au/)","UpdateFrequency":"When needed","Tags":["atmosphere","aws-pds","climate","climate model","climate projections","climate risk","environmental","extreme weather","hydrology","machine learning","meteorological","simulations","weather"],"License":"[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)","Citation":null,"Resources":[{"Description":"BARPA S3 bucket","ARN":"arn:aws:s3:::bom-opendata-climate/BARPA/","Region":"ap-southeast-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://bom-opendata-climate.s3.amazonaws.com/index.html#BARPA/)"]},{"Description":"Notification of new data in the bom-opendata-climate bucket","ARN":"arn:aws:sns:ap-southeast-2:314272288789:bom-opendata-climate-object_created","Region":"ap-southeast-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset BARPA","URL":"https://github.com/xcastagn/open-data-examples","NotebookURL":"https://github.com/xcastagn/open-data-examples/blob/master/get-to-know-a-dataset-barpa.ipynb","AuthorName":"Australian Bureau of Meteorology","AuthorURL":"https://www.bom.gov.au/"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-05-19","RegistryEntryLastModified":"2026-09-22","Slug":"australian-bureau-of-meteorology-barpa","Sources":["awslabs-open-data-registry"]}
{"Name":"Bureau of Meteorology Atmospheric Regional Reanalysis for Australia - Version 1 (BARRA)","Description":"The Bureau of Meteorology high-resolution Regional Reanalysis for Australia (BARRA) is a high-resolution multi-decadal regional atmospheric reanalysis. The reanalysis provides information about surface conditions (such as temperature, precipitation, wind speed and direction, humidity, evaporation and soil moisture), information on multiple pressure and model levels, and information on solar radiation and cloud cover. \n\nThe reanalysis suite is based on the Australian Community Climate Earth-System Simulator (ACCESS) and extends 70 levels (up to 80 km) into the atmosphere. \nIt is nested within the required boundary and/or initial conditions provided by ERA-Interim reanalysis, Operational SST and Sea Ice Analysis, and the Bureau offline soil moisture reanalysis. The region covered by the reanalysis is the Australian continent, and the surrounding region including parts of southeast Asia, New Zealand, and south to the ice edge of the Antarctic continent. \n\nThe suite contains 5 datasets.The \"regional\" dataset contains about 100 parameters, available at hourly time steps at approximately 12-km resolution. The 4 \"subdomain\" datasets, (South-West Western Australia, South Australia, Eastern New South Wales, and Tasmania), are dynamically downscaled analyses at 1.5-km resolution. These have been developed using multiple convective-scale (1.5-km) downscaling analyses driven by the 12-km system. It can be used for weather and climate research, case studies of past weather events, studying climate variability. BARRA includes:\n- **BARRA-R (Regional) Product**: BARRA-R is a moderate-resolution (12 km) regional reanalysis over Australia and the surrounding regions. The data set includes hourly data for temperature, moisture, wind and flux variables at sub-surface, surface, and pressure levels, and heights above surface.\n- **BARRA-AD (Adelaide) Product**: Downscaled (1.5km) regional atmospheric reanalysis over South Australia centred at Adelaide.\n- **BARRA-PH (Perth) Product**: Downscaled (1.5km) regional atmospheric reanalysis over south-western Western Australia at Perth.\n- **BARRA-SY (Sydney) Product**: Downscaled (1.5km) regional atmospheric reanalysis over eastern New South Wales including Sydney.\n- **BARRA-TA (Tasmania) Product**: Downscaled (1.5km) regional atmospheric reanalysis over Tasmania.\n","Documentation":"[BARRA 1](https://thredds.nci.org.au/thredds/fileServer/cj37/BARRA/FAQ_BARRA_August_2020.pdf)","Contact":"data_governance_office@bom.gov.au","ManagedBy":"[Bureau of Meteorology](https://www.bom.gov.au/)","UpdateFrequency":"No longer updated","Tags":["atmosphere","aws-pds","climate","climate model","climate projections","climate risk","environmental","extreme weather","hydrology","machine learning","meteorological","simulations","weather"],"License":"[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)","Citation":null,"Resources":[{"Description":"BARRA S3 bucket","ARN":"arn:aws:s3:::bom-opendata-climate/BARRA/","Region":"ap-southeast-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://bom-opendata-climate.s3.amazonaws.com/index.html#BARRA/)"]},{"Description":"Notification of new data in the bom-opendata-climate bucket","ARN":"arn:aws:sns:ap-southeast-2:314272288789:bom-opendata-climate-object_created","Region":"ap-southeast-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset BARRA","URL":"https://github.com/xcastagn/open-data-examples","NotebookURL":"https://github.com/xcastagn/open-data-examples/blob/master/get-to-know-a-dataset-barra_1.ipynb","AuthorName":"Australian Bureau of Meteorology","AuthorURL":"https://www.bom.gov.au/"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-05-19","RegistryEntryLastModified":"2026-09-22","Slug":"australian-bureau-of-meteorology-barra-1","Sources":["awslabs-open-data-registry"]}
{"Name":"Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia - Version 2 (BARRA2)","Description":"BARRA2 presents a moderate-resolution regional atmospheric reanalysis (BARPA-R2/RE2) and a kilometre-scale downscaled reanalysis (BARRA-C2) over Australia and surrounding regions, spanning from January 1979 to present day. \n\nBARRA2 can be used for weather and climate research, case studies of past weather events, studying climate variability and developing climatology products, driving impact modelling (e.g., hydrological, coastal, and fire), evaluation and calibration of other modelling work (e.g., seasonal and climate projections). BARRA2 includes:\n- **BARRA-R2 (Regional Version 2) Product**: BARRA-R2 is a moderate-resolution (12 km) regional atmospheric reanalysis over Australia and the surrounding regions including parts of southeast Asia and New Zealand. The dataset includes hourly, 3-hourly, daily and monthly data for temperature, moisture, wind and flux variables at sub-surface, surface, and pressure levels, and heights above surface. The vertical levels include 1000, 950, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10 hPa pressure levels and 10, 50, 100, 150, 200, 250 and 1500 metres above surface.\n- **BARRA-R2 Convective Parameters Product**: This BARPA-R2 based dataset includes hourly data for a collection of convective parameters over Australia. These diagnostics used to characterise environments favourable for thunderstorms and their associated hazards (heavy rainfall, large hail, damaging wind gusts, and tornadoes). These include parcel-based indices such as convective available potential energy (CAPE) and convective inhibition (CIN) for surface-based (SB), mixed-layer (ML), and most-unstable (MU) parcels; thermodynamic parameters such as precipitable water, saturation fraction, and lapse rates for various layers; and kinematic parameters such as bulk wind difference (BWD) and storm-relative helicity (SRH) for both fixed and \"effective\" layers. Daily and monthly mean and maximum values are also available for SBCAPE and SBCIN.\n- **BARRA-RE2 (Regional Ensemble Version 2) Product**: BARRA-RE2 is a moderate-resolution (24 km) downscaled ensemble for BARRA-R2 generated with ERA5 perturbations, over Australia and the surrounding regions including parts of southeast Asia and New Zealand. The dataset includes hourly, 3-hourly, daily and monthly data for temperature, moisture, wind and flux variables at sub-surface, surface, pressure levels, and heights above surface, for the 22 time-lagged member ensemble. The vertical levels include 1000, 950, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10 hPa pressure levels and 10, 50, 100, 150, 200, 250 and 1500 metres above surface.\n- **BARRA-RE2 Convective Parameters Product**: The BARRA-RE2 based dataset includes sub-daily data for a collection of convective parameters over Australia.\n- **BARRA-C2 (Convective-Scale Version 2) Product**: BARRA-C2 is a kilometre-scale (4.4 km) downscaled reanalysis, over Australia. The dataset includes 20-minute, hourly, 3-hourly, daily and monthly data for temperature, moisture, wind and flux variables at sub-surface, surface, pressure levels, and heights above surface. There are additional variables unique to convection-permitting models, namely lightning flash rate, updraft helicity, and derived radar reflectivity. The vertical levels include 1000, 975, 950, 925, 900, 850, 800, 750, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10 hPa pressure levels and 10, 50, 100, 150, 200, 250 and 1500 metres above surface. \nThe dataset also includes 27 convective parameters that characterise environments favourable for thunderstorms and their associated hazards (heavy rainfall, large hail, damaging wind gusts, and tornadoes).  \nCaution is advised when using these data as convective parameters are more commonly computed from coarse-resolution reanalyses with parameterised convection.  \n\nPlease see the [BARRA2 FAQs](https://opus.nci.org.au/x/mgDADw) and [Parameter Descriptions](https://opus.nci.org.au/x/n4IlF) for more information.\n","Documentation":"[BARRA 2](https://opus.nci.org.au/spaces/NDP/pages/264241166/BOM+BARRA2+ob53)","Contact":"data_governance_office@bom.gov.au","ManagedBy":"[Bureau of Meteorology](https://www.bom.gov.au/)","UpdateFrequency":"Yearly","Tags":["atmosphere","aws-pds","climate","climate model","climate projections","climate risk","environmental","extreme weather","hydrology","machine learning","meteorological","simulations","weather"],"License":"[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)","Citation":null,"Resources":[{"Description":"BARRA2 S3 bucket","ARN":"arn:aws:s3:::bom-opendata-climate/BARRA2/","Region":"ap-southeast-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://bom-opendata-climate.s3.amazonaws.com/index.html#BARRA2/)"]},{"Description":"Notification of new data in the bom-opendata-climate bucket","ARN":"arn:aws:sns:ap-southeast-2:314272288789:bom-opendata-climate-object_created","Region":"ap-southeast-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset BARRA","URL":"https://github.com/xcastagn/open-data-examples","NotebookURL":"https://github.com/xcastagn/open-data-examples/blob/master/get-to-know-a-dataset-barra_2.ipynb","AuthorName":"Australian Bureau of Meteorology","AuthorURL":"https://www.bom.gov.au/"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-05-19","RegistryEntryLastModified":"2026-09-22","Slug":"australian-bureau-of-meteorology-barra-2","Sources":["awslabs-open-data-registry"]}
{"Name":"Autonomic Aging: A dataset to quantify changes of cardiovascular autonomic function during healthy aging","Description":"Autonomic function regulating blood pressure and cardiac rhythm progressively declines with increasing age. Impaired cardiovascular control promotes a variety of age-related conditions such as dementia, or Alzheimer’s disease. This study aims to provide a database of high-resolution biological signals to describe the effect of healthy aging on cardiovascular regulation. Electrocardiogram and continuous non-invasive blood pressure signals were recorded simultaneously at rest in 1,121 healthy volunteers.","Documentation":"https://doi.org/10.13026/2hsy-t491","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Open Database License v1.0","Resources":[{"Description":"https://doi.org/10.13026/2hsy-t491","ARN":"arn:aws:s3:::physionet-open/autonomic-aging-cardiovascular/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"autonomic-aging-cardiovascular","Sources":["awslabs-open-data-registry"]}
{"Name":"Active Visual Semantics","Description":"The Active Visual Semantics (AVS) Dataset is a multimodal neuroimaging dataset combining magnetoencephalography (MEG), eye-tracking, and structural MRI, recorded from 5 participants (sub-01-sub-05) as they actively explored 4,080 natural scenes (subsampled from the Natural Scenes Dataset, NSD) across 10 recording sessions each, yielding more than 200,000 fixation epochs in total. Unlike neuroimaging datasets that rely on passive viewing with enforced central fixation, AVS captures brain activity during active, self-directed scene exploration, including natural saccades and fixations. A semantic scene-captioning task on 25% of trials links gaze behaviour to scene understanding and memory. For each session we provide eye-movement-locked MEG epochs (fixation- and saccade-locked; epochs for other events such as scene onset can be easily recomputed), raw and preprocessed eye-tracking data, per-fixation object category labels, human ratings of whether fixation targets were mentioned in participants' scene captions, pupil dynamics, and defaced structural MRI scans. Individualised head stabilisation casts, together with the structural scans, enable precise source reconstruction at the single-participant level across sessions. Source-reconstruction derivatives (FreeSurfer cortical surfaces, BEM models, forward solutions) are provided for every subject and are directly usable as an MNE-Python SUBJECTS_DIR. The dataset is accompanied by the open-source pyAVS Python package for loading, preprocessing, and analysis.\n","Documentation":"https://kietzmannlab.uni-osnabrueck.de/avs/","Contact":"Philip Sulewski (phsulewski@gmail.com)","ManagedBy":"[Kietzmann Lab](https://kietzmannlab.uni-osnabrueck.de) at Universität Osnabrück","UpdateFrequency":"Initial release as a single batch, including caption-to-object matching tables and scene-sampling metadata. No fixed update schedule.\n","Tags":["aws-pds","brain images","computer vision","electrophysiology","life sciences","machine learning","magnetic resonance imaging","natural language processing","neuroimaging","neuroscience"],"License":"[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MEG, eye-tracking, MRI/FreeSurfer derivatives, and behavioural data from the Active Visual Semantics (AVS) dataset.","ARN":"arn:aws:s3:::kietzmannlab-avs","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"New-object notifications for the kietzmannlab-avs S3 bucket (from AWS's public-dataset CloudFormation template's default SNS topic).","ARN":"arn:aws:sns:us-west-2:406194432886:kietzmannlab-avs-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: Active Visual Semantics (AVS)","URL":"https://github.com/KietzmannLab/pyavs/blob/main/examples/get-to-know-a-dataset.ipynb","AuthorName":"Philip Sulewski"},{"Title":"pyAVS Colab Quickstart","URL":"https://colab.research.google.com/github/KietzmannLab/pyavs/blob/main/examples/pyavs_colab_quickstart.ipynb","NotebookURL":"https://raw.githubusercontent.com/KietzmannLab/pyavs/main/examples/pyavs_colab_quickstart.ipynb","AuthorName":"Philip Sulewski"}],"Publications":[{"Title":"Fixation duration on natural scenes is explained by memory encoding not processing demand","URL":"https://doi.org/10.1038/s41593-026-02285-1","AuthorName":"Philip Sulewski et al."}]},"RegistryEntryAdded":"2026-08-25","RegistryEntryLastModified":"2026-08-25","Slug":"avs","Sources":["awslabs-open-data-registry"]}
{"Name":"COVID-19 Data Lake","Description":"A centralized repository of up-to-date and curated datasets on or related to the spread and characteristics of the novel corona virus (SARS-CoV-2) and its associated illness, COVID-19. Globally, there are several efforts underway to gather this data, and we are working with partners to make this crucial data freely available and keep it up-to-date. Hosted on the AWS cloud, we have seeded our curated data lake with COVID-19 case tracking data from Johns Hopkins and The New York Times, hospital bed availability from Definitive Healthcare, and over 45,000 research articles about COVID-19 and related coronaviruses from the Allen Institute for AI.","Documentation":"https://aws.amazon.com/blogs/big-data/a-public-data-lake-for-analysis-of-covid-19-data/","Contact":"aws-covid-19-data-lake@amazon.com","ManagedBy":"[Amazon Web Services](https://aws.amazon.com/)","UpdateFrequency":"Periodically","Tags":["amazon.science","bioinformatics","biology","coronavirus","COVID-19","health","life sciences","medicine","MERS","SARS"],"License":"Varies by dataset","Resources":[{"Description":"Collected COVID-19 related datasets","ARN":"arn:aws:s3:::covid19-lake","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://covid19-lake.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"A public data lake for analysis of COVID-19 data","URL":"https://aws.amazon.com/blogs/big-data/a-public-data-lake-for-analysis-of-covid-19-data/","AuthorName":"AWS Data Lake Team","AuthorURL":"https://aws.amazon.com/big-data/datalakes-and-analytics/","Services":["AWS Glue","Amazon Athena","Amazon Comprehend","Amazon Kendra","Amazon QuickSight"]},{"Title":"Exploring the public AWS COVID-19 data lake","URL":"https://aws.amazon.com/blogs/big-data/exploring-the-public-aws-covid-19-data-lake/","AuthorName":"AWS Data Lake Team","AuthorURL":"https://aws.amazon.com/big-data/datalakes-and-analytics/","Services":["Amazon Athena","AWS Glue","Amazon SageMaker"]},{"Title":"How to use SQL to query data in S3 Bucket with Amazon Athena and AWS SDK for .NET","URL":"https://github.com/aws-samples/query-data-in-s3-with-amazon-athena-and-aws-sdk-for-dotnet","AuthorName":"AWS ProServe US West Applications Team","AuthorURL":"https://github.com/orgs/aws-samples/teams/aws-proserve-us-west-applications","Services":["Amazon Athena","AWS Glue","Amazon S3","AWS CloudFormation"]}],"Tools & Applications":[{"Title":"CloudFormation template for Glue Catalog table definitions","URL":"https://us-east-2.console.aws.amazon.com/cloudformation/home?region=us-east-2#/stacks/create/review?templateURL=https://covid19-lake.s3.us-east-2.amazonaws.com/cfn/CovidLakeStack.template.json&stackName=CovidLakeStack","AuthorName":"AWS Data Lake Team","AuthorURL":"https://aws.amazon.com/big-data/datalakes-and-analytics/"},{"Title":"Explore the COVID-19 data lake public S3 bucket","URL":"https://go.aws/covid-19-data-lake","AuthorName":"AWS Data Lake Team","AuthorURL":"https://aws.amazon.com/big-data/datalakes-and-analytics/"}]},"RegistryEntryAdded":"2020-04-20","RegistryEntryLastModified":"2025-02-04","Slug":"aws-covid19-lake","Sources":["awslabs-open-data-registry"]}
{"Name":"AWS iGenomes","Description":"Common reference genomes hosted on AWS S3. Can be used when aligning and analysing raw DNA sequencing data.","Documentation":"https://ewels.github.io/AWS-iGenomes/","Contact":"https://github.com/ewels/AWS-iGenomes/issues","ManagedBy":"[SciLifeLab](https://opensource.scilifelab.se/)","UpdateFrequency":"New data are added when available.","Tags":["agriculture","amazon.science","biology","Caenorhabditis elegans","Danio rerio","genetic","genomic","Homo sapiens","life sciences","Mus musculus","Rattus norvegicus","reference index"],"License":"Multiple - please see [data origins](https://github.com/ewels/AWS-iGenomes#data-origin).","Resources":[{"Description":"AWS-iGenomes S3 Bucket","ARN":"arn:aws:s3:::ngi-igenomes","Region":"eu-west-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"nf-core analysis pipelines","URL":"http://nf-co.re/","AuthorName":"Phil Ewels","AuthorURL":"http://phil.ewels.co.uk/"}]},"RegistryEntryAdded":"2018-08-31","RegistryEntryLastModified":"2024-03-27","Slug":"aws-igenomes","Sources":["awslabs-open-data-registry"]}
{"Name":"AWS Public Blockchain Data","Description":"The AWS Public Blockchain Data initiative provides free access to blockchain datasets through collaboration with data providers. The data is optimized for analytics by being transformed into compressed Parquet files, partitioned by date for efficient querying.\n\n#### Datasets\n\n| Blockchain dataset | Maintained by | Path |\n| --- | --- | --- |\n| Bitcoin | AWS | `s3://aws-public-blockchain/v1.0/btc/` |\n| Ethereum | AWS | `s3://aws-public-blockchain/v1.0/eth/` |\n| Aptos | SonarX | `s3://aws-public-blockchain/v1.1/sonarx/aptos/` |\n| Arbitrum | SonarX | `s3://aws-public-blockchain/v1.1/sonarx/arbitrum/` |\n| Base | SonarX | `s3://aws-public-blockchain/v1.1/sonarx/base/` |\n| BNB Chain | BNB Chain | `s3://aws-public-blockchain/v1.1/bnb/` |\n| Cronos | Cronos | `s3://aws-public-blockchain/v1.1/cronos/` |\n| Hyperliquid | Hyperliquid | `s3://aws-public-blockchain/v1.1/hyperliquid/` |\n| Lighter Core | Lighter | `s3://aws-public-blockchain/v1.1/lighter/core/` |\n| Lighter on Robinhood Chain | Lighter | `s3://aws-public-blockchain/v1.1/lighter/robinhood/` |\n| Provenance | SonarX | `s3://aws-public-blockchain/v1.1/sonarx/provenance/` |\n| Stellar ([XDR files](https://developers.stellar.org/docs/learn/fundamentals/data-format/xdr)) | Stellar | `s3://aws-public-blockchain/v1.1/stellar/` |\n| The Open Network (TON) | TON | `s3://aws-public-blockchain/v1.1/ton/` |\n| XRP Ledger | SonarX | `s3://aws-public-blockchain/v1.1/sonarx/xrp/` |\n\n#### Become a Data Provider\n\nWe welcome additional blockchain data providers to join this initiative. If you're interested in contributing datasets to the AWS Public Blockchain Data program, please contact our team at [aws-public-blockchain@amazon.com](mailto:aws-public-blockchain@amazon.com).\n","Documentation":"https://github.com/aws-samples/digital-assets-examples/blob/main/analytics/","Contact":"aws-blockchain-data@amazon.com","ManagedBy":"[Amazon Web Services](https://aws.amazon.com/)","UpdateFrequency":"New data is delivered daily to the current date folders Parquet files.","Tags":["aws-pds","blockchain","web3"],"License":"https://github.com/aws-samples/digital-assets-examples/blob/main/LICENSE","Resources":[{"Description":"AWS Public Blockchain Data","ARN":"arn:aws:s3:::aws-public-blockchain","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://aws-public-blockchain.s3.us-east-2.amazonaws.com/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"Access Bitcoin and Ethereum open datasets for cross-chain analytics","URL":"https://aws.amazon.com/blogs/database/access-bitcoin-and-ethereum-open-datasets-for-cross-chain-analytics/","AuthorName":"Oliver Steffmann, Bhaskar Ravat, Sreeji Gopal, and Stefan Dicker"},{"Title":"An Interpretable Approach to Money Laundering Detection in Transaction Graphs using Pass-Through Templates","URL":"https://arxiv.org/abs/2609.20737","AuthorName":"Paolo Climaco"},{"Title":"Dynamic strategy for adaptive block size optimization in blockchain technology","URL":"https://link.springer.com/article/10.1007/s43621-025-01749-x","AuthorName":"Shafique Ahmed Awan et al."},{"Title":"Exploring Arbitrum Data: Analyze L2 Activity with AWS Public Blockchain Datasets","URL":"https://repost.aws/articles/ARpnBONglsT2e6D-hZZmxVvA/exploring-arbitrum-data-analyze-l2-activity-with-aws-public-blockchain-datasets","AuthorName":"Simon Goldberg, Everton Fraga"},{"Title":"Exploring Lighter Data: Analyze Perpetual Futures Activity with AWS Public Blockchain Datasets","URL":"https://repost.aws/articles/ARHvHW0NszQx67-yn5Lf408w/exploring-lighter-data-analyze-perpetual-futures-activity-with-aws-public-blockchain-datasets","AuthorName":"Simon Goldberg, Everton Fraga"},{"Title":"FEDS Notes - Primary and Secondary Markets for Stablecoins","URL":"https://www.federalreserve.gov/econres/notes/feds-notes/primary-and-secondary-markets-for-stablecoins-20240223.html","AuthorName":"Cy Watsky, Jeffrey Allen, Hamzah Daud, Jochen Demuth, Daniel Little, Megan Rodden, Amber Seira"},{"Title":"Frontiers of Statistics in Science and Engineering: 2035 and Beyond","URL":"https://doi.org/10.17226/29292","AuthorName":"National Academies of Sciences, Engineering, and Medicine"},{"Title":"Game-Theoretic Principles and Enhanced Simulated Annealing for Multi-Resource Allocation in Blockchain Networks","URL":"https://doi.org/10.59232/DST-V4I3P102","AuthorName":"Hieu Huynh Thanh"},{"Title":"Nested Data-Type Encodings in FastLanes (Master's Thesis, Technical University of Munich)","URL":"https://homepages.cwi.nl/~boncz/msc/2024-ZiyaMukhtarov.pdf","AuthorName":"Ziya Mukhtarov"},{"Title":"New datasets added to the AWS Public Blockchain Datasets — available for analytics and research","URL":"https://repost.aws/articles/AR3gztQGeSS8CfaKNNeyYwsQ","AuthorName":"Everton Fraga, Simon Goldberg"},{"Title":"Preparing Blockchain Transaction Data for AI- Based AML Detection: A Reproducible Cloud Pipeline","URL":"https://doi.org/10.1109/ACIT69831.2026.11700136","AuthorName":"Bogdan Adamyk et al."},{"Title":"Regulating Decentralized Systems: Evidence from Sanctions on Tornado Cash","URL":"https://www.newyorkfed.org/research/staff_reports/sr1112","AuthorName":"Anders Brownworth, Jon Durfee, Michael Junho Lee, Antoine Martin"},{"Title":"Unlocking XRP Ledger Data: Comprehensive Analysis with AWS Public Blockchain Datasets","URL":"https://repost.aws/articles/ARg_zMIXlhTG2hSDFZDfF6hQ/unlocking-xrp-ledger-data-comprehensive-analysis-with-aws-public-blockchain-datasets","AuthorName":"Simon Goldberg, Everton Fraga"},{"Title":"Why Monthly Disclosure Cannot Identify the Distribution of Unrealized Gains","URL":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6781319","AuthorName":"Avaneendra Trivedi"}]},"RegistryEntryAdded":"2022-09-23","RegistryEntryLastModified":"2026-11-06","Slug":"aws-public-blockchain","Sources":["awslabs-open-data-registry"]}
{"Name":"Harvard Electroencephalography Database","Description":"The Harvard EEG Database will encompass data gathered from four hospitals affiliated with Harvard University:Massachusetts General Hospital (MGH), Brigham and Women's Hospital (BWH), Beth Israel Deaconess Medical Center (BIDMC), and Boston Children's Hospital (BCH).","Documentation":"More documentation can be found [here](https://doi.org/10.60508/cw6j-s785)","Contact":"contact@bdsp.io","ManagedBy":"[Brain Data Science Platform](https://bdsp.io/)","UpdateFrequency":"New data is added as soon as it is available.","Tags":["aws-pds","bioinformatics","deep learning","life sciences","machine learning","medicine","neurophysiology","neuroscience"],"License":"BDSP Restricted Health Data License 1.0.0 \"[BDSP Licence](https://bdsp.io/content/harvard-eeg-db/view-license/1.0/); [Data User Agreement:](https://bdsp.io/content/harvard-eeg-db/view-dua/1.0/)\"","Resources":[{"Description":"Harvard Electroencephalography Database","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-eeg-access-point/EEG","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://doi.org/10.60508/7651-3337"}],"DataAtWork":{"Tools & Applications":[{"Title":"Harvard-EEG-Database-Tools","URL":"https://github.com/bdsp-core/Harvard-EEG-Database-Tools","AuthorName":"Brain Data Science Platform (BDSP)","AuthorURL":"https://bdsp.io"}],"Publications":[{"Title":"Harvard Electroencephalography Database","URL":"https://doi.org/10.60508/7651-3337","AuthorName":"Zafar, S., Loddenkemper, T., Lee, J. W., Cole, A., Goldenholz, D., Peters, J., et al."}]},"RegistryEntryAdded":"2023-06-20","RegistryEntryLastModified":"2023-06-20","Slug":"bdsp-harvard-eeg","Sources":["awslabs-open-data-registry"]}
{"Name":"Harvard-Emory ECG Database","Description":"The Harvard-Emory ECG database (HEEDB) is a large collection of 12-lead electrocardiography (ECG) recordings, prepared through a collaboration between Harvard University and Emory University investigators.","Documentation":"More documentation can be found [here](https://doi.org/10.60508/g072-7n95)","Contact":"contact@bdsp.io","ManagedBy":"[Brain Data Science Platform](https://bdsp.io/)","UpdateFrequency":"New data is added as soon as it is available.","Tags":["aws-pds","bioinformatics","deep learning","life sciences","machine learning","medicine","neurophysiology","neuroscience"],"License":"BDSP Restricted Health Data License 1.0.0 \"[BDSP Licence](https://bdsp.io/content/heedb/view-license/1.0/); [Data User Agreement:](https://bdsp.io/content/heedb/view-dua/1.0/)\"","Resources":[{"Description":"Harvard-Emory ECG Database","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-ecg-accesspoint/ECG/","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://doi.org/10.60508/g072-7n95"}],"DataAtWork":{"Tools & Applications":[{"Title":"WFDB Software Package","URL":"https://doi.org/10.13026/gjvw-1m31","AuthorName":"Moody, G., Pollard, T., & Moody, B.","AuthorURL":"https://www.physionet.org"}],"Publications":[{"Title":"Harvard Electroencephalography Database","URL":"https://doi.org/10.60508/g072-7n95","AuthorName":"Moura Junior, V.; Reyna, M.; Hong, S.; Gupta, A.; Ghanta, M.; Sameni, R., et al."}]},"RegistryEntryAdded":"2023-09-11","RegistryEntryLastModified":"2023-09-11","Slug":"bdsp-heedb","Sources":["awslabs-open-data-registry"]}
{"Name":"The Human Sleep Project","Description":"The Human Sleep Project (HSP) sleep physiology dataset is a growing collection of clinical polysomnography (PSG) recordings. Beginning with PSG recordings from from ~15K patients evaluated at the Massachusetts General Hospital, the HSP will grow over the coming years to include data from >200K patients, as well as people evaluated outside of the clinical setting. This data is being used to develop CAISR (Complete AI Sleep Report), a collection of deep neural networks,  rule-based algorithms, and signal processing approaches designed to provide better-than-human detection of conventional PSG scoring metrics, including sleep stages, arousals, apnea and hypopnea events and their subtypes, and periodic limb movements. Beyond conventional scoring, the HSP dataset is intended to support research seeking to identify \"hidden\" information within the brain's activity during sleep that can be used to directly measure brain health. These brain health indicators include measures of risk for common neurologic diseases, including cerebrovascular disease, and Alzheimer's disease and related neurodegenerative diseases of aging; and indicators of response to therapies, including lifestyle interventions (e.g. diet, meditation, exercise) and pharmacologic interventions. These data are shared via the BDSP (Brain Data Science Platform, a resource developed by an international coalition of investigators that aggregates and harmonizes a wide range of large-scale human clinical neuroscience data to support research aimed at improving diagnosis, treatment, and prevention of neurologic disease, and promotion of brain health. The summary data provided here are released for the benefit of the wider scientific community without restriction on use.","Documentation":"More documentation can be found [here](https://doi.org/10.60508/r0t9-5r24)","Contact":"contact@bdsp.io","ManagedBy":"[Brain Data Science Platform](https://bdsp.io/)","UpdateFrequency":"New data is added as soon as it is available.","Tags":["aws-pds","bioinformatics","deep learning","life sciences","machine learning","medicine","neurophysiology","neuroscience"],"License":"[BDSP Restricted Health Data License 1.0.0](https://bdsp.io/content/hsp/view-license/1.0/); [terms of use](https://github.com/bdsp-core/bdsp-license-and-dua/blob/main/terms-of-use.md)","Resources":[{"Description":"The Human Sleep Project (HSP) sleep physiology dataset is a growing collection of clinical polysomnography (PSG) recordings. Beginning with PSG recordings from from ~19K patients evaluated at the Massachusetts General Hospital, the HSP will grow over the coming years to include data from >200K patients, as well as people evaluated outside of the clinical setting.","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-psg-access-point/PSG/","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://doi.org/10.60508/r0t9-5r24"}],"DataAtWork":{"Tools & Applications":[{"Title":"The Human Sleep Project","URL":"https://doi.org/10.60508/r0t9-5r24","AuthorName":"Westover MB, Moura Junior V, Thomas R, Cash S, Nasiri S, Sun H, et al.","AuthorURL":"https://bdsp.io"}],"Publications":[{"Title":"A cost-effectiveness analysis of nasal surgery to increase CPAP compliance in sleep apnea patients with nasal obstruction. The Laryngoscope. 2017 Apr;127(4):977-983. PMCID: PMC5483184.","URL":"https://pubmed.ncbi.nlm.nih.gov/27653626/","AuthorName":"Kempfle J, Westover MB, Bianchi MT."},{"Title":"A Systematic Review and Meta-Analysis Examining the Impact of Sleep Disturbance on Postoperative Delirium. Critical Care Medicine. 2018 Dec;46(12):e1204-e1212. PMCID: PMC6274586.","URL":"https://pubmed.ncbi.nlm.nih.gov/30222634/","AuthorName":"Fadayomi AB, Ibala R, Bilotta F, Westover MB, Akeju O."},{"Title":"Age estimation from sleep studies using deep learning predicts life expectancy. NPJ Digit Med. 2022 Jul 22;5(1):103. doi: 10.1038/s41746-022-00630-9. PMCID: PMC9307657.","URL":"https://pubmed.ncbi.nlm.nih.gov/35869169/","AuthorName":"Brink-Kjaer A, Leary EB, Sun H, Westover MB, Stone KL, Peppard PE, et al."},{"Title":"Algorithm for automatic detection of self-similarity and prediction of residual central respiratory events during continuous positive airway pressure. Sleep. 2021 Apr 9;44(4):zsaa215. doi: 10.1093/sleep/zsaa215. PMCID: PMC8631077.","URL":"https://pubmed.ncbi.nlm.nih.gov/33057718/","AuthorName":"Oppersma E, Ganglberger W, Sun H, Thomas RJ*, Westover MB*"},{"Title":"Alternative remedies for insomnia: a proposed method for personalized therapeutic trials. Nature and Science of Sleep. 2017;9:97-108. PMCID: PMC5364017.","URL":"https://pubmed.ncbi.nlm.nih.gov/28360539/","AuthorName":"Romero K, Goparaju B, Russo K, Westover MB, Bianchi MT."},{"Title":"Applications of a Capacitor-Based Respiratory Position Sensing Device: Implications for Radiation Therapy. Austin Journal of Medical Oncology 2014;1(2). PMCID: PMC6956860.","URL":"https://pubmed.ncbi.nlm.nih.gov/31934681/","AuthorName":"Weng Y, Westover MB, Speier C, Sharp G, Bianchi MT, Westover KD."},{"Title":"Artificial Intelligence in Sleep Medicine: An American Academy of Sleep Medicine Position Statement. Journal of Clinical Sleep Medicine. 2020 Feb 5. doi: 10.5664/jcsm.8288. PMCID: PMC7161449.","URL":"https://pubmed.ncbi.nlm.nih.gov/32022674/","AuthorName":"Goldstein CA, Berry RB, Kent DT, Kristo DA, Seixas AA, Redline S, et al."},{"Title":"Artificial intelligence in sleep medicine: Background and implications for clinicians. Journal of Clinical Sleep Medicine. 2020 Feb 17. doi: 10.5664/jcsm.8388. PMCID: PMC7161463.","URL":"https://pubmed.ncbi.nlm.nih.gov/32065113/","AuthorName":"Goldstein CA, Berry RB, Kent DT, Kristo DA, Seixas AA, Redline S, et al."},{"Title":"Automated Scoring of Respiratory Events in Sleep with a Single Effort Belt and Deep Neural Networks. IEEE Transactions on Biomedical Engineering. 2021 Dec 20;PP. doi: 10.1109/TBME.2021.3136753. Epub ahead of print. PMCID: PMC9119908.","URL":"https://pubmed.ncbi.nlm.nih.gov/34928786/","AuthorName":"Nassi TE, Ganglberger W, Sun H, Bucklin AA, Biswal S, van Putten MJAM, et al."},{"Title":"Automated Sleep Apnea Quantification Based on Respiratory Movement. International Journal of Medical Sciences 2014; 11(8):796-802. PMCID: PMC4057486.","URL":"https://pubmed.ncbi.nlm.nih.gov/24936142/","AuthorName":"Bianchi MT, Lipoma T, Darling C, Alameddine Y, Westover MB."},{"Title":"Brain Age from the Electroencephalogram of Sleep. Neurobiol Aging. 2019 Feb;74:112-120. PMCID: PMC6478501.","URL":"https://pubmed.ncbi.nlm.nih.gov/30448611/","AuthorName":"Sun H, Paixao L, Oliva JT, Goparaju B, Carvalho DZ, van Leeuwen KG, et al."},{"Title":"Classification algorithms for predicting sleepiness and sleep apnea severity. Journal of Sleep Research. 2012 Feb;21(1):101-12. PMCID: PMC3698244.","URL":"https://pubmed.ncbi.nlm.nih.gov/21752133/","AuthorName":"Eiseman NA, Westover MB, Mietus JE, Thomas RJ, Bianchi MT"},{"Title":"Computational Evidence for a Competitive Thalamocortical Model of Spikes and Spindle Activity in Rolandic Epilepsy. Front Comput Neurosci. 2021 Jun 18;15:680549. doi: 10.3389/fncom.2021.680549. PMCID: PMC8249809.","URL":"https://pubmed.ncbi.nlm.nih.gov/34220477/","AuthorName":"Li Q, Westover MB, Zhang R, Chu CJ."},{"Title":"Data-Driven Modeling of Sleep States from EEG.  Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'12). 2012;2012:5090-3. PMCID: PMC6788787.","URL":"https://pubmed.ncbi.nlm.nih.gov/23367073/","AuthorName":"Van Esbroeck A, Westover MB"},{"Title":"Decision modeling in sleep apnea: the critical roles of pre-test probability, cost of untreated OSA, and time horizon.  Journal of Clinical Sleep Medicine. 2016 Mar;12(3):409-418.  PMCID: PMC4773629.","URL":"https://pubmed.ncbi.nlm.nih.gov/26518699/","AuthorName":"Moro M, Westover MB,  Kelly J, Bianchi MT."},{"Title":"Dementia Detection from Brain Activity During Sleep. Sleep. Nov 30:zsac286. doi: 10.1093/sleep/zsac286. Epub ahead of print. PMID: 36448766.*","URL":"https://pubmed.ncbi.nlm.nih.gov/36448766/","AuthorName":"Ye E*, Sun H*, Krishnamurthy PV, Adra N, Ganglberger W, Thomas RJ, et al."},{"Title":"Effects of cholinergic neuromodulation on thalamocortical rhythms during NREM sleep: a model study. Frontiers in Computational Neuroscience. 2020 Jan 23;13:100. doi: 10.3389/fncom.2019.00100. eCollection 2019. PMCID: PMC6990259.","URL":"https://pubmed.ncbi.nlm.nih.gov/32038215/","AuthorName":"Li Q, Song JL, Li SH, Westover MB, Zhang R."},{"Title":"Emergence of stable functional networks in long-term human EEG. The Journal of Neuroscience, Feb 2012; 32(8): 2703-2713. PMCID: PMC3361717.","URL":"https://pubmed.ncbi.nlm.nih.gov/22357854/","AuthorName":"Chu-Shore C, Kramer MA,  Pathmanathan J, Bianchi MT, Westover MB,  Wizon L, et al."},{"Title":"Excess Brain Age Reflected in the Electroencephalogram of Sleep Predicts Reduced Life Expectancy. Neurobiology of Aging. 2019 Dec 23. pii: S0197-4580(19)30443-9. doi: 10.1016/j.neurobiolaging.2019.12.015. PMCID: PMC7085452.","URL":"https://pubmed.ncbi.nlm.nih.gov/31932049/","AuthorName":"Paixao L, Sikka P, Sun H, Jain A, Hogan J, Thomas RJ, et al."},{"Title":"Expert-level automated sleep staging of long-term scalp EEG recordings using deep learning. SLEEP. June 2020;zsaa112. doi:10.1093/sleep/zsaa112. PMCID: PMC7686563.","URL":"https://pubmed.ncbi.nlm.nih.gov/32478820/","AuthorName":"Jaoude MA, Sun H, Pellerin KR, Pavlova M, Sarkis RA, Cash SS, et al."},{"Title":"Expert-level Sleep Scoring with Deep Neural Networks. Journal of the American Medical Informatics Association (JAMIA). 2018 Dec 1;25(12):1643-1650. PMCID: PMC6289549.","URL":"https://pubmed.ncbi.nlm.nih.gov/30445569/","AuthorName":"Biswal S, Kulas J, Sun H, Goparaju B, Westover MB*, Bianchi MT*, et al."},{"Title":"Focal sleep spindle deficits reveal focal thalamocortical dysfunction and predict cognitive deficits in childhood epilepsy with centrotemporal spikes. Journal of Neuroscience. 2021 Jan 14:JN-RM-2009-20. doi: 10.1523/JNEUROSCI.2009-20.2020. Epub ahead of print. PMCID: PMC8115887.","URL":"https://pubmed.ncbi.nlm.nih.gov/33468567/","AuthorName":"Kramer M, Stoyell S, Chinappen D, Ostrowski L, Spencer E, Morgan A, et al."},{"Title":"High prevalence of sleep-disordered breathing in the intensive care unit - a cross-sectional study. Sleep Breath. 2022 Aug 16. doi: 10.1007/s11325-022-02698-9. Epub ahead of print. PMID: 35971023.","URL":"https://pubmed.ncbi.nlm.nih.gov/35971023/","AuthorName":"Bucklin AA, Ganglberger W, Quadri SA, Tesh RA, Adra N, Da Silva Cardoso M, et al."},{"Title":"HIV Increases Sleep-based Brain Age Despite Antiretroviral Therapy. SLEEP. 2021 Mar 30:zsab058. doi: 10.1093/sleep/zsab058. Epub ahead of print. PMCID: PMC8361332.","URL":"https://pubmed.ncbi.nlm.nih.gov/33783511/","AuthorName":"Leone MJ*, Sun H*, Boutros CL, Liu L, Ye E, Sullivan L, et al."},{"Title":"How Machine Learning is Powering Neuroimaging to Improve Brain Health. Neuroinformatics. 2022 Mar 28:10.1007/s12021-022-09572-9. doi: 10.1007/s12021-022-09572-9. Epub ahead of print. PMID: 35347570; PMCID: PMC9515245.","URL":"https://pubmed.ncbi.nlm.nih.gov/35347570/","AuthorName":"Singh NM, Harrod JB, Subramanian S, Robinson M, Chang K, Cetin-Karayumak S, et al."},{"Title":"Insomnia and morning motor vehicle accidents: A decision analysis of the risk of hypnotics versus the risk of untreated insomnia. Journal of Clinical Psychopharmacology. 2014 Jun;34(3):400-402. PMCID: PMC6794095.","URL":"https://pubmed.ncbi.nlm.nih.gov/24743722/","AuthorName":"Bianchi MT, Westover MB."},{"Title":"Large-Scale Automated Sleep Staging. Sleep. 2017 Oct 1;40(10). doi: 10.1093/sleep/zsx139. PMCID: PMC6251659.","URL":"https://pubmed.ncbi.nlm.nih.gov/29029305/","AuthorName":"Sun H, Jian J, Goparaju B, Huang GB, Sourina O, Bianchi MT, et al."},{"Title":"Night-to-Night Variability of Sleep Electroencephalography-Based Brain Age Measurements. Clinical Neurophysiology. 2020 Oct 29;132(1):1-12. doi: 10.1016/j.clinph.2020.09.029. Epub ahead of print. PMCID: PMC7855943.","URL":"https://pubmed.ncbi.nlm.nih.gov/33248430/","AuthorName":"Hogan J, Sun H, Thomas RT, Westover MB."},{"Title":"Optimal Spindle Detection Parameters for Predicting Cognitive Performance. Sleep. 2022 Jan 4:zsac001. doi: 10.1093/sleep/zsac001. Epub ahead of print. PMCID: PMC8996023.","URL":"https://pubmed.ncbi.nlm.nih.gov/34984446/","AuthorName":"Adra N, Sun H, Ganglberger W, Ye EM, Dümmer LW, Tesh RA, et al."},{"Title":"Power Law versus Exponential State Transition Dynamics:  Application to Sleep-Wake Architecture. PLoSONE  5(12):  e14204. Dec. 2, 2010. PMCID: PMC2996311.","URL":"https://pubmed.ncbi.nlm.nih.gov/21151998/","AuthorName":"Chu-Shore J, Westover MB, Bianchi MT"},{"Title":"Sleep Apnea and Respiratory Anomaly Detection from a Wearable Band and Oxygen Saturation. Sleep and Breathing. 2021 Aug 18. doi: 10.1007/s11325-021-02465-2. Online ahead of print. PMCID: PMC8854446.","URL":"https://pubmed.ncbi.nlm.nih.gov/34409545/","AuthorName":"Ganglberger W, Bucklin AA, Tesh R, Cardoso MDS, Sun H, Leone M, , et al."},{"Title":"Sleep EEG-based Brain Age Index is a Biomarker for Dementia. JAMA Network Open. 2020 Sep 1;3(9):e2017357. doi: 10.1001/jamanetworkopen.2020.17357. PMID: 32986106; PMCID: PMC7522697.","URL":"https://pubmed.ncbi.nlm.nih.gov/32986106/","AuthorName":"Ye E, Sun H, Leone MJ, Paixao L, Thomas RJ, Lam AD, et al."},{"Title":"Sleep Staging from Electrocardiography and Respiration with Deep Learning. SLEEP. Sleep. 2020 Jul 13;43(7):zsz306. doi: 10.1093/sleep/zsz306. PMCID: PMC7355395.","URL":"https://pubmed.ncbi.nlm.nih.gov/31863111/","AuthorName":"Sun H, Ganglberger W, Panneerselvam E, Leone MJ, Quadri SA, Goparaju B, et al."},{"Title":"The Challenge of Undiagnosed Sleep Apnea in Low-Risk Populations: A Decision Analysis. Military Medicine 2014 Aug;179(8S):47-54. PMCID: PMC6788752.","URL":"https://pubmed.ncbi.nlm.nih.gov/25102549/","AuthorName":"Bianchi MT, Hershman S, Bahadoran M, Ferguson M, Westover MB."},{"Title":"The Impact of Body Posture and Sleep Stages on Sleep Apnea Severity in Adults.  Journal of Clinical Sleep Medicine. 2012 Dec 15;8(6):655-66A. PMCID: PMC3501662.","URL":"https://pubmed.ncbi.nlm.nih.gov/23243399/","AuthorName":"Eiseman NA, Westover MB, Ellenbogen JM, Bianchi MT"},{"Title":"The sleep and wake electroencephalogram over the lifespan. Neurobiol Aging. 2023 Jan 19;124:60-70. doi: 10.1016/j.neurobiolaging.2023.01.006. Epub ahead of print. PMID: 36739622.","URL":"https://pubmed.ncbi.nlm.nih.gov/36739622/","AuthorName":"Sun H, Ye E, Paixao L, Ganglberger W, Chu CJ, Zhang C, et al."}]},"RegistryEntryAdded":"2023-05-26","RegistryEntryLastModified":"2023-05-26","Slug":"bdsp-hsp","Sources":["awslabs-open-data-registry"]}
{"Name":"I-CARE:International Cardiac Arrest REsearch consortium Electroencephalography Database","Description":"The International Cardiac Arrest REsearch consortium (I-CARE) Database includes baseline clinical information and continuous electroencephalography (EEG) recordings from 1,020 comatose patients with a diagnosis of cardiac arrest who were admitted to an intensive care unit from seven academic hospitals in the U.S. and Europe. Patients were monitored with 18 bipolar EEG channels over hours to days for the diagnosis of seizures and for neurological prognostication. Long-term neurological function was determined using the Cerebral Performance Category scale.","Documentation":"More documentation can be found [here](https://doi.org/10.60508/xwye-v214)","Contact":"contact@bdsp.io","ManagedBy":"[Brain Data Science Platform](https://bdsp.io/)","UpdateFrequency":"New data is added as soon as it is available.","Tags":["aws-pds","bioinformatics","deep learning","life sciences","machine learning","medicine","neurophysiology","neuroscience"],"License":"BDSP Restricted Health Data License 1.0.0 \"[BDSP Licence](https://bdsp.io/content/bdsp-icare/view-license/1.0/); [Data User Agreement:](https://bdsp.io/content/bdsp-icare/view-dua/1.0/)\"","Resources":[{"Description":"This database consists of clinical and EEG data from adult patients with out-of-hospital or in-hospital cardiac arrest who had return of heart function (i.e., return of spontaneous circulation [ROSC]) but remained comatose - defined as inability to follow verbal commands and a Glasgow Coma Score inferior or equal to 8. The initial database release contains data from 607 patients - this is the public training set for the 2023 PhysioNet Challenge. This database release does not contain data from the remaining 413 patients that we are retaining as the hidden validation and test sets for the Challenge. Algorithms developed using the I-CARE training set can be submitted to the Brain Data Science Platform (BDSP) team for evaluation using the held-out dataset (bdsp.io).","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-icare-access-point/ICARE_train/","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://doi.org/10.60508/xwye-v214"}],"DataAtWork":{"Tools & Applications":[{"Title":"WFDB Software Package","URL":"https://doi.org/10.13026/gjvw-1m31","AuthorName":"Moody, G., Pollard, T., & Moody, B.","AuthorURL":"https://www.physionet.org"}],"Publications":[{"Title":"I-CARE:International Cardiac Arrest REsearch consortium Electroencephalography Database","URL":"https://doi.org/10.60508/xwye-v214","AuthorName":"Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval M, Kandhare P, Karukonda V, et al."},{"Title":"The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database","URL":"https://doi.org/10.1101/2023.08.28.23294672","AuthorName":"Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval M, Kandhare P, Karukonda V, et al."}]},"RegistryEntryAdded":"2023-11-19","RegistryEntryLastModified":"2023-11-19","Slug":"bdsp-icare","Sources":["awslabs-open-data-registry"]}
{"Name":"SPaRCNet data:Seizures, Rhythmic and Periodic Patterns in ICU Electroencephalography","Description":"The IIIC dataset includes  50,697 labeled EEG samples from 2,711 patients' and 6,095 EEGs that were annotated by physician experts from 18 institutions. These samples were used to train SPaRCNet (Seizures, Periodic and Rhythmic Continuum patterns Deep Neural Network), a computer program that classifies IIIC events with an accuracy matching clinical experts.","Documentation":"More documentation can be found [here](https://doi.org/10.60508/cw6j-s785)","Contact":"admin@bdsp.io","ManagedBy":"[Brain Data Science Platform](https://bdsp.io/)","UpdateFrequency":"New data is added as soon as it is available.","Tags":["aws-pds","bioinformatics","deep learning","life sciences","machine learning","medicine","neurophysiology","neuroscience"],"License":"BDSP Restricted Health Data License 1.0.0 \"[BDSP Licence](https://bdsp.io/content/bdsp-sparcnet/view-license/1.1/); [Data User Agreement:](https://bdsp.io/content/bdsp-sparcnet/view-dua/1.1/)\"","Resources":[{"Description":"ICU EEG Dataset","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-sparcnet-access-point","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://doi.org/10.60508/cw6j-s785"}],"DataAtWork":{"Tools & Applications":[{"Title":"IIIC-SPaRCNet Github Repository","URL":"https://github.com/bdsp-core/ICU_EEG_Neuro_Prognosis","AuthorName":"Brain Data Science Platform (BDSP)","AuthorURL":"https://bdsp.io"}],"Publications":[{"Title":"Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation","URL":"https://pubmed.ncbi.nlm.nih.gov/36878708/","AuthorName":"Jing J, Ge W, Hong S, Fernandes MB, Lin Z, Yang C et al., et al."},{"Title":"SPaRCNet data:Seizures, Rhythmic and Periodic Patterns in ICU Electroencephalography","URL":"https://doi.org/10.60508/cw6j-s785","AuthorName":"Jing, J., Ge, W., Struck, A. F., Fernandes, M., Hong, S., An, S., et al."}]},"RegistryEntryAdded":"2023-05-26","RegistryEntryLastModified":"2023-05-26","Slug":"bdsp-sparcnet","Sources":["awslabs-open-data-registry"]}
{"Name":"Brain Data Science Database 3","Description":"This collection unifies multiple brain datasets spanning critical care, sleep medicine, cardiopulmonary health, infectious diseases, and other aspects of clinical neuroscience. It includes large-scale electroencephalography (EEG) and polysomnography (PSG) recordings, brain imaging data (MRI, CT, PET), and electronic health records (EHR) data supporting research in areas such as epilepsy, delirium, coma, sleep depth, sleep-related breathing disorders, burst suppression, meditation, subarachnoid hemorrhage, cardiac arrest, and neuroinfectious diseases. All data is de-identified and includes algorithmic tools and reproducible code. Access requires (1) submitting proof of CITI training certification (research ethics training for human subjects research - see https://bdsp.io/about/citi-course/); (2) signing a data use agreement that prohibits re-identification, requires data security, restricts sharing, limits use to non-commercial research, and mandates reporting any potentially identifying information.","Documentation":"https://bdsp.io/about/database/#credentialed","Contact":"contact@bdsp.io","ManagedBy":"[Brain Data Science Platform](https://bdsp.io/)","UpdateFrequency":"New data is added as soon as it is available.","Tags":["aws-pds","bioinformatics","deep learning","life sciences","machine learning","medicine","neurophysiology","neuroscience"],"License":"[BDSP Restricted Health Data License 1.0.0](https://bdsp.io/content/heedb/view-license/4.0/)","Citation":"Refer to individual project citations in documentation","Resources":[{"Description":"The Human Sleep Project (HSP)","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-credentialed-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/hsp)"]},{"Description":"Boston Children’s Hospital Sleep Corpus","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-credentialed-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/l8c86mgywuneuy74ae71)"]},{"Description":"Harvard Electroencephalography Database","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-credentialed-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/harvard-eeg-db)"]},{"Description":"Harvard-Emory ECG Database","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-credentialed-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/heedb/4.0/)"]},{"Description":"NIDX - Identifying Neuroinfectious Diseases in Electronic Health Records","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-credentialed-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/nidx/1.0/)"]},{"Description":"Automated phenotyping of mild cognitive impairment and dementias using electronic health records - Data and Code","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-credentialed-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/mci-ad-nlp/1.0/)"]},{"Description":"Identification of patients with epilepsy using automated electronic health records phenotyping - Data and Code","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-credentialed-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/nax-epilepsy-nlp/1.0/)"]}],"RegistryEntryAdded":"2025-09-09","RegistryEntryLastModified":"2026-06-04","Slug":"bdsp_credentialed_projects","Sources":["awslabs-open-data-registry"]}
{"Name":"Brain Data Science Database 1","Description":"This collection unifies multiple brain datasets spanning critical care, sleep medicine, cardiopulmonary health, infectious diseases, and other aspects of clinical neuroscience. It includes a variety of types of clinical neuroscience data including electroencephalography (EEG) and polysomnography (PSG) recordings, and supporting data to enable research in diverse areas of clinical neuroscience such as epilepsy, delirium, coma, and sleep medicine. All data is de-identified and includes code to reproduce results in accompanying research publications. The data is available for non-commercial research use and is open access.","Documentation":"https://bdsp.io/about/database/#open","Contact":"contact@bdsp.io","ManagedBy":"[Brain Data Science Platform](https://bdsp.io/)","UpdateFrequency":"New data is added as soon as it is available.","Tags":["aws-pds","bioinformatics","deep learning","life sciences","machine learning","medicine","neurophysiology","neuroscience"],"License":"[Open Data Commons Attribution License v1.0](https://bdsp.io/content/rhofbas/view-license/1.0/)","Citation":"Refer to individual project citations in documentation","Resources":[{"Description":"Assessing Risk of Health Outcomes From Brain Activity in Sleep","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-open-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/rhofbas/1.0/)"]},{"Description":"The Human Burst Suppression Electroencephalogram of Deep Hypothermia","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-open-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/dhypothermia/1.0/)"]},{"Description":"Visual EEG-Based Grading of Delirium Severity and Associations with Clinical Outcomes (VE-CAM-S)","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-open-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/ve-cam-s/1.0/)"]}],"RegistryEntryAdded":"2025-09-09","RegistryEntryLastModified":"2026-06-04","Slug":"bdsp_open_projects","Sources":["awslabs-open-data-registry"]}
{"Name":"Brain Data Science Database 2","Description":"This collection unifies multiple brain datasets spanning critical care, sleep medicine, cardiopulmonary health, infectious diseases, and other aspects of clinical neuroscience. It includes large-scale electroencephalography (EEG) and polysomnography (PSG) recordings, brain imaging data (MRI, CT, PET), and supporting data to enable research in diverse areas of clinical neuroscience such as epilepsy, delirium, coma, sleep depth, sleep-related breathing disorders, meditation, subarachnoid hemorrhage, cardiac arrest, neuroinfectious diseases, and audiology. All data is de-identified and includes algorithmic tools and reproducible code. Access requires signing a data use agreement that prohibits re-identification, requires data security, restricts sharing, limits use to non-commercial research, and mandates reporting any potentially identifying information.","Documentation":"https://bdsp.io/about/database/#restricted","Contact":"contact@bdsp.io","ManagedBy":"[Brain Data Science Platform](https://bdsp.io/)","UpdateFrequency":"New data is added as soon as it is available.","Tags":["aws-pds","bioinformatics","deep learning","life sciences","machine learning","medicine","neurophysiology","neuroscience"],"License":"[BDSP Restricted Health Data License 1.0.0](https://bdsp.io/content/saheeg/view-license/1.0/)","Citation":"Refer to individual project citations in documentation","Resources":[{"Description":"Automated EEG-based prediction of delayed cerebral ischemia after subarachnoid hemorrhage","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/saheeg)"]},{"Description":"Cyclops - Automated detection of interictal epileptiform discharges with few EEG channels","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/cyclops)"]},{"Description":"E-CAM-S - Physiological Assessment of Delirium Severity via EEG","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/e-cam-s)"]},{"Description":"I-CARE - International Cardiac Arrest Research consortium EEG Database","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/bdsp-icare)"]},{"Description":"Morphological Prediction of CPAP Associated Acute Respiratory Instability","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/selfsim)"]},{"Description":"Ordinal Sleep Depth - Data and Code","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/ordinal-sleep-depth)"]},{"Description":"Quantifying Loop Gain using Dynamical Modelling of Ventilatory Control","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/hlg-data-code)"]},{"Description":"Real-Time Segmentation of Burst Suppression Patterns in Critical Care EEG Monitoring","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/bsupp)"]},{"Description":"Sleep Recordings from Wearable Devices in Meditators","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/meditate)"]},{"Description":"SPaRCNet - Seizures, Rhythmic and Periodic Patterns in ICU EEG","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/bdsp-sparcnet)"]},{"Description":"SpikeNet 2.0 - Deep learning model for epileptiform discharges in EEG","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/spikenet2)"]},{"Description":"TEEGLLTEEG - This EEG Looks Like That EEG","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/teegllteeg)"]},{"Description":"Targeted Metabolomics in the REGARDS Stroke Case-Cohort Study","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/tmrsccs)"]},{"Description":"Cerebrospinal Fluid Testing for Neuroinvasive West Nile Virus (CSF-WNV dataset)","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/csf-wnv/1.0.0/)"]},{"Description":"Continuous EEG Predicts Delayed Cerebral Ischemia After SAH","ARN":"arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-restricted-access-point/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore Dataset](https://bdsp.io/content/ceeg-sah-hemorrhage-dind/1.0/)"]}],"RegistryEntryAdded":"2025-09-09","RegistryEntryLastModified":"2026-06-04","Slug":"bdsp_restricted_projects","Sources":["awslabs-open-data-registry"]}
{"Name":"Beat Acute Myeloid Leukemia (AML) 1.0","Description":"Beat AML 1.0 is a collaborative research program involving 11 academic medical centers who worked\ncollectively to better understand  drugs and drug combinations that should be prioritized for\nfurther development within clinical and/or molecular subsets of acute myeloid leukemia (AML)\npatients. Beat AML 1.0 provides the largest-to-date dataset on primary acute myeloid leukemia\nsamples offering genomic, clinical, and drug response.\n\nThis dataset contains open Clinical Supplement and RNA-Seq Gene Expression Quantification data.\n\nThis dataset also contains controlled Whole Exome Sequencing (WXS) and RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation,\nWXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification  \n","Documentation":"https://www.cancer.gov/about-nci/organization/ccg/blog/2019/beataml\n","Contact":"dcf-support@datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genetic","genomic","Homo sapiens","life sciences","STRIDES"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"BEATAML1.0-COHORT RNA-Seq Gene Expression Quantification","ARN":"arn:aws:s3:::gdc-beataml1-cohort-phs001657-2-open","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"BEATAML1.0-CRENOLANIB Clinical Supplement","ARN":"arn:aws:s3:::gdc-beataml1.0-crenolanib-phs001628-2-open","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, RNA-Seq\nSplice Junction Quantification\n","ARN":"arn:aws:s3:::gdc-beataml1-cohort-phs001657-2-controlled","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001657.v1.p1&phv=417530&phd=&pha=&pht=9391&phvf=&phdf=&phaf=&phtf=&dssp=1&consent=&temp=1"}],"DataAtWork":{"Tools & Applications":[{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"}],"Publications":[{"Title":"Clinical resistance to crenolanib in acute myeloid leukemia due to diverse molecular\nmechanisms\n","URL":"https://www.ncbi.nlm.nih.gov/pubmed/30651561","AuthorName":"Zhang H, Savage S, Schultz AR, Bottomly D, White L, Segerdell E, et al."},{"Title":"Functional Genomic Landscape of Acute Myeloid Leukemia","URL":"https://www.ncbi.nlm.nih.gov/pubmed/30333627","AuthorName":"Jeffrey W. Tyner, Cristina E. Tognon, Dan Bottomly et al."}]},"RegistryEntryAdded":"2020-04-09","RegistryEntryLastModified":"2020-10-05","Slug":"beataml","Sources":["awslabs-open-data-registry"]}
{"Name":"ECMWF IFS Analysis N320 - Beyond Weather Icechunk Zarr","Description":"The ECMWF Integrated Forecasting System (IFS) operational analysis, every 6 hours from 2011\nto 2026, on the native N320 reduced Gaussian grid (542,080 points, about 31 km). It contains a\nselection of variables: 6 upper-air variables on 18 pressure levels, 22 surface variables, and\nthe land-sea mask and orography of each IFS cycle. This is the input set of data-driven\nweather models such as ECMWF's AIFS, for training, fine-tuning and verification. Stored as\nversioned Icechunk Zarr, chunked by timestep for fast random access.\n","Documentation":"https://github.com/Beyond-Weather-Git/open-data-examples/blob/main/ifs-analysis-n320/README.md","Contact":"https://github.com/Beyond-Weather-Git/open-data-examples/issues","ManagedBy":"[Beyond Weather](https://beyond-weather.com)","UpdateFrequency":"Not currently updated. The archive covers 2011-01-01 to 2026-09-30.","Tags":["atmosphere","aws-pds","forecast","global","machine learning","meteorological","weather","zarr"],"License":"[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/),\nfollowing the [ECMWF terms of use](https://apps.ecmwf.int/datasets/licences/general/).\nCopyright © 2011-2026 European Centre for Medium-Range Weather Forecasts (ECMWF). Source: www.ecmwf.int.\nECMWF does not accept any liability whatsoever for any error or omission in the data, their availability,\nor for any loss or damage arising from their use. Modified by Beyond Weather: selection of variables and\nlevels, interpolation to the N320 grid by ECMWF MARS, conversion from GRIB to Icechunk Zarr, renaming of\nvariables, and lossy bit rounding. Beyond Weather provides its modifications and hosting as-is, without\nwarranty, to the fullest extent permitted by law. This modified dataset is not endorsed by, sponsored by\nor affiliated with ECMWF.\n","Citation":"Contains modified ECMWF IFS operational analysis data, Copyright © 2011-2026 European Centre for\nMedium-Range Weather Forecasts (ECMWF), www.ecmwf.int, licensed under CC BY 4.0.\n","Resources":[{"Description":"IFS operational analysis 2011-2026, 6-hourly, N320 reduced Gaussian grid, Zarr v3 in an Icechunk repository","ARN":"arn:aws:s3:::beyond-weather-ecmwf-ifs-analysis","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://beyond-weather-ecmwf-ifs-analysis.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new ECMWF IFS Analysis N320 - Beyond Weather Icechunk Zarr data","ARN":"arn:aws:sns:us-east-2:239830941214:beyond-weather-ecmwf-ifs-analysis-object_created","Region":"us-east-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: ECMWF IFS Analysis N320 - Beyond Weather Icechunk Zarr","URL":"https://github.com/Beyond-Weather-Git/open-data-examples/blob/main/ifs-analysis-n320/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/Beyond-Weather-Git/open-data-examples/blob/main/ifs-analysis-n320/get-to-know-a-dataset.ipynb","AuthorName":"Beyond Weather","AuthorURL":"https://beyond-weather.com"}],"Tools & Applications":[{"Title":"Anemoi - framework for data-driven weather forecasting","URL":"https://anemoi.readthedocs.io","AuthorName":"ECMWF and partners"},{"Title":"earthkit-regrid - regridding of reduced Gaussian grids","URL":"https://github.com/ecmwf/earthkit-regrid","AuthorName":"ECMWF"},{"Title":"icechunk","URL":"https://icechunk.io","AuthorName":"Earthmover","AuthorURL":"https://earthmover.io"},{"Title":"xarray","URL":"https://docs.xarray.dev/","AuthorName":"xarray Developers"}],"Publications":[{"Title":"Changes to the forecasting system (IFS cycle history)","URL":"https://confluence.ecmwf.int/display/FCST/Changes+to+the+forecasting+system","AuthorName":"ECMWF"},{"Title":"IFS Documentation","URL":"https://www.ecmwf.int/en/publications/ifs-documentation","AuthorName":"ECMWF"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-10-01","RegistryEntryLastModified":"2026-10-16","Slug":"beyond-weather-ecmwf-ifs-analysis","Sources":["awslabs-open-data-registry"]}
{"Name":"2018 IEEE BHI and BSN Data Challenge","Description":"In collaboration with the IEEE Conference on Biomedical and Health Informatics (BHI) 2018 and the IEEE Conference on Body Sensor Networks (BSN), we are hosting a challenge to explore real clinical questions in critically ill patients using the MIMIC-III database. Participants in the challenge will be invited to present at the BHI & BSN Annual Conference in Las Vegas, USA (4-7 March 2018): https://bhi-bsn.embs.org/2018/","Documentation":"https://doi.org/10.13026/v1jk-ax96","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"MIT License","Resources":[{"Description":"https://doi.org/10.13026/v1jk-ax96","ARN":"arn:aws:s3:::physionet-open/bhi-2018-challenge/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"bhi-2018-challenge","Sources":["awslabs-open-data-registry"]}
{"Name":"Biodiversity Heritage Library Metadata and Page Images","Description":"The Biodiversity Heritage Library (BHL) is the world’s largest open access digital library for biodiversity literature and archives. BHL operates as a worldwide consortium of natural history, botanical, research, and national libraries working together to digitize the natural history literature held in their collections and make it freely available for open access.","Documentation":"Documentation can be found at <a href=\"https://github.com/gbhl/bhl-open-data\">our GitHub repository</a>.","Contact":"feedback@biodiversitylibrary.org","ManagedBy":"[The Biodiversity Heritage Library](https://biodiversitylibrary.org/)","UpdateFrequency":"Metadata is updated monthly. Images are updated weekly.","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["biodiversity","bioinformatics","life sciences"],"License":"Public Domain, CC0, or Creative Commons. Exact licenses are found in the related metadta files and <a href=\"https://github.com/gbhl/bhl-open-data\">documentation</a>.","Citation":null,"Resources":[{"Description":"Image files (JPEG-2000) and associated metadata describing the image and the book or article that contains the image.","ARN":"arn:aws:s3:::bhl-open-data","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"BHLIndex","URL":"https://github.com/gnames/bhlindex","AuthorName":"Global Names","AuthorURL":"https://globalnames.org/"},{"Title":"BioStor","URL":"https://biostor.org/","AuthorName":"Roderic Page","AuthorURL":"https://scholar.google.com/citations?user=4Z5WABAAAAAJ&hl=en"}],"Publications":[{"Title":"AI models are getting better and better at reading handwriting, but how can we find handwritten text to begin with?","URL":"https://doi.org/10.25573/data.23523495.v1","AuthorName":"Mike Trizna, JJ Dearborn","AuthorURL":"https://datascience.si.edu/people/mike-trizna"},{"Title":"Understanding BHL Through Metadata: Patterns of Bio-Diverse Knowledge Production","URL":"https://blog.biodiversitylibrary.org/2021/11/understanding-bhl-through-metadata-patterns-of-bio-diverse-knowledge-production.html","AuthorName":"Lidia Ponce de la Vega","AuthorURL":"https://lidiapv.com/"},{"Title":"Unearthing the Past for a Sustainable Future: Extracting and transforming data in the Biodiversity Heritage Library for climate action","URL":"https://doi.org/10.3897/biss.7.112436","AuthorName":"Dearborn J, Lichtenberg M, Richard J, deVeer J, Trizna M, Mika K","AuthorURL":"https://biss.pensoft.net/article/112436/list/9/"}]},"RegistryEntryAdded":"2024-09-05","RegistryEntryLastModified":"2025-07-09","Slug":"bhl-open-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Brain Hemorrhage Extended (BHX): Bounding box extrapolation from thick to thin slice CT images","Description":"BHX is a public available dataset with bounding box annotations for 5 types of acute hemorrhage as an extension of the qure.ai CQ500 dataset. This dataset intends to provide data resources to help advance hemorrhage detection towards machine learning localization tasks. Key Points: BHX contains up to 39,668 bounding boxes in 23,409 images annotated for hemorrhage, out of a total of ~170k images from qure.ai CQ500 dataset. The dataset was built through an efficient method to obtain automatic annotated images (thin slices) from sparse initial labeling (thick slices). We hope this dataset can contribute to further develop and validate machine learning algorithms, ultimately helping to diagnose intracranial hemorrhage.","Documentation":"https://doi.org/10.13026/9cft-hg92","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution-ShareAlike 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/9cft-hg92","ARN":"arn:aws:s3:::physionet-open/bhx-brain-bounding-box/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"bhx-brain-bounding-box","Sources":["awslabs-open-data-registry"]}
{"Name":"BIG IDEAs Lab Glycemic Variability and Wearable Device Data","Description":"This study aimed to determine the feasibility and effectiveness of wearable devices in detecting early physiological changes prior to the development of prediabetes [1-3]. The study generated digital biomarkers for remote, mHealth-based prediabetes and hyperglycemia risk to classify which individuals should undergo further clinical testing. The primary inclusion criteria were subjects aged 35-65 years, inclusive, including only post-menopausal females, with a point of care A1C measurement between 5.2-6.4%, inclusive. Blood was collected during the study for measurement of glucose, hemoglobin A1C, lipoproteins, and triglycerides. Participants wore a Dexcom 6 continuous glucose monitor (CGM) and an Empatica E4 wristband for 10 days while receiving a standardized breakfast meal every other day. At the end of the 10 days, the participant returned to the clinic for an oral glucose tolerance test (OGTT). Research data collected includes physiological measurements from wearable devices such as heart rate, accelerometry, and electrodermal conductance.","Documentation":"https://doi.org/10.13026/aw6y-fc44","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/aw6y-fc44","ARN":"arn:aws:s3:::physionet-open/big-ideas-glycemic-wearable/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"big-ideas-glycemic-wearable","Sources":["awslabs-open-data-registry"]}
{"Name":"bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset","Description":"Brain–computer interfaces (BCIs) have wide-ranging applications as solutions for replacing or substituting neural output that has been lost because of severe neuromuscular injury or disease, such as individuals with late-stage amyotrophic lateral sclerosis (ALS). The P300-based BCI is one of the most commonly researched BCI for communication. This BCI dataset is curated from data originally generated from previous visual P300-based BCI speller studies, which include single- and multi-session experiments under a wide range of conditions. The BCI data are provided in an enriched and standardised format with BCI data elements that align with developing IEEE P2731 Working Group standards for BCI data to facilitate reusability. The data files, provided in open European Data Format ‘plus’, contain: i) electroencephalography (EEG) signals; ii) the BCI encoder, target characters and stimulus event markers for P300 event related potential analysis; iii) BCI spelling outcomes and feedback event markers for error related potential analysis; and if available, iv) self-reported demographics (age, sex, race, ethnicity); v) ALS diagnosis and a revised ALS Functional Rating Scale score obtained from medical records; and vi) eye tracker signals.","Documentation":"https://doi.org/10.13026/0byy-ry86","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/0byy-ry86","ARN":"arn:aws:s3:::physionet-open/bigp3bci/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"bigp3bci","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"Amazon is no longer hosting this Data Lakehouse Ready dataset","Name":"Binding DB - Data Lakehouse Ready","Description":"This a parquet representation of The Binding Database's [Full BindingDB Database Dump](https://www.bindingdb.org/bind/chemsearch/marvin/SDFdownload.jsp?all_download=yes) that you can query straight from Athena in under 60 seconds (no Oracle database required). The Binding Database projects aims to make experimental data on the noncovalent association of molecules in solution searchable via the world wide web. The initial focus is on biomolecular systems, but data on host-guest and supramolecular systems are also important and being included over time. It is expected that the enhanced access to data provided by this resource will facilitate drug-discovery, the design of self-assembling systems, and the development of predictive computer models of binding. The Binding Database is based at the Skaggs School of Pharmacy and Pharmaceutical Sciences at the University of California, San Diego, La Jolla, CA.","Documentation":"https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_install.md","Contact":"https://github.com/aws-samples/data-lake-as-code/issues","ManagedBy":"[Amazon Web Services](https://aws.amazon.com/)","UpdateFrequency":"Within 2 months after an new BindingDB release.","Tags":["aws-pds","chemistry","genetic","genomic","molecule","life sciences","biotech blueprint","parquet"],"License":"https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_attributions.txt","Resources":[{"Description":"Binding DB 2020-11-14. Information on BindingDB releases can be found [here](https://www.bindingdb.org/bind/chemsearch/marvin/SDFdownload.jsp?all_download=yes).","ARN":"arn:aws:s3:::aws-roda-hcls-datalake/binding_db/","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Data Lake as Code Deployment Guide","URL":"https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_install.md","AuthorName":"AWS Biotech Blueprints Team","Services":["Amazon Athena","AWS Glue","AWS Lake Formation"]}],"Publications":[{"Title":"Data Lake as Code, Featuring ChEMBL and Open Targets","URL":"https://aws.amazon.com/blogs/startups/a-data-lake-as-code-featuring-chembl-and-opentargets/","AuthorName":"Paul Underwood"}]},"RegistryEntryAdded":"2020-11-19","RegistryEntryLastModified":"2025-02-04","Slug":"binding-db","Sources":["awslabs-open-data-registry"]}
{"Name":"Model Benchmarking","Description":"This dataset includes data and models relevant to benchmarking multimodal biological models. The data has been sourced and curated by a team of experts at Biohub and is provided as part of these datasets only when it is not publicly available or requires transformation to support effective model benchmarking.","Documentation":"https://virtualcellmodels.cziscience.com/benchmarks","Contact":"data@chanzuckerberg.com","ManagedBy":"[Biohub](https://biohub.org)","UpdateFrequency":"Update cadence may vary for each resource","Tags":["benchmark","Biohub","biology","biomolecular modeling","cell biology","life sciences","machine learning","model"],"License":"CC0 1.0","Resources":[{"Description":"Biohub Benchmarking data","ARN":"arn:aws:s3:::cz-benchmarks-data","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Evaluating SubCell and Related Imaging Models","URL":"https://virtualcellmodels.cziscience.com/micro-pub/evaluating-subcell","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"}],"Publications":[{"Title":"Tabula Sapiens reveals transcription factor expression, senescence effects, and sex-specific features in cell types from 28 human organs and tissues","URL":"https://doi.org/10.1101/2024.12.03.626516","AuthorName":"Tabula Sapiens Consortium et al."},{"Title":"The molecular evolution of spermatogenesis across mammals","URL":"https://doi.org/10.1038/s41586-022-05547-7","AuthorName":"Murat, F., et al."}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-05-13","RegistryEntryLastModified":"2026-05-13","Slug":"biohub-benchmarking","Sources":["awslabs-open-data-registry"]}
{"Name":"CELLxGENE Discover Census","Description":"CELLxGENE Discover ([cellxgene.cziscience.com](https://cellxgene.cziscience.com/)) is a free-to-use platform for the exploration, analysis, and retrieval of single-cell data. CELLxGENE Discover hosts the largest aggregation of standardized single-cell data from the major human and mouse tissues, with modalities that include gene expression, chromatin accessibility, DNA methylation, and spatial transcriptomics.\nThis year, CELLxGENE Discover has made available all of its human and mouse RNA single-cell data through Census (https://chanzuckerberg.github.io/cellxgene-census/) – a free-to-use service with an API and data that allows for querying its single-cell data corpus directly from Python or R. \nThe API uses a new technology, TileDB-SOMA, that allows for efficient and low-latency querying. The data are fully standardized and hosted publicly for free access, and they are composed by a count matrix of tens of millions of cells (observations) by >60 k genes (features) accompanied by standard cell metadata variables (e.g. cell type, tissue, sequencing technology, donor id, etc)  and gene metadata that includes GENCODE-based IDs and gene names. While these data are built from hundreds of datasets, the APIs enable convenient cell- and gene-based filtering to obtain any slice of interest in a matter of seconds. All data can be quickly transformed to NumPy, Pandas, Anndata, Seurat, or R base objects. We created data loaders for the data to be directly used by PyTorch for modeling at scale.\nIn addition, all the source dataset files in H5AD format are also available for retrieval. \n","Documentation":"https://chanzuckerberg.github.io/cellxgene-census/","Contact":"cellxgene@chanzuckerberg.com","ManagedBy":"[Biohub](https://biohub.org)","UpdateFrequency":"New releases are published weekly. Long-term supported (LTS) releases are published every 6 months.","Tags":["aws-pds","Biohub","bioinformatics","cell biology","life sciences","single-cell transcriptomics","transcriptomics"],"License":"CC-BY 4.0","Resources":[{"Description":"CELLxGENE Discover Census Data","ARN":"arn:aws:s3:::cellxgene-census-public-us-west-2/cell-census","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Computing on the cell by gene matrix using online (incremental) algorithms","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/api_demo/census_compute_over_X.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/api_demo/census_compute_over_X.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Experimental Highly Variable Genes API","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/experimental/highly_variable_genes.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/experimental/highly_variable_genes.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Explore CELLxGENE Discover Census in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/e18fa2ac-0148-5dee-9275-f91f5c5597b2/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Exploring all data from a tissue","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/analysis_demo/comp_bio_explore_and_load_lung_data.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/analysis_demo/comp_bio_explore_and_load_lung_data.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Exploring pre-calculated summary cell counts","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/api_demo/census_summary_cell_counts.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/api_demo/census_summary_cell_counts.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Exploring the Census Datasets table","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/api_demo/census_datasets.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/api_demo/census_datasets.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Genes measured in each cell (dataset presence matrix)","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/api_demo/census_dataset_presence.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/api_demo/census_dataset_presence.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Integrating multi-dataset slices of data","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/analysis_demo/comp_bio_data_integration_scvi.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/analysis_demo/comp_bio_data_integration_scvi.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Learning about the CELLxGENE Census","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/analysis_demo/comp_bio_census_info.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/analysis_demo/comp_bio_census_info.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Normalizing full-length gene sequencing data","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/analysis_demo/comp_bio_normalizing_full_gene_sequencing.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/analysis_demo/comp_bio_normalizing_full_gene_sequencing.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Out-of-core (incremental) mean and variance calculation","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/experimental/mean_variance.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/experimental/mean_variance.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Querying and fetching the single-cell data and cell/gene metadata.","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/api_demo/census_query_extract.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/api_demo/census_query_extract.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Querying data using the gget cellxgene module","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/api_demo/census_gget_demo.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/api_demo/census_gget_demo.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Summarizing cell and gene metadata","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/analysis_demo/comp_bio_summarize_axis_query.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/analysis_demo/comp_bio_summarize_axis_query.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Training a PyTorch Model","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/experimental/pytorch.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/experimental/pytorch.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Understanding and filtering out duplicate cells","URL":"https://chanzuckerberg.github.io/cellxgene-census/notebooks/api_demo/census_duplicated_cells.html","NotebookURL":"https://github.com/chanzuckerberg/cellxgene-census/blob/main/api/python/notebooks/api_demo/census_duplicated_cells.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"}],"Tools & Applications":[{"Title":"Census Python API","URL":"https://chanzuckerberg.github.io/cellxgene-census/python-api.html","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Census R API","URL":"https://chanzuckerberg.github.io/cellxgene-census/r/index.html","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"}],"Publications":[{"Title":"CELLxGENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data","URL":"https://www.biorxiv.org/content/10.1101/2023.10.30.563174v1","AuthorName":"Biohub Single-Cell Biology et al."},{"Title":"scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI.","URL":"https://www.biorxiv.org/content/10.1101/2023.04.30.538439v2","AuthorName":"Haotian Cui, et al."}]},"RegistryEntryAdded":"2026-05-13","RegistryEntryLastModified":"2026-11-03","Slug":"biohub-cellxgene-census","Sources":["awslabs-open-data-registry"]}
{"Name":"ESM Atlas — Protein Features and Structures","Description":"The ESM Atlas is a large-scale public dataset of computational outputs generated by ESMC and ESMFold2, derived from a deduplicated set of over 6.8 billion publicly available protein sequences spanning all domains of life — including viral proteins and previously unannotated sequences representing metagenomic dark matter sampled from a wide range of biomes. The dataset includes two primary components: sparse autoencoder (SAE) features for ~6.8 billion proteins, capturing interpretable biological representations from the ESMC 6B model, and predicted three-dimensional protein structures for ~1.1 billion proteins generated using ESMFold2. Proteins are organized into 7.7 million clusters based on SAE feature similarity, enabling functional grouping across the protein universe. The dataset is accessible via AWS CLI. A companion data explorer is available at https://biohub.ai/esmc/atlas.","Documentation":"https://biohub.ai/esmc/atlas","Contact":"support@biohub.org","ManagedBy":"[Biohub](https://biohub.org)","UpdateFrequency":"This is the v1.0 release of the dataset. Updates will be announced via the dataset documentation page.","Tags":["aws-pds","Biohub","bioinformatics","life sciences","machine learning","metagenomics","protein","structural biology"],"License":"**Atlas Data**<br/><br/>\n\nUnless otherwise noted below under “Datasets Incorporating Third-Party Data,” data in the Atlas is compiled and made available by Chan Zuckerberg Biohub, Inc. under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This permits use, distribution, and adaptation of the data for any purpose, provided that appropriate attribution is given. A copy of the license is available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/).\n\n<br/><br/>**Datasets Incorporating Third-Party Data**<br/><br/>\n\nThe “Sequences” and “All Data” datasets include data from third-party sources in addition to data generated by Biohub. These specific datasets are compiled and made available by Chan Zuckerberg Biohub, Inc. and are provided under the Creative Commons Attribution Share Alike 4.0 International (CC BY SA 4.0) license. A copy of the license is available at [https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/).\n\n<br/><br/>These datasets include data from the following sources:\n\n| Data Set | Source | License |\n| --- | --- | --- |\n| UniRef | [uniprot.org/uniref](https://www.uniprot.org/uniref) | [CC BY 4.0](https://www.uniprot.org/help/license) |\n| UniProt Archive (UniParc) | [ftp.uniprot.org/pub/databases/uniprot/uniparc/](https://ftp.uniprot.org/pub/databases/uniprot/current_release/uniparc/fasta/active/) | [CC BY 4.0](https://www.uniprot.org/help/license) |\n| MGnify (2022_05) | [ftp.ebi.ac.uk/pub/databases/metagenomics/peptide_database/2022_05/](https://ftp.ebi.ac.uk/pub/databases/metagenomics/peptide_database/2022_05/) | [CC0 1.0](https://ftp.ebi.ac.uk/pub/databases/metagenomics/peptide_database/2022_05/LICENCE.txt) |\n| MGnify (2024_04) | [ftp.ebi.ac.uk/pub/databases/metagenomics/peptide_database/2024_04/](https://ftp.ebi.ac.uk/pub/databases/metagenomics/peptide_database/2024_04/) | [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) |\n| UHGG | [ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/human-gut/](https://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/human-gut/v2.0.2/protein_catalogue/uhgp-100.tar.gz) | [EMBL-EBI Terms of Use](https://www.ebi.ac.uk/about/terms-of-use) |\n| SPIRE | [spire.embl.de](https://spire.embl.de/) | [CC BY-SA 4.0](https://spire.embl.de/faqs) |\n| Joint Genome Institute IMGM Dataset | [img.jgi.doe.gov](https://img.jgi.doe.gov/) | [JGI custom license](https://jgi.doe.gov/disclaimer) |\n| JGI IMG/M NR | [genome.jgi.doe.gov/portal/IMG_NR/](https://genome.jgi.doe.gov/portal/IMG_NR/IMG_NR.home.html) | [JGI custom license](https://jgi.doe.gov/disclaimer) |\n| JGI IMG/VR | [genome.jgi.doe.gov/portal/IMG_VR/](https://genome.jgi.doe.gov/portal/IMG_VR/IMG_VR.home.html) | [JGI custom license](https://jgi.doe.gov/disclaimer) |\n| JGI IMG UMAG (Prok) | [genome.jgi.doe.gov/portal/IMG-UMAG/](https://genome.jgi.doe.gov/portal/IMG-UMAG/IMG-UMAG.home.html) | [JGI custom license](https://jgi.doe.gov/disclaimer) |\n| JGI IMG UMAG (Euk) | [genome.jgi.doe.gov/portal/IMG-UMAG/](https://genome.jgi.doe.gov/portal/IMG-UMAG/IMG-UMAG.home.html) | [JGI custom license](https://jgi.doe.gov/disclaimer) |\n| Protein Data Bank (PDB) | [files.wwpdb.org/pub/pdb/data/assemblies/mmCIF/](https://files.wwpdb.org/pub/pdb/data/assemblies/mmCIF/) | [CC0 1.0](https://www.wwpdb.org/about/usage-policies) |\n| AlphaFold Protein Structure Database | [alphafold.ebi.ac.uk](https://alphafold.ebi.ac.uk/) | [CC BY 4.0](https://alphafold.ebi.ac.uk/assets/License-Disclaimer.pdf) |\n","Resources":[{"Description":"ESM Atlas — Protein sequences, SAE features, and predicted 3D structures","ARN":"arn:aws:s3:::esm-protein-atlas","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Embedding sequences with ESMC","URL":"https://colab.research.google.com/github/biohub/esm/blob/main/cookbook/tutorials/embed.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Fine-tuning ESMC","URL":"https://colab.research.google.com/github/biohub/esm/blob/main/cookbook/tutorials/esmc_finetune.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Folding with ESMFold2","URL":"https://colab.research.google.com/github/biohub/esm/blob/main/cookbook/tutorials/esmfold2.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Layer sweep for enzyme function classification","URL":"https://colab.research.google.com/github/biohub/esm/blob/main/cookbook/tutorials/esmc_layer_sweep.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Understanding proteins with SAE features","URL":"https://colab.research.google.com/github/biohub/esm/blob/main/cookbook/tutorials/esmc_sae_feature_interpretation.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Zero-shot entropy and mutation analysis","URL":"https://colab.research.google.com/github/biohub/esm/blob/main/cookbook/tutorials/esmc_mutation_scoring.ipynb","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"}]},"RegistryEntryAdded":"2026-05-27","RegistryEntryLastModified":"2026-11-02","Slug":"biohub-esm-atlas","Sources":["awslabs-open-data-registry"]}
{"Name":"Imaging BSD licensed data and models","Description":"This dataset contains a diverse range of imaging biological data and models. The data is sourced and curated by a team of experts at Biohub and is made available as part of these datasets only when it is not publicly accessible or requires transformations to support model training.","Documentation":"https://virtualcellmodels.cziscience.com/models","Contact":"data@chanzuckerberg.com","ManagedBy":"[Biohub](https://biohub.org)","UpdateFrequency":"Update cadence may vary for each resource","Tags":["biodiversity","Biohub","bioinformatics","biology","biomolecular modeling","brain images","cell biology","cell imaging","imaging","life sciences","machine learning","microscopy","model","protein","zarr"],"License":"[BSD-3-Clause License](https://opensource.org/license/bsd-3-clause)","Resources":[{"Description":"Cytoland model","ARN":"arn:aws:s3:::czi-cytoland","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Documentation for Cytoland","URL":"https://virtualcellmodels.cziscience.com/model/01961244-1970-7851-a4b9-fdbfa2fba9b2","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Quickstart Tutorial for Cytoland","URL":"https://virtualcellmodels.cziscience.com/quickstart/cytoland-quickstart","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"}],"Publications":[{"Title":"Cytoland: robust virtual staining of landmark organelles","URL":"https://www.biorxiv.org/content/10.1101/2024.05.31.596901v3","AuthorName":"Liu, Hirata-Miyasaki, et al."}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-05-13","RegistryEntryLastModified":"2026-05-13","Slug":"biohub-imaging-bsd","Sources":["awslabs-open-data-registry"]}
{"Name":"Imaging MIT Licensed data and models","Description":"This dataset contains a diverse range of imaging biological data and models. The data is sourced and curated by a team of experts at Biohub and is made available as part of these datasets only when it is not publicly accessible or requires transformations to support model training.","Documentation":"https://virtualcellmodels.cziscience.com/models","Contact":"data@chanzuckerberg.com","ManagedBy":"[Biohub](https://biohub.org)","UpdateFrequency":"Update cadence may vary for each resource","Tags":["biodiversity","Biohub","bioinformatics","biology","biomolecular modeling","brain images","cell biology","cell imaging","imaging","life sciences","machine learning","microscopy","model","protein","zarr"],"License":"[MIT License](https://opensource.org/license/mit)","Resources":[{"Description":"CELL-Diff model","ARN":"arn:aws:s3:::czi-celldiff-public","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Subcell model","ARN":"arn:aws:s3:::czi-subcell-public","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Documentation for CELL-Diff","URL":"https://virtualcellmodels.cziscience.com/model/0193323e-c230-7157-a420-e8ecef363ae9","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Documentation for SubCell","URL":"https://virtualcellmodels.cziscience.com/model/0193323e-ebd5-727c-bb32-87ed8f737213","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Quickstart Tutorial for CELL-Diff","URL":"https://virtualcellmodels.cziscience.com/quickstart/cell-diff-quickstart","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Quickstart Tutorial for SubCell","URL":"https://virtualcellmodels.cziscience.com/tutorial/subcell-tutorial","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"}],"Publications":[{"Title":"CELL-Diff: Unified diffusion modeling for protein sequences and microscopy images","URL":"https://www.biorxiv.org/content/10.1101/2024.10.15.618585v1","AuthorName":"Zheng Dihan, Bo Huang"},{"Title":"SubCell: Vision foundation models for microscopy capture single-cell biology","URL":"https://www.biorxiv.org/content/10.1101/2024.12.06.627299v1","AuthorName":"Ankit Gupta, Zoe Wefers, Konstantin Kahnert, Jan N Hansen, William D. Leineweber, Anthony Cesnik, Dan Lu, Ulrika Axelsson, Frederic Ballllosera Navarro, Theofanis Karaletsos, Emma Lundberg"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-05-13","RegistryEntryLastModified":"2026-05-13","Slug":"biohub-imaging-mit","Sources":["awslabs-open-data-registry"]}
{"Name":"Transcriptomic MIT Licensed data and models","Description":"This dataset contains a transcriptomics biological data and models. The models embed transcriptomic data and facilitate transcriptomic analysis. The data is sourced and curated by a team of experts at Biohub and is made available as part of these datasets only when it is not publicly accessible or requires transformations to support model training.","Documentation":"https://virtualcellmodels.cziscience.com/models","Contact":"data@chanzuckerberg.com","ManagedBy":"[Biohub](https://biohub.org)","UpdateFrequency":"Update cadence may vary for each resource","Tags":["biodiversity","Biohub","biology","biomolecular modeling","cell biology","hdf5","life sciences","machine learning","model","protein","transcriptomics"],"License":"[MIT License](https://opensource.org/license/mit)","Resources":[{"Description":"scGenePT model & training data","ARN":"arn:aws:s3:::czi-scgenept-public","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"TranscriptFormer model","ARN":"arn:aws:s3:::czi-transcriptformer","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Documentation for scGenePT","URL":"https://virtualcellmodels.cziscience.com/model/01936eb7-dba3-7f2e-b71a-463a7b173120","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Documentation for Transcriptformer","URL":"https://virtualcellmodels.cziscience.com/model/01966441-339f-77f7-aa06-f67636f865dc","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Quickstart Tutorial for Transcriptformer","URL":"https://virtualcellmodels.cziscience.com/quickstart/transcriptformer-quickstart","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"scGenePT Perturbation Prediction Tutorial","URL":"https://virtualcellmodels.cziscience.com/tutorial/scgenept-tutorial","AuthorName":"Biohub"}],"Publications":[{"Title":"A Cross-Species Generative Cell Atlas Across 1.5 Billion Years of Evolution: The TranscriptFormer Single-cell Model","URL":"https://www.biorxiv.org/content/10.1101/2025.04.25.650731v1","AuthorName":"Pearce, J. D., et. al."},{"Title":"scGenePT: Is language all you need for modeling single-cell perturbations?","URL":"https://www.biorxiv.org/content/10.1101/2024.10.23.619972v1","AuthorName":"Ana-Maria Istrate, Donghui Li, Theofanis Karaletsos"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-05-13","RegistryEntryLastModified":"2026-05-13","Slug":"biohub-transcriptomics-mit","Sources":["awslabs-open-data-registry"]}
{"Name":"BioLiP","Description":"BioLiP is a semi-manually curated database for high-quality, biologically relevant ligand-protein binding interactions. The structure data are collected primarily from the Protein Data Bank (PDB), with biological insights mined from literature and other specific databases. BioLiP aims to construct the most comprehensive and accurate database for serving the needs of ligand-protein docking, virtual ligand screening and protein function annotation.","Documentation":"https://zhanggroup.org/BioLiP","Contact":"zhanglab@zhanggroup.org","ManagedBy":"[Zhang Lab](https://zhanggroup.org/)","UpdateFrequency":"No regular schedule; updated upon availability of major dataset revisions","Tags":["bioinformatics","chemistry","life sciences","molecular docking","molecule","protein","structural biology"],"License":"No explicit license stated (publicly available for academic and research use).","Citation":"Chengxin Zhang, Xi Zhang, Peter L Freddolino, and Yang Zhang. BioLiP2: an updated structure database for biologically relevent ligand-protein interactions, Nucleic Acids Research, gkad630 (2023).","Resources":[{"Description":"BioLiP dataset","ARN":"arn:aws:s3:::biolip","Region":"ap-southeast-1","Type":"S3 Bucket"},{"Description":"BioLiP interaction structures","ARN":"arn:aws:s3:::biolip/weekly","Region":"ap-southeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"BioLiP API usage","URL":"https://zhanggroup.org/BioLiP/help.html","AuthorName":"Zhang Lab"}],"Publications":[{"Title":"BioLiP2: an updated structure database for biologically relevant ligand-protein interactions","URL":"https://academic.oup.com/nar/article/52/D1/D404/7233921","AuthorName":"Chengxin Zhang, Xi Zhang, Peter L Freddolino, and Yang Zhang"},{"Title":"BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions","URL":"https://academic.oup.com/nar/article/41/D1/D1096/1074898","AuthorName":"Jianyi Yang, Ambrish Roy, and Yang Zhang"}]},"RegistryEntryAdded":"2025-08-11","RegistryEntryLastModified":"2025-08-26","Slug":"biolip","Sources":["awslabs-open-data-registry"]}
{"Name":"Nighttime-Fire-Flare","Description":"Detection of nighttime combustion (fire and gas flaring) from daily top of atmosphere data from NASA's Black Marble VNP46A1 product using VIIRS Day/Night Band and VIIRS thermal bands.","Documentation":"https://docs.google.com/document/d/e/2PACX-1vSeDj3BbmvKCrFY3aimZ7-0eMhEN0OFIFk1JDTv3Fvfe4BjfFTwuS8OSMuSGa5vDs2fQKjlrJN8Ut2X/pub","Contact":"schakraborty@usra.edu","ManagedBy":"[USRA](https://srijachakraborty.com/) and [NASA Black Marble](https://blackmarble.gsfc.nasa.gov/)","UpdateFrequency":"New combustion detections are added whenever it is available and with model updates.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["anomaly detection","aws-pds","classification","disaster response","earth observation","environmental","NASA SMD AI","satellite imagery","socioeconomic","urban"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"NASA Black Marble Combustion Detections, Earth Science Training Data","ARN":"arn:aws:s3:::us-west-2.opendata.source.coop/vnp46a1_thermal_anomaly/","Region":"us-west-2","Type":"S3 Bucket","Explore":null}],"DataAtWork":{"Publications":[{"Title":"NASA SMD AI Workshop Report","URL":"https://science.nasa.gov/files/atoms/files/NASA%20SMD%20AI%20Workshop%2021%20(spreads)%20(1).pdf","AuthorName":"SMD Artificial Intelligence Machine Learning (AI/ML) Working Group"},{"Title":"Potentially underestimated gas flaring activities—a new approach to detect combustion using machine learning and NASA's Black Marble product suite","URL":"https://doi.org/10.1088/1748-9326/acb6a7","AuthorName":"Srija Chakraborty, Tomohiro Oda, Virginia Kalb, Zhuosen Wang, Miguel O Román"}]},"RegistryEntryAdded":"2023-05-24","RegistryEntryLastModified":"2025-07-09","Slug":"black_marble_combustion","Sources":["awslabs-open-data-registry"]}
{"Name":"Blended TROPOMI+GOSAT Satellite Data Product for Atmospheric Methane","Description":"A dataset of satellite retrievals of atmospheric methane that extends from 30 April 2018 to present.","Documentation":"https://github.com/nicholasbalasus/write_blended_files/blob/main/PUM.md","Contact":"nicholasbalasus@g.harvard.edu","ManagedBy":"Nicholas Balasus","UpdateFrequency":"Monthly","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","climate","environmental","satellite imagery"],"License":"There are no restrictions on the use of this data, but please contact nicholasbalasus@g.harvard.edu before its use in a publication.","Citation":"Please cite Balasus et al. (2023).","Resources":[{"Description":"Blended TROPOMI+GOSAT netCDF files","ARN":"arn:aws:s3:::blended-tropomi-gosat-methane","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://s3-us-west-2.amazonaws.com/blended-tropomi-gosat-methane/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Downloading the full data record from AWS","URL":"https://github.com/nicholasbalasus/write_blended_files/blob/main/resources/aws_tutorial_2.ipynb","AuthorName":"Nicholas Balasus","AuthorURL":"https://nicholasbalasus.github.io","Services":["Amazon SageMaker Studio Lab"]},{"Title":"Plotting one month of blended TROPOMI+GOSAT methane retrievals","URL":"https://github.com/nicholasbalasus/write_blended_files/blob/main/resources/aws_tutorial_1.ipynb","AuthorName":"Nicholas Balasus","AuthorURL":"https://nicholasbalasus.github.io","Services":["Amazon SageMaker Studio Lab"]}],"Publications":[{"Title":"A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases","URL":"https://doi.org/10.5194/amt-16-3787-2023","AuthorName":"N. Balasus, D.J. Jacob, A. Lorente, J.D. Maasakkers, R.J. Parker, H. Boesch, Z. Chen, M.M. Kelp, H. Nesser, D.J. Varon"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2024-02-02","RegistryEntryLastModified":"2025-07-09","Slug":"blended-tropomi-gosat-methane","Sources":["awslabs-open-data-registry"]}
{"Name":"IWMI DIWASA Blue ET for Africa","Description":"Blue evapotranspiration (Blue ET) is the portion of ET derived from blue water sources, including surface water (rivers, lakes, reservoirs) and groundwater used for irrigation. It is a key component of blue water fluxes in water accounting. Blue ET consists of evaporation from irrigated fields, transpiration from irrigated crops, and water lost from artificial storage. It helps assess water productivity in irrigated agriculture, quantify consumptive water use, and support sustainable water resource management, particularly in water-scarce regions.","Documentation":"https://iwmi.africageoportal.com/pages/continent-africa","Contact":"iwmiwaplus@gmail.com","ManagedBy":"[IWMI](https://www.iwmi.org/)","UpdateFrequency":"None","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","evapotranspiration","ground water","irrigated cropland","surface water","water"],"License":"Creative Commons open license","Resources":[{"Description":"Monthly Blue ET for Africa","ARN":"arn:aws:s3:::iwmi-datasets/Water_accounting_plus/Africa/Incremental_ET_M/","Region":"af-south-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://iwmi-datasets.s3.af-south-1.amazonaws.com/Cropland_partition/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Analysis of IWMI’s Water Data Products through Digital Earth Africa","URL":"https://learn.digitalearthafrica.org/courses/course-v1:IWMI+DIWASA1+2024_10/about","AuthorName":"A.T. Haile, E.T. Negash, K. Mubea, M. Tadesse","AuthorURL":"https://github.com/iwmiwaplus"}],"Tools & Applications":[{"Title":"Multi-Scale Water Accounting in the Volta Basin","URL":"https://public.tableau.com/app/profile/iwmi.wa/viz/Voltabasinvertical/Merged?publish=yes","AuthorName":"iwmiwaplus","AuthorURL":"https://public.tableau.com/app/profile/iwmi.wa"}]},"RegistryEntryAdded":"2025-02-11","RegistryEntryLastModified":"2025-07-09","Slug":"blue_et","Sources":["awslabs-open-data-registry"]}
{"Name":"Blue Brain Open Data","Description":"The Blue Brain Open Data represents an extensive neuroscience dataset encompassing a diverse range of data types, including experimental, model, and simulation data, along with images and videos depicting reconstructed neurons and brain regions.","Documentation":"https://github.com/BlueBrain/OpenData","Contact":"info@openbraininstitute.org","ManagedBy":"Open Brain Institute","UpdateFrequency":"No updates","Tags":["brain images","brain models","electrophysiology","ion channels","life sciences","microcircuit modeling and simulation","morphological reconstructions","Mus musculus","neuroscience","simulation neuroscience","single neuron models"],"License":"CC-BY-4.0","Resources":[{"Description":"Data files representing neurological tissue structures","ARN":"arn:aws:s3:::openbluebrain","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2025-01-07","RegistryEntryLastModified":"2025-07-04","Slug":"bluebrain_opendata","Sources":["awslabs-open-data-registry"]}
{"Name":"Baby Open Brains (BOBs) Repository on AWS","Description":"Manually curated and reviewed infant brain segmentations and accompanying T1w and T2w images for a range of 1-9 month old participants from the Baby Connectome Project (BCP)","Documentation":"https://bobsrepository.readthedocs.io/latest/","Contact":"Eric Feczko (feczk001@umn.edu) & Sally M. Stoyell (stoye003@umn.edu)","ManagedBy":"Masonic Institute for the Developing Brain (MIDB) Open Data Initiative","UpdateFrequency":"The repository is updated when: (1) all brain segmentations have undergone further rounds of manual correction, which may include refinement of existing ROIs and/or delineation of additional FreeSurfer ROIs and/or (2) new manually refined data from different age ranges and/or source studies becomes available","Tags":["life sciences","magnetic resonance imaging","neuroimaging","neuroscience","nifti","pediatric","segmentation"],"License":"CC-By Attribution 4.0 International","Citation":"DOI 10.17605/OSF.IO/WDR78","Resources":[{"Description":"BOBs Repository data","ARN":"arn:aws:s3:::bobsrepository","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://bobsrepository.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"View or Download the BOBS Repository","URL":"https://bobsrepository.readthedocs.io/data_access/","AuthorName":"Lucille A. Moore","AuthorURL":"https://github.com/LuciMoore"}],"Tools & Applications":[{"Title":"BIBsnet","URL":"https://zenodo.org/records/10679463","AuthorName":"Timothy J. Hendrickson et al."}],"Publications":[{"Title":"BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans","URL":"https://doi.org/10.1101/2023.03.22.533696","AuthorName":"Timothy J. Hendrickson et al.","AuthorURL":"https://orcid.org/0000-0001-6862-6526"}]},"RegistryEntryAdded":"2024-03-25","RegistryEntryLastModified":"2026-08-26","Slug":"bobsrepository","Sources":["awslabs-open-data-registry"]}
{"Name":"Body Sway When Standing and Listening to Music Modified to Reinforce Virtual Reality Environment Motion","Description":"The increased likelihood of falls as a consequence of aging or disease is generally associated with rising levels of body sway during stance. The use of virtual reality (VR) for studying the role in vision in regulating body sway is well established, however the extent to which music incorporated into a virtual environment can influence sway is not understood. This dataset was collected as part of a study to explore the hypothesis that music manipulated to match VR motion provided by an Oculus Rift head mounted display can lead to increased levels of body sway. Twenty-eight subjects stood for 60 s on a balance platform that measured anterior posterior (AP) and medial lateral (ML) center of pressure movement (indicators of body sway). While standing, the subjects experienced combinations of 3 visual conditions (VR translation in the AP direction at 0.1 Hz, no translation, and eyes closed) and 4 music conditions (Mozart’s Jupiter Symphony modified to scale volume at 0.1 Hz and 0.25 Hz, unmodified music, and no music) for a total of 12 trials. Analysis of the data using frequency domain measures may reveal the extent to which music influenced body sway.","Documentation":"https://doi.org/10.13026/x32c-cz47","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/x32c-cz47","ARN":"arn:aws:s3:::physionet-open/body-sway-music-vr/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"body-sway-music-vr","Sources":["awslabs-open-data-registry"]}
{"Name":"BodyM Dataset","Description":"The first large public body measurement dataset including 8978 frontal and lateral\nsilhouettes for 2505 real subjects, paired with height, weight and 14 body\nmeasurements. The following artifacts are made available for each subject.\n  - Subject Height\n  - Subject Weight\n  - Subject Gender\n  - Two black-and-white silhouette images of subject standing in frontal and side pose\n  respectively with full body in view.\n  - 14 body measurements in cm - {ankle girth, arm-length, bicep girth, calf girth,\n                                chest girth, forearm girth, height, hip girth, leg-length,\n                                shoulder-breadth, shoulder-to-crotch length, thigh girth,\n                                waist girth, wrist girth}\n\nThe data is split into 3 sets - Training, Test Set A, Test Set B. For the training and\nTest-A sets, subjects are photographed and 3D-scanned by in a lab by technicians. For\nthe Test-B set, subjects are scanned in the lab, but photographed in a less-controlled\nenvironment with diverse camera orientations and lighting conditions, to simulate\nin-the-wild image capture. Some subjects have been photographed more than once with\ndifferent clothing in order to test robustness of the dataset.\n","Documentation":"https://adversarialbodysim.github.io/","Contact":"Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6riTWmh-cGw/aws-open-data) and use the `AWS Open Data` tag.","ManagedBy":"Amazon","UpdateFrequency":"None","Tags":["amazon.science","computer vision","deep learning"],"License":"Creative Commons Attribution-Non Commercial 4.0 International Public License - https://creativecommons.org/licenses/by/4.0/legalcode","Resources":[{"Description":"This S3 bucket has height, weight, gender, measurements and two silhouette images for each type of data","ARN":"arn:aws:s3:::amazon-bodym","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Human Body Measurement Estimation with Adversarial Augmentation","URL":"https://www.amazon.science/publications/human-body-measurement-estimation-with-adversarial-augmentation","AuthorName":"Nataniel Ruiz, Miriam Bellver, Timo Bolkart, Ambuj Arora, Ming C. Lin, Javier Romero and Raja Bala"}]},"RegistryEntryAdded":"2022-10-06","RegistryEntryLastModified":"2024-03-27","Slug":"bodym","Sources":["awslabs-open-data-registry"]}
{"Name":"Boltz-1 Training Data","Description":"This is the data used to train the Boltz-1 model. It contains the following datasets:\n  - Our pre-processed version of the Protein Data Bank\n  - Our pre-processed version of the multiple sequence alignment data for each protein chain\n  - The raw multiple sequence alginment data.\n  - A pre-computed symmetry file for symmetry correction during training\n","Documentation":"https://github.com/jwohlwend/boltz/blob/main/docs/training.md","Contact":"jwohlwend@csail.mit.edu","ManagedBy":"MIT CSAIL - Regina Barzilay Group","UpdateFrequency":"None","Tags":["deep learning","life sciences","molecular docking","open source software","protein folding"],"License":"MIT License","Resources":[{"Description":"S3 Bucket containing the Boltz-1 data","ARN":"arn:aws:s3:::boltz1","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Boltz-1: Democratizing Biomolecular Interaction Modeling","URL":"https://www.biorxiv.org/content/10.1101/2024.11.19.624167v1.full.pdf","AuthorName":"J Wohlwend, G Corso, S Passaro, M Reveiz, K Leidal, W Swiderski, T Portnoi, I Chinn, J Silterra, T Jaakkola, R Barzilay"}]},"RegistryEntryAdded":"2024-12-23","RegistryEntryLastModified":"2024-12-23","Slug":"boltz1","Sources":["awslabs-open-data-registry"]}
{"Name":"Boreas Autonomous Driving Dataset","Description":"This autonomous driving dataset includes data from a 128-beam Velodyne Alpha-Prime lidar, a 5MP Blackfly camera, a 360-degree Navtech radar, and post-processed Applanix POS LV GNSS data. This dataset was collect in various weather conditions (sun, rain, snow) over the course of a year. The intended purpose of this dataset is to enable benchmarking of long-term all-weather odometry and metric localization across various sensor types. In the future, we hope to also support an object detection benchmark.","Documentation":"https://github.com/utiasASRL/pyboreas/blob/master/DATA_REFERENCE.md","Contact":"boreas@robotics.utias.utoronto.ca","ManagedBy":"[ASRL](http://asrl.utias.utoronto.ca)","UpdateFrequency":"New driving sequences will be added as they are collected.","Collabs":{"ASDI":{"Tags":["infrastructure"]}},"Tags":["autonomous vehicles","aws-pds","computer vision","lidar","robotics"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)","Resources":[{"Description":"Boreas dataset","ARN":"arn:aws:s3:::boreas","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Introduction to Visualizing Sensor Types (Jupyter notebook)","URL":"https://github.com/utiasASRL/pyboreas/blob/master/pyboreas/tutorials/intro.ipynb","AuthorName":"Keenan Burnett","Services":["Amazon SageMaker"]},{"Title":"Project Lidar onto Camera Frames (Jupyter notebook)","URL":"https://github.com/utiasASRL/pyboreas/blob/master/pyboreas/tutorials/lidar_camera_projection.ipynb","AuthorName":"Keenan Burnett","Services":["Amazon SageMaker"]}],"Publications":[{"Title":"Are We Ready for Radar to Replace Lidar in All-Weather Mapping and Localization?","URL":"https://arxiv.org/abs/2203.10174","AuthorName":"K Burnett, Y Wu, D J Yoon, A P Schoellig, T D Barfoot"},{"Title":"Boreas: A multi-season autonomous driving dataset","URL":"https://arxiv.org/abs/2203.10168","AuthorName":"K Burnett, D J Yoon, Y Wu, A Z Li, H Zhang, S Lu, J Qian, W Tseng, A Lambert, K YK Leung, A P Schoellig, Timothy D Barfoot"},{"Title":"Do we need to compensate for motion distortion and doppler effects in spinning radar navigation?","URL":"https://www.dynsyslab.org/wp-content/papercite-data/pdf/burnett-ral21.pdf","AuthorName":"K Burnett, A P Schoellig, T D Barfoot"},{"Title":"Need for Speed: Fast Correspondence-Free Lidar Odometry Using Doppler Velocity","URL":"https://arxiv.org/abs/2303.06511","AuthorName":"D J Yoon, K Burnett, J Laconte, Y Chen, H Vhavle, S Kammel, J Reuther, T D Barfoot"},{"Title":"Picking up speed: Continuous-time Lidar-only odometry using doppler velocity measurements","URL":"https://arxiv.org/abs/2209.03304","AuthorName":"Y Wu, D J Yoon, K Burnett, S Kammel, Y Chen, H Vhavle, T D Barfoot"},{"Title":"Radar odometry combining probabilistic estimation and unsupervised feature learning","URL":"https://arxiv.org/pdf/2105.14152.pdf","AuthorName":"K Burnett, D J Yoon, A P Schoellig, T D Barfoot"}]},"RegistryEntryAdded":"2021-10-19","RegistryEntryLastModified":"2025-07-09","Slug":"boreas","Sources":["awslabs-open-data-registry"]}
{"Name":"BossDB Open Neuroimagery Datasets","Description":"This data ecosystem, Brain Observatory Storage Service & Database (BossDB), contains several neuro-imaging datasets across multiple modalities and scales, ranging from nanoscale (electron microscopy), to microscale (cleared lightsheet microscopy and array tomography), and mesoscale (structural and functional magnetic resonance imaging). Additionally, many of the datasets include dense segmentation and meshes.","Contact":"brock.wester@jhuapl.edu","Documentation":"https://bossdb.org/","ManagedBy":"[Johns Hopkins University Applied Physics Laboratory](https://https://jhuapl.edu)","UpdateFrequency":"New datasets are added as soon as it is available. Minor updates on existing datasets occur sporadically.","Tags":["aws-pds","calcium imaging","electron microscopy","imaging","life sciences","light-sheet microscopy","magnetic resonance imaging","neuroimaging","neuroscience","volumetric imaging","x-ray","x-ray microtomography","x-ray tomography"],"License":"Creative Commons 4.0 International (CC BY 4.0); Creative Commons CC0 1.0 Universal (CC0-1.0); Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)","Resources":[{"Description":"Large 3D volumes of neuroimaging data and image processing products such as segmentation and reconstructed meshes","ARN":"arn:aws:s3:::bossdb-open-data","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Data access and download","URL":"https://bossdb.org/get-started","AuthorName":"Jordan Matelsky","AuthorURL":"https://github.com/j6k4m8"},{"Title":"Explore BossDB MICrONS Data in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/1c7c66ed-2ddb-4704-b21c-0def2ac3bf91/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Get To Know A Dataset - BossDB","URL":"https://github.com/aplbrain/bossdb_cookbook/blob/main/notebooks/Get-to-know-a-dataset-template.ipynb","NotebookURL":"https://github.com/aplbrain/bossdb_cookbook/blob/main/notebooks/Get-to-know-a-dataset-template.ipynb","AuthorName":"BossDB Team","AuthorURL":"https://github.com/aplbrain"}],"Tools & Applications":[{"Title":"bossDB","URL":"https://bossdb.org","AuthorName":"bossDB Team"},{"Title":"CloudVolume","URL":"https://github.com/seung-lab/cloud-volume","AuthorName":"Seung Lab"}],"Publications":[{"Title":"A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data","URL":"https://doi.org/10.1038/s41592-018-0181-1","AuthorName":"J. T. Vogelstein, E. Perlman, B. Falk, A. Baden, W. Gray Roncal, V. Chandrashekhar, F. Collman, S. Seshamani, J. L. Patsolic, K. Lillaney, M. Kazhdan, R. Hider, D. Pryor, J. Matelsky, T. Gion, P. Manavalan, B. Wester, M. Chevillet, E. T. Trautman, K. Khairy, E. Bridgeford, D. M. Kleissas, D. J. Tward, A. K. Crow, B. Hsueh, M. A. Wright, M. I. Miller, S. J. Smith, R. J. Vogelstein, K. Deisseroth, and R. Burns"},{"Title":"intern: Integrated Toolkit for Extensible and Reproducible Neuroscience","URL":"https://www.biorxiv.org/content/10.1101/2020.05.15.098707v1","AuthorName":"Jordan K Matelsky, Luis Rodriguez, Daniel Xenes, Timothy Gion, Robert Hider Jr., Brock Wester, William Gray-Roncal","AuthorURL":"https://bossdb.org/publications"},{"Title":"The Block Object Storage Service (bossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery","URL":"https://doi.org/10.1101/217745v2","AuthorName":"Robert Hider Jr., Dean M. Kleissas, Derek Pryor, Timothy Gion, Luis Rodriguez, Jordan Matelsky, William Gray-Roncal, Brock Wester","AuthorURL":"https://bossdb.org/publications"}]},"RegistryEntryAdded":"2021-06-24","RegistryEntryLastModified":"2026-11-03","Slug":"bossdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Biological and Physical Sciences (BPS) Microscopy Benchmark Training Dataset","Description":"Fluorescence microscopy images of individual nuclei from mouse fibroblast cells, irradiated with Fe particles or X-rays with fluorescent foci indicating 53BP1 positivity, a marker of DNA damage. These are maximum intensity projections of 9-layer microscopy Z-stacks.","Documentation":"https://docs.google.com/document/d/e/2PACX-1vTIjUPenLxVX0stErsBbK884QMJW_Ur1mqHJ9K3KIZl3klT90cxHDppsEvz5Z6Skdu13X8tzghqyWcN/pub","Contact":"lauren.m.sanders@nasa.gov","ManagedBy":"[NASA](https://osdr.nasa.gov/)","UpdateFrequency":"New fluorescence microscopy mouse fibroblast nuclei data is added whenever it is available.","Tags":["aws-pds","fluorescence imaging","GeneLab","genetic","genetic maps","life sciences","microscopy","NASA SMD AI"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"NASA BPS Microscopy Benchmark Training Data","ARN":"arn:aws:s3:::nasa-bps-training-data/Microscopy/","Region":"us-west-2","Type":"S3 Bucket","Explore":null}],"DataAtWork":{"Publications":[{"Title":"Dose, LET and Strain Dependence of Radiation-Induced 53BP1 Foci in 15 Mouse Strains Ex Vivo Introducing Novel DNA Damage Metrics","URL":"https://doi.org/10.1667/RR15338.1","AuthorName":"Sébastien Penninckx, Egle Cekanaviciute, Charlotte Degorre, Elodie Guiet, Louise Viger, Stéphane Lucasb, Sylvain V. Costes"},{"Title":"NASA SMD AI Workshop Report","URL":"https://science.nasa.gov/files/atoms/files/NASA%20SMD%20AI%20Workshop%2021%20(spreads)%20(1).pdf","AuthorName":"SMD Artificial Intelligence (AI) Initiative"}]},"RegistryEntryAdded":"2023-01-14","RegistryEntryLastModified":"2024-12-20","Slug":"bps_microscopy","Sources":["awslabs-open-data-registry"]}
{"Name":"Biological and Physical Sciences (BPS) RNA Sequencing Benchmark Training Dataset","Description":"RNA sequencing data from spaceflown and control mouse liver samples, sourced from NASA GeneLab and augmented with generative adversarial network.","Documentation":"https://docs.google.com/document/d/e/2PACX-1vSvHfRYav25TVSJfQE2MvEKJIM7LUnpmAyRQlrkamgwiWb9nKYNlSYu3_KvHmzUYkNQNbfpoB27t4oQ/pub","Contact":"lauren.m.sanders@nasa.gov","ManagedBy":"[NASA](https://osdr.nasa.gov/)","UpdateFrequency":"New spaceflight liver RNA sequencing data is added whenever it is available.","Tags":["aws-pds","gene expression","GeneLab","genetic","genetic maps","life sciences","NASA SMD AI","space biology"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"NASA BPS RNA Sequencing Benchmark Training Data","ARN":"arn:aws:s3:::nasa-bps-training-data/RNAsequencing/","Region":"us-west-2","Type":"S3 Bucket","Explore":null}],"DataAtWork":{"Publications":[{"Title":"Adversarial generation of gene expression data","URL":"https://doi.org/10.1093/bioinformatics/btab035","AuthorName":"Ramon Viñas, Helena Andrés-Terré, Pietro Liò, Kevin Bryson"},{"Title":"NASA SMD AI Workshop Report","URL":"https://science.nasa.gov/files/atoms/files/NASA%20SMD%20AI%20Workshop%2021%20(spreads)%20(1).pdf","AuthorName":"SMD Artificial Intelligence (AI) Initiative"}]},"RegistryEntryAdded":"2023-01-14","RegistryEntryLastModified":"2024-12-20","Slug":"bps_rnaseq","Sources":["awslabs-open-data-registry"]}
{"Name":"Blood Pressure in Salt-Sensitive Dahl Rats","Description":"This database contains continuous blood pressure recordings for 9 Dahl SS rats and 6 Dahl SS.13BN rats, under high and low salt conditions.","Documentation":"https://doi.org/10.13026/C20597","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C20597","ARN":"arn:aws:s3:::physionet-open/bpssrat/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"bpssrat","Sources":["awslabs-open-data-registry"]}
{"Name":"BraiDyn-BC: Cued lever-pull task dataset","Description":"The BraiDyn-BC (Brain Dynamics underlying emergence of Behavioral Change) Database offers an extensive, multimodal dataset that links\nwide-field calcium imaging of the mouse neocortex to comprehensive behavioral measurements during a behavioral task.\nAs one of the contents in this database, we newly provide a dataset that includes 15 sessions spanning two weeks of motor skill learning, \nin which 25 mice were trained to pull a lever to obtain water rewards.\nSimultaneous high-speed videography captures body, facial, and eye movements, and environmental parameters are monitored.\nThe dataset also features resting-state cortical activity and sensory-evoked responses, enhancing its utility for both learning-related and\nsensory-driven neural dynamics studies.\nData are formatted in accordance with the Neurodata Without Borders (NWB) standard, ensuring compatibility with existing analysis tools and\nadherence to the FAIR principles.\nThis resource enables in-depth investigations into the neural mechanisms underlying behavior and learning.\nThe platform encourages collaborative research, supporting the exploration of rapid within-session learning effects, long-term behavioral adaptations, and neural circuit dynamics.\n","Documentation":"https://doi.org/10.1101/2025.02.03.631599","Contact":"Ken Nakae (ken.nakae@gmail.com)","ManagedBy":"[BraiDyn-BC Database Project](https://boatneck-weeder-7b7.notion.site/BraiDyn-BC-Database-303cf08c89f94d81bb2eaed4c3c50345)","UpdateFrequency":"NA","Tags":["aws-pds","calcium imaging","imaging","life sciences","Mus musculus","neuroscience","video"],"License":"Creative Commons Attribution 4.0 International (CC-BY 4.0)","Resources":[{"Description":"BraiDyn-BC - Cued lever-pull task dataset","ARN":"arn:aws:s3:::braidyn-bc-buckets","Region":"ap-northeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Detailed usage tutorials on Google Colab","URL":"https://drive.google.com/drive/folders/1QciTJd3tXkEGhz6782czB2dEO3fafm8M","AuthorName":"Keisuke Sehara","AuthorURL":"https://orcid.org/0000-0003-4368-8143"}],"Tools & Applications":[{"Title":"A set of libraries used for generating the dataset","URL":"https://github.com/BraiDyn-BC/bdbc-data-pipeline","AuthorName":"Keisuke Sehara, Ryo Aoki, Shoya Sugimoto"}],"Publications":[{"Title":"A multimodal dataset linking wide-field calcium imaging to behavior changes in mice during an operant lever-pull task","URL":"https://doi.org/10.1101/2025.02.03.631599","AuthorName":"Kondo M, Sehara K, Harukuni R, Aoki R, Sugimoto S, Tanaka YR, Matsuzaki M, Nakae K","AuthorURL":null}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-04-14","RegistryEntryLastModified":"2025-10-29","Slug":"braidyn-bc_cued-lever-pull","Sources":["awslabs-open-data-registry"]}
{"Name":"Brain Encoding Response Generator (BERG)","Description":"Brain Encoding Response Generator (BERG) is a resource consisting of multiple pre-trained encoding models of the brain and an accompanying Python package to generate accurate in silico neural responses to arbitrary stimuli with just a few lines of code.","Documentation":"https://brain-encoding-response-generator.readthedocs.io/en/latest/index.html","Contact":"brain.berg.info@gmail.com","ManagedBy":"[Neural Dynamics of Visual Cognition](https://www.ewi-psy.fu-berlin.de/en/psychologie/arbeitsbereiche/neural_dyn_of_vis_cog/index.html), [CVAI](https://www.cvai.cs.uni-frankfurt.de/)","UpdateFrequency":"Updates will be released on a per-model or dataset basis as new models are trained and tested.","Tags":["brain models","computer vision","deep learning","life sciences","machine learning","neuroimaging","neuroscience"],"License":"CC BY-NC 4.0. For Terms & Conditions, see https://brain-encoding-response-generator.readthedocs.io/en/latest/about/terms_and_conditions.html","Citation":"Please cite the Brain Encoding Response Generator using the format provided here https://brain-encoding-response-generator.readthedocs.io/en/latest/about/citation.html","Resources":[{"Description":"Pre-trained neural encoding models","ARN":"arn:aws:s3:::brain-encoding-response-generator","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://brain-encoding-response-generator.s3.us-west-2.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"In-Silico EEG Data Tutorial","URL":"https://colab.research.google.com/drive/1uF5nr1pyg0_my3gULj3w5y0nuq5gZjhL#scrollTo=9IY581JgYIUN","NotebookURL":"https://github.com/gifale95/BERG/blob/main/tutorials/berg_eeg_tutorial.ipynb","AuthorName":"Alessandro Gifford","AuthorURL":"https://www.alegifford.com/","Services":["Amazon S3"]},{"Title":"In-Silico fMRI Data Tutorial","URL":"https://colab.research.google.com/drive/1w4opmM9h8Oe1NWlwIDuLuDIGuIXj9UaV#scrollTo=ObuCJgZsYB-G","NotebookURL":"https://github.com/gifale95/BERG/blob/main/tutorials/berg_fmri_tutorial.ipynb","AuthorName":"Alessandro Gifford","AuthorURL":"https://www.alegifford.com/","Services":["Amazon S3"]},{"Title":"Quickstart Tutorial","URL":"https://colab.research.google.com/drive/1JS4um1eS4Ml983lUNQgEw4544_Lc5Qn0#scrollTo=ObuCJgZsYB-G","NotebookURL":"https://github.com/gifale95/BERG/blob/main/tutorials/berg_quickstart.ipynb","AuthorName":"Domenic Bersch","Services":["Amazon S3"]}],"Tools & Applications":[{"Title":"Brain Encoding Response Generator (BERG)","URL":"https://github.com/gifale95/BERG","AuthorName":"Alessandro Gifford","AuthorURL":"https://www.alegifford.com/"}],"Publications":[{"Title":"The Brain Encoding Response Generator","URL":"https://www.alegifford.com/projects/berg/","AuthorName":"Alessandro Gifford","AuthorURL":"https://www.alegifford.com/"}]},"ADXCategories":["Healthcare & Life Sciences Data","Public Sector Data"],"RegistryEntryAdded":"2025-06-05","RegistryEntryLastModified":"2025-06-06","Slug":"brain-encoding-response-generator","Sources":["awslabs-open-data-registry"]}
{"Name":"BrainGlobe Atlases","Description":"BrainGlobe provides an archive and standardised interface to anatomical atlases from multiple species. This dataset includes these atlases, and other data (e.g. sample neuroanatomy data) to allow the greatest use of the atlases.","Documentation":"https://brainglobe.info/documentation/brainglobe-atlasapi/usage/atlas-details.html","Contact":"hello@brainglobe.info","ManagedBy":"[BrainGlobe](https://brainglobe.info/)","UpdateFrequency":"When new atlases are packaged","Tags":["aws-pds","biology","digital preservation","Homo sapiens","image processing","imaging","life sciences","light-sheet microscopy","magnetic resonance imaging","medical imaging","microscopy","Mus musculus","neurobiology","neuroimaging","neuroscience","Rattus norvegicus","volumetric imaging","zarr"],"License":"Creative Commons CC0 1.0 Universal","Citation":"Claudi et al., (2020). BrainGlobe Atlas API: a common interface for neuroanatomical atlases. Journal of Open Source Software, 5(54), 2668, https://doi.org/10.21105/joss.02668","Resources":[{"Description":"Atlases and sample data in a public bucket","ARN":"arn:aws:s3:::brainglobe","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for new BrainGlobe data","ARN":"arn:aws:sns:us-west-2:865567910455:brainglobe-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Interacting with cloud atlas data through Python","URL":"https://brainglobe.info/aws_examples/explore_atlas.html","AuthorName":"Alessandro Felder","AuthorURL":"https://github.com/alessandrofelder"}],"Publications":[{"Title":"BrainGlobe Atlas API: a common interface for neuroanatomical atlases","URL":"https://doi.org/10.21105/joss.02668","AuthorName":"Federico Claudi, Luigi Petrucco, Adam L. Tyson et al.","AuthorURL":"https://brainglobe.info/"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-10-20","RegistryEntryLastModified":"2025-12-15","Slug":"brainglobe","Sources":["awslabs-open-data-registry"]}
{"Name":"Brain/MINDS Marmoset Connectivity Resource on AWS","Description":"Brain/MINDS Marmoset Connectivity Resource (BMCR) is a resource that provides access to anterograde and retrograde neuronal tracer data, made available by Brain/MINDS project. It is currently restricted to injections into the prefrontal cortex of a marmoset brain but is planned to include injections into entire cortical areas and representative subcortical brain regions.","Documentation":"https://bmcr.brainminds.jp/","Contact":"https://dataportal.brainminds.jp/contact-us","ManagedBy":"[RIKEN Center for Brain Science](https://cbs.riken.jp)","UpdateFrequency":"As new data become available.","Tags":["brain images","imaging","life sciences","microscopy","neurobiology","neuroimaging","neuroscience","nifti","non-human primate"],"License":"Creative Commons Attribution 4.0 International","Resources":[{"Description":"NIfTI data for marmoset brains and metadata","ARN":"arn:aws:s3:::brainminds-marmoset-connectivity","Region":"ap-northeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Explorer Tutorials","URL":"https://bmcr.brainminds.jp/#ExplorerTutorial","AuthorName":"Henrik Skibbe"}],"Tools & Applications":[{"Title":"BMCR website","URL":"https://bmcr.brainminds.jp/","AuthorName":"Henrik Skibbe"},{"Title":"Marmoset PFC connectome","URL":"https://dataportal.brainminds.jp/marmoset-tracer-injection","AuthorName":"Akiya Watakabe, Henrik Skibbe and Tetsuo Yamamori"}],"Publications":[{"Title":"Local and long-distance organization of prefrontal cortex circuits in the marmoset brain.","URL":"https://doi.org/10.1016/j.neuron.2023.04.028","AuthorName":"Watakabe A, Skibbe H, Nakae K, Abe H, Ichinohe N, Rachmadi MF, Wang J, Takaji M, Mizukami H, Woodward A, Gong R, Hata J, Van Essen DC, Okano H, Ishii S, Yamamori T."},{"Title":"The Brain/MINDS Marmoset Connectivity Resource - An open-access platform for cellular-level tracing and tractography in the primate brain","URL":"https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002158","AuthorName":"H. Skibbe, M.F. Rachmadi, K. Nakae, C. E. Gutierrez, J. Hata, H. Tsukada, C. Poon, K. Doya, P. Majka, M. G. P. Rosa, M. Schlachter, H. Okano, T. Yamamori, S. Ishii, M. Reisert, A. Watakabe."}]},"RegistryEntryAdded":"2025-05-22","RegistryEntryLastModified":"2025-06-06","Slug":"brainminds-marmoset-connectivity","Sources":["awslabs-open-data-registry"]}
{"Name":"BrainSeq - Neurogenomics to Drive Novel Target Discovery for Neuropsychiatric Disorders","Description":"This ambitious project seeks to characterize the genetic and epigenetic regulation of multiple facets of transcription in distinct brain regions across the human lifespan in samples of major neuropsychiatric disorders and controls. Initially focused on schizophrenia and mood disorders, the goal of this consortium is to elucidate the underlying molecular mechanisms of genetic associations with the goal of identifying novel therapeutic targets. The consortium currently consists of seven pharmaceutical companies and a not-for-profit medical research institution working as a precompetitive team to generate and analyze publicly available archival brain genomic data related to neuropsychiatric illness.\n","Documentation":"https://www.synapse.org/#!Synapse:syn12299750/wiki/551288","Contact":"lcolladotor@gmail.com","ManagedBy":"Sage Bionetworks","UpdateFrequency":"Never","Tags":["aws-pds","gene expression","genotyping","life sciences","transcriptomics"],"License":"CC BY","Citation":"Jaffe AE, Straub RE, Shin JH, Tao R, Gao Y, Collado-Torres L, Kam-Thong T, Xi HS, Quan J, Chen Q, Colantuoni C, Ulrich WS, Maher BJ, Deep-Soboslay A; BrainSeq Consortium; Cross AJ, Brandon NJ, Leek JT, Hyde TM, Kleinman JE, Weinberger DR. Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis. Nat Neurosci. 2018 Aug;21(8):1117-1125. doi: 10.1038/s41593-018-0197-y. Epub 2018 Jul 26.\n","Resources":[{"Description":"Phase 1: DLPFC polyA+ RNA-seq on 738 subjects spanning the lifespan and three main psychiatric diagnostic groups (Schizophrenia, Major Depression Disorder, and Bipolar Disorder).\n","ARN":"arn:aws:s3:::brainseq.opendata.sagebase.org","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://doi.org/10.7303/syn12299750"}],"DataAtWork":{"Tools & Applications":[{"Title":"BrainSeq Phase1 DLPFC","URL":"https://eqtl.brainseq.org/phase1/","AuthorName":"Jaffe A, Straub R, Shin J, Tao R, Gao Y, Collado-Torres L, et al","AuthorURL":"https://github.com/LieberInstitute/brainseq_phase1"}],"Publications":[{"Title":"Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis","URL":"https://doi.org/10.1038/s41593-018-0197-y","AuthorName":"Jaffe A, Straub R, Shin J, Tao R, Gao Y, Collado-Torres L, et al","AuthorURL":"https://orcid.org/0000-0003-2409-2969"}]},"DeprecatedNotice":null,"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-04-13","RegistryEntryLastModified":"2026-04-29","Slug":"brainseq","Sources":["awslabs-open-data-registry"]}
{"Name":"Earth Observation Data Cubes for Brazil","Description":"Earth observation (EO) data cubes produced from analysis-ready data (ARD) of CBERS-4, Sentinel-2 A/B and Landsat-8 satellite images for Brazil. The datacubes are regular in time and use a hierarchical tiling system. Further details are described in [Ferreira et al. (2020)](https://www.mdpi.com/2072-4292/12/24/4033).","Documentation":"http://brazildatacube.org/en/home-page-2/","Contact":"brazildatacube@inpe.br","ManagedBy":"[INPE - Brazil Data Cube](http://brazildatacube.org/)","UpdateFrequency":"New EO data cubes are added as soon as there are produced by the Brazil Data Cube project.","DeprecatedNotice":"This dataset is deprecated and will be removed from AWS Open Data in the near future. If you have any questions or require assistance, please contact us at [data.support@inpe.br].","Tags":["aws-pds","cog","earth observation","geoscience","geospatial","image processing","open source software","satellite imagery","stac"],"License":"The EO data cubes are produced from free and open images of CBERS-4, Landsat-8 and Sentinel-2 satellites. Data usage is subject to Terms and Conditions for the use and distribution of [Landsat data](https://www.usgs.gov/centers/eros/data-citation?qt-science_support_page_related_con=0#qt-science_support_page_related_con), Legal Notice on the use and distribution of [Sentinel-2 data](https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice/) and License for use and distribution of [CBERS data](https://creativecommons.org/licenses/by-sa/3.0/). When using data produced by the Brazil Data Cube, please acknowledge the work of the Brazilian National Institute for Space Research (INPE) in producing such data.\nCBERS-4 image courtesy of the National Institute for Space Research (INPE).\nLandsat-8 image courtesy of the U.S. Geological Survey.\nSentinel-2 (ESA) image courtesy of the Copernicus SciHub.\nSentinel-2 Cloud-Optimized GeoTIFFs data 2017-2018, was accessed on June/2021 from https://registry.opendata.aws/sentinel-2-l2a-cogs.\n","Resources":[{"Description":"Earth Observation Data Cubes for Brazil - Sentinel 2A/2B","ARN":"arn:aws:s3:::bdc-sentinel-2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[BDC STAC V0.9.0 endpoint](https://bdc-sentinel-2.s3.us-west-2.amazonaws.com/catalog.json)"]},{"Description":"Earth Observation Data Cubes for Brazil - CBERS 4","ARN":"arn:aws:s3:::bdc-cbers","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[BDC STAC V0.9.0 endpoint](https://bdc-cbers.s3.us-west-2.amazonaws.com/catalog.json)"]},{"Description":"Notifications for new EO Data Cubes Sentinel-2 scenes","ARN":"arn:aws:sns:us-west-2:896627514407:bdc-sentinel-2-object_created","Region":"us-west-2","Type":"SNS Topic"},{"Description":"Notifications for new EO Data Cubes CBERS scenes","ARN":"arn:aws:sns:us-west-2:896627514407:bdc-cbers-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Tile Map Service to view Image Collections from Brazil Data Cube Catalog","URL":"https://github.com/brazil-data-cube/code-gallery/blob/master/jupyter/Python/tiler/bdc-tiler_introduction.ipynb","AuthorName":"Brazil Data Cube","AuthorURL":"https://brazil-data-cube.github.io"},{"Title":"Using Python to Access Image Collections Data (Jupyter notebook)","URL":"https://github.com/brazil-data-cube/code-gallery/blob/master/jupyter/Python/stac/stac-aws-introduction.ipynb","AuthorName":"Brazil Data Cube","AuthorURL":"https://brazil-data-cube.github.io/"}],"Tools & Applications":[{"Title":"cube-builder-aws - Application to generate data cubes on AWS environment.","URL":"https://github.com/brazil-data-cube/cube-builder-aws","AuthorName":"Brazil Data Cube","AuthorURL":"https://brazil-data-cube.github.io"},{"Title":"rstac - R library to query and download Image Collections from Brazil Data Cube Catalog on Amazon S3","URL":"https://github.com/brazil-data-cube/rstac","AuthorName":"Brazil Data Cube","AuthorURL":"https://brazil-data-cube.github.io"}],"Publications":[{"Title":"Building Earth Observation Data Cubes on AWS","URL":"https://www.proquest.com/openview/070d2a753cc88d26535c98293171a5ac/1?","AuthorName":"Ferreira, K R; Queiroz, G R; Marujo, R F B; Costa, R W. "},{"Title":"Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products.","URL":"https://www.mdpi.com/2072-4292/12/24/4033","AuthorName":"K. R. Ferreira, et al."},{"Title":"Using Remote Sensing Images and Cloud Services on AWS to Improve Land Use and Cover Monitoring","URL":"https://ieeexplore.ieee.org/abstract/document/9165649","AuthorName":"K. R. Ferreira, et al."}]},"RegistryEntryAdded":"2021-12-22","RegistryEntryLastModified":"2025-05-21","Slug":"brazil-data-cubes","Sources":["awslabs-open-data-registry"]}
{"Name":"Genome Aggregation Database (gnomAD)","Description":"The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use.\nThe v4.1 data set (GRCh38) spans 730,947 exome sequences and 76,215 whole-genome sequences from unrelated individuals, of [diverse ancestries](https://gnomad.broadinstitute.org/stats#diversity), sequenced sequenced as part of various disease-specific and population genetic studies.\nThe gnomAD Principal Investigators and team can be found [here](https://gnomad.broadinstitute.org/team), and the groups that have contributed data to the current release are listed [here](https://gnomad.broadinstitute.org/about).\nSign up for the gnomAD mailing list [here](http://broad.io/gnomad_list).\n","Documentation":"https://gnomad.broadinstitute.org/about","Contact":"gnomad@broadinstitute.org","ManagedBy":"gnomAD Production Team at the Broad Institute","UpdateFrequency":"Data from new releases are made public as soon as they are available. New releases, including both minor and major versions, have historically been issued on the order of once per year.","Tags":["aws-pds","bioinformatics","genetic","genomic","life sciences","population","population genetics","short read sequencing","whole genome sequencing"],"License":"[MIT](https://github.com/broadinstitute/gnomad_methods/blob/master/LICENSE); [terms of use](https://gnomad.broadinstitute.org/terms)","Resources":[{"Description":"gnomAD summary data aggregated from large-scale human genome and exome sequencing projects.","ARN":"arn:aws:s3:::gnomad-public-us-east-1","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"gnomAD quality control GitHub repository","URL":"https://github.com/broadinstitute/gnomad_qc","AuthorName":"gnomAD Production Team","AuthorURL":"https://github.com/orgs/broadinstitute/teams/gnomad/members"},{"Title":"Hail","URL":"https://hail.is/","AuthorName":"Hail Team","AuthorURL":"https://hail.is/"},{"Title":"Hail utilities for gnomAD","URL":"https://pypi.org/project/gnomad/","AuthorName":"gnomAD Production Team","AuthorURL":"https://github.com/orgs/broadinstitute/teams/gnomad/members"}],"Publications":[{"Title":"A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625, 92–100 (2024)","URL":"https://doi.org/10.1038/s41586-023-06045-0","AuthorName":"Chen, S., Francioli, L. C., Goodrich, J. K., Collins, R. L., Wang, Q., Alföldi, J., Watts, N. A., Vittal, C., Gauthier, L. D., Poterba, T., Wilson, M. W., Tarasova, Y., Phu, W., Yohannes, M. T., Koenig, Z., Farjoun, Y., Banks, E., Donnelly, S., Gabriel, S., Gupta, N., Ferriera, S., Tolonen, C., Novod, S., Bergelson, L., Roazen, D., Ruano-Rubio, V., Covarrubias, M., Llanwarne, C., Petrillo, N., Wade, G., Jeandet, T., Munshi, R., Tibbetts, K., gnomAD Project Consortium, O’Donnell-Luria, A., Solomonson, M., Seed, C., Martin, A. R., Talkowski, M. E., Rehm, H. L., Daly, M. J., Tiao, G., Neale, B. M., MacArthur, D. G. & Karczewski, K. J."},{"Title":"A structural variation reference for medical and population genetics. Nature 581, 444–451 (2020)","URL":"https://doi.org/10.1038/s41586-020-2287-8","AuthorName":"Collins, R. L., Brand, H., Karczewski, K. J., Zhao, X., Alföldi, J., Francioli, L. C., Khera, A. V., Lowther, C., Gauthier, L. D., Wang, H., Watts, N. A., Solomonson, M., O’Donnell-Luria, A., Baumann, A., Munshi, R., Walker, M., Whelan, C., Huang, Y., Brookings, T., ... Talkowski, M. E."},{"Title":"Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016)","URL":"https://doi.org/10.1038/nature19057","AuthorName":"Lek, M., Karczewski, K., Minikel, E. et al."},{"Title":"Characterising the loss-of-function impact of 5’ untranslated region variants in 15,708 individuals. Nature Communications 11, 2523 (2020)","URL":"https://doi.org/10.1038/s41467-019-10717-9","AuthorName":"Whiffin, N., Karczewski, K. J., Zhang, X., Chothani, S., Smith, M. J., Gareth Evans, D., Roberts, A. M., Quaife, N. M., Schafer, S., Rackham, O., Alföldi, J., O’Donnell-Luria, A. H., Francioli, L. C., Genome Aggregation Database (gnomAD) Production Team, Genome Aggregation Database (gnomAD) Consortium, Cook, S. A., Barton, P. J. R., MacArthur, D. G., & Ware, J. S."},{"Title":"Evaluating potential drug targets through human loss-of-function genetic variation. Nature 581, 459–464 (2020)","URL":"https://doi.org/10.1038/s41586-020-2267-z","AuthorName":"Minikel, E. V., Karczewski, K. J., Martin, H. C., Cummings, B. B., Whiffin, N., Rhodes, D., Alföldi, J., Trembath, R. C., van Heel, D. A., Daly, M. J., Genome Aggregation Database Production Team, Genome Aggregation Database Consortium, Schreiber, S. L., & MacArthur, D. G."},{"Title":"gnomAD v2.1","URL":"https://gnomad.broadinstitute.org/blog/2018-10-gnomad-v2-1/","AuthorName":"Laurent Francioli, Grace Tiao, Konrad Karczewski, Matthew Solomonson, Nick Watts"},{"Title":"gnomAD v3.0","URL":"https://gnomad.broadinstitute.org/blog/2019-10-gnomad-v3-0/","AuthorName":"Laurent Francioli, Daniel MacArthur"},{"Title":"Landscape of multi-nucleotide variants in 125,748 human exomes and 15,708 genomes. Nature Communications 11, 2539 (2020)","URL":"https://doi.org/10.1038/s41467-019-12438-5","AuthorName":"Wang, Q., Pierce-Hoffman, E., Cummings, B. B., Karczewski, K. J., Alföldi, J., Francioli, L. C., Gauthier, L. D., Hill, A. J., O’Donnell-Luria, A. H., Genome Aggregation Database (gnomAD) Production Team, Genome Aggregation Database (gnomAD) Consortium, & MacArthur, D. G."},{"Title":"Technical artifact drives apparent deviation from Hardy-Weinberg equilibrium at CCR5-∆32 and other variants in gnomAD. bioRxiv (p. 784157)","URL":"https://doi.org/10.1101/784157","AuthorName":"Karczewski, K. J., Gauthier, L. D., Daly, M. J."},{"Title":"The effect of LRRK2 loss-of-function variants in humans. Nature Medicine (2020)","URL":"https://doi.org/10.1038/s41591-020-0893-5","AuthorName":"Whiffin, N., Armean, I. M., Kleinman, A., Marshall, J. L., Minikel, E. V., Goodrich, J. K., Quaife, N. M., Cole, J. B., Wang, Q., Karczewski, K. J., Cummings, B. B., Francioli, L., Laricchia, K., Guan, A., Alipanahi, B., Morrison, P., Baptista, M. A. S., Merchant, K. M., Genome Aggregation Database Production Team, ... MacArthur, D. G."},{"Title":"The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020)","URL":"https://doi.org/10.1038/s41586-020-2308-7","AuthorName":"Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., Collins, R. L., Laricchia, K. M., Ganna, A., Birnbaum, D. P., Gauthier, L. D., Brand, H., Solomonson, M., Watts, N. A., Rhodes, D., Singer-Berk, M., England, E. M., Seaby, E. G., Kosmicki, J. A., ... MacArthur, D. G."},{"Title":"Transcript expression-aware annotation improves rare variant interpretation. Nature 581, 452–458 (2020)","URL":"https://doi.org/10.1038/s41586-020-2329-2","AuthorName":"Cummings, B. B., Karczewski, K. J., Kosmicki, J. A., Seaby, E. G., Watts, N. A., Singer-Berk, M., Mudge, J. M., Karjalainen, J., Kyle Satterstrom, F., O’Donnell-Luria, A., Poterba, T., Seed, C., Solomonson, M., Alföldi, J., The Genome Aggregation Database Production Team, The Genome Aggregation Database Consortium, Daly, M. J., & MacArthur, D. G."}]},"RegistryEntryAdded":"2020-09-17","RegistryEntryLastModified":"2024-04-17","Slug":"broad-gnomad","Sources":["awslabs-open-data-registry"]}
{"Name":"UK Biobank Pan-Ancestry Summary Statistics","Description":"A multi-ancestry analysis of 7,221 phenotypes using a generalized mixed model association testing framework, spanning 16,119 genome-wide association studies. We provide standard meta-analysis across all populations and with a leave-one-population-out approach for each trait. The data are provided in tsv format (per phenotype) and Hail MatrixTable (all phenotypes and variants). Metadata is provided in phenotype and variant manifests.","Documentation":"https://pan.ukbb.broadinstitute.org","Contact":"ukb.diverse.gwas@gmail.com","ManagedBy":"Analytic and Translational Genetics Unit, Massachusetts General Hospital and the Broad Institute","UpdateFrequency":"Occasional","Tags":["aws-pds","genetic","genome wide association study","genomic","life sciences","population genetics"],"License":"CC BY-4.0 (usage may be restricted by UK Biobank, more details on the \"[Downloads page](https://pan.ukbb.broadinstitute.org/downloads)\")","Resources":[{"Description":"Summary statistics from Genome Wide Association Studies (GWASes) of multiple ancestries in the UK Biobank, in TSV and Hail formats","ARN":"arn:aws:s3:::pan-ukb-us-east-1","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Hail Tutorials","URL":"https://hail.is/docs/0.2/tutorials-landing.html","AuthorName":"Hail Team","AuthorURL":"https://hail.is/"}],"Tools & Applications":[{"Title":"Hail","URL":"https://hail.is","AuthorName":"Hail Team","AuthorURL":"https://hail.is/"},{"Title":"Hail on AWS Quick Start","URL":"https://aws.amazon.com/quickstart/architecture/hail/","AuthorName":"Amazon Web Services and PrivoIT"}],"Publications":[{"Title":"Pan-ancestry genetic analysis of the UK Biobank","URL":"https://pan.ukbb.broadinstitute.org/","AuthorName":"Pan UKBB Team"}]},"RegistryEntryAdded":"2020-09-08","RegistryEntryLastModified":"2020-10-23","Slug":"broad-pan-ukb","Sources":["awslabs-open-data-registry"]}
{"Name":"Broad Genome References","Description":"Broad maintained human genome reference builds hg19/hg38 and decoy references.","Documentation":"https://s3.amazonaws.com/broad-references/broad-references-readme.html","Contact":"hensonc@broadinstitute.org","ManagedBy":"Broad Institute","UpdateFrequency":"Monthly","Tags":["aws-pds","bioinformatics","biology","cancer","genetic","genomic","Homo sapiens","life sciences","reference index"],"License":"CC0 1.0 Universal (CC0 1.0) Public Domain Dedication","Resources":[{"Description":"This dataset includes two human genome references assembled by the Genome Reference Consortium: Hg19 and Hg38.","ARN":"arn:aws:s3:::broad-references","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Using Amazon FSx for Lustre for Genomics Workflows on AWS","URL":"https://aws.amazon.com/blogs/storage/using-amazon-fsx-for-lustre-for-genomics-workflows-on-aws/","AuthorName":"W. Lee Pang","AuthorURL":"https://www.linkedin.com/in/lee-pang-a039a26/","Services":["AWS Batch","Amazon FSx"]}],"Publications":[{"Title":"Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA","URL":"https://www.cell.com/cell-reports-methods/pdf/S2667-2375(21)00165-X.pdf","AuthorName":"Willey J. C., Morrison T. B., Austermiller B., Crawford E. E., et al (2021)"}]},"RegistryEntryAdded":"2018-10-04","RegistryEntryLastModified":"2025-05-22","Slug":"broad-references","Sources":["awslabs-open-data-registry"]}
{"Name":"Brugada-HUCA: 12-Lead ECG Recordings for the Study of Brugada Syndrome","Description":"We present a novel dataset of 12-lead electrocardiogram (ECG) recordings developed to support the study and classification of Brugada syndrome, a rare but potentially fatal cardiac arrhythmia. The data were collected retrospectively from patients evaluated at the Cardiology Department of the Hospital Universitario Central de Asturias (HUCA) and reviewed by clinical experts. Diagnostic labels were assigned according to internationally accepted criteria. The dataset comprises 363 subjects in total, including 76 patients diagnosed with Brugada syndrome and 287 healthy control subjects. Each recording is accompanied by diagnostic information. This resource aims to facilitate the development and validation of machine learning algorithms and computational tools for ECG interpretation, supporting improved early detection and clinical decision-making. The dataset, fully anonymized in accordance with ethical and privacy standards, provides a reproducible foundation for collaborative research into Brugada syndrome and related cardiac conditions.","Documentation":"https://doi.org/10.13026/0m2w-dy83","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution-ShareAlike 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/0m2w-dy83","ARN":"arn:aws:s3:::physionet-open/brugada-huca/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"brugada-huca","Sources":["awslabs-open-data-registry"]}
{"Name":"BUSCO Datasets","Description":"Lineage datasets for use with BUSCO software package. Each dataset contains HMM profiles for clade specific, universal, single-copy marker genes. Datasets are available across archaea, bacteria, eukaryota and virus domains. The repository also includes necessary data files for phylogenetic placement of an input assembly.","Documentation":"https://busco.ezlab.org/busco_userguide.html#lineage-datasets","Contact":"https://gitlab.com/ezlab/busco/-/issues","ManagedBy":"Computational Evolutionary Genomics Group, University of Geneva","UpdateFrequency":"New datasets are released to correspond with updates in OrthoDB versions. Maintenance updates occur a few times a year if necessary to fix any bugs or update metadata.","Tags":["assembly","aws-pds","bacteria","bioinformatics","genomic","life sciences","metagenomics","open source software","protein","virus"],"License":"The BUSCO datasets are licensed under the Creative Commons Attribution-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nd/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. Any use of these datasets for analyses in a publication or product must include the citation of the corresponding paper - https://doi.org/10.1093/molbev/msab199","Citation":null,"Resources":[{"Description":"BUSCO datasets and companion files for use with BUSCO pipeline","ARN":"arn:aws:s3:::busco-data","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Notifications for new BUSCO data","ARN":"arn:aws:sns:us-east-1:622022425660:my-dataset-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"BUSCO - from QC to gene prediction and phylogenomics","URL":"https://www.youtube.com/watch?v=9SjVY3BT8JU","AuthorName":"Matthew Berkeley","AuthorURL":"https://github.com/berkelem","Services":null}],"Publications":[{"Title":"BUSCO - assessing genomic data quality and beyond.","URL":"https://currentprotocols.onlinelibrary.wiley.com/doi/full/10.1002/cpz1.323","AuthorName":"Mosè Manni, Matthew R. Berkeley, Mathieu Seppey, Evgeny M. Zdobnov"},{"Title":"BUSCO Update - Novel and Streamlined Workflows along with Broader and Deeper Phylogenetic Coverage for Scoring of Eukaryotic, Prokaryotic, and Viral Genomes.","URL":"https://academic.oup.com/mbe/article/38/10/4647/6329644?login=true","AuthorName":"Mosè Manni, Matthew R Berkeley, Mathieu Seppey, Felipe A Simão, Evgeny M Zdobnov"},{"Title":"OrthoDB and BUSCO update - annotation of orthologs with wider sampling of genomes.","URL":"https://academic.oup.com/nar/article/53/D1/D516/7899526?login=true","AuthorName":"Fredrik Tegenfeldt, Dmitry Kuznetsov, Mosè Manni, Matthew Berkeley, Evgeny M Zdobnov, Evgenia V Kriventseva"}]},"DeprecatedNotice":null,"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-07-08","RegistryEntryLastModified":"2025-08-18","Slug":"busco-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Brno University of Technology ECG Signal Database with Annotations of P Wave (BUT PDB)","Description":"Brno University of Technology ECG Signal Database with Annotations of P Wave (BUT PDB) is an ECG signal database with marked peaks of P waves created by the cardiology team at the Department of Biomedical Engineering, Brno University of Technology. The database consists of 50 2-minute 2-lead ECG signal records with various types of pathology. The ECGs were selected from three existing databases of ECG signal - the MIT-BIH Arrhythmia Database, the MIT-BIH Supraventricular Arrhythmia Database, and the Long Term AF Database. The P waves positions were manually annotated by two ECG experts in all 50 records. Each record contains also annotation of positions and types of QRS complexes (from original database) and dominant diagnosis (pathology) present in record. This database is created for the development, evaluation and objective comparison of P wave detection algorithms.","Documentation":"https://doi.org/10.13026/hwvj-5b53","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/hwvj-5b53","ARN":"arn:aws:s3:::physionet-open/but-pdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"but-pdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Brno University of Technology Smartphone PPG Database (BUT PPG)","Description":"Brno University of Technology Smartphone PPG Database (BUT PPG) is a database created by the cardiology team at the Department of Biomedical Engineering, Brno University of Technology, for the purpose of evaluating PPG quality and estimation of heart rate (HR). The data comprises 3,888 10-second recordings of PPGs and associated ECG signals used for determination of reference HR. All of the signals contain QRS complex positions, most signals contain moreover accelerometric data (ACC), and single-value annotations of blood pressure, blood oxygen saturation and glycaemia. The data were collected from 50 subjects (25 female, 25 male) aged between 19 to 76 years at rest and during various types of movement. Recordings were carried out between August 2020 and December 2021. PPG data were collected by smartphones Xiaomi Mi9 and Huawei P20 Pro with sampling frequency of 30 Hz. Reference ECG signals and ACC data were recorded using a mobile recorder Bittium Faros 360 or 180 with a sampling frequency of 1,000 Hz (ECG) and 100 Hz (ACC). Each PPG signal includes annotation of quality and reference HR. PPG signal quality is indicated binary: 1 indicates good quality for HR estimation, 0 indicates signals where HR cannot be detected reliably and thus these signals are unsuitable for any analysis.","Documentation":"https://doi.org/10.13026/tn53-8153","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/tn53-8153","ARN":"arn:aws:s3:::physionet-open/butppg/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"butppg","Sources":["awslabs-open-data-registry"]}
{"Name":"Brno University of Technology ECG Quality Database (BUT QDB)","Description":"BUT QDB is a database created by the cardiology team at the Department of Biomedical Engineering, Brno University of Technology, for the purpose of evaluating ECG quality. The data comprises 18 long-term recordings of single-lead ECGs and associated 3-axis accelerometer data, collected from 15 subjects (9 female, 6 male) aged between 21 to 83 years. Recordings were carried out between August 2018 and October 2019 while the subjects were undertaking ordinary everyday activities (\"free-living conditions\"). Data was collected using a mobile ECG and accelerometer (Bittium Faros 180) with a sampling frequency of 1,000 Hz for ECG signals and 100 Hz for accelerometer signals. The minimum length of recording is 24 hours. Three signals were fully annotated in terms of ECG signal quality. The remaining 15 signals were annotated in two selected segments, each of 20 minutes in duration. Furthermore, five additional segments of poor signal quality were also annotated. Signal quality was classified as follows: Class 1 indicates that all ECG significant waveforms (P wave, T wave and QRS complex) are clearly visible and their onsets and offsets can be detected reliably; Class 2 indicates that the noise level is increased and ECG significant points cannot be reliably detected, but the signal enables reliable QRS detection; Class 3 indicates that QRS complexes cannot be detected reliably and the signal is unsuitable for any further analysis.","Documentation":"https://doi.org/10.13026/kah4-0w24","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/kah4-0w24","ARN":"arn:aws:s3:::physionet-open/butqdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"butqdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Cloud to Street - Microsoft Flood and Clouds Dataset","Description":"This dataset consists of chips of Sentinel-1 and Sentinel-2 satellite data. Each Sentinel-1 chip contains a corresponding label for water and each Sentinel-2 chip contains a corresponding label for water and clouds. Data is stored in folders by a unique event identifier as the folder name. Within each event folder there are subfolders for Sentinel-1 (s1) and Sentinel-2 (s2) data. Each chip is contained in its own sub-folder with the folder name being the source image id, followed by a unique chip identifier consisting of a hyphenated set of 5 numbers. All bands of the satellite data, as well as the labels, and overview images are contained within the chip folder.","Documentation":"https://www.drivendata.org/competitions/81/detect-flood-water/","Contact":"support@cloudtostreet.info","ManagedBy":"[Radiant Earth Foundation](https://radiant.earth/)","UpdateFrequency":"Not updated","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","cog","computer vision","deep learning","earth observation","floods","geospatial","machine learning","satellite imagery","synthetic aperture radar"],"License":"CC-BY-4.0 https://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Flood and Cloud Training Dataset","ARN":"arn:aws:s3:::radiant-mlhub/c2smsfloods","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2022-02-01","RegistryEntryLastModified":"2022-10-12","Slug":"c2smsfloods","Sources":["awslabs-open-data-registry"]}
{"Name":"Caenorabditis Diversity Natural Resource","Description":"The Caenorhabditis Natural Diversity Resource (CaeNDR) is a data repository and analysis hub of wild strains of selfing Caenhorabditis species C. elegans, C. briggsae, and C. tropicalis from around the world to facilitate discovery of genetic variation across all three species through genome-wide association mappings to correlate genotype with phenotype and identify genetic variation underlying quantitative traits.","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/caendr","Contact":"https://caendr.org/about/contact-us","ManagedBy":"The Andersen Lab, Johns Hopkins University, Baltimore, MD, USA","UpdateFrequency":"Annually","Tags":["aws-pds","bam","bioinformatics","biology","Caenorhabditis elegans","fastq","gatk-sv","genetic maps","genome","genome wide association study","genomic","life sciences","short read sequencing","variant annotation","vcf"],"License":"All CaeNDR data is available under the MIT License. Use of the data should be cited in the usual way, with current details available at https://caendr.org/#citationModal","Resources":[{"Description":"https://caendr.org/faq","ARN":"arn:aws:s3:::caendr","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Data Releases - C. briggsae","URL":"https://caendr.org/data/data-release/c-briggsae/latest","AuthorName":"Erik Andersen"},{"Title":"Data Releases - C. elegans","URL":"https://caendr.org/data/data-release/c-elegans/latest","AuthorName":"Erik Andersen"},{"Title":"Data Releases - C. tropicalis","URL":"https://caendr.org/data/data-release/c-tropicalis/latest","AuthorName":"Erik Andersen"},{"Title":"FAQ - AWS API","URL":"https://caendr.org/faq","AuthorName":"Erik Andersen"}],"Publications":[{"Title":"CaeNDR, the Ceanorhabditis Natural Diversity Resource","URL":"https://doi.org/10.1093/nar/gkad887","AuthorName":"Crombie TA, McKeown R, Moya ND, Evans KS, Widmayer SJ, LaGrassa V, et al."}]},"ADXCategories":["Resources Data","Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-01-21","RegistryEntryLastModified":"2025-04-29","Slug":"caendr","Sources":["awslabs-open-data-registry"]}
{"Name":"Co-Produced Climate Data to Support California's Resilience Investments","Description":"Downscaled future and historical climate projections for California and her environs in support of California's Fifth Climate Assessment","Documentation":"https://analytics.cal-adapt.org/data/","Contact":"analytics@cal-adapt.org","ManagedBy":"Cal-Adapt: Analytics Engine (by Eagle Rock Analytics, Inc.)","UpdateFrequency":"Infrequent, Irregular","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","climate","climate model","earth observation","geoscience","geospatial","meteorological","simulations","weather","zarr"],"License":"Varies, see dataset specific metadata","Resources":[{"Description":"Data catalog","ARN":"arn:aws:s3:::cadcat","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://cadcat.s3.amazonaws.com/index.html)","[Data Catalog](https://cadcat.s3.amazonaws.com/cae-zarr.csv)"]}],"DataAtWork":{"Tutorials":[{"Title":"Analyzing Data Through a Global Warming Level Lens","URL":"https://github.com/cal-adapt/cae-notebooks/blob/main/analysis/warming_level_methods.ipynb","NotebookURL":"https://github.com/cal-adapt/cae-notebooks/blob/main/analysis/warming_level_methods.ipynb","AuthorName":"Cal-Adapt Analytics Engine Team","AuthorURL":"https://github.com/cal-adaptj"},{"Title":"List Data Hosted on The Cal-Adapt Data Catalog","URL":"https://nbviewer.org/urls/cadcat.s3.amazonaws.com/catalog.ipynb","NotebookURL":"https://nbviewer.org/urls/cadcat.s3.amazonaws.com/catalog.ipynb","AuthorName":"Cal-Adapt Analytics Engine Team","AuthorURL":"https://github.com/cal-adapt"},{"Title":"Tutorial on Data Access Using `climakitae`","URL":"https://github.com/cal-adapt/cae-notebooks/blob/main/data-access/basic_data_access.ipynb","NotebookURL":"https://github.com/cal-adapt/cae-notebooks/blob/main/data-access/basic_data_access.ipynb","AuthorName":"Cal-Adapt Analytics Engine Team","AuthorURL":"https://github.com/cal-adapt"}],"Tools & Applications":[{"Title":"Analytics Engine Data Catalog","URL":"https://analytics.cal-adapt.org/data/access/","AuthorName":"Cal-Adapt: Analytics Engine Team","AuthorURL":"https://github.com/cal-adapt"},{"Title":"AWS Command Line Interface (CLI)","URL":"https://analytics.cal-adapt.org/data/access/","AuthorName":"Cal-Adapt: Analytics Engine Team","AuthorURL":"https://github.com/cal-adapt"},{"Title":"AWS Explorer Functions","URL":"https://analytics.cal-adapt.org/data/access/","AuthorName":"Cal-Adapt: Analytics Engine Team","AuthorURL":"https://github.com/cal-adapt"},{"Title":"Cal-Adapt Data Download Tool (DDT)","URL":"https://analytics.cal-adapt.org/data/access/","AuthorName":"Cal-Adapt: Analytics Engine Team","AuthorURL":"https://github.com/cal-adapt"},{"Title":"Direct Connection in Xarray with S3 Path","URL":"https://analytics.cal-adapt.org/data/access/","AuthorName":"Cal-Adapt: Analytics Engine Team","AuthorURL":"https://github.com/cal-adapt"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2022-08-01","RegistryEntryLastModified":"2026-01-23","Slug":"caladapt-coproduced-climate-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Wildfire Projections to Support Climate Resilience","Description":"Wildfire projections for California and her environs in support of California's Fifth  Climate Assessment supported with historical weather observations and renewable energy  capacity profiles for grid operations.","Documentation":"https://analytics.cal-adapt.org/data/","Contact":"analytics@cal-adapt.org","ManagedBy":"Cal-Adapt: Analytics Engine (by Eagle Rock Analytics, Inc.)","UpdateFrequency":"Infrequent, Irregular","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","climate","climate model","climate projections","disaster response","electricity","energy","environmental","geospatial","meteorological","solar","sustainability","weather"],"License":"Varies, see dataset specific metadata","Citation":null,"Resources":[{"Description":"Wildfire Projections to Support Climate Resilience, see https://analytics.cal-adapt.org/data/ for more information.","ARN":"arn:aws:s3:::wfclimres","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Interactive Data Access and Visualization","URL":"https://github.com/cal-adapt/cae-notebooks/blob/main/data-access/renewables_data_access.ipynb","NotebookURL":"https://github.com/cal-adapt/cae-notebooks/blob/main/data-access/renewables_data_access.ipynb","AuthorName":"Cal-Adapt: Analytics Engine Team","AuthorURL":"https://github.com/cal-adapt"}],"Tools & Applications":[{"Title":"Analytics Engine Data Catalog","URL":"https://analytics.cal-adapt.org/data/access/","AuthorName":"Cal-Adapt: Analytics Engine Team","AuthorURL":"https://github.com/cal-adapt"},{"Title":"AWS Command Line Interface (CLI)","URL":"https://analytics.cal-adapt.org/data/access/","AuthorName":"Cal-Adapt: Analytics Engine Team","AuthorURL":"https://github.com/cal-adapt"},{"Title":"AWS Explorer Functions","URL":"https://analytics.cal-adapt.org/data/access/","AuthorName":"Cal-Adapt: Analytics Engine Team","AuthorURL":"https://github.com/cal-adapt"},{"Title":"Cal-Adapt Data Download Tool (DDT)","URL":"https://analytics.cal-adapt.org/data/access/","AuthorName":"Cal-Adapt: Analytics Engine Team","AuthorURL":"https://github.com/cal-adapt"},{"Title":"Direct Connection in Xarray with S3 Path","URL":"https://analytics.cal-adapt.org/data/access/","AuthorName":"Cal-Adapt: Analytics Engine Team","AuthorURL":"https://github.com/cal-adapt"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-04-02","RegistryEntryLastModified":"2026-01-23","Slug":"caladapt-wildfire-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"Wide-field calcium imaging sleep state database","Description":"A collection of wide-field calcium imaging (WFCI) sleep and wake recordings collected from twelve transgenic mice expressing GCaMP6f in excitatory neurons. Each mouse underwent a three-hour undisturbed WFCI recording session where wake, REM (rapid eye movement) sleep and NREM (non-REM) sleep was recorded. Each WFCI recording is manually scored by sleep scoring experts in 10-second epochs as wake, NREM or REM by use of adjunct EEG/EMG. The dataset contains annotated WFCI recordings, brain mask and the Paxinos atlas used for defining the brain regions. The dataset was collected as part of a study evaluating a deep learning-based automated sleep state classification method.","Documentation":"https://doi.org/10.13026/prp1-2d11","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/prp1-2d11","ARN":"arn:aws:s3:::physionet-open/calcium-imaging-sleep-state/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"calcium-imaging-sleep-state","Sources":["awslabs-open-data-registry"]}
{"Name":"CAncer MEtastases in LYmph nOdes challeNge (CAMELYON) Dataset","Description":"\"This dataset contains the all data for the [CAncer MEtastases in LYmph nOdes challeNge or CAMELYON](https://camelyon17.grand-challenge.org). CAMELYON was the first challenge using whole-slide images in computational pathology and aimed to help pathologists identify breast cancer metastases in sentinel lymph nodes. Lymph node metastases are extremely important to find, as they indicate that the cancer is no longer localized and systemic treatment might be warranted. Searching for these metastases in H&E-stained tissue is difficult and time-consuming and AI algorithms can play a role in helping make this faster and more accurate.\n","Documentation":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6007545/","Contact":"https://camelyon17.grand-challenge.org/","ManagedBy":"Radboud University Medical Center","UpdateFrequency":"As required","Tags":["aws-pds","cancer","computational pathology","computer vision","deep learning","grand-challenge.org","histopathology","life sciences"],"License":"CC0","Resources":[{"Description":"Whole slide images with corresponding annotations including tumor, stroma and tumor infiltrating lymphocytes","ARN":"arn:aws:s3:::camelyon-dataset","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Explore CAMELYON in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/034f9a56-4c3a-527b-8a5e-f40aeba78657/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"}],"Tools & Applications":[{"Title":"ASAP Viewer","URL":"https://computationalpathologygroup.github.io/ASAP/","AuthorName":"Geert Litjens","AuthorURL":"https://www.diagnijmegen.nl/people/geert-litjens/"},{"Title":"CAMELYON Challenge","URL":"https://camelyon17.grand-challenge.org/","AuthorName":"Diagnostic Image Analysis Group, Radboudumc, Nijmegen","AuthorURL":"https://www.diagnijmegen.nl/"}],"Publications":[{"Title":"1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset","URL":"https://academic.oup.com/gigascience/article/7/6/giy065/5026175","AuthorName":"Geert Litjens, et al.","AuthorURL":"https://www.diagnijmegen.nl/people/geert-litjens/"},{"Title":"Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer","URL":"https://jamanetwork.com/journals/jama/fullarticle/2665774","AuthorName":"Babak Ehteshami Bejnordi, et al.","AuthorURL":"https://www.linkedin.com/in/babakint"},{"Title":"From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge","URL":"https://ieeexplore.ieee.org/document/8447230/","AuthorName":"Peter Bandi, et al.","AuthorURL":"https://www.diagnijmegen.nl/people/peter-bandi/"}]},"RegistryEntryAdded":"2022-10-06","RegistryEntryLastModified":"2026-11-03","Slug":"camelyon","Sources":["awslabs-open-data-registry"]}
{"Name":"CanElevation - Canada Digital Elevation Models","Description":"The Canadian DEM represents the current coverage of elevation data available. This dataset includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived products. This dataset includes a 1m, 2m and 30m DEM. The 1m and 2 m products are a combination of DEM data generated from airborne LiDAR and optical digital images. The 30 m DEM integrates data from the Copernicus DEM acquired during the TanDEM-X Mission, with the DEM data derived from airborne lidar and provides a complete coverage for Canada. \n<br/>\n<br/>\nLe modèle numérique d’élévation (MNE) canadien représente la couverture actuelle des données d’élévation disponibles. Ce jeu de données comprend un Modèle Numérique de Terrain (MNT), un Modèle Numérique de Surface (MNS) et d’autres produits dérivés. Ce jeu de données propose des MNE de résolution 1 m, 2 m et 30 m. Les produits 1 m et 2 m sont issus d’une combinaison de données MNE générées à partir de LiDAR aéroporté et d’images numériques optiques. Le MNE de 30 m intègre des données provenant du MNE Copernicus acquis lors de la mission TanDEM-X, ainsi que les données MNE issues du LiDAR aéroporté, ce qui permet d’assurer une couverture complète du Canada. \n","Documentation":"[Medium Resolution Digital Elevation Model - MRDEM](https://open.canada.ca/data/en/dataset/18752265-bda3-498c-a4ba-9dfe68cb98da) [High Resolution Digital Elevation Model Mosaic](https://open.canada.ca/data/en/dataset/0fe65119-e96e-4a57-8bfe-9d9245fba06b) ","Contact":"geoinfo@nrcan-rncan.gc.ca","ManagedBy":"[Natural Resources Canada](https://nrcan.gc.ca/)","UpdateFrequency":"The dataset is updated as new DEM models becomes available.\n<br/>\nL'ensemble de données est mis à jour à mesure que des nouveaux modèles numérique d'élévation deviennent disponibles.\n","Tags":["aws-pds","canada","dem","dsm","dtm","elevation","geospatial","land","stac"],"License":"[Open Government License (OGL)](https://open.canada.ca/en/open-government-licence-canada)","Resources":[{"Description":"Mosaic of High Resolution Digital Elevation Model (HRDEM) at 1m / Mosaïque de Modèle numérique d'élévation de haute résolution (MNEHR) à 1m","ARN":"arn:aws:s3:::canelevation-dem/hrdem-mosaic-1m/","Region":"ca-central-1","Type":"S3 Bucket","Explore":["[STAC catalog](https://datacube.services.geo.ca/stac/api/search?collections=hrdem-mosaic-1m)","[STAC Browser by Radiant Earth](https://radiantearth.github.io/stac-browser/#/external/datacube.services.geo.ca/stac/api/collections/hrdem-mosaic-1m)","[Browse Bucket](https://canelevation-dem.s3.ca-central-1.amazonaws.com/index.html)"]},{"Description":"Mosaic of High Resolution Digital Elevation Model (HRDEM) at 2m / Mosaïque de Modèle numérique d'élévation de haute résolution (MNEHR) à 2m","ARN":"arn:aws:s3:::canelevation-dem/hrdem-mosaic-2m/","Region":"ca-central-1","Type":"S3 Bucket","Explore":["[STAC catalog](https://datacube.services.geo.ca/stac/api/search?collections=hrdem-mosaic-2m)","[STAC Browser by Radiant Earth](https://radiantearth.github.io/stac-browser/#/external/datacube.services.geo.ca/stac/api/collections/hrdem-mosaic-2m)","[Browse Bucket](https://canelevation-dem.s3.ca-central-1.amazonaws.com/index.html)"]},{"Description":"Medium Resolution Digital Elevation Model (MRDEM). Modèle numérique d'élévation de moyenne résolution (MNEMR)","ARN":"arn:aws:s3:::canelevation-dem/mrdem-30/","Region":"ca-central-1","Type":"S3 Bucket","Explore":["[STAC catalog](https://datacube.services.geo.ca/stac/api/search?collections=mrdem-30)","[STAC Browser by Radiant Earth](https://radiantearth.github.io/stac-browser/#/external/datacube.services.geo.ca/stac/api/collections/mrdem-30)","[Browse Bucket](https://canelevation-dem.s3.ca-central-1.amazonaws.com/index.html)"]},{"Description":"Mosaic of High Resolution Digital Elevation Model (HRDEM) by LiDAR acquisition project. Mosaïque de Modèle numérique d'élévation de haute résolution (MNEHR) par project d'acquisition LiDAR.","ARN":"arn:aws:s3:::canelevation-dem/hrdem-lidar/","Region":"ca-central-1","Type":"S3 Bucket","Explore":["[STAC catalog](https://datacube.services.geo.ca/stac/api/search?collections=hrdem-lidar)","[STAC Browser by Radiant Earth](https://radiantearth.github.io/stac-browser/#/external/datacube.services.geo.ca/stac/api/collections/hrdem-lidar)","[Browse Bucket](https://canelevation-dem.s3.ca-central-1.amazonaws.com/index.html)"]},{"Description":"High-Resolution Digital Elevation Model (HRDEM) generated from optical stereo imagery for Northern Canada / Modèle numérique d'élévation haute résolution (MNEHR) généré à partir de couple stéréo d'imagerie optique pour le nord du Canada","ARN":"arn:aws:s3:::canelevation-dem/hrdem-arcticdem/","Region":"ca-central-1","Type":"S3 Bucket","Explore":["[STAC catalog](https://datacube.services.geo.ca/stac/api/search?collections=hrdem-arcticdem)","[STAC Browser by Radiant Earth](https://radiantearth.github.io/stac-browser/#/external/datacube.services.geo.ca/stac/api/collections/hrdem-arcticdem)","[Browse Bucket](https://canelevation-dem.s3.ca-central-1.amazonaws.com/index.html)"]},{"Description":"Notifications for Canada Digital Elevation Models.","ARN":"arn:aws:sns:ca-central-1:675987781521:canelevation-dem-create-object","Region":"ca-central-1","Type":"SNS Topic"},{"Description":"Notifications for mosaic of High Resolution Digital Elevation Model (HRDEM) at 1m.","ARN":"arn:aws:sns:ca-central-1:675987781521:canelevation-dem-hrdem-mosaic-1m-create-object","Region":"ca-central-1","Type":"SNS Topic"},{"Description":"Notifications for mosaic of High Resolution Digital Elevation Model (HRDEM) at 2m.","ARN":"arn:aws:sns:ca-central-1:675987781521:canelevation-dem-hrdem-mosaic-2m-create-object","Region":"ca-central-1","Type":"SNS Topic"},{"Description":"Notifications for Medium Resolution Digital Elevation Model (MRDEM).","ARN":"arn:aws:sns:ca-central-1:675987781521:canelevation-dem-mrdem-30-create-object","Region":"ca-central-1","Type":"SNS Topic"},{"Description":"Notifications for High Resolution Digital Elevation Model (HRDEM) by LiDAR acquisition project.","ARN":"arn:aws:sns:ca-central-1:675987781521:canelevation-dem-hrdem-lidar-create-object","Region":"ca-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Cloud-Optimized Geospatial Data Access","URL":"https://nrcan.github.io/cloud-optimized-geospatial/","AuthorName":"NRCan"}],"Publications":[{"Title":"Descriptor: Medium Resolution Digital Elevation Model From Natural Resources Canada’s CanElevation Series (MRDEM-30)","URL":"https://doi.org/10.1109/IEEEDATA.2025.3576318","AuthorName":"H. McGrath et al."}]},"RegistryEntryAdded":"2025-08-07","RegistryEntryLastModified":"2025-11-25","Slug":"canelevation-dem","Sources":["awslabs-open-data-registry"]}
{"Name":"CanElevation - LiDAR Point Clouds","Description":"The [LiDAR Point Clouds](https://open.canada.ca/data/en/dataset/7069387e-9986-4297-9f55-0288e9676947) is a product that is part of the CanElevation Series created to support the [National Elevation Data Strategy](https://natural-resources.canada.ca/maps-tools-publications/satellite-elevation-air-photos/national-elevation-data-strategy) implemented by NRCan.\nThis product contains point clouds from various airborne LiDAR acquisition projects conducted in Canada. These airborne LiDAR acquisition projects may have been conducted by NRCan or by various partners. The LiDAR point cloud data is licensed under an [open government license](https://open.canada.ca/en/open-government-licence-canada) and has been incorporated into the National Elevation Data Strategy.\nPoint cloud files are distributed by LiDAR acquisition project without integration between projects.\nThe point cloud files are distributed using the compressed .LAZ / [Cloud Optimized Point Cloud (COPC)](https://copc.io/) format. The COPC open format is an octree reorganization of the data inside a .LAZ 1.4 file. It allows efficient use and visualization rendering via HTTP calls (e.g. via the web), while offering the capabilities specific to the compressed .LAZ format which is already well established in the industry. Point cloud files are therefore both downloadable for local use and viewable via URL links from a cloud computing environment.\nThe reference system used for all point clouds in the product is NAD83(CSRS), epoch 2010. The projection used is the UTM projection with the corresponding zone. Elevations are orthometric and expressed in reference to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013).\n<br/> <br/>\nLe produit [Nuages de points lidar](https://ouvert.canada.ca/data/fr/dataset/7069387e-9986-4297-9f55-0288e9676947) fait partie de la Série CanÉlévation créée pour appuyer la [Stratégie nationale de données d’élévation](https://ressources-naturelles.canada.ca/carte-outils-publications/imagerie-satellitaire-donnees-elevation-photos-aeriennes/strategie-nationale-donnees-elevation) mise en oeuvre par Ressources naturelles Canada (RNCan).\nCe produit contient les nuages de points obtenus lors de divers projets d’acquisition par lidar aéroporté réalisés au Canada. Ces projets d’acquisition par lidar aéroporté peuvent avoir été réalisés par RNCan ou par divers partenaires. Les données de nuages de points lidar ont une licence de type [gouvernement ouvert](https://ouvert.canada.ca/fr/licence-du-gouvernement-ouvert-canada) et ont été intégrés à la Stratégie nationale de données d’élévation.\nLes fichiers de nuages de points sont distribués par projet d'acquisition et sans intégration entre les projets.\nLes fichiers de nuages de points sont distribués en format compressé .LAZ / [Cloud Optimized Point Cloud (COPC)](https://copc.io/). Le format ouvert COPC est une réorganisation en octree des données à l’intérieur même d’un fichier .LAZ 1.4. Il permet une utilisation et un rendu de visualisation efficace via des appels HTTP (ex : via le web), tout en offrant les capacités propres au format .LAZ compressé qui est déjà bien établi dans l’industrie. Les fichiers de nuages de points sont donc autant téléchargeables pour une utilisation locale que visualisables via des liens URL provenant d’un environnement infonuagique.\nLe système de référence utilisé pour tous les nuages de points du produit est le NAD83(SCRS), époque 2010. La projection utilisée est la projection UTM avec le fuseau correspondant. Les élévations sont orthométriques et exprimées par rapport au Système canadien de référence altimétrique de 2013 (CGVD2013).","Documentation":"https://open.canada.ca/data/en/dataset/7069387e-9986-4297-9f55-0288e9676947","Contact":"geoinfo@nrcan-rncan.gc.ca","ManagedBy":"[Natural Resources Canada](https://www.nrcan.gc.ca/)","UpdateFrequency":"The dataset is updated as new LiDAR data becomes available.\nL'ensemble de données est mis à jour à mesure que de nouvelles données LiDAR deviennent disponibles.","Tags":["aws-pds","elevation","floods","geospatial","land","lidar","urban"],"License":"The LiDAR Point Clouds dataset is available free of charge under the [Open Government License - Canada](https://open.canada.ca/en/open-government-licence-canada).\nLe jeu de données Nuages de points LiDAR est disponible gratuitement sous la [licence du gouvernement ouvert - Canada](https://ouvert.canada.ca/fr/licence-du-gouvernement-ouvert-canada).","Resources":[{"Description":"LiDAR Point Clouds. Nuages de points lidar","ARN":"arn:aws:s3:::canelevation-lidar-point-clouds","Region":"ca-central-1","Type":"S3 Bucket","Explore":["[LiDAR Data on Open Canada](https://open.canada.ca/data/en/dataset/7069387e-9986-4297-9f55-0288e9676947)","[Browse Bucket](https://canelevation-lidar-point-clouds.s3.ca-central-1.amazonaws.com/pointclouds_nuagespoints/index.html#pointclouds_nuagespoints/)"]}],"DataAtWork":{"Tutorials":[{"Title":"How to generate a digital elevation model (DEM) from a lidar point cloud in COPC LAZ format | Comment générer un modèle numérique d'élévation (MNE) à partir d'un nuage de point lidar en format COPC LAZ","URL":"https://nrcan.github.io/CanElevation/pointclouds/dem-from-copc-lidar/","NotebookURL":"https://nrcan.github.io/CanElevation/pointclouds/DEM_from_COPC_lidar_EN.ipynb","AuthorName":"NRCan"},{"Title":"Identify projects and lidar tiles covering a region of interest | Déterminer les projets et les tuiles lidars couvrant une région d'intérêt","URL":"https://nrcan.github.io/CanElevation/pointclouds/projects-tiles-by-aoi/","NotebookURL":"https://nrcan.github.io/CanElevation/pointclouds/Get_Projects_Tiles_by_AOI_EN.ipynb","AuthorName":"NRCan"},{"Title":"Using the LiDAR Point Clouds - CanElevation Series product in QGIS | Utilisation du produit Nuages de points lidar - Série CanÉlévation dans QGIS","URL":"https://nrcan.github.io/CanElevation/pointclouds/qgis-interactive/","AuthorName":"NRCan"}]},"RegistryEntryAdded":"2025-04-28","RegistryEntryLastModified":"2025-11-25","Slug":"canelevation-pointcloud","Sources":["awslabs-open-data-registry"]}
{"Name":"CANOE (Canadian Aquatic Navigation for Observation of the Environment) Dataset","Description":"This autonomous marine navigation dataset includes data from a 360-degree Navtech radar, a 128-beam Ouster OS1 lidar with integrated IMU, a Teledyne Bumblebee stereo camera, Oculus M3000d imaging sonar, motor inputs, and GNSS. This dataset was collected on a lake and reservoir in Ontario, Canada. The intended purpose of this dataset is to enable the development and benchmarking of autonomous navigation algorithms in aquatic environments. In the future, we hope to release localization and odometry benchmarks.","Documentation":"https://github.com/utiasASRL/pycanoe/blob/main/DATA_REFERENCE.md","Contact":"canoe@robotics.utias.utoronto.ca","ManagedBy":"[Autonomous Space Robotics Laboratory (ASRL)](http://asrl.utias.utoronto.ca)","UpdateFrequency":"New sequences will be added as they are collected.","Tags":["autonomous vehicles","aws-pds","computer vision","lidar","radar","robotics"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)","Citation":null,"Resources":[{"Description":"Canoe Dataset","ARN":"arn:aws:s3:::canoe-data","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for new canoe data","ARN":"arn:aws:sns:us-west-2:455373446640:canoe-data-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get to Know a Dataset: Canoe","URL":"https://github.com/utiasASRL/pycanoe/blob/main/tutorials/get-to-know-dataset-canoe.ipynb","NotebookURL":"https://github.com/utiasASRL/pycanoe/blob/main/tutorials/get-to-know-dataset-canoe.ipynb","AuthorName":"Mia Thomas"}]},"DeprecatedNotice":null,"ADXCategories":["Automotive Data","Environmental Data"],"RegistryEntryAdded":"2026-02-02","RegistryEntryLastModified":"2026-04-06","Slug":"canoe","Sources":["awslabs-open-data-registry"]}
{"Name":"Capella Space Synthetic Aperture Radar (SAR) Open Dataset","Description":"Open Synthetic Aperture Radar (SAR) data from Capella Space. Capella Space is an information services company\n  that provides on-demand, industry-leading, high-resolution synthetic aperture radar (SAR) Earth observation\n  imagery. Through a constellation of small satellites, Capella provides easy access to frequent, timely, and\n  flexible information affecting dozens of industries worldwide. Capella's high-resolution SAR satellites are\n  matched with unparalleled infrastructure to deliver reliable global insights that sharpen our understanding\n  of the changing world – improving decisions about commerce, conservation, and security on Earth. Learn more\n  at www.capellaspace.com.\n","Documentation":"Documentation is available under [support.capellaspace.com](https://support.capellaspace.com/)","Contact":"opendata@capellaspace.com","ManagedBy":"[Capella Space](https://www.capellaspace.com/)","UpdateFrequency":"New data is added quarterly.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","cog","computer vision","earth observation","geospatial","image processing","satellite imagery","stac","synthetic aperture radar"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)\n","Resources":[{"Description":"Capella Space Open Data in COG format","ARN":"arn:aws:s3:::capella-open-data/data/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC Catalog](https://capella-open-data.s3.us-west-2.amazonaws.com/stac/catalog.json)","[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/capella-open-data.s3.us-west-2.amazonaws.com/stac/catalog.json)"]},{"Description":"Capella Space Open Data in TileDB format","ARN":"arn:aws:s3:::capella-open-data/data/tiledb/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Tutorials":[{"Title":"Scaling GEO Images in QGIS","URL":"https://support.capellaspace.com/hc/en-us/articles/7124025490964-Scaling-GEO-Images-in-QGIS","AuthorName":"Capella Space","AuthorURL":"https://www.capellaspace.com/"}],"Tools & Applications":[{"Title":"Python SDK for api.capellaspace.com","URL":"https://capella-console-client.readthedocs.io/en/main/","AuthorName":"Capella Space","AuthorURL":"https://www.capellaspace.com/"},{"Title":"Single Look Complex data reader for Capella SLC images - python module to convert Capella SLC data into an amplitude image.","URL":"https://github.com/capellaspace/tools/tree/master/capella-slc-reader","AuthorName":"Capella Space","AuthorURL":"https://www.capellaspace.com/"}],"Publications":[{"Title":"Analyzing LiDAR and SAR data with Capella Space and TileDB","URL":"https://tiledb.com/blog/analyzing-lidar-and-sar-data-with-capella-space-and-tiledb","AuthorName":"Stavros Papadopoulos"},{"Title":"Open SAR data and scalable analytics","URL":"https://medium.com/tiledb/open-sar-data-and-scalable-analytics-bfd1a5257e9","AuthorName":"Norman Barker"},{"Title":"Radar Generalized Image Quality Equation Applied to Capella Open Dataset","URL":"https://ieeexplore.ieee.org/document/9764201/","AuthorName":"Wade Schwartzkopf, Jason Brown, Gordon Farquharson, Craig Stringham, Michael Duersch, Jordan Heemskerk"}]},"RegistryEntryAdded":"2022-07-07","RegistryEntryLastModified":"2023-02-13","Slug":"capella_opendata","Sources":["awslabs-open-data-registry"]}
{"Name":"CAP Sleep Database","Description":"The CAP Sleep Database is a collection of 108 polysomnographic recordings registered at the Sleep Disorders Center of the Ospedale Maggiore of Parma, Italy. The waveforms (contained in the .edf files of the database) include at least 3 EEG channels (F3 or F4, C3 or C4 and O1 or O2, referred to A1 or A2), EOG (2 channels), EMG of the submentalis muscle, bilateral anterior tibial EMG, respiration signals (airflow, abdominal and thoracic effort and SaO2) and EKG. Additional traces include EEG bipolar traces, according to the 10-20 international system (Fp1-F3, F3-C3, C3-P3, P3-O1 and/or Fp2-F4, F4-C4, C4-P4, P4-O2).","Documentation":"https://doi.org/10.13026/C2VC79","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2VC79","ARN":"arn:aws:s3:::physionet-open/capslpdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"capslpdb","Sources":["awslabs-open-data-registry"]}
{"Name":"CarbonPDF","Description":"A carbon question-answering (QA) dataset specifically designed to facilitate the extraction and analysis of data from real-world carbon reports of computing products. The dataset features annotated metadata, a variety of numerical reasoning tasks, and structured derivations to ensure accurate processing of fragmented and inconsistent information.","Documentation":"https://github.com/pittcps/carbonpdf-dataset","Contact":"kaz81@pitt.edu","ManagedBy":"Pittcps lab","UpdateFrequency":"Data for a new company is added once collected.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","csv","environmental","industry","information retrieval","product comparison"],"License":"CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"A component-level product carbon footprint dataset and a corresponding question-answering dataset based on it","ARN":"arn:aws:s3:::carbonpdf","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Explore](https://github.com/pittcps/carbonpdf-dataset)"]}],"ADXCategories":["Environmental Data","Manufacturing Data"],"RegistryEntryAdded":"2025-07-10","RegistryEntryLastModified":"2025-07-17","Slug":"carbonpdf","Sources":["awslabs-open-data-registry"]}
{"Name":"Epicardially attached cardiac accelerometer data from canines and porcines","Description":"The database contains acceleration signals from accelerometers attached directly on the epicardium in canines and porcines during various interventions that altered cardiac function and myocardial motion. The tri-axial accelerometer was attached in the anterior, apical region of the left ventricle (LV). The database contains a total of 289 recordings from 19 canines and 27 porcines with an average duration of 15 seconds, ranging from 5 to 30 seconds. Acceleration data was recorded simultaneously with ECG and LV pressure (LVP).","Documentation":"https://doi.org/10.13026/5xmr-zd88","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/5xmr-zd88","ARN":"arn:aws:s3:::physionet-open/cardiac-accel-canine-porcine/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"cardiac-accel-canine-porcine","Sources":["awslabs-open-data-registry"]}
{"Name":"Two-tiered response of cardiorespiratory-cerebrovascular networks to orthostatic challenge","Description":"The authors used this dataset to evaluate the responsiveness of the cardiorespiratory-cerebrovascular networks by capturing linear and nonlinear interdependencies to postural changes. Ten young healthy adults participated in our study between December 2009 and February 2010 at Semmelweis University, Faculty of Medicine. Non-invasive measurements of arterial blood pressure (ABP), cardiac cycle duration (derived from ABP signals), breath-to-breath interval (capnography), cerebral blood flow velocity (BFV, recorded by transcranial Doppler sonography), and cerebral hemodynamics (hemoglobin signals monitored by near-infrared spectroscopy) were performed for 30-minutes in resting state, followed by a one-minute stand-up and a one-minute sit-down period. BFV and ABP signals were calibrated, and the dataset contains values in cm/s and mmHg, respectively. NIRS measurement yielded intensity values (raw signals) from which change of HbO (oxyhemoglobin), HbR (deoxyhemoglobin) and HbT (total hemoglobin) concentration in tissue can be calculated (in micromol/liter). Raw NIRS signals were synchronized to BFV, ABP and capnography time series yielding 33 minutes of resampled data (sampling frequency: ~ 3 Hz).","Documentation":"https://doi.org/10.13026/73w7-x881","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"PhysioNet Contributor Review Health Data License 1.5.0","Resources":[{"Description":"https://doi.org/10.13026/73w7-x881","ARN":"arn:aws:s3:::physionet-open/cardioresp-response-orthostat/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"cardioresp-response-orthostat","Sources":["awslabs-open-data-registry"]}
{"Name":"CartoStore","Description":"Cross-Platform Repository for High-resolution Spatial Transcriptomics Datasets.\n","Documentation":"[CartoStore Overview](https://github.com/seqscope/cartostore)","Contact":"hmkang@umich.edu","ManagedBy":"[Hyun Min Kang](https://scholar.google.com/citations?user=8e0jy0IAAAAJ&hl=en)","UpdateFrequency":"Monthly","Tags":["bioinformatics","genomic","life sciences","spatial omics","spatial transcriptomics"],"License":"\"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)\"\n","Citation":"CartoStore by Hyun Min Kang's lab at the University of Michigan School of Public Health.\nProvided by Kang lab and accessed [DAY MONTH YEAR].\n","Resources":[{"Description":"PMTile, YAML, JSON, and TSV files","ARN":"arn:aws:s3:::cartostore","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Cartloader Documentation","URL":"https://seqscope.github.io/cartloader","AuthorName":"Hyun Min Kang and Weiqiu Cheng"},{"Title":"CartoStore Overview","URL":"https://github.com/seqscope/cartostore/blob/main/README.md","AuthorName":"Hyun Min Kang and Weiqiu Cheng"},{"Title":"Example CartoStore Repository for Xenium Breast Cancer Dataset","URL":"https://zenodo.org/records/15649152","AuthorName":"Hyun Min Kang and Weiqiu Cheng"}]},"RegistryEntryAdded":"2025-06-30","RegistryEntryLastModified":"2025-08-06","Slug":"cartostore","Sources":["awslabs-open-data-registry"]}
{"Name":"Public Utility Data Liberation Project","Description":"The Public Utility Data Liberation Project (PUDL) provides analysis-ready U.S. energy\nsystem data in bulk for programmatic use. Sources include the U.S. Energy\nInformation Administration (EIA), the Environmental Protection Agency (EPA), the\nFederal Energy Regulatory Commission (FERC), the Pipeline and Hazardous Materials\nSafety Administration (PHMSA), the Securities and Exchange Commission (SEC).  The\nprimary focus is on the electricity sector, with additional data on the natural gas\nsystem and energy company financial reporting.\n","Documentation":"https://catalystcoop-pudl.readthedocs.io/en/latest/","Contact":"Create a [GitHub discussion](https://github.com/orgs/catalyst-cooperative/discussions).\nSign up for [office hours](https://calend.ly/catalyst-cooperative/pudl-office-hours).\nEmail us at [hello@catalyst.coop](mailto:hello@catalyst.coop).\n","ManagedBy":"[Catalyst Cooperative](https://catalyst.coop/)","UpdateFrequency":"New outputs published nightly as part of our continuous integration process.\nNew data is integrated on a rolling basis as it is published by the original sources.\nAgencies typically release data monthly, quarterly, or yearly.\nNew versioned releases of PUDL are published and archived at least once per quarter.\n","Tags":["aws-pds","economics","electricity","energy","energy modeling","environmental","geospatial","government records","industrial","industry","infrastructure","market data","parquet","regulatory","solar","sqlite","us","utilities"],"License":"[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) (Creative Commons Attribution 4.0 International License).","Resources":[{"Description":"PUDL Nightly Build Outputs","ARN":"arn:aws:s3:::pudl.catalyst.coop/nightly","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"PUDL Stable Release Outputs","ARN":"arn:aws:s3:::pudl.catalyst.coop/stable","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"FERC Electric Quarterly Reports (EQR)","ARN":"arn:aws:s3:::pudl.catalyst.coop/ferceqr","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"EIA-930 Sanity Checks","URL":"https://www.kaggle.com/code/catalystcooperative/03-eia-930-sanity-checks","AuthorName":"Catalyst Cooperative","AuthorURL":"https://catalyst.coop/","NotebookURL":"https://github.com/catalyst-cooperative/pudl-examples/blob/main/03-eia930-sanity-checks.ipynb"},{"Title":"FERC-714 Electricity Demand Forecast Biases","URL":"https://www.kaggle.com/code/catalystcooperative/05-ferc-714-electricity-demand-forecast-biases","AuthorName":"Catalyst Cooperative","AuthorURL":"https://catalyst.coop/","NotebookURL":"https://github.com/catalyst-cooperative/pudl-examples/blob/main/05-ferc714-electricity-demand-forecast-biases.ipynb"},{"Title":"PUDL Data Access","URL":"https://www.kaggle.com/code/catalystcooperative/01-pudl-data-access","AuthorName":"Catalyst Cooperative","AuthorURL":"https://catalyst.coop/","NotebookURL":"https://github.com/catalyst-cooperative/pudl-examples/blob/main/01-pudl-data-access.ipynb"},{"Title":"PUDL Imputed Electricity Demand","URL":"https://www.kaggle.com/code/catalystcooperative/06-pudl-imputed-electricity-demand","AuthorName":"Catalyst Cooperative","AuthorURL":"https://catalyst.coop/","NotebookURL":"https://github.com/catalyst-cooperative/pudl-examples/blob/main/06-pudl-imputed-demand.ipynb"},{"Title":"Renewable Generation Profiles","URL":"https://www.kaggle.com/code/catalystcooperative/04-renewable-generation-profiles","AuthorName":"Catalyst Cooperative","AuthorURL":"https://catalyst.coop/","NotebookURL":"https://github.com/catalyst-cooperative/pudl-examples/blob/main/04-renewable-generation-profiles.ipynb"},{"Title":"State Hourly Electricity Demand","URL":"https://www.kaggle.com/code/catalystcooperative/02-state-hourly-electricity-demand","AuthorName":"Catalyst Cooperative","AuthorURL":"https://catalyst.coop/","NotebookURL":"https://github.com/catalyst-cooperative/pudl-examples/blob/main/02-state-hourly-electricity-demand.ipynb"}]},"ADXCategories":["Environmental Data","Resources Data"],"RegistryEntryAdded":"2023-01-03","RegistryEntryLastModified":"2026-01-21","Slug":"catalyst-cooperative-pudl","Sources":["awslabs-open-data-registry"]}
{"Name":"CBERS on AWS","Description":"Imagery acquired\nby the China-Brazil Earth Resources Satellite (CBERS), 4 and 4A.\nThe\nimage files are recorded and processed by Instituto Nacional de Pesquisas\nEspaciais (INPE) and are converted to Cloud Optimized Geotiff\nformat in order to optimize its use for cloud based applications.\nContains all CBERS-4 MUX, AWFI, PAN5M and\nPAN10M scenes acquired since\nthe start of the satellite mission and is daily updated with\nnew scenes.\nCBERS-4A MUX Level 4 (Orthorectified) scenes are being\ningested starting from 04-13-2021. CBERS-4A WFI Level 4 (Orthorectified)\nscenes are being ingested starting from 10-12-2022.\nCBERS-4A WPM Level 4 (Orthorectified) scenes are being ingested starting from 03-27-2022.\n","Documentation":"https://github.com/fredliporace/cbers-on-aws","Contact":"https://lists.osgeo.org/mailman/listinfo/cbers-pds","ManagedBy":"[Frederico Liporace](https://github.com/fredliporace)","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","disaster response","earth observation","geospatial","imaging","satellite imagery","stac"],"License":"https://creativecommons.org/licenses/by-sa/3.0/","Resources":[{"Description":"CBERS imagery (COG files, quicklooks, metadata)","ARN":"arn:aws:s3:::brazil-eosats","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://stac.scitekno.com.br/v100)","[stacindex](https://stacindex.org/catalogs/cbers)"]},{"Description":"STAC static catalog","ARN":"arn:aws:s3:::br-eo-stac-1-0-0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false},{"Description":"Notifications for new CBERS 4A quicklooks, all sensors","ARN":"arn:aws:sns:us-west-2:599544552497:NewCB4AQuicklook","Region":"us-west-2","Type":"SNS Topic"},{"Description":"Notifications for new CBERS 4 quicklooks, all sensors","ARN":"arn:aws:sns:us-west-2:599544552497:NewCB4Quicklook","Region":"us-west-2","Type":"SNS Topic"},{"Description":"Topic that receives STAC V1.0.0 items as new scenes are ingested","ARN":"arn:aws:sns:us-west-2:769537946825:br-eo-stac-prod-stacitemtopic4BCE3141-Z8he7LYjqXFe","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Keeping a SpatioTemporal Asset Catalog (STAC) Up To Date with SNS/SQS","URL":"https://aws.amazon.com/blogs/publicsector/keeping-a-spatiotemporal-asset-catalog-stac-up-to-date-with-sns-sqs/","AuthorName":"Frederico Liporace","Services":["Amazon SNS","AWS Lambda","Amazon DynamoDB"]}],"Tools & Applications":[{"Title":"aws-sat-api-py","URL":"https://github.com/RemotePixel/aws-sat-api-py","AuthorName":"Remote Pixel","AuthorURL":"http://remotepixel.ca/"},{"Title":"CBERS static STAC catalog served by stac-browser","URL":"https://cbers.stac.cloud","AuthorName":"Radiant Earth","AuthorURL":"https://github.com/radiantearth/stac-browser"},{"Title":"CBERS timelapse GIF generator","URL":"https://github.com/fredliporace/cbersgif","AuthorName":"Frederico Liporace","AuthorURL":"https://github.com/fredliporace"},{"Title":"cbers-tiler","URL":"https://github.com/mapbox/cbers-tiler","AuthorName":"Mapbox","AuthorURL":"https://www.mapbox.com/"},{"Title":"EOS Land Viewer","URL":"https://eos.com/landviewer/","AuthorName":"Earth Observing System","AuthorURL":"https://eos.com/"},{"Title":"Forest Monitor","URL":"http://brazildatacube.dpi.inpe.br/forest-monitor/","AuthorName":"Brazil Datacube, INPE","AuthorURL":"http://brazildatacube.org/"},{"Title":"rio-tiler","URL":"https://github.com/mapbox/rio-tiler","AuthorName":"Mapbox","AuthorURL":"https://www.mapbox.com/"},{"Title":"STAC V1.0.0 endpoint","URL":"https://stac.scitekno.com.br/v100","AuthorName":"Scitekno","AuthorURL":"https://github.com/fredliporace/cbers-2-stac"}],"Publications":[{"Title":"Using Remote Sensing Images and Cloud Services on AWS to Improve Land Use and Cover Monitoring","URL":"https://ieeexplore.ieee.org/abstract/document/9165649","AuthorName":"K. R. Ferreira, et al."}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2025-03-27","Slug":"cbers","Sources":["awslabs-open-data-registry"]}
{"Name":"Chalmers Cloud Ice Climatology","Description":"The Chalmers Cloud Ice Climatology (CCIC) is a novel, deep-learning-based climate record of ice-particle concentrations in the atmosphere. CCIC results are available at high spatial and temporal resolution (0.07° / 3 h from 1983, 0.036° / 30 min from 2000) and thus ideally suited for evaluating high-resolution weather and climate models or studying individual weather systems.","Documentation":"https://ccic.readthedocs.io","Contact":"https://github.com/see-geo/ccic","ManagedBy":"[Geoscience and Remote Sensing at Chalmers University of Technology](https://www.chalmers.se/en/departments/see/research/geo)","UpdateFrequency":"Quarterly","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","climate","deep learning","environmental","exploration","geophysics","geoscience","geospatial","global","ice","planetary","satellite imagery","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Citation":"Please refer to https://ccic.readthedocs.io/en/latest/intro.html#citing-ccic for instructions on how to cite the CCIC data.","Resources":[{"Description":"CCIC total ice water path and 2D cloud probability in Zarr format","ARN":"arn:aws:s3:::chalmerscloudiceclimatology","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://chalmerscloudiceclimatology.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Reading CCIC data","URL":"https://ccic.readthedocs.io/en/latest/getting_started.html#reading-ccic-data","AuthorName":"Adrià Amell & Simon Pfreundschuh","Services":["Amazon S3"]},{"Title":"Storm tracking with CCIC","URL":"https://ccic.readthedocs.io/en/latest/applications/storm_tracking.html","AuthorName":"Julia Kukulies","AuthorURL":"https://juliakukulies.github.io/","Services":["Amazon S3"]}],"Tools & Applications":[{"Title":"CCIC on PyPI -- software for training and processing the CCIC retrievals and to read CCIC Zarr files","URL":"https://pypi.org/project/ccic/","AuthorName":"Simon Pfreundschuh","AuthorURL":"https://github.com/SEE-GEO/ccic"}],"Publications":[{"Title":"The Chalmers Cloud Ice Climatology: Retrieval implementation and validation","URL":"https://doi.org/10.5194/egusphere-2023-1953","AuthorName":"Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson"}]},"RegistryEntryAdded":"2024-02-07","RegistryEntryLastModified":"2025-07-09","Slug":"ccic","Sources":["awslabs-open-data-registry"]}
{"Name":"Cancer Cell Line Encyclopedia (CCLE)","Description":"The Cancer Cell Line Encyclopedia (CCLE) project is an effort to conduct a detailed genetic\ncharacterization of a large panel of human cancer cell lines. The CCLE provides public access to\ngenomic data, visualization and analysis for over 1100 cancer cell lines. This dataset contains\nRNA-Seq Aligned Reads, WXS Aligned Reads, and WGS Aligned Reads data.\n","Documentation":"https://portals.broadinstitute.org/ccle/about","Contact":"dcf-support@datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genetic","genomic","Homo sapiens","life sciences","STRIDES","transcriptomics","whole genome sequencing"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"RNA-Seq Aligned Reads, WXS Aligned Reads, WGS Aligned Reads","ARN":"arn:aws:s3:::gdc-ccle-2-open","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Broad Institute Cancer Cell Line Encyclopedia","URL":"https://portals.broadinstitute.org/ccle","AuthorName":"The Broad Institute of MIT & Harvard","AuthorURL":"https://www.broadinstitute.org/"},{"Title":"Cancer Genomics Cloud","URL":"https://cgc-accounts.sbgenomics.com/","AuthorName":"Seven Bridges","AuthorURL":"https://docs.sevenbridges.com/docs/ccle-data"},{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/legacy-archive/","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"},{"Title":"ISB CGC BigQuery tables","URL":"https://console.cloud.google.com/bigquery?p=isb-cgc&d=CCLE_bioclin_v0&page=dataset","AuthorName":"Institute for Systems Biology","AuthorURL":"https://isb-cancer-genomics-cloud.readthedocs.io/en/latest/sections/data/CCLE_top.html\n"}],"Publications":[{"Title":"Next-generation characterization of the Cancer Cell Line Encyclopedia","URL":"https://doi.org/10.1038/s41586-019-1186-3","AuthorName":"Ghandi, M., Huang F. et al."},{"Title":"Pharmacogenomic agreement between two cancer cell line data sets","URL":"https://doi.org/10.1038/nature15736","AuthorName":"The Cancer Cell Line Encyclopedia Consortium & The Genomics of Drug Sensitivity in Cancer\nConsortium\n"},{"Title":"The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug\nsensitivity\n","URL":"https://doi.org/10.1038/nature11003","AuthorName":"Barretina Caponigro Stransky et al."},{"Title":"The landscape of cancer cell line metabolism","URL":"https://doi.org/10.1038/s41591-019-0404-8","AuthorName":"Li, H. et al."}]},"RegistryEntryAdded":"2020-04-07","RegistryEntryLastModified":"2022-11-08","Slug":"ccle","Sources":["awslabs-open-data-registry"]}
{"Name":"CCRS MODIS albedo over Canada | Albédo MODIS du CCT couvrant le Canada","Description":"Times series of 10-day spectral and broadband albedo products derived at 250-m spatial resolution over Canadian territory and neighboring areas produced at the Canada Centre for Remote Sensing (CCRS) since February 2000 using MODIS L1B C6.1 swath imagery as input. The imagery for all spectral bands was downscaled and re-projected into the Lambert Conformal Conic (LCC) projection at 250-m spatial resolution.  The area size is 5,700 km x 4,800 km (22,800 pixel x 19,200 lines).\nSéries temporelles de produits d’albédo spectral et à large bande générés à des intervalles de 10 jours avec une résolution spatiale de 250 m, couvrant le territoire canadien et les régions voisines. Ces produits sont élaborés par le Centre canadien de télédétection (CCT) depuis février 2000 à partir des images MODIS L1B C6.1. Les images de toutes les bandes spectrales ont été rééchantillonnées et reprojetées en projection conforme de Lambert (LCC) à une résolution spatiale de 250 m. La zone couverte est d’environ 5 700 km par 4 800 km (22 800 pixels par 19 200 lignes).\n","Documentation":"https://data.eodms-sgdot.nrcan-rncan.gc.ca/public/CCRS/Trishchenko_MODIS_Albedo","Contact":"alexander.trichtchenko@nrcan-rncan.gc.ca","ManagedBy":"Canada Centre for Remote Sensing (CCRS), Canada Centre for Mapping and Earth Observation (CCMEO), Department of Natural Resources Canada (NRCan) https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-remote-sensing https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-mapping-earth-observation","UpdateFrequency":"Semi-annually, until the end of MODIS operations\nDeux fois par an, jusqu'à la fin des opérations MODIS\n","Tags":["analysis ready data","aws-pds","broadband","cog","earth observation","satellite imagery"],"License":"Creative Commons Licence. Creative Commons BY 4.0 https://creativecommons.org/licenses/by/4.0/","Citation":"Trishchenko, Alexander P. 2025. CCRS MODIS albedo over Canada at 250-m resolution and 10-day intervals.","Resources":[{"Description":"CCRS MODIS Albedo, Cloud Optimized GeoTIFF (COG) images","ARN":"arn:aws:s3:::ccrs-modis-albedo","Region":"ca-central-1","Type":"S3 Bucket"},{"Description":"Notifications for new CCRS MODIS Albedo data","ARN":"arn:aws:sns:ca-central-1:675987781521:ccrs-modis-albedo-object_created","Region":"ca-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset - CCRS MODIS Albedo at 250-m resolution and 10-day intervals","URL":"https://github.com/OpsCCRS/AWS-Open-Data-Registry-Preparation/blob/main/CCRSMODISAlbedo/get-to-know-a-dataset-CCRSMODISAlbedo.ipynb","AuthorName":"Alexander Trichtchenko","AuthorURL":"https://profils-profiles.science.gc.ca/en/profile/alexander-p-trishchenko","NotebookURL":"https://github.com/OpsCCRS/AWS-Open-Data-Registry-Preparation/blob/main/CCRSMODISAlbedo/get-to-know-a-dataset-CCRSMODISAlbedo.ipynb"}],"Publications":[{"Title":"A method for downscaling MODIS land channels to 250-m spatial resolution using adaptive regression and normalization","URL":"https://doi.org/10.1117/12.689157","AuthorName":"Trishchenko, A.P., Luo, Y., Khlopenkov, K.V."},{"Title":"A raster version of the circumpolar Arctic vegetation map (CAVM)","URL":"https://doi.org/10.1016/J.RSE.2019.111297","AuthorName":"Raynolds, M.K., Walker, D.A., Balser, A., Bay, C., Campbell, M., Cherosov, M.M., Dani�ls, F.J.A., Eidesen, P.B., Ermokhina, K.A., Frost, G.V., Jedrzejek, B., Jorgenson, M.T., Kennedy, B.E., Kholod, S.S., Lavrinenko, I.A., Lavrinenko, O.V., Magn�sson, B., Matveyeva, N.V., Met�salemsson, S., Nilsen, L., Olthof, I., Pospelov, I.N., Pospelova, E.B., Pouliot, D., Razzhivin, V., Schaepman-Strub, G., ?Sib�k, J., Telyatnikov, M.Y., Troeva, E."},{"Title":"An approach for developing surface albedo product from seven MODIS land bands at 250m spatial resolution over Canada and the Arctic circumpolar region","URL":"https://lpvs.gsfc.nasa.gov/LPV_meetings/Beijing09/Luo_MODIS_Albedo_Product.pdf","AuthorName":"Luo, Y., Trishchenko, A.P., Khlopenkov, K.V."},{"Title":"Annual mapping of large Forest disturbances across Canada's forests using 250 m MODIS imagery from 2000 to 2011","URL":"https://doi.org/10.1139/cjfr-2014-0229","AuthorName":"Guindon, L., Bernier, P.Y., Beaudoin, A., Pouliot, D., Villemaire, P., Hall, R.J., Latifovic, R., St-Amant, R."},{"Title":"Annual minimum snow/ice extent variations over Greenland since 2000:ice sheet, peripheral areas, and relation to ice mass balance","URL":"https://doi.org/10.1175/BAMS-D-22-0244.1","AuthorName":"Trishchenko, A.P., Ungureanu, C."},{"Title":"Arctic circumpolar mosaic at 250m spatial resolution for IPY by fusion of MODIS/TERRA land bands B1-B7","URL":"https://doi.org/10.1080/01431160802348119","AuthorName":"Trishchenko, A.P., Luo, Y., Khlopenkov, K.V., Park, W.M., Wang, S."},{"Title":"Boreal lichen woodlands: a possible negative feedback to climate change in eastern North America","URL":"https://doi.org/10.1016/j.agrformet.2010.12.013","AuthorName":"Bernier, P.Y., Desjardins, R.L., Karimi-Zindashty, Y., Worth, D., Beaudoin, A., Luo, Y., Wang, S."},{"Title":"CCRS MODIS albedo over Canada on GEO.ca | Albédo MODIS du CCT couvrant le Canada sur GEO.ca","URL":"https://app.geo.ca/en-ca/map-browser/record/28b6d950-a92b-427a-8f20-7adc1415c5dd","AuthorName":"Canada Centre for Remote Sensing | Centre canadien de télédétection","AuthorURL":"https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-remote-sensing"},{"Title":"Clear-Sky Composites over Canada from Visible Infrared Imaging Radiometer Suite:Continuing MODIS Time Series into the Future","URL":"https://doi.org/10.1080/07038992.2019.1601006","AuthorName":"Trishchenko, A.P."},{"Title":"Cumulative changes in minimum snow/ice extent over Canada and Northern USA for 2000-2023","URL":"https://doi.org/10.1080/07038992.2024.2371359","AuthorName":"Trishchenko, A.P., Ungureanu, C."},{"Title":"Detection of North American land cover change between 2005 and 2010 with 250m MODIS data","URL":"https://www.researchgate.net/publication/286156544_Detection_of_North_American_land_cover_change_between_2005_and_2010_with_250m_MODIS_Data","AuthorName":"Colditz, R.R., Pouliot, D., Llamas, R.M., Homer, C., Latifovic, R., Ressl, R.A., Tovar, C.M., Hern�ndez, A.V., Richardson, K."},{"Title":"Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America","URL":"https://doi.org/10.1016/j.rse.2008.06.010","AuthorName":"Luo, Y., Trishchenko, A.P., Khlopenkov, K.V."},{"Title":"Implementation and Evaluation of Concurrent Gradient Search Method for Reprojection of MODIS Level 1B Imagery","URL":"https://doi.org/10.1109/TGRS.2008.916633","AuthorName":"Khlopenkov, K.V., and Trishchenko, A.P."},{"Title":"Influence of two management practices in the Canadian Prairies on radiative forcing","URL":"https://doi.org/10.1016/j.scitotenv.2020.142701","AuthorName":"Liu, J., Worth, D.E., Desjardins, R.L., Haak, D., McConkey, B., Cerkowniak, D."},{"Title":"Landfast ice mapping using MODIS clear-sky composites:application for the Banks Island coastline in Beaufort Sea and comparison with Canadian Ice Service data","URL":"https://doi.org/10.1080/07038992.2021.1909466","AuthorName":"Trishchenko, A.P., Luo, Y."},{"Title":"Landfast ice properties over the Beaufort Sea region in 2000-2019 from MODIS and Canadian Ice Service data","URL":"https://doi.org/10.1139/cjes-2021-0011","AuthorName":"Trishchenko, A.P., Kostylev, V.E., Luo, Y., Ungureanu, C., Whalen, D., Li, J."},{"Title":"Minimum snow/ice extent over the Northern circumpolar landmass in 2000-19:how much snow survives the summer melt?","URL":"https://doi.org/10.1175/BAMS-D-20-0177.1","AuthorName":"Trishchenko, A.P., Ungureanu, C."},{"Title":"MODIS Surface Albedo and Surface Reflectance Dataset. Format Description.","URL":"https://data.eodms-sgdot.nrcan-rncan.gc.ca/public/CCRS/Trishchenko_MODIS_Albedo/","AuthorName":"Trishchenko, Alexander P., Ungureanu, Calin"},{"Title":"Perennial snow and ice variations (2000-2008) in the Arctic circumpolar land area from satellite observations","URL":"https://doi.org/10.1029/2010JF001664","AuthorName":"Fontana F.M.A., Trishchenko A.P., Luo Y., Khlopenkov K.V., Nussbaumer S.U., Wunderle S."},{"Title":"Probability of the annual minimum snow and ice (MSI) presence over Canada","URL":"https://open.canada.ca/data/en/dataset/808b84a1-6356-4103-a8e9-db46d5c20fcf","AuthorName":"Trishchenko, Alexander P."},{"Title":"Surface bidirectional reflectance and albedo properties derived by a land cover based approach from the MODIS observations.","URL":"https://doi.org/10.1029/2004JD004741","AuthorName":"Luo, Y., Trishchenko, Alexander P., Latifovic, R., Li, Z."},{"Title":"Variations of annual minimum snow and ice extent over Canada and neighbouring landmass derived from MODIS 250-m imagery for 2000-2014","URL":"https://doi.org/10.1080/07038992.2016.1166043","AuthorName":"Trishchenko, A.P., Leblanc, S.G., Wang, S., Li, J., Ungureanu, C., Luo, Y., Khlopenkov, K.V., Fontana, F., 2016"},{"Title":"Warm season snow/ice probability maps from modis and viirs sensors over Canada","URL":"https://doi.org/10.1109/IGARSS.2018.8519558","AuthorName":"Trishchenko, Alexander P., Ungureanu, Calin"}]},"RegistryEntryAdded":"2025-12-16","RegistryEntryLastModified":"2026-03-16","Slug":"ccrsmodisalbedo","Sources":["awslabs-open-data-registry"]}
{"Name":"CCRS MODIS Clear-Sky Composites over Canada | Composites MODIS sous ciel dégagé du CCT couvrant le Canada","Description":"The MODIS Clear-Sky TOA Composites dataset over Canada consists of 10-day composite time series at 250m resolution, produced by CCRS since February 2000. It includes clear-sky composites for MODIS bands B1–B7, primarily for land applications, with separate forward (FRW) and backward (BKW) scattering products for each period. All imagery is downscaled and reprojected to Lambert Conformal Conic over a 5,700 × 4,800km area. The processing system was developed by CCRS to maximize the quality of MODIS L1B swath imagery at northern latitudes. Le jeu de données MODIS de réflectance au sommet de l’atmosphère (TOA) en ciel clair couvrant le Canada, produit par le CCT depuis février 2000, consiste en des séries temporelles de composites sur 10 jours à une résolution de 250 m. Il comprend des composites en ciel clair pour les bandes MODIS B1 à B7, principalement destinés aux applications terrestres, avec des produits distincts de diffusion vers l’avant (FRW) et vers l’arrière (BKW) pour chaque période. Toutes les images sont rééchantillonnées et reprojetées en projection conique conforme de Lambert sur une zone de 5700 × 4800 km. Le système de traitement a été développé par le CCT afin d’optimiser la qualité des images en fauchée MODIS L1B aux latitudes nordiques.","Documentation":"https://data.eodms-sgdot.nrcan-rncan.gc.ca/public/CCRS/Trishchenko_MODIS_Albedo","Contact":"alexander.trichtchenko@nrcan-rncan.gc.ca","ManagedBy":"Canada Centre for Remote Sensing (CCRS), Canada Centre for Mapping and Earth Observation (CCMEO), Department of Natural Resources Canada (NRCan) https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-remote-sensing https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-mapping-earth-observation","UpdateFrequency":"Semi-annually, until the end of MODIS operations Deux fois par an, jusqu'à la fin des opérations MODIS","Tags":["analysis ready data","aws-pds","cog","earth observation","satellite imagery"],"License":"Creative Commons Licence. Creative Commons BY 4.0 https://creativecommons.org/licenses/by/4.0/","Citation":"Trishchenko, Alexander P. 2026. CCRS MODIS Clear-Sky Composites over Canada at 250-m resolution","Resources":[{"Description":"CCRS MODIS Clear-Sky Composites, Cloud Optimized GeoTIFF (COG) images","ARN":"arn:aws:s3:::ccrs-modis-composite","Region":"ca-central-1","Type":"S3 Bucket"},{"Description":"Notifications for new CCRS MODIS Clear-Sky Composites data","ARN":"arn:aws:sns:ca-central-1:675987781521:ccrs-modis-composite-object_created","Region":"ca-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset - CCRS MODIS Clear-Sky Composites over Canada","URL":"https://github.com/OpsCCRS/AWS-Open-Data-Registry-Preparation/blob/main/CCRSMODISComposite/get-to-know-a-dataset-CCRSMODISComposite.ipynb","AuthorName":"Alexander Trishchenko","NotebookURL":"https://github.com/OpsCCRS/AWS-Open-Data-Registry-Preparation/blob/main/CCRSMODISComposite/get-to-know-a-dataset-CCRSMODISComposite.ipynb"}],"Publications":[{"Title":"A method for downscaling MODIS land channels to 250-m spatial resolution using adaptive regression and normalization","URL":"https://doi.org/10.1117/12.689157","AuthorName":"Trishchenko, A.P., Luo, Y., Khlopenkov, K.V."},{"Title":"A raster version of the circumpolar Arctic vegetation map (CAVM)","URL":"https://doi.org/10.1016/J.RSE.2019.111297","AuthorName":"Raynolds, M.K., Walker, D.A., Balser, A., Bay, C., Campbell, M., Cherosov, M.M., Daniëls, F.J.A., Eidesen, P.B., Ermokhina, K.A., Frost, G.V., Jedrzejek, B., Jorgenson, M.T., Kennedy, B.E., Kholod, S.S., Lavrinenko, I.A., Lavrinenko, O.V., Magnússon, B., Matveyeva, N.V., Metúsalemsson, S., Nilsen, L., Olthof, I., Pospelov, I.N., Pospelova, E.B., Pouliot, D., Razzhivin, V., Schaepman-Strub, G., ˇSibík, J., Telyatnikov, M.Y., Troeva, E."},{"Title":"Annual mapping of large Forest disturbances across Canada's forests using 250 m MODIS imagery from 2000 to 2011","URL":"https://doi.org/10.1139/cjfr-2014-0229","AuthorName":"Guindon, L., Bernier, P.Y., Beaudoin, A., Pouliot, D., Villemaire, P., Hall, R.J., Latifovic, R., St-Amant, R."},{"Title":"Boreal lichen woodlands:a possible negative feedback to climate change in eastern North America","URL":"https://doi.org/10.1016/j.agrformet.2010.12.013","AuthorName":"Bernier, P.Y., Desjardins, R.L., Karimi-Zindashty, Y., Worth, D., Beaudoin, A., Luo, Y., Wang, S."},{"Title":"CCRS MODIS Clear-Sky Composites over Canada on GEO.ca | Composites MODIS sous ciel dégagé du CCT couvrant le Canada sur GEO.ca","URL":"https://app.geo.ca/en-ca/map-browser/record/7c954356-a099-2925-5b00-023eb775b90f","AuthorName":"Canada Centre for Remote Sensing | Centre canadien de télédétection","AuthorURL":"https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-remote-sensing"},{"Title":"Climate impacts of radiative forcing driven by agricultural land-use and land-cover changes in northern Canada","URL":"https://doi.org/10.1016/j.jenvman.2025.128201","AuthorName":"Pashanejad, E., Liu, J., Shrestha, B., Hannam, K., Bittman, S., Desjardins, R., Daneshfar, B., Trishchenko, A.P."},{"Title":"Cumulative changes in minimum snow/ice extent over Canada and Northern USA for 2000-2023","URL":"https://doi.org/10.1080/07038992.2024.2371359","AuthorName":"Trishchenko, A.P., Ungureanu, C."},{"Title":"Detection of North American land cover change between 2005 and 2010 with 250m MODIS data","URL":"https://www.researchgate.net/publication/286156544_Detection_of_North_American_land_cover_change_between_2005_and_2010_with_250m_MODIS_Data","AuthorName":"Colditz, R.R., Pouliot, D., Llamas, R.M., Homer, C., Latifovic, R., Ressl, R.A., Tovar, C.M., Hern�ndez, A.V., Richardson, K."},{"Title":"Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America","URL":"https://doi.org/10.1016/j.rse.2008.06.010","AuthorName":"Luo, Y., Trishchenko, A.P., Khlopenkov, K.V."},{"Title":"Implementation and Evaluation of Concurrent Gradient Search Method for Reprojection of MODIS Level 1B Imagery","URL":"https://doi.org/10.1109/TGRS.2008.916633","AuthorName":"Khlopenkov, K.V., and Trishchenko, A.P."},{"Title":"Influence of two management practices in the Canadian Prairies on radiative forcing","URL":"https://doi.org/10.1016/j.scitotenv.2020.142701","AuthorName":"Liu, J., Worth, D.E., Desjardins, R.L., Haak, D., McConkey, B., Cerkowniak, D."},{"Title":"Landfast ice mapping using MODIS clear-sky composites:application for the Banks Island coastline in Beaufort Sea and comparison with Canadian Ice Service data","URL":"https://doi.org/10.1080/07038992.2021.1909466","AuthorName":"Trishchenko, A.P., Luo, Y."},{"Title":"Landfast ice properties over the Beaufort Sea region in 2000-2019 from MODIS and Canadian Ice Service data","URL":"https://doi.org/10.1139/cjes-2021-0011","AuthorName":"Trishchenko, A.P., Kostylev, V.E., Luo, Y., Ungureanu, C., Whalen, D., Li, J."}]},"RegistryEntryAdded":"2026-06-02","RegistryEntryLastModified":"2026-10-29","Slug":"ccrsmodiscomposite","Sources":["awslabs-open-data-registry"]}
{"Name":"MIT-BIH ECG Compression Test Database","Description":"This database contains 168 short ECG recordings (20.48 seconds each) selected to pose a variety of challenges for ECG compressors, in particular for lossy compression methods.","Documentation":"https://doi.org/10.13026/C2QP41","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2QP41","ARN":"arn:aws:s3:::physionet-open/cdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"cdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Cerebromicrovascular Disease in Elderly with Diabetes","Description":"This dataset was collected as part of a prospective observational study to evaluate the effects of type 2 diabetes mellitus (DM) on cerebral vasoregulation, perfusion and functional outcomes, measured by blood flow responses to hypocapnia and hypercapnia, Valsalva maneuver, head-up tilt, and sit-to-stand test. The dataset comprises of observations from 69 diabetic and control participants (aged 55 to 75 years) with continuous measurements of cerebral blood flow using transcranial Doppler and MRI (magnetic resonance imaging), heart rate, blood pressure, and respiratory parameters, balance, walking, laboratory and retinopathy measures at baseline, and 41 subjects who completed the two-years of follow-up. Regional gray, white matter and cerebrospinal fluid volumes were quantified using a segmentation method applied on T1- and T2- weighted images and perfusion maps, using a continuous arterial spin labeling (CASL) images at 3 Tesla MRI. White matter integrity was determined from fluid attenuated inversion recovery (FLAIR) and diffusion tensor imaging (DTI) MRI. Dynamics of cerebral vasoregulation to CO2 challenge and orthostatic stress were measured using Transcranial Doppler ultrasound (TCD).","Documentation":"https://doi.org/10.13026/00bm-0x81","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/00bm-0x81","ARN":"arn:aws:s3:::physionet-open/cded/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"cded","Sources":["awslabs-open-data-registry"]}
{"Name":"Combined measurement of ECG, Breathing and Seismocardiograms","Description":"This database contains the combined measurement of ECG, breathing and seismocardiogram from 20 healthy volunteers. The purpose of the database was twofold: To check if slight errors in the detection of the RR time series when measuring it using two different leads are influenced by breathing. To compare the RR time series obtained from the ECG and its surrogate measure obtained from the seismocardiogram or SCG (and to optimize beat detectors for SCG).","Documentation":"https://doi.org/10.13026/C2KW23","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2KW23","ARN":"arn:aws:s3:::physionet-open/cebsdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"cebsdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Cell Painting Image Collection","Description":"The Cell Painting Image Collection is a collection of freely\ndownloadable microscopy image sets. Cell Painting is an\nunbiased high throughput imaging assay used to analyze\nperturbations in cell models. In addition to the images\nthemselves, each set includes a description of the biological\napplication and some type of \"ground truth\" (expected results).\nResearchers are encouraged to use these image sets as reference\npoints when developing, testing, and publishing new image\nanalysis algorithms for the life sciences. We hope that the\nthis data set will lead to a better understanding of which\nmethods are best for various biological image analysis\napplications.\n","Documentation":"https://github.com/cytodata/cytodata-hackathon-2018","Contact":"Post on https://forum.image.sc/ and tag with \"cellpainting\"","ManagedBy":"The Broad Institute","UpdateFrequency":"irregularly","Tags":["aws-pds","biology","cell imaging","cell painting","fluorescence imaging","high-throughput imaging","imaging","life sciences","microscopy"],"License":"CC0 1.0 Universal (CC0 1.0) Public Domain Dedication","Resources":[{"Description":"Images, extracted features and aggregated profiles are available as a S3 bucket","ARN":"arn:aws:s3:::cytodata","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Accelerating Drug Discovery with high-throughput Cell Painting on AWS","URL":"https://lifesciences-resources.awscloud.com/healthcare-life-sciences-aws-for-industries/accelerating-drug-discovery-with-high-throughput-cell-painting-on-aws","AuthorName":"Chris Kaspar"}],"Publications":[{"Title":"Example submission for the 2018 CytoData Hackathon (in R and Python)","URL":"https://github.com/cytodata/cytodata-hackathon-2018/tree/master/cytodata-toolkit/","AuthorName":"Juan Caicedo, Tim Becker","AuthorURL":"broadinstitute.org"}]},"RegistryEntryAdded":"2018-10-05","RegistryEntryLastModified":"2023-12-15","Slug":"cell-painting-image-collection","Sources":["awslabs-open-data-registry"]}
{"Name":"Cell Painting Gallery","Description":"The Cell Painting Gallery is a collection of image datasets created using the [Cell Painting](https://pubmed.ncbi.nlm.nih.gov/27560178/) assay. \nThe images of cells are captured by microscopy imaging, and reveal the response of various labeled cell components to whatever treatments are tested, which can include genetic perturbations, chemicals or drugs, or different cell types. \nThe datasets can be used for diverse applications in basic biology and pharmaceutical research, such as identifying disease-associated phenotypes, understanding disease mechanisms, and predicting a drug’s activity, toxicity, or mechanism of action ([Chandrasekaran et al 2020](https://carpenter-singh-lab.broadinstitute.org/files/anne/files/141_Chandrasekaran_NatRevDrugDiscov_2020.pdf)). \nThis collection is maintained by the [Carpenter–Singh lab](https://carpenter-singh-lab.broadinstitute.org/) and the [Cimini lab](https://cimini-lab.broadinstitute.org/) at the [Broad Institute](https://www.broadinstitute.org/).\nA human-friendly listing of datasets, instructions for accessing them, and other documentation is at the [corresponding GitHub page](https://github.com/broadinstitute/cellpainting-gallery) about the Gallery.\n","Documentation":"https://github.com/broadinstitute/cellpainting-gallery","Contact":"cellpainting@broadinstitute.org","ManagedBy":"Carpenter-Singh and Cimini Labs at the Broad Institute","UpdateFrequency":"Typically when an associated publication is posted on biorxiv","Tags":["aws-pds","bioinformatics","biology","cancer","cell biology","cell imaging","cell painting","chemical biology","computer vision","csv","deep learning","fluorescence imaging","genetic","high-throughput imaging","image processing","image-based profiling","imaging","life sciences","machine learning","medicine","microscopy","organelle"],"License":"CC0 1.0 Universal (CC0 1.0) Public Domain Dedication, but please do cite the corresponding publication for each dataset, as listed [here](https://github.com/broadinstitute/cellpainting-gallery#citation).","Resources":[{"Description":"Cell Painting data, comprising fluorescence microscopy cell images (TIFF), extracted features (CSV), and associated metadata (CSV and TXT).","ARN":"arn:aws:s3:::cellpainting-gallery","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Documentation](https://github.com/broadinstitute/cellpainting-gallery)","[Browse Bucket](https://cellpainting-gallery.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new Cell Painting Gallery data","ARN":"arn:aws:sns:us-east-1:309624411020:cellpainting-repository-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Cell Painting wiki","URL":"https://broad.io/cellpaintingwiki","AuthorName":"Multiple Authors","AuthorURL":"https://www.broadinstitute.org/imaging"},{"Title":"Center for Open Bioimage Analysis (COBA) YouTube Channel - video tutorials of CellProfiler and other softwares","URL":"https://www.youtube.com/c/COBACenterforOpenBioimageAnalysis/videos","AuthorName":"Multiple Authors","AuthorURL":"https://openbioimageanalysis.org"},{"Title":"Image-based Profiling Handbook - for processing image-based profiling datasets using CellProfiler and pycytominer","URL":"https://cytomining.github.io/profiling-handbook/","AuthorName":"Multiple Authors","AuthorURL":"https://github.com/cytomining/profiling-handbook/graphs/contributors"},{"Title":"Image-based profiling introductory exercise - data and an exercise on exploring image-based profiles, including understanding the various data levels","URL":"https://github.com/broadinstitute/BBBC021_Morpheus_Exercise/blob/main/MorpheusExerciseWritten.pdf","AuthorName":"Beth Cimini","AuthorURL":"https://github.com/broadinstitute/BBBC021_Morpheus_Exercise/graphs/contributors"},{"Title":"Scientific Community Image Forum - for software-oriented aspects of scientific imaging including analysis, processing, and acquisition","URL":"https://forum.image.sc","AuthorName":"Multiple Authors","AuthorURL":"https://openbioimageanalysis.org"}],"Tools & Applications":[{"Title":"Accelerating Drug Discovery with high-throughput Cell Painting on AWS","URL":"https://lifesciences-resources.awscloud.com/healthcare-life-sciences-aws-for-industries/accelerating-drug-discovery-with-high-throughput-cell-painting-on-aws","AuthorName":"Chris Kaspar"},{"Title":"Deep Profiler - Morphological profiling using deep learning","URL":"https://github.com/cytomining/DeepProfiler","AuthorName":"Multiple Authors","AuthorURL":"https://github.com/cytomining/DeepProfiler/graphs/contributors"},{"Title":"Distributed CellProfiler - Run encapsulated docker containers with CellProfiler in the Amazon Web Services infrastructure","URL":"https://github.com/CellProfiler/Distributed-CellProfiler","AuthorName":"Multiple Authors","AuthorURL":"https://github.com/CellProfiler/Distributed-CellProfiler/graphs/contributors"},{"Title":"Image-based Profiling Recipe","URL":"https://github.com/cytomining/profiling-recipe","AuthorName":"Multiple Authors","AuthorURL":"https://github.com/cytomining/profiling-recipe/graphs/contributors"},{"Title":"Pycytominer - Data processing functions for profiling perturbations","URL":"https://github.com/cytomining/pycytominer","AuthorName":"Multiple Authors","AuthorURL":"https://github.com/cytomining/pycytominer/graphs/contributors"}],"Publications":[{"Title":"A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay","URL":"https://academic.oup.com/gigascience/article/6/12/1/2865213","AuthorName":"Bray M-A, Gustafsdottir SM, Rohban MH, Singh S, Ljosa V, Sokolnicki KL, Bittker JA, Bodycombe NE, Dancik V, Hasaka TP, Hon CS, Kemp MM, Li K, Walpita D, Wawer MJ, Golub TR, Schreiber SL, Clemons PA, Shamji AF, & Carpenter AE"},{"Title":"Cell Painting predicts impact of lung cancer variants","URL":"https://www.molbiolcell.org/doi/10.1091/mbc.E21-11-0538","AuthorName":"Caicedo JC, Arevalo J, Piccioni F, Bray MA, Hartland CL, Wu X, Brooks AN, Berger AH, Boehm JS, Carpenter AE, & Singh S"},{"Title":"Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes","URL":"https://pubmed.ncbi.nlm.nih.gov/27560178/","AuthorName":"Bray M-A, Singh S, Han H, Davis CT, Borgeson B, Hartland C, Kost-Alimova M, Gustafsdottir SM, Gibson CC, & Carpenter AE"},{"Title":"Morphological Profiles of RNAi-Induced Gene Knockdown Are Highly Reproducible but Dominated by Seed Effects","URL":"http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0131370","AuthorName":"Singh S, Wu X, Ljosa V, Bray M-A, Piccioni F, Root DE, Doench JG, Boehm JS, & Carpenter AE"},{"Title":"Multiplex Cytological Profiling Assay to Measure Diverse Cellular States","URL":"http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0080999","AuthorName":"Gustafsdottir SM, Ljosa V, Sokolnicki KL, Wilson JA, Walpita D, Kemp MM, Seiler KP, Carrel HA, Golub TR, Schreiber SL, Clemons PA, Carpenter AE, and Shamji AF"},{"Title":"Systematic morphological profiling of human gene and allele function via Cell Painting","URL":"https://elifesciences.org/content/6/e24060","AuthorName":"Rohban MH, Singh S, Wu X, Berthet JB, Bray M-A, Shrestha Y, Varelas X, Boehm JS, & Carpenter AE"},{"Title":"Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling","URL":"http://www.pnas.org/content/111/30/10911","AuthorName":"Wawer MJ, Li K, Gustafsdottir SM, Ljosa V, BodycombeNE, Marton MA, Sokolnicki KL, Bray M-A, Kemp MM, Winchester E, Taylor B, Grant GB, Hon CSK, Duvall JR, Wilson JA, Bittker JA, Dancik V, Narayan R, Subramanian A, Winckler W, Golub TR, Carpenter AE, Shamji AF, Schreiber SL, & Clemons PA"}]},"RegistryEntryAdded":"2022-06-19","RegistryEntryLastModified":"2025-03-17","Slug":"cellpainting-gallery","Sources":["awslabs-open-data-registry"]}
{"Name":"Estimating Confidence Intervals for 2020 Census Statistics Using Approximate Monte Carlo Simulation (2010 Census Proof of Concept)","Description":"The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Approximate Monte Carlo (AMC) method seed Privacy Protected Microdata File (PPMF0) and PPMF replicates (PPMF1, PPMF2, ..., PPMF25) are a set of microdata files intended for use in estimating the magnitude of error(s) introduced by the 2020  Decennial Census Disclosure Avoidance System (DAS) into the Redistricting and DHC products. The PPMF0 was created by executing the 2020 DAS TopDown Algorithm (TDA) using the confidential  2010 Census Edited File (CEF) as the initial input; the replicates were then created by executing the 2020 DAS TDA repeatedly with the PPMF0 as its initial input.  Inspired by analogy to the use of bootstrap methods in non-private contexts, U.S. Census Bureau (USCB) researchers explored whether simple calculations  based on comparing each PPMFi to the PPMF0 could be used to reliably estimate the scale of errors introduced by the 2020 DAS, and generally found this approach  worked well. <br/> <br/> The PPMF0 and PPMFi files contained here are provided so that external researchers can estimate properties of DAS-introduced error without privileged  access to internal USCB-curated data sets; further information on the estimation methodology can be found in [Ashmead et. al 2024](https://github.com/uscensusbureau/AMC_Confidence_Intervals/blob/main/Approx_Monte_Carlo_confidence_interval_paper.pdf).  <br/> <br/> The 2010 DHC AMC seed PPMF0 and PPMF replicates have been cleared for public dissemination by the USCB Disclosure Review Board (CBDRB-FY24-DSEP-0002).  The 2010 PPMF0 included in these files was produced using the same parameters and settings as were used to produce the 2010 Demonstration Data Product  Suite (2023-04-03) PPMF, but represents an independent execution of the TopDown Algorithm. The PPMF0 and PPMF replicates contain all Person and Units attributes necessary to produce the Redistricting and DHC publications for both the United States and  Puerto Rico, and include geographic detail down to the Census Block level. They do not include attributes specific to either the Detailed DHC-A or Detailed DHC-B  products; in particular, data on Major Race (e.g., White Alone) is included, but data on Detailed Race (e.g., Cambodian) is not included in the PPMF0 and replicates. <br/> <br/> The [2020 AMC replicate files](https://registry.opendata.aws/census-2020-amc-mdf-replicates/) for estimating confidence intervals for the official 2020 Census statistics are available.  \n ","Documentation":"[AMC Replicates README (Updated 8/5/2024)](https://uscb-2020-product-releases.s3.amazonaws.com/decennial/amc/2010/mdf/2010-dhc-mdf-replicates/README.html)","Contact":"2020DAS@census.gov","ManagedBy":"[United States Census Bureau](http://www.census.gov/)","UpdateFrequency":"Not Updated","Tags":["age","approximate monte carlo","approximate monte carlo replicates","census","demographic and housing characteristics file","dhc","differential privacy","disclosure avoidance","ethnicity","group quarters","hispanic","household type","housing","housing units","latino","microdata","noisy measurements","population","race","redistricting","relation-to-householder","single year of age","voting age"],"License":"CC0 1.0 Universal","Resources":[{"Description":"2010 Census Production Settings Demographic and Housing Characteristics Approximate Monte Carlo method seed Privacy Protected Microdata File and PPMF replicates","ARN":"arn:aws:s3:::uscb-2020-product-releases/decennial/amc/2010/mdf/2010-dhc-mdf-replicates","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Census Open Data S3 Inventory","ARN":"arn:aws:s3:::uscb-opendata-inventory","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Estimating Confidence Intervals Using Approximate Monte Carlo Simulation Iterations (Jupyter Notebook)","URL":"https://github.com/uscensusbureau/AMC_Confidence_Intervals/tree/main","AuthorName":"Ashmead, R., Hawes, M. B., Pritts, M., Zhuravlev, P., Keller, S. A."}],"Publications":[{"Title":"An Approximate Monte Carlo Simulation Method for Estimating Uncertainty and Constructing Confidence Intervals for 2020 Census Statistics","URL":"https://github.com/uscensusbureau/AMC_Confidence_Intervals/blob/main/Approx_Monte_Carlo_confidence_interval_paper.pdf","AuthorName":"Ashmead, R., Hawes, M. B., Pritts, M., Zhuravlev, P., Keller, S. A."}]},"RegistryEntryAdded":"2024-04-12","RegistryEntryLastModified":"2024-10-08","Slug":"census-2010-amc-mdf-replicates","Sources":["awslabs-open-data-registry"]}
{"Name":"2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File","Description":"The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File (2023-06-30) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File.  The NMF consists of the full set of privacy-protected statistical queries (counts of individuals or housing units with particular combinations of characteristics) of confidential 2010 Census data relating to the 2010 Demonstration Data Products Suite – Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File – Production Settings (2023-04-03). These statistical queries, called “noisy measurements” were produced under the zero-Concentrated Differential Privacy framework (Bun, M. and Steinke, T [2016] https://arxiv.org/abs/1605.02065; see also Dwork C. and Roth, A. [2014] https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] https://arxiv.org/abs/2004.00010), which added positive or negative integer-valued noise to each of the resulting counts. The noisy measurements are an intermediate stage of the TDA prior to the post-processing the TDA then performs to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these 2010 Census demonstration data to enable data users to evaluate the expected impact of disclosure avoidance variability on 2020 Census data. The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File (2023-04-03) has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004). <br/> <br/> The 2010 Census Production Settings Demographic and Housing Characteristics Demonstration Noisy Measurement File includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2010 Census Edited File (CEF), which includes confidential data initially collected in the 2010 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) (https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/). As these 2010 Census demonstration data are intended to support study of the design and expected impacts of the 2020 Disclosure Avoidance System, the 2010 CEF records were pre-processed before application of the zCDP framework. This pre-processing converted the 2010 CEF records into the input-file format, response codes, and tabulation categories used for the 2020 Census, which differ in substantive ways from the format, response codes, and tabulation categories originally used for the 2010 Census. <br/> <br/> The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints—information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) —are provided.","Documentation":"[2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File (2023-06-22) README File](https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/2010%20DHC%20NMF%202023-06-22%20README.html)","Contact":"2020DAS@census.gov","ManagedBy":"[United States Census Bureau](http://www.census.gov/)","UpdateFrequency":"Not Updated","Tags":["aws-pds","census","differential privacy","disclosure avoidance","ethnicity","group quarters","hispanic","housing","housing units","latino","noisy measurements","population","race","redistricting","voting age"],"License":"CC0 1.0 Universal","Citation":"Cumings-Menon, R., Ashmead, R., Kifer, D., Leclerc, P., Spence, M., Zhuravlev, P., & Abowd, J. M. (2023). 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File [https://registry.opendata.aws/census-2010-dhc-nmf/].","Resources":[{"Description":"2010 Census Demographic and Housing Characteristics Noisy Measurement File","ARN":"arn:aws:s3:::uscb-2020-product-releases/decennial/dhc/2010/nmf/2010-dhc-nmf-county-partitioned","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Census Open Data S3 Inventory","ARN":"arn:aws:s3:::uscb-opendata-inventory","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"2010 Census Summary File 1 Technical Documentation","AuthorName":"U.S. Census Bureau","URL":"https://www2.census.gov/programs-surveys/decennial/2010/technical-documentation/complete-tech-docs/summary-file/sf1.pdf"},{"Title":"Computing Confidence Intervals Using the 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File (2023-04-03)","URL":"https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/Computing_Confidence_Intervals_Using_the_2010_Census_Redistricting_NMF.html","AuthorName":"Cumings-Menon, R., Hawes, M., and Spence, M. (2023) \"(Jupyter notebook explaining how to calculate estimates and confidence intervals from the noisy measurement files)\""},{"Title":"DAS 2020 Redistricting Production Code Release","AuthorName":"U.S. Census Bureau (Public GitHub repository for the 2020 Census DAS, vintaged as of the commit used to produce the official production run of the Redistricting product. The zCDP framework NMFs were generated in a for-internal-use-only pickled (https://docs.python.org/3/library/pickle.html; https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.SparkContext.pickleFile.html) form as a byproduct of the use of this code. A stand-alone script was developed and used to convert these internal-use NMFs into the Parquet format used in this product (that script is not yet publicly available.)","URL":"https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code"},{"Title":"Geographic Spines in the 2020 Census Disclosure Avoidance System","URL":"https://doi.org/10.48550/arXiv.2203.16654","AuthorName":"Abowd, J., Ashmead, R., Cumings-Menon, R., Garfinkel, S., Heineck, M., Heiss, C., Johns, R., Kifer, D., Leclerc, P., Machanavajjhala, A., Moran, B., Sexton, W., Spence, M., Zhuravlev, P."},{"Title":"The 2020 Census Disclosure Avoidance System Topdown Algorithm","URL":"https://doi.org/10.1162/99608f92.529e3cb9","AuthorName":"Abowd, J., Ashmead, R., Cumings-Menon, R., Garfinkel, S., Heineck, M., Heiss, C., Johns, R., Kifer, D., Leclerc, P., Machanavajjhala, A., Moran, B., Sexton, W., Spence, M., Zhuravlev, P."}]},"RegistryEntryAdded":"2023-09-08","RegistryEntryLastModified":"2024-07-12","Slug":"census-2010-dhc-nmf","Sources":["awslabs-open-data-registry"]}
{"Name":"2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File","Description":"The 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File (2023-04-03) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File.\n<br/>\n<br/>\nThe NMF consists of the full set of privacy-protected statistical queries (counts of individuals or housing units with particular combinations of characteristics) of confidential 2010 Census data relating to the redistricting data portion of the 2010 Demonstration Data Products Suite – Redistricting and Demographic and Housing Characteristics File – Production Settings (2023-04-03). These statistical queries, called “noisy measurements” were produced under the zero-Concentrated Differential Privacy framework (Bun, M. and Steinke, T [2016] https://arxiv.org/abs/1605.02065; see also Dwork C. and Roth, A. [2014] https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] https://arxiv.org/abs/2004.00010), which added positive or negative integer-valued noise to each of the resulting counts. The noisy measurements are an intermediate stage of the TDA prior to the post-processing the TDA then performs to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these 2010 Census demonstration data to enable data users to evaluate the expected impact of disclosure avoidance variability on 2020 Census data. The 2010 Census Production Settings Redistricting Data (P.L.94-171) Demonstration Noisy Measurement File (2023-04-03) has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004).\n<br/>\n<br/>\nThe data includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2010 Census Edited File (CEF), which includes confidential data initially collected in the 2010 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) (https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/). As these 2010 Census demonstration data are intended to support study of the design and expected impacts of the 2020 Disclosure Avoidance System, the 2010 CEF records were pre-processed before application of the zCDP framework. This pre-processing converted the 2010 CEF records into the input-file format, response codes, and tabulation categories used for the 2020 Census, which differ in substantive ways from the format, response codes, and tabulation categories originally used for the 2010 Census.\n<br/>\n<br/>\nThe NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints—information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) —are provided.\n","Documentation":"[2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File (2023-04-03) README File](https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/2010%20Redistricting%20NMF%202023-11-10_1.pdf)","Contact":"2020DAS@census.gov","ManagedBy":"[United States Census Bureau](http://www.census.gov/)","UpdateFrequency":"Last updated November 10, 2023: Modifications to identifiers within the parquet metadata used to support internal tracking of source data.","Tags":["aws-pds","census","differential privacy","disclosure avoidance","ethnicity","group quarters","hispanic","housing","housing units","latino","noisy measurements","population","race","redistricting","voting age"],"License":"CC0 1.0 Universal","Citation":"Abowd, J. M., Ashmead, R., Cumings-Menon, R., Garfinkel, S., Heineck, M., Heiss, C., Johns, R., Kifer, D., Leclerc, P., Machanavajjhala, A., Moran, B., Sexton, W., Spence, M., & Zhuravlev, P. (2023). 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File [https://registry.opendata.aws/census-2010-pl94-nmf/].","Resources":[{"Description":"The 2010 Census Redistricting Data (P.L. 94-171) Noisy Measurement File","ARN":"arn:aws:s3:::uscb-2020-product-releases/decennial/redistricting/2010/nmf/2010-pl94-nmf-state-partitioned","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Census Open Data S3 Inventory","ARN":"arn:aws:s3:::uscb-opendata-inventory","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Geographic Spines in the 2020 Census Disclosure Avoidance System","URL":"https://doi.org/10.48550/arXiv.2203.16654","AuthorName":"Abowd, J., Ashmead, R., Cumings-Menon, R., Garfinkel, S., Heineck, M., Heiss, C., Johns, R., Kifer, D., Leclerc, P., Machanavajjhala, A., Moran, B., Sexton, W., Spence, M., Zhuravlev, P."},{"Title":"The 2020 Census Disclosure Avoidance System Topdown Algorithm","URL":"https://doi.org/10.1162/99608f92.529e3cb9","AuthorName":"Abowd, J., Ashmead, R., Cumings-Menon, R., Garfinkel, S., Heineck, M., Heiss, C., Johns, R., Kifer, D., Leclerc, P., Machanavajjhala, A., Moran, B., Sexton, W., Spence, M., Zhuravlev, P."}]},"RegistryEntryAdded":"2023-09-08","RegistryEntryLastModified":"2024-07-12","Slug":"census-2010-pl94-nmf","Sources":["awslabs-open-data-registry"]}
{"Name":"Estimating Confidence Intervals for 2020 Census Statistics Using Approximate Monte Carlo Simulation (2020 Census Production Run)","Description":"The 2020 Census Production Settings Demographic and Housing Characteristics (DHC) Approximate Monte Carlo (AMC) method seed Privacy Protected Microdata File (PPMF0) and PPMF replicates (PPMF1, PPMF2, ..., PPMF50) are a set of microdata files intended for use in estimating the magnitude of error(s) introduced by the 2020  Census Disclosure Avoidance System (DAS) into the 2020 Census Redistricting Data Summary File (P.L. 94-171), the Demographic and Housing Characteristics File, and the Demographic Profile.  <br/> <br/> The PPMF0 was the source of the publicly released, official 2020 Census data products referenced above, and was created by executing the 2020 DAS TopDown Algorithm (TDA) using the confidential  2020 Census Edited File (CEF) as the initial input; the official location for the PPMF0 is [on the United States Census Bureau FTP server](https://www2.census.gov/programs-surveys/decennial/2020/data/privacy-protected-microdata-file/), but we also include a copy of it here for convenience. The replicates were then created by executing the 2020 DAS TDA repeatedly with the PPMF0 as its initial input.  <br/> <br/> Inspired by analogy to the use of bootstrap methods in non-private contexts, U.S. Census Bureau (USCB) researchers explored whether simple calculations  based on comparing each PPMFi to the PPMF0 could be used to reliably estimate the scale of errors introduced by the 2020 DAS, and generally found this approach  worked well. <br/> <br/> The PPMF0 and PPMFi files contained here are provided so that external researchers can estimate properties of DAS-introduced error without privileged  access to internal USCB-curated data sets; further information on the estimation methodology can be found in [Ashmead et. al 2024](https://github.com/uscensusbureau/AMC_Confidence_Intervals/blob/main/Approx_Monte_Carlo_confidence_interval_paper.pdf).  <br/> <br/> The 2020 DHC AMC seed PPMF0 and PPMF replicates have been cleared for public dissemination by the USCB Disclosure Review Board (CBDRB-FY22-DSEP-004).  The PPMF0 and PPMF replicates contain all Person and Units attributes necessary to produce the 2020 Census Redistricting Data Summary File (P.L. 94-171), the Demographic and Housing Characteristics File, and the Demographic Profile for both the United States and  Puerto Rico, and include geographic detail down to the Census Block level. They do not include attributes specific to either the Detailed DHC-A or Detailed DHC-B  products; in particular, data on Major Race (e.g., White Alone) is included, but data on Detailed Race (e.g., Cambodian) is not included in the PPMF0 and replicates.","Documentation":"[AMC Replicates README (Updated 9/17/2024)](https://uscb-2020-product-releases.s3.amazonaws.com/decennial/amc/2020/mdf/2020-dhc-mdf-replicates/ppmf/README.html)","Contact":"2020DAS@census.gov","ManagedBy":"[United States Census Bureau](http://www.census.gov/)","UpdateFrequency":"Not Updated","Tags":["2020 census","age","approximate monte carlo","approximate monte carlo replicates","census","decennial census","demographic and housing characteristics file","dhc","differential privacy","disclosure avoidance","ethnicity","group quarters","hispanic","household type","housing","housing units","latino","microdata","noisy measurements","population","race","redistricting","relation-to-householder","single year of age","voting age"],"License":"CC0 1.0 Universal","Resources":[{"Description":"2020 Census Production Settings Demographic and Housing Characteristics Approximate Monte Carlo method seed Privacy Protected Microdata File and PPMF replicates","ARN":"arn:aws:s3:::uscb-2020-product-releases/decennial/amc/2020/mdf/2020-dhc-mdf-replicates","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Census Open Data S3 Inventory","ARN":"arn:aws:s3:::uscb-opendata-inventory","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Estimating Confidence Intervals Using Approximate Monte Carlo Simulation Iterations (Jupyter Notebook)","URL":"https://github.com/uscensusbureau/AMC_Confidence_Intervals/tree/main","AuthorName":"Ashmead, R., Hawes, M. B., Pritts, M., Zhuravlev, P., Keller, S. A."}],"Publications":[{"Title":"An Approximate Monte Carlo Simulation Method for Estimating Uncertainty and Constructing Confidence Intervals for 2020 Census Statistics","URL":"https://github.com/uscensusbureau/AMC_Confidence_Intervals/blob/main/Approx_Monte_Carlo_confidence_interval_paper.pdf","AuthorName":"Ashmead, R., Hawes, M. B., Pritts, M., Zhuravlev, P., Keller, S. A."}]},"RegistryEntryAdded":"2024-09-28","RegistryEntryLastModified":"2024-10-08","Slug":"census-2020-amc-mdf-replicates","Sources":["awslabs-open-data-registry"]}
{"Name":"2020 Census Demographic and Housing Characteristics (DHC) Noisy Measurement File","Description":"The 2020 Census Demographic and Housing Characteristics Noisy Measurement File is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022], and implemented in [primitives.py](https://github.com/uscensusbureau/DAS_2020_DHC_Production_Code/blob/main/das_decennial/programs/engine/primitives.py)). The 2020 Census Demographic and Housing Characteristics Noisy Measurement File includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] ), which added positive or negative integer-valued noise to each of the resulting counts. These are estimated counts of individuals and housing units included in the 2020 Census Edited File (CEF), which includes confidential data collected in the 2020 Census of Population and Housing.\n<br/>\n<br/>\nThe noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the Census Demographic and Housing Characteristics Summary File. In addition to the noisy measurements, constraints based on invariant calculations --- counts computed without noise --- are also included (with the exception of the state-level total populations, which can be sourced separately from data.census.gov).\n<br/>\n<br/>\nThe Noisy Measurement File was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File.\n<br/>\n<br/>\nThe noisy measurements are produced in an early stage of the TDA. Afterward, these noisy measurements are post-processed to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these noisy measurements to enable data users to evaluate the impact of disclosure avoidance variability on 2020 Census data. The 2020 Census Demographic and Housing Characteristics (DHC) Noisy Measurement File has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004).\n<br/>\n<br/>\n","Documentation":"[Demographic and Housing Characteristics (DHC) Noisy Measurement File (2023-10-23) README File](https://www2.census.gov/programs-surveys/decennial/2020/data/demographic-and-housing-characteristics-file/00-2020-DHC-Noisy-Measurement-File/2020_DHC_NMF_README.html)","Contact":"2020DAS@census.gov","ManagedBy":"[United States Census Bureau](http://www.census.gov/)","UpdateFrequency":"Not Updated","Tags":["aws-pds","census","differential privacy","disclosure avoidance","ethnicity","group quarters","housing","housing units","noisy measurements","population","race","redistricting","voting age"],"License":"CC0 1.0 Universal","Citation":"Cumings-Menon, R., Ashmead, R., Kifer, D., Leclerc, P., Spence, M., Zhuravlev, P., & Abowd, J. M. (2023). 2020 Census Demographic and Housing Characteristics (DHC) Noisy Measurement File [https://registry.opendata.aws/census-2020-dhc-nmf/].","Resources":[{"Description":"The 2020 Census Demographic and Housing Characteristics Noisy Measurement File","ARN":"arn:aws:s3:::uscb-2020-product-releases/decennial/dhc/2020/nmf/2020-dhc-nmf","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Census Open Data S3 Inventory","ARN":"arn:aws:s3:::uscb-opendata-inventory","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"2020 Census Demographic and Housing Characteristics File Technical Documentation","URL":"https://www.census.gov/programs-surveys/decennial-census/technical-documentation/complete-technical-documents.html#dhc-and-dp","AuthorName":"U.S. Census Bureau. Note that the zCDP framework NMFs were generated in a for-internal-use-only pickled (https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.SparkContext.pickleFile.html) form as a byproduct of the use of this code. A stand-alone script was developed and used to convert these internal-use NMFs into the Parquet format used in this product (that script is not yet publicly available)."},{"Title":"Computing Confidence Intervals Using the 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File (2023-04-03) (Jupyter notebook explaining how to calculate estimates and confidence intervals from the noisy measurement files)","URL":"https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/Computing_Confidence_Intervals_Using_the_2010_Census_Redistricting_NMF.html","AuthorName":"Cumings-Menon, R., Hawes, M., and Spence, M. (2023)"},{"Title":"DAS 2020 DHC Production Code Release","URL":"https://github.com/uscensusbureau/DAS_2020_DHC_Production_Code/tree/main","AuthorName":"U.S. Census Bureau"},{"Title":"Geographic Spines in the 2020 Census Disclosure Avoidance System","URL":"https://doi.org/10.48550/arXiv.2203.16654","AuthorName":"Abowd, J., Ashmead, R., Cumings-Menon, R., Garfinkel, S., Heineck, M., Heiss, C., Johns, R., Kifer, D., Leclerc, P., Machanavajjhala, A., Moran, B., Sexton, W., Spence, M., Zhuravlev, P."},{"Title":"The 2020 Census Disclosure Avoidance System Topdown Algorithm","URL":"https://doi.org/10.1162/99608f92.529e3cb9","AuthorName":"Abowd, J., Ashmead, R., Cumings-Menon, R., Garfinkel, S., Heineck, M., Heiss, C., Johns, R., Kifer, D., Leclerc, P., Machanavajjhala, A., Moran, B., Sexton, W., Spence, M., Zhuravlev, P."}]},"RegistryEntryAdded":"2023-10-17","RegistryEntryLastModified":"2024-07-12","Slug":"census-2020-dhc-nmf","Sources":["awslabs-open-data-registry"]}
{"Name":"2020 Redistricting Data File Least Squares Estimates","Description":"The 2020 Redistricting Data File Least Squares Estimates data product provides count estimates, and their standard deviations, for each tabulation that was published as part of the persons universe of the 2020 Redistricting Data File for the US, state, county, and tract geographic levels. These estimates are computed using the generalized least squares (GLS) estimator using as input the publicly available 2020 Census persons universe noisy measurement files for both [the Redistricting Data File](https://registry.opendata.aws/census-2020-pl94-nmf/) and [the Demographic and Housing Characteristics File](https://registry.opendata.aws/census-2020-dhc-nmf/). The algorithm used to compute this estimate is described in more detail in [Least Squares Estimation For Hierarchical Data](https://arxiv.org/abs/2404.13164). As described in more detail in this paper and the README below, the primary goal of this data product is to provide the data required to compute confidence intervals for the 2020 Census Redistricting Data File published counts that account for the uncertainty in these published counts due to the disclosure avoidance methods applied to 2020 Census tabulations. In other words, these confidence intervals do not account for other sources of error, such as those due to enumeration errors.\n<br/>\n<br/>\n","Documentation":"[2020 Redistricting Data File Least Squares Estimates README File](https://github.com/uscensusbureau/DAS_2020_GLS_Uncertainty_Evaluation/blob/main/README.md)","Contact":"2020DAS@census.gov","ManagedBy":"[United States Census Bureau](http://www.census.gov/)","UpdateFrequency":"Not Updated","Tags":["aws-pds","census","confidence intervals","differential privacy","disclosure avoidance","ethnicity","group quarters","housing","housing units","least squares","noisy measurements","population","race","redistricting","voting age"],"License":"CC0 1.0 Universal","Citation":"Recommended citation: U.S. Census Bureau. 2020 Redistricting Data File Least Squares Estimates, https://registry.opendata.aws/census-2020-pls94-gls/, accessed on DATE.","Resources":[{"Description":"2020 Redistricting Data File Least Squares Estimates File","ARN":"arn:aws:s3:::uscb-2020-product-releases/decennial/gls/2020/redistricting-gls-estimates","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Census Open Data S3 Inventory","ARN":"arn:aws:s3:::uscb-opendata-inventory/","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2025-06-03","RegistryEntryLastModified":"2025-06-03","Slug":"census-2020-pl94-gls","Sources":["awslabs-open-data-registry"]}
{"Name":"2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File","Description":"The 2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File (NMF) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9, and implemented in the [DAS 2020 Redistricting Production Code](https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code)). The NMF was generated using [the Census Bureau's implementation](https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code/blob/289ee463936a6f0efcf2e378abe410ec01d0e140/source/programs/engine/primitives.py#L183) of the [Discrete Gaussian Mechanism](https://arxiv.org/abs/2004.00010), calibrated to satisfy [zero-Concentrated Differential Privacy](https://arxiv.org/abs/1605.02065) with [bounded neighbors](https://dl.acm.org/doi/10.1145/1989323.1989345).\n<br/>\n  <br/>\nThe NMF values, called **noisy measurements** are the output of applying the Discrete Gaussian Mechanism to counts from the 2020 Census Edited File (CEF). They are generally inconsistent with one another (for example, in a county composed of two tracts, the noisy measurement for the county's total population may not equal the sum of the noisy measurements of the two tracts' total population), and frequently negative (especially when the population being measured was small), but are integer-valued. The NMF was later post-processed as part of the DAS code to take the form of microdata and to satisfy various constraints. The NMF documented here contains both the noisy measurements themselves as well as the data needed to represent the DAS constraints; thus, the NMF could be used to reproduce the steps taken by the DAS code to produce microdata from the noisy measurements by applying [the production code base](https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code/blob/289ee463936a6f0efcf2e378abe410ec01d0e140/source/).\n<br/>\n<br/>\nThe 2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2020 Census Edited File (CEF), which includes confidential data initially collected in the 2020 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the [2020 Census Redistricting Data (P.L. 94-171) Summary File](https://www.census.gov/programs-surveys/decennial-census/about/rdo/summary-files.html).\n<br/>\n<br/>\nThe NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints--information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2020 Census Redistricting Data (P.L. 94-171) Summary File --are provided.\n","Documentation":"[2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File README File](https://www2.census.gov/programs-surveys/decennial/2020/data/01-Redistricting_File--PL_94-171/00-2020-Redistricting-Noisy-Measurement-File/2020%20Redistricting%20NMF%202023-06-15%20README.html)","Contact":"2020DAS@census.gov","ManagedBy":"[United States Census Bureau](http://www.census.gov/)","UpdateFrequency":"Not Updated","Tags":["aws-pds","census","differential privacy","disclosure avoidance","ethnicity","group quarters","housing","housing units","noisy measurements","population","race","redistricting","voting age"],"License":"CC0 1.0 Universal","Citation":"Abowd, J. M., Ashmead, R., Cumings-Menon, R., Garfinkel, S., Heineck, M., Heiss, C., Johns, R., Kifer, D., Leclerc, P., Machanavajjhala, A., Moran, B., Sexton, W., Spence, M., & Zhuravlev, P. (2023). 2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File [https://registry.opendata.aws/census-2020-pl94-nmf/].","Resources":[{"Description":"The 2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File","ARN":"arn:aws:s3:::uscb-2020-product-releases/decennial/redistricting/2020/nmf/2020-pl94-nmf-state-partitioned","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Census Open Data S3 Inventory","ARN":"arn:aws:s3:::uscb-opendata-inventory","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Geographic Spines in the 2020 Census Disclosure Avoidance System","URL":"https://doi.org/10.48550/arXiv.2203.16654","AuthorName":"Abowd, J., Ashmead, R., Cumings-Menon, R., Garfinkel, S., Heineck, M., Heiss, C., Johns, R., Kifer, D., Leclerc, P., Machanavajjhala, A., Moran, B., Sexton, W., Spence, M., Zhuravlev, P."},{"Title":"The 2020 Census Disclosure Avoidance System Topdown Algorithm","URL":"https://doi.org/10.1162/99608f92.529e3cb9","AuthorName":"Abowd, J., Ashmead, R., Cumings-Menon, R., Garfinkel, S., Heineck, M., Heiss, C., Johns, R., Kifer, D., Leclerc, P., Machanavajjhala, A., Moran, B., Sexton, W., Spence, M., Zhuravlev, P."}]},"RegistryEntryAdded":"2023-09-29","RegistryEntryLastModified":"2024-07-12","Slug":"census-2020-pl94-nmf","Sources":["awslabs-open-data-registry"]}
{"Name":"U.S. Census ACS PUMS","Description":"U.S. Census Bureau American Community Survey (ACS) Public Use Microdata Sample (PUMS) available in a linked data format using the Resource Description Framework (RDF) data model.","Documentation":"https://docs.data.world/uscensus/#american-community-survey-linked-open-data","Contact":"https://docs.data.world/uscensus/#60---contact","ManagedBy":"Data.world","UpdateFrequency":"Yearly, after ACS 1-year PUMS raw data are released","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aws-pds","census","statistics","survey"],"License":"[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"PUMS data in Turtle - Terse RDF Triple Language (.ttl) format along with ontologies and other complementary data.","ARN":"arn:aws:s3:::dataworld-linked-acs","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Setting up Blazegraph on EC2","URL":"https://docs.data.world/uscensus/#50---getting-started","AuthorName":"data.world","AuthorURL":"https://data.world/"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2023-02-28","Slug":"census-dataworld-pums","Sources":["awslabs-open-data-registry"]}
{"Name":"Cerebral Vasoregulation in Diabetes","Description":"This observational study evaluated the effects of type 2 diabetes on cerebral vasoregulation and functional outcomes, measured by blood flow responses to hypocapnia and hypercapnia, Valsalva maneuver, head-up tilt, and sit-to-stand test. This dataset contains 37 diabetic participants and 49 controls (aged 55 to 75 years) with continuous measurements of cerebral blood flow using transcranial Doppler and MRI, heart rate, blood pressure, and respiratory parameters, balance, walking, laboratory and retinopathy measures.","Documentation":"https://doi.org/10.13026/m40k-4758","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/m40k-4758","ARN":"arn:aws:s3:::physionet-open/cerebral-vasoreg-diabetes/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"cerebral-vasoreg-diabetes","Sources":["awslabs-open-data-registry"]}
{"Name":"CESM-HR","Description":"This dataset provides several global fields describing the state of atmosphere, ocean, land and ice from a high-resolution (0.1o for the ocean/ice models 0.25o for the land/atmosphere models) numerical earth system model, the Community Earth System Model (CESM, https://www.cesm.ucar.edu/). Texas A&M University (TAMU) and National Center for Atmospheric Research together with international partners collaboratively carried out a large set of high-resolution climate simulations, including a 500-year long preindustrial control simulation (PI-CTRL) described here. The CESM uses dynamic equations with a climatological (observations, long-term averaged) initial state of the earth system and the preindustrial greenhouse gas forcing (kept constant at the 1850 conditions) for making this PI-CTRL. Compared to standard low-resolution (1o) CMIP-class simulations, this high-resolution simulation dataset enables researchers to explore the contribution from processes at a wide range of spatial scales, from mesoscales to global scales, in shaping various observed climate phenomena with better accuracy.","Documentation":"https://github.com/MESACLIP/cesm-hr-pi-ctrl-aws","Contact":"ping@tamu.edu","ManagedBy":"[TAMU](https://www.tamu.com/)","UpdateFrequency":"Rare. The CESM-HR PI-CTRL experiment is complete. Updates are expected only if any issues with the copy of the data on the AWS is reported in the future. Other experiments will be shared in the future according to the availability of resources.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","climate","climate model","climate projections","CMIP6","ocean circulation","ocean currents","ocean sea surface height","ocean simulation","ocean velocity"],"License":"This dataset is created in collaboration with NCAR and the NCAR’s “Creative Commons Attribution 4.0 International license” used for their CESM2 LENS data product on AWS (https://www.ucar.edu/terms-of-use/data) will be applicable for the CESM-HR PI-CTRL datasets too.","Resources":[{"Description":"CESM-HR PI-CTRL data","ARN":"arn:aws:s3:::cesm-hr-pi-ctrl","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](http://pi-ctrl-bucket.s3-website.us-east-2.amazonaws.com)"]}],"DataAtWork":{"Tutorials":[{"Title":"CESM-HR Tutorial","URL":"https://github.com/MESACLIP/cesm-hr-pi-ctrl-aws/tree/main/Tutorials","AuthorName":"Jaison Kurian","AuthorURL":"https://github.com/jaisonkn"}],"Tools & Applications":[{"Title":"CESM-HR Tools and Applications","URL":"https://github.com/MESACLIP/cesm-hr-pi-ctrl-aws/tree/main/Tutorials","AuthorName":"Jaison Kurian","AuthorURL":"https://github.com/jaisonkn"}],"Publications":[{"Title":"An Unprecedented Set of High‐Resolution Earth System Simulations for Understanding Multiscale Interactions in Climate Variability and Change","URL":"https://doi.org/10.1029/2020MS002298","AuthorName":"Chang et al. (2020)"},{"Title":"High-resolution modelling identifies the Bering Strait’s role in amplified Arctic warming","URL":"https://doi.org/10.1038/s41558-024-02008-z","AuthorName":"Xu et al. (2024)"},{"Title":"Uncertain future of sustainable fisheries environment in eastern boundary upwelling zones under climate change","URL":"https://doi.org/10.1038/s43247-023-00681-0","AuthorName":"Chang et al. (2023)"}]},"RegistryEntryAdded":"2024-05-13","RegistryEntryLastModified":"2025-07-09","Slug":"cesm-hr","Sources":["awslabs-open-data-registry"]}
{"Name":"Cancer Genome Characterization Initiatives - Burkitt Lymphoma, HIV+ Cervical Cancer","Description":"The Cancer Genome Characterization Initiatives (CGCI) program supports cutting-edge genomics research of adult and pediatric cancers. CGCI investigators develop and apply advanced sequencing methods that examine genomes, exomes, and transcriptomes within various types of tumors. The program includes Burkitt Lymphoma Genome Sequencing Project (BLGSP) project and HIV+ Tumor Molecular Characterization Project - Cervical Cancer (HTMCP-CC) project.\nThe dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, and miRNA Expression Quantification data.\nThis dataset also contains controlled WGS/Targeted Sequencing/RNA-Seq/miRNA-Seq Aligned Reads, and RNA-Seq Splice Junction Quantification\n","Documentation":"https://ocg.cancer.gov/programs/cgci","Contact":"dcf-support@datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genomic","life sciences","STRIDES","transcriptomics"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq\nIsoform Expression Quantification, miRNA-Seq miRNA Expression Quantification \n","ARN":"arn:aws:s3:::gdc-cgci-blgsp-phs000235-2-open","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq\nIsoform Expression Quantification, miRNA-Seq miRNA Expression Quantification \n","ARN":"arn:aws:s3:::gdc-cgci-phs000235-2-open","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"WGS/Targeted Sequencing/RNA-Seq/miRNA-Seq Aligned Reads, RNA-Seq Splice Junction\nQuantification \n","ARN":"arn:aws:s3:::gdc-cgci-phs000235-2-controlled","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000235.v14.p2"}],"DataAtWork":{"Tools & Applications":[{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/CGCI-BLGSP","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"}],"Publications":[{"Title":"Genome-wide discovery of somatic coding and noncoding mutations in pediatric endemic and\nsporadic Burkitt lymphoma\n","URL":"https://doi.org/10.1182/blood-2018-09-871418","AuthorName":"Grande B. M., Gerhard D. S., Jiang A., Griner N. B., Abramson J. S., Alexander T. B., et al.\n"}]},"RegistryEntryAdded":"2020-06-24","RegistryEntryLastModified":"2020-10-05","Slug":"cgci","Sources":["awslabs-open-data-registry"]}
{"Name":"CCAFS-Climate Data","Description":"High resolution climate data to help assess the impacts of climate change primarily on agriculture. These open access datasets of climate projections will help researchers make climate change impact assessments.","Documentation":"http://www.ccafs-climate.org","Contact":"http://www.ccafs-climate.org/contact/","UpdateFrequency":"Every three months","ManagedBy":"[International Center for Tropical Agriculture](https://ciat.cgiar.org/)","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","climate","food security","sustainability"],"License":"Creative Commons Attribution-NonCommercial 4.0 International License http://creativecommons.org/licenses/by-nc/4.0/","Resources":[{"Description":"ARC GRID, and ARC ASCII format compressed","ARN":"arn:aws:s3:::cgiardata","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2022-06-14","Slug":"cgiardata","Sources":["awslabs-open-data-registry"]}
{"Name":"CGMacros: a scientific dataset for personalized nutrition and diet monitoring","Description":"We present CGMacros, a dataset containing multimodal information from two continuous glucose monitors (CGM), food macronutrients, food photographs, and physical activity, in addition to anonymized participant demographics, anthropometric measurements and health parameters from blood analyses and gut microbiome profiles. CGMacros contains data from 45 study participants (15 healthy adults, 16 with pre-diabetes, and 14 with Type 2 diabetes) who consumed meals with varying and known macronutrient compositions in a free-living setting for ten consecutive days. To our knowledge, this is the first database of its kind to be made publicly available. CGMacros, and larger publicly available datasets that we hope may follow, are essential to democratize academic research in personalized nutrition and algorithmic approaches to automated diet monitoring.","Documentation":"https://doi.org/10.13026/3z8q-x658","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/3z8q-x658","ARN":"arn:aws:s3:::physionet-open/cgmacros/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"cgmacros","Sources":["awslabs-open-data-registry"]}
{"Name":"Detecting and Quantifying Apnea Based on the ECG: The PhysioNet/Computing in Cardiology Challenge 2000","Description":"Obstructive sleep apnea (intermittent cessation of breathing) is a common problem with major health implications, ranging from excessive daytime drowsiness to serious cardiac arrhythmias. Obstructive sleep apnea is associated with increased risks of high blood pressure, myocardial infarction, and stroke, and with increased mortality rates. Standard methods for detecting and quantifying sleep apnea are based on respiration monitoring, which often disturbs or interferes with sleep and is generally expensive. A number of studies during the past 15 years have hinted at the possibility of detecting sleep apnea using features of the electrocardiogram. Such approaches are minimally intrusive, inexpensive, and may be particularly well-suited for screening. The major obstacle to use of such methods is that careful quantitative comparisons of their accuracy against that of conventional techniques for apnea detection have not been published. We therefore offer a challenge to the biomedical research community: demonstrate the efficacy of ECG-based methods for apnea detection using a large, well-characterized, and representative set of data. The goal of the contest is to stimulate effort and advance the state of the art in this clinically significant problem, and to foster both friendly competition and wide-ranging collaborations. We will award prizes of US$500 to the most successful entrant in each of two events.1","Documentation":"https://physionet.org/content/challenge-2000/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/challenge-2000/","ARN":"arn:aws:s3:::physionet-open/challenge-2000/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2000","Sources":["awslabs-open-data-registry"]}
{"Name":"Predicting Paroxysmal Atrial Fibrillation/Flutter: The PhysioNet/Computing in Cardiology Challenge 2001","Description":"Following the success of the first Computers in Cardiology Challenge, we are pleased to offer a new challenge from PhysioNet and Computers in Cardiology 2001. The challenge is to develop a fully automated method to predict the onset of paroxysmal atrial fibrillation/flutter (PAF), based on the ECG prior to the event. The goal of the contest is to stimulate effort and advance the state of the art in this clinically significant problem, and to foster both friendly competition and wide-ranging collaborations.","Documentation":"https://physionet.org/content/challenge-2001/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/challenge-2001/","ARN":"arn:aws:s3:::physionet-open/challenge-2001/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2001","Sources":["awslabs-open-data-registry"]}
{"Name":"Distinguishing Ischemic from Non-Ischemic ST Changes: The PhysioNet/Computing in Cardiology Challenge 2003","Description":"For the fourth annual PhysioNet/Computers in Cardiology Challenge, we propose a provocative question of considerable clinical interest: Is it possible to tell the difference between transient ST changes in the ECG that are due to myocardial ischemia, and those that are not?","Documentation":"https://physionet.org/content/challenge-2003/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/challenge-2003/","ARN":"arn:aws:s3:::physionet-open/challenge-2003/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2003","Sources":["awslabs-open-data-registry"]}
{"Name":"The First Five Challenges Revisited: The PhysioNet/Computing in Cardiology Challenge 2005","Description":"Our popular series of challenges presented in cooperation with Computers in Cardiology continues this year in a new mode. Participants are invited to choose one of the five previous challenges as a topic for a follow-up study: Detecting sleep apnea from the ECG Predicting paroxysmal atrial fibrillation RR interval time series modeling Distinguishing ischemic from non-ischemic ST changes Spontaneous termination of atrial fibrillation The problems posed by these challenges have continued to interest many researchers long after the final deadlines have passed. We encourage you to read the papers published by previous challenge participants in the Computers in Cardiology proceedings and elsewhere, and to build upon this work in pursuit of even better solutions. This year, we will recognize the most outstanding papers inspired by a previous challenge, with special awards to be presented at Computers in Cardiology. With knowledge gained from previously published papers, can you advance the state of the art further, either by combining efforts with other participants or by entirely new methods?","Documentation":"https://physionet.org/content/challenge-2005/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/challenge-2005/","ARN":"arn:aws:s3:::physionet-open/challenge-2005/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2005","Sources":["awslabs-open-data-registry"]}
{"Name":"QT Interval Measurement: The PhysioNet/Computing in Cardiology Challenge 2006","Description":"The seventh annual PhysioNet/Computers in Cardiology Challenge addresses a question of high clinical interest: Can the QT interval be measured by fully automated methods with an accuracy acceptable for clinical evaluations?","Documentation":"https://physionet.org/content/challenge-2006/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/challenge-2006/","ARN":"arn:aws:s3:::physionet-open/challenge-2006/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2006","Sources":["awslabs-open-data-registry"]}
{"Name":"Electrocardiographic Imaging of Myocardial Infarction: The PhysioNet/Computing in Cardiology Challenge 2007","Description":"The aim of Challenge 2007 is to establish how well one can characterize the location and extent of moderate to large, relatively compact infarcts using electrocardiographic evidence (supplemented by a model of the torso geometry and conductivity), in comparison with a \"gold standard\" expert analysis of gadolinium-enhanced MRI data.","Documentation":"https://physionet.org/content/challenge-2007/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/challenge-2007/","ARN":"arn:aws:s3:::physionet-open/challenge-2007/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2007","Sources":["awslabs-open-data-registry"]}
{"Name":"Detecting and Quantifying T-Wave Alternans: The PhysioNet/Computing in Cardiology Challenge 2008","Description":"The ninth annual PhysioNet/Computers in Cardiology Challenge aims to improve understanding of methods for identification and analysis of T-wave alternans in the ECG.","Documentation":"https://physionet.org/content/challenge-2008/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/challenge-2008/","ARN":"arn:aws:s3:::physionet-open/challenge-2008/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2008","Sources":["awslabs-open-data-registry"]}
{"Name":"Improving the Quality of ECGs Collected using Mobile Phones: The PhysioNet/Computing in Cardiology Challenge 2011","Description":"The aim of the PhysioNet/Computing in Cardiology Challenge 2011 is to develop an efficient algorithm able to run in near real-time within a mobile phone, that can provide useful feedback to a layperson in the process of acquiring a diagnostically useful ECG recording.","Documentation":"https://physionet.org/content/challenge-2011/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/challenge-2011/","ARN":"arn:aws:s3:::physionet-open/challenge-2011/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2011","Sources":["awslabs-open-data-registry"]}
{"Name":"Predicting Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012","Description":"The focus of the PhysioNet/CinC Challenge 2012 is to develop methods for patient-specific prediction of in-hospital mortality. Participants will use information collected during the first two days of an ICU stay to predict which patients survive their hospitalizations, and which patients do not.","Documentation":"https://physionet.org/content/challenge-2012/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/challenge-2012/","ARN":"arn:aws:s3:::physionet-open/challenge-2012/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2012","Sources":["awslabs-open-data-registry"]}
{"Name":"Noninvasive Fetal ECG: The PhysioNet/Computing in Cardiology Challenge 2013","Description":"The aim of this year's PhysioNet/Computing in Cardiology Challenge is to encourage development of accurate algorithms for locating QRS complexes and estimating the QT interval in noninvasive FECG signals.","Documentation":"https://physionet.org/content/challenge-2013/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/challenge-2013/","ARN":"arn:aws:s3:::physionet-open/challenge-2013/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2013","Sources":["awslabs-open-data-registry"]}
{"Name":"Classification of Heart Sound Recordings: The PhysioNet/Computing in Cardiology Challenge 2016","Description":"The 2016 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify heart sound recordings collected from a variety of clinical or nonclinical (such as in-home visits) environments. The aim is to identify, from a single short recording (10-60s) from a single precordial location, whether the subject of the recording should be referred on for an expert diagnosis.","Documentation":"https://physionet.org/content/challenge-2016/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/challenge-2016/","ARN":"arn:aws:s3:::physionet-open/challenge-2016/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2016","Sources":["awslabs-open-data-registry"]}
{"Name":"AF Classification from a Short Single Lead ECG Recording: The PhysioNet/Computing in Cardiology Challenge 2017","Description":"The 2017 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify, from a single short ECG lead recording (between 30 s and 60 s in length), whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an alternative rhythm, or is too noisy to be classified.","Documentation":"https://doi.org/10.13026/d3hm-sf11","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/d3hm-sf11","ARN":"arn:aws:s3:::physionet-open/challenge-2017/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2017","Sources":["awslabs-open-data-registry"]}
{"Name":"You Snooze You Win: The PhysioNet/Computing in Cardiology Challenge 2018","Description":"The goal of the challenge is use information from the available signals to correctly classify target arousal regions.","Documentation":"https://doi.org/10.13026/6phb-r450","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/6phb-r450","ARN":"arn:aws:s3:::physionet-open/challenge-2018/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2018","Sources":["awslabs-open-data-registry"]}
{"Name":"Early Prediction of Sepsis from Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019","Description":"Sepsis is a life-threatening condition that occurs when the body's response to infection causes tissue damage, organ failure, or death (Singer et al., 2016). In the U.S., nearly 1.7 million people develop sepsis and 270,000 people die from sepsis each year; over one third of people who die in U.S. hospitals have sepsis (CDC). Internationally, an estimated 30 million people develop sepsis and 6 million people die from sepsis each year; an estimated 4.2 million newborns and children are affected (WHO). Sepsis costs U.S. hospitals more than any other health condition at $24 billion (13% of U.S. healthcare expenses) a year, and a majority of these costs are for sepsis patients that were not diagnosed at admission (Paoli et al., 2018). Sepsis costs are even greater globally with the developing world at most risk. Altogether, sepsis is a major public health issue responsible for significant morbidity, mortality, and healthcare expenses. Early detection and antibiotic treatment of sepsis are critical for improving sepsis outcomes, where each hour of delayed treatment has been associated with roughly an 4-8% increase in mortality (Kumar et al., 2006; Seymour et al., 2017). To help address this problem, clinicians have proposed new definitions for sepsis (Singer et al., 2016), but the fundamental need to detect and treat sepsis early still remains, and basic questions about the limits of early detection remain unanswered. The PhysioNet/Computing in Cardiology Challenge 2019 provides an opportunity to address these questions.","Documentation":"https://doi.org/10.13026/v64v-d857","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/v64v-d857","ARN":"arn:aws:s3:::physionet-open/challenge-2019/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2019","Sources":["awslabs-open-data-registry"]}
{"Name":"Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021","Description":"The electrocardiogram (ECG) is a non-invasive representation of the electrical activity of the heart. Although the twelve-lead ECG is the standard diagnostic screening system for many cardiological issues, the limited accessibility of twelve-lead ECG devices provides a rationale for smaller, lower-cost, and easier to use devices. While single-lead ECGs are limiting [1], reduced-lead ECG systems hold promise, with evidence that subsets of the standard twelve leads can capture useful information [2], [3], [4] and even be comparable to twelve-lead ECGs in some limited contexts. In 2017 we challenged the public to classify AF from a single-lead ECG, and in 2020 we challenged the public to diagnose a much larger number of cardiac problems using twelve-lead recordings. However, there is limited evidence to demonstrate the utility of reduced-lead ECGs for capturing a wide range of diagnostic information.\n\nIn this year&rsquo;s Challenge, we ask the following question: &lsquo;Will two do?&rsquo; This year&rsquo;s Challenge builds on last year&rsquo;s Challenge [5], which asked participants to classify cardiac abnormalities from twelve-lead ECGs. We are asking you to build an algorithm that can classify cardiac abnormalities from twelve-lead, six-lead, four-lead, three-lead, and two-lead ECGs. We will test each algorithm on databases of these reduced-lead ECGs, and the differences in performances of the algorithms on these databases will reveal the utility of reduced-lead ECGs in comparison to standard twelve-lead EGCs.","Documentation":"https://doi.org/10.13026/34va-7q14","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/34va-7q14","ARN":"arn:aws:s3:::physionet-open/challenge-2021/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2024-11-12","RegistryEntryLastModified":"2026-06-05","Slug":"challenge-2021","Sources":["awslabs-open-data-registry"]}
{"Name":"Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022","Description":"Congenital heart diseases affect about 1% of newborns, representing an important morbidity and mortality factor for several severe conditions, including advanced heart failure [1]. In a 2019 survey, it was estimated that congenital heart diseases affect over 500,000 children in East Africa [2], and about 8 in every 1000 live births [3]. Acquired heart diseases include rheumatic fever and the Kawasaki disease, the former being a serious public health problem in developing regions, e.g., rural Brazil [4]. Several regions of developing countries have difficulties in diagnosing and treating both congenital and acquired heart conditions in children. This is mainly due to the lack of infrastructure and cardiology specialists in geographically large areas and difficulty in accessing health services. In addition, the current COVID-19 pandemic poses new difficulties in the clinical evaluation of patients by delaying important in-person patient-doctor contacts, negatively impacting screening and monitoring activities. A non-invasive assessment of the mechanical function of the heart, performed at point-of-care settings, can provide early information regarding congenital and acquired heart diseases in children. The lack of early diagnoses of these conditions represents a major public health problem, especially in underprivileged countries with high birth rates [5, 6, 7]. In particular, cardiac auscultation and the analysis of the phonocardiogram (PCG) can unveil fundamental clinical information regarding heart malfunctioning caused by congenital and acquired heart disease in pediatric populations. This is achieved by detecting abnormal sound waves, or heart murmurs, in the PCG signal. Murmurs are abnormal waves generated by turbulent blood flow in cardiac and vascular structures. They are closely associated with specific diseases such as septal defects, failure of ductus arteriosus closure in newborns, and defective cardiac valves. Succedent the 2016 Challenge, which focused on classifying normal vs. abnormal heart sounds from a single short recording from a single precordial location [8, 9], this year’s Challenge is devoted to detecting the presence or absence of murmurs from multiple heart sound recordings from multiple auscultation locations, as well as detecting the clinical outcomes.","Documentation":"https://doi.org/10.13026/t49p-5v35","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/t49p-5v35","ARN":"arn:aws:s3:::physionet-open/challenge-2022/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"challenge-2022","Sources":["awslabs-open-data-registry"]}
{"Name":"CHAMMI-75","Description":"Quantifying cell morphology using images and machine learning models has proven to be a powerful tool to study the response of cells to treatments. \nHowever, the models used to quantify cellular morphology are typically trained with a single microscopy imaging type and under controlled experimental conditions. \nThis results in specialized models that cannot be reused across biological studies because the technical specifications do not match (e.g., different number of channels), \nor because the target experimental conditions are out of distribution. We have created CHAMMI-75, a large-scale dataset containing 2.8 million multi-channel, \nhigh-resolution images curated from 75 diverse, publicly available biological studies. This dataset is useful to investigate and develop channel-adaptive models, \nwhich could process microscopy images of varying technical specifications and regardless of the number of channels. By breaking the limitations of existing models, \nCHAMMI-75 is an invaluable resource for creating the next generation of foundation models for image-based biological research.  \n","Documentation":"https://morgridge.org/research/labs/caicedo/chammi-75/","Contact":"Juan Caicedo, juan.caicedo@wisc.edu","ManagedBy":"Morgridge Institute for Research","UpdateFrequency":"Every 2 years","Tags":["aws-pds","biology","cell imaging","fluorescence imaging","high-throughput imaging","imaging","life sciences","machine learning","microscopy"],"License":"CC BY 4.0 License","Citation":"Agrawal, V., Peters, J., Thompson, T. N., Sanian, M. V., Pham, C., Moshkov, N., Kazi, A., Pillai, A., Freeman, J., Kang, B., et al. (2025). CHAMMI-75: pre-training multi-channel models with heterogeneous microscopy images. arXiv preprint arXiv:2512.20833.","Resources":[{"Description":"CHAMMI-75 Dataset: Images, training set and evaluation set available in an S3 bucket","ARN":"arn:aws:s3:::chammi-data","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: CHAMMI-75","URL":"https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/","NotebookURL":"https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/get-to-know-a-dataset-template.ipynb","AuthorName":"Vidit Agrawal, Juan Caicedo"},{"Title":"Running CHAMMI-75 Evaluation Benchmarks","URL":"https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/","NotebookURL":"https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/running-benchmarks.ipynb","AuthorName":"Vidit Agrawal, Juan Caicedo"}],"Tools & Applications":[{"Title":"CHAMMI Benchmarking Source Code","URL":"https://github.com/chaudatascience/channel_adaptive_models","AuthorName":"Chau Pham"},{"Title":"CHAMMI-75 Source Code","URL":"https://github.com/CaicedoLab/CHAMMI-75","AuthorName":"Vidit Agrawal"},{"Title":"CHAMMI-75 Website","URL":"https://morgridge.org/research/labs/caicedo/chammi-75/","AuthorName":"Vidit Agrawal, Juan Caicedo"},{"Title":"MorphEm Model (Trained on CHAMMI-75)","URL":"https://huggingface.co/CaicedoLab/MorphEm","AuthorName":"Vidit Agrawal, John Peters, Juan Caicedo"}],"Publications":[{"Title":"CHAMMI-75: pre-training multi-channel models with heterogeneous microscopy images","URL":"https://arxiv.org/abs/2512.20833","AuthorName":"Vidit Agrawal, John Peters, Tyler N. Thompson, Mohammad Vali Sanian, Chau Pham, Nikita Moshkov, Arshad Kazi, Aditya Pillai, Jack Freeman, Byunguk Kang, Samouil L. Farhi, Ernest Fraenkel, Ron Stewart, Lassi Paavolainen, Bryan A. Plummer, Juan C. Caicedo"},{"Title":"CHAMMI: A benchmark for channel-adaptive models in microscopy imaging","URL":"https://neurips.cc/virtual/2023/poster/73620","AuthorName":"Zitong Sam Chen, Chau Pham, Siqi Wang, Michael Doron, Nikita Moshkov, Bryan Plummer, Juan C. Caicedo"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-10-28","RegistryEntryLastModified":"2026-02-03","Slug":"chammi","Sources":["awslabs-open-data-registry"]}
{"Name":"Is the normal heart rate chaotic?","Description":"In its June 2008 issue, the editors of Chaos announced a new feature, \"Controversial Topics in Nonlinear Dynamics\". The first controversial topic to be aired is \"Is the Normal Heart Rate Chaotic?\". Since major impediments to achieving consensus on the statistical properties of biologic time series have included the lack of open access time series, PhysioNet has provided a set of 15 heart beat (RR-interval) time series in health and disease (congestive heart failure and atrial fibrillation). Each time series is about 24 hours long (roughly 100,000 intervals), and is provided as a text file for analysis and as an image for visual review. All of the time series were derived from continuous ambulatory (Holter) electrocardiograms. We encourage participants in the Chaos Controversies issue to use the 15 RR interval time series provided here as a common focus of analysis.","Documentation":"https://doi.org/10.13026/6gb6-8446","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Open Database License v1.0","Resources":[{"Description":"https://doi.org/10.13026/6gb6-8446","ARN":"arn:aws:s3:::physionet-open/chaos-heart-rate/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"chaos-heart-rate","Sources":["awslabs-open-data-registry"]}
{"Name":"Cerebral Haemodynamic Autoregulatory Information System GUI","Description":"Acute Brain injury (ABI) is a devastating event requiring intensive acute treatment and post-injury rehabilitation, both delivered for indeterminate periods of time. For severe ABIs, acute treatment is aimed at stabilizing the patient to prevent secondary brain injury from ischemia and swelling. This requires a balance between adequate levels of cerebral blood flow and safely low intracranial pressure (ICP) - a task normally done by autoregulatory (AR) processes of the brain. In the absence of normal AR, hemodynamic stability is difficult to maintain because there are no reliable predictors to guide treatment. Thus, guidelines for triage and discharge are somewhat arbitrary for ABI and the need for a Clinical Decision Support (CDS) system for neurotrauma is widely recognized. Prerequisite to a CDS is a large database of patient records and efficient means to extract meaningful information from them. CHARIS will systematize the analysis of relevant physiological signals, and will embody data-driven algorithms to search for potential predictors of acute clinical events.","Documentation":"https://physionet.org/content/charis-gui/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/charis-gui/","ARN":"arn:aws:s3:::physionet-open/charis-gui/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"charis-gui","Sources":["awslabs-open-data-registry"]}
{"Name":"CHARIS database","Description":"The CHARIS database contains multi-channel recordings of ECG, arterial blood pressure (ABP), and intracranial pressure (ICP) of patients diagnosed with traumatic brain injury (TBI). The data is contributed by members of the CHARIS project which aims to systematize the analysis of relevant physiological signals, and create data-driven algorithms to search for potential predictors of acute clinical events for patients with acute brain injury.","Documentation":"https://doi.org/10.13026/C24G6F","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C24G6F","ARN":"arn:aws:s3:::physionet-open/charisdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"charisdb","Sources":["awslabs-open-data-registry"]}
{"Name":"CHB-MIT Scalp EEG Database","Description":"This database, collected at the Children’s Hospital Boston, consists of EEG recordings from pediatric subjects with intractable seizures. Subjects were monitored for up to several days following withdrawal of anti-seizure medication in order to characterize their seizures and assess their candidacy for surgical intervention. The recordings are grouped into 23 cases and were collected from 22 subjects (5 males, ages 3–22; and 17 females, ages 1.5–19).","Documentation":"https://doi.org/10.13026/C2K01R","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2K01R","ARN":"arn:aws:s3:::physionet-open/chbmit/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"chbmit","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"Amazon is no longer hosting this Data Lakehouse Ready dataset","Name":"ChEMBL - Data Lakehouse Ready","Description":"ChEMBL is a manually curated database of bioactive molecules with drug-like properties. It brings together chemical, bioactivity and genomic data to aid the translation of genomic information into effective new drugs. This representation of ChEMBL is stored in Parquet format and most easily utilized through Amazon Athena. Follow the documentation for install instructions (< 2 minute install). New ChEMBL releases occur sporadically; the most up to date information on ChEMBL releases can be found [here](https://chembl.gitbook.io/chembl-interface-documentation/downloads).","Documentation":"https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_install.md","Contact":"https://github.com/aws-samples/data-lake-as-code/issues","ManagedBy":"[Amazon Web Services](https://aws.amazon.com/)","UpdateFrequency":"Upon request. We try to keep it updated to every odd version.","Tags":["chemistry","genomic","molecule","life sciences","biotech blueprint","parquet"],"License":"https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_attributions.txt","Resources":[{"Description":"ChEMBL 29","ARN":"arn:aws:s3:::aws-roda-hcls-datalake/chembl_29/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"ChEMBL 27","ARN":"arn:aws:s3:::aws-roda-hcls-datalake/chembl_27/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"ChEMBL 25","ARN":"arn:aws:s3:::aws-roda-hcls-datalake/chembl_25/","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Data Lake as Code Deployment Guide","URL":"https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_install.md","AuthorName":"AWS Biotech Blueprints Team","Services":["Amazon Athena","AWS Glue","AWS Lake Formation"]}],"Publications":[{"Title":"Data Lake as Code, Featuring ChEMBL and Open Targets","URL":"https://aws.amazon.com/blogs/startups/a-data-lake-as-code-featuring-chembl-and-opentargets/","AuthorName":"Paul Underwood"}]},"RegistryEntryAdded":"2021-01-19","RegistryEntryLastModified":"2025-02-04","Slug":"chembl","Sources":["awslabs-open-data-registry"]}
{"Name":"Congestive Heart Failure RR Interval Database","Description":"This database includes beat annotation files for 29 long-term ECG recordings of subjects aged 34 to 79, with congestive heart failure (NYHA classes I, II, and III).","Documentation":"https://doi.org/10.13026/C2F598","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2F598","ARN":"arn:aws:s3:::physionet-open/chf2db/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"chf2db","Sources":["awslabs-open-data-registry"]}
{"Name":"BIDMC Congestive Heart Failure Database","Description":"This database includes long-term ECG recordings from 15 subjects (11 men, aged 22 to 71, and 4 women, aged 54 to 63) with severe congestive heart failure (NYHA class 3–4). This group of subjects was part of a larger study group receiving conventional medical therapy prior to receiving the oral inotropic agent, milrinone.","Documentation":"https://doi.org/10.13026/C29G60","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C29G60","ARN":"arn:aws:s3:::physionet-open/chfdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"chfdb","Sources":["awslabs-open-data-registry"]}
{"Name":"CHIMERA","Description":"This dataset contains the training data for the [CHIMERA - Combining HIstology, Medical imaging (radiology) and molEcular data for medical pRognosis and diAgnosis](https://chimera.grand-challenge.org/) challenge. The CHIMERA Challenge aims to advance precision medicine in cancer care by addressing the critical need for multimodal data integration. Despite significant progress in AI, integrating transcriptomics, pathology, and radiology across clinical departments remains a complex challenge. Clinicians are faced with large, heterogeneous datasets that are difficult to analyze effectively. AI has the potential to unify multimodal data, but several technical barriers remain, such as defining appropriate fusion stages and handling missing modalities.","Documentation":"https://chimera.grand-challenge.org/","Contact":"nadieh.khalili@radboudumc.nl","ManagedBy":"Radboud University Medical Center","UpdateFrequency":"As required","Tags":["aws-pds","cancer","computational pathology","computer vision","deep learning","digital pathology","grand-challenge.org","histopathology","life sciences","machine learning","medical image computing","medical imaging"],"License":"CC BY-NC-SA 4.0","Resources":[{"Description":"Task 1 includes paired MRI and H&E-stained whole slide images with corresponding clinical data and biochemical recurrence information. Task 2 includes H&E slides, clinical data, and recurrence status in bladder cancer.  Task 3 includes H&E slides, RNA-seq clinical data, and recurrence status in bladder cancer.","ARN":"arn:aws:s3:::chimera-challenge","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"CHIMERA Challenge","URL":"https://chimera.grand-challenge.org/","AuthorName":"Computational Pathology Group Radboudumc, Nijmegen","AuthorURL":"https://www.computationalpathologygroup.eu/"}]},"ADXCategories":["Healthcare & Life Sciences Data","Public Sector Data"],"RegistryEntryAdded":"2025-04-08","RegistryEntryLastModified":"2025-05-01","Slug":"chimera","Sources":["awslabs-open-data-registry"]}
{"Name":"The CirCor DigiScope Phonocardiogram Dataset","Description":"A total number of 5272 heart sound recordings were collected from the main four auscultation locations of 1568 subjects, aged between 0 and 21 years (mean ± STD = 6.1 ± 4.3 years), with a duration between 4.8 to 80.4 seconds (mean ± STD = 22.9 ± 7.4 s), totaling more than 33.5 hours of recording. Each cardiac murmur in the dataset has been annotated in detail by a human annotator, in terms of timing, shape, pitch, grading, quality and location. Moreover, segmentation annotations regarding the location of fundamental heart sounds (S1 and S2) in the recordings have been obtained using a semi-supervised scheme. The segmentation annotations were performed by voting between three state-of-the-art machine-based algorithms. An expert annotator later studied the consensus and mismatches between the algorithms beat-by-beat and performed a manual annotation whenever the algorithms had disagreed or were not acceptable for the expert. To date, the dataset is the largest publicly available pediatric heart sound dataset, supporting deeper research on the topic of auscultation-based health recommendation systems. The dataset is being used in the George B. Moody PhysioNet Challenge 2022 on Heart Murmur Detection from Phonocardiogram Recordings.","Documentation":"https://doi.org/10.13026/tshs-mw03","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/tshs-mw03","ARN":"arn:aws:s3:::physionet-open/circor-heart-sound/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"circor-heart-sound","Sources":["awslabs-open-data-registry"]}
{"Name":"CitrusFarm Dataset","Description":"CitrusFarm is a multimodal agricultural robotics dataset that provides both multispectral images and navigational sensor data for localization, mapping and crop monitoring tasks. \n1) It was collected by a wheeled mobile robot in the Agricultural Experimental Station at the University of California Riverside in the summer of 2023. \n2) It offers a total of nine sensing modalities, including stereo RGB, depth, monochrome, near-infrared and thermal images, as well as wheel odometry, LiDAR, IMU and GPS-RTK data. \n3) It comprises seven sequences collected from three citrus tree fields, featuring various tree species at different growth stages, distinctive planting patterns, as well as varying daylight conditions. \n4) It spans a total operation time of 1.7 hours, covers a total distance of 7.5 km, and constitutes 1.3 TB of data.\n","Documentation":"https://ucr-robotics.github.io/Citrus-Farm-Dataset/","Contact":"Hanzhe Teng (hteng007@ucr.edu), Konstantinos Karydis (kkarydis@ece.ucr.edu)","ManagedBy":"[Autonomous Robots and Control Systems Lab](https://sites.google.com/view/arcs-lab)","UpdateFrequency":"NA","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["agriculture","aws-pds","computer vision","IMU","lidar","life sciences","localization","mapping","robotics"],"License":"Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).","Resources":[{"Description":"CitrusFarm Dataset sequences","ARN":"arn:aws:s3:::ucr-robotics/citrus-farm-dataset","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Python script to download this dataset","URL":"https://ucr-robotics.github.io/Citrus-Farm-Dataset/download.html","AuthorName":"Hanzhe Teng et al."},{"Title":"Python scripts used in the data collection and post-processing","URL":"https://ucr-robotics.github.io/Citrus-Farm-Dataset/tools.html","AuthorName":"Hanzhe Teng et al."}],"Publications":[{"Title":"Multimodal Dataset for Localization, Mapping and Crop Monitoring in Citrus Tree Farms","URL":"https://link.springer.com/chapter/10.1007/978-3-031-47969-4_44","AuthorName":"Hanzhe Teng, Yipeng Wang, Xiaoao Song and Konstantinos Karydis"}]},"RegistryEntryAdded":"2023-12-07","RegistryEntryLastModified":"2026-06-05","Slug":"citrus-farm","Sources":["awslabs-open-data-registry"]}
{"Name":"CIViC (Clinical Interpretation of Variants in Cancer)","Description":"Precision medicine refers to the use of prevention and treatment strategies that are tailored to the unique features of each individual and their disease. In the context of cancer this might involve the identification of specific mutations shown to predict response to a targeted therapy. The biomedical literature describing these associations is large and growing rapidly. Currently these interpretations exist largely in private or encumbered databases resulting in extensive repetition of effort. Realizing precision medicine will require this information to be centralized, debated and interpreted for application in the clinic. CIViC is an open access, open source, community-driven web resource for Clinical Interpretation of Variants in Cancer. Our goal is to enable precision medicine by providing an educational forum for dissemination of knowledge and active discussion of the clinical significance of cancer genome alterations.","Documentation":"https://docs.civicdb.org/","Contact":"help@civicdb.org","ManagedBy":"The Griffith Lab at Washington University School of Medicine","UpdateFrequency":"First of each month","Tags":["aws-pds","cancer","genetic","genomic","life sciences","vcf"],"License":"[CC0](https://creativecommons.org/publicdomain/zero/1.0/)","Resources":[{"Description":"Monthly CIViC data dumps including feature, variant, assertion, and evidence tables.","ARN":"arn:aws:s3:::civic-aws-opendata","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for monthly CIViC data availability.","ARN":"arn:aws:sns:us-west-2:182652044697:civic-aws-opendata-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"A community approach to the cancer-variant-interpretation bottleneck","URL":"https://www.nature.com/articles/s43018-022-00379-w","AuthorName":"Kilannin Krysiak et al.","AuthorURL":"https://griffithlab.org/"},{"Title":"CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer","URL":"https://www.nature.com/articles/ng.3774","AuthorName":"Malachi Griffith et al.","AuthorURL":"https://griffithlab.org/"},{"Title":"CIViCdb 2022: evolution of an open-access cancer variant interpretation knowledgebase","URL":"https://academic.oup.com/nar/article/51/D1/D1230/6827107","AuthorName":"Kilannin Krysiak et al.","AuthorURL":"https://griffithlab.org/"},{"Title":"CIViCpy: A Python Software Development and Analysis Toolkit for the CIViC Knowledgebase","URL":"https://ascopubs.org/doi/pdf/10.1200/CCI.19.00127","AuthorName":"Alex H Wagner et al.","AuthorURL":"https://www.nationwidechildrens.org/specialties/institute-for-genomic-medicine/research-labs/wagner-lab"},{"Title":"Standard operating procedure for curation and clinical interpretation of variants in cancer","URL":"https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-019-0687-x","AuthorName":"Arpad M Danos et al.","AuthorURL":"https://griffithlab.org/"}],"Tools & Applications":[{"Title":"CIViCpy - an analysis toolkit and SDK for extracting and analyzing knowledge from the CIViC knowledgebase","URL":"https://docs.civicpy.org/","AuthorName":"The CIViCpy Team","AuthorURL":"https://github.com/griffithlab/civicpy/graphs/contributors"}],"Tutorials":[{"Title":"Introduction to Clinical Interpretation of Somatic Cancer Variants","URL":"https://course.civicdb.org/","AuthorName":"The Griffith Lab","AuthorURL":"https://griffithlab.org/"}]},"RegistryEntryAdded":"2021-04-20","RegistryEntryLastModified":"2025-04-08","Slug":"civic","Sources":["awslabs-open-data-registry"]}
{"Name":"Clay Model v0 Embeddings","Description":"Machine learning model embeddings dataset providing pre-computed feature representations for satellite and aerial imagery analysis.","Documentation":"https://source.coop/repositories/clay/clay-model-v0-embeddings/description","Contact":"contact@madewithclay.org","ManagedBy":"[Source Cooperative](https://source.coop/)","UpdateFrequency":"As new model versions become available","Tags":["aerial imagery","computer vision","earth observation","imaging","machine learning","satellite imagery"],"License":"Creative Commons Attribution 4.0 International License","Citation":"Clay Model v0 Embeddings. Source Cooperative. https://source.coop/repositories/clay/clay-model-v0-embeddings/description","Resources":[{"Description":"Clay Model v0 Embeddings S3 Bucket","ARN":"arn:aws:s3:::us-west-2.opendata.source.coop/clay/clay-model-v0-embeddings","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://source.coop/clay/clay-model-v0-embeddings/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Revolutionizing earth observation with geospatial foundation models on AWS","URL":"https://aws.amazon.com/blogs/machine-learning/revolutionizing-earth-observation-with-geospatial-foundation-models-on-aws/","AuthorName":"Karsten Schroer, Bishesh Adhikari, and Iza Moise"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-07-02","RegistryEntryLastModified":"2026-03-24","Slug":"clay-model-v0-embeddings","Sources":["awslabs-open-data-registry"]}
{"Name":"Clay v1.5 NAIP-2","Description":"National Agriculture Imagery Program (NAIP) dataset providing high-resolution aerial imagery for agricultural monitoring, land use analysis, and natural resource management.","Documentation":"https://source.coop/repositories/clay/clay-v1-5-naip-2/description","Contact":"contact@madewithclay.org","ManagedBy":"[Source Cooperative](https://source.coop/)","UpdateFrequency":"As new NAIP data becomes available","Tags":["aerial imagery","agriculture","environmental","land use","natural resource"],"License":"Creative Commons Attribution 4.0 International License","Citation":"Clay v1.5 NAIP-2. Source Cooperative. https://source.coop/repositories/clay/clay-v1-5-naip-2/description","Resources":[{"Description":"Clay v1.5 NAIP-2 S3 Bucket","ARN":"arn:aws:s3:::us-west-2.opendata.source.coop/clay/clay-v1-5-naip-2","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://source.coop/clay/clay-v1-5-naip-2/)"]}],"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-07-02","RegistryEntryLastModified":"2025-07-02","Slug":"clay-v1-5-naip-2","Sources":["awslabs-open-data-registry"]}
{"Name":"Clay v1.5 Sentinel-2","Description":"Sentinel-2 satellite imagery dataset providing high-resolution optical data for land monitoring, agriculture, and environmental applications.","Documentation":"https://source.coop/repositories/clay/clay-v1-5-sentinel2/description","Contact":"contact@madewithclay.org","ManagedBy":"[Source Cooperative](https://source.coop/)","UpdateFrequency":"As new Sentinel-2 data becomes available","Tags":["agriculture","earth observation","environmental","land use","satellite imagery"],"License":"Creative Commons Attribution 4.0 International License","Citation":"Clay v1.5 Sentinel-2. Source Cooperative. https://source.coop/repositories/clay/clay-v1-5-sentinel2/description","Resources":[{"Description":"Clay v1.5 Sentinel-2 S3 Bucket","ARN":"arn:aws:s3:::us-west-2.opendata.source.coop/clay/clay-v1-5-sentinel2","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://source.coop/repositories/clay/clay-v1-5-sentinel2/description)"]}],"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-07-02","RegistryEntryLastModified":"2025-07-02","Slug":"clay-v1-5-sentinel2","Sources":["awslabs-open-data-registry"]}
{"Name":"Clinical Ultrasound Image Repository","Description":"Generic Clinical Ultrasound Data from Random Subjects acquired for Clinical Reasons, to be used for Developing Artificial Intelligence Applications. This dataset is complete with 2000 studies from 2000 subjects (one third each from abdominal, cardiac, and OB/GYN cases)","Documentation":"https://clinical-ultrasound-image-repository.s3.amazonaws.com/index.html","Contact":"shuver@nvidia.com","ManagedBy":"[MONAI Development Team](https://github.com/Project-MONAI/MONAI)","UpdateFrequency":"This is a static dataset; however, tutorials and resources will be updated as they are developed.","Tags":["aws-pds","life sciences","machine learning","medical imaging","medicine"],"License":"[CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)","Resources":[{"Description":"Clinical Ultrasound Image Repository","ARN":"arn:aws:s3:::clinical-ultrasound-image-repository","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://clinical-ultrasound-image-repository.s3.amazonaws.com/download.html)"]}],"RegistryEntryAdded":"2025-09-17","RegistryEntryLastModified":"2025-09-18","Slug":"clinical-ultrasound-image-data","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"Amazon is no longer hosting this Data Lakehouse Ready dataset","Name":"ClinVar - Data Lakehouse Ready","Description":"ClinVar is a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. ClinVar thus facilitates access to and communication about the relationships asserted between human variation and observed health status, and the history of that interpretation. ClinVar processes submissions reporting variants found in patient samples, assertions made regarding their clinical significance, information about the submitter, and other supporting data. The alleles described in submissions are mapped to reference sequences, and reported according to the HGVS standard. ClinVar then presents the data for interactive users as well as those wishing to use ClinVar in daily workflows and other local applications. ClinVar works in collaboration with interested organizations to meet the needs of the medical genetics community as efficiently and effectively as possible. This representation of ClinVar is stored in Parquet format and most easily utilized through Amazon Athena. Follow the documentation link for install instructions (< 2 minute install).","Documentation":"https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_install.md","Contact":"https://github.com/aws-samples/data-lake-as-code/issues","ManagedBy":"[Amazon Web Services](https://aws.amazon.com/)","UpdateFrequency":"Every Sunday at 1AM UTC","Tags":["chemistry","genetic","genomic","life sciences","biotech blueprint","parquet"],"License":"https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_attributions.txt","Resources":[{"Description":"ClinVar","ARN":"arn:aws:s3:::aws-roda-hcls-datalake/clinvar_summary_variants/","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Data Lake as Code Deployment Guide","URL":"https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_install.md","AuthorName":"AWS Biotech Blueprints Team","Services":["Amazon Athena","AWS Glue","AWS Lake Formation"]}],"Publications":[{"Title":"Data Lake as Code, Featuring ChEMBL and Open Targets","URL":"https://aws.amazon.com/blogs/startups/a-data-lake-as-code-featuring-chembl-and-opentargets/","AuthorName":"Paul Underwood"}]},"RegistryEntryAdded":"2021-02-02","RegistryEntryLastModified":"2025-02-04","Slug":"clinvar","Sources":["awslabs-open-data-registry"]}
{"Name":"CMAS Data Warehouse","Description":"CMAS Data Warehouse on AWS collects and disseminates meteorology, emissions and air quality model input and output for Community Multiscale Air Quality (CMAQ) Model Applications. This dataset is available as part of the AWS Open Data Program, therefore egress fees are not charged to either the host or the person downloading the data.  This S3 bucket is maintained as a public service by the University of North Carolina's CMAS Center, the US EPA’s Office of Research and Development, and the US EPA’s Office of Air and Radiation.  Metadata and DOIs for datasets included in the CMAS Data Warehouse are available from the CMAS Dataverse site: https://dataverse.unc.edu/dataverse/cmascenter \n","Documentation":"https://dataverse.unc.edu/dataverse/cmascenter","Contact":"cmas@unc.edu","ManagedBy":"[CMAS CENTER](https://cmascenter.org/)","UpdateFrequency":"New data is added as soon as it is available.","Collabs":{"ASDI":{"Tags":["air quality"]}},"Tags":["air quality","aws-pds","climate","cloud computing","environmental","geospatial","meteorological","model","MPAS","netcdf","open source software"],"License":"There are no restrictions on the use of this data. US EPA License (https://pasteur.epa.gov/license/sciencehub-license.html)","Resources":[{"Description":"CMAQ Release Benchmark Data for Easy Download","ARN":"arn:aws:s3:::cmaq-release-benchmark-data-for-easy-download","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://cmaq-release-benchmark-data-for-easy-download.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in CMAQ Release Benchmark Data for Easy Download","ARN":"arn:aws:sns:us-east-1:703786580975:cmaq-release-benchmark-data-for-easy-download-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"CMAQ CONUS-2 Benchmark Data","ARN":"arn:aws:s3:::cmas-cmaq-conus2-benchmark","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://cmas-cmaq-conus2-benchmark.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in CMAQ CONUS-2 Benchmark 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Bucket](https://cmas-amet.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in AMET Data","ARN":"arn:aws:sns:us-east-1:703786580975:cmas-amet-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"CMAS WWLLN Lightning Data","ARN":"arn:aws:s3:::cmas-wwlln-lightning","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://cmas-wwlln-lightning.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in CMAS WWLLN Lightning Data","ARN":"arn:aws:sns:us-east-1:703786580975:cmas-wwlln-lightning-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"EQUATES EPA’s Air QUAlity TimE Series Project Data","ARN":"arn:aws:s3:::cmas-equates","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://cmas-equates.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in EQUATES EPA’s Air QUAlity TimE Series Project Data","ARN":"arn:aws:sns:us-east-1:703786580975:cmas-equates-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"Community Multiscale Air Quality (CMAQ) 2019 3D Gridded and Column data from the EPA's Air Quality Time Series (EQUATES) Project","ARN":"arn:aws:s3:::epa-equates-v1","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://epa-equates-v1.s3.amazonaws.com/index.html)","[EPA CMAQ 2019 3D Gridded and Column data from EQUATES Project](https://aws.amazon.com/marketplace/pp/prodview-kziefcewnxcxe?sr=0-5&ref_=beagle&applicationId=AWSMPContessa)"]},{"Description":"CMAQ v6 2022 Data","ARN":"arn:aws:s3:::cmaq-v6-2022-data","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://cmaq-v6-2022-data.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in CMAQ v6 2022 Data","ARN":"arn:aws:sns:us-east-1:703786580975:cmaq-v6-2022-data-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"CMAQ 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Platform","ARN":"arn:aws:sns:us-east-1:703786580975:cmaq-12us4-cracmm3-modeling-platform-2023-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"CMAQ Model Versions 5.5 CRACMM2 Input Data (2022r1) -- 12/22/2021 - 12/31/2022 12km CONUS","ARN":"arn:aws:s3:::cmaq-12us1-cracmm2-modeling-platform-2022","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://cmaq-12us1-cracmm2-modeling-platform-2022.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in CMAQ Model Versions 5.5 CRACMM2 Input Data (2022r1) -- 12/22/2021 - 12/31/2022 12km CONUS","ARN":"arn:aws:sns:us-east-1:703786580975:cmaq-12us1-cracmm2-modeling-platform-2022-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"CMAQ Model Versions 5.5 CRACMM1-2 Input Data -- 12/22/2019 - 12/31/2020 12km CONUS","ARN":"arn:aws:s3:::cmaq-12us1-cracmm1-modeling-platform-2020","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://cmaq-12us1-cracmm1-modeling-platform-2020.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in CMAQ Model Versions 5.5 CRACMM1-2 Input Data","ARN":"arn:aws:sns:us-east-1:703786580975:cmaq-12us1-cracmm1-modeling-platform-2020-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"EPA 2022 Modeling Platform","ARN":"arn:aws:s3:::epa-2022-modeling-platform","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://epa-2022-modeling-platform.s3.amazonaws.com/index.html)","[OAQPS 2022 Modeling Platform](https://registry.opendata.aws/epa-2022-modeling-platform/)"]},{"Description":"CMAQ 2021 Modeling Platform","ARN":"arn:aws:s3:::2021platform","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://2021platform.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in 2021platform","ARN":"arn:aws:sns:us-east-1:703786580975:2021platform-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"CMAQ 2019 Modeling Platform","ARN":"arn:aws:s3:::cmaq-2019-modeling-platform","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://cmaq-2019-modeling-platform.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in CMAQ 2019 Modeling Platform","ARN":"arn:aws:sns:us-east-1:703786580975:cmaq-2019-modeling-platform-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"CMAQ 2018 Modeling Platform","ARN":"arn:aws:s3:::cmas-cmaq-modeling-platform-2018","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://cmas-cmaq-modeling-platform-2018.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in CMAQ 2018 Modeling Platform","ARN":"arn:aws:sns:us-east-1:703786580975:cmas-cmaq-modeling-platform-2018-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"2020 Modeling Platform","ARN":"arn:aws:s3:::2020platform","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://2020platform.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in 2020 Modeling Platform","ARN":"arn:aws:sns:us-east-1:703786580975:2020platform-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"2019 Modeling Platform","ARN":"arn:aws:s3:::2019platform","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://2019platform.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in 2019platform","ARN":"arn:aws:sns:us-east-1:703786580975:2019platform-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"2018v2 Modeling Platform","ARN":"arn:aws:s3:::2018v2platform","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://2018v2platform.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in 2018v2 Modeling Platform","ARN":"arn:aws:sns:us-east-1:703786580975:2018v2platform-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"2016v3 Modeling Platform","ARN":"arn:aws:s3:::2016v3platform","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://2016v3platform.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in 2016v3 Modeling Platform","ARN":"arn:aws:sns:us-east-1:703786580975:2016v3platform-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"SMOKE Test Case","ARN":"arn:aws:s3:::cmas-smoke-testcase","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://cmas-smoke-testcase.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in SMOKE Test Case","ARN":"arn:aws:sns:us-east-1:703786580975:cmas-smoke-testcase-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"SMOKE 2016 Modeling Platform","ARN":"arn:aws:s3:::cmas-smoke-modeling-platform-2016","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://cmas-smoke-modeling-platform-2016.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in SMOKE 2016 Modeling Platform","ARN":"arn:aws:sns:us-east-1:703786580975:cmas-smoke-modeling-platform-2016-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"NEMO 1km Emissions Data for CONUS 2017","ARN":"arn:aws:s3:::nemo-1km-emissions-data","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://nemo-1km-emissions-data.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data in NEMO 1km Emissions Data for CONUS 2017","ARN":"arn:aws:sns:us-east-1:703786580975:nemo-1km-emissions-data-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"AMET Documentation","URL":"https://github.com/USEPA/AMET/blob/master/docs/README.md","AuthorName":"USEPA","AuthorURL":"https://www.epa.gov/cmaq/forms/contact-us-about-community-multiscale-air-quality-modeling-system"},{"Title":"CMAQ Installation and Benchmarking Tutorial","URL":"https://github.com/USEPA/CMAQ/blob/main/DOCS/Users_Guide/Tutorials/CMAQ_UG_tutorial_benchmark.md","AuthorName":"USEPA","AuthorURL":"https://github.com/USEPA/CMAQ"},{"Title":"CMAQ on AWS HPC Workshop","URL":"https://catalog.workshops.aws/cmaq-tutorial/en-US/","AuthorName":"CMAS Center, U.S. EPA Office of Research and Development and AWS Workshop Studio","AuthorURL":"https://catalog.workshops.aws/","Services":["AWS ParallelCluster"]},{"Title":"Documentation for CMAQ on AWS ParallelCluster","URL":"https://pcluster-cmaq.readthedocs.io/en/latest/index.html#","AuthorName":"CMAS Center","AuthorURL":"https://github.com/CMASCenter/pcluster-cmaq/","Services":["AWS ParallelCluster"]},{"Title":"EQUATES Benchmark","URL":"https://github.com/lizadams/EQUATES_BENCHMARK","AuthorName":"Liz Adams","AuthorURL":"https://github.com/lizadams/"}]},"RegistryEntryAdded":"2022-04-26","RegistryEntryLastModified":"2026-10-08","Slug":"cmas-data-warehouse","Sources":["awslabs-open-data-registry"]}
{"Name":"Hybrid statistical-dynamic downscaling based on multi-model ensembles in Southeast Asia","Description":"GCMs under CMIP6 have been widely used to investigate climate change impacts and put forward associated adaptation and mitigation strategies. However, the relatively coarse spatial resolutions (usually 100~300km) preclude their direct applications at regional scales, which are exactly where the analysis (e.g., hydrological model simulation) is performed. To bridge this gap, a typical approach is to ‘refine’ the information from GCMs through regional climate downscaling experiments, which can be conducted statistically, dynamically, or a combination thereof. Statistical downscaling establishes relationships between large-scale climate indicators and small-scale climate variables in the reference (historical) period. Subsequently, these relationships are kept unchanged in the future and used to predict the future variables. On the other hand, dynamical downscaling operates based on the physical processes and the associated interactions in the climate systems and thus can produce a full set of regional climate simulations (e.g., temperature and precipitation fields) that are dynamically consistent. However, traditional dynamical downscaling contains significant biases that are transferred from GCMs and may be enhanced during the process of downscaling, thus degrading the downscaled results. One promising approach to remove these biases is the hybrid statistical-dynamical downscaling method, where GCMs are firstly bias-corrected, and subsequently used as lower and lateral boundary conditions to drive the regional climate models (RCMs).\n\nIn this work, we apply a hybrid statistical-dynamical downscaling method, following the approach of Xu et al. 2021. The bias-corrected dataset is adjusted to resemble ERA5-based mean climate and interannual variance, and with a non-linear trend from the ensemble mean of the 14 CMIP6 models. The dataset spans a historical period of 1979–2014 and future scenarios (SSP585) of 2015–2100, with a temporal scale of six-hour.\n\nThe main contributions of this dataset are twofold. First, we provide the open-source and high-resolution (12.5km: Southeast Asia; 2.5km:Southern Malay Peninsula; 500m: Singapore, as shown in the following Figures) datasets, including precipitation, wind, temperature, radiation, etc. Second, through our experiment, this bias-corrected and downscaled dataset is of exceptional quality compared to that of the existing dynamical scaling work (e.g., CORDEX) in southeast Asia in terms of its ability to reproduce regional climate extremes, spatial patterns, etc. This dataset will be useful for policy-makers and researchers to establish the necessary pathways for resilient planning in order to mitigate the dire impacts of climate change.\n","Documentation":"https://sgcale.github.io/resource/data/","Contact":"For any questions regarding dataset, email Professor Xiaogang He at hexg@nus.edu.sg.","ManagedBy":"[PREP-NexT Lab](https://github.com/PREP-NexT)","UpdateFrequency":"Update when needed.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","climate","netcdf","precipitation"],"License":"All the code in this repository is [MIT](https://choosealicense.com/licenses/mit/) licensed, but we request that you please provide attribution if reusing any of our digital content (graphics, logo, copy, etc.).","Resources":[{"Description":"We are releasing a bias-corrected and downscaled dataset based on 14 Coupled Model Intercomparison Project 6 (CMIP6) global climate models (GCMs) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. More details please refer to [this link](https://sgcale.github.io/research/climate-downscaling/).\n","ARN":"arn:aws:s3:::cmip6-wrf-southeastasia","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://cmip6-wrf-southeastasia.s3.us-west-2.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2023-10-30","RegistryEntryLastModified":"2026-04-30","Slug":"cmip6-era5-hybrid-southeast-asia","Sources":["awslabs-open-data-registry"]}
{"Name":"Coupled Model Intercomparison Project 6","Description":"The sixth phase of global coupled ocean-atmosphere general circulation model ensemble. \n<br /><br />\n","Documentation":"https://pangeo-data.github.io/pangeo-cmip6-cloud/, https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6","Contact":"If you have any feedback on the CMIP6 data available on AWS please email sustainability-data-initiative@amazon.com. We are acepting requests for additional CMIP6 variables and/or models to be made available to AWS but cannot guarantee that your request will be fulfilled. We will prioritize requests that bring value to the largest number of users. Note that we are not providing technical support through this email account.<br/><br/>\nWe also seek to identify case studies on how CMIP6 data is being used and will be featuring those stories in future publications and events. If you are interested in seeing your story highlighted, please share it with the ASDI team here: sustainability-data-initiative@amazon.com.\n","ManagedBy":"ESGF and Pangeo","UpdateFrequency":"Core CMIP6 datasets are added as soon as they are available.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","atmosphere","aws-pds","climate","earth observation","environmental","model","oceans","simulations","weather"],"License":"See [docs] (https://pangeo-data.github.io/pangeo-cmip6-cloud/licensing_citation.html) for further info","Resources":[{"Description":"Zarr formatted data","ARN":"arn:aws:s3:::cmip6-pds","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://cmip6-pds.s3.amazonaws.com/index.html#CMIP6/)","[Data Catalog](https://cmip6-pds.s3.amazonaws.com/pangeo-cmip6.csv)"]},{"Description":"Netcdf formatted data managed by the Earth System Grid Federation","ARN":"arn:aws:s3:::esgf-world","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://esgf-world.s3.amazonaws.com/index.html)","[Data Catalog](https://cmip6-nc.s3.amazonaws.com/esgf-world.csv.gz)"]}],"DataAtWork":{"Tutorials":[{"Title":"Analyze terabyte-scale geospatial datasets with Dask and Jupyter on AWS","URL":"https://aws.amazon.com/blogs/publicsector/analyze-terabyte-scale-geospatial-datasets-with-dask-and-jupyter-on-aws/","AuthorName":"Ethan Fahy and Zac Flamig","Services":["Amazon EC2","Amazon EKS","Amazon S3","AWS CloudFormation"]},{"Title":"Comparing CMIP6 Zarr vs NetCDF Holdings","URL":"https://nbviewer.jupyter.org/github/aradhakrishnanGFDL/gfdl-aws-analysis/blob/master/examples/a1r-CompareAWS-NCvsZarr.ipynb","AuthorName":"Aparna Radhakrishnan","Services":["Amazon S3"]},{"Title":"Finding CMIP6 data using intake-esm and plotting time series for points","URL":"https://nbviewer.jupyter.org/github/awslabs/amazon-asdi/blob/main/examples/cmip6/notebooks/cmip6_airtemperature.ipynb","AuthorName":"Zac Flamig","Services":["AWS CloudFormation","Amazon SageMaker"]},{"Title":"Getting Started with CMIP6 Data","URL":"https://github.com/aradhakrishnanGFDL/gfdl-aws-analysis/blob/community/doc/quickstartdoc.md","AuthorName":"Aparna Radhakrishnan"},{"Title":"Processing CMIP6 data in Zarr format with Dask & AWS Fargate","URL":"https://nbviewer.jupyter.org/github/awslabs/amazon-asdi/blob/main/examples/dask/notebooks/cmip6_zarr.ipynb","AuthorName":"Zac Flamig","Services":["AWS CloudFormation","AWS Fargate","Amazon SageMaker"]}],"Publications":[{"Title":"CMIP6 Data Informational Session Video Recording","URL":"https://www.youtube.com/watch?v=C0UhiiGgbWA","AuthorName":"ASDI"},{"Title":"Special issue | Coupled Model Intercomparison Project Phase 6 (CMIP6) Experimental Design and Organization","URL":"https://gmd.copernicus.org/articles/special_issue590.html","AuthorName":"V. Eyring"}]},"RegistryEntryAdded":"2020-08-12","RegistryEntryLastModified":"2025-02-04","Slug":"cmip6","Sources":["awslabs-open-data-registry"]}
{"Name":"CMS 2008-2010 Data Entrepreneurs’ Synthetic Public Use File (DE-SynPUF) in OMOP Common Data Model","Description":"DE-SynPUF is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,300,000 persom (2.3m) data sets in the [OMOP Common Data Model](https://www.ohdsi.org/data-standardization/) format.  The DE-SynPUF was created with the goal of providing a realistic set of claims data in the public domain while providing the very highest degree of protection to the Medicare beneficiaries’ protected health information.  The purposes of the DE-SynPUF are to: \n1. allow data entrepreneurs to develop and create software and applications that may eventually be applied to actual CMS claims data;\n2. train researchers on the use and complexity of conducting analyses with CMS claims data prior to initiating the process to obtain access to actual CMS data; and,\n3. support safe data mining innovations that may reveal unanticipated knowledge gains while preserving beneficiary privacy.\nThe files have been designed so that programs and procedures created on the DE-SynPUF will function on CMS Limited Data Sets. The data structure of the Medicare DE-SynPUF is very similar to the CMS Limited Data Sets, but with a smaller number of variables.  The DE-SynPUF also provides a robust set of metadata on the CMS claims data that have not been previously available in the public domain.  Although the DE-SynPUF has very limited inferential research value to draw conclusions about Medicare beneficiaries due to the synthetic processes used to create the file, the Medicare DE-SynPUF does increase access to a realistic Medicare claims data file in a timely and less expensive manner to spur the innovation necessary to achieve the goals of better care for beneficiaries and improve the health of the population.\n","Documentation":"https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/SynPUFs/DE_Syn_PUF","Contact":"Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6riTWmh-cGw/aws-open-data) and use the `AWS Open Data` tag.","ManagedBy":"[Amazon Web Sevices](https://aws.amazon.com/)","UpdateFrequency":"Not updated","Tags":["amazon.science","bioinformatics","health","life sciences","natural language processing","us"],"License":"https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/SynPUFs/Downloads/SynPUF_DUG.pdf","Resources":[{"Description":"Project data files","ARN":"arn:aws:s3:::synpuf-omop","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Create data science environments on AWS for health analysis using OHDSI","URL":"https://aws.amazon.com/blogs/big-data/creating-data-science-environments-on-aws-for-health-analysis-using-ohdsi/","AuthorName":"James Wiggins","Services":["AWS CloudFormation","Amazon EC2","Amazon RDS"]},{"Title":"Map clinical notes to the OMOP Common Data Model and healthcare ontologies using Amazon Comprehend Medical","URL":"https://aws.amazon.com/blogs/machine-learning/map-clinical-notes-to-the-omop-common-data-model-and-healthcare-ontologies-using-amazon-comprehend-medical/","AuthorName":"James Wiggins","Services":["Amazon Comprehend","AWS CloudFormation","Amazon EC2","Amazon RDS"]},{"Title":"Predict patient health outcomes using OHDSI and machine learning on AWS","URL":"https://aws.amazon.com/blogs/industries/predict-patient-health-outcomes-using-ohdsi-and-machine-learning-on-aws/","AuthorName":"James Wiggins","Services":["AWS CloudFormation","Amazon EC2","Amazon RDS"]}],"Tools & Applications":[{"Title":"OHDSIonAWS","URL":"https://github.com/OHDSI/OHDSIonAWS","AuthorName":"James Wiggins"}]},"RegistryEntryAdded":"2023-01-03","RegistryEntryLastModified":"2025-02-04","Slug":"cmsdesynpuf-omop","Sources":["awslabs-open-data-registry"]}
{"Name":"USGS COAWST (Coupled Ocean Atmosphere Wave and Sediment Transport) Forecast Model Archive, US East and Gulf Coasts","Description":"The COAWST modeling system has been used to simulate ocean, wave and sediment transport processes along the of US East Coast and Gulf of Mexico. The grid has a horizontal resolution of approximately 5km and is resolved with 16 vertical terrain following levels. The model has been executed on a daily basis since August 2009 with outputs written every hour. This archive contains model output from 2009-08-21 to 2022-06-17.","Documentation":"https://www.sciencebase.gov/catalog/item/610acd4fd34ef8d7056893da","Contact":"jbzambon@fathomscience.com","ManagedBy":"Fathom Science","UpdateFrequency":"None","Citation":"Warner, J.C., and Kalra, T.S., 2022, Collection of COAWST model forecast for the US East Coast and Gulf of Mexico, U.S. Geological Survey data release, https://doi.org/10.5066/P903KPBJ","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","oceans"],"License":"CC0","Resources":[{"Description":"A virtual Icechunk version 2 repository pointing at a collection of NetCDF4 files","ARN":"arn:aws:s3:::usgs-coawst","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"COAWST Icechunk Creation and Explorer Notebooks","URL":"https://github.com/fs-jbzambon/opendata-coawst/tree/main#notebooks","NotebookURL":"https://github.com/fs-jbzambon/opendata-coawst/blob/main/COAWST_explore_icechunk.ipynb","AuthorName":"Rich Signell","AuthorURL":"https://about.me/rich.signell","Services":null}],"Publications":[{"Title":"Coupled-Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Modeling System, U.S. Geological Survey Software Release, 23 April 2019","URL":"https://doi.org/10.5066/P9NQUAOW","AuthorName":"Warner, J.C., Ganju, N.K., Sherwood, C.R., Kalra, T.S., Aretxabaleta, A., He, R., Zambon, J., and Kumar, N."}]},"RegistryEntryAdded":"2023-11-28","RegistryEntryLastModified":"2026-05-04","Slug":"coawst","Sources":["awslabs-open-data-registry"]}
{"Name":"COBRA","Description":"This page describes the COBRA (Classification Of Basal cell carcinoma, Risky skin cancers and Abnormalities) skin pathology dataset, which comprises over 7000 histopathology whole-slide-images related to the diagnosis of basal cell carcinoma skin cancer, the most commonly diagnosed cancer. The dataset includes biopsies and excisions and is divided into four groups. The first group contains about 2,500 BCC biopsies with subtype labels, while the second group includes 2,500 non-BCC biopsies with different types of skin dysplasia. The third group has 1,000 labelled risky cancer biopsies, including rare and dangerous types like melanoma, squamous cell carcinoma, and Merkel cell carcinoma. Finally, the fourth group contains 1,000 BCC excisions with or without free tumor margins, with 300 fully annotated. The dataset will be released over 2023 in stages, starting with the BCC and non-BCC biopsies, followed by BCC excisions, and finally the risky cancer biopsies.\n","Documentation":"https://daangeijs.github.io/cobra/","Contact":"https://www.computationalpathologygroup.eu/members/daan-geijs/","ManagedBy":"Radboud University Medical Center","UpdateFrequency":"As required","Tags":["aws-pds","cancer","computational pathology","computer vision","deep learning","histopathology","life sciences"],"License":"CC BY-SA-NC 4.0","Resources":[{"Description":"Whole slide images with corresponding labels including skin cancer and basal cell carcinoma risk class.","ARN":"arn:aws:s3:::cobra-pathology","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"ASAP Viewer","URL":"https://computationalpathologygroup.github.io/ASAP/","AuthorName":"Geert Litjens","AuthorURL":"https://www.diagnijmegen.nl/people/geert-litjens/"},{"Title":"COBRA Tools & Labels","URL":"https://github.com/daangeijs/cobra","AuthorName":"Diagnostic Image Analysis Group, Radboudumc, Nijmegen","AuthorURL":"https://www.diagnijmegen.nl/"},{"Title":"Whole slide data - python package for working with wholeslide images and annotations files","URL":"https://github.com/DIAGNijmegen/pathology-whole-slide-data","AuthorName":"Diagnostic Image Analysis Group, Radboudumc, Nijmegen","AuthorURL":"https://www.diagnijmegen.nl/"}]},"RegistryEntryAdded":"2023-03-21","RegistryEntryLastModified":"2023-03-21","Slug":"cobra","Sources":["awslabs-open-data-registry"]}
{"Name":"Multilingual Name Entity Recognition (NER) Datasets with Gazetteer","Description":"Name Entity Recognition datasets containing short sentences and queries with low-context,\nincluding LOWNER, MSQ-NER, ORCAS-NER and Gazetteers (1.67 million entities).\nThis release contains the multilingual versions of the datasets in [Low Context Name Entity Recognition (NER) Datasets with Gazetteer](https://registry.opendata.aws/lowcontext-ner-gaz/).\n","Documentation":"https://code-mixed-ner.s3.amazonaws.com/readme.html","Contact":"njfn@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"N/A","Tags":["amazon.science","natural language processing"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Data file","ARN":"arn:aws:s3:::code-mixed-ner","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Gazetteer Enhanced Named Entity Recognition for Code-Mixed Web Queries","URL":"https://www.amazon.science/publications/gazetteer-enhanced-named-entity-recognition-for-code-mixed-web-queries","AuthorName":"Besnik Fetahu, Anjie Fang, Oleg Rokhlenko and Shervin Malmasi"}]},"RegistryEntryAdded":"2021-05-12","RegistryEntryLastModified":"2022-07-06","Slug":"code-mixed-ner","Sources":["awslabs-open-data-registry"]}
{"Name":"State of Colorado Basemap and Foundational Geospatial Data","Description":"The State of Colorado has gathered foundational geospatial datasets and created a statewide basemap","Documentation":"https://docs.google.com/document/d/1lkoULpFQtO5NqVtt-y_0HSNc5LK-3K8Yqz1ZGk2nGig/edit?usp=sharing","Contact":"oit_gis@state.co.us","ManagedBy":"State of Colorado Governor's Office of Information Technology (OIT) GIS team","UpdateFrequency":"Periodically","Tags":["aws-pds","geospatial","mapping"],"License":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","Resources":[{"Description":"Colorado Basemap and Foundational Geospatial Data","ARN":"arn:aws:s3:::colorado-public-geospatial-data","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for new Colorado basemap and foundational geospatial data","ARN":"arn:aws:sns:us-west-2:180294215083:colorado-public-geospatial-data-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Colorado AWS Open Data Basemap and Foundational Geospatial Data Guide","URL":"https://docs.google.com/document/d/19KTe_RXxvcWaA7r7_4LSKsoH2DB0xP1fVkbj9bdxG0Q/edit?usp=sharing","AuthorName":"State of Colorado OIT-GIS","AuthorURL":"https://geodata.colorado.gov/"},{"Title":"Colorado Public Basemap and Foundational Geospatial Data s3 Browser","URL":"https://colorado-public-geospatial-data.s3.us-west-2.amazonaws.com/index.html","AuthorName":"State of Colorado OIT-GIS","AuthorURL":"https://geodata.colorado.gov/"},{"Title":"Get to Know A Dataset: Colorado Basemap and Foundational Geospatial Data","URL":"https://github.com/OIT-GIS/open-data-examples/blob/main/colorado-basemap-and-foundational-geospatial-data/get-to-know-a-dataset.ipynb","AuthorName":"State of Colorado OIT-GIS","AuthorURL":"https://geodata.colorado.gov/"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2026-06-25","RegistryEntryLastModified":"2026-07-22","Slug":"colorado-basemap-and-foundational-geospatial-data","Sources":["awslabs-open-data-registry"]}
{"Name":"State of Colorado Elevation Data","Description":"The State of Colorado has gathered public historical elevation data.","Documentation":"https://docs.google.com/document/d/1HMO-d4cCrBvFa2F6-N3lhP6rkezlvBmSUFA5S8t_ekQ/edit?usp=sharing","Contact":"oit_gis@state.co.us","ManagedBy":"State of Colorado Governors Office of Information Technology OIT GIS team","UpdateFrequency":"Periodically","Tags":["aws-pds","geospatial","imaging","mapping"],"License":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","Resources":[{"Description":"Colorado Elevation Data (LiDAR)","ARN":"arn:aws:s3:::colorado-public-elevation-data","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for new Colorado Elevation data","ARN":"arn:aws:sns:us-west-2:180294215083:colorado-public-elevation-data-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Colorado AWS Open Data Elevation Data Guide","URL":"https://docs.google.com/document/d/1pAHZB6SgSE4QTawEbSnIIHpxVCTBg-IjQ6X9KJP28BM/edit?usp=sharing","AuthorName":"State of Colorado OIT-GIS","AuthorURL":"https://geodata.colorado.gov/"},{"Title":"Colorado Public Elevation Data s3 Browser","URL":"https://colorado-public-elevation-data.s3.amazonaws.com/index.html","AuthorName":"State of Colorado OIT-GIS","AuthorURL":"https://geodata.colorado.gov/"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2025-09-03","RegistryEntryLastModified":"2025-09-26","Slug":"colorado-elevation-data","Sources":["awslabs-open-data-registry"]}
{"Name":"State of Colorado Imagery","Description":"The State of Colorado has gathered public historical imagery ranging from 2005 to 2021.","Documentation":"https://docs.google.com/document/d/1YDHignUj9lQTMw2J-SqA96MTP8KmJYtk2ZKKC2ZYuPE/edit?usp=sharing","Contact":"oit_gis@state.co.us","ManagedBy":"State of Colorado, Governor's Office of Information Technology OIT GIS team","UpdateFrequency":"Periodically","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aerial imagery","aws-pds","geospatial","imaging","mapping"],"License":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","Resources":[{"Description":"The State of Colorado historic public aerial imagery. Currently, NAIP is available from 2005 and 2009-2021. The National Agriculture Imagery Program is a project managed by the U.S. Department of Agriculture created to collect leaf-on imagery for the United States during peak growing seasons. The files are available as GeoTIFFs. From 2005-2017 they have a one meter resolution. After that, it is a 60cm resolution. DRAPP (Denver Regional Aerial Photgraphy Project) is available from 2010-2020. It is available in 3, 6, and 12in resolutions (except 2012).","ARN":"arn:aws:s3:::colorado-public-imagery","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for real-time data updates","ARN":"arn:aws:sns:us-west-2:180294215083:colorado-public-imagery-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Colorado AWS Open Imagery Guide","URL":"https://docs.google.com/document/d/15GjCSWSzst82FZMqBqdGV0rt6FKJzt03NlQYdWwsLGE/edit?usp=sharing","AuthorName":"State of Colorado OIT-GIS","AuthorURL":"https://geodata.colorado.gov/"}],"Tools & Applications":[{"Title":"Colorado Public Imagery Downloader","URL":"https://gis.colorado.gov/imagery/","AuthorName":"State of Colorado OIT-GIS","AuthorURL":"https://geodata.colorado.gov/"},{"Title":"Colorado Public Imagery s3 Browser","URL":"https://colorado-public-imagery.s3.amazonaws.com/index.html","AuthorName":"State of Colorado OIT-GIS","AuthorURL":"https://geodata.colorado.gov/"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2025-05-05","RegistryEntryLastModified":"2026-04-08","Slug":"colorado-imagery","Sources":["awslabs-open-data-registry"]}
{"Name":"Common Crawl","Description":"A corpus of web crawl data composed of over 300 billion web pages.","Documentation":"https://commoncrawl.org/get-started","Contact":"https://commoncrawl.org/contact-us","ManagedBy":"[Common Crawl](https://commoncrawl.org/)","UpdateFrequency":"Monthly","Tags":["aws-pds","encyclopedic","internet","natural language processing","web archive"],"License":"This data is available for anyone to use under the [Common Crawl Terms of Use](https://commoncrawl.org/terms-of-use/)","Resources":[{"Description":"Crawl data (WARC and ARC format)","ARN":"arn:aws:s3:::commoncrawl","Region":"us-east-1","Type":"S3 Bucket","AccountRequired":true}],"DataAtWork":{"Tutorials":[{"Title":"Analysing Petabytes of Websites","URL":"http://tech.marksblogg.com/petabytes-of-website-data-spark-emr.html","AuthorName":"Mark Litwintschik","Services":["Amazon EMR"]},{"Title":"Common Crawl Index Athena","URL":"https://skeptric.com/common-crawl-index-athena/","AuthorName":"Edward Ross","Services":["Amazon Athena"]},{"Title":"Get To Know A Dataset - Common Crawl","URL":"https://github.com/commoncrawl/whirlwind-python-notebook","NotebookURL":"https://github.com/commoncrawl/whirlwind-python-notebook/blob/main/aws-ccf-dataset.ipynb","AuthorName":"Common Crawl Foundation","AuthorURL":"https://commoncrawl.org/"},{"Title":"Index to WARC Files and URLs in Columnar Format","URL":"https://commoncrawl.org/blog/index-to-warc-files-and-urls-in-columnar-format","AuthorName":"Sebastian Nagel","Services":["Amazon Athena"]},{"Title":"Large-scale graph mining with Spark","URL":"https://towardsdatascience.com/large-scale-graph-mining-with-spark-750995050656","AuthorName":"Win Suen","AuthorURL":"https://github.com/wsuen/pygotham2018_graphmining"},{"Title":"One click to download all the web pages you may want","URL":"https://medium.com/@jaderd/one-click-to-download-exactly-the-web-pages-you-may-want-no-matter-how-many-they-are-d4834265a0a3","AuthorName":"Jader Dias","Services":["Amazon Athena","AWS Lambda"]},{"Title":"Search the Common Crawl Using Lambda Functions","URL":"https://github.com/andresriancho/cc-lambda","AuthorName":"Andres Riancho","Services":["AWS Lambda"]}],"Tools & Applications":[{"Title":"All Around The World: The Common Crawl Dataset - Attack Surface Research","URL":"https://labs.watchtowr.com/all-around-the-world-the-common-crawl-dataset/","AuthorName":"Aliz Hammond","AuthorURL":"https://labs.watchtowr.com/"},{"Title":"CCNet: Extracting high quality monolingual datasets from web crawl data","URL":"https://arxiv.org/abs/1911.00359","AuthorName":"Facebook AI Research","AuthorURL":"https://github.com/facebookresearch/cc_net"},{"Title":"Dresden Web Table Corpus (DWTC)","URL":"https://wwwdb.inf.tu-dresden.de/research-projects/dresden-web-table-corpus/","AuthorName":"Database Systems Group Dresden","AuthorURL":"https://wwwdb.inf.tu-dresden.de/"},{"Title":"Glove: Global vectors for word representation","AuthorName":"Jeffrey Pennington, Richard Socher, Christopher D. Manning","URL":"https://aclanthology.org/D14-1162.pdf"},{"Title":"Learning word vectors for 157 languages","URL":"https://www.aclweb.org/anthology/L18-1550","AuthorName":"Facebook AI Research","AuthorURL":"https://fasttext.cc/docs/en/crawl-vectors.html"},{"Title":"Ransacking your password reset tokens","URL":"https://positive.security/blog/ransack-data-exfiltration","AuthorName":"Lukas Euler"},{"Title":"Search the html across 25 billion websites for passive reconnaissance using common crawl","AuthorName":"Ryan Elkins","URL":"https://medium.com/@brevityinmotion/search-the-html-across-25-billion-websites-for-passive-reconnaissance-using-common-crawl-7fe109250b83"}],"Publications":[{"Title":"Asynchronous pipeline for processing huge corpora on medium to low resource infrastructures","URL":"https://hal.inria.fr/hal-02148693","AuthorName":"Pedro Javier Ortiz Suárez, Benoît Sagot, Laurent Romary","AuthorURL":"https://oscar-corpus.com/"},{"Title":"Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl","URL":"https://arxiv.org/pdf/1710.01779.pdf","AuthorName":"Alexander Panchenko, Eugen Ruppert, Stefano Faralli, Simone Paolo Ponzetto, Chris Biemann"},{"Title":"C4Corpus: Multilingual Web-Size Corpus with Free License","URL":"http://www.lrec-conf.org/proceedings/lrec2016/pdf/388_Paper.pdf","AuthorName":"Ivan Habernal, Omnia Zayed, Iryna Gurevych","AuthorURL":"https://dkpro.github.io/dkpro-c4corpus/"},{"Title":"CC-News-En: A large English news corpus","URL":"https://doi.org/10.1145/3340531.3412762","AuthorName":"Joel Mackenzie, Rodger Benham, Matthias Petri, Johanne R. Trippas, J. Shane Culpepper, Alistair Moffat"},{"Title":"CCAligned: A Massive collection of cross-lingual web-document pairs","URL":"https://www.aclweb.org/anthology/2020.emnlp-main.480","AuthorName":"Ahmed El-Kishky, Vishrav Chaudhary, Francisco Guzmán, Philipp Koehn","AuthorURL":"http://www.statmt.org/cc-aligned/"},{"Title":"Coyo-700m: Image-text pair dataset","URL":"https://github.com/kakaobrain/coyo-dataset","AuthorName":"Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, Saehoon Kim"},{"Title":"Defending against neural fake news","URL":"http://papers.nips.cc/paper/9106-defending-against-neural-fake-news.pdf","AuthorName":"Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, et al","AuthorURL":"https://rowanzellers.com/grover/"},{"Title":"Index fun","URL":"https://psuter.net/2019/07/07/z-index","AuthorName":"Philippe Suter"},{"Title":"LAION-5B: An open large-scale dataset for training next generation image-text models","URL":"https://arxiv.org/abs/2210.0840","AuthorName":"Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, et al"},{"Title":"Language is not all you need: aligning perception with language models","URL":"https://arxiv.org/abs/2302.14045","AuthorName":"Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, et al"},{"Title":"Language models are few-shot learners","URL":"https://arxiv.org/abs/2005.14165","AuthorName":"Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, et al"},{"Title":"Large-scale analysis of style injection by relative path overwrite","URL":"https://doi.org/10.1145/3178876.3186090","AuthorName":"Sajjad Arshad, Seyed Ali Mirheidari, Tobias Lauinger, Bruno Crispo, Engin Kirda, William Robertson"},{"Title":"LLaMA: open and efficient foundation language models","URL":"https://arxiv.org/abs/2302.13971","AuthorName":"Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, et al"},{"Title":"Mapping languages: The Corpus of Global Language Use","URL":"https://doi.org/10.1007/s10579-020-09489-2","AuthorName":"Jonathan Dunn","AuthorURL":"https://www.earthlings.io/"},{"Title":"mT5: A massively multilingual pre-trained text-to-text transformer","URL":"https://arxiv.org/abs/2010.11934","AuthorName":"Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, et al"},{"Title":"Multimodal C4: an open, billion-scale corpus of images interleaved with text","URL":"https://arxiv.org/abs/2304.06939","AuthorName":"Wanrong Zhu, Jack Hessel, Anas Awadalla, Samir Yitzhak Gadre, Jesse Dodge, Alex Fang, et al"},{"Title":"N-gram counts and language models from the Common Crawl","URL":"http://www.lrec-conf.org/proceedings/lrec2014/pdf/1097_Paper.pdf","AuthorName":"Christian Buck, Kenneth Heafield, Bas van Ooyen","AuthorURL":"http://statmt.org/ngrams/"},{"Title":"No Language Left Behind: scaling human-centered machine translation","URL":"https://arxiv.org/abs/2207.04672","AuthorName":"Costa-jussà, Marta R., James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, et al"},{"Title":"Of using Common Crawl to play Family Feud","URL":"https://fulmicoton.com/posts/commoncrawl/","AuthorName":"Paul Masurel"},{"Title":"On the impact of publicly available news and information transfer to financial markets","URL":"https://arxiv.org/abs/2010.12002","AuthorName":"Metod Jazbec, Barna Pásztor, Felix Faltings, Nino Antulov-Fantulin, Petter N. Kolm"},{"Title":"Using open data to predict market movements","URL":"https://education.emc.com/content/dam/dell-emc/documents/en-us/2017KS_Ravinder-Using_Open_Data_to_Predict_Market_Movements.pdf","AuthorName":"DELL EMC"},{"Title":"Web Data Commons - RDFa, microdata, and microformat data sets","URL":"http://webdatacommons.org/structureddata/","AuthorName":"Christian Bizer, Robert Meusel, Anna Primpeli"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2025-12-08","Slug":"commoncrawl","Sources":["awslabs-open-data-registry"]}
{"Name":"Common Screens","Description":"A corpus of web screenshot and metadata data composed of over 70 million websites.","Documentation":"https://commonscreens.com/?page_id=1492","Contact":"admin@commonscreens.com","ManagedBy":"[Common Screens](https://commonscreens.com/)","UpdateFrequency":"Monthly","Tags":["aws-pds","encyclopedic","internet","natural language processing"],"License":"[Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Common Screens (jpeg and csv format)","ARN":"arn:aws:s3:::common-screens","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Cloudfront CDN distribution for hotlinking screenshots","Host":"dqh5x5k6xg3n1.cloudfront.net","Region":"us-west-2","Type":"CloudFront Distribution"}],"DataAtWork":{"Tutorials":[{"Title":"IAB Text Classification","URL":"https://commonscreens.com/?page_id=2512","AuthorName":"Common Screens"}]},"RegistryEntryAdded":"2022-12-06","RegistryEntryLastModified":"2022-12-06","Slug":"comonscreens","Sources":["awslabs-open-data-registry"]}
{"Name":"ConfUSIus Functional Ultrasound Imaging Dataset Collection","Description":"ConfUSIus Functional Ultrasound Imaging Dataset Collection is a curated set of public\nfunctional ultrasound imaging (fUSI) datasets and derived templates for neuroscience.\nThe collection brings together data from multiple studies and laboratories and makes it\navailable for programmatic access through stable cloud paths and the ConfUSIus Python\ntoolkit. Where possible, datasets are organized or re-exported using fUSI-BIDS/BIDS\nconventions; original formats, derivatives, provenance, and licenses vary by source\ndataset.\n","Documentation":"https://confusius.tools/latest/user-guide/datasets/","Contact":"maintainers@confusius.tools","ManagedBy":"[ConfUSIus](https://confusius.tools/)","UpdateFrequency":"Updated as new openly redistributable functional ultrasound imaging datasets or corrected metadata are incorporated into the ConfUSIus dataset collection.","Tags":["aws-pds","biology","imaging","life sciences","Mus musculus","neurobiology","neuroimaging","neuroscience","nifti","Rattus norvegicus","signal processing","volumetric imaging"],"License":"Licenses vary by source dataset and include CC BY 4.0, CC0 1.0, and CC BY-NC 4.0. Users must comply with the license and citation requirements for each source dataset. See the documentation for per-dataset license and provenance details.","Citation":"Please cite ConfUSIus when using the access tooling, and cite the original publication and dataset associated with each source dataset used.","Resources":[{"Description":"Functional ultrasound imaging datasets and templates organized into immutable releases under datasets/<dataset-id>/<version>/ and templates/<template-id>/<version>/. Each release includes a manifest.json with file sizes and SHA-256 checksums; last_versions.conf lists the latest versions. Most imaging data are NIfTI files with BIDS/fUSI-BIDS metadata; some datasets include derivatives, tabular metadata, videos, or source files. Public read access requires no AWS credentials.","ARN":"arn:aws:s3:::confusius-datasets","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for newly created objects in the ConfUSIus dataset collection S3 bucket.","ARN":"arn:aws:sns:us-west-2:837109648923:confusius-datasets-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"First-level GLM analysis of fUSI data","URL":"https://confusius.tools/latest/examples/_built/glm/first_level/","AuthorName":"ConfUSIus developers","AuthorURL":"https://confusius.tools/"},{"Title":"Get To Know A Dataset: ConfUSIus Functional Ultrasound Imaging Dataset Collection","URL":"https://confusius.tools/latest/examples/_built/io/dataset_collection/","NotebookURL":"https://github.com/confusius-tools/confusius-docs/blob/main/0.8.0/examples/_built/io/dataset_collection.ipynb","AuthorName":"ConfUSIus developers","AuthorURL":"https://github.com/confusius-tools","Services":["Amazon S3"]},{"Title":"Registration of two sessions from the same subject","URL":"https://confusius.tools/latest/examples/_built/registration/register_volume_same_subject/","AuthorName":"ConfUSIus developers","AuthorURL":"https://confusius.tools/"},{"Title":"Searchlight decoding of a continuous variable","URL":"https://confusius.tools/latest/examples/_built/decoding/searchlight_speed/","AuthorName":"ConfUSIus developers","AuthorURL":"https://confusius.tools/"}],"Tools & Applications":[{"Title":"ConfUSIus","URL":"https://confusius.tools/","AuthorName":"ConfUSIus developers","AuthorURL":"https://github.com/confusius-tools/confusius"}],"Publications":[{"Title":"A queen odour mediates reproductive suppression in a eusocial mammal","URL":"https://doi.org/10.1038/s41586-026-10772-5","AuthorName":"Khallaf MA et al."},{"Title":"A vascular code for speed in the spatial navigation system","URL":"https://doi.org/10.1016/j.celrep.2025.116791","AuthorName":"Cybis Pereira F et al."},{"Title":"Brainwide blood volume reflects opposing neural populations","URL":"https://doi.org/10.1038/s41586-026-10350-9","AuthorName":"Landemard A et al."},{"Title":"Induction of haemodynamic travelling waves by glial-related vasomotion in a rat model of neuroinflammation","URL":"https://doi.org/10.1016/j.ebiom.2025.105777","AuthorName":"Pereira M et al."},{"Title":"Neural correlates of blood flow measured by ultrasound","URL":"https://doi.org/10.1016/j.neuron.2022.02.012","AuthorName":"Nunez-Elizalde AO et al."},{"Title":"Structural and dynamic embedding of the mouse functional connectome revealed by functional ultrasound imaging","URL":"https://doi.org/10.64898/2026.02.05.704055","AuthorName":"Pepe C et al."},{"Title":"Whole-brain functional ultrasound imaging in awake head-fixed mice","URL":"https://doi.org/10.1038/s41596-021-00548-8","AuthorName":"Brunner C, Grillet M et al."}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-13","RegistryEntryLastModified":"2026-10-16","Slug":"confusius","Sources":["awslabs-open-data-registry"]}
{"Name":"CogWear: Can we detect cognitive effort with consumer-grade wearables?","Description":"The CogWear database consists of two datasets: pilot and survey gamification experiment. The pilot dataset includes physiological signals from 11 volunteers captured by three wearable devices (Empatica E4 watch, Samsung Galaxy Watch4 and Muse S EEG headband) during cognitively demanding task (Stroop test) and baseline condition (no engagement in any activity). The pilot dataset was collected to evaluate the experimental setup and the signal captured by the wearables. In particular, can we detect when people perform a cognitively demanding task using data from consumer-grade smartwatches. The survey gamification experiment dataset includes physiological signal collected from 13 volunteers by three wearable devices (Empatica E4 watch, Samsung Galaxy Watch4 and Muse S EEG headband) during two sessions. In both sessions participants, as in the pilot study, engaged in a cognitively demanding task then rested and did not perform any tasks and finally filled in four mobile surveys related to work and wellbeing (GRIT, PANAS, HAPPINES and NFR). The mobile surveys were either gamified or not. The volunteers were divided into two groups: group 1 filled in gamified surveys in the first session and non-gamified surveys in the second session, group 2 had the opposite order. There was a two-week break time between the sessions. This dataset was captured to investigate whether simple gamification has an impact on the cognitive burden of filling in the surveys.","Documentation":"https://doi.org/10.13026/5f6t-b637","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Open Database License v1.0","Resources":[{"Description":"https://doi.org/10.13026/5f6t-b637","ARN":"arn:aws:s3:::physionet-open/consumer-grade-wearables/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"consumer-grade-wearables","Sources":["awslabs-open-data-registry"]}
{"Name":"Copernicus Digital Elevation Model (DEM)","Description":"The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. We provide two instances of Copernicus DEM named GLO-30 Public and GLO-90. GLO-90 provides worldwide coverage at 90 meters. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that in both cases ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs and comes from Copernicus DEM 2021 release.\n","Documentation":"https://copernicus-dem-30m.s3.amazonaws.com/readme.html","Contact":"https://forum.sentinel-hub.com/c/aws-copdem/28","ManagedBy":"[Sinergise](https://www.sinergise.com/)","UpdateFrequency":"None, except GLO-30 Public can be updated if the public tile list changes.","Collabs":{"ASDI":{"Tags":["elevation"]}},"Tags":["agriculture","aws-pds","cog","disaster response","earth observation","elevation","geospatial","satellite imagery"],"License":"GLO-30 Public and GLO-90 are available on a free basis for the general public under the terms and conditions of the Licence found on [here](https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM).\n","Resources":[{"Description":"GLO-30 Public in S3 bucket. The list of tiles covering specific countries that are not yet released to the public is available [here](https://spacedata.copernicus.eu/documents/20123/121239/Non-released-tiles_GLO-30_PUBLIC_Dec+%282%29.xlsx).","ARN":"arn:aws:s3:::copernicus-dem-30m","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://copernicus-dem-30m-stac.s3.amazonaws.com/)"]},{"Description":"GLO-90 in S3 bucket.","ARN":"arn:aws:s3:::copernicus-dem-90m","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://copernicus-dem-90m-stac.s3.amazonaws.com/)"]}],"DataAtWork":{"Tutorials":[{"Title":"How to Calculate Slope and Aspect from the Copernicus DEM in Python","URL":"https://www.spatialworkflow.io/calculate-slope-aspect-python/","NotebookURL":"https://www.spatialworkflow.io/notebooks/calculate-slope-aspect-python.ipynb","AuthorName":"Spatial Workflow","AuthorURL":"https://www.spatialworkflow.io/"}],"Tools & Applications":[{"Title":"EO Browser","URL":"http://apps.sentinel-hub.com/eo-browser/","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"},{"Title":"Sentinel Hub WMS/WMTS/WCS Service and Process API","URL":"http://www.sentinel-hub.com/","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"}]},"RegistryEntryAdded":"2020-12-14","RegistryEntryLastModified":"2026-10-16","Slug":"copernicus-dem","Sources":["awslabs-open-data-registry"]}
{"Name":"Community coral reef image classification training data","Description":"Community-sourced repository of coral reef image classification training data, including continually updated confirmed annotations from [MERMAID](https://datamermaid.org/)","Documentation":"https://github.com/data-mermaid/image-classification-open-data","Contact":"contact@datamermaid.org","ManagedBy":"[MERMAID](https://datamermaid.org/)","UpdateFrequency":"Each partner organization updates on their own cadence. MERMAID updates once per day.","Tags":["aws-pds","coastal","conservation","coral reef","csv","global","machine learning","marine","parquet","survey"],"License":"[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)","Resources":[{"Description":"The coral-reef-training AWS S3 bucket provides a single, open, well-structured, growing, community-sourced repository of coral reef image classification training data. Hosted at s3://coral-reef-training, this bucket supports global efforts in coral reef conservation through standardized, machine-learning-ready imagery and annotations.\nThe bucket serves as the image storage backend for MERMAID’s image classification workflows and to distribute confirmed and scrubbed MERMAID coral reef image data, but it also provides a shared location where partners including CoralNet can contribute to and benefit from collective ML model development, each according to its own data structures and policies. Data in the bucket is free and open for public access; only contributing organizations have write access to their own data prefixes.\nBy centralizing and standardizing coral reef image data, this initiative accelerates collaboration across scientific, conservation, and machine learning communities and facilitates the creation of a common, evolving image classification model for coral reefs worldwide.","ARN":"arn:aws:s3:::coral-reef-training","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://coral-reef-training.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"MERMAID Image Classification Open Data Tutorial - Python version","URL":"https://data-mermaid.github.io/image-classification-open-data/image-classification-open-data-tutorial_Python.html","AuthorName":"Domazetoski V, Caldwell I","AuthorURL":"https://github.com/ViktorDomazetoski, https://github.com/ircaldwell"},{"Title":"MERMAID Image Classification Open Data Tutorial - R version","URL":"https://data-mermaid.github.io/image-classification-open-data/image-classification-open-data-tutorial_R.html","AuthorName":"Caldwell I","AuthorURL":"https://github.com/ircaldwell"}],"Tools & Applications":[{"Title":"MERMAID Collect","URL":"https://app.datamermaid.org/","AuthorName":"MERMAID","AuthorURL":"https://datamermaid.org/"},{"Title":"MERMAID Explore","URL":"https://explore.datamermaid.org/","AuthorName":"MERMAID","AuthorURL":"https://datamermaid.org/"}]},"RegistryEntryAdded":"2025-09-11","RegistryEntryLastModified":"2025-09-15","Slug":"coralreef-image-classification-training","Sources":["awslabs-open-data-registry"]}
{"Name":"COVID-19 Open Research Dataset (CORD-19)","Description":"Full-text and metadata dataset of COVID-19 and coronavirus-related research articles optimized for machine readability.","Documentation":"https://pages.semanticscholar.org/coronavirus-research","Contact":"partnerships@allenai.org","ManagedBy":"Allen Institute for AI","UpdateFrequency":"Weekly","Tags":["aws-pds","coronavirus","COVID-19","life sciences","MERS","SARS"],"License":"Open (see license file for details)","Resources":[{"Description":"S3 bucket with CORD-19 dataset files","ARN":"arn:aws:s3:::ai2-semanticscholar-cord-19","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"COVID-19 Open Research Dataset Challenge (CORD-19)","URL":"https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge","AuthorName":"Kaggle"}]},"RegistryEntryAdded":"2020-03-19","RegistryEntryLastModified":"2024-03-26","Slug":"cord-19","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"The provider of this dataset no longer provides this data through this mechanism. If there are any questions, reach out through the contact mechanism listed below.","Name":"Cornell EAS Data Lake","Description":"Earth & Atmospheric Sciences at Cornell University has created a public data lake of climate data. The data is stored in columnar storage formats (ORC) to make it straightforward to query using standard tools like Amazon Athena or Apache Spark. The data itself is originally intended to be used for building decision support tools for farmers and digital agriculture. The first dataset is the historical NDFD / NDGD data distributed by NCEP / NOAA / NWS. The NDFD (National Digital Forecast Database) and NDGD (National Digital Guidance Database) contain gridded forecasts and observations at 2.5km resolution for the Contiguous United States (CONUS). There are also 5km grids for several smaller US regions and non-continguous territories, such as Hawaii, Guam, Puerto Rico and Alaska. NOAA distributes archives of the NDFD/NDGD via its NOAA Operational Model Archive and Distribution System (NOMADS) in Grib2 format. The data has been converted to ORC to optimize storage space and to, more importantly, simplify data access via standard data analytics tools.\n","Documentation":"https://datalake.eas.cornell.edu/","Contact":"digitalag@cornell.edu","ManagedBy":"Not currently managed","UpdateFrequency":"Hourly","Tags":["agriculture","aws-pds","climate","earth observation","elevation","environmental","geospatial","mapping","meteorological","weather"],"License":"https://datalake.eas.cornell.edu/license.txt","Resources":[{"Description":"Cornell EAS Data Lake","ARN":"arn:aws:s3:::cornell-eas-data-lake","Region":"us-east-2","Type":"S3 Bucket"},{"Description":"Cornell EAS Data Lake Notifications. Used to send human readable information about updates to the EAS Data Lake.","ARN":"arn:aws:sns:us-east-2:003709786761:cornell-eas-data-lake-human","Region":"us-east-2","Type":"SNS Topic"},{"Description":"Cornell EAS Data Lake Automation Notifications. Used to send JSON notifications to automated build pipelines and ETL jobs when the EAS Data Lake is updated.","ARN":"arn:aws:sns:us-east-2:003709786761:cornell-eas-data-lake","Region":"us-east-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Cornell EAS Data Lake Setup Tutorial","URL":"https://datalake.eas.cornell.edu/tutorial.html","AuthorName":"Marty Sullivan","AuthorURL":"http://martysullivan.com"}],"Tools & Applications":null,"Publications":null},"RegistryEntryAdded":"2018-07-12","RegistryEntryLastModified":"2023-02-28","Slug":"cornell-eas-data-lake","Sources":["awslabs-open-data-registry"]}
{"Name":"Japanese Tokenizer Dictionaries","Description":"Japanese Tokenizer Dictionaries for use with MeCab.","Documentation":"This dataset includes dictionaries for tokenization and morphological\nanalysis of Japanese for use with MeCab. This includes NINJAL's UniDic, a\nmodified smaller version of UniDic for situations that require it, and the\nlegacy IPADic dictionary.\n","Contact":"polm@cotonoha.io","ManagedBy":"Cotonoha","UpdateFrequency":"Infrequently (typically less than once a year)","Tags":["aws-pds","csv","japanese","natural language processing"],"License":"Versions of Unidic offered here are available under the GPL/LGPL/BSD license.\n\nIPADic is offered under a unique BSD-like license. See below. \n\n    https://github.com/polm/ipadic-py/blob/master/ipadic/dicdir/COPYING\n","Resources":[{"Description":"Dictionary Files","ARN":"arn:aws:s3:::cotonoha-dic","Region":"ap-northeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Fugashi Word Count Tutorial","URL":"https://github.com/polm/fugashi-sagemaker-demo/blob/master/fugashi%20wordcount.ipynb","AuthorName":"Paul O'Leary McCann","AuthorURL":"https://cotonoha.io","Services":["Amazon SageMaker"]}],"Tools & Applications":[{"Title":"unidic-py","URL":"https://github.com/polm/unidic-py","AuthorName":"Paul O'Leary McCann","AuthorURL":"https://cotonoha.io"}],"Publications":[{"Title":"How to Tokenize Japanese in Python","URL":"https://www.dampfkraft.com/nlp/how-to-tokenize-japanese.html","AuthorName":"Paul O'Leary McCann","AuthorURL":"https://dampfkraft.com"}]},"RegistryEntryAdded":"2020-09-11","RegistryEntryLastModified":"2025-02-04","Slug":"cotonoha-dic","Sources":["awslabs-open-data-registry"]}
{"Name":"CoversBR","Description":"CoversBR is the first large audio database with, predominantly, Brazilian music for the tasks of Covers Song\nIdentification (CSI) and Live Song Identifications (LSI). Due to copyright restrictions audios of\nthe songs cannot be made available, however metadata and files of features have public access. Audio\nstreamings captured from radio and TV channels for the live song identification task will be made public.\nCoversBR is composed of metadata and features extracted from 102298 songs, distributed in 26366\ngroups of covers/versions, with an average of 3.88 versions per group. The entire collection adds up to a total of\napproximately 7070 hours and the average song length is 240 seconds (4 minutes).\n  \n","Documentation":"https://github.com/SPLab-IT/CoversBR","Contact":"https://github.com/SPLab-IT/CoversBR/issues","ManagedBy":"Dirceu G Silva","UpdateFrequency":"New metadata, songs features files and audio streamings for live song identification will be added as soon as available.","Tags":["aws-pds","copyright monitoring","cover song identification","live song identification","music","music features dataset","music information retrieval","music recognition"],"License":"The code in this repository is licensed under Apache 2.0\n\nThe metadata and the pre-extracted features are licensed under CC BY-NC-SA 4.0\n","Resources":[{"Description":"metadata of songs, features files and audio streaming","ARN":"arn:aws:s3:::covers-song-br","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Getting started with Cover Song Identification and Genre classification","URL":"https://github.com/aws-samples/aws-music-genre-classification","NotebookURL":"https://github.com/aws-samples/aws-music-genre-classification/blob/main/music-genre-classification-with-coversbr-dataset.ipynb","AuthorName":"Gabriel Bella Martini","Services":["Amazon SageMaker Studio Lab"]},{"Title":"Using the (CoversBR) dataset","URL":"https://github.com/SPLab-IT/CoversBR#using-the-dataset---tutorial","AuthorName":"Dirceu Silva, Atila Xavier, Edgard Moraes, Marco Grivet and Fernando Perdigão","AuthorURL":"https://github.com/SPLab-IT/CoversBR"}]},"RegistryEntryAdded":"2020-10-05","RegistryEntryLastModified":"2025-02-04","Slug":"covers-br","Sources":["awslabs-open-data-registry"]}
{"Name":"CPAP Pressure and Flow Data from a Local Trial of 30 Adults at the University of Canterbury","Description":"A pressure and flow dataset for CPAP (continuous positive airway pressure) breathing obtained from 30 subjects for model-based identification of patient-specific lung mechanics using a specially designed sensor system comprising an array of differential pressure sensors (Sensirion AG SDP816-125PA) and gauge pressure sensors (NXP MPVZ4006GW7U). Relevant medical information was collected using a questionnaire, including: sex; age; weight; height; smoking history; and history of asthma. Subjects were tasked with breathing at five different rates (including passive) from very slow to very fast breathing, matched to an online pacing sound and video, at two different levels of CPAP (4 and 7 cmH2O) for between 50 and 180 seconds depending on rate. Each data set for a breathing rate comprises ~17 breaths, including rest periods between breathing rates and CPAP levels. Data with significant observed sensor error was removed during processing. A better-fitted mask would be expected to reduce error in fully capturing expiration by reducing leaks around the masks seal with the face. The sensors measure inspiration and expiration using separate unidirectional differential flow sensors, which are combined in post-processing to for complete breathing data.","Documentation":"https://doi.org/10.13026/xfae-vv63","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution-ShareAlike 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/xfae-vv63","ARN":"arn:aws:s3:::physionet-open/cpap-data-canterbury/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"cpap-data-canterbury","Sources":["awslabs-open-data-registry"]}
{"Name":"Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings: The 4th China Physiological Signal Challenge 2021","Description":"Atrial fibrillation (AF) is the most frequent arrhythmia, but paroxysmal atrial fibrillation (PAF) often remains unrecognized. Early detection of PAF is of great value for AF surgery options, drug intervention and diagnosis and treatment of various clinical complications. Although accurate detection of paroxysmal AF is very important, there is currently no algorithm that can efficiently measure the onset and end of AF episode in dynamic or wearable ECGs. Previous AF detection algorithms usually focus on the classification of AF rhythm instead of locating the onsets and ends of AF episodes. Thus, the clinical significance for the personalized treatment and management of AF patients is limited. The identification of AF rhythm is also influenced by other abnormal rhythms in clinical applications. The 4th China Physiological Signal Challenge 2021 (CPSC 2021) aims to encourage the development of algorithms for searching the AF episodes in dynamic ECG records. A new dynamic ECG database was constructed to encourage the development of more efficient and robust algorithms for PAF detection. We also develop a new scoring metric to evaluate detection methods for PAF events.","Documentation":"https://doi.org/10.13026/ksya-qw89","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/ksya-qw89","ARN":"arn:aws:s3:::physionet-open/cpsc2021/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"cpsc2021","Sources":["awslabs-open-data-registry"]}
{"Name":"Clinical Proteomic Tumor Analysis Consortium 2 (CPTAC-2)","Description":"The Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a national effort to accelerate the\nunderstanding of the molecular basis of cancer through the application of large-scale proteome and\ngenome analysis, or proteogenomics. CPTAC-2 is the Phase II of the CPTAC Initiative (2011-2016).\nDatasets contain open RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression\nQuantification, and miRNA Expression Quantification data.\n","Documentation":"https://cptac-data-portal.georgetown.edu/cptac/public?scope=Phase+II+%2525","Contact":"dcf-support@datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genomic","life sciences","STRIDES","transcriptomics"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq\nmiRNA Expression Quantification\n","ARN":"arn:aws:s3:::gdc-cptac-2-phs000892-2-open","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Cancer Genomics Cloud","URL":"https://cgc-accounts.sbgenomics.com/","AuthorName":"Seven Bridges","AuthorURL":"http://www.cancergenomicscloud.org/"},{"Title":"CPTAC Data Portal","URL":"https://proteomics.cancer.gov/data-portal","AuthorName":"National Cancer Institute","AuthorURL":"https://proteomics.cancer.gov/"},{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/CPTAC-2","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"},{"Title":"Proteomic Data Commons","URL":"https://pdc.esacinc.com/pdc/browse","AuthorName":"National Cancer Institute","AuthorURL":"https://pdc.esacinc.com/pdc"}],"Publications":[{"Title":"Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer","URL":"https://doi.org/10.1016/j.cell.2016.05.069","AuthorName":"Hui Zhang, Tao Liu, Zhen Zhang, Samuel H. Payne, Bai Zhang, Jason E. McDermott, Jian-Ying\nZhou, Vladislav A. Petyuk, Li Chen, Debjit Ray, Shisheng Sun, Feng Yang, Lijun Chen, Jing\nWang, Punit Shah, Seong Won Cha, Paul Aiyetan, Sunghee Woo, Yuan Tian, Marina A. Gritsenko,\nTherese R. Clauss, Caitlin Choi, Matthew E. Monroe, Stefani Thomas, Song Nie, Chaochao Wu,\nRonald J. Moore, Kun-Hsing Yu, David L. Tabb, David Fenyö, Vineet Bafna, Yue Wang, Henry\nRodriguez, Emily S. Boja, Tara Hiltke, Robert C. Rivers, Lori Sokoll, Heng Zhu, Ie-Ming\nShih, Leslie Cope, Akhilesh Pandey, Bing Zhang, Michael P. Snyder, Douglas A. Levine,\nRichard D. Smith, Daniel W. Chan, Karin D. Rodland, the CPTAC Investigators\n"},{"Title":"Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities","URL":"https://doi.org/10.1016/j.cell.2019.03.030","AuthorName":"Suhas Vasaikar, Chen Huang, Xiaojing Wang. Vladislav A. Petyuk, Sara R. Savage, Bo Wen,\nYongchao Dou, Yun Zhang, Zhiao Shi, Osama A. Arshad, Marina A. Gritsenko, Lisa J. Zimmerman,\nJason E. McDermott, Therese R. Clauss, Ronald J. Moore, Rui Zhao, Matthew E. Monroe, Yi-Ting\nWang, Matthew C. Chambers, Robbert J.C. Slebos, Ken S. Lau, Qianxing Mo, Li Ding, Matthew\nEllis, Mathangi Thiagarajan, Christopher R. Kinsinger, Henry Rodriguez, Richard D. Smith,\nKarin D. Rodland, Daniel C. Liebler, Tao Liu, Bing Zhang, Clinical Proteomic Tumor Analysis\nConsortium\n"},{"Title":"Proteomic analysis of colon and rectal carcinoma using standard and customized databases\n","URL":"https://doi.org/10.1038/sdata.2015.22","AuthorName":"Slebos RJ, Wang X, Wang X, Zhang B, Tabb DL, Liebler DC"}]},"RegistryEntryAdded":"2020-04-07","RegistryEntryLastModified":"2020-10-05","Slug":"cptac-2","Sources":["awslabs-open-data-registry"]}
{"Name":"Clinical Proteomic Tumor Analysis Consortium 3 (CPTAC-3)","Description":"The Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a national effort to accelerate the\nunderstanding of the molecular basis of cancer through the application of large-scale proteome and\ngenome analysis, or proteogenomics. CPTAC-3 is the Phase III of the CPTAC Initiative. The dataset\ncontains open RNA-Seq Gene Expression Quantification data.\n","Documentation":"https://cptac-data-portal.georgetown.edu/cptac/public?scope=Phase+III","Contact":"dcf-support@datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genomic","life sciences","STRIDES","transcriptomics"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"RNA-Seq Gene Expression Quantification","ARN":"arn:aws:s3:::gdc-cptac-phs001287-2-open","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Cancer Genomics Cloud","URL":"https://cgc-accounts.sbgenomics.com/","AuthorName":"Seven Bridges","AuthorURL":"http://www.cancergenomicscloud.org/"},{"Title":"CPTAC Data Portal","URL":"https://proteomics.cancer.gov/data-portal","AuthorName":"National Cancer Institute","AuthorURL":"https://proteomics.cancer.gov/"},{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/CPTAC-3","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"},{"Title":"Proteomic Data Commons","URL":"https://pdc.esacinc.com/pdc/browse","AuthorName":"National Cancer Institute","AuthorURL":"https://pdc.esacinc.com/pdc"}],"Publications":[{"Title":"Evaluation of NCI-7 Cell Line Panel as a Reference Material for Clinical Proteomics","URL":"https://www.ncbi.nlm.nih.gov/pubmed/29718670","AuthorName":"Clark DJ, Hu Y, Bocik W, Chen L, Schnaubelt M, Roberts R, Shah P, Whiteley G, Zhang H\n"},{"Title":"Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma","URL":"https://doi.org/10.1016/j.cell.2019.10.007","AuthorName":"Clark DJ, Dhanasekaran SM, Petralia F, Pan J, Song X, Hu Y, da Veiga Leprevost F, Reva B,\nLih TM, Chang HY, Ma W, Huang C, Ricketts CJ, Chen L1, Krek A, Li Y, Rykunov D, Li QK, Chen\nLS, Ozbek U, Vasaikar S, Wu Y, Yoo S, Chowdhury S, Wyczalkowski MA, Ji J, Schnaubelt M, Kong\nA, Sethuraman S, Avtonomov DM, Ao M, Colaprico A, Cao S, Cho KC, Kalayci S, Ma S, Liu W,\nRuggles K, Calinawan A, Gümüş ZH, Geizler D, Kawaler E, Teo GC, Wen B, Zhang Y, Keegan S, Li\nK, Chen F, Edwards N, Pierorazio PM, Chen XS, Pavlovich CP, Hakimi AA, Brominski G, Hsieh\nJJ, Antczak A, Omelchenko T, Lubinski J, Wiznerowicz M, Linehan WM, Kinsinger CR,\nThiagarajan M, Boja ES, Mesri M, Hiltke T, Robles AI, Rodriguez H, Qian J, Fenyö D, Zhang B,\nDing L, Schadt E, Chinnaiyan AM, Zhang Z, Omenn GS, Cieslik M, Chan DW, Nesvizhskii AI, Wang\nP, Zhang H; Clinical Proteomic Tumor Analysis Consortium\n"}]},"RegistryEntryAdded":"2020-04-07","RegistryEntryLastModified":"2020-10-05","Slug":"cptac-3","Sources":["awslabs-open-data-registry"]}
{"Name":"Open VLF: Scientific Open Data Initiative for CRAAM's SAVNET and AWESOME VLF Data.","Description":"This platform is maintained by [CRAAM](https://www.mackenzie.br/centro-de-radio-astronomia-e-astrofisica-mackenzie) (Mackenzie Radio Astronomy and Astrophysics Center), a research center operated by [UPM](https://www.mackenzie.br/) (Mackenzie Presbyterian University) and [INPE](https://www.gov.br/inpe) (National Institute for Space Research), to provide public and free access for researchers, students, and the interested public to VLF (Very Low Frequency) data from CRAAM's antenna systems. Amazon AWS supports all data stored through the [AWS Open Data Program](https://aws.amazon.com/pt/opendata/).\nVery Low Frequency (VLF) signals can be used for navigation services, communication with submarines, and are a powerful tool to study the low-altitude Earth's ionosphere, atmospheric and geophysics phenomena, space weather, magnetic field, and solar flares. Here, we provide historical and updated VLF data from two of CRAAM’s antenna systems, the SAVNET and AWESOME systems.\nThe SAVNET consists of a network of 11 VLF receiving/tracking stations located in Latin America and Antarctica. Five stations are in Brazil, three in Peru, one in Argentina, one in Mexico, and one in the Brazilian Antarctic Research Station Comandante Ferraz. The CRAAM AWESOME systems include two antennas in Brazil and Antarctica. Data since 2002 from both systems are available, with occasional gaps during periods when data collection is interrupted.\nData provided here is stored in different formats (.mat for AWESOME antenna system and .fits for SAVNET system). For documentation and details, please visit the project website [Open VLF](https://open-vlf.web.app).\n","Documentation":"[Open VLF](https://open-vlf.web.app)","Contact":"[Open VLF Feedback](https://open-vlf.web.app/markdown/the-project)","ManagedBy":"[CRAAM Mackenzie](https://www.mackenzie.br/centro-de-radio-astronomia-e-astrofisica-mackenzie)","UpdateFrequency":"Various. Data since 2006, and still updated. Follow the announcements and what is new on the project website [Open VLF](https://open-vlf.web.app).","Tags":["archives","astronomy","atmosphere","aws-pds","aws-pds","global","open source software","signal processing"],"License":"There are no restrictions on the use of this data.","Citation":"","Resources":[{"Description":"The [Open VLF Files](https://open-vlf.web.app). Total size of 73.6 GB","ARN":"arn:aws:s3:::craam-files-bucket","Region":"sa-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"All source code, program utilities, and file layouts.","URL":"https://github.com/open-vlf","AuthorName":"Kauffmann, DHV; Santiago, LS; Oliveira, R de."},{"Title":"Open VLF platform","URL":"https://open-vlf.web.app","AuthorName":"CRAAM","AuthorURL":"https://www.mackenzie.br/centro-de-radio-astronomia-e-astrofisica-mackenzie"}],"Publications":[{"Title":"Open VLF: Scientific Open Data Initiative for CRAAM's SAVNET and AWESOME VLF Data (2023).","URL":"https://open-vlf.web.app","AuthorName":"Kauffmann, DHV; Santiago, LS; Raulin, JP; Correia, E; Oliveira, R de."}]},"RegistryEntryAdded":"2023-10-30","RegistryEntryLastModified":"2026-06-05","Slug":"craam-open-vlf","Sources":["awslabs-open-data-registry"]}
{"Name":"Collaborative Research in Computational Neuroscience","Description":"CRCNS.org is a repository of high quality neuroscience datasets which are expected to be\nuseful for testing computational models of the brain and new analysis methods.  The data\ninclude physiological recordings from sensory and memory systems, as well as eye movement\nand other behavioral data.  The repository and sharing of the data sets was initially funded\nby the NSF CRCNS (Collaborative Research in Computational Neuroscience) program.\n","Documentation":"https://crcns.org/data-sets","Contact":"jteeters@berkeley.edu","ManagedBy":"[UC Berkeley](http://www.berkeley.edu/)","UpdateFrequency":"Once the data is in place, it will probably be updated rarely (just a few times each year).","Tags":["biology","brain images","Drosophila melanogaster","electrophysiology","fluorescence imaging","Homo sapiens","imaging","life sciences","magnetic resonance imaging","neuroimaging","neurophysiology","neuroscience","simulation neuroscience"],"License":"Most contributors of individual datasets request that the dataset and paper describing the data be cited if the data is used for a publication.  Specific conditions for using each data set are given on the webpage describing the data at https://crcns.org/data-sets.","Resources":[{"Description":"All datasets","ARN":"arn:aws:s3:::crcnsarchive","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Exploring CRCNS Datasets in a Browser-Based Notebook on Scigantic","URL":"https://scigantic.com/archive/a852e22f-0786-5b8c-b401-20ef8aafc99f/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Get To Know a Dataset: Collaborative Research in Computational Neuroscience","URL":"https://github.com/jeffteeters/open-data-examples/blob/main/crcnsarchive/get-to-know-a-dataset.ipynb","AuthorName":"Jeff Teeters","AuthorURL":"https://redwood.berkeley.edu/people/jeff-teeters/"}],"Publications":[{"Title":"Data Sharing for Computational Neuroscience","URL":"https://link.springer.com/article/10.1007/s12021-008-9009-y","AuthorName":"Jeffrey L. Teeters, Kenneth D. Harris, K. Jarrod Millman, Bruno A. Olshausen & Friedrich T. Sommer"}]},"RegistryEntryAdded":"2026-07-06","RegistryEntryLastModified":"2026-07-17","Slug":"crcnsarchive","Sources":["awslabs-open-data-registry"]}
{"Name":"CAST RR Interval Sub-Study Database","Description":"The Cardiac Arrhythmia Suppression Trial (CAST) was a landmark NHLBI-sponsored study designed to test the hypothesis that the suppression of asymptomatic or mildly symptomatic ventricular premature complexes (PVCs) in survivors of myocardial infarction (MI) would decrease the number of deaths from ventricular arrhythmias and improve survival. Enrollment required an acute MI within the preceding 2 years and 6 or more PVCs per hour during a pre-treatment (qualifying) long-term ECG (Holter) recording. Those subjects enrolled within 90 days of the index MI were required to have left ventricular ejection fractions less than or equal to 55%, while those enrolled after this 90 day window were required to have an ejection fraction less than or equal to 40%. CAST enrolled 3,549 patients in all.","Documentation":"https://doi.org/10.13026/C25P42","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C25P42","ARN":"arn:aws:s3:::physionet-open/crisdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"crisdb","Sources":["awslabs-open-data-registry"]}
{"Name":"IWMI DIWASA Rainfed and Irrigated Cropland Map for Africa","Description":"A framework integrating the Budyko model has been developed to distinguish between rainfed and irrigated cropland areas across Africa. This expands on remote sensing land cover products available for agricultural water studies in Africa and thereby helps address the need for deeper insights into cropland patterns. Validation against an independent dataset revealed an overall accuracy of 73% with high precision and specificity scores. These results validate the framework’s effectiveness in identifying irrigated areas while minimizing errors in misclassifying rainfed areas as irrigated.","Documentation":"https://github.com/iwmiwaplus/ODR/tree/master/Partitioned%20Croplands","Contact":"iwmiwaplus@gmail.com","ManagedBy":"[IWMI](https://www.iwmi.org/)","UpdateFrequency":"None","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["agriculture","cropland partitioning","irrigated cropland","land cover","land use","rainfed cropland"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"high-confidence cropland map (HCCM)","ARN":"arn:aws:s3:::iwmi-datasets/Cropland_partition/HCCM/","Region":"af-south-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://iwmi-datasets.s3.af-south-1.amazonaws.com/Cropland_partition/index.html)"]},{"Description":"Cropland partitioning all data","ARN":"arn:aws:s3:::iwmi-datasets/Cropland_partition/","Region":"af-south-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://iwmi-datasets.s3.af-south-1.amazonaws.com/Cropland_partition/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Cropland percentage","URL":"https://github.com/iwmiwaplus/ODR/tree/master/Partitioned%20Croplands/Tutorials","AuthorName":"iwmiwaplus","AuthorURL":"https://github.com/iwmiwaplus"}],"Tools & Applications":[{"Title":"Water use in Awash basin","URL":"https://github.com/iwmiwaplus/ODR/blob/master/Partitioned%20Croplands/Applications/Awash_cropland%20partitioning.pdf","AuthorName":"A. Owusu, K. Akpoti, M. Leh, N. Velpuri","AuthorURL":"https://github.com/iwmiwaplus"}],"Publications":[{"Title":"A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale","URL":"https://doi.org/10.1016/J.JAG.2023.103607","AuthorName":"Owusu, A., Kagone, S., Leh, M., Velpuri, N. M., Gumma, M. K., Ghansah, B., Thilina-Prabhath, P., Akpoti, K., Mekonnen, K., Tinonetsana, P., & Mohammed, I."},{"Title":"Rainfed and Irrigated Cropland Areas for Africa","URL":"https://doi.org/10.5066/P9N4R7SF","AuthorName":"Owusu, A., Kagone, S., Leh, M., and Velpuri, N.M."}]},"RegistryEntryAdded":"2024-10-09","RegistryEntryLastModified":"2025-07-09","Slug":"cropland_partitioining","Sources":["awslabs-open-data-registry"]}
{"Name":"Cryo-EM SPA Workflow Records","Description":"The “Cryo-EM SPA Workflow Records” contains all outputs of all processing steps involved in cryogenic electron microscopy (cryo-EM) single particle analysis (SPA), including both intermediate and final output data. The primary focus will be on data generated by RELION and CryoSPARC, two widely used software packages for :Cryo-EM SPA. These records will be archived systematically. To ensure the data remains reproducible while minimizing storage demands, large-sized files that can be regenerated will be excluded prior to registration. The aim is to retain only the essential metadata, processing parameters, and representative outputs that allow for full reconstruction of the analysis pipeline. This approach balances the need for long-term data preservation with practical considerations for storage capacity. Through this effort, we seek to enhance transparency and reproducibility in cryo-EM research by providing a structured and accessible record of the analysis process. Importantly, the use of this dataset is intended to facilitate the development of future AI algorithms in the field of Cryo-EM.","Documentation":"https://github.com/KEK-SBRC-CryoEM/cryoem-spa-workflow-records","Contact":"toshio.moriya@kek.jp","ManagedBy":"[High Energy Accelerator Research Organization (KEK) Structural Biology Research Center (SBRC)](https://www2.kek.jp/imss/sbrc/eng/)","UpdateFrequency":"New releases are published on a rolling basis. Please contact the team via email for any questions.","Tags":["aws-pds","life sciences","machine learning","structural biology"],"License":"CC0","Resources":[{"Description":"Cryo-EM SPA Workflow Records","ARN":"arn:aws:s3:::cryoem-spa-workflow-records-public","Region":"ap-northeast-1","Type":"S3 Bucket","Explore":["[Website](https://sites.google.com/sbrc.jp/gotocloud-doc)"]}],"DataAtWork":{"Tutorials":[{"Title":"Building AI Training Datasets Using Intermediate Data on AWS","URL":"https://github.com/KEK-SBRC-CryoEM/cryoem-spa-workflow-records#4-tutorial-building-ai-training-datasets-using-intermediate-data-on-aws","AuthorName":"Toshio Moriya","AuthorURL":"https://orcid.org/0000-0001-7226-5487"}],"Publications":[{"Title":"GoToCloud optimization of cloud computing environment for accelerating cryo-EM structure-based drug design","URL":"https://www.nature.com/articles/s42003-024-07031-6","AuthorName":"Toshio Moriya, et al.","AuthorURL":"https://orcid.org/0000-0001-7226-5487"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-05-01","RegistryEntryLastModified":"2026-05-05","Slug":"cryoem-spa-workflow-records","Sources":["awslabs-open-data-registry"]}
{"Name":"CryoET Data Portal","Description":"Cryo-electron tomography (cryoET) is a powerful technique for visualizing 3D structures of cellular macromolecules at near atomic resolution in their native environment. Observing the inner workings of cells in context enables better understanding about the function of healthy cells and the changes associated with disease. However, the analysis of cryoET data remains a significant bottleneck, particularly the annotation of macromolecules within a set of tomograms, which often requires a laborious and time-consuming process of manual labelling that can take months to complete. Given the current success of machine learning (ML) methods for image analysis, it seems likely that ML will have a significant impact on resolving this bottleneck. The scientific community has expressed the need to encourage further ML algorithm development by providing large training sets of annotated cryoET data in standardized formats. In response to this, we (Biohub) have established the CryoET Data Portal (https://cryoetdataportal.czscience.com/) to provide biologists and developers open access to high-quality, standardized, annotated data that can be readily used to retrain or develop new annotation models and algorithms.","Documentation":"https://chanzuckerberg.github.io/cryoet-data-portal/cryoet_data_portal_docsite_quick_start.html","Contact":"cryoetdataportal@chanzuckerberg.com","ManagedBy":"[Biohub](https://biohub.org)","UpdateFrequency":"New releases are published on a rolling basis. Please contact the team via email for any questions.","Tags":["aws-pds","Biohub","cell biology","cryo electron tomography","electron tomography","life sciences","machine learning","segmentation","structural biology"],"License":"CC0","Resources":[{"Description":"CryoET Data Portal","ARN":"arn:aws:s3:::cryoet-data-portal-public","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Website](https://cryoetdataportal.czscience.com/)"]},{"Description":"Globally cached distribution of the CryoET Data Portal dataset","Region":"us-west-2","Type":"CloudFront Distribution","Host":"https://files.cryoetdataportal.cziscience.com"}],"DataAtWork":{"Tutorials":[{"Title":"A Beginner's Guide of the CryoET Data Portal in a Browser-Based Notebook on Scigantic","URL":"https://scigantic.com/archive/20c2e9e3-1d5e-53c1-bbfd-6bbaf6ac736b/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com/"},{"Title":"Downloading data via the AWS CLI","URL":"https://chanzuckerberg.github.io/cryoet-data-portal/dev/cryoet_data_portal_docsite_aws.html","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Examples of working with the portal API","URL":"https://chanzuckerberg.github.io/cryoet-data-portal/dev/cryoet_data_portal_docsite_examples.html","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"},{"Title":"Predicting sample boundaries","URL":"https://chanzuckerberg.github.io/cryoet-data-portal/dev/tutorial_sample_boundaries.html","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"}],"Tools & Applications":[{"Title":"Python API for CryoET Data Portal","URL":"https://chanzuckerberg.github.io/cryoet-data-portal/python-api.html","AuthorName":"Biohub","AuthorURL":"https://github.com/chanzuckerberg/"}],"Publications":[{"Title":"Cryo-ET reveals the in situ architecture of the polar tube invasion apparatus from microsporidian parasites","URL":"https://www.biorxiv.org/content/10.1101/2024.07.13.603322v1","AuthorName":"Mahrukh Usmani, et al.","AuthorURL":"https://scholar.google.com/scholar?q=Mahrukh+Usmani"}]},"RegistryEntryAdded":"2024-08-14","RegistryEntryLastModified":"2026-05-13","Slug":"cryoet-data-portal","Sources":["awslabs-open-data-registry"]}
{"Name":"A Realistic Cyber Defense Dataset (CSE-CIC-IDS2018)","Description":"This dataset is the result of a collaborative project between the Communications Security Establishment (CSE) and The Canadian Institute for Cybersecurity (CIC) that use the notion of profiles to generate cybersecurity dataset in a systematic manner. It incluides a detailed description of intrusions along with abstract distribution models for applications, protocols, or lower level network entities. The dataset includes seven different attack scenarios, namely Brute-force, Heartbleed, Botnet, DoS, DDoS, Web attacks, and infiltration of the network from inside. The attacking infrastructure includes 50 machines and the victim organization has 5 departments includes 420 PCs and 30 servers. This dataset includes the network traffic and log files of each machine from the victim side, along with 80 network traffic features extracted from captured traffic using CICFlowMeter-V3.\nFor more information on the creation of this dataset, see this paper by researchers at the Canadian Institute for Cybersecurity (CIC) and the University of New Brunswick (UNB): [Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization](http://www.scitepress.org/Papers/2018/66398/66398.pdf).\n","Documentation":"http://www.unb.ca/cic/datasets/ids-2018.html","Contact":"CIC@unb.ca","UpdateFrequency":"Annually","ManagedBy":"Canadian Institute for Cybersecurity","Tags":["aws-pds","aws-pds","cyber security","internet","intrusion detection","network traffic"],"License":"http://www.unb.ca/cic/datasets/ids-2018.html","Resources":[{"Description":"Network traffic and log files","ARN":"arn:aws:s3:::cse-cic-ids2018","Region":"ca-central-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2018-10-07","RegistryEntryLastModified":"2022-06-14","Slug":"cse-cic-ids2018","Sources":["awslabs-open-data-registry"]}
{"Name":"CAFE60 reanalysis","Description":"The CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1 (CAFE60v1) provides a large ensemble retrospective analysis of the global climate system from 1960 to present with sufficiently many realizations and at spatio-temporal resolutions suitable to enable probabilistic climate studies. Using a variant of the ensemble Kalman filter, 96 climate state estimates are generated over the most recent six decades. These state estimates are constrained by monthly mean ocean, atmosphere and sea ice observations such that their trajectories track the observed state while enabling estimation of the uncertainties in the approximations to the retrospective mean climate over recent decades. Strongly coupled data assimilation (SCDA) is implemented via an ensemble transform Kalman filter in order to constrain a general circulation climate model to observations. Satellite (altimetry, sea surface temperature, sea ice concentration) and in situ ocean temperature and salinity profiles are directly assimilated each month, whereas atmospheric observations are sub-sampled from the JRA55 atmospheric reanalysis. Strong coupling is implemented via explicit cross domain covariances between ocean, atmosphere, sea ice and ocean biogeochemistry. Atmospheric and surface ocean fields are available at daily resolution and monthly resolution for the land, subsurface ocean and sea ice. The system also produces a complete data archive of initial conditions potentially enabling individual forecasts for all members each month over the 60 year period. The size of the ensemble and application of strongly coupled data assimilation lead to new insights for future reanalyses. CAFE60v1 has been validated in comparison to empirical indices of the major climate teleconnections and blocking from various reanalysis products (ERA5, JRA55, NCEP NR1). Estimates of the large scale ocean structure and transports have been compared to those derived from gridded observational products (WOA18, HadISST, ERSSTv5) and climate model projections (CMIP). Sea ice (extent, concentration and variability) and land surface (precipitation and surface air temperatures) are also compared to a variety of model (ERA5, CMIP) and observational (GPCP, AWAP, HadCRU4, GIOMAS, NSIDC, HadISST) products. This analysis shows that CAFE60v1 is a useful, comprehensive and unique data resource for studying internal climate variability and predictability, including the recent climate response to anthropogenic forcing on multi-year to decadal time scales.","Documentation":"https://data.csiro.au/dap/ws/v2/collections/49803/support/4029","Contact":"Terence.O'Kane@csiro.au","ManagedBy":"[CSIRO](http://csiro.au/)","UpdateFrequency":"6 Monthly (Approx)","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","climate","sustainability"],"License":"Creative Commons Attribution-ShareAlike 4.0 International Licence","Resources":[{"Description":"CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1 (CAFE60v1) data","ARN":"arn:aws:s3:::cafe60-reanalysis-dataset-aws-open-data","Region":"ap-southeast-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://cafe60-reanalysis-dataset-aws-open-data.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for updates to data","ARN":"arn:aws:sns:ap-southeast-2:970429975021:Cafe60-Data-Changes","Region":"ap-southeast-2","Type":"SNS Topic"}],"RegistryEntryAdded":"2021-07-07","RegistryEntryLastModified":"2021-07-07","Slug":"csiro-cafe60","Sources":["awslabs-open-data-registry"]}
{"Name":"Global aboveground biomass (AGB), 100m by CTrees.org","Description":"CTrees' global aboveground biomass (AGB) estimates, annually for 2000-2025 at a standard spatial resolution of 100 meters.\n","Documentation":"[Project overview](https://app.earthmover.io/marketplace/69e00e1c21faca8bf36879d2)","Contact":"info@ctrees.org","ManagedBy":"[CTrees](https://ctrees.org/)","UpdateFrequency":"Annual","Tags":["aws-pds","cog","conservation","deep learning","earth observation","environmental","geospatial","image processing","land cover","satellite imagery","zarr"],"License":"https://creativecommons.org/licenses/by/4.0/\n","Citation":"@misc{https://doi.org/10.82924/7vmb-zv66, doi = {10.82924/7VMB-ZV66}, url = {https://app.earthmover.io/marketplace/69e00e1c21faca8bf36879d2}, author = {Yang, Yan and Saatchi, Sassan and Kwon, Nicholas and Liu, Z and Lin, Wen Tao and Dalagnol, Ricardo and Takougoum Sagang, Le Bienfaiteur and Li, Shuang}, keywords = {FOS: Earth and related environmental sciences}, title = {CTrees Global Aboveground Biomass}, publisher = {Earthmover PBC (United States)}, year = {2026}, copyright = {Creative Commons Attribution 4.0 International} }","Resources":[{"Description":"IceChunk Repository with global AGB Density at 100m resolution from CTrees.org.","ARN":"arn:aws:s3:::ctrees-agb-100m-global/agb_100m_global/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Access Via Github Repo](https://github.com/ctrees-products/open-data-examples/blob/main/CTrees_Above_Ground_Biomass_Data_Access.ipynb)","[Access Via Earthmover](https://app.earthmover.io/marketplace/69e00e1c21faca8bf36879d2)"]},{"Description":"Cloud-optimized GeoTIFF files for global AGB Density and Uncertainty at 100m resolution from CTrees.org.","ARN":"arn:aws:s3:::ctrees-agb-100m-global/cogs/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse CTrees Bucket](https://ctrees-agb-100m-global.s3.us-west-2.amazonaws.com/cogs/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"CTrees - Above Ground Biomass Data Access.ipynb","URL":"https://github.com/ctrees-products/open-data-examples/blob/main/CTrees_Above_Ground_Biomass_Data_Access.ipynb","AuthorName":"Naomi Provost"}],"Publications":[{"Title":"Preprint | From Snapshot Maps to Continuous Monitoring of Global Forest Carbon at 100 m Resolution (2000-2025)","AuthorName":"Yan Yang, Ricardo Dalagnol, Wentao Lin, Nicholas Kwon, Zhihua Liu, Sassan Saatchi, Le Bienfaiteur Sagang","URL":"https://doi.org/10.31223/X5KJ4Q"},{"Title":"Webinar | From Snapshot to System: Building a Global Aboveground Biomass Monitoring Framework","AuthorName":"Yan Yang and Sassan Saatchi","URL":"youtube.com/watch?v=DBg88wAXKIo"}]},"RegistryEntryAdded":"2026-05-05","RegistryEntryLastModified":"2026-06-10","Slug":"ctrees-agb-100m-global","Sources":["awslabs-open-data-registry"]}
{"Name":"Canopy Tree Height Map for the Amazon Forest (mean height composite 2020-2024) by CTrees.org","Description":"Mean canopy Tree Height for the Amazon Forest on the period 2020-2024 at 4.78 m of spatial resolution. Created using a deep learning model on high-resolution Planet imagery from the Norway's International Climate and Forest Initiative (NICFI) Satellite Data Program. From the original research paper https://doi.org/10.48550/arXiv.2501.10600\n","Documentation":"[Project overview](https://ctrees.org/products/tree-level)","Contact":"info@ctrees.org","ManagedBy":"[CTrees](https://ctrees.org/)","UpdateFrequency":"TBD","Tags":["aws-pds","cog","conservation","deep learning","earth observation","environmental","geospatial","image processing","land cover","lidar","satellite imagery"],"License":"Our canopy height map is a derivative product of Planet-NICFI and follows the same licence: [NICFI licensing agreement](https://planet.widen.net/s/zfdpf8qxwk/participantlicenseagreement_nicfi_2024)","Citation":"Fabien H Wagner, Ricardo Dalagnol, Griffin Carter, Mayumi CM Hirye, Shivraj Gill, Le Bienfaiteur Sagang Takougoum, Samuel Favrichon, Michael Keller, Jean PHB Ometto, Lorena Alves, Cynthia Creze, Stephanie P George-Chacon, Shuang Li, Zhihua Liu, Adugna Mullissa, Yan Yang, Erone G Santos, Sarah R Worden, Martin Brandt, Philippe Ciais, Stephen C Hagen, Sassan Saatchi. (2025). High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model. arXiv. eprint 2501.10600. URL https://arxiv.org/abs/2501.10600","Resources":[{"Description":"Cloud-optimized GeoTIFF files with names corresponding to the tiling system of the Norway's International Climate and Forest Initiative (NICFI) Satellite Data Program.","ARN":"arn:aws:s3:::ctrees-amazon-canopy-height","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse CTrees Bucket](https://ctrees-amazon-canopy-height.s3.us-west-2.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"How to download the CTrees Amazon Canopy Height Map","URL":"https://ctrees-amazon-canopy-height.s3.us-west-2.amazonaws.com/tutorial.html","AuthorName":"Fabien H Wagner"}],"Publications":[{"Title":"High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model","URL":"https://doi.org/10.48550/arXiv.2501.10600","AuthorName":"Fabien H Wagner, Ricardo Dalagnol, Griffin Carter, Mayumi CM Hirye, Shivraj Gill, Le Bienfaiteur Sagang Takougoum, Samuel Favrichon, Michael Keller, Jean PHB Ometto, Lorena Alves, Cynthia Creze, Stephanie P George-Chacon, Shuang Li, Zhihua Liu, Adugna Mullissa, Yan Yang, Erone G Santos, Sarah R Worden, Martin Brandt, Philippe Ciais, Stephen C Hagen, Sassan Saatchi"},{"Title":"Is this the largest tree in the Amazon? A Q&A with CTrees scientist Fabien Wagner","URL":"https://ctrees.org/news/largest-tree-amazon-with-fabien-wagner-63","AuthorName":"Rachel Kovinsky"}]},"RegistryEntryAdded":"2025-05-14","RegistryEntryLastModified":"2025-06-04","Slug":"ctrees-amazon-canopy-height","Sources":["awslabs-open-data-registry"]}
{"Name":"Sub-Meter Canopy Tree Height of California in 2020 by CTrees.org","Description":"Canopy Tree Height maps for California in 2020. Created using a deep learning model on very-high-resolution airborne imagery from the National Agriculture Imagery Program (NAIP) by United States Department of Agriculture (USDA).\n","Documentation":"[Project overview](https://ctrees.org/products/tree-level)","Contact":"info@ctrees.org","ManagedBy":"[CTrees](https://ctrees.org/)","UpdateFrequency":"TBD","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["aerial imagery","aws-pds","cog","conservation","deep learning","earth observation","environmental","geospatial","image processing","land cover"],"License":"https://creativecommons.org/licenses/by/4.0/\n","Citation":"Fabien H. Wagner, Sophia Roberts, Alison L. Ritz, Griffin Carter, Ricardo Dalagnol, Samuel Favrichon, Mayumi C.M. Hirye, Martin Brandt, Philippe Ciais, Sassan Saatchi, Sub-meter tree height mapping of California using aerial images and LiDAR-informed U-Net model, Remote Sensing of Environment, 2024, https://doi.org/10.1016/j.rse.2024.114099.","Resources":[{"Description":"Cloud-optimized GeoTIFF files with names corresponding to image of California for the year 2020 from the National Agriculture Imagery Program (NAIP) - United States Department of Agriculture (USDA) [NAIP](s3://naip-analytic/).","ARN":"arn:aws:s3:::ctrees-tree-height-ca-2020/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse CTrees Bucket](https://ctrees-tree-height-ca-2020.s3.us-west-2.amazonaws.com/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"Canopy Height Unlocked: California Forest Resources Detailed in New Tree-level Map","URL":"https://ctrees.org/news/california-tree-height-map-34","AuthorName":"Daniel Melling"},{"Title":"Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Model","URL":"https://doi.org/10.1016/j.rse.2024.114099","AuthorName":"Fabien H Wagner, Sophia Roberts, Alison L Ritz, Griffin Carter, Ricardo Dalagnol, Samuel Favrichon, Mayumi CM Hirye, Martin Brandt, Philippe Ciais and Sassan Saatchi"}]},"RegistryEntryAdded":"2024-03-18","RegistryEntryLastModified":"2025-07-09","Slug":"ctrees-california-vhr-tree-height","Sources":["awslabs-open-data-registry"]}
{"Name":"Clinical Trial Sequencing Project - Diffuse Large B-Cell Lymphoma","Description":"The goal of the project is to identify recurrent genetic alterations (mutations, deletions,\namplifications, rearrangements) and/or gene expression signatures. National Cancer Institute (NCI)\nutilized whole genome sequencing and/or whole exome sequencing in conjunction with transcriptome\nsequencing. The samples were processed and submitted for genomic characterization using pipelines\nand procedures established within The Cancer Genome Analysis (TCGA) project.\n","Documentation":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001175.v2.p2\n","Contact":"dcf-support@datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genomic","life sciences","STRIDES","transcriptomics","whole genome sequencing"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"RNA-Seq Gene Expression Quantification","ARN":"arn:aws:s3:::gdc-ctsp-phs001175-2-open","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/CTSP-DLBCL1","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"}],"Publications":[{"Title":"A multiprotein supercomplex controlling oncogenic signalling in lymphoma","URL":"https://www.ncbi.nlm.nih.gov/pubmed?cmd=DetailsSearch&term=29925955[PMID]","AuthorName":"Phelan JD, Young RM, Webster DE, Roulland S, Wright GW, Kasbekar M, Shaffer AL 3rd,\nCeribelli M, Wang JQ, Schmitz R, Nakagawa M, Bachy E, Huang DW, Ji Y, Chen L, Yang Y, Zhao\nH, Yu X, Xu W, Palisoc MM, Valadez RR, Davies-Hill T, Wilson WH, Chan WC, Jaffe ES, Gascoyne\nRD, Campo E, Rosenwald A, Ott G, Delabie J, Rimsza LM, Rodriguez FJ, Estephan F, Holdhoff M,\nKruhlak MJ, Hewitt SM, Thomas CJ, Pittaluga S, Oellerich T, Staudt LM\n"},{"Title":"Genetics and Pathogenesis of Diffuse Large B Cell Lymphoma","URL":"https://doi.org/10.1056/NEJMoa1801445","AuthorName":"Roland Schmitz, Ph.D., George W. Wright, Ph.D., Da Wei Huang, M.D., Calvin A.  Johnson,\nPh.D., James D. Phelan, Ph.D., James Q. Wang, Ph.D., Sandrine Roulland, Ph.D., Monica\nKasbekar, Ph.D., Ryan M. Young, Ph.D., Arthur L. Shaffer, Ph.D., Daniel J. Hodson, M.D.,\nPh.D., Wenming Xiao, Ph.D., et al.\n"}]},"RegistryEntryAdded":"2020-04-07","RegistryEntryLastModified":"2020-10-05","Slug":"ctsp-dlbcl","Sources":["awslabs-open-data-registry"]}
{"Name":"CTU-CHB Intrapartum Cardiotocography Database","Description":"This database, from the Czech Technical University (CTU) in Prague and the University Hospital in Brno (UHB), contains 552 cardiotocography (CTG) recordings, which were carefully selected from 9164 recordings collected between 2010 and 2012 at UHB.","Documentation":"https://doi.org/10.13026/C22013","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C22013","ARN":"arn:aws:s3:::physionet-open/ctu-uhb-ctgdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ctu-uhb-ctgdb","Sources":["awslabs-open-data-registry"]}
{"Name":"CU Ventricular Tachyarrhythmia Database","Description":"This database includes 35 eight-minute ECG recordings of human subjects who experienced episodes of sustained ventricular tachycardia, ventricular flutter, and ventricular fibrillation.","Documentation":"https://doi.org/10.13026/C2X59M","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2X59M","ARN":"arn:aws:s3:::physionet-open/cudb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"cudb","Sources":["awslabs-open-data-registry"]}
{"Name":"CUILESS2016","Description":"The Concept Unique Identifier (CUI)-less database contains a corpus of \"CUI-less\" concepts taken from the SemEval2015 Task 14 that have been assigned CUIs. The annotation process allows assignment of CUIS from any Unified Medical Language System (UMLS) semantic group and compositional normalization using more than one CUI per disease entity. Concepts are mapped to SNOMED CT as represented in the September 2016 version found in Unified Medical Language System (UMLS) 2016AB.","Documentation":"https://doi.org/10.13026/C2F66G","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2F66G","ARN":"arn:aws:s3:::physionet-open/cuiless16/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"cuiless16","Sources":["awslabs-open-data-registry"]}
{"Name":"Cerebral Vasoregulation in Elderly with Stroke","Description":"This database contains multimodal data from a large study investigating the effects of ischemic stroke on cerebral vasoregulation. The cross sectional study compared 60 subjects who suffered strokes, to 60 control subjects, collecting the following data for each patient across multiple days: transcranial doppler of cerebral arteries, 24-h blood pressure numerics, high resolution waveforms (ECG, blood pressure, CO2 and respiration) during various movement tasks, 24-h ECG, EMG, and accelerometer recordings, and gait pressure recordings during a walking test.","Documentation":"https://doi.org/10.13026/C2DW96","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2DW96","ARN":"arn:aws:s3:::physionet-open/cves/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"cves","Sources":["awslabs-open-data-registry"]}
{"Name":"CVSim","Description":"CVSim is a lumped-parameter model of the human cardiovascular system that has been developed and used for research and for teaching quantitative physiology courses at MIT and Harvard Medical School since 1984. The versions presented here have a graphical user interface implemented in Java, with computational components in C.","Documentation":"https://physionet.org/content/cvsim/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/cvsim/","ARN":"arn:aws:s3:::physionet-open/cvsim/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"cvsim","Sources":["awslabs-open-data-registry"]}
{"Name":"Central Weather Administration OpenData","Description":"Various kinds of weather raw data and charts from Central Weather Administration.","Documentation":"https://opendata.cwa.gov.tw/devManual/insrtuction","Contact":"od@cwa.gov.tw","ManagedBy":"[Central Weather Administration](https://www.cwa.gov.tw/)","UpdateFrequency":"Data is updated as soon as newer one is available.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","climate","earth observation","earthquakes","satellite imagery","weather"],"License":"http://data.gov.tw/license","Resources":[{"Description":"CWA data lake","ARN":"arn:aws:s3:::cwaopendata","Region":"ap-northeast-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2023-09-11","RegistryEntryLastModified":"2025-07-09","Slug":"cwa_opendata","Sources":["awslabs-open-data-registry"]}
{"DeprecatedNotice":"Central Weather Bureau OpenData dataset is now Central Weather Administration OpenData dataset. Please refer to Central Weather Administration OpenData dataset <a href=\"https://registry.opendata.aws/cwa_opendata/\">here</a>","Name":"Central Weather Bureau OpenData","Description":"Various kinds of weather raw data and charts from Central Weather Bureau.","Documentation":"https://opendata.cwb.gov.tw/devManual/insrtuction","Contact":"od@cwb.gov.tw","ManagedBy":"[Central Weather Bureau](https://www.cwb.gov.tw/)","UpdateFrequency":"Data is updated as soon as newer one is available.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["aws-pds","climate","earth observation","earthquakes","satellite imagery","weather"],"License":"http://data.gov.tw/license","Resources":[{"Description":"CWB data lake","ARN":"arn:aws:s3:::cwbopendata","Region":"ap-northeast-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2021-09-02","RegistryEntryLastModified":"2024-10-14","Slug":"cwb_opendata","Sources":["awslabs-open-data-registry"]}
{"Name":"OpenCell on AWS","Description":"The OpenCell project is a proteome-scale effort to measure the localization and interactions of human proteins using high-throughput genome engineering to endogenously tag thousands of proteins in the human proteome. This dataset consists of the raw confocal fluorescence microscopy images for all tagged cell lines in the OpenCell library.These images can be interpreted both individually, to determine the localization of particular proteins of interest,  and in aggregate, by training machine learning models to classify or quantify subcellular localization patterns.\n","Documentation":"https://opencell.czbiohub.org/download","Contact":"opencell@czbiohub.org","ManagedBy":"[Biohub](https://www.biohub.org)","UpdateFrequency":"This is the final version of the dataset.","Tags":["aws-pds","Biohub","biology","cell biology","cell imaging","computer vision","fluorescence imaging","imaging","life sciences","machine learning","microscopy"],"License":"CC BY-SA 4.0","Resources":[{"Description":"Live-cell confocal fluorescence microscopy images of the OpenCell library of fluorescently tagged cell lines","ARN":"arn:aws:s3:::czb-opencell","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"cytoself (an unsupervised ML model to quantify localization patterns)","URL":"https://github.com/royerlab/cytoself","AuthorName":"Hirofumi Kobayashi, Keith C. Cheveralls, Manuel D. Leonetti, Loic A. Royer","AuthorURL":"https://www.royerlab.org"},{"Title":"OpenCell web portal","URL":"https://opencell.czbiohub.org","AuthorName":"OpenCell team","AuthorURL":"https://www.czbiohub.org/manuel-leonetti-intracellular-architecture"}],"Publications":[{"Title":"OpenCell: proteome-scale endogenous tagging enables the cartography of human cellular organization","URL":"https://www.biorxiv.org/content/10.1101/2021.03.29.437450v1","AuthorName":"Nathan H. Cho, Keith C. Cheveralls, Andreas-David Brunner, Kibeom Kim, André C. Michaelis, Preethi Raghavan, et al.","AuthorURL":"https://www.czbiohub.org/manuel-leonetti-intracellular-architecture"},{"Title":"Self-Supervised Deep-Learning Encodes High-Resolution Features of Protein Subcellular Localization","URL":"https://www.biorxiv.org/content/10.1101/2021.03.29.437595v1","AuthorName":"Hirofumi Kobayashi, Keith C. Cheveralls, Manuel D. Leonetti, Loic A. Royer","AuthorURL":"https://www.royerlab.org"}]},"RegistryEntryAdded":"2021-08-05","RegistryEntryLastModified":"2026-05-13","Slug":"czb-opencell","Sources":["awslabs-open-data-registry"]}
{"Name":"Distributed Archives for Neurophysiology Data Integration (DANDI)","Description":"DANDI is a public archive of neurophysiology datasets, including raw and processed data,\nand associated software containers. Datasets are shared according to Creative Commons\nCC0 or CC-BY licenses. This US BRAIN Initiative supported archive provides a broad range\nof cellular neurophysiology data including intracellular and extracellular electrophysiology,\noptophysiology, calcium imaging, fiber photometry, behavioral time-series, and images from\nimmunostaining experiments, from over 20 species.\n\nData is organized using community standards:\n[NWB - Neurodata Without Borders](https://www.nwb.org/nwb-neurophysiology/),\n[BIDS - Brain Imaging Data Structure](https://bids.neuroimaging.io/),\n[NGFF - Next Generation File Format](https://ngff.openmicroscopy.org/) for Zarr-based imaging data, and\n[NIDM - Neuro Imaging Data Model](http://nidm.nidash.org/).\n\nThe S3 bucket is organized as follows:\n- **dandisets/** - Metadata and manifests for each Dandiset version; manifests reference keys\n  under blobs/ or zarrs/ for actual data. See [DANDI schema](https://github.com/dandi/schema/)\n  for manifest format specifications.\n- **blobs/** - Deduplicated binary data (NWB files) indexed by content hash.\n- **zarrs/** - Zarr arrays for large imaging datasets.\n\nDevelopment of DANDI is supported by the National Institute of Mental Health.\n","Documentation":"https://dandiarchive.org","Contact":"[DANDI Archive Help Desk](https://github.com/dandi/helpdesk/issues/new/choose)","ManagedBy":"[DANDI Archive](https://about.dandiarchive.org/team)","UpdateFrequency":"New datasets deposited every month","Tags":["aws-pds","biology","calcium imaging","cell imaging","electrophysiology","hdf5","life sciences","neuroimaging","neurophysiology","neuroscience","zarr"],"License":"CC0, CC-BY","Resources":[{"Description":"All datasets","ARN":"arn:aws:s3:::dandiarchive","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"DANDI Example Notebooks","URL":"https://dandi.github.io/example-notebooks/","AuthorName":"DANDI Project","AuthorURL":"https://dandiarchive.org/"},{"Title":"DANDI User Guide - Downloading and Using Data","URL":"https://docs.dandiarchive.org/user-guide-using/using/","AuthorName":"DANDI Project","AuthorURL":"https://dandiarchive.org/"},{"Title":"Explore a DANDI Dandiset in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/40091f78-f576-5b0f-b146-4ec976afcb89/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Get To Know A Dataset - DANDI","URL":"https://github.com/dandi/example-notebooks/blob/master/tutorials/open_data_quick_start_2026/Get-to-know-a-Dandiset.ipynb","NotebookURL":"https://github.com/dandi/example-notebooks/blob/master/tutorials/open_data_quick_start_2026/Get-to-know-a-Dandiset.ipynb","AuthorName":"DANDI Team","AuthorURL":"https://github.com/dandi"}],"Tools & Applications":[{"Title":"DANDI API","URL":"https://api.dandiarchive.org/swagger/","AuthorName":"DANDI Project","AuthorURL":"https://dandiarchive.org/"},{"Title":"DANDI CLI and Python API","URL":"https://github.com/dandi/dandi-cli","AuthorName":"DANDI Project","AuthorURL":"https://dandiarchive.org/"},{"Title":"DANDI JupyterHub","URL":"https://hub.dandiarchive.org/","AuthorName":"DANDI Project","AuthorURL":"https://dandiarchive.org/"},{"Title":"DANDI Web Interface","URL":"https://dandiarchive.org/","AuthorName":"DANDI Project","AuthorURL":"https://dandiarchive.org/"},{"Title":"ITK/VTK Viewer for OME-Zarr","URL":"https://kitware.github.io/itk-vtk-viewer/","AuthorName":"Kitware","AuthorURL":"https://www.kitware.com/"},{"Title":"Neuroglancer","URL":"https://github.com/google/neuroglancer","AuthorName":"Google","AuthorURL":"https://google.github.io/neuroglancer/"},{"Title":"Neurosift - Interactive NWB Viewer","URL":"https://neurosift.app/","AuthorName":"Flatiron Institute","AuthorURL":"https://www.simonsfoundation.org/flatiron/"},{"Title":"NWB Explorer","URL":"https://nwbexplorer.opensourcebrain.org/","AuthorName":"Open Source Brain","AuthorURL":"https://metacell.us/"}],"Publications":[{"Title":"A comparison of neuroelectrophysiology databases","URL":"https://www.nature.com/articles/s41597-023-02614-0","AuthorName":"Subash P, Gray A, Bhattacharyya B, et al."},{"Title":"Facilitating analysis of open neurophysiology data on the DANDI Archive using large language model tools","URL":"https://www.nature.com/articles/s41597-025-06285-x","AuthorName":"Magland JF, Ly R, Rübel O, Dichter B"},{"Title":"Neurosift: DANDI exploration and NWB visualization in the browser","URL":"https://joss.theoj.org/papers/10.21105/joss.06590","AuthorName":"Magland J, Soules J, Baker C, Dichter B"}]},"RegistryEntryAdded":"2020-09-21","RegistryEntryLastModified":"2026-11-03","Slug":"dandiarchive","Sources":["awslabs-open-data-registry"]}
{"Name":"DARPA Invisible Headlights Dataset","Description":"\"The DARPA Invisible Headlights Dataset is a large-scale multi-sensor dataset annotated for autonomous, off-road navigation in challenging off-road environments. It features simultaneously collected off-road imagery from multispectral, hyperspectral, polarimetric, and broadband sensors spanning wave-lengths from the visible spectrum to long-wave infrared and provides aligned LIDAR data for ground-truth shape. Camera calibrations, LiDAR registrations, and traversability annotations for a subset of the data are available.\"\n","Documentation":"https://www.kitware.com/darpa-headlights-dataset/","Contact":"ihdataset-aws@kitware.com","ManagedBy":"[Kitware](http://www.kitware.com/)","UpdateFrequency":"No updates anticipated at this time.\n","Tags":["autonomous vehicles","aws-pds","broadband","computer vision","lidar","machine learning","segmentation","us"],"License":"This work is licensed under CC BY 4.0. This dataset was developed with funding from the Defense Advanced Research Projects Agency (DARPA). Distribution Statement A: Approved for public release. Distribution is unlimited.  We hope this dataset can facilitate future research. If you find this data useful, please cite our WACV 2024 publication.","Citation":"\"@inproceedings{yellin2024concurrent,\ntitle={Concurrent Band Selection and Traversability Estimation From Long-Wave Hyperspectral Imagery in Off-Road Settings},\nauthor={Yellin, Florence and McCloskey, Scott and Hill, Cole and Smith, Eric and Clipp, Brian},\nbooktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},\npages={7483--7492},\nyear={2024}\n}\"\n","Resources":[{"Description":"Various types of multispectral, hyperspectral, polarimetric, and broadband imagery (i.e. jpg, bsq, hdr, envi, raw, bmp); LiDAR data (i.e. pcap, las); and metadata (i.e., txt, xlsx, csv).","ARN":"arn:aws:s3:::ihdataset-01","Region":"us-east-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2024-02-09","RegistryEntryLastModified":"2024-02-22","Slug":"darpa-invisible-headlights","Sources":["awslabs-open-data-registry"]}
{"Name":"Data to Science Catalog","Description":"A user-generated geospatial data collection maintained by the Data to Science  platform. Contributions vary by project, but typically include cloud-optimized  datasets such as Cloud-Optimized GeoTIFFs (COGs) and Cloud-Optimized Point  Clouds (COPCs), designed for efficient streaming, visualization, and analysis  in modern geospatial applications.\n","Documentation":"https://docs.gdsl.org/data-to-science-user-manual/","Contact":"jinha@purdue.edu","ManagedBy":"Geospatial Data Science Lab at Purdue University","UpdateFrequency":"None","Tags":["aerial imagery","agriculture","aws-pds","cog","dsm","dtm","earth observation","geospatial","high-throughput imaging","image processing","lidar","mapping","stac","tiff"],"License":"CC-BY-SA-4.0 (https://creativecommons.org/licenses/by-sa/4.0/)","Citation":null,"Resources":[{"Description":"Storage for Data to Science open datasets","ARN":"arn:aws:s3:::data-to-science-opendata-bucket","Region":"us-east-2","Type":"S3 Bucket"},{"Description":"Notifications for new Data to Science data","ARN":"arn:aws:sns:us-east-2:244573782505:data-to-science-opendata-bucket-object_created","Region":"us-east-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Data to Science Notebook Tutorials","URL":"https://py.d2s.org/guides/","NotebookURL":null,"AuthorName":"GDSL","AuthorURL":"https://gdsl.org","Services":null},{"Title":"Data to Science User Manual","URL":"https://docs.gdsl.org/data-to-science-user-manual/","NotebookURL":null,"AuthorName":"GDSL","AuthorURL":"https://gdsl.org","Services":null}],"Tools & Applications":[{"Title":"D2S Browser","URL":"https://plugins.qgis.org/plugins/d2s_browser/","AuthorName":"GDSL","AuthorURL":"https://gdsl.org"},{"Title":"Data to Science Web Application","URL":"https://aws.d2s.org","AuthorName":"GDSL","AuthorURL":"https://gdsl.org"}]},"DeprecatedNotice":null,"ADXCategories":null,"RegistryEntryAdded":"2026-01-29","RegistryEntryLastModified":"2026-02-05","Slug":"data-to-science","Sources":["awslabs-open-data-registry"]}
{"Name":"High Resolution Canopy Height Maps by WRI and Meta","Description":"Global and regional Canopy Height Maps (CHM). Created using machine learning models on high-resolution worldwide Maxar satellite imagery. \n","Documentation":"https://github.com/facebookresearch/HighResCanopyHeight","Contact":"dataforgood@meta.com","ManagedBy":"[Meta](https://dataforgood.fb.com/)","UpdateFrequency":"TBD","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aerial imagery","agriculture","aws-pds","climate","cog","earth observation","geospatial","image processing","land cover","machine learning","satellite imagery"],"License":"https://creativecommons.org/licenses/by/4.0/\n","Citation":"Meta and World Resources Institude (WRI) - 2024. High Resolution Canopy Height Maps (CHM). Source imagery for CHM © 2016 [Maxar](https://www.maxar.com/products/imagery-basemaps). Accessed DAY MONTH YEAR.","Resources":[{"Description":"Global Canopy Height maps. GeoTIFF files and Geojson files with observation date","ARN":"arn:aws:s3:::dataforgood-fb-data/forests/v1/alsgedi_global_v6_float/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"California Canopy Height maps. GeoTIFF files and Geojson files with observation date","ARN":"arn:aws:s3:::dataforgood-fb-data/forests/v1/California/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Sao Paulo Canopy Height maps. GeoTIFF files and Geojson files with observation date","ARN":"arn:aws:s3:::dataforgood-fb-data/forests/v1/sao_paulo/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Sub Saharan Africa Height maps. GeoTIFF files and Geojson files with observation date","ARN":"arn:aws:s3:::dataforgood-fb-data/forests/v1/subsaharan_africa/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Model weights","ARN":"arn:aws:s3:::dataforgood-fb-data/forests/v1/models/","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"BlockMap: any real place as a Minecraft world, with trees placed and sized from the canopy height map","URL":"https://blockmap.app/how-worlds-are-made#trees","AuthorName":"Nikandr Software Limited","AuthorURL":"https://nikandrsoftware.com"},{"Title":"Global Canopy Height on Earth Engine","URL":"https://meta-forest-monitoring-okw37.projects.earthengine.app/view/canopyheight","AuthorName":"Meta and WRI"}],"Publications":[{"Title":"Every tree counts: Large-scale mapping of canopy height at the resolution of individual trees","URL":"https://research.facebook.com/blog/2023/4/every-tree-counts-large-scale-mapping-of-canopy-height-at-the-resolution-of-individual-trees/","AuthorName":"Jamie Tolan, Camille Couprie, and Tracy Johns"},{"Title":"Sub-meter resolution canopy height maps using self-supervised learning and a vision transformer trained on Aerial and GEDI Lidar","URL":"https://www.sciencedirect.com/science/article/pii/S003442572300439X","AuthorName":"Jamie Tolan, Hung-I Yang, Ben Nosarzewski, Guillaume Couairon, Huy Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, Theo Moutakanni, Piotr Bojanowski, Tracy Johns, Brian White, Tobias Tiecke, Camille Couprie"},{"Title":"Using Artificial Intelligence to Map the Earth’s Forests","URL":"https://sustainability.fb.com/blog/2024/04/22/using-artificial-intelligence-to-map-the-earths-forests/","AuthorName":"Jamie Tolan, Camille Couprie, John Brandt, Justine Spore, Tobias Tiecke, Tracy Johns and Patrick Nease"}]},"RegistryEntryAdded":"2023-03-20","RegistryEntryLastModified":"2026-11-01","Slug":"dataforgood-fb-forests","Sources":["awslabs-open-data-registry"]}
{"Name":"Version 2 High Resolution Canopy Height Maps by WRI and Meta","Description":"Version 2 Global and regional Canopy Height Maps (CHMv2). Created using machine learning models on high-resolution worldwide Vantor satellite imagery. \n","Documentation":"https://github.com/facebookresearch/dinov3","Contact":"dataforgood@meta.com","ManagedBy":"[Meta](https://dataforgood.fb.com/)","UpdateFrequency":"TBD","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aerial imagery","agriculture","aws-pds","climate","cog","earth observation","geospatial","image processing","land cover","machine learning","satellite imagery"],"License":"https://creativecommons.org/licenses/by/4.0/\n","Citation":"Meta and World Resources Institude (WRI) - 2026. Version 2 High Resolution Canopy Height Maps (CHMv2). Source imagery for CHM © 2016 [Vantor](https://vantor.com/product/vivid/mosaic/). Accessed DAY MONTH YEAR.","Resources":[{"Description":"Global Canopy Height maps. GeoTIFF files and Geojson files with observation date","ARN":"arn:aws:s3:::dataforgood-fb-data/forests/v2/global/dinov3_global_chm_v2_ml3/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Notifications for new Version 2 High Resolution Canopy Height Maps by WRI and Meta data","ARN":"arn:aws:sns:us-east-1:632571768781:dataforgood-fb-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset - CHMv2","URL":"https://github.com/facebookresearch/dinov3/blob/main/notebooks/chmv2_dataset_exploration.ipynb","NotebookURL":"https://github.com/facebookresearch/dinov3/blob/main/notebooks/chmv2-get_to_know_a_dataset.ipynb","AuthorName":"Meta"}],"Tools & Applications":[{"Title":"Global Canopy Height on Earth Engine","URL":"https://meta-forest-monitoring-okw37.projects.earthengine.app/view/canopyheight","AuthorName":"Meta and WRI"}],"Publications":[{"Title":"CHMv2: Improvements in Global Canopy Height Mapping using DINOv3","URL":"https://arxiv.org/abs/2603.06382","AuthorName":"John Brandt, Seungeun Yi, Jamie Tolan, Xinyuan Li, Peter Potapov,Jessica Ertel, Justine Spore, Huy V. Vo, Michael Ramamonjisoa, Patrick Labatut, Piotr Bojanowski, and Camille Couprie"},{"Title":"DINOv3","URL":"https://arxiv.org/abs/2508.10104","AuthorName":"Oriane Siméoni, Huy V. Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, Francisco Massa, Daniel Haziza, Luca Wehrstedt, Jianyuan Wang, Timothée Darcet, Théo Moutakanni, Leonel Sentana, Claire Roberts, Andrea Vedaldi, Jamie Tolan, John Brandt, Camille Couprie, Julien Mairal, Hervé Jégou, Patrick Labatut, Piotr Bojanowski"}]},"RegistryEntryAdded":"2026-03-09","RegistryEntryLastModified":"2026-04-06","Slug":"dataforgood-fb-forestsv2","Sources":["awslabs-open-data-registry"]}
{"Name":"High Resolution Population Density Maps + Demographic Estimates by CIESIN and Meta","Description":"Population data for a selection of countries, allocated to 1 arcsecond blocks and provided in a combination of CSV\nand Cloud-optimized GeoTIFF files. This refines [CIESIN’s Gridded Population of the World](https://sedac.ciesin.columbia.edu/data/collection/gpw-v4)\nusing machine learning models on high-resolution worldwide Maxar satellite imagery. CIESIN population counts aggregated from worldwide census\ndata are allocated to blocks where imagery appears to contain buildings.\n","Documentation":"[Project overview](https://dataforgood.facebook.com/dfg/docs/methodology-high-resolution-population-density-maps) and [instructions for use with AWS Athena](https://dataforgood.facebook.com/dfg/docs/using-hrsl-public-dataset-with-athena)","Contact":"disastermaps@fb.com","ManagedBy":"[Meta](https://dataforgood.fb.com/)","UpdateFrequency":"Quarterly","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aerial imagery","aws-pds","demographics","disaster response","geospatial","image processing","machine learning","population","satellite imagery"],"License":"https://creativecommons.org/licenses/by/4.0/\n","Citation":"Meta and Center for International Earth Science Information Network - CIESIN - Columbia University. 2022. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 [Maxar](https://www.maxar.com/products/imagery-basemaps). Accessed DAY MONTH YEAR.","Resources":[{"Description":"Cloud-optimized GeoTIFF files","ARN":"arn:aws:s3:::dataforgood-fb-data/hrsl-cogs/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"CSV files","ARN":"arn:aws:s3:::dataforgood-fb-data/csv/","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Investigating environmental characteristics of US cities using publicly available ASDI datasets","URL":"https://github.com/aws-samples/aws-asdi-cities-smsl-notebook","NotebookURL":"https://github.com/aws-samples/aws-asdi-cities-smsl-notebook/blob/main/ASDI_Cities_Demo.ipynb","AuthorName":"Darren Ko","Services":["Amazon SageMaker Studio Lab"]}]},"RegistryEntryAdded":"2019-07-08","RegistryEntryLastModified":"2025-02-04","Slug":"dataforgood-fb-hrsl","Sources":["awslabs-open-data-registry"]}
{"Name":"Daylight Map Distribution of OpenStreetMap","Description":"Daylight is a complete distribution of global, open map data that’s freely available with support from community and professional mapmakers. Meta combines the work of global contributors to projects like OpenStreetMap with quality and consistency checks from Daylight mapping partners to create a free, stable, and easy-to-use street-scale global map. <br/><br/>\nThe Daylight Map Distribution contains a validated subset of the OpenStreetMap database. In addition to the standard OpenStreetMap PBF format, Daylight is available in two parquet formats that are optimized for AWS Athena including geometries (Points, LineStrings, Polygons, or MultiPolygons). First, **Daylight OSM Features** contains the nearly 1B renderable OSM features. Second, **Daylight OSM Elements** contains all of OSM, including all 7B nodes without attributes, and relations that do not contain geometries, such as turn restrictions. <br/><br/> \nDaylight Earth Table is a new data schema that classifies OpenStreetMap-style tags into a 3-level ontology (theme, class, subclass) and is the result of running the earth table classification over the latest release (v1.18) of the Daylight Map Distribution. The Daylight Earth Table is available as parquet files on Amazon S3. \n","Documentation":"[Project Website](https://daylightmap.org)","Contact":"osm@fb.com","ManagedBy":"[Meta](https://dataforgood.fb.com/)","UpdateFrequency":"Quarterly","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aws-pds","disaster response","geospatial","mapping","osm"],"License":"[Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/)\n","Resources":[{"Description":"Daylight OSM PBF Files","ARN":"arn:aws:s3:::daylight-map-distribution/release/","Region":"us-west-1","Type":"S3 Bucket"},{"Description":"New OSM PBF Notification","ARN":"arn:aws:sns:us-west-1:632571768781:Daylight","Region":"us-west-1","Type":"SNS Topic"},{"Description":"Daylight OSM Features (Parquet)","ARN":"arn:aws:s3:::daylight-openstreetmap/parquet/osm_features","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Daylight OSM Elements (Parquet)","ARN":"arn:aws:s3:::daylight-openstreetmap/parquet/osm_elements","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"New Parquet File Notification","ARN":"arn:aws:sns:us-west-2:632571768781:Analysis_Ready_Daylight","Region":"us-west-2","Type":"SNS Topic"},{"Description":"Daylight Earth Table (Parquet)","ARN":"arn:aws:s3:::daylight-openstreetmap/earth","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Daylight Earth Tables","URL":"https://daylightmap.org/earth/","AuthorName":"Daylight Contributors","Services":["Amazon Athena"]},{"Title":"Loading the Daylight Map Distribution OpenStreetMap Features into AWS Athena","URL":"https://gist.github.com/jenningsanderson/3e42a99dcb8f760038ad8aa47ea38ce8","AuthorName":"Jennings Anderson","Services":["Amazon Athena"]},{"Title":"Querying the Daylight OpenStreetMap Distribution with Amazon Athena","URL":"https://aws.amazon.com/blogs/publicsector/querying-daylight-openstreetmap-distribution-amazon-athena/","AuthorName":"Jennings Anderson and Mike Jeffe","AuthorURL":"http://jenningsanderson.com","Services":["Amazon Athena"]}],"Publications":[{"Title":"Daylight Earth Tables (NACIS 2022)","URL":"https://daylightmap.org/assets/nacis_2022_daylight_earth_table.pdf","AuthorName":"Jennings Anderson, Jonah Adkins, Jacob Wasserman"},{"Title":"Increasing OpenStreetMap Data Accessibility with the Analysis-Ready Daylight Distribution of OpenStreetMap: A Demonstration of Cloud-Based Assessments of Global Building Completeness","URL":"https://www.youtube.com/watch?v=rVBGLCFKAtw&t=161s","AuthorName":"Jennings Anderson and Timmera Whaley Omidire"}]},"RegistryEntryAdded":"2021-12-16","RegistryEntryLastModified":"2025-02-04","Slug":"daylight-osm","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"Please see [here](https://registry.opendata.aws/dc-lidar) for the latest content about this dataset.","Name":"District of Columbia - Classified Point Cloud LiDAR","Description":"\"LiDAR point cloud data for Washington, DC is available for anyone to use on Amazon S3.\nThis dataset, managed by the Office of the Chief Technology Officer (OCTO), through the\ndirection of the District of Columbia GIS program, contains tiled point cloud data for\nthe entire District along with associated metadata.\"\n","Documentation":"[2015 data](https://github.com/awslabs/open-data-docs/tree/main/docs/dc-lidar-2015), [2018 data](https://github.com/awslabs/open-data-docs/tree/main/docs/dc-lidar-2018)","Contact":"dcgis@dc.gov","UpdateFrequency":"The most recent data is from 2018 and 2015 data is available as well. A new data acquisition is planned for 2020.","ManagedBy":"[Washington DC government](https://dc.gov/)","Tags":["aws-pds","geospatial","cities","us-dc","disaster response"],"License":"See Washington, DC [Terms of Use](https://dc.gov/page/terms-and-conditions-use)","Resources":[{"Description":"LAS, XML, SHP","ARN":"arn:aws:s3:::dc-lidar-2015","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"LAS, XML","ARN":"arn:aws:s3:::dc-lidar-2018","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":null,"Tools & Applications":null,"Publications":null},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2022-11-08","Slug":"dc-lidar-2015","Sources":["awslabs-open-data-registry"]}
{"Name":"District of Columbia - Classified Point Cloud LiDAR","Description":"LiDAR point cloud data for Washington, DC is available for anyone to use on Amazon S3.\nThis dataset, managed by the Office of the Chief Technology Officer (OCTO), through the\ndirection of the District of Columbia GIS program, contains tiled point cloud data for\nthe entire District along with associated metadata.\n","Documentation":"[2015 data](https://github.com/awslabs/open-data-docs/tree/main/docs/dc-lidar-2015), [2018 data](https://github.com/awslabs/open-data-docs/tree/main/docs/dc-lidar-2018)","Contact":"dcgis@dc.gov","UpdateFrequency":"The most recent data is from 2018 and 2015 data is available as well. A new data acquisition is planned for 2020.","ManagedBy":"[Washington DC government](https://dc.gov/)","Collabs":{"ASDI":{"Tags":["elevation"]}},"Tags":["aws-pds","cities","disaster response","geospatial","us-dc"],"License":"See Washington, DC [Terms of Use](https://dc.gov/page/terms-and-conditions-use)","Resources":[{"Description":"LAS, XML, SHP","ARN":"arn:aws:s3:::dc-lidar-2015","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://dc-lidar-2015.s3.amazonaws.com/index.html)"]},{"Description":"LAS, XML","ARN":"arn:aws:s3:::dc-lidar-2018","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://dc-lidar-2018.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2019-05-31","RegistryEntryLastModified":"2022-06-14","Slug":"dc-lidar","Sources":["awslabs-open-data-registry"]}
{"Name":"The MIT Supercloud Dataset","Description":"Collection of parsed datacenter logs and time series data of hardware utilization from the MIT Supercloud system.","Documentation":"https://github.com/MIT-AI-Accelerator/datacenter-challenge","Contact":"mit-dcc@mit.edu","ManagedBy":"Siddharth Samsi","UpdateFrequency":"Data will be updated annually","Tags":["aws-pds","cloud computing","datacenter","energy","HPC","workload analysis"],"License":"http://creativecommons.org/licenses/by-nc-nd/4.0/","Citation":"Samsi, Siddharth, Weiss, Matthew, Bestor, David, et al. \"The MIT Supercloud Dataset.\" 2021 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2021.","Resources":[{"Description":"The MIT Supercloud Dataset","ARN":"arn:aws:s3:::mit-supercloud-dataset","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Generating the labelled dataset","URL":"https://nbviewer.org/github/MIT-AI-Accelerator/MIT-Supercloud-Dataset/blob/main/notebooks/Intro-to-MIT-Supercloud-Dataset.ipynb","NotebookURL":"https://github.com/MIT-AI-Accelerator/MIT-Supercloud-Dataset/blob/main/notebooks/Intro-to-MIT-Supercloud-Dataset.ipynb","AuthorName":"Matthew Weiss","AuthorURL":"https://github.com/MIT-AI-Accelerator/workload-classification","Services":["Amazon SageMaker Studio Lab"]},{"Title":"Introduction to the MIT Supercloud Dataset","URL":"https://nbviewer.org/github/MIT-AI-Accelerator/MIT-Supercloud-Dataset/blob/main/notebooks/Intro-to-MIT-Supercloud-Dataset.ipynb","AuthorName":"Matthew Weiss","NotebookURL":"https://github.com/MIT-AI-Accelerator/MIT-Supercloud-Dataset/blob/main/notebooks/Intro-to-MIT-Supercloud-Dataset.ipynb","Services":["Amazon SageMaker Studio Lab"]}],"Tools & Applications":[{"Title":"The MIT Supercloud Workload Classification Challenge","URL":"https://github.com/MIT-AI-Accelerator/workload-classification","AuthorName":"Benny J. Tang, Qiqi Chen, Matthew L. Weiss, et. al."}],"Publications":[{"Title":"AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications","URL":"https://doi.ieeecomputersociety.org/10.1109/HPCA53966.2022.00093","AuthorName":"Baolin Li, Rohin Arora, Siddharth Samsi, et. al.","AuthorURL":"https://github.com/boringlee24/HPCA22_SuperCloud"},{"Title":"The MIT Supercloud Dataset","URL":"https://arxiv.org/abs/2108.02037","AuthorName":"Siddharth Samsi, Matthew Weiss, David Bestor, et. al."},{"Title":"The MIT Supercloud Workload Classification Challenge","URL":"https://arxiv.org/abs/2204.05839","AuthorName":"Benny J. Tang, Qiqi Chen, Matthew L. Weiss, et. al.","AuthorURL":"https://github.com/MIT-AI-Accelerator/workload-classification"}]},"RegistryEntryAdded":"2022-10-18","RegistryEntryLastModified":"2025-02-04","Slug":"dcc","Sources":["awslabs-open-data-registry"]}
{"Name":"Data Chromatix","Description":"Analysis of biomedical time series plays a key role in clinical management and basic investigation. However, most conventional monitors streaming data in real-time show only the most recent values, not referenced to past dynamics. The proposed visualization method (termed “data chromatix”) was developed to address this challenge by bringing memory of the system’s past behavior into the current display window. The function DataChromatix.m (version 1.0) assigns a color to each data point of a time series. The color is determined by the values of a normalized histogram (estimated probability density function) computed from a pre-selected segment of the data. The algorithm receives the time series as input and generates a video of its colorized version as it would look on a typical monitor display, as well as a static graph of the entire colorized signal.","Documentation":"https://physionet.org/content/dchromatix/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/dchromatix/","ARN":"arn:aws:s3:::physionet-open/dchromatix/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"dchromatix","Sources":["awslabs-open-data-registry"]}
{"Name":"Dynamical Density Delay Maps","Description":"This project contains Matlab functions for visualizing the behavior of complex systems by means of the dynamical density delay mapping (\"D3-Map\") technique . This simple-to-implement visualization method provides an animated representation of a system's dynamics.","Documentation":"https://physionet.org/content/dddmaps/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/dddmaps/","ARN":"arn:aws:s3:::physionet-open/dddmaps/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"dddmaps","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa ALOS PALSAR, ALOS-2 PALSAR-2 and JERS-1","Description":"The ALOS/PALSAR annual mosaic is a global 25 m resolution dataset that combines data from many images captured by JAXA’s PALSAR and PALSAR-2 sensors on ALOS-1 and ALOS-2 satellites respectively. This product contains radar measurement in L-band and in HH and HV polarizations. It has a spatial resolution of 25 m and is available annually for 2007 to 2010 (ALOS/PALSAR) and 2015 to 2020 (ALOS-2/PALSAR-2).\nThe JERS annual mosaic is generated from images acquired by the SAR sensor on the Japanese Earth Resources Satellite-1 (JERS-1) satellite. This product contains radar measurement in L-band and HH polarization. It has a spatial resolution of 25 m and is available for 1996.\nThis mosaic data is part of a global dataset provided by the Japan Aerospace Exploration Agency (JAXA) Earth Observation Research Center.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/ALOS_PALSAR_annual_mosaic_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"As available, generally annually.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac","synthetic aperture radar"],"License":"Data is available for free under the [terms of use](https://earth.jaxa.jp/policy/en.html).\n","Resources":[{"Description":"ALOS PALSAR ALOS-2 PALSAR-2 data","ARN":"arn:aws:s3:::deafrica-input-datasets/alos_palsar_mosaic","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/alos_palsar_mosaic)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)","ARN":"arn:aws:s3:::deafrica-input-datasets-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent contain all object creation events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-input-datasets-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer (ALOS PALSAR and ALOS-2 PALSAR-2)","URL":"https://explorer.digitalearth.africa/products/alos_palsar_mosaic","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer (JERS)","URL":"https://explorer.digitalearth.africa/products/jers_sar_mosaic","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}],"Publications":[{"Title":"Introduction to DE Africa","URL":"https://youtu.be/Wkf7N6O9jJQ","AuthorName":"Dr Fang Yuan"}]},"RegistryEntryAdded":"2021-12-08","RegistryEntryLastModified":"2023-02-28","Slug":"deafrica-alos-jers","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa CHIRPS Rainfall","Description":"Digital Earth Africa (DE Africa) provides free and open access to a copy of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) monthly and daily products over Africa. The CHIRPS rainfall maps are produced and provided by the Climate Hazards Center in collaboration with the US Geological Survey, and use both rain gauge and satellite observations.\nThe CHIRPS-2.0 Africa Monthly dataset is regularly indexed to DE Africa from the CHIRPS monthly data. The CHIRPS-2.0 Africa Daily dataset is likewise indexed from the CHIRPS daily data. Both products have been converted to cloud-opitmized GeoTIFFs, and can be accessed through DE Africa’s Open Data Cube. This means the full archive of CHIRPS daily and monthly rainfall can be easily used for inspection or analysis across DE Africa platforms, including the user-interactive DE Africa Map.\nFor more information on the dataset, see the [CHIRPS website](https://www.chc.ucsb.edu/data/chirps).\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/CHIRPS_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"Monthly.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","climate","cog","deafrica","earth observation","food security","geospatial","meteorological","satellite imagery","stac","sustainability"],"License":"To the extent possible under the law, Pete Peterson has waived all copyright and related or neighboring rights to CHIRPS. CHIRPS data is in the public domain as registered with Creative Commons.\n","Resources":[{"Description":"CHIRPS daily rainfall","ARN":"arn:aws:s3:::deafrica-input-datasets/rainfall_chirps_daily","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/rainfall_chirps_daily)"]},{"Description":"CHIRPS monthly rainfall","ARN":"arn:aws:s3:::deafrica-input-datasets/rainfall_chirps_monthly","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/rainfall_chirps_monthly)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)","ARN":"arn:aws:s3:::deafrica-input-datasets-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent contain all object creation events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-input-datasets-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Rainfall - Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)","URL":"https://docs.digitalearthafrica.org/en/latest/sandbox/notebooks/Datasets/Rainfall_CHIRPS.html","AuthorName":"Digital Earth Africa Contributors"}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer (CHIRPS daily rainfall)","URL":"https://explorer.digitalearth.africa/products/rainfall_chirps_daily","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer (CHIRPS monthly rainfall)","URL":"https://explorer.digitalearth.africa/products/rainfall_chirps_monthly","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}],"Publications":[{"Title":"Introduction to DE Africa","URL":"https://youtu.be/Wkf7N6O9jJQ","AuthorName":"Dr Fang Yuan"},{"Title":"The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes","URL":"https://www.nature.com/articles/sdata201566","AuthorName":"Chris Funk, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, James Rowland, Laura Harrison, Andrew Hoell and Joel Michaelsen"}]},"RegistryEntryAdded":"2022-07-25","RegistryEntryLastModified":"2022-10-26","Slug":"deafrica-chirps","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa - Copernicus Global Land Service - Lake Water Quality","Description":"The Copernicus Global Land Service – Lake Water Quality products offer a comprehensive, satellite-derived monitoring system for assessing key water quality indicators in major large lakes, typically those greater than 50 hectares. These datasets are generated using optical satellite sensors, primarily Sentinel-2 MSI and Sentinel-3 OLCI, with earlier archives derived from Envisat MERIS. Spanning multiple spatial resolutions (100 m and 300 m) and temporal scales (10-day composites), they support both near-real-time and retrospective assessments of inland water quality.\n\nKey parameters include surface reflectance, turbidity, total suspended matter (TSM), chlorophyll-a concentration, trophic state index, and floating cyanobacteria risk—all essential for monitoring eutrophication, ecological health, and harmful algal blooms (HABs). The datasets cover the period from 2002 to the present, providing long-term continuity for environmental monitoring and scientific research, with focused coverage in Europe and Africa.\n\nAll products are delivered using standardized geospatial grids (EPSG:4326) and include quality flags, detailed metadata, and validation against in situ observations to ensure reliability. Continuous improvements across product versions—such as enhanced atmospheric correction and updated retrieval algorithms—have significantly improved accuracy and usability. In addition, comprehensive user manuals, technical documentation, and support materials are available, making the data highly accessible to researchers, policymakers, and environmental managers.\n\nDigital Earth Africa (DE Africa) hosts these datasets for the African region, providing free and open access to both the data and associated tools.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/CGLM_Lake_Water_Quality_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"New scene-level data is added regularly, as the Lake Water Quality (LWQ) datasets are updated every 10 days (dekadal composites), with near-real-time versions typically available within 3 to 4 days after satellite acquisition.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac","water"],"License":"DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n","Resources":[{"Description":"Lake Water Quality 2019-2024 (raster 100 m), 10-daily – version 1","ARN":"arn:aws:s3:::deafrica-input-datasets/cgls_lwq100_2019_2024","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/cgls_lwq100_2019_2024)"]},{"Description":"Lake Water Quality 2024 - present (raster 100 m), 10-daily – version 2","ARN":"arn:aws:s3:::deafrica-input-datasets/cgls_lwq100_2024_nrt","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/cgls_lwq100_2024_nrt)"]},{"Description":"Lake Water Quality 2002-2012 (raster 300 m), 10-daily – version 1","ARN":"arn:aws:s3:::deafrica-services/cgls_lwq300_2002_2012","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/cgls_lwq300_2002_2012)"]},{"Description":"Lake Water Quality 2016-2024 (raster 300 m), 10-daily – version 1","ARN":"arn:aws:s3:::deafrica-services/cgls_lwq300_2016_2024","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/cgls_lwq300_2016_2024)"]},{"Description":"Lake Water Quality 2024 - present (raster 300 m), 10-daily – version 2","ARN":"arn:aws:s3:::deafrica-services/cgls_lwq300_2024_nrt","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/cgls_lwq300_2024_nrt)"]}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer ( Lake Water Quality 2002-2012 (raster 300 m), 10-daily – version 1)","URL":"https://explorer.digitalearth.africa/products/cgls_lwq300_2002_2012","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer ( Lake Water Quality 2016-2024 (raster 300 m), 10-daily – version 1)","URL":"https://explorer.digitalearth.africa/products/cgls_lwq300_2016_2024","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer ( Lake Water Quality 2024 - present (raster 100 m), 10-daily – version 2)","URL":"https://explorer.digitalearth.africa/products/cgls_lwq100_2024_nrt","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer ( Lake Water Quality 2024 - present (raster 300 m), global, 10-daily – version 2)","URL":"https://explorer.digitalearth.africa/products/cgls_lwq300_2024_nrt","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer (Lake Water Quality 2019-2024 (raster 100 m), 10-daily – version 1)","URL":"https://explorer.digitalearth.africa/products/cgls_lwq100_2019_2024","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}]},"RegistryEntryAdded":"2025-07-03","RegistryEntryLastModified":"2025-07-03","Slug":"deafrica-clgm-lwq","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa Coastlines","Description":"Africa's long and dynamic coastline is subject to a wide range of pressures, including extreme weather and climate, sea level rise and human development. Understanding how the coastline responds to these pressures is crucial to managing this region, from social, environmental and economic perspectives.\nThe Digital Earth Africa Coastlines (provisional) is a continental dataset that includes annual shorelines and rates of coastal change along the entire African coastline from 2000 to the present.\nThe product combines satellite data from the Digital Earth Africa program with tidal modelling to map the typical location of the coastline at mean sea level for each year. The product enables trends of coastal erosion and growth to be examined annually at both a local and continental scale, and for patterns of coastal change to be mapped historically and updated regularly as data continues to be acquired. This allows current rates of coastal change to be compared with that observed in previous years or decades.\nThe ability to map shoreline positions for each year provides valuable insights into whether changes to the coastline are the result of particular events or actions, or a process of more gradual change over time. This information can enable scientists, managers and policy makers to assess impacts from the range of drivers impacting the coastlines and potentially assist planning and forecasting for future scenarios.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/Coastlines_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"To be defined.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","climate","coastal","deafrica","earth observation","geospatial","satellite imagery","sustainability"],"License":"DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n","Resources":[{"Description":"DE Africa Coastlines","ARN":"arn:aws:s3:::deafrica-services/coastlines","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)","ARN":"arn:aws:s3:::deafrica-services-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by the s3 bucket for all object create events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-services-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Monitoring coastal erosion along Africa’s coastline","URL":"https://docs.digitalearthafrica.org/en/latest/sandbox/notebooks/Real_world_examples/Coastal_erosion.html","AuthorName":"Digital Earth Africa Contributors"}],"Tools & Applications":[{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Web Feature Service","URL":"https://geoserver.digitalearth.africa/geoserver/ows?service=wfs&version=2.0.0&request=GetCapabilities","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Web Map Service","URL":"https://geoserver.digitalearth.africa/geoserver/ows?service=wms&version=1.3.0&request=GetCapabilities","AuthorName":"Digital Earth Africa Contributors"}],"Publications":[{"Title":"Introducing the Digital Earth Africa Coastlines Service","URL":"https://www.youtube.com/watch?v=k7S49TX7SHE","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Mapping Australia's dynamic coastline at mean sea level using three decades of Landsat imagery","URL":"https://doi.org/10.1016/j.rse.2021.112734","AuthorName":"Robbi Bishop-Taylor, Rachel Nanson, Stephen Sagar, Leo Lymburner"},{"Title":"Monitoring coastal erosion at Saly Portudal resort, Mbour-Senegal","URL":"https://www.digitalearthafrica.org/media-center/blog/monitoring-coastal-erosion-saly-portudal-resort-mbour-senegal","AuthorName":"Ndeye Fatou SANE, Lissong DIOP, Mamadou Lamine NDIAYE, Amadou SALL, Dr. Moussa SALL"}]},"RegistryEntryAdded":"2022-10-19","RegistryEntryLastModified":"2022-10-30","Slug":"deafrica-coastlines","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa Cropland Extent Map (2019)","Description":"Digital Earth Africa's cropland extent map (2019) shows the estimated location of croplands in Africa for the period January to December 2019. Cropland is defined as: \"a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvest-able at least once within the 12 months after the sowing/planting date.\" This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation. \nThis provisional cropland extent map has a resolution of 10m, and was built using Copernicus Sentinel-2 satellite images from 2019. The cropland extent map was produced using extensive training data from regions across Africa, coupled with a Random Forest machine learning model. The continental service contains maps built separately for eight Agro-Ecological Zones (AEZs). For a detailed exploration of the methods used to produce the cropland extent map, read the Jupyter Notebooks in DE Africa’s crop-mask GitHub repository.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/Cropland_extent_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"To be defined.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","earth observation","food security","geospatial","satellite imagery","stac","sustainability"],"License":"DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n","Resources":[{"Description":"Cropland extent map 2019","ARN":"arn:aws:s3:::deafrica-services/crop_mask","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/crop_mask)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)","ARN":"arn:aws:s3:::deafrica-services-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by the s3 bucket for all object create events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-services-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer (Cropland Extent Map)","URL":"https://explorer.digitalearth.africa/products/crop_mask","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}],"Publications":[{"Title":"Co-Production of a 10-m Cropland Extent Map for Continental Africa using Sentinel-2, Cloud Computing, and the Open-Data-Cube","URL":"https://agu2021fallmeeting-agu.ipostersessions.com/Default.aspx?s=BA-E4-89-84-15-50-25-89-AE-A8-EF-C5-32-7D-19-EE","AuthorName":"Chad Burton, Fang Yuan, Chong Ee-Faye, Meghan Halabisky, David Ongo, Fatou Mar, Victor Addabor, Bako Mamane and Sena Adimou"},{"Title":"Cropland Extent is now available for the entire African continent","URL":"https://www.digitalearthafrica.org/media-center/blog/cropland-extent-now-available-entire-african-continent","AuthorName":"Digital Earth Africa Contributors"}]},"RegistryEntryAdded":"2022-07-25","RegistryEntryLastModified":"2022-11-08","Slug":"deafrica-crop-extent","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa Fractional Cover","Description":"Fractional cover (FC) describes the landscape in terms of coverage by green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.) and bare soil. It provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The product is derived from Landsat satellite data, using an algorithm developed by the [Joint Remote Sensing Research Program](https://www.jrsrp.org.au/). \nDigital Earth Africa's FC service has two components. Fractional Cover is estimated from each Landsat scene, providing measurements from individual days. Fractional Cover Annual Summary (Percentiles) provides 10th, 50th, and 90th percentiles estimated independently for the green vegetation, non-green vegetation, and bare soil fractions observed in each calendar year (1st of January - 31st December).\nWhile the scene based Fractional Cover can be used to study dynamic processes, the annual summaries make it easier to analyse year to year changes. The percentiles provide robust estimates of the low, median and high proportion values observed for each cover type in a year, which can be used to characterise the land cover.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/Fractional_Cover_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"New scene-level data is added as new Landsat data is available. New summaries are available soon after data is available for a year.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac","sustainability"],"License":"DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n","Resources":[{"Description":"Fractional Cover data","ARN":"arn:aws:s3:::deafrica-services/fc_ls","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/fc_ls)"]},{"Description":"Fractional Cover Annual Summary data","ARN":"arn:aws:s3:::deafrica-services/fc_ls_summary_annual","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/fc_ls_summary_annual)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)","ARN":"arn:aws:s3:::deafrica-services-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item","ARN":"arn:aws:sns:af-south-1:565417506782:deafrica-landsat-wofs","Region":"af-south-1","Type":"SNS Topic"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by the s3 bucket for all object create events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-services-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer (Fractional Cover Annual Summary)","URL":"https://explorer.digitalearth.africa/products/fc_ls_summary_annual","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer (Fractional Cover)","URL":"https://explorer.digitalearth.africa/products/fc_ls","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}],"Publications":[{"Title":"Introduction to DE Africa","URL":"https://youtu.be/Wkf7N6O9jJQ","AuthorName":"Dr Fang Yuan"}]},"RegistryEntryAdded":"2022-07-25","RegistryEntryLastModified":"2022-07-25","Slug":"deafrica-fractional-cover","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa GeoMAD","Description":"GeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes.\nThe geomedian component combines measurements collected over the specified timeframe to produce one representative, multispectral measurement for every pixel unit of the African continent. The end result is a comprehensive dataset that can be used to generate true-colour images for visual inspection of anthropogenic or natural landmarks. The full spectral dataset can be used to develop more complex algorithms.\nFor each pixel, invalid data is discarded, and remaining observations are mathematically summarised using the geomedian statistic. Flyover coverage provided by collecting data over a period of time also helps scope intermittently cloudy areas.\nVariations between the geomedian and the individual measurements are captured by the three Median Absolute Deviation (MAD) layers. These are higher-order statistical measurements calculating variation relative to the geomedian. The MAD layers can be used on their own or together with geomedian to gain insights about the land surface, and understand change over time.\nCalculating GeoMAD over different timeframes and sensors provides a range of insights to the environment. An annual timeframe allows better correction for cloud cover and reduces artifacts for comparison over multiple years. A semiannual timeframe, for example six-month blocks, better captures seasonal variation within one year, but can also be used to compare equivalent periods from different years. Likewise, Landsat sensors allows full utility of the surface reflectance archive dating back to 1984, while more recent Sentinel-2 data provides higher-frequency flyovers and better resolution.\nThe Digital Earth Africa GeoMAD service currently provides annual and six-month semiannual datasets, with separate services for Landsat and Sentinel-2 sensors.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"GeoMADs for Sentinel-2 and Landsat are updated as their respective time periods are available.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac"],"License":"DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n","Resources":[{"Description":"Sentinel-2 GeoMAD (Annual) data","ARN":"arn:aws:s3:::deafrica-services/gm_s2_annual","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_s2_annual)"]},{"Description":"Sentinel-2 GeoMAD (Semi-Annual) data","ARN":"arn:aws:s3:::deafrica-services/gm_s2_semiannual","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_s2_semiannual)"]},{"Description":"Landsat 8 GeoMAD (Annual) data","ARN":"arn:aws:s3:::deafrica-services/gm_ls8_annual","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_ls8_annual)"]},{"Description":"Landsat 5 and 7 GeoMAD (Annual) data","ARN":"arn:aws:s3:::deafrica-services/gm_ls5_ls7_annual","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_ls5_ls7_annual)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)","ARN":"arn:aws:s3:::deafrica-services-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by deafrica-services bucket contain all object create events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-services-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer (Landsat 5 and 7 GeoMAD)","URL":"https://explorer.digitalearth.africa/products/gm_ls5_ls7_annual","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer (Landsat 8 GeoMAD)","URL":"https://explorer.digitalearth.africa/products/gm_ls8_annual","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer (Sentinel-2 Annual GeoMAD)","URL":"https://explorer.digitalearth.africa/products/gm_s2_annual","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer (Sentinel-2 Semi-Annual GeoMAD)","URL":"https://explorer.digitalearth.africa/products/gm_s2_semiannual","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}],"Publications":[{"Title":"Introduction to DE Africa","URL":"https://youtu.be/Wkf7N6O9jJQ","AuthorName":"Dr Fang Yuan"}]},"RegistryEntryAdded":"2021-12-08","RegistryEntryLastModified":"2023-02-28","Slug":"deafrica-geomad","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa Landsat Collection 2 Level 2","Description":"Digital Earth Africa (DE Africa) provides free and open access to a copy of Landsat Collection 2 Level-2 products over Africa. These products are produced and provided by the United States Geological Survey (USGS).\nThe Landsat series of Earth Observation satellites, jointly led by USGS and NASA, have been continuously acquiring images of the Earth’s land surface since 1972. DE Africa provides data from Landsat 5, 7 and 8 satellites, including historical observations dating back to late 1980s and regularly updated new acquisitions.\nNew Level-2 Landsat 7 and Landsat 8 data are available after 15 to 27 days from acquisition. See Landsat Collection 2 Generation Timeline for details.\nUSGS Landsat Collection 2 was released early 2021 and offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant. This internationally recognized certification ensures these products have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.html https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_ST_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"New Landsat data are added regularly, usually within a few hours of them being available in the usgs-landsat bucket.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac"],"License":"There are no restrictions on Landsat data downloaded from the USGS; it can be used or \nredistributed as desired. USGS request that you include a [statement of the data source](https://www.usgs.gov/centers/eros/data-citation?qt-science_support_page_related_con=0#qt-science_support_page_related_con) \nwhen citing, copying, or reprinting USGS Landsat data or images.\n","Resources":[{"Description":"Landsat scenes and metadata","ARN":"arn:aws:s3:::deafrica-landsat","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents) files","ARN":"arn:aws:s3:::deafrica-landsat-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC ST and SR record for each new Item","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-landsat-scene-topic","Region":"af-south-1","Type":"SNS Topic"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by deafrica-landsat s3 bucket all object create events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-landsat-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer (LS5 Surface Reflectance)","URL":"https://explorer.digitalearth.africa/products/ls5_sr","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer (LS5 Surface Temperature)","URL":"https://explorer.digitalearth.africa/products/ls5_st","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer (LS7 Surface Reflectance)","URL":"https://explorer.digitalearth.africa/products/ls7_sr","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer (LS7 Surface Temperature)","URL":"https://explorer.digitalearth.africa/products/ls7_st","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer (LS8 Surface Reflectance)","URL":"https://explorer.digitalearth.africa/products/ls8_sr","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer (LS8 Surface Temperature)","URL":"https://explorer.digitalearth.africa/products/ls8_st","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"ESRI"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}],"Publications":[{"Title":"Introduction to DE Africa","URL":"https://youtu.be/Wkf7N6O9jJQ","AuthorName":"Dr Fang Yuan"}]},"RegistryEntryAdded":"2021-04-19","RegistryEntryLastModified":"2023-02-28","Slug":"deafrica-landsat","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa Global Mangrove Watch","Description":"The Global Mangrove Watch (GMW) dataset is a result of the collaboration between Aberystwyth University (U.K.), solo Earth Observation (soloEO; Japan), Wetlands International the World Conservation Monitoring Centre (UNEP-WCMC) and the Japan Aerospace Exploration Agency (JAXA). The primary objective of producing this dataset is to provide countries lacking a national mangrove monitoring system with first cut mangrove extent and change maps, to help safeguard against further mangrove forest loss and degradation.\nThe Global Mangrove Watch dataset (version 2) consists of a global baseline map of mangroves for 2010 and changes from this baseline for six epochs i.e. 1996, 2007, 2008, 2009, 2015 and 2016. Annual maps are planned from 2018 and onwards. The dataset can be used to identify mangrove ecosystems and monitor changes in mangrove extent. This is important in applications such as quantifying ‘blue carbon’, mitigating risks from natural disasters, and prioritising restoration activities. For more information on the Global Watch Mangrove product see the Global Mangrove Watch website.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/Global_Mangrove_Watch_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"To be defined.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","coastal","cog","deafrica","earth observation","geospatial","land cover","natural resource","satellite imagery","stac","sustainability"],"License":"DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n","Resources":[{"Description":"Global Mangrove Watch","ARN":"arn:aws:s3:::deafrica-input-datasets/gmw","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gmw)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)","ARN":"arn:aws:s3:::deafrica-services-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by the s3 bucket for all object create events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-input-datasets-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Global Mangrove Watch Notebook","URL":"https://docs.digitalearthafrica.org/en/latest/sandbox/notebooks/Datasets/Global_Mangrove_Watch.html","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Monitoring Mangrove Extents Notebook","URL":"https://docs.digitalearthafrica.org/en/latest/sandbox/notebooks/Real_world_examples/Mangrove_analysis.html","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer (Global Mangrove Watch)","URL":"https://explorer.digitalearth.africa/products/gmw","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Global Mangrove Watch online platform","URL":"https://www.globalmangrovewatch.org/","AuthorName":"Global Mangrove Alliance","AuthorURL":"https://www.mangrovealliance.org/"}],"Publications":[{"Title":"Climate Next: How data and community can save Zanzibar’s mangroves","URL":"https://www.aboutamazon.com/news/aws/climate-next-how-data-and-community-can-save-zanzibars-mangroves","AuthorName":"Amazon Staff"},{"Title":"Time series for nature: Preserving mangroves in Zanzibar","URL":"https://www.digitalearthafrica.org/why-digital-earth-africa/impact-stories/time-series-nature-preserving-mangroves-zanzibar","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Zanzibar: The Essential Mangrove | Climate Next by AWS","URL":"https://www.youtube.com/watch?v=FVmcEaemfmA","AuthorName":"Amazon Web Services (AWS)"}]},"RegistryEntryAdded":"2022-10-28","RegistryEntryLastModified":"2022-10-31","Slug":"deafrica-mangrove","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa Monthly Normalised Difference Vegetation Index (NDVI) Anomaly","Description":"Digital Earth Africa’s Monthly NDVI Anomaly service provides estimate of vegetation condition, for each caldendar month, against the long-term baseline condition measured for the month from 1984 to 2020 in the NDVI Climatology.\n\n  A standardised anomaly is calculated by subtracting the long-term mean from an observation of interest and then dividing the result by the long-term standard deviation. Positive NDVI anomaly values indicate vegetation is greener than average conditions, and are usually due to increased rainfall in a region. Negative values indicate additional plant stress relative to the long-term average. The NDVI anomaly service is therefore effective for understanding the extent, intensity and impact of a drought.Abrupt and significant negative anomalies may also be caused by fire disturbance.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/NDVI_Anomaly_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"From September 2022, the Monthly NDVI Anomaly is generated as a low latency product, i.e. anomaly for a month is generated on the 5th day of the following month. This ensures data is available shortly after the end of a month and all Landat 9 and Sentinel-2 observations are included. Not all landsat 8 observations for the month will be used, because the Landsat 8 Surface Refelectance product from USGS has a latency of over 2 weeks ([see Landsat Collection 2 Generation Timeline (https://www.usgs.gov/media/images/landsat-collection-2-generation-timeline)).","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac"],"License":"DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n","Resources":[{"Description":"Monthly Normalised Difference Vegetation Index (NDVI) Anomaly","ARN":"arn:aws:s3:::deafrica-services/ndvi_anomaly","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/ndvi_anomaly)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)","ARN":"arn:aws:s3:::deafrica-services-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item","ARN":"arn:aws:sns:af-south-1:565417506782:deafrica-landsat-wofs","Region":"af-south-1","Type":"SNS Topic"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by the s3 bucket for all object create events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-services-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer (Monthly Normalised Difference Vegetation Index (NDVI) Anomaly)","URL":"https://explorer.digitalearth.africa/products/ndvi_anomaly","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}],"Publications":[{"Title":"Digital Earth Africa releases new Rolling Monthly GeoMAD continental service","URL":"https://www.digitalearthafrica.org/media-center/blog/digital-earth-africa-releases-new-rolling-monthly-geomad-continental-service","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Mean NDVI and Anomalies","URL":"https://www.digitalearthafrica.org/platform-resources/services/mean-ndvi-and-anomalies","AuthorName":"Digital Earth Africa Contributors"}]},"RegistryEntryAdded":"2024-05-29","RegistryEntryLastModified":"2024-12-18","Slug":"deafrica-ndvi_anomaly","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa Normalised Difference Vegetation Index (NDVI) Climatology","Description":"Digital Earth Africa’s NDVI climatology product represents the long-term average baseline condition of vegetation for every Landsat pixel over the African continent. Both mean and standard deviation NDVI climatologies are available for each calender month.\n\nSome key features of the product are:\n\n  - NDVI climatologies were developed using harmonized Landsat 5,7,and 8 satellite imagery.\n  - Mean and standard deviation NDVI climatologies are produced for each calender month, using a temporal baseline period from 1984-2020 (inclusive)\n  - Datasets have a spatial resolution of 30 metres\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/ndvi_climatology_ls.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"N/A.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac"],"License":"DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n","Resources":[{"Description":"Normalised Difference Vegetation Index (NDVI) Climatology","ARN":"arn:aws:s3:::deafrica-services/ndvi_climatology_ls","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/ndvi_climatology_ls)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)","ARN":"arn:aws:s3:::deafrica-services-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item","ARN":"arn:aws:sns:af-south-1:565417506782:deafrica-landsat-wofs","Region":"af-south-1","Type":"SNS Topic"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by the s3 bucket for all object create events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-services-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer(Normalised Difference Vegetation Index (NDVI) Climatology)","URL":"https://explorer.digitalearth.africa/products/ndvi_climatology_ls","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}],"Publications":[{"Title":"Mean NDVI and Anomalies","URL":"https://www.digitalearthafrica.org/platform-resources/services/mean-ndvi-and-anomalies","AuthorName":"Digital Earth Africa Contributors"}]},"RegistryEntryAdded":"2024-05-29","RegistryEntryLastModified":"2024-12-18","Slug":"deafrica-ndvi_climatology_ls","Sources":["awslabs-open-data-registry"]}
{"Name":"Sentinel-1 Monthly Mosaic","Description":"Synthetic Aperture Radar (SAR) sensor have the advantage of operating at wavelengths not impeded by cloud cover and can acquire data over a site during the day or night. The Sentinel-1 mission, part of the Copernicus joint initiative by the European Commission (EC) and the European Space Agency (ESA), provides reliable and repeated wide-area monitoring using its SAR instrument.Sentinel-1 Monthly Mosaics are analysis-ready product of individual Sentinel-1 acquisitions. Sentinel-1 monthly mosaics are generated from Radiometric Terrain Corrected (RTC) backscatter data, with variations from changing observation geometries mitigated. RTC images acquired within a calendar month are combined using a multitemporal compositing algorithm. This algorithm calculates a weighted average of valid pixels, assigning higher weights to pixels with higher local resolution (e.g., slopes facing away from the sensor). This local resolution weighting approach minimizes noise and improves spatial homogeneity in the composites. Sinergise (Planet Labs) processed and indexed the product on the DE Africa platform\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-1_Monthly_Mosaic_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"N/A.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac","synthetic aperture radar"],"License":"Access to S1 Monthly Mosaic data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice).\n","Resources":[{"Description":"S1 Monthly Mosaic tiles and metadata","ARN":"arn:aws:s3:::deafrica-sentinel-1","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/products/s1_monthly_mosaic)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Digital Earth Africa Explorer","URL":"https://explorer.digitalearth.africa/products/s1_monthly_mosaic/extents","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}]},"RegistryEntryAdded":"2025-04-07","RegistryEntryLastModified":"2026-07-16","Slug":"deafrica-sentinel-1-mosaic","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa Sentinel-1 Radiometrically Terrain Corrected","Description":"DE Africa’s Sentinel-1 backscatter product is developed to be compliant with the CEOS Analysis Ready Data for Land (CARD4L) specifications.\nThe Sentinel-1 mission, composed of a constellation of two C-band Synthetic Aperture Radar (SAR) satellites, are operated by European Space Agency (ESA) as part of the Copernicus Programme. The mission currently collects data every 12 days over Africa at a spatial resolution of approximately 20 m.\nRadar backscatter measures the amount of microwave radiation reflected back to the sensor from the ground surface. This measurement is sensitive to surface roughness, moisture content and viewing geometry. DE Africa provides Sentinel-1 backscatter as Radiometrically Terrain Corrected (RTC) gamma-0 (γ0) where variation due to changing observation geometries has been mitigated.\nThe dual polarisation backcastter time series can be used in applications for forests, agriculture, wetlands and land cover classification. SAR’s ability to ‘see through’ clouds makes it critical for mapping and monitoring land cover changes in the wet tropics.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-1_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"New Sentinel-1 data are added regularly.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac","synthetic aperture radar"],"License":"Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice).\n","Resources":[{"Description":"Sentinel-1 tiles and metadata","ARN":"arn:aws:s3:::deafrica-sentinel-1","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s1_rtc)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)","ARN":"arn:aws:s3:::deafrica-sentinel-1-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-1-scene-topic","Region":"af-south-1","Type":"SNS Topic"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by deafrica-sentinel-1 s3 bucket all object create events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-1-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Water detection with Sentinel-1","URL":"https://docs.digitalearthafrica.org/en/latest/sandbox/notebooks/Real_world_examples/Radar_water_detection.html","NotebookURL":"https://github.com/mseehaber/dea_sagemaker/blob/master/s1rtc-load-analysis.ipynb","AuthorName":"Madeleine Seehaber","Services":["Amazon SageMaker Studio Lab"]}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer","URL":"https://explorer.digitalearth.africa/products/s1_rtc/extents","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}],"Publications":[{"Title":"An Operational Analysis Ready Radar Backscatter Dataset for the African Continent","URL":"https://doi.org/10.3390/rs14020351","AuthorName":"Fang Yuan, Marko Repse, Alex Leith, Ake Rosenqvist, Grega Milcinski, Negin F. Moghaddam, Tishampati Dhar, Chad Burton, Lisa Hall, Cedric Jorand and Adam Lewis"},{"Title":"Introduction to DE Africa","URL":"https://youtu.be/Wkf7N6O9jJQ","AuthorName":"Dr Fang Yuan"}]},"RegistryEntryAdded":"2021-04-19","RegistryEntryLastModified":"2026-07-16","Slug":"deafrica-sentinel-1","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa Sentinel-2 Level-2A Surface Reflectance Collection 1","Description":"The Sentinel-2 mission is part of the European Union Copernicus programme for Earth observations. Sentinel-2 consists of twin satellites, Sentinel-2A (launched 23 June 2015) and Sentinel-2B (launched 7 March 2017). The two satellites have the same orbit, but 180° apart for optimal coverage and data delivery. Their combined data is used in the Digital Earth Africa Sentinel-2 product.\nTogether, they cover all Earth’s land surfaces, large islands, inland and coastal waters every 3-5 days.\nSentinel-2 data is tiered by level of pre-processing. Level-0, Level-1A and Level-1B data contain raw data from the satellites, with little to no pre-processing. Level-1C data is surface reflectance measured at the top of the atmosphere. This is processed using the Sen2Cor algorithm to give Level-2A, the bottom-of-atmosphere reflectance (Obregón et al, 2019). Level-2A data is the most ideal for research activities as it allows further analysis without applying additional atmospheric corrections.\nDigital Earth Africa Sentinel-2 Level-2A Surface Reflectance Collection 1 is the Sentinel-2 product processed for enhanced calibration and consistent time series between Sentinel-2A and Sentinel-2B. Digital Earth Africa does not host any lower-level Sentinel-2 data.\n\n\nNote that this data is a subset of the Sentinel-2 COGs dataset.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-2_Level-2A_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"New Sentinel-2 scenes are added regularly, usually within few hours after they are available on Copernicus OpenHub.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac"],"License":"Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice).\n","Resources":[{"Description":"Sentinel-2 scenes and metadata","ARN":"arn:aws:s3:::deafrica-sentinel-2-l2a-c1/sentinel-2-c1-l2a","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s2_l2a_c1)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)","ARN":"arn:aws:s3:::deafrica-sentinel-2-l2a-c1/sentinel-2-c1-l2a-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-2-l2a-c1-scene-topic","Region":"af-south-1","Type":"SNS Topic"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by deafrica-sentinel-2-l2a-c1 s3 bucket all object create events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-2-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Downloading and streaming data using STAC metadata","URL":"https://docs.digitalearthafrica.org/en/latest/sandbox/notebooks/Frequently_used_code/Downloading_data_with_STAC.html","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Use Sentinel-2-C1 data in the Open Data Cube","URL":"https://github.com/opendatacube/cube-in-a-box","AuthorName":"Alex Leith"}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer","URL":"https://explorer.digitalearth.africa/products/s2_l2a_c1/extents","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}],"Publications":[{"Title":"Introduction to DE Africa","URL":"https://youtu.be/Wkf7N6O9jJQ","AuthorName":"Dr Fang Yuan"},{"Title":"S2 Processing","URL":"https://sentiwiki.copernicus.eu/web/s2-processing#S2Processing-Collection-1ProcessingBaselineS2-Processing-Collection-Processing-Baseline","AuthorName":"Sentiwiki"}]},"RegistryEntryAdded":"2025-04-08","RegistryEntryLastModified":"2026-07-16","Slug":"deafrica-sentinel-2-c1","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa Sentinel-2 Level-2A","Description":"The Sentinel-2 mission is part of the European Union Copernicus programme for Earth observations. Sentinel-2 consists of twin satellites, Sentinel-2A (launched 23 June 2015) and Sentinel-2B (launched 7 March 2017). The two satellites have the same orbit, but 180° apart for optimal coverage and data delivery. Their combined data is used in the Digital Earth Africa Sentinel-2 product.\nTogether, they cover all Earth’s land surfaces, large islands, inland and coastal waters every 3-5 days.\nSentinel-2 data is tiered by level of pre-processing. Level-0, Level-1A and Level-1B data contain raw data from the satellites, with little to no pre-processing. Level-1C data is surface reflectance measured at the top of the atmosphere. This is processed using the Sen2Cor algorithm to give Level-2A, the bottom-of-atmosphere reflectance (Obregón et al, 2019). Level-2A data is the most ideal for research activities as it allows further analysis without applying additional atmospheric corrections.\nThe Digital Earth Africa Sentinel-2 dataset contains Level-2A data of the African continent. Digital Earth Africa does not host any lower-level Sentinel-2 data.\nNote that this data is a subset of the Sentinel-2 COGs dataset.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-2_Level-2A_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"New Sentinel-2 scenes are added regularly, usually within few hours after they are available on Copernicus OpenHub.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac"],"License":"Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice).\n","Resources":[{"Description":"Sentinel-2 scenes and metadata","ARN":"arn:aws:s3:::deafrica-sentinel-2","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s2_l2a)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)","ARN":"arn:aws:s3:::deafrica-sentinel-2-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-2-scene-topic","Region":"af-south-1","Type":"SNS Topic"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by deafrica-sentinel-2 s3 bucket all object create events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-2-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Downloading and streaming data using STAC metadata","URL":"https://docs.digitalearthafrica.org/en/latest/sandbox/notebooks/Frequently_used_code/Downloading_data_with_STAC.html","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Use Sentinel-2 data in the Open Data Cube","URL":"https://github.com/opendatacube/cube-in-a-box","AuthorName":"Alex Leith"}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer","URL":"https://explorer.digitalearth.africa/products/s2_l2a/extents","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}],"Publications":[{"Title":"Introduction to DE Africa","URL":"https://youtu.be/Wkf7N6O9jJQ","AuthorName":"Dr Fang Yuan"}]},"RegistryEntryAdded":"2021-04-19","RegistryEntryLastModified":"2026-07-16","Slug":"deafrica-sentinel-2","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa Sentinel-5P TROPOMI Level-2","Description":"Sentinel-5P is an Earth observation satellite launched in 2017 and operated by the European Space Agency under the Copernicus Programme. It was developed as a precursor mission to the future Sentinel-5 satellite to ensure continuity of atmospheric composition observations following earlier European missions such as SCIAMACHY, GOME, and GOME-2. The core objective of Sentinel-5P is to provide near-real-time, global measurements of atmospheric composition and air quality, supporting scientific research, environmental monitoring, and policy implementation related to air pollution, climate change, ozone depletion, and human health. Its datasets enable scientists to track pollutants and trace gases spatially and temporally across the globe with continuous daily coverage. Level-2 products represent geophysical variables derived from calibrated radiance measurements and converted into physically meaningful atmospheric quantities. These products describe key atmospheric components—trace gases, aerosols, and cloud properties—that are essential for applications in air-quality monitoring, weather forecasting, and climate-system analysis.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-5P_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"New data are added regularly, usually within a few hours of them being available.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["air quality","aws-pds","cog","deafrica","earth observation","geospatial","natural resource","satellite imagery","stac"],"License":"DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n","Resources":[{"Description":"Sentinel-5p TROPOMI Level 2 UV Aerosol Index","ARN":"arn:aws:s3:::deafrica-sentinel-5p-aer-ai/Sentinel-5p/TROPOMI/TROPO_L2_AI/","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s5p_tropomi_l2_aer_ai)"]},{"Description":"Sentinel-5p TROPOMI Level 2 Methane (CH4) total column","ARN":"arn:aws:s3:::deafrica-sentinel-5p-ch4/Sentinel-5p/TROPOMI/TROPO_L2_CH4/","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s5p_tropomi_l2_ch4)"]},{"Description":"Sentinel-5p TROPOMI Level 2 Cloud fraction, albedo, top pressure","ARN":"arn:aws:s3:::deafrica-sentinel-5p-cloud/Sentinel-5p/TROPOMI/TROPO_L2_CLOUD/","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s5p_tropomi_l2_cloud)"]},{"Description":"Sentinel-5p TROPOMI Level 2 Carbon Monoxide (CO) total column","ARN":"arn:aws:s3:::deafrica-sentinel-5p-co/Sentinel-5p/TROPOMI/TROPO_L2_CO/","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s5p_tropomi_l2_co)"]},{"Description":"Sentinel-5p TROPOMI Level 2 Formaldehyde (HCHO) total column","ARN":"arn:aws:s3:::deafrica-sentinel-5p-hcho/Sentinel-5p/TROPOMI/TROPO_L2_HCHO/","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s5p_tropomi_l2_hcho)"]},{"Description":"Sentinel-5p TROPOMI Level 2 Nitrogen Dioxide (NO2), total and tropospheric columns","ARN":"arn:aws:s3:::deafrica-sentinel-5p-no2/Sentinel-5p/TROPOMI/TROPO_L2_NO2/","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s5p_tropomi_l2_no2)"]},{"Description":"Sentinel-5p TROPOMI Level 2 Ozone (O3) total column","ARN":"arn:aws:s3:::deafrica-sentinel-5p-o3/Sentinel-5p/TROPOMI/TROPO_L2_O3/","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s5p_tropomi_l2_o3)"]},{"Description":"Sentinel-5p TROPOMI Level 2 Sulfur Dioxide (SO2) total column","ARN":"arn:aws:s3:::deafrica-sentinel-5p-so2/Sentinel-5p/TROPOMI/TROPO_L2_SO2/","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s5p_tropomi_l2_so2)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)","ARN":"arn:aws:s3:::deafrica-services-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"Notifications for new Sentinel 5P data","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-5p-scene-topic","Region":"af-south-1","Type":"SNS Topic"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by the s3 bucket for all object create events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-services-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Get to Know a Dataset: [DE Africa Sentinel 5P TROPOMI Level-2]","URL":"https://github.com/digitalearthafrica/open-data-examples/tree/main/open-data-examples/DEafrica_sentinel_5p","AuthorName":"Digital Earth Africa Contributors"}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer","URL":"https://explorer.digitalearth.africa/products/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}]},"RegistryEntryAdded":"2026-01-20","RegistryEntryLastModified":"2026-09-18","Slug":"deafrica-sentinel5p","Sources":["awslabs-open-data-registry"]}
{"Name":"DE Africa Waterbodies Monitoring Service","Description":"The Digital Earth Africa continental Waterbodies Monitoring Service identifies more than 700,000 water bodies from over three decades of satellite observations. This service maps persistent and seasonal water bodies and the change in their water surface area over time. Mapped water bodies may include, but are not limited to, lakes, ponds, man-made reservoirs, wetlands, and segments of some river systems.On a local, regional, and continental scale, this service helps improve our understanding of surface water dynamics and water availability and can be used for monitoring water bodies such as wetlands, lakes and dams in remote and/or inaccessible locations.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/Waterbodies_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"Single historical extent derived from the full temporal range.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac","water"],"License":"DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n","Resources":[{"Description":"DE Africa Waterbodies","ARN":"arn:aws:s3:::deafrica-services/waterbodies","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/waterbodies)"]}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org","AuthorName":"Digital Earth Africa Contributors"}],"Tools & Applications":[{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}],"Publications":[{"Title":"Waterbodies Monitoring Service","URL":"https://www.digitalearthafrica.org/node/601","AuthorName":"Digital Earth Africa Contributors"}]},"RegistryEntryAdded":"2024-12-18","RegistryEntryLastModified":"2024-12-19","Slug":"deafrica-waterbodies","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Africa Water Observations from Space","Description":"Water Observations from Space (WOfS) is a service that draws on satellite imagery to provide historical surface water observations of the whole African continent. WOfS allows users to understand the location and movement of inland and coastal water present in the African landscape. It shows where water is usually present; where it is seldom observed; and where inundation of the surface has been observed by satellite.\nThey are generated using the WOfS classification algorithm on Landsat satellite data. There are several WOfS products available for the African continent including scene-level data and annual or all time summaries.\n","Documentation":"https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_WOfS_specs.html","Contact":"helpdesk@digitalearthafrica.org","ManagedBy":"[Digital Earth Africa](https://www.digitalearthafrica.org/)","UpdateFrequency":"New scene-level data is added as new Landsat data is available. New summaries are available soon after data is available for a year.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac","water"],"License":"DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n","Resources":[{"Description":"Water Observations from Space data","ARN":"arn:aws:s3:::deafrica-services/wofs_ls","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls)"]},{"Description":"Water Observations from Space Annual Summary data","ARN":"arn:aws:s3:::deafrica-services/wofs_ls_summary_annual","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls_summary_annual)"]},{"Description":"Water Observations from Space All-Time Summary data","ARN":"arn:aws:s3:::deafrica-services/wofs_ls_summary_alltime","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls_summary_alltime)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)","ARN":"arn:aws:s3:::deafrica-services-inventory","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item","ARN":"arn:aws:sns:af-south-1:565417506782:deafrica-landsat-wofs","Region":"af-south-1","Type":"SNS Topic"},{"Description":"Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by the s3 bucket for all object create events.","ARN":"arn:aws:sns:af-south-1:543785577597:deafrica-services-topic","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Digital Earth Africa Training","URL":"http://learn.digitalearthafrica.org/","AuthorName":"Digital Earth Africa Contributors"}],"Tools & Applications":[{"Title":"Digital Earth Africa Explorer (Water Observations from Space All-Time Summary)","URL":"https://explorer.digitalearth.africa/products/wofs_ls_summary_alltime","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer (Water Observations from Space Annual Summary)","URL":"https://explorer.digitalearth.africa/products/wofs_ls_summary_annual","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Explorer (Water Observations from Space)","URL":"https://explorer.digitalearth.africa/products/wofs_ls","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Geoportal","URL":"https://www.africageoportal.com/pages/digital-earth-africa","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Map","URL":"https://maps.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Notebook Repo","URL":"https://github.com/digitalearthafrica/deafrica-sandbox-notebooks","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa Sandbox","URL":"https://sandbox.digitalearth.africa/","AuthorName":"Digital Earth Africa Contributors"},{"Title":"Digital Earth Africa web services","URL":"https://ows.digitalearth.africa","AuthorName":"Digital Earth Africa Contributors"}],"Publications":[{"Title":"Analysing effects of drought on inundation extent and vegetation cover dynamics in the Okavango Delta","URL":"https://agu2021fallmeeting-agu.ipostersessions.com/?s=7A-85-3A-E6-1F-5F-19-87-39-23-90-F1-DC-57-5C-E9","AuthorName":"Kelebogile Mfundisi, Kenneth Mubea, Fang Yuan, Chad Burton and Edward Boamah"},{"Title":"Water Observations from Space: accurate maps of surface water through time for the continent of Africa","URL":"https://agu2021fallmeeting-agu.ipostersessions.com/Default.aspx?s=E9-AF-34-FE-69-38-18-49-15-E0-0F-89-21-74-C0-29","AuthorName":"Meghan Halabisky, Kenneth Mubea, Fatou Mar, Fang Yuan, Chad Burton, Eloise Birchall, Negin F. Moghaddam, Sena Ghislain Adimou, Bako Mamane, David Ongo, Edward Boamah, Ee-Faye Chong, Nikita Gandhi, Alex Leith, Lisa Hall and Adam Lewis"}]},"RegistryEntryAdded":"2021-12-08","RegistryEntryLastModified":"2023-02-28","Slug":"deafrica-wofs","Sources":["awslabs-open-data-registry"]}
{"Name":"DeepDrug Protein Embeddings Bank (DPEB)","Description":"DPEB is a multimodal database of human protein embeddings integrating four biologically complementary representations—AlphaFold2, BioEmbeddings, ESM-2, and ProtVec—designed for enhanced protein-protein interaction prediction and functional classification.","Documentation":"https://github.com/deepdrugai/DPEB","Contact":"https://github.com/deepdrugai/DPEB/issues","ManagedBy":"Louisiana State University","UpdateFrequency":"Initial release; maintained for at least 2 years with updates planned based on new embedding models and protein coverage.","Tags":["aws-pds","bioinformatics","life sciences","machine learning","protein","structural biology"],"License":"MIT","Citation":"Sajol MSI et al. DeepDrug Protein Embeddings Bank (DPEB) was accessed on [DATE] at https://registry.opendata.aws/dpeb","Resources":[{"Description":"Multimodal human protein embeddings (AlphaFold2, BioEmbeddings, ESM-2, ProtVec) with JSONL-formatted metadata containing FASTA, UniProt IDs, and embeddings.","ARN":"arn:aws:s3:::deepdrug-dpeb","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Aggregating and Clustering AlphaFold2 Embeddings from DPEB","URL":"https://github.com/deepdrugai/DPEB/tree/main","AuthorName":"Md. Saiful Islam Sajol","AuthorURL":"https://github.com/deepdrugai"}],"Tools & Applications":[{"Title":"DPEB Explorer Tool","URL":"https://github.com/deepdrugai/DPEB","AuthorName":"DeepDrug Lab","AuthorURL":"https://github.com/deepdrugai"}],"Publications":[{"Title":"A Multimodal Human Protein Embeddings Database - DeepDrug Protein Embeddings Bank (DPEB)","URL":"https://doi.org/10.XXXX/nar.dpeb2025","AuthorName":"Sajol MSI, Rajasekaran M, Bess A, Alvin C, Mukhopadhyay S","AuthorURL":"https://github.com/deepdrugai/DPEB"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-09-25","RegistryEntryLastModified":"2025-09-25","Slug":"deepdrug-dpeb","Sources":["awslabs-open-data-registry"]}
{"Name":"Dendritic Consortium Multimodal Dataset","Description":"The Dendritic Consortium provides a multimodal dataset integrating calcium and voltage imaging, electrophysiology, electron microscopy, proteomics, and computational models of Baz1a pyramidal neurons in the mouse primary visual cortex (V1).","Documentation":"https://github.com/jpcastanoo/aws-open-data-dendritic-consortium","Contact":"dendriticconsortium@gmail.com","ManagedBy":"Dendritic Consortium","UpdateFrequency":"Continuously updated as new experimental and computational data are generated.","Tags":["aws-pds","brain images","brain models","electron microscopy","electrophysiology","imaging","life sciences","Mus musculus","neurobiology","neuroimaging","neurophysiology","neuroscience","simulation neuroscience","single neuron models"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"Multimodal dataset from Baz1a pyramidal neurons in mouse V1, including TIFF, ABF, MAT, CSV, PNG, PY, HOC, and SWC files.","ARN":"arn:aws:s3:::dendritic-consortium","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for new Dendritic Consortium data","ARN":"arn:aws:sns:us-west-2:662855374544:dendritic-consortium-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Download and Visualize Data from the Dendritic Consortium Dataset","URL":"https://github.com/jpcastanoo/aws-open-data-dendritic-consortium/tree/main/tutorials","AuthorName":"Dendritic Consortium","AuthorURL":"https://github.com/jpcastanoo/aws-open-data-dendritic-consortium"}],"Tools & Applications":[{"Title":"Dendritic Consortium Database","URL":"https://dendritic-consortium.vercel.app/database","AuthorName":"Dendritic Consortium","AuthorURL":"https://dendritic-consortium.vercel.app"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-06-30","RegistryEntryLastModified":"2025-08-18","Slug":"dendritic-consortium","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Pacific Annual Shorelines (Landsat, 30 m)","Description":"This dataset provides annual coastline positions across the Pacific\nregion derived from Landsat satellite imagery. It captures long-term\nshoreline change by mapping coastal boundaries over time, providing\ninsights into processes including erosion (where landmass area\ndecreases) and accretion or deposition (where landmass area increases).\nThe dataset supports coastal change analysis, erosion monitoring, and\nclimate adaptation planning.\n\nAnnual shoreline vectors are extracted and composited from Landsat\nimagery, and are published as a vector time series in both GeoPackage\nand PMTiles formats. A WMTS service is also available at https://tileser\nver.prod.digitalearthpacific.io/styles/coastlines/wmts.xml.\n","Documentation":"https://digitalearthpacific.org/#/applications","Contact":"dep@spc.int","ManagedBy":"[Pacific Community (SPC)](https://www.spc.int/)","UpdateFrequency":"Annually","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","climate","coastal","earth observation","environmental","geoscience","geospatial"],"License":"Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n","Resources":[{"Description":"Annual Shorelines (Landsat, 30 m) vector data in GeoPackage and PMTiles formats","ARN":"arn:aws:s3:::dep-public-data/dep_ls_coastlines","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_ls_coastlines)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Digital Earth Pacific Coastline Change Dashboard","URL":"https://dashboards.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"},{"Title":"Digital Earth Pacific Map","URL":"https://maps.digitalearthpacific.org","AuthorName":"Digital Earth Pacific Contributors"},{"Title":"Digital Earth Pacific STAC Browser","URL":"https://stac-browser.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"}],"Publications":[{"Title":"Digital Earth Pacific Open Data Access Documentation","URL":"https://digitalearthpacific.github.io/data-access/","AuthorName":"digitalearthpacific","AuthorURL":"https://github.com/digitalearthpacific"}]},"RegistryEntryAdded":"2024-06-14","RegistryEntryLastModified":"2026-09-30","Slug":"dep-coastlines","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Pacific Fractional Land Cover (Landsat, 30 m)","Description":"Fractional Land Cover represents the percentage of bare soil,\nphotosynthetic vegetation, and non-photosynthetic vegetation across the\nPacific region, derived from multi-mission Landsat surface reflectance\nimagery using spectral unmixing. The dataset supports long-term\nenvironmental monitoring, land cover change analysis, ecosystem\nmonitoring, trend analysis, and land degradation assessment. It\ncomprises two products:\n\n- Scene-based Fractional Land Cover: per-scene estimates of bare soil\n  (bs), photosynthetic vegetation (pv) and non-photosynthetic vegetation\n  (npv) fractions, plus unmixing error (ue), from 1984 onwards.\n- Annual Summary: yearly statistical summaries (10th, 50th and 90th\n  percentiles) of each cover fraction, along with counts of valid\n  observations.\n","Documentation":"https://digitalearthpacific.org/#/applications","Contact":"dep@spc.int","ManagedBy":"[Pacific Community (SPC)](https://www.spc.int/)","UpdateFrequency":"Scene-based data is added as new Landsat scenes are acquired; summaries are updated annually","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["agriculture","aws-pds","earth observation","environmental","geoscience","geospatial","land cover","sustainability"],"License":"Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n","Resources":[{"Description":"Fractional Land Cover, Scene-based (Landsat, 30 m)","ARN":"arn:aws:s3:::dep-public-data/dep_ls_fc","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_ls_fc)"]},{"Description":"Fractional Land Cover Annual Summary (Landsat, 30 m)","ARN":"arn:aws:s3:::dep-public-data/dep_ls_fc_summary_annual","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_ls_fc_summary_annual)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Digital Earth Pacific Map","URL":"https://maps.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"},{"Title":"Digital Earth Pacific STAC Browser","URL":"https://stac-browser.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"}],"Publications":[{"Title":"Digital Earth Pacific Open Data Access Documentation","URL":"https://digitalearthpacific.github.io/data-access/","AuthorName":"digitalearthpacific","AuthorURL":"https://github.com/digitalearthpacific"}]},"RegistryEntryAdded":"2026-09-30","RegistryEntryLastModified":"2026-09-30","Slug":"dep-fractional-cover","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Pacific Intertidal Elevation and Exposure (Sentinel-2/Landsat, 30 m)","Description":"This dataset provides spatial information on intertidal elevation and\nexposure across Pacific Island coastlines derived from multi-sensor\nsatellite observations. It includes elevation estimates and exposure\nmetrics indicating the proportion of time areas are exposed within the\nintertidal zone. The dataset supports coastal monitoring, habitat\nmapping, and shoreline dynamics analysis.\n\nIt is produced by combining Landsat and Sentinel observations to model\nintertidal elevation and calculate exposure metrics.\n","Documentation":"https://digitalearthpacific.org/#/applications","Contact":"dep@spc.int","ManagedBy":"[Pacific Community (SPC)](https://www.spc.int/)","UpdateFrequency":"Irregularly, as new versions are produced","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","coastal","earth observation","elevation","geoscience","geospatial","marine"],"License":"Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n","Resources":[{"Description":"Intertidal Elevation and Exposure (Sentinel-2/Landsat, 30 m)","ARN":"arn:aws:s3:::dep-public-data/dep_s2ls_intertidal","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_s2ls_intertidal)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Digital Earth Pacific Map","URL":"https://maps.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"},{"Title":"Digital Earth Pacific STAC Browser","URL":"https://stac-browser.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"}],"Publications":[{"Title":"Digital Earth Pacific Open Data Access Documentation","URL":"https://digitalearthpacific.github.io/data-access/","AuthorName":"digitalearthpacific","AuthorURL":"https://github.com/digitalearthpacific"}]},"RegistryEntryAdded":"2026-09-30","RegistryEntryLastModified":"2026-09-30","Slug":"dep-intertidal","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Pacific Annual Surface Reflectance Composite (Landsat, GeoMAD, 30 m)","Description":"This dataset provides annual cloud-free composite imagery derived from\nLandsat surface reflectance observations across the Pacific region using\ngeometric median and absolute deviation (GeoMAD) methods. The data are\nmasked for cloud, shadows and other image artefacts using the associated\npixel quality product to help provide as clear a set of observations as\npossible from which to calculate the medians.\n\nAlong with the geometric median surface reflectance bands, the GeoMAD\nincludes three measures of variability (Euclidean, spectral and Bray-\nCurtis median absolute deviations: emad, smad and bcmad) and a count of\nclear observations. The dataset is available annually from 2000 and\nsupports monitoring of vegetation, land cover, and coastal environments.\n","Documentation":"https://digitalearthpacific.org/#/applications","Contact":"dep@spc.int","ManagedBy":"[Pacific Community (SPC)](https://www.spc.int/)","UpdateFrequency":"Annually","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","earth observation","geoscience","geospatial","land cover","satellite imagery"],"License":"Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n","Resources":[{"Description":"Annual Surface Reflectance Composite (Landsat, GeoMAD, 30 m) Cloud Optimized GeoTIFFs","ARN":"arn:aws:s3:::dep-public-data/dep_ls_geomad","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_ls_geomad)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Digital Earth Pacific Map","URL":"https://maps.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"},{"Title":"Digital Earth Pacific STAC Browser","URL":"https://stac-browser.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"}],"Publications":[{"Title":"Digital Earth Pacific Open Data Access Documentation","URL":"https://digitalearthpacific.github.io/data-access/","AuthorName":"digitalearthpacific","AuthorURL":"https://github.com/digitalearthpacific"}]},"RegistryEntryAdded":"2024-06-14","RegistryEntryLastModified":"2026-09-30","Slug":"dep-ls-geomads","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Pacific Mangrove Extent, Density, Change and Condition (Sentinel-2, 10 m)","Description":"These datasets map mangrove ecosystems across Pacific Island Countries\nand Territories (PICTs) using Sentinel-2 satellite observations,\nsupporting coastal protection, carbon accounting, biodiversity\nassessment, resilience planning, and ecosystem accounting. They comprise\ntwo products:\n\n- Mangrove Extent and Density: an extension of the Global Mangrove Watch\n  (GMW v3, 2020) baseline, classifying mangrove areas annually from 2017\n  into closed (high-density), open (lower density) and non-mangrove\n  categories.\n- Mangrove Change and Condition (AMMI): analytical indicators describing\n  changes and condition of mangrove ecosystems, built on the satellite-\n  derived mangrove extent data to identify patterns of ecosystem change\n  over time.\n","Documentation":"https://digitalearthpacific.org/#/applications","Contact":"dep@spc.int","ManagedBy":"[Pacific Community (SPC)](https://www.spc.int/)","UpdateFrequency":"Annually","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["aws-pds","biodiversity","carbon","climate","coastal","earth observation","environmental","geoscience","geospatial"],"License":"Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n","Resources":[{"Description":"Mangrove Extent and Density (Sentinel-2, 10 m)","ARN":"arn:aws:s3:::dep-public-data/dep_s2_mangroves","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_s2_mangroves)"]},{"Description":"Mangrove Change and Condition, AMMI (Sentinel-2, 10 m)","ARN":"arn:aws:s3:::dep-public-data/dep_s2_ammi","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_s2_ammi)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Digital Earth Pacific Map","URL":"https://maps.digitalearthpacific.org/#share=s-ux1MvvWAfZueW286xCPv","AuthorName":"Digital Earth Pacific Contributors"},{"Title":"Digital Earth Pacific STAC Browser","URL":"https://stac-browser.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"}],"Publications":[{"Title":"Digital Earth Pacific Open Data Access Documentation","URL":"https://digitalearthpacific.github.io/data-access/","AuthorName":"digitalearthpacific","AuthorURL":"https://github.com/digitalearthpacific"}]},"RegistryEntryAdded":"2024-06-14","RegistryEntryLastModified":"2026-09-30","Slug":"dep-mangroves","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Pacific Annual Radar Composite (Sentinel-1, GeoMAD, 10 m)","Description":"This dataset provides annual radar-based composite imagery derived from\nSentinel-1 observations across the Pacific region. Sentinel-1 carries a\nC-band Synthetic Aperture Radar (SAR), which acquires data day and night\nand in all weather conditions, so the composite is independent of cloud\ncover.\n\nMulti-temporal Sentinel-1 backscatter observations (VV and VH\npolarisations) are aggregated into annual geometric median composites,\nalong with median absolute deviation measures (emad, smad and bcmad) and\na count of observations. The dataset is available annually from 2017 and\nsupports monitoring of land, water, and environmental change.\n","Documentation":"https://digitalearthpacific.org/#/applications","Contact":"dep@spc.int","ManagedBy":"[Pacific Community (SPC)](https://www.spc.int/)","UpdateFrequency":"Annually","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","climate","earth observation","environmental","geoscience","geospatial","satellite imagery"],"License":"Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n","Resources":[{"Description":"Annual Radar Composite (Sentinel-1, GeoMAD, 10 m) Cloud Optimized GeoTIFFs","ARN":"arn:aws:s3:::dep-public-data/dep_s1_geomad","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_s1_geomad)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Digital Earth Pacific Map","URL":"https://maps.digitalearthpacific.org","AuthorName":"Digital Earth Pacific Contributors"},{"Title":"Digital Earth Pacific STAC Browser","URL":"https://stac-browser.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"}],"Publications":[{"Title":"Digital Earth Pacific Open Data Access Documentation","URL":"https://digitalearthpacific.github.io/data-access/","AuthorName":"digitalearthpacific","AuthorURL":"https://github.com/digitalearthpacific"}]},"RegistryEntryAdded":"2026-09-30","RegistryEntryLastModified":"2026-09-30","Slug":"dep-s1-geomads","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Pacific Annual Surface Reflectance Composite (Sentinel-2, GeoMAD, 10 m)","Description":"This dataset provides annual cloud-free composite imagery derived from\nSentinel-2 observations across the Pacific region using geometric median\nand absolute deviation (GeoMAD) methods. The geometric median uses a\nmore robust method of determining the median observation than a simple\nmedian, and represents stable surface conditions.\n\nAlong with the median observation, the GeoMAD produces three measures of\nvariance, or absolute deviations (emad, smad and bcmad), which help to\nunderstand how the data change over the time period. For example, some\nareas, such as bare rock, will change very little, whereas crop land\nwill change more. The dataset is available annually from 2017 and\nsupports monitoring of vegetation, land cover, and coastal environments.\n","Documentation":"https://digitalearthpacific.org/#/applications","Contact":"dep@spc.int","ManagedBy":"[Pacific Community (SPC)](https://www.spc.int/)","UpdateFrequency":"Annually","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","earth observation","geoscience","geospatial","land cover","satellite imagery"],"License":"Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n","Resources":[{"Description":"Annual Surface Reflectance Composite (Sentinel-2, GeoMAD, 10 m) Cloud Optimized GeoTIFFs","ARN":"arn:aws:s3:::dep-public-data/dep_s2_geomad","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_s2_geomad)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Digital Earth Pacific Map","URL":"https://maps.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"},{"Title":"Digital Earth Pacific STAC Browser","URL":"https://stac-browser.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"}],"Publications":[{"Title":"Digital Earth Pacific Open Data Access Documentation","URL":"https://digitalearthpacific.github.io/data-access/","AuthorName":"digitalearthpacific","AuthorURL":"https://github.com/digitalearthpacific"}]},"RegistryEntryAdded":"2024-06-14","RegistryEntryLastModified":"2026-09-30","Slug":"dep-s2-geomads","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Pacific Satellite-Derived Bathymetry (Sentinel-2, 10 m)","Description":"This dataset provides bathymetric estimates for optically shallow, clear\ncoastal waters across the Pacific region using satellite imagery. It\nenables mapping of seabed depth in shallow coastal areas (typically down\nto depths where the seafloor remains optically visible, up to 30 m\ndepth) where traditional survey data may be limited. The dataset\nsupports marine spatial planning, coastal management, and environmental\nmonitoring.\n\nIt is derived from multispectral Sentinel-2 imagery using bathymetric\ninversion methods to estimate shallow water depth, and includes depth,\nmean, standard deviation and observation count layers.\n","Documentation":"https://digitalearthpacific.org/#/applications","Contact":"dep@spc.int","ManagedBy":"[Pacific Community (SPC)](https://www.spc.int/)","UpdateFrequency":"Irregularly, as new versions are produced","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","bathymetry","coastal","earth observation","geoscience","geospatial","marine","oceans"],"License":"Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n","Resources":[{"Description":"Satellite-Derived Bathymetry (Sentinel-2, 10 m)","ARN":"arn:aws:s3:::dep-public-data/dep_s2_sdb","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_s2_sdb)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Digital Earth Pacific Map","URL":"https://maps.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"},{"Title":"Digital Earth Pacific STAC Browser","URL":"https://stac-browser.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"}],"Publications":[{"Title":"Digital Earth Pacific Open Data Access Documentation","URL":"https://digitalearthpacific.github.io/data-access/","AuthorName":"digitalearthpacific","AuthorURL":"https://github.com/digitalearthpacific"}]},"RegistryEntryAdded":"2026-09-30","RegistryEntryLastModified":"2026-09-30","Slug":"dep-satellite-derived-bathymetry","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Pacific Seagrass Extent, Machine Learning (Sentinel-2, 10 m)","Description":"This dataset maps the distribution of seagrass ecosystems across the\nPacific region using Sentinel-2 imagery. It applies machine learning\ntechniques to classify seagrass extent at high spatial resolution, and\nincludes seagrass probability, classification and thresholded extent\nlayers from 2017 onwards. The dataset supports marine habitat mapping,\nbiodiversity monitoring, and coastal ecosystem management.\n","Documentation":"https://digitalearthpacific.org/#/applications","Contact":"dep@spc.int","ManagedBy":"[Pacific Community (SPC)](https://www.spc.int/)","UpdateFrequency":"Annually","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["aws-pds","biodiversity","coastal","earth observation","geoscience","geospatial","marine","oceans"],"License":"Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n","Resources":[{"Description":"Seagrass Extent, Machine Learning (Sentinel-2, 10 m)","ARN":"arn:aws:s3:::dep-public-data/dep_s2_seagrass","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_s2_seagrass)"]}],"DataAtWork":{"Tutorials":[{"Title":"Exploring Digital Earth Pacific Seagrass Extent in a Browser-Based Notebook on Scigantic","URL":"https://scigantic.com/archive/3f69cfcd-61a5-5e3a-acee-d3ada3b40521/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"}],"Tools & Applications":[{"Title":"Digital Earth Pacific Map","URL":"https://maps.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"},{"Title":"Digital Earth Pacific STAC Browser","URL":"https://stac-browser.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"}],"Publications":[{"Title":"Digital Earth Pacific Open Data Access Documentation","URL":"https://digitalearthpacific.github.io/data-access/","AuthorName":"digitalearthpacific","AuthorURL":"https://github.com/digitalearthpacific"}]},"RegistryEntryAdded":"2026-09-30","RegistryEntryLastModified":"2026-10-14","Slug":"dep-seagrass","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Pacific Vegetation Height, Satellite-Derived Canopy Height (Sentinel-2, 10 m)","Description":"This dataset provides spatial estimates of vegetation height across the\nPacific region derived from Sentinel-2 imagery using modelling\napproaches to estimate vegetation canopy height. It includes height and\nconfidence layers from 2020 onwards, enabling quarterly analysis of\nforest structure, biomass, and ecosystem condition. The dataset supports\napplications in land management, carbon monitoring, and biodiversity\nassessment.\n","Documentation":"https://digitalearthpacific.org/#/applications","Contact":"dep@spc.int","ManagedBy":"[Pacific Community (SPC)](https://www.spc.int/)","UpdateFrequency":"Quarterly","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","biodiversity","carbon","earth observation","environmental","geoscience","geospatial","land cover"],"License":"Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n","Resources":[{"Description":"Vegetation Height, Satellite-Derived Canopy Height (Sentinel-2, 10 m)","ARN":"arn:aws:s3:::dep-public-data/dep_s2_vegheight","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_s2_vegheight)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Digital Earth Pacific Map","URL":"https://maps.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"},{"Title":"Digital Earth Pacific STAC Browser","URL":"https://stac-browser.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"}],"Publications":[{"Title":"Digital Earth Pacific Open Data Access Documentation","URL":"https://digitalearthpacific.github.io/data-access/","AuthorName":"digitalearthpacific","AuthorURL":"https://github.com/digitalearthpacific"}]},"RegistryEntryAdded":"2026-09-30","RegistryEntryLastModified":"2026-09-30","Slug":"dep-vegetation-height","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Earth Pacific Water Observations from Space (WOfS, Landsat, 30 m)","Description":"Water Observations from Space (WOfS) maps the presence of surface water\nacross the Pacific region using Landsat satellite imagery, highlighting\nwhere water is usually present and where it is rarely present. The\ndataset supports flood mapping, drought monitoring, hydrological\nanalysis, and water resource management. It comprises three products:\n\n- Scene-based Surface Water: per-scene classification of surface water\n  presence, derived from Landsat imagery using water detection\n  algorithms, from 1984 onwards.\n- Annual Summary: annual statistics of wet and clear observation counts\n  and water frequency, capturing interannual variability in water\n  extent.\n- All-Time Summary: the frequency of water presence across all available\n  observations, highlighting persistent and seasonal water bodies.\n","Documentation":"https://digitalearthpacific.org/#/applications","Contact":"dep@spc.int","ManagedBy":"[Pacific Community (SPC)](https://www.spc.int/)","UpdateFrequency":"Scene-based data is added as new Landsat scenes are acquired; summaries are updated annually","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","disaster response","earth observation","environmental","geoscience","geospatial","surface water","water"],"License":"Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n","Resources":[{"Description":"Water Observations from Space (WOfS) Scene-based Surface Water (Landsat, 30 m)","ARN":"arn:aws:s3:::dep-public-data/dep_ls_wofl","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_ls_wofl)"]},{"Description":"Water Observations from Space (WOfS) Annual Summary (Landsat, 30 m)","ARN":"arn:aws:s3:::dep-public-data/dep_ls_wofs_summary_annual","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_ls_wofs_summary_annual)"]},{"Description":"Water Observations from Space (WOfS) All-Time Summary (Landsat, 30 m)","ARN":"arn:aws:s3:::dep-public-data/dep_ls_wofs_summary_alltime","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.digitalearthpacific.org/collections/dep_ls_wofs_summary_alltime)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Digital Earth Pacific Map","URL":"https://maps.digitalearthpacific.org","AuthorName":"Digital Earth Pacific Contributors"},{"Title":"Digital Earth Pacific STAC Browser","URL":"https://stac-browser.digitalearthpacific.org/","AuthorName":"Digital Earth Pacific Contributors"}],"Publications":[{"Title":"Digital Earth Pacific Open Data Access Documentation","URL":"https://digitalearthpacific.github.io/data-access/","AuthorName":"digitalearthpacific","AuthorURL":"https://github.com/digitalearthpacific"}]},"RegistryEntryAdded":"2024-06-14","RegistryEntryLastModified":"2026-09-30","Slug":"dep-wofs","Sources":["awslabs-open-data-registry"]}
{"Name":"The Cancer Dependency Map (DepMap) Cancer Cell Line Encyclopedia (CCLE) Dataset","Description":"This dataset consists of whole genome sequencing (WGS), whole exome sequencing (WES), and RNA sequencing files generated from ~1000 cancer cell lines described in Ghandi et al., 2019.","Documentation":"https://github.com/broadinstitute/depmap-omics-ccle","Contact":"https://forum.depmap.org","ManagedBy":"[Broad Institute](https://www.broadinstitute.org/)","UpdateFrequency":"occasionally (as additional sequencings are generated for publicly-releasible CCLE models)","Tags":["aws-pds","bam","bioinformatics","biology","cancer","genetic","genomic","Homo sapiens","life sciences","short read sequencing","transcriptomics","whole exome sequencing","whole genome sequencing"],"License":"By downloading this data you agree to our [Terms and Conditions](https://depmap.org/portal/terms/)","Citation":"Ghandi, Huang, Jané-Valbuena et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019). https://doi.org/10.1038/s41586-019-1186-3","Resources":[{"Description":"CRAM/BAM files (and their corresponding CRAI/BAI indexes) for RNA, WES, and WGS samples released by The Cancer Dependency Map (DepMap) as part of the Cancer Cell Line Encyclopedia (CCLE) project","ARN":"arn:aws:s3:::depmap-omics-ccle","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Notifications for new depmap-omics-ccle data","ARN":"arn:aws:sns:us-east-1:019511184952:depmap-omics-ccle-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"DepMap Omics CCLE data on the AWS Open Data Registry","URL":"https://github.com/broadinstitute/depmap-omics-ccle","AuthorName":"Devin McCabe"}],"Tools & Applications":[{"Title":"Cancer Cell Line Encyclopedia (CCLE)","URL":"https://sites.broadinstitute.org/ccle","AuthorName":"Ghandi, Huang, Jané-Valbuena et al."},{"Title":"The Cancer Dependency Map (DepMap)","URL":"https://depmap.org","AuthorName":"Arafeh, Shibue, Dempster et al."}],"Publications":[{"Title":"Bridging the gap between cancer cell line models and tumours using gene expression data","URL":"https://www.nature.com/articles/s41416-021-01359-0","AuthorName":"Noorbakhsh, Vazquez & McFarland"},{"Title":"Genetic dependencies associated with transcription factor activities in human cancer cell lines","URL":"https://www.sciencedirect.com/science/article/pii/S2211124724005035","AuthorName":"Thatikonda, Supper, Wachter et al."},{"Title":"Integrated cross-study datasets of genetic dependencies in cancer","URL":"https://www.nature.com/articles/s41467-021-21898-7","AuthorName":"Pacini, Dempster, Boyle et al."},{"Title":"Machine learning multi-omics analysis reveals cancer driver dysregulation in pan-cancer cell lines compared to primary tumors","URL":"https://www.nature.com/articles/s42003-022-04075-4","AuthorName":"Sanders, Chandra, Zebarjadi et al."},{"Title":"Next-generation characterization of the Cancer Cell Line Encyclopedia","URL":"https://www.nature.com/articles/s41586-019-1186-3","AuthorName":"Ghandi, Huang, Jané-Valbuena et al."},{"Title":"Partial gene suppression improves identification of cancer vulnerabilities when CRISPR-Cas9 knockout is pan-lethal","URL":"https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-03020-w","AuthorName":"Krill-Burger, Dempster, Borah et al."},{"Title":"The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks","URL":"https://link.springer.com/article/10.1186/s13059-023-02877-1","AuthorName":"Ben Guebila, Wang, Lopes-Ramos et al."},{"Title":"The present and future of the Cancer Dependency Map","URL":"https://www.nature.com/articles/s41568-024-00763-x","AuthorName":"Arafeh, Shibue, Dempster et al.","AuthorURL":"https://depmap.org"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-09-25","RegistryEntryLastModified":"2026-02-12","Slug":"depmap-omics-ccle","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"The provider of this dataset will no longer maintain this dataset. Please direct inquiries about this dataset directly to Deutsche Börse.","Name":"Deutsche Börse Public Dataset","Description":"The Deutsche Börse Public Data Set consists of trade data aggregated to one minute intervals from the Eurex and Xetra trading systems. It provides the initial price, lowest price, highest price, final price and volume for every minute of the trading day, and for every tradeable security.  If you need higher resolution data, including untraded price movements, please refer to our historical market data product [here](http://www.eurexchange.com/exchange-en/market-data/historical-data).  Also, be sure to check out our [developer's portal](https://console.developer.deutsche-boerse.com/).","Documentation":"https://github.com/Deutsche-Boerse/dbg-pds","Contact":"pds@deutsche-boerse.com","ManagedBy":"Not currently managed","UpdateFrequency":"The data is updated every minute during trading hours.","Tags":["aws-pds","market data","financial markets","trading"],"License":"Non-commercial (NC) - licensees may copy, distribute, display, and perform the work and make derivative works and remixes based on it only for non-commercial purposes.","Resources":[{"Description":"Eurex PDS Files","ARN":"arn:aws:s3:::deutsche-boerse-eurex-pds","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"Xetra PDS Files","ARN":"arn:aws:s3:::deutsche-boerse-xetra-pds","Region":"eu-central-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Stock Price Movement Prediction Using The Deutsche Börse Public Dataset & Machine Learning","URL":"https://github.com/Originate/dbg-pds-tensorflow-demo","AuthorName":"Originate","AuthorURL":"https://www.originate.com"},{"Title":"10 visualizations to try in Amazon QuickSight with sample data","URL":"https://aws.amazon.com/blogs/big-data/10-visualizations-to-try-in-amazon-quicksight-with-sample-data/","AuthorName":"AWS Big Data Blog","AuthorURL":"https://aws.amazon.com/blogs/big-data/"}],"Tools & Applications":[{"Title":"Streaming XETRA Data Using Apache Spark","URL":"http://live.thermobook.net/dataStreamingWeb","AuthorName":"Thermobook","AuthorURL":"http://www.thermobook.net"}],"Publications":null},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2022-07-07","Slug":"deutsche-boerse-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"Detrended Fluctuation Analysis","Description":"The method of detrended fluctuation analysis has proven useful in revealing the extent of long-range correlations in time series.","Documentation":"https://physionet.org/content/dfa/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/dfa/","ARN":"arn:aws:s3:::physionet-open/dfa/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"dfa","Sources":["awslabs-open-data-registry"]}
{"Name":"DHARANI Developing Human-Brain Atlas","Description":"We introduce DHARANI, the first online platform with three-dimensional (3D) histological reconstructions of the developing human brain from 14 to 24 gestational weeks (GW) across the five fetal brains. DHARANI features 5132 Nissl, hematoxylin and eosin stained, 20 µm coronal and sagittal sections, postmortem MRI, and a neuroanatomical atlas with 466 annotated sections covering ∼500 brain structures. It is accessible online at https://brainportal.humanbrain.in/publicview/index.html. The 3D reconstruction enables a volumetric view of the fetal brain, allowing visualization in all three planes akin to MRI, previously unachievable with histological datasets from the fetal brain. This allowed qualitative assessment of the growth of brain regions and layers throughout the second trimester. “DHARANI” documents the initiation of sulci, with the lateral fissure, calcarine, parieto-occipital, and cingulate sulci, at 14 GW. The central and postcentral sulci appear by 24 GW; however, cytoarchitectonic boundaries become visible before sulcal patterns. Cortical plate (CP) lamination begins at 24 GW in the parietal and occipital cortices. The frontal cortex lacks lamination at 24 GW, although putative Betz cells are already visible and show early patterning in the intermediate zone. The cell-sparse layer between the CP and subplate, containing late migratory neurons, begins in the orbital cortex at 14 GW and reaches the frontal cortex by 17 GW. The appearance of the honeycomb pattern in the occipital and parietal cortex occurs after 14 GW. Additionally, we describe the development of the thalamic pregeniculate with the rotation of the lateral geniculate nucleus. Cerebellar nuclei and an early Purkinje cell layer appear by 21 GW in the already foliated cerebellar cortex. The collection of > 650,000 images have been made available in this Open Data bucket to enable efficient access and analysis of the this dataset.\n","Contact":"keerthi@htic.iitm.ac.in","Documentation":"https://github.com/SGBC-IITM/DHARANI_data_tutorial/blob/main/README.md","ManagedBy":"[Sudha Gopalakrishnan Brain Centre, IIT Madras](http://iitm.humanbrain.in/)","UpdateFrequency":"Never","Tags":["brain images","computer vision","life sciences","microscopy","neurobiology","segmentation"],"License":"CC-by-4.0","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::dharani-fetal-brain-atlas","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing DHARANI data","URL":"https://github.com/SGBC-IITM/DHARANI_data_tutorial","AuthorName":"SGBC-IITM","AuthorURL":"iitm.humanbrain.in"}],"Tools & Applications":[{"Title":"DHARANI Web Portal","URL":"https://dharani.humanbrain.in","AuthorName":"SGBC-IITM","AuthorURL":"iitm.humanbrain.in"}],"Publications":[{"Title":"DHARANI- A 3D Developing Human-brain Atlas Resource to Advance Neuroscience Internationally -- Integrated Multimodal Imaging and High-resolution Histology of the second trimester","URL":"https://onlinelibrary.wiley.com/doi/toc/10.1002/(ISSN)1096-9861.DHARANI-A-3D-Developing-Human-brain","AuthorName":"Verma, et al.","AuthorURL":"iitm.humanbrain.in"}]},"RegistryEntryAdded":"2025-03-11","RegistryEntryLastModified":"2025-04-28","Slug":"dharani-brain-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue","Description":"This bucket contains the checkpoints used to reproduce the baseline results reported in the DialoGLUE benchmark hosted\non EvalAI (https://evalai.cloudcv.org/web/challenges/challenge-page/708/overview). The associated scripts for using the checkpoints are located here:\nhttps://github.com/alexa/dialoglue. The associated paper describing the benchmark and checkpoints is here: https://arxiv.org/abs/2009.13570.\nThe provided checkpoints include the CONVBERT model, a BERT-esque model trained on a large open-domain conversational\ndataset. It also includes the CONVBERT-DG and BERT-DG checkpoints described in the linked\npaper.\n","Documentation":"https://github.com/alexa/dialoglue","Contact":"mihaeric@amazon.com, mehrishikib@gmail.com, hakkanit@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not currently being updated","Tags":["amazon.science","conversation data","machine learning","natural language processing"],"License":"[CDLA-Sharing](https://cdla.io/sharing-1-0/)","Resources":[{"Description":"DialoGLUE checkpoints","ARN":"arn:aws:s3:::dialoglue","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://dialoglue.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue","URL":"https://arxiv.org/abs/2009.13570","AuthorName":"Shikib Mehri, Mihail Eric, Dilek Hakkani-Tur"}]},"RegistryEntryAdded":"2020-10-09","RegistryEntryLastModified":"2022-06-06","Slug":"dialoglue","Sources":["awslabs-open-data-registry"]}
{"Name":"Knowledge Portal Network Bottom-line Genetic Associations","Description":"At the Knowledge Portal Network, we aggregate and analyze genetic association results for a wide range of diseases and traits. For any given disease, a large number of individual genetic association datasets may have been generated. To make these results more interpretable, we meta-analyze all datasets for each phenotype, using a method that we term \"bottom-line integrative analysis\". Here we provide the bottom-line summary statistic files for public download.","Documentation":"https://kp4cd.org/bottom-line_associations","Contact":"help@kp4cd.org","ManagedBy":"Jason Flannick lab, Broad Institute of MIT and Harvard","UpdateFrequency":"Three times per year","Tags":["genetic","genome wide association study","life sciences"],"License":"https://a2f.hugeamp.org/policies.html","Citation":"Knowledge Portal Network Bottom-line Genetic Associations for [PHENOTYPE] were accessed on [DATE] at registry.opendata.aws/[dataset]","Resources":[{"Description":"Knowledge Portal Bottom-line Genetic Associations","ARN":"arn:aws:s3:::dig-open-bottom-line-analysis","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Tutorial: Use cases for the Knowledge Portal Network bottom-line genetic associations","URL":"https://kp4cd.org/bottom-line_tutorial","AuthorName":"Jason Flannick","AuthorURL":"https://www.flannicklab.org/"}],"Publications":[{"Title":"Cardiovascular Disease Knowledge Portal: A Community Resource for Cardiovascular Disease Research","URL":"https://www.ahajournals.org/doi/10.1161/CIRCGEN.123.004181?","AuthorName":"Costanzo MC, Roselli C, Brandes M, Duby M, Hoang Q, Jang D, et al."},{"Title":"Leveraging type 1 diabetes human genetic and genomic data in the T1D knowledge portal","URL":"https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002233","AuthorName":"Kudtarkar P, Costanzo MC, Sun Y, Jang D, Koesterer R, Mychaleckyj JC, et al."},{"Title":"The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits","URL":"https://www.sciencedirect.com/science/article/pii/S1550413123000797?","AuthorName":"Costanzo MC, von Grotthuss M, Massung J, Jang D, Caulkins L, Koesterer R, et al."}]},"RegistryEntryAdded":"2025-05-06","RegistryEntryLastModified":"2025-05-06","Slug":"dig-open-analysis-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Digital Data Real-Time Ingestion Utility","Description":"We present a Python-based utility for real-time ingestion of digital physiological signals and study enrollment metadata into an Oracle database. The application combines filesystem event monitoring, a GUI data entry form, and thin-mode Oracle connectivity to ensure timely and accurate data capture at the point of file creation. The system is designed for research assistants to enter metadata for digital signal recordings (e.g., ECG, PCG, and other physiological datasets), with automated database field assignments (e.g., study_date, study_id) and compliance with pre-defined enumerations and constraints. The utility follows best practices and aligns with high institutional and regulatory standards for digital data quality and integrity, ensuring robust, reproducible, and traceable research data collection. Key features include real-time linkage between digital physiological recordings and their corresponding metadata, ensuring synchronized and traceable data entry. The use of the Oracle thin client enables lightweight and efficient database integration, while the GUI-based control of data input minimizes human errors during metadata collection. All entries are securely timestamped to maintain a comprehensive audit trail and support full accountability. From a security standpoint, database credentials should be stored in environment variables or .env files with restricted access, user accounts should be limited to INSERT privileges, and read-only database views are recommended for downstream data consumers to preserve data integrity and confidentiality.","Documentation":"https://doi.org/10.13026/jt51-cs74","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"MIT License","Resources":[{"Description":"https://doi.org/10.13026/jt51-cs74","ARN":"arn:aws:s3:::physionet-open/digital-data-ingestion-utility/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"digital-data-ingestion-utility","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"Name":"Maxar Open Data Program","Description":"Pre and post event high-resolution satellite imagery in support of emergency planning, risk assessment, monitoring of staging areas and emergency response, damage assessment, and recovery. Also includes crowdsourced damage assessments for major, sudden onset disasters.\n","Documentation":"https://www.maxar.com/open-data","Contact":"https://www.maxar.com/open-data","UpdateFrequency":"New data is released in response to activations.","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["earth observation","disaster response","geospatial","satellite imagery","cog"],"License":"Creative Commons Attribution Non Commercial 4.0","ManagedBy":"[Maxar](https://www.maxar.com/)","Resources":[{"Description":"Imagery and metadata","Host":"opendata.digitalglobe.com","Region":"us-east-1","Type":"CloudFront Distribution"}],"DeprecatedNotice":"The CloudFront distribution has been retired but the original data is still available in S3. However beginning in 2022 the Open Data imagery was moved to the Maxar ARD pipeline that provides cloud-optimized imagery and metadata. See https://registry.opendata.aws/maxar-open-data/ for more information.","RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2023-02-28","Slug":"digital-globe-open-data","Sources":["awslabs-open-data-registry"]}
{"Name":"DigitalCorpora","Description":"Disk images, memory dumps, network packet captures, and files for use in digital forensics research and education. All of this information is accessible through the digitalcorpora.org website, and made available at s3://digitalcorpora/. Some of these datasets implement scenarios that were performed by students, faculty, and others acting *in persona*. As such, the information is synthetic and may be used without prior authorization or IRB approval. Details of these datasets can be found at http://www.simson.net/clips/academic/2009.DFRWS.Corpora.pdf","Contact":"Digital Corpora <admin@digitalcorpora.org>","ManagedBy":"[Simson Garfinkel](https://simson.net/)","Documentation":"https://digitalcorpora.org","UpdateFrequency":"Quarterly","Tags":["aws-pds","computer forensics","computer security","CSI","cyber security","digital forensics","image processing","imaging","information retrieval","internet","intrusion detection","machine learning","machine translation","text analysis"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"Digital Corpora Dataset","ARN":"arn:aws:s3:::digitalcorpora","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Bringing Science to Digital Forensics with Standardized Forensic Corpora","URL":"http://www.simson.net/clips/academic/2009.DFRWS.Corpora.pdf","AuthorURL":"https://simson.net/","AuthorName":"Garfinkel, Farrell, Roussev and Dinolt"},{"Title":"Creating Realistic Corpora for Forensic and Security Education","URL":"http://simson.net/clips/academic/2011.ADFSL.Corpora.pdf","AuthorName":"Woods, K., Christopher Lee, Simson Garfinkel, David Dittrich, Adam Russel, Kris Kearton","AuthorURL":"https://simson.net/"}]},"RegistryEntryAdded":"2021-01-23","RegistryEntryLastModified":"2021-02-05","Slug":"digitalcorpora","Sources":["awslabs-open-data-registry"]}
{"Name":"Danish Meteorological Institute (DMI) Reanalysis dataset v0.5","Description":"DANRA is a high-resolution meteorological reanalysis dataset for Denmark and Northwestern Europe covering the period September 1990 to December 2023","Documentation":"https://dmidk.github.io/danradocs/intro.html","Contact":"https://www.dmi.dk/kontakt","ManagedBy":"[Danish Meteorological Institute](https://www.dmi.dk/)","UpdateFrequency":"Not updated","Collabs":{"ASDI":{"Tags":["climate","weather"]}},"Tags":["air temperature","atmosphere","aws-pds","geospatial","global","land","meteorological","model","near-surface air temperature","near-surface relative humidity","near-surface specific humidity","water","weather","zarr"],"License":"DMI Reanalysis dataset v0.5 is distributed under the [Creative Commons License CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode.en)","Resources":[{"Description":"DMI Reanalysis dataset v0.5","ARN":"arn:aws:s3:::dmi-danra-05","Region":"eu-north-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"DANRA figures","URL":"https://dmidk.github.io/danradocs/notebooks/paper-figures.html","NotebookURL":"https://dmidk.github.io/danradocs/_sources/notebooks/paper-figures.ipynb","AuthorName":"Danish Meteorological Institute","AuthorURL":"https://www.dmi.dk/","Services":["Amazon S3"]},{"Title":"Looking at distributions","URL":"https://dmidk.github.io/danradocs/notebooks/distributions.html","NotebookURL":"https://dmidk.github.io/danradocs/_sources/notebooks/distributions.ipynb","AuthorName":"Danish Meteorological Institute","AuthorURL":"https://www.dmi.dk/","Services":["Amazon S3"]}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-09-29","RegistryEntryLastModified":"2025-10-02","Slug":"dmi-danra-05","Sources":["awslabs-open-data-registry"]}
{"Name":"Danish Meteorological Institute (DMI) Open Data Forecasts","Description":"DMI forecast data consist of various models where each model contains different set of parameters relating to a specific domain like ocean (WAM), storm flooding (DKSS) or weather (HARMONIE)","Documentation":"https://www.dmi.dk/friedata/dokumentation/forecast-data","Contact":"https://www.dmi.dk/friedata/dokumentation/api-status-contact","ManagedBy":"[Danish Meteorological Institute](https://www.dmi.dk/)","UpdateFrequency":"Every hour, 3 hours or 6 hours depending on model","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["air temperature","atmosphere","aws-pds","forecast","meteorological","model","near-surface air temperature","near-surface relative humidity","near-surface specific humidity","ocean circulation","ocean currents","ocean sea surface height","ocean simulation","ocean velocity","oceans","time series forecasting","weather"],"License":"DMI's Open Data are distributed under the [Creative Commons License CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode.en)","Resources":[{"Description":"DMI's Open Data Forecasts","ARN":"arn:aws:s3:::dmi-opendata","Region":"eu-north-1","Type":"S3 Bucket","Explore":["[Explore dataset](https://dmi-opendata.s3.eu-north-1.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Guide to processing forecast data with Python","URL":"https://github.com/dmidk/dmi-opendata-forecastdata-guide/","NotebookURL":"https://github.com/dmidk/dmi-opendata-forecastdata-guide/blob/main/graph-2m-temperature.ipynb","AuthorName":"Danish Meteorological Institute","AuthorURL":"https://www.dmi.dk/","Services":["Amazon S3"]}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-03-12","RegistryEntryLastModified":"2025-11-20","Slug":"dmi-opendata","Sources":["awslabs-open-data-registry"]}
{"Name":"Defense Meteorology Satellite Program (DMSP) Auroral Particle Flux","Description":"The United States Air Force (USAF) Defense Meteorological Satellite Program (DMSP) SSJ precipitating particle instrument measures in-situ total flux and energy distribution of electrons and ions at low earth orbit. These precipitating particles are of interest for space weather operations and research, in part because they produce aurora during normal and very strong geomagnetic storms. This dataset contains both sensor-level raw data (as detailed in Redmon et al. 2017) and a high-level machine-learning-ready data product.","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/dmspssj","Contact":"delores.knipp@colorado.edu","ManagedBy":"Space Weather Technology, Research and Education Center (TREC) at University of Colorado, Boulder","UpdateFrequency":"Infrequent","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","earth observation","geospatial","solar","space weather"],"License":"This data is in the '[public domain](https://creativecommons.org/publicdomain/zero/1.0/)'","Resources":[{"Description":"DMSP Auroral Particle Flux","ARN":"arn:aws:s3:::dmspssj","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"ssj_latbin: Create and read ML-ready DMSP particle precipitation data","URL":"https://github.com/lkilcommons/ssj_latbin","AuthorName":"Liam Kilcommons","AuthorURL":"https://www.linkedin.com/in/liam-kilcommons-69187449/"}]},"RegistryEntryAdded":"2022-04-25","RegistryEntryLastModified":"2022-11-08","Slug":"dmspssj","Sources":["awslabs-open-data-registry"]}
{"Name":"DNAStack COVID19 SRA Data","Description":"The [Sequence Read Archive (SRA)](https://www.ncbi.nlm.nih.gov/sra/) is the primary archive of high-throughput sequencing data, hosted by the National Institutes of Health (NIH). The SRA represents the largest publicly available repository of SARS-CoV-2 sequencing data. This dataset was created by DNAstack using SARS-CoV-2 sequencing data sourced from the SRA. Where possible, raw sequence data were processed by DNAstack through a unified bioinformatics pipeline to produce genome assemblies and variant calls. The use of a standardized workflow to produce this harmonized dataset allows public data generated using different methodologies to be combined and compared for a more powerful global analysis of available SARS-CoV-2 data, allowing researchers rapid access to aggregated downstream results for accelerated insight generation. Methodology: Reads from the SRA were extracted in FASTQ format, then entered into a different pipeline depending on the sequencing technology used to create the reads: the [ARTIC protocol](https://artic.network/ncov-2019/ncov2019-bioinformatics-sop.html) for Oxford Nanopore-derived reads; the [SIGNAL pipeline](https://github.com/jaleezyy/covid-19-signal) for paired-end Illumina reads; and the [CoSA pipeline](https://github.com/PacificBiosciences/CoSA) (using [DeepVariant](https://github.com/google/deepvariant) for variant calling) for PacBio reads. Briefly, reads were primer-trimmed and aligned to the SARS-CoV-2 reference genome, following which contiguous regions were assembled and variant sites were called. [Pangolin](https://github.com/cov-lineages/pangolin) was then used to assign viral lineage based on the assembled genome.\n","Documentation":"https://github.com/DNAstack/dnastack-open-data","Contact":"[DNAstack](bioinformatics@dnastack.com)","ManagedBy":"[DNAstack](https://dnastack.com/)","UpdateFrequency":"Rolling","Tags":["aws-pds","bam","bioinformatics","coronavirus","COVID-19","fasta","fastq","genetic","genomic","global","health","life sciences","long read sequencing","SARS-CoV-2","vcf","virus","whole genome sequencing"],"License":"[DNAstack terms of use](https://dnastack.com/terms-of-use/)","Resources":[{"Description":"SARS-CoV-2 raw sequencing and output data (FASTQ, BAM, FASTA, VCF)","ARN":"arn:aws:s3:::dnastack-covid-19-sra-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse bucket](https://dnastack-covid-19-sra-data.s3.amazonaws.com/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Viral lineage assignment","URL":"https://github.com/DNAstack/dnastack-open-data/tree/master/tutorials/assign_lineage","AuthorName":"Heather Ward","Services":["AWS Batch","Amazon EC2","Amazon S3"]}],"Tools & Applications":[{"Title":"Viral AI","URL":"https://viral.ai/collections/ncbi-sra","AuthorName":"DNAstack"}]},"RegistryEntryAdded":"2022-01-21","RegistryEntryLastModified":"2025-02-04","Slug":"dnastack-covid-19-sra-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Stress Recognition in Automobile Drivers","Description":"This database, contributed to PhysioNet by its creator, Jennifer Healey, contains a collection of multiparameter recordings from healthy volunteers, taken while they were driving on a prescribed route including city streets and highways in and around Boston, Massachusetts. The objective of the study for which these data were collected was to investigate the feasibility of automated recognition of stress on the basis of the recorded signals, which include ECG, EMG (right trapezius), GSR (galvanic skin resistance) measured on the hand and foot, and respiration.","Documentation":"https://doi.org/10.13026/C2SG6B","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2SG6B","ARN":"arn:aws:s3:::physionet-open/drivedb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"drivedb","Sources":["awslabs-open-data-registry"]}
{"Name":"DynaCell","Description":"DynaCell is an evaluation framework for dynamic 3D virtual staining of live cells.\nThe dataset pairs label-free transmitted-light volumes (phase reconstructions from brightfield z-stacks) with fluorescence ground truth for four organelles (nucleus, cell membrane,\nendoplasmic reticulum, mitochondria) across three conditions\n(uninfected, Zika-infected, Dengue-infected).\n\nThe v1 release contains images of A549 human lung adenocarcinoma cells acquired on\nthe Mantis correlative label-free and light-sheet fluorescence microscope at Biohub\n(24 OZX stores, 262 FOVs, ~407 GB), split into train and test sets across 4 organelle\nmarkers and 3 conditions.\n\nA v1.1 release will add the WTC-11 hiPSC component: cells from the Allen Institute\nhiPSC single-cell image dataset (Viana et al., Nature 2023), reprocessed as paired\nlabel-free and confocal fluorescence volumes. The iPSC dataset is redistributed under\nthe Allen Institute Terms of Use (https://www.allencell.org/terms-of-use.html), which\nrequires citation of the original dataset and limits use to noncommercial research.\n\nAll data are stored as RFC-9 zipped OME-Zarr (.ozx) archives following OME-NGFF v0.5,\nreadable with iohub (https://github.com/czbiohub-sf/iohub). Machine-readable metadata\n(Croissant JSON-LD with Responsible AI fields, per the NeurIPS Datasets & Benchmarks\ntrack) is published at s3://dynacell/v1/metadata/croissant.json.\n","Documentation":"https://github.com/mehta-lab/VisCy/tree/dynacell-models/applications/dynacell","Contact":"shalin.mehta@biohub.org","ManagedBy":"[Biohub](https://www.biohub.org/)","UpdateFrequency":"As needed - v1 (A549) is frozen; future releases will expand the dataset. Registry Entry Added 2026-05-04, Last Modified 2026-06-15.","Tags":["aws-pds","benchmark","Biohub","biology","cell biology","cell imaging","computer vision","fluorescence imaging","image-based profiling","life sciences","machine learning","microscopy","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"DynaCell v1 release: paired label-free and fluorescence 3D+time volumes\nin RFC-9 zipped OME-Zarr (.ozx) format. Contains biohub-a549 (train/test\nsplits, 4 markers x 3 conditions = 24 stores), Croissant 1.1 metadata\nwith Responsible AI fields, and placeholders for forthcoming demo\nsamples, trained model checkpoints, and supplementary movies.\n","ARN":"arn:aws:s3:::dynacell","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Get to know DynaCell v1 — loading and exploring the dataset","URL":"https://github.com/mehta-lab/VisCy/blob/dynacell-models/applications/dynacell/examples/notebooks/get-to-know-dynacell-dataset.ipynb","AuthorName":"Computational Imaging Group at Biohub","AuthorURL":"https://www.biohub.org/comp-micro"}],"Tools & Applications":[{"Title":"DynaCell virtual-staining demo (interactive Hugging Face Space)","URL":"https://huggingface.co/spaces/biohub/dynacell","AuthorName":"Computational Imaging Group at Biohub","AuthorURL":"https://www.biohub.org/comp-micro"},{"Title":"iohub — OME-Zarr and OZX I/O library","URL":"https://github.com/czbiohub-sf/iohub","AuthorName":"Computational Imaging Group at Biohub","AuthorURL":"https://www.biohub.org/comp-micro"},{"Title":"VisCy — training, prediction, and evaluation pipelines for DynaCell","URL":"https://github.com/mehta-lab/VisCy/tree/dynacell-models/applications/dynacell","AuthorName":"Computational Imaging Group at Biohub","AuthorURL":"https://www.biohub.org/comp-micro"},{"Title":"waveorder — phase reconstruction from label-free microscopy","URL":"https://github.com/mehta-lab/waveorder","AuthorName":"Computational Imaging Group at Biohub","AuthorURL":"https://www.biohub.org/comp-micro"}],"Publications":[{"Title":"DynaCell: an Evaluation Framework for Dynamic 3D Virtual Staining of Live Cells","URL":"https://github.com/mehta-lab/VisCy/tree/dynacell-models/applications/dynacell","AuthorName":"Kalinin, Zheng, Theodoro, Ivanov, Hirata-Miyasaki, Lee, Liu, Varra, Chandler, Pradeep, Liu, Leonetti, Arias, Huang, Mehta","AuthorURL":"https://www.biohub.org/comp-micro"}]},"RegistryEntryAdded":"2026-05-02","RegistryEntryLastModified":"2026-06-16","Slug":"dynacell","Sources":["awslabs-open-data-registry"]}
{"Name":"DWD ICON-EU - dynamical.org Icechunk Zarr","Description":"ICON-EU is a regional weather forecast model operated by Deutscher Wetterdienst (DWD), Germany's national meteorological service. ICON-EU is a nested configuration of DWD's global ICON (Icosahedral Non-hydrostatic) model that provides high-resolution forecasts over Europe.\n\nThese datasets have been translated to cloud-optimized Icechunk Zarr format by [dynamical.org](https://dynamical.org).\n\n- [DWD ICON-EU forecast, 5 day](https://dynamical.org/catalog/dwd-icon-eu-forecast-5-day/) - High-resolution weather forecasts for Europe from the ICON-EU model operated by Deutscher Wetterdienst (DWD).\n","Documentation":"https://dynamical.org/catalog/","Contact":"feedback@dynamical.org","ManagedBy":"[dynamical.org](https://dynamical.org)","UpdateFrequency":"DWD ICON-EU forecast, 5 day: Forecasts initialized every 6 hours","Tags":["atmosphere","aws-pds","climate","forecast","meteorological","weather","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"DWD ICON-EU Icechunk Zarr data","ARN":"arn:aws:s3:::dynamical-dwd-icon-eu","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://dynamical-dwd-icon-eu.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for dataset updates","ARN":"arn:aws:sns:us-west-2:761136292730:dynamical-dwd-icon-eu-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"DWD ICON-EU forecast, 5 day — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/dwd-icon-eu-forecast-5-day.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/dwd-icon-eu-forecast-5-day.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-04-21","RegistryEntryLastModified":"2026-07-09","Slug":"dynamical-dwd-icon-eu","Sources":["awslabs-open-data-registry"]}
{"Name":"ECCC HRDPS - dynamical.org Icechunk Zarr","Description":"The High Resolution Deterministic Prediction System (HRDPS) is the 2.5 km limited-area weather forecast model operated by Environment and Climate Change Canada (ECCC), Canada's national meteorological service. Its continental domain covers Canada and the northern United States at a resolution that captures fine-scale weather features.\n\nThese datasets have been translated to cloud-optimized Icechunk Zarr format by [dynamical.org](https://dynamical.org).\n\n- [ECCC HRDPS forecast](https://dynamical.org/catalog/eccc-hrdps-forecast/) - Weather forecasts from the High Resolution Deterministic Prediction System (HRDPS) continental domain, operated by Environment and Climate Change Canada (ECCC).\n","Documentation":"https://dynamical.org/catalog/","Contact":"feedback@dynamical.org","ManagedBy":"[dynamical.org](https://dynamical.org)","UpdateFrequency":"ECCC HRDPS forecast: Forecasts initialized every 6 hours","Tags":["atmosphere","aws-pds","climate","forecast","meteorological","weather","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ECCC HRDPS Icechunk Zarr data","ARN":"arn:aws:s3:::dynamical-eccc-hrdps","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://dynamical-eccc-hrdps.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for dataset updates","ARN":"arn:aws:sns:us-west-2:761136292730:dynamical-eccc-hrdps-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"ECCC HRDPS forecast — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/eccc-hrdps-forecast.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/eccc-hrdps-forecast.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-09-01","RegistryEntryLastModified":"2026-09-01","Slug":"dynamical-eccc-hrdps","Sources":["awslabs-open-data-registry"]}
{"Name":"ECMWF AIFS ENS - dynamical.org Icechunk Zarr","Description":"The Artificial Intelligence Forecasting System (AIFS) is a data driven forecast model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). AIFS ENS is the ensemble configuration of AIFS, containing 51 ensemble members. AIFS is trained on ECMWF's ERA5 re-analysis and ECMWF's operational numerical weather prediction (NWP) analyses.\n\nThese datasets have been translated to cloud-optimized Icechunk Zarr format by [dynamical.org](https://dynamical.org).\n\n- [ECMWF AIFS ENS forecast](https://dynamical.org/catalog/ecmwf-aifs-ens-forecast/) - Ensemble weather forecasts from the ECMWF Artificial Intelligence Forecasting System (AIFS) ENS model.\n","Documentation":"https://dynamical.org/catalog/","Contact":"feedback@dynamical.org","ManagedBy":"[dynamical.org](https://dynamical.org)","UpdateFrequency":"ECMWF AIFS ENS forecast: Forecasts initialized every 6 hours","Tags":["atmosphere","aws-pds","climate","forecast","meteorological","weather","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ECMWF AIFS ENS Icechunk Zarr data","ARN":"arn:aws:s3:::dynamical-ecmwf-aifs-ens","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://dynamical-ecmwf-aifs-ens.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for dataset updates","ARN":"arn:aws:sns:us-west-2:761136292730:dynamical-ecmwf-aifs-ens-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"ECMWF AIFS ENS forecast — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/ecmwf-aifs-ens-forecast.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/ecmwf-aifs-ens-forecast.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-05-21","RegistryEntryLastModified":"2026-09-21","Slug":"dynamical-ecmwf-aifs-ens","Sources":["awslabs-open-data-registry"]}
{"Name":"ECMWF AIFS Single - dynamical.org Icechunk Zarr","Description":"The Artificial Intelligence Forecasting System (AIFS) is a data driven forecast model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). This is the non-ensemble configuration of AIFS that produces a single forecast trace. AIFS is trained on ECMWF's ERA5 re-analysis and ECMWF's operational numerical weather prediction (NWP) analyses.\n\nThese datasets have been translated to cloud-optimized Icechunk Zarr format by [dynamical.org](https://dynamical.org).\n\n- [ECMWF AIFS Single forecast](https://dynamical.org/catalog/ecmwf-aifs-single-forecast/) - Weather forecasts from the ECMWF Artificial Intelligence Forecasting System (AIFS) Single model.\n- [ECMWF AIFS Single forecast, virtual](https://dynamical.org/catalog/ecmwf-aifs-single-forecast-virtual/) - Weather forecasts from the ECMWF Artificial Intelligence Forecasting System (AIFS) Single model.\n","Documentation":"https://dynamical.org/catalog/","Contact":"feedback@dynamical.org","ManagedBy":"[dynamical.org](https://dynamical.org)","UpdateFrequency":"ECMWF AIFS Single forecast: Forecasts initialized every 6 hours; ECMWF AIFS Single forecast, virtual: Forecasts initialized every 6 hours","Tags":["atmosphere","aws-pds","climate","forecast","meteorological","weather","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ECMWF AIFS Single Icechunk Zarr data","ARN":"arn:aws:s3:::dynamical-ecmwf-aifs-single","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://dynamical-ecmwf-aifs-single.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for dataset updates","ARN":"arn:aws:sns:us-west-2:761136292730:dynamical-ecmwf-aifs-single-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"ECMWF AIFS Single forecast — Heating degree days: GFS vs AIFS","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gfs%2Becmwf-aifs-hdd.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gfs%2Becmwf-aifs-hdd.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"ECMWF AIFS Single forecast — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/ecmwf-aifs-single-forecast.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/ecmwf-aifs-single-forecast.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"ECMWF AIFS Single forecast, virtual — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/ecmwf-aifs-single-forecast-virtual.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/ecmwf-aifs-single-forecast-virtual.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-03-30","RegistryEntryLastModified":"2026-09-28","Slug":"dynamical-ecmwf-aifs-single","Sources":["awslabs-open-data-registry"]}
{"Name":"ECMWF IFS ENS - dynamical.org Icechunk Zarr","Description":"The Integrated Forecasting System (IFS) is a global forecast model developed by ECMWF. ENS is an ensemble configuration of IFS: 51 ensemble members in the medium range and 101 in the sub-seasonal (extended) range. IFS consists of a numerical model of the Earth system, which includes an atmospheric model at its heart, coupled with models of other Earth system components such as the ocean. The data assimilation system combines the latest weather observations with a recent forecast to obtain the best possible estimate of the current state of the Earth system.\n\nThese datasets have been translated to cloud-optimized Icechunk Zarr format by [dynamical.org](https://dynamical.org).\n\n- [ECMWF IFS ENS forecast, 15 day, 0.25 degree](https://dynamical.org/catalog/ecmwf-ifs-ens-forecast-15-day-0-25-degree/) - Ensemble weather forecasts from the ECMWF Integrated Forecasting System (IFS).\n- [ECMWF IFS ENS forecast, 46 day, daily, 1.5 degree](https://dynamical.org/catalog/ecmwf-ifs-ens-forecast-46-day-daily-1-5-degree/) - Sub-seasonal-range ensemble weather forecasts from the ECMWF Integrated Forecasting System (IFS).\n- [ECMWF IFS ENS forecast, 46 day, 6 hourly, 1.5 degree](https://dynamical.org/catalog/ecmwf-ifs-ens-forecast-46-day-6-hourly-1-5-degree/) - Sub-seasonal-range ensemble weather forecasts from the ECMWF Integrated Forecasting System (IFS).\n","Documentation":"https://dynamical.org/catalog/","Contact":"feedback@dynamical.org","ManagedBy":"[dynamical.org](https://dynamical.org)","UpdateFrequency":"ECMWF IFS ENS forecast, 15 day, 0.25 degree: Forecasts initialized every 24 hours; ECMWF IFS ENS forecast, 46 day, daily, 1.5 degree: Forecasts initialized every 24 hours; ECMWF IFS ENS forecast, 46 day, 6 hourly, 1.5 degree: Forecasts initialized every 24 hours","Tags":["atmosphere","aws-pds","climate","forecast","meteorological","weather","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ECMWF IFS ENS Icechunk Zarr data","ARN":"arn:aws:s3:::dynamical-ecmwf-ifs-ens","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://dynamical-ecmwf-ifs-ens.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for dataset updates","ARN":"arn:aws:sns:us-west-2:761136292730:dynamical-ecmwf-ifs-ens-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"ECMWF IFS ENS forecast, 15 day, 0.25 degree — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/ecmwf-ifs-ens-forecast-15-day-0-25-degree.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/ecmwf-ifs-ens-forecast-15-day-0-25-degree.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"ECMWF IFS ENS forecast, 46 day, 6 hourly, 1.5 degree — ECMWF IFS ENS 46 day, daily and 6 hourly","URL":"https://github.com/dynamical-org/notebooks/blob/main/ecmwf-ifs-ens-forecast-46-day-1-5-degree.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/ecmwf-ifs-ens-forecast-46-day-1-5-degree.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"ECMWF IFS ENS forecast, 46 day, daily, 1.5 degree — ECMWF IFS ENS 46 day, daily and 6 hourly","URL":"https://github.com/dynamical-org/notebooks/blob/main/ecmwf-ifs-ens-forecast-46-day-1-5-degree.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/ecmwf-ifs-ens-forecast-46-day-1-5-degree.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-11-25","RegistryEntryLastModified":"2026-09-23","Slug":"dynamical-ecmwf-ifs-ens","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA IMERG - dynamical.org Icechunk Zarr","Description":"The Integrated Multi-satellitE Retrievals for GPM (IMERG) is a NASA algorithm that merges precipitation estimates from the constellation of passive microwave satellites in the Global Precipitation Measurement (GPM) mission with microwave-calibrated infrared estimates and monthly gauge analyses to produce a global, gridded, half-hourly precipitation record. IMERG intercalibrates, merges, and interpolates these inputs onto a 0.1 degree grid spanning the TRMM and GPM satellite eras.\n\nThese datasets have been translated to cloud-optimized Icechunk Zarr format by [dynamical.org](https://dynamical.org).\n\n- [NASA IMERG analysis, early](https://dynamical.org/catalog/nasa-imerg-analysis-early/) - Global half-hourly precipitation from NASA GPM IMERG Early Run, version 07.\n- [NASA IMERG analysis, late](https://dynamical.org/catalog/nasa-imerg-analysis-late/) - Global half-hourly precipitation from NASA GPM IMERG Late Run, version 07.\n","Documentation":"https://dynamical.org/catalog/","Contact":"feedback@dynamical.org","ManagedBy":"[dynamical.org](https://dynamical.org)","UpdateFrequency":"NASA IMERG analysis, early: 30 minutes; NASA IMERG analysis, late: 30 minutes","Tags":["atmosphere","aws-pds","climate","forecast","meteorological","weather","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NASA IMERG Icechunk Zarr data","ARN":"arn:aws:s3:::dynamical-nasa-imerg","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://dynamical-nasa-imerg.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for dataset updates","ARN":"arn:aws:sns:us-west-2:761136292730:dynamical-nasa-imerg-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"NASA IMERG analysis, early — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/nasa-imerg-analysis-early.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/nasa-imerg-analysis-early.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"NASA IMERG analysis, late — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/nasa-imerg-analysis-late.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/nasa-imerg-analysis-late.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-07-25","RegistryEntryLastModified":"2026-09-21","Slug":"dynamical-nasa-imerg","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA GEFS - dynamical.org Icechunk Zarr","Description":"The Global Ensemble Forecast System (GEFS) is a National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) weather forecast model. GEFS creates 31 separate forecasts (ensemble members) to describe the range of forecast uncertainty.\n\nThese datasets have been translated to cloud-optimized Icechunk Zarr format by [dynamical.org](https://dynamical.org).\n\n- [NOAA GEFS forecast, 35 day](https://dynamical.org/catalog/noaa-gefs-forecast-35-day/) - Weather forecasts from the Global Ensemble Forecast System (GEFS) operated by NOAA NWS NCEP.\n- [NOAA GEFS analysis](https://dynamical.org/catalog/noaa-gefs-analysis/) - Weather analysis from the Global Ensemble Forecast System (GEFS) operated by NOAA NWS NCEP.\n- [NOAA GEFS analysis, 0.25 degree, virtual](https://dynamical.org/catalog/noaa-gefs-analysis-0-25-degree-virtual/) - Weather analysis from the Global Ensemble Forecast System (GEFS) operated by NOAA NWS NCEP.\n- [NOAA GEFS forecast, 10 day, 0.25 degree, virtual](https://dynamical.org/catalog/noaa-gefs-forecast-10-day-0-25-degree-virtual/) - Weather forecasts from the Global Ensemble Forecast System (GEFS) operated by NOAA NWS NCEP.\n- [NOAA GEFS forecast, 16 day, 0.5 degree, virtual](https://dynamical.org/catalog/noaa-gefs-forecast-16-day-0-5-degree-virtual/) - Weather forecasts from the Global Ensemble Forecast System (GEFS) operated by NOAA NWS NCEP.\n- [NOAA GEFS forecast, 35 day, 0.5 degree, virtual](https://dynamical.org/catalog/noaa-gefs-forecast-35-day-0-5-degree-virtual/) - Weather forecasts from the Global Ensemble Forecast System (GEFS) operated by NOAA NWS NCEP.\n","Documentation":"https://dynamical.org/catalog/","Contact":"feedback@dynamical.org","ManagedBy":"[dynamical.org](https://dynamical.org)","UpdateFrequency":"NOAA GEFS forecast, 35 day: Forecasts initialized every 24 hours; NOAA GEFS analysis: 3.0 hours; NOAA GEFS analysis, 0.25 degree, virtual: 3 hours; NOAA GEFS forecast, 10 day, 0.25 degree, virtual: Forecasts initialized every 6 hours; NOAA GEFS forecast, 16 day, 0.5 degree, virtual: Forecasts initialized every 6 hours; NOAA GEFS forecast, 35 day, 0.5 degree, virtual: Forecasts initialized every 24 hours","Tags":["atmosphere","aws-pds","climate","forecast","meteorological","weather","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NOAA GEFS Icechunk Zarr data","ARN":"arn:aws:s3:::dynamical-noaa-gefs","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://dynamical-noaa-gefs.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for dataset updates","ARN":"arn:aws:sns:us-west-2:761136292730:dynamical-noaa-gefs-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"NOAA GEFS analysis — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gefs-analysis.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gefs-analysis.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"NOAA GEFS analysis, 0.25 degree, virtual — GEFS forecasts and analysis, virtual","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gefs-forecast-analysis-virtual.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gefs-forecast-analysis-virtual.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"NOAA GEFS forecast, 10 day, 0.25 degree, virtual — GEFS forecasts and analysis, virtual","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gefs-forecast-analysis-virtual.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gefs-forecast-analysis-virtual.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"NOAA GEFS forecast, 16 day, 0.5 degree, virtual — GEFS forecasts and analysis, virtual","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gefs-forecast-analysis-virtual.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gefs-forecast-analysis-virtual.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"NOAA GEFS forecast, 35 day — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gefs-forecast-35-day.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gefs-forecast-35-day.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"NOAA GEFS forecast, 35 day, 0.5 degree, virtual — GEFS forecasts and analysis, virtual","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gefs-forecast-analysis-virtual.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gefs-forecast-analysis-virtual.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-01-15","RegistryEntryLastModified":"2026-10-07","Slug":"dynamical-noaa-gefs","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA GFS - dynamical.org Icechunk Zarr","Description":"The Global Forecast System (GFS) is a National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) weather forecast model that generates data for dozens of atmospheric and land-soil variables, including temperatures, winds, precipitation, soil moisture, and atmospheric ozone concentration. The system couples four separate models (atmosphere, ocean model, land/soil model, and sea ice) that work together to depict weather conditions.\n\nThese datasets have been translated to cloud-optimized Icechunk Zarr format by [dynamical.org](https://dynamical.org).\n\n- [NOAA GFS analysis](https://dynamical.org/catalog/noaa-gfs-analysis/) - Weather analysis from the Global Forecast System (GFS) operated by NOAA NWS NCEP.\n- [NOAA GFS forecast](https://dynamical.org/catalog/noaa-gfs-forecast/) - Weather forecasts from the Global Forecast System (GFS) operated by NOAA NWS NCEP.\n- [NOAA GFS analysis, virtual](https://dynamical.org/catalog/noaa-gfs-analysis-virtual/) - Weather analysis from the Global Forecast System (GFS) operated by NOAA NWS NCEP.\n- [NOAA GFS forecast, virtual](https://dynamical.org/catalog/noaa-gfs-forecast-virtual/) - Weather forecasts from the Global Forecast System (GFS) operated by NOAA NWS NCEP.\n","Documentation":"https://dynamical.org/catalog/","Contact":"feedback@dynamical.org","ManagedBy":"[dynamical.org](https://dynamical.org)","UpdateFrequency":"NOAA GFS analysis: 1 hour; NOAA GFS forecast: Forecasts initialized every 6 hours; NOAA GFS analysis, virtual: 1 hour; NOAA GFS forecast, virtual: Forecasts initialized every 6 hours","Tags":["atmosphere","aws-pds","climate","forecast","meteorological","weather","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NOAA GFS Icechunk Zarr data","ARN":"arn:aws:s3:::dynamical-noaa-gfs","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://dynamical-noaa-gfs.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for dataset updates","ARN":"arn:aws:sns:us-west-2:761136292730:dynamical-noaa-gfs-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"NOAA GFS analysis — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gfs-analysis.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gfs-analysis.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"NOAA GFS analysis, virtual — GFS forecast and analysis, virtual","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gfs-forecast-analysis-virtual.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gfs-forecast-analysis-virtual.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"NOAA GFS forecast — Heating degree days: GFS vs AIFS","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gfs%2Becmwf-aifs-hdd.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gfs%2Becmwf-aifs-hdd.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"NOAA GFS forecast — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gfs-forecast.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gfs-forecast.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"NOAA GFS forecast, virtual — GFS forecast and analysis, virtual","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gfs-forecast-analysis-virtual.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-gfs-forecast-analysis-virtual.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-11-25","RegistryEntryLastModified":"2026-10-07","Slug":"dynamical-noaa-gfs","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA HRRR - dynamical.org Icechunk Zarr","Description":"The High-Resolution Rapid Refresh (HRRR) is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.\n\nThese datasets have been translated to cloud-optimized Icechunk Zarr format by [dynamical.org](https://dynamical.org).\n\n- [NOAA HRRR forecast, 18 hour, virtual](https://dynamical.org/catalog/noaa-hrrr-forecast-18-hour-virtual/) - Weather forecasts from the High-Resolution Rapid Refresh (HRRR) model operated by NOAA NWS NCEP.\n- [NOAA HRRR forecast, 48 hour](https://dynamical.org/catalog/noaa-hrrr-forecast-48-hour/) - Weather forecasts from the High-Resolution Rapid Refresh (HRRR) model operated by NOAA NWS NCEP.\n- [NOAA HRRR forecast, 48 hour, virtual](https://dynamical.org/catalog/noaa-hrrr-forecast-48-hour-virtual/) - Weather forecasts from the High-Resolution Rapid Refresh (HRRR) model operated by NOAA NWS NCEP.\n- [NOAA HRRR analysis](https://dynamical.org/catalog/noaa-hrrr-analysis/) - Analysis data from the High-Resolution Rapid Refresh (HRRR) model operated by NOAA NWS NCEP.\n- [NOAA HRRR analysis, virtual](https://dynamical.org/catalog/noaa-hrrr-analysis-virtual/) - Analysis data from the High-Resolution Rapid Refresh (HRRR) model operated by NOAA NWS NCEP.\n","Documentation":"https://dynamical.org/catalog/","Contact":"feedback@dynamical.org","ManagedBy":"[dynamical.org](https://dynamical.org)","UpdateFrequency":"NOAA HRRR forecast, 18 hour, virtual: Forecasts initialized every hour; NOAA HRRR forecast, 48 hour: Forecasts initialized every 6 hours; NOAA HRRR forecast, 48 hour, virtual: Forecasts initialized every 6 hours; NOAA HRRR analysis: 1 hour; NOAA HRRR analysis, virtual: 1 hour","Tags":["atmosphere","aws-pds","climate","forecast","meteorological","weather","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NOAA HRRR Icechunk Zarr data","ARN":"arn:aws:s3:::dynamical-noaa-hrrr","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://dynamical-noaa-hrrr.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for dataset updates","ARN":"arn:aws:sns:us-west-2:761136292730:dynamical-noaa-hrrr-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"NOAA HRRR analysis — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-hrrr-analysis.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-hrrr-analysis.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"NOAA HRRR analysis, virtual — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-hrrr-analysis-virtual.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-hrrr-analysis-virtual.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"NOAA HRRR forecast, 18 hour, virtual — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-hrrr-forecast-18-hour-virtual.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-hrrr-forecast-18-hour-virtual.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"NOAA HRRR forecast, 48 hour — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-hrrr-forecast-48-hour.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-hrrr-forecast-48-hour.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"},{"Title":"NOAA HRRR forecast, 48 hour, virtual — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-hrrr-forecast-48-hour-virtual.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-hrrr-forecast-48-hour-virtual.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-11-25","RegistryEntryLastModified":"2026-09-01","Slug":"dynamical-noaa-hrrr","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA MRMS - dynamical.org Icechunk Zarr","Description":"The NOAA Multi-Radar/Multi-Sensor System (MRMS) integrates data from multiple radars and radar networks, surface observations, numerical weather prediction (NWP) models, and climatology to generate seamless, high spatio-temporal resolution mosaics at low latency focused on hail, wind, tornado, quantitative precipitation estimations, convection, icing, and turbulence.\n\nThese datasets have been translated to cloud-optimized Icechunk Zarr format by [dynamical.org](https://dynamical.org).\n\n- [NOAA MRMS CONUS analysis, hourly](https://dynamical.org/catalog/noaa-mrms-conus-analysis-hourly/) - Hourly precipitation analysis from the Multi-Radar Multi-Sensor (MRMS) system operated by NOAA NWS NCEP.\n","Documentation":"https://dynamical.org/catalog/","Contact":"feedback@dynamical.org","ManagedBy":"[dynamical.org](https://dynamical.org)","UpdateFrequency":"NOAA MRMS CONUS analysis, hourly: 1 hour","Tags":["atmosphere","aws-pds","climate","forecast","meteorological","weather","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NOAA MRMS Icechunk Zarr data","ARN":"arn:aws:s3:::dynamical-noaa-mrms","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://dynamical-noaa-mrms.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for dataset updates","ARN":"arn:aws:sns:us-west-2:761136292730:dynamical-noaa-mrms-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"NOAA MRMS CONUS analysis, hourly — Quickstart","URL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-mrms-conus-analysis-hourly.ipynb","NotebookURL":"https://github.com/dynamical-org/notebooks/blob/main/noaa-mrms-conus-analysis-hourly.ipynb","AuthorName":"dynamical.org","AuthorURL":"https://dynamical.org"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-03-11","RegistryEntryLastModified":"2026-07-09","Slug":"dynamical-noaa-mrms","Sources":["awslabs-open-data-registry"]}
{"Name":"TJweather Global-Regional Integrated Numerical Weather Prediction System Dataset (SD3)","Description":"The TJweather Global-Regional Integrated Numerical Weather Prediction System Dataset provides high-resolution weather forecasts powered by Tianji Weather's next-generation Super Dynamics on Cube (SD3) framework. The system integrates a cubed-sphere grid, non-hydrostatic dynamical core, heterogeneous computing architecture, and Global Free-Zoom technology, enabling seamless transition from ~12 km global forecasts to 5 km refined simulations over Asia without traditional nesting artifacts.\nTJ-NWP provides forecasts extending up to 45 days, with reliable predictive skill reaching approximately 10 days. It demonstrates strong performance in complex terrain, extreme weather events, and subseasonal forecasting. The system delivers a comprehensive suite of meteorological, land-surface, and environmental variables across global to regional scales, including temperature, wind, precipitation, tropical cyclone tracks, and dust concentration.\n","Documentation":"https://github.com/Tianji-Weather/aws-opendata-docs/blob/main/tj-nwp-descriptive-documentation.md","Contact":"For any questions regarding the dataset, please visit [tjweather website](https://www.tjweather.com/en) or email us at contact@tjweather.com.","ManagedBy":"[Tianji Weather Science and Technology Company](https://www.tjweather.com/en)","UpdateFrequency":"Daily","Tags":["atmosphere","aws-pds","forecast","meteorological","model","netcdf","weather"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"TianJi Global-Regional Integrated Numerical Weather Prediction System Dataset","ARN":"arn:aws:s3:::tj-nwp","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://tj-nwp.s3.us-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for dataset updates","ARN":"arn:aws:sns:us-west-2:051370880159:tj-nwp-object_created","Region":"us-west-2","Type":"SNS Topic"}],"ADXCategories":["Environmental Data"],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: TJ-NWP","URL":"https://github.com/Tianji-Weather/aws-opendata-docs/blob/main/get-to-know-a-dataset-tj-nwp.ipynb","NotebookURL":"https://github.com/Tianji-Weather/aws-opendata-docs/blob/main/get-to-know-a-dataset-tj-nwp.ipynb","AuthorName":"Tianji Weather","AuthorURL":"https://www.tjweather.com/en"}]},"RegistryEntryAdded":"2026-05-20","RegistryEntryLastModified":"2026-08-10","Slug":"dynamical-tj-nwp","Sources":["awslabs-open-data-registry"]}
{"Name":"E11bio PRISM","Description":"This dataset was generated using E11.bio's PRISM technology (Protein Reconstruction and Identification through Multiplexing),\na platform that combines viral barcoding, expansion microscopy, and iterative immunolabeling for large-scale neuronal reconstruction.\n\nNeurons in the mouse hippocampal CA3 were transduced with a library of adeno-associated viruses (AAVs)\nencoding diverse “protein bits”—small epitope tags that act as combinatorial barcodes. \nTissue was then processed with an expansion microscopy protocol, physically enlarging the sample ~5×\nto achieve an effective voxel size of ~35 × 35 × 80 nm.\nAcross multiple cycles of staining, imaging, and antibody stripping, the same expanded tissue was repeatedly labeled,\nenabling iterative immunostaining for dozens of molecular targets.\n\nThe dataset includes:\n1) Light microscopy data of multiplexed brain tissue\n2) Segmentations of cell morphology and protein expression in the tissue\n3) Files for faster visualization of the data (e.g. precomputed format)\n4) Additional supporting files (e.g. model predictions, manual annotations etc.)\n","Documentation":"https://github.com/e11bio/e11-open-data","Contact":"hello@e11.bio","ManagedBy":"[E11.bio](https://e11.bio)","UpdateFrequency":"As required","Tags":["aws-pds","bioinformatics","biology","brain images","cell imaging","computer vision","fluorescence imaging","high-throughput imaging","image processing","imaging","ion channels","life sciences","machine learning","microscopy","morphological reconstructions","Mus musculus","neurobiology","neuroimaging","neuroscience","protein","segmentation","zarr"],"License":"https://e11.bio/terms-of-use","Resources":[{"Description":"Data files in a public bucket","ARN":"arn:aws:s3:::e11bio-prism","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"E11.Bio PRISM OpenData","URL":"https://github.com/e11bio/e11-open-data","NotebookURL":null,"AuthorName":"Arlo Sheridan & Johan Winnubst","AuthorURL":"https://e11.bio/team"}],"Tools & Applications":[{"Title":"Volara","URL":"https://github.com/e11bio/volara","AuthorName":"Arlo Sheridan & Will Patton","AuthorURL":"https://e11.bio/team"}]},"RegistryEntryAdded":"2025-09-25","RegistryEntryLastModified":"2025-09-25","Slug":"e11bio-prism","Sources":["awslabs-open-data-registry"]}
{"Name":"Essential-Web v1.0: 24T tokens of organized web data","Description":"A 24-trillion-token dataset in which every document is annotated with a twelve-category taxonomy covering topic, format, content complexity, and quality.","Documentation":"https://huggingface.co/datasets/EssentialAI/essential-web-v1.0","Contact":"research@essential.ai","ManagedBy":"[EssentialAI](https://www.essential.ai)","UpdateFrequency":"Not updated","Tags":["aws-pds","machine learning","natural language processing","text analysis","web archive"],"License":"Essential-Web-v1.0 contributions are made available under the [ODC attribution license](https://opendatacommons.org/licenses/by/odc_by_1.0_public_text.txt); however, users should also abide by the [Common Crawl - Terms of Use](https://commoncrawl.org/terms-of-use). We do not alter the license of any of the underlying data.","Resources":[{"Description":"Essential-Web v1.0: 24T tokens of organized web data","ARN":"arn:aws:s3:::essential-web-v1.0","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for new Essential-Web v1.0 data","ARN":"arn:aws:sns:us-west-2:021391128517:essential-web-v10-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"Essential-Web v1.0: 24T tokens of organized web data","URL":"https://arxiv.org/abs/2506.14111","AuthorName":"Andrew Hojel, Michael Pust, Tim Romanski, Yash Vanjani, Ritvik Kapila, Mohit Parmar et al.","AuthorURL":"https://arxiv.org/abs/2506.14111"}]},"RegistryEntryAdded":"2025-07-01","RegistryEntryLastModified":"2025-09-11","Slug":"eai-essential-web-v1","Sources":["awslabs-open-data-registry"]}
{"Name":"Evoked Auditory Responses in Heading Impaired","Description":"The Evoked Auditory Responses in Hearing Impaired database contains evoked Auditory Brainstem Response (ABR) and Otoacoustic Emission (OAE) recordings in eight hearing impaired listeners, in response to tone-burst stimuli across a wide range of levels.","Documentation":"https://doi.org/10.13026/C2Z664","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2Z664","ARN":"arn:aws:s3:::physionet-open/earh/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"earh","Sources":["awslabs-open-data-registry"]}
{"Name":"Evoked Auditory Responses in Normals","Description":"This database, created and contributed by Michael J. Epstein and Ikaro Silva, was generated as part of a study examining evoked potentials and loudness growth. The database consists of two sets of physiological signals: auditory brainstem response (ABR) and otoacoustic emissions (OAE) and two sets of psychoacoustical estimates of loudness as a function of peak sound pressure level (peSPL).","Documentation":"https://doi.org/10.13026/C2NP4D","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2NP4D","ARN":"arn:aws:s3:::physionet-open/earndb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"earndb","Sources":["awslabs-open-data-registry"]}
{"Name":"Icechunk ERA5","Description":"<b>An analysis-ready, cloud-optimized (ARCO) edition of ERA5, the fifth-generation global\nreanalysis from the Copernicus Climate Change Service (C3S) at ECMWF. Provided as a single\nIcechunk + Zarr v3 store on AWS S3, this version enables efficient, scalable access to global\nreanalysis data for weather, climate, and ML/AI applications. Managed by [Earthmover](https://earthmover.io).</b>\n<br /><br />\n\nERA5 provides hourly estimates of atmospheric, land-surface, and ocean variables from 1940 to\n2025 on a ~31 km (0.25°) global latitude-longitude grid. This Earthmover edition delivers 35\nsingle-level (surface) variables, 8 pressure-level variables on 13 isobaric levels\n(50–1000 hPa), and a curated 500 hPa geopotential slice, all with CF-1.7 compliant metadata.\n<br /><br />\n\nThe store is organized into three top-level groups (single, pressure, and 500hPa). Each group\nis provided with two complementary chunking strategies that hold identical data: a \"spatial\"\nlayout (one full global map per hour) optimized for map-style and regional queries, and a\n\"temporal\" layout (long time series in small spatial tiles) optimized for time-series\nextraction, location-based analysis, and ML training workflows. Arrays are stored as Float32\nwith PCodec compression for high compression ratios.\nOnly lossless compression has been applied; data values are bit-for-bit identical to raw ECMWF NetCDFs. \n<br /><br />\n\nThe data is provided in the [Icechunk](https://icechunk.io/en/stable/overview/) format.\nIcechunk is an open source tensor storage engine that augments Zarr with transactional updates,\nversion history, and an optimized, Rust-based I/O path for maximum performance in the cloud.\n<br /><br />\n\nAn interactive browser for the data is available via the Earthmover Data Marketplace:\n[https://app.earthmover.io/marketplace/6a19bcfe9aa6e97720a2fad2?tab=dataset](https://app.earthmover.io/marketplace/6a19bcfe9aa6e97720a2fad2?tab=dataset)\n<br /><br />\n\nEarthmover maintains two complementary ERA5 datasets:\n- This one, updated at quarterly frequency, available free under an open license.\n- A low-latency version with the same variables, but updated daily with the latest\n  ERA5T near-real-time data. This is available at a competitive price and comes with a strong SLA.\n  See [ERA5 (Daily Updates)](https://app.earthmover.io/marketplace/6a18ae1ba1c8feafd01f2b76) on\n  the Earthmover Data Marketplace.\n\nIf your organization is using ERA5 in a serious operational context, please consider subscribing\nto the low-latency version of this dataset.\nPaid subscribers for the low-latency dataset are what make it sustainable for us to provide this\nhigh-quality free, open dataset. \nContact [sales@earthmover.io](mailto:sales@earthmover.io) to learn more.\n<br /><br />\n\nThe ERA5 dataset is public and can be opened directly from S3 with the\n[Icechunk](https://icechunk.io/) and [Xarray](https://xarray.pydata.org/) libraries:\n<br /><br />\n```python\nimport icechunk\nimport xarray as xr\n\nstorage = icechunk.s3_storage(\n    bucket=\"earthmover-icechunk-era5\",\n    prefix=\"icechunkV2\",\n    region=\"us-east-1\",\n    anonymous=True,\n)\nrepo = icechunk.Repository.open(storage)\nsession = repo.readonly_session(\"main\")\nds = xr.open_zarr(session.store, group=\"single/spatial\", consolidated=False, chunks=None)\n```\n<br /><br />\n\nAlternatively, you can use the Arraylake client for a more concise syntax.\n<br /><br />\n```python\nfrom arraylake import Client\nimport xarray as xr\n\nclient = Client()\nclient.login()\n\nrepo = client.get_repo(\"earthmover-public/era5\")\nsession = repo.readonly_session(\"main\")\nds = xr.open_zarr(session.store, group=\"single/spatial\", consolidated=False, chunks=None)\n```\n<br /><br />\n\nThe dataset is assembled from the NSF NCAR Curated ERA5 archive and the Copernicus Climate Data\nStore. It is widely used for ML/AI training, climate and weather research, extreme-event\nanalysis, and applications such as renewable energy siting.\n","Documentation":"https://app.earthmover.io/marketplace/6a19bcfe9aa6e97720a2fad2","Contact":"hello@earthmover.io","ManagedBy":"[Earthmover](https://earthmover.io)","UpdateFrequency":"Quarterly (every three months); historical reanalysis covering 1940 to 2025","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","climate","data assimilation","energy","forecast","geoscience","geospatial","land","machine learning","meteorological","model","weather","zarr"],"License":"Creative Commons Attribution 4.0 International (CC-BY 4.0) - https://creativecommons.org/licenses/by/4.0/","Citation":"This dataset was generated using Copernicus Climate Change Service information 2026.\nCopernicus Climate Change Service, Climate Data Store, (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: https://doi.org/10.24381/cds.adbb2d47 (Accessed in 2026).\nCopernicus Climate Change Service, Climate Data Store, (2023): ERA5 hourly data on pressure levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: https://doi.org/10.24381/cds.bd0915c6 (Accessed in 2026).\nHistorical backfill from the NSF NCAR Curated ERA5 archive: European Centre for Medium-Range Weather Forecasts. 2019, updated monthly. ERA5 Reanalysis (0.25 Degree Latitude-Longitude Grid). Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. https://doi.org/10.5065/BH6N-5N20.\n","Resources":[{"Description":"ERA5 Zarr / Icechunk Data (Cloud-Optimized) - Icechunk store at s3://earthmover-icechunk-era5/icechunkV2","ARN":"arn:aws:s3:::earthmover-icechunk-era5/icechunkV2","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://earthmover-icechunk-era5.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for the ERA5 Zarr + Icechunk dataset","ARN":"arn:aws:sns:us-east-1:871265417473:earthmover-icechunk-era5-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"ERA5 sample data - access examples and documentation","URL":"https://docs.earthmover.io/sample-data/era5","AuthorName":"Earthmover","AuthorURL":"https://earthmover.io"}],"Tools & Applications":[{"Title":"era5-qc - verify ERA5 data against the original ECMWF CDS source","URL":"https://github.com/earth-mover/era5-qc","AuthorName":"Earthmover","AuthorURL":"https://earthmover.io"}],"Publications":[{"Title":"Low Latency Icechunk ERA5 Now Available on the Earthmover Data Marketplace","URL":"https://www.earthmover.io/blog/low-latency-era5-icechunk-marketplace","AuthorName":"Ryan Abernathey","AuthorURL":"https://www.earthmover.io/blog/author/ryan-abernathey"},{"Title":"The ERA5 global reanalysis","URL":"https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803","AuthorName":"Hersbach et al 2020","AuthorURL":"https://orcid.org/0000-0001-5330-7071"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-08-27","RegistryEntryLastModified":"2026-06-22","Slug":"earthmover-era5","Sources":["awslabs-open-data-registry"]}
{"Name":"EarthScope Geophysical Data","Description":"This dataset contains a subset of the geophysical data repositories\nof the National Science Foundation's (NSF) National Geophysical\nFacility (NGF) managed by the EarthScope Consortium.\nAll data sets contain recordings, or derivatives thereof, from networks of\ngeophysical sensors.  The seismic data in this set are in miniSEED format.\n","Documentation":"https://docs.earthscope.org/sponsored-open-data","Contact":"help@earthscope.org","ManagedBy":"[EarthScope Consortium](https://earthscope.org)","UpdateFrequency":"Daily","Tags":["aws-pds","climate","earth observation","earth observation","earthquakes","geophysics","geoscience","machine learning","seismology"],"License":"When using these data please cite both the network owners, identified by network code,\nand NSF's NGF facility operated by EarthScope.  Seismic network operators and owners specify the\nlicense for their data, and an appropriate citation, in their registration information which\nis available at: https://fdsn.org/networks/.  When no license has been declared by the owner,\nthe facility distributes the data with a license of CC-BY-4.0. The facility operation can be\ncited using the instructions here: https://www.earthscope.org/how-to-cite/.\n","Resources":[{"Description":"Geophysical data from NSF's NGF facility operated by EarthScope","ARN":"arn:aws:s3:::earthscope-geophysical-data","Region":"us-east-2","Type":"S3 Bucket"},{"Description":"Notifications for new EarthScope Geophysical data","ARN":"arn:aws:sns:us-east-2:161057982967:earthscope-geophysical-data-object_created","Region":"us-east-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"EarthScope Geophysical Data - Sponsored Open Data Tutorial","URL":"https://docs.earthscope.org/sponsored-open-data#getting-started","AuthorName":"EarthScope Consortium","AuthorURL":"https://earthscope.org"}]},"RegistryEntryAdded":"2025-05-28","RegistryEntryLastModified":"2026-10-02","Slug":"earthscope-geophysical-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Global Seasonal Sentinel-1 Interferometric Coherence and Backscatter Data Set","Description":"This data set is the first-of-its-kind spatial representation of multi-seasonal, global SAR repeat-pass interferometric coherence and backscatter signatures. Global coverage comprises all land masses and ice sheets from 82 degrees northern to 79 degrees southern latitude. The data set is derived from high-resolution multi-temporal repeat-pass interferometric processing of about 205,000 Sentinel-1 Single-Look-Complex data acquired in Interferometric Wide-Swath mode (Sentinel-1 IW mode) from 1-Dec-2019 to 30-Nov-2020. The data set was developed by [Earth Big Data LLC](https://earthbigdata.com) and [Gamma Remote Sensing AG](https://www.gamma-rs.ch), under contract for [NASA's Jet Propulsion Laboratory](https://jpl.nasa.gov). The data set covers four sets of seasonal (DJF/MAM/JJA/SON) metrics: 1) Median 6-, 12-, 18-, 24-, 36-, and 48-day repeat coherence estimates for C-band VV and HH polarized data, 2) Mean backscatter (gamma naught) for VV, VH, HH, and HV polarizations, 3) Seasonal coherence decay model parameters rho, tau, and rmse, 4) Local incidence and layover/shadow regions for all relative orbits (175 orbits). Note that in the data set filenames the seasons were referred to as northern hemisphere winter (DJF), spring (MAM), summer (JJA), and fall (SON). The data set is available in two main components: 1) 1x1 degree tiles. Each tile contains GeoTiffs at 3 arcsec pixel spacing of all metrics available in the tile. (s3://sentinel-1-global-coherence-earthbigdata/data/tiles/), 2) Global mosaicked tiles as cloud optimized GeoTIFFs (COG) at 0.01 degree pixel spacing (s3://sentinel-1-global-coherence-earthbigdata/data/mosaics/) for each of the computed metrics.\n","Documentation":"http://sentinel-1-global-coherence-earthbigdata.s3-website-us-west-2.amazonaws.com/","Contact":"For questions regarding data methodology or delivery, contact info@earthbigdata.com.","ManagedBy":"[Earth Big Data LLC](https://earthbigdata.com/)","UpdateFrequency":"The data set covers the time period from 1-Dec-2019 to 30-Nov-2020. No updates are currently produced.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","earth observation","earthquakes","ecosystems","environmental","geology","geophysics","geospatial","global","infrastructure","mapping","natural resource","satellite imagery","synthetic aperture radar","urban"],"License":"The use of these data fall under the terms and conditions of the [Creative Commons Attribution 4.0 International Public License](https://creativecommons.org/licenses/by/4.0/legalcode). Contains modified Copernicus Sentinel data. -- Any use of this data set in publications or sharing should use this citation: \"Global Seasonal Data Set of Interferometric Coherence and Backscatter from one Year of Sentinel-1 C-band SAR Data\". Josef Kellndorfer, Oliver Cartus, Marco Lavalle, Christophe Magnard, Shadi Oveisgharan, Batu Osmanoglu, Paul Rosen, Urs Wegmüller. doi: In Review. Accessed on [https://registry.opendata.aws/ebd-sentinel-1-global-coherence-backscatter/](https://registry.opendata.aws/ebd-sentinel-1-global-coherence-backscatter/)\n","Resources":[{"Description":"1x1 degree tiled data and metadata in a S3 bucket, Global [VRT](https://gdal.org/drivers/raster/vrt.html) mosaics (.vrt) with external overview files (.ovr) (search for vrts with bucket/prefix sentinel-1-global-coherence-earthbigdata/data/tiles/Global)","ARN":"arn:aws:s3:::sentinel-1-global-coherence-earthbigdata/data/tiles","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Global mosaics at 0.01 degree pixel spacing as cloud optimized GeoTIFFs in a S3 bucket","ARN":"arn:aws:s3:::sentinel-1-global-coherence-earthbigdata/data/mosaics","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Jupyter Notebook to access and visualize global mosaics of the global data set","URL":"https://github.com/EarthBigData/openSAR","AuthorName":"Josef Kellndorfer","AuthorURL":"https://earthbigdata.com"},{"Title":"Jupyter Notebook to access and visualize sub regions of the global data set","URL":"https://github.com/EarthBigData/openSAR","AuthorName":"Josef Kellndorfer","AuthorURL":"https://earthbigdata.com"}],"Publications":[{"Title":"Generating Global Temporal Coherence Maps from one year of Sentinel-1 C-band data, ESA Fringe 2021 Poster (Youtube)","URL":"https://www.youtube.com/watch?v=U3wbC6UWqx4","AuthorName":"Oliver Cartus, Josef Kellndorfer, Shadi Oveisgharan, Batu Osmanoglu, Paul Rosen, Urs Wegmüller"},{"Title":"Global seasonal Sentinel-1 interferometric coherence and backscatter data set","URL":"https://doi.org/10.1038/s41597-022-01189-6","AuthorName":"Josef Kellndorfer, Oliver Cartus, Marco Lavalle, Christophe Magnard, Pietro Milillo, Shadi Oveisgharan, Batu Osmanoglu, Paul A. Rosen, Urs Wegmüller"},{"Title":"Webinar: The new era of SAR Time Series Analysis and Visualization: Cloud meets Big SAR Data. IEEE GRSS Bay Area Chapter (Dec. 3rd 2021)","URL":"https://www.youtube.com/watch?v=ZR3L9l7WcHU","AuthorName":"Josef Kellndorfer"}]},"RegistryEntryAdded":"2021-09-09","RegistryEntryLastModified":"2022-11-08","Slug":"ebd-sentinel-1-global-coherence-backscatter","Sources":["awslabs-open-data-registry"]}
{"Name":"A large scale 12-lead electrocardiogram database for arrhythmia study","Description":"This newly inaugurated research database for 12-lead electrocardiogram (ECG) signals was created under the auspices of Chapman University, Shaoxing People’s Hospital (Shaoxing Hospital Zhejiang University School of Medicine), and Ningbo First Hospital. It aims to enable the scientific community in conducting new studies on arrhythmia and other cardiovascular conditions. Certain types of arrhythmias, such as atrial fibrillation, have a pronounced negative impact on public health, quality of life, and medical expenditures. As a non-invasive test, ECG is a major and vital diagnostic tool for detecting these conditions. This practice, however, generates large amounts of data, the analysis of which requires considerable time and effort by human experts. Modern machine learning and statistical tools can be trained on high quality, large data to achieve exceptional levels of automated diagnostic accuracy. Thus, we collected and disseminated this novel database that contains 12-lead ECGs of 45,152 patients with a 500 Hz sampling rate that features multiple common rhythms and additional cardiovascular conditions, all labeled by professional experts. The dataset can be used to design, compare, and fine-tune new and classical statistical and machine learning techniques in studies focused on arrhythmia and other cardiovascular conditions.","Documentation":"https://doi.org/10.13026/wgex-er52","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/wgex-er52","ARN":"arn:aws:s3:::physionet-open/ecg-arrhythmia/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ecg-arrhythmia","Sources":["awslabs-open-data-registry"]}
{"Name":"ECG Fragment Database for the Exploration of Dangerous Arrhythmia","Description":"The database contains a set of 2 second fragments of ECG signals with rhythm disturbances, which are grouped into separate classes according to the degree of threat to the patient's life. It is intended for practical use to develop and test the efficiency of algorithms for detecting dangerous arrhythmias for continuous monitoring systems. The “MIT-BIH Malignant Ventricular Ectopy Database” (MVED) was selected as the primary source of ECG records. This database contains heart rhythm disturbances necessary for research and is widely used for testing and comparative analysis of various algorithms for detecting arrhythmia threats. The signal is a modified limb lead II (MLII).","Documentation":"https://doi.org/10.13026/kpfg-xs25","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/kpfg-xs25","ARN":"arn:aws:s3:::physionet-open/ecg-fragment-high-risk-label/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ecg-fragment-high-risk-label","Sources":["awslabs-open-data-registry"]}
{"Name":"Automated Detection and Removal of Pacing Artifacts from ECG Signal","Description":"Pacing artifacts in electrocardiogram (ECG) signals can interfere with accurate waveform analysis, making their detection and removal essential for reliable interpretation. We present an algorithm and provide MATLAB code to detect and remove pacing artifacts from raw digital 12-lead ECG recordings. The algorithm was developed using the Chronic Renal Insufficiency Cohort (CRIC) data, which includes 493 paced ECGs, and validated on an external dataset (273 paced ECGs). A graphical user interface (GUI) was designed to allow user-friendly quality control of automated algorithm performance. Users can adjust settings to achieve the desired result: complete removal of the pacing spike without removing the ECG waveform. The algorithm automatically detected and removed pacing artifacts in 82.8% of the 10-second ECG files in the validation set, while the remaining files required user input. The overall sensitivity and specificity for pacing artifact detection were 98.4% and 76.5%, respectively. In summary, the proposed algorithm effectively detects and removes ECG pacing artifacts with high automation, thereby minimizing the need for manual fine-tuning.","Documentation":"https://doi.org/10.13026/hq2h-8p89","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"MIT License","Resources":[{"Description":"https://doi.org/10.13026/hq2h-8p89","ARN":"arn:aws:s3:::physionet-open/ecg-pacing-artifact-removal/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ecg-pacing-artifact-removal","Sources":["awslabs-open-data-registry"]}
{"Name":"Electrocardiogram, skin conductance and respiration from spider-fearful individuals watching spider video clips","Description":"This project contains electrocardiogram, skin conductance and respiration as raw data (unfiltered, unprocessed) recorded from consented, spider-fearful individuals using the wearable BITalino biosignal measurement device (PLUX – Wireless Biosignals S.A., Lisbon, Portugal) with the sampling rate set to 100 Hz per channel having 10-bit resolution. All subjects were introduced to the exposure procedure and rationale that is mainly based on the principles of the one-session exposure treatment. The main focus of this study was to investigate if the use of an HRV biofeedback intervention could be a promising therapeutic add-on to exposure therapy for specific phobias. Biosignals were collected at the Saarland University (Germany) from July 2017 to July 2018, and there are 57 subjects in total. The dataset may support studies on mental health and psychophysiology related to other anxiety disorders, such as fear of flying, social phobia and posttraumatic stress disorder.","Documentation":"https://doi.org/10.13026/sq6q-zg04","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/sq6q-zg04","ARN":"arn:aws:s3:::physionet-open/ecg-spider-clip/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ecg-spider-clip","Sources":["awslabs-open-data-registry"]}
{"Name":"CiPA ECG Validation Study","Description":"ECG effects of ranolazine, verapamil, lopinavir+ritonavir, chloroquine, dofetilide, diltiazem, and dofetilide+diltiazem in a small sample size clinical study. The ECGCIPA database contains multi-channel ECG recordings of 60 subjects participating in the CiPA ECG validation study.","Documentation":"https://doi.org/10.13026/C2967M","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2967M","ARN":"arn:aws:s3:::physionet-open/ecgcipa/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ecgcipa","Sources":["awslabs-open-data-registry"]}
{"Name":"ECG-ID Database","Description":"The database contains 310 ECG recordings, obtained from 90 persons. Each recording contains: ECG lead I, recorded for 20 seconds, digitized at 500 Hz with 12-bit resolution over a nominal ±10 mV range; 10 annotated beats (unaudited R- and T-wave peaks annotations from an automated detector); information (in the .hea file for the record) containing age, gender and recording date.","Documentation":"https://doi.org/10.13026/C2J01F","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2J01F","ARN":"arn:aws:s3:::physionet-open/ecgiddb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ecgiddb","Sources":["awslabs-open-data-registry"]}
{"Name":"ECG-Kit","Description":"This toolbox is a collection of Matlab tools that Mariano Llamedo Soria used, adapted or developed during his PhD and post-doc work with the Besicos group at University of Zaragoza, Spain and at the National Technological University of Buenos Aires, Argentina.","Documentation":"https://physionet.org/content/ecgkit/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/ecgkit/","ARN":"arn:aws:s3:::physionet-open/ecgkit/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ecgkit","Sources":["awslabs-open-data-registry"]}
{"Name":"ECGPUWAVE","Description":"ecgpuwave analyses an ECG signal from the specified record, detecting the QRS complexes and locating the beginning, peak, and end of the P, QRS, and ST-T waveforms. The output of ecgpuwave is written as a standard WFDB-format annotation file associated with the specified annotator.","Documentation":"https://physionet.org/content/ecgpuwave/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/ecgpuwave/","ARN":"arn:aws:s3:::physionet-open/ecgpuwave/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ecgpuwave","Sources":["awslabs-open-data-registry"]}
{"Name":"ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine","Description":"The ECGRDVQ database contains multi-channel ECG recordings of 22 healthy subjects partaking in a randomized, double‐blind, 5‐period crossover clinical trial aimed at comparing the effects of four known QT prolonging drugs versus placebo on electrophysiological and other clinical parameters.","Documentation":"https://doi.org/10.13026/C2HP45","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2HP45","ARN":"arn:aws:s3:::physionet-open/ecgrdvq/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ecgrdvq","Sources":["awslabs-open-data-registry"]}
{"Name":"ECGSYN - A realistic ECG waveform generator","Description":"ECGSYN generates a synthesized ECG signal with user-settable mean heart rate, number of beats, sampling frequency, waveform morphology (P, Q, R, S, and T timing, amplitude,and duration), standard deviation of the RR interval, and LF/HF ratio (a measure of the relative contributions of the low and high frequency components of the RR time series to total heart rate variability).","Documentation":"https://physionet.org/content/ecgsyn/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"GNU General Public License version 3","Resources":[{"Description":"https://physionet.org/content/ecgsyn/","ARN":"arn:aws:s3:::physionet-open/ecgsyn/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ecgsyn","Sources":["awslabs-open-data-registry"]}
{"Name":"ECG waveform generator for Matlab/Octave","Description":"ECGwaveGen generates a synthesized ECG signal with user-settable heart rate, signal duration, sampling frequency, QRS amplitude and duration, and T-wave amplitude; it uses QRSpulse to create premature beats followed by compensatory pauses.","Documentation":"https://physionet.org/content/ecgwavegen/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/ecgwavegen/","ARN":"arn:aws:s3:::physionet-open/ecgwavegen/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ecgwavegen","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"#### The provider of this dataset will no longer maintain it, but has instead worked with NSF NCAR to rehost the dataset here: [https://registry.opendata.aws/nsf-ncar-era5/](https://registry.opendata.aws/nsf-ncar-era5/)\n","Name":"ECMWF ERA5 Reanalysis","Description":"ERA5 is the fifth generation of ECMWF atmospheric reanalyses of the global climate, and the first reanalysis produced as an operational service. It utilizes the best available observation data from satellites and in-situ stations, which are assimilated and processed using ECMWF's Integrated Forecast System (IFS) Cycle 41r2.\nThe dataset provides all essential atmospheric meteorological parameters like, but not limited to, air temperature, pressure and wind at different altitudes, along with surface parameters like rainfall, soil moisture content and sea parameters like sea-surface temperature and wave height.\nERA5 provides data at a considerably higher spatial and temporal resolution than its legacy counterpart ERA-Interim. ERA5 consists of high resolution version with 31 km horizontal resolution, and a reduced resolution ensemble version with 10 members. It is currently available since 2008, but will be continuously extended backwards, first until 1979 and then to 1950.\nLearn more about ERA5 in Jon Olauson's paper [ERA5: The new champion of wind power modelling?](https://www.researchgate.net/publication/320084119_ERA5_The_new_champion_of_wind_power_modelling).\n","Documentation":"https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md","Contact":"etoodu@intertrust.com","ManagedBy":"[Intertrust](https://www.intertrust.com/)","UpdateFrequency":"Monthly","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","agriculture","climate","earth observation","meteorological","weather"],"License":"Generated using Copernicus Climate Change Service Information 2018. See http://apps.ecmwf.int/datasets/licences/copernicus/ for additional information.","Resources":[{"Description":"ERA5 Reanalysis NetCDF 4 files","ARN":"arn:aws:s3:::era5-pds","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing ERA5 Data on S3 Using Boto","URL":"https://github.com/planet-os/notebooks/blob/master/aws/era5-s3-via-boto.ipynb","AuthorName":"Intertrust Technologies Corporation","AuthorURL":"https://www.intertrust.com/"},{"Title":"ERA5 tutorial using the Planet OS API","URL":"https://github.com/planet-os/notebooks/blob/master/api-examples/ERA5_tutorial.ipynb","AuthorName":"Intertrust Technologies Corporation","AuthorURL":"https://www.intertrust.com/"},{"Title":"Processing ERA5 data in NetCDF Format","URL":"https://nbviewer.jupyter.org/github/awslabs/amazon-asdi/blob/main/examples/dask/notebooks/era5.ipynb","AuthorName":"Zac Flamig"},{"Title":"Processing ERA5 data in Zarr Format","URL":"https://nbviewer.jupyter.org/github/awslabs/amazon-asdi/blob/main/examples/dask/notebooks/era5_zarr.ipynb","AuthorName":"Zac Flamig"},{"Title":"Intro to Amazon EMR Studio","URL":"https://www.youtube.com/watch?v=rZ3zeJ6WKPY&list=RDCMUCd6MoB9NC6uYN2grvUNT-Zg","AuthorName":"Damon Cortesi","AuthorURL":"https://dacort.dev/","Services":["Amazon EMR"]}],"Tools & Applications":null,"Publications":[{"Title":"Potential changes in cooling degree day under different global warming levels and shared socioeconomic pathways in West Africa","AuthorName":"Oluwarotimi Delano Thierry Odou, Heidi Heinrichs Ursula, Rabani Adamou, Thierry Godjo, and Mounkaila S Moussa","URL":"https://iopscience.iop.org/article/10.1088/1748-9326/acbc8f"},{"Title":"Temperature and salinity trends in the northern limit of the Canary Current Upwelling System","AuthorName":"P. Otero, A. Cabrero, F. Alonso-Pérez, J. Gago, E. Nogueira","URL":"https://doi.org/10.1016/j.scitotenv.2023.165791"}]},"RegistryEntryAdded":"2018-07-11","RegistryEntryLastModified":"2026-07-16","Slug":"ecmwf-era5","Sources":["awslabs-open-data-registry"]}
{"Name":"ECMWF real-time forecasts","Description":"These products are a subset of the ECMWF real-time forecast data and are made available to the public free of charge. They are based on the medium-range (high-resolution and ensembles) forecast models.\nNote: The ECMWF Open Data Portal provides a rolling archive (most recent forecast runs), while the AWS replica bucket is updated as new data are published and may retain older data conventions/versions over time.\n","Documentation":"[User Documentation](https://confluence.ecmwf.int/display/DAC/ECMWF+open+data%3A+real-time+forecasts+from+IFS+and+AIFS)","Contact":"https://support.ecmwf.int","ManagedBy":"[European Centre for Medium-Range Weather Forecasts](https://www.ecmwf.int/)","UpdateFrequency":"IFS data is published according to the [real-time dissemination schedule](https://confluence.ecmwf.int/display/DAC/Dissemination+schedule), while AIFS data is published as soon as it is available for dissemination.\n","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["air temperature","atmosphere","aws-pds","meteorological","near-surface air temperature","near-surface relative humidity","near-surface specific humidity","precipitation","weather"],"License":"This ECMWF data is published under a Creative Commons Attribution 4.0 International license ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) and the [ECMWF Terms of Use](https://apps.ecmwf.int/datasets/licences/general/)","Citation":"https://doi.org/10.21957/open-data","Resources":[{"Description":"Access ECMWF's open-data for real-time forecasts","ARN":"arn:aws:s3:::ecmwf-forecasts","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"Globally cached CloudFront distribution of the ecmwf-forecasts dataset","Host":"d2zvc0wgha4k2l.cloudfront.net","Region":"eu-central-1","Type":"CloudFront Distribution"}],"DataAtWork":{"Tutorials":[{"Title":"A collection of Jupyter Notebooks that make use of the ecmwf-opendata package. This package simplifies the download of ECMWF open data.","URL":"https://github.com/ecmwf/notebook-examples/tree/master/opencharts","AuthorName":"ECMWF","AuthorURL":"https://www.ecmwf.int/"}],"Tools & Applications":[{"Title":"A package to simplify the download of ECMWF open data. It implements a request-based interface to the dataset using ECMWF's MARS language to select meteorological fields, similar to the existing ecmwf-api-client Python package.","URL":"https://github.com/ecmwf/ecmwf-opendata","AuthorName":"ECMWF","AuthorURL":"https://www.ecmwf.int/"},{"Title":"Herbie: A Python package to retrieve NWP data.","URL":"https://github.com/blaylockbk/Herbie","AuthorName":"Brian Blaylock","AuthorURL":"https://github.com/blaylockbk"}],"Publications":[{"Title":"Open Data Roadmap - Transition to Open Data","URL":"https://www.ecmwf.int/node/29013","AuthorName":"ECMWF","AuthorURL":"https://www.ecmwf.int/"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2023-04-18","RegistryEntryLastModified":"2026-09-24","Slug":"ecmwf-forecasts","Sources":["awslabs-open-data-registry"]}
{"Name":"ECMWF static datasets","Description":"A collection of curated, static ECMWF forecast datasets capturing notable weather events. Data are organised as <event>/<period>/<YYYYMMDD>/<product> following ECMWF naming conventions.\n","Documentation":"[ECMWF Open and Free Datasets](https://confluence.ecmwf.int/x/2UykDQ)","Contact":"https://support.ecmwf.int","ManagedBy":"[European Centre for Medium-Range Weather Forecasts](https://www.ecmwf.int/)","UpdateFrequency":"Updated as new curated event datasets are published.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","climate","meteorological","weather"],"License":"This ECMWF data is published under a Creative Commons Attribution 4.0 International license ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) and the [ECMWF Terms of Use](https://apps.ecmwf.int/datasets/licences/general/)","Resources":[{"Description":"Curated static ECMWF datasets","ARN":"arn:aws:s3:::ecmwf-static-datasets","Region":"eu-central-1","Type":"S3 Bucket"}],"ADXCategories":["Environmental Data"],"DataAtWork":{"Tutorials":[{"Title":"European heatwave, June 2026 - dataset documentation","URL":"https://confluence.ecmwf.int/x/XLQnKQ","AuthorName":"ECMWF"},{"Title":"European heatwave, June 2026 - Jupyter notebook: accessing and plotting the Extreme Forecast Index (EFI) for 2 m temperature","URL":"https://github.com/ecmwf/notebook-examples/blob/master/opendata/heatwave-efi-2t-aws.ipynb","AuthorName":"ECMWF","AuthorURL":"https://www.ecmwf.int/"}],"Publications":[{"Title":"European heatwave, June 2026 - dataset citation (DOI)","URL":"https://doi.org/10.5281/zenodo.21224650","AuthorName":"ECMWF"}]},"RegistryEntryAdded":"2026-07-07","RegistryEntryLastModified":"2026-09-24","Slug":"ecmwf-static-datasets","Sources":["awslabs-open-data-registry"]}
{"Name":"European ST-T Database","Description":"The European ST-T Database is intended to be used for evaluation of algorithms for analysis of ST and T-wave changes. This database consists of 90 annotated excerpts of ambulatory ECG recordings from 79 subjects. The subjects were 70 men aged 30 to 84, and 8 women aged 55 to 71.","Documentation":"https://doi.org/10.13026/C2D59Z","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2D59Z","ARN":"arn:aws:s3:::physionet-open/edb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"edb","Sources":["awslabs-open-data-registry"]}
{"Name":"edf-anonymize","Description":"edf-anonymize reads an EDF or EDF+ file (input), writing an anonymized copy of it as output.","Documentation":"https://physionet.org/content/edf-anonymize/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/edf-anonymize/","ARN":"arn:aws:s3:::physionet-open/edf-anonymize/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"edf-anonymize","Sources":["awslabs-open-data-registry"]}
{"Name":"ECG-Derived Respiration","Description":"The ECG-Derived Respiration (EDR) technique is an approach to obtain a respiration signal from an ECG.. It is based on the observation that the positions of ECG electrodes on the chest surface move relative to the heart, and transthoracic impedance varies, as the lungs fill and empty. Thus the lead axes vary at different points in the respiratory cycle, and any sufficiently precise measurement of the mean cardiac electrical axis shows variations that are correlated with respiration. This method is particularly effective if two or more ECG signals are available, but a single lead is sufficient. The EDR can be obtained even in the context of congestive heart failure, in which RSA may be absent.","Documentation":"https://physionet.org/content/edr/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/edr/","ARN":"arn:aws:s3:::physionet-open/edr/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"edr","Sources":["awslabs-open-data-registry"]}
{"Name":"Electroencephalogram and eye-gaze datasets for robot-assisted surgery performance evaluation","Description":"Surgical training and patient safety depend on objective and accurate performance evaluation. Robot-assisted surgery (RAS) is newer and its training less well defined, but resources are not easily accessed to support data collection from participants with various RAS skill levels. This work describes and makes available for public use electroencephalogram (EEG) and eye-gaze data collected from 25 participants performing RAS simulator tasks with varying levels of RAS experience.Twenty-seven tasks from six modules of the da Vinci®;Skills Simulator ™, which are standard elements of most RAS surgical skills training curricula, were taken into consideration. The dataset contains performance scores (scale: 0 to 100) generated by the simulator after each exercise was completed. Each task was completed by the participants at least twice, and if they weren't able to complete it with a score of 70 (out of 100) on at least one try, they tried again until they did. The National Institute of Biomedical Imaging and Bioengineering (NIBIB) and Roswell Park Comprehensive Cancer Center (RPCCC) supported the collection of data for use in RAS training, which is why the dataset has been named the NIBIB-RPCCC-RAS performance evaluation dataset. The dataset contains 1636 EEG recordings (in the \"*.edf\" format), 1559 eye-gaze recordings (in the \"*.csv\" format), performance scores, and participant demographic data, such as age, gender, and dominant hand. To our knowledge, this is the first shared dataset that contains EEG and eye-gaze data from participants with a variety of RAS experiences who performed all tasks included in the da Vinci simulator modules.","Documentation":"https://doi.org/10.13026/qj5m-n649","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/qj5m-n649","ARN":"arn:aws:s3:::physionet-open/eeg-eye-gaze-data/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"eeg-eye-gaze-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Integration of EEG and Eye-Gaze Datasets for Performance Evaluation in Fundamentals of Laparoscopic Surgery (FLS) Tasks","Description":"Laparoscopic surgery represents a significant advancement in healthcare, aiming to enhance patient outcomes. However, evaluating performance in laparoscopic surgery tasks can be challenging due to subjective assessments. To address this, this study provides a comprehensive description and public access to electroencephalogram (EEG) and eye-gaze data collected from 25 participants with varying levels of robotic surgery experience. The participants performed Fundamentals of Laparoscopic Surgery (FLS) tasks using a trainer box (Pyxus®), focusing on three standard FLS elements. The dataset, named the NIBIB-RPCCC-FLS dataset, includes performance scores assessed by a rater after each task attempt. Each participant completed the tasks five times, with some subjects performing them only twice. The data collection received support from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and Roswell Park Comprehensive Cancer Center (RPCCC), with the goal of facilitating objective performance evaluation in FLS tasks. The dataset consists of 315 EEG recordings in the \".edf\" format, 315 eye-gaze recordings in the \".csv\" format, performance scores, and participant demographic data, such as age, gender, dominant hand, and dominant eye. Notably, this dataset is the first shared collection to include EEG and eye-gaze data from participants with different experiences in robotic surgery who performed FLS tasks.","Documentation":"https://doi.org/10.13026/kyjw-p786","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/kyjw-p786","ARN":"arn:aws:s3:::physionet-open/eeg-eye-gaze-for-fls-tasks/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"eeg-eye-gaze-for-fls-tasks","Sources":["awslabs-open-data-registry"]}
{"Name":"EEGDash on AWS","Description":"The EEG-DaSh (EEG Data Sharing) data archive is a large-scale data-sharing resource for magnetoencephalography and electroencephalography (MEEG) data hosted at the Swartz Center for Computational Neuroscience (SCCN), UC San Diego. It provides curated, BIDS-formatted datasets for neuroscience research, machine learning, and deep learning applications. The archive spans three S3 buckets: (1) the EEGDash bucket for data served through the EEGDash platform, (2) the NEMAR archive containing datasets contributed through the NEMAR (Neuroelectromagnetic Data Archive and Tools Resource) platform, which serves as the upstream data source for EEGDash, and (3) a competition-specific collection of datasets adapted and preprocessed for machine learning benchmarks and competitions.\n","Documentation":"https://eegdash.org","Contact":"adelorme@ucsd.edu","ManagedBy":"[Swartz Center for Computational Neuroscience](https://sccn.ucsd.edu/)","UpdateFrequency":"About once a week","Tags":["aws-pds","deep learning","life sciences","machine learning","neuroimaging","neuroscience"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"EEGDash data","ARN":"arn:aws:s3:::eegdash","Region":"us-east-2","Type":"S3 Bucket"},{"Description":"NEMAR datasets - primary archive of MEEG datasets contributed through the NEMAR platform","ARN":"arn:aws:s3:::nemar","Region":"us-east-2","Type":"S3 Bucket"},{"Description":"Competition datasets - MEEG datasets adapted and preprocessed for machine learning benchmarks","ARN":"arn:aws:s3:::nmdatasets","Region":"us-east-2","Type":"S3 Bucket"},{"Description":"Notifications for EEGDash","ARN":"arn:aws:sns:us-east-2:191754232783:eegdash-nemar","Region":"us-east-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Deep Learning on EEGDash Data example","URL":"https://eegdash.org","AuthorName":"SCCN","AuthorURL":"https://github.com/sccn"}],"Tools & Applications":[{"Title":"eegdash on pypi.python.org - Python module to query and download EEGDash data from Amazon S3","URL":"https://pypi.org/project/eegdash/","AuthorName":"Young Truong","AuthorURL":"https://github.com/dungscout96"},{"Title":"EEGLAB - An open source environment for electrophysiological signal processing","URL":"https://eeglab.org","AuthorName":"SCCN","AuthorURL":"https://sccn.ucsd.edu/"},{"Title":"NEMAR - Neuroelectromagnetic Data Archive and Tools Resource","URL":"https://nemar.org","AuthorName":"SCCN","AuthorURL":"https://sccn.ucsd.edu/"}],"Publications":[{"Title":"NEMAR: an open access data, tools, and compute resource operating on neuroelectromagnetic data","URL":"https://doi.org/10.1093/database/baac096","AuthorName":"A. Delorme, D. Truong, C. Youn, T. Mullen, A. Smetanin, S. Bhatt, S. Makeig"}]},"RegistryEntryAdded":"2024-10-29","RegistryEntryLastModified":"2026-04-01","Slug":"eegdash","Sources":["awslabs-open-data-registry"]}
{"Name":"EEG During Mental Arithmetic Tasks","Description":"The database contains EEG recordings of subjects before and during the performance of mental arithmetic tasks.","Documentation":"https://doi.org/10.13026/C2JQ1P","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2JQ1P","ARN":"arn:aws:s3:::physionet-open/eegmat/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"eegmat","Sources":["awslabs-open-data-registry"]}
{"Name":"EEG Motor Movement/Imagery Dataset","Description":"The EEG Motor Movement/Imagery Dataset provides 64-channel electroencephalography recordings collected with the BCI2000 system during real and imagined motor tasks. Participants completed 14 experimental runs, including two one-minute baseline recordings with eyes open and eyes closed, followed by three repetitions of four two-minute task conditions. These conditions involved either executing or imagining unilateral fist movements in response to left/right visual targets, or executing or imagining bilateral fist or foot movements in response to top/bottom visual targets. EEG signals were recorded according to the international 10-10 electrode placement system at a sampling rate of 160 Hz and are provided in EDF+ format with accompanying annotation channels. Event labels identify rest periods and task onsets using three codes: T0 for rest, T1 for left-fist or both-fists movement/imagery depending on the run type, and T2 for right-fist or both-feet movement/imagery. The dataset supports research in brain-computer interfaces, motor imagery classification, movement-related EEG dynamics, and the development of signal-processing and machine-learning methods for neural decoding.","Documentation":"https://doi.org/10.13026/C28G6P","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C28G6P","ARN":"arn:aws:s3:::physionet-open/eegmmidb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"eegmmidb","Sources":["awslabs-open-data-registry"]}
{"Name":"Icelandic 16-electrode Electrohysterogram Database","Description":"This database consists of 122 16-electrode EHG recordings performed on 45 pregnant women. The recordings were performed between 2008 and 2010 in Iceland.","Documentation":"https://doi.org/10.13026/C2159J","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2159J","ARN":"arn:aws:s3:::physionet-open/ehgdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ehgdb","Sources":["awslabs-open-data-registry"]}
{"Name":"elDORS (v1) Database","Description":"The elDORS (elevated Database Of RNA Sequences) database provides a massive-scale, \nupdated repository of RNA sequence data curated from diverse genomic, non-coding RNA, \nand metagenomic sources. Following a rigorous hierarchical redundancy removal workflow, \nthe dataset features two distinct tiers: the baseline elDORS_raw database (elDORS_raw_v1) containing \n~1.91 billion sequences, and a clustered and optimized elDORS version (elDORS_v1) containing ~1.32 billion sequences \n(clustered at 80% sequence identity and 80% overlap, keeping sequences between 10 and 4096 nucleotides). \nTo bypass severe network bottlenecks, the database is distributed in raw chunked formats, \ncompressed archives, and pre-indexed pipeline builds optimized for immediate drop-in compatibility \nwith deep RNA multiple sequence alignment pipelines rMSA and RNAcmap3.\n","Documentation":"https://github.com/duttan710/open-data-examples/blob/main/elDORS_v1_Documentation.md","Contact":"https://github.com/duttan710/open-data-examples/issues","ManagedBy":"[Vicens Lab, University of Houston](https://www.uh.edu/nsm/cnrcs/people/faculty/vicens-lab/)","UpdateFrequency":"Static / Versioned Release (v1.0)","Tags":["bioinformatics","genomic","life sciences","metagenomics","msa","RNA-seq","structural biology"],"License":"CC BY 4.0","Citation":"Dutta, N., & Vicens, Q. (2026). elDORS - An elevated Database Of RNA Sequences. [PREPRINT] bioRxiv. [https://www.biorxiv.org/content/10.64898/2026.07.10.737016v1](https://www.biorxiv.org/content/10.64898/2026.07.10.737016v1)","Resources":[{"Description":"The complete elDORS v1 S3 bucket organized into four distinct database formats:\n1. Raw Sequence Distribution (/elDORS_v1_raw) - 1.2 TB baseline unclustered FASTA format sequence database.\n2. Clustered version (/elDORS_v1) - elDORS (clustered at 80% identity and 80% sequence overlap); the optimized database contains ~1.32 billion sequences (within the sequence length range (10≤length≤4096)); Both the 170 GB single-volume (.fasta.gz) file and multi-volume (total 170GB) distribution as ~9 GB sequence-aware chunks (.fasta.gz) are provided. \n3. rMSA-Optimized Build (/rMSA_optimized_elDORS) - 1.0 TB uncompressed FASTA and BLAST format files optimized for using the database with the rMSA pipeline.\n4. RNAcmap3-Optimized Build (/RNAcmap3_optimized_elDORS) - 861 GB multi-volume BLAST and FASTA format files optimized for the RNAcmap3 split-strategy pipeline.\n","ARN":"arn:aws:s3:::eldors-v1-database","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://eldors-v1-database.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Get to Know the elDORS (v1) Database","URL":"https://github.com/duttan710/elDORS-aws-opendata/blob/main/elDORS_v1_Database/get-to-know-a-dataset.ipynb","AuthorName":"Nivedita Dutta, Vicens Lab"}],"Publications":[{"Title":"elDORS: An elevated Database Of RNA Sequences","URL":"https://www.biorxiv.org/content/10.64898/2026.07.10.737016v1","AuthorName":"Nivedita Dutta, Quentin Vicens"}]},"RegistryEntryAdded":"2026-08-06","RegistryEntryLastModified":"2026-10-13","Slug":"eldors_v1","Sources":["awslabs-open-data-registry"]}
{"Name":"Sounds of Central African landscapes","Description":"Archival soundscapes recorded in the rainforest landscapes of\nCentral Africa, with a focus on the vocalizations of African forest\nelephants (Loxodonta cyclotis).\n","Documentation":"https://elephantlisteningproject.org/congo-soundscapes-public-database/","Contact":"elephant-lp@cornell.edu","ManagedBy":"Center for Conservation Bioacoustics, Cornell University (https://elephantlisteningproject.org)","UpdateFrequency":"New sound data spanning 4-month time periods added as soon as possible","Collabs":{"ASDI":{"Tags":["ecosystems"]}},"Tags":["aws-pds","biodiversity","biology","ecosystems","geospatial","land","life sciences","natural resource","survey"],"License":"These sound files are freely available for scientific study and\nexploration, including for the development of detection\nalgorithms. Derivative works based on use of these sounds\n(e.g. publications, reports, published detection algorithms, media\npieces) must include proper attribution to the \"Cornell Elephant\nListening Project.\"\n\nThese sounds are not available for any media use including, but not\nlimited to, advertising, promotion, radio or movie production,\nwithout express permission from the owners.\n","Resources":[{"Description":"Nouabale-Ndoki landscape sound data","ARN":"arn:aws:s3:::congo8khz-pnnn","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Listen to the rainforest chorus that's helping scientists protect African elephants","URL":"https://www.aboutamazon.com/news/sustainability/listen-to-the-rainforest-chorus-thats-helping-scientists-protect-african-elephants","AuthorName":"Amazon Staff"},{"Title":"You can now hear rainforest sounds worldwide-here's why that matters","URL":"https://www.nationalgeographic.com/animals/article/researchers-share-more-than-a-million-hours-of-rainforest-recordings","AuthorName":"Rachel Fobar"}]},"RegistryEntryAdded":"2020-08-20","RegistryEntryLastModified":"2022-01-11","Slug":"elp-nouabale-landscape","Sources":["awslabs-open-data-registry"]}
{"Name":"EMBER Open Datasets","Description":"This is data from, Ecosystem for Multi-modal Brain-behavior Experimentation and Research (EMBER), It contains time series behavioral and neuroscience data from animal and deidentified human subjects across multiple modalities.","Documentation":"https://emberarchive.org/","Contact":"brock.wester@jhuapl.edu","ManagedBy":"[Johns Hopkins University Applied Physics Laboratory](https://www.jhuapl.edu)","UpdateFrequency":"New datasets are added as soon as it is available. Minor updates on existing datasets occur sporadically.","Tags":["activity detection","activity recognition","analytics","aws-pds","bioinformatics","brain images","brain models","cloud computing","computer vision","deep learning","electrophysiology","GPS","h5","hdf5","Homo sapiens","json","life sciences","localization","machine learning","magnetic resonance imaging","Mus musculus","neurobiology","neuroimaging","neurophysiology","neuroscience","non-human primate","signal processing","speech processing","zarr"],"License":"Creative Commons 4.0 International (CC BY 4.0)","Resources":[{"Description":"Time series neurophysiology and behavioral data from animal and human (deidentified)","ARN":"arn:aws:s3:::ember-open-data","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Mapping the landscape of social behavior","URL":"https://pubmed.ncbi.nlm.nih.gov/40043703/","AuthorName":"Ugne Klibaite, Tianqing Li, Diego Aldarondo, Jumana F Akoad, Bence P Ölveczky, Timothy W Dunn."}],"Tutorials":[{"Title":"Get To Know A Dataset - EMBER","URL":"https://github.com/aplbrain/BBQS-EMBER-cookbook/blob/main/examples/get-to-know-a-dataset-ember.ipynb","NotebookURL":"https://github.com/aplbrain/BBQS-EMBER-cookbook/blob/main/examples/get-to-know-a-dataset-ember.ipynb","AuthorName":"EMBER Team","AuthorURL":"https://github.com/aplbrain"}]},"RegistryEntryAdded":"2025-03-27","RegistryEntryLastModified":"2026-01-16","Slug":"ember","Sources":["awslabs-open-data-registry"]}
{"Name":"Exposure Mapping Component (EMC) EMC-BUILT","Description":"The EMC-BUILT product consists of three datasets, including EMC-BUILT-S (gridded built-up surface estimates), EMC-BUILT-S-NRES (non-residential built-up surface estimates) and EMC-BUILT-GREENNESS (NDVI within and nearby the built-up domain). This global dataset has been produced for the year 2022 and is based on Sentinel-2 multispectral data.","Documentation":"https://human-settlement.emergency.copernicus.eu/emc_built_s.php","Contact":"JRC-GHSL-DATA@ec.europa.eu","ManagedBy":"[JRC](https://joint-research-centre.ec.europa.eu)","UpdateFrequency":"NA","Tags":["classification","cog","disaster response","earth observation","geoscience","geospatial","global","land cover","land use","mapping","planetary","tiff","tiles"],"License":"GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.","Resources":[{"Description":"EMC built-up surface raster layer in World Mollweide (EPSG:54009) at 10m, 100m and 1000m spatial resolution; also available in WGS 1984 (EPSG:4326) at 3 and 30 arcsecond spatial resolution, globally for the year 2022.","ARN":"arn:aws:s3:::jrc-ghsl/emc-built-s","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"EMC “non-residential” (NRES) built-up surface raster layer in World Mollweide (EPSG:54009) at 10m, 100m and 1000m spatial resolution; also available in WGS 1984 (EPSG:4326) at 3 and 30 arcsecond spatial resolution, globally for the year 2022.","ARN":"arn:aws:s3:::jrc-ghsl/emc-built-s-nres","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"The EMC Greenness layer represents the Normalized Difference Vegetation Index (NDVI) within and around built-up areas. The gridded data are available in World Mollweide projection (EPSG:54009) at 10m, 100m and 1000m spatial resolution; also available in WGS 1984 (EPSG:4326) at 3 and 30 arcsecond spatial resolution, globally for the year 2022.","ARN":"arn:aws:s3:::jrc-ghsl/emc-built-greenness","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:eu-central-1:291461994469:jrc-ghsl-object_created","Region":"eu-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: EMC BUILT","URL":"https://github.com/lucamaff/open-data-examples/tree/main/emc-built","NotebookURL":"https://github.com/lucamaff/open-data-examples/blob/main/emc-built/get-to-know-a-dataset.ipynb","AuthorName":"MAFFENINI Luca","AuthorURL":"https://github.com/lucamaff"}],"Publications":[{"Title":"Copernicus Exposure Mapping Component Built-up product 2022","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC144315","AuthorName":"KEMPER Thomas, POLITIS Panagiotis, MARI RIVERO Ines, RIFFLER Michael, LEITNER Armin, RALSER Stefan, POGLITSCH Eva, SCHLEICHER Christian, FAUCQUEUR Loic, PENNEC Alexandre, MARCONCINI Mattia, ESCH Thomas, JULEA Andreea Maria, MELCHIORRI Michele"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2026-09-25","RegistryEntryLastModified":"2026-10-14","Slug":"emc-built","Sources":["awslabs-open-data-registry"]}
{"Name":"Ensemble Meteorological Dataset for Planet Earth, EM-Earth","Description":"EM-Earth provides data for precipitation, mean air temperature, air temperature range, and dew-point temperature at 0.1° spatial resolution over global land areas from 1950 to 2019. EM-Earth provides hourly/daily deterministic estimates, and daily probabilistic estimates (25 ensemble members), to meet the diverse requirements of hydrometeorological applications.","Documentation":"https://doi.org/10.20383/102.0547","Contact":"shervan.gharari@usask.ca","ManagedBy":"[Computational Hydrology at the University of Saskatchewan](https://uofs-comphyd.github.io/)","UpdateFrequency":"N/A","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","meteorological","near-surface air temperature","netcdf","precipitation"],"License":"Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/","Citation":"https://doi.org/10.1175/BAMS-D-21-0106.1","Resources":[{"Description":"Ensemble Meteorological Dataset for Planet Earth, EM-Earth","ARN":"arn:aws:s3:::emearth","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://emearth.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"EM-Earth: The Ensemble Meteorological Dataset for Planet Earth","URL":"https://doi.org/10.1175/BAMS-D-21-0106.1","AuthorName":"Guoqiang Tang, Martyn P. Clark & Simon Michael Papalexiou"}]},"RegistryEntryAdded":"2023-02-08","RegistryEntryLastModified":"2025-07-09","Slug":"emearth","Sources":["awslabs-open-data-registry"]}
{"Name":"Examples of Electromyograms","Description":"An electromyogram (EMG) is a common clinical test used to assess function of muscles and the nerves that control them. EMG studies are used to help in the diagnosis and management of disorders such as the muscular dystrophies and neuropathies. Nerve conduction studies that measure how well and how fast the nerves conduct impulses are often performed in conjunction with EMG studies. Examples of EMG studies (courtesy of Seward Rutkove, MD, Department of Neurology, Beth Israel Deaconess Medical Center/Harvard Medical School) are given here.","Documentation":"https://doi.org/10.13026/C24S3D","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C24S3D","ARN":"arn:aws:s3:::physionet-open/emgdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"emgdb","Sources":["awslabs-open-data-registry"]}
{"Name":"EMory BrEast Imaging Dataset (EMBED)","Description":"EMBED is a racially diverse mammography dataset containing 3.4M screening and diagnostic images from 110,000 patients collected from 2013-2020, with an\nequal representation of black and white women. The dataset is comprised of 2D, synthetic 2D (C-view), and 3D (digital breast tomosynthesis, i.e. DBT)\nimages. It contains 60,000 annotated lesions linked to structured imaging descriptors and ground truth pathologic outcomes grouped into six severity\nclasses. This release represents 20% of the total 2D and C-view dataset and is available for research use. DBT, US, and MRI exams will be added at a\nlater date.</br></br>\nAcknowledgements - We would like to thank Glendor, Inc and MD.ai for assistance with image de-identification.\n","Documentation":"https://docs.hitilab.com/docs/datasets/embed","Contact":"hari.trivedi@emory.edu","ManagedBy":"Health Innovation and Translational Informatics Lab at Emory University (hitilab.com)","UpdateFrequency":"New data to be added annually","Tags":["aws-pds","bias","biology","cancer","health","imaging","life sciences","mammography","x-ray"],"License":"[Custom License](https://github.com/Emory-HITI/EMBED_Open_Data/blob/main/EMBED_license.md)","Citation":"In addition, please cite the following publication - https://pubs.rsna.org/doi/epdf/10.1148/ryai.220047","Resources":[{"Description":"EMBED dataset and tables","ARN":"arn:aws:s3:::embed-dataset-open","Region":"us-west-2","Type":"S3 Bucket","ControlledAccess":"https://data.hitilab.com/"}],"DataAtWork":{"Tutorials":[{"Title":"Screening Label Assignment Example","URL":"https://github.com/Emory-HITI/EMBED_Open_Data/blob/43e76483284a87b07d33982fc673082b5e2d41c9/resources/notebooks/screening_label_assignment.ipynb","AuthorName":"Emory-HITI"}],"Publications":[{"Title":"The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4M Screening and Diagnostic Mammograms","URL":"https://pubs.rsna.org/doi/epdf/10.1148/ryai.220047","AuthorName":"Jiwoong J. Jeong, Brianna L. Vey, Ananth Reddy, Thomas Kim, Thiago Santos, Ramon Correa, Raman Dutt, Marina Mosunjac, Gabriela Oprea-Ilies, Geoffrey Smith, Minjae Woo, Christopher R. McAdams, Mary S. Newell, Imon Banerjee, Judy Gichoya, Hari Trivedi","AuthorURL":"https://scholar.google.com/citations?hl=en&user=_VYSm7YAAAAJ"}]},"DeprecatedNotice":null,"RegistryEntryAdded":"2023-01-19","RegistryEntryLastModified":"2026-09-08","Slug":"emory-breast-imaging-dataset-embed","Sources":["awslabs-open-data-registry"]}
{"Name":"Encyclopedia of DNA Elements (ENCODE)","Description":"The Encyclopedia of DNA Elements (ENCODE) Consortium is an international collaboration of\nresearch groups funded by the National Human Genome Research Institute (NHGRI). The goal\nof ENCODE is to build a comprehensive parts list of functional elements in the human genome,\nincluding elements that act at the protein and RNA levels, and regulatory elements that\ncontrol cells and circumstances in which a gene is active. ENCODE investigators employ a\nvariety of assays and methods to identify functional elements. The discovery and annotation\nof gene elements is accomplished primarily by sequencing a diverse range of RNA sources,\ncomparative genomics, integrative bioinformatic methods, and human curation. Regulatory\nelements are typically investigated through DNA hypersensitivity assays, assays of\nDNA methylation, and immunoprecipitation (IP) of proteins that interact with DNA and RNA,\ni.e., modified histones, transcription factors, chromatin regulators, and\nRNA-binding proteins, followed by sequencing.\n","Contact":"encode-help@lists.stanford.edu","ManagedBy":"ENCODE Data Coordinating Center","Documentation":"https://www.encodeproject.org","UpdateFrequency":"Daily","Tags":["aws-pds","bioinformatics","biology","genetic","genomic","life sciences"],"License":"External data users may freely download, analyze, and publish results based on any ENCODE data without restrictions.","Resources":[{"Description":"Released and archived ENCODE data","ARN":"arn:aws:s3:::encode-public","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"ENCODE CTCF ChIP-seq data correlation across different cell types","URL":"https://github.com/ENCODE-DCC/encode-data-usage-examples/blob/master/ctcf_chip_seq_cross_cell_type_correlation.ipynb","AuthorName":"Paul Sud","AuthorURL":"https://github.com/p-sud"},{"Title":"Exploring ENCODE data from EC2 with Jupyter notebook","URL":"https://github.com/ENCODE-DCC/encode-data-usage-examples/blob/master/mount_s3_bucket_and_run_jupyter_on_ec2.ipynb","AuthorName":"Keenan Graham","AuthorURL":"https://github.com/keenangraham"},{"Title":"Ingesting ENCODE data into TileDB with S3 backend","URL":"https://github.com/ENCODE-DCC/encode-data-usage-examples/blob/master/ingest_encode_data_tile_db_with_s3_backend.ipynb","AuthorName":"Otto Jolanki","AuthorURL":"https://github.com/ottojolanki"}],"Publications":[{"Title":"New developments on the Encyclopedia of DNA Elements (ENCODE) data portal","URL":"https://academic.oup.com/nar/article/48/D1/D882/5622708#190992715","AuthorName":"Luo et al 2020","AuthorURL":"http://orcid.org/0000-0001-6948-2042"}]},"RegistryEntryAdded":"2019-04-09","RegistryEntryLastModified":"2022-05-19","Slug":"encode-project","Sources":["awslabs-open-data-registry"]}
{"Name":"ENHANCE.PET 1.6k - Whole-/Total-Body [18F]FDG-PET/CT with CT-Derived Segmentations","Description":"Open, multi-center dataset of 1,597 whole-/total-body FDG-PET/CT studies with 130 CT-derived, expert-verified anatomical segmentations per scan (~250 GB). Provided as anonymized NIfTI (PET, CT, labels) with spreadsheet metadata. Designed for segmentation benchmarking, multi-organ analysis, radiomics, and PET/CT AI research.\n","Documentation":"https://github.com/ENHANCE-PET/MOOSE/blob/main/DATA_CARD.md","Contact":"Lalith.shiyam@med.uni-muenchen.de","ManagedBy":"ENHANCE.PET initiative (LMU Klinikum & partners)","UpdateFrequency":"Ad hoc (new releases aligned with additional cohort availability)","Tags":["cancer","life sciences","medical imaging","nifti","radiology","segmentation"],"License":"Dataset licensing per originating site: - AutoPET Challenge: CC BY-NC 4.0 (non-commercial use) - University Hospital Leipzig: CC BY 4.0 - Azienda Ospedaliero Universitaria Careggi: CC BY 4.0 Software (MOOSE): Apache-2.0.\n","Citation":"Ferrara D. et al. (2025). Sharing a whole-/total-body [18F]FDG-PET/CT dataset with CT-derived segmentations - an ENHANCE.PET initiative. https://doi.org/10.21203/rs.3.rs-7169062/v2\n","Resources":[{"Description":"ENHANCE.PET 1.6k public S3 bucket","ARN":"arn:aws:s3:::enhance-pet-1-6k","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://enhance-pet-1-6k.s3.us-west-2.amazonaws.com/)"]},{"Description":"SNS topic for ENHANCE.PET 1.6k S3 object creation events","ARN":"arn:aws:sns:us-west-2:602670427264:enhance-pet-1-6k-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Dataset Organization & AWS Access (MOOSE CLI)","URL":"https://github.com/ENHANCE-PET/MOOSE/blob/main/DATA_CARD.md","AuthorName":"ENHANCE.PET Team","AuthorURL":"https://enhance.pet/"}],"Tools & Applications":[{"Title":"MOOSE (Multi-organ objective segmentation tool)","URL":"https://github.com/ENHANCE-PET/MOOSE","AuthorName":"ENHANCE.PET (QIMP Team)","AuthorURL":"https://enhance.pet/"}],"Publications":[{"Title":"Sharing a whole-/total-body [18F]FDG-PET/CT dataset with CT-derived segmentations - an ENHANCE.PET initiative","URL":"https://doi.org/10.21203/rs.3.rs-7169062/v2","AuthorName":"Ferrara, D.; Pires, M.; Gutschmayer, S.; Yu, J.; Abdelhafez, Y. G.; Abenavoli, E.; Badawi, R. D.; Chaudhari, A. J.; Chen, M. S.; Cherry, S. R.; Frille, A.; Geist, B. K.; Grüenert, S.; Hacker, M.; Hesse, S.; Kerkhoff, T.; Linder, P.; Pappisch, J.; Pusitz, S.; Raslan, O. A.; Rausch, I.; Raychaudhuri, S. P.; Sabri, O.; Schmidt, F.; Sciagrà, R.; Spencer, B.; Wang, G.; Wirtz, H.; Beyer, T.; Sundar, L. K. S.","AuthorURL":"https://orcid.org/0000-0002-8711-8081"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-08-13","RegistryEntryLastModified":"2025-08-14","Slug":"enhance-pet-1-6k","Sources":["awslabs-open-data-registry"]}
{"Name":"End of Term Web Archive Dataset","Description":"The End of Term Web Archive (EOT) captures and saves U.S. Government websites at the end of presidential administrations.  The EOT has thus far preserved websites from administration changes in 2008, 2012, 2016, 2020  and 2024.  Data from these web crawls have been made openly available in several formats in this dataset.\n","Documentation":"https://eotarchive.org/data/","Contact":"Mark Phillips <mark.phillips@unt.edu>, Sawood Alam <sawood@archive.org>","ManagedBy":"[End of Term Web Archive](https://eotarchive.org)","UpdateFrequency":"Every four years after a US Presidential Election","Tags":["archives","aws-pds","internet","natural language processing","web archive"],"License":"There are no restrictions on the use, access, and/or download of data from the End of Term Web Archive Dataset.  We request that you cite the End of Term Web Archive project when using the data provided from this dataset.  <br/><br/> [Creative Commons Zero](https://creativecommons.org/publicdomain/zero/1.0/)\n","Resources":[{"Description":"Web Archive Crawl Data (WARC and ARC formats)","ARN":"arn:aws:s3:::eotarchive","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Moving the End of Term Web Archive to the Cloud to Encourage Research Use and Reuse","URL":"https://digital.library.unt.edu/ark:/67531/metadc1998717/","AuthorName":"Mark Phillips and Sawood Alam"}]},"RegistryEntryAdded":"2023-01-31","RegistryEntryLastModified":"2025-08-26","Slug":"eot-web-archive","Sources":["awslabs-open-data-registry"]}
{"Name":"OSAP 2022 Modeling Platform ","Description":"The data are part of the 2022 Modeling Platform used to support regulatory actions and technical analyses conducted by the EPA's Office of State Air Partnerships (OSAP).  Specifically, this data includes Weather Research and Forecasting Model (v4.4.2) conducted at a 12-km resolution over the Continental United States (12US). MCIP-processed files and wrfcamx-processed (12US1 domain) are also available as part of this dataset  to assist in the use of emissions processing and photochemical modeling. These files  may be used in downstream applications to generate emissions, photochemical  modeling, or dispersion modeling inputs. Additionally, lateral boundary condition files  generated using GEOS-CF at 36-km with results translated from GEOS-Chem species available  in GEOS-CF to CMAQ cb6/ae7. Simulations for boundary conditions covering the northern  hemisphere are also provided. 12US2 lateral boundary condition files are also generated based  on 36US3 CMAQ model run outputs. These simulations were conducted using CMAQ v5.4 and GEOS-Chem v14.0.1. 2022v1 CMAQ-ready emissions are provided for a 36km grid over North America (36US3)  and two 12km grids (12SU1 and 12US2). In addition, 2022v1 CAMx-ready emissions are provided  for a 12km grid over North America (12US2). See the documentation for pictures of the grids.  The types of emissions data provided include point sources, nonpoint sources, mobiles sources,  fires, lightning NOx, and biogenic emissions. Input files for computing biogenic emissions,  lightning NOx emissions and bi-directional deposition inline within CMAQ are also provided.  Ozone column and photolysis rate input files for CAMx model run are also provided. The related  CMAQ and CAMx run scripts are also available now. One day sample outputs for CMAQ and CAMx on the 12US2 domain are also now available. README text files are included at multiple  levels within the directory structure to explain files at that level. For more information  about the emissions, see the below documentation or the 2022v1 web page: https://www.epa.gov/air-emissions-modeling/2022v1-emissions-modeling-platform","Documentation":"2022 WRF Modeling TSD:  https://bit.ly/2022WRF\n2022 Emissions Base Case: https://bit.ly/2022Emissions\n2022 v1 36US3 model performance: https://bit.ly/36US3_2022\n2022 Regulatory Modeling Platform: Air Quality Modeling v1: https://bit.ly/2022Platformv1","Contact":"Misenis.Chris@epa.gov","ManagedBy":"U.S. Environmental Protection Agency (https://www.epa.gov)","UpdateFrequency":"As needed","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["air quality","aws-pds","environmental","meteorological","regulatory","weather"],"License":"These datasets are products of the U.S. Government and are intended for public access and use. Unless otherwise specified, all data produced by the U.S EPA is, by default, in the public domain and are not subject to domestic copyright protection under 17 U.S.C. § 105. More details on the U.S. Public Domain license are available here: http://www.usa.gov/publicdomain/label/1.0/","Citation":"WRF Modeling: US EPA, 2024, \"Meteorological Model Performance for Annual 2022 Simulation WRF v4.4.2\" Emissions Modeling: US EPA, 2024, \"Documentation of 2022 Base Year Emissions Released August 2024\" Air Quality Modeling: US EPA, 2026, \"Technical Support Document (TSD): 2022 Regulatory Modeling Platform: Air Quality  Modeling Version 1\"","Resources":[{"Description":"The 2022 WRF output are stored as uncompressed netcdf/hdf5 formatted files in  the /WRF directory. The 2022 MCIP output are stored as uncompressed netcdf/hdf5  formatted files in IOAPI format in the /MCIP directory. The wrfcamx files are stored  as uncompressed netcdf files in the /wrfcamx directory. Information on the model  projection and grid structure is contained in the header information of the  netcdf file. The netcdf files can be opened and manipulated using software programs  that can read and write netcdf formatted files (e.g. Fortran, R, Python). The WRF files are daily files containing hourly data beginning at 00UTC through 23UTC for each modeled day. For more information: https://www2.mmm.ucar.edu/wrf/users/ The MCIP files are daily files with multiple files for each day. For more information about what each MCIP file contains, please see the following GitHub entry: https://github.com/USEPA/CMAQ/blob/main/PREP/mcip/README.md For more information about what each wrfcamx file contains, please see the README file in the wrfcamx source code file available from Ramboll at:  https://www.camx.com/getmedia/wrfcamx_v5.2.10Jan22.tgz The 2022v1 emissions data are stored as uncompressed netcdf files in the /emis directory.  Year 2022 CMAQ-ready emissions are provided under the folder emis/2022hc_cb6_22m.  Year 2022 CAMx-ready emissions are provided under the folder emis/CAMx. The 2022v1 12US2 boundary conditions are stored as uncompressed netecdf files in  bcon/12US2_CMAQ_BCON and bcon/ HEMI_CMAQ_12US2_CAMxBC. The 2022v1 12US2 and 36US3  EPIC data are stored as uncompressed netecdf files in CMAQ_ancillary_inputs/EPIC. The 2022v1 12US2 and 36US3 lightning data are stored as uncompressed netecdf  files in CMAQ_ancillary_inputs/Lightning_data. The 2022v1 12US2 and 36US3 ozone  column data as uncompressed txt files in CAMx_ancillary_inputs/ozone_col. The 2022v1  12US2 and 36US3 photolysis rate data are stored as uncompressed files in  CAMx_ancillary_inputs/photolysis_rate. The 2022v1 12US2 and 36US3 model run  scripts are stored in Model_jobs/.","ARN":"arn:aws:s3:::epa-2022-modeling-platform","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://epa-2022-modeling-platform.s3.amazonaws.com/index.html)"]},{"Description":"Notification for the 2022 Modeling Platform bucket","ARN":"arn:aws:sns:us-east-1:127085394039:epa-2022-modeling-platform-object_created","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2024-04-19","RegistryEntryLastModified":"2026-04-15","Slug":"epa-2022-modeling-platform","Sources":["awslabs-open-data-registry"]}
{"Name":"U.S. Environmental Protection Agency (EPA) Center for Computational Toxicology and Exposure High Throughput Transcriptomics Data","Description":"High-throughput transcriptomics (HTTr) data generated by US EPA Office of Research and Development, Center for Computational Toxicology and Exposure (CCTE), Biomolecular and Computational Toxicology Division.  All data is generated using TempO-Seq targeted RNA-seq technology from in vitro cell culture systems.\n","Documentation":"https://www.epa.gov/chemical-research/high-throughput-profiling-using-transcriptomics-and-high-content-imaging","Contact":"everett.logan@epa.gov","ManagedBy":"U.S. Environmental Protection Agency (https://www.epa.gov)","UpdateFrequency":"Quarterly","Tags":["aws-pds","bioinformatics","fastq","gene expression","transcriptomics"],"License":"These datasets are products of the U.S. Government and are intended for public access and use. Unless otherwise specified, all data produced by the U.S EPA is, by default, in the public domain and are not subject to domestic copyright protection under 17 U.S.C.   105. More details on the U.S. Public Domain license are available here: http://www.usa.gov/publicdomain/label/1.0/\n","Citation":"U.S. EPA, 2024, \"U.S. EPA CCTE High-throughput transcriptomics (HTTr) data 2021-2024\"","Resources":[{"Description":"HTTr TempO-Seq data","ARN":"arn:aws:s3:::epa-ccte-httr","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://epa-ccte-httr.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for HTTR bucket","ARN":"arn:aws:sns:us-east-1:127085394039:epa-ccte-httr-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"HTTr pipeline","URL":"https://github.com/USEPA/CompTox-httrpl","AuthorName":"Logan Everett"}],"Publications":[{"Title":"Benchmark Dose Modeling Approaches for Volatile Organic Chemicals Using a Novel Air-Liquid Interface In Vitro Exposure System","URL":"https://doi.org/10.1093/toxsci/kfac040","AuthorName":"Speen A., Murray J., Krantz Q., Davies D., Evansky P., Harrill J., et al"},{"Title":"Combining phenotypic profiling and targeted RNA-Seq reveals linkages between transcriptional perturbations and chemical effects on cell morphology: Retinoic acid as an example\n","URL":"https://doi.org/10.1016/j.taap.2022.116032","AuthorName":"Nyffeler J., Willis C., Harris F., Taylor L., Judson R., Everett L., et al"},{"Title":"Exploring the Effects of Experimental Parameters and Data Modeling Approaches on In Vitro Transcriptomic Point-of-Departure Estimates","URL":"https://doi.org/10.1016/j.tox.2023.153694","AuthorName":"Harrill J., Everett L., Haggard D., Bundy J., Willis C., Shah I., et al"},{"Title":"High-Throughput Transcriptomics of Water Extracts Detects Reductions in Biological Activity with Water Treatment Processes","URL":"https://doi.org/10.1021/acs.est.3c07525","AuthorName":"Rogers J., Leusch F., Chambers B., Daniels K., Everett L., Judson R., et al"},{"Title":"High-Throughput Transcriptomics Platform for Screening Environmental Chemicals","URL":"https://doi.org/10.1093/toxsci/kfab009","AuthorName":"Harrill J., Everett L., Haggard D., Sheffield T., Bundy J., Willis C., et al"},{"Title":"High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells","URL":"https://doi.org/10.1016/j.taap.2024.117073","AuthorName":"Bundy J., Everett L., Rogers J., Nyffeler J., Byrd G., Culbreth M., et al"},{"Title":"Signature analysis of high-throughput transcriptomics screening data for mechanistic inference and chemical grouping","URL":"https://doi.org/10.1093/toxsci/kfae108","AuthorName":"Harrill J., Everett L., Haggard D., Word L., Bundy J., Chambers B., et al"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-06-02","RegistryEntryLastModified":"2025-06-02","Slug":"epa-ccte-httr","Sources":["awslabs-open-data-registry"]}
{"Name":"EPA Dynamically Downscaled Ensemble (EDDE) Version 1","Description":"The data are a subset of the EPA Dynamically Downscaled Ensemble (EDDE),  Version 1. EDDE is a collection of physics-based modeled data that represent 3D atmospheric conditions for historical and future periods under different scenarios. The EDDE Version 1 datasets cover the contiguous United States at a horizontal grid spacing of 36 kilometers at hourly increments. EDDE Version 1 includes simulations that have been dynamically downscaled from multiple global climate models (GCMs) under both mid- and high-emission scenarios from the Fifth Coupled Model Intercomparison Project (CMIP5) using the Weather Research and Forecasting (WRF) model. Scenarios were downscaled from the Community Earth System Model (CESM) and the Geophysical Fluid Dynamics Laboratory (GFDL) Coupled Model version 3 (CM3). Simulations followed the historical periods 1975-2005 (CESM only) and 1995-2005 (both CESM and CM3), and Representative Concentration Pathways (RCP) 4.5 for 2025-2100 (CESM only), RCP6.0 for 2025-2055 (CESM only), and RCP8.5 (both CESM and CM3). Data are in Network Common Data Form (netCDF) version 4, which is used in atmospheric modeling. The EDDE data in netCDF are further written to adhere to principles of Climate and Forecasting System (CF) Compliance to enhance portability and interoperability with data from other sources. The files are self-describing with metadata included in the netCDF header. Data reference: https://doi.org/10.23719/1530964 Some peer-reviewed publications that describe EDDE (without the name \"EDDE\"): https://doi.org/10.1080/10962247.2021.1970048 https://doi.org/10.5194/acp-18-15471-2018 https://doi.org/10.1080/10962247.2014.996270 https://doi.org/10.1175/JCLI-D-15-0233.1 https://doi.org/10.1002/2014JD021785","Documentation":"EDDE Version 1 can be referenced using https://doi.org/10.23719/1530964 Please see the Data Use Statement, technical updates, and additional background information on the EDDE webpage, accessible from: https://www.epa.gov/climate-research/climate-change-tools-protect-public-health-and-environment This dataset was most recently updated in January 2025.","Contact":"spero.tanya@epa.gov; mallard.megan@epa.gov","ManagedBy":"U.S. Environmental Protection Agency (https://www.epa.gov)","UpdateFrequency":"Quarterly","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","air quality","air temperature","atmosphere","aws-pds","climate","climate model","climate projections","CMIP5","CMIP6","ecosystems","elevation","environmental","Eulerian","events","floods","fluid dynamics","geoscience","geospatial","hdf5","health","HPC","hydrology","infrastructure","land cover","land use","meteorological","model","near-surface air temperature","near-surface relative humidity","near-surface specific humidity","netcdf","open source software","physics","post-processing","precipitation","radiation","simulations","us","water","weather"],"License":"These datasets are products of the U.S. Government and are intended for public access and use. Unless otherwise specified, all data produced by the U.S EPA is, by default, in the public domain and are not subject to domestic copyright protection under 17 U.S.C. § 105. More details on the U.S. Public Domain license are available here: http://www.usa.gov/publicdomain/label/1.0/","Citation":"US EPA, 2024, \"EPA Dynamically Downscaled Ensemble (EDDE), Version 1\",  https://doi.org/10.23719/1530964.","Resources":[{"Description":"EDDE data are compressed netCDF (https://unidata.ucar.edu/software/netcdf/) files that conform to Climate and Forecast (CF) metdata conventions  (https://cfconventions.org). The data can be ingested, processed, and visualized using standard netCDF utility routines in various programming languages. Information on the model projection and grid structure is contained in the header information of each netCDF file. Two timekeeping variables are in the files. The variable \"time\" conforms to CF conventions using the \"hours since\" construct, where the reference time is given as the initialization of the simulation. The \"mtime\" variable is a user-readable string that aligns with \"time\" expressed as YYYY-MM-DD_HH:MI:SS. Additional information can be found at https://www.epa.gov/climate-research/climate-change-tools-protect-public-health-and-environment","ARN":"arn:aws:s3:::epa-edde","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://epa-edde.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for EDDE bucket","ARN":"arn:aws:sns:us-east-1:127085394039:epa-edde-object_created","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2024-07-15","RegistryEntryLastModified":"2025-07-09","Slug":"epa-edde-v1","Sources":["awslabs-open-data-registry"]}
{"Name":"EPA Dynamically Downscaled Ensemble (EDDE) Version 2","Description":"The data are a subset of the EPA Dynamically Downscaled Ensemble (EDDE),  Version 2. EDDE is a collection of physics-based modeled data that represent  3D atmospheric conditions for historical and future periods under different  scenarios. The EDDE Version 2 datasets cover the contiguous United States at  a horizontal grid spacing of 12 kilometers at hourly increments. EDDE Version  2 includes simulations that have been dynamically downscaled from multiple  global climate models (GCMs) under multiple emission scenarios from the  Sixth Coupled Model Intercomparison Project (CMIP6) using the Weather  Research and Forecasting (WRF) model. Scenarios were downscaled from the  Max Planck Institute (MPI) Earth System Model (ESM) version 1.2 High  Resolution (MPI-ESM1-2-HR) and the Norwegian Earth System Model version 2  Medium resolution (NorESM2-MM). Simulations cover the historical period  1985-2014 and a future period of 2025-2100 under Shared Socioeconomic  Pathway (SSP) 2-4.5 and SSP3-7.0. Data are in Network Common Data Form (netCDF)  version 4, which is used in atmospheric modeling. The EDDE data in netCDF are  further written to adhere to principles of Climate and Forecasting System (CF)  Compliance to enhance portability and interoperability with data from other sources. The files are self-describing with metadata included in the netCDF  header. Data reference: https://doi.org/10.23719/1531941   Some peer-reviewed publications that describe EDDE (note that publications  prior to 2025 do not use the name \"EDDE\"):  https://doi.org/10.1175/BAMS-D-24-0251.1  https://doi.org/10.1016/j.dib.2025.112070  https://doi.org/10.1080/10962247.2021.1970048  https://doi.org/10.5194/acp-18-15471-2018  https://doi.org/10.1080/10962247.2014.996270  https://doi.org/10.1175/JCLI-D-15-0233.1  https://doi.org/10.1002/2014JD021785 https://doi.org/10.5194/gmd-19-579-2026 ","Documentation":"EDDE can be referenced using https://doi.org/10.23719/1531941 Please see the Data Use Statement, technical updates, and additional background information on the EDDE webpage, accessible from: https://www.epa.gov/climate-research/climate-change-tools-protect-public-health-and-environment This dataset was most recently updated in July 2026.","Contact":"mallard.megan@epa.gov","ManagedBy":"U.S. Environmental Protection Agency (https://www.epa.gov)","UpdateFrequency":"Quarterly","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","air quality","air temperature","atmosphere","aws-pds","climate","climate model","climate projections","CMIP5","CMIP6","ecosystems","elevation","environmental","Eulerian","events","floods","fluid dynamics","geoscience","geospatial","hdf5","health","HPC","hydrology","infrastructure","land cover","land use","meteorological","model","near-surface air temperature","near-surface relative humidity","near-surface specific humidity","netcdf","open source software","physics","post-processing","precipitation","radiation","simulations","us","water","weather"],"License":"These datasets are products of the U.S. Government and are intended for public access and use. Unless otherwise specified, all data produced by the U.S EPA is, by default, in the public domain and are not subject to domestic copyright protection under 17 U.S.C. § 105. More details on the U.S. Public Domain license are available here: http://www.usa.gov/publicdomain/label/1.0/","Citation":"US EPA, 2024, \"EPA Dynamically Downscaled Ensemble (EDDE), Version 2\",  https://doi.org/10.23719/1531941.","Resources":[{"Description":"EDDE data are compressed netCDF (https://unidata.ucar.edu/software/netcdf/) files that conform to Climate and Forecast (CF) metdata conventions  (https://cfconventions.org). The data can be ingested, processed, and visualized using standard netCDF utility routines in various programming languages. Information on the model projection and grid structure is contained in the header information of each netCDF file. Two timekeeping variables are in the files. The variable \"time\" conforms to CF conventions using the \"hours since\" construct, where the reference time is given as the initialization of the simulation. The \"mtime\" variable is a user-readable string that aligns with \"time\" expressed as YYYY-MM-DD_HH:MI:SS. Additional information can be found at https://www.epa.gov/climate-research/climate-change-tools-protect-public-health-and-environment","ARN":"arn:aws:s3:::epa-edde-v2","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://epa-edde-v2.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for EDDE bucket","ARN":"arn:aws:sns:us-east-1:127085394039:epa-edde-v2-object_created","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2025-01-15","RegistryEntryLastModified":"2026-07-28","Slug":"epa-edde-v2","Sources":["awslabs-open-data-registry"]}
{"Name":"EMBER Modeling Files","Description":"These data are being released to support states in wildfire Exceptional Events demonstrations. EPA released fire impacts modeling data for the 2023 fire season called the Expedited Modeling of Burn Events Results (EMBER) in December 2024. EPA has updated the EMBER Dataset Tool on the EPA website to include summary tables and graphics four new years of EMBER data: 2021, 2022, 2024, and 2025. As part of the effort to update the EMBER data, we are posting the full model input and output files publicly here to aid state, local, and Tribal air agencies in constructing any potential Exceptional Events demonstrations. For more information about EMBER, see the following web page: https://www.epa.gov/air-quality-analysis/expedited-modeling-burn-events-results-ember","Documentation":"Dataset Description for 2023: https://www.sciencedirect.com/science/article/pii/S2352340924011703 Dataset Description for 2021, 2022, 2024, 2025: https://www.epa.gov/system/files/documents/2026-06/development-of-the-expedited-modeling-of-burn-events-results-ember-datasets-for-2021-2022-2024-and-2025.pdf","Contact":"Simon.Heather@epa.gov","ManagedBy":"U.S. Environmental Protection Agency (https://www.epa.gov)","UpdateFrequency":"As needed","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["air quality","aws-pds","environmental","regulatory"],"License":"These datasets are products of the U.S. Government and are intended for public access and use. Unless otherwise specified, all data produced by the U.S EPA is, by default, in the public domain and are not subject to domestic copyright protection under 17 U.S.C. § 105. More details on the U.S. Public Domain license are available here: http://www.usa.gov/publicdomain/label/1.0/","Citation":"EMBER Modeling Files was accessed on DATE from https://registry.opendata.aws/epa-ember. US EPA, 2026, \"Development of the Expedited Modeling of Burn Events Results (EMBER) Datasets for 2021, 2022, 2024, and 2025\", 454R26002","Resources":[{"Description":"The data are organized into five yearly folders (`2021 data`, `2022 data`, `2023 data`, `2024 data`, and `2025 data`), each with the same directory structure:\n\n```\n$YYYY (four digit year)\n└── CMAQ\n    ├── Emissions\n    │   ├── Anthropogenic\n    │   └── fires\n    ├── Inputs\n    │   ├── ICBC\n    │   └── MCIP\n    └── Outputs\n        └── columndata\n             ├── basecase\n             └── zerofires\n```\n\nAll files are NetCDF version 3 formatted files using I/O API (IOAPI) data structures.\n\n#### Emissions input files\n\nEmissions are split into two main directories, **Anthropogenic** and **Fires**.<br>\n<br>\n\n**Anthropogenic** includes all anthropogenic emissions, used in the base simulation as well as both zero-out simulations. The same anthropogenic emissions are used for the 2021, 2022 and 2023 simulations, so the files are only provided in the 2021 directory (the 2022 and 2023 directories contain a README pointing there). Likewise, the same emissions are used for 2024 and 2025 for all sectors except ptegu. All emissions files from sectors other than ptegu are only provided in the 2024 directory (the 2025 directory contains a README pointing there).\n<br><br>\nSubfolders:\n<br>\n- `Beis_inline` — auxiliary files needed to run CMAQ with online biogenic emissions calculations\n- `merged_nobeis_norwc` — gridded emissions: all gridded sources except residential wood combustion and biogenic sources\n- `rwc` — gridded emissions: residential wood combustion\n- `pt_oilgas` — point source emissions: oil and gas exploration and production\n- `ptegu` — point source emissions: electric generating units\n- `cmv_c3` or `cmv_c3_36` — point source emissions: large ocean-going C3 class commercial ships\n- `cmv_c1c2` or `cmv_c3_36` — point source emissions: smaller C1 and C2 class commercial ships\n- `othpt` or `canmex_point` — point source emissions: sources located in Mexico and Canada\n- `ptagfire` (2024 only) — point source emissions: agricultural burning sources\n- `ptnonipm` (2021 and 2024) and `ptnonipm_hr` (2024 only) — point source emissions not included in another category; `ptnonipm_hr` comes from sources with hourly CEMS data\n\n**Fires** includes day-specific fire emissions for each simulation year.\n<br><br>\nSubfolders:\n<br>\n- `ptfire_othna` (2021 and 2022) — wildland and agricultural fires in Canada and Mexico\n- `ptfire-canada` (2023, 2024 and 2025) — wildfires in Canada\n- `ptfire_mexona` (2024 and 2025) — wildland and agricultural fires in Mexico\n- `ptfire-rx` — prescribed fires in the United States\n- `ptfire-wild` — wildfires in the United States\n\nPoint source sectors include `inln_mole` files (emissions magnitudes) and `stack_groups` files (emissions location/stack characteristics). Gridded sectors include only `emis_mole` files (gridded emissions magnitudes).\n\n#### Meteorological input files\n\nFound in `$YYYY/CMAQ/inputs/MCIP`. Files are NetCDF version 3 formatted files using I/O API (IOAPI) data structures, provided for every day of the simulation. File type and variable descriptions: https://github.com/USEPA/CMAQ/tree/main/PREP/mcip\n\n#### Initial and boundary condition files\n\nFound in `$YYYY/CMAQ/inputs/ICBC`. Files are NetCDF version 3 formatted files using I/O API (IOAPI) data structures. Initial conditions are provided for April 1 of each simulation year; boundary condition files are provided for every day of the simulation. Descriptions: https://github.com/USEPA/CMAQ/tree/main/PREP/bcon and https://github.com/USEPA/CMAQ/tree/main/PREP/icon\n\n#### Gridded model output files\n\nFor each simulation year, gridded daily layer-1 model outputs are provided for three simulations (base, zero fires, and zero Canada and Mexico fires) under `$YYYY/CMAQ/outputs/`:\n\n```\n2021/CMAQ/outputs/\n  Base:                  HR2DAY_CMAQv55_cb6r5_ae7_aq.36US3.35.basecase2021_2021_O3_PM25_01Apr2021to30Sep2021.nc\n  Zero fires:            HR2DAY_CMAQv55_cb6r5_ae7_aq.36US3.35.noFires2021_2021_O3_PM25_01Apr2021to30Sep2021.nc\n  Zero CA/MX fires:      HR2DAY_CMAQv55_cb6r5_ae7_aq.36US3.35.noCN-MXfires2021_2021_O3_PM25_01Apr2021to30Sep2021.nc\n\n2022/CMAQ/outputs/\n  Base:                  HR2DAY_CMAQv55_cb6r5_ae7_aq.36US3.35.basecase2022_2022_O3_PM25_01Apr2022to30Sep2022.nc\n  Zero fires:            HR2DAY_CMAQv55_cb6r5_ae7_aq.36US3.35.noFires2022_2022_O3_PM25_01Apr2022to30Sep2022.nc\n  Zero CA/MX fires:      HR2DAY_CMAQv55_cb6r5_ae7_aq.36US3.35.noCN-MXfires2022_2022_O3_PM25_01Apr2022to30Sep2022.nc\n\n2023/CMAQ/outputs/\n  Base:                  HR2DAY_CMAQv54_cb6r5_ae7_aq.36US3.35.basecase2023_2023_O3_PM25_11Apr2023to29Sep2023.nc\n  Zero fires:            HR2DAY_CMAQv54_cb6r5_ae7_aq.36US3.35.basecase2023_nofires_2023_O3_PM25_11Apr2023to29Sep2023.nc\n  Zero CA fires:         HR2DAY_CMAQv54_cb6r5_ae7_aq.36US3.35.basecase2023_noCAfires_2023_O3_PM25_11Apr2023to29Sep2023.nc\n\n2024/CMAQ/outputs/\n  Base:                  hr2day_merged_2024.2024basecase_AMD_36US3_35.v55_m3dry.Apr-Sep.keyvars.ncf\n  Zero fires:            hr2day_merged_2024.EMBER_noFires_AMD_36US3_35.v55_m3dry.Apr-Sep.keyvars.ncf\n  Zero CA/MX fires:      hr2day_merged_2024.EMBER_noCA-MXfires_AMD_36US3_35.v55_m3dry.Apr-Sep.keyvars.ncf\n\n2025/CMAQ/outputs/\n  Base:                  hr2day_merged_2025.2025basecase_AMD_36US3_35.v55_m3dry.Apr-Sep.keyvars.ncf\n  Zero fires:            hr2day_merged_2025.EMBER_noFires_AMD_36US3_35.v55_m3dry.Apr-Sep.keyvars.ncf\n  Zero CA/MX fires:      hr2day_merged_2025.EMBER_noCA-MXfires_AMD_36US3_35.v55_m3dry.Apr-Sep.keyvars.ncf\n```\n\nEach output file is a NetCDF version 3 formatted file using I/O API (IOAPI) data structures, containing daily gridded concentrations from layer 1 of the model simulation, with the following variables:\n\n- `O3_MDA8` — maximum daily 8-hour ozone concentrations\n- `ATOTIJ_AVG` — 24-hour average total PM2.5 concentrations\n\nAdditionally, integrated column data is provided for the base and zero fires simulations from each year for a subset of species: NO2, CO, tropospheric ozone and formaldehyde. Aerosol Optical Depth (AOD) at 550 nm is provided for 2024 and 2025 simulations only. Column integrals include all CMAQ model layers that encompass pressures levels less than 200 hPa. Layers with mid-level pressures less than 200 hPa are excluded because they most likely include the stratosphere, which is not the target of EMBER and was treated differently in 2025 than other years. Files are NetCDF version 3 formatted files using I/O API data structures (IOAPI) and are found in the directory structure given below:\n<br><br>\n`$YYYY/CMAQ/outputs/columndata/`\n<br>\n- `basecase/CCTM_EMBER_basecase_COLUMN_36US3_$YYYY$MM$DD.nc`\n- `zerofires/CCTM_EMBER_ZeroFires_COLUMN_36US3_$YYYY$MM$DD.nc`\n","ARN":"arn:aws:s3:::epa-ember","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://epa-ember.s3.amazonaws.com/index.html)"]},{"Description":"Notification for the Ember bucket","ARN":"arn:aws:sns:us-east-1:127085394039:epa-ember-object_created","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2026-06-12","RegistryEntryLastModified":"2026-09-22","Slug":"epa-ember","Sources":["awslabs-open-data-registry"]}
{"Name":"Community Multiscale Air Quality (CMAQ) 2019 3D Gridded and Column data from the EPA's Air Quality Time Series (EQUATES) Project","Description":"The data are part of EPA’s Air Quality Time Series (EQUATES) Project.  The data consist of hourly gridded pollutant concentrations estimates by the Community Multiscale Air Quality (CMAQ) model version 5.3.2 (https://doi.org/10.15139/S3/F2KJSK) for January 1 – December 31, 2019.  Model data is provided for two spatial domains :  the Northern Hemisphere (108 km x 108km horizontal grid spacing) and the Contiguous United States including parts of Canada and Mexico (12km x 12km horizontal grid spacing).  Two types of hourly data are provided: three-dimensional air pollutant concentrations and vertical column pollutant totals. Previous studies have used this type of CMAQ 3D and vertical column air quality data to evaluate the modeling system, created model-observed ‘fused’ surfaces, and to analyze spatial and temporal changes in air quality in the upper atmosphere, e.g., https://doi.org/10.1016/j.envint.2019.104909; https://doi.org/10.5194/acp-17-12449-2017; https://doi.org/10.1029/2006JD008085; https://doi.org/10.5194/acp-15-9997-2015.","Documentation":"EQUATES data DOI: https://doi.org/10.15139/S3/F2KJSK. Please see the Data Use Statement if you plan to use this data for your own research. Additional information may be found on the EQUATES home page (www.epa.gov/cmaq/equates). For questions or issues please use this User Support Forum: https://forum.cmascenter.org/t/about-the-equates-category/2723","Contact":"CMAQ_Team@epa.gov","ManagedBy":"U.S. Environmental Protection Agency (https://www.epa.gov)","UpdateFrequency":"Annual","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["air quality","atmosphere","aws-pds","model"],"License":"These datasets are products of the U.S. Government and are intended for public access and use. Unless otherwise specified, all data produced by the U.S EPA is, by default, in the public domain and are not subject to domestic copyright protection under 17 U.S.C. § 105. More details on the U.S. Public Domain license are available here: http://www.usa.gov/publicdomain/label/1.0/","Citation":"US EPA, 2021, \"EQUATESv1.0: Emissions, WRF/MCIP, CMAQv5.3.2 Data -- 2002-2019 US_12km and NHEMI_108km\", https://doi.org/10.15139/S3/F2KJSK, UNC Dataverse, V5","Resources":[{"Description":"The 2019 CMAQ output are stored as compressed netcdf/hdf5 formatted files using I/O API data structures (https://www.cmascenter.org/ioapi/). Information on the model projection and grid structure is contained in the header information of the netcdf file. The netcdf files can be opened and manipulated using I/O API utilities (e.g. M3XTRACT, M3WNDW) or other software programs that can read and write netcdf formatted files (e.g. Fortran, R, Python).","ARN":"arn:aws:s3:::epa-equates-v1","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://epa-equates-v1.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for EQUATES bucket","ARN":"arn:aws:sns:us-east-1:127085394039:epa-equates-v1-object_created","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2024-03-02","RegistryEntryLastModified":"2025-07-09","Slug":"epa-equates-v1","Sources":["awslabs-open-data-registry"]}
{"Name":"EPA Hourly Prognostic Meteorological Data","Description":"The data are hourly outputs from the Weather Research and Forecasting (WRF) model generated by the EPA's Office of State Air Partnerships (OSAP), Air Quality Assessment Division, Air Quality Modeling Branch. These data were generated at a 12-km resolution over the Continental United States (12US), beginning for the year 2017 and continuing annually through 2023. These files are intended for use in a broad range of  air quality applications, but specifically may be used in dispersion modeling applications that would benefit from the use of the Mesoscale Model Interface (MMIF) tool  (https://www.epa.gov/scram/air-quality-dispersion-modeling-related-model-support-programs#mmif) which translates prognostic meteorological data into formats suitable for use with AERMOD, CALPUFF, or SCICHEM. The individual files are less than 1GB in size, which allows for the use of the MMIF tool in a Windows environment. These data are anticipated to be updated annually so the 3 most-recent years are available for use. Additionally, model-observation paired files (by quarter) are included to aid in the performance evaluation that is necessary  for use of these data in regulatory applications per Appendix W to 40 CFR Part 51. ","Documentation":"2017 WRF Modeling TSD: https://bit.ly/4AHmsDx 2022 WRF Modeling TSD:  https://bit.ly/2022WRF","Contact":"Misenis.Chris@epa.gov","ManagedBy":"U.S. Environmental Protection Agency (https://www.epa.gov)","UpdateFrequency":"Annually","Tags":["air quality","aws-pds","environmental","meteorological","regulatory","weather"],"License":"These datasets are products of the U.S. Government and are intended for public access and use. Unless otherwise specified, all data produced by the U.S EPA is, by default, in the public domain and are not subject to domestic copyright protection under 17 U.S.C. § 105. More details on the U.S. Public Domain license are available here: http://www.usa.gov/publicdomain/label/1.0/","Citation":"WRF Modeling: US EPA, 2026, \"Hourly Prognostic Meteorological Data\"","Resources":[{"Description":"The WRF output are stored as uncompressed netcdf/hdf5 formatted files in  directories corresponding to the specific years of interest. The model-obs paired files are stored as comma-delimited files in the year-specific directories.","ARN":"arn:aws:s3:::epa-hourly-prognostic-meteorology","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://epa-hourly-prognostic-meteorology.s3.amazonaws.com/index.html)"]},{"Description":"Notification for the EPA Hourly Prognostic Meteorological Data bucket","ARN":"arn:aws:sns:us-east-1:127085394039:epa-hourly-prognostic-meteorology-object_created","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2025-10-29","RegistryEntryLastModified":"2026-10-01","Slug":"epa-hourly-prognostic-meteorology","Sources":["awslabs-open-data-registry"]}
{"Name":"EPA Risk-Screening Environmental Indicators","Description":"Detailed air model results from EPA’s Risk-Screening Environmental Indicators (RSEI) model.","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/epa-rsei-pds","Contact":"https://www.epa.gov/rsei/forms/contact-us-about-rsei-model","ManagedBy":"U.S. Environmental Protection Agency (https://www.epa.gov)","UpdateFrequency":"Updated infrequently","Collabs":{"ASDI":{"Tags":["air quality"]}},"Tags":["aws-pds","environmental"],"License":"US Government work","Resources":[{"Description":"RSEI Microdata","ARN":"arn:aws:s3:::epa-rsei-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://epa-rsei-pds.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2025-04-21","Slug":"epa-rsei-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"EPHNOGRAM: A Simultaneous Electrocardiogram and Phonocardiogram Database","Description":"The electro-phono-cardiogram (EPHNOGRAM) project focused on the development of low-cost and low-power devices for recording simultaneous electrocardiogram (ECG) and phonocardiogram (ECG) data, with auxiliary channels for capturing environmental audio noise, which could be used for PCG quality enhancement through signal processing. The current database, recorded by version 2.1 of the developed hardware, has been acquired from 24 healthy adults aged between 23 and 29 (average: 25.4 ± 1.9 years) in 30min stress-test sessions during resting, walking, running and biking conditions, using indoor fitness center equipment. The dataset also contains several 30s sample records acquired during rest conditions. This data is useful for simultaneous multi-modal analysis of ECG and PCG. It provides interesting insights into the inter-relationship between the mechanical and electrical mechanisms of the heart, under rest and physical activity.","Documentation":"https://doi.org/10.13026/tjtq-5911","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/tjtq-5911","ARN":"arn:aws:s3:::physionet-open/ephnogram/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ephnogram","Sources":["awslabs-open-data-registry"]}
{"Name":"Epilepsy.Science","Description":"Epilepsy.Science comprise a set of datasets focused on Epilepsy Research that\nspan both Clinical Data and Pre-clinical data. Datasets are contributed by the \nEpilepsy Research community and published using a standardized structure and \nmetadata. Clinical datasets include de-identified subject information, EEG, and\nclinical imaging.\n","Documentation":"https://epilepsy.science/about","Contact":"support@pennsieve.net","ManagedBy":"[Pennsieve Labs](https://discover.pennsieve.io/)","UpdateFrequency":"Continuously as new data is published","Tags":["aws-pds","bioinformatics","electrophysiology","life sciences","medicine","neuroscience"],"License":"Creative Commons BY 4.0","Resources":[{"Description":"Epilepsy.Science Published Data Bucket","ARN":"arn:aws:s3:::edots-prod-aod-discover-publish50-use1","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Datasets](https://epilepsy.science/data)"]}],"DataAtWork":{"Tutorials":[{"Title":"Pennsieve Open Repositories","URL":"https://docs.pennsieve.io/docs/open-repositories","AuthorName":"Pennsieve"},{"Title":"Submitting a dataset proposal","URL":"https://docs.pennsieve.io/docs/submitting-a-dataset-proposal","AuthorName":"Pennsieve"}],"Tools & Applications":[{"Title":"The Epilepsy.Science Portal","URL":"https://epilepsy.science","AuthorName":"Joost Wagenaar, Brandon Westover, Kathryn Davis, Nishant Sinha, Brian Litt","AuthorURL":"https://epilepsy.science/about"},{"Title":"The Pennsieve Data Management Platform","URL":"https://app.pennsieve.io","AuthorName":"Joost Wagenaar","AuthorURL":"https://discover.pennsieve.io/about/team"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-12-03","RegistryEntryLastModified":"2026-03-02","Slug":"epilepsy-science","Sources":["awslabs-open-data-registry"]}
{"Name":"Epoch of Reionization Dataset","Description":"The data are from observations with the Murchison Widefield Array (MWA) which is a\nSquare Kilometer Array (SKA) precursor in Western Australia.  This particular\ndataset is from the Epoch of Reionization project which is a key science driver\nof the SKA. Nearly 2PB of such observations have been recorded to date, this is\na small subset of that which has been exported from the MWA data archive in\nPerth and made available to the public on AWS.  The data were taken to detect\nsignatures of the first stars and galaxies forming and the effect of these early\nstars and galaxies on the evolution of the universe.\n","Documentation":"http://danielcjacobs.com/research/epoch-of-reionization-aws-public-dataset/","Contact":"brynah (at) phys.washington.edu","ManagedBy":"University of washington Radio Cosmology Group","UpdateFrequency":"Irregularly","Tags":["astronomy","aws-pds"],"License":"BSD 2-Clause","Resources":[{"Description":"Selected MWA EoR data (uvfits format) from 2013-2015","ARN":"arn:aws:s3:::mwapublic","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2019-02-15","RegistryEntryLastModified":"2019-08-16","Slug":"epoch-of-reionization","Sources":["awslabs-open-data-registry"]}
{"Name":"ERA5-for-WRF Open Data on AWS","Description":"ERA5 reanalysis data on AWS, preprocessed for use with the Weather Research and Forecasting (WRF) model.","Documentation":"https://github.com/moptis/era5-for-wrf/","Contact":"info@veer.eco","ManagedBy":"[Veer Renewables](http://www.veer.eco/)","UpdateFrequency":"Monthly.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","electricity","meteorological","model","sustainability","weather"],"License":"CC BY-SA 4.0","Resources":[{"Description":"ERA5-for-WRF Data","ARN":"arn:aws:s3:::era5-for-wrf","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"ERA5-for-WRF Tutorials","URL":"https://github.com/moptis/era5-for-wrf/","AuthorName":"Veer Renewables","AuthorURL":"https://veer.eco"}]},"RegistryEntryAdded":"2024-05-23","RegistryEntryLastModified":"2025-07-09","Slug":"era5-for-wrf","Sources":["awslabs-open-data-registry"]}
{"Name":"ERP-based Brain-Computer Interface recordings","Description":"This dataset — created and contributed by Luca Citi, Riccardo Poli, and Caterina Cinel — was generated as part of a study aimed at identifying the factors limiting the performance of brain-computer interfaces (BCIs) based on event-related potentials (ERPs), in order to improve the transfer rate and the usability of these interfaces. In this study, each participant was asked to spell a total of 20 characters using a traditional matrix speller (shown at left). Each record in the dataset contains the signals, triggers and annotations corresponding to a single run, i.e. the spelling of a single character. Target characters were randomly chosen before the beginning of the run. Each row and column of the standard 6x6 matrix of characters was randomly intensified without replacement for 100 ms with a gap of 50 ms, leading to a stimulus onset asynchrony (SOA) of 150 ms. During a run, each row and column flashed approximately 20 times. During that period, subjects were asked to focus on the target character and to mentally count the number of times it was highlighted. From subject 03 onwards, subjects were asked to report their final count at the end of the run.","Documentation":"https://doi.org/10.13026/C2101S","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2101S","ARN":"arn:aws:s3:::physionet-open/erpbci/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"erpbci","Sources":["awslabs-open-data-registry"]}
{"Name":"ESA WorldCover Sentinel-1 and Sentinel-2 10m Annual Composites","Description":"The WorldCover 10m Annual Composites were produced, as part of the European Space Agency (ESA) WorldCover project, from the yearly Copernicus Sentinel-1 and Sentinel-2 archives for both years 2020 and 2021. These global mosaics consists of four products composites. A Sentinel-2 RGBNIR yearly median composite for bands B02, B03, B04, B08. A Sentinel-2 SWIR yearly median composite for bands B11 and B12. A Sentinel-2 NDVI yearly percentiles composite (NDVI 90th, NDVI 50th NDVI 10th percentiles). A Sentinel-1 GAMMA0 yearly median composite for bands VV, VH and VH/VV (power scaled). Each product is delivered as a series of Cloud-Optimized GeoTIFFs (COGs) in WSG84 projection in a grid of 1 by 1 degrees and at 0.3 arc seconds resolution (approx. 10m), except for the SWIR composite which is delivered at 0.6 arc seconds (approx. 20m). The Sentinel-2 composites were produced from the L2A archive. The GAMMA0 composite was produced by pre-processing the Sentinel-1 GRD products using [GAMMA software](https://www.gamma-rs.ch/).","Documentation":"More information is available on the products' [GitHub](https://github.com/ESA-WorldCover/esa-worldcover-datasets) and on WorldCover project [website](https://esa-worldcover.org/en/data-access).","Contact":"https://esa-worldcover.org/en/contact","ManagedBy":"[VITO](https://vito.be)","UpdateFrequency":"Not updated.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","disaster response","earth observation","geospatial","land cover","land use","machine learning","mapping","natural resource","satellite imagery","stac","sustainability","synthetic aperture radar"],"License":"CC-BY 4.0","Citation":"To cite the dataset in a publication please refer to the [Citation section](https://esa-worldcover.org/en/data-access)/[DOI v200](https://doi.org/10.5281/zenodo.7254221)/[DOI v100](https://doi.org/10.5281/zenodo.5571936).","Resources":[{"Description":"ESA WorldCover S2 composites. The bucket contains the 3 Sentinel-2 L2A annual composites for the years 2020 and 2021. A 4 bands RGBNIR yearly median composite (B02, B03, B04, B08), a 2 bands SWIR yearly median composite (B11, B12) and a 3 bands NDVI yearly percentiles composite (NDVI p90, NDVI p50, NDVI p10).","ARN":"arn:aws:s3:::esa-worldcover-s2","Region":"eu-central-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Products Grid](https://esa-worldcover.s3.eu-central-1.amazonaws.com/esa_worldcover_grid_composites.fgb)","[WorldCover Viewer](https://viewer.esa-worldcover.org/worldcover/?layer=WORLDCOVER_2021_S2_NDVI)"]},{"Description":"ESA WorldCover S1 composites. The bucket contains 1 Sentinel-1 annual composite for the years 2020 and 2021. A 3 bands GAMMA0 yearly median composite (VV, VH, VH/VV), power scaled.","ARN":"arn:aws:s3:::esa-worldcover-s1","Region":"eu-central-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Products Grid](https://esa-worldcover.s3.eu-central-1.amazonaws.com/esa_worldcover_grid_composites.fgb)","[WorldCover Viewer](https://viewer.esa-worldcover.org/worldcover/?layer=WORLDCOVER_2021_S1_VVVHratio)"]}],"DataAtWork":{"Tutorials":[{"Title":"Exploring the datasets","URL":"https://github.com/ESA-WorldCover/esa-worldcover-datasets","AuthorName":"VITO","AuthorURL":"https://esa-worldcover.org"}],"Tools & Applications":[{"Title":"WorldCover Viewer","URL":"https://viewer.esa-worldcover.org/worldcover/","AuthorName":"VITO","AuthorURL":"https://esa-worldcover.org/en"}],"Publications":[{"Title":"ESA WorldCover 10 m 2021 v200","URL":"https://doi.org/10.5281/zenodo.7254221","AuthorName":"Zanaga, D., Van De Kerchove, R.,Daems, D.,De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.E., Xu, P., Ramoino, F., Arino, O."},{"Title":"ESA WorldCover 10 m 2021 v200 - Product User Manual","URL":"https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf","AuthorName":"VITO"},{"Title":"Release of the 10 m WorldCover map","URL":"https://blog.vito.be/remotesensing/release-of-the-10-m-worldcover-map","AuthorName":"Ruben Van De Kerchove"}]},"RegistryEntryAdded":"2023-06-20","RegistryEntryLastModified":"2023-06-20","Slug":"esa-worldcover-vito-composites","Sources":["awslabs-open-data-registry"]}
{"Name":"ESA WorldCover","Description":"The European Space Agency (ESA) WorldCover product provides global land cover maps for 2020 & 2021 at 10 m resolution based on Copernicus Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes and has been generated in the framework of the ESA WorldCover project, part of the 5th Earth Observation Envelope Programme (EOEP-5) of the European Space Agency. A first version of the product (v100), containing the 2020 map was released in October 2021. The 2021 map was released in October 2022 using an improved algorithm (v200). The WorldCover 2020 and 2021 maps were generated with different algorithm versions and therefore changes between the maps should be treated with caution as these contain both real land cover changes as well as changes due to the used algorithms.","Documentation":"Documentation is available [here](https://esa-worldcover.org/en/data-access).","Contact":"https://esa-worldcover.org/en/contact","ManagedBy":"[VITO](https://vito.be)","UpdateFrequency":"Yearly.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","disaster response","earth observation","geospatial","land cover","land use","machine learning","mapping","natural resource","satellite imagery","stac","sustainability","synthetic aperture radar"],"License":"CC-BY 4.0","Citation":"To cite the dataset in a publication please refer to the [Citation section](https://esa-worldcover.org/en/data-access)/[DOI v200](https://doi.org/10.5281/zenodo.7254221)/[DOI v100](https://doi.org/10.5281/zenodo.5571936).","Resources":[{"Description":"ESA WorldCover in a S3 bucket","ARN":"arn:aws:s3:::esa-worldcover","Region":"eu-central-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC endpoint](https://services.terrascope.be/stac/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Exploring the dataset (STAC API examples)","URL":"https://github.com/ESA-WorldCover/esa-worldcover-datasets","AuthorName":"VITO","AuthorURL":"https://esa-worldcover.org"},{"Title":"Global Fire Spread Prediction System","URL":"https://github.com/SatelliteVu/SatelliteVu-AWS-Disaster-Response-Hackathon","AuthorName":"SatelliteVu","AuthorURL":"https://www.satellitevu.com/"}],"Tools & Applications":[{"Title":"BlockMap: any real place as a Minecraft world, with the ground chosen from ESA WorldCover land cover","URL":"https://blockmap.app/how-worlds-are-made#ground","AuthorName":"Nikandr Software Limited","AuthorURL":"https://nikandrsoftware.com"},{"Title":"ESA Viewer 2020","URL":"https://worldcover2020.esa.int/viewer","AuthorName":"ESA","AuthorURL":"https://worldcover2020.esa.int/"},{"Title":"ESA Viewer 2021","URL":"https://worldcover2021.esa.int/viewer","AuthorName":"ESA","AuthorURL":"https://worldcover2021.esa.int/"},{"Title":"TerraScope Viewer","URL":"https://viewer.terrascope.be/","AuthorName":"TerraScope","AuthorURL":"https://terrascope.be/en/about-us"},{"Title":"WorldCover GEE App","URL":"https://vitorsveg.users.earthengine.app/view/worldcover","AuthorName":"VITO","AuthorURL":"https://esa-worldcover.org"},{"Title":"WorldCover Viewer","URL":"https://viewer.esa-worldcover.org/worldcover/","AuthorName":"VITO","AuthorURL":"https://esa-worldcover.org/en"}],"Publications":[{"Title":"ESA WorldCover 10 m 2020 v100","URL":"https://doi.org/10.5281/zenodo.5571936","AuthorName":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, Linlin, Tsendbazar, N.E., Ramoino, F., Arino, O., 2021."},{"Title":"ESA WorldCover 10 m 2020 v100 - Product User Manual","URL":"https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf","AuthorName":"VITO"},{"Title":"ESA WorldCover 10 m 2021 v200","URL":"https://doi.org/10.5281/zenodo.7254221","AuthorName":"Zanaga, D., Van De Kerchove, R.,Daems, D.,De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.E., Xu, P., Ramoino, F., Arino, O."},{"Title":"ESA WorldCover 10 m 2021 v200 - Product User Manual","URL":"https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf","AuthorName":"VITO"},{"Title":"Fusing GEDI with earth observation data for large area aboveground biomass mapping","URL":"https://doi.org/10.1016/j.jag.2022.103108","AuthorName":"Yuri Shendryk"},{"Title":"Release of the 10 m WorldCover map","URL":"https://blog.vito.be/remotesensing/release-of-the-10-m-worldcover-map","AuthorName":"Ruben Van De Kerchove"},{"Title":"The world's most populated and greenest megacities (and how we found out)","URL":"https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/mapping/worlds-greenest-megacities/","AuthorName":"Michael Dangermond, Emily Meriam"},{"Title":"WorldCover taking it to the next level","URL":"https://blog.vito.be/remotesensing/worldcover2021","AuthorName":"Ruben Van De Kerchove"}]},"RegistryEntryAdded":"2022-10-28","RegistryEntryLastModified":"2026-11-01","Slug":"esa-worldcover-vito","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"This dataset listing has been deprecated with the release of the 2021 & 2020 [ESA WorldCover](https://registry.opendata.aws/esa-worldcover-vito) dataset. For more background on this see this [blog post](https://blog.vito.be/remotesensing/worldcover2021).","Name":"ESA WorldCover","Description":"The European Space Agency (ESA) WorldCover is a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data then provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land cover map almost in real time. WorldCover Map has been produced for 2020 (01 January to 31 December) with a global coverage as part of the 5th Earth Observation Envelope Programme (EOEP-5). It provides valuable information for applications such as biodiversity, food security, carbon assessment and climate modelling. More information can be found on the [WorldCover website](https://esa-worldcover.org/en) and the product [User Manual](https://esa-worldcover.s3.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf).","Documentation":"Documentation is available [here](https://esa-worldcover.s3.amazonaws.com/readme.html).","Contact":"https://esa-worldcover.org/en/contact","ManagedBy":"[Sinergise on behalf of VITO](https://vito.be)","UpdateFrequency":"Not yet defined.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","earth observation","agriculture","satellite imagery","geospatial","natural resource","cog","disaster response","mapping","synthetic aperture radar"],"License":"CC-BY 4.0, for Citation see [Citation section](https://esa-worldcover.org/en/data-access) / [DOI](https://doi.org/10.5281/zenodo.5571936)\n","Resources":[{"Description":"ESA WorldCover in a S3 bucket","ARN":"arn:aws:s3:::esa-worldcover","Region":"eu-central-1","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Tutorials":[{"Title":"Global Fire Spread Prediction System","URL":"https://github.com/SatelliteVu/SatelliteVu-AWS-Disaster-Response-Hackathon","AuthorName":"SatelliteVu","AuthorURL":"https://www.satellitevu.com/"}],"Tools & Applications":[{"Title":"WorldCover Viewer","URL":"https://viewer.esa-worldcover.org/worldcover/ta","AuthorName":"ESA/Vito","AuthorURL":"https://esa-worldcover.org/en"},{"Title":"TerraScope Viewer","URL":"https://viewer.terrascope.be/","AuthorName":"TerraScope","AuthorURL":"https://terrascope.be/en/about-us"}],"Publications":[{"Title":"Release of the 10 m WorldCover map","URL":"https://blog.vito.be/remotesensing/release-of-the-10-m-worldcover-map","AuthorName":"Ruben Van De Kerchove"},{"Title":"Remote Sensing Mapping of Build-Up Land with Noisy Label via Fault-Tolerant Learning","URL":"https://doi.org/10.3390/rs14092263","AuthorName":"Gang Xu, Yongjun Fang, Min Deng, Geng Sun and Jie Chen."}]},"RegistryEntryAdded":"2021-10-25","RegistryEntryLastModified":"2023-02-28","Slug":"esa-worldcover","Sources":["awslabs-open-data-registry"]}
{"Name":"Euclid Quick Release 1 (Q1)","Description":"Euclid launched in July 2023 as a European Space Agency (ESA) mission with involvement by NASA. The primary science goals of Euclid are to better understand the composition and evolution of the dark Universe. The Euclid mission will provide space-based imaging and spectroscopy as well as supporting ground-based imaging to achieve these primary goals. These data will be archived by multiple global repositories, including IRSA, where they will support transformational work in many areas of astrophysics. Euclid Quick Release 1 (Q1) consists of ~30 TB of imaging, spectroscopy, and catalogs covering four non-contiguous fields: Euclid Deep Field North (22.9 sq deg), Euclid Deep Field Fornax (12.1 sq deg), Euclid Deep Field South (28.1 sq deg), and LDN1641.","Documentation":"https://irsa.ipac.caltech.edu/data/Euclid/docs/overview_q1.html","Contact":"https://irsa.ipac.caltech.edu/docs/help_desk.html","ManagedBy":"NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech","UpdateFrequency":"Euclid Q1 has been finalized and will not be updated. The data may be presented in new ways as the products become available.","Tags":["astronomy","imaging","object detection","satellite imagery","survey"],"License":"https://irsa.ipac.caltech.edu/data_use_terms.html","Citation":"If you use Euclid Q1, please follow the citation instructions provided at [https://irsa.ipac.caltech.edu/data/Euclid/docs/overview_q1.html](https://irsa.ipac.caltech.edu/data/Euclid/docs/overview_q1.html) and [https://irsa.ipac.caltech.edu/ack.html](https://irsa.ipac.caltech.edu/ack.html).","Resources":[{"Description":"MER: Level 2 multiwavelength space- and ground-based mosaics. FITS format, organized by Tile ID.","ARN":"arn:aws:s3:::nasa-irsa-euclid-q1/q1/MER","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"MER_SEG: Segmentation maps of detections made on the MER mosaics. FITS format, organized by Tile ID.","ARN":"arn:aws:s3:::nasa-irsa-euclid-q1/q1/MER_SEG","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"NIR: Level 2 YJH NISP calibrated frames. FITS format, organized by Observation ID.","ARN":"arn:aws:s3:::nasa-irsa-euclid-q1/q1/NIR","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"VIS: Level 2 VIS calibrated frames. FITS format, organized by Observation ID.","ARN":"arn:aws:s3:::nasa-irsa-euclid-q1/q1/VIS","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"SIR: 2D spectroscopic science frames and 1D spectra. FITS format, organized by Observation ID.","ARN":"arn:aws:s3:::nasa-irsa-euclid-q1/q1/SIR","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"VMPZ: Level 3 visibility masks. FITS format.","ARN":"arn:aws:s3:::nasa-irsa-euclid-q1/q1/VMPZ","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"RAW: Level 1 VIS and NISP raw frames. FITS format, organized by Observation ID.","ARN":"arn:aws:s3:::nasa-irsa-euclid-q1/q1/RAW","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"Catalogs: MER final catalog, photometric redshift catalogs, spectroscopic catalogs organized by Tile ID. NIR and VIS catalogs of calibrated frame detections, organized by Observation ID. FITS format.","ARN":"arn:aws:s3:::nasa-irsa-euclid-q1/q1/catalogs","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false}],"DataAtWork":{"Tutorials":[{"Title":"Notebook Tutorials","URL":"https://irsa.ipac.caltech.edu/docs/notebooks/#accessing-euclid-data","AuthorName":"Caltech/IPAC-IRSA","AuthorURL":"https://irsa.ipac.caltech.edu"}]},"RegistryEntryAdded":"2025-04-09","RegistryEntryLastModified":"2025-04-09","Slug":"euclid-q1","Sources":["awslabs-open-data-registry"]}
{"Name":"EURO-CORDEX - European component of the Coordinated Regional Downscaling Experiment","Description":"The EURO-CORDEX dataset contains regional climate model data for Europe, for use in impacts, decision-making, and climate science. \nCurrently, the bucket contains monthly datasets of 2m air temperature downscaled from CMIP5 global model datasets using different\nregional climate models.\n","Documentation":"https://www.euro-cordex.net/060378/index.php.en","Contact":"cordex-aws@hereon.de","ManagedBy":"[Helmholtz Centre Hereon / GERICS](https://www.climate-service-center.de)","UpdateFrequency":"We will add more datasets on demand.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","climate","climate model","geospatial","model","zarr"],"License":"https://is-enes-data.github.io/cordex_terms_of_use.pdf","Resources":[{"Description":"Project data files","ARN":"arn:aws:s3:::euro-cordex","Region":"eu-central-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Intake-ESM Catalog","URL":"https://euro-cordex.s3.eu-central-1.amazonaws.com/catalog/CORDEX-CMIP5.json","AuthorName":"Lars Buntemeyer"},{"Title":"Jupyter Book","URL":"https://wcrp-cordex.github.io/cordex-tutorials/aws-access.html","AuthorName":"Lars Buntemeyer"}],"Publications":[{"Title":"Beyond ESGF - Regional climate model datasets in the cloud on AWS S3","URL":"https://doi.org/10.5281/zenodo.8059419","AuthorName":"Lars Buntemeyer"}]},"RegistryEntryAdded":"2023-05-15","RegistryEntryLastModified":"2024-03-26","Slug":"euro-cordex","Sources":["awslabs-open-data-registry"]}
{"Name":"EVAL_ST Tool","Description":"The EVAL_ST tool is an open source tool to evaluate and compare performance and robustness of ST episode detection algorithms. The tool supports all standard and other relevant performance measures, aggregate gross and average statistics, and bootstrap statistical procedure to predict real-world clinical performance. The tool (written in C) is compilable an a wide variety of platforms and contains an additional graphic user interface module (LessTif/Motif environment) for use on the LINUX/UNIX operating systems.","Documentation":"https://doi.org/10.13026/C2495B","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2495B","ARN":"arn:aws:s3:::physionet-open/eval-st/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"eval-st","Sources":["awslabs-open-data-registry"]}
{"Name":"Exceptional Responders Initiative","Description":"The Exceptional Responders Initiative is a pilot study to investigate the underlying molecular factors driving exceptional treatment responses of cancer patients to drug therapies. Study researchers will examine molecular profiles of tumors from patients either enrolled in a clinical trial for an investigational drug(s) and who achieved an exceptional response relative to other trial participants, or who achieved an exceptional response to a non-investigational chemotherapy. An exceptional response is defined as achievement of either a complete response or a partial response for at least 6 months duration in a trial or treatment where the overall response rate is < 10%. The hope is to discover underlying molecular features that can be further investigated and may eventually predict benefit from a given drug or class of drugs for a particular patient.\nThis pilot project will successfully characterize approximately 100 cases of tumor tissue and, when available, case-matched germline DNA. All samples will undergo whole exome sequencing, and cases with sufficient nucleic acids will undergo additional analyses (e.g. whole genome sequencing, mRNA-sequencing, mi RNA sequencing, promoter methylation analysis, SNP etc). Each case will be annotated with demographic and clinical information, along with follow-up information minimally sufficient to correlate molecular profiles with response. Both retrospective and prospective collections will be considered. The project will also accept sequencing data and clinical data from patients who have had sequencing performed outside of this project. All data will be de-identified and placed in a controlled access database so other investigators may use them for additional insights.\nClinically annotated tissue specimens meeting the criteria will be provided by groups participating in the Exceptional Cases Initiative to a Biospecimen Core Resource (BCR), which will perform quality control on the tissues, and will use a standard operating procedure to isolate nucleic acids. The nucleic acids will be shipped to a sequencing center to perform whole exome sequencing and analysis. These findings will be made available to the broader cancer research community in a controlled access database.","Documentation":"https://ftp.ncbi.nlm.nih.gov/dbgap/studies/phs001145/phs001145.v1.p1","Contact":"support@nci-gdc.datacommons.io","ManagedBy":"“[Center for Translational Data Science at The University of Chicago] (https://ctds.uchicago.edu/)”","UpdateFrequency":"Genomic Data Commons is the source of truth for this dataset and it offers monthly releases, although this particular dataset may not be updated at every release","Tags":["aws-pds","cancer","epigenomics","genomic","life sciences","STRIDES","transcriptomics","whole exome sequencing","whole genome sequencing"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"Sequencing Reads, Transcriptome Profiling, Simple Nucleotide Variation, Copy Number Variation, DNA Methylation, Clinical, Biospecimen, WXS, RNA-Seq, Targeted Sequencing","ARN":"arn:aws:s3:::gdc-exceptional-responders-er-phs001145-2-open","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"GDC Legacy Archive","URL":"https://portal.gdc.cancer.gov/legacy-archive/","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"},{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"}],"Publications":[{"Title":"The Exceptional Responders Initiative: Feasibility of a National Cancer Institute Pilot Study","URL":"https://doi.org/10.1093/jnci/djaa061","AuthorName":"Barbara A. Conley, Lou Staudt, et al."}]},"RegistryEntryAdded":"2023-06-21","RegistryEntryLastModified":"2023-06-21","Slug":"exceptional-responders","Sources":["awslabs-open-data-registry"]}
{"Name":"Recordings excluded from the NSR DB","Description":"The recordings in this collection were originally selected for inclusion in the MIT-BIH Normal Sinus Rhythm Database, but were excluded after study revealed low-grade arrhythmias.","Documentation":"https://doi.org/10.13026/C2W88Z","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2W88Z","ARN":"arn:aws:s3:::physionet-open/excluded/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"excluded","Sources":["awslabs-open-data-registry"]}
{"Name":"Eye Tracking Dataset for the 12-Lead Electrocardiogram Interpretation of Medical Practitioners and Students","Description":"This dataset contains collected eye tracking data from medical practitioners and students with different expertise levels to understand their electrocardiogram interpretation behavior. These medical practitioners encounter electrocardiograms during their daily medical practice. The end goal of the collection of this dataset was to use it for analysis to uncover insights about key best practices as well as pitfall in the ECG interpretation process. The data consists of quantitative eye tracking measurements for different eye gaze features for 63 participants. These participants belong to six expertise categories, namely: medical students, nurses, technicians, residents, fellows and finally consultants. Each one of these participants contributed to the data collection by interpreting ten different electrocardiograms. The eye tracking data includes the eye fixations count and duration, the eye gaze duration, and finally the fixations revisitations. It was collected at 60 frames per second using a Tobii Pro X2-60. Each participant was allowed a timeframe of 30 seconds to interpret each one of the ten ECGs. The collected data was processed taking into consideration two defined area of interest distributions. This enabled the extraction of metrics specifically tailored for the 12-lead electrocardiogram.","Documentation":"https://doi.org/10.13026/gsr5-8b11","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Open Database License v1.0","Resources":[{"Description":"https://doi.org/10.13026/gsr5-8b11","ARN":"arn:aws:s3:::physionet-open/eye-tracking-ecg/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"eye-tracking-ecg","Sources":["awslabs-open-data-registry"]}
{"Name":"Facial and oral temperature data from a large set of human subject volunteers","Description":"To evaluate the benefit of implementing standardized deployment and acquisition practices in the measurement of elevated body temperature (EBT) with infrared thermographs (IRTs), we conducted a clinical study with more than one thousand subjects. Subject oral temperatures were measured and facial thermal images captured with two evaluated IRTs. Based on the thermal images, temperatures from different locations on the face were extracted based on developed method and are listed in six CSV file as the open database. All data in these files has been de-identified. Based on these data, we published two main clinical study papers, with a focus on IRT effectiveness for EBT measurements and metrics for evaluating IRT clinical accuracy, respectively. Further analysis of this database can still be valuable.","Documentation":"https://doi.org/10.13026/3bhc-9065","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Zero 1.0 Universal Public Domain Dedication","Resources":[{"Description":"https://doi.org/10.13026/3bhc-9065","ARN":"arn:aws:s3:::physionet-open/face-oral-temp-data/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"face-oral-temp-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Fantasia Database","Description":"Twenty young (21 - 34 years old) and twenty elderly (68 - 85 years old) rigorously-screened healthy subjects underwent 120 minutes of continuous supine resting while continuous electrocardiographic (ECG), and respiration signals were collected; in half of each group, the recordings also include an uncalibrated continuous non-invasive blood pressure signal. Each subgroup of subjects includes equal numbers of men and women.","Documentation":"https://doi.org/10.13026/C2RG61","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2RG61","ARN":"arn:aws:s3:::physionet-open/fantasia/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"fantasia","Sources":["awslabs-open-data-registry"]}
{"Name":"FashionLocalTriplets","Description":"Fine-grained localized visual similarity and search for fashion.","Documentation":"https://fashionlocaltriplets.s3.amazonaws.com/Documentation.md","Contact":"Nikhil Garg (nikhgarg@amazon.de), Yuan Zhang (yuaa@amazon.com)","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not updated","Tags":["amazon.science","computer vision","machine learning"],"License":"Creative Commons Non-Commercial 4.0","Resources":[{"Description":"Image objects and metadata","ARN":"arn:aws:s3:::fashionlocaltriplets","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2021-06-03","RegistryEntryLastModified":"2022-06-06","Slug":"fashionlocaltriplets","Sources":["awslabs-open-data-registry"]}
{"Name":"COCO - Common Objects in Context - fast.ai datasets","Description":"COCO is a large-scale object detection, segmentation, and captioning dataset.\nThis is part of the fast.ai datasets collection hosted by AWS for convenience\nof fast.ai students. If you use this dataset in your research please cite\narXiv:1405.0312 [cs.CV].\n","Documentation":"http://course.fast.ai/datasets","Contact":"info@fast.ai","ManagedBy":"[fast.ai](http://www.fast.ai/)","UpdateFrequency":"As required","Tags":["aws-pds","computer vision","deep learning","machine learning"],"License":"Creative Commons http://cocodataset.org/#termsofuse","Resources":[{"Description":"Datasets","ARN":"arn:aws:s3:::fast-ai-coco","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2018-10-09","RegistryEntryLastModified":"2019-08-16","Slug":"fast-ai-coco","Sources":["awslabs-open-data-registry"]}
{"Name":"Image classification - fast.ai datasets","Description":"Some of the most important datasets for image classification research, including\nCIFAR 10 and 100, Caltech 101, MNIST, Food-101, Oxford-102-Flowers, Oxford-IIIT-Pets,\nand Stanford-Cars.  This is part of the fast.ai datasets collection hosted by\nAWS for convenience of fast.ai students. See documentation link for citation and\nlicense details for each dataset.\n","Documentation":"http://course.fast.ai/datasets","Contact":"info@fast.ai","ManagedBy":"[fast.ai](http://www.fast.ai/)","UpdateFrequency":"As required","Tags":["aws-pds","computer vision","deep learning","machine learning"],"License":"Varies by dataset - see documentation link","Resources":[{"Description":"Datasets","ARN":"arn:aws:s3:::fast-ai-imageclas","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Oxford-IIIT Pet Image Classification on Amazon SageMaker","URL":"https://github.com/aws/amazon-sagemaker-examples/blob/master/advanced_functionality/fastai_oxford_pets/fastai_lesson1_sagemaker_example.ipynb\n","AuthorName":"AWS","AuthorURL":"https://aws.amazon.com/sagemaker/"}]},"RegistryEntryAdded":"2018-10-09","RegistryEntryLastModified":"2021-04-29","Slug":"fast-ai-imageclas","Sources":["awslabs-open-data-registry"]}
{"Name":"Image localization  - fast.ai datasets","Description":"Some of the most important datasets for image localization  research, including\nCamvid and PASCAL VOC (2007 and 2012). This is part of the fast.ai datasets\ncollection hosted by AWS for convenience of fast.ai students. See\ndocumentation link for citation and license details for each dataset.\n","Documentation":"http://course.fast.ai/datasets","Contact":"info@fast.ai","ManagedBy":"[fast.ai](http://www.fast.ai/)","UpdateFrequency":"As required","Tags":["aws-pds","computer vision","deep learning","machine learning"],"License":"Varies by dataset - see documentation link","Resources":[{"Description":"Datasets","ARN":"arn:aws:s3:::fast-ai-imagelocal","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2018-10-09","RegistryEntryLastModified":"2019-08-16","Slug":"fast-ai-imagelocal","Sources":["awslabs-open-data-registry"]}
{"Name":"NLP - fast.ai datasets","Description":"Some of the most important datasets for NLP, with a focus on classification, including\nIMDb, AG-News, Amazon Reviews (polarity and full), Yelp Reviews (polarity and\nfull), Dbpedia, Sogou News (Pinyin), Yahoo Answers, Wikitext 2 and Wikitext\n103, and ACL-2010 French-English 10^9 corpus.  This is part of the\nfast.ai datasets collection hosted by AWS for convenience of fast.ai\nstudents. See documentation link for citation and license details for each\ndataset.\n","Documentation":"http://course.fast.ai/datasets","Contact":"info@fast.ai","ManagedBy":"[fast.ai](http://www.fast.ai/)","UpdateFrequency":"As required","Tags":["aws-pds","deep learning","machine learning","natural language processing"],"License":"Varies by dataset - see documentation link","Resources":[{"Description":"Datasets","ARN":"arn:aws:s3:::fast-ai-nlp","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2018-10-09","RegistryEntryLastModified":"2019-08-16","Slug":"fast-ai-nlp","Sources":["awslabs-open-data-registry"]}
{"Name":"FathomNet","Description":"FathomNet is a global, open-source database of ocean imagery and AI-ready annotations. It aggregates more than three decades of imagery from remotely operated vehicles, autonomous underwater vehicles, drop cameras, divers, and citizen scientists using the FathomVerse apps. Images carry standardized metadata (time, location, depth, environmental measurements, provenance, and taxonomic concepts) and expert, community, and machine-generated annotations, including classifications and bounding boxes. With more than 480,000 images and 1.2 million bounding boxes, FathomNet reflects the real conditions of ocean science (variable illumination, occlusion, complex backgrounds, unusual viewpoints, and long-tailed taxonomic distributions) and is used to train, test, and benchmark computer vision models for detection, classification, and biodiversity monitoring.\n","Documentation":"https://github.com/fathomnet/fathomnet-open-data","Contact":"fathomnet@mbari.org","ManagedBy":"[Monterey Bay Aquarium Research Institute](https://www.mbari.org/)","UpdateFrequency":"Ad hoc, as new imagery and annotations are contributed and reviewed.","Tags":["aws-pds","biodiversity","classification","computer vision","deep learning","ecosystems","imaging","labeled","life sciences","machine learning","marine","object detection","oceans","parquet"],"License":"There is no single license for FathomNet. Each contributor chooses the licenses for their own images and annotations, and that license is attached to every record: image_license on each image and annotation_license on each bounding box in the metadata tables. An image and its annotations can be licensed differently. Accepted licenses are documented at https://database.fathomnet.org/fathomnet/#/license.\n","Citation":"Katija, K., Orenstein, E., Schlining, B., Lundsten, L., Barnard, K., Sainz, G., Boulais, O., Cromwell, M., Butler, E., Woodward, B., and Bell, K. L. C. (2022). FathomNet: A global image database for enabling artificial intelligence in the ocean. Scientific Reports, 12, 15914. https://doi.org/10.1038/s41598-022-19939-2\n","Resources":[{"Description":"Original FathomNet images (JPEG, PNG, TIFF) under images/ and 512 px JPEG thumbnails under thumbnails/, keyed by image UUID. Metadata for images, bounding boxes, tags, and collections (Darwin Core) is provided as Parquet under metadata/, versioned by release date, with COCO JSON exports under exports/. Every image and annotation record includes the license its contributor chose. Source video under video/ is planned.\n","ARN":"arn:aws:s3:::fathomnet-pds","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for new FathomNet objects","ARN":"arn:aws:sns:us-west-2:639589055367:fathomnet-pds-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: FathomNet","URL":"https://github.com/fathomnet/fathomnet-open-data/blob/main/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/fathomnet/fathomnet-open-data/blob/main/get-to-know-a-dataset.ipynb","AuthorName":"MBARI","AuthorURL":"https://www.mbari.org/"}],"Tools & Applications":[{"Title":"FathomNet Database","URL":"https://database.fathomnet.org","AuthorName":"MBARI","AuthorURL":"https://www.mbari.org/"},{"Title":"fathomnet-py - Python client for the FathomNet API","URL":"https://github.com/fathomnet/fathomnet-py","AuthorName":"MBARI","AuthorURL":"https://www.mbari.org/"}],"Publications":[{"Title":"FathomNet: A global image database for enabling artificial intelligence in the ocean","URL":"https://doi.org/10.1038/s41598-022-19939-2","AuthorName":"Katija K, Orenstein E, Schlining B, Lundsten L, Barnard K, Sainz G, et al."}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-11-11","RegistryEntryLastModified":"2026-11-11","Slug":"fathomnet","Sources":["awslabs-open-data-registry"]}
{"Name":"International Neuroimaging Data-Sharing Initiative (INDI)","Description":"This bucket contains multiple neuroimaging datasets that are part of the International Neuroimaging Data-Sharing Initiative. Raw human and non-human primate neuroimaging data include 1) Structural MRI; 2) Functional MRI; 3) Diffusion Tensor Imaging; 4) Electroencephalogram (EEG)\nIn addition to the raw data, preprocessed data is also included for some datasets.\nA complete list of the available datasets can be seen in the documentation lonk provided below. \n","Documentation":"http://fcon_1000.projects.nitrc.org/","Contact":"[FCP-INDI](alexandre.franco@childmind.org)","ManagedBy":"[Child Mind Institute](https://childmind.org/our-research/)","UpdateFrequency":"Each dataset within INDI has its own release schedule. See release date and frequency for [each dataset](http://fcon_1000.projects.nitrc.org/indi/IndiPro.html)","Tags":["aws-pds","Homo sapiens","imaging","life sciences","magnetic resonance imaging","neuroimaging","neuroscience"],"License":"ODC-By v1.0 for imaging data and BSD 3-Clause for CPAC, unless otherwise specified","Resources":[{"Description":"Neuroimaging data for the International Neuroimaging Data-Sharing Initiative (INDI)","ARN":"arn:aws:s3:::fcp-indi","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Downloading FCP-INDI Neuroimaging Data from Amazon S3","URL":"https://fcon_1000.projects.nitrc.org/indi/s3/index.html","AuthorName":"INDI"}],"Tools & Applications":[{"Title":"Configurable Pipeline for the Analysis of Connectomes (C-PAC)","URL":"https://fcp-indi.github.io/docs/user/index.html#","AuthorName":"[INDI C-PAC Team](https://fcp-indi.github.io/)"}],"Publications":[{"Title":"Accelerating the Evolution of Nonhuman Primate Neuroimaging","URL":"https://www.sciencedirect.com/science/article/pii/S089662731931089X","AuthorName":"M.P. Milham, C. Petkov"},{"Title":"An Open Resource for Non-human Primate Imaging","URL":"https://www.sciencedirect.com/science/article/pii/S0896627318307682","AuthorName":"M.P. Milham, L. Ai, ..., C.E. Schroeder"},{"Title":"An open resource for transdiagnostic research in pediatric mental health and learning disorders","URL":"https://www.nature.com/articles/sdata2017181","AuthorName":"L.M. Alexander, J. Escalera, ..., M.P. Milham"},{"Title":"Assessment of the impact of shared brain imaging data on the scientific literature","URL":"https://www.nature.com/articles/s41467-018-04976-1","AuthorName":"M.P. Milham, R.C. Craddock, ..., A. Klein"},{"Title":"Enhancing studies of the connectome in autism using the autism brain imaging data exchange II.","URL":"https://www.nature.com/articles/sdata201710","AuthorName":"A. Di Martino, D. O'Connor, M.P. Milham"},{"Title":"Making data sharing work: The FCP/INDI experience","URL":"https://www.sciencedirect.com/science/article/abs/pii/S1053811912010671?via%3Dihub","AuthorName":"M. Mennes, B.B. Biswal, F.X. Castellanos, M.P. Milham"},{"Title":"The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism.","URL":"https://www.nature.com/articles/mp201378","AuthorName":"A. Di Martino, C-G Yan, ..., M.P. Milham"},{"Title":"The Healthy Brain Network Serial Scanning Initiative: a resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions","URL":"https://academic.oup.com/gigascience/article/6/2/giw011/2865212","AuthorName":"D. O'Connor, N.V. Potler, ..., M.P. Milham"},{"Title":"The NKI-Rockland sample: a model for accelerating the pace of discovery science in psychiatry","URL":"https://www.frontiersin.org/articles/10.3389/fnins.2012.00152/full","AuthorName":"K.B. Nooner, S.J. Colcombe, ..., M.P. Milham","AuthorURL":null}]},"RegistryEntryAdded":"2020-07-01","RegistryEntryLastModified":"2021-10-22","Slug":"fcp-indi","Sources":["awslabs-open-data-registry"]}
{"Name":"FECGSYN Toolbox","Description":"The FECGSYN toolbox is a reference open-source platform for NI-FECG research, product of a collaboration between the Department of Engineering Science, University of Oxford (DES-OX), the Institute of Biomedical Engineering, TU Dresden (IBMT-TUD) and the Biomedical Engineering Faculty at the Technion Israel Institute of Technology (BME-IIT).","Documentation":"https://physionet.org/content/fecgsyn/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/fecgsyn/","ARN":"arn:aws:s3:::physionet-open/fecgsyn/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"fecgsyn","Sources":["awslabs-open-data-registry"]}
{"Name":"Fetal ECG Synthetic Database","Description":"The FECGSYNDB is a large database of simulated adult and non-invasive fetal ECG (NI-FECG) signals, which provides a robust resource that enables reproducible research in the field. The data is generated using the FECGSYN simulator (visit website).","Documentation":"https://doi.org/10.13026/C21P4T","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C21P4T","ARN":"arn:aws:s3:::physionet-open/fecgsyndb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"fecgsyndb","Sources":["awslabs-open-data-registry"]}
{"Name":"Indian Institute of Science Fetal Heart Sound Database (IIScFHSDB)","Description":"The Indian Institute of Science Fetal Heart Sound Database (IIScFHSDB) has 60 fetal phonocardiography (fPCG) recordings obtained from expecting mothers of age 18-27 years of age with gestation weeks 30-40. The recordings are made at St. John’s Hospital, Bangalore with an electronic stethoscope. The data is collected from the antenatal and labor wards by placing the electronic stethoscope on the lower abdomen of the subject. The fPCG recordings are obtained from 60 subjects with the gain of 500X and the average duration of the recordings is 8 minutes with a sampling frequency of 2 kHz.","Documentation":"https://doi.org/10.13026/9vvw-cx05","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/9vvw-cx05","ARN":"arn:aws:s3:::physionet-open/fetalheartsounddata/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"fetalheartsounddata","Sources":["awslabs-open-data-registry"]}
{"Name":"FLAb: Fitness Landscapes for Antibodies","Description":"FLAb is the largest publicly available therapeutic antibody dataset designed to train and benchmark protein AI models. It provides open-access, high-quality developability data on diverse therapeutic properties, including expression, thermostability, immunogenicity, aggregation, polyreactivity, binding affinity, and pharmacokinetics.","Documentation":"https://github.com/Graylab/FLAb/blob/main/README.md","Contact":"mchungy1@jhu.edu","ManagedBy":"[Jeffrey Gray Lab, Johns Hopkins University](https://graylab.jhu.edu/)","UpdateFrequency":"Any new public release of antibody developabilty data is deposited into FLAb","Tags":["aws-pds","life sciences","machine learning","protein","protein template"],"License":"https://creativecommons.org/licenses/by/4.0/","Citation":"FLAb was accessed on [DATE] at registry.opendata.aws/flab","Resources":[{"Description":"Antibody developabiltiy data in CSV format","ARN":"arn:aws:s3:::graylab-flab","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"FLAb tutorial: Benchmarking a protein language model for antibody expression prediction","URL":"https://github.com/Graylab/FLAb/blob/main/examples/FLAb_ZeroShotExample_IgLM_Expression.ipynb","AuthorName":"Michael Chungyoun","AuthorURL":"https://www.linkedin.com/in/mfc12/"}],"Publications":[{"Title":"FLAb: Benchmarking deep learning methods for antibody fitness prediction","URL":"https://doi.org/10.1101/2024.01.13.575504","AuthorName":"Michael Chungyoun and Jeffrey J. Gray","AuthorURL":"https://www.linkedin.com/in/mfc12/"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-08-25","RegistryEntryLastModified":"2025-12-04","Slug":"flab","Sources":["awslabs-open-data-registry"]}
{"Name":"Foundation Medicine Adult Cancer Clinical Dataset (FM-AD)","Description":"The Foundation Medicine Adult Cancer Clinical Dataset (FM-AD) is a study conducted by Foundation\nMedicine Inc (FMI). Genomic profiling data for approximately 18,000 adult patients with a diverse\narray of cancers was generated using FoundationeOne, FMI's commercially available, comprehensive\ngenomic profiling assay. This dataset contains open Clinical and Biospecimen data.\n","Documentation":"https://gdc.cancer.gov/about-gdc/contributed-genomic-data-cancer-research/foundation-medicine/foundation-medicine\n","Contact":"dcf-support@datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is the source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genomic","life sciences"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"Biospecimen Supplement, Clinical Supplement","ARN":"arn:aws:s3:::gdc-fm-ad-phs001179-2-open","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/FM-AD","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"}],"Publications":[{"Title":"High-Throughput Genomic Profiling of Adult Solid Tumors Reveals Novel Insights into Cancer\nPathogenesis\n","URL":"https://www.ncbi.nlm.nih.gov/pubmed?cmd=DetailsSearch&term=28235761[PMID]","AuthorName":"Ryan J. Hartmaier, Lee A. Albacker, Juliann Chmielecki, Mark Bailey, Jie He, Michael E.\nGoldberg, Shakti Ramkissoon, James Suh, Julia A. Elvin, Samuel Chiacchia, Garrett M.\nFrampton, Jeffrey S. Ross, Vincent Miller, Philip J. Stephens and Doron Lipson\n"},{"Title":"Targeted next-generation sequencing of advanced prostate cancer identifies potential\ntherapeutic targets and disease heterogeneity.\n","URL":"https://www.ncbi.nlm.nih.gov/pubmed?cmd=DetailsSearch&term=22981675[PMID]","AuthorName":"Beltran H, Yelensky R, Frampton GM, Park K, Downing SR, MacDonald TY, Jarosz M, Lipson D,\nTagawa ST, Nanus DM, Stephens PJ, Mosquera JM, Cronin MT, Rubin MA\n"}]},"RegistryEntryAdded":"2020-04-07","RegistryEntryLastModified":"2024-12-20","Slug":"fm-ad","Sources":["awslabs-open-data-registry"]}
{"Name":"Finnish Meteorological Institute Weather Radar Data","Description":"The up-to-date weather radar from the FMI radar network is available as Open Data. The data contain both single radar data along with composites over Finland in GeoTIFF and HDF5-formats. Available composite parameters consist of radar reflectivity (DBZ), rainfall intensity (RR), and precipitation accumulation of 1, 12, and 24 hours. Single radar parameters consist of radar reflectivity (DBZ), radial velocity (VRAD), rain classification (HCLASS), and Cloud top height (ETOP 20). Raw volume data from singe radars are also provided in HDF5 format with ODIM 2.3 conventions. Radar data becomes available as soon as it's received from the radar and pre-processed into deliverable formats. Typically the most recent radar data was collected less than 5 minutes ago.","Documentation":"http://en.ilmatieteenlaitos.fi/radar-data-on-aws-s3","ManagedBy":"[Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/)","Contact":"avoin-data@fmi.fi","UpdateFrequency":"5 minutes","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","earth observation","meteorological","weather"],"License":"Creative Commons Attribution 4.0 International (CC BY 4.0)","Resources":[{"Description":"Radar data as GeoTIFF","ARN":"arn:aws:s3:::fmi-opendata-radar-geotiff","Region":"eu-west-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://fmi-opendata-radar-geotiff.s3.amazonaws.com/index.html)"]},{"Description":"Volume data as HDF5","ARN":"arn:aws:s3:::fmi-opendata-radar-volume-hdf5","Region":"eu-west-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://fmi-opendata-radar-volume-hdf5.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new GeoTIFF data","ARN":"arn:aws:sns:eu-west-1:916174725480:fmi-opendata-radar-geotiff-object_created","Region":"eu-west-1","Type":"SNS Topic"},{"Description":"Notifications for new volume data","ARN":"arn:aws:sns:eu-west-1:916174725480:fmi-opendata-radar-volume-hdf5-object_created","Region":"eu-west-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Handling data with QGIS","URL":"https://github.com/fmidev/opendata-resources/tree/master/examples/qgis/radar-composite","AuthorName":"Markus Peura"},{"Title":"Processing GeoTIFF data with python","URL":"https://github.com/fmidev/opendata-resources/blob/master/examples/python/Accumulated_prec_amount_from_radar_GeoTiff.ipynb","AuthorName":"Roope Tervo"},{"Title":"Processing HDF5 data with python","URL":"https://github.com/fmidev/opendata-resources/blob/master/examples/python/radar-rhi-from-hdf5.ipynb","AuthorName":"Roope Tervo"}]},"RegistryEntryAdded":"2020-10-06","RegistryEntryLastModified":"2023-02-28","Slug":"fmi-radar","Sources":["awslabs-open-data-registry"]}
{"Name":"Foldingathome COVID-19 Datasets","Description":"[Folding@home](http://foldingathome.org) is a massively distributed computing project that uses biomolecular simulations to investigate the [molecular origins of disease](https://foldingathome.org/diseases/) and accelerate the discovery of new therapies. Run by the [Folding@home Consortium](https://foldingathome.org/about/the-foldinghome-consortium/), a worldwide network of research laboratories focusing on a variety of different diseases, Folding@home seeks to address problems in human health on a scale that is infeasible by another other means, sharing the results of these large-scale studies with the research community through [peer-reviewed publications](https://foldingathome.org/papers-results/) and publicly shared datasets. During the [COVID-19 epidemic](https://en.wikipedia.org/wiki/Coronavirus_disease_2019), Folding@home focused its resources on understanding the vulnerabilities in [SARS-CoV-2](https://en.wikipedia.org/wiki/Severe_acute_respiratory_syndrome_coronavirus_2), the virus that causes COVID-19 disease, and working closely with a number of experimental collaborators to accelerate progress toward effective therapies for treating COVID-19 and ending the pandemic. In the process, it created the world's first [exascale distributed computing resource](https://doi.org/10.1101/2020.06.27.175430), enabling it to generate valuable scientific datasets of unprecedented size. More information about Folding@home's COVID-19 research activities at the [Folding@home COVID-19 page](https://foldingathome.org/diseases/infectious-diseases/covid-19/). In addition to working directly with experimental collaborators and rapidly sharing new research findings through preprint servers, Folding@home has joined other researchers in committing to [rapidly share all COVID-19 research data](https://doi.org/10.1021/acs.jcim.0c00319), and has joined forces with [AWS](https://aws.amazon.com/) and the [Molecular Sciences Software Institute (MolSSI)](http://molssi.org) to share datasets of unprecedented side through the [AWS Open Data Registry](https://registry.opendata.aws/), indexing these massive datasets via the [MolSSI COVID-19 Molecular Structure and Therapeutics Hub](https://covid.molssi.org/). The complete index of all Folding@home datasets can be found [here](https://covid.molssi.org//org-contributions/#folding--home). This repository contains several major datasets from this effort and comprises the single largest collection of molecular simulation data ever released.\n","Documentation":"https://github.com/FoldingAtHome/coronavirus","Contact":"[Folding@home](https://foldingathome.org/contact-us/)","ManagedBy":"Folding@home","UpdateFrequency":"Datasets will be updated periodically as additional simulations are completed.","Tags":["alchemical free energy calculations","aws-pds","biomolecular modeling","coronavirus","COVID-19","foldingathome","health","life sciences","molecular dynamics","protein","SARS-CoV-2","simulations","structural biology"],"License":"[CC0](https://creativecommons.org/share-your-work/public-domain/cc0/)","Resources":[{"Description":"Simulations of SARS-CoV-2 and associated host proteins, with emphasis on discovering druggable cryptic pockets, documented at the [MolSSI COVID Hub](https://covid.molssi.org//simulations/#foldinghome-simulations-of-the-sars-cov-2-spike-protein-spike-spike-binding).","ARN":"arn:aws:s3:::fah-public-data-covid19-cryptic-pockets","Region":"us-east-2","Type":"S3 Bucket"},{"Description":"Simulations of SARS-CoV-2 and associated host proteins relevant to neutralization by therapeutic antibodies, documented at the [MolSSI COVID Hub](https://covid.molssi.org//simulations/#foldinghome-simulations-of-the-sars-cov-2-spike-rbd-bound-to-human-ace2).","ARN":"arn:aws:s3:::fah-public-data-covid19-antibodies","Region":"us-east-2","Type":"S3 Bucket"},{"Description":"Absolute free energy calculations of small molecules from the [COVID Moonshot](http://postera.ai), documented at the [MolSSI COVID Hub](https://covid.molssi.org//simulations/#foldinghome-expanded-ensemble-absolute-free-energy-calculations-of-potential-small-molecule-inhibitors-of-the-sars-cov-2-main-protease-from-the-covid-moonshot-3clpro-3clpro-protease-activity)","ARN":"arn:aws:s3:::fah-public-data-covid19-absolute-free-energy","Region":"us-east-2","Type":"S3 Bucket"},{"Description":"Simulations of target proteins and bound small molecules from the [COVID Moonshot](http://postera.ai), documented at the [MolSSI COVID Hub](https://covid.molssi.org//simulations/#foldinghome-sars-cov-2-main-protease-apo-monomer-simulations-3clpro-3clpro-protease-activity)","ARN":"arn:aws:s3:::fah-public-data-covid19-moonshot-dynamics","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":" SARS-CoV-2 spike RBD with N501Y mutation bound to human ACE2 (953.7 µs)","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-the-sars-cov-2-spike-rbd-with-n501y-mutation-bound-to-human-ace2","AuthorName":"The Chodera lab at the Memorial Sloan Kettering Cancer Center","AuthorURL":"https://choderalab.org"},{"Title":"SARS-CoV-2 COVID Moonshot absolute free energy calculations","URL":"https://covid.molssi.org//simulations/#foldinghome-expanded-ensemble-absolute-free-energy-calculations-of-potential-small-molecule-inhibitors-of-the-sars-cov-2-main-protease-from-the-covid-moonshot-3clpro-3clpro-protease-activity","AuthorName":"The Voelz lab at Temple University","AuthorURL":"http://www.voelzlab.org/"},{"Title":"SARS-CoV-2 main viral protease (Mpro, 3CLPro, nsp5) dimer simulations: A 2.9 ms dataset of the SARS-CoV-2 main viral protease (apo, dimer) in search of cryptic pockets","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-nsp5-3clpro-3clpro-protease-activity","AuthorName":"The Bowman lab at Washington University in St. Louis","AuthorURL":"https://bowmanlab.biochem.wustl.edu"},{"Title":"SARS-CoV-2 main viral protease (Mpro, 3CLPro, nsp5) monomer simulations: A 2.6 ms equilibrium dataset of the SARS-CoV-2 main viral protease (apo, monomer)","URL":"https://covid.molssi.org//simulations/#foldinghome-sars-cov-2-main-protease-apo-monomer-simulations-3clpro-3clpro-protease-activity","AuthorName":"The Chodera lab at MSKCC","AuthorURL":"https://choderalab.org"},{"Title":"SARS-CoV-2 main viral protease (Mpro, 3CLPro, nsp5) monomer simulations: A 6.4 ms dataset of the SARS-CoV-2 main viral protease (apo, monomer) in search of cryptic pockets","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-nsp5-3clpro-3clpro-protease-activity","AuthorName":"The Bowman lab at Washington University in St. Louis","AuthorURL":"https://bowmanlab.biochem.wustl.edu"},{"Title":"SARS-CoV-2 nsp10 dataset: A 6.1 ms dataset of the SARS-CoV-2 nsp10 protein in search of cryptic pockets","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-nsp10-nsp10-nil","AuthorName":"The Bowman lab at Washington University in St. Louis","AuthorURL":"https://bowmanlab.biochem.wustl.edu"},{"Title":"SARS-CoV-2 nsp3 macrodomain dataset: An 11 ms dataset of the SARS-CoV-2 nsp3 macrodomain in search of cryptic pockets","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-nsp3-macrodomain-macrodomain-host-immune-response","AuthorName":"The Bowman lab at Washington University in St. Louis","AuthorURL":"https://bowmanlab.biochem.wustl.edu"},{"Title":"SARS-CoV-2 nsp3 pl2pro domain dataset: An 731 µs dataset of the SARS-CoV-2 nsp3 pl2pro domain in search of cryptic pockets","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-nsp3-pl2pro-domain-plpro-plpro-protease-activity","AuthorName":"The Bowman lab at Washington University in St. Louis","AuthorURL":"https://bowmanlab.biochem.wustl.edu"},{"Title":"SARS-CoV-2 nsp7 simulations: A 3.7 ms dataset of the SARS-CoV-2 nsp7 protein in search of cryptic pockets","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-nsp7-nsp7-nil","AuthorName":"The Bowman lab at Washington University in St. Louis","AuthorURL":"https://bowmanlab.biochem.wustl.edu"},{"Title":"SARS-CoV-2 nsp8 simulations: A 1.8 ms dataset of the SARS-CoV-2 nsp8 protein in search of cryptic pockets","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-nsp8-nsp8-nil","AuthorName":"The Bowman lab at Washington University in St. Louis","AuthorURL":"https://bowmanlab.biochem.wustl.edu"},{"Title":"SARS-CoV-2 nsp9 simulations: A 9 ms dataset of the SARS-CoV-2 nsp9 protein in search of cryptic pockets","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-nsp9-nsp9-nil","AuthorName":"The Bowman lab at Washington University in St. Louis","AuthorURL":"https://bowmanlab.biochem.wustl.edu"},{"Title":"SARS-CoV-2 RNA polymerase (nsp12, RdRP) dataset: A 3.4 ms dataset of the SARS-CoV-2 nsp12 protein in search of cryptic pockets","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-nsp12-rdrp-nil","AuthorName":"The Bowman lab at Washington University in St. Louis","AuthorURL":"https://bowmanlab.biochem.wustl.edu"},{"Title":"SARS-CoV-2 spike protein dataset: A 1.2 ms dataset of the SARS-CoV-2 spike protein in search of cryptic pockets","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-the-sars-cov-2-spike-protein-spike-spike-binding","AuthorName":"The Bowman lab at Washington University in St. Louis","AuthorURL":"https://bowmanlab.biochem.wustl.edu"},{"Title":"SARS-CoV-2 spike RBD (with glycosylation) (1.8 ms)","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-the-apo-sars-cov-2-spike-rbd-with-glycosylation","AuthorName":"The Chodera lab at the Memorial Sloan Kettering Cancer Center","AuthorURL":"https://choderalab.org"},{"Title":"SARS-CoV-2 spike RBD (without glycosylation) (1.9 ms)","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-the-apo-sars-cov-2-spike-rbd-without-glycosylation","AuthorName":"The Chodera lab at the Memorial Sloan Kettering Cancer Center","AuthorURL":"https://choderalab.org"},{"Title":"SARS-CoV-2 spike RBD bound to human ACE2 receptor (173.8 us): Wild-type and mutant simulations","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-the-sars-cov-2-spike-rbd-bound-to-human-ace2","AuthorName":"The Chodera lab at the Memorial Sloan Kettering Cancer Center","AuthorURL":"https://choderalab.org"},{"Title":"SARS-CoV-2 spike RBD bound to monoclonal antibody S2H97 (623.7 us)","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-the-sars-cov-2-spike-rbd-bound-to-monoclonal-antibody-s2h97","AuthorName":"The Chodera lab at the Memorial Sloan Kettering Cancer Center","AuthorURL":"https://choderalab.org"},{"Title":"SARS-CoV-2 spike RBD bound to monoclonal antibody S309 (1.1 ms)","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-the-sars-cov-2-spike-rbd-bound-to-monoclonal-antibody-s309","AuthorName":"The Chodera lab at the Memorial Sloan Kettering Cancer Center","AuthorURL":"https://choderalab.org"},{"Title":"SARS-CoV-2 spike RBD with P337A mutation bound to monoclonal antibody S309 (907.0 µs)","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-the-sars-cov-2-spike-rbd-with-p337a-mutation-bound-to-monoclonal-antibody-s309","AuthorName":"The Chodera lab at the Memorial Sloan Kettering Cancer Center","AuthorURL":"https://choderalab.org"},{"Title":"SARS-CoV-2 spike RBD with P337L mutation bound to monoclonal antibody S309 (923.2 µs)","URL":"https://covid.molssi.org//simulations/#foldinghome-simulations-of-the-sars-cov-2-spike-rbd-with-p337l-mutation-bound-to-monoclonal-antibody-s309","AuthorName":"The Chodera lab at the Memorial Sloan Kettering Cancer Center","AuthorURL":"https://choderalab.org"}],"Tutorials":[{"Title":"Folding@home COVID-19 efforts","URL":"https://github.com/FoldingAtHome/coronavirus/blob/master/README.md","AuthorName":"Folding@home Consortium"}],"Publications":[{"Title":"Circulating SARS-CoV-2 spike N439K variants maintain fitness while evading antibody-mediated immunity","URL":"https://doi.org/10.1016/j.cell.2021.01.037","AuthorName":"Emma C. Thomson, Laura E, Rosen, James G. Shepherd, et al."},{"Title":"SARS-CoV-2 RBD antibodies that maximize breadth and resistance to escape","URL":"https://doi.org/10.1038/s41586-021-03807-6","AuthorName":"Tyler N. Starr, Nadine Czudnochowski, Zhuoming Liu, et al."},{"Title":"SARS-CoV-2 Simulations Go Exascale to Capture Spike Opening and Reveal Cryptic Pockets Across the Proteome","URL":"https://doi.org/10.1101/2020.06.27.175430","AuthorName":"Maxwell I. Zimmerman, Justin R. Porter, Michael D. Ward, Sukrit Singh, Neha Vithani, Artur Meller, Upasana L. Mallimadugula, Catherine E. Kuhn, Jonathan H. Borowsky,  View ORCID ProfileRafal P. Wiewiora, Matthew F. D. Hurley, Aoife M Harbison, Carl A Fogarty, Joseph E. Coffland, Elisa Fadda, Vincent A. Voelz, John D. Chodera, Gregory R. Bowman"}]},"RegistryEntryAdded":"2020-10-07","RegistryEntryLastModified":"2022-11-08","Slug":"foldingathome-covid19","Sources":["awslabs-open-data-registry"]}
{"Name":"FoMo - A Multi-Season Dataset for Robot Navigation in Forêt Montmorency","Description":"The FoMo dataset is a multi-season collection recorded in a boreal forest environment, featuring deep snow, off-road terrain, steep slopes, and highly variable weather. It provides synchronized multi-modal sensor data—including two lidars (RoboSense and Leishen), an FMCW radar (Navtech), stereo and monocular cameras, dual IMUs, wheel odometry, power data, calibration sequences, and precise ground-truth trajectories via GNSS-PPK fusion.\nDesigned to support research on robust robot autonomy under adverse conditions, FoMo includes repeated traversals of six trajectories of varying complexity for long-term SLAM and odometry evaluation, as well as rich metadata such as one-minute weather station measurements.\nThe dataset is intended to challenge state-of-the-art SLAM, localization, traversability analysis, and multi-season robotics research.\n","Documentation":"https://fomo.norlab.ulaval.ca/overview","Contact":"dataset@norlab.ulaval.ca","ManagedBy":"[Norlab, Université Laval](https://norlab.ulaval.ca)","UpdateFrequency":"The dataset is considered complete and stable. Minor updates or corrections may occur, but they are expected to be infrequent.","Tags":["autonomous vehicles","aws-pds","benchmark","computer vision","environmental","extreme weather","geospatial","GNSS","IMU","lidar","localization","mapping","meteorological","perception","radar","RINEX","robotics","signal processing"],"License":"Creative Commons Attribution 4.0 International (CC BY 4.0). See https://creativecommons.org/licenses/by/4.0/\n","Resources":[{"Description":"Primary S3 bucket containing the full FoMo dataset. Data are structured by deployment and trajectory folders,  each containing lidar scans, radar images, stereo and monocular camera frames, audio, IMU logs, odometry, GNSS-PPK trajectories,  calibration sequences, metadata files, and ROS2/mcap-compatible exports.\n","ARN":"arn:aws:s3:::fomo-dataset","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://fomo-dataset.s3.amazonaws.com/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset - FoMo","URL":"https://github.com/norlab-ulaval/fomo-dataset-tutorials","NotebookURL":"https://github.com/norlab-ulaval/fomo-dataset-tutorials/blob/main/get_to_know_fomo-dataset.ipynb","AuthorName":"Norlab, Université Laval","AuthorURL":"https://norlab.ulaval.ca"}],"Tools & Applications":[{"Title":"FoMo Dataset Website","URL":"https://fomo.norlab.ulaval.ca/","AuthorName":"Norlab, Université Laval","AuthorURL":"https://norlab.ulaval.ca"},{"Title":"FoMo SDK (Rust & Python)","URL":"https://github.com/norlab-ulaval/fomo-sdk","AuthorName":"Norlab, Université Laval","AuthorURL":"https://norlab.ulaval.ca"}],"Publications":[{"Title":"Toward teach and repeat across seasonal deep snow accumulation (FoMo introduction paper)","URL":"https://arxiv.org/abs/2603.08433","AuthorName":"Boxan et al."}]},"ADXCategories":["Environmental Data","Automotive Data"],"RegistryEntryAdded":"2025-12-02","RegistryEntryLastModified":"2026-04-10","Slug":"fomo-norlab","Sources":["awslabs-open-data-registry"]}
{"Name":"Ford Multi-AV Seasonal Dataset","Description":"This research presents a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles at different days and times during 2017-18. The vehicles The vehicles were manually driven on an average route of 66 km in Michigan that included a mix of driving scenarios like the Detroit Airport, freeways, city-centres, university campus and suburban neighbourhood, etc. Each vehicle used in this data collection  is a Ford Fusion outfitted with an Applanix POS-LV inertial measurement unit (IMU), four HDL-32E Velodyne 3D-lidar scanners, 6 Point Grey 1.3 MP Cameras arranged on the rooftop for 360 degree coverage and 1 Pointgrey 5 MP camera mounted behind the windsheild for forward field of view. We present the seasonal variation in weather, lighting, construction and traffic conditions experienced in dynamic urban environments. This dataset can help design robust algorithms for autonomous vehicles and multi-agent systems. Each log in the dataset is time-stamped and contains raw data from all the sensors, calibration values, pose trajectory, ground truth pose, and 3D maps. All data is available in Rosbag format that can be visualized, modified and applied using the open-source Robot Operating System (ROS).","Contact":"avdata.ford.com","Documentation":"avdata.ford.com","ManagedBy":"[Ford Motor Company](https://avdata.ford.com)","UpdateFrequency":"New data will be added until the entire dataset is released online.","Collabs":{"ASDI":{"Tags":["infrastructure"]}},"Tags":["autonomous vehicles","aws-pds","computer vision","lidar","mapping","robotics","transportation","urban","weather"],"License":"This data is intended for non-commercial academic use only. It is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.","Resources":[{"Description":"All data","ARN":"arn:aws:s3:::ford-multi-av-seasonal","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Autonomous Driving Data Service (ADDS)","URL":"https://github.com/aws-samples/amazon-eks-autonomous-driving-data-service","AuthorName":"Ajay Vohra, Amazon","Services":["Amazon EKS","Amazon Redshift","Amazon S3","Amazon FSx"]},{"Title":"Ford AV Dataset Tutorial","URL":"https://github.com/Ford/AVData","AuthorName":"Ford Motor Company","AuthorURL":null}]},"RegistryEntryAdded":"2020-01-20","RegistryEntryLastModified":"2025-07-09","Slug":"ford-multi-av-seasonal","Sources":["awslabs-open-data-registry"]}
{"Name":"Fetal PCG Database","Description":"The project collects a series of 26 fetal phonocardiographic (PCG) signals from different pregnant women during the last months of their singleton physiological pregnancies (gestational week between 31 and 40).","Documentation":"https://doi.org/10.13026/C2PD5B","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2PD5B","ARN":"arn:aws:s3:::physionet-open/fpcgdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"fpcgdb","Sources":["awslabs-open-data-registry"]}
{"Name":"RNA structure by fragmentation frequency","Description":"The fragSTRUC project devises a software to extract RNA secondary structure information from Illumina datasets, based on divalent ions in standard RNA-seq library preparation fragmenting sequences at non-base-paired regions of RNA.","Documentation":"https://github.com/yuukiiwa/RNA_structure_by_fragmentation_frequency","Contact":"[fragSTRUC team](https://github.com/yuukiiwa/RNA_structure_by_fragmentation_frequency)","ManagedBy":"The Genome Institute of Singapore (https://www.a-star.edu.sg/gis) and UMass Chan Medical School's RNA Therapeutics Institute (https://www.umassmed.edu/rti/)","UpdateFrequency":"Datasets will be updated periodically as additional data is generated.","Tags":["aws-pds","bioinformatics","genomic","life sciences","transcriptomics"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Citation":"In addition, please cite Yuk Kei Wan and Leonard Schärfen Hidden structural information in RNA sequencing data.","Resources":[{"Description":"RNA structure by fragmentation frequency","ARN":"arn:aws:s3:::frag-struc","Region":"ap-southeast-1","Type":"S3 Bucket","Explore":["[Browse Bucket](http://frag-struc.s3-website-ap-southeast-1.amazonaws.com/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Accessing the fragSTRUC dataset on AWS","URL":"https://github.com/yuukiiwa/RNA_structure_by_fragmentation_frequency/blob/main/README.md","AuthorName":"Yuk Kei Wan and Leonard Schärfen"}],"Tools & Applications":[{"Title":"fragSTRUC: RNA structure by fragmentation frequency","URL":"https://github.com/lschaerfen/fragstruc","AuthorName":"Yuk Kei Wan and Leonard Schärfen"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-12-31","RegistryEntryLastModified":"2026-01-02","Slug":"frag-struc","Sources":["awslabs-open-data-registry"]}
{"Name":"UMASSD-FVCOM-GOM3-Hindcast","Description":"The Finite Volume Community Ocean Model (FVCOM) was used to simulate ocean water levels, velocity, temperature and salinity over a multi-decadal period (1984-present) in the waters of the Northeast US including the Gulf of Maine.  The model was configured and run by the Dr. Changshen Chen, Director of the Marine Ecosystems Dynamics Modeling Laboratory in the School for Marine Science & Technology at the University of Massachusetts Dartmouth.  The triangular mesh has a varying horizontal resolution from several hundred meters inshore to several kilometers offshore, and 45 terrain-following vertical layers.  The model output was saved at hourly intervals from 2009-08-21 to 2022-06-17.","Documentation":"https://en.wikipedia.org/wiki/Finite_Volume_Community_Ocean_Model","Contact":"rich@opensciencecomputing.com","ManagedBy":"Open Science Computing, LLC","UpdateFrequency":"None","Citation":"https://web.archive.org/web/20161229211546id_/http://fvcom.smast.umassd.edu/wp-content/uploads/2013/11/MITSG_12-25.pdf","Tags":["aws-pds","oceans"],"License":"CC0","Resources":[{"Description":"A collection of NetCDF files, kerchunk-generated Parquet reference files, and an Intake catalog","ARN":"arn:aws:s3:::umassd-fvcom","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Notifications for new UMASSD-FVCOM-GOM3 data","ARN":"arn:aws:sns:us-east-1:273354670095:umassd-fvcom-object-creation","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"FVCOM Explorer Notebook","URL":"https://github.com/opensciencecomputing/umassd-fvcom/blob/main/fvcom_gom3_explore.ipynb","NotebookURL":"https://github.com/opensciencecomputing/umassd-fvcom/blob/main/fvcom_gom3_explore.ipynb","AuthorName":"Rich Signell","AuthorURL":"https://about.me/rich.signell","Services":null}],"Publications":[{"Title":"An Unstructured Grid, Finite Volume, Three Dimensional, Primitive Equations Ocean Model with Application to Coastal Ocean and Estuaries","URL":"https://doi.org/10.1175/1520-0426(2003)020%3C0159:AUGFVT%3E2.0.CO;2","AuthorName":"Changsheng Chen, Hedong Liu, and Robert C. Beardsley"}]},"RegistryEntryAdded":"2025-03-03","RegistryEntryLastModified":"2026-04-03","Slug":"fvcom_gom3","Sources":["awslabs-open-data-registry"]}
{"Name":"Grid Algorithms and Data Analytics Library (GADAL)","Description":"The aim of this project is to create an easy-to-use platform where various types of analytics can be performed on a wide range of electrical grid datasets. The aim is to establish an open-source library of algorithms that universities, national labs and other developers can contribute to which can be used on both open-source and proprietary grid data to improve the analysis of electrical distribution systems for the grid modeling community. OEDI Systems Integration (SI) is a grid algorithms and data analytics API created to standardize how data is sent between different modules that are run as part of a co-simulation.\n","Documentation":"https://github.com/openEDI/GADAL","Contact":"https://github.com/openEDI/GADAL/issues","ManagedBy":"[National Renewable Energy Laboratory](https://www.nrel.gov/)","UpdateFrequency":"As needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["aws-pds","energy","environmental","model","sustainability"],"License":"Creative Commons Attribution 3.0 United States License","Resources":[{"Description":"[Sample IEEE123 Bus system for OEDI SI](https://data.openei.org/submissions/5773)","ARN":"arn:aws:s3:::gadal/gadal_ieee123/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gadal&prefix=gadal_ieee123%2F)"]}],"RegistryEntryAdded":"2023-02-02","RegistryEntryLastModified":"2023-02-02","Slug":"gadal","Sources":["awslabs-open-data-registry"]}
{"Name":"Gait Maturation Database","Description":"The data contained here are from 50 healthy children ranging in age from 40 months to 163 months. Each data file is named with a subject identifier (1-50) and the subject's age (e.g., the file name 2-45.txt indicates that subject #2 is 45 months old). Each data file contains two columns of data in text format. The first column is the time (in seconds), and the second column is the stride interval (in seconds) or the gait cycle duration, the time from heel strike to heel strike of the same foot.","Documentation":"https://doi.org/10.13026/C2H014","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2H014","ARN":"arn:aws:s3:::physionet-open/gait-maturation-db/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"gait-maturation-db","Sources":["awslabs-open-data-registry"]}
{"Name":"Gait in Aging and Disease Database","Description":"Walking stride interval time series included are from 15 subjects: 5 healthy young adults (23 - 29 years old), 5 healthy old adults (71 - 77 years old), and 5 older adults (60 - 77 years old) with Parkinson's disease. The file name indicates old (o), young (y) or Parkinson's disease (pd). For the old and young subjects, the age (in years) is also included in the filename.","Documentation":"https://doi.org/10.13026/C2C889","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2C889","ARN":"arn:aws:s3:::physionet-open/gaitdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"gaitdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Gait in Neurodegenerative Disease Database","Description":"Neuro-degenerative disease often affects gait and mobility. To understand better the pathophysiology of these diseases and to improve our ability to measure responses to therapeutic interventions, it may be helpful to quantify gait dynamics accurately. The records in this database are from patients with Parkinson's disease (n = 15), Huntington's disease (n = 20), or amyotrophic lateral sclerosis (n = 13). Records from 16 healthy control subjects are also included here.","Documentation":"https://doi.org/10.13026/C27G6C","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C27G6C","ARN":"arn:aws:s3:::physionet-open/gaitndd/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"gaitndd","Sources":["awslabs-open-data-registry"]}
{"Name":"Gait in Parkinson's Disease","Description":"This database contains measures of gait from 93 patients with idiopathic PD (mean age: 66.3 years; 63% men), and 73 healthy controls (mean age: 66.3 years; 55% men). The database includes the vertical ground reaction force records of subjects as they walked at their usual, self-selected pace for approximately 2 minutes on level ground.","Documentation":"https://doi.org/10.13026/C24H3N","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C24H3N","ARN":"arn:aws:s3:::physionet-open/gaitpdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"gaitpdb","Sources":["awslabs-open-data-registry"]}
{"Name":"GATK Structural Variation (SV) Data","Description":"This dataset holds the data needed to run a [structural variation discovery pipeline](https://github.com/broadinstitute/gatk-sv)\nfor Illumina short-read whole-genome sequencing (WGS) data in AWS.\n","Documentation":"https://github.com/LokaHQ/aws-open-data/blob/main/gatk-sv/documentation.md","Contact":"awsdata@loka.com","ManagedBy":"[Loka Inc.](https://loka.com/)","UpdateFrequency":"Every 3 months","Tags":["aws-pds","bioinformatics","biology","cromwell","gatk-sv","genetic","genomic","life sciences","structural variation"],"License":"https://github.com/LokaHQ/aws-open-data/blob/main/LICENSE","Resources":[{"Description":"This dataset contains, among others, the following data:\n\n- Illumina short-read whole-genome CRAMs or BAMs, aligned to hg38 with [bwa-mem](https://github.com/lh3/bwa). BAMs must also be indexed.\n- Indexed GVCFs produced by GATK HaplotypeCaller, or a jointly genotyped VCF.\n- Family structure definitions file in [PED format](https://gatk.broadinstitute.org/hc/en-us/articles/360035531972-PED-Pedigree-format).\n- Reference files from the human reference genome Hg38.\n","ARN":"arn:aws:s3:::gatk-sv-data-us-east-2","Region":"us-east-2","Type":"S3 Bucket"},{"Description":"This dataset contains, among others, the following data:\n\n- Illumina short-read whole-genome CRAMs or BAMs, aligned to hg38 with [bwa-mem](https://github.com/lh3/bwa). BAMs must also be indexed.\n- Indexed GVCFs produced by GATK HaplotypeCaller, or a jointly genotyped VCF.\n- Family structure definitions file in [PED format](https://gatk.broadinstitute.org/hc/en-us/articles/360035531972-PED-Pedigree-format).\n- Reference files from the human reference genome Hg38.\n","ARN":"arn:aws:s3:::gatk-sv-data-us-east-1","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Structural Variant Analysis on AWS with Amazon FSx for Lustre","URL":"https://aws.amazon.com/blogs/industries/using-structural-variant-analysis-on-aws-with-amazon-fsx-for-lustre-in-novel-therapeutic-discovery/","AuthorName":"Goldfinch Bio and Loka Inc."}],"Tutorials":[{"Title":"AWS Setup & Execution","URL":"https://github.com/goldfinchbio/aws-gatk-sv","AuthorName":"Goldfinch Bio and Loka Inc."}]},"RegistryEntryAdded":"2022-04-01","RegistryEntryLastModified":"2026-07-09","Slug":"gatk-sv-data","Sources":["awslabs-open-data-registry"]}
{"Name":"GATK Test Data","Description":"The GATK test data resource bundle is a collection of files for resequencing human genomic data with the\nBroad Institute's [Genome Analysis Toolkit (GATK)](https://software.broadinstitute.org/gatk/).\n","Documentation":"https://s3.amazonaws.com/gatk-test-data/gatk-test-data-readme.html","Contact":"https://gatkforums.broadinstitute.org/gatk","ManagedBy":"Broad Institute","UpdateFrequency":"Every 3 months","Tags":["aws-pds","bioinformatics","biology","cancer","genetic","genomic","life sciences"],"License":"CC0 1.0 Universal (CC0 1.0) Public Domain Dedication","Resources":[{"Description":"The contents of this dataset is multi-modal and includes various types of genomic data, such as CRAMs/BAMs, whole-genome sequencing (WGS) data, exome data, RNA data, etc.","ARN":"arn:aws:s3:::gatk-test-data","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2018-10-04","RegistryEntryLastModified":"2025-05-22","Slug":"gatk-test-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Global Biodiversity Information Facility (GBIF) Species Occurrences","Description":"The Global Biodiversity Information Facility (GBIF) is an international network and data infrastructure funded by the world's governments providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences, growing daily. The GBIF occurrence dataset combines data from a wide array of sources including specimen-related data from natural history museums, observations from citizen science networks and environment recording schemes. While these data are constantly changing at GBIF.org, periodic snapshots are taken and made available on AWS.","Documentation":"Documentation can be found [here](https://github.com/gbif/occurrence/blob/master/aws-public-data.md). You can learn more about GBIF [here](https://www.gbif.org).","Contact":"helpdesk@gbif.org","ManagedBy":"The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org))","UpdateFrequency":"Snapshots of GBIF are taken on a monthly basis","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["aws-pds","biodiversity","bioinformatics","conservation","earth observation","life sciences"],"License":"[CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [terms of use](https://www.gbif.org/terms).","Citation":"In addition, include the relevant DOI found from [the table](https://www.gbif.org/occurrence-snapshots). For more information on how to cite GBIF datasets, please refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines).","Resources":[{"Description":"GBIF species occurrence data in Parquet format (af-south-1 region).","Explore":["[Browse bucket](https://gbif-open-data-af-south-1.s3.af-south-1.amazonaws.com/index.html)"],"ARN":"arn:aws:s3:::gbif-open-data-af-south-1","Region":"af-south-1","Type":"S3 Bucket"},{"Description":"GBIF species occurrence data in Parquet format (ap-southeast-2 region).","Explore":["[Browse bucket](https://gbif-open-data-ap-southeast-2.s3.ap-southeast-2.amazonaws.com/index.html)"],"ARN":"arn:aws:s3:::gbif-open-data-ap-southeast-2","Region":"ap-southeast-2","Type":"S3 Bucket"},{"Description":"GBIF species occurrence data in Parquet format (eu-central-1 region).","Explore":["[Browse bucket](https://gbif-open-data-eu-central-1.s3.eu-central-1.amazonaws.com/index.html)"],"ARN":"arn:aws:s3:::gbif-open-data-eu-central-1","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"GBIF species occurrence data in Parquet format (sa-east-1 region).","Explore":["[Browse bucket](https://gbif-open-data-sa-east-1.s3.sa-east-1.amazonaws.com/index.html)"],"ARN":"arn:aws:s3:::gbif-open-data-sa-east-1","Region":"sa-east-1","Type":"S3 Bucket"},{"Description":"GBIF species occurrence data in Parquet format (us-east-1 region).","Explore":["[Browse bucket](https://gbif-open-data-us-east-1.s3.us-east-1.amazonaws.com/index.html)"],"ARN":"arn:aws:s3:::gbif-open-data-us-east-1","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"GBIF species occurrence data in Parquet format (af-south-1 region).","ARN":"arn:aws:sns:af-south-1:288719126026:gbif-open-data-af-south-1-object_created","Region":"af-south-1","Type":"SNS Topic"},{"Description":"GBIF species occurrence data in Parquet format (ap-southeast-2 region).","ARN":"arn:aws:sns:af-south-1:288719126026:gbif-open-data-ap-southeast-2-object_created","Region":"ap-southeast-2","Type":"SNS Topic"},{"Description":"GBIF species occurrence data in Parquet format (eu-central-1 region).","ARN":"arn:aws:sns:af-south-1:288719126026:gbif-open-data-eu-central-1-object_created","Region":"eu-central-1","Type":"SNS Topic"},{"Description":"GBIF species occurrence data in Parquet format (sa-east-1 region).","ARN":"arn:aws:sns:af-south-1:288719126026:gbif-open-data-sa-east-1-object_created","Region":"sa-east-1","Type":"SNS Topic"},{"Description":"GBIF species occurrence data in Parquet format (us-east-1 region).","ARN":"arn:aws:sns:af-south-1:288719126026:gbif-open-data-us-east-1-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"GBIF and Apache-Spark on AWS tutorial","URL":"https://data-blog.gbif.org/post/aws-and-gbif/","AuthorName":"John Waller","Services":["Amazon EMR"]}]},"RegistryEntryAdded":"2021-05-17","RegistryEntryLastModified":"2025-02-04","Slug":"gbif","Sources":["awslabs-open-data-registry"]}
{"Name":"Global Carbon Budget Data","Description":"The Global Carbon Budget (GCB) is recognised globally as the most comprehensive report on global carbon emissions and sinks. This dataset, updated every year, includes estimates of land and ocean carbon fluxes from the suite of models used in the report.","Documentation":"https://globalcarbonbudgetdata.org/","Contact":"For any data queries please contact gcbo-data@exeter.ac.uk.","ManagedBy":"Global Carbon Budget Office at the University of Exeter, UK","UpdateFrequency":"Annual","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["climate","land","oceans"],"License":"Open Data. There are no restrictions on the use of this data.","Citation":null,"Resources":[{"Description":"Global Carbon Budget Open Data - Trendy","ARN":"arn:aws:s3:::gcbo-opendata","Region":"eu-west-2","Type":"S3 Bucket","Explore":null}],"DataAtWork":{"Publications":[{"Title":"Global Carbon Budget 2023","URL":"https://essd.copernicus.org/articles/15/5301/2023/","AuthorName":"Pierre Friedlingstein, Michael O’Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson,Simone Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O´Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, Bo Zheng"}]},"ADXCategories":null,"RegistryEntryAdded":"2024-09-26","RegistryEntryLastModified":"2024-09-30","Slug":"gcbo-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"Global Database of Events, Language and Tone (GDELT)","Description":"This project monitors the world's broadcast, print,\nand web news from nearly every corner of every country in\nover 100 languages and identifies the people, locations,\norganizations, counts, themes, sources, emotions,\nquotes, images and events driving our global society every\nsecond of every day.\n","Documentation":"http://www.gdeltproject.org/","Contact":"http://www.gdeltproject.org/about.html#contact","UpdateFrequency":"Not currently being updated","ManagedBy":"Unmanaged","Tags":["aws-pds","disaster response","events"],"License":"http://www.gdeltproject.org/about.html#termsofuse","Resources":[{"Description":"Project data files","ARN":"arn:aws:s3:::gdelt-open-data","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Notifications for new data","ARN":"arn:aws:sns:us-east-1:928094251383:gdelt-csv","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Analysing Brexit Coverage In The Media Over Time","URL":"https://www.linkedin.com/pulse/media-brexit-story-so-far-may-corbyn-less-shambles-than-mark-chopping/","AuthorName":"Mark Chopping","AuthorURL":"https://www.kognitio.com"},{"Title":"Bootstrapping GeoMesa HBase on AWS S3","URL":"http://www.geomesa.org/documentation/tutorials/geomesa-hbase-s3-on-aws.html","AuthorName":"Commonwealth Computer Research, Inc.","AuthorURL":"https://www.ccri.com"},{"Title":"Creating PySpark DataFrame from CSV in AWS S3 in EMR","URL":"https://gist.github.com/jakechen/6955f2de51212163312b6430555b8e0b","AuthorName":"Jake Chen","AuthorURL":"https://github.com/jakechen"},{"Title":"Exploring GDELT with Athena","URL":"http://blog.julien.org/2017/03/exploring-gdelt-data-set-with-amazon.html","AuthorName":"Julien Simon","AuthorURL":"https://twitter.com/julsimon"},{"Title":"How to partition your geospatial data lake for analysis with Amazon Redshift","URL":"https://aws.amazon.com/blogs/publicsector/how-partition-geospatial-data-lake-analysis-amazon-redshift/","AuthorName":"Jeff DeMuth, Luke Wells, and Nemanja Boric"},{"Title":"Running R on Amazon Athena","URL":"https://aws.amazon.com/blogs/big-data/running-r-on-amazon-athena/","AuthorName":"Gopal Wunnava","AuthorURL":"https://www.linkedin.com/in/gopal-wunnava-b11a77/"}],"Tools & Applications":[{"Title":"Globe Events","URL":"http://live.thermobook.net/globe-events/","AuthorName":"thermobook","AuthorURL":"http://www.thermobook.net"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2022-07-28","Slug":"gdelt","Sources":["awslabs-open-data-registry"]}
{"Name":"Department of Energy’s Geothermal Data Repository (GDR) Data Lake","Description":"Data released from projects funded by the Department of Energy's Geothermal \nTechnologies Office (DOE GTO) that are too large or complex to be conveniently \naccessed by traditional means. The GDR data lake aims to improve and automate \naccess of high-value geothermal data sets, making data actionable and discoverable \nby researchers and industry to accelerate analysis and advance innovation. \nThis data lake is a sister-data lake to the Department of Energy’s Open Energy Data \nInitiative (OEDI) Data Lake.\n","Documentation":"https://github.com/openEDI/documentation/","Contact":"https://github.com/openEDI/documentation/issues","ManagedBy":"[National Laboratory of the Rockies](https://www.nrel.gov/)","UpdateFrequency":"As needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["aws-pds","energy","geothermal"],"License":"Creative Commons Attribution 4.0 United States License","Resources":[{"Description":"[Data Catalog of the Geothermal Data Repository (GDR)](https://gdr.openei.org/)","ARN":"arn:aws:s3:::gdr-data-lake/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gdr-data-lake)"]},{"Description":"[EGS Collab Experiment 1: Continuous Active-Source Seismic Monitoring (CASSM) Data](https://gdr.openei.org/submissions/1368)","ARN":"arn:aws:s3:::gdr-data-lake/egs_collab/experiment_1/EGS_CASSM","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gdr-data-lake&prefix=egs_collab%2Fexperiment_1%2FEGS_CASSM%2F)"]},{"Description":"[Imperial Valley Dark Fiber Project Continuous DAS Data](https://gdr.openei.org/submissions/1499)","ARN":"arn:aws:s3:::gdr-data-lake/imperialvalleydas","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gdr-data-lake&prefix=imperialvalleydas%2F)"]},{"Description":"[Seismic Survey 2016 Data at San Emidio Nevada](https://gdr.openei.org/submissions/1395)","ARN":"arn:aws:s3:::gdr-data-lake/wholescale/subter_seismic/san_emidio","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gdr-data-lake&prefix=wholescale%2Fsubter_seismic%2Fsan_emidio%2F)"]},{"Description":"[WHOLESCALE: Seismic Survey Data from San Emidio Nevada 2021](https://gdr.openei.org/submissions/1478)","ARN":"arn:aws:s3:::gdr-data-lake/wholescale/2021_seismic","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gdr-data-lake&prefix=wholescale%2F2021_seismic%2F)"]},{"Description":"[Seismic Survey 2016 Data at Crescent Valley, Nevada](https://gdr.openei.org/submissions/1567)","ARN":"arn:aws:s3:::gdr-data-lake/wholescale/subter_seismic/crescent_valley","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gdr-data-lake&prefix=wholescale%2Fsubter_seismic%2Fcrescent_valley%2F)"]},{"Description":"[Utah FORGE: Neubrex Well 16B(78)-32 DAS Data - April 2024](https://gdr.openei.org/submissions/1680)","ARN":"arn:aws:s3:::gdr-data-lake/FORGE/DAS/april_2024/Neubrex/16b_continuous","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gdr-data-lake&prefix=FORGE%2FDAS%2Fapril_2024%2FNeubrex%2F16b_continuous%2F)"]},{"Description":"[EGS Collab Experiment 2: Distributed Fiber Optic Acoustic Data (DAS)](https://gdr.openei.org/submissions/1606)","ARN":"arn:aws:s3:::gdr-data-lake/egs_collab/experiment_2/DAS/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gdr-data-lake&prefix=egs_collab%2Fexperiment_2%2FDAS%2F)"]},{"Description":"[EGS Collab Experiment 2: Microseismic Monitoring](https://gdr.openei.org/submissions/1607)","ARN":"arn:aws:s3:::gdr-data-lake/egs_collab/experiment_2/passive_seismic/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gdr-data-lake&prefix=egs_collab%2Fexperiment_2%2Fpassive_seismic%2F)"]},{"Description":"[Soda Lake Geothermal: Raw 3D and 3C Seismic-Reflection Data from 2010 Survey](https://gdr.openei.org/submissions/1655)","ARN":"arn:aws:s3:::gdr-data-lake/soda_lake/raw_seismic/2010","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gdr-data-lake&prefix=soda_lake%2Fraw_seismic%2F2010%2F)"]},{"Description":"[EGS Collab Experiment 2: Continuous Active Source Seismic Monitoring (CASSM)](https://gdr.openei.org/submissions/1700)","ARN":"arn:aws:s3:::gdr-data-lake/egs_collab/experiment_2/active_seismic/CASSM","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gdr-data-lake&prefix=egs_collab%2Fexperiment_2%2Factive_seismic%2FCASSM%2F)"]},{"Description":"[Utah FORGE: Continuous Downhole Geophone Data from April 2024 Stimulations](https://gdr.openei.org/submissions/1725)","ARN":"arn:aws:s3:::gdr-data-lake/FORGE/Geophone/april_2024/GES","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gdr-data-lake&prefix=FORGE%2FGeophone%2Fapril_2024%2FGES%2F)"]},{"Description":"[Utah FORGE: Triggered DAS Data from April 2024 Stimulations](https://gdr.openei.org/submissions/1725)","ARN":"arn:aws:s3:::gdr-data-lake/FORGE/DAS/april_2024/Neubrex/16b_triggered","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gdr-data-lake&prefix=FORGE%2FDAS%2Fapril_2024%2FNeubrex%2F16b_triggered%2F)"]}],"DataAtWork":{"Tutorials":[{"Title":"Dark Fiber Jupyter Notebook Tutorial","URL":"https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo_Distributed_Acoustic_Sensing_(DAS)_Data_SEGY.ipynb","AuthorName":"A. Nayak, A. Lowney","AuthorURL":"https://orcid.org/0000-0001-7913-7189"}],"Publications":[{"Title":"The Geothermal Data Repository: Ten Years of Supporting the Geothermal Industry with Open Access to Geothermal Data","URL":"https://www.nrel.gov/docs/fy22osti/82837.pdf","AuthorName":"J. Weers, A. Anderson, N. Taverna"},{"Title":"The Imperial Valley Dark Fiber Project: Toward Seismic Studies Using DAS and Telecom Infrastructure for Geothermal Applications","URL":"https://doi.org/10.1785/0220220072","AuthorName":"J. Ajo-Franklin, V. Rodrigues Tribaldos, A. Nayak, et al."}]},"RegistryEntryAdded":"2023-12-04","RegistryEntryLastModified":"2025-12-08","Slug":"gdr-data-lake","Sources":["awslabs-open-data-registry"]}
{"Name":"Multi-Anatomy Post-Surgical Magnetic Resonance Dataset (MAPSMR)","Description":"The MAPSMR dataset is a multi-organ, post-surgical MRI benchmark dataset focused on organ absence and altered anatomy after common abdominal and pelvic surgeries. The dataset includes cases such as cholecystectomy, prostatectomy, nephrectomy, colectomy, hepatectomy, and related procedures, with annotations identifying surgically absent organs and post-treatment anatomical changes.","Documentation":"https://github.com/fastestimator/gehcai-mapsmr","Contact":"AIFoundationModels@gehealthcare.com","ManagedBy":"[GE Healthcare](https://www.gehealthcare.com)","UpdateFrequency":"Not updated","Tags":["aws-pds","life sciences","machine learning","magnetic resonance imaging","medical imaging"],"License":"[CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/)","Citation":"Yang, Zhijian, et al. Decipher-MR - a vision-language foundation model for 3D MRI representations. npj Digital Medicine (2026).","Resources":[{"Description":"Missing organ data files in s3","ARN":"arn:aws:s3:::gehcai-mapsmr","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Get to know a dataset - GEHCAI-MAPSMR","URL":"https://github.com/fastestimator/gehcai-mapsmr/blob/main/get-to-know-a-dataset.ipynb","AuthorName":"https://github.com/fastestimator"}],"Publications":[{"Title":"Decipher-MR: a vision-language foundation model for 3D MRI representations","URL":"https://www.nature.com/articles/s41746-026-02596-4","AuthorName":"Zhijian Yang, Noel DSouza, Istvan Megyeri, Xiaojian Xu, Amin Honarmandi Shandiz, Farzin Haddadpour, Krisztian Koos, Laszlo Rusko, Emanuele Valeriano, Bharadwaj Swaminathan, Lei Wu, Parminder Bhatia, Taha Kass-Hout, Erhan Bas"}]},"RegistryEntryAdded":"2026-04-13","RegistryEntryLastModified":"2026-04-16","Slug":"gehcai-mapsmr","Sources":["awslabs-open-data-registry"]}
{"Name":"Genome Ark","Description":"The Genome Ark hosts genomic information for the Vertebrate Genomes Project (VGP) and other related projects. The VGP is an international collaboration that aims to generate complete and near error-free reference genomes for all extant vertebrate species. These genomes will be used to address fundamental questions in biology and disease, to identify species most genetically at risk for extinction, and to preserve genetic information of life.","Documentation":"https://vertebrategenomesproject.org","Contact":"[Dr. Erich Jarvis](ejarvis@rockefeller.edu)","ManagedBy":"The Genome10K community of scientists","UpdateFrequency":"Data will be continually updated as it is generated.","Tags":["aws-pds","biodiversity","bioinformatics","biology","conservation","genetic","genomic","life sciences"],"License":"[G10K Data Use Policy](https://genome10k.soe.ucsc.edu/data-use-policies/)","Resources":[{"Description":"https://vgp.github.io","ARN":"arn:aws:s3:::genomeark","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://genomeark.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2019-02-21","RegistryEntryLastModified":"2021-08-16","Slug":"genomeark","Sources":["awslabs-open-data-registry"]}
{"Name":"GenomeKit genomic data","Description":"GenomeKit is Deep Genomics’ Python library for fast and easy access to genomic resources such as sequence, data tracks, and annotations. The goal is to let machine learning researchers build data sets easily, and to be creative about how those data sets are designed. Out of the box, GenomeKit provides access to pre-built optimized genomic data files that are required for its operation.","Documentation":"https://deepgenomics.github.io/GenomeKit/data_org.html","Contact":"https://github.com/deepgenomics/GenomeKit/issues","ManagedBy":"Deep Genomics","UpdateFrequency":"Data is updated when popular new genome versions (assemblies or annotations) are released","Tags":["bioinformatics","genome","genomic","Homo sapiens","life sciences","Mus musculus","non-human primate","open source software","Rattus norvegicus","variant annotation"],"License":"Apache License Version 2.0 https://www.apache.org/licenses/LICENSE-2.0","Resources":[{"Description":"Optimized data files required to query genomic data using GenomeKit (Assemblies, annotations, etc)","ARN":"arn:aws:s3:::genomekit-public-dg","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Quickstart","URL":"https://deepgenomics.github.io/GenomeKit/quickstart.html","AuthorName":"Deep Genomics"}],"Tools & Applications":[{"Title":"An RNA foundation model enables discovery of disease mechanisms and candidate therapeutics","URL":"https://www.biorxiv.org/content/10.1101/2023.09.20.558508v1","AuthorName":"Albi Celaj, Alice Jiexin Gao, Tammy T.Y. Lau, Erle M. Holgersen, Alston Lo, Varun Lodaya, Christopher B. Cole, Robert E. Denroche, Carl Spickett, Omar Wagih, Pedro O. Pinheiro, Parth Vora, Pedrum Mohammadi-Shemirani, Steve Chan, Zach Nussbaum, Xi Zhang, Helen Zhu, Easwaran Ramamurthy, Bhargav Kanuparthi, Michael Iacocca, Diane Ly, Ken Kron, Marta Verby, Kahlin Cheung-Ong, Zvi Shalev, Brandon Vaz, Sakshi Bhargava, Farhan Yusuf, Sharon Samuel, Sabriyeh Alibai, Zahra Baghestani, Xinwen He, Kirsten Krastel, Oladipo Oladapo, Amrudha Mohan, Arathi Shanavas, Magdalena Bugno, Jovanka Bogojeski, Frank Schmitges, Carolyn Kim, Solomon Grant, Rachana Jayaraman, Tehmina Masud, Amit Deshwar, Shreshth Gandhi, Brendan J. Frey","AuthorURL":"https://www.biorxiv.org/content/10.1101/2023.09.20.558508v1.article-info"}]},"RegistryEntryAdded":"2024-12-26","RegistryEntryLastModified":"2024-12-26","Slug":"genomekit","Sources":["awslabs-open-data-registry"]}
{"Name":"Genoxus Annotation","Description":"Genoxus Annotation is a harmonized and curated collection of human genetic variant databases designed to support accurate and salable variant annotation. Variant annotation following genetic testing such as whole genome sequencing (WGS) or whole exome sequencing (WES) is a critical step in identifying and interpreting disease-associated genetic factors. As sequencing technologies continue to generate large volumes of genomic data, robust and well-structured annotation resources are essential for translating raw variant calls into clinically meaningful insights.\nGenoxus Annotation v1.0 integrates data from NCBI ClinVar. ClinVar provides curated information on the clinical significance of a broad spectrum of genetic variants including single nucleotide variants (SNVs), insertions (INS), deletions (DEL), INDELs, copy number variations (CNVs), and structural variants (SVs) along with their associated diseases and traits.\nGWAS catalog complements ClinVar by focusing primarily on SNVs identified through genome wide association studies, linking common variants to complex diseases and phenotype traits. (GWAS data is planned in a future release.) By harmonizing variant representations, standardizing disease terminology, and consolidating evidence across sources, Genoxus Annotation provides a unified framework that streamlines variant interpretation for research and clinical applications.","Documentation":"https://genoxuslabs.com/genoxusannotation/","Contact":"https://genoxuslabs.com/contact/","ManagedBy":"Genoxus Labs","UpdateFrequency":"Annually and as needed","Tags":["aws-pds","genetic","genomic","life sciences","variant annotation","whole exome sequencing","whole genome sequencing"],"License":"[Creative Commons BY 4 (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"https://genoxuslabs.com/genoxusannotation/","ARN":"arn:aws:s3:::genoxus-annotation-release","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for new genoxus-annotation-release data","ARN":"arn:aws:sns:us-west-2:918792379224:Genoxus-Annotation-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get to Know a Dataset - Genoxus Annotation","URL":"https://github.com/davidmaigenoxus/Genoxus-Annotation/blob/main/GenoxusTutorial.ipynb","AuthorName":"David Mai @ Genoxus Labs","AuthorURL":"https://github.com/davidmaigenoxus"}],"Tools & Applications":[{"Title":"Genoxus Annotation","URL":"https://github.com/davidmaigenoxus/Genoxus-Annotation","AuthorName":"David Mai @ Genoxus Labs","AuthorURL":"https://github.com/davidmaigenoxus"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-04-02","RegistryEntryLastModified":"2026-06-04","Slug":"genoxus-annotation","Sources":["awslabs-open-data-registry"]}
{"Name":"GeoJSON Files for Geo-TIDE","Description":"GeoJSON files for the MIT Climate & Sustainability Consortium's Geospatial Trucking Industry Decarbonization Explorer","Documentation":"https://github.com/mcsc-impact-climate/Geo-TIDE-datasets","Contact":"mcsc@mit.edu","ManagedBy":"MIT Climate & Sustainability Consortium","UpdateFrequency":"Quarterly","Collabs":{"ASDI":{"Tags":["sustainability"]}},"Tags":["aws-pds","electricity","energy","environmental","geospatial","supply chain","sustainability","transportation"],"License":"Creative Commons Attribution 4.0 International","Citation":"Eamer, D., Borrero, M., Bashir, N., & MIT Climate & Sustainability Consortium. (2025). GeoJSON files for the MCSC's Trucking Industry Decarbonization Explorer (Geo-TIDE) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15851359","Resources":[{"Description":"GeoJSON Files for Geo-TIDE","ARN":"arn:aws:s3:::mcsc-geotide-geojson-files/geojson_files/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://mcsc-geotide-geojson-files.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Geo-TIDE access and getting-started exercises","URL":"https://danikam16.wixsite.com/mysite/post/accessing-and-using-the-mcsc-s-interactive-geospatial-decision-support-tool-for-trucking-fleet-decar","AuthorName":"Danika Eamer and Helena De Figueiredo Valente","AuthorURL":"https://github.com/danikam","Services":["Amazon S3"]},{"Title":"Which logistics facilities should a return-to-base carrier target for fleet electrification and chargers?","URL":"https://docs.google.com/presentation/d/e/2PACX-1vQZccVHZVT1QRNdhCRRI810UxGvCD3hJhxIE4CzBDbhNr9iecHV5lp2Rv87x6rik1wrCiXXUq0WfuBk/pub","AuthorName":"Danika Eamer","AuthorURL":"https://github.com/danikam","Services":["Amazon S3"]},{"Title":"Which routes should a dry-van carrier prioritize for investment in battery electric or hydrogen trucks?","URL":"https://danikam16.wixsite.com/mysite/post/user-case-studies-for-interactive-geospatial-trucking-fleet-decision-support","AuthorName":"Danika Eamer and Helena De Figueiredo Valente","AuthorURL":"https://github.com/danikam","Services":["Amazon S3"]}],"Tools & Applications":[{"Title":"MCSC Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE)","URL":"https://climatedata.mit.edu/faf5/transportation","AuthorName":"Danika Eamer, Brilant Kasami, Brooke Bao, and MIT Climate & Sustainability Consortium","AuthorURL":"https://impactclimate.mit.edu"}],"Publications":[{"Title":"Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE): Technical Guide and Methodology","URL":"https://dspace.mit.edu/handle/1721.1/159069","AuthorName":"Eamer, D., Borrero, M., Bao, B., Kasami, B., and De Figueiredo Valente, H.","AuthorURL":"https://impactclimate.mit.edu"},{"Title":"Thought Experiment to Explore Potential Savings from Pooled Charging Infrastructure Investment","URL":"https://dspace.mit.edu/handle/1721.1/153617","AuthorName":"Eamer, D. and Borrero, M.","AuthorURL":"https://impactclimate.mit.edu"}]},"ADXCategories":["Resources Data"],"RegistryEntryAdded":"2025-07-09","RegistryEntryLastModified":"2025-07-10","Slug":"geo_tide_geojsons","Sources":["awslabs-open-data-registry"]}
{"Name":"GEOGLOWS Hydrological Model Version 2","Description":"GEOGLOWS is the Group on Earth Observation's Global Water Sustainability Program. It coordinates efforts from public \nand private entities to make application ready river data more accessible and sustainably available to underdeveloped \nregions. The GEOGLOWS Hydrological Model provides a retrospective and daily forecast of global river discharge at 7 \nmillion river sub-basins. The stream network is a hydrologically conditioned subset of the TDX-Hydro streams and \nbasins data produced by the United State's National Geospatial Intelligence Agency. The daily forecast provides 3 \nhourly average discharge in a 51 member ensemble and 15 day lead time derived from the ECMWF Integrated Forecast \nSystem (IFS). The retrospective simulation is derived from ERA5 climate reanalysis data and provides daily average \nstreamflow beginning on 1 January 1940. New forecasts are uploaded daily and the retrospective simulation is updated \nweekly on Sundays to keep the lag time between 5 and 12 days.<br><br>\nThe geoglows-v2 bucket contains: (1) model configuration files used to generate the simulations, (2) the GIS streams \ndatasets used by the model, (3) the GIS streams datasets optimized for visualizations used by Esri's Living Atlas \nlayer, (4) several supporting table of metadata including country names, river names, hydrological properties used for\nmodeling.<br><br>\nThe geoglows-v2-forecasts bucket contains: (1) daily 15 forecasts in zarr format optimized for time series queries of \nall ensemble members in the prediction, (2) CSV formatted summary files optimized for producing time series animated \nweb maps for the entire global streams dataset.<br><br>\nThe geoglows-v2-retrospective bucket contains: (1) the model retrospective outputs in (1a) zarr format optimized for \ntime series queries of up to a few hundred rivers on demand as well as (1b) in netCDF format best for bulk downloading \nthe dataset, (2) estimated return period flows for all 7 million million rivers (2a) in zarr format optimized for \nreading subsets of the dataset as well as (2b) in netCDF format best for bulk downloading. (3) The initialization files \nproduced at the end of each incremental simulation useful for restarting the model from a specific date.\n","Documentation":"https://training.geoglows.org","Contact":"https://groups.google.com/g/geoglows","ManagedBy":"Riley Hales","UpdateFrequency":"Monthly","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","geopackage","hydrography","hydrologic model","hydrology","simulations","zarr"],"License":"[Creative Commons BY 4 (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)","Citation":"https://doi.org/10.1111/jfr3.12859","Resources":[{"Description":"GEOGLOWS Version 2","ARN":"arn:aws:s3:::geoglows-v2","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](http://geoglows-v2.s3-website-us-west-2.amazonaws.com)"]},{"Description":"GEOGLOWS V2 Forecasts","ARN":"arn:aws:s3:::geoglows-v2-forecasts","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](http://geoglows-v2-forecasts.s3-website-us-west-2.amazonaws.com)"]},{"Description":"GEOGLOWS V2 Retrospective Simulation","ARN":"arn:aws:s3:::geoglows-v2-retrospective","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](http://geoglows-v2-retrospective.s3-website-us-west-2.amazonaws.com)"]}],"DataAtWork":{"Tutorials":[{"Title":"River Forecast System V2 background, training and tutorials","URL":"https://training.geoglows.org","AuthorName":"Riley Hales","AuthorURL":"https://hales.app"}],"Tools & Applications":[{"Title":"GEOGLOWS Hydroviewer","URL":"https://hydroviewer.geoglows.org","AuthorName":"Riley Hales","AuthorURL":"https://hales.app"},{"Title":"pygeoglows","URL":"https://geoglows.readthedocs.io/en/latest/","AuthorName":"Riley Hales","AuthorURL":"https://hales.app"}],"Publications":[{"Title":"Advancing global hydrologic modeling with the GEOGloWS ECMWF streamflow service","URL":"https://doi.org/10.1111/jfr3.12859","AuthorName":"Hales et al."}]},"DeprecatedNotice":null,"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2023-11-01","RegistryEntryLastModified":"2025-07-09","Slug":"geoglows-v2","Sources":["awslabs-open-data-registry"]}
{"Name":"GeoNadir FAIR Data","Description":"<br/>\n<br/>\nGeoNadir FAIR Data is an open collection of uncrewed aerial vehicle (UAV or drone) mapping datasets\ncontributed through the GeoNadir platform and published as cloud-native\ngeospatial files on Amazon S3.\n<br/>\n<br/>\nEach dataset represents one drone survey. Every dataset provides a normalized\nmetadata file and a ZIP package of the original raw images. Where available,\na dataset may also include an RGB orthomosaic, a multispectral orthomosaic,\na Digital Surface Model (DSM), and a Digital Terrain Model (DTM), each stored\nas a Cloud Optimized GeoTIFF (COG) that can be read directly by GDAL,\nRasterio, QGIS, and other cloud-native geospatial tools without downloading\nthe full file.\n<br/>\n<br/>\nThe files are addressable directly on Amazon S3 by a stable key convention, so\nthe collection is fully usable today with standard tools such as boto3,\nRasterio, GDAL, and QGIS: list the datasets prefix, read each survey's\nmetadata file, and open its COGs by their known keys.\n<br/>\n<br/>\nA static STAC 1.1 catalog describes every dataset as a STAC Item and can be\nbrowsed with STAC Browser or read with pystac and other STAC clients. A STAC\nAPI for server-side search (by capture date, area of interest, and metadata\nfields such as IUCN habitat classification) is planned.\n<br/>\n<br/>\nThe collection includes only datasets that GeoNadir is able to publish\npublicly under the Creative Commons Attribution 4.0\nInternational license (CC-BY-4.0) license. Private workspace data and unpublished\ndatasets are out of scope for this collection.\n","Documentation":"https://github.com/Geonadir/open-data-examples/blob/main/OPEN_DATA_STRUCTURE.md","Contact":"opendata@geonadir.com","ManagedBy":"[GeoNadir](https://geonadir.com/)","UpdateFrequency":"New drone mapping datasets are added as they are contributed and cleared for open publication.","Tags":["aerial imagery","agriculture","aws-pds","biodiversity","coastal","cog","conservation","coral reef","dem","dsm","dtm","earth observation","ecosystems","elevation","environmental","geospatial","global","land cover","land use","mapping","marine","stac","sustainability","wildlife"],"License":"This dataset is licensed under the Creative Commons Attribution 4.0\nInternational license (CC-BY-4.0). You are free to share and adapt the\nmaterial for any purpose, provided you give appropriate credit to GeoNadir\nand the contributing data provider. Full license text is available at\nhttps://creativecommons.org/licenses/by/4.0/\n","Citation":"GeoNadir FAIR Data was accessed on DATE from https://registry.opendata.aws/geonadir-fair-data. GeoNadir. 2026. GeoNadir FAIR Data: open drone mapping datasets.","Resources":[{"Description":"GeoNadir FAIR Data drone mapping datasets (Cloud Optimized GeoTIFFs, raw image ZIPs, and per-dataset JSON metadata)","ARN":"arn:aws:s3:::geonadir-fair-data","Region":"ap-southeast-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://geonadir-fair-data.s3.ap-southeast-2.amazonaws.com/index.html)","[Browse GeoNadir FAIR Data](https://data.geonadir.com/fairgeo)","[STAC catalog](https://data.geonadir.com/stac/catalog.json)","[Browse with STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.geonadir.com/stac/catalog.json)"]},{"Description":"New object notifications for GeoNadir FAIR Data (Lambda and SQS protocols only)","ARN":"arn:aws:sns:ap-southeast-2:209003640346:geonadir-fair-data-object_created","Region":"ap-southeast-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: GeoNadir FAIR Data","URL":"https://github.com/Geonadir/open-data-examples/blob/main/datasets/geonadir-fair-data/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/Geonadir/open-data-examples/blob/main/datasets/geonadir-fair-data/get-to-know-a-dataset.ipynb","AuthorName":"GeoNadir","AuthorURL":"https://geonadir.com/","Services":["Amazon S3"]}]},"RegistryEntryAdded":"2026-08-31","RegistryEntryLastModified":"2026-10-28","Slug":"geonadir-fair-data","Sources":["awslabs-open-data-registry"]}
{"Name":"GeoNet Aotearoa New Zealand Data","Description":"GeoNet provides geological hazard information for Aotearoa New Zealand. This dataset contains data and products recorded by the GeoNet sensor network. <br /> <br /> GNSS (Global Navigation Satellite System) data include raw data in proprietary and Receiver Independent Exchange Format (RINEX) and local tie-in survey conducted during equipment changes, more details can be found on [the GeoNet geodetic page](https://www.geonet.org.nz/data/types/geodetic) website. <br /> Coastal gauge data include relative measurement of sea level measured by tsunami monitoring gauges. Raw and quality control data are provided in CREX format (Character Form for the Representtion and eXchange of metereological data), more details can be found on [the GeoNet coastal tsunami monitoring gauges page](https://www.geonet.org.nz/data/types/tidal_gauges). <br /> Camera images data include webcam images from the GeoNet Volcano monitoring network and Built Environment Instrumentation Programme, more details can be found on [the GeoNet camera page](https://www.geonet.org.nz/data/types/camera). <br /> Waveform data include raw data from weak and strong motion instruments of the GeoNet seismic networks, more details can be found on [the GeoNet seismic waveform page](https://www.geonet.org.nz/data/types/seismic_waveforms). <br /> Seismic data products include strong motion derived data, more details can be found on [the GeoNet Strong Motion products page](https://strongmotion.geonet.org.nz). <br /> Time Series data products include derived time series data from a subgroup of the GeoNet sensor network. Data are in compressed comma separated format (csv.gz), more details can be found on [the GeoNet tilde website page](https://tilde.geonet.org.nz/ui/data-exploration). <br /> <br />","Documentation":"https://github.com/GeoNet/data-tutorials/tree/main/AWS_Open_Data","Contact":"For questions regarding data content or quality, please visit [the GeoNet site](https://www.geonet.org.nz/), contact info@geonet.org.nz or use GitHub issue on [GeoNet/help](https://github.com/GeoNet/help).","ManagedBy":"[GeoNet](https://www.geonet.org.nz/)","UpdateFrequency":"Daily for majority of datasets.","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aws-pds","broadband","coastal","Continuously Operating Reference Station (CORS)","earthquakes","geophysics","geoscience","geoscience","GNSS","GPS","oceans","RINEX","seismology"],"License":"GeoNet data are made available free of charge under '[CC3 licence](https://creativecommons.org/licenses/by/3.0/nz/)' to facilitate research and risk assessment. Please acknowledge the GeoNet programme and its sponsors when using the data, see '[GeoNet Data Policy](https://www.geonet.org.nz/policy)'.","Resources":[{"Description":"Data files","ARN":"arn:aws:s3:::geonet-open-data","Type":"S3 Bucket","Region":"ap-southeast-2"}],"DataAtWork":{"Tutorials":[{"Title":"Available GeoNet GNSS event high rate data","URL":"https://doi.org/10.21420/NQYC-EG34","AuthorName":"GNS Science - Te Pū Ao"},{"Title":"GeoNet Automatically Collected Landslide Data","URL":"https://doi.org/10.21420/A0JV-6W73","AuthorName":"GNS Science - Te Pū Ao"},{"Title":"GeoNet Automatically Collected Volcano Data","URL":"https://doi.org/10.21420/EN0F-XY29","AuthorName":"GNS Science - Te Pū Ao"},{"Title":"GeoNet Coastal Tsunami Gauge Digital Waveform Dataset","URL":"https://doi.org/10.21420/EJ6W-RC96","AuthorName":"GNS Science - Te Pū Ao"},{"Title":"GeoNet continuous GNSS network RAW files","URL":"https://doi.org/10.21420/TMHV-0T46","AuthorName":"GNS Science - Te Pū Ao"},{"Title":"GeoNet continuous GNSS network RINEX","URL":"https://doi.org/10.21420/RXKE-AZ44","AuthorName":"GNS Science - Te Pū Ao"},{"Title":"GeoNet datasets available via AWS Open Data Programme","URL":"https://www.geonet.org.nz/data/access/aws","AuthorName":"GNS Science - Te Pū Ao"},{"Title":"GeoNet Deep-ocean Assessment and Reporting of Tsunami (DART) Dataset","URL":"https://doi.org/10.21420/8TCZ-TV02","AuthorName":"GNS Science - Te Pū Ao"},{"Title":"GeoNet Seismic Digital Waveform dataset","URL":"https://doi.org/10.21420/G19Y-9D40","AuthorName":"GNS Science - Te Pū Ao"},{"Title":"GeoNet Strong Motion Data Products","URL":"https://doi.org/10.21420/X0MD-MV58","AuthorName":"GNS Science - Te Pū Ao"}],"Publications":[{"Title":"GNS Science Te Pū Ao Joins the AWS Open Data Sponsorship Program to Better Prepare for Natural Disasters","URL":"https://www.aboutamazon.com.au/news/aws/gns-science-te-pu-ao-joins-the-aws-open-data-sponsorship-program-to-better-prepare-for-natural-disasters","AuthorName":"Amazon Staff"},{"Title":"Site characterisation of GeoNet stations for the New Zealand Strong Motion Database","URL":"https://doi.org/10.5459/bnzsee.50.1.39-49","AuthorName":"A. Kaiser, C. Van Houtte, N. Perrin, L. Wotherspoon, G. McVerry"},{"Title":"The development and evolution of the GeoNet and PositioNZ GNSS continuously operating network in New Zealand","URL":"https://doi.org/10.1080/00288306.2015.1127821","AuthorName":"P. Gentle; K. Gledhill; G. Blick"},{"Title":"The New Zealand National Seismograph Network","URL":"https://doi.org/10.1785/gssrl.82.1.9","AuthorName":"T. Petersen, K. Gledhill, M. Chadwick, N. Gale, J. Ristau"},{"Title":"Tsunami Forecasting and Monitoring in New Zealand","URL":"https://doi.org/10.1007/s00024-010-0223-9","AuthorName":"W. Power; N. Gale"}]},"RegistryEntryAdded":"2021-11-15","RegistryEntryLastModified":"2023-01-05","Slug":"geonet","Sources":["awslabs-open-data-registry"]}
{"Name":"GEOS-Chem Input Data","Description":"Input data for the GEOS-Chem Chemical Transport Model, includes NASA/GMAO MERRA-2 and GEOS-FP [meteorological products](https://geos-chem.readthedocs.io/en/latest/gcclassic-user-guide/input-overview.html#met), [chemistry input data](https://geos-chem.readthedocs.io/en/latest/gcclassic-user-guide/input-overview.html#chemistry-input-data), [emissions input data](https://geos-chem.readthedocs.io/en/latest/gcclassic-user-guide/input-overview.html#emis-inputs), and other smaller datasets such as model [initial conditions](https://geos-chem.readthedocs.io/en/latest/gcclassic-user-guide/input-overview.html#initial-conditions-input-data>).","Documentation":"https://geos-chem.readthedocs.io/en/stable/geos-chem-shared-docs/doc/gcid-portal-overview.html","Contact":"https://geoschem.github.io/support-team.html","UpdateFrequency":"New meteorological and emission data will be added when available.","ManagedBy":"[GEOS-Chem Support Team](https://geoschem.github.io/support-team.html)","Collabs":{"ASDI":{"Tags":["air quality"]}},"Tags":["air quality","atmosphere","aws-pds","chemistry","climate","environmental","meteorological","model","weather"],"License":"https://geoschem.github.io/license.html","Resources":[{"Description":"Top-level directory for all GEOS-Chem data.","ARN":"arn:aws:s3:::geos-chem","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://geos-chem.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"GCHP Quickstart Guide","URL":"https://gchp.readthedocs.io/en/stable/getting-started/quick-start.html","AuthorName":"GEOS-Chem Support Team","AuthorURL":"http://geos-chem.org/support-team"},{"Title":"GEOS-Chem Classic Quickstart Guide","URL":"https://geos-chem.readthedocs.io/en/stable/getting-started/quick-start.html","AuthorName":"GEOS-Chem Support Team","AuthorURL":"http://geos-chem.org/support-team"},{"Title":"GEOS-Chem Input Data on AWS cloud","URL":"https://geos-chem.readthedocs.io/en/stable/geos-chem-shared-docs/doc/gcid-portal-overview.html","AuthorName":"GEOS-Chem Support Team","AuthorURL":"http://geos-chem.org/support-team"},{"Title":"Integrated Methane Inversion (IMI) Quickstart Guide","URL":"https://imi.readthedocs.io/en/latest/getting-started/quick-start.html","AuthorName":"Daniel Varon et al.","AuthorURL":"http://carboninversion.com/people"}],"Publications":[{"Title":"Enabling high-performance cloud computing for Earth science modeling on over a thousand cores: application to the GEOS-Chem atmospheric chemistry model","URL":"https://doi.org/10.1029/2020MS002064","AuthorName":"Jiawei Zhuang et al."},{"Title":"Enabling Immediate Access to Earth Science Models through Cloud Computing: Application to the GEOS-Chem Model","URL":"https://doi.org/10.1175/BAMS-D-18-0243.1","AuthorName":"Jiawei Zhuang et al."},{"Title":"Improved advection, resolution, performance, and community access in the new generation (version 13) of the high-performance GEOS-Chem global atmospheric chemistry model (GCHP)","URL":"https://doi.org/10.5194/gmd-15-8731-2022","AuthorName":"Randall V. Martin et al."}]},"RegistryEntryAdded":"2018-04-19","RegistryEntryLastModified":"2025-02-25","Slug":"geoschem-input-data","Sources":["awslabs-open-data-registry"]}
{"Name":"GEOS-Chem Nested Input Data","Description":"Input data for nested-grid simulations using the GEOS-Chem Chemical Transport Model. This includes the NASA/GMAO MERRA-2 and GEOS-FP [meteorological products](https://geos-chem.readthedocs.io/en/latest/gcclassic-user-guide/input-overview.html#met), the [HEMCO emission inventories](https://geos-chem.readthedocs.io/en/latest/gcclassic-user-guide/input-overview.html#emis-inputs), and other small data such as [model initial conditions](https://geos-chem.readthedocs.io/en/latest/gcclassic-user-guide/restart-files.html).","Documentation":"https://geos-chem.readthedocs.io/en/stable/geos-chem-shared-docs/doc/gcid-special-portals.html#geos-chem-nested-input-data","Contact":"http://geos-chem.org/support-team","UpdateFrequency":"New meteorological and emission data will be added when available.","ManagedBy":"[GEOS-Chem Support Team](https://geoschem.github.io/support-team.html)","Collabs":{"ASDI":{"Tags":["air quality"]}},"Tags":["air quality","atmosphere","aws-pds","chemistry","climate","environmental","meteorological","model","weather"],"License":"http://geos-chem.org/license","Resources":[{"Description":"Top-level directory for all GEOS-Chem nested-grid data.","ARN":"arn:aws:s3:::gcgrid","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://s3.amazonaws.com/gcgrid/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"GCHP Quickstart Guide","URL":"https://gchp.readthedocs.io/en/latest/getting-started/quick-start.html","AuthorName":"GEOS-Chem Support Team","AuthorURL":"http://geos-chem.org/support-team"},{"Title":"GEOS-Chem Classic Quickstart Guide","URL":"https://geos-chem.readthedocs.io/en/latest/getting-started/quick-start.html","AuthorName":"GEOS-Chem Support Team","AuthorURL":"http://geos-chem.org/support-team"},{"Title":"GEOS-Chem input data on AWS cloud","URL":"https://geos-chem.readthedocs.io/en/latest/geos-chem-shared-docs/supplemental-guides/geos-chem-input-data-on-aws.html","AuthorName":"GEOS-Chem Support Team","AuthorURL":"http://geos-chem.org/support-team"},{"Title":"GEOS-Chem nested input data","URL":"https://geos-chem.readthedocs.io/en/stable/geos-chem-shared-docs/doc/gcid-special-portals.html#geos-chem-nested-input-data","AuthorName":"GEOS-Chem Support Team","AuthorURL":"http://geos-chem.org/support-team"}],"Tools & Applications":[{"Title":"Integrated Methane Inversion","URL":"https://carboninversion.com","AuthorName":"Daniel Varon et al.","AuthorURL":"https://carboninversion.com/people/"}],"Publications":[{"Title":"Enabling high-performance cloud computing for Earth science modeling on over a thousand cores: application to the GEOS-Chem atmospheric chemistry model","URL":"https://doi.org/10.1029/2020MS002064","AuthorName":"Jiawei Zhuang et al."},{"Title":"Enabling Immediate Access to Earth Science Models through Cloud Computing: Application to the GEOS-Chem Model","URL":"https://doi.org/10.1175/BAMS-D-18-0243.1","AuthorName":"Jiawei Zhuang et al."}]},"RegistryEntryAdded":"2024-07-11","RegistryEntryLastModified":"2025-02-25","Slug":"geoschem-nested-input-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Global gridded estimates of the dominant age of the built stock (GHS-AGE)","Description":"GHS-AGE consists of spatial raster datasets representing global, gridded estimates of the dominant age of the built stock. GHS-AGE has been derived from a multi-temporal stack of gridded estimates of built-up surface (GHS-BUILT-S R2023A, 1975-2020) that has derived from integrated processing of Sentinel-2 and historical Landsat multispectral data. GHS-AGE estimates represent the epoch in which 50% of the built-up surface of 2020 has been exceeded for the first time, for each grid cell. GHS-AGE enables the global, fine-grained modelling of approximate building construction epoch information using a globally consistent methodology.","Documentation":"https://data.jrc.ec.europa.eu/dataset/d503bb56-9884-4e4d-bb8f-d86711d9f749","Contact":"JRC-GHSL-DATA@ec.europa.eu","ManagedBy":"[JRC](https://joint-research-centre.ec.europa.eu)","UpdateFrequency":"NA","Tags":["age","classification","cog","disaster response","earth observation","geoscience","geospatial","global","mapping","planetary","tiff","tiles"],"License":"GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.","Resources":[{"Description":"GHS-AGE is available in World Mollweide Equal Area projection (ESRI:54009), at 100 m and 1000 m resolution, and for two levels of temporal resolution (i.e., 5-year and 10-year intervals). Class codes for 10-year intervals: 0: not built-up; 1: median age < 1980; 2: 1980 - 1990, 3: 1990 - 2000, 4: 2000 - 2010, 5: 2010 - 2020. Class codes for 5-year intervals: 0: not built-up; 1: median age < 1975; 2: 1975 - 1980,..., 10: 2010 - 2020.","ARN":"arn:aws:s3:::jrc-ghsl/ghs-age","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:eu-central-1:291461994469:jrc-ghsl-object_created","Region":"eu-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: GHS-AGE","URL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-age","NotebookURL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-age/get-to-know-a-dataset.ipynb","AuthorName":"MAFFENINI Luca","AuthorURL":"https://github.com/lucamaff"}],"Publications":[{"Title":"GHS-AGE: Global gridded estimates of the dominant age of the built stock (1975-2020)","URL":"https://dx.doi.org/10.2760/6443992","AuthorName":"UHL, Johannes H., POLITIS, Panagiotis, PESARESI, Martino"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2026-10-12","RegistryEntryLastModified":"2026-10-14","Slug":"ghs-age","Sources":["awslabs-open-data-registry"]}
{"Name":"GHSL morphological settlement zones and characteristics (GHS-BUILT-C)","Description":"GHS-BUILT-C delineates the boundaries of the human settlements at 10m resolution and describes their inner characteristics in terms of the morphology of the built environment and the functional use. These Morphological Settlement Zones (MSZ) delineate the spatial domain of human settlements at a neighboring scale of approx. 100m, based on the spatial generalization of the built-up surfaces. GHS-BUILT-C also includes within-settlement open spaces. MSZ, open spaces, and built spaces class abstractions are derived by morphological spatial filtering and are further classified according to vegetation intensity, water surfaces (GHS_LAND_GLOBE_R2022A), road surfaces (OSM highways), functional use (GHS-BUILT-C_FUN_GLOBE_R2023A), and building height (GHS-BUILT-H_GLOBE_R2023A). The FUN subdataset provides a residential vs. non-residential functional classification of the built domain.","Documentation":"https://human-settlement.emergency.copernicus.eu/ghs_buC2023.php","Contact":"JRC-GHSL-DATA@ec.europa.eu","ManagedBy":"[JRC](https://joint-research-centre.ec.europa.eu)","UpdateFrequency":"NA","Tags":["cities","classification","cog","disaster response","earth observation","geoscience","geospatial","global","land use","planetary","tiff","tiles"],"License":"GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.","Resources":[{"Description":"GHSL Morphological Settlement Zone (MSZ) delineation and within-settlement classification in World Mollweide (EPSG:54009) at 10m spatial resolution, globally for the year 2018.","ARN":"arn:aws:s3:::jrc-ghsl/ghs-built-c-msz","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"GHSL gridded residential vs. non-residential functional classification in World Mollweide (EPSG:54009) at 10m spatial resolution, globally for the year 2018.","ARN":"arn:aws:s3:::jrc-ghsl/ghs-built-c-fun","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:eu-central-1:291461994469:jrc-ghsl-object_created","Region":"eu-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: GHS BUILT-C","URL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-built-c","NotebookURL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-built-c/get-to-know-a-dataset.ipynb","AuthorName":"MAFFENINI Luca","AuthorURL":"https://github.com/lucamaff"}],"Tools & Applications":[{"Title":"Global visualisation","URL":"https://human-settlement.emergency.copernicus.eu/visualisation.php","AuthorName":"TOMMASI Pierpaolo, MAFFENINI Luca"}],"Publications":[{"Title":"Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and Population Survey data","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC136539","AuthorName":"PESARESI Martino, SCHIAVINA Marcello, POLITIS Panagiotis, FREIRE Sergio, GOCH Katarzyna, UHL Johannes H., CARIOLI Alessandra, CORBANE Christina, DIJKSTRA Lewis, FLORIO Pietro, FRIEDRICH Hannah K., GAO Jing, LEYK Stefan, LINLIN Lu, MAFFENINI Luca, MARI-RIVERO Ines, MELCHIORRI Michele, SYRRIS Vasileios, VAN DEN HOEK Jamon, KEMPER Thomas"},{"Title":"Generalized vertical components of built-up areas from global Digital Elevation Models by multi-scale linear regression modelling","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC117825","AuthorName":"PESARESI Martino, CORBAN Christina, REN Chao, EDWARD Ng"},{"Title":"GHSL Data Package 2023","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC133256","AuthorName":"SCHIAVINA Marcello, MELCHIORRI Michele, PESARESI Martino, POLITIS Panagiotis, CARNEIRO FREIRE Sergio Manuel, MAFFENINI Luca, FLORIO Pietro, EHRLICH Daniele, GOCH Katarzyna, CARIOLI Alessandra, UHL Johannes, TOMMASI Pierpaolo, KEMPER Thomas"},{"Title":"The Generalised Settlement Area: mapping the Earth surface in the vicinity of built-up areas","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC113584","AuthorName":"FLORCZYK Aneta, MELCHIORI Michele, ZEIDLER Julian, CORBAN Christina, SCHIAVINA Marcello, CARNEIRO FREIRE Sergio Manuel, SABO Filip, POLITIS Panagiotis, ESCH Thomas, PESARESI Martino"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2026-09-25","RegistryEntryLastModified":"2026-10-14","Slug":"ghs-built-c","Sources":["awslabs-open-data-registry"]}
{"Name":"GHSL global building height estimates (GHS-BUILT-H)","Description":"GHS-BUILT-H depicts the depicts the global spatial distribution of building heights extracted from global digital elevation models (DEMs) and Sentinel-2 satellite imagery. The datasets consist of two products: 1) Average of the Net Building Height (ANBH), representing the mean building height per grid cell, in meters, 2) Average of the Gross Building Height (AGBH), in cubic meters per surface unit. The input DEMs are the ALOS World 3D - 30m (2006-2011) and the Shuttle Radar Topography Mission 30m (timestamp 2000). The building heights extracted from these sources are updated using shadow markers extracted from more recent Sentinel-2 imagery for the year 2018 (GHS-composite-S2 R2020A).\n","Documentation":"https://human-settlement.emergency.copernicus.eu/ghs_buH2023.php","Contact":"JRC-GHSL-DATA@ec.europa.eu","ManagedBy":"[JRC](https://joint-research-centre.ec.europa.eu)","UpdateFrequency":"NA","Tags":["cities","classification","cog","disaster response","earth observation","elevation","geoscience","geospatial","global","mapping","planetary","tiff","tiles"],"License":"GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.","Resources":[{"Description":"Average gross building height estimates in World Mollweide (EPSG:54009) at 100m spatial resolution; also available in WGS 1984 (EPSG:4326) at 3 arcsecond spatial resolution, globally for the year 2018.","ARN":"arn:aws:s3:::jrc-ghsl/ghs-built-h-agbh","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"Average net building height estimates in World Mollweide (EPSG:54009) at 100m spatial resolution; also available in WGS 1984 (EPSG:4326) at 3 arcsecond spatial resolution, globally for the year 2018.","ARN":"arn:aws:s3:::jrc-ghsl/ghs-built-h-anbh","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:eu-central-1:291461994469:jrc-ghsl-object_created","Region":"eu-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: GHS BUILT-H","URL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-built-h","NotebookURL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-built-h/get-to-know-a-dataset.ipynb","AuthorName":"MAFFENINI Luca","AuthorURL":"https://github.com/lucamaff"}],"Publications":[{"Title":"Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and Population Survey data","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC136539","AuthorName":"PESARESI Martino, SCHIAVINA Marcello, POLITIS Panagiotis, FREIRE Sergio, GOCH Katarzyna, UHL Johannes H., CARIOLI Alessandra, CORBANE Christina, DIJKSTRA Lewis, FLORIO Pietro, FRIEDRICH Hannah K., GAO Jing, LEYK Stefan, LINLIN Lu, MAFFENINI Luca, MARI-RIVERO Ines, MELCHIORRI Michele, SYRRIS Vasileios, VAN DEN HOEK Jamon, KEMPER Thomas"},{"Title":"Generalized vertical components of built-up areas from global Digital Elevation Models by multi-scale linear regression modelling","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC117825","AuthorName":"PESARESI Martino, CORBAN Christina, REN Chao, EDWARD Ng"},{"Title":"GHSL Data Package 2023","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC133256","AuthorName":"SCHIAVINA Marcello, MELCHIORRI Michele, PESARESI Martino, POLITIS Panagiotis, CARNEIRO FREIRE Sergio Manuel, MAFFENINI Luca, FLORIO Pietro, EHRLICH Daniele, GOCH Katarzyna, CARIOLI Alessandra, UHL Johannes, TOMMASI Pierpaolo, KEMPER Thomas"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2026-09-25","RegistryEntryLastModified":"2026-10-14","Slug":"ghs-built-h","Sources":["awslabs-open-data-registry"]}
{"Name":"GHSL global built-up surface estimates (GHS-BUILT-S)","Description":"GHS-BUILT-S depicts the global distribution of built-up surface from 1975 to 2030, measured in square metres per grid cell. The dataset reports both the total built-up surface and the surface of predominantly non-residential (NRES) structures.","Documentation":"https://human-settlement.emergency.copernicus.eu/ghs_buS2023.php","Contact":"JRC-GHSL-DATA@ec.europa.eu","ManagedBy":"[JRC](https://joint-research-centre.ec.europa.eu)","UpdateFrequency":"NA","Tags":["classification","cog","disaster response","earth observation","geoscience","geospatial","global","land cover","land use","mapping","planetary","tiff","tiles"],"License":"GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.","Resources":[{"Description":"GHS-BUILT-S global gridded built-up surface estimates in World Mollweide projection (EPSG:54009) at 10m, 100m and 1000m spatial resolution; also available in WGS 1984 (EPSG:4326) at spatial resolutions of 3 and 30 arcseconds. Global spatial extent, temporal extent from 1975 to 2030 in 5-year steps for the 100 and 1000-m data, while the 10-m data are available for 2018.","ARN":"arn:aws:s3:::jrc-ghsl/ghs-built-s","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"GHS-BUILT-S_NRES global gridded built-up surface estimates for the \"non-residential\" (NRES) domain in World Mollweide projection (EPSG:54009) at 10m, 100m and 1000m spatial resolution; also available in WGS 1984 (EPSG:4326) at spatial resolutions of 3 and 30 arcseconds. Global spatial extent, temporal extent from 1975 to 2030 in 5-year steps for the 100 and 1000-m data, while the 10-m data are available for 2018.","ARN":"arn:aws:s3:::jrc-ghsl/ghs-built-s-nres","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:eu-central-1:291461994469:jrc-ghsl-object_created","Region":"eu-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: GHS BUILT-S","URL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-built-s","NotebookURL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-built-s/get-to-know-a-dataset.ipynb","AuthorName":"MAFFENINI Luca","AuthorURL":"https://github.com/lucamaff"}],"Tools & Applications":[{"Title":"Global visualisation","URL":"https://human-settlement.emergency.copernicus.eu/visualisation.php","AuthorName":"TOMMASI Pierpaolo, MAFFENINI Luca"}],"Publications":[{"Title":"Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and Population Survey data","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC136539","AuthorName":"PESARESI Martino, SCHIAVINA Marcello, POLITIS Panagiotis, FREIRE Sergio, GOCH Katarzyna, UHL Johannes H., CARIOLI Alessandra, CORBANE Christina, DIJKSTRA Lewis, FLORIO Pietro, FRIEDRICH Hannah K., GAO Jing, LEYK Stefan, LINLIN Lu, MAFFENINI Luca, MARI-RIVERO Ines, MELCHIORRI Michele, SYRRIS Vasileios, VAN DEN HOEK Jamon, KEMPER Thomas"},{"Title":"Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC99996","AuthorName":"PESARESI Martino, CORBAN Christina, JULEA Andreea Maria, FLORCZYK Aneta, SYRRIS Vasileios, SOILLE Pierre"},{"Title":"GHSL Data Package 2023","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC133256","AuthorName":"SCHIAVINA Marcello, MELCHIORRI Michele, PESARESI Martino, POLITIS Panagiotis, CARNEIRO FREIRE Sergio Manuel, MAFFENINI Luca, FLORIO Pietro, EHRLICH Daniele, GOCH Katarzyna, CARIOLI Alessandra, UHL Johannes, TOMMASI Pierpaolo, KEMPER Thomas"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2026-09-25","RegistryEntryLastModified":"2026-10-14","Slug":"ghs-built-s","Sources":["awslabs-open-data-registry"]}
{"Name":"GHSL global building volume estimates (GHS-BUILT-V)","Description":"GHS-BUILT-V depicts the global distribution of building volume from 1975 to 2030, measured in cubic metres per grid cell. The dataset reports both the total building volume and the volume of predominantly non-residential (NRES) structures.","Documentation":"https://human-settlement.emergency.copernicus.eu/ghs_buV2023.php","Contact":"JRC-GHSL-DATA@ec.europa.eu","ManagedBy":"[JRC](https://joint-research-centre.ec.europa.eu)","UpdateFrequency":"NA","Tags":["classification","cog","disaster response","earth observation","geoscience","geospatial","global","land cover","land use","mapping","planetary","tiff","tiles"],"License":"GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.","Resources":[{"Description":"GHS-BUILT-V global gridded building volume estimates in World Mollweide projection (EPSG:54009) at 100m and 1000m spatial resolution; also available in WGS 1984 (EPSG:4326) at spatial resolutions of 3 and 30 arcseconds. Global spatial extent, temporal extent from 1975 to 2030 in 5-year steps.","ARN":"arn:aws:s3:::jrc-ghsl/ghs-built-v","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"GHS-BUILT-V-NRES global gridded building volume estimates for the \"non-residential\" (NRES) domain in World Mollweide projection (EPSG:54009) at 100m and 1000m spatial resolution; also available in WGS 1984 (EPSG:4326) at spatial resolutions of 3 and 30 arcseconds. Global spatial extent, temporal extent from 1975 to 2030 in 5-year steps.","ARN":"arn:aws:s3:::jrc-ghsl/ghs-built-v-nres","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:eu-central-1:291461994469:jrc-ghsl-object_created","Region":"eu-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: GHS BUILT-V","URL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-built-v","NotebookURL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-built-v/get-to-know-a-dataset.ipynb","AuthorName":"MAFFENINI Luca","AuthorURL":"https://github.com/lucamaff"}],"Tools & Applications":[{"Title":"Global visualisation","URL":"https://human-settlement.emergency.copernicus.eu/visualisation.php","AuthorName":"TOMMASI Pierpaolo, MAFFENINI Luca"}],"Publications":[{"Title":"Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and Population Survey data","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC136539","AuthorName":"PESARESI Martino, SCHIAVINA Marcello, POLITIS Panagiotis, FREIRE Sergio, GOCH Katarzyna, UHL Johannes H., CARIOLI Alessandra, CORBANE Christina, DIJKSTRA Lewis, FLORIO Pietro, FRIEDRICH Hannah K., GAO Jing, LEYK Stefan, LINLIN Lu, MAFFENINI Luca, MARI-RIVERO Ines, MELCHIORRI Michele, SYRRIS Vasileios, VAN DEN HOEK Jamon, KEMPER Thomas"},{"Title":"Generalized vertical components of built-up areas from global Digital Elevation Models by multi-scale linear regression modelling","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC117825","AuthorName":"PESARESI Martino, CORBAN Christina, REN Chao, EDWARD Ng"},{"Title":"GHSL Data Package 2023","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC133256","AuthorName":"SCHIAVINA Marcello, MELCHIORRI Michele, PESARESI Martino, POLITIS Panagiotis, CARNEIRO FREIRE Sergio Manuel, MAFFENINI Luca, FLORIO Pietro, EHRLICH Daniele, GOCH Katarzyna, CARIOLI Alessandra, UHL Johannes, TOMMASI Pierpaolo, KEMPER Thomas"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2026-09-25","RegistryEntryLastModified":"2026-10-14","Slug":"ghs-built-v","Sources":["awslabs-open-data-registry"]}
{"Name":"GHSL gridded land surface area estimates (GHS-LAND)","Description":"GHS-LAND represents the fraction of land surface per grid cell, derived from a Sentinel-2 multispectral data composite from 2018 and OpenStreetMap (OSM) data.","Documentation":"https://human-settlement.emergency.copernicus.eu/ghs_land2022.php","Contact":"JRC-GHSL-DATA@ec.europa.eu","ManagedBy":"[JRC](https://joint-research-centre.ec.europa.eu)","UpdateFrequency":"NA","Tags":["classification","cog","disaster response","earth observation","geoscience","geospatial","global","land","mapping","planetary","surface water","tiff","tiles"],"License":"GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.","Resources":[{"Description":"The GHSL gridded land surface raster layers in World Mollweide projection (EPSG:54009) at 10m, 100m and 1000m spatial resolution, globally for the year 2018.","ARN":"arn:aws:s3:::jrc-ghsl/ghs-land","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:eu-central-1:291461994469:jrc-ghsl-object_created","Region":"eu-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: GHS LAND","URL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-land","NotebookURL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-land/get-to-know-a-dataset.ipynb","AuthorName":"MAFFENINI Luca","AuthorURL":"https://github.com/lucamaff"}],"Publications":[{"Title":"Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and Population Survey data","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC136539","AuthorName":"PESARESI Martino, SCHIAVINA Marcello, POLITIS Panagiotis, FREIRE Sergio, GOCH Katarzyna, UHL Johannes H., CARIOLI Alessandra, CORBANE Christina, DIJKSTRA Lewis, FLORIO Pietro, FRIEDRICH Hannah K., GAO Jing, LEYK Stefan, LINLIN Lu, MAFFENINI Luca, MARI-RIVERO Ines, MELCHIORRI Michele, SYRRIS Vasileios, VAN DEN HOEK Jamon, KEMPER Thomas"},{"Title":"GHSL Data Package 2022","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC129516","AuthorName":"SCHIAVINA Marcello, MELCHIORRI Michele, PESARESI Martino, POLITIS Panagiotis, CARNEIRO FREIRE Sergio Manuel, MAFFENINI Luca, FLORIO Pietro, EHRLICH Daniele, GOCH Katarzyna, TOMMASI Pierpaolo, KEMPER Thomas"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2026-09-25","RegistryEntryLastModified":"2026-10-14","Slug":"ghs-land","Sources":["awslabs-open-data-registry"]}
{"Name":"GHSL gridded population estimates (GHS-POP)","Description":"These gridded datasets depict the distribution of residential population, expressed by number of persons per 100-m and 1-km cell, in 5-year steps from 1975 to 2030, Residential population estimates between 1975 and 2020 and short-term spatially explicit projections to 2025 and 2030 are derived from CIESIN GPWv4.11 data which were disaggregated from census or administrative units to grid cells, informed by the distribution, volume, and classification of built-up areas as mapped in the Global Human Settlement Layer (GHSL) datasets for each point in time.","Documentation":"https://human-settlement.emergency.copernicus.eu/ghs_pop2023.php","Contact":"JRC-GHSL-DATA@ec.europa.eu","ManagedBy":"[JRC](https://joint-research-centre.ec.europa.eu)","UpdateFrequency":"NA","Tags":["census","cog","demographics","demography","disaster response","earth observation","geoscience","geospatial","global","mapping","planetary","population","tiff","tiles"],"License":"GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.","Resources":[{"Description":"GHSL gridded population raster layers in World Mollweide projection (EPSG:54009) at 100m and 1000m spatial resolution; also available in WGS 1984 (EPSG:4326) at 3 and 30 arcsecond spatial resolution, globally from 1975 to 2030 in 5-year steps.","ARN":"arn:aws:s3:::jrc-ghsl/ghs-pop","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:eu-central-1:291461994469:jrc-ghsl-object_created","Region":"eu-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: GHS POP","URL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-pop","NotebookURL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-pop/get-to-know-a-dataset.ipynb","AuthorName":"MAFFENINI Luca","AuthorURL":"https://github.com/lucamaff"},{"Title":"Mapping Population and Built-Up Surface with GHSL in a Browser-Based Notebook on Scigantic","URL":"https://scigantic.com/archive/8043c357-eb10-5012-b52f-0846c894aaf5/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"}],"Tools & Applications":[{"Title":"Global visualisation","URL":"https://human-settlement.emergency.copernicus.eu/visualisation.php","AuthorName":"TOMMASI Pierpaolo, MAFFENINI Luca"}],"Publications":[{"Title":"Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and Population Survey data","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC136539","AuthorName":"PESARESI Martino, SCHIAVINA Marcello, POLITIS Panagiotis, FREIRE Sergio, GOCH Katarzyna, UHL Johannes H., CARIOLI Alessandra, CORBANE Christina, DIJKSTRA Lewis, FLORIO Pietro, FRIEDRICH Hannah K., GAO Jing, LEYK Stefan, LINLIN Lu, MAFFENINI Luca, MARI-RIVERO Ines, MELCHIORRI Michele, SYRRIS Vasileios, VAN DEN HOEK Jamon, KEMPER Thomas"},{"Title":"GHSL Data Package 2023","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC133256","AuthorName":"SCHIAVINA Marcello, MELCHIORRI Michele, PESARESI Martino, POLITIS Panagiotis, CARNEIRO FREIRE Sergio Manuel, MAFFENINI Luca, FLORIO Pietro, EHRLICH Daniele, GOCH Katarzyna, CARIOLI Alessandra, UHL Johannes, TOMMASI Pierpaolo, KEMPER Thomas"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2026-09-25","RegistryEntryLastModified":"2026-10-14","Slug":"ghs-pop","Sources":["awslabs-open-data-registry"]}
{"Name":"GHSL Degree of Urbanisation settlement model (GHS-SMOD)","Description":"These gridded datasets represent the “Degree of Urbanisation” (DEGURBA) settlement model per 1-km grid cell from 1975 to 2030 in 5-year time steps, derived from the GHS-POP R2023A gridded population estimates and GHS-BUILT-S gridded estimates of built-up surface. The DEGURBA methodology has been endorsed by the UN Statistical Commission and categorizes the Earth's surface into either three broad classes (level 1) or seven more detailed classes (level 2), capturing the spectrum from rural to urban areas. For instance, level one differentiates between urban centres, peri-urban or semi-dense areas, and rural areas. The Degree of Urbanisation method is based on segmentation of gridded population data (GHS-POP R2023A) and gridded built-up surface estimates from GHS-BUILT-S R2023A). These datasets represent the Degree of Urbanisation settlement model at the detailed level 2. The level-1 classification can be obtained by class aggregation. Class codes: 10 (water), 11 (Very low density rural area), 12 (Low density rural area), 13 (Rural cluster), 21 (Suburban or periurban area), 22 (Semi-dense urban cluster), 23 (Dense urban cluster), 30 (Urban centre).\n","Documentation":"https://human-settlement.emergency.copernicus.eu/ghs_smod2023.php","Contact":"JRC-GHSL-DATA@ec.europa.eu","ManagedBy":"[JRC](https://joint-research-centre.ec.europa.eu)","UpdateFrequency":"NA","Tags":["aws-pds","census","cities","classification","cog","demographics","demography","disaster response","earth observation","geoscience","geospatial","global","land use","mapping","planetary","population","tiff","tiles"],"License":"GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.","Resources":[{"Description":"The GHSL Degree of Urbanisation settlement model in World Mollweide projection (EPSG:54009) at 1km spatial resolution; also available in WGS 1984 (EPSG:4326) at 30 arcsecond spatial resolution, globally from 1975 to 2030 in 5-year time steps.","ARN":"arn:aws:s3:::jrc-ghsl/ghs-smod","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:eu-central-1:291461994469:jrc-ghsl-object_created","Region":"eu-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: GHS SMOD","URL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-smod","NotebookURL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-smod/get-to-know-a-dataset.ipynb","AuthorName":"MAFFENINI Luca","AuthorURL":"https://github.com/lucamaff"}],"Tools & Applications":[{"Title":"Global visualisation","URL":"https://human-settlement.emergency.copernicus.eu/visualisation.php","AuthorName":"TOMMASI Pierpaolo, MAFFENINI Luca"}],"Publications":[{"Title":"Applying the degree of urbanisation - A methodological manual to define cities, towns and rural areas for international comparisons","URL":"https://doi.org/10.2785/706535","AuthorName":"Eurostat (European Commission)"},{"Title":"GHSL Data Package 2023","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC133256","AuthorName":"SCHIAVINA Marcello, MELCHIORRI Michele, PESARESI Martino, POLITIS Panagiotis, CARNEIRO FREIRE Sergio Manuel, MAFFENINI Luca, FLORIO Pietro, EHRLICH Daniele, GOCH Katarzyna, CARIOLI Alessandra, UHL Johannes, TOMMASI Pierpaolo, KEMPER Thomas"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2026-09-25","RegistryEntryLastModified":"2026-10-14","Slug":"ghs-smod","Sources":["awslabs-open-data-registry"]}
{"Name":"GHSL World Urbanisation Prospects projected gridded built-up surface estimates (GHS-WUP-BUILT-S)","Description":"Gridded datasets representing estimates of the distribution of built-up surface from 1975 to 2100 in 5-year time steps. The built-up surface estimates from 1975 to 2020 are obtained from the GHS-BUILT-S R2023 product, while the projected estimates from 2025 to 2100 are computed using the CRISP model, assuming a stable share of non-residential built-up surface while modelling the growth of the residential component of the total built-up surface.","Documentation":"https://human-settlement.emergency.copernicus.eu/ghs_wup_built_s_r2025a.php","Contact":"JRC-GHSL-DATA@ec.europa.eu","ManagedBy":"[JRC](https://joint-research-centre.ec.europa.eu)","UpdateFrequency":"NA","Tags":["classification","cog","disaster response","earth observation","geoscience","geospatial","global","land cover","land use","mapping","planetary","tiff","tiles"],"License":"GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.","Resources":[{"Description":"Spatial raster datasets in World Mollweide projection (EPSG:54009) at 1km spatial resolution; also available in WGS 1984 (EPSG:4326) at 30 arcsecond spatial resolution. Data are available globally from 1975 to 2100 in 5-year steps and represent the estimated total built-up surface per grid cell in square metres.","ARN":"arn:aws:s3:::jrc-ghsl/ghs-wup-built-s","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:eu-central-1:291461994469:jrc-ghsl-object_created","Region":"eu-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: GHS WUP BUILT-S","URL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-wup-built-s","NotebookURL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-wup-built-s/get-to-know-a-dataset.ipynb","AuthorName":"MAFFENINI Luca","AuthorURL":"https://github.com/lucamaff"}],"Tools & Applications":[{"Title":"Raster data visualization","URL":"https://human-settlement.emergency.copernicus.eu/raster.php","AuthorName":"TOMMASI Pierpaolo, MAFFENINI Luca"}],"Publications":[{"Title":"GHSL WUP Projections Data Package 2025","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC144209","AuthorName":"SCHIAVINA Marcello, MELCHIORRI Michele, MARI RIVERO Ines, FLORIO Pietro, FREIRE Sergio, JACOBS-CRISIONI Chris, MAFFENINI Luca, POLITIS Panagiotis, UHL Johannes H., CARIOLI Alessandra, PESARESI Martino, DIJKSTRA Lewis, KEMPER Thomas"},{"Title":"Introducing the CRISP model to downscale future population projections","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC143916","AuthorName":"JACOBS-CRISIONI Chris, SCHIAVINA Marcello, KRASNODEBSKA Katarzyna, DIJKSTRA Lewis, CLAASSENS Jip, HILFERINK Maarten, VAN DER WIELEN Thijmen, KOOMEN Eric"},{"Title":"Population by degree of urbanization and by urban agglomeration from 1950 to 2100","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC144219","AuthorName":"JACOBS-CRISIONI Chris, SCHIAVINA Marcello, ALESSANDRINI Alfredo, DIJKSTRA Lewis"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2026-09-25","RegistryEntryLastModified":"2026-10-14","Slug":"ghs-wup-built-s","Sources":["awslabs-open-data-registry"]}
{"Name":"GHSL World Urbanisation Prospects Projected Degree of Urbanisation (GHS-WUP-DEGURBA)","Description":"These gridded datasets represent the “Degree of Urbanisation” (DEGURBA) settlement classification per 1-km grid cell from 1975 to 2100 in 5-year time steps, derived from the GHS-WUP-POP gridded population projections. The DEGURBA methodology classifies human settlements into three to seven classes along the rural-urban continuum and provides a globally consistent way to delineate cities, towns, sub- and periurban settlements, villages, and scattered rural settlements. Class codes: 10 (water), 11 (Very low density rural area), 12 (Low density rural area), 13 (Rural cluster), 21 (Suburban or periurban area), 22 (Semi-dense urban cluster), 23 (Dense urban cluster), 30 (Urban centre).\n","Documentation":"https://human-settlement.emergency.copernicus.eu/ghs_wup_degurba_r2025a.php","Contact":"JRC-GHSL-DATA@ec.europa.eu","ManagedBy":"[JRC](https://joint-research-centre.ec.europa.eu)","UpdateFrequency":"NA","Tags":["census","cities","classification","cog","demographics","demography","disaster response","earth observation","geoscience","geospatial","global","land use","mapping","planetary","population","tiff","tiles"],"License":"GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.","Resources":[{"Description":"Degree of Urbanisation settlement class codes at 1 km resolution in five-year steps from 1975 to 2100 in World Mollweide projection (EPSG:54009)","ARN":"arn:aws:s3:::jrc-ghsl/ghs-wup-degurba","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:eu-central-1:291461994469:jrc-ghsl-object_created","Region":"eu-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: GHS WUP DEGURBA","URL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-wup-degurba","NotebookURL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-wup-degurba/get-to-know-a-dataset.ipynb","AuthorName":"MAFFENINI Luca","AuthorURL":"https://github.com/lucamaff"}],"Tools & Applications":[{"Title":"Raster data visualization","URL":"https://human-settlement.emergency.copernicus.eu/raster.php","AuthorName":"TOMMASI Pierpaolo, MAFFENINI Luca"}],"Publications":[{"Title":"GHSL WUP Projections Data Package 2025","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC144209","AuthorName":"SCHIAVINA Marcello, MELCHIORRI Michele, MARI RIVERO Ines, FLORIO Pietro, FREIRE Sergio, JACOBS-CRISIONI Chris, MAFFENINI Luca, POLITIS Panagiotis, UHL Johannes H., CARIOLI Alessandra, PESARESI Martino, DIJKSTRA Lewis, KEMPER Thomas"},{"Title":"Introducing the CRISP model to downscale future population projections","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC143916","AuthorName":"JACOBS-CRISIONI Chris, SCHIAVINA Marcello, KRASNODEBSKA Katarzyna, DIJKSTRA Lewis, CLAASSENS Jip, HILFERINK Maarten, VAN DER WIELEN Thijmen, KOOMEN Eric"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2026-09-25","RegistryEntryLastModified":"2026-10-14","Slug":"ghs-wup-degurba","Sources":["awslabs-open-data-registry"]}
{"Name":"GHSL World Urbanisation Prospects projected gridded population estimates (GHS-WUP-POP)","Description":"Spatially explicit, gridded projections of human population from 1975 to 2100 at 1 km spatial resolution, reported as persons per sqkm in 5-year intervals. Residential population estimates between 1975 and 2020 are derived from raw global census data harmonized by CIESIN for the Gridded Population of the World (GPWv4.11) dataset, combined with population trends obtained from the urban agglomeration time series of the UN World Urbanisation Prospects 2018 (UN WUP 2018 – F22). The population projections for 2025 to 2100 are computed according to the 2000-2020 local growth rates in subnational functional areas converging to the projected national growth rates for 2100 (UN World Population Prospects, 2024) and are downscaled to 1-km grid cells using the CRISP methodology.","Documentation":"https://human-settlement.emergency.copernicus.eu/ghs_wup_pop_r2025a.php","Contact":"JRC-GHSL-DATA@ec.europa.eu","ManagedBy":"[JRC](https://joint-research-centre.ec.europa.eu)","UpdateFrequency":"NA","Tags":["census","cog","demographics","demography","disaster response","earth observation","geoscience","geospatial","global","mapping","planetary","population","tiff","tiles"],"License":"GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.","Resources":[{"Description":"Gridded datasets in World Mollweide projection (EPSG:54009) at 1 km spatial resolution; also available in WGS 1984 (EPSG:4326) at 30 arcsecond spatial resolution, globally from 1975 to 2100 in 5-year steps. Grid cell values represent the projected distribution of human population, in number of persons per cell.","ARN":"arn:aws:s3:::jrc-ghsl/ghs-wup-pop","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://jrc-ghsl.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:eu-central-1:291461994469:jrc-ghsl-object_created","Region":"eu-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: GHS WUP POP","URL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-wup-pop","NotebookURL":"https://github.com/lucamaff/open-data-examples/tree/main/ghs-wup-pop/get-to-know-a-dataset.ipynb","AuthorName":"MAFFENINI Luca","AuthorURL":"https://github.com/lucamaff"}],"Tools & Applications":[{"Title":"Raster data visualization","URL":"https://human-settlement.emergency.copernicus.eu/raster.php","AuthorName":"TOMMASI Pierpaolo, MAFFENINI Luca"}],"Publications":[{"Title":"GHSL WUP Projections Data Package 2025","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC144209","AuthorName":"SCHIAVINA Marcello, MELCHIORRI Michele, MARI RIVERO Ines, FLORIO Pietro, FREIRE Sergio, JACOBS-CRISIONI Chris, MAFFENINI Luca, POLITIS Panagiotis, UHL Johannes H., CARIOLI Alessandra, PESARESI Martino, DIJKSTRA Lewis, KEMPER Thomas"},{"Title":"Introducing the CRISP model to downscale future population projections","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC143916","AuthorName":"JACOBS-CRISIONI Chris, SCHIAVINA Marcello, KRASNODEBSKA Katarzyna, DIJKSTRA Lewis, CLAASSENS Jip, HILFERINK Maarten, VAN DER WIELEN Thijmen, KOOMEN Eric"},{"Title":"Population by degree of urbanization and by urban agglomeration from 1950 to 2100","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC144219","AuthorName":"JACOBS-CRISIONI Chris, SCHIAVINA Marcello, ALESSANDRINI Alfredo, DIJKSTRA Lewis"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2026-09-25","RegistryEntryLastModified":"2026-10-14","Slug":"ghs-wup-pop","Sources":["awslabs-open-data-registry"]}
{"Name":"Genome in a Bottle on AWS","Description":"Several reference genomes to enable translation of whole human genome sequencing to clinical practice. On 11/12/2020 these data were updated to reflect the most [up to date GIAB release](https://www.nist.gov/programs-projects/genome-bottle).","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/giab","Contact":"http://genomeinabottle.org/","ManagedBy":"[National Institute of Standards and Technology](https://www.nist.gov/)","UpdateFrequency":"New data are added as soon as they are available.","Tags":["aws-pds","genetic","genomic","life sciences","reference index","vcf"],"License":"There are no restrictions on the use of this data. More information on citation is available [here](https://www.nist.gov/programs-projects/genome-bottle).\n","Resources":[{"Description":"GIAB release data, last updated 11/12/2020.","ARN":"arn:aws:s3:::giab","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"GA4GH Benchmarking Tools","URL":"https://github.com/ga4gh/benchmarking-tools","AuthorName":"GA4GH Benchmarking Team"},{"Title":"The Genome in a Bottle Github Project","URL":"https://github.com/genome-in-a-bottle/about_GIAB","AuthorName":"Genome In A Bottle Consortium"}],"Publications":[{"Title":"Extensive sequencing of seven human genomes to characterize benchmark reference materials","URL":"https://www.nature.com/articles/sdata201625","AuthorName":"Zook J et al (2016)"},{"Title":"High-coverage, long-read sequencing of Han Chinese trio reference samples","URL":"https://www.nature.com/articles/s41597-019-0098-2","AuthorName":"Wang Y et al (2019)"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2022-06-15","Slug":"giab","Sources":["awslabs-open-data-registry"]}
{"Name":"GLAD Landsat ARD","Description":"The Landsat Analysis Ready Data (ARD) created by the Global Land Analysis and Discovery Lab (GLAD) at the University of Maryland serves as a spatially and temporally consistent input for land cover mapping and change detection at global to local scales. The GLAD ARD represents a 16-day time series of globally consistent, tiled Landsat normalized surface reflectance from 1997 to the present operationally updated every 16 days. Only data from 2020 to present available on the AWS, older data is available through the UMD API.","Documentation":"https://glad.umd.edu/ard/home","Contact":"potapov.peter@gmail.com","ManagedBy":"[Global Land Analysis and Discovery Lab](https://glad.umd.edu/)","UpdateFrequency":"New 16-day composites are added monthly.","Tags":["agriculture","cog","earth observation","geospatial","natural resource","satellite imagery"],"License":"The GLAD ARD imposes no restrictions on subsequent redistribution or use, provided proper citation is given following the Creative Commons Attribution License (CC BY).","Resources":[{"Description":"16-day image composite tiles","ARN":"arn:aws:s3:::glad.landsat.ard/data/tiles/","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"GLAD ARD Data Format","URL":"https://glad.umd.edu/ard/home","AuthorName":"Peter Potapov","AuthorURL":"https://glad.umd.edu/"}],"Tools & Applications":[{"Title":"GLAD Landsat ARD Tools","URL":"https://glad.umd.edu/ard/home","AuthorName":"Peter Potapov","AuthorURL":"https://glad.umd.edu/"}],"Publications":[{"Title":"Landsat analysis ready data for global land cover and land cover change mapping","URL":"https://www.mdpi.com/2072-4292/12/3/426","AuthorName":"Potapov, P., Hansen, M.C., Kommareddy, I., Kommareddy, A., Turubanova, S., Pickens, A., Adusei, B., Tyukavina A., Ying, Q."}]},"Citation":"Potapov, P., Hansen, M.C., Kommareddy, I., Kommareddy, A., Turubanova, S., Pickens, A., Adusei, B., Tyukavina A., Ying, Q. (2020) Landsat analysis ready data for global land cover and land cover change mapping, Remote Sens. 2020, 12, 426; doi:10.3390/rs12030426","RegistryEntryAdded":"2025-06-08","RegistryEntryLastModified":"2025-06-08","Slug":"glad-landsat-ard","Sources":["awslabs-open-data-registry"]}
{"Name":"Global 30m Height Above Nearest Drainage (HAND)","Description":"Height Above Nearest Drainage (HAND) is a terrain model that normalizes topography to the relative heights along the drainage network and is used to describe the relative soil gravitational potentials or the local drainage potentials. Each pixel value represents the vertical distance to the nearest drainage. The HAND data provides near-worldwide land coverage at 30 meters and was produced from the 2021 release of the Copernicus GLO-30 Public DEM as distributed in the [Registry of Open Data on AWS](https://registry.opendata.aws/copernicus-dem/).\n","Documentation":"https://glo-30-hand.s3.us-west-2.amazonaws.com/readme.html","Contact":"https://asf.alaska.edu/asf/contact-us/","ManagedBy":"[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)","UpdateFrequency":"None, except HAND may be updated if the[ Copernicus GLO-30 Public](https://registry.opendata.aws/copernicus-dem/) dataset is updated.\n","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["agriculture","aws-pds","cog","disaster response","elevation","geospatial","hydrology","satellite imagery","stac"],"License":"Copyright 2022 Alaska Satellite Facility (ASF). Produced using the Copernicus WorldDEM™-30 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. The use of the HAND data falls under the terms and conditions of the  [Creative Commons Zero (CC0) 1.0 Universal License](https://creativecommons.org/publicdomain/zero/1.0/).\n","Citation":null,"Resources":[{"Description":"GLO-30 HAND S3 bucket","ARN":"arn:aws:s3:::glo-30-hand","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://stac.asf.alaska.edu/collections/glo-30-hand)","[Via STAC Browser](https://radiantearth.github.io/stac-browser/#/external/stac.asf.alaska.edu/collections/glo-30-hand)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:us-west-2:879002409890:glo-30-hand-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Generate your own HAND data in Python","URL":"https://github.com/ASFHyP3/OpenData/blob/main/glo-30-hand/generate-hand-notebook.ipynb","NotebookURL":"https://github.com/ASFHyP3/OpenData/blob/main/glo-30-hand/generate-hand-notebook.ipynb","AuthorName":"Alaska Satellite Facility","AuthorURL":"https://asf.alaska.edu/"},{"Title":"Search the GLO-30 HAND Catalog in Python","URL":"https://github.com/ASFHyP3/OpenData/blob/main/glo-30-hand/search-hand-notebook.ipynb","NotebookURL":"https://github.com/ASFHyP3/OpenData/blob/main/glo-30-hand/search-hand-notebook.ipynb","AuthorName":"Alaska Satellite Facility","AuthorURL":"https://asf.alaska.edu/"}],"Tools & Applications":[{"Title":"GLO-30 HAND ImageServer","URL":"https://gis.asf.alaska.edu/arcgis/rest/services/GlobalHAND/GLO30_HAND/ImageServer","AuthorName":"Alaska Satellite Facility","AuthorURL":"https://asf.alaska.edu/"},{"Title":"GLO-30 HAND Webmap","URL":"https://asf-daac.maps.arcgis.com/apps/mapviewer/index.html?webmap=6b82a2e4ccd343d5ba73dc04d386e4ee","AuthorName":"Alaska Satellite Facility","AuthorURL":"https://asf.alaska.edu/"}],"Publications":[{"Title":"AGU 2022: A New Global 30m HAND Dataset to Support Hydrological Services and Applications","URL":"https://docs.google.com/presentation/d/1GzHn4bQrqqKj6DFFXwQ9zIvN7-etSjyIpHz3fQmxO0s/edit#slide=id.p","AuthorName":"Joseph H. Kennedy, et al.","AuthorURL":"https://jhkennedy.org/"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2023-11-20","RegistryEntryLastModified":"2025-07-09","Slug":"glo-30-hand","Sources":["awslabs-open-data-registry"]}
{"Name":"A Global Drought and Flood Catalogue from 1950 to 2016","Description":"Hydrological extremes, in the form of droughts and floods, have impacts on a wide range of sectors including water availability, food security, and energy production, among others. Given continuing large impacts of droughts and floods and the expectation for significant regional changes projected in the future, there is an urgent need to provide estimates of past events and their future risk, globally. However, current estimates of hydrological extremes are not robust and accurate enough, due to lack of long-term data records, standardized methods for event identification, geographical inconsistencies and data uncertainties. To tackle these challenges, we develop the first Global Drought and Flood Catalogue (GDFC) [He et al., 2020] for 1950-2016 by merging the latest in situ and remote-sensing datasets with state-of-the-art land surface and hydrodynamic modeling to provide a continuous and consistent estimate of the terrestrial water cycle and its extremes. This GDFC also includes an unprecedented level of detailed analysis of drought and large-scale flood events using a multivariate risk assessment framework, which incorporates regional spatial-temporal characteristics (i.e., duration, spatial extent, severity) and global hazard maps for different return periods. This catalogue forms a basis for analyzing the changing risk of droughts and floods and can underscore national and international climate change assessments and provide a key reference for climate change studies and climate model evaluations.\n","Documentation":"https://prep-next.github.io/data/GDFC/index.html","Contact":"For any questions regrading dataset, email Professor Xiaogang He at hexg@nus.edu.sg.","ManagedBy":"[PREP-NexT Lab](https://github.com/PREP-NexT)","UpdateFrequency":"No future updates planned.","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aws-pds","floods","global","near-surface air temperature","near-surface specific humidity","netcdf","precipitation"],"License":"GDFC archive is made available under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).\n","Citation":"He, X., M. Pan, Z. Wei, E. F. Wood, and J. Sheffield (2020), A Global Drought and Flood Catalogue from 1950 to 2016. Bulletin of the American Meteorological Society, doi:10.1175/BAMS-D-18-0269.1","Resources":[{"Description":"Global Drought and Flood Catalogue from 1950 to 2016 archive. Files are in netcdf and csv format. The GDFC delivers relevant products, including continental drought and pluvial catalogues, global-scale drought and flood (pluvial and fluvial) risk maps, long-term meteorological and agricultural standardized indices, underlying meteorological forcings and land surface hydrological fluxes and states. More details please refer to [this link](https://prep-next.github.io/data/GDFC/products.html).\n","ARN":"arn:aws:s3:::global-drought-flood-catalogue","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://global-drought-flood-catalogue.s3.us-west-2.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Data introduction included in the GDFC","URL":"https://prep-next.github.io/data/GDFC/products.html","AuthorName":"Xiaogang He","AuthorURL":"http://hydro.iis.u-tokyo.ac.jp/~hexg/cv.html"}],"Publications":[{"Title":"A Global Drought and Flood Catalogue from 1950 to 2016","URL":"https://doi.org/10.1175/BAMS-D-18-0269.1","AuthorName":"He, X., M. Pan, Z. Wei, E. F. Wood, and J. Sheffield"}]},"RegistryEntryAdded":"2022-12-22","RegistryEntryLastModified":"2025-07-09","Slug":"global-drought-flood-catalogue","Sources":["awslabs-open-data-registry"]}
{"Name":"The Genome Modeling System","Description":"The Genome Institute at Washington University has developed a high-throughput, fault-tolerant analysis information management system called the Genome Modeling System (GMS), capable of executing complex, interdependent, and automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. GMS includes a full system image with software and services, expandable from one workstation to a large compute cluster.","Documentation":"https://github.com/genome/gms/wiki","Contact":"https://github.com/genome/gms/issues","ManagedBy":"Genome Institute at the Washington University School of Medicine in St. Louis","UpdateFrequency":"Not updated","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["aws-pds","genetic","genomic","life sciences"],"License":"[GNU Lesser General Public License v3.0](https://github.com/genome/gms/blob/ubuntu-12.04/LICENSE)","Resources":[{"Description":"https://gmsdata.s3.amazonaws.com/index.html","ARN":"arn:aws:s3:::gmsdata","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://gmsdata.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2025-07-09","Slug":"gmsdata","Sources":["awslabs-open-data-registry"]}
{"Name":"Generalized Multiscale Entropy Analysis","Description":"The method of generalized multiscale entropy (GMSE) analysis is useful for investigating complexity in physiologic signals and other series that have correlations at multiple (time) scales. It represents a generalization of the original method of multiscale entropy analysis.","Documentation":"https://physionet.org/content/gmse/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/gmse/","ARN":"arn:aws:s3:::physionet-open/gmse/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"gmse","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"Amazon is no longer hosting this Data Lakehouse Ready dataset","Name":"Genome Aggregation Database (gnomAD) - Data Lakehouse Ready","Description":"The [Genome Aggregation Database (gnomAD)](https://gnomad.broadinstitute.org/) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects\nSign up for the gnomAD mailing list [here](http://broad.io/gnomad_list). This dataset was derived from summary data from gnomAD release 3.1, available on the [Registry of Open Data on AWS](https://registry.opendata.aws/broad-gnomad/) for ready enrollment into the [Data Lake as Code](https://github.com/aws-samples/data-lake-as-code/tree/blog).\n","Documentation":"https://github.com/aws-samples/data-lake-as-code/tree/roda#readme","Contact":"https://github.com/aws-samples/data-lake-as-code/issues","ManagedBy":"[Amazon Web Services](https://aws.amazon.com/)","UpdateFrequency":"Not updated","Tags":["biology","bioinformatics","biotech blueprint","genomic","genetic","life sciences","parquet","population genetics","vcf","whole genome sequencing"],"License":"[MIT](https://github.com/broadinstitute/gnomad_methods/blob/master/LICENSE); [terms of use](https://gnomad.broadinstitute.org/terms)","Resources":[{"Description":"Parquet representations of gnomAD summary data aggregated from gnomAD release 3.1","ARN":"arn:aws:s3:::aws-roda-hcls-datalake/gnomad","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Sample Queries on the 1000 Genomes, gnomAD and ClinVar data Lake","URL":"https://github.com/aws-samples/aws-genomics-datalake/blob/main/1000Genomes.ipynb","AuthorName":"Sujaya Srinivasan","Services":["Amazon Athena","AWS Glue"]}],"Tools & Applications":null,"Publications":null},"RegistryEntryAdded":"2021-09-13","RegistryEntryLastModified":"2025-02-04","Slug":"gnomad-data-lakehouse-ready","Sources":["awslabs-open-data-registry"]}
{"Name":"Earth Radio Occultation","Description":"This is an updating archive of radio occultation (RO) data using the transmitters of the Global Navigation Satellite Systems (GNSS) as generated and processed by the COSMIC DAAC (ucar), the Jet Propulsion Laboratory (jpl) of the California Institute of Technology, and the Radio Occultation Meteorology Satellite Application Facility (romsaf). The contributions for ucar and romsaf are currently active.\n\nThis dataset is funded by the NASA Earth Science Data Systems and the Advancing Collaborative Connections for Earth System Science (ACCESS) 2019 program.","Documentation":"http://github.com/gnss-ro/aws-opendata","Contact":"Stephen Leroy (sleroy@aer.com)","ManagedBy":"Verisk Atmospheric and Environmental Research, Inc.","UpdateFrequency":"The dataset is updated monthly for UCAR and ROM SAF contributions only. The update frequency for the JPL contribution is to be determined.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","climate","earth observation","global","signal processing","weather"],"License":"A Creative Commons open-use licence (https://www.ucar.edu/terms-of-use/data) applies to the data contributed by UCAR; the EUMETSAT Data Policy (https://www.eumetsat.int/eumetsat-data-licensing) applies to the data contributed by the ROM SAF; and a creative commons license (https://creativecommmons.org/licenses/by/4.0/legalcode) applied to the data contributed by the NASA JPL of the California Institute of Technology. The source of the data is designated in the attribute 'processing_center' and the license in the attribute 'data_use_license'.","Resources":[{"Description":"Three types of data exist in a directory hierarchy that includes the contributing processing center, the radio occultation mission, the file type, and the date: (1) Calibrated excess phase (calibratedPhase), a type of level 1b data product; (2) retrievals of bending angle, refractivity, dry temperature and dry pressure (refractivityRetrieval) as functions of geopotential height, a type of level 2a data product; and (3) atmospheric retrievals of pressure, temperature, and water vapor as functions of geopotential height (atmosphericRetrieval), a type of level 2b data product. In addition, a suite of JSON files containing the records that constitute of database of the contents of the archive. RO data can be queried, subsetted and downloaded using the Python API [awsgnssroutils](https://pypi.org/project/awsgnssroutils/).","ARN":"arn:aws:s3:::gnss-ro-data","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://gnss-ro-data.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Tutorial Demonstrations for Working with GNSS Radio Occultation Data in AWS Open Data Registry archive","URL":"http://github.com/gnss-ro/aws-opendata/tree/master/tutorials/","AuthorName":"Stephen Leroy, Amy McVey, Hailing Zhang, Chi Ao","AuthorURL":null,"Services":["Amazon S3","Amazon EC2","AWS Batch"]}],"Tools & Applications":[{"Title":"Utilities for Handling Data in the AWS Open Data Registry archive of GNSS Radio Occultation Data","URL":"http://github.com/gnss-ro/aws-opendata/tree/master/awsgnssroutils/","AuthorName":"Stephen Leroy, Amy McVey","AuthorURL":"http://www.aer.com"}],"Publications":[{"Title":"A technical description of atmospheric sounding by GPS occultation","URL":"http://doi.org/10.1016/S1364-6826(01)00114-6","AuthorName":"Hajj, G.A. et al.","AuthorURL":null},{"Title":"Observing Earth's atmosphere with radio occultation measurements using the Global Positioning System","URL":"doi.org/10.1029/97JD01569","AuthorName":"Kursinski, E.R. et al.","AuthorURL":null}]},"RegistryEntryAdded":"2022-05-24","RegistryEntryLastModified":"2026-07-09","Slug":"gnss-ro-opendata","Sources":["awslabs-open-data-registry"]}
{"Name":"Google Brain Genomics Sequencing Dataset for Benchmarking and Development","Description":"To facilitate benchmarking and development, the Google Brain group has sequenced 9 human samples covering the Genome in a Bottle truth sets on different sequencing instruments, sequencing modalities (Illumina short read and Pacific BioSciences long read), sample preparation protocols, and for whole genome and whole exome capture. The original source of these data are [gs://google-brain-genomics-public](https://console.cloud.google.com/storage/browser/brain-genomics-public/research/sequencing;tab=objects?prefix=&forceOnObjectsSortingFiltering=false).","Documentation":"https://genomics-benchmark-datasets.s3.amazonaws.com/README.txt","Contact":"genomics-benchmark-datasets@amazon.com","ManagedBy":"Amazon Web Services","UpdateFrequency":"Occasionally as new derived files (alignment files or variant call files) are generated","Tags":["amazon.science","bioinformatics","fastq","genetic","genomic","life sciences","long read sequencing","short read sequencing","whole exome sequencing","whole genome sequencing"],"License":"[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)","Resources":[{"Description":"FASTQ files for nine human samples comprising three parent-child trios at 40X 30X, and 20X coverage for whole genome sequencing and 100X, 75X, and 50X coverage for whole exome sequencing. Four samples are also sequenced with PacBio HiFi as described in Baid et al 2020. Note that this S3 bucket only contains FASTQ files; BAM and VCF files are available at [gs://google-brain-genomics-public](https://console.cloud.google.com/storage/browser/brain-genomics-public/research/sequencing;tab=objects?prefix=&forceOnObjectsSortingFiltering=false).","ARN":"arn:aws:s3:::genomics-benchmark-datasets/google-brain","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"An Extensive Sequence Dataset of Gold-Standard Samples for Benchmarking and Development","URL":"https://www.biorxiv.org/content/10.1101/2020.12.11.422022v1.full","AuthorName":"Baid G., Nattestad M., Kolesnikov A., Goel S., Yang H., Chang P., and Carroll A (2020)"}]},"RegistryEntryAdded":"2021-08-24","RegistryEntryLastModified":"2024-03-27","Slug":"google-brain-genomics-public","Sources":["awslabs-open-data-registry"]}
{"Name":"Google Books Ngrams","Description":"N-grams are fixed size tuples of items. In this case the items are words extracted from the Google Books corpus. The n specifies the number of elements in the tuple, so a 5-gram contains five words or characters. The n-grams in this dataset were produced by passing a sliding window of the text of books and outputting a record for each new token.","Documentation":"http://books.google.com/ngrams/","Contact":"https://books.google.com/ngrams","UpdateFrequency":"Not updated","ManagedBy":"Not managed","Tags":["amazon.science","natural language processing"],"License":"Creative Commons Attribution 3.0 Unported License","Resources":[{"Description":"A data set containing Google Books n-gram corpora in a Hadoop friendly file format.","ARN":"arn:aws:s3:::datasets.elasticmapreduce/ngrams/books/","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2024-03-27","Slug":"google-ngrams","Sources":["awslabs-open-data-registry"]}
{"Name":"GRABMyoFlow - Dataset extension","Description":"This dataset is an extension of the Gesture Recognition and Biometrics Electromyogram (GRABMyo) dataset, a comprehensive collection of surface electromyography (sEMG) signals from the forearm and wrist while performing static gestures, designed for hand gesture recognition (HGR) and biometric authentication research. The study extends the dataset in two key aspects: 1) by adding wrist-only recordings from 20 newly recruited healthy adults, and 2) by including a dynamic trial at the end of each session. The extension study follows the protocol established in the original study to ensure methodological consistency and reliable data acquisition. Combined with prior wrist recordings, the extended dataset now includes data from 63 participants, significantly increasing the sample size. Signal strength and quality in the new recordings have been validated to match the standards of the original dataset, ensuring uniformity across all subjects. The dynamic gesture recordings included in the extension capture the natural transitions between hand poses, better simulating real-world movement scenarios. These dynamic data enable new avenues for continuous authentication and gesture-event segmentation, directly addressing prior limitations of static-only datasets. Data acquisition employed the established electrode configuration, and open access to the data in the waveform database (WFDB)-format is provided to support the widespread use of research. This enriched dataset enhances both the scale and temporal complexity of the GRABMyo dataset, delivering a robust resource for advancing sEMG-based HGR for human-computer interaction or biometric systems research.","Documentation":"https://doi.org/10.13026/ah52-zq51","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/ah52-zq51","ARN":"arn:aws:s3:::physionet-open/grabmyo-flow/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"grabmyo-flow","Sources":["awslabs-open-data-registry"]}
{"Name":"Gesture Recognition and Biometrics ElectroMyogram (GRABMyo)","Description":"We present the Gesture Recognition and Biometrics ElectroMyogram (GRABMyo) dataset, an open-access dataset of electromyogram (EMG) recordings collected from the wrist and forearm muscles while performing hand gestures. Data were collected from 43 healthy participants (age range: 24-35 years) on three different days while performing 16 hand and finger gestures in identical experimental sessions on each day. The GRABMyo dataset can be used for research on: 1) EMG-based biometrics for personal identification and verification, and 2) EMG-based gesture recognition for neurorehabilitation and home applications. The large sample size will provide sufficient power for establishing results, specifically for applications such as biometrics. Further, it will be useful for subject-independent applications such as generalized classification models for gesture recognition. The multiday recordings will provide results that will be consistent over a longer duration, crucial for the reliability of EMG-based wearable devices.","Documentation":"https://doi.org/10.13026/89dm-f662","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/89dm-f662","ARN":"arn:aws:s3:::physionet-open/grabmyo/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"grabmyo","Sources":["awslabs-open-data-registry"]}
{"Name":"Gradient Algorithm","Description":"In finding an optimal stimulus waveform for inducing switches in neuronal states, analytical techniques from optimal control theory are often found to be difficult to use or extremely time intensive. Here, we present the code for a gradient-based algorithm approach that has been used to find energetically optimal stimulus waveforms to trigger an action potential in the Hodgkin-Huxley model as well as initiating and repressing repetitive firing in the FitzHugh-Nagumo models. These two models serve just as examples and the code can be easily adapted to any other system.","Documentation":"https://physionet.org/content/gradient-algorithm/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/gradient-algorithm/","ARN":"arn:aws:s3:::physionet-open/gradient-algorithm/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"gradient-algorithm","Sources":["awslabs-open-data-registry"]}
{"Name":"GRAF Reforecast","Description":"A zarr-formatted dataset of 1836 reforecast cases (approx. 5 years) from The Weather Company GRAF (Global high-Resolution Atmospheric Forecasting) model, a version of the National Center for Atmospheric Research (NCAR) Model for Predictions Across Scales (MPAS). GRAF is global, but the configuration for this reforecast had a mesh refinement to approx. 4 km over the US, Caribbean Basin, and Europe, and 15 km elsewhere. This model was designed to run much of its computation on graphical processing units, with this development assisted by NVIDIA. The 1836 cases (approx. 5 years) were generated from ECMWF reanalyses (ERA5) for initial condition dates spanning more than 20 years, 2004-2024. These dates of the chosen initial conditions mostly selected based on high-impact weather in the contiguous US (CONUS) and Caribbean. Sampling in this way spanned a wider range of interesting, high-impact weather scenarios than were there five contiguous years of data. GRAF reforecasts were mostly run to +27 h lead time, assuming a 3-h for spin up followed by a full diurnal cycle. Data were saved in zarr format on the native model vertical coordinate. Most fields were saved at 15-min intervals, though several precipitation variables were saved at 5-min cadence.","Documentation":"[Documentation](https://docs.google.com/forms/d/e/1FAIpQLSejRyG2CXrfcmrX7g_iFhc3RF-n3ZzmPQdVieSDwTzLNkR-_w/viewform)","Contact":"graf.reforecast@weather.com","ManagedBy":"[The Weather Company](https://www.weathercompany.com/)","UpdateFrequency":"One time push only","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["atmosphere","cloud amount","ERA5","forecast","geoscience","geospatial","model","MPAS","near-surface air temperature","near-surface relative humidity","precipitation","visibility","weather","wind speeds","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GRAF Reforecast dataset","ARN":"arn:aws:s3:::twc-graf-reforecast","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://s3-us-west-2.amazonaws.com/twc-graf-reforecast/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"Global reforecasts from MPAS “GRAF” with mesh refinement over the US and Europe","URL":"https://cesoc.net/wp-content/uploads/2024/08/GRAF-reforecast-Hamill-CESOC24.pdf","AuthorName":"Thomas M. Hamill, Raghu Raj Prasanna Kumar, Karthik Kashinath2, Carl Ponder, Mike Pritchard, Tao Ge, Akshay Subramanian, Jaideep Pathak, John Wong, Brett Wilt, Peter Neilley"}]},"RegistryEntryAdded":"2025-06-09","RegistryEntryLastModified":"2025-07-08","Slug":"graf-reforecast","Sources":["awslabs-open-data-registry"]}
{"Name":"IWMI DIWASA Green ET for Africa","Description":"Green evapotranspiration (Green ET) is the portion of ET derived from green water, which includes soil moisture and rainfall used by vegetation. It represents a key component of green water fluxes in water accounting. Green ET consists of evaporation from soil moisture in non-irrigated areas, transpiration from rainfed crops and natural vegetation, and interception losses from precipitation on vegetation. It plays a crucial role in rainfed agriculture, drought monitoring, and sustainable water management by tracking how rainfall supports plant growth.","Documentation":"https://iwmi.africageoportal.com/pages/continent-africa","Contact":"iwmiwaplus@gmail.com","ManagedBy":"[IWMI](https://www.iwmi.org/)","UpdateFrequency":"None","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["evapotranspiration","interception loss","rainfed cropland","soil moisture","water"],"License":"Creative commons open license","Resources":[{"Description":"Monthly Green ET for Africa","ARN":"arn:aws:s3:::iwmi-datasets/Water_accounting_plus/Africa/Rainfall_ET_M/","Region":"af-south-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://iwmi-datasets.s3.af-south-1.amazonaws.com/Cropland_partition/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Analysis of IWMI’s Water Data Products through Digital Earth Africa","URL":"https://learn.digitalearthafrica.org/courses/course-v1:IWMI+DIWASA1+2024_10/about","AuthorName":"A.T. Haile, E.T. Negash, K. Mubea, M. Tadesse","AuthorURL":"https://github.com/iwmiwaplus"}],"Tools & Applications":[{"Title":"Multi-Scale Water Accounting in the Volta Basin","URL":"https://public.tableau.com/app/profile/iwmi.wa/viz/Voltabasinvertical/Merged?publish=yes","AuthorName":"iwmiwaplus","AuthorURL":"https://public.tableau.com/app/profile/iwmi.wa"}]},"RegistryEntryAdded":"2025-02-11","RegistryEntryLastModified":"2025-07-09","Slug":"green_et","Sources":["awslabs-open-data-registry"]}
{"Name":"Gretel Synthetic Safety Alignment Dataset","Description":"A comprehensive dataset designed for aligning language models with safety and ethical guidelines. Contains 8,361 curated triplets of prompts, responses, and safe responses across various risk categories. Each entry includes safety scores, judge reasoning, and harm probability assessments, making it valuable for model alignment, testing, and benchmarking.","Documentation":"https://huggingface.co/datasets/gretelai/gretel-safety-alignment-en-v1","Contact":"https://gretel.ai/discord","ManagedBy":"[Gretel.ai](https://gretel.ai)","UpdateFrequency":"Static dataset, version 1.0 (Released December 2024)","Tags":["ai safety","machine learning","natural language processing","synthetic data"],"License":"Apache License 2.0 (https://www.apache.org/licenses/LICENSE-2.0)","Citation":"@dataset{gretelai_gretel-safety-alignment-en-v1, title = {Gretel Synthetic Safety Alignment Dataset}, year = {2024}, month = {12}, publisher = {Gretel}, url = {https://huggingface.co/datasets/gretelai/gretel-safety-alignment-en-v1}}","Resources":[{"Description":"The dataset is available as three files - train with 6,000 records, validation with 1,200 records, and test with 1,161 records. Each file is in parquet format and contains 14 columns including prompts, responses, safety scores, and probability assessments. Generated using Apache 2.0 licensed models including ibm-granite/granite-3.0-8b, Qwen2.5-7B, and Mistral-Nemo-Instruct-2407.","ARN":"arn:aws:s3:::gretel-datasets-public/gretel-synthetic-safety-alignment-en-v1","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://gretel-datasets-public.s3.amazonaws.com/gretel-synthetic-safety-alignment-en-v1/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Building Datasets to Enable Safer AI Responses","URL":"https://gretel.ai/blog/gretel-open-synthetic-safety-dataset","AuthorName":"Gretel.ai","AuthorURL":"https://gretel.ai","Services":[]},{"Title":"Getting Started with the Gretel Safety Alignment Dataset","URL":"https://huggingface.co/datasets/gretelai/gretel-safety-alignment-en-v1","AuthorName":"Gretel.ai","AuthorURL":"https://gretel.ai","Services":[]}],"Tools & Applications":[{"Title":"Gretel Navigator Data Designer","URL":"https://gretel.ai/blog/build-high-quality-datasets-for-ai-using-gretel-navigator","AuthorName":"Gretel.ai","AuthorURL":"https://gretel.ai"}]},"ADXCategories":["Financial Services Data","Media & Entertainment Data"],"RegistryEntryAdded":"2025-01-21","RegistryEntryLastModified":"2025-01-21","Slug":"gretel-synthetic-safety-alignment-en-v1","Sources":["awslabs-open-data-registry"]}
{"Name":"OpenEEW","Description":"Grillo has developed an IoT-based earthquake early-warning system,\nwith sensors currently deployed in Mexico, Chile, Puerto Rico and Costa Rica,\nand is now opening its entire archive of unprocessed accelerometer\ndata to the world to encourage the development of new algorithms\ncapable of rapidly detecting and characterizing earthquakes in\nreal time.\n","Documentation":"https://github.com/openeew/openeew","Contact":"hello@openeew.com","ManagedBy":"[Grillo](https://grillo.io/)","UpdateFrequency":"Approximately every 5 minutes","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aws-pds","disaster response","earth observation","earthquakes"],"License":"https://github.com/openeew/openeew#license","Resources":[{"Description":"OpenEEW","ARN":"arn:aws:s3:::grillo-openeew","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://grillo-openeew.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Analyzing a magnitude 7.2 earthquake in Mexico using Python","URL":"https://blog.grillo.io/analyzing-a-magnitude-7-2-earthquake-in-mexico-using-python-6272a4ff63e3","AuthorName":"Michael Allman","AuthorURL":"https://blog.grillo.io/"},{"Title":"Detect earthquakes in Chile in real time — from anywhere in the world","URL":"https://blog.grillo.io/detect-earthquakes-in-chile-in-real-time-from-anywhere-in-the-world-ff7386368f41","AuthorName":"Michael Allman","AuthorURL":"https://blog.grillo.io/"}],"Tools & Applications":[{"Title":"OpenEEW library for Python","URL":"https://github.com/openeew/openeew-python","AuthorName":"Grillo","AuthorURL":"https://grillo.io/"}],"Publications":[{"Title":"Evaluation of the Grillo sensor, a low-cost accelerometer for IoT-based Real-time seismology","URL":"https://doi.org/10.31223/X5XS47","AuthorName":"Vaclav Matej Kuna, Diego Melgar, Andres Meira"},{"Title":"How Grillo Built a Low-Cost Earthquake Early Warning System on AWS","URL":"https://aws.amazon.com/blogs/aws/how-grillo-built-a-low-cost-earthquake-early-warning-system-on-aws/","AuthorName":"Marcia Villalba"},{"Title":"NepalEEW: Testing the feasibility of an Earthquake Early Warning System in Nepal","URL":"https://library.seg.org/doi/epdfplus/10.1190/image2022-3751420.1","AuthorName":"Vaclav M. Kuna"}]},"RegistryEntryAdded":"2019-04-09","RegistryEntryLastModified":"2023-02-28","Slug":"grillo-openeew","Sources":["awslabs-open-data-registry"]}
{"Name":"Geospatial Information Center","Description":"This dataset curates and distributes, in an AWS-friendly manner, only those publicly available datasets registered on the Geospatial Information Center (front.geospatial.jp) that explicitly specify a Creative Commons (CC) license.\nThe collection includes a wide range of geospatial data types useful for analysis and application development, such as 3D city/building models (e.g., CityGML), elevation and terrain data (e.g., DEM), vector datasets for administrative boundaries, land use, and facilities, and disaster-related geospatial data (e.g., hazard information and damage scenarios).\n","Documentation":"https://github.com/aigidjp/opendata-gsic/blob/main/README.md","Contact":"gsic-admin@aigid.jp","ManagedBy":"[AIGID](https://aigid.jp/)","UpdateFrequency":"New datasets and updates are added as they become available in the Geospatial Information Center.","Tags":["aws-pds","cities","civic","csv","dem","disaster response","dsm","dtm","elevation","geopackage","geospatial","hazard","infrastructure","japanese","json","land","land use","lidar","mapping","metadata","tiff","tiles","transportation","urban","xml"],"License":"Creative Commons Attribution 4.0 International (CC-BY 4.0)","Resources":[{"Description":"Geospatial Information Center","ARN":"arn:aws:s3:::gsic-opendata","Region":"ap-northeast-1","Type":"S3 Bucket"},{"Description":"Notifications for the Geospatial Information Center","ARN":"arn:aws:sns:ap-northeast-1:334298573822:gsic-opendata-object_created","Region":"ap-northeast-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get to Know a Dataset:[Geospatial Information Center]","URL":"https://github.com/aigidjp/opendata-gsic/blob/main/tutorials/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/aigidjp/opendata-gsic/blob/main/tutorials/get-to-know-a-dataset.ipynb","AuthorName":"AIGID","AuthorURL":"https://aigid.jp/"}]},"RegistryEntryAdded":"2026-05-26","RegistryEntryLastModified":"2026-06-12","Slug":"gsic-opendata","Sources":["awslabs-open-data-registry"]}
{"Name":"Garvan Institute Long Read Sequencing Benchmark Data","Description":"The dataset contains reference samples that will be useful for benchmarking and comparing bioinformatics tools for genome analysis. Examples include: NA12878 (HG001) and NA24385 (HG002) sequenced on an Oxford Nanopore Technologies (ONT) PromethION using the latest R10.4.1 flowcells; and, UHR RNA (direct-RNA) on an ONT PromethION using the latest RNA004 flowcells. Raw signal data output by the sequencer is provided for these datasets in BLOW5 format, and can be rebasecalled when basecalling software updates bring accuracy and feature improvements over the years. Raw signal data is not only for rebasecalling, but also can be used for emerging bioinformatics tools that directly analyse raw signal data. We also provide the basecalled data alongside the raw signal data.","Documentation":"https://github.com/GenTechGp/gtgseq","Contact":"[gtgseq team](https://github.com/GenTechGp/gtgseq/issues)","ManagedBy":"Genomic Technologies Group, Garvan Institute of Medical Research (https://www.garvan.org.au/research/labs-groups/genomic-technologies-lab)","UpdateFrequency":"We plan to extend this open dataset with additional samples, including sequencing runs from vendors other than ONT. We will continue to provide updated basecalls when there is a major update to the basecalling software.","Tags":["aws-pds","bioinformatics","genomic","life sciences","long read sequencing"],"License":"[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)","Citation":"Gamaarachchi, H., Samarakoon, H., Jenner, S.P. et al. Fast nanopore sequencing data analysis with SLOW5. Nat Biotechnol 40, 1026–1029 (2022). https://doi.org/10.1038/s41587-021-01147-4","Resources":[{"Description":"Currently, the dataset contains two reference samples that will be useful for benchmarking and comparing bioinformatics tools for genome analysis. Raw signal data output by the sequencer is provided for these datasets in BLOW5 format. Basecalls are available in bgzip compressed FASTQ format. Alignments are available in BAM format.  More information can be found at https://github.com/GenTechGp/gtgseq/blob/main/docs/data.md.","ARN":"arn:aws:s3:::gtgseq","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://gtgseq.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Directly processing on an s3fs mount","URL":"https://github.com/GenTechGp/gtgseq/blob/main/docs/mount.md","AuthorName":"Hasindu Gamaarachchi"},{"Title":"Fetching subsets with slow5curl and samtools","URL":"https://github.com/GenTechGp/gtgseq/blob/main/docs/slow5curl.md","AuthorName":"Bonson Wong"}],"Tools & Applications":[{"Title":"buttery-eel: The buttery eel - a slow5 guppy basecaller wrapper","URL":"https://github.com/Psy-Fer/buttery-eel","AuthorName":"Samarakoon, H., Ferguson, J.M., Gamaarachchi H. et al."},{"Title":"Slow5curl: library and tool for accessing remote BLOW5 files.","URL":"https://github.com/BonsonW/slow5curl","AuthorName":"Wong, B., et al."},{"Title":"Slow5lib: toolkit slow5lib is a software library for reading & writing SLOW5 files.","URL":"https://github.com/hasindu2008/slow5lib","AuthorName":"Gamaarachchi, H., Samarakoon, H., Jenner, S.P. et al."},{"Title":"Slow5tools: toolkit for converting (FAST5 <-> SLOW5), compressing, viewing, indexing and manipulating data in SLOW5 format.","URL":"https://github.com/hasindu2008/slow5tools","AuthorName":"Samarakoon, H., Ferguson, J.M., Jenner, S.P. et al."}],"Publications":[{"Title":"Accelerated nanopore basecalling with SLOW5 data format.","URL":"https://doi.org/10.1093/bioinformatics/btad352","AuthorName":"Samarakoon, H., Ferguson, J.M., Gamaarachchi H. et al."},{"Title":"Fast nanopore sequencing data analysis with SLOW5.","URL":"https://doi.org/10.1038/s41587-021-01147-4","AuthorName":"Gamaarachchi, H., Samarakoon, H., Jenner, S.P. et al."},{"Title":"Flexible and efficient handling of nanopore sequencing signal data with slow5tools.","URL":"https://doi.org/10.1186/s13059-023-02910-3","AuthorName":"Samarakoon, H., Ferguson, J.M., Jenner, S.P. et al."},{"Title":"Streamlining remote nanopore data access with slow5curl","URL":"https://doi.org/10.1093/gigascience/giae016","AuthorName":"Wong, B., Ferguson, J.M., Do, J.Y. et al."}]},"RegistryEntryAdded":"2023-06-23","RegistryEntryLastModified":"2025-04-25","Slug":"gtgseq","Sources":["awslabs-open-data-registry"]}
{"Name":"Gulfwide Avian Colony Monitoring Survey Photos","Description":"For this project, The Water Institute (the Institute) and subcontractor Colibri Ecological Consulting, LLC (Colibri) utilized established methods and protocols capable of assessing changes of colonial waterbird populations and their important habitats within individual states and the broader northern Gulf of Mexico region.  Data collection activities included:\n    Aerial Photographic Nest Surveys: Implementation of fixed-wing aircraft surveys intended to assess waterbird colonies and document associated nesting within select portions of the northern Gulf of Mexico. Additional detail is provided on the Survey Protocols page of this portal. \n    Nest Dotting Analyses: Review and analysis of aerial photographic nest surveys (2010-2013, 2015, 2018, and 2021) with the intention of documenting the breeding population size and associated nesting for each species at each colony. Additional detail is provided on the Dotting Protocols page of this portal. \n","Documentation":"https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3570220a53647","Contact":"avaiandataaws@thewaterinstitute.org","ManagedBy":"[CPRA](https://coastal.la.gov/) and [The Water Institute](https://thewaterinstitute.org/)","UpdateFrequency":"~2 years","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["aws-pds","biology","conservation","ecosystems","environmental","labeled","life sciences","object detection"],"License":"Creative Commons BY-SA","Resources":[{"Description":"High resolution(5184 x 3456) images are provided in jpg format (compression quality level 98%).\nThe avian-monitoring folder includes the high resolution photos, the dotting process screenshots, the dotting information (birds and nest counts by species), and thumbnails of the subset of the images referenced on those dataset. Files in this subfolder have been renamed and organized to have a common naming convension across the years. \nThe top level `High Resolution Images` includes all the high resolution images (even the ones not referenced in the dotting process) with their original filenames. \n","ARN":"arn:aws:s3:::twi-aviandata","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Explore dataset](https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3570220a53647/page/Data-Explorer/)","[README](https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3570220a53647/page/Project-Information/)","[Data processing notebook](https://github.com/waterinstitute/avian_data_ingestor/blob/master/doc/Metadata%20for%20DottedImages.ipynb)"]}],"RegistryEntryAdded":"2023-10-09","RegistryEntryLastModified":"2026-06-05","Slug":"gulfwide-avian-monitoring","Sources":["awslabs-open-data-registry"]}
{"Name":"Guy's Breast Cancer Lymph Nodes (GRAPE)","Description":"This is a retrospective dataset of 1523 H&E-stained whole slide images (WSI) of lymph nodes from breast cancer patients. The cohort consisted of 177 patients (122 LN-positive - metastasis was reported in at least 1 LN - and 55 LN-negative patients) with invasive breast carcinoma treated between 1984 and 2002 at Guy’s Hospital London, UK. Slides were scanned and digitised at 40x magnification (0.23 µm/pixel), NanoZoomer H.T2.0 2.0-HT (Hamamatsu Photonics UK, Ltd, Welwyn Garden City, UK). WSIs are in .ndpi format.","Documentation":"https://github.com/cancerbioinformatics/GRAPE","Contact":"https://github.com/cancerbioinformatics/GRAPE/issues","ManagedBy":"http://www.cancerbioinformatics.co.uk","UpdateFrequency":"New data will be added as soon as it's available","Tags":["aws-pds","biology","breast cancer","cancer","computational pathology","histopathology","life sciences"],"License":"This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.","Resources":[{"Description":"H&E-stained WSI lymph node .ndpi images in a public bucket","ARN":"arn:aws:s3:::grape-lns","Region":"eu-north-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2023-04-07","RegistryEntryLastModified":"2025-01-23","Slug":"guys-breast-cancer-lymph-nodes","Sources":["awslabs-open-data-registry"]}
{"Name":"Variant Effect Predictor (VEP) and the Loss-Of-Function Transcript Effect Estimator (LOFTEE) Plugin","Description":"VEP determines the effect of genetic variants (SNPs, insertions, deletions, CNVs or structural variants) on genes, transcripts, and protein sequence, as well as regulatory regions. The European Bioinformatics Institute produces the VEP tool/db and releases updates every 1 - 6 months. The latest release contains 267 genomes from 232 species containing 5567663 protein coding genes. This dataset hosts the last 5 releases for human, rat, and zebrafish. Also, it hosts the required reference files for the Loss-Of-Function Transcript Effect Estimator (LOFTEE) plugin as it is commonly used with VEP.","Documentation":"https://hail-vep-pipeline.public.tennex.io/","Contact":"https://www.tennex.io/contact","ManagedBy":"[Tennex](https://www.tennex.io/)","UpdateFrequency":"New packages are added as soon as they are available and confirmed to work with recent versions of Hail.","Tags":["aws-pds","genome wide association study","genomic","life sciences","loftee","vep"],"License":"[VEP](https://uswest.ensembl.org/info/about/publications.html) use is governed by the Apache 2.0 licenses, and [LOFTEE](https://github.com/konradjk/loftee/blob/master/LICENSE) use is governed by the MIT license.\n","Resources":[{"Description":"VEP and LOFTEE data","ARN":"arn:aws:s3:::hail-vep-pipeline","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Hail","URL":"https://hail.is/","AuthorName":"Neale Lab","AuthorURL":"https://www.nealelab.is/"},{"Title":"Loss-Of-Function Transcript Effect Estimator (LOFTEE)","URL":"https://github.com/konradjk/loftee","AuthorName":"Konrad Karczewski","AuthorURL":"https://github.com/konradjk"},{"Title":"Variant Effect Predictor (VEP)","URL":"http://uswest.ensembl.org/info/docs/tools/vep/index.html","AuthorName":"Ensembl","AuthorURL":"http://uswest.ensembl.org/info/docs/tools/vep/index.html"}]},"RegistryEntryAdded":"2020-04-15","RegistryEntryLastModified":"2022-03-25","Slug":"hail-vep-pipeline","Sources":["awslabs-open-data-registry"]}
{"Name":"Human Balance Evaluation Database","Description":"The HBEDB contains force platform recordings from subjects undergoing stabilography tests. The subjects performed standing tasks under four different conditions: with their eyes opened or closed, while standing on a rigid or unstable surface. Each condition was tested three times, with the order of the conditions being randomized among subjects. A total of 1930 trials performed by 163 different subjects are given in this database.","Documentation":"https://doi.org/10.13026/C2WW2W","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2WW2W","ARN":"arn:aws:s3:::physionet-open/hbedb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"hbedb","Sources":["awslabs-open-data-registry"]}
{"Name":"Human Cancer Models Initiative (HCMI) Cancer Model Development Center","Description":"The Human Cancer Models Initiative (HCMI) is an international consortium that is generating novel,\nnext-generation, tumor-derived culture models annotated with genomic and clinical data.\nHCMI-developed models and related data are available as a community resource. The NCI is\ncontributing to the initiative by supporting four Cancer Model Development Centers (CMDCs).  CMDCs\nare tasked with producing next-generation cancer models from clinical samples. The cancer models\ninclude tumor types that are rare, originate from patients from underrepresented populations, lack\nprecision therapy, or lack cancer model tools. Throughout the development process, the CMDCs\nutilize stringent internal QC measures to ensure both clinical and molecular integrity. These\nmodels are then annotated with clinical and genomic data and are available as a community\nresource.\n","Documentation":"https://ocg.cancer.gov/programs/hcmi/nci-cancer-model-development","Contact":"dcf-support@datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genomic","life sciences","STRIDES","whole genome sequencing"],"License":"NIH Genomic Data Sharing Policy https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"Biospecimen Supplement, RNA-Seq Gene Expression Quantification","ARN":"arn:aws:s3:::gdc-hcmi-cmdc-phs001486-2-open","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/HCMI-CMDC","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"}]},"RegistryEntryAdded":"2020-04-07","RegistryEntryLastModified":"2020-10-05","Slug":"hcmi-cmdc","Sources":["awslabs-open-data-registry"]}
{"Name":"The Human Connectome Project","Description":"The Human Connectome Project (HCP Young Adult, HCP-YA) is mapping the healthy human connectome by collecting and freely distributing neuroimaging and behavioral data on 1,200 normal young adults, aged 22-35.","Documentation":"http://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release","Contact":"hcp-users@humanconnectome.org","ManagedBy":"[Connectome Coordination Facility](https://www.humanconnectome.org/ccf-staff)","UpdateFrequency":"Uncertain","Tags":["aws-pds","biology","imaging","life sciences","neurobiology","neuroimaging","neuroscience"],"License":"[HCP Data Use Agreement](https://www.humanconnectome.org/storage/app/media/data_use_terms/DataUseTerms-HCP-Open-Access-26Apr2013.pdf)","Resources":[{"Description":"https://www.humanconnectome.org/study/hcp-young-adult/overview","ARN":"arn:aws:s3:::hcp-openaccess","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://wiki.humanconnectome.org/docs/How%20To%20Connect%20to%20Connectome%20Data%20via%20AWS.html"}],"DataAtWork":{"Tutorials":[{"Title":"Exploring the Human Connectom","URL":"https://store.humanconnectome.org/courses/2019/exploring-the-human-connectome.php","AuthorName":"The Human Connectome Project"}],"Tools & Applications":[{"Title":"The Human Connectome Workbench","URL":"https://www.humanconnectome.org/software/connectome-workbench","AuthorName":"The Human Connectome Project"}],"Publications":[{"Title":"The Human Connectome Project: A retrospective","URL":"https://www.sciencedirect.com/science/article/pii/S1053811921008168","AuthorName":"Elam JS, Glasser MF, Harms MP, Sotiropoulos SN, Andersson JL, Burgess GC, Curtiss SW, et al."},{"Title":"The minimal preprocessing pipelines for the Human Connectome Project","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3720813/","AuthorName":"Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, Xu J, Jbabdi S, et al."},{"Title":"The WU-Minn Human Connectome Project: an overview.","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3724347/","AuthorName":"Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil, K, and the WU-Minn HCP Consortium."}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2024-04-08","Slug":"hcp-openaccess","Sources":["awslabs-open-data-registry"]}
{"Name":"Open Access Dataset and Toolbox of High-Density Surface Electromyogram Recordings","Description":"We provide an open access dataset of High densitY Surface Electromyogram (HD-sEMG) Recordings (named \"Hyser\"). We acquired data from 20 subjects with each subject participating in our experiment twice on separate days following the same experiment paradigm. Our Hyser dataset contains five sub-datasets: (1) pattern recognition (PR) dataset acquired during 34 hand gestures, (2) maximal voluntary muscle contraction (MVC) dataset while subjects contracted each individual finger, (3) one-degree of freedom (DoF) dataset acquired during force-varying contraction of each individual finger, (4) N-DoF dataset acquired during prescribed contractions of combinations of multiple fingers, and (5) random task dataset acquired during random contraction of combinations of fingers without any prescribed force trajectory. Sub-dataset 1 can be used for gesture recognition studies. Sub-datasets 2-5 also recorded individual finger forces, thus can be used for studies on proportional control of neuroprostheses.","Documentation":"https://doi.org/10.13026/hxan-pe94","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/hxan-pe94","ARN":"arn:aws:s3:::physionet-open/hd-semg/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"hd-semg","Sources":["awslabs-open-data-registry"]}
{"Name":"Software for computing Heart Rate Fragmentation","Description":"Heart rate fragmentation (HRF) is a new method for assessing neuroautonomic integrity based on the analysis of short-term (high-frequency [HF]) heart rate dynamics. The code (in AWK) provided here is for the computation of three different metrics, PIP, PNNLS, and PNNSS introduced in 2017 by Costa and colleagues. The motivation for developing HRF was the recognition that changes in heart rate (HR) above the respiratory frequency not only inflate the value of traditional heart rate variability (HRV) metrics, such as rMSSD and HF power, but are a dynamical marker of cardioautonomic dysfunction. In healthy subjects, HR increases with inspiration and decreases with expiration, defining a “high-frequency” pattern referred to as respiratory sinus arrhythmia. This physiologic oscillation is driven primarily by changes in vagal tone. In contexts in which cardio-respiratory coupling is degraded, e.g., with aging and disease, non-vagally mediated oscillations whose frequencies are higher than the breathing frequency may develop. Non-vagally mediated fluctuations are characterized by an excess of reversals in HR acceleration. The simplest measure of HRF is the percentage of “inflection points,” (PIP) i.e., of changes in HR acceleration sign. Of key importance is fact that traditional short-term HRV measures can be high for two opposite reasons, one healthy – respiratory sinus arrhythmia – consistent with high vagal tone modulation, and one pathologic – fragmented rhythms – consistent with decreased/impaired vagal activity. Increased HRF has been shown to be independently associated with higher risk of incident major adverse cardiovascular events and atrial fibrillation. These results have been independently confirmed, as described in Background section.","Documentation":"https://doi.org/10.13026/0mzj-gn98","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"GNU General Public License version 3","Resources":[{"Description":"https://doi.org/10.13026/0mzj-gn98","ARN":"arn:aws:s3:::physionet-open/heart-rate-fragmentation-code/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"heart-rate-fragmentation-code","Sources":["awslabs-open-data-registry"]}
{"Name":"HeartCycle: A comprehensive dataset of synchronized impedance cardiography and echocardiography for hemodynamic predictions","Description":"The \"HeartCycle\" dataset offers a comprehensive collection of synchronized impedance cardiography (ICG) and echocardiography (ECHO) signals, supplemented with finger photoplethysmography (PPG), heart sounds, and electrocardiography (ECG) data from 17 healthy volunteers. Collected during the HeartCycle project (FP7-216695), this dataset aims to address biases in the ICG waveform, particularly the ABEXYOZ complex, where the B and X points do not precisely align with the aortic valve opening and closing notches. The biases in B and X point detection are critical for hemodynamic prediction because these characteristic points are used to calculate essential diagnostic parameters, including systolic time intervals (PEP and LVET), contractility, stroke volume, and cardiac output. By providing synchronized ICG and ECHO signals, researchers can better understand these biases and develop more accurate models for hemodynamic parameter computation. The dataset is stored in HDF5 format, facilitating the storage of complex data structures and easy access to various physiological parameters. It is ideal for developing machine learning models to enhance the detection of characteristic points in ICG signals. For instance, machine learning models can be used to detect characteristic points for improved left ventricular ejection time (LVET) estimation or mapping the ICG signal to the different mechanical events in the cardiac cycle using the ECHO as a reference. Detailed metadata and usage notes are included to support data utilization across different software environments. Ethical approval was obtained from the University of Coimbra Hospital's ethics committee, and informed consent was provided by all participants.","Documentation":"https://doi.org/10.13026/z865-eb23","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/z865-eb23","ARN":"arn:aws:s3:::physionet-open/heartcycle/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"heartcycle","Sources":["awslabs-open-data-registry"]}
{"Name":"Hecatomb Databases","Description":"Preprocessed databases for use with the Hecatomb pipeline for viral and phage sequence annotation.","Documentation":"https://hecatomb.readthedocs.io/en/latest/","Contact":"shandley@wustl.edu","ManagedBy":"[Washington University in St. Louis](https://wustl.edu/)","UpdateFrequency":"Every 6 to 12 months","Tags":["aws-pds","bioinformatics","genetic","genomic","life sciences","metagenomics","virus","whole genome sequencing"],"License":"[MIT](https://opensource.org/licenses/MIT)","Resources":[{"Description":"Hecatomb Database Resources","ARN":"arn:aws:s3:::hecatombdatabases","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Explore Bucket](https://hecatombdatabases.s3.us-west-2.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"The Hecatomb Tutorial","URL":"https://hecatomb.readthedocs.io/en/latest/tutorialPt1/","AuthorName":"Michael Roach","AuthorURL":"https://bioinf.cc"}],"Publications":[{"Title":"No Evidence Known Viruses Play a Role in the Pathogenesis of Onchocerciasis-Associated Epilepsy. An Explorative Metagenomic Case-Control Study","URL":"https://doi.org/10.3390/pathogens10070787","AuthorName":"Michael Roach,Adrian Cantu,Melissa Krizia Vieri,Matthew Cotten,Paul Kellam,My Phan,Lia van der Hoek,Michel Mandro,Floribert Tepage,Germain Mambandu,Gisele Musinya,Anne Laudisoit,Robert Colebunders,Robert Edwards, John L. Mokili","AuthorURL":"https://bioinf.cc"}]},"RegistryEntryAdded":"2021-12-24","RegistryEntryLastModified":"2021-12-24","Slug":"hecatomb","Sources":["awslabs-open-data-registry"]}
{"Name":"Helpful Sentences from Reviews","Description":"A collection of sentences extracted from customer reviews labeled with their helpfulness score.","Documentation":"https://helpful-sentences-from-reviews.s3.amazonaws.com/README.md","Contact":"gkutiel@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not updated","Tags":["amazon.science","information retrieval","json","natural language processing","text analysis"],"License":"This data is available for anyone to use under the terms of the CDLA-Sharing license, which is available [here](https://cdla.dev/sharing-1-0/)","Resources":[{"Description":"Helpful Sentences from Reviews","ARN":"arn:aws:s3:::helpful-sentences-from-reviews","Region":"us-east-1","Type":"S3 Bucket","Explore":["[train.json](https://helpful-sentences-from-reviews.s3.amazonaws.com/train.json)","[test.json](https://helpful-sentences-from-reviews.s3.amazonaws.com/test.json)"]}],"DataAtWork":{"Publications":[{"Title":"Identifying Helpful Sentences in Product Reviews","URL":"https://arxiv.org/abs/2104.09792","AuthorName":"Iftah Gamzu et al (2021)"}]},"RegistryEntryAdded":"2021-04-30","RegistryEntryLastModified":"2022-06-06","Slug":"helpful-sentences-from-reviews","Sources":["awslabs-open-data-registry"]}
{"Name":"HESTIA Aggregated Agri-food Life Cycle Assessment (LCA) Data","Description":"National and global weighted averages of the environmental impacts of agricultural and food\nproduction, aggregated from the farm-level cycles published on the HESTIA platform.\n\nEach release is a self-contained snapshot in JSON-LD: validated aggregated impact assessments with\nthe cycles they describe, those cycles' sites, and every glossary term and bibliographic source any\nof them reference, so a release resolves without leaving the bucket. Alongside it sits a flat\nParquet mirror of the mid-point and end-point indicators, for querying without parsing JSON-LD, and\nan index mapping product to country to year to aggregation.\n\nThe 2025-05-01 release covers 70 products across 37 countries, spanning 1990-2025, with 54\nindicators computed under 18 characterisation models (ReCiPe, LC-Impact, Environmental Footprint\nand others). Releases are added here once they are freely available to everyone.\n","Documentation":"https://www.hestia.earth/guide/guide-accessing-data-aws-open-data","Contact":"community@hestia.earth","ManagedBy":"[HESTIA](https://www.hestia.earth/), University of Oxford","UpdateFrequency":"With each HESTIA data release, roughly two to three per year. A release is added here 182 days after its version date, once it is freely available to everyone.","Collabs":{},"Tags":["agriculture","aws-pds","carbon","climate","environmental","food security","json","life sciences","parquet","sustainability"],"License":"Creative Commons Attribution 4.0 International (CC-BY 4.0).\nhttps://creativecommons.org/licenses/by/4.0/\n","Resources":[{"Description":"Aggregated agri-food LCA data as JSON-LD and Parquet, one folder per release.","ARN":"arn:aws:s3:::hestia-aggregated-data","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Bucket layout and how to find data](https://www.hestia.earth/guide/guide-accessing-data-aws-open-data)","[Browse Bucket](https://hestia-aggregated-data.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"New object notifications, SQS and Lambda protocols only. A release is uploaded object by object and its manifest is written last, so an event for data/jsonld/releases/<version>/manifest.json means that release is complete.","ARN":"arn:aws:sns:eu-west-2:568256617230:hestia-aggregated-data-object-created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Exploring HESTIA Aggregated LCA Data in a Browser-Based Notebook on Scigantic","URL":"https://scigantic.com/archive/9118e197-ea8f-5a5e-91dd-d32ff7ed085e/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Get to know the HESTIA aggregated agri-food LCA dataset","URL":"https://github.com/hestia-earth/hestia-aws-open-data/blob/main/get-to-know-hestia-aggregated-data.ipynb","NotebookURL":"https://github.com/hestia-earth/hestia-aws-open-data/blob/main/get-to-know-hestia-aggregated-data.ipynb","AuthorName":"HESTIA","AuthorURL":"https://www.hestia.earth/","Services":["Amazon S3"]}],"Tools & Applications":[{"Title":"HESTIA platform","URL":"https://www.hestia.earth/","AuthorName":"HESTIA","AuthorURL":"https://www.hestia.earth/"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-08-13","RegistryEntryLastModified":"2026-10-14","Slug":"hestia-aggregated-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Hillel Yaffe Glaucoma Dataset (HYGD): A Gold-Standard Annotated Fundus Dataset for Glaucoma Detection","Description":"Glaucomatous optic neuropathy (GON) is a leading cause of irreversible blindness worldwide, affecting an estimated 64.3 million people globally with projections reaching 111.8 million by 2040. Approximately 50% of cases remain undiagnosed until advanced stages when vision loss becomes noticeable. Traditional diagnosis requires comprehensive ophthalmic examinations by specialists, creating accessibility barriers in many regions. The Hillel Yaffe Glaucoma Dataset (HYGD) addresses a critical limitation in existing GON datasets: the lack of gold-standard annotations. Unlike most publicly available datasets where glaucoma labels are determined solely from digital fundus images (DFIs), HYGD's labels are based on comprehensive ophthalmic examinations, including visual acuity assessment, intraocular pressure measurement, optical coherence tomography (OCT), visual field tests, and at least one year of follow-up monitoring. The dataset is structured to include both the DFIs and a labels file containing patient IDs, GON classifications, and image quality scores. All DFIs were taken using a TOPCON DRI OCT Triton retinal camera with a 45° FOV and underwent deidentification and standardization processing. HYGD enables researchers to train and benchmark models on rigorously annotated data, potentially improving their reliability. This dataset serves as a valuable resource for developing generalizable models that can function across diverse patient populations and clinical settings, ultimately supporting earlier detection and treatment of GON.","Documentation":"https://doi.org/10.13026/m92s-0z95","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/m92s-0z95","ARN":"arn:aws:s3:::physionet-open/hillel-yaffe-glaucoma-dataset/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"hillel-yaffe-glaucoma-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"HIRLAM Weather Model","Description":"HIRLAM (High Resolution Limited Area Model) is an operational synoptic and mesoscale weather prediction model managed by the Finnish Meteorological Institute.","Documentation":"http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3","ManagedBy":"[Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/)","Contact":"avoin-data@fmi.fi","UpdateFrequency":"The data is updated four times a day with analysis hours 00, 06, 12 and 18. Corresponding model runs are available roughly five hours after analysis time (~ after model run has started).","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","earth observation","meteorological","weather"],"License":"Creative Commons Attribution 4.0 International (CC BY 4.0)","Resources":[{"Description":"Surface GRIB files","ARN":"arn:aws:s3:::fmi-opendata-rcrhirlam-surface-grib","Region":"eu-west-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://fmi-opendata-rcrhirlam-surface-grib.s3.amazonaws.com/index.html)"]},{"Description":"Pressure GRIB files","ARN":"arn:aws:s3:::fmi-opendata-rcrhirlam-pressure-grib","Region":"eu-west-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://fmi-opendata-rcrhirlam-pressure-grib.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new surface data","ARN":"arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-rcrhirlam-surface-grib","Region":"eu-west-1","Type":"SNS Topic"},{"Description":"Notifications for new pressure data","ARN":"arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-rcrhirlam-pressure-grib","Region":"eu-west-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2023-02-28","Slug":"hirlam","Sources":["awslabs-open-data-registry"]}
{"Name":"Haaglanden Medisch Centrum sleep staging database","Description":"A collection of 151 whole-night polysomnographic (PSG) sleep recordings (85 Male, 66 Female, mean Age of 53.9 ± 15.4) collected during 2018 at the Haaglanden Medisch Centrum (HMC, The Netherlands) sleep center. Patient recordings were randomly selected and include a heterogeneous population which was referred for PSG examination on the context of different sleep disorders. The dataset contains electroencephalographic (EEG), electrooculographic (EOG), chin electromyographic (EMG), and electrocardiographic (ECG) activity, as well as event annotations corresponding to scoring of sleep patterns (hypnogram) performed by sleep technicians at HMC. The dataset was collected as part of a study evaluating the generalization performance of an automatic sleep scoring algorithm across multiple heterogeneous datasets.","Documentation":"https://doi.org/10.13026/t79q-fr32","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/t79q-fr32","ARN":"arn:aws:s3:::physionet-open/hmc-sleep-staging/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"hmc-sleep-staging","Sources":["awslabs-open-data-registry"]}
{"Name":"Human PanGenomics Project","Description":"This dataset includes sequencing data, assemblies, and analyses for the offspring of ten parent-offspring trios.","Documentation":"https://github.com/human-pangenomics/hpgp-data","Contact":"https://github.com/human-pangenomics/hpgp-data/issues","ManagedBy":"(Human Pangenome Reference Consortium)[https://humanpangenome.org/]","UpdateFrequency":"Data will be added and updated as technologies improve or new data uses are encountered.","Tags":["aws-pds","cram","fast5","fastq","genetic","genomic","life sciences"],"License":"[Creative Commons CC0 1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/deed.en)","Resources":[{"Description":"Human PanGenomics Project","ARN":"arn:aws:s3:::human-pangenomics","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://human-pangenomics.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes","URL":"https://www.nature.com/articles/s41587-020-0503-6","AuthorName":"Shafin et al (2020)"}]},"RegistryEntryAdded":"2019-04-08","RegistryEntryLastModified":"2022-06-15","Slug":"hpgp-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Epigenomes of the Human Pangenome Reference Consortium (HPRC) Release 2","Description":"The Human Pangenome Reference Consortium (HPRC) Release 2 represents a landmark achievement in genomics, providing high-quality phased genome assemblies from over 200 individuals with comprehensive functional genomics data. The HPRC Epigenome Browser provides researchers a way to explore all epigenomics data generated by release 2. The HPRC Epigenome Browser (HPRCEB) is a modern, interactive web portal that democratizes access to HPRC Release 2 epigenomics data through an intuitive interface supporting genome selection, data visualization, and bulk download capabilities. The portal integrates genome assemblies, DNA methylation profiles, gene expression data, and chromatin accessibility measurements across diverse populations, enabling researchers to efficiently identify and retrieve datasets matching their specific research needs.\n","Contact":"dli23@wustl.edu","ManagedBy":"Ting Wang Lab (https://wang.wustl.edu/)","Documentation":"https://epigenome.humanpangenome.org/?tab=tutorials","UpdateFrequency":"Annual. The repository will be updated with each new batch of data as it is generated and released under the next HPRC yearly cycle.","Tags":["aws-pds","bioinformatics","biology","epigenomics","genetic","genomic","life sciences"],"License":"External data users may freely download, analyze, and publish results based on any HPRC data provided here without restrictions.","Resources":[{"Description":"HPRC Epigenome Browser","ARN":"arn:aws:s3:::hprc-epigenome","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"\"Get To Know A Dataset: HPRC Epigenome\"\n","URL":"https://github.com/twlab/open-data-examples/blob/main/get-to-know-hprc-epigenome.ipynb","AuthorName":"HPRC Epigenome Browser","AuthorURL":"https://epigenome.humanpangenome.org/"}],"Publications":[{"Title":"\"Modbed track: Visualization of modified bases in single-molecule sequencing\"\n","URL":"https://www.sciencedirect.com/science/article/pii/S2666979X23002999?via%3Dihub","AuthorName":"Daofeng Li, Xiaoyu Zhuo, Jessica K. Harrison, Shane Liu, Ting Wang"},{"Title":"A draft human pangenome reference","URL":"https://doi.org/10.1038/s41586-023-05896-x","AuthorName":"Liao, WW., Asri, M., Ebler, J. et al."},{"Title":"WashU Epigenome Browser update 2025","URL":"https://doi.org/10.1093/nar/gkaf387","AuthorName":"Chanrung Seng, Shane Liu, Wenjin Zhang, Xiaoyu Zhuo, Daofeng Li, Ting Wang"}]},"RegistryEntryAdded":"2025-11-05","RegistryEntryLastModified":"2026-08-04","Slug":"hprc-epigenome","Sources":["awslabs-open-data-registry"]}
{"Name":"Homeland Security and Infrastructure US Cities","Description":"The U.S. Cities elevation data collection program supported the US Department of Homeland Security Homeland Security and Infrastructure Program (HSIP). As part of the HSIP Program, there were 133+ U.S. cities that had imagery and LiDAR collected to provide the Homeland Security, Homeland Defense, and Emergency Preparedness, Response and Recovery (EPR&R) community with common operational, geospatially enabled baseline data needed to analyze threat, support critical infrastructure protection and expedite readiness, response and recovery in the event of a man-made or natural disaster. As a part of that, for some time, recurring LiDAR data was also being collected by a joint agreement of NGA and other federal agencies and the HIFLDS Working Group. The publicly released data excluded US Military Installation coverage, but it is provided in as is. These collects were acquired by contract using commercial collection companies. Some metadata information about the collection can be found at USGS at https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/Non_Standard_Contributed/NGA_US_Cities/Topeka_KS/NGA%20133%20US%20Cities%20Data%20Disclaimer%20and%20Explanation%20Readme.pdf","Documentation":"https://github.com/hobuinc/hsip-lidar","Contact":"https://github.com/hobuinc/hsip-lidar","ManagedBy":"[Hobu, Inc.](https://hobu.co)","UpdateFrequency":"Periodically","Collabs":{"ASDI":{"Tags":["infrastructure"]}},"Tags":["aws-pds","disaster response","elevation","geospatial","lidar"],"License":"US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map","Resources":[{"Description":"A [Requester Pays](https://docs.aws.amazon.com/AmazonS3/latest/dev/RequesterPaysBuckets.html) Bucket of HSIP data including building footprints, LiDAR, ortho imagery, and vegetation delination.","ARN":"arn:aws:s3:::usgs-lidar-uscities","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":true}],"RegistryEntryAdded":"2022-09-07","RegistryEntryLastModified":"2022-09-20","Slug":"hsip-lidar-us-cities","Sources":["awslabs-open-data-registry"]}
{"Name":"Logistic Regression-HSMM-based Heart Sound Segmentation","Description":"The identification of the exact positions of the first and second heart sounds within a phonocardiogram (PCG), or heart sound segmentation, is an essential step in the automatic analysis of heart sound recordings, allowing for the classification of pathological events. While threshold-based segmentation methods have shown modest success, probabilistic models, such as hidden Markov models, have recently been shown to surpass the capabilities of previous methods. Segmentation performance is further improved when apriori information about the expected duration of the states is incorporated into the model, such as in a hidden semi-Markov model (HSMM). This code provides Matlab algorithms to perform HSMM-based heart sound segmentation.","Documentation":"https://doi.org/10.13026/vnt9-kf93","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"GNU General Public License version 3","Resources":[{"Description":"https://doi.org/10.13026/vnt9-kf93","ARN":"arn:aws:s3:::physionet-open/hss/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"hss","Sources":["awslabs-open-data-registry"]}
{"Name":"National Herbarium of Israel","Description":"Our collection encompasses approximately one million vascular plant specimens from the Mediterranean and Middle East biodiversity hotspot, representing flora from Israel, Jordan, Hermon, Sinai, Egypt, the Caucasus, Arabia, North Africa, and throughout the Mediterranean basin. This scientifically significant repository includes published voucher specimens, original specimens used for \"Flora Palaestina\" illustrations, and critical references for the Israeli gene bank collections. The ongoing digitization process captures high-resolution images of each specimen while systematically incorporating label information into our computerized catalog. This virtual herbarium will democratize access to these valuable botanical resources, enabling global researchers to examine specimens in exceptional detail from anywhere in the world. Beyond preservation, this digital transformation unlocks new research possibilities through computational analysis of both visual specimen characteristics and associated metadata. The dataset will serve as a foundational resource for advancing botanical research, ecological modeling, taxonomic investigation, historical analysis, and numerous other scientific disciplines concerned with plant biodiversity in this ecologically and historically significant region.","Documentation":"https://bit.ly/HUJVirtualHerbarium","Contact":"Eyal.Ben-Hur@mail.huji.ac.il","ManagedBy":"National Natural History Collections, The Hebrew University of Jerusalem","UpdateFrequency":"Monthly","Tags":["aws-pds","biodiversity","biology","climate","digital preservation","environmental","image processing","imaging","life sciences"],"License":"CC-BY-SA 4.0","Citation":"Vascular plants - Herbarium of The National Natural History Collections was accessed on DATE from https://registry.opendata.aws/huj-herbarium.","Resources":[{"Description":"HUJ Herbarium Collection Images","ARN":"arn:aws:s3:::hujinnhc/specify_assets/","Region":"il-central-1","Type":"S3 Bucket","Explore":null}],"DataAtWork":{"Tutorials":[{"Title":"How to use AWS S3 bucket to explore our public images dataset","URL":"https://bit.ly/HUJimages","NotebookURL":null,"AuthorName":"Eyal Ben-Hur","AuthorURL":null}]},"DeprecatedNotice":null,"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-05-20","RegistryEntryLastModified":"2025-10-21","Slug":"huj-herbarium","Sources":["awslabs-open-data-registry"]}
{"Name":"The Human Microbiome Project","Description":"The NIH-funded Human Microbiome Project (HMP) is a collaborative effort of over 300 scientists from more than 80 organizations to comprehensively characterize the microbial communities inhabiting the human body and elucidate their role in human health and disease. To accomplish this task, microbial community samples were isolated from a cohort of 300 healthy adult human subjects at 18 specific sites within five regions of the body (oral cavity, airways, urogenital track, skin, and gut). Targeted sequencing of the 16S bacterial marker gene and/or whole metagenome shotgun sequencing was performed for thousands of these samples. In addition, whole genome sequences were generated for isolate strains collected from human body sites to act as reference organisms for analysis. Finally, 16S marker and whole metagenome sequencing was also done on additional samples from people suffering from several disease conditions.","Documentation":"https://commonfund.nih.gov/hmp","Contact":"https://commonfund.nih.gov/hmp/related_activities","ManagedBy":"[The National Institutes of Health Office of Strategic Coordination - The Common Fund](https://commonfund.nih.gov/hmp)","UpdateFrequency":"Uncertain","Tags":["amino acid","aws-pds","fasta","fastq","genetic","genomic","life sciences","metagenomics","microbiome"],"License":"The data is publicly available to the community free of charge.","Resources":[{"Description":"https://aws.amazon.com/datasets/human-microbiome-project/","ARN":"arn:aws:s3:::human-microbiome-project","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"New microbe genomic variants in patients fecal community following surgical disruption of the upper human gastrointestinal tract","URL":"https://www.sciencedirect.com/science/article/pii/S2452231718300289","AuthorName":"Ranjit Kumar, Jayleen Grams, Daniel I. Chu, David K.Crossman, Richard Stahl, Peter Eipers, et al"},{"Title":"Strains, functions and dynamics in the expanded Human Microbiome Project","URL":"https://www.nature.com/articles/nature23889","AuthorName":"Jason Lloyd-Price, Anup Mahurkar, Gholamali Rahnavard, Jonathan Crabtree, Joshua Orvis, A. Brantley Hall, et al."},{"Title":"The Human Microbiome Project","URL":"https://www.nature.com/articles/nature06244","AuthorName":"Peter J. Turnbaugh, Ruth E. Ley, Micah Hamady, Claire M. Fraser-Liggett, Rob Knight & Jeffrey I. Gordon"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2022-06-15","Slug":"human-microbiome-project","Sources":["awslabs-open-data-registry"]}
{"Name":"Human Cell Atlas","Description":"The Human Cell Atlas (HCA) is a collaborative community of international scientists. Our mission is to create comprehensive reference maps of all the cells in the human body as a basis for both understanding human health and diagnosing, monitoring, and treating disease. The HCA registry has more than one thousand member scientists from hundreds of institutions around the world. The project is steered and governed by an Organizing Committee, co-chaired by Aviv Regev and Sarah Teichmann.","Documentation":"https://data.humancellatlas.org/","Contact":"https://data.humancellatlas.org/contact","ManagedBy":"UC Santa Cruz Genomics Institute, University of California, Santa Cruz, UCSC","UpdateFrequency":"Monthly","Tags":["biology","cell biology","cell imaging","gene expression","genome","genomic","Homo sapiens","life sciences","Mus musculus","single-cell transcriptomics","transcriptomics"],"License":"https://data.humancellatlas.org/about/data-use-agreement","Citation":"The URL for the HCA Data Portal, https://data.humancellatlas.org, can be used as the citation for data obtained from the HCA. Alternatively, you can cite: Regev A., et al. 2017. The Human Cell Atlas. Elife. Dec 5;6. pii: e27041. doi: 10.7554/eLife.27041.","Resources":[{"Description":"An S3 bucket containing all publicly accessible data files in  the Human Cell Atlas. The bucket layout and access procedures are  documented at\nhttps://github.com/DataBiosphere/azul/blob/develop/docs/mirror.rst\nand metadata can be viewed at\nhttps://explore.data.humancellatlas.org/\nor accessed programmatically at\nhttps://service.azul.data.humancellatlas.org/","ARN":"arn:aws:s3:::humancellatlas","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Data Browser UI](https://explore.data.humancellatlas.org/)","[Azul REST Web Service](https://service.azul.data.humancellatlas.org/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset - Human Cell Atlas","URL":"https://github.com/DataBiosphere/azul/blob/develop/docs/get-to-know-a-dataset-humancellatlas.ipynb","NotebookURL":"https://github.com/DataBiosphere/azul/blob/develop/docs/get-to-know-a-dataset-humancellatlas.ipynb","AuthorName":"UC Santa Cruz Genomics Institute","AuthorURL":"https://genomics.ucsc.edu/"}],"Publications":[{"Title":"The Human Cell Atlas from a cell census to a unified foundation model","URL":"https://www.nature.com/articles/s41586-024-08338-4","AuthorName":"Jennifer E. Rood, Samantha Wynne, Lucia Robson, Anna Hupalowska, John Randell, Sarah A. Teichmann & Aviv Regev"},{"Title":"The Human Cell Atlas White Paper","URL":"https://arxiv.org/abs/1810.05192","AuthorName":"Aviv Regev, Sarah Teichmann, Orit Rozenblatt-Rosen, Michael Stubbington, Kristin Ardlie, Ido Amit, Paola Arlotta, Gary Bader, Christophe Benoist, Moshe Biton, Bernd Bodenmiller, Benoit Bruneau, Peter Campbell, Mary Carmichael, Piero Carninci, Leslie Castelo-Soccio, Menna Clatworthy, Hans Clevers, Christian Conrad, Roland Eils, Jeremy Freeman, Lars Fugger, Berthold Goettgens, Daniel Graham, Anna Greka, Nir Hacohen, Muzlifah Haniffa, Ingo Helbig, Robert Heuckeroth, Sekar Kathiresan, Seung Kim, Allon Klein, Bartha Knoppers, Arnold Kriegstein, Eric Lander, Jane Lee, Ed Lein, Sten Linnarsson, Evan Macosko, Sonya MacParland, Robert Majovski, Partha Majumder, John Marioni, Ian McGilvray, Miriam Merad, Musa Mhlanga, Shalin Naik, Martijn Nawijn, Garry Nolan, Benedict Paten, Dana Pe'er, Anthony Philippakis, Chris Ponting, Steve Quake, Jayaraj Rajagopal, Nikolaus Rajewsky, Wolf Reik, Jennifer Rood, Kourosh Saeb-Parsy, Herbert Schiller, Steve Scott, Alex Shalek, Ehud Shapiro, Jay Shin, Kenneth Skeldon, Michael Stratton, Jenna Streicher, Henk Stunnenberg, Kai Tan, Deanne Taylor, Adrian Thorogood, Ludovic Vallier, Alexander van Oudenaarden, Fiona Watt, Wilko Weicher, Jonathan Weissman, Andrew Wells, Barbara Wold, Ramnik Xavier, Xiaowei Zhuang, Human Cell Atlas Organizing Committee"},{"Title":"The Human Cell Atlas: towards a first draft atlas","URL":"https://www.nature.com/collections/jccbbdahji","AuthorName":"Various authors"},{"Title":"The Human Cell Atlas: towards a first draft atlas","URL":"https://www.nature.com/immersive/d42859-024-00060-5/index.html","AuthorName":"Various authors"},{"Title":"The network effect: studying COVID-19 pathology with the Human Cell Atlas","URL":"https://www.nature.com/articles/s41580-020-0267-3","AuthorName":"Sarah Teichmann, Aviv Regev"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-04-18","RegistryEntryLastModified":"2026-10-05","Slug":"humancellatlas","Sources":["awslabs-open-data-registry"]}
{"Name":"Humor Detection from Product Question Answering Systems","Description":"This dataset provides labeled humor detection from product question answering systems.\nThe dataset contains 3 csv files: [Humorous.csv](https://humor-detection-pds.s3-us-west-2.amazonaws.com/Humorous.csv) \ncontaining the humorous product questions, \n[Non-humorous-unbiased.csv](https://humor-detection-pds.s3-us-west-2.amazonaws.com/Non-humorous-unbiased.csv) containing \nthe non-humorous prodcut questions from the same products as the humorous one, and, \n[Non-humorous-biased.csv](https://humor-detection-pds.s3-us-west-2.amazonaws.com/Non-humours-biased.csv) containing \nthe non-humorous prodcut questions from randomly selected products. \nEach file has the question, product description, image url to the product, \nand a label if the question is humorous or not.\n","Documentation":"https://humor-detection-pds.s3-us-west-2.amazonaws.com/README.txt","Contact":"yftahz@amazon.com, ekravi@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not currently being updated","Tags":["amazon.science","machine learning","natural language processing"],"License":"[CDLA-Sharing](https://cdla.io/sharing-1-0/)","Resources":[{"Description":"Humor Detection Dataset","ARN":"arn:aws:s3:::humor-detection-pds","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://humor-detection-pds.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"Humor Detection in Product Question Answering Systems.","URL":"https://sigir.org/sigir2020/","AuthorName":"Yftah Ziser, Elad Kravi & David Carmel"}]},"RegistryEntryAdded":"2020-05-11","RegistryEntryLastModified":"2022-06-06","Slug":"humor-detection","Sources":["awslabs-open-data-registry"]}
{"Name":"Humor patterns used for querying Alexa traffic","Description":"Humor patterns used for querying Alexa traffic when creating the taxonomy described in the paper \"“Alexa, Do You Want to Build a Snowman?” Characterizing Playful Requests to Conversational Agents\"  by Shani C., Libov A., Tolmach S., Lewin-Eytan L., Maarek Y., and Shahaf D. (CHI LBW 2022). These patterns corrospond to the researchers' hypotheses regarding what humor types are likely to appear in Alexa traffic. These patterns were used for querying Alexa traffic to evaluate these hypotheses.","Documentation":"Documented in [this paper](https://camps.aptaracorp.com/ACM_PMS/PMS/ACM/CHI22EXTENDEDABSTRACTS/454/3754dba1-93c0-11ec-b613-166a08e17233/OUT/chi22extendedabstracts-454.html)","Contact":"chen.shani@mail.huji.ac.il, alibov@amazon.com","UpdateFrequency":"Not currently updated","ManagedBy":"[Amazon](https://www.amazon.com/)","Tags":["amazon.science","dialog","machine learning","natural language processing"],"License":"CDLA-Sharing license","Citation":null,"Resources":[{"Description":"Humor patterns","ARN":"arn:aws:s3:::amazon-humor-patterns","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"“Alexa, Do You Want to Build a Snowman?” Characterizing Playful Requests to Conversational Agents","URL":"https://camps.aptaracorp.com/ACM_PMS/PMS/ACM/CHI22EXTENDEDABSTRACTS/454/3754dba1-93c0-11ec-b613-166a08e17233/OUT/chi22extendedabstracts-454.html#fn3","AuthorName":"Shani C., Libov A., Tolmach S., Lewin-Eytan L., Maarek Y., and Shahaf D."}]},"RegistryEntryAdded":"2022-04-27","RegistryEntryLastModified":"2022-11-08","Slug":"humor-patterns","Sources":["awslabs-open-data-registry"]}
{"Name":"HYCOM-OceanTrack Integrated HYCOM Eulerian Fields and Lagrangian Trajectories Dataset","Description":"A combined dataset of simulated ocean sea surface height, near-surface velocities, and particle trajectories from a global 1/25th degree HYbrid Coordinate Ocean Model (HYCOM) 1-year run.","Documentation":"https://github.com/selipot/hycom-oceantrack","Contact":"https://github.com/selipot/hycom-oceantrack/issues","ManagedBy":"Shane Elipot","UpdateFrequency":"Not updated","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","drifters","Eulerian","HYCOM","Lagrangian","numerical particle","ocean circulation","ocean currents","ocean sea surface height","ocean simulation","ocean velocity","oceans"],"License":"There are no restrictions on the use of this data.","Citation":"Pending","Resources":[{"Description":"HYCOM Eulerian and Lagrangian velocity and sea surface height","ARN":"arn:aws:s3:::hycom-global-drifters","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://hycom-global-drifters.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"HYCOM Eulerian tutorial","URL":"https://github.com/selipot/hycom-oceantrack/tree/main/tutorials/","NotebookURL":"https://github.com/selipot/hycom-oceantrack/tree/main/tutorials/hycom_eulerian_tutorial.ipynb","AuthorName":"Shane Elipot","AuthorURL":"https://selipot.github.io","Services":null},{"Title":"HYCOM Lagrangian tutorial","URL":"https://github.com/selipot/hycom-oceantrack/tree/main/tutorials/","NotebookURL":"https://github.com/selipot/hycom-oceantrack/tree/main/tutorials/hycom_lagrangian_tutorial.ipynb","AuthorName":"Shane Elipot","AuthorURL":"https://selipot.github.io","Services":null}],"Tools & Applications":[{"Title":"clouddrift","URL":"https://github.com/Cloud-Drift/clouddrift","AuthorName":"Shane Elipot","AuthorURL":"https://selipot.github.io"}],"Publications":[{"Title":"An integrated dataset of near-surface Eulerian fields and Lagrangian trajectories from an ocean model","URL":"https://doi.org/10.1038/s41597-024-03813-z","AuthorName":"Shane Elipot"},{"Title":"HYCOM-OceanTrack: HYCOM-OceanTrack: Integrated HYCOM Eulerian Fields and Lagrangian Trajectories Dataset (v1.0)","URL":"https://doi.org/10.5281/zenodo.12796468","AuthorName":"Shane Elipot"},{"Title":"Near-Surface Oceanic Kinetic Energy Distributions From Drifter Observations and Numerical Models","URL":"https://dx.doi.org/10.1029/2022JC018551","AuthorName":"Brian K. Arbic","AuthorURL":"https://arbic.earth.lsa.umich.edu"}]},"DeprecatedNotice":null,"ADXCategories":null,"RegistryEntryAdded":"2024-02-07","RegistryEntryLastModified":"2025-07-09","Slug":"hycom-global-drifters","Sources":["awslabs-open-data-registry"]}
{"Name":"HYbrid Coordinate Ocean Model Global Ocean Forecast System Reanalysis","Description":"Global Ocean Forecasting System (GOFS) 3.1 output on the GLBv0.08 grid. The resolution is 0.08° resolution between 40°S and 40°N, 0.04° poleward of these latitudes. The temportal frequenct is 3 hourly. This data was created by the Naval Research Laboratory: Ocean Dynamics and Prediction Branch.","Documentation":"https://www.hycom.org/dataserver/gofs-3pt1/reanalysis","Contact":"help@hycom.org","ManagedBy":"[COAPS](https://www.coaps.fsu.edu/)","UpdateFrequency":"Static Dataset Covering 1994-01-01 to 2015-12-31","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","global","oceans"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"GOFS 3.1: 41-layer HYCOM + NCODA Global 1/12° Reanalysis","ARN":"arn:aws:s3:::hycom-gofs-3pt1-reanalysis","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"HYCOM-examples repository","URL":"https://github.com/HYCOM/HYCOM-examples","AuthorName":"Dr. Alexandra Bozec","AuthorURL":"https://www.coaps.fsu.edu/alexandra-bozec"}],"Tools & Applications":[{"Title":"HYCOM-tools repository","URL":"https://github.com/HYCOM/HYCOM-tools","AuthorName":"Dr. Alan Wallcraft","AuthorURL":"https://www.coaps.fsu.edu/alan-wallcraft"}],"Publications":[{"Title":"GOFS 3.1 Validation Test Report","URL":"https://www7320.nrlssc.navy.mil/pubs/2017/metzger-2017.pdf","AuthorName":"E. Joseph Metzger, Naval Research Laboratory"},{"Title":"Operational multivariate ocean data assimilation","URL":"https://doi.org/10.1256/qj.05.105","AuthorName":"James A. Cummings"},{"Title":"Validation Test Report for the Improved Synthetic Ocean Profile (ISOP) System, Part I: Synthetic Profile Methods and Algorithm","URL":"https://apps.dtic.mil/sti/pdfs/ADA585251.pdf","AuthorName":"NAVAL RESEARCH LAB STENNIS DETACHMENT STENNIS SPACE CENTER MS OCEANOGRAPHY DIV"},{"Title":"Variational Data Assimilation for the Global Ocean","URL":"https://doi.org/10.1007/978-3-642-35088-7_13","AuthorName":"James A. Cummings, Ole Martin Smedstad"}]},"RegistryEntryAdded":"2023-03-09","RegistryEntryLastModified":"2025-07-09","Slug":"hycom-gofs-3pt1-reanalysis","Sources":["awslabs-open-data-registry"]}
{"Name":"Intracardiac Atrial Fibrillation Database","Description":"This database consists of endocardial recordings from the right atria of 8 patients in atrial fibrillation or flutter. A decapolar catheter with 2-5-2mm spacing (7mm spacing between bipoles) was placed in four separate regions of the heart. In each region, the 5 bipolar signals were recorded along with 3 surface ECG leads.","Documentation":"https://doi.org/10.13026/C23S33","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C23S33","ARN":"arn:aws:s3:::physionet-open/iafdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"iafdb","Sources":["awslabs-open-data-registry"]}
{"Name":"IBL Neuropixels Brainwide Map on AWS","Description":"Electrophysiological recordings of mouse brain activity acquired during a decision making task in multiple autism mice models.","Documentation":"https://docs.internationalbrainlab.org/notebooks_external/2025_data_release_autism_noel.html","Contact":"info@internationalbrainlab.org","ManagedBy":"[International Brain Laboratory](https://www.internationalbrainlab.com)","UpdateFrequency":"TBD","Tags":["autism spectrum disorder","aws-pds","life sciences","Mus musculus","neurophysiology","neuroscience","open source software"],"License":"CC-BY 4.0","Resources":[{"Description":"Project data in public bucket","ARN":"arn:aws:s3:::ibl-brain-wide-map-public","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Download the public data via ONE","URL":"https://docs.internationalbrainlab.org/notebooks_external/data_download.html","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"Find data associated with a release or publication","URL":"https://docs.internationalbrainlab.org/notebooks_external/data_download.html#Find-data-associated-with-a-release-or-publication","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"Intermediate Datasets and Analysis Code","URL":"https://osf.io/fap2s/ and https://osf.io/fap2s/wiki/home/","AuthorName":"Noel et al."},{"Title":"Loading Data","URL":"https://docs.internationalbrainlab.org/loading_examples.html","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"}],"Publications":[{"Title":"A common computational and neural anomaly across mouse models of autism","URL":"https://doi.org/10.1038/s41593-025-01965-8","AuthorName":"Noel et al."}]},"RegistryEntryAdded":"2025-09-06","RegistryEntryLastModified":"2025-09-08","Slug":"ibl-autism","Sources":["awslabs-open-data-registry"]}
{"Name":"IBL Behavioral Data on AWS","Description":"Behavioral data of mice performing a decision-making task, associated with 2020 publication of the IBL.","Documentation":"https://docs.internationalbrainlab.org/notebooks_external/2021_data_release_behavior.html","Contact":"info@internationalbrainlab.org","ManagedBy":"[International Brain Laboratory](https://www.internationalbrainlab.com)","UpdateFrequency":"TBD","Tags":["aws-pds","life sciences","Mus musculus","neurophysiology","neuroscience","open source software"],"License":"CC-BY 4.0","Resources":[{"Description":"Project data in public bucket","ARN":"arn:aws:s3:::ibl-brain-wide-map-public","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"ONE-api on pypi.python.org - python API to query and download neurophysiology data","URL":"https://pypi.org/project/ONE-api/","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"}],"Tutorials":[{"Title":"Download the public data via ONE","URL":"https://docs.internationalbrainlab.org/notebooks_external/data_download.html","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"Exploring the IBL behavior data","URL":"https://colab.research.google.com/drive/14OtLUEiyhrPTPsXetg8VQrTk1CLhBCW5#scrollTo=c6OU9gI9G775","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"Find data associated with a release or publication","URL":"https://docs.internationalbrainlab.org/notebooks_external/data_download.html#Find-data-associated-with-a-release-or-publication","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"Loading Data","URL":"https://docs.internationalbrainlab.org/loading_examples.html","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"}],"Publications":[{"Title":"Standardized and reproducible measurement of decision-making in mice","URL":"https://doi.org/10.7554/eLife.63711","AuthorName":"International Brain Laboratory et al.","AuthorURL":"www.internationalbrainlab.com"}]},"RegistryEntryAdded":"2023-01-09","RegistryEntryLastModified":"2025-09-06","Slug":"ibl-behaviour","Sources":["awslabs-open-data-registry"]}
{"Name":"BrainWideBench - IBL Benchmark for Neural Foundation Models in Neuroscience","Description":"BrainWideBench is a large-scale, multi-task benchmark for evaluating neural foundation models, derived from the International Brain Laboratory (IBL) Brainwide Map dataset. It contains preprocessed Neuropixels recordings from 139 mice spanning 276 brain regions during a standardized visual decision-making task. The benchmark is organized around three complementary task suites: behavioral decoding (TS1), neural activity prediction (TS2), and brain region identification (TS3). Data are processed into a standardized format using the torch_brain library and include spike-sorted neural recordings, behavioral signals (wheel movements, paw kinematics, whisker motion energy, lick detection), trial structure, stimulus parameters, and anatomical annotations. A strict animal-level train/evaluation split separates 126 pretraining animals (423 sessions) from 13 held-out evaluation animals (29 sessions), enabling reproducible evaluation of across-animal generalization.\n","Documentation":"https://brainbench-org.github.io/ibl-bwb/","Contact":"info@internationalbrainlab.org","ManagedBy":"[International Brain Laboratory](https://www.internationalbrainlab.com)","UpdateFrequency":"As needed with benchmark releases","Tags":["aws-pds","benchmark","life sciences","machine learning","Mus musculus","neurophysiology","neuroscience","open source software"],"License":"CC-BY 4.0","Resources":[{"Description":"Preprocessed benchmark data in HDF5 format, including spike-sorted neural recordings, behavioral signals, anatomical region labels, and quality-control metadata for all sessions. Organized by animal and session.\n","ARN":"arn:aws:s3:::brain-wide-bench","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://brain-wide-bench.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new brain-wide-bench data","ARN":"arn:aws:sns:us-east-1:842577843587:brain-wide-bench-sns","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"BrainWideBench - benchmark code with training and evaluation pipelines for all task suites","URL":"https://github.com/brainbench-org/ibl-bwb","AuthorName":"BrainWideBench team","AuthorURL":"https://github.com/brainbench-org"},{"Title":"BrainWideBench Leaderboard - benchmark results and model rankings","URL":"https://bwb.iblcore.org","AuthorName":"BrainWideBench team","AuthorURL":"https://github.com/brainbench-org"},{"Title":"ONE-api on pypi.python.org - Python API to query and download IBL neurophysiology data","URL":"https://pypi.org/project/ONE-api/","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"torch_brain - Python library for loading and processing the BrainWideBench data","URL":"https://torch-brain.readthedocs.io/en/latest/","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"}],"Publications":[{"Title":"A brain-wide map of neural activity during complex behaviour","URL":"https://doi.org/10.1038/s41586-025-09235-0","AuthorName":"International Brain Laboratory et al.","AuthorURL":"https://www.internationalbrainlab.com"},{"Title":"BrainWideBench: Benchmarking large-scale pretraining and across-animal transfer in multi-region neural recordings","URL":"https://arxiv.org/abs/2609.22064","AuthorName":"International Brain Laboratory et al.","AuthorURL":"https://www.internationalbrainlab.com"}]},"RegistryEntryAdded":"2026-05-14","RegistryEntryLastModified":"2026-09-29","Slug":"ibl-brain-wide-bench","Sources":["awslabs-open-data-registry"]}
{"Name":"IBL Neuropixels Brainwide Map on AWS","Description":"Electrophysiological recordings of mouse brain activity acquired during a decision making task.","Documentation":"https://docs.internationalbrainlab.org/notebooks_external/2025_data_release_brainwidemap.html","Contact":"info@internationalbrainlab.org","ManagedBy":"[International Brain Laboratory](https://www.internationalbrainlab.com)","UpdateFrequency":"TBD","Tags":["aws-pds","life sciences","Mus musculus","neurophysiology","neuroscience","open source software"],"License":"CC-BY 4.0","Resources":[{"Description":"Project data in public bucket","ARN":"arn:aws:s3:::ibl-brain-wide-map-public","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Notifications for new IBL data release","ARN":"arn:aws:sns:us-east-1:842577843587:ibl-brain-wide-map-public-sns","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"Data Explorer","URL":"https://viz.internationalbrainlab.org/app","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"ONE-api on pypi.python.org - python API to query and download neurophysiology data","URL":"https://pypi.org/project/ONE-api/","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"}],"Tutorials":[{"Title":"Download the public data via ONE","URL":"https://docs.internationalbrainlab.org/notebooks_external/data_download.html","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"Example analysis of the brain-wide map dataset","URL":"https://colab.research.google.com/drive/1th3MRZGHMSaeAvGmKGJQ84rBk8eEI4Fu","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"Find data associated with a release or publication","URL":"https://docs.internationalbrainlab.org/notebooks_external/data_download.html#Find-data-associated-with-a-release-or-publication","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"Introduction to IBL and the brain-wide map dataset","URL":"https://colab.research.google.com/drive/1Ua-NlpYYZCIOF56xbsT9YR71Enkotd-b#scrollTo=7XzVVlhsVHMK","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"Loading Data","URL":"https://docs.internationalbrainlab.org/loading_examples.html","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"Using IBL data with ONE","URL":"https://colab.research.google.com/drive/1y3sRI1wC7qbWqN6skvulzPOp6xw8tLm7#scrollTo=GSvi21Dn84wJ","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"}],"Publications":[{"Title":"A brain-wide map of neural activity during complex behaviour","URL":"https://doi.org/10.1038/s41586-025-09235-0","AuthorName":"International Brain Laboratory et al.","AuthorURL":"www.internationalbrainlab.com"},{"Title":"Brain-wide representations of prior information in mouse decision-making","URL":"https://doi.org/10.1038/s41586-025-09226-1","AuthorName":"International Brain Laboratory et al.","AuthorURL":"www.internationalbrainlab.com"}]},"RegistryEntryAdded":"2021-10-04","RegistryEntryLastModified":"2026-09-29","Slug":"ibl-brain-wide-map","Sources":["awslabs-open-data-registry"]}
{"Name":"IBL Neuropixels Reproducible Ephys Data on AWS","Description":"Electrophysiological recordings acquired using Neuropixels probes in different mice and labs, targeting the same brain locations (including posterior parietal cortex, hippocampus, and thalamus).","Documentation":"https://docs.internationalbrainlab.org/notebooks_external/2024_data_release_repro_ephys.html","Contact":"info@internationalbrainlab.org","ManagedBy":"[International Brain Laboratory](https://www.internationalbrainlab.com)","UpdateFrequency":"TBD","Tags":["aws-pds","life sciences","Mus musculus","neurophysiology","neuroscience","open source software"],"License":"CC-BY 4.0","Resources":[{"Description":"Project data in public bucket","ARN":"arn:aws:s3:::ibl-brain-wide-map-public","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Data Explorer","URL":"https://viz.internationalbrainlab.org/app","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"ONE-api on pypi.python.org - python API to query and download neurophysiology data","URL":"https://pypi.org/project/ONE-api/","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"}],"Tutorials":[{"Title":"Compute the RIGOR metrics","URL":"https://github.com/int-brain-lab/paper-reproducible-ephys/blob/2397f2cf5b92689f39e94ef7d8f76f0a7e2bd2a7/RIGOR_script.ipynb","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"Download the public data via ONE","URL":"https://docs.internationalbrainlab.org/notebooks_external/data_download.html","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"Find data associated with a release or publication","URL":"https://docs.internationalbrainlab.org/notebooks_external/data_download.html#Find-data-associated-with-a-release-or-publication","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"},{"Title":"Loading Data","URL":"https://docs.internationalbrainlab.org/loading_examples.html","AuthorName":"IBL Data Architecture Working Group","AuthorURL":"https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members"}],"Publications":[{"Title":"Reproducibility of in vivo electrophysiological measurements in mice","URL":"https://doi.org/10.7554/eLife.100840.1","AuthorName":"International Brain Laboratory et al.","AuthorURL":"www.internationalbrainlab.com"}]},"RegistryEntryAdded":"2023-01-09","RegistryEntryLastModified":"2025-09-06","Slug":"ibl-reproducible-ephys","Sources":["awslabs-open-data-registry"]}
{"Name":"Information-Based Similarity","Description":"IBS is a novel method for comparing time series based on linguistic analysis of \"word\" frequency and rank order statistics. It has been applied successfully in a variety of applications, including analysis of interbeat (RR) intervals, classification of the SARS coronavirus, and study of literary works of disputed authorship.","Documentation":"https://physionet.org/content/ibs/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/ibs/","ARN":"arn:aws:s3:::physionet-open/ibs/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ibs","Sources":["awslabs-open-data-registry"]}
{"Name":"Induced Cesarean EHG DataSet (ICEHG DS): Electrohysterogram records of pregnancies ending in induced and cesarean delivery","Description":"The surface electrohysterogram (EHG) has emerged as a promising diagnostic tool for non-invasive automated preterm birth prediction. The existing automated preterm birth prediction methods rely on the use of uterine EHG records coming from spontaneous preterm and term deliveries, and are indifferent to term induced and cesarean deliveries. In order to enhance current publicly available pool of term EHG records, we developed a new EHG dataset containing 126 30-minute EHG records, recorded either early (23rd week), or later during pregnancy (31st week), of those pregnancies that were expected to end in spontaneous term delivery, but ended in induced or cesarean section delivery. The records were collected at the University Medical Center Ljubljana, Ljubljana, Slovenia. The dataset includes 38 and 43, early and later, induced; 11 and 8, early and later, cesarean; and 13 and 13, early and later, induced-cesarean records. This database enables better understanding of the underlying physiological mechanisms involved during pregnancies ending in induced and cesarean deliveries, and provides more robust and realistic assessment of the performance of automated preterm birth prediction.","Documentation":"https://doi.org/10.13026/zw34-n382","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Open Database License v1.0","Resources":[{"Description":"https://doi.org/10.13026/zw34-n382","ARN":"arn:aws:s3:::physionet-open/icehg-ds/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"icehg-ds","Sources":["awslabs-open-data-registry"]}
{"Name":"Icentia11k Single Lead Continuous Raw Electrocardiogram Dataset","Description":"This is a dataset of continuous raw electrocardiogram (ECG) signals containing 11 thousand patients and 2 billion labelled beats. The signals were recorded with a 16-bit resolution at 250Hz with a fixed chest mounted single lead probe for up to 2 weeks. The average age of the patient is 62.2±17.4 years. 20 technologists annotated each beat's type (Normal, Premature Atrial Contraction, Premature Ventricular contraction) and rhythm (Normal Sinusal Rhythm, Atrial Fibrillation, Atrial Flutter).","Documentation":"https://doi.org/10.13026/kk0v-r952","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/kk0v-r952","ARN":"arn:aws:s3:::physionet-open/icentia11k-continuous-ecg/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"icentia11k-continuous-ecg","Sources":["awslabs-open-data-registry"]}
{"Name":"ICEYE Synthetic Aperture Radar (SAR) Open Dataset","Description":"ICEYE operates the world’s largest constellation of synthetic aperture radar (SAR) satellites, delivering unmatched access to persistent, high-resolution Earth observation data regardless of time of day or weather conditions. The ICEYE Open Dataset makes a curated selection of SAR imagery publicly available to promote research, innovation, and education in the geospatial community. ICEYE’s constellation enables rapid revisit rates and flexible imaging modes, unlocking insights into natural disasters, climate monitoring, infrastructure, and more.\n\nLearn more at [www.iceye.com](https://www.iceye.com).\n","Documentation":"Documentation is available at the [ICEYE website](https://www.iceye.com/open-data-initiative) and the [ICEYE Product Documentation](https://sar.iceye.com/latest/opendata/opendata/).","Contact":"customer@iceye.com","ManagedBy":"[ICEYE](https://www.iceye.com)","UpdateFrequency":"New data is added frequently.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","computer vision","disaster response","earth observation","geospatial","image processing","satellite imagery","stac","synthetic aperture radar"],"License":"The data is provided under the Creative Commons License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), which gives the user the right to share, copy, and redistribute the material in any medium or format, as well as adapt, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given to the original creator.\n","Resources":[{"Description":"ICEYE Open SAR Data","ARN":"arn:aws:s3:::iceye-open-data-catalog","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse bucket](http://iceye-open-data-catalog.s3-website-us-west-2.amazonaws.com)","[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/iceye-open-data-catalog.s3-us-west-2.amazonaws.com/catalog.json)"]},{"Description":"Notification for new ICEYE Open SAR Data","ARN":"arn:aws:sns:us-west-2:058264311954:iceye-open-data-catalog-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"ICEYE Product Documentation","URL":"https://sar.iceye.com","AuthorName":"ICEYE","AuthorURL":"https://www.iceye.com"}]},"RegistryEntryAdded":"2025-08-06","RegistryEntryLastModified":"2026-04-14","Slug":"iceye-opendata","Sources":["awslabs-open-data-registry"]}
{"Name":"ICGC on AWS","Description":"The International Cancer Genome Consortium (ICGC) coordinates projects with the common aim of accelerating research into the causes and control of cancer. The PanCancer Analysis of Whole Genomes (PCAWG) study is an international collaboration to identify common patterns of mutation in whole genomes from ICGC. More than 2,400 consistently analyzed genomes corresponding to over 1,100 unique ICGC donors are now freely available on Amazon S3 to credentialed researchers subject to ICGC data sharing policies.","Documentation":"https://dcc.icgc.org/icgc-in-the-cloud/aws","Contact":"dcc-support@icgc.org","ManagedBy":"[International Cancer Genome Collaboratory](https://dcc.icgc.org/)","UpdateFrequency":"New data is added as soon as it is available.","Tags":["aws-pds","bam","cancer","genetic","genomic","life sciences","vcf"],"License":"Data use is subject to the access and publication polices of the source. Distribution of the data is subject to ICGC Trusted Partner Approval.  More information on terms of use is available at https://icgc.org/daco","Resources":[{"Description":"BAM and VCF files from the The PanCancer Analysis of Whole Genomes (PCAWG) study.","ARN":"arn:aws:s3:::icgc","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://icgc.org/daco"},{"Description":"This public Amazon S3 bucket contains analysis metadata in XML format for genome analysis results. More information at http://oicr.icgc.meta.s3.amazonaws.com/metadata/README","ARN":"arn:aws:s3:::oicr.icgc.meta/metadata","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Raw sequencing and other primary data from non-TCGA ICGC projects","ARN":"arn:aws:s3:::oicr.icgc","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://docs.icgc.org/download/guide/"}],"DataAtWork":{"Tutorials":[{"Title":"AWS Pancancer Workflow Launcher","URL":"https://github.com/ICGC-TCGA-PanCancer/cli/wiki","AuthorName":"Solomon Shorser","AuthorURL":"https://github.com/solomonshorser"}],"Publications":[{"Title":"Analyses of non-coding somatic drivers in 2,693 cancer whole genomes","URL":"https://www.nature.com/articles/s41586-020-1965-x","AuthorName":"Rheinbay et al (2020)","AuthorURL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=Rheinbay%20E%5BAuthor%5D&cauthor=true&cauthor_uid=28658208"},{"Title":"Genomic basis of RNA alterations in cancer","URL":"https://www.nature.com/articles/s41586-020-1970-0","AuthorName":"PCAWG Transcriptome Core Group et al. (2020)","AuthorURL":"https://icgc.org/committees-and-working-groups#71954"},{"Title":"Pan-cancer analysis of whole genomes","URL":"https://www.nature.com/articles/s41586-020-1969-6","AuthorName":"The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium (2020)","AuthorURL":"https://dcc.icgc.org/pcawg"},{"Title":"Patterns of somatic structural variation in human cancer genomes","URL":"https://www.nature.com/articles/s41586-019-1913-9","AuthorName":"Li et al. (2020)","AuthorURL":"https://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Yilong%20Li"},{"Title":"The evolutionary history of 2,658 cancers","URL":"https://www.nature.com/articles/s41586-019-1907-7","AuthorName":"Gerstung et al (2020)","AuthorURL":"https://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Moritz%20Gerstung"},{"Title":"The repertoire of mutational signatures in human cancer","URL":"https://www.nature.com/articles/s41586-020-1943-3","AuthorName":"Alexandrov et al (2020)","AuthorURL":"https://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Ludmil%20B.%20Alexandrov"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2022-07-22","Slug":"icgc","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"The provider of this dataset no longer provides this data through this mechanism. If there are any questions, reach out through the contact mechanism listed below.","Name":"IChangeMyCity Complaints Data from Janaagraha","Description":"The [IChangeMyCity](https://www.ichangemycity.com) project provides insight into the complaints raised by citizens from diffent cities of India related to the issues in their neighbourhoods and the resolution of the same by the civic bodies.","Documentation":"http://ichangemycity-janaagraha-complaints-data-aws.s3.ap-south-1.amazonaws.com/readme.html","Contact":"data@janaagraha.org","ManagedBy":"Not currently managed","UpdateFrequency":"Daily","Tags":["aws-pds","cities","civic","complaints"],"License":"There are no restrictions on the use of data received from IChangeMyCity.com. More information on licensing and IChangeMyCity data citation is available from IChangeMyCity.com","Resources":[{"Description":"IChangeMyCity Complaints","ARN":"arn:aws:s3:::ichangemycity-janaagraha-complaints-data-aws","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":null,"Tools & Applications":null,"Publications":null},"RegistryEntryAdded":"2018-10-15","RegistryEntryLastModified":"2022-07-07","Slug":"ichangemycity","Sources":["awslabs-open-data-registry"]}
{"Name":"IDEAM - Colombian Radar Network","Description":"Historical and one-day delay data from the IDEAM radar network.","Documentation":"https://www.ideam.gov.co/nuestra-entidad/servicio-de-pronosticos-y-alertas/guia-de-descarga-y-visualizacion-de-datos-de-radar-meteorologico-de-ideam","Contact":"ospa@idem.gov.co, contacto@ideam.gov.co","ManagedBy":"[IDEAM](https://www.ideam.gov.co/)","UpdateFrequency":"Updated level II data is added as soon as it is available.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","earth observation","meteorological","natural resource","weather"],"License":"Creative Commons Attribution 4.0 International (CC BY 4.0)","Resources":[{"Description":"Level II data","ARN":"arn:aws:s3:::s3-radaresideam","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Ciencia de Datos Hidrometeorológicos con Python","URL":"https://projectpythia.org/AtmosCol-2023/radares","AuthorName":"Alfonso Ladino, Nicole Rivera, Max Grover"},{"Title":"Guía de Descarga y Visualización Datos de Radar Meteorológico de IDEAM","URL":"https://www.ideam.gov.co/nuestra-entidad/servicio-de-pronosticos-y-alertas/guia-de-descarga-y-visualizacion-de-datos-de-radar-meteorologico-de-ideam","AuthorName":"IDEAM"},{"Title":"Read and plot Sigmet files available on AWS using Xradar","URL":"https://docs.openradarscience.org/projects/xradar/en/stable/notebooks/Read-plot-Sigmet-data-from-AWS.html","AuthorName":"Alfonso Ladino"},{"Title":"Specific Differential Phase (KDP) retrieval methods comparison","URL":"https://projectpythia.org/radar-cookbook/notebooks/example-workflows/kdp-comparison/","AuthorName":"Alfonso Ladino, Max Grover"}]},"RegistryEntryAdded":"2020-10-30","RegistryEntryLastModified":"2026-07-22","Slug":"ideam-radares","Sources":["awslabs-open-data-registry"]}
{"Name":"Industry Documents Library (IDL) Data on AWS","Description":"The IDL Data on AWS contain the metadata, documents, and extracted text for all of the documents in the UCSF Industry Documents Library, a growing corpus of internal corporate records and other documents arising from the tobacco, opioids, chemical, drug, food, fossil fuel industries.","Documentation":"https://ucsf-idl-dataset.s3.amazonaws.com/index.html","Contact":"industrydocuments@ucsf.edu","ManagedBy":"University of California, San Francisco (UCSF)","UpdateFrequency":"monthly","Tags":["archives","chemistry","fossil fuel","health","life sciences","medicine","pharmaceutical","text analysis","txt"],"License":"https://www.industrydocuments.ucsf.edu/help/copyright/","Citation":null,"Resources":[{"Description":"Raw data from the Industry Documents Library, including documents in various file formats (PDF, Excel, PowerPoint, etc.), OCR or other extracted text from these files, and corresponding metadata for each document.","ARN":"arn:aws:s3:::ucsf-idl-dataset","Region":"us-east-1","Type":"S3 Bucket","Explore":null},{"Description":"Notifications for new UCSF Industry Documents data","ARN":"arn:aws:sns:us-east-1:661235013495:ucsf-idl-dataset-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DeprecatedNotice":null,"ADXCategories":["Healthcare & Life Sciences Data"],"DataAtWork":{"Tutorials":[{"Title":"IDL Notebook Tutorials","URL":"https://github.com/industrydocuments/industrydocuments.github.io/tree/main/src/open-data-examples/get-to-know-idl-dataset.ipynb","AuthorName":"UCSF/Industry Documents Library","AuthorURL":"https://www.industrydocuments.ucsf.edu/"}]},"RegistryEntryAdded":"2025-12-08","RegistryEntryLastModified":"2026-08-12","Slug":"idl","Sources":["awslabs-open-data-registry"]}
{"Name":"Planette ECMWF IFS S2S Reforecast Data","Description":"Global IFS-CY49R1 S2S reforecasts: 10-member ensemble, 46-day lead, 1.5° atmosphere/surface and 1° ocean, 2006–2025.\n\nThis dataset is derived from the ECMWF Data Store (S2S on-the-fly reforecasts) and published by Planette as a single cloud-native Icechunk/Zarr data cube at s3://planette-ifs-46-day/reforecasts for efficient analysis with xarray.\n","Documentation":"https://github.com/PlanetteAI/planette_ifs_archive/blob/main/README.md","Contact":"kyle.rasch@planette.ai","ManagedBy":"Planette.ai","UpdateFrequency":"Monthly","Collabs":{"ASDI":{"Tags":["climate","weather","forecast"]}},"Tags":["aws-pds","climate","earth observation","weather"],"License":"This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/legalcode\n","Citation":"European Centre for Medium-Range Weather Forecasts (2026): S2S global sub-seasonal to seasonal ensemble real-time forecasts from multiple NWP centres since 2015. ECMWF Data Store.\nhttps://ecds.ecmwf.int/datasets/s2s-reforecasts?tab=overview\n","Resources":[{"Description":"ECMWF IFS Hindcasts (Zarr format)","ARN":"arn:aws:s3:::planette-ifs-46-day","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Exploring Planette IFS S2S Reforecasts in a Browser-Based Notebook on Scigantic","URL":"https://scigantic.com/archive/5ebad309-ec2e-5c8e-b169-b0fcb7c1c3cd/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Get To Know A Dataset IFS","URL":"https://github.com/PlanetteAI/planette_ifs_archive/blob/main/Get_To_Know_A_Dataset_IFS.ipynb","AuthorName":"Kyle Rasch","AuthorURL":"https://github.com/kyle-rasch"}],"Tools & Applications":[{"Title":"icechunk","URL":"https://github.com/earth-mover/icechunk","AuthorName":"earth-mover"},{"Title":"xarray","URL":"https://docs.xarray.dev/","AuthorName":"xarray Developers"},{"Title":"zarr-python","URL":"https://zarr.dev/","AuthorName":"zarr Developers"}],"Publications":[{"Title":"ECMWF S2S Reforecasts Documentation","URL":"https://ecds.ecmwf.int/datasets/s2s-reforecasts?tab=documentation","AuthorName":"European Centre for Medium-Range Weather Forecasts"},{"Title":"The Sub-seasonal to Seasonal (S2S) Prediction Project Database.","URL":"https://doi.org/10.1175/BAMS-D-16-0017.1","AuthorName":"Vitart et al."}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-08-18","RegistryEntryLastModified":"2026-08-29","Slug":"ifs-s2s-forecast-data","Sources":["awslabs-open-data-registry"]}
{"Name":"The Impact of Variation on Function Consortium (IGVF)","Description":"The IGVF (Impact of Genomic Variation on Function) Consortium aims to understand how genomic variation affects genome function, \nwhich in turn impacts phenotype. The NHGRI is funding this collaborative program that brings together teams of investigators who \nwill use state-of-the-art experimental and computational approaches to model, predict, characterize and map genome function, how \ngenome function shapes phenotype, and how these processes are affected by genomic variation. These joint efforts will produce a \ncatalog of the impact of genomic variants on genome function and phenotypes.  \nThe Data Corpus consists of single-cell Genomics experiments (both single modal, and multimodal, typically snRNA-seq and snATAC-seq), \nCharacterization experiments using Massively Parallel Reporter Assays (MPRAs) and CRISPR-screens along with a variety of protein mutatation \nassays, and Predictive Models.\nThere are a huge variety of files in IGVF that are stored in the AWS OpenData Set so we recommend using the [metadata file]() or browsing the [IGVF Data Portal](https://data.igvf.org)\n","Contact":"igvf-portal-help@lists.stanford.edu","ManagedBy":"IGVF Data Administration and Coordination Center at Stanford University","Documentation":"https://data.igvf.org/general-help","UpdateFrequency":"Daily","Tags":["aws-pds","bioinformatics","biology","genetic","genomic","life sciences"],"License":"CC BY 4.0 - https://creativecommons.org/licenses/by/4.0/ - You are free to share and adapt this data with proper attribution","Resources":[{"Description":"Released and Archived IGVF Data Files","ARN":"arn:aws:s3:::igvf-public","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for new IGVF data","ARN":"arn:aws:sns:us-west-2:407227577691:igvf-public-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Ingesting IGVF Data into TileDB with S3 backend","URL":"https://github.com/IGVF-DACC/igvf-data-usage-examples/blob/master/ingest_igvf_h5ad_data_to_anndata_and_tiledb.ipynb","AuthorName":"Otto Jolanki","AuthorURL":"https://github.com/ottojolanki"},{"Title":"Load AnnData files from IGVF into scanpy and view the UMAPs","URL":"https://github.com/IGVF-DACC/igvf-data-usage-examples/blob/master/igvf-scanpy.ipynb","AuthorName":"Ben Hitz","AuthorURL":"https://github.com/hitz"}],"Tools & Applications":[{"Title":"The IGVF Catalog","URL":"https://catalog.igvf.org","AuthorName":"The IGVF Consortium","AuthorURL":"www.igvf.org"}],"Publications":[{"Title":"Deciphering the impact of genomic variation on function","URL":"https://www-nature-com.stanford.idm.oclc.org/articles/s41586-024-07510-0","AuthorName":"Jesse M. Engreitz and The IGVF Consortium","AuthorURL":"https://orcid.org/0000-0002-5754-1719"}]},"RegistryEntryAdded":"2025-09-11","RegistryEntryLastModified":"2025-12-04","Slug":"igvf-consortium","Sources":["awslabs-open-data-registry"]}
{"Name":"iHART Whole Genome Sequencing Data Set","Description":"iHART is the [Hartwell Foundation](http://www.thehartwellfoundation.org/)’s Autism Research and Technology Initiative. This release contains whole genome data from over 1000 families with 2 or more children with autism, of which biomaterials were provided by the Autism Genetic Resource Exchange ([AGRE](http://research.agre.org/)).","Documentation":"http://www.ihart.org/data","Contact":"ihart2-org@stanford.edu","ManagedBy":"[Stanford University](https://wall-lab.stanford.edu/projects/ihart/)","UpdateFrequency":"The dataset may be updated with additional or corrected data on a need-to-update basis.","Tags":["autism spectrum disorder","aws-pds","bam","genetic","genomic","life sciences","vcf","whole genome sequencing"],"License":"Data use is subject to the access and publication polices of the iHART. More information on terms of use is available at [iHART website](http://www.ihart.org/)","Resources":[{"Description":"BAM, gVCF, and VCF files from The iHART whole genome sequencing study, Phase I","ARN":"arn:aws:s3:::ihart-release","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"http://www.ihart.org/access"},{"Description":"BAM, gVCF, and VCF files from The iHART whole genome sequencing study, Phase II","ARN":"arn:aws:s3:::ihart-main","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"http://www.ihart.org/access"},{"Description":"Cram, gVCF, and VCF files from The iHART whole genome sequencing study, Phase I+II, GRCh38","ARN":"arn:aws:s3:::ihart-hg38","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"http://www.ihart.org/access"},{"Description":"gVCF and VCF files from The iHART whole genome sequencing study, control data sets","ARN":"arn:aws:s3:::ihart-psp","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"http://www.ihart.org/access"},{"Description":"gVCF and VCF files from The iHART whole genome sequencing study, control data sets","ARN":"arn:aws:s3:::ihart-brain","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"http://www.ihart.org/access"}],"DataAtWork":{"Publications":[{"Title":"Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks","URL":"https://www.cell.com/cell/fulltext/S0092-8674(19)30780-9","AuthorName":"Ruzzo et al. (2020)"}]},"RegistryEntryAdded":"2020-03-12","RegistryEntryLastModified":"2022-09-22","Slug":"ihart","Sources":["awslabs-open-data-registry"]}
{"Name":"1000 Genomes Phase 3 Reanalysis with DRAGEN 3.5, 3.7, 4.0, 4.2, and 4.4","Description":"#### Overview\n\nThis dataset contains alignment files and small variant (includes single nucleotide variants (SNV) and indels), copy number variant (CNV), short tandem repeat (i.e., repeat expansion; STR), structural variant (SV) and other variant call files from the [1000 Genomes Project (1KGP) Phase 3 dataset](https://www.internationalgenome.org/) (3,202 individuals, 602 trios) using Illumina DRAGEN v3.5.7b, v3.7.6, v4.0.3, v4.2.7, and v4.4.7 software.\nAll DRAGEN analyses were performed in the cloud using the [Illumina Connected Analytics](https://www.illumina.com/products/by-type/informatics-products/connected-analytics.html) bioinformatics platform powered by Amazon Web Services (see ['Data solution empowering population genomics'](https://www.illumina.com/science/genomics-research/articles/data-solution-empowering-population-genomics-research.html) for more information).\nThe v3.7.6, v4.2.7, and v4.4.7 datasets include results from trio small variant, *de novo* structural variant, and *de novo* copy number variant calls on 602 trio families comprised of members from the 1KGP Phase 3 dataset.\nTrio repeat expansion calling was included in the v3.7.6 dataset only.\nJoint cohort analysis was also performed on the entire 1KGP sample dataset for the v3.7.6, v4.0.3, v4.2.7, and v4.4.7 re-analyses using [DRAGEN Iterative gVCF Genotyper](https://www.illumina.com/products/by-type/informatics-products/dragen-secondary-analysis/iterative-GVCF-genotyper.html) v3.8.3, v4.2.0, v4.2.7, v4.4.7, respectively (see ['Genotyping variants at population scale using DRAGEN gVCF Genotyper'](https://www.illumina.com/science/genomics-research/articles/gVCF-Genotyper.html) and ['Population Genotyping'](https://help.dragen.illumina.com/product-guide/dragen-v4.4/dragen-dna-pipeline/iterative-gvcf-genotyper)).\n\n#### DRAGEN Versions\n\n##### v3.7\n\n[User Guide](https://support.illumina.com/content/dam/illumina-support/documents/documentation/software_documentation/dragen-bio-it/Illumina-DRAGEN-Bio-IT-Platform-User-Guide-1000000141465-00.pdf) | [Release Notes](https://www.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/Illumina-DRAGEN-Bio-IT-Platform-3.7-Release-Notes-1000000142362-v00.pdf)\n\nImprovements and new features in the v3.7.6 individual samples analyses include *CYP2D6* variant calling (see '[Overcoming high homology to detect variation in CYP21A2 with whole-genome sequencing in DRAGEN](https://www.illumina.com/science/genomics-research/articles/CYP21A2.html)') and joint detection and use of graph-based hg19 and hg38 reference hash tables (see ['DRAGEN Wins at PrecisionFDA Truth Challenge V2 Showcase Accuracy Gains from Alt-aware Mapping and Graph Reference Genomes'](https://www.illumina.com/science/genomics-research/dragen-wins-precisionfda-challenge-showcase-accuracy-gains.html) and ['Demystifying the versions of GRCh38/hg38 reference genomes, how they are used in DRAGEN and their impact on accuracy'](https://www.illumina.com/science/genomics-research/articles/dragen-demystifying-reference-genomes.html) for details).\n\n##### v4.0\n\n[User Guide](https://support-docs.illumina.com/SW/DRAGEN_v40/Content/SW/FrontPages/DRAGEN.htm) | [Release Notes](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200024449_01_DRAGEN-4.0-Customer-Release-Notes.pdf)\n\nThe DRAGEN v4.0.3 dataset features improved small variant calling accuracy due to utilization of a newly integrated [machine learning functionality](https://support-docs.illumina.com/SW/dragen_v42/Content/SW/DRAGEN/ml_for_vc.htm?Highlight=dragen-ml) with an updated graph based reference for difficult to map regions (see ['DRAGEN Sets New Standard for Data Accuracy in PrecisionFDA Benchmark Data. Optimizing Variant Calling Performance with Illumina Machine Learning and DRAGEN Graph'](https://www.illumina.com/science/genomics-research/articles/dragen-shines-again-precisionfda-truth-challenge-v2.html)); accuracy and runtime improvements in the SV caller; new targeted callers including *CYP2B6*, *GBA*, *SMN* and a Star Allele PGx caller; and an expanded catalog for use with Expansion Hunter STR caller.\n\n##### v4.2\n\n[User Guide](https://support-docs.illumina.com/SW/dragen_v42/Content/SW/FrontPages/DRAGEN.htm) | [Release Notes](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200040845_02_DRAGEN-4.2-Customer-Release-Notes.pdf)\n\nDRAGEN v4.2.7 offers significant accuracy improvements in small variant, CNV, and SV calling, includes new targeted callers (*HBA*, *LPA*, *RH*, *CYP21A2*, *SMN* silent carrier variant), and supports Star Allele calling for five additional pharmacogenes (*BCHE*, *ABCG2*, *NAT2*, *F5*, and *UGT2B17*).\nThese are further improved by upgraded machine learning models.\nSee [DRAGEN 4.2: Enhanced machine learning, new targeted callers, and more](https://developer.illumina.com/news-updates/dragen-4-2-enhanced-machine-learning-new-targeted-callers-and-more) for further details on these and other enchancements.\n\n##### v4.4\n\n[User Guide](https://help.dragen.illumina.com/product-guide/dragen-v4.4) | [Release Notes](https://www.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200068065_00_DRAGEN-4_4_4-Customer-Release-Notes.pdf)\n\nDRAGEN v4.4.7 boosts the speed and accuracy of all callers via the official release of an optimized pangenome graph reference ('[The quest for accuracy gains in the dark regions of the genomes: Presenting the DRAGEN multigenome mapper and pangenome reference updates in version 4.3](https://www.illumina.com/science/genomics-research/articles/second-gen-multigenome-mapping.html)').\nNamely, SV calling accuracy is substantially increased via the implementation of a multigenome mapper capable of exploiting the power of a pangenome reference.\nRuntime is further reduced by supporting AWS F2 EC2 instances ([Enabling Rapid Genomic and Multiomic Data Analysis with Illumina DRAGEN™ v4.4 on Amazon EC2 F2 Instances](https://aws.amazon.com/blogs/hpc/enabling-rapid-genomic-and-multiomic-data-analysis-with-illumina-dragen-v4-4-on-amazon-ec2-f2-instances/))\n\n#### Annotation\n\nStarting with the v4.0.3 reanalysis, annotation using the Illumina Connected Annotations (also known as Illumina Annotation Engine or Nirvana) was included as part of the analysis (see [Illumina Connected Annotations documentation](https://help.dragen.illumina.com/product-guide/dragen-v4.4/nirvana) for more information).\nFor the v4.0.3, v4.2.7, and v4.4.7 datasets, annotation was performed on the merged small variant VCF generated by the DRAGEN Iterative gVCF Genotyper for the entire 1KGP cohort.\nFor v4.2.7 and v4.4.7, annotation was also performed on the merged CNV, SV, and STR VCFs for the entire cohort.\n","Contact":"[Illumina, Inc.](mailto:support@illumina.com)","Documentation":"[DRAGEN Support Resources](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform.html)","ManagedBy":"[Illumina, Inc.](https://www.illumina.com/products/by-type/informatics-products/dragen-bio-it-platform.html)","UpdateFrequency":"Files may be updated subsequent to changes to the 1000 Genomes Project data set or select new DRAGEN features or offerings.","Tags":["aws-pds","bam","bioinformatics","biology","cram","genetic","genomic","genotyping","life sciences","machine learning","population genetics","short read sequencing","structural variation","tertiary analysis","variant annotation","whole genome sequencing"],"License":"TBD","Resources":[{"Description":"BAM, SNV-vcf, SNV-gvcf, STR-vcf, STR-bam, SV-vcf, ROH-vcf, CNV-vcf, CNV-bw, metrics  and other supporting files from DRAGEN v3.5.6b analyses in a public S3 bucket.","ARN":"arn:aws:s3:::1000genomes-dragen","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"BAM, SNV-vcf, SNV-gvcf, STR-vcf, STR-bam, SV-vcf, ROH-vcf, CNV-vcf, CNV-bw, cyp2d6-tsv, metrics and other supporting files from DRAGEN v3.7.6 analyses in a public S3 bucket.","ARN":"arn:aws:s3:::1000genomes-dragen-3.7.6","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"BAM, SNV-vcf, SNV-gvcf, STR-vcf, STR-bam, SV-vcf, ROH-vcf, CNV-vcf, CNV-bw, cyp2d6-tsv, metrics and other supporting files from DRAGEN v3.7.6 analyses in a public S3 bucket. This is a clone of the 1000genomes-dragen-3.7.6 bucket in the us-east-1 region.","ARN":"arn:aws:s3:::1000genomes-dragen-v3.7.6","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"CRAM, SNV-vcf, SNV-gvcf, STR-vcf, STR-bam, SV-vcf, ROH-vcf, CNV-vcf, CNV-bw, cyp2b6-tsv, cyp2d6-tsv, gba-tsv, smn-tsv, star-allele-tsv, metrics and other supporting files from DRAGEN v4.0.3 analyses and Nirvana Annotation in a public S3 bucket.","ARN":"arn:aws:s3:::1000genomes-dragen-v4.0.3","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"CRAM, SNV-vcf, SNV-gvcf, STR-vcf, STR-bam, SV-vcf, ROH-vcf, CNV-vcf, CNV-bw, cyp2b6-tsv, cyp2d6-tsv, gba-tsv, smn-tsv, star-allele-tsv, hla-tsv, gvcf, json, metrics and other supporting files from DRAGEN v4.2.7 analyses and Nirvana Annotation in a public S3 bucket.","ARN":"arn:aws:s3:::1000genomes-dragen-v4-2-7","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"CRAM, SNV-vcf, SNV-gvcf, STR-vcf, STR-bam, SV-vcf, ROH-vcf, CNV-vcf, CNV-bw, cyp2b6-tsv, cyp2d6-tsv, gba-tsv, smn-tsv, star-allele-tsv, hla-tsv, gvcf, json, metrics and other supporting files from DRAGEN v4.4.7 analyses and Nirvana Annotation in a public S3 bucket.","ARN":"arn:aws:s3:::1000genomes-dragen-v4-4-7","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"DRAGEN on AWS","URL":"https://help.dragen.illumina.com/reference/dragen-multi-cloud/dragen-on-aws","AuthorName":"Illumina Inc.","AuthorURL":"https://www.illumina.com/"},{"Title":"End-to-End User Flow: DRAGEN Analysis","URL":"https://help.ica.illumina.com/tutorials/end-to-end-1","AuthorName":"Illumina Inc.","AuthorURL":"https://www.illumina.com/"}],"Tools & Applications":[{"Title":"DRAGEN Bio-IT Platform","URL":"https://www.illumina.com/products/by-type/informatics-products/dragen-secondary-analysis.html","AuthorName":"Illumina Inc.","AuthorURL":"https://www.illumina.com/"},{"Title":"DRAGEN Iterative gVCF Genotyper","URL":"https://www.illumina.com/products/by-type/informatics-products/dragen-secondary-analysis/iterative-GVCF-genotyper.html","AuthorName":"Illumina Inc.","AuthorURL":"https://www.illumina.com/"},{"Title":"DRAGEN Support Resources","URL":"https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform.html","AuthorName":"Illumina Inc.","AuthorURL":"https://www.illumina.com/"},{"Title":"Illumina Connected Analytics","URL":"https://www.illumina.com/products/by-type/informatics-products/connected-analytics.html","AuthorName":"Illumina Inc.","AuthorURL":"https://www.illumina.com/"},{"Title":"Illumina Connected Analytics User Guide","URL":"https://help.ica.illumina.com","AuthorName":"Illumina Inc.","AuthorURL":"https://www.illumina.com/"},{"Title":"Nirvana Documentation","URL":"https://illumina.github.io/NirvanaDocumentation/","AuthorName":"Illumina Inc.","AuthorURL":"https://www.illumina.com/"}],"Publications":[{"Title":"Accurate and efficient calling of small and large variants from popgen datasets using the DRAGEN Bio-IT Platform","URL":"https://www.illumina.com/science/genomics-research/articles/popgen-variant-calling-with-dragen.html","AuthorName":"Illumina Inc. (2021)"},{"Title":"Data solution empowering population genomics","URL":"https://www.illumina.com/science/genomics-research/articles/data-solution-empowering-population-genomics-research.html","AuthorName":"Illumina Inc. (2021)"},{"Title":"Demystifying the versions of GRCh38/hg38 reference genomes, how they are used in DRAGEN and their impact on accuracy","URL":"https://www.illumina.com/science/genomics-research/articles/dragen-demystifying-reference-genomes.html","AuthorName":"Illumina Inc. (2021)"},{"Title":"DRAGEN Sets New Standard for Data Accuracy in PrecisionFDA Benchmark Data. Optimizing Variant Calling Performance with Illumina Machine Learning and DRAGEN Graph","URL":"https://www.illumina.com/science/genomics-research/articles/dragen-shines-again-precisionfda-truth-challenge-v2.html","AuthorName":"Illumina Inc. (2022)"},{"Title":"DRAGEN Wins at PrecisionFDA Truth Challenge V2 Showcase Accuracy Gains from Alt-aware Mapping and Graph Reference Genomes","URL":"https://www.illumina.com/science/genomics-research/dragen-wins-precisionfda-challenge-showcase-accuracy-gains.html","AuthorName":"Illumina Inc. (2020)"},{"Title":"Genotyping variants at population scale using DRAGEN gVCF Genotyper","URL":"https://www.illumina.com/science/genomics-research/articles/gVCF-Genotyper.html","AuthorName":"Illumina Inc. (2023)"},{"Title":"Overcoming high homology to detect variation in *CYP21A2* with whole-genome sequencing in DRAGEN","URL":"https://www.illumina.com/science/genomics-research/articles/CYP21A2.html","AuthorName":"Illumina Inc. (2023)"},{"Title":"PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions","URL":"https://www.sciencedirect.com/science/article/pii/S2666979X22000581?via%3Dihub","AuthorName":"Olson et al (2020)"},{"Title":"Unveiling Illumina Connected Annotations: A breakthrough in genomic annotation","URL":"https://www.illumina.com/science/genomics-research/articles/Connected-Annotations-blog.html","AuthorName":"Illumina Inc. (2024)"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2020-10-28","RegistryEntryLastModified":"2026-07-09","Slug":"ilmn-dragen-1kgp","Sources":["awslabs-open-data-registry"]}
{"Name":"YAOKI Lunar Rover IM-2 Mission Telemetry and Imagery Dataset","Description":"Mission telemetry and imagery from Dymon's YAOKI Lunar rover, which was deployed aboard the Intuitive Machines IM-2 lander to the Lunar south pole (lat=84.79059, lon=29.19567). JAOPS conducted operations between 2025-03-07 02:17:15+00.00 and 2025-03-07 04:33:04+00.00 using the open source Yamcs Mission Control System. This dataset contains telemetry received from the rover, relevant lander telemetry shared by the IM-2 team, telecommands sent to the rover, and images captured by the rover's camera.  The Yamcs archive allows replay of the original mission control environment. \n","Documentation":"https://github.com/jaops-space/im2-yaoki-yamcs-public","Contact":"Louis Burtz (ljburtz@jaops.com), Shinichiro Nakajima (s_nakajima@dymon.co.jp), Zack Gainsforth (data@openlunar.org)","ManagedBy":"[JAOPS Inc.](https://www.jaops.com/)","UpdateFrequency":"The dataset is static. The IM-2 / YAOKI mission concluded in March 2025; no further updates are planned.","Tags":["astronomy","aws-pds","exploration","lunar","planetary","robotics"],"License":"CC-BY-4.0","Citation":"JAOPS and Dymon Co., Ltd. (2025). Dymon YAOKI Lunar Rover Mission Data (IM-2).  Registry of Open Data on AWS.","Resources":[{"Description":"Browse YAOKI Lunar Rover IM-2 Mission Telemetry and Imagery Dataset","ARN":"arn:aws:s3:::im2-yaoki-rover-public","Region":"ap-northeast-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://im2-yaoki-rover-public.s3.ap-northeast-1.amazonaws.com/index.html)"]},{"Description":"Time series data stored as parquet for primary telemetry (TM) and telecommand (TC) channels.\n","ARN":"arn:aws:s3:::im2-yaoki-rover-public/timeseries/*","Region":"ap-northeast-1","Type":"S3 Bucket"},{"Description":"Lunar surface imagery captured by the YAOKI imager, provided both as raw sensor\npayloads (.raw) and reconstructed PNG images.\n","ARN":"arn:aws:s3:::im2-yaoki-rover-public/images/*","Region":"ap-northeast-1","Type":"S3 Bucket"},{"Description":"Complete mission archive: Yamcs RocksDB data store containing all received\ntelemetry packets and parameters, sent telecommands, and engineering events from\nthe YAOKI rover and the relayed IM-2 lander telemetry. \n","ARN":"arn:aws:s3:::im2-yaoki-rover-public/yamcs-data/*","Region":"ap-northeast-1","Type":"S3 Bucket"},{"Description":"Notifications for YAOKI data updates","ARN":"arn:aws:sns:ap-northeast-1:103242399928:im2-yaoki-rover-public-object_created","Region":"ap-northeast-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: YAOKI Lunar Rover IM-2 Mission Telemetry and Imagery Dataset","URL":"https://github.com/jaops-space/im2-yaoki-yamcs-public/blob/main/analysis/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/jaops-space/im2-yaoki-yamcs-public/blob/main/analysis/get-to-know-a-dataset.ipynb","AuthorName":"JAOPS","AuthorURL":"https://www.jaops.com/"},{"Title":"Images from the YAOKI rover camera","URL":"https://github.com/jaops-space/im2-yaoki-yamcs-public/blob/main/analysis/yaoki_images.ipynb","NotebookURL":"https://github.com/jaops-space/im2-yaoki-yamcs-public/blob/main/analysis/yaoki_images.ipynb","AuthorName":"JAOPS","AuthorURL":"https://www.jaops.com/"},{"Title":"Plotting the YAOKI telemetry and telecommands","URL":"https://github.com/jaops-space/im2-yaoki-yamcs-public/blob/main/analysis/yaoki_tmtc.ipynb","NotebookURL":"https://github.com/jaops-space/im2-yaoki-yamcs-public/blob/main/analysis/yaoki_tmtc.ipynb","AuthorName":"JAOPS","AuthorURL":"https://www.jaops.com/"},{"Title":"Running the YAOKI Yamcs mission control stack locally via Docker","URL":"https://github.com/jaops-space/im2-yaoki-yamcs-public#option-2-docker-setup","AuthorName":"JAOPS","AuthorURL":"https://www.jaops.com/"}],"Tools & Applications":[{"Title":"Yamcs Mission Control System","URL":"https://yamcs.org/","AuthorName":"Yamcs Organization","AuthorURL":"https://github.com/yamcs/yamcs"}],"Publications":[{"Title":"Analysis of the Data from the YAOKI Rover on the IM-2 Mission to the Lunar South Pole","URL":"https://im2-yaoki-rover-public.s3.ap-northeast-1.amazonaws.com/PublicRelease_YAOKI_IM2_data_analysis_by_JAOPS.pdf","AuthorName":"Louis Burtz"},{"Title":"Data Release Announcement Presentation Slides","URL":"https://im2-yaoki-rover-public.s3.ap-northeast-1.amazonaws.com/PublicRelease_Overview_Lunar_Ledger_YAOKI_Dymon_JAOPS.pdf","AuthorName":"Shinichiro Nakajima, Alejandro Sela, Louis Burtz, Zack Gainsforth"}]},"RegistryEntryAdded":"2026-07-12","RegistryEntryLastModified":"2026-09-09","Slug":"im2-yaoki-rover","Sources":["awslabs-open-data-registry"]}
{"Name":"Samples of MR Images","Description":"These magnetic resonance angiography (MRA) images show coronal slices acquired from consecutive anteroposterior positions within the torso. The study was performed on a 1.5T General Electric (GE) Signa imaging system with gadolinium-contrast-enhancement for visualization of the cardiopulmonary vasculature. The pulse sequence used was a 3D time-of-flight fast spoiled gradient recalled acquisition in steady state (FSPGR, TR=6.3, TE=1.4, NEX=1, FOV = 40cm, slice thickness = 1.2mm).","Documentation":"https://doi.org/10.13026/C2001G","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2001G","ARN":"arn:aws:s3:::physionet-open/images/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"images","Sources":["awslabs-open-data-registry"]}
{"Name":"Indiana Statewide Elevation Catalog","Description":"The State of Indiana Geographic Information Office and IOT Office of Technology manage a series of digital LiDAR LAS files stored in AWS, dating back to the 2011-2013 collection and including the NRCS-funded 2016-2020 collection. These LiDAR datasets are available as uncompressed LAS files, for cloud storage and access. Each year's data is organized into a tile grid scheme covering the entire geography of Indiana, ensuring easy access and efficient processing. The tiles' naming reflects each tile's lower left coordinate, facilitating accurate data management and retrieval. The AWS storage solution ensures that these extensive datasets are readily accessible for analysis and application across various projects.\n","Documentation":"https://elevation.gio.in.gov/","Contact":"sscholer@iot.in.gov","ManagedBy":"Indiana Geographic Information Office","UpdateFrequency":"The State of Indiana has another four-year cycle of collecting orthoimagery and Lidar starting in 2025 and continuing through 2028.  The collections are designated by counties in three groups that cover Indiana, South to North.  These areas are frequently referred to as Tiers in the other documentation.  For example, tier 1 (Central 3rd) extends from Harrison County in the South to Elkhart and St. Joseph County in the North, while Tier 2 consists of the counties to the eastern side of the State, and Tier 3 is those counties to the western side of the State.","Collabs":{"ASDI":{"Tags":["elevation"]}},"Tags":["agriculture","aws-pds","earth observation","geospatial","imaging","lidar","mapping","natural resource","sustainability"],"License":"Access to Indiana Geographic Information Office Lidar is governed by Creative Commons 0 (CC0): https://creativecommons.org/publicdomain/zero/1.0/legalcode","Resources":[{"Description":"State of Indiana Elevation archive.","ARN":"arn:aws:s3:::giselevationingov","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"ArcGIS Online Indiana Lidar Viewer","URL":"https://indianamap-inmap.hub.arcgis.com/maps/ff98e3834d464619bd5c8974b0038a13/about","AuthorName":"Indiana Geographic Information Office (IGIO)"},{"Title":"IGIO Elevation Opendata S3 Browser","URL":"https://giselevationingov.s3.amazonaws.com/index.html","AuthorName":"Indiana Geographic Information Office (IGIO)"}],"Publications":[{"Title":"Recording of 2025 - 2028 Indiana Imagery and Elevation Program Presentation","URL":"https://elevation.gio.in.gov/pages/resources","AuthorName":"Indiana Geographic Information Office (IGIO)"}]},"RegistryEntryAdded":"2024-11-06","RegistryEntryLastModified":"2025-07-09","Slug":"in-elevation","Sources":["awslabs-open-data-registry"]}
{"Name":"In-Gauge and En-Gage: Occupants' Behaviour, Engagement, Emotion, and Comfort Indoors with Heterogeneous Sensors and Wearables","Description":"We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia. The datasets contained two elements: (1) In-Gauge dataset: we conducted a 5-month longitudinal field study using two outdoor weather stations, as well as indoor weather stations in 17 classrooms and temperature sensors on the vents of occupant-controlled room air-conditioners; these were collated into individual datasets for each classroom at a 5-minute logging frequency, including additional data on occupant presence. (2) En-Gage dataset: we tracked 23 students and 6 teachers in a 4-week cross-sectional study En-Gage, using wearable sensors to log physiological data (electrodermal activity, heart rate, blood column pulse, skin temperature, 3-axis acceleration), as well as daily surveys to query the occupants' thermal comfort, learning engagement, emotions and seating behaviours. Overall, the combined dataset could be used to analyse the relationships between indoor/outdoor climates and students' behaviours/mental states on campus, which provide opportunities for the future design of intelligent feedback systems to benefit both students and staff.","Documentation":"https://doi.org/10.13026/srm3-7z33","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/srm3-7z33","ARN":"arn:aws:s3:::physionet-open/in-gauge-and-en-gage/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"in-gauge-and-en-gage","Sources":["awslabs-open-data-registry"]}
{"Name":"Indiana Statewide Digital Aerial Imagery Catalog","Description":"The State of Indiana Geographic Information Office and IOT Office of Technology manage a series of digital orthophotography dating back to 2005.  Every year's worth of imagery is available as Cloud Optimized GeoTIFF (COG) files, original GeoTIFF, and other compressed deliverables such as ECW and MrSID.  Additionally, each imagery year is organized into a tile grid scheme covering the entire geography of Indiana.  All years of imagery are tiled from a 5,000 ft grid or sub tiles depending upon the resolution of the imagery.  The naming of the tiles reflects the lower left coordinate from the image.\n","Documentation":"https://imagery.gio.in.gov/","Contact":"sscholer@iot.in.gov","ManagedBy":"Indiana Geographic Information Office","UpdateFrequency":"The State of Indiana has had a 4-year cycle collecting imagery.  The collections are designated by counties in three groups that cover Indiana, South to North.  These areas are frequently referred to as Tiers in the other documentation.  For example, tier 1 (Central 3rd) extends from Harrison County in the South to Elkhart and St. Joseph County in the North, while Tier 2 consists of the counties to the eastern side of the State, and Tier 3 is those counties to the western side of the State.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aerial imagery","agriculture","aws-pds","cog","earth observation","geospatial","imaging","mapping","natural resource","sustainability"],"License":"Access to Indiana Geographic Information Office Orthoimagery is governed by Creative Commons 0 (CC0): https://creativecommons.org/publicdomain/zero/1.0/legalcode","Resources":[{"Description":"State of Indiana digital orthophotography archive.","ARN":"arn:aws:s3:::gisimageryingov","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"ArcGIS Online Indiana Orthoimagery Viewer","URL":"https://indianamap-inmap.hub.arcgis.com/datasets/61d4dc991c154af49ad7c1d675182a4f/explore","AuthorName":"Indiana Geographic Information Office (IGIO)"},{"Title":"IGIO Imagery Opendata S3 Browser","URL":"https://gisimageryingov.s3.amazonaws.com/index.html","AuthorName":"Indiana Geographic Information Office (IGIO)"}],"Publications":[{"Title":"Recording of 2025 - 2028 Indiana Orthoimagery Program Presentation","URL":"https://imagery.gio.in.gov/pages/resources","AuthorName":"Indiana Geographic Information Office (IGIO)"}]},"RegistryEntryAdded":"2022-12-16","RegistryEntryLastModified":"2025-07-09","Slug":"in-imagery","Sources":["awslabs-open-data-registry"]}
{"Name":"Indiana Statewide Leaf-on Digital Aerial Imagery Catalog","Description":"The State of Indiana Geographic Information Office (GIO) provides a statewide, enhanced NAIP, high-resolution 6-in (15 cm) leaf-on digital orthoimagery catalog. Captured during the summer of 2025, this imagery features an enhanced horizontal accuracy. Primary deliverables are hosted as standard GeoTIFF (.tif), JPEG, and highly performant Cloud Optimized GeoTIFF (COG) files to optimize cloud-native streaming and raster rendering.\n\nManaged by the Indiana Geographic Information Office (IGIO) and integrated directly into the State’s open data imagery repository on AWS, this public-domain dataset is freely accessible without licensing restrictions. It supports direct downloads, seamless streaming via REST services, and native compatibility with web maps, GIS platforms, and desktop CAD applications. Additionally, this repository is SpatioTemporal Asset Catalog (STAC) enabled.\n","Documentation":"https://imagery.gio.in.gov/","Contact":"sscholer@iot.in.gov","ManagedBy":"Indiana Geographic Information Office","UpdateFrequency":"The 2025 acquisition was published and integrated into the AWS Open Data repository in mid 2026. Future leaf-on updates are contingent upon leveraging upcoming USDA NAIP collection schedules for Indiana (the next tentatively planned for 2027), subject to securing collaborative funding.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aerial imagery","agriculture","aws-pds","cog","earth observation","geospatial","imaging","mapping","natural resource","sustainability"],"License":"Access to Indiana Geographic Information Office Leaf-on Orthoimagery is governed by Creative Commons 0 (CC0): https://creativecommons.org/publicdomain/zero/1.0/legalcode","Resources":[{"Description":"State of Indiana digital leaf-on orthophotography archive.","ARN":"arn:aws:s3:::gisimageryleafoningov","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"ArcGIS Online Indiana Orthoimagery Viewer","URL":"https://indianamap-inmap.hub.arcgis.com/datasets/61d4dc991c154af49ad7c1d675182a4f/explore","AuthorName":"Indiana Geographic Information Office (IGIO)"},{"Title":"IGIO Imagery Opendata S3 Browser","URL":"https://gisimageryleafoningov.s3.amazonaws.com/index.html","AuthorName":"Indiana Geographic Information Office (IGIO)"}],"Publications":[{"Title":"Indiana Imagery Resouse Pages","URL":"https://imagery.gio.in.gov/","AuthorName":"Indiana Geographic Information Office (IGIO)"}]},"RegistryEntryAdded":"2026-06-10","RegistryEntryLastModified":"2026-06-10","Slug":"in-imageryleafon","Sources":["awslabs-open-data-registry"]}
{"Name":"iNaturalist Licensed Observation Images","Description":"iNaturalist is a community science effort in which participants share observations of living organisms that they encounter and document with photographic evidence, location, and date. The community works together reviewing these images to identify these observations to species. This collection represents the licensed images accompanying iNaturalist observations.","Documentation":"Documentation can be found <a href=\"https://github.com/inaturalist/inaturalist-open-data/tree/documentation\">here</a>. You can learn more about iNaturalist <a href=\"https://www.inaturalist.org/\">here</a>.","Contact":"help@inaturalist.org","ManagedBy":"[iNaturalist](https://www.inaturalist.org/) is an independent, tax-exempt, 501(c)(3), not-for-profit organization based in the United States of America (EIN/Tax ID: 92-1296468).","UpdateFrequency":"Images are posted in real time, and we are currently copying over images from existing observations. Metadata is updated monthly. More information on the metadata can be found in the [documentation](https://github.com/inaturalist/inaturalist-open-data)","Collabs":{"ASDI":{"Tags":["ecosystems"]}},"Tags":["aws-pds","biodiversity","bioinformatics","conservation","earth observation","life sciences"],"License":"Creative Commons or Public Domain (CC0), varying by image. More information on how to query and properly treat licenses can be found in the <a href=\"https://github.com/inaturalist/inaturalist-open-data\">documentation</a>","Resources":[{"Description":"Image files (e.g. JPEG) associated with metadata describing the observation associated with the image file.","ARN":"arn:aws:s3:::inaturalist-open-data","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2021-03-22","RegistryEntryLastModified":"2023-12-22","Slug":"inaturalist-open-data","Sources":["awslabs-open-data-registry"]}
{"Name":"St Petersburg INCART 12-lead Arrhythmia Database","Description":"This database consists of 75 annotated recordings extracted from 32 Holter records. Each record is 30 minutes long and contains 12 standard leads, each sampled at 257 Hz, with gains varying from 250 to 1100 analog-to-digital converter units per millivolt.","Documentation":"https://doi.org/10.13026/C2V88N","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2V88N","ARN":"arn:aws:s3:::physionet-open/incartdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"incartdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Indian High Court Judgments","Description":"This dataset contains judgements from the Indian High Courts, downloaded from ecourts website. It contains judgments of 25 high courts, along with raw metadata (in json format) and structured metadata (in parquet format). Judgments from the website are further compressed to optimize for size (care has been taken to not have any loss of data either in content or in visual appearance). Tar files are also made available in addition to the individual pdf files to make it easier for bulk download.","Documentation":"https://github.com/vanga/indian-high-court-judgments/blob/main/opendata/docs/dataset.md","Contact":"contact@dattam.in","ManagedBy":"[Dattam Labs](https://dattam.in)","UpdateFrequency":"Quarterly","Tags":["legal data"],"License":"CC-BY-4.0","Resources":[{"Description":"S3 bucket containing the judgments","ARN":"arn:aws:s3:::indian-high-court-judgments","Region":"ap-south-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Extracting text from judgment PDFs","URL":"https://github.com/vanga/indian-high-court-judgments/blob/main/opendata/tutorials/README.md","AuthorName":"Himanshu Mukkera","AuthorURL":"https://github.com/HimanshuReddy15","Services":["Amazon S3"]},{"Title":"Using AWS Athena to query the metadata","URL":"https://github.com/vanga/indian-high-court-judgments/blob/main/opendata/tutorials/ATHENA.md","AuthorName":"Himanshu Mukkera","AuthorURL":"https://github.com/HimanshuReddy15","Services":["Amazon Athena"]}]},"RegistryEntryAdded":"2025-05-13","RegistryEntryLastModified":"2025-05-29","Slug":"indian-high-court-judgments","Sources":["awslabs-open-data-registry"]}
{"Name":"Indian Supreme Court Judgments","Description":"This dataset contains judgements from the Indian Supreme Court, downloaded from ecourts website. It contains judgments from 1950 to 2025, along with raw metadata (in json format) and structured metadata in parquet format. Judgments are available in both English and regional Indian languages in zip format for easier download.","Documentation":"https://github.com/vanga/indian-supreme-court-judgments/blob/main/opendata/docs/dataset.md","Contact":"contact@dattam.in","ManagedBy":"[Dattam Labs](https://dattam.in)","UpdateFrequency":"Bi-monthly","Tags":["aws-pds","legal data"],"License":"CC-BY-4.0","Resources":[{"Description":"S3 bucket containing the judgments","ARN":"arn:aws:s3:::indian-supreme-court-judgments","Region":"ap-south-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Dataset Overview and Usage Examples","URL":"https://github.com/vanga/indian-supreme-court-judgments/blob/main/opendata/tutorials/README.md","AuthorName":"Nihesh Rachakonda","AuthorURL":"https://github.com/rnihesh","Services":["Amazon S3"]},{"Title":"Using AWS Athena to query the metadata","URL":"https://github.com/vanga/indian-supreme-court-judgments/blob/main/opendata/tutorials/ATHENA.md","AuthorName":"Nihesh Rachakonda","AuthorURL":"https://github.com/rnihesh","Services":["Amazon Athena"]}]},"RegistryEntryAdded":"2025-07-30","RegistryEntryLastModified":"2025-08-06","Slug":"indian-supreme-court-judgments","Sources":["awslabs-open-data-registry"]}
{"Name":"Infant 3T/7T Precision Imaging","Description":"Infant 3T/7T MRI precision imaging data, including raw BIDS, preprocessed Infant fMRIPrep derivatives, and XCP-D derivatives (N=6 highly sampled infant participants)","Documentation":"https://github.com/DCAN-Labs/infant_3T_7T_precision_imaging/tree/main#infant-3t7t-precision-imaging-data","Contact":"Julia Moser (moser297@umn.edu)","ManagedBy":"Masonic Institute for the Developing Brain (MIDB) Open Data Initiative","UpdateFrequency":"The repository will potentially be updated in the future as new data is acquired or current data is processed with updated processing pipelines/workflows.","Tags":["life sciences","magnetic resonance imaging","neuroimaging","neuroscience","pediatric"],"License":"[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Infant 3T/7T Precision Imaging Data","ARN":"arn:aws:s3:::infant-3t-7t-precision-mri","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://infant-3t-7t-precision-mri.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Get to Know Dataset - Infant 3T/7T Precision Imaging","URL":"https://github.com/DCAN-Labs/infant_3T_7T_precision_imaging/blob/main/tutorial/get-to-know-infant7T-dataset.ipynb","AuthorName":"Julia Moser","AuthorURL":"https://github.com/moser297"},{"Title":"Infant 3T/7T Precision Imaging Data on AWS","URL":"https://github.com/DCAN-Labs/infant_3T_7T_precision_imaging/tree/main#infant-3t7t-precision-imaging-data","AuthorName":"Julia Moser","AuthorURL":"https://github.com/moser297"}],"Tools & Applications":[{"Title":"Infant 3T/7T Precision Imaging Codebase","URL":"https://github.com/DCAN-Labs/infant_3T_7T_precision_imaging","AuthorName":"Julia Moser"}],"Publications":[{"Title":"Precision functional imaging in infants using multi-echo fMRI at 7T","URL":"https://www.biorxiv.org/content/10.1101/2025.11.09.687453v1","AuthorName":"Julia Moser et al.","AuthorURL":"https://orcid.org/0000-0002-6219-415X"}]},"RegistryEntryAdded":"2026-08-24","RegistryEntryLastModified":"2026-08-24","Slug":"infant-3t-7t-precision-mri","Sources":["awslabs-open-data-registry"]}
{"Name":"Safety and Preliminary Efficacy of Intranasal Insulin for Cognitive Impairment in Parkinson Disease and Multiple System Atrophy","Description":"This dataset was collected as part of a study that aimed to determine the effects of intranasal insulin (INI) on cognition and motor performance in PD. The study was a proof of concept, randomized, double-blinded, placebo-controlled trial evaluating the effects of 40 international units (IU) of insulin or saline once daily for four weeks on cognitive and functional performance. Of 16 subjects enrolled, eight in the INI group and six in the placebo group completed verbal fluency (FAS), Unified Parkinson Disease Scale (UPDRS), and modified Hoehn and Yahr scale (HY, PD severity) at baseline and post-treatment and were included in the analyses.","Documentation":"https://doi.org/10.13026/bn1x-my19","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/bn1x-my19","ARN":"arn:aws:s3:::physionet-open/inipdmsa/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"inipdmsa","Sources":["awslabs-open-data-registry"]}
{"Name":"InRad COVID-19 X-Ray and CT Scans","Description":"This dataset is a collection of anonymized thoracic radiographs (X-Rays) and computed tomography (CT) scans of patients with suspected COVID-19. Images are acommpanied by a positive or negative diagnosis for SARS-CoV2 infection via RT-PCR. These images were provided by Hospital das Clínicas da Universidade de São Paulo, Hospital Sirio-Libanes, and by Laboratory Fleury.","Documentation":"https://docs.google.com/document/d/1isuyQnszSio3JUZ1_3C4DTPC1fvaJGAR4ziTWGRi8K4/edit?usp=sharing","Contact":"inlab.inrad@hc.fm.usp.br","ManagedBy":"[Faculdade de Medicina da Universidade de São Paulo Institute of Radiology (InRad)](http://inrad.hc.fm.usp.br)","UpdateFrequency":"As Necessary","Tags":["aws-pds","bioinformatics","coronavirus","COVID-19","health","life sciences","medicine","SARS"],"License":"Creative Commons Attribution 4.0 International (CC BY 4.0)","Resources":[{"Description":"Radiographs in .png format CT-Scans in .nii format. Object names include a hash MD5. Hash MD5 are matched to a positive or negative RT-PCR test for SARS-CoV-2 or a non-COVID diagnosis as defined by codes that can be found in the accompanying file or the above documentation.","ARN":"arn:aws:s3:::inlab-opendata-covid-anonymized-images","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2021-06-21","RegistryEntryLastModified":"2021-06-21","Slug":"inlab-covid-19-images-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"AgricultureVision","Description":"Agriculture-Vision aims to be a publicly available large-scale aerial agricultural image dataset that is high-resolution, multi-band, and with multiple types of patterns annotated by agronomy experts.  The original dataset affiliated with the 2020 CVPR paper includes 94,986 512x512images sampled from 3,432 farmlands with nine types of annotations: double plant, drydown, endrow, nutrient deficiency, planter skip, storm damage, water, waterway and weed cluster.  All of these patterns have substantial impacts on field conditions and the final yield. These farmland images were captured between 2017 and 2019 across multiple growing seasons in numerous farming locations in the US.  Each field image contains four color channels: Near-infrared (NIR), Red, Green and Blue.  We first randomly split the 3,432 farmland images with a 6/2/2 train/val/test ratio. We then assign each sampled image to the split of the farmland image they are cropped from. This guarantees that no cropped images from the same farmland will appear in multiple splits in the final dataset.  The generated (supervised) Agriculture-Vision dataset thus contains 56,944/18,334/19,708 train/val/test images.\nAdditionally, we continue to grow this dataset.  In 2021 as a part of the [Prize Challenge at CVPR](https://www.agriculture-vision.com/agriculture-vision-2021/prize-challenge-2021), we have added sequences of full-field imagery across 52 fields to promote the use of weakly supervised methods.\n","Documentation":"https://arxiv.org/abs/2001.01306","Contact":"support@intelinair.com","ManagedBy":"Intelinair, Inc.","UpdateFrequency":"Periodically","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["aerial imagery","agriculture","aws-pds","computer vision","deep learning","machine learning"],"License":"Provided in the bucket.","Resources":[{"Description":"Original dataset affiliated with the 2020 CVPR paper.  Dataset provided as a series of tar.gz files with data for each year and an associated json file dscribing the train/validation/test split.","ARN":"arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_paper_2020","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false},{"Description":"Dataset affiliated with the 2021 CVPR Agricutlure Vision Workshop.   This includes both the supervised and additional raw imagery.  The supervised portion is split train-val-test.   The full-field imagery is given as a series of folders where each folder corresponds to a field, and the images contained are named according to the date of collection. This is the high-resolution-only subset of cvpr_challenge_2021_full.\n","ARN":"arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_challenge_2021","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false},{"Description":"Dataset affiliated with the 2021 CVPR Agricutlure Vision Workshop. This includes both the supervised and additional raw imagery, both high-resolution (10cm/pixel) and low-resolution (sentinel-1 10m/pixel) imagery.   The supervised portion is split train-val-test.  The full-field imagery is given as a series of folders where each folder corresponds to a field, and the images contained are named according to the date of collection.   High-resolution images are named `<MMDDYY>_<channel_high>.tif` Low-resolution images are named `<MMDDYY>_gamma0_<polarization>_low.tif`.\n","ARN":"arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_challenge_2021_full","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Publications":[{"Title":"Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis","URL":"https://arxiv.org/abs/2001.01306","AuthorName":"Mang Tik Chiu, Xingqian Xu, Yunchao Wei, Zilong Huang, Alexander Schwing, Robert Brunner, Hrant Khachatrian, Hovnatan Karapetyan, Ivan Dozier, Greg Rose, David Wilson, Adrian Tudor, Naira Hovakimyan, Thomas S. Huang, Honghui Shi"},{"Title":"The 2nd International Workshop and Prize Challenge on Agriculture-Vision, Challenges & Opportunities for Computer Vision in Agricutlure","URL":"https://www.agriculture-vision.com/","AuthorName":"Humphrey Shi, Naira Hovakimyan, Jennifer Hobbs, Ed Delp, Melba Crawford, Zhen Li, David Clifford, Jim Yuan, Mang Tik Chiu, Xingqian Xu"}]},"RegistryEntryAdded":"2021-03-25","RegistryEntryLastModified":"2026-07-16","Slug":"intelinair_agriculture_vision","Sources":["awslabs-open-data-registry"]}
{"Name":"Corn Kernel Counting Dataset","Description":"Dataset associated with the March 2021 Frontiers in Robotics and AI paper \"Broad Dataset and Methods for Counting and Localization of On-Ear Corn Kernels\", DOI: 10.3389/frobt.2021.627009","Documentation":"https://www.frontiersin.org/articles/10.3389/frobt.2021.627009/abstract","Contact":"support@intelinair.com","ManagedBy":"Intelinair, Inc.","UpdateFrequency":"Periodically","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["agriculture","aws-pds","computer vision","machine learning"],"License":"Provided in the bucket.","Resources":[{"Description":"Folder contains the terms of use, paper, and zip file of data in COOC format. Each kernel is annotated with an instance mask and appropriate class. The dataset is broken into three subcomponents.  The \"base\" (i.e. \"corn\") subset contains images from a wide variety of domains such as those you might find on the internet.  The \"narrow\" subset contains images with a single ear as would be anticipated in the application.  The \"many\" dataset resembles the base dataset but has a huge number of ears and kernels in each image. Images were annotated for (healthy) kernels, diseased kernels, “incomplete” (i.e. not fully matured) kernels, barren tips (i.e where the top of the ear does not produce kernels), and “kernel areas” where on-ear kernels were166present but resolution/lighting prevented annotation. \n","ARN":"arn:aws:s3:::intelinair-data-releases/corn-kernel-counting","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Publications":[{"Title":"Broad Dataset and Methods for Counting and Localization of On-Ear Corn Kernels","URL":"https://www.frontiersin.org/articles/10.3389/frobt.2021.627009/abstract","AuthorName":"Jennifer Hobbs, Vachik Khachatryan, Barathwaj Anandan, Harutyun Hovhannisyan, David Wilson"}]},"RegistryEntryAdded":"2021-05-04","RegistryEntryLastModified":"2025-07-09","Slug":"intelinair_corn_kernel_counting","Sources":["awslabs-open-data-registry"]}
{"Name":"Longitudinal Nutrient Deficiency","Description":"Dataset associated with the 2021 AAAI Paper- Detection and Prediction of Nutrient Deficiency Stress using Longitudinal Aerial Imagery.  The dataset contains 3 image sequences of aerial imagery from 386 farm parcels which have been annotated for nutrient deficiency stress.","Documentation":"https://arxiv.org/abs/2012.09654","Contact":"support@intelinair.com","ManagedBy":"Intelinair, Inc.","UpdateFrequency":"Periodically","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["aerial imagery","agriculture","aws-pds","computer vision","deep learning","machine learning"],"License":"Provided in the bucket.","Resources":[{"Description":"Folder contains the terms of use, paper, and zip file of data.","ARN":"arn:aws:s3:::intelinair-data-releases/longitudinal-nutrient-deficiency","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Publications":[{"Title":"Detection and Prediction of Nutrient Deficiency Stress using Longitudinal Aerial Imagery","URL":"https://arxiv.org/abs/2012.09654","AuthorName":"Saba Dadsetan, Gisele Rose, Naira Hovakimyan, Jennifer Hobbs"}]},"RegistryEntryAdded":"2021-03-25","RegistryEntryLastModified":"2025-07-09","Slug":"intelinair_longitudinal_nutrient_deficiency","Sources":["awslabs-open-data-registry"]}
{"Name":"Interhemispheric Cortex Connectivity Microscopy Dataset","Description":"This dataset contains high-resolution ExA-SPIM (Expansion-Assisted Selective Plane Illumination Microscopy) light-sheet imaging data of interhemispheric cortical connectivity in the mouse brain. Whole-brain volumes are acquired at near-isotropic sub-micron resolution following tissue expansion and clearing, enabling visualization and reconstruction of individual callosal projection neurons and their axonal arbors spanning both hemispheres. Data is stored in OME-Zarr format for efficient cloud-based access and multiscale visualization.","Documentation":"https://github.com/florenthaiss/open-data-examples/tree/main/interhemispheric-cortex-connectivity","Contact":"florent.haiss@pasteur.fr","ManagedBy":"[Institut Pasteur](https://www.pasteur.fr)","UpdateFrequency":"Periodically as new specimens are imaged","Tags":["aws-pds","image processing","imaging","life sciences","microscopy","Mus musculus","neurobiology","neuroimaging","neuroscience"],"License":"Creative Commons Attribution 4.0 International (CC-BY-4.0)","Citation":"Neural Circuit Dynamics and Decision Making Laboratory, Institut Pasteur - Mouse Neuroanatomy Data.","Resources":[{"Description":"ExA-SPIM light-sheet microscopy volumes of mouse brains in OME-Zarr format, with sidecar JSON metadata files describing subjects, acquisition parameters, and processing provenance. Organized as a flat list of data assets following AIND-style naming conventions.","ARN":"arn:aws:s3:::haisslab","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"Notifications published whenever new objects are added to the dataset bucket. Subscribe to receive alerts as new specimens are imaged and uploaded.","ARN":"arn:aws:sns:eu-west-3:253260001075:haisslab-object_created","Region":"eu-west-3","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Exploring ExA-SPIM Mouse Brain Volumes in a Browser-Based Notebook on Scigantic","URL":"https://scigantic.com/archive/6e0ee0b7-5063-5b7e-8b4d-d461194ea42a/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Get to know the Interhemispheric Cortex Connectivity dataset","URL":"https://github.com/florenthaiss/open-data-examples/blob/main/interhemispheric-cortex-connectivity/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/florenthaiss/open-data-examples/blob/main/interhemispheric-cortex-connectivity/get-to-know-a-dataset.ipynb","AuthorName":"Florent Haiss","AuthorURL":"https://github.com/florenthaiss"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-07-06","RegistryEntryLastModified":"2026-10-14","Slug":"interhemispheric-cortex-connectivity","Sources":["awslabs-open-data-registry"]}
{"Name":"10m Annual Land Use Land Cover (9-class)","Description":"This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use and land cover (LULC)\nderived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2024. \n\nEach map is a composite of LULC predictions for 9 classes throughout the year\nin order to generate a representative snapshot of each year. \n\nThis dataset was generated by Impact Observatory, which used billions of human-labeled pixels \n(curated by the National Geographic Society) to train a deep learning model for land classification. \nEach global map was produced by applying this model to the Sentinel-2 annual scene collections\nfrom the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. \n\nThese maps have been improved from Impact Observatory’s previous release and provide\na relative reduction in the amount of anomalous change between classes,\nparticularly between “Bare” and any of the vegetative classes\n“Trees,” “Crops,” “Flooded Vegetation,” and “Rangeland”.\nThis updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery. \n\nData can be accessed directly from the Registry of Open Data on AWS, from the [STAC 1.0.0 endpoint](https://api.impactobservatory.com/stac-aws/collections/io-10m-annual-lulc/items), or from the [IO Store](https://www.impactobservatory.com/maps-for-good/) for a specific Area of Interest (AOI).   \n","Documentation":"https://www.impactobservatory.com/global_maps","Contact":"hello@impactobservatory.com","ManagedBy":"[Impact Observatory](https://www.impactobservatory.com/)","UpdateFrequency":"A new year is made available annually, each January. A new time series was provided July 2023 to reduce anomalous change. Data for calendar year 2024 was released in June 2025.","Collabs":{"ASDI":{"Tags":["ecosystems"]}},"Tags":["aws-pds","cog","earth observation","environmental","geospatial","land cover","land use","machine learning","mapping","planetary","satellite imagery","stac","sustainability"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"10m Annual Land Use Land Cover (9-class)","ARN":"arn:aws:s3:::io-10m-annual-lulc","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC 1.0.0 endpoint](https://api.impactobservatory.com/stac-aws/collections/io-10m-annual-lulc/items)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"View the dataset on UN Biodiversity Lab Map Viewer","URL":"https://map.unbiodiversitylab.org/earth?basemap=grayscale&coordinates=20,0,2&layers=10m-annual-land-use-land-cover-9-class-01_100","AuthorName":"United Nations Development Programme"}],"Publications":[{"Title":"Global land use / land cover with Sentinel 2 and deep learning","URL":"https://ieeexplore.ieee.org/document/9553499","AuthorName":"K. Karra, C. Kontgis, Z. Statman-Weil, J. C. Mazzariello, M. Mathis and S. P. Brumby"},{"Title":"Mapping the world with unmatched frequency","URL":"https://medium.com/impactobservatoryinc/mapping-the-world-with-unmatched-frequency-54a83527f138","AuthorName":"Mark Hannel"},{"Title":"The world's most populated and greenest megacities (and how we found out)","URL":"https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/mapping/worlds-greenest-megacities/","AuthorName":"Esri"},{"Title":"These maps from satellite data show how much Earth has changed in only five years","URL":"https://www.fastcompany.com/90729824/these-maps-from-satellite-data-show-how-much-earth-has-changed-in-only-five-years","AuthorName":"Fast Company"},{"Title":"‘Very Dire’: Devastated by Floods, Pakistan Faces Looming Food Crisis","URL":"https://www.nytimes.com/2022/09/11/world/asia/pakistan-floods-food-crisis.html","AuthorName":"New York Times"}]},"RegistryEntryAdded":"2022-12-14","RegistryEntryLastModified":"2025-06-24","Slug":"io-lulc","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"The provider of this dataset will no longer maintain this dataset, and the historic data will no longer be available via the Registry of Open Data on AWS.  We are open to talking with anyone else who might be willing to provide this dataset to the community.  Contact <a href=\"mailto:opendata@amazon.com\">opendata@amazon.com</a>","Name":"IRS 990 Filings","Description":"**On December 16, 2021 the IRS announced that it would [discontinue updates to the IRS 990 Filings dataset on AWS, starting December 31, 2021](https://www.irs.gov/newsroom/irs-makes-tax-exempt-organization-search-primary-source-to-get-exempt-organization-data).**\n\nThe IRS has requested public inquiries be directed to +1-800-829-1040.\n\nMachine-readable data from certain electronic 990 forms filed with the IRS from 2013 to present.\n","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/irs-990","Contact":"https://www.irs.gov/e-file-providers/e-file-for-charities-and-non-profits","ManagedBy":"The Internal Revenue Service","UpdateFrequency":"New filings are added regularly","Tags":["aws-pds","regulatory","statistics"],"License":"None","Resources":[{"Description":"xml, json, csv","ARN":"arn:aws:s3:::irs-form-990","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Tutorial on using the IRS 990 e-file dataset","URL":"https://appliednonprofitresearch.com/posts/2018/06/the-irs-990-e-file-dataset-getting-to-the-chocolatey-center-of-data-deliciousness/","AuthorName":"Applied Nonprofit Research","AuthorURL":"https://www.appliednonprofitresearch.com/"}],"Tools & Applications":[{"Title":"Parse 990 XML using IRSx","URL":"https://github.com/jsfenfen/990-xml-reader","AuthorName":"Jacob Fenton","AuthorURL":"http://jacobfenton.com/"},{"Title":"aws-irs-990-explorer","URL":"http://irs-990-explorer.chrisgherbert.com","AuthorName":"Chris Herbert","AuthorURL":"http://chrisgherbert.com/"},{"Title":"990_long","URL":"https://github.com/CharityNavigator/990_long","AuthorName":"Charity Navigator","AuthorURL":"https://www.charitynavigator.org/"},{"Title":"Non Profit Light","URL":"http://nonprofitlight.com","AuthorName":"Non Profit Light"},{"Title":"Guide to Open Data for Nonprofit Research","URL":"https://lecy.github.io/Open-Data-for-Nonprofit-Research/","AuthorName":"lecy","AuthorURL":"https://github.com/lecy"},{"Title":"Nonprofit Explorer","URL":"https://projects.propublica.org/nonprofits/","AuthorName":"ProPublica","AuthorURL":"https://propublica.org"},{"Title":"Open990","URL":"https://www.open990.com/","AuthorName":"990 Consulting, LLC","AuthorURL":"https://www.990consulting.com/"},{"Title":"Grantmakers.io","URL":"https://www.grantmakers.io","AuthorName":"Chad Kruse","AuthorURL":"https://www.chadkruse.com/"},{"Title":"npo_classifier: Automated coding using machine-learning and remapping the U.S. nonprofit sector","URL":"https://github.com/ma-ji/npo_classifier","AuthorName":"Ji Ma","AuthorURL":"https://jima.me/"},{"Title":"Nonprofit Organization NTEE Code Finder & UN SDG Classification","URL":"https://x4i.org/nonprofit-ntee-code-finder","AuthorName":"X4Impact","AuthorURL":"https://x4i.org"}],"Publications":null},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2022-08-03","Slug":"irs990","Sources":["awslabs-open-data-registry"]}
{"Name":"iSDAsoil","Description":"iSDAsoil is a resource containing soil property predictions for the entire African continent, generated using machine learning. Maps for over 20 different soil properties have been created at 2 different depths (0-20 and 20-50cm).  Soil property predictions were made using machine learning coupled with remote sensing data and a training set of over 100,000 analyzed soil samples. Included in this dataset are images of predicted soil properties, model error and satellite covariates used in the mapping process.","Documentation":"https://www.isda-africa.com/isdasoil/isdasoil-on-aws/","Contact":"info@isda-africa.com","ManagedBy":"[Innovative Solutions for Decision Agriculture (iSDA)](https://www.isda-africa.com/)","UpdateFrequency":"Based upon the availability of new data","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["agriculture","analytics","aws-pds","biodiversity","conservation","deep learning","food security","geospatial","machine learning","satellite imagery"],"License":"CC-BY 4.0","Resources":[{"Description":"iSDAsoil data and covariates","ARN":"arn:aws:s3:::isdasoil","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse data using the STAC viewer](https://isdasoil.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"iSDAsoil Python tutorial","URL":"https://github.com/iSDA-Africa/isdasoil-tutorial","AuthorName":"Matt Miller","AuthorURL":"https://github.com/mmiller8878","Services":["AWS CloudFormation","Amazon SageMaker"]}],"Tools & Applications":[{"Title":"iSDAsoil homepage - view soil property maps online","URL":"https://www.isda-africa.com/isdasoil/","AuthorName":"iSDA"},{"Title":"iSDAsoil liming demo app on Observable","URL":"https://observablehq.com/d/1c48938b8a4f5da3","AuthorName":"Jamie Collinson","AuthorURL":"https://jamiecollinson.com/"}],"Publications":[{"Title":"African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning","URL":"https://www.nature.com/articles/s41598-021-85639-y","AuthorName":"Tomislav Hengl, Matthew A. E. Miller, Josip Križan, Keith D. Shepherd, Andrew Sila, Milan Kilibarda, Ognjen Antonijević, Luka Glušica, Achim Dobermann, Stephan M. Haefele, Steve P. McGrath, Gifty E. Acquah, Jamie Collinson, Leandro Parente, Mohammadreza Sheykhmousa, Kazuki Saito, Jean-Martial Johnson, Jordan Chamberlin, Francis B. T. Silatsa, Martin Yemefack, John Wendt, Robert A. MacMillan, Ichsani Wheeler & Jonathan Crouch"}]},"RegistryEntryAdded":"2021-05-07","RegistryEntryLastModified":"2025-02-04","Slug":"isdasoil","Sources":["awslabs-open-data-registry"]}
{"Name":"ISERV","Description":"ISS SERVIR Environmental Research and Visualization System (ISERV) was a fully-automated prototype camera aboard the International Space Station that was tasked to capture high-resolution Earth imagery of specific locations at 3-7 frames per second. In the course of its regular operations during 2013 and 2014, ISERV's camera acquired images that can be used primaliry in use is environmental and disaster management.","Documentation":"https://stacindex.org/collections/nasa-iserv","Contact":"support@radiant.earth","ManagedBy":"[Radiant Earth Foundation](https://www.radiant.earth/)","UpdateFrequency":"Not updated","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","earth observation","environmental","geospatial","satellite imagery"],"License":"The data is released under a ODC Public Domain Dedication & License 1.0 ([PDDL-1.0](https://spdx.org/licenses/PDDL-1.0.html)).","Resources":[{"Description":"ISERV Optical Imagery","ARN":"arn:aws:s3:::nasa-iserv","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2020-09-01","RegistryEntryLastModified":"2023-02-28","Slug":"iserv","Sources":["awslabs-open-data-registry"]}
{"Name":"International Skin Imaging Collaboration (ISIC) Archive","Description":"A public-access archive of skin lesion images, supporting teaching, research, and the development and evaluation of diagnostic algorithms.","Documentation":"https://www.isic-archive.com/","Contact":"support@isic-archive.com","ManagedBy":"International Skin Imaging Collaboration (ISIC)","UpdateFrequency":"Upon new data ingest from contributors.","Tags":["biology","cancer","classification","computational pathology","dicom","grand-challenge.org","health","Homo sapiens","imaging","life sciences","machine learning","medical image computing","medical imaging","medicine","microscopy","segmentation"],"License":"Creative Commons licenses (CC-0, CC-BY, or CC-BY-NC) are defined per-image.","Resources":[{"Description":"Images of skin lesions and associated metadata","ARN":"arn:aws:s3:::isic-archive","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Notifications of new data","ARN":"arn:aws:sns:us-east-1:024848456264:isic-archive-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"ISIC Archive Data Dictionary","URL":"https://www.isic-archive.com/data-dictionary","NotebookURL":null,"AuthorName":"International Skin Imaging Collaboration (ISIC)"}],"Tools & Applications":[{"Title":"ISIC Archive Gallery","URL":"https://gallery.isic-archive.com","AuthorName":"International Skin Imaging Collaboration (ISIC)"},{"Title":"isic-cli - The official command line tool for interacting with the ISIC Archive","URL":"https://pypi.org/project/isic-cli","AuthorName":"International Skin Imaging Collaboration (ISIC)"}],"Publications":[{"Title":"A patient-centric dataset of images and metadata for identifying melanomas using clinical context","URL":"https://doi.org/10.1038/s41597-021-00815-z","AuthorName":"Rotemberg V, Kurtansky N, Betz-Stablein B, Caffery L, Chousakos E, Codella N, et al"},{"Title":"Human surface anatomy terminology for dermatology: a Delphi consensus from the International Skin Imaging Collaboration","URL":"https://doi.org/10.1111/jdv.16855","AuthorName":"Navarrete-Dechent C, Liopyris K, Molenda M, Braun R, Curiel-Lewandrowski C, Dusza S, et al"},{"Title":"International Skin Imaging Collaboration - Designated Diagnoses (ISIC-DX): Consensus terminology for lesion diagnostic labeling","URL":"https://doi.org/10.1111/jdv.20055","AuthorName":"Scope A, Liopyris K, Weber J, Barnhill R, Braun R, Curiel-Lewandrowski C, et al"},{"Title":"The SLICE-3D dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection","URL":"https://doi.org/10.1038/s41597-024-03743-w","AuthorName":"Kurtansky N, D'Alessandro B, Gillis M, Betz-Stablein B, Cerminara S, Garcia R, et al"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-01-17","RegistryEntryLastModified":"2025-01-17","Slug":"isic-archive","Sources":["awslabs-open-data-registry"]}
{"Name":"Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE)","Description":"The Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project has a singular mission: to accelerate ice sheet and glacier research by producing globally comprehensive, high resolution, low latency, temporally dense, multi-sensor records of land ice and ice shelf change while minimizing barriers between the data and the user.\nITS_LIVE data currently consists of NetCDF Level 2 scene-pair ice flow products posted to a standard 120 m grid derived from Landsat 4/5/7/8/9, Sentinel-2 optical scenes, and Sentinel-1 SAR scenes. We have processed all land-ice intersecting image pairs separated by ≤546 days for optical and ≤12 for SAR sensors, for all missions from each mission start to the end of 2022.\nThe ITS_LIVE project will continue processing new optical and radar scene pairs as they come in, and will soon increase production to include SAR image pairs separated by more than 12 days. We also expect to extend processing to include ASTER, RADARSAT, and NISAR imagery. Further, all scene pair data are combined into a set of Zarr datacubes that are cloud-optimized for time-series analysis, with a suite of user tools available to query the data effectively.\nThe ITS_LIVE project is also producing a suite of Level 3 products, including quarterly and annual ice-sheet and regional velocity mosaics as Cloud-Optimized GeoTIFFs, standardized records of elevation change rates for Greenland and Antarctica, and ice shelf and tidewater glacier front positions.\nFor more information about the ITS_LIVE project, please see <https://its-live.jpl.nasa.gov/>.\n","Documentation":"https://its-live-data.s3.us-west-2.amazonaws.com/README.html","Contact":"If you have questions about the data itself or the processing methods used, please post in the [ITS_LIVE Community gitter](https://app.gitter.im/#/room/#its_live_community:gitter.im).\nIf you have questions about how the data is managed on AWS, please email the [ASF Tools Team](mailto:uaf-asf-apd@alaska.edu).\n","ManagedBy":"[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)","UpdateFrequency":"Up to daily, as new satellite imagery is made available.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","cog","earth observation","geophysics","geospatial","global","ice","netcdf","satellite imagery","stac","zarr"],"License":"[Creative Commons Zero (CC0) 1.0 Universal License](https://creativecommons.org/publicdomain/zero/1.0/)","Citation":"https://its-live.jpl.nasa.gov/#how-to-cite","Resources":[{"Description":"ITS_LIVE Data S3 Bucket","ARN":"arn:aws:s3:::its-live-data","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://its-live-data.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:us-west-2:367587189974:its-live-data-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"ITS_LIVE Point Data Access","URL":"https://github.com/nasa-jpl/its_live","NotebookURL":"https://github.com/nasa-jpl/its_live/blob/main/notebooks/itslive-notebook.ipynb","AuthorName":"Maria Liukis, Alex S. Gardner, Luis A. López, Mark Fahnestock, and Joseph H. Kennedy","AuthorURL":"https://its-live.jpl.nasa.gov/#people"},{"Title":"Using xarray to examine cloud-based glacier surface velocity data","URL":"https://e-marshall.github.io/itslive/intro.html","AuthorName":"Emma Marshall","AuthorURL":"https://github.com/e-marshall"}],"Tools & Applications":[{"Title":"A serverless React-Leaflet website to plot and share ITS_LIVE data","URL":"https://mappin.itsliveiceflow.science/","AuthorName":"Jacob Fahnestock","AuthorURL":"www.linkedin.com/in/fahnestockj"},{"Title":"Chad Greene's ITS_LIVE Matlab collection","URL":"https://github.com/chadagreene/ITS_LIVE","AuthorName":"Chad A. Greene","AuthorURL":"http://www.chadagreene.com/"},{"Title":"ITS_LIVE Data Access API","URL":"https://nsidc.org/apps/itslive-search/docs#/","AuthorName":"NSIDC","AuthorURL":"https://nsidc.org"},{"Title":"ITS_LIVE Data Access Portal","URL":"https://nsidc.org/apps/itslive/","AuthorName":"NSIDC","AuthorURL":"https://nsidc.org"},{"Title":"ITS_LIVEpy: A Python client for ITS_LIVE glacier velocity data","URL":"https://github.com/nasa-jpl/itslive-py","AuthorName":"Luis A. López and Mark Fahnestock","AuthorURL":"https://its-live.jpl.nasa.gov/#people"},{"Title":"ITS_LIVE_TOOL: A package designed to aid users working with the ITS_LIVE data","URL":"https://github.com/vdevauxchupin/ITS_LIVE_TOOL","AuthorName":"Victor Devaux-Chupin and Emma Marshall","AuthorURL":"https://github.com/vdevauxchupin/ITS_LIVE_TOOL/graphs/contributors"}],"Publications":[{"Title":"Antarctic calving loss rivals ice-shelf thinning","URL":"https://doi.org/10.1038/s41586-022-05037-w","AuthorName":"Greene, C.A., A.S. Gardner, N.-J. Schlegel, and A.D. Fraser"},{"Title":"Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement. Remote Sensing","URL":"https://doi.org/10.3390/rs13040749","AuthorName":"Lei, Y., A.S. Gardner, and P. Agram"},{"Title":"Detecting seasonal ice dynamics in satellite images","URL":"https://doi.org/10.5194/tc-14-4365-2020","AuthorName":"Greene, C.A., A.S. Gardner, and L.C. Andrews"},{"Title":"Elevation change of the Antarctic Ice Sheet: 1985 to 2020","URL":"https://doi.org/10.5194/essd-14-3573-2022","AuthorName":"Nilsson, J., A.S. Gardner, and F.S. Paolo"},{"Title":"Increased West Antarctic and unchanged East Antarctic ice discharge over the last 7 years","URL":"https://doi.org/10.5194/tc-12-521-2018","AuthorName":"Gardner, A. S., G. Moholdt, T. Scambos, M. Fahnstock, S. Ligtenberg, M. van den Broeke, and J. Nilsson"},{"Title":"Processing methodology for the ITS_LIVE Sentinel-1 ice velocity products","URL":"https://doi.org/10.5194/essd-14-5111-2022","AuthorName":"Lei, Y., A.S. Gardner, and P. Agram"},{"Title":"Ubiquitous acceleration in Greenland Ice Sheet calving from 1985 to 2022","URL":"https://doi.org/10.1038/s41586-023-06863-2","AuthorName":"Greene, C.A., A.S. Gardner, M. Wood, and J.K. Cuzzone"},{"Title":"Widespread slowdown in thinning rates of West Antarctic Ice Shelves","URL":"https://doi.org/10.5194/tc-17-3409-2023","AuthorName":"Paolo, F. S., A.S. Gardner, C.A. Greene, J. Nilsson, M.P. Schodlok, N.-J. Schlegel, and H.A. Fricker"}]},"DeprecatedNotice":null,"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2024-01-23","RegistryEntryLastModified":"2025-07-09","Slug":"its-live-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Cell Organelle Segmentation in Electron Microscopy (COSEM) on AWS","Description":"High resolution images of subcellular structures.","Documentation":"https://github.com/janelia-cosem/aws-opendata/blob/master/cosem-introduction.md","Contact":"COSEMData@janelia.hhmi.org","ManagedBy":"[Janelia Research Campus](https://www.janelia.org/)","UpdateFrequency":"New datasets and derived data are added as soon as they are available.","Tags":["aws-pds","cell biology","computer vision","electron microscopy","imaging","life sciences","organelle"],"License":"CC-BY-4.0","Resources":[{"Description":"Raw FIB-SEM datasets and derived data.","ARN":"arn:aws:s3:::janelia-cosem-datasets","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Machine learning models for organelle prediction.","ARN":"arn:aws:s3:::janelia-cosem-networks","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Correlative three-dimensional super-resolution and block-face electron microscopy of whole vitreously frozen cells.","URL":"https://science.sciencemag.org/content/367/6475/eaaz5357","AuthorName":"David P. Hoffman, Gleb Shtengel, C. Shan Xu, Kirby R. Campbell, Melanie Freeman, Lei Wang, Daniel E. Milkie, H. Amalia Pasolli, Nirmala Iyer, John A. Bogovic, Daniel R. Stabley, Abbas Shirinifard, Song Pang, David Peale, Kathy Schaefer, Wim Pomp, Chi-Lun Chang, Jennifer Lippincott-Schwartz, Tom Kirchhausen1, David J. Solecki, Eric Betzig, Harald F. Hess"},{"Title":"Enhanced FIB-SEM systems for large-volume 3D imaging","URL":"https://elifesciences.org/articles/25916","AuthorName":"C. Shan Xu, Kenneth J. Hayworth, Zhiyuan Lu, Patricia Grob, Ahmed M. Hassan, José G. García-Cerdán, Krishna K. Niyogi, Eva Nogales, Richard J. Weinberg, Harald F. Hess."},{"Title":"Whole-cell organelle segmentation in volume electron microscopy","URL":"https://www.nature.com/articles/s41586-021-03977-3","AuthorName":"Lisa Heinrich, Davis Bennett, David Ackerman, Woohyun Park, Jon Bogovic, Nils Eckstein, et al."}]},"RegistryEntryAdded":"2020-04-14","RegistryEntryLastModified":"2022-05-19","Slug":"janelia-cosem","Sources":["awslabs-open-data-registry"]}
{"Name":"Fly Brain Anatomy: FlyLight Gen1 and Split-GAL4 Imagery","Description":"This data set, made available by Janelia's FlyLight project, consists of fluorescence images \nof Drosophila melanogaster driver lines, aligned to standard templates, and stored in formats \nsuitable for rapid searching in the cloud. Additional data will be added as it is published. \n","Documentation":"https://open.quiltdata.com/b/janelia-flylight-imagery","Contact":"flylight@janelia.hhmi.org","ManagedBy":"[Janelia Research Campus](https://www.janelia.org)","UpdateFrequency":"As required","Tags":["aws-pds","biology","fluorescence imaging","image processing","imaging","life sciences","microscopy","neurobiology","neuroimaging","neuroscience"],"License":"[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Imagery and Metadata","ARN":"arn:aws:s3:::janelia-flylight-imagery","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Alignment Templates","ARN":"arn:aws:s3:::janelia-flylight-templates","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Color Depth MIPs","ARN":"arn:aws:s3:::janelia-flylight-color-depth","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"File Operations on AWS S3","URL":"https://github.com/JaneliaSciComp/open-data-flylight/blob/master/tutorials/File_operations_on_AWS_S3.ipynb","AuthorName":"Rob Svirskas"},{"Title":"Scaling Neuroscience Research on AWS","URL":"https://aws.amazon.com/blogs/architecture/scaling-neuroscience-research-on-aws/","AuthorName":"Konrad Rokicki","Services":["AWS Lambda","AWS AppSync","Amazon DynamoDB","AWS Step Functions"]},{"Title":"Tutorial for neuronbridger (R API)","URL":"http://natverse.org/neuronbridger/","AuthorName":"Alexander Bates"},{"Title":"Using Imagery on AWS S3","URL":"https://github.com/JaneliaSciComp/open-data-flylight/blob/master/tutorials/Using_imagery_on_AWS_S3.ipynb","AuthorName":"Rob Svirskas"}],"Tools & Applications":[{"Title":"Color Depth Search Fiji Plugin","URL":"https://github.com/JaneliaSciComp/ColorMIP_Mask_Search","AuthorName":"Hideo Otsuna"},{"Title":"Fly Light Split-GAL4 Driver Collection","URL":"http://splitgal4.janelia.org/cgi-bin/splitgal4.cgi","AuthorName":"Rob Svirskas"},{"Title":"FlyLight Project Website","URL":"https://www.janelia.org/project-team/flylight","AuthorName":"Geoffrey Meissner"},{"Title":"NeuronBridge","URL":"https://neuronbridge.janelia.org","AuthorName":"Jody Clements, Rob Svirskas, Hideo Otsuna, Cristian Goina, Konrad Rokicki"}],"Publications":[{"Title":"A GAL4-Driver Line Resource for Drosophila Neurobiology","URL":"https://doi.org/10.1016/j.celrep.2012.09.011","AuthorName":"Arnim Jenett, Gerald M Rubin, Teri-TB Ngo, David Shepherd, Christine Murphy, Heather Dionne, Barret D Pfeiffer, Amanda Cavallaro, Donald Hall, Jennifer Jeter, Nirmala Iyer, Dona Fetter, Joanna H Hausenfluck, Hanchuan Peng, Eric T Trautman, Robert R Svirskas, Eugene W Myers, Zbigniew R Iwinski, Yoshinori Aso, Gina M DePasquale, Adrianne Enos, Phuson Hulamm, Shing Chun Benny Lam, Hsing-Hsi Li, Todd R Laverty, Fuhui Long, Lei Qu, Sean D Murphy, Konrad Rokicki, Todd Safford, Kshiti Shaw, Julie H Simpson, Allison Sowell, Susana Tae, Yang Yu, Christopher T Zugates"},{"Title":"An image resource of subdivided Drosophila GAL4-driver expression patterns for neuron-level searches","URL":"https://doi.org/10.1101/2020.05.29.080473","AuthorName":"Geoffrey W Meissner, Zachary Dorman, Aljoscha Nern, Kaitlyn Forster, Theresa Gibney, Jennifer Jeter, Lauren Johnson, Yisheng He, Kelley Lee, Brian Melton, Brianna Yarbrough, Jody Clements, Cristian Goina, Hideo Otsuna, Konrad Rokicki, Robert R Svirskas, Yoshinori Aso, Gwyneth M Card, Barry J Dickson, Erica Ehrhardt, Jens Goldammer, Masayoshi Ito, Wyatt Korff, Ryo Minegishi, Shigehiro Namiki, Gerald M Rubin, Gabriella Sterne, Tanya Wolff, Oz Malkesman, FlyLight Project Team"},{"Title":"An unbiased template of the Drosophila brain and ventral nerve cord","URL":"https://doi.org/10.1101/376384","AuthorName":"John A Bogovic, Hideo Otsuna, Larissa Heinrich, Masayoshi Ito, Jennifer Jeter, Geoffrey Meissner, Aljoscha Nern, Jennifer Colonell, Oz Malkesman, Kei Ito, Stephan Saalfeld"},{"Title":"Color depth MIP mask search: a new tool to expedite Split-GAL4 creation","URL":"https://doi.org/10.1101/318006","AuthorName":"Hideo Otsuna, Masayoshi Ito, Takashi Kawase"},{"Title":"The neuronal architecture of the mushroom body provides a logic for associative learning","URL":"https://doi.org/10.7554/eLife.04577","AuthorName":"Yoshinori Aso, Daisuke Hattori, Yang Yu, Rebecca M Johnston, Nirmala A Iyer, Teri-TB Ngo, Heather Dionne, LF Abbott, Richard Axel, Hiromu Tanimoto, Gerald M Rubin"}]},"RegistryEntryAdded":"2020-03-19","RegistryEntryLastModified":"2025-02-04","Slug":"janelia-flylight","Sources":["awslabs-open-data-registry"]}
{"Name":"Mouse Brain Anatomy: MouseLight Imagery","Description":"This data set, made available by Janelia's MouseLight project, consists of \nimages and neuron annotations of the Mus musculus brain, stored in formats suitable\nfor viewing and annotation using the HortaCloud cloud-based annotation system.\n","Documentation":"https://open.quiltdata.com/b/janelia-mouselight-imagery","Contact":"hortacloud@janelia.hhmi.org","ManagedBy":"[Janelia Research Campus](https://www.janelia.org)","UpdateFrequency":"As required","Tags":["aws-pds","biology","fluorescence imaging","image processing","imaging","life sciences","microscopy","neurobiology","neuroimaging","neuroscience"],"License":"[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Imagery and Metadata","ARN":"arn:aws:s3:::janelia-mouselight-imagery","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"HortaCloud","URL":"https://hortacloud.janelia.org/","AuthorName":"David Schauder, Donald J. Olbris, Jody Clements, Cristian Goina, Robert R. Svirskas, Konrad Rokicki"},{"Title":"MouseLight NeuronBrowser","URL":"http://ml-neuronbrowser.janelia.org/","AuthorName":"Tiago A. Ferreira, Jayaram Chandrashekar"},{"Title":"MouseLight Project Website","URL":"https://www.janelia.org/project-team/mouselight","AuthorName":"Tiago A. Ferreira, Jayaram Chandrashekar"}],"Publications":[{"Title":"Reconstruction of 1,000 Projection Neurons Reveals New Cell Types and Organization of Long-Range Connectivity in the Mouse Brain","URL":"https://doi.org/10.1016/j.cell.2019.07.042","AuthorName":"Johan Winnubst, Erhan Bas, Tiago A. Ferreira, Zhuhao Wu, Michael N. Economo, Patrick Edson, Ben J. Arthur, Christopher Bruns, Konrad Rokicki, David Schauder, Donald J. Olbris, Sean D. Murphy, David G. Ackerman, Cameron Arshadi, Perry Baldwin, Regina Blake, Ahmad Elsayed, Mashtura Hasan, Daniel Ramirez, Bruno Dos Santos, Monet Weldon, Amina Zafar, Joshua T. Dudman, Charles R. Gerfen, Adam W. Hantman, Wyatt Korff, Scott M. Sternson, Nelson Spruston, Karel Svoboda, Jayaram Chandrashekar"}]},"RegistryEntryAdded":"2022-07-26","RegistryEntryLastModified":"2022-07-26","Slug":"janelia-mouselight","Sources":["awslabs-open-data-registry"]}
{"Name":"Japan Prefectures, 3D Point Cloud Data","Description":"This dataset comprises high-precision 3D point cloud data that covers all prefectures throughout Japan.\nThe data is produced through aerial laser surveys, airborne laser bathymetry, and mobile mapping systems, representing the culmination of many years of dedicated effort.\nThis data will be visualized and analyzed for use in infrastructure maintenance, disaster prevention measures, and autonomous vehicle driving.\n","Documentation":"https://github.com/aigidjp/opendata_japan_pointcloud/blob/main/README.md","Contact":"japan-pointcloud@aigid.jp","ManagedBy":"[AIGID](https://aigid.jp/)","UpdateFrequency":"Currently not scheduled","Tags":["aws-pds","disaster response","elevation","geospatial","japanese","land","lidar","mapping"],"License":"Creative Commons Attribution 4.0 International (CC-BY 4.0)","Resources":[{"Description":"Point Cloud Data for Prefectures Across Japan","ARN":"arn:aws:s3:::japan-pointcloud","Region":"ap-northeast-1","Type":"S3 Bucket"},{"Description":"Notifications for new japan-pointcloud data","ARN":"arn:aws:sns:ap-northeast-1:250546175908:japan-pointcloud-object_created","Region":"ap-northeast-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Tutorial of handling LAS format point cloud data","URL":"https://github.com/aigidjp/opendata_japan_pointcloud/blob/main/tutorials/README.md","AuthorName":"AIGID"}]},"RegistryEntryAdded":"2025-11-21","RegistryEntryLastModified":"2025-12-09","Slug":"japan_pointcloud","Sources":["awslabs-open-data-registry"]}
{"Name":"PALSAR-2 ScanSAR CARD4L (L2.2)","Description":"The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km.\n  The SAR imagery was ortho-rectificatied and slope corrected using the ALOS World 3D - 30 m (AW3D30) Digital Surface Model.\n  Polarization data are stored as 16-bit digital numbers (DN).\n  The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation:\n  γ0 = 10*log10(DN2) - 83.0 dB\n  CARD4L stands for CEOS Analysis Ready Data for Land (Level 2.2) data are ortho-rectified and radiometrically terrain-corrected.\n  This dataset is compatible with the [Committee on Earth Observation (CEOS)](https://ceos.org/) [Analysis Ready Data for LAND (CARD4L)](https://ceos.org/ard/files/PFS/NRB/v5.5/CARD4L-PFS_NRB_v5.5.pdf) standard.<br/><br/>\n  \n","Documentation":"https://www.eorc.jaxa.jp/ALOS/en/dataset/palsar2_l22_e.htm","Contact":"aproject@jaxa.jp","ManagedBy":"[JAXA](https://www.jaxa.jp/)","UpdateFrequency":"Every month after 42 days observed","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac","sustainability","synthetic aperture radar"],"License":"Data is available for free under the [terms of use](https://earth.jaxa.jp/policy/en.html).\n","Resources":[{"Description":"PALSAR-2 ScanSAR CARD4L","ARN":"arn:aws:s3:::jaxaalos2/palsar2/L2.2/Africa/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Publications":[{"Title":"ALOS series Open and Free Data","URL":"https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm","AuthorName":"JAXA EORC"}]},"RegistryEntryAdded":"2022-12-09","RegistryEntryLastModified":"2023-03-01","Slug":"jaxa-alos-palsar2-scansar","Sources":["awslabs-open-data-registry"]}
{"Name":"JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Digital Terrain Models","Description":"The Japan Aerospace EXploration Agency (JAXA) SELenological and ENgineering Explorer (SELENE) mission’s Kaguya spacecraft was launched on September 14, 2007 and science operations around the Moon started October 20, 2007. The primary mission in a circular polar orbit 100-km above the surface lasted from October 20, 2007 until October 31, 2008. An extended mission was then conducted in lower orbits (averaging 50km above the surface) from November 1, 2008 until the SELENE mission ended with Kaguya impacting the Moon on June 10, 2009. These data are digital terrain models derived using the NASA Ames Stereo Pipeline (ASP) and the Kaguya stereoscopic data. Digital terrain models (DTMs) in this data set were bundle adjusted and aligned to Lunar Orbiter Laser Altimeter (LOLA) shot data. The sensor model intrinsics used for these data have been re-estimated to reduce inter-DTM horizontal and vertical errors. Data are controlled to LOLA using the ASP pc_align program. Data co-register at orthoimage resolution (11-37 meters per pixel). At image resolution horizontal offsets are measurable. Horizontal precision is measures to be better than 30 meters per pixel on average. Vertical errors are mean centered to zero. An assessment of overlapping DTMs showed vertical precision on the order of 4 meters. Spacecraft jitter and unmodelled lense distortion at the observation edges is believed to be the major contributor to the vertical errors. To create these analysis ready data, we have taken the ASP genreated data, map projected the data to a stereopair centered orthographic projection and converted to a Cloud Optimized GeoTiff (COG) for online streaming. These data use a priori spacecraft ephemerides (for the nominal mission) and improved, but not controlled spacecraft ephemerides for the extended mission. These data co-register with other LOLA controlled data to the aforementioned horizontal and vertical accuracies.\n","Documentation":"https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/","Contact":"https://answers.usgs.gov/","ManagedBy":"[NASA](https://www.nasa.gov)","UpdateFrequency":"The Kaguya/SELENE mission has completed. At least one update to this dataset is planned to address identified issues with the nominal swath width evening observations.","Tags":["aws-pds","cog","elevation","planetary","stac"],"License":"[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)","Citation":"https://doi.org/10.5066/P13D7VMG","Resources":[{"Description":"Digital terrain models, orthoimages, shaded reliefs, and quality assurance documents","ARN":"arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/usgs_dtms/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_usgs_dtms)"]}],"DataAtWork":{"Tutorials":[{"Title":"Discovering and Downloading Data via the Command Line","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/cli/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Discovering and Downloading Data with Python","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Querying for Data in an ROI and Loading it into QGIS","URL":"https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"}],"Tools & Applications":[{"Title":"PySTAC Client","URL":"https://github.com/stac-utils/pystac-client","AuthorName":"PySTAC-Client Contributors","AuthorURL":"https://github.com/stac-utils/pystac-client/graphs/contributors"}]},"RegistryEntryAdded":"2024-07-19","RegistryEntryLastModified":"2024-07-19","Slug":"jaxa-usgs-nasa-kaguya-tc-dtms","Sources":["awslabs-open-data-registry"]}
{"Name":"JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations","Description":"The Japan Aerospace EXploration Agency (JAXA) SELenological and ENgineering Explorer (SELENE) mission’s Kaguya spacecraft was launched on September 14, 2007 and science operations around the Moon started October 20, 2007. The primary mission in a circular polar orbit 100-km above the surface lasted from October 20, 2007 until October 31, 2008. An extended mission was then conducted in lower orbits (averaging 50km above the surface) from November 1, 2008 until the SELENE mission ended with Kaguya impacting the Moon on June 10, 2009. These data were collected in monoscopic observing mode. To create these analysis ready data, we have taken the JAXA Data ARchives and Transmission System (DARTS) archived data, map projected the data to equirectangular or polar stereographic (pole centered) based on the center latitude of the observation, and converted to a Cloud Optimized GeoTiff (COG) for online streaming. These data use a priori spacecraft ephemerides (for the nominal mission) and improved, but not controlled spacecraft ephemerides for the extended mission. Therefore, these uncontrolled data are not guaranteed to co-register with other data sets.\n","Documentation":"https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/","Contact":"https://answers.usgs.gov/","ManagedBy":"[NASA](https://www.nasa.gov)","UpdateFrequency":"The Kaguya/SELENE mission has completed. No updates to this dataset are planned.","Tags":["aws-pds","cog","planetary","satellite imagery","stac"],"License":"[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)","Citation":"https://doi.org/10.5066/P9SH5YNV","Resources":[{"Description":"Scenes and metadata for monoscopic observing mode","ARN":"arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/monoscopic/uncontrolled/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_monoscopic_uncontrolled_observations)"]},{"Description":"Scenes and metadata for stereoscopic observing mode","ARN":"arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/stereoscopic/uncontrolled/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_stereoscopic_uncontrolled_observations)"]},{"Description":"Scenes and metadata for spectral profiler (spsupport) observing mode","ARN":"arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/spsupport/uncontrolled/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_spsupport_uncontrolled_observations)"]}],"DataAtWork":{"Tutorials":[{"Title":"Discovering and Downloading Data via the Command Line","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/cli/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Discovering and Downloading Data with Python","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Querying for Data in an ROI and Loading it into QGIS","URL":"https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"}],"Tools & Applications":[{"Title":"PySTAC Client","URL":"https://github.com/stac-utils/pystac-client","AuthorName":"PySTAC-Client Contributors","AuthorURL":"https://github.com/stac-utils/pystac-client/graphs/contributors"}]},"RegistryEntryAdded":"2023-09-14","RegistryEntryLastModified":"2024-07-19","Slug":"jaxa-usgs-nasa-kaguya-tc","Sources":["awslabs-open-data-registry"]}
{"Name":"Cloud Indexes for Bowtie, Kraken, HISAT, and Centrifuge","Description":"Genomic tools use reference databases as indexes to operate quickly and efficiently, analogous to how web search engines use indexes for fast querying. Here, we aggregate genomic, pan-genomic and metagenomic indexes for analysis of sequencing data.","Documentation":"https://benlangmead.github.io/aws-indexes/","Contact":"https://github.com/BenLangmead/aws-indexes/issues","ManagedBy":"Langmead Lab at Johns Hopkins University & Kim Lab at University of Texas Southwestern","UpdateFrequency":"As new data becomes available; roughly quarterly","Tags":["aws-pds","bioinformatics","biology","genomic","life sciences","mapping","medicine","reference index","whole genome sequencing"],"License":"Public Domain","Resources":[{"Description":"This bucket contains genomic indexes for Bowtie, Kraken, HISAT, and Centrifuge.","ARN":"arn:aws:s3:::genome-idx","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Table of contents for tutorials for constituent tools","URL":"https://benlangmead.github.io/aws-indexes/tutorial","AuthorName":"Ben Langmead","AuthorURL":"https://benlangmead.github.io/aws-indexes"}],"Publications":[{"Title":"Reducing reference bias using multiple population reference genomes","URL":"https://www.biorxiv.org/content/10.1101/2020.03.03.975219v4.full.pdf","AuthorName":"Chen et al (2020)"}]},"RegistryEntryAdded":"2020-06-26","RegistryEntryLastModified":"2024-12-20","Slug":"jhu-indexes","Sources":["awslabs-open-data-registry"]}
{"Name":"Indexes for Kaiju","Description":"This dataset comprises pre-built indexes for the bioinformatics software Kaiju, which is used for taxonomic classification of metagenomic sequencing data. Various indexes for different source reference databases are available.","Documentation":"https://bioinformatics-centre.github.io/kaiju/downloads.html","Contact":"https://github.com/bioinformatics-centre/kaiju/issues","ManagedBy":"[Peter Menzel](https://github.com/pmenzel)","UpdateFrequency":"roughly yearly","Tags":["aws-pds","bioinformatics","biology","genomic","life sciences","metagenomics","microbiome","reference index","whole genome sequencing"],"License":"Public Domain","Resources":[{"Description":"This AWS S3 bucket contains pre-built indexes for Kaiju.","ARN":"arn:aws:s3:::kaiju-idx","Region":"eu-central-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Quickstart Tutorial for downloading the index files and running Kaiju.","URL":"https://github.com/bioinformatics-centre/kaiju/blob/master/Quickstart.md","AuthorName":"Peter Menzel","AuthorURL":"https://github.com/pmenzel"}],"Publications":[{"Title":"Fast and sensitive taxonomic classification for metagenomics with Kaiju","URL":"https://www.nature.com/articles/ncomms11257","AuthorName":"Peter Menzel et al (2016)"}]},"RegistryEntryAdded":"2023-06-27","RegistryEntryLastModified":"2023-06-27","Slug":"kaiju-indexes","Sources":["awslabs-open-data-registry"]}
{"Name":"Kanagawa, 3D Point Cloud Data","Description":"This dataset comprises high-precision 3D point cloud data that encompasses the entire Kanagawa prefecture in Japan.\nThe data is produced through aerial laser survey, airborne laser bathymetry and mobile mapping systems, the culmination of many years of dedicated effort.\nThis data will be visualized and analyzed for use in infrastructure maintenance, disaster prevention measures and autonomous vehicle driving.\n","Documentation":"https://github.com/aigidjp/opendata_kanagawa_pointcloud/blob/main/README.md","Contact":"kanagawa-pointcloud@aigid.jp","ManagedBy":"[AIGID](https://aigid.jp/)","UpdateFrequency":"Currently not scheduled","Tags":["aws-pds","disaster response","elevation","geospatial","japanese","land","lidar","mapping"],"License":"Creative Commons Attribution 4.0 International (CC-BY 4.0)","Resources":[{"Description":"Point Cloud Data of Kanagawa Prefecture, Japan","ARN":"arn:aws:s3:::kanagawa-pointcloud","Region":"ap-northeast-1","Type":"S3 Bucket"},{"Description":"Notifications for new kanagawa-pointcloud data","ARN":"arn:aws:sns:ap-northeast-1:895319340027:kanagawa-pointcloud_created","Region":"ap-northeast-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Tutorial of handling LAS format point cloud data","URL":"https://github.com/aigidjp/opendata_kanagawa_pointcloud/blob/main/tutorials/README.md","AuthorName":"AIGID"}]},"RegistryEntryAdded":"2025-11-12","RegistryEntryLastModified":"2025-12-09","Slug":"kanagawa_pointcloud","Sources":["awslabs-open-data-registry"]}
{"Name":"Kardashev Open U.S. Grid Dataset","Description":"Harmonized, cloud-optimized U.S. power grid data derived from public ISO, RTO, and federal\nsources. Kardashev Labs normalizes interconnection queues, ERCOT GIS filing history,\nERCOT large-load queue observations, locational marginal prices, fuel mix, grid demand,\nrenewable curtailment, carbon intensity, and related reference tables into Parquet with\ndocumented schemas and source provenance.\n\nThe dataset is designed for researchers, journalists, and energy analysts who need\nmachine-readable history across U.S. electricity markets without building their own\ningest pipelines. Kardashev-produced schemas, metadata, and derived tables are openly\nlicensed. Upstream source terms are documented per dataset; bulk republication of\nrestricted third-party feeds is excluded from the open release.\n\nLive API and tools: https://data.kardashevlabs.org\n","Documentation":"https://github.com/kardashev-lab/kardashev-data/blob/main/docs/open-data-dataset.md","Contact":"ashutosh@kardashevlabs.org","ManagedBy":"[Kardashev Labs](https://kardashevlabs.org)","UpdateFrequency":"Daily to monthly, depending on source cadence","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["aws-pds","electricity","energy","environmental","geospatial","sustainability"],"License":"[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Kardashev Open U.S. Grid Dataset (Parquet, JSON schema, README)","ARN":"arn:aws:s3:::kardashev-open-us-grid","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://kardashev-open-us-grid.s3.us-west-2.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Exploring the ERCOT Interconnection Queue in a Browser-Based Notebook on Scigantic","URL":"https://scigantic.com/archive/e4a3676d-02cb-54e3-96d5-a115ab9aa7f2/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Get To Know A Dataset: Kardashev Open U.S. Grid Dataset","URL":"https://github.com/kardashev-lab/open-data-examples/blob/main/kardashev-open-us-grid-dataset/get-to-know-a-dataset.ipynb","AuthorName":"Ashutosh Mathore","AuthorURL":"https://kardashevlabs.org","Services":["Amazon S3"]}],"Tools & Applications":[{"Title":"ERCOT Large Load Tracker","URL":"https://large-load-tracker.kardashevlabs.org","AuthorName":"Kardashev Labs","AuthorURL":"https://kardashevlabs.org"},{"Title":"Interconnection Queue Tracker","URL":"https://interconnection-queue.kardashevlabs.org","AuthorName":"Kardashev Labs","AuthorURL":"https://kardashevlabs.org"},{"Title":"Kardashev Labs data API","URL":"https://data.kardashevlabs.org/docs","AuthorName":"Kardashev Labs","AuthorURL":"https://kardashevlabs.org"},{"Title":"kardashev Python package","URL":"https://github.com/kardashev-lab/kardashev-py","AuthorName":"Kardashev Labs","AuthorURL":"https://kardashevlabs.org"}]},"RegistryEntryAdded":"2026-09-10","RegistryEntryLastModified":"2026-10-14","Slug":"kardashev-open-us-grid-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"Gabriella Miller Kids First Pediatric Research Program (Kids First)","Description":"The NIH Common Fund's Gabriella Miller Kids First Pediatric Research Program’s (“Kids First”) vision is to “alleviate suffering from childhood cancer and structural birth defects by fostering collaborative research to uncover the etiology of these diseases and by supporting data sharing within the pediatric research community.” The program continues to generate and share whole genome sequence data from thousands of children affected by these conditions, ranging from rare pediatric cancers, such as osteosarcoma, to more prevalent diagnoses, such as congenital heart defects. In 2018, Kids First launched the Gabriella Miller Kids First Data Resource Center, charged with building a large-scale data platform supporting clinical and genetic data from these patients and their families in order to accelerate discovery and ultimately clinical impact. Researchers can search, access, aggregate, and analyze these data through the Kids First Data Resource Portal. Additionally, by using cloud-based individual workspaces in CAVATICA, a data analysis and sharing computation platform, researchers can cross-analyze Kids First data with data from other efforts, such as NCI’s TARGET program and consortia-based datasets like the Children’s Brain Tumor Tissue Consortium (CBTTC).\nKids First is made available on AWS via the [NIH STRIDES Initiative](https://aws.amazon.com/blogs/publicsector/aws-and-national-institutes-of-health-collaborate-to-accelerate-discoveries-with-strides-initiative/).\n","Documentation":"https://kidsfirstdrc.org/","Contact":"support@kidsfirstdrc.org","ManagedBy":"[The Gabriella Miller Kids First Data Resource Center at the Children's Hospital of Philadelphia](https://kidsfirstdrc.org/)","UpdateFrequency":"Data is updated on a rolling basis by the KFDRC to make data available as\nrapidly as possible under the NIH Genomic Data Sharing Policy. \n","Tags":["aws-pds","cancer","genetic","genomic","Homo sapiens","life sciences","pediatric","STRIDES","structural birth defect","whole genome sequencing"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"Kids First: Pediatric Research Project on the Genomic Analysis of Congenital Diaphragmatic Hernia","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-46sk55a3","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001110.v2.p1"},{"Description":"National Heart, Lung, and Blood Institute (NHLBI) Bench to Bassinet Program: The Gabriella Miller Kids First Pediatric Research Program of the Pediatric Cardiac Genetics Consortium (PCGC)","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-preasa7s","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001138.v3.p2"},{"Description":"Gabriella Miller Kids First (GMKF) Pediatric Research Program in Susceptibility to Ewing Sarcoma Based on Germline Risk and Familial History of Cancer","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-ygva0e1c","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001228"},{"Description":"Kids First: Genomic Studies of Orofacial Cleft Birth Defects","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-9pyzahhe","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001168.v2.p2"},{"Description":"GMKF: Kids First Pediatric Research Program on Congenital Cranial Dysinnervation Disorders and Related Birth Defects","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-dztb5hrr","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001247.v1.p1"},{"Description":"Kids First: Pediatric Research Project on Adolescent Idiopathic Scoliosis","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-rm8afw0r","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001410.v1.p1"},{"Description":"Disorders/Differences of Sex Development (DSD) Study Performed at UCLA in Collaboration with the DSD-Translational Research Network (DSD-TRN), with the Support of the Gabriella Miller Kids First Pediatric Research Program","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-6fpyjqbr","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001178.v1.p1"},{"Description":"Kids First: Genomics of Orofacial Cleft Birth Defects in Latin American Families","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-r0eprsgs","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001420.v1.p1"},{"Description":"Discovering the Genetic Basis of Human Neuroblastoma: A Gabriella Miller Kids First Pediatric Research Program (Kids First) Project","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-dypmehhf","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001436.v1.p1"},{"Description":"Gabriella Miller Kids First Pediatric Research Program in Enchondromatoses and Related Malignant Tumors","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-7nq9151j","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001987.v1.p1"},{"Description":"Kids First Pediatric Research Study in Familial Predisposition to Hematopoietic Malignancies (SJFAMILY-HM)","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-vttshwv4","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001738.v1.p1"},{"Description":"Gabriella Miller Kids First Pediatric Research Program in Whole Genome Sequencing of African and Asian Orofacial Clefts Case-Parent Triads","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-dk0krwk8","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001997.v1.p1"},{"Description":"Kids First: Whole Genome Sequencing of Nonsyndromic Craniosynostosis","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-p445achv","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001806.v1.p1"},{"Description":"Gabriella Miller Kids First Pediatric Research Program in Germline and Somatic Variants in Myeloid Malignancies in Children","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-pet7q6f2","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002187.v1.p1"},{"Description":"Kids First and INCLUDE: Down Syndrome, Heart Defects, and Acute Lymphoblastic Leukemia","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-z6mwd3h0","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002330.v1.p1"},{"Description":"Kids First: Genetics of Kidney and Urinary Tract Malformations","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-nmvv8a1y","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002162.v2.p1"},{"Description":"Gabriella Miller Kids First Pediatric Research Program in Genetics at the Intersection of Childhood Cancer and Birth Defects","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-jws3v24d","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001846.v1.p1"},{"Description":"Pediatric Brain Tumor Atlas: PNOC","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-m3dbxd12","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002517.v1.p1"},{"Description":"Kids First Pediatric Research Study in Familial Predisposition to Hematopoietic Malignancies (SJFAMILY-HM)","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-w0v965xz","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001738.v1.p1"},{"Description":"Gabriella Miller Kids First Pediatric Research Program: An Integrated Clinical and Genomic Analysis of Treatment Failure in Pediatric Osteosarcoma","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-zxjffmef","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001714.v1.p1"},{"Description":"Chordoma Foundation Dataset","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-7spqtt8m","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://cavatica.sbgenomics.com/p/datasets#cavatica/cbttc-cdhm-pa-01"},{"Description":"Clinical Sequencing Exploratory Research Consortium: Incorporation of Genomic Sequencing into Pediatric Cancer Care","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-zfgdg5ys","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001683.v2.p1"},{"Description":"Kids First: Genomic Analysis of Esophageal Atresia and Tracheoesophageal Fistulas and Associated Congenital Anomalies","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-0tyvy1tw","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002161.v1.p1"},{"Description":"Gabriella Miller Kids First Pediatric Research Program in Bladder Exstrophy, Epispadias, Complex (BEEC)","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-z0d9n23x","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002173.v1.p1"},{"Description":"Children's Brain Tumor Network","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-bhjxbdqk","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002517.v1.p1"},{"Description":"Kids First: Structural Defects of The Neural Tube","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-15a2mqq9","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002591.v1.p1"},{"Description":"Gabriella Miller Kids First Pediatric Research Project in Cornelia de Lange Syndrome, Related Diagnosis and Structural Birth Defects","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-2cekq05v","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002174.v1.p1"},{"Description":"Gabriella Miller Kids First Pediatric Research Program in Pediatric T-cell Acute Lymphoblastic Leukemia","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-aq9kvn5p","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002276.v2.p1"},{"Description":"Gabriella Miller Kids First Pediatric Research Program in Craniofacial Microsomia","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-b8x3c1mx","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002130.v1.p1"},{"Description":"Gabriella Miller Kids First Pediatric Research Project in Microtia in Hispanic Populations","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-dz4gpqx6","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002172.v1.p1"},{"Description":"The Collaboration to Assess Risk and Identify LoNG-term outcomes for Children with COVID (CARING for Children with COVID) - NICHD-2019-POP02: Pharmacokinetics, Pharmacodynamics, and Safety Profile of Understudied Drugs Administered to Children per Standard of Care","ARN":"arn:aws:s3:::kf-study-us-east-1-prd-sd-ffvq3t38","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002577.v1.p1"}],"DataAtWork":{"Tools & Applications":[{"Title":"CAVATICA","URL":"http://cavatica.org","AuthorName":"Seven Bridges Genomics","AuthorURL":"http://www.sevenbridges.com"},{"Title":"Kids First DRC Portal","URL":"https://portal.kidsfirstdrc.org","AuthorName":"Kids First DRC","AuthorURL":"https://kidsfirstdrc.org"},{"Title":"Kids First DRC Source Code","URL":"https://github.com/kids-first/","AuthorName":"Kids First DRC","AuthorURL":"https://kidsfirstdrc.org"},{"Title":"PedcBioPortal","URL":"https://pedcbioportal.kidsfirstdrc.org","AuthorName":"cBioPortal","AuthorURL":"https://www.cbioportal.org/"}],"Publications":[{"Title":"Clinically Relevant and Minimally Invasive Tumor Surveillance of Pediatric Diffuse Midline Gliomas Using Patient-Derived Liquid Biopsy","URL":"https://pubmed.ncbi.nlm.nih.gov/30322880/","AuthorName":"Eshini Panditharatna, Lindsay B Kilburn, et al."},{"Title":"Decreased ACKR3 (CXCR7) function causes oculomotor synkinesis in mice and humans.","URL":"http://www.ncbi.nlm.nih.gov/pubmed/31211835","AuthorName":"Mary C Whitman, Noriko Miyake, et al."},{"Title":"Deleterious de novo variants of X-linked ZC4H2 in females cause a variable phenotype with neurogenic arthrogryposis multiplex congenita.","URL":"http://www.ncbi.nlm.nih.gov/pubmed/31206972","AuthorName":"Suzanna G M Frints, Friederike Hennig, et al."},{"Title":"Development and Clinical Validation of a Large Fusion Gene Panel for Pediatric Cancers.","URL":"http://www.ncbi.nlm.nih.gov/pubmed/31255796","AuthorName":"Fengqi Chang, Fumin Lin, et al."},{"Title":"Elucidation of de novo small insertion/deletion biology with parent-of-origin phasing.","URL":"http://www.ncbi.nlm.nih.gov/pubmed/31898844","AuthorName":"Allison H Seiden, Felix Richter, et al."},{"Title":"Genome-Wide Association Study Identifies a Susceptibility Locus for Comitant Esotropia and Suggests a Parent-of-Origin Effect","URL":"https://pubmed.ncbi.nlm.nih.gov/30098192/","AuthorName":"Sherin Shaaban, Sarah MacKinnon et al."},{"Title":"Genome-wide Enrichment of De Novo Coding Mutations in Orofacial Cleft Trios.","URL":"https://pubmed.ncbi.nlm.nih.gov/32574564/","AuthorName":"Madison R Bishop, Kimberly K Diaz Perez, et al."},{"Title":"Genomic Analyses Implicate Noncoding De Novo Variants in Congenital Heart Disease.","URL":"https://pubmed.ncbi.nlm.nih.gov/32601476/","AuthorName":"Felix Richter, Sarah U Morton, et al."},{"Title":"Germline 16p11.2 Microdeletion Predisposes to Neuroblastoma.","URL":"http://www.ncbi.nlm.nih.gov/pubmed/31474320","AuthorName":"Laura Egolf, Zalman Vaksman, et al."},{"Title":"Germline microsatellite genotypes differentiate children with medulloblastoma.","URL":"http://www.ncbi.nlm.nih.gov/pubmed/31562520","AuthorName":"Samuel Rivero-Hinojosa, Nicholas Kinney, et al."},{"Title":"MACF1 Mutations Encoding Highly Conserved Zinc-Binding Residues of the GAR Domain Cause Defects in Neuronal Migration and Axon Guidance","URL":"https://pubmed.ncbi.nlm.nih.gov/30471716/","AuthorName":"William B Dobyns, Kimberly A Aldinger, et al."},{"Title":"MAGEL2-Related Disorders: A study and case series.","URL":"http://www.ncbi.nlm.nih.gov/pubmed/31397880","AuthorName":"Jameson Patak, James Gilfert, et al."},{"Title":"Phenotype delineation of ZNF462 related syndrome.","URL":"http://www.ncbi.nlm.nih.gov/pubmed/31361404","AuthorName":"Paul Kruszka, Tommy Hu, et al."},{"Title":"The Pediatric Cell Atlas: Defining the Growth Phase of Human Development at Single-Cell Resolution.","URL":"http://www.ncbi.nlm.nih.gov/pubmed/30930166","AuthorName":"Deanne M Taylor, Bruce J Aronow, et al."},{"Title":"Whole genome sequencing of orofacial cleft trios from the Gabriella Miller Kids First Pediatric Research Consortium identifies a new locus on chromosome 21.","URL":"http://www.ncbi.nlm.nih.gov/pubmed/31848685","AuthorName":"Nandita Mukhopadhyay, Madison Bishop, et al."}]},"RegistryEntryAdded":"2020-07-06","RegistryEntryLastModified":"2023-03-19","Slug":"kids-first","Sources":["awslabs-open-data-registry"]}
{"Name":"Kiel Cardio Database","Description":"The Kiel Cardio Database, which includes magnetocardiography (MCG) measurements of seven healthy male volunteers, is presented in this publication. QuSpin QZFM Gen 1 sensors were used in a magnetically shielded chamber at the University of Kiel. Each subject underwent 25 one-minute measurements, resulting in magnetic field recordings at 200 different positions for each subject. The dataset is intended to facilitate MCG research by making this expensive method accessible to researchers without the necessary equipment. Future versions of the database will expand its utility for cardiac research and arrhythmogenic tissue localization by incorporating electrocardiography (ECG), magneto-tomographic imaging (MRI), and annotation data.","Documentation":"https://doi.org/10.13026/q6r2-zz68","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution-ShareAlike 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/q6r2-zz68","ARN":"arn:aws:s3:::physionet-open/kiel-cardio/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"kiel-cardio","Sources":["awslabs-open-data-registry"]}
{"Name":"KINECAL","Description":"The field of human action recognition has made great strides in recent years, much helped by the availability of a wide variety of datasets that use Kinect to record human movement. Conversely, progress towards using Kinect in clinical practice has been hampered by the lack of appropriate data. In particular, datasets that contain clinically significant movements and appropriate metadata. This paper proposes a dataset to address this issue, namely KINECAL. It contains the recordings of 90 individuals carrying out 11 movements, commonly used in the clinical assessment of balance. The dataset contains relevant metadata, including clinical labelling, falls history and postural sway metrics. In addition, the recordings were made in both the lab and in informal settings.","Documentation":"https://doi.org/10.13026/vvkp-ct80","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Zero 1.0 Universal Public Domain Dedication","Resources":[{"Description":"https://doi.org/10.13026/vvkp-ct80","ARN":"arn:aws:s3:::physionet-open/kinecal/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"kinecal","Sources":["awslabs-open-data-registry"]}
{"Name":"KITTI Vision Benchmark Suite","Description":"Dataset and benchmarks for computer vision research in the context of autonomous driving. The dataset has been recorded in and around the city of Karlsruhe, Germany using the mobile platform AnnieWay (VW station wagon) which has been equipped with several RGB and monochrome cameras, a Velodyne HDL 64 laser scanner as well as an accurate RTK corrected GPS/IMU localization unit. The dataset has been created for computer vision and machine learning research on stereo, optical flow, visual odometry, semantic segmentation, semantic instance segmentation, road segmentation, single image depth prediction, depth map completion, 2D and 3D object detection and object tracking. In addition, several raw data recordings are provided. The datasets are captured by driving around the mid-size city of Karlsruhe, in rural areas and on highways. Up to 15 cars and 30 pedestrians are visible per image.","Documentation":"http://www.cvlibs.net/datasets/kitti/","Contact":"http://www.cvlibs.net/people.php","UpdateFrequency":"Not updated","ManagedBy":"[Max Planck Campus Tübingen](http://tue.mpg.de/)","Tags":["autonomous vehicles","aws-pds","computer vision","deep learning","machine learning","robotics"],"License":"Creative Commons Attribution-NonCommercial-ShareAlike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/","Resources":[{"Description":"http://www.cvlibs.net/datasets/kitti/raw_data.php","ARN":"arn:aws:s3:::avg-kitti","Region":"eu-central-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2018-10-02","RegistryEntryLastModified":"2022-06-14","Slug":"kitti","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"Klarna has deprecated the product page dataset. The dataset is no longer available for download.","Name":"The Klarna Product-Page Dataset","Description":"A collection of 51,701 product pages from 8175 e-commerce websites across 8 markets (US, GB, SE, NL, FI, NO, DE, AT) with 5 manually labelled elements, specifically, the product price, name and image, add-to-cart and go-to-cart buttons.\nThe dataset was collected between 2018 and 2019 and is made available has MHTML and as WebTraversalLibrary-format snapshots.\n","Documentation":"https://github.com/klarna/product-page-dataset","Contact":"https://github.com/klarna/product-page-dataset/issues, stefan.magureanu@klarna.com, riccardo.risuleo@klarna.com, alexandra.hotti@klarna.com","ManagedBy":"Web Automation Research, Klarna","UpdateFrequency":"The dataset is not expected to update frequently.","Tags":["aws-pds","internet","natural language processing","computer vision","commerce","deep learning","machine learning","information retrieval","graph"],"License":"CC BY-NC-SA","Resources":[{"Description":"Bucket containing the two datasets (one in the MHTML and one in the WTL snapshot formats) as tar-balls.","ARN":"arn:aws:s3:::klarna-research-public-datasets/","Region":"eu-west-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2021-10-20","RegistryEntryLastModified":"2025-04-11","Slug":"klarna_productpage_dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"Kraken2 NCBI RefSeq Complete V205 database on AWS","Description":"Database for use with Kraken2 (taxonomic annotation of metagenomic sequencing reads) including all NCBI RefSeq genomes available in release V205","Documentation":"https://github.com/R-Wright-1/kraken_metaphlan_comparison/wiki","Contact":"robyn.wright@dal.ca","ManagedBy":"Robyn Wright","UpdateFrequency":"This database is currently what was published in our 2023 paper comparing the performance of MetaPhlAn3 and Kraken2, but an updated database may be added in the future.","Tags":["aws-pds","benchmark","bioinformatics","life sciences","metagenomics","microbiome"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"Kraken2 NCBI RefSeq Complete V205 database","ARN":"arn:aws:s3:::kraken2-ncbi-refseq-complete-v205","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://kraken2-ncbi-refseq-complete-v205.s3.amazonaws.com/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Using an Amazon Machine Image for analysing samples with Kraken2","URL":"https://github.com/R-Wright-1/kraken_metaphlan_comparison/wiki/Using-an-Amazon-Machine-Image-for-analysing-samples-with-Kraken2","Services":["Amazon EC2"],"AuthorName":"Robyn Wright","AuthorURL":"https://github.com/R-Wright-1"}],"Tools & Applications":[{"Title":"Kraken2","URL":"https://github.com/DerrickWood/kraken2/wiki/Manual","AuthorName":"Derrick Wood, Jennifer Lu and Ben Langmead","AuthorURL":"https://github.com/DerrickWood/"}],"Publications":[{"Title":"From defaults to databases: parameter and database choice dramatically impact the performance of metagenomic taxonomic classification tools","URL":"https://doi.org/10.1099/mgen.0.000949","AuthorName":"Robyn J. Wright, Andre M. Comeau and Morgan G.I. Langille"}]},"RegistryEntryAdded":"2023-08-31","RegistryEntryLastModified":"2025-02-04","Slug":"kraken2-ncbi-refseq-complete-v205","Sources":["awslabs-open-data-registry"]}
{"Name":"Reference Indexes for krepp","Description":"krepp is an alignment-free method for estimating distances and phylogenetic placement of individual reads to many thousands of reference genomes in a scalable manner using k-mers. This dataset includes k-mer-based indexes consisting of ultra-large reference genome sets that can be efficiently analyzed using krepp.","Documentation":"https://github.com/bo1929/krepp/wiki/Available-reference-indexes","Contact":"https://github.com/bo1929/krepp/issues","ManagedBy":"Mirarab Lab at UC San Diego","UpdateFrequency":"Quarterly or as new data becomes available","Tags":["aws-pds","bioinformatics","life sciences","metagenomics","microbiome","reference index"],"License":"GPL-3.0 license. Use of the data should be cited in the usual way, following https://github.com/bo1929/krepp/tree/master?tab=readme-ov-file#citation.","Resources":[{"Description":"This dataset contains genomic indexes for various reference datasets in binary format. Using krepp, you can perform distance estimation and phylogenetic placement with respect to these indexes.","ARN":"arn:aws:s3:::kreppref","Region":"us-west-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Tutorial for using krepp indexes for metagenomic sequence analysis.","URL":"https://github.com/bo1929/krepp/wiki/Tutorial","AuthorName":"Ali Osman Berk Sapci","AuthorURL":"https://bo1929.github.io/"}],"Publications":[{"Title":"A k-mer-based maximum likelihood method for estimating distances of reads to genomes enables genome-wide phylogenetic placement.","URL":"https://www.biorxiv.org/content/10.1101/2025.01.20.633730v2","AuthorName":"Sapci et al. (2024)"}]},"RegistryEntryAdded":"2025-10-30","RegistryEntryLastModified":"2025-10-30","Slug":"kreppref","Sources":["awslabs-open-data-registry"]}
{"Name":"KyFromAbove on AWS","Description":"The KyFromAbove initiative is focused on building and maintaining a current basemap for Kentucky that can meet the needs of its users at the state, federal, local, and regional level. A common basemap, including current color leaf-off aerial photography and elevation data (LiDAR), reduces the cost of developing GIS applications, promotes data sharing, and add efficiencies to many business processes. All basemap data acquired through this effort is made available in the public domain. KyFromAbove acquires aerial imagery and LiDAR during leaf-off conditions in the Commonwealth. The imagery typically ranges from 6-inches to 3-inches in resolution and is available from the kyfromabove Amazon S3 bucket in a Cloud Optimized GeoTiff format. LiDAR data acquired by the program is USGS Quality Level 2 (QL2). Digital Elevation Models (DEMs) at a 2-foot resolution, point cloud data in an LAS format, spot elevations in a geopackage format, and contours in a geopackage format are also available from the kyfromabove Amazon S3 bucket. KyFromAbove LiDAR and imagery data resources are managed in a Kentucky-specific 5000x5000 foot grid (FIPS:1600) (EPSG:3089). More details about the program can be found at (https://kyfromabove.ky.gov/).","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove","Contact":"More information regarding the KyFromAbove program can be found at https://kyfromabove.ky.gov. If you have specific questions please contact - kyfromabove@ky.gov.","ManagedBy":"[Kentucky Division of Geographic Information](https://kygeonet.ky.gov)","UpdateFrequency":"KyFromAbove data are typically updated on an annual basis. Each year, a portion of the state is acquired with an overall update cycle of every three to four years. This update cadance is determined by both funding and the length of leaf-off conditions in a given year. This catalog currently includes imagery and LiDAR data from 2010 through 2025 for most products.","Collabs":{"ASDI":{"Tags":["elevation"]}},"Tags":["aerial imagery","aws-pds","cog","disaster response","dtm","earth observation","elevation","geopackage","geospatial","lidar","mapping","stac","tiff","tiles"],"License":"Public Domain with Attribution\n","Resources":[{"Description":"Elevation and imagery data resources for the Commonwealth of Kentucky are organized in this bucket. Elevation data are available in Cloud Optimized GeoTIFF (COG) and Geopackage formats depending on the data type. Imagery data is also available in COG format. A Cloud Optimized GeoTIFF (COG) is a GeoTIFF file optimized for hosting on a HTTP file server. COG has an internal organization that enables more efficient workflows on the cloud by supporting HTTP GET range requests, where just parts of a file are requested and returned.","ARN":"arn:aws:s3:::kyfromabove","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html)","[StacAPI Endpoint](https://spved5ihrl.execute-api.us-west-2.amazonaws.com/)","[Catalog Explorer](https://kygeonet.ky.gov/catalogexplorer)"]},{"Description":"KyFromAbove Topographic Contours, digital elevation models, point cloud, spot elevations and the KyTopo Map Series quadrangles can be found in this bucket.","ARN":"arn:aws:s3:::kyfromabove/elevation/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/)"]},{"Description":"Topographic contours created from the KyFromAbove Phase 1 LiDAR-derived digital elevation model (DEM) in geopackage and Esri file geodatabase format. There are four data resources in this folder - 1) KyTopo contours at a 10-foot interval primarily for Western and Central Kentucky, 2) KyTopo contours at a 20-foot interval primarily for Central and Eastern Kentucky, 3) KyTopo contours at a 40-foot interval for Eastern Kentucky, and 4) KyTopo contours at a 5-foot interval for the entire Commonwealth.","ARN":"arn:aws:s3:::kyfromabove/elevation/Contours/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/Contours/)"]},{"Description":"LiDAR-derived digital elevation models (DEM) for the Commonwealth of Kentucky organized in a 5000x5000 foot grid. There are currently three data resources in this folder - 1) Phase 1 LiDAR-derived DEMs at a 5 foot resolution, 2) Phase 2 LiDAR-derived DEMs at a 2 foot resolution, and 3) Phase 3 LiDAR-derived DEMs at a 2 foot resolution. All data has been converted to a Cloud Optimized GeoTIFF (COG) format. Phase 3 efforts are still underway.","ARN":"arn:aws:s3:::kyfromabove/elevation/DEM/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/DEM/)"]},{"Description":"There are three data resources in this folder - 1) KyTopo Map Series quadrangles in a COG format, 2) KyTopo Map Series quadrangles with all collar information in a non-georeferenced PNG format for printing on a standard ARCH-D sized sheet, and 3) the KyTopo Map Series quadrangles tile grid in a geopackage format. The COGs were created using GDAL with JPEG compression at a 90% quality setting and the default 512x512 tile setting.","ARN":"arn:aws:s3:::kyfromabove/elevation/KyTopoMapSeries/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/KyTopoMapSeries/)"]},{"Description":"LiDAR-derived Point Cloud tiles for the Commonwealth of Kentucky organized in a 5000x5000 foot grid. There  currently two complete resources in this folder -  1) Phase 1 LiDAR-derived point clouds in LAZ format and 2) Phase 2 LiDAR-derived Point Clouds in COPC format. Phase 3 is partially availabe in COPC format while efforts are still ongoing.","ARN":"arn:aws:s3:::kyfromabove/elevation/PointCloud/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/PointCloud/)"]},{"Description":"The data in this bucket includes spot elevations for the entire Commonwealth of Kentucky generated from the KyFromAbove Phase 1 LiDAR-derived digital elevation model (DEM) in a geopackage format. ArcGIS was used to create this dataset. Spot elevations for Phase 2 and Phase 3 will be generated upon completion of each Phase.","ARN":"arn:aws:s3:::kyfromabove/elevation/SpotElevations/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/SpotElevations/)"]},{"Description":"KyFromAbove aerial imagery, both nadir and oblique views, can be found in this bucket. Ortho imagery is available for Phases 1, 3, and 3.  Oblique imagery is available for Phase 3 only.","ARN":"arn:aws:s3:::kyfromabove/imagery/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/)"]},{"Description":"KyFromAbove ortho imagery for the Commonwealth of Kentucky organized in a 5000x5000 foot grid. Each image tile has been converted to a COG format. Phase 1 and 2 data are organized by acquisition year and are currently available for use. Phase 3 is organized by year and season, as imagery was acquired during the fall and spring leaf-off seasons as sun angle permitted.","ARN":"arn:aws:s3:::kyfromabove/imagery/orthos/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/orthos/)"]},{"Description":"KyFromAbove oblique imagery can be found in this folder. The four oblique views associated with each ortho image are provided in a 3-band (RGB) COG format using the default 512x512 tile setting. There are no oblique images available for Phases 1 and 2. Phase 3 data is available for the entire state. It is organized by year and season (where Season1 = Spring and Season2 = Fall) as imagery was acquired during the fall and spring leaf-off seasons as sun angle and weather conditions permitted.","ARN":"arn:aws:s3:::kyfromabove/imagery/obliques/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[KyFromAbove Explorer](https://explore.kyfromabove.ky.gov/)","[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/obliques/)"]}],"DataAtWork":{"Tutorials":[{"Title":"KyFromAbove Bulk Download Area of Interest from Tile Index","URL":"https://github.com/ianhorn/kyfromabove-on-aws-examples/blob/main/examples/clip_tiles_to_boundary.ipynb","NotebookURL":"https://github.com/ianhorn/kyfromabove-on-aws-examples/blob/main/examples/clip_tiles_to_boundary.ipynb","AuthorName":"Ian Horn","AuthorURL":"https://www.linkedin.com/in/ian-horn-503b1022/","Services":["Amazon S3"]},{"Title":"KyFromAbove County Bulk Download","URL":"https://github.com/ianhorn/kyfromabove-on-aws-examples/blob/main/examples/bulk_download_county.ipynb","NotebookURL":"https://github.com/ianhorn/kyfromabove-on-aws-examples/blob/main/examples/bulk_download_county.ipynb","AuthorName":"Ian Horn","AuthorURL":"https://www.linkedin.com/in/ian-horn-503b1022/","Services":["Amazon S3"]}],"Tools & Applications":[{"Title":"Kentucky From Above SpatioTemporal Asset Catalog","URL":"https://kygeonet.ky.gov/stac","AuthorName":"Ian Horn, Ky Div. of Geographic Information"},{"Title":"KyFromAbove Catalog Explorer","URL":"https://kygeonet.ky.gov/catalogexplorer","AuthorName":"Boyd Shearer, Ky Div. of Geographic Information","AuthorURL":"https://www.linkedin.com/in/boydshearer/"},{"Title":"KyFromAbove Explorer","URL":"https://explore.kyfromabove.ky.gov/","AuthorName":"NV5","AuthorURL":"https://www.nv5.com/geospatial/"}],"Publications":[{"Title":"A New View of Kentucky's Cities","URL":"https://www.mydigitalpublication.com/publication/?m=16892&i=816848&view=articleBrowser&article_id=4737566&ver=html5","AuthorName":"Kent Anness"},{"Title":"KyFromAbove is democratizing key data in Kentucky","URL":"https://www.geoweeknews.com/news/kyfromabove-aerial-imagery-lidar-data-topography-elevation","AuthorName":"Matt Collins"},{"Title":"KyTopo! Kentucky's New Topographic Map Series","URL":"https://www.esri.com/about/newsroom/arcuser/kytopo-kentuckys-new-topographic-map-series/","AuthorName":"Kent Anness"},{"Title":"NV5 Earns 3 Excellence Awards at MAPPS 2026","URL":"https://lidarnews.com/news/nv5-earns-3-awards-at-2026-mapps-conference/","AuthorName":"LiDAR News"}]},"RegistryEntryAdded":"2023-08-22","RegistryEntryLastModified":"2026-04-16","Slug":"kyfromabove","Sources":["awslabs-open-data-registry"]}
{"Name":"Voices Obscured in Complex Environmental Settings (VOiCES)","Description":"VOiCES is a speech corpus recorded in acoustically challenging settings,\nusing distant microphone recording. Speech was recorded in real rooms with various\nacoustic features (reverb, echo, HVAC systems, outside noise, etc.). Adversarial noise,\neither television, music, or babble, was concurrently played with clean speech.\nData was recorded using multiple microphones strategically placed\nthroughout the room. The corpus includes audio recordings, orthographic transcriptions,\nand speaker labels.\n","Documentation":"https://voices18.github.io/","Contact":"https://github.com/voices18/utilities/issues","UpdateFrequency":"Data from two additional rooms will be added to the corpus Fall 2018.","ManagedBy":"[In-Q-Tel](https://www.iqt.org/)","Tags":["automatic speech recognition","aws-pds","denoising","machine learning","speaker identification","speech processing"],"License":"Creative Commons BY 4.0 (see [here](htpps://voices.lab41.org) for more details)","Resources":[{"Description":"wav audio files, orthographic transcriptions, and speaker ID","ARN":"arn:aws:s3:::lab41openaudiocorpus","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Getting started with VOiCES data","URL":"https://github.com/voices18/utilities/blob/master/Using%20VOiCES%20corpus%20tutorial.ipynb","AuthorName":"M.A. Barrios"}]},"RegistryEntryAdded":"2018-06-27","RegistryEntryLastModified":"2022-06-14","Slug":"lab41-sri-voices","Sources":["awslabs-open-data-registry"]}
{"Name":"Low Altitude Disaster Imagery (LADI) Dataset","Description":"The Low Altitude Disaster Imagery (LADI) Dataset consists of human and machine annotated airborne images collected by the Civil Air Patrol in support of various disaster responses from 2015-2023. Two key distinctions are the low altitude, oblique perspective of the imagery and disaster-related features, which are rarely featured in computer vision benchmarks and datasets.","Documentation":"https://github.com/LADI-Dataset/ladi-overview","Contact":"ladi-dataset-admin@mit.edu","ManagedBy":"[MIT Lincoln Laboratory Humanitarian Assistance and Disaster Relief group](https://www.ll.mit.edu/r-d/biotechnology-and-human-systems/humanitarian-assistance-and-disaster-relief-systems)","UpdateFrequency":"Periodically","License":"Creative Commons Attribution 4.0 International (CC BY 4.0)","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aerial imagery","aws-pds","coastal","computer vision","disaster response","earth observation","earthquakes","geospatial","image processing","imaging","infrastructure","land","machine learning","mapping","natural resource","seismology","transportation","urban","water"],"Resources":[{"Description":"LADI dataset: images and labels","ARN":"arn:aws:s3:::ladi","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"LADI v1 Tutorials","URL":"https://github.com/LADI-Dataset/ladi-tutorial","AuthorName":"Andrew Weinert, Jianyu Mao, Kiana Harris, Nae-Rong Chang, Caleb Pennell, Yiming Ren, Ryan Earley, Nadia Dimitrova","AuthorURL":"https://github.com/LADI-Dataset"},{"Title":"LADI v2 Overview","URL":"https://github.com/LADI-Dataset/ladi-overview","AuthorName":"Jeffrey Liu, Sam Scheele, Katherine Picchione","AuthorURL":"https://github.com/LADI-Dataset"}],"Tools & Applications":[{"Title":"NIST TRECVID 2020 - Disaster Scene Description and Indexing (DSDI)","URL":"https://www-nlpir.nist.gov/projects/tv2020/dsdi.html","AuthorName":"TREC Video Retrieval Evaluation (TRECVID)"},{"Title":"Remote Sensing for Disaster Response Course","URL":"https://github.com/bwsi-hadr","AuthorName":"Beaver Works Summer Institute","AuthorURL":"https://beaverworks.ll.mit.edu/CMS/bw/bwsi"}],"Publications":[{"Title":"Accelerate disaster response with computer vision for satellite imagery using Amazon SageMaker and Amazon Augmented AI","URL":"https://aws.amazon.com/blogs/machine-learning/accelerate-disaster-response-with-computer-vision-for-satellite-imagery-using-amazon-sagemaker-and-amazon-augmented-ai/","AuthorName":"Vamshi Krishna Enabothala, Morgan Dutton, and Sandeep Verma"},{"Title":"An overview on the evaluated video retrieval tasks at TRECVID 2022","URL":"https://arxiv.org/abs/2306.13118","AuthorName":"George Awad, Keith Curtis, Asad Butt, Jonathan Fiscus, Afzal Godil, Yooyoung Lee, Andrew Delgado, Eliot Godard, Lukas Diduch, Jeffrey Liu, Yvette Graham, Georges Quenot"},{"Title":"Evaluating Multiple Video Understanding and Retrieval Tasks at TRECVID 2021","URL":"https://www.nist.gov/publications/evaluating-multiple-video-understanding-and-retrieval-tasks-trecvid-2021","AuthorName":"George Awad, Asad Butt, Keith Curtis, Jonathan G. Fiscus, Afzal A. Godil, Yooyoung Lee, Andrew Delgado, Eliot Godard, Baptiste Chocot, Lukas Diduch, Jeffrey Liu, Yvette Graham, Gareth Jones, Georges Quenot"},{"Title":"Large Scale Organization and Inference of an Imagery Dataset for Public Safety","URL":"https://doi.org/10.1109/HPEC.2019.8916437","AuthorName":"Jeffrey Liu, David Strohschein, Siddharth Samsi, Andrew Weinert"},{"Title":"Train and Deploy an Image Classifier for Disaster Response","URL":"https://arxiv.org/abs/2005.05495","AuthorName":"Jianyu Mao, Kiana Harris, Nae-Rong Chang, Caleb Pennell, Yiming Ren"},{"Title":"TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domains","URL":"https://arxiv.org/abs/2104.13473","AuthorName":"George Awad, Asad A. Butt, Keith Curtis, Jonathan Fiscus, Afzal Godil, Yooyoung Lee, Andrew Delgado, Jesse Zhang, Eliot Godard, Baptiste Chocot, Lukas Diduch, Jeffrey Liu, Alan F. Smeaton, Yvette Graham, Gareth J. F. Jones, Wessel Kraaij, Georges Quenot"},{"Title":"Video Testing at the FirstNet Innovation and Test Lab Using a Public Safety Dataset","URL":"https://doi.org/10.1109/LNET.2019.2955833","AuthorName":"Chris Budny, Jeffrey Liu, Andrew Weinert"}]},"RegistryEntryAdded":"2020-10-01","RegistryEntryLastModified":"2025-07-09","Slug":"ladi","Sources":["awslabs-open-data-registry"]}
{"Name":"LAFC-Evict: Learning-Augmented Cache Eviction Dataset","Description":"LAFC-Evict is a derived, tabular research dataset of counterfactual supervision labels for learning-augmented cache-eviction research. Each row describes one candidate object considered for eviction at one full-cache-miss decision point, together with engineered state/candidate features and labels describing what would happen to the cache miss rate over a short future horizon if that specific candidate were evicted. The current AWS release, v1.0, contains five trace families: cloudphysics, metacdn, metakv, twemcache, and wiki2018. It includes 277,995,072 candidate rows, 2,363,286 decision-view rows, and a 1,000,000-row pairwise sample. LAFC-Evict is a generated/derived artifact and does not redistribute raw upstream trace logs, raw Wikimedia pageview rows, raw page titles, or other third-party raw data. The historical v0.3 wiki2018-only AWS publication remains preserved at the bucket root for reproducibility; v1.0 is available under the versioned prefix s3://lafc-evict-open-data/v1.0/.","Documentation":"https://huggingface.co/datasets/SoroushVahidi/lafc-evict","Contact":"sv96@njit.edu","ManagedBy":"Soroush Vahidi","UpdateFrequency":"No fixed schedule. Updated on major dataset releases; the current AWS version is v1.0, published 2026-09-24.","Tags":["benchmark","machine learning","parquet"],"License":"[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)","Citation":"Please cite the dataset release and, where relevant, the upstream raw data sources used to derive the published features and labels. Dataset: Vahidi, S. \"LAFC-Evict: Learning-Augmented Cache Eviction Dataset (v1.0).\" 2026. https://huggingface.co/datasets/SoroushVahidi/lafc-evict Associated public preprint, without claiming peer review: Vahidi, S. \"Decision-aligned eviction-value prediction for robust learning-augmented caching.\" SSRN abstract ID 6636732, 2026. https://ssrn.com/abstract=6636732","Resources":[{"Description":"LAFC-Evict S3 bucket. Current v1.0 release is under s3://lafc-evict-open-data/v1.0/; historical v0.3 objects remain preserved at the bucket root.","ARN":"arn:aws:s3:::lafc-evict-open-data","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for new objects created in the LAFC-Evict public bucket","ARN":"arn:aws:sns:us-west-2:761705104187:lafc-evict-open-data-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: LAFC-Evict","URL":"https://github.com/SoroushVahidi/lafc-evict-tutorial/blob/main/get-to-know-a-dataset.ipynb","NotebookURL":"https://raw.githubusercontent.com/SoroushVahidi/lafc-evict-tutorial/main/get-to-know-a-dataset.ipynb","AuthorName":"Soroush Vahidi","AuthorURL":"https://github.com/SoroushVahidi","Services":["Amazon S3"]}],"Tools & Applications":[{"Title":"LAFC-Evict dataset-generation pipeline (Augmented-caching)","URL":"https://github.com/SoroushVahidi/Augmented-caching","AuthorName":"Soroush Vahidi","AuthorURL":"https://github.com/SoroushVahidi"}]},"RegistryEntryAdded":"2026-09-24","RegistryEntryLastModified":"2026-09-24","Slug":"lafc-evict","Sources":["awslabs-open-data-registry"]}
{"Name":"LAION-fMRI","Description":"LAION-fMRI is one of the largest and most comprehensive high-resolution imaging datasets of brain responses to visually-presented photographs which we built to uncover how the human brain sees and understands the world. Five participants viewed over 25,000 unique natural images in 165 hours across 165 sessions, capturing hundreds of thousands of brain responses every second with an ultra-high-field 7-Tesla MRI scanner. This extraordinary scale means results can be replicated across individuals and generalized across a very broad range of visual experiences, overcoming critical barriers that previous datasets simply could not. The images span everything from everyday photographs (sampled from 120 million image-text pairs) to abstract shapes and visual illusions, ensuring the dataset covers the full breadth of human visual experience. Every image was measured multiple times, delivering exceptional signal quality and setting new standards for the field. Beyond functional brain scans, the dataset includes rich complementary data (eye-tracking, behavioral responses, detailed maps of each participant's visual brain architecture, and cutting-edge white matter fiber tracking), making it possibly the most deeply characterized neuroimaging resources assembled to date. LAION-fMRI provides the foundation for building next-generation AI vision models grounded in real human brain data, fundamentally bridging the gap between biological and artificial intelligence.","Documentation":"https://laion-fmri.hebartlab.com","Contact":"hebart@cbs.mpg.de","ManagedBy":"[Hebart Lab](https://hebartlab.com)","UpdateFrequency":"Infrequent. Supplemental fMRI sessions and additional metadata will be released after the launch release, otherwise updates are limited to corrections.","Tags":["aws-pds","brain images","computer vision","deep learning","imaging","life sciences","machine learning","magnetic resonance imaging","neuroimaging","neuroscience","nifti"],"License":"CC0 1.0 for the fMRI data, derivatives, and metadata (https://creativecommons.org/publicdomain/zero/1.0/). Raw stimulus images require accepting a Data Use Agreement: https://laion-fmri.hebartlab.com/request","Resources":[{"Description":"BIDS-organized 7T fMRI data (NIfTI) with derivatives including preprocessed data, ROIs, single-trial beta estimates from GLMsingle, retinotopy maps, functional localizers, and precision diffusion MRI tractography.","ARN":"arn:aws:s3:::laion-fmri","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: LAION-fMRI","URL":"https://github.com/ViCCo-Group/LAION-fMRI/blob/main/get-to-know-a-dataset.ipynb","AuthorName":"ViCCo Group","AuthorURL":"https://github.com/ViCCo-Group"},{"Title":"LAION-fMRI documentation, tutorial and example code","URL":"https://github.com/ViCCo-Group/LAION-fMRI","AuthorName":"ViCCo Group","AuthorURL":"https://github.com/ViCCo-Group"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-04-08","RegistryEntryLastModified":"2026-09-15","Slug":"laion-fmri","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"This dataset listing is deprecated in favor of [USGS-Landsat](https://registry.opendata.aws/usgs-landsat/). Please refer to [USGS cloud access mechanism](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-data-access) for more details.","Name":"Landsat 8","Description":"An ongoing collection of satellite imagery of all land on Earth produced by\nthe Landsat 8 satellite.\n","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/landsat-pds","Contact":"https://lists.osgeo.org/mailman/listinfo/landsat-pds","ManagedBy":"[Planet](https://www.planet.com/)","UpdateFrequency":"New Landsat 8 scenes are added regularly as soon as they are available.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","agriculture","earth observation","satellite imagery","geospatial","natural resource","disaster response"],"License":"There are no restrictions on the use of data received from the U.S. Geological Survey's Earth Resources Observation and Science (EROS) Center or NASA's Land Processes Distributed Active Archive Center (LP DAAC), unless expressly identified prior to or at the time of receipt. More information on licensing and Landsat data citation is available from USGS.\n","Resources":[{"Description":"Scenes and metadata","ARN":"arn:aws:s3:::landsat-pds","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://landsat-pds.s3.amazonaws.com/index.html)","[Earth Search STAC Catalog](https://earth-search.aws.element84.com/v1/collections/landsat-c2-l2)"]},{"Description":"New scene notifications","ARN":"arn:aws:sns:us-west-2:274514004127:NewSceneHTML","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"A Gentle Introduction to GDAL Part 4: Working with Satellite Data","URL":"https://medium.com/planet-stories/a-gentle-introduction-to-gdal-part-4-working-with-satellite-data-d3835b5e2971","AuthorName":"Planet","AuthorURL":"https://www.planet.com/"},{"Title":"Exploring the Chile wildfires with Landsat and Sentinel-2 imagery","URL":"https://www.gearthblog.com/blog/archives/2017/02/exploring-chile-wildfires-landsat-sentinel-2-imagery.html","AuthorName":"Timothy Whitehead"},{"Title":"Using Vector tiles and AWS Lambda, we can build a really simple API to get Landsat and Sentinel images","URL":"https://remotepixel.ca/blog/searchapi_20171211.html","AuthorName":"Remote Pixel","AuthorURL":"https://remotepixel.ca/"},{"Title":"Integrate imagery from the full Landsat archive into your own apps, maps, and analysis with Landsat image services","URL":"http://www.arcgis.com/home/group.html?id=a74dff13f1be4b2ba7264c3315c57077#overview","AuthorName":"Esri","AuthorURL":"https://www.esri.com/en-us/home"},{"Title":"Using Cloud-Optimized GeoTIFF","URL":"http://www.acgeospatial.co.uk/cog-part-2-python/","AuthorName":"acgeospatial","AuthorURL":"http://www.acgeospatial.co.uk/"}],"Tools & Applications":[{"Title":"landsatlive.live","URL":"https://landsatlive.live","AuthorName":"Development Seed","AuthorURL":"https://developmentseed.org/"},{"Title":"EOS Land Viewer","URL":"https://eos.com/landviewer/","AuthorName":"Earth Observing System","AuthorURL":"https://eos.com/"},{"Title":"Aggi: the fire detecting Twitter bot","URL":"https://twitter.com/aggibot","AuthorName":"Aggi","AuthorURL":"https://twitter.com/aggibot"},{"Title":"COG-Explorer - View Cloud Optimized GeoTIFF images in the browser directly from object storage","URL":"https://geotiffjs.github.io/cog-explorer/","AuthorName":"EOX","AuthorURL":"https://eox.at/"},{"Title":"Spectator - tracking Landsat 8, accessing the data and quick preview","URL":"https://spectator.earth/","AuthorName":"Spectator","AuthorURL":"https://spectator.earth/"},{"Title":"Apps for exploring and analyzing Landsat imagery on the fly","URL":"http://www.esri.com/landing-pages/software/landsat/unlock-earths-secrets","AuthorName":"Esri","AuthorURL":"https://www.esri.com/en-us/home"},{"Title":"Development Seed Geolambda","URL":"https://developmentseed.org/blog/2017/08/17/geolambdas/","AuthorName":"Matthew Hanson","AuthorURL":"https://developmentseed.org/team/matt-hanson/"},{"Title":"FME Landsat-8 on AWS Reader","URL":"https://docs.safe.com/fme/html/FME_Desktop_Documentation/FME_ReadersWriters/landsat8aws/landsat8aws.htm","AuthorName":"Safe Software","AuthorURL":"https://www.safe.com/"},{"Title":"FME Landsat-8/Sentinel-2 File Selector","URL":"http://demos.fmeserver.com/landsat-sentinel-selector/","AuthorName":"Safe Software","AuthorURL":"https://www.safe.com/"},{"Title":"Sentinel Hub WMS/WMTS/WCS Service for Landsat","URL":"http://www.sentinel-hub.com/apps/wms","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"},{"Title":"Sentinel Playground for Landsat","URL":"https://apps.sentinel-hub.com/sentinel-playground/?source=L8","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"},{"Title":"Dynamic map tiling with Cloud-Optimized GeoTIFFs","URL":"https://kylebarron.dev/blog/cog-mosaic/overview","AuthorName":"Kyle Barron"}],"Publications":[{"Title":"Using Remote Sensing Images and Cloud Services on AWS to Improve Land Use and Cover Monitoring","URL":"https://ieeexplore.ieee.org/abstract/document/9165649","AuthorName":"K. R. Ferreira, et al."}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2023-08-04","Slug":"landsat-8","Sources":["awslabs-open-data-registry"]}
{"Name":"Legal Entity Identifier (LEI) and Legal Entity Reference Data (LE-RD)","Description":"The Legal Entity Identifier (LEI) is a 20-character, alpha-numeric code based on the ISO 17442 standard developed by the International Organization for Standardization (ISO). It connects to key reference information that enables clear and unique identification of legal entities participating in financial transactions. Each LEI contains information about an entity’s ownership structure and thus answers the questions of 'who is who’ and ‘who owns whom’. Simply put, the publicly available LEI data pool can be regarded as a global directory, which greatly enhances transparency in the global marketplace. The Financial Stability Board (FSB) has reiterated that global LEI adoption underpins “multiple financial stability objectives” such as improved risk management in firms as well as better assessment of micro and macro prudential risks. As a result, it promotes market integrity while containing market abuse and financial fraud. Last but not least, LEI rollout “supports higher quality and accuracy of financial data overall”. The publicly available LEI data pool is a unique key to standardized information on legal entities globally. The data is registered and regularly verified according to protocols and procedures established by the Regulatory Oversight Committee. In cooperation with its partners in the Global LEI System, the Global Legal Entity Identifier Foundation (GLEIF) continues to focus on further optimizing the quality, reliability and usability of LEI data, empowering market participants to benefit from the wealth of information available with the LEI population. The drivers of the LEI initiative, i.e. the Group of 20, the FSB and many regulators around the world, have emphasized the need to make the LEI a broad public good. The Global LEI Index, made available by GLEIF, greatly contributes to meeting this objective. It puts the complete LEI data at the disposal of any interested party, conveniently and free of charge. The benefits for the wider business community to be generated with the Global LEI Index grow in line with the rate of LEI adoption. To maximize the benefits of entity identification across financial markets and beyond, firms are therefore encouraged to engage in the process and get their own LEI. Obtaining an LEI is easy. Registrants simply contact their preferred business partner from the list of LEI issuing organizations available on the GLEIF website.\n","Documentation":"https://www.gleif.org/en/lei-data/gleif-golden-copy/download-the-golden-copy#/","Contact":"info@gleif.org","ManagedBy":"[GLEIF](http://www.gleif.org/)","UpdateFrequency":"Three times daily (about every 8 hours)","Collabs":{"ASDI":{"Tags":["socioeconomic"]}},"Tags":["analytics","aws-pds","blockchain","climate","commerce","copyright monitoring","csv","financial markets","governance","government spending","json","market data","socioeconomic","statistics","transparency","xml"],"License":"Creative Commons (CC0) license","Resources":[{"Description":"Legal Entity Identifier (LEI) and Legal Entity Reference Data (LE-RD)","ARN":"arn:aws:s3:::gleif","Region":"eu-central-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"AWS hosts new open dataset to help businesses identify climate finance risks and investments","URL":"https://aws.amazon.com/blogs/publicsector/aws-hosts-new-open-dataset-help-businesses-identify-climate-finance-risks-investments/","AuthorName":"AWS Public Sector Blog Team"}]},"RegistryEntryAdded":"2021-09-29","RegistryEntryLastModified":"2022-05-31","Slug":"lei","Sources":["awslabs-open-data-registry"]}
{"Name":"Leipzig Heart Center ECG-Database: Arrhythmias in Children and Patients with Congenital Heart Disease","Description":"Interpretation of Electrocardiograms (ECG) is increasingly complemented by algorithms. These algorithms are based on large datasets. This ECG database consists of children and adults with congenital heart defects (CHD) including many arrhythmia annotations. This dataset, notable for its manual annotations and inclusion of intracardiac electrograms alongside traditional 12-lead ECGs, offers 1075.85 minutes of recordings (in total 113924 annotated beats) that capture various cardiac rhythms and arrhythmias such as supraventricular tachycardia and ventricular tachycardia. The data were meticulously collected from patients undergoing electrophysiological studies, with subsequent annotations by expert reviewers using the LightWAVE® software. The significance of this database lies in its focus on pediatric arrhythmias and arrhythmias of patients with congenital heart defect areas currently underrepresented in existing datasets, which predominantly feature adult pathologies. This resource aims to enhance algorithmic development for ECG interpretation, leveraging machine learning to improve diagnosis and treatment outcomes in these sensitive groups. The dataset not only serves as a critical tool for developing precision medicine but also sets a precedent for future expansions to include a broader spectrum of congenital heart defects conditions, thereby supporting the evolution of cardiac care through advanced computational techniques.","Documentation":"https://doi.org/10.13026/7a4j-vn37","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/7a4j-vn37","ARN":"arn:aws:s3:::physionet-open/leipzig-heart-center-ecg/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"leipzig-heart-center-ecg","Sources":["awslabs-open-data-registry"]}
{"Name":"LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes challenge (LEOPARD) Dataset","Description":"\"This dataset contains the all data for the [LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes challenge or LEOPARD](https://leopard.grand-challenge.org/).Prostate cancer, impacting 1.4 million men annually, is a prevalent malignancy ([H. Sung et al., 2021](https://acsjournals.onlinelibrary.wiley.com/doi/10.3322/caac.21660)). A substantial number of these individuals undergo prostatectomy as the primary curative treatment. The efficacy of this surgery is assessed, in part, by monitoring the concentration of prostate-specific antigen (PSA) in the bloodstream. While the role of PSA in prostate cancer screening is debatable ([W. F. Clark et al., 2018;](https://jamanetwork.com/journals/jama/fullarticle/2680553) [E. A. M. Heijnsdijk et al., 2018](https://onlinelibrary.wiley.com/doi/full/10.1002/ijc.31102)), it serves as a valuable biomarker for postprostatectomy follow-up in patients. Following successful surgery, PSA concentration is typically undetectable (<0.1 ng/mL) within 4-6 weeks ([S. S. Goonewardene et al., 2014](https://wchh.onlinelibrary.wiley.com/doi/abs/10.1002/tre.407)). However, approximately 30% of patients experience biochemical recurrence, signifying the resurgence of prostate cancer cells. This recurrence serves as a prognostic indicator for progression to clinical metastases and eventual prostate cancer-related mortality ([C. L. Amling, 2014;](https://pubmed.ncbi.nlm.nih.gov/10840432/) [S. J. Freedland et al., 2005;](https://jamanetwork.com/journals/jama/fullarticle/201291) [M. Han et al., 2001;](https://www.sciencedirect.com/science/article/abs/pii/S0094014305701634?via%3Dihub) [T. Van den Broeck et al., 2001](https://www.sciencedirect.com/science/article/pii/S0302283818307528?via%3Dihub). Current clinical practices gauge the risk of biochemical recurrence by considering the International Society of Urological Pathology (ISUP) grade, PSA value at diagnosis, and TNM staging criteria ([J. I. Epstein et al., 2016](https://journals.lww.com/ajsp/abstract/2016/02000/the_2014_international_society_of_urological.10.aspx)). A recent European consensus guideline suggests categorizing patients into low-risk, intermediate-risk, and high-risk groups based on these factors ([N. Mottet et al., 2021](https://www.sciencedirect.com/science/article/abs/pii/S0302283820307697?via%3Dihub)). Notably, a high ISUP grade independently assigns a patient to the intermediate (grade 2/3) or high-risk group (grade 4/5). The Gleason growth patterns, representing morphological patterns of prostate cancer, are used to categorize cancerous tissue into ISUP grade groups ([J. I. Epstein, 2010;](https://www.auajournals.org/doi/10.1016/j.juro.2009.10.046) [P. M. Pierorazio et al., 2013;](https://bjui-journals.onlinelibrary.wiley.com/doi/10.1111/j.1464-410X.2012.11611.x) [G. J. L. H. van Leenders et al., 2020;](https://journals.lww.com/ajsp/fulltext/2020/08000/the_2019_international_society_of_urological.1.aspx) [J. I. Epstein et al., 2016](https://www.sciencedirect.com/science/article/abs/pii/S0302283815005576?via%3Dihub)). However, the ISUP grade has limitations, such as grading disagreement among pathologists ([J. I. Epstein et al., 2016](https://www.sciencedirect.com/science/article/abs/pii/S0302283815005576?via%3Dihub)) and coarse descriptors of tissue morphology. Recently, deep learning was shown ([H. Pinckaers et al., 2022](https://www.nature.com/articles/s43856-022-00126-3); [O. Eminaga et. al., 2024](https://www.nature.com/articles/s44303-023-00005-z)) to be able to predict the biochemical recurrence of prostate cancer.  Hypothesizing that deep learning could uncover finer morphological features' prognostic value, we are organizing the LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes (LEOPARD) challenge. The goal of this challenge is to yield top-performance deep learning solutions to predict the time to biochemical recurrence from H&E-stained histopathological tissue sections, i.e. based on morphological features.\n","Documentation":"https://leopard.grand-challenge.org/","Contact":"https://leopard.grand-challenge.org/","ManagedBy":"Radboud University Medical Center","UpdateFrequency":"As required","Tags":["aws-pds","cancer","computational pathology","computer vision","deep learning","grand-challenge.org","histopathology","life sciences"],"License":"CC BY NC SA","Resources":[{"Description":"H\\&E stained prostatectomy whole slide images with corresponding labels of recurrence event evidence (1/0) and time to recurrence or last followup in years.","ARN":"arn:aws:s3:::leopard-challenge","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"ASAP Viewer","URL":"https://computationalpathologygroup.github.io/ASAP/","AuthorName":"Geert Litjens","AuthorURL":"https://www.diagnijmegen.nl/people/geert-litjens/"},{"Title":"LEOPARD Challenge","URL":"https://leopard.grand-challenge.org/","AuthorName":"Diagnostic Image Analysis Group, Radboudumc, Nijmegen","AuthorURL":"https://www.diagnijmegen.nl/"}]},"RegistryEntryAdded":"2024-11-06","RegistryEntryLastModified":"2025-04-10","Slug":"leopard","Sources":["awslabs-open-data-registry"]}
{"Name":"LGND Clay v1.5 Sentinel-2","Description":"A global dataset of Clay v1.5 embeddings for Sentinel2.","Documentation":"https://source.coop/clay/lgnd-clay-v1-5-sentinel-2-l2a","Contact":"contact@madewithclay.org","ManagedBy":"[Source Cooperative](https://source.coop/)","UpdateFrequency":"As new data versions become available","Tags":["aws-pds","computer vision","earth observation","imaging","machine learning","satellite imagery"],"License":"Creative Commons Attribution 4.0 International License","Citation":"Clay v1.5 Sentinel-2. Source Cooperative. https://source.coop/clay/lgnd-clay-v1-5-sentinel-2-l2a","Resources":[{"Description":"Clay v1.5 Sentinel-2","ARN":"arn:aws:s3:::us-west-2.opendata.source.coop/clay/lgnd-clay-v1-5-sentinel-2-l2a","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://source.coop/clay/lgnd-clay-v1-5-sentinel-2-l2a)"]},{"Description":"Notifications for new data as it arrives","ARN":"arn:aws:sns:us-west-2:417712557820:source_coop-clay-lgnd-clay-v1-5-sentinel-2-l2a_new-object","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"Applying geospatial foundation models to reforestation site selection","URL":"https://sustainabilityexchange.amazon.com/data-initiative/case-study/applying-geospatial-foundation-models-to-reforestation-site-selection","AuthorName":"Amazon Sustainability Data Initiative"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-02-01","RegistryEntryLastModified":"2026-03-19","Slug":"lgnd-clay-v1-5-sentinel2","Sources":["awslabs-open-data-registry"]}
{"Name":"Sanborn Maps Data Package","Description":"The dataset contains metadata records for 50,600 maps from the [Sanborn Fire Insurance Maps collection](https://www.loc.gov/collections/sanborn-maps/) and their corresponding 440,048 JPEG images. The Sanborn collection at Library of Congress includes over fifty thousand editions of fire insurance maps comprising almost seven hundred thousand individual sheets. The Library of Congress holdings represent the largest extant collection of maps produced by the Sanborn Map Company.","Documentation":"https://data.labs.loc.gov/sanborn/","Contact":"For curatorial questions about the content of the collection and formats, contact the Library of Congress Geography and Map Division at https://ask.loc.gov/map-geography. For technical questions about access, contact LC-Labs@loc.gov","ManagedBy":"[Library of Congress](https://www.loc.gov/)","UpdateFrequency":"As new and significant changes to the underlying digital collection occurs","Tags":["archives","aws-pds","cities","computer vision","conservation","cultural preservation","culture","demographics","digital assets","geospatial","history","housing","land use","mapping","urban"],"License":"The content of the Library of Congress online Sanborn Maps Collection is in the public domain and is free to use and reuse. For more information, see https://www.loc.gov/collections/sanborn-maps/about-this-collection/rights-and-access/.","Resources":[{"Description":"Sanborn Maps data","ARN":"arn:aws:s3:::loc-sanborn-maps","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket by State](https://loc-sanborn-maps.s3.amazonaws.com/maps-by-state/index.html)","[README](https://loc-sanborn-maps.s3.amazonaws.com/README.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Fire Insurance Maps at the Library of Congress: A Resource Guide","URL":"https://guides.loc.gov/fire-insurance-maps/introduction","AuthorName":"Julie Stoner, Reference Librarian, Geography and Map Division, Library of Congress"},{"Title":"README data cover sheet","URL":"https://loc-sanborn-maps.s3.amazonaws.com/README.html","AuthorName":"Library of Congress"},{"Title":"Sanborn Map Data Python Tutorial (Jupyter notebook)","URL":"https://libraryofcongress.github.io/data-exploration/Data%20Packages/sanborn.html","AuthorName":"Library of Congress","AuthorURL":"https://github.com/LibraryOfCongress"}],"Tools & Applications":[{"Title":"Sanborn Atlas Volume Finder","URL":"https://loc.maps.arcgis.com/apps/instant/media/index.html?appid=0cb2c04324a0413081e1b793ea18f854","AuthorName":"Julie Stoner and Meagan Snow, Geography and Map Division, Library of Congress","AuthorURL":"https://github.com/aarande"}],"Publications":[{"Title":"Introduction to the Collection","URL":"https://www.loc.gov/collections/sanborn-maps/articles-and-essays/introduction-to-the-collection/","AuthorName":"Walter W. Ristow"}]},"RegistryEntryAdded":"2025-07-10","RegistryEntryLastModified":"2025-07-17","Slug":"loc-sanborn-maps","Sources":["awslabs-open-data-registry"]}
{"Name":"LOFAR ELAIS-N1 cycle 2 observations on AWS","Description":"These data correspond to the [International LOFAR Telescope](http://www.lofar.org/) observations of the sky field ELAIS-N1 (16:10:01 +54:30:36) during the cycle 2 of observations. There are 11 runs of about 8 hours each plus the corresponding observation of the calibration targets before and after the target field. The data are measurement sets ([MS](https://casa.nrao.edu/Memos/229.html)) containing the cross-correlated data and metadata divided in 371 frequency sub-bands per target centred at ~150 MHz.","Contact":"https://www.lofarcloud.uk/","Documentation":"https://www.lofarcloud.uk/data.html","UpdateFrequency":"Not updated","ManagedBy":"Institute for Astronomy, University of Edinburgh","Tags":["astronomy","aws-pds","imaging","survey"],"License":"The data are considered \"LOFAR data in the public domain\" and their use must adhere to the [LOFAR data policy](https://www.astron.nl/radio-observatory/observing-proposals/lofar-data-policy/lofar-data-policy). Any publication based on these data must contain the following acknowledgement text: \"This paper is based (in part) on data obtained with the International LOFAR Telescope (ILT) under project code LC2_024. LOFAR (van Haarlem et al. 2013) is the Low Frequency Array designed and constructed by ASTRON. It has observing, data processing, and data storage facilities in several countries, that are owned by various parties (each with their own funding sources), and that are collectively operated by the ILT foundation under a joint scientific policy. The ILT resources have benefitted from the following recent major funding sources: CNRS-INSU, Observatoire de Paris and Université d'Orléans, France; BMBF, MIWF-NRW, MPG, Germany; Science Foundation Ireland (SFI), Department of Business, Enterprise and Innovation (DBEI), Ireland; NWO, The Netherlands; The Science and Technology Facilities Council, UK; Ministry of Science and Higher Education, Poland\"","Resources":[{"Description":"LOFAR ELAIS-N1 cycle 2 observations","ARN":"arn:aws:s3:::lofar-elais-n1","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Calibration of LOFAR ELAIS-N1 data in the Amazon cloud","URL":"https://www.lofarcloud.uk","AuthorName":"J. Sabater","AuthorURL":"https://github.com/nudomarinero"}]},"RegistryEntryAdded":"2019-02-05","RegistryEntryLastModified":"2022-06-14","Slug":"lofar-elais-n1","Sources":["awslabs-open-data-registry"]}
{"Name":"LongBench - cross-platform reference dataset profiling cancer cell lines with bulk and single-cell approaches","Description":"LongBench is a comprehensive benchmark dataset of the latest long-read transcriptomics technologies from Oxford Nanopore (ON) and Pacific Biosciences, alongside a comparison with next-generation sequencing from Illumina. We generated bulk and single-cell libraries from lung cancer cell lines which include different cancer subtypes to capture real biological variation. To further compare and assess sequencing platform performance, Sequins and SIRVs (Set 4) synthetic spike-ins have been included.\n","Documentation":"https://github.com/mritchielab/LongBench.io","Contact":"mritchie@wehi.edu.au","ManagedBy":"Richie Lab, Walter and Eliza Hall Institute of Medical Research","UpdateFrequency":"New data will be added as soon as they are available.","Tags":["aws-pds","bam","benchmark","bioinformatics","cancer","fastq","life sciences","long read sequencing","short read sequencing","single-cell transcriptomics","vcf"],"License":"CC BY-4.0","Resources":[{"Description":"Bulk, single-cell, and single-nucleus RNA-seq data from the LongBench project, covering eight human lung cancer cell lines. Bulk sequencing (FASTQ) was performed on ONT PCR-cDNA, ONT direct RNA (including pod5 files for RNA modification analysis), PacBio Kinnex, and Illumina platforms. Single-cell and single-nucleus sequencing (FASTQ) was performed on ONT PCR-cDNA, PacBio Kinnex, and Illumina platforms. Aligned reads (BAM), variant calls (VCF), and processed gene expression data are also provided, along with reference genome annotations (GTF and FASTA).","ARN":"arn:aws:s3:::longbench-data","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Benchmarking long-read DE gene and transcript analysis with edgeR","URL":"https://mritchielab.github.io/LongBench.io/bulk-de-benchmarking/","AuthorName":"Yupei You"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-08-29","RegistryEntryLastModified":"2025-10-31","Slug":"longbench","Sources":["awslabs-open-data-registry"]}
{"Name":"Low Context Name Entity Recognition (NER) Datasets with Gazetteer","Description":"See https://lowcontext-ner-gaz.s3.amazonaws.com/readme.html","Documentation":"https://lowcontext-ner-gaz.s3.amazonaws.com/readme.html","Contact":"njfn@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"N/A","Tags":["amazon.science","natural language processing"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Data files","ARN":"arn:aws:s3:::lowcontext-ner-gaz","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input","URL":"https://www.amazon.science/publications/gemnet-effective-gated-gazetteer-representations-for-recognizing-complex-entities-in-low-context-input","AuthorName":"Tao Meng, Anjie Fang, Oleg Rokhlenko and Shervin Malmasi"}]},"RegistryEntryAdded":"2021-04-30","RegistryEntryLastModified":"2022-07-06","Slug":"lowcontext-ner-gaz","Sources":["awslabs-open-data-registry"]}
{"Name":"Long Term AF Database","Description":"This database includes 84 long-term ECG recordings of subjects with paroxysmal or sustained atrial fibrillation (AF). Each record contains two simultaneously recorded ECG signals digitized at 128 Hz with 12-bit resolution over a 20 mV range; record durations vary but are typically 24 to 25 hours.","Documentation":"https://doi.org/10.13026/C2QG6Q","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2QG6Q","ARN":"arn:aws:s3:::physionet-open/ltafdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ltafdb","Sources":["awslabs-open-data-registry"]}
{"Name":"MIT-BIH Long-Term ECG Database","Description":"The MIT-BIH Long-Term ECG Database is a collection of 7 long-duration electrocardiogram (ECG) recordings (14 to 22 hours each), with manually reviewed beat annotations. The database was primarily developed to support research in the field of cardiac arrhythmia detection and analysis, and is a collaborative effort between the Massachusetts Institute of Technology (MIT) and the Beth Israel Hospital (BIH).","Documentation":"https://doi.org/10.13026/C2KS3F","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2KS3F","ARN":"arn:aws:s3:::physionet-open/ltdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ltdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Long Term Movement Monitoring Database","Description":"The LTMM database contains 3-day 3D accelerometer recordings of 71 elder community residents, used to study gait, stability, and fall risk.","Documentation":"https://doi.org/10.13026/C2S59C","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2S59C","ARN":"arn:aws:s3:::physionet-open/ltmm/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ltmm","Sources":["awslabs-open-data-registry"]}
{"Name":"Learning to Rank and Filter - community question answering","Description":"This dataset provides product related questions and answers, including answers' quality labels, as as part of the paper 'IR Evaluation and Learning in the Presence of Forbidden Documents'.","Documentation":"https://ltrf-cqa-dataset.s3-us-west-2.amazonaws.com/readme.txt","Contact":"ekravi@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not currently being updated.","Tags":["amazon.science","machine learning","natural language processing"],"License":"[CDLA-Permissive](https://cdla.dev/permissive-1-0/)","Resources":[{"Description":"S3 bucket with dataset","ARN":"arn:aws:s3:::ltrf-cqa-dataset","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"URL":"https://sigir.org/sigir2022/","AuthorName":"David Carmel, Nachshon Cohen, Amir Ingber & Elad Kravi","Title":"IR Evaluation and Learning in the Presence of Forbidden Documents"}]},"RegistryEntryAdded":"2022-04-21","RegistryEntryLastModified":"2022-04-21","Slug":"ltrf-cqa-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"EEG Signals from an RSVP Task","Description":"This project contains EEG data from 11 healthy participants upon rapid presentation of images through the Rapid Serial Visual Presentation (RSVP) protocol at speeds of 5, 6, and 10 Hz.","Documentation":"https://doi.org/10.13026/C2KX0P","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2KX0P","ARN":"arn:aws:s3:::physionet-open/ltrsvp/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ltrsvp","Sources":["awslabs-open-data-registry"]}
{"Name":"Integrative Analysis of Lung Adenocarcinoma in Environment and Genetics Lung cancer Etiology (Phase 2)","Description":"We performed whole genome sequencing and whole exome sequencing of 31 lung adenocarcinoma (LUAD) samples from the Environment And Genetics in Lung cancer Etiology (EAGLE) study. The EAGLE study is made available on AWS via the NIH STRIDES Initiative (https://aws.amazon.com/blogs/publicsector/aws-and-national-institutes-of-health-collaborate-to-accelerate-discoveries-with-strides-initiative/).\n","Documentation":"https://ftp.ncbi.nlm.nih.gov/dbgap/studies/phs001239/phs001239.v1.p1","Contact":"support@nci-gdc.datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases, although this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","epigenomics","genomic","life sciences","STRIDES","whole exome sequencing","whole genome sequencing"],"License":"(NIH Genomic Data Sharing Policy)[https://gdc.cancer.gov/access-data/data-access-policies]","Resources":[{"Description":"Whole Genome Sequencing, Whole Exome Sequencing\n","ARN":"arn:aws:s3:::gdc-cddp-eagle-1-phs001239-2-open","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"EAGLE - Environment And Genetics in Lung cancer Etiology","URL":"https://dceg.cancer.gov/research/who-we-study/cancer-cases-controls/eagle-study#:~:text=Environment%20And%20Genetics%20in%20Lung%20cancer%20Etiology%20(EAGLE)%20was%20developed,%2C%20clinical%2C%20and%20behavioral%20data.","AuthorName":"National Cancer Institute Division of Cancer Epidemiology and Genetics","AuthorURL":"https://dceg.cancer.gov/"},{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"}]},"RegistryEntryAdded":"2023-04-27","RegistryEntryLastModified":"2023-04-27","Slug":"luad-eagle","Sources":["awslabs-open-data-registry"]}
{"Name":"Lobachevsky University Electrocardiography Database","Description":"Lobachevsky University Electrocardiography Database (LUDB) is an ECG signal database with marked boundaries and peaks of P, T waves and QRS complexes. The database consists of 200 10-second 12-lead ECG signal records representing different morphologies of the ECG signal. The ECGs were collected from healthy volunteers and patients of the Nizhny Novgorod City Hospital No 5 in 2017–2018. The patients had various cardiovascular diseases while some of them had pacemakers. The boundaries of P, T waves and QRS complexes were manually annotated by cardiologists for all 200 records. Also, each record is annotated with the corresponding diagnosis. The database can be used for educational purposes as well as for training and testing algorithms for ECG delineation, i.e. for automatic detection of boundaries and peaks of P, T waves and QRS complexes.","Documentation":"https://doi.org/10.13026/eegm-h675","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/eegm-h675","ARN":"arn:aws:s3:::physionet-open/ludb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ludb","Sources":["awslabs-open-data-registry"]}
{"Name":"Louisiana Watershed Initiative (LWI) Model Data","Description":"Geographic (land cover, land elevation, etc.), meteorologic (pluvial, wind, etc.),  hydrologic (fluvial, tidal, etc.), hydrodynamic (water surface elevations, flow velocities), and built environment (structures, levees, floodgates, culverts) data used as inputs to and  outputs from numerical modeling software for the prediction of flood risk in stochastic and probabilistic frameworks. This data was collected from open sources, such as from the  National Oceanographic and Atmospheric Administration (NOAA) or the  United States Geological Survey (USGS). The format of these data is modified to suit the needs of the modeling program and software, and then used to predict flooding in Louisiana across a range of scenarios. The modeling software used to predict flooding which utilizes and creates this data is freely available from the  United States Army Corps of Engineers Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and River Analysis System (HEC-RAS). All data is made public by the State of Louisiana for the benefit of its citizens. This flood prediction data can be used by federal, state, and local decision makers as well as private citizens to assess the flood risk they face and make sound science-based decisions for response and adaptation.\n","Contact":"endmc@thewaterinstitute.org","ManagedBy":"The Water Institute","UpdateFrequency":"yearly","Documentation":"https://watershed.la.gov/modeling-program","Tags":["aws-pds","bathymetry","climate","coastal","disaster response","elevation","floods","forecast","geospatial","hydrologic model","hydrology","infrastructure","land cover","land use","mapping","meteorological","model","open source software","precipitation","simulations","sustainability","water","weather"],"License":"https://creativecommons.org/licenses/by/4.0/ with attribution to Louisiana Watershed Council","Citation":"Louisiana Watershed Initiative Model Data. Louisiana Watershed Council. 2025. https://lwi.endmc.org/","Resources":[{"Description":"Model Applications and Simulations","ARN":"arn:aws:s3:::lwi-model-data","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse the bucket](https://lwi-model-data.s3.amazonaws.com/index.html)","[Disover the data and models. The ID of the resources can be used to explore the data on the AWS bucket.](https://lwi.endmc.org/)"]}],"DataAtWork":{"Tutorials":[{"Title":"ENDMC Documentation","URL":"https://lwi.endmc.org/help/help_index","AuthorName":"The Water Institute","AuthorURL":"https://thewaterinstitute.org/"}],"Tools & Applications":[{"Title":"go-consequences","URL":"https://github.com/USACE/go-consequences","AuthorName":"United States Army Corps of Engineers","AuthorURL":"https://www.usace.army.mil/"},{"Title":"HEC-FIA","URL":"https://www.hec.usace.army.mil/software/hec-fia/","AuthorName":"United States Army Corps of Engineers","AuthorURL":"https://www.usace.army.mil/"},{"Title":"HEC-FIA documentation","URL":"https://www.hec.usace.army.mil/software/hec-fia/documentation.aspx","AuthorName":"United States Army Corps of Engineers","AuthorURL":"https://www.usace.army.mil/"},{"Title":"HEC-HMS","URL":"https://www.hec.usace.army.mil/software/hec-hms/","AuthorName":"United States Army Corps of Engineers","AuthorURL":"https://www.usace.army.mil/"},{"Title":"HEC-HMS documentation","URL":"https://www.hec.usace.army.mil/software/hec-hms/documentation.aspx","AuthorName":"United States Army Corps of Engineers","AuthorURL":"https://www.usace.army.mil/"},{"Title":"HEC-RAS","URL":"https://www.hec.usace.army.mil/software/hec-ras/","AuthorName":"United States Army Corps of Engineers","AuthorURL":"https://www.usace.army.mil/"},{"Title":"HEC-RAS documentation","URL":"https://www.hec.usace.army.mil/software/hec-ras/documentation.aspx","AuthorName":"United States Army Corps of Engineers","AuthorURL":"https://www.usace.army.mil/"},{"Title":"LWI ENDMC Datan and Model Catalog.","URL":"https://lwi.endmc.org/","AuthorName":"The Water Institute","AuthorURL":"https://thewaterinstitute.org/"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2024-12-13","RegistryEntryLastModified":"2025-01-08","Slug":"lwi-model-data","Sources":["awslabs-open-data-registry"]}
{"Name":"A practical method for calculating Lyapunov exponents from small data sets","Description":"Detecting the presence of chaos in a dynamical system is an important problem that is solved by measuring the largest Lyapunov exponent. Lyapunov exponents quantify the exponential divergence of initially close state-space trajectories and estimate the amount of chaos in a system.","Documentation":"https://physionet.org/content/lyapunov/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/lyapunov/","ARN":"arn:aws:s3:::physionet-open/lyapunov/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"lyapunov","Sources":["awslabs-open-data-registry"]}
{"Name":"Multi-robot, Multi-Sensor, Multi-Environment Event Dataset (M3ED)","Description":"M3ED is the first multi-sensor event camera (EC) dataset focused on high-speed dynamic motions in robotics applications. M3ED provides high-quality synchronized data from multiple platforms (car, legged robot, UAV), operating in challenging conditions such as off-road trails, dense forests, and performing aggressive flight maneuvers. M3ED also covers demanding operational scenarios for EC, such as high egomotion and multiple independently moving objects. M3ED includes high-resolution stereo EC (1280×720), grayscale and RGB cameras, a high-quality IMU, a 64-beam LiDAR, and RTK localization.","Documentation":"You can access the overview of the data [here](https://m3ed.io/overview/).","Contact":"https://m3ed.io/contact-us/","ManagedBy":"[Daniilidis Group](https://www.grasp.upenn.edu/people/kostas-daniilidis/), [KumarRobotics](https://www.kumarrobotics.org/)","UpdateFrequency":"The dataset will be uploaded sporadically, when bugs are found and new features are implemented (see [updates](https://m3ed.io/#updates)).","Tags":["autonomous vehicles","aws-pds","computer vision","deep learning","event camera","global shutter camera","GNSS","GPS","h5","hdf5","IMU","lidar","machine learning","perception","robotics","RTK"],"License":"Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)","Resources":[{"Description":"Multi-robot, Multi-Sensor, Multi-Environment Event Dataset (M3ED)","ARN":"arn:aws:s3:::m3ed-dist","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dataset","URL":"https://openaccess.thecvf.com/content/CVPR2023W/EventVision/papers/Chaney_M3ED_Multi-Robot_Multi-Sensor_Multi-Environment_Event_Dataset_CVPRW_2023_paper.pdf","AuthorName":"Chaney K, Cladera F, et al."}]},"ADXCategories":null,"RegistryEntryAdded":"2023-06-29","RegistryEntryLastModified":"2024-03-26","Slug":"m3ed","Sources":["awslabs-open-data-registry"]}
{"Name":"Motion Artifact Contaminated ECG Database","Description":"Short duration ECG signals are recorded from a healthy 25-year-old male performing different physical activities to study the effect of motion artifacts on ECG signals and their sparsity.","Documentation":"https://doi.org/10.13026/C2JP4G","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2JP4G","ARN":"arn:aws:s3:::physionet-open/macecgdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"macecgdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Golden Retriever Lifetime Study: Whole genome genotyping of Golden Retrievers on Axiom HD Arrays","Description":"Morris Animal Foundation’s Golden Retriever Lifetime Study is a longitudinal, prospective study following 3044 golden retrievers. The Study’s purpose is to identify the nutritional, environmental, lifestyle and genetic risk factors for cancer and other diseases. The Golden Oldie’s study enrolled an additional cohort of golden retrievers that had reached the age of 12 years or older and had not yet been diagnosed with a malignant cancer. This population can be used as a control group for conditions with high mortality in younger age. This dataset contains the data for ~1.1 million genetic markers from 3224 unique individual dogs processed using Axiom Canine Genotyping Array Sets A and B. Of the 3244 individual samples, 3024 were from the Golden Retriever Lifetime Study cohort and 200 from the Golden Oldies cohort. \n","Documentation":"https://datacommons.morrisanimalfoundation.org/artisanal_dataset/151","Contact":"datacommons@morrisanimalfoundation.org","ManagedBy":"[Morris Animal Foundation](https://www.morrisanimalfoundation.org/)","UpdateFrequency":"Static","Tags":["aws-pds","genome","genotyping","golden retriever lifetime study","life sciences","morris animal foundation"],"License":"CC-BY-SA","Resources":[{"Description":"Two zip files containing genotyping data from Axiom Array sets A and B, as well as data from the combination of both arrays.","ARN":"arn:aws:s3:::mafgrlsgenome","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Tutorials":[{"Title":"GRLS GWAS Tutorial","URL":"https://morrisanimalfoundation.github.io/grGWAS/","AuthorName":"Tamer Mansour"}],"Publications":[{"Title":"Cohort profile: The Golden Retriever Lifetime Study (GRLS)","URL":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0269425","AuthorName":"Julia Labadie, Brenna Swafford, Mara DePena, Kathy Tietje, Rodney Page, Janet Patterson-Kane"},{"Title":"The Golden Retriever Lifetime Study: establishing an observational cohort study with translational relevance for human health","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4581032/","AuthorName":"Michael K. Guy, Rodney L. Page, Wayne A. Jensen, Patricia N. Olson, J. David Haworth, Erin E. Searfoss, and Diane E. Brown"}]},"RegistryEntryAdded":"2023-10-18","RegistryEntryLastModified":"2024-12-20","Slug":"maf-genome","Sources":["awslabs-open-data-registry"]}
{"Name":"MAN TruckScenes","Description":"A large scale multimodal dataset for Autonomous Trucking. Sensor data was recorded with a heavy truck from MAN equipped with 6 lidars, 6 radars, 4 cameras and a high-precision GNSS. MAN TruckScenes allows the research community to come into contact with truck-specific challenges, such as trailer occlusions, novel sensor perspectives, and terminal environments for the first time.\nIt comprises more than 740 scenes of 20s each within a multitude of different environmental conditions. Bounding boxes are available for 27 object classes, 15 attributes, and a range of more than 230m. The scenes are tagged according to 34 distinct scene tags, and all objects are tracked throughout the scene to promote a wide range of applications.","Documentation":"https://www.man.eu/truckscenes","Contact":"truckscenes@man.eu","ManagedBy":"[MAN Truck and Bus SE](https://www.man.eu)","UpdateFrequency":"The dataset may be updated with additional or corrected data on a need-to-update basis.","Tags":["autonomous vehicles","computer vision","deep learning","GPS","IMU","lidar","logistics","machine learning","object detection","object tracking","perception","radar","robotics","transportation"],"License":"[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)","Resources":[{"Description":"MAN TruckScenes mini","ARN":"arn:aws:s3:::man-truckscenes/release/mini/","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"MAN TruckScenes trainval","ARN":"arn:aws:s3:::man-truckscenes/release/trainval/","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"MAN TruckScenes test","ARN":"arn:aws:s3:::man-truckscenes/release/test/","Region":"eu-central-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"TruckScenes devkit tutorial","URL":"https://github.com/TUMFTM/truckscenes-devkit/tree/main","NotebookURL":"https://github.com/TUMFTM/truckscenes-devkit/blob/main/tutorials/truckscenes_tutorial.ipynb","AuthorName":"Felix Fent, Fabian Kuttenreich, Florian Ruch, Farija Rizwin","AuthorURL":null,"Services":null}],"Tools & Applications":[{"Title":"PyPi package","URL":"https://pypi.org/project/truckscenes-devkit/","AuthorName":"Felix Fent, Fabian Kuttenreich, Florian Ruch, Farija Rizwin","AuthorURL":null},{"Title":"TruckScenes devkit","URL":"https://github.com/TUMFTM/truckscenes-devkit","AuthorName":"Felix Fent, Fabian Kuttenreich, Florian Ruch, Farija Rizwin","AuthorURL":null}],"Publications":[{"Title":"MANTruckScenes: A multimodal dataset for autonomous trucking in diverse conditions","URL":"https://arxiv.org/pdf/2407.07462","AuthorName":"Felix Fent, Fabian Kuttenreich, Florian Ruch, Farija Rizwin, et al","AuthorURL":null}]},"ADXCategories":["Automotive Data","Environmental Data"],"RegistryEntryAdded":"2024-12-05","RegistryEntryLastModified":"2024-12-05","Slug":"man-truckscenes","Sources":["awslabs-open-data-registry"]}
{"Name":"High resolution, annual cropland and landcover maps for selected African countries","Description":"High resolution, annual cropland and landcover maps for selected African countries developed by [Clark University](https://clarku.edu)'s [Agricultural Impacts Research Group](https://agroimpacts.info/) using various machine learning approaches applied to Planet imagery, including field boundary and cultivated frequency maps, as well as multi-class land cover.\n","Documentation":"https://github.com/agroimpacts/mapping-africa","Contact":"mappingafrica@clarku.edu","ManagedBy":"[The Agricultural Impacts Research Group](https://agroimpacts.info/)","UpdateFrequency":"New maps are added as developed","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["agriculture","aws-pds","cog","deep learning","labeled","land cover","machine learning","satellite imagery"],"License":"[Planet NICFI participant license agreement](https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf)","Resources":[{"Description":"Field boundary and land cover maps","ARN":"arn:aws:s3:::mappingafrica","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and downloading data","URL":"https://github.com/agroimpacts/mapping-africa","NotebookURL":null,"AuthorName":"Rahebe Abedi","AuthorURL":"https://github.com/Rahebe22"}],"Publications":[{"Title":"A super-ensemble approach to map land cover types with high resolution over data-sparse African savanna landscapes","URL":"https://www.sciencedirect.com/science/article/pii/S1569843222003405","AuthorName":"Song et al. (2023)"},{"Title":"Final report-Phase 1: Creating open agricultural maps and ground truth data to better deliver farm extension services","URL":"https://cropanalytics.net/wp-content/uploads/2022/04/FarmerlineClark-Report-Feb-2022-002.pdf","AuthorName":"Estes et al (2022)"},{"Title":"Final report-Phase 2: Creating next generation field boundary and crop type maps: Rigorous multi-scale groundtruth provides sustainable extension services for smallholders","URL":"https://cropanalytics.net/wp-content/uploads/2022/11/Farmerline-Clark-Round-2-Report-V2-Nov-8-2022.pdf","AuthorName":"Wussah et al (2022)"},{"Title":"High resolution, annual maps of field boundaries for smallholder-dominated croplands at national scales","URL":"https://www.frontiersin.org/article/10.3389/frai.2021.744863","AuthorName":"Estes et al. (2022)"}]},"RegistryEntryAdded":"2024-07-19","RegistryEntryLastModified":"2025-07-09","Slug":"mapping-africa","Sources":["awslabs-open-data-registry"]}
{"Name":"Department of Energy's Marine Energy Data Lake","Description":"Data released from projects funded by the Department of Energy's Water Power Technologies Office (DOE WPTO) \nthat are too large or complex to be conveniently accessed by traditional means. The Marine Energy data lake \naims to improve and automate access of high-value MHK data sets, making data actionable and discoverable by \nresearchers and industry to accelerate analysis and advance innovation. \nThis data lake is a sister-data lake to the Department of Energy’s Open Energy Data Initiative (OEDI) data lake. \n","Documentation":"https://github.com/openEDI/documentation/","Contact":"https://github.com/openEDI/documentation/issues","ManagedBy":"[National Laboratory of the Rockies](https://www.nrel.gov/)","UpdateFrequency":"As needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["aws-pds","energy","marine","water"],"License":"Creative Commons Attribution 4.0 United States License","Resources":[{"Description":"[Data Catalog of the Marine and Hydrokinetic Data Repository](https://mhkdr.openei.org/)","ARN":"arn:aws:s3:::marine-energy-data/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=marine-energy-data)"]},{"Description":"[Tidal Resource Data from Sequim Bay Inlet, WA, August 2020](https://mhkdr.openei.org/submissions/395)","ARN":"arn:aws:s3:::marine-energy-data/primre/sequim-tidal-resource/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=marine-energy-data&prefix=primre%2Fsequim-tidal-resource%2F)"]},{"Description":"[PacWave Site Observations](https://mhkdr.openei.org/submissions/596)","ARN":"arn:aws:s3:::marine-energy-data/pacwave/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=marine-energy-data&prefix=pacwave%2F)"]},{"Description":"[Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) Dataset](https://mhkdr.openei.org/submissions/507)","ARN":"arn:aws:s3:::marine-energy-data/umsli/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=marine-energy-data&prefix=umsli%2F)"]},{"Description":"[High Resolution Tidal Hindcast (US Tidal) Dataset](https://mhkdr.openei.org/submissions/632)","ARN":"arn:aws:s3:::marine-energy-data/us-tidal/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=marine-energy-data&prefix=us-tidal%2F)"]},{"Description":"[Water Velocity and Turbulence Data from Sequim Bay, WA, 2016 - 2025](https://mhkdr.openei.org/submissions/662)","ARN":"arn:aws:s3:::marine-energy-data/primre/sequim-tidal-resource-deployments/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=marine-energy-data&prefix=primre%2Fsequim-tidal-resource-deployments%2F)"]},{"Description":"[TEAMER: Acoustics UW Tidal Turbine](https://mhkdr.openei.org/submissions/611)","ARN":"arn:aws:s3:::marine-energy-data/uw-passive-acoustics/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=marine-energy-data&prefix=uw-passive-acoustics%2F)"]},{"Description":"NREL MOIS Data for NWEI Azura","ARN":"arn:aws:s3:::marine-energy-data/mois/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=marine-energy-data&prefix=mois%2F)"]},{"Description":"[Ocean Surface Wave Model Hindcast Data-US East Coast, Gulf of Mexico, Caribbean Sea (Puerto Rico & US Virgin Islands)](https://mhkdr.openei.org/submissions/629)","ARN":"arn:aws:s3:::marine-energy-data/swan/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=marine-energy-data&prefix=swan%2F)"]}],"RegistryEntryAdded":"2023-12-04","RegistryEntryLastModified":"2026-10-22","Slug":"marine-energy-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Gaia DR3","Description":"[Gaia DR3 data](https://www.cosmos.esa.int/web/gaia/dr3) were originally released by the European Space Agency in December 2020. This [HATS](https://hats.readthedocs.io/en/stable)-formatted catalog was produced by the LSST Interdisciplinary Network for Collaboration and Computing. The GAIA HATS Datasets are specifically designed for efficient spatial cross-matching with other HATS-format catalogs, whether within the same archive or across distributed archive data centers. This enables astronomers to perform complex analyses, such as identifying correlations or overlaps between datasets from different surveys. Users can leverage [LSDB (Large-Scale Database)](https://docs.lsdb.io/en/latest/), a scalable spatial analysis library, to execute precise, high-performance operations like cone searches or cross-matching. \n","Documentation":"https://docs.lsdb.io/en/latest/index.html","Contact":"archive@stsci.edu","ManagedBy":"[Space Telescope Science Institute](http://www.stsci.edu/)","Citation":"Please see [the LSDB citation page](https://docs.lsdb.io/en/latest/citation.html) if using LSDB for an academic publication. Please also [cite the Gaia team](https://gea.esac.esa.int/archive/documentation/GDR3/Miscellaneous/sec_credit_and_citation_instructions/).","UpdateFrequency":"Never","Tags":["astronomy"],"License":"Attribution required.","Resources":[{"Description":"Gaia DR3 HATS-Formatted Files","ARN":"arn:aws:s3:::stpubdata/gaia/gaia_dr3/public/hats","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false},{"Description":"Notifications for new Gaia data","ARN":"arn:aws:sns:us-east-1:879230861493:stpubdata/gaia","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Dark Energy Survey / Gaia DR3 Crossmatch","URL":"https://docs.lsdb.io/en/stable/tutorials/pre_executed/des-gaia.html","AuthorName":"LSDB Collaboration"},{"Title":"GAIA HATS Tutorial","URL":"https://github.com/spacetelescope/odr-tutorials/blob/main/mast-gaia-dr3/get-to-know-a-dataset-HATS-Gaia-DR3-SourceCat.ipynb","AuthorName":"MAST Staff"}]},"RegistryEntryAdded":"2026-01-29","RegistryEntryLastModified":"2026-08-13","Slug":"mast-gaia-dr3","Sources":["awslabs-open-data-registry"]}
{"Name":"Galaxy Evolution Explorer Satellite (GALEX)","Description":"The Galaxy Evolution Explorer Satellite (GALEX) was a NASA mission led by the California Institute of Technology, whose primary goal was to investigate how star formation in galaxies evolved from the early universe up to the present. GALEX used microchannel plate detectors to obtain direct images in the near-UV (NUV) and far-UV (FUV), and a grism to disperse light for low resolution spectroscopy.\n","Contact":"archive@stsci.edu","Documentation":"https://archive.stsci.edu/missions-and-data/galex","ManagedBy":"[Space Telescope Science Institute](http://www.stsci.edu/)","Citation":"See the [Legacy Missions acknowledgement](https://archive.stsci.edu/publishing/mission-acknowledgements#section-c3c6c066-474e-4a5c-96ca-5fd3fe025b6b) for full citation instructions.","UpdateFrequency":"Never","Tags":["astronomy","aws-pds"],"License":"Public Domain. Attribution required for refereed scientific papers.","Resources":[{"Description":"Galex Project data files","ARN":"arn:aws:s3:::stpubdata/galex","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false}],"RegistryEntryAdded":"2026-01-29","RegistryEntryLastModified":"2026-01-29","Slug":"mast-galex","Sources":["awslabs-open-data-registry"]}
{"Name":"Hubble Space Telescope","Description":"The Hubble Space Telescope (HST) is one of the most productive scientific instruments ever created. This dataset contains calibrated and raw data for all currently active instruments on HST: ACS, COS, STIS, WFC3, and FGS.\n","Documentation":"https://archive.stsci.edu/missions-and-data/hst","Contact":"archive@stsci.edu","ManagedBy":"[Space Telescope Science Institute](http://www.stsci.edu/)","Citation":"Please see the [HST mission acknowledgement page](https://archive.stsci.edu/publishing/mission-acknowledgements#section-e10746cc-090f-423d-ba4d-c02db088f9c4) for full citation instructions.","UpdateFrequency":"Hourly","Tags":["astronomy","aws-pds"],"License":"Public domain. Attribution required for refereed scientific papers.","Resources":[{"Description":"HST Mission data files","ARN":"arn:aws:s3:::stpubdata/hst","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false},{"Description":"Notifications for new data","ARN":"arn:aws:sns:us-east-1:879230861493:stpubdata/hst","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2026-01-29","RegistryEntryLastModified":"2026-01-29","Slug":"mast-hst","Sources":["awslabs-open-data-registry"]}
{"Name":"The JWST Advanced Extragalactic Survey JADES","Description":"JADES is an infrared imaging and multi-object spectroscopy survey focused on two deep fields: the Hubble Deep Field (GOODS-N) \nand Hubble Ultra Deep Field (GOODS-S). JADES conducted NIRCam imaging in 8-10 bands, covering about 42 square arcminutes to very \ndeep limits (fainter than 30th magnitude) with an average of about 100 hours of total exposure time, and then another 167 square arcminutes\nto a typical exposure time of 25 hrs. Coordinated parallels with the MIRI instrument extend this imaging further into the infrared in smaller regions.\nJADES then performed extensive NIRSpec spectroscopy with over 5000 targets on 31 separate pointings, including two deep exposures of\n55 hrs spread across 5 dispersers. The JADES team added additional imaging and spectroscopy in Cycles 1, 2, and 3 from a number of \naffiliated guest observer programs.\n","Contact":"archive@stsci.edu","Documentation":"https://archive.stsci.edu/hlsp/jades","ManagedBy":"[Space Telescope Science Institute](http://www.stsci.edu/)","Citation":"Please see the [JADES data page](https://jades-survey.github.io/scientists/data.html) for full instructions on\nusing JADES data in published works.\n","UpdateFrequency":"Never","Tags":["astronomy","aws-pds"],"License":"All HLSPs hosted at MAST are subject to a [CC By 4.0 license](https://creativecommons.org/licenses/by/4.0/).","Resources":[{"Description":"JADES files","ARN":"arn:aws:s3:::stpubdata/mast/hlsp/jades","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Tools & Applications":[{"Title":"TIKE: a free, Jupyter-based cloud platform to access and analyze MAST's AWS timeseries data","URL":"https://timeseries.science.stsci.edu","AuthorName":"MAST Staff"}]},"RegistryEntryAdded":"2026-01-29","RegistryEntryLastModified":"2026-02-18","Slug":"mast-jades","Sources":["awslabs-open-data-registry"]}
{"Name":"James Webb Space Telescope (JWST)","Description":"The James Webb Space Telescope (JWST) is the world's next flagship infrared observatory led by NASA with its partners, ESA (European Space Agency), and CSA (Canadian Space Agency). JWST offers scientists the opportunity to observe galaxy evolution, the formation of stars and planets, exoplanetary systems, and our own solar system, in ways never before possible.\n","Documentation":"https://archive.stsci.edu/missions-and-data/jwst","Contact":"archive@stsci.edu","ManagedBy":"[Space Telescope Science Institute](http://www.stsci.edu/)","Citation":"Please see the [JWST mission acknowledgement page](https://archive.stsci.edu/publishing/mission-acknowledgements#section-6e75d209-e28f-4808-9289-881e83c379f7) for full citation instructions.","UpdateFrequency":"Hourly","Tags":["astronomy","aws-pds"],"License":"Public domain. Attribution required for refereed scientific papers.","Resources":[{"Description":"JWST Public Data files","ARN":"arn:aws:s3:::stpubdata/jwst","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false},{"Description":"Notifications for new data","ARN":"arn:aws:sns:us-east-1:879230861493:stpubdata/jwst","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Getting Started with JWST Data (JDox)","URL":"https://jwst-docs.stsci.edu/getting-started-with-jwst-data","AuthorName":"STScI"},{"Title":"MAST Archive Manual for JWST Data","URL":"https://outerspace.stsci.edu/display/MASTDOCS/JWST+Archive+Manual","AuthorName":"MAST, STScI"}]},"RegistryEntryAdded":"2026-01-29","RegistryEntryLastModified":"2026-02-18","Slug":"mast-jwst","Sources":["awslabs-open-data-registry"]}
{"Name":"K2 Mission Data","Description":"The K2 mission observed 100 square degrees for 80 days each across 20 different pointings along the ecliptic, collecting high-precision photometry for a selection of targets within each field. The mission began when the original Kepler mission ended due to loss of the second reaction wheel in 2013.\n","Documentation":"https://archive.stsci.edu/missions-and-data/k2","Contact":"archive@stsci.edu","ManagedBy":"[Space Telescope Science Institute](http://www.stsci.edu/)","Citation":"Please see the [Kepler/K2 acknowledgement page](https://archive.stsci.edu/publishing/mission-acknowledgements#section-179a61b6-6595-49fc-ae11-69695a895a1e) for full citation instructions.","UpdateFrequency":"Never","Tags":["astronomy","aws-pds"],"License":"Public domain. Attribution required for refereed scientific papers.","Resources":[{"Description":"K2 Mission data files","ARN":"arn:aws:s3:::stpubdata/k2","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false}],"RegistryEntryAdded":"2026-01-29","RegistryEntryLastModified":"2026-01-29","Slug":"mast-k2","Sources":["awslabs-open-data-registry"]}
{"Name":"Kepler Mission Data","Description":"The Kepler mission observed the brightness of more than 180,000 stars near the Cygnus constellation at a 30 minute cadence for 4 years in order to find transiting exoplanets, study variable stars, and find eclipsing binaries.\n","Documentation":"https://archive.stsci.edu/missions-and-data/kepler","Contact":"archive@stsci.edu","ManagedBy":"[Space Telescope Science Institute](http://www.stsci.edu/)","Citation":"Please see the [Kepler/K2 acknowledgement page](https://archive.stsci.edu/publishing/mission-acknowledgements#section-179a61b6-6595-49fc-ae11-69695a895a1e) for full citation instructions.","UpdateFrequency":"Never","Tags":["astronomy","aws-pds"],"License":"Public domain. Attribution required for refereed scientific papers.","Resources":[{"Description":"Kepler Mission data files","ARN":"arn:aws:s3:::stpubdata/kepler","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false}],"RegistryEntryAdded":"2026-03-20","RegistryEntryLastModified":"2026-03-20","Slug":"mast-kepler","Sources":["awslabs-open-data-registry"]}
{"Name":"Pan-STARRS PS1 Survey","Description":"The PS1 surveys used a 1.8 meter telescope and its 1.4 Gigapixel camera to image the sky in five broadband filters. The largest of these surveys provides coverage of the entire sky north of -30 degrees declination, with approximately 10 observation epochs across 3 years in each filter.\n","Documentation":"https://outerspace.stsci.edu/display/PANSTARRS/","Contact":"archive@stsci.edu","ManagedBy":"[Space Telescope Science Institute](http://www.stsci.edu/)","Citation":"Please see the [PanSTARRS acknowledgement page](https://archive.stsci.edu/publishing/mission-acknowledgements#section-895d38a0-86b3-4143-b521-6cc3312701f9) for full citation instructions.","UpdateFrequency":"Never","Tags":["astronomy","aws-pds"],"License":"STScI hereby grants the non-exclusive, royalty-free, non-transferable, worldwide right and license to use, reproduce, and publicly display in all media data from the PS1 surveys.","Resources":[{"Description":"PS1 DR1 and DR2 image files","ARN":"arn:aws:s3:::stpubdata/panstarrs/ps1","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false}],"RegistryEntryAdded":"2026-01-29","RegistryEntryLastModified":"2026-01-29","Slug":"mast-panstarrs","Sources":["awslabs-open-data-registry"]}
{"Name":"MIT Quick-Look Pipeline (QLP)","Description":"The MIT Quick-Look Pipeline (QLP) light curves are derived from [TESS](https://archive.stsci.edu/missions-and-data/tess) Full Frame Images (FFIs). \nQuick-Look products provide users an early look at TESS data before final mission products are available at the archive.\n","Documentation":"https://archive.stsci.edu/hlsp/qlp","Contact":"archive@stsci.edu","ManagedBy":"[Space Telescope Science Institute](http://www.stsci.edu/)","Citation":"If using this data in published research, visit the [QLP HLSP page](https://archive.stsci.edu/hlsp/qlp) for the appropriate citation.","UpdateFrequency":"Hourly","Tags":["astronomy","aws-pds"],"License":"All HLSPs hosted at MAST are subject to a [CC By 4.0 license](https://creativecommons.org/licenses/by/4.0/).","Resources":[{"Description":"QLP files","ARN":"arn:aws:s3:::stpubdata/mast/hlsp/qlp","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false},{"Description":"Notifications for new data","ARN":"arn:aws:sns:us-east-1:879230861493:stpubdata/mast/hlsp/qlp","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"TIKE: a free, Jupyter-based cloud platform to access and analyze MAST's AWS timeseries data","URL":"https://timeseries.science.stsci.edu","AuthorName":"MAST Staff"}],"Publications":[{"Title":"Photometry of 10 Million Stars from the First Two Years of TESS Full Frame Images: Part I","URL":"https://ui.adsabs.harvard.edu/abs/2020RNAAS...4..204H/abstract","AuthorName":"Huang et al."},{"Title":"Photometry of 10 Million Stars from the First Two Years of TESS Full Frame Images: Part II","URL":"https://ui.adsabs.harvard.edu/abs/2020RNAAS...4..206H/abstract","AuthorName":"Huang et al."},{"Title":"Quick-look Pipeline Light Curves for 5.7 Million Stars Observed Over the Second Year of TESS' First Extended Mission","URL":"https://ui.adsabs.harvard.edu/abs/2022RNAAS...6..236K/abstract","AuthorName":"Kunimoto et al."},{"Title":"Quick-look Pipeline Lightcurves for 9.1 Million Stars Observed over the First Year of the TESS Extended Mission","URL":"https://ui.adsabs.harvard.edu/abs/2021RNAAS...5..234K/abstract","AuthorName":"Kunimoto et al."}]},"RegistryEntryAdded":"2026-08-06","RegistryEntryLastModified":"2026-09-22","Slug":"mast-qlp","Sources":["awslabs-open-data-registry"]}
{"Name":"TESS-SPOC","Description":"The data products for the TESS-SPOC FFI targets are the same as for the [TESS](https://archive.stsci.edu/missions-and-data/tess)\ntwo-minute cadence targets: calibrated target pixel files, simple aperture photometry (SAP) flux time series, presearch data conditioning\ncorrected (PDCSAP) flux time series, and cotrending basis vectors (CBV). Since TESS-SPOC relies on FFIs, data are sampled at the FFI cadence.\n","Contact":"archive@stsci.edu","Documentation":"https://archive.stsci.edu/hlsp/tess-spoc","ManagedBy":"[Space Telescope Science Institute](http://www.stsci.edu/)","Citation":"Please cite [Caldwell et. al 2020](https://ui.adsabs.harvard.edu/abs/2020RNAAS...4..201C/abstract) when using TESS-SPOC data in published works.","UpdateFrequency":"Monthly","Tags":["astronomy","aws-pds"],"License":"All HLSPs hosted at MAST are subject to a [CC By 4.0 license](https://creativecommons.org/licenses/by/4.0/).","Resources":[{"Description":"TESS-SPOC files","ARN":"arn:aws:s3:::stpubdata/mast/hlsp/tess-spoc","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false},{"Description":"Notifications for new data","ARN":"arn:aws:sns:us-east-1:879230861493:stpubdata/mast/hlsp/tess-spoc","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"TIKE: a free, Jupyter-based cloud platform to access and analyze MAST's AWS timeseries data","URL":"https://timeseries.science.stsci.edu","AuthorName":"MAST Staff"}],"Publications":[{"Title":"TESS Science Processing Operations Center FFI Target List Products","URL":"https://ui.adsabs.harvard.edu/link_gateway/2020RNAAS...4..201C/doi:10.3847/2515-5172/abc9b3","AuthorName":"Caldwell et. al"}]},"RegistryEntryAdded":"2026-01-29","RegistryEntryLastModified":"2026-02-18","Slug":"mast-tess-spoc","Sources":["awslabs-open-data-registry"]}
{"Name":"Transiting Exoplanet Survey Satellite (TESS)","Description":"The Transiting Exoplanet Survey Satellite (TESS) is a multi-year survey that has discovered exoplanets in orbit around bright stars across the entire sky using high-precision photometry. The survey also enables a wide variety of stellar astrophysics, solar system science, and extragalactic variability studies. More information about TESS is available at [MAST](https://archive.stsci.edu/missions-and-data/tess) and the [TESS Science Support Center](https://heasarc.gsfc.nasa.gov/docs/tess/).\n","Documentation":"https://archive.stsci.edu/missions-and-data/tess","Contact":"archive@stsci.edu","ManagedBy":"[Space Telescope Science Institute](http://www.stsci.edu/)","Citation":"Please see the [TESS acknowledgement page](https://archive.stsci.edu/publishing/mission-acknowledgements#section-fc68bc22-9270-4dda-bc47-07ec6ffd2d27) for full citation instructions.","UpdateFrequency":"Monthly","Tags":["astronomy","aws-pds"],"License":"Public domain. Attribution required for refereed scientific papers.","Resources":[{"Description":"TESS Mission data files","ARN":"arn:aws:s3:::stpubdata/tess","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false},{"Description":"Notifications for new data","ARN":"arn:aws:sns:us-east-1:879230861493:stpubdata/tess","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"TIKE - a free, Jupyter-based cloud platform to access and analyze MAST's AWS TESS data","URL":"https://timeseries.science.stsci.edu","AuthorName":"MAST Staff"}],"Publications":[{"Title":"TESS Archive Manual","URL":"https://outerspace.stsci.edu/display/TESS","AuthorName":"MAST Staff"}]},"RegistryEntryAdded":"2026-01-29","RegistryEntryLastModified":"2026-01-29","Slug":"mast-tess","Sources":["awslabs-open-data-registry"]}
{"Name":"TESS-GAIA Light Curve (TGLC)","Description":"TESS-Gaia Light Curve (TGLC) is a PSF-based TESS full-frame image (FFI) light curve product. Using Gaia DR3 as priors, the team forward models the FFIs with the effective point spread function to remove contamination from nearby stars. The resulting light curves show a photometric precision closely tracking the pre-launch prediction of the noise level: TGLC's photometric precision consistently reaches ≲2% at 16th TESS magnitude even in crowded fields, demonstrating excellent decontamination and deblending power.\n","Documentation":"https://archive.stsci.edu/hlsp/tglc","Contact":"archive@stsci.edu","ManagedBy":"[Space Telescope Science Institute](http://www.stsci.edu/)","Citation":"Please cite [Han & Brandt 2023](https://ui.adsabs.harvard.edu/abs/2023AJ....165...71H/abstract) when using TGLC data in published works.","UpdateFrequency":"Monthly","Tags":["astronomy","aws-pds"],"License":"All HLSPs hosted at MAST are subject to a [CC By 4.0 license](https://creativecommons.org/licenses/by/4.0/).","Resources":[{"Description":"TGLC Files","ARN":"arn:aws:s3:::stpubdata/mast/hlsp/tglc","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false},{"Description":"Notifications for new data","ARN":"arn:aws:sns:us-east-1:879230861493:stpubdata/mast/hlsp/tglc","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"TGLC in the Cloud Jupyter Notebook","URL":"https://github.com/spacetelescope/tike_content/blob/main/content/notebooks/tglc/tglc.ipynb","AuthorName":"MAST Staff"}],"Tools & Applications":[{"Title":"TIKE - a free, Jupyter-based cloud platform to access and analyze MAST's AWS timeseries data","URL":"https://timeseries.science.stsci.edu","AuthorName":"MAST Staff"}],"Publications":[{"Title":"TESS-Gaia Light Curve, A PSF-based TESS FFI Light-curve Product","URL":"https://ui.adsabs.harvard.edu/link_gateway/2023AJ....165...71H/doi:10.3847/1538-3881/acaaa7","AuthorName":"Han, T. and Brandt, T."}]},"RegistryEntryAdded":"2026-01-29","RegistryEntryLastModified":"2026-01-29","Slug":"mast-tglc","Sources":["awslabs-open-data-registry"]}
{"Name":"Materials Project Data","Description":"Materials Project is an open database of computed materials properties aiming to accelerate materials science research. The resources in this OpenData dataset contain the raw, parsed, and build data products.","Documentation":"https://docs.materialsproject.org","Contact":"materialsproject@lbl.gov","ManagedBy":"[Materials Project](https://materialsproject.org)","UpdateFrequency":"New versions and objects added as we continuously calculate, parse and build new materials and their properties.","Tags":["aws-pds","chemistry","cloud computing","data assimilation","digital assets","digital preservation","energy","environmental","free software","genome","HPC","information retrieval","infrastructure","json","machine learning","materials science","molecular dynamics","molecule","open source software","physics","post-processing","x-ray crystallography"],"License":"[Materials Project Terms of Use](https://materialsproject.org/about/terms)","Resources":[{"Description":"Raw Data","ARN":"arn:aws:s3:::materialsproject-raw","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Parsed Data","ARN":"arn:aws:s3:::materialsproject-parsed","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://materialsproject-parsed.s3.amazonaws.com/index.html)"]},{"Description":"Build Data","ARN":"arn:aws:s3:::materialsproject-build","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://materialsproject-build.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Getting Started with the Materials API (MAPI)","URL":"https://docs.materialsproject.org/downloading-data/using-the-api","AuthorName":"Materials Project","AuthorURL":"https://materialsproject.org"},{"Title":"Materials Project Data on AWS OpenData","URL":"https://docs.materialsproject.org/downloading-data/aws-opendata","AuthorName":"Materials Project","AuthorURL":"https://materialsproject.org"}],"Tools & Applications":[{"Title":"Atomate2","URL":"https://materialsproject.github.io/atomate2","AuthorName":"Materials Project","AuthorURL":"https://materialsproject.org"},{"Title":"FireWorks","URL":"https://materialsproject.github.io/fireworks","AuthorName":"Materials Project","AuthorURL":"https://materialsproject.org"},{"Title":"MP API python client","URL":"https://pypi.org/project/mp-api/","AuthorName":"Materials Project","AuthorURL":"https://materialsproject.org"},{"Title":"Pymatgen","URL":"https://pymatgen.org","AuthorName":"Materials Project","AuthorURL":"https://materialsproject.org"}],"Publications":[{"Title":"The Materials Project: A materials genome approach to accelerating materials innovation","URL":"https://pubs.aip.org/aip/apm/article/1/1/011002/119685/Commentary-The-Materials-Project-A-materials","AuthorName":"Jain A., Ong S.P., Hautier G., et al."}]},"ADXCategories":["Public Sector Data","Environmental Data"],"RegistryEntryAdded":"2023-09-18","RegistryEntryLastModified":"2023-09-19","Slug":"materials-project","Sources":["awslabs-open-data-registry"]}
{"Name":"Visceral adipose tissue measurements during pregnancy","Description":"This database is from a cohort study of pregnant women up to 20 weeks of pregnancy and followed until delivery. Measurement of maternal visceral adipose tissue (VAT) was performed during routine obstetric ultrasound. At this time, the biometric evaluation was also performed, and information obtained from prenatal care. Gestational outcomes, including gestational diabetes mellitus (GDM), were obtained through evaluating the patients' medical records at the hospitals where they took place. The data was collected as part of a study that sought to evaluate if maternal VAT could predict GDM at time of delivery. Variables included in this dataset are maternal age, previous DM, blood pressure (in the same day of VAT measurement), VAT (in the periumbilical region), gestational age at time of inclusion, number of pregnancies, level of first fasting glucose, pre-gestational body mass index (BMI). Also, the pregnancy outcomes: gestational age at birth, type of delivery (vaginal or caesarean section), child birth weight and the diagnosis of GDM.","Documentation":"https://doi.org/10.13026/p729-7p53","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/p729-7p53","ARN":"arn:aws:s3:::physionet-open/maternal-visceral-adipose/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"maternal-visceral-adipose","Sources":["awslabs-open-data-registry"]}
{"Name":"Maxar Open Data Program","Description":"Pre and post event high-resolution satellite imagery in support of emergency planning, risk assessment, \nmonitoring of staging areas and emergency response, damage assessment, and recovery. These images are generated\nusing the [Maxar ARD](https://ard.maxar.com/docs) pipeline, tiled on an organized grid in analysis-ready\ncloud-optimized formats.\n","Documentation":"https://www.maxar.com/open-data","Contact":"https://www.maxar.com/open-data","UpdateFrequency":"New data is released in response to activations. Older data may be migrated to the ARD format as needed.","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aws-pds","cog","disaster response","earth observation","geospatial","satellite imagery","stac"],"License":"Creative Commons Attribution Non Commercial 4.0","ManagedBy":"[Maxar](https://www.maxar.com/)","Resources":[{"Description":"Imagery and metadata","ARN":"arn:aws:s3:::maxar-opendata","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/maxar-opendata.s3.dualstack.us-west-2.amazonaws.com/events/catalog.json)","[STAC Catalog](https://stacindex.org/catalogs/maxar-open-data-catalog-ard-format#/)"]}],"DataAtWork":{"Tutorials":[{"Title":"ARD and Command Line Tools","URL":"https://ard.maxar.com/docs/working-with-ard/command-line-tools/","AuthorName":"Maxar Open Data","AuthorURL":"https://www.maxar.com/open-data"},{"Title":"ARD Deliverables and File Structure","URL":"https://ard.maxar.com/docs/ard-order-delivery/about-ard-order-delivery/","AuthorName":"Maxar Open Data","AuthorURL":"https://www.maxar.com/open-data"},{"Title":"Data Access (SDK tutorial)","URL":"https://ard.maxar.com/docs/sdk/sdk/data-access/","AuthorName":"Maxar Open Data","AuthorURL":"https://www.maxar.com/open-data"},{"Title":"Maxar Open Data - Leafmap","URL":"https://leafmap.org/notebooks/67_maxar_open_data/","AuthorName":"Qiusheng Wu","AuthorURL":"https://geography.utk.edu/about-us/faculty/dr-qiusheng-wu/"},{"Title":"Visualizing Maxar Open Data with SageMaker Studio Lab","URL":"https://github.com/giswqs/maxar-open-data/","NotebookURL":"https://github.com/giswqs/maxar-open-data/blob/master/examples/maxar_open_data.ipynb","AuthorName":"Qiusheng Wu","AuthorURL":"https://geography.utk.edu/about-us/faculty/dr-qiusheng-wu/","Services":["Amazon SageMaker Studio Lab"]},{"Title":"Visualizing Turkey & Syria Earthquake Maxar Open Data with SageMaker Studio Lab","URL":"https://github.com/giswqs/maxar-open-data/","NotebookURL":"https://github.com/giswqs/maxar-open-data/blob/master/examples/turkey_earthquake.ipynb","AuthorName":"Qiusheng Wu","AuthorURL":"https://geography.utk.edu/about-us/faculty/dr-qiusheng-wu/","Services":["Amazon SageMaker Studio Lab"]}],"Tools & Applications":[{"Title":"Maxar ARD SDK (max-ard)","URL":"https://ard.maxar.com/docs/sdk/","AuthorName":"Maxar Open Data","AuthorURL":"https://www.maxar.com/open-data"},{"Title":"MGP Xpress","URL":"https://xpress.maxar.com/","AuthorName":"MGP Xpress","AuthorURL":"https://xpress-docs.maxar.com/Home.htm#"}],"Publications":[{"Title":" Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami","URL":"https://doi.org/10.1080/19475705.2022.2147455","AuthorName":"Riantini Virtriana et al."},{"Title":"Disaster, Infrastructure and Participatory Knowledge The Planetary Response Network","URL":"https://eprints.lancs.ac.uk/id/eprint/167032/1/Simmons_et_al_2022_CSTP_PRN_Paper_Special_Issue_Accepted_Version.pdf","AuthorName":"Brooke Simmons, Chris Lintott, Steven Reece, et al."},{"Title":"Using Data from Earth Observation to Support Sustainable Development Indicators: An Analysis of the Literature and Challenges for the Future","URL":"https://doi.org/10.3390/su14031191","AuthorName":"Ana Andries, Stephen Morse, Richard J. Murphy, Jim Lynch, and Emma R. Woolliams"}]},"RegistryEntryAdded":"2022-10-10","RegistryEntryLastModified":"2025-02-04","Slug":"maxar-open-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Marginal Build Emissions Rates (MBERs) for Electricity","Description":"The Climate TRACE coalition has developed and maintains free global hourly Build Margin data, also known as MBERs, that are compliant with the Greenhouse Gas Protocol's Project Protocol electricity sector guidance, Guidelines for Grid-Connected Electricity Projects (\"GHGP Guidelines\").","Documentation":"https://github.com/WattTime/mbers-open-data/blob/main/MBER_Data_Summary_and_Methodology.pdf","Contact":"The annual and hourly MBERs data are created and maintained by the Climate TRACE coalition of nonprofits, universities, and tech companies. The largest contributors to the coalition's electricity sector work are WattTime, Transition Zero, Global Energy Monitor, Pixel Scientia Labs, Planet Labs, and Georgetown University. For questions or more information about MBER data, contact coalition@ClimateTRACE.org or visit https://climatetrace.org/contact.","ManagedBy":"Climate TRACE","UpdateFrequency":"Annually","Tags":["aws-pds","carbon","climate","csv","electricity","energy","energy modeling","environmental"],"License":"All data are free and provided without license restrictions.","Citation":"Marginal Build Emissions Rates (MBERs) for Electricity. Climate TRACE. [DATE]. URL: https://www.gem.wiki/MBERs","Resources":[{"Description":"Marginal Build Emissions Rates (MBERs) for Electricity CSV Data","ARN":"arn:aws:s3:::mbers-open-data","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"MBER Orientation and Tutorial","URL":"https://github.com/WattTime/mbers-open-data/blob/main/MBER_Orientation_and_Tutorial.pdf","AuthorName":"Climate TRACE"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-06-17","RegistryEntryLastModified":"2025-07-10","Slug":"mbers-open-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Genomic Characterization of Metastatic Castration Resistant Prostate Cancer","Description":"Biopsies of castration resistant prostate cancer metastases were subjected to whole genome sequencing (WGS), along with RNA-sequencing (RNA-Seq). The overarching goal of the study is to illuminate molecular mechanisms of acquired resistance to therapeutic agents, and particularly androgen signaling inhibitors, in the treatment of metastatic castration resistant prostate cancer (mCRPC). This study is made available on AWS via the NIH STRIDES Initiative.\n","Documentation":"https://isb-cancer-genomics-cloud.readthedocs.io/en/latest/sections/data/WCDT_about.html","Contact":"support@nci-gdc.datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases, although this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genomic","life sciences","STRIDES","whole genome sequencing"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Citation":"dbGaP accession phs001648.v2.p1","Resources":[{"Description":"Whole Genome Sequencing, RNA Sequencing\n","ARN":"arn:aws:s3:::gdc-wcdt-mcrpc-phs001648-2-open","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"}],"Publications":[{"Title":"Genomic characterization of metastatic castration-resistant prostate cancer patients undergoing PSMA radioligand therapy: A single-center experience","URL":"https://doi.org/10.1002/pros.24450","AuthorName":"Swayamjeet Satapathy, Chandan K Das, et al."}]},"RegistryEntryAdded":"2023-05-31","RegistryEntryLastModified":"2023-05-31","Slug":"mcrpc","Sources":["awslabs-open-data-registry"]}
{"Name":"Heart Rate Oscillations during Meditation","Description":"Heart rate time series for 5 different groups of healthy subjects performing meditation techniques.","Documentation":"https://doi.org/10.13026/C2B880","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2B880","ARN":"arn:aws:s3:::physionet-open/meditation/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"meditation","Sources":["awslabs-open-data-registry"]}
{"Name":"MeerKAT Radio Observations of ESO 137-006","Description":"MeerKAT radio telescope visibility data for the radio galaxy ESO 137-006, located in the\nNorma galaxy cluster (Abell 3627) at a distance of approximately 70 megaparsecs. The\nobservations cover two frequency bands (980–1080 MHz and 1356–1440 MHz) and comprise\n14 hours of on-target integration time taken in May 2019 using all 64 MeerKAT antennas.\nThese data led to the discovery of multiple collimated synchrotron threads (CSTs) — narrow,\nthread-like structures of radio emission connecting the galaxy's radio lobes — a configuration\nnot previously observed in radio galaxies (Ramatsoku et al. 2020, A&A 636, L1).\n\nThe data are stored in Zarr format, implementing the CASA Measurement Set v2 data model.\nThey are cloud-native and can be accessed directly from S3 using the dask-ms Python library\nwithout prior download. The dataset consists of three Measurement Sets: one primary\ncalibrator and two target-field observations. Each target dataset is about 1.1TB compressed\n(~3TB uncompressed).\n\nA key community challenge associated with this dataset is performing distributed radio\ninterferometric imaging (gridding, FFT, deconvolution) at scale directly on cloud storage —\na workflow directly relevant to future Square Kilometre Array (SKA) pipelines.\n","Documentation":"https://github.com/ratt-ru/ratt-public-open-data-tutorial/blob/main/documentation.md","Contact":"sperkins@sarao.ac.za","ManagedBy":"[South African Radio Astronomy Observatory (SARAO)](https://www.sarao.ac.za)","UpdateFrequency":"Not updated","Tags":["archives","astronomy","aws-pds","imaging","signal processing","survey","zarr"],"License":"Creative Commons Attribution 4.0 International (CC BY 4.0).\nAttribution required for refereed scientific publications — please cite\nRamatsoku et al. (2020), A&A 636, L1, https://doi.org/10.1051/0004-6361/202037800.\n","Citation":"Ramatsoku, M. et al. (2020). Collimated synchrotron threads linking the radio lobes of ESO 137-006. Astronomy & Astrophysics, 636, L1. https://doi.org/10.1051/0004-6361/202037800","Resources":[{"Description":"MeerKAT Measurement Sets for ESO 137-006 in Zarr format. Includes one primary\ncalibrator dataset (ms1_primary.zarr) and two target-field datasets\n(ms1_target.zarr, ms2_target.zarr). Each Measurement Set contains raw and\ncalibrated visibilities, flags, weights, UVW coordinates, and antenna/timing\nmetadata, stored as chunked Zarr arrays accessible via dask-ms.\n","ARN":"arn:aws:s3:::ratt-public-data/ESO137","Region":"af-south-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse dataset](https://ratt-public-data.s3.af-south-1.amazonaws.com/index.html#ESO137/)"]},{"Description":"Update notifications for s3://ratt-public-data. Users can subscribe to this SNS topic with [AWS Lambda](https://aws.amazon.com/lambda/) or [AWS Simple Queue Service](https://aws.amazon.com/sqs/).","ARN":"arn:aws:sns:af-south-1:666850927229:ratt-public-data-object_created","Region":"af-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: MeerKAT ESO 137-006","URL":"https://github.com/ratt-ru/ratt-public-open-data-tutorial/blob/main/get-to-know-the-eso137-006-dataset.ipynb","NotebookURL":"https://github.com/ratt-ru/ratt-public-open-data-tutorial/blob/main/get-to-know-the-eso137-006-dataset.ipynb","AuthorName":"Simon Perkins","AuthorURL":"https://github.com/ratt-ru"}],"Publications":[{"Title":"Collimated synchrotron threads linking the radio lobes of ESO 137-006","URL":"https://doi.org/10.1051/0004-6361/202037800","AuthorName":"Ramatsoku, M. et al.","AuthorURL":"https://www.aanda.org/articles/aa/full_html/2020/04/aa37800-20/aa37800-20.html"}]},"RegistryEntryAdded":"2026-05-25","RegistryEntryLastModified":"2026-07-06","Slug":"meerkat-eso137-006","Sources":["awslabs-open-data-registry"]}
{"Name":"MegaScenes","Description":"The MegaScenes Dataset is an extensive collection of around 430k scenes, featuring over 100k structure-from-motion reconstructions and over 2 million registered images. MegaScenes includes a diverse array of scenes, such as minarets, building interiors, statues, bridges, towers, religious buildings, and natural landscapes. The images of these scenes are captured under varying conditions, including different times of day, various weather and illumination, and from different devices with distinct camera intrinsics.","Documentation":"https://megascenes.github.io","Contact":"jt664@cornell.edu; gene@cs.cornell.edu","ManagedBy":"Cornell University (https://www.cs.cornell.edu/~snavely/)","UpdateFrequency":"The dataset will be updated periodically.","Tags":["benchmark","computer vision","deep learning","internet"],"License":"Creative Commons Attribution 4.0 International License. The photos have their own licenses.","Resources":[{"Description":"The MegaScenes Dataset is an extensive collection of around 430k scenes, featuring over 100k structure-from-motion reconstructions and over 2 million registered images. The format of the reconstructions follows COLMAP.","ARN":"arn:aws:s3:::megascenes","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"MegaScenes: Scene-Level View Synthesis at Scale","URL":"https://megascenes.github.io","AuthorName":"Tung J., Chou G., Cai R., Yang, G., Zhang K., Wetzstein G., et al."}],"Tools & Applications":[{"Title":"MegaScenes: Scene-Level View Synthesis at Scale","URL":"https://megascenes.github.io","AuthorName":"Tung J., Chou G., Cai R., Yang, G., Zhang K., Wetzstein G., et al."}],"Publications":[{"Title":"MegaScenes: Scene-Level View Synthesis at Scale","URL":"https://megascenes.github.io","AuthorName":"Tung J., Chou G., Cai R., Yang, G., Zhang K., Wetzstein G., et al."}]},"RegistryEntryAdded":"2024-05-09","RegistryEntryLastModified":"2024-05-10","Slug":"megascenes","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office Blended Probabilistic Forecast – Global gridded percentiles","Description":"This product provides percentile weather forecasts. The grid resolution is approximately 20km and covers the whole globe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format.\n\nBlended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks.\n\nThis is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities.\n\nThis info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future.\n\n#### How percentiles work\n\nEnsemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as percentiles, particularly when they want to see where each member of an ensemble sits within the full range of possible outcomes.\n\nPercentiles are generated from an ensemble forecast by first sorting all the members of that ensemble, for example from the coldest temperature to the warmest. To then identify a particular percentile forecast (e.g. the 10th percentile), we find the temperature at which 10% of the ensemble members predict colder conditions. As only 10% of the ensemble members are predicting a lower temperature than the 10th percentile forecast, this indicates that it is giving a relatively low forecast of temperature. Similarly, 90% of ensemble members would predict a lower temperature than the 90th percentile forecast, indicating that it is giving a relatively high forecast of temperature.\n\n#### Precipitation percentiles should be used cautiously\n\nPrecipitation percentiles are potentially useful for experts. But we don't recommend that most people use them, as they are hard to interpret.\n\nPrecipitation is spatially noisy (i.e. it can vary a lot over small distances), especially when it is showery. This means that the individual ensemble forecasts that the percentiles are generated from are likely to have their heaviest precipitation positioned in different places. As a result, a high percentile (e.g. 95th) will pick up the high values from all those different locations and make it appear that heavy rain could occur over a wider area than is physically plausible. In other words, the spatial extent of the precipitation when using a high percentile is not physically realistic. High percentiles can be very useful for finding out what the values at the higher end may be, but not how they are spatially organised.\n\nLikewise, the spatial extent of the precipitation will be greatly reduced for the lower percentiles. If there are differences between the ensemble forecasts about where it will rain or not, it is possible that a low percentile precipitation field may show zero precipitation everywhere. Again, that is potentially misleading because it suggests it might be dry everywhere, which is not what the individual forecasts are necessarily saying. It is better to view different percentile maps together (5th, 50th, 95th) to get a more informative impression.\n\nEven the 50th percentile can be misleading as, by definition, it will never include the highest predicted values. Nor is there any guarantee that the peaks in the 50th percentile grid will align with the peaks in the 95th percentile grid.\n\n#### About the grid\n\nThe grid resolution for Best Probabilistic Forecast Global gridded probabilities is approximately 20km and covers the whole globe.\n\nNumerical weather prediction (NWP) models generate forecasts for each grid point within a geographical area of interest. Each of these gridded forecasts corresponds to a particular diagnostic (e.g. precipitation rate) at a particular time. IMPROVER then takes an ensemble of these gridded forecasts and applies post-processing techniques to enhance them and represent them probabilistically. The resulting grid of values represents probabilities of exceeding or falling below a particular threshold.\n\n| Aspect | Values |\n| --- | --- |\n| Projection | Equirectangular Latitude-Longitude |\n| Standard parallel | 48.16° N |\n| Reference datum | earth_radius = 6371229.0 m |\n| Nominal resolution | 20 km or N640 |\n| North-South spacing | 0.1875° (20.85 km) |\n| East-West spacing | 0.28125° (~20.86 km - UK, 31.27 km - equator) |\n| North-West corner | 89.90625°N, 179.859375°W |\n| South-West corner | 89.90625°S, 179.859375°W |\n| South-East corner | 89.90625°S, 179.859375°E |\n| North-East corner | 89.90625°N, 179.859375°E |\n| East-West points | 1280 |\n| North-South points | 960 |\n| Grid type | Arakawa A |\n\n#### Parameters and timesteps\n\nThere are 38 weather parameters available including:\n\n- Cloud\n- Temperature\n- Pressure\n- Humidity\n- Visibility\n- Precipitation rate and accumulations (see note above about care required with use)\n- UV\n- Wind\n\nFor most parameters, the following timesteps are available:\n\n- Every hour from 0 to 120 hours\n- Every 3 hours from 123 to 192 hours\n\nHowever, timesteps vary significantly for some parameters. Check the [parameter documentation](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/csv/data/blended-probabilistic-forecast-parameter-catalogue.xlsx) for more details.\n\n#### Latency\n\nData is made available shortly after the blend time.\n\n#### Archive length\n\nData is available for the past 30 days.\n\n#### Business needs\n\nThis product supports risk-based decision-making by providing uncertainty ranges rather than single deterministic values. Typical uses include:\n\n- assessing uncertainty for operational planning\n- evaluating weather-related risk thresholds\n- deriving deterministic products (e.g. 50th percentile) from probabilistic outputs\n\nGridded forecasts show how a diagnostic varies spatially across a domain at a given time. By using a time series of gridded fields, you can determine how a weather diagnostic is expected to evolve across a geographic area.\n\nIf you need a forecast for a specific location, a spot forecast may suit your needs better than gridded data. Spot Blended Probabilistic Forecasts are also available as percentiles and probabilities for both the UK and Global domains.\n","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nPlease email our Service Desk at: servicedesk@metoffice.gov.uk and let them know which dataset you are using and that it’s from the Registry of Open Data on AWS.\n\nService desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"4 times each day at around 00, 06, 12 and 18 UTC.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["air temperature","atmosphere","aws-pds","forecast","geoscience","geospatial","model","near-surface air temperature","near-surface relative humidity","netcdf","weather"],"License":"This product is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Resources":[{"Description":"Met Office Blended Probabilistic Forecast – Global gridded percentiles","ARN":"arn:aws:s3:::met-office-global-gridded-percentiles","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-global-gridded-percentiles.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new Met Office Blended Probabilistic Forecast – Global gridded percentiles Data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-global-gridded-percentiles-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Introduction - IMPROVER Documentation","URL":"https://improver.readthedocs.io/en/latest/metadata/Introduction.html","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"Numerical weather prediction models","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"The Met Office Unified Model","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2026-04-29","RegistryEntryLastModified":"2026-07-20","Slug":"met-office-bpf-global-gridded-percentiles","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office Blended Probabilistic Forecast – Global gridded probabilities","Description":"This product provides gridded probabilistic weather forecasts. The grid resolution is approximately 20km and covers the whole globe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format.\n\nBlended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks.\n\nThis is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities.\n\nThis info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future.\n\n#### How probabilities work\n\nEnsemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as probabilities, particularly when they're interested in specific thresholds.\n\nProbabilities are generated from an ensemble forecast by counting how many members of that ensemble exceed a particular threshold value. For example, if the threshold for screen temperature is 5°C, and 9 out of 18 ensemble members show a screen temperature above 5°C, there is a 50% chance of temperatures exceeding 5°C.\n\n#### About the grid\n\nThe grid resolution for Blended Probabilistic Forecast Global gridded probabilities is approximately 20km and covers the whole globe.\n\nNumerical weather prediction (NWP) models generate forecasts for each grid point within a geographical area of interest. Each of these gridded forecasts corresponds to a particular diagnostic (e.g. precipitation rate) at a particular time. IMPROVER then takes an ensemble of these gridded forecasts and applies post-processing techniques to enhance them and represent them probabilistically. The resulting grid of values represents probabilities of exceeding or falling below a particular threshold.\n\n| Aspect | Values |\n| --- | --- |\n| Projection | Equirectangular Latitude-Longitude |\n| Standard parallel | 48.16° N |\n| Reference datum | earth_radius = 6371229.0 m |\n| Nominal resolution | 20 km or N640 |\n| North-South spacing | 0.1875° (20.85 km) |\n| East-West spacing | 0.28125° (~20.86 km - UK, 31.27 km - equator) |\n| North-West corner | 89.90625°N, 179.859375°W |\n| South-West corner | 89.90625°S, 179.859375°W |\n| South-East corner | 89.90625°S, 179.859375°E |\n| North-East corner | 89.90625°N, 179.859375°E |\n| East-West points | 1280 |\n| North-South points | 960 |\n| Grid type | Arakawa A |\n\n#### Parameters and timesteps\n\nThere are 31 weather parameters available including:\n\n- Cloud\n- Lightning\n- Humidity\n- Visibility\n- Precipitation rate and accumulations\n- UV\n- Wind\n\nFor most parameters, the following timesteps are available:\n\n- Every hour from 0 to 120 hours\n- Every 3 hours from 123 to 192 hours\n\nHowever, timesteps vary significantly for some parameters. Check the [parameter documentation](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/csv/data/blended-probabilistic-forecast-parameter-catalogue.xlsx) for more details.\n\n#### Latency\n\nData is made available shortly after the blend time.\n\n#### Archive length\n\nData is available for the past 30 days.\n\n#### Business needs\n\nThis product supports risk-based decision-making by providing uncertainty ranges rather than single deterministic values. Typical uses include:\n\n- assessing uncertainty for operational planning\n- evaluating weather-related risk thresholds\n- deriving deterministic products (e.g. 50th percentile) from probabilistic outputs\n\nGridded forecasts show how a diagnostic varies spatially across a domain at a given time. By using a time series of gridded fields, you can determine how a weather diagnostic is expected to evolve across a geographic area.\n\nIf you need a forecast for a specific location, a spot forecast may suit your needs better than gridded data. Spot Blended Probabilistic Forecasts are also available as percentiles and probabilities for both the UK and Global domains.\n","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nPlease email our Service Desk at: servicedesk@metoffice.gov.uk and let them know which dataset you are using and that it’s from the Registry of Open Data on AWS.\n\nService desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"4 times each day at around 00, 06, 12 and 18 UTC.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["air temperature","atmosphere","aws-pds","forecast","geoscience","geospatial","model","near-surface air temperature","near-surface relative humidity","netcdf","weather"],"License":"This product is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Resources":[{"Description":"Met Office Blended Probabilistic Forecast – Global gridded probabilities","ARN":"arn:aws:s3:::met-office-global-gridded-probabilities","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-global-gridded-probabilities.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new Met Office Blended Probabilistic Forecast – Global gridded probabilities Data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-global-gridded-probabilities-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Introduction - IMPROVER Documentation","URL":"https://improver.readthedocs.io/en/latest/metadata/Introduction.html","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"Numerical weather prediction models","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"The Met Office Unified Model","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2026-04-29","RegistryEntryLastModified":"2026-07-20","Slug":"met-office-bpf-global-gridded-probabilities","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office Blended Probabilistic Forecast – Global spot percentiles","Description":"This product provides percentile weather forecasts for 5,956 sites (or spots) across the globe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format.\n<br><br>\n\nBlended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks. \n<br><br>\n\nThis is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities. \n<br><br>\n\nThis info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future.\n<br><br>\n\n<b>How percentiles work</b>\n<br>\n\nEnsemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as percentiles, particularly when they want to see where each member of an ensemble sits within the full range of possible outcomes. \n<br><br>\nPercentiles are generated from an ensemble forecast by first sorting all the members of that ensemble, for example from the coldest temperature to the warmest. To then identify a particular percentile forecast (e.g. the 10th percentile), we find the temperature at which 10% of the ensemble members predict colder conditions. As only 10% of the ensemble members are predicting a lower temperature than the 10th percentile forecast, this indicates that it is giving a relatively low forecast of temperature. Similarly, 90% of ensemble members would predict a lower temperature than the 90th percentile forecast, indicating that it is giving a relatively high forecast of temperature. \n<br><br>\n\n<b>Precipitation percentiles should be used cautiously</b> \n<br>\nPrecipitation percentiles are potentially useful for experts. But we don't recommend that most people use them, as they are hard to interpret. \n<br><br>\n\nPrecipitation is spatially noisy (i.e. it can vary a lot over small distances), especially when it is showery. This means that the individual ensemble forecasts that the percentiles are generated from are likely to have their heaviest precipitation positioned in different places. As a result, a high percentile (e.g. 95th) will pick up the high values from all those different locations and make it appear that heavy rain could occur over a wider area than is physically plausible. In other words, the spatial extent of the precipitation when using a high percentile is not physically realistic. High percentiles can be very useful for finding out what the values at the higher end may be, but not how they are spatially organised. \n<br><br>\n\nLikewise, the spatial extent of the precipitation will be greatly reduced for the lower percentiles. If there are differences between the ensemble forecasts about where it will rain or not, it is possible that a low percentile precipitation field may show zero precipitation everywhere. Again, that is potentially misleading because it suggests it might be dry everywhere, which is not what the individual forecasts are necessarily saying. It is better to view different percentile maps together (5th, 50th, 95th) to get a more informative impression. \n<br><br>\n\nEven the 50th percentile can be misleading as, by definition, it will never include the highest predicted values. Nor is there any guarantee that the peaks in the 50th percentile grid will align with the peaks in the 95th percentile grid. \n<br><br>\n\n<b>How spot data works</b> \n<br>\nSpot data is derived by extracting site‑specific forecasts from post‑processed gridded forecasts. \n<br><br>\n\nIMPROVER operates on two domains: \n  - the UK domain, which primarily covers the region around the United Kingdom, Ireland and parts of Western Europe \n  - the Global domain, which covers the whole world \n\nWithin each domain, IMPROVER post-processes most forecasts on a grid, though the resolution of this grid differs between the two domains. Site-specific forecasts are drawn from these grids at the end of the post-processing chains. Sites within the UK domain draw forecasts from the high-resolution UK domain grid, as this should provide the best possible forecast, whereas sites outside of this domain draw from the lower resolution Global grid. The two domains are represented in two separate datasets. \n<br><br>\n\n<b>Global domain sites</b>\n<br>\n\nThere are 5,956 sites in the Global domain. (The Global domain excludes the British Isles and parts of Western Europe. For sites in the UK, Ireland and the parts of Western Europe not included in the Global domain, please see the UK Spot Percentiles dataset.) \n<br><br>\n\nSite-specific forecasts in the Global domain are calculated from the MOGREPS-G model alone. However this model is time-lagged to provide additional forecast spread and reduce inter-cycle variation. \n<br><br>\n\n<b>Parameters and timesteps</b>\n<br>\nThere are 68 weather parameters available including:  \n  - Cloud\n  - Temperature\n  - Pressure\n  - Humidity\n  - Visibility\n  - Precipitation rate and accumulations (see note above about care required with use)\n  - UV\n  - Wind\n<br><br>\n\nFor most parameters, the following timesteps are available: \n  - Every hour from 0 to 120 hours \n  - Every 3 hours from 123 to 192 hours \n<br><br>\n\nHowever, timesteps vary significantly for some parameters. Check the [parameter documentation](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/csv/data/blended-probabilistic-forecast-parameter-catalogue.xlsx) for more details. \n<br><br>\n\nThis dataset also contains deterministic “weather symbol” parameters. These are designed to complement the percentiles information in cases where the user wants to extract a single “deterministic” forecast. The 50th percentile works well for some parameters, but others are better represented by the weather symbol. Check the [parameter documentation](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/csv/data/blended-probabilistic-forecast-parameter-catalogue.xlsx) for guidance on where this applies. \n<br><br>\n\n<b>Latency</b>\n<br>\nData is made available shortly after the blend time. \n<br><br>\n\n<b>Archive length</b>\n<br>\nData is available for the past 30 days. \n<br><br>\n\n<b>Business needs</b>\n<br>\nThis product supports risk-based decision-making by providing uncertainty ranges rather than single deterministic values. Typical uses include: \n  - assessing uncertainty for operational planning\n  - evaluating weather-related risk thresholds\n  - deriving deterministic products (e.g. 50th percentile) from probabilistic outputs\n<br><br>\n\nSpot forecasts provide information about weather diagnostics at single sites. By using a time series of these forecasts, you can determine how a weather diagnostic is expected to evolve at a particular location. This is ideal for users if you are not moving spatially and are instead interested in how conditions are evolving at your location. This is the kind of time-series information you see in the Met Office app when you look at a forecast for your home address.  \n<br><br>\n\nGridded forecasts show how a diagnostic varies spatially across a domain at a given time. By using a time series of gridded fields, you can determine how a weather diagnostic is expected to evolve across a geographic area. So you may find them more useful if you need to consider moving spatially. This kind of product is very familiar from television broadcast weather forecasts. Gridded Blended Probabilistic Forecasts are also available as percentiles and probabilities for both the UK and Global domains.  \n","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nPlease email our Service Desk at: servicedesk@metoffice.gov.uk and let them know which dataset you are using and that it’s from the Registry of Open Data on AWS.\n<br><br>\nService desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"4 times each day at around 00, 06, 12 and 18 UTC.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["air temperature","atmosphere","aws-pds","forecast","geoscience","geospatial","model","near-surface air temperature","near-surface relative humidity","netcdf","weather"],"License":"This product is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Resources":[{"Description":"Met Office Blended Probabilistic Forecast – Global spot percentiles","ARN":"arn:aws:s3:::met-office-global-spot-percentiles","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-global-spot-percentiles.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new Met Office Blended Probabilistic Forecast – Global spot percentiles Data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-global-spot-percentiles-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Introduction - IMPROVER Documentation","URL":"https://improver.readthedocs.io/en/latest/metadata/Introduction.html","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"Numerical weather prediction models","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"The Met Office Unified Model","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2026-04-29","RegistryEntryLastModified":"2026-07-08","Slug":"met-office-bpf-global-spot-percentiles","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office Blended Probabilistic Forecast – Global spot probabilities","Description":"This product provides probabilistic weather forecasts for 5,956 sites (or spots) across the globe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format. \n<br><br>\n\nBlended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks. \n<br><br>\n\nThis is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities. \n<br><br>\n\nThis info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future. \n<br><br>\n\n<b>How probabilities work</b>\n<br>\n\nEnsemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as probabilities, particularly when they're interested in specific thresholds. \n<br><br>\n\nProbabilities are generated from an ensemble forecast by counting how many members of that ensemble exceed a particular threshold value. For example, if the threshold for screen temperature is 5°C, and 9 out of 18 ensemble members show a screen temperature above 5°C, there is a 50% chance of temperatures exceeding 5°C. \n<br><br>\n\n<b>How spot data works</b>\n<br>\nSpot data is derived by extracting site‑specific forecasts from post‑processed gridded forecasts. \n<br><br>\n\nIMPROVER operates on two domains: \n  - the UK domain, which primarily covers the region around the United Kingdom, Ireland and parts of Western Europe\n  - the Global domain, which covers the whole world\n<br><br>\n\nWithin each domain, IMPROVER post-processes most forecasts on a grid, though the resolution of this grid differs between the two domains. Site-specific forecasts are drawn from these grids at the end of the post-processing chains. Sites within the UK domain draw forecasts from the high-resolution UK domain grid, as this should provide the best possible forecast, whereas sites outside of this domain draw from the lower resolution Global grid. The two domains are represented in two separate datasets. \n<br><br>\n\n<b>Global domain sites</b>\n<br>\nThere are 5,956 sites in the Global domain. (The Global domain excludes the British Isles and parts of Western Europe. For sites in the UK, Ireland and the parts of Western Europe not included in the Global domain, please see the UK Spot Percentiles dataset.) \n<br><br>\n\nSite-specific forecasts in the Global domain are calculated from the MOGREPS-G model alone. However this model is time-lagged to provide additional forecast spread and reduce inter-cycle variation. \n<br><br>\n\n<b>Parameters and timesteps</b>\n<br>\nThere are 33 weather parameters available including:\n  - Cloud\n  - Lightning\n  - Pressure\n  - Humidity\n  - Visibility\n  - Precipitation rate and accumulations\n  - UV\n  - Wind\n<br><br>\n\nFor most parameters, the following timesteps are available:\n- Every hour from 0 to 120 hours\n- Every 3 hours from 123 to 192 hours\n<br><br>\n\nHowever, timesteps vary significantly for some parameters. Check the [parameter documentation](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/csv/data/blended-probabilistic-forecast-parameter-catalogue.xlsx) for more details. \n<br><br>\n\n<b>Latency</b>\n<br>\nData is made available shortly after the model blend time. \n<br><br>\n\n<b>Archive length</b>\n<br>\nData is available for the past 30 days. \n<br><br>\n\n<b>Business needs</b>\n<br>\nThis product supports risk-based decision-making by providing uncertainty ranges rather than single deterministic values. Typical uses include: \n- assessing uncertainty for operational planning\n- evaluating weather-related risk thresholds\n- deriving deterministic products (e.g. 50th percentile) from probabilistic outputs \n<br><br>\n\nSpot forecasts provide information about weather diagnostics at single sites. By using a time series of these forecasts, you can determine how a weather diagnostic is expected to evolve at a particular location. This is ideal for users if you are not moving spatially and are instead interested in how conditions are evolving at your location. This is the kind of time-series information you see in the Met Office app when you look at a forecast for your home address.  \n<br><br>\n\nGridded forecasts show how a diagnostic varies spatially across a domain at a given time. By using a time series of gridded fields, you can determine how a weather diagnostic is expected to evolve across a geographic area. So you may find them more useful if you need to consider moving spatially. This kind of product is very familiar from television broadcast weather forecasts. Gridded Blended Probabilistic Forecasts are also available as percentiles and probabilities for both the UK and Global domains.\n","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nPlease email our Service Desk at: servicedesk@metoffice.gov.uk and let them know which dataset you are using and that it’s from the Registry of Open Data on AWS.\n<br><br>\nService desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"4 times each day at around 00, 06, 12 and 18 UTC.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["air temperature","atmosphere","aws-pds","forecast","geoscience","geospatial","model","near-surface air temperature","near-surface relative humidity","netcdf","weather"],"License":"This product is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Resources":[{"Description":"Met Office Blended Probabilistic Forecast - Global spot probabilities","ARN":"arn:aws:s3:::met-office-global-spot-probabilities","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-global-spot-probabilities.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new Met Office Blended Probabilistic Forecast – Global spot probabilities Data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-global-spot-probabilities-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Introduction - IMPROVER Documentation","URL":"https://improver.readthedocs.io/en/latest/metadata/Introduction.html","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"Numerical weather prediction models","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"The Met Office Unified Model","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2026-04-29","RegistryEntryLastModified":"2026-07-08","Slug":"met-office-bpf-global-spot-probabilities","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office Blended Probabilistic Forecast – UK gridded percentiles","Description":"This product provides gridded percentile weather forecasts. The grid resolution is approximately 2km and covers the UK and parts of Western Europe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format.\n\nBlended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks.\n\nThis is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities.\n\nThis info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future.\n\n#### How percentiles work\n\nEnsemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as percentiles, particularly when they want to see where each member of an ensemble sits within the full range of possible outcomes.\n\nPercentiles are generated from an ensemble forecast by first sorting all the members of that ensemble, for example from the coldest temperature to the warmest. To then identify a particular percentile forecast (e.g. the 10th percentile), we find the temperature at which 10% of the ensemble members predict colder conditions. As only 10% of the ensemble members are predicting a lower temperature than the 10th percentile forecast, this indicates that it is giving a relatively low forecast of temperature. Similarly, 90% of ensemble members would predict a lower temperature than the 90th percentile forecast, indicating that it is giving a relatively high forecast of temperature.\n\n#### Precipitation percentiles should be used cautiously\n\nPrecipitation percentiles are potentially useful for experts. But we don't recommend that most people use them, as they are hard to interpret.\n\nPrecipitation is spatially noisy (i.e. it can vary a lot over small distances), especially when it is showery. This means that the individual ensemble forecasts that the percentiles are generated from are likely to have their heaviest precipitation positioned in different places. As a result, a high percentile (e.g. 95th) will pick up the high values from all those different locations and make it appear that heavy rain could occur over a wider area than is physically plausible. In other words, the spatial extent of the precipitation when using a high percentile is not physically realistic. High percentiles can be very useful for finding out what the values at the higher end may be, but not how they are spatially organised.\n\nLikewise, the spatial extent of the precipitation will be greatly reduced for the lower percentiles. If there are differences between the ensemble forecasts about where it will rain or not, it is possible that a low percentile precipitation field may show zero precipitation everywhere. Again, that is potentially misleading because it suggests it might be dry everywhere, which is not what the individual forecasts are necessarily saying. It is better to view different percentile maps together (5th, 50th, 95th) to get a more informative impression.\n\nEven the 50th percentile can be misleading as, by definition, it will never include the highest predicted values. Nor is there any guarantee that the peaks in the 50th percentile grid will align with the peaks in the 95th percentile grid.\n\n#### About the grid\n\nThe grid resolution is approximately 2km and covers the UK and parts of Western Europe.\n\nNumerical weather prediction (NWP) models generate forecasts for each grid point within a geographical area of interest. Each of these gridded forecasts corresponds to a particular diagnostic (e.g. precipitation rate) at a particular time. IMPROVER then takes an ensemble of these gridded forecasts and applies post-processing techniques to enhance them and represent them probabilistically. The resulting grid of values represents probabilities of exceeding or falling below a particular threshold.\n\n| Aspect | Values |\n| --- | --- |\n| Projection | Lambert Azimuthal Equal Area |\n| Longitude of Prime Meridian | 0.0° W |\n| Reference datum | ETRS89 - uses the GRS80 as a reference ellipsoid. |\n| Nominal resolution | 2 km |\n| Extreme West | -1158 km |\n| Extreme East | 924 km |\n| Extreme North | 902 km |\n| Extreme South | -1036 km |\n| Northwest Corner (Long, Lat) | -24.5099247, 61.31885886 |\n| Northeast Corner (Long, Lat) | 15.27976922, 61.9206868 |\n| Southwest Corner (Long, Lat) | -17.11712928, 44.51715281 |\n| Southeast Corner (Long, Lat) | 9.21255933, 44.899873 |\n| Semi Major Axis | 6378.137 km |\n| Semi MinorAxis | 6356.75231414036 km |\n| Longitude of Projection Origin | 2.5° W |\n| Latitude of Projection Origin | 54.9° N |\n| False Eastings | 0.0 km |\n| False Northings | 0.0 km |\n| East-West points | 1042 |\n| North-South points | 970 |\n| Grid type | Arakawa A |\n\n#### Parameters and timesteps\n\nThere are 44 weather parameters available including:\n\n- Cloud\n- Temperature\n- Pressure\n- Humidity\n- Visibility\n- Precipitation rate and accumulations (see note above about care required with use)\n- UV\n- Wind\n\nFor most parameters, the following timesteps are available:\n\n- Every hour from 0 to 120 hours\n- Every 3 hours from 123 to 186 hours\n\nHowever, timesteps vary significantly for some parameters. Check the [parameter documentation](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/csv/data/blended-probabilistic-forecast-parameter-catalogue.xlsx) for more details.\n\n#### Latency\n\nData is made available shortly after the model blend time.\n\n#### Archive length\n\nData is available for the past 30 days.\n\n#### Business needs\n\nThis product supports risk-based decision-making by providing uncertainty ranges rather than single deterministic values. Typical uses include:\n\n- assessing uncertainty for operational planning\n- evaluating weather-related risk thresholds\n- deriving deterministic products (e.g. 50th percentile) from probabilistic outputs\n\nGridded forecasts show how a diagnostic varies spatially across a domain at a given time. By using a time series of gridded fields, you can determine how a weather diagnostic is expected to evolve across a geographic area.\n\nIf you need a forecast for a specific location, a spot forecast may suit your needs better than gridded data. Spot Blended Probabilistic Forecasts are also available as percentiles and probabilities for both the UK and Global domains.\n","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nservicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"4 times each day at around 00, 06, 12 and 18 UTC.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["air temperature","atmosphere","aws-pds","forecast","geoscience","geospatial","model","near-surface air temperature","near-surface relative humidity","netcdf","weather"],"License":"This product is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Resources":[{"Description":"Met Office UK Gridded Probabilistic Blended Percentiles Forecast","ARN":"arn:aws:s3:::met-office-uk-gridded-percentiles","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-uk-gridded-percentiles.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new UK Gridded Blended Probabilistic Forecast Data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-uk-gridded-percentiles-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Introduction - IMPROVER Documentation","URL":"https://improver.readthedocs.io/en/latest/metadata/Introduction.html","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"Numerical weather prediction models","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"The Met Office Unified Model","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2026-04-29","RegistryEntryLastModified":"2026-07-20","Slug":"met-office-bpf-uk-gridded-percentiles","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office Blended Probabilistic Forecast – UK gridded probabilities","Description":"This product provides gridded probabilistic weather forecasts. The grid resolution is approximately 2km and covers the UK and parts of Western Europe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format.\n\nBlended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks.\n\nThis is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities.\n\nThis info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future.\n\n#### How probabilities work\n\nEnsemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as probabilities, particularly when they're interested in specific thresholds.\n\nProbabilities are generated from an ensemble forecast by counting how many members of that ensemble exceed a particular threshold value. For example, if the threshold for screen temperature is 5°C, and 9 out of 18 ensemble members show a screen temperature above 5°C, there is a 50% chance of temperatures exceeding 5°C.\n\n#### About the grid\n\nThe grid resolution is approximately 2km and covers the UK and parts of Western Europe.\n\nNumerical weather prediction (NWP) models generate forecasts for each grid point within a geographical area of interest. Each of these gridded forecasts corresponds to a particular diagnostic (e.g. precipitation rate) at a particular time. IMPROVER then takes an ensemble of these gridded forecasts and applies post-processing techniques to enhance them and represent them probabilistically. The resulting grid of values represents probabilities of exceeding or falling below a particular threshold.\n\n| Aspect | Values |\n| --- | --- |\n| Projection | Lambert Azimuthal Equal Area |\n| Longitude of Prime Meridian | 0.0° W |\n| Reference datum | ETRS89 - uses the GRS80 as a reference ellipsoid. |\n| Nominal resolution | 2 km |\n| Extreme West | -1158 km |\n| Extreme East | 924 km |\n| Extreme North | 902 km |\n| Extreme South | -1036 km |\n| Northwest Corner (Long, Lat) | -24.5099247, 61.31885886 |\n| Northeast Corner (Long, Lat) | 15.27976922, 61.9206868 |\n| Southwest Corner (Long, Lat) | -17.11712928, 44.51715281 |\n| Southeast Corner (Long, Lat) | 9.21255933, 44.899873 |\n| Semi Major Axis | 6378.137 km |\n| Semi MinorAxis | 6356.75231414036 km |\n| Longitude of Projection Origin | 2.5° W |\n| Latitude of Projection Origin | 54.9° N |\n| False Eastings | 0.0 km |\n| False Northings | 0.0 km |\n| East-West points | 1042 |\n| North-South points | 970 |\n| Grid type | Arakawa A |\n\n#### Parameters and timesteps\n\nThere are 44 weather parameters available including:\n\n- Cloud\n- Temperature\n- Pressure\n- Humidity\n- Visibility\n- Precipitation rate and accumulations\n- Wind\n\nFor most parameters, the following timesteps are available:\n\n- Every hour from 0 to 120 hours\n- Every 3 hours from 123 to 186 hours\n\nHowever, timesteps vary significantly for some parameters. Check the [parameter documentation](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/csv/data/blended-probabilistic-forecast-parameter-catalogue.xlsx) for more details.\n\n#### Latency\n\nData is made available shortly after the model blend time.\n\n#### Archive length\n\nData is available for the past 30 days.\n\n#### Business needs\n\nThis product supports risk-based decision-making by providing uncertainty ranges rather than single deterministic values. Typical uses include:\n\n- assessing uncertainty for operational planning\n- evaluating weather-related risk thresholds\n- deriving deterministic products (e.g. 50th percentile) from probabilistic outputs\n\nGridded forecasts show how a diagnostic varies spatially across a domain at a given time. By using a time series of gridded fields, you can determine how a weather diagnostic is expected to evolve across a geographic area.\n\nIf you need a forecast for a specific location, a spot forecast may suit your needs better than gridded data. Spot Blended Probabilistic Forecasts are also available as percentiles and probabilities for both the UK and Global domains.\n","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nPlease email our Service Desk at: servicedesk@metoffice.gov.uk and let them know which dataset you are using and that it’s from the Registry of Open Data on AWS.\n\nService desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"The data is continuously updated by blending multiple sources. The latest version is what was available at the latest blend time.\n\nWe check for new model data every 15 minutes and include it in the blend as it becomes available. For the first 5 days of the forecast, we include data from models that run 24 updates per day. Beyond day 5, we get new model data every 6 hours.\n\nThis means that the data comes through as a continuous stream of rolling updates, rather than a coherent set of data from a single model run. Instead of waiting for a complete set of data, it's best to simply take the latest version of each timestep and parameter available at the moment you refresh. Some elements may update frequently while others won't change as regularly, and that’s expected.\n","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["air temperature","atmosphere","aws-pds","forecast","geoscience","geospatial","model","near-surface air temperature","near-surface relative humidity","netcdf","weather"],"License":"This product is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Resources":[{"Description":"Met Office Blended Probabilistic Forecast – UK gridded probabilities","ARN":"arn:aws:s3:::met-office-uk-gridded-probabilities","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-uk-gridded-probabilities.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new Met Office Blended Probabilistic Forecast – UK gridded probabilities Data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-uk-gridded-probabilities-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Introduction - IMPROVER Documentation","URL":"https://improver.readthedocs.io/en/latest/metadata/Introduction.html","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"Numerical weather prediction models","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"The Met Office Unified Model","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2026-04-29","RegistryEntryLastModified":"2026-07-20","Slug":"met-office-bpf-uk-gridded-probabilities","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office Blended Probabilistic Forecast – UK Spot Percentiles","Description":"This product provides percentile weather forecasts for 7,213 sites (or spots) across the United Kingdom, Ireland and parts of Western Europe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format. \n<br><br>\n\nBlended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks. \n<br><br>\n\nThis is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities. \n<br><br>\n\nThis info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future. \n<br><br>\n\n<b>How percentiles work</b>\n<br>\nEnsemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as percentiles, particularly when they want to see where each member of an ensemble sits within the full range of possible outcomes. \n<br><br>\n\nPercentiles are generated from an ensemble forecast by first sorting all the members of that ensemble, for example from the coldest temperature to the warmest. To then identify a particular percentile forecast (e.g. the 10th percentile), we find the temperature at which 10% of the ensemble members predict colder conditions. As only 10% of the ensemble members are predicting a lower temperature than the 10th percentile forecast, this indicates that it is giving a relatively low forecast of temperature. Similarly, 90% of ensemble members would predict a lower temperature than the 90th percentile forecast, indicating that it is giving a relatively high forecast of temperature. \n<br><br>\n\n<b>Precipitation percentiles should be used cautiously</b>\n<br>\nPrecipitation percentiles are potentially useful for experts. But we don't recommend that most people use them, as they are hard to interpret. \n<br><br>\n\nPrecipitation is spatially noisy (i.e. it can vary a lot over small distances), especially when it is showery. This means that the individual ensemble forecasts that the percentiles are generated from are likely to have their heaviest precipitation positioned in different places. As a result, a high percentile (e.g. 95th) will pick up the high values from all those different locations and make it appear that heavy rain could occur over a wider area than is physically plausible. In other words, the spatial extent of the precipitation when using a high percentile is not physically realistic. High percentiles can be very useful for finding out what the values at the higher end may be, but not how they are spatially organised. \n<br><br>\n\nLikewise, the spatial extent of the precipitation will be greatly reduced for the lower percentiles. If there are differences between the ensemble forecasts about where it will rain or not, it is possible that a low percentile precipitation field may show zero precipitation everywhere. Again, that is potentially misleading because it suggests it might be dry everywhere, which is not what the individual forecasts are necessarily saying. It is better to view different percentile maps together (5th, 50th, 95th) to get a more informative impression. \n<br><br>\n\nEven the 50th percentile can be misleading as, by definition, it will never include the highest predicted values. Nor is there any guarantee that the peaks in the 50th percentile grid will align with the peaks in the 95th percentile grid. \n<br><br>\n\n<b>How spot data works</b>\n<br>\nSpot data is derived by extracting site‑specific forecasts from post‑processed gridded forecasts. \n<br><br>\n\nIMPROVER operates on two domains: \n- the UK domain, which primarily covers the region around the United Kingdom, Ireland and parts of Western Europe \n- the Global domain, which covers the whole world \n<br><br>\n\nWithin each domain, IMPROVER post-processes most forecasts on a grid, though the resolution of this grid differs between the two domains. Site-specific forecasts are drawn from these grids at the end of the post-processing chains.  \n<br><br>\n\n<b>UK domain sites</b>\n<br>\nThere are 7,213 forecast sites in the UK domain. IMPROVER calculates forecasts for these sites from UK domain gridded forecasts. These gridded forecasts are generated from a lead-time-dependent blend of up to 4 forecasting models: an extrapolation nowcast (MONOW), UKV, MOGREPS-UK, and MOGREPS-G. The blend can also involve multiple cycles of a model at different lead times. \n<br><br>\n\n<b>Parameters and timesteps</b>\n<br>\nThere are 61 weather parameters available including:  \n- Cloud\n- Temperature\n- Pressure\n- Humidity\n- Visibility\n- Precipitation rate and accumulations\n- UV\n- Wind  \n<br><br>\n\nFor most parameters, the following timesteps are available: \n- Every hour from 0 to 120 hours \n- Every 3 hours from 123 to 186 hours \n<br><br>\n\nHowever, timesteps vary significantly for some parameters. Check the [parameter documentation](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/csv/data/blended-probabilistic-forecast-parameter-catalogue.xlsx) for more details. \n<br><br>\n\n<b>Latency</b>\n<br>\nData is made available shortly after the model blend time. \n<br><br>\n\n<b>Archive length</b>\n<br>\nData is available for the past 30 days. \n<br><br>\n\n<b>Business needs</b>  \n<br>\nThis product supports risk-based decision-making by providing uncertainty ranges rather than single deterministic values. Typical uses include: \n- assessing uncertainty for operational planning\n- evaluating weather-related risk thresholds\n- deriving deterministic products (e.g. 50th percentile) from probabilistic outputs \n<br><br>\n\nSpot forecasts provide information about weather diagnostics at single sites. By using a time series of these forecasts, you can determine how a weather diagnostic is expected to evolve at a particular location. This is ideal for users if you are not moving spatially and are instead interested in how conditions are evolving at your location. This is the kind of time-series information you see in the Met Office app when you look at a forecast for your home address.\n<br><br>\n\nGridded forecasts show how a diagnostic varies spatially across a domain at a given time. By using a time series of gridded fields, you can determine how a weather diagnostic is expected to evolve across a geographic area. So you may find them more useful if you need to consider moving spatially. This kind of product is very familiar from television broadcast weather forecasts. Gridded Blended Probabilistic Forecasts are also available as percentiles and probabilities for both the UK and Global domains. \n","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nPlease email our Service Desk at: servicedesk@metoffice.gov.uk and let them know which dataset you are using and that it’s from the Registry of Open Data on AWS.\n<br><br>\nService desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"The data is continuously updated by blending multiple sources. The latest version is what was available at the latest blend time.\n<br><br>\nWe check for new model data every 15 minutes and include it in the blend as it becomes available. For the first 5 days of the forecast, we include data from models that run 24 updates per day. Beyond day 5, we get new model data every 6 hours. \n<br><br>\nThis means that the data comes through as a continuous stream of rolling updates, rather than a coherent set of data from a single model run. Instead of waiting for a complete set of data, it's best to simply take the latest version of each timestep and parameter available at the moment you refresh. Some elements may update frequently while others won't change as regularly, and that’s expected.\n","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["air temperature","atmosphere","aws-pds","forecast","geoscience","geospatial","model","near-surface air temperature","near-surface relative humidity","netcdf","weather"],"License":"This product is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Resources":[{"Description":"Met Office Blended Probabilistic Forecast – UK Spot Percentiles","ARN":"arn:aws:s3:::met-office-uk-spot-percentiles","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-uk-spot-percentiles.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new Met Office Blended Probabilistic Forecast – UK Spot Percentiles Data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-uk-spot-percentiles-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Introduction - IMPROVER Documentation","URL":"https://improver.readthedocs.io/en/latest/metadata/Introduction.html","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"Numerical weather prediction models","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"The Met Office Unified Model","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2026-04-29","RegistryEntryLastModified":"2026-07-08","Slug":"met-office-bpf-uk-spot-percentiles","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office Blended Probabilistic Forecast – UK Spot Probabilities","Description":"This product provides probabilistic weather forecasts for 7,213 sites (or spots) across the United Kingdom, Ireland and parts of Western Europe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format.\n<br><br>\n\nBlended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks. \n<br><br>\n\nThis is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities. \n<br><br>\n\nThis info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future. \n<br><br>\n\n<b>How probabilities work</b>\n<br>\nEnsemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as probabilities, particularly when they're interested in specific thresholds. \n<br><br>\n\nProbabilities are generated from an ensemble forecast by counting how many members of that ensemble exceed a particular threshold value. For example, if the threshold for screen temperature is 5°C, and 9 out of 18 ensemble members show a screen temperature above 5°C, there is a 50% chance of temperatures exceeding 5°C. \n<br><br>\n\n<b>How spot data works</b>\n<br>\nSpot data is derived by extracting site‑specific forecasts from post‑processed gridded forecasts. \n<br><br>\n\nIMPROVER operates on two domains: \n- the UK domain, which primarily covers the region around the United Kingdom, Ireland and parts of Western Europe \n- the Global domain, which covers the whole world \n<br><br>\n\nWithin each domain, IMPROVER post-processes most forecasts on a grid, though the resolution of this grid differs between the two domains. Site-specific forecasts are drawn from these grids at the end of the post-processing chains.  \n<br><br>\n\n<b>UK domain sites</b>\n<br>\nThere are 7,213 forecast sites in the UK domain. IMPROVER calculates forecasts for these sites from UK domain gridded forecasts. These gridded forecasts are generated from a lead-time-dependent blend of up to 4 forecasting models: an extrapolation nowcast (MONOW), UKV, MOGREPS-UK, and MOGREPS-G. The blend can also involve multiple cycles of a model at different lead times. \n<br><br>\n\n<b>Parameters and timesteps</b>\n<br>\nThere are 61 weather parameters available including:  \n- Cloud\n- Temperature\n- Pressure\n- Humidity\n- Visibility\n- Precipitation rate and accumulations\n- UV\n- Wind  \n<br><br>\n\nFor most parameters, the following timesteps are available: \n- Every hour from 0 to 120 hours \n- Every 3 hours from 123 to 186 hours \n<br><br>\n\nHowever, timesteps vary significantly for some parameters. Check the [parameter documentation](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/csv/data/blended-probabilistic-forecast-parameter-catalogue.xlsx) for more details. \n<br><br>\n\nThis dataset also contains deterministic “weather symbol” parameters. These are designed to complement the percentiles information in cases where the user wants to extract a single “deterministic” forecast. The 50th percentile works well for some parameters, but others are better represented by the weather symbol. Check the [parameter documentation](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/csv/data/blended-probabilistic-forecast-parameter-catalogue.xlsx) for guidance on where this applies. \n<br><br>\n\n<b>Latency</b>\n<br>\nData is made available shortly after the model blend time. \n<br><br>\n\n<b>Archive length</b>\n<br>\nData is available for the past 30 days. \n<br><br>\n\n<b>Business needs</b>  \n<br>\nThis product supports risk-based decision-making by providing uncertainty ranges rather than single deterministic values. Typical uses include: \n- assessing uncertainty for operational planning\n- evaluating weather-related risk thresholds\n- deriving deterministic products (e.g. 50th percentile) from probabilistic outputs \n<br><br>\n\nSpot forecasts provide information about weather diagnostics at single sites. By using a time series of these forecasts, you can determine how a weather diagnostic is expected to evolve at a particular location. This is ideal for users if you are not moving spatially and are instead interested in how conditions are evolving at your location. This is the kind of time-series information you see in the Met Office app when you look at a forecast for your home address.\n<br><br>\n\nGridded forecasts show how a diagnostic varies spatially across a domain at a given time. By using a time series of gridded fields, you can determine how a weather diagnostic is expected to evolve across a geographic area. So you may find them more useful if you need to consider moving spatially. This kind of product is very familiar from television broadcast weather forecasts. Gridded Blended Probabilistic Forecasts are also available as percentiles and probabilities for both the UK and Global domains. \n","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nPlease email our Service Desk at: servicedesk@metoffice.gov.uk and let them know which dataset you are using and that it’s from the Registry of Open Data on AWS.\n<br><br>\nService desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"The data is continuously updated by blending multiple sources. The latest version is what was available at the latest blend time.\n<br><br>\nWe check for new model data every 15 minutes and include it in the blend as it becomes available. For the first 5 days of the forecast, we include data from models that run 24 updates per day. Beyond day 5, we get new model data every 6 hours. \n<br><br>\nThis means that the data comes through as a continuous stream of rolling updates, rather than a coherent set of data from a single model run. Instead of waiting for a complete set of data, it's best to simply take the latest version of each timestep and parameter available at the moment you refresh. Some elements may update frequently while others won't change as regularly, and that’s expected.\n","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["air temperature","atmosphere","aws-pds","forecast","geoscience","geospatial","model","near-surface air temperature","near-surface relative humidity","netcdf","weather"],"License":"This product is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Resources":[{"Description":"Met Office Blended Probabilistic Forecast – UK Spot Probabilities","ARN":"arn:aws:s3:::met-office-uk-spot-probabilities","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-uk-spot-probabilities.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new Met Office Blended Probabilistic Forecast – UK Spot Probabilities Data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-uk-spot-probabilities-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Introduction - IMPROVER Documentation","URL":"https://improver.readthedocs.io/en/latest/metadata/Introduction.html","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"Numerical weather prediction models","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"The Met Office Unified Model","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2026-04-29","RegistryEntryLastModified":"2026-07-08","Slug":"met-office-bpf-uk-spot-probabilities","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office Global DCPP hindcast data from CMIP6","Description":"This product provides global climate hindcast data on the native grids of the HadGEM3-GC31-MM model. The atmospheric and land surface data is presented on an N216 lat-lon grid (432 x 324 longitude/latitudes, 0.833 x 0.556 degrees). The ocean and sea ice data is presented on the ORCA025 tripolar grid (primarily 0.25 degrees; 1440 x 1205 longitude/latitudes).\n<br><br>\n\nThe data consists of model output from the [Hadley Centre Global Environment Model in the Global Coupled configuration 3.1](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019MS001995) (HadGEM3-GC3.1), as part of the [Decadal Climate Prediction Project](https://gmd.copernicus.org/articles/9/3751/2016/)  dcppA-hindcast experiment within the sixth Coupled Model Intercomparison Project (CMIP6).\n<br><br>\n\nThe hindcasts (climate simulations initialised from historical data at a specified point in time) start in 1960 through to 2015. Each is typically 10 years long with 10 ensemble members for each start date. An ensemble of simulations was run, using different initial conditions for each member, to allow users of the data to understand and quantify levels of uncertainty.\n<br><br>\n\nThis data has previously been published to [ESGF](https://esgf-ui.ceda.ac.uk/search?project=CMIP6&activeFacets=%7B%22institution_id%22%3A%22MOHC%22%2C%22activity_id%22%3A%22DCPP%22%2C%22experiment_id%22%3A%22dcppA-hindcast%22%7D) as part of CMIP6 and is presented here in an optimised netCDF format through application of the [CMIP7 repacking tools](https://github.com/NCAS-CMS/cmip7_repack).\n<br><br>\n\nVariables included are documented on the [github wiki](https://github.com/MetOffice/ASDI-CMIP6-DCPP-hindcast/wiki). This also includes a table describing associated metadata.\n<br><br>\n\n## Parameters, timesteps, levels and forecast periods \n\nVariables are presented in the same form as published in CMIP6, with a single data variable per NetCDF4 file including coordinate data and global attribute metadata. The [github wiki](https://github.com/MetOffice/ASDI-CMIP6-DCPP-hindcast/wiki#table-of-variables) describes the list of variables included along with their definition. \n<br><br>\n\nThe start date and ensemble member identifier (variant_label) are encoded in the “member_id” field (as described in [Taylor et al](https://zenodo.org/records/15670624)). For example, the third ensemble member for the 1970 start date will have a member_id of `s1970-r3i1p1f2` and corresponding data will be found under “CMIP6/DCPP/MOHC/HadGEM3-GC31-MM/dcppA-hindcast/s1970-r3i1p1f2”. \n<br><br>\nOther general information on CMIP6 data standards can be found [here](https://wcrp-cmip.github.io/cmip7-guidance/docs/CMIP6/). \n\n## Archive length \n\nSimulations with start dates from 1960 through to 2018 are included and each simulation is approximately 10 years in length. \n\n## Business needs \n\nThis data can be used to evaluate the utility of decadal forecast data for both academic and commercial purposes. DCPP activities, through CMIP and the Met Office’s role as the [WMO Lead Centre for Annual-to-Decadal Climate Prediction](https://hadleyserver.metoffice.gov.uk/wmolc/https://hadleyserver.metoffice.gov.uk/wmolc/), will continue to provide similar data and updates to future CMIP exercises (CMIP6Plus, CMIP7).\n","Documentation":"(https://github.com/MetOffice/ASDI-CMIP6-DCPP-hindcast)","Contact":"https://github.com/MetOffice/ASDI-CMIP6-DCPP-hindcast","ManagedBy":"[Met Office](https://www.metoffice.gov.uk)","UpdateFrequency":"Rare once complete","Tags":["atmosphere","aws-pds","climate","climate model","CMIP6","geospatial","ice","land","model","oceans","sustainability"],"License":"CMIP6 data included is licensed under CC-BY 4.0 (see [here](https://wcrp-cmip.github.io/CMIP6_CVs/docs/CMIP6_source_id_licenses.html).","Citation":"Hermanson, Leon (2020). MOHC HadGEM3-GC31-MM model output prepared for CMIP6 DCPP dcppA-hindcast. Version YYYYMMDD[1]. Distributed via ASDI, equivalent to ESGF data published via doi:[10.22033/ESGF/CMIP6.5892](http://doi.org/10.22033/ESGF/CMIP6.5892)\n\n[1] please use the version number corresponding to the data\n","Resources":[{"Description":"CMIP6 standards-compliant netCDF data","ARN":"arn:aws:s3:::met-office-cmip6","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://met-office-cmip6.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Global mean temperature calculation from a subset of hindcasts.","URL":"https://github.com/MetOffice/ASDI-CMIP6-DCPP-hindcast/tree/main/examples/get-to-know-met-office-cmip6-data.ipynb","NotebookURL":"https://github.com/MetOffice/ASDI-CMIP6-DCPP-hindcast/tree/main/examples/get-to-know-met-office-cmip6-data.ipynb","AuthorName":"matthew-mizielinski","AuthorURL":"https://github.com/matthew-mizielinski"}]},"DeprecatedNotice":null,"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-08-28","RegistryEntryLastModified":"2026-09-03","Slug":"met-office-cmip6","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office Global Deterministic 10km on a 2-year rolling archive","Description":"A numerical weather prediction forecast for the whole globe, with a resolution of approximately 0.09 degrees i.e. 10km (2,560 x 1,920 grid points). The data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes.<br><br>\n\nThe global deterministic model is a global configuration of the Unified Model, which is the Met Office’s flagship Numerical Weather Prediction model. The model’s initial state is kept close to the real atmosphere using hybrid 4D-Var data assimilation.<br><br>\n\nThe archive contains data from the past two years. The data is typically available approximately 3 to 6 hours after the model run time.<br><br>\n\nThe following timesteps are available for most parameters:\n- every hour from 0 to 54 hours\n- every 3 hours from 57 to 144 hours\n- every 6 hours from 150 to 168 hours\n","UpdateFrequency":"The model is run four times each day, with forecast reference times of 00:00, 06:00, 12:00 and 18:00 (UTC).<br><br>\n\nThe runs at 00:00 and 12:00 provide data for the next 168 hours. The runs at 06:00 and 18:00 provide data for the next 67 hours.<br><br>\n\nThe forecast reference time represents the nominal data time or start time of a model forecast run, rather than the time when the data is available.\n","License":"British Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nservicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["air temperature","atmosphere","aws-pds","forecast","geoscience","geospatial","model","near-surface air temperature","near-surface relative humidity","netcdf","weather"],"Resources":[{"Description":"Met Office Global Deterministic 10km on a 2-year rolling archive","ARN":"arn:aws:s3:::met-office-atmospheric-model-data","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-atmospheric-model-data.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new atmospheric model data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-atmospheric-model-data-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Numerical weather prediction models","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"The Met Office Unified Model","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2024-07-22","RegistryEntryLastModified":"2026-07-08","Slug":"met-office-global-deterministic","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office Global Ensemble Prediction System (MOGREPS-G) on a 30-day rolling archive","Description":"A numerical weather prediction model that produces forecasts for the whole globe up to a week ahead. The projection used is the Equirectangular Latitude-Longitude and the grid resolution is 20km. The data is available as NetCDF files. It's offered on a free, unsupported basis, so we don’t recommend using it for any critical business purposes.<br><br>\n\nMet Office Global Ensemble Prediction System (MOGREPS-G) is a global configuration of the Unified Model, which is the Met Office's flagship Numerical Weather Prediction model.<br><br>\n\nThe archive contains 30 days of data. The data is typically available approximately 10-11 hours after the model run time.<br><br>\n\nThe following timesteps are available:\n- Every hour from 0 to 54 hours or from 0 to 132 hours (depending on the parameter)\n- Every 3 hours between 57 to 198 hours or from 135 to 198 hours (depending on the parameter) for data prior to late January 2026. Data produced after this run out to 246 hours.\n","UpdateFrequency":"The MOGREPS-G model runs four times per day at 00:00, 06:00, 12:00 and 18:00 UTC.","License":"British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nservicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["air temperature","atmosphere","aws-pds","forecast","geoscience","geospatial","global","meteorological","model","near-surface air temperature","near-surface relative humidity","netcdf","weather"],"Resources":[{"Description":"Met Office Global Ensemble Prediction System (MOGREPS-G) on a 30-day rolling archive","ARN":"arn:aws:s3:::met-office-global-ensemble-model-data","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-global-ensemble-model-data.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new global ensemble model data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-global-ensemble-model-data-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Numerical weather prediction models","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"The Met Office Unified Model","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"What is an ensemble forecast?","URL":"https://www.metoffice.gov.uk/research/weather/ensemble-forecasting/what-is-an-ensemble-forecast","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2024-12-13","RegistryEntryLastModified":"2026-07-08","Slug":"met-office-global-ensemble","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office Global Ocean model on a 2-year rolling archive","Description":"The Global Ocean component of the Met Office Global Coupled Atmosphere-Land-Ocean-Ice system which has been running in operations since May 2022. The system provides a global physical analysis and coupled forecast products providing 3D daily mean fields of temperature and salinity, zonal and meridional velocities; 2D daily mean fields of sea surface height, bottom temperature, mixed layer depth, sea ice fraction, sea ice thickness and sea ice zonal and meridional velocities; and instantaneous hourly fields for sea surface height, sea surface temperature and surface currents. The Met Office Global Coupled Atmosphere-Land-Ocean-Ice system is comprised of both deterministic and ensemble forecasting systems both of which are coupled to an interactive ocean. Ocean and ice analysis and forecast products are currently generated from the deterministic system once a day with forecast with lead time out to 7 days, interpolated onto a regular latitude-longitude grid products by our Global Marine Post Processing (MaPP-GL) system.","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nservicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"The Global Ocean model runs once daily. T+00h is the midnight prior to production, the following are produced. T-48h to T-24h best estimate; T-24h to T+00h analyses; 7 day forecast (from T+00h).","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["aws-pds","forecast","geoscience","geospatial","global","marine","model","netcdf","ocean sea surface height","oceans","weather"],"License":"British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Resources":[{"Description":"Met Office Global Ocean model on a 2-year rolling archive","ARN":"arn:aws:s3:::met-office-global-ocean-model-data","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-global-ocean-model-data.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new global ocean model data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-global-ocean-model-data-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Coupled forecasting development","URL":"https://www.metoffice.gov.uk/research/weather/ocean-forecasting/coupled-development","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"Ocean models","URL":"https://www.metoffice.gov.uk/services/government/defence-and-security/ocean-models","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2025-02-05","RegistryEntryLastModified":"2026-07-08","Slug":"met-office-global-ocean","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office Global Wave model on a 2-year rolling archive","Description":"The Met Office runs global wave forecast models to support marine safety and operational decision making. Met Office configurations are developed to be run using the community wave model WAVEWATCH IIITM. The global wave configuration is designed to generate accurate forecasts for open waters of the world’s oceans and larger seas. The Met Office wave models are forced using wind data from the Met Office Global Atmospheric Hi-Res Model. The global wave model is run to provide a five day outlook for wave characteristics defining height, period and direction of waves within a given sea-state. The model uses a refined grid system in order to better represent fetch in constrained sea areas and blocking effects of islands and headlands. Please note this is a public beta and therefore a non-operational service.","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nservicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"The Global Wave model runs at 00:00, 12:00 (UTC) to T+144h, and at 06:00, 18:00 (UTC) to T+66h.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["aws-pds","forecast","geoscience","geospatial","global","marine","model","netcdf","ocean sea surface height","oceans","weather"],"License":"British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Resources":[{"Description":"Met Office Global Wave model on a 2-year rolling archive","ARN":"arn:aws:s3:::met-office-global-wave-model-data","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-global-wave-model-data.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new global wave model data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-global-wave-model-data-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Coupled forecasting development","URL":"https://www.metoffice.gov.uk/research/weather/ocean-forecasting/coupled-development","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"Ocean models","URL":"https://www.metoffice.gov.uk/services/government/defence-and-security/ocean-models","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2025-02-10","RegistryEntryLastModified":"2026-07-08","Slug":"met-office-global-wave","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office NWS Ocean model on a 2-year rolling archive","Description":"The Northwest European continental shelf physical ocean model predicts temperature, salinity and circulation for waters surrounding the UK. \nOcean physics analysis provides a 6-day forecast for the North-West European Atlantic shelf at 1.5km resolution:<br/>\n - 33 depth levels<br/>\n - Currents<br/>\n - Salinity<br/>\n - Temperature<br/>\n - Mixing Layer Thickness\n","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nservicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"The NWS Ocean model runs once daily.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["aws-pds","forecast","geoscience","geospatial","marine","model","netcdf","ocean sea surface height","oceans","weather"],"License":"British Crown copyright 2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Resources":[{"Description":"Met Office NWS Ocean model on a 2-year rolling archive","ARN":"arn:aws:s3:::met-office-nws-ocean-model-data","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-nws-ocean-model-data.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new NWS ocean model data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-nws-ocean-model-data-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Coupled forecasting development","URL":"https://www.metoffice.gov.uk/research/weather/ocean-forecasting/coupled-development","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"Ocean models","URL":"https://www.metoffice.gov.uk/services/government/defence-and-security/ocean-models","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2025-05-22","RegistryEntryLastModified":"2026-07-09","Slug":"met-office-nws-ocean","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office NWS Wave model on a 2-year rolling archive","Description":"Northwest European continental shelf regional wave model predicting sea-state and various sea and swell wave characteristics for waters surrounding the UK.\n\nThe Met Office runs global and regional wave forecast models to support marine safety and operational decision making.\nMet Office configurations are developed to be run using the community wave model WAVEWATCH IIITM.\nThe global wave configuration is designed to generate accurate forecasts for open waters of the world's oceans and larger seas,\nwhilst regional configurations are run in order to improve accuracy closer to the coast.\nThe Met Office wave models are forced using wind data from the Met Office Global Atmospheric Hi-Res Model and, where\nappropriate in regional configurations, currents from the Met Office shelf seas model.\n\nMet Office NWS Wave (UK wave) Model 6 day forecast to provide an outlook for wave characteristics defining height, period and direction of waves within a given sea-state.\nThe model uses a refined grid system in order to better represent fetch in constrained sea areas and blocking effects of\nislands and headlands. Data are released at the model's base resolution of 0.030303 degrees longitude by 0.0135135 degrees latitude\napproximating to a 1.5km grid with a daily update cycle\tat 00, 06, 12, 18z\n","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nservicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"The model runs at 00:00, 06:00, 12:00, 18:00 (UTC) from T-48h to T+143h","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["aws-pds","forecast","geoscience","geospatial","marine","model","netcdf","ocean sea surface height","oceans","weather"],"License":"British Crown copyright 2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Resources":[{"Description":"Met Office NWS Wave model on a 2-year rolling archive","ARN":"arn:aws:s3:::met-office-nws-wave-model-data","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-nws-wave-model-data.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new NWS wave model data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-nws-wave-model-data-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Coupled forecasting development","URL":"https://www.metoffice.gov.uk/research/weather/ocean-forecasting/coupled-development","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"Ocean models","URL":"https://www.metoffice.gov.uk/services/government/defence-and-security/ocean-models","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2025-05-22","RegistryEntryLastModified":"2026-07-09","Slug":"met-office-nws-wave","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office UK Deterministic (UKV)2km on a 2-year rolling archive","Description":"A high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, projected on to a 2km horizontal grid. The data is available as NetCDF files. It's offered on a free, unsupported basis, so we don’t recommend using it for any critical business purposes.<br><br>\n\nBased on the Met Office UKV model, which is a deterministic, numerical weather prediction model for the UK and Ireland. It is a UK configuration of the Unified Model, which is the Met Office’s flagship Numerical Weather Prediction model.<br><br>\n\nThe archive contains data from the past two years. The data is typically available approximately 3 to 6 hours after the model run time.<br><br>\n\nThe following timesteps are available:\n- every hour from 0 to 54 hours (for most parameters)\n- every 3 hours from 57 to 120 hours<br><br>\n\nFor some parameters, data is available for multiple vertical height or pressure levels through the atmosphere.\n","UpdateFrequency":"There are three lengths of model run, each with its own update frequency:\n- Nowcast: forecasts the next 12 hours and runs at 0100, 0200, 0400, 0500, 0700, 0800, 1000, 1100, 1300, 1400, 1600, 1700, 1900, 2000, 2200 and 2300 UTC.\n- Short: forecasts the next 54 hours and runs at 0000, 0600, 0900, 1200, 1800 and 2100 UTC.\n- Medium: forecasts the next 120 hours and runs at 0300 and 1500 UTC.\n","License":"British Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nservicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["air temperature","atmosphere","aws-pds","forecast","geoscience","geospatial","model","near-surface air temperature","near-surface relative humidity","netcdf","weather"],"Resources":[{"Description":"Met Office UK Deterministic (UKV)2km on a 2-year rolling archive","ARN":"arn:aws:s3:::met-office-atmospheric-model-data","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-atmospheric-model-data.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new atmospheric model data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-atmospheric-model-data-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Numerical weather prediction models","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"The Met Office Unified Model","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2024-07-22","RegistryEntryLastModified":"2026-07-08","Slug":"met-office-uk-deterministic","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office Global and Regional Ensemble Prediction System - UK (MOGREPS-UK) on a 30-day rolling archive","Description":"A numerical weather prediction model that produces forecasts for the UK for the next 5 days. Parameters including temperature, pressure, wind, humidity, etc. are forecast at grid points separated by about 2.2 km, and the model has multiple vertical levels. The data is available as NetCDF files. It's offered on a free, unsupported basis, so we don’t recommend using it for any critical business purposes.<br><br>\n\nMet Office Global and Regional Ensemble Prediction System - UK (MOGREPS-UK) is a UK configuration of the Unified Model, which is the Met Office's flagship Numerical Weather Prediction model.<br><br>\n\nThe archive contains 30 days of data. The data is typically available approximately 10-11 hours after the model run time.<br><br>\n\nFor most parameters, timesteps are provided every hour from 0 to 126 hours\n","UpdateFrequency":"The MOGREPS-UK model runs 24 times per day.","License":"British Crown copyright 2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nservicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["air temperature","atmosphere","aws-pds","forecast","geoscience","geospatial","global","meteorological","model","near-surface air temperature","near-surface relative humidity","netcdf","weather"],"Resources":[{"Description":"Met Office Global and Regional Ensemble Prediction System - UK (MOGREPS-UK) on a 30-day rolling archive","ARN":"arn:aws:s3:::met-office-uk-ensemble-model-data","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-uk-ensemble-model-data.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new UK ensemble model data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-uk-ensemble-model-data-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Numerical weather prediction models","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"The Met Office Unified Model","URL":"https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"},{"Title":"What is an ensemble forecast?","URL":"https://www.metoffice.gov.uk/research/weather/ensemble-forecasting/what-is-an-ensemble-forecast","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}],"Tools & Applications":[{"Title":"Iris","URL":"https://scitools-iris.readthedocs.io/en/stable/userguide/index.html","AuthorName":"Iris Contributors","AuthorURL":"https://scitools-iris.readthedocs.io/en/stable/copyright.html"}]},"RegistryEntryAdded":"2025-04-24","RegistryEntryLastModified":"2026-07-08","Slug":"met-office-uk-ensemble","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office UK Land Surface Observations","Description":"Land surface weather observations for 31 parameters from over 250 locations across the Met Office UK land observation network. The data is available as CSV files. You can use it to monitor the latest weather affecting a specific location so you can plan for your business or operations.\n<br><br>\nThe observations are produced every minute and transmitted to the Amazon Registry of Open Data every hour. They’re available for a rolling 7-day period (168 hours).\n<br>\nAll locations in the observation network are within the bounding box:\n - -15 (West)\n - 48 (South)\n - 5 (East)\n - 62 (North)\nOn average they are about 40km apart, distributed uniformly to detect as many weather features as possible.\n<br><br>\n31 meteorological parameters are measured across the observation network. But not all locations record all parameters. Check the documentation for the full list of parameters.\n","UpdateFrequency":"60 minutes of new data for all locations is ingested into the Amazon Registry of Open Data every hour. For example, at 12:00 new data will arrive for observations collected between 10:00-1100. This is due to the quality checks required to verify data for public consumption.","License":"British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nservicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["atmosphere","aws-pds","csv","geospatial","precipitation","weather"],"Resources":[{"Description":"Met Office UK Land Surface Observations","ARN":"arn:aws:s3:::met-office-land-observations-data","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-land-observations-data.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new UK land surface observations data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-land-observations-data-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"UK Land Surface Observations Usage Examples","URL":"https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/services/data/met-office-uk-land-surface-observations-asdi-pdf-document-2.0.pdf","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}]},"RegistryEntryAdded":"2026-02-25","RegistryEntryLastModified":"2026-07-08","Slug":"met-office-uk-land-observations","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office UK Marine Observations","Description":"Marine surface weather observations for 32 parameters from 69 locations across the Met Office marine observation network. Observations are available for a rolling 7-day period (168 hours). The data is available as CSV files.<br><br>\nThe data comes from moored buoys, light vessels and ships with automatic weather stations onboard. Buoys and light vessels are static and you can view their locations on the [Met Office Marine Observations page](https://weather.metoffice.gov.uk/specialist-forecasts/coast-and-sea/observations). You can use the data to monitor the latest weather affecting a specific marine location so you can plan for your business or operations.<br><br>\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities. \n","UpdateFrequency":"Every hour, at 15 minutes past the hour, we retrieve a single set of parameter values for each location. For example, at 12:15 new data is retrieved for observations collected between 11:00 and 12:00. This is due to the quality checks required to verify data for public consumption and global network latency.","License":"British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nservicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["atmosphere","aws-pds","csv","geospatial","precipitation","weather"],"Resources":[{"Description":"Met Office UK Marine Observations","ARN":"arn:aws:s3:::met-office-marine-observations-data","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-marine-observations-data.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new UK marine observations data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-marine-observations-data-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"UK Marine Observations Usage Examples","URL":"https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/services/data/pdf-documentation_met-office-uk-marine-observations-on-asdi.pdf","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}]},"RegistryEntryAdded":"2026-04-08","RegistryEntryLastModified":"2026-07-08","Slug":"met-office-uk-marine-observations","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office UK Radar Observations on a 2-year rolling archive","Description":"The United Kingdom Composite, Surface Rain Rate Estimate is an international radar composite produced by Met Office (UK). This is a composite, radar reflectivity derived, surface rain rate estimate product in HDF5 code from stations covering the United Kingdom.","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nservicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST).  As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"Four images per hour (every 15 minutes). The data is available within 20 minutes of the validity time of the product.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["atmosphere","aws-pds","geospatial","h5","hdf5","precipitation","radar","weather"],"License":"British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)","Resources":[{"Description":"Met Office UK Radar Observations on a 2-year rolling archive","ARN":"arn:aws:s3:::met-office-radar-obs-data","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse bucket](https://met-office-radar-obs-data.s3.eu-west-2.amazonaws.com/index.html)"]},{"Description":"Notifications for new radar observations data","ARN":"arn:aws:sns:eu-west-2:633885181284:met-office-radar-obs-data-object_created","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Radar","URL":"https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/research/library-and-archive/library/publications/factsheets/factsheet_15-weather-radar-2020_2023.pdf","AuthorName":"Met Office","AuthorURL":"https://www.metoffice.gov.uk/"}]},"RegistryEntryAdded":"2025-03-19","RegistryEntryLastModified":"2026-07-08","Slug":"met-office-uk-radar-observations","Sources":["awslabs-open-data-registry"]}
{"Name":"Met Office UK Earth System Model (UKESM1) ARISE-SAI geoengineering experiment data","Description":"Data from the UK Earth System Model (UKESM1) ARISE-SAI experiment. The UKESM1 ARISE-SAI experiment explores the impacts of geoengineering via the injection of sulphur dioxide (SO2) into the stratosphere in order to keep global mean surface air temperature near 1.5 C above the pre-industrial climate. Data includes a five member ensemble of simulations with SO2 injection plus a five member ensemble of SSP2-4.5 simulations from CMIP6 to serve as a reference data set","Documentation":"(https://github.com/MetOffice/arise-cmor-tables)","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nhttps://github.com/MetOffice/arise-cmor-tables/issues\n","ManagedBy":"[Met Office](https://www.metoffice.gov.uk)","UpdateFrequency":"Rare once complete","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","climate","climate model","CMIP6","geospatial","ice","land","model","oceans","sustainability"],"License":"CMIP6 data included is licensed under CC-BY 4.0 (see [here](https://wcrp-cmip.github.io/CMIP6_CVs/docs/CMIP6_source_id_licenses.html). Additional ARISE-SAI data is licenced under the [Open Government Licence v3](https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/)","Citation":null,"Resources":[{"Description":"CMIP6 standards-compliant netCDF data","ARN":"arn:aws:s3:::met-office-ukesm1-arise","Region":"eu-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://met-office-ukesm1-arise.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Examples coming soon","URL":"https://github.com/MetOffice/arise-cmor-tables/tree/master/examples","AuthorName":"MetOffice"}]},"DeprecatedNotice":null,"ADXCategories":null,"RegistryEntryAdded":"2023-10-17","RegistryEntryLastModified":"2026-07-08","Slug":"met-office-ukesm1-arise","Sources":["awslabs-open-data-registry"]}
{"Name":"MetaGraph Sequence Indexes","Description":"The MetaGraph Sequence Indexes dataset comprises full-text searchable index files for raw sequencing data hosted in major public repositories. These include the European Nucleotide Archive (ENA) managed by the European Bioinformatics Institute (EMBL-EBI), the Sequence Read Archive (SRA) maintained by the National Center for Biotechnology Information (NCBI), and the DNA Data Bank of Japan (DDBJ) Sequence Read Archive (DRA).\n\nAll index files can be used with the MetaGraph framework for sequence search. Indexes can be jointly used for aggregated search in the cloud or can be individually downloaded for search using local hardware. \n","Documentation":"Documentation of the dataset available under https://github.com/ratschlab/metagraph-open-data\nDocumentation of the MetaGraph framework is available under https://metagraph.ethz.ch/\n","Contact":"Please open an issue under https://github.com/ratschlab/metagraph-open-data/issues","ManagedBy":"[Biomedical Informatics Lab, ETH Zurich, Switzerland](https://bmi.inf.ethz.ch)","UpdateFrequency":"Continuously as new sequencing data becomes available.","Tags":["analysis ready data","biodiversity","bioinformatics","biology","fasta","genome","genomic","graph","information retrieval","life sciences","medicine","metagenomics","microbiome","transcriptomics","whole exome sequencing","whole genome sequencing"],"License":"[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)","Citation":"Karasikov M, Mustafa H, Danciu D, Zimmermann M, Barber C, Raetsch G, Kahles A. Indexing All Life’s Known Biological Sequences. Preprint (2024). doi: [10.1101/2020.10.01.322164](https://www.biorxiv.org/content/10.1101/2020.10.01.322164v4)","Resources":[{"Description":"MetaGraph Sequence Indexes","ARN":"arn:aws:s3:::metagraph","Region":"eu-west-1","Type":"S3 Bucket","Explore":["[Content Browser](https://metagraph.s3-eu-west-1.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Usage within AWS","URL":"https://github.com/ratschlab/metagraph-open-data?tab=readme-ov-file#usage-within-aws","AuthorName":"Oleksandr Kulkov","AuthorURL":"https://bmi.inf.ethz.ch/people/person/oleksandr-kulkov","Services":["AWS CloudFormation","AWS Step Functions","AWS Lambda","AWS Batch","Amazon S3","Amazon SNS"]}],"Tools & Applications":[{"Title":"CloudFormation stack with a Step Function for dataset queries via AWS Batch","URL":"https://github.com/ratschlab/metagraph-open-data","AuthorName":"Oleksandr Kulkov","AuthorURL":"https://bmi.inf.ethz.ch/people/person/oleksandr-kulkov"}],"Publications":[{"Title":"A global metagenomic map of urban microbiomes and antimicrobial resistance","URL":"https://www.cell.com/cell/fulltext/S0092-8674(21)00585-7","AuthorName":"Danko D, Bezdan D, Afshin EE, Ahsanuddin S, Bhattacharya C, Butler DJ, Chng KE, Donnellan D, Hecht J, Jackson K, Kuchin K, Karasikov M, Lyons A, Mak L, Meleshko D, Mustafa H, et al."}]},"ADXCategories":["Healthcare & Life Sciences Data","Resources Data"],"RegistryEntryAdded":"2025-04-17","RegistryEntryLastModified":"2025-05-08","Slug":"metagraph","Sources":["awslabs-open-data-registry"]}
{"Name":"Atmospheric Models from Météo-France","Description":"Global and high-resolution regional atmospheric models from Météo-France.\n\n- ARPEGE World covers the entire world at a base horizontal resolution of 0.5° (~55km) between grid points, it predicts weather out up to 114 hours in the future.\n\n- ARPEGE Europe covers Europe and North-Africa at a base horizontal resolution of 0.1° (~11km) between grid points, it predicts weather out up to 114 hours in the future.\n\n- AROME France covers France at a base horizontal resolution of 0.025° (~2.5km) between grid points, it predicts weather out up to 42 hours in the future.\n\n- AROME France HD covers France and neighborhood at a base horizontal resolution of 0.01° (~1.5km) between grid points, it predicts weather out up to 42 hours in the future.\n\nDozens of atmospheric variables are available through this datase: temperatures, winds, precipitation...\n\nOur work is based on open-data from Météo-France, but we are not affiliated or endorsed by Météo-France.\n","Documentation":"https://mf-models-on-aws.org","Contact":"contact@openmeteodata.com","ManagedBy":"[OpenMeteoData](https://openmeteodata.com)","UpdateFrequency":"Every 6 hours","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","disaster response","earth observation","environmental","meteorological","model","weather"],"License":"https://mf-models-on-aws.org/en/doc/license","Resources":[{"Description":"Atmospheric Models from Météo-France","ARN":"arn:aws:s3:::mf-nwp-models","Region":"eu-west-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://mf-nwp-models.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Windguru.cz","URL":"https://windguru.cz","AuthorName":"Windguru","AuthorURL":"https://windguru.cz"},{"Title":"Windy.com","URL":"https://windy.com","AuthorName":"Windy","AuthorURL":"https://windy.com"}]},"RegistryEntryAdded":"2019-04-10","RegistryEntryLastModified":"2022-11-08","Slug":"meteo-france-models","Sources":["awslabs-open-data-registry"]}
{"Name":"Multiview Extended Video with Activities (MEVA)","Description":"The Multiview Extended Video with Activities (MEVA) dataset consists\nvideo data of human activity, both scripted and unscripted,\ncollected with roughly 100 actors over several weeks.  The data was\ncollected with 29 cameras with overlapping and non-overlapping\nfields of view. The current release consists of about 328 hours\n(516GB, 4259 clips) of video data, as well as 4.6 hours (26GB) of\nUAV data. Other data includes GPS tracks of actors, camera models,\nand a site map. We have also released annotations for roughly 184 hours of\ndata. Further updates are planned.\n","Documentation":"http://mevadata.org","Contact":"mevadata@kitware.com","ManagedBy":"[Kitware](http://www.kitware.com/)","UpdateFrequency":"We anticipate two or three updates as more\nvideo data is released to the public.\n","Tags":["aws-pds","computer vision","urban","us","video"],"License":"http://mevadata.org/resources/MEVA-data-license.txt","Resources":[{"Description":"AVI video clips and collection site map","ARN":"arn:aws:s3:::mevadata-public-01","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"ActEV: Activities in Extended Video","URL":"https://actev.nist.gov/","AuthorName":"National Institute of Standards and Technology (NIST)"},{"Title":"MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection","URL":"https://openaccess.thecvf.com/content/WACV2021/html/Corona_MEVA_A_Large-Scale_Multiview_Multimodal_Video_Dataset_for_Activity_Detection_WACV_2021_paper.html","AuthorName":"Kellie Corona, Katie Osterdahl, Roderic Collins, Anthony Hoogs"},{"Title":"TinyAction Challenge: Recognizing Real-world Low-resolution Activities in Videos","URL":"https://arxiv.org/abs/2107.11494","AuthorName":"Praveen Tirupattur, Aayush J Rana, Tushar Sangam, Shruti Vyas, Yogesh S Rawat, Mubarak Shah"}]},"RegistryEntryAdded":"2019-09-06","RegistryEntryLastModified":"2022-02-17","Slug":"mevadata","Sources":["awslabs-open-data-registry"]}
{"Name":"MGH/MF Waveform Database","Description":"The Massachusetts General Hospital/Marquette Foundation (MGH/MF) Waveform Database is a comprehensive collection of electronic recordings of hemodynamic and electrocardiographic waveforms of stable and unstable patients in critical care units, operating rooms, and cardiac catheterization laboratories. It is the result of a collaboration between physicians, biomedical engineers and nurses at the Massachusetts General Hospital. The database consists of recordings from 250 patients and represents a broad spectrum of physiologic and pathophysiologic states. Individual recordings vary in length from 12 to 86 minutes, and in most cases are about an hour long.","Documentation":"https://doi.org/10.13026/C26K5Q","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C26K5Q","ARN":"arn:aws:s3:::physionet-open/mghdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"mghdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Influence of the MHD effect on 12-lead and 3-lead ECGs recorded in 1T to 7T MRI scanners","Description":"This ECG dataset was acquired in different magnetic resonance imaging (MRI) scanners to study the magnetohydrodynamic (MHD) effect. The MHD effect, which is caused an interaction of the MRI’s strong static magnetic field and the patient’s blood flow, superimposes the ECG signal during MRI exams. As a consequence, a detailed morphological analysis of the ECG (e.g. of the P wave, ST segment or the T wave) is prevented. The MHD effect might be a useful signal for extracting further physiological information, e.g. about blood flow or stroke volume. The 12-lead and 3-lead ECG signals were acquired from different subjects in various MRI scanners with magnetic field strengths ranging from 1T up to 7T. All signals were manually annotated.","Documentation":"https://doi.org/10.13026/05td-jn37","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/05td-jn37","ARN":"arn:aws:s3:::physionet-open/mhd-effect-ecg-mri/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"mhd-effect-ecg-mri","Sources":["awslabs-open-data-registry"]}
{"Name":"MIMIC-IV demo data in the Medical Event Data Standard (MEDS)","Description":"This dataset is an automated ETL conversion of the MIMIC-IV Clinical Database Demo into the Medical Event Data Standard (MEDS). MEDS is a data schema for storing streams of medical events such as those sourced from Electronic Health Records or claims records. MEDS is intentionally a minimal standard, designed for maximum interoperability across datasets, existing tools, and model architectures. By providing a simple standardization layer between datasets and model-specific code, MEDS is intended to help make machine learning research for EHR data more reproducible, robust, computationally performant, and collaborative.","Documentation":"https://doi.org/10.13026/t2y8-ea41","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Open Database License v1.0","Resources":[{"Description":"https://doi.org/10.13026/t2y8-ea41","ARN":"arn:aws:s3:::physionet-open/mimic-iv-demo-meds/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"mimic-iv-demo-meds","Sources":["awslabs-open-data-registry"]}
{"Name":"MIMIC-IV Clinical Database Demo","Description":"The Medical Information Mart for Intensive Care (MIMIC)-IV&nbsp;database is comprised&nbsp;of&nbsp;deidentified electronic health records for patients admitted to the Beth Israel Deaconess Medical Center. Access to MIMIC-IV is limited to credentialed users. Here, we have provided an openly-available demo of MIMIC-IV containing&nbsp;a subset of&nbsp;100 patients. The dataset includes similar content to MIMIC-IV, but excludes free-text clinical notes. The demo may be useful for running workshops&nbsp;and for&nbsp;assessing whether the MIMIC-IV is appropriate for a study before making an access request.","Documentation":"https://doi.org/10.13026/dp1f-ex47","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Open Database License v1.0","Resources":[{"Description":"https://doi.org/10.13026/dp1f-ex47","ARN":"arn:aws:s3:::physionet-open/mimic-iv-demo/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2024-11-12","RegistryEntryLastModified":"2026-06-05","Slug":"mimic-iv-demo","Sources":["awslabs-open-data-registry"]}
{"Name":"MIMIC-IV-ECG: Diagnostic Electrocardiogram Matched Subset","Description":"The MIMIC-IV-ECG module contains approximately 800,000 diagnostic electrocardiograms across nearly 160,000 unique patients. These diagnostic ECGs use 12 leads and are 10 seconds in length. They are sampled at 500 Hz. This subset contains all of the ECGs for patients who appear in the MIMIC-IV Clinical Database. When a cardiologist report is available for a given ECG, we provide the needed information to link the waveform to the report. The patients in MIMIC-IV-ECG have been matched against the MIMIC-IV Clinical Database, making it possible to link to information across the MIMIC-IV modules.","Documentation":"https://doi.org/10.13026/4nqg-sb35","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Open Database License v1.0","Resources":[{"Description":"https://doi.org/10.13026/4nqg-sb35","ARN":"arn:aws:s3:::physionet-open/mimic-iv-ecg/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2024-11-12","RegistryEntryLastModified":"2026-06-05","Slug":"mimic-iv-ecg","Sources":["awslabs-open-data-registry"]}
{"Name":"MIMIC-IV Clinical Database Demo on FHIR","Description":"Interoperability of healthcare data has become increasingly important given the increase in deployment of data driven algorithms in clinical settings. The Fast Healthcare Interoperability Resources (FHIR) standard has emerged as a promising mechanism to share healthcare data across vendors in real-time and batch settings. Real-world datasets available in FHIR would accelerate research and development of data-driven algorithms. Existing datasets in FHIR are primarily synthetic, and cover a limited number of resources. To address this gap, we have reformatted the Medical Information Mart for Intensive Care (MIMIC)-IV Clinical Database Demo into FHIR. The MIMIC clinical databases have received wide adoption and the constituent data are understood by the community. As much as possible, we adhered to the base resources with minimal extensions. Alongside the dataset, we publish openly available code allowing researchers to quickly build upon our work. Translating MIMIC-IV into FHIR provides a benchmark dataset for institutions to experiment with FHIR based tools, and we hope this resource supports adoption and use of FHIR.","Documentation":"https://physionet.org/content/mimic-iv-fhir-demo/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Open Database License v1.0","Resources":[{"Description":"https://physionet.org/content/mimic-iv-fhir-demo/","ARN":"arn:aws:s3:::physionet-open/mimic-iv-fhir-demo/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"mimic-iv-fhir-demo","Sources":["awslabs-open-data-registry"]}
{"Name":"MIMIC-III (‘Medical Information Mart for Intensive Care’)","Description":"MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, \nsingle-center database comprising information relating to patients \nadmitted to critical care units at a large tertiary care hospital. \nData includes vital signs, medications, laboratory measurements, \nobservations and notes charted by care providers, fluid balance, \nprocedure codes, diagnostic codes, imaging reports, hospital length \nof stay, survival data, and more. The database supports applications \nincluding academic and industrial research, quality improvement initiatives, \nand higher education coursework. The MIMIC-III dataset is freely-available. \nResearchers seeking to use the database must formally request access. For details, see \n[the getting started page](https://mimic.physionet.org/gettingstarted/access/).  Once you \nhave a PhysioNet account, you must enable access to the MIMIC-III dataset from your \nAWS account.  To do this, please [input your AWS account number](https://physionet.org/settings/cloud/), and \n[request access to the MIMIC-III Clinical Database on AWS](https://physionet.org/projects/mimiciii/1.4/request_access/2).\n","Documentation":"https://mimic.physionet.org/","Contact":"https://mimic.physionet.org/help/","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","bioinformatics","health","life sciences","natural language processing","us"],"License":"https://physionet.org/content/mimiciii/view-license/1.4/","Resources":[{"Description":"Project data files","ARN":"arn:aws:s3:::mimic-iii-physionet","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://mimic.physionet.org/gettingstarted/access/#request-access-to-mimic-iii"}],"DataAtWork":{"Tutorials":[{"Title":"Building predictive disease models using Amazon SageMaker with Amazon HealthLake normalized data","URL":"https://aws.amazon.com/blogs/machine-learning/building-predictive-disease-models-using-amazon-sagemaker-with-amazon-healthlake-normalized-data/","AuthorName":"Ujjwal Ratan, Nihir Chadderwala, and Parminder Bhatia","Services":["AWS Glue","AWS HealthLake","Amazon SageMaker"]},{"Title":"Perform biomedical informatics without a database using MIMIC-III data and Amazon Athena","URL":"https://aws.amazon.com/blogs/big-data/perform-biomedical-informatics-without-a-database-using-mimic-iii-data-and-amazon-athena/","AuthorName":"James Wiggins, Alistair Johnson","Services":["Amazon Athena","AWS Glue"]}],"Tools & Applications":[{"Title":"MIMIC-code GitHub repository","URL":"https://github.com/MIT-LCP/mimic-code","AuthorName":"Alistair Johnson","AuthorURL":"https://github.com/alistairewj"}]},"RegistryEntryAdded":"2019-08-13","RegistryEntryLastModified":"2026-11-05","Slug":"mimiciii","Sources":["awslabs-open-data-registry"]}
{"Name":"mirrulations","Description":"The regulations.gov website allows users to view proposed rules and supporting documents for the federal rule-making process.  In addition, users can post and view comments about those proposed rules.  The site contains about 27 million pieces of text and binary data, but the API that provides access only allows a user to obtain one thousand items per hour.  As a result, it would take approximately 3 years to download all the data.\nMirrulations (MIRRor of regULATIONS.gov) is a system that uses a collection of donated API keys to create a mirror of the data.  In addition, for each pdf in the dataset the system extracts available text and stores it as a separate text file.  It has successfully downloaded all historic data and continues to run and collect data each hour.\nThis data is valuable for data journalism.  As a public record of the rule-making process, it contains insights into the workings of federal agencies.  This dataset provides a single text corpus of the federal rule-making process.","Documentation":"https://github.com/MoravianUniversity/mirrulations-data/blob/main/structure.md","Contact":"colemanb@moravian.edu","ManagedBy":"[Moravian University Computer Science](https://www.moravian.edu/computer-science)","UpdateFrequency":"Hourly","Tags":["government records"],"License":"https://creativecommons.org/publicdomain/mark/1.0/deed.en","Resources":[{"Description":"Mirrulations dataset","ARN":"arn:aws:s3:::mirrulations","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Extracting Comment Sentiment from Public Comments","URL":"https://github.com/MoravianUniversity/mirrulations-data","AuthorName":"Ben Coleman","AuthorURL":"https://www.moravian.edu/computer-science/faculty","Services":null}]},"RegistryEntryAdded":"2024-06-14","RegistryEntryLastModified":"2024-06-14","Slug":"mirrulations","Sources":["awslabs-open-data-registry"]}
{"Name":"The MIT ROSMAP Single-Nucleus Multiomics Study (MIT_ROSMAP_Multiomics)","Description":"The MIT ROSMAP Single-Nucleus Multiomics Study provides single-nucleus\ntranscriptomic and chromatin accessibility data generated from postmortem\nbrain tissue from participants in the Religious Orders Study and Rush Memory\nand Aging Project (ROSMAP). The study includes single-nucleus RNA sequencing,\nsingle-nucleus ATAC sequencing, and paired multiome data from 427 prefrontal\ncortex samples, as well as single-nucleus RNA sequencing data from multiple\nbrain regions. These data support investigation of cell-type-specific\nmolecular changes, cellular vulnerability, cognitive resilience, and\nAlzheimer's disease progression.\n","Documentation":"https://doi.org/10.7303/9618323","Contact":"https://www.synapse.org/Synapse:syn2580853/discussion/default","ManagedBy":"Sage Bionetworks","UpdateFrequency":"AD Knowledge Portal Data Releases are published monthly (https://news.adknowledgeportal.org/). Updates to specific studies are dependent on data contributor and ingress timelines.","Tags":["aws-pds","bioinformatics","biology","epigenomics","gene expression","Homo sapiens","life sciences","medicine","neurobiology","neuroscience","RNA-seq","single-cell transcriptomics","transcriptomics"],"License":"https://adknowledgeportal.synapse.org/Explore/Studies/DetailsPage/StudyDetails?Study=syn52293417#Acknowledgement","Citation":"The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (NIH-NIA, U24AG061340).","Resources":[{"Description":"This study provides controlled-access single-nucleus RNA sequencing, single-nucleus ATAC sequencing, paired RNA and chromatin-accessibility multiome data, and related metadata generated from postmortem brain tissue from ROSMAP participants.","ARN":"arn:aws:s3:::mit-rosmap.opendata.sagebase.org","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://adknowledgeportal.synapse.org/Data%20Access"}],"DataAtWork":{"Publications":[{"Title":"Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer's disease pathology","URL":"https://doi.org/10.1016/j.cell.2023.08.039","AuthorName":"Mathys H, Peng Z, Boix CA, Victor MB, Leary N, Babu S, et al."}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-09-23","RegistryEntryLastModified":"2026-10-05","Slug":"mit-rosmap","Sources":["awslabs-open-data-registry"]}
{"Name":"MIT-BIH Arrhythmia Database","Description":"The MIT-BIH Arrhythmia Database contains 48 approximately half-hour, two-channel ambulatory electrocardiogram (ECG) recordings from 47 subjects. The recordings were collected at Boston's Beth Israel Hospital between 1975 and 1979 and include both randomly selected excerpts and recordings selected to represent less common, clinically significant arrhythmias. Signals were digitized at 360 samples per second per channel with 11-bit resolution over a 10 mV range. Each recording includes expert-reviewed reference annotations, comprising approximately 110,000 beat annotations across the database. First distributed in 1980, the database provides a shared resource for developing and evaluating arrhythmia detection algorithms and investigating cardiac dynamics.","Documentation":"https://doi.org/10.13026/C2F305","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2F305","ARN":"arn:aws:s3:::physionet-open/mitdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"mitdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Multiscale Multifractal Analysis","Description":"Multiscale multifractal analysis (MMA) (Gierałtowski et al., 2012) is a time series analysis method, designed to describe scaling properties of fluctuations within the signal analyzed. The main result of this procedure is the so called Hurst surface h(q,s) , which is a dependence of the local Hurst exponent h (fluctuation scaling exponent) on the multifractal parameter q (Kantelhardt et al., 2002) and the scale of observation s (data window width).","Documentation":"https://physionet.org/content/mma/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/mma/","ARN":"arn:aws:s3:::physionet-open/mma/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"mma","Sources":["awslabs-open-data-registry"]}
{"Name":"Multilevel Monitoring of Activity and Sleep in Healthy People","Description":"Multilevel Monitoring of Activity and Sleep in Healthy people (MMASH) dataset provides 24 hours of continuous beat-to-beat heart data, triaxial accelerometer data, sleep quality, physical activity and psychological characteristics (i.e., anxiety status, stress events and emotions) for 22 healthy participants. Moreover, saliva bio-markers (i.e.cortisol and melatonin) and activity log were also provided in this dataset. The MMASH dataset will enable researchers to test the correlations between physical activity, sleep quality, and psychological characteristics.","Documentation":"https://doi.org/10.13026/cerq-fc86","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Open Database License v1.0","Resources":[{"Description":"https://doi.org/10.13026/cerq-fc86","ARN":"arn:aws:s3:::physionet-open/mmash/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"mmash","Sources":["awslabs-open-data-registry"]}
{"Name":"The Massively Multilingual Image Dataset (MMID)","Description":"MMID is a large-scale, massively multilingual dataset of images paired with the words they represent collected at the [University of Pennsylvania](https://upenn.edu).\nThe dataset is doubly parallel: for each language, words are stored parallel to images that represent the word, _and_ parallel to the word's translation into English (and corresponding images.)\n","Documentation":"https://multilingual-images.org/doc.html","Contact":"mmid-users@googlegroups.com","ManagedBy":"[Penn NLP](https://github.com/penn-nlp)","UpdateFrequency":"Language data is added as it is ready for distribution.","Tags":["aws-pds","computer vision","machine learning","machine translation","natural language processing"],"License":"See citation instructions at http://multilingual-images.org","Resources":[{"Description":"Images for words in various languages, packaged by in .tar archives by each language.\n","ARN":"arn:aws:s3:::mmid-pds","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2019-01-23","RegistryEntryLastModified":"2019-08-16","Slug":"mmid","Sources":["awslabs-open-data-registry"]}
{"Name":"CoMMpass from the Multiple Myeloma Research Foundation","Description":"The Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile study is the Multiple Myeloma Research Foundation (MMRF)’s landmark personalized medicine initiative.  CoMMpass is a\nlongitudinal observation study of around 1000 newly diagnosed myeloma patients receiving various\nstandard approved treatments. The MMRF’s vision is to track the treatment and results for each\nCoMMpass patient so that someday the information can be used to guide decisions for newly\ndiagnosed patients. CoMMpass checked on patients every 6 months for 8 years, collecting tissue\nsamples, genetic, information, quality of life and various disease and clinical outcomes. The\nstudy has produced one of the largest genomic and clinical datasets of a single disease.\n","Documentation":"https://gdc.cancer.gov/about-gdc/contributed-genomic-data-cancer-research/foundation-medicine/multiple-myeloma-research-foundation-mmrf\n","Contact":"dcf-support@datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genetic","genomic","life sciences","STRIDES","whole genome sequencing"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"RNA-Seq Gene Expression Quantification","ARN":"arn:aws:s3:::gdc-mmrf-commpass-phs000748-2-open","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/MMRF-COMMPASS","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"}],"Publications":[{"Title":"\"Identification of Initiating Trunk Mutations and Distinct Molecular Subtypes: An Interim\nAnalysis of the Mmrf Commpass Study\"\n","URL":"https://doi.org/10.1182/blood.V126.23.722.722","AuthorName":"Jonathan J Keats, PhD, Gil Speyer, Legendre Christophe, Christofferson Austin, Kristi\nStephenson, BS, Ahmet Kurdoglu, Megan Russell, Aldrich Jessica, Cuyugan Lori, Jonathan\nAdkins, Jackie McDonald, Adrienne Helland, Alex Blanski, Meghan Hodges, Dan Rohrer, Sundar\nJagannath, MD, David Siegel, MD PhD, Ravi Vij, MD MBA, Gregory Orloff, MD, Todd Zimmerman,\nMD, Ruben Niesvizky, MD, Darla Liles, MD, Joseph W. Fay, Jeffrey L. Wolf, MD PhD, Robert M.\nRifkin, Norma C Gutierrez, The MMRF CoMMpass Network, Jen Toups, Mary Derome, MS, Winnie\nLiang, PhD, Seunchan Kim, Daniel Auclair, PhD, Pamela G. Kidd, MD, Scott Jewell, PhD, John\nDavid Carpten, PhD, Sagar Lonial, MD\n"},{"Title":"\"Interim Analysis Of The MMRF CoMMpass Trial: a Longitudinal Study In Multiple Myeloma\nRelating Clinical Outcomes To Genomic and Immunophenotypic Profiles\"\n","URL":"https://doi.org/10.1182/blood.V122.21.532.532","AuthorName":"Keats JJ, Craig DW, Liang W, Venkata Y, Kurdoglu A, Aldrich J, Auclair D, Allen K, Harrison\nB, Jewell S, Kidd PG, Correll M, Jagannath S, Siegel DS, Vij R, Orloff G, Zimmerman TM, MMRF\nCoMMpass Network, Capone W, Carpten J, Lonial S.\n"},{"Title":"\"Interim Analysis of the Mmrf Commpass Trial: Identification of Novel Rearrangements\nPotentially Associated with Disease Initiation and Progression\"\n","URL":"https://doi.org/10.1182/blood.V124.21.722.722","AuthorName":"Sagar Lonial, MD, Venkata D Yellapantula, Winnie Liang, PhD, Ahmet Kurdoglu, BS, Jessica\nAldrich, MSc, Christophe M. Legendre, MD, Kristi Stephenson, Jonathan Adkins, Jackie\nMcDonald, Adrienne Helland, Megan Russell, Austin Christofferson, Lori Cuyugan, Dan Rohrer,\nAlex Blanski, Meghan Hodges, Mmrf CoMMpass Network, Mary Derome, Daniel Auclair, PhD, Pamela\nG. Kidd, MD, Scott Jewell, PhD, David Craig, PhD, John Carpten, PhD, Jonathan J. Keats, PhD\n"},{"Title":"\"Molecular Predictors of Outcome and Drug Response in Multiple Myeloma: An Interim Analysis\nof the Mmrf CoMMpass Study\"\n","URL":"https://doi.org/10.1182/blood.V128.22.194.194","AuthorName":"Jonathan J Keats, PhD, Gil Speyer, Austin Christofferson, Christophe Legendre, PhD, Jessica\nAldrich, Megan Russell, Lori Cuyugan, Jonathan Adkins, Alex Blanski, Meghan Hodges, Dan\nRohrer, Sundar Jagannath, MD, Ravi Vij, MD, Gregory Orloff, MD, Todd Zimmerman, MD, Ruben\nNiesvizky, MD, Darla Liles, MD, Joseph W. Fay, Jeffrey L. Wolf, MD, Robert M Rifkin, Norma C\nGutierrez, MD PhD, Mmrf CoMMpass Network, Jennifer Yesil, MS, Mary Derome, MS, Seungchan\nKim, PhD, Winnie Liang, PhD, Pamela G. Kidd, MD, Scott Jewell, PhD, John David Carpten, PhD,\nDaniel Auclair, PhD, Sagar Lonial, MD FACP\n"}]},"RegistryEntryAdded":"2020-04-08","RegistryEntryLastModified":"2024-12-20","Slug":"mmrf-commpass","Sources":["awslabs-open-data-registry"]}
{"Name":"Missouri Statewide Aerial Imagery Collection","Description":"The Missouri Spatial Data Information Service (MSDIS) is responsible for the storage and distribution of digital spatial data, aerial photography, and LiDAR related to the state of Missouri. \n","Documentation":"https://data-msdis.opendata.arcgis.com","Contact":"msdismail@missouri.edu","ManagedBy":"Missouri Spatial Data Information Service","UpdateFrequency":"Missouri's imagery cycle is based on funding availability.","Tags":["aerial imagery","agriculture","aws-pds","earth observation","geospatial","imaging","mapping","natural resource","sustainability"],"License":"Access to Missouri aerial imagery distributed by the Missouri Spatial Data Information Service (MSDIS) is governed by Creative Commons BY 4.0 (CC BY 4.0):: https://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Aerial imagery of the state of Missouri.","ARN":"arn:aws:s3:::mo-gis-imagery","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://mo-gis-imagery.s3.us-west-2.amazonaws.com/index.html)"]},{"Description":"Notification for Missouri Statewide Aerial Imagery Collection.","ARN":"arn:aws:sns:us-west-2:092382576634:mo-gis-imagery-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get to Know A Dataset: Missouri Statewide Imagery Collection","URL":"https://github.com/TVought/mo-gis-opendata/blob/main/mo-gis-imagery/get-to-know-a-dataset-mo-gis-imagery.ipynb","NotebookURL":"https://github.com/TVought/mo-gis-opendata/blob/main/mo-gis-imagery/get-to-know-a-dataset-mo-gis-imagery.ipynb","AuthorName":"Missouri Spatial Data Information Service","AuthorURL":"https://data-msdis.opendata.arcgis.com/"}],"Tools & Applications":[{"Title":"2023 Missouri Imagery Webinar","URL":"https://msdis-archive.missouri.edu/archive/MSDIS_Outbox/2022_2023_ImageryWebinars/07%20March%202023%20Webinar.mp4","AuthorName":"Missouri GIS Advisory Council"},{"Title":"Fly MO Imagery","URL":"https://www.flymoimagery.com/","AuthorName":"Missouri Office of Geospatial Information"},{"Title":"The Missouri Map","URL":"https://arcg.is/0bSreG0","AuthorName":"Missouri Spatial Data Information Service"}]},"RegistryEntryAdded":"2026-10-13","RegistryEntryLastModified":"2026-10-13","Slug":"mo-gis-imagery","Sources":["awslabs-open-data-registry"]}
{"Name":"MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4","Description":"Data from the Moderate Resolution Imaging Spectroradiometer (MODIS), managed by\nthe U.S. Geological Survey and NASA. Five products are included:\nMCD43A4 (MODIS/Terra and Aqua Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid),\nMOD11A1 (MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid),\nMYD11A1 (MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid),\nMOD13A1 (MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid),\nand MYD13A1 (MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid).\nMCD43A4 has global coverage, all time (~21 years).\nThe other products have ~11 years of global coverage.  All data files are in single-band\ncloud-optimized GeoTIFF (COG) format.\n","Documentation":"Documentation is available for this data at the [s22s/astraea-opendata GitHub repository](https://github.com/s22s/astraea-opendata) maintained by Astraea.","Contact":"https://astraea.earth/","ManagedBy":"[Astraea](https://astraea.earth/)","UpdateFrequency":"New scenes are added daily.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","disaster response","geospatial","natural resource","satellite imagery"],"License":"There are no restrictions on the use of data, unless expressly identified prior to or at the time of receipt.","Resources":[{"Description":"Imagery and metadata","ARN":"arn:aws:s3:::astraea-opendata","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":true},{"Description":"New data notifications","ARN":"arn:aws:sns:us-west-2:791757209086:astraea-opendata-events","Region":"us-west-2","Type":"SNS Topic"},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents) files for MCD43A4 (ORC)","ARN":"arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MCD43A4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":true},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents) files for MOD11A1 (ORC)","ARN":"arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MOD11A1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":true},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents) files for MYD11A1 (ORC)","ARN":"arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MYD11A1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":true},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents) files for MOD13A1 (ORC)","ARN":"arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MOD13A1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":true},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents) files for MYD13A1 (ORC)","ARN":"arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MYD13A1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":true}],"DataAtWork":{"Tools & Applications":[{"Title":"Astraea Earth OnDemand","URL":"https://astraea.earth/earth-ondemand/","AuthorName":"Astraea, Inc.","AuthorURL":"https://astraea.earth"}]},"RegistryEntryAdded":"2019-11-04","RegistryEntryLastModified":"2024-03-26","Slug":"modis-astraea","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"The provider of this dataset no longer provides this data through this mechanism. If there are any questions, please reach out to <a href=\"mailto:opendata@amazon.com\">opendata@amazon.com</a>.","Name":"MODIS","Description":"The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/modis-pds","Contact":"No contact","ManagedBy":"Not currently managed","UpdateFrequency":"Daily","Tags":["aws-pds","agriculture","geospatial","satellite imagery","natural resource","disaster response"],"License":"There are no restrictions on the use of data, unless expressly identified prior to or at the time of receipt.","Resources":[{"Description":"Imagery and metadata","ARN":"arn:aws:s3:::modis-pds","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"New data notifications","ARN":"arn:aws:sns:us-west-2:552188055668:New_MODIS_Scene","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":null,"Tools & Applications":[{"Title":"Sentinel Hub WMS/WMTS/WCS Service for MODIS","URL":"http://www.sentinel-hub.com/apps/wms","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"},{"Title":"Sentinel Playground for MODIS","URL":"https://apps.sentinel-hub.com/sentinel-playground/?source=Modis&lat=37.274052809979054&lng=-78.8818359375&zoom=7","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"},{"Title":"EOS Land Viewer","URL":"https://eos.com/landviewer/","AuthorName":"Earth Observing System","AuthorURL":"https://eos.com/"}],"Publications":null},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2023-02-28","Slug":"modis","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"This dataset listing is no longer active. Please go [here](https://registry.opendata.aws/uk-met-office/) for current information.* Archive data from the UK Met Office Global and Regional Ensemble Prediction System (MOGREPS) available on Amazon S3.","Name":"UK Met Office Global and Regional Weather Forecasts","Description":"Data from two models is available: MOEGREPS-UK, a high resolution weather forecast covering the United Kingdom, and MOGREPS-G, a global weather forecast.","Documentation":"http://www.metoffice.gov.uk/research/weather/ensemble-forecasting/mogreps","Contact":"https://www.metoffice.gov.uk/about-us/contact","ManagedBy":"[UK Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"Not currently updated","Tags":["aws-pds","agriculture","earth observation","climate","weather","meteorological"],"License":"This data is free to use for non-commercial research purposes only under the terms of the [Non-Commercial Government Licence](http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/non-commercial-government-licence.htm), and not for profit making applications. You must ensure that you acknowledge the source of this data as prescribed by this licence. No ongoing level of service, availability or support is implied and data may be changed or removed without notice.","Resources":[{"Description":"Global weather forecast","ARN":"arn:aws:s3:::mogreps-g","Region":"eu-west-2","Type":"S3 Bucket"},{"Description":"High resolution weather forecast covering the United Kingdom","ARN":"arn:aws:s3:::mogreps-uk","Region":"eu-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":null,"Tools & Applications":null,"Publications":null},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2023-02-28","Slug":"mogreps","Sources":["awslabs-open-data-registry"]}
{"Name":"COVID-19 Molecular Structure and Therapeutics Hub","Description":"Aggregating critical information to accelerate drug discovery for the molecular modeling and simulation community.\nA community-driven data repository and curation service for molecular structures, models, therapeutics, and\nsimulations related to computational research related to therapeutic opportunities for COVID-19\n(caused by the SARS-CoV-2 coronavirus).\n","Documentation":"https://covid.molssi.org/","Contact":"info@molssi.org","ManagedBy":"[Molecular Sciences Software Institute (MolSSI)](https://molssi.org/) and [BioExcel](https://bioexcel.eu/)","UpdateFrequency":"Data contributions come from external researchers and groups at a roughly weekly cadence.","Tags":["aws-pds","bioinformatics","biology","coronavirus","COVID-19","life sciences","molecular docking","pharmaceutical"],"License":"Most data will be in an open license provided by the contributing individual(s). Neither MolSSI nor BioExcel\nhold the license(s) for data in the data set unless explicitly noted.\n","Resources":[{"Description":"Data storage of for the MolSSI and BioExcel COVID-19 Hub. Includes atomistic structures (often as PDB),\nrefined models (as PDB or higher resolution molecular file types), simulations (file types a function of the\nsimulation program, usually compressed as zip or tarball), drug databases of conformers and tautomers\n(primarily compressed mol2 files), and any additional supporting files necessary to reproduce or process the data\nincluded in the the compressed package (e.g. simulation inputs).\n","ARN":"arn:aws:s3:::molssi-bioexcel-covid-19-structure-therapeutics-hub","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2020-06-29","RegistryEntryLastModified":"2020-10-23","Slug":"molssi-covid19-hub","Sources":["awslabs-open-data-registry"]}
{"Name":"MONKEY","Description":"This dataset contains the training data for the [Machine learning for Optimal detection of iNflammatory cells in the KidnEY or MONKEY](https://monkey.grand-challenge.org/) challenge. The MONKEY challenge focuses on the automated detection and classification of inflammatory cells, specifically monocytes and lymphocytes, in kidney transplant biopsies using Periodic acid-Schiff (PAS) stained whole-slide images (WSI). It contains 80 WSI, collected from 4 different pathology institutes, with annotated regions of interest. For each WSI up to 3 different PAS scans and one IHC slide scan are available. This dataset and challenge support the development of AI models that can aid in the diagnostic process, reduce pathologists’ workload, and improve patient outcomes in renal transplantation.","Documentation":"https://monkey.grand-challenge.org/","Contact":"linda.studer@radboudumc.nl","ManagedBy":"Radboud University Medical Center","UpdateFrequency":"As required","Tags":["aws-pds","cancer","classification","computational pathology","computer vision","deep learning","digital pathology","grand-challenge.org","histopathology","imaging","life sciences","machine learning","medical image computing","medical imaging"],"License":"CC BY-NC-SA 4.0","Resources":[{"Description":"PAS- and IHC-stained whole slide images with corresponding dot annotaions for inflammatory cells (monocytes and lymphocytes) in regions of interest.","ARN":"arn:aws:s3:::monkey-training","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"ASAP Viewer","URL":"https://computationalpathologygroup.github.io/ASAP/","AuthorName":"Geert Litjens","AuthorURL":"https://www.diagnijmegen.nl/people/geert-litjens/"},{"Title":"MONKEY Challenge","URL":"https://monkey.grand-challenge.org/","AuthorName":"Computational Pathology Group Radboudumc, Nijmegen","AuthorURL":"https://www.computationalpathologygroup.eu/"},{"Title":"MONKEY challenge tutorials","URL":"https://github.com/computationalpathologygroup/monkey-challenge","AuthorName":"Computational Pathology Group Radboudumc, Nijmegen","AuthorURL":"https://www.computationalpathologygroup.eu/"},{"Title":"Structured description of the MONKEY Challenge","URL":"https://zenodo.org/records/13794656","AuthorName":"Linda Studer, Dominique van Midden, Fazael Ayatollahi, Jeroen van der Laak","AuthorURL":"https://www.computationalpathologygroup.eu/"},{"Title":"Whole slide data - python package for working with wholeslide images and annotations files","URL":"https://github.com/DIAGNijmegen/pathology-whole-slide-data","AuthorName":"Diagnostic Image Analysis Group, Radboudumc, Nijmegen","AuthorURL":"https://www.diagnijmegen.nl/"}]},"ADXCategories":["Healthcare & Life Sciences Data","Public Sector Data"],"RegistryEntryAdded":"2024-08-30","RegistryEntryLastModified":"2024-09-24","Slug":"monkey","Sources":["awslabs-open-data-registry"]}
{"Name":"Meta-Organized Stimuli And fMRI Imaging data for Computational modeling (MOSAIC)","Description":"This extensible dataset, MOSAIC, aggregates individual functional magnetic resonance imaging (fMRI) datasets by leveraging a shared preprocessing pipeline and stimulus curation procedure. This dataset aggregation procedure achieves the scale necessary for neural network training and the diversity needed for generalizable results.","Documentation":"https://blahner.github.io/MOSAICfmri/","Contact":"blahner@mit.edu","ManagedBy":"Massachusetts Institute of Technology, Georgia Tech","UpdateFrequency":"New data is uploaded as researchers preprocess their fMRI data according to MOSAIC format and submit.","Tags":["aws-pds","brain images","brain models","hdf5","life sciences","machine learning","neuroimaging","neuroscience"],"License":"CC BY 4.0","Citation":null,"Resources":[{"Description":"HDF5 files containing preprocessed fMRI data","ARN":"arn:aws:s3:::mosaicfmri","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://mosaicfmri.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Download MOSAIC data, visualize fMRI responses, load and run brain-optimized models (Jupyter notebook)","URL":"https://github.com/murtylab/mosaic-dataset/blob/master/examples/mosaic-starter.ipynb","NotebookURL":"https://github.com/murtylab/mosaic-dataset/blob/master/examples/mosaic-starter.ipynb","AuthorName":"Mayukh Deb"},{"Title":"Load HDF5 file (Jupyter notebook)","URL":"https://github.com/blahner/mosaic-preprocessing/blob/main/src/fmriDatasetPreparation/create_hdf5/load_hdf5.ipynb","NotebookURL":"https://github.com/blahner/mosaic-preprocessing/blob/main/src/fmriDatasetPreparation/create_hdf5/load_hdf5.ipynb","AuthorName":"Benjamin Lahner"},{"Title":"MOSAIC Python package (mosaic-dataset)","URL":"https://pypi.org/project/mosaic-dataset/","AuthorName":"Mayukh Deb"},{"Title":"Preprocess fMRI datasets with MOSAIC shared pipeline","URL":"https://github.com/blahner/mosaic-preprocessing","AuthorName":"Benjamin Lahner"},{"Title":"Run a synthetic localizer experiment using MOSAIC's brain-optimized models (Jupyter notebook)","URL":"https://github.com/murtylab/mosaic-dataset/blob/master/examples/mosaic_synthetic_localizer.ipynb","NotebookURL":"https://github.com/murtylab/mosaic-dataset/blob/master/examples/mosaic_synthetic_localizer.ipynb","AuthorName":"Benjamin Lahner"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-09-07","RegistryEntryLastModified":"2026-06-05","Slug":"mosaic","Sources":["awslabs-open-data-registry"]}
{"Name":"Motion Artifact Contaminated fNIRS and EEG Data","Description":"This data collection, contributed to PhysioBank by Kevin Sweeney and colleagues at the National University of Ireland in Maynooth, contains examples of functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG) recordings that have been created for evaluating artifact removal methods.","Documentation":"https://doi.org/10.13026/C2988P","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2988P","ARN":"arn:aws:s3:::physionet-open/motion-artifact/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"motion-artifact","Sources":["awslabs-open-data-registry"]}
{"Name":"nuPlan","Description":"nuPlan is the world's first large-scale planning benchmark for autonomous driving.","Documentation":"https://nuplan.org","Contact":"https://nuplan.org","ManagedBy":"[Motional, Inc.](https://motional.com)","UpdateFrequency":"Finalized","Tags":["autonomous vehicles","aws-pds","lidar","robotics","transportation","urban"],"License":"[Commercial](https://www.nuscenes.org/terms-of-use)","Resources":[{"Description":"Globally cached distribution of the nuPlan Dataset. Web frontend is available to browse the dataset.\n","Host":"https://d1qinkmu0ju04f.cloudfront.net","Region":"ap-northeast-1","Type":"CloudFront Distribution"},{"Description":"nuPlan Dataset\n","ARN":"arn:aws:s3:::motional-nuplan","Region":"ap-northeast-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://motional-nuplan.s3.ap-northeast-1.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"nuPlan Advacned Model Training","URL":"https://colab.research.google.com/github/motional/nuplan-devkit/","NotebookURL":"https://github.com/motional/nuplan-devkit/blob/master/tutorials/nuplan_advanced_model_training.ipynb","AuthorName":"Motional","AuthorURL":null,"Services":null},{"Title":"nuPlan Framework","URL":"https://colab.research.google.com/github/motional/nuplan-devkit/","NotebookURL":"https://github.com/motional/nuplan-devkit/blob/master/tutorials/nuplan_framework.ipynb","AuthorName":"Motional","AuthorURL":null,"Services":null},{"Title":"nuPlan Planner Tutorial","URL":"https://colab.research.google.com/github/motional/nuplan-devkit/","NotebookURL":"https://github.com/motional/nuplan-devkit/blob/master/tutorials/nuplan_planner_tutorial.ipynb","AuthorName":"Motional","AuthorURL":null,"Services":null},{"Title":"nuPlan Scenario Visualization","URL":"https://colab.research.google.com/github/motional/nuplan-devkit/","NotebookURL":"https://github.com/motional/nuplan-devkit/blob/master/tutorials/nuplan_scenario_visualization.ipynb","AuthorName":"Motional","AuthorURL":null,"Services":null},{"Title":"nuPlan Sensor Data Tutorial","URL":"https://colab.research.google.com/github/motional/nuplan-devkit/","NotebookURL":"https://github.com/motional/nuplan-devkit/blob/master/tutorials/nuplan_sensor_data_tutorial.ipynb","AuthorName":"Motional","AuthorURL":null,"Services":null}],"Tools & Applications":[{"Title":"nuPlan devkit","URL":"https://github.com/motional/nuplan-devkit","AuthorName":"Motional","AuthorURL":null}],"Publications":[{"Title":"NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles","URL":"https://arxiv.org/abs/2106.11810","AuthorName":"Holger Caesar, Juraj Kabzan, Kok Seang Tan, Whye Kit Fong, Eric Wolff, Alex Lang, Luke Fletcher, Oscar Beijbom, Sammy Omari","AuthorURL":null}]},"ADXCategories":["Automotive Data"],"RegistryEntryAdded":"2024-01-11","RegistryEntryLastModified":"2024-02-15","Slug":"motional-nuplan","Sources":["awslabs-open-data-registry"]}
{"Name":"nuScenes","Description":"Public large-scale dataset for autonomous driving. It enables researchers to study challenging urban driving situations using the full sensor suite of a real self-driving car.","Documentation":"https://www.nuscenes.org","Contact":"https://www.nuscenes.org","ManagedBy":"[Motional, Inc.](https://motional.com)","UpdateFrequency":"Finalized","Tags":["autonomous vehicles","aws-pds","computer vision","lidar","robotics","transportation","urban"],"License":"[Commercial](https://www.nuscenes.org/terms-of-use)","Resources":[{"Description":"Globally cached distribution of the nuScenes Dataset. Web frontend is available to browse the dataset.","Host":"https://d36yt3mvayqw5m.cloudfront.net","Region":"ap-northeast-1","Type":"CloudFront Distribution"},{"Description":"nuScenes Dataset","ARN":"arn:aws:s3:::motional-nuscenes","Region":"ap-northeast-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://motional-nuscenes.s3.ap-northeast-1.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"nuImages devkit tutorial","URL":"https://colab.research.google.com/github/nutonomy/nuscenes-devkit/","NotebookURL":"https://github.com/nutonomy/nuscenes-devkit/blob/master/python-sdk/tutorials/nuimages_tutorial.ipynb","AuthorName":"Motional","AuthorURL":null,"Services":null},{"Title":"nuScenes CAN bus tutorial","URL":"https://colab.research.google.com/github/nutonomy/nuscenes-devkit/","NotebookURL":"https://github.com/nutonomy/nuscenes-devkit/blob/master/python-sdk/tutorials/can_bus_tutorial.ipynb","AuthorName":"Motional","AuthorURL":null,"Services":null},{"Title":"nuScenes devkit tutorial","URL":"https://colab.research.google.com/github/nutonomy/nuscenes-devkit/","NotebookURL":"https://github.com/nutonomy/nuscenes-devkit/blob/master/python-sdk/tutorials/nuscenes_tutorial.ipynb","AuthorName":"Motional","AuthorURL":null,"Services":null},{"Title":"nuScenes lidarseg and panoptic tutorial","URL":"https://colab.research.google.com/github/nutonomy/nuscenes-devkit/","NotebookURL":"https://github.com/nutonomy/nuscenes-devkit/blob/master/python-sdk/tutorials/nuscenes_lidarseg_panoptic_tutorial.ipynb","AuthorName":"Motional","AuthorURL":null,"Services":null},{"Title":"nuScenes Map Expansion Tutorial","URL":"https://colab.research.google.com/github/nutonomy/nuscenes-devkit/","NotebookURL":"https://github.com/nutonomy/nuscenes-devkit/blob/master/python-sdk/tutorials/map_expansion_tutorial.ipynb","AuthorName":"Motional","AuthorURL":null,"Services":null},{"Title":"nuScenes prediction tutorial","URL":"https://colab.research.google.com/github/nutonomy/nuscenes-devkit/","NotebookURL":"https://github.com/nutonomy/nuscenes-devkit/blob/master/python-sdk/tutorials/prediction_tutorial.ipynb","AuthorName":"Motional","AuthorURL":null,"Services":null}],"Tools & Applications":[{"Title":"nuScenes devkit","URL":"https://github.com/nutonomy/nuscenes-devkit","AuthorName":"Motional","AuthorURL":null}],"Publications":[{"Title":"nuScenes: A multimodal dataset for autonomous driving","URL":"https://arxiv.org/abs/1903.11027","AuthorName":"Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom","AuthorURL":null},{"Title":"Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking","URL":"https://arxiv.org/abs/2109.03805","AuthorName":"Whye Kit Fong, Rohit Mohan, Juana Valeria Hurtado, Lubing Zhou, Holger Caesar, Oscar Beijbom, Abhinav Valada","AuthorURL":null}]},"ADXCategories":["Automotive Data"],"RegistryEntryAdded":"2024-01-15","RegistryEntryLastModified":"2024-02-15","Slug":"motional-nuscenes","Sources":["awslabs-open-data-registry"]}
{"Name":"Longitudinal Cylinder Rearing Behavioral Data in a Mouse Stroke Model Across Multiple Drug Treatments","Description":"This dataset includes longitudinal data from 59 mice assigned to four drug treatment groups (lemborexant 10 mg/kg, lemborexant 30 mg/kg, zolpidem 30 mg/kg, and vehicle), assessed at five timepoints: baseline (pre-treatment), maximal deficit (post-stroke 48 hours), and weeks 2, 4, and 8 post-treatment. Designed to evaluate drug efficacy over time, the dataset captures functional or physiological changes across stroke acute and recovery phases via the behavioral test of cylinder rearing. The shared data specifically comprises 295 video recordings (.mp4) of these sessions, paired with corresponding frame-based annotation files (.csv) that classify paw usage (left, right, both, or neither) for each frame.","Documentation":"https://doi.org/10.13026/8bnm-6e37","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/8bnm-6e37","ARN":"arn:aws:s3:::physionet-open/mouse-stroke-cylinder-rearing/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"mouse-stroke-cylinder-rearing","Sources":["awslabs-open-data-registry"]}
{"Name":"Molecular Profiling to Predict Response to Treatment (phs001965)","Description":"The Molecular Profiling to Predict Response to Treatment (MP2PRT) program is part of the NCI's Cancer Moonshot Initiative. The aim of this program is the retrospective characterization and analysis of biospecimens collected from completed NCI-sponsored trials of the National Clinical Trials Network and the NCI Community Oncology Research Program. This study, titled \"Identification of Genetic Changes Associated with Relapse and/or Adaptive Resistance in Patients Registered as Favorable Histology Wilms Tumor on AREN03B2\", performs genomic characterization (WGS 30X, Total RNAseq, miRNAseq) on a discovery set of 70 trio cases (normal tissue, tumor tissue at time of diagnosis, tumor tissue at time of relapse) from patients who relapsed with Favorable Histology Wilms Tumor. Prioritized findings from the discovery set will be validated using Targeted Sequencing in an independent validation set of 47 relapse samples. The MP2PRT study is made available on AWS via the NIH STRIDES Initiative (https://aws.amazon.com/blogs/publicsector/aws-and-national-institutes-of-health-collaborate-to-accelerate-discoveries-with-strides-initiative/).\n","Documentation":"https://datacatalog.ccdi.cancer.gov/dataset/GDC-MP2PRT","Contact":"support@nci-gdc.datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genomic","life sciences","STRIDES","whole genome sequencing"],"License":"[NIH Genomic Data Sharing Policy](https://gdc.cancer.gov/access-data/data-access-policies)","Citation":"In addition, please cite the following grants in any publication that cites this data: U10CA180886 (NCTN Operations Center Grant); 1U24-CA196173 (Human Specimen Banking in NCI-Sponsored Clinical Trials); U10CA180899 (NCTN Statistics & Data Center)","Resources":[{"Description":"miRNA Sequencing, Total RNA Sequencing, Whole Genome Sequencing, Targeted Sequencing\n","ARN":"arn:aws:s3:::gdc-mp2prt-wt-phs001965-2-open","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Childhood Cancer Data Initiative Data Catalog","URL":"https://datacatalog.ccdi.cancer.gov/dataset/GDC-MP2PRT","AuthorName":"National Cancer Institute","AuthorURL":"https://datacatalog.ccdi.cancer.gov/"},{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"}],"Publications":[{"Title":"Finding the way to Wilms tumor by comparing the primary and relapse tumor samples","URL":"https://doi.org/10.1016/j.xcrm.2022.100667","AuthorName":"Filippo Spreafico, Sara Ciceri, et al."},{"Title":"Genetic changes associated with relapse in favorable histology Wilms tumor: A Children's Oncology Group AREN03B2 study","URL":"https://doi.org/10.1016/j.xcrm.2022.100644","AuthorName":"Samantha Gadd, Vicki Huff, et al."}]},"RegistryEntryAdded":"2023-04-27","RegistryEntryLastModified":"2023-04-27","Slug":"mp2prt","Sources":["awslabs-open-data-registry"]}
{"Name":"Multiscale Poincare Plots","Description":"Poincaré delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these complex time series, we introduced a methodology called multiscale Poincaré (MSP) plots.","Documentation":"https://physionet.org/content/mpp/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/mpp/","ARN":"arn:aws:s3:::physionet-open/mpp/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"mpp","Sources":["awslabs-open-data-registry"]}
{"Name":"Emory Knee Radiograph (MRKR) dataset","Description":"The Emory Knee Radiograph (MRKR) dataset is a large, demographically diverse collection of\n503,261 knee radiographs from 83,011 patients, 40% of which are African American. This dataset\nprovides imaging data in DICOM format along with detailed clinical information, including patient-\nreported pain scores, diagnostic codes, and procedural codes, which are not commonly available in\nsimilar datasets. The MRKR dataset also features imaging metadata such as image laterality, view type,\nand presence of hardware, enhancing its value for research and model development. MRKR addresses\nsignificant gaps in existing datasets by offering a more representative sample for studying osteoarthritis\nand related outcomes, particularly among minority populations, thereby providing a valuable resource\nfor clinicians and researchers.\nAcknowledgments - We would like to thank MD.ai for assistance with image de-identification.\n","Documentation":"https://github.com/Emory-HITI/MRKR","Contact":"bprice9@emory.edu, bpri0002@radiology.ufl.edu, hari.trivedi@emory.edu","ManagedBy":"Health Innovation and Translational Informatics Lab at Emory University (hitilab.com)","UpdateFrequency":"New data is added as soon as it is available.","Tags":["aws-pds","bioinformatics","biology","computer vision","csv","health","imaging","labeled","life sciences","machine learning","medical image computing","medical imaging","radiology","x-ray"],"License":"CC-BY-SA","Resources":[{"Description":"MRKR dataset and tables","ARN":"arn:aws:s3:::emory-mrkr-dataset","Region":"us-west-2","Type":"S3 Bucket","ControlledAccess":"https://data.hitilab.com/auth/register"}],"DataAtWork":{"Tutorials":[{"Title":"Example Notebook","URL":"https://github.com/Emory-HITI/MRKR/blob/main/example_notebook.ipynb","AuthorName":"Emory-HITI"}],"Publications":[{"Title":"Emory Knee Radiograph Dataset","URL":"https://arxiv.org/abs/2411.00866","AuthorName":"Brandon Price, Jason Adleberg, Kaesha Thomas, Zach Zaiman, Aawez Mansuri, Beatrice Brown-Mulry, Chima Okecheukwu, Judy Gichoya, Hari Trivedi."}]},"RegistryEntryAdded":"2024-11-04","RegistryEntryLastModified":"2026-02-11","Slug":"mrkr","Sources":["awslabs-open-data-registry"]}
{"Name":"Medical Segmentation Decathlon","Description":"With recent advances in machine learning, semantic segmentation algorithms are becoming increasingly general purpose and translatable to unseen tasks. Many key algorithmic advances in the field of medical imaging are commonly validated on a small number of tasks, limiting our understanding of the generalisability of the proposed contributions. A model which works out-of-the-box on many tasks, in the spirit of AutoML, would have a tremendous impact on healthcare. The field of medical imaging is also missing a fully open source and comprehensive benchmark for general purpose algorithmic validation and testing covering a large span of challenges, such as: small data, unbalanced labels, large-ranging object scales, multi-class labels, and multimodal imaging, etc. This challenge and dataset aims to provide such resource through the open sourcing of large medical imaging datasets on several highly different tasks, and by standardising the analysis and validation process.","Documentation":"http://medicaldecathlon.com","Contact":"[Medical Decathlon Organisers](medicaldecathlon@gmail.com)","ManagedBy":"[MONAI Development Team](https://github.com/Project-MONAI/MONAI)","UpdateFrequency":"This is a static dataset; however, tutorials and resources will be updated as they are developed.","Tags":["aws-pds","computed tomography","health","imaging","life sciences","magnetic resonance imaging","medicine","nifti","segmentation"],"License":"[CC-BY-SA 4.0 International](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Ten tasks from the Medical Segmentation Decathlon Challenge. Tasks are organized by organ system and pathology, as follow, Liver Tumours; Brain Tumours; Hippocampus; Lung Tumours; Prostate; Cardiac; Pancreas Tumour; Colon Cancer; Hepatic Vasculature; Spleen. Tasks are provided in both tar.gz and uncompressed format.","ARN":"arn:aws:s3:::msd-for-monai","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"This is a mirror of s3://msd-for-monai in eu-west-2.","ARN":"arn:aws:s3:::msd-for-monai-eu","Region":"eu-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Pytorch-Integrated MSD Data Loader","URL":"https://github.com/Project-MONAI/tutorials","AuthorName":"MONAI Development Team","AuthorURL":"https://github.com/Project-MONAI","Services":null}],"Tools & Applications":[{"Title":"MONAI: Getting Started","URL":"https://monai.io/start.html","AuthorName":"MONAI Development Team","AuthorURL":"https://github.com/Project-MONAI"}],"Publications":[{"Title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","URL":"https://arxiv.org/pdf/1902.09063.pdf","AuthorName":"Simpson A. L., Antonelli M., Bakas S., Bilello M., Farahana K., van Ginneken B., et al"}]},"RegistryEntryAdded":"2020-08-14","RegistryEntryLastModified":"2022-11-08","Slug":"msd","Sources":["awslabs-open-data-registry"]}
{"Name":"Multiscale Entropy Analysis","Description":"The method of multiscale entropy (MSE) analysis is useful for investigating complexity in physiologic signals and other series that have correlations at multiple (time) scales.","Documentation":"https://physionet.org/content/mse/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/mse/","ARN":"arn:aws:s3:::physionet-open/mse/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"mse","Sources":["awslabs-open-data-registry"]}
{"Name":"The Mount Sinai PsychAD Study (MSSM_PsychAD)","Description":"The Mount Sinai PsychAD Study provides population-scale single-nucleus RNA\nsequencing data from the dorsolateral prefrontal cortex of 1,494 postmortem\nbrain donors. The study encompasses more than 6.3 million nuclei from\nindividuals with neurodegenerative diseases, serious mental illnesses, and\nneurotypical controls. Represented conditions include Alzheimer's disease,\nParkinson's disease, schizophrenia, bipolar disorder, diffuse Lewy body\ndementia, vascular dementia, and additional neurological and psychiatric\ndisorders. The study also includes genotype, clinical, neuropathological, and\nneuropsychiatric symptom data where available.\n","Documentation":"https://doi.org/10.7303/9618136","Contact":"https://www.synapse.org/Synapse:syn2580853/discussion/default","ManagedBy":"Sage Bionetworks","UpdateFrequency":"AD Knowledge Portal Data Releases are published monthly (https://news.adknowledgeportal.org/). Updates to specific studies are dependent on data contributor and ingress timelines.","Tags":["aws-pds","bioinformatics","biology","gene expression","genomic","genotyping","Homo sapiens","life sciences","medicine","neurobiology","neuroscience","RNA-seq","single-cell transcriptomics","transcriptomics"],"License":"https://adknowledgeportal.synapse.org/Explore/Studies/DetailsPage/StudyDetails?Study=syn52160016#Acknowledgement","Citation":"The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (NIH-NIA, U24AG061340).","Resources":[{"Description":"This study provides controlled-access genotype, single-nucleus RNA sequencing, clinical, neuropathological, neuropsychiatric symptom, and related metadata from 1,494 postmortem brain donors representing neurodegenerative diseases, serious mental illnesses, and neurotypical controls.","ARN":"arn:aws:s3:::mssm-psych-ad.opendata.sagebase.org","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://adknowledgeportal.synapse.org/Data%20Access"}],"DataAtWork":{"Publications":[{"Title":"Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics","URL":"https://doi.org/10.1186/s13059-018-1603-1","AuthorName":"Stoeckius M, Zheng S, Houck-Loomis B, Hao S, Yeung BZ, Mauck WM 3rd, et al."},{"Title":"Population-scale cross-disorder atlas of the human prefrontal cortex at single-cell resolution","URL":"https://www.nature.com/articles/s41597-025-04687-5","AuthorName":"Fullard JF, Prashant NM, Lee D, Mathur D, Therrien K, Hong A, et al."}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-09-23","RegistryEntryLastModified":"2026-10-05","Slug":"mssm-psych-ad","Sources":["awslabs-open-data-registry"]}
{"Name":"MAMEM SSVEP Database","Description":"Released by the Information Technologies Institute (CERTH-ITI) and powered by MAMEM HORIZON 2020, the MSSVEP database contains EEG recordings of 11 subjects under the stimulation of flickering lights, used to study the steady state visually evoked potentials. The data is divided into three sets in which the experimental procedures and capturing devices differ.","Documentation":"https://doi.org/10.13026/C29591","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C29591","ARN":"arn:aws:s3:::physionet-open/mssvepdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"mssvepdb","Sources":["awslabs-open-data-registry"]}
{"Name":"A multi-camera and multimodal dataset for posture and gait analysis","Description":"Gait and posture analysis while using assisting robotic devices is of utmost importance to attain effective assistance. This work provides a multi-camera, multimodal, and detailed dataset for vision-based applications using a wheeled robotic walker equipped with a pair of affordable cameras. Depth data was acquired at 30 fps from a total of 14 healthy participants walking at 3 different gait speeds, across 3 different walking scenarios/paths at 3 different locations. Simultaneously, accurate skeleton joint data was recorded using an inertial-based commercial motion capture system that provides a reliable ground-truth for classical or novel (i.e., machine learning-based) vision-based applications. In total, the database contains approximately 166K frames of synchronized data, which amounts to 92 minutes of total recording time. This dataset may contribute to the development and evaluation of: i) classic or data-driven vision-based pose estimation algorithms; ii) applications in human detection and tracking, and movement forecasting; iii) and gait/posture metrics analysis using a rehabilitation device.","Documentation":"https://doi.org/10.13026/fyxw-n385","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/fyxw-n385","ARN":"arn:aws:s3:::physionet-open/multi-gait-posture/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"multi-gait-posture","Sources":["awslabs-open-data-registry"]}
{"Name":"Multi Token Completion","Description":"This dataset provides masked sentences and multi-token phrases that were masked-out of these sentences.\nWe offer 3 datasets: a general purpose dataset extracted from the Wikipedia and Books corpora, and 2 additional datasets extracted from pubmed abstracts.\nAs for the pubmed data, please be aware that the dataset does not reflect the most current/accurate data available from NLM (it is not being updated).\nFor these datasets, the columns provided for each datapoint are as follows:\ntext- the original sentence\nspan- the span (phrase) which is masked out\nspan_lower- the lowercase version of span\nrange- the range in the text string which will be masked out (this is important because span might appear more than once in text)\nfreq- the corpus  frequency of span_lower\nmasked_text- the masked version of text, span is replaced with [MASK]\nAdditinaly, we provide a small (3K) dataset with human annotations.\n","Documentation":"https://multi-token-completion.s3.amazonaws.com/README.txt","Contact":"guyk@amazon.com, orenk@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not currently being updated","Tags":["amazon.science","machine learning","natural language processing"],"License":"Datasets are published under [CC-NC-SA-3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/).\nHuman evaluation is published under [CC-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/).\n","Resources":[{"Description":"multi-token-completion Datasets","ARN":"arn:aws:s3:::multi-token-completion","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Simple and Effective Multi-Token Completion from Masked Language Models","AuthorName":"Oren Kalinsky, Guy Kushilevitz, Alex Libov & Yoav Goldberg","URL":"https://2023.eacl.org/"}]},"RegistryEntryAdded":"2023-02-06","RegistryEntryLastModified":"2023-02-07","Slug":"multi-token-completion","Sources":["awslabs-open-data-registry"]}
{"Name":"MultiCoNER Datasets","Description":"MultiCoNER 1 is a large multilingual dataset (11 languages) for Named Entity Recognition. It is designed to represent some of the contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities such as movie titles, and long-tail entity distributions. MultiCoNER 2 is a large multilingual dataset (12 languages) for fine grained Named Entity Recognition. Its fine-grained taxonomy contains 36 NE classes, representing real-world challenges for NER, where named entities, apart from the surface form, context represents a critical role in distinguishing between the different fine-grained types (e.g. Scientist vs. Athlete). Furthermore, the test data of MultiCoNER 2 contains noisy instances, where the noise has been applied to both context tokens as well as the entity tokens. The noise includes typing errors at character level based on keyboard layouts in the the different languages.\n","Documentation":"https://multiconer.s3.us-west-2.amazonaws.com/readme.html","Contact":"besnikf@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"N/A","Tags":["natural language processing"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MultiCoNER 1 Data files","ARN":"arn:aws:s3:::multiconer/multiconer2022/","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"MultiCoNER 2 Data files","ARN":"arn:aws:s3:::multiconer/multiconer2023/","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition","URL":"https://assets.amazon.science/95/4c/47c2afd44236ac1a05d184e7813e/dynamic-gazetteer-integration-in-multilingual-models-for-cross-lingual-and-cross-domain-named-entity-recognition.pdf","AuthorName":"Besnik Fetahu, Anjie Fang, Oleg Rokhlenko and Shervin Malmasi"},{"Title":"Gazetteer Enhanced Named Entity Recognition for Code-Mixed Web Queries","URL":"https://www.amazon.science/publications/gazetteer-enhanced-named-entity-recognition-for-code-mixed-web-queries","AuthorName":"Besnik Fetahu, Anjie Fang, Oleg Rokhlenko and Shervin Malmasi"},{"Title":"GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input","URL":"https://www.amazon.science/publications/gemnet-effective-gated-gazetteer-representations-for-recognizing-complex-entities-in-low-context-input","AuthorName":"Tao Meng, Anjie Fang, Oleg Rokhlenko and Shervin Malmasi"},{"Title":"MultiCoNER: A Large-scale Multilingual Dataset for Complex Named Entity Recognition","URL":"https://aclanthology.org/2022.coling-1.334.pdf","AuthorName":"Shervin Malmasi, Anjie Fang, Besnik Fetahu, Sudipta Kar, Oleg Rokhlenko"}]},"RegistryEntryAdded":"2022-03-24","RegistryEntryLastModified":"2023-02-17","Slug":"multiconer","Sources":["awslabs-open-data-registry"]}
{"Name":"Software for Analysis of Multifractal Time Series","Description":"Software for calculating multifractal partitions and moments of a time series.","Documentation":"https://physionet.org/content/multifractal/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/multifractal/","ARN":"arn:aws:s3:::physionet-open/multifractal/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"multifractal","Sources":["awslabs-open-data-registry"]}
{"Name":"Multimedia Commons","Description":"The Multimedia Commons is a collection of audio and visual features computed for the nearly 100 million Creative Commons-licensed Flickr images and videos in the YFCC100M dataset from Yahoo! Labs, along with ground-truth annotations for selected subsets. The International Computer Science Institute (ICSI) and Lawrence Livermore National Laboratory are producing and distributing a core set of derived feature sets and annotations as part of an effort to enable large-scale video search capabilities. They have released this feature corpus into the public domain, under Creative Commons License 0, so it is free for anyone to use for any purpose.","Documentation":"https://multimediacommons.wordpress.com/yfcc100m-core-dataset/","Contact":"multimedia-commons@icsi.berkeley.edu","UpdateFrequency":"Not updated.","ManagedBy":"[Multimedia Commons](http://mmcommons.org/)","Tags":["aws-pds","computer vision","machine learning","multimedia","video"],"License":"The International Computer Science Institute and Lawrence Livermore National Laboratory have released the feature corpus and annotations under Creative Commons 0 (public domain), so there are no restrictions on use. More information on licensing and citation of the original metadata and the underlying images and videos is available from Yahoo! Labs at https://webscope.sandbox.yahoo.com/catalog.php?datatype=i&did=67","Resources":[{"Description":"This directory has 99,171,688 image files and 787,479 video files. The videos add up to around 8,081 hours, with an average video length of 37s and a median length of 28s.","ARN":"arn:aws:s3:::multimedia-commons","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://multimedia-commons.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2022-06-14","Slug":"multimedia-commons","Sources":["awslabs-open-data-registry"]}
{"Name":"A multimodal gait dataset of brain activity, muscle activity, kinematics and ground forces in young adults","Description":"Gait is a fundamental motor function, and its analysis is essential for understanding locomotor control, rehabilitation, and the early detection of neurological and musculoskeletal disorders. While many datasets capture either biomechanical or neural aspects of gait, publicly available multimodal datasets that integrate brain, muscle, kinematic, and ground reaction force recordings remain scarce. This limitation restricts advances in modeling neuromechanical interactions and the development of machine learning approaches for gait classification and rehabilitation technologies. To address this gap, we provide a comprehensive dataset of treadmill walking from 59 healthy adults with a mean age of 24 ± 5 years, representing both sexes and different body mass index categories. Participants walked at three controlled speeds (0.5, 0.75, and 1.0 m/s), with synchronized recordings from scalp electroencephalography, surface electromyography of 12 lower-limb muscles, inertial sensors capturing kinematics, and bilateral force plates measuring three-dimensional forces, moments, and center of pressure. The dataset enables investigations into brain-body interactions, speed-dependent adaptations, and neuromechanical variability, while supporting the benchmarking of computational models for gait analysis.","Documentation":"https://doi.org/10.13026/r0ea-7161","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/r0ea-7161","ARN":"arn:aws:s3:::physionet-open/multimodal-gait-dataset/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"multimodal-gait-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"A Multi-Modal Satellite Imagery Dataset for Public Health Analysis in Colombia","Description":"We introduce a cost-effective public health analysis solution for low- and middle-income countries—the Multi-Modal Satellite Imagery Dataset in Colombia. By leveraging high-quality, spatiotemporally aligned satellite images and corresponding metadata, the dataset integrates economic, demographic, meteorological, and epidemiological data. Employing a single forwards and a forward-backward technique ensures clear satellite images with minimal cloud cover for every epi-week, significantly enhancing overall data quality. The extraction process utilizes the satellite extractor package powered by the SentinelHub API, resulting in a comprehensive dataset of 12,636 satellite images from 81 municipalities in Colombia between 2016 and 2018, along with relevant metadata. Beyond expediting public health data analysis across diverse locations and timeframes, this versatile framework consistently captures multimodal features. Its applications extend to various realms in multimodal AI, encompassing deforestation monitoring, forecasting education indices, water quality assessment, tracking extreme climatic events, addressing epidemic illnesses, and optimizing precision agriculture.","Documentation":"https://doi.org/10.13026/xr5s-xe24","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Zero 1.0 Universal Public Domain Dedication","Resources":[{"Description":"https://doi.org/10.13026/xr5s-xe24","ARN":"arn:aws:s3:::physionet-open/multimodal-satellite-data/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"multimodal-satellite-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Multi-Scale Ultra High Resolution (MUR) Sea Surface Temperature (SST)","Description":"A global, gap-free, gridded, daily 1 km Sea Surface Temperature (SST) dataset created by merging multiple Level-2 satellite SST datasets. Those input datasets include the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 (AMSR-2) on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. Data are available from 2002 to present in Zarr format. The original source of the MUR data is the NASA JPL Physical Oceanography DAAC.","Documentation":"https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1","Contact":"podaac@podaac.jpl.nasa.gov","ManagedBy":"[Farallon Institute](https://faralloninstitute.org)","UpdateFrequency":"The temporal extent of the Zarr store is 2002-06-01 to 2020-01-20.","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","climate","earth observation","environmental","natural resource","oceans","satellite imagery","water","weather"],"License":"There are no restrictions on the use of these data.","Resources":[{"Description":"MUR Level 4 SST dataset in [Zarr](https://zarr.readthedocs.io/en/stable/) format. The zarr-v1/ directory contains a zarr store chunked (5, 1799, 3600) along the dimensions (time, lat, lon).","ARN":"arn:aws:s3:::mur-sst/zarr-v1","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Python Jupyter Notebooks","URL":"https://github.com/pangeo-gallery/osm2020tutorial","AuthorName":"Chelle Gentemann, Rich Signell","AuthorURL":"https://faralloninstitute.org"},{"Title":"Python Reader Software","URL":"https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/python/","AuthorName":"PO.DAAC","AuthorURL":"https://podaac.jpl.nasa.gov"}],"Tools & Applications":[{"Title":"HTTPS server","URL":"https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1","AuthorName":"PO.DAAC","AuthorURL":"https://podaac.jpl.nasa.gov"},{"Title":"OPeNDAP server","URL":"https://opendap.jpl.nasa.gov/opendap/OceanTemperature/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/contents.html","AuthorName":"Physical Oceanography Distributed Active Archive Center (PO.DAAC)","AuthorURL":"https://podaac.jpl.nasa.gov"},{"Title":"State of the Ocean (SOTO) server","URL":"https://podaac-tools.jpl.nasa.gov/soto","AuthorName":"PO.DAAC","AuthorURL":"https://podaac.jpl.nasa.gov"},{"Title":"THREDDS server","URL":"http://thredds.jpl.nasa.gov/thredds/catalog_ghrsst_gds2.html?dataset=MUR-JPL-L4-GLOB-v4.1","AuthorName":"PO.DAAC","AuthorURL":"https://podaac.jpl.nasa.gov"},{"Title":"Web discovery service","URL":"https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHGMR-4FJ04","AuthorName":"PO.DAAC","AuthorURL":"https://podaac.jpl.nasa.gov"}],"Publications":[{"Title":"A multi-scale high-resolution analysis of global sea surface temperature","URL":"https://doi.org/10.1016/j.rse.2017.07.029","AuthorName":"Chin, Toshio Michael, Jorge Vazquez-Cuervo, and Edward M. Armstrong"},{"Title":"GHRSST Data Processing Specification","URL":"https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf","AuthorName":"GHRSST Project"},{"Title":"Improving our knowledge about the oceans by providing cloud-based access to large datasets","URL":"https://aws.amazon.com/blogs/publicsector/improving-knowledge-oceans-providing-cloud-based-access-large-datasets/","AuthorName":"Chelle Gentemann"}]},"RegistryEntryAdded":"2020-01-29","RegistryEntryLastModified":"2023-02-28","Slug":"mur","Sources":["awslabs-open-data-registry"]}
{"Name":"MICRO Motion capture data from groups of participants standing still to auditory stimuli (2012)","Description":"This dataset comprises head motion observations collected as part of an experiment in which a group of people were asked to stand still for 6 minutes, with the first 3 minutes in silence, followed by 3 minutes with music. Head motion was captured in units of mm at 100Hz using a Qualisys infra-red optical system. The experiment was carried out at the University of Oslo on March 8th 2012 from a total of 113 participants. Code to read and process these files is available. The paper corresponding to the work is Jensenius et al., \"The Musical Influence on People's Micromotion when Standing Still in Groups\", Proceedings of the 14th Sound and Music Computing Conference (2017).","Documentation":"https://doi.org/10.13026/4h9d-gf10","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/4h9d-gf10","ARN":"arn:aws:s3:::physionet-open/music-motion-2012/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"music-motion-2012","Sources":["awslabs-open-data-registry"]}
{"Name":"MICRO Motion capture data from groups of participants standing still to auditory stimuli (2015)","Description":"This project contains head movement data recorded from groups of participants asked to stand as still as possible and presented with a series of auditory stimuli. Data was collected in units of mm with a Qualisys motion capture system at 100Hz. Data was collected at the University of Oslo on March 12th 2015 from a total of 108 participants. Code to read and process these files has been made publicly available.","Documentation":"https://doi.org/10.13026/dfv0-sb95","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/dfv0-sb95","ARN":"arn:aws:s3:::physionet-open/music-motion-2015/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"music-motion-2015","Sources":["awslabs-open-data-registry"]}
{"Name":"MUSIC (Sudden Cardiac Death in Chronic Heart Failure)","Description":"The MUSIC (MUerte Subita en Insuficiencia Cardiaca) study is a prospective, multicentre, longitudinal study designed to assess risk predictors of cardiac mortality and sudden cardiac death (SCD) in ambulatory patients with chronic heart failure (CHF). The study population consisted of 992 patients with CHF consecutively enrolled from the specialized HF clinics of eight University Spanish Hospitals between April 2003 and December 2004, and followed up for a median of 44 months (until November 2008). All patients had a 3-lead resting electrocardiogram (ECG), a 24 h, 2-(4%) or 3-lead (96%) Holter ECG, chest X-ray, echocardiography, and blood laboratory parameters performed at enrolment. Primary outcomes were cardiac death, either sudden cardiac death (SCD) or pump failure death (PFD) at the end of the follow-up period.","Documentation":"https://doi.org/10.13026/z3m7-rf58","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Open Database License v1.0","Resources":[{"Description":"https://doi.org/10.13026/z3m7-rf58","ARN":"arn:aws:s3:::physionet-open/music-sudden-cardiac-death/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"music-sudden-cardiac-death","Sources":["awslabs-open-data-registry"]}
{"Name":"Spontaneous Ventricular Tachyarrhythmia Database","Description":"This database contains 135 pairs of RR interval time series, recorded by implanted cardioverter defibrillators (Medtronic Jewel PlusTM ICD 7218) in 78 subjects. Each series contains between 986 and 1022 RR intervals. One series of each pair includes a spontaneous episode of ventricular tachycardia (VT) or ventricular fibrillation (VF), and the other is a sample of the intrinsic (usually sinus) rhythm.","Documentation":"https://doi.org/10.13026/C25K5D","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C25K5D","ARN":"arn:aws:s3:::physionet-open/mvtdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"mvtdb","Sources":["awslabs-open-data-registry"]}
{"Name":"MWIS VR Instances","Description":"Large-scale node-weighted conflict graphs for maximum weight independent set solvers","Documentation":"https://mwis-vr-instances.s3.amazonaws.com/vrInstances.pdf","Contact":"resendem@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Infrequent","Tags":["amazon.science","graph","traffic","transportation"],"License":"MIT-0","Resources":[{"Description":"Conflict graphs for maximum weight independent set","ARN":"arn:aws:s3:::mwis-vr-instances","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2021-05-25","RegistryEntryLastModified":"2022-06-06","Slug":"mwis-vr-instances","Sources":["awslabs-open-data-registry"]}
{"Name":"Myocardial perfusion scintigraphy image database","Description":"This database provides a collection of myocardial perfusion scintigraphy images in DICOM format with all metadata and segmentations (masks) in NIfTI format. The images were obtained from patients undergoing scintigraphy examinations to investigate cardiac conditions such as ischemia and myocardial infarction. The dataset encompasses a diversity of clinical cases, including various perfusion patterns and underlying cardiac conditions. All images have been properly anonymized, and the age range of the patients is from 20 to 90 years. This database represents a valuable source of information for researchers and healthcare professionals interested in the analysis and diagnosis of cardiac diseases. Moreover, it serves as a foundation for the development and validation of image processing algorithms and artificial intelligence techniques applied to cardiovascular medicine. Available for free on the PhysioNet platform, its aim is to promote collaboration and advance research in nuclear cardiology and cardiovascular medicine, while ensuring the replicability of studies.","Documentation":"https://doi.org/10.13026/ce2z-dw74","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Open Database License v1.0","Resources":[{"Description":"https://doi.org/10.13026/ce2z-dw74","ARN":"arn:aws:s3:::physionet-open/myocardial-perfusion-spect/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"myocardial-perfusion-spect","Sources":["awslabs-open-data-registry"]}
{"Name":"NAIP on AWS","Description":"The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. This \"leaf-on\" imagery andtypically ranges from 30 centimeters to 100 centimeters in resolution and is available from the naip-analytic Amazon S3 bucket as 4-band (RGB + NIR) imagery in MRF format, on naip-source Amazon S3 bucket as 4-band (RGB + NIR) in uncompressed Raw GeoTiff format and naip-visualization as 3-band (RGB) Cloud Optimized GeoTiff format. More details on [NAIP](https://naip-usdaonline.hub.arcgis.com/)","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/naip","Contact":"imagerycontent@esri.com","ManagedBy":"[Esri](https://www.esri.com/en-us/home)","UpdateFrequency":"NAIP data is provided state by state at varying time intervals. Each year, a variable number of states are updated with an overall update cycle of every two to three years for each state. This catalog currently includes NAIP imagery from 2010 through 2023 for most products.","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["aerial imagery","agriculture","aws-pds","cog","earth observation","geospatial","natural resource","regulatory"],"License":"Public Domain with Attribution\n","Resources":[{"Description":"The data in this bucket is 4-band (RGB + NIR) in MRF format and Cloud Optimized GeoTIFF (COG) processed by Esri. The MRF format is a cloud-optimized open data format that provides fast access to data on S3. MRF can be accessed using GDAL and most applications build using GDAL, but also directly by web applications. Data is provided as 512x512 tiles, with reduced-resolution pyramids created using 2x sampling by averaging. Finally, data compression is efficient and lossless (meaning the data values here are the same as the source). A Cloud Optimized GeoTIFF (COG) is a GeoTIFF file optimized for hosting on a HTTP file server. COG has an internal organization that enables more efficient workflows on the cloud by supporting HTTP GET range requests, where just parts of a file are requested and returned. The bucket is set as a Requester Pays bucket.","ARN":"arn:aws:s3:::naip-analytic","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":true},{"Description":"The data in this bucket is Original Imagery in Geotiff format 4-band (RGB + NIR) uncompressed no overviews and metadata in a Requester Pays bucket","ARN":"arn:aws:s3:::naip-source","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":true},{"Description":"The data in this bucket is 3 band RGB in Geotiff format. It is converted and managed by Esri. This imagery is provided as Cloud Optimized GeoTIFFs, has been compressed using YCbCr JPEG with quality 85. It is provided as 512x512 tiles, with pyramids created using 2x sampling by averaging. The bucket is set as a Requester Pays bucket.","ARN":"arn:aws:s3:::naip-visualization","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":true}],"DataAtWork":{"Tutorials":[{"Title":"Urban Tree Detection","URL":"https://github.com/jonathanventura/urban-tree-detection","AuthorName":"Jonathan Ventura"}],"Tools & Applications":[{"Title":"3D Terrain Imagery Draping Tool using USDA NAIP and USGS Seamless COG DEM","URL":"https://github.com/mwkorver/deckgl-s3-cog-s1m","AuthorName":"Mark Korver","AuthorURL":"https://www.linkedin.com/in/markkorver/"},{"Title":"EOS Land Viewer","URL":"https://eos.com/landviewer/","AuthorName":"Earth Observing System","AuthorURL":"https://eos.com/"},{"Title":"VoyagerSearch showing off Batch + NAIP","URL":"https://www.voyagersearch.com/using-aws-batch-to-generate-image-thumbnails-for-voyager","AuthorName":"Voyager","AuthorURL":"https://www.voyagersearch.com/"}],"Publications":[{"Title":"Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery","URL":"https://doi.org/10.48550/arXiv.2208.10607","AuthorName":"Jonathan Ventura, Milo Honsberger, Cameron Gonsalves, Julian Rice, Camille Pawlak, Natalie L.R. Love, Skyler Han, Viet Nguyen, Keilana Sugano, Jacqueline Doremus, G. Andrew Fricker, Jenn Yost, Matt Ritter"}]},"RegistryEntryAdded":"2018-07-11","RegistryEntryLastModified":"2026-07-16","Slug":"naip","Sources":["awslabs-open-data-registry"]}
{"Name":"Nanopore Reference Human Genome","Description":"This dataset includes the sequencing and assembly of a reference standard human genome (GM12878) using the MinION nanopore sequencing instrument with the R9.4 1D chemistry.","Documentation":"https://github.com/nanopore-wgs-consortium/NA12878","Contact":"https://github.com/nanopore-wgs-consortium/NA12878/issues","ManagedBy":"Nanopore Whole Genome Sequencing Consortium","UpdateFrequency":"Data will be added as methodology improves or new data uses are encountered.","Tags":["aws-pds","genetic","genomic","life sciences","whole genome sequencing"],"License":"Nanopore Human Reference data is released under the Creative Commons CC-BY license and allows free, full and open access to all. For more details please refer to the data reuse and license section of the documentation.","Resources":[{"Description":"Nanopore Reference Human Genome","ARN":"arn:aws:s3:::nanopore-human-wgs","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://nanopore-human-wgs.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2022-06-15","Slug":"nanopore","Sources":["awslabs-open-data-registry"]}
{"Name":"NapierOne Mixed File Dataset","Description":"NapierOne is a modern cybersecurity mixed file data set, primarily aimed at, but not limited to, ransomware detection and forensic analysis. The dataset contains over 500,000 distinct files, representing 44 distinct popular file types. It was designed to address the known deficiency in research reproducibility and improve consistency by facilitating research replication and repeatability. The data set was inspired by the Govdocs1 data set and it is intended that ‘NapierOne’ be used as a complement to this original data set. An investigation was performed with the goal of determining the common files types currently in use. No specific research was found that explicitly provided this information, so an alternative consensus approach was employed. This involved combining the findings from multiple sources of file type usage into an overall ranked list. After which 5,000 real-world example files were gathered, and a specific data subset was created, for each of the common file types identified. In some circumstances, multiple data subsets were created for a specific file type, each subset representing a specific characteristic for that file type. For example, there are multiple data subsets for the ZIP file type with each subset containing examples of a specific compression method. Ransomware execution tends to produce files that have high entropy, so examples of file types that naturally have this attribute are also present. The resulting entire data set comprises of more than 90 separate data subsets divided between 44 distinct file types, resulting in over 500,000 unique files in total. Currently, the data set contains examples of the following file types APK, BIN, BMP, CSS, CSV, DOC, DOCX, DWG, ELF, EPS,EPUB, EXE, GIF, GZIP, HTML, ICS, JS, JPG, JSON, MKV, MP3, MP4, ODS, OXPS, PDF, PNG, PPT, PPTX, PS1, RAR, SVG, TAR, TIF, TXT, WEBP, XLS, XLSX, XML, ZIP, ZLIB, 7Zip","Documentation":"https://github.com/simonrdavies/NapierOne","Contact":"Simon Davies  s.davies@napier.ac.uk Richard Macfarlane  R.Macfarlane@napier.ac.uk William J. Buchanan b.buchanan@napier.ac.uk","ManagedBy":"[School of Computing at Edinburgh Napier University](https://www.napier.ac.uk/about-us/our-schools/school-of-computing/)","UpdateFrequency":"Data will be added as methodology improves or new common or required file types are encountered.","Tags":["aws-pds","computer forensics","computer security","cyber security","digital forensics","malware","mixed file dataset","ransomware"],"License":"NapierOne is released under the Edinburgh Napier University License Agreement and allows free, full and open access to all. For more details please refer to the License and Attribution section of the documentation. The license agreement means that you are free to Copy, publish, distribute and transmit the Information; Adapt the Information; Exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. You must (where you do any of the above) Acknowledge the source of the information in your product.","Resources":[{"Description":"NapierOne Mixed File Dataset","ARN":"arn:aws:s3:::napierone.com","Region":"eu-north-1","Type":"S3 Bucket","Explore":["[Browse Bucket](http://napierone.com.s3-website.eu-north-1.amazonaws.com)"]}],"DataAtWork":{"Tutorials":[{"Title":"napierOne use examples","URL":"https://github.com/simonrdavies/NapierOne/blob/main/tutorials.md","AuthorName":"s.davies","AuthorURL":"https://github.com/simonrdavies/NapierOne/"}],"Publications":[{"Title":"Exploring the Need For an Updated Mixed File Research Data Set","URL":"https://doi.org/10.1109/ICEET53442.2021.9659618","AuthorName":"Simon R.Davies, Richard Macfarlane, William J.Buchanan."},{"Title":"NapierOne - A modern mixed file data set alternative to Govdocs1","URL":"https://doi.org/10.1016/j.fsidi.2021.301330","AuthorName":"Simon R.Davies, Richard Macfarlane, William J.Buchanan"}]},"RegistryEntryAdded":"2021-09-07","RegistryEntryLastModified":"2022-06-06","Slug":"napierone","Sources":["awslabs-open-data-registry"]}
{"Name":"1940 Census Population Schedules, Enumeration District Maps, and Enumeration District Descriptions","Description":"The 1940 Census population schedules were created by the Bureau of the Census in an attempt to enumerate every person living in the United States on April 1, 1940, although some persons were missed. The 1940 census population schedules were digitized by the National Archives and Records Administration (NARA) and released publicly on April 2, 2012.\nThe 1940 Census enumeration district maps contain maps of counties, cities, and other minor civil divisions that show enumeration districts, census tracts, and related boundaries and numbers used for each census. The coverage is nation wide and includes territorial areas.\nThe 1940 Census enumeration district descriptions contain written descriptions of census districts, subdivisions, and enumeration districts.\n","Documentation":"https://www.archives.gov/developer/1940-census","Contact":"public.dataset.program@nara.gov","ManagedBy":"National Archives and Records Administration (NARA)","UpdateFrequency":"Not updated","Collabs":{"ASDI":{"Tags":["socioeconomic"]}},"Tags":["1940 census","archives","aws-pds","census","demography","nara"],"License":"US Government work","Resources":[{"Description":"1940 Census","ARN":"arn:aws:s3:::nara-1940-census","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"1940 Census on the AWS Registry of Open Data","URL":"https://www.archives.gov/developer/1940-census","AuthorName":"National Archives and Records Administration"}],"Tools & Applications":[{"Title":"National Archives 1940 Census","URL":"https://1940census.archives.gov","AuthorName":"National Archives and Records Administration","AuthorURL":"https://www.archives.gov"}]},"RegistryEntryAdded":"2021-04-15","RegistryEntryLastModified":"2021-12-28","Slug":"nara-1940-census","Sources":["awslabs-open-data-registry"]}
{"Name":"1950 Census Population Schedules, Enumeration District Maps, and Enumeration District Descriptions","Description":"The 1950 Census population schedules were created by the Bureau of the Census in an attempt to enumerate every person living in the United States on April 1, 1950, although some persons were missed. The 1950 census population schedules were digitized by the National Archives and Records Administration (NARA) and released publicly on April 1, 2022.\nThe 1950 Census enumeration district maps contain maps of counties, cities, and other minor civil divisions that show enumeration districts, census tracts, and related boundaries and numbers used for each census. The coverage is nation wide and includes territorial areas.\nThe 1950 Census enumeration district descriptions contain written descriptions of census districts, subdivisions, and enumeration districts.\n","Documentation":"https://www.archives.gov/developer/1950-census","Contact":"public.dataset.program@nara.gov","ManagedBy":"National Archives and Records Administration (NARA)","UpdateFrequency":"Not updated","Collabs":{"ASDI":{"Tags":["socioeconomic"]}},"Tags":["1950 census","archives","aws-pds","census","demography","nara"],"License":"US Government work","Resources":[{"Description":"1950 Census","ARN":"arn:aws:s3:::nara-1950-census","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"1950 Census on the AWS Registry of Open Data","URL":"https://www.archives.gov/developer/1950-census","AuthorName":"National Archives and Records Administration"}],"Tools & Applications":[{"Title":"National Archives 1950 Census","URL":"https://1950census.archives.gov","AuthorName":"National Archives and Records Administration","AuthorURL":"https://www.archives.gov"}]},"RegistryEntryAdded":"2022-03-31","RegistryEntryLastModified":"2022-03-31","Slug":"nara-1950-census","Sources":["awslabs-open-data-registry"]}
{"Name":"National Archives Catalog","Description":"The National Archives Catalog dataset contains all of the descriptions; authority records; digitized and electronic records; and tags, transcriptions and comments for NARA’s archival holdings available in the Catalog.","Documentation":"https://www.archives.gov/developer/national-archives-catalog-dataset","Contact":"public.dataset.program@nara.gov","ManagedBy":"National Archives and Records Administration (NARA)","UpdateFrequency":"Biannual","Tags":["archives","aws-pds","government records","nara","national archives catalog"],"License":"US Government work","Resources":[{"Description":"National Archives Catalog","ARN":"arn:aws:s3:::nara-national-archives-catalog","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"National Archives Catalog on the AWS Registry of Open Data","URL":"https://www.archives.gov/developer/national-archives-catalog-dataset","AuthorName":"National Archives and Records Administration"}],"Tools & Applications":[{"Title":"National Archives Catalog","URL":"https://catalog.archives.gov","AuthorName":"National Archives and Records Administration","AuthorURL":"https://www.archives.gov"}]},"RegistryEntryAdded":"2021-03-24","RegistryEntryLastModified":"2022-01-04","Slug":"nara-national-archives-catalog","Sources":["awslabs-open-data-registry"]}
{"Name":"National Archives Nationalsozialistische Deutsche Arbeiterpartei (NSDAP)","Description":"The NSDAP dataset on the AWS Registry of Open Data includes over 16 million digital objects and metadata, including Textract-generated OCR text, from two portions (MFKL and MFOK) of microfilm publication A3340 - Records Relating to Membership in the Nationalsozialistische Deutsche Arbeiterpartei (NSDAP).","Documentation":"https://github.com/usnationalarchives/nsdap","ManagedBy":"National Archives and Records Administration (NARA)\nSee all datasets managed by [National Archives and Records Administration (NARA)](https://registry.opendata.aws/?search=managedBy:national%20archives%20and%20records%20administration).\n","Contact":"public.dataset.program@nara.gov","Citation":"Records Relating to Membership in the Nationalsozialistische Deutsche Arbeiterpartei (National Socialist German Labor Party (NSDAP)), 1927-1945 (National Archives Identifier 120443610 was accessed on DATE from https://registry.opendata.aws/nara-nsdap.","UpdateFrequency":"Not updated","Tags":["archives","aws-pds","government records","nara","national archives catalog"],"License":"US Government work","Resources":[{"Description":"Nationalsozialistische Deutsche Arbeiterpartei (NSDAP) (A3340)","ARN":"arn:aws:s3:::nara-nsdap","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Instructions for Accessing NSDAP Dataset on the AWS Registry of Open Data by National Archives and Records Administration (on GitHub)","URL":"https://github.com/usnationalarchives/nsdap","AuthorName":"National Archives and Records Administration"}],"Tools & Applications":[{"Title":"Catalog Description: Records Relating to Membership in the Nationalsozialistische Deutsche Arbeiterpartei (National Socialist German Labor Party (NSDAP)), 1927-1945 (A3340)","URL":"https://catalog.archives.gov/id/12044361","AuthorName":"National Archives and Records Administration","AuthorURL":"https://www.archives.gov"}]},"RegistryEntryAdded":"2026-08-24","RegistryEntryLastModified":"2026-08-24","Slug":"nara-nsdap","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 1993_AN_NASA Project","Description":"This data set contains spot elevation measurements of Arctic, Greenland, Antarctic, and Patagonia sea ice and ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation.\n<br>\n\n#### BLATM2\nThis data set contains resampled and smoothed elevation measurements of Arctic and Antarctic sea ice, as well as Greenland, Arctic, Patagonia, and Antarctic region land ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/blatm1b/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"BLATM1B v1 - This data set contains spot elevation measurements of Arctic, Greenland, Antarctic, and Patagonia sea ice and ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE-Related/BLATM1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"BLATM2 v1 - This data set contains resampled and smoothed elevation measurements of Arctic and Antarctic sea ice, as well as Greenland, Arctic, Patagonia, and Antarctic region land ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE-Related/BLATM2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-1993-an-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 1993_GR_NASA Project","Description":"This data set contains depth sounder measurements of ice elevation, ice surface, ice bottom, and ice thickness over Greenland and Antarctica, acquired by the Multichannel Coherent Radar Depth Sounder (MCoRDS).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/brmcr2/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"BRMCR2 v1 - This data set contains depth sounder measurements of ice elevation, ice surface, ice bottom, and ice thickness over Greenland and Antarctica, acquired by the Multichannel Coherent Radar Depth Sounder (MCoRDS).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE-Related/BRMCR2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-1993-gr-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2007_GR_NASA Project","Description":"This data set contains surface elevation data over Greenland measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/blvis2/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice","lidar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"BLVIS2 v1 - This data set contains surface elevation data over Greenland measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/BLVIS2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2007-gr-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2008_AN_UTIG Project","Description":"This data set contains vertical acceleration values for Antarctica using the BGM-3 Gravimeter. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.\n<br>\n\n#### IGBGM2\nThis data set contains free air anomaly measurements taken over Antarctica using the BGM-3 Gravimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.\n<br>\n\n#### IR1HI1B\nThis data set contains Antarctica radar sounder echo strength profiles from the Hi-Capability Radar Sounder (HiCARS) Version 1 instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which was funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.\n<br>\n\n#### IR1HI2\nThis data set contains ice thickness, surface and bed elevation, and echo strength measurements taken over Antarctica using the Hi-Capability Airborne Radar Sounder (HiCARS) instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.\n<br>\n\n#### IR2HI1B\nThis data set contains Antarctica radar sounder echo strength profiles from the Hi-Capability Radar Sounder (HiCARS) Version 2 instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which was funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.\n<br>\n\n#### IR2HI2\nThis data set contains ice thickness, surface and bed elevation, and echo strength measurements taken over Antarctica using the Hi-Capability Airborne Radar Sounder (HiCARS) instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which was funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.\n<br>\n\n#### IAPRS1B\nThis data set contains static pressure values for Antarctica using the Paroscientific Digiquartz Transmitter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.\n<br>\n\n#### ILUTP1B\nThis data set contains laser ranges, returned pulses, and deviation for returned pulses in Antarctica and Greenland using the Riegl Laser Altimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA's Operation IceBridge.\n<br>\n\n#### ILUTP2\nThis data set contains surface range values for Antarctica and Greenland derived from measurements captured by the Riegl Laser Altimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.\n<br>\n\n#### IMFGM1B\nThis data set contains time-registered Level-1B field readings taken over Antarctica using the Watson-Gyro Fluxgate Magnetometer instrument. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### IGZLS1B\nThis data set contains vertical, cross body, and along body acceleration values for geophysical survey flights in Antarctica using the ZLS Dynamic Gravity Meter. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/igbgm1b/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","elevation","ice","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"IGBGM1B v1 - This data set contains vertical acceleration values for Antarctica using the BGM-3 Gravimeter. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IGBGM1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IGBGM2 v1 - This data set contains free air anomaly measurements taken over Antarctica using the BGM-3 Gravimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IGBGM2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IR1HI1B v1 - This data set contains Antarctica radar sounder echo strength profiles from the Hi-Capability Radar Sounder (HiCARS) Version 1 instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which was funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IR1HI1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IR1HI2 v1 - This data set contains ice thickness, surface and bed elevation, and echo strength measurements taken over Antarctica using the Hi-Capability Airborne Radar Sounder (HiCARS) instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IR1HI2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IR2HI1B v1 - This data set contains Antarctica radar sounder echo strength profiles from the Hi-Capability Radar Sounder (HiCARS) Version 2 instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which was funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IR2HI1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IR2HI2 v1 - This data set contains ice thickness, surface and bed elevation, and echo strength measurements taken over Antarctica using the Hi-Capability Airborne Radar Sounder (HiCARS) instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which was funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IR2HI2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IAPRS1B v1 - This data set contains static pressure values for Antarctica using the Paroscientific Digiquartz Transmitter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IAPRS1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILUTP1B v1 - This data set contains laser ranges, returned pulses, and deviation for returned pulses in Antarctica and Greenland using the Riegl Laser Altimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA's Operation IceBridge.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILUTP1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILUTP2 v1 - This data set contains surface range values for Antarctica and Greenland derived from measurements captured by the Riegl Laser Altimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILUTP2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IMFGM1B v1 - This data set contains time-registered Level-1B field readings taken over Antarctica using the Watson-Gyro Fluxgate Magnetometer instrument. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IMFGM1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IGZLS1B v1 - This data set contains vertical, cross body, and along body acceleration values for geophysical survey flights in Antarctica using the ZLS Dynamic Gravity Meter. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IGZLS1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2008-an-utig","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2009_AK_NASA Project","Description":"This data set represents a collection of orthorectified images that were created using the NASA Ames Stereo Pipeline. The final images were obtained by processing stereo images from the IceBridge DMS L0 Raw Imagery data set, along with NASA's Land, Vegetation, and Ice Sensor (LVIS) and Airborne Topographic Mapper (ATM) lidar data from the IceBridge LVIS L2 Geolocated Surface Elevation Product and IceBridge ATM L1B Elevation and Return Strength data sets, respectively. The closely related data set IceBridge DMS L3 Ames Stereo Pipeline Photogrammetric DEM provides the corresponding digital elevation models (DEMs) in GeoTIFF format.\n<br>\n\n#### IODEM3\nThis data set represents a collection of digital elevation models (DEMs) that were created using the NASA Ames Stereo Pipeline. The final DEMs were obtained by processing stereo images from the IceBridge DMS L0 Raw Imagery data set, along with NASA's Land, Vegetation, and Ice Sensor (LVIS) and Airborne Topographic Mapper (ATM) lidar data from the IceBridge LVIS L2 Geolocated Surface Elevation Product and IceBridge ATM L1B Elevation and Return Strength data sets, respectively. The closely related data set IceBridge DMS L3 Ames Stereo Pipeline Orthorectified Images provides the corresponding orthorectified images in GeoTIFF format.\n<br>\n\n#### IRWIS2\nThis data set contains depth sounder measurements of elevation, surface, bottom, and thickness for Alaska taken from the Warm Ice Sounding Explorer (WISE). The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/iodim3/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice","lidar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"IODIM3 v1 - This data set represents a collection of orthorectified images that were created using the NASA Ames Stereo Pipeline. The final images were obtained by processing stereo images from the IceBridge DMS L0 Raw Imagery data set, along with NASA's Land, Vegetation, and Ice Sensor (LVIS) and Airborne Topographic Mapper (ATM) lidar data from the IceBridge LVIS L2 Geolocated Surface Elevation Product and IceBridge ATM L1B Elevation and Return Strength data sets, respectively.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IODIM3/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IODEM3 v1 - This data set represents a collection of digital elevation models (DEMs) that were created using the NASA Ames Stereo Pipeline. The final DEMs were obtained by processing stereo images from the IceBridge DMS L0 Raw Imagery data set, along with NASA's Land, Vegetation, and Ice Sensor (LVIS) and Airborne Topographic Mapper (ATM) lidar data from the IceBridge LVIS L2 Geolocated Surface Elevation Product and IceBridge ATM L1B Elevation and Return Strength data sets, respectively.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IODEM3/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IRWIS2 v1 - This data set contains depth sounder measurements of elevation, surface, bottom, and thickness for Alaska taken from the Warm Ice Sounding Explorer (WISE). The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IRWIS2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2009-ak-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2009_AK_UAF Project","Description":"This data set contains flight reports from NASA Operation IceBridge Greenland, Arctic, Antarctic, and Alaska missions. Flight reports contain information on region, mission, aircraft model, flight data, purpose of flight, and on-board sensors. The flight reports were collected as part of Operation IceBridge funded aircraft survey campaigns. The corresponding flight lines can be found in the IceBridge L1B Thinned Flight Lines (IPFLT1B) data set.\n<br>\n\n#### ILAKP1B\nThis data set contains surface profiles of Alaska Glaciers acquired using the airborne University of Alaska Fairbanks (UAF) Glacier Lidar system. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### ILAKS1B\nThis data set contains scanning laser altimetry data points of Alaskan glaciers and parts of East and West Antarctica acquired by the airborne University of Alaska Fairbanks (UAF) Glacier Lidar system. The data were collected as part of NASA Operation IceBridge funded campaigns.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/ipflr1b/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","lidar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"IPFLR1B v1 - This data set contains flight reports from NASA Operation IceBridge Greenland, Arctic, Antarctic, and Alaska missions. Flight reports contain information on region, mission, aircraft model, flight data, purpose of flight, and on-board sensors.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IPFLR1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILAKP1B v1 - This data set contains surface profiles of Alaska Glaciers acquired using the airborne University of Alaska Fairbanks (UAF) Glacier Lidar system. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILAKP1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILAKS1B v1 - This data set contains scanning laser altimetry data points of Alaskan glaciers and parts of East and West Antarctica acquired by the airborne University of Alaska Fairbanks (UAF) Glacier Lidar system. The data were collected as part of NASA Operation IceBridge funded campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILAKS1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2009-ak-uaf","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2009_AN_CRESIS Project","Description":"This data set contains depth sounder measurements of ice elevation, ice surface, ice bottom, and ice thickness for Greenland and Antarctica taken from the Multichannel Coherent Radar Depth Sounder (MCoRDS). The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/irmcr2/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"IRMCR2 v1 - This data set contains depth sounder measurements of ice elevation, ice surface, ice bottom, and ice thickness for Greenland and Antarctica taken from the Multichannel Coherent Radar Depth Sounder (MCoRDS). The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IRMCR2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2009-an-cresis","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2009_AN_NASA Project","Description":"This data set contains radar echograms taken over Greenland and Antarctica using the Center for Remote Sensing of Ice Sheets (CReSIS) Accumulation Radar instrument. The data were collected as part of Operation IceBridge funded campaigns.\n<br>\n\n#### ILATM1B\nThis data set contains spot elevation measurements of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### ILATM2\nThis data set contains resampled and smoothed elevation measurements of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region land ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### IDHDT4\nThis data set contains surface elevation rate of change measurements derived from IceBridge and Pre-IceBridge Airborne Topographic Mapper (ATM) widescan elevation measurements data for Arctic and Antarctic missions flown under NASA's Operation IceBridge (OIB) and Arctic Ice Mapping (AIM) projects.\n<br>\n\n#### IOCAM1B\nThis data set contains images taken with the Continuous Airborne Mapping By Optical Translator (CAMBOT) over Antarctica and Greenland.\n<br>\n\n#### IODCC0\nThis data set contains camera calibration reports for IceBridge Digital Mapping System (DMS) missions flown over Antarctica and Greenland.\n<br>\n\n#### IODMS1B\nThis data set contains Level-1B imagery taken from the Digital Mapping System (DMS) over Greenland and Antarctica. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### IODMS3\nThe IceBridge DMS L3 Photogrammetric DEM (IODMS3) data set contains gridded digital elevation models and orthorectified images of Greenland and Antarctica derived from the Digital Mapping System (DMS). The data were collected as part of Operation IceBridge funded campaigns.\n<br>\n\n#### IAKST1B\nThis data set contains surface temperature measurements of Arctic and Antarctic sea ice and land ice acquired by the Heitronics KT19.85 Series II Infrared Radiation Pyrometer. For flights with the NASA DC-8 aircraft, the National Suborbital Research Center (NSRC) operated the instrument and created the data product. For flights with the NASA P-3 and other aircraft, the instrument was operated by the Wallops Flight Facility (WFF) as part of the ATM instrument suite. The data were collected as part of the Operation IceBridge funded survey campaigns.\n<br>\n\n#### IRKUB1B\nThis data set contains elevation and surface measurements over Greenland, the Arctic, and Antarctica, as well as flight path charts and echogram images acquired using the Center for Remote Sensing of Ice Sheets (CReSIS) Ku-Band Radar Altimeter.\n<br>\n\n#### IDCSI4\nThis data set contains derived geophysical data products including sea ice freeboard, snow depth, and sea ice thickness measurements in Greenland and Antarctica retrieved from IceBridge Snow Radar, Digital Mapping System (DMS), Continuous Airborne Mapping By Optical Translator (CAMBOT), and Airborne Topographic Mapper (ATM) data sets. The data were collected as part of Operation IceBridge funded campaigns.\n<br>\n\n#### ILVIS0\nThis data set contains raw Inertial Measurement Unit (IMU), Global Positioning System (GPS), and camera data over Greenland, Antarctica, and Alaska measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of Operation IceBridge funded campaigns, including the Arctic Radiation - IceBridge Sea and Ice Experiment (ARISE).\n<br>\n\n#### IOLVIS1A\nThis data set contains geotagged images taken over Greenland and Antarctica by the NASA Digital Mapping Camera paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of Operation IceBridge funded campaigns.\n<br>\n\n#### ILVIS1B\nThis data set contains return energy waveform data measured over Greenland, Alaska, and Antarctica by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of Operation IceBridge funded campaigns.\n<br>\n\n#### ILVIS2\nThis data set contains surface elevation data over Greenland, Alaska, and Antarctica, measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA Operation IceBridge funded campaigns.\n<br>\n\n#### IGGRV1B\nThis data set contains gravity measurements, including acceleration data in three orthogonal directions, from the Sander Geophysics AIRGrav airborne gravity system. Gravity data include latitude and Eotvos-corrected values, as well as free air correction at various along-flight-line time filtering scales. The data were collected as part of Operation IceBridge funded campaigns.\n<br>\n\n#### IRSNO1B\nThis data set contains radar echograms taken from the Center for Remote Sensing of Ice Sheets (CReSIS) ultra wide-band snow radar over land and sea ice in the Arctic and Antarctic. In addition, airborne snow measurements were taken during 10 flights over Alaska mountains, ice fields, and glaciers at the end of May 2018 by a compact CReSIS FMCW radar system installed on a Single Otter aircraft. The data were collected as part of Operation IceBridge funded campaigns.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/iracc1b/versions/2","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","earth observation","elevation","ice","lidar","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"IRACC1B v2 - This data set contains radar echograms taken over Greenland and Antarctica using the Center for Remote Sensing of Ice Sheets (CReSIS) Accumulation Radar instrument. The data were collected as part of Operation IceBridge funded campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IRACC1B/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILATM1B v1 - This data set contains spot elevation measurements of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILATM1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILATM2 v2 - This data set contains resampled and smoothed elevation measurements of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region land ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILATM2/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IDHDT4 v1 - This data set contains surface elevation rate of change measurements derived from IceBridge and Pre-IceBridge Airborne Topographic Mapper (ATM) widescan elevation measurements data for Arctic and Antarctic missions flown under NASA's Operation IceBridge (OIB) and Arctic Ice Mapping (AIM) projects.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IDHDT4/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IOCAM1B v1 - This data set contains images taken with the Continuous Airborne Mapping By Optical Translator (CAMBOT) over Antarctica and Greenland.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IOCAM1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IODCC0 v1 - This data set contains camera calibration reports for IceBridge Digital Mapping System (DMS) missions flown over Antarctica and Greenland.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IODCC0/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IODMS1B v1 - This data set contains Level-1B imagery taken from the Digital Mapping System (DMS) over Greenland and Antarctica. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IODMS1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IODMS3 v1 - The IceBridge DMS L3 Photogrammetric DEM (IODMS3) data set contains gridded digital elevation models and orthorectified images of Greenland and Antarctica derived from the Digital Mapping System (DMS). The data were collected as part of Operation IceBridge funded campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IODMS3/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IAKST1B v1 - This data set contains surface temperature measurements of Arctic and Antarctic sea ice and land ice acquired by the Heitronics KT19.85 Series II Infrared Radiation Pyrometer. For flights with the NASA DC-8 aircraft, the National Suborbital Research Center (NSRC) operated the instrument and created the data product.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IAKST1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IRKUB1B v2 - This data set contains elevation and surface measurements over Greenland, the Arctic, and Antarctica, as well as flight path charts and echogram images acquired using the Center for Remote Sensing of Ice Sheets (CReSIS) Ku-Band Radar Altimeter.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IRKUB1B/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IDCSI4 v1 - This data set contains derived geophysical data products including sea ice freeboard, snow depth, and sea ice thickness measurements in Greenland and Antarctica retrieved from IceBridge Snow Radar, Digital Mapping System (DMS), Continuous Airborne Mapping By Optical Translator (CAMBOT), and Airborne Topographic Mapper (ATM) data sets. The data were collected as part of Operation IceBridge funded campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IDCSI4/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILVIS0 v1 - This data set contains raw Inertial Measurement Unit (IMU), Global Positioning System (GPS), and camera data over Greenland, Antarctica, and Alaska measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of Operation IceBridge funded campaigns, including the Arctic Radiation - IceBridge Sea and Ice Experiment (ARISE).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/ILVIS0/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IOLVIS1A v1 - This data set contains geotagged images taken over Greenland and Antarctica by the NASA Digital Mapping Camera paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of Operation IceBridge funded campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/IOLVIS1A/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILVIS1B v2 - This data set contains return energy waveform data measured over Greenland, Alaska, and Antarctica by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of Operation IceBridge funded campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/ILVIS1B/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILVIS2 v1 - This data set contains surface elevation data over Greenland, Alaska, and Antarctica, measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA Operation IceBridge funded campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/ILVIS2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IGGRV1B v2 - This data set contains gravity measurements, including acceleration data in three orthogonal directions, from the Sander Geophysics AIRGrav airborne gravity system. Gravity data include latitude and Eotvos-corrected values, as well as free air correction at various along-flight-line time filtering scales.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IGGRV1B/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IRSNO1B v2 - This data set contains radar echograms taken from the Center for Remote Sensing of Ice Sheets (CReSIS) ultra wide-band snow radar over land and sea ice in the Arctic and Antarctic. In addition, airborne snow measurements were taken during 10 flights over Alaska mountains, ice fields, and glaciers at the end of May 2018 by a compact CReSIS FMCW radar system installed on a Single Otter aircraft.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IRSNO1B/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2009-an-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2009_GR_NASA Project","Description":"This data set contains contains Greenland ice thickness measurements acquired using the Pathfinder Advanced Radar Ice Sounder (PARIS). The data were collected as part of Operation IceBridge funded campaigns.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/irpar2/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"IRPAR2 v1 - This data set contains contains Greenland ice thickness measurements acquired using the Pathfinder Advanced Radar Ice Sounder (PARIS). The data were collected as part of Operation IceBridge funded campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IRPAR2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2009-gr-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2010_AN_NASA Project","Description":"This data set contains Level-3 tomographic ice thickness measurements and ice thickness errors over areas of Greenland and Antarctica. Two of the data files additionally provide bed elevation measurements. The data were derived from measurements taken by the Center for Remote Sensing of Ice Sheets (CReSIS) Multichannel Coherent Radar Depth Sounder (MCoRDS) instrument and were collected as part of NASA Operation IceBridge funded campaigns.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/irtit3/versions/2","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","earth observation","elevation","ice","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"IRTIT3 v2 - This data set contains Level-3 tomographic ice thickness measurements and ice thickness errors over areas of Greenland and Antarctica. Two of the data files additionally provide bed elevation measurements.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IRTIT3/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2010-an-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2010_AN_UTIG Project","Description":"This data set contains geolocated surface elevation measurements captured over Antarctica using the Sigma Space Mapping Photon Counting Lidar and Riegl Laser Altimeter. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.\n<br>\n\n#### ILSNP1B\nThis data set contains nadir photon counting data captured over Antarctica using the Sigma Space photon counting lidar. Position and orientation data are included. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/ilsnp4/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","elevation","ice","lidar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ILSNP4 v1 - This data set contains geolocated surface elevation measurements captured over Antarctica using the Sigma Space Mapping Photon Counting Lidar and Riegl Laser Altimeter. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILSNP4/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILSNP1B v1 - This data set contains nadir photon counting data captured over Antarctica using the Sigma Space photon counting lidar. Position and orientation data are included.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILSNP1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2010-an-utig","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2010_GR_NASA Project","Description":"This data set contains reprocessed images depicting labels that indicate the sea ice surface category, created by processing IceBridge DMS L0 Raw Imagery with the Open Source Sea-ice Processing Algorithm. The images are provided as TIFF files (.tif). Additional metadata are provided as CSV text files (.csv), which are available as a single zip file named RDSISC4_metadata.zip. An orthorectified version of this data set is available as IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Orthorectified Images.\n<br>\n\n#### RDSISCO4\nThis data set contains reprocessed, orthorectified images depicting labels that indicate the sea ice surface category, created by processing IceBridge DMS L0 Raw Imagery (IODMS0) with the Open Source Sea-ice Processing Algorithm. Orthorectification was done using digital elevation models from the IceBridge DMS L3 Ames Stereo Pipeline Photogrammetric DEM (IODEM3) collection. The standard (non-orthorectified) images are available as IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Images (RDSISC4).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/rdsisc4/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"RDSISC4 v1 - This data set contains reprocessed images depicting labels that indicate the sea ice surface category, created by processing IceBridge DMS L0 Raw Imagery with the Open Source Sea-ice Processing Algorithm. The images are provided as TIFF files (.tif).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE-Related/RDSISC4/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"RDSISCO4 v1 - This data set contains reprocessed, orthorectified images depicting labels that indicate the sea ice surface category, created by processing IceBridge DMS L0 Raw Imagery (IODMS0) with the Open Source Sea-ice Processing Algorithm. Orthorectification was done using digital elevation models from the IceBridge DMS L3 Ames Stereo Pipeline Photogrammetric DEM (IODEM3) collection.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE-Related/RDSISCO4/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2010-gr-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2011_AN_NASA Project","Description":"This data set contains surface temperature measurements of Arctic and Antarctic sea ice and land ice acquired by the Heitronics KT19.85 Series II Infrared Radiation Pyrometer. The instrument is operated by the Wallops Flight Facility (WFF) as part of the ATM instrument suite. The data were collected as part of the Operation IceBridge funded survey campaigns.\n<br>\n\n#### ILNSA1B\nThis data set contains spot elevation measurements of Greenland, Arctic, and Antarctic sea ice acquired using the NASA Airborne Topographic Mapper (ATM) 4CT3 narrow scan instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/iakst1b/versions/2","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"IAKST1B v2 - This data set contains surface temperature measurements of Arctic and Antarctic sea ice and land ice acquired by the Heitronics KT19.85 Series II Infrared Radiation Pyrometer. The instrument is operated by the Wallops Flight Facility (WFF) as part of the ATM instrument suite.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IAKST1B/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILNSA1B v1 - This data set contains spot elevation measurements of Greenland, Arctic, and Antarctic sea ice acquired using the NASA Airborne Topographic Mapper (ATM) 4CT3 narrow scan instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILNSA1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2011-an-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2011_AN_UTIG Project","Description":"This data set contains geolocated photon elevations captured over Antarctica using the Sigma Space photon counting lidar. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/ilsig1b/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","elevation","ice","lidar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ILSIG1B v1 - This data set contains geolocated photon elevations captured over Antarctica using the Sigma Space photon counting lidar. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILSIG1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2011-an-utig","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2012_AN_NASA Project","Description":"This data set contains radar echograms taken from the Center for Remote Sensing of Ice Sheets (CReSIS) ultra Multichannel Coherent Radar Depth Sounder (MCoRDS) over land and sea ice in the Arctic and Antarctic.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/irmcr1b/versions/2","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","earth observation","ice","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"IRMCR1B v2 - This data set contains radar echograms taken from the Center for Remote Sensing of Ice Sheets (CReSIS) ultra Multichannel Coherent Radar Depth Sounder (MCoRDS) over land and sea ice in the Arctic and Antarctic.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IRMCR1B/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2012-an-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2012_AN_UTIG Project","Description":"This data set contains vertical acceleration values for Antarctica using the CMG 1A dynamic gravity meter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.\n<br>\n\n#### IGCMG2\nThis data set contains geolocated free air gravity disturbances derived from measurements taken over Antarctica using the GT-1A gravity meter S-019. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/igcmg1b/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"IGCMG1B v1 - This data set contains vertical acceleration values for Antarctica using the CMG 1A dynamic gravity meter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IGCMG1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IGCMG2 v1 - This data set contains geolocated free air gravity disturbances derived from measurements taken over Antarctica using the GT-1A gravity meter S-019. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IGCMG2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2012-an-utig","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2012_GR_GBMF Project","Description":"This data set contains Greenland and Antarctica gravity measurements taken from the Sander Geophysics AIRGrav airborne gravity system.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/rggrv1b/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"RGGRV1B v1 - This data set contains Greenland and Antarctica gravity measurements taken from the Sander Geophysics AIRGrav airborne gravity system.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE-Related/RGGRV1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2012-gr-gbmf","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2013_AK_UAF Project","Description":"This data set contains radar echograms acquired by the University of Alaska Fairbanks High-Frequency Radar Sounder over select glaciers in Alaska.\n<br>\n\n#### IRUAFHF2\nThis data set contains measurements of glacier surface elevation, bed elevation, and ice thickness for Alaska and Northwestern Canada. Glacier surface elevation is derived from IceBridge UAF Lidar Scanner L1B Geolocated Surface Elevation Triplets (ILAKS1B). Radar bed returns are sourced from IceBridge UAF L1B HF Geolocated Radar Echo Strength Profiles (IRUAFHF1B) to provide glacier bed elevation and ice thickness. Radar-derived ice thickness and glacier bed elevation are provided as per-trace measurements from the corresponding L1B radar echo strength profile (referencing the WGS 84 ellipsoid).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/iruafhf1b/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice","lidar","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"IRUAFHF1B v1 - This data set contains radar echograms acquired by the University of Alaska Fairbanks High-Frequency Radar Sounder over select glaciers in Alaska.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IRUAFHF1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IRUAFHF2 v1 - This data set contains measurements of glacier surface elevation, bed elevation, and ice thickness for Alaska and Northwestern Canada. Glacier surface elevation is derived from IceBridge UAF Lidar Scanner L1B Geolocated Surface Elevation Triplets (ILAKS1B).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IRUAFHF2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2013-ak-uaf","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2013_AN_NASA Project","Description":"This data set contains spot elevation measurements of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### ILVGH1B\nThis data set contains energy waveform data measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter, aboard the Global Hawk Unmanned Aerial Vehicle. The data were collected as part of NASA Operation IceBridge funded campaigns.\n<br>\n\n#### ILVGH2\nThis data set contains surface elevation data measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter, aboard the Global Hawk Unmanned Aerial Vehicle. The data were collected as part of NASA Operation IceBridge funded campaigns.\n<br>\n\n#### ILNSA1B\nThis data set contains spot elevation measurements of Greenland, Arctic, and Antarctic sea ice acquired using the NASA Airborne Topographic Mapper (ATM) narrow-swath instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### IMCS31B\nThis data set contains magnetic field readings taken over Antarctica using the Scintrex CS-3 Cesium Magnetometer instrument. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/ilatm1b/versions/2","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice","lidar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ILATM1B v2 - This data set contains spot elevation measurements of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILATM1B/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILVGH1B v1 - This data set contains energy waveform data measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter, aboard the Global Hawk Unmanned Aerial Vehicle. The data were collected as part of NASA Operation IceBridge funded campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/ILVGH1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILVGH2 v1 - This data set contains surface elevation data measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter, aboard the Global Hawk Unmanned Aerial Vehicle. The data were collected as part of NASA Operation IceBridge funded campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/ILVGH2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILNSA1B v2 - This data set contains spot elevation measurements of Greenland, Arctic, and Antarctic sea ice acquired using the NASA Airborne Topographic Mapper (ATM) narrow-swath instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILNSA1B/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IMCS31B v2 - This data set contains magnetic field readings taken over Antarctica using the Scintrex CS-3 Cesium Magnetometer instrument. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IMCS31B/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2013-an-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2015_AK_UAF Project","Description":"This data set contains radar echograms acquired by the Arizona Radio-Echo Sounder (ARES) over select glaciers in Alaska.\n<br>\n\n#### IRARES2\nThis data set contains glacier surface elevation, bed elevation, and ice thickness measurements for Alaska and Northwestern Canada. Glacier surface elevation is derived from IceBridge UAF Lidar Scanner L1B Geolocated Surface Elevation Triplets (ILAKS1B). Radar bed returns are taken from IceBridge ARES L1B Geolocated Radar Echo Strength Profiles (IRARES1B).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/irares1b/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice","lidar","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"IRARES1B v1 - This data set contains radar echograms acquired by the Arizona Radio-Echo Sounder (ARES) over select glaciers in Alaska.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IRARES1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IRARES2 v1 - This data set contains glacier surface elevation, bed elevation, and ice thickness measurements for Alaska and Northwestern Canada. Glacier surface elevation is derived from IceBridge UAF Lidar Scanner L1B Geolocated Surface Elevation Triplets (ILAKS1B).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IRARES2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2015-ak-uaf","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2015_AN_NASA Project","Description":"This data set contains geotagged images captured by NASA Digital Mapping Cameras, which were mounted alongside the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/olvis1a/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","lidar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OLVIS1A v1 - This data set contains geotagged images captured by NASA Digital Mapping Cameras, which were mounted alongside the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/OLVIS1A/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2015-an-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2017_AN_NASA Project","Description":"This data set contains spot elevation measurements with corresponding waveforms of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### ILATMGR\nThis data set reports surface grain size estimates of snow and ice using waveform measurements from NASA's Airborne Topographic Mapper (ATM) narrow-swath and wide-swath instrumentation over the Greenland ice sheet and surrounding sea ice.\n<br>\n\n#### ILNSAW1B\nThis data set contains spot elevation measurements with corresponding waveforms of Greenland, Arctic, and Antarctic sea ice. The data complement the IceBridge ATM L1B Near-Infrared Waveforms data, which are measured at near-infrared wavelength. The data were acquired as part of aircraft survey campaigns funded by Operation IceBridge.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/ilatmw1b/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ILATMW1B v1 - This data set contains spot elevation measurements with corresponding waveforms of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILATMW1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILATMGR v1 - This data set reports surface grain size estimates of snow and ice using waveform measurements from NASA's Airborne Topographic Mapper (ATM) narrow-swath and wide-swath instrumentation over the Greenland ice sheet and surrounding sea ice.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILATMGR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILNSAW1B v1 - This data set contains spot elevation measurements with corresponding waveforms of Greenland, Arctic, and Antarctic sea ice. The data complement the IceBridge ATM L1B Near-Infrared Waveforms data, which are measured at near-infrared wavelength.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILNSAW1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2017-an-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2017_GR_NASA Project","Description":"This data set contains gravity measurements taken over Greenland and Antarctica by the Lamont-Doherty Earth Observatory (LDEO) Gravimeter Suite. The data were collected as part of Operation IceBridge funded campaigns.\n<br>\n\n#### ILVIS2\nThis data set contains surface elevation data over parts of Greenland, measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA Operation IceBridge funded campaigns.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/iglgs1b/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice","lidar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"IGLGS1B v1 - This data set contains gravity measurements taken over Greenland and Antarctica by the Lamont-Doherty Earth Observatory (LDEO) Gravimeter Suite. The data were collected as part of Operation IceBridge funded campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IGLGS1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ILVIS2 v2 - This data set contains surface elevation data over parts of Greenland, measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA Operation IceBridge funded campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/ILVIS2/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2017-gr-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA 2018_AN_NASA Project","Description":"This data set contains geolocated waveforms of Greenland, Arctic, and Antarctic sea ice measured by the Airborne Topographic Mapper (ATM) near-infrared (NIR) lidar. The data complement, and are intended to be used with, the IceBridge Narrow Swath ATM L1B Elevation and Return Strength with Waveforms data, which are measured at green wavelength. The data were acquired as part of aircraft survey campaigns funded by Operation IceBridge.\n<br>\n\n#### IOCAM0\nThis data set contains raw images and associated aircraft position and attitude data, taken over Antarctica and Greenland by the Continuous Airborne Mapping By Optical Translator (CAMBOT), part of the Airborne Topographic Mapper (ATM) instrument suite. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### IOCAM1B\nThis data set contains high-resolution imagery taken with the Continuous Airborne Mapping By Optical Translator (CAMBOT) system over Antarctica and Greenland. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/ilnirw1b/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice","lidar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ILNIRW1B v1 - This data set contains geolocated waveforms of Greenland, Arctic, and Antarctic sea ice measured by the Airborne Topographic Mapper (ATM) near-infrared (NIR) lidar. The data complement, and are intended to be used with, the IceBridge Narrow Swath ATM L1B Elevation and Return Strength with Waveforms data, which are measured at green wavelength.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/ILNIRW1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IOCAM0 v1 - This data set contains raw images and associated aircraft position and attitude data, taken over Antarctica and Greenland by the Continuous Airborne Mapping By Optical Translator (CAMBOT), part of the Airborne Topographic Mapper (ATM) instrument suite. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IOCAM0/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IOCAM1B v2 - This data set contains high-resolution imagery taken with the Continuous Airborne Mapping By Optical Translator (CAMBOT) system over Antarctica and Greenland. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IOCAM1B/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-2018-an-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ABLE-2 Project","Description":"ABLE-2A_Aerosol_AircraftInSitu_Electra_Data is the in-situ aerosol data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.\n<br>\n\n#### ABLE-2A_TraceGas_AircraftInSitu_Electra_Data\nABLE-2A_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data using chemiluminescence, gas traps, cryogenic air samples, and IR lasers are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.\n<br>\n\n#### ABLE-2A_MetNav_AircraftInSitu_Electra_Data\nABLE-2A_MetNav_AircraftInSitu_Electra_Data is the in-situ meteorology and navigational data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.\n<br>\n\n#### ABLE-2A_Ground_Data\nABLE-2A_Ground_Data is the ground site data collected during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.\n<br>\n\n#### ABLE-2A_Merge_Data\nABLE-2A_Merge_Data is the merge data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.\n<br>\n\n#### ABLE-2A_Sondes_Data\nABLE-2A_Sondes_Data is the radiosonde and rawinsonde data collected during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.\n<br>\n\n#### ABLE-2B_Aerosol_AircraftInSitu_Electra_Data\nABLE-2B_Aerosol_AircraftInSitu_Electra_Data is the in-situ aerosol data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.\n<br>\n\n#### ABLE-2B_TraceGas_AircraftInSitu_Electra_Data\nABLE-2B_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data using chemiluminescence, gas traps, and grab samples are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.\n<br>\n\n#### ABLE-2B_MetNav_AircraftInSitu_Electra_Data\nABLE-2B_MetNav_AircraftInSitu_Electra_Data is the in-situ meteorology and navigational data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.\n<br>\n\n#### ABLE-2B_Ground_Data\nABLE-2B_Ground_Data is the ground data collected during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data using grab samples are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.\n<br>\n\n#### ABLE-2B_Merge_Data\nABLE-2B_Merge_Data is the merge data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data using chemiluminescence, gas traps, and grab samples are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.\n<br>\n\n#### ABLE-2B_Sondes_Data\nABLE-2B_Sondes_Data is the rawinsonde and tethered balloon data collected during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www-gte.larc.nasa.gov/gte_hmpg.htm#table","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","lidar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ABLE-2A_Aerosol_AircraftInSitu_Electra_Data v1 - ABLE-2A_Aerosol_AircraftInSitu_Electra_Data is the in-situ aerosol data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ABLE/ABLE-2A_Aerosol_AircraftInSitu_Electra_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ABLE-2A_TraceGas_AircraftInSitu_Electra_Data v1 - ABLE-2A_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data using chemiluminescence, gas traps, cryogenic air samples, and IR lasers are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ABLE/ABLE-2A_TraceGas_AircraftInSitu_Electra_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ABLE-2A_MetNav_AircraftInSitu_Electra_Data v1 - ABLE-2A_MetNav_AircraftInSitu_Electra_Data is the in-situ meteorology and navigational data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ABLE/ABLE-2A_MetNav_AircraftInSitu_Electra_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ABLE-2A_Ground_Data v1 - ABLE-2A_Ground_Data is the ground site data collected during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ABLE/ABLE-2A_Ground_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ABLE-2A_Merge_Data v1 - ABLE-2A_Merge_Data is the merge data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ABLE/ABLE-2A_Merge_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ABLE-2A_Sondes_Data v1 - ABLE-2A_Sondes_Data is the radiosonde and rawinsonde data collected during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ABLE/ABLE-2A_Sondes_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ABLE-2B_Aerosol_AircraftInSitu_Electra_Data v1 - ABLE-2B_Aerosol_AircraftInSitu_Electra_Data is the in-situ aerosol data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ABLE/ABLE-2B_Aerosol_AircraftInSitu_Electra_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ABLE-2B_TraceGas_AircraftInSitu_Electra_Data v1 - ABLE-2B_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data using chemiluminescence, gas traps, and grab samples are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ABLE/ABLE-2B_TraceGas_AircraftInSitu_Electra_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ABLE-2B_MetNav_AircraftInSitu_Electra_Data v1 - ABLE-2B_MetNav_AircraftInSitu_Electra_Data is the in-situ meteorology and navigational data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ABLE/ABLE-2B_MetNav_AircraftInSitu_Electra_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ABLE-2B_Ground_Data v1 - ABLE-2B_Ground_Data is the ground data collected during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data using grab samples are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ABLE/ABLE-2B_Ground_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ABLE-2B_Merge_Data v1 - ABLE-2B_Merge_Data is the merge data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data using chemiluminescence, gas traps, and grab samples are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ABLE/ABLE-2B_Merge_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ABLE-2B_Sondes_Data v1 - ABLE-2B_Sondes_Data is the rawinsonde and tethered balloon data collected during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ABLE/ABLE-2B_Sondes_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-able-2","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ABLE-3 Project","Description":"ABLE-3A_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3A (ABLE-3A) suborbital campaign. Data using grab samples, gas chromatography, and Laser Induced Fluorescence (LIF) are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.\n<br>\n\n#### ABLE-3A_MetNav_AircraftInSitu_Electra_Data\nABLE-3A_MetNav_AircraftInSitu_Electra_Data is the in-situ meteorology and navigational data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3A (ABLE-3A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.\n<br>\n\n#### ABLE-3A_AircraftRemoteSensing_Electra_DIAL_Data\nABLE-3A_AircraftRemoteSensing_Electra_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3A (ABLE-3A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.\n<br>\n\n#### ABLE-3A_Ground_Data\nABLE-3A_Ground_Data is the ground site data collected during the Arctic Boundary Layer Expedition - 3A (ABLE-3A) suborbital campaign. Data from the Harvard CO2 instrument and mist chambers are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.\n<br>\n\n#### ABLE-3A_Merge_Data\nABLE-3A_Merge_Data is the merge data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3A (ABLE-3A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.\n<br>\n\n#### ABLE-3A_Trajectory_Data\nABLE-3A_Trajectory_Data is the trajectory data collected during the Arctic Boundary Layer Expedition - 3A (ABLE-3A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.\n<br>\n\n#### ABLE-3B_Aerosol_AircraftInSitu_Electra_Data\nABLE-3B_Aerosol_AircraftInSitu_Electra_Data is the in-situ aerosol data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3B (ABLE-3B) suborbital campaign. Data using mist chambers and teflon filters are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.\n<br>\n\n#### ABLE-3B_TraceGas_AircraftInSitu_Electra_Data\nABLE-3B_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3B (ABLE-3B) suborbital campaign. Data using grab samples, gas chromatography, Laser Induced Fluorescence (LIF), and the Differential Absorption CO, CH4, N2O Measurements (DACOM) instrument are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.\n<br>\n\n#### ABLE-3B_MetNav_AircraftInSitu_Electra_Data\nABLE-3B_MetNav_AircraftInSitu_Electra_Data is the in-situ meteorological and navigational data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3B (ABLE-3B) suborbital campaign. Data from the Turbulent Air Motion Measurement System (TAMMS) instrument are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.\n<br>\n\n#### ABLE-3B_AircraftRemoteSensing_Electra_DIAL_Data\nABLE-3B_AircraftRemoteSensing_Electra_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3B (ABLE-3B) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.\n<br>\n\n#### ABLE-3B_Ground_Data\nABLE-3B_Ground_Data is the ground site data collected during the Arctic Boundary Layer Expedition - 3B (ABLE-3B) suborbital campaign. Data from the High-Altitude Fast-Response CO2 Analyzer (Harvard CO2) instrument are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.\n<br>\n\n#### ABLE-3B_Merge_Data\nABLE-3B_Merge_Data is the merge data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3B (ABLE-3B) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.\n<br>\n\n#### ABLE-3B_Trajectory_Data\nABLE-3B_Trajectory_Data is the trajectory data collected during the Arctic Boundary Layer Expedition - 3B (ABLE-3B) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www-gte.larc.nasa.gov/gte_hmpg.htm#table","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","climate","lidar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ABLE-3A_TraceGas_AircraftInSitu_Electra_Data v1 - ABLE-3A_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3A (ABLE-3A) suborbital campaign. Data using grab samples, gas chromatography, and Laser Induced Fluorescence (LIF) are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ABLE/ABLE-3A_TraceGas_AircraftInSitu_Electra_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ABLE-3A_MetNav_AircraftInSitu_Electra_Data v1 - ABLE-3A_MetNav_AircraftInSitu_Electra_Data is the in-situ meteorology and navigational data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3A (ABLE-3A) suborbital campaign. 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{"Name":"NASA ABoVE Project","Description":"This document presents the Concise Experiment Plan for NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) to serve as a guide to the Program as it identifies the research to be conducted under this study. Research for ABoVE will link field-based, process-level studies with geospatial data products derived from airborne and satellite remote sensing, providing a foundation for improving the analysis and modeling capabilities needed to understand and predict ecosystem responses and societal implications. The ABoVE Concise Experiment Plan (ACEP) outlines the conceptual basis for the Field Campaign and expresses the compelling rationale explaining the scientific and societal importance of the study. It presents both the science questions driving ABoVE research as well as the top-level requirements for a study design to address them.\n<br>\n\n#### Arctic_Boreal_CO2_Flux_V2_2448\nThis dataset is a synthesis of terrestrial and freshwater CO2 and CH4 fluxes from the Arctic-Boreal region aggregated to monthly timesteps. The dataset, known as ABCFlux v2, includes 1,028 unique sites and spans 1984-2024 with the majority of observations occurring after 1999. ABCFlux v2 includes surface-atmosphere CO2 fluxes of net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (Reco) alongside CH4 fluxes. For aquatic ecosystems, CH4 fluxes were split into diffusive and ebullitive flux pathways and included potential emissions from transient storage in the water column, alongside CO2 and CH4 concentrations dissolved in the surface water. Fluxes were measured through a variety of methods including chamber and eddy covariance techniques alongside bubble traps, ice-surveys, and concentration-based turbulence-driven modelling in aquatic ecosystems. Supporting variables include methodological metadata (e.g., gap-filling methods, number of chamber measurement days), environmental measurements (e.g., air, water, and soil temperatures), and site-level attributes (e.g., permafrost thaw status, disturbance history). Compared with version 1, this v2 dataset includes additional variables to represent detailed descriptions of plant functional types, deep soil temperatures (<10 cm), and permafrost thaw presence or absence in the top two meters. Data pertaining to CH4 fluxes were also added that were not included in previous version. The data are provided in comma separated values (CSV) format.\n<br>\n\n#### ABOLVIS1A\nThis data set contains geotagged images collected over Alaska and Western Canada. The images were taken by the NASA Digital Mapping Camera, paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE).\n<br>\n\n#### ABLVIS1B\nThis data set contains return energy waveform data over Alaska and Western Canada measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE).\n<br>\n\n#### ABLVIS2\nThis data set contains surface elevation data over Alaska and Western Canada measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE).\n<br>\n\n#### ABoVE_ASCENDS_XCO2_2050\nThis dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2), methane (CH4), and water vapor concentrations, plus air temperature, pressure, relative humidity, and wind speed values over Alaska and the Yukon and Northwest Territories of Canada during 2017-07-20 to 2017-08-08. Measurements were taken onboard a DC-8 aircraft during this Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) airborne deployment over portions of the Arctic-Boreal Vulnerability Experiment (ABoVE) domain. CO2 and CH4 were measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument. Water vapor and relative humidity were measured with Diode Laser Hydrometer. Measurements of column-averaged dry-air mixing ratio CO2 measurements (XCO2) were taken with the CO2 Sounder Lidar instrument. The airborne CO2 Sounder is a pulsed, multi-wavelength Integrated Path Differential Absorption lidar. It estimates XCO2 in the nadir path from the aircraft to the scattering surface by measuring the shape of the 1572.33 nm CO2 absorption line. The data were collected in order to capture the spatial and temporal dynamics of the northern high latitude carbon cycle as part of ABoVE and are provided in ICARTT file format.\n<br>\n\n#### ABoVE_ASCENDS_Backscatter_2051\nThis dataset provides atmospheric backscattering coefficient profiles collected during Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) deployments from 2017-07-20 to 2017-08-08 over Alaska, U.S., and the Yukon and Northwest Territories of Canada. These profiles were measured by the CO2 Sounder Lidar instrument carried on a DC-8 aircraft. The airborne CO2 Sounder is a pulsed, multi-wavelength Integrated Path Differential Absorption lidar that estimates column-averaged dry-air CO2 mixing ratio (XCO2) in the nadir path from the aircraft to the scattering surface. In addition to XCO2, the lidar receiver recorded the time-resolved atmospheric backscatter signal strength as the laser pulses propagated through the atmosphere. Raw lidar data were converted to the atmospheric backscatter cross-section product and the two-way atmosphere transmission, also known as attenuated backscatter profiles. These ASCENDS flights were coordinated with the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) campaign and are provided in ICARTT format.\n<br>\n\n#### ABoVE_ASCENDS_Merge_2114\nThis dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2), methane (CH4), water vapor concentrations, air temperature, pressure, and wind speed and direction as well as airborne remote sensing measurements of column average CO2 collected during Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) deployments from 2017-07-20 to 2017-08-08 over Alaska, US, and the Yukon and Northwest Territories of Canada. CO2 and CH4 were measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument. Water vapor and relative humidity were measured with Diode Laser Hydrometer. Measurements were taken onboard a DC-8 aircraft. The ASCENDS flights were coordinated with the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) campaign. The data are provided in ICARTT format along with an archive of flight videos.\n<br>\n\n#### ABoVE_PBand_SAR_1657\nThis dataset provides estimates of soil geophysical properties derived from Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band polarimetric synthetic aperture radar (PolSAR) data collected in August and October of 2014, 2015, and 2017 over 12 study sites (with some exceptions) across Northern Alaska. Soil properties reported include the active layer thickness (ALT), dielectric constant, soil moisture profile, surface roughness, and their respective uncertainty estimates at 30-m spatial resolution over the 12 flight transects. Most of the study sites are located within the continuous permafrost zone and where the aboveground vegetation consisting mainly of dwarf shrub and tussock/sedge/moss tundra has a minimal impact on P-band radar backscatter.\n<br>\n\n#### Soil_ActiveLayer_Properties_AK_2315\nThis dataset provides soil active layer characteristics from nine locations across Alaska. Soil samples were collected in 2016 except for one site which was sampled in 2018. Soil cores were collected from each site using a steel barrel and plastic sample tube attached to a hand drill. At the majority of sites, samples were taken from each end of three 30-m transects (i.e. samples collected at the 0 m and 30 m location of each transect). The entire thawed horizon (active layer) was sampled where possible, and the length of cores varies among sites. Cores were kept frozen until analysis in the lab. Samples were sectioned by horizon (organic and mineral), and the organic horizon was split into subsections so that no section was longer than approximately 10 cm. Coarse roots were removed, dried and weighed. Soils were measured for gravimetric water content, percent soil organic matter (SOM), pH, and bulk density. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma-separated values (CSV) format.\n<br>\n\n#### Permafrost_ActiveLayer_NSlope_1759\nThis dataset provides in situ soil measurements including soil dielectric properties, temperature, and moisture profiles, active layer thickness (ALT), and measurements of soil organic matter, bulk density, porosity, texture, and coarse root biomass. Samples were collected from the surface to permafrost table in soil pits at selected sites along the Dalton Highway in Northern Alaska. From North to South, the study sites include Franklin Bluffs, Sagwon, Happy Valley, Ice Cut, and Imnavait Creek. Measurements were made from August 22 to August 26, 2018. The purpose of the field campaign was to characterize the dielectric properties of permafrost active layer soils in support of the NASA Arctic and Boreal Vulnerability Experiment (ABoVE) Airborne Campaign.\n<br>\n\n#### ABoVE_ReSALT_InSAR_PolSAR_V3_2004\nThis dataset provides estimates of seasonal subsidence, active layer thickness (ALT), the vertical soil moisture profile, and uncertainties at a 30 m resolution for 51 sites across the ABoVE domain, including 39 sites in Alaska and 12 sites in Northwest Canada. The ALT and soil moisture profile retrievals simultaneously use L- and P-band synthetic aperture radar (SAR) data acquired by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instruments during the 2017 Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign. The data are provided in NetCDF Version 4 format along with a python script for estimating soil volumetric water content from data.\n<br>\n\n#### Sat_ActiveLayer_Thickness_Maps_1760\nThis dataset provides annual estimates of active layer thickness (ALT) at 1 km resolution across Alaska from 2001-2015. The ALT was estimated using a remote sensing-based soil process model incorporating global satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover extent (SCE), and Soil Moisture Active and Passive (SMAP) satellite soil moisture records. The study area covers the majority land area of Alaska except for areas of perennial ice/snow cover or open water. The ALT was defined as the maximum soil thawing depth throughout the year. The mean ALT and mean uncertainty from 2001 to 2015 are also provided.\n<br>\n\n#### Photos_ThermokarstLakes_AK_1845\nThis dataset includes high resolution orthophotographs of 21 lakes in the region of Fairbanks, Alaska, USA. Aerial photographs were taken on October 8, 2014, three days after lake-ice formation. These photographs were used to identify open holes in lake ice that indicate the location of hotspot seeps associated with the releases of methane from thawing permafrost. Aerial photography can be used to measure changes in lake areas and to observe patterns in the formation of lake ice and other early winter lake conditions.\n<br>\n\n#### ABoVE_Open_Water_Map_1643\nThis dataset contains georeferenced three-band orthomosaics of green, red, and near-infrared (NIR) digital imagery at 1m resolution collected over selected surface waters across Alaska and Canada between July 9 and August 17, 2017. The orthomosaics were generated from individual images collected by a Cirrus Designs Digital Camera System (DCS) mounted on a Beechcraft Super King Air B200 aircraft from approximately 8-11 km altitude. Flights were over the following areas: Saskatchewan River, Saskatoon, Inuvik, Yukon River including Yukon Flats, Sagavanirktok River, Arctic Coastal Plain, Old Crow Flats, Peace-Athabasca Delta, Slave River, Athabasca River, Yellowknife, Great Slave Lake, Mackenzie River and Delta, Daring Lake, and other selected locations. Most locations were imaged twice during two flight campaigns in Canada and Alaska extending roughly SE-NW then NW-SE up to a month apart. The data were georeferenced using 303 ground control points (GCPs) across the study region.\n<br>\n\n#### ABoVE_AirSWOT_Radar_Data_1646\nThis dataset provides AirSWOT (Surface Water and Ocean Topography) Ka-band (35.75 GHz) radar data products collected from an airborne platform over parts of Alaska and Canada during the period 2017-07-09 to 2017-08-17. Flights targeted specific surface water features, including rivers, lakes, ponds, and wetlands in the ABoVE domain. The radar data include six products: elevation (above the WGS84 ellipsoid), incidence angle, magnitude (backscatter), interferometric correlation (coherence), DHDPHI (incidence angle dependent height sensitivity), and error (estimated height random error, 1-sigma standard deviation). The flight lines were selected to span a full spectrum of permafrost conditions (permafrost-free to continuous permafrost, low to high ground ice content), ecosystems, climatic regions, topographic relief, and geological substrates across the ABoVE domain to investigate surface water responses to thawing permafrost and spatial and temporal variability in terrestrial water storage by measuring elevation and extent of surface waters. The data are provided in two forms: 1) the original output (outer-swath products only) at 3.6 m2 resolution in UTM coordinates from the AirSWOT processing group at the Jet Propulsion Laboratory (JPL), and 2) the ABoVE Projection at 3.6 m2 resolution, clipped to the ABoVE reference grid tiles using the C grid.\n<br>\n\n#### AirSWOT_Orthomosaic_WaterMask_1655\nThis dataset provides NASA AirSWOT Ka-band (35.75 GHz) radar interferometry data products for water surface elevation (WSE), a derived color-infrared (CIR) digital image orthomosaic, and derived lake/wetland and river channel water masks at 3.6 x 3.6 m resolution for a study area of ~3,300 km2 in the Yukon Flats Basin (YFB) in eastern interior Alaska. The data were collected during a flight over the region on June 15, 2015.These data were collected to validate AirSWOT WSE mappings and to improve the understanding of surface water flow through complex Arctic-Boreal wetland systems.\n<br>\n\n#### ABoVE_AirSWOT_Water_Mask_1707\nThis dataset provides 1) a conservative open water mask for future water surface elevation (WSE) extraction from the co-registered AirSWOT Ka-band interferometry data, and 2) high-resolution (1 m) water body distribution maps for water bodies greater than 40 m2 along the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) foundational flight lines. The masks and maps were derived from georeferenced three-band orthomosaics generated from individual images collected during the flights and a semi-automated water classification algorithm based on the Normalized Difference Water Index (NDWI). In total, 3,167 km2 of open water were mapped from 23,380 km2 of flight lines spanning 23 degrees of latitude. Detected water body sizes range from 40 m2 to 15 km2. The image tiles were georeferenced using manually selected ground control points (GCPs). Comparison with manually digitized open water boundaries yields an overall open-water classification accuracy of 98.0%.\n<br>\n\n#### Alaska_Lake_Pond_Occurrence_2399\nThe Alaska Lake and Pond Occurrence Dataset (ALPOD) is a spatially explicit map of lakes and ponds across Alaska and their seasonal fluctuations. The core product is an open water occurrence raster that: (a) separates lakes and ponds from other components of the landscape (e.g., rivers and wetlands); (b) is built from Sentinel-2 imagery and has 10-m resolution; and (c) records the percentages of time that each pixel was open water and attached to a lake or pond during the 2016-2021 ice-free seasons at near-daily temporal resolution. The number of water bodies depends on the chosen occurrence threshold, but a conservative estimate is that ALPOD maps over 800,000 lakes and ponds larger than 0.001 km2. The lake occurrence rasters are tiled by UTM zone and latitude. ALPOD also includes a vector product defined using a 25% occurrence threshold. ALPOD was created using a U-Net lake identification model and manual inspection to produce a maximum possible lake extent mask. This mask serves as the region of interest for an adaptive NDWI threshold water classification algorithm written in Google Earth Engine (GEE), which was used to classify open water within the maximum lake mask in every available cloud- and ice-free Sentinel-2 image during the study period. ALPOD is suitable for investigations of individual water bodies as well as lake and pond patterns across Alaska. The data are provided in GeoTIFF and shapefile formats.\n<br>\n\n#### Alder_Shrub_Soil_Alaska_V2_2300\nThis dataset holds measures of vegetative cover and soil characteristics for sites in interior Alaska, U.S., along the James W. Dalton Highway (Alaska Route 11). The field data were collected during August in 2018 and 2019 to study the expansion of shrub cover, particularly alders (Alnus spp.) in tundra ecosystems and the potential impact of shrubs on soil properties. Samples were measured along transects at 5- to 10-m intervals. Soil samples were collected and analyzed in the laboratory. Vegetation variables include percent cover of mosses, lichens, graminoid species, shrubs, alder, birch (Betula spp.), and willow (Salix spp.) along with the biomass, size, and age structure of alder. An allometric model to estimate alder biomass was developed. Soil metrics include moisture content, conductivity, bulk density, carbon and nitrogen content and isotope ratios. The data include the maximum annual Normalized Difference Vegetation Index (NDVI) for 2019 and the trend in maximum NDVI for 2000-2020. This is version 2 of this dataset.The data are provided in comma-separated values (CSV) format.\n<br>\n\n#### ABoVE_MODIS_MAIAC_Reflectance_1858\nThis dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances across the ABoVE domain in Alaska and western Canada from 2000 to 2017. Using random forests (RF), a machine-learning approach, the original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) to reduce artifacts and variability due to angular effects. The original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12) were preserved. The resulting surface reflectance data are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena. The data cover 11 different Terra and Aqua satellite MODIS MAIAC tiles.\n<br>\n\n#### Annual_30m_AGB_1808\nThis dataset provides estimated annual aboveground biomass (AGB) density for live woody (tree and shrub) species and corresponding standard errors at a 30 m spatial resolution for the boreal forest biome portion of the Core Study Domain of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) Project (Alaska and Canada) over the time period 1984-2014. The data were derived from a time series of Landsat-5 and Landsat-7 surface reflectance imagery and full-waveform lidar returns from the Geoscience Laser Altimeter System (GLAS) flown onboard IceSAT from 2004 to 2008. The Change Detection and Classification (CCDC) model-fitting algorithm was used to estimate the seasonal variability in surface reflectance, and AGB density data were produced by applying allometric equations to the GLAS lidar data. A Gradient Boosted Machines machine learning algorithm was used to predict annual AGB density across the study domain given the seasonal variability in surface reflectance and other predictors. The data received statistical smoothing to reduce noise and uncertainty was estimated at the pixel level. These data contribute to the characterization of how biomass stocks are responding to climate and disturbance in boreal forests.\n<br>\n\n#### Annual_Seasonality_Greenness_1698\nThis dataset provides annual maps of the timing of spring onset with leaf emergence, of autumn onset with leaf senescence, and of peak greenness for each 30 m pixel derived from Landsat time series of Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) observations from 1984 to 2014. The ABoVE core domain includes 169 ABoVE grid tiles across Alaska, USA and Alberta, British Columbia, Northwest Territories, Nunavut, Saskatchewan, and Yukon, Canada. The data provided for deriving seasonality includes the total number of cloud-free observations, r-squared values between observed and spline-predicted Enhanced Vegetation Index (EVI), long-term average minimum EVI, long-term average maximum EVI, long-term average spring onset, long-term average autumn onset, annual spring onset, and annual autumn onset. The data provided for peak greenness includes annual peak Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), annual composite red reflectance, annual composite NIR reflectance, annual composite shortwave infrared reflectance (band 6, SWIR1), annual composite shortwave infrared reflectance (band 7, SWIR2), number of dates used to calculate composites, and day of year of associated maximum composite.\n<br>\n\n#### Annual_Thaw_Slump_1724\nThis dataset provides a time series of spatial data showing the expansion of a thaw slump on the East Fork Chandalar River near the community of Venetie, Alaska, from 2008 through 2017. The erosion of vegetated areas along the river was documented by manually digitizing imagery from ESRI basemaps and Landsat 5 (TM), 7 (ETM+), and 8 (OLI), using the band combination of shortwave infrared 2, shortwave infrared 1, and red.\n<br>\n\n#### ABoVE_Atmospheric_Flask_Data_1717\nThis dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e. \"concentrations\") from flights over Alaska and the Yukon and Northwest Territories of Canada during the Arctic Carbon Aircraft Profile (Arctic-CAP) monthly sampling campaigns from April-November 2017. The data were derived from laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard the aircraft. During each of the six monthly campaigns, flights over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain included 25 vertical profiles, from the surface up to 6 km altitude, at locations selected to complement regular long-term vertical profiles, remote sensing data, and ground-based flux tower measurements. Measurements were initiated by the aircraft pilot at predetermined locations within each profile in order to evenly distribute flask sampling points throughout each flight. A total of 408 flask samples were collected during 55 individual flights. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.\n<br>\n\n#### ABoVE_Arctic_CAP_1658\nThis dataset provides in situ airborne measurements of atmospheric carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), and water vapor concentrations, plus air temperature, pressure, relative humidity, and wind speed values over Alaska and the Yukon and Northwest Territories of Canada during the Arctic Carbon Aircraft Profile (Arctic-CAP) monthly sampling campaigns from April-November 2017. Observations have been averaged to a 10-second interval and are reported with the number of samples (N) and standard deviation. During each of the six monthly campaigns, flights over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain included 25 vertical profiles, from the surface up to 6 km altitude, at locations selected to complement regular long-term vertical profiles, remote sensing data, and ground-based flux tower measurements.\n<br>\n\n#### AVHRR_Fire_Products_1545\nThis dataset provides annual forest fire burned area and daily hotspot products developed using data acquired from the Advanced Very-High-Resolution Radiometer (AVHRR) instruments carried aboard two NOAA polar-orbiting satellites (NOAA-11 and NOAA-14). The fire products were generated over 12 fire seasons (1st May - 31st October) from 1989-2000 across North America at 1-km resolution and subset to the ABoVE spatial domain of Alaska and Canada.\n<br>\n\n#### ABoVE_Airborne_AV3_V1_2388\nThis dataset includes L1B radiance and L2A surface reflectance imagery acquired by the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument over portions of Alaska and northwestern Canada in 2023. These data were collected for the Arctic-Boreal Vulnerability Experiment (ABoVE) project. NASA's AVIRIS-3 is a spectral mapping system that measures reflected radiance at 7.4-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 390-2500 nm. The AVIRIS-3 sensor has a 40 degree instantaneous field of view with 1234 pixels, providing altitude dependent ground sampling distances from 12 m to sub meter range. This dataset represents one part of a multi-sensor airborne sampling campaign conducted by eleven different aircraft teams for ABoVE. The imagery data are provided in ENVI format along with a GeoJSON showing imagery boundaries. These data were specifically processed for the ABoVE project.\n<br>\n\n#### ABoVE_Airborne_AVIRIS_NG_V3_2362\nThis dataset supersedes the previously published ABoVE AVIRIS-NG Level 2 surface reflectance files for 2017-2019 surveys of Alaska and northwestern Canada. It also includes previously unpublished L1 radiance and L2 reflectance for the 2021 surveys in Iceland when COVID-era policies prevented normal ABoVE flights, and the 2022 surveys, which returned to the ABoVE domain. The dataset comprises ~1700 individual flight lines covering ~120,000 km2 with a nominal spatial resolution of 5 m. Sampling includes individual transects to capture key gradients like the tundra-taiga ecotone and raster maps of key study areas like the CHARS Greiner watershed, the Mackenzie Delta, and the Utqiagvik/Point Barrow area. AVIRIS-NG measures reflected radiance in 425 bands at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Measurements were radiometrically and geometrically calibrated. This dataset represents one part of a multi-sensor airborne sampling campaign conducted by eleven different aircraft teams for ABoVE. The imagery data are provided in ENVI format along with a RGB composite image for each flight line and shapefiles showing imagery boundaries.\n<br>\n\n#### IMERG_Precip_Canada_Alaska_2097\nThis dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global coverage. This dataset bias-corrected IMERG's HQprecipitation precipitation estimates, which are based on passive microwave (PMW)-only retrievals, using a linear regression method. This method utilizes empirical measurements from rain gauge stations from the Global Historical Climatology Network (GHCN) and a digital elevation model. This bias correction approach improves estimates at elevations above 500 m a.s.l., which are typically underestimated.\n<br>\n\n#### Boreal_Forest_Survey_Fairbanks_2390\nThis dataset includes five metrics of forest resilience (recruitment, invasives, permafrost change, tree damage, and radial growth) at five recently burned forest sites (2010-2019) near Fairbanks, Alaska. The sites were imaged by the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS-NG) in 2017 and 2022 during the Arctic-Boreal Vulnerability Experiment (ABoVE). Field measurements were conducted in 2021. Random forest (RF) vegetation classification models constructed from key hyperspectral bands were validated with ground-truthing (GT) of 44 measured plots and 45 geotagged plots. GT included stem densities, understory cover, soil characteristics, radial growth of 51 spruce trees from cores, and visual damage assays of 668 conifers and deciduous trees. There are 10 data files in comma-separated values format (CSV) in this dataset.\n<br>\n\n#### NWT_Burn_Severity_Maps_1694\nThis dataset provides maps at 30-m resolution of landscape surface burn severity (surface litter and soil organic layers) from the 2014-2015 fires in the Northwest Territories and Northern Alberta, Canada. The maps were derived from Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) imagery and two separate multiple linear regression models trained with field data; one for the Plains and a second for the Shield ecoregion. Field observations were used to estimate area burned in each of five severity classes (unburned, singed, light, moderate, severely burned) in six stratified randomly selected plots of 10 x 10-m in size across a 1-ha site. Using this five class scale a burn severity index (BSI) for each 1-ha site was calculated using multiple weighted and averaged field parameters. Pre- and post-fire phenologically paired Landsat 8 images were used to model the five discrete severity classes using midpoints as breaks.\n<br>\n\n#### Wildfires_NWT_Canada_1548\nThis data set provides a fire progression map for year 2015 and measures of burn severity and vegetation community biophysical data collected from areas that were burned by wildfires in 2014 and 2015 in the Northwest Territories, Canada. Field data collected in 2016 include an estimate of burn severity, woody seedling/sprouting data, soil moisture, peat depth, thaw depth, and vegetation cover for selected sites.\n<br>\n\n#### Wildfires_2014_NWT_Canada_1307\nThis data set provides peatland landcover classification maps, fire progression maps, and vegetation community biophysical data collected from areas that were burned by wildfire in 2014 in the Northwest Territories, Canada. The peatland maps include peatland type (bog, fen, marsh, swamp) and level of biomass (open, forested). The fire progression maps enabled an assessment of wildfire progression rates at a daily time scale. Field data, collected in 2015, include an estimate of burn severity, woody seedling/sprouting data, soil moisture, and tree diameter and height of burned sites and similar vegetation characterization at landcover validation sites.\n<br>\n\n#### Burned_Area_Depth_AK_CA_2063\nThis dataset provides annual gridded estimates of fire locations and associated burn fraction per pixel for Alaska and Canada at approximately 500 m spatial resolution for the period 2001-2019. Gridded predictions of carbon combustion and burn depth for the same period within the ABoVE extended domain using the burn area maps and field data are also available. Fire locations and date of burn (DOB) were detected by MODIS-derived active fire products. Burned area was primarily estimated from finer-scale Landsat imagery using a differenced Normalized Burn Ratio (dNBR) algorithm and upscaled to an approximate 500 m MODIS resolution. Aboveground combustion, belowground combustion, and burn depth were statistically modeled at the pixel level for every mapped burned pixel in the ABoVE extended domain based on field observations across Alaska and western Canada. Predictor variables included remotely sensed indicators of fire severity, topography, soils, climate, and fire weather. Quality flags for burned area and combustion are available. Fire is the dominant disturbance agent in Alaskan and Canadian boreal ecosystems and releases large amounts of carbon into the atmosphere. These data are useful for studies of disturbance, fire ecology, and carbon cycling in boreal ecosystems.\n<br>\n\n#### Southern_Boreal_Plot_Attribute_1740\nThis dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas. Stands were characterized through field observations and sampling of the vegetative community (i.e., tree species, abundance, and biophysical measurements, stand age, coarse woody debris, history of fires or logging), soils (i.e., soil moisture class, unburned and burned soil organic layer depth, samples for bulk density and carbon analyses), and basic landscape geophysical traits. From these results, the pre-fire carbon stocks and carbon combustion values from both the above- and below-ground pools were estimated using a combination of linear and mixed-effects modeling and were calibrated against carbons stocks from the unburned stands. Estimates of uncertainty were generated for above- and below-ground carbon stocks and combustion values using a Monte Carlo framework paired with classic uncertainty propagation techniques.\n<br>\n\n#### Wildfire_Effects_Spruce_Field_1595\nThis dataset provides the results of field observations of soil characteristics and depth to permafrost, survey results for Composite Burn Index (CBI) determination, and Landsat-derived estimates of Relative Difference Normalized Burn Ratio (RdNBR) for 38 burned and unburned forest sites near Tanana, Alaska in 2017. Forests in the study area, at the confluence of the Yukon and Tanana Rivers about 200 km west of Fairbanks, are predominately black spruce on wetter soils and white spruce on drier soils. The burned areas were from wildfires that occurred in the summer of 2015.\n<br>\n\n#### ABoVE_Soil_Radiocarbon_NWT_1664\nThis dataset provides field data from boreal forests in the Northwest Territories (NWT), Canada, that were burned by wildfires in 2014. During fieldwork in 2015, 211 burned plots were established. From these plots, thirty-two forest plots were selected that were dominated by black spruce and were representative of the full moisture gradient across the landscape, ranging from xeric to sub-hygric. Plot observations included slope, aspect, and moisture. At each plot, one intact organic soil profile associated with a specific burn depth was selected and analyzed for carbon content and radiocarbon (14C) values at specific profile depth increments to assess legacy carbon presence and combustion. Vegetation observations included tree density. Stand age at the time of the fire was determined from tree-ring counts. Estimates of pre-fire below and aboveground carbon pools were derived. The percent of total NWT wildfire burned area comprising of \"young\" stands (less than 60 years old at time of fire) was estimated.\n<br>\n\n#### Seasonality_Tundra_Vegetation_1606\nThis dataset provides a summary of potential climate drivers of Arctic tundra vegetation productivity that have been compiled for growing seasons from 1982 to 2015. The scale of interest is the entire pan-arctic non-alpine tundra and the continental subdivisions of the North American and the Eurasian Arctic North of 70 degrees. These climate drivers include (1) maximum normalized difference vegetation index (MaxNDVI) and time-integrated NDVI (TI-NDVI), (2) summer sea ice concentrations, (3) oceanic heat content, (4) land surface temperature, and (5) summer warmth index (SWI). Data are provided variously as timeseries and weekly and bi-weekly averages over selected time ranges and study regions with calculated trends and trend significance. Data collected over 33 years were compiled to observe seasonal trends of vegetation productivity and to detect dynamics between arctic vegetation and climate drivers.\n<br>\n\n#### AK_North_Slope_NEE_CH4_Flux_1562\nThis dataset provides CO2 and CH4 fluxes and meteorological parameters from five eddy covariance (EC) tower sites located at Barrow (three sites), Atqasuk (ATQ) and Ivotuk (IVO), Alaska. These locations form a 300-km north-south transect across Alaska's North Slope. Flux measurements include CO2, CH4, and H2O fluxes plus sensible and latent heat fluxes. Meteorological data include air temperature, wind speed, rain, soil temperature, PAR, radiation, soil water content, RH, ground heat fluxes, and air pressure. All data are reported at half-hourly intervals and cover the period 2015-01-01 to 2017-03-09.\n<br>\n\n#### MODIS_MAIAC_Reflectance_1700\nThis dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances by two methods at each of 62 flux tower sites (1 km x 1 km area) across the ABoVE domain in Alaska and western Canada from 2000 to 2015/2016. The original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) using two independent algorithms: the first based on the original BRDF (Bidirectional Reflectance Distribution Function) parameters provided by the MAIAC team, and the second based on a machine learning approach (random forests). The corrected data preserve the original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12). The resulting tower site sub-daily timeseries of angular corrected surface reflectances are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena.\n<br>\n\n#### Dall_Sheep_Population_Dynamics_1640\nThis dataset contains estimated annual average Dall sheep (Ovis dalli dalli) lamb-to-ewe ratios for each year from 2000-2015 across the full species range in Alaska and Northwestern Canada. Sheep population data are from surveys conducted over the 14 major mountain ranges encompassing the range of Dall sheep. For this study, the mountain ranges were divided into 24 mountain units due to differing climate gradients. Estimated covariate environmental and climate data used to examine the relationship between environmental conditions and Dall sheep population performance (per mountain unit) are also provided and include precipitation, temperature, snow cover, elevation, and distance to the center of the range.\n<br>\n\n#### Dall_Sheep_Snowpack_1602\nThis dataset provides daily estimates of snow depth and snow density for the study area in Lake Clark National Park and Preserve (LCNPP), Alaska. The data were generated using SnowModel and used as snow covariates along with landscape covariates in modeling efforts to study Dall sheep movements in response to dynamic snow conditions. Thirty adult Dall sheep (12 male, 18 female) were captured and outfitted with global positioning system (GPS) collars programmed to acquire locations every seven hours. Given the individual sheep locations, their distances to land cover (e.g., shrub, forest, glacier), landscape characteristics (e.g., elevation, terrain ruggedness index (TRI), vector ruggedness measure (VRM), slope, and aspect), snow depth and density, MODIS normalized difference snow index (NDSI), and other covariates were determined and are provided in the environmental data file. The snow density and depth data are provided at 25-m, 100-m, 500-m, 2000-m, and 10000-m grid resolutions, at 1-day increments, and cover the period September 1, 2005 through August 31, 2008. The sheep, snow, and landscape data cover the years 2006, 2007, and 2008.\n<br>\n\n#### Snowpack_Dall_Sheep_Track_1583\nThis dataset contains Dall sheep (Ovis dalli dalli) hoof sinking depths in snow tracks, and snow depth, hardness, and density measurements in snow pits adjacent to the tracks. Snow measurements were collected between March 19-22, 2017 at sites on Jaeger Mesa in the Wrangell Mountains (WRST), Alaska. Estimated sheep age classes and track site coordinates are also provided.\n<br>\n\n#### ABoVE_Planning_Field_Sites_1582\nThis dataset provides a listing of the ~6,700 field sites used in planning the ABoVE Airborne Campaign (AAC) for 2017. The sites included point, polygon, and line locations that were used in determining the 2017 AAC flight paths. We intend this compilation to assist investigators in understanding the flight line choices and as a method for investigators to identify ground locations used in the airborne campaign. Data users may also search for the underlying data available at each of these locations. Site descriptors include name, coordinates, principal investigators with emails, data types, long-term archive locations, and links to project descriptions.\n<br>\n\n#### Frac_FuelComponent_Maps_Tundra_1761\nThis dataset provides maps of the distribution of three major wildland fire fuel types at 30 m spatial resolution covering the Alaskan arctic tundra, circa 2015. The three fuel components include woody (evergreen and deciduous shrubs), herbaceous (sedges and grasses), and nonvascular species (mosses and lichens). Multi-seasonal and multispectral mosaics were first developed at 30 m resolution using Landsat 8 surface reflectance data collected from 2013 to 2017. The spectral information from Landsat mosaics was combined with field observations from representative tundra vegetation plots collected during multiple field trips to model the fractional cover of fuel type components. An improved vegetation mask for shrub and graminoid-dominated tundra was developed using random forest classification and is also included. The final fractional cover maps were developed using the trained model with the multi-seasonal and multi-spectral Landsat mosaics across the entire Alaskan tundra.\n<br>\n\n#### Great_Slave_Lake_Ecosystem_Map_1695\nThis dataset provides an ecosystem type map at 12.5 meter pixel spacing and 0.2 ha minimum mapping unit for the area surrounding Great Slave Lake, Northwest Territories, Canada for the time period 1997 to 2011. The map includes nine classes for peatland, wetland, and upland areas derived from a Random Forest classification trained on multi-date, multi-sensor remote sensing images across the study extent, and using field data and high-resolution Worldview-2 image interpretation for training and validation. The nine classes are: Water, Marsh, Swamp, Open Fen, Treed Fen, Bog, Upland Deciduous, Upland Conifer, and Sparsely Vegetated. A tenth map class identifies areas of historical fires (prior to 2011) that are currently undergoing post-fire successional revegetation. This dataset provides an ecosystem type map of the area before the large fire season of 2014 to better understand the effects of fires in the area.\n<br>\n\n#### End_of_Season_Snow_Depth_1702\nThis dataset provides 20,582 snow depth measurements collected at six sites near Fairbanks, Alaska, USA. Measurements were made during March or April from 2014-2019. The sites were located at or near Goldstream, Creamer's Field, APEX, the Permafrost Tunnel and Farmer's Loop. The US Army Corps of Engineers, Cold Regions Research and Engineering Laboratory (CRREL) owns and operates facilities at the Permafrost Tunnel and Farmer's Loop. The sites are suitable for manipulation experiments, installing permanent equipment, and establishing long-term measurements.\n<br>\n\n#### Environmental_Disturbances_AK_1705\nThis dataset provides descriptions and photos of environmental conditions that impacted availability to subsistence resources by residents in nine rural communities within the Yukon River basin of Interior Alaska. The data (photos) were collected by citizens (harvesters) residing in the communities while engaged in subsistence harvesting activities. The data include descriptions of the environmental condition captured in the photo, photo date, an explanation of how the condition influenced travel and access to resources, the subsistence activity when the photo was taken, effects of the environmental condition on the participant's safety, and the participant's observations regarding frequency and extent of the condition. A sensitivity metric was derived that incorporated the adaptive capacity of the participants to environmental conditions. The observations are for the period February 2016 - June 2017.\n<br>\n\n#### Effect_Environment_Moose_1739\nThis dataset provides daily and annual air temperature, river water level, and leaf drop dates coincident with the moose (Alces alces) hunting season (September) for the area surrounding the rural communities of Nulato, Koyukuk, Kaltag, Galena, Ruby, Huslia, and Hughes in interior Alaska, USA, over the period 2000-2016. The main objective of the study was to assess how the environmental conditions impacted the success of hunters who rely on moose as a subsistence resource.\n<br>\n\n#### ABoVE_Frac_Open_Water_1362\nThis data set provides land surface fractional open water cover maps for two overlapping regions: the entire pan-Arctic region (latitude > 45 degrees) and the Arctic-Boreal Vulnerability Experiment (ABoVE) domain across Alaska and Canada. The data are a 10-day averaged time step at 5-km spatial resolution for the period 2002-2015. Data represent the aerial portion of a grid cell covered by open water. The data were produced using high frequency (89 GHz) brightness temperatures from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), with other ancillary inputs from AMSR-E/AMSR2 25-km products and the Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting data record for fractional water is suitable for documenting open water patterns and inundation dynamics in boreal-Arctic ecosystems experiencing rapid climate change.\n<br>\n\n#### Maps_AGB_North_Slope_AK_1565\nThis dataset includes 30-m gridded estimates of total plant aboveground biomass (AGB), the shrub AGB, and the shrub dominance (shrub/plant AGB) for non-water portions of the Beaufort Coastal Plain and Brooks Foothills ecoregions of the North Slope of Alaska. The estimates were derived by linking biomass harvests from 28 published field site datasets with NDVI from a regional Landsat mosaic derived from Landsat 5 and 7 satellite imagery. The data cover the period 2007-06-01 to 2016-08-31.\n<br>\n\n#### Snow_Cover_Extent_and_Depth_1757\nThis dataset provides estimates of maximum snow cover extent (SCE) and snow depth for each 8-day composite period from 2001 to 2017 at 1 km resolution across Alaska. The study area covers the majority land area of Alaska except for areas covered by perennial ice/snow or open water. A downscaling scheme was used in which Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis 0.5 degree snow depth data were interpolated to a finer 1 km spatial grid. In the methods used, the downscaling scheme incorporated MODIS SCE (MOD10A2) to better account for the influence of local topography on the 1km snow distribution patterns. For MODIS cloud-contaminated pixels, persistent and patchy cloud cover conditions were improved by applying an elevation-based spatial filtering algorithm to predict snow occurrence. Cloud-free MODIS SCE data were then used to downscale MERRA-2 snow depth data. For each snow-covered 1 km pixel indicated by the MODIS data, the snow depth was estimated based on the snow depth of the neighboring MERRA-2 0.5 grid cell, with weights predicted using a spatial filter.\n<br>\n\n#### Historical_Lake_Shorelines_AK_1859\nThis dataset includes maps of historical lake shorelines with derived lake areas in the southern portion of the Goldstream Valley and the surrounding landscape north of Fairbanks, Alaska, USA. Historical lake margins were mapped for 1949, 1967, and 1985 using 1 m aerial photographs available through U.S. Geological Survey Earth Explorer, and for 2009 using 2.5 m SPOT satellite image mosaics. The study area was a 214 km<sup>2</sup> area of Pleistocene-aged yedoma permafrost in the southern portion of the Goldstream Valley. An increasing number of thermokarst lakes and ponds, from 130&ndash;275 per year, were identified over the entire study period. Anthropogenic lakes, formed by mining peat, gravel, and gold concentrated in the northwestern extent of Goldstream Valley, were excluded.\n<br>\n\n#### IceFraction_WaterBodies_ACP_AK_2451\nThis dataset contains ice fraction data at 1-km spatial resolution and approximately 6-day temporal resolution for small water bodies (900 m2 to 25 km2) across the Arctic Coastal Plain of Alaska (ACP) from 2017 to 2023. The data were developed using Sentinel-1 Synthetic Aperture Radar (SAR) imagery, texture features, and temperature data. Ice fraction is summarized for 1-km grid cells. Ice cover of water bodies in the northern high latitudes (NHL) is highly sensitive to the changing climate, and its dynamics exert substantial impacts on the NHL ecosystems, hydrological processes, and carbon cycle. Compared with the Google Dynamic World (DW) product derived from Sentinel-2 observations, this dataset shows high consistency with DW (R =0.91, RMSE = 0.19) while having enhanced temporal coverage due to fewer constraints from solar illumination, cloud cover, and atmospheric conditions.\n<br>\n\n#### Fire_Ignitions_Locations_AK_CA_2316\nThis dataset provides daily fire ignition locations and timing for boreal fires in Alaska, U.S., and Canada between 2001 and 2019. The fire ignition locations and timing are extracted from the ABoVE Fire Emission Database; however, the temperate prairies of Canada, the Atlantic Highlands, and Mixed Wood Plains were not included. Fires were detected from Landsat differenced normalized burn ratio (dNBR) and the daily MODIS burned area and active fire products. Detections by dNBR were limited to fire perimeters from national fire databases. Fire ignition locations were retrieved using a local minimum within the fire perimeters. However, when fire locations were confounded due to simultaneous active fire detections, the fire ignition location was set as the centroid of these pixels. A spatial uncertainty equaling the standard deviation of the pixels' coordinates and the nominal nadir of 1000 m was applied to the fire ignition location. The temporal resolution of the ignition timing is within one day. Data is provided in comma separated values (CSV) and shapefile formats.\n<br>\n\n#### BurnedArea_Emissions_AK_YT_NWT_1812\nThis dataset provides estimates of daily burned area, carbon emissions, and uncertainty, and daily fire ignition locations for boreal fires in Alaska, U.S., and in the Yukon and Northwest Territories, Canada. The data are at 500 m resolution for the 18-year period from 2001-2018. Burned area was retrieved from combining fire perimeter data from the Alaskan and Canadian Large Fire Databases with surface reflectance and active fire data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6. Per-pixel carbon consumption was estimated based on a statistical relationship between field estimates of pyrogenic consumption and several environmental variables. To derive the carbon consumption estimates, the approach from Alaskan Fire Emissions Database (AKFED) was updated and extended for the period 2001-2018. Fire weather variables, temperature, and the drought code complemented remotely sensed tree cover and burn severity as model predictors. Fire ignition location and timing were extracted from the daily burned area maps.\n<br>\n\n#### ABoVE_L1_P_SAR_1800\nThis dataset provides Level 1 (L1) polarimetric radar backscattering coefficient (Sigma-0 or S-0), multi-look complex, polarimetrically calibrated, and georeferenced data products from the UAVSAR P-band SAR radar instrument collected over 74 study sites across Alaska, USA, and western Canada. The radar instrument is a fully polarimetric P-band (ultra-high frequency) SAR operating in the 420-440 MHz band. The flight campaigns took place periodically in May-August 2017 onboard a NASA Gulfstream-III aircraft. Each set of products was produced from a data take (i.e., acquisition) of the UAVSAR P-band SAR radar instrument, where one data take is equivalent to one flight line over a site. Two to four data takes were sought for each site, although for some sites as few as one or as many as six are provided. There were a total of 139 data takes over the 74 sites.\n<br>\n\n#### Alaska_Lake_Pond_Maps_2134\nThis dataset provides polygon spatial files of lake and pond extents for three sub-regions of Interior Alaska's boreal forest, and one tundra region located in Alaska's Yukon-Kuskokwim Delta. Files provide lake and pond extents of standing water without wetland vegetation or other obstructions with a minimum area of 0.01 ha. Water extents were derived from Planet Labs PlanetScope imagery with resolution of 3.125 m. A deep learning model (U-Net) was applied to PlanetScope orthotile imagery from Planet Labs' Dove-R and Super Dove satellites. The U-Net model used the red, green, blue, and near-infrared bands along with a slope raster derived from a 30-m digital elevation model (DEM) as inputs. The U-Net detected water bodies in all available cloud-free images from the snow-free period (May-September) of 2019-2021. Water body data are provided as 3-year composites (2019-2021) for all four regions and monthly climatological composites (May-September) over 2019-2021 for the three boreal forest regions. The composite water files indicate the presence of open, standing water in >40% of valid PlanetScope observations for a given composite time-slice. Files are provided in shapefile format.\n<br>\n\n#### Lake_Wetland_Classes_UAVSAR_1883\nThis dataset contains a high-resolution land cover classification focused on water and wetland vegetation classes over three NASA ABoVE Campaign regions: Yukon Flats, Alaska, USA; the Peace-Athabasca Delta, Alberta; and the Canadian Shield, Northwest Territories (NWT), Canada. The product was derived from L-band synthetic aperture radar (SAR) acquisitions from the airborne NASA UAVSAR instrument in 2017-2019. The classification was trained and validated from field visits, UAV images, satellite imagery as well as other ABoVE datasets. Classifications in all regions are provided as both preliminary 13-class versions and final, simplified 5-class versions. Training and test data used for the classifier are also included as well as characteristics of lakes in the study area. This land cover classification was developed to support a project focusing on potential methane emissions from the shallow near-shore, or littoral, regions of lakes. The emergent aquatic vegetation classes can be used as a proxy for these littoral zones. Wetland vegetation classifications are provided as gridded raster files with an approximately 5-meter spatial resolution and aligned with the original UAVSAR footprints. Composite mosaics that aggregate these UAVSAR scenes by region and day of acquisition, if applicable, are also provided. Classifications in all regions are provided as both preliminary 13-class versions and final 5-class versions.\n<br>\n\n#### ABoVE_GrowingSeason_Lake_Color_1866\nThis dataset provides an annual time series of Landsat green surface reflectance and the derived annual trend during the growing season (June and July) for 472,890 lakes across the ABoVE Extended Study Domain from 1984 to 2019. The reflectance data are from Landsat-5, Landsat-7, and Landsat-8 sensors for the green band (center wavelength 560 nm). Over 270,000 Landsat scenes were evaluated and quality assured to be cloud-free and over water. Lakes were selected from HydroLAKES, a global database of lakes of at least 10 ha. Lake surface reflectance was extracted from a 3-by-3-pixel area centered on each lake centroid from the selected Landsat scenes determined from lake polygons. This dataset demonstrates changes in lake color over time in the arctic and boreal regions of North America. Color is relevant for understanding physical, ecological, and biogeochemical processes in some of the world&rsquo;s highest concentrations of lakes where climate change may have significant impacts.\n<br>\n\n#### Lakes_Ponds_Weekly_Occurrence_2430\nThis dataset provides weekly surface water occurrence across lakes and ponds in six study regions: the Alaskan Coastal Plain, Yukon Flats, Yukon Kuskokwim Delta, Mackenzie River Delta, Tuktoyaktuk Peninsula, and Anderson Plain. The study regions are in Alaska, US, and Yukon and Northwest Territories, Canada. This binary (water or not water) raster product is built from Sentinel-2 imagery and a maximum lake extent vector product also produced from Sentinel-2 imagery. When cloud-free imagery was available, raster files were produced at 10 m spatial resolution and weekly temporal resolution during the ice-free season (May-September) for the period 2016-2023. Open water was differentiated from land using an adaptive NDWI threshold water classification algorithm and then clipped to the lake and ponds vector product so that only lake and pond water is reported. The data are provided in netCDF format along with two Jupyter notebooks holding code used for this analysis.\n<br>\n\n#### Methane_Flux_BigTrail_Lake_2393\nThis dataset provides field data and gridded land cover and terrain data for an area north of Big Trail Lake, an active thermokarst lake in Goldstream Valley (near Fairbanks, Alaska, USA). Field samples were collected at 56 locations across the study area in June 2021 and August 2022. Field data include chamber-based methane (CH4) and carbon dioxide (CO2) flux, soil moisture, soil temperature, soil pH, vegetation communities, meteorological data, and soil samples. The soil samples were used to conduct SIMPER microbial community analysis and soil chemical analyses and to quantify methanogen and methanotroph relative abundance. Land cover classifications (10 cm spatial resolution) were derived from supervised random forest classifications using RedEdge-MX and multiSPEC-4C multispectral drone imagery, normalized difference vegetation index (NDVI), and USGS 3D Elevation Program (3DEP) digital surface model (DSM) data. RedEdge-MX drone imagery was collected in July 2021 and multiSPEC-4C drone imagery was collected in August 2019. Gridded microtopography and slope estimates were derived at 1 m spatial resolution using USGS 3DEP digital terrain model (DTM) data. Gridded products are provided in cloud-optimized GeoTIFF format, field data are provided in comma-separated values format, and sample location photos in JPEG format are contained within a compressed file.\n<br>\n\n#### Tundra_Greeness_Temp_Trends_1893\nThis dataset provides annual tundra greenness and summer air temperatures at a resolution of 50 km over the pan-Arctic tundra biome above 31.5 degrees over the time period 1985 to 2016. Annual tundra greenness was assessed using the maximum Normalized Difference Vegetation Index (NDVImax) derived from surface reflectance measured by sensors on the Landsat satellites. Summer air temperatures were quantified using the Summer Warmth Index (SWI) derived from an ensemble of five global temperature datasets. Tabular data include NDVImax, SWI, and estimates of uncertainty using Monte Carlo simulations at 45,334 vegetated sampling sites. Raster data provide (1) annual SWI from 1985 to 2016; (2) temporal trends in annual NDVImax and SWI from 1985 to 2016 and from 2000 to 2016; and (3) temporal correlations between annual NDVImax - SWI during these two periods. Each raster also includes estimates of uncertainty that were generated using Monte Carlo simulations. This dataset provides a new pan-Arctic product for assessing inter-annual variability in tundra using moderate resolution observations from the Landsat satellites.\n<br>\n\n#### BorealForest_Greenness_Trends_2023\nThis dataset provides information on interannual trends in annual maximum vegetation greenness from 1985 to 2019 for recently undisturbed areas in the boreal forest biome. Multi-decadal changes in remotely sensed vegetation greenness provide evidence of an emerging boreal biome shift driven by climate warming. Annual maximum vegetation greenness was assessed at about 100,000 random sample locations using an ensemble of spectral vegetation indices (NDVI, EVI2, kNDVI, and NIRv) derived from Landsat products. The dataset provides raster data summarizing vegetation greenness trends for sample locations stratified by Ecological Land Unit in GeoTIFF format. These raster data span the circum-hemispheric boreal forest biome between 45 to 70 degrees north at 300 m resolution. Estimates of uncertainty were generated using Monte Carlo simulations. Interannual trends in annual maximum vegetation greenness from 1985 to 2019 and 2000 to 2019 are provided for sample locations with adequate data for time series analysis; these data are in comma-separated values (CSV) format.\n<br>\n\n#### ABoVE_AGBD_Uncertainty_Maps_2465\nThis dataset provides annual aboveground biomass (AGB) maps and associated uncertainty maps for Alaska and Canada from 1984 to 2022 at ~30 m resolution (0.00027 degrees). The dataset was derived using predictors from synthetic spectral features from Landsat Collection 2 and Continuous Change Detection and Classification algorithm. Extensive collections of ground plots (n = 45,002) and airborne lidar data (n = 421,942) were compiled for reference AGB in order to calibrate AGB models using Extreme Gradient Boosting (XGBoost) per ecoregion. Fifty AGB predictions were derived, of which the mean and standard deviation was used as per-pixel AGB prediction and uncertainty, respectively. The dataset can promote better understanding of carbon dynamics across arctic and boreal regions of North America. The data are provided in cloud optimized GeoTIFF format.\n<br>\n\n#### ABoVE_ForestDisturbance_Agents_1924\nThis dataset provides spatial data on disturbance agents of fire, insects, and logging in the Arctic Boreal Vulnerability Experiment (ABoVE) core domain at an annual time step from 1987-2012 and 30 m resolution. Using a time-series of Landsat data, the three disturbance types were identified by abrupt changes in Tasseled Cap (dTC) indices of brightness, greenness, and wetness. Disturbances were detected by a Continuous Change Detection and Classification (CCDC) harmonic regression model applied to the time series. The dTC indices and disturbance results are provided.\n<br>\n\n#### Annual_Landcover_ABoVE_1691\nThis dataset provides two 30-m resolution time series products of annual land cover classifications over the Arctic Boreal Vulnerability Experiment (ABoVE) core domain for each year of the period 1984-2014. The data are the annual dominant plant functional type in a given 30-m pixel derived from Landsat surface reflectance, landcover training data mapped across the ABoVE domain (using Random Forests modeling, with clustering and interpretation of field photography) and very high resolution imagery to assign land cover classifications. One product has a 15-class land cover classification that breaks out forest and shrub types into several additional classes; the other product provides a simplified, 10-class approach. Classification accuracy assessment results are provided per year. Assessments were based on a probability-based random sample of reference data that supported statistically robust estimation of areas and uncertainties in mapped areas.\n<br>\n\n#### Boreal_LandCoverClasses_AK_CA_2423\nThis dataset contains a 30-m resolution time series of annual land cover classifications as the dominant plant functional type class for all of boreal Alaska and Canada from 1986 to 2020. The data were derived from a time series of Landsat Collection 2 Surface Reflectance and processed using the Continuous Change Detection and Classification (CCDC) algorithm. This dataset includes a nine-class land cover scheme. Classification accuracy was assessed using a probability-based random sample, ensuring statistically robust area estimates and uncertainty measures. The classifications were produced using a supervised Random Forest classification model and Canadian National Forest Inventory photo plot data. The data are provided in multiband GeoTIFF file format and distributed by tile in the ABoVE Level B grid.\n<br>\n\n#### ABoVE_Fire_Severity_dNBR_1564\nThis dataset contains differenced Normalized Burned Ratio (dNBR) at 30-m resolution calculated for burn scars from fires that occurred within the Arctic Boreal and Vulnerability Experiment (ABoVE) Project domain in Alaska and Canada during 1985-2015. The fire perimeters were obtained from the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) fire occurrence datasets. Only burns with an area larger than 200-ha were included. The dNBR for each burn scar at 30-m pixel resolution was derived from pre- and post-burn Landsat 5, 7, and 8 scenes within a 5-km buffered area surrounding each burn scar using Landsat LEDAPS surface reflection image pairs.\n<br>\n\n#### Last_Day_Spring_Snow_1528\nThis dataset provides the last day of spring snow cover for most of Alaska and the Yukon Territory for 2000 through 2016. The data are based on the MODIS daily snow cover fraction product (MODSCAG) and are provided at 500-m resolution. Pixels in the daily snow cover fraction grids from April 1 through July 31 were flagged as \"Snow\" if the snow fraction exceeded 0.15, resulting in a time series of binary daily snow cover grids for each year. The annual last day of spring snow for each pixel was identified by day of the year ranging from 91 (April 1) to 183 (July 2).\n<br>\n\n#### ABoVE_Footprints_WRF_AK_NWCa_1896\nThis dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) Footprint data products for receptors (observations) located at positions along flight paths and at various fixed observing sites at circumpolar locations at northern latitudes during 2016-2019. Each aircraft and station position is treated as an independent receptor in the WRF-STILT model in order to simulate the land surface influence on observed atmospheric constituents. The footprints are independent of chemical species and can be applied to different flux models and incorporated into formal inversion frameworks. The particle trajectories that determine the footprint field are constrained only by the outer edges of the WRF modeling domain. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by the thawing of Arctic permafrost.\n<br>\n\n#### ABoVE_Particles_WRF_AK_NWCa_1895\nThis dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory files for receptors located at positions along flight paths and at various fixed observing sites at circumpolar locations above 45 degrees North during 2016-2019. The particle files describe the motion of particles released backward in time over a 10-day period. The particle files are separated into archives by platform type (some platforms are combined) and can be characterized as either low resolution or high resolution depending on whether the subsequent footprint fields were generated on a circumpolar 0.5-degree grid (low-resolution) or both 0.5-degree and 0.1-degree grids (high-resolution). The platforms include flux towers at fixed sites, laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard aircraft, and observations by NASA's Orbiting Carbon Observatory-2 satellite. These particle files were thinned to retain particle location information only when the particles have non-zero contributions to the corresponding footprint field. These particle files are used to compute the footprint fields available in a companion dataset. The particle trajectories that determine the footprint field are constrained only by the outer edges of the WRF modeling domain. Likewise, the companion footprint files are provided on a regular latitude-longitude grid. This dataset extends previous research on the atmospheric transport of land-surface emissions of greenhouse gases by the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) project. In particular, the content of the low-resolution particle files is similar to those for the CARVE dataset.\n<br>\n\n#### ABoVE_Forage_Lichen_Maps_1867\nThis dataset provides modeled estimates of lichen ground cover at 30 m resolution across the Fortymile study area in interior eastern Alaska, U.S., and the Yukon Territory, Canada, for the nominal year 2015. The mapped lichens are important winter forage for the nine resident caribou (Rangifer tarandus) herds in the region. A random forest modeling approach with vegetation inputs and environmental and spectral predictors was used to estimate lichen cover for 2015. Input data for the model were aggregated from historical in-situ vegetation plots, visual aerial surveys, and recent unmanned aerial system (UAS) imagery to align with 30 m resolution Landsat pixels over the 583,200 km2 study area. The model was also used to estimate lichen cover for the year 2000 by applying the trained model to historical Landsat imagery. An estimate of lichen volume in 2015, based on a published algorithm, is also provided. In addition, site-level presence-absence maps at <1 m resolution and site-level lichen cover maps at both 2 m and 30 resolution are provided. Site-level data were derived from high-resolution RGB imagery collected in summer 2017 from UASs at 22 forested and alpine sites across interior Alaska and western Yukon. Due to the use of two unique UAS imagers at 7 sites, there are 29 data collections across the 22 sites. Each UAS data collection is associated with three data files. These landscape-scale maps could be useful for understanding trends in lichen abundance and distribution, as well as for caribou research, management, and conservation.\n<br>\n\n#### GPP_MODIS_Alaska_Canada_2024\nThis dataset contains gridded estimations of daily ecosystem Gross Primary Production (GPP) in grams of carbon per day at a 1 km2 spatial resolution over Alaska and Canada from 2000-01-01 to 2018-01-01. Daily estimates of GPP were derived from a light-curve model that was fitted and validated over a network of ABoVE domain Ameriflux flux towers then upscaled using MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) data to span the extended ABoVE domain. In general, the methods involved three steps; the first step involved collecting and processing mainly carbon-flux site-level data, the second step involved the analysis and correction of site-level MAIAC data, and the final step developed a framework to produce large-scale estimates of GPP. The light-curve parameter model was generated by upscaling from flux tower sub-daily temporal resolution by deconvolving the GPP variable into 3 components: the absorbed photosynthetically active radiation (aPAR), the maximum GPP or maximum photosynthetic capacity (GPPmax), and the photosynthetic limitation or amount of light needed to reach maximum capacity (PPFDmax). GPPmax and PPFDmax were related to satellite reflectance measurements sampled at the daily scale. GPP over the extended ABoVE domain was estimated at a daily resolution from the light-curve parameter model using MODIS MAIAC daily reflectance as input. This framework allows large-scale estimates of phenology and evaluation of ecosystem sensitivity to climate change.\n<br>\n\n#### ABoVE_LVIS_VegetationStructure_1923\nThis dataset provides Level 3 (L3) footprint-level gridded metrics and attributes collected from NASA's Land, Vegetation, and Ice Sensor (LVIS)-Facility instrument for each flightline from 2017 and 2019. In 2017, the LVIS-Facility instrument was flown at a nominal flight altitude of 28,000 ft onboard a Dynamic Aviation Super King Air B200T. In 2019, the LVIS-Facility and LVIS-Classic instruments were flown at a nominal flight altitude of 41,000 feet onboard the NASA Gulfstream V. LVIS data are collected as waveforms over footprints of ~10-m diameter. The L3 data include grids of canopy relative height (RH), complexity, canopy cover (CC), ground elevation, and the number of LVIS footprints available to produce a pixel's estimate.. These 30-m resolution grids describe the vertical column of the vegetation canopy in detail with relative canopy height metrics and are enriched with an additional set of canopy cover estimates at a variety of height thresholds. The LVIS-Facility instrument 2017 and 2019 acquisitions span Arctic, boreal, temperate, and sub-tropical landscapes in support of a variety of Arctic-Boreal Vulnerability Experiment (ABoVE)- and Global Ecosystem Dynamics Investigation (GEDI)-related science. In the ABoVE study domain of arctic and boreal Alaska and Western Canada, some of these acquisitions coincide spatially with legacy small-footprint airborne lidar. Data are included for the ABoVE domain and also for the continental U.S. and central America in support of GEDI calibration and validation. Data files are provided in GeoTIFF format and one geopackage file shows flightlines.\n<br>\n\n#### Methane_Ebullition_Lakes_AK_1861\nThis dataset includes maps of the locations and number of methane ebullition hotspots in 15 frozen lakes in the southern portion of the Goldstream Valley and the surrounding landscape just north of Fairbanks, Alaska, USA. Hotspots were identified from early winter high resolution aerial photographs acquired three days after lake-ice formation in October 2014. Hotspot ebullition seeps are defined as point-sources of high ebullition that release methane from lake sediments year-round. High rates of bubbling impede ice formation. In early winter, bubbling leads to dark, round open holes in lake ice which were visible in the aerial photos. This project investigated the role of theromkarst lakes in thawing of permafrost and mobilization of organic carbon in frozen soils.\n<br>\n\n#### CH4_Flux_BigTrail_Goldstream_1778\nThis dataset provides diffusive methane (CH4) fluxes collected from two thermokarst lakes in the Goldstream Valley, north of Fairbanks in interior Alaska. Fluxes were collected from the littoral zones, adjacent shoreline, and upland vegetation. The data were collected during July 2018. Measurements were made using a mobile, closed chamber technique where chamber air was recirculated through a Los Gatos Research (LGR) Ultraportable Cavity Ring-down Spectrometer. The chamber was large enough to enclose emergent and upland vegetation up to 1.5 m in height, allowing plant-facilitated fluxes to be measured. These in situ measurements were used to verify spatial patterns in methane flux (i.e., exponential decay with distance from water) detected by NASA's Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG).\n<br>\n\n#### AK_Yukon_PFT_TopCover_2032\nThis dataset contains data files of modeled top cover estimates by plant functional type (PFT) for the Arctic and Boreal Alaska and Yukon regions. Estimates are presented for single years at 5-year intervals from 1985 to 2020. Also included are root mean square error (RMSE) and source year, which indicate the specific year from which pixels in the top cover maps were derived. Plant functional types include conifer trees, broadleaf trees, deciduous shrubs, evergreen shrubs, graminoids, forbs, and light macrolichens. Estimates were derived through the combination of two stochastic gradient-boosting models that used environmental and spectral covariates. Environmental covariates represented topographic, climatic, permafrost, hydrographic, and phenological gradients, and spectral covariates were based on Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) data collected between 1984-2020. These maps catalog widespread changes in the distribution of PFTs occurring in the Arctic and boreal forest ecosystems, such as tundra shrub expansion, due to the intensification of disturbances such as fire and climate-driven vegetation dynamics.\n<br>\n\n#### MODIS_CCaN_NDVI_Trends_Alaska_1666\nThis dataset provides the average Normalized Difference Vegetation Index (NDVI) at 1-km resolution over the north slope of Alaska, USA, for the growing season (June-August) of each year from 2000-2015, and NDVI trends for the same period. The dataset presents growing-season averages and trends from two sources: 1) derived from 1-km, 8-day data from the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD13A2) product, and 2) predicted by the Coupled Carbon and Nitrogen model (CCaN). CCaN is a mass balance carbon and nitrogen model that was driven by 1-km MODIS surface temperature and climate data for the North Slope of Alaska and parameterized using model-data fusion, where model predictions were ecologically constrained with historical ecological ground and satellite-based data.\n<br>\n\n#### Albedo_Boreal_North_America_1605\nThis dataset contains MODIS-derived daily mean shortwave blue sky albedo for northern North America (i.e., Canada and Alaska) and a set of quality control flags for each albedo value to aid in user interpretation. The data cover the period of February 24, 2000 through April 22, 2017. The blue sky albedo data were derived from the MODIS 500-m version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters MCD43A1 dataset (MCD43A1.006, https://doi.org/10.5067/MODIS/MCD43A1.006) (Schaaf & Wang, 2015a, please refer to the MCD43 documentation and user guides for more information). Blue sky refers to albedo calculated under real-world conditions with a combination of both diffuse and direct lighting based on atmospheric and view-geometry conditions. Daily mean albedo was calculated by averaging hourly instantaneous blue sky albedo values weighted by the solar insolation for each time interval. Potter et al. (2019, https://doi.org/10.1111/gcb.14888) is the associated paper for this dataset. Note the actual extent of the dataset in Figure 1 of the User Guide. Users are encouraged to refer to the User Guide for further important information about the use of this dataset.\n<br>\n\n#### Alaska_Yukon_NDVI_1614\nThis dataset provides the maximum Normalized Difference Vegetation Index (NDVI) at 1-km resolution over northern Alaska, USA and the Yukon Territory, Canada for each year from 2002-2017, as well as a 16 year maximum NDVI product. MODIS products MOD13Q1 and MYD13Q1 from Collection 6 were acquired at 250-m pixel size from June 1-August 30 of each year. Within each growing season from 2002-2017, the maximum NDVI was determined for each pixel. These maximum NDVI values were then aggregated to 1-km by selecting the maximum NDVI from the sixteen 250-m pixels values nested within each 1-km pixel. A long-term 16-year maximum NDVI was then derived from the time series of annual maximum NDVI values.\n<br>\n\n#### Monthly_Hydrological_Fluxes_1647\nThis dataset provides modeled estimates of monthly hydrological fluxes at 0.25-degree resolution over Alaska and Canada for the years 1979-2018. The estimates were derived from the Variable Infiltration Capacity (VIC) macroscale hydrological model version 4.1.2 with water and energy balance schemes at 0.25-degree spatial and daily temporal resolution for this 38-year period. The gridded output data products are monthly average water balance variables including precipitation (P), evapotranspiration (E), 'P minus E', evaporation, soil moisture in three soil layers, base flow and runoff, snow depth, snow water equivalent (SWE), and snow sublimation, and energy balance variables including surface temperature, albedo, latent and sensible heat flux, ground heat flux, short- and long-wave and other radiative fluxes. The daily modeled values for precipitation and evapotranspiration were also aggregated to water years and precipitation was also aggregated to a 30-year climate normal average.\n<br>\n\n#### ABoVE_Uncertainty_Maps_1652\nThis dataset provides estimates of the uncertainty in components of the carbon cycle including: soil carbon stock, autotrophic respiration (Ra), heterotrophic respiration (Rh), net ecosystem exchange (NEE), net primary production (NPP), and gross primary productivity (GPP) across the entire ABoVE Study Domain at 0.5-degree resolution for the reference year 2003. The uncertainties were calculated from the multi-model (n = 20) disagreement, i.e. standard deviation, from the Trends in Net Land Atmosphere Carbon Exchanges program (TRENDY) and the North American Carbon Program (NACP) regional synthesis model outputs averaged to annual means. This total uncertainty integrates both structural uncertainty of land-surface physics among models as well as inherent parametric uncertainty introduced within models, and uncertainty from forcing data.\n<br>\n\n#### Vegetation_greenness_trend_1576\nThis dataset provides the summer NDVI trend and trend significance for the period 1984-2012 over Alaska and Canada. The NDVI were calculated per-pixel from all available peak-summer 30-m Landsat 5 and 7 surface reflectance data for the period. NDVI time series were assembled for each 30-m land location (i.e., non-water, non-snow), from observations that were unaffected by clouds as indicated by data-quality masks and following additional processing to remove anomalous NDVI values. A simple linear regression via ordinary least squares was applied to the per-pixel NDVI time series. The slope of the regression was taken as the annual NDVI trend (unit NDVI change per year) and is reported in the \"trend\" data files. A Student's t-test was used to assess the significance of the trend and the per-pixel significance is reported in the \"trend_sig\" data files. A significant positive slope indicates a greening trend, and a significant negative slope indicates a browning trend.\n<br>\n\n#### Chlorophyll_Fluorescence_Data_1785\nThis dataset provides the results of in situ measurements of needle-level chlorophyll fluorescence (ChlF) obtained from a pulse amplitude modulated (PAM) fluorometer from evergreen needleleaf forested sites one in Alaska and one in Idaho. Measured light-adapted minimal fluorescence (Ft) is reported as the quantum yield of fluorescence and light-adapted variable fluorescence over maximal fluorescence (Fv/Fm) and is reported as the quantum yield of photosystem II. Also reported for both sites are two modeled irradiance products: (1) the top-of-canopy instantaneous irradiance (W/m2) and (2) needle-level irradiance (W/m2) that was modeled to account for shadow casting and canopy orientation in modulating direct radiation. Both products were modeled to be contemporaneous with ChlF observations. At the Idaho site only, needle-level irradiance (W/m2) was measured in situ with a handheld pyranometer. The Alaska field site is located in the northern latitudinal forest-tundra ecotone (FTE) near the Dalton Highway in Northern Alaska. Thirty-six Picea glauca (white spruce) trees were sampled on 2017-07-07 to 2017-07-08. The Idaho field site is located in a montane forest near McCall, Idaho. Ten selected Abies grandis (grand fir) trees were sampled on 2019-07-05 to 2019-07-06. Measurement of needle-level ChlF occurred during clear-sky conditions such that the canopies experienced a broad range of variability in sunlit-shading patterns across the day during these near-solstice periods.\n<br>\n\n#### SnowMeltDuration_PMicrowave_1843\nThis dataset provides the annual period of snowpack melting (i.e., snow melt duration, SMD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. SMD is the number of days between the main melt onset date (MMOD) and the last day of seasonal snow cover when the melting of snow is complete. These dates were derived from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). This dataset documents variability in SMD across space and the 31-year temporal period. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies.\n<br>\n\n#### Main_Melt_Onset_Dates_1841\nThis dataset provides the annual date of snowpack seasonal beginning melt (i.e., main melt onset date, MMOD) across northwest Canada, Alaska, US, and parts of far eastern Russia at 6.25-km resolution for the period 1988-2023. MMOD was derived from the daily 19V (K-band) and 37V (Ka-band) GHz bands from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). The PMW MMOD dataset was validated using the transition date from Freeze Degree Days (FDD) to Thaw Degree Days (TDD) from in situ air temperature observations from 31 SNOw TELemetry network (SNOTEL) stations, and compared to an established Freeze-Thaw ESDR (FT-ESDR) spring onset date record. The resulting MMOD data record is suitable for documenting the spatial-temporal impacts of MMOD variability in ecosystem services, wildlife movements, and hydrologic processes within the ABoVE (Arctic Boreal Vulnerability Experiment) domain. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). No coastal mask is used for the 2017-2023 data. The full data are included, and data users should be aware that values adjacent to large water bodies can be adversely affected.\n<br>\n\n#### Canada_Boreal_Forest_Greenness_1587\nThis dataset provides a 28-year time series of peak greenness (NDVI) data derived from Landsat 5 TM imagery over the boreal forest region of Canada. Landsat 5 TM scenes were collected for 46 selected sidelap sites along gradients in climate, tree cover, and disturbance history from 1984 to 2011. Peak-greenness reflectance was computed for 30-m Landsat pixels using the maximum normalized difference vegetation index (NDVI) along with the normalized burn ratio (NBR) during the period between days of the year (DOY) 180 and 204. To facilitate trend analysis at each site, the NDVI and NBR data of the 30-m Landsat pixels were regridded to the coarser MODIS 500-m (463.3-m) spatial scale to reduce the effects of missing data and to enhance the significance of the trend. The regridded NDVI and NBR 28-year time series data at 500-m resolution are provided for each of the 46 sites. Two trend analyses were run on the 500-m resolution data and are reported for each site. Supplemental site metadata are also provided, including the number of valid Landsat pixels, land cover composition, and disturbance history, for each 500-m pixel.\n<br>\n\n#### Permafrost_Thaw_Depth_YK_1598\nThis dataset provides field observations of thaw depth and dominant vegetation types, a LiDAR-derived elevation map, and permafrost distribution and probability maps for an area on the coastal plain of the Yukon-Kuskokwim Delta (YKD), in western Alaska, USA. Field data were collected during July 8-17, 2016 to parameterize and to validate the derived permafrost maps. The YKD is in the sporadic to isolated permafrost zone where permafrost forms extensive elevated plateaus on abandoned floodplains. The region is extremely flat and vulnerable to eustatic sea-level rise and inland storm surges. These high-resolution permafrost maps support landscape change analyses and assessments of the impacts of climate change on permafrost in this region of high biological productivity, critical wildlife habitats, and subsistence-based human economy.\n<br>\n\n#### Radial_Growth_PRI_1781\nThis dataset provides simultaneous in-situ measurements of the photochemical reflectance index (PRI) and radial tree growth of selected white spruce trees (Picea glauca (Moench) Voss) at the northern treeline in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. PRI and dendrometer measurements were simultaneously collected on 29 trees from six plots spaced along a 5.5 km transect from south to north where tree density becomes increasingly sparse. Measurements were made throughout the 2018 and 2019 growing seasons (May 1 to September 15) with a sampling interval of 5 minutes. The data were collected to better understand the suitability of the PRI to remotely track radial tree growth dynamics.\n<br>\n\n#### Veg_Soil_Tundra_Burned_Area_2119\nThis dataset includes field measurements from 26 burned and unburned transects established in 2008 in the region of the Anaktuvuk River tundra fire on the Arctic Slope of Alaska, US. Measurements include plant cover by species, shrub and tussock density, thaw depth, and soil depth. This wildfire occurred in 2007, and sampling took place in 2008-2011 and in 2017.\n<br>\n\n#### ABoVE_NWT_2017_Field_Data_1771\nThis dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2017 from 11 study sites in the ABoVE Study area. The 11 study areas contained 28 sites that were burned by wildfires in 2014 and 2015, and 10 unburned sites in the Northwest Territories (NWT), Canada. Burned sites included peatland and upland. These field data include assessment of burn severity, vegetation inventories, ground cover, diameter and height for trees and shrubs, seedling and sprouting cover, soil moisture, and depth of unfrozen soil. Plot sizes were 10 m x 10 m with smaller subplots for selected measurements. Similar data were collected for these sites in the years 2015-2019 and are available in related separate datasets. Field data are provided in CSV format. The dataset includes digital photographs (in JPEG format) of vegetation conditions at sampling sites.\n<br>\n\n#### Wildfires_NWT_Canada_2018_1703\nThis dataset provides vegetation community characteristics and biophysical data collected in 2018 from areas that were burned by wildfire in 2014 and 2015, and from nine unburned validation sites in the Northwest Territories, Canada. The data include vegetation inventories, ground cover, regrowth, tree diameter and height, and woody seedling/sprouting data at burned sites, and similar vegetation community characterization at unburned validation sites. Additional measurements included soil moisture, collected for validation of the UAVSAR airborne collection, and depth to frozen ground at the nine unburned sites. This 2018 fieldwork completes four years of field sampling at the wildfire areas.\n<br>\n\n#### Wildfires_NWT_Canada_2019_1900\nThis dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2019 from 11 study areas, which contained 28 sites that were burned by wildfires in 2014 and 2015, and 14 unburned sites in the Northwest Territories (NWT), Canada. Burn sites included peatland and upland. These field data include vegetation inventories, ground cover, as well as diameter and height for trees and shrubs in the unburned sites. Similar data were collected for the unburned sites in the years 2015-18 and are available in related separate datasets. In 2019, the focus was on woody and non-woody seedling/sprouting regrowth data in the burned sites. Additional measurements collected at all sites included total peat depth, soil moisture, and active layer thickness (ALT). Soil moisture and ALT were collected for validation of the UAVSAR airborne collection and Radarsat-2 overpasses. This 2019 fieldwork completes five years of field sampling at the wildfire areas.\n<br>\n\n#### Rain-on-Snow_Data_V2_2415\nThis dataset provides maps of rain-on-snow (ROS) events across Alaska for the individual cold season months from November to March using observations from two space-borne passive microwave radiometers: (a) the Advanced Microwave Scanning Radiometer for EOS and Advanced Microwave Scanning Radiometer 2 (AMSR-E/2) from 2002 to 2023; and (b) the Special Sensor Microwave Imager and the Special Sensor Microwave Imager Sounder (SSMI/S) from 1988 to 2020. Considering the differences in sensor overpass time, observation geometry, and ancillary snow cover data, the AMSR-E/2 and SSMI/S-based ROS records were generated separately. ROS events were defined as changes in surface snow wetness and isothermal states induced by atmospheric processes associated with winter rainfall or latent heat exchange. The data are summations of the number of days with ROS events per pixel at 6-km spatial resolution per month or per 5-month water year. Winter months are when snowmelt from solar illumination is minimal and snow cover is widespread and relatively consistent throughout the region. The data are provided in GeoTIFF format.\n<br>\n\n#### Reflectance_Spectra_Alaska_1685\nThis dataset reports full-spectrum (350-2500 nm) reflectance measurements of diverse plant communities at the plot-level and individual plant species at the leaf-level, at multiple sites across northern Alaska during the 2017 and 2018 summer field seasons. Plot-level reflectance data (1 m2) include an assemblage of vascular and non-vascular species comprising tundra plant communities, while leaf-level scans are specific to one particular tundra species. Reflectance measurements were collected using a HR-1024i spectrometer and data were calibrated using a Spectralon white reference panel during sampling to correct for changing light conditions. Sampling methods and data and metadata structure follow that of the Ecological Spectral Information System (EcoSIS) Spectral Library.\n<br>\n\n#### River_Ice_Breakup_Freezeup_1697\nThis dataset provides estimates of river ice breakup and freeze-up stages along selected reaches of the Yukon and Tanana Rivers in the Yukon River Basin in interior Alaska from 1972-2016. Time series of Landsat satellite images were visually interpreted to identify the day of year and characteristics of the different stages of river ice seasonality. The stages of breakup or freeze-up were distinguished from one another based on the spatial extent and patterns of open water and ice cover. Images were displayed as false color composites, with the shortwave infrared (SWIR), near infrared (NIR), and green bands represented by red, green, and blue.\n<br>\n\n#### Erosion_Vegetation_Yukon_1616\nThis dataset provides a time series of riverbank erosion and vegetation colonization along reaches of the Yukon River (3 study areas), Tanana and Nenana Rivers (1 area), and Chandalar River (1 area) in interior Alaska over the period 1984-2017. The change data were derived from selected 30-m images from Landsat TM, Landsat ETM+, and Landsat Operational Land Imager (OLI) surface reflectance products. Image classification used the Normalized Differenced Vegetation Index (NDVI) with an NDVI threshold of 0.2 to differentiate vegetated from non-vegetated pixels. Images were assigned to one of seven or eight multiyear intervals, within the 1984-2017 overall range, for each study area. Time intervals vary by study site. Change detection identified shifts from one time interval to the next: changes from vegetated to non-vegetated classes were considered riverbank erosion and changes from non-vegetated to vegetated classes were considered vegetation colonization.\n<br>\n\n#### SAR_Methane_Ebullition_AK_1790\nThis dataset provides Synthetic Aperture Radar (SAR) estimates of lake-source methane ebullition flux in mg CH4/m2/d for thousands of lakes in five regions across Alaska. The study regions include the Atqasuk, Barrow Peninsula, Fairbanks, northern Seward Peninsula, and Toolik. L-band SAR backscatter values for early winter lake ice scenes were collected from 2007 to 2010 over 5,143 lakes using the Phased Array type L-band Synthetic Aperture Radar (PALSAR) instrument on the Advanced Land Observing Satellite (ALOS-1) satellite. The backscatter data were combined with field measurements of methane ebullition from 48 study lakes across the five regions to obtain a volumetric flux estimate for each lake. Mean methane gas-fractions from each region were applied to the SAR-based volumetric fluxes to obtain an estimate of methane ebullition mass flux per lake. The data files contain lake perimeters and the lake-specific attributes of lake area, SAR backscatter values and standard errors, volumetric flux with standard errors, mean percent of methane from gas samples, and methane ebullition mass flux.\n<br>\n\n#### Dissolved_Gases_Alaska_Rivers_2360\nThis dataset provides dissolved carbon dioxide (CO2) and methane (CH4) concentrations alongside their stable and radiocarbon isotopic compositions within the Arctic Sagavanirktok and Kuparuk River watersheds located on the North Slope of Alaska. The data were collected during the spring, fall, and summer seasons in 2022. In field separation of the bulk gaseous components (N2, CO2, and CH4) from the liquid phase was achieved using a degassing membrane contactor. Laboratory isotopic analyses were conducted at the W. M. Keck Carbon Cycle Accelerator Mass Spectrometer facility at UC Irvine. This collection aims to provide insights into the seasonal dynamics of greenhouse gas emissions in these critical Arctic environments, thereby contributing valuable information for climate change research and monitoring programs. The data are provided in comma separated values (CSV) format.\n<br>\n\n#### Active_Layer_Thaw_Depths_1701\nThis dataset provides soil active layer thaw depth measurements collected along transects at three sites near Fairbanks, Alaska, USA. Measurements were made during the late summers of 2014-2018. The sites were located at Creamer's Field, the Permafrost Tunnel, and Farmer's Loop (two transects). Vegetation ecotypes along the transects are also reported. The US Army Corps of Engineers, Cold Regions Research and Engineering Laboratory (CRREL) owns and operates facilities at the Permafrost Tunnel and Farmer's Loop. The sites are suitable for manipulation experiments, installing permanent equipment, and establishing long-term measurements.\n<br>\n\n#### ABoVE_Soil_ThawDepth_Moisture_1903\nThis dataset provides soil thaw depth and moisture (STDM) measurements and dielectric properties measured by different research teams at sites in Alaska, U.S., and the Northwest Territories, Canada. There are multiple observations per site and 352,719 total observations. The dataset includes 206,000 observations of active layer thickness measured by mechanical probing (6.0%) or ground penetrating radar (GPR) (94.0%). Approximately 16,000 volumetric water content measurements were collected using GPR (22.1%), Hydrosense I and II probes (75.3%), and DualEM (2.6%). Metadata includes the location, time, geospatial coordinates, technique, measurement teams. Measurements were typically collected in August and September near the end of the thaw season and cover the period 2008-06-22 to 2020-08-15.\n<br>\n\n#### ABoVE_Soil_ThawMoisture_2369\nThis dataset provides soil thaw depth and moisture measurements and dielectric properties measured by different research teams at sites in Alaska, U.S., and the Northwest Territories, Canada. There are multiple observations per site and 528,703 total observations. The dataset includes 223,230 observations of active layer thickness measured by mechanical probing (9.8%) or ground penetrating radar (GPR) (90.2%). Approximately 179,154 volumetric water content (VWC) measurements were collected using GPR (2.0%), HydroSense I and II probes (8.8%), in situ loggers (89.2%), and DualEM (<1.0%). Metadata includes the location, time, geospatial coordinates, sampling technique, measurement teams, and field team contact information. Measurements were typically collected in August and September near the end of the thaw season and cover the period from 2005 to 2022. This dataset, referred to as field measurements of Soil Moisture and Active layer Thickness (SMALT) (Schaefer et al., 2021), was developed for work in Clayton et al. (2021). It has been expanded in Version 2 to comprise a comprehensive dataset of NASA Arctic-Boreal Vulnerability Experiment (ABoVE) field campaign measurements of soil moisture and active layer thickness, including logger data of relevance to Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) acquisitions. The data are provided in comma separated values (CSV) format.\n<br>\n\n#### ALT_Soil_Collection_Protocols_2373\nThis dataset contains soil moisture sampling protocols and calibration algorithms for Campbell Scientific Hydrosense-I and II units used at burned and unburned sites in the ABoVE project domain. The soil moisture sampling protocols document provides guidance for sampling in situ soil moisture to relate to synthetic aperture radar (SAR) data collections. Two additional documents describe algorithms for calibrating handheld Hydrosense units used to measure volumetric water content in soils. One calibration document applies to organic soils in fire-affected sites of the Northwest Territories and Alaska. A second document provides calibrations for samples collected in non-burned tundra, boreal bog, fen, upland deciduous and conifer sites located in Alberta and tundra sites in Alaska. The information is provided in portable document format (PDF).\n<br>\n\n#### Soil_Temp_Moisture_Alaska_1869\nThis dataset provides soil temperature and volumetric water content (VWC) measurements at 15 cm depth collected at 12 selected boreal and tundra sites located across Alaska. Each site is equipped with a HOBO MicroStation Data Logger that hosts two soil temperature sensors (HOBO S-TMB-M006 Temperature Smart Sensor), and two soil moisture sensors (HOBO S-SMD-M005 10HS Soil Moisture Smart Sensor). Each sensor was installed horizontally at a depth of 15 cm within the soil profile. Samples of soil from seven sites were taken to a laboratory for determination of site-specific soil moisture sensor calibration curves to correct raw measurements. Data were nominally recorded at an hourly frequency and downloaded from the sites at least annually for the period 2016-08-11 to 2023-09-02, but data coverage varies by site. These measurements were collected at the same sites as previously archived CO2 efflux and thaw depth data. The data are provided in comma-separated values (CSV) format.\n<br>\n\n#### USArray_Ground_Temperature_1680\nThis dataset includes soil temperature profile measurements taken at 63 monitoring sites associated with the USArray program, located across the NASA ABoVE domain in interior Alaska. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m from 2016-2021 using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. This station data complement an existing temperature monitoring network, allowing for better characterization of ground temperatures and permafrost conditions in northern and western Alaska. The temperature measurements are provided for each site in 64 data files in comma-separated values (.csv) format. Site descriptive data are also provided for soil, vegetation, and location.\n<br>\n\n#### Soil_Temperature_Profiles_AK_1767\nThis dataset includes soil temperature profile measurements taken at 16 monitoring sites in Alaska, USA, and at one site in Yukon, Canada. The six sites are collocated with seismic stations of the USArray program. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m. Measurements were made from 2016-2018 at two sites, 2017-2019 at four sites, and 2018-2019 at 11 sites using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska.\n<br>\n\n#### Post_Fire_C_Emissions_1787\nThis dataset provides spatial estimates of carbon combustion from all 2015 wildfire burned areas across Saskatchewan, Canada, on a 30-m grid. Carbon combustion (kg C/m2) was derived from post-fire field measurements of carbon stocks completed in 2016 at 47 stands that burned during three 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in adjacent areas. The study areas covered two ecozones (Boreal Plains and Boreal Shield), two stand-replacing history types (fire and timber harvest), three soil moisture classes (xeric, mesic, and subhygric), and three stand dominance classifications (coniferous, deciduous, and mixed). To spatially extrapolate estimates of combustion to all 2015 fires in Saskatchewan, a predictive radial support vector machine model was trained on the 47 burned stands with associated environmental variables and geospatial predictors and applied to historical fire areas. The dataset also includes uncertainty estimates represented as per pixel standard deviations of model estimates derived using a Monte Carlo analysis.\n<br>\n\n#### ABoVE_reference_grid_1367\nThe Arctic - Boreal Vulnerability Experiment (ABoVE) has developed two standardized spatial data products to expedite coordination of research activities and to facilitate data interoperability. First, the ABoVE Study Domain encompasses the Arctic and boreal regions of Alaska, USA, and the western provinces of Canada, North America. Core and Extended study regions have been designated within this Domain and are provided in both a vector representation (Shapefile) and a raster representation (GeoTIFF at 1,000-meter pixel resolution). Second, a Standard Reference Grid System has been developed to cover the entire Study Domain and also extends to the eastern portion of North America. This Reference Grid is provided as a nested pair of polygon grids at scales of 240- and 30-meter spatial resolution. A shapefile is provided for each grid. Note that the designated standard projection for the all ABoVE products is the Canadian Albers Equal Area projection.\n<br>\n\n#### ABoVE_reference_grid_v2_1527\nThe Arctic - Boreal Vulnerability Experiment (ABoVE) has developed two standardized spatial data products to expedite coordination of research activities and to facilitate data interoperability. The ABoVE Study Domain encompasses the Arctic and boreal regions of Alaska, USA, and the western provinces of Canada, North America. Core and Extended study regions have been designated within this Domain and are provided in a vector representation (Shapefile), a raster representation (GeoTIFF at 1,000-meter spatial resolution), and a NetCDF file. A standard Reference Grid System has been developed to cover the entire Study Domain and extends to the eastern portion of North America. This Reference Grid is provided as nested polygon grids at scales of 240, 30, and 5-meter spatial resolution. The 5-meter grid is new in Version 2. Note that the designated standard projection for all ABoVE products is the Canadian Albers Equal Area projection.\n<br>\n\n#### Interior_Alaska_Subsistence_1725\nThis dataset provide maps to show the search and harvest areas used by community residents for all subsistence resources combined across Interior Alaska for the years 2011 through 2017. The maps show the extent of areas used by residents for those communities where data collection and research has occurred; it is not a comprehensive use map for the entire area. The maps are a composite of data collected by the Division of Subsistence, Alaska Department of Fish and Game using standardized methods where respondents indicated the search areas for species harvested, the amounts harvested, and the location and months of harvest. These data are important for research, analysis, and regulatory assessment.\n<br>\n\n#### Decadal_Water_Maps_1324\nThis data set provides the location and extent of surface water (open water not including vegetated wetlands) for the entire Boreal and Tundra regions of North America for three epochs, centered on 1991, 2001, and 2011. Each of the products were generated with at least three years of ice-free Landsat imagery. The data are at 30-m resolution and were derived from time series of Landsat 4 and 5 Thematic Mapper (TM) data and Landsat 7 Enhanced Thematic Mapper (ETM+) covering all of Alaska and all provinces of Canada. The overall goal was to generate a map of the nominal extent of water for a given epoch, where nominal is neither the maximum nor the minimum but rather a representative extent for that time period.\n<br>\n\n#### ABoVE_Plot_Data_Burned_Sites_1744\nThis dataset is a synthesis of field plot characterization data, derived above-ground and below-ground combusted carbon, and acquired Fire Weather Index (FWI) System components for burned boreal forest sites across Alaska, USA, the Northwest Territories, and Saskatchewan, Canada from 1983-2016. Unburned plot data are also included. Compiled plot-level characterization data include stand age, disturbance history, tree density, and tree biophysical measurements for calculation of the above-ground (ag) and below-ground (bg) biomass/carbon pools, pre-fire and residual post-fire soil organic layer (SOL) depths and estimates of combustion of tree structural classes. The measured slope and aspect for each site and an assigned moisture class based on topography are also provided. Data from 1019 burned and 152 unburned sites are included. From the estimates of combusted ag and bg carbon pools and SOL losses, the total carbon combusted, the proportion of pre-fire carbon combusted, and the proportion of total carbon combusted were calculated for each plot. FWI System components including moisture and drought codes and indices of fire danger were obtained for each plot from existing data sources based on the plot location, year of burn, and a dynamic start-up date (day of burn, DOB) from the global fire weather database. Data for soil characteristics are included in a separate file.\n<br>\n\n#### PostFire_Tree_Regeneration_1955\nThis dataset is a synthesis of species-specific pre- and post-fire tree stem density estimates, field plot characterization data, and acquired climate moisture deficit data for sites from Alaska, USA eastward to Quebec, Canada in fires that burned between 1989 and 2014. Data are from 1,538 sites across 58 fire perimeters encompassing 4.52 Mha of forest and all major boreal ecozones in North America. To be included in this synthesis, a site had to contain information on species-specific post-fire seedling densities. This included sites where seedlings had been counted 2-13 years post-fire, a timeframe over which there was little change in relative dominance of species based on densities. Plot characterization data includes stand age, site drainage, disturbance history, crown combustion severity, seedbed conditions, and stand structural attributes. Gridded values of Hargreaves Climate Moisture Deficit (CMD) were obtained for each plot where plot coordinates were available. These values included 30-year normals (1981-2010) and CMD in the two years immediately following the fire year. CMD anomalies were calculated as the difference between the 30-year normal and the single year values for each of the first two years after a fire. These synthesis data are provided in comma-separated values (CSV) format.\n<br>\n\n#### LiDAR_Tundra_Forest_AK_1782\nThis dataset provides terrestrial lidar scanning (TLS) point cloud data collected at 10 research plots along the forest-tundra ecotone (FTE) in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. Data were collected in mid-June 2016. Data were acquired for each plot from multiple scan positions with a Leica ScanStation C10 green wavelength laser instrument. After processing the point spacing is < 1 cm. TLS enables resolution of 3-dimensional landscape features that can be used to derive ecologically important metrics of canopy structure and surface topography at high spatial resolution.\n<br>\n\n#### ABoVE_Thaw_Depth_1579\nThis dataset provides thaw depth measurements made at seven locations across Alaska, during August 2016, June and September 2017, and July-August 2018. Three of the locations are paired unburned-burned sites. At each site, three 30-meter transects were established and thaw depth was measured at 1-meter increments along each transect using a 1.15 m T-shaped thaw depth probe. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma-separated values (CSV) format.\n<br>\n\n#### Boreal_CanopyCover_StandAge_2012\nThis dataset contains Landsat-derived locally-calibrated estimates of tree canopy cover (TCC) and forest stand age across global boreal forests from 1984-2020 in Cloud-Optimized GeoTIFF (.tif) format. These raster data span the circum-hemispheric boreal forest biome between 47 to 73 degrees north at 30 m resolution. Machine learning models calibrated with data from the World Reference System 2 were used to predict TCC from Landsat data at 30-m spatial resolution at annual temporal resolution. Through analysis of TCC time series, forest change estimates of stand age from 1984-2020 were developed. The broad spatial and temporal coverage of these data provide insight into forest and carbon dynamics of the global boreal forest system. Boreal forests store a large proportion of global soil and biomass carbon and have experienced disproportionately high levels of warming over the past century.\n<br>\n\n#### AK_Tundra_PFT_FractionalCover_1830\nThis dataset provides predicted continuous-field cover for tundra plant functional types (PFTs), across ~125,000 km2 of Alaska's North Slope at 30-m resolution. The data cover the period 2010-07-01 to 2015-08-31. The data were derived using a random forest data-mining algorithm, predictors derived from Landsat satellite observations (surface reflectance composites for ~15-day periods from May-August), and field vegetation cover and site characterization data spanning bioclimatic and geomorphic gradients. The field vegetation cover was stratified by nine PFTs, plus open water, bare ground and litter, and using the cover metrics total cover (areal cover including the understory) and top cover (uppermost canopy or ground cover), resulting in a total of 19 field cover types. The field data and predictor values at the field sites are also included.\n<br>\n\n#### NorthSlope_NEE_TVPRM_1920\nThis dataset includes hourly net ecosystem exchange (NEE) simulated by the Tundra Vegetation Photosynthesis and Respiration Model (TVPRM) at 30 km horizontal resolution for the Alaskan North Slope for 2008-2017. TVPRM calculates tundra NEE from air temperature, soil temperature, photosynthetically active radiation (PAR), and solar-induced chlorophyll fluorescence (SIF) using functional relationships derived from eddy covariance tower measurements. These relationships were then scaled over the region using gridded meteorology and a vegetation map. The site-level CO2 fluxes fell into two distinct ecosystem groups: inland tundra (ICS, ICT, ICH, IVO) and coastal tundra (ATQ, BES, BEO, CMDL). The expanded modeling framework allowed for the easy substitution of ecological behaviors and environmental drivers, including the choice of representative inland tundra site, coastal tundra site, vegetation map (CAVM, RasterCAVM, or ABoVE-LC), meteorological reanalysis product (NARR or ERA5), and SIF product (GOME2, GOSIF, or CSIF). Using all of these variations generated an ensemble of 288 different TVPRM simulations of regional CO2 flux and one additional simulation option with added aquatic and zero curtain fluxes (AqZC).\n<br>\n\n#### ALT_Maps_AK_CA_2332\nThe dataset consists of maps of estimated Active Layer Thickness (ALT) at 30-m resolution throughout the northern half of Alaska for the years 2014, 2015, and 2017. The maps were generated by using a machine learning-based regression and a set of spatial data layers to upscale ALT from narrow swaths of ALT that were retrieved from airborne high-resolution P-band Polarimetric Synthetic Aperture Radar (PolSAR) imagery. The data are provided in cloud-optimized GeoTIFF format.\n<br>\n\n#### ABoVE_Izaviknek_Field_Data_1772\nThis dataset provides ecological field data that were collected during July 2017 and July 2018 from 43 plots spanning gradients of fire history in the upland tundra of the Yukon-Kuskokwim (Y-K) Delta, Alaska. Plot-level data include vegetation species composition and structure, leaf area index (LAI), topography, thaw-depth, and soil characteristics collected at plots burned in 1971-1972, 1985, 2006-2007, 2015, or unburned controls. Vegetation species were sampled along transects using the vegetation point-intercept (VPI) sampling approach and summarized by three metrics of vegetation cover: (1) top-hit cover, (2) any-hit cover, and (3) multi-hit cover. Each metric is the total number of hits for a species divided by the total number of sample points. The VPI any-hit cover metric data were combined with Landsat imagery to develop fractional maps of any-hit cover for four aggregated plant functional types (PFTs); bryophytes, herbs, lichen, and shrubs for the upland tundra area. Photographs of vegetation transects and soil pits are included as companion files.\n<br>\n\n#### InundationMap_YkFlats_PeaceAth_1901\nThis dataset provides time series of wetland inundation coverage maps and corresponding inundation frequency maps at ~10-meter resolution estimated every 12 days during the free-water period (May to October) for the years 2017-2019 over the Yukon Flats (YK) portion of the Yukon River, Alaska, USA, and the Peace-Athabasca Delta (PAD), Alberta, Canada. Wetland inundation coverage was determined by a two-step modified decision-tree classification approach that first used Sentinel-1 C-band SAR to identify likely inundated areas across a study site and was followed by a decision-tree classification step with C-band SAR backscatter statistics thresholds to distinguish among different inundation components. The result of this process was five classes for each inundation map, namely Open Water (OW), Floating Plants (FP), Emergent Plants (EP), Flooded Vegetation (FV), and Dry Land (DRY). After all the individual (every 12 days) inundation coverage maps were derived for a study site, they were generalized to two-class maps which maintained only inundation status. These generalized maps were then stacked and summarized to produce the inundation frequency map for the site. In these maps, higher values signify more frequently inundated areas, with the maximum value representing permanently inundated pixels. The Sentinel-1 inundation mapping capability demonstrated here provided frequent, broad-scale mapping of different wetland inundation components. Integration of such products with process-based methane (CH4) models would improve simulation of CH4 emissions from wetlands.\n<br>\n\n#### Ecosystem_Map_SRD_PAD_1947\nThis dataset provides ecosystem-types for the Slave River Delta (SRD) and Peace-Athabasca Delta (PAD), Canada, for the time periods circa 2007 and circa 2017. The image resolution is 12.5 m with 0.2-hectare minimum mapping unit. Included are an 18-class modified Enhanced Wetland Classification (EWC) scheme for wetland, peatland, and upland areas. Classes were derived from a Random Forest classification trained on multi-seasonal moderate-resolution images and synthetic aperture radar (SAR) imagery sourced from aerial and satellite sensors, field data, and calculated indices. Indices included Height Above Nearest Drainage (HAND) and Topographic Position Index (TPI), both derived from a digital elevation model, to differentiate between land cover types. The c. 2007 remote sensing data were comprised of early and late growing season Landsat-5, ERS2, L-Band PALSAR from 2006 to 2010 and growing season Landsat thermal composites. The c. 2017 remote sensing data were comprised of early and late growing season Landsat-8 and L-Band PALSAR-2 from 2017 to 2019, Sentinel-1 June VV and VH mean and standard deviations, and growing season Landsat thermal composites. Elevation indices from multi-resolution TPI and HAND were created from the Japan Aerospace Exploration Agency Advanced Land Observing Satellite 30 m Global Spatial Data Model. Also included are the images used for classification and the classification error matrices for each map and time period. Data are provided in GeoTIFF and GeoPackage file formats.\n<br>\n\n#### Wetland_VegClassification_PAD_2069\nThis dataset contains land cover classification focused on water and wetland vegetation communities over the Peace-Athabasca Delta, Canada. Four classification maps with 5-m resolution were derived various combinations of Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) acquired in July and September 2019, and a historical LiDAR archive data. The maps include 10 land cover classes, including open water, emergent aquatic vegetation types, terrestrial vegetation, and forest. Based on field data, the best performing model, which combined all three data sources, achieved an overall accuracy of 93.5%. The land cover maps are provided in GeoTIFF format along with polygons of AVIRIS-NG, UAVSAR, and LiDAR footprints in shapefile and KML formats.\n<br>\n\n#### WhiteSpruce_Leaf_Traits_Alaska_2124\nThis dataset provides measurements of gas exchange (light response curves, Kok curves and ACi curves), leaf traits (carbon, nitrogen, and specific leaf area), leaf pigments (Chlorophyll a, Chlorophyll b and Carotenoids), the photochemical reflectance index (PRI), and average photosynthetic photon flux density as collected from hemispherical photographs. Data were collected on white spruce trees (Picea glauca (Moench) Voss) growing at the northern edge of the species' distribution in Alaska and at the southern edge of the species' distribution in Black Rock Forest (BRF), New York. Measurements were taken at high and low canopy positions on each tree at both sites during the 2017 growing season (2017-06-19 to 2017-07-20). Gas exchange, leaf trait, pigment and spectral measurements were obtained using a portable photosynthesis system (LI-6800, LI-COR, Lincoln, NE). Photochemical reflectance index was determined using a spectroradiometer, and hemispherical photographs were taken with a digital camera. These data were collected to better understand how vertical canopy gradients in photosynthetic physiology change from the southernmost to the northernmost range extremes of white spruce. The data are provided in comma-separated value (CSV) format.\n<br>\n\n#### Fire_Emissions_NWT_1561\nThis dataset provides estimates of wildfire carbon emissions and uncertainties at 30-m resolution, and measurements collected at burned and unburned field plots from the 2014 wildfire sites near Yellowknife, Northwest Territories (NWT), Canada. Field data were collected at 211 burned plots in 2015 and include site characteristics, tree cover and species, basal area, delta normalized burn ratio (dNBR), plot characteristics, soil carbon, and carbon combusted. Data were collected at 36 unburned plots with characteristics similar to the burned plots in 2016. The emission estimates were derived from a statistical modeling approach based on measurements of carbon consumption at the 211 burned field plots located in seven independent burn scars. Estimates include uncertainty of field observations of aboveground and belowground combustion, as well as prediction uncertainty from a multiplicative regression model. To apply the model across all 2014 NWT fire perimeters, the final model covariates were re-gridded to a common 30-m grid defined by the Arctic Boreal and Vulnerability Experiment (ABoVE) Project. The regression model was then applied to burned pixels defined by a threshold of Landsat-derived differenced Normalized Burn Ratio (dNBR) within fire perimeters. Derived carbon emissions and uncertainty in g/m2 are provided for each 30-m grid cell. The modeled NWT domain encompasses 29 tiles within the ABoVE 30-m reference grid system.\n<br>\n\n#### Wildfires_Date_of_Burning_1559\nThis dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021.\n<br>\n\n#### Wolves_Denning_Pups_Climate_1846\nThis dataset provides annual gray wolf (Canis lupus) denning spatial information and timing, associated climatic and phenologic metrics, and reproductive success (i.e., pup survival) in wolf populations across areas of western Canada and Alaska within the NASA ABoVE Core Domain. The study encompasses 18 years between the period 2000-2017. Wolves were captured from eight populations following standard animal care protocols and released with Global Positioning System (GPS) collars. Data from 388 wolves were used to estimate den initiation dates (n=227 dens of 106 packs) and reproductive success in the eight populations. Each population was monitored from 1 to 12 years between 2000 and 2017. Denning parturition phenology was measured each year as the number of calendar days from January 1st to the initiation date of each documented denning event. Reproductive success was determined as to whether pups survived through the end of August following a reproductive event. To evaluate the effect of climate factors on reproductive phenology, aggregated seasonal climate metrics for temperature, precipitation, and snow water equivalent based on three biological seasons for seasonal wolf home ranges were produced. Normalized Difference Vegetation Index (NDVI) time-series data were used to estimate phenological metrics such as the start of the growing season (SOS), length of the growing season (LOS), and time-integrated NDVI (tiNDVI), and were summarized for the populations' home range.\n<br>\n\n#### Arctic_Winter_Respiration_v2_1762\nThis dataset provides soil-surface carbon dioxide (CO2) efflux derived from measurements of soil respiration with forced diffusion (FD) chambers. Soil Respiration Stations (SRS) were installed at 11 boreal and tundra sites along a broad south-to-north transect starting from near Fairbanks in interior Alaska and extending to Atqasuk in northern Alaska. Each SRS measures soil respiration and ambient atmospheric CO2 concentrations with a forced diffusion (FD) chamber to derive soil CO2 flux. The SRS also measures soil CO2 concentrations and temperatures using instrumented chambers buried at 5, 10, and 15 cm depths in the soil profile. At the highest measurement frequency, data are collected hourly, and during the lowest winter frequency, every 48 hours. The data include flux values and running median filtered values from the two or three FD chambers at each site. Soil CO2 and temperature profile data (beginning June 2017) were collected beginning 2016-08-18 through 2023-09-02. This dataset updates four sites with extended temporal coverage. As of this publication, sampling is continuing, and new data will be added as available.\n<br>\n\n#### Boreal_AGB_Density_ICESat2_2186\nThis dataset provides estimates of Aboveground dry woody Biomass Density (AGBD) for high northern latitude forests at a 30-m spatial resolution. It is designed both for boreal-wide mapping and filling the northern spatial data gap from NASA's Global Ecosystem Dynamics Investigation (GEDI) project. Mapping forest aboveground biomass is essential for understanding, monitoring, and managing forest carbon stocks toward climate change mitigation. The AGBD estimates cover the extent of high latitude boreal forests and extend southward to 50 degrees latitude outside the boreal zone. AGBD was predicted using two modeling steps: 1) Ordinary Least Squares (OLS) regression related field plot measurements of AGBD to NASA's ICESat-2 30-m lidar samples, and 2) random forest models were used to extend estimates beyond the field plots by relating ICESat-2 AGBD predictions to wall-to-wall covariate stacks from Harmonized Landsat Sentinel-2 (HLS) and the Copernicus DEM. Per-pixel uncertainties are estimated from bootstrapping both models. Non-vegetated areas (e.g. built up, water, rock, ice) were masked out. HLS composites and ICESat-2 data were from 2019-2021; three years of conditions were aggregated into the circa 2020 map. ICESat-2 data were filtered to include only strong beams, growing seasons (June through September), solar elevations less than 5 degrees, snow free land (snow flag set to 1), and \"msw_flag\" equal to 0 (clear skies and no observed atmospheric scattering). ICESat-2's ATL08 product was resampled to a 30-m spatial resolution to better match both the field plots and mapped pixels. HLS data (L30HLS) were used to create a greenest pixel composite of growing season multispectral data, which was then used to compute a suite of vegetation indices: NDVI, NDWI, NBR, NBR2, TCW, TCG. These were then used, in combination with the slope and elevation data from the Copernicus DEM product, to predict 30-m AGBD per 90-km tile. Estimates of mean AGBD with standard deviation are provided in cloud-optimized GeoTIFF (CoG) format. Training data are in comma-separated values (CSV) format. A polygon map of data tiles is included as a GeoPackage file and a Shapefile.\n<br>\n\n#### AGB_Great_Slave_Lake_NWT_2365\nThis dataset holds aboveground biomass (ABG) estimates for areas in the Great Slave Lake Region in the Northwest Territories of Canada for 2019. ABG was estimated from L-band synthetic aperture radar (SAR) data obtained from JAXA's Advanced Land Observing Satellite-2 (ALOS-2/PALSAR-2) and supplemented with data from NASA's airborne Uninhabited Aerial Vehicle SAR (UAVSAR) instrument. SAR data were collected from 2017 to 2021. In situ AGB measurements at 14 plots sampled in 2019 were used to calibrate a logarithmic regression to relate the radar datasets to in situ AGB data. Then, AGB was mapped over available ALOS-2 tiles. The estimates are provided in 20-Mg ha-1 bins at 100-m resolution in cloud optimized GeoTIFF format.\n<br>\n\n#### YKDelta_EnvChange_InfoExchange_1894\nThis dataset provides a booklet documenting the discussions and outcomes from a knowledge-exchange meeting with Yup'ik elders from the Yukon-Kuskokwim Delta (YKD), western Alaska, community members, and natural scientist to discuss landscape and weather changes that have been observed in their homelands. The meeting was held during November 14-16, 2018. Yup'ik participants represented several YKD villages that occupy very different biophysical environments, and they have lifelong perspectives of environmental conditions and change that predate the era of Earth-observing satellites by many decades. Nearly 16 hours of discussion and testimonials from YKD elders were recorded during the meeting. The booklet is structured according to the environmental change processes that were discussed (e.g., coastal flooding, permafrost thaw, shrub expansion, climate change) and includes narrative summaries, quotations from participants, graphical illustrations, and examples of the field- and remote-sensing-based scientific findings, and map products developed as part of the larger ABoVE project.\n<br>\n\n#### Alaska_Arctic_Tundra_Veg_Map_1353\nThis data set provides the spatial distributions of vegetation types, geobotanical characteristics, and physiographic features for the Arctic tundra region of Alaska for the period 1993-2005. Specific attributes include dominant vegetation, bioclimate subzones, floristic subprovinces, landscape types, lake coverage, and substrate chemistry. This data set generally includes areas North and West of the forest boundary and excludes areas that have a boreal flora such as the Aleutian Islands and alpine tundra regions south of treeline.\n<br>\n\n#### Arctic_Boreal_Burned_Area_V2_2328\nThis dataset provides annual cumulative end-of-season burned area in circumpolar boreal forests and tundra for the years 2002-2022. The data were generated using the Arctic Boreal Burned Area (ABBA) version 2 algorithm with MODIS collection 6 products. The algorithm is based on Normalized Burned Ratio differencing (dNBR) and is designed specifically to capture late season fires. The annual MODIS Vegetation Continuous Fields (VCF) 250-m Collection 5.1 (MOD44B) product allowed for additional vegetation-dependent dNBR thresholds within the algorithm's processing steps. The spatial domain is circumpolar regions above 50 degrees north latitude. The data are provided in cloud-optimized GeoTIFF format with 463-m resolution.\n<br>\n\n#### North_Slope_Veg_Plots_1536\nThis dataset provides vegetation cover and environmental plot and soil data collected at flux tower sites of the North Slope Arctic System Science/Land-Atmosphere-Ice Interactions (ARCSS/LAII) project in August of 1995 and 1996. The 19 ARCSS/LAII flux tower sites are located along a North-South transect from near Prudhoe Bay to the foothills of the Brooks Range on the North Slope of Alaska. At 17 of the flux tower sites, one or more vegetation plots (29 total) were established and measurements including (1) plant species cover for the major vegetation types using the Braun-Blanquet approach, (2) plot environmental data, and (3) soil profile descriptions were recorded. In addition, at all 19 sites, plant growth form composition and cover were surveyed using a point sampling technique along multiple transects within selected plots.\n<br>\n\n#### Arrigetch_Peaks_Veg_Plots_1358\nThis data set provides environmental and vegetation data collected between 1978 and 1981 from 439 study plots at Arrigetch Peaks research site, located in the Gates of the Arctic National Park and Preserve in the Endicott Mountains of the central Brooks Range, Alaska. Plots varied between 1 and 50 square meters in size and were located in 13 broad habitat types across the glaciated landscape. Environmental data include aspect, elevation, and cover of bare soil, rock, soil crust, and litter. Species data are described according to the Braun-Blanquet system. This product brings together for easy reference all the available information collected from the vegetation plots in the Arrigetch Peaks region of Alaska.\n<br>\n\n#### Atqasuk_Veg_Plots_1371\nThis data set provides vegetation species abundance data collected in 1975 from 60 sites on the Arctic Coastal Plain near Atqasuk, Alaska, as well as environmental and species data for 31 of the sites that were revisited in 2000 and 2010. The study sites are located on Arctic tundra near the Meade River, about 60 miles southwest of Barrow. Data includes baseline plot information for vegetation and site factors for the study plots subjectively located in 9 plant communities. Specific attributes include: site characteristics such as altitude, slope, aspect, and topographic position; soil pH and organic layer depth; and dominant plant communities. This product brings together for easy reference all of the available information collected from the plots that has been used for the classification, mapping, and analysis of geo-botanical factors at the Atqasuk research sites and across Alaska.\n<br>\n\n#### Frost_Boils_Veg_Plots_1361\nThis data set describes the environment, soil, and vegetation on nonsorted circles and earth hummocks at seven study sites along a N-S-transect from the Arctic Ocean to the Arctic Foothills based on data collected from 2000 to 2006. The study sites are located along the Dalton Highway, beginning in Prudhoe Bay, on the North Slope of Alaska. These frost-boil features are important landscape components of the arctic tundra. Data include the baseline plot information for vegetation, soils, and site factors for 117 study plots subjectively located in areas of homogeneous, representative vegetation on frost-heave features surrounding stable tundra. Nine community types were identified in three bioclimate subzones. Vegetation was classified according to the Braun-Blanquet system.\n<br>\n\n#### Happy_Valley_Veg_Plots_1354\nThis dataset provides environmental, soil, and vegetation data collected in July 1994 from 56 study plots at the Happy Valley research site, located along the Sagavanirktok River in a glaciated valley of the northern Arctic Foothills of the Brooks Range. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in 17 plant communities that occur in 5 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools, soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geo-botanical factors in the Happy Valley region and across Alaska.\n<br>\n\n#### Imnavait_Creek_Veg_Plots_1356\nThis dataset provides environmental, soil, and vegetation data collected during the periods of August 1984 and August-September 1985 from 84 study plots at the Imnavait Creek research site. Imnavait Creek is located in a shallow basin at the foothills of the central Brooks Range. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in 14 plant communities that occur in 19 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geo-botanical factors in the Imnavait Creek region and across Alaska.\n<br>\n\n#### Nome_Veg_Plots_1372\nThis data set provides environmental, soil, and vegetation data collected in July and August 1951 from 80 study plots in the Nome River Valley about 10 miles northeast of Nome, Alaska on the Seward Peninsula. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in plant communities that were found to occur in 5 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species and cover, and soil characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping and analysis of geo-botanical factors in the Nome River Valley and across Alaska.\n<br>\n\n#### Oumalik_Veg_plots_1506\nThis data set provides environmental, soil, and vegetation data collected between 1983 and 1985 from 87 study plots near an abandoned test oil well in Oumalik, Alaska. Specific attributes include dominant vegetation, species, and cover, soil chemistry, physical characteristics, moisture, and organic matter, as well as site disturbance from various sources. The vegetation sampling sites were chosen to represent the full range of vegetation in the area with replication, and for uniformity in floristic composition and environmental conditions.\n<br>\n\n#### Prudhoe_Bay_Veg_Plots_1360\nThis data set provides environmental, soil, and vegetation data collected between 1973 and 1980 from 89 study plots in the Prudhoe Bay region of Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for study plots subjectively located in 43 plant communities and 4 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation, species, and cover; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for classification, mapping, and analysis of geobotanical factors in the Prudhoe Bay region and across Alaska.\n<br>\n\n#### Toolik_Lake_Veg_Plots_1333\nThis dataset provides environmental, soil, and vegetation data collected in August 1989 from 81 study plots at the Toolik Lake research site, located in the southern Arctic Foothills of the Brooks Range, Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in 26 communities and 4 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geobotanical factors in the Toolik Lake region and across Alaska.\n<br>\n\n#### Umiat_Veg_Plots_1370\nThis data set provides vegetation cover and plot data collected during the periods of July and August, 1951, from 51 stands (areas of homogeneous vegetation communities) in the the Umiat region of Alaska, on the Colville River. The Umiat area is within the Northern Foothills section of the Arctic Foothills province on the slope north of the Brooks Range. Data include vegetation species, percent cover classes, soil moisture, topographic position, slope, aspect, and plot shape and size.\n<br>\n\n#### ATLAS_Veg_Plots_1541\nThis data set provides environmental, soil, and vegetation data collected from study sites on the North Slope and Seward Peninsula of Alaska during the Arctic Transition in Land-Atmosphere System (ATLAS) project. ATLAS-1 sites on the North Slope, located in Barrow, Atqasuk, Oumalik, and Ivotuk, were sampled in 1998-1999. ATLAS-2 sites located at Council and Quartz Creek on the Seward Peninsula were sampled in 2000. Specific attributes include dominant vegetation species and cover, biomass, soil chemistry and moisture, leaf area index (LAI), normalized difference vegetation index (NDVI), topography and elevation, and plant cover abundance.\n<br>\n\n#### Barrow_Tundra_Veg_Plots_1535\nThis data set provides vegetation cover and environmental plot data collected as part of the International Biological Program (IBP), U. S. Tundra Biome Program, in Barrow, Alaska in 1972. Forty-three (43) plots were assessed for estimated percent land cover by species and plot data including moisture, topographic position, slope, aspect, shape, and soil data. In 1999, 2008, and 2010, 33 of the same plots were resampled for these same measures as part of the IPY \"Back to the Future: Resampling old research sites to assess changes in high latitude terrestrial ecosystem structure and function\" project. The tundra at Barrow is considered coastal tundra located in the most northern region of North Slope and is characterized by various microtopographic features such as polygons, as well as many ponds and lakes.\n<br>\n\n#### Barrow_NGEE_Arctic_Veg_Plots_1505\nThis data set provides vegetation cover and environmental plot data collected on the Barrow Environmental Observatory (BEO), Barrow, Alaska in 2012. Forty-eight 1 x 1 m plots were established in homogenous plant communities along two perpendicular transects across ice wedge polygon geomorphic features on the BEO. Plots were distinguished as to their location within the polygons as center, edge, or trough. Vegetation data were originally collected by the U.S. Department of Energy (DOE) Next-Generation Ecosystem Experiment (NGEE) Arctic Project as part of a larger study to understand the response of Arctic terrestrial ecosystems to climate change.\n<br>\n\n#### Pingo_Veg_Plots_1507\nThis data set provides vegetation species and vegetation plot data collected between 1983 and 1985 from 293 study plots on 41 pingos on the North Slope of Alaska. The pingos were located within the Arctic Coastal Plain in the Kuparuk, Prudhoe Bay, Kadleroshilik, and Toolik River areas. Specific attributes include dominant vegetation species, cover, soil pH, moisture, and physical characteristics of the plots.\n<br>\n\n#### Tundra_Fire_Veg_Plots_1547\nThis dataset provides environmental and vegetation data collected in late June and July of 2011 and of 2012 from study plots located in tundra fire scars and adjacent unburned tundra areas on the Seward Peninsula and the northern foothills of the Brooks Range in Arctic Alaska. The surveys focused on upland tundra settings and provide information on vegetative differences between the burned and unburned sites. The sampling design established a chronosequence of sites that varied in time since last fire to better understand post-fire vegetation successional trajectories. Complete species lists and their cover abundance data are provided for both study areas. Environmental data include the baseline plot descriptive information for vegetation, soils, and site factors. No soil samples were collected.\n<br>\n\n#### Flux_Tower_Zona_Veg_Plots_1546\nThis data set provides vegetation, environmental, and soil data collected from plots located in the footprints of eddy covariance flux towers along a 300 km north-south latitudinal gradient from Barrow, to Atqasuk, and to Ivotuk across the North Slope of Alaska in 2014. Within each of the five flux tower footprints, 1x1-m quadrats were placed subjectively within widespread habitat or micro-habitat types to map the dominant vegetation communities and site environmental factors. Specific attributes included species cover data and environmental, soil and spectral data (active layer thaw depth, moss layer depth, organic horizon layer depth, standing water depth, soil moisture status, vegetation height, LAI).\n<br>\n\n#### Canadian_West_Arctic_Veg_Plots_1543\nThis dataset provides vegetation, soil, and plot characteristics for 154 study plots located at three sites across the Richardson Mountains, Northwest Territories (NWT), and the British Mountains, Yukon Territory (YT). Study sites in the NWT included areas near Canoe Lake and Divided Lake; the study site in the YT was near Trout Lake. Specific attributes include dominant vegetation, species cover, and the physical characteristics of the plot areas. A soil pit was dug at each plot and the physical and chemical characteristics were determined for soil horizons. The data were collected in June, July, and August of 1965 and July and August of 1966.\n<br>\n\n#### Arctic_Network_Veg_plots_1542\nThis dataset provides environmental, soil, and vegetation data collected at selected locations in the parks and preserves of the National Park Service (NPS) Arctic Network (ARCN) between 2002 and 2008. The ARCN includes five national parks and preserves in northern Alaska encompassing 19.5 million acres and represents some of the wildest, most undisturbed areas left on earth: The Bering Land Bridge National Preserve, Cape Krusenstern National Monument, Gates of the Arctic National Park and Preserve, Kobuk Valley National Park, and the Noatak National Preserve. The sampling sites were chosen to represent the full range of vegetation in the area with replication, and for uniformity in floristic composition and environmental conditions and were positioned on transects along toposequences within major physiographic units (riverine, lacustrine, lowland, upland, subalpine and alpine). Specific attributes include dominant vegetation, species, and cover, soil chemistry, physical characteristics, moisture, and organic matter, as well as site disturbance from various sources.\n<br>\n\n#### Willow_Veg_Plots_1368\nThis data set provides environmental, soil, and vegetation data collected in July and August 1997 from 85 study plots in willow shrub communities located along a north-south transect from the Brooks Range to Prudhoe Bay on the North Slope of Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in three broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geobotanical factors in the region and across Alaska.\n<br>\n\n#### Barter_Barrow_Veg_Plots_1534\nThis dataset provides vegetation cover and environmental plot and soil data collected at two U.S. Air Force sites at Barter Island (BI) and Point Barrow (B), on the coastal North Slope of Alaska, in 1994. At Barter Island, 31 plots, and 30 plots at Barrow, were subjectively located in 14 plant communities. The investigation was part of a larger study initiated by the United States Congress to provide an opportunity to enhance the stewardship of the natural and cultural resources land under Department of Defense jurisdiction. These two sites were characterized to build an inventory of the biotic communities to compare them to historic communities.\n<br>\n\n#### Unalaska_Veg_Plots_1375\nThis data set provides environmental, soil, and vegetation data collected during August 2007 from 69 study plots at the Unalaska Island research site, and one plot on Amaknak Island. The study sites are within the eastern Aleutian Islands, Alaska, USA. Data includes the plot information for vegetation, soils, and site characteristics for the study plots subjectively located in 11 plant communities that occur in six broad habitat types. Specific attributes include: dominant vegetation species and cover, soil chemistry, moisture, organic matter, topography, and elevation. Cover-abundance was estimated for all vascular plants, bryophytes, and macrolichens according to the nine-point ordinal scale of Westhoff and van der Maarel (1973).\n<br>\n\n#### Poplar_Veg_Plots_1376\nThis data set provides vegetation cover and environmental plot data collected from 32 balsam poplar (Populus balsamifera L., Salicaceae) vegetation plots located on the Arctic Slope of Alaska and in the interior boreal forests of Alaska and the Yukon from 2003 to 2005. The estimated percent land cover by species per plot are according to the older Braun-Blanquet cover-abundance scale. Plot data includes moisture, topographic position, slope, aspect, shape, and soil data.\n<br>\n\n#### Prudhoe_Bay_ArcSEES_Veg_Plots_1555\nThis dataset provides environmental, soil, and vegetation data collected from study plots in the vicinity of Lake Colleen off the Spine Road at Prudhoe Bay, Alaska, during August of 2014. Data include vegetation species, leaf area index (LAI), percent cover classes, soil moisture and color, and plot characteristics including geology, topographic position, slope, aspect, and plot disturbance.\n<br>\n\n#### LandCover_Great_Lakes_Basin_2440\nThis dataset holds maps of land cover and wetland ecotype for the Great Lakes Basin (GLB) circa 2010. The data were derived from multi-season L-band SAR, Landsat 5 optical imagery, and thousands of field training data samples. The dataset includes detailed landcover maps of shorelines of each of five Great Lakes along with a land cover map of the entire GLB. This is the first binational map of the GLB that specifically depicts native wetland ecotypes and monocultures of invasive wetland plants (Phragmites australis and Typha spp.). The goal of the effort was to improve understanding of nutrient flow and transport of water across the landscape to the coasts and the processes at the land-water interface in wetland ecosystems that lead to the success of invasive species. The data are provided in Cloud-optimized GeoTIFF (COG), shapefile, comma separated values (CSV), and text file formats. An associated report is included in PDF format.\n<br>\n\n#### Plot_Data_Noatak_Seward_AK_1919\nThis dataset includes field measurements from unburned and burned 10 m x 10 m and 1 m x 1 m plots in the Noatak, Seward, and North Slope regions of the Alaskan tundra during July through August in the years 2016-2018. The data include vegetation coverage, soil moisture, soil temperature, soil thickness, thaw depth, and weather measurements. Measurements were recorded using ocular assessments and standard equipment. Plot photographs are included.\n<br>\n\n#### Arctic_Vegetation_Maps_1323\nThis data set provides the spatial distributions of vegetation types, geobotanical characteristics, and physiographic features for the circumpolar Arctic tundra biome for the period 1982-2003. Specific attributes include dominant vegetation, bioclimate subzones, floristic subprovinces, landscape types, lake coverage, Arctic treeline, elevation, and substrate chemistry data. Vegetation indices, trends, and biomass estimate products for the circumpolar Arctic through 2010 are also provided.\n<br>\n\n#### Boreal_AGB_Density_ICESat2_V3_2437\nThis dataset provides estimates of aboveground dry woody biomass density (AGBD) and vegetation height for high northern latitude forests at a 30-m spatial resolution for the year 2020, accounting for >30% of global forest area. The estimates were derived with state-of-the-art earth observation datasets collected from space, including lidar observations from NASA's ICESat-2 and imagery from NASA's Harmonized Landsat/Sentinel-2 project. They are designed for circumpolar boreal-wide mapping from local to global scales and provide the northern component of global forest structure estimates, to which complementary estimates from NASA's Global Ecosystem Dynamics Investigation (GEDI) mission contribute temperate and tropical portions. The AGBD and height predictions cover the extent of high latitude boreal forests and shrublands, and while they extend southward outside the boreal domain nominally to ~50 degrees N they are intended to contribute to global estimates northward from 51.6 degrees N.\n<br>\n\n#### CH4_CO2_WaterBodies_YK_Delta_2178\nThis dataset provides estimates of carbon dioxide (CO2) and methane (CH4) diffusive fluxes from waterbodies, and watershed landcover data for the central-interior of the Yukon-Kuskokwim Delta (YK delta), Alaska. Dissolved concentrations of methane and carbon dioxide were predicted using an integrated terrestrial-aquatic approach to scale observations based on landscape and waterbody remote sensing drivers. The observations include ~300 samples of surface water dissolved gases collected in July 2016-2019 from the central region of the YK Delta, Alaska. A machine learning model was used to generate estimated fluxes. Model inputs include Sentinel-2 MSI with derived normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), an Arctic digital elevation model (DEM) with derived slope and flow accumulation, Sentinel-1 C-band July and December VV and VH composites, and a landcover map. Waterbody size, shape, and reflectance were determined using object-based image analysis in Google Earth Engine. Landscape-level input data were averaged in non-nested sub-basins calculated using the System for Automated Geoscientific Analyses (SAGA) \"channel network\" algorithm at three threshold sizes. Cross validation was used to tune and select variables for gradient boosting models. The trained gradient boosting models were then used to predict dissolved methane and carbon dioxide in all waterbodies (~17,000) in the region. These aquatic concentrations were converted to fluxes using an average gas transfer velocity from observations (0.33 m/d). The data are provided in GeoTIFF and shapefile formats.\n<br>\n\n#### ABoVE_SnowModel_Data_2105\nThis dataset provides daily SnowModel simulation outputs on a 3-km grid for the period 1 September 1980 through 31 August 2020, covering the Core ABoVE Domain. The daily outputs include: air temperature (deg C), relative humidity (%), wind speed (m/s), wind direction (deg from True North), total precipitation (rain+snow) (m), rainfall (m), snowfall (m), snow melt (m), snow sublimation (m), runoff (m), surface temperature (deg C), bulk snowpack thermal resistance (K/W), snow depth (m), snow density (kg/m3), and snow-water-equivalent (SWE) depth (m). Model data inputs included land cover and topography, ground-based observations of snow, remote sensing observations of snow from satellites and aircraft, and meteorological forcing data from weather stations and reanalysis data. The SnowModel includes the processing modules MicroMet, Enbal, SnowDunes, SnowAssin, SnowPack, and SnowTran-3D. The data are provided in NetCDF format.\n<br>\n\n#### DeciduousFractionl_CanopyCover_2296\nThis dataset holds deciduous fraction and tree canopy cover at 30-m resolution over the North American boreal domain for 1992 to 2015. Deciduous fraction is the areal percentage of deciduous trees relative to all tree canopy cover within a pixel, and tree canopy cover is the areal percentage of a pixel that is covered by tree canopy. Deciduous fraction values are valid only for pixels with tree canopy cover >25 percent. Normalized difference vegetation index (NDVI)-based median-value image composites were derived from Landsat 5, 7, and 8 Collection 1 surface reflectance datasets for years 1987-1997, 1998-2002, 2003-2007, 2008-2012, and 2013-2018 to create composites for nominal years 1992, 2000, 2005, 2010, and 2015, respectively. These image composites were prepared for early spring, mid-summer, and mid-to-late fall seasons to identify key differences in deciduous and evergreen green-up amplitudes. Random Forest (RF) regression models were used to derive deciduous fraction and tree canopy cover from the image composites. These models were trained with data from in-situ samples across Alaska and Canada from a variety of studies. Seventy percent of the in-situ samples were used for training and 30% for validation. Per-pixel uncertainty for both deciduous fraction and tree canopy cover are included and were based on one standard deviation of output values across all decision trees in the RF regression. These datasets were developed as part of NASA's ABoVE project to capture forest composition changes over the North American boreal domain across the last several decades. The data are provided in GeoTIFF format.\n<br>\n\n#### ArcticTreeLine_Dendrometry_Env_2185\nThis dataset provides in situ measurements of radial tree growth of selected white spruce (Picea glauca) and black spruce (Picea mariana) trees, as well as simultaneous in situ measurements of environmental variables (air temperature, air pressure, relative humidity, soil temperature, volumetric water content, and solar irradiance) at two Arctic treeline sites: one in the Brooks Range of Alaska (AK), USA, and the other near Inuvik, Northwest Territories (NWT), Canada. In AK, 36 trees were monitored from June 7, 2016 to September 13, 2019, and in NWT, 24 trees were monitored from July 5, 2017 to July 25, 2019 with a sampling interval of 5- or 20-minutes for radial tree growth and 5-minutes for all environmental variables. The dendrometer data included in this dataset are only those gathered from 2016-2017. Dendrometer data from 2018-2019 are available from a related dataset. The data were collected to better understand the influence of environmental variables on radial tree growth dynamics. The data are provided in comma-separated values (CSV) format.\n<br>\n\n#### FieldData_Alaska_Tundra_2177\nThis dataset, titled the Synthesized Alaskan Tundra Field Database (SATFiD), provides a comprehensive collection of in-situ field data compiled from 37 existing datasets resulting from field surveys conducted at Alaska tundra sites between 1972 to 2020. The data were harmonized prior to being included in this dataset. The variables include active layer thickness, vegetation cover (by plant functional types), soil moisture and temperatures, as well as the wildfire history. SATFiD provides a unique lens into various long-term ecological processes within the tundra (such as the fire-permafrost-vegetation interactions) under a rapidly changing climate.\n<br>\n\n#### Prudhoe_Bay_Veg_Maps_1387\nThis data set provides a collection of maps of geoecological characteristics of areas within the Beechey Point quadrangle near Prudhoe Bay on the North slope of Alaska: a geobotanical atlas of the Prudhoe Bay region, a land cover map of the Beechey Point quadrangle, and cumulative impact maps in the Prudhoe Bay Oilfield for ten dates from 1968 to 2010. The geobotanical atlas is based on aerial photographs and covers 145 square kilometers of the Prudhoe Bay Oilfield. The land cover map of the Beechey Point quadrangle was derived from the Landsat multispectral scanner, aerial photography, and other field and cartographic methods. The cumulative impact maps of the Prudhoe Bay Oilfield show historical infrastructure and natural changes digitized from aerial photos taken in each successive analysis year (1968, 1970, 1972, 1973, 1977, 1979, 1983, 1990, 2001, and 2010). Nine geoecological attributes are included: dominant vegetation, secondary vegetation, tertiary vegetation, percentage open water, landform, dominant surface form, secondary surface form, dominant soil, and secondary soil. These data document environmental changes in an Arctic region that is affected by both climate change and rapid industrial development.\n<br>\n\n#### GeoCryoAI_PermafrostThaw_CFlux_2371\nThis dataset provides model code, input data, sample results, and documentation for an artificial intelligence-driven model, GeoCryoAI. GeoCryoAI is a hybridized process-constrained ensemble learning framework consisting of stacked convolutionally layered long short-term memory-encoded recurrent neural networks. The purpose of GeoCryoAI is to quantify permafrost thaw dynamics and greenhouse gas emissions in Alaska. The dataset includes pre-processed input data (i.e., thaw depth, active layer thickness, thaw subsidence; CO2 flux, CH4 flux) acquired from in situ measurements (e.g., CALM, GTNP, ITEX, SMALT STDM, ReSALT, AmeriFlux, NEON), remote sensing platforms (e.g., UAVSAR, AVIRIS-NG), and process-based modeling products. Field data were included to quantify CO2 and CH4 flux (e.g., chamber, eddy-covariance, and tall-tower measurements via flux tower networks) and active layer thickness (e.g., mechanical probing, borehole temperatures, ground-penetrating radar). These measurements were resampled to a 1-km grid, standardized, transformed, and assimilated into GeoCryoAI, a framework that simultaneously ingests, scales, and analyzes input data after resolving disparate spatiotemporal sampling and data densities. Model outputs were generated from two process-based models: SIBBORK-TTE derived thaw subsidence and TCFM-Arctic generated carbon flux outputs. The objective was to quantify how the Arctic is changing in response to climate change and how evidence of the permafrost carbon feedback may contribute toward a better understanding of the uncertainty of nonlinear feedbacks and their impact on the earth system.\n<br>\n\n#### ABoVE_CO2_CH4_Flux_Estimates_2121\nThis dataset provides gridded estimates of gross primary productivity (GPP), ecosystem respiration (Reco), net ecosystem CO2 exchange (NEE = Reco - GPP), and methane (CH4) emissions from tundra and boreal wetland soils, across the pan-Arctic and Boreal zone (>49 degrees north) at 1-km spatial resolution. The data were produced through simulations of the Arctic Terrestrial Carbon Flux Model (TCFM-Arctic) and are provided at the daily time step for the years 2003-2015. TCFM-Arctic uses a light-use efficiency approach driven by satellite estimates of FPAR (fraction of absorbed photosynthetically active radiation) to estimate GPP, and autotrophic respiration (Rauto) is estimated as a fraction of GPP. Heterotrophic respiration (Rhetero) is estimated using decomposition rates with environmental constraints applied to three near-surface soil organic carbon (SOC) pools, and Reco is determined as the sum of Ra and Rh. Methane production is estimated using optimal CH4 production rates with environmental constraints applied to the labile carbon pool, and transfer of CH4 from the soil to the atmosphere is modeled through vegetation, soil diffusion, and water ebullition pathways. The model estimates were calibrated and evaluated using >60 tower eddy covariance (EC) sites. Baseline carbon pools were initialized by continuously cycling (spinning-up) the model for 1,000 model years using recent climatology from 1985 to 2002 to reach a dynamic steady-state between estimated net primary productivity (NPP = GPP - Rauto) and near-surface SOC pools. The TCFM-Arctic simulations were extended to the full Arctic-boreal domain at a 1-km spatial resolution using land cover maps representing high latitude vegetation communities. The data are provided in NetCDF and comma-separated values (CSV) formats.\n<br>\n\n#### Soil_Carbon_Flux_Maps_1683\nThis dataset provides gridded estimates of soil CO2 flux (g C m-2 d-1) for the winter non-growing season (NGS) across pan-Arctic and Boreal permafrost regions (>49 Deg N), at 25 km spatial resolution. The data are the daily average flux over a monthly period for two climate periods: the baseline climate period represents 2003-2018 and the future climate scenarios period represents 2018-2100 under Representative Concentration Pathways (RCP) 4.5 and 8.5. The data were produced by applying a Boosted Regression Tree machine learning approach to create gridded estimates of emissions based on in situ observations of NGS fluxes provided in a related dataset. The resulting monthly average flux data records can be used to calculate annual NGS soil CO2 flux budgets from 2003-2100.\n<br>\n\n#### Vegetation_Photos_Toolik_Lake_1718\nThis dataset contains 731 ground-based nadir vegetation community and ground surface photographs of selected field plots taken as ground reference data for vegetation classification studies at three areas near Toolik Lake, Alaska during the summers of 2014 and 2015. The largest area, 'Toolik', (approximately 6 km2) covers research areas near Toolik Field Station at Toolik Lake, including Arctic LTER installations. The other two areas are each roughly 3 km2: the 'Pipeline' area: a stretch of the Trans-Alaska Pipeline, and the 'Imnavait' area: along Imnavait Creek roughly 10 km east of Toolik Lake.\n<br>\n\n#### Shrub_Biomass_Toolik_Lake_AK_1573\nThis dataset contains estimates for aboveground shrub biomass and uncertainty at high spatial resolution (0.80-m) across three research areas near Toolik Lake, Alaska. The estimates for August of 2013 were generated and mapped using Random Forest modeling with input variables of optimized LiDAR-derived canopy volume and height, mean NDVI from 4-band RGB color and near-IR orthophotographs, and harvested biomass data. Uncertainty in the final shrub biomass maps was quantified by producing separate maps showing the coefficient of variation (CV) of the Random Forest map estimates. Shrub biomass was harvested at Toolik Lake in 2014 and used to optimize inputs and validate the final model and these biomass data are also provided.\n<br>\n\n#### Vegetation_Maps_Toolik_Lake_1690\nThis dataset contains vegetation community maps at 20 cm resolution for three landscapes near the Toolik Lake research area in the northern foothills of the Brooks Range, Alaska, USA. The maps were built using a Random Forest modeling approach using predictor layers derived from airborne lidar data and high-resolution digital airborne imagery collected in 2013, and vegetation community training data collected from 800 reference field plots across the lidar footprints in 2014 and 2015. Vegetation community descriptions were based on the commonly used classifications of existing Toolik area vegetation maps.\n<br>\n\n#### LakeBathymetry_Model_NSlope_AK_2243\nThis dataset provides lake bathymetry maps derived from Landsat surface reflectance products for a portion of the North Slope area of Alaska. A random forest regression algorithm was used to generate depths for each point identified as being part of a lake, creating depth prediction files for each Landsat scene available for the study period: 2016-07-01 to 2018-08-31. These products are fitted to the ABoVE standard projection and reference grid to make them easily scalable and geometrically compatible with other products in the ABoVE study domain. The data are provided in cloud-optimized GeoTIFF (COG) format.\n<br>\n\n#### Lakes_Ponds_Inundation_YKDelta_2450\nThis dataset provides high resolution (3 m) maps delineating open water and partial inundation in the Yukon-Kuskokwim Delta (YKD), Alaska, and a database that tracks rapid changes in surface area for individual lakes and ponds over 2019-2023. The dataset was built using daily PlanetScope imagery (3 m), allowing for tracking changes in surface water extent at high spatial and temporal resolutions. Two types of maps were produced; (1) open water lakes and ponds and (2) partial inundation, defined as areas that partially contain water (e.g. emergent vegetation, flooded vegetation, or saturated soil). Open water lakes and ponds were mapped using a convolutional neural network, and partial inundation was classified using a pixel-based thresholding approach. For both map categories, monthly climatological composites for the snow and ice-free period are provided. For open water lake and pond maps, monthly composites are provided as well. An accompanying database includes the geometry and surface area time series for each mapped water body.\n<br>\n\n#### Northern_Alaska_Veg_Maps_1359\nThis data set provides four land cover and ecosystem classification maps for northern Alaska. The maps were produced for several projects and from different data sources including Landsat imagery and existing maps and models, and cover a range of ecosystem and vegetation classes. The data used to derive the maps covered the period 1976-08-04 to 2014-09-01.\n<br>\n\n#### Seward_Peninsula_Veg_Maps_1363\nThis data set provides two landcover and vegetation maps for the Seward Peninsula, Alaska. These maps were produced from existing maps, Landsat imagery, and color infrared aerial photography covering the period 1976-06-01 to 1999-09-01.\n<br>\n\n#### Arctic_Wildlife_Refuge_Veg_Map_1384\nThis data set provides a landcover map with 16 landcover classes for the northern coastal plain of the the Arctic National Wildlife Refuge (ANWR) on the North Slope of Alaska. The map was derived from Landsat Thematic Mapper (Landsat TM) data, Digital Elevation Models (DEMs), aerial photographs, existing maps, and extensive ground-truthing. The data used to derive the map cover the period 1982 to 1993.\n<br>\n\n#### ABoVE_Domain_Projected_LULC_2353\nThis dataset provides projections of land use and land cover (LULC) change within the Arctic Boreal Vulnerability Experiment (ABoVE) domain, spanning from 2015 to 2100 with a spatial resolution of 0.25 degrees. It includes LULC change under two Shared Socioeconomic Pathways (SSP126 and SSP585) derived from Global Change Analysis Model (GCAM) at an annual scale. The specific land types include: needleleaf evergreen tree-temperate, needleleaf evergreen tree-boreal, needleleaf deciduous tree-boreal, broadleaf evergreen tree-tropical, broadleaf evergreen tree-temperate, broadleaf deciduous tree-tropical, broadleaf deciduous tree-temperate, broadleaf deciduous tree-boreal, broadleaf evergreen shrub-temperate, broadleaf deciduous shrub-temperate, broadleaf deciduous shrub-boreal, C3 arctic grass, C3 grass, C4 grass, and C3 unmanaged rainfed crop. The data were generated by integrating regional LULC projections from GCAM with high-resolution MODIS land cover data and applying two alternative spatial downscaling models: FLUS and Demeter. Data are provided in NetCDF format.\n<br>\n\n#### LVISC1B\nThis data set contains Level-1B geolocated return energy waveforms collected by the NASA Land, Vegetation, and Ice Sensor (LVIS) Facility, an imaging lidar and camera sensor suite.\n<br>\n\n#### LVISC2\nThis data set contains Level-2 geolocated surface elevation and canopy height measurements collected by the NASA Land, Vegetation, and Ice Sensor (LVIS) Facility, an imaging lidar and camera sensor suite.\n<br>\n\n#### CO2Fluxes_Arctic_Boreal_Domain_2377\nThis dataset provides gridded estimates of gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem CO2 exchange (NEE) across the circumpolar terrestrial Arctic-boreal region at a 1-km spatial resolution. Monthly CO2 flux data from 2001 to 2020 were generated using terrestrial eddy covariance and chamber CO2 flux observations, combined with geospatial meteorological, remote sensing, topographical and soil data, all within a random forest modeling framework. Aggregated average annual NEE, average annual NEE with direct fire emissions added based on the Global Fire Emissions Database (GFED) product, and temporal trends in annual NEE rasters over 2002-2020 are also included. The data are provided in NetCDF and GeoTIFF formats.\n<br>\n\n#### Imnavait_Creek_Veg_Maps_1385\nThis dataset provides the spatial distribution of vegetation types, soil carbon, and physiographic features in the Imnavait Creek area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology. Data are also provided on the research grids for georeferencing. The map data are from a variety of sources and encompass the period 1970-06-01 to 2015-08-31.\n<br>\n\n#### Kuparuk_Veg_Maps_1378\nThis data set provides a collection of vegetation, landscape, geobotanical, elevation, hydrology, and geologic maps for the Kuparuk River Basin, North Slope, Alaska. The maps cover either (1) the entire Kuparuk River Basin, from the headwaters on the north side of the Brooks Range to the Beaufort Sea coast, or (2) the selected Upper Kuparuk River Region including the Toolik Lake and Imnavait Creek research areas. The maps were produced from imagery and existing geobotanical maps covering the period 1976-08-04 to 2008-12-31.\n<br>\n\n#### Toolik_Lake_Area_Veg_Maps_1380\nThis data set provides the spatial distributions of vegetation types, soil carbon, and physiographic features in the Toolik Lake area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology.\n<br>\n\n#### North_Slope_Transect_Veg_Maps_1386\nThis dataset includes vegetation cover maps, Normalized Difference Vegetation Index (NDVI) maps, snow depth and thaw depth data that were obtained as part of a biocomplexity project on the North Slope of Alaska, USA, and the Northwest Territories (NWT), Canada. In Alaska, seven sites are located along the Dalton Highway and in the Prudhoe Bay Oilfield area, forming a transect across the climate gradient of the North Slope. From South to North, the sites are Happy Valley, Sagwon (an acidic and nonacidic site), Franklin Bluffs, Deadhorse, West Dock and Howe Island. Four sites are in the NWT, forming a latitudinal gradient from South to North; the sites include Inuvik, Green Cabin, Mould Bay, and Isachsen.\n<br>\n\n#### CH4_Fluxes_ThermokarstLakes_AK_1870\nThis dataset provides methane fluxes from hot-spot and non-hot spot differing surfaces at Big Trail Lake (BTL) in the Goldstream Valley near Fairbanks, AK, USA. Measurements were taken at a remotely-sensed methane hotspot on the shoreline of a pond, adjacent to BTL with a Los Gatos Ultra-Portable Greenhouse Gas Analyzer (UGGA), and from various non-hotspot surfaces representative of the broader thermokarst lake ecosystem with bucket chambers. All data were collected between 2019-07-04 and 2019-12-04 during the daytime hours of 09:35-17:32 local time. A ground-based CH4 enhancement survey was performed on 2019-07-06 between 13:25-17:15 Alaska Daylight Time (AKDT), approximately two hours following an AVIRIS-NG overflight and hotspot detection at the Eastside Pond. Methane flux is reported in units of both mmol CH4 m-2 hr-1 and mg CH4 m-2 d-1. Flux errors are quantified for each\n<br>\n\n#### ABoVE_Annual_Veg_Resilience_2374\nThis dataset provides estimates of vegetation resilience in the Arctic Boreal Vulnerability Experiment (ABoVE) core domain at annual time steps for 2000-2019 and at 300-m spatial resolution. Vegetation resilience is defined as the recovery rate from deviations, due to climate perturbations or disturbances, to the equilibrium state. It is quantified as the negative temporal lag-1 autocorrelation of Enhanced Vegetation Index (EVI). Using a time series of MODIS EVI, the vegetation resilience was estimated using a Bayesian dynamic linear model. The mapped vegetation resilience was derived from Terra EVI products (MOD13Q1v061) across 175 ABoVE B grid tiles over the ABoVE core domain. The estimated mean resilience, upper boundary, and lower boundary results are provided for each tile in cloud optimized GeoTIFF (COG) format.\n<br>\n\n#### NDVI_Forest_Structure_1797\nThis dataset provides leaf area index (LAI), tree species and canopy cover, normalized difference vegetation index (NDVI), and NDVI trends for boreal forests in interior Alaska, U.S. These data were collected to investigate NDVI trends with forest structure and composition as influenced by disturbance and succession. The data are from 102 sites surveyed in 2017 and 2018 and include locations with and without a fire since 1940. A time series of NDVI was developed from Landsat (1999-2018) to measure NDVI trends. The field data cover the period 2017-08-29 to 2018-08-20. The surveyed forest stands spanned a distance of over 425 km across interior Alaska. The sites were selected before visiting the field to include locations with and without a fire since 1940. Recently burned sites were selected to span a range of years since fire, while sites without a recent fire were selected to include a range of Landsat NDVI trends. For each year, the median NDVI during the growing season was calculated. Then, a simple linear regression trend was calculated for years 1999-2018.\n<br>\n\n#### Passive_Microwave_Snowoff_Data_1711\nThis dataset provides annual maps of the snowoff (SO) date from 1988-2023 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product. The spatial domain was intended to match MODIS Alaska Snow Metrics and extend its temporal fidelity beyond the MODIS era. SO date estimates were compared to snow depth measurements collected at SNOTEL stations across Alaska and to three SO datasets derived from MODIS, Landsat, and the Interactive Multisensor Snow and Ice Mapping System (IMS). The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012).\n<br>\n\n#### Post_Fire_SOC_NWT_2235\nThis dataset provides site moisture, soil organic layer thickness, soil organic carbon, nonvascular plant functional group, stand dominance, ecozone, time-after-fire, jack pine proportion, and deciduous proportion for 511 forested plots spanning ~140,000 km2 across two ecozones of the Northwest Territories, Canada (NWT). The plots were established during 2015-2018 across 41 wildfire scars and unburned areas (no burn history prior to 1965), with 317 plots in the Plains and 194 plots in the Shield regions. At each plot, two adjacent 30-m transects were established 2 m apart, running north from the plot origin. Soil organic layer (SOL) depth (cm) was measured every 3 m and the mean was taken from the 10 measurements to calculate a plot-level SOL thickness. Three soil organic layer profiles were destructively sampled at 0, 12, and 24 m using a corer that was custom designed for NWT soils. Within the transects, all stems taller than 1.37 m were identified to species to calculate tree density (stems / m2). Nonvascular plant percent cover was identified to functional group at five, 1-m2 quadrats spaced 6 m apart along the belt transect. A subset of 2,067 of 5,137 total increments from 1,803 profiles from 421 plots were analyzed for total percent C using a CHN analyzer. Time-after-fire was established using fire history records. For older plots where no known fire history is recorded, tree age was used. Data are for the period 2015-06-11 to 2018-08-24 and are provided in comma-separated values (CSV) format.\n<br>\n\n#### Gas_Seeps_Lakes_Fairbanks_SAR_2394\nThis dataset includes maps and locations of potential gas seepage in lakes within the immediate and surrounding area around Fairbanks, Alaska, a region underlain by discontinuous permafrost and characterized by thermokarst lake formation. Gas bubbling from lakes in the Fairbanks area is usually rich in methane, a potent greenhouse gas that is difficult to quantify. A new remote sensing method was used for detecting potential gas seepage, defined as areas of suspected perennial ebullition, by using a previously ground-truthed seep that appeared as a high-backscatter perennial feature in winter L-band Synthetic Aperture Radar (SAR) data from 2006-2011. Based on threshold values determined by the sigma-naught backscatter of this training seep, 1,690 areas of potential seepage were detected in 459 lakes out of 658 lakes analyzed. Results were validated through a different ground-truthed seep. The data are provided in shapefile format.\n<br>\n\n#### ALT_GPR_Barrow_1355\nThis data set provides estimates of Active Layer Thickness (ALT) determined with ground-based measurements, and calculated soil volumetric water content (VWC) at four selected sites around Barrow, Alaska in August 2013. ALT was determined using a ground-penetrating radar (GPR) system and traditional mechanical probing. Calculated uncertainties are also included. GPR measurements were taken along four transects of varying length (approx. 1 to 7 km). Mechanical probing included several high-density surveys (every 1 m within 100-m survey line) along each GPR transect. VWC of the active layer soil was calculated at 3-8 calibration points per site where the probe measurement was exactly co-located with a GPR trace.\n<br>\n\n#### Alaska_L4_WRF_STILT_Footprints_1544\nThis dataset provides Stochastic Time-Inverted Lagrangian Transport model outputs for receptors located at the NOAA Barrow Alaska Observatory for 12 selected years (15 August to 15 October) across the 30-year, 1982 to 2011, study timeframe. Meteorological fields from version 3.5.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the \"receptor\" location), to create the adjoint of the transport model in the form of a \"footprint\" field. The footprint, with units of mixing ratio (ppm --- CO2; ppb --- CH4) per (umol m-2 s-1 --- CO2; nmol m-2 s-1 --- CH4), quantifies the influence of upwind surface fluxes on concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. The simulation results included in this dataset are crucial for understanding changes in Arctic carbon cycling and are part of a retrospective analysis to link changes in atmospheric composition at Arctic receptor sites with shifts in ecosystem structure and function. Each file provides the surface influence-function footprints on a lat/lon/time grid from WRF-STILT simulations for the receptor location.\n<br>\n\n#### ReSALT_ALT_GPR_1265\nThis data set includes estimates of permafrost Active Layer Thickness (ALT; cm), and calculated uncertainties, derived using a ground-penetrating radar (GPR) system in the field in August 2014 near Toolik Lake and Happy Valley on the North Slope of Alaska. GPR measurements were taken along 10 transects of varying length (approx. 1 to 7 km). Traditional ALT estimates from mechanical probing every 100 to 500 m along each transect are also included. These data are suitable for future studies of how ALT varies over relatively large geological features, such as hills and valleys, wetland areas, and drained lake basins.\n<br>\n\n#### PreABoVE_AirMOSS_L1_Alaska_1678\nThis data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multi-look complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over 10 study sites across Northern Alaska, USA. Flight campaigns took place in August 2014, October 2014, April 2015, August 2015, September 2015, and October 2015. The acquired L1 P-band radar backscatter data will be used to derive estimates of soil water content and permafrost state at the study sites.\n<br>\n\n#### ReSALT_InSAR_Barrow_1266\nActive layer thickness (ALT) is a critical parameter for monitoring the status of permafrost that is typically measured at specific locations using probing, in situ temperature sensors, or other ground-based observations. The thickness of the active layer is the average annual thaw depth, in permafrost areas, due to solar heating of the surface. This data set includes the mean Remotely Sensed Active Layer Thickness (ReSALT) over years 2006 to 2011 for the region near Barrow, Alaska. The data were produced by an Interferometric Synthetic Aperture Radar (InSAR) technique that measures seasonal surface subsidence and infers ALT. ReSALT estimates were validated by comparison with ground-based ALT obtained using probing and Ground Penetrating Radar at multiple sites. These results indicate remote sensing techniques based on InSAR could be an effective way to measure and monitor ALT over large areas on the Arctic coastal plain. These data provide gridded (30-m) estimates of active layer thickness (cm; ALT) and seasonal subsidence (cm), as well as calculated uncertainty in each of these parameters. This data set was developed in support of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) field campaign. The data are presented in one netCDF (.nc) file.\n<br>\n\n#### Freeze_Thaw_NorthernHemisphere_2323\nThis dataset provides a probabilistic freeze/thaw (FT) data record from 2016 to 2020 for the Northern Hemisphere derived using a deep learning model (U-Net). The model was informed by satellite multi-frequency microwave brightness temperature retrievals from the NASA SMAP (Soil Moisture Active Passive) and JAXA AMSR2 (Advanced Microwave Scanning Radiometer 2) radiometers, and trained using daily soil temperature observations from Northern Hemisphere weather stations and global reanalysis data (ERA-5). Unlike other available FT data records that provide only a binary classification of frozen or non-frozen conditions, this product includes both binary FT and continuous variable estimates of the probability of thawed conditions. This product is designed to complement other established binary FT data records, including the NASA FT Earth System Data Record and SMAP Level 3 FT operational products, by providing a probabilistic FT variable with enhanced accuracy and sensitivity to near-surface (<=5 cm depth) soil FT condition. The data are provided in cloud optimized GeoTIFF (COG) format.\n<br>\n\n#### PermafrostThaw_CarbonEmissions_1872\nThis dataset consists of an ensemble of model projections from 1901 to 2299 for the northern hemisphere permafrost domain. The model projections include monthly average values for a common set of diagnostic outputs at a spatial resolution of 0.5 x 0.5 degrees latitude and longitude. The model simulations resulted from a synthesis effort organized by the Permafrost Carbon Network to evaluate the impacts of climate change on the carbon cycle in permafrost regions in the high northern latitudes. The model teams used different historical input weather data, but most used driver data developed by the Climate Research Unit - National Centers for Environmental Prediction (CRUNCEP) as modified for the Multiscale Terrestrial Model Intercomparison Project (MsTMIP). The teams scaled the driver data for the projections using output from global climate models from the fifth Coupled Model Intercomparison Project (CMIP5). The synthesis evaluated the terrestrial carbon cycle in the modern era and projected future emissions of carbon under two climate warming scenarios: Representative Concentration Pathways 4.5 and 8.5 (RCP45 and RCP85) from CMIP5. RCP45 represents emissions resulting in a global climate close to the target climate in the Paris Accord. RCP85 represents unconstrained greenhouse gas emissions.\n<br>\n\n#### GPP_COS_Conductance_SoilFluxes_2324\nThis dataset provides outputs from the Simple Biosphere Model (v 4.2). Products include hourly 0.5-degree gridded fluxes of gross primary productivity (GPP), respiration, carbonyl sulfide (COS) uptake by vegetation and soil, along with conductance of COS (apparent mesophyll and total), stomatal conductance of water and partial pressure of CO2 in the canopy air space, leaf surface, interior and chloroplast. The data are separated by plant functional type (PFT). Fluxes have dimensions of latitude, longitude, time, and plant functional type. Model output spans 53N to 90N latitude and 180W to 180E longitude over years 2000 to 2020. The data are provided in NetCDF version 4 format.\n<br>\n\n#### Daily_FineParticulateMatter_AK_2157\nThe dataset provides simulated PM2.5 concentration estimates over Alaska, U.S. PM2.5 (particulate matter with diameter <= 2.5 microns) concentrations in air (micrograms m-3) are gridded at 0.1-degree resolution for May to September for the years 2001 through 2015. The data were created in a modeling process utilizing the Wildland Fire Emissions Inventory System (WFEIS), the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, and multiple models including the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The data are provided in GeoTIFF format.\n<br>\n\n#### Snow_Depth_Data_Images_1656\nThis dataset includes data from late-March snow surveys and hourly digital camera images from two study areas within the Wrangell St Elias National Park, Alaska. These data comprise snow density, stratigraphy, and temperature profiles obtained by snow pits; and snow depth data obtained from transects between snow pits. Daily snow depths, adjacent to each pit, were derived from hourly camera images of snow stakes placed adjacent to each pit. These data were collected to constrain and validate a physically-based, spatially-distributed snow evolution model used to simulate snow conditions in Dall sheep habitat. The two study areas are both located within the Jacksina Park Unit (JPU). The first study area, surveyed in 2017, included the northern end of Jaeger Mesa and an area near Rambler mine in the North East of the JPU. The second study area, surveyed in 2018, was within the upper watershed of Pass Creek in the North of the JPU. The remote cameras operated from September 2016 to August 2017 on Jaeger Mesa/Rambler Mine and from September 2017 to July 2018 at Pass Creek.\n<br>\n\n#### Snow_Wildlife_Tracks_AK_WA_2188\nThis dataset contains three field seasons of snow-wildlife observations conducted at 707 sites from January 2021 to March 2023 in Washington and Alaska, spanning a broad range of snow conditions. Relatively fresh tracks (usually <24 h) of common large mammal predators (bobcats, coyotes, cougars, and wolves) and their ungulate prey (caribou, Dall sheep, moose, mule deer, and white-tailed deer) were investigated to determine how snow affects predator-prey interactions. The track sink depth and dimensions (width and length) of three consecutive footprints were measured from one individual. Age class was recorded for moose based either on visual confirmation of an individual creating snow tracks or based on track dimensions. The ability to differentiate age classes for smaller ungulates was more uncertain, so age classes for deer, caribou, or sheep were not specified. Animal gait was identified using a simple classification scheme. Data also include animal species, snow density, hardness, total ice, surface temperature, and vegetation type. To best capture snow hardness, surface penetrability and hand-hardness were measured throughout the snowpack. The data are provided in comma-separated values (CSV) format.\n<br>\n\n#### Arctic_Soil_Properties_2149\nThis dataset provides lab-measured soil properties, including soil water matric potential, soil dielectric properties, soil electrical conductivity, corresponding soil moisture. The dataset also includes the basic soil physical properties such as soil organic matter, bulk density, porosity, fiber content, root biomass, and mineral texture. Soil samples were collected from August 21 to August 27, 2018, from the surface to permafrost table in soil pits at nine sites along the Dalton Highway in northern and central regions of Alaska. Permittivity and soil electrical conductivity measurements were conducted using METER TEROS 12 probes. Soil moisture measurements were made with a TEROS 21 probe. The measurements were conducted in the lab over the span of three years. The purpose of soil collection and lab measurements was to develop an integrated framework that relates the hydrological properties to dielectric properties of permafrost active layer soil in support of the NASA Arctic and Boreal Vulnerability Experiment (ABoVE) Airborne Campaign.\n<br>\n\n#### ABoVE_Soil_Respiration_Maps_1935\nThis dataset provides gridded estimates of carbon dioxide (CO2) emissions from soil respiration occurring within permafrost-affected tundra and boreal ecosystems of Alaska and Northwest Canada at a 300 m spatial resolution for the period 2016-08-18 to 2018-09-12. The estimates include monthly average CO2 flux (gCO2 C m-2 d-1), daily average CO2 flux and error estimates by season (Autumn, Winter, Spring, Summer), estimates of annual offset of CO2 uptake (i.e., vegetation GPP), annual budgets of vegetation gross primary productivity (GPP; gCO2 C m-2 yr-1), and the fraction of open (non-vegetated) water within each 300 m grid cell. Belowground sources of respiration (i.e., root and microbial) are included. The gridded soil CO2 estimates were obtained using seasonal Random Forest models, information from remote sensing, and a new compilation of in-situ soil CO2 flux from Soil Respiration Stations and eddy covariance towers. The flux tower data are provided along with daily gap-filled flux observations for each Soil Respiration station forced diffusion (FD) chamber record. The data cover the NASA ABoVE Domain.\n<br>\n\n#### TundraTransect_VegRefl_Soil_2232\nThis dataset provides visible-near infrared spectral reflectance, descriptions of vegetation cover, surface temperature, the total fraction of absorbed photosynthetically active radiation (fAPAR, 2001 only), permafrost active layer depth, elevation, and soil temperature at 5 cm depth. Measurements were made at every meter along a 100-m transect aligned mainly in an east-west direction, located approximately 300 m southeast of the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) baseline observatory near Utqiagvik, Alaska. Reflectance measurements were collected at nearly weekly intervals through the growing seasons of 2000 to 2002 to describe characteristics of green-up, peak growth, and senescence. Reflectance measurements were also collected once near peak growth in 2022. Ancillary measurements were collected at intervals through the 2001 and 2002 growing seasons.\n<br>\n\n#### ArcticTreeLine_Spruce_CO2_WV_1948\nThis dataset provides in situ measurements of needle-level gas-exchange and leaf traits from Picea glauca (white spruce) from a field site located in the northern latitudinal forest-tundra ecotone (FTE) near the Dalton Highway in northern Alaska, and from one study site located in Black Rock Forest, New York, USA. Measurements were collected with an open flow portable photosynthesis system (Li6400XT) and custom-built temperature-controlled cuvette. Respiration as a function of leaf temperature was measured continuously as the needle temperature was ramped from approximately 5 to 65 degrees C, at a constant rate of 1 degree C per minute. Additional data include tree diameter at breast height (dbh), leaf area, photosynthetic rate, intercellular C02, conductance to H20, tree height, and data from raw temperature curves. Results are reported on both a leaf area and leaf mass basis. The data are for the period 2018-06-06 to 2018-06-23 and are provided in comma-separated (CSV) format.\n<br>\n\n#### ABoVE_SAR_Surveys_2150\nThis dataset contains tables containing Airborne flight metadata from synthetic aperture radar (SAR) surveys from 2012 to 2022 in Alaska and Canada. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne SAR surveys of over 120,000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. Legacy lines acquired between 2012 and 2015 by other projects are included for completeness and to enable longer times series creation. The data files and companion file contain L-band and P-band airborne SAR metadata acquired during the ABoVE airborne campaigns. Included are detailed descriptions of ~80 SAR flight lines and how each fits into the ABoVE experimental design. Extensive maps, tables, and hyperlinks give direct access to every flight plan as well as individual flight lines. This entry is a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.\n<br>\n\n#### Nongrowing_Season_CO2_Flux_1692\nThis dataset provides a synthesis of winter ( September-April) in situ soil CO2 flux measurement data from locations across pan-Arctic and Boreal permafrost regions. The in situ data were compiled from 66 published and 21 unpublished studies conducted from 1989-2017. The data sources (publication references) are provided. Sampling sites spanned pan-Arctic Boreal and tundra regions (>53 Deg N) in continuous, discontinuous, and isolated/sporadic permafrost zones. The CO2 flux measurements were aggregated at the monthly level, or seasonally when monthly data were not available, and are reported as the daily average (g C m-2 day-1) over the interval. Soil moisture and temperature data plus environmental and ecological model driver data (e.g., vegetation type and productivity, soil substrate availability) are also included based on gridded satellite remote sensing and reanalysis sources.\n<br>\n\n#### Arctic_Boreal_CO2_Flux_1934\nThis Arctic-Boreal CO2 fluxes (ABCflux) dataset contains monthly aggregates of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity (GPP) and ecosystem respiration. Over 70 supporting variables describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. The data contained in this ABCflux dataset form a standardized monthly database of Arctic-Boreal CO2 fluxes (i.e., ABCflux Database) and include 244 sites and 6,309 monthly observations; 136 sites and 2,217 monthly observations represent tundra, and 108 sites and 4,092 observations represent the boreal biome. The data are for the period 1989 to 2020.\n<br>\n\n#### CubeSat_Arctic_Boreal_LakeArea_1667\nThis dataset provides near-daily lake area timeseries for 85,358 lakes across four study areas in Northern Canada and Alaska, USA, between May 1 and October 1, 2017. These lake area estimates were produced using digital images from newly developed Planet Labs CubeSats, small satellites with a 4-band (blue, green, red, near-infrared) camera payload. In constellation, CubeSats collected imagery at very high spatial (3-5m) and temporal (near-daily) resolution. From the imagery, each lake's mean, minimum, and maximum areas and seasonal dynamism were derived. The dataset covers four Arctic-Boreal regions: the Yukon Flats Basin (YFB) in eastern interior Alaska, and the Mackenzie River Valley (MRV), Canadian Shield Transect (CST), and Hudson Bay Lowland (HBL) in Canada.\n<br>\n\n#### TowerBased_PhotoSpec_SIF_SK_CA_1887\nThis dataset includes daily averaged solar-induced chlorophyll fluorescence (SIF) in the red (680-686 nm) and far-red (745-758 nm) wavelength ranges, relative SIF (SIF/Intensity), chlorophyll-carotenoid index (CCI), photochemical reflectance index (PRI), near-infrared vegetation index (NIRv), and normalized difference vegetation index (NDVI) for both black spruce (Picea mariana) and larch (Larix laricina) targets. The study site (Southern Old Black Spruce, SOBS Fluxnet ID CA-Obs) is located near the southern limit of the boreal forest ecotone in Saskatchewan, Canada. Data were collected for the spring transition in both 2019 and 2020 using PhotoSpec. Species-specific averages were calculated over each 30-minute period, then averaged again to report daily averages of SIF relative and reflectance measurements for both black spruce and larch.\n<br>\n\n#### CircumArctic_Trends_Hotspots_2322\nThis dataset provides estimates of trends in temperature, moisture, and vegetation changes over the circumpolar Arctic. Time series trends were measured by the Theil-Sen slope and associated p-values for a variety of variables including 2-meter air temperature, precipitation, soil moisture, non-frozen season days, permafrost active layer thickness, snow cover, vapor pressure deficit, land surface water fraction, normalized difference vegetation index (NDVI), and vegetation optical depth. Trends were measured annually and over specific seasons of spring (March to May), summer (June to August), autumn (September to November) and winter (December to February), and for the 1980-2020 and 1997-2020 time periods, depending on the variable and original data availability. Emerging hotspots of change were identified for the same variables and seasons, but only over the 1997-2020 period. In addition, a multivariate ranking was used to create combined hotspot layers to show areas of substantial changes in the thermal environment, moisture, and vegetation; these themes reflect landscape changes considered to be detrimental (e.g., a threat) to ecosystems and human populations. Ancillary files provide the boundaries of study regions, Brown permafrost regions, and a land cover product. The data are provided in cloud optimized GeoTIFF (COG) and shapefile formats.\n<br>\n\n#### Tundra_Leaf_Spectra_2005\nThis dataset provides leaf-level visible-near infrared spectral reflectance, chlorophyll fluorescence spectra, species, plant functional type (PFT), and chlorophyll content of common high latitude plant samples collected near Fairbanks, Utqiagvik, and Toolik, Alaska, U.S., during the summers of 2019, 2020, and 2021. A FluoWat leaf clip was used to measure leaf-level visible-near infrared spectral reflectance and chlorophyll fluorescence spectra. Fluorescence yield (Fyield) was calculated as the ratio of the emitted fluorescence divided by the absorbed radiation for the wavelengths from 400 nm up to the wavelength of the cut off for the FluoWat low pass filter (either 650 or 700 nm). Chlorophyll content of samples was measured using a CCM-300 Chlorophyll Content. The data are provided in comma-separated values (.csv) format.\n<br>\n\n#### TundraVeg_Reflectance_Soil_CO2_1960\nThis dataset provides measurements at tundra plots collected near Utqiagvik and Atqasuk, AK, including visible-near infrared spectral reflectance, chamber gas exchange measurements of CO2, pulse amplitude modulated (PAM) fluorometry, chlorophyll pigment contents, along with surface temperature, permafrost active layer depth, and soil temperature at 5 cm, through the growing seasons of 2001 and 2002. At all plots, spectral reflectance was measured using a portable spectrometer configured with a straight fiber optic foreoptic, surface temperatures were measured using a handheld Everest Infrared Thermometer, and thaw depth (or active layer depth) was measured using a metal rod graduated in centimeter intervals. At small plots (~15 cm) at Utqiagvik (referred to as Patch plots) chambers were constructed that enclosed an individual patch to determine photosynthetic rate and estimate respiration rate (made by covering the chamber in a dark cloth). Efficiency using PAM fluorometer, ambient yield estimations, and rapid light curve measurements were taken. Chlorophyll concentration was measured with a portable spectrometer configured as a spectrophotometer. At larger plots (approximately 1 m2) which were part of the International Tundra EXperiment (ITEX plots) at Utqiagvik (referred to as Barrow) and Atqasuk, a sub-sample of five control and five warmed plots at each site were fitted with 0.45 m diameter polyvinyl chloride collars for chamber flux measurements. To determine the total fraction of absorbed photosynthetically active radiation (fAPAR), a series of photosynthetically active radiation (PAR) measurements were made using a custom-made light bar consisting of a linear array of GaAsP sensors mounted within an aluminum U-bar under a white plastic diffuser. In addition, a visual estimate was made of the fraction of standing dead vegetation based on percent cover. The data are provided in comma-separated values (.csv) format. In addition, photographs of plots and instruments are provided.\n<br>\n\n#### UAVSAR_Wetland_Peace-Athabasca_2442\nThis dataset holds UAVSAR imagery products for the Peace-Athabasca Delta region of Canada. UAVSAR data were collected for two flight lines on five dates from 2017 to 2022 and processed to NISAR-like products with spatial resolution of approximately 10 m. Products include double bounce, volume, odd, and helical scatter components. In addition, radiometrically terrain corrected HH backscatter, HV backscatter, and the ratio of HH to HV, as well as the local incidence angle in radians and a valid data mask are provided. These data can be used to estimate the extent of inundation of wetlands in this region. The data are provided in cloud optimized GeoTIFF format.\n<br>\n\n#### Understory_Veg_Biomass_Alaska_2340\nThis dataset provides measurements of vegetation biomass from 11 locations across Alaska during 2016 to 2018. Vegetation was harvested from plots that were located at the end of previously established 30-m transects at each site, except at one site where plots were randomly selected. Vascular vegetation was clipped from 50 cm x 50 cm plots, and non-vascular vegetation was clipped from 25 cm x 25 cm plots. All harvested vegetation was sorted by functional group or by species where identification was possible. The sorted vegetation was dried and then weighed to determine biomass. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma separated values (CSV) format.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://above.nasa.gov","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","cog","earth observation","elevation","geospatial","hydrology","ice","land cover","lidar","netcdf","oceans","precipitation","radar","satellite imagery","soil moisture","stac","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ABoVE_Concise_Experiment_Plan_1617 v1.1 - This document presents the Concise Experiment Plan for NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) to serve as a guide to the Program as it identifies the research to be conducted under this study. Research for ABoVE will link field-based, process-level studies with geospatial data products derived from airborne and satellite remote sensing, providing a foundation for improving the analysis and modeling capabilities needed to understand and predict ecosystem responses and societal implications.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Concise_Experiment_Plan/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Arctic_Boreal_CO2_Flux_V2_2448 v1 - This dataset is a synthesis of terrestrial and freshwater CO2 and CH4 fluxes from the Arctic-Boreal region aggregated to monthly timesteps. The dataset, known as ABCFlux v2, includes 1,028 unique sites and spans 1984-2024 with the majority of observations occurring after 1999.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Arctic_Boreal_CO2_Flux_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABOLVIS1A v1 - This data set contains geotagged images collected over Alaska and Western Canada. The images were taken by the NASA Digital Mapping Camera, paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/ABOLVIS1A/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ABLVIS1B v1 - This data set contains return energy waveform data over Alaska and Western Canada measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/ABLVIS1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ABLVIS2 v1 - This data set contains surface elevation data over Alaska and Western Canada measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/ABLVIS2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ABoVE_ASCENDS_XCO2_2050 v1 - This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2), methane (CH4), and water vapor concentrations, plus air temperature, pressure, relative humidity, and wind speed values over Alaska and the Yukon and Northwest Territories of Canada during 2017-07-20 to 2017-08-08. Measurements were taken onboard a DC-8 aircraft during this Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) airborne deployment over portions of the Arctic-Boreal Vulnerability Experiment (ABoVE) domain.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_ASCENDS_XCO2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_ASCENDS_Backscatter_2051 v1 - This dataset provides atmospheric backscattering coefficient profiles collected during Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) deployments from 2017-07-20 to 2017-08-08 over Alaska, U.S., and the Yukon and Northwest Territories of Canada. These profiles were measured by the CO2 Sounder Lidar instrument carried on a DC-8 aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_ASCENDS_Backscatter/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_ASCENDS_Merge_2114 v1 - This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2), methane (CH4), water vapor concentrations, air temperature, pressure, and wind speed and direction as well as airborne remote sensing measurements of column average CO2 collected during Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) deployments from 2017-07-20 to 2017-08-08 over Alaska, US, and the Yukon and Northwest Territories of Canada. CO2 and CH4 were measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_ASCENDS_Merge/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_PBand_SAR_1657 v1 - This dataset provides estimates of soil geophysical properties derived from Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band polarimetric synthetic aperture radar (PolSAR) data collected in August and October of 2014, 2015, and 2017 over 12 study sites (with some exceptions) across Northern Alaska. Soil properties reported include the active layer thickness (ALT), dielectric constant, soil moisture profile, surface roughness, and their respective uncertainty estimates at 30-m spatial resolution over the 12 flight transects.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_PBand_SAR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Soil_ActiveLayer_Properties_AK_2315 v1 - This dataset provides soil active layer characteristics from nine locations across Alaska. Soil samples were collected in 2016 except for one site which was sampled in 2018.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Soil_ActiveLayer_Properties_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Permafrost_ActiveLayer_NSlope_1759 v1 - This dataset provides in situ soil measurements including soil dielectric properties, temperature, and moisture profiles, active layer thickness (ALT), and measurements of soil organic matter, bulk density, porosity, texture, and coarse root biomass. Samples were collected from the surface to permafrost table in soil pits at selected sites along the Dalton Highway in Northern Alaska.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Permafrost_ActiveLayer_NSlope/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_ReSALT_InSAR_PolSAR_V3_2004 v3 - This dataset provides estimates of seasonal subsidence, active layer thickness (ALT), the vertical soil moisture profile, and uncertainties at a 30 m resolution for 51 sites across the ABoVE domain, including 39 sites in Alaska and 12 sites in Northwest Canada. The ALT and soil moisture profile retrievals simultaneously use L- and P-band synthetic aperture radar (SAR) data acquired by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instruments during the 2017 Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_ReSALT_InSAR_PolSAR_V3/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Sat_ActiveLayer_Thickness_Maps_1760 v1 - This dataset provides annual estimates of active layer thickness (ALT) at 1 km resolution across Alaska from 2001-2015. The ALT was estimated using a remote sensing-based soil process model incorporating global satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover extent (SCE), and Soil Moisture Active and Passive (SMAP) satellite soil moisture records.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Sat_ActiveLayer_Thickness_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Photos_ThermokarstLakes_AK_1845 v1 - This dataset includes high resolution orthophotographs of 21 lakes in the region of Fairbanks, Alaska, USA. Aerial photographs were taken on October 8, 2014, three days after lake-ice formation.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Photos_ThermokarstLakes_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Open_Water_Map_1643 v1 - This dataset contains georeferenced three-band orthomosaics of green, red, and near-infrared (NIR) digital imagery at 1m resolution collected over selected surface waters across Alaska and Canada between July 9 and August 17, 2017. The orthomosaics were generated from individual images collected by a Cirrus Designs Digital Camera System (DCS) mounted on a Beechcraft Super King Air B200 aircraft from approximately 8-11 km altitude.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Open_Water_Map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_AirSWOT_Radar_Data_1646 v1 - This dataset provides AirSWOT (Surface Water and Ocean Topography) Ka-band (35.75 GHz) radar data products collected from an airborne platform over parts of Alaska and Canada during the period 2017-07-09 to 2017-08-17. Flights targeted specific surface water features, including rivers, lakes, ponds, and wetlands in the ABoVE domain.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_AirSWOT_Radar_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirSWOT_Orthomosaic_WaterMask_1655 v1 - This dataset provides NASA AirSWOT Ka-band (35.75 GHz) radar interferometry data products for water surface elevation (WSE), a derived color-infrared (CIR) digital image orthomosaic, and derived lake/wetland and river channel water masks at 3.6 x 3.6 m resolution for a study area of ~3,300 km2 in the Yukon Flats Basin (YFB) in eastern interior Alaska. The data were collected during a flight over the region on June 15, 2015.These data were collected to validate AirSWOT WSE mappings and to improve the understanding of surface water flow through complex Arctic-Boreal wetland systems.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/AirSWOT_Orthomosaic_WaterMask/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_AirSWOT_Water_Mask_1707 v1 - This dataset provides 1) a conservative open water mask for future water surface elevation (WSE) extraction from the co-registered AirSWOT Ka-band interferometry data, and 2) high-resolution (1 m) water body distribution maps for water bodies greater than 40 m2 along the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) foundational flight lines. The masks and maps were derived from georeferenced three-band orthomosaics generated from individual images collected during the flights and a semi-automated water classification algorithm based on the Normalized Difference Water Index (NDWI).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_AirSWOT_Water_Mask/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Alaska_Lake_Pond_Occurrence_2399 v1 - The Alaska Lake and Pond Occurrence Dataset (ALPOD) is a spatially explicit map of lakes and ponds across Alaska and their seasonal fluctuations. The core product is an open water occurrence raster that: (a) separates lakes and ponds from other components of the landscape (e.g., rivers and wetlands); (b) is built from Sentinel-2 imagery and has 10-m resolution; and (c) records the percentages of time that each pixel was open water and attached to a lake or pond during the 2016-2021 ice-free seasons at near-daily temporal resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Alaska_Lake_Pond_Occurrence/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Alder_Shrub_Soil_Alaska_V2_2300 v2 - This dataset holds measures of vegetative cover and soil characteristics for sites in interior Alaska, U.S., along the James W. Dalton Highway (Alaska Route 11).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Alder_Shrub_Soil_Alaska_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_MODIS_MAIAC_Reflectance_1858 v1 - This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances across the ABoVE domain in Alaska and western Canada from 2000 to 2017. Using random forests (RF), a machine-learning approach, the original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) to reduce artifacts and variability due to angular effects.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_MODIS_MAIAC_Reflectance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Annual_30m_AGB_1808 v1 - This dataset provides estimated annual aboveground biomass (AGB) density for live woody (tree and shrub) species and corresponding standard errors at a 30 m spatial resolution for the boreal forest biome portion of the Core Study Domain of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) Project (Alaska and Canada) over the time period 1984-2014. The data were derived from a time series of Landsat-5 and Landsat-7 surface reflectance imagery and full-waveform lidar returns from the Geoscience Laser Altimeter System (GLAS) flown onboard IceSAT from 2004 to 2008.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Annual_30m_AGB/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Annual_Seasonality_Greenness_1698 v1 - This dataset provides annual maps of the timing of spring onset with leaf emergence, of autumn onset with leaf senescence, and of peak greenness for each 30 m pixel derived from Landsat time series of Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) observations from 1984 to 2014. The ABoVE core domain includes 169 ABoVE grid tiles across Alaska, USA and Alberta, British Columbia, Northwest Territories, Nunavut, Saskatchewan, and Yukon, Canada.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Annual_Seasonality_Greenness/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Annual_Thaw_Slump_1724 v1 - This dataset provides a time series of spatial data showing the expansion of a thaw slump on the East Fork Chandalar River near the community of Venetie, Alaska, from 2008 through 2017. The erosion of vegetated areas along the river was documented by manually digitizing imagery from ESRI basemaps and Landsat 5 (TM), 7 (ETM+), and 8 (OLI), using the band combination of shortwave infrared 2, shortwave infrared 1, and red.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Annual_Thaw_Slump/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Atmospheric_Flask_Data_1717 v1 - This dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e. \"concentrations\") from flights over Alaska and the Yukon and Northwest Territories of Canada during the Arctic Carbon Aircraft Profile (Arctic-CAP) monthly sampling campaigns from April-November 2017.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Atmospheric_Flask_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Arctic_CAP_1658 v1 - This dataset provides in situ airborne measurements of atmospheric carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), and water vapor concentrations, plus air temperature, pressure, relative humidity, and wind speed values over Alaska and the Yukon and Northwest Territories of Canada during the Arctic Carbon Aircraft Profile (Arctic-CAP) monthly sampling campaigns from April-November 2017. Observations have been averaged to a 10-second interval and are reported with the number of samples (N) and standard deviation.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Arctic_CAP/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRR_Fire_Products_1545 v1 - This dataset provides annual forest fire burned area and daily hotspot products developed using data acquired from the Advanced Very-High-Resolution Radiometer (AVHRR) instruments carried aboard two NOAA polar-orbiting satellites (NOAA-11 and NOAA-14). The fire products were generated over 12 fire seasons (1st May - 31st October) from 1989-2000 across North America at 1-km resolution and subset to the ABoVE spatial domain of Alaska and Canada.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/AVHRR_Fire_Products/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Airborne_AV3_V1_2388 v1 - This dataset includes L1B radiance and L2A surface reflectance imagery acquired by the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument over portions of Alaska and northwestern Canada in 2023. These data were collected for the Arctic-Boreal Vulnerability Experiment (ABoVE) project.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Airborne_AV3_V1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Airborne_AVIRIS_NG_V3_2362 v3 - This dataset supersedes the previously published ABoVE AVIRIS-NG Level 2 surface reflectance files for 2017-2019 surveys of Alaska and northwestern Canada. It also includes previously unpublished L1 radiance and L2 reflectance for the 2021 surveys in Iceland when COVID-era policies prevented normal ABoVE flights, and the 2022 surveys, which returned to the ABoVE domain.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Airborne_AVIRIS_NG_V3/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"IMERG_Precip_Canada_Alaska_2097 v1 - This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (.tif) format.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/IMERG_Precip_Canada_Alaska/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Boreal_Forest_Survey_Fairbanks_2390 v1 - This dataset includes five metrics of forest resilience (recruitment, invasives, permafrost change, tree damage, and radial growth) at five recently burned forest sites (2010-2019) near Fairbanks, Alaska. The sites were imaged by the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS-NG) in 2017 and 2022 during the Arctic-Boreal Vulnerability Experiment (ABoVE).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Boreal_Forest_Survey_Fairbanks/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NWT_Burn_Severity_Maps_1694 v1 - This dataset provides maps at 30-m resolution of landscape surface burn severity (surface litter and soil organic layers) from the 2014-2015 fires in the Northwest Territories and Northern Alberta, Canada. The maps were derived from Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) imagery and two separate multiple linear regression models trained with field data; one for the Plains and a second for the Shield ecoregion.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/NWT_Burn_Severity_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Wildfires_NWT_Canada_1548 v1 - This data set provides a fire progression map for year 2015 and measures of burn severity and vegetation community biophysical data collected from areas that were burned by wildfires in 2014 and 2015 in the Northwest Territories, Canada. Field data collected in 2016 include an estimate of burn severity, woody seedling/sprouting data, soil moisture, peat depth, thaw depth, and vegetation cover for selected sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Wildfires_NWT_Canada/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Wildfires_2014_NWT_Canada_1307 v1 - This data set provides peatland landcover classification maps, fire progression maps, and vegetation community biophysical data collected from areas that were burned by wildfire in 2014 in the Northwest Territories, Canada. The peatland maps include peatland type (bog, fen, marsh, swamp) and level of biomass (open, forested).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Wildfires_2014_NWT_Canada/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Burned_Area_Depth_AK_CA_2063 v1 - This dataset provides annual gridded estimates of fire locations and associated burn fraction per pixel for Alaska and Canada at approximately 500 m spatial resolution for the period 2001-2019. Gridded predictions of carbon combustion and burn depth for the same period within the ABoVE extended domain using the burn area maps and field data are also available.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Burned_Area_Depth_AK_CA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Southern_Boreal_Plot_Attribute_1740 v1 - This dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Southern_Boreal_Plot_Attribute/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Wildfire_Effects_Spruce_Field_1595 v1 - This dataset provides the results of field observations of soil characteristics and depth to permafrost, survey results for Composite Burn Index (CBI) determination, and Landsat-derived estimates of Relative Difference Normalized Burn Ratio (RdNBR) for 38 burned and unburned forest sites near Tanana, Alaska in 2017. Forests in the study area, at the confluence of the Yukon and Tanana Rivers about 200 km west of Fairbanks, are predominately black spruce on wetter soils and white spruce on drier soils.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Wildfire_Effects_Spruce_Field/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Soil_Radiocarbon_NWT_1664 v1 - This dataset provides field data from boreal forests in the Northwest Territories (NWT), Canada, that were burned by wildfires in 2014. During fieldwork in 2015, 211 burned plots were established.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Soil_Radiocarbon_NWT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Seasonality_Tundra_Vegetation_1606 v1 - This dataset provides a summary of potential climate drivers of Arctic tundra vegetation productivity that have been compiled for growing seasons from 1982 to 2015. The scale of interest is the entire pan-arctic non-alpine tundra and the continental subdivisions of the North American and the Eurasian Arctic North of 70 degrees.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Seasonality_Tundra_Vegetation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AK_North_Slope_NEE_CH4_Flux_1562 v1 - This dataset provides CO2 and CH4 fluxes and meteorological parameters from five eddy covariance (EC) tower sites located at Barrow (three sites), Atqasuk (ATQ) and Ivotuk (IVO), Alaska. These locations form a 300-km north-south transect across Alaska's North Slope.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/AK_North_Slope_NEE_CH4_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MODIS_MAIAC_Reflectance_1700 v1 - This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances by two methods at each of 62 flux tower sites (1 km x 1 km area) across the ABoVE domain in Alaska and western Canada from 2000 to 2015/2016. The original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) using two independent algorithms: the first based on the original BRDF (Bidirectional Reflectance Distribution Function) parameters provided by the MAIAC t...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/MODIS_MAIAC_Reflectance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Dall_Sheep_Population_Dynamics_1640 v1 - This dataset contains estimated annual average Dall sheep (Ovis dalli dalli) lamb-to-ewe ratios for each year from 2000-2015 across the full species range in Alaska and Northwestern Canada. Sheep population data are from surveys conducted over the 14 major mountain ranges encompassing the range of Dall sheep.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Dall_Sheep_Population_Dynamics/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Dall_Sheep_Snowpack_1602 v1 - This dataset provides daily estimates of snow depth and snow density for the study area in Lake Clark National Park and Preserve (LCNPP), Alaska. The data were generated using SnowModel and used as snow covariates along with landscape covariates in modeling efforts to study Dall sheep movements in response to dynamic snow conditions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Dall_Sheep_Snowpack/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Snowpack_Dall_Sheep_Track_1583 v1 - This dataset contains Dall sheep (Ovis dalli dalli) hoof sinking depths in snow tracks, and snow depth, hardness, and density measurements in snow pits adjacent to the tracks. Snow measurements were collected between March 19-22, 2017 at sites on Jaeger Mesa in the Wrangell Mountains (WRST), Alaska.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Snowpack_Dall_Sheep_Track/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Planning_Field_Sites_1582 v1 - This dataset provides a listing of the ~6,700 field sites used in planning the ABoVE Airborne Campaign (AAC) for 2017. The sites included point, polygon, and line locations that were used in determining the 2017 AAC flight paths.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Planning_Field_Sites/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Frac_FuelComponent_Maps_Tundra_1761 v1 - This dataset provides maps of the distribution of three major wildland fire fuel types at 30 m spatial resolution covering the Alaskan arctic tundra, circa 2015. The three fuel components include woody (evergreen and deciduous shrubs), herbaceous (sedges and grasses), and nonvascular species (mosses and lichens).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Frac_FuelComponent_Maps_Tundra/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Great_Slave_Lake_Ecosystem_Map_1695 v1 - This dataset provides an ecosystem type map at 12.5 meter pixel spacing and 0.2 ha minimum mapping unit for the area surrounding Great Slave Lake, Northwest Territories, Canada for the time period 1997 to 2011. The map includes nine classes for peatland, wetland, and upland areas derived from a Random Forest classification trained on multi-date, multi-sensor remote sensing images across the study extent, and using field data and high-resolution Worldview-2 image interpretation for training and validation.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Great_Slave_Lake_Ecosystem_Map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"End_of_Season_Snow_Depth_1702 v1 - This dataset provides 20,582 snow depth measurements collected at six sites near Fairbanks, Alaska, USA. Measurements were made during March or April from 2014-2019.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/End_of_Season_Snow_Depth/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Environmental_Disturbances_AK_1705 v1 - This dataset provides descriptions and photos of environmental conditions that impacted availability to subsistence resources by residents in nine rural communities within the Yukon River basin of Interior Alaska. The data (photos) were collected by citizens (harvesters) residing in the communities while engaged in subsistence harvesting activities.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Environmental_Disturbances_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Effect_Environment_Moose_1739 v1 - This dataset provides daily and annual air temperature, river water level, and leaf drop dates coincident with the moose (Alces alces) hunting season (September) for the area surrounding the rural communities of Nulato, Koyukuk, Kaltag, Galena, Ruby, Huslia, and Hughes in interior Alaska, USA, over the period 2000-2016. The main objective of the study was to assess how the environmental conditions impacted the success of hunters who rely on moose as a subsistence resource.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Effect_Environment_Moose/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Frac_Open_Water_1362 v1 - This data set provides land surface fractional open water cover maps for two overlapping regions: the entire pan-Arctic region (latitude > 45 degrees) and the Arctic-Boreal Vulnerability Experiment (ABoVE) domain across Alaska and Canada. The data are a 10-day averaged time step at 5-km spatial resolution for the period 2002-2015.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Frac_Open_Water/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Maps_AGB_North_Slope_AK_1565 v1 - This dataset includes 30-m gridded estimates of total plant aboveground biomass (AGB), the shrub AGB, and the shrub dominance (shrub/plant AGB) for non-water portions of the Beaufort Coastal Plain and Brooks Foothills ecoregions of the North Slope of Alaska. The estimates were derived by linking biomass harvests from 28 published field site datasets with NDVI from a regional Landsat mosaic derived from Landsat 5 and 7 satellite imagery.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Maps_AGB_North_Slope_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Snow_Cover_Extent_and_Depth_1757 v1 - This dataset provides estimates of maximum snow cover extent (SCE) and snow depth for each 8-day composite period from 2001 to 2017 at 1 km resolution across Alaska. The study area covers the majority land area of Alaska except for areas covered by perennial ice/snow or open water.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Snow_Cover_Extent_and_Depth/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Historical_Lake_Shorelines_AK_1859 v1 - This dataset includes maps of historical lake shorelines with derived lake areas in the southern portion of the Goldstream Valley and the surrounding landscape north of Fairbanks, Alaska, USA. Historical lake margins were mapped for 1949, 1967, and 1985 using 1 m aerial photographs available through U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Historical_Lake_Shorelines_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"IceFraction_WaterBodies_ACP_AK_2451 v1 - This dataset contains ice fraction data at 1-km spatial resolution and approximately 6-day temporal resolution for small water bodies (900 m2 to 25 km2) across the Arctic Coastal Plain of Alaska (ACP) from 2017 to 2023. The data were developed using Sentinel-1 Synthetic Aperture Radar (SAR) imagery, texture features, and temperature data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/IceFraction_WaterBodies_ACP_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Fire_Ignitions_Locations_AK_CA_2316 v1 - This dataset provides daily fire ignition locations and timing for boreal fires in Alaska, U.S., and Canada between 2001 and 2019. The fire ignition locations and timing are extracted from the ABoVE Fire Emission Database; however, the temperate prairies of Canada, the Atlantic Highlands, and Mixed Wood Plains were not included.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Fire_Ignitions_Locations_AK_CA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BurnedArea_Emissions_AK_YT_NWT_1812 v2 - This dataset provides estimates of daily burned area, carbon emissions, and uncertainty, and daily fire ignition locations for boreal fires in Alaska, U.S., and in the Yukon and Northwest Territories, Canada. The data are at 500 m resolution for the 18-year period from 2001-2018.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/BurnedArea_Emissions_AK_YT_NWT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_L1_P_SAR_1800 v1 - This dataset provides Level 1 (L1) polarimetric radar backscattering coefficient (Sigma-0 or S-0), multi-look complex, polarimetrically calibrated, and georeferenced data products from the UAVSAR P-band SAR radar instrument collected over 74 study sites across Alaska, USA, and western Canada. The radar instrument is a fully polarimetric P-band (ultra-high frequency) SAR operating in the 420-440 MHz band.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_L1_P_SAR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Alaska_Lake_Pond_Maps_2134 v1 - This dataset provides polygon spatial files of lake and pond extents for three sub-regions of Interior Alaska's boreal forest, and one tundra region located in Alaska's Yukon-Kuskokwim Delta. Files provide lake and pond extents of standing water without wetland vegetation or other obstructions with a minimum area of 0.01 ha.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Alaska_Lake_Pond_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Lake_Wetland_Classes_UAVSAR_1883 v1 - This dataset contains a high-resolution land cover classification focused on water and wetland vegetation classes over three NASA ABoVE Campaign regions: Yukon Flats, Alaska, USA; the Peace-Athabasca Delta, Alberta; and the Canadian Shield, Northwest Territories (NWT), Canada. The product was derived from L-band synthetic aperture radar (SAR) acquisitions from the airborne NASA UAVSAR instrument in 2017-2019.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Lake_Wetland_Classes_UAVSAR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_GrowingSeason_Lake_Color_1866 v1 - This dataset provides an annual time series of Landsat green surface reflectance and the derived annual trend during the growing season (June and July) for 472,890 lakes across the ABoVE Extended Study Domain from 1984 to 2019. The reflectance data are from Landsat-5, Landsat-7, and Landsat-8 sensors for the green band (center wavelength 560 nm).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_GrowingSeason_Lake_Color/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Lakes_Ponds_Weekly_Occurrence_2430 v1 - This dataset provides weekly surface water occurrence across lakes and ponds in six study regions: the Alaskan Coastal Plain, Yukon Flats, Yukon Kuskokwim Delta, Mackenzie River Delta, Tuktoyaktuk Peninsula, and Anderson Plain. The study regions are in Alaska, US, and Yukon and Northwest Territories, Canada.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Lakes_Ponds_Weekly_Occurrence/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Methane_Flux_BigTrail_Lake_2393 v1 - This dataset provides field data and gridded land cover and terrain data for an area north of Big Trail Lake, an active thermokarst lake in Goldstream Valley (near Fairbanks, Alaska, USA). Field samples were collected at 56 locations across the study area in June 2021 and August 2022.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Methane_Flux_BigTrail_Lake/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Tundra_Greeness_Temp_Trends_1893 v1 - This dataset provides annual tundra greenness and summer air temperatures at a resolution of 50 km over the pan-Arctic tundra biome above 31.5 degrees over the time period 1985 to 2016. Annual tundra greenness was assessed using the maximum Normalized Difference Vegetation Index (NDVImax) derived from surface reflectance measured by sensors on the Landsat satellites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Tundra_Greeness_Temp_Trends/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BorealForest_Greenness_Trends_2023 v1 - This dataset provides information on interannual trends in annual maximum vegetation greenness from 1985 to 2019 for recently undisturbed areas in the boreal forest biome. Multi-decadal changes in remotely sensed vegetation greenness provide evidence of an emerging boreal biome shift driven by climate warming.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/BorealForest_Greenness_Trends/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_AGBD_Uncertainty_Maps_2465 v1 - This dataset provides annual aboveground biomass (AGB) maps and associated uncertainty maps for Alaska and Canada from 1984 to 2022 at ~30 m resolution (0.00027 degrees). The dataset was derived using predictors from synthetic spectral features from Landsat Collection 2 and Continuous Change Detection and Classification algorithm.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_AGBD_Uncertainty_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_ForestDisturbance_Agents_1924 v1 - This dataset provides spatial data on disturbance agents of fire, insects, and logging in the Arctic Boreal Vulnerability Experiment (ABoVE) core domain at an annual time step from 1987-2012 and 30 m resolution. Using a time-series of Landsat data, the three disturbance types were identified by abrupt changes in Tasseled Cap (dTC) indices of brightness, greenness, and wetness.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_ForestDisturbance_Agents/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Annual_Landcover_ABoVE_1691 v1 - This dataset provides two 30-m resolution time series products of annual land cover classifications over the Arctic Boreal Vulnerability Experiment (ABoVE) core domain for each year of the period 1984-2014. The data are the annual dominant plant functional type in a given 30-m pixel derived from Landsat surface reflectance, landcover training data mapped across the ABoVE domain (using Random Forests modeling, with clustering and interpretation of field photography) and very high resolution imagery to assign land cover classifications.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Annual_Landcover_ABoVE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Boreal_LandCoverClasses_AK_CA_2423 v1 - This dataset contains a 30-m resolution time series of annual land cover classifications as the dominant plant functional type class for all of boreal Alaska and Canada from 1986 to 2020. The data were derived from a time series of Landsat Collection 2 Surface Reflectance and processed using the Continuous Change Detection and Classification (CCDC) algorithm.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Boreal_LandCoverClasses_AK_CA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Fire_Severity_dNBR_1564 v1 - This dataset contains differenced Normalized Burned Ratio (dNBR) at 30-m resolution calculated for burn scars from fires that occurred within the Arctic Boreal and Vulnerability Experiment (ABoVE) Project domain in Alaska and Canada during 1985-2015. The fire perimeters were obtained from the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) fire occurrence datasets.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Fire_Severity_dNBR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Last_Day_Spring_Snow_1528 v1 - This dataset provides the last day of spring snow cover for most of Alaska and the Yukon Territory for 2000 through 2016. The data are based on the MODIS daily snow cover fraction product (MODSCAG) and are provided at 500-m resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Last_Day_Spring_Snow/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Footprints_WRF_AK_NWCa_1896 v1 - This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) Footprint data products for receptors (observations) located at positions along flight paths and at various fixed observing sites at circumpolar locations at northern latitudes during 2016-2019. Each aircraft and station position is treated as an independent receptor in the WRF-STILT model in order to simulate the land surface influence on observed atmospheric constituents.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Footprints_WRF_AK_NWCa/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Particles_WRF_AK_NWCa_1895 v1 - This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory files for receptors located at positions along flight paths and at various fixed observing sites at circumpolar locations above 45 degrees North during 2016-2019. The particle files describe the motion of particles released backward in time over a 10-day period.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Particles_WRF_AK_NWCa/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Forage_Lichen_Maps_1867 v1 - This dataset provides modeled estimates of lichen ground cover at 30 m resolution across the Fortymile study area in interior eastern Alaska, U.S., and the Yukon Territory, Canada, for the nominal year 2015. The mapped lichens are important winter forage for the nine resident caribou (Rangifer tarandus) herds in the region.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Forage_Lichen_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GPP_MODIS_Alaska_Canada_2024 v1 - This dataset contains gridded estimations of daily ecosystem Gross Primary Production (GPP) in grams of carbon per day at a 1 km2 spatial resolution over Alaska and Canada from 2000-01-01 to 2018-01-01. Daily estimates of GPP were derived from a light-curve model that was fitted and validated over a network of ABoVE domain Ameriflux flux towers then upscaled using MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) data to span the extended ABoVE domain.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/GPP_MODIS_Alaska_Canada/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_LVIS_VegetationStructure_1923 v1 - This dataset provides Level 3 (L3) footprint-level gridded metrics and attributes collected from NASA's Land, Vegetation, and Ice Sensor (LVIS)-Facility instrument for each flightline from 2017 and 2019. In 2017, the LVIS-Facility instrument was flown at a nominal flight altitude of 28,000 ft onboard a Dynamic Aviation Super King Air B200T.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_LVIS_VegetationStructure/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Methane_Ebullition_Lakes_AK_1861 v1 - This dataset includes maps of the locations and number of methane ebullition hotspots in 15 frozen lakes in the southern portion of the Goldstream Valley and the surrounding landscape just north of Fairbanks, Alaska, USA. Hotspots were identified from early winter high resolution aerial photographs acquired three days after lake-ice formation in October 2014.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Methane_Ebullition_Lakes_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CH4_Flux_BigTrail_Goldstream_1778 v1 - This dataset provides diffusive methane (CH4) fluxes collected from two thermokarst lakes in the Goldstream Valley, north of Fairbanks in interior Alaska. Fluxes were collected from the littoral zones, adjacent shoreline, and upland vegetation.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/CH4_Flux_BigTrail_Goldstream/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AK_Yukon_PFT_TopCover_2032 v1.1 - This dataset contains data files of modeled top cover estimates by plant functional type (PFT) for the Arctic and Boreal Alaska and Yukon regions. Estimates are presented for single years at 5-year intervals from 1985 to 2020.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/AK_Yukon_PFT_TopCover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MODIS_CCaN_NDVI_Trends_Alaska_1666 v1 - This dataset provides the average Normalized Difference Vegetation Index (NDVI) at 1-km resolution over the north slope of Alaska, USA, for the growing season (June-August) of each year from 2000-2015, and NDVI trends for the same period. The dataset presents growing-season averages and trends from two sources: 1) derived from 1-km, 8-day data from the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD13A2) product, and 2) predicted by the Coupled Carbon and Nitrogen model (CCaN).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/MODIS_CCaN_NDVI_Trends_Alaska/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Albedo_Boreal_North_America_1605 v1.1 - This dataset contains MODIS-derived daily mean shortwave blue sky albedo for northern North America (i.e., Canada and Alaska) and a set of quality control flags for each albedo value to aid in user interpretation. The data cover the period of February 24, 2000 through April 22, 2017.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Albedo_Boreal_North_America/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Alaska_Yukon_NDVI_1614 v1 - This dataset provides the maximum Normalized Difference Vegetation Index (NDVI) at 1-km resolution over northern Alaska, USA and the Yukon Territory, Canada for each year from 2002-2017, as well as a 16 year maximum NDVI product. MODIS products MOD13Q1 and MYD13Q1 from Collection 6 were acquired at 250-m pixel size from June 1-August 30 of each year.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Alaska_Yukon_NDVI/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Monthly_Hydrological_Fluxes_1647 v1 - This dataset provides modeled estimates of monthly hydrological fluxes at 0.25-degree resolution over Alaska and Canada for the years 1979-2018. The estimates were derived from the Variable Infiltration Capacity (VIC) macroscale hydrological model version 4.1.2 with water and energy balance schemes at 0.25-degree spatial and daily temporal resolution for this 38-year period.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Monthly_Hydrological_Fluxes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Uncertainty_Maps_1652 v1 - This dataset provides estimates of the uncertainty in components of the carbon cycle including: soil carbon stock, autotrophic respiration (Ra), heterotrophic respiration (Rh), net ecosystem exchange (NEE), net primary production (NPP), and gross primary productivity (GPP) across the entire ABoVE Study Domain at 0.5-degree resolution for the reference year 2003. The uncertainties were calculated from the multi-model (n = 20) disagreement, i.e.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Uncertainty_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Vegetation_greenness_trend_1576 v1 - This dataset provides the summer NDVI trend and trend significance for the period 1984-2012 over Alaska and Canada. The NDVI were calculated per-pixel from all available peak-summer 30-m Landsat 5 and 7 surface reflectance data for the period.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Vegetation_greenness_trend/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Chlorophyll_Fluorescence_Data_1785 v1 - This dataset provides the results of in situ measurements of needle-level chlorophyll fluorescence (ChlF) obtained from a pulse amplitude modulated (PAM) fluorometer from evergreen needleleaf forested sites one in Alaska and one in Idaho. Measured light-adapted minimal fluorescence (Ft) is reported as the quantum yield of fluorescence and light-adapted variable fluorescence over maximal fluorescence (Fv/Fm) and is reported as the quantum yield of photosystem II.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Chlorophyll_Fluorescence_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SnowMeltDuration_PMicrowave_1843 v1.1 - This dataset provides the annual period of snowpack melting (i.e., snow melt duration, SMD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. SMD is the number of days between the main melt onset date (MMOD) and the last day of seasonal snow cover when the melting of snow is complete.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/SnowMeltDuration_PMicrowave/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Main_Melt_Onset_Dates_1841 v1.0 - This dataset provides the annual date of snowpack seasonal beginning melt (i.e., main melt onset date, MMOD) across northwest Canada, Alaska, US, and parts of far eastern Russia at 6.25-km resolution for the period 1988-2023. MMOD was derived from the daily 19V (K-band) and 37V (Ka-band) GHz bands from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Main_Melt_Onset_Dates/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Canada_Boreal_Forest_Greenness_1587 v1 - This dataset provides a 28-year time series of peak greenness (NDVI) data derived from Landsat 5 TM imagery over the boreal forest region of Canada. Landsat 5 TM scenes were collected for 46 selected sidelap sites along gradients in climate, tree cover, and disturbance history from 1984 to 2011.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Canada_Boreal_Forest_Greenness/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Permafrost_Thaw_Depth_YK_1598 v1 - This dataset provides field observations of thaw depth and dominant vegetation types, a LiDAR-derived elevation map, and permafrost distribution and probability maps for an area on the coastal plain of the Yukon-Kuskokwim Delta (YKD), in western Alaska, USA. Field data were collected during July 8-17, 2016 to parameterize and to validate the derived permafrost maps.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Permafrost_Thaw_Depth_YK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Radial_Growth_PRI_1781 v1 - This dataset provides simultaneous in-situ measurements of the photochemical reflectance index (PRI) and radial tree growth of selected white spruce trees (Picea glauca (Moench) Voss) at the northern treeline in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. PRI and dendrometer measurements were simultaneously collected on 29 trees from six plots spaced along a 5.5 km transect from south to north where tree density becomes increasingly sparse.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Radial_Growth_PRI/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Veg_Soil_Tundra_Burned_Area_2119 v1 - This dataset includes field measurements from 26 burned and unburned transects established in 2008 in the region of the Anaktuvuk River tundra fire on the Arctic Slope of Alaska, US. Measurements include plant cover by species, shrub and tussock density, thaw depth, and soil depth.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Veg_Soil_Tundra_Burned_Area/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_NWT_2017_Field_Data_1771 v1 - This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2017 from 11 study sites in the ABoVE Study area. The 11 study areas contained 28 sites that were burned by wildfires in 2014 and 2015, and 10 unburned sites in the Northwest Territories (NWT), Canada.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_NWT_2017_Field_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Wildfires_NWT_Canada_2018_1703 v1 - This dataset provides vegetation community characteristics and biophysical data collected in 2018 from areas that were burned by wildfire in 2014 and 2015, and from nine unburned validation sites in the Northwest Territories, Canada. The data include vegetation inventories, ground cover, regrowth, tree diameter and height, and woody seedling/sprouting data at burned sites, and similar vegetation community characterization at unburned validation sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Wildfires_NWT_Canada_2018/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Wildfires_NWT_Canada_2019_1900 v1 - This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2019 from 11 study areas, which contained 28 sites that were burned by wildfires in 2014 and 2015, and 14 unburned sites in the Northwest Territories (NWT), Canada. Burn sites included peatland and upland.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Wildfires_NWT_Canada_2019/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Rain-on-Snow_Data_V2_2415 v2 - This dataset provides maps of rain-on-snow (ROS) events across Alaska for the individual cold season months from November to March using observations from two space-borne passive microwave radiometers: (a) the Advanced Microwave Scanning Radiometer for EOS and Advanced Microwave Scanning Radiometer 2 (AMSR-E/2) from 2002 to 2023; and (b) the Special Sensor Microwave Imager and the Special Sensor Microwave Imager Sounder (SSMI/S) from 1988 to 2020. Considering the differences in sensor overpass time, observation geometry, and ancillary snow cover data, the AMSR-E/2 and SSMI/S-based ROS recor...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Rain-on-Snow_Data_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Reflectance_Spectra_Alaska_1685 v1 - This dataset reports full-spectrum (350-2500 nm) reflectance measurements of diverse plant communities at the plot-level and individual plant species at the leaf-level, at multiple sites across northern Alaska during the 2017 and 2018 summer field seasons. Plot-level reflectance data (1 m2) include an assemblage of vascular and non-vascular species comprising tundra plant communities, while leaf-level scans are specific to one particular tundra species.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Reflectance_Spectra_Alaska/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"River_Ice_Breakup_Freezeup_1697 v1 - This dataset provides estimates of river ice breakup and freeze-up stages along selected reaches of the Yukon and Tanana Rivers in the Yukon River Basin in interior Alaska from 1972-2016. Time series of Landsat satellite images were visually interpreted to identify the day of year and characteristics of the different stages of river ice seasonality.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/River_Ice_Breakup_Freezeup/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Erosion_Vegetation_Yukon_1616 v1 - This dataset provides a time series of riverbank erosion and vegetation colonization along reaches of the Yukon River (3 study areas), Tanana and Nenana Rivers (1 area), and Chandalar River (1 area) in interior Alaska over the period 1984-2017. The change data were derived from selected 30-m images from Landsat TM, Landsat ETM+, and Landsat Operational Land Imager (OLI) surface reflectance products.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Erosion_Vegetation_Yukon/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SAR_Methane_Ebullition_AK_1790 v1 - This dataset provides Synthetic Aperture Radar (SAR) estimates of lake-source methane ebullition flux in mg CH4/m2/d for thousands of lakes in five regions across Alaska. The study regions include the Atqasuk, Barrow Peninsula, Fairbanks, northern Seward Peninsula, and Toolik.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/SAR_Methane_Ebullition_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Dissolved_Gases_Alaska_Rivers_2360 v1 - This dataset provides dissolved carbon dioxide (CO2) and methane (CH4) concentrations alongside their stable and radiocarbon isotopic compositions within the Arctic Sagavanirktok and Kuparuk River watersheds located on the North Slope of Alaska. The data were collected during the spring, fall, and summer seasons in 2022.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Dissolved_Gases_Alaska_Rivers/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Active_Layer_Thaw_Depths_1701 v1 - This dataset provides soil active layer thaw depth measurements collected along transects at three sites near Fairbanks, Alaska, USA. Measurements were made during the late summers of 2014-2018.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Active_Layer_Thaw_Depths/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Soil_ThawDepth_Moisture_1903 v1 - This dataset provides soil thaw depth and moisture (STDM) measurements and dielectric properties measured by different research teams at sites in Alaska, U.S., and the Northwest Territories, Canada. There are multiple observations per site and 352,719 total observations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Soil_ThawDepth_Moisture/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Soil_ThawMoisture_2369 v1 - This dataset provides soil thaw depth and moisture measurements and dielectric properties measured by different research teams at sites in Alaska, U.S., and the Northwest Territories, Canada. There are multiple observations per site and 528,703 total observations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Soil_ThawMoisture/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ALT_Soil_Collection_Protocols_2373 v1 - This dataset contains soil moisture sampling protocols and calibration algorithms for Campbell Scientific Hydrosense-I and II units used at burned and unburned sites in the ABoVE project domain. The soil moisture sampling protocols document provides guidance for sampling in situ soil moisture to relate to synthetic aperture radar (SAR) data collections.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ALT_Soil_Collection_Protocols/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Soil_Temp_Moisture_Alaska_1869 v1 - This dataset provides soil temperature and volumetric water content (VWC) measurements at 15 cm depth collected at 12 selected boreal and tundra sites located across Alaska. Each site is equipped with a HOBO MicroStation Data Logger that hosts two soil temperature sensors (HOBO S-TMB-M006 Temperature Smart Sensor), and two soil moisture sensors (HOBO S-SMD-M005 10HS Soil Moisture Smart Sensor).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Soil_Temp_Moisture_Alaska/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"USArray_Ground_Temperature_1680 v1.1 - This dataset includes soil temperature profile measurements taken at 63 monitoring sites associated with the USArray program, located across the NASA ABoVE domain in interior Alaska. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/USArray_Ground_Temperature/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Soil_Temperature_Profiles_AK_1767 v1 - This dataset includes soil temperature profile measurements taken at 16 monitoring sites in Alaska, USA, and at one site in Yukon, Canada. The six sites are collocated with seismic stations of the USArray program.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Soil_Temperature_Profiles_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Post_Fire_C_Emissions_1787 v1 - This dataset provides spatial estimates of carbon combustion from all 2015 wildfire burned areas across Saskatchewan, Canada, on a 30-m grid. Carbon combustion (kg C/m2) was derived from post-fire field measurements of carbon stocks completed in 2016 at 47 stands that burned during three 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in adjacent areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Post_Fire_C_Emissions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_reference_grid_1367 v1 - The Arctic - Boreal Vulnerability Experiment (ABoVE) has developed two standardized spatial data products to expedite coordination of research activities and to facilitate data interoperability. First, the ABoVE Study Domain encompasses the Arctic and boreal regions of Alaska, USA, and the western provinces of Canada, North America.","ARN":"arn:aws:s3:::above-reference-grid-1367","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_reference_grid_v2_1527 v2.1 - The Arctic - Boreal Vulnerability Experiment (ABoVE) has developed two standardized spatial data products to expedite coordination of research activities and to facilitate data interoperability. The ABoVE Study Domain encompasses the Arctic and boreal regions of Alaska, USA, and the western provinces of Canada, North America.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_reference_grid_v2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Interior_Alaska_Subsistence_1725 v1 - This dataset provide maps to show the search and harvest areas used by community residents for all subsistence resources combined across Interior Alaska for the years 2011 through 2017. The maps show the extent of areas used by residents for those communities where data collection and research has occurred; it is not a comprehensive use map for the entire area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Interior_Alaska_Subsistence/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Decadal_Water_Maps_1324 v1.1 - This data set provides the location and extent of surface water (open water not including vegetated wetlands) for the entire Boreal and Tundra regions of North America for three epochs, centered on 1991, 2001, and 2011. Each of the products were generated with at least three years of ice-free Landsat imagery.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Decadal_Water_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Plot_Data_Burned_Sites_1744 v1 - This dataset is a synthesis of field plot characterization data, derived above-ground and below-ground combusted carbon, and acquired Fire Weather Index (FWI) System components for burned boreal forest sites across Alaska, USA, the Northwest Territories, and Saskatchewan, Canada from 1983-2016. Unburned plot data are also included.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Plot_Data_Burned_Sites/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PostFire_Tree_Regeneration_1955 v1.1 - This dataset is a synthesis of species-specific pre- and post-fire tree stem density estimates, field plot characterization data, and acquired climate moisture deficit data for sites from Alaska, USA eastward to Quebec, Canada in fires that burned between 1989 and 2014. Data are from 1,538 sites across 58 fire perimeters encompassing 4.52 Mha of forest and all major boreal ecozones in North America.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/PostFire_Tree_Regeneration/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LiDAR_Tundra_Forest_AK_1782 v1 - This dataset provides terrestrial lidar scanning (TLS) point cloud data collected at 10 research plots along the forest-tundra ecotone (FTE) in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. Data were collected in mid-June 2016.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/LiDAR_Tundra_Forest_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Thaw_Depth_1579 v1.0 - This dataset provides thaw depth measurements made at seven locations across Alaska, during August 2016, June and September 2017, and July-August 2018. Three of the locations are paired unburned-burned sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Thaw_Depth/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Boreal_CanopyCover_StandAge_2012 v1 - This dataset contains Landsat-derived locally-calibrated estimates of tree canopy cover (TCC) and forest stand age across global boreal forests from 1984-2020 in Cloud-Optimized GeoTIFF (.tif) format. These raster data span the circum-hemispheric boreal forest biome between 47 to 73 degrees north at 30 m resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Boreal_CanopyCover_StandAge/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AK_Tundra_PFT_FractionalCover_1830 v1 - This dataset provides predicted continuous-field cover for tundra plant functional types (PFTs), across ~125,000 km2 of Alaska's North Slope at 30-m resolution. The data cover the period 2010-07-01 to 2015-08-31.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/AK_Tundra_PFT_FractionalCover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NorthSlope_NEE_TVPRM_1920 v1 - This dataset includes hourly net ecosystem exchange (NEE) simulated by the Tundra Vegetation Photosynthesis and Respiration Model (TVPRM) at 30 km horizontal resolution for the Alaskan North Slope for 2008-2017. TVPRM calculates tundra NEE from air temperature, soil temperature, photosynthetically active radiation (PAR), and solar-induced chlorophyll fluorescence (SIF) using functional relationships derived from eddy covariance tower measurements.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/NorthSlope_NEE_TVPRM/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ALT_Maps_AK_CA_2332 v1 - The dataset consists of maps of estimated Active Layer Thickness (ALT) at 30-m resolution throughout the northern half of Alaska for the years 2014, 2015, and 2017. The maps were generated by using a machine learning-based regression and a set of spatial data layers to upscale ALT from narrow swaths of ALT that were retrieved from airborne high-resolution P-band Polarimetric Synthetic Aperture Radar (PolSAR) imagery.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ALT_Maps_AK_CA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Izaviknek_Field_Data_1772 v1 - This dataset provides ecological field data that were collected during July 2017 and July 2018 from 43 plots spanning gradients of fire history in the upland tundra of the Yukon-Kuskokwim (Y-K) Delta, Alaska. Plot-level data include vegetation species composition and structure, leaf area index (LAI), topography, thaw-depth, and soil characteristics collected at plots burned in 1971-1972, 1985, 2006-2007, 2015, or unburned controls.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Izaviknek_Field_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"InundationMap_YkFlats_PeaceAth_1901 v1 - This dataset provides time series of wetland inundation coverage maps and corresponding inundation frequency maps at ~10-meter resolution estimated every 12 days during the free-water period (May to October) for the years 2017-2019 over the Yukon Flats (YK) portion of the Yukon River, Alaska, USA, and the Peace-Athabasca Delta (PAD), Alberta, Canada. Wetland inundation coverage was determined by a two-step modified decision-tree classification approach that first used Sentinel-1 C-band SAR to identify likely inundated areas across a study site and was followed by a decision-tree classificat...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/InundationMap_YkFlats_PeaceAth/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Ecosystem_Map_SRD_PAD_1947 v1 - This dataset provides ecosystem-types for the Slave River Delta (SRD) and Peace-Athabasca Delta (PAD), Canada, for the time periods circa 2007 and circa 2017. The image resolution is 12.5 m with 0.2-hectare minimum mapping unit.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Ecosystem_Map_SRD_PAD/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Wetland_VegClassification_PAD_2069 v1 - This dataset contains land cover classification focused on water and wetland vegetation communities over the Peace-Athabasca Delta, Canada. Four classification maps with 5-m resolution were derived various combinations of Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) acquired in July and September 2019, and a historical LiDAR archive data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Wetland_VegClassification_PAD/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"WhiteSpruce_Leaf_Traits_Alaska_2124 v1 - This dataset provides measurements of gas exchange (light response curves, Kok curves and ACi curves), leaf traits (carbon, nitrogen, and specific leaf area), leaf pigments (Chlorophyll a, Chlorophyll b and Carotenoids), the photochemical reflectance index (PRI), and average photosynthetic photon flux density as collected from hemispherical photographs. Data were collected on white spruce trees (Picea glauca (Moench) Voss) growing at the northern edge of the species' distribution in Alaska and at the southern edge of the species' distribution in Black Rock Forest (BRF), New York.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/WhiteSpruce_Leaf_Traits_Alaska/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Fire_Emissions_NWT_1561 v1 - This dataset provides estimates of wildfire carbon emissions and uncertainties at 30-m resolution, and measurements collected at burned and unburned field plots from the 2014 wildfire sites near Yellowknife, Northwest Territories (NWT), Canada. Field data were collected at 211 burned plots in 2015 and include site characteristics, tree cover and species, basal area, delta normalized burn ratio (dNBR), plot characteristics, soil carbon, and carbon combusted.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Fire_Emissions_NWT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Wildfires_Date_of_Burning_1559 v1.1 - This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Wildfires_Date_of_Burning/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Wolves_Denning_Pups_Climate_1846 v1 - This dataset provides annual gray wolf (Canis lupus) denning spatial information and timing, associated climatic and phenologic metrics, and reproductive success (i.e., pup survival) in wolf populations across areas of western Canada and Alaska within the NASA ABoVE Core Domain. The study encompasses 18 years between the period 2000-2017.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Wolves_Denning_Pups_Climate/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Arctic_Winter_Respiration_v2_1762 v2.1 - This dataset provides soil-surface carbon dioxide (CO2) efflux derived from measurements of soil respiration with forced diffusion (FD) chambers. Soil Respiration Stations (SRS) were installed at 11 boreal and tundra sites along a broad south-to-north transect starting from near Fairbanks in interior Alaska and extending to Atqasuk in northern Alaska.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Arctic_Winter_Respiration_v2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Boreal_AGB_Density_ICESat2_2186 v1 - This dataset provides estimates of Aboveground dry woody Biomass Density (AGBD) for high northern latitude forests at a 30-m spatial resolution. It is designed both for boreal-wide mapping and filling the northern spatial data gap from NASA's Global Ecosystem Dynamics Investigation (GEDI) project.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Boreal_AGB_Density_ICESat2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AGB_Great_Slave_Lake_NWT_2365 v1 - This dataset holds aboveground biomass (ABG) estimates for areas in the Great Slave Lake Region in the Northwest Territories of Canada for 2019. ABG was estimated from L-band synthetic aperture radar (SAR) data obtained from JAXA's Advanced Land Observing Satellite-2 (ALOS-2/PALSAR-2) and supplemented with data from NASA's airborne Uninhabited Aerial Vehicle SAR (UAVSAR) instrument.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/AGB_Great_Slave_Lake_NWT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"YKDelta_EnvChange_InfoExchange_1894 v1 - This dataset provides a booklet documenting the discussions and outcomes from a knowledge-exchange meeting with Yup'ik elders from the Yukon-Kuskokwim Delta (YKD), western Alaska, community members, and natural scientist to discuss landscape and weather changes that have been observed in their homelands. The meeting was held during November 14-16, 2018.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/YKDelta_EnvChange_InfoExchange/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Alaska_Arctic_Tundra_Veg_Map_1353 v1 - This data set provides the spatial distributions of vegetation types, geobotanical characteristics, and physiographic features for the Arctic tundra region of Alaska for the period 1993-2005. Specific attributes include dominant vegetation, bioclimate subzones, floristic subprovinces, landscape types, lake coverage, and substrate chemistry.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Alaska_Arctic_Tundra_Veg_Map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Arctic_Boreal_Burned_Area_V2_2328 v2 - This dataset provides annual cumulative end-of-season burned area in circumpolar boreal forests and tundra for the years 2002-2022. The data were generated using the Arctic Boreal Burned Area (ABBA) version 2 algorithm with MODIS collection 6 products.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Arctic_Boreal_Burned_Area_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"North_Slope_Veg_Plots_1536 v1 - This dataset provides vegetation cover and environmental plot and soil data collected at flux tower sites of the North Slope Arctic System Science/Land-Atmosphere-Ice Interactions (ARCSS/LAII) project in August of 1995 and 1996. The 19 ARCSS/LAII flux tower sites are located along a North-South transect from near Prudhoe Bay to the foothills of the Brooks Range on the North Slope of Alaska.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/North_Slope_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Arrigetch_Peaks_Veg_Plots_1358 v1 - This data set provides environmental and vegetation data collected between 1978 and 1981 from 439 study plots at Arrigetch Peaks research site, located in the Gates of the Arctic National Park and Preserve in the Endicott Mountains of the central Brooks Range, Alaska. Plots varied between 1 and 50 square meters in size and were located in 13 broad habitat types across the glaciated landscape.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Arrigetch_Peaks_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Atqasuk_Veg_Plots_1371 v1 - This data set provides vegetation species abundance data collected in 1975 from 60 sites on the Arctic Coastal Plain near Atqasuk, Alaska, as well as environmental and species data for 31 of the sites that were revisited in 2000 and 2010. The study sites are located on Arctic tundra near the Meade River, about 60 miles southwest of Barrow.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Atqasuk_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Frost_Boils_Veg_Plots_1361 v1 - This data set describes the environment, soil, and vegetation on nonsorted circles and earth hummocks at seven study sites along a N-S-transect from the Arctic Ocean to the Arctic Foothills based on data collected from 2000 to 2006. The study sites are located along the Dalton Highway, beginning in Prudhoe Bay, on the North Slope of Alaska.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Frost_Boils_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Happy_Valley_Veg_Plots_1354 v1 - This dataset provides environmental, soil, and vegetation data collected in July 1994 from 56 study plots at the Happy Valley research site, located along the Sagavanirktok River in a glaciated valley of the northern Arctic Foothills of the Brooks Range. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in 17 plant communities that occur in 5 broad habitat types across the glaciated landscape.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Happy_Valley_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Imnavait_Creek_Veg_Plots_1356 v1 - This dataset provides environmental, soil, and vegetation data collected during the periods of August 1984 and August-September 1985 from 84 study plots at the Imnavait Creek research site. Imnavait Creek is located in a shallow basin at the foothills of the central Brooks Range.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Imnavait_Creek_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Nome_Veg_Plots_1372 v1 - This data set provides environmental, soil, and vegetation data collected in July and August 1951 from 80 study plots in the Nome River Valley about 10 miles northeast of Nome, Alaska on the Seward Peninsula. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in plant communities that were found to occur in 5 broad habitat types across the glaciated landscape.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Nome_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Oumalik_Veg_plots_1506 v1 - This data set provides environmental, soil, and vegetation data collected between 1983 and 1985 from 87 study plots near an abandoned test oil well in Oumalik, Alaska. Specific attributes include dominant vegetation, species, and cover, soil chemistry, physical characteristics, moisture, and organic matter, as well as site disturbance from various sources.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Oumalik_Veg_plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Prudhoe_Bay_Veg_Plots_1360 v1 - This data set provides environmental, soil, and vegetation data collected between 1973 and 1980 from 89 study plots in the Prudhoe Bay region of Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for study plots subjectively located in 43 plant communities and 4 broad habitat types across the glaciated landscape.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Prudhoe_Bay_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Toolik_Lake_Veg_Plots_1333 v1 - This dataset provides environmental, soil, and vegetation data collected in August 1989 from 81 study plots at the Toolik Lake research site, located in the southern Arctic Foothills of the Brooks Range, Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in 26 communities and 4 broad habitat types across the glaciated landscape.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Toolik_Lake_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Umiat_Veg_Plots_1370 v1 - This data set provides vegetation cover and plot data collected during the periods of July and August, 1951, from 51 stands (areas of homogeneous vegetation communities) in the the Umiat region of Alaska, on the Colville River. The Umiat area is within the Northern Foothills section of the Arctic Foothills province on the slope north of the Brooks Range.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Umiat_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATLAS_Veg_Plots_1541 v1 - This data set provides environmental, soil, and vegetation data collected from study sites on the North Slope and Seward Peninsula of Alaska during the Arctic Transition in Land-Atmosphere System (ATLAS) project. ATLAS-1 sites on the North Slope, located in Barrow, Atqasuk, Oumalik, and Ivotuk, were sampled in 1998-1999.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ATLAS_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Barrow_Tundra_Veg_Plots_1535 v1 - This data set provides vegetation cover and environmental plot data collected as part of the International Biological Program (IBP), U. S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Barrow_Tundra_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Barrow_NGEE_Arctic_Veg_Plots_1505 v1 - This data set provides vegetation cover and environmental plot data collected on the Barrow Environmental Observatory (BEO), Barrow, Alaska in 2012. Forty-eight 1 x 1 m plots were established in homogenous plant communities along two perpendicular transects across ice wedge polygon geomorphic features on the BEO.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Barrow_NGEE_Arctic_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Pingo_Veg_Plots_1507 v1 - This data set provides vegetation species and vegetation plot data collected between 1983 and 1985 from 293 study plots on 41 pingos on the North Slope of Alaska. The pingos were located within the Arctic Coastal Plain in the Kuparuk, Prudhoe Bay, Kadleroshilik, and Toolik River areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Pingo_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Tundra_Fire_Veg_Plots_1547 v1 - This dataset provides environmental and vegetation data collected in late June and July of 2011 and of 2012 from study plots located in tundra fire scars and adjacent unburned tundra areas on the Seward Peninsula and the northern foothills of the Brooks Range in Arctic Alaska. The surveys focused on upland tundra settings and provide information on vegetative differences between the burned and unburned sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Tundra_Fire_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Flux_Tower_Zona_Veg_Plots_1546 v1 - This data set provides vegetation, environmental, and soil data collected from plots located in the footprints of eddy covariance flux towers along a 300 km north-south latitudinal gradient from Barrow, to Atqasuk, and to Ivotuk across the North Slope of Alaska in 2014. Within each of the five flux tower footprints, 1x1-m quadrats were placed subjectively within widespread habitat or micro-habitat types to map the dominant vegetation communities and site environmental factors.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Flux_Tower_Zona_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Canadian_West_Arctic_Veg_Plots_1543 v1 - This dataset provides vegetation, soil, and plot characteristics for 154 study plots located at three sites across the Richardson Mountains, Northwest Territories (NWT), and the British Mountains, Yukon Territory (YT). Study sites in the NWT included areas near Canoe Lake and Divided Lake; the study site in the YT was near Trout Lake.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Canadian_West_Arctic_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Arctic_Network_Veg_plots_1542 v1 - This dataset provides environmental, soil, and vegetation data collected at selected locations in the parks and preserves of the National Park Service (NPS) Arctic Network (ARCN) between 2002 and 2008. The ARCN includes five national parks and preserves in northern Alaska encompassing 19.5 million acres and represents some of the wildest, most undisturbed areas left on earth: The Bering Land Bridge National Preserve, Cape Krusenstern National Monument, Gates of the Arctic National Park and Preserve, Kobuk Valley National Park, and the Noatak National Preserve.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Arctic_Network_Veg_plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Willow_Veg_Plots_1368 v1 - This data set provides environmental, soil, and vegetation data collected in July and August 1997 from 85 study plots in willow shrub communities located along a north-south transect from the Brooks Range to Prudhoe Bay on the North Slope of Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in three broad habitat types across the glaciated landscape.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Willow_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Barter_Barrow_Veg_Plots_1534 v1 - This dataset provides vegetation cover and environmental plot and soil data collected at two U.S. Air Force sites at Barter Island (BI) and Point Barrow (B), on the coastal North Slope of Alaska, in 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Barter_Barrow_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Unalaska_Veg_Plots_1375 v1 - This data set provides environmental, soil, and vegetation data collected during August 2007 from 69 study plots at the Unalaska Island research site, and one plot on Amaknak Island. The study sites are within the eastern Aleutian Islands, Alaska, USA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Unalaska_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Poplar_Veg_Plots_1376 v1 - This data set provides vegetation cover and environmental plot data collected from 32 balsam poplar (Populus balsamifera L., Salicaceae) vegetation plots located on the Arctic Slope of Alaska and in the interior boreal forests of Alaska and the Yukon from 2003 to 2005. The estimated percent land cover by species per plot are according to the older Braun-Blanquet cover-abundance scale.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Poplar_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Prudhoe_Bay_ArcSEES_Veg_Plots_1555 v1 - This dataset provides environmental, soil, and vegetation data collected from study plots in the vicinity of Lake Colleen off the Spine Road at Prudhoe Bay, Alaska, during August of 2014. Data include vegetation species, leaf area index (LAI), percent cover classes, soil moisture and color, and plot characteristics including geology, topographic position, slope, aspect, and plot disturbance.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Prudhoe_Bay_ArcSEES_Veg_Plots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LandCover_Great_Lakes_Basin_2440 v1 - This dataset holds maps of land cover and wetland ecotype for the Great Lakes Basin (GLB) circa 2010. The data were derived from multi-season L-band SAR, Landsat 5 optical imagery, and thousands of field training data samples.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/LandCover_Great_Lakes_Basin/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Plot_Data_Noatak_Seward_AK_1919 v1 - This dataset includes field measurements from unburned and burned 10 m x 10 m and 1 m x 1 m plots in the Noatak, Seward, and North Slope regions of the Alaskan tundra during July through August in the years 2016-2018. The data include vegetation coverage, soil moisture, soil temperature, soil thickness, thaw depth, and weather measurements.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Plot_Data_Noatak_Seward_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Arctic_Vegetation_Maps_1323 v1 - This data set provides the spatial distributions of vegetation types, geobotanical characteristics, and physiographic features for the circumpolar Arctic tundra biome for the period 1982-2003. Specific attributes include dominant vegetation, bioclimate subzones, floristic subprovinces, landscape types, lake coverage, Arctic treeline, elevation, and substrate chemistry data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Arctic_Vegetation_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Boreal_AGB_Density_ICESat2_V3_2437 v3 - This dataset provides estimates of aboveground dry woody biomass density (AGBD) and vegetation height for high northern latitude forests at a 30-m spatial resolution for the year 2020, accounting for >30% of global forest area. The estimates were derived with state-of-the-art earth observation datasets collected from space, including lidar observations from NASA's ICESat-2 and imagery from NASA's Harmonized Landsat/Sentinel-2 project.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Boreal_AGB_Density_ICESat2_V3/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CH4_CO2_WaterBodies_YK_Delta_2178 v1 - This dataset provides estimates of carbon dioxide (CO2) and methane (CH4) diffusive fluxes from waterbodies, and watershed landcover data for the central-interior of the Yukon-Kuskokwim Delta (YK delta), Alaska. Dissolved concentrations of methane and carbon dioxide were predicted using an integrated terrestrial-aquatic approach to scale observations based on landscape and waterbody remote sensing drivers.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/CH4_CO2_WaterBodies_YK_Delta/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_SnowModel_Data_2105 v1 - This dataset provides daily SnowModel simulation outputs on a 3-km grid for the period 1 September 1980 through 31 August 2020, covering the Core ABoVE Domain. The daily outputs include: air temperature (deg C), relative humidity (%), wind speed (m/s), wind direction (deg from True North), total precipitation (rain+snow) (m), rainfall (m), snowfall (m), snow melt (m), snow sublimation (m), runoff (m), surface temperature (deg C), bulk snowpack thermal resistance (K/W), snow depth (m), snow density (kg/m3), and snow-water-equivalent (SWE) depth (m).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_SnowModel_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeciduousFractionl_CanopyCover_2296 v1 - This dataset holds deciduous fraction and tree canopy cover at 30-m resolution over the North American boreal domain for 1992 to 2015. Deciduous fraction is the areal percentage of deciduous trees relative to all tree canopy cover within a pixel, and tree canopy cover is the areal percentage of a pixel that is covered by tree canopy.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/DeciduousFractionl_CanopyCover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ArcticTreeLine_Dendrometry_Env_2185 v1 - This dataset provides in situ measurements of radial tree growth of selected white spruce (Picea glauca) and black spruce (Picea mariana) trees, as well as simultaneous in situ measurements of environmental variables (air temperature, air pressure, relative humidity, soil temperature, volumetric water content, and solar irradiance) at two Arctic treeline sites: one in the Brooks Range of Alaska (AK), USA, and the other near Inuvik, Northwest Territories (NWT), Canada. In AK, 36 trees were monitored from June 7, 2016 to September 13, 2019, and in NWT, 24 trees were monitored from July 5, 201...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ArcticTreeLine_Dendrometry_Env/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"FieldData_Alaska_Tundra_2177 v1 - This dataset, titled the Synthesized Alaskan Tundra Field Database (SATFiD), provides a comprehensive collection of in-situ field data compiled from 37 existing datasets resulting from field surveys conducted at Alaska tundra sites between 1972 to 2020. The data were harmonized prior to being included in this dataset.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/FieldData_Alaska_Tundra/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Prudhoe_Bay_Veg_Maps_1387 v1 - This data set provides a collection of maps of geoecological characteristics of areas within the Beechey Point quadrangle near Prudhoe Bay on the North slope of Alaska: a geobotanical atlas of the Prudhoe Bay region, a land cover map of the Beechey Point quadrangle, and cumulative impact maps in the Prudhoe Bay Oilfield for ten dates from 1968 to 2010. The geobotanical atlas is based on aerial photographs and covers 145 square kilometers of the Prudhoe Bay Oilfield.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Prudhoe_Bay_Veg_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GeoCryoAI_PermafrostThaw_CFlux_2371 v1 - This dataset provides model code, input data, sample results, and documentation for an artificial intelligence-driven model, GeoCryoAI. GeoCryoAI is a hybridized process-constrained ensemble learning framework consisting of stacked convolutionally layered long short-term memory-encoded recurrent neural networks.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/GeoCryoAI_PermafrostThaw_CFlux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_CO2_CH4_Flux_Estimates_2121 v1 - This dataset provides gridded estimates of gross primary productivity (GPP), ecosystem respiration (Reco), net ecosystem CO2 exchange (NEE = Reco - GPP), and methane (CH4) emissions from tundra and boreal wetland soils, across the pan-Arctic and Boreal zone (>49 degrees north) at 1-km spatial resolution. The data were produced through simulations of the Arctic Terrestrial Carbon Flux Model (TCFM-Arctic) and are provided at the daily time step for the years 2003-2015.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_CO2_CH4_Flux_Estimates/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Soil_Carbon_Flux_Maps_1683 v1 - This dataset provides gridded estimates of soil CO2 flux (g C m-2 d-1) for the winter non-growing season (NGS) across pan-Arctic and Boreal permafrost regions (>49 Deg N), at 25 km spatial resolution. The data are the daily average flux over a monthly period for two climate periods: the baseline climate period represents 2003-2018 and the future climate scenarios period represents 2018-2100 under Representative Concentration Pathways (RCP) 4.5 and 8.5.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Soil_Carbon_Flux_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Vegetation_Photos_Toolik_Lake_1718 v1 - This dataset contains 731 ground-based nadir vegetation community and ground surface photographs of selected field plots taken as ground reference data for vegetation classification studies at three areas near Toolik Lake, Alaska during the summers of 2014 and 2015. The largest area, 'Toolik', (approximately 6 km2) covers research areas near Toolik Field Station at Toolik Lake, including Arctic LTER installations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Vegetation_Photos_Toolik_Lake/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Shrub_Biomass_Toolik_Lake_AK_1573 v1 - This dataset contains estimates for aboveground shrub biomass and uncertainty at high spatial resolution (0.80-m) across three research areas near Toolik Lake, Alaska. The estimates for August of 2013 were generated and mapped using Random Forest modeling with input variables of optimized LiDAR-derived canopy volume and height, mean NDVI from 4-band RGB color and near-IR orthophotographs, and harvested biomass data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Shrub_Biomass_Toolik_Lake_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Vegetation_Maps_Toolik_Lake_1690 v1 - This dataset contains vegetation community maps at 20 cm resolution for three landscapes near the Toolik Lake research area in the northern foothills of the Brooks Range, Alaska, USA. The maps were built using a Random Forest modeling approach using predictor layers derived from airborne lidar data and high-resolution digital airborne imagery collected in 2013, and vegetation community training data collected from 800 reference field plots across the lidar footprints in 2014 and 2015.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Vegetation_Maps_Toolik_Lake/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LakeBathymetry_Model_NSlope_AK_2243 v1 - This dataset provides lake bathymetry maps derived from Landsat surface reflectance products for a portion of the North Slope area of Alaska. A random forest regression algorithm was used to generate depths for each point identified as being part of a lake, creating depth prediction files for each Landsat scene available for the study period: 2016-07-01 to 2018-08-31.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/LakeBathymetry_Model_NSlope_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Lakes_Ponds_Inundation_YKDelta_2450 v1 - This dataset provides high resolution (3 m) maps delineating open water and partial inundation in the Yukon-Kuskokwim Delta (YKD), Alaska, and a database that tracks rapid changes in surface area for individual lakes and ponds over 2019-2023. The dataset was built using daily PlanetScope imagery (3 m), allowing for tracking changes in surface water extent at high spatial and temporal resolutions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Lakes_Ponds_Inundation_YKDelta/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Northern_Alaska_Veg_Maps_1359 v1 - This data set provides four land cover and ecosystem classification maps for northern Alaska. The maps were produced for several projects and from different data sources including Landsat imagery and existing maps and models, and cover a range of ecosystem and vegetation classes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Northern_Alaska_Veg_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Seward_Peninsula_Veg_Maps_1363 v1 - This data set provides two landcover and vegetation maps for the Seward Peninsula, Alaska. These maps were produced from existing maps, Landsat imagery, and color infrared aerial photography covering the period 1976-06-01 to 1999-09-01.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Seward_Peninsula_Veg_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Arctic_Wildlife_Refuge_Veg_Map_1384 v1 - This data set provides a landcover map with 16 landcover classes for the northern coastal plain of the the Arctic National Wildlife Refuge (ANWR) on the North Slope of Alaska. The map was derived from Landsat Thematic Mapper (Landsat TM) data, Digital Elevation Models (DEMs), aerial photographs, existing maps, and extensive ground-truthing.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Arctic_Wildlife_Refuge_Veg_Map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Domain_Projected_LULC_2353 v1 - This dataset provides projections of land use and land cover (LULC) change within the Arctic Boreal Vulnerability Experiment (ABoVE) domain, spanning from 2015 to 2100 with a spatial resolution of 0.25 degrees. It includes LULC change under two Shared Socioeconomic Pathways (SSP126 and SSP585) derived from Global Change Analysis Model (GCAM) at an annual scale.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Domain_Projected_LULC/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LVISC1B v1 - This data set contains Level-1B geolocated return energy waveforms collected by the NASA Land, Vegetation, and Ice Sensor (LVIS) Facility, an imaging lidar and camera sensor suite.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/LVISC1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"LVISC2 v1 - This data set contains Level-2 geolocated surface elevation and canopy height measurements collected by the NASA Land, Vegetation, and Ice Sensor (LVIS) Facility, an imaging lidar and camera sensor suite.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/LVISC2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CO2Fluxes_Arctic_Boreal_Domain_2377 v1 - This dataset provides gridded estimates of gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem CO2 exchange (NEE) across the circumpolar terrestrial Arctic-boreal region at a 1-km spatial resolution. Monthly CO2 flux data from 2001 to 2020 were generated using terrestrial eddy covariance and chamber CO2 flux observations, combined with geospatial meteorological, remote sensing, topographical and soil data, all within a random forest modeling framework.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/CO2Fluxes_Arctic_Boreal_Domain/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Imnavait_Creek_Veg_Maps_1385 v1 - This dataset provides the spatial distribution of vegetation types, soil carbon, and physiographic features in the Imnavait Creek area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Imnavait_Creek_Veg_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Kuparuk_Veg_Maps_1378 v1 - This data set provides a collection of vegetation, landscape, geobotanical, elevation, hydrology, and geologic maps for the Kuparuk River Basin, North Slope, Alaska. The maps cover either (1) the entire Kuparuk River Basin, from the headwaters on the north side of the Brooks Range to the Beaufort Sea coast, or (2) the selected Upper Kuparuk River Region including the Toolik Lake and Imnavait Creek research areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Kuparuk_Veg_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Toolik_Lake_Area_Veg_Maps_1380 v1 - This data set provides the spatial distributions of vegetation types, soil carbon, and physiographic features in the Toolik Lake area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Toolik_Lake_Area_Veg_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"North_Slope_Transect_Veg_Maps_1386 v1 - This dataset includes vegetation cover maps, Normalized Difference Vegetation Index (NDVI) maps, snow depth and thaw depth data that were obtained as part of a biocomplexity project on the North Slope of Alaska, USA, and the Northwest Territories (NWT), Canada. In Alaska, seven sites are located along the Dalton Highway and in the Prudhoe Bay Oilfield area, forming a transect across the climate gradient of the North Slope.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/North_Slope_Transect_Veg_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CH4_Fluxes_ThermokarstLakes_AK_1870 v1 - This dataset provides methane fluxes from hot-spot and non-hot spot differing surfaces at Big Trail Lake (BTL) in the Goldstream Valley near Fairbanks, AK, USA. Measurements were taken at a remotely-sensed methane hotspot on the shoreline of a pond, adjacent to BTL with a Los Gatos Ultra-Portable Greenhouse Gas Analyzer (UGGA), and from various non-hotspot surfaces representative of the broader thermokarst lake ecosystem with bucket chambers.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/CH4_Fluxes_ThermokarstLakes_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Annual_Veg_Resilience_2374 v1 - This dataset provides estimates of vegetation resilience in the Arctic Boreal Vulnerability Experiment (ABoVE) core domain at annual time steps for 2000-2019 and at 300-m spatial resolution. Vegetation resilience is defined as the recovery rate from deviations, due to climate perturbations or disturbances, to the equilibrium state.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Annual_Veg_Resilience/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NDVI_Forest_Structure_1797 v1 - This dataset provides leaf area index (LAI), tree species and canopy cover, normalized difference vegetation index (NDVI), and NDVI trends for boreal forests in interior Alaska, U.S. These data were collected to investigate NDVI trends with forest structure and composition as influenced by disturbance and succession.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/NDVI_Forest_Structure/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Passive_Microwave_Snowoff_Data_1711 v1.0 - This dataset provides annual maps of the snowoff (SO) date from 1988-2023 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Passive_Microwave_Snowoff_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Post_Fire_SOC_NWT_2235 v1 - This dataset provides site moisture, soil organic layer thickness, soil organic carbon, nonvascular plant functional group, stand dominance, ecozone, time-after-fire, jack pine proportion, and deciduous proportion for 511 forested plots spanning ~140,000 km2 across two ecozones of the Northwest Territories, Canada (NWT). The plots were established during 2015-2018 across 41 wildfire scars and unburned areas (no burn history prior to 1965), with 317 plots in the Plains and 194 plots in the Shield regions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Post_Fire_SOC_NWT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Gas_Seeps_Lakes_Fairbanks_SAR_2394 v1 - This dataset includes maps and locations of potential gas seepage in lakes within the immediate and surrounding area around Fairbanks, Alaska, a region underlain by discontinuous permafrost and characterized by thermokarst lake formation. Gas bubbling from lakes in the Fairbanks area is usually rich in methane, a potent greenhouse gas that is difficult to quantify.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Gas_Seeps_Lakes_Fairbanks_SAR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ALT_GPR_Barrow_1355 v1 - This data set provides estimates of Active Layer Thickness (ALT) determined with ground-based measurements, and calculated soil volumetric water content (VWC) at four selected sites around Barrow, Alaska in August 2013. ALT was determined using a ground-penetrating radar (GPR) system and traditional mechanical probing.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ALT_GPR_Barrow/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Alaska_L4_WRF_STILT_Footprints_1544 v1 - This dataset provides Stochastic Time-Inverted Lagrangian Transport model outputs for receptors located at the NOAA Barrow Alaska Observatory for 12 selected years (15 August to 15 October) across the 30-year, 1982 to 2011, study timeframe. Meteorological fields from version 3.5.1 of the Weather Research and Forecasting model are used to drive STILT.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Alaska_L4_WRF_STILT_Footprints/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ReSALT_ALT_GPR_1265 v1 - This data set includes estimates of permafrost Active Layer Thickness (ALT; cm), and calculated uncertainties, derived using a ground-penetrating radar (GPR) system in the field in August 2014 near Toolik Lake and Happy Valley on the North Slope of Alaska. GPR measurements were taken along 10 transects of varying length (approx.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ReSALT_ALT_GPR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreABoVE_AirMOSS_L1_Alaska_1678 v1 - This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multi-look complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over 10 study sites across Northern Alaska, USA. Flight campaigns took place in August 2014, October 2014, April 2015, August 2015, September 2015, and October 2015.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/PreABoVE_AirMOSS_L1_Alaska/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ReSALT_InSAR_Barrow_1266 v1 - Active layer thickness (ALT) is a critical parameter for monitoring the status of permafrost that is typically measured at specific locations using probing, in situ temperature sensors, or other ground-based observations. The thickness of the active layer is the average annual thaw depth, in permafrost areas, due to solar heating of the surface.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ReSALT_InSAR_Barrow/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Freeze_Thaw_NorthernHemisphere_2323 v1 - This dataset provides a probabilistic freeze/thaw (FT) data record from 2016 to 2020 for the Northern Hemisphere derived using a deep learning model (U-Net). The model was informed by satellite multi-frequency microwave brightness temperature retrievals from the NASA SMAP (Soil Moisture Active Passive) and JAXA AMSR2 (Advanced Microwave Scanning Radiometer 2) radiometers, and trained using daily soil temperature observations from Northern Hemisphere weather stations and global reanalysis data (ERA-5).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Freeze_Thaw_NorthernHemisphere/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PermafrostThaw_CarbonEmissions_1872 v1 - This dataset consists of an ensemble of model projections from 1901 to 2299 for the northern hemisphere permafrost domain. The model projections include monthly average values for a common set of diagnostic outputs at a spatial resolution of 0.5 x 0.5 degrees latitude and longitude.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/PermafrostThaw_CarbonEmissions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GPP_COS_Conductance_SoilFluxes_2324 v1 - This dataset provides outputs from the Simple Biosphere Model (v 4.2). Products include hourly 0.5-degree gridded fluxes of gross primary productivity (GPP), respiration, carbonyl sulfide (COS) uptake by vegetation and soil, along with conductance of COS (apparent mesophyll and total), stomatal conductance of water and partial pressure of CO2 in the canopy air space, leaf surface, interior and chloroplast.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/GPP_COS_Conductance_SoilFluxes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Daily_FineParticulateMatter_AK_2157 v1 - The dataset provides simulated PM2.5 concentration estimates over Alaska, U.S. PM2.5 (particulate matter with diameter <= 2.5 microns) concentrations in air (micrograms m-3) are gridded at 0.1-degree resolution for May to September for the years 2001 through 2015.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Daily_FineParticulateMatter_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Snow_Depth_Data_Images_1656 v1 - This dataset includes data from late-March snow surveys and hourly digital camera images from two study areas within the Wrangell St Elias National Park, Alaska. These data comprise snow density, stratigraphy, and temperature profiles obtained by snow pits; and snow depth data obtained from transects between snow pits.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Snow_Depth_Data_Images/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Snow_Wildlife_Tracks_AK_WA_2188 v1 - This dataset contains three field seasons of snow-wildlife observations conducted at 707 sites from January 2021 to March 2023 in Washington and Alaska, spanning a broad range of snow conditions. Relatively fresh tracks (usually <24 h) of common large mammal predators (bobcats, coyotes, cougars, and wolves) and their ungulate prey (caribou, Dall sheep, moose, mule deer, and white-tailed deer) were investigated to determine how snow affects predator-prey interactions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Snow_Wildlife_Tracks_AK_WA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Arctic_Soil_Properties_2149 v1 - This dataset provides lab-measured soil properties, including soil water matric potential, soil dielectric properties, soil electrical conductivity, corresponding soil moisture. The dataset also includes the basic soil physical properties such as soil organic matter, bulk density, porosity, fiber content, root biomass, and mineral texture.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Arctic_Soil_Properties/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_Soil_Respiration_Maps_1935 v1 - This dataset provides gridded estimates of carbon dioxide (CO2) emissions from soil respiration occurring within permafrost-affected tundra and boreal ecosystems of Alaska and Northwest Canada at a 300 m spatial resolution for the period 2016-08-18 to 2018-09-12. The estimates include monthly average CO2 flux (gCO2 C m-2 d-1), daily average CO2 flux and error estimates by season (Autumn, Winter, Spring, Summer), estimates of annual offset of CO2 uptake (i.e., vegetation GPP), annual budgets of vegetation gross primary productivity (GPP; gCO2 C m-2 yr-1), and the fraction of open (non-vegeta...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_Soil_Respiration_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TundraTransect_VegRefl_Soil_2232 v1 - This dataset provides visible-near infrared spectral reflectance, descriptions of vegetation cover, surface temperature, the total fraction of absorbed photosynthetically active radiation (fAPAR, 2001 only), permafrost active layer depth, elevation, and soil temperature at 5 cm depth. Measurements were made at every meter along a 100-m transect aligned mainly in an east-west direction, located approximately 300 m southeast of the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) baseline observatory near Utqiagvik, Alaska.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/TundraTransect_VegRefl_Soil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ArcticTreeLine_Spruce_CO2_WV_1948 v1 - This dataset provides in situ measurements of needle-level gas-exchange and leaf traits from Picea glauca (white spruce) from a field site located in the northern latitudinal forest-tundra ecotone (FTE) near the Dalton Highway in northern Alaska, and from one study site located in Black Rock Forest, New York, USA. Measurements were collected with an open flow portable photosynthesis system (Li6400XT) and custom-built temperature-controlled cuvette.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ArcticTreeLine_Spruce_CO2_WV/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABoVE_SAR_Surveys_2150 v1 - This dataset contains tables containing Airborne flight metadata from synthetic aperture radar (SAR) surveys from 2012 to 2022 in Alaska and Canada. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne SAR surveys of over 120,000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/ABoVE_SAR_Surveys/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Nongrowing_Season_CO2_Flux_1692 v1 - This dataset provides a synthesis of winter ( September-April) in situ soil CO2 flux measurement data from locations across pan-Arctic and Boreal permafrost regions. The in situ data were compiled from 66 published and 21 unpublished studies conducted from 1989-2017.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Nongrowing_Season_CO2_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Arctic_Boreal_CO2_Flux_1934 v1 - This Arctic-Boreal CO2 fluxes (ABCflux) dataset contains monthly aggregates of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity (GPP) and ecosystem respiration. Over 70 supporting variables describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Arctic_Boreal_CO2_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CubeSat_Arctic_Boreal_LakeArea_1667 v1 - This dataset provides near-daily lake area timeseries for 85,358 lakes across four study areas in Northern Canada and Alaska, USA, between May 1 and October 1, 2017. These lake area estimates were produced using digital images from newly developed Planet Labs CubeSats, small satellites with a 4-band (blue, green, red, near-infrared) camera payload.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/CubeSat_Arctic_Boreal_LakeArea/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TowerBased_PhotoSpec_SIF_SK_CA_1887 v1 - This dataset includes daily averaged solar-induced chlorophyll fluorescence (SIF) in the red (680-686 nm) and far-red (745-758 nm) wavelength ranges, relative SIF (SIF/Intensity), chlorophyll-carotenoid index (CCI), photochemical reflectance index (PRI), near-infrared vegetation index (NIRv), and normalized difference vegetation index (NDVI) for both black spruce (Picea mariana) and larch (Larix laricina) targets. The study site (Southern Old Black Spruce, SOBS Fluxnet ID CA-Obs) is located near the southern limit of the boreal forest ecotone in Saskatchewan, Canada.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/TowerBased_PhotoSpec_SIF_SK_CA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CircumArctic_Trends_Hotspots_2322 v1 - This dataset provides estimates of trends in temperature, moisture, and vegetation changes over the circumpolar Arctic. Time series trends were measured by the Theil-Sen slope and associated p-values for a variety of variables including 2-meter air temperature, precipitation, soil moisture, non-frozen season days, permafrost active layer thickness, snow cover, vapor pressure deficit, land surface water fraction, normalized difference vegetation index (NDVI), and vegetation optical depth.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/CircumArctic_Trends_Hotspots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Tundra_Leaf_Spectra_2005 v1 - This dataset provides leaf-level visible-near infrared spectral reflectance, chlorophyll fluorescence spectra, species, plant functional type (PFT), and chlorophyll content of common high latitude plant samples collected near Fairbanks, Utqiagvik, and Toolik, Alaska, U.S., during the summers of 2019, 2020, and 2021. A FluoWat leaf clip was used to measure leaf-level visible-near infrared spectral reflectance and chlorophyll fluorescence spectra.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Tundra_Leaf_Spectra/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TundraVeg_Reflectance_Soil_CO2_1960 v1 - This dataset provides measurements at tundra plots collected near Utqiagvik and Atqasuk, AK, including visible-near infrared spectral reflectance, chamber gas exchange measurements of CO2, pulse amplitude modulated (PAM) fluorometry, chlorophyll pigment contents, along with surface temperature, permafrost active layer depth, and soil temperature at 5 cm, through the growing seasons of 2001 and 2002. At all plots, spectral reflectance was measured using a portable spectrometer configured with a straight fiber optic foreoptic, surface temperatures were measured using a handheld Everest Infrar...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/TundraVeg_Reflectance_Soil_CO2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"UAVSAR_Wetland_Peace-Athabasca_2442 v1 - This dataset holds UAVSAR imagery products for the Peace-Athabasca Delta region of Canada. UAVSAR data were collected for two flight lines on five dates from 2017 to 2022 and processed to NISAR-like products with spatial resolution of approximately 10 m.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/UAVSAR_Wetland_Peace-Athabasca/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Understory_Veg_Biomass_Alaska_2340 v1 - This dataset provides measurements of vegetation biomass from 11 locations across Alaska during 2016 to 2018. Vegetation was harvested from plots that were located at the end of previously established 30-m transects at each site, except at one site where plots were randomly selected.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/Understory_Veg_Biomass_Alaska/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-above","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ACCP Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### plotchem_420\nThe purpose of these measurements was to determine plot-level average leaf concentrations of nitrogen, lignin, cellulose, etc. in order to investigate how AVIRIS reflectance measurements vary with chemistry. The plot-level leaf chemistry values were calculated from green leaf chemistry values and litterfall sample weights.\n<br>\n\n#### leafchem_421\nAs part of NASA's Accelerated Canopy Chemistry Program (ACCP) analyses were performed for the determination of carbon constituents and nitrogen content in fresh forest foliage. Samples were analyzed using a series of extraction's that yielded different carbon constituents: non-polar, polar, cellulose and lignin. Nitrogen analyses were conducted using a standard combustion procedure. Approximately 1000 leaf samples were collected from 5 geographically distinct sites and were analyzed at the University of New Hampshire to ensure consistency in analysis. Results were used as a calibration set for Visible/NIR reflectance and the estimation of canopy carbon and nitrogen concentrations.\n<br>\n\n#### canopychem_422\nThis data set describes the nitrogen and chlorophyll content of small, monospecific canopies formed from seedlings of Douglas-fir (Pseudotsuga menziesii) and bigleaf maple (Acer macrophyllum). The trees were provided different levels of fertilization in order to produce canopies with varying nitrogen and chlorophyll concentration. For the Douglas-fir, fertilization was provided during the dormant season, so there were no differences in growth or leaf area among canopies, and canopies were at a constant density with varying foliar chemistry. For the maple, seedlings were aggregated at various densities, producing a matrix of leaf area as well as chemistry variations. Before destructive analysis for foliar chemistry, canopy reflectance was measured under natural sunlight (see ACCP Seedling Canopy Reflectance Spectra Data).\n<br>\n\n#### canopyspec_423\nThis is a data set of spectral reflectance (400-2500 nm) of small, monospecific canopies formed from seedlings of Douglas-fir (Pseudotsuga menziesii) and bigleaf maple (Acer macrophyllum). The trees were provided different levels of fertilization in order to produce canopies with varying nitrogen and chlorophyll concentration. For the Douglas-fir, fertilization was provided during the dormant season, so there were no differences in growth or leaf area among canopies, and canopies were at a constant density with varying foliar chemistry. For the maple, seedlings were aggregated at various densities, producing a matrix of leaf area as well as chemistry variations. Canopy reflectance was measured under natural sunlight, and canopies were then destructively analyzed for chemical content and leaf area (see ACCP Seedling Canopy Chemistry Data).\n<br>\n\n#### plotspec_544\nAVIRIS image scenes were acquired in 1992 over ACCP sites. Pixels that coincided with field study plots were extracted and reflectance values were correlated with estimated canopy carbon and nitrogen content.\n<br>\n\n#### leafspec_424\nThe leaf spectra datasets contain visible and near infrared reflectance spectra data for both fresh and dry leaf samples collected in the ACCP. These samples are from Blackhawk Island, WI, Harvard Forest, MA, Howland, ME, Jasper Ridge, CA field sites and the Douglas fir and bigleaf maple seedling canopy study sites. Data reported for each sample is absorbance [log(1/Reflectance)] from 400-2498nm at 2nm intervals and a resolution of 10nm. These data were collected for the purpose of determining the relationship of foliar chemical concentrations with visible and near infrared wavelength reflectance spectra.. Both multiple linear regression and partial least square regression techniques have been used to relate lab chemistry data to spectral reflectance. ORNL DAAC maintains information on the entire ACCP.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/ACCP/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"plotchem_420 v1 - The purpose of these measurements was to determine plot-level average leaf concentrations of nitrogen, lignin, cellulose, etc. in order to investigate how AVIRIS reflectance measurements vary with chemistry.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/accp/plotchem/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"leafchem_421 v1 - As part of NASA''s Accelerated Canopy Chemistry Program (ACCP) analyses were performed for the determination of carbon constituents and nitrogen content in fresh forest foliage. Samples were analyzed using a series of extraction''s that yielded different carbon constituents: non-polar, polar, cellulose and lignin.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/accp/leafchem/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"canopychem_422 v1 - This data set describes the nitrogen and chlorophyll content of small, monospecific canopies formed from seedlings of Douglas-fir (Pseudotsuga menziesii) and bigleaf maple (Acer macrophyllum). The trees were provided different levels of fertilization in order to produce canopies with varying nitrogen and chlorophyll concentration.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/accp/canopychem/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"canopyspec_423 v1 - This is a data set of spectral reflectance (400-2500 nm) of small, monospecific canopies formed from seedlings of Douglas-fir (Pseudotsuga menziesii) and bigleaf maple (Acer macrophyllum). The trees were provided different levels of fertilization in order to produce canopies with varying nitrogen and chlorophyll concentration.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/accp/canopyspec/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"plotspec_544 v1 - AVIRIS image scenes were acquired in 1992 over ACCP sites. Pixels that coincided with field study plots were extracted and reflectance values were correlated with estimated canopy carbon and nitrogen content.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/accp/plotspec/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"leafspec_424 v1 - The leaf spectra datasets contain visible and near infrared reflectance spectra data for both fresh and dry leaf samples collected in the ACCP. These samples are from Blackhawk Island, WI, Harvard Forest, MA, Howland, ME, Jasper Ridge, CA field sites and the Douglas fir and bigleaf maple seedling canopy study sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/accp/leafspec/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-accp","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ACRIM III Project","Description":"Launch and mission info for NASA's AcrimSat Earth satellite, which for 14 years monitored solar radiation and its effects on Earth's atmosphere and climate change.\n<br>\n\n#### ACR3L2DM\nACR3L2DM_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Daily Mean Data version 1 product consists of Level 2 total solar irradiance in the form of daily means gathered by the ACRIM III instrument on the ACRIMSAT satellite. The daily means are constructed from the shutter cycle results for each day.\n<br>\n\n#### ACR3L2SC\nACR3L2SC_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Shutter Cycle Data version 1 product contains Level 2 total solar irradiance in the form of shutter cycles gathered by the ACRIM instrument on the ACRIMSAT satellite.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.jpl.nasa.gov/missions/active-cavity-irradiance-monitor-satellite-acrimsat/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ACR3L2DM v1 - ACR3L2DM_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Daily Mean Data version 1 product consists of Level 2 total solar irradiance in the form of daily means gathered by the ACRIM III instrument on the ACRIMSAT satellite. The daily means are constructed from the shutter cycle results for each day.","ARN":"arn:aws:s3:::asdc-prod-protected/ACRIM_III/ACR3L2DM_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ACR3L2SC v1 - ACR3L2SC_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Shutter Cycle Data version 1 product contains Level 2 total solar irradiance in the form of shutter cycles gathered by the ACRIM instrument on the ACRIMSAT satellite.","ARN":"arn:aws:s3:::asdc-prod-protected/ACRIM_III/ACR3L2SC_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-acrim-iii","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ACT-America Project","Description":"The ACT-America Campaign Catalog provides information about the airborne campaigns of the Atmospheric Carbon and Transport (ACT-America) project. ACT-America advanced atmospheric greenhouse gas inversions to a high level of accuracy and precision through new methods and models that improved knowledge of atmospheric transport, prior flux models, and space-based observations. The catalog compiles flight details for the five campaigns conducted during Summer 2016, Winter 2017, Fall 2017, Spring 2018, and Summer 2019 (2016-05-27 to 2019-07-26) across three regions of the eastern and central United States. Data include flight dates, regions, objectives, weather conditions, instrument status, aircraft flight paths, detailed weather reports, and measurement summary figures. A total of 121 research flights were conducted within the five six-week seasonal campaigns by each of the two instrumented aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Wallops Flight Facility's C-130 Hercules. During 1,140 flight hours remote and in situ sensors onboard the two research aircraft measured greenhouse gas mole fractions, trace gases, and thermodynamic variables across a variety of continental surfaces and atmospheric conditions to study the transport and fluxes of atmospheric carbon dioxide and methane. As noted in the Flight_patterns_staus field, there were flights when both aircraft flew directly under Orbiting Carbon Observatory-2 (OCO-2) overpasses to evaluate the ability of OCO-2 to observe high-resolution atmospheric CO2 variations. The C-130 aircraft was also equipped with active remote sensing instruments for planetary boundary layer height detection and column greenhouse gas measurements. The data are provided in comma separated values (CSV), compressed Keyhole Markup Language (KMZ) formats along with figures as JPEG and Portable Network Graphics images.\n<br>\n\n#### CPL_ABL_Top_Height_1825\nThis dataset consists of the atmospheric boundary layer (ABL) top heights and the altitudes of the two additional aerosol layers (in km above mean sea level) derived from Cloud Physics Lidar (CPL) measurements using the Haar wavelet transform method. The CPL instrument was deployed onboard NASA's C-130 aircraft to obtain aerosol backscatter profiles during four ACT-America field campaigns (Summer 2016, Winter 2017, Fall 2017, and Spring 2018). CPL is a backscatter lidar designed to operate simultaneously at three wavelengths. The profiles were collected at 4-second temporal and 30 m vertical resolutions. The time resolution of the provided CPL-derived ABL top heights and other aerosol layers are 8 seconds.\n<br>\n\n#### ACT_CASA_Ensemble_Prior_Fluxes_1675\nThis data set provides gridded, model-derived gross primary productivity (GPP), ecosystem respiration (RECO), and net ecosystem exchange (NEE) of CO2 biogenic fluxes and their uncertainties at monthly and 3-hourly time scales over 2003-2019 on a 463-m spatial resolution grid for the conterminous United States (CONUS) and on both 5-km and half-degree spatial resolution grids for North America (NA). The biogeochemical model Carnegie Ames Stanford Approach (CASA) was used.\n<br>\n\n#### HALO_LiDAR_AOP_ML_Heights_1833\nThis dataset provides measurements from the High Altitude Lidar Observatory (HALO) instrument, an airborne multi-function Differential Absorption Lidar (DIAL) and High Spectral Resolution Lidar (HSRL), operating at 532 nm and 1064 nm wavelengths onboard a C-130 aircraft during the June and July 2019 ACT-America campaign. The flights took place over eastern and central North America based from Shreveport, Louisiana; Lincoln, Nebraska; and NASA Wallops Flight Facility located on the eastern shore of Virginia. HALO data were sampled at 0.5 s temporal and 1.25 m vertical resolutions. The data include profiles of aerosol optical properties (AOP), distributions of mixed layer heights (MLH), columns of tropospheric methane, and navigation parameters. The data are provided in HDF5 format along with PNG images and a companion files in Portable Document (.pdf) format.\n<br>\n\n#### ACTAMERICA_MFLL_L1_1817\nThis dataset provides Level 1 (L1) remotely sensed differential absorption optical depth (DAOD) measurements made through the Multi-Functional Fiber Laser Lidar (MFLL; Harris Corporation) during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the United States for the Atmospheric Carbon and Transport (ACT-America) project. DAOD were measured at 0.1 second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with MFLL. The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1 second column CO2 reporting frequency, are included, but not limited to, latitude, longitude, altitude, and attitude. Data users should note that a Level 2 (L2) MFLL data product is available (related dataset) that contains all data variables (plus the column-average CO2) included in this L1 MFLL data product but has undergone additional processing and calibrations and is recommended for most use cases.\n<br>\n\n#### ACTAMERICA_Hskping_1574\nThis dataset provides aircraft navigational parameters and related meteorological data (often referred to as \"housekeeping\" data) in support of the research activities for the two aircrafts that flew for the NASA Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spans five years and includes five 6-week intensive field campaigns covering all 4 seasons and 3 regions of the central and eastern United States. Two instrumented aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. During these flights, aircraft positional, meteorological, and environmental data are recorded by a variety of instruments. For this dataset, measurements include, but are not limited to: latitude, longitude, altitude, ground speed, air temperature, and wind speed and direction. These data are incorporated into related ACT-America flight-instrumented datasets to provide geotrajectory file information for position, attitude, and altitude awareness of instrumented sampling.\n<br>\n\n#### Insitu_Tower_Greenhouse_Gas_1798\nThis dataset provides Level 1 (L1) in situ atmospheric carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) concentrations as measured on a network of instrumented communications towers across the central and eastern USA operated by the Atmospheric Carbon and Transport-America (ACT-America) project. There were 11 towers instrumented with cavity ring-down spectrometers (CRDS; Picarro Inc.) with measurements beginning in January 2015 and continuing to October 2019. The measurement period varied by tower site. The Picarro analyzers continuously measured total CH4, isotopic ratio of CH4, CO2, CO, and other greenhouse gas concentrations. Not all species were measured at all sites. Complete tower location, elevation, instrument height, and date/time information are also provided. Determination of greenhouse gas fluxes and uncertainty bounds is essential for the evaluation of the effectiveness of mitigation strategies. These L1 data are raw instrument outputs from the Picarro instruments. A Level 2 (L2) product derived from this L1 data is available and generally would be the preferred data for most use cases.\n<br>\n\n#### ACTAMERICA_PICARRO_1556\nThis dataset provides atmospheric carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), water vapor (H2O), and ozone (O3) concentrations collected during airborne campaigns conducted by the Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spanned 4 years and included five 6-week airborne campaigns covering all 4 seasons and 3 regions of the central and eastern United States. This dataset provides results from all five campaigns, including Summer 2016, Winter 2017, Fall 2017, Spring 2018, and Summer 2019. Two instrumented aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. CO2, CO, CH4, and H2O were collected with an infrared cavity ring-down spectrometer system (CRDS; Picarro Inc.). Ozone data were collected with a dual beam differential UV absorption ozone monitor (Model 205; 2B Technologies). Both aircraft hosted identical arrays of in situ sensors. Complete aircraft flight information including, but not limited to, latitude, longitude, altitude, and meteorological conditions are also provided.\n<br>\n\n#### ACTAMERICA_PFP_1575\nThis dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e., concentrations) from airborne campaigns over North America for the NASA Atmospheric Carbon and Transport - America (ACT-America) project. ACT-America's mission spanned five years and included five six-week field campaigns covering all four seasons and three regions of the central and eastern United States. Two instrumented aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. The data were derived from laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard the two ACT-America aircraft. Approximately 10 - 12 discrete flask samples were captured during each of the 195 flights. This dataset provides results from all five campaigns, including Summer 2016, Winter 2017, Fall 2017, Spring 2018, and Summer 2019.\n<br>\n\n#### ACTAMERICA-PICARRO_Ground_1568\nThis dataset provides atmospheric carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) concentrations as measured on a network of instrumented communications towers operated by the Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spans five years and includes five 6-week intensive field campaigns covering all 4 seasons and 3 regions of the central and eastern United States. Tower-based measurements began in early 2015 and are continuously collecting CO2, CO, and CH4 data to characterize ground-level (>100 m) carbon background conditions to support the periodic airborne measurement campaigns and transport modeling conducted by ACT-America. The towers are instrumented with infrared cavity ring-down spectrometer systems (CRDS; Picarro Inc.). Data are reported for the highest sampling port on each tower. The averaging interval standard deviation and uncertainty derived from periodic flask sample to in-situ measurement comparisons are provided. Complete tower location, elevation, instrument height, and date/time information are also provided.\n<br>\n\n#### ACTAMERICA_MFFLL_1649\nThis dataset provides Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the United States for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at 0.1 second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1 second column CO2 reporting frequency, are included, but not limited to, latitude, longitude, altitude, and attitude. Processing for this Level 2 (L2) product included additional processing and calibration procedures described in this document as applied to retrieval of column CO2 from L1 MFLL data. Data users should use this L2 data unless different CO2 retrieval criteria are preferred.\n<br>\n\n#### MFLL_XCO2_Range_10Hz_1892\nThis dataset provides a direct subset (i.e., the Lite version) of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1-second column CO2 reporting frequency, is included, but not limited to, latitude, longitude, altitude, and attitude.\n<br>\n\n#### MFLL_CO2_Weighting_Functions_1891\nThis dataset provides vertical weighting function coefficients of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. The MFLL-measured column-averaged CO2 values have certain distinct vertical weights on CO2 profiles depending on the meteorological conditions and the wavelengths used at the measurement time and location. This product includes the instrument location at the time of measurement in geographic coordinates and altitude, along with a vector of weighting function values representing conditions along the nadir direction.\n<br>\n\n#### ACTAMERICA_Merge_1593\nThis dataset provides merged data products acquired during flights over the central and eastern United States as part of the Atmospheric Carbon and Transport - America (ACT-America) project. Two aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. The merged data products are composed of continuous in situ measurements of atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), ozone (O3), and ethane (C2H6, B200 aircraft only) that were averaged to uniform intervals and merged with aircraft navigation and meteorological variables as well as trace gas concentrations from discrete flask samples collected with the Programmable Flask Package (PFP). These merged data products provide integrated measurements at intervals useful to the modeling community for studying the transport and fluxes of atmospheric carbon dioxide and methane across North America.\n<br>\n\n#### Profile_based_PBL_heights_1706\nThis dataset provides profile-based estimates of the height to the top of the planetary boundary layer (PBL), also known as the atmospheric boundary layer (ABL), in meters above mean sea level estimated from meteorological measurements acquired during ascending or descending vertical profile flight segments during NASA's Atmospheric Carbon and Transport - America (ACT-America) airborne campaign. ACT-America flights sampled the atmosphere over the central and eastern United States seasonally from 2016 - 2019. Two aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions.\n<br>\n\n#### ACTAMERICA_WRF_Chem_Output_1884\nThis dataset includes hourly output from the WRF-Chem simulation model for North America at a resolution of 27 km for 2016-06-29 through 2019-07-31. WRF-Chem is the Weather Research and Forecasting (WRF) model coupled with Chemistry. The output provides baseline conditions for comparison to data from ACT-America airborne campaigns conducted to study atmospheric CO2 and CH4 from 2016 to 2019. The WRF-Chem (v. 3.6.1) model was driven by meteorological conditions and sea-surface temperatures. The output includes 50 vertical layers up to atmospheric pressure of 50 hPa with 20 levels in the lowest 1 km. It provides information for understanding the fluxes and atmospheric transport of carbon dioxide (CO2), methane (CH4), and ethane (C2H6).\n<br>\n\n#### FLEXPART_Influence_Functions_2018\nThis dataset contains a set of Lagrangian particle dispersion simulations of carbon dioxide concentrations using the FLEXible PARTicle (FLEXPART) model. FLEXPART quantified the source-receptor relationships, so-called \"influence functions\", in a backward mode. The simulations were constructed for five Atmospheric Carbon and Transport America (ACT-America) deployments over the eastern U.S. that occurred in 2016-2019. Each receptor of the influence function is the 30-second or 10-minute interval along flight tracks, characterized by a box with boundaries between the maximum and minimum latitude/longitude as well as between the maximum and minimum altitudes during the interval. Each receptor box released 5,000 particles and simulated their transport and dispersion backward for 10 or 20 days. The simulations were driven by 27-km meteorology provided by the WRF-Chem simulation or by ERA-Interim data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Background levels of carbon dioxide were obtained from CarbonTracker and OCO-2 v9 MIP. The data are provided in netCDF and FLEXPART binary formats.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://actamerica.ornl.gov","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","earth observation","elevation","hdf","hdf5","lidar","netcdf","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ACTAMERICA_Campaign_Catalog_1862 v1 - The ACT-America Campaign Catalog provides information about the airborne campaigns of the Atmospheric Carbon and Transport (ACT-America) project. ACT-America advanced atmospheric greenhouse gas inversions to a high level of accuracy and precision through new methods and models that improved knowledge of atmospheric transport, prior flux models, and space-based observations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/ACTAMERICA_Campaign_Catalog/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CPL_ABL_Top_Height_1825 v1 - This dataset consists of the atmospheric boundary layer (ABL) top heights and the altitudes of the two additional aerosol layers (in km above mean sea level) derived from Cloud Physics Lidar (CPL) measurements using the Haar wavelet transform method. The CPL instrument was deployed onboard NASA's C-130 aircraft to obtain aerosol backscatter profiles during four ACT-America field campaigns (Summer 2016, Winter 2017, Fall 2017, and Spring 2018).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/CPL_ABL_Top_Height/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ACT_CASA_Ensemble_Prior_Fluxes_1675 v1.1 - This data set provides gridded, model-derived gross primary productivity (GPP), ecosystem respiration (RECO), and net ecosystem exchange (NEE) of CO2 biogenic fluxes and their uncertainties at monthly and 3-hourly time scales over 2003-2019 on a 463-m spatial resolution grid for the conterminous United States (CONUS) and on both 5-km and half-degree spatial resolution grids for North America (NA). The biogeochemical model Carnegie Ames Stanford Approach (CASA) was used.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/ACT_CASA_Ensemble_Prior_Fluxes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"HALO_LiDAR_AOP_ML_Heights_1833 v1 - This dataset provides measurements from the High Altitude Lidar Observatory (HALO) instrument, an airborne multi-function Differential Absorption Lidar (DIAL) and High Spectral Resolution Lidar (HSRL), operating at 532 nm and 1064 nm wavelengths onboard a C-130 aircraft during the June and July 2019 ACT-America campaign. The flights took place over eastern and central North America based from Shreveport, Louisiana; Lincoln, Nebraska; and NASA Wallops Flight Facility located on the eastern shore of Virginia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/HALO_LiDAR_AOP_ML_Heights/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ACTAMERICA_MFLL_L1_1817 v1 - This dataset provides Level 1 (L1) remotely sensed differential absorption optical depth (DAOD) measurements made through the Multi-Functional Fiber Laser Lidar (MFLL; Harris Corporation) during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the United States for the Atmospheric Carbon and Transport (ACT-America) project. DAOD were measured at 0.1 second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with MFLL.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/ACTAMERICA_MFLL_L1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ACTAMERICA_Hskping_1574 v1.1 - This dataset provides aircraft navigational parameters and related meteorological data (often referred to as \"housekeeping\" data) in support of the research activities for the two aircrafts that flew for the NASA Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spans five years and includes five 6-week intensive field campaigns covering all 4 seasons and 3 regions of the central and eastern United States.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/ACTAMERICA_Hskping/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Insitu_Tower_Greenhouse_Gas_1798 v1 - This dataset provides Level 1 (L1) in situ atmospheric carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) concentrations as measured on a network of instrumented communications towers across the central and eastern USA operated by the Atmospheric Carbon and Transport-America (ACT-America) project. There were 11 towers instrumented with cavity ring-down spectrometers (CRDS; Picarro Inc.) with measurements beginning in January 2015 and continuing to October 2019.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/Insitu_Tower_Greenhouse_Gas/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ACTAMERICA_PICARRO_1556 v1.2 - This dataset provides atmospheric carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), water vapor (H2O), and ozone (O3) concentrations collected during airborne campaigns conducted by the Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spanned 4 years and included five 6-week airborne campaigns covering all 4 seasons and 3 regions of the central and eastern United States.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/ACTAMERICA_PICARRO/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ACTAMERICA_PFP_1575 v1.2 - This dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e., concentrations) from airborne campaigns over North America for the NASA Atmospheric Carbon and Transport - America (ACT-America) project. ACT-America's mission spanned five years and included five six-week field campaigns covering all four seasons and three regions of the central and eastern United States.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/ACTAMERICA_PFP/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ACTAMERICA-PICARRO_Ground_1568 v1.1 - This dataset provides atmospheric carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) concentrations as measured on a network of instrumented communications towers operated by the Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spans five years and includes five 6-week intensive field campaigns covering all 4 seasons and 3 regions of the central and eastern United States.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/ACTAMERICA-PICARRO_Ground/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ACTAMERICA_MFFLL_1649 v1.1 - This dataset provides Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the United States for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at 0.1 second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/ACTAMERICA_MFFLL/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MFLL_XCO2_Range_10Hz_1892 v1 - This dataset provides a direct subset (i.e., the Lite version) of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/MFLL_XCO2_Range_10Hz/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MFLL_CO2_Weighting_Functions_1891 v1 - This dataset provides vertical weighting function coefficients of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/MFLL_CO2_Weighting_Functions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ACTAMERICA_Merge_1593 v1.2 - This dataset provides merged data products acquired during flights over the central and eastern United States as part of the Atmospheric Carbon and Transport - America (ACT-America) project. Two aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/ACTAMERICA_Merge/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Profile_based_PBL_heights_1706 v1.1 - This dataset provides profile-based estimates of the height to the top of the planetary boundary layer (PBL), also known as the atmospheric boundary layer (ABL), in meters above mean sea level estimated from meteorological measurements acquired during ascending or descending vertical profile flight segments during NASA's Atmospheric Carbon and Transport - America (ACT-America) airborne campaign. ACT-America flights sampled the atmosphere over the central and eastern United States seasonally from 2016 - 2019.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/Profile_based_PBL_heights/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ACTAMERICA_WRF_Chem_Output_1884 v1 - This dataset includes hourly output from the WRF-Chem simulation model for North America at a resolution of 27 km for 2016-06-29 through 2019-07-31. WRF-Chem is the Weather Research and Forecasting (WRF) model coupled with Chemistry.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/ACTAMERICA_WRF_Chem_Output/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"FLEXPART_Influence_Functions_2018 v1 - This dataset contains a set of Lagrangian particle dispersion simulations of carbon dioxide concentrations using the FLEXible PARTicle (FLEXPART) model. FLEXPART quantified the source-receptor relationships, so-called \"influence functions\", in a backward mode.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/actamerica/FLEXPART_Influence_Functions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-act-america","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA AEHYP Project","Description":"The Airborne Hyperspectral Reflectance Indian Cave Nebraska Multi-Day (AEHYPICNE1M) data are from the Indian Cave Forest Global Earth Observatory ([ForestGeo](https://forestgeo.si.edu/)) plot in Indian Cave State Park in southeastern Nebraska. The data have a spatial resolution of 1 meter (m) and fall in the spectral range of 400-1000 nanometers (nm). The data can be used by researchers in developing new capabilities for the remote sensing of forest diversity and function, as well as a global biodiversity monitoring system. The [Indian Cave ForestGeo](https://forestgeo.si.edu/sites/north-america/indian-cave) plot airborne data were collected on September 6, 2019, and August 4, 2022. The images were collected by the Nebraska Earth Observatory (NEO), using an AisaKESTREL (Specim, Oulu, Finland) hyperspectral pushbroom sensor mounted on a Piper Saratoga fixed-wing aircraft. The AEHYPICNE1M product provides images with 178 hyperspectral bands that have been radiometrically, geometrically, and atmospherically corrected.\n<br>\n\n#### AEHYP1TPPOK\nThe Airborne Hyperspectral Reflectance data are from The Nature Conservancy’s Tallgrass Prairie Preserve in northeastern Oklahoma. The data have a spatial resolution of 1 meter (m) and fall in the spectral range of 400-2450 nanometers (nm). These data can be used to develop approaches for studying grassland biodiversity. The Nature Conservancy’s Tallgrass Prairie Preserve airborne data were collected on three separate occasions: August 3, 2020, July 31, 2021, and July 23, 2022. The images were collected by SpecTIR Remote Sensing (SRS) Division, contracted by Oklahoma State University, using an AisaFENIX 1K (Specim, Oulu, Finland) pushbroom sensor mounted on a Twin Commander 500 fixed wing aircraft. The AEHYP1TPPOK product provides orthorectified individual flight lines of 323 hyperspectral bands that have been radiometrically, geometrically, and atmospherically corrected.\n<br>\n\n#### AEHYPCCMN300MM\nThe Airborne Hyperspectral Reflectance image mosaic is of the Long-Term Ecological Research (LTER) site at Cedar Creek Ecosystem Science Reserve (CCESR), Minnesota (AEHYPCCMN300MM). The image is at approximate 1 meter and resampled to 300 millimeter (0.3 meter) spatial resolution during processing in the 400 to 970 nanometer (nm) spectral range. This image can be used to understand the optical diversity-biodiversity relationship and investigate the spatial sensitivity of the relationship at local scales. Airborne data for the LTER Cedar Creek site were collected on August 2, 2014, using an AisaEAGLE airborne hyperspectral system mounted on a fixed-wing Piper Saratoga aircraft. The images were collected from a height of 1,540 m and spectral binning (approximately 10 nm) increased the signal-to-noise ratio of the data. Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) software was used for atmospheric correction in converting radiance to reflectance. Airborne data covered all the prairie grassland plots in the biodiversity (BioDIV) experiment and the Biodiversity and Climate experiment. The data were mosaicked as one image. Provided in the AEHYPCCMN300MM product are 64 hyperspectral bands in an Environment for Visualizing Images (ENVI) format.\n<br>\n\n#### AEHYPWRNE1M\nThe Airborne Hyperspectral Reflectance image mosaic is of the long-term prairie restoration and biodiversity study site led by the Nature Conservancy near Wood River, Nebraska (AEHYPWRNE1M). The approximately 1-meter (m) spatial resolution image was resampled in the 397 to 1004 nanometer (nm) spectral range. This image can be used to detect grassland biodiversity over time to help develop a regional biodiversity monitoring program. Airborne data for the long-term prairie restoration and biodiversity Wood River site were collected on four separate occasions: August 23, 2017; June 27, 2018; August 24, 2018; October 16, 2018. The images were collected using an AisaKESTREL compact hyperspectral pushbroom sensor. Provided in the AEHYPWRNE1M product are 178 hyperspectral bands that have been radiometrically, geometrically, and atmospherically corrected.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/centers/lp-daac","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","earth observation"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AEHYPICNE1M v001 - The Airborne Hyperspectral Reflectance Indian Cave Nebraska Multi-Day (AEHYPICNE1M) data are from the Indian Cave Forest Global Earth Observatory ([ForestGeo](https://forestgeo.si.edu/)) plot in Indian Cave State Park in southeastern Nebraska. The data have a spatial resolution of 1 meter (m) and fall in the spectral range of 400-1000 nanometers (nm).","ARN":"arn:aws:s3:::lp-prod-protected/AEHYPICNE1M.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AEHYP1TPPOK v001 - The Airborne Hyperspectral Reflectance data are from The Nature Conservancy’s Tallgrass Prairie Preserve in northeastern Oklahoma. The data have a spatial resolution of 1 meter (m) and fall in the spectral range of 400-2450 nanometers (nm).","ARN":"arn:aws:s3:::lp-prod-protected/AEHYP1TPPOK.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AEHYPCCMN300MM v001 - The Airborne Hyperspectral Reflectance image mosaic is of the Long-Term Ecological Research (LTER) site at Cedar Creek Ecosystem Science Reserve (CCESR), Minnesota (AEHYPCCMN300MM). The image is at approximate 1 meter and resampled to 300 millimeter (0.3 meter) spatial resolution during processing in the 400 to 970 nanometer (nm) spectral range.","ARN":"arn:aws:s3:::lp-prod-protected/AEHYPCCMN300MM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AEHYPWRNE1M v001 - The Airborne Hyperspectral Reflectance image mosaic is of the long-term prairie restoration and biodiversity study site led by the Nature Conservancy near Wood River, Nebraska (AEHYPWRNE1M). The approximately 1-meter (m) spatial resolution image was resampled in the 397 to 1004 nanometer (nm) spectral range.","ARN":"arn:aws:s3:::lp-prod-protected/AEHYPWRNE1M.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-aehyp","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA AfriSAR Project","Description":"This data set contains geotagged images collected over Gabon, Africa. The images were taken by the NASA Digital Mapping Camera paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR.\n<br>\n\n#### AFLVIS1B\nThis data set contains return energy waveform data over Gabon, Africa. The measurements were taken by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR.\n<br>\n\n#### AFLVIS2\nThis data set contains surface elevation data over Gabon, Africa. The measurements were taken by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR.\n<br>\n\n#### AfriSAR_AGB_Maps_1681\nThis dataset provides gridded estimates of aboveground biomass (AGB) for four sites in Gabon at 0.25 ha (50 m) resolution derived with field measurements and airborne LiDAR data collected from 2010 to 2016. The sites represent a mix of forested, savannah, and some agricultural and disturbed landcover types: Lope site, within Lope National Park; Mabounie, mostly forested site; Mondah Forest, protected area; and the Rabi forest site, part of the Smithsonian Institution of Global Earth Observatories world-wide network of forest plots. Plot-level biophysical measurements of tree diameter and tree height (or estimated by allometry) were performed at 1 ha and 0.25 ha scales on multiple plots at each site and used to derive AGB for each tree and then summed for each plot. Aerial LiDAR scans were used to construct digital elevation models (DEM) and digital surface models (DSM), and then the DEM and DSM were used to construct a canopy height model (CHM) at 1 m resolution. After checking site-plot locations against the CHM, mean canopy height (MCH) was computed over each 0.25 ha. A single regression model relating MCH and AGB estimates, incorporating local height based on the trunk DBH (HD) relationships, was produced for all sites and combined with the CHM layer to construct biomass maps at 0.25 ha resolution.\n<br>\n\n#### AfriSAR_LVIS_Footprint_Cover_1591\nThis dataset includes footprint-level canopy structure products derived from data collected using NASA's Land, Vegetation, and Ice Sensor (LVIS) during flights over five forested sites in Gabon during February and March 2016. Three types of canopy structure information are included for each flight: 1) vertical profiles of canopy cover fraction in 1-meter bins, 2) vertical profiles of plant area index (PAI) in 1-meter bins, and 3) footprint summary data of total recorded energy, leaf area index, canopy cover fraction, and vertical foliage profiles in 10-meter bins. Canopy structure metrics are provided for each waveform (20-m footprint) collected by the LVIS instrument. These data were collected by NASA as part of the AfriSAR project. AfriSAR is a NASA collaboration with the European Space Agency (ESA), German Aerospace Center (DLR), and the Gabonese Space Agency (AGEOS) that is collecting data useful for deriving forest canopy structure and will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle.\n<br>\n\n#### Polarimetric_CT_1601\nThis dataset contains forest vertical structure and associated uncertainty products derived by applying multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) and Polarimetric Coherence Tomographic SAR (PCT or PC-TomoSAR) techniques. The data were collected from multiple repeat-pass flights over Gabonese forests using the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument in February-March 2016. In addition, supplementary data products based on various intermediate parameters of the UAVSAR data are provided and include radar backscatter, coherence, and viewing and terrain geometry. These data were collected by NASA as part of the joint NASA/ESA AfriSAR campaign.\n<br>\n\n#### Afrisar_LVIS_Biomass_VProfiles_1775\nThis dataset contains gridded forest characterization products derived from full-waveform lidar data acquired by NASA's airborne Land, Vegetation, and Ice Sensor (LVIS) instrument for five forested sites in Gabon, Africa, during the 2016 NASA-ESA AfriSAR campaign. The LVIS lidar instrument was flown over study sites in Lope, Mondah/Akanda, Pongara, Rabi, and Mabouni from February to March 2016. Derived canopy cover, canopy heights, bare ground elevation, plant area index (PAI), and foliage height diversity (FHD), and respective uncertainties are provided at a 25 m resolution for each of the five study sites. Aboveground biomass density (AGBD) and uncertainty were modeled at 50 m and 100 m resolutions for the Lope, Mondah, and Mabounie sites using field inventory data and waveform height and cover metrics. Lidar grid cell data collection statistics (i.e., number of shots and flight lines) and a data mask are also included. This research leverages high-quality forest inventory datasets collected during the AfriSAR campaign for one of the least studied and most unique forest ecosystems in the world.\n<br>\n\n#### AfriSAR_Mondah_Field_Data_1580\nThis dataset provides plot-level estimates of basal area, aboveground biomass, number of trees, maximum tree height, and basal-area-weighted wood specific gravity that were derived from observations of nearly 6,700 individual trees including tree family, species, DBH, the height of each tree, and their x, y location within 25 x 25 m subplots. These field data were collected from 15 1-hectare plots located across the Mondah Forest of Gabon as part of the AfriSAR Campaign in 2016. These biophysical and biomass data were used for training models to derive the AfriSAR remote sensing-based aboveground biomass products.\n<br>\n\n#### Polarimetric_height_profile_1577\nThis dataset provides height profiles derived from UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar; JPL) data acquired over Lope National Park and Rabi Forest in Gabon as part of the AfriSAR campaign in 2016. These data were produced using synthetic aperture radar tomography (TomoSAR), a method that reveals three-dimensional forest structures by extending the conventional two-dimensional imaging capabilities of radars using multiple images acquired from slightly different antenna positions. AfriSAR was an airborne campaign that collected radar, lidar, and field measurements of forests in Gabon, West Africa, as part of a collaborative mission between NASA, the European Space Agency, and the Gabonese Space Agency. These data will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle, such as the Global Ecosystem Dynamics Investigation (GEDI).\n<br>\n\n#### PolInSAR_Canopy_Height_1589\nThis dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library. 2) Canopy height was derived from a fusion of PolInSAR and Land, Vegetation, and Ice Sensor (LVIS) Lidar data. This dataset also includes various intermediate parameters of the PolInSAR data (including radar backscatter, coherence, and viewing and terrain geometry) which provide additional insight into the input data used to invert the RVoG model and accuracy of the canopy height estimates. The AfriSAR campaign was flown from 2016-02-27 to 2016-03-08. AfriSAR data were collected by NASA, in collaboration with the European Space Agency (ESA) and the Gabonese Space Agency.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/afolvis1a/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","earth observation","elevation","ice","lidar","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AFOLVIS1A v1 - This data set contains geotagged images collected over Gabon, Africa. The images were taken by the NASA Digital Mapping Camera paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/AFOLVIS1A/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AFLVIS1B v1 - This data set contains return energy waveform data over Gabon, Africa. The measurements were taken by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/AFLVIS1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AFLVIS2 v1 - This data set contains surface elevation data over Gabon, Africa. The measurements were taken by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/LVIS/AFLVIS2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AfriSAR_AGB_Maps_1681 v1 - This dataset provides gridded estimates of aboveground biomass (AGB) for four sites in Gabon at 0.25 ha (50 m) resolution derived with field measurements and airborne LiDAR data collected from 2010 to 2016. The sites represent a mix of forested, savannah, and some agricultural and disturbed landcover types: Lope site, within Lope National Park; Mabounie, mostly forested site; Mondah Forest, protected area; and the Rabi forest site, part of the Smithsonian Institution of Global Earth Observatories world-wide network of forest plots.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/afrisar/AfriSAR_AGB_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AfriSAR_LVIS_Footprint_Cover_1591 v1 - This dataset includes footprint-level canopy structure products derived from data collected using NASA''s Land, Vegetation, and Ice Sensor (LVIS) during flights over five forested sites in Gabon during February and March 2016. Three types of canopy structure information are included for each flight: 1) vertical profiles of canopy cover fraction in 1-meter bins, 2) vertical profiles of plant area index (PAI) in 1-meter bins, and 3) footprint summary data of total recorded energy, leaf area index, canopy cover fraction, and vertical foliage profiles in 10-meter bins.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/afrisar/AfriSAR_LVIS_Footprint_Cover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Polarimetric_CT_1601 v1 - This dataset contains forest vertical structure and associated uncertainty products derived by applying multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) and Polarimetric Coherence Tomographic SAR (PCT or PC-TomoSAR) techniques. The data were collected from multiple repeat-pass flights over Gabonese forests using the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument in February-March 2016.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/afrisar/Polarimetric_CT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Afrisar_LVIS_Biomass_VProfiles_1775 v1 - This dataset contains gridded forest characterization products derived from full-waveform lidar data acquired by NASA's airborne Land, Vegetation, and Ice Sensor (LVIS) instrument for five forested sites in Gabon, Africa, during the 2016 NASA-ESA AfriSAR campaign. The LVIS lidar instrument was flown over study sites in Lope, Mondah/Akanda, Pongara, Rabi, and Mabouni from February to March 2016.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/afrisar/Afrisar_LVIS_Biomass_VProfiles/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AfriSAR_Mondah_Field_Data_1580 v1 - This dataset provides plot-level estimates of basal area, aboveground biomass, number of trees, maximum tree height, and basal-area-weighted wood specific gravity that were derived from observations of nearly 6,700 individual trees including tree family, species, DBH, the height of each tree, and their x, y location within 25 x 25 m subplots. These field data were collected from 15 1-hectare plots located across the Mondah Forest of Gabon as part of the AfriSAR Campaign in 2016.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/afrisar/AfriSAR_Mondah_Field_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Polarimetric_height_profile_1577 v1 - This dataset provides height profiles derived from UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar; JPL) data acquired over Lope National Park and Rabi Forest in Gabon as part of the AfriSAR campaign in 2016. These data were produced using synthetic aperture radar tomography (TomoSAR), a method that reveals three-dimensional forest structures by extending the conventional two-dimensional imaging capabilities of radars using multiple images acquired from slightly different antenna positions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/afrisar/Polarimetric_height_profile/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PolInSAR_Canopy_Height_1589 v1 - This dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/afrisar/PolInSAR_Canopy_Height/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-afrisar","Sources":["awslabs-open-data-registry"]}
{"Name":"AIRS/Aqua L1B Infrared (IR) geolocated and calibrated radiances V005 (AIRIBRAD) at GES DISC","Description":"WARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space.\n\nThe thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the “spectral_freq” parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Maximum BT shifts are approximately +- 0.5 K, although many channels experience far smaller BT shifts. Approximately 1803 channels have BT shifts of less than 0.1 K and 575 channels are now shifted in BT by more than 0.1 K, while 231 of these channels have BT shifts greater than 0.2 K.\n\nUsers of the L1b v5 product who are concerned that these shifts may impact their science investigations and applications are encouraged to switch to the AIRS L1c v6.7.4 product, which, among many other improvements, converts the spectra to a fixed frequency grid.          END OF WARNING.\n\nThe Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1B data set contains AIRS calibrated and geolocated radiances in milliWatts/m^2/cm^-1/steradian for 2378 infrared channels in the 3.74 to 15.4 micron region of t he spectrum. The AIRS instrument is co-aligned with AMSU-A so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. The AIRIBRAD_005 products are stored in files (often referred to as \"granules\") that contain 6 minutes of data, 90 footprints across track by 135 lines along track.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/YZEXEVN4JGGJ","Contact":"GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov","ManagedBy":"NASA","UpdateFrequency":"From 2002-08-30 to Ongoing","Tags":["atmosphere","aws-pds","datacenter","earth observation","global","hdf","ice","land","metadata","opendap","orbit"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AIRS/Aqua L1B Infrared (IR) geolocated and calibrated radiances V005 (AIRIBRAD) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level1/AIRIBRAD.005/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-airibrad","Sources":["awslabs-open-data-registry"]}
{"Name":"AIRS/Aqua L1C Infrared (IR) resampled and corrected radiances V6.7 (AIRICRAD) at GES DISC","Description":"The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1C data set contains AIRS infrared calibrated and geolocated radiances in W/m2/micron/ster. This data set is generated from AIRS level 1B data. The spectral coverage of L1C data is from 3.74 to 15.4 mm. The nominal spectral resolution lambda / delta lambda = 1200. The spectrum is sampled twice per spectral resolution element in a total of 2645 spectral channels. A day of AIRS data is divided into 240 granules (scenes) each of 6-minute duration. For the AIRS IR measurements, an individual granule contains 135 pixels across-track and 90 along-track pixels; there are total of 135 x 90 = 12,150 pixels per granule. AIRS employs a 49.5 degree crosstrack scanning with a 1.1 degree instantaneous field of view (IFOV) to provide twice daily coverage of essentially the entire globe in a 1:30 PM sun synchronous orbit with the 13.5 x 13.5 km2 spatial resolution at nadir. The L1C swath products are derived from the L1B swath products. The primary purpose of the level 1C is to generate the spectra of radiances without spectral gaps caused by the instrument design and bad spectral points. The AIRS L1C data can be used for comparative (with other IR measurements) studies and for weather-climate research.\n\nThis is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct\nto this page. For this collection the switchover occurred on June 1, 2020.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/VWD3DRC07UEN","Contact":"GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/","ManagedBy":"NASA","UpdateFrequency":"From 2002-08-30 to Ongoing","Tags":["atmosphere","aws-pds","climate","datacenter","earth observation","global","hdf","metadata","opendap","orbit"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AIRS/Aqua L1C Infrared (IR) resampled and corrected radiances V6.7 (AIRICRAD) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level1/AIRICRAD.6.7/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"AIRS Level-1C and applications to cross-calibration with MODIS and CrIS","URL":"https://doi.org/10.1117/12.2061967","AuthorName":"Evan M. Manning, Hartmut H. Aumann, and Ali Behrangi"},{"Title":"AIRS version 6.6 and version 7 level-1C products","URL":"https://doi.org/10.1117/12.2529400","AuthorName":"Evan M. Manning, L. Larrabee Strow, and Hartmut H. Aumann"},{"Title":"Radiometric Stability Validation of 17 Years of AIRS Data Using Sea Surface Temperatures.","URL":"https://doi.org/10.1029/2019GL085098","AuthorName":"Aumann, H.H. Brogerg, S.,Manning, E., and Pagaino, T."},{"Title":"Updates to the absolute radiometric accuracy of the AIRS on Aqua","URL":"https://doi.org/10.1117/12.2324605","AuthorName":"Pagano, T.S., Aumann, H.H., Broberg, S., Manning, E., Overoye, K., and Weiler, M."},{"Title":"Validation of the Atmospheric Infrared Sounder radiative transfer algorithm","URL":"https://doi.org/10.1029/2005JD006146","AuthorName":"Strow, L.L, Hannon, S.E, De-Souza Machado, S., Motteler, H.E., and Tobin, D.C."}],"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-airicrad","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA AirMOSS Project","Description":"This dataset provides in situ measurements of soil temperature, moisture, conductivity, measured diameter of tree at breast height (DBH) and total height collected at the Harvard Forest, Petersham, Massachusetts, USA, during October 2012 and July - August 2013. These measurements were collected in support of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project to validate root-zone soil measurements and carbon flux model estimates.\n<br>\n\n#### AirMOSS_L1_Sigma0_BERMS_1406\nThis data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the BERMS (Boreal Ecosystem Research and Monitoring Sites), in Saskatchewan, Canada. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.\n<br>\n\n#### AirMOSS_L1_Sigma0_Chamel_1407\nThis data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Chamela Biological Station, in Jalisco, Mexico. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.\n<br>\n\n#### AirMOSS_L1_Sigma0_DukeFr_1408\nThis data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Duke Forest site in North Carolina. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.\n<br>\n\n#### AirMOSS_L1_Sigma0_Harvrd_1409\nThis data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Harvard Forest site in Massachusetts. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.\n<br>\n\n#### AirMOSS_L1_Sigma0_Howlnd_1410\nThis data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Howland Forest site in Maine. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.\n<br>\n\n#### AirMOSS_L1_Sigma0_LaSelv_1411\nThis data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the La Selva Biological Station in Costa Rica. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.\n<br>\n\n#### AirMOSS_L1_Sigma0_Metoli_1412\nThis data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Metolius site in Oregon. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.\n<br>\n\n#### AirMOSS_L1_Sigma0_Moisst_1413\nThis data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the MOISST site in Oklahoma. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.\n<br>\n\n#### AirMOSS_L1_Sigma0_TonziR_1414\nThis data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Tonzi Ranch site in California. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.\n<br>\n\n#### AirMOSS_L1_Sigma0_Walnut_1415\nThis data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Walnut Gulch site in Arizona. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.\n<br>\n\n#### AirMOSS_L2_Carbon_Flux_1420\nThis data set contains carbon flux measurements recorded by an aircraft at the Duke, Harvard, and Howland Forest sites during the summers of 2012-2014 as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Frequent measurements of CO2 and H2O were obtained using a cavity ring down spectrometer on board the Airborne Laboratory for Atmospheric Research, operated by Purdue University. Estimates of surface CO2 flux, sensible and latent heat fluxes, their corresponding uncertainties, and average wind speed and direction are provided for each of the 26 flights.\n<br>\n\n#### AirMOSS_L2_Inground_Soil_Moist_1416\nThis data set provides level 2 (L2) hourly volumetric (cm3/cm3) soil moisture profiles from in-ground sensors at seven North American sites as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Three profiles were installed at each site, sampling at seven different depths per profile (2 cm to 80 cm). Initial sampling began at three sites in September 2011 and additional sites were added during 2012 and 2013. All sampling concluded in December 2015. The AirMOSS project used an airborne radar instrument to estimate root-zone soil moisture at 10 study sites across North America. These in-ground soil moisture data were collected to calibrate and validate the AirMOSS data.\n<br>\n\n#### AirMOSS_L2_Precipitation_1417\nThis data set provides level 2 (L2) calibrated hourly precipitation (cm/hr) from rain gauges at seven North American sites as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Three gauges were installed at each site. Initial sampling began at three sites in September 2011 and additional sites were added during 2012 and 2013. All sampling concluded in December 2015. The AirMOSS project used an airborne radar instrument to estimate root-zone soil moisture at 10 study sites across North America. These precipitation data were collected in conjunction with in-ground soil moisture data in order to calibrate and validate the AirMOSS data.\n<br>\n\n#### AirMOSS_L2_3_RZ_Soil_Moisture_1418\nThis data set provides level 2/3 root zone soil moisture (RZSM) estimates at multiple depths at 90-m spatial resolution from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over ten sites across North America. AirMOSS produces estimates of RZSM with data from a P-band synthetic aperture radar (SAR) flown on a NASA Gulfstream-III aircraft. The resulting soil moisture estimates capture the effects of gradients of soil, topography, and vegetation heterogeneity over an area of approximately 100km x 25km at each of the study sites. AirMOSS flight campaigns took place at least biannually from 2012 to 2015 at each site.\n<br>\n\n#### AirMOSS_L4_Daily_NEE_1422\nThis data set provides Level 4 daily estimates of Net Ecosystem Exchange (NEE) of CO2 at a spatial resolution of 30 arc-seconds (~1 km) for seven of the sites covered by the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) flights, each site spanning ~2500 km2. The daily NEE estimates are generally available from October 2012 through October 2014, although the exact time ranges vary by site. The AirMOSS L4 daily NEE were produced by the Ecosystem Demography Biosphere Model (ED2) augmented by the AirMOSS-derived L2/3 root zone soil moisture data as an additional input. The AirMOSS soil moisture data were used to estimate the sensitivity of carbon fluxes to soil moisture and to diagnose and improve estimation and prediction of NEE by constraining the model's predictions of soil moisture and its impact on above- and below-ground fluxes.\n<br>\n\n#### AirMOSS_L4_Regional_NEE_1423\nThis data set provides Level 4 estimates of Net Ecosystem Exchange (NEE) of CO2 across the conterminous USA at a spatial resolution of 50 km. Modeled estimates are provided at hourly and monthly temporal resolutions, from January 2012 through October 2014. The AirMOSS L4 Regional NEE data were produced by the Ecosystem Demography Biosphere Model (ED2) augmented by the AirMOSS-derived L2/3 root zone soil moisture data as an additional input. The AirMOSS soil moisture data were used to estimate the sensitivity of carbon fluxes to soil moisture and to diagnose and improve estimation and prediction of NEE by constraining the model's predictions of soil moisture and its impact on above- and below-ground fluxes.\n<br>\n\n#### AirMOSS_L4_RZ_Soil_Moisture_1421\nThis data set provides hourly gridded soil moisture estimates derived from hydrologic modeling at nine AirMOSS sites across North America. The AirMOSS L4 RZSM product represents a temporal interpolation of intermittent AirMOSS L2/3 RZSM retrievals into a temporally-continuous, multi-layer, hourly soil moisture product. The L4 RZSM data have the same spatial resolution (3-arcsecs or ~100 m), and the same temporal coverage (generally Fall 2012 through Fall 2015), as the underlying L2/3 RZSM data. The L4 RZSM data were produced by the integration of the Level 2/3 product and other ancillary information into the Penn State Integrated Hydrologic Model (PIHM). Many key applications for AirMOSS data products, including the calculation of net ecosystem exchange (NEE), require temporally continuous RZSM estimates such as those provided here.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://airmoss.ornl.gov","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","precipitation","radar","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AirMOSS_Field_Data_Harvard_1677 v1 - This dataset provides in situ measurements of soil temperature, moisture, conductivity, measured diameter of tree at breast height (DBH) and total height collected at the Harvard Forest, Petersham, Massachusetts, USA, during October 2012 and July - August 2013. These measurements were collected in support of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project to validate root-zone soil measurements and carbon flux model estimates.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_Field_Data_Harvard/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L1_Sigma0_BERMS_1406 v1 - This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the BERMS (Boreal Ecosystem Research and Monitoring Sites), in Saskatchewan, Canada. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L1_Sigma0_BERMS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L1_Sigma0_Chamel_1407 v1 - This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Chamela Biological Station, in Jalisco, Mexico. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L1_Sigma0_Chamel/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L1_Sigma0_DukeFr_1408 v1 - This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Duke Forest site in North Carolina. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L1_Sigma0_DukeFr/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L1_Sigma0_Harvrd_1409 v1 - This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Harvard Forest site in Massachusetts. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L1_Sigma0_Harvrd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L1_Sigma0_Howlnd_1410 v1 - This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Howland Forest site in Maine. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L1_Sigma0_Howlnd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L1_Sigma0_LaSelv_1411 v1 - This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the La Selva Biological Station in Costa Rica. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L1_Sigma0_LaSelv/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L1_Sigma0_Metoli_1412 v1 - This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Metolius site in Oregon. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L1_Sigma0_Metoli/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L1_Sigma0_Moisst_1413 v1 - This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the MOISST site in Oklahoma. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L1_Sigma0_Moisst/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L1_Sigma0_TonziR_1414 v1 - This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Tonzi Ranch site in California. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L1_Sigma0_TonziR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L1_Sigma0_Walnut_1415 v1 - This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Walnut Gulch site in Arizona. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L1_Sigma0_Walnut/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L2_Carbon_Flux_1420 v1 - This data set contains carbon flux measurements recorded by an aircraft at the Duke, Harvard, and Howland Forest sites during the summers of 2012-2014 as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Frequent measurements of CO2 and H2O were obtained using a cavity ring down spectrometer on board the Airborne Laboratory for Atmospheric Research, operated by Purdue University.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L2_Carbon_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L2_Inground_Soil_Moist_1416 v1 - This data set provides level 2 (L2) hourly volumetric (cm3/cm3) soil moisture profiles from in-ground sensors at seven North American sites as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Three profiles were installed at each site, sampling at seven different depths per profile (2 cm to 80 cm).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L2_Inground_Soil_Moist/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L2_Precipitation_1417 v1 - This data set provides level 2 (L2) calibrated hourly precipitation (cm/hr) from rain gauges at seven North American sites as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Three gauges were installed at each site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L2_Precipitation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L2_3_RZ_Soil_Moisture_1418 v1.1 - This data set provides level 2/3 root zone soil moisture (RZSM) estimates at multiple depths at 90-m spatial resolution from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over ten sites across North America. AirMOSS produces estimates of RZSM with data from a P-band synthetic aperture radar (SAR) flown on a NASA Gulfstream-III aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L2_3_RZ_Soil_Moisture/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L4_Daily_NEE_1422 v1 - This data set provides Level 4 daily estimates of Net Ecosystem Exchange (NEE) of CO2 at a spatial resolution of 30 arc-seconds (~1 km) for seven of the sites covered by the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) flights, each site spanning ~2500 km2. The daily NEE estimates are generally available from October 2012 through October 2014, although the exact time ranges vary by site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L4_Daily_NEE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L4_Regional_NEE_1423 v1 - This data set provides Level 4 estimates of Net Ecosystem Exchange (NEE) of CO2 across the conterminous USA at a spatial resolution of 50 km. Modeled estimates are provided at hourly and monthly temporal resolutions, from January 2012 through October 2014.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L4_Regional_NEE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AirMOSS_L4_RZ_Soil_Moisture_1421 v1 - This data set provides hourly gridded soil moisture estimates derived from hydrologic modeling at nine AirMOSS sites across North America. The AirMOSS L4 RZSM product represents a temporal interpolation of intermittent AirMOSS L2/3 RZSM retrievals into a temporally-continuous, multi-layer, hourly soil moisture product.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/airmoss/campaign/AirMOSS_L4_RZ_Soil_Moisture/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-airmoss","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA amsr-2 Project","Description":"This AMSR Unified global ocean data set reports integrated water vapor and cloud liquid water content in the atmospheric column, plus 10-meter sea surface wind speeds. The data are derived from AMSR-E and AMSR2 brightness temperature observations that have been resampled by the Japan Aerospace Exploration Agency (JAXA) to facilitate an intercalibrated (i.e., “unified”) AMSR-E/AMSR2 data record. Ancillary files, including product history, quality assessment (QA), and file-specific metadata are also available.\n<br>\n\n#### AU_Rain\nThis AMSR-E/AMSR2 Unified Level-2B data set reports instantaneous surface precipitation rates and types (over land and ocean) and precipitation profiles (over ocean). The data are derived by applying the AMSR-E/AMSR2 unified algorithm to L1R data obtained by the Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) and AMSR2 instruments.\n<br>\n\n#### AU_Land\nThis Level-2B AMSR Unified product contains surface soil moisture at 25 km resolution, estimated from AMSR-E and AMSR2 brightness temperature observations using two different approaches: the Normalized Polarization Difference (NPD) algorithm and the Single Channel Algorithm (SCA). Data are provided in the NSIDC EASE-Grid Global projection. Soil moisture estimates are derived from AMSR-E and AMSR2 data which have been spatially resampled by the Japan Aerospace Exploration Agency (JAXA) to intercalibrate, or “unify,” the two sensors.\n<br>\n\n#### AU_SI12\nThis data set reports average daily horizontally and vertically polarized brightness temperatures; sea ice concentrations; and snow depths over sea ice on 12.5 km resolution north and south polar stereographic grids. Ungridded sea ice motion data are also available for the Arctic. The data are derived from observations acquired by the Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) and AMSR2 instruments that have been spatially resampled by the Japan Aerospace Exploration Agency (JAXA) to unify the AMSR-E and AMSR2 missions. Brightness temperatures are available at 18.7 GHz, 23.8 GHz, 36.5 GHz and 89.0 GHz for ascending and descending orbits, and as a single daily average. Sea ice concentrations are generated with the Enhanced NASA Team (NT2) algorithm; a separate variable reports the daily sea ice concentration difference between the legacy AMSR Basic Bootstrap Algorithm (ABA) and the NT2.\n<br>\n\n#### AU_SI25\nThis data set reports average daily horizontally and vertically polarized brightness temperatures and sea ice concentrations on 25 km resolution north and south polar stereographic grids. The data are derived from observations acquired by the Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) and AMSR2 instruments that have been spatially resampled by the Japan Aerospace Exploration Agency (JAXA) to unify the AMSR-E and AMSR2 missions. Brightness temperatures are available at 6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz and 89.0 GHz for ascending and descending orbits, and as a single daily average. Sea ice concentrations are generated with the Enhanced NASA Team (NT2) algorithm; a separate variable reports the daily sea ice concentration difference between the legacy AMSR Basic Bootstrap Algorithm (ABA) and the NT2.\n<br>\n\n#### AU_SI6\nThis data set reports 6.25 km resolution, horizontally and vertically polarized average daily 89.0 GHz brightness temperatures for the Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) and AMSR2 instruments. The data are provided on north and south polar stereographic grids. The brightness temperatures are derived from Level 1R data that has been spatially resampled by the Japan Aerospace Exploration Agency (JAXA) to unify AMSR-E and AMSR2 observations.\n<br>\n\n#### AU_5DSno\nThis AMSR-E/AMSR2 Unified Level-3 data set provides estimates of the 5-day snow water equivalent (SWE) at 25 km resolution, derived from brightness temperature measurements acquired by the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 instruments. SWE data are provided on the azimuthal 25 km Equal-Area Scalable Earth Grid (EASE-Grid) for both the Northern and Southern Hemisphere. The data were generated from input brightness temperatures which were resampled by the Japan Aerospace Exploration Agency (JAXA) to intercalibrate, or unify, the AMSR-E and AMSR2 missions.\n<br>\n\n#### AU_DySno\nThis AMSR-E/AMSR2 Unified Level-3 data set provides daily estimates of snow water equivalent (SWE) at 25 km resolution, derived from brightness temperature measurements acquired by the Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) and AMSR2 instruments. SWE data are provided on the azimuthal 25 km Equal-Area Scalable Earth Grid (EASE-Grid) for both the Northern and Southern Hemisphere. The data were generated from input brightness temperatures which were resampled by the Japan Aerospace Exploration Agency (JAXA) to intercalibrate, or unify, the AMSR-E and AMSR2 missions.\n<br>\n\n#### AU_DyOcn\nThe AMSR-E/AMSR2 Unified L3 Global Daily Ascending/Descending .25 x .25 deg Ocean Grids data set (AU_DyOcn) reports daily estimates of water vapor, cloud liquid water content, and surface wind speed over the ocean on a global 0.25° × 0.25° resolution grid. The data are derived from the AMSR-E/AMSR2 Unified L2B Global Swath Ocean Products, Version 1 data set. Sea surface temperatures from the NOAA 1/4° Daily Optimum Interpolation Sea Surface Temperature (OISST) product are also included.\n<br>\n\n#### AU_MoSno\nThis AMSR-E/AMSR2 Unified Level-3 (L3) data set provides monthly estimates of snow water equivalent (SWE) derived from brightness temperature measurements acquired by the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 instruments. SWE data are provided on the azimuthal 25 km Equal-Area Scalable Earth Grid (EASE-Grid) for both the Northern and Southern Hemisphere. The data were generated from input brightness temperatures which were resampled by the Japan Aerospace Exploration Agency (JAXA) to intercalibrate, or unify, the AMSR-E and AMSR2 missions.\n<br>\n\n#### AU_MoOcn\nThe AMSR-E/AMSR2 Unified L3 Global Monthly Ascending/Descending .25x.25 deg Ocean Grids data set is a gridded product that reports monthly estimates of water vapor, cloud liquid water content, and surface wind speed over the ocean. The data are derived from resampled Near Real-Time (NRT) Level-1R data provided by Japan Aerospace Exploration Agency (JAXA). Sea surface temperatures from the NOAA 1/4° Daily Optimum Interpolation Sea Surface Temperature (OISST) product are also included.\n<br>\n\n#### AU_WkOcn\nThe AMSR-E/AMSR2 Unified L3 Global Weekly Ascending/Descending .25 x .25 deg Ocean Grids data set (AU_WkOcn) reports weekly estimates of water vapor, cloud liquid water content, and surface wind speed over the ocean on a global 0.25° × 0.25° resolution grid. The data are derived from the AMSR-E/AMSR2 Unified L2B Global Swath Ocean Products, Version 1 data set. Sea surface temperatures from the NOAA 1/4° Daily Optimum Interpolation Sea Surface Temperature (OISST) product are also included.\n<br>\n\n#### NSIDC-0477\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains a global record of the daily freeze/thaw status of the landscape. The record is derived from radiometric brightness temperatures acquired between 1979 and 2021 by four satellite-based, passive microwave sensors: the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), the Special Sensor Microwave Imager/Sounder (SSMIS), and the Advanced Microwave Scanning Radiometer 2 (AMSR2).\n<br>\n\n#### NISE_A2\nThe Near-real-time Ice and Snow Extent (NISE) Advanced Microwave Scanning Radiometer 2 (AMSR2) data set provides daily, global maps of sea ice concentrations and snow extent. The data provides a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/au_ocean/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","oceans","precipitation","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AU_Ocean v1 - This AMSR Unified global ocean data set reports integrated water vapor and cloud liquid water content in the atmospheric column, plus 10-meter sea surface wind speeds. The data are derived from AMSR-E and AMSR2 brightness temperature observations that have been resampled by the Japan Aerospace Exploration Agency (JAXA) to facilitate an intercalibrated (i.e., “unified”) AMSR-E/AMSR2 data record.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AU_Ocean/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AU_Rain v1 - This AMSR-E/AMSR2 Unified Level-2B data set reports instantaneous surface precipitation rates and types (over land and ocean) and precipitation profiles (over ocean). The data are derived by applying the AMSR-E/AMSR2 unified algorithm to L1R data obtained by the Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) and AMSR2 instruments.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AU_Rain/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AU_Land v1 - This Level-2B AMSR Unified product contains surface soil moisture at 25 km resolution, estimated from AMSR-E and AMSR2 brightness temperature observations using two different approaches: the Normalized Polarization Difference (NPD) algorithm and the Single Channel Algorithm (SCA). Data are provided in the NSIDC EASE-Grid Global projection.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AU_Land/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AU_SI12 v1 - This data set reports average daily horizontally and vertically polarized brightness temperatures; sea ice concentrations; and snow depths over sea ice on 12.5 km resolution north and south polar stereographic grids. Ungridded sea ice motion data are also available for the Arctic.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AU_SI12/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AU_SI25 v1 - This data set reports average daily horizontally and vertically polarized brightness temperatures and sea ice concentrations on 25 km resolution north and south polar stereographic grids. The data are derived from observations acquired by the Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) and AMSR2 instruments that have been spatially resampled by the Japan Aerospace Exploration Agency (JAXA) to unify the AMSR-E and AMSR2 missions.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AU_SI25/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AU_SI6 v1 - This data set reports 6.25 km resolution, horizontally and vertically polarized average daily 89.0 GHz brightness temperatures for the Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) and AMSR2 instruments. The data are provided on north and south polar stereographic grids.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AU_SI6/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AU_5DSno v1 - This AMSR-E/AMSR2 Unified Level-3 data set provides estimates of the 5-day snow water equivalent (SWE) at 25 km resolution, derived from brightness temperature measurements acquired by the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 instruments. SWE data are provided on the azimuthal 25 km Equal-Area Scalable Earth Grid (EASE-Grid) for both the Northern and Southern Hemisphere.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AU_5DSno/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AU_DySno v1 - This AMSR-E/AMSR2 Unified Level-3 data set provides daily estimates of snow water equivalent (SWE) at 25 km resolution, derived from brightness temperature measurements acquired by the Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) and AMSR2 instruments. SWE data are provided on the azimuthal 25 km Equal-Area Scalable Earth Grid (EASE-Grid) for both the Northern and Southern Hemisphere.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AU_DySno/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AU_DyOcn v1 - The AMSR-E/AMSR2 Unified L3 Global Daily Ascending/Descending .25 x .25 deg Ocean Grids data set (AU_DyOcn) reports daily estimates of water vapor, cloud liquid water content, and surface wind speed over the ocean on a global 0.25° × 0.25° resolution grid. The data are derived from the AMSR-E/AMSR2 Unified L2B Global Swath Ocean Products, Version 1 data set.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AU_DyOcn/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AU_MoSno v1 - This AMSR-E/AMSR2 Unified Level-3 (L3) data set provides monthly estimates of snow water equivalent (SWE) derived from brightness temperature measurements acquired by the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 instruments. SWE data are provided on the azimuthal 25 km Equal-Area Scalable Earth Grid (EASE-Grid) for both the Northern and Southern Hemisphere.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AU_MoSno/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AU_MoOcn v1 - The AMSR-E/AMSR2 Unified L3 Global Monthly Ascending/Descending .25x.25 deg Ocean Grids data set is a gridded product that reports monthly estimates of water vapor, cloud liquid water content, and surface wind speed over the ocean. The data are derived from resampled Near Real-Time (NRT) Level-1R data provided by Japan Aerospace Exploration Agency (JAXA).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AU_MoOcn/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AU_WkOcn v1 - The AMSR-E/AMSR2 Unified L3 Global Weekly Ascending/Descending .25 x .25 deg Ocean Grids data set (AU_WkOcn) reports weekly estimates of water vapor, cloud liquid water content, and surface wind speed over the ocean on a global 0.25° × 0.25° resolution grid. The data are derived from the AMSR-E/AMSR2 Unified L2B Global Swath Ocean Products, Version 1 data set.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AU_WkOcn/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0477 v5 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains a global record of the daily freeze/thaw status of the landscape. The record is derived from radiometric brightness temperatures acquired between 1979 and 2021 by four satellite-based, passive microwave sensors: the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), the Special Sensor Microwave Imager/Sounder (SSMIS), and the Advanced Microwave Scanning Radiometer 2 (AMSR2).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0477/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NISE_A2 v1 - The Near-real-time Ice and Snow Extent (NISE) Advanced Microwave Scanning Radiometer 2 (AMSR2) data set provides daily, global maps of sea ice concentrations and snow extent. The data provides a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/OTHR/NISE_A2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-amsr-2","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA amsr-e Project","Description":"These Level-3 Snow Water Equivalent (SWE) data sets contain SWE data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids).\n<br>\n\n#### AE_SI12\nThis Level-3 gridded product (AE_SI12) includes brightness temperatures at 18.7 through 89.0 GHz, sea ice concentration, and snow depth over sea ice.\n<br>\n\n#### AE_SI12\nThis data set (AE_SI12) reports average daily, 12.5 km resolution, horizontally and vertically polarized brightness temperatures (Tb) at four frequencies—18.7 GHz, 23.8 GHz, 36.5 GHz and 89.0 GHz. It also reports: daily sea ice concentrations derived with the Enhanced NASA Team (NT2) algorithm; daily sea ice concentration differences between NT2 and the legacy AMSR Basic Bootstrap Algorithm (BBA); and daily snow depths over sea ice. Data are provided on Northern and Southern Hemisphere, polar stereographic grids. These data are derived from observations acquired by the Advanced Microwave Scanning Radiometer for EOS (AMSR-E).\n<br>\n\n#### AE_SI25\nThis Level-3 gridded product (AE_SI25) includes brightness temperatures at 6.9 through 89.0 GHz and sea ice concentrations.\n<br>\n\n#### AE_SI25\nThis data set (AE_SI25) reports average daily, 25 km resolution, horizontally and vertically polarized brightness temperatures (Tb) at six frequencies—6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz—plus sea ice concentrations and sea ice concentration differences between the Enhanced NASA Team (NT2) algorithm and the legacy AMSR Basic Bootstrap Algorithm (BBA). Data are provided on Northern and Southern Hemisphere, polar stereographic grids. These data are derived from observations acquired by the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) on board the NASA Aqua satellite.\n<br>\n\n#### AE_SI6\nThis Level-3 gridded product (AE_SI6) includes brightness temperatures at 89.0 GHz. Data are mapped to a polar stereographic grid at 6.25 km spatial resolution. This product is an intermediate product during processing of AMSR-E Level-3 sea ice products at 12.5 km and 25 km resolution.\n<br>\n\n#### AE_SI6\nThis data set (AE_SI6) reports average daily, 6.25 km resolution, horizontally and vertically polarized brightness temperatures (Tbs) at 89.0 GHz. Data are provided on Northern and Southern Hemisphere, polar stereographic grids. Tbs are derived from observations acquired by the Advanced Microwave Scanning Radiometer for EOS (AMSR-E). This data set is created as an intermediate product while generating the AMSR E Level 3 12.5 km (AE_SI12) and 25 km (AE_SI25) sea ice products.\n<br>\n\n#### AE_SID\nThis product provides 6.25 km sea ice drift grids for the Northern and Southern Hemispheres.\n<br>\n\n#### AE_DyOcn\nThe Level-3 daily product (AE_DyOcn), weekly product (AE_WkOcn), and monthly product (AE_MoOcn) include SST, near-surface wind speed, columnar water vapor, and columnar cloud liquid water over oceans in a 0.25 degree by 0.25 degree grid, generated from AE_Ocean.\n<br>\n\n#### AE_Land3\nThis gridded Level-3 land surface product (AE_Land3) includes daily measurements of surface soil moisture and vegetation/roughness water content interpretive information, as well as brightness temperatures and quality control variables. Ancillary data include time, geolocation, and quality assessment.\n<br>\n\n#### AMSREL1A\nAMSR-E Level-1A observation counts are processed from Level-0 science packet data by the Japan Aerospace Exploration Agency (JAXA) Earth Observation Center (EOC) in Japan.\n<br>\n\n#### AE_L2A\nThe AMSR-E Level-2A product (AE_L2A) contains daily 50 minute half-orbit swath brightness temperatures for six channels ranging from 6.9 GHz through 89 GHz. Data are resampled to spatial resolutions ranging from 5.4 km to 56 km. Each file is packaged with geolocation and quality information as well as ancillary data.\n<br>\n\n#### AE_Ocean\nThis daily Level-2B swath data set includes Sea Surface Temperature (SST), Near-Surface Wind Speed, Columnar Water Vapor, and Cloud liquid Water data arrays, and was used as input to generate the following daily, weekly, and monthly Level-3 gridded ocean products; AE_DyOcn, AE_WkOcn, and AE_MoOcn.\n<br>\n\n#### AE_Rain\nThe AMSR-E/Aqua Level-2B precipitation product includes instantaneous surface precipitation rate and type over ice-free/snow-free land and ocean between 89.24 degrees north and south latitudes at at 10 km spatial resolution along the track and 5 km spatial resolution along the scan. The data are generated by the GPROF 2010 Version 2 algorithm using Version 3 of the AMSR-E Level-2A Brightness Temperatures.\n<br>\n\n#### AE_Rain\nThe AMSR-E/Aqua Level-2B precipitation product includes instantaneous surface precipitation rate and type over ice-free/snow-free land and ocean between 89.24 degrees north and south latitudes at at 10 km spatial resolution along the track and 5 km spatial resolution along the scan. The data are generated by the GPROF 2017 algorithm using Version 4 of the AMSR-E Level-2A Brightness Temperatures.\n<br>\n\n#### AE_Land\nThis Level-2B land surface product (AE_Land) includes daily measurements of surface soil moisture, vegetation/roughness water content interpretive information, and quality control variables. Ancillary data include time, geolocation, and quality assessment.\n<br>\n\n#### AE_Land\nThis data set contains gridded estimates of soil moisture in the top 1 cm of soil, averaged over the AMSR-E retrieval footprint. Soil moisture is estimated from AMSR-E/Aqua L2A brightness temperature (Tb) measurements using two different approaches: the Normalized Polarization Difference algorithm (NPD) and the Single Channel Algorithm (SCA). Ancillary data are also provided to help interpret the soil moisture observations, including vegetation roughness, observation counts for various surface conditions, and QA flags.\n<br>\n\n#### AE_RnGd\nThis Level-3 rainfall accumulation product (AE_RnGd) consists of two grids of 28 rows by 72 columns of monthly averaged rainfall accumulation over ocean and land. Both grids are 5 degree by 5 degree resolution.\n<br>\n\n#### NSIDC-0079\nThis sea ice concentration data set was derived using measurements from the Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 satellite and from the Special Sensor Microwave/Imager (SSM/I) sensors on the Defense Meteorological Satellite Program's (DMSP) -F8, -F11, and -F13 satellites. Measurements from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard DMSP-F17 are also included. The data set has been generated using the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) Bootstrap Algorithm with daily varying tie-points. Daily (every other day prior to July 1987) and monthly data are available for both the north and south polar regions. Data are gridded on the SSM/I polar stereographic grid (25 x 25 km) and provided in two-byte integer format. Data coverage began on 01 November 1978 and is ongoing through the most current processing, with updated data processed several times annually.\n<br>\n\n#### NSIDC-0630\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, is an improved, enhanced-resolution, gridded passive microwave Earth System Data Record (ESDR) for monitoring cryospheric and hydrologic time series from SMMR, SSM/I-SSMIS, and AMSR-E. It is derived from the most mature and available Level-2 satellite passive microwave records from 1978 through the present.\n<br>\n\n#### NSIDC-0738\nThis data set contains twice-daily, enhanced-resolution brightness temperature data derived from the SMAP radiometer. Data are available on the Northern Hemisphere, Southern Hemisphere, Temperate, and Mid-Latitude (sub-set of Global) EASE-Grid 2.0 projections and on the 3 km, 3.125 km, 9 km, 25 km, and 36 km resolution grids. This data set applies the same SIR technique used to derive brightness temperatures from the SMMR, AMSR-E, and SSM/I-SSMIS sensors and is a companion product for the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR data set (DOI: 10.5067/MEASURES/CRYOSPHERE/NSIDC-0630.001).\n<br>\n\n#### NSIDC-0738\nThis data set contains twice-daily, enhanced-resolution brightness temperature data produced by image reconstruction of the standard SMAP product, SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures (SPL1BTB). Data are available in the Northern Hemisphere, Southern Hemisphere, Temperate, and Mid-Latitude (subset of Global) EASE-Grid 2.0 projections and on the 3 km, 3.125 km, 9 km, 25 km, and 36 km resolution grids. This is a companion product for the Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR data set. The same SIR technique is applied to derive brightness temperatures from the SMMR, AMSR-E, and SSM/I-SSMIS sensors as for that data set.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/ae_5dsno/versions/2","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","earth observation","ice","oceans","precipitation","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AE_5DSno v2 - These Level-3 Snow Water Equivalent (SWE) data sets contain SWE data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_5DSno/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_SI12 v3 - This Level-3 gridded product (AE_SI12) includes brightness temperatures at 18.7 through 89.0 GHz, sea ice concentration, and snow depth over sea ice.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_SI12/3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_SI12 v4 - This data set (AE_SI12) reports average daily, 12.5 km resolution, horizontally and vertically polarized brightness temperatures (Tb) at four frequencies—18.7 GHz, 23.8 GHz, 36.5 GHz and 89.0 GHz. It also reports: daily sea ice concentrations derived with the Enhanced NASA Team (NT2) algorithm; daily sea ice concentration differences between NT2 and the legacy AMSR Basic Bootstrap Algorithm (BBA); and daily snow depths over sea ice.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_SI12/4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_SI25 v3 - This Level-3 gridded product (AE_SI25) includes brightness temperatures at 6.9 through 89.0 GHz and sea ice concentrations.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_SI25/3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_SI25 v4 - This data set (AE_SI25) reports average daily, 25 km resolution, horizontally and vertically polarized brightness temperatures (Tb) at six frequencies—6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz—plus sea ice concentrations and sea ice concentration differences between the Enhanced NASA Team (NT2) algorithm and the legacy AMSR Basic Bootstrap Algorithm (BBA). Data are provided on Northern and Southern Hemisphere, polar stereographic grids.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_SI25/4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_SI6 v3 - This Level-3 gridded product (AE_SI6) includes brightness temperatures at 89.0 GHz. Data are mapped to a polar stereographic grid at 6.25 km spatial resolution.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_SI6/3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_SI6 v4 - This data set (AE_SI6) reports average daily, 6.25 km resolution, horizontally and vertically polarized brightness temperatures (Tbs) at 89.0 GHz. Data are provided on Northern and Southern Hemisphere, polar stereographic grids.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_SI6/4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_SID v1 - This product provides 6.25 km sea ice drift grids for the Northern and Southern Hemispheres.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_SID/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_DyOcn v2 - The Level-3 daily product (AE_DyOcn), weekly product (AE_WkOcn), and monthly product (AE_MoOcn) include SST, near-surface wind speed, columnar water vapor, and columnar cloud liquid water over oceans in a 0.25 degree by 0.25 degree grid, generated from AE_Ocean.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_DyOcn/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_Land3 v2 - This gridded Level-3 land surface product (AE_Land3) includes daily measurements of surface soil moisture and vegetation/roughness water content interpretive information, as well as brightness temperatures and quality control variables. Ancillary data include time, geolocation, and quality assessment.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_Land3/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AMSREL1A v3 - AMSR-E Level-1A observation counts are processed from Level-0 science packet data by the Japan Aerospace Exploration Agency (JAXA) Earth Observation Center (EOC) in Japan.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AMSREL1A/3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_L2A v4 - The AMSR-E Level-2A product (AE_L2A) contains daily 50 minute half-orbit swath brightness temperatures for six channels ranging from 6.9 GHz through 89 GHz. Data are resampled to spatial resolutions ranging from 5.4 km to 56 km.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_L2A/4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_Ocean v2 - This daily Level-2B swath data set includes Sea Surface Temperature (SST), Near-Surface Wind Speed, Columnar Water Vapor, and Cloud liquid Water data arrays, and was used as input to generate the following daily, weekly, and monthly Level-3 gridded ocean products; AE_DyOcn, AE_WkOcn, and AE_MoOcn.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_Ocean/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_Rain v3 - The AMSR-E/Aqua Level-2B precipitation product includes instantaneous surface precipitation rate and type over ice-free/snow-free land and ocean between 89.24 degrees north and south latitudes at at 10 km spatial resolution along the track and 5 km spatial resolution along the scan. The data are generated by the GPROF 2010 Version 2 algorithm using Version 3 of the AMSR-E Level-2A Brightness Temperatures.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_Rain/3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_Rain v4 - The AMSR-E/Aqua Level-2B precipitation product includes instantaneous surface precipitation rate and type over ice-free/snow-free land and ocean between 89.24 degrees north and south latitudes at at 10 km spatial resolution along the track and 5 km spatial resolution along the scan. The data are generated by the GPROF 2017 algorithm using Version 4 of the AMSR-E Level-2A Brightness Temperatures.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_Rain/4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_Land v2 - This Level-2B land surface product (AE_Land) includes daily measurements of surface soil moisture, vegetation/roughness water content interpretive information, and quality control variables. Ancillary data include time, geolocation, and quality assessment.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_Land/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_Land v3 - This data set contains gridded estimates of soil moisture in the top 1 cm of soil, averaged over the AMSR-E retrieval footprint. Soil moisture is estimated from AMSR-E/Aqua L2A brightness temperature (Tb) measurements using two different approaches: the Normalized Polarization Difference algorithm (NPD) and the Single Channel Algorithm (SCA).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_Land/3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AE_RnGd v2 - This Level-3 rainfall accumulation product (AE_RnGd) consists of two grids of 28 rows by 72 columns of monthly averaged rainfall accumulation over ocean and land. Both grids are 5 degree by 5 degree resolution.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AMSA/AE_RnGd/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0079 v4 - This sea ice concentration data set was derived using measurements from the Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 satellite and from the Special Sensor Microwave/Imager (SSM/I) sensors on the Defense Meteorological Satellite Program's (DMSP) -F8, -F11, and -F13 satellites. Measurements from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard DMSP-F17 are also included.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/PM/NSIDC-0079/4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0630 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, is an improved, enhanced-resolution, gridded passive microwave Earth System Data Record (ESDR) for monitoring cryospheric and hydrologic time series from SMMR, SSM/I-SSMIS, and AMSR-E. It is derived from the most mature and available Level-2 satellite passive microwave records from 1978 through the present.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/PM/NSIDC-0630/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0738 v2 - This data set contains twice-daily, enhanced-resolution brightness temperature data derived from the SMAP radiometer. Data are available on the Northern Hemisphere, Southern Hemisphere, Temperate, and Mid-Latitude (sub-set of Global) EASE-Grid 2.0 projections and on the 3 km, 3.125 km, 9 km, 25 km, and 36 km resolution grids.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-Related/NSIDC-0738/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0738 v3 - This data set contains twice-daily, enhanced-resolution brightness temperature data produced by image reconstruction of the standard SMAP product, SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures (SPL1BTB). Data are available in the Northern Hemisphere, Southern Hemisphere, Temperate, and Mid-Latitude (subset of Global) EASE-Grid 2.0 projections and on the 3 km, 3.125 km, 9 km, 25 km, and 36 km resolution grids.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-Related/NSIDC-0738/3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-amsr-e","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Applications Technology Satellite Project","Description":"GVHRRATS6IMIR is the Geosynchronous Very High Resolution Radiometer (GVHRR) Black and White Infrared Images on 70mm Film data product from the sixth Applications Technology Satellite (ATS-6). This set of IR imagery (10.5 to 12.5 micrometer, with an 11 km footprint at the sub-satellite point) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title at the bottom of the image and a gray scale on the right boundary that represents brightness temperatures. The title contains the satellite identification, receiving station, spectral band, picture number, picture type, pixel scale, sector number, and date. The ATS-6 satellite was in a geosynchronous orbit parked at 95W viewing the hemisphere below the satellite. The GVHRR experiment returned data from launch until August 15, 1974 when it became inoperable, The PI was William E. Shenk from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00092 (old ID 74-039A-08B).\n<br>\n\n#### GVHRRATS6IMVIS\nGVHRRATS6IMVIS is the Geosynchronous Very High Resolution Radiometer (GVHRR) Black and White Visible Images on Film data product from the sixth Applications Technology Satellite (ATS-6). This set of visible imagery (0.55 to 0.75 micrometer, with a 5.5 km footprint at the sub-satellite point) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title at the bottom of the image and a gray scale on the right boundary that represents brightness temperatures. The title contains the satellite identification, receiving station, spectral band, picture number, picture type, pixel scale, sector number, and date. The ATS-6 satellite was in a geosynchronous orbit parked at 95W viewing the hemisphere below the satellite. The GVHRR experiment returned data from launch until August 15, 1974 when it became inoperable, The PI was William E. Shenk from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00047 (old ID 74-039A-08A).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/GVHRRATS6IMIR_001.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GVHRRATS6IMIR v001 - GVHRRATS6IMIR is the Geosynchronous Very High Resolution Radiometer (GVHRR) Black and White Infrared Images on 70mm Film data product from the sixth Applications Technology Satellite (ATS-6). This set of IR imagery (10.5 to 12.5 micrometer, with an 11 km footprint at the sub-satellite point) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/ATS/GVHRRATS6IMIR.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GVHRRATS6IMVIS v001 - GVHRRATS6IMVIS is the Geosynchronous Very High Resolution Radiometer (GVHRR) Black and White Visible Images on Film data product from the sixth Applications Technology Satellite (ATS-6). This set of visible imagery (0.55 to 0.75 micrometer, with a 5.5 km footprint at the sub-satellite point) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/ATS/GVHRRATS6IMVIS.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-applications-technology-satellite","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Aqua Project","Description":"AIRS is a facility instrument whose goal is to support climate research and improve weather forecasting Launched into Earth-orbit on May 4, 2002, the Atmospheric Infrared Sounder, AIRS, moves climate research and weather prediction into the 21st century.\n<br>\n\n#### AIRXAMAP\nThe Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. The AIRS Granule Map Product consists of images of granule coverage in PDF and JPG format. The images are daily ones but updated every 6 minutes to capture any new available granule. Granules are assembled by ascending, descending, in north and south hemisphere, and the maps are in global cylindrical projection and satellite projection for better view.\n<br>\n\n#### AIRIBRAD\nWARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space. The thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the “spectral_freq” parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Maximum BT shifts are approximately +- 0.5 K, although many channels experience far smaller BT shifts. Approximately 1803 channels have BT shifts of less than 0.1 K and 575 channels are now shifted in BT by more than 0.1 K, while 231 of these channels have BT shifts greater than 0.2 K. Users of the L1b v5 product who are concerned that these shifts may impact their science investigations and applications are encouraged to switch to the AIRS L1c v6.7.4 product, which, among many other improvements, converts the spectra to a fixed frequency grid. END OF WARNING. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1B data set contains AIRS calibrated and geolocated radiances in milliWatts/m^2/cm^-1/steradian for 2378 infrared channels in the 3.74 to 15.4 micron region of t he spectrum. The AIRS instrument is co-aligned with AMSU-A so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. The AIRIBRAD_005 products are stored in files (often referred to as \"granules\") that contain 6 minutes of data, 90 footprints across track by 135 lines along track.\n<br>\n\n#### AIRABRAD_NRT\nThe AMSU-A Level 1B Near Real Time (NRT) product (AIRABRAD_NRT_005) differs from the routine product (AIRABRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AMSU-A instrument is co-aligned with AIRS so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. AMSU-A is primarily a temperature sounder that provides atmospheric information in the presence of clouds, which can be used to correct the AIRS infrared measurements for the effects of clouds. This is possible because non-precipitating clouds are for the most part transparent to microwave radiation, in contrast to visible and infrared radiation which are strongly scattered and absorbed by clouds. AMSU-A1 has 13 channels from 50 - 90 GHz and AMSU-A2 has 2 channels from 23 - 32 GHz. The AIRABRAD_NRT_005 products are stored in files (often referred to as \"granules\") that contain 6 minutes of data, 30 footprints across track by 45 lines along track.\n<br>\n\n#### AIRIBQAP_NRT\nThe AIRS Level 1B Near Real Time (NRT) product (AIRIBQAP_NRT_005) differs from the routine product (AIRIBQAP_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude. The AIRS IR Level 1B QA Subset contains Quality Assurance (QA) parameters that a user of may use to filter AIRS IR Level 1B radiance data to create a subset of analysis. QA parameters indicate quality of granule-per-channel, scan-per-channel, field of view, and channel and should be accessed before any data of analysis. It also contains \"glintlat\", \"glintlon\", and \"sun_glint_distant\" that users can use to check for possibility of solar glint contamination.\n<br>\n\n#### AIRVBRAD_NRT\nThe AIRS Visible/Near Infrared (VIS/NIR) Level 1B Near Real Time (NRT) product (AIRVBRAD_NRT_005) differs from the routine product (AIRVBRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The AIRS VIS/NIR level 1B data set contains visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian for 4 channels in the 0.4 to 1.0 um region of the spectrum. The spectral range of the VIS/NIR channels are as follows: Channel 1 0.41 um - 0.44 um, Channel 2 0.58 um - 0.68 um, Channel 3 0.71 um - 0.92 um, Channel 4 0.49 um - 0.94 um. The AIRVBRAD_NRT_005 products are stored in files (often referred to as \"granules\") that contain 6 minutes of data, 90 footprints across track by 135 lines along track. The VIS/NIR granules are only produced in the daytime so there will always be fewer VIS/NIR granules than Infrared or microwave granules.\n<br>\n\n#### AIRVBQAP_NRT\nThe AIRS Level 1B Near Real Time (NRT) product (AIRVBQAP_NRT_005) differs from the routine product (AIRVBQAP_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) Visible/Near Infrared (VIS/NIR) instrument in combination with the AIRS Infrared Spectrometer, the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB) constitute an innovative atmospheric sounding group aboard the Earth Observing System (EOS) Aqua platform in a near-polar Sun-synchronous orbit with a 1:30 AM/PM equator crossing time and an ~705 km altitude. The AIRS Visible/Near Infrared (VIS/NIR) Level 1B QA Subset contains Quality Assurance (QA) parameters that a may use of filter AIRS VIS/NIR Level 1B radiance data to create a subset of analysis. It includes \"state\" that user should check before using any VIS/NIR Level 1B data radiance and \"glintlat\", \"glintlon\", and \"sun_glint_distant\" that users can use to check for possibility of solar glint contamination. AIRS VIS/NIR Level 1B radiance data can be found in AIRVBRAD.\n<br>\n\n#### AIRS2CCF_NRT\nThe Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) product (AIRS2CCF_NRT_006) differs from the routine product (AIRS2CCF_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file. The AIRS2CCF_NRT_006 products are stored in files (often referred to as \"granules\") that contain 6 minutes of data, 30 footprints across track by 45 lines along track.\n<br>\n\n#### AIRS2RET_NRT\nThe Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) product (AIRS2RET_NRT_006) differs from the routine product (AIRS2RET_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### AIRS2SUP_NRT\nThe Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Support Retrieval (AIRS-only) product (AIRS2SUP_NRT_006) differs from the routine product (AIRS2SUP_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 scanlines. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### AIRG2SSD_IRonly\nThe Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This precipitation estimate from AIRS IR only is using a TOVS-like algorithm, and is intended for merging into the precipitation product of the Global Precipitation Climatology Project (GPCP). The precipitation estimate from AIRS Level 2 Support product, which are 6-min swath granules (240 per day) are combined here into one daily \"Level 2G\" global grid with dimensions (24x1440x720). Thus every hour is a \"layer\", and the resulting grid cell size is 0.25 degree (~25 km). Thus the grid size is made to fit TRMM products. Since AIRS precipitation is retrieved at AMSU footprint resolution, which is about 45 km at nadir, many grid cells in this 0.25-deg grid are \"empty\". The data are stored such that the first line is the South Pole. The geolocation information for every hour-layer is also provided in the file.\n<br>\n\n#### AIRX3C28\nAtmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 8-day Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is 8-day gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3, 8-day, Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers an 8-day period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the 8-day period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean.\n<br>\n\n#### LPRM_AMSRE_SOILM2\nAMSR-E/Aqua surface soil moisture (LPRM) L2B V002 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), using the Land Parameter Retrieval Model (LPRM). Each swath is packaged with associated geolocation fields. The data set covers the period from June 2002 to October 2011 (when the AMSR-E on the NASA EOS Aqua satellite stopped producing data due to a problem with the rotation of its antenna), at the spatial resolution (nominally 56 and 38 km, respectively) of AMSR-E's C and X bands (6.9 and 10.7 GHz, respectively). The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR-E's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the AMSR-E resampled brightness temperatures (AE_L2A) product, archived at the National Snow and Ice Data Center (NSIDC).\n<br>\n\n#### LPRM_AMSRE_A_SOILM3\nAMSR-E/Aqua surface soil moisture (LPRM) L3 1 day 25 km x 25 km ascending V002 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the daytime product. The data set covers the period from June 2002 to October 2011 (when the AMSR-E on the NASA EOS Aqua satellite stopped producing data due to a problem with the rotation of its antenna). The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR-E's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the AMSR-E resampled brightness temperatures (AE_L2A) product, daytime passes, as processed using LPRM (i.e., LPRM/AMSR-E/Aqua L2B product, LPRM_AMSRE_SOILM2_V002).\n<br>\n\n#### LPRM_AMSRE_D_SOILM3\nAMSR-E/Aqua surface soil moisture (LPRM) L3 1 day 25 km x 25 km descending V002 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the nighttime product. The data set covers the period from June 2002 to October 2011 (when the AMSR-E on the NASA EOS Aqua satellite stopped producing data due to a problem with the rotation of its antenna). The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR-E's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the AMSR-E resampled brightness temperatures (AE_L2A) product, nighttime passes, as processed using LPRM (i.e., LPRM/AMSR-E/Aqua L2B product, LPRM_AMSRE_SOILM2_V002).\n<br>\n\n#### SNDRAQIL2JSFRET\nThe Joint Single Footprint Retrieval Algorithm (JoSFRA) Level-2 geophysical parameters include estimates of atmospheric temperature and water vapor profiles, cloud properties, and surface temperature. These are retrieved from infrared spectra observed by the Atmospheric Infrared Sounder (AIRS) instrument on the Aqua satellite. AIRS is a grating spectrometer aboard Aqua, the second Earth Observing System (EOS) polar-orbiting platform. AIRS is co-boresited with the Advanced Microwave Sounding Unit (AMSU) also on Aqua. Horizontal resolutions are 50 km for AMSU and 13.5 km for AIRS. The JoSFRA algorithm uses an optimal-estimation scheme and retrieves geophysical quantities from AIRS thermal infrared spectra at their native horizontal resolution. Cloud observations from MODIS are used in the forward model without recourse to a cloud-cleared state. JOSFRA retrievals provide improved spatial resolution (13.5 km vs 50 km for cloud-cleared retrievals) and information content quantification, making them well-suited for process studies. JoSFRA retrievals are particularly useful in cases where high resolution (finer than 45 km) is needed or is beneficial, such as regions of strong horizontal gradients in water vapor. Use of JoSFRA retrievals is recommended under medium to low cloud cover. AIRS observations provide near-global coverage twice daily (around 1:30 am and pm local time) since August 30, 2002. An AIRS granule includes 6 minutes of data, 90 AIRS (30 AMSU) footprints across the orbit track by 135 AIRS (45 AMSU) footprints along track. Each day includes 240 granules, with an orbit repeat cycle of approximately 16 days. For the initial release of Version 2 JoSFRA, a limited test data set is provided. Future releases will expand the dataset. The initial dataset includes full global coverage data for two 5-day periods: January 13-17, 2011 and July 13-17, 2011, the Marine ARM GPCI Investigation of Clouds (MAGIC) (Lewis and Teixeira, EOS, 2015) test campaign in the Pacific Ocean with all 6-minute granules that overlap the box bounded by 20-35 degrees North latitude and 120-160 West longitude, June 1, 2012 – September 30, 2013, select granules from the years 2002-2007 where correlative data were available. The locations include Dept. of Energy (DOE) Atmospheric Radiation Measurement (ARM) sites at the North Slope of Alaska (NSA), Southern Great Plains (SGP), and Tropical Western Pacific (TWP), as well as scientific field campaigns.\n<br>\n\n#### AIRSIL3MSOLR\nThis L3 Spectral Outgoing Longwave Radiation (OLR) is derived using the AIRS radiances to compute spectral fluxes based on an algorithm developed by Xianglei Huang at the University of Michigan. It uses data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft. The Aqua AIRS Huang Level-3 Spectral OLR product contains OLR parameters derived from the AIRS version 6 data: all-sky and clear-sky OLR both spectrally resolved at 10 cm-1 bandwidth and integrated to a single value per grid square. This is monthly product on a 2x2 degree latitude/longitude grid.\n<br>\n\n#### AER_DBDT_D10KM_L3_MODIS_AQUA\nThis High-Resolution (0.1 x 0.1 degree) Level 3 daily Aerosol Optical Depth (AOD) product is generated by combining two Moderate Resolution Imaging Spectroradiometer (MODIS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the AQUA satellite. This dataset is provided in daily files ranging from 2002-07-04 to the present. The spatial coverage is global and the dataset is gridded at 0.1 x 0.1 degree spatial resolution. The data are generated using Level 2 AOD retrieved using DT and DB algorithms. The product provides multiple options for using data either from DT or DB or combined. Depending on user need and application, they can choose one or more relevant parameter. The pixels with highest quality as recommended by science teams are only considered in these averaging. In addition to averaged AOD at 0.1 x 0.1 degree resolution, standard deviation and number of pixels averaged from each algorithm are also provided. Average sensor zenith angle is also provided for additional filtering of the data. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### AER_DBDT_M10KM_L3_MODIS_AQUA\nThis High-Resolution (0.1 x 0.1 degree) Level 3 monthly Aerosol Optical Depth (AOD) product is generated by combining two Moderate Resolution Imaging Spectroradiometer (MODIS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the AQUA satellite. This dataset is provided in monthly files ranging from July 2002 to the present. The spatial coverage is global and the dataset is gridded at 0.1 x 0.1 degree spatial resolution. The data are generated using Level 2 AOD retrieved using DT and DB algorithms. The product provides multiple options for using data either from DT or DB or combined. Depending on user need and application, they can choose one or more relevant parameter. The pixels with highest quality as recommended by science teams are only considered in these averaging. In addition to averaged AOD at 0.1 x 0.1 degree resolution, standard deviation and number of pixels averaged from each algorithm are also provided. Average sensor zenith angle is also provided for additional filtering of the data. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### AIRX2RET\nThe Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS combination with the Advanced Microwave Sounding Unit (AMSU) constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities are also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### AIRX2SUP\nThe Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU), AIRS constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### AIRS2CCF_NRT\nThe Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) product (AIRS2CCF_NRT_7.0) differs from the routine product (AIRS2CCF_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file. The AIRS2CCF_NRT_7.0 products are stored in files (often referred to as \"granules\") that contain 6 minutes of data, 30 footprints across track by 45 lines along track.\n<br>\n\n#### AIRS2RET_NRT\nThe Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) product (AIRS2RET_NRT_7.0) differs from the routine product (AIRS2RET_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in several AMSU channels started to increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### AIRS2SUP_NRT\nThe Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Support Retrieval (AIRS-only) product (AIRS2SUP_NRT_7.0) differs from the routine product (AIRS2SUP_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is product produced using AIRS IR only because the radiometric noise in several AMSU channels started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 scanlines. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### AIRX3STD\nThe Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) AIRS constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South at 1:30 AM local time) or ascending (equatorial crossing South to North at 1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. The value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the box. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.\n<br>\n\n#### AIRS3STD\nThe Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South at 1:30 AM local time) or ascending (equatorial crossing South to North at 1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. The value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the box.\n<br>\n\n#### AIRS3STM\nThe Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS Only Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.\n<br>\n\n#### AIRSAC3MNH3\nThe mass concentration ammonia in the atmosphere, consists of products generated for the study of atmospheric ammonia. Atmospheric ammonia is an important component of the global nitrogen cycle. In the troposphere, ammonia reacts rapidly with acids such as sulfuric and nitric to form fine particulate matter. These ammonium containing aerosols affect Earth's radiative balance, both directly by scattering incoming radiation and indirectly as cloud condensation nuclei. Major sources of atmospheric ammonia involve agricultural activities including animal husbandry, especially concentrated animal feeding operations and fertilizer use. Major sinks of atmospheric ammonia involve dry deposition and wet removal by precipitation, as well as conversion to particulate ammonium by reaction with acids. Measurements of ambient NH3 are sparse, but satellites provide a means to monitor atmospheric composition globally. Using the AIRS/AMSU satellite this algorithm provides monthly measurements of derived atmospheric NH3 for September 2002 through August 2016.\n<br>\n\n#### MYDFDS_CLM_GLB_L3\nVersion 1 is the current version of the dataset. This collection MYDFDS_CLM_GLB_L3 provides level 3 climatological monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚. It is derived from Level 2, the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products Collection 6.1 from Aqua (MYD04_L2). The dataset is the climatological monthly mean for each month averaged over 2003 to 2022. The FDS is calculated as the number of days per month when the daily dust optical depth is greater than a threshold optical depth (e.g., 0.025) with two quality flags: the lowest (1) and highest (3). It is advised to use flag 1, which is of lower quality, over dust source regions, and flag 3 over remote areas or polluted regions. Eight thresholds (0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 2) are saved separately in eight files. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### MYD04_L2\nThe MODIS/Aqua Aerosol 5-Min L2 Swath 10km product (MYD04_L2) provides full global coverage of aerosol properties from the Dark Target (DT) and Deep Blue (DB) algorithms. The DT algorithm is applied over ocean and dark land (e.g., vegetation), while the DB algorithm now covers the entire land areas including both dark and bright surfaces. Both results are provided on a 10x10 pixel scale (10 km at nadir). Each MYD04_L2 product file covers a five-minute time interval. The output grid is 135 pixels in width by 203 pixels in length. Every tenth file has an output grid size of 135 by 204 pixels. MYD04_L2 product files are stored in Hierarchical Data Format (HDF-EOS). The new Collection 6.1 (C61) MYD04_L2 product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals. The MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land. In Collection 5 and in earlier collections, there was only one aerosol product (MYD04_L2) at 10km (at nadir) spatial resolution. Starting from C6, the Dark Target (DT) Aerosol algorithm team provided a new 3 km spatial resolution product (MYD04_3k) intended for the air quality community. For more information visit the MODIS Atmosphere website at: https://modis-atmos.gsfc.nasa.gov/products/aerosol And, for C6.1 changes and updates, visit: https://modis-atmosphere.gsfc.nasa.gov/documentation/collection-61\n<br>\n\n#### MYD04_3K\nThe new Collection 6.1 (C61) MODIS/Aqua Aerosol 5 Min L2 Swath 3km (MYD04_3K) product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals. The MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land. In Collection 5, and earlier collections, there was only one aerosol product (MYD04_L2) at 10km (at nadir) spatial resolution. Starting from C6, the Dark Target (DT) Aerosol algorithm team provided a new 3 km spatial resolution product (MYD04_3k) intended for the air quality community. The MYD04_3K product is based on the same algorithm and Look up Tables as the standard Dark Target aerosol product. Because of finer resolution, subtle differences are made in selecting pixels for retrieval and in determining QA. The only differences between the existing 10km algorithm and the new 3km algorithm are: 1) the size of the pixel-arrays defining each retrieval box ( 6x6 retrieval boxes of 36 pixels at 0.5km resolution for 3km algorithm as oppose to 20x20 retrieval boxes of 400 pixels at 0.5km resolution for 10km product); 2) the minimum percentage of \"good\" pixels required for a retrieval (a minimum of 5 pixels over ocean and 6 pixels over land instead of a minimum of 10 pixels over ocean or 12 pixels over land for 10km product retrieval); 3) the 10km algorithm attempts a \"poor quality\" retrieval while 3km algorithm does not. Everything else is the same between two products. For more information on C6.1 changes and updates, visit the MODIS Atmosphere website at: https://modis-atmosphere.gsfc.nasa.gov/documentation/collection-61\n<br>\n\n#### MYD08_E3\nThe MODIS/Aqua Aerosol Cloud Water Vapor Ozone 8-Day L3 Global 1Deg CMG product (MYD08_E3) contains 8-Day 1 degree x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. The MYD08_E3 contains nearly 1000 statistical datasets (SDS's) that are derived from the Level-3 MODIS Atmosphere Daily Global Product. Statistics are computed over a 1 degree equal-angle lat-lon grid that spans an 8-Day interval. Since the grid cells are 1 degree by 1 degree, the output grid is always 360 pixels in width and 180 pixels in length. MYD08_E3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. The MODIS 8-Day Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution. For more information about the MYD08_E3 product, please visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/eight-day\n<br>\n\n#### MYD08_D3\nThe MODIS/Aqua Aerosol Cloud Water Vapor Ozone Daily L3 Global 1Deg CMG product (MYD08_D3) contains daily 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. The MYD08_D3 contains roughly 600 statistical datasets that are derived from approximately 80 scientific parameters from four Level-2 MODIS Atmosphere Products: MOD04_L2, MOD05_L2, MOD06_L2, and MOD07_L2. Statistics are computed over a 1 degree equal-angle lat-lon grid that spans a 24-hour (0000 to 2400 Greenwich Mean Time) interval. Since the grid cells are 1 degree by 1 degree, the output grid is always 360 pixels in width and 180 pixels in length. MYD08_D3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. The MODIS Daily Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution. For more information about the MYD08_D3 product, please visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/daily\n<br>\n\n#### MYD08_M3\nThe MODIS/Aqua Aerosol Cloud Water Vapor Ozone Monthly L3 Global 1Deg CMG product (MYD08_M3) contains monthly 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. The MYD08_M3 contains roughly 800 statistical datasets that are derived from the Level-3 MODIS Atmosphere Daily Global Product. Statistics are sorted into 1x1 degree cells on an equal-angle grid that spans a (calendar) monthly interval and then summarized over the globe. MYD08_M3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. The MODIS monthly Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution. For more information about the MYD08_M3 product, please visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/monthly\n<br>\n\n#### MYDATML2\nThe MODIS/Aqua Aerosol, Cloud and Water Vapor Subset 5-Min L2 Swath 5km and 10km (MYDATML2) product contains a combination of key high interest science parameters. The ATML2 product provides a subset of datasets from the suite of atmosphere team products on both a 10 km scale (aerosols) and 5km scale (native 5 km cloud properties and a 5x5 pixel sample of the 1km cloud datasets). The ATML2 product employs the same 5x5 pixel sampling scheme for the 1km native resolution Level 2 products as is used in the MOD08 Level 3 global aggregated product, an approach that has been shown to retain statistical integrity for multi-day aggregations. The C6 significantly increases the number of datasets included in the ATML2 product, including the full suite of QA datasets. Since the ATML2 data granule file size is significantly smaller than the combined size of the individual L2 products, and because the 1 km pixel sampling is consistent with the L3 algorithm, the ATML2 product is a more practical means for the user community to develop research L3 algorithms for their own specific purposes. For more information, visit the MODIS Atmosphere website at: https://modis-atmos.gsfc.nasa.gov/products/joint-atm\n<br>\n\n#### MYD09\nThe MODIS/Aqua Atmospherically Corrected Surface Reflectance 5-Min L2 Swath 250m, 500m, 1km (MYD09) product is computed from the MODIS Level 1B land bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648 nm, 858 nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm, respectively). The product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption. The surface-reflectance product is the input for product generation for several land products: vegetation Indices (VIs), Bidirectional Reflectance Distribution Function (BRDF), thermal anomaly, snow/ice, and Fraction of Photosynthetically Active Radiation/Leaf Area Index (FPAR/LAI).\n<br>\n\n#### MYD021KM\nThe MODIS/Aqua Calibrated Radiances 5Min L1B Swath 1km data set contains calibrated and geolocated at-aperture radiances for 36 discrete bands located in the 0.4 to 14.4 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which during processing are converted to geophysical units of W / (m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously. The shortname for this product is MYD021KM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical file size is approximately 115 MB. Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world. See the MODIS Characterization Support Team webpage for more C6 product information at: https://mcst.gsfc.nasa.gov/l1b/product-information or visit Science Team homepage at: https://modis.gsfc.nasa.gov/data/dataprod/\n<br>\n\n#### MYD02QKM\nThe MODIS/Terra Calibrated Radiances 5-Min L1B Swath 250m data set contains calibrated and geolocated at-aperture radiances for 2 discrete bands located in the 0.62 to 0.88 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which during processing are converted to geophysical units of W / (m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. Separate L1B products are available for the five 500m resolution channels (MYD02HKM) and the twenty-nine 1km resolution channels (MYD021KM). For the 500m product, there are actually seven channels available since the data from the two 250 m channels have been aggregated to 500m resolution. Similarly, for the 1km product, all 36 MODIS channels are available since the data from the two 250m and five 500m channels have been aggregated into equivalent 1km pixel values. Spatial resolution for pixels at nadir is 250 m, degrading to 1.2 km in the along-scan direction and 0.5 km in the along-track direction for pixels located at the scan extremes. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 250 m granule will contain a scene built from 203 scans sampled 5416 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 40 along-track spatial elements for the 250 m channels, the scene will be composed of (5416 x 8120) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 17 degrees scan angle. The shortname for this product is MYD02QKM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical file size will be approximately 140 MB and the total daily volume is around 22GB. Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world. See the MODIS Characterization Support Team webpage for more C6.1 product information at: http://mcst.gsfc.nasa.gov/l1b/product-information or visit Science Team homepage at: http://modis.gsfc.nasa.gov/data/dataprod/\n<br>\n\n#### MYD02HKM\nThe MODIS/Aqua Calibrated Radiances 5Min L1B Swath 500m data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously. Channels 1 and 2 have 250 m resolution, channels 3 through 7 have 500 m resolution. However, for the MODIS L1B 500 m product, the 250 m band radiance data and their associated uncertainties have been aggregated to 500m resolution. Thus the entire channel data set has been co-registered to the same spatial scale in the 500 m product. Separate L1B products are available for the 250 m resolution channels (MYD02QKM) and 1 km resolution channels (MYD021KM). For the latter product, the 250 m and 500 m channel data (bands 1 through 7) have been aggregated into equivalent 1 km pixel values. Spatial resolution for pixels at nadir is 500 km, degrading to 2.4 km in the along-scan direction at the scan extremes. However, thanks to the overlapping of consecutive swaths and respectively pixels there, the resulting resolution at the scan extremes is about 1 km. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 500 m granule will contain a scene built from 203 scans sampled 2708 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 20 along-track spatial elements for the 500 m channels, the scene will be composed of (2708 x 4060) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 20 degrees scan angle. To summarize, the MODIS L1B 500 m data product consists of: 1. Calibrated radiances, uncertainties and number of samples for (2) 250 m reflected solar bands aggregated to 500 m resolution 2. Calibrated radiances and uncertainties for (5) 500 m reflected solar bands 3. Geolocation for 1km pixels, that must be interpolated to get 500 m pixel locations. For the relationship of 1km pixels to 500m pixels, see the Geolocation ATBD https://modis.gsfc.nasa.gov/data/atbd/atbd_mod28_v3.pdf. 4. Calibration data for all channels (scale and offset) 5. Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization users requiring all geolocation and solar/satellite geometry fields at 1km resolution can obtain the separate MODIS Level 1 Geolocation product (MYD03) from LAADS https://ladsweb.modaps.eosdis.nasa.gov/ . The shortname for this product is MYD02HKM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical MYD02HKM file size is approximately 135 MB. Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world. See the MODIS Characterization Support Team webpage for more C6 product information at: https://mcst.gsfc.nasa.gov/l1b/product-information or visit Science Team homepage at: https://modis.gsfc.nasa.gov/data/dataprod/\n<br>\n\n#### CLDMSK_L2_MODIS_Aqua\nThe MODIS-VIIRS Cloud Mask (MVCM) is designed to facilitate continuity in cloud detection between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Suomi NPP spacecraft. To establish continuity, this MODIS MVCM product does not use an algorithm identical to that used in the standard MODIS product (MOD35/MYD35). The MVCM-MODIS Cloud Mask product is Aqua MOIDS Level-2, 5-Min Swath product generated at 1000 m (at nadir) spatial resolution. The algorithm employs a series of visible through infrared threshold and consistency tests to specify confidence that an unobstructed view of the Earth's surface has been observed. Radiometrically-accurate radiances are required, thus holes in the cloud mask will appear wherever the input radiances are incomplete or of poor quality. For more information consult Product Page at: https://cimss.ssec.wisc.edu/MVCM/\n<br>\n\n#### MYD35_L2\nThe MODIS/Aqua Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km product consists of global cloud mask quality assurance and other ancillary parameters. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence levels that an unobstructed view of the Earth's surface is observed. An indication of shadows affecting the scene is also provided. The 250-m cloud mask flags are based on the visible channel data only. Radiometrically accurate radiances are required, so holes in the cloud mask will appear wherever the input radiances are incomplete or of poor quality. The shortname for this Level-2 MODIS cloud mask product is MYD35_L2. The MYD35_L2 product files are stored in Hierarchical Data Format (HDF-EOS). This product consists of 9 parameters and each of these parameters are stored as a Scientific Data Set (SDS) within the HDF-EOS file. The Cloud Mask and Quality Assurance SDS's are stored at 1 kilometer pixel resolution. All other SDS's (those relating to time, geolocation, and viewing geometry) are stored at 5 kilometer pixel resolution. For more information about the MYD35_L2 product, visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/cloud-mask\n<br>\n\n#### CLDPROP_L2_MODIS_Aqua\nThe MODIS/Aqua Cloud Properties 5-min L2 Swath 1km product is designed to facilitate continuity in cloud properties between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Suomi NPP spacecraft. To establish continuity, this MODIS Cloud Properties product does not use algorithms identical to those used in the standard MODIS product (MOD06/MYD06). The product consists of cloud optical and physical parameters derived using observations in visible through infrared spectral channels. MODIS infrared channels that are common with VIIRS are primarily used to derive cloud-top temperature, cloud-top height, effective emissivity, an infrared cloud phase product (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. The MODIS solar reflectances channels are primarily used to derive cloud optical thickness, particle effective radius, water path, and to inform the phase used in the optical retrievals. The MODIS Cloud Properties product is a Level-2 product generated at 1 km (at nadir) spatial resolution. The current version-1.1 of the Level-2 CLDPROP product collection is corrected to address an issue with the cloud optical properties’ thermodynamic phase that caused erroneous liquid water cloud phase results.\n<br>\n\n#### CLDPROPCOSP_D3_MODIS_Aqua\nThe MODIS/Aqua Cloud Properties COSP Level 3 daily, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_D3_MODIS_Aqua. It contains MODIS Aqua cloud mask, cloud top, and cloud optical retrieval data over daily timeframe. It provides a set of custom cloud-related parameters for better comparison with climate model output. The “COSP” acronym in the short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. Provided in netCDF4 format, it contains 32 aggregated science data sets (SDS/parameters). Consult the CLDPROPCOSP User Guide for details regarding how the L3 daily statistics are computed, and to learn more about the gridding and sampling protocols specific to this product and a number of other topics germane to the user community. The collection of this product starts from July 4, 2002 and includes 365 granules each calendar year.\n<br>\n\n#### CLDPROPCOSP_M3_MODIS_Aqua\nThe MODIS/Aqua Cloud Properties COSP Level 3 monthly, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_M3_MODIS_Aqua. It contains MODIS Aqua cloud mask, cloud top, and cloud optical retrieval data over monthly timeframe. It provides a set of custom cloud-related parameters for better comparison with climate model output. The “COSP” acronym in the short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. Provided in netCDF4 format, it contains 32 aggregated science data sets (SDS/parameters). Consult the CLDPROPCOSP User Guide for details regarding how the L3 daily statistics are computed, and to learn more about the gridding and sampling protocols specific to this product and a number of other topics germane to the user community. The collection of this product starts from August 1, 2002 and includes 12 granules each calendar year.\n<br>\n\n#### CLDPROP_D3_MODIS_Aqua\nThe Cloud Properties Level-3 gridded product is designed to facilitate continuity in cloud property statistics between the MODIS on the Aqua and Terra platforms and the common continuity products generated for the VIIRS (Visible Infrared Imaging Radiometer Suite) and the MODIS Aqua instruments. CLDPROP Level-3 statistical routines include scalar and histograms (1-D and 2-D) that are calculated identically to statistical datasets in the MODIS standard Level-3 product (MOD08 and MYD08 for MODIS Terra and Aqua, respectively). In addition, the same dataset names are used for all common datasets provided in both the continuity and standard Level-3 files.\n<br>\n\n#### MYD06_L2\nThe MODIS/Aqua Clouds 5-Min L2 Swath 1km and 5km product consists of cloud optical and physical parameters. The cloud optical parameters are generated at 1km and cloud top (physical) parameters are generated at 5km resolution. These parameters are derived using remotely sensed infrared, visible and near infrared solar reflected radiances. MODIS infrared channel radiances are used to derive cloud top temperature, cloud top height, effective emissivity, cloud phase (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. MODIS visible radiances are used to derive cloud optical thickness and effective particle radius and cloud shadow effects. Near infrared solar reflected radiance provides additional information in the retrieval of cloud particle phase (ice vs. water, clouds vs. snow). The shortname for this level-2 MODIS cloud product is MYD06_L2. The MYD06_L2 consists of parameters at a spatial resolution of either 1- km or 5-km (at nadir). Each MYD06_L2 product file covers a five-minute time interval. This means that for 5-km resolution parameters, the output grid is 270 pixels in width by 406 pixels in length. C6.1 changes for the cloud optical property retrievals are low-impact, and are limited primarily to ancillary product usage, the Quality Assurance (QA), and handling of cloud top (CT) properties fill values; no updates to retrieval science are implemented. The MODIS Cloud Product is used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial (1 kilometer) resolution. For more information about the MYD06_L2 product, visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/cloud\n<br>\n\n#### MYD03\nThe MODIS/Aqua Geolocation Fields 5-Min L1A Swath 1km are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily (in Collection 6 and later, information is provided to calculate 500m geolocation fields). The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit, the instrument telemetry and the digital elevation model. The geolocation fields include geodetic Latitude, Longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1 km sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors of any of the 36 MODIS bands. This product is used as input by a large number of subsequent MODIS products, particularly the products produced by the Land team. The short name for this product is MYD03. Each file is roughly 30 MB in size, and the total data volume is approximately 8 GB/day. See the MODIS Science Team homepage for more data set information: https://modis.gsfc.nasa.gov/data/dataprod/\n<br>\n\n#### MYD17A2H\nThe MYD17A2H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD17A2H Version 6.1](https://doi.org/10.5067/MODIS/MYD17A2H.061) data product. The MYD17A2H Version 6 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP minus the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD17A2H\nThe MYD17A2H Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP minus the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD17A2HGF\nThe MYD17A2HGF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD17A2HGF Version 6.1](https://doi.org/10.5067/MODIS/MYD17A2HGF.061) data product. The MYD17A2HGF Version 6 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day [MYD15A2H](https://doi.org/10.5067/modis/myd15a2h.006) is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year. Known Issues: Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD17A2HGF\nThe MYD17A2HGF Version 6.1 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day [MYD15A2H](https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year. Known Issues: Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD21\nThe MYD21 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD21 Version 6.1](https://doi.org/10.5067/MODIS/MYD21.061) data product. The MYD21 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MYD21 Land Surface Temperature (LST) algorithm differs from the [MYD11](https://doi.org/10.5067/modis/myd11_l2.006) algorithm in that the MYD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. MYD21 products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD21\nThe MYD21 Version 6.1 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MYD21 Land Surface Temperature (LST) algorithm differs from the [MYD11](https://doi.org/10.5067/modis/myd11_l2.061) algorithm in that the MYD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD21C2\nA new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the [MYD11](https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21C2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free [MYD21A1D](http://doi.org/10.5067/MODIS/MYD21A1D.061) and [MYD21A1N](http://doi.org/10.5067/MODIS/MYD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD21A2\nThe MYD21A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD21A2 Version 6.1](https://doi.org/10.5067/MODIS/MYD21A2.061) data product. A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the [MYD11](https://doi.org/10.5067/modis/myd11_l2.006) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21A2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free [MYD21A1D](http://dx.doi.org/10.5067/MODIS/MYD21A1D.006) and [MYD21A1N](http://dx.doi.org/10.5067/MODIS/MYD21A1N.006) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. MYD21A2 products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD21A2\nA suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the [MYD11](https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21A2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free [MYD21A1D](http://doi.org/10.5067/MODIS/MYD21A1D.061) and [MYD21A1N](http://doi.org/10.5067/MODIS/MYD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD21C1\nA new suite of MODIS Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 LST algorithm differs from the algorithm of the [MYD11](https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to retrieve dynamically both the LST and spectral emissivity simultaneously from the three MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21C1 Version 6.1 dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily [MYD21](http://doi.org/10.5067/MODIS/MYD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21C1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The MYD21C1 product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD21A1D\nThe MYD21A1D Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD21A1D Version 6.1](https://doi.org/10.5067/MODIS/MYD21A1D.061) data product. A new suite of MODIS Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6. The MYD21 LST algorithm differs from the algorithm of the [MYD11](https://doi.org/10.5067/modis/myd11_l2.006) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to retrieve dynamically both the LST and spectral emissivity simultaneously from the three MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21A1D dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily [MYD21](http://doi.org/10.5067/MODIS/MYD21.006) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The MYD21A1D product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. MYD21A1D products are available two months after acquisition due to latency of data inputs. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document [ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD21A1D\nA suite of MODIS Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 LST algorithm differs from the algorithm of the [MYD11](https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to retrieve dynamically both the LST and spectral emissivity simultaneously from the three MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21A1D dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily [MYD21](http://doi.org/10.5067/MODIS/MYD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The MYD21A1D product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD21A1N\nThe MYD21A1N Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD21A1N Version 6.1](https://doi.org/10.5067/MODIS/MYD21A1N.061) data product. A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the [MYD11](https://doi.org/10.5067/modis/myd11_l2.006) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily [MYD21](http://dx.doi.org/10.5067/MODIS/MYD21.006) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MYD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. MYD21A1N products are available two months after acquisition due to latency of data inputs. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD11_L2\nThe MYD11_L2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11_L2 Version 6.1](https://doi.org/10.5067/MODIS/MYD11_L2.061) data product. The MYD11_L2 Version 6 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MYD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MYD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD11_L2\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 5-Minute (MYD11_L2) Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MYD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MYD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD11C2\nThe MYD11C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11C2 Version 6.1](https://doi.org/10.5067/MODIS/MYD11C2.061) data product. The MYD11C2 Version 6 product provides Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe. The LST&E values in the MYD11C2 product are derived by compositing and averaging the values from the corresponding eight [MYD11C1](https://doi.org/10.5067/MODIS/MYD11C1.006) daily files. The MYD11C2 granule consists of 17 layers. Each MYD11C2 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD11C2\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11C2) Version 6.1 product provides Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe. The LST&E values in the MYD11C2 product are derived by compositing and averaging the values from the corresponding eight [MYD11C1](https://doi.org/10.5067/MODIS/MYD11C1.061) daily files. The MYD11C2 granule consists of 17 layers. Each MYD11C2 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD11A2\nThe MYD11A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11A2 Version 6.1](https://doi.org/10.5067/MODIS/MYD11A2.061) data product. The MYD11A2 Version 6 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MYD11A2 is a simple average of all the corresponding [MYD11A1](https://doi.org/10.5067/MODIS/MYD11A1.006) LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. Known Issues For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD11A2\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11A2) Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MYD11A2 is a simple average of all the corresponding [MYD11A1](https://doi.org/10.5067/MODIS/MYD11A1.061) LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD11B2\nThe MYD11B2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11B2 Version 6.1](https://doi.org/10.5067/MODIS/MYD11B2.061) data product. The MYD11B2 Version 6 product provides an average 8-day per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each temperature and emissivity pixel value in the MYD11B2 is a simple average of all the corresponding values from the LST&E values from the [MYD11B1](https://doi.org/10.5067/MODIS/MYD11B1.006) product collected during that 8-day period. Each MYD11B2 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km [MYD11_L2](https://doi.org/10.5067/MODIS/MYD11_L2.006) swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD11B2\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11B2) Version 6.1 product provides an average 8-day per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each temperature and emissivity pixel value in the MYD11B2 is a simple average of all the corresponding values from the LST&E values from the [MYD11B1](https://doi.org/10.5067/MODIS/MYD11B1.061) product collected during that 8-day period. Each MYD11B2 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km [MYD11_L2](https://doi.org/10.5067/MODIS/MYD11_L2.061) swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD11C1\nThe MYD11C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11C1 Version 6.1](https://doi.org/10.5067/MODIS/MYD11C1.061) data product. The MYD11C1 Version 6 product provides daily Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). The MYD11C1 product is directly derived from the [MYD11B1](https://doi.org/10.5067/MODIS/MYD11B1.006) product. A CMG granule follows a Geographic grid, having 7,200 columns and 3,600 rows, which represent the entire globe. Each MYD11C1 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the grid. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD11C1\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MYD11C1) Version 6.1 product provides daily Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). The MYD11C1 product is directly derived from the [MYD11B1](https://doi.org/10.5067/MODIS/MYD11B1.061) product. A CMG granule follows a Geographic grid, having 7,200 columns and 3,600 rows, which represent the entire globe. Each MYD11C1 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the grid. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD11A1\nThe MYD11A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11A1 Version 6.1](https://doi.org/10.5067/MODIS/MYD11A1.061) data product. The MYD11A1 Version 6 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the [MYD11_L2](https://doi.org/10.5067/MODIS/MYD11_L2.006) swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD11A1\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MYD11A1) Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the [MYD11_L2](https://doi.org/10.5067/MODIS/MYD11_L2.061) swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD11B1\nThe MYD11B1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11B1 Version 6.1](https://doi.org/10.5067/MODIS/MYD11B1.061) data product. The MYD11B1 Version 6 product provides daily per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each MOD11B1 granule consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the tile. Unique to the MYD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km [MYD11_L2](https://doi.org/10.5067/MODIS/MYD11_L2.006) swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD11B1\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MYD11B1) Version 6.1 product provides daily per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each MOD11B1 granule consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the tile. Unique to the MYD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km [MYD11_L2](https://doi.org/10.5067/MODIS/MYD11_L2.061) swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD11C3\nThe MYD11C3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11C3 Version 6.1](https://doi.org/10.5067/MODIS/MYD11C3.061) data product. The MYD11C3 Version 6 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe. The LST&E values in the MYD11C3 product are derived by compositing and averaging the values from the corresponding month of [MYD11C1](https://doi.org/10.5067/MODIS/MYD11C1.006) daily files. Each MYD11C3 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD11C3\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Monthly (MYD11C3) Version 6.1 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe. The LST&E values in the MYD11C3 product are derived by compositing and averaging the values from the corresponding month of [MYD11C1](https://doi.org/10.5067/MODIS/MYD11C1.061) daily files. Each MYD11C3 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD11B3\nThe MYD11B3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11B3 Version 6.1](https://doi.org/10.5067/MODIS/MYD11B3.061) data product. The MYD11B3 Version 6 product provides average monthly per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each LST&E pixel value in the MYD11B3 is a simple average of all the corresponding values from the [MYD11B1](https://doi.org/10.5067/MODIS/MYD11B1.006) collected during the month period. Each MYD11B3 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MYD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km [MYD11_L2](https://doi.org/10.5067/MODIS/MYD11_L2.006) swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD11B3\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Monthly (MYD11B3) Version 6.1 product provides average monthly per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each LST&E pixel value in the MYD11B3 is a simple average of all the corresponding values from the [MYD11B1](https://doi.org/10.5067/MODIS/MYD11B1.061) collected during the month period. Each MYD11B3 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MYD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km [MYD11_L2](https://doi.org/10.5067/MODIS/MYD11_L2.061) swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD15A2H\nThe MYD15A2H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD15A2H Version 6.1](https://doi.org/10.5067/MODIS/MYD15A2H.061) data product. The MYD15A2H Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the “best” pixel available from all the acquisitions of the Aqua sensor from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nanometers (nm)) absorbed by the green elements of a vegetation canopy. Science Datasets (SDS) in the Level 4 (L4) MYD15A2H product include LAI, FPAR, two quality layers, and standard deviation for LAI and FPAR. Two low resolution browse images, LAI and FPAR, are also available for each MYD15A2H granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD15A2H\nThe MYD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the \"best\" pixel available from all the acquisitions of the Aqua sensor from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nanometers (nm)) absorbed by the green elements of a vegetation canopy. Science Datasets (SDS) in the Level 4 (L4) MYD15A2H product include LAI, FPAR, two quality layers, and standard deviation for LAI and FPAR. Two low resolution browse images, LAI and FPAR, are also available for each MYD15A2H granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD16A2\nThe MYD16A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD16A2 Version 6.1](https://doi.org/10.5067/MODIS/MYD16A2.061) data product. The MYD16A2 Version 6 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MYD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2 granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period depending on the year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD16A2\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) MYD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MYD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2 granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period depending on the year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD16A2GF\nThe MYD16A2GF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD16A2GF Version 6.1](https://doi.org/10.5067/MODIS/MYD16A2GF.061) data product. The MYD16A2GF Version 6 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MYD16A2GF will be generated at the end of each year when the entire yearly 8-day [MYD15A2H](https://doi.org/10.5067/MODIS/MYD15A2H.006) is available. Hence, the gap-filled MYD16A2GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A2GF in near-real time because it will be generated only at the end of a given year. Provided in the MYD16A2GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. The last acquisition period of each year is a 5- or 6-day composite period, depending on the year. Known Issues: Operational and uncertainty issues are provided on page 13 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD16A2GF\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) MYD16A2GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MYD16A2GF will be generated at the end of each year when the entire yearly 8-day [MYD15A2H](https://doi.org/10.5067/MODIS/MYD15A2H.061) is available. Hence, the gap-filled MYD16A2GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A2GF in near-real time because it will be generated only at the end of a given year. Provided in the MYD16A2GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. The last acquisition period of each year is a 5 or 6-day composite period, depending on the year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD16A3GF\nThe MYD16A3GF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD16A3GF Version 6.1](https://doi.org/10.5067/MODIS/MYD16A3GF.061) data product. The MYD16A3GF Version 6 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MYD16A3GF will be generated at the end of each year when the entire yearly 8-day [MYD15A2H](https://doi.org/10.5067/MODIS/MYD15A2H.006) is available. Hence, the gap-filled MYD16A3GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A3GF in near-real time because it will be generated only at the end of a given year. Provided in the MYD16A3GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A3GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD16A3GF\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) MYD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MYD16A3GF will be generated at the end of each year when the entire yearly 8-day [MYD15A2H](https://doi.org/10.5067/MODIS/MYD15A2H.061) is available. Hence, the gap-filled MYD16A3GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A3GF in near-real time because it will be generated only at the end of a given year. Provided in the MYD16A3GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A3GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD16A3\nThe MYD16A3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD16A3GF Version 6.1](https://doi.org/10.5067/MODIS/MYD16A3GF.061) data product. The MYD16A3 Version 6 Evapotranspiration/Latent Heat Flux product is a yearly composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MYD16A3 product are layers for yearly Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A3 granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6). Improvements/Changes from Previous Versions * Spatial resolution of Version 6 products has increased to nominal 500 m from nominal 1,000 m in Version 5. * Version 5 data products were previously distributed by the Numerical Terradynamic Simulation Group at the University of Montana. The Version 6 products are a continuation of this project.\n<br>\n\n#### MYD17A3HGF\nThe MYD17A3HGF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD17A3HGF Version 6.1](https://doi.org/10.5067/MODIS/MYD17A3HGF.061) data product. The MYD17A3HGF Version 6 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MYD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). The MYD17A3HGF will be generated at the end of each year when the entire yearly 8-day [MYD15A2H](https://doi.org/10.5067/modis/myd15a2h.006) is available. Hence, the gap-filled MYD17A3HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A3HGF in near-real time because it will be generated only at the end of a given year. Known Issues: Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD17A3HGF\nThe MYD17A3HGF Version 6.1 product provides information about annual Gross and Net Primary Production (GPP and NPP) at 500 meter (m) pixel resolution. Annual Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and NPP is derived from the sum of all 8-day GPP and Net Photosynthesis (PSN) products ([MYD17A2H](https://doi.org/10.5067/MODIS/MYD17A2H.061)) from the given year. The PSN value is the difference of the GPP and the Maintenance Respiration (MR). The MYD17A3HGF will be generated at the end of each year when the entire yearly 8-day [MYD15A2H](https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A3HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A3HGF in near-real time because it will be generated only at the end of a given year. Known Issues: Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD17A3H\nThe MYD17A3H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD17A3HGF Version 6.1](https://doi.org/10.5067/MODIS/MYD17A3HGF.061) data product. The MYD17A3H Version 6 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products ([MYD17A2H](https://doi.org/10.5067/MODIS/MYD17A2H.006)) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). Knonw Issues * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6). * Forward processing of Aqua MODIS Net Primary Production Yearly L4 Global 500 m SIN Grid products was discontinued on January 3, 2004, due to unexpected errors in the input data. Users are encouraged to use the MYD17A3HGF Version 6.1 data product.\n<br>\n\n#### MYDOCGA\nThe MYDOCGA Version 6 data product was decommissioned on July 31, 2023. The MYDOCGA Version 6 Level 2 Gridded Lite (L2G-lite) Ocean Reflectance product provides an estimate of the surface spectral reflectance data from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) bands 8 through 16. Data have been corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. MYDOCGA is a daily land product with a pixel size of 1 kilometer (km). The product is referred to as ocean reflectance because bands 8 through 16 are used primarily to produce ocean products. The MYDOCGA, as with other L2G data sets, stores the “best available pixel” from all the qualifying observations in the first layer and any subsequent observations are stored in either a full or compact format layer within the Hierarchical Data Format (HDF) file. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6). Improvements/Changes from Previous Versions * New product for MODIS Version 6.\n<br>\n\n#### MYD09Q1\nThe MYD09Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD09Q1 Version 6.1](https://doi.org/10.5067/MODIS/MYD09Q1.061) data product. The MYD09Q1 Version 6 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. Known Issues: The Collection 6 MODIS Land Surface Reflectance product (MYD09) may [incorrectly flag retrievals as ‘High Aerosol’](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=174) over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD09Q1\nThe MYD09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. Known Issues: Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded seriously after launch and presently a majority of the Band 6 detectors are non-functional. Science users should read and use the non-functional detector flags and decide for themselves the optimum manner to handle non-functional detector \"gaps\" for their products. For complete information please refer to the [MODIS Characterization Support Team (MCST) website](https://mcst.gsfc.nasa.gov/time-dependent-list-non-functional-or-noisy-detector). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD09A1\nThe MYD09A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD09A1 Version 6.1](https://doi.org/10.5067/MODIS/MYD09A1.061) data product. The Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua MYD09A1 Version 6 product provides an estimate of the surface spectral reflectance of Aqua MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are a quality variable and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. Known Issues: The Collection 6 MODIS Land Surface Reflectance product (MYD09) may [incorrectly flag retrievals as ‘High Aerosol’](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=174) over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD09A1\nThe Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua MYD09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are a quality layer and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. Known Issues: Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded seriously after launch and presently a majority of the Band 6 detectors are non-functional. Science users should read and use the non-functional detector flags and decide for themselves the optimum manner to handle non-functional detector \"gaps\" for their products. For complete information please refer to the [MODIS Characterization Support Team (MCST) website](https://mcst.gsfc.nasa.gov/time-dependent-list-non-functional-or-noisy-detector). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD09GA\nThe MYD09GA Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD09GA Version 6.1](https://doi.org/10.5067/MODIS/MYD09GA.061) data product. The MYD09GA Version 6 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 km observation bands and geolocation flags. The reflectance layers from the MYD09GA are used as the source data for many of the MODIS land products. Known Issues: The Collection 6 MODIS Land Surface Reflectance product (MYD09) may [incorrectly flag retrievals as ‘High Aerosol’](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=174) over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD09GA\nThe MYD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer observation bands and geolocation flags. The reflectance layers from the MYD09GA are used as the source data for many of the MODIS land products. Known Issues: Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded seriously after launch and presently a majority of the Band 6 detectors are non-functional. Science users should read and use the non-functional detector flags and decide for themselves the optimum manner to handle non-functional detector \"gaps\" for their products. For complete information please refer to the [MODIS Characterization Support Team (MCST) website](https://mcst.gsfc.nasa.gov/time-dependent-list-non-functional-or-noisy-detector). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD09GQ\nThe MYD09GQ Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD09GQ Version 6.1](https://doi.org/10.5067/MODIS/MYD09GQ.061) data product. The MYD09GQ Version 6 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MYD09GA). Known Issues: The Collection 6 MODIS Land Surface Reflectance product (MYD09) may [incorrectly flag retrievals as ‘High Aerosol’](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=174) over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD09GQ\nThe MYD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MYD09GA). Known Issues: Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded seriously after launch and presently a majority of the Band 6 detectors are non-functional. Science users should read and use the non-functional detector flags and decide for themselves the optimum manner to handle non-functional detector \"gaps\" for their products. For complete information please refer to the [MODIS Characterization Support Team (MCST) website](https://mcst.gsfc.nasa.gov/time-dependent-list-non-functional-or-noisy-detector). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD09CMG\nThe MYD09CMG Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD09CMG Version 6.1](https://doi.org/10.5067/MODIS/MYD09CMG.061) data product. The MYD09CMG Version 6 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, resampled to 5600 meter (m) pixel resolution and corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. The MOD09CMG data product provides 25 layers including MODIS bands 1 through 7; Brightness Temperature data from thermal bands 20, 21, 31, and 32; along with Quality Assurance (QA) and observation bands. This product is based on a Climate Modeling Grid (CMG) for use in climate simulation models. Known Issues: The Collection 6 MODIS Land Surface Reflectance product (MYD09) may [incorrectly flag retrievals as ‘High Aerosol’](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=174) over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD09CMG\nThe MYD09CMG Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, resampled to 5600 meter (m) pixel resolution and corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. The MOD09CMG data product provides 25 layers including MODIS bands 1 through 7; Brightness Temperature data from thermal bands 20, 21, 31, and 32; along with Quality Assurance (QA) and observation bands. This product is based on a Climate Modeling Grid (CMG) for use in climate simulation models. Known Issues: A [striping anomaly in Band 5](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=939) of the MYD09CMG V61 product has been identified, caused by a dead detector in the AQUA Band 5, affecting data accuracy throughout the Aqua MODIS mission. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD07_L2\nThe MODIS/Aqua Temperature and Water Vapor Profiles 5-Min L2 Swath 5km (MYD07_L2) product consists of a numbers of parameters related to atmospheric stability, atmospheric temperature and moisture profiles, total atmospheric water vapor, and total ozone. All of these parameters are produced for both daytime and nighttime conditions at 5-km pixel resolution when at least 9 Field Of View (FOV) are cloud free. The MODIS total-ozone burden is an estimate of the total-column tropospheric and stratospheric ozone content. The MODIS atmospheric stability consists of three daily Level 2 atmospheric stability indices. The Total Totals (TT), the Lifted Index (LI), and the K index (K) are each computed using the infrared temperature- and moisture-profile data, also derived as part of MYD07. The MODIS temperature and moisture profiles are produced at 20 vertical levels. The MODIS atmospheric water-vapor product is an estimate of the total tropospheric column water vapor made from integrated MODIS infrared retrievals of atmospheric moisture profiles in clear scenes. Additional information is available at: https://modis-atmos.gsfc.nasa.gov/products/atm-profile.\n<br>\n\n#### MYD14\nThe MYD14 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD14 Version 6.1](https://doi.org/10.5067/MODIS/MYD14.061) data product. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire MYD14 Version 6 product is produced daily in 5-minute temporal satellite increments (swaths). The MYD14 product is used to generate all of the higher level fire products, but can also be used to identify fires and other thermal anomalies, such as volcanoes. Each swath of data is approximately 2,030 kilometers along track (long), and 2,300 kilometers across track (wide). Known Issues: Known issues are described on the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6) and in Section 7.2 of the User Guide which covers Pre-November 2000 Data Quality, Detection Confidence, Flagging of Static Sources, and the August 2020 MODIS Aqua Outage.\n<br>\n\n#### MYD14\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire (MYD14) Version 6.1 product is produced daily in 5-minute temporal satellite increments (swaths). The MYD14 product is used to generate all of the higher level fire products, but can also be used to identify fires and other thermal anomalies, such as volcanoes. Each swath of data is approximately 2,030 kilometers along track (long), and 2,300 kilometers across track (wide). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD14A2\nThe MYD14A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD14A2 Version 6.1](https://doi.org/10.5067/MODIS/MYD14A2.061) data product. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day (MYD14A2) Version 6 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MYD14A2 gridded composite contains maximum value of individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators. Known Issues: Known issues are described on the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6) and in Section 7.2 of the User Guide which covers Pre-November 2000 Data Quality, Detection Confidence, Flagging of Static Sources, and the August 2020 MODIS Aqua Outage.\n<br>\n\n#### MYD14A2\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day (MYD14A2) Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MYD14A2 gridded composite contains maximum value of individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD14A1\nThe MYD14A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD14A1 Version 6.1](https://doi.org/10.5067/MODIS/MYD14A1.061) data product. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily (MYD14A1) Version 6 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MYD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire-radiative-power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition. Known Issues: Known issues are described on the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6) and in Section 7.2 of the User Guide which covers Pre-November 2000 Data Quality, Detection Confidence, Flagging of Static Sources, and the August 2020 MODIS Aqua Outage.\n<br>\n\n#### MYD14A1\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily (MYD14A1) Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MYD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire-radiative-power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYDTBGA\nThe MYDTBGA Version 6 data product was decommissioned on July 31, 2023. The MYDTBGA Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 Gridded Lite (L2G-lite) Thermal Band product consists of brightness temperature data from Aqua MODIS bands 20, 31, and 32 and albedo data from band 20 along with the orbit and granule pointer fields. MYDTBGA is a daily product with a pixel size of 1 kilometer (km). The MYDTBGA, as with other L2G data sets, stores the “best available pixel” from all the qualifying observations in the first layer and any subsequent observations are stored in either full or a compact layers within the Hierarchical Data Format (HDF) file. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6). Improvements/Changes from Previous Versions * New product for MODIS Version 6.\n<br>\n\n#### MYD05_L2\nThe MODIS/Aqua Total Precipitable Water Vapor 5-Min L2 Swath 1km and 5km (MYD05_L2) product consists of atmospheric column water-vapor amounts. This product is derived from data collected by the Moderate-Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite. There are two different algorithms used to derive total precipitable water vapor in this data product: a near-infrared algorithm and an infrared algorithm. The near-infrared algorithm relies on observations of reflected solar radiation in MODIS's near-infrared channels, thus, the near-infrared retrievals are only produced during the daytime over surfaces that reflect near-infrared energy. As a result, the near-infrared algorithm is only applied over clear, cloud free land areas of the globe and above clouds over both the land and ocean. Over clear ocean areas, water-vapor estimates are provided over extended sun glint areas. Data produced by the near-infrared algorithm are generated at a 1-km spatial resolution. The other algorithm is the infrared algorithm which can be used to derive atmospheric precipitable water vapor profiles during both day and night. Data from the infrared algorithm are generated at a 5-km spatial resolution when at least nine field of views (FOVs) are cloud free. The infrared-derived precipitable water vapor is generated as a component of product MYD07, and is simply added to product MYD05 for convenience. There are two MODIS Precipitable Water Vapor products: MOD05_L2, containing data collected from the Terra platform; and MYD05_L2, containing data collected from the Aqua platform. This dataset has a short name of MYD05_L2 and provides data from the Aqua platform only. The MODIS Adaptive Processing System (MODAPS) is currently generating an improved version 6.1 (061) for all MODIS Level-1 (L1) and higher-level Level-2 (L2) & Level-3 (L3) Atmosphere Team products. The decision to create a new improved Collection 6.1 (061) was driven by the need to address a number of issues in the current Collection 6 (006) Level-1B (L1B) data, which have a negative impact in varying degrees in downstream products. It should be noted that the near-infrared algorithm refinement for this product is no longer being supported by NASA and as such there has been no update to this algorithm for Collection 6.1. For more information, visit the MODIS Atmosphere website at: https://modis-atmos.gsfc.nasa.gov/products/water-vapor\n<br>\n\n#### MYD13C1\nThe MYD13C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD13C1 Version 6.1](https://doi.org/10.5067/MODIS/MYD13C1.061) data product. The MYD13C1 Version 6 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The Climate Modeling Grid (CMG) consists 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MYD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer [MYD13A2](https://doi.org/10.5067/MODIS/MYD13A2.006) data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C1 has data fields for NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues: The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MYD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=174) MYD13 Vegetation Index data products particularly over arid bright surfaces. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD13C1\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MYD13C1) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The Climate Modeling Grid (CMG) consists 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MYD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer [MYD13A2](https://doi.org/10.5067/MODIS/MYD13A2.061) data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C1 has data fields for NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD13A2\nThe MYD13A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD13A2 Version 6.1](https://doi.org/10.5067/MODIS/MYD13A2.061) data product. The MYD13A2 Version 6 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: The following issues have been detected: * Unexpected missing data in the last cycles of each year. * Incorrect instances of \"NoData\" and spikes in NDVI values. * VI Usefulness Bits are not correctly assigned. * For instances where the VI Quality (bits 0-1) is flagged as good and the VI Usefulness (bits 2-5) indicates the same pixels have the lowest usefulness score, users are advised to disregard the usefulness score. * The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MYD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=174) MYD13 Vegetation Index data products particularly over arid bright surfaces. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD13A2\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MYD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD13Q1\nThe MYD13Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD13Q1 Version 6.1](https://doi.org/10.5067/MODIS/MYD13Q1.061) data product. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MYD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: The following issues have been detected: * Unexpected missing data in the last cycles of each year. * Incorrect instances of \"NoData\" and spikes in NDVI values. * VI Usefulness Bits are not correctly assigned. * For instances where the VI Quality (bits 0-1) is flagged as good and the VI Usefulness (bits 2-5) indicates the same pixels have the lowest usefulness score, users are advised to disregard the usefulness score. * The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MYD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=174) MYD13 Vegetation Index data products particularly over arid bright surfaces. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD13Q1\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MYD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD13A1\nThe MYD13A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD13A1 Version 6.1](https://doi.org/10.5067/MODIS/MYD13A1.061) data product. The MYD13A1 Version 6 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: The following issues have been detected: * Unexpected missing data in the last cycles of each year. * Incorrect instances of \"NoData\" and spikes in NDVI values. * VI Usefulness Bits are not correctly assigned. * For instances where the VI Quality (bits 0-1) is flagged as good and the VI Usefulness (bits 2-5) indicates the same pixels have the lowest usefulness score, users are advised to disregard the usefulness score. * The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MYD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=174) MYD13 Vegetation Index data products particularly over arid bright surfaces. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD13A1\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MYD13A1) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD13C2\nThe MYD13C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD13C2 Version 6.1](https://doi.org/10.5067/MODIS/MYD13C2.061) data product. The MYD13C2 Version 6 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the [MYD13A2](https://doi.org/10.5067/MODIS/MYD13A2.006) products that overlap the month and employs a weighted temporal average. Global MYD13C1 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C2 has data fields for the NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues: The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MYD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=174) MYD13 Vegetation Index data products particularly over arid bright surfaces. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD13C2\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Monthly (MYD13C2) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the [MYD13A2](https://doi.org/10.5067/MODIS/MYD13A2.061) products that overlap the month and employs a weighted temporal average. Global MYD13C1 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C2 has data fields for the NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD13A3\nThe MYD13A3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD13A3 Version 6.1](https://doi.org/10.5067/MODIS/MYD13A3.061) data product. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13A3) Version 6 data are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the [MYD13A2](https://doi.org/10.5067/MODIS/MYD13A2.006) products that overlap the month and employs a weighted temporal average. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes an Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin clouds. The MODIS NDVI and EVI products are computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes as well as global and regional climate. Additional applications include characterizing land surface biophysical properties and processes, such as primary production and land cover conversion. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as three observation layers. Known Issues: The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MYD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=174) MYD13 Vegetation Index data products particularly over arid bright surfaces. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).\n<br>\n\n#### MYD13A3\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13A3) Version 6.1 data are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the [MYD13A2](https://doi.org/10.5067/MODIS/MYD13A2.061) products that overlap the month and employs a weighted temporal average. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes an Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin clouds. The MODIS NDVI and EVI products are computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes as well as global and regional climate. Additional applications include characterizing land surface biophysical properties and processes, such as primary production and land cover conversion. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as three observation layers. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD28C2\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir 8-Day Level 3 (L3) Global (MYD28C2) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MYD28C2 Version 6.1 data product provides an 8-day time series of surface area, elevation, and water storage. Datasets are combined with pre-established [Area-Elevation (A-E) curves](https://doi.org/10.1016/j.rse.2020.111831) and image classifications of near-infrared (NIR) reflectance from the surface reflectance product acquired by the Aqua satellite ([MYD09Q1](https://lpdaac.usgs.gov/products/myd09q1v061/)). The MYD28C2 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir area, elevation, and storage capacity. Known Issues: Water occurrence images generally show smaller surface area dynamics in high latitude regions, creating pixels with low occurrence values that have relatively large uncertainties. In addition, the quality of raw water area classification can be affected by lake ice coverage typically creating an overestimation of surface area in the enhancement algorithm. This issue will be addressed in a future release of the enhancement algorithm. For additional information about known issues, refer to Section 4 in the User Guide and [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYD28C3\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir Monthly Level 3 (L3) Global (MYD28C3) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MYD28C3 Version 6.1 data product is a composite of the 8-day area classifications from [MYD28C2](https://lpdaac.usgs.gov/products/myd28c2v061/), which is converted to provide monthly elevation and water storage. [Lake Temperature and Evaporation Model (LTEM)](https://doi.org/10.1016/j.rse.2020.112104) via MODIS Land Surface Temperature (LST) ([MYD21](https://lpdaac.usgs.gov/products/myd21v061/)) and meteorological data from [Global Land Data Assimilation System (GLDAS)](https://earth.gsfc.nasa.gov/hydro/data/gldas-global-land-data-assimilation-system-data) are used to produce monthly evaporation rates and volume losses. The MYD28C3 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), monthly reservoir area, elevation, storage capacity, evaporation rate, and evaporation volume. Known Issues: Water occurrence images generally show smaller surface area dynamics in high latitude regions, creating pixels with low occurrence values that have relatively large uncertainties. In addition, the quality of raw water area classification can be affected by lake ice coverage typically creating an overestimation of surface area in the enhancement algorithm. This issue will be addressed in a future release of the enhancement algorithm. For additional information about known issues, refer to Section 4 in the User Guide and [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n<br>\n\n#### MYDFDS_MON_GLB_L3\nVersion 1 is the current version of the dataset. This collection MYDFDS_MON_GLB_L3 provides level 3 monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚. It is derived from Level 2, the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products Collection 6.1 from Aqua (MYD04_L2). The dataset covers the monthly mean from 2003 to 2022. The FDS is calculated as the number of days per month when the daily dust optical depth is greater than a threshold optical depth (e.g., 0.025) with two quality flags: the lowest (1) and highest (3). It is advised to use flag 1, which is of lower quality, over dust source regions, and flag 3 over remote areas or polluted regions. Eight thresholds (0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 2) are saved separately in eight files. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### SNDRAQIML2CCPRET\nWARNING: To users of the derived product “co_mmr_midtrop” (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply “co_mmr_midtrop” by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (“mol_lay/co_mol_lay”) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) and AMSU (Advanced Microwave Sounding Unit). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS in combination with the AMSU constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km. The CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### SNDRAQIML2CCPCCR\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) and AMSU (Advanced Microwave Sounding Unit). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS in combination with the AMSU constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### SNDRAQIML2PLEVCPS\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder)/AMSU (Advanced Microwave Sounding Unit) instruments aboard the Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using data. They include including gas mixing ratio profiles, column totals, surface values, tropopause properties, and relative humidity, together with per-field quality flagging. The profiles are specified at the surface and layer boundaries and are estimated from layer amounts using the L2 algorithm An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.\n<br>\n\n#### SNDRAQIML3SDCCP\nWARNING: Users of the derived product “co_mmr_midtrop” (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply “co_mmr_midtrop” by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (“mol_lay/co_mol_lay”) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) and AMSU (Advanced Microwave Sounding Unit). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS in combination with the AMSU constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km. The CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid. This daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the specific quality control (QC) methodology. Specific QC accepts profile level from the top of the atmosphere down to the level where the QC determines that it is still good. Below this level, the data is rejected.\n<br>\n\n#### SNDRAQIL2CPS\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS CLIMCAPS Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km. The CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid. An AIRS level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### SNDRAQIL2PLEVCPS\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) instrument aboard the Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using data. They include including gas mixing ratio profiles, column totals, surface values, tropopause properties, and relative humidity, together with per-field quality flagging. The profiles are specified at the surface and layer boundaries and are estimated from layer amounts using the L2 algorithm An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.\n<br>\n\n#### SNDRAQIML1BCALSUBSUM\nThe Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. AIRS/Aqua Level-1C calibration subset including clear cases, special calibration sites, random nadir spots, and high clouds. Infrared temperature sounders generate a large amount of Level-1B spectral data. For example, the AIRS instrument with 2378 channels, its visible light component and AMSU with 15 channels create 3x240 files each day, for a total of over 500 MB of data. The purpose of the Calibration Data Subsets is extract key information from these data into a few daily files to: 1. Facilitate a quick evaluation of the absolute calibration of the instruments. 2. Facilitate an assessment of the instrument performance under clear, cloudy, and extreme hot and cold conditions. 3. Facilitate the evaluation of instrument trends and their significance relative to climate trends. 4. Facilitate the comparison of AIRS with CrIS using their equivalent data subsets. The output files are constructed from Level-1B or Level-1C IR and MW brightness or antenna temperatures. Each file contains selected observations taken from a nominal 24-hour period. The “summary” product includes a large set of cases of interest, including all identified spectra that match selection criteria detailed below for clear, special cloud classes, etc. These amount to about 10% of all spectra. But for each selected case only brightness temperatures (BTs) for selected key channels are saved.\n<br>\n\n#### SNDR13CHRP1\nThe Climate Hyperspectral Infrared Radiance Product (CHIRP) is a Level 1 radiance product derived from Atmospheric Infrared Sounder (AIRS) on EOS-AQUA and the Cross-Track Infrared Sounders (CrIS) on the SNPP and JPSS-1+ platforms. (JPSS-1 is also called NOAA-20). CHIRP provides a consistent spectral response function (SRF) across all instruments. Inter-instrument radiometric offsets are removed with SNPP-CrIS chosen as the \"standard\". CHIRP follows the original instrument storage, i.e., granule in, granule out, and contains all information needed for retrievals (including cross-track, along-track, fov id, etc.). This version of CHIRP, SNDR13CHRP1, is the primary CHIRP product which provides a full radiance record from 2002 onwards starting with AIRS from September 1, 2002 to August 30, 2016, switching to the SNPP CrIS instrument on September 1, 2016. The SNDR13CHRP1 product then switches to using JPSS-1 CrIS radiances starting September 1, 2018. This selection of time periods provides the best match to times when the microwave sounders on each of these platforms exhibited good performance and avoids long outages (such as SNPP CrIS in early Spring 2019). CHIRP is available for AIRS, CrIS-SNPP, and CrIS-JPSS-1 for time periods not used in the product distributed here, and are named SNDR13CHRP1AQCal, SNDR13CHRP1SNCal, and SNDR13CHRP1J1Cal respectively.\n<br>\n\n#### SNDRAQIL3SSDFCNSAT\nThis data set provides an estimate of the surface air temperature. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.\n<br>\n\n#### SNDRAQIL3SSDFCVPD\nThis data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.\n<br>\n\n#### SNDR13IML3SSDFCVPD\nThe Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.\n<br>\n\n#### MYDFDS_SDV_GLB_L3\nVersion 1 is the current version of the dataset. This collection MYDFDS_SDV_GLB_L3 provides level 3 standard deviation of climatological monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚. It is derived from Level 2, the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products Collection 6.1 from Aqua (MYD04_L2). The dataset is the standard deviation of climatological monthly mean for each month over 2003 to 2022. The FDS is calculated as the number of days per month when the daily dust optical depth is greater than a threshold optical depth (e.g., 0.025) with two quality flags: the lowest (1) and highest (3). It is advised to use flag 1, which is of lower quality, over dust source regions, and flag 3 over remote areas or polluted regions. Eight thresholds (0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 2) are saved separately in eight files. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://airs.jpl.nasa.gov/index.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","earth observation","elevation","hdf","ice","land cover","netcdf","oceans","precipitation","satellite imagery","soil moisture","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AIRXAMAP v005 - The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level1/AIRXAMAP.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRIBRAD v005 - WARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level1/AIRIBRAD.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRABRAD_NRT v005 - The AMSU-A Level 1B Near Real Time (NRT) product (AIRABRAD_NRT_005) differs from the routine product (AIRABRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_NRT/AIRABRAD_NRT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRIBQAP_NRT v005 - The AIRS Level 1B Near Real Time (NRT) product (AIRIBQAP_NRT_005) differs from the routine product (AIRIBQAP_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_NRT/AIRIBQAP_NRT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRVBRAD_NRT v005 - The AIRS Visible/Near Infrared (VIS/NIR) Level 1B Near Real Time (NRT) product (AIRVBRAD_NRT_005) differs from the routine product (AIRVBRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products d...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_NRT/AIRVBRAD_NRT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRVBQAP_NRT v005 - The AIRS Level 1B Near Real Time (NRT) product (AIRVBQAP_NRT_005) differs from the routine product (AIRVBQAP_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_NRT/AIRVBQAP_NRT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRS2CCF_NRT v006 - The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) product (AIRS2CCF_NRT_006) differs from the routine product (AIRS2CCF_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_NRT/AIRS2CCF_NRT.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRS2RET_NRT v006 - The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) product (AIRS2RET_NRT_006) differs from the routine product (AIRS2RET_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surf...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_NRT/AIRS2RET_NRT.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRS2SUP_NRT v006 - The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Support Retrieval (AIRS-only) product (AIRS2SUP_NRT_006) differs from the routine product (AIRS2SUP_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climat...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_NRT/AIRS2SUP_NRT.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRG2SSD_IRonly v006 - The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This precipitation estimate from AIRS IR only is using a TOVS-like algorithm, and is intended for merging into the precipitation product of the Global Precipitation Climatology Project (GPCP).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level2/AIRG2SSD_IRonly.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRX3C28 v005 - Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level3/AIRX3C28.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LPRM_AMSRE_SOILM2 v002 - AMSR-E/Aqua surface soil moisture (LPRM) L2B V002 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_AMSRE_SOILM2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LPRM_AMSRE_A_SOILM3 v002 - AMSR-E/Aqua surface soil moisture (LPRM) L3 1 day 25 km x 25 km ascending V002 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_AMSRE_A_SOILM3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LPRM_AMSRE_D_SOILM3 v002 - AMSR-E/Aqua surface soil moisture (LPRM) L3 1 day 25 km x 25 km descending V002 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_AMSRE_D_SOILM3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRAQIL2JSFRET v2 - The Joint Single Footprint Retrieval Algorithm (JoSFRA) Level-2 geophysical parameters include estimates of atmospheric temperature and water vapor profiles, cloud properties, and surface temperature. These are retrieved from infrared spectra observed by the Atmospheric Infrared Sounder (AIRS) instrument on the Aqua satellite.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/JoSFRA/SNDRAQIL2JSFRET.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRSIL3MSOLR v6.1 - This L3 Spectral Outgoing Longwave Radiation (OLR) is derived using the AIRS radiances to compute spectral fluxes based on an algorithm developed by Xianglei Huang at the University of Michigan. It uses data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level3/AIRSIL3MSOLR.6.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AER_DBDT_D10KM_L3_MODIS_AQUA v001 - This High-Resolution (0.1 x 0.1 degree) Level 3 daily Aerosol Optical Depth (AOD) product is generated by combining two Moderate Resolution Imaging Spectroradiometer (MODIS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the AQUA satellite. This dataset is provided in daily files ranging from 2002-07-04 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TASNPP_AOD_Level3/AER_DBDT_D10KM_L3_MODIS_AQUA.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AER_DBDT_M10KM_L3_MODIS_AQUA v001 - This High-Resolution (0.1 x 0.1 degree) Level 3 monthly Aerosol Optical Depth (AOD) product is generated by combining two Moderate Resolution Imaging Spectroradiometer (MODIS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the AQUA satellite. This dataset is provided in monthly files ranging from July 2002 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TASNPP_AOD_Level3/AER_DBDT_M10KM_L3_MODIS_AQUA.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRX2RET v7.0 - The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS combination with the Advanced Microwave Sounding Unit (AMSU) constitutes an innovative atmospheric sounding group of infrared and microwave sensors.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level2/AIRX2RET.7.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRX2SUP v7.0 - The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU), AIRS constitutes an innovative atmospheric sounding group of infrared and microwave sensors.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level2/AIRX2SUP.7.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRS2CCF_NRT v7.0 - The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) product (AIRS2CCF_NRT_7.0) differs from the routine product (AIRS2CCF_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_NRT/AIRS2CCF_NRT.7.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRS2RET_NRT v7.0 - The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) product (AIRS2RET_NRT_7.0) differs from the routine product (AIRS2RET_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surf...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_NRT/AIRS2RET_NRT.7.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRS2SUP_NRT v7.0 - The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Support Retrieval (AIRS-only) product (AIRS2SUP_NRT_7.0) differs from the routine product (AIRS2SUP_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climat...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_NRT/AIRS2SUP_NRT.7.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRX3STD v7.0 - The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) AIRS constitutes an innovative atmospheric sounding group of infrared and microwave sensors.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level3/AIRX3STD.7.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRS3STD v7.0 - The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level3/AIRS3STD.7.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRS3STM v7.0 - The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS Only Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level3/AIRS3STM.7.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AIRSAC3MNH3 v3 - The mass concentration ammonia in the atmosphere, consists of products generated for the study of atmospheric ammonia. Atmospheric ammonia is an important component of the global nitrogen cycle.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level3/AIRSAC3MNH3.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MYDFDS_CLM_GLB_L3 v1 - Version 1 is the current version of the dataset. This collection MYDFDS_CLM_GLB_L3 provides level 3 climatological monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/DustStorm/MYDFDS_CLM_GLB_L3.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MYD04_L2 v6.1 - The MODIS/Aqua Aerosol 5-Min L2 Swath 10km product (MYD04_L2) provides full global coverage of aerosol properties from the Dark Target (DT) and Deep Blue (DB) algorithms. The DT algorithm is applied over ocean and dark land (e.g., vegetation), while the DB algorithm now covers the entire land areas including both dark and bright surfaces.","ARN":"arn:aws:s3:::prod-lads/MYD04_L2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD04_3K v6.1 - The new Collection 6.1 (C61) MODIS/Aqua Aerosol 5 Min L2 Swath 3km (MYD04_3K) product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals. The MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land.","ARN":"arn:aws:s3:::prod-lads/MYD04_3K","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD08_E3 v6.1 - The MODIS/Aqua Aerosol Cloud Water Vapor Ozone 8-Day L3 Global 1Deg CMG product (MYD08_E3) contains 8-Day 1 degree x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter.","ARN":"arn:aws:s3:::prod-lads/MYD08_E3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD08_D3 v6.1 - The MODIS/Aqua Aerosol Cloud Water Vapor Ozone Daily L3 Global 1Deg CMG product (MYD08_D3) contains daily 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter.","ARN":"arn:aws:s3:::prod-lads/MYD08_D3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD08_M3 v6.1 - The MODIS/Aqua Aerosol Cloud Water Vapor Ozone Monthly L3 Global 1Deg CMG product (MYD08_M3) contains monthly 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter.","ARN":"arn:aws:s3:::prod-lads/MYD08_M3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYDATML2 v6.1 - The MODIS/Aqua Aerosol, Cloud and Water Vapor Subset 5-Min L2 Swath 5km and 10km (MYDATML2) product contains a combination of key high interest science parameters. The ATML2 product provides a subset of datasets from the suite of atmosphere team products on both a 10 km scale (aerosols) and 5km scale (native 5 km cloud properties and a 5x5 pixel sample of the 1km cloud datasets).","ARN":"arn:aws:s3:::prod-lads/MYDATML2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD09 v6.1 - The MODIS/Aqua Atmospherically Corrected Surface Reflectance 5-Min L2 Swath 250m, 500m, 1km (MYD09) product is computed from the MODIS Level 1B land bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648 nm, 858 nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm, respectively). The product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption.","ARN":"arn:aws:s3:::prod-lads/MYD09","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD021KM v6.1 - The MODIS/Aqua Calibrated Radiances 5Min L1B Swath 1km data set contains calibrated and geolocated at-aperture radiances for 36 discrete bands located in the 0.4 to 14.4 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which during processing are converted to geophysical units of W / (m^2 um sr).","ARN":"arn:aws:s3:::prod-lads/MYD021KM","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD02QKM v6.1 - The MODIS/Terra Calibrated Radiances 5-Min L1B Swath 250m data set contains calibrated and geolocated at-aperture radiances for 2 discrete bands located in the 0.62 to 0.88 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which during processing are converted to geophysical units of W / (m^2 um sr).","ARN":"arn:aws:s3:::prod-lads/MYD02QKM","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD02HKM v6.1 - The MODIS/Aqua Calibrated Radiances 5Min L1B Swath 500m data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr).","ARN":"arn:aws:s3:::prod-lads/MYD02HKM","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDMSK_L2_MODIS_Aqua v1 - The MODIS-VIIRS Cloud Mask (MVCM) is designed to facilitate continuity in cloud detection between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Suomi NPP spacecraft. To establish continuity, this MODIS MVCM product does not use an algorithm identical to that used in the standard MODIS product (MOD35/MYD35).","ARN":"arn:aws:s3:::prod-lads/CLDMSK_L2_MODIS_Aqua","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD35_L2 v6.1 - The MODIS/Aqua Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km product consists of global cloud mask quality assurance and other ancillary parameters. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence levels that an unobstructed view of the Earth's surface is observed.","ARN":"arn:aws:s3:::prod-lads/MYD35_L2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDPROP_L2_MODIS_Aqua v1.1 - The MODIS/Aqua Cloud Properties 5-min L2 Swath 1km product is designed to facilitate continuity in cloud properties between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Suomi NPP spacecraft. To establish continuity, this MODIS Cloud Properties product does not use algorithms identical to those used in the standard MODIS product (MOD06/MYD06).","ARN":"arn:aws:s3:::prod-lads/CLDPROP_L2_MODIS_Aqua","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDPROPCOSP_D3_MODIS_Aqua v1.1 - The MODIS/Aqua Cloud Properties COSP Level 3 daily, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_D3_MODIS_Aqua. It contains MODIS Aqua cloud mask, cloud top, and cloud optical retrieval data over daily timeframe.","ARN":"arn:aws:s3:::prod-lads/CLDPROPCOSP_D3_MODIS_Aqua","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDPROPCOSP_M3_MODIS_Aqua v1.1 - The MODIS/Aqua Cloud Properties COSP Level 3 monthly, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_M3_MODIS_Aqua. It contains MODIS Aqua cloud mask, cloud top, and cloud optical retrieval data over monthly timeframe.","ARN":"arn:aws:s3:::prod-lads/CLDPROPCOSP_M3_MODIS_Aqua","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDPROP_D3_MODIS_Aqua v1.1 - The Cloud Properties Level-3 gridded product is designed to facilitate continuity in cloud property statistics between the MODIS on the Aqua and Terra platforms and the common continuity products generated for the VIIRS (Visible Infrared Imaging Radiometer Suite) and the MODIS Aqua instruments. CLDPROP Level-3 statistical routines include scalar and histograms (1-D and 2-D) that are calculated identically to statistical datasets in the MODIS standard Level-3 product (MOD08 and MYD08 for MODIS Terra and Aqua, respectively).","ARN":"arn:aws:s3:::prod-lads/CLDPROP_D3_MODIS_Aqua","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD06_L2 v6.1 - The MODIS/Aqua Clouds 5-Min L2 Swath 1km and 5km product consists of cloud optical and physical parameters. The cloud optical parameters are generated at 1km and cloud top (physical) parameters are generated at 5km resolution.","ARN":"arn:aws:s3:::prod-lads/MYD06_L2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD03 v6.1 - The MODIS/Aqua Geolocation Fields 5-Min L1A Swath 1km are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily (in Collection 6 and later, information is provided to calculate 500m geolocation fields). The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface.","ARN":"arn:aws:s3:::prod-lads/MYD03","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD17A2H v006 - The MYD17A2H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD17A2H Version 6.1](https://doi.org/10.5067/MODIS/MYD17A2H.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD17A2H.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD17A2H v061 - The MYD17A2H Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN).","ARN":"arn:aws:s3:::lp-prod-protected/MYD17A2H.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD17A2HGF v006 - The MYD17A2HGF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD17A2HGF Version 6.1](https://doi.org/10.5067/MODIS/MYD17A2HGF.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD17A2HGF.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD17A2HGF v061 - The MYD17A2HGF Version 6.1 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN).","ARN":"arn:aws:s3:::lp-prod-protected/MYD17A2HGF.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD21 v006 - The MYD21 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD21 Version 6.1](https://doi.org/10.5067/MODIS/MYD21.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD21.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD21 v061 - The MYD21 Version 6.1 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters.","ARN":"arn:aws:s3:::lp-prod-protected/MYD21.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD21C2 v061 - A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the [MYD11](https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique.","ARN":"arn:aws:s3:::lp-prod-protected/MYD21C2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD21A2 v006 - The MYD21A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD21A2 Version 6.1](https://doi.org/10.5067/MODIS/MYD21A2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD21A2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD21A2 v061 - A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the [MYD11](https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique.","ARN":"arn:aws:s3:::lp-prod-protected/MYD21A2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD21C1 v061 - A new suite of MODIS Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 LST algorithm differs from the algorithm of the [MYD11](https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique.","ARN":"arn:aws:s3:::lp-prod-protected/MYD21C1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD21A1D v006 - The MYD21A1D Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD21A1D Version 6.1](https://doi.org/10.5067/MODIS/MYD21A1D.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD21A1D.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD21A1D v061 - A suite of MODIS Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 LST algorithm differs from the algorithm of the [MYD11](https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique.","ARN":"arn:aws:s3:::lp-prod-protected/MYD21A1D.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD21A1N v006 - The MYD21A1N Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD21A1N Version 6.1](https://doi.org/10.5067/MODIS/MYD21A1N.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD21A1N.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11_L2 v006 - The MYD11_L2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11_L2 Version 6.1](https://doi.org/10.5067/MODIS/MYD11_L2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11_L2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11_L2 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 5-Minute (MYD11_L2) Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11_L2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11C2 v006 - The MYD11C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11C2 Version 6.1](https://doi.org/10.5067/MODIS/MYD11C2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11C2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11C2 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11C2) Version 6.1 product provides Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11C2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11A2 v006 - The MYD11A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11A2 Version 6.1](https://doi.org/10.5067/MODIS/MYD11A2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11A2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11A2 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11A2) Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MYD11A2 is a simple average of all the corresponding [MYD11A1](https://doi.org/10.5067/MODIS/MYD11A1.061) LST pixels collected within that 8-day period.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11A2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11B2 v006 - The MYD11B2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11B2 Version 6.1](https://doi.org/10.5067/MODIS/MYD11B2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11B2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11B2 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11B2) Version 6.1 product provides an average 8-day per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each temperature and emissivity pixel value in the MYD11B2 is a simple average of all the corresponding values from the LST&E values from the [MYD11B1](https://doi.org/10.5067/MODIS/MYD11B1.061) product collected during that 8-day period.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11B2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11C1 v006 - The MYD11C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11C1 Version 6.1](https://doi.org/10.5067/MODIS/MYD11C1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11C1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11C1 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MYD11C1) Version 6.1 product provides daily Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). The MYD11C1 product is directly derived from the [MYD11B1](https://doi.org/10.5067/MODIS/MYD11B1.061) product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11C1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11A1 v006 - The MYD11A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11A1 Version 6.1](https://doi.org/10.5067/MODIS/MYD11A1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11A1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11A1 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MYD11A1) Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the [MYD11_L2](https://doi.org/10.5067/MODIS/MYD11_L2.061) swath product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11A1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11B1 v006 - The MYD11B1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11B1 Version 6.1](https://doi.org/10.5067/MODIS/MYD11B1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11B1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11B1 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MYD11B1) Version 6.1 product provides daily per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each MOD11B1 granule consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the per...","ARN":"arn:aws:s3:::lp-prod-protected/MYD11B1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11C3 v006 - The MYD11C3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11C3 Version 6.1](https://doi.org/10.5067/MODIS/MYD11C3.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11C3.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11C3 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Monthly (MYD11C3) Version 6.1 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11C3.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11B3 v006 - The MYD11B3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD11B3 Version 6.1](https://doi.org/10.5067/MODIS/MYD11B3.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11B3.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD11B3 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Monthly (MYD11B3) Version 6.1 product provides average monthly per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each LST&E pixel value in the MYD11B3 is a simple average of all the corresponding values from the [MYD11B1](https://doi.org/10.5067/MODIS/MYD11B1.061) collected during the month period.","ARN":"arn:aws:s3:::lp-prod-protected/MYD11B3.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD15A2H v006 - The MYD15A2H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD15A2H Version 6.1](https://doi.org/10.5067/MODIS/MYD15A2H.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD15A2H.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD15A2H v061 - The MYD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the \"best\" pixel available from all the acquisitions of the Aqua sensor from within the 8-day period.","ARN":"arn:aws:s3:::lp-prod-protected/MYD15A2H.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD16A2 v006 - The MYD16A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD16A2 Version 6.1](https://doi.org/10.5067/MODIS/MYD16A2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD16A2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD16A2 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) MYD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover.","ARN":"arn:aws:s3:::lp-prod-protected/MYD16A2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD16A2GF v006 - The MYD16A2GF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD16A2GF Version 6.1](https://doi.org/10.5067/MODIS/MYD16A2GF.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD16A2GF.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD16A2GF v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) MYD16A2GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover.","ARN":"arn:aws:s3:::lp-prod-protected/MYD16A2GF.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD16A3GF v006 - The MYD16A3GF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD16A3GF Version 6.1](https://doi.org/10.5067/MODIS/MYD16A3GF.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD16A3GF.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD16A3GF v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) MYD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover.","ARN":"arn:aws:s3:::lp-prod-protected/MYD16A3GF.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD16A3 v006 - The MYD16A3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD16A3GF Version 6.1](https://doi.org/10.5067/MODIS/MYD16A3GF.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD16A3.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD17A3HGF v006 - The MYD17A3HGF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD17A3HGF Version 6.1](https://doi.org/10.5067/MODIS/MYD17A3HGF.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD17A3HGF.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD17A3HGF v061 - The MYD17A3HGF Version 6.1 product provides information about annual Gross and Net Primary Production (GPP and NPP) at 500 meter (m) pixel resolution. Annual Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and NPP is derived from the sum of all 8-day GPP and Net Photosynthesis (PSN) products ([MYD17A2H](https://doi.org/10.5067/MODIS/MYD17A2H.061)) from the given year.","ARN":"arn:aws:s3:::lp-prod-protected/MYD17A3HGF.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD17A3H v006 - The MYD17A3H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD17A3HGF Version 6.1](https://doi.org/10.5067/MODIS/MYD17A3HGF.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD17A3H.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYDOCGA v006 - The MYDOCGA Version 6 data product was decommissioned on July 31, 2023. The MYDOCGA Version 6 Level 2 Gridded Lite (L2G-lite) Ocean Reflectance product provides an estimate of the surface spectral reflectance data from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) bands 8 through 16.","ARN":"arn:aws:s3:::lp-prod-protected/MYDOCGA.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD09Q1 v006 - The MYD09Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD09Q1 Version 6.1](https://doi.org/10.5067/MODIS/MYD09Q1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD09Q1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD09Q1 v061 - The MYD09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers.","ARN":"arn:aws:s3:::lp-prod-protected/MYD09Q1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD09A1 v006 - The MYD09A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD09A1 Version 6.1](https://doi.org/10.5067/MODIS/MYD09A1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD09A1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD09A1 v061 - The Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua MYD09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are a quality layer and four observation bands.","ARN":"arn:aws:s3:::lp-prod-protected/MYD09A1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD09GA v006 - The MYD09GA Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD09GA Version 6.1](https://doi.org/10.5067/MODIS/MYD09GA.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD09GA.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD09GA v061 - The MYD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer observation bands and geolocation flags.","ARN":"arn:aws:s3:::lp-prod-protected/MYD09GA.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD09GQ v006 - The MYD09GQ Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD09GQ Version 6.1](https://doi.org/10.5067/MODIS/MYD09GQ.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD09GQ.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD09GQ v061 - The MYD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m bands are the Quality Assurance (QA) layer and five observation layers.","ARN":"arn:aws:s3:::lp-prod-protected/MYD09GQ.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD09CMG v006 - The MYD09CMG Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD09CMG Version 6.1](https://doi.org/10.5067/MODIS/MYD09CMG.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD09CMG.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD09CMG v061 - The MYD09CMG Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, resampled to 5600 meter (m) pixel resolution and corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. The MOD09CMG data product provides 25 layers including MODIS bands 1 through 7; Brightness Temperature data from thermal bands 20, 21, 31, and 32; along with Quality Assurance (QA) and observation bands.","ARN":"arn:aws:s3:::lp-prod-protected/MYD09CMG.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD07_L2 v6.1 - The MODIS/Aqua Temperature and Water Vapor Profiles 5-Min L2 Swath 5km (MYD07_L2) product consists of a numbers of parameters related to atmospheric stability, atmospheric temperature and moisture profiles, total atmospheric water vapor, and total ozone. All of these parameters are produced for both daytime and nighttime conditions at 5-km pixel resolution when at least 9 Field Of View (FOV) are cloud free.","ARN":"arn:aws:s3:::prod-lads/MYD07_L2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD14 v006 - The MYD14 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD14 Version 6.1](https://doi.org/10.5067/MODIS/MYD14.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD14.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD14 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire (MYD14) Version 6.1 product is produced daily in 5-minute temporal satellite increments (swaths). The MYD14 product is used to generate all of the higher level fire products, but can also be used to identify fires and other thermal anomalies, such as volcanoes.","ARN":"arn:aws:s3:::lp-prod-protected/MYD14.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD14A2 v006 - The MYD14A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD14A2 Version 6.1](https://doi.org/10.5067/MODIS/MYD14A2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD14A2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD14A2 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day (MYD14A2) Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MYD14A2 gridded composite contains maximum value of individual fire pixel classes detected during the eight days of acquisition.","ARN":"arn:aws:s3:::lp-prod-protected/MYD14A2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD14A1 v006 - The MYD14A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD14A1 Version 6.1](https://doi.org/10.5067/MODIS/MYD14A1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD14A1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD14A1 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily (MYD14A1) Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MYD14A1 contains eight consecutive days of fire data conveniently packaged into a single file.","ARN":"arn:aws:s3:::lp-prod-protected/MYD14A1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYDTBGA v006 - The MYDTBGA Version 6 data product was decommissioned on July 31, 2023. The MYDTBGA Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 Gridded Lite (L2G-lite) Thermal Band product consists of brightness temperature data from Aqua MODIS bands 20, 31, and 32 and albedo data from band 20 along with the orbit and granule pointer fields.","ARN":"arn:aws:s3:::lp-prod-protected/MYDTBGA.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD05_L2 v6.1 - The MODIS/Aqua Total Precipitable Water Vapor 5-Min L2 Swath 1km and 5km (MYD05_L2) product consists of atmospheric column water-vapor amounts. This product is derived from data collected by the Moderate-Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite.","ARN":"arn:aws:s3:::prod-lads/MYD05_L2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD13C1 v006 - The MYD13C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD13C1 Version 6.1](https://doi.org/10.5067/MODIS/MYD13C1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD13C1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD13C1 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MYD13C1) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers.","ARN":"arn:aws:s3:::lp-prod-protected/MYD13C1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD13A2 v006 - The MYD13A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD13A2 Version 6.1](https://doi.org/10.5067/MODIS/MYD13A2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD13A2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD13A2 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MYD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers.","ARN":"arn:aws:s3:::lp-prod-protected/MYD13A2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD13Q1 v006 - The MYD13Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD13Q1 Version 6.1](https://doi.org/10.5067/MODIS/MYD13Q1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD13Q1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD13Q1 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MYD13Q1 product provides two primary vegetation layers.","ARN":"arn:aws:s3:::lp-prod-protected/MYD13Q1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD13A1 v006 - The MYD13A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD13A1 Version 6.1](https://doi.org/10.5067/MODIS/MYD13A1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD13A1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD13A1 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MYD13A1) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers.","ARN":"arn:aws:s3:::lp-prod-protected/MYD13A1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD13C2 v006 - The MYD13C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD13C2 Version 6.1](https://doi.org/10.5067/MODIS/MYD13C2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD13C2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD13C2 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Monthly (MYD13C2) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers.","ARN":"arn:aws:s3:::lp-prod-protected/MYD13C2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD13A3 v006 - The MYD13A3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD13A3 Version 6.1](https://doi.org/10.5067/MODIS/MYD13A3.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MYD13A3.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD13A3 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13A3) Version 6.1 data are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the [MYD13A2](https://doi.org/10.5067/MODIS/MYD13A2.061) products that overlap the month and employs a weighted temporal average.","ARN":"arn:aws:s3:::lp-prod-protected/MYD13A3.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD28C2 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir 8-Day Level 3 (L3) Global (MYD28C2) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MYD28C2 Version 6.1 data product provides an 8-day time series of surface area, elevation, and water storage.","ARN":"arn:aws:s3:::lp-prod-protected/MYD28C2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD28C3 v061 - The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir Monthly Level 3 (L3) Global (MYD28C3) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MYD28C3 Version 6.1 data product is a composite of the 8-day area classifications from [MYD28C2](https://lpdaac.usgs.gov/products/myd28c2v061/), which is converted to provide monthly elevation and water storage.","ARN":"arn:aws:s3:::lp-prod-protected/MYD28C3.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYDFDS_MON_GLB_L3 v1 - Version 1 is the current version of the dataset. This collection MYDFDS_MON_GLB_L3 provides level 3 monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/DustStorm/MYDFDS_MON_GLB_L3.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRAQIML2CCPRET v2 - WARNING: To users of the derived product “co_mmr_midtrop” (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_Sounder_Level2/SNDRAQIML2CCPRET.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRAQIML2CCPCCR v2 - The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) and AMSU (Advanced Microwave Sounding Unit). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_Sounder_Level2/SNDRAQIML2CCPCCR.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRAQIML2PLEVCPS v2.1 - The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder)/AMSU (Advanced Microwave Sounding Unit) instruments aboard the Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_Sounder_Level2/SNDRAQIML2PLEVCPS.2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRAQIML3SDCCP v2 - WARNING: Users of the derived product “co_mmr_midtrop” (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_Sounder_Level3/SNDRAQIML3SDCCP.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRAQIL2CPS v2.1 - The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_Sounder_Level2/SNDRAQIL2CPS.2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRAQIL2PLEVCPS v2.1 - The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) instrument aboard the Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_Sounder_Level2/SNDRAQIL2PLEVCPS.2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRAQIML1BCALSUBSUM v2 - The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. AIRS/Aqua Level-1C calibration subset including clear cases, special calibration sites, random nadir spots, and high clouds.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Calibration_Subsets/SNDRAQIML1BCALSUBSUM.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDR13CHRP1 v2 - The Climate Hyperspectral Infrared Radiance Product (CHIRP) is a Level 1 radiance product derived from Atmospheric Infrared Sounder (AIRS) on EOS-AQUA and the Cross-Track Infrared Sounders (CrIS) on the SNPP and JPSS-1+ platforms. (JPSS-1 is also called NOAA-20).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CHIRP/SNDR13CHRP1.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRAQIL3SSDFCNSAT v2 - This data set provides an estimate of the surface air temperature. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Data_Fusion/SNDRAQIL3SSDFCNSAT.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRAQIL3SSDFCVPD v2 - This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Data_Fusion/SNDRAQIL3SSDFCVPD.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDR13IML3SSDFCVPD v2 - The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Data_Fusion/SNDR13IML3SSDFCVPD.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MYDFDS_SDV_GLB_L3 v1 - Version 1 is the current version of the dataset. This collection MYDFDS_SDV_GLB_L3 provides level 3 standard deviation of climatological monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/DustStorm/MYDFDS_SDV_GLB_L3.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-aqua","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA AQUARIUS SAC-D Project","Description":"The version 5.0 Aquarius CAP Level 2 product contains the fourth release of the AQUARIUS/SAC-D orbital/swath data based on the Combined Active Passive (CAP) algorithm. CAP is a P.I. produced dataset developed and provided by JPL. This Level 2 dataset contains sea surface salinity (SSS), wind speed and wind direction data derived from 3 different radiometers and the onboard scatterometer. The CAP algorithm simultaneously retrieves the salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. Each L2 data file covers one 98 minute orbit. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_RAINCORRECTED_CAP_7DAY_V5\nVersion 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the 7-Day running mean sea surface salinity (SSS) rain corrected V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_CAP_7DAY_V5\nVersion 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the 7-Day running mean sea surface salinity (SSS) V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_ANCILLARY_CELESTIALSKY_V1\nThis datasets contains three maps of L-band (wavelength = 21 cm) brightness temperature of the celestial sky (\"Galaxy\") used in the processing of the NASA Aquarius instrument data. The maps report Sky brightness temperatures in Kelvin gridded on the Earth Centered Inertial (ECI) reference frame epoch J2000. They are sampled over 721 Declinations between -90 degrees and +90 degrees and 1441 Right Ascensions between 0 degrees and 360 degrees, all evenly spaced at 0.25 degrees intervals. The brightness temperatures are assumed temporally invariant and polarization has been neglected. They include microwave continuum and atomic hydrogen line (HI) emissions. The maps differ only in how the strong radio source Cassiopeia A has been included into the whole sky background surveys: 1/ TB_no_Cas_A does not include Cassiopeia A and reports only the whole Sky surveys. 2/ TB_Cas_A_1cell spread Cas A total flux homogeneously over 1 map grid cell (i.e. 9.8572E-6 sr). 3/ TB_Cas_A_beam spreads Cas A over surrounding grid cells using a convolution by a Gaussian beam with HPBW of 35 arcmin (equivalent to the instrument used for the Sky surveys). Cassiopeia A is a supernova remnant (SNR) in the constellation Cassiopeia and the brightest extra-solar radio source in the sky at frequencies above 1.\n<br>\n\n#### AQUARIUS_L2_SSS_V5\nThe version 5.0 Aquarius Level 2 product is the official third release of the orbital/swath data from AQUARIUS/SAC-D mission. The Aquarius Level 2 data set contains sea surface salinity (SSS) and wind speed data derived from 3 different radiometers and the onboard scatterometer. Included also in the Level 2 data are the horizontal and vertical brightness temperatures (TH and TV) for each radiometer, ancillary data, flags, converted telemetry and navigation data. Each data file covers one 98 minute orbit. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. Enhancements to the version 5.0 Level 2 data relative to v4.0 include: improvement of the salinity retrieval geophysical model for SST bias, estimates of SSS uncertainties (systematic and random components), and inclusion of a new spiciness variable.\n<br>\n\n#### AQUARIUS_L3_ANCILLARY_SST_SMI_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, 7-Day, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the 28-Day running mean, ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.\n<br>\n\n#### AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY-RUNNINGMEAN_V5\nAquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the 7-Day running mean ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.\n<br>\n\n#### AQUARIUS_L3_ANCILLARY_SST_SMI_ANNUAL_V5\nAquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, Annual, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the Annual ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.\n<br>\n\n#### AQUARIUS_L3_ANCILLARY_SST_SMIA_DAILY_V5\nAquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, Daily, and Daily time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the Daily, ascending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SM_SMIA_MONTHLY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending rain-flagged sea surface salinity smoothed product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SM_SMID_MONTHLY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending rain-flagged sea surface salinity smoothed product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SM_SMI_MONTHLY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly rain-flagged sea surface salinity smoothed product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMI_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day running mean rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMI_ANNUAL_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMIA_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMIA_7DAY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMIA_ANNUAL_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMIA_DAILY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMIA_CUMULATIVE_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission cummulative, Ascending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMIA_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMIA_3MONTH_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMI_DAILY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMID_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Descending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMID_7DAY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Descending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMID_ANNUAL_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMID_DAILY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMID_CUMULATIVE_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission cummulative, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onbard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMID_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMID_3MONTH_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Descending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMI_CUMULATIVE_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission cummulative rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius dataset. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMI_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS-RainFlagged_SMI_3MONTH_V5\nAquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMI_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMI_7DAY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMI_7DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day running mean, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMI_ANNUAL_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMIA_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMIA_7DAY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMIA_ANNUAL_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMIA_DAILY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMIA_CUMULATIVE_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMIA_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Ascending sea surface density product for version 5.0. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMIA_MONTHLY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMIA_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMIA_3MONTH_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMI_DAILY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMID_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMID_7DAY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMID_ANNUAL_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMID_DAILY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Descending sea surface density product for version 5.0 of the Aquarius data set. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMID_CUMULATIVE_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMID_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onbard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMID_MONTHLY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMID_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMID_3MONTH_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMI_CUMULATIVE_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, sea surface density product for version 5.0 of the Aquarius data set. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMI_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMI_MONTHLY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMI_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_DENSITY_SMI_3MONTH_V5\nAquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SM_SMIA_MONTHLY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending sea surface salinity smoothed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SM_SMID_MONTHLY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending sea surface salinity smoothed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SM_SMI_MONTHLY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly sea surface salinity smoothed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMI_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMI_7DAY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMI_7DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day running mean sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMI_ANNUAL_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMIA_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMIA_7DAY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMIA_ANNUAL_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMIA_DAILY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMIA_CUMULATIVE_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Ascending sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMIA_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMIA_MONTHLY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMIA_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMIA_3MONTH_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMI_DAILY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMID_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMID_7DAY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMID_ANNUAL_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMID_DAILY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMID_CUMULATIVE_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Descending sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMID_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMID_MONTHLY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMID_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMID_3MONTH_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMI_CUMULATIVE_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMI_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMI_MONTHLY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMI_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SSS_SMI_3MONTH_V5\nAquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMI_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, sea surface spiciness product for version 5.0 of the Aquarius data set. The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMI_7DAY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMI_7DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day running mean sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMI_ANNUAL_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMIA_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface salinity spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMIA_7DAY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMIA_ANNUAL_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMIA_DAILY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMIA_CUMULATIVE_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMIA_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Ascending sea surface spiciness product for version 5.0. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMIA_MONTHLY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMIA_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMIA_3MONTH_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMI_DAILY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMID_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMID_7DAY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMID_ANNUAL_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMID_DAILY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMID_CUMULATIVE_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMID_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onbard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMID_MONTHLY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMID_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMID_3MONTH_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMI_CUMULATIVE_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMI_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMI_MONTHLY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly sea surface spiciness product for version 5.0 of the Aquarius dataset. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMI_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_SPICINESS_SMI_3MONTH_V5\nAquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMI_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMI_7DAY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMI_7DAY-RUNNINGMEAN_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonaltime scales. This particular data set is the 7-Day running mean wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMI_ANNUAL_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMIA_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMIA_7DAY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMIA_ANNUAL_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMIA_DAILY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMIA_CUMULATIVE_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Ascending wind speed product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMIA_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMIA_3MONTH_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMI_DAILY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMID_28DAY-RUNNINGMEAN_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMID_7DAY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMID_ANNUAL_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMID_DAILY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMID_CUMULATIVE_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Descending wind speed product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMID_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMID_3MONTH_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMI_CUMULATIVE_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative wind speed product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY-CLIMATOLOGY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMI_SEASONAL-CLIMATOLOGY_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_L3_WIND_SPEED_SMI_3MONTH_V5\nAquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.\n<br>\n\n#### AQUARIUS_ANCILLARY_RFI_V1\nAquarius ancillary Radio Frequency Interference (RFI) product used in ADPS mission processing contains monthly-averaged Radio Frequency Interference (RFI) data for ascending/descending passes as detected by the Aquarius radiometers and scatterometer. The data is available for ascending (northward) and descending (southward) passes of the satellite only and ascending/descending passes combined. The values stored in this product are the percentage of radiometer and scatterometer measurements identified as corrupted by interference by the RFI detection algorithms [1,2] within each data point, averaged over one month. An additional RFI flag [3] is used to identify locations where the measured brightness temperature over land exceeds the expected limits of surface emissivity. This flag is not used to remove samples from further processing, but, in generating the radiometer RFI data, 100% RFI is assigned to points where this flag is raised. This product can be used to reproduce the RFI maps available on the Aquarius website at University of Maine (https://aquarius.umaine.edu/cgi/gal_radiometer.htm for the radiometer, and https://aquarius.umaine.edu/cgi/gal_scatterometer.htm for the scatterometer), by plotting the variables Rad_RFI_percent_AscDes_AllBeams and Scat_RFI_percent_AscDes_AllBeams on the latitude/longitude grid. Additionally, the user can generate maps by using only a particular beam or only ascending passes, for example. All combinations of beams and ascending/descending passes are possible. This product contains information about RFI, but it is also relevant for the retrieved Sea Surface Salinity (SSS). Over the ocean, the RFI percentage in this product corresponds to the amount of raw measurements discarded due to RFI contamination before SSS retrieval. Therefore, maps of the RFI percentage can give the user an indication of where RFI is more likely to have affected the quality of SSS retrievals, for a particular month, or for a series of months.\n<br>\n\n#### AQUARIUS_L4_OISSS_IPRC_7DAY_V5\nThe IPRC/SOEST Aquarius OI-SSS v5 product is a level 4, near-global, 0.5 degree spatial resolution, 7-day, optimally interpolated salinity dataset based on version 5.0 of the AQUARIUS/SAC-D level 2 mission data. This is a PI led dataset produced at the International Pacific Research Center (IPRC) at the University of Hawaii (Manoa) School of Ocean and Earth Science and Technology. The optimal interpolation (OI) mapping procedure used to create this product corrects for systematic spatial biases in Aquarius SSS data with respect to near-surface in situ salinity observations and takes into account available statistical information about the signal and noise, specific to the Aquarius instrument. Bias fields are constructed by differencing in situ from Aquarius derived SSS fields obtained separately using ascending and descending satellite observations for each of the three Aquarius beams, and by removal of small-scale noise and low-pass filtering along-track using a two-dimensional Hanning window procedures prior to application of the OI algorithm. Additional enhancements for this new version of the product include: 1) The V5.0 (end-of mission) version of Aquarius Level-2 (swath) SSS data are used as input data for the OI SSS analysis. 2) The source of the first guess fields has changed from the APDRC Argo-derived SSS product to the average of four different in-situ based SSS products. 3) The bias correction algorithm has changed to adjust SSS retrievals for large-scale systematic biases on a repeat-track basis. 4) New, less restrictive thresholds are implemented to filter observations for land and ice contamination, thus improving coverage in the coastal areas and semi-enclosed seas. 5) Level-2 RFI masks for descending and ascending satellite passes are used to discard observations in specific geographic zones where excessive ascending-descending differences are observed due to contamination from undetected RFI. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. The Aquarius polar orbit is sun synchronous at 657 km with a 6 pm, ascending node, and has a 7-Day repeat cycle.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AQUARIUS_L2_SSS_CAP_V5 v5.0 - The version 5.0 Aquarius CAP Level 2 product contains the fourth release of the AQUARIUS/SAC-D orbital/swath data based on the Combined Active Passive (CAP) algorithm. CAP is a P.I.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L2_SSS_CAP_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS_RAINCORRECTED_CAP_7DAY_V5 v5.0 - Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS_RAINCORRECTED_CAP_7DAY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS_CAP_7DAY_V5 v5.0 - Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS_CAP_7DAY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_ANCILLARY_CELESTIALSKY_V1 v1 - This datasets contains three maps of L-band (wavelength = 21 cm) brightness temperature of the celestial sky (\"Galaxy\") used in the processing of the NASA Aquarius instrument data. The maps report Sky brightness temperatures in Kelvin gridded on the Earth Centered Inertial (ECI) reference frame epoch J2000.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_ANCILLARY_CELESTIALSKY_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L2_SSS_V5 v5.0 - The version 5.0 Aquarius Level 2 product is the official third release of the orbital/swath data from AQUARIUS/SAC-D mission. The Aquarius Level 2 data set contains sea surface salinity (SSS) and wind speed data derived from 3 different radiometers and the onboard scatterometer.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L2_SSS_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_ANCILLARY_SST_SMI_28DAY-RUNNINGMEAN_V5 v5.0 - Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, 7-Day, and annual time intervals as Aquarius L3 standard salinity and wind speed products.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_ANCILLARY_SST_SMI_28DAY-RUNNINGMEAN_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY-RUNNINGMEAN_V5 v5.0 - Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY-RUNNINGMEAN_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_ANCILLARY_SST_SMI_ANNUAL_V5 v5.0 - Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, Annual, and annual time intervals as Aquarius L3 standard salinity and wind speed products.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_ANCILLARY_SST_SMI_ANNUAL_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_ANCILLARY_SST_SMIA_DAILY_V5 v5.0 - Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, Daily, and Daily time intervals as Aquarius L3 standard salinity and wind speed products.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_ANCILLARY_SST_SMIA_DAILY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS-RainFlagged_SM_SMIA_MONTHLY_V5 v5.0 - Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending rain-flagged sea surface salinity smoothed product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS-RainFlagged_SM_SMIA_MONTHLY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS-RainFlagged_SM_SMID_MONTHLY_V5 v5.0 - Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending rain-flagged sea surface salinity smoothed product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS-RainFlagged_SM_SMID_MONTHLY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS-RainFlagged_SM_SMI_MONTHLY_V5 v5.0 - Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly rain-flagged sea surface salinity smoothed product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS-RainFlagged_SM_SMI_MONTHLY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS-RainFlagged_SMI_28DAY-RUNNINGMEAN_V5 v5.0 - Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS-RainFlagged_SMI_28DAY-RUNNINGMEAN_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY_V5 v5.0 - Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY-RUNNINGMEAN_V5 v5.0 - Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day running mean rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY-RUNNINGMEAN_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS-RainFlagged_SMI_ANNUAL_V5 v5.0 - Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS-RainFlagged_SMI_ANNUAL_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS-RainFlagged_SMIA_28DAY-RUNNINGMEAN_V5 v5.0 - Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS-RainFlagged_SMIA_28DAY-RUNNINGMEAN_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS-RainFlagged_SMIA_7DAY_V5 v5.0 - Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS-RainFlagged_SMIA_7DAY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS-RainFlagged_SMIA_ANNUAL_V5 v5.0 - Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS-RainFlagged_SMIA_ANNUAL_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS-RainFlagged_SMIA_DAILY_V5 v5.0 - Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS-RainFlagged_SMIA_DAILY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS-RainFlagged_SMIA_CUMULATIVE_V5 v5.0 - Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission cummulative, Ascending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS-RainFlagged_SMIA_CUMULATIVE_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY-CLIMATOLOGY_V5 v5.0 - Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY-CLIMATOLOGY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY_V5 v5.0 - Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. 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This particular data set is the Daily, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SPICINESS_SMIA_DAILY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SPICINESS_SMIA_CUMULATIVE_V5 v5.0 - Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. 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This particular data set is the Seasonal, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SPICINESS_SMIA_3MONTH_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SPICINESS_SMI_DAILY_V5 v5.0 - Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. 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This particular data set is the 28-Day running mean, Descending sea surface spiciness product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SPICINESS_SMID_28DAY-RUNNINGMEAN_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SPICINESS_SMID_7DAY_V5 v5.0 - Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Descending sea surface spiciness product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SPICINESS_SMID_7DAY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SPICINESS_SMID_ANNUAL_V5 v5.0 - Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. 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This particular data set is the Daily, Descending sea surface spiciness product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SPICINESS_SMID_DAILY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SPICINESS_SMID_CUMULATIVE_V5 v5.0 - Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. 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This particular data set is the monthly climatology, Descending sea surface spiciness product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SPICINESS_SMID_MONTHLY-CLIMATOLOGY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SPICINESS_SMID_MONTHLY_V5 v5.0 - Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending sea surface spiciness product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SPICINESS_SMID_MONTHLY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SPICINESS_SMID_SEASONAL-CLIMATOLOGY_V5 v5.0 - Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Descending sea surface spiciness product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SPICINESS_SMID_SEASONAL-CLIMATOLOGY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SPICINESS_SMID_3MONTH_V5 v5.0 - Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Descending sea surface spiciness product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SPICINESS_SMID_3MONTH_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SPICINESS_SMI_CUMULATIVE_V5 v5.0 - Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative sea surface spiciness product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SPICINESS_SMI_CUMULATIVE_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SPICINESS_SMI_MONTHLY-CLIMATOLOGY_V5 v5.0 - Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology sea surface spiciness product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SPICINESS_SMI_MONTHLY-CLIMATOLOGY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SPICINESS_SMI_MONTHLY_V5 v5.0 - Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. 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This particular data set is the seasonal climatology sea surface spiciness product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SPICINESS_SMI_SEASONAL-CLIMATOLOGY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_SPICINESS_SMI_3MONTH_V5 v5.0 - Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal sea surface spiciness product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_SPICINESS_SMI_3MONTH_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMI_28DAY-RUNNINGMEAN_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMI_28DAY-RUNNINGMEAN_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMI_7DAY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMI_7DAY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMI_7DAY-RUNNINGMEAN_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonaltime scales. This particular data set is the 7-Day running mean wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMI_7DAY-RUNNINGMEAN_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMI_ANNUAL_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMI_ANNUAL_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMIA_28DAY-RUNNINGMEAN_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMIA_28DAY-RUNNINGMEAN_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMIA_7DAY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMIA_7DAY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMIA_ANNUAL_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMIA_ANNUAL_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMIA_DAILY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMIA_DAILY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMIA_CUMULATIVE_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Ascending wind speed product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMIA_CUMULATIVE_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY-CLIMATOLOGY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY-CLIMATOLOGY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMIA_SEASONAL-CLIMATOLOGY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMIA_SEASONAL-CLIMATOLOGY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMIA_3MONTH_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMIA_3MONTH_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMI_DAILY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMI_DAILY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMID_28DAY-RUNNINGMEAN_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMID_28DAY-RUNNINGMEAN_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMID_7DAY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMID_7DAY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMID_ANNUAL_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMID_ANNUAL_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMID_DAILY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMID_DAILY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMID_CUMULATIVE_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Descending wind speed product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMID_CUMULATIVE_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY-CLIMATOLOGY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY-CLIMATOLOGY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMID_SEASONAL-CLIMATOLOGY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMID_SEASONAL-CLIMATOLOGY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMID_3MONTH_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMID_3MONTH_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMI_CUMULATIVE_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative wind speed product for version 5.0 of the Aquarius data set.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMI_CUMULATIVE_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY-CLIMATOLOGY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY-CLIMATOLOGY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMI_SEASONAL-CLIMATOLOGY_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMI_SEASONAL-CLIMATOLOGY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L3_WIND_SPEED_SMI_3MONTH_V5 v5.0 - Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L3_WIND_SPEED_SMI_3MONTH_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_ANCILLARY_RFI_V1 v1 - Aquarius ancillary Radio Frequency Interference (RFI) product used in ADPS mission processing contains monthly-averaged Radio Frequency Interference (RFI) data for ascending/descending passes as detected by the Aquarius radiometers and scatterometer. The data is available for ascending (northward) and descending (southward) passes of the satellite only and ascending/descending passes combined.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_ANCILLARY_RFI_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AQUARIUS_L4_OISSS_IPRC_7DAY_V5 v5.0 - The IPRC/SOEST Aquarius OI-SSS v5 product is a level 4, near-global, 0.5 degree spatial resolution, 7-day, optimally interpolated salinity dataset based on version 5.0 of the AQUARIUS/SAC-D level 2 mission data. This is a PI led dataset produced at the International Pacific Research Center (IPRC) at the University of Hawaii (Manoa) School of Ocean and Earth Science and Technology.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AQUARIUS_L4_OISSS_IPRC_7DAY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-aquarius-sac-d","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ARCSIX Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### ARCSIX_Analysis_LaRC-G3_Data\nARCSIX_Analysis_LaRC-G3_Data is the analysis data collected onboard the LaRC G-III aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### ARCSIX_Dropsondes_LaRC-G3_Data\nARCSIX_Dropsondes_LaRC-G3_Data is the dropsonde data collected onboard the LaRC G-III aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the Airborne Vertical Atmosphere Profiling System (AVAPS) is featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### ARCSIX_AircraftRemoteSensing_LaRC-G3_HALO_Data\nARCSIX_AircraftRemoteSensing_LaRC-G3_HALO_Data is the High Altitude Lidar Observatory (HALO) data collected onboard the LaRC G-III aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### ARCSIX_MetNav_AircraftInSitu_LaRC-G3_Data\nARCSIX_MetNav_AircraftInSitu_LaRC-G3_Data is the in-situ meteorology and navigation data collected onboard the LaRC G-III aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the N520NA Meteorological and Navigation Facility Instrumentation is featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### ARCSIX_Cloud_AircraftInSitu_Learjet_Data\nARCSIX_Cloud_AircraftInSitu_Learjet_Data is the in-situ cloud data collected onboard the Learjet aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the 2D-Gray Particle Probe (2D-Gray), 2D-Stereo Particle Probe (2DS), Fast Cloud Droplet Probe (FCDP), High Volume Precipitation Spectrometer (HVPS), and the Nevzorov Water Vapor Probe are featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### ARCSIX_MetNav_AircraftInSitu_Learjet_Data\nARCSIX_MetNav_AircraftInSitu_Learjet_Data is the in-situ meteorology and navigation data collected onboard the Learjet aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### ARCSIX_AircraftRemoteSensing_Learjet_KPR_Data\nARCSIX_AircraftRemoteSensing_Learjet_KPR_Data is the Ka-Band Probe Radar-Radiometer (KPR) data collected onboard the Learjet aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### ARCSIX_AircraftRemoteSensing_P3B_MARLi_Data\nARCSIX_AircraftRemoteSensing_P3B_MARLi_Data contains data collected by the Multi-function Airborne Raman Lidar (MARLi) onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### ARCSIX_AircraftRemoteSensing_P3B_RSP_Data\nARCSIX_AircraftRemoteSensing_P3B_RSP_Data contains data collected by the Research Scanning Polarimeter (RSP) onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### ARCSIX_Analysis_P3B_Data\nARCSIX_Analysis_P3B_Data is the analysis data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### ARCSIX_Aerosol_AircraftInSitu_P3B_Data\nARCSIX_Aerosol_AircraftInSitu_P3B_Data is the in-situ aerosol data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the Nucleation Mode Aerosol Size Spectrometer, Portable Optical Particle Spetrometer (POPS), Ultra-High Sensitivity Aerosol Spectrometer (UHSAS), Time-of-Flight Aerosol Mass Spectrometer (ToF-AMS), Differential Aerosol Sizing and Hygroscopicity Spectrometer Probe (DASH-SP), Fast Integrated Mobility Spectrometer (FIMS), Aerodynamic Particle Sizer (APS), Cloud Condensation Nuclei Counter (CCN), TSI Condensation Particle Counter 3772 (TSI CPC-3772), TSI-3563 Nephelometer, and the Single Particle Soot Photometer (SP2) are featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### ARCSIX_Cloud_AircraftInSitu_P3B_Data\nARCSIX_Cloud_AircraftInSitu_P3B_Data is the in-situ cloud data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the 2D-Stereo Particle Probe (2DS), Fast Cloud Droplet Probe (FCDP), High Volume Precipitation Spectrometer (HVPS), and the Continuous Flow Diffusion Chamber (CFDC) are featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### ARCSIX_MetNav_AircraftInSitu_P3B_Data\nARCSIX_MetNav_AircraftInSitu_P3B_Data is the in-situ meteorology and navigation data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the Diode Laser Hygrometer (DLH) and the UNS-1Fw Flight Management System are featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### ARCSIX_Radiation_AircraftInSitu_P3B_Data\nARCSIX_Radiation_AircraftInSitu_P3B_Data is the in-situ radiation data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the Broadband Radiometer (BBR), Hyper-Spectral Radiometer, Solar Spectral Flux Radiometers (SSFR), and the G-band Vapor Radiometer (GVR) are featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### ARCSIX_TraceGas_AircraftInSitu_P3B_Data\nARCSIX_TraceGas_AircraftInSitu_P3B_Data is the in-situ trace gas data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the PICARRO G2401-m Gas Concentration Analyzer and the 2B Technologies Model 205 Ozone Monitor are featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/ARCSIX/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","earth observation","ice","lidar","precipitation","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ARCSIX_Analysis_LaRC-G3_Data v1 - ARCSIX_Analysis_LaRC-G3_Data is the analysis data collected onboard the LaRC G-III aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_Analysis_LaRC-G3_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ARCSIX_Dropsondes_LaRC-G3_Data v1 - ARCSIX_Dropsondes_LaRC-G3_Data is the dropsonde data collected onboard the LaRC G-III aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the Airborne Vertical Atmosphere Profiling System (AVAPS) is featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_Dropsondes_LaRC-G3_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ARCSIX_AircraftRemoteSensing_LaRC-G3_HALO_Data v1 - ARCSIX_AircraftRemoteSensing_LaRC-G3_HALO_Data is the High Altitude Lidar Observatory (HALO) data collected onboard the LaRC G-III aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_AircraftRemoteSensing_LaRC-G3_HALO_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ARCSIX_MetNav_AircraftInSitu_LaRC-G3_Data v1 - ARCSIX_MetNav_AircraftInSitu_LaRC-G3_Data is the in-situ meteorology and navigation data collected onboard the LaRC G-III aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the N520NA Meteorological and Navigation Facility Instrumentation is featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_MetNav_AircraftInSitu_LaRC-G3_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ARCSIX_Cloud_AircraftInSitu_Learjet_Data v1 - ARCSIX_Cloud_AircraftInSitu_Learjet_Data is the in-situ cloud data collected onboard the Learjet aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the 2D-Gray Particle Probe (2D-Gray), 2D-Stereo Particle Probe (2DS), Fast Cloud Droplet Probe (FCDP), High Volume Precipitation Spectrometer (HVPS), and the Nevzorov Water Vapor Probe are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_Cloud_AircraftInSitu_Learjet_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ARCSIX_MetNav_AircraftInSitu_Learjet_Data v1 - ARCSIX_MetNav_AircraftInSitu_Learjet_Data is the in-situ meteorology and navigation data collected onboard the Learjet aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_MetNav_AircraftInSitu_Learjet_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ARCSIX_AircraftRemoteSensing_Learjet_KPR_Data v1 - ARCSIX_AircraftRemoteSensing_Learjet_KPR_Data is the Ka-Band Probe Radar-Radiometer (KPR) data collected onboard the Learjet aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_AircraftRemoteSensing_Learjet_KPR_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ARCSIX_AircraftRemoteSensing_P3B_MARLi_Data v1 - ARCSIX_AircraftRemoteSensing_P3B_MARLi_Data contains data collected by the Multi-function Airborne Raman Lidar (MARLi) onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_AircraftRemoteSensing_P3B_MARLi_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ARCSIX_AircraftRemoteSensing_P3B_RSP_Data v1 - ARCSIX_AircraftRemoteSensing_P3B_RSP_Data contains data collected by the Research Scanning Polarimeter (RSP) onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_AircraftRemoteSensing_P3B_RSP_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ARCSIX_Analysis_P3B_Data v1 - ARCSIX_Analysis_P3B_Data is the analysis data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_Analysis_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ARCSIX_Aerosol_AircraftInSitu_P3B_Data v1 - ARCSIX_Aerosol_AircraftInSitu_P3B_Data is the in-situ aerosol data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the Nucleation Mode Aerosol Size Spectrometer, Portable Optical Particle Spetrometer (POPS), Ultra-High Sensitivity Aerosol Spectrometer (UHSAS), Time-of-Flight Aerosol Mass Spectrometer (ToF-AMS), Differential Aerosol Sizing and Hygroscopicity Spectrometer Probe (DASH-SP), Fast Integrated Mobility Spectrometer (FIMS), Aerodynamic Particle Sizer (APS), Cloud Condensation Nuclei Counter (CC...","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_Aerosol_AircraftInSitu_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ARCSIX_Cloud_AircraftInSitu_P3B_Data v1 - ARCSIX_Cloud_AircraftInSitu_P3B_Data is the in-situ cloud data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the 2D-Stereo Particle Probe (2DS), Fast Cloud Droplet Probe (FCDP), High Volume Precipitation Spectrometer (HVPS), and the Continuous Flow Diffusion Chamber (CFDC) are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_Cloud_AircraftInSitu_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ARCSIX_MetNav_AircraftInSitu_P3B_Data v1 - ARCSIX_MetNav_AircraftInSitu_P3B_Data is the in-situ meteorology and navigation data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the Diode Laser Hygrometer (DLH) and the UNS-1Fw Flight Management System are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_MetNav_AircraftInSitu_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ARCSIX_Radiation_AircraftInSitu_P3B_Data v1 - ARCSIX_Radiation_AircraftInSitu_P3B_Data is the in-situ radiation data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the Broadband Radiometer (BBR), Hyper-Spectral Radiometer, Solar Spectral Flux Radiometers (SSFR), and the G-band Vapor Radiometer (GVR) are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_Radiation_AircraftInSitu_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ARCSIX_TraceGas_AircraftInSitu_P3B_Data v1 - ARCSIX_TraceGas_AircraftInSitu_P3B_Data is the in-situ trace gas data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the PICARRO G2401-m Gas Concentration Analyzer and the 2B Technologies Model 205 Ozone Monitor are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ARCSIX/ARCSIX_TraceGas_AircraftInSitu_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-arcsix","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ASCENDS Airborne Project","Description":"This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2) over California and Nevada on February 10-11, 2016. Measurements were taken onboard a DC-8 aircraft during this Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) airborne deployment. CO2 was measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument while over California and Nevada. The objective of this deployment was to assess the performance of the 2016 version of the CO2 Sounder LiDAR. The two flights were flown to compare results from an experimental LiDAR sensor with the AVOCET instrument. Aircraft navigation and flight meteorological data are also provided. The data are provided in ICARTT and comma-separated values (CSV) formats.\n<br>\n\n#### ASCENDS_LAS_IN_Sept_2014_2116\nThis dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2) over Indianapolis, Indiana (IN) on September 3, 2014 during the morning commuter period with heavy traffic emissions. Stationary source emissions are also included. The observed CO2 plume downwind of the urban area, along with the prevailing wind speed and direction, enabled estimations of emission rates. CO2 was measured with an airborne CO2 Laser Absorption Spectrometer (JPL CO2LAS) developed at NASA's Jet Propulsion Laboratory (JPL) to demonstrate the airborne Integrated Path Differential-Absorption (IPDA) lidar technique as a stepping stone to a capability for global measurements of CO2 concentrations from space. The CO2LAS measures the weighted, column averaged carbon dioxide between the aircraft and the ground using a continuous-wave heterodyne technique. The instrument operates at a 2.05 micron wavelength optimized for enhancing sensitivity to boundary layer carbon dioxide. Measurements were taken onboard a DC-8 aircraft during this Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) airborne deployment. The data are provided in HDF-5 format.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","hdf","lidar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ASCENDS_AVOCET_CA_NV_Feb_2016_2115 v1 - This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2) over California and Nevada on February 10-11, 2016. Measurements were taken onboard a DC-8 aircraft during this Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) airborne deployment.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/ascends/ASCENDS_AVOCET_CA_NV_Feb_2016/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ASCENDS_LAS_IN_Sept_2014_2116 v1 - This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2) over Indianapolis, Indiana (IN) on September 3, 2014 during the morning commuter period with heavy traffic emissions. Stationary source emissions are also included.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/ascends/ASCENDS_LAS_IN_Sept_2014/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-ascends-airborne","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ASIA-AQ Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### ASIA-AQ_Analysis_DC8_Data\nASIA-AQ_Analysis_DC8_Data are analysis flag files derived from data products onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data flags identify plumes likely influenced by biomass burning and other combustion processes as well as identifies different air mass layers. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.\n<br>\n\n#### ASIA-AQ_Aerosol_AircraftInSitu_DC8_Data\nASIA-AQ_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Transmission Electron Microscopy (TEM), Aerosol Mass Spectrometer (AMS), Single Particle Soot Photometer (DMT SP2), Ultra-High Sensitivity Aerosol Spectrometer (DMT UHSAS), Scanning Mobility Particle Sizer (SMPS), TSI-3563 Nephelometer, Cloud Condensation Nuclei (CCN) counter, and Condensation Particle Counter (CPC) are featured in this collection. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.\n<br>\n\n#### ASIA-AQ_Cloud_AircraftInSitu_DC8_Data\nASIA-AQ_Cloud_AircraftInSitu_DC8_Data is the in-situ cloud data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Cloud Droplet Probe (CDP), and Cloud Particle Spectrometer with Polarized Detection (CPSPD) are featured in this collection. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.\n<br>\n\n#### ASIA-AQ_MetNav_AircraftInSitu_DC8_Data\nASIA-AQ_MetNav_AircraftInSitu_DC8_Data is the in-situ meteorology and navigation data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Diode Laser Hygrometer (DLH) and the Meteorological Measurement System (MMS) are featured in this collection. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.\n<br>\n\n#### ASIA-AQ_TraceGas_AircraftInSitu_DC8_Data\nASIA-AQ_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Trace Organic Gas Analyzer (TOGA), Whole Air Sampler (WAS), Quantum Cascade Tunable Infrared Differential Absorption Spectrometer (QC-TILDAS), Chemical Ionization Time-of-Flight Mass Spectrometer (CIT-ToF-CIMS), Differential Absorption CO, CH4, N2O Measurements (DACOM), LI-7000 Closed Path CO2/H2O Gas Analyzer (LI-7000), Los Gatos Research CO/CO2/H2O Analyzer (LGR), Airborne Cavity Enhanced Spectrometer (ACES), MIRO Multi-compound Gas Analyzer (MIRO MGA), Compact Airborne Nitrogen diOxide Experiment (CANOE), Rapid Ozone Experiment (ROZE), Proton Transfer Mass Spectrometer (PTR-MS), In Situ Airborne Formaldehyde (ISAF), and Open-Path Ammonia Laser Spectrometer (OPALS) are featured in this collection. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.\n<br>\n\n#### ASIA-AQ_jValue_AircraftInSitu_DC8_Data\nASIA-AQ_jValue_AircraftInSitu_DC8_Data is the in-situ photolysis rates collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the CCD-based Actinic Flux Spectroradiometer (CAFS) is featured in this collection. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.\n<br>\n\n#### ASIA-AQ_AircraftRemoteSensing_LaRC-G3_GCAS_Data\nASIA-AQ_AircraftRemoteSensing_LaRC-G3_GCAS_Data is the Geostationary Coastal and Air Pollution Event (GEO-CAPE) Airborne Simulator Data (GCAS) data collected onboard the NASA LaRC G-III aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.\n<br>\n\n#### ASIA-AQ_AircraftRemoteSensing_LaRC-G3_HSRL2_Data\nASIA-AQ_AircraftRemoteSensing_LaRC-G3_HSRL2_Data is the High Spectral Resolution Lidar (HSRL) data collected onboard the NASA LaRC G-III aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/ASIA-AQ/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","earth observation","lidar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ASIA-AQ_Analysis_DC8_Data v1 - ASIA-AQ_Analysis_DC8_Data are analysis flag files derived from data products onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data flags identify plumes likely influenced by biomass burning and other combustion processes as well as identifies different air mass layers.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ASIA-AQ/ASIA-AQ_Analysis_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ASIA-AQ_Aerosol_AircraftInSitu_DC8_Data v1 - ASIA-AQ_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Transmission Electron Microscopy (TEM), Aerosol Mass Spectrometer (AMS), Single Particle Soot Photometer (DMT SP2), Ultra-High Sensitivity Aerosol Spectrometer (DMT UHSAS), Scanning Mobility Particle Sizer (SMPS), TSI-3563 Nephelometer, Cloud Condensation Nuclei (CCN) counter, and Condensation Particle Counter (CPC) are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ASIA-AQ/ASIA-AQ_Aerosol_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ASIA-AQ_Cloud_AircraftInSitu_DC8_Data v1 - ASIA-AQ_Cloud_AircraftInSitu_DC8_Data is the in-situ cloud data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Cloud Droplet Probe (CDP), and Cloud Particle Spectrometer with Polarized Detection (CPSPD) are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ASIA-AQ/ASIA-AQ_Cloud_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ASIA-AQ_MetNav_AircraftInSitu_DC8_Data v1 - ASIA-AQ_MetNav_AircraftInSitu_DC8_Data is the in-situ meteorology and navigation data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Diode Laser Hygrometer (DLH) and the Meteorological Measurement System (MMS) are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ASIA-AQ/ASIA-AQ_MetNav_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ASIA-AQ_TraceGas_AircraftInSitu_DC8_Data v1 - ASIA-AQ_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Trace Organic Gas Analyzer (TOGA), Whole Air Sampler (WAS), Quantum Cascade Tunable Infrared Differential Absorption Spectrometer (QC-TILDAS), Chemical Ionization Time-of-Flight Mass Spectrometer (CIT-ToF-CIMS), Differential Absorption CO, CH4, N2O Measurements (DACOM), LI-7000 Closed Path CO2/H2O Gas Analyzer (LI-7000), Los Gatos Research CO/CO2/H2O Analyzer (LGR), Airborne Cavity En...","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ASIA-AQ/ASIA-AQ_TraceGas_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ASIA-AQ_jValue_AircraftInSitu_DC8_Data v1 - ASIA-AQ_jValue_AircraftInSitu_DC8_Data is the in-situ photolysis rates collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the CCD-based Actinic Flux Spectroradiometer (CAFS) is featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ASIA-AQ/ASIA-AQ_jValue_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ASIA-AQ_AircraftRemoteSensing_LaRC-G3_GCAS_Data v1 - ASIA-AQ_AircraftRemoteSensing_LaRC-G3_GCAS_Data is the Geostationary Coastal and Air Pollution Event (GEO-CAPE) Airborne Simulator Data (GCAS) data collected onboard the NASA LaRC G-III aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ASIA-AQ/ASIA-AQ_AircraftRemoteSensing_LaRC-G3_GCAS_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ASIA-AQ_AircraftRemoteSensing_LaRC-G3_HSRL2_Data v1 - ASIA-AQ_AircraftRemoteSensing_LaRC-G3_HSRL2_Data is the High Spectral Resolution Lidar (HSRL) data collected onboard the NASA LaRC G-III aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/ASIA-AQ/ASIA-AQ_AircraftRemoteSensing_LaRC-G3_HSRL2_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-asia-aq","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ASTER GED Project","Description":"The AG1kmB Version 3 dataset was decommissioned as of December 14, 2016. Users are encouraged to use the ASTER Global Emissivity Dataset 1-kilometer [AG1km](https://doi.org/10.5067/Community/ASTER_GED/AG1km.003) dataset in HDF5. The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07 atmospheric profiles and the MODerate spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model. This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. The HDF5 version of the data are available for distribution, please see [AG1km](https://doi.org/10.5067/Community/ASTER_GED/AG1km.003) for more information. The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. Knonw Issues * Known issues are provided in Section 4 of the User Guide.\n<br>\n\n#### AG100B\nThe AG100B Version 3 dataset was decommissioned as of December 14, 2016. Users are encouraged to use the ASTER Global Emissivity Dataset 100-meter [AG100 Version 3](https://doi.org/10.5067/Community/ASTER_GED/AG100.003) dataset in HDF5. The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07 atmospheric profiles and the MODerate Spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model. This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. The HDF5 version of the data are available for distribution, please see [AG100](https://doi.org/10.5067/Community/ASTER_GED/AG100.003) for more information. The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. Known Issues: Known Issues are provided in Section 4 of the ASTER GED User Guide.\n<br>\n\n#### AG1km\nThe Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07_L2 atmospheric profiles and the MODerate Spectral resolution TRANsmittance (MODTRAN 5.2) radiative transfer model. This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. Known Issues: Known issues are provided in Section 4 of the User Guide.\n<br>\n\n#### AG100\nAdvanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07_L2 atmospheric profiles and the MODerate spectral resolution TRANsmittance (MODTRAN 5.2 radiative transfer model). This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. AG100 data are available globally at spatial resolution of 100 meters. The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. Known Issues: Known issues are provided in Section 4, starting on page 8 of the User Guide.\n<br>\n\n#### AG5KMMOH\nThe AG5KMMOH Version 4 dataset was decommissioned as of December 14, 2016. Users are encouraged to use [Version 4.1 ASTER Global Emissivity Dataset, Monthly, 0.05 degree, HDF5](https://doi.org/10.5067/Community/ASTER_GED/AG5KMMOH.041). The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is a collection of monthly files for each year of global emissivity. The ASTER GED data products are generated for 2000 through 2015 using the ASTER Temperature Emissivity Separation (TES) algorithm atmospheric correction method. This algorithm method uses Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheric Profiles product [MOD07](https://doi.org/10.5067/MODIS/MOD07_L2.006) and the MODTRAN 5.2 radiative transfer model along with the snow cover data from the standard monthly MODIS/Terra snow cover monthly global 0.05 degree product [MOD10CM](https://doi.org/10.5067/MODIS/MOD10CM.006), and vegetation information from the MODIS monthly gridded Normalized Difference Vegetation Index (NDVI) product [MOD13C2](https://doi.org/10.5067/MODIS/MOD13C2.006). The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product.\n<br>\n\n#### AG5KMMOH\nThe Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is a collection of monthly files (see known issues for gaps) for each year of global emissivity. The ASTER GED data products are generated for 2000 through 2015 using the ASTER Temperature Emissivity Separation (TES) algorithm atmospheric correction method. This algorithm method uses Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheric Profiles product MOD07 and the MODerate spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model along with the snow cover data from the standard monthly MODIS/Terra snow cover monthly global 0.05 degree product [MOD10CM](https://doi.org/10.5067/MODIS/MOD10CM.006), and vegetation information from the MODIS monthly gridded NDVI product [MOD13C2](https://doi.org/10.5067/MODIS/MOD13C2.006). ASTER GED Monthly V041 data products are offered in Hierarchical Data Format 5 (HDF5). The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. Known Issues: Since ASTER GED Monthly V041 uses MOD10CM as an input, there will be some months that were unable to be created due to missing data. Please see the [MODIS/Terra Data Outages Webpage](https://landweb.modaps.eosdis.nasa.gov/knownissue?as=61) for more information.\n<br>\n\n#### AG5KMMON\nThe AG5KMMOH Version 4 dataset was decommissioned as of December 14, 2016. Users are encouraged to use [Version 4.1 of ASTER Global Emissivity Dataset, Monthly, 0.05 degree, HDF5](https://doi.org/10.5067/Community/ASTER_GED/AG5KMMOH.041). The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is a collection of monthly files (see known issues for gaps) for each year of global emissivity. The ASTER GED data products are generated for 2000 through 2015 using the ASTER Temperature Emissivity Separation (TES) algorithm atmospheric correction method. This algorithm method uses Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheric Profiles product [MOD07](https://doi.org/10.5067/MODIS/MOD07_L2.006) and the MODerate spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model along with the snow cover data from the standard monthly MODIS/Terra snow cover monthly global 0.05 degree product [MOD10CM](https://doi.org/10.5067/MODIS/MOD10CM.006), and vegetation information from the MODIS monthly gridded Normalized Difference Vegetation Index (NDVI) product [MOD13C2](https://doi.org/10.5067/MODIS/MOD13C2.006). The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/centers/lp-daac","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","hdf","hdf5","ice","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AG1kmB v003 - The AG1kmB Version 3 dataset was decommissioned as of December 14, 2016. Users are encouraged to use the ASTER Global Emissivity Dataset 1-kilometer [AG1km](https://doi.org/10.5067/Community/ASTER_GED/AG1km.003) dataset in HDF5.","ARN":"arn:aws:s3:::lp-prod-protected/AG1kmB.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AG100B v003 - The AG100B Version 3 dataset was decommissioned as of December 14, 2016. Users are encouraged to use the ASTER Global Emissivity Dataset 100-meter [AG100 Version 3](https://doi.org/10.5067/Community/ASTER_GED/AG100.003) dataset in HDF5.","ARN":"arn:aws:s3:::lp-prod-protected/AG100B.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AG1km v003 - The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07_L2 atmospheric profiles and the MODerate Spectral resolution TRANsmittance (MODTRAN 5.2) radiative transfer model. This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008.","ARN":"arn:aws:s3:::lp-prod-protected/AG1km.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AG100 v003 - Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07_L2 atmospheric profiles and the MODerate spectral resolution TRANsmittance (MODTRAN 5.2 radiative transfer model). This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008.","ARN":"arn:aws:s3:::lp-prod-protected/AG100.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AG5KMMOH v004 - The AG5KMMOH Version 4 dataset was decommissioned as of December 14, 2016. Users are encouraged to use [Version 4.1 ASTER Global Emissivity Dataset, Monthly, 0.05 degree, HDF5](https://doi.org/10.5067/Community/ASTER_GED/AG5KMMOH.041).","ARN":"arn:aws:s3:::ag5kmmoh","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AG5KMMOH v041 - The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is a collection of monthly files (see known issues for gaps) for each year of global emissivity. The ASTER GED data products are generated for 2000 through 2015 using the ASTER Temperature Emissivity Separation (TES) algorithm atmospheric correction method.","ARN":"arn:aws:s3:::lp-prod-protected/AG5KMMOH.041","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AG5KMMON v004 - The AG5KMMOH Version 4 dataset was decommissioned as of December 14, 2016. Users are encouraged to use [Version 4.1 of ASTER Global Emissivity Dataset, Monthly, 0.05 degree, HDF5](https://doi.org/10.5067/Community/ASTER_GED/AG5KMMOH.041).","ARN":"arn:aws:s3:::ag5kmmon","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-aster-ged","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA aster Project","Description":"This dataset contains a simulated rasterized water surface elevation and inundation-extent product to be provided by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent. This is a derived product through resampling the upstream dataset L2_HR_PIXC_V1 and L2_HR_PIXCVEC_V1 onto a uniform grid over the North America continent. A uniform grid is superimposed onto the pixel cloud from the source products, and all pixel-cloud samples within each grid cell are aggregated to produce a single value per raster cell. The raster data are produced geographically fixed tiles at resolutions of 100 m and 250 m in a Universal Transverse Mercator projection grid. Note that this is a simulated SWOT product and not suited for any scientific exploration.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/data/instruments/aster","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","oceans"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1 v1.0 - This dataset contains a simulated rasterized water surface elevation and inundation-extent product to be provided by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-aster","Sources":["awslabs-open-data-registry"]}
{"Name":"ASTER Level 1T Precision Terrain Corrected Registered At-Sensor Radiance V004","Description":"The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B ([AST_L1B](https://doi.org/10.5067/ASTER/AST_L1B.004)) that has been geometrically corrected and rotated to a north-up UTM projection. The AST_L1T is created from a single resampling of the corresponding ASTER L1A ([AST_L1A](https://doi.org/10.5067/ASTER/AST_L1A.004)) product. The bands available in the AST_L1T depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T dataset does not include the aft-looking VNIR band 3. The AST_L1T product has a spatial resolution of 15 meters (m) for the VNIR bands, 30 m for the SWIR bands, and 90 m for the TIR bands.\n\nThe precision terrain correction process incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover).\n\nFor daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution as both a text file and single band browse images with the valid GCPs overlaid.\n\nThis multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depends on the bands available in the corresponding [AST_L1A](https://doi.org/10.5067/ASTER/AST_L1A.004) dataset.\n\nKnown Issues\n\n* Since October 1, 2017, a correction addresses zero-filled scans in low-latitude, ascending orbit (nighttime) TIR data. Additional details are available in the ASTER L1T User Advisory.\n* Data from the SWIR bands collected after April 2008 may show anomalous saturation and striping. See the ASTER SWIR User Advisory for further information.\n\nImprovements/Changes from Previous Versions\n\n* Enhanced Geolocation Accuracy: Version 4 uses Collection 2 Ground Control Points (GCPs) compared against Global Land Survey (GLS) 2000 standards to improve positional accuracy.\n* Radiometric Calibration Update: Version 4 applies Radiometric Calibration Coefficient Version 5 (RCC V5) to improve the radiometric accuracy of the raw DNs, based on research by [Tsuchida and others (2020)](https://doi.org/10.3390/rs12030427), published in Remote Sensing.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/ASTER/AST_L1T.004","Contact":"User Services: lpdaac@usgs.gov. Home Page: https://www.earthdata.nasa.gov/centers/lp-daac/contact","ManagedBy":"NASA","UpdateFrequency":"From 2000-03-04 to Ongoing (Varies)","Tags":["aws-pds","cog","cog","earth observation","global","land","orbit"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ASTER Level 1T Precision Terrain Corrected Registered At-Sensor Radiance V004.","ARN":"arn:aws:s3:::lp-prod-protected/AST_L1T.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Download Files from S3 Using boto3","URL":"https://github.com/nasa/LPDAAC-Data-Resources/blob/7f58ad3abeca7f0d17637ddb812642c0120a57ab/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb","AuthorName":"LPDAAC"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-astl1t","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ATDD Project","Description":"This is a subset of AMSR-E rain rate product along CloudSat field of view track. The goal of the subset is to select and return AMSR-E data that are within -100 km across the CloudSat track. Thus resultant subset swath is 45 pixels cross-track. Apart from that, all efforts are made to preserve the original HDF-EOS formatting of the source full-sized data. The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA EOS Aqua satellite provides global passive microwave measurements of terrestrial, oceanic, and atmospheric variables for the investigation of water and energy cycles. The original, full-sized, product is Level-2B swath product (AE_Rain), and it contains instantaneous measurements of rain rate and rain type (convective vs. stratiform), generated from Level-2A brightness temperatures (AE_L2A). The Goddard Space Flight Center (GSFC) Profiling algorithm determines rain rate and type over ocean areas, and a Modified GSFC Profiling algorithm over land. Data are stored in HDF-EOS (HDF4) format, and are available from 18 June 2002 until the AMSR-E instrument was turned off due to antenna problems in October 2011.\n<br>\n\n#### MAC04S0\nThis is the narrow-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the narrow-swath subset is to select and return MODIS data that are within +-5 km across the CloudSat track. I.e. the resultant MODIS subset swath is sought to be about 10 km cross-track.However, the original MYD04_L2 has 10-km pixels. Thus, MAC04S0 cross-track width is 2 pixels, the closest on either side of CloudSat track, and the resultant cross-track swath width is about 20 km.Along-track, all MODIS pixels from the original product are preserved. In the stardard product, the MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties (e.g., optical thickness and size distribution), mass concentration, look-up table derived reflected and transmitted fluxes, as well as quality assurance and other ancillary parameters, globally over ocean and near globally over land. (The shortname for this product is MAC04S0).\n<br>\n\n#### MAC04S1\nThis is the wide-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the wide-swath subset is to select and return MODIS data that are within +-100 km across the CloudSat track. I.e. the resultant MODIS subset swath is sought to be about 200 km cross-track. However, the original MYD04_L2 has 10-km pixels. Thus, MAC04S1 cross-track width is 21 pixels, and the resultant cross-track swath width is about 200 km. Along-track, all MODIS pixels from the original product are preserved. In the standard product, the MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties (e.g., optical thickness and size distribution), mass concentration, look-up table derived reflected and transmitted fluxes, as well as quality assurance and other ancillary parameters, globally over ocean and near globally over land. (The shortname for this product is MAC04S1).\n<br>\n\n#### MAM04S0\nThis is the MODIS/Aqua subset along MLS field of view track. The goal of the subset is to select and return MODIS data that are within +-100 km across the MLS track. I.e. the resultant MODIS subset swath is sought to be about 200 km cross-track. However, the original MYD04_L2 has 10-km pixels. Thus, MAM04S0 cross-track width is 21 pixels, and the resultant cross-track swath width is about 200 km. Along-track, all MODIS pixels from the original product are preserved. In the stardard product, the MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties (e.g., optical thickness and size distribution), mass concentration, look-up table derived reflected and transmitted fluxes, as well as quality assurance and other ancillary parameters, globally over ocean and near globally over land. (The shortname for this product is MAM04S0).\n<br>\n\n#### MAM03S0\nThis is the MODIS/Aqua subset along the Microwave Limb Sounder (MLS) field of view track. The goal of the subset is to select and return MODIS data that are within +-100 km across the MLS track. I.e. the resultant MODIS subset swath is about 200 km cross-track. Thus, MAM03S0 cross-track width is 201 pixels. Along-track, all MODIS pixels from the original product are preserved. In the standard product, geolocation fields are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily. The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit, the instrument telemetry and the digital elevation model. The geolocation fields include geodetic Latitude, Longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1 km sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors of any of the 36 MODIS bands. This product is used as input by a large number of subsequent MODIS products, particularly the products produced by the Land team. (The shortname for this product is MAM03S0).\n<br>\n\n#### OMCLDO2_CPR\nThis the OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) subset along CloudSat track, for the purposes of the A-Train mission. The original product uses the DOAS technique method. This level-2 global cloud product at the pixel resolution (13x24 km2 at nadir) is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track. This product contains cloud pressure, cloud fraction, slant column O2-O2 and O3, ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this Level-2 OMI cloud pressure and fraction (O2-O2 absorption) subset along CloudSat track product is OMCLDO2_CPR)\n<br>\n\n#### OMCLDRR_CPR\nThis is the OMI/Aura Cloud Pressure and Fraction (Raman Scattering) subset along CloudSat tracks, for the purposes of the A-Train mission. The original data product uses the Rotational Raman Scattering method. This level-2 global cloud product provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). The goal of this subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this Level-2 OMI cloud pressure and fraction subset along CloudSat tracks product is OMCLDRR_CPR)\n<br>\n\n#### OMAERUV_CPR\nThis is a CloudSat-collocated subset of the original OMI product OMAERUV, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated OMI Level 2 near-UV aerosol subset is OMAERUV_CPR_003)\n<br>\n\n#### OMNO2_CPR\nThis is a CloudSat-collocated subset of the original product OMNO2, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated OMI Level 2 NO2 subset is OMNO2_CPR_V003)\n<br>\n\n#### OMTO3_CPR\nThis is a CloudSat-collocated subset of the original product OMTO3, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated OMI Level 2 Total Ozone Column subset is OMTO3_CPR_V003)\n<br>\n\n#### OMSO2_CPR\nThis is a CloudSat-collocated subset of the original product OMSO2, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated subset of the original product OMSO2 Product is OMSO2_CPR_V003) This document describes the original OMI SO2 product (OMSO2) produced from global mode UV measurements of the Ozone Monitoring Instrument (OMI). OMI was launched on July 15, 2004 on the EOS Aura satellite, which is in a sun-synchronous ascending polar orbit with 1:45pm local equator crossing time. The data collection started on August 17, 2004 (orbit 482) and continues to this day with only minor data gaps. The minimum SO2 mass detectable by OMI is about two orders of magnitude smaller than the detection threshold of the legacy Total Ozone Mapping Spectrometer (TOMS) SO2 data (1978-2005) [Krueger et al 1995]. This is due to smaller OMI footprint and the use of wavelengths better optimized for separating O3 from SO2. The product file, called a data granule, covers the sunlit portion of the orbit with an approximately 2600 km wide swath containing 60 pixels per viewing line. During normal operations, 14 or 15 granules are produced daily, providing fully contiguous coverage of the globe. Currently, OMSO2 products are not produced when OMI goes into the \"zoom mode\" for one day every 452 orbits (~32 days). For each OMI pixel we provide 4 different estimates of the column density of SO2 in Dobson Units (1DU=2.69x10^16 molecules/cm2) obtained by making different assumptions about the vertical distribution of the SO2. However, it is important to note that in most cases the precise vertical distribution of SO2 is unimportant. The users can use either the SO2 plume height, or the center of mass altitude (CMA) derived from SO2 vertical distribution, to interpolate between the 4 values: 1)Planetary Boundary Layer (PBL) SO2 column (ColumnAmountSO2_PBL), corresponding to CMA of 0.9 km. 2)Lower tropospheric SO2 column (ColumnAmountSO2_TRL), corresponding to CMA of 2.5 km. 3)Middle tropospheric SO2 column, (ColumnAmountSO2_TRM), usually produced by volcanic degassing, corresponding to CMA of 7.5 km, 4)Upper tropospheric and Stratospheric SO2 column (ColumnAmountSO2_STL), usually produced by explosive volcanic eruption, corresponding to CMA of 17 km. The accuracy and precision of the derived SO2 columns vary significantly with the SO2 CMA and column amount, observational geometry, and slant column ozone. OMI becomes more sensitive to SO2 above clouds and snow/ice, and less sensitive to SO2 below clouds. Preliminary error estimates are discussed below (see Data Quality Assessment). OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 9 Mbytes.\n<br>\n\n#### PARASOLRB_CPR\nThis is the POLDER/Parasol Level-2 Radiation Budget Subset, collocated with the CloudSat track. The subset is processed at the A-Train Data Depot of the GES DISC, NASA. The algorithm first converts the original POLDER binary data, which is Level-2 but nevertheless in a sinusoidal grid, into HDF4 format, and thus stores the full-sized data in HDF4. Then, it calculates the CloudSat ground track coordinates, and proceeds to extract the closest POLDER grid cells. Along with the extraction, the algorithm re-orders the subset grid cells in a line-by-line fashion, so that the output subset is in array format and resembles a swath. This array has a cross-track dimension of 11 columns. That makes about 200-km-wide coverage. All original parameters are preserved in the subset. As it is collocated with CloudSat, the subset is automatically collocated with CALIPSO as well.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/about/competitive-programs/access/a-train-data-depot","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","hdf","ice","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AMSRERR_CPR v002 - This is a subset of AMSR-E rain rate product along CloudSat field of view track. The goal of the subset is to select and return AMSR-E data that are within -100 km across the CloudSat track.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/AMSRE/AMSRERR_CPR.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MAC04S0 v002 - This is the narrow-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the narrow-swath subset is to select and return MODIS data that are within +-5 km across the CloudSat track.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MAC/MAC04S0.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MAC04S1 v002 - This is the wide-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the wide-swath subset is to select and return MODIS data that are within +-100 km across the CloudSat track.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MAC/MAC04S1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MAM04S0 v002 - This is the MODIS/Aqua subset along MLS field of view track. The goal of the subset is to select and return MODIS data that are within +-100 km across the MLS track.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MAM/MAM04S0.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MAM03S0 v002 - This is the MODIS/Aqua subset along the Microwave Limb Sounder (MLS) field of view track. The goal of the subset is to select and return MODIS data that are within +-100 km across the MLS track.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MAM/MAM03S0.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMCLDO2_CPR v003 - This the OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) subset along CloudSat track, for the purposes of the A-Train mission. The original product uses the DOAS technique method.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OMI/OMCLDO2_CPR.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMCLDRR_CPR v003 - This is the OMI/Aura Cloud Pressure and Fraction (Raman Scattering) subset along CloudSat tracks, for the purposes of the A-Train mission. The original data product uses the Rotational Raman Scattering method.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OMI/OMCLDRR_CPR.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMAERUV_CPR v003 - This is a CloudSat-collocated subset of the original OMI product OMAERUV, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OMI/OMAERUV_CPR.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMNO2_CPR v003 - This is a CloudSat-collocated subset of the original product OMNO2, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OMI/OMNO2_CPR.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMTO3_CPR v003 - This is a CloudSat-collocated subset of the original product OMTO3, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OMI/OMTO3_CPR.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMSO2_CPR v003 - This is a CloudSat-collocated subset of the original product OMSO2, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OMI/OMSO2_CPR.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"PARASOLRB_CPR v001 - This is the POLDER/Parasol Level-2 Radiation Budget Subset, collocated with the CloudSat track. The subset is processed at the A-Train Data Depot of the GES DISC, NASA.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/PARASOL/PARASOLRB_CPR.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-atdd","Sources":["awslabs-open-data-registry"]}
{"Name":"ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006","Description":"This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/ATLAS/ATL03.006","Contact":"Email: nsidc@nsidc.org. Home Page: https://nsidc.org/daac","ManagedBy":"NASA","UpdateFrequency":"From 2018-10-13 to Ongoing","Tags":["atmosphere","aws-pds","datacenter","earth observation","global","hdf","ice","land","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL03/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and working with ICESat-2 data in the cloud","URL":"https://github.com/nsidc/NSIDC-Data-Tutorials/blob/main/notebooks/ICESat-2_Cloud_Access/ATL06-direct-access.ipynb","AuthorName":"Andy Barrett, Jennifer Roebuck, Amy Steiker"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-atl03","Sources":["awslabs-open-data-registry"]}
{"Name":"ATLAS/ICESat-2 L3A Land and Vegetation Height V006","Description":"This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/ATLAS/ATL08.006","Contact":"NASA NSIDC DAAC: nsidc@nsidc.org. Home Page: https://nsidc.org/daac","ManagedBy":"NASA","UpdateFrequency":"From 2018-10-14 to Ongoing","Tags":["atmosphere","aws-pds","datacenter","earth observation","global","hdf","ice","land"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ATLAS/ICESat-2 L3A Land and Vegetation Height V006.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL08/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing and working with ICESat-2 data in the cloud","URL":"https://github.com/nsidc/NSIDC-Data-Tutorials/blob/main/notebooks/ICESat-2_Cloud_Access/ATL06-direct-access_rendered.ipynb","AuthorName":"Andy Barrett, Jennifer Roebuck, and Amy Steiker."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-atl08","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ATLAS Project","Description":"The Shuttle Solar Backscatter Ultraviolet (SSBUV) Level-2 Ozone data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flying on the NOAA satellites. Data are available in the ASCII AMES text format. Ozone profiles of the upper atmosphere and total column ozone values are available for the following time periods: Flight #1: 1989 October 19, 20, 21. Flight #2: 1990 October 7, 8, 9. Flight #3: 1991 August 3, 4, 5, 6. Flight #4: 1992 March 29, 31. Flight #5: 1993 April 9, 11, 13, 15, 16. Flight #6: 1994 March 14, 15, 17. Flight #7: 1994 November 5, 7, 10, 13. Flight #8: 1996 January 12, 16, 18. SSBUV measures spectral ultraviolet radiances backscattered by the earth's atmosphere. For the ozone measurements the instrument steps over wavelengths between 252.2 and 339.99 nm while viewing the earth in the nadir position (50 km x 50 km footprint at nadir) at 19 pressure levels between 0.3 mb and 100 mb.\n<br>\n\n#### SSBUVIRR\nThe Shuttle Solar Backscatter Ultraviolet (SSBUV) level-2 irradiance data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flown on the NOAA polar orbiting satellites. Data are available in an ASCII text format. UV irradiance data are available for the following days from the eight missions: Flight #1: 1989 October 19, 20, 21 Flight #2: 1990 October 7, 8, 9 Flight #3: 1991 August 3, 4, 5, 6 Flight #4: 1992 March 29, 30 Flight #5: 1993 April 9, 11, 13, 15, 16 Flight #6: 1994 March 14, 15, 17 Flight #7: 1994 November 5, 7, 10, 13 Flight #8: 1996 January 12, 16, 18 The Shuttle SBUV (SSBUV) instrument measured solar spectral UV irradiance during the maximum and declining phase of solar cycle 22. The SSBUV data accurately represent the absolute solar UV irradiance between 200-405 nm, and also show the long-term variations during eight flights between October 1989 and January 1996. These data have been used to correct long-term sensitivity changes in the NOAA-11 SBUV/2 data, which provide a near-daily record of solar UV variations over the 170-400 nm region between December 1988 and October 1994. These data demonstrate the evolution of short-term solar UV activity during solar cycle 22.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/SSBUVO3_008.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SSBUVO3 v008 - The Shuttle Solar Backscatter Ultraviolet (SSBUV) Level-2 Ozone data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flying on the NOAA satellites.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Shuttle_SBUV_Level2/SSBUVO3.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SSBUVIRR v008 - The Shuttle Solar Backscatter Ultraviolet (SSBUV) level-2 irradiance data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flown on the NOAA polar orbiting satellites.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Shuttle_SBUV_Level2/SSBUVIRR.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-atlas","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ATom Project","Description":"This dataset provides observations collected during eleven airborne campaigns from 2006&ndash;2017 and associated input and output from nine widely used chemical transport models (CTMs). The airborne campaigns include ARCTAS-A, ARCTAS-B, ATom-1 and ATom-2, CalNex, DC3, INTEX-B, KORUS-AQ, MILAGRO, SEAC4RS, and WINTER, and they sampled mainly tropospheric air over the conterminous U.S. and the state of Alaska, Mexico, Canada, Greenland, and South Korea and remote areas over the Arctic, Pacific, Southern, and Atlantic Oceans. The CTMs are the AM4.1, CCSM4, GEOS-5, GEOS-Chem TOMAS, GEOS-Chem v10, GEOS-Chem v12, GISS-MATRIX, GISS-ModelE, and TM4-ECPL-F, and the output includes sulfate, nitrate, temperature, specific humidity, mixing ratio of ammonium, the volume mixing ratio of nitric acid, surface pressure, gas-phase ammonia, gas-phase nitric acid, pressure, total ammonium, etc. The observations were collected in-situ from a variety of instruments, including the Aerosol Microphysical Properties (AMP), HR Aerodyne Aerosol Mass Spectrometer (AMS), CIT Chemical Ionization Mass Spectrometer (CIMS), diode laser hygrometer (DLH), a mist chamber/ion chromatography system (MC/IC), Particle Analysis by Laser Mass Spectrometer (PALMS), Single Particle Soot Photometer (SP2), and UCI Whole Air Sampler (WAS). In-situ data also include latitude, longitude, and pressure. These observations were used to investigate how aerosol pH and ammonium balance change from polluted to remote regions, such as over oceans, and were compared to predictions from the CTMs.\n<br>\n\n#### ATom_CAFS_Instrument_Data_V2_1933\nThis dataset contains actinic flux and photolysis frequencies for photodissociation reactions for a variety of chemical species during the four ATom campaigns. Spectrally resolved actinic flux was measured by the down- and up-welling Charged-coupled device Actinic Flux Spectroradiometers (CAFS) from approximately 280-650 nm. Photolysis frequencies were calculated from the actinic flux and published cross sections and quantum yield values for atmospherically relevant molecules. Solar radiation drives the chemistry of the atmosphere, including the evolution of ozone, greenhouse gases, biomass burning, and other anthropogenic and natural trace constituents.\n<br>\n\n#### ATom_SOAP_Instrument_Data_1898\nThis dataset contains one-second aerosol extinction and absorption measurements from the Spectrometers for Optical Aerosol Properties (SOAP) instrument aboard the NASA DC-8 aircraft during the ATom-4 campaign that occurred in 2018. SOAP is a compact, low maintenance instrument that measures aerosol extinction and absorption at 532 nm. Aerosol extinction is measured by cavity ringdown spectroscopy and aerosol absorption by photoacoustic spectroscopy. Extinction is measured with sufficient precision and accuracy for the remote atmosphere. The absorption measurements are valid only in strongly absorbing cases, such as in dilute plumes from wildfire smoke. The absorption and extinction of visible light by aerosol particles is a major component of the earth's radiation budget, strongly affecting climate. Highly absorbing particles directly heat the atmosphere, while particles that scatter light tend to cool the atmosphere. Extinction is the sum of absorption and scattering; in most cases scattering represents >90% of extinction, with absorption making up the remainder. These aerosol-radiation interactions also alter air temperature and the rates of photochemical reactions.\n<br>\n\n#### ATom_HIPPO_ORCAS_1788\nThis dataset provides calculated age of air (AoA) and the argon/nitrogen (Ar/N2) ratio (per meg) from stratospheric flask samples and simultaneous high-frequency measurements of nitrous oxide (N2O), carbon dioxide (CO2), ozone (O3), methane (CH4), and carbon monoxide (CO) compiled from three airborne projects. The trace gases were used to identify 235 flask samples with stratospheric influence collected by the Medusa Whole Air Sampler and to calculate AoA using a new N2O-AoA relationship developed using a Markov Chain Monte Carlo algorithm. The data span a wide range of latitudes poleward of 40 degrees in both the Northern and Southern Hemispheres and cover the period 2009-01-10 to 2018-05-21.\n<br>\n\n#### ATom_nav_1613\nThis dataset provides flight track and aircraft navigation data from the NASA Atmospheric Tomography Mission (ATom). Flight track information is available for the four ATom campaigns: ATom-1, ATom-2, ATom-3, and ATom-4. Each ATom campaign consists of multiple individual flights and flight navigational information is recorded in 10-second intervals. Data available for each flight includes research flight number, date, and start and stop time of each 10-second interval. In addition, latitude, longitude, altitude, pressure and temperature is included at each 10-second interval. NASA's ATom campaign deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. During each campaign, flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. One intended use of this flight track data is to facilitate to mapping model results from global models onto the precise ATom flight tracks for comparison.\n<br>\n\n#### ATom_Rad_Measurements_ARMAS_1906\nThis dataset contains Level 2 (L2) absorbed radiation dose rates in silicon from the Automated Radiation Measurements for Aerospace Safety (ARMAS) system along ATom flight paths for the ATom-1 campaign conducted in July and August 2016. Absorbed dose rates measure how much energy is deposited in matter by ionizing radiation per unit time. The radiation sources can be from galactic cosmic rays, solar energetic particles, or Van Allen radiation belt energetic particles. Radiation can have adverse effects on human tissue and aerospace electronics, as well as profound effects on chemical species in the atmosphere, making them important to consider in atmospheric modeling and analyses. In this context, the derived ambient equivalent dose rates are also provided and relate the absorbed dose in human tissue to the effective biological damage of the radiation through a radiation weighting factor. In addition, visualizations of absorbed radiation dose for ATom-1 flight paths are included. The visualizations show the absorbed dose rates in silicon in the upper panel and the 3D representation of the flight in the bottom panel.\n<br>\n\n#### ATom_FlightTrack_Influences_1889\nThis dataset contains back trajectories, boundary layer influences, and convective influences of air parcels along NASA DC-8 aircraft's flight tracks during the four ATom campaigns that occurred from 2016 to 2018. Back trajectories were interpolated using National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) meteorology. Back trajectory analysis determines the origin of air masses by modeling the path of an air parcel backward in time. It can be used to better understand the sources of atmospheric compounds. Boundary layer Influences were determined based on 30 Day Back Trajectories. The atmospheric boundary layer is the lowest part of the troposphere that is directly influenced by earth's surface. The boundary layer influences wind patterns and thus the dispersal of pollutants and other atmospheric compounds of interest. Convective influences were based on 10 Day Back Trajectories and NASA Langley cloud products. Convective influences model the effects of convection on the movement of water vapor through the atmosphere, which influences cloud behavior.\n<br>\n\n#### ATom_Carbon_Aerosol_Loadings_1618\nThis dataset provides black carbon (BC) mass mixing ratios (in units of ng BC / kg air) measured during NASA's Atmospheric Tomography (ATom)-1 flight campaign during July and August 2016. The BC-core masses of BC-containing aerosol particles were measured using a Single Particle Soot Photometer (SP2). Conversion to mass mixing ratio (MMR) is achieved by monitoring sample flow. Influences in air mass composition were determined using the Particle Analysis by Laser Mass Spectrometry (PALMS) instruments. Also included here are data from the Cloud, Aerosol and Precipitation Spectrometer (CAPS) instrument which are used to identify measurements taken while in clouds. Finally, the associated latitude, longitude, altitude, and the timestamp of each measurement are included. All data are at ten seconds resolution. ATom-1 flights originated from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America.\n<br>\n\n#### ATom_CESM2_1878\nThis dataset contains CAM-chem (Community Atmosphere Model with Chemistry) model outputs along ATom flight tracks. CAM-chem is a component of the Community Earth System Model Version 2 (CESM2) and is used for simulations of global tropospheric and stratospheric atmospheric composition and for studies of chemistry-climate interactions. In general, CAM-chem uses the MOZART chemical mechanism, with various choices of complexity for tropospheric and stratospheric chemistry. For this dataset, CAM-chem used the MOZART-TS1 chemical mechanism, and the model was nudged to reanalysis meteorology from MERRA2.\n<br>\n\n#### ATom_CAPSVienna_Data_1981\nThis dataset contains cloud type and coarse aerosol contents measured by the University of Vienna's second-generation Cloud Aerosol and Precipitation Spectrometer (CAPS) instrument mounted to the NASA DC-8 aircraft during the four ATom campaigns that occurred from 2016 to 2018. CAPS measures particle size distributions in a size range between nominally 0.5 micrometers and 960 micrometers. The sizes range between approximately 0.5 and 50 micrometers is covered by the optical particle counter component of CAPS-the Cloud and Aerosol Spectrometer with Depolarization Detection (CAS-DPOL). The sizes range from 15 to 930 micrometers is measured with the optical array probe called Cloud imaging Probe (CIP). Cloud types are determined using an algorithm developed to detect and classify clouds using measurements of CAPS. Relative humidity and temperature are considered by the algorithm. The cloud indicator provides a classification on a 1 Hz basis and separates data in cloud-free, aerosol-cloud transition regime (ACTR), liquid clouds, clouds in the mixed-phase temperature regime (MPTR), and cirrus clouds. The coarse aerosol product provides cloud and aerosol particle number concentrations at standard pressure (1013.25 hPa) and standard temperature (273.15 K) in selected size ranges. Particle sizes refer to ammonium sulfate optical equivalent diameter (m=1.52 + 0.0i).\n<br>\n\n#### ATom_Picarro_Instrument_Data_1732\nThis dataset contains atmospheric measurements of CO2, CH4, and CO mixing ratios made with a Picarro G2401 spectrometer during the four ATom campaigns. Picarro G2401 uses Wavelength-Scanned Cavity Ring Down Spectroscopy (WS-CRDS), a time-based measurement utilizing a near-infrared laser to measure a spectral signature of the molecule. For the ATom mission, the Picarro instrument was modified in the laboratory to operate across the full pressure altitude range of flight campaigns. The instrument was also modified to have a shorter measurement interval.\n<br>\n\n#### ATom_Mapping_OH_Troposphere_1669\nThis dataset provides profile-integrated column densities of formaldehyde (HCHO), hydroxyl (OH), and OH production rates, diel tropospheric mean OH concentrations, and uncertainties that were derived from direct observation data from selected profiles of NASA Atmospheric Tomography (ATom) mission 1 and 2 flights for the period July 29, 2016 to February 21, 2017. These calculated products were combined with coincident HCHO column retrievals from the Ozone Monitoring Instrument (OMI) to scale and extend the profile results to a global gridded (0.5 deg latitude x 0.625 deg longitude) product. In addition to OMI formaldehyde column data, model output products from the Global Modeling Initiative (GMI) including average tropopause height, scaling factor, column air mass, and column-average formaldehyde photolysis frequency are provided. The GMI model output products were used in calculations and are included for user convenience.\n<br>\n\n#### ATom_Aerosol_Properties_V2_2111\nThis dataset contains comprehensive measurements of aerosol microphysical, chemical, and optical properties derived for both dry and ambient conditions from in situ measurements made during the four ATom campaigns. The dataset includes composition-resolved size distributions the integrated mass of sulfate, organics, nitrate, sea salt, dust, black carbon, and other compounds in coarse and fine fractions; extinction and absorption coefficients from each species at both dry and ambient conditions; asymmetry parameters; Angstrom exponents; and fitted lognormal functions to describe the size distribution. Optical parameters are calculated for 10 wavelengths from the near UV to the near IR, and size distributions range from 3 nm to 50 um in diameter. One file contains these data at 1-minute time intervals. Another file contains a subset of these data averaged into 1-km vertical bins for each vertical profile the aircraft made, as well as composition-resolved integrated aerosol optical depth derived from each profile. The concentration of cloud condensation nuclei is calculated for 5 supersaturations.\n<br>\n\n#### ATom_FullModel_DataStream_1877\nThis dataset provides Modeling Data Stream (MDS) and Reactivity Data Stream (RDS) products for each of the four ATom campaigns conducted from 2016 to 2018. MDS files contain the atmospheric constituents needed to model the RDS of the air parcels along ATom flight paths. The MDS is a continuous data stream (every 10 seconds) of the atmospheric content of these key chemical species derived from the in-situ measurements collected along ATom flight paths (as reported in the comprehensive related dataset ATom: Merged Atmospheric Chemistry, Trace Gases, and Aerosols). Values for chemical species measured by multiple instruments were selected from the instrument with better coverage and/or greater precision. Missing values were filled using interpolation for short gaps. For long gaps owing to instrument failure, values were estimated using multiple linear regressions from comparable parallel flights from other ATom campaigns. All species were flagged for instrument source and values were flagged for gap-filling status. In combination, MDS and RDS provide, in essence, a photochemical climatology for each air parcel along ATom flight paths containing the reactive species that control the loss of methane and the production and loss of ozone.\n<br>\n\n#### ATom_Forward_Flight_Videos_1938\nThis dataset contains images taken from the front of the NASA DC-8 aircraft during the first three ATom campaigns from 2016-2017. Images were taken with an Axis P1357 High Definition camera with a Theia TH138A wide-angle lens. These images were then stitched together at a 10-second frequency into an MP4 (.mp4) video for each flight. The forward camera shows the visible atmosphere that DC-8 flew through, allowing the in situ measurements to be placed in the context of cloud fields, smoke and haze layers, and boundary layers.\n<br>\n\n#### ATom_Clouds_Aerosols_2250\nThis dataset is the basis for the development of the Cloud Indicator, a novel algorithm that automatically detects and classifies measurement periods inside clouds. The included data were used in the analysis and development of figures for the related publication. The Cloud Indicator algorithm was developed based on particle size distribution measurements from a second-generation Cloud, Aerosol, and Precipitation Spectrometer (CAPS) combined with measurements of relative humidity and temperature from other sensors, to automatically detect flight sequences in clouds and classify the cloud type. Measurements were collected on 2016-08-20 as part of the Atmospheric Tomography Mission (ATom-1) Campaign and on 2017-04-20 as part of the Absorbing aerosol layers in a changing climate: aging, LIFEtime and dynamics (A-LIFE) project. As an additional criterion for the Cloud Indicator, a cloud-aerosol volume factor was established to ensure a precise and robust distinction between clouds and aerosol layers such as mineral dust or biomass burning to reduce misclassifications. Data are provided in netCDF (.nc) format.\n<br>\n\n#### ATom_Mineral_Dust_Cirrus_Cloud_2006\nThis dataset provides: (1) In situ dust aerosol concentration measurements over remote tropical Pacific and Atlantic Oceans by NOAA Particle Analysis by Laser Mass Spectrometry (PALMS) airborne single-particle mass spectrometer combined with Aerosol Microphysical Properties (AMP) aerosol size spectrometers. Measurements were made aboard the NASA DC8 aircraft during the four ATom campaigns that occurred from 2016 to 2018 (2) Model output of dust and meteorology from the CESM global transport model extracted at the time and location of the aircraft; (3) Model output of dust, other aerosol, and meteorology from the GEOS global transport model extracted at the time and location of the aircraft; (4) CESM model global output of dust and meteorology for dust emitted by specific source regions; (5) NCEP Global Forecast System forward trajectories of air parcels initiated at the time and location of the aircraft; and (6) The location and properties of cirrus clouds formed along the forward trajectories simulated using a parcel model. These data have been applied to better understand the role of mineral dust in cirrus cloud formation.\n<br>\n\n#### Flight_Environment_Parameters_1909\nThis dataset contains flight dynamics and environmental parameters (often referred to as housekeeping) specific to the DC-8 aircraft as collected from an assortment of instruments across all four ATom campaigns flown from 2016 through 2018. Measurements include aircraft position, altitude, speed, wind parameters, air temperature, and atmospheric and cabin pressure. These data can be used to understand the interior and exterior conditions and positioning of the DC-8 aircraft at 1-second resolution.\n<br>\n\n#### Interpolated_Met_Products_1876\nThis dataset provides modeled meteorological conditions and tagged-CO tracer concentrations along ATom flight paths derived from the Goddard Earth Observing System Version 5 (GEOS-5) data assimilation products from the Global Modeling and Assimilation Office (GMAO) at NASA's Goddard Space Flight Center. The GMAO \"GEOS fp\" forward processing system ingests satellite, ground-based, and airborne data, using a sophisticated model along with the data's statistical properties to obtain global three-dimensional data gridded fields at regular time intervals. These data are from the GMAO model output that were fitted to the ATom flight tracks by interpolating the GMAO model output to the horizontal ATom flight tracks for each of the 4 ATom Deployments. The dataset also provides tagged-CO tracer concentrations, which represent the contribution of specific regional sources to the total simulated CO. The data products produced are consistent with both the original measurements and the physical laws governing the atmosphere. To provide some meteorological context for the ATom flights, the GEOS5 gridded data are interpolated in space and time to the flight tracks.\n<br>\n\n#### ATom_Photolysis_Rates_1651\nThis dataset provides the results from nine global chemistry-climate or chemistry-transport models that estimated gridded values of atmospheric photolytic rates (J values) for ozone (O3), designated J-O1D, and nitrogen dioxide (NO2), designated J-NO2, under cloudy and clear sky scenarios. Each model produced global 4-D fields (latitude by longitude by pressure for 24 hours) for one day in mid-August 2016 (nominally) of results from two simulations: first using their standard treatment of clouds (all sky or cloudy) and a second with clouds and aerosols removed (clear sky). Model resolution ranges from 0.5 to 2.5 degrees. Observed J-O1D and J-NO2 values from the first ATom deployment (29 July - 23 August 2016) were collected with the Charged-coupled device Actinic Flux Spectroradiometer (CAFS) instrument. The ATom CAFS measurements are 3-second averages along the flight path for selected remote areas over the tropical and northern Pacific Ocean. Both all-sky (cloudy) and synthesized clear-sky J values are provided. (3) Additional data are included for clouds and ozone column plus other cloudy and clear sky parameters for the same remote areas of the tropical and northern Pacific Ocean. These auxiliary data are provided for use with included MATLAB scripts to reproduce the plots and analyses performed in the related publication by Hall et al. (2018). Note that while the analyses in the related publication were limited to the Pacific basin, the global model data are archived with this dataset.\n<br>\n\n#### ATom_GlobalModelInitiative_CTM_1897\nThis dataset contains Global Modeling Initiative (GMI) Chemical Transport Model (CTM) outputs from the four Atom campaigns. GMI simulations of the ATom flight periods have a horizontal resolution of 1.0 x 1.25 degrees, with output every 15 minutes. The ICARTT files are generated by spatially and temporally interpolating the output to the ATom flight track. Vertical interpolation is linear in log-pressure. The netCDF files provide three-dimensional&nbsp;(3D) GMI simulation output for the region surrounding the flight track every 15 minutes at the original model resolution. GMI is a 3-D CTM that includes full chemistry for both the troposphere and stratosphere. GMI simulates the concentrations of many of the species measured during ATom.\n<br>\n\n#### ATom_DLH_Instrument_Data_V2_1937\nThis dataset provides the concentrations of water measured by the Diode Laser Hygrometer (DLH) flown on the NASA DC-8 during the ATom 1-4 campaigns from 2016 - 2018. The DLH measures the water vapor in the atmosphere by wavelength modulated differential absorption spectroscopy of an isolated rovibrational line. The measurements include water vapor mixing ratio in parts-per-million-by-volume (ppmv) and relative humidity in percent. Relative humidity, both with respect to liquid water and with respect to ice, are quantities derived from measurements of water vapor mixing ratio as well as ambient temperature and pressure.\n<br>\n\n#### ATom_CIT_Instrument_Data_V2_1927\nThis dataset provides the concentrations of gas-phase organic and inorganic analytes measured by the California Institute of Technology (CIT) Chemical Ionization Mass Spectrometer (CIMS), or CIT-CIMS, flown on the NASA DC-8 aircraft during the four ATom campaigns. The CIT-CIMS employs CF3O-ion chemistry with two independent mass spectrometers (compact time-of-flight and triple quadrupole) to enable sensitive and specific measurements of atmospheric trace gases. The measurements include hydrogen peroxide (H2O2), hydrogen cyanide (HCN), nitric acid (HNO3), methyl hydrogen peroxide (CH3OOH), peroxyacetic acid (C2O3H4), peroxynitric acid (HO2NO2), and sulfur dioxide (SO2), in units of parts-per-trillion-by-volume.\n<br>\n\n#### ATom_Aerosols_Meteorology_1684\nThis dataset provides (1) the results of in situ aerosol particle property measurements collected over remote tropical areas of both Pacific and Atlantic Oceans during the NASA airborne Atmospheric Tomography (ATom) campaigns for ATom-1 and ATom-2 and (2) modeled outputs of comparable aerosol properties, atmospheric chemistry and meteorology at 70 m resolution from four chemical-transport models matched to the location and time of the aircraft measurements.\n<br>\n\n#### Airborne_Insitu_Measurements_1784\nThis dataset provides results of selected in-situ measurements of airflow and aerosol particles collected during the following airborne campaigns: NASA Atmospheric Tomography (ATom), Saharan Aerosol Long-range Transport and Aerosol-Cloud-interaction Experiment (SALTRACE), and Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics (A-LIFE). The airborne campaigns were conducted between 2013-06-10 and 2018-05-21. Depending upon the aircraft instrumentation per flight and campaign, the data include aircraft position, relative humidity, temperature, pressure, angle of attack (AOA), the probe location, true and probe air speeds, and aerosol particle diameters as extracted from Cloud Imaging Probe (CIP) images for the ATom and A-LIFE flights. Also provided are the results of combining the airborne data with numerical modeling to simulate particle sampling efficiency. Simulations investigated how airflow around wing-mounted instruments affected sampling efficiency and the induced errors for different realistic flight conditions.\n<br>\n\n#### ATom_WAS_Instrument_Data_1751\nThis dataset provides atmospheric concentrations of halocarbons and hydrocarbons measured by the UC-Irvine Whole Air Sampler (WAS) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The analysis of samples from the UCI WAS provides measurements of more than 50 trace gases, including C2-C10 NMHCs, C1-C2 halocarbons, C1-C5 alkyl nitrates, and selected sulfur compounds. Species were identified and measured using an established technique of airborne whole air sampling followed by laboratory analysis using gas chromatography (GC) with flame ionization detection (FID), and mass spectrometric detection (MSD). The ATom mission deployed an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018.\n<br>\n\n#### ATom_NOyO3_Instrument_Data_1734\nThis dataset provides in situ concentrations of nitric oxide (NO), nitrogen dioxide (NO2), total reactive nitrogen oxides (NOy), and ozone (O3) measured by the NOAA Nitrogen Oxides and Ozone (NOyO3) 4-channel chemiluminescence (CL) instrument during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. NOyO3 provides fast-response, specific, high precision, and calibrated measurements of nitrogen oxides and ozone at a spatial resolution of better than 100 m. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.\n<br>\n\n#### ATom_SP2_Instrument_Data_1672\nThis dataset provides the refractory black carbon mass concentration at one-second resolution measured by the Single Particle Soot Photometer (NOAA SP2) instrument during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The SP2 is a laser-induced incandescence instrument primarily used for measuring the black carbon mass content of individual particles.\n<br>\n\n#### ATom_AO2_Instrument_Data_V2_1880\nThis dataset provides in situ atmospheric oxygen and carbon dioxide concentrations measured by the NCAR Airborne Oxygen Instrument (AO2) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The AO2 Instrument measures O2 concentration using a vacuum-ultraviolet absorption technique. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for a systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.\n<br>\n\n#### ATom_AMP_Instrument_Data_1671\nThis dataset provides the number, surface area, and volume concentrations and size distributions of dry aerosol particles measured by the Aerosol Microphysical Properties (AMP) instrument package during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. Five instruments--two nucleation-mode aerosol size spectrometers (NMASS), two ultra-high sensitivity aerosol spectrometers (UHSAS), and a laser aerosol spectrometer (LAS)--comprise the AMP package. The AMP payload provides size distributions with up to one-second time resolution for dry aerosol particles between 0.003 and 4.8 microns in diameter.\n<br>\n\n#### ATom_GT_CIMS_Instrument_Data_1715\nThis dataset provides measurements of two important components of photochemical smog - peroxyacetyl nitrate (PAN) and peroxyl propionyl nitrate (PPN)- measured by the Georgia Tech Chemical Ionization Mass Spectrometer (GT-CIMS) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The GT-CIMS measures reactive nitrogen species in the lower atmosphere. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.\n<br>\n\n#### ATom_HR-AMS_Instrument_Data_1716\nThis dataset provides the atmospheric concentrations of separated ions from inorganic and organic species measured by the High-Resolution Aerosol Mass Spectrometer (HR-AMS) collected during flights of the NASA ATom Mission. Data are available from all four ATom Campaigns. The HR-AMS detects non-refractory submicron aerosol composition by impaction on a vaporizer at 600 degrees C, followed by electron ionization and time-of-flight mass spectral analysis. The measurements include chemically speciated submicron non-refractory particulate mass at a one second and 60 second resolution, and the size distribution of chemically speciated submicron non-refractory particulate mass at 60 second resolution.\n<br>\n\n#### NOAA_ToF_CIMS_Instrument_Data_1921\nThis dataset provides the mixing ratios of reactive nitrogen and halogen species measured by the NOAA Iodide Ion Time-of-Flight Chemical Ionization Mass Spectrometer (NOAA CIMS) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission for ATom-3 and ATom-4 campaigns. The NOAA CIMS uses chemical ionization mass spectrometric detection of gas phase organic and inorganic analytes via I- adduct formation. Measurements for ATom include N2O5 (dinitrogen pentoxide), ClNO2 (chloro nitrite), Cl2 (Chlorine), HCOOH (formic acid), C2H4O3S (hydroperoxymethyl thioformate), BrCl (bromine monochloride), BrCN (cyanogen bromide), and BrO (bromine monoxide). ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2-13 km altitude. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.\n<br>\n\n#### ATom_PFP_Instrument_Data_1746\nThis dataset provides mole fractions of atmospheric trace gases measured by the Programmable Flask Package (PFP) Whole Air Sampler during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The PFP whole air sampler provides a means of automated or manual filling of glass flasks. The sampler is designed to remove excess water vapor from the sampled air and compress it without contamination into ~1-liter volumes. These flasks are analyzed at the NOAA's Global Monitoring Division laboratory for trace gases and at the INSTAR's Staple Isotope Lab laboratory for isotopes of methane. Analysis of standardized PFP samples can measure more than 60 trace gases including N2O, SF6, H2, CS2, OCS, CO2, CH4, CO, CFCs, HCFCs, HFCs, Solvents, Methyl Halides, Hydrocarbons and Perfluorocarbons. The ATom mission deployed an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018.\n<br>\n\n#### ATom_ISAF_Instrument_Data_1730\nThis dataset provides the atmospheric volume mixing ratio of formaldehyde measured during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. The NASA In Situ Airborne Formaldehyde (ISAF) instrument, based at the Goddard Space Flight Center, measures formaldehyde on high-altitude NASA aircraft. The instrument uses laser-induced fluorescence (LIF) to obtain the high detection sensitivity needed to detect formaldehyde in the upper troposphere and lower stratosphere where abundances are 10 parts per trillion. LIF also enables a fast time response needed to measure the abundance of formaldehyde in the finely structured outflow of convective storms. These measurements of formaldehyde will be used elucidate mechanisms of convective transport and quantify the effects of boundary layer pollutants on the ozone photochemistry and cloud microphysics of the upper atmosphere.\n<br>\n\n#### ATom_Particulate_Iodine_1773\nThis dataset provides mass concentrations of particulate iodine as measured by the High-Resolution Aerosol Mass Spectrometer (HR-AMS) during the first two deployments of the NASA Atmospheric Tomography airborne missions (ATom-1 and ATom-2) in 2016 and 2017, respectively. The data provided in this dataset result from a reanalysis of the initial HR-AMS data based on post-mission calibrations and are reported at 1-minute resolution. The dataset also includes the fractions of the main ions (I+, HI+, and I2+) that can be used to ascertain the oxidation state of iodine in particles. Each observation includes an air mass classification flag (tropospheric or stratospheric conditions) based on collocated in situ water vapor and ozone measurements and positional data from the HR-AMS data feed.\n<br>\n\n#### ATom_Medusa_Instrument_Data_V2_1881\nThis dataset provides O2/N2, CO2, Ar/N2, and stable isotope ratios of CO2 measured in flasks collected by the Medusa Whole Air Sampler during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. ATom deployed an extensive gas and aerosol payload on the NASA DC-8 aircraft for a systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Medusa collected 32 cryogenically dried, flow, and pressure-controlled samples per flight. The samples are collected by an automated sampler into 1.5 L glass flasks that integrate over 25 seconds. Medusa provides discretely-sampled comparisons for onboard in situ O2/N2 ratio and CO2 measurements and unique measurements of Ar/N2 and 13C, 14C, and 18O isotopologues of CO2. Medusa flasks are analyzed on a sector-magnet mass spectrometer and a LiCor non-dispersive infrared CO2 analyzer by the Scripps O2 Program at Scripps Institution of Oceanography.\n<br>\n\n#### ATom_SP2_LAM_FeOx_MMR_1828\nThis dataset provides mass mixing ratios and number density of light-absorbing metallic aerosols (LAM) in the size range 180-1290 nm obtained with the NOAA Single Particle Soot Photometer (SP2) during the four deployments of the NASA Atmospheric Tomography (ATom) airborne mission from 2016-2018. The NOAA SP2 detects light absorbing aerosols, such as black carbon (BC), via laser-induced incandescence to provide real-time in situ quantification of refractory aerosol mass and number density. The percent of LAM aerosols attributed to anthropogenic iron oxides (FeOx) by mass is also provided.\n<br>\n\n#### ATom_ATHOS_Instrument_Data_V2_1930\nThis dataset provides the mixing ratios of hydrogen oxides measured by the Airborne Tropospheric Hydrogen Oxides Sensor (ATHOS) during the ATom 1-4 campaigns. ATHOS uses laser-induced fluorescence (LIF) to measure hydroxide (OH) and hydroperoxyl (HO2) simultaneously. The measurements include OH and HO2 mixing ratios and the OH interference determined by chemical removal of OH. The reactivity of OH is measured by the OH Reactivity (OHR) instrument using the discharge flow method and is integrated into the ATHOS electronics. These data provide insights into the oxidative state of the global atmosphere. These data are useful for testing the oxidation chemistry in models and other analytical methods being developed to deduce the atmosphere's oxidative state.\n<br>\n\n#### ATom_MMS_Instrument_Data_1731\nThis dataset contains measurements from the Meteorological Measurement System (MMS) instrument from the four ATom campaigns. MMS is a state-of-the-art instrument for measuring accurate, high resolution in situ airborne state parameters (pressure, temperature, turbulence index, and the 3-dimensional wind vector). These key measurements enable our understanding of atmospheric dynamics, chemistry, and microphysical processes. The MMS is used to investigate atmospheric mesoscale (gravity and mountain lee waves) and microscale (turbulence) phenomena. An accurate characterization of the turbulence phenomenon is important for the understanding of dynamic processes in the atmosphere, such as the behavior of buoyant plumes within cirrus clouds, diffusions of chemical species within wake vortices generated by jet aircraft, and microphysical processes in breaking gravity waves. Accurate temperature and pressure data are needed to evaluate chemical reaction rates as well as to determine accurate mixing ratios. Accurate wind field data establish a detailed relationship with the various constituents and the measured wind also verifies numerical models used to evaluate air mass origin.\n<br>\n\n#### ATom_PALMS_Instrument_Data_1733\nThis dataset contains single-particle aerosol composition as measured by the Particle Analysis by Laser Mass Spectrometry (PALMS) instrument during the four ATom campaigns from 2016-2018. Single aerosol particles are classified into several particle types, including: mixed sulfate/organic nitrate, biomass burning, elemental carbon, mineral/metallic, meteoric material, alkali salt, sea salt, heavy oil combustion, and others. Particle types are reported as raw number fractions and as absolute mass concentrations. PALMS measures aerosol composition for particles from diameter ~100 to 5000 nm, with most of the particle data in the size range ~150 to 3000 nm. Also included are absolute aerosol concentrations measured by a modified Laser Aerosol Spectrometer (LAS). Integrated number, surface area, and volume concentrations from LAS are reported over multiple size ranges.\n<br>\n\n#### ATom_QCLS_Instrument_Data_V2_1932\nThis dataset provides atmospheric concentrations of CO2, CH4, CO, and N2O measured by the Harvard Quantum Cascade Laser System (QCLS) instruments during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The QCLS (DUAL and CO2) instrument package contains two separate optical assemblies and calibration systems, and a common data system and power supply. The QCLS DUAL instrument simultaneously measures CO, CH4, and N2O concentrations, in situ, using two thermoelectrically cooled pulsed-quantum cascade lasers light sources, a multiple pass absorption cell, and two liquid nitrogen-cooled solid-state detectors. The QCLS CO2 instrument measures CO2 concentrations in situ using a thermoelectrically cooled pulsed-quantum cascade laser light source, gas cells, and liquid nitrogen cooled solid-state detectors. The CO2 mixing ratio of air flowing through the sample gas cell is determined by measuring absorption from a single infrared transition line at 4.32 microns relative to a reference gas of known concentration.\n<br>\n\n#### ATom_UCATS_Instrument_Data_1750\nThis dataset, collected with the Unmanned Aircraft Systems (UAS) Chromatograph for Atmospheric Trace Species (UCATS), provides atmospheric concentrations of nitrous oxide (N2O), sulfur hexafluoride (SF6), methane (CH4), hydrogen (H2), carbon monoxide (CO), water vapor (H2O), and ozone (O3). The UCATS system is three different instruments in one enclosure: a two-channel chromatograph with electron capture detectors (one measures N2O and SF6, the other measures CH4, H2 and CO), a tunable diode laser instrument for H2O, and a dual-beam O3 photometer.\n<br>\n\n#### ATom_SAGA_Instrument_Data_1748\nSoluble acidic gases and aerosols (SAGA) were collected with two related installations; a mist chamber/ion chromatography (MC/IC) system and a paired bulk aerosol system. The MC/IC system measures in situ atmospheric distributions of nitric acid (plus < 1 um NO3 aerosol) and fine (< 1 um) aerosol sulfate at an approximately 80-second interval. The paired bulk aerosol system collects particulates onto filters for subsequent analysis. Collected filters were first extracted with water to obtain the water-soluble (WS) constituents and then extracted again using methanol to collect the methanol soluble (MS) fraction. The light absorption of filtered extracts was measured from 300 to 700 nm. Ion chromatography on aqueous extracts of the bulk aerosol samples collected on Teflon filters were used to quantify soluble ions (Cl-, Br-, NO3-, SO42-, C2O42-, Na+, NH4+, K+, Ca+, and Mg+). The SAGA system is provided by the University of New Hampshire (UNH).\n<br>\n\n#### ATom_merge_1581\nThis dataset provides information on greenhouse gases and human-produced air pollution, including atmospheric concentrations of carbon dioxide (CO2), methane (CH4), tropospheric ozone (O3), and black carbon (BC) aerosols, collected during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. This dataset includes merged data from all instruments plus additional data such as numbered profiles and distance flown. Merged data have been created for seven different sampling intervals. In the case of data obtained over longer time intervals (e.g. flask data), the merge files provide (weighted) averages to match the sampling intervals. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for a systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate. Profiles of the reactive gases will also provide critical information for the validation of satellite data, particularly in remote areas where in situ data is lacking. Complete aircraft flight information including, but not limited to, latitude, longitude, and altitude are also provided. This data release provides results from all instruments on all four ATom flight campaigns.\n<br>\n\n#### ATom_NMASS_Data_1607\nThis dataset provides extensive calibration and in-flight performance data for two nucleation mode aerosol size spectrometer (NMASS) instruments utilized in the NASA Atmospheric Tomography Mission (ATom). Each NMASS has five condensation particle counters (CPCs) that detect particles above a different minimum size, determined by the maximum vapor supersaturation encountered by the particles. Operated in parallel, the CPCs provide continuous concentrations of particles in different cumulative size classes between 3 and 60 nm. Knowing the response function of each CPC, numerical inversion techniques were applied to recover size distributions from the continuous concentrations. Data provided include: NMASS counting efficiencies and diameters of calibration aerosols, inverted particle size distributions; comparisons of NMASS and Scanning Mobility Particle Sizer (SMPS) results; and performance at flows, temperatures, and pressures measured by both NMASSs and comparison with Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) concentrations collected on board the NASA DC-8 aircraft during an ATom flight in February 2017.\n<br>\n\n#### ATom_CO_GEOS_1604\nThis dataset contains carbon monoxide (CO) observations at 10-second intervals from flights during the ATom-1 campaign in 2016 and simulated CO concentrations from the Goddard Earth Observing System version 5 (GEOS-5) model for the corresponding locations along the ATom flight tracks. The Atmospheric Tomography Mission (ATom) is a NASA Earth Venture Suborbital-2 mission studying the impact of human-produced air pollution on greenhouse gases and on chemically reactive gases in the atmosphere. The airborne observations were collected using the Quantum Cascade Laser System (QCLS) instrument, a high-frequency laser spectroscopy instrument for in situ atmospheric gas sampling. This dataset provides a direct comparison of observational and simulated CO that will be used to inform future atmospheric modeling experiments. The dataset also contains simulated tagged-CO tracer concentrations, which represent the contribution of specific regional sources to the total simulated CO. This dataset contributes to one of the ATom mission objectives to create an observation-based chemical climatology of important atmospheric constituents and their reactivity in the remote troposphere.\n<br>\n\n#### ATom_Organic_Aerossols_1795\nThis dataset provides airborne in situ observations of submicron organic aerosol (OA) mass concentrations during the first (mid-2016) and second (early-2017) global deployments of the Atmospheric Tomography Mission (ATom), as well as modeled submicron OA mass concentrations along the flight tracks from global chemistry models that implement a variety of commonly used representations of OA sources and chemistry. In situ observations include non-refractory submicron aerosols measured by the High-Resolution Aerosol Mass Spectrometer (HR-AMS), aerosol volume concentrations measured by the Aerosol Microphysical Properties package (AMP), black carbon mass content measured by the Single Particle Soot Photometer (NOAA SP2), and refractory and non-refractory aerosol composition measured by the Particle Analysis By Laser Mass Spectrometry (PALMS). Both observed and modeled data are provided at a 60-second temporal resolution. The data are provided in netCDF format.\n<br>\n\n#### ATom_Ozonesonde_InstrumentData_1910\nThis dataset contains ozone measurements from the Ozonesonde instrument in Antarctica, Hawaii, and Fiji taken during the Atom-4 campaign. The Electrochemical Concentration Cell (ECC) Ozonesonde is a balloon-borne instrument that collects ozone concentrations paired with a radiosonde to collect additional meteorological info along a vertical profile (as a result, unlike other ATom data, this dataset is not associated with DC-8). The balloon can ascend to altitudes of 35 km before bursting. Ozone in the stratosphere helps reduce UV radiation that reaches Earth's surface; however, ozone at ground level can negatively influence respiratory health.\n<br>\n\n#### ATom_Simulated_Data_1597\nThis dataset provides a simulated data stream representative of an Atmospheric Tomography mission (ATom) data collection flight and also modeled reactivities for ozone (O3) production and loss and methane (CH4) loss from six global atmospheric chemistry models: CAM, GEOS-Chem, GFDL, GISS-E2.1, GMI, and UCI. The simulated data include concentrations of selected atmospheric trace gases for 14,880 air parcels along a simulated north-south ATom flight path along 180-degrees longitude over the Pacific basin. Each of the six models produced ozone production and loss and methane loss reactivities initialized using the simulated data beginning with five different days in August (8-01, 8-06, 8-11, 8-16, 8-21). Modeled years for each individual model varied from 1997 to 2016.\n<br>\n\n#### ATom_SO2_LIF_Instrument_Data_1890\nThis dataset provides concentrations of sulfur dioxide (SO2) measured by the Laser Induced Fluorescence Instrumentation for Sulfur Dioxide (SO2-LIF) on the ATom-4 campaign in April and May 2018. The LIF-SO2 instrument detects SO2 at the single-part per trillion level using red-shifted laser-induced fluorescence. Measurements are reported at 1-second intervals along the flight paths. Sources of SO2 atmosphere from natural sources include volcanic eruptions and wildfires; however, most anthropogenic sources, such as fossil fuel combustion, arise. SO2 influences some negative health and environmental impacts and is an important precursor of aerosols in the nucleation of new particles globally.\n<br>\n\n#### ATom_PANTHER_Instrument_Data_1914\nThis dataset contains measurements of various trace gases from the PAN and Trace Hydrohalocarbon ExpeRiment (PANTHER) across the four ATom campaigns. PANTHER uses Electron Capture Detection and Gas Chromatography (ECD-GC) and Mass Selective Detection and Gas Chromatography (MSD-GC) to measure numerous trace gases, including methyl halides, HCFCs, PAN, N2O, SF6, CFC-12, CFC-11, Halon 1211, methyl chloroform, carbon tetrachloride.\n<br>\n\n#### ATom_UHSAS_Data_1619\nThis dataset provides extensive calibration and in-flight performance data for two Ultra-High Sensitivity Aerosol Spectrometers (UHSAS) used for particle size distribution and volatility measurements during the NASA Atmospheric Tomography Mission (ATom) airborne campaign. UHSAS-1 was equipped with a compact thermodenuder operating at 300 degrees C and UHSAS-2 was operated without a thermodenuder to determine the number and volume fraction of volatile particles. Laboratory studies utilized aerosols from limonene ozonolysis (limon), atomization of ammonium sulfate (AS), and atomization of 2-diethylhexyl (dioctyl) sebacate (DOS). Data include: UHSAS detection efficiency, sizing calibration, performance at a range of pressures and at a range of thermodenuder temperatures, comparison of UHSAS-2 and condensation particle counter (CPC) particle number concentrations, comparisons of UHSAS-1 and UHSAS-2 for dry particle number concentration, surface area and volume collected onboard of a NASA DC-8 aircraft during August 2016, and dry aerosol size distributions for thermodenuded and non-thermodenuded instrument collected in February 2017.\n<br>\n\n#### Aerosol_Sulfate_LowermostStrat_1868\nThis dataset consists of (a) selected aerosol and gas-phase observations made on all four deployments of NASA Atmospheric Tomography Mission (ATom), (b) thermodynamic properties related to aerosol formation derived from these measurements, (c) 48-h back trajectories for ATom-4 observations, and (d) output from the Model of Aerosols and Ions in the Atmosphere (MAIA). ATom observations, thermodynamics, and back trajectories were inputs for MAIA model runs. MAIA runs focused on data from ATom-4 deployment, and output includes aerosol formation rates, and ultrafine particle size distributions and number concentrations in the lowermost stratosphere (LMS). ATom 1-4 deployments included all four seasons from 2016 to 2018. This investigation sought to understand how new particle formation (NPF) can occur in the LMS, factors influencing the amount of NPF, and other potential sources of ultrafine aerosols in this region of the atmosphere. The data are provided in comma-separated value (CSV) format.\n<br>\n\n#### ATom_TOGA_Instrument_Data_V2_1936\nThis dataset provides concentrations of volatile organic compounds (VOCs) measured by the Trace Organic Gas Analyzer (TOGA) during the four ATom campaigns. These data are relevant to the impact of human-produced air pollution on greenhouse gases and on chemically reactive gases in the atmosphere. Specific data were obtained for radical precursors, tracers of anthropogenic and biogenic activities, tracers of urban and biomass combustion emissions, products of oxidative processing, precursors to aerosol formation, and compounds important for aerosol modification and transformation. TOGA measures a wide range of VOCs with high sensitivity (ppt or lower), frequency (2-minutes), accuracy (often 15% or better), and precision (<3%).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/data/projects/atom","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","ice","netcdf","oceans","precipitation","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ATom_Modeled_Observed_Data_1857 v1 - This dataset provides observations collected during eleven airborne campaigns from 2006&ndash;2017 and associated input and output from nine widely used chemical transport models (CTMs). The airborne campaigns include ARCTAS-A, ARCTAS-B, ATom-1 and ATom-2, CalNex, DC3, INTEX-B, KORUS-AQ, MILAGRO, SEAC4RS, and WINTER, and they sampled mainly tropospheric air over the conterminous U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Modeled_Observed_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_CAFS_Instrument_Data_V2_1933 v2 - This dataset contains actinic flux and photolysis frequencies for photodissociation reactions for a variety of chemical species during the four ATom campaigns. Spectrally resolved actinic flux was measured by the down- and up-welling Charged-coupled device Actinic Flux Spectroradiometers (CAFS) from approximately 280-650 nm.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_CAFS_Instrument_Data_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_SOAP_Instrument_Data_1898 v1 - This dataset contains one-second aerosol extinction and absorption measurements from the Spectrometers for Optical Aerosol Properties (SOAP) instrument aboard the NASA DC-8 aircraft during the ATom-4 campaign that occurred in 2018. SOAP is a compact, low maintenance instrument that measures aerosol extinction and absorption at 532 nm.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_SOAP_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_HIPPO_ORCAS_1788 v1 - This dataset provides calculated age of air (AoA) and the argon/nitrogen (Ar/N2) ratio (per meg) from stratospheric flask samples and simultaneous high-frequency measurements of nitrous oxide (N2O), carbon dioxide (CO2), ozone (O3), methane (CH4), and carbon monoxide (CO) compiled from three airborne projects. The trace gases were used to identify 235 flask samples with stratospheric influence collected by the Medusa Whole Air Sampler and to calculate AoA using a new N2O-AoA relationship developed using a Markov Chain Monte Carlo algorithm.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_HIPPO_ORCAS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_nav_1613 v1 - This dataset provides flight track and aircraft navigation data from the NASA Atmospheric Tomography Mission (ATom). Flight track information is available for the four ATom campaigns: ATom-1, ATom-2, ATom-3, and ATom-4.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_nav/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Rad_Measurements_ARMAS_1906 v1 - This dataset contains Level 2 (L2) absorbed radiation dose rates in silicon from the Automated Radiation Measurements for Aerospace Safety (ARMAS) system along ATom flight paths for the ATom-1 campaign conducted in July and August 2016. Absorbed dose rates measure how much energy is deposited in matter by ionizing radiation per unit time.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Rad_Measurements_ARMAS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_FlightTrack_Influences_1889 v1 - This dataset contains back trajectories, boundary layer influences, and convective influences of air parcels along NASA DC-8 aircraft's flight tracks during the four ATom campaigns that occurred from 2016 to 2018. Back trajectories were interpolated using National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) meteorology.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_FlightTrack_Influences/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Carbon_Aerosol_Loadings_1618 v1 - This dataset provides black carbon (BC) mass mixing ratios (in units of ng BC / kg air) measured during NASA's Atmospheric Tomography (ATom)-1 flight campaign during July and August 2016. The BC-core masses of BC-containing aerosol particles were measured using a Single Particle Soot Photometer (SP2).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Carbon_Aerosol_Loadings/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_CESM2_1878 v1 - This dataset contains CAM-chem (Community Atmosphere Model with Chemistry) model outputs along ATom flight tracks. CAM-chem is a component of the Community Earth System Model Version 2 (CESM2) and is used for simulations of global tropospheric and stratospheric atmospheric composition and for studies of chemistry-climate interactions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_CESM2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_CAPSVienna_Data_1981 v1 - This dataset contains cloud type and coarse aerosol contents measured by the University of Vienna's second-generation Cloud Aerosol and Precipitation Spectrometer (CAPS) instrument mounted to the NASA DC-8 aircraft during the four ATom campaigns that occurred from 2016 to 2018. CAPS measures particle size distributions in a size range between nominally 0.5 micrometers and 960 micrometers.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_CAPSVienna_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Picarro_Instrument_Data_1732 v1 - This dataset contains atmospheric measurements of CO2, CH4, and CO mixing ratios made with a Picarro G2401 spectrometer during the four ATom campaigns. Picarro G2401 uses Wavelength-Scanned Cavity Ring Down Spectroscopy (WS-CRDS), a time-based measurement utilizing a near-infrared laser to measure a spectral signature of the molecule.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Picarro_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Mapping_OH_Troposphere_1669 v1 - This dataset provides profile-integrated column densities of formaldehyde (HCHO), hydroxyl (OH), and OH production rates, diel tropospheric mean OH concentrations, and uncertainties that were derived from direct observation data from selected profiles of NASA Atmospheric Tomography (ATom) mission 1 and 2 flights for the period July 29, 2016 to February 21, 2017. These calculated products were combined with coincident HCHO column retrievals from the Ozone Monitoring Instrument (OMI) to scale and extend the profile results to a global gridded (0.5 deg latitude x 0.625 deg longitude) product.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Mapping_OH_Troposphere/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Aerosol_Properties_V2_2111 v2.1 - This dataset contains comprehensive measurements of aerosol microphysical, chemical, and optical properties derived for both dry and ambient conditions from in situ measurements made during the four ATom campaigns. The dataset includes composition-resolved size distributions the integrated mass of sulfate, organics, nitrate, sea salt, dust, black carbon, and other compounds in coarse and fine fractions; extinction and absorption coefficients from each species at both dry and ambient conditions; asymmetry parameters; Angstrom exponents; and fitted lognormal functions to describe the size dis...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Aerosol_Properties_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_FullModel_DataStream_1877 v1 - This dataset provides Modeling Data Stream (MDS) and Reactivity Data Stream (RDS) products for each of the four ATom campaigns conducted from 2016 to 2018. MDS files contain the atmospheric constituents needed to model the RDS of the air parcels along ATom flight paths.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_FullModel_DataStream/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Forward_Flight_Videos_1938 v1 - This dataset contains images taken from the front of the NASA DC-8 aircraft during the first three ATom campaigns from 2016-2017. Images were taken with an Axis P1357 High Definition camera with a Theia TH138A wide-angle lens.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Forward_Flight_Videos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Clouds_Aerosols_2250 v1 - This dataset is the basis for the development of the Cloud Indicator, a novel algorithm that automatically detects and classifies measurement periods inside clouds. The included data were used in the analysis and development of figures for the related publication.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Clouds_Aerosols/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Mineral_Dust_Cirrus_Cloud_2006 v1 - This dataset provides: (1) In situ dust aerosol concentration measurements over remote tropical Pacific and Atlantic Oceans by NOAA Particle Analysis by Laser Mass Spectrometry (PALMS) airborne single-particle mass spectrometer combined with Aerosol Microphysical Properties (AMP) aerosol size spectrometers. Measurements were made aboard the NASA DC8 aircraft during the four ATom campaigns that occurred from 2016 to 2018 (2) Model output of dust and meteorology from the CESM global transport model extracted at the time and location of the aircraft; (3) Model output of dust, other aerosol, an...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Mineral_Dust_Cirrus_Cloud/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Flight_Environment_Parameters_1909 v1 - This dataset contains flight dynamics and environmental parameters (often referred to as housekeeping) specific to the DC-8 aircraft as collected from an assortment of instruments across all four ATom campaigns flown from 2016 through 2018. Measurements include aircraft position, altitude, speed, wind parameters, air temperature, and atmospheric and cabin pressure.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/Flight_Environment_Parameters/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Interpolated_Met_Products_1876 v1 - This dataset provides modeled meteorological conditions and tagged-CO tracer concentrations along ATom flight paths derived from the Goddard Earth Observing System Version 5 (GEOS-5) data assimilation products from the Global Modeling and Assimilation Office (GMAO) at NASA's Goddard Space Flight Center. The GMAO \"GEOS fp\" forward processing system ingests satellite, ground-based, and airborne data, using a sophisticated model along with the data's statistical properties to obtain global three-dimensional data gridded fields at regular time intervals.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/Interpolated_Met_Products/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Photolysis_Rates_1651 v1 - This dataset provides the results from nine global chemistry-climate or chemistry-transport models that estimated gridded values of atmospheric photolytic rates (J values) for ozone (O3), designated J-O1D, and nitrogen dioxide (NO2), designated J-NO2, under cloudy and clear sky scenarios. Each model produced global 4-D fields (latitude by longitude by pressure for 24 hours) for one day in mid-August 2016 (nominally) of results from two simulations: first using their standard treatment of clouds (all sky or cloudy) and a second with clouds and aerosols removed (clear sky).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Photolysis_Rates/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_GlobalModelInitiative_CTM_1897 v1 - This dataset contains Global Modeling Initiative (GMI) Chemical Transport Model (CTM) outputs from the four Atom campaigns. GMI simulations of the ATom flight periods have a horizontal resolution of 1.0 x 1.25 degrees, with output every 15 minutes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_GlobalModelInitiative_CTM/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_DLH_Instrument_Data_V2_1937 v2 - This dataset provides the concentrations of water measured by the Diode Laser Hygrometer (DLH) flown on the NASA DC-8 during the ATom 1-4 campaigns from 2016 - 2018. The DLH measures the water vapor in the atmosphere by wavelength modulated differential absorption spectroscopy of an isolated rovibrational line.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_DLH_Instrument_Data_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_CIT_Instrument_Data_V2_1927 v2 - This dataset provides the concentrations of gas-phase organic and inorganic analytes measured by the California Institute of Technology (CIT) Chemical Ionization Mass Spectrometer (CIMS), or CIT-CIMS, flown on the NASA DC-8 aircraft during the four ATom campaigns. The CIT-CIMS employs CF3O-ion chemistry with two independent mass spectrometers (compact time-of-flight and triple quadrupole) to enable sensitive and specific measurements of atmospheric trace gases.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_CIT_Instrument_Data_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Aerosols_Meteorology_1684 v1 - This dataset provides (1) the results of in situ aerosol particle property measurements collected over remote tropical areas of both Pacific and Atlantic Oceans during the NASA airborne Atmospheric Tomography (ATom) campaigns for ATom-1 and ATom-2 and (2) modeled outputs of comparable aerosol properties, atmospheric chemistry and meteorology at 70 m resolution from four chemical-transport models matched to the location and time of the aircraft measurements.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Aerosols_Meteorology/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Airborne_Insitu_Measurements_1784 v1 - This dataset provides results of selected in-situ measurements of airflow and aerosol particles collected during the following airborne campaigns: NASA Atmospheric Tomography (ATom), Saharan Aerosol Long-range Transport and Aerosol-Cloud-interaction Experiment (SALTRACE), and Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics (A-LIFE). The airborne campaigns were conducted between 2013-06-10 and 2018-05-21.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/Airborne_Insitu_Measurements/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_WAS_Instrument_Data_1751 v1 - This dataset provides atmospheric concentrations of halocarbons and hydrocarbons measured by the UC-Irvine Whole Air Sampler (WAS) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The analysis of samples from the UCI WAS provides measurements of more than 50 trace gases, including C2-C10 NMHCs, C1-C2 halocarbons, C1-C5 alkyl nitrates, and selected sulfur compounds.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_WAS_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_NOyO3_Instrument_Data_1734 v1 - This dataset provides in situ concentrations of nitric oxide (NO), nitrogen dioxide (NO2), total reactive nitrogen oxides (NOy), and ozone (O3) measured by the NOAA Nitrogen Oxides and Ozone (NOyO3) 4-channel chemiluminescence (CL) instrument during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. NOyO3 provides fast-response, specific, high precision, and calibrated measurements of nitrogen oxides and ozone at a spatial resolution of better than 100 m.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_NOyO3_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_SP2_Instrument_Data_1672 v1 - This dataset provides the refractory black carbon mass concentration at one-second resolution measured by the Single Particle Soot Photometer (NOAA SP2) instrument during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The SP2 is a laser-induced incandescence instrument primarily used for measuring the black carbon mass content of individual particles.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_SP2_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_AO2_Instrument_Data_V2_1880 v2 - This dataset provides in situ atmospheric oxygen and carbon dioxide concentrations measured by the NCAR Airborne Oxygen Instrument (AO2) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The AO2 Instrument measures O2 concentration using a vacuum-ultraviolet absorption technique.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_AO2_Instrument_Data_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_AMP_Instrument_Data_1671 v1 - This dataset provides the number, surface area, and volume concentrations and size distributions of dry aerosol particles measured by the Aerosol Microphysical Properties (AMP) instrument package during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. Five instruments--two nucleation-mode aerosol size spectrometers (NMASS), two ultra-high sensitivity aerosol spectrometers (UHSAS), and a laser aerosol spectrometer (LAS)--comprise the AMP package.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_AMP_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_GT_CIMS_Instrument_Data_1715 v1 - This dataset provides measurements of two important components of photochemical smog - peroxyacetyl nitrate (PAN) and peroxyl propionyl nitrate (PPN)- measured by the Georgia Tech Chemical Ionization Mass Spectrometer (GT-CIMS) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The GT-CIMS measures reactive nitrogen species in the lower atmosphere.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_GT_CIMS_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_HR-AMS_Instrument_Data_1716 v1.1 - This dataset provides the atmospheric concentrations of separated ions from inorganic and organic species measured by the High-Resolution Aerosol Mass Spectrometer (HR-AMS) collected during flights of the NASA ATom Mission. Data are available from all four ATom Campaigns.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_HR-AMS_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NOAA_ToF_CIMS_Instrument_Data_1921 v2 - This dataset provides the mixing ratios of reactive nitrogen and halogen species measured by the NOAA Iodide Ion Time-of-Flight Chemical Ionization Mass Spectrometer (NOAA CIMS) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission for ATom-3 and ATom-4 campaigns. The NOAA CIMS uses chemical ionization mass spectrometric detection of gas phase organic and inorganic analytes via I- adduct formation.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/NOAA_ToF_CIMS_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_PFP_Instrument_Data_1746 v1 - This dataset provides mole fractions of atmospheric trace gases measured by the Programmable Flask Package (PFP) Whole Air Sampler during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The PFP whole air sampler provides a means of automated or manual filling of glass flasks.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_PFP_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_ISAF_Instrument_Data_1730 v1 - This dataset provides the atmospheric volume mixing ratio of formaldehyde measured during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_ISAF_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Particulate_Iodine_1773 v1 - This dataset provides mass concentrations of particulate iodine as measured by the High-Resolution Aerosol Mass Spectrometer (HR-AMS) during the first two deployments of the NASA Atmospheric Tomography airborne missions (ATom-1 and ATom-2) in 2016 and 2017, respectively. The data provided in this dataset result from a reanalysis of the initial HR-AMS data based on post-mission calibrations and are reported at 1-minute resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Particulate_Iodine/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Medusa_Instrument_Data_V2_1881 v2 - This dataset provides O2/N2, CO2, Ar/N2, and stable isotope ratios of CO2 measured in flasks collected by the Medusa Whole Air Sampler during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. ATom deployed an extensive gas and aerosol payload on the NASA DC-8 aircraft for a systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Medusa_Instrument_Data_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_SP2_LAM_FeOx_MMR_1828 v1 - This dataset provides mass mixing ratios and number density of light-absorbing metallic aerosols (LAM) in the size range 180-1290 nm obtained with the NOAA Single Particle Soot Photometer (SP2) during the four deployments of the NASA Atmospheric Tomography (ATom) airborne mission from 2016-2018. The NOAA SP2 detects light absorbing aerosols, such as black carbon (BC), via laser-induced incandescence to provide real-time in situ quantification of refractory aerosol mass and number density.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_SP2_LAM_FeOx_MMR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_ATHOS_Instrument_Data_V2_1930 v2 - This dataset provides the mixing ratios of hydrogen oxides measured by the Airborne Tropospheric Hydrogen Oxides Sensor (ATHOS) during the ATom 1-4 campaigns. ATHOS uses laser-induced fluorescence (LIF) to measure hydroxide (OH) and hydroperoxyl (HO2) simultaneously.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_ATHOS_Instrument_Data_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_MMS_Instrument_Data_1731 v1 - This dataset contains measurements from the Meteorological Measurement System (MMS) instrument from the four ATom campaigns. MMS is a state-of-the-art instrument for measuring accurate, high resolution in situ airborne state parameters (pressure, temperature, turbulence index, and the 3-dimensional wind vector).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_MMS_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_PALMS_Instrument_Data_1733 v1 - This dataset contains single-particle aerosol composition as measured by the Particle Analysis by Laser Mass Spectrometry (PALMS) instrument during the four ATom campaigns from 2016-2018. Single aerosol particles are classified into several particle types, including: mixed sulfate/organic nitrate, biomass burning, elemental carbon, mineral/metallic, meteoric material, alkali salt, sea salt, heavy oil combustion, and others.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_PALMS_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_QCLS_Instrument_Data_V2_1932 v2 - This dataset provides atmospheric concentrations of CO2, CH4, CO, and N2O measured by the Harvard Quantum Cascade Laser System (QCLS) instruments during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The QCLS (DUAL and CO2) instrument package contains two separate optical assemblies and calibration systems, and a common data system and power supply.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_QCLS_Instrument_Data_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_UCATS_Instrument_Data_1750 v1 - This dataset, collected with the Unmanned Aircraft Systems (UAS) Chromatograph for Atmospheric Trace Species (UCATS), provides atmospheric concentrations of nitrous oxide (N2O), sulfur hexafluoride (SF6), methane (CH4), hydrogen (H2), carbon monoxide (CO), water vapor (H2O), and ozone (O3). The UCATS system is three different instruments in one enclosure: a two-channel chromatograph with electron capture detectors (one measures N2O and SF6, the other measures CH4, H2 and CO), a tunable diode laser instrument for H2O, and a dual-beam O3 photometer.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_UCATS_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_SAGA_Instrument_Data_1748 v1 - Soluble acidic gases and aerosols (SAGA) were collected with two related installations; a mist chamber/ion chromatography (MC/IC) system and a paired bulk aerosol system. The MC/IC system measures in situ atmospheric distributions of nitric acid (plus < 1 um NO3 aerosol) and fine (< 1 um) aerosol sulfate at an approximately 80-second interval.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_SAGA_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_merge_1581 v1.5 - This dataset provides information on greenhouse gases and human-produced air pollution, including atmospheric concentrations of carbon dioxide (CO2), methane (CH4), tropospheric ozone (O3), and black carbon (BC) aerosols, collected during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. This dataset includes merged data from all instruments plus additional data such as numbered profiles and distance flown.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_merge/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_NMASS_Data_1607 v1 - This dataset provides extensive calibration and in-flight performance data for two nucleation mode aerosol size spectrometer (NMASS) instruments utilized in the NASA Atmospheric Tomography Mission (ATom). Each NMASS has five condensation particle counters (CPCs) that detect particles above a different minimum size, determined by the maximum vapor supersaturation encountered by the particles.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_NMASS_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_CO_GEOS_1604 v1 - This dataset contains carbon monoxide (CO) observations at 10-second intervals from flights during the ATom-1 campaign in 2016 and simulated CO concentrations from the Goddard Earth Observing System version 5 (GEOS-5) model for the corresponding locations along the ATom flight tracks. The Atmospheric Tomography Mission (ATom) is a NASA Earth Venture Suborbital-2 mission studying the impact of human-produced air pollution on greenhouse gases and on chemically reactive gases in the atmosphere.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_CO_GEOS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Organic_Aerossols_1795 v1 - This dataset provides airborne in situ observations of submicron organic aerosol (OA) mass concentrations during the first (mid-2016) and second (early-2017) global deployments of the Atmospheric Tomography Mission (ATom), as well as modeled submicron OA mass concentrations along the flight tracks from global chemistry models that implement a variety of commonly used representations of OA sources and chemistry. In situ observations include non-refractory submicron aerosols measured by the High-Resolution Aerosol Mass Spectrometer (HR-AMS), aerosol volume concentrations measured by the Aeros...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Organic_Aerossols/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Ozonesonde_InstrumentData_1910 v1 - This dataset contains ozone measurements from the Ozonesonde instrument in Antarctica, Hawaii, and Fiji taken during the Atom-4 campaign. The Electrochemical Concentration Cell (ECC) Ozonesonde is a balloon-borne instrument that collects ozone concentrations paired with a radiosonde to collect additional meteorological info along a vertical profile (as a result, unlike other ATom data, this dataset is not associated with DC-8).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Ozonesonde_InstrumentData/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_Simulated_Data_1597 v1 - This dataset provides a simulated data stream representative of an Atmospheric Tomography mission (ATom) data collection flight and also modeled reactivities for ozone (O3) production and loss and methane (CH4) loss from six global atmospheric chemistry models: CAM, GEOS-Chem, GFDL, GISS-E2.1, GMI, and UCI. The simulated data include concentrations of selected atmospheric trace gases for 14,880 air parcels along a simulated north-south ATom flight path along 180-degrees longitude over the Pacific basin.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_Simulated_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_SO2_LIF_Instrument_Data_1890 v1 - This dataset provides concentrations of sulfur dioxide (SO2) measured by the Laser Induced Fluorescence Instrumentation for Sulfur Dioxide (SO2-LIF) on the ATom-4 campaign in April and May 2018. The LIF-SO2 instrument detects SO2 at the single-part per trillion level using red-shifted laser-induced fluorescence.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_SO2_LIF_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_PANTHER_Instrument_Data_1914 v1 - This dataset contains measurements of various trace gases from the PAN and Trace Hydrohalocarbon ExpeRiment (PANTHER) across the four ATom campaigns. PANTHER uses Electron Capture Detection and Gas Chromatography (ECD-GC) and Mass Selective Detection and Gas Chromatography (MSD-GC) to measure numerous trace gases, including methyl halides, HCFCs, PAN, N2O, SF6, CFC-12, CFC-11, Halon 1211, methyl chloroform, carbon tetrachloride.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_PANTHER_Instrument_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_UHSAS_Data_1619 v1 - This dataset provides extensive calibration and in-flight performance data for two Ultra-High Sensitivity Aerosol Spectrometers (UHSAS) used for particle size distribution and volatility measurements during the NASA Atmospheric Tomography Mission (ATom) airborne campaign. UHSAS-1 was equipped with a compact thermodenuder operating at 300 degrees C and UHSAS-2 was operated without a thermodenuder to determine the number and volume fraction of volatile particles.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_UHSAS_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Aerosol_Sulfate_LowermostStrat_1868 v1 - This dataset consists of (a) selected aerosol and gas-phase observations made on all four deployments of NASA Atmospheric Tomography Mission (ATom), (b) thermodynamic properties related to aerosol formation derived from these measurements, (c) 48-h back trajectories for ATom-4 observations, and (d) output from the Model of Aerosols and Ions in the Atmosphere (MAIA). ATom observations, thermodynamics, and back trajectories were inputs for MAIA model runs.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/Aerosol_Sulfate_LowermostStrat/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ATom_TOGA_Instrument_Data_V2_1936 v2 - This dataset provides concentrations of volatile organic compounds (VOCs) measured by the Trace Organic Gas Analyzer (TOGA) during the four ATom campaigns. These data are relevant to the impact of human-produced air pollution on greenhouse gases and on chemically reactive gases in the atmosphere.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/atom/ATom_TOGA_Instrument_Data_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-atom","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ATOMIC Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### SAILDRONE_ATOMIC\nSaildrone is a wind and solar powered unmanned surface vehicle (USV) capable of long distance deployments lasting up to 12 months and providing high quality, near real-time, multivariate surface ocean and atmospheric observations while transiting at typical speeds of 3-5 knots. The drone is autonomous in that it may be guided remotely from land while being completely wind driven. The saildrone ATOMIC (Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign) campaign involved the deployment of a fleet of saildrones, jointly funded by NASA and NOAA, in the Atlantic waters offshore of Barbados over a 45 day period from 17 January to 2 March 2020. The goal was to understand the Ocean-Atmosphere interaction particularly over the mesoscale ocean eddies in that region. The saildrones were equipped with a suite of instruments that included a CTD, IR pyrometer, fluorometer, dissolved oxygen sensor, anemometer, barometer, and Acoustic Doppler Current Profiler (ADCP). Additionally, four temperature data loggers were positioned vertically along hull to provide further information on thermal variability near the ocean surface. This Saildrone ATOMIC dataset is comprised of two data files for each of the three NASA-funded saildrones deployed, one for the surface observations and one for the ADCP measuements. The surface data files contain saildrone platform telemetry and near-surface observational data (air temperature, sea surface skin and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, wind speed and direction) spanning the entire cruise at 1 minute temporal resolution. The ADCP files for each saildrone are at 5 minute resolution for the duration of the deployments. All data files are in netCDF format and CF/ACDD compliant consistent with the NOAA/NCEI specification.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/ATOMIC/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","netcdf","oceans"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SAILDRONE_ATOMIC v1.0 - Saildrone is a wind and solar powered unmanned surface vehicle (USV) capable of long distance deployments lasting up to 12 months and providing high quality, near real-time, multivariate surface ocean and atmospheric observations while transiting at typical speeds of 3-5 knots. The drone is autonomous in that it may be guided remotely from land while being completely wind driven.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SAILDRONE_ATOMIC","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-atomic","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Aura Project","Description":"Aura (Latin for breeze) obtains measurements of ozone, aerosols and key gases throughout the atmosphere.\n<br>\n\n#### OMUFPMET\nThe GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUFPMET) product provides selected meteorlogical fields from the GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include layer pressure thickness, surface pressure, vertical temperature profiles, surface potential, and mid-layer pressure along with geolocation info. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPMET. The OMI ancillary products were developed to provide supplementary information for use with the OMI collection 4 L1B data sets. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. The OMUFPMET files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.\n<br>\n\n#### OMVFPMET\nThe GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVFPMET) product provides selected meteorlogical fields from the GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include layer pressure thickness, surface pressure, vertical temperature profiles, surface potential, and mid-layer pressure along with geolocation info. The OMI team also provides a corresponding product for the OMI UV2 swath, OMUFPMET. The OMI ancillary products were developed to provide supplementary information for use with the OMI collection 4 L1B data sets. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. The OMVFPMET files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.\n<br>\n\n#### OMUFPSLV\nThe GEOS-5 FP-IT 3D Time-Averaged Single-Level Diagnostics Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUFPSLV) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include boundary layer top pressure, tropopause pressure, surface pressure, surface skin temperature, and vertical wind profiles at 10m. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPSLV. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. The OMUFPSLV files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.\n<br>\n\n#### OMVFPSLV\nThe GEOS-5 FP-IT 3D Time-Averaged Single-Level Diagnostics Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVFPSLV) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include boundary layer top pressure, tropopause pressure, surface pressure, surface skin temperature, and vertical wind profiles at 10m. The OMI team also provides a corresponding product for the OMI UV2 swath, OMUFPSLV. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. The OMVFPSLV files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.\n<br>\n\n#### OMUFPITMET\nThe GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura UV-2 1-Orbit L2 Support Swath 13x24km (OMUFPITMET) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include surface pressure, vertical temperature profiles, surface and vertical wind profiles, tropopause pressure, boundary layer top pressure, and surface geopotenial. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPITMET. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. To reduce the size of each orbital file, FP-IT data fields with a vertical dimension of 72 layers have been reduced to 47 layers in OMUFPITMET by combining layers above the troposphere. The OMUFPITMET files are in netCDF4 format which is compatible with most HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.\n<br>\n\n#### OMVFPITMET\nThe GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura VIS 1-Orbit L2 Support Swath 13x24km (OMVFPITMET) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI VIS swath. The fields in this product include surface pressure, vertical temperature profiles, surface and vertical wind profiles, tropopause pressure, boundary layer top pressure, and surface geopotenial. The OMI team also provides a corresponding product for the OMI UV-2 swath, OMUFPITMET. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI VIS spatial resolution is 13km x 24km at nadir. To reduce the size of each orbital file, FP-IT data fields with a vertical dimension of 72 layers have been reduced to 47 layers in OMVFPITMET by combining layers above the troposphere. The OMVFPITMET files are in netCDF4 format which is compatible with most HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.\n<br>\n\n#### HIRMLS3IWC\nHIRMLS3IWC is the Joint EOS High Resolution Dynamics Limb Sounder (HIRDLS) and Microwave Limb Sounder (MLS) monthly 10 degreee lat x 20 degreee lon gridded product for ice water content (IWC) data. This is version 2 released to the public, with the original input coming from v3.3 MLS and v7 HIRDLS. The grid spatial coverage is near-global (-80 to +90 degrees latitude). The product contains HIRDLS and MLS IWC data for the time of the HIRDLS mission from February 1, 2005 through December 31, 2007. The useful vertical range of the data is from 215 to 82 hPa for both HIRDLS and MLS, and the vertical resolution is about 1.5 km for HIRDLS and 3 km for MLS. Users of the HIRMLS3IWC data product should read the Version 2 HIRDLS-MLS Level 3 IWC Data Description and Quality document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF5. The data file contains two grid objects (one with HIRDLS data, the other with MLS data), each with a set of data fields, attributes, and metadata. Each grid contains data fields with IWC values, and the HIRDLS grid includes data fields with volume density, cloud top pressure and frequency of clouds. Time, latitude and vertical pressure information are also included in each grid.\n<br>\n\n#### HIRDLS2\nThe \"HIRDLS/Aura Level 2 Geophysical Parameters\" data product (HIRDLS2) contains an entire day's worth of Level-2 vertical profiles of O3, HNO3, H2O, CFC-11, CFC-12, N2O, NO2, N2O5, ClONO2, temperature, geopotential height, and aerosol extinction at 12.1 and 8.3 microns, as well as cloud top pressure. HIRDLS measured infrared emissions in 21 channels ranging from 6.12 to 17.76 microns in the upper troposphere, stratosphere and mesosphere. Data are available for the ~3 year mission from January 29, 2005 until March 17, 2008. Observations of the Earth's atmosphere were only made from the far azimuth scan (away from sun side) resulting in limited data coverage from +80 to -64 degrees latitude. The useful vertical range of the data depends on the measured species, and are provided on 24 levels per decade of pressure corresponding to about 1 km vertical resolution. The current and final version of this product is 7. Of the original targeted species, only CH4 was not retrieved in this version. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a single swath object with one day of data (measured species and species precision), geolocation fields (e.g. time, latitude, longitude, pressure), and swath attributes, along with file level metadata. Each file contains approximately 5600 profile scans.\n<br>\n\n#### H3ZFCCLONO2\nThe \"HIRDLS/Aura Level 3 Chlorine Nitrate (ClONO2) Zonal Fourier Coefficients\" version 7 data product (H3ZFCCLONO2) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 100 to 1.0 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.\n<br>\n\n#### H3ZFCCFC11\nThe \"HIRDLS/Aura Level 3 Chlorofluorocarbon-11 (CFC-11) Zonal Fourier Coefficients\" version 7 data product (H3ZFCCFC11) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the CFC-11 data is 316 to 17.8 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.\n<br>\n\n#### H3ZFCCFC12\nThe \"HIRDLS/Aura Level 3 Chlorofluorocarbon-12 (CFC-12) Zonal Fourier Coefficients\" version 7 data product (H3ZFCCFC12) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the CFC-12 data is 316 to 8.3 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.\n<br>\n\n#### HIR3SCOL\nHIR3SCOL is the EOS High Resolution Dynamics Limb Sounder (HIRDLS/Aura) level 3 daily gridded 1 x 1 deg. stratospheric columns of NO2 (nitrogen dioxide) data product. The data are gridded at 1 x 1 degree resolution from +80 to -64 degrees latitude. The stratospheric column is computed from data at 57 to 1.0 hPa. The product consists of one file spanning the entire ~3 year HIRDLS mission from January 22, 2005 through March 17, 2008. Users of the HIR3SCOL data product should read the Version 7 HIRDLS Data Description and Quality document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF5. The data file contains one grid object with data fields, attributes, and metadata.\n<br>\n\n#### H3ZFCN2O5\nThe \"HIRDLS/Aura Level 3 Dinitrogen Pentoxide (N2O5) Zonal Fourier Coefficients\" version 7 data product (H3ZFCN2O) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 82.5 to 1.0 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.\n<br>\n\n#### H3ZFC12MEXT\nThe \"HIRDLS/Aura Level 3 Extinction at 12.1 Microns Zonal Fourier Coefficients\" version 7 data product (H3ZFC12MEXT) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 215 to 20 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.\n<br>\n\n#### H3ZFC8MEXT\nThe \"HIRDLS/Aura Level 3 Extinction at 8.3 Microns Zonal Fourier Coefficients\" version 7 data product (H3ZFC8MEXT) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 215 to 20 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.\n<br>\n\n#### H3ZFCGPH\nThe \"HIRDLS/Aura Level 3 Geopotential Height Zonal Fourier Coefficients\" version 7 data product (H3ZFCGPH) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 1000 to 0.01 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.\n<br>\n\n#### H3ZFCHNO3\nThe \"HIRDLS/Aura Level 3 Nitric Acid (HNO3) Zonal Fourier Coefficients\" version 7 data product (H3ZFCHNO3) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 215 to 5.1 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.\n<br>\n\n#### H3ZFCNO2\nThe \"HIRDLS/Aura Level 3 Nitrogen Dioxide (NO2) Zonal Fourier Coefficients\" version 7 data product (H3ZFCNO2) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 100 to 5.1 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.\n<br>\n\n#### H3ZFCN2O\nThe \"HIRDLS/Aura Level 3 Nitrous Oxide (N2O) Zonal Fourier Coefficients\" version 7 data product (H3ZFCN2O) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 100 to 5.1 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.\n<br>\n\n#### H3ZFCO3\nThe \"HIRDLS/Aura Level 3 Ozone (O3) Zonal Fourier Coefficients\" version 7 data product (H3ZFCO3) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 422 to 0.1 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.\n<br>\n\n#### H3ZFCT\nThe \"HIRDLS/Aura Level 3 Temperature Zonal Fourier Coefficients\" version 7 data product (H3ZFCT) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 1000 to 0.0042 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.\n<br>\n\n#### H3ZFCH2O\nThe \"HIRDLS/Aura Level 3 Water Vapor (H2O) Zonal Fourier Coefficients\" version 7 data product (H3ZFCH2O) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 200 to 10 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.\n<br>\n\n#### ML3DZMBRO\nML3DZMBRO is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-85 degrees to 85 degrees latitude) spaced every 10 degrees in latitude. The recommended useful vertical range is between 10 and 4.64 hPa, and the vertical resolution is about 5 km. Users of the ML3DZMBRO data product should read the MLS Radiance Average Retrievals (RAR) BrO Product Guideline document, as well as section 3.2 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 4 network Common Data Form (netCDF4), which is built on the version 5 Hierarchical Data Format, or HDF5. The netCDF4 files follow the Climate and Forecast (CF) metadata conventions. Each file contains two zonal means objects or groups, one with data from the ascending part of the MLS orbit, the other with the descending data. Each zonal means object contains the average, error (precision), solar zenith angle, and local solar time for each latitude band and pressure level. Files also contain metadata attributes describing the data and product.\n<br>\n\n#### ML3DZMHO2\nML3DZMHO2 is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-85 degrees to 85 degrees latitude) spaced every 10 degrees in latitude. The recommended useful vertical range is between 21.5 to 0.0464 hPa, and the vertical resolution is about 5 km. Users of the ML3DZMHO2 data product should read the MLS Radiance Average Retrievals (RAR) Product Guideline document, as well as section 3.2 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 4 network Common Data Form (netCDF4), which is built on the version 5 Hierarchical Data Format, or HDF5. The netCDF4 files follow the Climate and Forecast (CF) metadata conventions. Each file contains two zonal means objects or groups, one with data from the daytime part of the MLS orbit, the other with the nighttime data. Each zonal means object contains the average, error (precision), solar zenith angle, and local solar time for each latitude band and pressure level. Files also contain metadata attributes describing the data and product.\n<br>\n\n#### ML1OA\nML1OA is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 orbit attitude and tangent point geolocation data. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1OA data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains orbital and attitude information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.\n<br>\n\n#### ML1OA\nML1OA is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 orbit attitude and tangent point geolocation data. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1OA data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains orbital and attitude information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.\n<br>\n\n#### ML1RADD\nML1RADD is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the digital autocorrelators. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADD data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.\n<br>\n\n#### ML1RADD\nML1RADD is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the digital autocorrelators. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADD data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.\n<br>\n\n#### ML1RADG\nML1RADG is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADG data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.\n<br>\n\n#### ML1RADG\nML1RADG is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADG data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.\n<br>\n\n#### ML1RADT\nML1RADT is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADG data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.\n<br>\n\n#### ML1RADT\nML1RADT is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADG data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.\n<br>\n\n#### ML2BRO\nML2BRO is the EOS Aura Microwave Limb Sounder (MLS) standard product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 10 and 3.16 hPa, and the vertical resolution is about 5.5 km (6 km at 3.16 hPa). Users of the ML2BRO data product should read section 3.2 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2BRO\nML2BRO is the EOS Aura Microwave Limb Sounder (MLS) standard product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 10 and 3.16 hPa, and the vertical resolution is about 5.5 km (6 km at 3.16 hPa). Users of the ML2BRO data product should read section 3.2 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2CO\nML2CO is the EOS Aura Microwave Limb Sounder (MLS) standard product for carbon monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML2CO data product should read section 3.7 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2CO\nML2CO is the EOS Aura Microwave Limb Sounder (MLS) standard product for carbon monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 215 and 0.00564 hPa, and the vertical resolution is about 6 km. Users of the ML2CO data product should read section 3.7 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2CLO\nML2CLO is the EOS Aura Microwave Limb Sounder (MLS) standard product for chlorine monoxide derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML2CLO data product should read section 3.6 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2CLO\nML2CLO is the EOS Aura Microwave Limb Sounder (MLS) standard product for chlorine monoxide derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML2CLO data product should read section 3.6 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2IWC\nML2IWC is the EOS Aura Microwave Limb Sounder (MLS) standard product for cloud ice water content derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML2IWC data product should read sections 3.15 and 3.16 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2IWC\nML2IWC is the EOS Aura Microwave Limb Sounder (MLS) standard product for cloud ice water content derived from radiances measured by the 240 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML2IWC data product should read sections 3.15 and 3.16 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2DGG\nML2DGG is the EOS Aura Microwave Limb Sounder (MLS) product containing geophysical diagnostic quantities pertaining directly to the standard geophysical data products, generally on a similar (or identical) grid, and at different spectral ranges. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). Vertical resolution varies between species and typically ranges from 3 - 6 km. Users of the ML2DGG data product should read the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contain swaths objects for each diagnostics measurement. Each swath has a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2DGG\nML2DGG is the EOS Aura Microwave Limb Sounder (MLS) product containing geophysical diagnostic quantities pertaining directly to the standard geophysical data products, generally on a similar (or identical) grid, and at different spectral ranges. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). Vertical resolution varies between species and typically ranges from 3 - 6 km. Users of the ML2DGG data product should read the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contain swaths objects for each diagnostics measurement. Each swath has a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2DGM\nML2DGM is the EOS Aura Microwave Limb Sounder (MLS) product containing the minor frame diagnostic quantities on a miscellaneous grid. These include items such as tangent pressure, chi-square describing various fits to the measured radiances, number of radiances used in various retrieval phases, etc. This product contains a second auxiliary file which includes cloud-induced radiances inferred for selected spectral channels. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). Vertical resolution varies between species and typically ranges from 3 - 6 km. Users of the ML2DGM data product should read the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 Hierarchical Data Format, or HDF5. Each file contains sets of HDF5 dataset objects (n-dimensional arrays) for each diagnostics measurement. The dataset objects represent data and geolocation fields; included in the file are file attributes and metadata. There are two files per day (MLS-Aura_L2AUX-DGM* and MLS-Aura_L2AUX-Cloud*).\n<br>\n\n#### ML2GPH\nML2GPH is the EOS Aura Microwave Limb Sounder (MLS) standard product for geopotential height derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML2GPH data product should read section 3.8 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2GPH\nML2GPH is the EOS Aura Microwave Limb Sounder (MLS) standard product for geopotential height derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML2GPH data product should read section 3.8 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2HCL\nML2HCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen chloride derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2HCL data product should read section 3.10 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2HCL\nML2HCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen chloride derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2HCL data product should read section 3.10 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2HCN\nML2HCN is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen cyanide derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML2HCN data product should read section 3.11 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2HCN\nML2HCN is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen cyanide derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML2HCN data product should read section 3.11 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2HO2\nML2HO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 21.5 to 0.0464 hPa, and the vertical resolution is about 5 km. Users of the ML2HO2 data product should read section 3.13 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2HO2\nML2HO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 21.5 to 0.0464 hPa, and the vertical resolution is about 5 km. Users of the ML2HO2 data product should read section 3.13 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2OH\nML2OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroxyl derived from radiances measured by the THz radiometer. The data version is 4.2. Data coverage is continuous from August 8, 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML2OH data product should read section 3.19 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2OH\nML2OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroxyl derived from radiances measured by the THz radiometer. The data version is 5.0. Data coverage is continuous from August 8, 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML2OH data product should read section 3.19 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2HOCL\nML2HOCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hypochlorous acid derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML2OHCL data product should read section 3.14 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2HOCL\nML2HOCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hypochlorous acid derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML2OHCL data product should read section 3.14 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2CH3OH\nAt this time it is recommended that these data not be used pending further validation.ML2CH3OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for methanol derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 100 hPa, and the vertical resolution range is about 4-5 km. Users of the ML2CH3OH data product should read section 3.5 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2CH3OH\nAt this time it is recommended that these data not be used pending further validation.ML2CH3OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for methanol derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 100 hPa, and the vertical resolution range is about 4-5 km. Users of the ML2CH3OH data product should read section 3.5 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2CH3CL\nML2CH3CL is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl chloride derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML2CH3CL data product should read section 3.3 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2CH3CL\nML2CH3CL is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl chloride derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML2CH3CL data product should read section 3.3 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2CH3CN\nML2CH3CN is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl cyanide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 46.4 and 1.0 hPa, and the vertical resolution ranges between ~5 km in the lower stratosphere and ~10 km in the upper stratosphere. Users of the ML2CH3CN data product should read section 3.4 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2CH3CN\nML2CH3CN is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl cyanide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 46.4 and 1.0 hPa, and the vertical resolution ranges between ~5 km in the lower stratosphere and ~10 km in the upper stratosphere. Users of the ML2CH3CN data product should read section 3.4 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2HNO3\nML2HNO3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitric acid derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML2HNO3 data product should read section 3.12 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2HNO3\nML2HNO3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitric acid derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML2HNO3 data product should read section 3.12 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2N2O\nML2N2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitrous oxide derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML2N2O data product should read section 3.17 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2N2O\nML2N2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitrous oxide derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML2N2O data product should read section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2O3\nML2O3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for ozone derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML2O3 data product should read section 3.18 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2O3\nML2O3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for ozone derived from radiances measured by the 240 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML2O3 data product should read section 3.18 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2RHI\nML2RHI is the EOS Aura Microwave Limb Sounder (MLS) standard product for relative humidity with respect to ice derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2RHI data product should read section 3.20 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2RHI\nML2RHI is the EOS Aura Microwave Limb Sounder (MLS) standard product for relative humidity with respect to ice derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2RHI data product should read section 3.20 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2SO2\nML2SO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for sulfur dioxide derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML2SO2 data product should read section 3.21 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2SO2\nML2SO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for sulfur dioxide derived from radiances measured by the 240 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML2SO2 data product should read section 3.21 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2T\nML2T is the EOS Aura Microwave Limb Sounder (MLS) standard product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2T data product should read section 3.22 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2T\nML2T is the EOS Aura Microwave Limb Sounder (MLS) standard product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2T data product should read section 3.22 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2H2O\nML2H2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for water vapor derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML2H2O data product should read section 3.9 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML2H2O\nML2H2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for water vapor derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML2H2O data product should read section 3.9 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.\n<br>\n\n#### ML3DZMBRO\nML3DZMBRO is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-85 degrees to 85 degrees latitude) spaced every 10 degrees in latitude. The recommended useful vertical range is between 10 and 4.64 hPa, and the vertical resolution is about 5 km. Users of the ML3DZMBRO data product should read the MLS Radiance Average Retrievals (RAR) BrO Product Guideline document, as well as section 3.2 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 4 network Common Data Form (netCDF4), which is built on the version 5 Hierarchical Data Format, or HDF5. The netCDF4 files follow the Climate and Forecast (CF) metadata conventions. Each file contains two zonal means objects or groups, one with data from the ascending part of the MLS orbit, the other with the descending data. Each zonal means object contains the average, error (precision), solar zenith angle, and local solar time for each latitude band and pressure level. Files also contain metadata attributes describing the data and product.\n<br>\n\n#### ML3DBCO\nML3DBCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3DBCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBCO\nML3DBCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3DBCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZCO\nML3DZCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3DZCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZCO\nML3DZCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3DZCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBCLO\nML3DBCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3DBCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBCLO\nML3DBCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3DBCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZCLO\nML3DZCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3DZCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZCLO\nML3DZCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3DZCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBIWC\nML3DBIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3DBIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBIWC\nML3DBIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3DBIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZIWC\nML3DZIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3DZIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZIWC\nML3DZIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3DZIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBGPH\nML3DBGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3DBGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBGPH\nML3DBGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3DBGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZGPH\nML3DZGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3DZGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZGPH\nML3DZGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3DZGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBHCL\nML3DBHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBHCL\nML3DBHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZHCL\nML3DZHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZHCL\nML3DZHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBHCN\nML3DBHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3DBHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBHCN\nML3DBHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3DBHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZHCN\nML3DZHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3DZHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZHCN\nML3DZHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3DZHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBOH\nML3DBOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 4.2. Data coverage is continuous from August 2, 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3DBOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBOH\nML3DBOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 5.1. Data coverage is continuous from August 2, 2005 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3DBOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZOH\nML3DZOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 4.2. Data coverage is continuous from August 2, 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3DZOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZOH\nML3DZOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 5.1. Data coverage is continuous from August 2, 2005 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3DZOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBHOCL\nML3DBHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3DBOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBHOCL\nML3DBHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3DBOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZHOCL\nML3DZHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3DZOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZHOCL\nML3DZHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3DZOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBCH3CL\nML3DBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3DBCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBCH3CL\nML3DBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3DBCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZCH3CL\nML3DZCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3DZCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZCH3CL\nML3DZCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3DZCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBHNO3\nML3DBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3DBHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBHNO3\nML3DBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3DBHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZHNO3\nML3DZHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3DZHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZHNO3\nML3DZHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3DZHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBN2O\nML3DBN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3DBN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBN2O\nML3DBN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3DBN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZN2O\nML3DZN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3DZN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZN2O\nML3DZN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3DZN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBO3\nML3DBO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3DBO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBO3\nML3DBO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3DBO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZO3\nML3DZO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3DZO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZO3\nML3DZO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3DZO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBRHI\nML3DBRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBRHI\nML3DBRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZRHI\nML3DZRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZRHI\nML3DZRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBSO2\nML3DBSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3DBSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBSO2\nML3DBSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3DBSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZSO2\nML3DZSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3DZSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZSO2\nML3DZSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3DZSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBT\nML3DBT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBT\nML3DBT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZT\nML3DZT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZT\nML3DZT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBH2O\nML3DBH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3DBH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DBH2O\nML3DBH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3DBH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZH2O\nML3DZH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3DZH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZH2O\nML3DZH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3DZH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3DZMHO2\nML3DZMHO2 is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-85 degrees to 85 degrees latitude) spaced every 10 degrees in latitude. The recommended useful vertical range is between 21.5 to 0.0464 hPa, and the vertical resolution is about 5 km. Users of the ML3DZMHO2 data product should read the MLS Radiance Average Retrievals (RAR) Product Guideline document, as well as section 3.2 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 4 network Common Data Form (netCDF4), which is built on the version 5 Hierarchical Data Format, or HDF5. The netCDF4 files follow the Climate and Forecast (CF) metadata conventions. Each file contains two zonal means objects or groups, one with data from the daytime part of the MLS orbit, the other with the nighttime data. Each zonal means object contains the average, error (precision), solar zenith angle, and local solar time for each latitude band and pressure level. Files also contain metadata attributes describing the data and product.\n<br>\n\n#### ML3MBCO\nML3MBCO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3MBCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBCO\nML3MBCO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3MBCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBCLO\nML3MBCLO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3MBCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBCLO\nML3MBCLO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3MBCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBIWC\nML3MBIWC is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3MBIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBIWC\nML3MBIWC is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3MBIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBGPH\nML3MBGPH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3MBGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBGPH\nML3MBGPH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3MBGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBHCL\nML3MBHCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBHCL\nML3MBHCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBHCN\nML3MBHCN is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3MBHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBHCN\nML3MBHCN is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3MBHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBOH\nML3MBOH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 4.2. Data coverage is continuous from August 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3MBOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBOH\nML3MBOH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 5.1. Data coverage is continuous from August 2005 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3MBOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBHOCL\nML3MBHOCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3MBOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBHOCL\nML3MBHOCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3MBOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBCH3CL\nML3MBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3MBCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBCH3CL\nML3MBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3MBCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBHNO3\nML3MBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3MBHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBHNO3\nML3MBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3MBHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBN2O\nML3MBN2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3MBN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBN2O\nML3MBN2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3MBN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs \"potential temperature\", lat vs \"potential temperature\" zonal mean, \"equivalent latitude\" vs \"potential temperature\" zonal mean, and vortex average vs \"potential temperature\". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBO3\nML3MBO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3MBO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBO3\nML3MBO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3MBO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBRHI\nML3MBRHI is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBRHI\nML3MBRHI is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBSO2\nML3MBSO2 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3MBSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBSO2\nML3MBSO2 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3MBSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBT\nML3MBT is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBT\nML3MBT is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBH2O\nML3MBH2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3MBH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML3MBH2O\nML3MBH2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3MBH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.\n<br>\n\n#### ML2CO_NRT\nML2CO_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for carbon monoxide (CO). This product contains CO profiles derived from the 240 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 215 to 0.1 hPa. The MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.\n<br>\n\n#### ML2HNO3_NRT\nML2HNO3_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for nitric acid (HNO3). This product contains HNO3 profiles derived from the 190 and 240 GHz regions. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 100 to 1.47 hPa. The MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.\n<br>\n\n#### ML2N2O_NRT\nML2N2O_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for nitrous oxide (N2O). This product contains N2O profiles derived from the 190 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 100 to 1 hPa. The MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.\n<br>\n\n#### ML2O3_NRT\nML2O3_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for ozone (O3). This product contains O3 profiles derived from the 240 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 261 to 0.1 hPa. The MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.\n<br>\n\n#### ML2SO2_NRT\nML2SO2_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for sulfur dioxide (SO2). This product contains SO2 profiles derived from the 190 and 240 GHz regions. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 215 to 10 hPa. The MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.\n<br>\n\n#### ML2T_NRT\nML2T_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for temperature. This product contains temperature profiles derived from the 118 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 215 to 0.001 hPa. The MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.\n<br>\n\n#### ML2H2O_NRT\nML2H2O_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for water vapor (H2O). This product contains H2O profiles derived from the 190 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 147 to 1 hPa. The MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.\n<br>\n\n#### OMAEROZ\nThe reprocessed OMI/Aura Level-2 Zoomed Aerosol data product OMAEROZ at 13x12 km resolution have been made available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access in March 2012. There are two Level-2 Aura OMI aerosol products OMAERO and OMAERUV. The OMAERUV product uses the near-UV algorithm. The OMAERO (13x24 km resolution) and OMAEROZ (13x12 km resolution) is based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. The multi-wavelength retrieval algorithm is developed by the KNMI OMI Team Scientists. Drs. Deborah Stein-Zweers, Martin Sneep and Pepijn Veefkind are now the key investigators of this product. The OMAEROZ products contain Aerosol Optical Depths, Single Scattering Albedo, Aerosol Type, Aerosol Layer Height, and other intermediate and ancillary parameters and geolocation information. The OMAEROZ files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). OMAEROZ data files are based on Zoomed Level 1B radiance observations which are made once a month. Thus there is one day of zoomed data (approximately 14 orbits) per month. The maximum file size for the OMAEROZ data is about 11 Mbytes. A Readme document containing brief algorithm description and known data quality related issues and file specifications are provided by the OMAERO Algorithm lead.\n<br>\n\n#### OMMYDAGEO\nThe OMI/Aura and MODIS/Aqua Aerosol Geo-colocation Product 1-Orbit L2 Swath 13x24 km (OMMYDAGEO) is a Level-2 orbital data product that links the MODIS/Aqua aerosol geo-coordinates at 3 and 10 km with the OMI indices along the OMI orbital track. This product allows users to match up MODIS granules with the OMI orbit for analysis and validation. It co-locates MODIS and OMI cloud and radiance information onto the OMI pixel. The OMMYDAGEO data files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the swath model, and follows the same conventions used by the other OMI Level-2 data products. Each file contains data for 5 minutes, corresponding to the MODIS granule from the daylit side of the orbit. There are on the order of about 140 swath files per day. The file size for the OMMYDAGEO data product is about 12 Megabytes.\n<br>\n\n#### OMMYDCLD\nThe OMI/Aura and MODIS/Aqua Merged Cloud Product 1-Orbit L2 Swath 13x24 km (OMMYDCLD) is a Level-2 orbital product that combines cloud parameters retrieved by the Ozone Mapping Instrument (OMI) on the Aura satellite with collocated statistical information for cloud parameters retrieved by the Moderate Resolution Imaging Spectrometer (MODIS) on the Aqua spacecraft. This product is designed to take advantage of the synergy between OMI and MODIS, which both fly on satellites in the NASA A-Train constellation of Earth-observing satellites that follow similar orbital tracks and collect near-simultaneous observations. This product can be used for cloud-clearing, detection of multi-layered clouds, and other applications that may exploit these multi-spectral measurements. The algorithm for the OMMYDCLD product co-locates daytime cloud parameters from MODIS onto the OMI visible (VIS) pixel for a given OMI orbit and generates statistical information from the collocated MODIS pixels. For each OMI granule, the orbit start and end times are used to select the corresponding 5-minute MODIS granules for processing. A contiguous list of MODIS granules spanning the full duration of the OMI orbit are selected based on the relative time lag between Aqua and Aura. The algorithm lead for this product is NASA OMI scientist Dr. Joanna Joiner. The OMMYDCLD data files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the swath model, and follows the same conventions used by the other OMI Level-2 data products. Each file contains data from the day lit portion of an orbit (about 53 minutes). There are approximately 14 orbits per day. The file size for the OMMYDCLD data product is about 8 Mbytes.\n<br>\n\n#### OMBRO\nThe Aura Ozone Monitoring Instrument (OMI) collection-3 Bromine Monoxide Product OMBRO from the Aura-OMI, is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The shortname for this Level-2 OMI total column BrO product is OMBRO. The algorithm leads for this product are the US OMI scientists Dr. Kelly Chance and Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA. The OMBRO product contains total vertical column BrO, standard errors (rms and sigma), quality flags, geolocation and other ancillary information. The OMBRO files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The average file size for the OMBRO data product is about 5 Mbytes.\n<br>\n\n#### OMOCLO\nThe Aura Ozone Monitoring Instrument (OMI) collection-3 Chlorine Dioxide Product OMOCLO is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The shortname for this Level-2 OMI total column OClO product is OMOCLO. The algorithm leads for this product are the US OMI scientists Dr. Kelly Chance and Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA. The OMOCLO product contains slant column OClO, standard errors (rms and sigma), quality flags, geolocation and other ancillary information. The OMOCLO files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMOCLO data product is about 20 MB.\n<br>\n\n#### OMCLDO2\nThe reprocessed OMI/Aura Level-2 cloud data product OMCLDO2 is now available from the NASA GoddardEarth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed in late 2011. OMI provides two cloud products based on two different algorithms, the Rotational Raman Scattering method, and O2-O2 absorption method using the DOAS technique. This level-2 global cloud product, with a pixel resolution of 13x24 km2at nadir, is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. This product contains cloud pressure, cloud fraction, slant column O2-O2, ozone, ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The lead scientist for this product is Dr. Pepijn Veefkind. The OMCLDO2 product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) and is roughly 15.096 MB in size. There are approximately 14 orbits per day thus the total data volume is approximately 200 GB/day.\n<br>\n\n#### OMCLDO2G\nThis Level-2G daily global gridded product OMCLDO2G is based on the pixel level OMI Level-2 CLDO2 product OMCLDO2. This level-2G global cloud product (OMCLDO2G) is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. This product contains cloud pressure, cloud fraction, slant column O2-O2 and Ozone, Ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The short name for this Level-2 OMI cloud product is OMCLDO2G and the lead scientist for this product and for OMCLDO2 (the data source of OMCLDO2G) is KNMI scientist Dr. Pepijn Veefkind. OMCLDO2G data product is a special Level-2 Global Gridded Product where pixel level data (OMCLDO2) are binned into 0.25x0.25 degree global grids. It contains the OMCLDO2 data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid are saved 'Without Averaging'. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMCLDO2G product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each daily data file contains data from the day lit portion of the orbits (~14 orbits) and is roughly 85 MB in size.\n<br>\n\n#### OMCLDO2Z\nThe reprocessed Aura Ozone Monitoring Instrument (OMI) Level-2 zoomed cloud data product OMCLDO2Z at 13x12 km resolution is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed late 2011. OMI provides two cloud products based on two different algorithms, the Rotational Raman Scattering method and O2-O2 absorption method using the DOAS technique. This level-2 zoomed cloud product at the pixel resolution (13x12 km2 at nadir) is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. This product contains effective cloud pressure, effective cloud fraction, slant column O2-O2; uncertainties in derived, parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Zoomed cloud product is OMCLDO2Z. The lead scientist for this product is Dr. Pepijn Veefkind. The OMCLDO2Z files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) and is roughly 20 MB in size. OMCLDO2Z data files are based on Zoomed Level 1B radiance observations which are made once a month. Thus there is one zoomed cloud product per month.\n<br>\n\n#### OMDOAO3\nThe second release of Aura Ozone Monitoring Instrument (OMI) Version 003 OMI/Aura Level-2 Total Column Ozone Data Product OMDOAO3 is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The data were processed in late 2011 using Algorithm or PGE version 1.2.3 and released in March 2012. OMI provides two total column ozone products based on two different algorithms. This level-2 global total column ozone product at the pixel resolution (13x24 km at nadir), is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI UV radiance values between 331.1 and 336.1 nm. In addition to the total ozone column this product also contains auxiliary , derived and ancillary input parameters e.g. ozone slant column density, ozone ghost column density, air mass factor, scene reflectivity, radiance over the DOAS fit window, root mean square of DAOS fit, cloud fraction, cloud radiance, cloud pressure, terrain height, geolocation, viewing angles and quality flags. The shortname for this Level-2 OMI total column ozone product is OMDOAO3. The lead scientist for this product is Dr. J. Pepijn Veefkind. The OMDOAO3 product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (approximately 53 minutes) and is approximately 11 MB in size. There are approximately 14 orbits per day.\n<br>\n\n#### OMDOAO3Z\nThe reprocessed Aura Ozone Monitoring Instrument (OMI) Level-2 Zoomed Ozone data product OMDOAO3Z at 13x12 km resolution is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed late 2011. OMI provides two sets of total column ozone products OMTO3 and OMDOAO3 which are based on two different algorithms. OMTO3 product is based on TOMS like ozone retrieval algorithm whereas OMDOAO3 total column ozone product is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm. The DOAS retrieval algorithm is developed by the KNMI OMI Scientist, Dr Pepijn Veefkind. Based on spatial resolutions, there are two DOAS algorithm based ozone products, OMDOAO3 (at 13x24 km resolution) and OMDOAO3Z (13x12 km resolution). In addition to the total ozone column values these DOAS based ozone products also contain some auxiliary derived and ancillary input parameters e.g. ozone slant column density, ozone ghost column density, air mass factor, scene reflectivity, radiance over the DOAS fit window, root mean square of DAOS fit, cloud fraction, cloud radiance, cloud pressure, terrain height, geolocation, viewing angles and quality flags. The shortname for this Level-2 OMI Zoomed Ozone product is OMDOAO3Z. The OMDOAO3Z files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). OMDOAO3Z data files are based on Zoomed Level 1B radiance observations which are made once a month. Thus there is one day of zoomed data (approximately 14 orbits) per month. The maximum file size for the OMDOAO3Z data is approximately 30 MB.\n<br>\n\n#### OMCLDRR\nThe reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.\n<br>\n\n#### OMCLDRR\nThis is the Aura Ozone Monitoring Instrument (OMI) Version 004 Level 2 Cloud Data Product OMCLDRR. OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.\n<br>\n\n#### OMCLDRRG\nThis Level-2G daily global gridded product OMCLDRRG is based on the pixel level OMI Level-2 CLDRR product OMCLDRR. This level-2G global cloud product (OMCLDRRG) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The algorithm lead for the products OMCLDRR and OMCLDRRG is NASA OMI scientist Dr. Joanna Joinner. OMCLDRRG data product is a special Level-2G Gridded Global Product where pixel level data (OMCLDRR)are binned into 0.25x0.25 degree global grids. It contains the OMCLDRR data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMCLDRRG data products are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (~14 orbits). The average file size for the OMCLDRRG data product is about 75 Mbytes.\n<br>\n\n#### OMHCHO\nThe Aura Ozone Monitoring Instrument (OMI) Version-3 Formaldehyde Product OMHCHO is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The shortname for this Level-2 OMI total column Formaldehyde product is OMHCHO. The algorithm leads for this product are the US OMI scientists Dr. Kelly Chance and Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA. The OMHCHO product contains total vertical column HCHO, standard errors (rms and sigma), quality flags, geolocation and other ancillary information. The OMHCHO files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The average file size for the OMHCHO data product is about 5 MB.\n<br>\n\n#### OMHCHOG\nThis Level-2G daily global gridded product OMHCHOG is based on the pixel level OMI Level-2 HCHO product OMHCHO. OMHCHOG data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree grids. It contains data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging (third dimension provides indexing for the data points in each small grid). Scientists can apply a data filtering scheme of their choice and create Level-3 global gridded products. The OMHCHOG data product contains almost all parameters (e.g. total vertical column HCHO, standard errors, quality flags, geolocation and ancillary information) that are contained in the OMHCHO product. The OMHCHOG data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portions of 14 to 15 orbits that cover the globe in a day. The average file size for the OMGCHOG data product is about 55 Mbytes.\n<br>\n\n#### OMHCHOd\nThe OMI/Aura Formaldehyde (HCHO) Total Column Daily L3 Weighted Mean Global 0.1deg Lat/Lon Grid (OMHCHOd). The formaldehyde values in each file are the average for 0.1 x 0.1 degree grid cell of cloud-screened total HCHO columns for a single day. Other variables included in the files are the weight of each grid cell, the standard error of column averages, mean albedo, mean cloud fraction, mean cloud pressure, and surface height. The weight information is useful for combining data from several files and reducing the noise of the retrievals by co-adding in the temporal or spatial dimensions. The OMHCHOd files are in the netCDF4 format which is compatible with most HDF5 readers and tools. Each file contains daily data from approximately 15 orbits. The maximum file size for the OMHCHOd data product is about 80 Mbytes.\n<br>\n\n#### OMGLER\nThe OMI/Aura Global Geometry-Dependent Surface LER 1-Orbit L2 Swath 13x24km product, or OMGLER, provides GLER, and the computed top-of-atmosphere (TOA) radiance from which GLER is derived, for the OMI field of view. The OMGLER data also contain a number of ancillary/input parameters for each OMI pixel used to compute TOA radiance. The primary intended use of the product is to provide surface reflectance information for OMI cloud, aerosol and trace gas algorithms. GLER is designed to easily replace commonly used LER climatologies within existing OMI algorithms. The product lead is Joanna J. Joiner (OMI US science team leader). The algorithm developer is Wenhan Qin. The OMGLER product file is produced in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains GLER data for the daylit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. Files are roughly 9 MB in size.\n<br>\n\n#### OMPIXCOR\nThe Version-3 Aura Ozone Monitoring Instrument (OMI) Pixel Corner Product, OMPIXCOR, is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The shortname for this Level-2 OMI product is OMPIXCOR. The algorithm lead for this product is the US OMI scientists Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA. The OMPIXCOR product contains ground locations of the OMI pixel corners in the global scanning mode. The motivation for the development of the OMI ground pixel corner products was the common need for: the visualization of derived OMI data products, the provision of ground pixel area for computations of trace gas emissions per area, the facilitation of the development of cross-platform pixel mapping applications (e.g., between OMI and MODIS), and to generally aid validation studies, to name just a few. The OMPIXCOR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) . There are approximately 14 orbits per day. The average file size for the OMPIXCOR data product is about 5 Mbytes.\n<br>\n\n#### OML1BIRR\nThe Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) UV Averaged Solar Irradiances product (shortname OML1BIRR) contains the averaged radiometrically calibrated irradiance measurements from the UV and VIS detectors. The OMI UV Band 1 (264-311 nm) has 159 wavelength bins, the UV Band 2 (307-383 nm) has 559 wavelength bins, and the VIS Band 3 (349-504 nm) has 751 wavelength bins. The data files are written in netCDF version 4 format and are usually made once per day when the Sun is within the solar port field-of-view just before the spacecraft moves into the night shadow at the north end of an orbit. The lead algorithm scientist for this product is Quintus Kleipool from the Royal Netherlands Meteorological Institude (KNMI).\n<br>\n\n#### OML1BIRR\nThe OMI Level 1B solar irradiance product is the radiometrically calibrated and geolocated measurements of the UV and Visible channels of the spectral solar irradiance. It is the averaged measurements of the solar irradiances over a single solar observation in the wavelength ranges of UV1 (264-311 nm, 159 channels), UV2 (307-383 nm, 557 channels) and VIS (349-504 nm, 751 channels). The data contain solar measurement products for both the global and the spatial zoom-in mode. This product only contains measurements obtained with the quartz volume diffuser and provides average of the individual measurements made along track to average out the solar elevation dependent bidirectional reflectance distribution function (BRDF) features of the diffuser. The shortname for this OMI Level-1B Product is OML1BIRR. The lead algorithm scientists for this product is Dr. Marcel Dobber from the Roayl Netherlands Meteorological Institude (KNMI). OMI calibrated and geolocated radiances for the UV and Visible channels, spectral irradiances, calibration measurements, and all derived geophysical atmospheric products are archived at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). OML1BIRR files are stored in the HDF4 based EOS Hierarchical Data Format. The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16 bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.\n<br>\n\n#### OML1BRUG\nThe Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View UV Radiance, Global-Mode (OML1BRUG) Version-3 product contains geo-located Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm conducted in the global measurement mode. In the standard global measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath for each of the 557 channels of UV2 (307-383 nm) and 30 ground pixels (13 km x 48 km at nadir) for the 159 channels of UV1 (264-311 nm). Each file contains data from the day lit portion of an orbit (~53 minutes) and is roughly 180 MB in size. There are approximately 14 orbits per day. Once a month, in one orbit, OMI performs dark measurements, it does not perform radiance measurements. In addition, OMI performs spatial zoom measurements one day per month. For that day, this product also contains UV2 measurements that are rebinned from the spatial zoom-in measurements. In original spatial zoom mode the nadir ground pixel size is 13 x 12 km and measurements are available only for the UV2 and VIS wavelengths (306 to 432 nm). The shortname for this OMI Level-1B Product is OML1BRUG. The lead algorithm scientist for this product is Dr. Marcel Dobber from the Royal Netherlands Meteorological Institude (KNMI). The OML1BRUG files are stored in the HDF4 based EOS Hierarchical Data Format (HDF-EOS). The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16 bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.\n<br>\n\n#### OML1BRUG\nThe Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Geolocated Earthshine UV Radiance, Global-mode (shortname OML1BRUG) Version 4 product contains geolocated Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm taken in the global measurement mode. In the global mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath (~2600 km) for each of the 557 channels of Band 2 (307-383 nm) and 30 ground pixels (13 km x 48 km at nadir) for the 159 channels of Band 1 (264-311 nm). There are approximately 14 files of orbital data per day. Each file contains data from the daylit portion of an orbit and is roughly 210 MB in size. This OML1BRUG global-mode product is occasionally unavailable when the instrument is collecting data in the zoom-mode or is making special calibration measurements. The data in the OML1BRUG files are stored in the Network Common Data Form (netCDF) format. The lead algorithm scientist for the OMI Level 1 products is Dr. Quintus Kleipool of the Royal Netherlands Meteorological Institude (KNMI).\n<br>\n\n#### OML1BRUZ\nThe Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View UV Radiance, Zoom-in-Mode (OML1BRUZ) Version-3 product contains geo-located Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm using spectral and spatial zoom-in measurement modes. In zoom-in measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath. Each file contains data from the day lit portion of an orbit (~60 minutes) and is roughly 215 MB in size. There are approximately 14 orbits per day. OMI performs spatial zoom-in measurements one day per month. For that day, this product also contains UV2 measurements that are rebinned from the spatial zoom-in measurements. The shortname for this OMI Level-1B Product is OML1BRUZ. The lead algorithm scientist for this product is Dr. Marcel Dobber from the Royal Netherlands Meteorological Institude (KNMI). The OML1BRUZ files are stored in HDF4 based EOS Hierarchical Data Format (HDF-EOS). The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16 bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.\n<br>\n\n#### OML1BRUZ\nThe Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Geolocated Earthshine UV Radiance, Zoom-mode (shortname OML1BRUZ) Version 4 product contains geolocated Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm taken in the global measurement mode. In the zoom-in mode, OMI observes 60 ground pixels (13 km x 12 km at nadir) across the swath (~750 km width) for each of the 557 channels of Band 2 (307-383 nm) and 30 ground pixels (13 km x 24 km at nadir) across the swath (~2600 km) for the 159 channels of Band 1 (264-311 nm). There are approximately 14 files of orbital data per day. Each file contains data from the daylit portion of an orbit and is roughly 210 MB in size. This OML1BRUZ zoom-in mode product is only available about once a month. The data in the OML1BRUZ files are stored in the Network Common Data Form (netCDF) format. The lead algorithm scientist for the OMI Level 1 products is Dr. Quintus Kleipool of the Royal Netherlands Meteorological Institude (KNMI).\n<br>\n\n#### OML1BRVG\nThe Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View VIS Radiance, Global-Mode (OML1BRVG) Version-3 product contains geo-located Earth view spectral radiances from the VIS detector in the wavelength range of 349 to 504 nm conducted in the global measurement mode. In the standard global measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath (13 km x 48 km at nadir). Each file contains data from the day lit portion of an orbit (~60 minutes) and is roughly 200 MB in size. There are approximately 14 orbits per day. Once a month, in one orbit, OMI performs dark measurements, it does not perform radiance measurements. In addition, OMI performs spatial zoom measurements one day per month. For that day, this product also contains UV2 measurements that are rebinned from the spatial zoom-in measurements. In original spatial zoom mode the nadir ground pixel size is 13 x 12 km and measurements are available only for the UV2 and VIS wavelengths (306 to 432 nm). The shortname for this OMI Level-1B Product is OML1BRVG. The lead algorithm scientist for this product is Dr. Marcel Dobber from the Royal Netherlands Meteorological Institude (KNMI). The OML1BRVG files are stored in the HDF4 based EOS Hierarchical Data Format (HDF-EOS). The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16-bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.\n<br>\n\n#### OML1BRVG\nThe Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Geolocated Earthshine VIS Radiance, Global-mode (shortname OML1BRVG) Version 4 product contains geolocated Earth view spectral radiances from the VIS detectors in the wavelength range of 349 to 504 nm taken in the global measurement mode. In the global mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath (~2600 km) for each of the 751 channels of Band 3 (349-504 nm). There are approximately 14 files of orbital data per day. Each file contains data from the daylit portion of an orbit and is roughly 240 MB in size. This OML1BRVG global-mode product is occasionally unavailable when the instrument is collecting data in the zoom-mode or is making special calibration measurements. The data in the OML1BRVG files are stored in the Network Common Data Form (netCDF) format. The lead algorithm scientist for the OMI Level 1 products is Dr. Quintus Kleipool of the Royal Netherlands Meteorological Institude (KNMI).\n<br>\n\n#### OML1BRVZ\nThe Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View VIS Radiance, Zoom-in-Mode (OML1BRVZ) Version-3 product contains geo-located Earth view spectral radiances from the VIS detectors in the wavelength range of 349 to 504 nm using spectral and spatial zoom-in measurement modes. In zoom-in measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath. Each file contains data from the day lit portion of an orbit (~60 minutes) and is roughly 190 MB in size. There are approximately 14 orbits per day. OMI performs spatial zoom-in measurements one day per month. For that day, this product also contains VIS measurements that are rebinned from the spatial zoom-in measurements. The lead algorithm scientist for this product is Dr. Marcel Dobber from the Royal Netherlands Meteorological Institude (KNMI). The OML1BRVZ files are stored in the HDF4 based EOS Hierarchical Data Format (HDF-EOS). The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16 bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.\n<br>\n\n#### OML1BRVZ\nThe Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Zoom-in Earthshine UV Radiance, Zoom-mode (shortname OML1BRVZ) Version 4 product contains geolocated Earth view spectral radiances from the VIS detectors in the wavelength range of 349 to 504 nm taken in the zoom-in measurement mode. In the zoom-in mode, OMI observes 60 ground pixels (13 km x 12 km at nadir) across the swath (~750 km width) for each of the 751 channels of Band 3 (349-504 nm). There are approximately 14 files of orbital data per day. Each file contains data from the daylit portion of an orbit and is roughly 240 MB in size. This OML1BRVZ zoom-in mode product is only available about once a month. The data in the OML1BRVZ files are stored in the Network Common Data Form (netCDF) format. The lead algorithm scientist for the OMI Level 1 products is Dr. Quintus Kleipool of the Royal Netherlands Meteorological Institude (KNMI).\n<br>\n\n#### OMAERO\nThe Level-2 Aura Ozone Monitoring Instrument (OMI) Aerosol Product (OMAERO) is now available from NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. This is the second public release of version 003. The data was re-processed in late 2011 using an improved algorithm (processing version 1.2.3.1). After some quick validation the reprocessed data was released to the public in March 2012. The shortname for this Level-2 Aerosol Product is OMAERO_V003. There are two Level-2 Aura OMI aerosol products OMAERUV and OMAERO. The OMAERUV product uses the near-UV algorithm. The OMAERO product is based on the multi-wavelength algorithm and that uses up to 20 wavelength bands between 331 nm and 500 nm. OMAERO retrieval algorithm is developed by the KNMI OMI Team Scientists. Drs. Deborah Stein-Zweers, Martin Sneep and Pepijn Veefkind are now the key investigators of this product. The OMAERO product contains Aerosol Optical Depths, Single Scattering Albedo, and other ancillary and geolocation information. The OMAERO files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERO data product is about 6 Mbytes.\n<br>\n\n#### OMAEROG\nThis Level-2G daily global gridded product OMAEROG is based on the pixel level OMI Level-2 Aerosol product OMAERO, based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. OMAEROG is a special Level-2 gridded product where pixel level products are binned into 0.25 x 0.25 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMAEROG data product contains almost all parameters that are in OMAERO. For example, in addition to the extinction optical depth and single scattering albedo, it also contains aerosol indices, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and extensive quality flags. The OMAEROG files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMAEROG data product is about 78 Mbytes.\n<br>\n\n#### OMAEROe\nThe OMI science team produces this Level-3 Aura/OMI Global Aerosol Data Products OMAEROe (0.25deg Lat/Lon grids). The OMAEROe product selects best aerosol value from the Level2G good quality data that are reported in each grid, based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. The selection criteria is based on the shortest optical path length (secant of solar zenith angle + secant of viewing zenith angle). The OMAEROe files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMAEROe data product is about 7 Mbytes. (The shortname for this Level-3 Global Gridded Aerosol Product is OMAEROe)\n<br>\n\n#### OMAERUV\nThe Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data are available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The shortname for this Level-2 near-UV Aerosol Product is OMAERUV_V003. The OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). The OMAERUV files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes.\n<br>\n\n#### OMAERUV\nThe Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product OMAERUV (Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Optical Depth, Aerosol Single Scattering Albedo, Absorption Optical Depth, UV Aerosol Index, and Aerosol Optical Depth over clouds at three wavelengths (354, 388, and 500 nm), and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). The OMAERUV files are stored in the version 4.0 Network Common Data Form (NetCDF). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 17 Mbytes.\n<br>\n\n#### OMAERUVG\nThis Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 AERUV product OMAERUV. This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 Aerosol product OMAERUV. OMAERUVG data product is a special Level-2 gridded product where pixel level products are binned into 0.25x0.25 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMAERUVG files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits mapped on the Global 0.25x0.25 deg Grids. The maximum file size for the OMAERUVG data product is about 50 Mbytes.\n<br>\n\n#### OMAERUVd\nThe OMI science team produces this Level-3 daily global gridded product OMAERUVd (1 deg Lat/Lon grids). The OMAERUVd product is produced with all data pixels that fall in a grid box with quality filtered and then averaged, based on the pixel level OMI Level-2 Aerosol data product OMAERUV. The OMAERUV data product is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data. The OMAERUVd data product contains extinction and absorption optical depths at three wavelenghts (355 nm, 388 nm and 500 nm). The OMAERUVd files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMAERUVd data product is about 0.2 Mbytes.\n<br>\n\n#### OMNO2\nThe Version 4.0 Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major V4.0 updates include: (1) use of a new daily and OMI field of view specific geometry dependent surface Lambertian Equivalent Reflectivity (GLER) product in both NO2 and cloud retrievals; (2) use of improved cloud parameters (effective cloud fraction and cloud optical centroid pressure) from a new cloud algorithm (OMCDO2N) that are retrieved consistently with NO2 using a new algorithm for O2-O2 slant column data and the GLER product for terrain reflectivity; (3) use of a more accurate terrain pressure calculated using OMI ground pixel-averaged terrain height and monthly mean GMI terrain pressure; and (4) improved treatment over snow/ice surfaces by using the concept of scene LER and scene pressure. The details can be found in the updated OMNO2 readme document (see Documentation). The OMNO2 product contains slant column NO2 (total amount along the average optical path from the sun into the atmosphere, and then toward the satellite), the total NO2 vertical column density (VCD), the stratospheric and tropospheric VCDs, air mass factors (AMFs), scattering weights for calculation of AMFs, and other ancillary data. The short name for the Level-2 swath type column NO2 products is OMNO2. Other OMNO2-associated NO2 products include the Level-2 gridded column product, OMNO2G, and the Level-3 gridded column product, OMNO2d. The OMNO2 files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each Level-2 file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMNO2 is ~24 MB.\n<br>\n\n#### OMNO2\nThe Collection 4, Version 5.0, Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major V5.0 updates include the use of: (1) improved Collection 4 Level-1B radiance and irradiance data in the retrieval of NO2 and O2-O2 slant columns; (2) a-priori NO2 profiles and other model-derived information from a high-resolution (~25 km) Global Modeling Initiative (GMI) simulation; (3) a new algorithm for improved de-striping and data flagging to correct for cross-track artifacts and row-anomaly; (4) updated geometry dependent surface Lambertian Equivalent Reflectivity (GLER) and consistently-retrieved O2-O2 cloud products; and (5) improved snow/ice database and treatment in the GLER, cloud, and NO2 algorithms. The details can be found in the updated OMNO2 readme document (see Documentation). The OMNO2 product contains NO2 slant column density (SCD), total NO2 vertical column density (VCD), the stratospheric and tropospheric VCDs, air mass factors (AMFs), scattering weights for AMF calculation, and other ancillary data used in the OMNO2 algorithm. The OMI NO2 files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). The short name for the Level-2 swath type column NO2 products is OMNO2. Each Level-2 file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMNO2 is ~21 MB. Other OMNO2-associated NO2 products include the Level-2 gridded column product, OMNO2G, and the Level-3 gridded column product, OMNO2d.\n<br>\n\n#### OMNO2d\nThis is Level-3 daily global gridded (0.25x0.25 degree) Nitrogen Dioxide Product (OMNO2d). OMNO2d data product is a Level-3 Gridded Product where pixel level data of good quality are binned and \"averaged\" into 0.25x0.25 degree global grids. This product contains Total column NO2 and Total Tropospheric Column NO2, for all atmospheric conditions, and for sky conditions where cloud fraction is less than 30 percent. Nitrogen dioxide is an important chemical species in both, the stratosphere where it plays a key role in ozone chemistry, and in the troposphere where it is a precursor to ozone production. In the troposphere, it is produced in various combustion processes and in lightning and is an indicator of poor air quality. The OMNO2d data are stored in version 5 EOS Hierarchical Data Format (HDF-EOS). Each file contains data from the day lit portion of the orbit (~14 orbits). The average file size for the OMNO2d data product is about 12 Mbytes.\n<br>\n\n#### OMNO2G\nThis Level-2G daily global gridded product OMNO2G is based on the pixel level OMI Level-2 NO2 product OMNO2. OMNO2G data product is a special Level-2 Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a rid box are saved Without Averaging. Nitrogen dioxide is an important chemical species in both the stratosphere, where it plays a key role in ozone chemistry, and in the troposphere, where it is a precursor to ozone production. In the troposphere, it is produced in various combustion processes and in lightning and is an indicator of poor air quality. The OMNO2G data product contains almost all parameters that are contained in OMNO2 product. The OMNO2G data are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of the orbit (~14 orbits). The average file size for the OMNO2G data product is about 115 Mbytes.\n<br>\n\n#### OMDOAO3G\nThis Level-2G daily global gridded product OMDOAO3G is based on the pixel level OMI Level-2 DOAO3 product OMDOAO3. This Level-2G global total column ozone product is derived from OMDOAO3 which is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm. In addition to the total ozone column this product also contains some auxiliary derived and ancillary input parameters, e.g. ozone slant column density, ozone ghost column density, etc. The short name for this Level-2 OMI ozone product is OMDOAO3G and the lead algorithm scientist for this product and for OMDOAO3 (the data source of OMDOAO3G) is Dr. Pepijn Veefkind from KNMI. The OMDOAO3G product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (approximately 14 orbits) and is roughly 80 MB in size.\n<br>\n\n#### OMDOAO3e\nThe OMI science team produces this Level-3 Aura/OMI Global OMDOAO3e Data Products (0.25deg Lat/Lon grids). This Level-3 global total column ozone product is derived from OMDOAO3 which is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm. In addition to the total ozone column (best quality data, satisfying the shortest path length) and its precision this product also contains some ancillary parameters such as cloud fraction, cloud height, etc. The short name for this Level-3 OMI ozone product is OMDOAO3e and the lead Algorithm scientist for this product and for OMDOAO3 (the data source of OMDOAO3e) is Dr. Pepijn Veefkind from KNMI. The OMDOAO3e product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (approximately 14 orbits) and is roughly 8 MB in size.\n<br>\n\n#### OMO3PR\nThe Aura Ozone Monitoring Instrument Level-2 Ozone Profile data product OMO3PR (Version 003) is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI Level-2 ozone profile product, OMO3PR at the pixel resolution 13x 48 km (at nadir), is based on the optimal estimation algorithm (Rodgers, 2000) with climatological ozone profiles as a-priori information. The OMO3PR retrieval algorithm uses spectral radiance values from the UV1 channel (270 nm to 308.5 nm) and from the first part of the UV2 channel (311.5 nm to 33 0 nm). OMO3PR product provides ozone values (in Dobson unit) for 18 atmospheric layers. It also provides a-priori ozone profile values, error covariance matrix, averaging kernel and some ancillary information such as time, latitude, longitude, solar zenith and viewing zenith angles and quality flags. The short name for this Level-2 OMI ozone profile product is OMO3PR. The lead scientist for this product is Dr. Johan de Haan. The OMO3PR product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (approximately 53 minutes). There are approximately 14 orbits per day thus the total data volume is approximately 150 GB/day.\n<br>\n\n#### OMTO3G\nThis Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time for the 24-hour period beginning at 00:00:00 UTC. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes.\n<br>\n\n#### OMTO3\nThe Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Version 003) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI provides two Level-2 (OMTO3 and OMDOAO3) total column ozone products at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB.\n<br>\n\n#### OMTO3\nThe Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Collection Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm at a pixel resolution of 13 x 24 km at nadir. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm for this product was originally developed by a team led by Dr. Pawan K. Bhartia at NASA Goddard Space Flight Center. The current product lead is Dr. Can Li. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB.\n<br>\n\n#### OMSO2e\nThe OMI science team produces this Level-3 Aura/OMI Global OMSO2e Data Products (0.25 degree Latitude/Longitude grids). In this Level-3 daily global SO2 data product, each grid contains only one observation of Total Column Density of SO2 in the Planetary Boundary Layer (PBL), based on an improved Principal Component Analysis (PCA) Algorithm. This single observation is the \"best pixel\", selected from all \"good\" L2 pixels of OMSO2 that overlap this grid and have UTC time between UTC times of 00:00:00 and 23:59:59.999. In addition to the SO2 Vertical column value some ancillary parameters, e.g., cloud fraction, terrain height, scene number, solar and satellite viewing angles, row anomaly flags, and quality flags have been also made available corresponding to the best selected SO2 data pixel in each grid. The OMSO2e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the grid model.\n<br>\n\n#### OMSO2\nThe Aura Ozone Monitoring Instrument (OMI) level 2 sulphur dioxide (SO2) total column product (OMSO2) has been updated with a principal component analysis (PCA)-based algorithm (v2) with new SO2 Jacobian lookup tables and a priori profiles that significantly improve retrievals for anthropogenic SO2. The data files (or granules) contain different estimates of the vertical column density (VCD) of SO2 depending on the users investigating anthropogenic or volcanic sources. Files also contain quality flags, geolocation and other ancillary information. The lead scientist for the OMSO2 product is Can Li. The OMSO2 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the daylit half of an orbit (~53 minutes). There are approximately 14 orbits per day. The resolution of the data is 13x24 km2 at nadir, with a swath width of 2600 km and 60 pixels per scan line every 2 seconds.\n<br>\n\n#### OMSO2G\nThis Level-2G daily global gridded product OMSO2G is based on the pixel level OMI Level-2 SO2 product OMSO2. OMSO2G data product is a special Level-2 gridded product where pixel level products are binned into 0.125x0.125 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999 . All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMSO2G data product contains almost all parameters that are contained in OMSO2 files. For example, in addition to three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm, and ancillary parameters, e.g., UV aerosol index, cloud fraction, cloud pressure, geolocation, solar and satellite viewing angles, and quality flags. The OMSO2G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 146 Mbytes.\n<br>\n\n#### OMLER\nThe OMI Earth Surface Reflectance Climatology product, OMLER (Global 0.5 degrees Lat/Lon grid) which is based on Version 003 Level-1B top of atmosphere upwelling radiance and incoming irradiance. OMI calibrated and geolocated radiances from 159 channels in UV1(264-311 nm), 557 channels in UV2 (307-383 nm) and 751 channels in VIS (349-504) spectral regions, spectral irradiances, calibration measurements, and all derived geophysical atmospheric products (Level-2 and 3) are archived at the NASA Goddard DAAC. OMLER spectral surface reflectance product contains monthly and yearly climatology of the Earth's surface Lambert Equivalent Reflectance (LER) for 23 wavelengths in the spectral range 309 to 500 nm, at a spatial resolution of 0.5 by 0.5 degrees. This LER is defined as the required reflectance of an isotropic surface needed to match the observed top of the atmosphere (TOA) reflectance in a pure Rayleigh scattering atmosphere under cloud free conditions and no aerosols. The climatology is based on statistical analysis of the three years of OMI version 03 radiance data (Oct 2004-Oct 2007). This product also provides minimum spectral surface reflectivity observed during the three year period. The OMLER product file is produced in the version 5 Hierarchical Data Format (HDF-EOS5). It is roughly 300 MB in size.\n<br>\n\n#### OMUVB\nThe Aura Ozone Monitoring Instrument (OMI) Version 003 Surface UV Irradiance Product (OMUVB) is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The shortname for this Level-2 OMI Surface UVB product is OMUVB. The algorithm scientists for this product are: Dr. Jari Hovila, Dr. Antii Arola and Dr. Johanna Taminnen. The OMUVB product contains erythemally weighted daily dose and dose rate, and spectral irradiances at 305, 310, 324, and 380 nm. It also contains quality flags, cloud optical depth, Lambertian Equivalent Reflectivity, Total Column Ozone amount, and other ancillary information. The OMUVB files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMUVB data product is about 10 Mbytes.\n<br>\n\n#### OMUVBG\nThis is Level-2G daily global gridded Aura-OMI Spectral Surface UVB Irradiance and Erythemal Dose product (OMUVBG). The OMUVBG is a special Level-2 Global Gridded type data Product (referred as Level 2G or L2G) where Level-2 or swath pixel data are binned (but not averaged)into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All ancillary parameters such as latitude, longitude, time, solar and viewing angles are also saved for each pixel. First two dimensions of each parameter correspond to spatial (Lat/Lon based) Grid ID and third dimension identifies the pixel or observed scene (referred as 'candidates' ID). Scientists can apply a data filtering scheme of their choice, average good quality pixels data in each grid and create their Level-3 products. The OMUVBG files are available in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from the day lit portion of the globe. The maximum file size for the OMUVBG data product is about 128 MBytes.\n<br>\n\n#### OMUVBd\nThis is Level-3 daily global gridded Aura-OMI Spectral Surface UVB Irradiance and Erythemal Dose product (OMUVBd). The OMUVBd product contains global erythemally weighted daily dose and erythemal dose rate at local solar noon at 1.0x1.0 deg grids. The OMUVBd files are available in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from the day lit portion of the globe. The maximum file size for the OMUVBd data product is about 5 MBytes.\n<br>\n\n#### OMTO3e\nThe OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles. The OMTO3e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes.\n<br>\n\n#### OMTO3d\nThe OMI science team produces this Level-3 daily global TOMS-Like Total Column Ozone gridded product OMTO3d (1 deg Lat/Lon grids). The OMTO3d product is produced by gridding and averaging only good quality level-2 total column ozone orbital swath data (OMTO3, based on the enhanced TOMS version-8 algorithm) on the 1x1 degree global grids. The OMTO3d files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3d data product is about 0.65 Mbytes.\n<br>\n\n#### OMPIXCORZ\nThe Version-3 Aura Ozone Monitoring Instrument (OMI) Pixel Corner Product in zoom-in mode, OMPIXCORZ, is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The shortname for this Level-2 OMI product is OMPIXCORZ. The algorithm lead for this product is the US OMI scientists Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA. The OMPIXCORZ product contains ground locations of the OMI pixel corners in the zoom-in scanning mode. The motivation for the development of the OMI ground pixel corner products was the common need for: the visualization of derived OMI data products, the provision of ground pixel area for computations of trace gas emissions per area, the facilitation of the development of cross-platform pixel mapping applications (e.g., between OMI and MODIS), and to generally aid validation studies, to name just a few. The OMPIXCORZ files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) . There are approximately 14 orbits approximately one day per month. The average file size for the OMPIXCORZ data product is about 8 Mbytes.\n<br>\n\n#### OMUANC\nThe Primary Ancillary Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area. The OMI team also provides a corresponding product for the OMI VIS swath, OMVANC. This product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. The OMUANC files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.\n<br>\n\n#### OMVANC\nThe Primary Ancillary Data Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area. The OMI team also provides a corresponding product for the OMI UV2 swath, OMUANC. This product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. The OMVANC files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://aura.gsfc.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","elevation","hdf","hdf5","ice","land cover","netcdf","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OMUFPMET v004 - The GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUFPMET) product provides selected meteorlogical fields from the GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include layer pressure thickness, surface pressure, vertical temperature profiles, surface potential, and mid-layer pressure along with geolocation info.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Ancillary/OMUFPMET.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMVFPMET v004 - The GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVFPMET) product provides selected meteorlogical fields from the GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include layer pressure thickness, surface pressure, vertical temperature profiles, surface potential, and mid-layer pressure along with geolocation info.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Ancillary/OMVFPMET.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMUFPSLV v004 - The GEOS-5 FP-IT 3D Time-Averaged Single-Level Diagnostics Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUFPSLV) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include boundary layer top pressure, tropopause pressure, surface pressure, surface skin temperature, and vertical wind profiles at 10m.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Ancillary/OMUFPSLV.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMVFPSLV v004 - The GEOS-5 FP-IT 3D Time-Averaged Single-Level Diagnostics Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVFPSLV) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include boundary layer top pressure, tropopause pressure, surface pressure, surface skin temperature, and vertical wind profiles at 10m.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Ancillary/OMVFPSLV.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMUFPITMET v003 - The GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura UV-2 1-Orbit L2 Support Swath 13x24km (OMUFPITMET) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include surface pressure, vertical temperature profiles, surface and vertical wind profiles, tropopause pressure, boundary layer top pressure, and surface geopotenial.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMUFPITMET.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMVFPITMET v003 - The GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura VIS 1-Orbit L2 Support Swath 13x24km (OMVFPITMET) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI VIS swath. The fields in this product include surface pressure, vertical temperature profiles, surface and vertical wind profiles, tropopause pressure, boundary layer top pressure, and surface geopotenial.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMVFPITMET.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HIRMLS3IWC v002 - HIRMLS3IWC is the Joint EOS High Resolution Dynamics Limb Sounder (HIRDLS) and Microwave Limb Sounder (MLS) monthly 10 degreee lat x 20 degreee lon gridded product for ice water content (IWC) data. This is version 2 released to the public, with the original input coming from v3.3 MLS and v7 HIRDLS.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_MLS_Level3/HIRMLS3IWC.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HIRDLS2 v007 - The \"HIRDLS/Aura Level 2 Geophysical Parameters\" data product (HIRDLS2) contains an entire day's worth of Level-2 vertical profiles of O3, HNO3, H2O, CFC-11, CFC-12, N2O, NO2, N2O5, ClONO2, temperature, geopotential height, and aerosol extinction at 12.1 and 8.3 microns, as well as cloud top pressure. HIRDLS measured infrared emissions in 21 channels ranging from 6.12 to 17.76 microns in the upper troposphere, stratosphere and mesosphere.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level2/HIRDLS2.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"H3ZFCCLONO2 v007 - The \"HIRDLS/Aura Level 3 Chlorine Nitrate (ClONO2) Zonal Fourier Coefficients\" version 7 data product (H3ZFCCLONO2) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level3/H3ZFCCLONO2.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"H3ZFCCFC11 v007 - The \"HIRDLS/Aura Level 3 Chlorofluorocarbon-11 (CFC-11) Zonal Fourier Coefficients\" version 7 data product (H3ZFCCFC11) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level3/H3ZFCCFC11.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"H3ZFCCFC12 v007 - The \"HIRDLS/Aura Level 3 Chlorofluorocarbon-12 (CFC-12) Zonal Fourier Coefficients\" version 7 data product (H3ZFCCFC12) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level3/H3ZFCCFC12.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HIR3SCOL v007 - HIR3SCOL is the EOS High Resolution Dynamics Limb Sounder (HIRDLS/Aura) level 3 daily gridded 1 x 1 deg. stratospheric columns of NO2 (nitrogen dioxide) data product.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level3/HIR3SCOL.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"H3ZFCN2O5 v007 - The \"HIRDLS/Aura Level 3 Dinitrogen Pentoxide (N2O5) Zonal Fourier Coefficients\" version 7 data product (H3ZFCN2O) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level3/H3ZFCN2O5.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"H3ZFC12MEXT v007 - The \"HIRDLS/Aura Level 3 Extinction at 12.1 Microns Zonal Fourier Coefficients\" version 7 data product (H3ZFC12MEXT) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level3/H3ZFC12MEXT.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"H3ZFC8MEXT v007 - The \"HIRDLS/Aura Level 3 Extinction at 8.3 Microns Zonal Fourier Coefficients\" version 7 data product (H3ZFC8MEXT) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level3/H3ZFC8MEXT.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"H3ZFCGPH v007 - The \"HIRDLS/Aura Level 3 Geopotential Height Zonal Fourier Coefficients\" version 7 data product (H3ZFCGPH) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level3/H3ZFCGPH.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"H3ZFCHNO3 v007 - The \"HIRDLS/Aura Level 3 Nitric Acid (HNO3) Zonal Fourier Coefficients\" version 7 data product (H3ZFCHNO3) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level3/H3ZFCHNO3.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"H3ZFCNO2 v007 - The \"HIRDLS/Aura Level 3 Nitrogen Dioxide (NO2) Zonal Fourier Coefficients\" version 7 data product (H3ZFCNO2) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level3/H3ZFCNO2.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"H3ZFCN2O v007 - The \"HIRDLS/Aura Level 3 Nitrous Oxide (N2O) Zonal Fourier Coefficients\" version 7 data product (H3ZFCN2O) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level3/H3ZFCN2O.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"H3ZFCO3 v007 - The \"HIRDLS/Aura Level 3 Ozone (O3) Zonal Fourier Coefficients\" version 7 data product (H3ZFCO3) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level3/H3ZFCO3.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"H3ZFCT v007 - The \"HIRDLS/Aura Level 3 Temperature Zonal Fourier Coefficients\" version 7 data product (H3ZFCT) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level3/H3ZFCT.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"H3ZFCH2O v007 - The \"HIRDLS/Aura Level 3 Water Vapor (H2O) Zonal Fourier Coefficients\" version 7 data product (H3ZFCH2O) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_HIRDLS_Level3/H3ZFCH2O.007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZMBRO v005 - ML3DZMBRO is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZMBRO.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZMHO2 v005 - ML3DZMHO2 is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZMHO2.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML1OA v004 - ML1OA is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 orbit attitude and tangent point geolocation data. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level1/ML1OA.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML1OA v005 - ML1OA is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 orbit attitude and tangent point geolocation data. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level1/ML1OA.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML1RADD v004 - ML1RADD is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the digital autocorrelators. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level1/ML1RADD.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML1RADD v005 - ML1RADD is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the digital autocorrelators. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level1/ML1RADD.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML1RADG v004 - ML1RADG is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level1/ML1RADG.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML1RADG v005 - ML1RADG is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level1/ML1RADG.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML1RADT v004 - ML1RADT is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level1/ML1RADT.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML1RADT v005 - ML1RADT is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level1/ML1RADT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2BRO v004 - ML2BRO is the EOS Aura Microwave Limb Sounder (MLS) standard product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2BRO.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2BRO v005 - ML2BRO is the EOS Aura Microwave Limb Sounder (MLS) standard product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2BRO.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2CO v004 - ML2CO is the EOS Aura Microwave Limb Sounder (MLS) standard product for carbon monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2CO.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2CO v005 - ML2CO is the EOS Aura Microwave Limb Sounder (MLS) standard product for carbon monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2CO.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2CLO v004 - ML2CLO is the EOS Aura Microwave Limb Sounder (MLS) standard product for chlorine monoxide derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2CLO.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2CLO v005 - ML2CLO is the EOS Aura Microwave Limb Sounder (MLS) standard product for chlorine monoxide derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2CLO.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2IWC v004 - ML2IWC is the EOS Aura Microwave Limb Sounder (MLS) standard product for cloud ice water content derived from radiances measured by the 240 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2IWC.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2IWC v005 - ML2IWC is the EOS Aura Microwave Limb Sounder (MLS) standard product for cloud ice water content derived from radiances measured by the 240 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2IWC.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2DGG v004 - ML2DGG is the EOS Aura Microwave Limb Sounder (MLS) product containing geophysical diagnostic quantities pertaining directly to the standard geophysical data products, generally on a similar (or identical) grid, and at different spectral ranges. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2DGG.004/2024","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2DGG v005 - ML2DGG is the EOS Aura Microwave Limb Sounder (MLS) product containing geophysical diagnostic quantities pertaining directly to the standard geophysical data products, generally on a similar (or identical) grid, and at different spectral ranges. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2DGG.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2DGM v004 - ML2DGM is the EOS Aura Microwave Limb Sounder (MLS) product containing the minor frame diagnostic quantities on a miscellaneous grid. These include items such as tangent pressure, chi-square describing various fits to the measured radiances, number of radiances used in various retrieval phases, etc.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2DGM.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2GPH v004 - ML2GPH is the EOS Aura Microwave Limb Sounder (MLS) standard product for geopotential height derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2GPH.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2GPH v005 - ML2GPH is the EOS Aura Microwave Limb Sounder (MLS) standard product for geopotential height derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2GPH.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2HCL v004 - ML2HCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen chloride derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2HCL.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2HCL v005 - ML2HCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen chloride derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2HCL.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2HCN v004 - ML2HCN is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen cyanide derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2HCN.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2HCN v005 - ML2HCN is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen cyanide derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2HCN.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2HO2 v004 - ML2HO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2HO2.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2HO2 v005 - ML2HO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2HO2.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2OH v004 - ML2OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroxyl derived from radiances measured by the THz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2OH.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2OH v005 - ML2OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroxyl derived from radiances measured by the THz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2OH.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2HOCL v004 - ML2HOCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hypochlorous acid derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2HOCL.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2HOCL v005 - ML2HOCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hypochlorous acid derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2HOCL.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2CH3OH v004 - At this time it is recommended that these data not be used pending further validation.ML2CH3OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for methanol derived from radiances measured by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2CH3OH.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2CH3OH v005 - At this time it is recommended that these data not be used pending further validation.ML2CH3OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for methanol derived from radiances measured by the 640 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2CH3OH.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2CH3CL v004 - ML2CH3CL is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl chloride derived from radiances measured by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2CH3CL.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2CH3CL v005 - ML2CH3CL is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl chloride derived from radiances measured by the 640 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2CH3CL.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2CH3CN v004 - ML2CH3CN is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl cyanide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2CH3CN.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2CH3CN v005 - ML2CH3CN is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl cyanide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2CH3CN.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2HNO3 v004 - ML2HNO3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitric acid derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2HNO3.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2HNO3 v005 - ML2HNO3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitric acid derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2HNO3.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2N2O v004 - ML2N2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitrous oxide derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2N2O.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2N2O v005 - ML2N2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitrous oxide derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2N2O.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2O3 v004 - ML2O3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for ozone derived from radiances measured by the 240 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2O3.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2O3 v005 - ML2O3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for ozone derived from radiances measured by the 240 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2O3.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2RHI v004 - ML2RHI is the EOS Aura Microwave Limb Sounder (MLS) standard product for relative humidity with respect to ice derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2RHI.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2RHI v005 - ML2RHI is the EOS Aura Microwave Limb Sounder (MLS) standard product for relative humidity with respect to ice derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2RHI.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2SO2 v004 - ML2SO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for sulfur dioxide derived from radiances measured by the 240 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2SO2.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2SO2 v005 - ML2SO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for sulfur dioxide derived from radiances measured by the 240 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2SO2.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2T v004 - ML2T is the EOS Aura Microwave Limb Sounder (MLS) standard product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2T.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2T v005 - ML2T is the EOS Aura Microwave Limb Sounder (MLS) standard product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2T.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2H2O v004 - ML2H2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for water vapor derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2H2O.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2H2O v005 - ML2H2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for water vapor derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level2/ML2H2O.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZMBRO v004 - ML3DZMBRO is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZMBRO.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBCO v004 - ML3DBCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBCO.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBCO v005 - ML3DBCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBCO.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZCO v004 - ML3DZCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZCO.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZCO v005 - ML3DZCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZCO.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBCLO v004 - ML3DBCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBCLO.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBCLO v005 - ML3DBCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBCLO.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZCLO v004 - ML3DZCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZCLO.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZCLO v005 - ML3DZCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZCLO.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBIWC v004 - ML3DBIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBIWC.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBIWC v005 - ML3DBIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBIWC.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZIWC v004 - ML3DZIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZIWC.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZIWC v005 - ML3DZIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZIWC.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBGPH v004 - ML3DBGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBGPH.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBGPH v005 - ML3DBGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBGPH.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZGPH v004 - ML3DZGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZGPH.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZGPH v005 - ML3DZGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZGPH.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBHCL v004 - ML3DBHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBHCL.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBHCL v005 - ML3DBHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBHCL.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZHCL v004 - ML3DZHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZHCL.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZHCL v005 - ML3DZHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZHCL.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBHCN v004 - ML3DBHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBHCN.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBHCN v005 - ML3DBHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBHCN.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZHCN v004 - ML3DZHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZHCN.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZHCN v005 - ML3DZHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZHCN.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBOH v004 - ML3DBOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBOH.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBOH v005 - ML3DBOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBOH.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZOH v004 - ML3DZOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZOH.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZOH v005 - ML3DZOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZOH.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBHOCL v004 - ML3DBHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBHOCL.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBHOCL v005 - ML3DBHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBHOCL.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZHOCL v004 - ML3DZHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZHOCL.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZHOCL v005 - ML3DZHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZHOCL.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBCH3CL v004 - ML3DBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBCH3CL.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBCH3CL v005 - ML3DBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBCH3CL.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZCH3CL v004 - ML3DZCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZCH3CL.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZCH3CL v005 - ML3DZCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZCH3CL.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBHNO3 v004 - ML3DBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBHNO3.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBHNO3 v005 - ML3DBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBHNO3.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZHNO3 v004 - ML3DZHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZHNO3.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZHNO3 v005 - ML3DZHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZHNO3.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBN2O v004 - ML3DBN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBN2O.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBN2O v005 - ML3DBN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBN2O.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZN2O v004 - ML3DZN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZN2O.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZN2O v005 - ML3DZN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZN2O.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBO3 v004 - ML3DBO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBO3.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBO3 v005 - ML3DBO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBO3.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZO3 v004 - ML3DZO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZO3.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZO3 v005 - ML3DZO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZO3.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBRHI v004 - ML3DBRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBRHI.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBRHI v005 - ML3DBRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBRHI.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZRHI v004 - ML3DZRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZRHI.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZRHI v005 - ML3DZRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZRHI.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBSO2 v004 - ML3DBSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBSO2.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBSO2 v005 - ML3DBSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBSO2.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZSO2 v004 - ML3DZSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZSO2.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZSO2 v005 - ML3DZSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZSO2.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBT v004 - ML3DBT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBT.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBT v005 - ML3DBT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZT v004 - ML3DZT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZT.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZT v005 - ML3DZT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBH2O v004 - ML3DBH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBH2O.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DBH2O v005 - ML3DBH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DBH2O.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZH2O v004 - ML3DZH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZH2O.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZH2O v005 - ML3DZH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZH2O.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3DZMHO2 v004 - ML3DZMHO2 is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3DZMHO2.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBCO v004 - ML3MBCO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBCO.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBCO v005 - ML3MBCO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBCO.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBCLO v004 - ML3MBCLO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBCLO.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBCLO v005 - ML3MBCLO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBCLO.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBIWC v004 - ML3MBIWC is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBIWC.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBIWC v005 - ML3MBIWC is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBIWC.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBGPH v004 - ML3MBGPH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBGPH.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBGPH v005 - ML3MBGPH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBGPH.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBHCL v004 - ML3MBHCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBHCL.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBHCL v005 - ML3MBHCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBHCL.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBHCN v004 - ML3MBHCN is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBHCN.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBHCN v005 - ML3MBHCN is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBHCN.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBOH v004 - ML3MBOH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBOH.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBOH v005 - ML3MBOH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBOH.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBHOCL v004 - ML3MBHOCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBHOCL.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBHOCL v005 - ML3MBHOCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBHOCL.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBCH3CL v004 - ML3MBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBCH3CL.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBCH3CL v005 - ML3MBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBCH3CL.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBHNO3 v004 - ML3MBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBHNO3.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBHNO3 v005 - ML3MBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBHNO3.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBN2O v004 - ML3MBN2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBN2O.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBN2O v005 - ML3MBN2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBN2O.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBO3 v004 - ML3MBO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBO3.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBO3 v005 - ML3MBO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBO3.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBRHI v004 - ML3MBRHI is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBRHI.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBRHI v005 - ML3MBRHI is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBRHI.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBSO2 v004 - ML3MBSO2 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBSO2.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBSO2 v005 - ML3MBSO2 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBSO2.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBT v004 - ML3MBT is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBT.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBT v005 - ML3MBT is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBH2O v004 - ML3MBH2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBH2O.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML3MBH2O v005 - ML3MBH2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_Level3/ML3MBH2O.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2CO_NRT v005 - ML2CO_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for carbon monoxide (CO). This product contains CO profiles derived from the 240 GHz region.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_NRT/ML2CO_NRT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2HNO3_NRT v005 - ML2HNO3_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for nitric acid (HNO3). This product contains HNO3 profiles derived from the 190 and 240 GHz regions.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_NRT/ML2HNO3_NRT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2N2O_NRT v005 - ML2N2O_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for nitrous oxide (N2O). This product contains N2O profiles derived from the 190 GHz region.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_NRT/ML2N2O_NRT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2O3_NRT v005 - ML2O3_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for ozone (O3). This product contains O3 profiles derived from the 240 GHz region.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_NRT/ML2O3_NRT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2SO2_NRT v005 - ML2SO2_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for sulfur dioxide (SO2). This product contains SO2 profiles derived from the 190 and 240 GHz regions.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_NRT/ML2SO2_NRT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2T_NRT v005 - ML2T_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for temperature. This product contains temperature profiles derived from the 118 GHz region.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_NRT/ML2T_NRT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ML2H2O_NRT v005 - ML2H2O_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for water vapor (H2O). This product contains H2O profiles derived from the 190 GHz region.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_MLS_NRT/ML2H2O_NRT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMAEROZ v003 - The reprocessed OMI/Aura Level-2 Zoomed Aerosol data product OMAEROZ at 13x12 km resolution have been made available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access in March 2012. There are two Level-2 Aura OMI aerosol products OMAERO and OMAERUV.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMAEROZ.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMMYDAGEO v003 - The OMI/Aura and MODIS/Aqua Aerosol Geo-colocation Product 1-Orbit L2 Swath 13x24 km (OMMYDAGEO) is a Level-2 orbital data product that links the MODIS/Aqua aerosol geo-coordinates at 3 and 10 km with the OMI indices along the OMI orbital track. This product allows users to match up MODIS granules with the OMI orbit for analysis and validation.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMMYDAGEO.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMMYDCLD v003 - The OMI/Aura and MODIS/Aqua Merged Cloud Product 1-Orbit L2 Swath 13x24 km (OMMYDCLD) is a Level-2 orbital product that combines cloud parameters retrieved by the Ozone Mapping Instrument (OMI) on the Aura satellite with collocated statistical information for cloud parameters retrieved by the Moderate Resolution Imaging Spectrometer (MODIS) on the Aqua spacecraft. This product is designed to take advantage of the synergy between OMI and MODIS, which both fly on satellites in the NASA A-Train constellation of Earth-observing satellites that follow similar orbital tracks and collect near-simu...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMMYDCLD.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMBRO v003 - The Aura Ozone Monitoring Instrument (OMI) collection-3 Bromine Monoxide Product OMBRO from the Aura-OMI, is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The shortname for this Level-2 OMI total column BrO product is OMBRO.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMBRO.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMOCLO v003 - The Aura Ozone Monitoring Instrument (OMI) collection-3 Chlorine Dioxide Product OMOCLO is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The shortname for this Level-2 OMI total column OClO product is OMOCLO.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMOCLO.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMCLDO2 v003 - The reprocessed OMI/Aura Level-2 cloud data product OMCLDO2 is now available from the NASA GoddardEarth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed in late 2011.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMCLDO2.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMCLDO2G v003 - This Level-2G daily global gridded product OMCLDO2G is based on the pixel level OMI Level-2 CLDO2 product OMCLDO2. This level-2G global cloud product (OMCLDO2G) is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2G/OMCLDO2G.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMCLDO2Z v003 - The reprocessed Aura Ozone Monitoring Instrument (OMI) Level-2 zoomed cloud data product OMCLDO2Z at 13x12 km resolution is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed late 2011.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMCLDO2Z.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMDOAO3 v003 - The second release of Aura Ozone Monitoring Instrument (OMI) Version 003 OMI/Aura Level-2 Total Column Ozone Data Product OMDOAO3 is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The data were processed in late 2011 using Algorithm or PGE version 1.2.3 and released in March 2012.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMDOAO3.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMDOAO3Z v003 - The reprocessed Aura Ozone Monitoring Instrument (OMI) Level-2 Zoomed Ozone data product OMDOAO3Z at 13x12 km resolution is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed late 2011.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMDOAO3Z.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMCLDRR v003 - The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMCLDRR.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMCLDRR v004 - This is the Aura Ozone Monitoring Instrument (OMI) Version 004 Level 2 Cloud Data Product OMCLDRR. OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMCLDRR.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMCLDRRG v003 - This Level-2G daily global gridded product OMCLDRRG is based on the pixel level OMI Level-2 CLDRR product OMCLDRR. This level-2G global cloud product (OMCLDRRG) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2G/OMCLDRRG.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMHCHO v003 - The Aura Ozone Monitoring Instrument (OMI) Version-3 Formaldehyde Product OMHCHO is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The shortname for this Level-2 OMI total column Formaldehyde product is OMHCHO.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMHCHO.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMHCHOG v003 - This Level-2G daily global gridded product OMHCHOG is based on the pixel level OMI Level-2 HCHO product OMHCHO. OMHCHOG data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree grids.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2G/OMHCHOG.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMHCHOd v003 - The OMI/Aura Formaldehyde (HCHO) Total Column Daily L3 Weighted Mean Global 0.1deg Lat/Lon Grid (OMHCHOd). The formaldehyde values in each file are the average for 0.1 x 0.1 degree grid cell of cloud-screened total HCHO columns for a single day.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level3/OMHCHOd.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMGLER v003 - The OMI/Aura Global Geometry-Dependent Surface LER 1-Orbit L2 Swath 13x24km product, or OMGLER, provides GLER, and the computed top-of-atmosphere (TOA) radiance from which GLER is derived, for the OMI field of view. The OMGLER data also contain a number of ancillary/input parameters for each OMI pixel used to compute TOA radiance.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMGLER.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPIXCOR v003 - The Version-3 Aura Ozone Monitoring Instrument (OMI) Pixel Corner Product, OMPIXCOR, is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The shortname for this Level-2 OMI product is OMPIXCOR.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMPIXCOR.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OML1BIRR v004 - The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) UV Averaged Solar Irradiances product (shortname OML1BIRR) contains the averaged radiometrically calibrated irradiance measurements from the UV and VIS detectors. The OMI UV Band 1 (264-311 nm) has 159 wavelength bins, the UV Band 2 (307-383 nm) has 559 wavelength bins, and the VIS Band 3 (349-504 nm) has 751 wavelength bins.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level1/OML1BIRR.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OML1BIRR v003 - The OMI Level 1B solar irradiance product is the radiometrically calibrated and geolocated measurements of the UV and Visible channels of the spectral solar irradiance. It is the averaged measurements of the solar irradiances over a single solar observation in the wavelength ranges of UV1 (264-311 nm, 159 channels), UV2 (307-383 nm, 557 channels) and VIS (349-504 nm, 751 channels).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level1/OML1BIRR.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OML1BRUG v003 - The Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View UV Radiance, Global-Mode (OML1BRUG) Version-3 product contains geo-located Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm conducted in the global measurement mode. In the standard global measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath for each of the 557 channels of UV2 (307-383 nm) and 30 ground pixels (13 km x 48 km at nadir) for the 159 channels of UV1 (264-311 nm).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level1/OML1BRUG.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OML1BRUG v004 - The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Geolocated Earthshine UV Radiance, Global-mode (shortname OML1BRUG) Version 4 product contains geolocated Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm taken in the global measurement mode. In the global mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath (~2600 km) for each of the 557 channels of Band 2 (307-383 nm) and 30 ground pixels (13 km x 48 km at nadir) for the 159 channels of Band 1 (264-311 nm).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level1/OML1BRUG.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OML1BRUZ v003 - The Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View UV Radiance, Zoom-in-Mode (OML1BRUZ) Version-3 product contains geo-located Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm using spectral and spatial zoom-in measurement modes. In zoom-in measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level1/OML1BRUZ.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OML1BRUZ v004 - The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Geolocated Earthshine UV Radiance, Zoom-mode (shortname OML1BRUZ) Version 4 product contains geolocated Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm taken in the global measurement mode. In the zoom-in mode, OMI observes 60 ground pixels (13 km x 12 km at nadir) across the swath (~750 km width) for each of the 557 channels of Band 2 (307-383 nm) and 30 ground pixels (13 km x 24 km at nadir) across the swath (~2600 km) for the 159 channels of Band 1 (264-311 nm).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level1/OML1BRUZ.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OML1BRVG v003 - The Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View VIS Radiance, Global-Mode (OML1BRVG) Version-3 product contains geo-located Earth view spectral radiances from the VIS detector in the wavelength range of 349 to 504 nm conducted in the global measurement mode. In the standard global measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath (13 km x 48 km at nadir).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level1/OML1BRVG.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OML1BRVG v004 - The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Geolocated Earthshine VIS Radiance, Global-mode (shortname OML1BRVG) Version 4 product contains geolocated Earth view spectral radiances from the VIS detectors in the wavelength range of 349 to 504 nm taken in the global measurement mode. In the global mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath (~2600 km) for each of the 751 channels of Band 3 (349-504 nm).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level1/OML1BRVG.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OML1BRVZ v003 - The Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View VIS Radiance, Zoom-in-Mode (OML1BRVZ) Version-3 product contains geo-located Earth view spectral radiances from the VIS detectors in the wavelength range of 349 to 504 nm using spectral and spatial zoom-in measurement modes. In zoom-in measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level1/OML1BRVZ.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OML1BRVZ v004 - The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Zoom-in Earthshine UV Radiance, Zoom-mode (shortname OML1BRVZ) Version 4 product contains geolocated Earth view spectral radiances from the VIS detectors in the wavelength range of 349 to 504 nm taken in the zoom-in measurement mode. In the zoom-in mode, OMI observes 60 ground pixels (13 km x 12 km at nadir) across the swath (~750 km width) for each of the 751 channels of Band 3 (349-504 nm).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level1/OML1BRVZ.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMAERO v003 - The Level-2 Aura Ozone Monitoring Instrument (OMI) Aerosol Product (OMAERO) is now available from NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. This is the second public release of version 003.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMAERO.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMAEROG v003 - This Level-2G daily global gridded product OMAEROG is based on the pixel level OMI Level-2 Aerosol product OMAERO, based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. OMAEROG is a special Level-2 gridded product where pixel level products are binned into 0.25 x 0.25 degree global grids.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2G/OMAEROG.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMAEROe v003 - The OMI science team produces this Level-3 Aura/OMI Global Aerosol Data Products OMAEROe (0.25deg Lat/Lon grids). The OMAEROe product selects best aerosol value from the Level2G good quality data that are reported in each grid, based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level3/OMAEROe.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMAERUV v003 - The Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data are available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMAERUV.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMAERUV v004 - The Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product OMAERUV (Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The OMAERUV retrieval algorithm is developed by the US OMI Team Scientists.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMAERUV.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMAERUVG v003 - This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 AERUV product OMAERUV. This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 Aerosol product OMAERUV.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2G/OMAERUVG.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMAERUVd v003 - The OMI science team produces this Level-3 daily global gridded product OMAERUVd (1 deg Lat/Lon grids). The OMAERUVd product is produced with all data pixels that fall in a grid box with quality filtered and then averaged, based on the pixel level OMI Level-2 Aerosol data product OMAERUV.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level3/OMAERUVd.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMNO2 v003 - The Version 4.0 Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major V4.0 updates include: (1) use of a new daily and OMI field of view specific geometry dependent surface Lambertian Equivalent Reflectivity (GLER) product in both NO2 and cloud retrievals; (2) use of improved cloud parameters (effective cloud fraction and cloud optical centroid pressure) from a new cloud algorithm (OMCDO2N) that are retrieved consistently with NO2 using a new algorithm for O2-O2...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMNO2.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMNO2 v004 - The Collection 4, Version 5.0, Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major V5.0 updates include the use of: (1) improved Collection 4 Level-1B radiance and irradiance data in the retrieval of NO2 and O2-O2 slant columns; (2) a-priori NO2 profiles and other model-derived information from a high-resolution (~25 km) Global Modeling Initiative (GMI) simulation; (3) a new algorithm for improved de-striping and data flagging to correct for cross-track ar...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMNO2.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMNO2d v004 - This is Level-3 daily global gridded (0.25x0.25 degree) Nitrogen Dioxide Product (OMNO2d). OMNO2d data product is a Level-3 Gridded Product where pixel level data of good quality are binned and \"averaged\" into 0.25x0.25 degree global grids.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level3/OMNO2d.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMNO2G v003 - This Level-2G daily global gridded product OMNO2G is based on the pixel level OMI Level-2 NO2 product OMNO2. OMNO2G data product is a special Level-2 Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2G/OMNO2G.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMDOAO3G v003 - This Level-2G daily global gridded product OMDOAO3G is based on the pixel level OMI Level-2 DOAO3 product OMDOAO3. This Level-2G global total column ozone product is derived from OMDOAO3 which is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2G/OMDOAO3G.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMDOAO3e v003 - The OMI science team produces this Level-3 Aura/OMI Global OMDOAO3e Data Products (0.25deg Lat/Lon grids). This Level-3 global total column ozone product is derived from OMDOAO3 which is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level3/OMDOAO3e.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMO3PR v003 - The Aura Ozone Monitoring Instrument Level-2 Ozone Profile data product OMO3PR (Version 003) is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI Level-2 ozone profile product, OMO3PR at the pixel resolution 13x 48 km (at nadir), is based on the optimal estimation algorithm (Rodgers, 2000) with climatological ozone profiles as a-priori information.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMO3PR.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMTO3G v004 - This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2G/OMTO3G.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMTO3 v003 - The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Version 003) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI provides two Level-2 (OMTO3 and OMDOAO3) total column ozone products at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMTO3.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMTO3 v004 - The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Collection Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm at a pixel resolution of 13 x 24 km at nadir.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMTO3.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMSO2e v003 - The OMI science team produces this Level-3 Aura/OMI Global OMSO2e Data Products (0.25 degree Latitude/Longitude grids). In this Level-3 daily global SO2 data product, each grid contains only one observation of Total Column Density of SO2 in the Planetary Boundary Layer (PBL), based on an improved Principal Component Analysis (PCA) Algorithm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level3/OMSO2e.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMSO2 v003 - The Aura Ozone Monitoring Instrument (OMI) level 2 sulphur dioxide (SO2) total column product (OMSO2) has been updated with a principal component analysis (PCA)-based algorithm (v2) with new SO2 Jacobian lookup tables and a priori profiles that significantly improve retrievals for anthropogenic SO2. The data files (or granules) contain different estimates of the vertical column density (VCD) of SO2 depending on the users investigating anthropogenic or volcanic sources.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMSO2.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMSO2G v003 - This Level-2G daily global gridded product OMSO2G is based on the pixel level OMI Level-2 SO2 product OMSO2. OMSO2G data product is a special Level-2 gridded product where pixel level products are binned into 0.125x0.125 degree global grids.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2G/OMSO2G.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMLER v003 - The OMI Earth Surface Reflectance Climatology product, OMLER (Global 0.5 degrees Lat/Lon grid) which is based on Version 003 Level-1B top of atmosphere upwelling radiance and incoming irradiance. OMI calibrated and geolocated radiances from 159 channels in UV1(264-311 nm), 557 channels in UV2 (307-383 nm) and 751 channels in VIS (349-504) spectral regions, spectral irradiances, calibration measurements, and all derived geophysical atmospheric products (Level-2 and 3) are archived at the NASA Goddard DAAC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level3/OMLER.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMUVB v003 - The Aura Ozone Monitoring Instrument (OMI) Version 003 Surface UV Irradiance Product (OMUVB) is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The shortname for this Level-2 OMI Surface UVB product is OMUVB.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMUVB.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMUVBG v003 - This is Level-2G daily global gridded Aura-OMI Spectral Surface UVB Irradiance and Erythemal Dose product (OMUVBG). The OMUVBG is a special Level-2 Global Gridded type data Product (referred as Level 2G or L2G) where Level-2 or swath pixel data are binned (but not averaged)into 0.25x0.25 degree global grids.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2G/OMUVBG.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMUVBd v003 - This is Level-3 daily global gridded Aura-OMI Spectral Surface UVB Irradiance and Erythemal Dose product (OMUVBd). The OMUVBd product contains global erythemally weighted daily dose and erythemal dose rate at local solar noon at 1.0x1.0 deg grids.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level3/OMUVBd.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMTO3e v004 - The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level3/OMTO3e.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMTO3d v004 - The OMI science team produces this Level-3 daily global TOMS-Like Total Column Ozone gridded product OMTO3d (1 deg Lat/Lon grids). The OMTO3d product is produced by gridding and averaging only good quality level-2 total column ozone orbital swath data (OMTO3, based on the enhanced TOMS version-8 algorithm) on the 1x1 degree global grids.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level3/OMTO3d.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPIXCORZ v003 - The Version-3 Aura Ozone Monitoring Instrument (OMI) Pixel Corner Product in zoom-in mode, OMPIXCORZ, is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The shortname for this Level-2 OMI product is OMPIXCORZ.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Level2/OMPIXCORZ.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMUANC v004 - The Primary Ancillary Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Ancillary/OMUANC.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMVANC v004 - The Primary Ancillary Data Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Aura_OMI_Ancillary/OMVANC.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-aura","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA AVIRIS Project","Description":"This dataset provides attributed geospatial and tabular information for identifying and querying flight lines of interest for the Airborne Visible InfraRed Imaging Spectrometer-Classic (AVIRIS-C) and Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) Facility Instrument collections. It includes attributed shapefile and GeoJSON files containing polygon representation of individual flights lines for all years and separate KMZ files for each year. These files allow users to visualize and query flight line locations using Geographic Information System (GIS) software. Tables of AVIRIS-C and AVIRIS-NG flight lines with attributed information include dates, bounding coordinates, site names, investigators involved, flight attributes, associated campaigns, and corresponding file names for associated L1B (radiance) and L2 (reflectance) files in the AVIRIS-C and AVIRIS-NG Facility Instrument Collections. Tabular information is also provided in comma-separated values (CSV) format.\n<br>\n\n#### AV3_L1B_RDN_2356\nThis dataset contains Level 1B (L1B) calibrated radiance images as well as observational geometry and illumination parameters from the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-3 is a spectral mapping system that measures reflected radiance at 7.4-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 390-2500 nm. The AVIRIS-3 sensor has a 40 degree instantaneous field of view with 1234 pixels, providing altitude dependent ground sampling distances from 12 m to sub meter range. This spectrometer measures radiance from surface and atmosphere and is extremely similar in design to the orbital Earth Surface Mineral Dust Source Investigation (EMIT) spectrometer. AVIRIS-3 has been designed to fly on a variety of aircraft platforms including the King Air B-200, Gulfstream III, Gulfstream V, and ER-2. For each flight line, two file types are included: calibrated radiance (RDN) and orthocorrected observation geometry and illumination (ORT) in netCDF format. Both file types include a geolocation lookup table (GLT) for georeferencing pixels in UTM and geographic coordinates. A band mask file indicates whether wavelengths were interpolated on a per pixel basis. In addition, ancillary files for each flight line are provided, including a quick look image in JPEG format and text files in YAML format that document processing algorithms and parameters used during production.\n<br>\n\n#### AV3_L2A_RFL_2357\nThis dataset contains Level 2A (L2A) surface reflectance images from the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-3 is a spectral mapping system that measures reflected radiance at 7.4-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 390-2500 nm. Surface hemispherical directional reflectance was derived from calibrated radiance using an optimal estimation algorithm. For each flight line, two file types are included: orthocorrected surface reflectance (RFL_ORT) and orthocorrected reflectance uncertainty (UNC_ORT) in netCDF format. Both file types include data projected in a UTM coordinate system. In addition, ancillary files for each flight line are provided, including a quick look image in GeoTIFF format and text files in YAML format that document processing algorithms and parameters used during production.\n<br>\n\n#### AV3_L2B_GHG_2358\nThis dataset contains Level 2B (L2b) enhancements of greenhouse gasses (GHG) derived from imagery collected by the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument. Products include methane and carbon dioxide enhancements, each with per-pixel uncertainties and sensitivities to the background. Concentration enhancements are estimated from radiance measurements using a column-wise adaptive matched filter approach, which searches each pixel's radiance spectrum for deviations that are characteristic of a GHG's absorption spectrum. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-3 is a spectral mapping system that measures reflected radiance at 7.4-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 390-2500 nm.\n<br>\n\n#### AVIRIS-Classic_L1B_Radiance_2155\nThis dataset contains Level 1B (L1B) orthocorrected, scaled radiance image files as well as files of observational geometry and illumination parameters and supporting sensor band information from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS-Classic) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-Classic is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-Classic measures reflected radiance in 224 contiguous bands at approximately 10-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 400-2500 nm. The AVIRIS-Classic sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub meter range. AVIRIS-Classic is flown on a variety of aircraft platforms including the Twin Otter, NASA's WB-57, and NASA's high altitude ER-2. Multiple file types are included for each flight line. The primary data files include: orthocorrected calibrated radiance image (img) files, geometric lookup table (glt) and orthocorrected observation geometry and illumination (obs_ort) files. Also included are unprojected files of input geometry (igm), and parameters relating to the geometry of observation and illumination (obs). Additional files provide information on spectral (spc) and radiometric calibration (rcc, gain), spatial resolution (geo), aircraft and sensor position (eph, nav), deployment notes (info), and data processing (plog). Quicklook images (jpeg) and polygon outlines of imagery footprints (kmz) are provided for each flight line. The primary AVIRIS-Classic L1B data are provided in ENVI binary format, which includes a flat binary file accompanied by a header (.hdr) file holding metadata in text format. The ancillary files include JPEG images, maps in Keyhole Markup Language (KML), and calibration files in binary and text formats. This archive currently includes data from 2004, and 2006 - 2025. Additional L1B data will be added as they become available. AVIRIS-Classic supports NASA Science and applications in many areas including plant composition and function, geology and soils, greenhouse gas mapping, and calibration of orbital platforms.\n<br>\n\n#### AVIRIS-Classic_L2_Reflectance_2154\nThis dataset contains Level 2 (L2) orthocorrected reflectance from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS-Classic) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-Classic is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-Classic measures reflected radiance in 224 contiguous bands at approximately 10-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 400-2500 nm. The AVIRIS-Classic sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub meter range. AVIRIS-Classic is flown on a variety of aircraft platforms including the Twin Otter, NASA's WB-57, and NASA's high altitude ER-2. For each flight line, two types of L2 data files may be included: (a) calibrated surface reflectance and (b) water vapor and optical absorption paths for liquid water and ice. The L2 data are provided in ENVI format, which includes a flat binary file accompanied by a header (.hdr) file holding metadata in text format. This archive currently includes data from 2008 - 2025. Additional AVIRIS-Classic facility instrument L2 data will be added as they become available. AVIRIS-Classic supports NASA Science and applications in many areas including plant composition and function, geology and soils, greenhouse gas mapping, and calibration of orbital platforms.\n<br>\n\n#### AVIRIS-NG_L1B_radiance_2095\nThis dataset contains Level 1B (L1B) orthocorrected, scaled radiance image files as well as files of observational geometry and illumination parameters and supporting sensor band information from the Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-NG is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-NG measures reflected radiance at 5-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 380-2510 nm. The AVIRIS-NG sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub-meter range. In this dataset, for each flight line, six file types are included: orthocorrected calibrated radiance image (img) files, geometric lookup table (glt) and orthocorrected observation geometry and illumination (obs_ort) files. Also included are unprojected files of input geometry (igm), parameters relating to the geometry of observation and illumination (obs), and orthocorrected locations of each pixel (loc). In addition, ancillary files for the flight line are provided, including quick look images and polygon outlines of imagery footprints. The AVIRIS-NG L1B data are provided in ENVI binary format, which includes a flat binary file accompanied by a header (.hdr) file holding metadata in text format. The ancillary files include JPEG images and maps in Keyhole Markup Language (KML). The AVIRIS-NG is flown on a variety of aircraft platforms including the Twin Otter, the King Air B-200, and NASA's high altitude ER-2. This archive currently includes data from 2014 - 2022. Additional AVIRIS-NG facility instrument L1B data will be added as they become available. AVIRIS-NG supports NASA Science and applications in many areas including plant composition and function, geology and soils, greenhouse gas mapping, and calibration of orbital platforms.\n<br>\n\n#### AVIRIS-NG_L2_Reflectance_2110\nThis dataset contains Level 2 (L2) orthocorrected reflectance from the Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The AVIRIS-NG is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-NG measures reflected radiance at 5-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 380-2510 nm. The AVIRIS-NG sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub-meter range. For each flight line, two types of L2 data files may be included: (a) calibrated surface reflectance and (b) water vapor and optical absorption paths for liquid water and ice. The L2 data are provided in ENVI format, which includes a flat binary file accompanied by a header (.hdr) file holding metadata in text format. The AVIRIS-NG is flown on a variety of aircraft platforms including the Twin Otter, the King Air B-200, and NASA's high altitude ER-2. This archive currently includes data from 2014 - 2022. Additional AVIRIS-NG facility instrument L2 data will be added as they become available. The AVIRIS-NG supports NASA Science and applications in many areas including plant composition and function, geology and soils, greenhouse gas mapping, and calibration of orbital platforms.\n<br>\n\n#### AVIRIS-NG_CH4_CO2_Plumes_2406\nThis dataset contains enhanced column-integrated methane (CH4) and carbon dioxide (CO2) (concentration lengths) acquired from 211 flight lines across North America imaged by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) between June 26 and August 15, 2023 for NASA's Synergistic TEMPO Air Quality Science (STAQS) campaign. Enhanced CH4 and CO2 concentrations were estimated from the L1B radiance measurements using a cluster matched filter approach. This algorithm searches each pixel's radiance spectrum for deviations that are characteristic of a greenhouses gas's absorption spectrum. A total of 177 greenhouse gas plumes were identified and delineated, of which 74 were CH4 and 103 were CO2. The data are provided in GeoTIFF and JSON formats.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","geospatial","ice","netcdf"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AVIRIS_FlightLine_Locator_2140 v1.0 - This dataset provides attributed geospatial and tabular information for identifying and querying flight lines of interest for the Airborne Visible InfraRed Imaging Spectrometer-Classic (AVIRIS-C) and Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) Facility Instrument collections. It includes attributed shapefile and GeoJSON files containing polygon representation of individual flights lines for all years and separate KMZ files for each year.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/aviris/AVIRIS_FlightLine_Locator/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AV3_L1B_RDN_2356 v1 - This dataset contains Level 1B (L1B) calibrated radiance images as well as observational geometry and illumination parameters from the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/aviris/AV3_L1B_RDN/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AV3_L2A_RFL_2357 v1 - This dataset contains Level 2A (L2A) surface reflectance images from the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/aviris/AV3_L2A_RFL/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AV3_L2B_GHG_2358 v1 - This dataset contains Level 2B (L2b) enhancements of greenhouse gasses (GHG) derived from imagery collected by the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument. Products include methane and carbon dioxide enhancements, each with per-pixel uncertainties and sensitivities to the background.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/aviris/AV3_L2B_GHG/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVIRIS-Classic_L1B_Radiance_2155 v1 - This dataset contains Level 1B (L1B) orthocorrected, scaled radiance image files as well as files of observational geometry and illumination parameters and supporting sensor band information from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS-Classic) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/aviris/AVIRIS-Classic_L1B_Radiance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVIRIS-Classic_L2_Reflectance_2154 v1 - This dataset contains Level 2 (L2) orthocorrected reflectance from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS-Classic) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/aviris/AVIRIS-Classic_L2_Reflectance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVIRIS-NG_L1B_radiance_2095 v1 - This dataset contains Level 1B (L1B) orthocorrected, scaled radiance image files as well as files of observational geometry and illumination parameters and supporting sensor band information from the Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/aviris/AVIRIS-NG_L1B_radiance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVIRIS-NG_L2_Reflectance_2110 v1 - This dataset contains Level 2 (L2) orthocorrected reflectance from the Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/aviris/AVIRIS-NG_L2_Reflectance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVIRIS-NG_CH4_CO2_Plumes_2406 v1 - This dataset contains enhanced column-integrated methane (CH4) and carbon dioxide (CO2) (concentration lengths) acquired from 211 flight lines across North America imaged by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) between June 26 and August 15, 2023 for NASA's Synergistic TEMPO Air Quality Science (STAQS) campaign. Enhanced CH4 and CO2 concentrations were estimated from the L1B radiance measurements using a cluster matched filter approach.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/aviris/AVIRIS-NG_CH4_CO2_Plumes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-aviris","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA AVISO Project","Description":"This dataset contains absolute dynamic topography (similar to sea level but with respect to the geoid) binned and averaged monthly on 1 degree grids. The coverage is from October 1992 to December 2010. These data were provided by AVISO (French space agency data provider) to support the CMIP5 (Coupled Model Intercomparison Project Phase 5) under the World Climate Research Program (WCRP) and was first made available via the JPL Earth System Grid. The dynamic topography are derived from sea surface height measured by several satellites including Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2, and referenced to the geoid. Along with this dataset, two additional ancillary data files are included in the same directory which contain the number of observations and standard error co-located on the same 1 degree grids.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AVISO_L4_DYN_TOPO_1DEG_1MO v1 - This dataset contains absolute dynamic topography (similar to sea level but with respect to the geoid) binned and averaged monthly on 1 degree grids. The coverage is from October 1992 to December 2010.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVISO_L4_DYN_TOPO_1DEG_1MO","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-aviso","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA AVUELO Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### AVUELO_AV3_L2A_Reflectance_2479\nThis dataset contains Level 2A (L2A) orthocorrected surface reflectance and uncertainty estimates from the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument acquired for the Airborne Validation Unified Experiment: Land to Ocean (AVUELO) project in 2025. This L2A data product was generated with a provisional calibration algorithm. AVUELO aims to advance the validation and calibration of spaceborne imaging spectroscopy (hyperspectral) data for tropical ecosystems by combining airborne imaging spectroscopy for terrestrial and marine sites in Panama and Costa Rica with contemporaneous field measurements and data collected in February 2025. The NASA AVIRIS-3 is a spectral mapping system that measures reflected radiance at ~7.4-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 390-2500 nm. The AVIRIS-3 sensor has a 40 degree instantaneous field of view with 1,234 pixels, providing altitude dependent ground sampling distances from 12 m to sub meter range. This spectrometer measures radiance from surface and atmosphere and is identical in design to the orbital Earth Surface Mineral Dust Source Investigation (EMIT) spectrometer. AVIRIS-3 was deployed on a King Air aircraft. Surface hemispherical directional reflectance was derived from calibrated radiance using an optimal estimation algorithm. For each flight scene, two file types are included: orthocorrected surface reflectance (RFL_ORT) and orthocorrected reflectance uncertainty (UNC_ORT) in netCDF format. Both file types include data projected in a UTM coordinate system. When available, a version 2 calibration Level 2A (L2A) of orthocorrected surface reflectance and uncertainty data for these same flight lines will be available in an AVIRIS-3 L2A Orthocorrected Surface Reflectance, Facility Instrument Collection, Version 2.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/AVUELO/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","netcdf","oceans"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AVUELO_AV3_L2A_Reflectance_2479 v1 - This dataset contains Level 2A (L2A) orthocorrected surface reflectance and uncertainty estimates from the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument acquired for the Airborne Validation Unified Experiment: Land to Ocean (AVUELO) project in 2025. This L2A data product was generated with a provisional calibration algorithm.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/avuelo/AVUELO_AV3_L2A_Reflectance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-avuelo","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA BigFoot Project","Description":"The BigFoot project gathered field data for selected EOS Land Validation Sites in North America from 1999 to 2003. Data collected and derived for varying intervals at the BigFoot sites and archived with this data set include FPAR, nitrogen content, allometry equations, root biomass, LAI, tree biomass, soil respiration, NPP, landcover images, and vegetation inventories.Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, and deciduous broadleaf forest; desert grassland and shrubland. The project collected multi-year, in situ measurements of ecosystem structure and functional characteristics related to the terrestrial carbon cycle at the sites listed in Table 1. Companion files include documentation of measurement data, site and plot locations (Figure 2), and plot photographs for the SEVI and TUND sites (Figure 3).BigFoot Project Background: Reflectance data from MODIS, the Moderate Resolution Imaging Spectrometer onboard NASA's Earth Observing System (EOS) satellites Terra and Aqua ( http://landval.gsfc.nasa.gov/MODIS/index.php ), was used to produce several science products including land cover, leaf area index (LAI), gross primary production (GPP), and net primary production (NPP). The overall goal of the BigFoot Project was to provide validation of these products. To do this, BigFoot combined ground measurements, additional high-resolution remote-sensing data, and ecosystem process models at six flux tower sites representing different biomes to evaluate the effects of the spatial and temporal patterns of ecosystem characteristics on MODIS products. BigFoot characterized up to a 7 x 7 km area (49 1-km MODIS pixels) surrounding the CO2 flux towers located at six of the nine BigFoot sites. The sampling design allowed the Project to examine scales and spatial patterns of these properties, the inter-annual variability and validity of MODIS products, and provided for a field-based ecological characterization of the flux tower footprint. BigFoot was funded by NASA's Terrestrial Ecology Program.\n<br>\n\n#### GPP_surfaces_749\nThe BigFoot project gathered Gross Primary Production (GPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. At this time we are archiving Northern Old Black Spruce (NOBS - BOREAS NSA, Canada) and Harvard Forest LTER (HARV - Massachusetts, USA) data collected in 2001.The GPP surfaces were produced by a spatial version of an ecosystem process model named, Biome-BGC. Inputs to the model included Landsat ETM+ derived Land Cover and LAI, tower derived meteorological variables, and a set of site level ecophysical parameters. The model was calibrated using field measured NPP and validated by tower derived estimates of GPP. For an in depth discussion of methods used to produce these surfaces, please see Turner et al. (2003).Each BigFoot GPP product covers a 7 x 7 km extent and consists of the GPP surface in BIP format (280 rows by 280 columns by 365 bands at 25 meter resolution) and an accompanying text file which provides metadata specific to the image (such as projection, data type, etc). Additional information on GPP surface development can be found on the BigFoot website at http://www.fsl.orst.edu/larse/bigfoot/ovr_mthd.html.BigFoot Project Background: Reflectance data from MODIS, the Moderate Resolution Imaging Spectrometer onboard NASA's Earth Observing System (EOS) satellite Terra (http://landval.gsfc.nasa.gov/MODIS/index.php), is used to produce several science products including land cover, leaf area index (LAI), gross primary production (GPP) and net primary production (NPP). The overall goal of the BigFoot Project was to provide validation of these products. To do this, BigFoot combined ground measurements, additional high resolution remote sensing data, and ecosystem process models at nine flux tower sites representing different biomes to evaluate the effects of the spatial and temporal patterns of ecosystem characteristics on MODIS products. BigFoot characterized up to a 7 x 7 km area (49 MODIS pixels) surrounding the CO2 flux towers located at each of the nine sites. We collected multi-year, in situ measurements of ecosystem structure and functional characteristics related to the terrestrial carbon cycle. Our sampling design allowed us to examine scales and spatial pattern of these properties, the inter-annual variability and validity of MODIS products, and provided for a field-based ecological characterization of the flux tower footprint. BigFoot was funded by NASA's Terrestrial Ecology Program.For more details on the BigFoot Project, please visit the website: http://www.fsl.orst.edu/larse/bigfoot/index.html.\n<br>\n\n#### Land_Cover_surfaces_748\nThe BigFoot project gathered data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. These surfaces were produced from Landsat ETM+ imagery to explicitly characterize the land cover at the BigFoot Sites to provide validation of the MODIS land cover product. The land cover scheme is consistent with the categories defined by the MOD12 IGBP (http://geography.bu.edu/landcover/userguidelc/index.html) strategy. Each BigFoot land cover product covers approximately a 7 x 7 km extent and consists of the land cover surface image in standard geotiff format, an accompanying text file which provides metadata specific to the image (such as projection, data type, class names, etc), and associated auxiliary and world files. For an in depth discussion of methods used to produce these surfaces, please see references.Additional information on land cover surface development can be found on the BigFoot website at http://www.fsl.orst.edu/larse/bigfoot/ovr_mthd.html.BigFoot Project Background:Reflectance data from MODIS, the Moderate Resolution Imaging Spectrometer onboard NASA's Earth Observing System (EOS) satellite Terra (http://landval.gsfc.nasa.gov/MODIS/index.php), is used to produce several science products including land cover, leaf area index (LAI) and net primary production (NPP). The overall goal of the BigFoot Project was to provide validation of these products. To do this, BigFoot combined ground measurements, additional high resolution remote sensing data, and ecosystem process models at nine flux tower sites representing different biomes to evaluate the effects of the spatial and temporal patterns of ecosystem characteristics on MODIS products. BigFoot characterized up to a 7 x 7 km area (49 MODIS pixels) surrounding the CO2 flux towers located at each of the nine sites. We collected multi-year, in situ measurements of ecosystem structure and functional characteristics related to the terrestrial carbon cycle. Our sampling design allowed us to examine scales and spatial pattern of these properties, the inter-annual variability and validity of MODIS products, and provided for a field-based ecological characterization of the flux tower footprint. BigFoot was funded by NASA's Terrestrial Ecology Program.\n<br>\n\n#### LAI_surfaces_747\nThe BigFoot project gathered leaf area index (LAI) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. LAI was measured at plots within each site for at least two years using standard direct and optical methods at each site. Direct measurement approaches included periodic area harvest for non-forest sites and application of allometric equations to tree diameter data for forest sites. LAI was also estimated indirectly using the Li-Cor LAI-2000 Plant Canopy Analyzers (Gower et al. 1999). LAI was measured three times each year at the forest sites and four to six times at other sites depending upon the phenology of LAI development for a given ecosystem. To develop LAI surfaces at any given site, the Landsat ETM+ image closest in date to maximum LAI was chosen as a reference and images from other dates radiometrically normalized to it. Each LAI surface has a grain of 25 meters and covers a 7 x 7 km extent. The data set consists of the LAI surface images in standard geotiff format, an accompanying text file which provides metadata specific to the image (such as projection, data type, class names, etc), and associated auxiliary and world files. Additional information on LAI measurements and surface development can be found on the BigFoot website at http://www.fsl.orst.edu/larse/bigfoot/ovr_mthd.html. BigFoot Project Background: Reflectance data from MODIS, the Moderate Resolution Imaging Spectrometer onboard NASA's Earth Observing System (EOS) satellite Terra (http://landval.gsfc.nasa.gov/MODIS/index.php), is used to produce several science products including land cover, leaf area index (LAI) and net primary production (NPP). The overall goal of the BigFoot Project was to provide validation of these products. To do this, BigFoot combined ground measurements, additional high resolution remote sensing data, and ecosystem process models at nine flux tower sites representing different biomes to evaluate the effects of the spatial and temporal patterns of ecosystem characteristics on MODIS products. BigFoot characterized up to a 7 x 7 km area (49 MODIS pixels) surrounding the CO2 flux towers located at each of the nine sites. We collected multi-year, in situ measurements of ecosystem structure and functional characteristics related to the terrestrial carbon cycle. Our sampling design allowed us to examine scales and spatial pattern of these properties, the inter-annual variability and validity of MODIS products, and provided for a field-based ecological characterization of the flux tower footprint. BigFoot was funded by NASA's Terrestrial Ecology Program.\n<br>\n\n#### Meteorological_1065\nThe BigFoot Project has compiled daily meteorological measurements for nine EOS Land Validation Sites located from Alaska to Brazil from 1991 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. The BigFoot Project needed meteorological data to run the ecosystem process models used for scaling GPP and NPP products, for monitoring interannual variability, and for model testing. Meteorological data were obtained from various agencies collecting data in the vicinity of the BigFoot sites and for more recent years, collected on co-located CO2 flux measurement towers. A comparable set of original measurements from all sites were aggregated to a common daily time step for use in the BIOME-BGC model.\n<br>\n\n#### NPP_surfaces_750\nThe BigFoot project gathered Net Primary Production (NPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. At this time we are archiving Northern Old Black Spruce (NOBS - BOREAS NSA, Canada) and Harvard Forest LTER (HARV - Massachusetts, USA) data collected in 2001.The NPP surfaces were produced by a spatial version of an ecosystem process model named, Biome-BGC. Inputs to the model included Landsat ETM+ derived Land Cover and LAI, tower derived meteorological variables, and a set of site-level ecophysical parameters. The model was calibrated using field measured NPP and validated by tower derived estimates of GPP. Each BigFoot NPP product covers a 7 x 7 km extent and consists of the NPP surface in ASCII Raster (BIL - Band Interleaved by Line) format (280 rows by 280 columns at 25 meter resolution) and an accompanying text file which provides metadata specific to the image (such as projection, data type, etc).Additional information on NPP surface development can be found on the BigFoot website at http://www.fsl.orst.edu/larse/bigfoot/ovr_mthd.html.BigFoot Project Background: Reflectance data from MODIS, the Moderate Resolution Imaging Spectrometer onboard NASA's Earth Observing System (EOS) satellite Terra (http://landval.gsfc.nasa.gov/MODIS/index.php), is used to produce several science products including land cover, leaf area index (LAI), gross primary production (GPP) and net primary production (NPP). The overall goal of the BigFoot Project was to provide validation of these products. To do this, BigFoot combined ground measurements, additional high resolution remote sensing data, and ecosystem process models at nine flux tower sites representing different biomes to evaluate the effects of the spatial and temporal patterns of ecosystem characteristics on MODIS products. BigFoot characterized up to a 7 x 7 km area (49 MODIS pixels) surrounding the CO2 flux towers located at each of the nine sites. We collected multi-year, in situ measurements of ecosystem structure and functional characteristics related to the terrestrial carbon cycle. Our sampling design allowed us to examine scales and spatial pattern of these properties, the inter-annual variability and validity of MODIS products, and provided for a field-based ecological characterization of the flux tower footprint. BigFoot was funded by NASA's Terrestrial Ecology Program.For more details on the BigFoot Project, please visit the website: http://www.fsl.orst.edu/larse/bigfoot/index.html.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/data/projects/bigfoot","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","earth observation","land cover","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Field_Measurements_868 v1 - The BigFoot project gathered field data for selected EOS Land Validation Sites in North America from 1999 to 2003. Data collected and derived for varying intervals at the BigFoot sites and archived with this data set include FPAR, nitrogen content, allometry equations, root biomass, LAI, tree biomass, soil respiration, NPP, landcover images, and vegetation inventories.Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, and deciduous broadleaf forest; desert grassland and shrubland.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/bigfoot_val/Field_Measurements/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GPP_surfaces_749 v1 - The BigFoot project gathered Gross Primary Production (GPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/bigfoot_val/GPP_surfaces/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Land_Cover_surfaces_748 v1 - The BigFoot project gathered data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/bigfoot_val/Land_Cover_surfaces/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LAI_surfaces_747 v1 - The BigFoot project gathered leaf area index (LAI) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/bigfoot_val/LAI_surfaces/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Meteorological_1065 v1 - The BigFoot Project has compiled daily meteorological measurements for nine EOS Land Validation Sites located from Alaska to Brazil from 1991 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/bigfoot_val/Meteorological/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_surfaces_750 v1 - The BigFoot project gathered Net Primary Production (NPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/bigfoot_val/NPP_surfaces/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-bigfoot","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Biodiversity Project","Description":"This dataset contains vegetation canopy metrics for the Greater Kruger National Park region of South Africa for 2007-2010 and 2015-2024. Metrics include relative height 98th percentile (RH98), fractional canopy cover, and foliage height diversity. This dataset contains vegetation canopy metrics for the Greater Kruger National Park region of South Africa. Metrics include relative height 98th percentile (RH98), fractional canopy cover, and foliage height diversity. They were derived by modeling a sample of Global Ecosystem Dynamics Investigation (GEDI) Level 2A Elevation and Height Metrics and Level 2B Canopy Cover and Vertical Profile Metrics at the 25-m footprint level with wall-to-wall spatial datasets including Landsat, PALSAR, topography derived from NASADEM, and soils data from iSDAsoil. Maps for 2023 and 2024 were created by applying the model trained from 2007-2022 data to PALSAR and Landsat data for 2023 and 2024. The data are suitable for analyzing change in woody canopy structure within the savanna ecosystems of this region. The data are provided in cloud optimized GeoTIFF format.\n<br>\n\n#### Acoustic_Data_SonomaCounty_CA_2341\nThis dataset holds in situ sound recordings from sites in Sonoma County, California, USA as part of the Soundscapes to Landscapes citizen science project. Recordings were collected from 2017 to 2022 during the bird breeding season (mid-March thru mid-July). Sites (n=1399) were selected across the county; locations were stratified with respect to topographic position and broad land use/land cover types, such as forest, shrubland, herbaceous, urban, agriculture, and riparian areas. Two types of automated recorders were used: Android-based smartphones with attached microphones and AudioMoths. Recorders were deployed at sites for at least 3 days, and programmed to record 1 min of every 10, thus providing temporal sampling through day and night. Each recording was saved in a waveform audio file format (.wav) with 16-bit digitization depth and 44.1 kHz or 48 kHz sampling rate for smartphone and AudioMoth recorders, respectively. The dataset also includes site information including site location when so permitted by landowners in tabular form and photographs of field sites.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","land cover","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GEDI_Canopy_Metrics_GreaterKNP_2401 v1 - This dataset contains vegetation canopy metrics for the Greater Kruger National Park region of South Africa for 2007-2010 and 2015-2024. Metrics include relative height 98th percentile (RH98), fractional canopy cover, and foliage height diversity.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/biodiversity/GEDI_Canopy_Metrics_GreaterKNP/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Acoustic_Data_SonomaCounty_CA_2341 v1 - This dataset holds in situ sound recordings from sites in Sonoma County, California, USA as part of the Soundscapes to Landscapes citizen science project. Recordings were collected from 2017 to 2022 during the bird breeding season (mid-March thru mid-July).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/biodiversity/Acoustic_Data_SonomaCounty_CA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-biodiversity","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA BioSCape Project","Description":"BioSCape\n<br>\n\n#### BioSCape_EstuaryVegHabitats_2441\nThis dataset provides vegetation information for 64 coastal wetland plots and coastal wetland extent maps for three habitat classes in 84 estuaries in South Africa. The vegetation plot data include vegetation species occurrence, percent cover, salinity, representative vegetation heights, porewater salinity, and ground temperature. In situ plot data were collected between October 27 and November 10, 2023, in Swartvlei Estuary, Wilderness Lakes, Knynsa Estuary, and Langebaan Lagoon within lands administered by the South African National Parks. The habitat extent maps were created for 84 estuaries across the Greater Cape Floristic Region. The classification is a single point in time representing the growing season when the plot data were collected (December 2023-January 2024). The habitat extent data were created with PlanetScope satellite classified with a U-Net semantic segmentation algorithm. The habitats classified are salt marsh, submerged aquatic vegetation, and reeds & sedges. Plot and habitat data were collected and created to inform analysis of the imaging spectroscopy and LiDAR data collected as part of the BioSCape field campaign. The data are provided in cloud optimized GeoTIFF, comma separated values, and geopackage formats.\n<br>\n\n#### BioSCape_VegPlots_Berg_Eerste_2425\nThis dataset contains vegetation plot survey data collected at 36 sites across the Berg and Eerste River catchments in the Western Cape, South Africa collected during 2022 and again in 2023 during the 2023 BioSCape Field Collection season. Sampling protocols were designed by the BioSCape Leadership team for use by multiple teams to generate standardized and comparable vegetation plot data across sites. Each plot was circular, 10 m in diameter, and representative of the vegetation community. Species were identified at each 1-m mark along the N-S and W-E transects, totaling 42 identifications per plot. Fractional ground cover of the plot, fractional vegetation dominance, and rarefaction counts for each plot were recorded at all plots from 2023. Data from 2022 have fractional ground cover of the NE quadrant and fractional vegetation dominance. These data are provided in comma separated values (CSV) format. The boundaries of the patches where the vegetation plots were located were recorded as polygons in included Keyhole Markup Language (KML) files.\n<br>\n\n#### BioSCape_AVNG_L2B_BRDF_GCFR_2385\nThis dataset holds corrected surface reflectance (L2B) from the Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument flown on a NASA Gulfstream III aircraft during the Biodiversity Survey of the Cape (BioSCape) project. AVIRIS-NG imagery was acquired in October - November 2023 over the Greater Cape Floristic Region (GCFR), South Africa. BioSCape is a multi-agency, NASA-led research project that integrates airborne imaging spectroscopy and lidar with a suite of measurements of biodiversity. L2B surface reflectance was derived from L1B radiance data in 425 bands, and this enhanced L2B product includes topographic, glint, and bidirectional reflectance distribution function (BRDF) corrections. Data were georeferenced and projected into UTM coordinates. These files hold the original native ancillary information relevant to the image, as well as the ancillary masks used in the creation and parameterization of the topography, glint, and BRDF corrections applied to enhance these image products. The data are provided in netCDF format.\n<br>\n\n#### BioSCape_ANG_V02_L3_RFL_Mosaic_2427\nThis dataset holds mosaics of resampled surface reflectance from the Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument for Biodiversity Survey of the Cape (BioSCape) project. AVIRIS-NG imagery was acquired in October - November 2023 over the Greater Cape Floristic Region (GCFR), South Africa. L2B surface reflectance data from these AVIRIS-NG collections were resampled to 5-m spatial resolution and mosaiced into a regular tile system of 807 tiles. A given tile includes multiple AVIRIS-NG scenes from multiple flight lines spanning multiple days. BioSCape is a multi-agency, NASA-led research project that integrates airborne imaging spectroscopy and lidar with a suite of measurements of biodiversity. The dataset includes surface reflectance with uncertainty for each tile in netCDF format along with an RGB quicklook image in TIFF format. A spatial index of mosaic grid ties is included in GeoJSON format.\n<br>\n\n#### Acoustic_Data_Cape_Floristic_2372\nThis dataset holds in situ sound recordings from sites in Greater Cape Floristic Region (GCFR), South Africa from June to December 2023. The recordings were collected as part of the Biodiversity Survey of the Cape (BioSCape) project, a multi-agency, NASA-led research project that integrates airborne imaging spectroscopy and lidar with a suite of measurements of biodiversity. BioSoundSCape is a BioSCape subproject seeking to relate ground-based measures of bioacoustic diversity to remote imagery. AudioMoth recorders were deployed at sites for 4 to 10 days of data collection (median = 7), and programmed to record 1 min of every 10, thus providing temporal sampling through day and night. Each recording was saved in a waveform audio file format with 16-bit digitization depth and a 48 kHz sampling rate. The recordings contain a wide range of environmental sounds such as biophony (e.g., birds, frogs, insects), anthropophony (e.g,. automobiles, airplanes) and geophony (e.g,. wind, rain). Sampling locations were stratified with respect to elevation, broad land use/land cover types, and time since wildfire disturbance. Most sites were within protected fynbos and Afromontane forest ecosystems. There were 505 sites in the wet season and 489 sites in the dry season, with most sites co-located between seasons. All sites were located within AVIRIS-NG hyperspectral acquisitions and 61% of sites were in LVIS lidar acquisitions. The dataset includes site information in tabular form and photographs of field sites.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://bioscape.io/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice","land cover","lidar","netcdf","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"BioSCape_EstuaryVegHabitats_2441 v1 - This dataset provides vegetation information for 64 coastal wetland plots and coastal wetland extent maps for three habitat classes in 84 estuaries in South Africa. The vegetation plot data include vegetation species occurrence, percent cover, salinity, representative vegetation heights, porewater salinity, and ground temperature.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/bioscape/BioSCape_EstuaryVegHabitats/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BioSCape_VegPlots_Berg_Eerste_2425 v1 - This dataset contains vegetation plot survey data collected at 36 sites across the Berg and Eerste River catchments in the Western Cape, South Africa collected during 2022 and again in 2023 during the 2023 BioSCape Field Collection season. Sampling protocols were designed by the BioSCape Leadership team for use by multiple teams to generate standardized and comparable vegetation plot data across sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/bioscape/BioSCape_VegPlots_Berg_Eerste/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BioSCape_AVNG_L2B_BRDF_GCFR_2385 v1 - This dataset holds corrected surface reflectance (L2B) from the Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument flown on a NASA Gulfstream III aircraft during the Biodiversity Survey of the Cape (BioSCape) project. AVIRIS-NG imagery was acquired in October - November 2023 over the Greater Cape Floristic Region (GCFR), South Africa.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/bioscape/BioSCape_AVNG_L2B_BRDF_GCFR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BioSCape_ANG_V02_L3_RFL_Mosaic_2427 v2 - This dataset holds mosaics of resampled surface reflectance from the Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument for Biodiversity Survey of the Cape (BioSCape) project. AVIRIS-NG imagery was acquired in October - November 2023 over the Greater Cape Floristic Region (GCFR), South Africa.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/bioscape/BioSCape_ANG_V02_L3_RFL_Mosaic/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Acoustic_Data_Cape_Floristic_2372 v1 - This dataset holds in situ sound recordings from sites in Greater Cape Floristic Region (GCFR), South Africa from June to December 2023. The recordings were collected as part of the Biodiversity Survey of the Cape (BioSCape) project, a multi-agency, NASA-led research project that integrates airborne imaging spectroscopy and lidar with a suite of measurements of biodiversity.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/bioscape/Acoustic_Data_Cape_Floristic/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-bioscape","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA BOREAS Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### h9rgbl94_229\nThis data set contains the measurements from the Belfort rain gauges at the BOREAS NSA andSSA. These measurements were submitted in 15-minute and 1-hour intervals. Only the 15-minute interval data set was loaded into the data base tables. Data were collected from the Belfort gauges from mid-April until mid-October in 1994, 1995, and 1996.\n<br>\n\n#### h9rgtb94_230\nThe BOREAS HYD-09 team collected several data sets containing precipitation and streamflow measurements over the BOREAS study areas. This data set contains the measurements from the tipping bucket rain gauges at the BOREAS NSA and SSA. These measurements were submitted in 15-minute and 1-hour intervals. Only the 15-minute interval data set was loaded into the data base tables. Data were collected from the tipping bucket gauges from mid-April until mid-October in 1994, 1995, and 1996.\n<br>\n\n#### boreas_aeshrday_235\nThis data set contains hourly and daily meteorological data from 23 meteorological stations across Canada from January 1975 to January 1997. The surface meteorology parameters include: date, time, temperature, precipitation, snow, snow depth, sea level pressure, station pressure, dew point, wind direction, wind speed, dry and wet bulb temperature, relative humidity, cloud opacity and cloud amount.\n<br>\n\n#### aes5davg_236\nThe Canadian Atmospheric Environment Service (AES) provided BOREAS with hourly and daily surface meteorological data from 23 of the AES meteorological stations located across Canada and upper air data from 1 station at The Pas, Manitoba. Due to copyright restrictions on the full resolution surface meteorological data, this data set contains 5-day average values for the surface parameters. The upper air data are provided in their full resolution form. The 5-day averaging was performed in order to create a data set that could be publicly distributed at no cost. Temporally, the surface meteorological data cover the period of January 1975 to December 1996 and the upper air data cover the period of January 1961 to November 1996.\n<br>\n\n#### mwlezflx_493\nThis data set contains measurements from the NOAA/ATDD Long-EZ Aircraft collected during the 1994 IFCs at the SSA. These measurements were made from various instruments mounted on the aircraft. The data that were collected include: aircraft altitude, wind direction, wind speed, air temperature, potential temperature, water mixing ratio, U and V components of wind velocity, static pressure, surface radiative temperature, downwelling and upwelling total radiation, downwelling and upwelling longwave radiation, net radiation, downwelling and upwelling PAR, greenness index, CO2 concentration, O3 concentration, and CH4 concentration. There are also various columns that indicate the standard deviation, skewness, kurtosis, and trend of some of these data.\n<br>\n\n#### faamwdat_237\nThe BOREAS AFM-02 team collected pass-by-pass fluxes (and many other statistics) for a large number of level (constant altitude), straight line passes used in a variety of flight patterns. The data were collected by the University of Wyoming King Air in 1994 BOREAS IFCs 1-3. Most of these data were collected at 60-70 m above ground level, but a significant number of passes were also flown at various levels in the planetary boundary layer, up to about the inversion height. This documentation concerns only the data from the straight and level passes that are presented as original (over the NSA and SSA) and moving window values (over the Transect). Another archive of King Air data is also available, containing data from all the soundings flown by the King Air 1994 IFCs 1-3.\n<br>\n\n#### afm2as94_494\nThe BOREAS AFM-02 team used the University of Wyoming King Air aircraft during IFCs 1, 2, and 3 in 1994 to collected pass-by-pass fluxes (and many other statistics) for the large number of level (constant altitude), straight line passes used in a variety of flight patterns. The data described here form a second set, namely soundings that were incorporated into nearly every research flight by the King Air in 1994. These soundings generally went from near the surface to above the inversion layer. Most were flown immediately after takeoff or immediately after finishing the last flux pattern of that particular day's flights. The parameters that were measured include wind direction, wind speed, west wind component (u), south wind component (v), static pressure, air dry bulb temperature, potential temperature, dewpoint, temperature, water vapor mixing ratio, and CO2 concentration. Data on the aircraft's location, attitude, and altitude during data collection are also provided.\n<br>\n\n#### afm3mw94_495\nThe BOREAS AFM-03 team used the NCAR Electra aircraft data to make measurements of the fluxes of momentum, sensible and latent heat, carbon dioxide, and ozone over the entire BOREAS region to tie together measurements made in both the SSA and the NSA. These data were also used to study the planetary boundary layer using both in situ and remote sensing measurements. This data set contains both the aircraft flux and the moving window data.\n<br>\n\n#### afm3as94_496\nThe BOREAS AFM-03 team used the NCAR Electra aircraft to make sounding measurements to study the planetary boundary layer using in situ and remote-sensing measurements. Measurements were made of wind speed and direction, air pressure and temperature, potential temperature, dewpoint, mixing ratio of H2O, CO2 concentration, and ozone concentration. Twenty-five research missions were flown over the NSA, SSA, and the transect during BOREAS IFCs 1, 2, and 3 during 1994. All missions had from 4 to 10 soundings through the top of the planetary boundary layer. This sounding data set contains all of the in situ vertical profiles through the boundary layer top that were made (with the exception of \"porpoise\" maneuvers). Data were recorded in 1-second time intervals.\n<br>\n\n#### afm4tofx_497\nThe BOREAS AFM-04 team used the NRC Twin Otter aircraft in 1994 and 1996 to make measurements in the boundary layer of the fluxes of sensible and latent heat, momentum, ozone, methane, and carbon dioxide, plus supporting meteorological parameters such as temperature, humidity, and wind speed and direction. Aircraft position, heading, and altitude were also recorded, as were several radiometric observations for use in interpretation of the data (greenness index, surface temperature, incoming and reflected radiation). Data were collected at both the NSA and SSA during the three 1994 IFCs and in July and August of 1996.\n<br>\n\n#### afm4toas_498\nThe BOREAS AFM-04 team used the NRC Twin Otter aircraft to make sounding measurements through the boundary layer. These measurements included concentrations of carbon dioxide and ozone, atmospheric pressure, dry bulb temperature, potential temperature, dewpoint temperature, calculated mixing ratio, and wind speed and direction. Aircraft position, heading, and altitude were also recorded. Data were collected at both the NSA and the SSA in 1994 and 1996.\n<br>\n\n#### aes_upl1_238\nThe BOREAS AFM-05 team collected and processed data from the numerous radiosonde flights during the project. The goals of the AFM-05 team were to provide large scale definition of the atmosphere by supplementing the existing AES aerological network, both temporally and spatially. This data set includes basic upper-air parameters collected from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.\n<br>\n\n#### afm06ihd_240\nThe BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environment Technology Laboratory (NOAA/ETL) operated a 915 MHz wind/Radio Acoustic Sounding System (RASS) profiler system in the Southern Study Area (SSA) near the Old Jack Pine (OJP) site. This data set provides boundary layer height information over the site. The data were collected from 21-May-1994 to 20-Sep-1994.\n<br>\n\n#### afm06ptd_241\nThe BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environment Technology Laboratory (NOAA/ETL) operated a 915 MHz wind/Radio Acoustic Sounding System (RASS) profiler system in the Southern Study Area (SSA) near the Old Jack Pine (OJP) tower from 21-May-1994 to 20-Sep-1994. The data set provides temperature profiles at 15 heights, containing the variables of virtual temperature, vertical velocity, the speed of sound, and w-bar.\n<br>\n\n#### afm06pwd_242\nThe BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environment Technology Laboratory (NOAA/ETL) operated a 915 MHz wind/Radio Acoustic Sounding System (RASS) profiler system in the Southern Study Area (SSA) near the Old Jack Pine (OJP) tower from 21-May-1994 to 20-Sep-1994. The data set provides wind profiles at 38 heights, containing the variables of wind speed, wind direction and the u-, v-, and w-components of the total wind.\n<br>\n\n#### afm6gifs_433\nThe BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environmental Technology Laboratory (NOAA/ETL) operated a 35 GHz cloud-sensing radar in the Northern Study Area (NSA) near the Old Jack Pine (OJP) tower from 16-Jul-1994 to 08-Aug-1994. This data set contains a time series of GIF (Graphical Interchange Format) images that show the structure of the lower atmosphere. Companion files include (1) an image inventory listing to inform users of the images that are available and (2)example thumbnail images.\n<br>\n\n#### afm06smd_243\nThe BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environment Technology Laboratory (NOAA/ETL) collected surface meteorological data from 21-May to 20-Sep-1994 near the SSA-OJP tower site.\n<br>\n\n#### ecmwf2_523\nThe BOREAS AFM-08 team focused on modeling efforts to improve the understanding of the diurnal evolution of the convective boundary layer over the boreal forest. This data set contains hourly data from the ECMWF operational model from below the soil surface to the top of the atmosphere, including the model fluxes at the surface. Spatially, the data cover a pair of the points that enclose the rawinsonde sites at Candle Lake, Saskatchewan, in the SSA and Thompson, Manitoba, in the NSA. Temporally, the data include the two time periods of 13-May-1994 to 30-Sept-1994 and 01-Mar-1996 to 31-Mar-1997.\n<br>\n\n#### afm11afr_244\nThis directory contains reports from the BOREAS AFM-11 team regarding quality control and sampling analysis of data collected by other AFM personnel using the Electra, Long-EZ, and Twin Otter aircraft. These reports are stored in Adobe Acrobat (.PDF) format and should be downloaded in binary mode.\n<br>\n\n#### avhrrlc1_434\nThis regional land cover classification is based on the use of multitemporal 1-km Advanced Very High Resolution Radiometer (AVHRR) National Oceanic and Atmospheric Administration (NOAA 11) data that were analyzed in combination with selected Landsat Thematic Mapper (TM) and extensive field observations within a 619-km by 821-km subset of the 1,000-km by 1,000-km BOReal Ecosystem-Atmosphere Study (BOREAS) region (Steyaert et al., 1997). Following the approach developed by Loveland et al. (1991) for 1-km AVHRR land cover mapping in the conterminous United States, monthly Normalized Difference Vegetation Index (NDVI) image composites (April-September 1992) of this subset in the BOREAS region were used in an unsupervised image cluster analysis algorithm to develop an initial set of seasonal land cover classes. Extensive ground data with Global Positioning System (GPS) georeferencing, observations from low-level aerial flights over remote areas, and selected Landsat image composites for the study areas were analyzed to split, aggregate, and label the spectral-temporal clusters throughout the BOREAS region. Landsat TM image composites (bands 5, 4, and 3) were available for the 100-km by 100-km Northern Study Area (NSA) and Southern Study Area (SSA). This AVHRR land cover product was compared with Landsat TM land cover classifications for the BOREAS study areas (Steyaert et al., 1997). Companion files include example thumbnail images that may be viewed and the image data files downloaded using a convenient viewer utility.\n<br>\n\n#### afm13afr_245\nFor the BOReal Ecosystem-Atmosphere Study (BOREAS) in 1994 and 1996, the Airborne Fluxes and Meteorology (AFM) group was involved in measurements (from different platforms and heights within the atmospheric boundary layer) of key atmospheric variables and several surface-related parameters that can be used to describe the evolution of the atmospheric boundary layer and the boundary layer fluxes of sensible heat, latent heat, momentum, and CO2. Specifically, the AFM-13 team was interested in analysis and interpretation of airborne flux observations over a 16-km by 16-km grid site in each of the BOREAS study areas. The primary data used in the investigation were collected using the Canadian Twin Otter aircraft, one among the many research aircraft flown in BOREAS. The main objectives of the AFM-13 investigations are to use the Twin Otter-based data with tower flux data to map spatial and temporal variations in the fluxes of heat, moisture, and CO2, and to define realistic footprint functions over the BOREAS sites, so that airborne observations are related to the correct ground surface with its biological and ecological characteristics. These maps are then compared to maps of remote sensing observations over the sites. It is hoped that these studies help to develop regional scale models of fluxes of sensible heat, latent heat, and CO2 for global monitoring of climate change. This document presents a brief summary of the Twin Otter grid sites, the measured data, the type of analysis carried out, and the preliminary results from the 1994 Intensive Field Campaigns (IFCs).\n<br>\n\n#### Saskatchewan_Soils_125m_SSA_1346\nThis data set provides soil descriptions for forested areas in the BOREAS southern study area (SSA) in central Saskatchewan, Canada provided by Agriculture Canada. The data contain soil code, modifiers, extent, and soil names for the primary, secondary, and tertiary soil units within each polygon.\n<br>\n\n#### calibgas_500\nIn order to improve the comparability of trace gas measurements made by various science teams, the BOReal Ecosystem-Atmosphere Study (BOREAS) obtained several cylinders of carbon dioxide (CO2) and methane (CH4) that were used as calibration standards. The cylinders were stored in the field laboratories established in Paddockwood, Saskatchewan (Southern Study Area) (SSA) and in Thompson, Manitoba (Northern Study Area) (NSA) from May 1994 until November 1996.\n<br>\n\n#### CanSIS_Regional_Soils_1347\nThis data set contains soils data from the Canada Soil Information System (CanSIS) in ESRI Shapefile format for the provinces of Saskatchewan and Manitoba. They are provided as part of the BOReal Ecosystem-Atmosphere Study (BOREAS) Staff Science GIS data collection program. Attribute tables provide the various soil data for the polygons. There is one attribute table for Saskatchewan and one for Manitoba. This data product may be useful to someone who is interested in studying this area at a regional scale.\n<br>\n\n#### dtms0bil_247\nThe level-0 Daedalus TMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas. This information includes detailed land cover and biophysical parameter maps such as FPAR and LAI. Two flights of the Daedalus TMS instrument were made onboard the ER-2 aircraft on September 16 and 17, 1994. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images that may be viewed and the image data files downloaded using a convenient viewer utility.\n<br>\n\n#### n_s_dem_248\nThese data were derived from the original DEMs produced by the BOREAS HYD-08 team. The original DEMs were in the UTM projection, while this product is projected in the AEAC projection (see Section 7 for further projection details). The pixel size of the data is 100 meters, which is appropriate for the 1:50,000- scale contours from which the DEMs were made. The original data were compiled from information available in the 1970s and 1980s. This data set covers the two MSAs that are contained within the SSA and the NSA.\n<br>\n\n#### srfmetmd_249\nIn 1995, the BOREAS science teams identified the need for a continuous surface meteorological and radiation data set to support flux and surface process modeling efforts. This data set contains actual, substituted, and interpolated 15-minute meteorological and radiation data compiled from several surface measurements sites over the BOREAS SSA and NSA. Temporally, the data cover 01-Jan-1994 to 31-Dec-1996.\n<br>\n\n#### elev_arc_250\nThis data set was prepared by BORIS staff by reformatting the original data into the ARC/INFO Generate format. The original data were received in SIF at a scale of 1:50,000. BORIS staff could not find a format document or commercial software for reading SIF; the BOREAS HYD-08 team provided some C source code that could read some of the SIF files. The data cover the BOREAS NSA and SSA. The original data were compiled from information available in the 1970s and 1980s.\n<br>\n\n#### er2flog_501\nDuring 1994 and 1996, digital and analog imaging instruments mounted on the NASA ER2 aircraft collected various remotely sensed data from the atmosphere and earth?s surface as part of the BOReal Ecosystem-Atmosphere Study (BOREAS) Intensive Field Campaigns (IFC). Personnel from the NASA Earth Resources Aircraft Program within the High-altitude Aircraft Branch at NASA Ames Research Center (ARC) compiled and published logs that document the BOREAS ER2 missions. These are standard flight reports that the ARC personnel produced routinely during their tenure.\n<br>\n\n#### BFO_dsp01_ccrs_tm_landcover_588\nThe objective of this land cover mosaic is to provide a data product that characterises the detailed land cover of a significant portion of the BOREAS Region. Seven Landsat-5 TM images have been assembled to completely cover the BOREAS Transect. Entire TM scenes were used to create this land cover map. A detailed classification scheme was employed, permitting the extraction of virtually all land cover information that can be discerned from digitally enhanced TM images. The data are provided in a binary image format file.\n<br>\n\n#### BFO_dsp01_ccrs_avhrr_landcover_589\nThis land cover product was produced by NBIOME to generate an up-to-date, spatially and temporally consistent land cover map of the landmass of Canada for use by scientists and other users interested in environmental information at national and regional scales. The data were acquired by CCRS and were provided to BORIS for use. This data set is gridded and was produced from 10-day composite data of surface parameters. Temporally, the 10-day compositing periods begin 11-April-95 and ends 31-Oct-95. Spatially, the data cover the entire landmass of Canada. The data are stored in binary image format files.\n<br>\n\n#### BFO_dsp04_ers_freeze-thaw_maps_590\nThe BOREAS DSP-4 team acquired and analyzed imaging radar data from the ESA's ERS-1 over a complete annual cycle at the BOREAS sites in Canada in 1994 to detect shifts in radar backscatter related to varying environmental conditions. Two independent transitions correlating with snow melt and soil thaw onset, and possible canopy thaw were revealed by the data. The results demonstrated that radar provides an ability to observe thaw transitions at the beginning of the growing season, which in turn helps constrain the length of the growing season. The data presented here are gridded maps of landscape freeze/thaw state derived from backscatter change maps. The backscatter change maps were computed from radar backscatter images covering the southern BOREAS sites. The freeze/thaw classifications were determined through application of a change detection threshold based on temporal backscatter change relative to a winter-time frozen reference state. The data are provided as both ASCII text and as binary image (.gif) format files.\n<br>\n\n#### BFO_dsp05_ccrs_avhrr_npp_591\nThe BOREAS DSP-5 team generated a NPP image over the BOREAS region from a process-based ecosystem model, the Boreal Ecosystem Productivity Simulator (BEPS). The NPP image was created from a series of composited AVHRR images from April 11 - September 10, 1994. This document describes how the NPP is generated . The NPP data are stored in a binary image file.\n<br>\n\n#### BFO_dsp06_casi_lai_cc_592\nThe LAI and canopy closure images over the BOREAS conifer flux tower sites were produced at a spatial resolution of 30 m using the Forest-Light Interaction Model. The data used were obtained by the CASI instrument in high spatial resolution mode in the winter of 1994. Additional high resolution LAI and canopy closure images were produced over the two black spruce flux tower sites using the FLIM-CLUS algorithm. The data are stored in binary image format.\n<br>\n\n#### BFO_dsp08_polder_surface_params_594\nThis data set contains maps of surface reflectance and ve getation parameters derived from imagery collected by the POLDER instrument over BOREAS conifer tower sites in the Southern Study Area(SSA) during 1994. The POLDER imagery proficed in this data set was collected on June 1, and July 21, 1994 from the NASA C-130 aircraft platform.\n<br>\n\n#### BFO_dsp09_moss_map_595\nBOREAS follow-on group DSP-9 mapped surface moss type at three scales (1 km, 30 m, and 10 m) based on observed associations between moss cover and land cover type. In the BOREAS Northern (NSA) and Southern (SSA) Study Areas, we utilized land cover derived from Landsat TM (30 m) and ground measurements/observations, soils maps, and field observations to establish associations between moss and land cover. At the BOREAS regional scale, the 1 km moss cover map was developed using a 1 km AVHRR land cover map for a 619 by 821 km subset of the BOREAS region. Our regional moss cover map is largely based on inferences from the 1 km land cover analysis and from ground observations in the study areas. The 30 m moss map covers the BOREAS Southern Study Area. The 10 m map covers the BOREAS NSA Old Black Spruce tower site.\n<br>\n\n#### BFO_dsp09_sask_fire_map_596\nThis data set contains a pair of raster images and a spreadsheet chronicling the most recent fire history of Saskatchewan from 1945 to 1996. This data set was developed from a series of ARC/INFO export files of annual fire data that were compiled and provided by the Saskatchewan Environment and Resource Management (SERM) Wildlife Branch.\n<br>\n\n#### BFO_dsp10_landcover_tm_reclassed_597\nThese images were produced by aggregating a reclassified version of the 30-m land cover Thematic Mapper classification by CCRS and are now available at multiple resolutions (10x5 minutes, and 30 minutes). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups. Characteristics of the individual products are described in the data set guide document.\n<br>\n\n#### BFO_dsp10_fpar_lai_1994_585\nExisting BOREAS gridded data sets were processed into projections and scales required by the Follow-on modeling teams. Existing TM and AVHRR based landcover maps, AVHRR-FPAR maps, AVHRR-LAI maps, moss cover maps, and a new peatland distribution map were regridded to scales of 2 km, 10 by 5 minute, and half-degree grids for use by the BOREAS Follow-On Carbon and Hydro-Meteorological modeling groups.\n<br>\n\n#### BFO_dsp10_landcover_598\nThese images were produced by aggregating the 1-km land cover classification by Lou Steyaert at multiple resolutions (2 km, 10x5 minutes, and 0.5 degree). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups to have a number of data sets available in common grid projections and scales for intercomparison studies. Characteristics of the individual products are described in the data set guide document.\n<br>\n\n#### BFO_dsp10_moss_cover_599\nExisting 1-km moss cover classifications were reprocessed and are now available at multiple resolutions (2 km, 10x5 minutes, and 0.5 degree). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups to have a number of data sets available in common grid projections and scales for intercomparison studies. Characteristics of the individual products are described in the data set guide document.\n<br>\n\n#### BFO_dsp10_ndvi_600\nThese images were produced by averaging the 1-km FASIR-NDVI maps by Jing Chen to a 10' (horizontal) by 5' (vertical) pixel size in a straight latitude/longitude grid. Each pixel represents the average NDVI of the 1-km pixels that fall in each 10' by 5' pixel, where more than 50% of the 1-km pixels in the 10' by 5' area are not cloud and are not missing. If more than 50% of the 1-km pixels are missing or cloudy, a value of 0 is assigned to the 10' by 5' pixel.\n<br>\n\n#### BFO_dsp10_peatlands_601\nThese images were produced by aggregating 1' gridded data layers derived from the polygon-based Peatlands of Canada Database (Tarnocai et al., 2000) to 10' (horizontal) by 5' (vertical) and to 0.5 degree by 0.5 degree (or 30' by 30') pixel sizes in straight latitude/longitude grids. See the Peatlands Map of Canada data set for more information on the original data product that this is based on.\n<br>\n\n#### BFO_dsp10_landcover_tm_mosaic_602\nExisting 30-m land cover Thematic Mapper classification by CCRS was aggregated and reprocessed and are now available at multiple resolutions (10x5 minutes and 30 minutes). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups. Characteristics of the individual products are described in the data set guide document.\n<br>\n\n#### BFO_flx01_derived_data_603\nThe BOREAS Follow-On FLX-01 team derived NEE, GEE, and Respiration using measured tower C02 flux measurements taken at the NSA-OBS site. The data provided contain half-hourly measurements as well as 4 and 5 day binned data sets. The derived data covers the period from March 1994 through the end of 1998.\n<br>\n\n#### BFO_flx01_flux_met_temp_604\nThe BOREAS Follow-On FLX-01 team collected tower flux, surface meteorological, and soil temperature data at the BOREAS NSA-OBS site continuously from March 1994 through December 1998. Data from March 1994 through October 1996 are included in the BOREAS TF-03 effort while data from the end of October 1996 through December 1998 are included in the BOREAS Follow-on FLX-01 effort.\n<br>\n\n#### BFO_flx03_area_avg_flux_586\nCalculations of area-averaged fluxes using extracted flux data from BORIS.\n<br>\n\n#### BFO_flx04_nsa_burn_flux_587\nTower flux and meteorological data were collected above 4 black spruce forest sites in the NSA that experienced stand-replacing wildfires in 1989,1981,1964 and 1930. At each site, 4-6 weeks of data were collected during the peak growing season (June-September) in either 1999 or 2000. Fluxes were measured using paired portable solar powered eddy flux systems. The data are part of an ongoing age sequence study that will result in year round eddy flux and meteorological measurements in seven sites that burned between 2 and 150 years ago.\n<br>\n\n#### BFO_hmet01_goes8_level2_srb_605\nThe BOREAS RSS-14 team collected and processed several Level-1 GOES-7 and GOES-8 image data sets for 1994-1996, and GOES-7 Level-2 for 1994 over the BOREAS study region. This data set contains shortwave and longwave radiation images at the surface and top of the atmosphere derived from collected GOES-8 data. These GOES-8 Level-2 data cover the period from 12-Feb-1996 to 22-Oct-1996. There are images missing from the temporal series. The main difference between this data set and 1994 data set is in the spatial coverage and the grid cell size.\n<br>\n\n#### BFO_hmet01_ssmi_precip_606\nA gridded data set has been assembled over the BOREAS hydro-meteorological study region that combines a precipitation data set based on a rain gauge network with precipitation estimates based on SSM/I satellite images. The result is an hourly precipitation data set covering 122 consecutive days beginning on June 1, 1996.\n<br>\n\n#### BFO_hmet02_gridded_met_phase2-3_607\nPhase II and III gridded data sets have been generated by an objective analysis scheme using all of the surface meteorological station data over BOREAS region for 1994-1996. Additionally, FSU GOES incoming solar radiation retrievals and BOREAS rain radar retrievals during portions of 1994 and 1996 were used when remote sensing products were available. Two Phase II, Northern Study Area (NSA) and Southern Study Area (SSA) grids, and one Phase III, Regional, gridded data sets have been assembled on a HOURLY time step. The meteorological variables in this data set are surface air pressure, air temperature, dew point temperature, wind speed, wind direction, precipitation, incoming solar (shortwave) radiation, and incoming infrared (longwave) radiation.\n<br>\n\n#### BFO_hmet03_hourly_met_p1_608\nPoint data developed from in situ observations at four flux tower sites were combined to produce continuous, above the canopy, meteorological forcing data sets. Data from the OA and OBS sites in the SSA and the Fen and OBS sites in the NSA were used to create continuous time series with a time step of one hour, covering the period from 1-Jan-1994 through 1-Dec-1996. Meteorological variables of interest are surface air pressure, air temperature, dew point temperature, wind speed, wind direction, precipitation, incoming solar (shortwave) radiation, and incoming infrared (longwave) radiation.\n<br>\n\n#### BFO_hmet04_src_nsa_96-98_609\nAs part of the BOREAS Follow-on, an extended period of data collection was supported in the NSA because of the continued efforts at the NSA-OBS site. This data set contains near-surface meteorological data collected and averaged over 15 minute intervals from two sites in the NSA, the SRC tower at the Thompson airport (YTH) and a temporary walkup wooden tower at the Old Black Spruce (OBS) tower site.\n<br>\n\n#### BFO_mod01_gridded_met_p3_daily_610\nThe Phase 3 gridded data sets provided by HMet-01 on an hourly time step have been converted to averaged daily files by the MOD-01 group to reduce the size and number of files used for input to some of the carbon models.\n<br>\n\n#### ssafcovr_251\nThis data set was prepared by BORIS staff by processing the original vector data into raster files. The original data were received as ARC/INFO coverages or as export files from SERM. The data include information on forest parameters for the BOREAS SSA MSA. The data are stored in binary, image format files.\n<br>\n\n#### nsafcovr_252\nThis data set was processed by BORIS staff from the original vector data of species, crown closure, cutting class, and site classification/subtype into raster files. The original polygon data were received from Linnet Graphics, the distributor of data for MNR. In the case of the species layer, the percentages of species composition were removed. This reduced the amount of information contained in the species layer of the gridded product, but it was necessary in order to make the gridded product easier to use. The original maps were produced from 1:15,840-scale aerial photography collected in 1988.\n<br>\n\n#### h01_shd_253\nThe BOREAS HYD-01 team made several measurements related to soil moisture and soil properties. These soil hydraulic properties were determined at the flux tower sites based on analysis of in situ tension infiltrometer tests and laboratory determined water retention from soil cores collected during the 1994-95 field campaigns. Results from this analysis are saturated hydraulic conductivity, and fitting parameters for the van Genuchten-Mualem soil hydraulic conductivity and water retention function at flux tower sites.\n<br>\n\n#### h01uncpd_254\nUnder-canopy precipitation measurements were made by the BOREAS HYD-01 science team in 1994, 1995, and 1996 at various flux tower sites in the NSA and SSA. In 1994, these data were collected at the NSA-OJP, NSA-YJP, SSA-OJP, and SSA-YJP sites. Starting in 1995 and ending in 1997, data were collected at the NSA-OBS, NSA-OJP, NSA-YJP, and SSA-OA. These data were collected to support HYD-01 research by measuring the amount of water that falls through the canopy and is intercepted by the ground or moss. These data coincide with volumetric soil moisture measurements made by HYD-01.\n<br>\n\n#### h01smpvd_255\nThe BOREAS HYD-01 team made measurements of volumetric soil moisture at the SSA and NSA tower flux sites in 1994 and at selected tower flux sites in 1995-97. Different methods were used to collect these measurements, including neutron probe and manual and automated TDR. In 1994, the measurements were made every other day at the NSA-OJP, NSA-YJP, NSA-OBS, NSA-Fen, SSA-OJP, SSA-YJP, SSA-Fen, SSA-YA, and SSA-OBS sites. In 1995-97, when automated equipment was deployed at NSA-OJP, NSA-YJP, NSA-OBS, SSA-OBS, and SSA-OA, the measurements were made as often as every hour.\n<br>\n\n#### h02swed_256\nThe surface meteorological data collected at the BOREAS tower and ancillary sites are being used as inputs to an energy balance model to monitor the amount of snow storage in the boreal forest region. The BOREAS HYD-02 team used snow water equivalent (SWE) derived from an energy balance model and in situ observed SWE to compare the SWE inferred from airborne and spaceborne microwave data, and to assess the accuracy of microwave retrieval algorithms. The major external measurements that are needed are snowpack temperature profiles, and in situ snow areal extent and snow water equivalent data. The data in this data set were collected during February 1994 and cover portions of the SSA, NSA, and the transect areas.\n<br>\n\n#### h03stdnd_257\nThe BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of stem density from a variety of sites. Stem density measurements were made during the FFC-W 1996 in the SSA only using standard techniques. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems.\n<br>\n\n#### h03candd_258\nThe BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of canopy density (closure) from a variety of sites. Canopy density measurements were made during the FFC-W and FFC-T 1994 in both the SSA and NSA using a forest densiometer. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems.\n<br>\n\n#### h03sd96d_259\nThe BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of snow depth, snow density in 3-cm intervals, an integrated snow pack density and snow water equivalent (SWE), and snow pack physical properties from snow pit evaluation taken in 1994 and 1996. The data were collected from several sites in both the SSA and the NSA. A variety of standard tools were used to measure the snowpack properties, including a meter stick (snow depth), a 100 cc snow density cutter, a dial stem thermometer and the Canadian snow sampler as used by HYD-04 to obtain a snow pack-integrated measure of SWE. This study was undertaken to predict spatial distributions of snow properties important to the hydrology, remote sensing signatures, and the transmissivity of gases through the snow.\n<br>\n\n#### h03dbhd_264\nThe BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of tree diameter at breast height (DBH) from a variety of sites. This study was undertaken to predict spatial distributions of energy transfer, snow properties important to the hydrology, remote sensing signatures, and transmissivity of gases through the snow and their relation to forests in boreal ecosystems.\n<br>\n\n#### h3scmet_265\nThe BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set includes measurements of wind speed and direction; air temperature; relative humidity; and canopy, trunk, and snow surface temperatures within three forest types. The data were collected in the SSA-OJP (1994) and SSA-OBS and SSA-OA (1996). Measurements were taken for 3 days in 1994 and 4 days at each site in 1996. These measurements were intended to be short term to allow the relationship between subcanopy measurements and those collected above the forest canopy to be determined. The subcanopy estimates of wind speed were used in a snow melt model to help predict the timing of snow ablation.\n<br>\n\n#### h03scrdd_266\nThe BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains solar radiation measurements from several pyranometers (solar radiometers) placed on the snow surface in jack pine (1994) and black spruce and aspen forests (1996). An array of radiometers was used to collect data for 3-4 consecutive days in each forest type to study the hypothesis that energy transfer and snow water equivalent would vary spatially as a function of canopy closure. The quality of the data is good, because the days were generally clear and the radiometers were checked daily to remove anything that landed on the radiometers.\n<br>\n\n#### h04assd_267\nThe BOREAS HYD-04 work was focused on collecting data during the winter field campaign (FFC-W) to improve the understanding of winter processes within the boreal forest. Airborne remote sensing data (gamma, passive microwave) were acquired along a series of flight lines established in the vicinity of the BOREAS study areas. Ground snow surveys were conducted along selected sections of these aircraft flight lines. These calibration segments were typically 10-20 km in length, and ground data were collected at 1- to 2-km intervals. The development and validation of remote sensing algorithms will provide the means to extend the knowledge of these processes and states from the local to the regional scale.\n<br>\n\n#### h04stsnd_268\nThe BOREAS HYD-04 work was focused on collecting data during the winter field campaign (FFC-W) to improve the understanding of winter processes within the boreal forest. Snow surveys were conducted at special snow courses throughout the 1993/94, 1994/95, 1995/96, and 1996/97 winter seasons. These snow courses were located in different boreal forest land cover types (i.e., old aspen, old black spruce, young jack pine, forest clearing, etc.) to document snow cover variations throughout the season as a function of different land cover. Measurements of snow depth, density, and water equivalent were acquired on or near the first and fifteenth of each month during the snow cover season. The development and validation of remote sensing algorithms will provide the means to extend the knowledge of these processes and states from the local to the regional scale. A specific thrust of the research is the development and validation of snow cover algorithms from airborne passive microwave measurements.\n<br>\n\n#### h5flxd_269\nThe BOREAS HYD-05 team collected tower flux, surface meteorological, and surface temperature data on a frozen lake (Namekus Lake) and in a mature jack pine forest in the Beartrap Creek watershed. Both sites were located in the BOREAS SSA. The objective of this study was to characterize the winter energy and water vapor fluxes, as well as related properties (such as snow density, depth, temperature, and melt) for forested and nonforested areas of the boreal forest. Data were collected on Namekus Lake in the winters of 1994 and 1996, and at Beartrap Creek in the winter of 1994 only.\n<br>\n\n#### h6acgsmd_270\nThis data set contains percent soil moisture (by weight) (and/or water content if there is a moss/humus layers measured from aircraft using a terrestrial gamma ray instrument. There is also data that indicates the location of the aircraft at the time it collected the terrestrial gamma ray data for the various flight lines and bins. The location information contains a list of coordinates that indicate the path of the aircraft for each bin. The data were collected during four time periods from September 1993 to September 1994 over the Southern Study Area (SSA) and two time periods from February to August 1994 over the Northern Study Area (NSA).\n<br>\n\n#### h06grsmd_271\nThis data set contains percent soil moisture ground measurements. These data were collected on the ground along the various flight lines flown in the Southern and Northern Study Areas (SSA and NSA) during 1994 by the gamma ray instrument. This data set contains information on the locations of field in-site measurements of soil moisture, depth of moss/humus layer, and water content of the moss/humus layer and contains information on soil conditions and vegetative cover around the sites.\n<br>\n\n#### h06grmsd_272\nThis data set contains water content measurements of the moss/humus layer, where it existed. These data were collected along various flight lines in the Southern and Northern Study Areas (SSA and NSA) during 1994.\n<br>\n\n#### h08gm94_273\nThe BOREAS HYD-08 team made measurements of surface hydrological processes that were collected at the NSA OBS Tower Flux site in 1994 and at Joey Lake, Manitoba, to support their research into point hydrological processes and the spatial variation of these processes. The data collected may be useful in characterizing canopy interception, drip, throughfall, moss interception, drainage, evaporation, and capacity during the growing season at daily temporal resolution. This particular data set contains the gravimetric moss moisture measurements from June to September 1994. A nested spatial sampling plan was implemented to support research into spatial variations of the measured hydrological processes and ultimately the impact of these variations on modeled carbon and water budgets.\n<br>\n\n#### h08gm96_274\nThe BOREAS HYD-08 team made measurements of surface hydrological processes that were collected at the SSA-OBS Tower Flux site in 1996 to support its research into point hydrological processes and the spatial variation of these processes. Data collected may be useful in characterizing canopy interception, drip, throughfall, moss interception, drainage, evaporation, and capacity during the growing season at daily temporal resolution. This particular data set contains the gravimetric moss moisture measurements from July to August 1996. To collect these data, a nested spatial sampling plan was implemented to support research into spatial variations of the measured hydrological processes and ultimately the impact of these variations on modeled carbon and water budgets.\n<br>\n\n#### h08gp96_275\nThe BOREAS HYD-08 team made measurements of surface hydrological processes at the SSA-OBS Tower Flux site to support its research into point hydrological processes and the spatial variation of these processes. Data collected may be useful in characterizing canopy interception, drip, throughfall, moss interception, drainage, evaporation, and capacity during the growing season at daily temporal resolution. This particular data set contains the gross precipitation measurements for July to August 1996. Gross precipitation is the precipitation that falls that is not intercepted by tree canopies.\n<br>\n\n#### h8utmdem_276\nThese DEMs were produced from digitized contours at a cell resolution of 100 meters. Vector contours of the area were used as input to a software package that interpolates between contours to create a DEM representing the terrain surface. The vector contours had a contour interval of 25 feet. The data cover the BOREAS MSAs of the SSA and NSA and are given in a UTM map projection.\n<br>\n\n#### h8thrfld_277\nThe BOREAS HYD-08 team made measurements of surface hydrological processes at the SSA (1996) and NSA OBS (1994) Tower Flux sites, supporting its research into point hydrological processes and the spatial variation of these processes. These data were collected during the 1994 and 1996 field campaigns. Data collected may be useful in characterizing canopy interception, drip, throughfall, moss interception, drainage, evaporation, and capacity during the growing season at daily temporal resolution. This particular data set contains the measurements of throughfall, which is the amount of precipitation that fell through the canopy. A nested spatial sampling plan was implemented to determine spatial variations of the measured hydrological processes and ultimately the impact of these variations on modeled carbon and water budgets.\n<br>\n\n#### h09rradi_278\nThe BOREAS HYD-09 team collected data on precipitation and streamflow over portions of the NSA and SSA. This data set contains Cartesian maps of rain accumulation for 1-hour and daily periods during the summer of 1994 over the SSA only (not the full view of the radar). A parallel set of 1-hour maps for the whole radar view has been prepared and is available upon request from the HYD-09 personnel. An incidental benefit of the areal selection was the elimination of some of the less accurate data, because for various reasons the radar rain estimates degrade considerably outside a range of about 100 km. The data are available in tabular ASCII files.\n<br>\n\n#### h09stmgd_279\nThese streamflow data were collected by the HYD-09 science team to support its research into meltwater supply to the soil during the spring melt period. These data were also collected for HYD-09's research into the evolution of soil moisture, evaporation, and runoff from the end of the snowmelt period through freeze up. Data were collected in the BOREAS SSA and NSA from April until October in 1994, 1995, and 1996. Gauges SW1 and NW1 were operated year-round; however, data may not be available for both gauges for all 3 years.\n<br>\n\n#### lsatmssd_435\nThe Earth Resources Technology Satellite (ERTS) Program launched the first of a series of satellites (ERTS-1) in 1972. Part of the NASA Earth Resources Survey Program, the ERTS Program and the ERTS satellites were later renamed Landsat to better represent the civil satellite program's prime emphasis on remote sensing of land resources. Landsat satellites 1 through 5 carry the MSS sensor. CCRS and BOREAS personnel gathered a set of MSS images from Landsat satellites 1, 2, 4 and 5 covering the dates of 21-Aug-1972 to 05-Sep-1988. The data are provided in binary image format files. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images that may be viewed and the image data files downloaded using a convenient viewer utility.\n<br>\n\n#### ltm_ii3a_280\nFor BOREAS, the level-3a Landsat TM data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI. Although very similar in content to the level-3s Landsat TM products, the level-3a images were created to provide users with a more usable BSQ format and to provide information that permitted direct determination of per-pixel latitude and longitude coordinates. Geographically, the level-3a images cover the BOREAS NSA and SSA. Temporally, the images cover the period of 22-Jun-1984 to 30-Jul-1996. The images are available in binary, image-format files. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images that may be viewed and the image data files downloaded using a convenient viewer utility.\n<br>\n\n#### ltm_ii3b_425\nFor BOREAS, the level-3b Landsat TM data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI. Although very similar in content to the level-3a Landsat TM products, the level-3b images were created to provide users with a directly usable at-sensor radiance image. Geographically, the level-3b images cover the BOREAS NSA and SSA. Temporally, the images cover the period of 22-Jun-1984 to 30-Jul-1996. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images that may be viewed using a convenient viewer utility.\n<br>\n\n#### ltm_ii3p_426\nFor BOREAS, the level-3p Landsat TM data were used to supplement the level-3s Landsat TM products. Along with the other remotely sensed images, the Landsat TM images were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI. Although very similar to the level-3s Landsat TM products, the level-3p images were processed with ground control information which improved the accuracy of the geographic coordinates provided. Geographically, the level-3p images cover the BOREAS NSA and SSA. Temporally, the four images cover the period of 20-Aug-1988 to 07-Jun-1994. Except for the 07-Jun-1994 image which contains 7 bands, the other three only contain 3 bands. Companion files include (1)an image inventory listing to inform users of the images that are available and (2) example thumbnail images that may be viewed using a convenient viewer utility.\n<br>\n\n#### ltm_ii3s_427\nFor BOREAS, the level-3s Landsat TM data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI. CCRS collected and supplied the level-3s images to BOREAS for use in the remote sensing research activities. Geographically, the bulk of the level-3s images cover the BOREAS NSA and SSA with a few images covering the area between the NSA and SSA. Temporally, the images cover the period of 22-Jun-1984 to 30-Jul-1996. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images that may be viewed using a convenient viewer utility.\n<br>\n\n#### aoci0bil_281\nThe level-0 AOCI imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas. The AOCI was the only remote sensing instrument flown with wavelength bands specific to the investigation of various aquatic parameters such as chlorophyll content and turbidity. Only one flight of the AOCI instrument was made onboard the ER-2 aircraft on 21-Jul-1994 over the SSA. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images that may be viewed and the image data files downloaded using a convenient viewer utility.\n<br>\n\n#### tims0bil_282\nFor BOREAS, the TIMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information over the primary study areas. The level-0 TIMS images cover the time periods of 16-Apr-1994 to 20-Apr-1994 and 06-Sep-1994 to 17-Sep-1994. The images are available in their original uncalibrated format. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images that may be viewed and the image data files downloaded using a convenient viewer utility.\n<br>\n\n#### maslv1b_560\nFor BOREAS, the MAS images, along with the other remotely sensed data, were collected to provide spatially-extensive information over the primary study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR (fraction of Photosynthetically Active Radiation), and LAI (Leaf Area Index). Collection of the MAS images occurred over the study areas during the 1994 field campaigns. The level-1B MAS data cover the dates of 21-Jul-1994, 24-Jul-1994, 04-Aug-1994 and 08-Aug-1994. The data are not geographically/geometrically corrected; however, files of relative X and Y coordinates for each image pixel were derived by using the C130 INS data in a MAS scan model.\n<br>\n\n#### tims1bsq_436\nThe BOREAS Staff Science Aircraft Data Acquisition Program focused on providing the research teams with the remotely sensed satellite data products they needed to compare and spatially extend point results. For BOREAS, the TIMS imagery, along with other aircraft images, was collected to provide spatially extensive information over the primary study areas. The level-1B TIMS images cover the time periods of 16-Apr-1994 to 20-Apr-1994 and 06-Sep-1994 to 17-Sep-1994. The system calibrated images are stored in binary image format files. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images.\n<br>\n\n#### mas_lv2_561\nThe BOREAS Staff Science Aircraft Data Acquisition Program focused on providing the research teams with the remotely sensed aircraft data products they needed to compare and spatially extend point results. The MAS images, along with other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes biophysical parameter maps such as surface reflectance and temperature. Collection of the MAS images occurred over the study areas during the 1994 field campaigns. The level-2 MAS data cover the dates of 21-Jul-1994, 24-Jul-1994, 04-Aug-1994 and 08-Aug-1994. The data are not geographically/geometrically corrected; however, files of relative X and Y coordinates for each image pixel were derived by using the C130 navigation data in a MAS scan model. The data are provided in binary image format files.\n<br>\n\n#### avhrrl3b_481\nThe BOREAS Staff Science Satellite Data Acquisition Program focused on providing the research teams with the remotely sensed satellite data products they needed to compare and spatially extend point results. Data acquired from the AVHRR instrument on the NOAA-9, -11, -12, and -14 satellites were processed and archived for the BOREAS region by the MRSC and BORIS. The data were acquired by CCRS and were provided for use by BOREAS researchers. A few winter acquisitions are available, but the archive contains primarily growing season imagery. These gridded, at-sensor radiance image data cover the period of 30-Jan-1994 to 18-Sep-1996. Geographically, the data cover the entire 1000 km x 1000 km BOREAS Region. The data are stored in binary image format files.\n<br>\n\n#### spot_3s_437\nFor BOREAS, the level-3s SPOT data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI. The SPOT images acquired for the BOREAS project were selected primarily to fill temporal gaps in the Landsat TM image data collection. CCRS collected and supplied the level-3s images to BORIS for use in the remote sensing research activities. Spatially, the level-3s images cover 60-by 60-km portions of the BOREAS NSA and SSA. Temporally, the images cover the period of 17-Apr-1994 to 30-Aug-1996. The images are available in binary image format files. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images.\n<br>\n\n#### avhrrl4b_438\nThe BOREAS Staff Science Satellite Data Acquisition Program focused on providing the research teams with the remotely sensed satellite data products they needed to compare and spatially extend point results. MRSC and BORIS personnel acquired, processed, and archived data from the AVHRR instruments on the NOAA-11 and -14 satellites. The AVHRR data were acquired by CCRS and were provided to BORIS for use by BOREAS researchers. These AVHRR level-4b data are gridded, 10-day composites of at-sensor radiance values produced from sets of single-day images. Temporally, the 10-day compositing periods begin 11-Apr-1994 and end 10-Sep-1994. Spatially, the data cover the entire BOREAS region. The data are stored in binary image format files. Companion files include example thumbnail images that may be viewed and the image data files downloaded using a convenient viewer utility.\n<br>\n\n#### ns001bil_440\nFor BOREAS, the NS001 TMS imagery, along with the other remotely sensed images, was collected in order to provide spatially extensive information over the primary study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI. Data collections occurred over the study areas during the 1994 field campaigns.Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images.\n<br>\n\n#### ns0012bq_482\nFor BOREAS, the NS001 TMS images, along with the other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI. Collection of the NS001 images occurred over the study areas during the 1994 field campaigns. The Level-2 NS001 data are atmospherically corrected versions of some of the best original NS001 imagery and cover the dates of 19-Apr-1994, 07-Jun-1994, 21-Jul-1994, 08-Aug-1994, and 16-Sep-1994. The data are not geographically/geometrically corrected; however, files of relative X and Y coordinates for each image pixel were derived by using the C130 INS data in an NS001 scan model. The data are provided in binary image format files. Note also that the top portion of the ASCII header file in each Level-2 NS001 image product indicates that the band 8 data are 'Scaled Reflectance' when in fact they are 'Scaled Temperatures.\n<br>\n\n#### panpfcov_283\nThis data set provides detailed canopy, understory, and ground cover, height, density, and condition information for PANP in the western portion of the BOREAS SSA in vector form.\n<br>\n\n#### reg_aeac_284\nThis data set is based on the GTOPO30 DEM produced by the USGS EDC. The BOREAS region (1,000km x 1000km) was extracted from the GTOPO30 data and reprojected by BOREAS staff into the AEAC projection. The pixel size of these data is 1 km.\n<br>\n\n#### regsoilr_285\nThis data set was gridded by BORIS staff from a vector data set received from Canadian Soil Information System (CanSIS). The original data came in two parts that covered Saskatchewan and Manitoba. The data were gridded and merged into one data set of 84 files covering the BOReal Ecosystem-Atmosphere Study (BOREAS) region. The data were gridded into the Albers Equal-Area Conic (AEAC) projection. Because the mapping of the two provinces was done separately in the original vector data, there may be discontinuities in some of the soil layers because of different interpretations of certain soil properties.\n<br>\n\n#### rss1para_286\nThe BOREAS RSS-01 team collected surface reflectance and transmittance data from three forested sites in the SSA. This data set contains averaged reflectance factors and transmitted radiances measured by the PARABOLA instrument at selected sites in the BOREAS SSA at different view angles and at three wavelength bands throughout the day. PARABOLA measurements were made during each of the three BOREAS IFCs during the growing season of 1994 at three SSA tower flux sites as well as during the FFC-T. Additional measurements were made in early and mid-1996 during the FFC-W and during IFC-2.\n<br>\n\n#### asasrefl_287\nAtmospherically-corrected bidirectional reflectance factor means for small homogeneous areas from several BOREAS sites were derived from multi-spectral, multi-angle imagery acquired by the Advanced Solidstate Array Spectroradiometer (ASAS) aboard the C-130 aircraft platform in 1994 and 1996.\n<br>\n\n#### asas_l1b_562\nThe BOREAS RSS-02 team used the ASAS instrument, mounted on the NASA C-130 aircraft, to create at-sensor radiance images of various sites as a function of spectral wavelength, view geometry (combinations of view zenith angle, view azimuth angle, solar zenith angle, and solar azimuth angle), and altitude. The level-1b ASAS images of the BOREAS study areas were collected from April to September 1994 and March to July 1996. The data are provided in binary image format files.\n<br>\n\n#### rs3atmos_288\nThe BOREAS RSS-03 team collected and processed helicopter-based measurements of atmospheric conditions to estimates of aerosol optical thickness and atmospheric water vapor. The automatic sun-tracking photometer for helicopters was deployed during all three IFC's of 1994 at numerous tower and auxiliary sites in both the NSA and SSA. Seven spectral channels (440, 540, 613, 670, 870 and 1030 am) were chosen to span the visible and NIR wavelengths and to avoid gaseous absorption. One additional channel, 940 nm, was selected to measure the water column abundance above the helicopter platform.\n<br>\n\n#### BOREAS_RSS-03_Snapshots_289\nThis data set provides images of boreal forests in central Canada collected over numerous tower and auxiliary sites during the BOREAS Intensive Field Campaigns (IFCs) in the Northern (NSA) and Southern Study Areas (SSA). The images were acquired by helicopter with VHS video cameras during the green-up, peak, and senescent stages of the growing season from May-September of 1994. These snapshots were generated from VHS imagery and converted to .jpg format.\n<br>\n\n#### rss3hmmr_290\nThe RSS-03 team acquired helicopter-based measurements of forested sites during BOREAS with a Barnes MMR. The data were collected in 1994 during the rhree BOREAS IFCs at numerous tower and auxiliary sites in both the NSA and SSA. The 15-degree FOV of the MMR yielded approximately 79 m from the 300 m altitude ground resolution. The MMR has seven spectral bands that are similar to the Landsat TM bands, ranging from the blue region to the thermal. Note:\n<br>\n\n#### rs3se590_291\nThe BOREAS RSS-03 team collected multiple remotely sensed data sets from the NASA UH-1 helicopter. This data set includes helicopter-based radiometric measurements of forested sites acquired during BOREAS made with an SE-590 processed to reflectance factors. The data used in this analysis were collected in 1994 during the three BOREAS IFCs at numerous tower and auxiliary sites in both the NSA and the SSA. The 15-degree FOV of the SE-590 yielded a ground resolution of approximately 79 m at the 300-m nominal altitude.\n<br>\n\n#### rss4lib_292\nThe BOREAS RSS-04 team focused its efforts on deriving estimates of LAI and leaf chlorophyll and nitrogen concentrations from remotely sensed data for input into the Forest BGC model. This data set contains measurements of jack pine (Pinus banksiana) needle biochemistry from the BOREAS SSA in July and August 1994. The data contain measurements of current and year-1 needle chlorophyll, nitrogen, lignin, cellulose, and water content for the OJP flux tower and nearby auxiliary sites. The data have been used to test a needle reflectance and transmittance model, LIBERTY (Dawson et al., in press). The source code for the model and modeled needle spectra for each of the sampled tower and auxiliary sites are provided as part of this data set. The LIBERTY model was developed and the predicted spectral data generated to parameterize a canopy reflectance model (North, 1996) for comparison with AVIRIS, POLDER, and PARABOLA data.\n<br>\n\n#### r04laifd_293\nThe RSS-04 team collected several data sets related to leaf, plant, and stand physical, optical, and chemical properties. This data set contains leaf area indices and FPAR measurements which were taken at the three conifer sites in the BOREAS SSA during August 1993 and at the jack pine tower flux and a subset of auxiliary sites during July and August 1994. The measurements were made with LAI-2000 and Ceptometer instruments. The measurements were taken for the purpose of model parameterization and to test empirical relationships that were hypothesized between biophysical parameters and remotely sensed data.\n<br>\n\n#### r07elaid_294\nThe BOREAS RSS-07 team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of LAI from remotely sensed images. Ground measurements of LAI and FPAR absorbed by the plant canopy were made using the LAI-2000 and TRAC optical instruments during focused periods from 09-AUG-1993 to 19-SEP-1994. The measurements were intensive at the NSA and SSA tower sites, but were made just once or twice at auxiliary sites.\n<br>\n\n#### rs7tmlai_441\nThe BOREAS RSS-07 team used Landsat TM images processed at CCRS to produce images of LAI for the BOREAS study areas. Two images acquired on June 6 and August 9, 1991 were used for the SSA, and one image acquired on June 9, 1994 was used for the NSA. The LAI images are based on ground measurements and Landsat TM RSR images. The data are stored in binary image-format files. Companion files include example thumbnail images that may be viewed and the image data files downloaded using a convenient viewer utility.\n<br>\n\n#### r7laifpa_442\nThe BOREAS RSS-07 team focused their efforts on developing and validating procedures and algorithms that would allow the retrieval of LAI from remotely sensed vegetation indices. This data set contains images of LAI and FPAR that were produced from the AVHRR Level-4c ten-day composite NDVI images produced at CCRS for the three summer IFCs in 1994. The algorithms were developed based on ground measurements and Landsat TM images (Chen and Cihlar, 1996; Chen, 1996b). The data are provided in binary image format files. Companion files include example thumbnail images that may be viewed and the image data files downloaded using a convenient viewer utility.\n<br>\n\n#### biomebgc_295\nBIOME-BGC is a general ecosystem process model designed to simulate biogeochemical and hydrologic processes across multiple scales (Running and Hunt, 1993). In this investigation, BIOME-BGC was used to estimate daily water and carbon budgets for the BOREAS tower flux sites for 1994. Carbon variables estimated by the model include gross primary production (i.e., net photosynthesis), maintenance and heterotrophic respiration, net primary production, and net ecosystem carbon exchange. Hydrologic variables estimated by the model include snowcover, evaporation, transpiration, evapotranspiration, soil moisture, and outflow. The information provided by the investigation includes input initialization and model output files for various sites in tabular ASCII format.\n<br>\n\n#### biomebg2_296\nThe BOREAS RSS-08 team performed research to evaluate the effect of seasonal weather and landcover heterogeneity on boreal forest regional water and carbon fluxes using a process level ecosystem model, BIOME-BGC, coupled with remote sensing-derived parameter maps of key state variables. This data set contains derived maps of landcover type and crown and stem biomass as model inputs to determine annual evapotranspiration, gross primary production, autotrophic respiration and net primary productivity within the BOREAS SSA-MSA, at a 30 m spatial resolution. Model runs were conducted over a 3 year period from 1994-1996, images are provided for each of those years.\n<br>\n\n#### rss8brdf_505\nGround BRDF measurements were acquired by the Remote Sensing Science (RSS)-08 team to aid in the development of advanced spectral vegetation indices. The RSS-08 team measured reflectances at the double-scaffold towers in the Southern Study Area (SSA) Old Black Spruce (OBS) and Old Aspen (OA) sites during IFC-3 in 1994. The RSS-08 team also acquired stereo photography at the SSA-OA, SSA-OJP, and SSA-OBS sites during IFC-3.\n<br>\n\n#### rss8snow_428\nThe BOREAS RSS-08 team utilized Landsat TM images to perform mapping of snow extent over the SSA. This data set consists of two Landsat TM images which were used to determine the snow-covered pixels over the BOREAS SSA on 18-Jan-1993 and on 06-Feb-1994. Companion files include example thumbnail images that may be viewed using a convenient viewer utility.\n<br>\n\n#### rss8digi_506\nThe RSS08 team acquired stereo photography from the double-scaffold towers at the Southern Study Area (SSA) Old Black Spruce (OBS), Old Aspen (OA), and Old Jack Pine (OJP) sites during IFC-3 in 1994. The imagery of the canopy was taken from various perspectives. The RSS08 team also measured BRDF at the SSA-OA and -OBS sites during IFC-3.\n<br>\n\n#### rss10tom_443\nThe BOREAS RSS-10 team investigated the magnitude of daily, seasonal, and yearly variations of PAR from ground and satellite observations. This data set contains satellite estimates of surface-incident photosynthetically active radiation (PAR, 400-700 nm, MJ m-2) at 1 degree spatial resolution. The spatial coverage is circumpolar from latitudes of 41 to 66 degrees N latitude. The temporal coverage is from May through September for years 1979 through 1989. Eleven-year statistics are also provided: mean, standard deviation, and coefficient of variation for 1979-1989. The PAR estimates were derived from the global gridded ultraviolet reflectivity data product (average of 360, 380 nm) from the Nimbus-7 Total Ozone Mapping Spectrometer (TOMS). Image mask data are provided for identifying the boreal forest zone, and ocean/land and snow/ice covered areas. The data are available as binary image format data files. Companion files include example thumbnail images that may be viewed and the image data files downloaded using a convenient viewer utility.\n<br>\n\n#### r11sunpd_297\nThe BOREAS RSS-11 team operated a network of five automated (Cimel) and two hand-held (Miami) solar radiometers from 1994 to 1996 during the BOREAS field campaigns. The data provide aerosol optical depth measurements, size distribution, phase function, and column water vapor amounts over points in northern Saskatchewan and Manitoba, Canada. The data are useful for the correction of remotely sensed aircraft and satellite images. The data are provided in tabular ASCII files.\n<br>\n\n#### sunphair_298\nThe BOREAS RSS-12 team collected both ground and airborne sunphotometer measurements for use in characterizing the aerosol optical properties of the atmosphere during the BOREAS data collection activities. These measurements are to be used to: 1) measure the magnitude and variability of the aerosol optical depth in both time and space; 2) determine the optical properties of the boreal aerosols; and 3) atmospherically correct remotely sensed data acquired during BOREAS. This data set contains airborne tracking sunphotometer data that were acquired from the C-130 aircraft during its flights over the BOREAS study areas. The data cover selected days and times from May to September 1994.\n<br>\n\n#### r12sunpd_299\nThe BOREAS RSS-12 team collected both ground and airborne sunphotometer measurements for use in characterizing the aerosol optical properties of the atmosphere during the BOREAS data collection activities. These measurements are to be used to: 1) measure the magnitude and variability of the aerosol optical depth in both time and space; 2) determine the optical properties of the boreal aerosols; and 3) atmospherically correct some remotely sensed data acquired during BOREAS. These data cover selected days and times from May to September 1994 and were taken from one of two ground sites near Candle Lake in the SSA. The data described in this document are from the field sunphotometer data.\n<br>\n\n#### goes71_444\nThe level-1 BOREAS GOES-7 image data were collected by Remote Sensing Science Team 14 (RSS-14) personnel at Florida State University (FSU) and delivered to BORIS. The data cover the period of 01-Jan-1994 through 08-Jul-1995, with partial to complete coverage on the majority of the days. The data include three bands with eight-bit pixel values. No major problems with the data have been identified. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images.\n<br>\n\n#### goes71a_300\nThe level-1a BOREAS GOES-7 image data was collected by Remote Sensing Science Team 14 (RSS-14) personnel at the Florida State University and processed to level-1a products by BORIS personnel. The primary objective for the GOES-7 images in 1994 was to collect visible, infrared (IR), and water vapor channel data covering the BOREAS region at a sufficiently high temporal frequency for subsequent use in analyzing weather events and deriving temporal surface radiation parameters and patterns that existed during the Focused and Intensive Field Campaigns (FFCs and IFCs). The transition and shifting of satellites from GOES-7 to GOES-8 in 1995 enabled good quality images to be acquired over the BOREAS region four times per day from January to June, giving a reasonable monitoring dataset. The data cover the period of 01-January-1994 through 08-July-1995 with partial to complete coverage on the majority of the days. The data include three-bands with eight-bit pixel values. No major problems with the data have been identified.\n<br>\n\n#### goes72_554\nThe BOREAS RSS-14 team collected and processed several GOES-7 and GOES-8 image data sets that covered the BOREAS study region. This data set contains images of shortwave and longwave radiation at the surface and top of the atmosphere derived from collected GOES-7 data. The data cover the time period of 05-Feb-1994 to 20-Sep-1994. The images missing from the temporal series were zero-filled to create a consistent sequence of files. The data are stored in binary image format files.\n<br>\n\n#### goes81_445\nThe BOREAS RSS-14 team collected and processed several GOES-7 and GOES-8 image data sets that covered the BOREAS study region. The level-1 BOREAS GOES-8 images are raw data values collected by RSS-14 personnel at FSU and delivered to BORIS. The data cover 14-Jul-1995 to 21-Sep-1995 and 01-Jan-1996 to 03-Oct-1996. The data start out containing three 8-bit spectral bands and end up containing five 10-bit spectral bands. No major problems with the data have been identified. The data are contained in binary image format files. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images.\n<br>\n\n#### goes81a_446\nThe BOREAS RSS-14 team collected and processed several GOES-7 and GOES-8 image data sets that covered the BOREAS study region. The level-1a GOES-8 images were created by BORIS personnel from the level-1 images delivered by FSU personnel. The data cover 14-Jul-1995 to 21-Sep-1995 and 12-Feb-1996 to 03-Oct-1996. The data start out as three bands with 8-bit pixel values and end up as five bands with 10-bit pixel values. No major problems with the data have been identified. The differences between the level-1 and level-1a GOES-8 data are the formatting and packaging of the data. The images missing from the temporal series of level-1 GOES-8 images were zero-filled by BORIS staff to create files consistent in size and format. In addition, BORIS staff packaged all the images of a given type from a given day into a single file, removed the header information from the individual level-1 files, and placed it into a single descriptive ASCII header file. The data are contained in binary image format files. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images.\n<br>\n\n#### rss14srb_447\nThe BOREAS RSS-14 team collected and processed GOES-7 and -8 images of the BOREAS region as part of their effort to characterize the incoming, reflected and emitted radiation at regional scales. This data set contains surface radiation parameters, such as net radiation and net solar radiation, that have been interpolated from GOES-7 images and AMS data onto the standard BOREAS mapping grid at a resolution of 5 km N-S and E-W. While some parameters are taken directly from the AMS data set, others have been corrected according to calibrations carried out during the second 1994 IFC-2. The corrected values, as well as the uncorrected values are included. For example, two values of net radiation are provided: an uncorrected value (Rn), and a value that has been corrected according to the calibrations (Rn-COR). The data are provided in binary image format data files. Companion files include example thumbnail images that may be viewed and the image data files downloaded using a convenient viewer utility.\n<br>\n\n#### rs15bmlc_483\nAs part of BOREAS, the RSS-15 team conducted an investigation using SIR-C , X-SAR and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape. The procedure involved image classification with SAR and Landsat TM data and development of simple mapping techniques using combinations of SAR channels. For the SSA, the SIR-C data used were acquired on 06-Oct-1994, and the Landsat TM data used were acquired on September 2, 1995. The maps of the NSA were developed from SIR-C data acquired on 13-Apr-1994.\n<br>\n\n#### rs16cm61_563\nThe BOREAS RSS-16 team used satellite and aircraft SAR data in conjunction with various ground measurements to determine the moisture regime of the boreal forest. RSS-16 assisted with the acquisition and ordering of NASA JPL AIRSAR data collected from the NASA DC-8 aircraft. The NASA JPL AIRSAR is a side-looking imaging radar system that utilizes the SAR principle to obtain high-resolution images that represent the radar backscatter of the imaged surface at different frequencies and polarizations. The information contained in each pixel of the AIRSAR data represents the radar backscatter for all possible combinations of horizontal and vertical transmit and receive polarizations (i.e., HH, HV, VH, and VV). Geographically, the data cover portions of the BOREAS SSA and NSA. Temporally, the data were acquired from 12-Aug-1993 to 31-Jul-1995. The level-3b AIRSAR CM data are in compressed Stokes matrix format, which has 10 bytes per pixel. From this data format, it is possible to synthesize a number of different radar backscatter measurements. The data are stored in binary image-format files.\n<br>\n\n#### rss17fth_484\nThe BOREAS RSS-17 team acquired and analyzed imaging radar data from the ESA's ERS-1 over a complete annual cycle at the BOREAS sites in Canada in 1994 to detect shifts in radar backscatter related to varying environmental conditions. Two independent transitions corresponding to soil thaw and possible canopy thaw were revealed by the data. The results demonstrated that radar provides an ability to observe thaw transitions at the beginning of the growing season, which in turn helps constrain the length of the growing season. The data set presented here includes change maps derived from radar backscatter images that were mosaicked together to cover the southern BOREAS sites. The image values used for calculating the changes are given relative to the reference mosaic image. Due to copyright issues, the 01-March-1994 reference image is not included on the CD-ROM and is not publically available. See the accompanying guide document for information about how to possibly acquire the data. The data are stored in binary image format files.\n<br>\n\n#### rs17diel_301\nThe BOREAS RSS-17 team acquired and analyzed imaging radar data from the ESA's ERS-1 over a complete annual cycle at the BOREAS sites in Canada in 1994 to detect shifts in radar backscatter related to varying environmental conditions. This data set consists of dielectric constant profile measurements from selected trees at various BOREAS flux tower sites. The relative dielectric constant was measured at C-band (frequency = 5 GHz) as a function of depth into the trunk of three trees at each site. Measurements were made during April 1994 with an Applied Microwave Corporation field PDP fitted with a 0.358-cm (0.141-inch) diameter coaxial probe tip.\n<br>\n\n#### rs7ssatd_302\nThe BOREAS RSS-17 team collected several data sets in support of its research in monitoring and analyzing environmental and phenological states using radar data. This data set consists of tree bole and soil temperature measurements from various BOREAS flux tower sites. Temperatures were measured with thermistors implanted in the hydroconductive tissue of the trunks of several trees at each site and at various depths in the soil. Data were stored on a data logger at intervals of either 1 or 2 hours. The majority of the data were acquired between early 1994 and early 1995. The primary product of this data set is the diurnal stem temperature measurements acquired for selected trees at five BOREAS tower sites.\n<br>\n\n#### rss17xyf_303\nAs part of its efforts to determine environmental and phenological states from radar imagery, the BOREAS RSS-17 team collected in situ tree xylem flow measurements for one growing season on five Picea mariana (black spruce) trees. The data were collected to obtain information on the temporal and spatial variability in water uptake by trees in the SSA-OBS (Picea mariana) stand in the BOREAS SSA. Temporally, the data were collected in 30-minute intervals for 120 days from 31-May-1994 until 27-September-1994.\n<br>\n\n#### RSS18_AVIRIS_L1B_449\nThis dataset holds Level 1B (L1B) radiance data collected by the AVIRIS-Classic instrument near Prince Albert, Saskatchewan, Canada, on August 14, 1996. This imagery was acquired for the Boreal Ecosystem-Atmosphere Study (BOREAS) project in the boreal forests of central Canada. BOREAS focused on improving the understanding of exchanges of radiative energy, sensible heat, water, CO2 and trace gases between the boreal forest and the lower atmosphere. NASA's AVIRIS-Classic is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-Classic measures reflected radiance in 224 contiguous bands at approximately 10-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 400-2500 nm. The AVIRIS-Classic sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub meter range. For these data, AVIRIS-Classic was deployed on NASA's ER-2 high altitude aircraft. These spectra are acquired as images with 20-meter spatial resolution, 11 km swath width, and flight lines up to 800 km in length. The measurements are spectrally, radiometrically, and geometrically calibrated. There are seven flight lines subdivided into 66 scenes. The dataset includes the radiance imagery cube for each scene along with calibration and navigation information. The radiance data are in instrument coordinates, georeferenced by center of each scan line, and provided in a binary file. Metadata are included in a mixture of binary and text file formats.\n<br>\n\n#### rss18opt_503\nThe BOREAS RSS-18 team collected ground-based sunphotometer data in support of AVIRIS remote sensing activities at the SSA. The following information was compiled by staff members of the BOReal Ecosystem-Atmosphere Study (BOREAS) Information System (BORIS) as part of their data documentation efforts.\n<br>\n\n#### r19cas94_537\nThe RSS-19 team collected CASI images from the Chieftain Navaho aircraft in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI was deployed as a site-specific optical sensor during BOREAS field campaigns. Image data were collected with CASI on 36 days during five field campaigns between February and September 1994, primarily at flux tower sites located at study sites near Thompson, Manitoba, and Prince Albert, Saskatchewan. A variety of CASI data collection strategies were used to meet the following scientific objectives: 1) canopy bidirectional reflectance, 2) canopy biochemistry, 3) spatial variability, and 4) estimates of up and downwelling PAR and spectral albedo, as well as changes along transects across lakes and transects NSA and SSA. The images are stored as binary image files.\n<br>\n\n#### und_refl_304\nOne objective of BOREAS is to further the understanding of the spectral bi-directional reflectance of typical boreal ecosystem stands in the visible/near-infrared regime. An essential input for any canopy BRDF model is an accurate estimate of the average understory reflectance, both for sunlit and shaded conditions. These variables can be expected to vary seasonally because of species-dependent differences in the phenological cycle of foliar display. In response to these requirements, the average understory reflectance for the flux tower sites of both the NSA (Thompson, Manitoba) and the SSA (Candle Lake, Saskatchewan) Study Areas (NSA and SSA) was observed throughout the year during five field campaigns. This was done by measuring the nadir reflectance (400 to 850 nm) of sunlit and shaded understory (vegetation and snow cover) along a surveyed LAI transect line (Chen, RSS-07) at each site near solar noon and documenting a average site reflectance. Comparisons between sites reveal differences in the green and infrared regions of the spectra, because of the differing species in the understory for each site. Temporal (seasonal) variation for each site was also observed, indicating the changing flora mixtures and changing spectral signatures as the understory matures during the growing season.\n<br>\n\n#### r19cas96_538\nThe RSS-19 team collected CASI images from the Chieftain Navaho aircraft in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI was deployed as a site-specific optical sensor as part of BOREAS. The overall objective of the CASI deployment was to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. In 1996, image data were collected with CASI on 15 days during a field campaign between 18-July and 01-August, primarily at flux tower sites located at study sites near Thompson, Manitoba, and Prince Albert, Saskatchewan. A subset of the data is available from the BOREAS Information System (BORIS). Radiance and at-ground modeled reflectance images have been provided. This subset of CASI-processed data corresponds to the data for CASI Mission 3 described in Section 4.1.3 of the complete guide document. A variety of CASI data collection strategies were used to meet the following scientific objectives: 1) canopy bidirectional reflectance, 2) canopy biochemistry, 3) spatial variability, and 4) estimates of up and downwelling PAR spectral albedo, as well as changes along transects across lakes at the southern site and transects between the NSA and SSA. The images are stored as binary image files.\n<br>\n\n#### rs20c130_305\nThis data set contains measurements of surface BRDF made by the POLDER instrument over several surface types (pine, spruce, fen) of the BOREAS SSA during the 1994 IFCs. Single-point BRDF values were acquired either from the NASA ARC C-130 aircraft or from a NASA WFF helicopter. A related data set collected from the helicopter platform is available as is POLDER imagery acquired from the C-130.\n<br>\n\n#### rs20helo_306\nThis data set contains measurements of surface BRDF made by the POLDER instrument over several sites (pine, spruce, fen) of the BOREAS study areas during 1994. Single-point BRDF values were acquired from NASA's WFF helicopter. A related data set collected from the C-130 platform is available as is POLDER imagery acquired from the C-130.\n<br>\n\n#### rs20prad_555\nThese data are a subset of images collected by the POLDER instrument over tower sites in the BOREAS study areas during the IFCs in 1994. The POLDER images presented here from the NASA/Ames C-130 aircraft are made available for illustration purposes only. The data are stored in binary image-format files.\n<br>\n\n#### saskffcc_307\nThe Saskatchewan Forest Fire Control Centre (SFFCC) provided surface meteorological data to BOREAS from their archive. This data set contains hourly surface meteorological data from 18 of the meteorological stations located across Saskatchewan. Included in this data are parameters of date, time, temperature, relative humidity, wind direction, wind speed and precipitation. Temporally, the data cover the period of May through September of 1994 and 1995.\n<br>\n\n#### BOREAS_SLICER_508\nScanning Lidar Imager of Canopies by Echo Recovery (SLICER) data were acquired in support of BOReal Ecosystem-Atmosphere Study (BOREAS) at all of the Tower Flux (TF) sites in the Southern and Northern Study Areas (SSA and NSA, respectively) and along transects between the study areas. Data were acquired on 5 days between 18 and 30 July 1996. Each coverage of a tower site is typically 40 km in length, with a minimum of 3 and a maximum of 10 lines across each tower oriented in a variety of azimuths. The SLICER data were acquired simultaneously with Advanced Solid-State Array Spectroradiometer (ASAS) hyperspectral, multiview angle images. The SLICER Level 3 products consist of binary files for each flight line with a data record for each laser shot composed of 13 parameters and a 600-byte waveform that is the raw record of the back scatter laser energy reflected from Earth's surface.\n<br>\n\n#### ssafcovv_509\nThe forest cover data provided by Saskatchewan Environment and Resource Management, Forestry Branch - Inventory Unit (SERM-FBIU) are basically a digital version of its 1:12,500 scale forest cover polygon maps. The data include information on forest parameters and cover the area in and near the BOREAS SSA, excluding the PANP. As a digital archive, however, changes within forest stands can be updated more readily. At the same time, it should be kept in mind that most of these digital forest cover data were acquired in 1993, and the data set has been static since that time.\n<br>\n\n#### saskfc1m_510\nThis data set is a condensed forest cover type digital map of Saskatchewan and is a product of the Saskatchewan Environment and Resource Management, Forestry Branch - Inventory Unit (SERM-FBIU). This map was generalized from SERM township maps of vegetation cover at an approximate scale of 1:63,000 (1 in. = 1 mile). The cover information was iteratively generalized until it was compiled on a 1:1,000,000 scale map base. This data set was prepared by SERM-FBIU. The data is a condensed forest cover type map of Saskatchewan at a scale of 1:1,000,000.\n<br>\n\n#### saskfire_308\nThis data set is a series of ARC/INFO export files of the fire history of Saskatchewan by year from 1945 to 1996, with a few missing years. The data set was compiled and provided by the Saskatchewan Environment and Resource Management (SERM) to Wildlife Branch.\n<br>\n\n#### geocoord_556\nIn an effort to properly document the sites and areas where data were collected, personnel of the BOReal Ecosystem-Atmosphere Study (BOREAS) Information System (BORIS) obtained and compiled geographic coordinate and other site information from several sources throughout the experiment period. The final set of information is organized into two data sets that provide geographic coordinate and site characteristic information for single sites and corner coordinates for standard geographic areas. The data are stored in two text files as American Standard Code for Information Interchange (ASCII) characters.\n<br>\n\n#### te1ch4fx_310\nThe BOREAS TE-01 team collected various data to characterize the soil-plant systems in the BOREAS SSA. Particular emphasis was placed on nutrient biochemistry, the stores and transfers of organic carbon and how the characteristics were related to measured methane fluxes. The overall transect in the Prince Albert National Park (Saskatchewan, Canada) included the major plant communities and related soils that occurred in that section of the boreal forest. Soil physical, chemical and biological measurements along the transect were used to characterize the static environment, which allowed them to be related to methane fluxes. Chamber techniques were used to provide a measure of methane production/uptake. Chamber measurements coupled with flask sampling were used to determine the seasonality of methane fluxes. This particular data set contains methane flux and soil profile methane concentration values from the SSA-OA site. The data were collected from 29-MAY to 17-SEP-1994.\n<br>\n\n#### soilte1r_312\nThis data set was gridded from vector layers of soil maps that were received from Dr. Darwin Anderson TE-01 who did the original soil mapping in the field during 1994. The vector layers were gridded into raster files that cover approximately 1 square kilometer over each of the tower sites in the SSA.\n<br>\n\n#### te01ssld_530\nThis data set was collected by TE-01 to provide a set of soil properties for BOREAS investigators in the SSA. The soil samples were collected at sets of soil pits. Each set of soil pits was in the vicinity of one of the five flux towers in the BOREAS SSA. The collected soil samples were sent to a lab, where the major soil properties were determined. These properties include, but are not limited to, soil horizon; dry soil color; pH; bulk density; total, organic, and inorganic carbon; electric conductivity; cation exchange capacity; exchangeable sodium, potassium, calcium, magnesium, and hydrogen; water content at 0.01, 0.033, and 1.5 MPascals; nitrogen; phosphorus; particle size distribution; texture; pH of the mineral soil and of the organic soil; extractable acid; and sulfur.\n<br>\n\n#### te2wdrs2_314\nThe BOREAS TE-02 team collected several data sets in support of its efforts to characterize and interpret information on the respiration of the foliage, roots, and wood of boreal vegetation. This data set contains measurements of wood respiration measured continuously (about once per hour) in the NSA during the growing season of 1994.\n<br>\n\n#### te2flrsp_315\nThe BOREAS TE-02 team collected several data sets in support of its efforts to characterize and interpret information on the respiration of the foliage, roots, and wood of boreal vegetation. This data set contains measurements of foliar respiration conducted in the NSA during the growing season of 1994.\n<br>\n\n#### te2rtrsp_316\nThe BOREAS TE-02 team collected several data sets in support of its efforts to characterize and interpret information on the respiration of the foliage, roots, and wood of boreal vegetation. This data set includes means of tree root respiration measurements on roots having diameters ranging from 0 to 2 mm conducted in the NSA during the growing season of 1994.\n<br>\n\n#### te2stsap_317\nThe BOREAS TE-02 team collected several data sets in support of its efforts to characterize and interpret information on the respiration of the foliage, roots, and wood of boreal vegetation. This data set contains measurements of growth and sapwood of the stems conducted in the NSA during the growing season of 1994.\n<br>\n\n#### te2wdrsp_318\nThe BOREAS TE-02 team collected several data sets in support of its efforts to characterize and interpret information on the respiration of the foliage, roots, and wood of boreal vegetation. This data set contains measurements of wood respiration conducted in the NSA during the growing season of 1994.\n<br>\n\n#### te04bbag_319\nThe BOREAS TE-04 team collected continuous records of gas exchange under ambient conditions from intact boreal forest trees in the BOREAS NSA from 23-Jul-1996 until 14-Aug-1996. These measurements can be used to test models of photosynthesis, stomatal conductance, and leaf respiration, such as SiB2 (Sellers et al., 1996) or the leaf model (Collatz et al., 1991), and programs can be obtained from the investigators.\n<br>\n\n#### te04gxda_320\nMeasurements of light, CO2, temperature, and humidity response curves were made by the BOREAS TE-04 team during the summary of 1994 using intact attached leaves of boreal forest species located in the BOREAS SSA. These measurements were conducted to calibrate models used to predict photosynthesis, stomatal conductance, and leaf respiration. The data can be used to construct plots of response functions or for parameterizing models. Parameter values suitable for application in SiB2 (Sellers et al., 1996) or the leaf model of Collatz et al. (1991) and programs can be obtained from the investigators.\n<br>\n\n#### te5airs_321\nThe BOREAS group TE-05 collected measurements in the NSA and SSA on gas exchange, gas composition and tree growth. This data set contains measurements of the concentration and stable carbon (13C/12C) and oxygen (18O/16O) isotope ratios of atmospheric CO2 in air samples collected at different heights within forest canopies. The data were collected to determine the influence of photosynthesis and respiration by the forest ecosystems on the concentration and stable isotope ratio of atmospheric CO2. These measurements were collected at the SSA during each IFC at OJP, OBS, and OA sites. Measurements were also collected at the NSA during each 1994 IFC at the OJP, T6R5S TE UBS, and T2Q6A TE OA sites. The stable isotope ratios are expressed using standard delta notation and in units of per mil. The isotope ratios are expressed relative to the international standard, PDB, for both carbon and oxygen samples.\n<br>\n\n#### te5co2pd_322\nThese data were collected by BOREAS TE-05 to provide detailed information within the canopy during times when TE-05 sampled canopy CO2 for carbon and oxygen isotope analysis. These measurements were made in both the NSA and SSA during each IFC at the OJP, OBS, UBS, and OA sites. CO2 profile data were not collected at SSA-OA during the first IFC.\n<br>\n\n#### te5lciso_323\nThe BOREAS group TE-05 collected measurements in the NSA and SSA on gas exchange, gas composition and tree growth. This documentation describes leaf carbon isotope data that were collected in 1993 and 1994 at the NSA and SSA OJP, OBS and NSA UBS sites. In addition, leaf carbon isotope data were collected in 1994 only at the NSA and SSA OA sites. These data were collected to provide seasonal integrated physiological information for 10 to 15 common species at these 6 BOREAS sites.\n<br>\n\n#### te5lgxd_324\nThe BOREAS group TE-05 collected measurements in the NSA and SSA on gas exchange, gas composition and tree growth. The leaf photosynthetic gas exchange data were collected in the BOREAS NSA and the SSA using a Li-Cor 6200 portable photosynthesis system. The data were collected to compare the photosynthetic capacity, stomatal conductance and leaf intercellular CO2 concentrations among the major tree species at the BOREAS sites. The data are average values from diurnal measurements on the upper canopy foliage (sun leaves).\n<br>\n\n#### te5soilr_325\nThe BOREAS group TE-05 collected measurements in the NSA and SSA on gas exchange, gas composition and tree growth. Soil respiration data collected in the BOREAS NSA and SSA to compare the soil respiration rates in different forest sites using a Li-Cor 6200 soil respiration chamber (model 6299).\n<br>\n\n#### te5metd_326\nThe BOREAS group TE-05 collected measurements in the NSA and SSA on gas exchange, gas composition and tree growth. Measurements of meteorological data, including air and soil temperature, RH, and PPFD were 30-minute intervals during the 1994 IFCs at various sites in the BOREAS NSA and SSA.\n<br>\n\n#### te5treer_327\nThese data include tree ring widths and cellulose carbon isotope data from coniferous trees collected at the BOREAS NSA and SSA by the BOREAS TE-05 team. Ring width data are provided for both Picea mariana and Pinus banksiana. The carbon isotope data are provided only for Pinus banksiana.\n<br>\n\n#### te6satns_328\nThe BOREAS TE-06 team collected several data sets to examine the influence of vegetation, climate, and their interactions on the major carbon fluxes for boreal forest species. This data set contains measurements of the air temperature at a single height and soil temperature at several depths in the NSA from 25-May to 8-Oct-1994. Chromel-Constantan thermocouple wires run by a miniprogrammable data logger (Model 21X, Campbell Scientific, Inc., Logan, UT) provided direct measurements of temperature.\n<br>\n\n#### te6allom_329\nThe BOREAS TE-06 team collected several data sets in support of its efforts to characterize and interpret information on the plant biomass, allometry, biometry, sapwood, leaf area index, net primatry production, soil temperature, leaf water potential, soil CO2 flux, and multivegetation imagery of boreal vegetation. This data set includes tree measurements conducted on the above gound biomass of trees in the BOREAS NSA and SSA during the growing seasons of 1994 and 1995 and the derived allometric relationships/equations.\n<br>\n\n#### te6bmflg_330\nThe BOREAS TE-06 team collected several data sets in support of its efforts to characterize and interpret information on the plant biomass, allometry, biometry, sapwood, leaf area index, net primary production, soil temperature, leaf water potential, soil CO2 flux, and multivegetation imagery of boreal vegetation. This data set contains measurements of estimates of the standing biomass and leaf area index for the plant species at the TF, CEV, and AUX sites in the SSA and NSA during the growing seasons of 1994 and 1995.\n<br>\n\n#### te6mltvg_331\nA newly developed ground-based canopy imaging system called an MVI was tested and used by the BOREAS TE-06 team to collect measurements of the canopy gap fraction (sky fraction), canopy gap-size distribution (size and frequency of gaps between foliage in canopy), branch architecture, and leaf angle distribution (fraction of leaf area in specific leaf inclination classes assuming azimuthal symmetry). Measurements of the canopy gap-size distribution are used to derive canopy clumping indices that can be used to adjust indirect LAI measurements made in nonrandom forests. These clumping factors will also help to describe the radiation penetration in clumped canopies more accurately by allowing for simple adjustments to Beer's law. Measurements of the above quantities were obtained at BOREAS NSA OJP site in IFC-2 in 1994, at the SSA OA in July 1995, and at the SSA OBS and SSA OA sites in IFC-2 in 1996. Modeling studies were also performed to further validate MVI measurements and to gain a more complete understanding of boreal forest canopy architecture. By using MVI measurements and Monte Carlo simulations, clumping indices as a function of zenith angle were derived for the three main boreal species studied during BOREAS.\n<br>\n\n#### te6npp_200\nThe BOREAS TE-06 team collected several data sets to examine the influence of vegetation, climate, and their interactions on the major carbon fluxes for boreal forest species. This data set contains estimates of the biomass produced by the plant species at the TF, CEV, and AUX sites in the SSA and NSA for a given year. Temporally, the data cover the years of 1985 to 1995. The plant biomass production (i.e., aboveground, belowground, understory, litterfall), spatial coverage, and temporal nature of measurements varied between the TF, CEV, and AUX sites as deemed necessary by BOREAS principal investigators.\n<br>\n\n#### te6h2opd_332\nThe BOREAS TE-06 team collected several data sets to examine the influence of vegetation, climate, and their interactions on the major carbon fluxes for boreal forest species. This data set contains summaries of predawn leaf water potentials and foliage moisture contents collected at the TF and CEV sites that had canopy access towers. The data was collected on a nearly weekly basis from early June to late August 1994 by TE-06, members of the BOREAS staff, and employees of Environment Canada.\n<br>\n\n#### te07dend_333\nThe BOREAS TE-07 team collected data sets in support of its efforts to characterize and interpret information on the sapflow and dendrology of boreal vegetation. This data set contains dendrology measurements, consisting of tree ring width and density taken at several points within each ring. Measurements were taken near the TE towers at the OJP and OBS sites in NSA. In the SSA, measurements were taken near the TE towers at the MIX, OBS, and OJP sites; at the AIM-13 and BMH-9 sites; and near the TF-YJP site. All data were collected during the summer of 1994. Note that the TE-07 dendrology data available for the ORNL DAAC are a summary and an inventoru of the full Canadian Forest Service (CFS) data set. Please see Section 1.5 of the complete data set reference document for information on obtaining the CFS data set.\n<br>\n\n#### te07sapf_334\nThe BOREAS TE-07 team collected data sets in support of its efforts to characterize and interpret information on the sap flow of boreal vegetation. The heat pulse method was used to monitor sap flow and to estimate rates of transpiration from aspen, black spruce, and mixed wood forests at the SSA-OA, MIX, SSA-OBS, and Batoche sites in Saskatchewan, Canada. Measurements were made at the various sites from May to Oct 1994, May to Oct 1995, and Apr to Oct 1996. A scaling procedure was used to estimate canopy transpiration rates from the sap flow measurements.\n<br>\n\n#### te08bchm_335\nThe BOREAS TE-08 team collected pigment density data from aspen bark and leaves from four sites within the BOREAS SSA from 24-May-1994 to 16-Jun-1994 (IFC-1), 19-Jul-1994 to 08-Aug-1994 (IFC 2), and 30-Aug-1994 to 19-Sep-1994 (IFC-3). One to nine trees from each site were sampled during the three IFCs. Each tree was sampled in five different locations for bark pigment properties: basal stem section, which was any bark sample taken below one-half the tree height; upper stem section, which was any bark sample taken from the main stem above one-half the tree height; bark taken from branches up to 3 years old; a 2-year old branch segment; and a 1-year old branch segment. Additionally, a limited number of leaves were collected. Bark samples were removed from the stem of the tree, placed in ziplock bags, and transported to UNH, where they were processed and analyzed by a spectrophotometer. In each data file, samples are identified by Site, date, Tree#, and Sample Location (see 1st paragraph above). Pigment density values are normalized to mg/m2. Density values for the following pigments are provided: Chl a, Chl b, Total Chl (Chl a+b), Carotenoids, Chl a to b ratio, and the Total Chl to carotenoids ratio.\n<br>\n\n#### te08bopt_336\nThe BOREAS TE-08 team collected in-lab spectral reflectance data for aspen bark and leaves from three sites within the BOREAS SSA from 24-May-1994 to 16-Jun-1994 (IFC 1), 19-Jul-1994 to 08-Aug-1994 (IFC 2), and 30-Aug-1994 to 19-Sep-1994 (IFC 3). One to nine trees from each site were sampled during the three IFCs. Each tree was sampled in five different locations for bark spectral properties: BS, US, BR, BT, and BO. Additionally, a limited number of LV were collected. Bark samples were removed from the stem of the tree and placed in ziplock bags for transport to UNH, where they were scanned with a spectroradiometer in a controlled environment. Each sample was scanned twice: the first set of measurements was made with the bark surface moistened, and the second set was made with the bark surface air-dried for a period of 30 minutes. These data represent continuous spectra of bark reflectance. Each sample was scanned three times, rotating the sample when possible. The reported values for each sample are an average over the three scans.\n<br>\n\n#### te09gxda_337\nThe gas exchange data of the Boreal Ecosystem-Atmosphere Study (BOREAS) Northern Study Area (NSA) were collected to characterize diurnal gas exchange and water potential of two canopy levels of five boreal canopy cover types: young and old jack pine (Pinus banksiana Lamb.), old aspen (Populus tremuloides Michx.), and lowland and upland black spruce (Picea mariana (Mill) B.S.P.). These data were collected between 27-May-1994 and 17-Sep-1994. The purpose of this study was threefold: 1) to provide in situ gas exchange data that will be used to validate models of photosynthetic responses to light, temperature, and carbon dioxide (CO2); 2) to compare the photosynthetic responses of different tree crown levels (upper and lower), and 3) to characterize the diurnal water potential curves for these sites to get an indication of the extent to which soil moisture supply to leaves might be limiting photosynthesis.\n<br>\n\n#### te9spref_338\nSpectral reflectance of the forest understory at the ground level, in three boreal forest sites of Northern Manitoba (56 degrees N latitude and 98 degrees W longitude), were obtained and analyzed. The objective of the study was to estimate light levels inside the forest canopy and to link these estimates with airborne images taken above the canopy, in order to tie the photosynthetic experiments and models with the remotely sensed measurements. The Boreal Ecosystem-Atmospheric Study (BOREAS) Terrestrial Ecosystem (TE)-09 project contained several sub-projects designed to work together to meet this goal: a high-resolution canopy modeling component, extensive measurements of canopy architecture and structure, photometric measurements inside the canopy, and spectral measurements of both the canopy and the understory.\n<br>\n\n#### te9bioav_339\nThe BOREAS TE-09 team collected several data sets related to chemical and photosynthetic properties of leaves. This data set contains canopy biochemistry data collected in 1994 in the NSA at the YJP, OJP, OBS, BS and OA sites including biochemistry lignin, nitrogen, cellulose, starch, and fiber concentrations. These data were collected to study the spatial and temporal changes in the canopy biochemistry of boreal forest cover types and how a high-resolution radiative transfer model in the mid-infrared could be applied in an effort to obtain better estimates of canopy biochemical properties using remote sensing.\n<br>\n\n#### te09cd_341\nThe Boreal Ecosystem-Atmosphere Study (BOREAS) Terrestrial Ecology Team #9 (TE-09) provided several data sets containing information about the state and response of boreal forest tree species. This data set contains information on the spatial density of chlorophyll in the leaves of three boreal tree species collected at five different sites at various times during 1994.\n<br>\n\n#### te09pnd_342\nThe BOREAS TE-09 team collected several data sets related to chemical and photosynthetic properties of leaves in boreal forest tree species. This data set describes the relationship between Photosynthetically Active Radiation (PAR) levels and foliage nitrogen in samples from six sites in the BOREAS NSA. This information is useful for modeling the vertical distribution of carbon fixation for these different forest types in the boreal forest. The data were collected to quantify the relationship between PAR and leaf nitrogen of black spruce, jack pine, and aspen.\n<br>\n\n#### te09npd_343\nThe BOREAS TE-09 team collected several data sets related to chemical and photosynthetic properties of leaves in boreal forest tree species. This data set describes the spatial and temporal relationship between foliage nitrogen concentration and photosynthetic capacity in the canopies of black spruce, jack pine, and aspen. The data were collected from June to September 1994 and are useful for modeling the vertical distribution of carbon fixation for different forest types in the boreal forest.\n<br>\n\n#### te09prd_344\nThe BOREAS TE-09 team collected several data sets related to chemical and photosynthetic properties of leaves. This data set describes (1) the response of leaf and shoot-level photosynthesis to ambient and intercellular CO2 concentration, temperature, and incident PAR for black spruce, jack pine, and aspen during the three IFCs in 1994 in the NSA; (2) the response of stomatal conductance to vapor pressure difference throughout the growing season of 1994; and (3) a range of shoot water potentials (controlled in the laboratory) for black spruce and jack pine.\n<br>\n\n#### te10lfch_345\nThe BOREAS TE-10 team collected several data sets in support of its efforts to characterize and interpret information on the reflectance, transmittance, gas exchange, chlorophyll content, carbon content, hydrogen content, and nitrogen content of boreal vegetation. This data set describes the relationship between sample location, age, chlorophyll content, and C-H-N concentrations at several sites in the SSA conducted during the growing seasons of 1994 and 1996.\n<br>\n\n#### te10lgxd_346\nThe BOREAS TE-10 team collected several data sets in support of its efforts to characterize and interpret information on the reflectance, transmittance, gas exchange, chlorophyll content, carbon content, hydrogen content, and nitrogen content of boreal vegetation. This data set contains measurements of assimilation, stomatal conductance, transpiration, internal CO2 concentration, and water use efficiency conducted in the SSA during the growing seasons of 1994 and 1996 using a portable gas exchange system.\n<br>\n\n#### te10lopt_531\nThe BOREAS TE-10 team collected several data sets in support of its efforts to characterize and interpret information on the reflectance, transmittance, gas exchange, oxygen evolution, and biochemical properties of boreal vegetation. This data set describes the spectral optical properties (reflectance and transmittance) of boreal forest conifers and broadleaf tree leaves as measured with a Spectron Engineering SE590 spectroradiometer at the SSA OBS, OJP, YJP, OA, OA-AUX, YA-AUX, and YA sites. The data were collected during the growing seasons of 1994 and 1996.\n<br>\n\n#### te10prd_347\nThe BOREAS TE-10 team collected several data sets in support of its efforts to characterize and interpret information on the gas exchange, reflectance, transmittance, chlorophyll content, carbon content, hydrogen content, nitrogen content, and photosynthetic response of boreal vegetation. This data set contains measurements of quantitative parameters and leaf photosynthetic response to increases in light conducted in the SSA during the growing seasons of 1994 and 1996 using an oxygen electrode system. Leaf photosynthetic responses were not collected in 1996.\n<br>\n\n#### te11lgxd_348\nThe BOREAS TE-11 team collected several data sets in support of its efforts to characterize and interpret information on the sapflow, gas exchange, and lichen photosynthesis of boreal vegetation and meteorological data of the area studied. This data set contains measurements of assimilation and transpiration conducted at the OJP site during the growing seasons of 1993 and 1994.\n<br>\n\n#### te11sapf_349\nThe BOREAS TE-11 team collected several data sets in support of its efforts to characterize and interpret information on the sap flow, gas exchange, and lichen photosynthesis of boreal vegetation and meteorological data of the area studied. This data set contains measurements of sap flow conducted at the SSA-OJP site in the growing seasons of 1993 and 1994.\n<br>\n\n#### te11smet_350\nThe BOReal Ecosystem-Atmosphere Study (BOREAS) Terrestrial Ecology (TE)-11 team collected several data sets in support of its efforts to characterize and interpret information on the sapflow, gas exchange, and lichen photosynthesis of boreal vegetation and meteorological data of the area studied. This data set contains the meteorological data taken in the BOREAS Southern Study Area (SSA) during 1994 and used by TE-11 for their sapflow and gas exchange data analysis.\n<br>\n\n#### te12parc_485\nThe BOREAS TE-12 team collected PAR data sets in support of its efforts to characterize and interpret information on shoot geometry, leaf optical properties, leaf water potential, and leaf gas exchange. The data were collected at the SSA-OBS site from 04-Jul-1996 to 25-Jul-1996.\n<br>\n\n#### te12lgex_351\nThe BOREAS TE-12 team collected several data sets in support of its efforts to characterize and interpret information on the reflectance, transmittance, and gas exchange of boreal vegetation. This data set contains measurements of leaf gas exchange conducted in the SSA during the growing seasons of 1994 and 1995 using a portable gas exchange system.\n<br>\n\n#### te12lod_352\nThe BOREAS TE-12 team collected several data sets in support of its efforts to characterize and interpret information on the reflectance, transmittance, and gas exchange of boreal vegetation. This data set contains measurements of hemispherical spectral reflectance and transmittance factors of individual leaves, needles (ages: current and past 2 years' growth, i.e., for 1993, the growing seasons of 1993, 1992, and 1991 were measured; in 1994, the growing seasons of 1994, 1993, and 1992 were measured), twigs (reflectance only), and substrate at near-normal incidence measured using a LI-COR LI-1800-12 integrating sphere attached to a Spectron Engineering SE590 spectroradiometer. Procedures of Daughtry et al. (1989) were followed. These procedures permitted measurement of samples that: 1) filled the entire integrating sphere sample port, and 2) were narrow with a length greater than the sample port diameter. Optical properties were measured at the SSA Fen,YJP, YA, and OBS sites.\n<br>\n\n#### te12sgd_353\nThe BOREAS TE-12 team collected shoot geometry data in 1993 and 1994 from Aspen, Jack Pine, and Black Spruce trees. Collections were made at the Southern Study Area FEN, YJP, OJP, OA, YA, MIX and OBS sites. A caliper was used to measure shoot and needle lengths and widths. A volume displacement procedure was used to measure the weight of the shoot or twig submerged in water.\n<br>\n\n#### te12h2op_354\nThe BOREAS TE-12 team collected water potential data in 1993 and 1994 from aspen, jack pine and black spruce leaves/needles. Collections were made at the SSA FEN, YJP, YA, OA, and OBS sites. Measurements were made using a pressure chamber on a platform in the field.\n<br>\n\n#### te13biom_355\nThe Boreal Ecosystem-Atmosphere Study (BOREAS) Terrestrial Ecology (TE)-13 team collected data on site characteristics, soil profiles, woody debris, overstory vegetation, and understory vegetation from approximately 100 sites in the Southern Study Area (SSA), Northern Study Area (NSA), and Transect Areas in the boreal forest. This data sets provides 3 reports published by the Canadian Forest Service for the BOREAS project in pdf file format.\n<br>\n\n#### te17pem_486\nA BOREAS version of the Global Production Efficiency Model (www.inform.umd.edu/glopem) was developed by TE-17 to generate maps of gross and net primary production, autotrophic respiration, and light use efficiency for the BOREAS region. This document provides basic information on the model and how the maps were generated. The data generated by the model are stored in binary image-format files.\n<br>\n\n#### biomdens_450\nThis biomass density image covers almost the entire BOREAS SSA. The pixels for which biomass density is computed include areas that are in conifer land cover classes only. The biomass density values represent the amount of overstory biomass (i.e. tree biomass only) per unit area. It is derived from a Landsat-5 TM image collected on 02-Sep-1994. The technique that was used to create this image is very similar to the technique that was used to create the physical classification of the SSA. The data are provided in a binary image file format. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images.\n<br>\n\n#### te18geos_532\nThe SAIL (Scattering from Arbitrarily Inclined Leaves) model was combined with the Jasinski geometric model to simulate canopy spectral reflectance and absorption of photosynthetically active radiation for discontinuous canopies. This model is called the GeoSail model. Tree shapes are described by cylinders or cones distributed over a plane. Spectral reflectance and transmittance of trees are calculated from the SAIL model to determine the reflectance of the three components used in the geometric model: illuminated canopy, illuminated background, shadowed canopy, and shadowed background. The model code is Fortran, sample input and output data are provided in ASCII text files.\n<br>\n\n#### ltmmaxln_429\nThe objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification. A standard supervised maximum likelihood approach was used to produce this classification. Companion files include example thumbnail images that may be viewed using a convenient viewer utility.\n<br>\n\n#### ltmmaxls_430\nA Landsat-5 TM image from 06-Aug-1990 was used to derive this classification. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the SSA. A standard supervised maximum likelihood approach was used to produce this classification. Companion files include example thumbnail images that may be viewed using a convenient viewer utility.\n<br>\n\n#### ltmphysn_431\nThe objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 21-Jun-1995 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes. Companion files include example thumbnail images that may be viewed and using a convenient viewer utility.\n<br>\n\n#### ltmphyss_432\nThe objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the SSA. A Landsat-5 TM image from 02-Sep-1994 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes. These data are provided in a binary image file format. Companion files include example thumbnail images that may be viewed and the image data files downloaded using a convenient viewer utility.\n<br>\n\n#### te18ls30_557\nThe BOREAS TE-18 team used a radiometric rectification process to produce standardized digital number (DN) values for a series of Landsat TM images of the BOREAS SSA and NSA in order to compare images that were collected under different atmospheric conditions. The images for each study area were referenced to an image that had very clear atmospheric qualities. The reference image for the SSA was collected on 02-Sep-1994, while the reference image for the NSA was collected on 21-Jun-1995. The 23 rectified images cover the period of 07-Jul-1985 to 18-Sep-1994 in the SSA and from 22-Jun-1984 to 09-Jun-1994 in the NSA. Each of the reference scenes had coincident atmospheric optical thickness measurements made by RSS-11. The radiometric rectification process is described in more detail by Hall et al. (1991). The original Landsat TM data were received from CCRS for use in the BOREAS project. The data are stored in binary image-format files. Due to the nature of the radiometric rectification process and copyright issues, these full resolution images may not be publicly distributed. However, a spatially degraded 60-m resolution version of the images is available on the BOREAS CD-ROM series. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images that may be viewed and the image data files downloaded using a convenient viewer utility.\n<br>\n\n#### te18ls60_564\nThe BOREAS TE-18 team used a radiometric rectification process to produce standardized digital number (DN) values for a series of Landsat TM images of the BOREAS SSA and NSA in order to compare images that were collected under different atmospheric conditions. The images for each study area were rectified by using a reference image that had clear atmospheric qualities. The reference image for the SSA was collected on 02-Sep-1994, while the reference image for the NSA was collected on 21-Jun-1995. The 23 rectified images cover the period of 07-Jul-1985 to 18-Sep-1994 in the SSA and 22-Jun-1984 to 09-Jun-1994 in the NSA.\n<br>\n\n#### te19modl_487\nThe BOREAS TE-19 team developed a model called the Spruce and Moss Model (SPAM) designed to simulate the daily carbon balance of a black spruce/moss boreal forest ecosystem. It is driven by daily weather conditions, and consists of four components: (1) soil climate; (2) tree photosynthesis and respiration; (3) moss photosynthesis and respiration; and (4) litter decomposition and associated heterotrophic respiration. The model simulates tree gross and net photosynthesis, wood respiration, live root respiration, moss gross and net photosynthesis, and heterotrophic respiration (decomposition of root litter, young needle and moss litter, and humus). These values can be combined to generate predictions of total site net ecosystem exchange of carbon (NEE), total soil dark respiration (live roots + heterotrophs + live moss), spruce and moss net productivity, and net carbon accumulation in the soil. The files include source code and sample input and output files in ASCII format.\n<br>\n\n#### te20sldn_356\nThis data set contains the major soil properties of soil samples collected at the tower flux sites in the NSA. The soil samples were collected by Hugo Veldhuis and his staff from the University of Manitoba. The mineral soil samples were largely analyzed by Barry Goetz, under the supervision of Dr. Harold Rostad at the University of Saskatchewan. The organic soil samples were largely analyzed by Peter Haluschak, under the supervision of Hugo Veldhuis at the Centre for Land and Biological Resources Research in Winnipeg, Manitoba. During the course of field investigation and mapping, selected surface and subsurface soil samples were collected for laboratory analysis. These samples were used as benchmark references for specific soil attributes in general soil characterization. Detailed soil sampling, description and laboratory analysis were performed on selected modal soils to provide examples of common soil physical and chemical characteristics in the study area. The soil properties that were determined include soil horizon; dry soil color; pH; bulk density; total, organic, and inorganic carbon; electric conductivity; cation exchange capacity; exchangeable sodium, potassium, calcium, magnesium, and hydrogen; water content at 0.01, 0.033, and 1.5 MPascals; nitrogen; phosphorus; particle size distribution; texture; pH of the mineral soil and of the organic soil; extractable acid; and sulfur.\n<br>\n\n#### te20site_488\nThe BOREAS TE-20 team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set contains measurements of site characteristics conducted in the SSA from 18-Jul-1994 to 30-Jul-1994.\n<br>\n\n#### soilt20r_357\nThis data set was gridded from vector layers of soil maps that were received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994. The vector layers were gridded into raster files that cover the NSA-MSA and tower sites.\n<br>\n\n#### soilt20v_533\nThe BOREAS TE-20 team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set contains vector layers of soil maps that were received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994. The vector layers were converted to ARC/INFO EXPORT files. These data cover 1-kilometer diameters around each of the NSA tower sites, and another layer covers the NSA-MSA.\n<br>\n\n#### te20supp_489\nThe BOREAS TE-20 team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set and documentation were compiled from field notes and other information provided by Hugo Veldhuis, who did the original soils mapping in the field during 1994. The information here describes the soils and landscape characteristics of the NSA-MSA and tower sites.\n<br>\n\n#### te21smet_358\nThe BOREAS TE-21 team collected data sets in support of its efforts to characterize and interpret information on the meteorology of boreal forest areas. Daily meteorological data were derived from half-hourly BOREAS TF and AMS mesonet measurements collected in the SSA and NSA for the period of 01-Jan-1994 until 31-Dec-1994.\n<br>\n\n#### te22allm_490\nThe BOREAS TE-22 team collected data sets in support of its efforts to characterize and interpret information on the forest structure of boreal vegetation in the SSA and NSA during the 1994 growing season.\n<br>\n\n#### te22ring_491\nThe BOREAS TE-22 team collected complete tree cores at several sites in the SSA and NSA in order to perform historical growth studies and relate the information to their modeling activities. The cores were collected during the summer of 1994 in the Northern and Southern Study Areas. A sample of the file types resulting from the analysis of the tree cores are provided.\n<br>\n\n#### te23arch_492\nThe BOREAS TE-23 team collected hemispherical photographs in support of its efforts to characterize and interpret information on estimates of canopy architecture and radiative transfer properties for most BOREAS study sites. Various OA, OBS, OJP, YJP, and YA sites in the boreal forest were measured from May to August 1994. The hemispherical photographs were used to derive values of LAI, Leaf angle, Gap fraction, and Clumping index. This documentation describes these derived values. The derived data are stored in tabular ASCII files. The hemispherical photographs are stored in the original set of 42 CD-ROMs, that were supplied by TE-23.\n<br>\n\n#### te23mapp_359\nThe BOREAS TE-23 team collected map plot data in support of its efforts to characterize and interpret information on canopy architecture and understory cover at the BOREAS tower flux sites and selected auxiliary sites from May to August 1994. Mapped plots (typical dimensions 50 m x 60 m) were set up and characterized at all BOREAS forested tower flux and selected auxiliary sites. Detailed measurement of the mapped plots included 1) stand characteristics (location, density, basal area); 2) map locations DBH of all trees; 3) detailed geometric measures of a subset of trees (height, crown dimensions); and 4) understory cover maps. The data are stored in tabular ASCII files.\n<br>\n\n#### tf01soil_511\nThe BOREAS TF-01 team collected several data sets in support of its efforts to characterize and interpret soil information at the SSA-OA tower site in 1994 as part of BOREAS. Data sets collected include soil respiration, temperature, moisture, and gravimetric data.\n<br>\n\n#### tf01tflx_512\nThe BOREAS TF-01 team collected energy, carbon dioxide, and momentum flux data above the canopy along with meteorological and soils data at the BOREAS SSA-OA site from mid-April to the end of the year for 1996.\n<br>\n\n#### tf01uflx_513\nThe BOREAS TF-01 team collected energy, carbon dioxide, and momentum flux data under the canopy along with meteorological and soils data at the BOREAS SSA-OA site from mid-October to mid-November of 1993 and throughout all of 1994.\n<br>\n\n#### tf1ch4_514\nThe BOREAS TF-01 team collected various trace gas and energy flux data in its efforts to characterize the temporal energy and gas exchanges that occurred over the SSA-OA site. This data set contains methane (CH4) and nitrous oxide (N2O) fluxes that were measured at the BOREAS SSA-OA site. These fluxes were measured from 16-Apr to 16-Sep-1994.\n<br>\n\n#### tf2met_504\nMembers of the BOREAS TF-02 team collected meteorological and ozone measurements from instruments mounted below a tethered balloon. These data were collected at the SSA-OA site to extend meteorological and ozone measurements made from the flux tower to heights of 300 m. The tethersonde operated during the fall of 1993 and the spring, summer, and fall of 1994.\n<br>\n\n#### tf02tflx_515\nThe BOREAS TF-02 team collected energy, carbon dioxide, water vapor, and momentum flux data above the canopy and in profiles through the canopy, along with meteorological data at the BOREAS SSA-OA site. Above-canopy measurements began in early February and ran through mid-September of 1994. Measurements were collected over a longer period of 1994 than most BOREAS flux sites. Daily precipitation data from several gauges were also collected.\n<br>\n\n#### tf3acco2_360\nThe BOREAS TF-03 and TGB-01 teams collected automated CO2 chamber flux data in their efforts to fully describe the CO2flux at the NSA-OBS site. This data set contains fluxes of CO2 at the NSA-OBS site measured using automated chambers. In addition to reporting the CO2flux, it reports chamber air temperature, moss temperature, and light levels during each measurement. The data set covers the period from 23-Sep-1995 through 26-Oct-1995 and from 28-May-1996 through 21-Oct-1996.\n<br>\n\n#### tf3tflxd_361\nThe BOREAS TF-03 team collected tower flux, surface meterological, and soil temperature data at the BOREAS NSA-OBS site continuously from the March 1994 through October 1996.\n<br>\n\n#### tf4ssafx_362\nThe BOREAS TF-04 team measured fluxes of carbon dioxide (CO2) and methane (CH4) across the soil-air interface in four ages of jack pine forest at the Southern Study Area of the Boreal Ecosystem Atmosphere Study (BOREAS) during August 1993 to March 1995. Gross and net flux of CO2 and flux of CH4 between soil and air are presented for 24 chamber sites in mature jack pine forest, 20-year old, 4- year old, and clear cut areas.\n<br>\n\n#### tf4ssasp_363\nThe BOREAS TF-04 team measured distributions of carbon dioxide (CO2) and methane (CH4) concentrations for the upper 5 meters of soil and unsaturated zone at the mature stand, upper 6 m at the 20-year old stand, and the upper 1m at the 8-year old stand and clear cut area at the Southern Study Area of the Boreal Ecosystem Atmosphere Study (BOREAS) during August 1993 to March 1995. Particle size and carbon content of the unsaturated deposits, precipitation, soil temperature and moisture, carbon and oxygen isotopes of soil CO2 and soil water chemistry are also presented.\n<br>\n\n#### tf04flux_451\nThe BOREAS TF-04 team collected energy, carbon dioxide, and water vapor flux data at the BOREAS SSA-YJP site during the growing season of 1994. In addition, meteorological data were collected both above and within the canopy.\n<br>\n\n#### tf5tflxd_364\nThe BOREAS TF-05 team collected tower flux data at the BOREAS Southern Study Area Old Jack Pine (SSA-OJP) site through the growing season of 1994.\n<br>\n\n#### tf6fxmet_365\nThe BOREAS TF-06 team collected surface energy flux and meteorology data at the SSA-YA site. The data characterize the energy flux and meteorological conditions at the site from 18-Jul to 20-Sep-1994. The data set does not contain any trace gas exchange measurements.\n<br>\n\n#### tf07flux_452\nThe BOREAS TF-07 team collected meteorological data as well as energy, carbon dioxide, water vapor, methane, and nitrous oxide flux data at the BOREAS SSA-OBS site. The data were collected from May 24 to September 19, 1994.\n<br>\n\n#### tf08ceil_453\nThe BOREAS TF-08 team used ceilometers to collect data on the fraction of the sky covered with clouds and the cloud height. Included with these data is the surface-based lifting condensation level, derived from temperature and humidity values acquired at the flux tower at the NSA-OJP site. Ceilometer data were collected at the NSA-OJP site in 1994 and at the NSA-OJP and SSA-OBS sites in 1996.\n<br>\n\n#### tf08tflx_516\nThe BOREAS TF-08 team collected energy, CO2, and water vapor flux data at the BOREAS NSA-OJP site during the growing season of 1994 and most of the year for 1996.\n<br>\n\n#### tf9brflx_366\nThe BOREAS TF-09 team collected data which describe carbon dioxide and water vapor fluxes from foliage at the BOREAS SSA-OBS site from 07-April through 23-November-1996.\n<br>\n\n#### tf9tflxd_367\nThe BOREAS TF-09 team collected energy, carbon dioxide and water vapor flux data at the BOREAS SSA-OBS site during the growing season of 1994 and most of the year for 1996. From the winter of 1995 to 1996, soil temperature data were also collected and provided.\n<br>\n\n#### tf10fxmt_368\nThe BOREAS TF-10 team collected tower flux and meteorological data at two sites, a fen and a young jack pine forest, near Thompson, Manitoba, Canada, as part of BOREAS. A preliminary data set was assembled in August 1993 while field testing the instrument packages, and at both sites data were collected from 15-Aug to 31-Aug. The main experimental period was in 1994, when continuous data were collected from 08-Apr to 23-Sept at the fen site. A very limited experiment was run in the spring/summer of 1995, when the fen site tower was operated from 08-Apr to 14-Jun in support of a hydrology experiment in an adjoining, feeder basin. Upon examination of the 1994 data set, it became clear that the behavior of the heat, water, and carbon dioxide fluxes throughout the whole growing season was an important scientific question, and that the 1994 data record was not sufficiently long to capture the character of the seasonal behavior of the fluxes. Thus, the fen site was operated in 1996 in order to collect data from spring melt to autumn freeze-up. Data were collected from 29-Apr to 05-Nov at the fen site. All variables are presented as 30-minute averages.\n<br>\n\n#### tf11biom_369\nThe BOREAS TF-11 team collected several data sets in their efforts to fully describe the flux and site characteristics at the SSA-Fen site. This data set contains plant cover, standing crop of plant biomass, and estimated net primary productivity at each chamber site at the end of the 1994 field season. The measurements were conducted as part of a 2x2 factorial experiment in which we added carbon (300 g m-2 as wheat straw) and nitrogen (6 g m-2 as urea) to four replicate locations in the vicinity of the TF-11 tower.\n<br>\n\n#### tf11conc_370\nThe BOREAS TF-11 team collected several data sets in their efforts to fully describe the flux and site characteristics at the SSA-Fen site. This data set contains temperature, pH, and concentration profiles of methane and carbon dioxide within the surface 50 cm of peat. The measurements were conducted as part of a 2x2 factorial experiment in which we added carbon (300 g m-2 as wheat straw) and nitrogen (6 g m-2 as urea) to four replicate locations in the vicinity of the TF-11 tower. The data set covers the period from the first week of June 1994 through the second week of September, 1994.\n<br>\n\n#### tf11flux_371\nThe BOREAS TF-11 team collected several data sets in their efforts to fully describe the flux and site characteristics at the SSA-Fen site. This data set contains fluxes of methane and carbon dioxide at the SSA fen site measured using static chambers. The measurements were conducted as part of a 2x2 factorial experiment in which we added carbon (300 g m-2 as wheat straw) and nitrogen (6 g m-2 as urea) to four replicate locations in the vicinity of the TF-11 tower. In addition to siting and treatment variables, it reports air temperature and water table height relative to the average peat surface during each measurement. The data set covers the period from the first week of June 1994 through the second week of September, 1994.\n<br>\n\n#### tf11dcom_372\nThe BOREAS TF-11 team collected several data sets in their efforts to fully describe the flux and site characteristics at the SSA-Fen site. This data set contains decomposition rates of a standard substrate (wheat straw) across treatments. The measurements were conducted as part of a 2x2 factorial experiment in which we added carbon (300 g m-2 as wheat straw) and nitrogen (6 g m-2 as urea) to four replicate locations in the vicinity of the TF-11 tower.\n<br>\n\n#### tf11sflm_455\nThe BOREAS TF-11 team gathered a variety of data to complement their tower flux measurements collected at the SSA Fen site. The data described in this document were made by the TF-11 team at the SSA Fen site to quantify the effect that the films observed to form on open water surfaces had on the transfer of carbon dioxide and methane from the water to the air. Measurements of fluxes of carbon dioxide and methane were made in 1994 and in 1996 using the chamber flux method. A gas chromatograph and a LI-COR LI-6200 were used to measure concentrations and to calculate the fluxes.\n<br>\n\n#### tf11leaf_456\nThe BOREAS TF-11 team gathered a variety of data to complement their tower flux measurements collected at the SSA Fen site. This data set contains single-leaf gas exchange data from the SSA Fen site during 1994 and 1995. These leaf gas exchange properties were measured for the dominant vascular plants using portable gas exchange systems.\n<br>\n\n#### tf11soil_457\nThe BOREAS TF-11 team gathered a variety of data to complement their tower flux measurements collected at the SSA Fen site. These data are soil surface CO2 flux data at the SSA Fen site from 27-May-1994 to 23-Sep-1994 and from 13-May-1995 to 3-Oct-1995. A portable gas exchange system was used to make these measurements.\n<br>\n\n#### tf11lai_458\nThe BOREAS TF-11 team gathered a variety of data to complement their tower flux measurements collected at the SSA Fen site. These data are LAI measurements made by the TF-11 team throughout the 1995 growing season. The data include the LAI of plants that fall into six categories: total, Carex spp., Betula pumila, Menyanthes trifoliata, Salix spp., and other vascular plants.\n<br>\n\n#### tf11tfx_373\nThe BOREAS TF-11 team collected energy, carbon dioxide, and methane flux data at the BOREAS SSA-Fen site during the growing seasons of 1994 and 1995.\n<br>\n\n#### tgb1ccfd_374\nChamber flux measurements were taken at the Northern Study Area (NSA) Old Jack Pine (OJP), Young Jack Pine (YJP), Old Black Spruce (OBS), and Beaver Pond (BP) sites during the summer of 1994. The purpose of these measurements was to examine the trace gas exchange between the atmosphere and the boreal soils. The BOREAS TGB-01 team made methane (CH4) and carbon dioxide (CO2) dark chamber flux measurements from 16- May-1994 through 13-Sep-1994. Gas samples were extracted approximately every 7 days from dark chambers and analyzed at the NSA lab facility.\n<br>\n\n#### tgb1cfd_375\nThe BOREAS TGB-01 team made numerous measurements of trace gas concentrations and fluxes at various NSA sites. This data set contains half-hourly averages of ambient methane (CH4) measurements and calculated fluxes for the NSA-TF in 1996 and the NSA-BP and NSA-OJP tower sites in 1994. The purpose of this study was to determine the CH4 flux from the study area by measuring ambient CH4 concentrations. This flux can then be compared to the chamber flux measurements taken at the same sites.\n<br>\n\n#### tgb1sfd_376\nChamber flux measurements were taken at the Boreal Ecosystem Atmosphere Study (BOREAS) Northern Study Area (NSA) Old Jack Pine (OJP) and Young Jack Pine (YJP) sites during the summer of 1994. The purpose of these measurements was to examine the trace gas exchange between the atmosphere and the boreal soils. The following is a description of the acquisition of data and the final datasets. The BOREAS TGB-01 team made sulfur hexaflouride (SF6) dark chamber flux measurements 16-May through 13-Sep-1994. Gas samples were extracted approximately every 7 days from dark chambers and analyzed at the NSA lab facility.\n<br>\n\n#### tgbfenfx_378\nThe BOREAS TGB-03 team collected methane (CH4) chamber flux measurements at the NSA fen site during May-September 1994 and June-October 1996. Gas samples were extracted approximately every 7 days from chambers and analyzed at the NSA lab facility.\n<br>\n\n#### tgbfenne_379\nThe BOREAS TGB-01 and TGB-03 teams collected several data sets that contributed to understanding the measured trace gas fluxes over sites in the NSA. This data set contains NEE measurements collected with chambers at the NSA fen in 1994 and 1996. Gas samples were extracted approximately every 7 days from chambers and analyzed at the NSA lab facility.\n<br>\n\n#### tgb3wd_380\nThe BOREAS TGB-01 and TGB-03 teams collected several data sets that contributed to understanding the measured trace gas fluxes over sites in the NSA. This data set contains continuous and manual measurements of water level, air and soil temperatures at the four subsites within the NSA Tower Fen site complex. The measurements were taken to understand the thermal and hydrological gradients associated with each plant community present in the fen. Measurements were taken from May to September 1994 and May to October 1996.\n<br>\n\n#### tgb3cofd_381\nThe BOREAS TGB-03 team collected methane and carbon dioxide (CH4, CO2) chamber flux measurements at the NSA Fen site, OBS, YJP, and auxiliary sites along Gillam Road and the 1989 burn site. Gas samples were extracted from chambers and analyzed at the NSA lab facility approximately every 7 days during May to September 1994 and June to October 1996.\n<br>\n\n#### tgb03doc_459\nThe BOREAS TGB-03 team collected Dissolved Organic Carbon (DOC) data during the summer of 1994 in the Northern Study Area. The purpose of this work was to establish the major sources, sinks and fluxes of dissolved organic carbon (DOC) in the Northern Study Area. Data on DOC concentrations taken from samples representing the major sources and sinks were to be combined with hydrologic measurements (e.g. precipitation, stream flow etc.) to calculate DOC fluxes.\n<br>\n\n#### tgb3plsp_382\nThe BOREAS TGB-03 team collected several data sets that contributed to understanding the measured trace gas fluxes over sites in the NSA. This data set contains information about the composition of plant species that were within the collars used to measure NEE. The species composition was identified to understand the differences in NEE among the various plant communities in the NSA fen. The data were collected in July of 1994 and 1996.\n<br>\n\n#### tgb4flux_460\nThe BOREAS TGB-04 team measured the exchange of heat, water, and CO2 between a boreal forest beaver pond and the atmosphere in the NSA for the ice-free period of BOREAS. The data cover the period of May 28 to September 18, 1994.\n<br>\n\n#### tgb4wsed_461\nThe BOREAS TGB-04 team collected several data sets in support of their flux tower measurements at the NSA Beaver Pond site. This data set contains water and sediment temperature data collected from May to September 1994.\n<br>\n\n#### tgb5nnfd_383\nThe BOREAS TGB-05 team made several measurements of trace gas concentrations and fluxes at various NSA sites. This data set contains biogenic soil emissions of nitric oxide and nitrous oxide that were measured over a wide range of spatial and temporal site parameters. Since very little is known about biogenic soil emissions of nitric oxide and nitrous oxide from the Boreal forest, the goal of the measurements was to characterize the biogenic soil fluxes of nitric oxide and nitrous oxide from black spruce and jack pine areas in the boreal forest. The diurnal variation and monthly variation of the emissions was examined as well as the impact of wetting through natural or artificial means. Temporally, the data cover mid-August 1993, June to August 1994, and mid-July 1995.\n<br>\n\n#### tgb5cflx_384\nThe BOREAS TGB-05 team collected a variety of trace gas concentration and flux measurements at several NSA sites. This data set contains carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) chamber flux measurements conducted in 1994 at upland forest sites that experienced stand- replacement fires. These measurements were acquired to understand the impact of fires on soil biogeochemistry and related changes in trace gas exchange in boreal forest soils. Relevant ancillary data, including data concerning the soil temperature, solar irradiance, and information from nearby unburned control sites, are included to provide a basis for modeling the regional impacts of fire and climate changes on trace gas biogeochemistry.\n<br>\n\n#### tgb5docd_385\nThe BOREAS TGB-05 team collected several data sets related to carbon and trace gas fluxes and concentrations in the NSA. This data set contains concentrations of dissolved organic and inorganic carbon species from water samples collected at various NSA sites. In particular, this set covers the NSA Tower Beaver Pond Site and the NSA Gillam Road Beaver Pond Site, including data from all visits to open water sampling locations during the BOREAS field campaigns from April to September 1994.\n<br>\n\n#### fhstmanr_386\nThis raster format data set covers the province of Manitoba. The data were gridded into the AEAC projection from the original vector data. The original vector data were produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000-scale maps. The locational accuracy of the data is considered fair to poor. When the locations of some fire boundaries were compared to Landsat TM images, they were found to be off by as much as a few kilometers. This problem should be kept in mind when using these data.\n<br>\n\n#### fhstmanv_387\nThis vector format data set covers the province of Manitoba and was produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000 scale maps. The locational accuracy of the data is considered fair to poor. When the locations of some fire boundaries were compared to Landsat TM images, they were found to be off by as much as a few kilometers.\n<br>\n\n#### tgb6chrc_388\nThe BOREAS TGB-06 team collected soil methane measurements at several sites in the SSA and NSA. This data set contains soil methane consumption (bacterial CH4 oxidation) and associated 13C fractionation effects in samples that were collected at various sites in 1994 and 1996 from enclosures (chambers). Methane 13C data in soil gas samples from the NSA YJP and OJP sites for 1994 and 1996 are also given. Additional data on the isotopic composition of methane (carbon and hydrogen isotopes) produced in the NSA beaver ponds and fen bog in 1993 and 1994 are given as well.\n<br>\n\n#### tgb7aaho_389\nThe BOREAS TGB-07 team measured the concentration and flux of several agricultural pesticides in air, rainwater, and dry deposition samples in order to determine the associated yearly deposition rates. This data set contains information on the ambient air concentration of seven herbicides [2,4-dichlorophenoxyacidic_acid (2,4-D), bromoxynil, dicamb, 2-methyl-4-chlorophenoxyacetic acid (MCPA), triallate, trifluralin, and diclop-methyl] known to appear in the atmosphere of the Canadian prairies. Also, the concentration of three herbicides (atrazine, alaclor and metolachlor), two groups of insecticides (lindane and breakdown products and dichlro-diphenyl-trichloroethane (DDT) and breakdown products), and several polychlorinated biphenyls commonly used in the central United States were measured. All of these chemicals are reported, in the literature, to be transported in the atmosphere. Many have been reported to occur in boreal and arctic food chains. The sampling was carried out from June 16 to August 13, 1993 and May 4 to July 20, 1994 at the BOREAS site in the Prince Albert National Park (Waskesiu).\n<br>\n\n#### tgb7ddho_390\nThe BOREAS TGB-07 team measured the concentration and flux of several agricultural pesticides in air, rainwater, and dry deposition samples in order to determine the associated yearly deposition rates. This data set contains information on the dry deposition flux of seven herbicides [2,4-dichlorophenoxyacidic_acid (2,4-D), bromoxynil, dicamb, 2-methyl-4-chlorophenoxyacetic acid (MCPA), triallate, trifluralin, and diclop-methyl] known to appear in the atmosphere of the Canadian prairies. Also, the concentration of three herbicides (atrazine, alaclor and metolachlor), two groups of insecticides (lindane and breakdown products and dichlro-diphenyl-trichloroethane (DDT) and breakdown products), and several polychlorinated biphenyls commonly used in the central United States were measured. All of these chemicals are reported, in the literature, to be transported in the atmosphere. Many have been reported to occur in boreal and arctic food chains. The sampling was carried out from June 16 to August 13, 1993 and May 4 to July 20, 1994 at the BOREAS site in the Prince Albert National Park (Waskesiu).\n<br>\n\n#### tgb7rwho_391\nThe BOREAS TGB-07 team measured the concentration and flux of several agricultural pesticides in air and rainwater samples in order to determine the associated yearly deposition rates. This data set contains information on the rainwater concentration of seven herbicides [2,4-dichlorophenoxyacidic_acid (2,4-D), bromoxynil, dicamb, 2-methyl-4-chlorophenoxyacetic acid (MCPA), triallate, trifluralin, and diclop-methyl] known to appear in the atmosphere of the Canadian prairies. Also, the concentration of three herbicides (atrazine, alaclor and metolachlor), two groups of insecticides (lindane and breakdown products and dichlro-diphenyl-trichloroethane (DDT) and breakdown products), and several polychlorinated biphenyls commonly used in the central United States were measured. All of these chemicals are reported, in the literature, to be transported in the atmosphere. Many have been reported to occur in boreal and arctic food chains. The sampling was carried out from June 16 to August 13, 1993 and May 4 to July 20, 1994 at the BOREAS site in the Prince Albert National Park (Waskesiu).\n<br>\n\n#### tgb8mono_392\nThe TGB-08 team collected data to investigate the controls over non-methane hydrocarbon (NMHC) fluxes from boreal forest tree species. This data set contains measurements of monoterpene concentrations in collected foliar gas emissions and foliar samples. The data were collected at the Old Jack Pine (OJP) and Old Black Spruce(OBS) tower-flux sites in the SSA and were the locus for the monoterpene emission measurements. These areas contained mature stands of jack pine and black spruce and were the focal sites in the BOREAS program for studies of biosphere/atmosphere exchange from these two habitat types. The OBS site is situated in a black spruce/sphagnum bog with the largest trees 155 years old and 10-15 m. tall. The OJP site is in a jack pine forest, 80 to 120 years old, which lies on a sandy bench of glacial outwash with the largest tree standing 15 m. tall. Temporally, the data cover the period of 24-May-94 to 19-Sep-94.\n<br>\n\n#### tgb8prds_393\nThe TGB-08 team collected data to investigate the controls over non-methane hydrocarbon (NMHC) fluxes from boreal forest tree species. This data set includes measurements of photosynthetic rates at mature Jack Pine and Black spruce sites. The two areas used in this research were in the Southern Study Area (SSA) of the BOREAS region: the SSA Old Jack Pine (OJP) and Old Black Spruce(OBS) tower-flux locations. These areas contained mature stands of jack pine and black spruce and were the focal sites in the BOREAS program for studies of biosphere/atmosphere exchange from these two habitat types. The OBS site is situated in a black spruce/sphagnum bog with the largest trees 155 years old and 10-15 m. tall. The OJP site is in a jack pine forest, 80 to 120 years old, which lies on a sandy bench of glacial outwash with the largest tree standing 15 m. tall. Temporally, the data cover the period of 24-May-94 to 19-Sep-94.\n<br>\n\n#### tgb8scds_394\nThe TGB-08 team collected data to investigate the controls over non-methane hydrocarbon (NMHC) fluxes from boreal forest tree species. This data set includes measurements of starch concentrations in foliar samples at mature Jack Pine and Black spruce sites. The two areas used in this research were in the Southern Study Area (SSA) of the BOREAS region: the SSA Old Jack Pine (OJP) and Old Black Spruce(OBS) tower-flux locations. These areas contained mature stands of jack pine and black spruce and were the focal sites in the BOREAS program for studies of biosphere/atmosphere exchange from these two habitat types. The OBS site is situated in a black spruce/sphagnum bog with the largest trees 155 years old and 10-15 m. tall. The OJP site is in a jack pine forest, 80 to 120 years old, which lies on a sandy bench of glacial outwash with the largest tree standing 15 m. tall. Temporally, the data cover the period of 24-May-94 to 19-Sep-94.\n<br>\n\n#### tgb9nmhc_395\nTHE BOREAS TGB-09 team collected data in order to inventory and quantify the anthropogenic and biogenic NMHC's over the BOREAS study areas. This data set contains concentration and mixing ratio values for several NMHC's collected at the BOREAS SSA from 27-MAY-1994 to 15-SEP-1994.\n<br>\n\n#### tgb10ocd_396\nThe BOREAS TGB-10 team collected several trace gas data sets in their efforts to determine the role of biogenic hydrocarbon emissions with respect to boreal forest carbon cycles. This data set contains measured peroxide (H2O2 and total organic peroxides (ROOH)) and ozone concentrations as well as, H2O2 and ROOH deposition velocities. These data were obtained at the SSA Old Jack Pine site from May to September 1994.\n<br>\n\n#### tgb10ofd_397\nThe BOREAS TGB-10 team collected several trace gas data sets in their efforts to determine the role of biogenic hydrocarbon emissions with respect to boreal forest carbon cycles. This oxidant data set contains measured peroxide (H2O2 and total organic peroxides (ROOH)) and ozone concentrations as well as, H2O2 and ROOH deposition velocities. These data were obtained at the SSA Old Jack Pine site during the summer of 1994.\n<br>\n\n#### tgb10voc_398\nThe BOREAS TGB-10 team collected several trace gas data sets in their efforts to determine the role of biogenic hydrocarbon emissions with respect to boreal forest carbon cycles. This data set contains measured VOC concentrations. These data were obtained at the SSA Old Jack Pine site from May to September 1994.\n<br>\n\n#### tgb12ci_399\nThe BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites to estimate the rates of carbon accumulation and turnover in each of the major vegetation types. This data set contains information on the carbon isotopic content of carbon dioxide sampled from soils.\n<br>\n\n#### tgb12rad_400\nThe BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites to estimate the rates of carbon accumulation and turnover in each of the major vegetation types. Sampling strategies for soils were designed to take advantage of local fire chronosequences, so that the accumulation of C in regrowing mosses could be determined. All the data are used to (1) calculate the inventory of C and N in moss and mineral soil layers at NSA sites (2) determine the rates of input and turnover (using both accumulation since the last stand-killing fire and radiocarbon data) and (3) link changes in soil respiration rate to shifts in the 14C content of soil CO2 to determine the average 'age' respired CO2. These 222Rn activity data were collected from 15-NOV-1993 to 16-AUG-1994 over the NSA sites.\n<br>\n\n#### tgb12rfd_401\nThe BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites to estimate the rates of carbon accumulation and turnover in each of the major vegetation types. Sampling strategies for soils were designed to take advantage of local fire chronosequences, so that the accumulation of carbon in new moss growth could be determined. All the data are used to 1) calculate the inventory of carbon and nitrogen in moss and mineral soil layers at NSA sites, 2) determine the rates of input and turnover (using both accumulation since the last stand-killing fire and radiocarbon data), and 3) link changes in soil respiration rate to shifts in the 14C content of soil CO2 to determine the average 'age' respired CO2. These 222Rn flux data were collected from 15-NOV-1993 to 16-AUG-1994 over the NSA sites.\n<br>\n\n#### tgb12cfd_517\nThe BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites. This data set provides: (1) estimates of soil carbon stocks by horizon based on soil survey data and analyses of data from individual soil profiles; (2) estimates of soil carbon fluxes based on stocks, fire history, drainage, and soil C inputs and decomposition constants based on field work using radiocarbon analyses; (3) fire history data estimating age ranges of time since last fire; (4) a raster image and an associated soils table file from which area-weighted maps of soil carbon and fluxes and fire history may be generated. This data set was created from raster files, soil polygon data files, and detailed lab analysis of soils data that were received from Hugo Veldhuis who did the original mapping in the field during 1994. Also used were soils data from Susan Trumbore and Jennifer Harden (BOREAS TGB-12). The binary raster file covers a 733 km^2 area within the NSA-MSA.\n<br>\n\n#### tgb12scd_402\nThe BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites to estimate the rates of carbon accumulation and turnover in each of the major vegetation types. TGB-12 data sets include soil properties at tower and selected auxiliary sites in the BOREAS NSA and data on the seasonal variations in the radiocarbon content of CO2 in the soil atmosphere at NSA tower sites. The sampling strategies for soils were designed to take advantage of local fire chronosequences, so that the accumulation of C in areas of moss regrowth could be determined. These data are used to calculate the inventory of C and N in moss and mineral soil layers at NSA sites and to determine the rates of input and turnover (using both accumulation since the last stand-killing fire and radiocarbon data). This data set includes physical parameters needed to determine carbon and nitrogen inventory in soils. The data were collected discontinuously from August 1993 to July 1996.\n<br>\n\n#### tgb12sci_558\nDuring 1993 and 1994, the BOReal Ecosystem-Atmosphere Study (BOREAS) Trace Gas and Biogeochemistry team #12 (TGB-12) collected several data sets to support their analysis of soil carbon content in the Northern Study Area (NSA). The primary data set (BOREAS TGB-12 Soil Carbon Data over the NSA) is described in some detail. Other ancillary information was stored and provided in two sets of soil pit description and surface vegetation transect files. In addition, a site description file provides more information on the positioning of the sampling sites. These files contain additional information that complements the data contained in the primary data set.\n<br>\n\n#### ams_cs93_403\nCanadian AES personnel collected data related to surface and atmospheric meteorological conditions over the BOREAS Region. This data set contains 15 minute meteorological data from 14 automated meteorology stations located across the BOREAS region. Included in this data are parameters of date, time, mean sea level pressure, station pressure, temperature, dewpoint, wind speed, resultant wind speed, resultant wind direction, peak wind, precipitation, maximum temperature in the last hour, minimum temperature in the last hour, pressure tendency, liquid precipitation in the last hour, relative humidity, precipitation from a weighing gauge, and snow depth. Temporally, the data cover the period of August 1993 to December 1996.\n<br>\n\n#### marsii94_407\nCanadian AES personnel collected several data sets related to surface and atmospheric meteorological conditions over the BOREAS region. This data set contains 15-minute meteorological data from six MARSII meteorology stations in the BOREAS region in Canada. Parameters include site, time, temperature, dewpoint, visibility, wind speed, wind gust, wind direction, two cloud groups, precipitation, and station pressure. Temporarily, the data cover the period of May to September 1994. Geographically, the stations are spread across the provinces of Saskatchewan and Manitoba.\n<br>\n\n#### readac_d_408\nCanadian AES personnel collected and processed data related to surface atmospheric meteorological conditions over the BOREAS region. This data set contains 15 minute meteorological data from one READAC meteorology station in Hudson Bay, Saskatchewan. Parameters include day, time, type of report, sky condition, visibility, mean sea level pressure, temperature, dewpoint, wind, altimeter, opacity, minimum and maximum visibility, station pressure, minimum and maximum air temperature, a wind group, precipitation, and precipitation in the last hour. The data were collected non-continuously from 24-May-1994 to 20-Sep-1994.\n<br>\n\n#### samsa94d_462\nThe Saskatchewan Research Council (SRC) collected surface meteorological and radiation data from December, 1993 until Decemb er 1996. The data set is comprised of the Suite A (meteorological and energy balance measurements) and Suite B (diffuse sol ar and longwave measurements) components. Suite A measurements were taken at each of ten sites and suite B measurements were made at five of the suite A sites. These data cover an area of roughly 1000 km by 1000 km (a large portion of northern Man itoba and northern Saskatchewan). The measurement network was designed to provide researchers with a sufficient record of n ear-surface meteorological and radiation measurements.\n<br>\n\n#### BOREAS_CDS_1350\nThis data set provides Boreal Ecosystem-Atmosphere Study (BOREAS) project information and data collected at selected sites in the boreal forest of Saskatchewan and Manitoba, Canada from 1993 through 1996. The data include surface, airborne, and satellite-based observations. Note that all of the data products on these CDs have been archived as separate BOREAS data sets by the ORNL DAAC and in many cases the published data are later versions. Users should search for BOREAS data among these individual data sets. These data were originally distributed on 12 CD-ROMs, but are now archived as 12 zip files to ensure historical completeness of the BOREAS data record.\n<br>\n\n#### soil125r_309\nThis data set consists of GIS layers that describe the soils of the BOREAS SSA. The original data were submitted as vector layers that were gridded by BOREAS staff to a 30-meter pixel size in the AEAC projection. These data layers include the soil code (which relates to the soil name), modifier (which also relates to the soil name), and extent (indicating the extent that this soil exists within the polygon). There are three sets of these layers representing the primary, secondary, and tertiary soil characteristics. Thus, there is a total of nine layers in this data set along with supporting files. The data are stored in binary, image format files.\n<br>\n\n#### tgb1ccsd_377\nThe BOREAS TGB-01 team made numerous measurements of trace gas concentrations and fluxes at various NSA sites. This data set contains methane (CH4) and carbon dioxide (CO2) concentrations in soil profiles from the NSA-OJP, NSA-OBS, NSA-YJP, and NSA-BP sites during the period of 23-May to 20-Sep-1994. The soil gas sampling profiles of CH4 and CO2 were completed to quantify controls on CO2 and CH4 fluxes in the boreal forest.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/BOREAS/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","earth observation","hydrology","ice","land cover","lidar","oceans","precipitation","radar","satellite imagery","soil moisture","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"h9rgbl94_229 v1 - This data set contains the measurements from the Belfort rain gauges at the BOREAS NSA andSSA. These measurements were submitted in 15-minute and 1-hour intervals.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h9rgbl94/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h9rgtb94_230 v1 - The BOREAS HYD-09 team collected several data sets containing precipitation and streamflow measurements over the BOREAS study areas. This data set contains the measurements from the tipping bucket rain gauges at the BOREAS NSA and SSA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h9rgtb94/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"boreas_aeshrday_235 v2 - This data set contains hourly and daily meteorological data from 23 meteorological stations across Canada from January 1975 to January 1997. The surface meteorology parameters include: date, time, temperature, precipitation, snow, snow depth, sea level pressure, station pressure, dew point, wind direction, wind speed, dry and wet bulb temperature, relative humidity, cloud opacity and cloud amount.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/boreas_aeshrday/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"aes5davg_236 v1 - The Canadian Atmospheric Environment Service (AES) provided BOREAS with hourly and daily surface meteorological data from 23 of the AES meteorological stations located across Canada and upper air data from 1 station at The Pas, Manitoba. Due to copyright restrictions on the full resolution surface meteorological data, this data set contains 5-day average values for the surface parameters.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/aes5davg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"mwlezflx_493 v1 - This data set contains measurements from the NOAA/ATDD Long-EZ Aircraft collected during the 1994 IFCs at the SSA. These measurements were made from various instruments mounted on the aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/mwlezflx/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"faamwdat_237 v1 - The BOREAS AFM-02 team collected pass-by-pass fluxes (and many other statistics) for a large number of level (constant altitude), straight line passes used in a variety of flight patterns. The data were collected by the University of Wyoming King Air in 1994 BOREAS IFCs 1-3.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/faamwdat/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"afm2as94_494 v1 - The BOREAS AFM-02 team used the University of Wyoming King Air aircraft during IFCs 1, 2, and 3 in 1994 to collected pass-by-pass fluxes (and many other statistics) for the large number of level (constant altitude), straight line passes used in a variety of flight patterns. The data described here form a second set, namely soundings that were incorporated into nearly every research flight by the King Air in 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/afm2as94/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"afm3mw94_495 v1 - The BOREAS AFM-03 team used the NCAR Electra aircraft data to make measurements of the fluxes of momentum, sensible and latent heat, carbon dioxide, and ozone over the entire BOREAS region to tie together measurements made in both the SSA and the NSA. These data were also used to study the planetary boundary layer using both in situ and remote sensing measurements.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/afm3mw94/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"afm3as94_496 v1 - The BOREAS AFM-03 team used the NCAR Electra aircraft to make sounding measurements to study the planetary boundary layer using in situ and remote-sensing measurements. Measurements were made of wind speed and direction, air pressure and temperature, potential temperature, dewpoint, mixing ratio of H2O, CO2 concentration, and ozone concentration.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/afm3as94/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"afm4tofx_497 v1 - The BOREAS AFM-04 team used the NRC Twin Otter aircraft in 1994 and 1996 to make measurements in the boundary layer of the fluxes of sensible and latent heat, momentum, ozone, methane, and carbon dioxide, plus supporting meteorological parameters such as temperature, humidity, and wind speed and direction. Aircraft position, heading, and altitude were also recorded, as were several radiometric observations for use in interpretation of the data (greenness index, surface temperature, incoming and reflected radiation).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/afm4tofx/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"afm4toas_498 v1 - The BOREAS AFM-04 team used the NRC Twin Otter aircraft to make sounding measurements through the boundary layer. These measurements included concentrations of carbon dioxide and ozone, atmospheric pressure, dry bulb temperature, potential temperature, dewpoint temperature, calculated mixing ratio, and wind speed and direction.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/afm4toas/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"aes_upl1_238 v1 - The BOREAS AFM-05 team collected and processed data from the numerous radiosonde flights during the project. The goals of the AFM-05 team were to provide large scale definition of the atmosphere by supplementing the existing AES aerological network, both temporally and spatially.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/aes_upl1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"afm06ihd_240 v1 - The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environment Technology Laboratory (NOAA/ETL) operated a 915 MHz wind/Radio Acoustic Sounding System (RASS) profiler system in the Southern Study Area (SSA) near the Old Jack Pine (OJP) site. This data set provides boundary layer height information over the site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/afm06ihd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"afm06ptd_241 v1 - The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environment Technology Laboratory (NOAA/ETL) operated a 915 MHz wind/Radio Acoustic Sounding System (RASS) profiler system in the Southern Study Area (SSA) near the Old Jack Pine (OJP) tower from 21-May-1994 to 20-Sep-1994. The data set provides temperature profiles at 15 heights, containing the variables of virtual temperature, vertical velocity, the speed of sound, and w-bar.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/afm06ptd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"afm06pwd_242 v1 - The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environment Technology Laboratory (NOAA/ETL) operated a 915 MHz wind/Radio Acoustic Sounding System (RASS) profiler system in the Southern Study Area (SSA) near the Old Jack Pine (OJP) tower from 21-May-1994 to 20-Sep-1994. The data set provides wind profiles at 38 heights, containing the variables of wind speed, wind direction and the u-, v-, and w-components of the total wind.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/afm06pwd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"afm6gifs_433 v1 - The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environmental Technology Laboratory (NOAA/ETL) operated a 35 GHz cloud-sensing radar in the Northern Study Area (NSA) near the Old Jack Pine (OJP) tower from 16-Jul-1994 to 08-Aug-1994. This data set contains a time series of GIF (Graphical Interchange Format) images that show the structure of the lower atmosphere.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/afm6gifs/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"afm06smd_243 v1 - The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environment Technology Laboratory (NOAA/ETL) collected surface meteorological data from 21-May to 20-Sep-1994 near the SSA-OJP tower site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/afm06smd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ecmwf2_523 v1 - The BOREAS AFM-08 team focused on modeling efforts to improve the understanding of the diurnal evolution of the convective boundary layer over the boreal forest. This data set contains hourly data from the ECMWF operational model from below the soil surface to the top of the atmosphere, including the model fluxes at the surface.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/ecmwf2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"afm11afr_244 v1 - This directory contains reports from the BOREAS AFM-11 team regarding quality control and sampling analysis of data collected by other AFM personnel using the Electra, Long-EZ, and Twin Otter aircraft. These reports are stored in Adobe Acrobat (.PDF) format and should be downloaded in binary mode.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/afm11afr/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"avhrrlc1_434 v1 - This regional land cover classification is based on the use of multitemporal 1-km Advanced Very High Resolution Radiometer (AVHRR) National Oceanic and Atmospheric Administration (NOAA 11) data that were analyzed in combination with selected Landsat Thematic Mapper (TM) and extensive field observations within a 619-km by 821-km subset of the 1,000-km by 1,000-km BOReal Ecosystem-Atmosphere Study (BOREAS) region (Steyaert et al., 1997). Following the approach developed by Loveland et al.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/avhrrlc1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"afm13afr_245 v1 - For the BOReal Ecosystem-Atmosphere Study (BOREAS) in 1994 and 1996, the Airborne Fluxes and Meteorology (AFM) group was involved in measurements (from different platforms and heights within the atmospheric boundary layer) of key atmospheric variables and several surface-related parameters that can be used to describe the evolution of the atmospheric boundary layer and the boundary layer fluxes of sensible heat, latent heat, momentum, and CO2. Specifically, the AFM-13 team was interested in analysis and interpretation of airborne flux observations over a 16-km by 16-km grid site in each of ...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/afm13afr/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Saskatchewan_Soils_125m_SSA_1346 v2 - This data set provides soil descriptions for forested areas in the BOREAS southern study area (SSA) in central Saskatchewan, Canada provided by Agriculture Canada. The data contain soil code, modifiers, extent, and soil names for the primary, secondary, and tertiary soil units within each polygon.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/Saskatchewan_Soils_125m_SSA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"calibgas_500 v1 - In order to improve the comparability of trace gas measurements made by various science teams, the BOReal Ecosystem-Atmosphere Study (BOREAS) obtained several cylinders of carbon dioxide (CO2) and methane (CH4) that were used as calibration standards. The cylinders were stored in the field laboratories established in Paddockwood, Saskatchewan (Southern Study Area) (SSA) and in Thompson, Manitoba (Northern Study Area) (NSA) from May 1994 until November 1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/GRAB_BAG/calibgas/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CanSIS_Regional_Soils_1347 v2 - This data set contains soils data from the Canada Soil Information System (CanSIS) in ESRI Shapefile format for the provinces of Saskatchewan and Manitoba. They are provided as part of the BOReal Ecosystem-Atmosphere Study (BOREAS) Staff Science GIS data collection program.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/CanSIS_Regional_Soils/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"dtms0bil_247 v1 - The level-0 Daedalus TMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas. This information includes detailed land cover and biophysical parameter maps such as FPAR and LAI.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/dtms0bil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"n_s_dem_248 v1 - These data were derived from the original DEMs produced by the BOREAS HYD-08 team. The original DEMs were in the UTM projection, while this product is projected in the AEAC projection (see Section 7 for further projection details).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/n_s_dem/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"srfmetmd_249 v1 - In 1995, the BOREAS science teams identified the need for a continuous surface meteorological and radiation data set to support flux and surface process modeling efforts. This data set contains actual, substituted, and interpolated 15-minute meteorological and radiation data compiled from several surface measurements sites over the BOREAS SSA and NSA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/srfmetmd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"elev_arc_250 v1 - This data set was prepared by BORIS staff by reformatting the original data into the ARC/INFO Generate format. The original data were received in SIF at a scale of 1:50,000.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/elev_arc/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"er2flog_501 v1 - During 1994 and 1996, digital and analog imaging instruments mounted on the NASA ER2 aircraft collected various remotely sensed data from the atmosphere and earth?s surface as part of the BOReal Ecosystem-Atmosphere Study (BOREAS) Intensive Field Campaigns (IFC). Personnel from the NASA Earth Resources Aircraft Program within the High-altitude Aircraft Branch at NASA Ames Research Center (ARC) compiled and published logs that document the BOREAS ER2 missions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/GRAB_BAG/er2flog/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp01_ccrs_tm_landcover_588 v1 - The objective of this land cover mosaic is to provide a data product that characterises the detailed land cover of a significant portion of the BOREAS Region. Seven Landsat-5 TM images have been assembled to completely cover the BOREAS Transect.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp01_ccrs_tm_landcover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp01_ccrs_avhrr_landcover_589 v1 - This land cover product was produced by NBIOME to generate an up-to-date, spatially and temporally consistent land cover map of the landmass of Canada for use by scientists and other users interested in environmental information at national and regional scales. The data were acquired by CCRS and were provided to BORIS for use.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp01_ccrs_avhrr_landcover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp04_ers_freeze-thaw_maps_590 v1 - The BOREAS DSP-4 team acquired and analyzed imaging radar data from the ESA's ERS-1 over a complete annual cycle at the BOREAS sites in Canada in 1994 to detect shifts in radar backscatter related to varying environmental conditions. Two independent transitions correlating with snow melt and soil thaw onset, and possible canopy thaw were revealed by the data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp04_ers_freeze-thaw_maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp05_ccrs_avhrr_npp_591 v1 - The BOREAS DSP-5 team generated a NPP image over the BOREAS region from a process-based ecosystem model, the Boreal Ecosystem Productivity Simulator (BEPS). The NPP image was created from a series of composited AVHRR images from April 11 - September 10, 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp05_ccrs_avhrr_npp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp06_casi_lai_cc_592 v1 - The LAI and canopy closure images over the BOREAS conifer flux tower sites were produced at a spatial resolution of 30 m using the Forest-Light Interaction Model. The data used were obtained by the CASI instrument in high spatial resolution mode in the winter of 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp06_casi_lai_cc/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp08_polder_surface_params_594 v1 - This data set contains maps of surface reflectance and ve getation parameters derived from imagery collected by the POLDER instrument over BOREAS conifer tower sites in the Southern Study Area(SSA) during 1994. The POLDER imagery proficed in this data set was collected on June 1, and July 21, 1994 from the NASA C-130 aircraft platform.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp08_polder_surface_params/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp09_moss_map_595 v1 - BOREAS follow-on group DSP-9 mapped surface moss type at three scales (1 km, 30 m, and 10 m) based on observed associations between moss cover and land cover type. In the BOREAS Northern (NSA) and Southern (SSA) Study Areas, we utilized land cover derived from Landsat TM (30 m) and ground measurements/observations, soils maps, and field observations to establish associations between moss and land cover.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp09_moss_map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp09_sask_fire_map_596 v1 - This data set contains a pair of raster images and a spreadsheet chronicling the most recent fire history of Saskatchewan from 1945 to 1996. This data set was developed from a series of ARC/INFO export files of annual fire data that were compiled and provided by the Saskatchewan Environment and Resource Management (SERM) Wildlife Branch.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp09_sask_fire_map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp10_landcover_tm_reclassed_597 v1 - These images were produced by aggregating a reclassified version of the 30-m land cover Thematic Mapper classification by CCRS and are now available at multiple resolutions (10x5 minutes, and 30 minutes). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp10_landcover_tm_reclassed/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp10_fpar_lai_1994_585 v1 - Existing BOREAS gridded data sets were processed into projections and scales required by the Follow-on modeling teams. Existing TM and AVHRR based landcover maps, AVHRR-FPAR maps, AVHRR-LAI maps, moss cover maps, and a new peatland distribution map were regridded to scales of 2 km, 10 by 5 minute, and half-degree grids for use by the BOREAS Follow-On Carbon and Hydro-Meteorological modeling groups.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp10_fpar_lai_1994/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp10_landcover_598 v1 - These images were produced by aggregating the 1-km land cover classification by Lou Steyaert at multiple resolutions (2 km, 10x5 minutes, and 0.5 degree). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups to have a number of data sets available in common grid projections and scales for intercomparison studies.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp10_landcover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp10_moss_cover_599 v1 - Existing 1-km moss cover classifications were reprocessed and are now available at multiple resolutions (2 km, 10x5 minutes, and 0.5 degree). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups to have a number of data sets available in common grid projections and scales for intercomparison studies.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp10_moss_cover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp10_ndvi_600 v1 - These images were produced by averaging the 1-km FASIR-NDVI maps by Jing Chen to a 10' (horizontal) by 5' (vertical) pixel size in a straight latitude/longitude grid. Each pixel represents the average NDVI of the 1-km pixels that fall in each 10' by 5' pixel, where more than 50% of the 1-km pixels in the 10' by 5' area are not cloud and are not missing.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp10_ndvi/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp10_peatlands_601 v1 - These images were produced by aggregating 1' gridded data layers derived from the polygon-based Peatlands of Canada Database (Tarnocai et al., 2000) to 10' (horizontal) by 5' (vertical) and to 0.5 degree by 0.5 degree (or 30' by 30') pixel sizes in straight latitude/longitude grids. See the Peatlands Map of Canada data set for more information on the original data product that this is based on.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp10_peatlands/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_dsp10_landcover_tm_mosaic_602 v1 - Existing 30-m land cover Thematic Mapper classification by CCRS was aggregated and reprocessed and are now available at multiple resolutions (10x5 minutes and 30 minutes). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/DSP/BFO_dsp10_landcover_tm_mosaic/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_flx01_derived_data_603 v1 - The BOREAS Follow-On FLX-01 team derived NEE, GEE, and Respiration using measured tower C02 flux measurements taken at the NSA-OBS site. The data provided contain half-hourly measurements as well as 4 and 5 day binned data sets.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/BFO_flx01_derived_data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_flx01_flux_met_temp_604 v1 - The BOREAS Follow-On FLX-01 team collected tower flux, surface meteorological, and soil temperature data at the BOREAS NSA-OBS site continuously from March 1994 through December 1998. Data from March 1994 through October 1996 are included in the BOREAS TF-03 effort while data from the end of October 1996 through December 1998 are included in the BOREAS Follow-on FLX-01 effort.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/BFO_flx01_flux_met_temp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_flx03_area_avg_flux_586 v1 - Calculations of area-averaged fluxes using extracted flux data from BORIS.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/BFO_flx03_area_avg_flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_flx04_nsa_burn_flux_587 v1 - Tower flux and meteorological data were collected above 4 black spruce forest sites in the NSA that experienced stand-replacing wildfires in 1989,1981,1964 and 1930. At each site, 4-6 weeks of data were collected during the peak growing season (June-September) in either 1999 or 2000.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/BFO_flx04_nsa_burn_flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_hmet01_goes8_level2_srb_605 v1 - The BOREAS RSS-14 team collected and processed several Level-1 GOES-7 and GOES-8 image data sets for 1994-1996, and GOES-7 Level-2 for 1994 over the BOREAS study region. This data set contains shortwave and longwave radiation images at the surface and top of the atmosphere derived from collected GOES-8 data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HMET/BFO_hmet01_goes8_level2_srb/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_hmet01_ssmi_precip_606 v1 - A gridded data set has been assembled over the BOREAS hydro-meteorological study region that combines a precipitation data set based on a rain gauge network with precipitation estimates based on SSM/I satellite images. The result is an hourly precipitation data set covering 122 consecutive days beginning on June 1, 1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HMET/BFO_hmet01_ssmi_precip/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_hmet02_gridded_met_phase2-3_607 v1 - Phase II and III gridded data sets have been generated by an objective analysis scheme using all of the surface meteorological station data over BOREAS region for 1994-1996. Additionally, FSU GOES incoming solar radiation retrievals and BOREAS rain radar retrievals during portions of 1994 and 1996 were used when remote sensing products were available.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HMET/BFO_hmet02_gridded_met_phase2-3/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_hmet03_hourly_met_p1_608 v1 - Point data developed from in situ observations at four flux tower sites were combined to produce continuous, above the canopy, meteorological forcing data sets. Data from the OA and OBS sites in the SSA and the Fen and OBS sites in the NSA were used to create continuous time series with a time step of one hour, covering the period from 1-Jan-1994 through 1-Dec-1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HMET/BFO_hmet03_hourly_met_p1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_hmet04_src_nsa_96-98_609 v1 - As part of the BOREAS Follow-on, an extended period of data collection was supported in the NSA because of the continued efforts at the NSA-OBS site. This data set contains near-surface meteorological data collected and averaged over 15 minute intervals from two sites in the NSA, the SRC tower at the Thompson airport (YTH) and a temporary walkup wooden tower at the Old Black Spruce (OBS) tower site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HMET/BFO_hmet04_src_nsa_96-98/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BFO_mod01_gridded_met_p3_daily_610 v1 - The Phase 3 gridded data sets provided by HMet-01 on an hourly time step have been converted to averaged daily files by the MOD-01 group to reduce the size and number of files used for input to some of the carbon models.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HMET/BFO_mod01_gridded_met_p3_daily/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ssafcovr_251 v1 - This data set was prepared by BORIS staff by processing the original vector data into raster files. The original data were received as ARC/INFO coverages or as export files from SERM.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/ssafcovr/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"nsafcovr_252 v1 - This data set was processed by BORIS staff from the original vector data of species, crown closure, cutting class, and site classification/subtype into raster files. The original polygon data were received from Linnet Graphics, the distributor of data for MNR.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/nsafcovr/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h01_shd_253 v1 - The BOREAS HYD-01 team made several measurements related to soil moisture and soil properties. These soil hydraulic properties were determined at the flux tower sites based on analysis of in situ tension infiltrometer tests and laboratory determined water retention from soil cores collected during the 1994-95 field campaigns.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h01_shd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h01uncpd_254 v1 - Under-canopy precipitation measurements were made by the BOREAS HYD-01 science team in 1994, 1995, and 1996 at various flux tower sites in the NSA and SSA. In 1994, these data were collected at the NSA-OJP, NSA-YJP, SSA-OJP, and SSA-YJP sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h01uncpd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h01smpvd_255 v1 - The BOREAS HYD-01 team made measurements of volumetric soil moisture at the SSA and NSA tower flux sites in 1994 and at selected tower flux sites in 1995-97. Different methods were used to collect these measurements, including neutron probe and manual and automated TDR.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h01smpvd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h02swed_256 v1 - The surface meteorological data collected at the BOREAS tower and ancillary sites are being used as inputs to an energy balance model to monitor the amount of snow storage in the boreal forest region. The BOREAS HYD-02 team used snow water equivalent (SWE) derived from an energy balance model and in situ observed SWE to compare the SWE inferred from airborne and spaceborne microwave data, and to assess the accuracy of microwave retrieval algorithms.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h02swed/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h03stdnd_257 v1 - The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of stem density from a variety of sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h03stdnd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h03candd_258 v1 - The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of canopy density (closure) from a variety of sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h03candd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h03sd96d_259 v1 - The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of snow depth, snow density in 3-cm intervals, an integrated snow pack density and snow water equivalent (SWE), and snow pack physical properties from snow pit evaluation taken in 1994 and 1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h03sd96d/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h03dbhd_264 v1 - The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of tree diameter at breast height (DBH) from a variety of sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h03dbhd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h3scmet_265 v1 - The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set includes measurements of wind speed and direction; air temperature; relative humidity; and canopy, trunk, and snow surface temperatures within three forest types.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h3scmet/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h03scrdd_266 v1 - The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set contains solar radiation measurements from several pyranometers (solar radiometers) placed on the snow surface in jack pine (1994) and black spruce and aspen forests (1996).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h03scrdd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h04assd_267 v1 - The BOREAS HYD-04 work was focused on collecting data during the winter field campaign (FFC-W) to improve the understanding of winter processes within the boreal forest. Airborne remote sensing data (gamma, passive microwave) were acquired along a series of flight lines established in the vicinity of the BOREAS study areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h04assd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h04stsnd_268 v1 - The BOREAS HYD-04 work was focused on collecting data during the winter field campaign (FFC-W) to improve the understanding of winter processes within the boreal forest. Snow surveys were conducted at special snow courses throughout the 1993/94, 1994/95, 1995/96, and 1996/97 winter seasons.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h04stsnd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h5flxd_269 v1 - The BOREAS HYD-05 team collected tower flux, surface meteorological, and surface temperature data on a frozen lake (Namekus Lake) and in a mature jack pine forest in the Beartrap Creek watershed. Both sites were located in the BOREAS SSA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h5flxd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h6acgsmd_270 v1 - This data set contains percent soil moisture (by weight) (and/or water content if there is a moss/humus layers measured from aircraft using a terrestrial gamma ray instrument. There is also data that indicates the location of the aircraft at the time it collected the terrestrial gamma ray data for the various flight lines and bins.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h6acgsmd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h06grsmd_271 v1 - This data set contains percent soil moisture ground measurements. These data were collected on the ground along the various flight lines flown in the Southern and Northern Study Areas (SSA and NSA) during 1994 by the gamma ray instrument.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h06grsmd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h06grmsd_272 v1 - This data set contains water content measurements of the moss/humus layer, where it existed. These data were collected along various flight lines in the Southern and Northern Study Areas (SSA and NSA) during 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h06grmsd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h08gm94_273 v1 - The BOREAS HYD-08 team made measurements of surface hydrological processes that were collected at the NSA OBS Tower Flux site in 1994 and at Joey Lake, Manitoba, to support their research into point hydrological processes and the spatial variation of these processes. The data collected may be useful in characterizing canopy interception, drip, throughfall, moss interception, drainage, evaporation, and capacity during the growing season at daily temporal resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h08gm94/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h08gm96_274 v1 - The BOREAS HYD-08 team made measurements of surface hydrological processes that were collected at the SSA-OBS Tower Flux site in 1996 to support its research into point hydrological processes and the spatial variation of these processes. Data collected may be useful in characterizing canopy interception, drip, throughfall, moss interception, drainage, evaporation, and capacity during the growing season at daily temporal resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h08gm96/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h08gp96_275 v1 - The BOREAS HYD-08 team made measurements of surface hydrological processes at the SSA-OBS Tower Flux site to support its research into point hydrological processes and the spatial variation of these processes. Data collected may be useful in characterizing canopy interception, drip, throughfall, moss interception, drainage, evaporation, and capacity during the growing season at daily temporal resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h08gp96/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h8utmdem_276 v1 - These DEMs were produced from digitized contours at a cell resolution of 100 meters. Vector contours of the area were used as input to a software package that interpolates between contours to create a DEM representing the terrain surface.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h8utmdem/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h8thrfld_277 v1 - The BOREAS HYD-08 team made measurements of surface hydrological processes at the SSA (1996) and NSA OBS (1994) Tower Flux sites, supporting its research into point hydrological processes and the spatial variation of these processes. These data were collected during the 1994 and 1996 field campaigns.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h8thrfld/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h09rradi_278 v1 - The BOREAS HYD-09 team collected data on precipitation and streamflow over portions of the NSA and SSA. This data set contains Cartesian maps of rain accumulation for 1-hour and daily periods during the summer of 1994 over the SSA only (not the full view of the radar).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h09rradi/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"h09stmgd_279 v1 - These streamflow data were collected by the HYD-09 science team to support its research into meltwater supply to the soil during the spring melt period. These data were also collected for HYD-09's research into the evolution of soil moisture, evaporation, and runoff from the end of the snowmelt period through freeze up.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/HYD/h09stmgd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"lsatmssd_435 v1 - The Earth Resources Technology Satellite (ERTS) Program launched the first of a series of satellites (ERTS-1) in 1972. Part of the NASA Earth Resources Survey Program, the ERTS Program and the ERTS satellites were later renamed Landsat to better represent the civil satellite program's prime emphasis on remote sensing of land resources.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/lsatmssd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ltm_ii3a_280 v1 - For BOREAS, the level-3a Landsat TM data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/ltm_ii3a/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ltm_ii3b_425 v1 - For BOREAS, the level-3b Landsat TM data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/ltm_ii3b/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ltm_ii3p_426 v1 - For BOREAS, the level-3p Landsat TM data were used to supplement the level-3s Landsat TM products. Along with the other remotely sensed images, the Landsat TM images were collected in order to provide spatially extensive information over the primary study areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/ltm_ii3p/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ltm_ii3s_427 v1 - For BOREAS, the level-3s Landsat TM data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/ltm_ii3s/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"aoci0bil_281 v1 - The level-0 AOCI imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas. The AOCI was the only remote sensing instrument flown with wavelength bands specific to the investigation of various aquatic parameters such as chlorophyll content and turbidity.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/aoci0bil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tims0bil_282 v1 - For BOREAS, the TIMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information over the primary study areas. The level-0 TIMS images cover the time periods of 16-Apr-1994 to 20-Apr-1994 and 06-Sep-1994 to 17-Sep-1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/tims0bil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"maslv1b_560 v1 - For BOREAS, the MAS images, along with the other remotely sensed data, were collected to provide spatially-extensive information over the primary study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR (fraction of Photosynthetically Active Radiation), and LAI (Leaf Area Index).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/maslv1b/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tims1bsq_436 v1 - The BOREAS Staff Science Aircraft Data Acquisition Program focused on providing the research teams with the remotely sensed satellite data products they needed to compare and spatially extend point results. For BOREAS, the TIMS imagery, along with other aircraft images, was collected to provide spatially extensive information over the primary study areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/tims1bsq/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"mas_lv2_561 v1 - The BOREAS Staff Science Aircraft Data Acquisition Program focused on providing the research teams with the remotely sensed aircraft data products they needed to compare and spatially extend point results. The MAS images, along with other remotely sensed data, were collected to provide spatially extensive information over the primary study areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/mas_lv2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"avhrrl3b_481 v1 - The BOREAS Staff Science Satellite Data Acquisition Program focused on providing the research teams with the remotely sensed satellite data products they needed to compare and spatially extend point results. Data acquired from the AVHRR instrument on the NOAA-9, -11, -12, and -14 satellites were processed and archived for the BOREAS region by the MRSC and BORIS.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/avhrrl3b/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"spot_3s_437 v1 - For BOREAS, the level-3s SPOT data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/spot_3s/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"avhrrl4b_438 v1 - The BOREAS Staff Science Satellite Data Acquisition Program focused on providing the research teams with the remotely sensed satellite data products they needed to compare and spatially extend point results. MRSC and BORIS personnel acquired, processed, and archived data from the AVHRR instruments on the NOAA-11 and -14 satellites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/avhrrl4b/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ns001bil_440 v1 - For BOREAS, the NS001 TMS imagery, along with the other remotely sensed images, was collected in order to provide spatially extensive information over the primary study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/ns001bil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ns0012bq_482 v1 - For BOREAS, the NS001 TMS images, along with the other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/ns0012bq/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"panpfcov_283 v1 - This data set provides detailed canopy, understory, and ground cover, height, density, and condition information for PANP in the western portion of the BOREAS SSA in vector form.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/panpfcov/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"reg_aeac_284 v1 - This data set is based on the GTOPO30 DEM produced by the USGS EDC. The BOREAS region (1,000km x 1000km) was extracted from the GTOPO30 data and reprojected by BOREAS staff into the AEAC projection.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/reg_aeac/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"regsoilr_285 v1 - This data set was gridded by BORIS staff from a vector data set received from Canadian Soil Information System (CanSIS). The original data came in two parts that covered Saskatchewan and Manitoba.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/regsoilr/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rss1para_286 v1 - The BOREAS RSS-01 team collected surface reflectance and transmittance data from three forested sites in the SSA. This data set contains averaged reflectance factors and transmitted radiances measured by the PARABOLA instrument at selected sites in the BOREAS SSA at different view angles and at three wavelength bands throughout the day.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rss1para/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"asasrefl_287 v1 - Atmospherically-corrected bidirectional reflectance factor means for small homogeneous areas from several BOREAS sites were derived from multi-spectral, multi-angle imagery acquired by the Advanced Solidstate Array Spectroradiometer (ASAS) aboard the C-130 aircraft platform in 1994 and 1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/asasrefl/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"asas_l1b_562 v1 - The BOREAS RSS-02 team used the ASAS instrument, mounted on the NASA C-130 aircraft, to create at-sensor radiance images of various sites as a function of spectral wavelength, view geometry (combinations of view zenith angle, view azimuth angle, solar zenith angle, and solar azimuth angle), and altitude. The level-1b ASAS images of the BOREAS study areas were collected from April to September 1994 and March to July 1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/asas_l1b/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rs3atmos_288 v1 - The BOREAS RSS-03 team collected and processed helicopter-based measurements of atmospheric conditions to estimates of aerosol optical thickness and atmospheric water vapor. The automatic sun-tracking photometer for helicopters was deployed during all three IFC's of 1994 at numerous tower and auxiliary sites in both the NSA and SSA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rs3atmos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BOREAS_RSS-03_Snapshots_289 v2 - This data set provides images of boreal forests in central Canada collected over numerous tower and auxiliary sites during the BOREAS Intensive Field Campaigns (IFCs) in the Northern (NSA) and Southern Study Areas (SSA). The images were acquired by helicopter with VHS video cameras during the green-up, peak, and senescent stages of the growing season from May-September of 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/BOREAS_RSS-03_Snapshots/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rss3hmmr_290 v1 - The RSS-03 team acquired helicopter-based measurements of forested sites during BOREAS with a Barnes MMR. The data were collected in 1994 during the rhree BOREAS IFCs at numerous tower and auxiliary sites in both the NSA and SSA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rss3hmmr/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rs3se590_291 v1 - The BOREAS RSS-03 team collected multiple remotely sensed data sets from the NASA UH-1 helicopter. This data set includes helicopter-based radiometric measurements of forested sites acquired during BOREAS made with an SE-590 processed to reflectance factors.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rs3se590/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rss4lib_292 v1 - The BOREAS RSS-04 team focused its efforts on deriving estimates of LAI and leaf chlorophyll and nitrogen concentrations from remotely sensed data for input into the Forest BGC model. This data set contains measurements of jack pine (Pinus banksiana) needle biochemistry from the BOREAS SSA in July and August 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rss4lib/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"r04laifd_293 v1 - The RSS-04 team collected several data sets related to leaf, plant, and stand physical, optical, and chemical properties. This data set contains leaf area indices and FPAR measurements which were taken at the three conifer sites in the BOREAS SSA during August 1993 and at the jack pine tower flux and a subset of auxiliary sites during July and August 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/r04laifd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"r07elaid_294 v1 - The BOREAS RSS-07 team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of LAI from remotely sensed images. Ground measurements of LAI and FPAR absorbed by the plant canopy were made using the LAI-2000 and TRAC optical instruments during focused periods from 09-AUG-1993 to 19-SEP-1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/r07elaid/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rs7tmlai_441 v1 - The BOREAS RSS-07 team used Landsat TM images processed at CCRS to produce images of LAI for the BOREAS study areas. Two images acquired on June 6 and August 9, 1991 were used for the SSA, and one image acquired on June 9, 1994 was used for the NSA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rs7tmlai/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"r7laifpa_442 v1 - The BOREAS RSS-07 team focused their efforts on developing and validating procedures and algorithms that would allow the retrieval of LAI from remotely sensed vegetation indices. This data set contains images of LAI and FPAR that were produced from the AVHRR Level-4c ten-day composite NDVI images produced at CCRS for the three summer IFCs in 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/r7laifpa/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"biomebgc_295 v1 - BIOME-BGC is a general ecosystem process model designed to simulate biogeochemical and hydrologic processes across multiple scales (Running and Hunt, 1993). In this investigation, BIOME-BGC was used to estimate daily water and carbon budgets for the BOREAS tower flux sites for 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/biomebgc/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"biomebg2_296 v1 - The BOREAS RSS-08 team performed research to evaluate the effect of seasonal weather and landcover heterogeneity on boreal forest regional water and carbon fluxes using a process level ecosystem model, BIOME-BGC, coupled with remote sensing-derived parameter maps of key state variables. This data set contains derived maps of landcover type and crown and stem biomass as model inputs to determine annual evapotranspiration, gross primary production, autotrophic respiration and net primary productivity within the BOREAS SSA-MSA, at a 30 m spatial resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/biomebg2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rss8brdf_505 v1 - Ground BRDF measurements were acquired by the Remote Sensing Science (RSS)-08 team to aid in the development of advanced spectral vegetation indices. The RSS-08 team measured reflectances at the double-scaffold towers in the Southern Study Area (SSA) Old Black Spruce (OBS) and Old Aspen (OA) sites during IFC-3 in 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/GRAB_BAG/rss8brdf/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rss8snow_428 v1 - The BOREAS RSS-08 team utilized Landsat TM images to perform mapping of snow extent over the SSA. This data set consists of two Landsat TM images which were used to determine the snow-covered pixels over the BOREAS SSA on 18-Jan-1993 and on 06-Feb-1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rss8snow/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rss8digi_506 v1 - The RSS08 team acquired stereo photography from the double-scaffold towers at the Southern Study Area (SSA) Old Black Spruce (OBS), Old Aspen (OA), and Old Jack Pine (OJP) sites during IFC-3 in 1994. The imagery of the canopy was taken from various perspectives.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/GRAB_BAG/rss8digi/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rss10tom_443 v1 - The BOREAS RSS-10 team investigated the magnitude of daily, seasonal, and yearly variations of PAR from ground and satellite observations. This data set contains satellite estimates of surface-incident photosynthetically active radiation (PAR, 400-700 nm, MJ m-2) at 1 degree spatial resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rss10tom/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"r11sunpd_297 v1 - The BOREAS RSS-11 team operated a network of five automated (Cimel) and two hand-held (Miami) solar radiometers from 1994 to 1996 during the BOREAS field campaigns. The data provide aerosol optical depth measurements, size distribution, phase function, and column water vapor amounts over points in northern Saskatchewan and Manitoba, Canada.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/r11sunpd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"sunphair_298 v1 - The BOREAS RSS-12 team collected both ground and airborne sunphotometer measurements for use in characterizing the aerosol optical properties of the atmosphere during the BOREAS data collection activities. These measurements are to be used to: 1) measure the magnitude and variability of the aerosol optical depth in both time and space; 2) determine the optical properties of the boreal aerosols; and 3) atmospherically correct remotely sensed data acquired during BOREAS.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/sunphair/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"r12sunpd_299 v1 - The BOREAS RSS-12 team collected both ground and airborne sunphotometer measurements for use in characterizing the aerosol optical properties of the atmosphere during the BOREAS data collection activities. These measurements are to be used to: 1) measure the magnitude and variability of the aerosol optical depth in both time and space; 2) determine the optical properties of the boreal aerosols; and 3) atmospherically correct some remotely sensed data acquired during BOREAS.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/r12sunpd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"goes71_444 v1 - The level-1 BOREAS GOES-7 image data were collected by Remote Sensing Science Team 14 (RSS-14) personnel at Florida State University (FSU) and delivered to BORIS. The data cover the period of 01-Jan-1994 through 08-Jul-1995, with partial to complete coverage on the majority of the days.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/goes71/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"goes71a_300 v1 - The level-1a BOREAS GOES-7 image data was collected by Remote Sensing Science Team 14 (RSS-14) personnel at the Florida State University and processed to level-1a products by BORIS personnel. The primary objective for the GOES-7 images in 1994 was to collect visible, infrared (IR), and water vapor channel data covering the BOREAS region at a sufficiently high temporal frequency for subsequent use in analyzing weather events and deriving temporal surface radiation parameters and patterns that existed during the Focused and Intensive Field Campaigns (FFCs and IFCs).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/goes71a/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"goes72_554 v1 - The BOREAS RSS-14 team collected and processed several GOES-7 and GOES-8 image data sets that covered the BOREAS study region. This data set contains images of shortwave and longwave radiation at the surface and top of the atmosphere derived from collected GOES-7 data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/goes72/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"goes81_445 v1 - The BOREAS RSS-14 team collected and processed several GOES-7 and GOES-8 image data sets that covered the BOREAS study region. The level-1 BOREAS GOES-8 images are raw data values collected by RSS-14 personnel at FSU and delivered to BORIS.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/goes81/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"goes81a_446 v1 - The BOREAS RSS-14 team collected and processed several GOES-7 and GOES-8 image data sets that covered the BOREAS study region. The level-1a GOES-8 images were created by BORIS personnel from the level-1 images delivered by FSU personnel.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/goes81a/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rss14srb_447 v1 - The BOREAS RSS-14 team collected and processed GOES-7 and -8 images of the BOREAS region as part of their effort to characterize the incoming, reflected and emitted radiation at regional scales. This data set contains surface radiation parameters, such as net radiation and net solar radiation, that have been interpolated from GOES-7 images and AMS data onto the standard BOREAS mapping grid at a resolution of 5 km N-S and E-W.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rss14srb/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rs15bmlc_483 v1 - As part of BOREAS, the RSS-15 team conducted an investigation using SIR-C , X-SAR and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rs15bmlc/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rs16cm61_563 v1 - The BOREAS RSS-16 team used satellite and aircraft SAR data in conjunction with various ground measurements to determine the moisture regime of the boreal forest. RSS-16 assisted with the acquisition and ordering of NASA JPL AIRSAR data collected from the NASA DC-8 aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rs16cm61/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rss17fth_484 v1 - The BOREAS RSS-17 team acquired and analyzed imaging radar data from the ESA's ERS-1 over a complete annual cycle at the BOREAS sites in Canada in 1994 to detect shifts in radar backscatter related to varying environmental conditions. Two independent transitions corresponding to soil thaw and possible canopy thaw were revealed by the data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rss17fth/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rs17diel_301 v1 - The BOREAS RSS-17 team acquired and analyzed imaging radar data from the ESA's ERS-1 over a complete annual cycle at the BOREAS sites in Canada in 1994 to detect shifts in radar backscatter related to varying environmental conditions. This data set consists of dielectric constant profile measurements from selected trees at various BOREAS flux tower sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rs17diel/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rs7ssatd_302 v1 - The BOREAS RSS-17 team collected several data sets in support of its research in monitoring and analyzing environmental and phenological states using radar data. This data set consists of tree bole and soil temperature measurements from various BOREAS flux tower sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rs7ssatd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rss17xyf_303 v1 - As part of its efforts to determine environmental and phenological states from radar imagery, the BOREAS RSS-17 team collected in situ tree xylem flow measurements for one growing season on five Picea mariana (black spruce) trees. The data were collected to obtain information on the temporal and spatial variability in water uptake by trees in the SSA-OBS (Picea mariana) stand in the BOREAS SSA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rss17xyf/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"RSS18_AVIRIS_L1B_449 v1 - This dataset holds Level 1B (L1B) radiance data collected by the AVIRIS-Classic instrument near Prince Albert, Saskatchewan, Canada, on August 14, 1996. This imagery was acquired for the Boreal Ecosystem-Atmosphere Study (BOREAS) project in the boreal forests of central Canada.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/RSS18_AVIRIS_L1B/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rss18opt_503 v1 - The BOREAS RSS-18 team collected ground-based sunphotometer data in support of AVIRIS remote sensing activities at the SSA. The following information was compiled by staff members of the BOReal Ecosystem-Atmosphere Study (BOREAS) Information System (BORIS) as part of their data documentation efforts.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/GRAB_BAG/rss18opt/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"r19cas94_537 v1 - The RSS-19 team collected CASI images from the Chieftain Navaho aircraft in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI was deployed as a site-specific optical sensor during BOREAS field campaigns.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/r19cas94/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"und_refl_304 v1 - One objective of BOREAS is to further the understanding of the spectral bi-directional reflectance of typical boreal ecosystem stands in the visible/near-infrared regime. An essential input for any canopy BRDF model is an accurate estimate of the average understory reflectance, both for sunlit and shaded conditions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/und_refl/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"r19cas96_538 v1 - The RSS-19 team collected CASI images from the Chieftain Navaho aircraft in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI was deployed as a site-specific optical sensor as part of BOREAS.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/r19cas96/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rs20c130_305 v1 - This data set contains measurements of surface BRDF made by the POLDER instrument over several surface types (pine, spruce, fen) of the BOREAS SSA during the 1994 IFCs. Single-point BRDF values were acquired either from the NASA ARC C-130 aircraft or from a NASA WFF helicopter.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rs20c130/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rs20helo_306 v1 - This data set contains measurements of surface BRDF made by the POLDER instrument over several sites (pine, spruce, fen) of the BOREAS study areas during 1994. Single-point BRDF values were acquired from NASA's WFF helicopter.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rs20helo/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rs20prad_555 v1 - These data are a subset of images collected by the POLDER instrument over tower sites in the BOREAS study areas during the IFCs in 1994. The POLDER images presented here from the NASA/Ames C-130 aircraft are made available for illustration purposes only.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/RSS/rs20prad/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"saskffcc_307 v1 - The Saskatchewan Forest Fire Control Centre (SFFCC) provided surface meteorological data to BOREAS from their archive. This data set contains hourly surface meteorological data from 18 of the meteorological stations located across Saskatchewan.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/saskffcc/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BOREAS_SLICER_508 v2 - Scanning Lidar Imager of Canopies by Echo Recovery (SLICER) data were acquired in support of BOReal Ecosystem-Atmosphere Study (BOREAS) at all of the Tower Flux (TF) sites in the Southern and Northern Study Areas (SSA and NSA, respectively) and along transects between the study areas. Data were acquired on 5 days between 18 and 30 July 1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/BOREAS_SLICER/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ssafcovv_509 v1 - The forest cover data provided by Saskatchewan Environment and Resource Management, Forestry Branch - Inventory Unit (SERM-FBIU) are basically a digital version of its 1:12,500 scale forest cover polygon maps. The data include information on forest parameters and cover the area in and near the BOREAS SSA, excluding the PANP.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/ssafcovv/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"saskfc1m_510 v1 - This data set is a condensed forest cover type digital map of Saskatchewan and is a product of the Saskatchewan Environment and Resource Management, Forestry Branch - Inventory Unit (SERM-FBIU). This map was generalized from SERM township maps of vegetation cover at an approximate scale of 1:63,000 (1 in.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/saskfc1m/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"saskfire_308 v1 - This data set is a series of ARC/INFO export files of the fire history of Saskatchewan by year from 1945 to 1996, with a few missing years. The data set was compiled and provided by the Saskatchewan Environment and Resource Management (SERM) to Wildlife Branch.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/saskfire/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"geocoord_556 v1 - In an effort to properly document the sites and areas where data were collected, personnel of the BOReal Ecosystem-Atmosphere Study (BOREAS) Information System (BORIS) obtained and compiled geographic coordinate and other site information from several sources throughout the experiment period. The final set of information is organized into two data sets that provide geographic coordinate and site characteristic information for single sites and corner coordinates for standard geographic areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/GRAB_BAG/geocoord/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te1ch4fx_310 v1 - The BOREAS TE-01 team collected various data to characterize the soil-plant systems in the BOREAS SSA. Particular emphasis was placed on nutrient biochemistry, the stores and transfers of organic carbon and how the characteristics were related to measured methane fluxes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te1ch4fx/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"soilte1r_312 v1 - This data set was gridded from vector layers of soil maps that were received from Dr. Darwin Anderson TE-01 who did the original soil mapping in the field during 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/soilte1r/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te01ssld_530 v1 - This data set was collected by TE-01 to provide a set of soil properties for BOREAS investigators in the SSA. The soil samples were collected at sets of soil pits.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te01ssld/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te2wdrs2_314 v1 - The BOREAS TE-02 team collected several data sets in support of its efforts to characterize and interpret information on the respiration of the foliage, roots, and wood of boreal vegetation. This data set contains measurements of wood respiration measured continuously (about once per hour) in the NSA during the growing season of 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te2wdrs2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te2flrsp_315 v1 - The BOREAS TE-02 team collected several data sets in support of its efforts to characterize and interpret information on the respiration of the foliage, roots, and wood of boreal vegetation. This data set contains measurements of foliar respiration conducted in the NSA during the growing season of 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te2flrsp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te2rtrsp_316 v1 - The BOREAS TE-02 team collected several data sets in support of its efforts to characterize and interpret information on the respiration of the foliage, roots, and wood of boreal vegetation. This data set includes means of tree root respiration measurements on roots having diameters ranging from 0 to 2 mm conducted in the NSA during the growing season of 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te2rtrsp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te2stsap_317 v1 - The BOREAS TE-02 team collected several data sets in support of its efforts to characterize and interpret information on the respiration of the foliage, roots, and wood of boreal vegetation. This data set contains measurements of growth and sapwood of the stems conducted in the NSA during the growing season of 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te2stsap/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te2wdrsp_318 v1 - The BOREAS TE-02 team collected several data sets in support of its efforts to characterize and interpret information on the respiration of the foliage, roots, and wood of boreal vegetation. This data set contains measurements of wood respiration conducted in the NSA during the growing season of 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te2wdrsp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te04bbag_319 v1 - The BOREAS TE-04 team collected continuous records of gas exchange under ambient conditions from intact boreal forest trees in the BOREAS NSA from 23-Jul-1996 until 14-Aug-1996. These measurements can be used to test models of photosynthesis, stomatal conductance, and leaf respiration, such as SiB2 (Sellers et al., 1996) or the leaf model (Collatz et al., 1991), and programs can be obtained from the investigators.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te04bbag/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te04gxda_320 v1 - Measurements of light, CO2, temperature, and humidity response curves were made by the BOREAS TE-04 team during the summary of 1994 using intact attached leaves of boreal forest species located in the BOREAS SSA. These measurements were conducted to calibrate models used to predict photosynthesis, stomatal conductance, and leaf respiration.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te04gxda/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te5airs_321 v1 - The BOREAS group TE-05 collected measurements in the NSA and SSA on gas exchange, gas composition and tree growth. This data set contains measurements of the concentration and stable carbon (13C/12C) and oxygen (18O/16O) isotope ratios of atmospheric CO2 in air samples collected at different heights within forest canopies.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te5airs/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te5co2pd_322 v1 - These data were collected by BOREAS TE-05 to provide detailed information within the canopy during times when TE-05 sampled canopy CO2 for carbon and oxygen isotope analysis. These measurements were made in both the NSA and SSA during each IFC at the OJP, OBS, UBS, and OA sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te5co2pd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te5lciso_323 v1 - The BOREAS group TE-05 collected measurements in the NSA and SSA on gas exchange, gas composition and tree growth. This documentation describes leaf carbon isotope data that were collected in 1993 and 1994 at the NSA and SSA OJP, OBS and NSA UBS sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te5lciso/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te5lgxd_324 v1 - The BOREAS group TE-05 collected measurements in the NSA and SSA on gas exchange, gas composition and tree growth. The leaf photosynthetic gas exchange data were collected in the BOREAS NSA and the SSA using a Li-Cor 6200 portable photosynthesis system.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te5lgxd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te5soilr_325 v1 - The BOREAS group TE-05 collected measurements in the NSA and SSA on gas exchange, gas composition and tree growth. Soil respiration data collected in the BOREAS NSA and SSA to compare the soil respiration rates in different forest sites using a Li-Cor 6200 soil respiration chamber (model 6299).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te5soilr/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te5metd_326 v1 - The BOREAS group TE-05 collected measurements in the NSA and SSA on gas exchange, gas composition and tree growth. Measurements of meteorological data, including air and soil temperature, RH, and PPFD were 30-minute intervals during the 1994 IFCs at various sites in the BOREAS NSA and SSA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te5metd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te5treer_327 v1 - These data include tree ring widths and cellulose carbon isotope data from coniferous trees collected at the BOREAS NSA and SSA by the BOREAS TE-05 team. Ring width data are provided for both Picea mariana and Pinus banksiana.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te5treer/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te6satns_328 v1 - The BOREAS TE-06 team collected several data sets to examine the influence of vegetation, climate, and their interactions on the major carbon fluxes for boreal forest species. This data set contains measurements of the air temperature at a single height and soil temperature at several depths in the NSA from 25-May to 8-Oct-1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te6satns/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te6allom_329 v1 - The BOREAS TE-06 team collected several data sets in support of its efforts to characterize and interpret information on the plant biomass, allometry, biometry, sapwood, leaf area index, net primatry production, soil temperature, leaf water potential, soil CO2 flux, and multivegetation imagery of boreal vegetation. This data set includes tree measurements conducted on the above gound biomass of trees in the BOREAS NSA and SSA during the growing seasons of 1994 and 1995 and the derived allometric relationships/equations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te6allom/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te6bmflg_330 v1 - The BOREAS TE-06 team collected several data sets in support of its efforts to characterize and interpret information on the plant biomass, allometry, biometry, sapwood, leaf area index, net primary production, soil temperature, leaf water potential, soil CO2 flux, and multivegetation imagery of boreal vegetation. This data set contains measurements of estimates of the standing biomass and leaf area index for the plant species at the TF, CEV, and AUX sites in the SSA and NSA during the growing seasons of 1994 and 1995.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te6bmflg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te6mltvg_331 v1 - A newly developed ground-based canopy imaging system called an MVI was tested and used by the BOREAS TE-06 team to collect measurements of the canopy gap fraction (sky fraction), canopy gap-size distribution (size and frequency of gaps between foliage in canopy), branch architecture, and leaf angle distribution (fraction of leaf area in specific leaf inclination classes assuming azimuthal symmetry). Measurements of the canopy gap-size distribution are used to derive canopy clumping indices that can be used to adjust indirect LAI measurements made in nonrandom forests.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te6mltvg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te6npp_200 v1 - The BOREAS TE-06 team collected several data sets to examine the influence of vegetation, climate, and their interactions on the major carbon fluxes for boreal forest species. This data set contains estimates of the biomass produced by the plant species at the TF, CEV, and AUX sites in the SSA and NSA for a given year.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te6npp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te6h2opd_332 v1 - The BOREAS TE-06 team collected several data sets to examine the influence of vegetation, climate, and their interactions on the major carbon fluxes for boreal forest species. This data set contains summaries of predawn leaf water potentials and foliage moisture contents collected at the TF and CEV sites that had canopy access towers.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te6h2opd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te07dend_333 v1 - The BOREAS TE-07 team collected data sets in support of its efforts to characterize and interpret information on the sapflow and dendrology of boreal vegetation. This data set contains dendrology measurements, consisting of tree ring width and density taken at several points within each ring.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te07dend/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te07sapf_334 v1 - The BOREAS TE-07 team collected data sets in support of its efforts to characterize and interpret information on the sap flow of boreal vegetation. The heat pulse method was used to monitor sap flow and to estimate rates of transpiration from aspen, black spruce, and mixed wood forests at the SSA-OA, MIX, SSA-OBS, and Batoche sites in Saskatchewan, Canada.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te07sapf/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te08bchm_335 v1 - The BOREAS TE-08 team collected pigment density data from aspen bark and leaves from four sites within the BOREAS SSA from 24-May-1994 to 16-Jun-1994 (IFC-1), 19-Jul-1994 to 08-Aug-1994 (IFC 2), and 30-Aug-1994 to 19-Sep-1994 (IFC-3). One to nine trees from each site were sampled during the three IFCs.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te08bchm/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te08bopt_336 v1 - The BOREAS TE-08 team collected in-lab spectral reflectance data for aspen bark and leaves from three sites within the BOREAS SSA from 24-May-1994 to 16-Jun-1994 (IFC 1), 19-Jul-1994 to 08-Aug-1994 (IFC 2), and 30-Aug-1994 to 19-Sep-1994 (IFC 3). One to nine trees from each site were sampled during the three IFCs.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te08bopt/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te09gxda_337 v1 - The gas exchange data of the Boreal Ecosystem-Atmosphere Study (BOREAS) Northern Study Area (NSA) were collected to characterize diurnal gas exchange and water potential of two canopy levels of five boreal canopy cover types: young and old jack pine (Pinus banksiana Lamb.), old aspen (Populus tremuloides Michx.), and lowland and upland black spruce (Picea mariana (Mill) B.S.P.). These data were collected between 27-May-1994 and 17-Sep-1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te09gxda/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te9spref_338 v1 - Spectral reflectance of the forest understory at the ground level, in three boreal forest sites of Northern Manitoba (56 degrees N latitude and 98 degrees W longitude), were obtained and analyzed. The objective of the study was to estimate light levels inside the forest canopy and to link these estimates with airborne images taken above the canopy, in order to tie the photosynthetic experiments and models with the remotely sensed measurements.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te9spref/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te9bioav_339 v1 - The BOREAS TE-09 team collected several data sets related to chemical and photosynthetic properties of leaves. This data set contains canopy biochemistry data collected in 1994 in the NSA at the YJP, OJP, OBS, BS and OA sites including biochemistry lignin, nitrogen, cellulose, starch, and fiber concentrations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te9bioav/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te09cd_341 v1 - The Boreal Ecosystem-Atmosphere Study (BOREAS) Terrestrial Ecology Team #9 (TE-09) provided several data sets containing information about the state and response of boreal forest tree species. This data set contains information on the spatial density of chlorophyll in the leaves of three boreal tree species collected at five different sites at various times during 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te09cd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te09pnd_342 v1 - The BOREAS TE-09 team collected several data sets related to chemical and photosynthetic properties of leaves in boreal forest tree species. This data set describes the relationship between Photosynthetically Active Radiation (PAR) levels and foliage nitrogen in samples from six sites in the BOREAS NSA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te09pnd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te09npd_343 v1 - The BOREAS TE-09 team collected several data sets related to chemical and photosynthetic properties of leaves in boreal forest tree species. This data set describes the spatial and temporal relationship between foliage nitrogen concentration and photosynthetic capacity in the canopies of black spruce, jack pine, and aspen.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te09npd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te09prd_344 v1 - The BOREAS TE-09 team collected several data sets related to chemical and photosynthetic properties of leaves. This data set describes (1) the response of leaf and shoot-level photosynthesis to ambient and intercellular CO2 concentration, temperature, and incident PAR for black spruce, jack pine, and aspen during the three IFCs in 1994 in the NSA; (2) the response of stomatal conductance to vapor pressure difference throughout the growing season of 1994; and (3) a range of shoot water potentials (controlled in the laboratory) for black spruce and jack pine.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te09prd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te10lfch_345 v1 - The BOREAS TE-10 team collected several data sets in support of its efforts to characterize and interpret information on the reflectance, transmittance, gas exchange, chlorophyll content, carbon content, hydrogen content, and nitrogen content of boreal vegetation. This data set describes the relationship between sample location, age, chlorophyll content, and C-H-N concentrations at several sites in the SSA conducted during the growing seasons of 1994 and 1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te10lfch/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te10lgxd_346 v1 - The BOREAS TE-10 team collected several data sets in support of its efforts to characterize and interpret information on the reflectance, transmittance, gas exchange, chlorophyll content, carbon content, hydrogen content, and nitrogen content of boreal vegetation. This data set contains measurements of assimilation, stomatal conductance, transpiration, internal CO2 concentration, and water use efficiency conducted in the SSA during the growing seasons of 1994 and 1996 using a portable gas exchange system.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te10lgxd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te10lopt_531 v1 - The BOREAS TE-10 team collected several data sets in support of its efforts to characterize and interpret information on the reflectance, transmittance, gas exchange, oxygen evolution, and biochemical properties of boreal vegetation. This data set describes the spectral optical properties (reflectance and transmittance) of boreal forest conifers and broadleaf tree leaves as measured with a Spectron Engineering SE590 spectroradiometer at the SSA OBS, OJP, YJP, OA, OA-AUX, YA-AUX, and YA sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te10lopt/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te10prd_347 v1 - The BOREAS TE-10 team collected several data sets in support of its efforts to characterize and interpret information on the gas exchange, reflectance, transmittance, chlorophyll content, carbon content, hydrogen content, nitrogen content, and photosynthetic response of boreal vegetation. This data set contains measurements of quantitative parameters and leaf photosynthetic response to increases in light conducted in the SSA during the growing seasons of 1994 and 1996 using an oxygen electrode system.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te10prd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te11lgxd_348 v1 - The BOREAS TE-11 team collected several data sets in support of its efforts to characterize and interpret information on the sapflow, gas exchange, and lichen photosynthesis of boreal vegetation and meteorological data of the area studied. This data set contains measurements of assimilation and transpiration conducted at the OJP site during the growing seasons of 1993 and 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te11lgxd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te11sapf_349 v1 - The BOREAS TE-11 team collected several data sets in support of its efforts to characterize and interpret information on the sap flow, gas exchange, and lichen photosynthesis of boreal vegetation and meteorological data of the area studied. This data set contains measurements of sap flow conducted at the SSA-OJP site in the growing seasons of 1993 and 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te11sapf/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te11smet_350 v1 - The BOReal Ecosystem-Atmosphere Study (BOREAS) Terrestrial Ecology (TE)-11 team collected several data sets in support of its efforts to characterize and interpret information on the sapflow, gas exchange, and lichen photosynthesis of boreal vegetation and meteorological data of the area studied. This data set contains the meteorological data taken in the BOREAS Southern Study Area (SSA) during 1994 and used by TE-11 for their sapflow and gas exchange data analysis.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te11smet/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te12parc_485 v1 - The BOREAS TE-12 team collected PAR data sets in support of its efforts to characterize and interpret information on shoot geometry, leaf optical properties, leaf water potential, and leaf gas exchange. The data were collected at the SSA-OBS site from 04-Jul-1996 to 25-Jul-1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te12parc/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te12lgex_351 v1 - The BOREAS TE-12 team collected several data sets in support of its efforts to characterize and interpret information on the reflectance, transmittance, and gas exchange of boreal vegetation. This data set contains measurements of leaf gas exchange conducted in the SSA during the growing seasons of 1994 and 1995 using a portable gas exchange system.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te12lgex/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te12lod_352 v1 - The BOREAS TE-12 team collected several data sets in support of its efforts to characterize and interpret information on the reflectance, transmittance, and gas exchange of boreal vegetation. This data set contains measurements of hemispherical spectral reflectance and transmittance factors of individual leaves, needles (ages: current and past 2 years'' growth, i.e., for 1993, the growing seasons of 1993, 1992, and 1991 were measured; in 1994, the growing seasons of 1994, 1993, and 1992 were measured), twigs (reflectance only), and substrate at near-normal incidence measured using a LI-COR L...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te12lod/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te12sgd_353 v1 - The BOREAS TE-12 team collected shoot geometry data in 1993 and 1994 from Aspen, Jack Pine, and Black Spruce trees. Collections were made at the Southern Study Area FEN, YJP, OJP, OA, YA, MIX and OBS sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te12sgd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te12h2op_354 v1 - The BOREAS TE-12 team collected water potential data in 1993 and 1994 from aspen, jack pine and black spruce leaves/needles. Collections were made at the SSA FEN, YJP, YA, OA, and OBS sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te12h2op/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te13biom_355 v2 - The Boreal Ecosystem-Atmosphere Study (BOREAS) Terrestrial Ecology (TE)-13 team collected data on site characteristics, soil profiles, woody debris, overstory vegetation, and understory vegetation from approximately 100 sites in the Southern Study Area (SSA), Northern Study Area (NSA), and Transect Areas in the boreal forest. This data sets provides 3 reports published by the Canadian Forest Service for the BOREAS project in pdf file format.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te13biom/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te17pem_486 v1 - A BOREAS version of the Global Production Efficiency Model (www.inform.umd.edu/glopem) was developed by TE-17 to generate maps of gross and net primary production, autotrophic respiration, and light use efficiency for the BOREAS region. This document provides basic information on the model and how the maps were generated.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te17pem/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"biomdens_450 v1 - This biomass density image covers almost the entire BOREAS SSA. The pixels for which biomass density is computed include areas that are in conifer land cover classes only.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/biomdens/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te18geos_532 v1 - The SAIL (Scattering from Arbitrarily Inclined Leaves) model was combined with the Jasinski geometric model to simulate canopy spectral reflectance and absorption of photosynthetically active radiation for discontinuous canopies. This model is called the GeoSail model.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te18geos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ltmmaxln_429 v1 - The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/ltmmaxln/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ltmmaxls_430 v1 - A Landsat-5 TM image from 06-Aug-1990 was used to derive this classification. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the SSA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/ltmmaxls/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ltmphysn_431 v1 - The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 21-Jun-1995 was used to derive the classification.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/ltmphysn/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ltmphyss_432 v1 - The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the SSA. A Landsat-5 TM image from 02-Sep-1994 was used to derive the classification.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/ltmphyss/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te18ls30_557 v1 - The BOREAS TE-18 team used a radiometric rectification process to produce standardized digital number (DN) values for a series of Landsat TM images of the BOREAS SSA and NSA in order to compare images that were collected under different atmospheric conditions. The images for each study area were referenced to an image that had very clear atmospheric qualities.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te18ls30/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te18ls60_564 v1 - The BOREAS TE-18 team used a radiometric rectification process to produce standardized digital number (DN) values for a series of Landsat TM images of the BOREAS SSA and NSA in order to compare images that were collected under different atmospheric conditions. The images for each study area were rectified by using a reference image that had clear atmospheric qualities.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te18ls60/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te19modl_487 v1 - The BOREAS TE-19 team developed a model called the Spruce and Moss Model (SPAM) designed to simulate the daily carbon balance of a black spruce/moss boreal forest ecosystem. It is driven by daily weather conditions, and consists of four components: (1) soil climate; (2) tree photosynthesis and respiration; (3) moss photosynthesis and respiration; and (4) litter decomposition and associated heterotrophic respiration.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te19modl/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te20sldn_356 v1 - This data set contains the major soil properties of soil samples collected at the tower flux sites in the NSA. The soil samples were collected by Hugo Veldhuis and his staff from the University of Manitoba.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te20sldn/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te20site_488 v1 - The BOREAS TE-20 team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set contains measurements of site characteristics conducted in the SSA from 18-Jul-1994 to 30-Jul-1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te20site/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"soilt20r_357 v1 - This data set was gridded from vector layers of soil maps that were received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/soilt20r/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"soilt20v_533 v1 - The BOREAS TE-20 team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set contains vector layers of soil maps that were received from Dr.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/soilt20v/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te20supp_489 v1 - The BOREAS TE-20 team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set and documentation were compiled from field notes and other information provided by Hugo Veldhuis, who did the original soils mapping in the field during 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te20supp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te21smet_358 v1 - The BOREAS TE-21 team collected data sets in support of its efforts to characterize and interpret information on the meteorology of boreal forest areas. Daily meteorological data were derived from half-hourly BOREAS TF and AMS mesonet measurements collected in the SSA and NSA for the period of 01-Jan-1994 until 31-Dec-1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te21smet/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te22allm_490 v1 - The BOREAS TE-22 team collected data sets in support of its efforts to characterize and interpret information on the forest structure of boreal vegetation in the SSA and NSA during the 1994 growing season.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te22allm/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te22ring_491 v1 - The BOREAS TE-22 team collected complete tree cores at several sites in the SSA and NSA in order to perform historical growth studies and relate the information to their modeling activities. The cores were collected during the summer of 1994 in the Northern and Southern Study Areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/GRAB_BAG/te22ring/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te23arch_492 v1 - The BOREAS TE-23 team collected hemispherical photographs in support of its efforts to characterize and interpret information on estimates of canopy architecture and radiative transfer properties for most BOREAS study sites. Various OA, OBS, OJP, YJP, and YA sites in the boreal forest were measured from May to August 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te23arch/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"te23mapp_359 v1 - The BOREAS TE-23 team collected map plot data in support of its efforts to characterize and interpret information on canopy architecture and understory cover at the BOREAS tower flux sites and selected auxiliary sites from May to August 1994. Mapped plots (typical dimensions 50 m x 60 m) were set up and characterized at all BOREAS forested tower flux and selected auxiliary sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TE/te23mapp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf01soil_511 v1 - The BOREAS TF-01 team collected several data sets in support of its efforts to characterize and interpret soil information at the SSA-OA tower site in 1994 as part of BOREAS. Data sets collected include soil respiration, temperature, moisture, and gravimetric data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf01soil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf01tflx_512 v1 - The BOREAS TF-01 team collected energy, carbon dioxide, and momentum flux data above the canopy along with meteorological and soils data at the BOREAS SSA-OA site from mid-April to the end of the year for 1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf01tflx/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf01uflx_513 v1 - The BOREAS TF-01 team collected energy, carbon dioxide, and momentum flux data under the canopy along with meteorological and soils data at the BOREAS SSA-OA site from mid-October to mid-November of 1993 and throughout all of 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf01uflx/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf1ch4_514 v1 - The BOREAS TF-01 team collected various trace gas and energy flux data in its efforts to characterize the temporal energy and gas exchanges that occurred over the SSA-OA site. This data set contains methane (CH4) and nitrous oxide (N2O) fluxes that were measured at the BOREAS SSA-OA site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf1ch4/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf2met_504 v1 - Members of the BOREAS TF-02 team collected meteorological and ozone measurements from instruments mounted below a tethered balloon. These data were collected at the SSA-OA site to extend meteorological and ozone measurements made from the flux tower to heights of 300 m.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf2met/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf02tflx_515 v1 - The BOREAS TF-02 team collected energy, carbon dioxide, water vapor, and momentum flux data above the canopy and in profiles through the canopy, along with meteorological data at the BOREAS SSA-OA site. Above-canopy measurements began in early February and ran through mid-September of 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf02tflx/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf3acco2_360 v1 - The BOREAS TF-03 and TGB-01 teams collected automated CO2 chamber flux data in their efforts to fully describe the CO2flux at the NSA-OBS site. This data set contains fluxes of CO2 at the NSA-OBS site measured using automated chambers.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf3acco2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf3tflxd_361 v1 - The BOREAS TF-03 team collected tower flux, surface meterological, and soil temperature data at the BOREAS NSA-OBS site continuously from the March 1994 through October 1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf3tflxd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf4ssafx_362 v1 - The BOREAS TF-04 team measured fluxes of carbon dioxide (CO2) and methane (CH4) across the soil-air interface in four ages of jack pine forest at the Southern Study Area of the Boreal Ecosystem Atmosphere Study (BOREAS) during August 1993 to March 1995. Gross and net flux of CO2 and flux of CH4 between soil and air are presented for 24 chamber sites in mature jack pine forest, 20-year old, 4- year old, and clear cut areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf4ssafx/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf4ssasp_363 v1 - The BOREAS TF-04 team measured distributions of carbon dioxide (CO2) and methane (CH4) concentrations for the upper 5 meters of soil and unsaturated zone at the mature stand, upper 6 m at the 20-year old stand, and the upper 1m at the 8-year old stand and clear cut area at the Southern Study Area of the Boreal Ecosystem Atmosphere Study (BOREAS) during August 1993 to March 1995. Particle size and carbon content of the unsaturated deposits, precipitation, soil temperature and moisture, carbon and oxygen isotopes of soil CO2 and soil water chemistry are also presented.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf4ssasp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf04flux_451 v1 - The BOREAS TF-04 team collected energy, carbon dioxide, and water vapor flux data at the BOREAS SSA-YJP site during the growing season of 1994. In addition, meteorological data were collected both above and within the canopy.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf04flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf5tflxd_364 v1 - The BOREAS TF-05 team collected tower flux data at the BOREAS Southern Study Area Old Jack Pine (SSA-OJP) site through the growing season of 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf5tflxd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf6fxmet_365 v1 - The BOREAS TF-06 team collected surface energy flux and meteorology data at the SSA-YA site. The data characterize the energy flux and meteorological conditions at the site from 18-Jul to 20-Sep-1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf6fxmet/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf07flux_452 v1 - The BOREAS TF-07 team collected meteorological data as well as energy, carbon dioxide, water vapor, methane, and nitrous oxide flux data at the BOREAS SSA-OBS site. The data were collected from May 24 to September 19, 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf07flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf08ceil_453 v1 - The BOREAS TF-08 team used ceilometers to collect data on the fraction of the sky covered with clouds and the cloud height. Included with these data is the surface-based lifting condensation level, derived from temperature and humidity values acquired at the flux tower at the NSA-OJP site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf08ceil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf08tflx_516 v1 - The BOREAS TF-08 team collected energy, CO2, and water vapor flux data at the BOREAS NSA-OJP site during the growing season of 1994 and most of the year for 1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf08tflx/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf9brflx_366 v1 - The BOREAS TF-09 team collected data which describe carbon dioxide and water vapor fluxes from foliage at the BOREAS SSA-OBS site from 07-April through 23-November-1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf9brflx/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf9tflxd_367 v1 - The BOREAS TF-09 team collected energy, carbon dioxide and water vapor flux data at the BOREAS SSA-OBS site during the growing season of 1994 and most of the year for 1996. From the winter of 1995 to 1996, soil temperature data were also collected and provided.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf9tflxd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf10fxmt_368 v1 - The BOREAS TF-10 team collected tower flux and meteorological data at two sites, a fen and a young jack pine forest, near Thompson, Manitoba, Canada, as part of BOREAS. A preliminary data set was assembled in August 1993 while field testing the instrument packages, and at both sites data were collected from 15-Aug to 31-Aug.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf10fxmt/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf11biom_369 v1 - The BOREAS TF-11 team collected several data sets in their efforts to fully describe the flux and site characteristics at the SSA-Fen site. This data set contains plant cover, standing crop of plant biomass, and estimated net primary productivity at each chamber site at the end of the 1994 field season.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf11biom/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf11conc_370 v1 - The BOREAS TF-11 team collected several data sets in their efforts to fully describe the flux and site characteristics at the SSA-Fen site. This data set contains temperature, pH, and concentration profiles of methane and carbon dioxide within the surface 50 cm of peat.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf11conc/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf11flux_371 v1 - The BOREAS TF-11 team collected several data sets in their efforts to fully describe the flux and site characteristics at the SSA-Fen site. This data set contains fluxes of methane and carbon dioxide at the SSA fen site measured using static chambers.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf11flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf11dcom_372 v1 - The BOREAS TF-11 team collected several data sets in their efforts to fully describe the flux and site characteristics at the SSA-Fen site. This data set contains decomposition rates of a standard substrate (wheat straw) across treatments.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf11dcom/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf11sflm_455 v1 - The BOREAS TF-11 team gathered a variety of data to complement their tower flux measurements collected at the SSA Fen site. The data described in this document were made by the TF-11 team at the SSA Fen site to quantify the effect that the films observed to form on open water surfaces had on the transfer of carbon dioxide and methane from the water to the air.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf11sflm/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf11leaf_456 v1 - The BOREAS TF-11 team gathered a variety of data to complement their tower flux measurements collected at the SSA Fen site. This data set contains single-leaf gas exchange data from the SSA Fen site during 1994 and 1995.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf11leaf/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf11soil_457 v1 - The BOREAS TF-11 team gathered a variety of data to complement their tower flux measurements collected at the SSA Fen site. These data are soil surface CO2 flux data at the SSA Fen site from 27-May-1994 to 23-Sep-1994 and from 13-May-1995 to 3-Oct-1995.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf11soil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf11lai_458 v1 - The BOREAS TF-11 team gathered a variety of data to complement their tower flux measurements collected at the SSA Fen site. These data are LAI measurements made by the TF-11 team throughout the 1995 growing season.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf11lai/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tf11tfx_373 v1 - The BOREAS TF-11 team collected energy, carbon dioxide, and methane flux data at the BOREAS SSA-Fen site during the growing seasons of 1994 and 1995.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TF/tf11tfx/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb1ccfd_374 v1 - Chamber flux measurements were taken at the Northern Study Area (NSA) Old Jack Pine (OJP), Young Jack Pine (YJP), Old Black Spruce (OBS), and Beaver Pond (BP) sites during the summer of 1994. The purpose of these measurements was to examine the trace gas exchange between the atmosphere and the boreal soils.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb1ccfd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb1cfd_375 v1 - The BOREAS TGB-01 team made numerous measurements of trace gas concentrations and fluxes at various NSA sites. This data set contains half-hourly averages of ambient methane (CH4) measurements and calculated fluxes for the NSA-TF in 1996 and the NSA-BP and NSA-OJP tower sites in 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb1cfd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb1sfd_376 v1 - Chamber flux measurements were taken at the Boreal Ecosystem Atmosphere Study (BOREAS) Northern Study Area (NSA) Old Jack Pine (OJP) and Young Jack Pine (YJP) sites during the summer of 1994. The purpose of these measurements was to examine the trace gas exchange between the atmosphere and the boreal soils.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb1sfd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgbfenfx_378 v1 - The BOREAS TGB-03 team collected methane (CH4) chamber flux measurements at the NSA fen site during May-September 1994 and June-October 1996. Gas samples were extracted approximately every 7 days from chambers and analyzed at the NSA lab facility.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgbfenfx/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgbfenne_379 v1 - The BOREAS TGB-01 and TGB-03 teams collected several data sets that contributed to understanding the measured trace gas fluxes over sites in the NSA. This data set contains NEE measurements collected with chambers at the NSA fen in 1994 and 1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgbfenne/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb3wd_380 v1 - The BOREAS TGB-01 and TGB-03 teams collected several data sets that contributed to understanding the measured trace gas fluxes over sites in the NSA. This data set contains continuous and manual measurements of water level, air and soil temperatures at the four subsites within the NSA Tower Fen site complex.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb3wd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb3cofd_381 v1 - The BOREAS TGB-03 team collected methane and carbon dioxide (CH4, CO2) chamber flux measurements at the NSA Fen site, OBS, YJP, and auxiliary sites along Gillam Road and the 1989 burn site. Gas samples were extracted from chambers and analyzed at the NSA lab facility approximately every 7 days during May to September 1994 and June to October 1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb3cofd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb03doc_459 v1 - The BOREAS TGB-03 team collected Dissolved Organic Carbon (DOC) data during the summer of 1994 in the Northern Study Area. The purpose of this work was to establish the major sources, sinks and fluxes of dissolved organic carbon (DOC) in the Northern Study Area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/GRAB_BAG/tgb03doc/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb3plsp_382 v1 - The BOREAS TGB-03 team collected several data sets that contributed to understanding the measured trace gas fluxes over sites in the NSA. This data set contains information about the composition of plant species that were within the collars used to measure NEE.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb3plsp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb4flux_460 v1 - The BOREAS TGB-04 team measured the exchange of heat, water, and CO2 between a boreal forest beaver pond and the atmosphere in the NSA for the ice-free period of BOREAS. The data cover the period of May 28 to September 18, 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb4flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb4wsed_461 v1 - The BOREAS TGB-04 team collected several data sets in support of their flux tower measurements at the NSA Beaver Pond site. This data set contains water and sediment temperature data collected from May to September 1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/GRAB_BAG/tgb4wsed/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb5nnfd_383 v1 - The BOREAS TGB-05 team made several measurements of trace gas concentrations and fluxes at various NSA sites. This data set contains biogenic soil emissions of nitric oxide and nitrous oxide that were measured over a wide range of spatial and temporal site parameters.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb5nnfd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb5cflx_384 v1 - The BOREAS TGB-05 team collected a variety of trace gas concentration and flux measurements at several NSA sites. This data set contains carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) chamber flux measurements conducted in 1994 at upland forest sites that experienced stand- replacement fires.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb5cflx/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb5docd_385 v1 - The BOREAS TGB-05 team collected several data sets related to carbon and trace gas fluxes and concentrations in the NSA. This data set contains concentrations of dissolved organic and inorganic carbon species from water samples collected at various NSA sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb5docd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fhstmanr_386 v1 - This raster format data set covers the province of Manitoba. The data were gridded into the AEAC projection from the original vector data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/fhstmanr/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fhstmanv_387 v1 - This vector format data set covers the province of Manitoba and was produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000 scale maps. The locational accuracy of the data is considered fair to poor.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/fhstmanv/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb6chrc_388 v1 - The BOREAS TGB-06 team collected soil methane measurements at several sites in the SSA and NSA. This data set contains soil methane consumption (bacterial CH4 oxidation) and associated 13C fractionation effects in samples that were collected at various sites in 1994 and 1996 from enclosures (chambers).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb6chrc/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb7aaho_389 v1 - The BOREAS TGB-07 team measured the concentration and flux of several agricultural pesticides in air, rainwater, and dry deposition samples in order to determine the associated yearly deposition rates. This data set contains information on the ambient air concentration of seven herbicides [2,4-dichlorophenoxyacidic_acid (2,4-D), bromoxynil, dicamb, 2-methyl-4-chlorophenoxyacetic acid (MCPA), triallate, trifluralin, and diclop-methyl] known to appear in the atmosphere of the Canadian prairies.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb7aaho/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb7ddho_390 v1 - The BOREAS TGB-07 team measured the concentration and flux of several agricultural pesticides in air, rainwater, and dry deposition samples in order to determine the associated yearly deposition rates. This data set contains information on the dry deposition flux of seven herbicides [2,4-dichlorophenoxyacidic_acid (2,4-D), bromoxynil, dicamb, 2-methyl-4-chlorophenoxyacetic acid (MCPA), triallate, trifluralin, and diclop-methyl] known to appear in the atmosphere of the Canadian prairies.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb7ddho/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb7rwho_391 v1 - The BOREAS TGB-07 team measured the concentration and flux of several agricultural pesticides in air and rainwater samples in order to determine the associated yearly deposition rates. This data set contains information on the rainwater concentration of seven herbicides [2,4-dichlorophenoxyacidic_acid (2,4-D), bromoxynil, dicamb, 2-methyl-4-chlorophenoxyacetic acid (MCPA), triallate, trifluralin, and diclop-methyl] known to appear in the atmosphere of the Canadian prairies.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb7rwho/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb8mono_392 v1 - The TGB-08 team collected data to investigate the controls over non-methane hydrocarbon (NMHC) fluxes from boreal forest tree species. This data set contains measurements of monoterpene concentrations in collected foliar gas emissions and foliar samples.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb8mono/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb8prds_393 v1 - The TGB-08 team collected data to investigate the controls over non-methane hydrocarbon (NMHC) fluxes from boreal forest tree species. This data set includes measurements of photosynthetic rates at mature Jack Pine and Black spruce sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb8prds/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb8scds_394 v1 - The TGB-08 team collected data to investigate the controls over non-methane hydrocarbon (NMHC) fluxes from boreal forest tree species. This data set includes measurements of starch concentrations in foliar samples at mature Jack Pine and Black spruce sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb8scds/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb9nmhc_395 v1 - THE BOREAS TGB-09 team collected data in order to inventory and quantify the anthropogenic and biogenic NMHC's over the BOREAS study areas. This data set contains concentration and mixing ratio values for several NMHC's collected at the BOREAS SSA from 27-MAY-1994 to 15-SEP-1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb9nmhc/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb10ocd_396 v1 - The BOREAS TGB-10 team collected several trace gas data sets in their efforts to determine the role of biogenic hydrocarbon emissions with respect to boreal forest carbon cycles. This data set contains measured peroxide (H2O2 and total organic peroxides (ROOH)) and ozone concentrations as well as, H2O2 and ROOH deposition velocities.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb10ocd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb10ofd_397 v1 - The BOREAS TGB-10 team collected several trace gas data sets in their efforts to determine the role of biogenic hydrocarbon emissions with respect to boreal forest carbon cycles. This oxidant data set contains measured peroxide (H2O2 and total organic peroxides (ROOH)) and ozone concentrations as well as, H2O2 and ROOH deposition velocities.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb10ofd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb10voc_398 v1 - The BOREAS TGB-10 team collected several trace gas data sets in their efforts to determine the role of biogenic hydrocarbon emissions with respect to boreal forest carbon cycles. This data set contains measured VOC concentrations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb10voc/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb12ci_399 v1 - The BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites to estimate the rates of carbon accumulation and turnover in each of the major vegetation types. This data set contains information on the carbon isotopic content of carbon dioxide sampled from soils.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb12ci/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb12rad_400 v1 - The BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites to estimate the rates of carbon accumulation and turnover in each of the major vegetation types. Sampling strategies for soils were designed to take advantage of local fire chronosequences, so that the accumulation of C in regrowing mosses could be determined.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb12rad/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb12rfd_401 v1 - The BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites to estimate the rates of carbon accumulation and turnover in each of the major vegetation types. Sampling strategies for soils were designed to take advantage of local fire chronosequences, so that the accumulation of carbon in new moss growth could be determined.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb12rfd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb12cfd_517 v1 - The BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites. This data set provides: (1) estimates of soil carbon stocks by horizon based on soil survey data and analyses of data from individual soil profiles; (2) estimates of soil carbon fluxes based on stocks, fire history, drainage, and soil C inputs and decomposition constants based on field work using radiocarbon analyses; (3) fire history data estimating age ranges of time since last fire; (4) a raster image and an associated soils table file f...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb12cfd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb12scd_402 v1 - The BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites to estimate the rates of carbon accumulation and turnover in each of the major vegetation types. TGB-12 data sets include soil properties at tower and selected auxiliary sites in the BOREAS NSA and data on the seasonal variations in the radiocarbon content of CO2 in the soil atmosphere at NSA tower sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb12scd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb12sci_558 v1 - During 1993 and 1994, the BOReal Ecosystem-Atmosphere Study (BOREAS) Trace Gas and Biogeochemistry team #12 (TGB-12) collected several data sets to support their analysis of soil carbon content in the Northern Study Area (NSA). The primary data set (BOREAS TGB-12 Soil Carbon Data over the NSA) is described in some detail.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/GRAB_BAG/tgb12sci/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ams_cs93_403 v1 - Canadian AES personnel collected data related to surface and atmospheric meteorological conditions over the BOREAS Region. This data set contains 15 minute meteorological data from 14 automated meteorology stations located across the BOREAS region.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/ams_cs93/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"marsii94_407 v1 - Canadian AES personnel collected several data sets related to surface and atmospheric meteorological conditions over the BOREAS region. This data set contains 15-minute meteorological data from six MARSII meteorology stations in the BOREAS region in Canada.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/marsii94/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"readac_d_408 v1 - Canadian AES personnel collected and processed data related to surface atmospheric meteorological conditions over the BOREAS region. This data set contains 15 minute meteorological data from one READAC meteorology station in Hudson Bay, Saskatchewan.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/readac_d/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"samsa94d_462 v1 - The Saskatchewan Research Council (SRC) collected surface meteorological and radiation data from December, 1993 until Decemb er 1996. The data set is comprised of the Suite A (meteorological and energy balance measurements) and Suite B (diffuse sol ar and longwave measurements) components.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/AFM/samsa94d/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BOREAS_CDS_1350 v1 - This data set provides Boreal Ecosystem-Atmosphere Study (BOREAS) project information and data collected at selected sites in the boreal forest of Saskatchewan and Manitoba, Canada from 1993 through 1996. The data include surface, airborne, and satellite-based observations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/BOREAS_CDS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"soil125r_309 v1 - This data set consists of GIS layers that describe the soils of the BOREAS SSA. The original data were submitted as vector layers that were gridded by BOREAS staff to a 30-meter pixel size in the AEAC projection.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/STAFF/soil125r/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tgb1ccsd_377 v1 - The BOREAS TGB-01 team made numerous measurements of trace gas concentrations and fluxes at various NSA sites. This data set contains methane (CH4) and carbon dioxide (CO2) concentrations in soil profiles from the NSA-OJP, NSA-OBS, NSA-YJP, and NSA-BP sites during the period of 23-May to 20-Sep-1994.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/boreas/TGB/tgb1ccsd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-boreas","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA CALIPSO Project","Description":"Earth Orbiter\n<br>\n\n#### CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00\nCAL_IIR_L3_GEWEX_Cloud-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) IIR Level 3 Global Energy and Water Cycle Experiment (GEWEX) Cloud, Standard Version 1-00 data product. Data for this product was collected using the CALIPSO Imaging Infrared Radiometer (IIR) instrument. This product reports global distributions of IIR cloud effective radius, water path averages, and histograms on a uniform 2-dimensional (2D) spatial grid. This product is designed to follow the general guidance of the GEWEX Cloud Assessment. Cloud amount, radiative temperature, effective emissivity, and optical depth characterize the cloud samples for which IIR microphysical retrievals are reported. Cloud properties are reported for ice clouds, liquid water clouds, and high ice clouds of layer pressure lower than 440 hPa. All level 3 parameters are derived from the IIR version 4 level 2 track products, with the temporal extent averaging one month. CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, Centre National d'Etudes Spatiales (CNES).\n<br>\n\n#### CAL_IIR_L3_GEWEX_Cloud-Standard-V2-00\nCAL_IIR_L3_GEWEX_Cloud-Standard-V2-00_V2-00 are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Infrared Imaging Radiometer (IIR) Level 3 Cloud products for the Global Energy and Water Cycle Experiment (GEWEX) Cloud Assessment. The IIR Level 3 GEWEX Cloud product reports global distributions of IIR cloud effective radius and water path averages and histograms on a uniform2-dimensional (2D) spatial grid. Cloud amount, radiative temperature, effective emissivity, and optical depth characterize the cloud samples for which IIR microphysical retrievals are reported. The statistics are reported for atmospheric columns containing only ice clouds, only liquid water clouds, and only high ice clouds of layer pressure lower than 440 hPa. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera(WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L0_PIVB-Standard-V1-00\nCAL_LID_L0_PIVB-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 0 Payload Instrument Verification and Block (PIVB), Version 1-00 data product. These data were collected intermittently between August 2016 and June 2023 using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The CALIPSO payload flight software, when commanded, creates Lidar Level 0 PIVB data packets for each of the three channels: 532 nm parallel, 532 nm perpendicular, and 1064 nm. These packets contain the altitude-dependent profiles (15 m resolution) of the raw backscatter signals acquired in the high and low gain channels over 15 consecutive laser pulses prior to being processed by CALIOP’s on-board profile averaging algorithm. Also included in the product are time and position information for each laser pulse, associated instrument engineering data, and an array containing the on-board measurement altitudes. No post-processing is done for the PIVB data, so the backscatter profiles have not been altitude-registered, geolocated, range-corrected, or calibrated. The PIVB data is not part of routine science data capture and is acquired only episodically throughout the latter portion of the CALIPSO mission. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the ImagingInfrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L1-Standard-V5-00\nCAL_LID_L1-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 1B profile data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP) instrument. The CALIOP Level 1B data product contains a half orbit (day or night) of calibrated and geolocated single-shot (highest resolution) lidar profiles, including 532 nm and1064 nm attenuated backscatter and depolarization ratio at 532 nm. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera(WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L2_01kmCLay-Standard-V4-51\nCAL_LID_L2_01kmCLay-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 1 km Cloud Layer, Version 4-51 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Within this layer product are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The lidar layer products contain column descriptors associated with several layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales). CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, called the C-Train.\n<br>\n\n#### CAL_LID_L2_01kmCLay-Standard-V5-00\nCAL_LID_L2_01kmCLay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 1 km Cloud Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Within this cloud layer product, generated at a horizontal resolution of 1 km, are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The cloud layer products consist of a sequence of column descriptors, each associated with a variable number of cloud layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., cloud layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L2_333mMLay-Standard-V5-00\nCAL_LID_L2_333mMLay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 333 m Merged Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Within this cloud and aerosol layer product, generated at a horizontal resolution of 333 m (single shot resolution), are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The merged layer products consist of a sequence of column descriptors, each associated with a variable number of cloud and aerosol layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., cloud and aerosol layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L2_05kmALay-Standard-V5-00\nCAL_LID_L2_05kmALay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Aerosol Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Within this aerosol layer product, generated at a horizontal resolution of 5 km, are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The aerosol layer products consist of a sequence of column descriptors, each associated with a variable number of cloud layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018,when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August1, 2023.\n<br>\n\n#### CAL_LID_L2_05kmMLay-Standard-V5-00\nCAL_LID_L2_05kmMLay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Merged Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Within this cloud and aerosol layer product, generated at a horizontal resolution of 5 km, are two general classes of data: Column Properties(including position data and viewing geometry) and Layer Properties. The merged layer products consist of a sequence of column descriptors, each associated with a variable number of cloud or aerosol layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., cloud or aerosol layers) identified within the column. Foreach feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera(WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L2_05kmCLay-Standard-V5-00\nCAL_LID_L2_05kmCLay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Cloud Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Within this cloud layer product, generated at a horizontal resolution of 5 km, are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The cloud layer products consist of a sequence of column descriptors, each associated with a variable number of cloud layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., cloud layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L2_05kmAPro-Standard-V5-00\nCAL_LID_L2_05kmAPro-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Aerosol Profile data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. This aerosol profile product reports vertical profiles of particulate extinction and backscatter, as well as additional information (e.g. particulate depolarization ratios) derived from these fundamental measurements. The aerosol profile products are reported at a uniform spatial resolution of 60 m vertically and 5 km horizontally, over a nominal altitude range from 30 km to -0.5 km. Due to constraints imposed by the on-board data averaging scheme, the vertical resolution of the aerosol profile data varies as a function of altitude. In the tropospheric region between 20 km to -0.5 km, the aerosol profile products are reported at a resolution of 60 m vertically, and in the stratospheric region(above 20-km), the aerosol profile products are reported at a resolution of 180 m vertically. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00\nCAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Blowing Snow - Antarctica, Version 1-00 data product. This product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and reports the distribution of blowing snow properties based on back-scatter retrievals over Antarctica. Data collection for this product is complete. CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D'Etudes Spatiales).\n<br>\n\n#### CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01\nCAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Blowing Snow - Antarctica, Version 1-01 data product. This product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and reports the distribution of blowing snow properties based on back-scatter retrievals over Antarctica. The version of this product was changed from 1-00 to 1-01 to account for a change in the operating system of the CALIPSO production cluster. Data collection for this product is complete. CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, Centre National d'Études Spatiales (CNES).\n<br>\n\n#### CAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00\nCAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Blowing Snow - Greenland, Version 1-00 data product. This product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and reports the distribution of blowing snow properties based on back-scatter retrievals over Greenland. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales). CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, called the C-Train.\n<br>\n\n#### CAL_LID_L2_05kmCPro-Standard-V5-00\nCAL_LID_L2_05kmCPro-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Cloud Profile data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. This cloud profile product reports vertical profiles of particulate extinction and backscatter, as well as additional information (e.g. particulate depolarization ratios) derived from these fundamental measurements. The cloud profile products are reported at a uniform spatial resolution of 60 m vertically and 5 km horizontally, over a nominal altitude range from 30 km to -0.5 km. Due to constraints imposed by the on-board data averaging scheme, the vertical resolution of the cloud profile data varies as a function of altitude. In the tropospheric region between 20 km to -0.5 km, the cloud profile products are reported at a resolution of 60 m vertically, and in the stratospheric region (above20-km), the cloud profile products are reported at a resolution of 180 m vertically. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km(428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L2_MLay_Diagnostic-Beta-V5-00\nCAL_LID_L2_MLay-Diagnostic-Beta-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2Merged Layer Diagnostic data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The contents of this merged layer product contain information already in the CAL_LID_L2_05kmMLay-Standard-V5-00 andCAL_LID_L2_333mMLay-Standard-V5-00 data products, but with additional diagnostic flags and parameters that were used by the science algorithms. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera(WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L2_PSCMask-Standard-V3-00\nCAL_LID_L2_PSCMask-Standard-V3-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 Polar Stratospheric Clouds (PSC) data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and describes the spatial distribution, optical properties, and composition of PSC layers observed. The product contains profiles of PSC presence, composition, optical properties, and meteorological information on a uniform 5-km horizontal x 180-m vertical grid along CALIPSO orbit tracks. Aura Microwave Limb Sounder (MLS) measurements of the primary PSC condensable vapors HNO3 and H2O and a number of parameters from the Aura MLS V2 Derived Meteorological Products (DMPs) are also included in this product. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L2_VFM-Standard-V5-00\nCAL_LID_L2_VFM-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 Vertical Feature Mask (VFM) data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The CALIOP Level 2 VFM data product contains scene classification and lidar lighting and land/water indicators. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera(WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L3_Cloud_Occurrence-Standard-V1-00\nCAL_LID_L3_Cloud_Occurrence-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Cloud Occurrence Data, Standard Version 1-00 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The degradation of the laser energies that started in September 2016 had a negative impact on the product, and because of this, generation and distribution ended in December 2016. Updated Lidar Level 2 data products and changes to the Lidar Level 3 Cloud Occurrence algorithm will need to be completed before a new release of this product is released. This product reports global distributions of clouds on a uniform spatial grid. All level 3 parameters are derived from the CALIPSO level 2 data, with a temporal average of one month. CALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES.\n<br>\n\n#### CAL_LID_L3_Cloud_Occurrence-Standard-V2-00\nCAL_LID_L3_Cloud_Occurrence-Standard-V2-00_V2-00 are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar(CALIOP) Level 3 Cloud Occurrence products. The Lidar Level 3 Cloud Occurrence product reports global distributions of cloud occurrence by counts on a uniform spatial grid. At each grid, the number of detected ice cloud samples is also reported as a histogram of ice cloud layer optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L3_GEWEX_Cloud-Standard-V1-00\nCAL_LID_L3_GEWEX_Cloud-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 3 Global Energy and Water Cycle Experiment (GEWEX) Cloud, Standard Version 1-00 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data collection for this product is complete. This product is a reformatted version of the CALIPSO contribution to the GEWEX cloud assessment of global cloud datasets from satellites. The data submitted by the CALIPSO team for this project had to conform to a specific format: yearly netCDF files organized by parameter. To be compatible with another publicly orderable lidar level 3 CALIPSO aerosol and cloud products reported as monthly HDF files, this new lidar level 3 CALIPSO GEWEX cloud product was created. These files report global distributions of cloud amount and cloud top as averages and histograms on a uniform 2-dimensional (2D) spatial grid. All level 3 parameters are derived from the CALIPSO version 4. x Level 2, 5 km cloud merged layer products, with a temporal averaging of one month. CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D’Etudes Spatiales).\n<br>\n\n#### CAL_LID_L3_Ice_Cloud-Standard-V1-00\nCAL_LID_L3_Ice_Cloud-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Ice Cloud Data, Standard Version 1-00 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The degradation of the laser energies that started in September 2016 had a negative impact on the product, and because of this, generation and distribution ended in December 2016. Updated Lidar Level 2 data products and changes to the Lidar Level 3 Ice Cloud algorithm will need to be completed before a new release of this product is released. This product reports global distributions of ice cloud extinction and ice water content histograms on a uniform spatial grid. All level 3 parameters are derived from the CALIPSO level 2, 5km cloud profile products, with a temporal average of one month. CALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES.\n<br>\n\n#### CAL_LID_L3_Stratospheric_APro-Standard-V1-00\nCAL_LID_L3_Stratospheric_APro-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Stratospheric Aerosol Profiles Standard Version 1-00 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V1.00 product ended on July 1, 2020 to support a change in the operating system of the CALIPSO production clusters. The V1.01 data product covers July 1, 2020, to current. The CALIPSO Lidar Level 3 stratospheric aerosol reports global distributions of 532nm total attenuated backscatter, extinction, attenuated scattering ratios, and stratospheric aerosol optical depths on a uniform spatial grid. All level 3 parameters are derived from the CALIPSO version 4 level 1 and level 2 5 km merged layer and version 3 level 2 polar stratospheric cloud data products, with a temporal averaging of one month. The primary outputs are reported in terms of 1) background only and 2) all aerosol. All features identified by the level 2 algorithms have been removed for background only. Only aerosol layers are considered for all aerosols, while clouds and polar stratospheric clouds are removed. CALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'études spatiales).\n<br>\n\n#### CAL_LID_L3_Stratospheric_APro-Standard-V1-01\nCAL_LID_L3_Stratospheric_APro-Standard-V1-01 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Stratospheric Aerosol Profiles Standard Version 1-01 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The version of this product was changed from 1-00 to 1-01 to account for a change in the operating system of the CALIPSO production cluster. Data collection for this product is ongoing. The CALIPSO Lidar Level 3 stratospheric aerosol reports global distributions of 532nm total attenuated backscatter, extinction, attenuated scattering ratios, and stratospheric aerosol optical depths on a uniform spatial grid. All level 3 parameters are derived from the CALIPSO version 4 level 1 and level 2 5 km merged layer and version 3 level 2 polar stratospheric cloud data products, with a temporal averaging of one month. The primary outputs are reported in terms of 1) background only and 2) all aerosol. All features identified by the level 2 algorithms have been removed for background only. Only aerosol layers are considered for all aerosols, while clouds and polar stratospheric clouds are removed. CALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'études spatiales).\n<br>\n\n#### CAL_LID_L3_Stratospheric_APro-Standard-V2-00\nCAL_LID_L3_Stratospheric_APro-Standard-V2-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Stratospheric Aerosol data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. This data product reports global distributions of 532 nm total attenuated backscatter, particulate backscatter, extinction, attenuated scattering ratios, and stratospheric aerosol optical depths on a uniform spatial grid. All parameters are derived from the version 5.00 CALIOP Level 1 and Level 2 and V2.00 CALIOP Polar Stratospheric Mask data products. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20\nCAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, All Sky Data, Standard Version 4-20 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to current. The CALIPSO lidar level 3 aerosol data product reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is intended to be a tropospheric product, so data are only reported below altitudes of 12 km. All level 3 parameters are derived from the version 4.20 CALIOP level 2 aerosol profile product and have been quality screened before averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distribution information are also included. Averaged profile data is reported for all aerosols, regardless of type, and for mineral dust aerosols only. Classification of dust is based on the aerosol-type flags in the level 2 profile product. To keep level 3 file sizes manageable, four different types of level 3 files are produced, depending on the sky condition and the temporal coverage of the data prior to averaging. Description of the Four Sky Conditions (Day, Night): 1) All Sky: All level 2 columns are averaged, regardless of cloud occurrence, 2) Cloud-Free: Only cloud-free level 2 columns are averaged, 3) Cloudy-Sky, Transparent: Only level 2 columns containing transparent clouds are averaged, and 4) Cloud-Sky, Opaque: Only level 2 columns containing opaque clouds are averaged CALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'études spatiales).\n<br>\n\n#### CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V5-00\nCAL_LID_L3_Tropospheric_APro_AllSky-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol, All-Sky, data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. This data product, generated separately between day and night, reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is a tropospheric product, so data are only reported below altitudes of 12 km. All parameters are derived from the version 5.00 CALIOP Level 2 data and have been quality screened prior to averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distributional information are also included. The All-Sky designate indicates that all level 2 columns are averaged, regardless of the occurrence of clouds. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20\nCAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, Cloud Free Data, Standard Version 4-20 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to current. The CALIPSO lidar level 3 aerosol data product reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is intended to be a tropospheric product, so data are only reported below altitudes of 12 km. All level 3 parameters are derived from the version 4.20 CALIOP level 2 aerosol profile product and have been quality screened before averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distribution information are also included. Averaged profile data is reported for all aerosols, regardless of type, and for mineral dust aerosols only. Classification of dust is based on the aerosol-type flags in the level 2 profile product. To keep level 3 file sizes manageable, four different types of level 3 files are produced, depending on the sky condition and the temporal coverage of the data prior to averaging. Description of the Four Sky Conditions (Day, Night) 1) All Sky: All level 2 columns are averaged, regardless of cloud occurrence 2) Cloud-Free: Only cloud-free level 2 columns are averaged 3) Cloudy-Sky, Transparent: Only level 2 columns containing transparent clouds are averaged 4) Cloud-Sky, Opaque: Only level 2 columns containing opaque clouds are averaged CALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D’Etudes Spatiales).\n<br>\n\n#### CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20\nCAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, Cloudy Sky Opaque Data, Standard Version 4-20 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to current. The CALIPSO lidar level 3 aerosol data product reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is intended to be a tropospheric product, so data are only reported below altitudes of 12km. All level 3 parameters are derived from the version 4.20 CALIOP level 2 aerosol profile product and have been quality screened before averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distribution information are also included. Averaged profile data is reported for all aerosols, regardless of type, and for mineral dust aerosols only. Classification of dust is based on the aerosol-type flags in the level 2 profile product. To keep level 3 file sizes manageable, four different types of level 3 files are produced, depending on the sky condition and the temporal coverage of the data before averaging. Description of the Four Sky Conditions (Day, Night): 1) All Sky: All level 2 columns are averaged, regardless of cloud occurrence 2) Cloud-Free: Only cloud-free level 2 columns are averaged 3) Cloudy-Sky, Transparent: Only level 2 columns containing transparent clouds are averaged 4) Cloud-Sky, Opaque: Only level 2 columns containing opaque clouds are averaged CALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D’Etudes Spatiales).\n<br>\n\n#### CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V5-00\nCAL_LID_L3_Tropospheric_APro_CloudSkyOpaque-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol, Cloudy Sky Opaque, data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. This data product, generated separately between day and night, reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is a tropospheric product, so data are only reported below altitudes of 12 km. All parameters are derived from the version 5.00 CALIOP Level 2 data and have been quality screened prior to averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distributional information are also included. The Cloudy Sky Opaque designate indicates that only level 2 columns containing opaque clouds are averaged. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20\nCAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, Cloudy Sky Transparent Data, Standard Version 4-20 data product. Data is collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data generation and distribution of this V4.20 product ended on July 1, 2020, to support a change in the operating system of the CALIPSO production clusters. The V4.21 data product covers July 1, 2020, to current. The CALIPSO lidar level 3 aerosol data product reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is intended to be a tropospheric product, so data are only reported below altitudes of 12 km. All level 3 parameters are derived from the version 4.20 CALIOP level 2 aerosol profile product and have been quality screened before averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distribution information are also included. Averaged profile data is reported for all aerosols, regardless of type, and for mineral dust aerosols only. Classification of dust is based on the aerosol-type flags in the level 2 profile product. To keep level 3 file sizes manageable, there are four different types of level 3 files produced, depending on the sky condition and the temporal coverage of the data before averaging: Description of the Four Sky Conditions (Day, Night) 1) All Sky: All level 2 columns are averaged, regardless of cloud occurrence, 2) Cloud-Free: Only cloud-free level 2 columns are averaged, 3) Cloudy-Sky, Transparent: Only level 2 columns containing transparent clouds are averaged, and 4) Cloud-Sky, Opaque: Only level 2 columns containing opaque clouds are averaged CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in formation with five other satellites in the international \"A-Train\" (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: the CALIOP, the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D'Etudes Spatiales).\n<br>\n\n#### CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V5-00\nCAL_LID_L3_Tropospheric_APro_CloudSkyTransparent-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol, Cloudy Sky Transparent, data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. This data product, generated separately between day and night, reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is a tropospheric product, so data are only reported below altitudes of 12km. All parameters are derived from the version 5.00 CALIOP Level 2 data and have been quality screened prior to averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532 nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distributional information are also included. The Cloudy Sky Transparent designate indicates that only level 2 columns containing transparent clouds are averaged. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_WFC_L1_Asm-Standard-V4-00\nCAL_WFC_L1_Asm-Standard-V4-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC)Level 1B Assembler Version 4-00 data product. The WFC Level 1B Assembler data products contain geolocated nighttime daily digital count and daily statistical data which comes from the calibration product. The Assembler takes the calibration data and calculates the statistics foreach pixel (mean, standard deviation, maximum, and minimum). CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles)above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera(WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.\n<br>\n\n#### CAL_WFC_L1_Cal-Standard-V4-00\nCAL_WFC_L1_Cal-Standard-V4-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC)Level 1B Calibration Version 4-00 data product. The WFC Level 1B Calibration data products contain geolocated nighttime digital count data. The calibration data is collected over a 25-second segment on the dark side of every orbit. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018,when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August1, 2023.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br><br>\n","Documentation":"https://science.nasa.gov/mission/calipso/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","earth observation","hdf","ice","lidar","netcdf","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00 vV1-00 - CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) IIR Level 3 Global Energy and Water Cycle Experiment (GEWEX) Cloud, Standard Version 1-00 data product. Data for this product was collected using the CALIPSO Imaging Infrared Radiometer (IIR) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00_V1-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_IIR_L3_GEWEX_Cloud-Standard-V2-00 vV2-00 - CAL_IIR_L3_GEWEX_Cloud-Standard-V2-00_V2-00 are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Infrared Imaging Radiometer (IIR) Level 3 Cloud products for the Global Energy and Water Cycle Experiment (GEWEX) Cloud Assessment. The IIR Level 3 GEWEX Cloud product reports global distributions of IIR cloud effective radius and water path averages and histograms on a uniform2-dimensional (2D) spatial grid.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_IIR_L3_GEWEX_Cloud-Standard-V2-00_V2-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L0_PIVB-Standard-V1-00 vV1-00 - CAL_LID_L0_PIVB-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 0 Payload Instrument Verification and Block (PIVB), Version 1-00 data product. These data were collected intermittently between August 2016 and June 2023 using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP).","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L0_PIVB-Standard-V1-00_V1-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L1-Standard-V5-00 vV5-00 - CAL_LID_L1-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 1B profile data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L1-Standard-V5-00_V5-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L2_01kmCLay-Standard-V4-51 vV4-51 - CAL_LID_L2_01kmCLay-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 1 km Cloud Layer, Version 4-51 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L2_01kmCLay-Standard-V4-51_V4-51","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L2_01kmCLay-Standard-V5-00 vV5-00 - CAL_LID_L2_01kmCLay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 1 km Cloud Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L2_01kmCLay-Standard-V5-00_V5-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L2_333mMLay-Standard-V5-00 vV5-00 - CAL_LID_L2_333mMLay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 333 m Merged Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L2_333mMLay-Standard-V5-00_V5-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L2_05kmALay-Standard-V5-00 vV5-00 - CAL_LID_L2_05kmALay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Aerosol Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L2_05kmALay-Standard-V5-00_V5-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L2_05kmMLay-Standard-V5-00 vV5-00 - CAL_LID_L2_05kmMLay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Merged Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L2_05kmMLay-Standard-V5-00_V5-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L2_05kmCLay-Standard-V5-00 vV5-00 - CAL_LID_L2_05kmCLay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Cloud Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L2_05kmCLay-Standard-V5-00_V5-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L2_05kmAPro-Standard-V5-00 vV5-00 - CAL_LID_L2_05kmAPro-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Aerosol Profile data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L2_05kmAPro-Standard-V5-00_V5-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00 vV1-00 - CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Blowing Snow - Antarctica, Version 1-00 data product. This product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and reports the distribution of blowing snow properties based on back-scatter retrievals over Antarctica.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-00_V1-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01 vV1-01 - CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Blowing Snow - Antarctica, Version 1-01 data product. This product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and reports the distribution of blowing snow properties based on back-scatter retrievals over Antarctica.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L2_BlowingSnow_Antarctica-Standard-V1-01_V1-01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00 vV1-00 - CAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Blowing Snow - Greenland, Version 1-00 data product. This product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and reports the distribution of blowing snow properties based on back-scatter retrievals over Greenland.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L2_BlowingSnow_Greenland-Standard-V1-00_V1-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L2_05kmCPro-Standard-V5-00 vV5-00 - CAL_LID_L2_05kmCPro-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Cloud Profile data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L2_05kmCPro-Standard-V5-00_V5-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L2_MLay_Diagnostic-Beta-V5-00 vV5-00 - CAL_LID_L2_MLay-Diagnostic-Beta-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2Merged Layer Diagnostic data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L2_MLay_Diagnostic-Beta-V5-00_V5-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L2_PSCMask-Standard-V3-00 vV3-00 - CAL_LID_L2_PSCMask-Standard-V3-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 Polar Stratospheric Clouds (PSC) data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and describes the spatial distribution, optical properties, and composition of PSC layers observed.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L2_PSCMask-Standard-V3-00_V3-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L2_VFM-Standard-V5-00 vV5-00 - CAL_LID_L2_VFM-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 Vertical Feature Mask (VFM) data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L2_VFM-Standard-V5-00_V5-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L3_Cloud_Occurrence-Standard-V1-00 vV1-00 - CAL_LID_L3_Cloud_Occurrence-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Cloud Occurrence Data, Standard Version 1-00 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L3_Cloud_Occurrence-Standard-V1-00_V1-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L3_Cloud_Occurrence-Standard-V2-00 vV2-00 - CAL_LID_L3_Cloud_Occurrence-Standard-V2-00_V2-00 are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar(CALIOP) Level 3 Cloud Occurrence products. The Lidar Level 3 Cloud Occurrence product reports global distributions of cloud occurrence by counts on a uniform spatial grid.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L3_Cloud_Occurrence-Standard-V2-00_V2-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L3_GEWEX_Cloud-Standard-V1-00 vV1-00 - CAL_LID_L3_GEWEX_Cloud-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 3 Global Energy and Water Cycle Experiment (GEWEX) Cloud, Standard Version 1-00 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L3_GEWEX_Cloud-Standard-V1-00_V1-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L3_Ice_Cloud-Standard-V1-00 vV1-00 - CAL_LID_L3_Ice_Cloud-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Ice Cloud Data, Standard Version 1-00 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L3_Ice_Cloud-Standard-V1-00_V1-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L3_Stratospheric_APro-Standard-V1-00 vV1-00 - CAL_LID_L3_Stratospheric_APro-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Stratospheric Aerosol Profiles Standard Version 1-00 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L3_Stratospheric_APro-Standard-V1-00_V1-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L3_Stratospheric_APro-Standard-V1-01 vV1-01 - CAL_LID_L3_Stratospheric_APro-Standard-V1-01 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Stratospheric Aerosol Profiles Standard Version 1-01 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L3_Stratospheric_APro-Standard-V1-01_V1-01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L3_Stratospheric_APro-Standard-V2-00 vV2-00 - CAL_LID_L3_Stratospheric_APro-Standard-V2-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Stratospheric Aerosol data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L3_Stratospheric_APro-Standard-V2-00_V2-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20 vV4-20 - CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, All Sky Data, Standard Version 4-20 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V4-20_V4-20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V5-00 vV5-00 - CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol, All-Sky, data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L3_Tropospheric_APro_AllSky-Standard-V5-00_V5-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20 vV4-20 - CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, Cloud Free Data, Standard Version 4-20 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-20_V4-20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20 vV4-20 - CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, Cloudy Sky Opaque Data, Standard Version 4-20 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V4-20_V4-20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V5-00 vV5-00 - CAL_LID_L3_Tropospheric_APro_CloudSkyOpaque-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol, Cloudy Sky Opaque, data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L3_Tropospheric_APro_CloudySkyOpaque-Standard-V5-00_V5-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20 vV4-20 - CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, Cloudy Sky Transparent Data, Standard Version 4-20 data product. Data is collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V4-20_V4-20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V5-00 vV5-00 - CAL_LID_L3_Tropospheric_APro_CloudSkyTransparent-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol, Cloudy Sky Transparent, data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_LID_L3_Tropospheric_APro_CloudySkyTransparent-Standard-V5-00_V5-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_WFC_L1_Asm-Standard-V4-00 vV4-00 - CAL_WFC_L1_Asm-Standard-V4-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC)Level 1B Assembler Version 4-00 data product. The WFC Level 1B Assembler data products contain geolocated nighttime daily digital count and daily statistical data which comes from the calibration product.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_WFC_L1_Asm-Standard-V4-00_V4-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CAL_WFC_L1_Cal-Standard-V4-00 vV4-00 - CAL_WFC_L1_Cal-Standard-V4-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Wide Field Camera (WFC)Level 1B Calibration Version 4-00 data product. The WFC Level 1B Calibration data products contain geolocated nighttime digital count data.","ARN":"arn:aws:s3:::asdc-prod-protected/CALIPSO/CAL_WFC_L1_Cal-Standard-V4-00_V4-00","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-calipso","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA CAR Project","Description":"CAR will fly in 2022-2025 for the NASA’s Student Airborne Science Activation (SaSa) project. GSFC scientists and engineers will operate CAR together with\n<br>\n\n#### CAR_ARCTAS_L1C\nARCTAS focuses on advancing understanding of the factors driving current changes in the Arctic region including transport of mid-latitude pollution, boreal forest fires, aerosol radiative forcing, and chemical processes. ARCTAS aimed to use detailed observations from aircraft to provide the validation, retrieval constraints, correlative data, and process information needed to better achieve the potential of satellites for Arctic research. The plan is for the combination of satellite and aircraft data to provide together powerful information for constraining and evaluating models of Arctic atmospheric composition and climate, and thus improve model projections of future change.\n<br>\n\n#### CAR_ARMCAS_BRDF\nThe Arctic Radiation Measurement in Column Atmosphere-surface System (ARMCAS) was a collaborative research effort between the Cloud and Aerosol Research (CAR) Group, Department of Atmospheric Sciences, University of Washington (led by Professor Peter V. Hobbs) and Drs. Michael King and Si-Chee Tsay of NASA/Goddard. The field portion of ARMCAS was based out of Deadhorse, Alaska, from June 3-15, 1995. Flights of the University of Washington's Convair C-131A research aircraft and NASA's ER-2 aircraft took place over the tundra of the North Slope and over the partially ice-covered Beaufort Sea. Several of these flights were closely coordinated in order to provide simultaneous in situ and remote sensing measurements of arctic clouds. This data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.\n<br>\n\n#### CAR_CLAMS_BRDF\nCLAMS is the Chesapeake Lighthouse and Aircraft Measurements for Satellites field campaign sponsored by CERES, MISR, MODIS-Atmospheres and the NASA/GEWEX Global Aerosol Climatology Project (GACP). The centerpiece of CLAMS is the Chesapeake Lighthouse sea platform 20 km east of Virginia Beach, at which NASA and NOAA make continuous, long-term measurements of radiation, meteorology, and ocean waves. Members of the CERES, MISR and MODIS instrument teams are collaborating to accomplish a common set of objectives tied to the validation of EOS data products. The CLAMS campaign took place in July-August 2001 to validate Terra data products from a shortwave closure experiment targeting clear (cloud-free) sky conditions and focused on obtaining: 1. more accurate spectral and broadband radiative fluxes at the surface and within the atmosphere, 2. characterization of ocean optics in the vicinity of the lighthouse, and 3. description of the atmospheric aerosol amounts, micro-physical and optical properties, and their variability. This data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.\n<br>\n\n#### CAR_CLASIC_BRDF\nCLASIC (Cloud and Land Surface Interaction Campaign) focuses on advancing the understanding of how land surface processes influence cumulus convection. CLASIC was conducted in the Southern Great Plains (SGP &#8211; a region comprising Kansas, Oklahoma, and Texas) of the United States during June 2007. The SGP site consists of in situ and remote-sensing instrument clusters arrayed across approximately 55,000 square miles (143,000 square kilometers) in north-central Oklahoma, making it the largest and most extensive climate research field site in the world. The CAR flew aboard Sky Research Jetstream-31 and measured spectral and angular distribution of scattered light by clouds and aerosols, and provided bidirectional reflectance of various surfaces, and imagery of cloud and Earth surface features. By making such diverse measurements, our goal is to widen the audience of potential end-users and to foster collaborations among campaign participants and with outside users. This data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.\n<br>\n\n#### CAR_CLASIC_L1C\nCLASIC (Cloud and Land Surface Interaction Campaign) focuses on advancing the understanding of how land surface processes influence cumulus convection. The CAR flew aboard Sky Research Jetstream-31 and measured spectral and angular distribution of scattered light by clouds and aerosols, and provided bidirectional reflectance of various surfaces, and imagery of cloud and Earth surface features. By making such diverse measurements, our goal is to widen the audience of potential end-users and to foster collaborations among campaign participants and with outside users.\n<br>\n\n#### CAR_DISCOVERAQ_L1C\nDISCOVER-AQ, a NASA Earth Venture program funded mission, stands for Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality. In recent years, progress in reaching air quality goals has begun to plateau for many locations. Furthermore, near-surface pollution is one of the most challenging problems for Earth observations from space. However, with an improved ability to monitor pollution from satellites from DISCOVER-AQ, scientists could make better air quality forecasts, more accurately determine the sources of pollutants in the air and more closely determine the fluctuations in emissions levels. In short, the more accurate data scientists have at hand, the better society is able to deal effectively with lingering pollution problems.\n<br>\n\n#### CAR_ECO3D_L1C\nThis study promotes the understanding of vegetation response to changing forcing factors such as climate, storm frequency, and management practices, and is directly traceable to missions such as MODIS, MISR, and ICESat-2.\n<br>\n\n#### CAR_ECO3D_BRDF\nThis study provide critical measurements on 3-dimensional structure of vegetation, which is important for quantifying the amount of carbon stored in biomass. It promotes the understanding of vegetation response to changing forcing factors such as climate, storm frequency, and management practices, and is directly traceable to missions such as MODIS, MISR, and ICESat-2.During the ECO-3D mission in 2011, the CAR instrument was flown aboard the NASA P-3 and obtained measurements of bidirectional reflectance distribution function (BRDF) over forests ranging from Boreal to tropical wetlands covering sites from Quebec to Southern Florida. This data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.\n<br>\n\n#### CAR_FIREACE_BRDF\nThe scientific objectives of FIRE/ACE are to study impact of Arctic clouds on radiation exchange between surface, atmosphere, and space, and the influence of surface characteristics of sea ice, leads, and ice melt ponds on these clouds. FIRE/ACE will attempt to document, understand, and predict the Arctic cloud-radiation feedbacks, including changes in cloud fraction and vertical distribution, water vapor cloud content, cloud particle concentration and size, and cloud phase as atmospheric temperature and chemical composition change. FIRE/ACE uses the data to focus on improving current climate model simulations of the Arctic climate, especially with respect to clouds and their effects on the surface energy budget. In addition, FIRE/ACE addresses a number of scientific questions dealing with radiation, cloud microphysics, and atmospheric chemistry. This data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.\n<br>\n\n#### CAR_INTEXB_L1C\nINTEX-B (Intercontinental Chemical Transport Experiment-Phase B) focuses on the long-range transport of pollution, global atmospheric photochemistry, and the effects of aerosols and clouds on radiation and climate. It has two phases: phase 1 of the study was performed in Mexico from March 1-20, 2006, and phase 2 was performed in April and May and focused on Asian City pollution outflow over the western Pacific.\n<br>\n\n#### CAR_KUWAITOILFIRE_BRDF\nCAR Kuwait Oil Fire mission measured bidirectional reflectance function of smoke from Kuwait oil fires during the Kuwait Oil Fire Smoke Experiment. Measurements were also taken over the Saudi Arabian desert with overlying desert dust, and Persian Gulf waters with some overlying aerosols. This experiment was a part of an international research effort in response to an environmental crisis, when over 600 oil wells in Kuwait were ignited by Iraqi forces in 1991. The resulting fires produced large plumes of smoke that had significant effects on the Persian Gulf region but limited global effects. Between May 16 and June 12, 1991, the Kuwait Oil Fire Smoke Experiment (KOFSE) was conducted in the Persian Gulf Region. The purpose of KOFSE was to determine the chemical and physical nature of the smoke and to investigate its potential effects on air quality, weather, and climate. This data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.\n<br>\n\n#### CAR_LEADEX_BRDF\nCAR LEADEX mission measured bidirectional reflectance functions for four common arctic surfaces: snow covered sea ice, melt season sea ice, snow covered tundra, and tundra shortly after snowmelt. The measurements show how the reflectance differs amongst the mentioned arctic surfaces and provides insights into the variability of albedo in the arctic. This data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.\n<br>\n\n#### CAR_SAFARI_BRDF\nThe Southern African Regional Science Initiative (SAFARI) 2000 is an international science field campaign aimed at developing a better understanding of the southern Africa earth-atmosphere-human system. The goal of SAFARI 2000 is to identify and understand the relationship between the physical, chemical, biological, and anthropogenic processes that underlie the biogeophysical and biogeochemical systems of southern Africa. Particular emphasis will be placed upon biogenic, pyrogenic, and anthropogenic emissions - their characterization and quantification, their transport and transformations in the atmosphere, their influence on regional climate and meteorology, their eventual deposition, and the effects of this deposition on ecosystems. This data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.\n<br>\n\n#### CAR_SCARA_BRDF\nThe objectives for the SCAR mission are to advance our knowledge of how the physical, chemical and radiative processes in our atmosphere are affected by sulfate aerosol and smoke from biomass burning; to improve our expertise at remotely sensing smoke, water vapor, clouds, vegetation and fires; and to assess the effects of deforestation and biomass burning on tropical landscapes. The SCAR-A campaign occurred in western Atlantic Ocean. This data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.\n<br>\n\n#### CAR_SCARA_L1C\nThe objectives for the SCAR mission are to advance our knowledge of how the physical, chemical and radiative processes in our atmosphere are affected by sulfate aerosol and smoke from biomass burning; to improve our expertise at remotely sensing smoke, water vapor, clouds, vegetation and fires; and to assess the effects of deforestation and biomass burning on tropical landscapes. The SCAR-B campaign occurred in western Atlantic Ocean.\n<br>\n\n#### CAR_SCARB_BRDF\nThe objectives for the SCAR mission are to advance our knowledge of how the physical, chemical and radiative processes in our atmosphere are affected by sulfate aerosol and smoke from biomass burning; to improve our expertise at remotely sensing smoke, water vapor, clouds, vegetation and fires; and to assess the effects of deforestation and biomass burning on tropical landscapes. The SCAR-B campaign occurred in Brazil. This data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.\n<br>\n\n#### CAR_SKUKUZA_BRDF\nCAR mission Skukuza measured bidirectional reflection functions over different natural surfaces and ecosystems in southern Africa. The measurements were conducted to characterize surface anisotropy in support of the CAR SAFARI mission and to validate products from NASA's Earth Observing System satellites. This data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.\n<br>\n\n#### CAR_SKUKUZA_L1C\nCAR mission Skukuza measured bidirectional reflection functions over different natural surfaces and ecosystems in southern Africa. The measurements were conducted to characterize surface anisotropy in support of the CAR SAFARI mission and to validate products from NASA’s Earth Observing System satellites.\n<br>\n\n#### CAR_SNOWEX17_BRDF\nSnowEx is a multi-year airborne project to help advance capabilities, and plan for a near-future space mission to monitor global seasonal snow water equivalent - currently an inconsistently collected and difficult-to-obtain data point that scientists say is critical to understanding the world's water resources. This data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.\n<br>\n\n#### CAR_SNOWEX17_L1C\nSnowEx is a multi-year airborne project to help advance capabilities, and plan for a near-future space mission to monitor global seasonal snow water equivalent — currently an inconsistently collected and difficult-to-obtain data point that scientists say is critical to understanding the world’s water resources.\n<br>\n\n#### CAR_TARFOX_BRDF\nCAR TARFOX mission collected data in the western Atlantic Ocean on the effects of tropospheric aerosols on radiation budgets in cloud free skies. The mission also measured the chemical, physical, and optical properties of aerosols. In July 1996, CAR data were collected aboard the University of Washington C-131A aircraft over the forested Great Dismal Swamp wetlands south of Norfolk, Virginia and the Atlantic Ocean approximately 340 km offshore of Richmond, Virginia. This data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://car.gsfc.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","earth observation","ice","oceans","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"CAR_ARCTAS_L1C v1 - ARCTAS focuses on advancing understanding of the factors driving current changes in the Arctic region including transport of mid-latitude pollution, boreal forest fires, aerosol radiative forcing, and chemical processes. ARCTAS aimed to use detailed observations from aircraft to provide the validation, retrieval constraints, correlative data, and process information needed to better achieve the potential of satellites for Arctic research.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_ARCTAS_L1C.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_ARMCAS_BRDF v2 - The Arctic Radiation Measurement in Column Atmosphere-surface System (ARMCAS) was a collaborative research effort between the Cloud and Aerosol Research (CAR) Group, Department of Atmospheric Sciences, University of Washington (led by Professor Peter V. Hobbs) and Drs.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_ARMCAS_BRDF.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_CLAMS_BRDF v2 - CLAMS is the Chesapeake Lighthouse and Aircraft Measurements for Satellites field campaign sponsored by CERES, MISR, MODIS-Atmospheres and the NASA/GEWEX Global Aerosol Climatology Project (GACP). The centerpiece of CLAMS is the Chesapeake Lighthouse sea platform 20 km east of Virginia Beach, at which NASA and NOAA make continuous, long-term measurements of radiation, meteorology, and ocean waves.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_CLAMS_BRDF.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_CLASIC_BRDF v2 - CLASIC (Cloud and Land Surface Interaction Campaign) focuses on advancing the understanding of how land surface processes influence cumulus convection. CLASIC was conducted in the Southern Great Plains (SGP &#8211; a region comprising Kansas, Oklahoma, and Texas) of the United States during June 2007.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_CLASIC_BRDF.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_CLASIC_L1C v1 - CLASIC (Cloud and Land Surface Interaction Campaign) focuses on advancing the understanding of how land surface processes influence cumulus convection. The CAR flew aboard Sky Research Jetstream-31 and measured spectral and angular distribution of scattered light by clouds and aerosols, and provided bidirectional reflectance of various surfaces, and imagery of cloud and Earth surface features.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_CLASIC_L1C.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_DISCOVERAQ_L1C v1 - DISCOVER-AQ, a NASA Earth Venture program funded mission, stands for Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality. In recent years, progress in reaching air quality goals has begun to plateau for many locations.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_DISCOVERAQ_L1C.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_ECO3D_L1C v1 - This study promotes the understanding of vegetation response to changing forcing factors such as climate, storm frequency, and management practices, and is directly traceable to missions such as MODIS, MISR, and ICESat-2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_ECO3D_L1C.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_ECO3D_BRDF v2 - This study provide critical measurements on 3-dimensional structure of vegetation, which is important for quantifying the amount of carbon stored in biomass. It promotes the understanding of vegetation response to changing forcing factors such as climate, storm frequency, and management practices, and is directly traceable to missions such as MODIS, MISR, and ICESat-2.During the ECO-3D mission in 2011, the CAR instrument was flown aboard the NASA P-3 and obtained measurements of bidirectional reflectance distribution function (BRDF) over forests ranging from Boreal to tropical wetlands cove...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_ECO3D_BRDF.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_FIREACE_BRDF v2 - The scientific objectives of FIRE/ACE are to study impact of Arctic clouds on radiation exchange between surface, atmosphere, and space, and the influence of surface characteristics of sea ice, leads, and ice melt ponds on these clouds. FIRE/ACE will attempt to document, understand, and predict the Arctic cloud-radiation feedbacks, including changes in cloud fraction and vertical distribution, water vapor cloud content, cloud particle concentration and size, and cloud phase as atmospheric temperature and chemical composition change.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_FIREACE_BRDF.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_INTEXB_L1C v1 - INTEX-B (Intercontinental Chemical Transport Experiment-Phase B) focuses on the long-range transport of pollution, global atmospheric photochemistry, and the effects of aerosols and clouds on radiation and climate. It has two phases: phase 1 of the study was performed in Mexico from March 1-20, 2006, and phase 2 was performed in April and May and focused on Asian City pollution outflow over the western Pacific.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_INTEXB_L1C.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_KUWAITOILFIRE_BRDF v2 - CAR Kuwait Oil Fire mission measured bidirectional reflectance function of smoke from Kuwait oil fires during the Kuwait Oil Fire Smoke Experiment. Measurements were also taken over the Saudi Arabian desert with overlying desert dust, and Persian Gulf waters with some overlying aerosols.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_KUWAITOILFIRE_BRDF.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_LEADEX_BRDF v2 - CAR LEADEX mission measured bidirectional reflectance functions for four common arctic surfaces: snow covered sea ice, melt season sea ice, snow covered tundra, and tundra shortly after snowmelt. The measurements show how the reflectance differs amongst the mentioned arctic surfaces and provides insights into the variability of albedo in the arctic.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_LEADEX_BRDF.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_SAFARI_BRDF v2 - The Southern African Regional Science Initiative (SAFARI) 2000 is an international science field campaign aimed at developing a better understanding of the southern Africa earth-atmosphere-human system. The goal of SAFARI 2000 is to identify and understand the relationship between the physical, chemical, biological, and anthropogenic processes that underlie the biogeophysical and biogeochemical systems of southern Africa.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_SAFARI_BRDF.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_SCARA_BRDF v2 - The objectives for the SCAR mission are to advance our knowledge of how the physical, chemical and radiative processes in our atmosphere are affected by sulfate aerosol and smoke from biomass burning; to improve our expertise at remotely sensing smoke, water vapor, clouds, vegetation and fires; and to assess the effects of deforestation and biomass burning on tropical landscapes. The SCAR-A campaign occurred in western Atlantic Ocean.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_SCARA_BRDF.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_SCARA_L1C v1 - The objectives for the SCAR mission are to advance our knowledge of how the physical, chemical and radiative processes in our atmosphere are affected by sulfate aerosol and smoke from biomass burning; to improve our expertise at remotely sensing smoke, water vapor, clouds, vegetation and fires; and to assess the effects of deforestation and biomass burning on tropical landscapes. The SCAR-B campaign occurred in western Atlantic Ocean.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_SCARA_L1C.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_SCARB_BRDF v2 - The objectives for the SCAR mission are to advance our knowledge of how the physical, chemical and radiative processes in our atmosphere are affected by sulfate aerosol and smoke from biomass burning; to improve our expertise at remotely sensing smoke, water vapor, clouds, vegetation and fires; and to assess the effects of deforestation and biomass burning on tropical landscapes. The SCAR-B campaign occurred in Brazil.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_SCARB_BRDF.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_SKUKUZA_BRDF v2 - CAR mission Skukuza measured bidirectional reflection functions over different natural surfaces and ecosystems in southern Africa. The measurements were conducted to characterize surface anisotropy in support of the CAR SAFARI mission and to validate products from NASA's Earth Observing System satellites.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_SKUKUZA_BRDF.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_SKUKUZA_L1C v1 - CAR mission Skukuza measured bidirectional reflection functions over different natural surfaces and ecosystems in southern Africa. The measurements were conducted to characterize surface anisotropy in support of the CAR SAFARI mission and to validate products from NASA’s Earth Observing System satellites.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_SKUKUZA_L1C.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_SNOWEX17_BRDF v2 - SnowEx is a multi-year airborne project to help advance capabilities, and plan for a near-future space mission to monitor global seasonal snow water equivalent - currently an inconsistently collected and difficult-to-obtain data point that scientists say is critical to understanding the world's water resources. This data set consists of observations made with the CAR instrument and includes values for bidirectional reflectance factor at varying spectral bands.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_SNOWEX17_BRDF.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_SNOWEX17_L1C v1 - SnowEx is a multi-year airborne project to help advance capabilities, and plan for a near-future space mission to monitor global seasonal snow water equivalent — currently an inconsistently collected and difficult-to-obtain data point that scientists say is critical to understanding the world’s water resources.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_SNOWEX17_L1C.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_TARFOX_BRDF v2 - CAR TARFOX mission collected data in the western Atlantic Ocean on the effects of tropospheric aerosols on radiation budgets in cloud free skies. The mission also measured the chemical, physical, and optical properties of aerosols.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CAR/CAR_TARFOX_BRDF.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-car","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA CARAFE Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### CARAFE_2016_2017_v2_2002\nThis dataset provides airborne eddy covariance (EC) fluxes of carbon dioxide, methane, sensible heat, and latent heat at high spatial resolution collected during the NASA Carbon Airborne Flux Experiment (CARAFE) airborne 2016 and 2017 campaigns. CARAFE utilized the NASA C-23 Sherpa aircraft with a suite of commercial and custom instrumentation. Deployment occurred across the Mid-Atlantic Region for the period 2016-09-07 through 2016-09-26 and 2017-05-03 through 2017-05-26. The data also include downwelling radiation, water vapor, pressure, temperature, wind, and aircraft navigation data. Airborne EC can quantify surface fluxes at local to regional scales, potentially helping to bridge gaps between top-down and bottom-up flux estimates and offering novel insights into biophysical and biogeochemical processes.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/CARAFE/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"CARAFE_2016_2017_v2_2002 v1.1 - This dataset provides airborne eddy covariance (EC) fluxes of carbon dioxide, methane, sensible heat, and latent heat at high spatial resolution collected during the NASA Carbon Airborne Flux Experiment (CARAFE) airborne 2016 and 2017 campaigns. CARAFE utilized the NASA C-23 Sherpa aircraft with a suite of commercial and custom instrumentation.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carafe/CARAFE_2016_2017_v2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-carafe","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA CARVE Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### Polar-VPRM_Alaskan-NEE_1314\nThis data set provides 3-hourly estimates of gross ecosystem CO2 exchange (GEE) and respiration (autotrophic and heterotrophic) for the state of Alaska from 2012 to 2014. The data were generated using the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM) and are provided at ~ 1 km2 [1/4-degree (longitude) by 1/6-degree (latitude)] pixel resolution. The PolarVPRM produces high-frequency estimates of GEE of CO2 for North American biomes from remotely-sensed data sets. For Alaska, the model used meteorological inputs from the North American regional re-analysis (NARR) and inputs of fractional snow cover and land surface water index (LSWI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Land surface greenness was factored into the model from three sources: 1) Enhanced Vegetation Index (EVI) from MODIS; 2) Solar Induced Florescence (SIF) from the Orbiting Carbon Observatory 2 (OCO-2); and 3) SIF from the Global Ozone Monitoring Experiment 2 (GOME-2). Three independent estimates of GEE are included in the data set, one for each source of greenness observations.\n<br>\n\n#### CH4_Aircraft_STILT_footprints_1300\nThis data set provides the results of (1) year-round measurements of methane (CH4) flux along with soil and air temperatures at five eddy covariance towers at sites located in the Alaskan Arctic tundra from June 2013 to December 2014 and (2) airborne CH4 and ozone (O3) measurements collected during Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) flight campaigns for years 2012 through 2014. The included site-level flux data at half-hourly intervals were calculated following standard eddy covariance data processing procedures. Also reported are daily mean methane flux, soil temperature with depth, and air temperature for each tower site. Also identified for each flux tower site were the \"zero curtain\" periods of extended cold when soil temperatures were poised near 0 degrees C. The reported CARVE airborne CH4 and O3 data were aggregated horizontally at 5 km intervals. Measurement heights are reported. These aircraft positions were treated as receptors in a Stochastic Time-Inverted Lagrangian Transport (STILT) model coupled with meteorology fields from the polar variant of the Weather and Research Forecasting model (WRF), in order to model the land surface influence on the aircraft-observed methane concentrations. The summed land surface influence on the aircraft data at each position is reported. For each airborne measurement, 2D surface influence fields (i.e. footprints) at two different spatial resolutions were derived using the WRF-STILT simulations. These gridded footprints are provided as netCDF formatted files. Regional C-CH4 fluxes were calculated from the CARVE CH4 data and footprints for the period 2012-2014 and are also included with this data set. Acknowledgements: Data collection efforts were funded by NSF ARCSS project \"Methane Loss From Arctic\" (ARCSS #1204263; http://www.nsf.gov/awardsearch/showAward?AWD_ID=1204263) and by NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE).\n<br>\n\n#### AKFED_V1_1282\nThis data set provides estimates of annual carbon emissions (kg carbon per square meter) from boreal fires at 450-m resolution for the state of Alaska between 2001 and 2013. To produce these data, daily burned area for 2001 to 2013 was mapped using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) combined with perimeters from the Alaska Large Fire Database. Carbon consumption was calibrated using available field measurements from black spruce forests in Alaska. Above- and below-ground carbon consumption were modeled based on environmental variables including elevation, day of burning within the fire season, pre-fire tree cover and the differenced normalized burn ratio (dNBR). Modeled uncertainties in carbon consumption are included in the data set. The derived burn area and carbon emissions product, referred to as the Alaskan Fire Emissions Database (AKFED), provides a resource for study of the environmental controls on daily fire dynamics, boreal fire emissions in biogeochemical models, and potential feedbacks from changing fire regimes. There are 26 data files in GeoTIFF (.tif) format with this data set. There are 13 .tifs for carbon consumption, one for each year, and 13 .tifs for carbon consumption uncertainty, one for each year.\n<br>\n\n#### Alaskan_CH4_CO2_Fluxes_1316\nThis data set provides hourly atmospheric concentrations of methane (CH4), carbon dioxide (CO2), and carbon monoxide (CO) as mole fractions, from January 2012 to December 2014 measured at the CARVE flux tower in Fox, Alaska (17 km north of Fairbanks) as part of NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). High-resolution meteorological fields from the Polar Weather Research and Forecasting (WRF) model coupled with the Stochastic Time-Inverted Lagrangian Transport model (WRF- STILT), along with the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM) were used to determine the influence region of the tower site and investigate the inter-annual and seasonal variability of regional fluxes of CO2 and CH4 in boreal Alaska using the tower observations. Modeled estimates of CH4, CO2, and CO background concentrations are provided. The WRF-STILT model \"footprints\" for the CARVE tower are provided with this data set.\n<br>\n\n#### CARVE_Reports_1434\nThis dataset includes detailed daily flight reports from each of the airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The reports include plots of the flight path, altitude, wind and weather conditions, IR and visible light images, and initial analysis of the atmospheric gas concentrations encountered along the flight. Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The CARVE measurements are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.\n<br>\n\n#### CARVE_Land_Thaw_State_1383\nThis data set provides daily 10 km resolution maps of the Alaskan and Arctic Boreal land surface state as either frozen, melting, or thawed. These data are generated from passive microwave radiometer observations made from 2003 through 2014 by the Advanced Microwave Scanning Radiometer (AMSR-E) and the Special Sensor Microwave Imager (SSM/I). Data products overlap with science data collections carried out during the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE).\n<br>\n\n#### CARVE_Ecosystem_CH4_Flux_1558\nThis dataset provides methane flux estimates derived from airborne measurements collected over Alaska and the western Yukon Territory during the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) between 2012 and 2014. The state-scale methane fluxes were calculated using a combination of atmospheric profiles and lagrangian transport modeling. The methane flux estimates were used in a simple linear regression model to estimate the fluxes from the tundra and boreal ecosystems. Methane fluxes were also used with a combination of environmental variables to derive a statistical relationship between domain-wide flux and soil temperature. Soil temperature products from North American Regional Reanalysis and derived parameters from a Boltmann-Arrhenius model were used to model methane flux and related uncertainties within the domain at monthly and daily frequencies.\n<br>\n\n#### BC_Aerosol_Dynamics_Alaska_1340\nThis data set provides measurements of the isotopic composition of black carbon and organic carbon aerosols collected at two locations in interior Alaska during the summer of 2013, as part of NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The delta14C end member of fire aerosol was derived and linked to soil elemental and isotopic composition in Alaskan boreal forests. Soil and aerosol measurements were used to estimate average depth of burn in Alaska during the summer of 2013.\n<br>\n\n#### CARVE_Photos_1435\nThis dataset contains photos taken by scientists aboard the CARVE aircraft during airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content.\n<br>\n\n#### CARVE_Videos_1433\nThis dataset contains videos captured by a camera mounted on the CARVE aircraft during airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content.\n<br>\n\n#### CARVE_L1_Infrared_1428\nThis data set provides earth referenced radiance counts measured by the Forward Looking Infrared (FLIR) camera aboard the CARVE aircraft between April 2013 and November 2015 for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The FLIR camera records images of the surface temperature while measuring concentrations of atmospheric carbon dioxide, methane, and ozone. Thermal images from the FLIR camera will be used to characterize land surfaces underlain by permafrost during specific phases in the freeze-thaw cycle. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.\n<br>\n\n#### CARVE_L1_FlightPath_Winds_1427\nThis data set provides high-frequency wind speed and direction data for the C-23 Sherpa aircraft during airborne campaigns over the Alaskan and Canadian Arctic as part of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data were collected in situ using the Aventech AIMMS-30 Airborne Wind Sensor onboard the aircraft and are presented at 1-second intervals throughout each flight. The Winds instrument was available for flights in year 2015 only. The measurements included in this data set are most useful when paired with the scientific data collected by other CARVE airborne instruments.\n<br>\n\n#### CARVE_L1_FlightPath_1425\nThis data set provides high-frequency geolocation, time, height, pitch, roll, and heading information for the C-23 Sherpa aircraft during airborne campaigns over the Alaskan and Canadian Arctic as part of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data were collected in situ using the Digital Air Data System (DADS) onboard the aircraft and are presented at 1-second intervals throughout each flight. Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The measurements included in this data set are useful for matching aircraft position with the scientific data collected by other CARVE airborne instruments.\n<br>\n\n#### CARVE_L1_Ground_Flux_1424\nThis data set provides ground in situ flux and meteorological science data from fixed instruments at three eddy covariance tower sites located in the Alaskan Arctic tundra. Real and gap-filled observations of carbon dioxide, methane, water vapor, and latent energy flux in addition to standard meteorological and environmental variables are reported at half-hourly intervals between 2011 and 2015 for sites at Atqasuk, Barrow, and Ivotuk, Alaska. The three sites form a 300-km north-south transect on the North Slope of Alaska, each site representing distinct Arctic vegetation communities. These tower measurements create a long-term record of one of the largest, most volatile carbon stocks on the planet. Observations from these towers are being used to determine the seasonal and inter-annual patterns of CO2 and CH4 flux, and their relationship to changes in environmental factors.\n<br>\n\n#### CARVE_L1_FTS_Spectra_1426\nThis data set contains Level 1 spectral radiance data collected using the Fourier Transform Spectrometer (FTS) during airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.\n<br>\n\n#### CARVE_L2_AtmosGas_Harvard_1403\nThis data set provides atmospheric carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) concentrations from airborne campaigns over the Alaskan and Canadian arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data were collected in situ using a four-species cavity ring-down spectrometer system (CRDS; Picarro Inc.) provided by Harvard University and are presented at 5-second intervals throughout each flight. The Harvard CRDS instrument only collected data in 2012-2014; no Harvard data are available for year 2015. Aircraft latitude, longitude, and altitude are also provided. CARVE flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.\n<br>\n\n#### CARVE_L2_AtmosGas_NOAA_1401\nThis data set provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and water vapor (H2O) concentrations from airborne campaigns over the Alaskan and Canadian arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data were collected in situ using a cavity ring-down spectrometer (CRDS; Picarro Inc.) and are presented at 2-second intervals throughout each flight. Aircraft latitude, longitude, and altitude are also provided. Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.\n<br>\n\n#### CARVE_L2_AtmosGas_Ground_1419\nThis data set provides atmospheric methane (CH4), carbon dioxide (CO2), and carbon monoxide (CO) dry air mole fractions and water vapor mole fractions (H2O) from continuous in situ measurements at the CARVE flux tower in Fox, Alaska between October 2011 and May 2015 for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Air was drawn from three different heights above the base of the tower (31.7 m, 17.1 m, and 4.9 m) and analyzed using a Picarro cavity ring-down spectrometer (CRDS). Measurements of ambient and sonic temperature, vertical and horizontal velocity, and atmospheric pressure are also included in the data set.\n<br>\n\n#### CARVE_L2_Flask_1404\nThis data set provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e. \"concentrations\") from airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The CARVE flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas abundances. The data were derived from laboratory measurements of whole air samples collected by a Programmable Flask Package (PFP) onboard the CARVE aircraft. Air samples were collected at strategic intervals to coincide with the overflight of a ground site of interest, or when interesting geophysical conditions were encountered. While most of these samples were collected near the surface in the planetary boundary layer (PBL), on almost every flight samples were also collected in the free troposphere. A minimum of 12 flask samples were collected per flight. Whole air samples collected in the PFPs were analyzed on automated systems at the NOAA Earth System Research Laboratory (ESRL) Global Monitoring Division in Boulder, CO, which also analyzes samples from the NOAA/ESRL Global Greenhouse Gas Reference Network. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.\n<br>\n\n#### CARVE_L2_Flask_Ground_1405\nThis data set provides atmospheric carbon dioxide, methane, carbon monoxide, molecular hydrogen, nitrous oxide, sulfur hexafluoride, and other trace gas mole fractions (i.e. \"concentrations\") from a flask sampling system at the CARVE flux tower in Fox, Alaska for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data were derived from laboratory measurements of whole air samples collected by a Programmable Flask Package (PFP) from the top of the tower at 32 m above ground level during late evening multiple times per month since January 2012. Whole air samples collected in the PFPs were analyzed on automated systems at the NOAA Earth System Research Laboratory (ESRL) Global Monitoring Division in Boulder, CO, which also analyzes samples from the NOAA/ESRL Global Greenhouse Gas Reference Network. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.\n<br>\n\n#### CARVE_L2_FTS_ColumnGas_1429\nThis data set provides total vertical column O2, CO2, CH4, CO, and H2O, as well as dry-air columns of CO2, CH4, CO, and H2O from airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data represent the Level 2 Quick Retrieval (L2QR) data product collected using the CARVE Fourier Transform Spectrometer (FTS). Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.\n<br>\n\n#### CARVE_L2_AtmosGas_Merge_1402\nThis data set provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), ozone (O3), and water vapor (H2O) concentrations from airborne campaigns over the Alaskan and Canadian arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). These data are merged and gap-filled outputs from two different cavity ring-down spectrometers (CRDS; Picarro Inc.) flown aboard the CARVE aircraft and are presented at 5-second intervals throughout each flight. Aircraft latitude, longitude, and altitude are also provided. Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.\n<br>\n\n#### CARVE_L4_WRF-STILT_Footprint_1431\nThis data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) Footprint data products for particle receptors located at positions along Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) flight paths (2012 - 2015) and various meteorological stations in Alaska and the Canadian Arctic. Each product consists of multiple NetCDF footprint files packaged as a TAR/GZIP file. These aircraft and station positions were treated as receptors in the WRF-STILT model in order to simulate the land surface influence on observed atmospheric constituents. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.\n<br>\n\n#### CARVE_L4_WRF-STILT_Particle_1430\nThis data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) model inputs for particle receptors located at positions along Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) flight paths (2012 - 2015) and various meteorological stations in Alaska and the Canadian Arctic. Each product consists of multiple NetCDF files packaged as a TAR/GZIP file. These data correspond to WRF-STILT model footprint data also generated by the CARVE science team.\n<br>\n\n#### Alaskan_CO2_Flux_1325\nThis data set reports monthly averages of atmospheric CO2 concentration from satellite and airborne observations between 2009 and 2013 and simulated present and future monthly concentrations and land-atmosphere CO2 flux for periods between 1990 and 2200. Atmospheric CO2 concentration measurements were obtained from Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) and NOAA Arctic Coast Guard (ACG) flights, the Greenhouse Gases Observing Satellite (GOSAT), and NOAA/ESRL vertical profile measurements at Poker Flat, Alaska (PFA). Present and future monthly CO2 concentrations and fluxes were simulated using the GEOS-Chem global tracer model and the Community Land Model, Version 4.5, for multiple regional flux and permafrost thaw scenarios.\n<br>\n\n#### AK_Regional_CO2_Flux_1389\nThis data set provides estimates of 3-hourly net ecosystem CO2 exchange (NEE) at 0.5-degree resolution over the state of Alaska for 2012-2014. The NEE estimates are the output are from Geostatistical Inverse Modeling of a subset of CARVE aircraft CO2 data, WRF-STILT footprints, and PVPRM-SIF data from flux towers (CRV: located in Fox, AK and BRW: located just outside Barrow, AK). Daily mean NEE is also provided as calculated for all of Alaska and for four sub-regions (0.5-degree resolution) that were defined across Alaska, based on general landcover type: North Slope Tundra, South and West Tundra, Boreal Forests, and Mixed (all other). Also provided are derived annual carbon budgets for (1) all of Alaska with defined contributions from biogenic, fossil fuel, and biomass burning sources and (2) annual biogenic carbon budgets for the four landcover-type regions of Alaska. Provided for completeness are the CARVE aircraft atmospheric measurement data used in estimating NEE.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/CARVE/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","elevation","ice","netcdf","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Polar-VPRM_Alaskan-NEE_1314 v1 - This data set provides 3-hourly estimates of gross ecosystem CO2 exchange (GEE) and respiration (autotrophic and heterotrophic) for the state of Alaska from 2012 to 2014. The data were generated using the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM) and are provided at ~ 1 km2 [1/4-degree (longitude) by 1/6-degree (latitude)] pixel resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/Polar-VPRM_Alaskan-NEE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CH4_Aircraft_STILT_footprints_1300 v1 - This data set provides the results of (1) year-round measurements of methane (CH4) flux along with soil and air temperatures at five eddy covariance towers at sites located in the Alaskan Arctic tundra from June 2013 to December 2014 and (2) airborne CH4 and ozone (O3) measurements collected during Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) flight campaigns for years 2012 through 2014. The included site-level flux data at half-hourly intervals were calculated following standard eddy covariance data processing procedures.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/CH4_Aircraft_STILT_footprints/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AKFED_V1_1282 v1 - This data set provides estimates of annual carbon emissions (kg carbon per square meter) from boreal fires at 450-m resolution for the state of Alaska between 2001 and 2013. To produce these data, daily burned area for 2001 to 2013 was mapped using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) combined with perimeters from the Alaska Large Fire Database.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/AKFED_V1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Alaskan_CH4_CO2_Fluxes_1316 v1 - This data set provides hourly atmospheric concentrations of methane (CH4), carbon dioxide (CO2), and carbon monoxide (CO) as mole fractions, from January 2012 to December 2014 measured at the CARVE flux tower in Fox, Alaska (17 km north of Fairbanks) as part of NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). High-resolution meteorological fields from the Polar Weather Research and Forecasting (WRF) model coupled with the Stochastic Time-Inverted Lagrangian Transport model (WRF- STILT), along with the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM) were...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/Alaskan_CH4_CO2_Fluxes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_Reports_1434 v1 - This dataset includes detailed daily flight reports from each of the airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The reports include plots of the flight path, altitude, wind and weather conditions, IR and visible light images, and initial analysis of the atmospheric gas concentrations encountered along the flight.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_Reports/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_Land_Thaw_State_1383 v1.1 - This data set provides daily 10 km resolution maps of the Alaskan and Arctic Boreal land surface state as either frozen, melting, or thawed. These data are generated from passive microwave radiometer observations made from 2003 through 2014 by the Advanced Microwave Scanning Radiometer (AMSR-E) and the Special Sensor Microwave Imager (SSM/I).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/CARVE_Land_Thaw_State/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_Ecosystem_CH4_Flux_1558 v1 - This dataset provides methane flux estimates derived from airborne measurements collected over Alaska and the western Yukon Territory during the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) between 2012 and 2014. The state-scale methane fluxes were calculated using a combination of atmospheric profiles and lagrangian transport modeling.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/CARVE_Ecosystem_CH4_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BC_Aerosol_Dynamics_Alaska_1340 v1 - This data set provides measurements of the isotopic composition of black carbon and organic carbon aerosols collected at two locations in interior Alaska during the summer of 2013, as part of NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The delta14C end member of fire aerosol was derived and linked to soil elemental and isotopic composition in Alaskan boreal forests.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/BC_Aerosol_Dynamics_Alaska/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_Photos_1435 v1 - This dataset contains photos taken by scientists aboard the CARVE aircraft during airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_Photos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_Videos_1433 v1 - This dataset contains videos captured by a camera mounted on the CARVE aircraft during airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_Videos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_L1_Infrared_1428 v1 - This data set provides earth referenced radiance counts measured by the Forward Looking Infrared (FLIR) camera aboard the CARVE aircraft between April 2013 and November 2015 for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The FLIR camera records images of the surface temperature while measuring concentrations of atmospheric carbon dioxide, methane, and ozone.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_L1_Infrared/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_L1_FlightPath_Winds_1427 v1 - This data set provides high-frequency wind speed and direction data for the C-23 Sherpa aircraft during airborne campaigns over the Alaskan and Canadian Arctic as part of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data were collected in situ using the Aventech AIMMS-30 Airborne Wind Sensor onboard the aircraft and are presented at 1-second intervals throughout each flight.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_L1_FlightPath_Winds/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_L1_FlightPath_1425 v1 - This data set provides high-frequency geolocation, time, height, pitch, roll, and heading information for the C-23 Sherpa aircraft during airborne campaigns over the Alaskan and Canadian Arctic as part of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data were collected in situ using the Digital Air Data System (DADS) onboard the aircraft and are presented at 1-second intervals throughout each flight.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_L1_FlightPath/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_L1_Ground_Flux_1424 v1 - This data set provides ground in situ flux and meteorological science data from fixed instruments at three eddy covariance tower sites located in the Alaskan Arctic tundra. Real and gap-filled observations of carbon dioxide, methane, water vapor, and latent energy flux in addition to standard meteorological and environmental variables are reported at half-hourly intervals between 2011 and 2015 for sites at Atqasuk, Barrow, and Ivotuk, Alaska.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_L1_Ground_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_L1_FTS_Spectra_1426 v1 - This data set contains Level 1 spectral radiance data collected using the Fourier Transform Spectrometer (FTS) during airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_L1_FTS_Spectra/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_L2_AtmosGas_Harvard_1403 v1 - This data set provides atmospheric carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) concentrations from airborne campaigns over the Alaskan and Canadian arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data were collected in situ using a four-species cavity ring-down spectrometer system (CRDS; Picarro Inc.) provided by Harvard University and are presented at 5-second intervals throughout each flight.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_L2_AtmosGas_Harvard/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_L2_AtmosGas_NOAA_1401 v1 - This data set provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and water vapor (H2O) concentrations from airborne campaigns over the Alaskan and Canadian arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data were collected in situ using a cavity ring-down spectrometer (CRDS; Picarro Inc.) and are presented at 2-second intervals throughout each flight.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_L2_AtmosGas_NOAA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_L2_AtmosGas_Ground_1419 v1 - This data set provides atmospheric methane (CH4), carbon dioxide (CO2), and carbon monoxide (CO) dry air mole fractions and water vapor mole fractions (H2O) from continuous in situ measurements at the CARVE flux tower in Fox, Alaska between October 2011 and May 2015 for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Air was drawn from three different heights above the base of the tower (31.7 m, 17.1 m, and 4.9 m) and analyzed using a Picarro cavity ring-down spectrometer (CRDS).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_L2_AtmosGas_Ground/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_L2_Flask_1404 v1 - This data set provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e. \"concentrations\") from airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_L2_Flask/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_L2_Flask_Ground_1405 v1 - This data set provides atmospheric carbon dioxide, methane, carbon monoxide, molecular hydrogen, nitrous oxide, sulfur hexafluoride, and other trace gas mole fractions (i.e. \"concentrations\") from a flask sampling system at the CARVE flux tower in Fox, Alaska for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_L2_Flask_Ground/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_L2_FTS_ColumnGas_1429 v1 - This data set provides total vertical column O2, CO2, CH4, CO, and H2O, as well as dry-air columns of CO2, CH4, CO, and H2O from airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data represent the Level 2 Quick Retrieval (L2QR) data product collected using the CARVE Fourier Transform Spectrometer (FTS).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_L2_FTS_ColumnGas/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_L2_AtmosGas_Merge_1402 v1 - This data set provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), ozone (O3), and water vapor (H2O) concentrations from airborne campaigns over the Alaskan and Canadian arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). These data are merged and gap-filled outputs from two different cavity ring-down spectrometers (CRDS; Picarro Inc.) flown aboard the CARVE aircraft and are presented at 5-second intervals throughout each flight.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_L2_AtmosGas_Merge/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_L4_WRF-STILT_Footprint_1431 v1.1 - This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) Footprint data products for particle receptors located at positions along Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) flight paths (2012 - 2015) and various meteorological stations in Alaska and the Canadian Arctic. Each product consists of multiple NetCDF footprint files packaged as a TAR/GZIP file.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_L4_WRF-STILT_Footprint/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CARVE_L4_WRF-STILT_Particle_1430 v1.1 - This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) model inputs for particle receptors located at positions along Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) flight paths (2012 - 2015) and various meteorological stations in Alaska and the Canadian Arctic. Each product consists of multiple NetCDF files packaged as a TAR/GZIP file.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/campaign/CARVE_L4_WRF-STILT_Particle/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Alaskan_CO2_Flux_1325 v1.1 - This data set reports monthly averages of atmospheric CO2 concentration from satellite and airborne observations between 2009 and 2013 and simulated present and future monthly concentrations and land-atmosphere CO2 flux for periods between 1990 and 2200. Atmospheric CO2 concentration measurements were obtained from Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) and NOAA Arctic Coast Guard (ACG) flights, the Greenhouse Gases Observing Satellite (GOSAT), and NOAA/ESRL vertical profile measurements at Poker Flat, Alaska (PFA).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/Alaskan_CO2_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AK_Regional_CO2_Flux_1389 v1 - This data set provides estimates of 3-hourly net ecosystem CO2 exchange (NEE) at 0.5-degree resolution over the state of Alaska for 2012-2014. The NEE estimates are the output are from Geostatistical Inverse Modeling of a subset of CARVE aircraft CO2 data, WRF-STILT footprints, and PVPRM-SIF data from flux towers (CRV: located in Fox, AK and BRW: located just outside Barrow, AK).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/carve/AK_Regional_CO2_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-carve","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA CERES Project","Description":"CER_BDS_Terra-FM2_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Bidirectional Scans (BDS) Terra Flight Model 2 (FM2) Edition 4 data product, which is collected using the CERES-FM2 instrument on the Terra platform. CER_BDS_Terra-FM2_Edition4 includes geolocated and calibrated Top of the Atmosphere (TOA) filtered radiances and other instrument data. Data collection for this product is ongoing. Each CERES BDS data product contains twenty-four hours of Level-1B data for each CERES scanner instrument mounted on each spacecraft. BDS includes samples of normal and short Earth scan elevation profiles in fixed and rotating azimuth modes (including space, internal calibration, and solar calibration views). BDS contains Level-0 raw (unconverted) and the geolocated converted science and instrument data. BDS contains additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data, converted digital status data, and parameters used in the radiance count conversion equations. CERES is a key Earth Observing System (EOS) program component. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-1Hour_Terra-MODIS\nCER_SYN1deg-1Hour_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds, and Aerosols 1-Hourly Terra Edition4A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites, CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer on Terra. Data collection for this product is complete. The CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated top-of-atmosphere (TOA) radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS and geostationary satellite cloud properties along with atmospheric profiles provided by GMAO. The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a one-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. The CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrow-band to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-1Hour_Terra-Aqua-MODIS\nCER_SYN1deg-1Hour_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO) Top-of-Atmosphere (TOA) Within-Atmosphere and Surface Fluxes, Clouds, and Aerosols 1-Hourly Terra-Aqua Edition4A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM3, FM4, CERES Scanner, and MODIS on Aqua. Data collection for this product is ongoing. The CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated top-of-atmosphere (TOA) radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a one-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. The CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrow-band to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-1Hour_Terra-NOAA20\nCER_SYN1deg-1Hour_Terra-NOAA20-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO) Top-of-Atmosphere (TOA) Within-Atmosphere and Surface Fluxes, Clouds, and Aerosols 1-Hourly Terra-Aqua Edition4A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM6, and VIIRS on NOAA-20. Data collection for this product is ongoing. The CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated top-of-atmosphere (TOA) radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a one-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. The CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrow-band to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-3Hour_Terra-MODIS\nCER_SYN1deg-3Hour_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols 3-Hourly Terra Edition4A data product, which was collected using Imaging Radiometers on the Geostationary Satellites platform and CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra. Data collection for this product is complete. The CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The calculated fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the NASA Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a three-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to accurately model variability between CERES observations. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-3Hour_Terra-Aqua-MODIS\nCER_SYN1deg-3Hour_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere, and Surface Fluxes, Clouds and Aerosols 3-Hourly Terra-Aqua Edition4A data product. The instruments and platforms used to collect this data include Imaging Radiometers on the Geostationary Satellites platform; CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and CERES FM3, CERES FM4, CERES Scanner, and MODIS on Aqua. Data collection for this product is in progress. CERES Synoptic (SYN) 1-degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The calculated fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the NASA Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a three-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to accurately model variability between CERES observations. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-Day_Terra-MODIS\nCER_SYN1deg-Day_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA) and Surface Fluxes, Clouds and Aerosols Daily Terra Edition4A data product. Data was collected using CERES Imaging Radiometers on Geostationary Satellites as well as CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer on Terra. Data collection for this product is complete. Note: It is highly recommended to use this product (CER_SYN1deg-Day_Terra-MODIS_Edition4A) in conjunction with CER_SYN1deg-Day_Terra-Aqua-MODIS_Edition4A when doing science-quality research. The CERES Synoptic Radiative Fluxes and Clouds (SYN) 1degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The calculated fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a daily temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. The CERES SYN1deg products use 1-hourly radiances and cloud property data from GEO imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrow-band to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-Day_Terra-Aqua-MODIS\nCER_SYN1deg-Day_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Daily Terra-Aqua Edition4A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer on Terra; and FM3, FM4 CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Aqua. Data collection for this product is ongoing. The CERES Synoptic Radiative Fluxes and Clouds (SYN) 1degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a daily temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. The CERES SYN1deg products use 1-hourly radiances and cloud property data from GEO imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-Day_Terra-NOAA20\nCER_SYN1deg-Day_Terra-NOAA20_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Daily Terra-NOAA20 Edition4A data product. Data was collected using the following instruments and platforms: Imaging Radiometers on the Geostationary Satellites platform, CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra; and CERES FM6 and VIIRS on NOAA-20. Data collection for this product is ongoing. The CERES Synoptic Radiative Fluxes and Clouds (SYN) 1degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident imager-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The calculated fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a daily temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. The CERES SYN1deg products use 1-hourly radiances and cloud property data from GEO imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Daily means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-Day_Terra-NPP\nCER_SYN1deg-Day_Terra-NPP_Edition1A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA) and Surface Fluxes, Clouds and Aerosols Daily Terra-Suomi National Polar-orbiting Partnership (NPP) Edition1A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer on Terra; and FM5, CERES Scanner, and Visible-Infrared Imager-Radiometer Suite (VIIRS) on Suomi-NPP. Data collection for this product is complete. The CERES Synoptic Radiative Fluxes and Clouds (SYN) 1degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product offers parameters on a daily temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. The CERES SYN1deg products use 1-hourly radiances and cloud property data from GEO imagers to accurately model variability between CERES observations. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrow-band to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-Month_Terra-Aqua-MODIS\nCER_SYN1deg-Month_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere, and Surface Fluxes, Clouds and Aerosols Monthly Terra-Aqua Edition4A data product. Data was collected using the following instruments and platforms: Imaging Radiometers on Geostationary Satellites platform, CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra; and CERES FM3, FM4, and MODIS on Aqua. Data collection for this product is ongoing. CERES Synoptic (SYN) 1-degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the NASA Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product offers parameters on a three-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to accurately model variability between CERES observations. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-Month_Terra-NOAA20\nCER_SYN1deg-Month_Terra-NOAA20_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere, and Surface Fluxes, Clouds and Aerosols Monthly Terra-NOAA20 Edition4A data product. Data was collected using the following instruments and platforms: Imaging Radiometers on the Geostationary Satellites platform, CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra; and CERES FM6 and VIIRS on NOAA-20. Data collection for this product is ongoing. CERES Synoptic (SYN) 1-degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, VIIRS, and geostationary satellite cloud properties along with atmospheric profiles provided by the NASA Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a three-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to accurately model variability between CERES observations. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a critical Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-Month_Terra-NPP\nCER_SYN1deg-Month_Terra-NPP_Edition1A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA) Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly Terra-Suomi National Polar-orbiting Partnership (NPP) Edition1A data product. Data was collected using the CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM5, CERES Scanner, and Visible-Infrared Imager-Radiometer Suite (VIIRS) on NPP. Data collection for this product is complete. The CERES SYN1deg products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a monthly temporal resolution on 1°-regional, zonal, and global spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. The CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-MHour_Terra-MODIS\nCER_SYN1deg-MHour_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly-Averaged 1-Hourly Terra Edition4A data product. Data was collected using the CERES Imaging Radiometers on the Geostationary Satellites platform and CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra platform. Data collection for this product is complete. The CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, and surface fluxes and computed fluxes that have been adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are made for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a monthly-averaged one-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. The CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique is used to ensure GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-MHour_Terra-Aqua-MODIS\nCER_SYN1deg-MHour_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly-Averaged 1-Hourly Terra-Aqua Edition4A data product. Data was collected using the CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM3, FM4 CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Aqua. Data collection for this product is ongoing. The CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product offers parameters on a monthly-averaged one-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. The CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-MHour_Terra-NOAA20\nCER_SYN1deg-MHour_Terra-NOAA20_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly-Averaged 1-Hourly Terra-Aqua Edition4A data product. Data was collected using the CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM6 CERES Scanner, and VIIRS on NOAA-20. Data collection for this product is ongoing. The CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a monthly-averaged one-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. The CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SYN1deg-Month_Terra-MODIS\nCER_SYN1deg-Month_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere and Surface Fluxes Clouds and Aerosols Monthly Terra Edition4A data product, which was collected using Imaging Radiometers on Geostationary Satellites platform as well as CERES Flight Model 1 (FM1), CERES FM2, and MODIS on Terra. Data collection for this product is complete. CERES Synoptic (SYN) 1-degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the NASA Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product offers parameters on a three-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to accurately model variability between CERES observations. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_CRS_Terra-FM2-MODIS\nCER_CRS_Terra-FM2-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Clouds and Radiative Swath (CRS) Terra Flight Model 2 (FM2) Moderate-Resolution Imaging Spectroradiometer (MODIS) Edition4A data product, which was collected using the CERES-FM2 instrument on the Terra platform. Please note that only a few variables from the SSF have been included and this product should be used in conjunction with the CER_SSF_Terra-FM2-MODIS_Edition4A product. The Clouds and Radiative Swath (CRS) product contains one hour of instantaneous CERES data for a single scanner instrument. The CRS contains geolocation, geometry, packet identification, and minimal cloud properties, and TOA fluxes from the CERES SSF product. For each CERES footprint on the Single Scanner Footprint (SSF), the CRS product also contains vertical flux profiles evaluated at six levels in the atmosphere: the surface, 850-, 500-, 200-, 70-, and 0.01-hPa for both clear-sky and total-sky. CERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999.\n<br>\n\n#### CER_FluxByCldTyp-Day_NOAA20-VIIRS\nCER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1B is the Clouds and the Earth's Radiant Energy System (CERES) Monthly Daytime Mean Regionally Averaged NOAA-20 Top-of-Atmosphere (TOA) Fluxes and Associated Cloud Properties Stratified by Optical Depth and Effective Pressure Edition 1B data product. Data was collected using CERES Flight Model 6 (FM6) and Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA-20. Data collection for this product is ongoing. CER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1B provides the monthly mean daytime CERES fluxes and CERES-VIIRS cloud properties that have been spatially gridded into 1° regions along both the NOAA-20 ground tracks where the TOA fluxes and cloud properties have been stratified by six cloud optical depth bins and seven cloud effective pressure layers. The CERES FluxByCldTyp-Day Edition1B product inputs Single Scanner Footprint (SSF) Edition1B footprint data. Within each footprint, all 1-km pixel-level VIIRS-retrieved cloud properties are stratified into three possible sub-footprint components: two cloud layers and a clear portion. The VIIRS channel radiances are converted to broadband (BB) radiances for each sub-footprint component. The CERES angular directional models are then applied to obtain BB fluxes. Each CERES sub-footprint cloud layer and associated fluxes are assigned to one of the 42 cloud types, similar to the stratification process in the CldTypHist product. FluxByCloudTyp is an hourly instantaneous gridded daytime-only product with a global extent. Each netCDF4 file covers a month. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_FluxByCldTyp-Day_NOAA20-VIIRS\nCER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1C is the Clouds and the Earth's Radiant Energy System (CERES) Monthly Daytime Mean Regionally Averaged NOAA-20 Top-of-Atmosphere (TOA) Fluxes and Associated Cloud Properties Stratified by Optical Depth and Effective Pressure Edition 1C data product. Data was collected using CERES Flight Model 6 (FM6) and Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA-20 Data collection for this product is ongoing. CER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1C provides the monthly mean daytime CERES fluxes and CERES-VIIRS cloud properties that have been spatially gridded into 1° regions along both the NOAA-20 ground tracks where the TOA fluxes and cloud properties have been stratified by six cloud optical depth bins and seven cloud effective pressure layers. The CERES FluxByCldTyp-Day Edition1C product uses Single Scanner Footprint (SSF) Edition1C footprint data as input. Within each footprint, all 1-km pixel-level VIIRS-retrieved cloud properties are stratified into three possible sub-footprint components: two cloud layers and a clear portion. For each sub-footprint component, the VIIRS channel radiances are converted to broadband (BB) radiances. The CERES angular directional models are then applied to obtain BB fluxes. Each of the CERES sub-footprint cloud layers and associated fluxes are assigned to one of the 42 cloud types, similarly to the stratification process in the CldTypHist product. FluxByCloudTyp is an hourly instantaneous gridded daytime-only product with a global extent. Each netCDF4 file covers a month. CERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CERES_EBAF\nCERES_EBAF_Edition4.1 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) and surface monthly means data in netCDF format Edition 4.1 data product. Data was collected using the CERES Scanner instruments on both the Terra and Aqua platforms. Data collection for this product is ongoing. CERES_EBAF_Edition4.1 data are monthly and climatological averages of TOA clear-sky (spatially complete) fluxes and all-sky fluxes, where the TOA net flux is constrained to the ocean heat storage. It also provides computed monthly mean surface radiative fluxes consistent with the CERES EBAF-TOA product and some basic cloud properties derived from MODIS. Cloud Radiative Effects are provided at both the TOA and surface as determined using a cloud-free profile in the Fu-Liou Radiative Transfer Model (RTM). Observed fluxes are obtained using cloud properties derived from narrow-band imagers onboard both EOS Terra and Aqua satellites, as well as geostationary satellites, to fully model the diurnal cycle of clouds. The computations are also based on meteorological assimilation data from the Goddard Earth Observing System (GEOS) Versions 5.4.1 models. Unlike other CERES Level 3 clear-sky regional data sets that contain clear-sky data gaps, the clear-sky fluxes in the EBAF-TOA product are regionally complete. The EBAF-TOA product is the CERES project's best estimate of the fluxes based on all available satellite platforms and input data. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CERES_EBAF\nCERES_EBAF_Edition4.2 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) and surface monthly means data in netCDF format Edition 4.2 data product. Data was collected using the CERES Scanner instruments on the Terra, Aqua, and NOAA-20 platforms for various periods. Data collection for this product is ongoing. CERES_EBAF_Edition4.2 data are monthly and climatological averages of TOA clear-sky (spatially complete) fluxes and all-sky fluxes, where the TOA net flux is constrained to the ocean heat storage. It also provides computed monthly mean surface radiative fluxes consistent with the CERES EBAF-TOA product and some basic cloud properties derived from colocated imagers. Cloud Radiative Effects are provided at both the TOA and surface as determined using a cloud-free profile in the Fu-Liou Radiative Transfer Model (RTM). Observed fluxes are obtained using cloud properties derived from narrow-band imagers onboard both EOS Terra and Aqua satellites and NOAA-20, as well as geostationary satellites, to fully model the diurnal cycle of clouds. The computations are also based on meteorological assimilation data from the Goddard Earth Observing System (GEOS) Versions 5.4.1 models. Unlike other CERES Level 3 clear-sky regional data sets that contain clear-sky data gaps, the clear-sky fluxes in the EBAF-TOA product are regionally complete. The EBAF-TOA product is the CERES project's best estimate of the fluxes based on all available satellite platforms and input data. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CERES_EBAF\nCERES_EBAF_Edition4.2.1 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) and surface monthly means data in netCDF format Edition 4.2.1 data product. Data was collected using the CERES Scanner instruments on the Terra, Aqua, and NOAA-20 platforms for various periods. Data collection for this product is ongoing. CERES_EBAF_Edition4.2.1 data are monthly and climatological averages of TOA clear-sky (spatially complete) fluxes and all-sky fluxes, where the TOA net flux is constrained to the ocean heat storage. It also provides computed monthly mean surface radiative fluxes consistent with the CERES EBAF-TOA product and some basic cloud properties derived from colocated imagers. Cloud Radiative Effects are supplied at the TOA and surface, as determined using a cloud-free profile in the Fu-Liou Radiative Transfer Model (RTM). Observed fluxes are obtained using cloud properties derived from narrow-band imagers onboard both EOS Terra and Aqua satellites and NOAA-20, as well as geostationary satellites, to model the diurnal cycle of clouds. The computations are also based on meteorological assimilation data from the Goddard Earth Observing System (GEOS) Versions 5.4.1 models. Unlike other CERES Level 3 clear-sky regional data sets that contain clear-sky data gaps, the clear-sky fluxes in the EBAF-TOA product are regionally complete. The EBAF-TOA product is the CERES project's best estimate of the fluxes based on all available satellite platforms and input data. CERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board the Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CERES_EBAF-TOA\nCERES_EBAF-TOA_Edition4.1 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Monthly means data in netCDF format Edition 4.1 data product. Data was collected using the CERES Scanner instruments on both the Terra and Aqua platforms. Data collection for this product is ongoing. CERES_EBAF-TOA_Edition4.1 data are monthly and climatological averages of TOA clear-sky (spatially complete) fluxes and all-sky fluxes, where the TOA net flux is constrained to the ocean heat storage. EBAF-TOA provides some basic cloud properties derived from Moderate-Resolution Imaging Spectroradiometer (MODIS) alongside TOA fluxes. Observed fluxes are obtained using cloud properties derived from narrow-band imagers onboard both Earth Observing System (EOS) Terra and Aqua satellites as well as geostationary satellites to more fully model the diurnal cycle of clouds. The computations are also based on meteorological assimilation data from the Goddard Earth Observing System (GEOS) Versions 5.4.1 models. Unlike other CERES Level 3 clear-sky regional data sets that contain clear-sky data gaps, the clear-sky fluxes in the EBAF-TOA product are regionally complete. The EBAF-TOA product is the CERES project's best estimate of the fluxes based on all available satellite platforms and input data. CERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CERES_EBAF-TOA\nCERES_EBAF-TOA_Edition4.2 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Monthly means data in netCDF format Edition 4.2 data product. Data was collected using the CERES Scanner instruments on the Terra, Aqua, and NOAA-20 platforms. Data collection for this product is ongoing. CERES_EBAF-TOA_Edition4.2 data are monthly and climatological averages of TOA clear-sky (spatially complete) fluxes and all-sky fluxes, where the TOA net flux is constrained to the ocean heat storage. EBAF-TOA provides some basic cloud properties derived from high-resolution imager data alongside TOA fluxes. The Moderate-Resolution Imaging Spectroradiometer (MODIS) imagers Terra and Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) are used for NOAA-20. Observed fluxes are obtained using cloud properties derived from narrow-band imagers onboard Earth Observing System (EOS) Terra and Aqua and NOAA-20 satellites and geostationary satellites to fully model the diurnal cycle of clouds. The computations are also based on meteorological assimilation data from the Goddard Earth Observing System (GEOS) Versions 5.4.1 models. Unlike other CERES Level 3 clear-sky regional data sets that contain clear-sky data gaps, the clear-sky fluxes in the EBAF-TOA product are regionally complete. The EBAF-TOA product is the CERES project's best estimate of the fluxes based on all available satellite platforms and input data. Only Terra data is used from March 2000 to June 2002; Terra and Aqua are combined from July 2002 until March 2022; and only NOAA-20 is used after March 2022. A correction created from an overlap period with time periods when both Terra and Aqua are available is used to adjust the single satellite periods. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CERES_EBAF-TOA\nCERES_EBAF-TOA_Edition4.2.1 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Monthly means data in netCDF format Edition 4.2.1 data product. Data was collected using the CERES Scanner instruments on the Terra, Aqua, and NOAA-20 platforms. Data collection for this product is ongoing. CERES_EBAF-TOA_Edition4.2.1 data are monthly and climatological averages of TOA clear-sky (spatially complete) fluxes and all-sky fluxes, where the TOA net flux is constrained to the ocean heat storage. EBAF-TOA provides some basic cloud properties derived from high-resolution imager data alongside TOA fluxes. The Moderate-Resolution Imaging Spectroradiometer (MODIS) imagers Terra and Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) are used for NOAA-20. Observed fluxes are obtained using cloud properties derived from narrow-band imagers onboard Earth Observing System (EOS) Terra and Aqua and NOAA-20 satellites and geostationary satellites to fully model the diurnal cycle of clouds. The computations are also based on meteorological assimilation data from the Goddard Earth Observing System (GEOS) Versions 5.4.1 models until March 2022 and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Unlike other CERES Level 3 clear-sky regional data sets that contain clear-sky data gaps, the clear-sky fluxes in the EBAF-TOA product are regionally complete. The EBAF-TOA product is the CERES project's best estimate of the fluxes based on all available satellite platforms and input data. Only Terra data is used from March 2000 to June 2002; Terra and Aqua are combined from July 2002 until March 2022; and only NOAA-20 is used after March 2022. A correction created from an overlap period with time periods when both Terra and Aqua are available is used to adjust the single satellite periods. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_ES9_NPP-FM5\nThe ERBE-like Monthly Regional Averages (ES-9) product contains a month of space and time-averaged Clouds and the Earth's Radiant Energy System (CERES) data for a single satellite using measurements from the primary cross-track instrument. All instantaneous shortwave and longwave fluxes at the Top-of-the-Atmosphere (TOA) from the CERES ES-8 product for a month are sorted by 2.5-degree spatial regions, by day number, and by the local hour of observation. The mean of the instantaneous fluxes for a given region-day-hour bin is determined and recorded on the ES-9, along with other flux statistics and scene information. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is \"like\" the algorithm used for the Earth Radiation Budget Experiment (ERBE). The ES-9 also contains hourly average fluxes for the month and an overall monthly average for each region. These average fluxes are given for both clear-sky and total-sky scenes. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The last CERES instrument (FM6) was launched on board the Joint Polar Satellite System 1 (JPSS-1) satellite on November 18, 2017.\n<br>\n\n#### CER_ES9_Aqua-Xtrk\nCER_ES9_Aqua-Xtrk_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Gridded Instantaneous Top-of-the-Atmosphere (TOA) Fluxes Aqua Cross-track Edition 4 data product, which was collected using the CERES-Flight Model (FM3) and FM4 instruments on the Aqua platform. Data collection for this product is ongoing. The ERBE-like Monthly Regional Averages (ES-9) products contain a month of space and time-averaged CERES data for a single satellite using measurements from the primary cross-track instrument. All instantaneous shortwave and longwave fluxes at the TOA from the CERES ES-8 product for a month are sorted by 2.5-degree spatial regions, by day number, and by the local hour of observation. The mean of the instantaneous fluxes for a given region-day-hour bin is determined and recorded on the ES-9, along with other flux statistics and scene information. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is \"like\" the algorithm used for ERBE. ES-9 also contains hourly average fluxes for the month and an overall monthly average for each region. These average fluxes are given for both clear-sky and total-sky scenes. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_ES9_Terra-Xtrk\nCER_ES9_Terra-Xtrk_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Gridded Instantaneous Top-of-the-Atmosphere (TOA) Fluxes Terra Cross-track Edition 4 data product, which was collected using the CERES Flight Model 1 (FM1) and FM2 instruments on the Terra platform. Data collection for this product is ongoing. The ERBE-like Monthly Regional Averages (ES-9) products contain a month of space and time-averaged CERES data for a single satellite using measurements from the primary cross-track instrument. All instantaneous shortwave and longwave fluxes at the TOA from the CERES ES-8 product for a month are sorted by 2.5-degree spatial regions, by day number, and by the local hour of observation. The mean of the instantaneous fluxes for a given region-day-hour bin is determined and recorded on the ES-9, along with other flux statistics and scene information. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is \"like\" the algorithm used for ERBE. ES-9 also contains hourly average fluxes for the month and an overall monthly average for each region. These average fluxes are given for both clear-sky and total-sky scenes. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_ES4_TRMM-PFM\nCER_ES4_TRMM-PFM_Edition2 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Monthly Geographical Averages Tropical Rainfall Measuring Mission (TRMM) proto flight model (PFM) Edition 2 data product. Data for this product was collected by the CERES PFM instrument on the TRMM platform. Data collection for this product is complete. CER_ES4_TRMM-PFM_Edition2 data are CERES instrument Top-of-the-Atmosphere (TOA) fluxes that used algorithms identical to those used by ERBE, averaged regionally, zonally, and globally. The ERBE-like Monthly Geographical Averages (ES-4) product contains a month of space and time-averaged CERES data for a single scanner instrument. The ES-4 is also produced for combinations of scanner instruments. For each observed 2.5-degree spatial region, the daily average, the hourly average over the month, and the overall monthly average of shortwave and long-wave (LW) fluxes at the Top-of-the-Atmosphere (TOA) from the CERES ES-9 product are spatially nested up from 2.5-degree regions to 5- and 10-degree regions, to 2.5-, 5-, and 10-degree zonal averages, and to global monthly averages. For each nested area, the albedo and net flux are given. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, PFM, was launched on November 27, 1997, as part of the TRMM. Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_ES9_TRMM-PFM\nCER_ES9_TRMM-PFM_Edition2 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Monthly Regional Averages Tropical Rainfall Measuring Mission (TRMM) proto flight model (PFM) Edition 2 data product. Data for this product was collected by the CERES-PFM on the Tropical Rainfall Measuring Mission (TRMM) platform. Data collection for this product is complete. CER_ES9_TRMM-PFM_Edition2 data are CERES instrument Top-of-the-Atmosphere (TOA) fluxes that used algorithms identical to those used by ERBE, regional averages of instantaneous footprint TOA fluxes only for the hours of satellite overpass (from ES-8 Level 2 product). The ERBE-like Monthly Regional Averages (ES-9) product contains a month of space and time-averaged CERES data for a single scanner instrument. The ES-9 is also produced for combinations of scanner instruments. All instantaneous short-wave and long-wave (LW) fluxes at the TOA from the CERES ES-8 product for a month are sorted by 2.5-degree spatial regions, by day number, and by the local hour of observation. The mean of the instantaneous fluxes for a given region-day-hour bin is determined and recorded on the ES-9, along with other flux statistics and scene information. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is like the algorithm used for the ERBE. The ES-9 also contains hourly average fluxes for the month and an overall monthly average for each region. These average fluxes are given for both clear-sky and total-sky scenes. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, PFM, was launched on November 27, 1997, as part of the TRMM. Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_ES4_NPP-FM5\nThe ERBE-like Monthly Geographical Averages (ES-4) product contains a month of space and time-averaged Clouds and the Earth's Radiant Energy System (CERES) data for a single satellite using measurements from the primary cross-track instrument. For each observed 2.5-degree spatial region, the daily average, the hourly average over the month, and the overall monthly average of shortwave and longwave fluxes at the Top-of-the-Atmosphere (TOA) from the CERES ES-9 product are spatially nested up from 2.5-degree regions to 5- and 10-degree regions, to 2.5-, 5-, and 10-degree zonal averages, and to global monthly averages. For each nested area, the albedo and net flux are given. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is \"like\" the algorithm used for the Earth Radiation Budget Experiment (ERBE). CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The last CERES instrument (FM6) was launched on board the Joint Polar Satellite System 1 (JPSS-1) satellite on November 18, 2017.\n<br>\n\n#### CER_ES4_Aqua-Xtrk\nCER_ES4_Aqua-Xtrk_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Time-Interpolated Top-of-the-Atmosphere (TOA) Fluxes Aqua Crosstrack Edition4 data product, which was collected using the CERES-FM3 and CERES-FM4 instruments on the Aqua platform. Data collection for this product is complete. The ERBE-like Monthly Geographical Averages (ES-4) product contains a month of space and time averaged CERES data for a single satellite using measurements from the primary crosstrack instrument. For each observed 2.5-degree spatial region, the daily average, the hourly average over the month, and the overall monthly average of shortwave and longwave fluxes at the TOA from the CERES ES-9 product are spatially nested up from 2.5-degree regions to 5- and 10-degree regions, to 2.5-, 5-, and 10-degree zonal averages, and to global monthly averages. For each nested area, the albedo and net flux are given. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is \"like\" the algorithm used for ERBE. CERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, protoflight model (PFM), was launched on November 27, 1997 as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_ES4_Terra-Xtrk\nCER_ES4_Terra-Xtrk_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Time-Interpolated Top-of-the-Atmosphere (TOA) Fluxes Terra Cross-track Edition 4 data product, which was collected using the CERES-Flight Model (FM1) and FM2 instruments on the Terra platform. Data collection for this product is complete. The ERBE-like Monthly Geographical Averages (ES-4) product contains a month of space and time-averaged CERES data for a single satellite using measurements from the primary cross-track instrument. For each observed 2.5-degree spatial region, the daily average, the hourly average over the month, and the overall monthly average of shortwave and longwave fluxes at the TOA from the CERES ES-9 product are spatially nested up from 2.5-degree regions to 5- and 10-degree regions, to 2.5-, 5-, and 10-degree zonal averages, and to global monthly averages. For each nested area, the albedo and net flux are given. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is \"like\" the algorithm used for ERBE. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful ERBE mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_FluxByCldTyp-Month_NOAA20-VIIRS\nCER_FluxByCldTyp-Month_NOAA20-VIIRS_Edition1C is the Clouds and the Earth's Radiant Energy System (CERES) Monthly Daytime Mean Regionally Averaged NOAA-20 Top-of-Atmosphere (TOA) Fluxes and Associated Cloud Properties Stratified by Optical Depth and Effective Pressure Edition 1C data product. Data was collected using CERES Flight Model 6 (FM6) and Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA-20 Data collection for this product is ongoing. CER_FluxByCldTyp-Month_NOAA20-VIIRS_Edition1C provides the monthly mean daytime CERES fluxes and CERES-VIIRS cloud properties that have been spatially gridded into 1° regions along the NOAA-20 ground track where the TOA fluxes and cloud properties have been stratified by six cloud optical depth bins and seven cloud effective pressure layers. The CERES FluxByCldTyp-Month Edition1C product uses Single Scanner Footprint (SSF) Edition1C footprint data as input. Within each footprint, all 1-km pixel-level VIIRS-retrieved cloud properties are stratified into three possible sub-footprint components: two cloud layers and a clear portion. For each sub-footprint component, the VIIRS channel radiances are converted to broadband (BB) radiances. The CERES angular directional models are then applied to obtain BB fluxes. Each of the CERES sub-footprint cloud layers and associated fluxes are assigned to one of the 42 cloud types, similarly to the stratification process in the CldTypHist product. FluxByCloudTyp is a monthly instantaneous gridded and averaged daytime-only product with a global extent. Each netCDF4 file covers a single day. CERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_FluxByCldTyp-Month_Terra-Aqua-MODIS\nCER_FluxByCldTyp-Month_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Monthly Daytime Mean Regionally Averaged Terra and Aqua Top-of-Atmosphere (TOA) Fluxes and Associated Cloud Properties Stratified by Optical Depth and Effective Pressure Edition 4A data product. Data was collected using CERES Flight Model 1 (FM1), FM2, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and FM3, FM4, and MODIS on Aqua. Data collection for this product is ongoing. CER_FluxByCldTyp-Month_Terra-Aqua-MODIS_Edition4A provides the monthly mean daytime CERES fluxes and CERES-Moderate-Resolution Imaging Spectroradiometer (MODIS) cloud properties that have been spatially gridded into 1° regions along both the Terra and Aqua ground tracks where the TOA fluxes and cloud properties have been stratified by six cloud optical depth bins and seven cloud effective pressure layers. The CERES FluxByCldTyp-Month Edition4A product inputs Single Scanner Footprint (SSF) Edition4A footprint data. All 1-km pixel-level MODIS-retrieved cloud properties within each footprint are stratified into three possible sub-footprint components: two cloud layers and a clear portion. The MODIS channel radiances are converted to broadband (BB) radiances for each sub-footprint component. The CERES angular directional models are then applied to obtain BB fluxes. Each CERES sub-footprint cloud layer and associated fluxes are assigned to one of the 42 cloud types, similar to the stratification process in the CldTypHist product. FluxByCloudTyp is an hourly instantaneous gridded daytime-only product with a global extent. Each netCDF4 file covers a single day. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SSF1deg-Hour_NOAA20-VIIRS\nThe Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint One Degree (SSF1deg) Hour provides regional averages of CERES Top of Atmosphere (TOA) fluxes, clouds derived from a co-located imager and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window wavelength bands. The incoming daily solar irradiance is from the Solar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa - 500 hPa, 500 hPa - 300 hPa, 300 hPa - 100 hPa) and a total of all layers. The aerosols are averaged instantaneous values from the co-located imager. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite on November 18, 2017.\n<br>\n\n#### CER_SSF1deg-Month_NPP-VIIRS\nCER_SSF1deg-Month_NPP-VIIRS_Edition2A is the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint One Degree (SSF1deg) Time-Interpolated Top-of-Atmosphere (TOA) Fluxes, Clouds and Aerosols Monthly Edition 2A data product, which was collected using the CERES-Flight Model 5 (FM5) and Visible-Infrared Imager-Radiometer Suite (VIIRS) instruments on the Suomi National Polar-orbiting Partnership (NPP) platform. Data collection for this product is in progress. CERES SSF1deg Month provides monthly averages of regional constant meteorology temporally interpolated CERES TOA fluxes, clouds derived from a co-located imager, and aerosols on a 1-degree latitude and longitude grid. One-degree zonally and global averaged values for the parameters are also provided. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window (WN) wavelength bands. The incoming solar daily irradiance is from the Solar Radiation and Climate Experiment (SORCE) Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa, - 500 hPa, 500 hPa, - 300 hPa, 300 hPa, - 100 hPa) and a total of all layers. The aerosols are averaged instantaneous values from the co-located imager. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SSF1deg-Day_NOAA20-VIIRS\nThe Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint One Degree (SSF1deg) Day provides daily averages of regional constant meteorology temporally interpolated CERES Top of Atmosphere (TOA) fluxes, clouds derived from a co-located imager and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window wavelength bands. The incoming daily solar irradiance is from the Solar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa - 500 hPa, 500 hPa - 300 hPa, 300 hPa - 100 hPa) and all layers. The aerosols are averaged instantaneous values from the co-located imager. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite on November 18, 2017.\n<br>\n\n#### CER_SSF1deg-Day_NOAA20-VIIRS\nCER_SSF1deg-Day_NOAA20-VIIRS_Edition1C is the NOAA-20 Clouds and the Earth's Radiant Energy System (CERES) Level 3 Single Scanner Footprint (SSF) Edition1C Top of Atmosphere (ToA) flux data product. The SSF One Degree (SSF1deg) Day provides daily averages on a 1-degree latitude and longitude global grid from the NOAA-20 CERES Flight Model 6 (FM-6) data. The last CERES instrument, FM-6, was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now known as NOAA-20, on November 18, 2017. The data product begins May 1, 2018. The product is distributed in monthly Hierarchical Data Format (HDF) 4 files. The file contains the daily mean for each day of the month and provides global coverage over a day. The SSF1deg-Day granule contains daily averages of regional CERES FM6 Earth-viewing Top of Atmosphere (ToA) shortwave and longwave fluxes. The fluxes are converted from the unfiltered CERES radiances at the footprint level using the co-located Visible Infrared Imaging Radiometer Suite (VIIRS) imager-defined scene. The footprint fluxes are gridded at hourly periods and then temporally interpolated assuming constant meteorology between measurements. The ToA fluxes are provided for both clear-sky and total sky conditions. The incoming daily solar irradiance is from the Solar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The VIIRS radiances are used with CERES-specific cloud mask and cloud property retrievals and are stratified into four atmospheric layers: surface to 700 mb, 700 mb to 500 mb, 500 to 300 mb, and above 300 mb along with the total. Each cloud layer has properties such as amount, height, temperature, pressure, optical depth, emissivity, phase, water path, and water particle size. The cloud properties are averaged for day and night (24-hour) and day-only periods. This product uses the instantaneous gridded product, CER_SSF1deg-Hour_NOAA20-VIIRS_Edition1C, as input.\n<br>\n\n#### CER_SSF1deg-Day_NPP-VIIRS\nCER_SSF1deg-Day_NPP-VIIRS_Edition2A is the Clouds and the Earth's Radiant Energy System (CERES) Time-Interpolated Top of Atmosphere (TOA) Fluxes, Clouds and Aerosols Daily SUOMI National Polar-orbiting Partnership (NPP) Edition 2A data product, which was collected using the CERES-Flight Model 5 (FM5) and Visible-Infrared Imager-Radiometer Suite (VIIRS) instruments on the Suomi NPP platform. Data collection for this product is ongoing. The CERES Single Scanner Footprint One Degree (SSF1deg) Daily product provides daily averages of regional constant meteorology temporally interpolated CERES TOA fluxes, clouds derived from a co-located imager, and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window (WN) wavelength bands. The incoming solar daily irradiance is from the Solar Radiation and Climate Experiment (SORCE), Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa, - 500 hPa, 500 hPa, - 300 hPa, 300 hPa, - 100 hPa) and a total of all layers. The aerosols are averaged instantaneous values from the co-located imager. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_SSF1deg-Month_NOAA20-VIIRS\nThe Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint One Degree (SSF1deg) Month provides monthly averages of regional constant meteorology temporally interpolated CERES Top of Atmosphere (TOA) fluxes, clouds derived from a co-located imager and aerosols on a 1-degree latitude and longitude grid. One-degree zonally and global averaged values for the parameters are also provided. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window wavelength bands. The incoming daily solar irradiance is from the Solar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa - 500 hPa, 500 hPa - 300 hPa, 300 hPa - 100 hPa) and a total of all layers. The aerosols are averaged instantaneous values from the co-located imager.CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The CERES instrument (FM5) was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite on November 18, 2017.\n<br>\n\n#### CER_SSF1deg-Month_NOAA20-VIIRS\nCER_SSF1deg-Month_NOAA20-VIIRS_Edition1C is the NOAA-20 Clouds and the Earth's Radiant Energy System (CERES) Level 3 Single Scanner Footprint (SSF) Edition1C Top of Atmosphere (ToA) flux data product. The SSF One Degree (SSF1deg) Month provides monthly averages on a 1-degree latitude and longitude global grid from the NOAA-20 CERES Flight Model 6 (FM6) data. The last CERES instrument, FM-6, was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now known as NOAA-20, on November 18, 2017. The data product begins May 1, 2018. The product is distributed in monthly Hierarchical Data Format (HDF) 4 files. The file contains the monthly mean and provides global coverage. The SSF1deg-Month granule contains the monthly averages of regional CERES FM6 Earth viewing Top of Atmosphere (ToA) shortwave and longwave fluxes. The fluxes are converted from the unfiltered CERES radiances at the footprint level using the co-located Visible Infrared Imaging Radiometer Suite (VIIRS) imager-defined scene. The footprint fluxes are gridded at hourly periods and then temporally interpolated assuming constant meteorology between measurements. The ToA fluxes are provided for both clear-sky and total sky conditions. The incoming daily solar irradiance is from the Solar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The VIIRS radiances are used with CERES-specific cloud mask and cloud property retrievals and are stratified into four atmospheric layers: surface to 700 mb, 700 mb to 500 mb, 500 to 300 mb, and above 300 mb along with the total. Each cloud layer has properties such as amount, height, temperature, pressure, optical depth, emissivity, phase, water path, and water particle size. The cloud properties are averaged for day and night (24-hour) and day-only periods. This product uses the instantaneous gridded product, CER_SSF1deg-Hour_NOAA20-VIIRS_Edition1C, as input.\n<br>\n\n#### CER_SSF1deg-Month_Terra-MODIS\nCER_SSF1deg-Month_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Time-Interpolated Top of Atmosphere (TOA) Fluxes, Clouds and Aerosols Monthly Terra Edition4A data product, which was collected using the CERES Flight Model 1 (FM1), FM2, and MODIS instruments on the Terra platform. Data collection for this product is in progress. CERES Single Scanner Footprint One Degree (SSF1deg) Day provides daily averages of regional constant meteorology temporally interpolated TOA fluxes, clouds derived from a co-located imager, and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode. TOA fluxes are provided for clear-sky and all-sky conditions for longwave (LW), shortwave (SW), and window (WN) wavelength bands. The incoming solar daily irradiance is from the SOlar Radiation and Climate Experiment (SORCE) and Total Solar Irradiance (TSI). The cloud properties are averaged for day and night (24-hour) and day-only periods. Cloud properties are stratified into four atmospheric layers (surface-700 hPa, 700 hPa, - 500 hPa, 500 hPa, - 300 hPa, 300 hPa - 100 hPa) and a total of all layers. The aerosols are averaged instantaneous values from the co-located imager. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_CldTypHist_GEO-MODIS-VIIRS\nCER_CldTypHist_GEO-MODIS-VIIRS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES)- Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) and hourly geostationary cloud properties stratified by the International Satellite Cloud Climatology Project (ISCCP) cloud types for day and night Edition 4A data product. Data collection is ongoing. The CERES-MODIS-VIIRS and hourly geostationary cloud properties (CldTypHist) data product contain monthly and one-hourly gridded regional mean cloud properties as a function of 18 cloud types, where the cloud properties are stratified by pressure, optical depth, and phase. Data is available day and night. The CldTypHist product combines cloud properties from Terra-MODIS (10:30 AM local equator crossing time LECT), NOAA20-VIIRS (1:30 PM LECT), and geostationary satellites (GEO) to provide the most diurnally complete product. The GEO cloud properties have been normalized with MODIS for diurnal consistency. The CERES MODIS-derived cloud properties are not the official NASA MODIS cloud retrievals but are based on the CERES cloud working group retrievals that are also available in other CERES products. Likewise, CERES-VIIRS cloud properties are not the official NASA VIIRS cloud retrievals but are based on the CERES cloud working group retrievals that are also available in other CERES products. The CERES MODIS-derived and VIIRS-derived cloud properties provide coverage from pole to pole. The hourly GEO cloud properties come from five satellites at 8km nominal resolution with coverage limited to equatorward of 60 degrees. The GEO cloud retrievals incorporate additional channels as they become available on improved geostationary satellites that replaced earlier ones in the time period. The geostationary calibration is normalized to Terra-MODIS. Each CldTypHist file covers a single month. CERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.\n<br>\n\n#### CER_GEO_Ed4_GOE15_NH\nCER_GEO_Ed4_GOE15_NH_V01.4 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 15 (GOES-15) over the Northern Hemisphere (NH) Version 1.4 data product. Data was collected using the GOES-15 Imager on the GOES-15 Platform. Note: Version 1.4 uses the Global Modeling and Assimilation Office (GMAO) Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis for atmospheric input; Version 1.2 used GMAO's GEOS-5.4.1. No changes have been made to the cloud retrieval algorithm. This data set comprises cloud micro-physical and radiation properties derived hourly from GOES-15 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. The data set is arranged as files for each hour in netCDF-4 format. The observations are at 4 km resolution (at nadir) and are sub-sampled to 8 km (taking every other line and pixel).\n<br>\n\n#### CER_GEO_Ed4_GOE17_NH\nCER_GEO_Ed4_GOE17_NH_V01.4 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 17 (GOES-17) over the Northern Hemisphere (NH) Version 1.4 data product. Data was collected using the Advanced Baseline Imager (ABI) on the GOES-17 Platform. Note: Version 1.4 uses the Global Modeling and Assimilation Office (GMAO) Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis for atmospheric input; Version 1.2 used GMAO's GEOS-5.4.1. No changes have been made to the cloud retrieval algorithm. This data set comprises cloud micro-physical and radiation properties derived hourly from GOES-17 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. The data set is arranged as files for each hour in netCDF-4 format. The observations are at 2 km resolution (at nadir) and are sub-sampled to 6 km (taking every third line and pixel).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br><br>\n","Documentation":"https://ceres.larc.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","elevation","hdf","ice","netcdf","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"CER_SYN1deg-1Hour_Terra-MODIS vEdition4A - CER_SYN1deg-1Hour_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds, and Aerosols 1-Hourly Terra Edition4A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites, CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer on Terra.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-1Hour_Terra-MODIS_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-1Hour_Terra-Aqua-MODIS vEdition4A - CER_SYN1deg-1Hour_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO) Top-of-Atmosphere (TOA) Within-Atmosphere and Surface Fluxes, Clouds, and Aerosols 1-Hourly Terra-Aqua Edition4A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM3, FM4, CERES Scanner, and MODIS on Aqua.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-1Hour_Terra-Aqua-MODIS_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-1Hour_Terra-NOAA20 vEdition4A - CER_SYN1deg-1Hour_Terra-NOAA20-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO) Top-of-Atmosphere (TOA) Within-Atmosphere and Surface Fluxes, Clouds, and Aerosols 1-Hourly Terra-Aqua Edition4A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM6, and VIIRS on NOAA-20.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-1Hour_Terra-NOAA20_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-3Hour_Terra-MODIS vEdition4A - CER_SYN1deg-3Hour_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols 3-Hourly Terra Edition4A data product, which was collected using Imaging Radiometers on the Geostationary Satellites platform and CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-3Hour_Terra-MODIS_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-3Hour_Terra-Aqua-MODIS vEdition4A - CER_SYN1deg-3Hour_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere, and Surface Fluxes, Clouds and Aerosols 3-Hourly Terra-Aqua Edition4A data product. The instruments and platforms used to collect this data include Imaging Radiometers on the Geostationary Satellites platform; CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and CERES FM3, CERES FM4, CERES Scanner, and MODIS on Aqua.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-3Hour_Terra-Aqua-MODIS_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-Day_Terra-MODIS vEdition4A - CER_SYN1deg-Day_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA) and Surface Fluxes, Clouds and Aerosols Daily Terra Edition4A data product. Data was collected using CERES Imaging Radiometers on Geostationary Satellites as well as CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer on Terra.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-Day_Terra-MODIS_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-Day_Terra-Aqua-MODIS vEdition4A - CER_SYN1deg-Day_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Daily Terra-Aqua Edition4A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer on Terra; and FM3, FM4 CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Aqua.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-Day_Terra-Aqua-MODIS_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-Day_Terra-NOAA20 vEdition4A - CER_SYN1deg-Day_Terra-NOAA20_Edition4A is the Clouds and the Earth''s Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Daily Terra-NOAA20 Edition4A data product. Data was collected using the following instruments and platforms: Imaging Radiometers on the Geostationary Satellites platform, CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra; and CERES FM6 and VIIRS on NOAA-20.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-Day_Terra-NOAA20_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-Day_Terra-NPP vEdition1A - CER_SYN1deg-Day_Terra-NPP_Edition1A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA) and Surface Fluxes, Clouds and Aerosols Daily Terra-Suomi National Polar-orbiting Partnership (NPP) Edition1A data product. Data was collected using several instruments on multiple platforms, including CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer on Terra; and FM5, CERES Scanner, and Visible-Infrared Imager-Radiometer Suite (VIIRS) on...","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-Day_Terra-NPP_Edition1A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-Month_Terra-Aqua-MODIS vEdition4A - CER_SYN1deg-Month_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth''s Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere, and Surface Fluxes, Clouds and Aerosols Monthly Terra-Aqua Edition4A data product. Data was collected using the following instruments and platforms: Imaging Radiometers on Geostationary Satellites platform, CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra; and CERES FM3, FM4, and MODIS on Aqua.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-Month_Terra-Aqua-MODIS_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-Month_Terra-NOAA20 vEdition4A - CER_SYN1deg-Month_Terra-NOAA20_Edition4A is the Clouds and the Earth''s Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere, and Surface Fluxes, Clouds and Aerosols Monthly Terra-NOAA20 Edition4A data product. Data was collected using the following instruments and platforms: Imaging Radiometers on the Geostationary Satellites platform, CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra; and CERES FM6 and VIIRS on NOAA-20.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-Month_Terra-NOAA20_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-Month_Terra-NPP vEdition1A - CER_SYN1deg-Month_Terra-NPP_Edition1A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA) Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly Terra-Suomi National Polar-orbiting Partnership (NPP) Edition1A data product. Data was collected using the CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM5, CERES Scanner, and Visible-Infrared Imager-Radiometer Suite (VIIRS) on NPP.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-Month_Terra-NPP_Edition1A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-MHour_Terra-MODIS vEdition4A - CER_SYN1deg-MHour_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly-Averaged 1-Hourly Terra Edition4A data product. Data was collected using the CERES Imaging Radiometers on the Geostationary Satellites platform and CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra platform.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-MHour_Terra-MODIS_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-MHour_Terra-Aqua-MODIS vEdition4A - CER_SYN1deg-MHour_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly-Averaged 1-Hourly Terra-Aqua Edition4A data product. Data was collected using the CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM3, FM4 CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Aqua.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-MHour_Terra-Aqua-MODIS_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-MHour_Terra-NOAA20 vEdition4A - CER_SYN1deg-MHour_Terra-NOAA20_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly-Averaged 1-Hourly Terra-Aqua Edition4A data product. Data was collected using the CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM6 CERES Scanner, and VIIRS on NOAA-20.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-MHour_Terra-NOAA20_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SYN1deg-Month_Terra-MODIS vEdition4A - CER_SYN1deg-Month_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere and Surface Fluxes Clouds and Aerosols Monthly Terra Edition4A data product, which was collected using Imaging Radiometers on Geostationary Satellites platform as well as CERES Flight Model 1 (FM1), CERES FM2, and MODIS on Terra. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SYN1deg-Month_Terra-MODIS_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_CRS_Terra-FM2-MODIS vEdition4A - CER_CRS_Terra-FM2-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Clouds and Radiative Swath (CRS) Terra Flight Model 2 (FM2) Moderate-Resolution Imaging Spectroradiometer (MODIS) Edition4A data product, which was collected using the CERES-FM2 instrument on the Terra platform. Please note that only a few variables from the SSF have been included and this product should be used in conjunction with the CER_SSF_Terra-FM2-MODIS_Edition4A product.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_CRS_Terra-FM2-MODIS_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_FluxByCldTyp-Day_NOAA20-VIIRS vEdition1B - CER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1B is the Clouds and the Earth's Radiant Energy System (CERES) Monthly Daytime Mean Regionally Averaged NOAA-20 Top-of-Atmosphere (TOA) Fluxes and Associated Cloud Properties Stratified by Optical Depth and Effective Pressure Edition 1B data product. Data was collected using CERES Flight Model 6 (FM6) and Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA-20.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_FluxByCldTyp-Day_NOAA20-VIIRS vEdition1C - CER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1C is the Clouds and the Earth's Radiant Energy System (CERES) Monthly Daytime Mean Regionally Averaged NOAA-20 Top-of-Atmosphere (TOA) Fluxes and Associated Cloud Properties Stratified by Optical Depth and Effective Pressure Edition 1C data product. Data was collected using CERES Flight Model 6 (FM6) and Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA-20 Data collection for this product is ongoing.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_FluxByCldTyp-Day_NOAA20-VIIRS_Edition1C","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CERES_EBAF vEdition4.1 - CERES_EBAF_Edition4.1 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) and surface monthly means data in netCDF format Edition 4.1 data product. Data was collected using the CERES Scanner instruments on both the Terra and Aqua platforms.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CERES_EBAF_Edition4.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CERES_EBAF vEdition4.2 - CERES_EBAF_Edition4.2 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) and surface monthly means data in netCDF format Edition 4.2 data product. Data was collected using the CERES Scanner instruments on the Terra, Aqua, and NOAA-20 platforms for various periods.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CERES_EBAF_Edition4.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CERES_EBAF vEdition4.2.1 - CERES_EBAF_Edition4.2.1 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) and surface monthly means data in netCDF format Edition 4.2.1 data product. Data was collected using the CERES Scanner instruments on the Terra, Aqua, and NOAA-20 platforms for various periods.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CERES_EBAF_Edition4.2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CERES_EBAF-TOA vEdition4.1 - CERES_EBAF-TOA_Edition4.1 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Monthly means data in netCDF format Edition 4.1 data product. Data was collected using the CERES Scanner instruments on both the Terra and Aqua platforms.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CERES_EBAF-TOA_Edition4.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CERES_EBAF-TOA vEdition4.2 - CERES_EBAF-TOA_Edition4.2 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Monthly means data in netCDF format Edition 4.2 data product. Data was collected using the CERES Scanner instruments on the Terra, Aqua, and NOAA-20 platforms.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CERES_EBAF-TOA_Edition4.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CERES_EBAF-TOA vEdition4.2.1 - CERES_EBAF-TOA_Edition4.2.1 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Monthly means data in netCDF format Edition 4.2.1 data product. Data was collected using the CERES Scanner instruments on the Terra, Aqua, and NOAA-20 platforms.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CERES_EBAF-TOA_Edition4.2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_ES9_NPP-FM5 vEdition2 - The ERBE-like Monthly Regional Averages (ES-9) product contains a month of space and time-averaged Clouds and the Earth's Radiant Energy System (CERES) data for a single satellite using measurements from the primary cross-track instrument. All instantaneous shortwave and longwave fluxes at the Top-of-the-Atmosphere (TOA) from the CERES ES-8 product for a month are sorted by 2.5-degree spatial regions, by day number, and by the local hour of observation.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_ES9_NPP-FM5_Edition2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_ES9_Aqua-Xtrk vEdition4 - CER_ES9_Aqua-Xtrk_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Gridded Instantaneous Top-of-the-Atmosphere (TOA) Fluxes Aqua Cross-track Edition 4 data product, which was collected using the CERES-Flight Model (FM3) and FM4 instruments on the Aqua platform. Data collection for this product is ongoing.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_ES9_Aqua-Xtrk_Edition4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_ES9_Terra-Xtrk vEdition4 - CER_ES9_Terra-Xtrk_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Gridded Instantaneous Top-of-the-Atmosphere (TOA) Fluxes Terra Cross-track Edition 4 data product, which was collected using the CERES Flight Model 1 (FM1) and FM2 instruments on the Terra platform. Data collection for this product is ongoing.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_ES9_Terra-Xtrk_Edition4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_ES4_TRMM-PFM vEdition2 - CER_ES4_TRMM-PFM_Edition2 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Monthly Geographical Averages Tropical Rainfall Measuring Mission (TRMM) proto flight model (PFM) Edition 2 data product. Data for this product was collected by the CERES PFM instrument on the TRMM platform.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_ES4_TRMM-PFM_Edition2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_ES9_TRMM-PFM vEdition2 - CER_ES9_TRMM-PFM_Edition2 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Monthly Regional Averages Tropical Rainfall Measuring Mission (TRMM) proto flight model (PFM) Edition 2 data product. Data for this product was collected by the CERES-PFM on the Tropical Rainfall Measuring Mission (TRMM) platform.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_ES9_TRMM-PFM_Edition2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_ES4_NPP-FM5 vEdition2 - The ERBE-like Monthly Geographical Averages (ES-4) product contains a month of space and time-averaged Clouds and the Earth's Radiant Energy System (CERES) data for a single satellite using measurements from the primary cross-track instrument. For each observed 2.5-degree spatial region, the daily average, the hourly average over the month, and the overall monthly average of shortwave and longwave fluxes at the Top-of-the-Atmosphere (TOA) from the CERES ES-9 product are spatially nested up from 2.5-degree regions to 5- and 10-degree regions, to 2.5-, 5-, and 10-degree zonal averages, and to...","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_ES4_NPP-FM5_Edition2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_ES4_Aqua-Xtrk vEdition4 - CER_ES4_Aqua-Xtrk_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Time-Interpolated Top-of-the-Atmosphere (TOA) Fluxes Aqua Crosstrack Edition4 data product, which was collected using the CERES-FM3 and CERES-FM4 instruments on the Aqua platform. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_ES4_Aqua-Xtrk_Edition4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_ES4_Terra-Xtrk vEdition4 - CER_ES4_Terra-Xtrk_Edition4 is the Clouds and the Earth's Radiant Energy System (CERES) Earth Radiation Budget Experiment (ERBE)-like Time-Interpolated Top-of-the-Atmosphere (TOA) Fluxes Terra Cross-track Edition 4 data product, which was collected using the CERES-Flight Model (FM1) and FM2 instruments on the Terra platform. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_ES4_Terra-Xtrk_Edition4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_FluxByCldTyp-Month_NOAA20-VIIRS vEdition1C - CER_FluxByCldTyp-Month_NOAA20-VIIRS_Edition1C is the Clouds and the Earth's Radiant Energy System (CERES) Monthly Daytime Mean Regionally Averaged NOAA-20 Top-of-Atmosphere (TOA) Fluxes and Associated Cloud Properties Stratified by Optical Depth and Effective Pressure Edition 1C data product. Data was collected using CERES Flight Model 6 (FM6) and Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA-20 Data collection for this product is ongoing.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_FluxByCldTyp-Month_NOAA20-VIIRS_Edition1C","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_FluxByCldTyp-Month_Terra-Aqua-MODIS vEdition4A - CER_FluxByCldTyp-Month_Terra-Aqua-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Monthly Daytime Mean Regionally Averaged Terra and Aqua Top-of-Atmosphere (TOA) Fluxes and Associated Cloud Properties Stratified by Optical Depth and Effective Pressure Edition 4A data product. Data was collected using CERES Flight Model 1 (FM1), FM2, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and FM3, FM4, and MODIS on Aqua.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_FluxByCldTyp-Month_Terra-Aqua-MODIS_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SSF1deg-Hour_NOAA20-VIIRS vEdition1B - The Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint One Degree (SSF1deg) Hour provides regional averages of CERES Top of Atmosphere (TOA) fluxes, clouds derived from a co-located imager and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SSF1deg-Hour_NOAA20-VIIRS_Edition1B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SSF1deg-Month_NPP-VIIRS vEdition2A - CER_SSF1deg-Month_NPP-VIIRS_Edition2A is the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint One Degree (SSF1deg) Time-Interpolated Top-of-Atmosphere (TOA) Fluxes, Clouds and Aerosols Monthly Edition 2A data product, which was collected using the CERES-Flight Model 5 (FM5) and Visible-Infrared Imager-Radiometer Suite (VIIRS) instruments on the Suomi National Polar-orbiting Partnership (NPP) platform. Data collection for this product is in progress.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SSF1deg-Month_NPP-VIIRS_Edition2A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SSF1deg-Day_NOAA20-VIIRS vEdition1B - The Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint One Degree (SSF1deg) Day provides daily averages of regional constant meteorology temporally interpolated CERES Top of Atmosphere (TOA) fluxes, clouds derived from a co-located imager and aerosols on a 1-degree latitude and longitude grid. This single satellite product uses the primary CERES instrument in cross-track mode.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SSF1deg-Day_NOAA20-VIIRS_Edition1B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SSF1deg-Day_NOAA20-VIIRS vEdition1C - CER_SSF1deg-Day_NOAA20-VIIRS_Edition1C is the NOAA-20 Clouds and the Earth's Radiant Energy System (CERES) Level 3 Single Scanner Footprint (SSF) Edition1C Top of Atmosphere (ToA) flux data product. The SSF One Degree (SSF1deg) Day provides daily averages on a 1-degree latitude and longitude global grid from the NOAA-20 CERES Flight Model 6 (FM-6) data.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SSF1deg-Day_NOAA20-VIIRS_Edition1C","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SSF1deg-Day_NPP-VIIRS vEdition2A - CER_SSF1deg-Day_NPP-VIIRS_Edition2A is the Clouds and the Earth's Radiant Energy System (CERES) Time-Interpolated Top of Atmosphere (TOA) Fluxes, Clouds and Aerosols Daily SUOMI National Polar-orbiting Partnership (NPP) Edition 2A data product, which was collected using the CERES-Flight Model 5 (FM5) and Visible-Infrared Imager-Radiometer Suite (VIIRS) instruments on the Suomi NPP platform. Data collection for this product is ongoing.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SSF1deg-Day_NPP-VIIRS_Edition2A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SSF1deg-Month_NOAA20-VIIRS vEdition1B - The Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint One Degree (SSF1deg) Month provides monthly averages of regional constant meteorology temporally interpolated CERES Top of Atmosphere (TOA) fluxes, clouds derived from a co-located imager and aerosols on a 1-degree latitude and longitude grid. One-degree zonally and global averaged values for the parameters are also provided.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SSF1deg-Month_NOAA20-VIIRS_Edition1B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SSF1deg-Month_NOAA20-VIIRS vEdition1C - CER_SSF1deg-Month_NOAA20-VIIRS_Edition1C is the NOAA-20 Clouds and the Earth's Radiant Energy System (CERES) Level 3 Single Scanner Footprint (SSF) Edition1C Top of Atmosphere (ToA) flux data product. The SSF One Degree (SSF1deg) Month provides monthly averages on a 1-degree latitude and longitude global grid from the NOAA-20 CERES Flight Model 6 (FM6) data.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SSF1deg-Month_NOAA20-VIIRS_Edition1C","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_SSF1deg-Month_Terra-MODIS vEdition4A - CER_SSF1deg-Month_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) Time-Interpolated Top of Atmosphere (TOA) Fluxes, Clouds and Aerosols Monthly Terra Edition4A data product, which was collected using the CERES Flight Model 1 (FM1), FM2, and MODIS instruments on the Terra platform. Data collection for this product is in progress.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_SSF1deg-Month_Terra-MODIS_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_CldTypHist_GEO-MODIS-VIIRS vEdition4A - CER_CldTypHist_GEO-MODIS-VIIRS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES)- Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) and hourly geostationary cloud properties stratified by the International Satellite Cloud Climatology Project (ISCCP) cloud types for day and night Edition 4A data product. Data collection is ongoing.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_CldTypHist_GEO-MODIS-VIIRS_Edition4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_GEO_Ed4_GOE15_NH vV01.4 - CER_GEO_Ed4_GOE15_NH_V01.4 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 15 (GOES-15) over the Northern Hemisphere (NH) Version 1.4 data product. Data was collected using the GOES-15 Imager on the GOES-15 Platform.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_GEO_Ed4_GOE15_NH_V01.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CER_GEO_Ed4_GOE17_NH vV01.4 - CER_GEO_Ed4_GOE17_NH_V01.4 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 17 (GOES-17) over the Northern Hemisphere (NH) Version 1.4 data product. Data was collected using the Advanced Baseline Imager (ABI) on the GOES-17 Platform.","ARN":"arn:aws:s3:::asdc-prod-protected/CERES/CER_GEO_Ed4_GOE17_NH_V01.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-ceres","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA chirps Project","Description":"This data set provides downscaled six-hourly atmospheric forcings from European Centre for Medium-Range Weather Forecasts (ECMWF) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation from 2003 to 2019 at a spatial resolution of ~1km across High Mountain Asia.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/hma_dm_6h/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","precipitation","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"HMA_DM_6H v1 - This data set provides downscaled six-hourly atmospheric forcings from European Centre for Medium-Range Weather Forecasts (ECMWF) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation from 2003 to 2019 at a spatial resolution of ~1km across High Mountain Asia.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_DM_6H/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-chirps","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA CIOSS Project","Description":"The Gridded Altimeter Fields with Enhanced Coastal Coverage data product contains Sea Surface Height Anomalies (SSHA or SLA) and zonal and meridional geostrophic velocities for the US west coast encompassing 35.25 deg-48.5 deg N latitude and 227.75 deg-248.5 deg E longitude. This annually updated data product extends from October 14, 1992 through November 4, 2009. SSHA and current velocities are derived from the AVISO quarter degree DT UPD MSLA version 3.0 grids, 0.75 deg and greater away from the coast. Values within 0.75 deg of the coast are derived from tide gauge observations and interpolated out to the altimeter filled region. Details on how these data are derived can be found in: Saraceno, M., P. T. Strub, and P. M. Kosro (2008), Estimates of sea surface height and near-surface alongshore coastal currents from combinations of altimeters and tide gauges, J. Geophys. Res., 113, C11013, doi:10.1029/2008JC004756.\n<br>\n\n#### ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_DAILY\nThe Gridded Altimeter Fields with Enhanced Coastal Coverage data product contains Sea Surface Height Anomalies (SSHA or SLA) and zonal and meridional geostrophic velocities for the US west coast encompassing 35.25 deg-48.5 deg N latitude and 227.75 deg-248.5 deg E longitude. This annually updated data product extends from October 14, 1992 through January 19, 2011. SSHA and current velocities are derived from the AVISO quarter degree DT UPD MSLA version 3.0 grids, 0.75 deg and greater away from the coast. Values within 0.75 deg of the coast are derived from tide gauge observations and interpolated out to the altimeter filled region. Details on how these data are derived can be found in: Saraceno, M., P. T. Strub, and P. M. Kosro (2008), Estimates of sea surface height and near-surface alongshore coastal currents from combinations of altimeters and tide gauges, J. Geophys. Res., 113, C11013, doi:10.1029/2008JC004756.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST v1 - The Gridded Altimeter Fields with Enhanced Coastal Coverage data product contains Sea Surface Height Anomalies (SSHA or SLA) and zonal and meridional geostrophic velocities for the US west coast encompassing 35.25 deg-48.5 deg N latitude and 227.75 deg-248.5 deg E longitude. This annually updated data product extends from October 14, 1992 through November 4, 2009.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_DAILY v1 - The Gridded Altimeter Fields with Enhanced Coastal Coverage data product contains Sea Surface Height Anomalies (SSHA or SLA) and zonal and meridional geostrophic velocities for the US west coast encompassing 35.25 deg-48.5 deg N latitude and 227.75 deg-248.5 deg E longitude. This annually updated data product extends from October 14, 1992 through January 19, 2011.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_DAILY","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-cioss","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA CITE-2 Project","Description":"CITE-2_Aerosol_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft Chemical Instrumentation Test and Evaluation - 2 (CITE-2) suborbital campaign. Data using forward scattering spectrometers are featured in this collection. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) to develop advanced instrumentation to measure critical atmospheric trace gases and quantify their sources, sinks, and distribution. Among those were the CITE missions, which had the overarching goal to test and evaluate the instruments developed for the GTE missions. To accomplish this objective, the CITE missions adopted the methodology of conducting intercomparisons of airborne measurements obtained for the same species by instruments utilizing fundamentally different detection principles (Hoell et al., 1990). The second phase of the CITE missions, CITE 2, was the first systematic evaluation of both instrumentation and photochemistry of odd nitrogen from an airborne platform (Hoell et al., 1990). Odd nitrogen is a crucial component of tropospheric chemistry. The common understanding of odd nitrogen chemistry at the time of GTE had come into question as being incomplete. Hoell et al. (1990) state that ground-based measurements showed there may be significant unidentified members of the NOy family and that “simple” odd nitrogen chemistry may not be fully understood, driving the objectives of CITE 2. CITE 2 was conducted in August 1986 with a focus on daytime odd nitrogen and the evaluation of instrumentation developed for measuring nitrogen species, specifically nitrogen dioxide (NO2), nitric acid (HNO3), and peroxyacetyl nitrate (PAN). Additional questions related to the abundance and partitioning among members of the odd nitrogen family were studied (Hoell et al., 1990). To accomplish its objectives, the CITE 2 team deployed the NASA Lockheed Electra aircraft equipped with a suite of instrumentation for the intercomparison of nitrogen species augmented with ancillary measurements such as temperature, dew point, wind velocity, aircraft position, CO, and ozone (O3). Ancillary measurements were added to the payload to answer questions related to tropospheric odd nitrogen budgets and photochemistry. Along with the airborne measurements, the CITE 2 campaign also relied on a ground-based evaluation of calibration standards for HNO3, NO2, PAN, and intercomparison of airborne ambient measurements of each species. Based at Moffett Field, California, CITE 2 took place over a four-week period conducting measurements over California and the eastern Pacific Ocean. While CITE 1 and CITE 2 used identical measurement techniques for NO2, the campaigns used different measurements techniques for the remaining species. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the CITE 2 overview paper. A collection of the publications based on CITE 2 observations are available in the Journal of Geophysical Research special issue: Global Tropospheric Experiment/Chemical Instrumentation Test & Evaluation.\n<br>\n\n#### CITE-2_TraceGas_AircraftInSitu_Electra_Data\nCITE-2_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft Chemical Instrumentation Test and Evaluation - 2 (CITE-2) suborbital campaign. Data using grab samples, gas chromatography, chemiluminescence, and laser differential absorption are featured in this collection. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) to develop advanced instrumentation to measure critical atmospheric trace gases and quantify their sources, sinks, and distribution. Among those were the CITE missions, which had the overarching goal to test and evaluate the instruments developed for the GTE missions. To accomplish this objective, the CITE missions adopted the methodology of conducting intercomparisons of airborne measurements obtained for the same species by instruments utilizing fundamentally different detection principles (Hoell et al., 1990). The second phase of the CITE missions, CITE 2, was the first systematic evaluation of both instrumentation and photochemistry of odd nitrogen from an airborne platform (Hoell et al., 1990). Odd nitrogen is a crucial component of tropospheric chemistry. The common understanding of odd nitrogen chemistry at the time of GTE had come into question as being incomplete. Hoell et al. (1990) state that ground-based measurements showed there may be significant unidentified members of the NOy family and that “simple” odd nitrogen chemistry may not be fully understood, driving the objectives of CITE 2. CITE 2 was conducted in August 1986 with a focus on daytime odd nitrogen and the evaluation of instrumentation developed for measuring nitrogen species, specifically nitrogen dioxide (NO2), nitric acid (HNO3), and peroxyacetyl nitrate (PAN). Additional questions related to the abundance and partitioning among members of the odd nitrogen family were studied (Hoell et al., 1990). To accomplish its objectives, the CITE 2 team deployed the NASA Lockheed Electra aircraft equipped with a suite of instrumentation for the intercomparison of nitrogen species augmented with ancillary measurements such as temperature, dew point, wind velocity, aircraft position, CO, and ozone (O3). Ancillary measurements were added to the payload to answer questions related to tropospheric odd nitrogen budgets and photochemistry. Along with the airborne measurements, the CITE 2 campaign also relied on a ground-based evaluation of calibration standards for HNO3, NO2, PAN, and intercomparison of airborne ambient measurements of each species. Based at Moffett Field, California, CITE 2 took place over a four-week period conducting measurements over California and the eastern Pacific Ocean. While CITE 1 and CITE 2 used identical measurement techniques for NO2, the campaigns used different measurements techniques for the remaining species. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the CITE 2 overview paper. A collection of the publications based on CITE 2 observations are available in the Journal of Geophysical Research special issue: Global Tropospheric Experiment/Chemical Instrumentation Test & Evaluation.\n<br>\n\n#### CITE-2_MetNav_AircraftInSitu_Electra_Data\nCITE-2_MetNav_AircraftInSitu_Electra_Data is the in-situ meteorology and navigational data collected onboard the NASA Electra aircraft Chemical Instrumentation Test and Evaluation - 2 (CITE-2) suborbital campaign. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) to develop advanced instrumentation to measure critical atmospheric trace gases and quantify their sources, sinks, and distribution. Among those were the CITE missions, which had the overarching goal to test and evaluate the instruments developed for the GTE missions. To accomplish this objective, the CITE missions adopted the methodology of conducting intercomparisons of airborne measurements obtained for the same species by instruments utilizing fundamentally different detection principles (Hoell et al., 1990). The second phase of the CITE missions, CITE 2, was the first systematic evaluation of both instrumentation and photochemistry of odd nitrogen from an airborne platform (Hoell et al., 1990). Odd nitrogen is a crucial component of tropospheric chemistry. The common understanding of odd nitrogen chemistry at the time of GTE had come into question as being incomplete. Hoell et al. (1990) state that ground-based measurements showed there may be significant unidentified members of the NOy family and that “simple” odd nitrogen chemistry may not be fully understood, driving the objectives of CITE 2. CITE 2 was conducted in August 1986 with a focus on daytime odd nitrogen and the evaluation of instrumentation developed for measuring nitrogen species, specifically nitrogen dioxide (NO2), nitric acid (HNO3), and peroxyacetyl nitrate (PAN). Additional questions related to the abundance and partitioning among members of the odd nitrogen family were studied (Hoell et al., 1990). To accomplish its objectives, the CITE 2 team deployed the NASA Lockheed Electra aircraft equipped with a suite of instrumentation for the intercomparison of nitrogen species augmented with ancillary measurements such as temperature, dew point, wind velocity, aircraft position, CO, and ozone (O3). Ancillary measurements were added to the payload to answer questions related to tropospheric odd nitrogen budgets and photochemistry. Along with the airborne measurements, the CITE 2 campaign also relied on a ground-based evaluation of calibration standards for HNO3, NO2, PAN, and intercomparison of airborne ambient measurements of each species. Based at Moffett Field, California, CITE 2 took place over a four-week period conducting measurements over California and the eastern Pacific Ocean. While CITE 1 and CITE 2 used identical measurement techniques for NO2, the campaigns used different measurements techniques for the remaining species. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the CITE 2 overview paper. A collection of the publications based on CITE 2 observations are available in the Journal of Geophysical Research special issue: Global Tropospheric Experiment/Chemical Instrumentation Test & Evaluation.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www-gte.larc.nasa.gov/gte_hmpg.htm#table","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","oceans"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"CITE-2_Aerosol_AircraftInSitu_Electra_Data v1 - CITE-2_Aerosol_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft Chemical Instrumentation Test and Evaluation - 2 (CITE-2) suborbital campaign. Data using forward scattering spectrometers are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/CITE-2/CITE-2_Aerosol_AircraftInSitu_Electra_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CITE-2_TraceGas_AircraftInSitu_Electra_Data v1 - CITE-2_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft Chemical Instrumentation Test and Evaluation - 2 (CITE-2) suborbital campaign. Data using grab samples, gas chromatography, chemiluminescence, and laser differential absorption are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/CITE-2/CITE-2_TraceGas_AircraftInSitu_Electra_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CITE-2_MetNav_AircraftInSitu_Electra_Data v1 - CITE-2_MetNav_AircraftInSitu_Electra_Data is the in-situ meteorology and navigational data collected onboard the NASA Electra aircraft Chemical Instrumentation Test and Evaluation - 2 (CITE-2) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/CITE-2/CITE-2_MetNav_AircraftInSitu_Electra_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-cite-2","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA CITE-3 Project","Description":"CITE-3_Aerosol_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft Chemical Instrumentation Test and Evaluation - 3 (CITE-3) suborbital campaign. Data using chemiluminescence are featured in this collection. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) to develop advanced instrumentation to measure critical atmospheric trace gases and quantify their sources, sinks, and distribution. Among those were the Chemical Instrumentation Test and Evaluation (CITE) missions, which had the overarching goal to test and evaluate the instruments developed for the GTE missions. To accomplish this objective, the CITE missions adopted the methodology of conducting intercomparisons of airborne measurements obtained for the same species by instruments utilizing fundamentally different detection principles (Hoell et al., 1990). The third phase of the CITE mission, CITE 3, occurred in the North and tropical Atlantic Ocean from August-September 1989. Its primary objective was to test and evaluate the capacity to collect reliable measurements of the following sulfur species: sulfur dioxide (SO2), dimethyl sulfide (DMS), carbonyl sulfide (COS), carbon disulfide (CS2), and hydrogen sulfide (H2S). The secondary objective of CITE 3 was to determine the abundance and distribution of major sulfur species over a wide range of atmospheric conditions, including altitude, solar flux levels, atmospheric mixing ratios, and surface source strengths of sulfur in a predominantly marine environment (Hoell et al., 1993). CITE 3 utilized the NASA Electra research aircraft equipped with a suite of instruments for sulfur and ancillary measurements. The two main objectives were addressed through intercomparisons of airborne measurements obtained for the same species but utilizing fundamentally different detection principles in order to have multiple measurements of each of the sulfur species. In some instances, the same detection principle was used, but the method of sample collection and/or analysis procedures were different. A complete payload had the potential of five techniques for SO2, six techniques for DMS, and three techniques for H2S, CS2, and COS. However, due to operational problems or team decisions to focus on specific species during flights, not all measurement techniques were employed at all times (Hoell et al., 1993). There were three phases of field activity in this campaign: (1) intercomparison of sulfur calibration standards, (2) measurements in ambient air over the Northern Atlantic Ocean, and (3) measurements in ambient air over the tropical Atlantic Ocean. Cite 3 was based out of NASA Wallops Flight Facility in Virginia, USA and Natal, Brazil. Each base of operation provided a different environment for intercomparison. Air masses at NAS Wallops Flight Facility were influenced by anthropogenic emissions along the eastern United States compared to the relatively clean marine boundary layer over the ocean off the coast of Natal. As part of the Natal deployment, ozonesondes were launched from the Natal area to provide data on the general state of the atmosphere as well as serve as a frame of reference when compared to the seasonally averaged ozone data from this site. seasonally averaged ozone data from this site. Sulfur gases and their reaction products play important roles in the chemistry of the global troposphere as well as the biogeochemical sulfur cycle. The sulfur database from CITE 3, and the results from both intercomparison studies and photochemical budget studies, significantly enhanced the ability to evaluate the confidence in the existing databases. Detailed description related to the motivation, implementation, and instrument payloads are available in the CITE 3 overview paper. A collection of the publications based on CITE 3 observations are available in the Journal of Geophysical Research special issue: Chemical Instrumentation Test and Evaluation (CITE 3).\n<br>\n\n#### CITE-3_TraceGas_AircraftInSitu_Electra_Data\nCITE-3_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft Chemical Instrumentation Test and Evaluation - 3 (CITE-3) suborbital campaign. Data using gas chromatography, laser induced photofragmentation, and the Differential Absorption CO, CH4, N2O Measurements (DACOM) instrument are featured in this collection. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) to develop advanced instrumentation to measure critical atmospheric trace gases and quantify their sources, sinks, and distribution. Among those were the Chemical Instrumentation Test and Evaluation (CITE) missions, which had the overarching goal to test and evaluate the instruments developed for the GTE missions. To accomplish this objective, the CITE missions adopted the methodology of conducting intercomparisons of airborne measurements obtained for the same species by instruments utilizing fundamentally different detection principles (Hoell et al., 1990). The third phase of the CITE mission, CITE 3, occurred in the North and tropical Atlantic Ocean from August-September 1989. Its primary objective was to test and evaluate the capacity to collect reliable measurements of the following sulfur species: sulfur dioxide (SO2), dimethyl sulfide (DMS), carbonyl sulfide (COS), carbon disulfide (CS2), and hydrogen sulfide (H2S). The secondary objective of CITE 3 was to determine the abundance and distribution of major sulfur species over a wide range of atmospheric conditions, including altitude, solar flux levels, atmospheric mixing ratios, and surface source strengths of sulfur in a predominantly marine environment (Hoell et al., 1993). CITE 3 utilized the NASA Electra research aircraft equipped with a suite of instruments for sulfur and ancillary measurements. The two main objectives were addressed through intercomparisons of airborne measurements obtained for the same species but utilizing fundamentally different detection principles in order to have multiple measurements of each of the sulfur species. In some instances, the same detection principle was used, but the method of sample collection and/or analysis procedures were different. A complete payload had the potential of five techniques for SO2, six techniques for DMS, and three techniques for H2S, CS2, and COS. However, due to operational problems or team decisions to focus on specific species during flights, not all measurement techniques were employed at all times (Hoell et al., 1993). There were three phases of field activity in this campaign: (1) intercomparison of sulfur calibration standards, (2) measurements in ambient air over the Northern Atlantic Ocean, and (3) measurements in ambient air over the tropical Atlantic Ocean. Cite 3 was based out of NASA Wallops Flight Facility in Virginia, USA and Natal, Brazil. Each base of operation provided a different environment for intercomparison. Air masses at NAS Wallops Flight Facility were influenced by anthropogenic emissions along the eastern United States compared to the relatively clean marine boundary layer over the ocean off the coast of Natal. As part of the Natal deployment, ozonesondes were launched from the Natal area to provide data on the general state of the atmosphere as well as serve as a frame of reference when compared to the seasonally averaged ozone data from this site. seasonally averaged ozone data from this site. Sulfur gases and their reaction products play important roles in the chemistry of the global troposphere as well as the biogeochemical sulfur cycle. The sulfur database from CITE 3, and the results from both intercomparison studies and photochemical budget studies, significantly enhanced the ability to evaluate the confidence in the existing databases. Detailed description related to the motivation, implementation, and instrument payloads are available in the CITE 3 overview paper. A collection of the publications based on CITE 3 observations are available in the Journal of Geophysical Research special issue: Chemical Instrumentation Test and Evaluation (CITE 3).\n<br>\n\n#### CITE-3_MetNav_AircraftInSitu_Electra_Data\nCITE-3_MetNav_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft Chemical Instrumentation Test and Evaluation - 3 (CITE-3) suborbital campaign. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) to develop advanced instrumentation to measure critical atmospheric trace gases and quantify their sources, sinks, and distribution. Among those were the Chemical Instrumentation Test and Evaluation (CITE) missions, which had the overarching goal to test and evaluate the instruments developed for the GTE missions. To accomplish this objective, the CITE missions adopted the methodology of conducting intercomparisons of airborne measurements obtained for the same species by instruments utilizing fundamentally different detection principles (Hoell et al., 1990). The third phase of the CITE mission, CITE 3, occurred in the North and tropical Atlantic Ocean from August-September 1989. Its primary objective was to test and evaluate the capacity to collect reliable measurements of the following sulfur species: sulfur dioxide (SO2), dimethyl sulfide (DMS), carbonyl sulfide (COS), carbon disulfide (CS2), and hydrogen sulfide (H2S). The secondary objective of CITE 3 was to determine the abundance and distribution of major sulfur species over a wide range of atmospheric conditions, including altitude, solar flux levels, atmospheric mixing ratios, and surface source strengths of sulfur in a predominantly marine environment (Hoell et al., 1993). CITE 3 utilized the NASA Electra research aircraft equipped with a suite of instruments for sulfur and ancillary measurements. The two main objectives were addressed through intercomparisons of airborne measurements obtained for the same species but utilizing fundamentally different detection principles in order to have multiple measurements of each of the sulfur species. In some instances, the same detection principle was used, but the method of sample collection and/or analysis procedures were different. A complete payload had the potential of five techniques for SO2, six techniques for DMS, and three techniques for H2S, CS2, and COS. However, due to operational problems or team decisions to focus on specific species during flights, not all measurement techniques were employed at all times (Hoell et al., 1993). There were three phases of field activity in this campaign: (1) intercomparison of sulfur calibration standards, (2) measurements in ambient air over the Northern Atlantic Ocean, and (3) measurements in ambient air over the tropical Atlantic Ocean. Cite 3 was based out of NASA Wallops Flight Facility in Virginia, USA and Natal, Brazil. Each base of operation provided a different environment for intercomparison. Air masses at NAS Wallops Flight Facility were influenced by anthropogenic emissions along the eastern United States compared to the relatively clean marine boundary layer over the ocean off the coast of Natal. As part of the Natal deployment, ozonesondes were launched from the Natal area to provide data on the general state of the atmosphere as well as serve as a frame of reference when compared to the seasonally averaged ozone data from this site. seasonally averaged ozone data from this site. Sulfur gases and their reaction products play important roles in the chemistry of the global troposphere as well as the biogeochemical sulfur cycle. The sulfur database from CITE 3, and the results from both intercomparison studies and photochemical budget studies, significantly enhanced the ability to evaluate the confidence in the existing databases. Detailed description related to the motivation, implementation, and instrument payloads are available in the CITE 3 overview paper. A collection of the publications based on CITE 3 observations are available in the Journal of Geophysical Research special issue: Chemical Instrumentation Test and Evaluation (CITE 3).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www-gte.larc.nasa.gov/gte_hmpg.htm#table","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","oceans"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"CITE-3_Aerosol_AircraftInSitu_Electra_Data v1 - CITE-3_Aerosol_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft Chemical Instrumentation Test and Evaluation - 3 (CITE-3) suborbital campaign. Data using chemiluminescence are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/CITE-3/CITE-3_Aerosol_AircraftInSitu_Electra_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CITE-3_TraceGas_AircraftInSitu_Electra_Data v1 - CITE-3_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft Chemical Instrumentation Test and Evaluation - 3 (CITE-3) suborbital campaign. Data using gas chromatography, laser induced photofragmentation, and the Differential Absorption CO, CH4, N2O Measurements (DACOM) instrument are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/CITE-3/CITE-3_TraceGas_AircraftInSitu_Electra_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CITE-3_MetNav_AircraftInSitu_Electra_Data v1 - CITE-3_MetNav_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft Chemical Instrumentation Test and Evaluation - 3 (CITE-3) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/CITE-3/CITE-3_MetNav_AircraftInSitu_Electra_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-cite-3","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA CLASIC07 Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### CL07SM\nThis data set is comprised of several parameters from in situ measurements collected for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).\n<br>\n\n#### CL07V\nThis data set includes in situ vegetation data collected during the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) campaign. Sampling was designed to coincide with satellite overpasses, such as Landsat's Thematic Mapper (TM) 5 and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Terra satellite (MODIS/Terra), which can be then used to estimate vegetation water content on the regional scale.\n<br>\n\n#### CL07LC\nThis data set consists of land cover classification data derived from satellite imagery as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07). ResourceSat-1 AWiFS images of the study area were retrieved for the period of April through August 2007. The land use classification image provides information about vegetation present in the study area at a resolution of 56 meters.\n<br>\n\n#### CL07PLBK\nThis data set contains backscatter data obtained by the Passive Active L-band System (PALS) microwave aircraft radar instrument as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).\n<br>\n\n#### CL07PLTB\nThis data set contains brightness temperature data obtained by the Passive Active L-band System (PALS) microwave aircraft radiometer instrument as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).\n<br>\n\n#### CL07ST\nThis data set contains soil texture data obtained for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07). The original data were extracted from a multi-layer soil characteristics database for the conterminous United States called CONUS-Soil and generated for the regional study area. Data are representative of the conditions present in the regional study area during the general timeline of the CLASIC07 campaign.\n<br>\n\n#### CL07VWC\nThe Vegetation Water Content (VWC) map for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) was derived by calculating Normalized Difference Water Index (NDWI) from ResourceSat-1 satellite imagery.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/CLASIC07/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","land cover","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"CL07SM v1 - This data set is comprised of several parameters from in situ measurements collected for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/CL07SM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CL07V v1 - This data set includes in situ vegetation data collected during the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) campaign. Sampling was designed to coincide with satellite overpasses, such as Landsat's Thematic Mapper (TM) 5 and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Terra satellite (MODIS/Terra), which can be then used to estimate vegetation water content on the regional scale.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/CL07V/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CL07LC v1 - This data set consists of land cover classification data derived from satellite imagery as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07). ResourceSat-1 AWiFS images of the study area were retrieved for the period of April through August 2007.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/CL07LC/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CL07PLBK v1 - This data set contains backscatter data obtained by the Passive Active L-band System (PALS) microwave aircraft radar instrument as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/CL07PLBK/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CL07PLTB v1 - This data set contains brightness temperature data obtained by the Passive Active L-band System (PALS) microwave aircraft radiometer instrument as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/CL07PLTB/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CL07ST v1 - This data set contains soil texture data obtained for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07). The original data were extracted from a multi-layer soil characteristics database for the conterminous United States called CONUS-Soil and generated for the regional study area.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/CL07ST/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CL07VWC v1 - The Vegetation Water Content (VWC) map for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) was derived by calculating Normalized Difference Water Index (NDWI) from ResourceSat-1 satellite imagery.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/CL07VWC/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-clasic07","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Climate Project","Description":"This dataset provides two 30-year climate normal data products for conditions during the last glacial maximum (LGM; ~18,000 years ago) and a modern time period (1975-2005) for the entire state of Alaska. The first set of products are monthly climate variable averages at 60 m resolution, including: minimum, maximum, and average temperatures, total precipitation, total surface radiation, rain, snow, potential evapotranspiration (PET), actual evapotranspiration (AET), and water deficit. The second set of products are annual summary climate variable averages for the same variables (excepting average temperature and rain) at 60m, 120m, 240m, 800m, 1km, 2km, 3km, 4km, 5km, 10km and 12km resolutions. The 30-year climate normal monthly averages were derived by topographically downscaling climate variables from existing coarse-resolution general circulation model outputs combined with local weather station data and digital surface models for Alaska for both the LGM and modern time periods at 60 m resolution. From this baseline, monthly averages for total surface radiation, rain, snow, potential evapotranspiration, actual evapotranspiration, and water deficit were also modeled. The annual averages are coarser resolution upsampled versions of the 60 m resolution monthly average data.\n<br>\n\n#### NASASatellite_Dev_Applications_2293\nThis dataset provides a presentation that highlights the role NASA research and researchers played in developing a wide range of significant, quantitative ecological applications of satellite data. The presentation by Dr Diane E. Wickland, former NASA Terrestrial Ecology Program Manager and Lead for NASA Carbon Cycle and Ecosystems Focus Area, provides a top-level overview from her perspective of the development and evolution of the program. Dr Wickland joined NASA in 1985 to manage a newly formed Terrestrial Ecosystems Program. Along with other NASA program managers, she was charged with reorienting the program to be less empirical and have a greater focus on first principles, and to prepare for a next generation of earth-observing satellites. As an ecologist, she thought that focusing on important ecological questions and recruiting practicing ecologists to the program would facilitate such a change in directions. The presentation emphasizes the early years of U.S. satellite remote sensing and covers a few highlights after 2005.\n<br>\n\n#### EastAnglia10YearMean_549\nThis is a data set of 10-year mean monthly surface climate data over global land areas, excluding Antarctica, for the period 1901-1990. The data set is gridded at 0.5 degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency. In constructing the monthly grids the authors used an anomaly approach which attempts to maximize station data in space and time (New et al., 2000). In this technique, grids of monthly historic anomalies are derived relative to a standard normal period. Station measurement data for the years 1961-1990, extracted from the monthly data holdings of the Climatic Research Unit and the Global Historic Climatology Network (GHCN), served as the normal period (New et al., 1999). The anomaly grids were then combined with high-resolution mean monthly climatology to arrive at fields of estimated historical monthly surface climate. Data users are encouraged to see the companion file New et al.(2000) for a complete description of this technique and potential applications and limitations of the data set. For additional information, refer to the IPCC Data Distribution Centre.\n<br>\n\n#### EastAnglia30YearMean_550\nThis is a data set of 30-year mean monthly surface climate data over global land areas, excluding Antarctica, for the period 1901-1960. The data set is gridded at 0.5 degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency. In constructing the monthly grids the authors used an anomaly approach which attempts to maximize station data in space and time (New et al., 2000). In this technique, grids of monthly historic anomalies are derived relative to a standard normal period. Station measurement data for the years 1961-1990, extracted from the monthly data holdings of the Climatic Research Unit and the Global Historic Climatology Network (GHCN), served as the normal period (New et al., 1999). The anomaly grids were then combined with high-resolution mean monthly climatology to arrive at fields of estimated historical monthly surface climate. Data users are encouraged to see the companion file New et al.(2000) for a complete description of this technique and potential applications and limitations of the data set. For additional information, refer to the IPCC Data Distribution Centre.\n<br>\n\n#### CramerLeemans_416\nThis database is a major update of the Leemans and Cramer database (Leemans and Cramer 1991). It currently contains monthly averages of mean temperature, temperature range, precipitation, rain days and sunshine hours for the terrestrial surface of the globe, gridded at 0.5 degree longitude/latitude resolution. It was generated from a large data base, using the partial thin-plate splining algorithm developed by Michael F. Hutchinson, Canberra (Hutchinson and Bischof 1983).\n<br>\n\n#### EastAngliaClimate_542\nThis is a dataset of mean monthly surface climate measurements over global land areas, excluding Antarctica, for the period 1961-1990. Values were interpolated from station data to a 0.5 degree latitude/longitude grid for several climatic parameters: precipitation and wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and minimum temperature can be determined), vapour pressure, sunshine, cloud cover, ground-frost frequency and windspeed. A description of the data files is provided as a companion file. For a complete documentation of the dataset, see New et al, 1999. Also refer to IPCC Data Distribution Centre.\n<br>\n\n#### CDIAC_NDP41_220\nThis data set contains monthly temperature, precipitation, sea-level pressure, and station-pressure data for thousands of meteorological stations worldwide. The database was compiled from pre-existing national, regional, and global collections of data as part of the Global Historical Climatology Network (GHCN) project, the goal of which is to produce, maintain, and make available a comprehensive global surface baseline climate data set for monitoring climate and detecting climate change. It contains data from roughly 6000 temperature stations, 7500 precipitation stations, 1800 sea level pressure stations, and 1800 station pressure stations. Each station has at least 10 years of data, 40% have more than 50 years of data. Spatial coverage is good over most of the globe, particularly for the United States and Europe. Data gaps are evident over the Amazon rainforest, the Sahara Desert, Greenland, and Antarctica.\n<br>\n\n#### global_N_deposition_maps_830\nThis data set provides global gridded estimates of atmospheric deposition of total inorganic nitrogen (N), NHx (NH3 and NH4+), and NOy (all oxidized forms of nitrogen other than N2O), in mg N/m2/year, for the years 1860 and 1993 and projections for the year 2050. The data set was generated using a global three-dimensional chemistry-transport model (TM3) with a spatial resolution of 5 degrees longitude by 3.75 degrees latitude (Jeuken et al., 2001; Lelieveld and Dentener, 2000). Nitrogen emissions estimates (Van Aardenne et al., 2001) and projection scenario data (IPCC, 1996; 2000) were used as input to the model. The model output grids were subdivided into 50 km x 50 km sub-grids to create spatially defined deposition maps. The gridded data were assigned to continental and marine regions using boundaries delineated on a world data coverage from ESRI (1993).The data are stored as ASCII text files (.txt), in tab delimited format. The data can be used to produce maps that illustrate both the temporal and spatial variability of atmospheric deposition of N, NHx, and NOy as well as the degree of alteration and regional heterogeneity in deposition through time. Nine data files are provided to produce the following maps:Global N Deposition (1860, 1993, and 2050)Global NHx Deposition (1860, 1993, and 2050)Global NOy Deposition (1860, 1993, and 2050).\n<br>\n\n#### GIS_EastAngliaClimateMonthly_551\nThis is a data set of mean monthly surface climate data over global land areas, excluding Antarctica, for nearly all of the twentieth century. The data set is gridded at 0.5 degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency. All variables have mean monthly values for the period 1901-1995, several have data as recent as 1998, and more data will be added by the data originators. In constructing the monthly grids the authors used an anomaly approach which attempts to maximize station data in space and time (New et al., 2000). In this technique, grids of monthly historic anomalies are derived relative to a standard normal period. Station measurement data for the years 1961-1990, extracted from the monthly data holdings of the Climatic Research Unit and the Global Historic Climatology Network (GHCN), served as the normal period (New et al., 1999). The anomaly grids were then combined with high-resolution mean monthly climatology to arrive at fields of estimated historical monthly surface climate. Data users are encouraged to see the companion file New et al. (2000) for a complete description of this technique and potential applications and limitations of the data set. For additional information, refer to the IPCC Data Distribution Centre. Access to the complete year-by-year monthly data set or to data more recent than posted here can be achieved by making a request with the Climate Impacts LINK Project at the Climatic Research Unit (email: d.viner@uea.ac.uk, web site: www.cru.uea.ac.uk/link ).\n<br>\n\n#### EastAngliaPrecip_417\nAn historical monthly precipitation dataset for global land areas from 1900 to 1996, gridded at two different resolutions (2.5 degrees latitude by 3.75 degrees longitude and 5 degrees latitude/longitude).\n<br>\n\n#### global_N_cycle_797\nNitrogen is a major nutrient in terrestrial ecosystems and an important catalyst in tropospheric photochemistry. Over the last century human activities have dramatically increased inputs of reactive nitrogen (Nr, the combination of oxidized, reduced and organically bound nitrogen) to the Earth system. Nitrogen cycle perturbations have compromised air quality and human health, acidified ecosystems, and degraded and eutrophied lakes and coastal estuaries [Vitousek et al., 1997a, 1997b; Rabalais, 2002; Howarth et al., 2003; Townsend et al., 2003; Galloway et al., 2004]. To begin to quantify the changes to the global N cycle, we have assembled key flux data and N2O mixing ratios from various sources. The data assembled from different sources includes fertilizer production from 1920-2004; manure production from 1860-2004; crop N fixation estimated for three time points, 1860, 1900, 1995; tropospheric N2O mixing ratios from ice core and firn measurements, and tropospheric concentrations to cover the time period from 1756-2004. The changing N2O concentrations provide an independent index of changes to the global N cycle, in much the same way that changing carbon dioxide concentrations provide an important constraint on the global carbon cycle. The changes to the global N cycle are driven by industrialization, as indicated by fossil fuel NOx emission, and by the intensification of agriculture, as indicted by fertilizer and manure production and crop N2 fixation. The data set and the science it reflects are by nature interdisciplinary. Making the data set available through the ORNL DAAC is an attempt to make the data set available to the considerable interdisciplinary community studying the N cycle.\n<br>\n\n#### Global_Lakes_Methane_2008\nThis dataset provides global gridded information on lake surface area and open water CH4 emissions at a resolution of 0.25-degree x 0.25-degree for an annual climatology representative of the average conditions from 2003 to 2015. A compilation of flux data from 575 individual lake systems and 893 aggregated flux values were used, and each flux measurement was classified into one of seven ecoclimatic types. Ice-cover-regulated emission seasonality was derived from satellite microwave observations of ice cover phenology and freeze-thaw dynamics. Global lake area was determined from the merger of HydroLAKES and Climate Change Initiative Inland-Water (CCI-IW) remote-sensing data, and lakes were classified into ecoclimatic regions to facilitate linking these types with ecosystem-specific CH4 measurements in the flux compilation. Exploratory estimates of fluxes associated with ice melt and with spring and fall water-column turnover are also included. The data are provided in NetCDF format.\n<br>\n\n#### EM27_XCO2_XCH4_XCO_AK_1831\nThis dataset provides ground-based column-averaged dry mole fractions (DMFs) of CO2 (xco2), CO (xco), CH4 (xch4), and N2O (xn2o) to supplement satellite-based observations of carbon dynamics of northern boreal ecosystems. Measurements were conducted with Bruker EM27/SUN Fourier transform spectrometers (FTS) at the University of Alaska Fairbanks (UAF) and two sites on the edges of the Tanana Flats wetlands to the south from 2016-08-04 to 2019-10-31. Single detectors were used during the first campaign at UAF in 2017, then two instruments were updated to dual detectors in early 2018 to allow retrieval of xco and xn2o. Data from additional FTS instruments, operated by Los Alamos National Laboratories (LANL), Karlsruhe Institute of Technology (KIT), and Jet Propulsion Laboratory (JPL), employed in these campaigns are included.\n<br>\n\n#### New_England_CH4_1311\nThis data set contains an inventory of natural and anthropogenic methane emissions for all counties in the six New England states of Connecticut, Rhode Island, Massachusetts, Vermont, New Hampshire, and Maine. The inventory represents a snapshot in time (circa 1990-1994) and provides emission estimates for multiple sources including wetlands, landfills, ruminant animals, residential wood combustion, fossil fuel combustion and use, animal manure, wastewater treatment, and natural gas transmission pipelines. Also included is the uptake or sink of methane in relatively well-drained upland soils.\n<br>\n\n#### nitrogen_deposition_730\nThis data set contains data for wet and dry nitrogen-species deposition for the United States and Western Europe. Deposition data were acquired directly from monitoring programs in the United States and Europe covering time periods from 1978-1994 for wet deposition and from 1989-1994 for dry deposition and evaluated using similar quality assurance criteria to ensure comparability. A standard geostatistical method (kriging) was used to interpolate data onto a 0.5 x 0.5 degree resolution map for wet and dry deposition. Analysis of N deposition for these regions was limited by sampling density, frequency, and coverage. These spatially explicit wet and dry N fluxes also provide a tool for verifying regional and global models of atmospheric chemistry and transport, and represent critical inputs into terrestrial models of biogeochemistry. These data can be used to construct continental scale N budgets and to evaluate recent modification of land-atmosphere N exchange and ecosystem function (Holland et al., 2004). Data files of the site monitoring locations and monthly deposition averages are available in ASCII space-delimited format, and 0.5 x 0.5 degree gridded deposition values are available in both ASCII space-delimited format and ASCII grid format. The 11 mapped data images are available in .jpg format as companion files (e.g., Figs. 1 and 2). The complete set of 11 derived nitrogen-species 0.5 x 0.5 degree deposition maps is also available in .pdf format in the companion file. Other companion files include quality assurance plans and operating manuals available from and maintained by the United States and European monitoring networks.\n<br>\n\n#### Snowmelt_timing_maps_V2_1712\nThis data set provides snowmelt timing maps (STMs), cloud interference maps, and a map with the count of calculated snowmelt timing values for North America. The STMs are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard 8-day composite snow-cover product MOD10A2 collection 6 for the period 2001-01-01 to 2018-12-31. The STMs were created by conducting a time-series analysis of the MOD10A2 snow maps to identify the DOY of snowmelt on a per-pixel basis. Snowmelt timing (no-snow) was defined as a snow-free reading following two consecutive snow-present readings for a given 500-m pixel. The count of STM values is also reported, which represents the number of years on record in the STMs from 2001-2018.\n<br>\n\n#### African_Rainfall_Patterns_1263\nThis data set describes rainfall distribution statistics over the African continent, including Madagascar. The rainfall estimates are based on data from the NASA Tropical Rainfall Measuring Mission (TRMM) measured between 1998 and 2012. Rainfall patterns were quantified using a gamma-based function and two Markov chain parameters with the aim to summarize the rainfall pattern to a small number of parameters and processes. These summary statistics are suitable for temporal downscaling. These data provide gridded (0.25 x 0.25-degree) estimates of 14-year mean monthly rainfall total amount (mm), frequency (count), intensity (mm/hr), and duration (hrs) of rainfall, as well as Markov chain and gamma-distribution parameters for use in temporal downscaling. The data are presented as a series of 12 netCDF (.nc) files.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","earth observation","ice","netcdf","precipitation","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Climate_Normals_Modern_LGM_AK_1663 v1 - This dataset provides two 30-year climate normal data products for conditions during the last glacial maximum (LGM; ~18,000 years ago) and a modern time period (1975-2005) for the entire state of Alaska. The first set of products are monthly climate variable averages at 60 m resolution, including: minimum, maximum, and average temperatures, total precipitation, total surface radiation, rain, snow, potential evapotranspiration (PET), actual evapotranspiration (AET), and water deficit.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/Climate_Normals_Modern_LGM_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NASASatellite_Dev_Applications_2293 v1 - This dataset provides a presentation that highlights the role NASA research and researchers played in developing a wide range of significant, quantitative ecological applications of satellite data. The presentation by Dr Diane E.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/NASASatellite_Dev_Applications/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"EastAnglia10YearMean_549 v1 - This is a data set of 10-year mean monthly surface climate data over global land areas, excluding Antarctica, for the period 1901-1990. The data set is gridded at 0.5 degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/EastAnglia10YearMean/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"EastAnglia30YearMean_550 v1 - This is a data set of 30-year mean monthly surface climate data over global land areas, excluding Antarctica, for the period 1901-1960. The data set is gridded at 0.5 degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/EastAnglia30YearMean/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CramerLeemans_416 v1 - This database is a major update of the Leemans and Cramer database (Leemans and Cramer 1991). It currently contains monthly averages of mean temperature, temperature range, precipitation, rain days and sunshine hours for the terrestrial surface of the globe, gridded at 0.5 degree longitude/latitude resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/CramerLeemans/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"EastAngliaClimate_542 v1 - This is a dataset of mean monthly surface climate measurements over global land areas, excluding Antarctica, for the period 1961-1990. Values were interpolated from station data to a 0.5 degree latitude/longitude grid for several climatic parameters: precipitation and wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and minimum temperature can be determined), vapour pressure, sunshine, cloud cover, ground-frost frequency and windspeed.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/EastAngliaClimate/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CDIAC_NDP41_220 v2 - This data set contains monthly temperature, precipitation, sea-level pressure, and station-pressure data for thousands of meteorological stations worldwide. The database was compiled from pre-existing national, regional, and global collections of data as part of the Global Historical Climatology Network (GHCN) project, the goal of which is to produce, maintain, and make available a comprehensive global surface baseline climate data set for monitoring climate and detecting climate change.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/CDIAC_NDP41/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"global_N_deposition_maps_830 v1 - This data set provides global gridded estimates of atmospheric deposition of total inorganic nitrogen (N), NHx (NH3 and NH4+), and NOy (all oxidized forms of nitrogen other than N2O), in mg N/m2/year, for the years 1860 and 1993 and projections for the year 2050. The data set was generated using a global three-dimensional chemistry-transport model (TM3) with a spatial resolution of 5 degrees longitude by 3.75 degrees latitude (Jeuken et al., 2001; Lelieveld and Dentener, 2000).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/global_N_deposition_maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GIS_EastAngliaClimateMonthly_551 v1 - This is a data set of mean monthly surface climate data over global land areas, excluding Antarctica, for nearly all of the twentieth century. The data set is gridded at 0.5 degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/GIS_EastAngliaClimateMonthly/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"EastAngliaPrecip_417 v1 - An historical monthly precipitation dataset for global land areas from 1900 to 1996, gridded at two different resolutions (2.5 degrees latitude by 3.75 degrees longitude and 5 degrees latitude/longitude).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/EastAngliaPrecip/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"global_N_cycle_797 v1 - Nitrogen is a major nutrient in terrestrial ecosystems and an important catalyst in tropospheric photochemistry. Over the last century human activities have dramatically increased inputs of reactive nitrogen (Nr, the combination of oxidized, reduced and organically bound nitrogen) to the Earth system.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/global_N_cycle/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Lakes_Methane_2008 v1 - This dataset provides global gridded information on lake surface area and open water CH4 emissions at a resolution of 0.25-degree x 0.25-degree for an annual climatology representative of the average conditions from 2003 to 2015. A compilation of flux data from 575 individual lake systems and 893 aggregated flux values were used, and each flux measurement was classified into one of seven ecoclimatic types.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/Global_Lakes_Methane/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"EM27_XCO2_XCH4_XCO_AK_1831 v1 - This dataset provides ground-based column-averaged dry mole fractions (DMFs) of CO2 (xco2), CO (xco), CH4 (xch4), and N2O (xn2o) to supplement satellite-based observations of carbon dynamics of northern boreal ecosystems. Measurements were conducted with Bruker EM27/SUN Fourier transform spectrometers (FTS) at the University of Alaska Fairbanks (UAF) and two sites on the edges of the Tanana Flats wetlands to the south from 2016-08-04 to 2019-10-31.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/EM27_XCO2_XCH4_XCO_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"New_England_CH4_1311 v1 - This data set contains an inventory of natural and anthropogenic methane emissions for all counties in the six New England states of Connecticut, Rhode Island, Massachusetts, Vermont, New Hampshire, and Maine. The inventory represents a snapshot in time (circa 1990-1994) and provides emission estimates for multiple sources including wetlands, landfills, ruminant animals, residential wood combustion, fossil fuel combustion and use, animal manure, wastewater treatment, and natural gas transmission pipelines.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/New_England_CH4/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"nitrogen_deposition_730 v1 - This data set contains data for wet and dry nitrogen-species deposition for the United States and Western Europe. Deposition data were acquired directly from monitoring programs in the United States and Europe covering time periods from 1978-1994 for wet deposition and from 1989-1994 for dry deposition and evaluated using similar quality assurance criteria to ensure comparability.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/nitrogen_deposition/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Snowmelt_timing_maps_V2_1712 v2 - This data set provides snowmelt timing maps (STMs), cloud interference maps, and a map with the count of calculated snowmelt timing values for North America. The STMs are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard 8-day composite snow-cover product MOD10A2 collection 6 for the period 2001-01-01 to 2018-12-31.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/Snowmelt_timing_maps_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"African_Rainfall_Patterns_1263 v1 - This data set describes rainfall distribution statistics over the African continent, including Madagascar. The rainfall estimates are based on data from the NASA Tropical Rainfall Measuring Mission (TRMM) measured between 1998 and 2012.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_climate/African_Rainfall_Patterns/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-climate","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA CMS Project","Description":"This dataset provides gridded estimates of aboveground biomass (AGB) for live dry woody vegetation density in the form of both stock for the baseline year 2003 and annual change in stock from 2003 to 2016. Data are at a spatial resolution of approximately 500 m (463.31 m; 21.47 ha) for three geographies: the biogeographical limit of the Amazon Basin, the country of Mexico, and a Pantropical belt from 40 degrees North to 30 degrees South latitudes. Estimates were derived from a multi-step modeling approach that combined field measurements with co-located LiDAR data from NASA ICESat Geoscience Laser Altimeter System (GLAS) to calibrate a machine-learning (ML) algorithm that generated spatially explicit annual estimates of AGB density. ML inputs included a suite of satellite and ancillary spatial predictor variables compiled as wall-to-wall raster mosaics, including MODIS products, WorldClim climate variables reflecting current (1960-1990) climatic conditions, and SoilGrids soil variables. The 14-year time series was analyzed at the grid cell (~500 m) level with a change point-fitting algorithm to quantify annual losses and gains in AGB. Estimates of AGB and change can be used to derive total losses, gains, and the net change in aboveground carbon density over the study period as well as annual estimates of carbon stock.\n<br>\n\n#### Salt_Marsh_Biomass_CONUS_2348\nThis dataset provides estimates of aboveground biomass (AGB) and salt marsh extent in the contiguous United States for 2020 and includes all coastal watersheds across the contiguous United States at 10-m resolution. Estimates were generated by XGBoost machine learning regression. Salt marsh extent was classified using an ensemble of XGBoost, random forests, and support vector machines, trained with salt marsh location identified with the National Wetland Inventory (NWI). The data are organized by Hydrologic Unit Code (HUC) 6-digit basin. Within each HUC, the spatial extent of salt marsh and its uncertainty were estimated by machine learning and input data from NWI maps, the National Elevation Dataset, along with Sentinel-1 and Sentinel-2 imagery. Estimates were compared to in situ biomass data from salt marshes in Georgia and Massachusetts. The data are provided in cloud-optimized GeoTIFF format.\n<br>\n\n#### Howland_Forest_Biomass_Map_2434\nThis dataset holds aboveground biomass (AGB) estimates at 10-m spatial resolution for the Howland Research Forest in central Maine for 2012, 2015, 2017, 2021, and 2023. Forest inventory data were collected using 50 fixed-area plot sampling during the summers of 2021, 2023, and 2024. Plots included permanent inventory plots around existing flux towers and additional plots to ensure representation of various forest conditions. Each plot had a radius of 7.98 m. In addition, leaf-off airborne LiDAR data were collected by the USGS 3DEP project in 2012, 2015, and 2023, and leaf-on data were obtained from the NASA G-LiHT project for 2017 and 2021. The LANDIS-II forest landscape model along with its Biomass Succession extension was used to simulate ecosystem dynamics in Howland Forest. Then, a random forest (RF) model was used to generate wall-to-wall biomass maps for the research forest from the LiDAR data. The RF model was calibrated from in situ AGB measurements from plots and simulated AGB values for the LiDAR acquisition years. Howland Research Forest is a low-elevation transitional forest dominated by spruce and hemlock, with conifer and northern hardwood species. The data are provided in cloud optimized GeoTIFF format.\n<br>\n\n#### Tidal_Marsh_Biomass_US_V1-1_1879\nThis dataset provides maps of aboveground tidal marsh biomass (g/m2) at 30 m resolution for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Estuarine and palustrine emergent tidal marsh areas were based on a 2010 NOAA Coastal Change Analysis Program (C-CAP) map. Aboveground biomass maps were generated from a random forest model driven by Landsat vegetation indices and a national scale dataset of field-measured aboveground biomass. The final model, driven by six Landsat vegetation indices, with the soil adjusted vegetation index as the most important, successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle, and growth form. Biomass can be converted to carbon stocks using a mean plant carbon content of 44.1%.\n<br>\n\n#### CMS_AGB_Landcover_Indonesia_1645\nThis dataset provides estimates of aboveground biomass, percent canopy cover, mean canopy height, landcover, and forest degradation index products for forests in Kalimantan, Indonesia (Island of Borneo) representative of conditions in late 2014. Data were combined from several sources including field sampling, airborne lidar, satellite measurements, a forest-type land cover map, and integrated into a random forest algorithm to produce these estimates.\n<br>\n\n#### CMS_iWED_V1_2452\nThis dataset provides an integrated Wildfire Event Dataset (iWED version 1) for wildfire events of 100 ha or more in area from 1992 to 2021 for the continental US. Fire information was compiled from a variety of state, regional, and federal-level agencies responsible for filing and archiving incident level reports. Additional information was obtained from the Monitoring Trends in Burn Severity (MTBS) program initiated in the mid-2000s. MTBS is being continuously updated as new satellite remote sensing data are collected and processed. The data are provided in comma separated values (CSV) format.\n<br>\n\n#### CMS_AGB_NW_USA_1719\nThis dataset provides annual maps of aboveground biomass (AGB, Mg/ha) for forests in Washington, Oregon, Idaho, and western Montana, USA, for the years 2000-2016, at a spatial resolution of 30 meters. Tree measurements were summarized with the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS) to estimate AGB in field plots contributed by stakeholders, then lidar was used to predict plot-level AGB using the Random Forests machine learning algorithm. The machine learning outputs were used to predict AGB from Landsat time series imagery processed through LandTrendr, climate metrics generated from 30-year climate normals, and topographic metrics generated from a 30-m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The non-forested pixels were masked using the PALSAR 2009 forest/nonforest mask.\n<br>\n\n#### Annual_Burned_Area_Maps_1708\nThis dataset provides maps of annual burned area for the part of Mawas conservation program in Central Kalimantan, Indonesia from 1997 through 2015. Landsat imagery (TM, ETM+, OLI/TIR) at 30 m resolution was obtained for this 19-year period, including the variables surface reflectance, brightness temperature, and pixel quality assurance, plus the indices NDVI, NDMI, NBR, NBR2, SAVI, and MSAVI. The MODIS active fire product (MCD14) was used to define when fires occurred. Random Forest classifications were used to separate burned and unburned 30-m pixels with inputs of composites of Landsat indices and thermal bands, based on the pre- and post-fire values.\n<br>\n\n#### CMS_WFEIS_CONUS-AK_1306\nThis data set contains annual modeled estimates of wildland fire emissions at 0.01 degree (~1-km) spatial resolution from the Wildland Fire Emissions Information System (WFEIS v0.5) for the conterminous U.S. (CONUS) and Alaska for 2001 through 2013. WFEIS is a web-based tool that provides resources to quantify emissions from past fires and output results as spatial data files (French et al., 2014). The data set includes emissions estimates of carbon (C), carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), other non-methane hydrocarbons (NMHC), and particulate matter (PM) as well as estimates of above-ground biomass, total fuel availability, and consumption estimates.\n<br>\n\n#### Blue_Carbon_Tidal_Wetland_Maps_2091\nThis dataset contains shapefiles showing location of tidal wetland parcels with the potential for net greenhouse gas removal if restored from current mapped condition to unimpeded tidal wetlands. These maps focus on managed lands in the contiguous United States along the ocean coasts and show impounded wetlands where reconnecting tidal flow could diminish methane production. The maps include current dominant wetland type, restoration category, potential removal of atmospheric greenhouse gases in units of mass carbon dioxide with estimates of uncertainty.\n<br>\n\n#### BlueFlux_AirborneObs_Florida_2327\nThis dataset includes airborne in situ measurements of greenhouse gas mixing ratios, meteorological parameters, and fluxes (CO2, CH4, latent heat fluxes, friction velocity, and convective velocity scale) calculated with wavelet transforms. CO2, CH4, CO, O3, and water vapor mixing ratios, and meteorological variables were obtained from a Beechcraft A90 King Air aircraft. Flights occurred on April 19-26 2022, October 14-20 2022, February 5-13 2023, and April 13-19 2023 as part of the BlueFlux campaign, funded by NASA's Carbon Monitoring System program. Measurements were made with several instruments, including a PICARRO 2401-m (0.5 Hz CO2/CH4/H2O/CO), PICARRO 2311-f (10 Hz CO2/CH4/H2O), NASA Rapid Ozone Experiment (ROZE, 10 Hz O3), and AIMMS-20 probe (3-D winds, meteorology, and aircraft location data). Flight lines span Everglades National Park (ENP) and Big Cypress National Preserve (BCNP) in southern Florida, USA. The measurements were used to calculate vertical fluxes of trace gases and heat via wavelet transform eddy covariance\n<br>\n\n#### BlueFlux_Tidal_River_Water_2333\nThis dataset provides dissolved carbon (dissolved inorganic carbon and dissolved organic carbon), greenhouse gases, dissolved organic matter optical, and hydrological (water temperature, pH, alkalinity, dissolved oxygen) data collected from the Shark and Harney tidal rivers in the Everglades, Florida, USA. The data were collected as part of the NASA Carbon Monitoring System (CMS) BlueFlux field campaigns over the 2022 wet season (October 2022) and 2023 dry season (March 2023). Data includes single-collection samples collected from sites along both rivers and samples collected by an autosampler at one site over multiple tidal cycles. The data are provided in comma-separated values (.csv) format.\n<br>\n\n#### BlueFlux_Gridded_CO2_CH4_2404\nThis dataset contains gridded estimates of carbon dioxide (CO2) and methane (CH4) fluxes at daily resolution covering the Southern Florida region from 2000 to 2024. Gridded CO2 and CH4 flux prototype products at 500-m spatial resolution were derived from a machine learning model based on eddy covariance (EC) measurements from 1) airborne fluxes collected seasonally with the NASA Carbon Airborne Flux Experiment (CARAFE) over the region during five flight deployments and 2) regional EC tower networks representing long term wetland ecosystem fluxes since 2004. Multiscale flux measurements were upscaled with remote sensing observations of MODIS optical reflectance using a bootstrap ensemble random forest modeling approach to predict daily mean flux intensity and uncertainty from February 2000 to August 2024. Prototypes of modeled, gridded greenhouse gas fluxes were developed as part of the NASA Carbon Monitoring System (CMS) BlueFlux Project. The data are provided in netCDF format.\n<br>\n\n#### TLS_Lidar_BlueFlux_Mangroves_2311\nThis dataset contains point clouds of three-dimensional (3D) mangrove forest structure and volume collected from 10 sites in Everglades National Park, Florida. Data were collected during NASA CMS \"Blueflux\" campaigns in March 2022, October 2022, and March 2023. Products were acquired using a RIEGL VZ-400i terrestrial laser scanner (TLS). TLS is a non-destructive and quantitative method for in situ 3D forest structure measuring and monitoring. Data are provided in LAS (.las) format.\n<br>\n\n#### Boreal_Arctic_Wetland_CH4_2351\nThis dataset provides an upscaled estimate of Boreal-Arctic wetland CH4 emissions at a weekly time scale from 2002 to 2021 at 0.5 by 0.5-degree spatial resolution. Ground truth data on wetland CH4 emissions from eddy covariance towers (139 site years) and chambers (168 site years) were used to train and validate a causality-guided machine learning model. The trained model was then used to estimate CH4 emissions at grid cells that have wetlands and located above 44 degrees north. The data are provided in netCDF format.\n<br>\n\n#### CMSFluxFire\nThis dataset provides the Carbon Flux for Fires. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxFossilFuelPrior\nThis dataset provides the Carbon Flux for Fossil Fuel Prior. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxOceanPrior\nThis dataset provides the Carbon Flux for Ocean Carbon Prior. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxFossilFuelPrior\nThis dataset provides the Prior for the Fossil Fuel Carbon Flux. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxNBE\nThis dataset provides the Carbon Flux from the Net Biome Exchange. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxNBEPrior\nThis dataset provides the Carbon Flux from the Net Biome Exchange Prior. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxLandPrior\nThis dataset provides the Prior for the Land Carbon Flux. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxNBE\nThis dataset provides the Carbon Flux for Posterior Net Biome Exchange (NBE). The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxOcean\nThis dataset provides the Posterior Carbon Flux for the Ocean. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxOceanPrior\nThis dataset provides the Prior for the Carbon Flux for Ocean. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxTotal\nThis dataset provides the Carbon Flux for Posterior Total Carbon. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxTotalPrior\nThis dataset provides the Prior for Total Carbon Flux. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxFossilfuel\nThis dataset provides the Carbon Flux for Fossil Fuel. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxOcean\nThis dataset provides the Carbon Flux for Ocean Carbon. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxTotalprior\nThis dataset provides the Carbon Flux for Prior Total Carbon. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxMISC\nThis dataset provides the Carbon Flux for Shipping, Aviation, and Chemical Sources. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSFluxNEE\nThis dataset provides the Carbon Flux from the Net Ecosystem Exchange. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSLakeHuronPPM\nMonthly Average primary production/carbon fixation data for Lake Huron. The primary production data is derived using MODIS imagery with model data. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSLakeHuronPPY\nYearly Average primary production/carbon fixation data for Lake Huron. The primary production data is derived using MODIS imagery with model data. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSLakeMichiganPPM\nMonthly Average primary production/carbon fixation data for Lake Michigan. The primary production data is derived using MODIS imagery with model data. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSLakeMichiganPPY\nYearly Average primary production/carbon fixation data for Lake Michigan. The primary production data is derived using MODIS imagery with model data. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSLakeSuperiorPPM\nMonthly Average primary production/carbon fixation data for Lake Superior. The primary production data is derived using MODIS imagery with model data. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMSLakeSuperiorPPY\nYearly Average primary production/carbon fixation data for Lake Superior. The primary production data is derived using MODIS imagery with model data. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMS_CONUS_Biomass_1752\nThis dataset provides annual estimates of six carbon pools, including forest aboveground live biomass, belowground biomass, aboveground dead biomass, belowground dead biomass, litter, and soil organic matter, across the conterminous United States (CONUS) for 2005, 2010, 2015, 2016, and 2017. Carbon stocks were estimated using a modified MaxEnt model. Measurements of pixel-specific site conditions from remote sensing data were combined with field inventory data from the U.S. Forest Service Forest Inventory and Analysis (FIA). Remote sensing data inputs included Thematic Mapper on Landsat 5, Operational Land Imager on Landsat 8, Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua, microwave radar measurements from Phased Array type L-band Synthetic Aperture Radar (PALSAR) on Advanced Land Observation Satellite (ALOS) and PALSAR-2 ALOS-2, airborne imagery from National Agriculture Imagery Program (NAIP), and the digital elevation model from the Shuttle Radar Topography Mission (SRTM). Data from satellite and airborne sources were co-registered on a common 100 m (1 ha) grid.\n<br>\n\n#### CMS_CTL_NA_GOSAT_FOOTPRINTS\nThis data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the GOSAT satellite. Meteorological fields from the WRF model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the \"receptor\" location), to create the adjoint of the transport model in the form of a \"footprint\" field. The footprint, with units of mixing ratio per surface flux, quantifies the influence of upwind surface fluxes on greenhouse gas concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. For each column observation location, the receptors are located at 23 discrete vertical levels throughout the atmospheric column. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.\n<br>\n\n#### CMS_CTL_NA_OCO2_FOOTPRINTS\nThis data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the OCO-2 satellite. Meteorological fields from the WRF model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the \"receptor\" location), to create the adjoint of the transport model in the form of a \"footprint\" field. The footprint, with units of mixing ratio per surface flux, quantifies the influence of upwind surface fluxes on greenhouse gas concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. For each column observation location, the receptors are located at 14 discrete vertical levels throughout the atmospheric column. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.\n<br>\n\n#### CMS_CTL_NA_TCCON_FOOTPRINTS\nThis data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the TCCON ground network. Meteorological fields from the WRF model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the \"receptor\" location), to create the adjoint of the transport model in the form of a \"footprint\" field. The footprint, with units of mixing ratio per surface flux, quantifies the influence of upwind surface fluxes on greenhouse gas concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. For each column observation location, the receptors are located at 23 discrete vertical levels throughout the atmospheric column. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.\n<br>\n\n#### CMS_Global_Forest_Age_2345\nThis dataset provides classes of global forests delineated by status/condition in 2020 at approximately 30-m resolution. The data support generating Tier 1 estimates for Aboveground dry woody Biomass Density (AGBD) in natural forests in the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Forest classes include primary, young secondary (<=20 years), and old secondary forests (>20 years). Classification was based on a Boolean combination of a suite of existing Earth Observation (EO) products of forest tree cover, height, age, and land use classification layers representing years 2000 to 2020. This forest status/condition classification prioritizes the reduction of potential errors of commission in the delineations by minimizing the inclusion of ambiguous pixels. Hence, it provides a conservative estimate of global forest area, identifying approximately 3.26 billion ha of forests worldwide. The classification was created on the collaborative open-science cloud-computing system, the ESA-NASA Multi-mission Analysis and Algorithm Platform (MAAP). The data are provided in cloud-optimized GeoTIFF format.\n<br>\n\n#### CMS_Simulated_SIF_NiwotRidge_1720\nThis dataset provides results for simulations of solar-induced chlorophyll fluorescence (SIF) implemented within the terrestrial biosphere Community Land Model (CLM 4.5) for Niwot Ridge, Colorado, USA, from 1998-2018. The data include outputs from three model simulations designed to test the importance of non-photochemical quenching (NPQ), that is, the absorbed light energy dissipated as heat, in determining seasonal SIF.\n<br>\n\n#### C_FluxStocks_CLM5_DART_WestUS_1856\nThis dataset provides monthly estimates of biomass stocks and land-atmosphere carbon exchange across the western United States at 0.95 degrees longitude x 1.25 degrees latitude grid resolution from 1998 through 2010. The data include outputs from two types of model simulations: (1) a \"free\" simulation which used Community Land Model (CLM5.0) simulations forced with meteorology appropriate for complex mountainous terrain, and (2) \"assimilation\" runs using the land surface data assimilation system (CLM5-DART). In assimilation runs, the CLM5 vegetation state is constrained by remotely sensed observations of leaf area index and aboveground biomass, which influenced biomass stocks and carbon fluxes.\n<br>\n\n#### CMS_GO_CH4_SEC_TDYC_NA\nMethane emissions are provided by sector in the contiguous United States (CONUS), Canada, and Mexico by inverse analysis of in situ (GLOBALVIEWplus CH4ObsPack) and satellite (GOSAT) atmospheric methane observations. The inversion uses as a prior estimate the national anthropogenic emission inventories for the three countries reported by the US Environmental Protection Agency (EPA), En- vironment and Climate Change Canada (ECCC), and the Instituto Nacional de Ecología y Cambio Climático (INECC) in Mexico to the United Nations Framework Convention on Climate Change (UNFCCC) and thus serves as an evaluation of these inventories in terms of their magnitudes and trends. Emissions are optimized with a Gaussian mixture model (GMM).\n<br>\n\n#### CMS_FluxEstimates_Aircraft_CO2_2336\nThis dataset provides gridded surface-atmosphere CO2 fluxes over North America from April 8 to November 18 during 2018 and 2019. Net ecosystem exchange (NEE) was estimated by the CMS-Flux-NA CO2 inversion system by assimilating in situ CO2 measurements and/or Orbiting Carbon Observatory (OCO-2) column-averaged CO2 retrievals. These data, along with imposed diurnal NEE variations, fossil fuel emissions, biomass burning, and biofuel emissions, are provided at 3-hour temporal resolution. The modeled co-samples of CO2 observed for aircraft flights are included for model evaluation. The data are provided in NetCDF version 4 format.\n<br>\n\n#### CMS_Mangrove_Biomass_Zambezi_1522\nThis dataset provides several estimates of aboveground biomass from various regressions and allometries for mangrove forest in the Zambezi River Delta, Mozambique. Plot level estimates of aboveground biomass are based on extensive tree biophysical measurements from field campaigns conducted in September and October of 2012 and 2013. Aboveground biomass estimates for the larger area of mangrove coverage within the delta are based on (1) the plot level data and (2) canopy structure data derived from airborne LiDAR surveys in 2014. The high-resolution canopy height model for the delta region derived from the airborne LiDAR data is also included.\n<br>\n\n#### CMS_LiDAR_AGB_PEF_2012_1318\nThis data set includes estimates of aboveground biomass (AGB) in 2012 from the Penobscot Experimental Forest (PEF) in Bradley, Maine. The AGB was modeled using LiDAR data gathered with the LiDAR Hyperspectral and Thermal Imager (G-LiHT) operated by Goddard Space Flight Center and field inventory data from 604 permanent Forest Inventory and Analysis (FIA) plots within the PEF. The estimates were produced through a novel modeling approach that accommodates temporal misalignment between field measurements and remotely sensed data by including multiple time-indexed measurements at plot locations to estimate changes in AGB.\n<br>\n\n#### Global_Riverine_N2O_Emissions_1791\nThis dataset provides modeled estimates of annual nitrous oxide (N2O) emissions at a coarse geographic scale (0.5 x 0.5 degree) for two sets of global rivers and streams covering the period of 1900-2016. Emissions (g N2O-N/yr) are provided for higher-order rivers and streams (>=4th order) and headwater streams (<4th order). The estimates were derived from a water transport model, the Model for Scale Adaptive River Transport (MOSART), coupled with the Dynamic Land Ecosystem Model (DLEM) to link hydrology and ecosystem processes pertaining to N2O flux and transport. Factors driving the model included climate, land use and land cover, and nitrogen inputs (i.e., fertilizer, deposition, manure, and sewage). Nitrogen discharges from streams and rivers to the ocean were calibrated from observations from 50 river basins across the globe.\n<br>\n\n#### Atmospheric_CO2_California_1641\nThis dataset provides measurements of atmospheric CO2 concentrations, carbon isotopes d13C and D14C, and fossil fuel CO2 (ffCO2) estimates from nine observation sites in California over three month-long campaigns in separate seasons of 2014-2015. ffCO2 was quantified based on the CO2 concentration and D14C. Simulations of ffCO2 at the sites and times of the observations were conducted with the Vulcan v2.2 fossil fuel emissions estimate for 2002 and the Weather Research and Forecasting - Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) atmospheric model. The observed and simulated ffCO2 were incorporated into Bayesian inverse estimates of ffCO2 to calculate California's ffCO2 emissions during the campaign period.\n<br>\n\n#### CMS_Methane_Emissions_Boston_1291\nThis data set provides average hourly measured, modeled enhancements, and background methane (CH4) concentrations, atmospheric ethane (C2H6) measurements, prior CH4 flux fields by sector, and a spatial reconstruction of natural gas (NG) consumption in Boston, Massachusetts and the surrounding region. Atmospheric CH4 concentrations were measured continuously from September 2012 through August 2013 at four locations and atmospheric ethane was measured continuously for several months during 2012-2014 at one location. Spatial models of prior CH4 emissions and natural gas consumption are given for an ~18,000 km^2 area centered on Boston, MA.\n<br>\n\n#### CMS_Global_Cropland_Carbon_1279\nThis data set provides global estimates of carbon fluxes associate with annual crop net primary production (NPP) and harvested biomass, annual uptake and release by humans and livestock, and the total annual estimate of net carbon exchange (NCE) derived from these carbon fluxes. NCE estimates are for the combined crop plant harvest and consumption/expiration of fodder by livestock and of food by humans. Estimation of carbon uptake and release from global agricultural production and consumption required compilation and analysis of inventory data from various sources for the years 2005-2011. The flux estimates were spatially distributed to a global 0.05-degree resolution grid using MODIS land cover data. The quantities of carbon flux in each gridcell are represented in two ways: (1) where the quantities of carbon distributed to each gridcell were divided by the total gridcell area, resulting in average carbon fluxes per unit of total area (g C/m2/yr), and (2), where annual carbon fluxes associated with a source were summed over all types for the gridcell (Mg C/yr). The total surface area of the grid cells is provided. There are eight data files in netCDF format (.nc4) with this data set -- two files (per area and per gridcell) for each of the four flux source types. Data for all years are in each .nc4 file.\n<br>\n\n#### CMS_WRF_Footprints_CO2_Signals_1381\nThis data set provides estimated CO2 emission signals for 16 regions (air quality basins) in California, USA, during the individual months of November 2010 and May 2011. The CO2 signals were predicted from simulated atmospheric CO2 observations and modeled fossil fuel emissions and biosphere CO2 fluxes. Data is also provided for the land surface in the larger modeling domain outside California. CO2 signals refer to the local enhancement or depletion in atmospheric CO2 concentration caused by fossil fuel emissions or biospheric exchange occurring within the region.\n<br>\n\n#### GPP_CONUS_TROPOMI_1875\nThis dataset includes estimates of gross primary production (GPP) for the conterminous U.S., for 2018-02-15 to 2021-10-15, based on measurements of solar-induced chlorophyll fluorescence from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5P satellite platform. GPP was estimated from rates of photosynthesis inferred from SIF using a linear model and ecosystem scaling factors from 102 AmeriFlux sites. Knowledge of the spatiotemporal patterns of GPP is necessary for understanding regional and global carbon budgets. Broad-scale estimates of GPP have typically relied upon carbon cycle models linking spatial patterns of vegetation with remotely sensed environmental data. SIF provides a means to directly estimate photosynthetic activity, and therefore, GPP. Recent deployments of satellite platforms that measure SIF provide near-real-time measurements and represent a breakthrough in measuring GPP on a global scale. Regular SIF measurements can detect spatially explicit ecosystem-level responses to climate events such as drought and flooding. This dataset includes spatially explicit estimates of GPP (g m-2 d-1), uncertainty in GPP, and related TROPOMI SIF measurements (mW m-2 sr-1 nm-1) at 500-m resolution. The data are provided in NetCDF format.\n<br>\n\n#### CMS_Pantropical_Forest_Biomass_1337\nThis data set provides estimates of pre-deforestation aboveground live woody biomass (AGLB) at 30-m resolution for deforested areas of tropical America, tropical Africa, and tropical Asia for the year 2000. The biomass estimates are only for areas where deforestation occurred during the period 2000 through 2012. These estimates represent biomass loss over this time period and can be used to derive average annual carbon emissions from tropical deforestation.\n<br>\n\n#### CMS_Daily_ET_MexFlux_1309\nThis data set provides daily average observations for evapotranspiration (measured and gap-filled), precipitation, net radiation, soil water content, air temperature, vapor pressure deficit, and normalized vegetation index (NDVI) from two water-limited shrubland sites for years 2008-2010. Both sites are located in the northwest part of Mexico and are part of the MexFlux network.\n<br>\n\n#### CMS_Fire_Weather_Data_AK_1509\nThis dataset provides daily fire weather indices for interior Alaska during the active fire seasons from 2001 to 2010. Data are gridded at 60-m resolution. The active fire season is defined as May 24-September 18 (days of the year 144-261) in this dataset. Fire weather is the use of meteorological parameters such as relative humidity, wind speed and direction, cloud cover, mixing heights, and soil moisture to determine whether conditions are favorable for fire growth and smoke dispersion. The six indices provided in this dataset are defined and produced following the methodology of the Canadian Forest Fire Weather Index System: Fine Fuel Moisture Code, Duff Moisture Code, Drought Code, Initial Spread Index, Buildup Index, Fire Weather Index. The dataset was developed following point source data interpolation from weather station observations.\n<br>\n\n#### FIA_Forest_Biomass_Estimates_1873\nThis dataset provides forest biomass estimates for the conterminous United States based on data from the USDA Forest Inventory and Analysis (FIA) program. FIA maintains uniformly measured field plots across the conterminous U.S. This dataset, derived from field survey data from 2009-2019, includes statistical estimates of biomass at the finest scale (64,000-hectare hexagons) allowed by FIA's sample density. Estimates include the mean (and standard error of the mean) biomass for both live and dead trees, calculated using three sets of allometric equations. There is also an estimate of the area of forestland in each hexagon. These data can be useful for assessing the accuracy of remotely sensed biomass estimates.\n<br>\n\n#### CMS_Forest_Productivity_1221\nNotice: This data set and guide were updated on June 30, 2014 to correct an error in the reported units. The data values were not changed.Spatially-gridded estimates of above ground biomass (AGB), net primary productivity (NPP), and net ecosystem productivity (NEP) are provided for forested areas of the conterminous United States (CONUS). Estimates of uncertainty are also provided for AGB and NEP. These data were derived by using Forest Inventory and Analysis (FIA) data to constrain forest growth rates in a Carnegie-Ames-Stanford Approach (CASA) carbon-cycle process model. Note that the data set does not include data for forests in the Northern Prairie States region (NPS; see Figure 3). These data provide a detailed estimate of carbon sources and sinks from recent forest disturbance and recovery across regions and forest types of the US. The data are presented as a series of ten NetCDF v4 (.nc4) files at two spatial scales (1-degree and 5-km spatial resolution) for the nominal year of 2005.\n<br>\n\n#### CMS_Forest_Carbon_Fluxes_1313\nThis data set provides maps of estimated carbon in forests of the 48 continental states of the US for the years 2005-2010. Carbon (termed committed carbon) stocks were estimated for forest aboveground biomass, belowground biomass, standing dead stems, and litter for the year 2005. Carbon emissions were estimated from land use conversion to agriculture, insect damage, logging, wind, and weather events in the forests for the years 2006 - 2010. Committed net carbon flux was estimated as the sum of carbon emissions and sequestration. The maps are provided at 100-m spatial resolution in GeoTIFF format. Average annual carbon estimates, by US county, for (1) emissions for the multiple disturbance sources, (2) sequestration, and (3) the committed net carbon flux are provided in an ESRI shapefile.\n<br>\n\n#### CMS_Landscapes_Brazil_Forests_1301\nThis data set provides measurements for diameter at breast height (DBH), commercial tree height, and total tree height for forest inventories taken at the Fazenda Cauaxi and the Fazenda Nova Neonita, Paragominas municipality, Para, Brazil. Also included for each tree are the common, family, and scientific name, coordinates, canopy position, crown radius, and for dead trees the decomposition status. These biophysical measurements were made at Fazenda Cauaxi during 2012 and 2014 and at the Fazenda Nova Neonita during 2013.\n<br>\n\n#### LIDAR_FOREST_CANOPY_HEIGHTS_1271\nThis data set provides estimates of forest canopy height derived from the Geoscience Laser Altimeter System (GLAS) LiDAR instrument that was aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite. A global GLAS waveform data set (n=12,336,553) from collection periods between October 2004 and March 2008 was processed to obtain canopy height estimates. Estimates of GLAS maximum canopy height and crown-area-weighted Lorey's height are provided for 18,578 statistically-selected globally distributed forested sites in a point shapefile. Country is included as a site attribute. Also provided is the average canopy height for the forested area of each country, plus the number of GLAS data footprints (shots), number of selected sample sites, and estimates of the variance for each country.\n<br>\n\n#### CMS_Global_Monthly_Wetland_CH4_1502\nThis data set provides global monthly wetland methane (CH4) emissions and uncertainty data products derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies. The data are at 0.5 by 0.5-degree resolution. Two model output data products are included in WetCHARTs v1.0: an output from the full ensemble for 2009-2010 and an output from a limited subset for 2001-2015. The intended use of the products is as a process-informed wetland CH4 emission and uncertainty data set for atmospheric chemistry and transport modelling (WetCHARTs).\n<br>\n\n#### MonthlyWetland_CH4_WetCHARTsV2_2346\nThis dataset provides global monthly wetland methane (CH4) emissions estimates at 0.5 by 0.5-degree resolution for the period 2001-01-01 to 2022-08-31 that were derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies that encompass the main sources of uncertainty in wetland CH4 emissions. There are 18 model configurations. WetCHARTs v1.3.3 is an updated product of WetCHARTs v1.3.1 dataset. The intended use of this product is as a process-informed wetland CH4 emission data set for atmospheric chemistry and transport modeling. Users can compare estimates by model configuration to explore variability and sensitivity with respect to ensemble members. The data are provided in netCDF format.\n<br>\n\n#### CMS_Global_Livestock_CH4_CO2_1329\nThis data set provides global annual carbon flux estimates, at 0.05-degree resolution, associated with livestock feed intake, manure, manure management, respiration, and enteric fermentation, summed over all livestock types. These fluxes can be summed across multiple grid cells to obtain totals for any given areas. These 2000-2013 flux estimates were based on livestock populations reported by the Food and Agriculture Organization (FAO) and the United States Department of Agriculture National Agricultural Statistics Service (USDA NASS), on coefficients provided by the Intergovernmental Panel on Climate Change (IPCC), and on additional coefficients developed by the authors.\n<br>\n\n#### CMS_Global_Mangrove_Forest_Ht_2251\nThis dataset characterizes canopy heights of mangrove-forested wetlands globally for 2015 at 12-m resolution. Estimates of maximum canopy height (height of the tallest tree) were derived from the German Space Agency's TanDEM-X data that produced global digital surface models. Also provided are Lidar estimates of canopy height based on the GEDI instrument, which were used for training and validation of the TanDEM-X estimates of forest height. The coverage of these data follows Global Mangrove Watch's mangrove extent maps. These spatially explicit maps of mangrove canopy height can be used to assess local-scale geophysical and environmental conditions that may regulate forest structure and carbon cycle dynamics. Maps revealed a wide range of canopy heights, including maximum values (>60 m) that surpass maximum heights of other forest types. Maps are provided in cloud optimized GeoTIFF format, and mangrove heights for individual GEDI tiles are compiled in a comma separated values (CSV) files.\n<br>\n\n#### CMS_WRF_Model_Products_1338\nThis data set contains estimated hourly CO2 atmospheric mole fractions and meteorological observations over North America for the year 2010 at a horizontal grid resolution of 27 km and vertical resolution from the surface to 50 hPa. The data are output from the Penn State WRF-Chem version of the Weather Research and Forecasting (WRF) model using lateral boundary conditions and surface fluxes from the CMS-Flux Inversion system.\n<br>\n\n#### GCAM_Land_Cover_2005-2095_1216\nThe data provided are annual land cover projections for years 2005 through 2095 generated by the Global Change Assessment Model (GCAM) Version 3.1. For the conterminous USA, the GCAM global gridded results were downscaled to ~5.6 km (0.05 degree) resolution. For each 5.6 x 5.6 km area, the annual land cover percentage comprised by each of the nineteen different land cover classes/plant functional types (PFTs) of the Community Land Model (CLM) (Table 1) are provided. Results are reported for GCAM runs of three scenarios of future human efforts towards climate mitigation as related to global carbon emissions, radiative forcing, and land cover change. Specific scenario conditions were 1) a reference scenario with no explicit climate mitigation efforts that reaches a radiative forcing level of over 7 W/m2 in 2100, 2) the 2.6 mitigation pathway (MP) scenario which is a very low emission scenario with a mid-century peak in radiative forcing at ~3 W/m2, declining to 2.6 W/m2 in 2100, and 3) the 4.5 MP scenario which stabilizes radiative forcing at 4.5 W/m2 (~ 650 ppm CO2-equivalent) before 2100. These downscaled land cover projections can be used to derive spatially explicit estimates of potential shifts in croplands, grasslands, shrub lands, and forest lands in each future climate scenario. Data are presented as three NetCDF v4 files (.nc4), one for each future climate scenario -- 2.6 MP, 4.5 MP, and GCAM reference).\n<br>\n\n#### Landcover_Colombian_Amazon_1783\nThis dataset provides annual maps of land cover classes for the Colombian Amazon from 2001 through 2016 that were created by classifying time segments detected by the Continuous Change Detection and Classification (CCDC) algorithm. The CCDC algorithm detected changes in Landsat pixel surface reflectance across the time series, and the time segments were classified into land cover types using a Random Forest classifier and manually collected training data. Annual maps of land cover were created for each Landsat scene and then post-processed and mosaicked. Land cover types include unclassified, forest, natural grasslands, urban, pastures, secondary forest, water, or highly reflective surfaces. The training data are not included with this dataset.\n<br>\n\n#### Sonoma_County_Forest_AGB_1764\nThis data set provides estimates of above-ground woody biomass and uncertainty at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha), were generated using a combination of airborne LiDAR data and field plot measurements with a parametric modeling approach. The relationship between field estimated and airborne LiDAR estimated aboveground biomass density is represented as a parametric model that predicts biomass as a function of canopy cover and 50th percentile and 90th percentile LiDAR heights at a 30-m resolution. To estimate uncertainty, the biomass model was re-fit 1,000 times through a sampling of the variance-covariance matrix of the fitted parametric model. This produced 1,000 estimates of biomass per pixel. The 5th and 95th percentiles, and the standard deviation of these pixel biomass estimates, were calculated.\n<br>\n\n#### CMS_Landscapes_Brazil_LiDAR_1302\nThis data set provides raw LiDAR point cloud data and derived Digital Terrain Models (DTMs) for five forested areas in the municipality of Paragominas, Para, Brazil, for the years 2012, 2013, and 2014. Data are included for two areas in Paragominas for 2013 and 2014, two areas for the Fazenda Cauaxi for 2012 and 2014, and for the Fazenda Andiroba for 2014. Shapefiles showing the LiDAR/DTM coverage areas are also provided for each of the areas.\n<br>\n\n#### CMS_LiDAR_Indonesia_1518\nThis dataset provides airborne LiDAR data collected over 90 sites totaling approximately 100,000 hectares of forested land in Kalimantan, Indonesia on the island of Borneo in late 2014. The data were collected as part of an effort to establish a national forest monitoring system for Indonesia that uses a combination of remote sensing and ground-based forest carbon inventory approaches.\n<br>\n\n#### CMS_LiDAR_Point_Cloud_Zambezi_1521\nThis data set provides high-resolution LiDAR point cloud data collected during surveys over mangrove forests in the Zambezi River Delta in Mozambique in May 2014. The data are arranged into 144 1- by 1-km tiles.\n<br>\n\n#### CMS_Maryland_AGB_Canopy_1320\nThis data set provides 30-meter gridded estimates of aboveground biomass (AGB), canopy height, and canopy coverage for the state of Maryland in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery to select 848 field sampling sites for biomass measurements. The field-based estimates were related to LiDAR height and volume metrics through random forests regression models across three physiographic regions of Maryland.\n<br>\n\n#### CMS_LiDAR_Biomass_CanHt_Sonoma_1523\nThis data set provides estimates of above-ground biomass (AGB), canopy height, and percent tree cover at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha) were generated using a combination of LiDAR data, field plot measurements, and random forest modeling approaches. Estimates of AGB uncertainty are also provided. Maximum canopy height and tree cover were derived from LiDAR data and high-resolution National Agriculture Imagery Program (NAIP) images.\n<br>\n\n#### CMS_LiDAR_Products_Indonesia_1540\nThis dataset provides canopy height and elevation data products derived from airborne LiDAR data collected over 90 sites on the island of Borneo in late 2014. The sites cover approximately 100,000 hectares of forested land in Kalimantan, Indonesia. The data were produced as part of an effort to improve a national forest monitoring system for Indonesia that uses a combination of remote sensing and ground-based forest carbon inventory approaches.\n<br>\n\n#### CMS_Pilot_Biomass_1257\nThese data consist of high-resolution maps of aboveground biomass at four forested sites in the US: Garcia River Tract in California, Anne Arundel and Howard Counties in Maryland, Parker Tract in North Carolina, and Hubbard Brook Experimental Forest in New Hampshire. Biomass maps were generated using a combination of field data (forest inventory and Lidar) and modeling approaches. Estimates of uncertainty are also provided for the Maryland site using two different modeling methodologies. These data provide estimates of aboveground biomass for the nominal year of 2011 at 20-50 meter resolution in units of megagrams of carbon per hectare (or acre for the Garcia Tract site). The data are presented as a series of 11 GeoTIFF (.tif) files.\n<br>\n\n#### CMS_Pennsylvania_Tree_Cover_1334\nThis data set provides high-resolution (1-m) tree canopy cover for states in the Northeast USA. State-level canopy cover data are currently available for Pennsylvania (data for nominal year 2008), Delaware (2014), and Maryland (2013). The data were derived with a rules-based expert system which facilitated integration of leaf-on LiDAR and imagery data into a single classification workflow, exploiting the spectral, height, and spatial information contained in the datasets. Additional states will be added as data processing is completed.\n<br>\n\n#### CMS_Mangrove_CanHt_Stand_Age_1377\nThis data set provides canopy height, land cover change, and stand age estimates for mangrove forests in the Rufiji River Delta in Tanzania. The estimates were derived from a canopy height model (CHM) using TanDEM-X imagery and Polarimetric SAR interferometry (Pol-InSAR) techniques. Landsat imagery circa 1990 and circa 2014 was used to estimate stand age between 1994 and 2014 and for forest land cover change modeling.\n<br>\n\n#### CMS_Mangrove_Canopy_Ht_Zambezi_1357\nThis data set provides high resolution canopy height estimates for mangrove forests in the Zambezi Delta, Mozambique, Africa. The estimates were derived from three separate canopy height models (CHM) using airborne Lidar data, stereophotogrammetry with WorldView 1 imagery, and Interferometric-Synthetic Aperture Radar (In-SAR) techniques with TanDEM-X imagery. The data cover the period 2011-10-14 to 2014-05-06.\n<br>\n\n#### CMS_Mangrove_Canopy_Height_1327\nThis data set provides canopy height estimates for mangrove forests at 0.6 x 0.6 m resolution in three study sites located in southeastern Mozambique, Africa: two sites on Inhaca Island and one in the Maputo Elephant Reserve, located in the southern province of Maputo for September, 2012. The estimates were derived from WorldView1 (WV-1) very high resolution (VHR) stereo images processed using the Ames Stereo Pipeline (ASP) digital surface model (DSM) tool.\n<br>\n\n#### CMS_Mangrove_Cover_1670\nThis dataset provides estimates of mangrove extent for 2016, and mangrove change (gain or loss) from 2000 to 2016, in major river delta regions of eight countries: Bangladesh, Gabon, Jamaica, Mozambique, Peru, Senegal, Tanzania, and Vietnam. For mangrove extent, a combination of Landsat 8 OLI, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) elevation data were used to create country-wide maps of mangrove landcover extent at a 30-m resolution. For mangrove change, the global mangrove map for 2000 (Giri et al., 2010) was used as the baseline. Normalized Difference Vegetation Indices (NDVI) were calculated for every cloud- and shadow-free pixel in the Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI collection and used to create an NDVI anomaly from 2000 to 2016. Areas of change (loss or gain) occurred at the extremes of the cumulative anomalies.\n<br>\n\n#### CMS_CO2_Fluxes_TBMO_1315\nThis data set provides global, gridded, model-derived net ecosystem exchange (NEE) of CO2 flux between the land and atmosphere at 3-hourly time steps over seven years (2004-2010) at three different spatial resolutions: 0.5 x 0.5 degree, 2.0 x 2.5 degrees, and 4.0 x 5.0 degrees (latitude/longitude). The 3-hourly data were derived from monthly NEE outputs of 15 global land surface models and four ensemble products in the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP).\n<br>\n\n#### CMS_SST_GPP_Mexico_1310\nThis data set provides data for MODIS-derived (1) gross primary productivity (GPP) for the years 2000-2010, (2) fraction of photosynthetically active radiation (fPAR) for the years 2003-2013, (3) sea surface temperature (SST) for the years 2003-2013, and (4) the NOAA-source Multivariate ENSO Index (MEI) data for the years 2003-2013 (as a measure of the El Nino/Southern Oscillation). The study areas were three transects on the Baja California Peninsula, Mexico, and the adjacent Pacific Ocean. The terrestrial transects, in order from North to South, West to East included Punta Colonet (three sites-PC1, PC2, PC3), Punta Abreojos (two sites-PA1, PA2), and Magdalena Bay (three sites-MB1, MB2, MB3).\n<br>\n\n#### CMS_Great_Basin_Biomass_1755\nThis dataset provides annual maps of live aboveground tree biomass (Mg/ha) for pinyon-juniper forests across the Great Basin of the Western USA for the years 2000-2016 at a spatial resolution of 30 meters. Biomass estimates are limited to areas of the Great Basin defined as a pinyon-juniper ecosystem type by the 2016 Landfire Existing Vegetation Type map. The estimates of biomass were based on a linear relationship with pinyon-juniper canopy cover and crown-based allometrics developed from field data in Nevada and Idaho. Canopy cover was estimated from remote sensing by using annual composites of Landsat imagery, which were temporally segmented with the LandTrendr algorithm, along with biologically-relevant climate variables, and topographic indices in a Random Forest regression model. Models of canopy cover were trained from semi-automatic extraction of tree crowns from 2011 - 2013 high resolution imagery (1 m) from the National Agriculture Imagery Program, which were validated with photo interpretation. Maps of the standard deviation of biomass estimates from decision trees in the Random Forest model are provided as an indicator of uncertainty. Biomass estimates were calibrated to estimates from the Forest Inventory and Analysis program (FIA) on an annual basis and corrections applied.\n<br>\n\n#### CMS_SABGOM_Model_Simulations_1510\nThis dataset contains monthly mean ocean surface physical and biogeochemical data for the Gulf of America simulated by the South Atlantic Bight and Gulf of America (SABGOM) model on a 5-km grid from 2005 to 2010. The simulated data include ocean surface salinity, temperature, dissolved inorganic nitrogen (DIN), dissolved inorganic carbon (DIC), partial pressure of CO2 (pCO2), air-sea CO2 flux, surface currents, and primary production. The SABGOM model is a coupled physical-biogeochemical model for studying circulation and biochemical cycling for the entire Gulf of America to achieve an improved understanding of marine ecosystem variations and their relations with three-dimensional ocean circulation in a gulf-wide context.\n<br>\n\n#### CMS_Soil_CO2_Efflux_1298\nThis data set provides the results of (1) monthly measurements of soil CO2 efflux, volumetric water content, and temperature, and (2) seasonal measurements of soil (porosity, bulk density, nitrogen (N) and carbon (C) content) and vegetation (leaf area index (LAI), litter and fine root biomass) properties in a water-limited ecosystem in Baja California, Mexico. Measurements and samples were collected from August 2011 to August 2012.\n<br>\n\n#### C_Pools_Fluxes_CONUS_1837\nThis dataset provides estimates of carbon pools, fluxes, and associated uncertainties across the contiguous USA (CONUS) at 0.5-degree resolution for all terrestrial land cover types. Carbon pools include labile carbon, foliar carbon, fine root, woody carbon, litter carbon, and soil organic carbon. Carbon fluxes include gross primary production (GPP), net primary production (NPP), net biome exchange, autotrophic respiration, and heterotrophic respiration. The modeled estimates are provided as monthly averages over the 16-year period, 2001 through 2016. The data were derived from the CARbon DAta MOdel fraMework (CARDAMOM) that included climate data, and above and below ground biomass maps of CONUS for the years 2005, 2010, 2015 and 2016 as input data sources to this model-data fusion framework. The input data were integrated into the CARDAMOM model to constrain on the terrestrial carbon and to specifically attribute changes of forest carbon stocks and spatial distributions of carbon emissions and removals across forested lands. United States Forest Service's Forest Inventory and Analysis (FIA) plot data were used to train models for the prediction of forest above-ground biomass (AGB).\n<br>\n\n#### Vermont_HighRes_LandCover_2072\nThis dataset contains estimates of tree canopy cover presence at high resolution (0.5m) across the state of Vermont for 2016 in Cloud-Optimized GeoTIFF (.tif) format. Tree canopy was derived from 2016 high-resolution remotely sensed data as part of the Vermont High-Resolution Land Cover mapping project. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected. Tree canopy assessments have been conducted for numerous communities throughout the U.S. where the results have been instrumental in helping to establish tree canopy goals.\n<br>\n\n#### Tree_Canopy_Cover_Mexico_V2_2445\nThis dataset provides 20-year tree cover (TC) estimates at 30-m spatial resolution for Mexico from 2000 to 2019 using Landsat time series, airborne LiDAR, and machine learning. The TC data (hereafter, CMS-TC) offers accurate and consistent national-scale percent tree cover estimates with an overall coefficient of determination (R squared) of 0.81 and a root mean square error (RMSE) of 11.90%. The CMS-TC product is essential for tracking tree cover and aboveground biomass changes, monitoring land use dynamics, supporting biodiversity conservation, and informing climate and land use policy decisions. The data are provided in GeoTIFF format.\n<br>\n\n#### Continuous_Lifeform_Maps_CONUS_1809\nThis dataset contains estimates of percent cover of tree, shrub, herb, and other (non-vegetation) lifeform classes and uncertainties for the conterminous U.S. (CONUS). The estimates were derived using quantile regression forest models and indicate the percent of ground covered by a vertical projection of each lifeform class ranging from 0 to 100 percent. Model input data included Landsat surface reflectance (SR) data and 165 airborne LiDAR datasets covering eight of the eleven terrestrial biomes of the conterminous U.S. and Alaska. Eighty-six of the LiDAR acquisitions are part of the NASA Goddard's LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) airborne imager data collection; the remaining 79 sites were acquired by the National Science Foundation's National Ecological Observatory Network Airborne Observation Platform (NEON AOP). Acquisitions were selected based on the availability of the SR data for each G-LiHT and NEON dataset. The data are annual estimates from 1984 to 2018 and were tiled (425 tiles) using the CONUS Landsat Analysis Ready Data (ARD) grid scheme. Data are provided in GeoTIFF format.\n<br>\n\n#### Uncertainty_US_Coastal_GHG_1650\nThis dataset provides maps of coastal wetland carbon and methane fluxes and coastal wetland surface elevation from 2006 to 2011 at 30 m resolution for coastal wetlands of the conterminous United States. Total coastal wetland carbon flux per year per pixel was calculated by combining maps of wetland type and change with soil, biomass, and methane flux data from a literature review. Uncertainty in carbon flux was estimated from 10,000 iterations of a Monte Carlo analysis. In addition to the uncertainty analysis, this dataset also provides a probabilistic map of the extent of tidal elevation, as well as the geospatial files used to create that surface, and a land cover and land cover change map of the coastal zone from 2006 to 2011 with accompanying estimated median soil, biomass, methane, and total CO2 equivalent annual fluxes, each with reported 95% confidence intervals, at 30 m resolution. Land cover was quantified using the Coastal Change Analysis Program (C-CAP), a Landsat-based land cover mapping product.\n<br>\n\n#### Niwot_Ridge_CNPAM_Fluorescence_1722\nThis dataset provides chlorophyll fluorescence measurements made on pine and spruce needle tissues at the Niwot Ridge AmeriFlux Core site (US-NR1) near Nederland, Colorado, USA, during the summers of 2017 and 2018. Two types of measurements were made using pulse-amplitude-modulation (PAM) fluorometry: the photosystem II (PSII) operating efficiency in the light (Fq'/Fm' at variable light levels), and the maximum quantum efficiency of PSII photochemistry (Fv/Fm) on dark-acclimated tissues. Chlorophyll fluorescence measurements were made to determine seasonality of photosynthetic performance at the needle level.\n<br>\n\n#### Niwot_Ridge_Pigment_1723\nThis dataset provides concentrations of pigments in pine and spruce needle tissues collected at the Niwot Ridge AmeriFlux Core site (US-NR1) near Nederland, Colorado, USA, during the summers of 2017 and 2018. Pigments measured included Chlorophyll A and B, Violaxanthin, Antheraxanthin, Zeaxanthin, Neoxanthin, Lutein, and beta-Carotene. Measurements were made on sun foliage from two canopy-access towers near the main flux tower, and in the laboratory on branches collected from those towers, every 4-8 weeks over the annual cycle. Due to canopy structure, a limited number of trees were accessible from the towers, preventing extensive replication. Pigments were extracted in acetone and analyzed by HPLC. The measurements were made to evaluate seasonal changes associated with the down-regulation of photosynthesis.\n<br>\n\n#### CMS_DARTE_V2_1735\nThis data set provides a 38-year, 1-km resolution inventory of annual on-road CO2 emissions for the conterminous United States based on roadway-level vehicle traffic data and state-specific emissions factors for multiple vehicle types on urban and rural roads as compiled in the Database of Road Transportation Emissions (DARTE). CO2 emissions from the on-road transportation sector are provided annually for 1980-2017 as a continuous surface at a spatial resolution of 1 km.\n<br>\n\n#### GCRW_DEM_2016_1793\nThis dataset contains four alternative digital elevation models (DEMs) at 1 m resolution and model performance statistical metrics for the Global Change Research Wetland (GCReW) site on the Rhode River, a tributary of the Chesapeake Bay in Maryland, USA, for the year 2016. Three DEMs were created by using different strategies for correcting positive biases in Light Detection and Ranging (LiDAR)-based DEMs that are common in tidal wetlands. These included (1) applying a single average offset based on a literature review, (2) using the LiDAR Elevation Correction with NDVI (LEAN)-method, and (3) applying plant community-specific offsets using a local vegetation cover map. Existing LiDAR data at 1 m resolution collected in 2011 was the basis for these DEMs. The fourth DEM was created by using Empirical Bayesian Kriging to extrapolate between measured ground points. The elevation is provided in meters relative to the North American Vertical Datum of 1988 (NAVD 88). To calibrate the four approaches, the elevation of the entire marsh complex was surveyed at 20 m x 20 m resolution to document the distribution of elevation relative to tidal datums from a single year. Two Trimble R8 real-time kinematic (RTK) GPS receivers were used to survey 525 points over the complex from July 26, 2016, to August 15, 2016. Relative plant cover was also documented. Tidal datums were calculated from the nearby Annapolis, MD tidal gauge located 13 km from GCReW.\n<br>\n\n#### Disturbance_Biomass_Maps_1679\nThis dataset provides derived disturbance history and predicted annual forest biomass maps at 30-m resolution for six selected Landsat scenes across the Conterminous United States (CONUS) for the period 1985-2014. The focus sites are in the following states: Colorado, Maine, Minnesota, Oregon, Pennsylvania, and South Carolina. These scenes were selected to represent a wide range of forest ecosystems, which ensured that a diversity of forest type groups and forest change processes (e.g., harvest, fire, insects, and urbanization) were included. Disturbance history was derived from a Landsat time-series for each site. Each disturbance is represented by year of detection, duration, and magnitude. The cause of the disturbance was not identified. Forest biomass was measured at field plots within each of the six sites and combined with airborne LiDAR data from each site to create land validation maps. Site biomass at 30-m resolution was estimated by developing Random Forest models that include site disturbance history with the land validation maps.\n<br>\n\n#### Cropland_Carbon_Fluxes_2125\nThis dataset contains daily estimates of carbon fluxes in croplands derived from the \"ecosys\" model covering a portion of the Midwestern US (Illinois, Indiana, and Iowa) at county-level resolution from 2001-2018. Ecosys simulates water, energy, carbon, and nutrient cycles simultaneously for various ecosystems, including agricultural systems at up to hourly resolution. Estimates include: gross primary productivity (GPP), net primary productivity (NPP), autotrophic respiration (Ra), heterotrophic respiration (Rh), or net ecosystem exchange (NEE). Data were generated by the ecosys model constrained by observational data, including USDA crop yield from USDA National Agricultural Statistics Service, and a remote-sensing-based SLOPE GPP product. Model performance was evaluated using observations from AmeriFlux towers at agricultural sites within the study area. Agriculture in the US Midwest produces significant quantities of corn and soybeans, which are key elements to the global food supply. The data are provided in shapefile format.\n<br>\n\n#### EF_Data_Mexico_1693\nThis dataset provides a map of the distribution of ecosystem functional types (EFTs) at 0.05 degree resolution across Mexico for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT for each pixel.\n<br>\n\n#### CMS_EFT_CONUS_1659\nThis dataset provides maps of the distribution of ecosystem functional types (EFTs) and the interannual variability of EFTs at 0.05 degree resolution across the conterminous United States (CONUS) for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT and interannual variability was calculated as the number of unique EFTs for each pixel.\n<br>\n\n#### DLEM_C_N_Export_1699\nThis dataset provides estimates for export and leaching of dissolved inorganic carbon (DIC), dissolved organic carbon (DIC), total organic carbon (TOC), particulate organic carbon (POC), ammonium (NH4+), nitrate (NO3-), and total organic nitrogen (TON) from the Mississippi River Basin (MRB) to the Gulf of Mexico. The estimates are provided for a historical period of 1901-2014, and a future period of 2010-2099 (carbon estimates only) under two scenarios of high and low levels of population growth, economy, and energy consumption, respectively. The estimates are from the Dynamic Land Ecosystem Model 2.0 (DLEM 2.0). These data are applicable to studying how changes in multiple environmental factors (e.g., fertilizer application, land-use changes, climate variability, atmospheric CO2 and N deposition) affect the dynamics of leaching and export to the Gulf of Mexico.\n<br>\n\n#### Fire_Emissions_Indonesia_2118\nThis dataset provides 10-minute fire emissions within 0.1-degree regularly spaced intervals across Indonesia from July 2015 to December 2020. The dataset was produced with a top-down approach based on fire radiative energy (FRE) and smoke aerosol emission coefficients (Ce) derived from multiple new-generation satellite observations. Specifically, the Ce values of peatland, tropical forest, cropland, or savanna and grassland were derived from fire radiative power (FRP) and emission rates of smoke aerosols based on Visible Infrared Imaging Radiometer Suite (VIIRS) active fire and aerosol products. FRE for each 0.1-degree interval was calculated from the diurnal FRP cycle that was reconstructed by fusing cloud-corrected FRP retrievals from the high temporal-resolution (10 mins) Himawari-8 Advanced Himawari Imager (AHI) with those from high spatial-resolution (375 m) VIIRS. This new dataset was named the Fused AHI-VIIRS based fire Emissions (FAVE). Fire emissions data are provided in comma-separated values (CSV) format with one file per month from July 2015 to December 2020. Each file includes variables of fire observation time, fire geographic location, classification, fire radiative energy, various fire emissions and related standard deviations.\n<br>\n\n#### CMS_Forest_Carbon_Maryland_1660\nThis dataset provides 90-m resolution maps of estimated forest aboveground biomass (Mg/ha) for nominal year 2011 and projections of carbon sequestration potential for the state of Maryland. Estimated biomass and sequestration potential were computed using the Ecosystem Demography (ED) model, which integrates data from multiple sources, including: climate variables from the North American Regional Reanalysis (NARR) Product, soil variables from the Soil Survey Geographic Database (SSURGO), land cover variables from airborne lidar, the National Agriculture Imagery Program (NAIP) and the National Land Cover Database (NLCD), and vegetation parameters from the Forest Inventory and Analysis (FIA) Program.\n<br>\n\n#### AGB_Carbon_Sequestration_RGGI_1922\nThis dataset provides 90 m estimates of forest aboveground biomass (Mg/ha) for nominal 2011 and projections of carbon sequestration potential for 11 states in the Regional Greenhouse Gas Initiative (RGGI) domain. The RGGI is a cooperative, market-based effort among States in the eastern United States. Estimated biomass and sequestration potential were computed using the Ecosystem Demography (ED) model. The ED Model integrates several key data including climate variables from Daymet and MERRA2 products; physical soil and hydraulic properties from Probabilistic Remapping of SSURGO (POLARIS) and CONUS-SOIL; land cover characteristics from airborne lidar, the National Agriculture Imagery Program (NAIP), and the National Land Cover Database (NLCD); and vegetation parameters from the Forest Inventory and Analysis (FIA) Program.\n<br>\n\n#### Maine_Forest_Biomass_Map_2435\nThis dataset holds estimates of forest aboveground biomass (AGB) for Maine, USA, in 2023. AGB was estimated using airborne LiDAR data from the USGS 3DEP project and a deep learning convolutional neural network (CNN) model. The airborne LiDAR datasets used in this mapping were collected in different years. The CNN model was calibrated using plot-level forest inventory data with precise location measurements and spectral indices derived from multiple remote sensing products. Stand-level biomass succession models, developed from the USDA Forest Service Forest Inventory and Analysis (FIA) data, were applied to project biomass estimates to the year 2023 with 10-m spatial resolution. The data are provided in GeoTIFF format.\n<br>\n\n#### Annual_Forest_AGB_Maryland_2384\nThis dataset includes estimates of annual forest aboveground biomass over the state of Maryland, USA, for the period 1984-2023. It was generated by a modeling approach that linked an ecosystem model called Ecosystem Demography (ED) model, airborne lidar data of canopy height in circa 2010, and the remote sensing based land cover change dataset (NAFD).\n<br>\n\n#### AGB_NEP_Disturbance_US_Forests_1829\nThis dataset, derived from the National Forest Carbon Monitoring System (NFCMS), provides estimates of forest carbon stocks and fluxes in the form of aboveground woody biomass (AGB), total live biomass, total ecosystem carbon, aboveground coarse woody debris (CWD), and net ecosystem productivity (NEP) as a function of the number of years since the most recent disturbance (i.e., stand age) for forests of the conterminous U.S. at a 30 m resolution for the benchmark years 1990, 2000, and 2010. The data were derived from an inventory-constrained version of the Carnegie-Ames-Stanford Approach (CASA) carbon cycle process model that accounts for disturbance processes for each combination of forest type, site productivity, and pre-disturbance biomass. Also provided are the core model data inputs including the year of the most recent disturbance according to the North American Forest Dynamics (NAFD) and the Monitoring Trends in Burn Severity (MTBS) data products; the type of disturbance; biomass estimates from the year 2000 according to the National Biomass and Carbon Dataset (NBCD); forest-type group; a site productivity classification; and the number of years since stand-replacing disturbance. The data are useful for a wide range of applications including monitoring and reporting recent dynamics of forest carbon across the conterminous U.S., assessment of recent trends with attribution to disturbance and regrowth drivers, conservation planning, and assessment of climate change mitigation opportunities within the forest sector.\n<br>\n\n#### Forest_Inventory_Brazil_2007\nThis dataset provides the complete catalog of forest inventory and biophysical measurements collected over selected forest research sites across the Amazon rainforest in Brazil between 2009 and 2018 for the Sustainable Landscapes Brazil Project. This dataset includes measurements for diameter at breast height (DBH), commercial tree height, and total tree height for forest inventories. Also included for each tree are the family, common and scientific names, coordinates, canopy position, crown radius, and for dead trees, the decomposition status. Aboveground biomass estimate is available for selected sites. The data are provided in comma-separated values (CSV) and shapefile formats. Sampling methodology for each site and year is described in companion files.\n<br>\n\n#### GEOS_CASAGFED_3H_NEE\nThis product provides 3 hourly average net ecosystem exchange (NEE) and gross ecosystem exchange (GEE) of Carbon derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA- GFED3) model. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### GEOS_CASAGFED_D_FIRE\nThis product provides Daily average wildfire emissions (FIRE) and fuel wood burning emissions (FUEL) derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA- GFED3) model. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### GEOS_CASAGFED_M_FLUX\nThis product provides Monthly average Net Primary Production (NPP), heterotrophic respiration (Rh), wildfire emissions (FIRE), and fuel wood burning emissions (FUEL) derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA- GFED3) model. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMS_Global_Fire_Atlas_1642\nThe Global Fire Atlas is a global dataset that tracks the day-to-day dynamics of individual fires to determine the timing and location of ignitions, fire size, duration, daily expansion, fire line length, speed, and direction of spread. These individual fire characteristics were derived based on the Global Fire Atlas algorithm and estimated day of burn information at 500-m resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 MCD64A1 burned area product. The algorithm identified 13.3 million individual fires (>=21 ha or 0.21 km2; the size of one MODIS pixel) over the 2003-2016 study period.\n<br>\n\n#### GlobFireCarbon\nThis dataset provides carbon monoxide and carbon dioxide flux from fires constrained by satellite observations. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMS_Global_Forest_AGC_2180\nThis dataset provides global gridded estimates of forest aboveground carbon stocks and potential fluxes at a 0.01-degree resolution. It was derived by initializing a newly developed global Ecosystem Demography model (ED v3.0) with novel remote sensing observations of tree canopy height collected by GEDI and ICESat-2, two NASA spaceborne lidar missions. A total of 3.77 billion lidar samples were used to generate gridded canopy height histograms that were then linked to ED simulations of canopy height and carbon dynamics during ecosystem succession. This process constrained representation of contemporary forest conditions and associated carbon stocks and fluxes in the model. Inputs that drove these simulations included meteorology, carbon dioxide levels, and soil properties. The data are provided in cloud-optimized GeoTIFF format.\n<br>\n\n#### Methane_Flaring_Sites_VIIRS_1874\nThis dataset contains annual global flare site surveys from 2012-2019 derived from Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar Partnership (SNPP) satellite. Gas flaring sites were identified from heat anomalies first estimated by the VIIRS Nightfire (VNF) algorithm from which high-temperature biomass burning and low-temperature gas flaring were separated based on temperature and persistence. Nightly observations for each flare site were drawn to determine their activity in the given calendar year. Data include flare location, temperature, and estimated flared gas volume; flaring data summarized by country; and KMZ files for viewing flaring locations in Google Earth. This dataset is valuable for measuring the current status of global gas flaring, which can have significant environmental impacts.\n<br>\n\n#### CMS_Global_Soil_Respiration_1736\nThis dataset provides six global gridded products at 1-km resolution of predicted annual soil respiration (Rs) and associated uncertainty, maps of the lower and upper quartiles of the prediction distributions, and two derived annual heterotrophic respiration (Rh) maps. A machine learning approach was used to derive the predicted Rs and uncertainty data using a quantile regression forest (QRF) algorithm trained with observations from the global Soil Respiration Database (SRDB) version 3 spanning from 1961 to 2011. The two Rh maps were derived from the predicted Rs with two different empirical equations. These products were produced to support carbon cycle research at local- to global-scales, and highlight the immense spatial variability of soil respiration and our ability to predict it across the globe.\n<br>\n\n#### SoilResp_HeterotrophicResp_1928\nThis dataset provides global gridded estimates of annual soil respiration (Rs) and soil heterotrophic respiration (Rh) and associated uncertainties at 1 km resolution. Mean soil respiration was estimated using a quantile regression forest model utilizing data from the global Soil Respiration Database Version 5 (SRDB-V5) and covariates of mean annual temperature, seasonal precipitation, and vegetative cover. The SRDB holds results of field studies of soil respiration from around the globe. A total of 4,115 records from 1,036 studies were selected from SRDB-V5. SRDB-V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. These soil respiration records were combined with global meteorological, land cover, and topographic data and then evaluated with variable selection using random forests. The standard deviation and coefficient of variation of Rs are included and were also derived from the same model. Global heterotrophic respiration was calculated from Rs estimates. The data are produced in part from SRDB-V5 inputs that cover the period 1961-2016.\n<br>\n\n#### GFEI_CH4\nThis is a global inventory of methane emissions from fuel exploitation (GFEI) created for the NASA Carbon Monitoring System (CMS). The emission sources represented in this dataset include fugitive emission sources from oil, gas, and coal exploitation following IPCC 2006 definitions and are estimated using bottom-up methods. The inventory emissions are based on individual country reports submitted in accordance with the United Nations Framework Convention on Climate Change (UNFCCC). For those countries that do not report, the emissions are estimated following IPCC 2006 methods. Emissions are allocated to infrastructure locations including mines, wells, pipelines, compressor stations, storage facilities, processing plants, and refineries. The purpose of the inventory is to be used as a prior estimate of fuel exploitation emissions in inverse modeling of atmospheric methane observations. GFEI only includes fugitive methane emissions from oil, gas, and coal exploitation activities and does not include any combustion emissions as defined in IPCC 2006 category 1A. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.\n<br>\n\n#### CMS_Global_Map_Mangrove_Canopy_1665\nThis dataset characterizes the global distribution, biomass, and canopy height of mangrove-forested wetlands based on remotely sensed and in situ field measurement data. Estimates of (1) mangrove aboveground biomass (AGB), (2) maximum canopy height (height of the tallest tree), and (3) basal-area weighted height (individual tree heights weighted in proportion to their basal area) for the nominal year 2000 were derived across a 30-meter resolution global mangrove ecotype extent map using remotely-sensed canopy height measurements and region-specific allometric models. Also provided are (4) in situ field measurement data for selected sites across a wide variety of forest structures (e.g., scrub, fringe, riverine and basin) in mangrove ecotypes of the global equatorial region. Within designated plots, selected trees were identified to species and diameter at breast height (DBH) and tree height was measured using a laser rangefinder or clinometer. Tree density (the number of stems) can be estimated for each plot and expressed per unit area. These data were used to derive plot-level allometry among AGB, basal area weighted height (Hba), and maximum canopy height (Hmax) and to validate the remotely sensed estimates.\n<br>\n\n#### CMS_Global_Mangrove_Loss_1768\nThis dataset provides estimates of the extent of mangrove loss, land cover change, and its anthropogenic or climatic drivers in three time periods: 2000-2005, 2005-2010, and 2010-2016. Landsat-based Normalized Difference Vegetation Index (NDVI) anomalies were used to determine loss extent in each period. The drivers of mangrove loss were determined by examining land cover changes using a random forest machine learning technique that considered change from mangrove to wet soil, dry soil, and water at each loss pixel. A series of decision trees used several global-scale land-use datasets to identify the ultimate driver of the mangrove loss. Loss drivers include commodity production (agriculture, aquaculture), settlement, erosion, extreme climatic events, and non-productive conversion. Maps of loss extent per period, mangrove land cover changes, and loss drivers are provided for each of 39 mangrove holding nations.\n<br>\n\n#### CMSGCH4F\nThis dataset provides global methane fluxes optimized with GOSAT data for 2010-2018. It is supported by the Carbon Monitoring System project. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.\n<br>\n\n#### Global_Salt_Marsh_Change_2122\nThis dataset provides global salt marsh change, including loss and gain for five-year periods from 2000-2019. Loss and gain at a 30 m spatial resolution were estimated with Normalized Difference Vegetation Index (NDVI) anomaly algorithm using Landsat 5, 7, and 8 collections within the known extent of salt marshes. The data are provided in cloud-optimized GeoTIFF format.\n<br>\n\n#### Global_Freshwater_CH4Emissions_2253\nThis dataset provides monthly globally gridded freshwater wetland methane emissions from 2001-2018 in nmol CH4 m-2 s-1, g C-CH4 m-2 d-1, and TgCH4 grid cell-1 month-1. The data were derived from a six-predictor random forest upscaling model (UpCH4) trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites covering bog (8), fen (8), marsh (10), swamp (6), and wet tundra (11) wetland classes and distributed across Arctic-boreal (20), temperate (16), and (sub)tropical (7) climate zones. Weekly mean CH4 fluxes were computed from half-hourly FLUXNET-CH4 Version 1.0 fluxes. Each grid cell CH4 flux prediction was weighted by fractional grid cell wetland extent to estimate CH4 emissions using the primary global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) product and an alternate Global Inundation Estimate from Multiple Satellites GIEMS version 2 global wetland map. Both WAD2M and GIEMS-2 maps were modified with several correction data layers to represent the monthly area covered by vegetated wetlands, excluding open water and coastal wetlands. The data products are: mean daily fluxes with no adjustment for wetland area (i.e., flux densities assuming hypothetical 100% wetland cover); mean daily fluxes adjusting for WAD2M or GIEMS-2 wetland area; and by-pixel monthly sum of freshwater wetland methane emissions adjusting for WAD2M or GIEMS-2 wetland area. The data are provided in NetCDF4 format.\n<br>\n\n#### Tidal_Marsh_Vegetation_US_1608\nThis dataset provides 30m resolution maps of the fraction of green vegetation within tidal marshes for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD; Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from a 1m classification of 2013 to 2015 National Agriculture Imagery Program (NAIP) images as tidal marsh green vegetation, non-vegetation, and open water. Using this high-resolution map, the percent of each class within Landsat pixel extents was calculated to produce a 30m fraction of green vegetation map for each region.\n<br>\n\n#### CStocks_Greenness_Mangroves_MX_1853\nThis dataset provides estimates of greenness trends, above- and belowground carbon stocks, and climate variables of the persistent mangrove forests on the coasts of Mexico (PMFM) at a 1 km resolution from 2001 through 2015. Data are available as one-time estimates or across the temporal range; typically as monthly summaries. One-time estimates of aboveground carbon and soil organic carbon stocks for the PMFM derived from existing sources are provided. Also included are the monthly mean normalized difference vegetation index (NDVI) from MOD13A3 used to derive greenness trends, monthly mean air temperature, and total monthly precipitation from Daymet for 2001-2015 across the PMFM. Other files include the distribution and coverage of PMFM across Mexico. Distributions are provided as four categories of PMFM: (1) Arid mangroves with Surface Water as main input, along the Gulf of California and Pacific Coast (ARsw); (2) humid mangroves with surface water input along the Pacific Coast (HUsw-Pa); (3) humid mangroves with surface water input along the coast of the Gulf of Mexico (HUsw-Gf); (4) humid mangroves with groundwater input along the Gulf of Mexico and Caribbean Sea (HUgw). These data provide a baseline for national monitoring programs, carbon accounting models, and greenness trends in coastal wetlands.\n<br>\n\n#### Tidal_Wetland_GPP_CONUS_1792\nThis dataset provides mapped tidal wetland gross primary production (GPP) estimates (g C/m2/day) derived from multiple wetland types at 250-m resolution across the conterminous United States at 16-day intervals from March 5, 2000, through November 17, 2019. GPP was derived with the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field-based eddy covariance (EC) tower GPP data into a single Bayesian framework along with Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets. Tidal wetlands are a critical component of global climate regulation. Tidal wetland-based carbon, or \"blue carbon,\" is a valued resource that is increasingly important for restoration and conservation purposes.\n<br>\n\n#### SIF_PAR_fPAR_US_Midwest_2018_1813\nThis dataset provides estimated solar-induced chlorophyll fluorescence (SIF) of specific vegetation types and total SIF under clear-sky and real/cloudy conditions at a resolution of 4 km for the Midwest USA. The estimates are 8-day averaged daily means over the 2018 crop growing season for the time period 2018-05-01 to 2018-09-29. SIF of a specific vegetation type (i.e., corn, soybean, grass/pasture, forest) was expressed as the product of photosynthetically active radiation (PAR), the fraction of photosynthetically active radiation absorbed by the canopy (fPAR), and canopy SIF yield (SIFyield) for each vegetation type. Uncertainty of each variable was also calculated and is provided. These components of the SIF model were derived using a TROPOspheric Monitoring Instrument (TROPOMI) dataset, the USDA National Agricultural Statistics Service Cropland Data Layer, and the MODIS MCD15A2H 8-day 500 m fPAR product. These data could be used to improve estimates of vegetation productivity and vegetation stress.\n<br>\n\n#### CMS_HR_MNA_CH4_FLUX\nThis data set contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by Greenhouse Gases Observing SATellite (GOSAT) observations. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMS_Landcover_Indonesia_1838\nThis dataset contains annual land use/cover (LUC) maps at 30 m resolution across Mawas, Central Kalimantan, Indonesia. There are six files, each representing a five-year interval over the period 1994-2019. An additional file for 2015 was created for accuracy assessment. A high-quality and low-cloud coverage image from Landsat 5 or Landsat 8 over each 5-year period was selected or composited for the January-August timeframe. Investigators used their knowledge to manually identify training polygons in these images for five LUC classes: peat swamp forest, tall shrubs/ secondary forest, low shrubs/ferns/grass, urban/bare land/open flooded areas, and river. Pixel values of Landsat Tier 1 surface reflectance products and selected indices were extracted for each LUC and used to predict LUC classes across the Mawas study area using the Classification and Regression Trees (CART) method. These data can be used to evaluate the relationship between fire occurrence and land cover type in the study site.\n<br>\n\n#### Estimated_Biomass_Stock_Amazon_1648\nThis dataset provides estimates of forest aboveground biomass for three study areas and the entire Paragominas municipality, in Para, Brazil, in 2012. Aboveground biomass (in megagrams of carbon per hectare) was measured for inventory plots within the study (focal) areas, and then assimilated and modeled with LiDAR and PALSAR metrics using gradient boosting machines (GBM) to predict spatially explicit forest aboveground biomass and uncertainties for the entire focal areas. The PALSAR data across the three focal areas was combined and used in a GBM model to predict forest aboveground biomass across the entire Paragominas municipality.\n<br>\n\n#### CMS_LiDAR_Biomass_MD_PA_DE_1538\nThis dataset provides 30-meter gridded estimates of aboveground biomass (AGB), forest canopy height, and canopy coverage for Maryland, Pennsylvania, and Delaware in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery in a model-based stratification that was used to select 848 sampling sites for AGB estimation. Field-based estimates were then related to LiDAR height and volume metrics through random forest regression models across three physiographic regions. Spatial errors were estimated at the pixel level using standard prediction intervals to assess the accuracy of the modeling approach. Estimates of biomass were further validated against the permanent network of FIA plots and compared with existing coarse resolution national biomass maps.\n<br>\n\n#### AGB_CanopyHt_Cover_NewEngland_1854\nThis dataset provides 30 m gridded estimates of aboveground biomass density (AGBD), forest canopy height, and tree canopy coverage for the New England Region of the U.S., including the state of Maine, Vermont, New Hampshire, Massachusetts, Connecticut, and Rhode Island, for the nominal year 2015. It is based on inputs from 1 m resolution Leaf-off LiDAR data collected from 2010 through 2015, high-resolution leaf-on agricultural imagery, and FIA plot-level measurements. Canopy height and tree cover were derived directly from LiDAR data while AGBD was estimated by statistical models that link remote sensing data and FIA plots at the pixel level. Error in AGBD was calculated at the 90% confidence interval. This approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA's spaceborne LiDAR missions.\n<br>\n\n#### Forest_AGB_NW_USA_1766\nThis dataset provides maps of aboveground forest biomass (AGB) of living trees and standing dead trees in Mg/ha across portions of Northwestern United States, including Washington, Oregon, Idaho, and Montana, at a spatial resolution of 30 m. Forest inventory data were compiled from 29 stakeholders that had overlapping lidar imagery. The collection totaled 3805 field plots with lidar imagery for 176 collections acquired between 2002 and 2016. Plot-level AGB estimates were calculated from tree measurements using the default allometric equations found in the Fire Fuels Extension (FFE) of the Forest Vegetation Simulator (FVS). The random forest algorithm was used to model AGB from lidar height and density metrics that were generated from the lidar returns within fixed-radius field plot footprints, gridded climate metrics obtained from the Climate-FVS Ready Data Server, and topographic estimates extracted from Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global elevation rasters. AGB was then mapped from the same lidar metrics gridded across the extent of the lidar collections at 30-m resolution. The standard deviation of estimated AGB of the terminal nodes from the random forest predictions was also mapped to show pixel-level model uncertainty. Note that the AGB estimates are, for the most part, a single snapshot in time and that the forest conditions are not necessarily representative of the larger study area.\n<br>\n\n#### LiDAR_Forest_Inventory_Brazil_1644\nThis dataset provides the complete catalog of point cloud data collected during LiDAR surveys over selected forest research sites across the Amazon rainforest in Brazil between 2008 and 2018 for the Sustainable Landscapes Brazil Project. Flight lines were selected to overfly key field research sites in the Brazilian states of Acre, Amazonas, Bahia, Goias, Mato Grosso, Para, Rondonia, Santa Catarina, and Sao Paulo. The point clouds have been georeferenced, noise-filtered, and corrected for misalignment of overlapping flight lines. They are provided in 1 km2 tiles. The data were collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time.\n<br>\n\n#### CMS_LiDAR_AGB_California_1537\nThis dataset provides estimates of aboveground biomass and spatially explicit uncertainty from 53 airborne LiDAR surveys of locations throughout California between 2005 and 2014. Aboveground biomass was estimated by performing individual tree crown detection and applying a customized \"remote sensing aware\" allometric equation to these individual trees. Aboveground biomass estimates and their uncertainties for each study area are provided in per-tree and gridded format. The canopy height models used for the tree detection and biomass estimation are also provided.\n<br>\n\n#### CMS_CH4_FLX_CA\nThis data set (CMS_CH4_FLX_CA) contains the yearly average methane (CH4) flux for Canada's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by ICF International in 2013. A related data set (CMS_CH4_FLX_MX) contains the yearly average methane (CH4) flux for Mexico's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by the Mexican Petrolium Institute in 2010. The Canadian emissions are concentrated in Alberta (gas production and processing) and the Mexican emissions are concentrated along the east coast (oil production). More details about the observations, algorithm, and scientific findings are described in Sheng et al. 2017. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMS_CH4_FLX_MX\nThis data set (CMS_CH4_FLX_MX) contains the yearly average methane (CH4) flux for Mexico's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by the Mexican Petrolium Institute in 2010. A related data set (CMS_CH4_FLX_CA) contains the yearly average methane (CH4) flux for Canada's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by ICF International in 2013. The Mexican emissions are concentrated along the east coast (oil production) and the Canadian emissions are concentrated in Alberta (gas production and processing). More details about the observations, algorithm, and scientific findings are described in Sheng et al. 2017. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMS_CH4_FLX_NAD\nThe CMS Methane (CH4) Flux for North America data set contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by Greenhouse Gases Observing SATellite (GOSAT) observations. The nested approach of the inversion enables large point sources to be resolved while aggregating regions with weak emissions and minimizing aggregation errors. The emission sources are separated into 12 different sectors as follows: Total, Oil/Gas, Coal, Cows, Waste (Landfills+ Wastewater), Biofuel, Rice, Other Anthropogenic, Biomass Burning, Wetlands, Soil Absorption, Other Natural. More details about the algorithm and error characterization can be found in Turner, Jacob, Wecht, et al. 2015. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### Methane_Ethane_MA_NH_1982\nThis dataset provides the hourly average of continuous atmospheric measurements of methane (CH4) from two urban sites and three boundary sites in and around Boston, Massachusetts, U.S., from September 2012-May 2020, measured with Picarro cavity ring down spectrometers (CRDS). Five-minute average atmospheric measurements of ethane (C2H6) and methane at Copley Square in Boston, MA, are also provided, with ethane measured with a laser spectrometer and methane measured with a Picarro CRDS. Background CH4 concentrations for the urban sites were determined using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model trajectories at the boundary of the study region based on measurements at three boundary sites and wind direction from the North American Mesoscale Forecast System (NAM) 12-kilometer meteorology.\n<br>\n\n#### CH4_Plume_AVIRIS-NG_1727\nThis dataset provides maps of methane (CH4) plumes along flight lines over identified methane point-source emitting infrastructure across the State of California, USA collected during 2016 and 2017. Methane plume locations were derived from Next-Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) overflights during the California Methane Survey. The survey was designed to cover at least 60% of the methane point source infrastructure in California guided by the Vista-CA dataset of identified locations of potential methane emitting facilities and infrastructure in three primary sectors (energy, agriculture, and waste). The purpose of the survey was to detect, quantify, and attribute point source emissions to specific infrastructure elements to improve the scientific understanding of regional methane budgets and to inform policy and planning activities that reduce methane emissions.\n<br>\n\n#### MICASA_FLUX_3H\nMiCASA is an extensive revision of CASA-GFED3. CASA-GFED3 derives from Potter et al. (1993), diverging in development since Randerson et al. (1996). CASA is a light use efficiency model: NPP is expressed as the product of photosynthetically active solar radiation, a light use efficiency parameter, scalars that capture temperature and moisture limitations, and fractional absorption of photosynthetically active radiation (fPAR) by the vegetation canopy derived from satellite data. Fire parameterization was incorporated into the model by van der Werf et al. (2004) leading to CASA-GFED3 after several revisions (van der Werf et al., 2006, 2010). Development of the GFED module has continued, now at GFED5 (Chen et al., 2023) with less focus on the CASA module. MiCASA diverges from GFED development at version 3, although future reconciliation is possible. Input datasets include air temperature, precipitation, incident solar radiation, a soil classification map, and several satellite derived products. These products are primarily based on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined datasets including land cover classification (MCD12Q1), burned area (MCD64A1), Nadir BRDF-Adjusted Reflectance (NBAR; MCD43A4), from which fPAR is derived, and tree/herbaceous/bare vegetated fractions from Terra only (MOD44B). Emissions due to fire and burning of coarse woody debris (fuel wood) are estimated separately.\n<br>\n\n#### SLOPE_GPP_CONUS_1786\nThis dataset contains estimated gross primary productivity (GPP), photosynthetically active radiation (PAR), soil adjusted near infrared reflectance of vegetation (SANIRv), the fraction of C4 crops in vegetation (fC4), and their uncertainties for the conterminous United States (CONUS) from 2000 to 2019. The daily estimates are SatelLite Only Photosynthesis Estimation (SLOPE) products at 250-m resolution. There are three distinct features of the GPP estimation algorithm: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR, (2) SLOPE couples gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRv (SANIRv) dataset, and (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. PAR, SANIRv and C4 fraction are used to drive a parsimonious model with only two parameters to estimate GPP, along with a quantitative uncertainty, on a per-pixel and daily basis. The slope GPP product has an R2 = 0.84 and a root-mean-square error (RMSE) of 1.65 gC m-2 d-1.\n<br>\n\n#### NASMo_TiAM_250m_2326\nThis NASMo-TiAM (North America Soil Moisture Dataset Derived from Time-Specific Adaptable Machine Learning Models) dataset holds gridded estimates of surface soil moisture (0-5 cm depth) at a spatial resolution of 250 meters over 16-day intervals from mid-2002 to December 2020 for North America. The model employed Random Forests to downscale coarse-resolution soil moisture estimates (0.25 deg) from the European Space Agency Climate Change Initiative (ESA CCI) based on their correlation with a set of static (terrain parameters, bulk density) and dynamic covariates (Normalized Difference Vegetation Index, land surface temperature). NASMo-TiAM 250m predictions were evaluated through cross-validation with ESA CCI reference data and independent ground-truth validation using North American Soil Moisture Database (NASMD) records. The data are provided in cloud optimized GeoTIFF format.\n<br>\n\n#### BGC_glider_GNATS\nThis dataset contains ocean biogeochemistry data from two Slocum gliders along the Gulf of Maine North Atlantic Time Series (GNATS) transect. The transect runs approximately east-west, with only a very minor change in latitude. The gliders are deployed on the western end of the transect, travel along the transect line to the eastern end, turn around and travel back along the transect to the western end, before being recovered. Each file contains data from one deployment (a glider “mission”), and thus contains both an eastbound and a westbound measurement of each variable. A full mission takes approximately 20 – 30 days. The data are gridded by longitude (0.01° intervals) and depth (1 m intervals). For more details on dataset preparation, see the Original Publication Citation and Data Processing Workflow below.\n<br>\n\n#### CMS_OCE_BGC_CCS\nA coupled physical-biogeochemical ocean model (the MITgcm with BLING biogeochemistry) is a least squares fit to all available ocean observations in the region of the California Current System. This is accomplished iteratively through the adjoint method, using the methodology developed by the Consortium for Estimating the Circulation and Climate of the Ocean (ECCO). The result is a physically realistic estimate of the ocean state. The model domain extends from 28N to 40N and from 130W to 114W. It has a 1/16-degree horizontal resolution (~7km) and 72 vertical levels. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.\n<br>\n\n#### CMS_Monthly_CO2_Gulf_1668\nThis dataset provides 1 km gridded monthly estimates of surface ocean partial pressure of CO2 (pCO2) and air-sea flux of CO2 (CO2 flux) for the northern Gulf of America for the period 2006 through 2010. Estimates of pCO2 were derived from MODIS/Aqua satellite imagery in combination with ship-based observations. Estimates of CO2 flux were derived from estimates of seawater pCO2, wind fields, and atmospheric pCO2.\n<br>\n\n#### Coastal_US_Elevation_Data_1844\nThis dataset provides maps of the elevation of coastal wetlands relative to tidal ranges for the conterminous United States (CONUS) at 30 m resolution for 2010. It also includes maps of tidal amplitude, relative sea-level rise for the period 1983-2001, and maps for coastal lands and low marsh areas based on the probability of being below the mean higher high tide water line for spring tides (MHHWS). Uncertainty layers for elevation maps are also provided.\n<br>\n\n#### CMS_Coastal_Wetland_Resilience_1839\nThis dataset provides information about the resilience of tidal wetlands to sea-level rise under three scenarios of global change. With rising seas, regularly inundated tidal wetlands may persist by vertical accretion of sediments (vertical resilience) and/or by migrating inland (lateral resilience), but local and regional conditions constrain these options. This dataset provides a vertical resilience index (VR) for coastal wetlands at 30 m resolution across the continental US predicted for 2100. The VR index was computed for current sea levels, local tidal dynamics, and coastal topography. It was also calculated for future sea levels predicted for 2100 by three IPCC Realized Concentration Pathway (RCP) scenarios: 2.5, 4.5, and 8.5. Moreover, the VR index incorporates estimated rates of sediment accretion. Relevant to lateral resiliency, the data include current and future tidal areas identified by mapping mean higher high water spring tide locations under the RCP scenarios. A shapefile outlining watershed units with tidal wetlands is included along with land cover classes for these areas for 1996 and 2011.\n<br>\n\n#### SatelliteDerived_Forest_Mexico_2320\nThis dataset provides a comparison of forest extent agreement from seven remote sensing-based products across Mexico. These satellite-derived products include European Space Agency 2020 Land Cover Map for Mexico (ESA), Globeland30 2020 (Globeland30), Commission for Environmental Cooperation 2015 Land Cover Map (CEC), Impact Observatory 2020 Land Cover Map (IO), NAIP Trained Mean Percent Cover Map (NEX-TC), Global Land Analysis and Discovery Global 2010 Tree Cover (Hansen-TC), and Global Forest Cover Change Tree Cover 30 m Global (GFCC-TC). All products included data at 10-30 m resolution and represented the state of forest or tree cover from 2010 to 2020. These seven products were chosen based on: a) feedback from end-users in Mexico; b) availability and FAIR (findable, accessible, interoperable, and replicable) data principles; and c) products representing different methodological approaches from global to regional scales. The combined agreement map documents forest cover for each satellite-derived product at 30-m resolution across Mexico. The data are in cloud optimized GeoTIFF format and cover the period 2010-2020. A shapefile is included that outlines Mexico mainland areas.\n<br>\n\n#### SiB4_Global_HalfDegree_Daily_1849\nThis dataset provides global daily output predicted by the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter. Daily output includes carbon, carbonyl sulfide, and energy fluxes; solar-induced fluorescence; carbon pools; soil moisture and temperatures in the top three layers; total column soil water and plant available water; and environmental potentials used to scale photosynthesis. The SiB4 output is per plant functional type (PFT) within each 0.5-degree grid cell. SiB4 partitions variable output to 15 PFTs in each grid cell that are indexed by the \"npft\" dimension (01-15) in each data file. The PFT three-character abbreviations (\"pft_names\" variable) are listed in the same order as the \"npft\" dimension. To combine the PFT-specific output into grid cell totals, users must compute the area-weighted mean across the vector of PFT-specific values for each cell. Fractional areal coverages are given in the \"pft_area\" variable for each cell.\n<br>\n\n#### SiB4_Global_HalfDegree_Hourly_1847\nThis dataset provides global hourly output predicted from the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter. Hourly output includes carbon fluxes, carbonyl sulfide (COS) fluxes, gross primary production, ecosystem respiration, solar-induced fluorescence (SIF), top-layer soil temperature and moisture, vegetation stress, photosynthetically active radiation (PAR), leaf and canopy-level carbon-dioxide partial pressures, and canopy conductance. The SiB4 output is per plant functional type (PFT) within each 0.5-degree grid cell. SiB4 partitions variable output to 15 PFTs in each grid cell that are indexed by the \"npft\" dimension (01-15) in each data file. The PFT three-character abbreviations (\"pft_names\" variable) are listed in the same order as the \"npft\" dimension. To combine the PFT-specific output into grid cell totals, users must compute the area-weighted mean across the vector of PFT-specific values for each cell. Fractional areal coverages are given in the \"pft_area\" variable for each cell.\n<br>\n\n#### SiB4_Global_HalfDegree_Monthly_1848\nThis dataset provides global monthly output predicted by the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter. Monthly output includes carbon, carbonyl sulfide (COS), and energy fluxes; solar-induced fluorescence (SIF); carbon pools; soil moisture and temperatures in the top three layers; total column soil water and plant available water; and environmental potentials used to scale photosynthesis. The SiB4 output is per plant functional type (PFT) within each 0.5-degree grid cell. SiB4 partitions variable output to 15 PFTs in each grid cell that are indexed by the \"npft\" dimension (01-15) in each data file. The PFT three-character abbreviations (\"pft_names\" variable) are listed in the same order as the \"npft\" dimension. To combine the PFT-specific output into grid cell totals, users must compute the area-weighted mean across the vector of PFT-specific values for each cell. Fractional areal coverages are given in the \"pft_area\" variable for each cell.\n<br>\n\n#### Crops_SIF_VegIndices_IL_NE_2136\nThis dataset contains half-hourly ground solar-induced chlorophyll fluorescence (SIF) and vegetation indices including NDVI, EVI, Red edge chlorophyll index, green chlorophyll index, and photochemical reflectance index at seven crop sites in Nebraska and Illinois for the period 2016-2021. Four sites were located at Eddy Covariance (EC) tower sites (sites US-Ne2, US-Ne3, US-UiB, and US-UiC), and three sites were located on private farms (sites Reifsteck, Rund, and Reinhart). The sites were either miscanthus, corn-soybean rotation or corn-corn-soybean rotation. The spectral data for SIF retrieval and hyperspectral reflectance for vegetation index calculation were collected by the FluoSpec2 system, installed near planting, and uninstalled after harvest to collect whole growing-season data. Raw nadir SIF at 760 nm from different algorithms (sFLD, 3FLD, iFLD, SFM) are included. SFM_nonlinear and SFM_linear represent the Spectral fitting method (SFM) with the assumption that fluorescence and reflectance change with wavelength non-linearly and linearly, respectively. Additional data include two SIF correction factors including calibration coefficient adjustment factor (f_cal_corr_QEPRO) and upscaling nadir SIF to eddy covariance footprint factor (ratio_EC footprint, SIF pixel), and measured FPAR from quantum sensors and Rededge NDVI calculated FPAR. The data are provided in comma-separated values (CSV) format.\n<br>\n\n#### Wetland_Soil_CarbonStocks_WA_2249\nThis dataset contains estimates of soil organic carbon stocks and wetland intrinsic potential (WIP) across the Hoh River Watershed in the Olympic Peninsula, WA, USA in 2012-2013. Estimates were derived from an equation based on wetland intrinsic potential and geology type (Stewart et al., 2023). Wetland intrinsic potential estimates the likelihood that that an area is a wetland using a random forest model built on vegetation, hydrology, and soil data (Halabisky et al., 2022). SOC estimates at 1 m and 30 cm, SOC standard deviations, and WIP are presented in Cloud-Optimized GeoTIFF (.tif) format at 4-m resolution. Also included are 36 field observations of SOC collected from 2020-08-01 to 2022-06-29. These are contained in a comma separated (.csv) file.\n<br>\n\n#### CMS_SOC_Mexico_1754\nThis dataset provides an estimate of soil organic carbon (SOC) in the top one meter of soil across Mexico at a 90-m resolution for the period 1999-2009. Carbon estimates (kg/m2) are based on a field data collection of 2852 soil profiles by the National Institute for Statistics and Geography (INEGI). The profile data were used for the development of a predictive model along with a set of environmental covariates that were harmonized in a regular grid of 90x90 m2 across all Mexican states. The base of reference was the digital elevation model (DEM) of the INEGI at 90-m spatial resolution. A model ensemble of regression trees with a recursive elimination of variables explained 54% of the total variability using a cross-validation technique of independent samples. The error associated with the predictive model estimates of SOC is provided. A summary of the total estimated SOC per state, statistical description of the modeled SOC data, and the number of pixels modeled for each state are also provided.\n<br>\n\n#### CMS_SOC_Mexico_CONUS_1737\nThis dataset provides two sets of gridded estimates of estimated soil organic carbon (SOC) and associated uncertainties for 0-30 cm topsoil layer in kg SOC/m2 at 250-m resolution across Mexico and the conterminous USA (CONUS). The first set of gridded SOC estimates, for the period 1991-2010, were derived using multi-source SOC field data and multiple environmental variables representative of the soil forming environment coupled with a machine learning approach (i.e., simulated annealing) and regression tree ensemble modeling for optimized SOC prediction. Predictions of gridded SOC and uncertainty based on multiple bulk density (BD) pedotransfer functions (PFTs) are also included. The second set of gridded SOC estimates, for the period 2009-2011, were derived from two fully independent validation field datasets from across both countries. Note that the same environmental variables and modeling approach used for the first set of estimates were applied to the second set to assess the models' sensitivity to multiple SOC data sources. The SOC field data for the first set of estimates are provided in this dataset and the other data sources, including the two independent validation field datasets, are referenced.\n<br>\n\n#### Country_SOC_Latin_America_1615\nThis dataset provides 5 x 5 km gridded estimates of soil organic carbon (SOC) across Latin America that were derived from existing point soil characterization data and compiled environmental prediction factors for SOC. This dataset is representative for the period between 1980 to 2000s corresponding with the highest density of observations available in the WoSIS system and the covariates used as prediction factors for soil organic carbon across Latin America. SOC stocks (kg/m2) were estimated for the SOC and bulk density point measurements and a spatially explicit measure of the SOC estimation error was also calculated. A modeling ensemble, using a linear combination of five statistical methods (regression Kriging, random forest, kernel weighted nearest neighbors, partial least squared regression and support vector machines) was applied to the SOC stock data at (1) country-specific and (2) regional scales to develop gridded SOC estimates (kg/m2) for all of Latin America. Uncertainty estimates are provided for the two model predictions based on independent model residuals and their full conditional response to the SOC prediction factors.\n<br>\n\n#### Tidal_Wetland_Soil_Carbon_1612\nThis dataset provides modeled estimates of soil carbon stocks for tidal wetland areas of the Conterminous United States (CONUS) for the period 2006-2010. Wetland areas were determined using both 2006-2010 Coastal Change Analysis Program (C-CAP) raster maps and the National Wetlands Inventory (NWI) vector data. All 30 x 30-meter C-CAP pixels were extracted that are coded as estuarine emergent, scrub/shrub, or forested in either 2006 or 2010. A soil database for model fitting and validation was compiled from 49 different studies with spatially explicit empirical depth profile data and associated metadata, totaling 1,959 soil cores from 18 of the 22 coastal states. Reported estimates of carbon stocks were derived with modeling approaches that included (1) applying a single average carbon stock value from the compiled soil core data, (2) applying models fit using the empirical data and applied spatially using soil, vegetation and salinity maps, (3) relying on independently generated soil carbon maps from The United States Department of Agriculture (USDA)'s Soil Survey Geographic Database (SSURGO), and the NWI that intersected with mapped tidal wetlands, and (4) using a version of SSURGO bias-corrected for bulk density. Comparisons of uncertainty, precision, and accuracy among these four approaches are also provided.\n<br>\n\n#### Tree_Canopy_Cover_Mexico_2137\nThe data set provides multi-year (2016-2018) percent tree cover (TC) estimates for entire Mexico at 30 m spatial resolution. The TC data (hereafter, NEX-TC) was derived from the 30 m Landsat Collection 1 product and a hierarchical deep learning approach (U-Net) developed in a previous CMS effort for the conterminous United States (CONUS) (Park et al., 2022). The hierarchical U-Net framework first developed a U-Net model for very high-resolution aerial images (NAIP) using training labels derived from previous work based on an interactive image segmentation tool and iterative updates with expert knowledge (Basu et al., 2015). The developed NAIP U-Net model and NAIP data produced 1-m NAIP TC across all lower 48 CONUS states. A Landsat U-Net model was developed for multi-year and large-scale TC mapping based on the very high-resolution NAIP TC made in the earlier stage. The Landsat U-Net model developed was adopted over the CONUS for testing its transferability, validation, and improvement across Mexico. This dataset provides national-scale percent tree cover estimates over Mexico and can be helpful for studies of carbon cycling, land cover and land use change, etc. The team has been working on improving temporal stability of the product and will update the product once the next version is ready to be shared.\n<br>\n\n#### High_Res_Tidal_Marsh_Veg_1609\nThis dataset provides maps of tidal marsh green vegetation, non-vegetation, and open water for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from current National Agriculture Imagery Program data (2013-2015) using object-based classification for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program (C-CAP) map. These 1m resolution maps were used to calculate the fraction of green vegetation within 30m Landsat pixels for the same tidal marsh regions and these data are provided in a related dataset.\n<br>\n\n#### Wetland_Salinity_Maps_2392\nThis dataset provides gridded average annual wetland salinity concentrations in practical salinity units (PSU) at 30-meter resolution within 24 coastal estuary sites in the United States predicted for 2020. Salinity in estuaries can serve as a proxy for sulfate concentration, which can inhibit methanogenesis. Data were derived from a hybrid approach to mapping salinity as a continuous variable using a combination of physical watershed and stream characteristics, optical remote sensing based on vegetation characteristics, and climate variables. Data are provided in cloud-optimized GeoTIFF format covering 33 Hydrologic Unit Code 8-digit (HUC8) watersheds to the extent of palustrine and estuarine wetlands as defined by NOAA's 2016 Coastal Change Analysis Program (C-CAP) Coastal Land Cover layer. Additionally, model outputs are provided in comma separated values (CSV) files, and code scripts are provided in a compressed (.zip) file.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","cog","earth observation","elevation","geospatial","hydrology","ice","land cover","lidar","netcdf","oceans","precipitation","radar","satellite imagery","soil moisture","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AGB_Pantropics_Amazon_Mexico_1824 v1 - This dataset provides gridded estimates of aboveground biomass (AGB) for live dry woody vegetation density in the form of both stock for the baseline year 2003 and annual change in stock from 2003 to 2016. Data are at a spatial resolution of approximately 500 m (463.31 m; 21.47 ha) for three geographies: the biogeographical limit of the Amazon Basin, the country of Mexico, and a Pantropical belt from 40 degrees North to 30 degrees South latitudes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/AGB_Pantropics_Amazon_Mexico/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Salt_Marsh_Biomass_CONUS_2348 v1 - This dataset provides estimates of aboveground biomass (AGB) and salt marsh extent in the contiguous United States for 2020 and includes all coastal watersheds across the contiguous United States at 10-m resolution. Estimates were generated by XGBoost machine learning regression.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Salt_Marsh_Biomass_CONUS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Howland_Forest_Biomass_Map_2434 v1 - This dataset holds aboveground biomass (AGB) estimates at 10-m spatial resolution for the Howland Research Forest in central Maine for 2012, 2015, 2017, 2021, and 2023. Forest inventory data were collected using 50 fixed-area plot sampling during the summers of 2021, 2023, and 2024.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Howland_Forest_Biomass_Map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Tidal_Marsh_Biomass_US_V1-1_1879 v1.1 - This dataset provides maps of aboveground tidal marsh biomass (g/m2) at 30 m resolution for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Estuarine and palustrine emergent tidal marsh areas were based on a 2010 NOAA Coastal Change Analysis Program (C-CAP) map.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Tidal_Marsh_Biomass_US_V1-1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_AGB_Landcover_Indonesia_1645 v1 - This dataset provides estimates of aboveground biomass, percent canopy cover, mean canopy height, landcover, and forest degradation index products for forests in Kalimantan, Indonesia (Island of Borneo) representative of conditions in late 2014. Data were combined from several sources including field sampling, airborne lidar, satellite measurements, a forest-type land cover map, and integrated into a random forest algorithm to produce these estimates.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_AGB_Landcover_Indonesia/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_iWED_V1_2452 v1 - This dataset provides an integrated Wildfire Event Dataset (iWED version 1) for wildfire events of 100 ha or more in area from 1992 to 2021 for the continental US. Fire information was compiled from a variety of state, regional, and federal-level agencies responsible for filing and archiving incident level reports.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_iWED_V1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_AGB_NW_USA_1719 v1 - This dataset provides annual maps of aboveground biomass (AGB, Mg/ha) for forests in Washington, Oregon, Idaho, and western Montana, USA, for the years 2000-2016, at a spatial resolution of 30 meters. Tree measurements were summarized with the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS) to estimate AGB in field plots contributed by stakeholders, then lidar was used to predict plot-level AGB using the Random Forests machine learning algorithm.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_AGB_NW_USA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Annual_Burned_Area_Maps_1708 v1 - This dataset provides maps of annual burned area for the part of Mawas conservation program in Central Kalimantan, Indonesia from 1997 through 2015. Landsat imagery (TM, ETM+, OLI/TIR) at 30 m resolution was obtained for this 19-year period, including the variables surface reflectance, brightness temperature, and pixel quality assurance, plus the indices NDVI, NDMI, NBR, NBR2, SAVI, and MSAVI.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Annual_Burned_Area_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_WFEIS_CONUS-AK_1306 v1 - This data set contains annual modeled estimates of wildland fire emissions at 0.01 degree (~1-km) spatial resolution from the Wildland Fire Emissions Information System (WFEIS v0.5) for the conterminous U.S. (CONUS) and Alaska for 2001 through 2013.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_WFEIS_CONUS-AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Blue_Carbon_Tidal_Wetland_Maps_2091 v1 - This dataset contains shapefiles showing location of tidal wetland parcels with the potential for net greenhouse gas removal if restored from current mapped condition to unimpeded tidal wetlands. These maps focus on managed lands in the contiguous United States along the ocean coasts and show impounded wetlands where reconnecting tidal flow could diminish methane production.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Blue_Carbon_Tidal_Wetland_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BlueFlux_AirborneObs_Florida_2327 v1 - This dataset includes airborne in situ measurements of greenhouse gas mixing ratios, meteorological parameters, and fluxes (CO2, CH4, latent heat fluxes, friction velocity, and convective velocity scale) calculated with wavelet transforms. CO2, CH4, CO, O3, and water vapor mixing ratios, and meteorological variables were obtained from a Beechcraft A90 King Air aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/BlueFlux_AirborneObs_Florida/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BlueFlux_Tidal_River_Water_2333 v1 - This dataset provides dissolved carbon (dissolved inorganic carbon and dissolved organic carbon), greenhouse gases, dissolved organic matter optical, and hydrological (water temperature, pH, alkalinity, dissolved oxygen) data collected from the Shark and Harney tidal rivers in the Everglades, Florida, USA. The data were collected as part of the NASA Carbon Monitoring System (CMS) BlueFlux field campaigns over the 2022 wet season (October 2022) and 2023 dry season (March 2023).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/BlueFlux_Tidal_River_Water/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BlueFlux_Gridded_CO2_CH4_2404 v1 - This dataset contains gridded estimates of carbon dioxide (CO2) and methane (CH4) fluxes at daily resolution covering the Southern Florida region from 2000 to 2024. Gridded CO2 and CH4 flux prototype products at 500-m spatial resolution were derived from a machine learning model based on eddy covariance (EC) measurements from 1) airborne fluxes collected seasonally with the NASA Carbon Airborne Flux Experiment (CARAFE) over the region during five flight deployments and 2) regional EC tower networks representing long term wetland ecosystem fluxes since 2004.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/BlueFlux_Gridded_CO2_CH4/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TLS_Lidar_BlueFlux_Mangroves_2311 v1 - This dataset contains point clouds of three-dimensional (3D) mangrove forest structure and volume collected from 10 sites in Everglades National Park, Florida. Data were collected during NASA CMS \"Blueflux\" campaigns in March 2022, October 2022, and March 2023.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/TLS_Lidar_BlueFlux_Mangroves/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Boreal_Arctic_Wetland_CH4_2351 v1 - This dataset provides an upscaled estimate of Boreal-Arctic wetland CH4 emissions at a weekly time scale from 2002 to 2021 at 0.5 by 0.5-degree spatial resolution. Ground truth data on wetland CH4 emissions from eddy covariance towers (139 site years) and chambers (168 site years) were used to train and validate a causality-guided machine learning model.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Boreal_Arctic_Wetland_CH4/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxFire v1 - This dataset provides the Carbon Flux for Fires. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxFire.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxFossilFuelPrior v2 - This dataset provides the Carbon Flux for Fossil Fuel Prior. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxFossilFuelPrior.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxOceanPrior v2 - This dataset provides the Carbon Flux for Ocean Carbon Prior. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxOceanPrior.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxFossilFuelPrior v3 - This dataset provides the Prior for the Fossil Fuel Carbon Flux. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxFossilFuelPrior.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxNBE v2 - This dataset provides the Carbon Flux from the Net Biome Exchange. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxNBE.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxNBEPrior v2 - This dataset provides the Carbon Flux from the Net Biome Exchange Prior. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxNBEPrior.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxLandPrior v3 - This dataset provides the Prior for the Land Carbon Flux. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxLandPrior.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxNBE v3 - This dataset provides the Carbon Flux for Posterior Net Biome Exchange (NBE). The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxNBE.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxOcean v3 - This dataset provides the Posterior Carbon Flux for the Ocean. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxOcean.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxOceanPrior v3 - This dataset provides the Prior for the Carbon Flux for Ocean. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxOceanPrior.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxTotal v2 - This dataset provides the Carbon Flux for Posterior Total Carbon. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxTotal.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxTotalPrior v3 - This dataset provides the Prior for Total Carbon Flux. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxTotalPrior.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxFossilfuel v1 - This dataset provides the Carbon Flux for Fossil Fuel. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxFossilfuel.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxOcean v1 - This dataset provides the Carbon Flux for Ocean Carbon. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxOcean.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxTotalprior v1 - This dataset provides the Carbon Flux for Prior Total Carbon. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxTotalprior.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxMISC v1 - This dataset provides the Carbon Flux for Shipping, Aviation, and Chemical Sources. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxMISC.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSFluxNEE v1 - This dataset provides the Carbon Flux from the Net Ecosystem Exchange. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSFluxNEE.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSLakeHuronPPM v1 - Monthly Average primary production/carbon fixation data for Lake Huron. The primary production data is derived using MODIS imagery with model data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSLakeHuronPPM.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSLakeHuronPPY v1 - Yearly Average primary production/carbon fixation data for Lake Huron. The primary production data is derived using MODIS imagery with model data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSLakeHuronPPY.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSLakeMichiganPPM v1 - Monthly Average primary production/carbon fixation data for Lake Michigan. The primary production data is derived using MODIS imagery with model data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSLakeMichiganPPM.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSLakeMichiganPPY v1 - Yearly Average primary production/carbon fixation data for Lake Michigan. The primary production data is derived using MODIS imagery with model data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSLakeMichiganPPY.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSLakeSuperiorPPM v1 - Monthly Average primary production/carbon fixation data for Lake Superior. The primary production data is derived using MODIS imagery with model data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSLakeSuperiorPPM.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMSLakeSuperiorPPY v1 - Yearly Average primary production/carbon fixation data for Lake Superior. The primary production data is derived using MODIS imagery with model data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSLakeSuperiorPPY.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_CONUS_Biomass_1752 v1 - This dataset provides annual estimates of six carbon pools, including forest aboveground live biomass, belowground biomass, aboveground dead biomass, belowground dead biomass, litter, and soil organic matter, across the conterminous United States (CONUS) for 2005, 2010, 2015, 2016, and 2017. Carbon stocks were estimated using a modified MaxEnt model.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_CONUS_Biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_CTL_NA_GOSAT_FOOTPRINTS v1 - This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the GOSAT satellite. Meteorological fields from the WRF model are used to drive STILT.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_CTL_NA_GOSAT_FOOTPRINTS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_CTL_NA_OCO2_FOOTPRINTS v1 - This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the OCO-2 satellite. Meteorological fields from the WRF model are used to drive STILT.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_CTL_NA_OCO2_FOOTPRINTS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_CTL_NA_TCCON_FOOTPRINTS v1 - This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the TCCON ground network. Meteorological fields from the WRF model are used to drive STILT.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_CTL_NA_TCCON_FOOTPRINTS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Global_Forest_Age_2345 v1 - This dataset provides classes of global forests delineated by status/condition in 2020 at approximately 30-m resolution. The data support generating Tier 1 estimates for Aboveground dry woody Biomass Density (AGBD) in natural forests in the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Forest_Age/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Simulated_SIF_NiwotRidge_1720 v1 - This dataset provides results for simulations of solar-induced chlorophyll fluorescence (SIF) implemented within the terrestrial biosphere Community Land Model (CLM 4.5) for Niwot Ridge, Colorado, USA, from 1998-2018. The data include outputs from three model simulations designed to test the importance of non-photochemical quenching (NPQ), that is, the absorbed light energy dissipated as heat, in determining seasonal SIF.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Simulated_SIF_NiwotRidge/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"C_FluxStocks_CLM5_DART_WestUS_1856 v1 - This dataset provides monthly estimates of biomass stocks and land-atmosphere carbon exchange across the western United States at 0.95 degrees longitude x 1.25 degrees latitude grid resolution from 1998 through 2010. The data include outputs from two types of model simulations: (1) a \"free\" simulation which used Community Land Model (CLM5.0) simulations forced with meteorology appropriate for complex mountainous terrain, and (2) \"assimilation\" runs using the land surface data assimilation system (CLM5-DART).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/C_FluxStocks_CLM5_DART_WestUS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_GO_CH4_SEC_TDYC_NA v1 - Methane emissions are provided by sector in the contiguous United States (CONUS), Canada, and Mexico by inverse analysis of in situ (GLOBALVIEWplus CH4ObsPack) and satellite (GOSAT) atmospheric methane observations. The inversion uses as a prior estimate the national anthropogenic emission inventories for the three countries reported by the US Environmental Protection Agency (EPA), En- vironment and Climate Change Canada (ECCC), and the Instituto Nacional de Ecología y Cambio Climático (INECC) in Mexico to the United Nations Framework Convention on Climate Change (UNFCCC) and thus serves as...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_GO_CH4_SEC_TDYC_NA.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_FluxEstimates_Aircraft_CO2_2336 v1 - This dataset provides gridded surface-atmosphere CO2 fluxes over North America from April 8 to November 18 during 2018 and 2019. Net ecosystem exchange (NEE) was estimated by the CMS-Flux-NA CO2 inversion system by assimilating in situ CO2 measurements and/or Orbiting Carbon Observatory (OCO-2) column-averaged CO2 retrievals.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_FluxEstimates_Aircraft_CO2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Mangrove_Biomass_Zambezi_1522 v1 - This dataset provides several estimates of aboveground biomass from various regressions and allometries for mangrove forest in the Zambezi River Delta, Mozambique. Plot level estimates of aboveground biomass are based on extensive tree biophysical measurements from field campaigns conducted in September and October of 2012 and 2013.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Mangrove_Biomass_Zambezi/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_LiDAR_AGB_PEF_2012_1318 v1 - This data set includes estimates of aboveground biomass (AGB) in 2012 from the Penobscot Experimental Forest (PEF) in Bradley, Maine. The AGB was modeled using LiDAR data gathered with the LiDAR Hyperspectral and Thermal Imager (G-LiHT) operated by Goddard Space Flight Center and field inventory data from 604 permanent Forest Inventory and Analysis (FIA) plots within the PEF.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_LiDAR_AGB_PEF_2012/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Riverine_N2O_Emissions_1791 v1 - This dataset provides modeled estimates of annual nitrous oxide (N2O) emissions at a coarse geographic scale (0.5 x 0.5 degree) for two sets of global rivers and streams covering the period of 1900-2016. Emissions (g N2O-N/yr) are provided for higher-order rivers and streams (>=4th order) and headwater streams (<4th order).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Global_Riverine_N2O_Emissions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Atmospheric_CO2_California_1641 v1 - This dataset provides measurements of atmospheric CO2 concentrations, carbon isotopes d13C and D14C, and fossil fuel CO2 (ffCO2) estimates from nine observation sites in California over three month-long campaigns in separate seasons of 2014-2015. ffCO2 was quantified based on the CO2 concentration and D14C.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Atmospheric_CO2_California/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Methane_Emissions_Boston_1291 v1 - This data set provides average hourly measured, modeled enhancements, and background methane (CH4) concentrations, atmospheric ethane (C2H6) measurements, prior CH4 flux fields by sector, and a spatial reconstruction of natural gas (NG) consumption in Boston, Massachusetts and the surrounding region. Atmospheric CH4 concentrations were measured continuously from September 2012 through August 2013 at four locations and atmospheric ethane was measured continuously for several months during 2012-2014 at one location.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Methane_Emissions_Boston/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Global_Cropland_Carbon_1279 v1 - This data set provides global estimates of carbon fluxes associate with annual crop net primary production (NPP) and harvested biomass, annual uptake and release by humans and livestock, and the total annual estimate of net carbon exchange (NCE) derived from these carbon fluxes. NCE estimates are for the combined crop plant harvest and consumption/expiration of fodder by livestock and of food by humans.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Cropland_Carbon/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_WRF_Footprints_CO2_Signals_1381 v1 - This data set provides estimated CO2 emission signals for 16 regions (air quality basins) in California, USA, during the individual months of November 2010 and May 2011. The CO2 signals were predicted from simulated atmospheric CO2 observations and modeled fossil fuel emissions and biosphere CO2 fluxes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_WRF_Footprints_CO2_Signals/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GPP_CONUS_TROPOMI_1875 v1 - This dataset includes estimates of gross primary production (GPP) for the conterminous U.S., for 2018-02-15 to 2021-10-15, based on measurements of solar-induced chlorophyll fluorescence from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5P satellite platform. GPP was estimated from rates of photosynthesis inferred from SIF using a linear model and ecosystem scaling factors from 102 AmeriFlux sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/GPP_CONUS_TROPOMI/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Pantropical_Forest_Biomass_1337 v1 - This data set provides estimates of pre-deforestation aboveground live woody biomass (AGLB) at 30-m resolution for deforested areas of tropical America, tropical Africa, and tropical Asia for the year 2000. The biomass estimates are only for areas where deforestation occurred during the period 2000 through 2012.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Pantropical_Forest_Biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Daily_ET_MexFlux_1309 v1 - This data set provides daily average observations for evapotranspiration (measured and gap-filled), precipitation, net radiation, soil water content, air temperature, vapor pressure deficit, and normalized vegetation index (NDVI) from two water-limited shrubland sites for years 2008-2010. Both sites are located in the northwest part of Mexico and are part of the MexFlux network.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Daily_ET_MexFlux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Fire_Weather_Data_AK_1509 v1 - This dataset provides daily fire weather indices for interior Alaska during the active fire seasons from 2001 to 2010. Data are gridded at 60-m resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Fire_Weather_Data_AK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"FIA_Forest_Biomass_Estimates_1873 v1 - This dataset provides forest biomass estimates for the conterminous United States based on data from the USDA Forest Inventory and Analysis (FIA) program. FIA maintains uniformly measured field plots across the conterminous U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/FIA_Forest_Biomass_Estimates/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Forest_Productivity_1221 v1 - Notice: This data set and guide were updated on June 30, 2014 to correct an error in the reported units. The data values were not changed.Spatially-gridded estimates of above ground biomass (AGB), net primary productivity (NPP), and net ecosystem productivity (NEP) are provided for forested areas of the conterminous United States (CONUS).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Forest_Productivity/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Forest_Carbon_Fluxes_1313 v1 - This data set provides maps of estimated carbon in forests of the 48 continental states of the US for the years 2005-2010. Carbon (termed committed carbon) stocks were estimated for forest aboveground biomass, belowground biomass, standing dead stems, and litter for the year 2005.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Forest_Carbon_Fluxes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Landscapes_Brazil_Forests_1301 v1 - This data set provides measurements for diameter at breast height (DBH), commercial tree height, and total tree height for forest inventories taken at the Fazenda Cauaxi and the Fazenda Nova Neonita, Paragominas municipality, Para, Brazil. Also included for each tree are the common, family, and scientific name, coordinates, canopy position, crown radius, and for dead trees the decomposition status.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Landscapes_Brazil_Forests/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LIDAR_FOREST_CANOPY_HEIGHTS_1271 v1 - This data set provides estimates of forest canopy height derived from the Geoscience Laser Altimeter System (GLAS) LiDAR instrument that was aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite. A global GLAS waveform data set (n=12,336,553) from collection periods between October 2004 and March 2008 was processed to obtain canopy height estimates.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/LIDAR_FOREST_CANOPY_HEIGHTS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Global_Monthly_Wetland_CH4_1502 v1 - This data set provides global monthly wetland methane (CH4) emissions and uncertainty data products derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies. The data are at 0.5 by 0.5-degree resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Monthly_Wetland_CH4/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MonthlyWetland_CH4_WetCHARTsV2_2346 v1.3.3 - This dataset provides global monthly wetland methane (CH4) emissions estimates at 0.5 by 0.5-degree resolution for the period 2001-01-01 to 2022-08-31 that were derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies that encompass the main sources of uncertainty in wetland CH4 emissions. There are 18 model configurations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/MonthlyWetland_CH4_WetCHARTsV2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Global_Livestock_CH4_CO2_1329 v2 - This data set provides global annual carbon flux estimates, at 0.05-degree resolution, associated with livestock feed intake, manure, manure management, respiration, and enteric fermentation, summed over all livestock types. These fluxes can be summed across multiple grid cells to obtain totals for any given areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Livestock_CH4_CO2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Global_Mangrove_Forest_Ht_2251 v1 - This dataset characterizes canopy heights of mangrove-forested wetlands globally for 2015 at 12-m resolution. Estimates of maximum canopy height (height of the tallest tree) were derived from the German Space Agency's TanDEM-X data that produced global digital surface models.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Mangrove_Forest_Ht/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_WRF_Model_Products_1338 v1 - This data set contains estimated hourly CO2 atmospheric mole fractions and meteorological observations over North America for the year 2010 at a horizontal grid resolution of 27 km and vertical resolution from the surface to 50 hPa. The data are output from the Penn State WRF-Chem version of the Weather Research and Forecasting (WRF) model using lateral boundary conditions and surface fluxes from the CMS-Flux Inversion system.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_WRF_Model_Products/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GCAM_Land_Cover_2005-2095_1216 v1 - The data provided are annual land cover projections for years 2005 through 2095 generated by the Global Change Assessment Model (GCAM) Version 3.1. For the conterminous USA, the GCAM global gridded results were downscaled to ~5.6 km (0.05 degree) resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/GCAM_Land_Cover_2005-2095/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Landcover_Colombian_Amazon_1783 v1 - This dataset provides annual maps of land cover classes for the Colombian Amazon from 2001 through 2016 that were created by classifying time segments detected by the Continuous Change Detection and Classification (CCDC) algorithm. The CCDC algorithm detected changes in Landsat pixel surface reflectance across the time series, and the time segments were classified into land cover types using a Random Forest classifier and manually collected training data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Landcover_Colombian_Amazon/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Sonoma_County_Forest_AGB_1764 v1 - This data set provides estimates of above-ground woody biomass and uncertainty at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha), were generated using a combination of airborne LiDAR data and field plot measurements with a parametric modeling approach.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Sonoma_County_Forest_AGB/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Landscapes_Brazil_LiDAR_1302 v1 - This data set provides raw LiDAR point cloud data and derived Digital Terrain Models (DTMs) for five forested areas in the municipality of Paragominas, Para, Brazil, for the years 2012, 2013, and 2014. Data are included for two areas in Paragominas for 2013 and 2014, two areas for the Fazenda Cauaxi for 2012 and 2014, and for the Fazenda Andiroba for 2014.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Landscapes_Brazil_LiDAR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_LiDAR_Indonesia_1518 v1 - This dataset provides airborne LiDAR data collected over 90 sites totaling approximately 100,000 hectares of forested land in Kalimantan, Indonesia on the island of Borneo in late 2014. The data were collected as part of an effort to establish a national forest monitoring system for Indonesia that uses a combination of remote sensing and ground-based forest carbon inventory approaches.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_LiDAR_Indonesia/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_LiDAR_Point_Cloud_Zambezi_1521 v1 - This data set provides high-resolution LiDAR point cloud data collected during surveys over mangrove forests in the Zambezi River Delta in Mozambique in May 2014. The data are arranged into 144 1- by 1-km tiles.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_LiDAR_Point_Cloud_Zambezi/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Maryland_AGB_Canopy_1320 v1 - This data set provides 30-meter gridded estimates of aboveground biomass (AGB), canopy height, and canopy coverage for the state of Maryland in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery to select 848 field sampling sites for biomass measurements.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Maryland_AGB_Canopy/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_LiDAR_Biomass_CanHt_Sonoma_1523 v1 - This data set provides estimates of above-ground biomass (AGB), canopy height, and percent tree cover at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha) were generated using a combination of LiDAR data, field plot measurements, and random forest modeling approaches.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_LiDAR_Biomass_CanHt_Sonoma/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_LiDAR_Products_Indonesia_1540 v1 - This dataset provides canopy height and elevation data products derived from airborne LiDAR data collected over 90 sites on the island of Borneo in late 2014. The sites cover approximately 100,000 hectares of forested land in Kalimantan, Indonesia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_LiDAR_Products_Indonesia/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Pilot_Biomass_1257 v1 - These data consist of high-resolution maps of aboveground biomass at four forested sites in the US: Garcia River Tract in California, Anne Arundel and Howard Counties in Maryland, Parker Tract in North Carolina, and Hubbard Brook Experimental Forest in New Hampshire. Biomass maps were generated using a combination of field data (forest inventory and Lidar) and modeling approaches.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Pilot_Biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Pennsylvania_Tree_Cover_1334 v1.1 - This data set provides high-resolution (1-m) tree canopy cover for states in the Northeast USA. State-level canopy cover data are currently available for Pennsylvania (data for nominal year 2008), Delaware (2014), and Maryland (2013).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Pennsylvania_Tree_Cover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Mangrove_CanHt_Stand_Age_1377 v1 - This data set provides canopy height, land cover change, and stand age estimates for mangrove forests in the Rufiji River Delta in Tanzania. The estimates were derived from a canopy height model (CHM) using TanDEM-X imagery and Polarimetric SAR interferometry (Pol-InSAR) techniques.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Mangrove_CanHt_Stand_Age/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Mangrove_Canopy_Ht_Zambezi_1357 v1 - This data set provides high resolution canopy height estimates for mangrove forests in the Zambezi Delta, Mozambique, Africa. The estimates were derived from three separate canopy height models (CHM) using airborne Lidar data, stereophotogrammetry with WorldView 1 imagery, and Interferometric-Synthetic Aperture Radar (In-SAR) techniques with TanDEM-X imagery.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Mangrove_Canopy_Ht_Zambezi/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Mangrove_Canopy_Height_1327 v1 - This data set provides canopy height estimates for mangrove forests at 0.6 x 0.6 m resolution in three study sites located in southeastern Mozambique, Africa: two sites on Inhaca Island and one in the Maputo Elephant Reserve, located in the southern province of Maputo for September, 2012. The estimates were derived from WorldView1 (WV-1) very high resolution (VHR) stereo images processed using the Ames Stereo Pipeline (ASP) digital surface model (DSM) tool.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Mangrove_Canopy_Height/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Mangrove_Cover_1670 v1.1 - This dataset provides estimates of mangrove extent for 2016, and mangrove change (gain or loss) from 2000 to 2016, in major river delta regions of eight countries: Bangladesh, Gabon, Jamaica, Mozambique, Peru, Senegal, Tanzania, and Vietnam. For mangrove extent, a combination of Landsat 8 OLI, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) elevation data were used to create country-wide maps of mangrove landcover extent at a 30-m resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Mangrove_Cover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_CO2_Fluxes_TBMO_1315 v1 - This data set provides global, gridded, model-derived net ecosystem exchange (NEE) of CO2 flux between the land and atmosphere at 3-hourly time steps over seven years (2004-2010) at three different spatial resolutions: 0.5 x 0.5 degree, 2.0 x 2.5 degrees, and 4.0 x 5.0 degrees (latitude/longitude). The 3-hourly data were derived from monthly NEE outputs of 15 global land surface models and four ensemble products in the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_CO2_Fluxes_TBMO/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_SST_GPP_Mexico_1310 v1 - This data set provides data for MODIS-derived (1) gross primary productivity (GPP) for the years 2000-2010, (2) fraction of photosynthetically active radiation (fPAR) for the years 2003-2013, (3) sea surface temperature (SST) for the years 2003-2013, and (4) the NOAA-source Multivariate ENSO Index (MEI) data for the years 2003-2013 (as a measure of the El Nino/Southern Oscillation). The study areas were three transects on the Baja California Peninsula, Mexico, and the adjacent Pacific Ocean.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_SST_GPP_Mexico/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Great_Basin_Biomass_1755 v1 - This dataset provides annual maps of live aboveground tree biomass (Mg/ha) for pinyon-juniper forests across the Great Basin of the Western USA for the years 2000-2016 at a spatial resolution of 30 meters. Biomass estimates are limited to areas of the Great Basin defined as a pinyon-juniper ecosystem type by the 2016 Landfire Existing Vegetation Type map.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Great_Basin_Biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_SABGOM_Model_Simulations_1510 v1 - This dataset contains monthly mean ocean surface physical and biogeochemical data for the Gulf of America simulated by the South Atlantic Bight and Gulf of America (SABGOM) model on a 5-km grid from 2005 to 2010. The simulated data include ocean surface salinity, temperature, dissolved inorganic nitrogen (DIN), dissolved inorganic carbon (DIC), partial pressure of CO2 (pCO2), air-sea CO2 flux, surface currents, and primary production.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_SABGOM_Model_Simulations/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Soil_CO2_Efflux_1298 v1 - This data set provides the results of (1) monthly measurements of soil CO2 efflux, volumetric water content, and temperature, and (2) seasonal measurements of soil (porosity, bulk density, nitrogen (N) and carbon (C) content) and vegetation (leaf area index (LAI), litter and fine root biomass) properties in a water-limited ecosystem in Baja California, Mexico. Measurements and samples were collected from August 2011 to August 2012.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Soil_CO2_Efflux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"C_Pools_Fluxes_CONUS_1837 v1 - This dataset provides estimates of carbon pools, fluxes, and associated uncertainties across the contiguous USA (CONUS) at 0.5-degree resolution for all terrestrial land cover types. Carbon pools include labile carbon, foliar carbon, fine root, woody carbon, litter carbon, and soil organic carbon.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/C_Pools_Fluxes_CONUS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Vermont_HighRes_LandCover_2072 v1 - This dataset contains estimates of tree canopy cover presence at high resolution (0.5m) across the state of Vermont for 2016 in Cloud-Optimized GeoTIFF (.tif) format. Tree canopy was derived from 2016 high-resolution remotely sensed data as part of the Vermont High-Resolution Land Cover mapping project.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Vermont_HighRes_LandCover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Tree_Canopy_Cover_Mexico_V2_2445 v2 - This dataset provides 20-year tree cover (TC) estimates at 30-m spatial resolution for Mexico from 2000 to 2019 using Landsat time series, airborne LiDAR, and machine learning. The TC data (hereafter, CMS-TC) offers accurate and consistent national-scale percent tree cover estimates with an overall coefficient of determination (R squared) of 0.81 and a root mean square error (RMSE) of 11.90%.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Tree_Canopy_Cover_Mexico_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Continuous_Lifeform_Maps_CONUS_1809 v1 - This dataset contains estimates of percent cover of tree, shrub, herb, and other (non-vegetation) lifeform classes and uncertainties for the conterminous U.S. (CONUS).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Continuous_Lifeform_Maps_CONUS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Uncertainty_US_Coastal_GHG_1650 v1 - This dataset provides maps of coastal wetland carbon and methane fluxes and coastal wetland surface elevation from 2006 to 2011 at 30 m resolution for coastal wetlands of the conterminous United States. Total coastal wetland carbon flux per year per pixel was calculated by combining maps of wetland type and change with soil, biomass, and methane flux data from a literature review.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Uncertainty_US_Coastal_GHG/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Niwot_Ridge_CNPAM_Fluorescence_1722 v1 - This dataset provides chlorophyll fluorescence measurements made on pine and spruce needle tissues at the Niwot Ridge AmeriFlux Core site (US-NR1) near Nederland, Colorado, USA, during the summers of 2017 and 2018. Two types of measurements were made using pulse-amplitude-modulation (PAM) fluorometry: the photosystem II (PSII) operating efficiency in the light (Fq''/Fm'' at variable light levels), and the maximum quantum efficiency of PSII photochemistry (Fv/Fm) on dark-acclimated tissues.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Niwot_Ridge_CNPAM_Fluorescence/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Niwot_Ridge_Pigment_1723 v1 - This dataset provides concentrations of pigments in pine and spruce needle tissues collected at the Niwot Ridge AmeriFlux Core site (US-NR1) near Nederland, Colorado, USA, during the summers of 2017 and 2018. Pigments measured included Chlorophyll A and B, Violaxanthin, Antheraxanthin, Zeaxanthin, Neoxanthin, Lutein, and beta-Carotene.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Niwot_Ridge_Pigment/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_DARTE_V2_1735 v2 - This data set provides a 38-year, 1-km resolution inventory of annual on-road CO2 emissions for the conterminous United States based on roadway-level vehicle traffic data and state-specific emissions factors for multiple vehicle types on urban and rural roads as compiled in the Database of Road Transportation Emissions (DARTE). CO2 emissions from the on-road transportation sector are provided annually for 1980-2017 as a continuous surface at a spatial resolution of 1 km.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_DARTE_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GCRW_DEM_2016_1793 v1 - This dataset contains four alternative digital elevation models (DEMs) at 1 m resolution and model performance statistical metrics for the Global Change Research Wetland (GCReW) site on the Rhode River, a tributary of the Chesapeake Bay in Maryland, USA, for the year 2016. Three DEMs were created by using different strategies for correcting positive biases in Light Detection and Ranging (LiDAR)-based DEMs that are common in tidal wetlands.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/GCRW_DEM_2016/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Disturbance_Biomass_Maps_1679 v1 - This dataset provides derived disturbance history and predicted annual forest biomass maps at 30-m resolution for six selected Landsat scenes across the Conterminous United States (CONUS) for the period 1985-2014. The focus sites are in the following states: Colorado, Maine, Minnesota, Oregon, Pennsylvania, and South Carolina.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Disturbance_Biomass_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Cropland_Carbon_Fluxes_2125 v1 - This dataset contains daily estimates of carbon fluxes in croplands derived from the \"ecosys\" model covering a portion of the Midwestern US (Illinois, Indiana, and Iowa) at county-level resolution from 2001-2018. Ecosys simulates water, energy, carbon, and nutrient cycles simultaneously for various ecosystems, including agricultural systems at up to hourly resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Cropland_Carbon_Fluxes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"EF_Data_Mexico_1693 v1 - This dataset provides a map of the distribution of ecosystem functional types (EFTs) at 0.05 degree resolution across Mexico for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/EF_Data_Mexico/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_EFT_CONUS_1659 v1 - This dataset provides maps of the distribution of ecosystem functional types (EFTs) and the interannual variability of EFTs at 0.05 degree resolution across the conterminous United States (CONUS) for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_EFT_CONUS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DLEM_C_N_Export_1699 v1 - This dataset provides estimates for export and leaching of dissolved inorganic carbon (DIC), dissolved organic carbon (DIC), total organic carbon (TOC), particulate organic carbon (POC), ammonium (NH4+), nitrate (NO3-), and total organic nitrogen (TON) from the Mississippi River Basin (MRB) to the Gulf of Mexico. The estimates are provided for a historical period of 1901-2014, and a future period of 2010-2099 (carbon estimates only) under two scenarios of high and low levels of population growth, economy, and energy consumption, respectively.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/DLEM_C_N_Export/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Fire_Emissions_Indonesia_2118 v1 - This dataset provides 10-minute fire emissions within 0.1-degree regularly spaced intervals across Indonesia from July 2015 to December 2020. The dataset was produced with a top-down approach based on fire radiative energy (FRE) and smoke aerosol emission coefficients (Ce) derived from multiple new-generation satellite observations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Fire_Emissions_Indonesia/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Forest_Carbon_Maryland_1660 v1 - This dataset provides 90-m resolution maps of estimated forest aboveground biomass (Mg/ha) for nominal year 2011 and projections of carbon sequestration potential for the state of Maryland. Estimated biomass and sequestration potential were computed using the Ecosystem Demography (ED) model, which integrates data from multiple sources, including: climate variables from the North American Regional Reanalysis (NARR) Product, soil variables from the Soil Survey Geographic Database (SSURGO), land cover variables from airborne lidar, the National Agriculture Imagery Program (NAIP) and the Nation...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Forest_Carbon_Maryland/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AGB_Carbon_Sequestration_RGGI_1922 v1 - This dataset provides 90 m estimates of forest aboveground biomass (Mg/ha) for nominal 2011 and projections of carbon sequestration potential for 11 states in the Regional Greenhouse Gas Initiative (RGGI) domain. The RGGI is a cooperative, market-based effort among States in the eastern United States.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/AGB_Carbon_Sequestration_RGGI/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Maine_Forest_Biomass_Map_2435 v1 - This dataset holds estimates of forest aboveground biomass (AGB) for Maine, USA, in 2023. AGB was estimated using airborne LiDAR data from the USGS 3DEP project and a deep learning convolutional neural network (CNN) model.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Maine_Forest_Biomass_Map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Annual_Forest_AGB_Maryland_2384 v1 - This dataset includes estimates of annual forest aboveground biomass over the state of Maryland, USA, for the period 1984-2023. It was generated by a modeling approach that linked an ecosystem model called Ecosystem Demography (ED) model, airborne lidar data of canopy height in circa 2010, and the remote sensing based land cover change dataset (NAFD).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Annual_Forest_AGB_Maryland/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AGB_NEP_Disturbance_US_Forests_1829 v2 - This dataset, derived from the National Forest Carbon Monitoring System (NFCMS), provides estimates of forest carbon stocks and fluxes in the form of aboveground woody biomass (AGB), total live biomass, total ecosystem carbon, aboveground coarse woody debris (CWD), and net ecosystem productivity (NEP) as a function of the number of years since the most recent disturbance (i.e., stand age) for forests of the conterminous U.S. at a 30 m resolution for the benchmark years 1990, 2000, and 2010.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/AGB_NEP_Disturbance_US_Forests/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Forest_Inventory_Brazil_2007 v1 - This dataset provides the complete catalog of forest inventory and biophysical measurements collected over selected forest research sites across the Amazon rainforest in Brazil between 2009 and 2018 for the Sustainable Landscapes Brazil Project. This dataset includes measurements for diameter at breast height (DBH), commercial tree height, and total tree height for forest inventories.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Forest_Inventory_Brazil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GEOS_CASAGFED_3H_NEE v2 - This product provides 3 hourly average net ecosystem exchange (NEE) and gross ecosystem exchange (GEE) of Carbon derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA- GFED3) model. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/GEOS_CASAGFED_3H_NEE.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GEOS_CASAGFED_D_FIRE v2 - This product provides Daily average wildfire emissions (FIRE) and fuel wood burning emissions (FUEL) derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA- GFED3) model. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/GEOS_CASAGFED_D_FIRE.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GEOS_CASAGFED_M_FLUX v2 - This product provides Monthly average Net Primary Production (NPP), heterotrophic respiration (Rh), wildfire emissions (FIRE), and fuel wood burning emissions (FUEL) derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA- GFED3) model. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/GEOS_CASAGFED_M_FLUX.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Global_Fire_Atlas_1642 v1 - The Global Fire Atlas is a global dataset that tracks the day-to-day dynamics of individual fires to determine the timing and location of ignitions, fire size, duration, daily expansion, fire line length, speed, and direction of spread. These individual fire characteristics were derived based on the Global Fire Atlas algorithm and estimated day of burn information at 500-m resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 MCD64A1 burned area product.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Fire_Atlas/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GlobFireCarbon v1 - This dataset provides carbon monoxide and carbon dioxide flux from fires constrained by satellite observations. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/GlobFireCarbon.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Global_Forest_AGC_2180 v1 - This dataset provides global gridded estimates of forest aboveground carbon stocks and potential fluxes at a 0.01-degree resolution. It was derived by initializing a newly developed global Ecosystem Demography model (ED v3.0) with novel remote sensing observations of tree canopy height collected by GEDI and ICESat-2, two NASA spaceborne lidar missions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Forest_AGC/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Methane_Flaring_Sites_VIIRS_1874 v1 - This dataset contains annual global flare site surveys from 2012-2019 derived from Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar Partnership (SNPP) satellite. Gas flaring sites were identified from heat anomalies first estimated by the VIIRS Nightfire (VNF) algorithm from which high-temperature biomass burning and low-temperature gas flaring were separated based on temperature and persistence.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Methane_Flaring_Sites_VIIRS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Global_Soil_Respiration_1736 v1 - This dataset provides six global gridded products at 1-km resolution of predicted annual soil respiration (Rs) and associated uncertainty, maps of the lower and upper quartiles of the prediction distributions, and two derived annual heterotrophic respiration (Rh) maps. A machine learning approach was used to derive the predicted Rs and uncertainty data using a quantile regression forest (QRF) algorithm trained with observations from the global Soil Respiration Database (SRDB) version 3 spanning from 1961 to 2011.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Soil_Respiration/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SoilResp_HeterotrophicResp_1928 v1 - This dataset provides global gridded estimates of annual soil respiration (Rs) and soil heterotrophic respiration (Rh) and associated uncertainties at 1 km resolution. Mean soil respiration was estimated using a quantile regression forest model utilizing data from the global Soil Respiration Database Version 5 (SRDB-V5) and covariates of mean annual temperature, seasonal precipitation, and vegetative cover.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/SoilResp_HeterotrophicResp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GFEI_CH4 v1 - This is a global inventory of methane emissions from fuel exploitation (GFEI) created for the NASA Carbon Monitoring System (CMS). The emission sources represented in this dataset include fugitive emission sources from oil, gas, and coal exploitation following IPCC 2006 definitions and are estimated using bottom-up methods.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/GFEI_CH4.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Global_Map_Mangrove_Canopy_1665 v1.3 - This dataset characterizes the global distribution, biomass, and canopy height of mangrove-forested wetlands based on remotely sensed and in situ field measurement data. Estimates of (1) mangrove aboveground biomass (AGB), (2) maximum canopy height (height of the tallest tree), and (3) basal-area weighted height (individual tree heights weighted in proportion to their basal area) for the nominal year 2000 were derived across a 30-meter resolution global mangrove ecotype extent map using remotely-sensed canopy height measurements and region-specific allometric models.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Map_Mangrove_Canopy/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Global_Mangrove_Loss_1768 v1 - This dataset provides estimates of the extent of mangrove loss, land cover change, and its anthropogenic or climatic drivers in three time periods: 2000-2005, 2005-2010, and 2010-2016. Landsat-based Normalized Difference Vegetation Index (NDVI) anomalies were used to determine loss extent in each period.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Global_Mangrove_Loss/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMSGCH4F v1 - This dataset provides global methane fluxes optimized with GOSAT data for 2010-2018. It is supported by the Carbon Monitoring System project.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMSGCH4F.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Salt_Marsh_Change_2122 v1 - This dataset provides global salt marsh change, including loss and gain for five-year periods from 2000-2019. Loss and gain at a 30 m spatial resolution were estimated with Normalized Difference Vegetation Index (NDVI) anomaly algorithm using Landsat 5, 7, and 8 collections within the known extent of salt marshes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Global_Salt_Marsh_Change/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Freshwater_CH4Emissions_2253 v1 - This dataset provides monthly globally gridded freshwater wetland methane emissions from 2001-2018 in nmol CH4 m-2 s-1, g C-CH4 m-2 d-1, and TgCH4 grid cell-1 month-1. The data were derived from a six-predictor random forest upscaling model (UpCH4) trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites covering bog (8), fen (8), marsh (10), swamp (6), and wet tundra (11) wetland classes and distributed across Arctic-boreal (20), temperate (16), and (sub)tropical (7) climate zones.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Global_Freshwater_CH4Emissions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Tidal_Marsh_Vegetation_US_1608 v1 - This dataset provides 30m resolution maps of the fraction of green vegetation within tidal marshes for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD; Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from a 1m classification of 2013 to 2015 National Agriculture Imagery Program (NAIP) images as tidal marsh green vegetation, non-vegetation, and open water.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Tidal_Marsh_Vegetation_US/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CStocks_Greenness_Mangroves_MX_1853 v1 - This dataset provides estimates of greenness trends, above- and belowground carbon stocks, and climate variables of the persistent mangrove forests on the coasts of Mexico (PMFM) at a 1 km resolution from 2001 through 2015. Data are available as one-time estimates or across the temporal range; typically as monthly summaries.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CStocks_Greenness_Mangroves_MX/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Tidal_Wetland_GPP_CONUS_1792 v1 - This dataset provides mapped tidal wetland gross primary production (GPP) estimates (g C/m2/day) derived from multiple wetland types at 250-m resolution across the conterminous United States at 16-day intervals from March 5, 2000, through November 17, 2019. GPP was derived with the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field-based eddy covariance (EC) tower GPP data into a single Bayesian framework along with Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Tidal_Wetland_GPP_CONUS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SIF_PAR_fPAR_US_Midwest_2018_1813 v1 - This dataset provides estimated solar-induced chlorophyll fluorescence (SIF) of specific vegetation types and total SIF under clear-sky and real/cloudy conditions at a resolution of 4 km for the Midwest USA. The estimates are 8-day averaged daily means over the 2018 crop growing season for the time period 2018-05-01 to 2018-09-29.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/SIF_PAR_fPAR_US_Midwest_2018/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_HR_MNA_CH4_FLUX v1 - This data set contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by Greenhouse Gases Observing SATellite (GOSAT) observations. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_HR_MNA_CH4_FLUX.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Landcover_Indonesia_1838 v1 - This dataset contains annual land use/cover (LUC) maps at 30 m resolution across Mawas, Central Kalimantan, Indonesia. There are six files, each representing a five-year interval over the period 1994-2019.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Landcover_Indonesia/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Estimated_Biomass_Stock_Amazon_1648 v1 - This dataset provides estimates of forest aboveground biomass for three study areas and the entire Paragominas municipality, in Para, Brazil, in 2012. Aboveground biomass (in megagrams of carbon per hectare) was measured for inventory plots within the study (focal) areas, and then assimilated and modeled with LiDAR and PALSAR metrics using gradient boosting machines (GBM) to predict spatially explicit forest aboveground biomass and uncertainties for the entire focal areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Estimated_Biomass_Stock_Amazon/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_LiDAR_Biomass_MD_PA_DE_1538 v2 - This dataset provides 30-meter gridded estimates of aboveground biomass (AGB), forest canopy height, and canopy coverage for Maryland, Pennsylvania, and Delaware in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery in a model-based stratification that was used to select 848 sampling sites for AGB estimation.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_LiDAR_Biomass_MD_PA_DE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AGB_CanopyHt_Cover_NewEngland_1854 v1 - This dataset provides 30 m gridded estimates of aboveground biomass density (AGBD), forest canopy height, and tree canopy coverage for the New England Region of the U.S., including the state of Maine, Vermont, New Hampshire, Massachusetts, Connecticut, and Rhode Island, for the nominal year 2015. It is based on inputs from 1 m resolution Leaf-off LiDAR data collected from 2010 through 2015, high-resolution leaf-on agricultural imagery, and FIA plot-level measurements.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/AGB_CanopyHt_Cover_NewEngland/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Forest_AGB_NW_USA_1766 v1 - This dataset provides maps of aboveground forest biomass (AGB) of living trees and standing dead trees in Mg/ha across portions of Northwestern United States, including Washington, Oregon, Idaho, and Montana, at a spatial resolution of 30 m. Forest inventory data were compiled from 29 stakeholders that had overlapping lidar imagery.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Forest_AGB_NW_USA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LiDAR_Forest_Inventory_Brazil_1644 v1 - This dataset provides the complete catalog of point cloud data collected during LiDAR surveys over selected forest research sites across the Amazon rainforest in Brazil between 2008 and 2018 for the Sustainable Landscapes Brazil Project. Flight lines were selected to overfly key field research sites in the Brazilian states of Acre, Amazonas, Bahia, Goias, Mato Grosso, Para, Rondonia, Santa Catarina, and Sao Paulo.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/LiDAR_Forest_Inventory_Brazil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_LiDAR_AGB_California_1537 v1 - This dataset provides estimates of aboveground biomass and spatially explicit uncertainty from 53 airborne LiDAR surveys of locations throughout California between 2005 and 2014. Aboveground biomass was estimated by performing individual tree crown detection and applying a customized \"remote sensing aware\" allometric equation to these individual trees.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_LiDAR_AGB_California/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_CH4_FLX_CA v1 - This data set (CMS_CH4_FLX_CA) contains the yearly average methane (CH4) flux for Canada's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by ICF International in 2013. A related data set (CMS_CH4_FLX_MX) contains the yearly average methane (CH4) flux for Mexico's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by the Mexican Petrolium Institute in 2010.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_CH4_FLX_CA.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_CH4_FLX_MX v1 - This data set (CMS_CH4_FLX_MX) contains the yearly average methane (CH4) flux for Mexico's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by the Mexican Petrolium Institute in 2010. A related data set (CMS_CH4_FLX_CA) contains the yearly average methane (CH4) flux for Canada's oil and gas systems based on a bottom up calculation of oil/gas emissions reported by ICF International in 2013.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_CH4_FLX_MX.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_CH4_FLX_NAD v1 - The CMS Methane (CH4) Flux for North America data set contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by Greenhouse Gases Observing SATellite (GOSAT) observations. The nested approach of the inversion enables large point sources to be resolved while aggregating regions with weak emissions and minimizing aggregation errors.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_CH4_FLX_NAD.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"Methane_Ethane_MA_NH_1982 v1 - This dataset provides the hourly average of continuous atmospheric measurements of methane (CH4) from two urban sites and three boundary sites in and around Boston, Massachusetts, U.S., from September 2012-May 2020, measured with Picarro cavity ring down spectrometers (CRDS). Five-minute average atmospheric measurements of ethane (C2H6) and methane at Copley Square in Boston, MA, are also provided, with ethane measured with a laser spectrometer and methane measured with a Picarro CRDS.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Methane_Ethane_MA_NH/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CH4_Plume_AVIRIS-NG_1727 v1 - This dataset provides maps of methane (CH4) plumes along flight lines over identified methane point-source emitting infrastructure across the State of California, USA collected during 2016 and 2017. Methane plume locations were derived from Next-Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) overflights during the California Methane Survey.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CH4_Plume_AVIRIS-NG/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MICASA_FLUX_3H v1 - MiCASA is an extensive revision of CASA-GFED3. CASA-GFED3 derives from Potter et al.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/MICASA_FLUX_3H.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SLOPE_GPP_CONUS_1786 v1 - This dataset contains estimated gross primary productivity (GPP), photosynthetically active radiation (PAR), soil adjusted near infrared reflectance of vegetation (SANIRv), the fraction of C4 crops in vegetation (fC4), and their uncertainties for the conterminous United States (CONUS) from 2000 to 2019. The daily estimates are SatelLite Only Photosynthesis Estimation (SLOPE) products at 250-m resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/SLOPE_GPP_CONUS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NASMo_TiAM_250m_2326 v1 - This NASMo-TiAM (North America Soil Moisture Dataset Derived from Time-Specific Adaptable Machine Learning Models) dataset holds gridded estimates of surface soil moisture (0-5 cm depth) at a spatial resolution of 250 meters over 16-day intervals from mid-2002 to December 2020 for North America. The model employed Random Forests to downscale coarse-resolution soil moisture estimates (0.25 deg) from the European Space Agency Climate Change Initiative (ESA CCI) based on their correlation with a set of static (terrain parameters, bulk density) and dynamic covariates (Normalized Difference Vege...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/NASMo_TiAM_250m/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BGC_glider_GNATS v1 - This dataset contains ocean biogeochemistry data from two Slocum gliders along the Gulf of Maine North Atlantic Time Series (GNATS) transect. The transect runs approximately east-west, with only a very minor change in latitude.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/BGC_glider_GNATS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_OCE_BGC_CCS v1 - A coupled physical-biogeochemical ocean model (the MITgcm with BLING biogeochemistry) is a least squares fit to all available ocean observations in the region of the California Current System. This is accomplished iteratively through the adjoint method, using the methodology developed by the Consortium for Estimating the Circulation and Climate of the Ocean (ECCO).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/CMS/CMS_OCE_BGC_CCS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Monthly_CO2_Gulf_1668 v1 - This dataset provides 1 km gridded monthly estimates of surface ocean partial pressure of CO2 (pCO2) and air-sea flux of CO2 (CO2 flux) for the northern Gulf of America for the period 2006 through 2010. Estimates of pCO2 were derived from MODIS/Aqua satellite imagery in combination with ship-based observations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Monthly_CO2_Gulf/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Coastal_US_Elevation_Data_1844 v1 - This dataset provides maps of the elevation of coastal wetlands relative to tidal ranges for the conterminous United States (CONUS) at 30 m resolution for 2010. It also includes maps of tidal amplitude, relative sea-level rise for the period 1983-2001, and maps for coastal lands and low marsh areas based on the probability of being below the mean higher high tide water line for spring tides (MHHWS).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Coastal_US_Elevation_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_Coastal_Wetland_Resilience_1839 v1 - This dataset provides information about the resilience of tidal wetlands to sea-level rise under three scenarios of global change. With rising seas, regularly inundated tidal wetlands may persist by vertical accretion of sediments (vertical resilience) and/or by migrating inland (lateral resilience), but local and regional conditions constrain these options.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_Coastal_Wetland_Resilience/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SatelliteDerived_Forest_Mexico_2320 v1 - This dataset provides a comparison of forest extent agreement from seven remote sensing-based products across Mexico. These satellite-derived products include European Space Agency 2020 Land Cover Map for Mexico (ESA), Globeland30 2020 (Globeland30), Commission for Environmental Cooperation 2015 Land Cover Map (CEC), Impact Observatory 2020 Land Cover Map (IO), NAIP Trained Mean Percent Cover Map (NEX-TC), Global Land Analysis and Discovery Global 2010 Tree Cover (Hansen-TC), and Global Forest Cover Change Tree Cover 30 m Global (GFCC-TC).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/SatelliteDerived_Forest_Mexico/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SiB4_Global_HalfDegree_Daily_1849 v1 - This dataset provides global daily output predicted by the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/SiB4_Global_HalfDegree_Daily/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SiB4_Global_HalfDegree_Hourly_1847 v1 - This dataset provides global hourly output predicted from the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/SiB4_Global_HalfDegree_Hourly/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SiB4_Global_HalfDegree_Monthly_1848 v1 - This dataset provides global monthly output predicted by the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/SiB4_Global_HalfDegree_Monthly/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Crops_SIF_VegIndices_IL_NE_2136 v1 - This dataset contains half-hourly ground solar-induced chlorophyll fluorescence (SIF) and vegetation indices including NDVI, EVI, Red edge chlorophyll index, green chlorophyll index, and photochemical reflectance index at seven crop sites in Nebraska and Illinois for the period 2016-2021. Four sites were located at Eddy Covariance (EC) tower sites (sites US-Ne2, US-Ne3, US-UiB, and US-UiC), and three sites were located on private farms (sites Reifsteck, Rund, and Reinhart).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Crops_SIF_VegIndices_IL_NE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Wetland_Soil_CarbonStocks_WA_2249 v1 - This dataset contains estimates of soil organic carbon stocks and wetland intrinsic potential (WIP) across the Hoh River Watershed in the Olympic Peninsula, WA, USA in 2012-2013. Estimates were derived from an equation based on wetland intrinsic potential and geology type (Stewart et al., 2023).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Wetland_Soil_CarbonStocks_WA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_SOC_Mexico_1754 v1 - This dataset provides an estimate of soil organic carbon (SOC) in the top one meter of soil across Mexico at a 90-m resolution for the period 1999-2009. Carbon estimates (kg/m2) are based on a field data collection of 2852 soil profiles by the National Institute for Statistics and Geography (INEGI).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_SOC_Mexico/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMS_SOC_Mexico_CONUS_1737 v1 - This dataset provides two sets of gridded estimates of estimated soil organic carbon (SOC) and associated uncertainties for 0-30 cm topsoil layer in kg SOC/m2 at 250-m resolution across Mexico and the conterminous USA (CONUS). The first set of gridded SOC estimates, for the period 1991-2010, were derived using multi-source SOC field data and multiple environmental variables representative of the soil forming environment coupled with a machine learning approach (i.e., simulated annealing) and regression tree ensemble modeling for optimized SOC prediction.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/CMS_SOC_Mexico_CONUS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Country_SOC_Latin_America_1615 v1 - This dataset provides 5 x 5 km gridded estimates of soil organic carbon (SOC) across Latin America that were derived from existing point soil characterization data and compiled environmental prediction factors for SOC. This dataset is representative for the period between 1980 to 2000s corresponding with the highest density of observations available in the WoSIS system and the covariates used as prediction factors for soil organic carbon across Latin America.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Country_SOC_Latin_America/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Tidal_Wetland_Soil_Carbon_1612 v1 - This dataset provides modeled estimates of soil carbon stocks for tidal wetland areas of the Conterminous United States (CONUS) for the period 2006-2010. Wetland areas were determined using both 2006-2010 Coastal Change Analysis Program (C-CAP) raster maps and the National Wetlands Inventory (NWI) vector data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Tidal_Wetland_Soil_Carbon/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Tree_Canopy_Cover_Mexico_2137 v1 - The data set provides multi-year (2016-2018) percent tree cover (TC) estimates for entire Mexico at 30 m spatial resolution. The TC data (hereafter, NEX-TC) was derived from the 30 m Landsat Collection 1 product and a hierarchical deep learning approach (U-Net) developed in a previous CMS effort for the conterminous United States (CONUS) (Park et al., 2022).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Tree_Canopy_Cover_Mexico/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"High_Res_Tidal_Marsh_Veg_1609 v1 - This dataset provides maps of tidal marsh green vegetation, non-vegetation, and open water for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from current National Agriculture Imagery Program data (2013-2015) using object-based classification for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program (C-CAP) map.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/High_Res_Tidal_Marsh_Veg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Wetland_Salinity_Maps_2392 v1 - This dataset provides gridded average annual wetland salinity concentrations in practical salinity units (PSU) at 30-meter resolution within 24 coastal estuary sites in the United States predicted for 2020. Salinity in estuaries can serve as a proxy for sulfate concentration, which can inhibit methanogenesis.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/cms/Wetland_Salinity_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-cms","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA COMEX Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### COMEX_AMOG_Mobile_Air_Surveys_2387\nThis dataset provides AutoMObile trace Gas Surveyor (AMOG) in situ relevant datasets collected during the CO2 and Methane EXperiment (COMEX) field campaign and afterwards. COMEX was conducted in the summer and fall 2014 to study strong methane, CH4, sources in S. California including animal husbandry, fossil fuel industrial (FFI) production, petroleum refining, landfills, and natural geological formations. The AMOG Surveyor is a mobile air quality laboratory built into a passenger car. AMOG Surveyor uses an asynchronous logging system based on a NMEA tagging protocol, termed a tagstream. Analyzers provide data at rates from 5 Hz to 0.016 Hz. Tagstream data are timestamped based on the time of the analyzer measurement, which does not take into account the air sample travel time in sample tubes and analyzers, termed layback time. This dataset contains Level 0 (L0) and Level 1 (L1) data. L0 data were analyzed and sensor data interpolated to a uniform timebase of 5 Hz, correcting for the layback time, the time of flight for air samples in the sample tube and analyzers. L1 data include true winds (corrected for vehicle velocity), with filtering of outliers, non-physical data, and gap filling. The data files are in HDF-5 (.h5) format.\n<br>\n\n#### COMEX_AVIRIS_Classic_Flights_2343\nThis dataset lists flight lines and provides data access links and contextual flight information for a subset of the AVIRIS-Classic Facility Instrument Collection that are associated with the CO2 and MEthane eXperiment (COMEX) Project. The COMEX Project was carried out May through September, 2014. AVIRIS-Classic Facility Instrument data are otherwise not replicated in this dataset. The COMEX Project utilized several measurement capabilities including the AVIRIS-Classic airborne facility instrument data to demonstrate that methane emissions associated with fossil fuel production activities in the Los Angeles, California area were of sufficient magnitude and size for space-based observations. These lists of the associated COMEX flights from the AVIRIS-Classic Facility Instrument provide flight lines and access information for the Level 1B Calibrated Radiance data and the Level 2 Calibrated Reflectance data.\n<br>\n\n#### COMEX_AVIRIS_NG_Flights_2342\nThis dataset lists flight lines and provides data access links and contextual flight information for a subset of the AVIRIS-NG Facility Instrument Collection that are associated with the CO2 and MEthane eXperiment (COMEX) Project. The COMEX Project was carried out May through September, 2014. AVIRIS-NG Facility Instrument data are otherwise not replicated in this dataset. The COMEX Project utilized several measurement capabilities including the AVIRIS-NG airborne facility instrument data to demonstrate that methane emissions associated with fossil fuel production activities in the Los Angeles, California area were of sufficient magnitude and size for space-based observations. These lists of the associated COMEX flights from the AVIRIS-NG Facility Instrument provide flight lines and access information for the Level 1B Calibrated Radiance data and the Level 2 Calibrated Reflectance data.\n<br>\n\n#### COMEX_AJAX_CO2_CH4_2347\nThis dataset provides information to access NASA Earthdata published flight data and flight information collected by the Alpha Jet Atmospheric eXperiment (AJAX) and associated with the COMEX project in 2014-2015. The file lists information for COMEX-related datasets that has been subsetted from AJAX collections archived through NASA's Atmospheric Science Data Center. AJAX data are not otherwise replicated in this dataset. AJAX is a partnership between NASA's Ames Research Center and H211, L.L.C., which conducted in-situ measurements over California, Nevada, and the coastal Pacific in support of satellite validation. During COMEX data collection, a Picarro greenhouse gas (GHG) sensor was mounted on an Alpha Jet, a tactical strike fighter developed by Dassault-Breguet and Dornier through a German-French NATO collaboration. The GHG sensor made repeat measurements in California and Nevada. In situ data included measurements of CO2, CH4, and H2O at 2 Hz or CH4 and H2O at 10 Hz with a strategy of characterizing atmospheric structure over ocean and land, and vertical profiles to at least 5000 m. Ancillary data, including O3, formaldehyde, and meteorological profiles, were also collected. This dataset provides filenames, spatiotemporal bounds, and download URLs for accessing these in situ data. This information is provided in comma separated values (CSV) format.\n<br>\n\n#### COMEX_LongwaveInfrared_Imagery_2331\nThis dataset provides calibrated at-sensor radiance, retrieved surface brightness temperature, and adaptive coherence estimator (ACE) detection imagery of methane, and a limited number of auxiliary gases collected with the Aerospace Corporation's Mako airborne longwave-IR hyperspectral imager flown during July 22-25, 2014 over a variety of methane generating sites in southern and central California (CA), U.S. These sites included animal husbandry and oil/gas production facilities. Specific study areas included the Coal Oil Point marine seep field off of Goleta, CA, the Kern River oil field complex at Bakersfield, CA, and the extensive stockyards in Chino, CA. The Kern River complex was acquired at 1-m ground sampling distance (GSD), while the other study areas were at 2-m GSD. Levels 1-3 data include single whisk data cubes (L1); at-sensor radiance and sensor performance (L2); surface brightness temperature and ACE detections for specific gases (L3). The data were collected in support of the NASA/ESA COMEX (CO2 and Methane EXperiment) campaign. The data are provided in ENVI and comma separated values (CSV) formats. Quicklook images are included for flight lines and molecule specific detections.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/COMEX/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","hdf","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"COMEX_AMOG_Mobile_Air_Surveys_2387 v1 - This dataset provides AutoMObile trace Gas Surveyor (AMOG) in situ relevant datasets collected during the CO2 and Methane EXperiment (COMEX) field campaign and afterwards. COMEX was conducted in the summer and fall 2014 to study strong methane, CH4, sources in S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/comex/COMEX_AMOG_Mobile_Air_Surveys/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"COMEX_AVIRIS_Classic_Flights_2343 v1 - This dataset lists flight lines and provides data access links and contextual flight information for a subset of the AVIRIS-Classic Facility Instrument Collection that are associated with the CO2 and MEthane eXperiment (COMEX) Project. The COMEX Project was carried out May through September, 2014.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/comex/COMEX_AVIRIS_Classic_Flights/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"COMEX_AVIRIS_NG_Flights_2342 v1 - This dataset lists flight lines and provides data access links and contextual flight information for a subset of the AVIRIS-NG Facility Instrument Collection that are associated with the CO2 and MEthane eXperiment (COMEX) Project. The COMEX Project was carried out May through September, 2014.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/comex/COMEX_AVIRIS_NG_Flights/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"COMEX_AJAX_CO2_CH4_2347 v1 - This dataset provides information to access NASA Earthdata published flight data and flight information collected by the Alpha Jet Atmospheric eXperiment (AJAX) and associated with the COMEX project in 2014-2015. The file lists information for COMEX-related datasets that has been subsetted from AJAX collections archived through NASA's Atmospheric Science Data Center.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/comex/COMEX_AJAX_CO2_CH4/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"COMEX_LongwaveInfrared_Imagery_2331 v1 - This dataset provides calibrated at-sensor radiance, retrieved surface brightness temperature, and adaptive coherence estimator (ACE) detection imagery of methane, and a limited number of auxiliary gases collected with the Aerospace Corporation's Mako airborne longwave-IR hyperspectral imager flown during July 22-25, 2014 over a variety of methane generating sites in southern and central California (CA), U.S. These sites included animal husbandry and oil/gas production facilities.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/comex/COMEX_LongwaveInfrared_Imagery/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-comex","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA COWVR-TEMPEST/STP-H8 Project","Description":"This data set includes satellite-based observations of calibrated, geo-located antenna temperature and brightness temperatures, along with the sensor telemetry used to derive those values. Brightness temperatures are derived from the microwave band frequencies 18.7 GHz, 23.8 GHz, and 34.5 GHz. This product is best suited for a cal/val user or sensor expert. These level 1c measurements make up the temperature sensor data record (TSDR) from the COWVR (Compact Ocean Wind Vector Radiometer) sensor aboard the international space station (ISS), starting in January 2022 forward-streaming to PO.DAAC till the planned mission end in December 2024. Its swath width is 1012 km and spatial resolution is <35 km. Data files in HDF5 format are available at roughly hourly frequency (the ISS orbit period is ~90 minutes), although note that the coverage shown in the thumbnail is for a full day. Files include calibration and flag data in addition to brightness temperatures. Version 10.0 is the first public release, and is named as such to be consistent with the internal version numbers of the project team prior to release <br><br> The COWVR sensor is a fully polarimetric, conically imaging microwave radiometer for measuring ocean surface wind vectors. It operates nominally on-orbit aboard the ISS and data are non-sun-synchronous. It was deployed as part of the Space Test Program - Houston 8 (STP-H8) technology demonstration mission. A successful COWVR mission will demonstrate a lower-cost sensor architecture (e.g. in comparison to WindSat) for providing imaging passive microwave data, including ocean surface vector wind products for the Department of Defense (DoD). COWVR was provided by the Jet Propulsion Laboratory and flown by the United States Space Force, Space Systems Command, Development Corps for Innovation and Prototyping.\n<br>\n\n#### COWVR_STPH8_L2_EDR_V10.0\nThis dataset includes satellite-based observations of geolocated surface wind vectors, precipitable water vapor, and integrated cloud liquid water, as well as the microwave brightness temperatures used to derive them. Theses measurements make up the environmental data record (EDR) from the COWVR (Compact Ocean Wind Vector Radiometer) sensor aboard the international space station (ISS), beginning in January 2022 with forward-streaming to PO.DAAC. Data over the satellite swath are available in HDF5 format with roughly one file per hour (the ISS orbit period is ~90 minutes), and coverage shown in the thumbnail is for a full day. Spatial resolution is roughly 35 km. The file metadata formats may be different than what an average user is familiar with – please see the User Guide to learn more. Version 10.0 is the first public release, and is named as such to be consistent with the internal version numbering of the project team prior to release. <br><br> The COWVR sensor is a fully polarimetric, conically imaging microwave radiometer for measuring ocean surface wind vectors. It operates nominally on-orbit aboard the ISS and data are non-sun-synchronous. It was deployed as part of the Space Test Program - Houston 8 (STP-H8) technology demonstration mission. A successful COWVR mission will demonstrate a lower-cost sensor architecture (e.g. in comparison to WindSat) for providing imaging passive microwave data, including ocean surface vector wind products for the Department of Defense (DoD). COWVR was provided by the Jet Propulsion Laboratory and flown by the United States Space Force, Space Systems Command, Development Corps for Innovation and Prototyping.\n<br>\n\n#### TEMPEST_STPH8_L1_TSDR_V10.0\nThis dataset includes satellite-based observations of calibrated, geo-located antenna temperature and brightness temperatures, along with the sensor telemetry used to derive those values. Brightness temperatures are derived from the microwave band frequencies 87, 164, 174, 178 and 181 GHz. This product is best suited for a cal/val user or sensor expert. These level 1c measurements make up the temperature sensor data record (TSDR) from the TEMPEST (Temporal Experiment for Storms and Tropical Systems) sensor aboard the international space station (ISS), starting in January 2022 forward-streaming to PO.DAAC till the planned mission end in December 2024. TEMPEST swath width is 1400 kilometers and resolution at nadir is 25 km for the 87 GHz channel and 13 km for the 180 GHz channels. Data files in HDF5 format are available at roughly hourly frequency (the ISS orbit period is ~90 minutes), although note that the coverage shown in the thumbnail is for a full day. Files include calibration and flag data in addition to brightness temperatures. Version 10.0 is the first public release, and is named as such to be consistent with the internal version numbering of the project team prior to release. <br><br> The TEMPEST instrument is a microwave radiometer deployed as part of the Space Test Program - Houston 8 (STP-H8) technology demonstration mission, with the primary objective of tropical cyclone intensity tracking. It operates nominally on-orbit aboard the ISS and data are non-sun-synchronous. A successful mission will demonstrate a lower-cost, lighter-weight sensor architecture for providing microwave data. TEMPEST was provided by the Jet Propulsion Laboratory and flown by the United States Space Force, Space Systems Command, Development Corps for Innovation and Prototyping.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","hdf","hdf5","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"COWVR_STPH8_L1_TSDR_V10.0 v10.0 - This data set includes satellite-based observations of calibrated, geo-located antenna temperature and brightness temperatures, along with the sensor telemetry used to derive those values. Brightness temperatures are derived from the microwave band frequencies 18.7 GHz, 23.8 GHz, and 34.5 GHz.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/COWVR_STPH8_L1_TSDR_V10.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"COWVR_STPH8_L2_EDR_V10.0 v10.0 - This dataset includes satellite-based observations of geolocated surface wind vectors, precipitable water vapor, and integrated cloud liquid water, as well as the microwave brightness temperatures used to derive them. Theses measurements make up the environmental data record (EDR) from the COWVR (Compact Ocean Wind Vector Radiometer) sensor aboard the international space station (ISS), beginning in January 2022 with forward-streaming to PO.DAAC.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/COWVR_STPH8_L2_EDR_V10.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"TEMPEST_STPH8_L1_TSDR_V10.0 v10.0 - This dataset includes satellite-based observations of calibrated, geo-located antenna temperature and brightness temperatures, along with the sensor telemetry used to derive those values. Brightness temperatures are derived from the microwave band frequencies 87, 164, 174, 178 and 181 GHz.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TEMPEST_STPH8_L1_TSDR_V10.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-cowvr-tempest-stp-h8","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA CSDA Project","Description":"The GeoEye-1 Level 1B Multispectral 4-Band L1B Satellite Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the GeoEye-1 satellite using the GeoEye-1 Imaging System across the global land surface from September 2008 to the present. This satellite imagery is in the visible and near-infrared waveband range with data in the blue, green, red, and near-infrared wavelengths. The imagery has a spatial resolution of 1.84m at nadir (1.65m before summer 2013) and has a temporal resolution of approximately 3 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.\n<br>\n\n#### GE01_Pan_L1B\nThe GeoEye-1 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the GeoEye-1 satellite using the GeoEye-1 Imaging System across the global land surface from September 2008 to the present. This data product includes panchromatic imagery with a spatial resolution of 0.46m at nadir (0.41m before summer 2013) and a temporal resolution of approximately 3 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.\n<br>\n\n#### WV01_Pan_L1B\nThe WorldView-1 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Panchromatic imagery is collected by the DigitalGlobe WorldView-1 satellite using the WorldView-60 camera across the global land surface from September 2007 to the present. Data have a spatial resolution of 0.5 meters at nadir and a temporal resolution of approximately 1.7 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.\n<br>\n\n#### WV02_MSI_L1B\nThe WorldView-2 Level 1B Multispectral 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-2 satellite using the WorldView-110 camera across the global land surface from October 2009 to the present. This satellite imagery is in the visible and near-infrared waveband range with data in the coastal, blue, green, yellow, red, red edge, and near-infrared (2 bands) wavelengths. It has a spatial resolution of 1.85m at nadir and a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.\n<br>\n\n#### WV02_Pan_L1B\nThe WorldView-2 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-2 satellite using the WorldView-110 camera across the global land surface from October 2009 to the present. This data product includes panchromatic imagery with a spatial resolution of 0.46m and a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.\n<br>\n\n#### WV02_MSI_L2A\nThe WorldView-2 Level 2A Multispectral 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-2 satellite using the WorldView-110 camera across the global land surface from October 2009 to the present. This satellite imagery is in the visible and near-infrared waveband range with data in the coastal, blue, green, yellow, red, red edge, and near-infrared (2 bands) wavelengths. It has a spatial resolution of 1.85m at nadir and a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. These level 2A data have been processed and undergone radiometric correction, sensor correction, projected to a plane using a map projection and datum, and has a coarse DEM applied. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.\n<br>\n\n#### WV03_MSI_L1B\nThe WorldView-3 Level 1B Multispectral 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-3 satellite using the WorldView-110 camera across the global land surface from August 2014 to the present. This satellite imagery is in a range of wavebands with data in the coastal, blue, green, yellow, red, red edge, and near-infrared (2 bands) wavelengths. The imagery has a spatial resolution of 1.24m at nadir and a temporal resolution of less than one day. The data are provided in National Imagery Transmission Format (NITF). This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.\n<br>\n\n#### WV03_MSI_L2A\nThe WorldView-3 Level 2A Multispectral 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-3 satellite using the WorldView-110 camera across the global land surface from August 2014 to the present. This satellite imagery is in a range of wavebands with data in the coastal, blue, green, yellow, red, red edge, and near-infrared (2 bands) wavelengths. The imagery has a spatial resolution of 1.24m at nadir and a temporal resolution of less than one day. The data are provided in National Imagery Transmission Format (NITF). These level 2A data have been processed and undergone radiometric correction, sensor correction, projected to a plane using a map projection and datum, and has a coarse DEM applied. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.\n<br>\n\n#### WV04_MSI_L1B\nThe WorldView-4 Multispectral 4-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe WorldView-4 satellite using the SpaceView-110 camera across the global land surface from December 2016 to January 2019. This satellite imagery is in the visible and near-infrared waveband range with data in the blue, green, red, and near-infrared wavelengths. The multispectral imagery has a spatial resolution of 1.24m at nadir and has a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a Maxar End User License Agreement for Worldview 4 imagery and investigators must be approved by the CSDA Program.\n<br>\n\n#### WV04_Pan_L1B\nThe WorldView-4 Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe WorldView-4 satellite using the WorldView-110 camera across the global land surface from December 2016 to January 2019. This data product includes panchromatic imagery with a spatial resolution of 0.31m at nadir and a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a Maxar End User License Agreement for Worldview 4 imagery and investigators must be approved by the CSDA Program.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.csdap.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/esds/csdap","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","geospatial","ice","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GE01_MSI_L1B v1 - The GeoEye-1 Level 1B Multispectral 4-Band L1B Satellite Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the GeoEye-1 satellite using the GeoEye-1 Imaging System across the global land surface from September 2008 to the present.","ARN":"arn:aws:s3:::csda-cumulus-prod-protected-5047/GE01_MSI_L1B___1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.csdap.earthdata.nasa.gov/s3credentials"},{"Description":"GE01_Pan_L1B v1 - The GeoEye-1 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the GeoEye-1 satellite using the GeoEye-1 Imaging System across the global land surface from September 2008 to the present.","ARN":"arn:aws:s3:::csda-cumulus-prod-protected-5047/GE01_Pan_L1B___1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.csdap.earthdata.nasa.gov/s3credentials"},{"Description":"WV01_Pan_L1B v1 - The WorldView-1 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Panchromatic imagery is collected by the DigitalGlobe WorldView-1 satellite using the WorldView-60 camera across the global land surface from September 2007 to the present.","ARN":"arn:aws:s3:::csda-cumulus-prod-protected-5047/WV01_Pan_L1B___1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.csdap.earthdata.nasa.gov/s3credentials"},{"Description":"WV02_MSI_L1B v1 - The WorldView-2 Level 1B Multispectral 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-2 satellite using the WorldView-110 camera across the global land surface from October 2009 to the present.","ARN":"arn:aws:s3:::csda-cumulus-prod-protected-5047/WV02_MSI_L1B___1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.csdap.earthdata.nasa.gov/s3credentials"},{"Description":"WV02_Pan_L1B v1 - The WorldView-2 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-2 satellite using the WorldView-110 camera across the global land surface from October 2009 to the present.","ARN":"arn:aws:s3:::csda-cumulus-prod-protected-5047/WV02_Pan_L1B___1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.csdap.earthdata.nasa.gov/s3credentials"},{"Description":"WV02_MSI_L2A v1 - The WorldView-2 Level 2A Multispectral 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-2 satellite using the WorldView-110 camera across the global land surface from October 2009 to the present.","ARN":"arn:aws:s3:::csda-cumulus-prod-protected-5047/WV02_MSI_L2A___1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.csdap.earthdata.nasa.gov/s3credentials"},{"Description":"WV03_MSI_L1B v1 - The WorldView-3 Level 1B Multispectral 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-3 satellite using the WorldView-110 camera across the global land surface from August 2014 to the present.","ARN":"arn:aws:s3:::csda-cumulus-prod-protected-5047/WV03_Pan_L1B___1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.csdap.earthdata.nasa.gov/s3credentials"},{"Description":"WV03_MSI_L2A v1 - The WorldView-3 Level 2A Multispectral 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-3 satellite using the WorldView-110 camera across the global land surface from August 2014 to the present.","ARN":"arn:aws:s3:::csda-cumulus-prod-protected-5047/WV03_MSI_L2A___1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.csdap.earthdata.nasa.gov/s3credentials"},{"Description":"WV04_MSI_L1B v1 - The WorldView-4 Multispectral 4-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe WorldView-4 satellite using the SpaceView-110 camera across the global land surface from December 2016 to January 2019.","ARN":"arn:aws:s3:::wv04-msi-l1b","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.csdap.earthdata.nasa.gov/s3credentials"},{"Description":"WV04_Pan_L1B v1 - The WorldView-4 Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe WorldView-4 satellite using the WorldView-110 camera across the global land surface from December 2016 to January 2019.","ARN":"arn:aws:s3:::wv04-pan-l1b","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.csdap.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-csda","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA CWIC Project","Description":"The The MODIS Near Real Time (NRT) product, MOD09Q1N provides Band 1 and 2 data at 250 meter resolution in a daily rolling 8-day gridded level-3 product in the Sinusoidal projection. Each MOD09Q1N pixel contains the best possible L2G observation during an 8-day period as selected on the basis of high observation coverage low view angle the absence of clouds or cloud shadow and aerosol loading. Science Data Sets provided for this product include reflectance values for Bands 1 and 2 and a quality rating.\n<br>\n\n#### SCAMSN6IM\nThe SCAMSN6IM data product consists of images of brightness temperatures, water vapor and temperature on 70 mm film strips from the Nimbus-6 Scanning Microwave Spectrometer. Each display contains eight vertical strips of data from one orbit. All strips have the same geographic coverage, but each represents a different parameter. The first three are brightness temperatures for channels 2 (31.65 GHz) and 3 (52.85 GHz) and their differences. The next two represent retrieved water vapor and liquid water from clouds or precipitation over the oceans, respectively. The remaining three strips on the right represent inverted mean temperatures for atmospheric layers 1000-500 mbar, 500-250 mbar, and 250-100 mbar, respectively. The first five parameters are displayed in 18-step gray levels, the values of which can be found in a table in each of the first five volumes of \"The Nimbus 6 Data Catalog.\" The last three parameters are displayed by contour bands (labeled on the side) that are spaced 4 K apart. Spatial resolution on the ground for the parameters varies from 145 km at nadir to 330 km at the scan extremes. The images are saved as TIFF digital files. About 3-5 months of images are archived into a ZIP file. Additional information can be found in section 2.4.1 of \"The Nimbus 6 User's Guide.\" The SCAMS experiment on Nimbus-6 is a follow on to the successful Nimbus-5 NEMS experiment. SCAMS continuously monitored emitted microwave radiation at frequencies of 22.235, 31.65, 52.85, 53.85 and 55.45 GHz. The three channels near the 5.0-mm oxygen absorption band were used primarily to deduce atmospheric temperature profiles. The two channels near 10 mm permitted water vapor and cloud water content over calm oceans to be estimated separately. The instrument, a Dicke-superheterodyne type, scanned +/- 45 degrees normal to the orbital plane with a 10 degree field of view. The three oxygen channels shared common signal and reference antennas. Both water vapor channels had their own signals and reference antennas. The absolute rms accuracy of the oxygen channels was better than 2 Kelvin and that of the water vapor channels better than 1 Kelvin. The SCAMS Principal Investigator was Prof. David H. Staelin from MIT. The Nimbus-6 SCAMS images are available from June 15, 1975 (day of year 166) through May 31, 1976 (day of year 152). This product was previously available from the NSSDC with the identifier ESAD-00200 (old ID 75-052A-10B).\n<br>\n\n#### TOVSB5NG\nLevel 3 parameters from HIRS/2 and MSU radiances using the Improved Initialization Inversion (3I) classification retrieval scheme by the Laboratoire de Meteorologie Dynamique (Ecole Polytechnique) averaged over 5 days and mapped on to a 1x1 degree grid. This data was run as part of the NASA TOVS Pathfinder project and designated as Path-B. This dataset contains data from the NOAA-10 satellite.\n<br>\n\n#### TOVSBDNG\nLevel 3 parameters from HIRS/2 and MSU radiances using the Improved Initialization Inversion (3I) classification retrieval scheme by the Laboratoire de Meteorologie Dynamique (Ecole Polytechnique) averaged over 1 day and mapped on to a 1x1 degree grid. This data was run as part of the NASA TOVS Pathfinder project and designated as Path-B. This dataset contains data from the NOAA-10 satellite.\n<br>\n\n#### TOVSBMNG\nLevel 3 parameters from HIRS/2 and MSU radiances using the Improved Initialization Inversion (3I) classification retrieval scheme by the Laboratoire de Meteorologie Dynamique (Ecole Polytechnique) averaged over 1 month and mapped on to a 1x1 degree grid. This data was run as part of the NASA TOVS Pathfinder project and designated as Path-B. This dataset contains data from the NOAA-10 satellite.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br><br>\n","Documentation":"http://modis.gsfc.nasa.gov/sci_team/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","oceans","precipitation","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SCAMSN6IM v001 - The SCAMSN6IM data product consists of images of brightness temperatures, water vapor and temperature on 70 mm film strips from the Nimbus-6 Scanning Microwave Spectrometer. Each display contains eight vertical strips of data from one orbit.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus6_SCAMS_Level2/SCAMSN6IM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSB5NG v01 - Level 3 parameters from HIRS/2 and MSU radiances using the Improved Initialization Inversion (3I) classification retrieval scheme by the Laboratoire de Meteorologie Dynamique (Ecole Polytechnique) averaged over 5 days and mapped on to a 1x1 degree grid. This data was run as part of the NASA TOVS Pathfinder project and designated as Path-B.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/tovs/TOVSB5NG","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSBDNG v01 - Level 3 parameters from HIRS/2 and MSU radiances using the Improved Initialization Inversion (3I) classification retrieval scheme by the Laboratoire de Meteorologie Dynamique (Ecole Polytechnique) averaged over 1 day and mapped on to a 1x1 degree grid. This data was run as part of the NASA TOVS Pathfinder project and designated as Path-B.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/tovs/TOVSBDNG","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSBMNG v01 - Level 3 parameters from HIRS/2 and MSU radiances using the Improved Initialization Inversion (3I) classification retrieval scheme by the Laboratoire de Meteorologie Dynamique (Ecole Polytechnique) averaged over 1 month and mapped on to a 1x1 degree grid. This data was run as part of the NASA TOVS Pathfinder project and designated as Path-B.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/tovs/TOVSBMNG","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-cwic","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA CYGNSS Project","Description":"This dataset contains the version 1.0 CYGNSS level 3 ocean microplastic concentration data record, which provides 18 netCDF files, each containing one month of daily gridded maps of microplastic number density (#/km^2). Microplastic concentration number density is indirectly estimated by an empirical relationship between ocean surface roughness and wind speed (Evans and Ruf, 2021). User caution is advised in regions containing independent, non-correlative factors affecting ocean surface roughness, such as anomalous atmospheric conditions within the Intertropical Convergence Zone, biogenic surfactants (such as algal blooms), oil spills, etc. This product reports microplastic concentration on a daily temporal and 0.25-degree latitude/longitude spatial grid with 30-day, 1 degree latitude/longitude feature resolution, as constrained by the binning and spatiotemporal averaging of the Mean Square Slope (MSS) anomaly (i.e., difference between measured and predicted ocean surface roughness for a given wind speed).\n<br>\n\n#### CYGNSS_L1_CAL_RAW_IF_V1.0\nThe CYGNSS Level 1 Calibrated Raw IF Version 1.0 dataset is produced by the CYGNSS Science Team of the University of Michigan, and it contains the first release, Version 1.0, of the CYGNSS Calibrated Raw Intermediate Frequency (IF) based L1 Product. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. <br><br> This product includes several established signal coherence detectors, including the power-ratio P<sub>ratio</sub>, complex zero-Doppler delay waveform and full entropy E<sub>full</sub>, and a novel fast entropy detector E<sub>fast</sub>. Both entropy detectors are provided with two temporal resolutions: 2 ms and 50 ms. Several scattered signal strength products are included: Signal-to-Noise Ratio SNR, reflected power P<sub>g</sub>, reflectivity Γ, and Normalized Bistatic Radar Cross-Section NBRCS. Each of these products is derived using a coherent integration time of T<sub>c </sub>= 1 ms and incoherent integration times of N<sub>inc</sub> = 1000, 500, 250, 100, 50, and 2 ms. Signal strength time series at the shorter (2 and 50 ms) times provides excellent detection of land-water transitions in heterogeneous scenes. Delay Doppler Maps (DDMs) are also generated with high delay (∆τ = 1/16 chip) and Doppler (∆f= 50 Hz) resolution. This suite of coherence detection methods can be used to detect the presence of small inland water bodies.\n<br>\n\n#### CYGNSS_L1_CDR_V1.0\nThis Level 1 (L1) dataset contains the Version 1.0 Climate Data Record (CDR) of the geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 2 months, depending on the availability of the MERRA wind speed reanalysis. The Version 1.0 CDR represents the first climate-quality release and is a collection of reanalysis products derived from the v2.1 Level 1 data. Calibration accuracy and long term stability are improved relative to the SDR v2.1 using a new trackwise correction algorithm which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds. Details of the algorithm are provide in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. The CDR exhibits improved calibration accuracy and stability over v2.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for variations in the transmit power level of the GPS signals measured by the CYGNSS bistatic radar receivers. By comparison, the v2.1 SDR L1 algorithm assumes a constant GPS transmit power, and variations in it can be misinterpreted as variations in the L1 data and in subsequent L2 science data products derived from them. The GPS constellation consists of several different satellite models (a.k.a. block types) and the level of transmit power variation differs between them. The more recent Block IIF models (which account for ~37% of the GPS constellation) have significantly larger variations than the older models and, for this reason, they have been screened out and not used to produce v2.1 L2 or L3 science data products. Trackwise correction eliminates the need for this screening so CDR L2 and L3 data products now include Block IIF samples. It should be noted that the trackwise correction algorithm cannot be successfully applied to all v2.1 SDR L1 data, so there is also some loss of samples that were present in v2.1. Overall, there is a significant increase in sampling and improvement in spatial coverage with the CDR products.\n<br>\n\n#### CYGNSS_L1_CDR_V1.1\nThis Level 1 (L1) dataset contains the Version 1.1 Climate Data Record (CDR) of the geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 1 month, depending on the availability of the MERRA wind speed reanalysis. The Version 1.1 CDR is a collection of reanalysis products derived from the SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 using the same trackwise correction algorithm as was used by CDR v1.0 (https://doi.org/10.5067/CYGNS-L1C10 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the LES. The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all v3.0 SDR L1 data, so there is also some loss of samples that were present in v3.0.\n<br>\n\n#### CYGNSS_L1_CDR_V1.2\nThis Level 1 (L1) dataset contains the Version 1.2 Climate Data Record (CDR) of the geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 1 week. The Version 1.2 CDR is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L1C11 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the LES. The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all v3.1 SDR L1 data, so there is also some loss of samples that were present in v3.1.\n<br>\n\n#### CYGNSS_L1_FULL_DDM\nThis Level 1 (L1) dataset contains the Full Delay Doppler Map (DDM) sensor data from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The primary CYGNSS instrument, also known as the Delay-Doppler Mapping Instrument (DDMI), measures the incoming radio frequency (RF) streams from three input antenna channels (2 nadir oriented science antennas and one zenith oriented navigation antenna) and processes them in real time into DDMs, which are two-dimensional maps of the signal scattered from the Earth surface as a function of propagation time delay and Doppler frequency shift. DDMs are normally sampled over a restricted range of delay and Doppler values centered on the values at the specular point of reflection. The bit resolution of scattered signal strength is also truncated by a lossy data compression algorithm. Full DDMs are sampled over a wider range of delay and Doppler values and retain their full (lossless) bit resolution. Full DDM data records are typically 10-15 min in duration and are initiated by ground commands to coincide with an overpass by one of the spacecraft of a target area of interest.\n<br>\n\n#### CYGNSS_L1_FULL_DDM_V3.0\nThis Level 1 (L1) dataset contains the Version 3.0 (v3.0) Full Delay Doppler Map (DDM) sensor data from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The primary CYGNSS instrument, also known as the Delay-Doppler Mapping Instrument (DDMI), measures the incoming radio frequency (RF) streams from three input antenna channels (2 nadir oriented science antennas and one zenith oriented navigation antenna) and processes them in real time into DDMs, which are two-dimensional maps of the signal scattered from the Earth surface as a function of propagation time delay and Doppler frequency shift. DDMs are normally sampled over a restricted range of delay and Doppler values centered on the values at the specular point of reflection. The bit resolution of scattered signal strength is also truncated by a lossy data compression algorithm. Full DDMs are sampled over a wider range of delay and Doppler values and retain their full (lossless) bit resolution. Full DDM data records are typically 10-15 min in duration and are initiated by ground commands to coincide with an overpass by one of the spacecraft of a target area of interest. This version supersedes the Full DDM Version 1.0 (https://doi.org/10.5067/CYGNS-L1FDD) for data retrieved during or after August 2018. For data retrieved prior to August 2018, users will need to continue using the Full DDM Version 1.0. This version links the Full DDMs to the CYGNSS v3.0 L1 files (https://doi.org/10.5067/CYGNS-L1X30) whereas the version 1.0 Full DDM linked the Full DDMs to the CYGNSS v2.1 L1 files (https://doi.org/10.5067/CYGNS-L1X21). The calibration of the Full DDMs has not been modified for this release.\n<br>\n\n#### CYGNSS_L1_RAW_IF\nThis Level 1 (L1) dataset contains the Raw Intermediate Frequency (IF) sensor data from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The primary CYGNSS instrument, also known as the Delay-Doppler Mapping Instrument (DDMI), digitizes the incoming radio frequency (RF) streams from three input antenna channels (2 nadir oriented science antennas and one zenith oriented navigation antenna). The Raw IF data included in this data record are the raw sensor counts, retrieved prior to any digital signal processing, thus providing the highest possible resolution in delay and doppler space allowing for the construction of high resolution Delay Doppler Map (DDM) data. Raw IF data records are 30-90 sec in duration, with 60 sec being typical, and are initiated by ground commands to coincide with an overpass by one of the spacecraft of a target area of interest.\n<br>\n\n#### CYGNSS_L1_V2.1\nThis Level 1 (L1) dataset contains the Version 2.1 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.0. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) data is now available when the CYGNSS satellites are rolled away from nadir during orbital high beta-angle periods, resulting in a significant amount of additional data; 2) correction to coordinate frames result in more accurate estimates of receiver antenna gain at the specular point; 3) improved calibration for analog-to-digital conversion results in better consistency between CYGNSS satellites measurements at nearly the same location and time; 4) improved GPS EIRP and transmit antenna pattern calibration results in significantly reduced PRN-dependence in the observables; 5) improved estimation of the location of the specular point within the DDM; 6) an altitude-dependent scattering area is used to normalize the scattering cross section (v2.0 used a simpler scattering area model that varied with incidence and azimuth angles but not altitude); 7) corrections added for noise floor-dependent biases in scattering cross section and leading edge slope of delay waveform observed in the v2.0 data. Users should also note that the receiver antenna pattern calibration is not applied per-DDM-bin in this v2.1 release.\n<br>\n\n#### CYGNSS_L1_V3.0\nThis Level 1 (L1) dataset contains the Version 3.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L1X21 . Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. Here is a summary of improvements the calibration and processing changes to the Version 3.0 data: 1) the transmitted GPS signal strength in the direction of the DDM scattering surface is determined in real time from measurements of the direct signal from the GPS satellite to the CYGNSS navigation receiver, allowing for the BRCS calibration to be corrected for variations in GPS transmit power; 2) the NBRCS has been validated using comparisons with a large population of modeled values derived from coincident ocean surface roughness spectra produced by the NOAA WAVEWATCH-3 oceanographic wave model; 3) L1 calibration parameters have been adjusted to produce a best fit to the model population.\n<br>\n\n#### CYGNSS_L1_V3.1\nThis Level 1 (L1) dataset contains the Version 3.1 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L1X30. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. Here is a summary of improvements the calibration and processing changes to the Version 3.1 data: The CYGNSS science antenna gain patterns have been adjusted to improve the accuracy of the ocean surface scattering cross section (a.k.a. the NBRCS) calibration. They are adjusted so that the annual average observed NBRCS matches the model-predicted average as derived from Wavewatch-3 estimates of the surface roughness with the appropriate spectral tail extension added to the roughness spectrum. The adjustment is made independently at each position in the science antenna pattern. A correction for coarse quantization effects by the on-board digital processor has also been added. This reduces the effects of radio frequency interference, which appeared as calibration biases in the v3.0 L1 NBRCS and retrieval biases in the v3.0 L2 wind speed that were persistent at certain locations.\n<br>\n\n#### CYGNSS_L1_V3.2\nThis CYGNSS Level 1 (L1) science data record dataset contains the version 3.2 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.1: https://doi.org/10.5067/CYGNS-L1X31. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. <br><br> The correction for coarse quantization effects that was implemented in v3.1 for the signal portion of the DDM has been updated to include a correction to the noise floor portion of the DDM. This update is found to improve the sensitivity to soil moisture over land and to have a minimal effect on the sensitivity to wind speed over ocean. An update is made to the correction for the temperature dependence of the receiver electronics. This update reduces slow variations in calibration bias associated with a ~60 day oscillation in the mean temperature of the satellites. L1 variables over land and ocean are now combined in common netcdf data files, with additional details added regarding the specular point calculation over land. Nadir (science) antenna pattern and NBRCS rescaling has been updated to improve the inter-satellite consistency of the L1 calibration.<br><br> The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38° N and 38° S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.\n<br>\n\n#### CYGNSS_L2_CDR_V1.0\nThis dataset contains the Version 1.0 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 2 months (or better) from the last recorded measurement time. The Version 1.0 CDR represents the first climate-quality release and is a collection of reanalysis products derived from the SDR v2.1 Level 1 data. Calibration accuracy and long term stability are improved relative to the SDR v2.1 using a new trackwise correction algorithm which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds. Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v2.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v2.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for variations in the transmit power level of the GPS signals measured by the CYGNSS bistatic radar receivers. The SDR v2.1 L1 algorithm assumes a constant GPS transmit power and variations in it can be misinterpreted as variations in the L1 data and in subsequent L2 science data products derived from them. The GPS constellation consists of several different satellite models (a.k.a. block types) and the level of transmit power variation differs between them. The more recent Block IIF models (which account for ~37% of the GPS constellation) have significantly larger variations than the older models and, for this reason, they have been screened out and not used to produce SDR v2.1 L2 or L3 science data products. Trackwise correction eliminates the need for this screening so CDR L2 and L3 data products now include Block IIF samples. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v2.1 L1 data so there is also some loss of samples that were present in SDR v2.1. Overall, there is a significant increase in sampling and improvement in spatial coverage with the CDR products.\n<br>\n\n#### CYGNSS_L2_CDR_V1.1\nThis dataset contains the Version 1.1 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. The Version 1.1 CDR represents is a collection of reanalysis products derived from the SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 (https://doi.org/10.5067/CYGNS-L2X30 ) using the same trackwise correction algorithm as was used by CDR v1.0 (https://doi.org/10.5067/CYGNS-L2C10 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.0 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. CDR v1.1 does not include a Young Seas with Limited Fetch (YSLF) wind speed product and investigators requiring wind speed measurements in and near the inner core of tropical cyclones should use the SDR v3.0 YSLF wind speed product. A YSLF wind speed product is omitted because the trackwise correction algorithm, which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds, is inherently biased toward fully developed sea state conditions. The constraint improves wind speed retrieval performance in fully developed seas but produces underestimates in YSLF conditions. It should also be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.0 L1 data so there is also some loss of samples that were present in SDR v3.0.\n<br>\n\n#### CYGNSS_L2_CDR_V1.2\nThis dataset contains the Version 1.2 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. The Version 1.2 CDR represents is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.1 (https://doi.org/10.5067/CYGNS-L2X31 ) using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L2C11 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.1 L1 data so there is also some loss of samples that were present in SDR v3.1.\n<br>\n\n#### CYGNSS_L2_SURFACE_FLUX_CDR_V1.0\nThis dataset contains the first release, Version 1.0, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 1-2 month latency from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The Cyclone Global Navigation Satellite System (CYGNSS) is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. The Coupled Ocean-Atmosphere Response Experiment (COARE) version 3.5 algorithm combines CYGNSS L2 CDR v1.0 ocean surface wind speed estimates with the auxiliary parameters provided by the NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) to produce latent and sensible heat fluxes and their respective transfer coefficients. More information on how the data is produced and validated can be found in the dataset user guide (see Documentation tab). More information on the CYGNSS mission, spacecraft, instrumentation and related datasets is available here: https://podaac.jpl.nasa.gov/CYGNSS. Additional information on the CYGNSS L2 CDR v1.0 wind speed dataset is available here: https://doi.org/10.5067/CYGNS-L2C10.\n<br>\n\n#### CYGNSS_L2_SURFACE_FLUX_CDR_V1.1\nThis dataset contains the first release, Version 1.1, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 1-2 month latency from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The Cyclone Global Navigation Satellite System (CYGNSS) is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. The Coupled Ocean-Atmosphere Response Experiment (COARE) version 3.5 algorithm combines CYGNSS L2 CDR v1.1 ocean surface wind speed estimates with the auxiliary parameters provided by the NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) to produce latent and sensible heat fluxes and their respective transfer coefficients. More information on how the data is produced and validated can be found in the dataset user guide (see Documentation tab). More information on the CYGNSS mission, spacecraft, instrumentation and related datasets is available here: https://podaac.jpl.nasa.gov/CYGNSS. Additional information on the CYGNSS L2 CDR v1.1 wind speed dataset is available here: https://doi.org/10.5067/CYGNS-L2C11.\n<br>\n\n#### CYGNSS_L2_SURFACE_FLUX_CDR_V1.2\nThis dataset contains the third release, Version 1.2, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 6-7 day latency from the Delay Doppler Mapping Instrument (DDMI) aboard the Cyclone Global Navigation Satellite System (CYGNSS) constellation. CYGNSS is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. The Coupled Ocean-Atmosphere Response Experiment (COARE) version 3.5 algorithm combines CYGNSS L2 CDR v1.2 ocean surface wind speed estimates with the auxiliary parameters provided by the European Centre for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA5) to produce latent and sensible heat fluxes and their respective transfer coefficients. More information on how the data is produced and validated can be found in the dataset user guide (see Documentation tab). More information on the CYGNSS mission, spacecraft, instrumentation and related datasets is available here: https://podaac.jpl.nasa.gov/CYGNSS . Additional information on the CYGNSS L2 CDR v1.2 wind speed dataset is available here: https://doi.org/10.5067/CYGNS-L2C12 .\n<br>\n\n#### CYGNSS_L2_SURFACE_FLUX_V1.0\nThis dataset contains the Version 1.0 CYGNSS Level 2 Ocean Surface Heat Flux Science Data Record, which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). Only one netCDF-4 data file is produced each day (each file containing data from a combination of up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. Version 1.0 represents the first release. The Cyclone Global Navigation Satellite System (CYGNSS), launched on 15 December 2016, is a NASA Earth System Science Pathfinder Mission that was launched with the purpose to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the CYGNSS observatories provide nearly gap-free Earth coverage with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. The 35 degree orbital inclination allows CYGNSS to measure ocean surface winds between approximately 38 degrees North and 38 degrees South latitude using an innovative combination of all-weather performance Global Positioning System (GPS) L-band ocean surface reflectometry to penetrate the clouds and heavy precipitation. The Coupled Ocean-Atmosphere Response Experiment (COARE) algorithm is what is used in this dataset to estimate the latent and sensible heat fluxes and their respective transfer coefficients. While COARE's initial intentions were for low to moderate wind speeds, the version used for this product, COARE 3.5, has been verified with direct in situ flux measurements for wind speeds up to 25 m/s. As CYGNSS does not provide air/sea temperature, humidity, surface pressure or density, the producer of this dataset obtains these values from the NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), which uses data assimilation to combine all available in situ and satellite observation data with an initial estimate of the atmospheric state, provided by a global atmospheric model. Since the MERRA-2 data is only updated on monthly intervals, this corresponding heat flux dataset is likewise updated on a monthly interval to reflect the latest data available from MERRA-2, thus accounting for measurement latency, with respect to CYGNSS observables, ranging from 1 to 2 months. The data from this release compares well with in situ buoy data, including: Kuroshio Extension Observatory (KEO), National Data Buoy Center (NDBC), Ocean Sustained Interdisciplinary Time-series Environment observation System (OceanSITES), Prediction and Research Moored Array in the Tropical Atlantic (PIRATA), Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA), and the Tropical Atmosphere Ocean (TAO) array. As this marks only the first data release, future work is expected to provide comparisons and validation with various field campaigns (e.g., PISTON, CAMP2Ex) as well as more buoy data, especially at higher flux estimates.\n<br>\n\n#### CYGNSS_L2_SURFACE_FLUX_V2.0\nThis dataset contains the Version 2.0 CYGNSS Level 2 Ocean Surface Heat Flux Science Data Record, which provides time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). Version 2.0 represents the second release of this product, which now uses CYGNSS Level 2 (L2) Science Data Record (SDR) Version 3.1 surface wind speeds and ECMWF Reanalysis, Version 5 (ERA5). Version 1.0 used CYGNSS L2 SDR Version 2.1 surface wind speeds and NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2). The Coupled Ocean-Atmosphere Response Experiment (COARE) algorithm is what is used in this dataset to estimate the latent and sensible heat fluxes and their respective transfer coefficients. While COARE's initial intentions were for low to moderate wind speeds, the version used for this product, COARE 3.5, has been verified with direct in situ flux measurements for wind speeds up to 25 m/s. As CYGNSS does not provide air/sea temperature, humidity, surface pressure or density, the producer of this dataset obtains these values from this dataset obtains these values from ERA5. The Cyclone Global Navigation Satellite System (CYGNSS), launched on 15 December 2016, is a NASA Earth System Science Pathfinder Mission that was launched with the purpose to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the CYGNSS observatories provide nearly gap-free Earth coverage with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. As a result of the CYGNSS constellation coverage, this data is made available from 1 August 2018 to present with an approximate 1 week latency in the netCDF-4 formatted data files, where each file contains data within a 24-hour UTC period from a combination of up to 8 unique CYGNSS spacecraft. More information on CYGNSS can be found on the CYGNSS mission page.\n<br>\n\n#### CYGNSS_L2_SURFACE_FLUX_V3.2\nThe CYGNSS level 2 ocean surface heat flux science data record version 3.2 dataset is provided as a service to the oceanographic and meteorological research communities on behalf of the CYGNSS Science Team in direct collaboration with the Cyclone Global Navigation Satellite System (CYGNSS) Mission. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. <br><br> This dataset provides time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). Version 3.2 uses CYGNSS Level 2 (L2) Science Data Record (SDR) Version 3.2 surface wind speeds and ECMWF Reanalysis, Version 5 (ERA5). The Coupled Ocean-Atmosphere Response Experiment (COARE) algorithm is what is used in this dataset to estimate the latent and sensible heat fluxes and their respective transfer coefficients. While COARE's initial intentions were for low to moderate wind speeds, the version used for this product, COARE 3.5, has been verified with direct in situ flux measurements for wind speeds up to 25 m/s. As CYGNSS does not provide air/sea temperature, humidity, surface pressure or density, the producer of this dataset obtains these values from this dataset obtains these values from ERA5. This dataset is made available from 1 August 2018 to present with an approximate 1 week latency in the netCDF-4 formatted data files, where each file contains data within a 24-hour UTC period from a combination of up to 8 unique CYGNSS spacecraft. More information on CYGNSS can be found on the CYGNSS mission page.\n<br>\n\n#### CYGNSS_L2_V2.1\nThis dataset contains the Version 2.1 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.0. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) revised Geophysical Model Functions (GMFs) for both Fully Developed Seas (FDS) and Young Seas with Limited Fetch conditions, to be consistent with the calibration changes made to the v2.1 Level 1 science data products.; 2) Revised covariance matrix between DDMA and LES versions of the FDS wind speed retrieval, used by the minimum variance estimator, resulting from changes made to the v2.1 Level 1 science data products; 3) Revised debiasing algorithm coefficients used by the FDS L2 retrieval algorithm, resulting from changes made to the v2.1 Level 2 science data products; 4) revised quality control (Q/C) flags related to the required level of consistency between DDMA and LES versions of the FDS wind speed retrieval (the errors in the two retrievals are now less correlated so larger discrepancies are allowed; if either retrieval is not available, the sample receives a fatal Q/C flag); 5) new Q/C flag related to the block type of the GPS satellite which provided the transmitted signal. Samples using block II-F signals receive a fatal Q/C flag due to the higher level of uncertainty in their radiated power; 6) revised wind speed uncertainty values as a function of RCG and wind speed, plus a new dependence of the uncertainty on GPS block type to reflect the higher uncertainty in GPS radiated power for block II-F satellites.\n<br>\n\n#### CYGNSS_L2_V3.0\nThis dataset contains the Version 3.0 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L2X21. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Here is a summary of processing changes reflected in the v3.0 data: 1) the changes to calibration and validation of the Level 1 Normalized Bistatic Radar Cross Section (NBRCS) necessitated updates to the Geophysical Model Functions (GMFs) used to retrieve wind speed; 2) the GMF for fully developed seas (FDS) conditions was generating using matchups between NBRCS measurements and coincident wind speeds produced by NASAs Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis model; 3) the GMF for young seas with limited fetch (YSLF) was generated using matchups between NBRCS and coincident wind speeds produced by NOAAs Hurricane Weather Research and Forecast (HWRF) System; 4) YSLF wind speed is a tapered linear combination of wind speeds derived from the FDS and YSLF GMFs, where the taper gives more weight to FDS at low wind speeds and more to YSLF at high wind speeds and accounts for the transition from FDS to YSLF sea state conditions near cyclonic storms; 5) re-introduces measurements using transmissions from previously discarded GPS satellite block types; in prior versions, Block II-F was completely discarded due to large variations in GPS transmit power. The real time transmit power monitoring and correction implemented in Level 1 v3.0 data now allows Block II-F signals to be used.\n<br>\n\n#### CYGNSS_L2_V3.1\nThis dataset contains the Version 3.1 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L2X30. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Here is a summary of processing changes reflected in the v3.1 data: The L2 Geophysical Model Functions (GMFs) that map L1 observables to ocean surface wind speed were rederived to be consistent with the v3.1 L1 calibration. The method used for deriving the GMFs is the same as for v3.0. A new correction has been added to both the Fully Developed Seas (FDS) and Young Seas Limited Fetch (YSLF) wind speed products that is a function of the Significant Wave Height (SWH) of the ocean surface. The correction is based on an observed correlation between the wind speed error and SWH. The SWH value used by the correction algorithm is the ERA5 reanalysis product, coincident in space and time with a CYGNSS measurement. The FDS and YSLF retrieval algorithms are otherwise the same as v3.0.\n<br>\n\n#### CYGNSS_L3_CDR_V1.0\nThis dataset contains the Version 1.0 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 2 month latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.0 CDR represents the first climate-quality release and is a collection of reanalysis products derived from the SDR v2.1 Level 1 data. Calibration accuracy and long term stability are improved relative to the SDR v2.1 using a new trackwise correction algorithm which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds. Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v2.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v2.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for variations in the transmit power level of the GPS signals measured by the CYGNSS bistatic radar receivers. The SDR v2.1 L1 algorithm assumes a constant GPS transmit power and variations in it can be misinterpreted as variations in the L1 data and in subsequent L2 science data products derived from them. The GPS constellation consists of several different satellite models (a.k.a. block types) and the level of transmit power variation differs between them. The more recent Block IIF models (which account for ~37% of the GPS constellation) have significantly larger variations than the older models and, for this reason, they have been screened out and not used to produce SDR v2.1 L2 or L3 science data products. Trackwise correction eliminates the need for this screening so CDR L2 and L3 data products now include Block IIF samples. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v2.1 L1 data so there is also some loss of samples that were present in SDR v2.1. Overall, there is a significant increase in sampling and improvement in spatial coverage with the CDR products.\n<br>\n\n#### CYGNSS_L3_CDR_V1.1\nThis dataset contains the Version 1.1 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 1 to 2 month latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.1 CDR is a collection of reanalysis products derived from the SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 (https://doi.org/10.5067/CYGNS-L3X30 ) using the same trackwise correction algorithm as was used by CDR v1.0 (https://doi.org/10.5067/CYGNS-L3C10 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.0 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. CDR v1.1 does not include a Young Seas with Limited Fetch (YSLF) wind speed product and investigators requiring wind speed measurements in and near the inner core of tropical cyclones should use the SDR v3.0 YSLF wind speed product. A YSLF wind speed product is omitted because the trackwise correction algorithm, which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds, is inherently biased toward fully developed sea state conditions. The constraint improves wind speed retrieval performance in fully developed seas but produces underestimates in YSLF conditions. It should also be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.0 L1 data so there is also some loss of samples that were present in SDR v3.0.\n<br>\n\n#### CYGNSS_L3_CDR_V1.2\nThis dataset contains the Version 1.2 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 5 days latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.2 CDR is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.1 (https://doi.org/10.5067/CYGNS-L3X31 ) using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L3C11 ), which was derived from SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.1 L1 data so there is also some loss of samples that were present in SDR v3.1.\n<br>\n\n#### CYGNSS_L3_MRG_NRT_V3.2\nThis dataset contains the version 3.2 CYGNSS Level 3 Merged (MRG) Science Data Record Near Real Time (NRT) Storm Wind Speed derived from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. It combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 Young Seas Limited Fetch (YSLF) winds for a region surrounding a given tropical cyclone (TC), with L2 Fully Developed Seas (FDS) winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid. <br><br> L3 MRG is a product which combines the L2 FDS and YSLF winds and eliminates the need to choose between them depending on sea state development and the proximity to storms. The data are provided in netCDF-4 format and starts from the June 11, 2024 through the present with an approximate latency between 2 and 24 hours . A tapered weighted averaging scheme is used centered on the 34-knot wind radius (R34) of the storm. The R34 value in each storm quadrant is also reported. The algorithm produces global (+/- 40 deg latitude) wind speeds reported on a 0.1x0.1 deg grid every 6 hours for each tropical cyclone, although some 6-hourly increments may be missing if there are an insufficient number of satellite overpasses of the storm during that time interval. The netCDF files are output on a storm-by-storm basis. <br><br> The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38° N and 38° S latitude. This range includes the critical latitude band for tropical cyclone formation and movement\n<br>\n\n#### CYGNSS_L3_MRG_NRT_V3.2.1\nThis dataset contains the version 3.2.1 CYGNSS Level 3 Merged (MRG) Science Data Record Near Real Time (NRT) Storm Wind Speed derived from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. It combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 Young Seas Limited Fetch (YSLF) winds for a region surrounding a given tropical cyclone (TC), with L2 Fully Developed Seas (FDS) winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid. <br><br> L3 MRG is a product which combines the L2 FDS and YSLF winds and eliminates the need to choose between them depending on sea state development and the proximity to storms. The data are provided in netCDF-4 format and starts from the September 1, 2024 through the present with an approximate latency between 2 and 24 hours. A tapered weighted averaging scheme is used centered on the 25 m/s wind radius of the storm. The 34 knot wind radius (R34) algorithm has been updated for v3.2.1 release to center around the National Hurricane Center or the Joint Typhoon Warning Center (NHC/JTWC) reported storm center instead of the CYGNSS Vmax location The algorithm produces global (+/- 40 deg latitude) wind speeds reported on a 0.1x0.1 deg grid every 6 hours for each tropical cyclone, although some 6-hourly increments may be missing if there are an insufficient number of satellite overpasses of the storm during that time interval. The netCDF files are output on a storm-by-storm basis. <br><br> The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38° N and 38° S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.\n<br>\n\n#### CYGNSS_L3_MRG_NRT_V3.2.2\nCYGNSS_L3_MRG_NRT_V3.2.2 This dataset contains the Near Real Time (NRT) version of the 3.2.2 CYGNSS Level 3 Merged (MRG) Science Data Record Wind Speed which combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 Young Seas Limited Fetch (YSLF) winds for a region surrounding a given tropical cyclone (TC), with L3 Fully Developed Seas (FDS) winds away from the TC center. The L3 MRG wind speeds are obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation and provided on a 0.1x0.1 degree latitude by longitude equirectangular grid. The NRT version of the L3 MRG product is intended to provide operational guidance via reduced latency compared to the standard CYGNSS L3 MRG product. In order to support this goal, as of v3.2.2 the averaging window has been reduced from the standard L3 MRG +/-6 hour window to +/-3 hours. <br><br> The L3 MRG product was developed to eliminate the need to choose between the FDS and YSLF winds depending on sea state development and the proximity to storms by providing a product that merges winds in areas of YSLF and FDS conditions into a single wind field. The L3 MRG NRT data are provided in netCDF-4 format and will extend from the 2025 storm season through the present with an approximate latency between 2 and 24 hours, compared to an approximately 6 day latency for the standard product. A tapered weighted averaging scheme is used for the merging, centered on the 25 m/s wind radius of the storm. The algorithm produces global (+/- 40 deg latitude) wind speeds reported on a 0.1x0.1 deg grid using wind speeds collected over a 6 hour window, centered 3 hours back from the most recent sample in the wind field. In contrast to the standard L3 MRG product, the NRT netCDF files are output for a single time. New files are produced for a storm as more recent data becomes available instead of a single file containing all the available wind fields over a storm’s lifecycle. Changes from the previous v3.2.1 version include a new algorithm for determining the 34-knot wind radii and the inclusion of 50-knot wind radii. Additionally, the 'epoch time' variable is now reported in 'seconds since' the epoch instead of 'hours since' the epoch. This change enables us to capture the exact timing of the L3 MRG Near Real Time (NRT) version time steps with full precision. <br><br> The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38° N and 38° S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.\n<br>\n\n#### CYGNSS_L3_MRG_V3.2\nThis dataset contains the version 3.2 CYGNSS level 3 science data record merged storm (MRG) wind speed which combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 YSLF winds for a region surrounding a given tropical cyclone (TC), with L3 FDS winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. <br><br> L3 MRG is a new product which combines the L2 FDS and Young Seas Limited Fetch (YSLF) winds and eliminates the need to choose between them depending on sea state development and the proximity to storms. The data are provided in netCDF-4 format and extend from 1 August 2018 to the present with an approximate 6 day latency. A tapered weighted averaging scheme is used centered on the 34-knot wind radius (R34) of the storm. The R34 value in each storm quadrant is also reported. The algorithm produces global (+/- 40 deg latitude) wind speeds reported on a 0.1x0.1 deg grid every 6 hours for each tropical cyclone, although some 6-hourly increments may be missing if there are an insufficient number of satellite overpasses of the storm during that time interval. The netcdf files are output on a storm-by-storm basis.<br><br> The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38° N and 38° S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.\n<br>\n\n#### CYGNSS_L3_MRG_V3.2.1\nThis dataset contains the version 3.2.1 CYGNSS level 3 science data record merged storm (MRG) wind speed which combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 Young Seas Limited Fetch (YSLF) winds for a region surrounding a given tropical cyclone (TC), with L3 Fully Developed Seas (FDS) winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. <br><br> L3 MRG combines the L2 FDS and Young Seas Limited Fetch (YSLF) winds and eliminates the need to choose between them depending on sea state development and the proximity to storms. The data are provided in netCDF-4 format and extend from 1 August 2018 to the present with an approximate 6 day latency. A tapered weighted averaging scheme is used centered on the 25 m/s wind radius of the storm. The 34 knot wind radius (R34) algorithm has been updated for v3.2.1 release to center around the National Hurricane Center or the Joint Typhoon Warning Center (NHC/JTWC) reported storm center instead of the CYGNSS Vmax location. The algorithm produces global (+/- 40 deg latitude) wind speeds reported on a 0.1x0.1 deg grid every 6 hours for each tropical cyclone, although some 6-hourly increments may be missing if there are an insufficient number of satellite overpasses of the storm during that time interval. The netCDF files are output on a storm-by-storm basis. <br><br> The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38° N and 38° S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.\n<br>\n\n#### CYGNSS_L3_MRG_V3.2.2\nThis dataset contains the version 3.2.2 CYGNSS level 3 science data record merged storm (MRG) wind speed which combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 Young Seas Limited Fetch (YSLF) winds for a region surrounding a given tropical cyclone (TC), with L3 Fully Developed Seas (FDS) winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. <br><br> L3 MRG combines the L2 FDS and Young Seas Limited Fetch (YSLF) winds and eliminates the need to choose between them depending on sea state development and the proximity to storms. The data are provided in netCDF-4 format and extend from 1 August 2018 to the present with an approximate 6 day latency. A tapered weighted averaging scheme is used centered on the 25 m/s wind radius of the storm. The 34 knot wind radius (R34) algorithm was updated for the v3.2.1 release to center around the National Hurricane Center or the Joint Typhoon Warning Center (NHC/JTWC) reported storm center instead of the CYGNSS Vmax location. The algorithm produces global (+/- 40 deg latitude) wind speeds reported on a 0.1x0.1 deg grid every 6 hours for each tropical cyclone, although some 6-hourly increments may be missing if there are an insufficient number of satellite overpasses of the storm during that time interval. The netCDF files are output on a storm-by-storm basis. <br><br> The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38° N and 38° S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.\n<br>\n\n#### CYGNSS_L3_MICROPLASTIC_V3.2\nThe CYGNSS L3 Ocean Microplastic Concentration V3.2 dataset is provided by the CYGNSS Science Team of the University of Michigan. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. <br><br> This dataset contains the version 3.2 CYGNSS Level 3 ocean microplastic concentration data record, which provides daily netCDF files, each file containing a gridded map of microplastic number density (#/km^2). Microplastic concentration number density is indirectly estimated by an empirical relationship between ocean surface roughness and wind speed (Evans and Ruf, 2021). User caution is advised in regions containing independent, non-correlative factors affecting ocean surface roughness, such as anomalous atmospheric conditions within the Intertropical Convergence Zone, biogenic surfactants (such as algal blooms), oil spills, etc. This product reports microplastic concentration on a daily temporal and 0.25-degree latitude/longitude spatial grid with 30-day, 1 degree latitude/longitude feature resolution, as constrained by the binning and spatial temporal averaging of the Mean Square Slope (MSS) anomaly (i.e., difference between measured and predicted ocean surface roughness for a given wind speed). Version 3.2 uses CYGNSS MSS measurements that are derived from updated v3.2 Level 1 scattering cross section data and has updated the parameterizations in the data processing algorithm to use v3.2 data correctly.\n<br>\n\n#### CYGNSS_L3_V2.1\nThis dataset contains the Version 2.1 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 2.0. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) first time availability of wind speeds using the Geophysical Model Function (GMF) calibrated for Young Seas with Limited Fetch (YSLF) conditions; 2) inherits all other improvements made to the version 2.1 Level 2 data intended to improve the quality of the wind speed retrievals and uncertainty estimates. For a full list of improvements to the version 2.1 Level 2 data, please refer to the following dataset information page: https://podaac.jpl.nasa.gov/dataset/CYGNSS_L2_V2.1\n<br>\n\n#### CYGNSS_L3_V3.0\nThis dataset contains the Version 3.0 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L3X21. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 3.0 release inherits all improvements made to the version 3.0 Level 2 data intended to improve the quality of the wind speed retrievals. For a full list of improvements to the version 3.0 Level 2 data, please refer to: https://doi.org/10.5067/CYGNS-L2X30.\n<br>\n\n#### CYGNSS_L3_V3.1\nThis dataset contains the Version 3.1 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L3X30. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 3.1 release inherits all improvements made to the version 3.1 Level 2 data intended to improve the quality of the wind speed retrievals. For a full list of improvements to the version 3.1 Level 2 data, please refer to: https://doi.org/10.5067/CYGNS-L2X31.\n<br>\n\n#### CYGNSS_L3_SOIL_MOISTURE_V3.2\nThe CYGNSS Level 3 Soil Moisture V3.2 dataset is provided by the CYGNSS Science Team of the University of Michigan. It estimates volumetric water content for soils between 0-5 cm depth at a 6-hour discretization for most of the subtropics from the V3.2 reflectivity measurements provided in the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32). CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. <br><br> The soil moisture retrieval algorithm is an update of the previous version developed by UCAR-CU using a linear regression of CYGNSS angle-normalized effective surface reflectivity trained against collocated SMAP soil moisture during the calibration period 8/1/2018 to 11/15/2023. The data are archived in daily files in netCDF-4 format. Volumetric soil moisture water content in units of cm3/cm3 is provided with two gridding resolutions, 9x9 km and 36x36 km. The variable SM_subdaily contains data reported in six hour intervals. The variable SM_daily provides a daily average. The time series covers the period from August 2018 to present.\n<br>\n\n#### CYGNSS_L3_S1.0\nThis dataset contains the Version 1.0 Cyclone Global Navigation Satellite System (CYGNSS) Level 3 Storm Centric Grid (SCG) Science Data Record (SDR) which provides the average wind speed combined from aggregated wind speed measurements made by the entire CYGNSS constellation whose specular points are located near a storm of interest in latitude, longitude and time. Data are provided on both a 0.1x0.1 degree latitude by longitude equirectangular grid and storm centric coordinates obtained from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. Storm centric coordinates are derived from the National Hurricane Center (NHC) Best Track dataset to produce a 6 hourly wind speed averaging window. A single netCDF-4 data file is produced for each storm. Each storm is uniquely identified by the year, storm basin, and a storm number. This dataset is intended for historical storm analysis, and as such, this dataset is periodically updated based on the availability of the NHC Best Track storm center information that is typically made available in April for the previous year's hurricane season. SCG files are produced for named storms, as defined by the NHC, that reach hurricane strength (i.e., having a maximum sustained wind speed of at least 65 knots). Due to the dependency on NHC Best Track data, the SCG files produced in this dataset are confined to storms in the Northern Hemisphere within the North Atlantic and East Pacific ocean regions. Wind speed inputs are provided by the CYGNSS Level 2 SDR Version 3.0 (https://doi.org/10.5067/CYGNS-L2X30 ).\n<br>\n\n#### CYGNSS_NOAA_L2_SWSP_25KM_V1.2\nThis dataset contains the Version 1.2 NOAA CYGNSS Level 2 Science Wind Speed Product Version 1.2 which provides the time-tagged and geolocated average wind speed (m/s) in 25x25 kilometer grid cells along the measurement tracks from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. This version corresponds to the second science-quality released through the PO.DAAC, as produced by NOAA/NESDIS using a specific geophysical model function (GMF version 1.0) and a track-wise debiasing algorithm as part of the wind speed retrieval process. The reported retrieval locations are determined by averaging the specular point locations falling within each 25 km grid cell. Version 1.2 includes four major updates compared to Version 1.1 ( https://doi.org/10.5067/CYGNN-22511 ), namely: 1) the inclusion of data associated to a spacecraft roll angle exceeding +/- 5 degrees; 2) an improved wind speed performance in the higher wind speed regime; 3) a full revision of the quality flags; 4) the inclusion of a wind speed retrieval error variable. Only one netCDF-4 data file is produced for each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Formatting of the data variables and metadata designed to be consistent with the netCDF-4 formatting provided by the legacy CYGNSS mission Level 2 wind speed science data record (SDR).\n<br>\n\n#### RONGOWAI_L1_SDR_V1.0\nThe Rongowai Level 1 Science Data Record Version 1.0 dataset is generated by the University of Auckland (UoA) Rongowai Science Payloads Operations Centre in New Zealand. This initiative is supported by NASA and the New Zealand Space Agency. The data collection process is conducted using the Next-generation receiver (NgRx) mounted on the Air New Zealand domestic aircraft Q300.<br><br> This Level 1 (L1) dataset contains the Version 1.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument onboard an Air New Zealand domestic Q300 (tail number ZK-NFA). 20 DDMs are contained within a typical L1 netcdf corresponding to 10 Left-Hand-Circularly Polarized (LHCP) and 10 Right-Hand-Circularly Polarized (RHCP) channels. Other useful scientific and engineering measurement parameters include the co- and cross-polarized Normalized Bistatic Radar Cross Section (NBRCS) of the specular point, the Leading Edge Slope (LES) of the integrated delay waveform and the normalized waveforms. The L1 dataset contains a number of other engineering and science measurement parameters, including coherence detection and a coherence state metric, sets of quality flags/indicators, error estimates, Fresnel-zone geometry estimates (and thereby the estimated per-sample spatial resolution) as well as a variety of timekeeping, and geolocation parameters. Each netCDF data files corresponds to a single flight between airports within New Zealand (flight durations typically range between 45 min and 1hr 30min with a median of 7 flights/day) and measurements are reported at 1 second increments. Latency is approximately 1 days (or better) from the last recorded measurement time.\n<br>\n\n#### CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2\nThe CYGNSS Level 3 UC Berkeley Watermask Record Version 3.2 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. <br><br> This dataset is derived from version 3.2 of the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32). This is an update from the previous watermask monthly product (https://doi.org/10.5067/CYGNS-L3W31) which derived from the CYGNSS L1 SDR v3.1 (https://doi.org/10.5067/CYGNS-L1X31). The new product provides daily binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with an approximate 6-day latency. The algorithm utilized data from up to 30 days prior to generate the daily map. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in daily files in netCDF-4 format and covers the period from September 2018 to present. <br><br> This product is recommended for operational use. For science applications, we recommend the use of the Berkeley-RWAWC monthly product instead: https://podaac.jpl.nasa.gov/dataset/CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1 Note that the daily product consist of maps constructed using the most recent 31 days of data to rapidly capture surface water dynamics without relying on historical data. While the oldest data within this 31 day-period is weighted less and replaced by newer observations as they become available, extreme flood events may still be detected with a delay due to the incorporation of prior days’ data into the algorithm. The incorporation of older data is necessary to maintain the spatial scale.\n<br>\n\n#### CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1\nThe CYGNSS Level 3 UC Berkeley Watermask Record Version 3.1 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. <br><br> This dataset is derived from version 3.1 of the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X31), and provides monthly binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with a 1-month latency. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in monthly files in netCDF-4 format and covers the period from August 2018 to present.\n<br>\n\n#### CYGNSS_L3_SOIL_MOISTURE_V1.0\nThe CYGNSS Level 3 Soil Moisture Product provides volumetric water content estimates for soils between 0-5 cm depth at a 6-hour discretization for most of the subtropics. The data were produced by CYGNSS investigators at the University Corporation for Atmospheric Research (UCAR) and the University Colorado at Boulder (CU), and derive from version 2.1 of the CYGNSS L1 SDR. The soil moisture algorithm uses collocated soil moisture retrievals from SMAP to calibrate CYGNSS observations from the same day. For a given location, a linear relationship between the SMAP soil moisture and CYGNSS reflectivity is determined and used to transform the CYGNSS observations into soil moisture. The data are archived in daily files in netCDF-4 format. Two soil moisture variables report the volumetric water content in units of cm3/cm3. The variable SM_subdaily includes up to four soil moisture estimates per day. Another variable SM_daily provides a daily average. The time series covers the period from March 2017 to present.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","ice","netcdf","oceans","precipitation","radar","satellite imagery","soil moisture","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"CYGNSS_L3_MICROPLASTIC_V1.0 v1.0 - This dataset contains the version 1.0 CYGNSS level 3 ocean microplastic concentration data record, which provides 18 netCDF files, each containing one month of daily gridded maps of microplastic number density (#/km^2). Microplastic concentration number density is indirectly estimated by an empirical relationship between ocean surface roughness and wind speed (Evans and Ruf, 2021).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_MICROPLASTIC_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L1_CAL_RAW_IF_V1.0 v1.0 - The CYGNSS Level 1 Calibrated Raw IF Version 1.0 dataset is produced by the CYGNSS Science Team of the University of Michigan, and it contains the first release, Version 1.0, of the CYGNSS Calibrated Raw Intermediate Frequency (IF) based L1 Product. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L1_CAL_RAW_IF_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L1_CDR_V1.0 v1.0 - This Level 1 (L1) dataset contains the Version 1.0 Climate Data Record (CDR) of the geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L1_CDR_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L1_CDR_V1.1 v1.1 - This Level 1 (L1) dataset contains the Version 1.1 Climate Data Record (CDR) of the geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L1_CDR_V1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L1_CDR_V1.2 v1.2 - This Level 1 (L1) dataset contains the Version 1.2 Climate Data Record (CDR) of the geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L1_CDR_V1.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L1_FULL_DDM v1.0 - This Level 1 (L1) dataset contains the Full Delay Doppler Map (DDM) sensor data from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The primary CYGNSS instrument, also known as the Delay-Doppler Mapping Instrument (DDMI), measures the incoming radio frequency (RF) streams from three input antenna channels (2 nadir oriented science antennas and one zenith oriented navigation antenna) and processes them in real time into DDMs, which are two-dimensional maps of the signal scattered from the Earth surface as a function of propagation time delay and Doppler frequ...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L1_FULL_DDM","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L1_FULL_DDM_V3.0 v3.0 - This Level 1 (L1) dataset contains the Version 3.0 (v3.0) Full Delay Doppler Map (DDM) sensor data from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The primary CYGNSS instrument, also known as the Delay-Doppler Mapping Instrument (DDMI), measures the incoming radio frequency (RF) streams from three input antenna channels (2 nadir oriented science antennas and one zenith oriented navigation antenna) and processes them in real time into DDMs, which are two-dimensional maps of the signal scattered from the Earth surface as a function of propagation time dela...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L1_FULL_DDM_V3.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L1_RAW_IF v1.0 - This Level 1 (L1) dataset contains the Raw Intermediate Frequency (IF) sensor data from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The primary CYGNSS instrument, also known as the Delay-Doppler Mapping Instrument (DDMI), digitizes the incoming radio frequency (RF) streams from three input antenna channels (2 nadir oriented science antennas and one zenith oriented navigation antenna).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L1_RAW_IF","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L1_V2.1 v2.1 - This Level 1 (L1) dataset contains the Version 2.1 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.0.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L1_V2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L1_V3.0 v3.0 - This Level 1 (L1) dataset contains the Version 3.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L1X21.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L1_V3.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L1_V3.1 v3.1 - This Level 1 (L1) dataset contains the Version 3.1 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L1X30.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L1_V3.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L1_V3.2 v3.2 - This CYGNSS Level 1 (L1) science data record dataset contains the version 3.2 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.1: https://doi.org/10.5067/CYGNS-L1X31.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L1_V3.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L2_CDR_V1.0 v1.0 - This dataset contains the Version 1.0 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L2_CDR_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L2_CDR_V1.1 v1.1 - This dataset contains the Version 1.1 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L2_CDR_V1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L2_CDR_V1.2 v1.2 - This dataset contains the Version 1.2 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L2_CDR_V1.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L2_SURFACE_FLUX_CDR_V1.0 v1.0 - This dataset contains the first release, Version 1.0, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 1-2 month latency from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The Cyclone Global Navigation Satellite System (CYGNSS) is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L2_SURFACE_FLUX_CDR_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L2_SURFACE_FLUX_CDR_V1.1 v1.1 - This dataset contains the first release, Version 1.1, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 1-2 month latency from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The Cyclone Global Navigation Satellite System (CYGNSS) is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L2_SURFACE_FLUX_CDR_V1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L2_SURFACE_FLUX_CDR_V1.2 v1.2 - This dataset contains the third release, Version 1.2, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 6-7 day latency from the Delay Doppler Mapping Instrument (DDMI) aboard the Cyclone Global Navigation Satellite System (CYGNSS) constellation. CYGNSS is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L2_SURFACE_FLUX_CDR_V1.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L2_SURFACE_FLUX_V1.0 v1.0 - This dataset contains the Version 1.0 CYGNSS Level 2 Ocean Surface Heat Flux Science Data Record, which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L2_SURFACE_FLUX_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L2_SURFACE_FLUX_V2.0 v2.0 - This dataset contains the Version 2.0 CYGNSS Level 2 Ocean Surface Heat Flux Science Data Record, which provides time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L2_SURFACE_FLUX_V2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L2_SURFACE_FLUX_V3.2 v3.2 - The CYGNSS level 2 ocean surface heat flux science data record version 3.2 dataset is provided as a service to the oceanographic and meteorological research communities on behalf of the CYGNSS Science Team in direct collaboration with the Cyclone Global Navigation Satellite System (CYGNSS) Mission. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L2_SURFACE_FLUX_V3.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L2_V2.1 v2.1 - This dataset contains the Version 2.1 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.0.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L2_V2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L2_V3.0 v3.0 - This dataset contains the Version 3.0 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L2X21.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L2_V3.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L2_V3.1 v3.1 - This dataset contains the Version 3.1 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L2X30.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L2_V3.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_CDR_V1.0 v1.0 - This dataset contains the Version 1.0 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_CDR_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_CDR_V1.1 v1.1 - This dataset contains the Version 1.1 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_CDR_V1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_CDR_V1.2 v1.2 - This dataset contains the Version 1.2 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_CDR_V1.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_MRG_NRT_V3.2 v3.2 - This dataset contains the version 3.2 CYGNSS Level 3 Merged (MRG) Science Data Record Near Real Time (NRT) Storm Wind Speed derived from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. It combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 Young Seas Limited Fetch (YSLF) winds for a region surrounding a given tropical cyclone (TC), with L2 Fully Developed Seas (FDS) winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_MRG_NRT_V3.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_MRG_NRT_V3.2.1 v3.2.1 - This dataset contains the version 3.2.1 CYGNSS Level 3 Merged (MRG) Science Data Record Near Real Time (NRT) Storm Wind Speed derived from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. It combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 Young Seas Limited Fetch (YSLF) winds for a region surrounding a given tropical cyclone (TC), with L2 Fully Developed Seas (FDS) winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_MRG_NRT_V3.2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_MRG_NRT_V3.2.2 v3.2.2 - CYGNSS_L3_MRG_NRT_V3.2.2 This dataset contains the Near Real Time (NRT) version of the 3.2.2 CYGNSS Level 3 Merged (MRG) Science Data Record Wind Speed which combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 Young Seas Limited Fetch (YSLF) winds for a region surrounding a given tropical cyclone (TC), with L3 Fully Developed Seas (FDS) winds away from the TC center. The L3 MRG wind speeds are obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation and provided on a 0.1x0.1 degree latitude by longitude equirectangular g...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_MRG_NRT_V3.2.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_MRG_V3.2 v3.2 - This dataset contains the version 3.2 CYGNSS level 3 science data record merged storm (MRG) wind speed which combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 YSLF winds for a region surrounding a given tropical cyclone (TC), with L3 FDS winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. <br><br> L3 MRG is a new product which combines the L2 FDS and Young Seas Limited Fetch (YSLF) winds and eliminates the need to choose ...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_MRG_V3.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_MRG_V3.2.1 v3.2.1 - This dataset contains the version 3.2.1 CYGNSS level 3 science data record merged storm (MRG) wind speed which combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 Young Seas Limited Fetch (YSLF) winds for a region surrounding a given tropical cyclone (TC), with L3 Fully Developed Seas (FDS) winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. <br><br> L3 MRG combines the L2 FDS and Young Seas Limited Fetch (YSLF) winds and e...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_MRG_V3.2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_MRG_V3.2.2 v3.2.2 - This dataset contains the version 3.2.2 CYGNSS level 3 science data record merged storm (MRG) wind speed which combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 Young Seas Limited Fetch (YSLF) winds for a region surrounding a given tropical cyclone (TC), with L3 Fully Developed Seas (FDS) winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. <br><br> L3 MRG combines the L2 FDS and Young Seas Limited Fetch (YSLF) winds and e...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_MRG_V3.2.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_MICROPLASTIC_V3.2 v3.2 - The CYGNSS L3 Ocean Microplastic Concentration V3.2 dataset is provided by the CYGNSS Science Team of the University of Michigan. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_MICROPLASTIC_V3.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_V2.1 v2.1 - This dataset contains the Version 2.1 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_V2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_V3.0 v3.0 - This dataset contains the Version 3.0 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_V3.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_V3.1 v3.1 - This dataset contains the Version 3.1 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_V3.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_SOIL_MOISTURE_V3.2 v3.2 - The CYGNSS Level 3 Soil Moisture V3.2 dataset is provided by the CYGNSS Science Team of the University of Michigan. It estimates volumetric water content for soils between 0-5 cm depth at a 6-hour discretization for most of the subtropics from the V3.2 reflectivity measurements provided in the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_SOIL_MOISTURE_V3.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_S1.0 v1.0 - This dataset contains the Version 1.0 Cyclone Global Navigation Satellite System (CYGNSS) Level 3 Storm Centric Grid (SCG) Science Data Record (SDR) which provides the average wind speed combined from aggregated wind speed measurements made by the entire CYGNSS constellation whose specular points are located near a storm of interest in latitude, longitude and time. Data are provided on both a 0.1x0.1 degree latitude by longitude equirectangular grid and storm centric coordinates obtained from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_S1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_NOAA_L2_SWSP_25KM_V1.2 v1.2 - This dataset contains the Version 1.2 NOAA CYGNSS Level 2 Science Wind Speed Product Version 1.2 which provides the time-tagged and geolocated average wind speed (m/s) in 25x25 kilometer grid cells along the measurement tracks from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. This version corresponds to the second science-quality released through the PO.DAAC, as produced by NOAA/NESDIS using a specific geophysical model function (GMF version 1.0) and a track-wise debiasing algorithm as part of the wind speed retrieval process.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_NOAA_L2_SWSP_25KM_V1.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"RONGOWAI_L1_SDR_V1.0 v1.0 - The Rongowai Level 1 Science Data Record Version 1.0 dataset is generated by the University of Auckland (UoA) Rongowai Science Payloads Operations Centre in New Zealand. This initiative is supported by NASA and the New Zealand Space Agency.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/RONGOWAI_L1_SDR_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2 v3.2 - The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.2 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1 v3.1 - The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.1 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CYGNSS_L3_SOIL_MOISTURE_V1.0 v1.0 - The CYGNSS Level 3 Soil Moisture Product provides volumetric water content estimates for soils between 0-5 cm depth at a 6-hour discretization for most of the subtropics. The data were produced by CYGNSS investigators at the University Corporation for Atmospheric Research (UCAR) and the University Colorado at Boulder (CU), and derive from version 2.1 of the CYGNSS L1 SDR.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CYGNSS_L3_SOIL_MOISTURE_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-07-16","Slug":"nasa-cygnss","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Daymet Project","Description":"This dataset provides annual climate summaries derived from Daymet Version 4 R1 daily data at a 1 km x 1 km spatial resolution for five Daymet variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable. Each data file is provided as a single year by variable and covers the same period of record as the Daymet V4 R1 daily data. The annual climatology files are derived from the larger datasets of daily weather parameters produced on a 1 km x 1 km grid for North America (including Canada, the United States, and Mexico), Hawaii, and Puerto Rico. Separate annual files are provided for the land areas of continental North America, Hawaii, and Puerto Rico. Data are distributed in standardized Climate and Forecast (CF)-compliant netCDF (.nc) and Cloud Optimized GeoTIFF (.tif) file formats. In Version 4 R1, all 2020 and 2021 files (60 total) were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.\n<br>\n\n#### Daymet_Daily_V4R1_2129\nThis dataset provides Daymet Version 4 R1 data as gridded estimates of daily weather parameters for North America, Hawaii, and Puerto Rico. Daymet variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset covers the period from January 1, 1980, to December 31 (or December 30 in leap years) of the most recent full calendar year for the Continental North America and Hawaii spatial regions. Data for Puerto Rico is available starting in 1950. Each subsequent year is processed individually at the close of a calendar year. Daymet variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Areas of Hawaii and Puerto Rico are available as files separate from the continental North America. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant netCDF file format. In Version 4 R1, all 2020 and 2021 files were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.\n<br>\n\n#### Daymet_Monthly_V4R1_2131\nThis dataset provides Daymet Version 4 R1 monthly climate summaries derived from Daymet Version 4 R1 daily data at a 1 km x 1 km spatial resolution for five Daymet variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Monthly averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and monthly totals are provided for the precipitation variable. Each data file is yearly by variable with 12 monthly time steps and covers the same period of record as the Daymet V4 R1 daily data. The monthly climatology files are derived from the larger datasets of daily weather parameters produced on a 1 km x 1 km grid for North America, Hawaii, and Puerto Rico. Separate monthly files are provided for the land areas of continental North America (Canada, the United States, and Mexico), Hawaii, and Puerto Rico. Data are distributed in standardized Climate and Forecast (CF)-compliant netCDF (.nc) and Cloud-Optimized GeoTIFF (.tif) formats. In Version 4 R1 (ver 4.1), all 2020 and 2021 files (60 total) were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.\n<br>\n\n#### Daymet_xval_V4R1_2132\nThis dataset reports the station-level daily weather observation data and the corresponding cross-validation results for three Daymet model parameters: minimum temperature (tmin), maximum temperature (tmax), and daily total precipitation (prcp) across continental North America (including Canada, the United States, and Mexico), Hawaii, and Puerto Rico. Each data file contains the daily observations and cross-validation results for one parameter for each modeled region and each year, that is, from 1980 to the current calendar year for stations across continental North America and Hawaii and from 1950 to the current year for Puerto Rico. Also included are corresponding station metadata files listing every surface weather station used in Daymet processing for each parameter, region, and year and containing the station name, station identification, latitude, and longitude. The data are provided in netCDF and text formats. In Version 4 R1, all 2020 and 2021 files were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.\n<br>\n\n#### Daymet_SubDaily_Puerto_Rico_1977\nTo support high spatial- and temporal-resolution land surface modeling, this dataset provides 3-hourly time step historic weather forcing at 1-km spatial resolution for Puerto Rico and surrounding islands. The latest Daymet V4 data provides gridded historic daily weather observation at 1-km spatial resolution from 1950 to present. Using sub-daily temporal information from two meteorological reanalysis datasets (GSWP3 and NARR), Daymet was further temporally downscaled to 3-hourly time steps and provided in the format required for land surface model simulations. The process of temporal downscaling preserves the relative magnitude in each sub-daily time step from GSWP3 and NARR while maintaining the total and average values from Daymet at each day. These result in two blended datasets: 1950-2014 Daymet-GSWP3 and 1979-2019 Daymet-NARR. Available variables include surface air temperature, precipitation, specific humidity, shortwave and longwave radiation, wind speed, and pressure. These data can be used as a high-resolution meteorological forcing dataset to support high-resolution land surface modeling where accurate meteorological forcing datasets built from historic observations and/or reanalysis datasets are desirable.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://daymet.ornl.gov","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","ice","netcdf","precipitation","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Daymet_Annual_V4R1_2130 v4.1 - This dataset provides annual climate summaries derived from Daymet Version 4 R1 daily data at a 1 km x 1 km spatial resolution for five Daymet variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/daymet/Daymet_Annual_V4R1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Daymet_Daily_V4R1_2129 v4.1 - This dataset provides Daymet Version 4 R1 data as gridded estimates of daily weather parameters for North America, Hawaii, and Puerto Rico. Daymet variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/daymet/Daymet_Daily_V4R1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Daymet_Monthly_V4R1_2131 v4.1 - This dataset provides Daymet Version 4 R1 monthly climate summaries derived from Daymet Version 4 R1 daily data at a 1 km x 1 km spatial resolution for five Daymet variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Monthly averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and monthly totals are provided for the precipitation variable.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/daymet/Daymet_Monthly_V4R1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Daymet_xval_V4R1_2132 v4.1 - This dataset reports the station-level daily weather observation data and the corresponding cross-validation results for three Daymet model parameters: minimum temperature (tmin), maximum temperature (tmax), and daily total precipitation (prcp) across continental North America (including Canada, the United States, and Mexico), Hawaii, and Puerto Rico. Each data file contains the daily observations and cross-validation results for one parameter for each modeled region and each year, that is, from 1980 to the current calendar year for stations across continental North America and Hawaii and f...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/daymet/Daymet_xval_V4R1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Daymet_SubDaily_Puerto_Rico_1977 v1 - To support high spatial- and temporal-resolution land surface modeling, this dataset provides 3-hourly time step historic weather forcing at 1-km spatial resolution for Puerto Rico and surrounding islands. The latest Daymet V4 data provides gridded historic daily weather observation at 1-km spatial resolution from 1950 to present.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/daymet/Daymet_SubDaily_Puerto_Rico/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-daymet","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Delta-X Project","Description":"This dataset contains estimates of forest aboveground biomass (AGB) across the Atchafalaya and Terrebonne Basins, Louisiana, US. AGB was derived from AVIRIS-NG surface reflectance and UAVSAR products. L2B BRDF-adjusted surface reflectance was produced after applying atmospheric correction to L2 Hemispherical-Directional surface reflectance from NASA's AVIRIS-NG instrument. A polarimetric decomposition of the UAVSAR Level 1 (L1) Single Look Complex (SLC) stack product was used. To estimate AGB, local pixel reflectance spectra and radar scattering component pixels coincident with in situ forest AGB plot data from Summer 2015 and Fall 2021 were used to generate a machine learning-based regression model. This model was then scaled to the AVIRIS-NG and UAVSAR mosaic data, in accordance with vegetation type, to map forest AGB across the Atchafalaya and Terrebonne Basins. The data are provided in cloud optimized GeoTIFF format.\n<br>\n\n#### DeltaX_L3_AVIRIS-NG_Veg_Types_2352\nThis dataset provides maps of vegetation types for the Atchafalaya and Terrebonne basins in coastal Louisiana, U.S., derived from NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) imagery acquired during spring and fall of 2021 for the Delta-X campaign. Vegetation types were classified from Level-2B BRDF-adjusted surface reflectance. Local pixel reflectance spectra coincident with herbaceous vegetation field samples and vegetation plot data from Louisiana's Coastwide Reference Monitoring System were used to generate a machine learning-based model to classify vegetation types. This model was then applied to the AVIRIS-NG mosaic imagery to map vegetation types across the Atchafalaya and Terrebonne Basins. The data are provided in cloud optimized GeoTIFF (COG) format.\n<br>\n\n#### DeltaX_AGB_AGN_V2_2237\nThis dataset contains total aboveground biomass (AGB) and necromass (AGN), and total carbon, total nitrogen, and total phosphorus content of aboveground biomass (AGB) and necromass (AGN) samples collected from herbaceous wetlands in the Atchafalaya and Terrebonne basins in southeastern coastal Louisiana during 2021. Field measurements were conducted at three sites in the Atchafalaya basin and three sites in the Terrebonne basin. Five of the sites are adjacent to sites from the Coastwide Reference Monitoring System (CRMS). The other site is located in Wax Lake Delta (WLD) without appropriate adjacent CRMS sites. All AGB and AGN plant material within each plot was clipped at soil level, stored in plastic bags, and transported to the laboratory for further analyses. Herbaceous wetland sites in both basins were chosen to represent a salinity gradient including freshwater, brackish, and saline ecosystems. These data cover the period 2021-03-19 to 2021-03-31 (spring) and 2021-08-19 to 2021-08-27 (fall).\n<br>\n\n#### DeltaX_Vegetation_Structure_V2_2240\nThis dataset provides mean stem diameter, mean height, dominant species, hydrogeomorphic zone (HGM), and stem density for vegetation in herbaceous wetlands collected in the Atchafalaya and Terrebonne basins in southeastern coastal Louisiana. The data were collected between 2021-03-21 to 2021-03-31 during the Delta-X Spring 2021 deployment, and from 2021-08-19 to 2021-08-27 during the Fall deployment. Field measurements were conducted at six sites in the Atchafalaya (N = 3) and Terrebonne (N = 3) basins. Five of the sites were adjacent to sites from the Coastwide Reference Monitoring System (CRMS), and the other site was in the Wax Lake Delta (WLD) without appropriate adjacent CRMS sites. Sites in both basins were chosen to represent a salinity gradient including freshwater, brackish, and saline ecosystems. At each herbaceous wetland site, duplicate sampling stations (30 m apart) were established parallel to the wetland edge at 25 and 50 m within the intertidal zone to capture within site variability in vegetation dynamics and soil properties. The data are provided in comma-separated values (.csv) format.\n<br>\n\n#### DeltaX_ADCP_Measurements_V2_2081\nThis dataset provides river discharge measurements collected at selected locations in the Atchafalaya and Terrebonne Basins within the Mississippi River Delta (MRD) floodplain in coastal Louisiana, USA. The measurements were made during the Delta-X 2021 field efforts from 2021-03-25 to 2021-04-11 (spring) and 2021-08-16 to 2021-09-25 (fall). Channel surveys were conducted with a Teledyne RiverPro acoustic doppler current profiler (ADCP) or a Sontek M9 RiverSurveyor ADCP on selected wide channels (>100 m wide) and a few selected narrow channels (approximately 10 m wide) near the Delta-X intensive study sites. River discharge was measured on cross-channel transects. Reported data include bathymetry, discharge (m3 s-1), and flow velocity.\n<br>\n\n#### DeltaX_L2_AirSWOT_WaterElev_V3_2350\nThis dataset contains Level 2 (L2) AirSWOT geocoded products, including estimated water surface elevation. The AirSWOT instrument is a Ka-band interferometer and for this study is flown on the King Air B200 platform. Data were collected during the DeltaX airborne campaign over the Atchafalaya and Terrebonne basins of the Mississippi River Delta, Louisiana, USA. Flights occurred during the Delta-X Spring 2021 deployment from 2021-03-26 to 2021-04-18 and the Delta-X Fall 2021 deployment from 2021-08-21 to 2021-09-12. AirSWOT is capable of producing high resolution (3.6 m) digital elevation models over land and water bodies using near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and produce continuous gridded elevation data. The instrument includes six antennas that form multiple baseline pairs for along-track and across-track interferometry. AirSWOT elevation data are useful for calibrating elevation and slopes along the main channels, as well as tying observations to open ocean tidal conditions and is an airborne calibration and validation instrument for the Surface Water and Ocean Topography (SWOT) satellite. This Version 3 dataset provides updated data files due to an updated Calumet survey that changed the water level by 0.138 m. This resulted in all the AirSWOT water levels changing by that same amount. For these L2 products, only the estimated water surface elevation in respect to the WGS84 ellipsoid surface, and estimated height above the NAVD88 (GEOID12B) vertical datum files changed. Note that data acquired on September 1 and September 5, 2021 do not meet the expected MAE in-situ comparison and should be used with caution. This dataset contains cloud optimized GeoTIFF rasters in UTM map coordinates for each flight line. In addition, a text file provides basic metadata, including flight line ID, start and end UTC times of data acquisition, processor version number, and the date and time of different processing stages.\n<br>\n\n#### DeltaX_L3_AirSWOT_WaterElev_V2_2349\nThis dataset contains water surface elevations at selected point locations generated from the AirSWOT data collected during the Spring and Fall 2021 Delta-X deployments over the Atchafalaya and Terrebonne basins in Louisiana, USA. AirSWOT uses near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and produce continuous gridded elevation data. The Level 3 (L3) data were created by masking land areas out of the AirSWOT Level 2 products, then filtering and averaging to the AirSWOT heights to produce water surface elevations at selected points throughout the scene. The AirSWOT elevation data are useful for calibrating elevation and slopes along the main channels, as well as tying observations to open ocean tidal conditions. AirSWOT performance in the floodplain was limited by the presence of vegetation and the very small slope characteristic of two dimensional floodplain discharge. Therefore, the bulk of the AirSWOT data collections were targeted at the larger channels, since the channel discharge provides the necessary boundary conditions for potential overflow to islands and floodplains. The data are provided in comma-separated values (CSV) format.\n<br>\n\n#### DeltaX_L1b_AirSWOT_1996\nThis dataset contains AirSWOT interferogram products collected during the 2021 Delta-X Campaign over the Atchafalaya and Terrebonne Basins of the Mississippi River Delta, Louisiana, USA from 2021-03-26 to 2021-04-18 (Spring) and 2021-08-21 to 2021-09-12 (Fall). AirSWOT uses near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and produce continuous gridded elevation data. AirSWOT elevation data is useful for calibrating elevation and slopes along the main channels, as well as tying observations to open ocean tidal conditions. The AirSWOT Level 1B (L1B) data products represent interferogram data in the radar coordinate system, not in georeferenced map coordinates. This is an earlier stage of data processing which is used to generate the later Level 2 and Level 3 data products which will contain georeferenced water heights and water height profiles for river channels in each basin. The data are provided in binary and text file formats.\n<br>\n\n#### DeltaX_ANUGA_Hydrodynamics_MRD_2310\nThis dataset comprises the primary inputs and outputs from the ANUGA hydrodynamic model for spring 2021 (2021-03-20 to 2021-04-04). These dates align with the 2021 Delta-X Spring Campaign. Data cover the Atchafalaya and Terrebonne basins of the Mississippi River Delta in southern Louisiana, USA. ANUGA is a 2D depth-integrated hydrodynamic model which uses the Finite Volume Method (FVM) to numerically solve the shallow water momentum and continuity equations for fluid flow in broad-scale geophysical systems. The inputs consist of a modified digital elevation (DEM) model, a spatial classification of the friction coefficient modified, and the model's unstructured grid with boundary condition locations. The model's outputs include two weeks of predictions of water levels and mean horizontal velocities at each mesh node at a 30-minute time step. Outputs are provided in NetCDF format, and inputs are provided in GeoTIFF and comma separated values (CSV) format. Also included are MP4 videos (.mp4) that provide visual summaries of the outputs.\n<br>\n\n#### DeltaX_L2A_AVIRIS-NG_BRDF_V3_2355\nThis data provides AVIRIS-NG Bidirectional Reflectance Distribution Function (BRDF) and sunglint-corrected surface spectral reflectance images over the Atchafalaya and Terrebonne basins of the Mississippi River Delta (MRD) of coastal Louisiana, USA. Flights were acquired during the Spring and Fall 2021 deployments of the Delta-X campaign. The imagery was acquired by the Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) from 2021-03-27 to 2021-04-06 and 2021-08-18 to 2021-09-25. Reflectance data are provided for each flight line. In addition, ten files of mosaicked flight lines, by time period and over four locations (labeled Terre, Atcha, TerreEast, and Bara), are included. Data are provided as binary ENVI image and header files. Only land pixels were corrected; mask files for the mosaic file coverage showing presence/absence of water and clouds are also included. For the Delta-X mission, these data serve to better understand rates of soil erosion, accretion, and creation in the delta system, with the goal of building better models of how river deltas will behave under relative sea level rise.\n<br>\n\n#### DeltaX_L1_AVIRIS_Radiance_1987\nThis dataset provides Level 1B (L1B) radiance products from NASA's Airborne Visible Infrared Imaging Spectrometer- Next Generation (AVIRIS-NG) instrument acquired over the Atchafalaya and Terrebonne basins of the Mississippi River Delta, Louisiana, USA during two deployments; spring and fall of 2021. All flights were flown on a Dynamic Aviation King Air B200. There are a combined 200 total flight lines for the spring and fall 2021 deployments; spring 2021 had 75 flight lines, fall 2021 had 175 flight lines. AVIRIS-NG measures reflected radiance at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Level 1B data are orthorectified calibrated radiance values in units of spectral radiance in which raw digital numbers (DNs) are translated to units of radiant intensity measured at the sensor. Measurements are radiometrically and geometrically calibrated and provided at approximately 5-meter spatial resolution, dependent on aircraft altitude. Additional flight line files include band information of observational geometry and illumination parameters, as well as geographic pixel locations and elevation. These L1B data are provided in ENVI file format. AVIRIS-NG Cal/Val, Level 2 and Level 3 products for the Pre-Delta-X and Delta-X missions are provided in related datasets.\n<br>\n\n#### DeltaX_L2_AVIRIS_Reflectance_1988\nThis dataset provides Level 2 (L2) atmospherically corrected surface reflectance data acquired from NASA's Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over regions of interest in the Atchafalaya and Terrebonne basins on the southern coast of Louisiana, United States. Data were collected as part of the Delta-X Spring and Fall 2021 deployments that occurred from 2021-03-27 to 2021-04-06 and from 2021-08-18 to 2021-08-25. Additionally, L2 data from flights flown specifically to capture the Significant Event of Hurricane Ida are provided. This includes 56 files from flights conducted following Hurricane Ida from 2021-09-23 to 2021-09-25. Hurricane Ida made landfall over this region on 2021-08-29. AVIRIS-NG is a pushbroom spectral mapping system with a high signal-to-noise ratio (SNR) designed for high performance imaging spectroscopy. AVIRIS-NG measures the wavelength range from 380 nm to 2510 nm with 5-nm sampling resolution. For this dataset, spatial resolution varies from 3.8-5.4 meters. For this campaign, the AVIRIS-NG instrument was deployed on the Dynamic Aviation King Air B200 platform. This dataset represents one part of a multisensor airborne sampling campaign conducted by different aircraft teams for the Delta-X Campaign. Data are provided in ENVI file format.\n<br>\n\n#### DeltaX_L2B_AVIRIS-NG_FracCover_2407\nThis dataset provides estimates of fractional vegetation cover across the Atchafalaya and Terrebonne basins. Atmospheric correction was applied to a L2B BRDF-adjusted surface reflectance product from AVIRIS-NG imagery to produce L2 Hemispherical-Directional surface reflectance. A spectral library was compiled using in situ spectra in the USGS Spectral Library (v7) to represent green vegetation, non-photosynthetic vegetation, and soil. Linear spectral unmixing was then applied to mosaics of AVIRIS-NG reflectance using these spectra to estimate the fractional composition (scaled 0-1) of each endmember type for all terrestrial pixels. In addition to the AVIRIS-NG imagery and in situ field spectra, this analysis incorporated field images collected using a GoPro digital camera coincident with the AVIRIS-NG flight lines.\n<br>\n\n#### DeltaX_L3_AVIRIS-NG_AGB_V3_2409\nThis dataset includes high-resolution (~5 m) gridded estimates of aboveground biomass (AGB), aboveground necromass (AGN), and aboveground net primary productivity (ANPP) for herbaceous vegetation in the Atchafalaya and Terrebonne basins of the Mississippi River Delta in coastal Louisiana, USA, for the fall and spring seasons of 2021. AGB, AGN and ANPP were estimated from Bidirectional Reflectance Distribution Function (BRDF)-adjusted surface reflectance products from NASA's Airborne Visible Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) acquired over the study area in April and August 2021. The BRDF-adjusted reflectance was derived from hemispherical-directional surface reflectance with atmospheric correction. Local pixel reflectance spectra coincident with herbaceous vegetation AGB field samples from both Spring and Fall 2021 collections were used to generate a machine learning-based regression model to estimate AGB. This model was then scaled to the AVIRIS-NG mosaic imagery in accordance with vegetation type to map herbaceous AGB across the Atchafalaya and Terrebonne Basins. The AGN field data, separated from the AGB samples, were also used in conjunction with AVIRIS-NG-derived fractional coverage to derive AGN maps again employing machine learning. The AGB and AGN products were used together to map ANPP across the Atchafalaya and Terrebonne Basins. The data are provided in cloud optimized GeoTIFF (COG) format. This Version 3 dataset replaces the files provided in Version 2.\n<br>\n\n#### DeltaX_AVIRIS-NG_L3_BGB_2456\nThis dataset contains high-resolution (~5 m) gridded estimates of belowground biomass (BGB) in the Atchafalaya and Terrebonne basins of southern Louisiana, U.S., in August 2021. The Level 3 (L3) AVIRIS-NG-derived herbaceous aboveground biomass (AGB) and necromass (AGN) Version 3 products were used to predict the BGB. The AGB estimates were derived from the L2B BRDF-adjusted surface reflectance product, following atmospheric correction to produce L2 Hemispherical-Directional surface reflectance. An Ordinary Least Squares Regression (OLSR) model was derived, trained on field sample measurements (n=40) of aboveground biomass (AGB), aboveground necromass (AGN), and the natural logarithm-transformed total nitrogen (N). This dataset constitutes all available field samples where AGB, AGN, total N, and BGB were measured concurrently. Belowground biomass is an essential component of organic accretion but cannot be directly measured by airborne instruments. This model infers BGB from its relationships with aboveground plant mass and foliar N concentrations. This model was applied to landscape-scale maps derived from AVIRIS-NG data to estimate BGB for the August 2021 peak biomass collection period. The data are provided in cloud optimized GeoTIFF (COG) format.\n<br>\n\n#### DeltaX_L3_AVIRIS-NG_Water_V3_2152\nThis dataset includes estimates of total suspended solids (TSS) concentration and turbidity for waters of the Atchafalaya River and Terrebonne Basins of the Mississippi River Delta (MRD) in coastal Louisiana. Estimates were derived from Level 2 (L2) BRDF-corrected imagery from NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). AVIRIS-NG imagery was collected from March 27-April 6 (spring) and August 20-25 (fall), 2021, as part of the 2021 Delta-X campaign. Algorithms for TSS and turbidity estimation were developed using in-situ remote-sensing reflectance measured at field sampling stations paired with in-situ measures of turbidity from a water quality probe and TSS from water samples. Using the in-situ data, a partial least squares regression (PLSR) model was developed for each AVIRIS-NG wavelength. A subset of the in-situ data, collected during relatively clear AVIRIS-NG overflights, was held out to validate the PLSR model. The PLSR algorithm was then applied to AVIRIS-NG imagery to retrieve TSS and turbidity across the study area. The measurement units for TSS and turbidity estimates are mg L-1 and Formazin Nephelometric Units (FNU), respectively, and the spatial resolution is 3.8 to 5.4 m as determined by the AVIRIS-NG imagery. The dataset includes binary cloud and water masks. These data quantify the mesoscale (i.e., on the order of 1 ha) patterns of soil accretion that control land loss and gain and predict the resilience of deltaic floodplains under projected relative sea-level rise. Gridded estimates are provided in netCDF format, and regression coefficients are included in a comma-separated values (CSV) file. This is Version 3 of this dataset. All previously released data were updated to the latest available versions.\n<br>\n\n#### DeltaX_Sediment_Grain_Size_V3_2379\nThis dataset includes sediment concentration and grain size distribution measurements from suspended and bed sediment samples collected in the Atchafalaya River and Terrebonne Basins as part of the Delta-X Spring campaign between March 25 and April 2, 2021, and the Delta-X Fall campaign between August 17 and 22, 2021. In addition, ten samples were collected during a field campaign in October 2019. Samples were collected in the main channels and the interior of Mike Island in the Wax Lake Delta, Louisiana and at site CRMS0421 inside the Terrebonne Basin. Sediment samples were collected from a boat using a Van Dorn sampler (for suspended sediment samples) or a Ponar bed sampler (for bed samples). Suspended sediment samples were collected from a boat drifting at approximately the same velocity as the water flow. One sample was collected per drift. Bed samples were collected in a similar fashion. Data include measurements of sediment grain size distribution, total sediment concentration, water temperature, flow velocity, salinity, and depth. This Version 3 updates Version 2 to include data from more samples and reprocessed laser diffraction grain size distributions with optimized sediment optical properties. The data is provided in comma separated values (CSV) format.\n<br>\n\n#### DeltaX_BGB_BGN_V2_2238\nThis dataset contains total belowground biomass (BGB) and necromass (BGN), and total carbon, total nitrogen, and total phosphorus content of samples collected from herbaceous wetlands in the Atchafalaya and Terrebonne basins of the Mississippi River Delta in southeastern coastal Louisiana, U.S., during March and August 2021. The data were collected during the Delta-X Spring and Fall deployments. Field measurements were conducted at three sites in the Atchafalaya basin and three sites in the Terrebonne basin. Five of the sites are adjacent to sites from the Coastwide Reference Monitoring System (CRMS). The other site is located in Wax Lake Delta (WLD) without appropriate adjacent CRMS sites. Herbaceous wetland sites in both basins were chosen to represent a salinity gradient including freshwater, brackish, and saline ecosystems. Root biomass samples were collected using a gouge soil auger.\n<br>\n\n#### DeltaX_ANUGA_AtchafalayaBasin_2306\nThis dataset provides ANUGA hydrodynamic modeling results and input run-scripts for the Atchafalaya basin in the Mississippi River Delta in southern Louisiana, USA, during three windows of time corresponding to the Delta-X and Pre-Delta-X field campaigns in fall 2016, spring 2021, and fall 2021. ANUGA is a 2D depth-integrated hydrodynamic model which uses the Finite Volume Method (FVM) to numerically solve the shallow water momentum and continuity equations for fluid flow in broad-scale geophysical systems. Each iteration of the model was extensively calibrated using a database of in-situ and remotely-sensed observations, including about 54 water level gauges, numerous water surface profiles collected by AirSWOT or lidar, and water level change measurements derived from UAVSAR. The model was forced using observational data collected from NOAA and USGS, and the model mesh was specifically designed to capture channel-island connectivity using high-resolution Planet Labs imagery spanning over a decade. In total, over a month of simulation outputs are included in this dataset, covering different seasons and hydrological conditions in the Atchafalaya and Wax Lake Delta systems. These model outputs can be leveraged with other Delta-X datasets to provide contextual information about water levels or flow velocities at different times or locations within the Atchafalaya basin, and the model codes provided can be used to simulate additional time periods for further analysis in this region. Model outputs are presented in NetCDF (.nc) format and run-scripts are in Python (.py) or contained in compressed (.zip) file format.\n<br>\n\n#### DeltaX_Delft3D_Atchafalaya_MRD_2302\nThis dataset contains the Delft3D model of the Atchafalaya Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall campaigns in 2021 and include hydrodynamics, waves, and sediment transport. Bottom friction was calibrated using AirSWOT water elevation data, while sediment parameters were calibrated using AVIRIS-NG Total Suspended Solids (TSS) data. All files required to run the simulations are included. Model output of water levels, velocity, and depth-averaged sediment concentration are provided for both campaigns as netCDF files. The dataset includes a netCDF file containing the annual inorganic mass accumulation rates derived from simulations.\n<br>\n\n#### DeltaX_Delft3D_Terrebonne_MRD_2301\nThis dataset contains the Delft3D model of the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall campaigns in 2021 and include hydrodynamics, waves, and sediment transport. Bottom friction was calibrated using AirSWOT water elevation data, while sediment parameters were calibrated using AVIRIS-NG Total Suspended Solids (TSS) data. All files required to run the simulations are included. The model's output of water levels, velocity, and depth-averaged sediment concentrations are provided for both campaigns as netCDF files. The dataset includes a netCDF file containing the annual inorganic mass accumulation rates derived through a storms analysis and modelling.\n<br>\n\n#### DeltaX_Delft3D_FM_HydroModel_2464\nThis dataset contains Delft3D Flexible Mesh (FM) hydrodynamic model setup and results for the Atchafalaya and Terrebonne basins in coastal Louisiana, U.S.. The model domain spans from Bayou Lafourche in eastern Terrebonne Bay, encompasses Atchafalaya Bay, and extends westward to Vermilion Bay. Separate simulations were conducted for spring and fall, each covering a 31-day period. Both simulations were calibrated and validated using Delta-X airborne remote sensing data and in situ measurements. The dataset includes the full set of hydrodynamic model input files for each seasonal simulation, along with a netCDF output file for each model run. Using the provided input files, users can reproduce the simulations and obtain results identical to those in the output netCDF files included in this dataset.\n<br>\n\n#### DeltaX_Delft3D_FM_Hydroperiod_2421\nThis dataset provides the weighted average hydroperiod derived from two Delft3D FM model simulations representing the low discharge (fall 2021) and high discharge (spring 2021) seasons. Each model was run for 31 days, excluding a 1-day warm-up period from the analysis. The weights for each model were derived from the long-term probability density function (PDF) of the Atchafalaya River discharge. Hydroperiod, which quantifies the frequency of inundation at each model grid cell, is computed by analyzing the water level time series and identifying periods when the water depth does not exceed the model-defined wet/dry threshold of 5 cm.\n<br>\n\n#### DeltaX_Delft3D_FM_Salinity_2420\nThis dataset provides the weighted averaged salinity derived from two Delft3D Flexible Mesh (FM) model simulations representing the low discharge (fall 2021) and high discharge (spring 2021) seasons. The weights for each model were determined using the probability density function of the Atchafalaya River discharge. Each model was run for 31 days, excluding a 1-day warm-up period from the analysis. Salinity is calculated by applying a weighted average to the salinity values at each grid cell in the final time step of each simulation. The data are provided in netCDF format.\n<br>\n\n#### DeltaX_Delft3D_294_Terrebonne_2303\nThis dataset contains the Delft3D model of the intensive site 294 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport. All files required to run the simulations are included. The model's output of water velocity and depth-averaged sediment concentrations are provided for both deployments. The dataset includes annual inorganic mass accumulation rates derived through modelling intra-annual variability in water levels and suspended sediment concentrations. The data are provided in netCDF format.\n<br>\n\n#### DeltaX_Delft3D_322_Terrebonne_2312\nThis dataset contains the Delft3D model of the intensive site 322 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport. All files required to run the simulations are included. The model's output of water velocity and depth-averaged sediment concentrations are provided for both deployments. The dataset includes annual inorganic mass accumulation rates derived through modelling intra-annual variability in water levels and suspended sediment concentrations. The data are provided in netCDF and ENVI formats.\n<br>\n\n#### DeltaX_Delft3D_396_Terrebonne_2314\nThis dataset contains the Delft3D model of the intensive site 396 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport. All files required to run the simulations are included. The model's output of water velocity and depth-averaged sediment concentrations are provided for both deployments. The dataset includes annual inorganic mass accumulation rates derived through modelling intra-annual variability in water levels and suspended sediment concentrations. The data are provided in netCDF format.\n<br>\n\n#### DeltaX_Delft3D_399_Terrebonne_2313\nThis dataset contains the Delft3D model of the intensive site 399 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport. All files required to run the simulations are included. The model's output of water velocity and depth-averaged sediment concentrations are provided for both deployments. The dataset includes annual inorganic mass accumulation rates derived through modelling intra-annual variability in water levels and suspended sediment concentrations. The data are provided in netCDF format.\n<br>\n\n#### DeltaX_Delft3D_421_Terrebonne_2304\nThis dataset contains the Delft3D model of the intensive site 421 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport. All files required to run the simulations are included. The model's output of water velocity and depth-averaged sediment concentrations are provided for both deployments. The dataset includes annual inorganic mass accumulation rates derived through modelling intra-annual variability in water levels and suspended sediment concentrations. The data are provided in netCDF format.\n<br>\n\n#### DeltaX_DEM_MRD_LA_2181\nThis dataset provides an updated digital elevation model (DEM) for the Atchafalaya and Terrebonne basins in coastal Louisiana, USA. The DEM is updated from the Pre-Delta-X DEM and extended to the full Delta-X study area. This DEM was developed from multiple data sources, including sonar data collected during Pre-Delta-X and Delta-X campaigns, bathymetric data from the Coastal Protection and Restoration Authority System-Wide Assessment and Monitoring System (CPRA SWAMP), and NOAA, and topography from the National Elevation Dataset and LiDAR from US Geological Survey (USGS). The provided data layers include the DEM, a binary water/land mask, data source flags, and eight layers with analysis weighting factors for each pixel. Elevation values are provided in meters with respect to the North American Vertical Datum of 1988 (NAVD88). The weighting factors indicate how each data source contributed to this multisource DEM. The data are provided in cloud-optimized GeoTIFF (CoG) format.\n<br>\n\n#### DeltaX_Ecogeomorphic_Products_2108\nThis product delineates the Mississippi River Delta (MRD) landscape into distinct ecogeomorphic cells, which are small contiguous areas of land with similar ecological and geomorphological characteristics. The study area is the Atchafalaya and Terrebonne basins of the MRD in southern Louisiana, U.S., which was the focus of NASA's 2021 Delta-X campaign. Each ecogeomorphic cell is a small homogeneous area of similar vegetation and elevation (or bathymetry). A \"cell\" typically consists of a cluster of contiguous pixels in a raster image, although cells of single pixels are present. The elevation was derived from the USGS Digital Elevation Model, and the vegetation was characterized by its spectral signature as measured by near infrared (NIR) reflectance and normalized difference vegetation index (NDVI). NIR and NDVI were computed from Sentinel-2 images acquired January through September 2021. The data are provided in shapefile and GeoTIFF formats. The vector shapefiles contain the distinct ecogeomorphic cells as polygons with unique labels (i.e., ID number). A raster image of these ecogeomorphic cells provided wherein the pixel values are the polygon labels from the shapefiles. The GeoTIFFs hold the mean and standard deviations of bathymetry, NIR, and NDVI spectral indices within each ecogeomorphic region (polygon). The raster data are provided with a spatial resolution of 0.000045 degrees (approximately 5 meters).\n<br>\n\n#### DeltaX_Feldspar_Sediment_V4_2381\nThis dataset provides elevation, hydrogeomorphic zone classification, soil carbon content, bulk density, organic matter content, and sediment accretion measurements collected at feldspar stations established near Louisiana's Coastwide Reference Monitoring Systems (CRMS) sites and on Mike Island in Wax Lake Delta (WLD). Feldspar stations were established to capture recent sediment deposition rates across hydrogeomorphic zones defined as discrete surface elevation ranges relative to NAVD88 (e.g., subtidal < -0.04 m, intertidal -0.04 m to 0.30 m, and supratidal > 0.30 m). Hydrogeomorphic zones classification was based on marsh surface elevation measurements acquired in November - December 2020 using a RTK GPS (Trible R12, using Geoid 18). Between two and four feldspar stations were deployed approximately 25 and 50 meters from a main channel to represent existing hydrogeomorphic zones in brackish and saline emergent marsh vegetation, brackish and saline ponds within emergent marshes, tidal freshwater emergent marshes, and forested swamps. Cryocore technique was used to determine recent sediment deposition. Soil samples were collected to determine organic and inorganic fractions and organic carbon content. The data cover the Delta-X field studies conducted from Fall 2020 through Fall 2023. The first feldspar markers were deployed in October of 2019. The data are provided in comma-separated values (CSV) format.\n<br>\n\n#### DeltaX_Foliar_Stable_Isotopes_2194\nThis dataset contains foliar tissue C and N bulk isotopic signatures (delta 13C, delta 15N) of dominant wetland herbaceous species collected at six sites in the Atchafalaya (N = 3) and Terrebonne (N = 3) basins in coastal Louisiana. Five of the sites are from the Coastwide Reference Monitoring System (CRMS) and one site is the Mike Island Site in the Wax Lake Delta (WLD). For the herbaceous wetland sites, Aboveground biomass (AGB) was harvested inside duplicate plots (0.25 m2), located 5 m apart at each sampling station. All plant material within each plot was clipped at soil level, stored in plastic bags, and transported to the laboratory for further analyses. In the lab, plant tissue (foliar) C and N bulk isotopic signatures were analyzed for two dominant plant species from each site using a Thermo Scientific Delta V Plus CF-IRMS coupled to a Carlo-Erba 1108 elemental analyzer via a ConFlo IV interface (Thermo Fisher Scientific, Waltham, MA, USA). The data were collected during 2021-08-19 to 2021-08-27 during the Delta-X Fall 2021 deployment.\n<br>\n\n#### DeltaX_Particle_Size_LISST_V2_2077\nThis dataset provides in situ measurements of beam attenuation coefficient at 670 nm, average suspended particle size, particle size distribution, and water temperature in surface waters (~0.5 m) of the Atchafalaya and Terrebonne Basins on the southern coast of Louisiana. The field studies were conducted in the Spring and Fall in support of the Delta-X mission and include measurements made in 2021 during March 25 - April 22 and August 14 - September 24. Measurements were made using the Sequoia Scientific Laser In-Situ Scattering and Transmissometer instrument (LISST-200X) in multiple channels of varying width (from a few meters to >100m), near Delta-X intensive study sites and in open bays and lakes and at a few locations in the nearshore Gulf of Mexico. The data are provided in comma-separated (CSV) format.\n<br>\n\n#### DeltaX_Insitu_WQ_Indicators_V2_2080\nThis dataset provides in situ measurements of water temperature (degrees C), salinity (PSU), turbidity (FNU), and chlorophyll-a fluorescence (RFU) in surface of the Atchafalaya River and Terrebonne Basins during the Spring (2021-03-25 to 2021-04-22) and Fall (2021-08-14 to 2021-09-24) field efforts by the Delta-X project. Field sampling was paused on August 25 and resumed on September 13, 2021, due to the landfall of Hurricane Ida on 2021-08-26 approximately 70 km east of the study site. Water quality changes caused by the hurricane were expected to be minimal. Measurements were collected in multiple channels of varying width (from a few meters to >100 m), near Delta-X intensive study sites, in open bays and lakes, and at a few locations in the nearshore Gulf of Mexico using either a YSI ProDSS water quality probe or a YSI EXO3 water quality probe.\n<br>\n\n#### DeltaX_Insitu_Reflectance_V3_2153\nThis dataset includes above water measurements of remote-sensing reflectance measured in situ at field sampling stations during the Delta-X 2021 field efforts. Measurements were collected in the Atchafalaya River and Terrebonne Basins on the southern coast of Louisiana from 2021-03-25 to 2021-04-22 (spring) and from 2021-08-14 to 2021-09-24 (fall). Field sampling was paused on 2021-08-25 and resumed on 2021-09-13 due to the landfall of Hurricane Ida on 2021-08-26 approximately 70 km east of the study site. Water quality changes caused by the hurricane were expected to be minimal. Reflectance was measured near-simultaneously with collection of field samples and in-water sediment parameters in multiple channels of varying width (from a few meters to >100 m), near Delta-X intensive study sites, in open bays and lakes, and at a few locations in the nearshore Gulf of Mexico. For each in situ collection, a handheld Portable SpectroRadiometer (PSR-1100f, Spectral Evolution) was used to measure radiance from: (a) a highly reflective (>99% reflectance) Lambertian Spectralon panel (b) from the sky, measured at 40 degrees from the solar zenith and at 135 degrees from the sun azimuthal plane, (c) from the water, measured at 40 degrees from nadir and at 135 degrees from the sun azimuthal plane. These measurements were used to calculate remote-sensing reflectance and the water-leaving radiance relative to downwelling irradiance, including a correction for the influence of reflected skylight. In Version 3, the data files with this dataset replace and update the data files in Version 2. A minor update was made to the code used to calculate the remote sensing reflectance (Rrs). These data are provided in comma-separated values (.csv) format.\n<br>\n\n#### DeltaX_H2O_Surface_Elevation_2086\nThis dataset contains in situ water level measurements collected at 49 different locations across the Atchafalaya and Terrebonne basins in the Mississippi River Delta (MRD) floodplain, and 49 files with the in situ measurements from raw water level converted to absolute water surface elevation with respect to NAVD88 (GEOID12B). There were 65 sites included in the study, however, data from 16 of the sites were corrupted, unretrievable from the instrument or the instrument was lost during deployment. Additionally, it contains water surface elevations collected by GNSS mounted to a boat while underway, and a summary file with site information for all 65 sites. Relative water level measurements were recorded every 15-20 minutes using commercial pressure transducers (Levelogger, Solinst) to measure water depth. Water surface elevation was determined by measuring an absolute height conversion at each sensor location using AirSWOT or a survey-grade global navigation satellite system (GNSS). These water level measurements calibrate and validate the Delta-X campaign's remote sensing observations and hydrodynamic models. The data are provided in comma separated values (CSV) and JPEG image formats.\n<br>\n\n#### DeltaX_Island_Channel_Model_2106\nThis dataset includes model code and output for a model that simulates changes in islands and small water channels of river delta systems in response to dynamics of sediment deposit, erosion, and changing water levels. Simulations demonstrate developmental cycles of secondary channels and how sediment dynamics can allow islands to build land vertically to keep pace with rising sea levels rather than passively drowning. The model was applied to the Mississippi River Delta as part of NASA's Delta-X project. Simulations were run for other river deltas, including the Amazon, Brahmputra, Danube, Magdalena, Nile, Orinoco, Parana, Rhine-Meuse, and Rhone rivers. The model code is provided in text format for MATLAB software. Files demonstrating initial model conditions and outputs are provided in binary MATLAB as well as NetCDF version 4 format.\n<br>\n\n#### DeltaX_TotalSubsidenceRate_MRD_2307\nThis dataset provides estimates of land subsidence rates for the Delta-X domain area within the Atchafalaya and Terrebonne basins for 2021. The study area is a portion of the Mississippi River Delta in coastal Louisiana, U.S. The total subsidence is calculated as the sum of deep and shallow vertical elevation change rates. The deep subsidence rate is based on information from the Coastal Protection and Restoration Authority (CPRA) of Louisiana, documented in the Phase-4 subsidence trend report prepared for and provided by CPRA (2022). The shallow subsidence is calculated for the Delta-X study area by interpolation of publicly available data provided by CPRA for their coast-wide estimation of shallow subsidence in the 2023 Coastal Master Plan. The total subsidence rates and the estimated uncertainty in the total subsidence rates are provided as separate files in cloud optimized GeoTIFF (COG) format at 30-m (0.0003 decimal degrees) resolution.\n<br>\n\n#### DeltaX_LandAccretion_WLD_2309\nThis dataset provides the Matlab sediment transport and land accretion model at Wax Lake Delta (WLD), Atchafalaya Basin, in coastal Louisiana. The data include the Matlab scripts that solve the advection and Exner equations to simulate the suspended sediment transport and accretion at WLD. The model requires modeled flow information from a separate ANUGA hydrodynamic model as inputs. For this study, ANUGA modeled flow information from the Delta-X Spring and Fall 2021 campaigns were used as inputs. The ANUGA output files are converted to variables used by this Matlab model using pre-processing tools. The main code calculates suspended sediment fluxes and accretion rates of mud and sand as a function of space and time. The cumulative sediment accretion from each campaign was then used to estimate an annualized land accretion map using a weighted-average formula presented. The final product, the one-yr upscaled land accretion map, is archived as a separate dataset.\n<br>\n\n#### DeltaX_LandAccretionMap_WLD_2308\nThis dataset provides sediment transport and land accretion model results at Wax Lake Delta (WLD), Atchafalaya Basin, in coastal Louisiana, USA. Data were simulated over the Delta-X Spring 2021 (2021-03-21 to 2021-04-03) and Fall 2021 (2021-08-14 to 2021-08-27) campaigns and the results are presented as annualized land accretion rate map. The model results for these two short-term campaigns are used to calculate the 1-year upscale land accretion rate at WLD in post-processing, which is also provided in this dataset. Model results for these two short-term campaigns were derived using inputs from an ANUGA hydrodynamic model. The Matlab sediment transport and land accretion model used to derive these data employs sediment transport theory that models floc behavior using a non-cohesive sediment transport framework. Data are presented in NetCDF (.nc) format.\n<br>\n\n#### DeltaX_MarshAccretion_NUMAR_2354\nThis dataset provides input data and model code to run the Marsh Accretion Rates (NUMAR) process model used to predict soil accretion rates and chemical properties for marsh sites in the Mississippi River Delta. NUMAR is a modification of the NUMAN model by Chen and Twilley (1999) that was developed for mangrove environments. This dataset provides Python code, input data in comma separated values (CSV) format, and documentation for installing and running the model in Portable Document Format (PDF).\n<br>\n\n#### DeltaX_NUMAR_Soil_Accretion_2368\nThis dataset holds modeled estimates of soil accretion for the Atchafalaya and Terrebonne basins in the Mississippi River Delta of coastal Louisiana, U.S. Soil accretion was predicted from 2021-2100 using the Numerical Understanding of Marsh Accretion Resilience (NUMAR) model. This process-based model is an adaptation of the NUMAN model that was modified for marsh environments. The input parameters were aggregated within ecogeomorphic cells, areas of similar vegetation and elevation. The dataset includes spatially explicit input values, description of important parameters, and a shapefile of model outputs.\n<br>\n\n#### DeltaX_Insitu_POC_2073\nThis dataset provides measurements of particulate organic carbon (POC) concentrations made on water samples collected during 2021 in surface waters of the Atchafalaya River and Terrebonne Basins, portions of the Mississippi River Delta in coastal Louisiana. Water samples were collected at ~0.5 m depth from surface during the spring (2021-03-25 to 2021-04-22) and fall (2021-08-14 to 2021-09-24) field efforts. Field sampling was paused on August 25 and resumed on September 13 due to the landfall of Hurricane Ida on 2021-08-26 approximately 70 km east of the study sites. Water quality changes in this dataset caused by the hurricane are expected to be minimal. Samples were collected in multiple channels of varying width (from a few meters to >100 m) near Delta-X intensive study sites, in open bays and lakes, and a few locations in the nearshore Gulf of Mexico. For each sample, the water sample volume was filtered (in triplicate) through 25-mm glass microfiber (GF/F) filters to retain the suspended particles. The amount of organic carbon retained on each filter was measured using an elemental carbon, hydrogen and nitrogen (CHN) analyzer and normalized by the volume of sample water filtered. The reported values in this dataset include the mean and standard deviation of POC measurements from three replicate samples collected at each site.\n<br>\n\n#### DeltaX_RTK_Elevation_2071\nThis dataset provides real-time kinematic (RTK) GPS elevation measurements, along with horizontal and vertical precision errors, obtained along transects near Louisiana's Coastwide Reference Monitoring Systems (CRMS) sites and on Mike Island in Wax Lake Delta (WLD). The data were collected during the Delta-X Spring Campaign from 2021-03-24 to 2021-04-02. The data are provided in comma-separated values (CSV) format.\n<br>\n\n#### DeltaX_BG_Root_Stable_Isotopes_2193\nThis dataset contains carbon-13 (13C) and nitrogen-15 (15N) isotopic signatures of belowground root biomass samples from herbaceous wetlands in the Atchafalaya and Terrebonne basins of the Mississippi River Delta in coastal Louisiana, U.S., during August 2021. The data were collected during the Delta-X Fall deployment. Field measurements were conducted at three sites in the Atchafalaya basin and three sites in the Terrebonne basin. Five of the sites are adjacent to sites from the Coastwide Reference Monitoring System (CRMS). The other site is located in Wax Lake Delta (WLD) without appropriate adjacent CRMS sites. Herbaceous wetland sites in both basins were chosen to represent a salinity gradient including freshwater, brackish, and saline ecosystems. Root biomass samples were collected using a gouge soil auger. Bulk isotopic signatures in living fine roots were measured with a mass spectrometer coupled to an elemental analyzer.\n<br>\n\n#### DeltaX_Sediment_Core_GrainSize_2382\nThis dataset provides grain size distribution measurements collected from sediment core samples on Mike Island in the Wax Lake Delta, Louisiana, as part of the Delta-X Spring campaign in March, 2021,and the Fall campaign during August, 2021. The data are for March 26 and 29, and August 18 and 24. Sediment cores were collected using a piston core, then volume-based grain size distribution for each sample was measured using a laser diffraction particle size analyzer. The data are provided in comma separated values (CSV) format.\n<br>\n\n#### DeltaX_Cs137_SoilAccretionRate_2380\nThis dataset holds measurements of Cesium-137 (137Cs) activity sampled from sites in the Atchafalaya and Terrebonne Basins on the southern coast of Louisiana, USA. Sediment cores were taken to validate modeled estimates of soil accretion rates. The NUMAR model was designed to predict soil properties and corresponding accretion rates in marsh environments. This study explored whether the probabilistic outcomes generated by the model align with accretion rates determined through field-based measurements using 137Cs activity. The model's probabilistic simulations were conducted for active and inactive basins, encompassing fresh, brackish, and saline sites. Aliquots of approximately 5-9 g of finely ground dry sediment were placed into vials of known geometry for direct gamma counting of 137Cs using high-purity Germanium (Ge) well detectors. The 137Cs dating technique is based on detecting the specific core section in which the radioactive isotope 137Cs peak activity occurs across a vertical soil profile. This peak corresponds to the peak fallout in 1963 deposited during the atmospheric nuclear testing and provides a timeline reference for estimating soil accumulation rates, sediment deposition, and marsh accretion. Therefore, to validate the NUMAR accretion results, 137Cs activity was measured at different core sections across a vertical soil profile to determine the peak corresponding to the peak fallout in 1963 due to atmospheric nuclear testing and estimate soil accretion rates. The data are provided in comma separated values (CSV) format.\n<br>\n\n#### DeltaX_Herb_WetlandSoil_V3_2239\nThis dataset contains properties of soil core samples for herbaceous wetlands collected in the Atchafalaya and Terrebonne basins in southeastern coastal Louisiana for the period 2021-03-21 to 2021-04-02 and on 2021-08-19. Field measurements were conducted at six sites in the Atchafalaya (N = 3) and Terrebonne (N = 3) basins. Five sites were adjacent to sites from the Coastwide Reference Monitoring System (CRMS). The other site is in the Wax Lake Delta (WLD) without appropriate adjacent CRMS sites. Herbaceous wetland sites in both basins were chosen to represent a salinity gradient including freshwater, brackish and saline ecosystems. Soil properties include bulk density, organic matter content, total densities of carbon, nitrogen, phosphorus, along with 13C and 15N isotopic signatures. The data are provided in comma-separated values (.csv) format.\n<br>\n\n#### DeltaX_Sonar_Bathymetry_2085\nThis dataset includes bathymetry data for water channels in a portion of the Mississippi River Delta (MRD) of coastal Louisiana. The data were collected using sonar during field efforts of the Delta-X Campaign taking place during 2021. In situ continuous surveys of channel bathymetry were conducted in the Atchafalaya and Terrebonne basins using either a Lowrance HDS-Live Fish Finder with Active Imaging 3-in-1 Transducer or a SonarMite Echo Sounder mounted on the side or back of the research boat. The sounder depth observations were delineated by the date, time, and location. These bathymetry measurements were used to generate a digital elevation model (DEM) through interpolation with ancillary DEMs. The data are provided in comma-separated values (CSV) format. A map of data collection routes is provided in compressed keyhole markup language (KMZ).\n<br>\n\n#### DeltaX_TSS_Concentration_V2_2075\nThis dataset provides measurements of total suspended solids concentrations (TSS) of surface waters in the Atchafalaya River and Terrebonne Basins during the spring (2021-03-25 to 2021-04-22) and fall (2021-08-14 to 2021-09-24) field efforts by the Delta-X project. Field sampling was paused on August 25 and resumed on September 13, 2021, due to the landfall of Hurricane Ida on 2021-08-26 approximately 70 km east of the study site. Water quality changes caused by the hurricane were expected to be minimal. Samples were collected from ~0.5 m of surface in multiple channels of varying width (from a few meters to >100 m), near Delta-X intensive study sites, in open bays and lakes, and at few locations in the nearshore Gulf of Mexico. For each collection, the water sample volume was filtered, and the suspended particles retained on the filter were weighed after drying. The TSS concentration was calculated as the difference of the filter weight (before and after filtration) divided by the volume filtered.\n<br>\n\n#### DeltaX_Turbidity_Data_V4_2241\nThis dataset provides turbidity measurements with co-located water and air pressure and temperature measurements in portions of the Mississippi River Delta, coastal Louisiana, US. Data were collected at five sites in Atchafalaya River Basin in Spring (2021-03-24 to 2021-04-02) and eight sites in the Atchafalaya River and Terrebonne Basins in Fall 2021 (2021-08-16 to 2021-08-27). In order to sample various hydrodynamic conditions, sensors were deployed at island edges, island interior, and other portions of wetlands. Sensors recorded turbidity, absolute pressure, and temperature. The Delta-X mission is a 5-year NASA Earth Venture Suborbital-3 mission to study the Mississippi River Delta in the United States, which is growing and sinking in different areas. River deltas and their wetlands are drowning as a result of sea level rise and reduced sediment inputs. The Delta-X mission will determine which parts will survive and continue to grow, and which parts will be lost. Delta-X begins with airborne and in situ data acquisition and carries through data analysis, model integration, and validation to predict the extent and spatial patterns of future deltaic land loss or gain. The data are provided in comma-separated values (CSV) files.\n<br>\n\n#### DeltaX_L1_UAVSAR_SLC_Stack_1984\nThis dataset contains UAVSAR Level 1 (L1) Single Look Complex (SLC) stack products for Delta-X flight lines acquired during 2021-03-27 to 2021-04-18 (spring) and 2021-09-03 to 2021-09-13 (fall). The data were collected by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III (C20) aircraft as part of the Delta-X campaign. The study area includes the Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Repeat pass interferometric synthetic aperture (InSAR) data are a standard UAVSAR product delivered by the UAVSAR processing team. These repeat pass SLC stack co-registered time series data were used as the underlying data for higher level data products. These higher level products provide a time series of water level changes and address a goal of the Delta-X campaign to measure water-level changes throughout wetlands. Data quality was assessed by comparing water elevation estimates with data from in situ water level gauges throughout the study area. These L1 data contain slant range single look complex (SLC), latitude/longitude/height, look vector, doppler, and metadata files. The data are provided in SLC stack format (.slc) with associated annotation (.ann), latitude-longitude-height (.llh), look vector (.lkv), and Doppler centroid-slant range (.dop) files. The single look complex (SLC) stacks are in the HH, HV, VH, and VV polarizations. The same area was sampled at approximately 30-minute intervals. The SLCs are not corrected for residual baseline (BU).\n<br>\n\n#### DeltaX_L1B_UAVSAR_WaterLevels_1979\nThis dataset contains UAVSAR Level 1B (L1B) interferometric products for Delta-X flight lines acquired during the 2021 Spring (2021-03-27 to 2021-04-18) and Fall (2021-09-03 to 2021-09-13) deployments. The data were collected by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III (C20) aircraft as part of the Delta-X campaign. The study area includes the Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Repeat pass interferometric synthetic aperture (InSAR) data are a standard UAVSAR product delivered by the UAVSAR processing team. For this dataset, a set of nearest-neighbor (NN), NN+1, and NN+2 co-registered VV-polarization interferograms were generated from the quad-polarization SLC stack level-1 (L1) product using a combination of the InSAR Scientific Computing Environment (ISCE), the statistical-cost, network-flow algorithm for phase unwrapping (SNAPHU), and previously developed python code. Data quality was assessed by comparing water elevation estimates with data from in situ water level gauges throughout the study area. The data are provided in non-georeferenced ENVI file format and include interferometric amplitude, wrapped interferometric phase, interferometric coherence, and unwrapped interferometric phase products. Geometry files for each flight line per field campaign with latitude, longitude, height and incidence angle information are also included. The goal of this campaign was to measure water-level changes throughout wetlands, and these data may be used to generate time series of water levels. The data are provided in ENVI format.\n<br>\n\n#### DeltaX_L2_UAVSAR_WaterLevels_2057\nThis dataset contains georeferenced UAVSAR Level 2 (L2) interferometric products for Delta-X flight lines acquired during the spring (2021-03-27 to 2021-04-18) and fall (2021-09-03 to 2021-09-13) deployments. This dataset provides water-level change observations throughout wetlands of the Atchafalaya and Terrebonne Basins, in Southern Louisiana, USA, within the Mississippi River Delta (MRD), and it may be used to generate time series analysis. The data were collected by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III (C20) aircraft as part of the Delta-X campaign. Water surface elevations were measured on multiple flights at 30-minute intervals. Data quality was assessed by comparing water elevation estimates with data from in situ water level gauges throughout the study area. The data include interferogram phase, interferogram amplitude, unwrapped interferogram phase, and coherence products. A series of quality assurance masks of troposphere-induced phase delay regions were generated for all SAR acquisition dates using a weather feature matching algorithm. Geometry files for each flight line per field campaign with latitude, longitude, height and incidence angle information are also included. The data are provided in ENVI format.\n<br>\n\n#### DeltaX_UAVSAR_L3_ChannelMap_V2_2344\nThis dataset provides gridded estimates of water channels for the Atchafalaya and Terrebonne basins of the Mississippi River Delta in Louisiana, U.S.A. The data show channels with open water that are as narrow as 10 m. These channel estimates were generated from the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Level 1B interferometric products in radar coordinates acquired in the Spring and Fall Delta-X deployments of 2021, which have a spatial resolution of approximately 6 m. UAVSAR is a polarimetric L-band synthetic aperture radar (SAR) flown on the NASA Gulfstream-III aircraft. The data are provided in cloud-optimized GeoTIFF format. The channel estimates can be used to define open water paths in hydrodynamic models and to evaluate model performance.\n<br>\n\n#### DeltaX_L3_UAVSAR_WaterLevels_2058\nThis dataset contains georeferenced InSAR-derived water level change maps for Delta-X flight lines acquired during the spring (2021-03-27 to 2021-04-18) and fall (2021-09-03 to 2021-09-13) deployments. Water-level change observations are provided throughout wetlands of the Atchafalaya and Terrebonne Basins, in Southern Louisiana, USA, within the Mississippi River Delta (MRD). The data were collected by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III (C20) aircraft as part of the Delta-X campaign. Water surface elevations were measured on multiple flights at 30-minute intervals. There are three types of gridded products available: temporalcoherence (which provide an index measuring quality of phase unwrapping ranging from 0 (poor) to 1 (correctly unwrapped)), waterlevelchange in centimeters (which provide cumulative changes in water levels at approximately 30-minute intervals), and waterlevelchange_ramp in centimeters (which provide a 2-dimensional linear trend in water-level estimates not related to changing water levels). The water-level change maps were estimated using the phase unwrapping corrected interferograms generated for nearest-neighbor (NN), NN+1, and NN+2 pairs for data acquired within a single flight (one day). This analysis was done for all flight lines. Water level changes are relative to the first sampling flight for that study area. Data quality was assessed by comparing water elevation estimates with data from in situ water level gauges throughout the study area. A series of quality assurance masks of troposphere-induced phase delay regions were generated for all SAR acquisition dates using a weather feature matching algorithm.\n<br>\n\n#### PreDeltaX_L3_AVIRIS_Biomass_1821\nThis dataset includes aboveground biomass (AGB) and vegetation of herbaceous and forest wetland at 5.4 m resolution across the Wax Lake Delta (WLD) in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Vegetation classes were derived from Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery acquired over the Atchafalaya Basin and the Terrebonne Basin in October 2016 in combination with a digital elevation model. The AVIRIS-NG surface reflectance data were also combined with L-band Uninhabited Airborne Vehicle Synthetic Aperture Radar (UAVSAR) HV backscatter and scattering component values from coincident vegetation sample sites to develop and test AGB models for emergent herbaceous and forested wetland vegetation. This study used the integrated airborne data from AVIRIS-NG and UAVSAR to assess the instruments' unique capabilities in combination for estimating AGB in coastal deltaic wetlands. The 5.4 m resolution vegetation classification map for the WLD study area was then used to apply the best models to estimate AGB across the WLD.\n<br>\n\n#### PreDeltaX_L3_AVIRIS_Sediment_1822\nThis dataset includes total suspended solids (TSS) at the water surface across the Atchafalaya and Terrebonne Basins in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. AVIRIS-NG, the Next Generation Airborne Visible to Infrared Imaging Spectrometer, acquired data over the study area in 2015 and 2016. The remote imageries were combined with coincident field measurements to develop and validate spatially explicit estimates at 3.7-5.4 m resolution of the concentration (mg/L) of TSS.\n<br>\n\n#### PreDeltaX_Sonar_Bathymetry_1807\nThis dataset provides water depths and water surface elevations collected during bathymetric surveys of the main channel of the Wax Lake Delta within the Mississippi River Delta (MRD) floodplain of coastal Louisiana, USA. The measurements were made during the Pre-Delta-X Campaign in Fall 2016. The in situ continuous (1 Hz) surveys of channel bathymetry were conducted using a SonarMite Hydrolite Single Beam Echo Sounder mounted on the side of the research boat. The sounder was located directly beneath the Septentrio global navigation satellite system (GNSS) antenna, about 30 cm below the water surface. The sounder depth observations were integrated with the GNSS location and elevation data into one data file per day for October 16-20. These bathymetry measurements were used to generate a merged digital elevation model (DEM) through interpolation with ancillary DEMs to expand the existing wetland DEM to include channels. The merged DEM product is distributed in GeoTIFF format.\n<br>\n\n#### PreDeltaX_UAVSAR_SLC_1816\nThis Level 1 (L1) dataset includes single look complex (SLC) stack products and co-registered interferograms in the HH (horizontal transmit and horizontal receive) polarization for the Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. The data were collected in October 2016 by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III aircraft as part of the Pre-Delta-X campaign. A single study region, flight line \"gulfco_12011\", was sampled six times at approximately 30-minute intervals to monitor changing water levels. The SLC stack product is a standard UAVSAR product delivered by the UAVSAR processing team. The L1 interferograms were generated from the SLC stacks.\n<br>\n\n#### PreDeltaX_L2_AirSWOT_1818\nThis dataset provides water surface elevations over the Wax Lake Delta in the Atchafalaya Basin in coastal Louisiana, USA, in May 2015. These Level 2 (L2) data were collected by AirSWOT, an airborne instrument employing near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and produce continuous gridded elevation data at 3.6 m resolution. Along with elevation estimates, the dataset includes measures of estimation errors, sensitivity, incidence angle, backscatter, and interferometric correlation. For this application, in situ water level data were added into the AirSWOT phase calibration procedure. These L2 data consist of a set of rasters in Universal Transverse Mercator (UTM) map coordinates for each of the 39 AirSWOT flight lines. These elevation data were later used for calculating elevation and slopes along the main channels in this wetland system, as well as tying observations to ocean tidal conditions.\n<br>\n\n#### PreDeltaX_L2_AVIRIS_SR_1826\nThis Level 2 (L2) dataset provides surface spectral reflectance data acquired over the Atchafalaya and Terrebonne basins of the Mississippi River Delta (MRD) of coastal Louisiana, USA, in 2015-2016. The data include georectified images with Lambertian-equivalent surface reflectance and composite mosaics with adjustments for Bidirectional Reflectance Distribution Function (BRDF) effects. The imagery was acquired by the Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG), as part of the Pre-Delta-X campaign.\n<br>\n\n#### PreDeltaX_L3_AirSWOT_WL_1819\nThis dataset contains water level profiles generated from the AirSWOT data collected in the Atchafalaya Basin in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Part of the Pre-Delta-X Campaign, AirSWOT used near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and uncertainty in May 2015. This Level 3 (L3) AirSWOT dataset is in the form of numerous profiles of water level along the Wax Lake Outlet.\n<br>\n\n#### PreDeltaX_ASO_LiDAR_WaterLevel_1820\nThis dataset contains lidar-derived water surface elevation profiles for river channels between Wax Lake, in the Atchafalaya River Basin of the Mississippi River Delta, and the Gulf of Mexico. The provided elevation profiles (i.e., water levels) were estimated using remotely sensed lidar data in combination with in situ field measurements of water levels for elevation calibration and to quantify uncertainty in estimates. The lidar data were collected during the Fall 2016 Pre-Delta-X Campaign using an Airborne Snow Observatory (ASO) lidar instrument. The results are time-specific water levels measured as elevation in meters with respect to the North American Vertical Datum 1988 (NAVD 88) geoid (or orthometric height) and the World Geodetic System 1984 (WGS 84) ellipsoidal surface (or ellipsoidal/GPS height) along the water channels in this drainage system.\n<br>\n\n#### PreDeltaX_ADCP_Measurements_1806\nThis dataset provides river discharge measurements collected at selected locations across the Atchafalaya River Basin, within the Mississippi River Delta (MRD) floodplain in coastal Louisiana, USA. The measurements were made during the Pre-Delta-X campaign on October 15 to 20, 2016. Seventy-five channel surveys were conducted with a SonTek RiverSurveyor M9 acoustic doppler current profiler (ADCP) on selected wide channels (~100 m) and a few selected (~10 m) narrow channels. ADCP data provide near-instantaneous estimates of river discharge across the sampled channels. Sites coincided with AirSWOT swaths in the Atchafalaya River Basin and water level measurement locations. This in situ dataset was used to calibrate and validate Delta-X hydrodynamic models.\n<br>\n\n#### PreDeltaX_Insitu_Reflectance_1804\nThis dataset provides measurements of in situ remote-sensing reflectance (Rrs; per steradian) of surface water across Atchafalaya Basin, southern coastal Louisiana, USA within Mississippi River Delta (MRD) floodplain. The in situ spectral reflectance measurements were made during the Pre-Delta-X campaign in Fall 2016 (October 14 to 2). Hand-held spectrometer measurements were collected from a boat at 35 locations selected to represent a range of suspended sediment concentrations and properties from a variety of hydrodynamic and physical settings typically encountered across the Atchafalaya basin. These 35 spectral reflectance measurements were collected at 24 unique sites that coincide with measurements of total suspended solids (TSS). The data serves two main purposes, to ground-truth the remote-sensing reflectance derived from NASA's Airborne Visible-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) instrument, and to calibrate and validate algorithms for the retrieval of TSS from AVIRIS-NG.\n<br>\n\n#### PreDeltaX_TSS_Concentration_1802\nThis dataset contains the total suspended solids (TSS) concentration of in situ water samples collected at selected sites across the Atchafalaya and Terrebonne Basins within the Mississippi River Delta (MRD) floodplain of coastal Louisiana, USA. The measurements were made during the Pre-Delta-X campaign in Spring 2015 and Fall 2016. The sampling sites spanned large and small channels at locations chosen to cover a representative range of suspended sediment concentrations from a variety of hydrodynamic and physical settings typically encountered across the basins. Water samples were collected by bottle just beneath the surface and stored on ice until filtering. The TSS concentration was calculated as the difference of the filter weight (before and after filtration) divided by the volume of sample filtered. Both TSS and in situ spectral reflectance measurements were collected at some sampling sites. Pre-Delta-X sampling focused on surface waters, where the TSS data are used as inputs for hydrodynamic models for sediment transport and to calibrate the model to convert AVIRIS-NG spectral reflectance measurements into TSS.\n<br>\n\n#### PreDeltaX_UAVSARChannelMaps_V2_2366\nThis dataset provides spatial data on water channels in the estuary of the Atchafalaya Basin of the Mississippi River Delta of coastal Louisiana. These Level-3 (L3) channel maps were developed from interferograms derived from Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data collected on 2016-10-16 (low tides) and 2016-10-17 (high tides). The channel maps define open water paths in hydrodynamic models and are used to evaluate model performance. This is version 2 of this dataset. Data are provided in cloud optimized GeoTIFF format.\n<br>\n\n#### PreDeltaX_UAVSAR_WaterLevel_1823\nThis dataset contains five maps of cumulative changes in water levels at 30-minute intervals over a 150-minute period on 2016-10-16 in the Atchafalaya Basin in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Water surface elevations were measured on six flights at 30-minute intervals, with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar (SAR) flown on the NASA Gulfstream-III aircraft. The five georeferenced maps at 6 m resolution show the cumulative change of water levels (cm) every 30 minutes relative to the first sampling flight. These Level 3 maps were generated using the InSAR time series Small Baseline Subsets (SBAS) algorithm implemented in the Generic InSAR Analysis Toolbox (GIAnT) toolbox and served to evaluate and compare hydrodynamic models.\n<br>\n\n#### PreDeltaX_Vegetation_Structure_1805\nThis dataset provides vegetation species, height, stem density and diameter, and species aboveground biomass (AGB) measurements collected at herbaceous and forested wetland sites across the Atchafalaya and Terrebonne basins within the Mississippi River Delta (MRD) floodplain in coastal Louisiana, USA. The measurements were made during the Pre-Delta-X campaign in Spring 2015 and Fall 2015. Vegetation height and density and diameter data are only provided for forested Atchafalaya sites during the spring collections. At the nine herbaceous wetland sites, a transect was established perpendicular to the wetland edge with replicate sample plots (0.25 m2, 5 m apart) located at 50, 100, and 150 m from the wetland edge to capture the range of vegetation structure, zonation, and composition. AGB was harvested inside the duplicate plots at each sampling location. At the six forested wetland sites, duplicate circular plots (10 m radius, 50 m apart) were established inside the forest approximately 30 m from the wetland edge. All trees with a diameter at breast height (DBH at 1.3 m) > 2.5 cm were measured within each plot and identified to species. The height of trees was measured with a laser range finder. AGB was estimated using species-specific allometric equations. Measurements were used to generate marsh and forested wetland coverage and biomass in response to seasonality within both basins. The data will be used to calibrate remote sensing data (e.g., UAVSAR, AVIRIS-NG) and hydrodynamics and sediment transport models.\n<br>\n\n#### PreDeltaX_Water_Level_Data_1801\nThis dataset provides absolute water level elevations derived for 10 locations across the Wax Lake Delta, Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Field measurements were made during the Pre-Delta-X campaign on October 13-20, 2016. Relative water level measurements were recorded every five minutes during a one-week period using in situ pressure transducers (Solinst) to measure water surface elevation change with millimeter accuracy. The Solinst system combines a total pressure transducer (TPT) and a temperature detector. Once underwater, the TPT measures the sum of the atmosphere and water pressure above the sensor. Atmospheric pressure fluctuations must be accounted for to obtain the height of the water column above the TPT. An absolute elevation correction was applied to the water level data using an iterative approach with the USGS Calumet Station water level height and Airborne Snow Observatory (ASO) lidar water level profiles. These Pre-Delta-X water level measurements served to calibrate and validate the campaign's remote sensing observations and hydrodynamic models.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://deltax.jpl.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","cog","earth observation","elevation","ice","land cover","lidar","netcdf","oceans","radar","satellite imagery","stac","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"DeltaX_AVIRIS-NG_UAVSAR_L3_AGB_2410 v1 - This dataset contains estimates of forest aboveground biomass (AGB) across the Atchafalaya and Terrebonne Basins, Louisiana, US. AGB was derived from AVIRIS-NG surface reflectance and UAVSAR products.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_AVIRIS-NG_UAVSAR_L3_AGB/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_L3_AVIRIS-NG_Veg_Types_2352 v1 - This dataset provides maps of vegetation types for the Atchafalaya and Terrebonne basins in coastal Louisiana, U.S., derived from NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) imagery acquired during spring and fall of 2021 for the Delta-X campaign. Vegetation types were classified from Level-2B BRDF-adjusted surface reflectance.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_L3_AVIRIS-NG_Veg_Types/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_AGB_AGN_V2_2237 v2 - This dataset contains total aboveground biomass (AGB) and necromass (AGN), and total carbon, total nitrogen, and total phosphorus content of aboveground biomass (AGB) and necromass (AGN) samples collected from herbaceous wetlands in the Atchafalaya and Terrebonne basins in southeastern coastal Louisiana during 2021. Field measurements were conducted at three sites in the Atchafalaya basin and three sites in the Terrebonne basin.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_AGB_AGN_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Vegetation_Structure_V2_2240 v2 - This dataset provides mean stem diameter, mean height, dominant species, hydrogeomorphic zone (HGM), and stem density for vegetation in herbaceous wetlands collected in the Atchafalaya and Terrebonne basins in southeastern coastal Louisiana. The data were collected between 2021-03-21 to 2021-03-31 during the Delta-X Spring 2021 deployment, and from 2021-08-19 to 2021-08-27 during the Fall deployment.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Vegetation_Structure_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_ADCP_Measurements_V2_2081 v2 - This dataset provides river discharge measurements collected at selected locations in the Atchafalaya and Terrebonne Basins within the Mississippi River Delta (MRD) floodplain in coastal Louisiana, USA. The measurements were made during the Delta-X 2021 field efforts from 2021-03-25 to 2021-04-11 (spring) and 2021-08-16 to 2021-09-25 (fall).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_ADCP_Measurements_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_L2_AirSWOT_WaterElev_V3_2350 v3 - This dataset contains Level 2 (L2) AirSWOT geocoded products, including estimated water surface elevation. The AirSWOT instrument is a Ka-band interferometer and for this study is flown on the King Air B200 platform.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_L2_AirSWOT_WaterElev_V3/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_L3_AirSWOT_WaterElev_V2_2349 v2 - This dataset contains water surface elevations at selected point locations generated from the AirSWOT data collected during the Spring and Fall 2021 Delta-X deployments over the Atchafalaya and Terrebonne basins in Louisiana, USA. AirSWOT uses near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and produce continuous gridded elevation data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_L3_AirSWOT_WaterElev_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_L1b_AirSWOT_1996 v1.1 - This dataset contains AirSWOT interferogram products collected during the 2021 Delta-X Campaign over the Atchafalaya and Terrebonne Basins of the Mississippi River Delta, Louisiana, USA from 2021-03-26 to 2021-04-18 (Spring) and 2021-08-21 to 2021-09-12 (Fall). AirSWOT uses near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and produce continuous gridded elevation data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_L1b_AirSWOT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_ANUGA_Hydrodynamics_MRD_2310 v1 - This dataset comprises the primary inputs and outputs from the ANUGA hydrodynamic model for spring 2021 (2021-03-20 to 2021-04-04). These dates align with the 2021 Delta-X Spring Campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_ANUGA_Hydrodynamics_MRD/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_L2A_AVIRIS-NG_BRDF_V3_2355 v3 - This data provides AVIRIS-NG Bidirectional Reflectance Distribution Function (BRDF) and sunglint-corrected surface spectral reflectance images over the Atchafalaya and Terrebonne basins of the Mississippi River Delta (MRD) of coastal Louisiana, USA. Flights were acquired during the Spring and Fall 2021 deployments of the Delta-X campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_L2A_AVIRIS-NG_BRDF_V3/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_L1_AVIRIS_Radiance_1987 v1 - This dataset provides Level 1B (L1B) radiance products from NASA's Airborne Visible Infrared Imaging Spectrometer- Next Generation (AVIRIS-NG) instrument acquired over the Atchafalaya and Terrebonne basins of the Mississippi River Delta, Louisiana, USA during two deployments; spring and fall of 2021. All flights were flown on a Dynamic Aviation King Air B200.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_L1_AVIRIS_Radiance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_L2_AVIRIS_Reflectance_1988 v1 - This dataset provides Level 2 (L2) atmospherically corrected surface reflectance data acquired from NASA's Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over regions of interest in the Atchafalaya and Terrebonne basins on the southern coast of Louisiana, United States. Data were collected as part of the Delta-X Spring and Fall 2021 deployments that occurred from 2021-03-27 to 2021-04-06 and from 2021-08-18 to 2021-08-25.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_L2_AVIRIS_Reflectance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_L2B_AVIRIS-NG_FracCover_2407 v1 - This dataset provides estimates of fractional vegetation cover across the Atchafalaya and Terrebonne basins. Atmospheric correction was applied to a L2B BRDF-adjusted surface reflectance product from AVIRIS-NG imagery to produce L2 Hemispherical-Directional surface reflectance.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_L2B_AVIRIS-NG_FracCover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_L3_AVIRIS-NG_AGB_V3_2409 v3 - This dataset includes high-resolution (~5 m) gridded estimates of aboveground biomass (AGB), aboveground necromass (AGN), and aboveground net primary productivity (ANPP) for herbaceous vegetation in the Atchafalaya and Terrebonne basins of the Mississippi River Delta in coastal Louisiana, USA, for the fall and spring seasons of 2021. AGB, AGN and ANPP were estimated from Bidirectional Reflectance Distribution Function (BRDF)-adjusted surface reflectance products from NASA's Airborne Visible Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) acquired over the study area in April and...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_L3_AVIRIS-NG_AGB_V3/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_AVIRIS-NG_L3_BGB_2456 v1 - This dataset contains high-resolution (~5 m) gridded estimates of belowground biomass (BGB) in the Atchafalaya and Terrebonne basins of southern Louisiana, U.S., in August 2021. The Level 3 (L3) AVIRIS-NG-derived herbaceous aboveground biomass (AGB) and necromass (AGN) Version 3 products were used to predict the BGB.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_AVIRIS-NG_L3_BGB/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_L3_AVIRIS-NG_Water_V3_2152 v3 - This dataset includes estimates of total suspended solids (TSS) concentration and turbidity for waters of the Atchafalaya River and Terrebonne Basins of the Mississippi River Delta (MRD) in coastal Louisiana. Estimates were derived from Level 2 (L2) BRDF-corrected imagery from NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_L3_AVIRIS-NG_Water_V3/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Sediment_Grain_Size_V3_2379 v3 - This dataset includes sediment concentration and grain size distribution measurements from suspended and bed sediment samples collected in the Atchafalaya River and Terrebonne Basins as part of the Delta-X Spring campaign between March 25 and April 2, 2021, and the Delta-X Fall campaign between August 17 and 22, 2021. In addition, ten samples were collected during a field campaign in October 2019.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Sediment_Grain_Size_V3/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_BGB_BGN_V2_2238 v2 - This dataset contains total belowground biomass (BGB) and necromass (BGN), and total carbon, total nitrogen, and total phosphorus content of samples collected from herbaceous wetlands in the Atchafalaya and Terrebonne basins of the Mississippi River Delta in southeastern coastal Louisiana, U.S., during March and August 2021. The data were collected during the Delta-X Spring and Fall deployments.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_BGB_BGN_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_ANUGA_AtchafalayaBasin_2306 v1 - This dataset provides ANUGA hydrodynamic modeling results and input run-scripts for the Atchafalaya basin in the Mississippi River Delta in southern Louisiana, USA, during three windows of time corresponding to the Delta-X and Pre-Delta-X field campaigns in fall 2016, spring 2021, and fall 2021. ANUGA is a 2D depth-integrated hydrodynamic model which uses the Finite Volume Method (FVM) to numerically solve the shallow water momentum and continuity equations for fluid flow in broad-scale geophysical systems.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_ANUGA_AtchafalayaBasin/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Delft3D_Atchafalaya_MRD_2302 v1 - This dataset contains the Delft3D model of the Atchafalaya Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall campaigns in 2021 and include hydrodynamics, waves, and sediment transport.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Delft3D_Atchafalaya_MRD/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Delft3D_Terrebonne_MRD_2301 v1 - This dataset contains the Delft3D model of the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall campaigns in 2021 and include hydrodynamics, waves, and sediment transport.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Delft3D_Terrebonne_MRD/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Delft3D_FM_HydroModel_2464 v1 - This dataset contains Delft3D Flexible Mesh (FM) hydrodynamic model setup and results for the Atchafalaya and Terrebonne basins in coastal Louisiana, U.S.. The model domain spans from Bayou Lafourche in eastern Terrebonne Bay, encompasses Atchafalaya Bay, and extends westward to Vermilion Bay.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Delft3D_FM_HydroModel/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Delft3D_FM_Hydroperiod_2421 v1 - This dataset provides the weighted average hydroperiod derived from two Delft3D FM model simulations representing the low discharge (fall 2021) and high discharge (spring 2021) seasons. Each model was run for 31 days, excluding a 1-day warm-up period from the analysis.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Delft3D_FM_Hydroperiod/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Delft3D_FM_Salinity_2420 v1 - This dataset provides the weighted averaged salinity derived from two Delft3D Flexible Mesh (FM) model simulations representing the low discharge (fall 2021) and high discharge (spring 2021) seasons. The weights for each model were determined using the probability density function of the Atchafalaya River discharge.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Delft3D_FM_Salinity/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Delft3D_294_Terrebonne_2303 v1 - This dataset contains the Delft3D model of the intensive site 294 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Delft3D_294_Terrebonne/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Delft3D_322_Terrebonne_2312 v1 - This dataset contains the Delft3D model of the intensive site 322 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Delft3D_322_Terrebonne/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Delft3D_396_Terrebonne_2314 v1 - This dataset contains the Delft3D model of the intensive site 396 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Delft3D_396_Terrebonne/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Delft3D_399_Terrebonne_2313 v1 - This dataset contains the Delft3D model of the intensive site 399 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Delft3D_399_Terrebonne/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Delft3D_421_Terrebonne_2304 v1 - This dataset contains the Delft3D model of the intensive site 421 in the Terrebonne Basin along the Mississippi River Delta (MRD) in coastal Louisiana. Simulations cover the Delta-X Spring and Fall deployments in 2021 and include hydrodynamics and sediment transport.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Delft3D_421_Terrebonne/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_DEM_MRD_LA_2181 v1 - This dataset provides an updated digital elevation model (DEM) for the Atchafalaya and Terrebonne basins in coastal Louisiana, USA. The DEM is updated from the Pre-Delta-X DEM and extended to the full Delta-X study area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_DEM_MRD_LA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Ecogeomorphic_Products_2108 v1 - This product delineates the Mississippi River Delta (MRD) landscape into distinct ecogeomorphic cells, which are small contiguous areas of land with similar ecological and geomorphological characteristics. The study area is the Atchafalaya and Terrebonne basins of the MRD in southern Louisiana, U.S., which was the focus of NASA's 2021 Delta-X campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Ecogeomorphic_Products/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Feldspar_Sediment_V4_2381 v4 - This dataset provides elevation, hydrogeomorphic zone classification, soil carbon content, bulk density, organic matter content, and sediment accretion measurements collected at feldspar stations established near Louisiana's Coastwide Reference Monitoring Systems (CRMS) sites and on Mike Island in Wax Lake Delta (WLD). Feldspar stations were established to capture recent sediment deposition rates across hydrogeomorphic zones defined as discrete surface elevation ranges relative to NAVD88 (e.g., subtidal < -0.04 m, intertidal -0.04 m to 0.30 m, and supratidal > 0.30 m).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Feldspar_Sediment_V4/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Foliar_Stable_Isotopes_2194 v1 - This dataset contains foliar tissue C and N bulk isotopic signatures (delta 13C, delta 15N) of dominant wetland herbaceous species collected at six sites in the Atchafalaya (N = 3) and Terrebonne (N = 3) basins in coastal Louisiana. Five of the sites are from the Coastwide Reference Monitoring System (CRMS) and one site is the Mike Island Site in the Wax Lake Delta (WLD).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Foliar_Stable_Isotopes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Particle_Size_LISST_V2_2077 v2 - This dataset provides in situ measurements of beam attenuation coefficient at 670 nm, average suspended particle size, particle size distribution, and water temperature in surface waters (~0.5 m) of the Atchafalaya and Terrebonne Basins on the southern coast of Louisiana. The field studies were conducted in the Spring and Fall in support of the Delta-X mission and include measurements made in 2021 during March 25 - April 22 and August 14 - September 24.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Particle_Size_LISST_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Insitu_WQ_Indicators_V2_2080 v2 - This dataset provides in situ measurements of water temperature (degrees C), salinity (PSU), turbidity (FNU), and chlorophyll-a fluorescence (RFU) in surface of the Atchafalaya River and Terrebonne Basins during the Spring (2021-03-25 to 2021-04-22) and Fall (2021-08-14 to 2021-09-24) field efforts by the Delta-X project. Field sampling was paused on August 25 and resumed on September 13, 2021, due to the landfall of Hurricane Ida on 2021-08-26 approximately 70 km east of the study site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Insitu_WQ_Indicators_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Insitu_Reflectance_V3_2153 v3 - This dataset includes above water measurements of remote-sensing reflectance measured in situ at field sampling stations during the Delta-X 2021 field efforts. Measurements were collected in the Atchafalaya River and Terrebonne Basins on the southern coast of Louisiana from 2021-03-25 to 2021-04-22 (spring) and from 2021-08-14 to 2021-09-24 (fall).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Insitu_Reflectance_V3/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_H2O_Surface_Elevation_2086 v1 - This dataset contains in situ water level measurements collected at 49 different locations across the Atchafalaya and Terrebonne basins in the Mississippi River Delta (MRD) floodplain, and 49 files with the in situ measurements from raw water level converted to absolute water surface elevation with respect to NAVD88 (GEOID12B). There were 65 sites included in the study, however, data from 16 of the sites were corrupted, unretrievable from the instrument or the instrument was lost during deployment.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_H2O_Surface_Elevation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Island_Channel_Model_2106 v1 - This dataset includes model code and output for a model that simulates changes in islands and small water channels of river delta systems in response to dynamics of sediment deposit, erosion, and changing water levels. Simulations demonstrate developmental cycles of secondary channels and how sediment dynamics can allow islands to build land vertically to keep pace with rising sea levels rather than passively drowning.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Island_Channel_Model/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_TotalSubsidenceRate_MRD_2307 v1 - This dataset provides estimates of land subsidence rates for the Delta-X domain area within the Atchafalaya and Terrebonne basins for 2021. The study area is a portion of the Mississippi River Delta in coastal Louisiana, U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_TotalSubsidenceRate_MRD/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_LandAccretion_WLD_2309 v1 - This dataset provides the Matlab sediment transport and land accretion model at Wax Lake Delta (WLD), Atchafalaya Basin, in coastal Louisiana. The data include the Matlab scripts that solve the advection and Exner equations to simulate the suspended sediment transport and accretion at WLD.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_LandAccretion_WLD/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_LandAccretionMap_WLD_2308 v1 - This dataset provides sediment transport and land accretion model results at Wax Lake Delta (WLD), Atchafalaya Basin, in coastal Louisiana, USA. Data were simulated over the Delta-X Spring 2021 (2021-03-21 to 2021-04-03) and Fall 2021 (2021-08-14 to 2021-08-27) campaigns and the results are presented as annualized land accretion rate map.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_LandAccretionMap_WLD/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_MarshAccretion_NUMAR_2354 v1 - This dataset provides input data and model code to run the Marsh Accretion Rates (NUMAR) process model used to predict soil accretion rates and chemical properties for marsh sites in the Mississippi River Delta. NUMAR is a modification of the NUMAN model by Chen and Twilley (1999) that was developed for mangrove environments.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_MarshAccretion_NUMAR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_NUMAR_Soil_Accretion_2368 v1 - This dataset holds modeled estimates of soil accretion for the Atchafalaya and Terrebonne basins in the Mississippi River Delta of coastal Louisiana, U.S. Soil accretion was predicted from 2021-2100 using the Numerical Understanding of Marsh Accretion Resilience (NUMAR) model.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_NUMAR_Soil_Accretion/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Insitu_POC_2073 v1 - This dataset provides measurements of particulate organic carbon (POC) concentrations made on water samples collected during 2021 in surface waters of the Atchafalaya River and Terrebonne Basins, portions of the Mississippi River Delta in coastal Louisiana. Water samples were collected at ~0.5 m depth from surface during the spring (2021-03-25 to 2021-04-22) and fall (2021-08-14 to 2021-09-24) field efforts.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Insitu_POC/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_RTK_Elevation_2071 v1 - This dataset provides real-time kinematic (RTK) GPS elevation measurements, along with horizontal and vertical precision errors, obtained along transects near Louisiana's Coastwide Reference Monitoring Systems (CRMS) sites and on Mike Island in Wax Lake Delta (WLD). The data were collected during the Delta-X Spring Campaign from 2021-03-24 to 2021-04-02.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_RTK_Elevation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_BG_Root_Stable_Isotopes_2193 v1 - This dataset contains carbon-13 (13C) and nitrogen-15 (15N) isotopic signatures of belowground root biomass samples from herbaceous wetlands in the Atchafalaya and Terrebonne basins of the Mississippi River Delta in coastal Louisiana, U.S., during August 2021. The data were collected during the Delta-X Fall deployment.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_BG_Root_Stable_Isotopes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Sediment_Core_GrainSize_2382 v1 - This dataset provides grain size distribution measurements collected from sediment core samples on Mike Island in the Wax Lake Delta, Louisiana, as part of the Delta-X Spring campaign in March, 2021,and the Fall campaign during August, 2021. The data are for March 26 and 29, and August 18 and 24.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Sediment_Core_GrainSize/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Cs137_SoilAccretionRate_2380 v1 - This dataset holds measurements of Cesium-137 (137Cs) activity sampled from sites in the Atchafalaya and Terrebonne Basins on the southern coast of Louisiana, USA. Sediment cores were taken to validate modeled estimates of soil accretion rates.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Cs137_SoilAccretionRate/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Herb_WetlandSoil_V3_2239 v3 - This dataset contains properties of soil core samples for herbaceous wetlands collected in the Atchafalaya and Terrebonne basins in southeastern coastal Louisiana for the period 2021-03-21 to 2021-04-02 and on 2021-08-19. Field measurements were conducted at six sites in the Atchafalaya (N = 3) and Terrebonne (N = 3) basins.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Herb_WetlandSoil_V3/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Sonar_Bathymetry_2085 v1 - This dataset includes bathymetry data for water channels in a portion of the Mississippi River Delta (MRD) of coastal Louisiana. The data were collected using sonar during field efforts of the Delta-X Campaign taking place during 2021.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Sonar_Bathymetry/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_TSS_Concentration_V2_2075 v2 - This dataset provides measurements of total suspended solids concentrations (TSS) of surface waters in the Atchafalaya River and Terrebonne Basins during the spring (2021-03-25 to 2021-04-22) and fall (2021-08-14 to 2021-09-24) field efforts by the Delta-X project. Field sampling was paused on August 25 and resumed on September 13, 2021, due to the landfall of Hurricane Ida on 2021-08-26 approximately 70 km east of the study site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_TSS_Concentration_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_Turbidity_Data_V4_2241 v4 - This dataset provides turbidity measurements with co-located water and air pressure and temperature measurements in portions of the Mississippi River Delta, coastal Louisiana, US. Data were collected at five sites in Atchafalaya River Basin in Spring (2021-03-24 to 2021-04-02) and eight sites in the Atchafalaya River and Terrebonne Basins in Fall 2021 (2021-08-16 to 2021-08-27).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_Turbidity_Data_V4/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_L1_UAVSAR_SLC_Stack_1984 v1.1 - This dataset contains UAVSAR Level 1 (L1) Single Look Complex (SLC) stack products for Delta-X flight lines acquired during 2021-03-27 to 2021-04-18 (spring) and 2021-09-03 to 2021-09-13 (fall). The data were collected by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III (C20) aircraft as part of the Delta-X campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_L1_UAVSAR_SLC_Stack/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_L1B_UAVSAR_WaterLevels_1979 v1.1 - This dataset contains UAVSAR Level 1B (L1B) interferometric products for Delta-X flight lines acquired during the 2021 Spring (2021-03-27 to 2021-04-18) and Fall (2021-09-03 to 2021-09-13) deployments. The data were collected by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III (C20) aircraft as part of the Delta-X campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_L1B_UAVSAR_WaterLevels/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_L2_UAVSAR_WaterLevels_2057 v1.1 - This dataset contains georeferenced UAVSAR Level 2 (L2) interferometric products for Delta-X flight lines acquired during the spring (2021-03-27 to 2021-04-18) and fall (2021-09-03 to 2021-09-13) deployments. This dataset provides water-level change observations throughout wetlands of the Atchafalaya and Terrebonne Basins, in Southern Louisiana, USA, within the Mississippi River Delta (MRD), and it may be used to generate time series analysis.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_L2_UAVSAR_WaterLevels/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_UAVSAR_L3_ChannelMap_V2_2344 v2 - This dataset provides gridded estimates of water channels for the Atchafalaya and Terrebonne basins of the Mississippi River Delta in Louisiana, U.S.A. The data show channels with open water that are as narrow as 10 m.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_UAVSAR_L3_ChannelMap_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DeltaX_L3_UAVSAR_WaterLevels_2058 v1.1 - This dataset contains georeferenced InSAR-derived water level change maps for Delta-X flight lines acquired during the spring (2021-03-27 to 2021-04-18) and fall (2021-09-03 to 2021-09-13) deployments. Water-level change observations are provided throughout wetlands of the Atchafalaya and Terrebonne Basins, in Southern Louisiana, USA, within the Mississippi River Delta (MRD).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/DeltaX_L3_UAVSAR_WaterLevels/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_L3_AVIRIS_Biomass_1821 v1 - This dataset includes aboveground biomass (AGB) and vegetation of herbaceous and forest wetland at 5.4 m resolution across the Wax Lake Delta (WLD) in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Vegetation classes were derived from Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery acquired over the Atchafalaya Basin and the Terrebonne Basin in October 2016 in combination with a digital elevation model.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_L3_AVIRIS_Biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_L3_AVIRIS_Sediment_1822 v1 - This dataset includes total suspended solids (TSS) at the water surface across the Atchafalaya and Terrebonne Basins in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. AVIRIS-NG, the Next Generation Airborne Visible to Infrared Imaging Spectrometer, acquired data over the study area in 2015 and 2016.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_L3_AVIRIS_Sediment/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_Sonar_Bathymetry_1807 v1 - This dataset provides water depths and water surface elevations collected during bathymetric surveys of the main channel of the Wax Lake Delta within the Mississippi River Delta (MRD) floodplain of coastal Louisiana, USA. The measurements were made during the Pre-Delta-X Campaign in Fall 2016.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_Sonar_Bathymetry/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_UAVSAR_SLC_1816 v1 - This Level 1 (L1) dataset includes single look complex (SLC) stack products and co-registered interferograms in the HH (horizontal transmit and horizontal receive) polarization for the Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. The data were collected in October 2016 by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III aircraft as part of the Pre-Delta-X campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_UAVSAR_SLC/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_L2_AirSWOT_1818 v1 - This dataset provides water surface elevations over the Wax Lake Delta in the Atchafalaya Basin in coastal Louisiana, USA, in May 2015. These Level 2 (L2) data were collected by AirSWOT, an airborne instrument employing near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and produce continuous gridded elevation data at 3.6 m resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_L2_AirSWOT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_L2_AVIRIS_SR_1826 v1 - This Level 2 (L2) dataset provides surface spectral reflectance data acquired over the Atchafalaya and Terrebonne basins of the Mississippi River Delta (MRD) of coastal Louisiana, USA, in 2015-2016. The data include georectified images with Lambertian-equivalent surface reflectance and composite mosaics with adjustments for Bidirectional Reflectance Distribution Function (BRDF) effects.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_L2_AVIRIS_SR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_L3_AirSWOT_WL_1819 v1 - This dataset contains water level profiles generated from the AirSWOT data collected in the Atchafalaya Basin in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Part of the Pre-Delta-X Campaign, AirSWOT used near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and uncertainty in May 2015.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_L3_AirSWOT_WL/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_ASO_LiDAR_WaterLevel_1820 v1 - This dataset contains lidar-derived water surface elevation profiles for river channels between Wax Lake, in the Atchafalaya River Basin of the Mississippi River Delta, and the Gulf of Mexico. The provided elevation profiles (i.e., water levels) were estimated using remotely sensed lidar data in combination with in situ field measurements of water levels for elevation calibration and to quantify uncertainty in estimates.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_ASO_LiDAR_WaterLevel/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_ADCP_Measurements_1806 v1 - This dataset provides river discharge measurements collected at selected locations across the Atchafalaya River Basin, within the Mississippi River Delta (MRD) floodplain in coastal Louisiana, USA. The measurements were made during the Pre-Delta-X campaign on October 15 to 20, 2016.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_ADCP_Measurements/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_Insitu_Reflectance_1804 v1 - This dataset provides measurements of in situ remote-sensing reflectance (Rrs; per steradian) of surface water across Atchafalaya Basin, southern coastal Louisiana, USA within Mississippi River Delta (MRD) floodplain. The in situ spectral reflectance measurements were made during the Pre-Delta-X campaign in Fall 2016 (October 14 to 2).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_Insitu_Reflectance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_TSS_Concentration_1802 v1 - This dataset contains the total suspended solids (TSS) concentration of in situ water samples collected at selected sites across the Atchafalaya and Terrebonne Basins within the Mississippi River Delta (MRD) floodplain of coastal Louisiana, USA. The measurements were made during the Pre-Delta-X campaign in Spring 2015 and Fall 2016.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_TSS_Concentration/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_UAVSARChannelMaps_V2_2366 v2 - This dataset provides spatial data on water channels in the estuary of the Atchafalaya Basin of the Mississippi River Delta of coastal Louisiana. These Level-3 (L3) channel maps were developed from interferograms derived from Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data collected on 2016-10-16 (low tides) and 2016-10-17 (high tides).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_UAVSARChannelMaps_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_UAVSAR_WaterLevel_1823 v1 - This dataset contains five maps of cumulative changes in water levels at 30-minute intervals over a 150-minute period on 2016-10-16 in the Atchafalaya Basin in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Water surface elevations were measured on six flights at 30-minute intervals, with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar (SAR) flown on the NASA Gulfstream-III aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_UAVSAR_WaterLevel/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_Vegetation_Structure_1805 v1 - This dataset provides vegetation species, height, stem density and diameter, and species aboveground biomass (AGB) measurements collected at herbaceous and forested wetland sites across the Atchafalaya and Terrebonne basins within the Mississippi River Delta (MRD) floodplain in coastal Louisiana, USA. The measurements were made during the Pre-Delta-X campaign in Spring 2015 and Fall 2015.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_Vegetation_Structure/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PreDeltaX_Water_Level_Data_1801 v1 - This dataset provides absolute water level elevations derived for 10 locations across the Wax Lake Delta, Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Field measurements were made during the Pre-Delta-X campaign on October 13-20, 2016.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/deltax/PreDeltaX_Water_Level_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-delta-x","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA DIS Project","Description":"The ORNL DAAC Spatial Data Access Tool (SDAT) is a suite of Web-based applications that enable users to visualize and download spatial data in user-selected spatial/temporal extents, file formats, and projections. SDAT incorporates Open Geospatial Consortium (OGC) standard Web services, including Web Coverage Service (WCS), Web Map Service (WMS), and Web Feature Service (WFS). The SDAT provides ORNL DAAC-archived data sets and additional relevant data products including agriculture, atmosphere, biosphere, climate indicators, human dimensions, land surface, oceans, terrestrial hydrosphere data types, and related model output data sets.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1388","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","geospatial","ice","oceans"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SDAT_1388 v1 - The ORNL DAAC Spatial Data Access Tool (SDAT) is a suite of Web-based applications that enable users to visualize and download spatial data in user-selected spatial/temporal extents, file formats, and projections. SDAT incorporates Open Geospatial Consortium (OGC) standard Web services, including Web Coverage Service (WCS), Web Map Service (WMS), and Web Feature Service (WFS).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/services/SDAT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-dis","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA DSCOVR Project","Description":"Deep Space Climate Observatory (DSCOVR) DSCOVR National Institute of Standards and Technology Advanced Radiometer (NISTAR) was explicitly designed to measure the global daytime radiation budget for an entire hemisphere using active cavity radiometers for three channels: total (0.2 - 100 um), SW (0.2 - 4.0 um), and near-infrared (0.7 - 4.0 um). To derive the Earth Radiation Budget (ERB) from NISTAR measurements, the Short Wave (SW) radiances need to be unfiltered first before they can be subtracted from the total to yield the Long Wave (LW) (4 - 100 um) radiances. Additionally, the Earth's surface and atmosphere are anisotropic reflectors and emitters, resulting in a relatively complex variation of radiance leaving the Earth as a function of viewing and illumination. Converting radiance to flux requires using angular distribution models (ADMs) to account for the emittance and reflectance anisotropies. The anisotropies are characterized for all Earth Polychromatic Imaging Camera (EPIC) pixels by using the Clouds and the Earth's Radiant Energy System (CERES) empirical angular distribution models (ADMs), which are functions of scene types which are defined using many variables including surface type, cloud amount, cloud phase, and optical depth, and water vapor. EPIC composite product is used to provide accurate scene-type information. The EPIC composites are generated from cloud property retrievals from Low Earth Orbit/Geostationary Equatorial Orbit (LEO/GEO) imagers mapped into the EPIC pixels. The EPIC composite also includes ancillary data (i.e., surface type, snow and ice map, skin temperature, precipitable water, etc.) needed for anisotropic factor selections. The anisotropies at the EPIC-pixel are then used to calculate the global mean SW and LW anisotropic factors, then convert the NISTAR SW and LW radiances to fluxes. This product contains the time series of the daytime Earth radiation budget derived from the NISTAR measurements.\n<br>\n\n#### DSCOVR_NISTAR_L1A\nDSCOVR_NISTAR_L1A is the Deep Space Climate Observatory (DSCOVR) National Institute of Standards & Technology Advanced Radiometer (NISTAR) Level 1A Radiance, Version 3 data product. NISTAR is a 4-band radiometer onboard THE National Oceanic and Atmospheric Administration's (NOAA) DSCOVR spacecraft located at the Earth-Sun Lagrange-1 (L-1) point, from which vantage it continuously measures the reflected and emitted radiances of the sunlit face of the Earth. These measurements provide an accurate energy balance measurement that improves our understanding of the Earth's radiation budget. NISTAR employs three electrical substitution radiometers and a photodiode to measure reflected sunlight and infrared emission from the Earth. NISTAR measures the absolute irradiance integrated over the entire sunlit face of Earth in four broadband channels minute-by-minute. NISTAR has a 1º field of view (FOV) that acts as one large pixel that encompasses the entire sunlit side of the Earth and a 7º field of regard. The four measurement bands and their uses are: 1) Total Radiation – 0.2 µm to 100 µm: total radiant power in the UV, visible, and infrared wavelengths emerging from Earth. 2) Total Solar Reflected – 0.2 µm to 4 µm: reflected solar radiance in UV, visible, and near-infrared wavelengths from Earth. 3) Near Infrared Solar Reflected – 0.7 µm to 4 µm: reflected near-infrared solar radiation from Earth. 4) Photodiode – 0.2 µm to 1.1 µm: tracks the stability of the filters and verifies co-alignment of NISTAR and EPIC. The Level 1A products have been converted to engineering units but retain one-to-one associations with the items in the raw telemetry from which they were derived. These data products are in HDF5 format.\n<br>\n\n#### DSCOVR_EPIC_L2_O3SO2AI\nRobust cloud products are critical for the Deep Space Climate Observatory (DSCOVR) to contribute significantly to climate studies. Building on our team’s track record in cloud detection, cloud property retrieval, oxygen band exploitation, and DSCOVR-related studies, we propose to develop a suite of algorithms for generating the operational Earth Polychromatic Imaging Camera (EPIC) cloud mask, cloud height, and cloud optical thickness products. Multichannel observations will be used for cloud masking; the cloud height will be developed with information from the oxygen A- and B- band pairs (780 nm vs. 779.5 nm and 680 nm vs. 687.75 nm); for the cloud optical thickness retrieval, we propose an approach that combines the EPIC 680 nm observations and numerical weather model outputs. Preliminary results from radiative transfer modeling and proxy data applications show that the proposed algorithms are viable. Product validation will be conducted by comparing EPIC observations/retrievals with counterparts from coexisting Low Earth Orbit (LEO) and Geosynchronous Earth Orbit (GEO) satellites. The proposed work will include a rigorous uncertainty analysis based on theoretical and computational radiative transfer modeling that complements standard validation activities with physics-based diagnostics. We also plan to evaluate and improve the calibration of the EPIC O2 A- and B-band absorption channels by tracking the instrument performance over known targets, such as cloud-free ocean and ice sheet surfaces. The deliverables for the proposed work include an Algorithm Theoretical Basis Document (ATBD) for peer review, products generated with the proposed algorithms, and supporting research articles. The data products, archived at the Atmospheric Science Data Center (ASDC) at the NASA Langley Research Center, will provide essential inputs needed for the community to apply EPIC observations to climate research and better interpret The National Institute of Standards and Technology Advanced Radiometer (NISTAR) observations. The proposed work directly responds to the solicitation to “develop and implement the necessary algorithms and processes to enable various data products from EPIC sunrise to sunset observations once on orbit” and improve “the calibration of EPIC based on in-flight data.”\n<br>\n\n#### DSCOVR_EPIC_L1A_MISC\nThe Deep Space Climate Observatory (DSCOVR) mission's Earth Polychromatic Imaging Camera (EPIC) Level 1A miscellaneous data products capture unique images of the Moon transiting across the Earth's disk, dark space or Jupiter from the spacecraft's position at Lagrange point L1. These specialized observations, stored in HDF5 format, contain calibrated radiance measurements across 10 spectral bands ranging from 317 nm (ultraviolet) to 780 nm (near-infrared). The images of the Moon transiting across the Earth's disk or appearing alongside Earth included in the miscellaneous dataset provide a distinctive perspective of the Earth-Moon system from approximately one million miles away, offering valuable information for Earth-Moon system dynamics.\n<br>\n\n#### DSCOVR_EPIC_L2_TO3\nDSCOVR_EPIC_L2_TO3_v03 is Level2 Total Ozone derived from the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) using Level 1b version 3 inputs and version 3 ozone retrieval algorithm. The measurements from four EPIC UV (ultraviolet) channels derive the global distributions of total ozone over the entire sunlit portion of the Earth. A new soft calibration technique developed based on scene matching with OMPS gives calibrated EPIC radiances. The calibrated EPIC radiances derive science-quality total ozone products from EPIC consistent with those from other UV instruments. The retrieval algorithm uses wavelength triplets and assumes that the scene reflectivity changes linearly with wavelength. Version 3 algorithm includes several key modifications aimed to improve total ozone retrievals: a) switch to Version 3 Level 1b product with improved geolocation registration, flat field, and dark counts corrections; b) replace OMI-based (Ozone Monitoring Instrument) cloud height climatology with the simultaneous EPIC A-Band cloud height; c) update absolute calibrations using polar orbiting the NASA OMPS SNPP ( Ozone Mapping and Profiler Suite / Suomi National Polar-orbiting Partnership Ozone); d) add corrections for ozone profile shape and temperature; e) update algorithm and error flags to filter data; f) add column weighting functions for each observation to facilitate error analysis. EPIC ozone retrievals accurately capture short-term synoptic changes in total column ozone. With EPIC measurements from DSCOVR's vantage point, synoptic ozone maps can be derived every 1-2 hours. Scene Reflectivity (clouds, aerosols, and surface) is derived from ozone retrieval. In conjunction with ozone, the scene reflectivity has been used to derive the amount of UV solar radiation reaching the ground, and surface UV Erythemal is also reported in these files.\n<br>\n\n#### DSCOVR_EPIC_L3_PAR\nDSCOVR_EPIC_L3_PAR_01 is the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 3 photosynthetically available radiation (PAR) version 1 data product. The EPIC observations of the Earth’s surface lit by the Sun made 13 times during the day in spectral bands centered on 443, 551, and 680 nm are used to estimate daily mean PAR at the ice-free ocean surface. PAR is defined as the quantum energy flux from the Sun in the 400-700 nm range. Daily mean PAR is the 24-hour averaged planar flux in that spectral range reaching the surface. It is expressed in E.m-2.d-1 (Einstein per meter squared per day). The factor required to convert E.m-2 d-1 units to mW.cm-2.µm-1 units are equal to 0.838 to an inaccuracy of a few percent regardless of meteorological conditions. The EPIC daily mean PAR product is generated on Plate Carrée (equal-angle) grid with an 18.4 km resolution at the equator and on an 18.4 km equal-area grid, i.e., the product is compatible with Ocean Biology Processing Group ocean color products. The EPIC PAR algorithm uses a budget approach, in which the solar irradiance reaching the surface is obtained by subtracting from the irradiance arriving at the top of the atmosphere (known), the irradiance reflected space (estimated from the EPIC Level 1b radiance data), taking into account atmospheric transmission (modeled). Clear and cloudy regions within a pixel do not need to be distinguished. This dismisses the need for often-arbitrary assumptions about cloudiness distribution and is therefore adapted to the relatively large EPIC pixels. A daily mean PAR is estimated on the source grid for each EPIC instantaneous daytime observation, assuming no cloudiness changes during the day, and the individual estimates are remapped and weight-averaged using the cosine of the Sun zenith angle. In the computations, wind speed, surface pressure, and water vapor amount are extracted from NCEP (National Centers for Environmental Prediction) Reanalysis 2 data, aerosol optical thickness, and angstrom coefficient from MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) data, and ozone amount from EPIC Level 2 data. Areas contaminated by sun glint are excluded using a threshold on sun glint reflectance calculated using wind data. Ice masking is based on NSIDC (National Snow and Ice Data Center) near real-time ice fraction data. Additional information about the EPIC ocean surface PAR products can be found at the NASA DSCOVR: EPIC website: https://epic.gsfc.nasa.gov/, under “Science -> Products -> Ocean Surface” (https://epic.gsfc.nasa.gov/science/products/ocean).\n<br>\n\n#### DSCOVR_EPIC_L3_PAR-IMAGE\nDSCOVR_EPIC_L3_PAR-image_01 is a view image showing data from DSCOVR_EPIC_L3_PAR, which is the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 3 photosynthetically available radiation (PAR) version 1 data product. The EPIC observations of the Earth’s surface lit by the Sun made 13 times during the day in spectral bands centered on 443, 551, and 680 nm are used to estimate daily mean PAR at the ice-free ocean surface. PAR is defined as the quantum energy flux from the Sun in the 400-700 nm range. Daily mean PAR is the 24-hour averaged planar flux in that spectral range reaching the surface. It is expressed in E.m-2.d-1 (Einstein per meter squared per day). The factor required to convert E.m-2 d-1 units to mW.cm-2.µm-1 units are equal to 0.838 to an inaccuracy of a few percent regardless of meteorological conditions. The EPIC daily mean PAR product is generated on Plate Carrée (equal-angle) grid with an 18.4 km resolution at the equator and on an 18.4 km equal-area grid, i.e., the product is compatible with Ocean Biology Processing Group ocean color products. The EPIC PAR algorithm uses a budget approach, in which the solar irradiance reaching the surface is obtained by subtracting from the irradiance arriving at the top of the atmosphere (known), the irradiance reflected space (estimated from the EPIC Level 1b radiance data), taking into account atmospheric transmission (modeled). Clear and cloudy regions within a pixel do not need to be distinguished. This dismisses the need for often-arbitrary assumptions about cloudiness distribution and is therefore adapted to the relatively large EPIC pixels. A daily mean PAR is estimated on the source grid for each EPIC instantaneous daytime observation, assuming no cloudiness changes during the day, and the individual estimates are remapped and weight-averaged using the cosine of the Sun zenith angle. In the computations, wind speed, surface pressure, and water vapor amount are extracted from NCEP (National Centers for Environmental Prediction) Reanalysis 2 data, aerosol optical thickness, and angstrom coefficient from MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) data, and ozone amount from EPIC Level 2 data. Areas contaminated by sun glint are excluded using a threshold on sun glint reflectance calculated using wind data. Ice masking is based on NSIDC (National Snow and Ice Data Center) near real-time ice fraction data. Additional information about the EPIC ocean surface PAR products can be found at the NASA DSCOVR: EPIC website: https://epic.gsfc.nasa.gov/, under “Science -> Products -> Ocean Surface” (https://epic.gsfc.nasa.gov/science/products/ocean).\n<br>\n\n#### DSCOVR_EPIC_L3_PAR\nEPIC Ocean Surface PAR The EPIC observations of the Earth’s surface lit by the Sun made 13 times during the day in spectral bands centered on 443, 551, and 680 nm are used to estimate daily mean photosynthetically available radiation (PAR) at the ice-free ocean surface. PAR is defined as the quantum energy flux from the Sun in the 400-700 nm range. Daily mean PAR is the 24-hour averaged planar flux in that spectral range reaching the surface. It is expressed in E.m-2.d-1 (Einstein per meter squared per day). The factor required to convert E.m-2 d-1 units to mW.cm-2.μm-1 units is equal to 0.838 to an inaccuracy of a few percent regardless of meteorological conditions. The EPIC daily mean PAR product is generated on Plate Carrée (equal-angle) grid with 18.4 km resolution at the equator and on 18.4 km equal-area grid, i.e., the product is compatible with Ocean Biology Processing Group ocean color products. The EPIC PAR algorithm uses a budget approach, in which the solar irradiance reaching the surface is obtained by subtracting from the irradiance arriving at the top of the atmosphere (known) the irradiance reflected to space (estimated from the EPIC Level 1b radiance data), taking into account atmospheric transmission (modeled). Clear and cloudy regions within a pixel do not need to be distinguished, which dismisses the need for often-arbitrary assumptions about cloudiness distribution and is therefore adapted to the relatively large EPIC pixels. A daily mean PAR is estimated on the source grid for each EPIC instantaneous daytime observation, assuming no cloudiness change during the day, and the individual estimates are remapped and weight-averaged using the cosine of the Sun zenith angle. In the computations, wind speed, surface pressure, and water vapor amount are extracted from NECP Reanalysis 2 data, aerosol optical thickness and angstrom coefficient fromMERRA-2 data, and ozone amount from EPIC Level 2 data. Areas contaminated by sun glint are excluded using a threshold on sun glint reflectance calculated using wind data. Ice masking is based on NSIDC near real time ice fraction data. Details about the algorithm are given in Frouinet al., (2018). Figure A1 gives an example of EPIC daily mean PAR product. Date is March 20, 2018(equinox); land is in black and sea ice in white. Values range from a few E.m-2.d-1at high latitudes to about 58 E.m-2.d-1 at equatorial and tropical latitudes, with atmospheric perturbances modulating the surface PAR field especially at middle latitudes. The EPIC ocean surface PAR products are available at the Atmospheric Science Data Center (ASDC) at NASA Langley Research Center: https://asdc.larc.nasa.gov. 4. Reference Robert Frouin, Jing Tan, Didier Ramon, Bryan Franz, Hiroshi Murakami, 2018: Estimating photosynthetically available radiation at the ocean surface from EPIC/DSCOVR data, Proc. SPIE 10778, Remote Sensing of the Open and Coastal Ocean and Inland Waters, 1077806 (24 October 2018); doi: 10.1117/12.2501675. Changes from version 1 1) Algorithm (consistent with PACE) Updated the calculation of atmospheric reflectance, gaseous transmittance, and atmospheric transmittance using LUTs method so that calculations are accurate at high Sun and view zenith angles; Updated the calculation of surface albedo (based on Jin et al., 2011); Updated the calculation of cloud/surface layer albedo. 2)Ancillary data Changed the sources of the ancillary data including wind speed, surface pressure, and water vapor from NCEP to MERRA2; Added cloud fraction from MERRA2, which is needed for computing direct/diffuse ratio hence surface albedo.\n<br>\n\n#### DSCOVR_EPIC_L2_COMPOSITE\nIn DSCOVR_EPIC_L2_composite_01, cloud property retrievals from multiple imagers on low Earth orbit (LEO) satellites (including MODIS, VIIRS, and AVHRR) and geostationary (GEO) satellites (including GOES-13 and -15, METEOSAT-7 and -10, MTSAT-2, and Himawari-8) are used to generate the composite. Based on the Ceres cloud detection and retrieval system, all cloud properties were determined using a standard set of algorithms, the Satellite ClOud and Radiation Property Retrieval System (SatCORPS). Cloud properties from these LEO/GEO imagers are optimally merged together to provide a seamless global composite product at 5-km resolution by using an aggregated rating that considers five parameters (nominal satellite resolution, pixel time relative to the Earth Polychromatic Imaging Camera (EPIC) observation time, viewing zenith angle, distance from day/night terminator, and sun glint factor) and selects the best observation at the time nearest to the EPIC measurements. About 72% of the LEO/GEO satellite overpass times are within one hour of the EPIC measurements, while 92% are within two hours of the EPIC measurements. The global composite data are then remapped into the EPIC Field of View (FOV) by convolving the high-resolution cloud properties with the EPIC point spread function (PSF) defined with a half-pixel accuracy to produce the EPIC composite. PSF-weighted radiances and cloud properties averages are computed separately for each cloud phase. Ancillary data (i.e., surface type, snow and ice map, skin temperature, precipitable water, etc.) needed for anisotropic factor selections are also included in the composite. These composite images are produced for each observation time of the EPIC instrument (typically 300 to 600 composites per month).\n<br>\n\n#### DSCOVR_EPIC_L2_MAIAC-DAILY\nDSOCVR EPIC_L2 MAIAC-Daily_01 contains plots of data generated from DSCOVR_EPIC_L2_MAIAC_03, the DSCOVR EPIC L2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) Version 03 data product. Data collection for this product is ongoing. The datasets visualized include Aerosol Layer Height (ALH), Aerosol Optical Depth, and Single Scattering Albedo at 340nm, 388nm, 443nm, 551 nm, 680nm, and 780nm. Level 2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) provides an interdisciplinary suite of products for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC). The current version 3 reports the following products: a) Atmosphere: cloud mask, global aerosol optical depth at 443nm and 551nm, fine mode fraction (over the ocean), aerosol layer height (ALH) globally, and spectral aerosol absorption for detected biomass burning or mineral dust aerosols. The absorption information includes single scattering albedo at 340-780nm range, imaginary refractive index at 680nm (k0), and Spectral Absorption Exponent (SAE) characterizing spectral increase of imaginary refractive index from Red towards UV wavelengths. The aerosol optical properties {AOD, ALH, k0, SAE} are retrieved simultaneously by matching EPIC measurements in the UV-NIR range, including atmospheric oxygen A- and B-bands. b) Land: atmospherically corrected spectral bidirectional reflectance factors (BRF) along with Lambertian surface reflectance and bidirectional reflectance distribution function (BRDF) for the backscattering view geometries of EPIC. The BRDF is represented by three parameters of the Ross-Thick Li-Sparse model. c) Ocean: Water leaving reflectance (non-dimensional) at Ultraviolet-Visible (UV-Vis) bands. The parameters are provided at 10 km resolution on a zonal sinusoidal grid with a 1—to 2-hour temporal frequency. MAIAC version 03 also provides gap-filled global composite products for the Normalized Difference Vegetation Index (NDVI) over land and water, leaving reflectance in 5 UV-Vis bands over the global ocean. The composite products represent a weighted running average where the weight of the latest observation is maximized towards the local noon and low aerosol conditions.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://asdc.larc.nasa.gov/project/DSCOVR","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","earth observation","hdf","hdf5","ice","oceans","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"DSCOVR_NISTAR_L2_FLX v01 - Deep Space Climate Observatory (DSCOVR) DSCOVR National Institute of Standards and Technology Advanced Radiometer (NISTAR) was explicitly designed to measure the global daytime radiation budget for an entire hemisphere using active cavity radiometers for three channels: total (0.2 - 100 um), SW (0.2 - 4.0 um), and near-infrared (0.7 - 4.0 um). To derive the Earth Radiation Budget (ERB) from NISTAR measurements, the Short Wave (SW) radiances need to be unfiltered first before they can be subtracted from the total to yield the Long Wave (LW) (4 - 100 um) radiances.","ARN":"arn:aws:s3:::asdc-prod-protected/DSCOVR/DSCOVR_NISTAR_L2_FLX_01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"DSCOVR_NISTAR_L1A v3 - DSCOVR_NISTAR_L1A is the Deep Space Climate Observatory (DSCOVR) National Institute of Standards & Technology Advanced Radiometer (NISTAR) Level 1A Radiance, Version 3 data product. NISTAR is a 4-band radiometer onboard THE National Oceanic and Atmospheric Administration's (NOAA) DSCOVR spacecraft located at the Earth-Sun Lagrange-1 (L-1) point, from which vantage it continuously measures the reflected and emitted radiances of the sunlit face of the Earth.","ARN":"arn:aws:s3:::asdc-prod-protected/DSCOVR/DSCOVR_NISTAR_L1A_3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"DSCOVR_EPIC_L2_O3SO2AI v02 - Robust cloud products are critical for the Deep Space Climate Observatory (DSCOVR) to contribute significantly to climate studies. Building on our team’s track record in cloud detection, cloud property retrieval, oxygen band exploitation, and DSCOVR-related studies, we propose to develop a suite of algorithms for generating the operational Earth Polychromatic Imaging Camera (EPIC) cloud mask, cloud height, and cloud optical thickness products.","ARN":"arn:aws:s3:::asdc-prod-protected/DSCOVR/DSCOVR_EPIC_L2_O3SO2AI_02","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"DSCOVR_EPIC_L1A_MISC vV1 - The Deep Space Climate Observatory (DSCOVR) mission's Earth Polychromatic Imaging Camera (EPIC) Level 1A miscellaneous data products capture unique images of the Moon transiting across the Earth's disk, dark space or Jupiter from the spacecraft's position at Lagrange point L1. These specialized observations, stored in HDF5 format, contain calibrated radiance measurements across 10 spectral bands ranging from 317 nm (ultraviolet) to 780 nm (near-infrared).","ARN":"arn:aws:s3:::asdc-prod-protected/DSCOVR/DSCOVR_EPIC_L1A_MISC_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"DSCOVR_EPIC_L2_TO3 v03 - DSCOVR_EPIC_L2_TO3_v03 is Level2 Total Ozone derived from the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) using Level 1b version 3 inputs and version 3 ozone retrieval algorithm. The measurements from four EPIC UV (ultraviolet) channels derive the global distributions of total ozone over the entire sunlit portion of the Earth.","ARN":"arn:aws:s3:::asdc-prod-protected/DSCOVR/DSCOVR_EPIC_L2_TO3_03","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"DSCOVR_EPIC_L3_PAR v01 - DSCOVR_EPIC_L3_PAR_01 is the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 3 photosynthetically available radiation (PAR) version 1 data product. The EPIC observations of the Earth’s surface lit by the Sun made 13 times during the day in spectral bands centered on 443, 551, and 680 nm are used to estimate daily mean PAR at the ice-free ocean surface.","ARN":"arn:aws:s3:::asdc-prod-protected/DSCOVR/DSCOVR_EPIC_L3_PAR_01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"DSCOVR_EPIC_L3_PAR-IMAGE v01 - DSCOVR_EPIC_L3_PAR-image_01 is a view image showing data from DSCOVR_EPIC_L3_PAR, which is the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 3 photosynthetically available radiation (PAR) version 1 data product. The EPIC observations of the Earth’s surface lit by the Sun made 13 times during the day in spectral bands centered on 443, 551, and 680 nm are used to estimate daily mean PAR at the ice-free ocean surface.","ARN":"arn:aws:s3:::asdc-prod-protected/DSCOVR/DSCOVR_EPIC_L3_PAR-IMAGE_01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"DSCOVR_EPIC_L3_PAR v02 - EPIC Ocean Surface PAR The EPIC observations of the Earth’s surface lit by the Sun made 13 times during the day in spectral bands centered on 443, 551, and 680 nm are used to estimate daily mean photosynthetically available radiation (PAR) at the ice-free ocean surface. PAR is defined as the quantum energy flux from the Sun in the 400-700 nm range.","ARN":"arn:aws:s3:::asdc-prod-protected/DSCOVR/DSCOVR_EPIC_L3_PAR_02","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"DSCOVR_EPIC_L2_COMPOSITE v01 - In DSCOVR_EPIC_L2_composite_01, cloud property retrievals from multiple imagers on low Earth orbit (LEO) satellites (including MODIS, VIIRS, and AVHRR) and geostationary (GEO) satellites (including GOES-13 and -15, METEOSAT-7 and -10, MTSAT-2, and Himawari-8) are used to generate the composite. Based on the Ceres cloud detection and retrieval system, all cloud properties were determined using a standard set of algorithms, the Satellite ClOud and Radiation Property Retrieval System (SatCORPS).","ARN":"arn:aws:s3:::asdc-prod-protected/DSCOVR/DSCOVR_EPIC_L2_COMPOSITE_01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"DSCOVR_EPIC_L2_MAIAC-DAILY v01 - DSOCVR EPIC_L2 MAIAC-Daily_01 contains plots of data generated from DSCOVR_EPIC_L2_MAIAC_03, the DSCOVR EPIC L2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) Version 03 data product. Data collection for this product is ongoing.","ARN":"arn:aws:s3:::asdc-prod-protected/DSCOVR/DSCOVR_EPIC_L2_MAIAC-DAILY_01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-dscovr","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ECCO Project","Description":"This dataset provides ancillary data for the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate, and is intended for expert users to reproduce the state estimate. The ancillary data include documentation files, files required to initialize the model, forcing fields, binary input grid files, observational data used to constrain the model, model equivalent of observed profiles, files related to atmospheric flux-forced experiments, and some script files. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds].\n<br>\n\n#### ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4\nThis dataset contains daily-averaged atmosphere surface temperature, humidity, wind, and pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_ATM_STATE_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged atmosphere surface temperature, humidity, winds, and pressure on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_ATM_STATE_05DEG_MONTHLY_V4R4\nThis dataset contains monthly-averaged atmosphere surface temperature, humidity, wind, and pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_ATM_STATE_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged atmosphere surface temperature, humidity, winds, and pressure on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged Gent-McWilliams ocean bolus transport streamfunction on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged Gent-McWilliams ocean bolus transport streamfunction on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_BOLUS_05DEG_DAILY_V4R4\nThis dataset contains daily-averaged Gent-McWilliams ocean bolus velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_BOLUS_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged Gent-McWilliams ocean bolus velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_BOLUS_05DEG_MONTHLY_V4R4\nThis dataset contains monthly-averaged Gent-McWilliams ocean bolus velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_BOLUS_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged Gent-McWilliams ocean bolus ocean velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_GEOMETRY_05DEG_V4R4\nThis dataset provides geometric parameters for the regular 0.5-degree lat-lon grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Parameters include areas and lengths of grid cell sides and the horizontal and vertical coordinates of grid cell centers and corners. Additional information related to the global domain geometry (e.g., bathymetry and land/ocean masks) are also included. However, users should note these domain geometry fields are approximations because they have been interpolated from the ECCO lat-lon-cap 90 (llc90) native model grid. Users interested in exact budget closure calculations for volume, heat, salt, or momentum should use ECCO fields provided on the llc90 grid. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_GEOMETRY_LLC0090GRID_V4R4\nThis dataset provides geometric parameters for the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Parameters include areas and lengths of grid cell sides; horizontal and vertical coordinates of grid cell centers and corners; grid rotation angles; and global domain geometry including bathymetry and land/ocean masks. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4\nThis dataset provides instantaneous hourly global mean atmospheric pressure from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4B\nThis dataset provides instantaneous hourly global mean atmospheric pressure from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4\nThis dataset provides daily-averaged global mean sea level from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4\nThis dataset provides monthly-averaged global mean sea level from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4\nThis dataset provides 3D coefficients for the Gent-McWilliams and Redi parameterizations and background vertical diffusivity interpolated to a regular 0.5-degree grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Each of these three time-invariant, spatially-varying terms are estimated during the ECCO V4r4 optimization. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4\nThis dataset provides 3D coefficients for the Gent-McWilliams and Redi parameterizations and background vertical diffusivity on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Each of these three time-invariant, spatially-varying terms are estimated during the ECCO V4r4 optimization. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_FRESH_FLUX_05DEG_DAILY_V4R4\nThis dataset contains daily-averaged ocean and sea-ice surface freshwater fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_FRESH_FLUX_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged ocean and sea-ice surface freshwater fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_FRESH_FLUX_05DEG_MONTHLY_V4R4\nThis dataset contains monthly-averaged ocean and sea-ice surface freshwater fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_FRESH_FLUX_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged ocean and sea-ice surface freshwater fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_HEAT_FLUX_05DEG_DAILY_V4R4\nThis dataset contains daily-averaged ocean and sea-ice surface heat fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged ocean and sea-ice surface heat fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4\nThis dataset contains monthly-averaged ocean and sea-ice surface heat fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged ocean and sea-ice surface heat fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_STRESS_05DEG_DAILY_V4R4\nThis dataset contains daily-averaged ocean and sea-ice surface stress interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged ocean and sea-ice surface stress on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_STRESS_05DEG_MONTHLY_V4R4\nThis dataset contains monthly-averaged ocean and sea-ice surface stress interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged ocean and sea-ice surface stress on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OBP_05DEG_DAILY_V4R4\nThis dataset contains daily-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OBP_05DEG_DAILY_V4R4B\nThis dataset contains daily-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B\nThis dataset provides daily-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OBP_05DEG_MONTHLY_V4R4\nThis dataset contains monthly-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OBP_05DEG_MONTHLY_V4R4B\nThis dataset contains monthly-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B\nThis dataset provides monthly-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OBP_LLC0090GRID_SNAPSHOT_V4R4\nThis dataset provides instantaneous ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_DENS_STRAT_PRESS_05DEG_DAILY_V4R4\nThis dataset contains daily-averaged ocean density, stratification, and hydrostatic pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged ocean density, stratification, and hydrostatic pressure on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_DENS_STRAT_PRESS_05DEG_MONTHLY_V4R4\nThis dataset contains monthly-averaged ocean density, stratification, and hydrostatic pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged ocean density, stratification, and hydrostatic pressure on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4\nThis dataset contains daily-averaged ocean mixed layer depth interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged ocean mixed layer depth on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_MIXED_LAYER_DEPTH_05DEG_MONTHLY_V4R4\nThis dataset contains monthly-averaged ocean mixed layer depth interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged ocean mixed layer depth on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4\nThis dataset contains daily-averaged ocean potential temperature and salinity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_TEMP_SALINITY_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports. ECCO V4r4 is a free-running solution of 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. V4r4 data constraints include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean potential temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4\nThis dataset contains monthly-averaged ocean potential temperature and salinity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports. ECCO V4r4 is a free-running solution of 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. V4r4 data constraints include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean potential temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_TEMP_SALINITY_LLC0090GRID_SNAPSHOT_V4R4\nThis dataset provides instantaneous ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports. ECCO V4r4 is a free-running solution of 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. V4r4 data constraints include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean potential temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged ocean three-dimensional momentum tendency on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged ocean three-dimensional momentum tendency on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged ocean three-dimensional potential temperature fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged ocean three-dimensional potential temperature fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged ocean three-dimensional salinity fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged ocean three-dimensional salinity fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged ocean three-dimensional volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged ocean three-dimensional volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_VEL_05DEG_DAILY_V4R4\nThis dataset contains daily-averaged ocean velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged ocean velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_VEL_05DEG_MONTHLY_V4R4\nThis dataset contains monthly-averaged ocean velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged ocean velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4\nThis dataset provides instantaneous hourly SBO core products from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4B\nThis dataset provides instantaneous hourly SBO core products from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SSH_05DEG_DAILY_V4R4\nThis dataset contains daily-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SSH_05DEG_DAILY_V4R4B\nThis dataset contains daily-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B\nThis dataset provides daily-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SSH_05DEG_MONTHLY_V4R4\nThis dataset contains monthly-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SSH_05DEG_MONTHLY_V4R4B\nThis dataset contains monthly-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4b revision 4 (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B\nThis dataset provides monthly-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SSH_LLC0090GRID_SNAPSHOT_V4R4\nThis dataset provides instantaneous dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include dynamic sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; dynamic sea surface temperature (SST) from satellite radiometers [AVHRR], dynamic sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_DAILY_V4R4\nThis dataset contains daily-averaged sea-ice and snow concentration and thickness interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_MONTHLY_V4R4\nThis dataset contains monthly-averaged sea-ice and snow concentration and thickness interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4\nThis dataset provides instantaneous sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged sea-ice and snow horizontal volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged sea-ice and snow horizontal volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged sea-ice salt plume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged sea-ice salt plume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4\nThis dataset contains daily-averaged sea-ice velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4\nThis dataset provides daily-averaged sea-ice velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4\nThis dataset contains monthly-averaged sea-ice velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4\nThis dataset provides monthly-averaged sea-ice velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4\nThis dataset provides instantaneous sea-ice velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","ice","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ECCO_L4_ANCILLARY_DATA_V4R4 vV4r4 - This dataset provides ancillary data for the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate, and is intended for expert users to reproduce the state estimate. The ancillary data include documentation files, files required to initialize the model, forcing fields, binary input grid files, observational data used to constrain the model, model equivalent of observed profiles, files related to atmospheric flux-forced experiments, and some script files.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_ANCILLARY_DATA_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4 vV4r4 - This dataset contains daily-averaged atmosphere surface temperature, humidity, wind, and pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_ATM_STATE_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged atmosphere surface temperature, humidity, winds, and pressure on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_ATM_STATE_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_ATM_STATE_05DEG_MONTHLY_V4R4 vV4r4 - This dataset contains monthly-averaged atmosphere surface temperature, humidity, wind, and pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_ATM_STATE_05DEG_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_ATM_STATE_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged atmosphere surface temperature, humidity, winds, and pressure on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_ATM_STATE_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged Gent-McWilliams ocean bolus transport streamfunction on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged Gent-McWilliams ocean bolus transport streamfunction on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_BOLUS_STREAMFUNCTION_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_BOLUS_05DEG_DAILY_V4R4 vV4r4 - This dataset contains daily-averaged Gent-McWilliams ocean bolus velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_BOLUS_05DEG_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_BOLUS_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged Gent-McWilliams ocean bolus velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_BOLUS_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_BOLUS_05DEG_MONTHLY_V4R4 vV4r4 - This dataset contains monthly-averaged Gent-McWilliams ocean bolus velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_BOLUS_05DEG_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_BOLUS_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged Gent-McWilliams ocean bolus ocean velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_BOLUS_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_GEOMETRY_05DEG_V4R4 vV4r4 - This dataset provides geometric parameters for the regular 0.5-degree lat-lon grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Parameters include areas and lengths of grid cell sides and the horizontal and vertical coordinates of grid cell centers and corners.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_GEOMETRY_05DEG_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_GEOMETRY_LLC0090GRID_V4R4 vV4r4 - This dataset provides geometric parameters for the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Parameters include areas and lengths of grid cell sides; horizontal and vertical coordinates of grid cell centers and corners; grid rotation angles; and global domain geometry including bathymetry and land/ocean masks.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_GEOMETRY_LLC0090GRID_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4 vV4r4 - This dataset provides instantaneous hourly global mean atmospheric pressure from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4B vV4r4b - This dataset provides instantaneous hourly global mean atmospheric pressure from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged global mean sea level from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged global mean sea level from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4 vV4r4 - This dataset provides 3D coefficients for the Gent-McWilliams and Redi parameterizations and background vertical diffusivity interpolated to a regular 0.5-degree grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Each of these three time-invariant, spatially-varying terms are estimated during the ECCO V4r4 optimization.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4 vV4r4 - This dataset provides 3D coefficients for the Gent-McWilliams and Redi parameterizations and background vertical diffusivity on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Each of these three time-invariant, spatially-varying terms are estimated during the ECCO V4r4 optimization.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_FRESH_FLUX_05DEG_DAILY_V4R4 vV4r4 - This dataset contains daily-averaged ocean and sea-ice surface freshwater fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_FRESH_FLUX_05DEG_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_FRESH_FLUX_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged ocean and sea-ice surface freshwater fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_FRESH_FLUX_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_FRESH_FLUX_05DEG_MONTHLY_V4R4 vV4r4 - This dataset contains monthly-averaged ocean and sea-ice surface freshwater fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_FRESH_FLUX_05DEG_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_FRESH_FLUX_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged ocean and sea-ice surface freshwater fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_FRESH_FLUX_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_HEAT_FLUX_05DEG_DAILY_V4R4 vV4r4 - This dataset contains daily-averaged ocean and sea-ice surface heat fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_HEAT_FLUX_05DEG_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged ocean and sea-ice surface heat fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4 vV4r4 - This dataset contains monthly-averaged ocean and sea-ice surface heat fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged ocean and sea-ice surface heat fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_STRESS_05DEG_DAILY_V4R4 vV4r4 - This dataset contains daily-averaged ocean and sea-ice surface stress interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_STRESS_05DEG_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged ocean and sea-ice surface stress on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_STRESS_05DEG_MONTHLY_V4R4 vV4r4 - This dataset contains monthly-averaged ocean and sea-ice surface stress interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_STRESS_05DEG_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged ocean and sea-ice surface stress on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OBP_05DEG_DAILY_V4R4 vV4r4 - This dataset contains daily-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OBP_05DEG_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OBP_05DEG_DAILY_V4R4B vV4r4b - This dataset contains daily-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OBP_05DEG_DAILY_V4R4B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B vV4r4b - This dataset provides daily-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OBP_05DEG_MONTHLY_V4R4 vV4r4 - This dataset contains monthly-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OBP_05DEG_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OBP_05DEG_MONTHLY_V4R4B vV4r4b - This dataset contains monthly-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OBP_05DEG_MONTHLY_V4R4B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B vV4r4b - This dataset provides monthly-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OBP_LLC0090GRID_SNAPSHOT_V4R4 vV4r4 - This dataset provides instantaneous ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OBP_LLC0090GRID_SNAPSHOT_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_DENS_STRAT_PRESS_05DEG_DAILY_V4R4 vV4r4 - This dataset contains daily-averaged ocean density, stratification, and hydrostatic pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_DENS_STRAT_PRESS_05DEG_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged ocean density, stratification, and hydrostatic pressure on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_DENS_STRAT_PRESS_05DEG_MONTHLY_V4R4 vV4r4 - This dataset contains monthly-averaged ocean density, stratification, and hydrostatic pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_DENS_STRAT_PRESS_05DEG_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged ocean density, stratification, and hydrostatic pressure on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4 vV4r4 - This dataset contains daily-averaged ocean mixed layer depth interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged ocean mixed layer depth on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_MIXED_LAYER_DEPTH_05DEG_MONTHLY_V4R4 vV4r4 - This dataset contains monthly-averaged ocean mixed layer depth interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged ocean mixed layer depth on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4 vV4r4 - This dataset contains daily-averaged ocean potential temperature and salinity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_TEMP_SALINITY_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_TEMP_SALINITY_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4 vV4r4 - This dataset contains monthly-averaged ocean potential temperature and salinity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_TEMP_SALINITY_LLC0090GRID_SNAPSHOT_V4R4 vV4r4 - This dataset provides instantaneous ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_TEMP_SALINITY_LLC0090GRID_SNAPSHOT_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged ocean three-dimensional momentum tendency on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged ocean three-dimensional momentum tendency on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged ocean three-dimensional potential temperature fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged ocean three-dimensional potential temperature fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged ocean three-dimensional salinity fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged ocean three-dimensional salinity fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged ocean three-dimensional volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged ocean three-dimensional volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_VEL_05DEG_DAILY_V4R4 vV4r4 - This dataset contains daily-averaged ocean velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_VEL_05DEG_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged ocean velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_VEL_05DEG_MONTHLY_V4R4 vV4r4 - This dataset contains monthly-averaged ocean velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_VEL_05DEG_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged ocean velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4 vV4r4 - This dataset provides instantaneous hourly SBO core products from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4B vV4r4b - This dataset provides instantaneous hourly SBO core products from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SSH_05DEG_DAILY_V4R4 vV4r4 - This dataset contains daily-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SSH_05DEG_DAILY_V4R4B vV4r4b - This dataset contains daily-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_DAILY_V4R4B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B vV4r4b - This dataset provides daily-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SSH_05DEG_MONTHLY_V4R4 vV4r4 - This dataset contains monthly-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SSH_05DEG_MONTHLY_V4R4B vV4r4b - This dataset contains monthly-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4b revision 4 (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B vV4r4b - This dataset provides monthly-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SSH_LLC0090GRID_SNAPSHOT_V4R4 vV4r4 - This dataset provides instantaneous dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_SNAPSHOT_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_DAILY_V4R4 vV4r4 - This dataset contains daily-averaged sea-ice and snow concentration and thickness interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_MONTHLY_V4R4 vV4r4 - This dataset contains monthly-averaged sea-ice and snow concentration and thickness interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4 vV4r4 - This dataset provides instantaneous sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged sea-ice and snow horizontal volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged sea-ice and snow horizontal volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged sea-ice salt plume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged sea-ice salt plume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4 vV4r4 - This dataset contains daily-averaged sea-ice velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4 vV4r4 - This dataset provides daily-averaged sea-ice velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4 vV4r4 - This dataset contains monthly-averaged sea-ice velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4 vV4r4 - This dataset provides monthly-averaged sea-ice velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4 vV4r4 - This dataset provides instantaneous sea-ice velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-ecco","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ECOSTRESS Project","Description":"The ECO1BRAD Version 1 data product was decommissioned on May 21, 2025. Users are encouraged to use the [ECO_L1CT_RAD](https://doi.org/10.5067/ECOSTRESS/ECO_L1CT_RAD.002) Version 2 and [ECO_L1CG_RAD](https://doi.org/10.5067/ECOSTRESS/ECO_L1CG_RAD.002) Version 2 data products. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. . The ECO1BRAD Version 1 data product provides at-sensor calibrated radiance values retrieved for five thermal infrared (TIR) bands operating between 8 and 12.5 microns. Additionally, the digital numbers (DN) for the shortwave infrared (SWIR) band are provided. The TIR bands are spatially co-registered to produce a variable spatial resolution between 70 meters (m) to 90 m at the edge of the swath. The ECO1BRAD data product is provided as swath data and does not contain geolocation information. The corresponding [ECO1BGEO](https://doi.org/10.5067/ECOSTRESS/ECO1BGEO.001) data product is required to georeference the ECO1BRAD data product. The geographic coverage of acquisitions for the ECO1BRAD Version 1 data product extends to areas outside of those indicated on the coverage map. However, corresponding higher-level products over these areas are not available at this time. The ECO1BRAD Version 1 data product contains variables of radiance values for the five TIR bands, DN values for the SWIR band, associated data quality indicators, and ancillary data. Known Issues: Cannot perform spatial query on ECO1BRAD in NASA Earthdata Search: ECO1BRAD does not contain spatial attributes, so granules cannot be searched by geographic location. Users should search for ECO1BRAD data products by orbit number instead. * Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Missing scan data/striping features: During testing, an instrument artifact was encountered in ECOSTRESS bands 1 and 5, resulting in missing values. A machine learning algorithm has been applied to interpolate missing values. For more information on the missing scan filling techniques and outcomes, see Section 3.3.2 of the User Guide. * Scan overlap: An overlap between ECOSTRESS scans results in a clear line overlap and repeating data. Additional information is available in Section 3.2 of the User Guide. * Scan flipping: Improvements to the visualization of the data to compensate for instrument orientation are discussed in Section 3.4 of the User Guide. * Cold bias: ECOSTRESS Level-1 Radiance data shows high correlation with in-situ ground measurements (R2 = 0.99 in all bands). Currently, ECOSTRESS has a cold bias of approximately 0.7 Kelvin (K), which will be corrected through calibration in future data releases. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n<br>\n\n#### ECO1BATT\nThe ECO1BATT Version 1 data product was decommissioned on April 22, 2025. Users are encouraged to use the [ECO_L1B_ATT](https://doi.org/10.5067/ECOSTRESS/ECO_L1B_ATT.002) Version 2 data product. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The ECO1BATT Version 1 data product provides both corrected and uncorrected attitude quaternions and spacecraft ephemeris data obtained from the ISS. The data are provided in 1 second intervals by the ISS, and each product file contains vectors from the duration of the orbit. The time elements are copied from the ISS raw data. The ECO1BATT Version 1 data product contains variables of corrected and uncorrected attitude quaternions, spacecraft ephemeris data including Earth-centered inertial (ECI) position and velocity, and associated time elements. Known Issues: Cannot perform spatial query on ECO1BATT in NASA Earthdata Search: ECO1BATT does not contain spatial attributes, so granules cannot be searched by geographic location. Users should search for ECO1BATT data products by orbit number instead. * Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n<br>\n\n#### ECO2CLD\nThe ECO2CLD Version 1 data product was decommissioned on May 21, 2025. Users are encouraged to use the [ECO_L2_CLOUD](https://doi.org/10.5067/ECOSTRESS/ECO_L2_CLOUD.002) Version 2 and [ECO_L2G_CLOUD](https://doi.org/10.5067/ECOSTRESS/ECO_L2G_CLOUD.002) Version 2 data products. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The ECO2CLD Version 1 data product provides a cloud mask that can be used to determine cloud cover for the ECO1BRAD, ECO2LSTE, ECO3ETPTJPL, ECO4ESIPTJPL, and ECO4WUE data products. The ECOSTRESS Level 2 cloud product is derived using the five calibrated thermal bands in a multispectral cloud-conservative thresholding approach. The details of the algorithm are provided in the Algorithm Theoretical Basis Document (ATBD). The corresponding [ECO1BGEO](https://doi.org/10.5067/ECOSTRESS/ECO1BGEO.001) data product is required to georeference the ECO2CLD data product. The ECO2CLD Version 1 data product contains a single cloud mask layer. Information on how to interpret the bit fields in the cloud mask is provided in section 3.1 of the User Guide. Known Issues: Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n<br>\n\n#### ECO4ESIALEXI\nThe ECO4ESIALEXI Version 1 data product was decommissioned on January 30, 2026. Users are encouraged to use the [ECO_L4G_ESI_ALEXI](https://doi.org/10.5067/ECOSTRESS/ECO_L4G_ESI_ALEXI.002) and [ECO_L4T_ESI_ALEXI](https://doi.org/10.5067/ECOSTRESS/ECO_L4T_ESI_ALEXI.002) Version 2 data product(s). The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The NASA Jet Propulsion Laboratory (JPL) ECO4ESIALEXI Version 1 data product provides the Evaporative Stress Index (ESI), which is computed from clear-sky estimates of the relative daily evapotranspiration (ET) fraction: ESI = ET/ETo, where ET is ETdaily from the ECOSTRESS Level 3 product and ETo is the reference ET. A description of the major components of the ECOSTRESS algorithm implemented in Version 1 of the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (DisALEXI) ESI code is provided in the Algorithm Theoretical Basis Document (ATBD). ESI applications include indicating agricultural drought and observing vegetation stress. ECO4ESIALEXI is available for CONUS at 70-meter (m) pixel resolution. The ECO4ESIALEXI Version 1 data product contains variables of daily evaporative stress index, evaporative stress index uncertainty, and associated quality flags. A low-resolution browse is also available showing daily ESI as a stretched image with a color ramp in JPEG format. Known Issues: Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only TIR bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n<br>\n\n#### ECO4ESIALEXIU\nThe ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The United States Department of Agriculture (USDA) ECO4ESIALEXIU Version 1 data product provides the Evaporative Stress Index (ESI), which is computed from clear-sky estimates of the relative daily evapotranspiration (ET) fraction: ESI = ET/ETo, where ET is ETdaily from the ECOSTRESS Level 3 product and ETo is the reference ET. A description of the major components of the ECOSTRESS algorithm implemented in Version 1 of the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (DisALEXI) ESI code is provided in the Algorithm Theoretical Basis Document (ATBD). ESI applications include indicating agricultural drought and observing vegetation stress. The dis-ALEXI USDA ESI product is generated on a UTM grid at a spatial resolution of 30 meters. The ECO4ESIALEXIU Version 1 data product contains variables of daily evaporative stress index, evaporative stress index uncertainty, and associated quality flags. A low-resolution browse is also available showing daily ESI as a stretched image with a color ramp in JPEG format. Known Issues: Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n<br>\n\n#### ECO4ESIPTJPL\nThe ECO4ESIPTJPL Version 1 data product was decommissioned on September 30, 2025. Users are encouraged to use the [ECO_L4T_ESI]( https://doi.org/10.5067/ECOSTRESS/ECO_L4T_ESI.002) and [ECO_L4G_ESI]( https://doi.org/10.5067/ECOSTRESS/ECO_L4G_ESI.002) Version 2 data products. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The ECO4ESIPTJPL Version 1 data product provides Evaporative Stress Index (ESI) data generated according to the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm described in the ECOSTRESS Level 4 (ESI_PT-JPL) Algorithm Theoretical Basis Document (ATBD). The ESI product is derived from the ratio of the Level 3 actual evapotranspiration (ET) to potential ET (PET) calculated as part of the algorithm. The ESI is an indicator of potential drought and plant water stress emphasizing areas of sub-optimal plant productivity. The ECO4ESIPTJPL Version 1 data product contains variables of ESI and PET. Known Issues: Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n<br>\n\n#### ECO3ETALEXI\nThe ECO3ETALEXI Version 1 data product was decommissioned on January 30th, 2026. Users are encouraged to use the [ECO_L3T_ET_ALEXI](https://doi.org/10.5067/ECOSTRESS/ECO_L3T_ET_ALEXI.002) and [ECO_L3G_ET_ALEXI](https://doi.org/10.5067/ECOSTRESS/ECO_L3G_ET_ALEXI.002) Version 2 data product(s). The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The NASA Jet Propulsion Laboratory (JPL) ECO3ETALEXI Version 1 data product provides estimates of daily evapotranspiration (ET) using the ECOSTRESS Level 2 (L2) land surface temperature and emissivity (LST&E) product, along with ancillary meteorological data and remotely sensed vegetation cover information. The ECO3ETALEXI data product is derived using a physics-based surface energy balance (SEB) algorithm, the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (DisALEXI). Described in the Algorithm Theoretical Basis Document (ATBD), DisALEXI is based on spatial disaggregation of regional-scale fluxes from the ALEXI SEB model. There are many approaches for spatially mapping ET; however, SEB methods are favored for remote sensing retrievals based on land-surface temperature. ALEXI was initially developed for managed landscapes and has now been evaluated in comparison with micrometeorological flux tower observations over crop, forest, grassland, wetland, and semiarid desert sites. Applications include crop water use, crop phenology monitoring, and drought early warning or water stress detection. ECO3ETALEXI is available for CONUS at 70-meter (m) pixel resolution. The ECO3ETALEXI Version 1 data product contains variables of daily ET, ET uncertainty, and associated quality flags. A low-resolution browse is also available showing daily ET as a stretched image with a color ramp in JPEG format. Known Issues: Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only TIR bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n<br>\n\n#### ECO3ETPTJPL\nECO3ETPTJPL Version 1 was deprecated on September 30, 2025. Users are encouraged to use the [ECO_L3G_JET](https://doi.org/10.5067/ECOSTRESS/ECO_L3G_JET.002) and [ECO_L3T_JET](https://doi.org/10.5067/ECOSTRESS/ECO_L3T_JET.002) Version 2 data products. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. ECO3ETPTJPL Version 1 is a Level 3 (L3) product that provides evapotranspiration (ET) generated from data acquired by the ECOSTRESS radiometer instrument according to the Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm described in the Algorithm Theoretical Basis Document (ATBD). The ET product is generated from the Level 2 data products for surface temperature and emissivity, the Level 1 geolocation information, and a significant number of ancillary data inputs from other sources. ET is set by various controls, including radiative and atmospheric demand, and environmental sensitivity, productivity, vegetation physiology, and phenology. PT-JPL is best utilized for natural ecosystems. The L3 ET product is used for creating the Level 4 products, Evaporative Stress Index (ESI) and Water Use Efficiency (WUE). The ECO3ETPTJPL Version 1 data product contains variables of instantaneous ET, daily ET, canopy transpiration, soil evaporation, ET uncertainty, and interception evaporation. Known Issues: Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n<br>\n\n#### ECO1BGEO\nThe ECO1BGEO Version 1 data product was decommissioned on January 30, 2026. Users are encouraged to use the [ECO_L1B_GEO](https://doi.org/10.5067/ECOSTRESS/ECO_L1B_GEO.002) Version 2 data product. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The ECO1BGEO Version 1 data product provides the geolocation information for the radiance values retrieved in the [ECO1BRAD](https://doi.org/10.5067/ecostress/eco1brad.001) Version 1 data product. The ECO1BGEO data product should be used to georeference the ECO1BRAD, ECO2CLD, ECO2LSTE, ECO3ANCQA, ECO3ETPTJPL, ECO4ESIPTJPL, and ECO4WUE data products. The geolocation processing corrects the ISS-reported ephemeris and attitude data by image matching with a global ortho-base derived from Landsat data, and then assigns latitude and longitude values to each of the Level 1 radiance pixels. When image matching is successful, the data are geolocated to better than 50 meter (m) accuracy. The ECO1BGEO data product is provided as swath data. The ECO1BGEO data product contains data variables for latitude and longitude values, solar and view geometry information, surface height, and the fraction of pixel on land versus water. Known Issues: Geolocation accuracy: In cases where scenes were not successfully matched with the ortho-base, the geolocation error is significantly larger, with the worst-case geolocation error for uncorrected data being at 7 kilometers (km). Within the metadata of the ECO1BGEO file, if the field \"L1GEOMetadata/OrbitCorrectionPerformed\" is \"True,\" the data was corrected, and geolocation accuracy should be better than 50 m. If this is \"False,\" then the data was processed without correcting the geolocation and will have up to 7 km geolocation error. * Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n<br>\n\n#### ECO_L2G_CLOUD\nThe ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. The ECOSTRESS Gridded Cloud Mask Instantaneous L2 Global 70 m (ECO_L2G_CLOUD) Version 2 data product is derived using a single-channel Bayesian cloud threshold with a look-up-table (LUT) approach. The ECO_L2G_CLOUD product provides a cloud mask that can be used to determine cloud cover for accurate land surface temperature and evapotranspiration estimation. This data product is a gridded version of the [ECO_L2_CLOUD](https://doi.org/10.5067/ECOSTRESS/ECO_L2_CLOUD.002) Version 2 product that was resampled using nearest neighbor, projected to a globally snapped 0.0006° grid, and repackaged as the ECO_L2G_CLOUD Version 2 data product. The ECO_L2G_CLOUD Version 2 data product contains two cloud mask layers: cloud confidence and final cloud mask. Information on how to interpret the cloud confidence and cloud mask layers is provided in Table 7 of the ECO_L2_CLOUD Version 2 User Guide. Known Issues: Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods. * Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the International Space Station (ISS), potentially impacting data quality of obstructed pixels. The 'FieldOfViewObstruction' metadata field is included in all Version 2 products to indicate possible obstructions: * Before October 24, 2024 (orbits prior to 35724): The field is present but was not populated and does not reliably identify affected scenes. * On or after October 24, 2024 (starting with orbit 35724): The field is populated and generally accurate, except for late December 2024, when a temporary processing error may have caused false positives. * A [list of scenes](https://lpdaac.usgs.gov/documents/2249/obst_all_sort.txt) confirmed to be affected by obstructions is available and is recommended for verifying historical data (before October 24, 2024) and scenes from late December 2024. * The ISS native pointing information is coarse relative to ECOSTRESS pixels, so ECOSTRESS geolocation is improved through image matching with a basemap. Metadata in the L1B_GEO file shows the success of this geolocation improvement, using categorizations \"best\", \"good\", \"suspect\", and \"poor\". We recommend that users use only \"best\" and \"good\" scenes for evaluations where geolocation is important (e.g., comparison to field sites). For some scenes, this metadata is not reflected in the higher-level products (e.g., land surface temperature, evapotranspiration, etc.). While this metadata is always available in the geolocation product, to save users additional download, we have produced a [summary text file](https://lpdaac.usgs.gov/documents/2253/qa_20250423-present.txt) that includes the geolocation quality flags for all scenes from launch to present. At a later date, all higher-level products will reflect the geolocation quality flag correctly (the field name is GeolocationAccuracyQA). *During the time period of May 15th, 2025, through July 1st, 2025, ECOSTRESS data was noisier than expected. Cycling the payload resolved the issue, but researchers should use all levels of ECOSTRESS data acquired during this time period with caution.\n<br>\n\n#### ECO3ANCQA\nThe ECO3ANCQA Version 1 data product was decommissioned on September 30, 2025. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The ECO3ANCQA Version 1 is a Level 3 (L3) product that provides Quality Assessment (QA) fields for all ancillary data used in L3 and Level 4 (L4) products generated by Jet Propulsion Laboratory (JPL). No quality flags are generated for the L3 or L4 products. Instead, the quality flags of the source data products are resampled by nearest neighbor onto the geolocation of the ECOSTRESS scene. A quality flag array for each input dataset, when available, is collected into the combined QA product. The ECO3ANCQA Version 1 data product contains layers of quality flags for ECOSTRESS cloud mask, Landsat 8, land cover type, albedo, MODIS Terra aerosol, MODIS Terra Cloud 1 km, MODIS Terra Cloud 5 km, MODIS Terra atmospheric profile, vegetation indices, MODIS Terra gross primary productivity, and MODIS water mask. Known Issues: Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n<br>\n\n#### ECO2LSTE\nThe ECO2LSTE Version 1 data product was decommissioned on May 21, 2025. Users are encouraged to use the [ECO_L2T_LSTE](https://doi.org/10.5067/ECOSTRESS/ECO_L2T_LSTE.002) Version 2 and [ECO_L2G_LSTE](https://doi.org/10.5067/ECOSTRESS/ECO_L2G_LSTE.002) Version 2 data products. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The ECO2LSTE Version 1 data product provides atmospherically corrected land surface temperature and emissivity (LST&E) values derived from five thermal infrared (TIR) bands. The ECO2LSTE data product was derived using a physics-based Temperature and Emissivity Separation (TES) algorithm. The ECO2LSTE is provided as swath data and has a spatial resolution of 70 meters (m). The corresponding [ECO1BGEO](https://doi.org/10.5067/ECOSTRESS/ECO1BGEO.001) data product is required to georeference the ECO2LSTE data product. The ECO2LSTE Version 1 data product contains variables of LST, emissivity for bands 1 through 5, quality control for LST&E, LST error, emissivity error for bands 1 through 5, wideband emissivity, and Precipitable Water Vapor (PWV). Known Issues: Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n<br>\n\n#### ECO1BMAPRAD\nThe ECO1BMAPRAD Version 1 data product was decommissioned on February 14, 2025. Users are encouraged to use the [ECO_L1CT_RAD](https://doi.org/10.5067/ECOSTRESS/ECO_L1CT_RAD.002) Version 2 and [ECO_L1CG_RAD](https://doi.org/10.5067/ECOSTRESS/ECO_L1CG_RAD.002) Version 2 data products. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. A map of the acquisition coverage can be found in Figure 2 on the [ECOSTRESS website](https://ecostress.jpl.nasa.gov/science). The ECO1BMAPRAD Version 1 data product combines the at-sensor calibrated radiance values retrieved for the ECO1BRAD data product and the geolocation information provided in the ECO1BGEO data product to produce a geotagged, resampled radiance product. The ECO1BMAPRAD data product is produced as a map registered product that is in a rotated geographic projection with a spatial resolution of 70 meters (m). The ECO1BMAPRAD data product accounts for the overlap and variable pixel size in the ECO1BRAD data product. The ECO1BMAPRAD Version 1 data product contains data variables including the radiance values for the five thermal infrared (TIR) bands, digital number (DN) values for the shortwave infrared (SWIR) band, associated data quality indicators, latitude and longitude values, solar and view geometry information, and surface height. Known Issues: Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Resampled data: The data has been resampled, so users interested in working with data closest to that acquired by the instrument may want to work with the swath products. * Missing scan data: During testing, an instrument artifact was encountered in ECOSTRESS bands 1 and 5, resulting in missing values. A machine learning algorithm has been applied to interpolate missing values. For more information on the missing scan filling techniques and outcomes, see Section 3.3.2 of the User Guide. * Cold bias: ECOSTRESS Level-1 Radiance data shows high correlation with in-situ ground measurements (R2 = 0.99 in all bands). Currently, ECOSTRESS has a cold bias of approximately 0.7 Kelvin (K), which will be corrected through calibration in future data releases. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n<br>\n\n#### ECO4WUE\nThe ECO4WUE Version 1 data product was deprecated on September 30, 2025. Users are encouraged to use the [ECO_L4T_WUE]( https://doi.org/10.5067/ECOSTRESS/ECO_L4T_WUE.002) and [ECO_L4G_WUE]( https://doi.org/10.5067/ECOSTRESS/ECO_L4G_WUE.002) Version 2 data products. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The ECO4WUE Version 1 data product provides Water Use Efficiency (WUE) data generated according to the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm described in the ECOSTRESS Level 4 WUE Algorithm Theoretical Basis Document (ATBD). WUE is the ratio of carbon stored by plants to water evaporated by plants. This ratio is given as grams of carbon stored per kilogram of water evaporated over the course of the day from sunrise to sunset on the day when the ECOSTRESS granule was acquired. The ECO4WUE Version 1 data product contains a single variable of water use efficiency. Known Issues: Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/centers/lp-daac","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","earth observation","ice","land cover","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ECO1BRAD v001 - The ECO1BRAD Version 1 data product was decommissioned on May 21, 2025. Users are encouraged to use the [ECO_L1CT_RAD](https://doi.org/10.5067/ECOSTRESS/ECO_L1CT_RAD.002) Version 2 and [ECO_L1CG_RAD](https://doi.org/10.5067/ECOSTRESS/ECO_L1CG_RAD.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/ECO1BRAD.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ECO1BATT v001 - The ECO1BATT Version 1 data product was decommissioned on April 22, 2025. Users are encouraged to use the [ECO_L1B_ATT](https://doi.org/10.5067/ECOSTRESS/ECO_L1B_ATT.002) Version 2 data product.","ARN":"arn:aws:s3:::lp-prod-protected/ECO1BATT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ECO2CLD v001 - The ECO2CLD Version 1 data product was decommissioned on May 21, 2025. Users are encouraged to use the [ECO_L2_CLOUD](https://doi.org/10.5067/ECOSTRESS/ECO_L2_CLOUD.002) Version 2 and [ECO_L2G_CLOUD](https://doi.org/10.5067/ECOSTRESS/ECO_L2G_CLOUD.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/ECO2CLD.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ECO4ESIALEXI v001 - The ECO4ESIALEXI Version 1 data product was decommissioned on January 30, 2026. Users are encouraged to use the [ECO_L4G_ESI_ALEXI](https://doi.org/10.5067/ECOSTRESS/ECO_L4G_ESI_ALEXI.002) and [ECO_L4T_ESI_ALEXI](https://doi.org/10.5067/ECOSTRESS/ECO_L4T_ESI_ALEXI.002) Version 2 data product(s).","ARN":"arn:aws:s3:::lp-prod-protected/ECO4ESIALEXI.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ECO4ESIALEXIU v001 - The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes.","ARN":"arn:aws:s3:::lp-prod-protected/ECO4ESIALEXIU.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ECO4ESIPTJPL v001 - The ECO4ESIPTJPL Version 1 data product was decommissioned on September 30, 2025. Users are encouraged to use the [ECO_L4T_ESI]( https://doi.org/10.5067/ECOSTRESS/ECO_L4T_ESI.002) and [ECO_L4G_ESI]( https://doi.org/10.5067/ECOSTRESS/ECO_L4G_ESI.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/ECO4ESIPTJPL.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ECO3ETALEXI v001 - The ECO3ETALEXI Version 1 data product was decommissioned on January 30th, 2026. Users are encouraged to use the [ECO_L3T_ET_ALEXI](https://doi.org/10.5067/ECOSTRESS/ECO_L3T_ET_ALEXI.002) and [ECO_L3G_ET_ALEXI](https://doi.org/10.5067/ECOSTRESS/ECO_L3G_ET_ALEXI.002) Version 2 data product(s).","ARN":"arn:aws:s3:::lp-prod-protected/ECO3ETALEXI.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ECO3ETPTJPL v001 - ECO3ETPTJPL Version 1 was deprecated on September 30, 2025. Users are encouraged to use the [ECO_L3G_JET](https://doi.org/10.5067/ECOSTRESS/ECO_L3G_JET.002) and [ECO_L3T_JET](https://doi.org/10.5067/ECOSTRESS/ECO_L3T_JET.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/ECO3ETPTJPL.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ECO1BGEO v001 - The ECO1BGEO Version 1 data product was decommissioned on January 30, 2026. Users are encouraged to use the [ECO_L1B_GEO](https://doi.org/10.5067/ECOSTRESS/ECO_L1B_GEO.002) Version 2 data product.","ARN":"arn:aws:s3:::lp-prod-protected/ECO1BGEO.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ECO_L2G_CLOUD v002 - The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes.","ARN":"arn:aws:s3:::lp-prod-protected/ECO_L2G_CLOUD.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ECO3ANCQA v001 - The ECO3ANCQA Version 1 data product was decommissioned on September 30, 2025. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress.","ARN":"arn:aws:s3:::lp-prod-protected/ECO3ANCQA.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ECO2LSTE v001 - The ECO2LSTE Version 1 data product was decommissioned on May 21, 2025. Users are encouraged to use the [ECO_L2T_LSTE](https://doi.org/10.5067/ECOSTRESS/ECO_L2T_LSTE.002) Version 2 and [ECO_L2G_LSTE](https://doi.org/10.5067/ECOSTRESS/ECO_L2G_LSTE.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/ECO2LSTE.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ECO1BMAPRAD v001 - The ECO1BMAPRAD Version 1 data product was decommissioned on February 14, 2025. Users are encouraged to use the [ECO_L1CT_RAD](https://doi.org/10.5067/ECOSTRESS/ECO_L1CT_RAD.002) Version 2 and [ECO_L1CG_RAD](https://doi.org/10.5067/ECOSTRESS/ECO_L1CG_RAD.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/ECO1BMAPRAD.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ECO4WUE v001 - The ECO4WUE Version 1 data product was deprecated on September 30, 2025. Users are encouraged to use the [ECO_L4T_WUE]( https://doi.org/10.5067/ECOSTRESS/ECO_L4T_WUE.002) and [ECO_L4G_WUE]( https://doi.org/10.5067/ECOSTRESS/ECO_L4G_WUE.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/ECO4WUE.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-ecostress","Sources":["awslabs-open-data-registry"]}
{"Name":"Mars Spectrometry: Detect Evidence for Past Habitability","Description":"NASA missions like the Curiosity and Perseverance rovers carry a rich array of instruments suited to collect data and build evidence towards answering if Mars ever had livable environmental conditions. These rovers can collect rock and soil samples and can take measurements that can be used to determine their chemical makeup. <br/><br/>\nBecause communication between rovers and Earth is severely constrained, with limited transfer rates and short daily communication windows, scientists have a limited time to analyze the data and make difficult inferences about the chemistry in order to prioritize the next operations and send those instructions back to the rover. <br/><br/>\nThis project aimed at building a model to automatically analyze evolved gas analysis mass spectrometry (EGA-MS) data collected for Mars exploration in order to help the scientists in their analysis of understanding the past habitability of Mars. <br/><br/>\nMore information are available at https://mars.nasa.gov/msl/spacecraft/instruments/sam/ and the data from Mars are available and described at https://pds-geosciences.wustl.edu/missions/msl/sam.htm. <br/><br/>\nWe request that you cite the following ackowledgement when using the data provided from this NASA Project: \"NASA provided support for the development of SAM. The datasets for these 2 challenges were provided by NASA Goddard Space Flight Center and NASA Johnson Space Center. They have been collected by the Sample Analysis at Mars (SAM) scientists and specifically processed for these challenges with the help of the scientists from NASA: Doug Archer, Charles Malespin, Caroline Freissinet, Stephanie Getty, Luoth Chou, Eric Lyness, and Victoria Da Poian, and the DrivenData team. Data from all SAM experiments are archived in the Planetary Data System (pds.nasa.gov).\"\n","Documentation":"https://pds-geosciences.wustl.edu/missions/msl/sam.htm","Contact":"victoria.dapoian@nasa.gov , eric.i.lyness@nasa.gov","ManagedBy":"NASA","UpdateFrequency":"Static dataset for the commercial data\nWhen more data is collected on the SAM instrument on Mars, the data will be available on the Planetary Data System website: Data from all SAM experiments are archived in the Planetary Data System (pds.nasa.gov). \n","Tags":["analytics","archives","aws-pds","deep learning","machine learning","NASA SMD AI","planetary"],"License":"There are no restrictions on the use, access, and/or download of data from the NASA project.","Resources":[{"Description":"EGAMS SAM Instrument Exploration Dataset","ARN":"arn:aws:s3:::egams-samdata-mlchallenge","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Publications":[{"Title":"Mars Spectrometry: Detect Evidence for Past Habitability (Challenge Results)","URL":"https://www.drivendata.org/competitions/93/nasa-mars-spectrometry/page/575/","AuthorName":"DrivenData team and NASA partners"}]},"RegistryEntryAdded":"2023-01-26","RegistryEntryLastModified":"2023-01-31","Slug":"nasa-ega","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA EMIT Project","Description":"The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of sunlit regions of interest between 52° N latitude and 52° S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) [EMIT Open Data Portal](https://earth.jpl.nasa.gov/emit/data/data-portal/coverage-and-forecasts/). The EMIT Level 1B At-Sensor Calibrated Radiance and Geolocation (EMITL1BRAD) Version 1 data product provides at-sensor calibrated radiance values along with observation data in a spatially raw, non-orthocorrected format. Each EMITL1BRAD granule consists of two Network Common Data Format 4 (NetCDF4) files at a spatial resolution of 60 meters (m): Radiance (EMIT_L1B_RAD) and Observation (EMIT_L1B_OBS). The Radiance file contains the at-sensor radiance measurements of 285 bands with a spectral range of 381-2493 nanometers (nm) and with a spectral resolution of ~7.5 nm, which are held within a single science dataset layer (SDS). The Observation file contains viewing and solar geometries, timing, topographic, and other information related to the observation. Each NetCDF4 file holds a location group containing geometric lookup tables (GLT), which are orthorectified images that provide relative x and y reference locations from the raw scene to allow for projection of the data. Along with the GLT layers, the files also contain latitude, longitude, and elevation layers. The latitude and longitude coordinates are presented using the World Geodetic System (WGS84) ellipsoid. The elevation data was obtained from Shuttle Radar Topography Mission v3 (SRTM v3) data and resampled to EMIT’s spatial resolution. Each granule is approximately 75 kilometers (km) by 75 km, nominal at the equator, with some granules at the end of an orbit segment reaching 150 km in length. Known Issues: Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe.\n<br>\n\n#### EMITL1BATT\nThe Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS). EMIT uses imaging spectroscopy to take mineralogical measurements of the sunlit regions of interest between 52° N latitude and 52° S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science [(VISIONS): EMIT Open Data Portal](https://earth.jpl.nasa.gov/emit/data/data-portal/coverage-and-forecasts/). The EMIT Level 1B Corrected Spacecraft Attitude and Ephemeris (EMITL1BATT) Version 1 data product provides both corrected and uncorrected attitude quaternions and spacecraft ephemeris data obtained from the ISS, including Earth-centered inertial (ECI) position and velocity, and associated time elements. The data are provided in 1 second intervals, and each product file contains vectors from the duration of the orbit. The time elements are copied from the ISS raw data. The data for each EMITL1BATT granule are delivered in a single Network Common Data Format 4 (netCDF-4) file. Known Issues: Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe.\n<br>\n\n#### EMITL2BCO2ENH\nThe Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52° N latitude and 52° S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) [EMIT Open Data Portal](https://earth.jpl.nasa.gov/emit/data/data-portal/coverage-and-forecasts/). In addition to its primary objective described above, EMIT has demonstrated the capacity to characterize carbon dioxide (CO2) and methane (CH4) point-source emissions by measuring gas absorption features in the short-wave infrared bands. The EMIT Level 2B Greenhouse Gas (GHG) series of products can be used to identify and quantify point source emissions. The EMIT Level 2B Carbon Dioxide Enhancement Data (EMITL2BCO2ENH) Version 1 data product is a total vertical column enhancement estimate of CO2 in parts per million meter (ppm m) based on an adaptive matched filter approach. EMITL2BCO2ENH provides per-pixel CO2 enhancement data used to identify CO2 plume complexes. The initial release of the EMITL2BCO2ENH data product will only include granules where CO2 plume complexes have been identified. Each granule contains one Cloud Optimized GeoTIFF (COG) file at a spatial resolution of 60 meters (m): Carbon Dioxide Enhancement (EMIT_L2B_CO2ENH). The EMITL2BCO2ENH COG file contains methane enhancement data based primarily on [EMITL1BRAD](https://doi.org/10.5067/EMIT/EMITL1BRAD.001) radiance values. Each granule is approximately 75 kilometers (km) by 75 km, nominal at the equator, with some granules near the end of an orbit segment reaching 150 km in length. Known Issues: Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe.\n<br>\n\n#### EMITL2BCO2ENH\nThe Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station. EMIT uses imaging spectroscopy to take measurements of sunlit regions of interest between 52° N latitude and 52° S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) [EMIT Open Data Portal](https://earth.jpl.nasa.gov/emit/data/data-portal/coverage-and-forecasts/). In addition to its primary objective described above, EMIT has demonstrated the capacity to characterize methane (CH4) and carbon dioxide (CO2) point-source emissions by measuring gas absorption features in the shortwave infrared bands. The EMIT Level 2B Carbon Dioxide Enhancement Data (EMITL2BCO2ENH) Version 2 data product is a total vertical column enhancement estimate of carbon dioxide in parts per million meter (ppm m) based on an adaptive matched filter approach. EMITL2BCO2ENH provides per-pixel carbon dioxide enhancement data used to identify carbon dioxide plume complexes, per-pixel carbon dioxide uncertainty due to sensor noise, and per-pixel carbon dioxide sensitivity that can be used to remove bias from the enhancement data. The EMITL2BCO2ENH Version 2 data product includes methane enhancement granules for all captured scenes, regardless of carbon dioxide plume complex identification. Each granule contains three Cloud Optimized GeoTIFF (COG) files at a spatial resolution of 60 meters (m): Carbon Dioxide Enhancement (EMIT_L2B_CO2ENH), Carbon Dioxide Uncertainty (EMIT_L2B_CO2UNCERT), and Carbon Dioxide Sensitivity (EMIT_L2B_CO2SENS). The EMITL2BCO2ENH COG files contain carbon dioxide enhancement data based primarily on [EMITL1BRAD](https://doi.org/10.5067/EMIT/EMITL1BRAD.001) radiance values. Each granule is approximately 75 kilometers (km) by 75 km, nominal at the equator, with some granules near the end of an orbit segment reaching 150 km in length. Known Issues: Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe.\n<br>\n\n#### EMITL2BMIN\nThe Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of the sunlit regions of interest between 52° N latitude and 52° S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) [EMIT Open Data Portal](https://earth.jpl.nasa.gov/emit/data/data-portal/coverage-and-forecasts/). The EMIT Level 2B Estimated Mineral Identification and Band Depth and Uncertainty (EMITL2BMIN) Version 1 data product provides estimated mineral identification and band depths in a spatially raw, non-orthocorrected format. Each EMITL2BMIN granule contains two Network Common Data Format 4 (NetCDF4) files at a spatial resolution of 60 meters (m): Mineral Identification (EMIT_L2B_MIN) and Mineral Uncertainty (EMIT_L2B_MINUNCERT). The EMIT_L2B_MIN file contains the band depth (the depth of the identified spectral feature) and the identified mineral for each pixel. Two spectral groups, which correspond to different regions of the spectra, are identified independently and often co-occur. These estimates are generated using the [Tetracorder system](https://www.usgs.gov/publications/tetracorder-user-guide-version-44) ([code](https://github.com/PSI-edu/spectroscopy-tetracorder)) and are based on [EMITL2ARFL](https://doi.org/10.5067/EMIT/EMITL2ARFL.001) reflectance values. The EMIT_L2B_MINUNCERT file provides band depth uncertainty estimates calculated using surface Reflectance Uncertainty values from the EMITL2ARFL data product. The band depth uncertainties are presented as standard deviations. The fit score for each mineral identification is also provided as the coefficient of determination (r<sup>2</sup>) of the match between the continuum normalized library reference and the continuum normalized observed spectrum. Associated metadata indicates the name and reference information for each identified mineral, and additional information about aggregating minerals into different categories is available in the [emit-sds-l2b repository](https://github.com/emit-sds/emit-sds-l2b) and will be available as subsequent data products. The EMITL2BMIN data product includes a total of 19 Science Dataset (SDS) layers. There are four layers for each of the Spectral Groups (Group 1 and Group 2): Mineral Identification, Band Depth, Band Depth Uncertainties, and Fit Score. Additional layers consist of geometric lookup table (GLT) x values, GLT y values, latitude, longitude, elevation, associated spectral library record, mineral name, URL for the spectral library description, spectral group, spectral library, and spectral group index. A browse image with Group 1 Band Depth, Group 2 Band Depth, Group 1 Band Depth Uncertainty, and Group 2 Band Depth Uncertainty is also included. Each granule is approximately 75 kilometers (km) by 75 km, nominal at the equator, with some granules at the end of an orbit segment reaching 150 km in length. Disclaimer This product is generated to support the EMIT mission objectives of constraining the sign of dust related radiative forcing. Ten mineral types are the core focus of this work: calcite, chlorite, dolomite, goethite, gypsum, hematite, illite+muscovite, kaolinite, montmorillonite, and vermiculite. A future product will aggregate these results for use in Earth System Models. Additional minerals are included in this product for transparency but were not the focus of this product. Further validation is required to use these additional mineral maps, particularly in the case of resource exploration. Similarly, the separation of minerals with similar spectral features, such as a fine-grained goethite and hematite, is an area of active research. The results presented here are an initial offering, but the precise categorization is likely to evolve over time, and the limits of what can and cannot be separated on the global scale is still being explored. The user is encouraged to read the Algorithm Theoretical Basis Document (ATBD) for more details. Known Issues: Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/data/instruments/emit-imaging-spectrometer","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","cog","elevation","netcdf","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"EMITL1BRAD v001 - The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of sunlit regions of interest between 52° N latitude and 52° S latitude.","ARN":"arn:aws:s3:::lp-prod-protected/EMITL1BRAD.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"EMITL1BATT v001 - The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS).","ARN":"arn:aws:s3:::lp-prod-protected/EMITL1BATT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"EMITL2BCO2ENH v001 - The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52° N latitude and 52° S latitude.","ARN":"arn:aws:s3:::lp-prod-protected/EMITL2BCO2ENH.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"EMITL2BCO2ENH v002 - The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station.","ARN":"arn:aws:s3:::lp-prod-protected/EMITL2BCO2ENH.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"EMITL2BMIN v001 - The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of the sunlit regions of interest between 52° N latitude and 52° S latitude.","ARN":"arn:aws:s3:::lp-prod-protected/EMITL2BMIN.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-emit","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA EOS LAND VAL Project","Description":"This data set provides field measurements of diameter, tree height, and crown dimensions for 1,513 trees in 30 plots at the La Selva Biological Station in Costa Rica. Fourteen of these plots were in undisturbed primary forest, six were in primary forest which had been selectively logged, seven were secondary forests, and three were abandoned pastures reverting to forest. The diameter and height data were used to calculate aboveground biomass for each of the 30 plots. The crown measurements were used to estimate a vertical profile for each plot, showing the vegetation volume in 1 meter increments from the ground to the top of the canopy. There are three comma-delimited data files and two shapefiles with this data set. The files contain the measurements and calculated biomass for the individual stems as well as the summary data at the plot level.\n<br>\n\n#### Maryland_Temperature_Humidity_1319\nThis data set describes the temperature and relative humidity at 12 locations around Goddard Space Flight Center in Greenbelt MD at 15 minute intervals between November 2013 and November 2015. These data were collected to study the impact of surface type on heating in a campus setting and to improve the understanding of urban heating and potential mitigation strategies on the campus scale. Sensors were mounted on posts at 2 m above surface and placed on 7 different surface types around the centre: asphalt parking lot, bright surface roof, grass field, forest, and stormwater mitigation features (bio-retention pond and rain garden). Data were also recorded in an office setting and a garage, both pre- and post-deployment, for calibration purposes. This dataset could be used to validate satellite-based study or could be used as a stand-alone study of the impact of surface type on heating in a campus setting.\n<br>\n\n#### LAI_Canada_816\nThis data set provides local LAI maps for the selected measured sites in Canada. These derived maps may also be useful for validating other LAI maps over these same sites given that the areas are protected from disturbance. The maps should be used for the given period of validity. The LAI data are suitable for use in modeling the carbon, water, energy, energy and trace gas exchange between the land surface and the atmosphere at regional scales. The data set may also be useful for monitoring changes in the land surface.The Leaf Area Index (LAI) maps are at 30-m resolution for the selected sites. LAI is defined here as half the total (all-sided) live foliage area per unit horizontal projected ground surface area. Overstory LAI corresponds to all tree foliage except for treeless areas where it corresponds to total foliage. The algorithms were developed from ground measurements and Landsat TM and ETM+ images (Fernandes et. al., 2003). A mask was developed using the Landsat ETM+/TM5 image and available land cover map to identify only those areas with land cover belonging to the sample land cover classes and with Landsat ETM+/TM5 spectral reflectance values that fell within the convex hull of the spectral reflectance values over the plots. LAI was mapped within the masked region using the Landsat ETM+/TM5 image and the developed transfer function. The final LAI map was scaled by a factor of 20 (offset 0). The LAI maps are in Tagged Image File Format (TIFF).\n<br>\n\n#### LAI_VALERI_Canada_829\nThis data set provide local LAI maps for the Larose (Ontario) site in Canada. These derived maps may also be useful for validating other LAI maps over this same site given that the area is protected from disturbance. The maps should be used for the given period of validity. The LAI data are suitable for use in modeling the carbon, water, energy, energy and trace gas exchange between the land surface and the atmosphere at regional scales. The dataset may also be useful for monitoring changes in the land surface. A complete description of producing the maps for the Larose site and the ground measurement campaign is provide in the companion document (Larose2003FTReport.pdf (link)).The Leaf Area Index (LAI) maps are at 30-m resolution for the 3x3-km Larose site. LAI is defined here as half the total (all-sided) live foliage area per unit horizontal projected ground surface area. Overstory LAI corresponds to all tree foliage except for treeless areas where it corresponds to total foliage. The algorithms were developed from ground measurements and Landsat TM and ETM+ images (Fernandes et. al., 2003). A mask was developed using the Landsat ETM+/TM5 image and available land cover maps to identify only those areas with land cover belonging to the sample land cover classes and with Landsat ETM+/TM5 spectral reflectance values that fell within the convex hull of the spectral reflectance values over the plots. LAI was mapped within the masked region using the Landsat ETM+/TM5 image and the developed transfer function. The final LAI map was scaled by a factor of 20 (offset 0). The LAI maps are in Tagged Image File Format (TIFF).The VALERI project (Validation of Land European Remote sensing Instruments) is dedicated to the validation of the products derived from medium resolution satellite sensors ( http://www.avignon.inra.fr/valeri/ ). The objectives of the VALERI project are: (1) to evaluate the absolute accuracy of the biophysical products (LAI, fAPAR, fCover) derived from large swath sensors (e.g., AVHRR, POLDER, VEGETATION, SEAWIFS, MSG, MERIS, AATSR, MODIS, MISR, GLI) using a range of possible algorithms; and (2) to inter-compare the products derived from different sensors and algorithms. For this purpose, the VALERI project has developed a network of sites distributed over the Earth’s surface and a methodology designed to directly measure the biophysical variables of interest at proper spatial and temporal scales.\n<br>\n\n#### sar_subsets_993\nThis data set provides Synthetic Aperture Radar (SAR) images for 42 selected sites from various terrestrial ecology and meteorological monitoring networks including FLUXNET, Ameriflux, Long Term Ecological Research (LTER), and the Greenland Climate Network (GC-Net). The data set contains at least one image for all 42 sites, and six sites have multiple images. See Table 1 for the sites and the temporal range of the available images. The scenes are in GeoTIFF format in Universal Transverse Mercator (UTM), WGS-84 projection, and 15-meter resolution. The SAR images are subset scenes of approximately 60 km x 70 km that include an established site in one of the monitoring networks. The spatial resolution of all scenes is 15 meters. These scenes are distributed as geotif files with appropriate projection information defined within the file.The acquisition mode for all data is the Fine Beam Double Polarization or FBD with the HH/HV polarization. The HH and HV channels are distributed as 3 channels to allow for an intuitive image display. The HH band is displayed in the red and blue channels and the HV band is displayed in the green channel. For some images only single polarization is available; these images are distributed as grayscale images. The source of the data is the PALSAR (Phased Array type L-band Synthetic Aperture Radar) sensor flying on the Advanced Land Observing Satellite (ALOS). The PALSAR data are in dual Polarization, HH+HV, mode. Bands HH (red and blue) and Band-HV (green) can be used to visualize land use patterns. The resulting images show vegetation in shades of green and barren land in shades of pink or purple.\n<br>\n\n#### SoilSCAPE_1339\nThis data set contains in-situ soil moisture profile and soil temperature data collected at 20-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites in four states (California, Arizona, Oklahoma, and Michigan) in the United States. SoilSCAPE used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data at up to 12 sites over varying durations since August 2011. At its maximum, the network consisted of over 200 wireless sensor installations (nodes), with a range of 6 to 27 nodes per site. The soil moisture sensors (EC-5 and 5-TM from Decagon Devices) were installed at three to four depths, nominally at 5, 20, and 50 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. Temperature sensors were installed at 5 cm depth at six of the sites. Data collection started in August 2011 and continues at eight sites through the present. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional (airborne, e.g. NASA's Airborne Microwave Observation of Subcanopy and Subsurface Mission - AirMOSS) and national (spaceborne, e.g. NASA's Soil Moisture Active Passive - SMAP) scales.\n<br>\n\n#### SoilSCAPE_V2_2049\nThis dataset contains in-situ soil moisture profile and soil temperature data collected at 30-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites since 2021 in the United States and New Zealand. The SoilSCAPE network has used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data over varying durations since 2011. Since 2021, the SoilSCAPE has upgraded the two previously active sites in Arizona and added several new sites in the United States and New Zealand. These new sites typically use the METER Teros-12 soil moisture sensor. At its maximum, the new network consisted of 57 wireless sensor installations (nodes), with a range of 6 to 8 nodes per site. Each SoilSCAPE site contains multiple wireless end-devices (EDs). Each ED supports up to five soil moisture probes typically installed at 5, 10, 20, and 30 cm below the surface. Sites in Arizona have soil moisture probes installed at up to 75 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional and national (e.g. NASA's Cyclone Global Navigation Satellite System - CYGNSS and Soil Moisture Active Passive - SMAP) scales. The data are provided in NetCDF format.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","earth observation","ice","land cover","netcdf","radar","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"LaSelva_Biomass_1215 v1 - This data set provides field measurements of diameter, tree height, and crown dimensions for 1,513 trees in 30 plots at the La Selva Biological Station in Costa Rica. Fourteen of these plots were in undisturbed primary forest, six were in primary forest which had been selectively logged, seven were secondary forests, and three were abandoned pastures reverting to forest.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/eos_land_val/LaSelva_Biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Maryland_Temperature_Humidity_1319 v1 - This data set describes the temperature and relative humidity at 12 locations around Goddard Space Flight Center in Greenbelt MD at 15 minute intervals between November 2013 and November 2015. These data were collected to study the impact of surface type on heating in a campus setting and to improve the understanding of urban heating and potential mitigation strategies on the campus scale.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/eos_land_val/Maryland_Temperature_Humidity/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LAI_Canada_816 v1 - This data set provides local LAI maps for the selected measured sites in Canada. These derived maps may also be useful for validating other LAI maps over these same sites given that the areas are protected from disturbance.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/eos_land_val/LAI_Canada/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LAI_VALERI_Canada_829 v1 - This data set provide local LAI maps for the Larose (Ontario) site in Canada. These derived maps may also be useful for validating other LAI maps over this same site given that the area is protected from disturbance.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/eos_land_val/LAI_VALERI_Canada/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"sar_subsets_993 v1 - This data set provides Synthetic Aperture Radar (SAR) images for 42 selected sites from various terrestrial ecology and meteorological monitoring networks including FLUXNET, Ameriflux, Long Term Ecological Research (LTER), and the Greenland Climate Network (GC-Net). The data set contains at least one image for all 42 sites, and six sites have multiple images.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/eos_land_val/sar_subsets/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SoilSCAPE_1339 v1 - This data set contains in-situ soil moisture profile and soil temperature data collected at 20-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites in four states (California, Arizona, Oklahoma, and Michigan) in the United States. SoilSCAPE used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data at up to 12 sites over varying durations since August 2011.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/eos_land_val/SoilSCAPE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SoilSCAPE_V2_2049 v2 - This dataset contains in-situ soil moisture profile and soil temperature data collected at 30-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites since 2021 in the United States and New Zealand. The SoilSCAPE network has used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data over varying durations since 2011.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/eos_land_val/SoilSCAPE_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-eos-land-val","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA EOS Project","Description":"AM1EPHNE is the Terra Near Real Time (NRT) 2-hour spacecraft Extrapolated ephemeris data file in native format. The file name format is the following: AM1EPHNE.Ayyyyddd.hhmm.vvv.yyyydddhhmmss where from left to right: E = Extrapolated; N = Native format; A = AM1 (Terra); yyyy = data year, ddd = Julian data day, hh = data hour, mm = data minute; vvv = Version ID; yyyy = production year, ddd = Julian production day, hh = production hour, mm = production minute, and ss = production second. Data set information:http://modis.gsfc.nasa.gov/sci_team/\n<br>\n\n#### MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0\nDay and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODAM-8D4N4\n<br>\n\n#### MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0\nDay and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-1D9D4\n<br>\n\n#### MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0\nTDay and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-AN9D4\n<br>\n\n#### MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0\nDay and night spatially gridded global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODST-MO4D4\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br><br>\n","Documentation":"http://modis.gsfc.nasa.gov/sci_team/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","earth observation","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0 v2019.0 - Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0 v2019.0 - Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MODIS_TERRA_L3_SST_THERMAL_DAILY_9KM_DAYTIME_V2019.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0 v2019.0 - TDay and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MODIS_TERRA_L3_SST_THERMAL_ANNUAL_9KM_DAYTIME_V2019.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0 v2019.0 - Day and night spatially gridded global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MODIS_TERRA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-eos","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA EOSDIS Project","Description":"This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 1 product containing spectra and runlog (i.e. ) information in a netCDF format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. The transmission spectra are ratioed from ATMOS high sun observations, on a scale of 0 to 1. Data files also include time, geolocation and other information. The data were collected during four space shuttle missions: STS-51B/Spacelab 3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number.\n<br>\n\n#### ATMOSL2AF\nThis is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical altitude (km) grid with data stored in an ASCII table using a FORTRAN friendly fixed field format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. Measured species include: H2O, CO2, O3, N2O, CO, CH4, NO and NO2 (both diurnally and not diurnally corrected), HNO3, HF, HCl, OCS, H2CO, HOCl, H2O2, HO2NO2, N2O5, ClONO2, HCN, CH3F, CH3Cl, CF4, CCl2F2, CCl3F, CCl4, COF2, C2H6, C2H2, N2, CHF2Cl, HCOOH, HDO, SF6 and CH3D reported at 100 levels from 0.5 to 99.5 km. Data files also include time, geolocation and other information. The data were collected during four space shuttle missions: STS-51B/Spacelab 3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number. A similar product (ATMOSL2AT) exists that contains these same data in a spreadsheet friendly tab delimited format.\n<br>\n\n#### ATMOSL2AT\nThis is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical altitude (km) grid with data stored in an ASCII table using a spreadsheet friendly tab delimited format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. Measured species include: H2O, CO2, O3, N2O, CO, CH4, NO and NO2 (both diurnally and not diurnally corrected), HNO3, HF, HCl, OCS, H2CO, HOCl, H2O2, HO2NO2, N2O5, ClONO2, HCN, CH3F, CH3Cl, CF4, CCl2F2, CCl3F, CCl4, COF2, C2H6, C2H2, N2, CHF2Cl, HCOOH, HDO, SF6 and CH3D reported at 100 levels from 0.5 to 99.5 km. Data files also include time, geolocation and other information. The data were collected during four space shuttle missions: STS-51B/Spacelab 3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number. A similar product (ATMOSL2AF) exists that contains these same data in a FORTRAN friendly fixed field format.\n<br>\n\n#### ATMOSL2TF\nThis is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical potential temperature (theta) grid with data stored in an ASCII table using a FORTRAN friendly fixed field format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. Measured species include: H2O, CO2, O3, N2O, CO, CH4, NO and NO2 (both diurnally and not diurnally corrected), HNO3, HF, HCl, OCS, H2CO, HOCl, H2O2, HO2NO2, N2O5, ClONO2, HCN, CH3F, CH3Cl, CF4, CCl2F2, CCl3F, CCl4, COF2, C2H6, C2H2, N2, CHF2Cl, HCOOH, HDO, SF6 and CH3D reported at 53 levels from 280 to 3950 K. Data files also include time, geolocation and other information. The data were collected during four space shuttle missions: STS-51B/Spacelab-3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number. A similar product (ATMOSL2TT) exists that contains these same data in a spreadsheet friendly tab delimited format.\n<br>\n\n#### ATMOSL2TT\nThis is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical potential temperature (theta) grid with data stored in an ASCII table using a spreadsheet friendly tab delimited format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. Measured species include: H2O, CO2, O3, N2O, CO, CH4, NO and NO2 (both diurnally and not diurnally corrected), HNO3, HF, HCl, OCS, H2CO, HOCl, H2O2, HO2NO2, N2O5, ClONO2, HCN, CH3F, CH3Cl, CF4, CCl2F2, CCl3F, CCl4, COF2, C2H6, C2H2, N2, CHF2Cl, HCOOH, HDO, SF6 and CH3D reported at 53 levels from 280 to 3950 K. Data files also include time, geolocation and other information. The data were collected during four space shuttle missions: STS-51B/Spacelab 3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number. A similar product (ATMOSL2TF) exists that contains these same data in a FORTRAN friendly fixed field format.\n<br>\n\n#### ATMOSL2PF\nThis is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical pressure (atm) grid with data stored in an ASCII table using a FORTRAN friendly fixed field format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. Measured species include: H2O, CO2, O3, N2O, CO, CH4, NO and NO2 (both diurnally and not diurnally corrected), HNO3, HF, HCl, OCS, H2CO, HOCl, H2O2, HO2NO2, N2O5, ClONO2, HCN, CH3F, CH3Cl, CF4, CCl2F2, CCl3F, CCl4, COF2, C2H6, C2H2, N2, CHF2Cl, HCOOH, HDO, SF6 and CH3D reported at 85 levels from 1 to 10-7 atm. Data files also include time, geolocation and other information. The data were collected during four space shuttle missions: STS-51B/Spacelab-3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number. A similar product (ATMOSL2PT) exists that contains these same data in a spreadsheet friendly tab delimited format.\n<br>\n\n#### ATMOSL2PT\nThis is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical pressure (atm) grid with data stored in an ASCII table using a spreadsheet friendly tab delimited format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. Measured species include: H2O, CO2, O3, N2O, CO, CH4, NO and NO2 (both diurnally and not diurnally corrected), HNO3, HF, HCl, OCS, H2CO, HOCl, H2O2, HO2NO2, N2O5, ClONO2, HCN, CH3F, CH3Cl, CF4, CCl2F2, CCl3F, CCl4, COF2, C2H6, C2H2, N2, CHF2Cl, HCOOH, HDO, SF6 and CH3D reported at 85 levels from 1 to 10-7 atm. Data files also include time, geolocation and other information. The data were collected during four space shuttle missions: STS-51B/Spacelab 3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number. A similar product (ATMOSL2PF) exists that contains these same data in a FORTRAN friendly fixed field format.\n<br>\n\n#### EOLE1\nThe Eole 1 Raw Temperature, Pressure and Location Data Near 200 mbar product was obtained from the experimenter and originally consisted of a BCD tape generated on a CDC 6600 computer, subsequently converted to ASCII characters. The data are arranged sequentially by orbit. Data from each orbit are contained in a single record and consist of a heading giving the orbit number, the number of balloons contacted, and a control number. Following the heading, the balloon number, date of observation, location, and ambient temperature and pressure are listed. A maximum of 25 balloon contacts may appear in a single record. Empty records with no balloon contacts have been omitted. These data were obtained from balloons near 200 mbar and are for the region between 30 deg S and 60 deg S. The upper level wind speed and direction can be generated from these data by comparing individual balloon locations obtained from successive orbits. Eole, also known as the Cooperative Application Satellite (CAS-A), was the the second French experimental relay and meteorological satellite and the first launched by NASA under a cooperative agreement with the Centre National d'Etudes Spatiales (CNES).\n<br>\n\n#### EXP7L1TRTWHT\nExplorer-7 Thermal Radiation Experiment Selected White Sensor Temperature (Nighttime) Values product contains the temperatures measured by the white sensor at night. The white sensor was designed to measure terrestrial radiation. There is a single file for the entire mission (Nov. 15, 1959 to May 24, 1960). The data were originally written on IBM 7094 machines to magnetic tapes. In addition to the temperature values, the file contains radiance, geolocation and orbit information. The data have been restored and are archived in their original IBM EBCDIC text format. The Explorer-7 satellite was successfully launched on October 13, 1959. The radius of the circle of coverage was about 23 deg (~2500 km) at perigee and 31.5 deg (~3500 km) at apogee. Half the radiation is received from an area below the satellite with a radius of 5.3 deg (545 km) at perigee and 9 deg (~1015 km) at apogee. The Thermal Radiation Experiment successfully returned the first set of Earth looking data from space. The instrument was operational from launch until Feb. 28, 1961. The Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00248 (old ID 59-009A-01A).\n<br>\n\n#### EXP7L1TRTALL\nExplorer-7 Thermal Radiation Experiment Temperature Values from All Sensors product contains temperature readings from all five bolometers in order to measure solar, reflected and terrestrial radiation. There are two files for the entire mission (Oct. 19, 1959 to April 16, 1960 and April 16, 1960 to June 4, 1960. Note there is no geolocation information included with these data. The data were originally written on IBM 7094 machines on magnetic tapes. The data have been restored and are archived in their original IBM 36-bit word binary format. The Explorer-7 satellite was successfully launched on October 13, 1959. The radius of the circle of coverage was about 23 deg (~2500 km) at perigee and 31.5 deg (~3500 km) at apogee. Half the radiation is received from an area below the satellite with a radius of 5.3 deg (545 km) at perigee and 9 deg (~1015 km) at apogee. The Thermal Radiation Experiment successfully returned the first set of Earth looking data from space. The instrument was operational from launch until Feb. 28, 1961. The Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00249 (old ID 59-009A-01B).\n<br>\n\n#### GEOS2OBSINPUTINTL\nGEOS2OBSINPUTINTL is the optical beacon system data product which contains reduced raw geodetic optical observations obtained by various international camera systems. These data were used as input to the Quality Control Program to create the product called the International Optical Beacon Pass Summary Data. The optical beacon system, used for geometric geodesy studies, consisted of four xenon flash tubes programmed to flash sequentially, in a series of five or seven flashes. Data are available for the time period from 1968-02-20 to 1968-10-03 in a single file with 1689 data records where each is a line of ASCII text.\\n\\nThe principal investigator for the Optical Beacon System experiment was R. E. Williston from APL. A previous version of this instrument flew on the first GEOS-1 satellite.\n<br>\n\n#### GEOS3STST\nGEOS3STST is the satellite-to-satellite tracking data product which contains observations, obtained from the S-band transponders on GEOS 3 relayed by the ATS 6 spacecraft to various ground stations, used for geodetic studies. Data are available for the time period from 1975-04-13 to 1976-04-28 in sixteen files, written in ASCII text, where each measurement is recorded as two lines of text.\\n\\nThe principal investigator for the Satellite-to-Satellite Tracking experiment was Indalecio Y. Galicinao from NASA/GSFC.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/about/esdis/eosdis","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","netcdf","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ATMOSL1 v3 - This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 1 product containing spectra and runlog (i.e. ) information in a netCDF format.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/ATMOS/ATMOSL1.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ATMOSL2AF v3 - This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical altitude (km) grid with data stored in an ASCII table using a FORTRAN friendly fixed field format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/ATMOS/ATMOSL2AF.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ATMOSL2AT v3 - This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical altitude (km) grid with data stored in an ASCII table using a spreadsheet friendly tab delimited format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/ATMOS/ATMOSL2AT.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ATMOSL2TF v3 - This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical potential temperature (theta) grid with data stored in an ASCII table using a FORTRAN friendly fixed field format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/ATMOS/ATMOSL2TF.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ATMOSL2TT v3 - This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical potential temperature (theta) grid with data stored in an ASCII table using a spreadsheet friendly tab delimited format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/ATMOS/ATMOSL2TT.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ATMOSL2PF v3 - This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical pressure (atm) grid with data stored in an ASCII table using a FORTRAN friendly fixed field format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/ATMOS/ATMOSL2PF.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ATMOSL2PT v3 - This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 2 product containing trace gases on a vertical pressure (atm) grid with data stored in an ASCII table using a spreadsheet friendly tab delimited format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/ATMOS/ATMOSL2PT.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"EOLE1 v001 - The Eole 1 Raw Temperature, Pressure and Location Data Near 200 mbar product was obtained from the experimenter and originally consisted of a BCD tape generated on a CDC 6600 computer, subsequently converted to ASCII characters. The data are arranged sequentially by orbit.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/EOLE1_Level1/EOLE1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"EXP7L1TRTWHT v001 - Explorer-7 Thermal Radiation Experiment Selected White Sensor Temperature (Nighttime) Values product contains the temperatures measured by the white sensor at night. The white sensor was designed to measure terrestrial radiation.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Explorer/EXP7L1TRTWHT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"EXP7L1TRTALL v001 - Explorer-7 Thermal Radiation Experiment Temperature Values from All Sensors product contains temperature readings from all five bolometers in order to measure solar, reflected and terrestrial radiation. There are two files for the entire mission (Oct.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Explorer/EXP7L1TRTALL.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GEOS2OBSINPUTINTL v001 - GEOS2OBSINPUTINTL is the optical beacon system data product which contains reduced raw geodetic optical observations obtained by various international camera systems. These data were used as input to the Quality Control Program to create the product called the International Optical Beacon Pass Summary Data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GEOS/GEOS2OBSINPUTINTL.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GEOS3STST v001 - GEOS3STST is the satellite-to-satellite tracking data product which contains observations, obtained from the S-band transponders on GEOS 3 relayed by the ATS 6 spacecraft to various ground stations, used for geodetic studies. Data are available for the time period from 1975-04-13 to 1976-04-28 in sixteen files, written in ASCII text, where each measurement is recorded as two lines of text.\n\nThe principal investigator for the Satellite-to-Satellite Tracking experiment was Indalecio Y.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GEOS/GEOS3STST.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-eosdis","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ESDIS Project","Description":"This is a global simulation of black carbon (BC) aerosol concentrations and daily deposition (wet+dry) from the FLEX-ible PARTicle (FLEXPART) Lagrangian particle dispersion model version 10.4. The FLEXPART model code are open source and freely available.\n<br>\n\n#### DUSTFLEXPART\nThis is a global simulation of mineral dust aerosol concentrations and daily deposition (wet+dry) from the FLEX-ible PARTicle (FLEXPART) Lagrangian particle dispersion model version 10.4. The FLEXPART model code are open source and freely available.\n<br>\n\n#### OCFLEXPART\nThis is a global simulation of organic carbon (OC) aerosol concentrations and daily deposition (wet+dry) from the FLEX-ible PARTicle (FLEXPART) Lagrangian particle dispersion model version 10.4. The FLEXPART model code are open source and freely available.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/BCFLEXPART_1.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"BCFLEXPART v1 - This is a global simulation of black carbon (BC) aerosol concentrations and daily deposition (wet+dry) from the FLEX-ible PARTicle (FLEXPART) Lagrangian particle dispersion model version 10.4. The FLEXPART model code are open source and freely available.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/FLEXPART/BCFLEXPART.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"DUSTFLEXPART v1 - This is a global simulation of mineral dust aerosol concentrations and daily deposition (wet+dry) from the FLEX-ible PARTicle (FLEXPART) Lagrangian particle dispersion model version 10.4. The FLEXPART model code are open source and freely available.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/FLEXPART/DUSTFLEXPART.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCFLEXPART v1 - This is a global simulation of organic carbon (OC) aerosol concentrations and daily deposition (wet+dry) from the FLEX-ible PARTicle (FLEXPART) Lagrangian particle dispersion model version 10.4. The FLEXPART model code are open source and freely available.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/FLEXPART/OCFLEXPART.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-esdis","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA FIFE Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### fife_hydrology_strm_15m_1\nThe Fifteen Minute Stream Flow Data from the USGS Data Set contains 15 minute stream flow data from the USGS station located 2.9 miles upstream from the mouth of Kings Creek. The record extends from April 1, 1979 through September 2, 1988. The purpose of this data set was to provide accurate measurements of the stream flow from Kings Creek so that a water budget analysis for the northwest quadrant of the FIFE study area could be performed. The stilling pipe installed at the USGS station operates on the principle that the water level in a standpipe at a specific location within a stream bed can be converted to a volume of water in the stream bed. The tracking of the change in stream height with time then enables the calculation of stream flow.\n<br>\n\n#### fife_sur_met_rain_30m_2\nThe FIFE Thirty Minute Rainfall Data Data Set contains data from thirty rain gauges located in the Kings Creek basin in the northwest corner of the FIFE study area during 1987. Reliability of the gauges were such that at any particular time, data from approximately 20 were recovered. The high temperatures and humidity, plus software problems in the loggers, resulted in data losses. The collected data were of high quality and sufficiently many gauges were working that the structure of the raincells can be observed from the gauge data.\n<br>\n\n#### fife_AF_dtrnd_nae_3\nThe purpose of this study was to develop alternatives to ground-based measurements in order to obtain information required to predict the effects of soil and land use on the fluxes of greenhouse gases, the surface energy balance, and the water balance. Satellite-based algorithms have been developed via flux measurements from an aircraft to estimate vegetation and soil conditions on a regional scale. The purpose of the Twin Otter FIFE flights was to make measurements in the boundary layer of the fluxes of sensible and latent heat, momentum, and carbon dioxide, plus supporting meteorological parameters such as temperature, humidity, wind speed, and direction. Aircraft position, heading, and altitude were also recorded, as were several radiometric observations for use in interpretation of these data. The Twin Otter aircraft allows steady flight trajectories at low airspeed (50-60 [m][sec^-1]) down to levels less than 10 m above the ground. The aircraft is instrumented to measure the contribution of flux densities of momentum, sensible, and latent heat, and CO2 over a frequency range of 0 to 5 Hz (MacPherson et al., 1981). All the flux measurements were obtained with the eddy-correlation method, wherein the aircraft is equipped with an inertial platform, accelerometers, and a gust probe for measurement of earth-relative gusts in the x, y, and z directions. Gusts in these dimensions are then correlated with each other for momentum fluxes and with fluctuations in other variables to obtain the various scalar fluxes, such as temperature (for sensible heat flux) and water vapor mixing ratio (for latent heat flux). The fluctuations in all variables were calculated with three different methods (the arithmetic means removed, the linear trends removed, or filtered with a high-pass recursive filter) prior to the eddy correlation calculations. This data set contains the linearly detrended data. Through this research, it is hoped that techniques can be developed to utilize satellite data for global monitoring of crop health and climate change.\n<br>\n\n#### fife_AF_dtrnd_wyo_4\nThe University of Wyoming (UW) King Air atmospheric boundary layer measurement missions were flown in 1987 during IFCs 3 and 4. This Boundary Layer Fluxes data set contains parameters that describe the environment in which the flux data were collected and the flux data itself . The fluctuations in all variables were calculated with three different methods (the arithmetic means removed, the linear trends removed, or filtered with a high-pass recursive filter) prior to the eddy correlation calculations. This data set contains the linearly detrended data. All the flux measurements were obtained with the eddy-correlation method, wherein the aircraft is equipped with an inertial platform, accelerometers, and a gust probe for measurement of earth-relative gusts in the x, y, and z directions. Gusts in these dimensions are then correlated with each other for momentum fluxes and with fluctuations in other variables to obtain the various scalar fluxes, such as temperature (for sensible heat flux) and water vapor mixing ratio (for latent heat flux). The summary of data calculated from each aircraft pass includes various statistics, correlations, and fluxes calculated after the time series for each variable with the linear trends removed.\n<br>\n\n#### fife_AF_dtrnd_ncar_5\nThe NCAR King Air participation in FIFE-1987 and FIFE-1989 was part of a coordinated atmospheric boundary layer component which included other aircraft, surface measurements, balloon-borne profiles, SODAR, and lidar remote sensing. The chief objective of the boundary layer component was to describe the structure of the atmospheric boundary layer over the FIFE study area, increase knowledge of the physical processes active in the daytime boundary layer, and explore the relationship of surface properties to the time and spatial variation in the structure of the boundary layer. The phenomena studied were the daytime convective boundary layer structure and physical processes. This study used airborne measurement of vertical and horizontal wind gusts, humidity, potential temperature, mean horizontal wind speed, and horizontal linear trends of temperature, humidity, radiation. Fluxes of sensible heat, moisture, and momentum were estimated from fast response wind gust, temperature, and humidity measurements; these fluxes were evaluated from data whose linear trend and mean were removed. In addition several radiation parameters were also measured.. Several radiation parameters were also measured (e.g., global short and longwave, upwelling, and downwelling). Altitude of the aircraft was measured by radar and pressure; radar was more accurate but was only valid below about 930 m. Geographical position was measured by an inertial navigation system. All level legs of a flight mission were flown at a constant pressure altitude, thus the altitude of the aircraft over the surface varied. In general, the detrended data set is of excellent overall quality with very little loss of data. Vertical winds were sampled at an effective rate of 5 samples per second instead of the customary 10 samples per second; this had negligible effect on the fluxes but could compromise estimates of turbulence dissipation. Fluxes were estimated using raw, detrended and high-pass filtered data. From extensive analysis the FIFE Boundary Layer Group recommends using the detrended data.\n<br>\n\n#### fife_AF_filtr_ncar_8\nThe NCAR King Air participation in FIFE-1987 and FIFE-1989 was part of a coordinated atmospheric boundary layer component which included other aircraft, surface measurements, balloon-borne profiles, and SODAR and lidar remote sensing. The chief objective of the boundary layer component was to describe the structure of the atmospheric boundary layer over the FIFE study area, increase knowledge of the physical processes active in the daytime boundary layer, and explore the relationship of surface properties to the time and spatial variation in the structure of the boundary layer. The phenomena studied were the daytime convective boundary layer structure and physical processes. This study used airborne measurement of vertical and horizontal wind gusts, humidity, potential temperature, mean horizontal wind speed, and horizontal linear trends of temperature, humidity, radiation. Fluxes of sensible heat, moisture, and momentum were estimated from fast response wind gust, temperature, and humidity measurements; these fluxes were evaluated from data which had been high pass filtered with a third order algorithm with a break point set at 0.012 Hz (5 km wavelength). Several radiation parameters were also measured (e.g., global short and longwave, upwelling, and downwelling). Altitude of the aircraft was measured by radar and pressure; radar was more accurate but was only valid below about 930 m. Geographical position was measured by an inertial navigation system. All level legs of a flight mission were flown at a constant pressure altitude, thus the altitude of the aircraft over the surface varied. In general, the data set is of excellent overall quality with very little loss of data. Vertical winds were sampled at an effective rate of 5 samples per second instead of the customary 10 samples per second; this had negligible effect on the fluxes but could compromise estimates of turbulence dissipation. From extensive analysis the FIFE Boundary Layer Group recommends using the detrended data rather than the filtered data.\n<br>\n\n#### fife_AF_raw_wyo_10\nThe University of Wyoming (UW) King Air atmospheric boundary layer measurement missions were flown in 1987 during IFCs 3 and 4. This Raw Boundary Layer Fluxes data set contains parameters that describe the environment in which the flux data were collected and the flux data itself. The fluctuations in all variables were calculated with three different methods (the arithmetic means removed, the linear trends removed, or filtered with a high-pass recursive filter) prior to the eddy correlation calculations. This data set contains the data with the arithmetic means removed (i.e., RAW). All the flux measurements were obtained with the eddy-correlation method, wherein the aircraft is equipped with an inertial platform, accelerometers, and a gust probe for measurement of earth-relative gusts in the x, y, and z directions. Gusts in these dimensions are then correlated with each other for momentum fluxes and with fluctuations in other variables to obtain the various scalar fluxes, such as temperature (for sensible heat flux) and water vapor mixing ratio (for latent heat flux). The summary of data calculated from each aircraft pass includes various statistics, correlations, and fluxes calculated after the time series for each variable with the arithmetic means removed.\n<br>\n\n#### fife_sur_met_ams_12\nAs part of the FIFE staff science data collection effort, the FIFE Information System (FIS) processed and archived 5 minute, near-surface radiometric and meteorological information collected by the Automated Meteorological Stations (AMS) distributed over the FIFE study area. The FIFE AMS Data Set contains the two output products created. The level-1 product contains unpacked 5 minute data. The level-1a product contains 30 minute averages of these data. All AMS stations were equipped to measure air temperature, humidity, wind speed, soil temperature, reflected solar radiation, net radiation, surface temperature, and precipitation. Two stations were augmented with extra radiation sensors to become super-AMS (SAMS). These stations measured total radiation, direct solar radiation, diffuse solar radiation, photosynthetically active radiation, and downward longwave radiation.\n<br>\n\n#### fife_atmos_ncdc_son_13\nThe NOAA Radiosonde Observations - 1989 (NCDC) Data Set contains radiosonde data obtained from the National Climatic Data Center (NCDC). These 396 days of data cover 13 months from October 1988 through October 1989. These data were collected using sondes released in Dodge City and Topeka Kansas, 337 km and 68 km, respectively, from the FIFE study area. Radiosonde observations were made to determine the pressure, temperature, and humidity from the surface to the point where the sounding was terminated. It is assumed that the use of these data is applicable to the FIFE study because these meteorological data are relatively stable in the horizontal domain. These data may be used as input to numerical models, as well as verification data for simulation studies.\n<br>\n\n#### fife_atmos_brut_drv_14\nThe FIFE Standard Pressure Level Radiosonde Data Set provides a set of standard level profiles (i.e., 5 mb pressure intervals) from over 450 radiosonde balloon flights, which occurred every one to three hours (daylight hours) during the FIFE IFCs. This derived profile data were computed to 5 mb pressure intervals through simple linear interpolation means. An assumption exists that a linear interpolation scheme may be used with sufficient accuracy to assign meteorological values at 5 mb pressure levels. Some errors are introduced using this method. Several new variables were computed from the original FIFE Radiosonde Data and are included in this derived data set. U (east-west) and V (north-south) winds have been computed from wind speed and direction, and potential temperature has been computed from pressure and temperature. These new parameters are desirable for initial conditions in numerical models as well as forcing functions in models, or as verification and comparison of numerical model's results.\n<br>\n\n#### fife_atmos_brut_son_15\nThe FIFE Radiosonde Data Set contains temperatures, wind speed, and temperature profiles in the atmospheric boundary layer measured by means of radiosondes that were analyzed in the framework of Monin-Obukhov similarity theory, with the objective of determining the regional surface heat flux. Profiles of temperature, humidity and wind velocity in the atmosphere were measured by means of intensive radiosoundings conducted approximately between 900 and 1800 CDST in northeastern Kansas during the five FIFE Intensive Field Campaigns in spring, summer and fall of 1987, and in the late summer of 1989. Some 445 radiosondes were released to generate the measurements needed to obtain profiles of wind velocity dry-bulb and wet-bulb temperature. The launch site was located near the northern edge of the experimental area to ensure that these profiles reflect surface conditions over the fetch of the experimental area in the general direction of the prevailing southerly wind. The raw radiosonde data described here have been corrected for sensor delays (see the FIFE Temperature and Humidity Profiles) and algorithm inconsistencies, (see the FIFE Radiosonde Wind Profiles) and have been interpolated to a set of standard pressure levels (see the FIFE Standard Pressure Level Radiosonde Data). These derived data sets are described separately.\n<br>\n\n#### fife_atmos_noaa_tov_16\nThe TOVS data were acquired from NOAA/NESDIS to monitor atmospheric conditions that occurred over the FIFE study area during 1987. The TOVS data were obtained from NESDIS in the standard TOVS sounding product format containing atmospheric sounding data for NOAA-9 and NOAA-10 satellites over the FIFE study area. The TOVS sounding products information is derived from three sensors which measure the intensity of upwelling radiation in the various spectral intervals that occur at maxima over broad layers and depths of the atmosphere. These radiance measurements are processed into Earth-located, calibrated radiance values, \"clear\" radiances (radiances corrected for cloud effects and angle-of-view), estimates of water vapor in three atmospheric layers (converted to precipitable water in these layers), mean temperatures for selected atmospheric layers, tropopause height and temperature estimates, and geopotential thickness of selected atmospheric layers.\n<br>\n\n#### fife_atmos_lidar_ht_17\nThe Volume Imaging LIDAR (VIL) system of the University of Wisconsin, operated during FIFE and all LIDAR return signals measured at a 90 degree elevation angle were averaged and stored in a file. From plots of those profiles, clouds up to 15 km AGL can be identified. By choosing appropriate reflectivity levels, the data from the University of Wisconsin LIDAR have been used to derive unique 2-D and 3-D views of the Atmospheric Boundary Layer (ABL) structure and the variations in that structure with time. Some of these views are available in the GRAB BAG directory on FIFE CD-ROM Volume 1. Color videos were also produced and are available from the Archive listed in Section 13.1. These views and videos provide important insights into many problems facing investigators in all aspects of FIFE, including scaling and the representativeness of point and line samples.\n<br>\n\n#### fife_atmos_sodar_18\nThe acoustic sounder (SODAR) maps the amplitude of backscattered acoustic energy associated with temperature fluctuations and thus thermal inversions in the atmosphere. The aim of the SODAR measurements was to provide estimates of the height of the mixed layer and the vertical dimensions of inversions within the lower kilometer of the atmosphere. A single, vertically pointing, conventional SODAR was operated at an acoustic frequency near 1500 Hz to detect the amplitude of backscattered acoustic energy. The thickness of an elevated inversion as seen by the SODAR is often smaller than the difference between the heights of the inversion top and bottom, because of oscillations in the heights that occur. The heights were estimated only for the inversions that were clearly associated with the active mixed layer. These data were collected at one location in the northwest quadrant of the FIFE study area during the first three Intensive Field Campaigns held in 1987.\n<br>\n\n#### fife_sur_flux_30_min_brl_19\nSurface flux measurements were made at selected sites within the FIFE area. Each surface flux station was capable of measuring the fluxes of net radiation, sensible heat, and latent heat. The Bowen ratio stations measured the soil heat flux as well. The surface flux and micrometeorological measurements available in this data set were collected from 15 locations within the FIFE study area between 1987 and 1989. Six automatic surface energy and radiation balance systems were operated continuously for 144 days from May 16 to October 16, 1987. Variables including net radiation, air temperature, vapor pressure and wind speed, were quite similar for the sites even though the sites were as much as 10 km apart and represented the four cardinal slopes and a top. The Bowen ratio was low during most of the season, increasing sharply toward the end of the season after a long dry spell. The average Bowen ratio was 0.35. About 72% of the available energy was converted into latent heat flux density. Since the data systems and instrumentation used were of similar design, the variability in results can be ascribed to treatment and locations. These results can be used to estimate the number of stations needed to represent a rolling prairie topography.\n<br>\n\n#### fife_sur_flux_30_min_brg_20\nThe Bowen Ratio Surface Flux Observations (GSFC) Data Set contains data collected using the Bowen Ratio Techniques. The major data collection effort was conducted in 1987 when 16 stationary sites were equipped with Bowen ratio equipment by different groups. Surface flux measurements were made at selected sites within the FIFE area. All measurements are from a single upland site that was grazed. This station measured the fluxes of net radiation, sensible heat, and latent heat and several micrometeorological parameters.\n<br>\n\n#### fife_sur_flux_30_min_brk_21\nThe Bowen Ratio Surface Flux Observations (KSU) Data Set contains surface flux measurements made at selected sites within the FIFE area. The sites were equipped with Bowen ratio equipment that was operated by several different groups. Each surface flux station was capable of measuring the fluxes of net radiation, latent heat and sensible heat. The Bowen ratio stations measured the soil heat flux as well. The components of the energy balance were determined with the Bowen Ratio Energy Balance (BREB) method. The BREB is a combination of the transport and the energy balance equations. The surface flux and micrometeorological measurements available in this data set were collected from 23 locations with 27 site identifiers from 1987 through 1989. Thirteen of these locations were instrumented with stationary bowen ratio systems which collected daily measurements for months. These systems were all located in the northwest quadrant of the FIFE study area within the Konza Prairie Natural Research Area. Ten locations were instrumented in 1987 for a few days at a time with a portable Bowen ratio system. This roving system visited all but the southeast quadrant of the FIFE study area.\n<br>\n\n#### fife_sur_flux_30_min_brs_22\nThe Bowen Ratio Surface Flux Observations (Smith) Data Set contains surface flux measurements made at selected sites within the FIFE area. The collection effort was conducted in 1987, 1988, and 1989 from sites equipped with Bowen ratio equipment operated by several different groups. Each surface flux station was capable of measuring the fluxes of net radiation, sensible heat, and latent heat using. The Bowen ratio stations measured the soil heat flux as well. The surface flux and micrometeorological measurements available in this data set were collected from 2 locations within the FIFE study area. One of these sites was located at the bottom of a valley while the other was on the top of a ridge. During the IFC's data were collected daily.\n<br>\n\n#### fife_sur_flux_30_min_brv_23\nThe Bowen Ratio Surface Flux Observations (UNL) Data Set contains surface flux and micrometeorolgical measurements collected at one location located in a flat area of uniform surface vegetation approximately in the center of the FIFE study area. The data collection effort was during the four Intensive Field Campaigns in the spring, summer, and fall of 1987 (May 28 - Oct 17). The Bowen ratio system that collected these data was designed to retrieve all major components of the surface energy budget along with a large set of measured and derived parameters describing the dynamical, thermodynamical, hydrological, and radiative properties of the ground surface and atmosphere surface layer.\n<br>\n\n#### fife_sur_flux_30_min_brw_24\nThe Bowen Ratio Surface Flux Observations (USGS) Data Set contains surface flux and micrometeorological collected from one location within the Northwest quadrant of the FIFE study area. Data were collected daily at this location only during the IFC's during the period from late May through mid-October, 1987. Each Bowen ratio station was capable of measuring the fluxes of net radiation, sensible heat, and latent heat. The Bowen ratio stations measured the soil heat flux as well.\n<br>\n\n#### fife_biology_pho_box_27\nThe Canopy Photosynthesis Data Set data were collected from five sites within the FIFE study area during July 1, 1987 through October 12, 1987. The objectives of the study were to estimate canopy photosynthetic rates, respiration rates, and bulk stomatal resistance. Photosynthesis was measured by monitoring the net exchange of CO2 from the canopy to the atmosphere while the canopy is enclosed in a Plexiglas chamber equipped with a LI-COR CO2 gas analyzer. The rate of CO2 concentration change over intervals of 10 - 20 seconds is measured. This CO2 concentration rate of change is used along with other factors (e.g., the amount of canopy area enclosed, the volume of the enclosure, and temperature) to estimate the net photosynthesis rate. The stomatal resistance and conductance is calculated from the total leaf resistance (i.e., calculated via the transpiration rate along with the leaf and air temperatures) minus the boundary layer resistance. Stomatal resistance of selected species of the grass used to estimate the total canopy resistance were measured independently using a leaf diffusion porometer. The results showed that estimated values of net CO2 flux varied between about 0.25 and 1.0 [mg][m^-2][sec^-1] during IFC-2 and IFC-3, and around zero during IFC-4. Resistances ranged from 80 [sec][m^-1] to 300 [sec] [m^-1] during IFC-2 and IFC-3, rising to very high values (greater than 1000 [sec][m^-1]) during IFC-4.\n<br>\n\n#### fife_sur_met_cld_cam_28\nThe FIFE Cloud Camera Data Data Set was collected to document distribution of clouds during FIFE, evaluate algorithms for identifying presence of thin cirrus, and popcorn cumulus clouds, and evaluate the impact of these clouds on retrieval of surface fluxes from satellite data. Clouds could be remotely sensed from both the surface and from satellites. Unlike surface properties, cloud parameters are incompletely retrieved from above or below; there is no ground truth for cloud retrieval algorithms. A camera fitted with a whole-sky (\"fish-eye\") lens and positioned so that it points directly upwards can capture a full horizon-to-horizon image of the sky dome. Careful film and filter combinations permit differentiation of cloud types. The mathematical mapping of a spherical surface onto a flat surface uses nomenclature from the cartographic community, where the development of techniques for mapping the surface of the earth has a long history. Cartographic projections are precise, mathematically defined 'mappings' and, as a consequence, this nomenclature has been adopted in describing whole-sky camera photographs (Herbert 1986; McGuffie and Henderson-Sellers 1989). Analysis of the camera data showed considerable temporal variability indicating that synoptic observation of cloud was not adequate. There is indication that the NOAA station report convention (clear, scattered, broken, and overcast) from the nearest synoptic NOAA surface stations (Manhattan Airport, and Fort Riley airfield) were used instead of true okta cloud observations, in the NOAA data.\n<br>\n\n#### fife_sur_met_rain_day_29\nThe FIFE Daily Rainfall Data Set contains daily precipitation values for 42 rain gauge stations within the Konza LTER portion of the FIFE site (i.e., Northwest quadrant). The data set is a composite of data collected by the LTER staff and the Princeton University group. The LTER staff collected daily precipitation data from 12 of the 42 rain gauge stations within this area with the Princeton University group collecting 30 minute precipitation data from the remaining 30 stations. LTER data was collected from April 1982 through December 1989. Data collected by the LTER staff was year round for some stations and from April 1 to October 31 for others. The Princeton University group collected data from May 1987 to October 1987. The Princeton University 30 minute precipitation data was converted to daily precipitation data by the FIS staff. At any particular time, data from approximately 20 of the 30 Princeton University stations were recovered. High temperatures and humidity, plus software problems in the rainfall data loggers, resulted in these data losses. The collected data were of high quality and enough gauges were working at all times so that rain cells could be observed using these data.\n<br>\n\n#### fife_sur_flux_30_min_eca_30\nThe Eddy Correlation Surface Flux Observations (Argonne) Data Set contains surface flux measurements made at selected sites within the FIFE study area. The surface area and aerological data described here were located at 2 sites in the northwest quadrant of the study area within the Konza Prairie Natural Research area. Both sites were located on hill tops that were burnt on a regular cycle. These data were collected daily only during the five Intensive Field Campaigns which were held during the growing season of 1987 and the projected summer dry down in 1989. Data were also collected using a portable eddy correlation system that moved to a variety of locations within the FIFE study area. These data were collected for a day or two at each location sometime between June 1, 1987 and October 13, 1987. Argonne National Laboratory did not measure radiation but concentrated on observations of turbulence quantities, primarily covariances and standard deviations of winds, temperature, water vapor, and other quantities. The surface fluxes and standard deviations of carbon dioxide and ozone were measured by Argonne so that the fluxes of mass could be related to each other, surface biophysical conditions, vegetative parameters, and the optical characteristics of the surface that could be detected by remote sensing.\n<br>\n\n#### fife_sur_flux_30_min_ecg_31\nThe Eddy Correlations Surface Flux Observations (GSFC) Data Set contains surface flux measurements made at selected sites within the FIFE study area. The surface flux and micrometeorological measurements in this data set were collected from a single location located in the southwest quadrant on a upland, grazed area. The data set contains data collected daily from June 26 - October 17, 1987 during the three Intensive Field Campaigns. No data is available between the campaigns. Micrometeorological techniques of eddy correlation and Bowen ratio were used in determining the fluxes of sensible heat, latent heat, and carbon dioxide in FIFE. Eddy correlation is a well-established technique that has the primary advantage of measuring turbulent diffusive fluxes directly across a near-horizontal plane above the surface. It requires a rigid platform unencumbered by significant aerodynamic obstacles. The fluxes of sensible and latent heat are computed as covariances of the fluctuations of vertical wind velocity with fluctuations of temperature and vapor density at the same point and time.\n<br>\n\n#### fife_sur_flux_30_min_ecb_32\nSurface flux measurements were made at selected sites within the FIFE study area. Each surface flux station was capable of measuring the fluxes of net radiation, sensible heat, and latent heat. The Eddy Correlation Surface Flux Observations (UK) Data Set contains surface flux and micrometeorological measurements collected from one location in the southwest quadrant of the FIFE study area. This location was grazed and had a gentle downhill slope to the southwest. Data were collected daily from May 14 - October 18, 1987, and from July 21 - August 16, 1989. The output from the Hydra sensors is sampled at 10 Hz and processed in real time to give hourly averages of sensible and latent heat flux and the friction velocity. The hourly mean values of net radiation, temperature, and of vapor pressure, provided in this data are a synthesis of the best measurements available for this site. The temperature measurement provided here is the preferred value for this site. This temperature was used to calculate the fluxes and some standard deviations. This is necessary because the sonic anemometer has a slightly temperature dependent calibration. The average soil heat flux measured at 5 mm depth is a weighted average value over three sample positions with dense, medium and sparse vegetation. A vegetation survey was made to assign weights to these three classes at this site. The spatial variability in this measurement at this (over) grazed site is particularly high and the three individual sensors commonly measure soil heat fluxes differing by factors of two or three. Some evidence suggests these data are providing a measurement of this component of the energy budget for this site which is biased low. Presumably this is because the limited number of sensors inadequately samples the points with low canopy density for this sparse crop cover.\n<br>\n\n#### fife_sur_flux_30_min_ecv_33\nSurface flux and micrometeorological measurements were collected at one site within the northwest quadrant near the center of the FIFE study area during all five of the Intensive Field Campaigns (four in 1987 and one in 1989). This site had historically been ungrazed but had recently been exposed to grazing. The station was capable of measuring the fluxes of net radiation, sensible heat and latent heat using an eddy correlation system. In addition, measurements of soil heat flux and several micrometeorological parameters were made.\n<br>\n\n#### FIFE_CD_V3_130\nThis data set provides aircraft-based NS001 Thematic Mapper Simulator (TMS) images of the study area associated with The First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) project conducted on the Konza Prairie in Kansas. The images were acquired during June 1987 to August 1989. The images in this data set were originally provided on the FIFE CD-ROM Volume 3.\n<br>\n\n#### fife_soilmstr_peck_gam_37\nAirborne soil moisture measurement is based on the difference between natural terrestrial gamma radiation flux measured for comparatively wet and dry soils. The presence of moisture in the soil causes an effective increase in the soil density resulting in an increased attenuation of the gamma flux for relatively wet soil and a correspondingly lower flux at the ground surface. As part of the FIFE experiment, natural terrestrial gamma radiation data over a network of 24 flight lines were collected. The data acquisition procedure was designed to accumulate and store spectral radiation data along a flight line from which estimates of soil moisture could be computed. Ground-based soil moisture measurements were used to make a one-time calibration of the natural terrestrial radioisotope signal over the flight line network. A time-series of airborne soil moisture measurements (to a depth of 20 cm) was compared to an extensive, independent data set of ground-based soil moisture measurements. Estimates for flight line segments were found to have an average RMS error of approximately 2.5 % soil moisture (Peck et al., 1990).\n<br>\n\n#### fife_sur_refl_gem_helo_38\nDuring the 1989 Intensive Field Campaign a Russian spectroradiometer, the Gemma, was used to collect visible and near infrared spectra of a variety of FIFE sites from a helicopter. Gemma measurements of selected study sites and laboratory measurements of a portable calibration light sphere were made. The helicopter missions were designed to provide characterizations of each FIFE site while providing FIFE study area coverage, and to provide an intermediate scale of sampling between that of the surface measurements and the higher altitude aircraft and spacecraft multispectral imaging devices. The Gemma spectroradiometer was mounted on the helicopter to allow a comparison between it and the SE-590 spectroradiometer and Modular Multiband Radiometer (MMR) over a number of sites.\n<br>\n\n#### fife_sur_met_hday_met_39\nThe FIFE Historic Daily Meteorology Data Data Set is one of the historical data sets used for the FIFE project. The data set contains data back to January, 1900. This data set was prepared for input into models, therefore, no leap days (February 29) are included. Daily weather observations of air temperature and precipitation were made by Kansas State University. The observations are made according to the procedures outlined by the National Weather Service (Anonymous 1989).\n<br>\n\n#### fife_sur_met_hmon_met_40\nThe FIFE Historic Monthly Meteorology Data Data Set is one of the historical data sets used for the FIFE project. This data set provides monthly precipitation values from January 1858 to December 1989 for Manhattan, Kansas adjacent to the FIFE study area. Daily weather observations of precipitation were made according to the procedures outlined by the National Weather Service by Kansas State University. The daily precipitation data were then summed to produce monthly precipitation.\n<br>\n\n#### fife_sur_refl_ltbr_ksu_41\nThe purpose of the Leaf Area Index and PAR Determined from KSU Light Bar Measurements study was to collect extensive non-destructive measurements of Leaf Area Index (LAI) at the flux sites during IFC-5 (August 1989). These data were collected at thirteen locations which were coincident with the surface flux measurements within the FIFE study area from July 3, 1989 through August 18, 1989. The various fractions of the Photosynthetically Active Radiation (PAR) (i.e., diffuse, reflected, transmitted and total) were measured using a Line Quantum meter from LI-COR Inc. From these fractions the ratio of reflected to total incoming PAR was computed. LAI can be estimated from light bar measurements of PAR transmittance from measurements above and below a vegetation canopy. The use of the light bar allows rapid, multiple, and repeatable measurements of LAI at the FIFE sites. This type of measurements could not be done using destructive measurements of LAI.\n<br>\n\n#### fife_sur_refl_ltbr_unl_42\nThe Leaf Area Index and PAR Determined from the UNL Light Bar Data were collected in 1987, 1988, and 1989. Incoming, reflected, and transmitted photosynthetically active radiation (PAR) was measured with a LI-COR LI-191SA line quantum sensor. Absorbed and intercepted PAR calculated from these measurements. The objectives of this research were to characterize bi-directional reflectance factor distributions, estimate surface albedo, determine the variability of reflected and emitted fluxes in selected spectral wavebands as a function of topography, vegetative community and management practice, determine the influence of plant water status on surface reflectance factors, and determine sun angle affects on radiation fluxes.\n<br>\n\n#### fife_sur_refl_lightwnd_43\nThe prairie is inherently variable and large numbers of samples are needed to obtain reliable estimates of the prairie agronomic characteristics. For the Indirect Leaf Area Index Obtained from the KSU Light Wand Study a limited number of destructive samples were supplemented by large numbers of rapid non-destructive estimates. For the non-destructive measurements the LI-COR Inc. LAI-2000 Plant Canopy Analyzer (light-wand) was used. This instrument measures transmittance of the canopy in the blue region of the spectrum, at five different zenith angles. These measurements were inverted to provide estimates of leaf area index, and mean inclination angle of the leaves to the zenith. Verification studies conducted by LI-COR Inc. indicated that the LAI-2000 Canopy Analyzer measurements were generally within 15% of values obtained by destructive sampling and measurement with a leaf area meter. As a rough guess even the destructive measurements are accurate only to within about 25% of the true sample leaf area value. In the opinion of the study team, the non-destructive measurements with the LAI-2000 are as good as the destructive measurements, and provide a better estimate of the site mean, because many more non-destructive measurement can be made.\n<br>\n\n#### fife_biology_leaf_ang_44\nThe Leaf Angle Data Data Set contains leaf angle distributions (LAD) obtained during the 1987 growing season for ten types of plant canopies, from the Konza Long-Term Ecological Research (LTER) area. These data were collected using a direct measurement technique (i.e., a Spatial Coordinate Apparatus (SCA)). The species selected were major species common on the prairie with the leaves were of sufficient size to allow SCA measurement. The objective of this study was to obtain detailed LAD information on the major canopy species of the tallgrass prairie and selected agricultural crops. The LAD information for specific canopies can be used as input for a canopy radiation model. Canopy leaf orientation is an important parameter for plant growth modeling. Four categories of zenith angle distributions were found among the 14 species. These were planophile, plagiophile, erectophile, and uniform. Some canopies were found to have non-uniform leaf azimuth angle distribution. Also there were deferences between the upper and lower parts of the canopies for some species.\n<br>\n\n#### fife_biology_pho_leaf_46\nThe objective of the Leaf Photosynthesis study was to measure the response of leaf photosynthesis and stomatal conductance to light, temperature, vapor pressure deficit, carbon dioxide and water potential for the most abundant C4 species at the FIFE study area. To this end, photosynthesis measurements were made on 6 days in June, July and August of 1987 at three different locations in the northwest quadrant of the FIFE study area. Leaf photosynthetic rate is measured by enclosing a leaf in a closed, transparent chamber and measuring the decrease in carbon dioxide concentration as a function of time. Light flux density is measured outside of the chamber and must be corrected for the chamber transmittance, which is 0.9. These data can be fit to various models of leaf photosynthesis and stomatal conductance by providing the responses to light, temperature leaf water potential, and carbon dioxide under field conditions on intact plants.\n<br>\n\n#### fife_sur_refl_unl_long_49\nThe Incoming Longwave Radiation Data from UNL Data Set was collected as part of a study of thermal radiant energy from vegetative canopies. These data were collected during the growing season of 1987 and 1989. The data measurements were made at 13 stations within 12 sitegrids scattered throughout the FIFE study area. Values for incoming longwave radiation were calculated using the radiometer chopper or detector temperature as a measure of air temperature. When determining surface temperatures from infrared thermometer measurements of the surface, the surface emissivity and the reflected component must be taken into account. The reflected component is dependent on the surface emissivity and the incoming longwave radiation.\n<br>\n\n#### fife_sur_refl_mmr_calb_51\nThe MMR Calibration Data Set contains radiance data collected in the summer of 1987 and in July and August of 1989 via a Modular Multiband Radiometer (MMR) instrument. The MMR instrument monitored a nearly lambertian calibration panel stationed near the center of the FIFE study area. The radiances recorded from this instrument can be used to monitor solar insolation and clouds. In some cases, these data were also used to calculate the reflectance factor for reflective radiances measured over vegetation using other MMR instruments located at other FIFE sites or mounted on a helicopter.\n<br>\n\n#### fife_sur_refl_mmr_grnd_52\nSurface reflectance factors, radiances, and temperatures were measured with a Mast-borne Modular Multiband Radiometer (MMR), predominantly in the solar principal plane, with nadir and off-nadir, view-zenith angles. The MMR was mounted on a portable mast in order to achieve a spatial sampling at a variety of sites as well as within each site. The portable mast alignment varied from the solar principal plane, to the azimuthal plane aligned perpendicular to the principal plane and aligned with the satellite azimuthal plane. Measurements were periodically collected with the MMR over a barium sulfate reference panel. Measurements were typically coordinated with aircraft and/or satellite overpasses. Solar radiation data at or near the specific site should be used to screen possible times of variable cloud cover.\n<br>\n\n#### fife_sur_refl_mmr_helo_53\nThe Surface Reflectance Measured with a Helicopter-borne MMR Data Set contains surface reflectance measurements in Landsat-TM bands at low to intermediate altitudes. The helicopter operated during all Intensive Field Campaigns (IFCs) and was available to support all satellite overpasses. The average flight time was 2 hours, during which an average of 11 FIFE sites plus one special target were covered. The helicopter missions were designed to provide a rapid means of intensively spectrally characterizing each FIFE site while providing FIFE study area coverage, and to provide an intermediate scale of sampling between that of the surface measurements and the higher altitude aircraft and spacecraft multispectral imaging devices. The Modular Multiband Radiometer (MMR) instrumentation was chosen to provide compatibility with surface-based radiometers and TM spacecraft sensors. Off-nadir measurements were made as a means of providing more accurate estimates of hemispherical reflectance and for use with bi-directional reflectance models.\n<br>\n\n#### fife_sur_refl_mmr_leaf_54\nThe Leaf Optical Properties from UNL Data Set contains leaf-level spectral observations acquired in situ with the Nebraska Multiband Leaf Radiometer (NMLR) coupled with a LiCor LI-1800-12 integrating sphere. The NMLR measured leaf reflectance and transmittance in the seven MMR bands. Data were collected in 1987, 1988, and 1989. During 1987, measurements were always made on the most recently expanded leaf of the selected plant. Measurements were made on a variety of forbs and grasses. During 1988, measurements were made on the most recently expanded leaf of the selected plant unless specified. Measurements were also made of older green, yellow and brown leaves on a plant. Measurements were usually made on grasses (i.e., Indian grass, Switch grass and Big bluestem). A few forbs were measured. The same leaf was sometimes measured throughout the day. During 1989, measurements were usually made on the most recently expanded leaf of the selected plant unless specified. Typically, leaves of the dominant grass species at a site were measured. At least two samples of each species were measured. Typically, during all collections (i.e., 1987 - 1989) an external light source with a restricted beam spot (slitted illuminator) was used to restrict the illumination spot on narrow grass leaves so that only leaf material was illuminated.\n<br>\n\n#### fife_biology_mow_biop_55\nThe Mowing Experiment Biophysical Measurements data set was collected to quantify the effects of grazing and nitrogen fertilization on primary productivity and plant chemistry. The data in this data set quantified the effects of foliage removal on plant net primary productivity (NPP), plant nutrient content and the effects of grazing pressure as simulated by mowing. Mean values and their variances are reported. Standing crop values reflect treatment effects of removing biomass periodically, but the productivity levels show the inverse effects, suggesting plant compensatory growth mechanisms. Grazing intensity was defined as the amount of leaf area remaining following defoliation. The latter was manipulated experimentally by mowing at several heights. Grazing frequency was defined as the number of times foliage removal occurred in each year and included grazing and mowing history as well as current mowing frequency.\n<br>\n\n#### fife_biology_mow_exo_56\nLight radiation striking a vegetative canopy interacts with individual phyto-elements (i.e., leaves, stems, branches) and the underlying substrate. The interaction depends on light quality, radiative form (direct or diffuse), illumination incidence angle, vegetative component optical properties and canopy architecture. Radiation is reflected, transmitted or absorbed. Mowing, grazing, and fertilization can affect the canopy architecture or optical properties of vegetation, thus changing the canopy reflectance. This study examined the response of spectral reflectance characteristics (using an Exotech radiometer) to canopies that were manipulated using simulated grazing and fertilization of plots. The spectral reflectance data set supports the original hypothesis of a curvilinear relationship between productivity and grazing intensity. Reflectances for the four MSS bands and the standard error for each are reported. These data were collected at two locations within the northwest quadrant of the FIFE study area during the growing season of 1987. Reflected radiation measurements were converted to radiances and reflectance factor. The reflectance factor is the ratio of the target reflected radiant flux to an ideal radiant flux reflected by an ideal Lambertian standard surface irradiated in exactly the same way as the target.\n<br>\n\n#### fife_atmos_nmc_upr_57\nThe Upper Air Derivative Data from NMC Data Set was derived from National Meteorological Center global upper air models. These models use a 6 hours intermittent assimilation method. In this method, the objective analysis is performed every 6 hours using a 6 hours forecast as an initial guess (Kanamitsu 1989). The National Meteorological Center (NMC) gridded upper air data was extracted from the NOAA operational analysis system and transmitted to the FIS. This contained spatially interpolated NMC upper air data calculated for four grid points of 381 km polar-stereograph over the FIFE area. FIS considers this a derived data set (i.e., not from original measurements).\n<br>\n\n#### fife_sur_met_noaa_sur_58\nThe FIFE Staff Science effort included the acquisition, processing and archiving of meteorological parameters of the atmosphere above the FIFE study area, which would furnish surface meteorological parameters from hourly reporting network for the FIFE area, and provide input data and/or verification data for numerical simulation models. Though the measurements presented in this data set were not taken precisely at the FIFE site, it is hypothesized that they present a representative horizontal cross-section of meteorological variables and sky conditions in and around the site. It is also realized that many of the variables presented in this data set are somewhat subjective and dependent on the skill (and biases) of the observer, such as estimates of cloud amount and height. The NOAA regional surface reports were extracted from the NOAA operational analysis system and transmitted to the FIS. This contained hourly surface meteorological data from selected stations as received from NESDIS for FIFE.\n<br>\n\n#### fife_sat_obs_ns001tms_59\nAs part of the FIFE staff science data processing effort, the FIFE Information System (FIS) extracted site average radiances from the level-1 NS001-TMS products. Data were collected by the NS001 during each of the FIFE IFC's. Selected flights were processed to level-1. The site averages were extracted from these processed images. Therefore, this data set contains a small number of observation dates for each site, but at the multiple angles provided by the grid pattern used during each flight. The data set can be used for canopy reflectance modeling studies. The site average radiances extracted from the NS001 imagery are instrument-corrected spectral radiances for each of the eight spectral bands. Geographic location and viewing and solar angles for each of 39 FIFE ground measurement sites are also included for each observation. The sensor calibrated radiance values were corrected using atmospheric aerosol optical thickness and gaseous absorption profile measurements, when available. The atmospheric correction algorithm of Fraser et al. (1989) was used to calculate reflectance in the visible and infrared channels. The thermal data are corrected using parameters derived from the Lowtran7 atmospheric path radiance model (Kneizys et al., 1988).\n<br>\n\n#### fife_optical_ot_calib_60\nThe objective of the Optical Thickness Calibration study was to compare aerosol optical thickness measurements derived from three different groups. These groups measured atmospheric transmission, in particular, the aerosol optical thickness, using at least three different instruments (e.g., a solar transmissometer SXM-2, a Reagan sunphotometer, and an airborne tracking sunphotometer) and three different analysis procedures.\n<br>\n\n#### fife_optical_ot_brug_62\nThe NIPS and Reagan Sunphotometer Optical Thickness study compared various ground and image-based techniques used to characterize the atmosphere. These data are used to remove atmospheric absorption and scattering from remote sensing scenes so that surface parameters can be retrieved. An evaluation of the effects of uncorrected atmospheric absorption and scattering on various vegetation indices and subsequent biophysical parameter estimations was also undertaken. These data can also be used to derive aerosol size distribution (King et al., 1978) and thereby estimate the phase function. Aerosol optical depths were recorded at various locations within the FIFE site. A Normal Incident Pyrheliometer (NIP) and a Reagan sunphotometer was used to collect data during the IFCs. These data showed that daily averages span a range of 0.05 to 0.28 in the mid-visible wavelength (Bruegge et al., 1992a). Diurnal variations were recorded. The afternoon optical depths are greater than those of the morning by as much as 0.07. These data are analyzed using the Langley technique. Rayleigh optical depth is subtracted, and aerosol, ozone, and water vapor abundance's simultaneously measured. In retrieving ozone, a Junge aerosol model is assumed, thus, the natural log of aerosol optical depth is linear with wavelength (Bruegge et al., 1992a). This contrasts with other experimental approaches used by investigators in which an ozone abundance is assumed (Halthore and Markham 1992). This approach allows measurement of aerosol, but is limited by the accuracy of the ozone data.\n<br>\n\n#### fife_optical_ot_c130_63\nThe data in the Sunphotometer Optical Thickness Data from C130 Aircraft data set were collected in June, July and August 1987, and in August 1989. The data was collected at selected locations within the FIFE study area. Atmospheric optical depths derived from measurements of solar radiation by the airborne suntracking sunphotometer are available in this data set. These data are necessary for atmospheric correction of data from Earth viewing airborne and satellite sensors in the visible and near infrared regions of the electromagnetic spectrum. The data show that atmospheric optical depth changes significantly both spatially and temporally. Variability in atmospheric optical properties and substantial differences in atmospheric optical properties during the data collection, emphasize the need to make quantitative measurements of atmospheric optical properties at the time of remote sensing data acquisition.\n<br>\n\n#### fife_optical_ot_frasr_64\nThe Solar Transmissometer Aerosol Optical Thickness Data Set contains optical thickness data that provide a measure of the effect of aerosols on the attenuation of radiation through the atmosphere at 8 discrete wavelength bands throughout the visible and near IR portion of the electromagnetic spectrum. These data were collected using a ground-based solar transmissometer in June and July of 1987, and July and August of 1989, at two stations in the FIFE study area. These data are used to provide atmospheric correction of remotely sensed data using radiative transfer models and to study aerosol particle size distribution (see Halthore and Markham 1992; King et al. 1978). These data are also used to infer the optical clarity of the atmosphere.\n<br>\n\n#### fife_optical_ot_staff_66\nAerosol optical thickness in conjunction with an atmospheric model can provide estimates of atmospheric effects on transmitted and reflected solar radiation. These effects can then be used to correct aircraft and satellite radiometric data. In FIFE, three sunphotometers were used to track the sun through a range of airmasses during the period of February 6, 1987 through October 31, 1989. The Aerosol Optical Thickness from GSFC Data Set were analyzed using the Langley technique. Rayleigh optical depth was subtracted, and aerosol, ozone, and water vapor abundance's simultaneously measured. In retrieving ozone a Junge aerosol model was assumed, thus the natural log of aerosol optical depth was linear with wavelength (Bruegge et al. 1992). This approach allows measurement of aerosol, but is limited by the accuracy of the ozone data.\n<br>\n\n#### fife_sur_refl_parabola_68\nThe Surface Reflectances Measured by the PARABOLA Data Set contains measurements from the Portable Apparatus or Rapid Acquisitions of Bi-directional Observations of Land and Atmosphere (PARABOLA) instrument. The focus of this research was to characterize the variation in vegetation reflectance as a function of solar and sensor viewing geometry, wavelength, and plant canopy biophysical characteristics. An understanding of these relationships is necessary for meaningful biophysical and ecological interpretations of measurements acquired from airborne and satellite sensors. The PARABOLA is able to measure these variations in reflectance because it measures at different viewing angles and at 3 spectral bands. The data are averaged reflectance factors of the Konza Prairie at different view angles and at 3 wavelength bands throughout the day. PARABOLA measurements were made during each of the 5 FIFE Intensive Field Campaigns from five locations within the FIFE study area.\n<br>\n\n#### fife_biology_plantpro_69\nThe focus of this study was to quantify the effects of foliage removal by cattle on plant net primary productivity (NPP). The Vegetation Biomass, Production and Consumption at Selected Sites Data Set contains mean values and their variances. During the growing season of 1987, portable cattle exclosures were used to quantify above-ground plant biomass dynamics at each of four sites. All sites had been grazed each year and burned frequently during the preceding 10 years. Biomass was measured inside portable exclosures, outside exclosures (in unprotected vegetation), and inside permanent exclosures. Exclosures were moved to previously unsampled locations within a distance of 10 m after samples were obtained, and these remained in place until the next sampling date.\n<br>\n\n#### fife_sur_refl_irt_helo_70\nThe Surface Radiant Temperature Measured with a Helicopter-borne Infrared Thermometer Data Set were collected for six days during July and August of 1989 to provide the radiant temperature of the FIFE sites and as a check of the thermal band on the MMR. The average and standard deviation of radiant temperature were measured with an Everest infrared thermometer. The Everest Series 4000 Infrared Thermometer (IRT) was mounted on the NASA Bell UH-1B helicopter in conjunction with the Barnes Multiband Modular Radiometer (MMR) and the Spectron Engineering SE590 Spectroradiometer for the 1989 field campaign. The IRT collected radiant temperature data as the helicopter hovered over individual sites within the FIFE study area.\n<br>\n\n#### fife_sur_refl_irt_mult_71\nThe Surface Temperatures Measured at Multiple Angles Data Set was collected at two locations within the northwest quadrant of the FIFE study area during July and August 1989. The data set contains hemispherical surface temperature, surface temperatures measured at several view zenith angles, and surface temperatures and at-view azimuth increments of 45 degrees. These data were collected using the Everest multiplexed infrared thermometers (IRT) Model 4000 and an Eppley Precision Infrared Radiometer Model PIR. Periodically measurements of the surface emissivity and incoming longwave radiation were also made. The purpose of this study was to characterize bi-directional reflectance factor distributions, estimate surface albedo from bi-directional reflectance factor and radiance data, determine the variability of reflected and emitted fluxes in selected spectral wavebands as a function of topography, vegetative community and management practice, determine the influence of plant water status on surface reflectance factors, and determine sun angle affects on radiation fluxes.\n<br>\n\n#### fife_sur_refl_irt_grnd_72\nThe Surface Temperatures from UNL Data Set contains surface temperatures collected between July 15 and August 11, 1989 at three FIFE area sites. These surface temperatures were measured with an Everest multiplexed infrared thermometer (IRT) Model 4000 predominantly in the solar principal plane, with nadir and off-nadir, view-zenith angles (mounted on the portable mast with the Barnes Model 12-1000 Modular Multiband Radiometer (MMR)). The purpose of this study was to determine the variability of emitted fluxes as a function of topography, vegetative community and management practice. Spatial and temporal sampling at sites 906 (2133-EVN), 916 (4439-EVN), and 966 (2437-EVN) was achieved. Measurements were typically coordinated with aircraft and/or satellite overpasses.\n<br>\n\n#### fife_atmos_noaa_son_73\nThe NOAA Radiosonde Observations Data Set contains data that were extracted from the NOAA operational analysis system and transmitted to the FIS. Data are available from July 1985 to October 1988, there are 1123 days of data during this period with data at twelve hour intervals. These data were collected using sondes released in Dodge City and Topeka, Kansas, 337 km and 68 km, respectively, from the FIFE site. Radiosonde observations were made to determine the pressure, temperature, and humidity from the surface to the point where the sounding was terminated.\n<br>\n\n#### fife_biology_root_bio_75\nThe FIFE Root Biomass data were collected from 16 locations within the FIFE study area during the 1987 growing season. They provide a measure of the below-ground biomass for the study area. Biomass reported as grams per square m assumes that the depth of the core samples is sufficient to include all root biomass under the surface to an infinite depth. Prairie vegetation does possess roots deeper than the 20 cm coring, however, the fraction of total root biomass below 20 cm is minuscule and safely ignored in a study of biomass.\n<br>\n\n#### fife_sat_obs_sat_coef_76\nThe atmospheric effects on the transmitted and reflected solar radiation should be factored into the estimation of geophysical and biophysical parameters from remotely-sensed data, so that appropriate correction schemes can be employed to infer reflectivity of the ground from satellite radiometric data. Some of the correction techniques require derived coefficients as inputs in the algorithms that perform the atmospheric correction. As part of the FIFE staff science data collection effort, the FIFE Information System (FIS) utilized atmospheric correction and related algorithms to generate coefficients for deriving corrected values from the FIFE level-1 image products. These coefficients were used by FIFE staff in calculating site reflectances from pixel values extracted from the level-1 imagery. The Fraser (Fraser et al., 1992) and LOWTRAN 7 (Kneizys et al., 1988) models were used for computation of coefficients used to correct radiances of scattered radiation measured by aircraft and/or satellite during FIFE. The Fraser algorithm is designed to compute the surface reflectance for a given measured radiance, or alternatively, the upward radiance at an arbitrary height when the surface reflectance is given. LOWTRAN 7 is a low-resolution propagation model and computer code for predicting atmospheric transmittance and background radiance from 0 to 50,000 [cm^-1] at a resolution of 20 [cm^-1].\n<br>\n\n#### fife_sat_obs_sat_avhr_77\nThe Advanced Very High Resolution Radiometer (AVHRR) is a four- or five-channel scanning radiometer capable of providing global daytime and nighttime sea-surface temperature and information about ice, snow, and clouds. The sensor measures emitted and reflected radiation in five channels (bands) of the electromagnetic spectrum. The Site Average Reflectances Extracted from AVHRR-LAC Imagery Data Set consists of averages of pixel extracts from AVHRR-LAC (1 km resolution) scenes that overlay the FIFE site. Average radiances for dates are available for the five sensor wavebands and average reflectance and exoatmospheric reflectances are available for wavebands 1 and 2. Site averages are clustered in 1987 and during the summer of 1989. Some data are also available for early 1988. The AVHRR is capable of operating in both real-time or recorded modes. Direct readout data were transmitted to ground stations of the automatic picture transmission (APT) class at low-resolution (4x4 km) and to ground stations of the high-resolution picture transmission (HRPT) class at high resolution (1x1 km). Data recorded on board were available for processing in the NOAA Central Computer Facility. They included local area coverage (LAC) data which were from selected portions of each orbit with a 1x1 km resolution. The precision of satellite remote sensing estimates of surface reflectance (Hall et al., 1992), calibrated and corrected for atmospheric effects, was no worse than about 1 percent absolute.\n<br>\n\n#### fife_sat_obs_sat_ltm_78\nThe Thematic Mapper sensor system was used to collect the original data between February 1987 and October 1989 from which this data set was produced. Landsat TM extract data contains the average instrument corrected spectral radiances for each of the seven spectral bands. In addition, the associated view and solar angles are available for each of 39 FIFE ground measurement sites. The Site Reflectances Extracted from Landsat TM Imagery Data Set also contains reflectance values and exoatmospheric reflectance values for these seven spectral bands. These reflectances were derived using the sensor calibrated radiances which were corrected for exoatmospheric effects using atmospheric aerosol optical thickness and gaseous absorption profile measurements, when available. The atmospheric correction algorithm of Fraser et al. (1989) was used to calculate reflectance in the visible and infrared channels. The thermal data were corrected using parameters derived from the Lowtran-7 atmospheric path radiance model (Kneizys et al. 1988).\n<br>\n\n#### fife_sat_obs_sat_spot_79\nThe Site Reflectances Extracted from SPOT HRV Imagery Data Set contains the average instrument corrected spectral radiances for each of the spectral bands (3 in XS and 1 in PAN) collected during the growing seasons of 1987, 1988, and 1989. In addition, the associated view angles and solar angles are available for each of 39 FIFE ground measurement sites. The data set also contains reflectances and exoatmospheric reflectances for these spectral bands. These reflectances were derived using the sensor calibrated radiance values corrected using atmospheric aerosol optical thickness and gaseous absorption profile measurements, when available. The atmospheric correction algorithm of Fraser et al. (1989) was used to calculate reflectance in the visible and infrared channels.\n<br>\n\n#### fife_sur_refl_se5_gsfc_81\nThe SE-590 Spectroradiometer Reflectance Factors from GSFC Data Set contains spectral data collected with the Spectron SE-590 Spectral Radiometer at selected FIFE sites located primarily on the Konza Prairie. These measurements were acquired in conjunction with the Surface Reflectances measured by the PARABOLA bi-directional measurements. Ground SE-590 data were acquired in all four 1987 Intensive Field Campaigns and in the 1989 Intensive Field Campaign. The ground SE-590 data were collected at approximately every 10 degree change in solar zenith angle (SZA) to characterize diurnal variations and/or simultaneous observations acquired by helicopter, airplane, or satellite over flights. The data were collected as wavelength intensity values which were converted to spectral radiances with instrument and campaign-specific calibration coefficients.\n<br>\n\n#### fife_sur_refl_se5_unl_82\nThe SE-590 Reflectance Factors and Radiances from UNL Data Set contains surface reflectance and viewing angle data that was collected at three sites within the FIFE study area via a SE590 mounted on a portable mast. All measurements were made on eleven days between July 15 and August 11, 1989. Measurements were typically coordinated with aircraft and/or satellite overpasses. On days when measurements were not made the bare soil was covered with a plastic mulch that allowed moisture to penetrate the surface but hindered the regrowth of the vegetation. Solar radiation data at or near the specific site should be used to screen possible times of variable cloud cover. Canopy, illumination, and viewing geometry are critical in determining the amount of reflected radiation received at the sensor. The measurements were predominantly made in the solar principal plane since the greatest variation in observed reflected radiation is expected to occur in that plane due to extremes in sunlit and shaded portions of the canopy (Norman and Walthall 1985). Reflected radiation measurements were converted to radiances and reflectance factor. Reflected radiation from a field reference panel corrected for non-perfect reflectance and sun angle was used as an estimate of the ideal Lambertian standard surface (Walter-Shea and Biehl 1990).\n<br>\n\n#### fife_sur_refl_se5_leaf_85\nThe SE-590 Leaf Level Spectral Observations from GSFC Data Set were acquired in situ with a Spectron SE590 spectroradiometer fitted with the 1 degree IFOV lens, and coupled with a LI-COR integrating sphere. The purpose in collecting SE590 leaf reflectance and transmittance data was to characterize the optical properties of the canopy components to gain a better understanding of how these optical properties contribute to canopy reflection and absorption of radiation. To measure the reflectance and transmittance of leaf surfaces an integrating sphere was used. The integrating sphere collected all of the radiation reflected from or transmitted through a surface. These data are the average spectral optical properties (i.e., reflectance, transmittance) and the standard deviations for the three dominant species found on each of three sites: 916 (i.e., Big Bluestem, Indiangrass, and Switchgrass), 906 (i.e., Big Bluestem, Indiangrass, and Switchgrass), and 26 (i.e., Big Bluestem, Lovegrass and Dropseed) during late July and early August, 1989. The average spectral reflectance and transmittance represent the mean values for the adaxial (top) and abaxial (bottom) sides of 4 - 10 leaves for wavelengths between 400 - 1050 nm at approximately 3 nm intervals.\n<br>\n\n#### fife_sur_refl_se5_helo_87\nThe SE-590 Reflectance Factors and Radiances Measured from a Helicopter Data Set were collected using the helicopter-borne SE-590 during Intensive Field Campaign 5 (IFC-5) in 1989. These data were collected at 17 different grid locations within the FIFE study area. Data were collected on 6 days from July 28, 1989 through August 8, 1989, when sky conditions were clear. The helicopter missions were designed to provide a means of spectrally characterizing each FIFE site and provide an intermediate scale of sampling between that of the surface measurements and the higher altitude aircraft and spacecraft multispectral imaging devices. The SE-590 instrumentation was chosen to provide compatibility with surface-based radiometers and TM spacecraft sensors. Off-nadir measurements were made as a means of providing more accurate estimates of hemispherical reflectance and for use with bi-directional reflectance models.\n<br>\n\n#### ffo_Betts_1987_ams_88\nThe Site Averaged AMS Data: 1987 (Betts) Data Set contains the site averaged product of the Portable Automatic Meteorological Station (AMS) data acquired during the 1987-1989 FIFE experiment. Data are in 30 minute time intervals in 1987.\n<br>\n\n#### ffo_Betts_1987-1989_ams_89\nThe Site Averaged AMS Data: 1987 - 1989 (Betts) Data Set contains the site averaged product of the Portable Automatic Meteorological Station (AMS) data acquired during the 1987-1989 FIFE experiment. Data are in 30 minute time intervals in 1987.\n<br>\n\n#### ffo_Betts_1988_ams_90\nThe Site Averaged AMS Data: 1988 (Betts) Data Set contains the site averaged product of the Portable Automatic Meteorological Station (AMS) data acquired during the 1987-1989 FIFE experiment. Data are in 30 minute time intervals in 1988.\n<br>\n\n#### ffo_Betts_1989_ams_91\nThe Site Averaged AMS Data: 1989 (Betts) Data Set contains the site averaged product of the Portable Automatic Meteorological Station (AMS) data acquired during the 1987-1989 FIFE experiment. Data are in 30 minute time intervals in 1989.\n<br>\n\n#### ffo_Betts_1987_afd_92\nThe Site Averaged Flux Data: 1987 (Betts) Data Set contains the site averaged product data collected by many PIs during the 1987-1989 FIFE experiment. This data set is a time series of 30-minute average variables for the periods May 27, 1987 - Oct 16, 1987.\n<br>\n\n#### ffo_Betts_1987-1989_afd_93\nThe Site Averaged Flux Data: 1987 (Betts) Data Set contains the site averaged product data collected by many PIs during the 1987-1989 FIFE experiment. Data are in 30 minute time intervals in 1987 and include the entire period 1987-1989.\n<br>\n\n#### ffo_Betts_1988_afd_94\nThe Site Averaged Flux Data: 1988 (Betts) Data Set contains the site averaged product data collected by many PIs during the 1987-1989 FIFE experiment. Data are in 30 minute time intervals and include data only for 1988.\n<br>\n\n#### ffo_Betts_1989_afd_95\nThe Site Averaged Flux Data: 1987 (Betts) Data Set contains the site averaged product data collected by many PIs during the 1987-1989 FIFE experiment. Data are in 30 minute time intervals and include data only for 1989.\n<br>\n\n#### ffo_Betts_1987_gsm_96\nThe Site Averaged Gravimetric Soil Moisture Data: 1987 (Betts) Data Set contains the site averaged product data collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. This data set includes only 1987 data.\n<br>\n\n#### ffo_Betts_1987-1989_gsm_97\nThe Site Averaged Gravimetric Soil Moisture Data: 1987 - 1989 (Betts) Data Set contains the site averaged product data collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. This data set includes data from May 20, 1987 through August 12, 1989.\n<br>\n\n#### ffo_Betts_1988_gsm_98\nThe Site Averaged Gravimetric Soil Moisture Data: 1988 (Betts) Data Set contains the site averaged product data collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. This data set includes only 1988 data.\n<br>\n\n#### ffo_Betts_1987_nsm_100\nThe Site Averaged Neutron Soil Moisture Data: 1987 (Betts) Data Set contains the site averaged product data of the neutron probe soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. This data set includes only 1987 data.\n<br>\n\n#### ffo_Betts_1987-1989_nsm_101\nThe Site Averaged Neutron Soil Moisture Data: 1987 - 1989 (Betts) Data Set contains the site averaged product data of the neutron probe soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. This data set includes the 1987 - 1989 data.\n<br>\n\n#### ffo_Betts_1988_nsm_102\nThe Site Averaged Neutron Soil Moisture Data: 1988 (Betts) Data Set contains the site averaged product data of the neutron probe soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. This data set includes only 1988 data.\n<br>\n\n#### ffo_Betts_1989_nsm_103\nThe Site Averaged Neutron Soil Moisture Data: 1989 (Betts) Data Set contains the site averaged product data of the neutron probe soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. This data set includes only 1989 data.\n<br>\n\n#### fife_soilprop_soildens_104\nSoil bulk density is defined as the ratio of the mass of dry solids to the bulk volume of the soil occupied by those dry solids. Bulk density of the soil is an important site characterization parameter since it changes for a given soil. It varies with structural condition of the soil, particularly that related to packing. The Soil Bulk Density Data Set contains bulk density of the soil based on dry weight at two depths, 0-10 cm and 10-20 cm. Samples were collected at 31 different locations within the FIFE study area during the growing season of 1987. Samples were collected primarily in the northwest quadrant of the study area but at least one sitegrid is located in each of the quadrants of the study area.\n<br>\n\n#### fife_biology_soil_co2_105\nIn the Soil Carbon Dioxide Flux study, a prototype gas exchange system and sensor were used to determine the soil surface flux of CO2 and associated parameters at the three FIFE supersites. The goal of this investigation was to characterize fluxes of carbon dioxide from the surface of the soil for a representative portion of the FIFE study area. These measurements are required to understand the carbon budget of the prairie and necessary for comparing vegetation models of photosynthesis with CO2 flux measurements by micrometeorological methods. The flux of the carbon dioxide from the surface of the soil is an important component of the carbon budget of a prairie ecosystem. The results from this study indicate that a soil chamber can be used to obtain reasonable estimates of soil surface carbon dioxide fluxes when operated in a closed system that is ported to the free atmosphere. Further, the flux of carbon dioxide from the soil surface of a grassland can be a large part of the carbon budget and should never be assumed to be negligible. Both soil temperature and soil water content are critical parameters for predicting soil surface CO2 flux, and leaf area index is a surrogate for the plant contribution through root respiration.\n<br>\n\n#### fife_biology_soil_gas_106\nNitrogen gas fluxes are important to ecosystem productivity and atmospheric chemistry. Scaling of these microscale fluxes to landscape and regional scales relevant to ecosystem and atmosphere-biosphere exchange questions is difficult. For FIFE, two approaches were explored to accomplish scaling. First the relationships between hourly and daily gas fluxes and soil moisture were established and then large area estimates of soil moisture from simulation models or a push broom microwave radiometer were used to scale data from experimental sites to larger areas. The second approach was to establish relationships between annual gas fluxes and plant productivity and then use large area data on plant productivity derived from SPOT images as a scaling tool. Both approaches were based on hypotheses and previous studies that established strong relationships between soil moisture and plant productivity and gas fluxes. FIFE Soil Gas Fluxes Using Soil Cores Data Set contains the daily flux rates of denitrification, nitrous oxide flux and carbon dioxide flux obtained from 10 sites at four sampling dates during 1987. Soil gas fluxes were measured using an intact extracted core technique. The data set includes estimates of in situ fluxes as well as denitrification fluxes measured in cores amended with either water or water plus nitrate. Analysis of relationships between daily flux rates and soil moisture and between annual fluxes and plant productivity are reported elsewhere (Groffman and Turner submitted to Ecology, Groffman and Wood in preparation). Analysis of the denitrification data, and evaluation of denitrification fluxes in the context of the ecosystem ecology of the FIFE study area are presented in Groffman et al. (1992).\n<br>\n\n#### fife_soilprop_soilhydc_107\nThe Soil Hydraulic Conductivity Data Set contains soil hydraulic conductivity, matric flux potential, and soil depth data collected during the 1989 FIFE soil properties investigation. The purpose of the 1989 FIFE soil properties investigation was to obtain more points on the soil moisture release curves for the soils at the FIFE stations, and provide values for the saturated hydraulic conductivity for the long-term water balance studies. In-situ measurements of field-saturated hydraulic conductivity were made using the constant well head permeameter method. These measurements were made at five sites, each representing a different soil series. The constant well head method for hydraulic conductivity involves augering a hole to the desired depth and measuring the steady state flow rate of water into the hole while maintaining a constant head of water inside the hole. Six measurements were made at each of the two soil depths at each site. The hydraulic conductivity measurements were made at the same depths and close to (less than 1 m away from) the location where samples for moisture release measurements were taken.\n<br>\n\n#### fife_soilmstr_soil_imp_108\nThe purpose of this research was to characterize the soil moisture distribution in the FIFE study area. Daily measurements of the soil dielectric properties were obtained at five locations throughout the FIFE study area during the 1987 Intensive Field Campaigns (IFC). Calculated soil volumetric water contents were compared with gravimetric soil moisture measurements collected at the same locations by the FIFE staff science team. Examination of the data revealed that the impedance probe is a more consistent source of time series information than traditional measurements, and is potentially more closely linked to the physical parameters. The dielectric constant of soil is a potentially sensitive indicator of soil moisture. Since, the magnetic permeability of all naturally occurring soils is near that of free space, dielectric measurements serve to fully characterize the electromagnetic response of soils. Many of the indirect methods of soil moisture measurement permit frequent or continuous measurements in the same place with only small expenditure of time. Thus, changes in water content with time can be approximated. The soil impedance is sensitive to the moisture content of the soil and can be used to calculate the volumetric water content of the soil. Soil impedence techniques using probes have been demonstrated to show small-scale diurnal variations that would be completely missed by small-scale spatial variations in the gravimetric sampling scheme. Furthermore, the basically non-destructive nature of the fixed probes minimize the impact of the sampling technique on the dynamic behavior of the region under study.\n<br>\n\n#### fife_soilmstr_peck_sm_109\nWater content measurements by gravimetric methods involve weighing a wet sample, removing the water via drying in an oven, and reweighing the sample to determine the amount of water removed. Water content then is obtained by dividing the difference between wet and dry masses by the mass of the dry sample to obtain the ratio of the mass of water to the mass of dry soil. When multiplied by 100, this becomes the percentage of water in the sample on a dry-mass (or, as often expressed, on a dry-weight) basis. Soil moisture determined using the gravimetric method was measured at 800 sites along 24 transects. These transects were over flown by the airborne Gamma Radiation System used to measure soil moisture. These data are useful for comparison of airborne and ground soil moisture data. This analysis for the airborne Gamma Radiation System, using completely independent soil moisture data showed that the root mean square error of 97 flights was 3.02 percent soil moisture, with a bias of less than 0.5 percent soil moisture (Carroll et al., 1988).\n<br>\n\n#### fife_soilmstr_sm_grav_110\nThe gravimetrical soil moisture data were collected from many stations spread over the FIFE study area. These data were collected to characterize the spatial and temporal patterns of moisture content of the soils over this area during and between the FIFE Intensive Field Campaigns. The aim of the FIFE soil moisture work was to characterize spatial and temporal patterns of soil moisture at the FIFE site, to validate and calibrate remote sensing measurements of soil water, and to evaluate alternative methods of measuring soil moisture both from the air and on the ground. A further goal was to develop techniques for comparing point and spatially continuous measurements of soil moisture. The FIFE soil moisture research was designed to advance the technology for characterizing soil moisture and contribute useful data to other FIFE investigations.\n<br>\n\n#### fife_soilmstr_sm_neut_111\nThe neutron-probe data present a series of measurements of volumetric water content in the soil profile compiled using the neutron method. These data were collected from throughout the FIFE study area from May 1987 through August 1989. The neutron method of measuring soil water content uses the principle of neutron thermalization. When both hydrogen and oxygen are considered, water has a marked effect on slowing or thermalizing neutrons. Thermal neutron density is easily measured with a detector, if the capture cross-section remains constant then the thermal neutron density may be calibrated against water concentration on a volume basis.\n<br>\n\n#### fife_soilprop_soil_rel_112\nThis data product was created based on the hypothesis that a variety of ground truth observations of soil moisture could be combined to estimate equal soil moisture contours across a large area (e.g., the FIFE area). A second stage required interpolating from these contours, using the methods of spatial statistics, to average soil moisture values at the nodes of a uniform grid. The grid node values in this product represent the average soil moisture of a 0.5 km x 0.5 km area centered at the node location. The correlation area method (CAM) was used to combine in situ measurements and airborne gamma remote sensing estimates to obtain areal averages of soil moisture. Information on biomass and the spatial distribution of vegetation in a model was also used to estimate soil moisture from PBMR measurements. Another simple method, using only ground soil moisture data, was also used to compute soil moisture from the PBMR measurements. All soil moisture data collected from the aircraft platforms and ground measurements were entered into the ARC/INFO GIS along with the UTM coordinates of each observation. All available and usable measurements of soil moisture were considered in an analysis that produced isolines of soil moisture.\n<br>\n\n#### fife_soilmstr_sm_tran_113\nThe aim of the FIFE soil moisture transect work was to characterize spatial and temporal patterns of soil moisture along selected transects at the FIFE study area. Two levels of ground data were collected to support the passive microwave (PBMR) flights over the Konza experimental area. The water content measurements were collected using gravimetric methods. Soil moisture measured along a transect is necessary to calibrate airborne moisture instruments or compare data obtained from them. Soil units in a landscape are inherently heterogeneous, which leads to variations in moisture content along an aircraft flight path on the ground. In order to reduce errors, values on the flight path were sampled at close intervals.\n<br>\n\n#### fife_sur_refl_soilrefl_114\nSoil reflectance properties are an important factor in determining landscape reflectance characteristics. No soil reflectance data were collected as part of the FIFE experiment. Therefore, the FIS staff choose spectra from soils similar to those in the FIFE study area from the atlas of soil reflectance properties (Stoner et al., 1980). The atlas represents a wide range of soil types, and FIS staff choose spectra from soils similar to those in the FIFE study area. The selection of spectra was based on soil particle size, organic carbon content, taxonomic classification, and geography of soils found in the FIFE study area. All measurements were made on uniformly moist, sieved soils, which were equilibrated for 24 hours at a one-tenth bar moisture tension. Soil reflectance was measured using an Exotech Model 20 C spectroradiometer adapted for indoor use with a reflectometer equipped with an artificial illumination source, transfer optics, and sample stage. Spectral readings were taken in 0.01 micrometer increments over the 0.52 to 2.32 micrometer wavelength range.\n<br>\n\n#### fife_soilprop_soilsurv_115\nThe objectives of this study were to collect soil survey information from the FIFE study area, determine the soil types at the FIFE sites, and characterize the physical and chemical properties of the soils. The Soil Properties Reference Information Data Set provide a description of the soils and their properties at the FIFE study sites as described by the U.S. Soil Conservation Service. Five stations representative of the Clime, Benfield, Dwight, Florence, and Tully soil types were selected, and a detailed description of the soil profile at each of these five sites was made. Soil samples from the surface down to bedrock were collected from the horizons and analyzed for bulk density, particle size distribution, moisture retention at 1/3 and 15 bar suctions, cation exchange capacity, and other chemical and physical properties, using standard procedures (Soil Survey Staff 1984).\n<br>\n\n#### fife_soilprop_soilther_116\nThe purpose of the 1989 FIFE soil properties investigation was to obtain a description of the thermal properties of the soils within the FIFE study area. Soil thermal conductivity measurements describe the soil properties which govern the flow of heat through the soil. The thermal conductivity is defined as the quantity of heat that flows through a unit area in a unit time under a unit temperature gradient. These measurements were made using a hot wire probe in situ at two depths at twenty six FIFE sites during October 1987. The measurements were taken using a long electrically heated wire enclosed in a cylindrical probe . The probe is placed in the soil, the wire is heated by running a current through it, and the temperature rise is measured with a thermocouple placed next to the wire. A plot of temperature versus the log of time can be used to derive the thermal conductivity. The results may require a correction factor to account for the dimensions of the probe.\n<br>\n\n#### fife_soilprop_soil_h2o_117\nDuring the 1989 FIFE field campaign, measurements were made of soil moisture release parameters and hydraulic conductivity. Bulk density and soil moisture release data were collected at five FIFE sites representing the major soil types in the FIFE study area. These data were used to model the porosity, saturated water potential, and the b-factor (the exponent of the power curve function) following the method of Clapp and Hormberger (1978). These soil moisture characteristics can be used to describe plant-available water and water movement through soils.\n<br>\n\n#### fife_biology_biomass_118\nThe FIFE Standing Crop and Nitrogen Content Data Set contains biomass and nitrogen concentration data for live and dead above-ground plant material collected along transects in watersheds within the FIFE study area. The transects were in watersheds that had undergone burning and grazing treatments. Point physical descriptors (elevation, slope, and soil depth) are also included in the data set. Substantial variation in biomass, and N accumulation occurred over time, with topography, and as a result of grazing and previous burning (Schimel et al. 1991a, Kittel et al. 1990, Turner et al. 1992, Davis et al. 1992).\n<br>\n\n#### fife_hydrology_strm_day_119\nThe Daily Stream Flow Amounts Data Set contains daily measurements of stream flow for the four LTER stations and for the USGS stream-flow station located on tributaries to Kings Creek. This data set contains measurements from April 1979 to September 1988 for the USGS station, and from June 1985 to December 1987 for the 4 LTER stations. Five stream-flow gauges were placed across creeks in the Long-Term Ecological Research (LTER) section of the FIFE study area. Four of these five stations were maintained and monitored by the LTER staff while the fifth was part of the USGS network of stream flow gauges. The V-throated flume and standpipes used at the LTER weirs operated on the principle that the height of the water level in a standpipe at a specific location within a weir of known dimensions can be converted to volume of water in the stream. The change of this instantaneous volume with time could then be used to compute volumetric stream flow. The stilling pipe installation at the USGS stations operates on the principle that the height of the water level in a standpipe at a specific location within a streambed can be converted to volume of water in the stream. The tracking of the change in stream height with time then enables the calculation of stream flow.\n<br>\n\n#### fife_hydrology_strm_st_120\nThe Storm Event Stream Flow Data Set were collected during storm events from five treatment areas within the Konza Prairie Long-Term Ecological Research (LTER) site located within the northwest quadrant of the FIFE study area. These data were recorded so that the hydrology of the streams draining the tallgrass prairie could be studied. Moreover, these data were collected to determine the effect of burn frequency of a watershed upon runoff. Data are available from June 14, 1985 through December 31, 1987. The V-throated flume and standpipes used at the LTER weirs operated on the principle that the height of the water level in the standpipe at a specific location within a weir of known dimensions can be converted to volume of water in the stream. The change of this instantaneous volume with time could then be used to compute volumetric stream flow. The V-notch, sharp-crested weir used in watershed 1D operated on the principle that water flowing past a point of known dimensions per unit time could be converted through standard equations to volumetric flow.\n<br>\n\n#### fife_sur_flux_basel_92_121\nThe FIFE Surface Flux Baseline 92 Derived Data Set was compiled from the original surface flux data collected during FIFE (i.e., no measurements were made specifically for this data set). This data set contains data collected from mid-May through mid-October, 1987 at 21 stations located within 19 sitegrids spread throughout the FIFE study area. For a description of the theory behind the original surface flux measurements see the documentation for each of the original surface flux data sets. Surface heat flux data routinely have erroneous jumps (i.e., spikes) in the latent and sensible heat flux time series in the early morning and evening hours due to small gradients in the measured data. A series of tests were developed to identify these spikes and flag them. Flux data obtained from Bowen ratio sites are also checked for energy imbalances. These data were also compared to model results. The consistency between these two methods is indicated in this data set.\n<br>\n\n#### fife_sur_met_ncdc_sur_122\nThe NOAA Regional Surface Data - 1989 (NCDC) Data Set contains hourly surface meteorological data for the FIFE area. Though the measurements presented in this data set were not taken precisely at the FIFE study area, it is hypothesized that they present a representative horizontal cross-section of meteorological variables and sky conditions in and around the site. It is also realized that many of the variables presented in this data set are somewhat subjective and dependent on the skill (and biases) of the observer, such as estimates of cloud amount and height. This data may be used as input data and/or verification data for numerical simulation models.\n<br>\n\n#### fife_sur_refl_unl_surf_123\nThe Surface Temperatures, Reflected and Emitted Radiation, and PAR from UNL Data Set contains surface temperatures at different view zenith and azimuth angles, net radiation, incoming and reflected photosynthetically active radiation, incoming and reflected shortwave radiation, and reflected and emitted longwave radiation. Surface temperatures were measured at a 30 degree view zenith angle with an Everest infrared thermometer (IRT) Model 112C and at approximately a 60 degree view zenith angle with a Scheduler Plant Stress Monitor at 4 view azimuths (predominantly 90 degree increments from the solar azimuth). The Scheduler also measured air temperature, relative humidity, and vapor pressure deficit. Net radiation was measured with a Radiation and Energy Balance Systems (REBS) net radiometer Model Q*3. Incoming shortwave radiation was measured with a horizontally mounted Eppley Precision Pyranometer Model PSP. Reflected shortwave radiation was measured with two (2) Eppley Precision pyranometers Model PSP usually mounted horizontally (at site 966 (2437-PSP) one PSP was mounted horizontally and the other was inclined parallel to the slope). Reflected and emitted longwave radiation were measured with a horizontally mounted Eppley Precision Infrared Radiometer Model PIR.\n<br>\n\n#### fife_atmos_tempprof_124\nThe temperature profile data included in this data set was derived from the FIFE radiosonde data collected during the summer and fall of 1987 and the late summer of 1989 by Dr. Wilfred H. Brutsaert. These intensive radiosonde flights allowed the measurement of the atmospheric profiles of potential temperature and specific humidity. These data have been corrected for sensor delays, algorithm inconsistencies and have been interpolated to a set of standard pressure levels.\n<br>\n\n#### fife_biology_leaf_h2o_126\nThe Total Leaf Tissue Water Potential Data Set was collected during the summer months of 1988 and 1989. The objective of this study was to determine the influence of plant water status on surface reflectance factors. Measurements were made at six stations on Indian grass, switch grass, Big bluestem, little bluestem, and tall dropseed. Leaf water potential measurements were usually made on the same leaf that optical measurements were made and on leaves of surrounding plants. Measurements were made on the most recently expanded leaf of the selected plant unless specified. Measurements were also made of older green and yellow leaves on a plant. Leaf water potential measurements can be linked with the leaf optical properties data if the plant number in both sets of data are known. Plant water potential values measured just before dawn will provide the highest plant water potential (smallest negative value) during the day and also provides a reasonable estimate of the soil water potential. It is hypothesized that as the leaf water potential decreases (large negative value) that there may be some change in the internal structure of the leaf that would be detectable in one or more of the Nebraska Multiband Leaf Radiometer (NMLR - instrument used during leaf optical measurements) wavebands. It is also hypothesized that the amounts of water in a leaf will be lowest at low water potential and that this might also be detectable with the NMLR especially in the mid-IR wavebands.\n<br>\n\n#### fife_biology_veg_biop_135\nThe Biophysical Properties of the Vegetation Data Set were collected as part of the larger FIFE Science effort to characterize the physical and biological properties of the sites within the FIFE study area over the life of the field experiment. These data were collected at 43 locations scattered throughout the FIFE study area between May 1987 and August 1989. The measurements of leaf area were based on an optical technique in which the area of the light beam obscured by the material under the beam is a measure of the surface area of that material relative to the total surface area that the beam covers. The resulting Leaf Area Indices (LAI) provide a relative measure of leaf area. These indices, when compared between plant samples provide an indirect and relative measure of plant biomass.\n<br>\n\n#### fife_biology_veg_spec_136\nThe Vegetation Species and Cover Abundance Data Set documents the species present at the FIFE staff data measurement sites. Percent cover is estimated for each species at approximately the time of the IFC's. Disturbances occur over a variety of spatial and temporal scales in North American grasslands, and interactions of these different disturbances affect community structure. Two types of disturbance commonly occur over large spatial scales in grasslands, namely, fire and grazing. Analysis of percent cover of dominant species indicated that composition and heterogeneity was significantly affected by grazing intensity and burning. The effects of disturbances on community structure are not additive, and may not be extrapolated from studies of single factors. The interpretation of patterns in natural communities is clearly scale dependent, and processes may act differently when viewed from different spatial or temporal scales. The effects of scale may not always be predictable; therefore, an understanding of pattern and process at one hierarchical level may not provide useful information about pattern and process at a different hierarchical level.\n<br>\n\n#### fife_biology_veg_ref_137\nThe Konza Natural Research Area is a tallgrass prairie in a biologically heterogeneous environment that is rich in native plant species. Species composition is extremely variable over sites because of the effects of both natural and anthropological factors. The FIFE Vegetation Species Reference Data Set is used to associate the plant species found on the Konza Prairie with both their common and Latin names, and to translate the species codes found in the FIFE vegetation data sets to their Latin and common names.\n<br>\n\n#### fife_atmos_wind_lid_138\nThe aim of this wind profile study was to derive wind profiles and momentum fluxes from the National Oceanic and Atmospheric Administration (NOAA)/Wave Propagation Laboratory (WPL) Doppler LIDAR, and compare LIDAR and airborne measurements of mean wind, turbulent structure, momentum flux, and heat flux. Another objective was to compare profiles of mean wind and temperature obtained from aircraft, balloon sondes, and wind LIDAR. These data were collected at one location near the center of the FIFE study area but in the northwest quadrant. Data were acquired for a two week period during June and July 1987. Pulsed Doppler LIDAR measures the radial (along-beam) velocity as a function of range using light-scattering particles in the air as tracers. When the LIDAR beam is directed straight upward and the backscattered return as a function of height is recorded, vertical aerosol profiles may be determined. Various pointing and scanning schemes permit measurement of a variety of mean and turbulent quantities based on assumptions about the flow. The remote-sensing character of LIDAR offers the ability to measure flow parameters simultaneously at all the heights in a profile. The winds were obtained with the VAD (Velocity Azimuth Display) technique. The LIDAR only operates above 500 m, therefore the wind profile begins above the ground surface. Data in the planetary boundary layer are usually continuous, but gaps appear occasionally in profiles extending to several kilometers. Profiles were unsmoothed, and the LIDAR's short pulse made adjacent data points almost independent.\n<br>\n\n#### fife_atmos_wind_son_139\nThe wind profile data described in this document were derived from the raw radiosonde data collected during FIFE by Dr. Wilfred H. Brutsaert during the summer and fall of 1987 and the late summer of 1989 The objective of this study was to calculate wind velocity and wind direction from successive horizontal positions of a radiosonde. These data have allowed the measurement of the atmospheric profiles of wind velocity and direction. The raw data have also been corrected for sensor delays and have been interpolated to a set of standard pressure levels. Successive horizontal positions of the radiosonde balloon in relation to its release point was used to calculate average wind speed and direction. The variables used to make these calculations were obtained from the FIFE Radiosonde Data. The balloon height was calculated by adding 10 m (i.e., the length of the string) to the height of the sonde. The horizontal distance of the sonde, together with the measured azimuth angle (also obtained from the FIFE Radiosonde Data), produced the horizontal position of the sonde. Finally, successive horizontal positions allowed the calculation of average wind velocity and direction over the interval. Note, as a result of the addition of 10 m for most flights, the height of the wind measurements in this data set is 10 meters higher than the companion values in the original FIFE Radiosonde Data.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/FIFE/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","earth observation","elevation","hydrology","ice","lidar","oceans","precipitation","radar","satellite imagery","soil moisture","stac","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"fife_hydrology_strm_15m_1 v1 - The Fifteen Minute Stream Flow Data from the USGS Data Set contains 15 minute stream flow data from the USGS station located 2.9 miles upstream from the mouth of Kings Creek. The record extends from April 1, 1979 through September 2, 1988.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_hydrology_strm_15m/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_met_rain_30m_2 v1 - The FIFE Thirty Minute Rainfall Data Data Set contains data from thirty rain gauges located in the Kings Creek basin in the northwest corner of the FIFE study area during 1987. Reliability of the gauges were such that at any particular time, data from approximately 20 were recovered.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_met_rain_30m/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_AF_dtrnd_nae_3 v1 - The purpose of this study was to develop alternatives to ground-based measurements in order to obtain information required to predict the effects of soil and land use on the fluxes of greenhouse gases, the surface energy balance, and the water balance. Satellite-based algorithms have been developed via flux measurements from an aircraft to estimate vegetation and soil conditions on a regional scale.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_AF_dtrnd_nae/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_AF_dtrnd_wyo_4 v1 - The University of Wyoming (UW) King Air atmospheric boundary layer measurement missions were flown in 1987 during IFCs 3 and 4. This Boundary Layer Fluxes data set contains parameters that describe the environment in which the flux data were collected and the flux data itself.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_AF_dtrnd_wyo/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_AF_dtrnd_ncar_5 v1 - The NCAR King Air participation in FIFE-1987 and FIFE-1989 was part of a coordinated atmospheric boundary layer component which included other aircraft, surface measurements, balloon-borne profiles, SODAR, and lidar remote sensing. The chief objective of the boundary layer component was to describe the structure of the atmospheric boundary layer over the FIFE study area, increase knowledge of the physical processes active in the daytime boundary layer, and explore the relationship of surface properties to the time and spatial variation in the structure of the boundary layer.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_AF_dtrnd_ncar/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_AF_filtr_ncar_8 v1 - The NCAR King Air participation in FIFE-1987 and FIFE-1989 was part of a coordinated atmospheric boundary layer component which included other aircraft, surface measurements, balloon-borne profiles, and SODAR and lidar remote sensing. The chief objective of the boundary layer component was to describe the structure of the atmospheric boundary layer over the FIFE study area, increase knowledge of the physical processes active in the daytime boundary layer, and explore the relationship of surface properties to the time and spatial variation in the structure of the boundary layer.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_AF_filtr_ncar/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_AF_raw_wyo_10 v1 - The University of Wyoming (UW) King Air atmospheric boundary layer measurement missions were flown in 1987 during IFCs 3 and 4. This Raw Boundary Layer Fluxes data set contains parameters that describe the environment in which the flux data were collected and the flux data itself.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_AF_raw_wyo/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_met_ams_12 v1 - As part of the FIFE staff science data collection effort, the FIFE Information System (FIS) processed and archived 5 minute, near-surface radiometric and meteorological information collected by the Automated Meteorological Stations (AMS) distributed over the FIFE study area. The FIFE AMS Data Set contains the two output products created.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_met_ams/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_atmos_ncdc_son_13 v1 - The NOAA Radiosonde Observations - 1989 (NCDC) Data Set contains radiosonde data obtained from the National Climatic Data Center (NCDC). These 396 days of data cover 13 months from October 1988 through October 1989.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_atmos_ncdc_son/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_atmos_brut_drv_14 v1 - The FIFE Standard Pressure Level Radiosonde Data Set provides a set of standard level profiles (i.e., 5 mb pressure intervals) from over 450 radiosonde balloon flights, which occurred every one to three hours (daylight hours) during the FIFE IFCs. This derived profile data were computed to 5 mb pressure intervals through simple linear interpolation means.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_atmos_brut_drv/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_atmos_brut_son_15 v1 - The FIFE Radiosonde Data Set contains temperatures, wind speed, and temperature profiles in the atmospheric boundary layer measured by means of radiosondes that were analyzed in the framework of Monin-Obukhov similarity theory, with the objective of determining the regional surface heat flux. Profiles of temperature, humidity and wind velocity in the atmosphere were measured by means of intensive radiosoundings conducted approximately between 900 and 1800 CDST in northeastern Kansas during the five FIFE Intensive Field Campaigns in spring, summer and fall of 1987, and in the late summer of ...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_atmos_brut_son/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_atmos_noaa_tov_16 v1 - The TOVS data were acquired from NOAA/NESDIS to monitor atmospheric conditions that occurred over the FIFE study area during 1987. The TOVS data were obtained from NESDIS in the standard TOVS sounding product format containing atmospheric sounding data for NOAA-9 and NOAA-10 satellites over the FIFE study area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_atmos_noaa_tov/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_atmos_lidar_ht_17 v1 - The Volume Imaging LIDAR (VIL) system of the University of Wisconsin, operated during FIFE and all LIDAR return signals measured at a 90 degree elevation angle were averaged and stored in a file. From plots of those profiles, clouds up to 15 km AGL can be identified.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_atmos_lidar_ht/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_atmos_sodar_18 v1 - The acoustic sounder (SODAR) maps the amplitude of backscattered acoustic energy associated with temperature fluctuations and thus thermal inversions in the atmosphere. The aim of the SODAR measurements was to provide estimates of the height of the mixed layer and the vertical dimensions of inversions within the lower kilometer of the atmosphere.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_atmos_sodar/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_flux_30_min_brl_19 v1 - Surface flux measurements were made at selected sites within the FIFE area. Each surface flux station was capable of measuring the fluxes of net radiation, sensible heat, and latent heat.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_flux_30_min_brl/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_flux_30_min_brg_20 v1 - The Bowen Ratio Surface Flux Observations (GSFC) Data Set contains data collected using the Bowen Ratio Techniques. The major data collection effort was conducted in 1987 when 16 stationary sites were equipped with Bowen ratio equipment by different groups.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_flux_30_min_brg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_flux_30_min_brk_21 v1 - The Bowen Ratio Surface Flux Observations (KSU) Data Set contains surface flux measurements made at selected sites within the FIFE area. The sites were equipped with Bowen ratio equipment that was operated by several different groups.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_flux_30_min_brk/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_flux_30_min_brs_22 v1 - The Bowen Ratio Surface Flux Observations (Smith) Data Set contains surface flux measurements made at selected sites within the FIFE area. The collection effort was conducted in 1987, 1988, and 1989 from sites equipped with Bowen ratio equipment operated by several different groups.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_flux_30_min_brs/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_flux_30_min_brv_23 v1 - The Bowen Ratio Surface Flux Observations (UNL) Data Set contains surface flux and micrometeorolgical measurements collected at one location located in a flat area of uniform surface vegetation approximately in the center of the FIFE study area. The data collection effort was during the four Intensive Field Campaigns in the spring, summer, and fall of 1987 (May 28 - Oct 17).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_flux_30_min_brv/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_flux_30_min_brw_24 v1 - The Bowen Ratio Surface Flux Observations (USGS) Data Set contains surface flux and micrometeorological collected from one location within the Northwest quadrant of the FIFE study area. Data were collected daily at this location only during the IFC's during the period from late May through mid-October, 1987.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_flux_30_min_brw/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_biology_pho_box_27 v1 - The Canopy Photosynthesis Data Set data were collected from five sites within the FIFE study area during July 1, 1987 through October 12, 1987. The objectives of the study were to estimate canopy photosynthetic rates, respiration rates, and bulk stomatal resistance.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_biology_pho_box/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_met_cld_cam_28 v1 - The FIFE Cloud Camera Data Data Set was collected to document distribution of clouds during FIFE, evaluate algorithms for identifying presence of thin cirrus, and popcorn cumulus clouds, and evaluate the impact of these clouds on retrieval of surface fluxes from satellite data. Clouds could be remotely sensed from both the surface and from satellites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_met_cld_cam/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_met_rain_day_29 v1 - The FIFE Daily Rainfall Data Set contains daily precipitation values for 42 rain gauge stations within the Konza LTER portion of the FIFE site (i.e., Northwest quadrant). The data set is a composite of data collected by the LTER staff and the Princeton University group.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_met_rain_day/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_flux_30_min_eca_30 v1 - The Eddy Correlation Surface Flux Observations (Argonne) Data Set contains surface flux measurements made at selected sites within the FIFE study area. The surface area and aerological data described here were located at 2 sites in the northwest quadrant of the study area within the Konza Prairie Natural Research area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_flux_30_min_eca/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_flux_30_min_ecg_31 v1 - The Eddy Correlations Surface Flux Observations (GSFC) Data Set contains surface flux measurements made at selected sites within the FIFE study area. The surface flux and micrometeorological measurements in this data set were collected from a single location located in the southwest quadrant on a upland, grazed area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_flux_30_min_ecg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_flux_30_min_ecb_32 v1 - Surface flux measurements were made at selected sites within the FIFE study area. Each surface flux station was capable of measuring the fluxes of net radiation, sensible heat, and latent heat.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_flux_30_min_ecb/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_flux_30_min_ecv_33 v1 - Surface flux and micrometeorological measurements were collected at one site within the northwest quadrant near the center of the FIFE study area during all five of the Intensive Field Campaigns (four in 1987 and one in 1989). This site had historically been ungrazed but had recently been exposed to grazing.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_flux_30_min_ecv/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"FIFE_CD_V3_130 v1 - This data set provides aircraft-based NS001 Thematic Mapper Simulator (TMS) images of the study area associated with The First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) project conducted on the Konza Prairie in Kansas. The images were acquired during June 1987 to August 1989.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/FIFE_CD_V3/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_soilmstr_peck_gam_37 v1 - Airborne soil moisture measurement is based on the difference between natural terrestrial gamma radiation flux measured for comparatively wet and dry soils. The presence of moisture in the soil causes an effective increase in the soil density resulting in an increased attenuation of the gamma flux for relatively wet soil and a correspondingly lower flux at the ground surface.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_soilmstr_peck_gam/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_gem_helo_38 v1 - During the 1989 Intensive Field Campaign a Russian spectroradiometer, the Gemma, was used to collect visible and near infrared spectra of a variety of FIFE sites from a helicopter. Gemma measurements of selected study sites and laboratory measurements of a portable calibration light sphere were made.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_gem_helo/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_met_hday_met_39 v1 - The FIFE Historic Daily Meteorology Data Data Set is one of the historical data sets used for the FIFE project. The data set contains data back to January, 1900.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_met_hday_met/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_met_hmon_met_40 v1 - The FIFE Historic Monthly Meteorology Data Data Set is one of the historical data sets used for the FIFE project. This data set provides monthly precipitation values from January 1858 to December 1989 for Manhattan, Kansas adjacent to the FIFE study area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_met_hmon_met/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_ltbr_ksu_41 v1 - The purpose of the Leaf Area Index and PAR Determined from KSU Light Bar Measurements study was to collect extensive non-destructive measurements of Leaf Area Index (LAI) at the flux sites during IFC-5 (August 1989). These data were collected at thirteen locations which were coincident with the surface flux measurements within the FIFE study area from July 3, 1989 through August 18, 1989.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_ltbr_ksu/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_ltbr_unl_42 v1 - The Leaf Area Index and PAR Determined from the UNL Light Bar Data were collected in 1987, 1988, and 1989. Incoming, reflected, and transmitted photosynthetically active radiation (PAR) was measured with a LI-COR LI-191SA line quantum sensor.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_ltbr_unl/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_lightwnd_43 v1 - The prairie is inherently variable and large numbers of samples are needed to obtain reliable estimates of the prairie agronomic characteristics. For the Indirect Leaf Area Index Obtained from the KSU Light Wand Study a limited number of destructive samples were supplemented by large numbers of rapid non-destructive estimates.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_lightwnd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_biology_leaf_ang_44 v1 - The Leaf Angle Data Data Set contains leaf angle distributions (LAD) obtained during the 1987 growing season for ten types of plant canopies, from the Konza Long-Term Ecological Research (LTER) area. These data were collected using a direct measurement technique (i.e., a Spatial Coordinate Apparatus (SCA)).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_biology_leaf_ang/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_biology_pho_leaf_46 v1 - The objective of the Leaf Photosynthesis study was to measure the response of leaf photosynthesis and stomatal conductance to light, temperature, vapor pressure deficit, carbon dioxide and water potential for the most abundant C4 species at the FIFE study area. To this end, photosynthesis measurements were made on 6 days in June, July and August of 1987 at three different locations in the northwest quadrant of the FIFE study area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_biology_pho_leaf/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_unl_long_49 v1 - The Incoming Longwave Radiation Data from UNL Data Set was collected as part of a study of thermal radiant energy from vegetative canopies. These data were collected during the growing season of 1987 and 1989.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_unl_long/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_mmr_calb_51 v1 - The MMR Calibration Data Set contains radiance data collected in the summer of 1987 and in July and August of 1989 via a Modular Multiband Radiometer (MMR) instrument. The MMR instrument monitored a nearly lambertian calibration panel stationed near the center of the FIFE study area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_mmr_calb/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_mmr_grnd_52 v1 - Surface reflectance factors, radiances, and temperatures were measured with a Mast-borne Modular Multiband Radiometer (MMR), predominantly in the solar principal plane, with nadir and off-nadir, view-zenith angles. The MMR was mounted on a portable mast in order to achieve a spatial sampling at a variety of sites as well as within each site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_mmr_grnd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_mmr_helo_53 v1 - The Surface Reflectance Measured with a Helicopter-borne MMR Data Set contains surface reflectance measurements in Landsat-TM bands at low to intermediate altitudes. The helicopter operated during all Intensive Field Campaigns (IFCs) and was available to support all satellite overpasses.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_mmr_helo/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_mmr_leaf_54 v1 - The Leaf Optical Properties from UNL Data Set contains leaf-level spectral observations acquired in situ with the Nebraska Multiband Leaf Radiometer (NMLR) coupled with a LiCor LI-1800-12 integrating sphere. The NMLR measured leaf reflectance and transmittance in the seven MMR bands.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_mmr_leaf/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_biology_mow_biop_55 v1 - The Mowing Experiment Biophysical Measurements data set was collected to quantify the effects of grazing and nitrogen fertilization on primary productivity and plant chemistry. The data in this data set quantified the effects of foliage removal on plant net primary productivity (NPP), plant nutrient content and the effects of grazing pressure as simulated by mowing.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_biology_mow_biop/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_biology_mow_exo_56 v1 - Light radiation striking a vegetative canopy interacts with individual phyto-elements (i.e., leaves, stems, branches) and the underlying substrate. The interaction depends on light quality, radiative form (direct or diffuse), illumination incidence angle, vegetative component optical properties and canopy architecture.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_biology_mow_exo/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_atmos_nmc_upr_57 v1 - The Upper Air Derivative Data from NMC Data Set was derived from National Meteorological Center global upper air models. These models use a 6 hours intermittent assimilation method.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_atmos_nmc_upr/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_met_noaa_sur_58 v1 - The FIFE Staff Science effort included the acquisition, processing and archiving of meteorological parameters of the atmosphere above the FIFE study area, which would furnish surface meteorological parameters from hourly reporting network for the FIFE area, and provide input data and/or verification data for numerical simulation models. Though the measurements presented in this data set were not taken precisely at the FIFE site, it is hypothesized that they present a representative horizontal cross-section of meteorological variables and sky conditions in and around the site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_met_noaa_sur/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sat_obs_ns001tms_59 v1 - As part of the FIFE staff science data processing effort, the FIFE Information System (FIS) extracted site average radiances from the level-1 NS001-TMS products. Data were collected by the NS001 during each of the FIFE IFC's.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sat_obs_ns001tms/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_optical_ot_calib_60 v1 - The objective of the Optical Thickness Calibration study was to compare aerosol optical thickness measurements derived from three different groups. These groups measured atmospheric transmission, in particular, the aerosol optical thickness, using at least three different instruments (e.g., a solar transmissometer SXM-2, a Reagan sunphotometer, and an airborne tracking sunphotometer) and three different analysis procedures.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_optical_ot_calib/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_optical_ot_brug_62 v1 - The NIPS and Reagan Sunphotometer Optical Thickness study compared various ground and image-based techniques used to characterize the atmosphere. These data are used to remove atmospheric absorption and scattering from remote sensing scenes so that surface parameters can be retrieved.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_optical_ot_brug/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_optical_ot_c130_63 v1 - The data in the Sunphotometer Optical Thickness Data from C130 Aircraft data set were collected in June, July and August 1987, and in August 1989. The data was collected at selected locations within the FIFE study area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_optical_ot_c130/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_optical_ot_frasr_64 v1 - The Solar Transmissometer Aerosol Optical Thickness Data Set contains optical thickness data that provide a measure of the effect of aerosols on the attenuation of radiation through the atmosphere at 8 discrete wavelength bands throughout the visible and near IR portion of the electromagnetic spectrum. These data were collected using a ground-based solar transmissometer in June and July of 1987, and July and August of 1989, at two stations in the FIFE study area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_optical_ot_frasr/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_optical_ot_staff_66 v1 - Aerosol optical thickness in conjunction with an atmospheric model can provide estimates of atmospheric effects on transmitted and reflected solar radiation. These effects can then be used to correct aircraft and satellite radiometric data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_optical_ot_staff/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_parabola_68 v1 - The Surface Reflectances Measured by the PARABOLA Data Set contains measurements from the Portable Apparatus or Rapid Acquisitions of Bi-directional Observations of Land and Atmosphere (PARABOLA) instrument. The focus of this research was to characterize the variation in vegetation reflectance as a function of solar and sensor viewing geometry, wavelength, and plant canopy biophysical characteristics.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_parabola/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_biology_plantpro_69 v1 - The focus of this study was to quantify the effects of foliage removal by cattle on plant net primary productivity (NPP). The Vegetation Biomass, Production and Consumption at Selected Sites Data Set contains mean values and their variances.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_biology_plantpro/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_irt_helo_70 v1 - The Surface Radiant Temperature Measured with a Helicopter-borne Infrared Thermometer Data Set were collected for six days during July and August of 1989 to provide the radiant temperature of the FIFE sites and as a check of the thermal band on the MMR. The average and standard deviation of radiant temperature were measured with an Everest infrared thermometer.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_irt_helo/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_irt_mult_71 v1 - The Surface Temperatures Measured at Multiple Angles Data Set was collected at two locations within the northwest quadrant of the FIFE study area during July and August 1989. The data set contains hemispherical surface temperature, surface temperatures measured at several view zenith angles, and surface temperatures and at-view azimuth increments of 45 degrees.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_irt_mult/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_irt_grnd_72 v1 - The Surface Temperatures from UNL Data Set contains surface temperatures collected between July 15 and August 11, 1989 at three FIFE area sites. These surface temperatures were measured with an Everest multiplexed infrared thermometer (IRT) Model 4000 predominantly in the solar principal plane, with nadir and off-nadir, view-zenith angles (mounted on the portable mast with the Barnes Model 12-1000 Modular Multiband Radiometer (MMR)).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_irt_grnd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_atmos_noaa_son_73 v1 - The NOAA Radiosonde Observations Data Set contains data that were extracted from the NOAA operational analysis system and transmitted to the FIS. Data are available from July 1985 to October 1988, there are 1123 days of data during this period with data at twelve hour intervals.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_atmos_noaa_son/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_biology_root_bio_75 v1 - The FIFE Root Biomass data were collected from 16 locations within the FIFE study area during the 1987 growing season. They provide a measure of the below-ground biomass for the study area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_biology_root_bio/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sat_obs_sat_coef_76 v1 - The atmospheric effects on the transmitted and reflected solar radiation should be factored into the estimation of geophysical and biophysical parameters from remotely-sensed data, so that appropriate correction schemes can be employed to infer reflectivity of the ground from satellite radiometric data. Some of the correction techniques require derived coefficients as inputs in the algorithms that perform the atmospheric correction.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sat_obs_sat_coef/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sat_obs_sat_avhr_77 v1 - The Advanced Very High Resolution Radiometer (AVHRR) is a four- or five-channel scanning radiometer capable of providing global daytime and nighttime sea-surface temperature and information about ice, snow, and clouds. The sensor measures emitted and reflected radiation in five channels (bands) of the electromagnetic spectrum.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sat_obs_sat_avhr/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sat_obs_sat_ltm_78 v1 - The Thematic Mapper sensor system was used to collect the original data between February 1987 and October 1989 from which this data set was produced. Landsat TM extract data contains the average instrument corrected spectral radiances for each of the seven spectral bands.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sat_obs_sat_ltm/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sat_obs_sat_spot_79 v1 - The Site Reflectances Extracted from SPOT HRV Imagery Data Set contains the average instrument corrected spectral radiances for each of the spectral bands (3 in XS and 1 in PAN) collected during the growing seasons of 1987, 1988, and 1989. In addition, the associated view angles and solar angles are available for each of 39 FIFE ground measurement sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sat_obs_sat_spot/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_se5_gsfc_81 v1 - The SE-590 Spectroradiometer Reflectance Factors from GSFC Data Set contains spectral data collected with the Spectron SE-590 Spectral Radiometer at selected FIFE sites located primarily on the Konza Prairie. These measurements were acquired in conjunction with the Surface Reflectances measured by the PARABOLA bi-directional measurements.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_se5_gsfc/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_se5_unl_82 v1 - The SE-590 Reflectance Factors and Radiances from UNL Data Set contains surface reflectance and viewing angle data that was collected at three sites within the FIFE study area via a SE590 mounted on a portable mast. All measurements were made on eleven days between July 15 and August 11, 1989.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_se5_unl/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_se5_leaf_85 v1 - The SE-590 Leaf Level Spectral Observations from GSFC Data Set were acquired in situ with a Spectron SE590 spectroradiometer fitted with the 1 degree IFOV lens, and coupled with a LI-COR integrating sphere. The purpose in collecting SE590 leaf reflectance and transmittance data was to characterize the optical properties of the canopy components to gain a better understanding of how these optical properties contribute to canopy reflection and absorption of radiation.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_se5_leaf/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_se5_helo_87 v1 - The SE-590 Reflectance Factors and Radiances Measured from a Helicopter Data Set were collected using the helicopter-borne SE-590 during Intensive Field Campaign 5 (IFC-5) in 1989. These data were collected at 17 different grid locations within the FIFE study area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_se5_helo/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1987_ams_88 v1 - The Site Averaged AMS Data: 1987 (Betts) Data Set contains the site averaged product of the Portable Automatic Meteorological Station (AMS) data acquired during the 1987-1989 FIFE experiment. Data are in 30 minute time intervals in 1987.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1987_ams/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1987-1989_ams_89 v1 - The Site Averaged AMS Data: 1987 - 1989 (Betts) Data Set contains the site averaged product of the Portable Automatic Meteorological Station (AMS) data acquired during the 1987-1989 FIFE experiment. Data are in 30 minute time intervals in 1987.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1987-1989_ams/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1988_ams_90 v1 - The Site Averaged AMS Data: 1988 (Betts) Data Set contains the site averaged product of the Portable Automatic Meteorological Station (AMS) data acquired during the 1987-1989 FIFE experiment. Data are in 30 minute time intervals in 1988.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1988_ams/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1989_ams_91 v1 - The Site Averaged AMS Data: 1989 (Betts) Data Set contains the site averaged product of the Portable Automatic Meteorological Station (AMS) data acquired during the 1987-1989 FIFE experiment. Data are in 30 minute time intervals in 1989.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1989_ams/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1987_afd_92 v1 - The Site Averaged Flux Data: 1987 (Betts) Data Set contains the site averaged product data collected by many PIs during the 1987-1989 FIFE experiment. This data set is a time series of 30-minute average variables for the periods May 27, 1987 - Oct 16, 1987.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1987_afd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1987-1989_afd_93 v1 - The Site Averaged Flux Data: 1987 (Betts) Data Set contains the site averaged product data collected by many PIs during the 1987-1989 FIFE experiment. Data are in 30 minute time intervals in 1987 and include the entire period 1987-1989.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1987-1989_afd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1988_afd_94 v1 - The Site Averaged Flux Data: 1988 (Betts) Data Set contains the site averaged product data collected by many PIs during the 1987-1989 FIFE experiment. Data are in 30 minute time intervals and include data only for 1988.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1988_afd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1989_afd_95 v1 - The Site Averaged Flux Data: 1987 (Betts) Data Set contains the site averaged product data collected by many PIs during the 1987-1989 FIFE experiment. Data are in 30 minute time intervals and include data only for 1989.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1989_afd/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1987_gsm_96 v1 - The Site Averaged Gravimetric Soil Moisture Data: 1987 (Betts) Data Set contains the site averaged product data collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1987_gsm/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1987-1989_gsm_97 v1 - The Site Averaged Gravimetric Soil Moisture Data: 1987 - 1989 (Betts) Data Set contains the site averaged product data collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1987-1989_gsm/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1988_gsm_98 v1 - The Site Averaged Gravimetric Soil Moisture Data: 1988 (Betts) Data Set contains the site averaged product data collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1988_gsm/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1987_nsm_100 v1 - The Site Averaged Neutron Soil Moisture Data: 1987 (Betts) Data Set contains the site averaged product data of the neutron probe soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1987_nsm/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1987-1989_nsm_101 v1 - The Site Averaged Neutron Soil Moisture Data: 1987 - 1989 (Betts) Data Set contains the site averaged product data of the neutron probe soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1987-1989_nsm/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1988_nsm_102 v1 - The Site Averaged Neutron Soil Moisture Data: 1988 (Betts) Data Set contains the site averaged product data of the neutron probe soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1988_nsm/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ffo_Betts_1989_nsm_103 v1 - The Site Averaged Neutron Soil Moisture Data: 1989 (Betts) Data Set contains the site averaged product data of the neutron probe soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/ffo_Betts_1989_nsm/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_soilprop_soildens_104 v1 - Soil bulk density is defined as the ratio of the mass of dry solids to the bulk volume of the soil occupied by those dry solids. Bulk density of the soil is an important site characterization parameter since it changes for a given soil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_soilprop_soildens/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_biology_soil_co2_105 v1 - In the Soil Carbon Dioxide Flux study, a prototype gas exchange system and sensor were used to determine the soil surface flux of CO2 and associated parameters at the three FIFE supersites. The goal of this investigation was to characterize fluxes of carbon dioxide from the surface of the soil for a representative portion of the FIFE study area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_biology_soil_co2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_biology_soil_gas_106 v1 - Nitrogen gas fluxes are important to ecosystem productivity and atmospheric chemistry. Scaling of these microscale fluxes to landscape and regional scales relevant to ecosystem and atmosphere-biosphere exchange questions is difficult.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_biology_soil_gas/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_soilprop_soilhydc_107 v1 - The Soil Hydraulic Conductivity Data Set contains soil hydraulic conductivity, matric flux potential, and soil depth data collected during the 1989 FIFE soil properties investigation. The purpose of the 1989 FIFE soil properties investigation was to obtain more points on the soil moisture release curves for the soils at the FIFE stations, and provide values for the saturated hydraulic conductivity for the long-term water balance studies.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_soilprop_soilhydc/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_soilmstr_soil_imp_108 v1 - The purpose of this research was to characterize the soil moisture distribution in the FIFE study area. Daily measurements of the soil dielectric properties were obtained at five locations throughout the FIFE study area during the 1987 Intensive Field Campaigns (IFC).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_soilmstr_soil_imp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_soilmstr_peck_sm_109 v1 - Water content measurements by gravimetric methods involve weighing a wet sample, removing the water via drying in an oven, and reweighing the sample to determine the amount of water removed. Water content then is obtained by dividing the difference between wet and dry masses by the mass of the dry sample to obtain the ratio of the mass of water to the mass of dry soil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_soilmstr_peck_sm/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_soilmstr_sm_grav_110 v1 - The gravimetrical soil moisture data were collected from many stations spread over the FIFE study area. These data were collected to characterize the spatial and temporal patterns of moisture content of the soils over this area during and between the FIFE Intensive Field Campaigns.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_soilmstr_sm_grav/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_soilmstr_sm_neut_111 v1 - The neutron-probe data present a series of measurements of volumetric water content in the soil profile compiled using the neutron method. These data were collected from throughout the FIFE study area from May 1987 through August 1989.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_soilmstr_sm_neut/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_soilprop_soil_rel_112 v1 - This data product was created based on the hypothesis that a variety of ground truth observations of soil moisture could be combined to estimate equal soil moisture contours across a large area (e.g., the FIFE area). A second stage required interpolating from these contours, using the methods of spatial statistics, to average soil moisture values at the nodes of a uniform grid.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_soilprop_soil_rel/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_soilmstr_sm_tran_113 v1 - The aim of the FIFE soil moisture transect work was to characterize spatial and temporal patterns of soil moisture along selected transects at the FIFE study area. Two levels of ground data were collected to support the passive microwave (PBMR) flights over the Konza experimental area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_soilmstr_sm_tran/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_soilrefl_114 v1 - Soil reflectance properties are an important factor in determining landscape reflectance characteristics. No soil reflectance data were collected as part of the FIFE experiment.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_soilrefl/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_soilprop_soilsurv_115 v1 - The objectives of this study were to collect soil survey information from the FIFE study area, determine the soil types at the FIFE sites, and characterize the physical and chemical properties of the soils. The Soil Properties Reference Information Data Set provide a description of the soils and their properties at the FIFE study sites as described by the U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_soilprop_soilsurv/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_soilprop_soilther_116 v1 - The purpose of the 1989 FIFE soil properties investigation was to obtain a description of the thermal properties of the soils within the FIFE study area. Soil thermal conductivity measurements describe the soil properties which govern the flow of heat through the soil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_soilprop_soilther/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_soilprop_soil_h2o_117 v1 - During the 1989 FIFE field campaign, measurements were made of soil moisture release parameters and hydraulic conductivity. Bulk density and soil moisture release data were collected at five FIFE sites representing the major soil types in the FIFE study area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_soilprop_soil_h2o/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_biology_biomass_118 v1 - The FIFE Standing Crop and Nitrogen Content Data Set contains biomass and nitrogen concentration data for live and dead above-ground plant material collected along transects in watersheds within the FIFE study area. The transects were in watersheds that had undergone burning and grazing treatments.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_biology_biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_hydrology_strm_day_119 v1 - The Daily Stream Flow Amounts Data Set contains daily measurements of stream flow for the four LTER stations and for the USGS stream-flow station located on tributaries to Kings Creek. This data set contains measurements from April 1979 to September 1988 for the USGS station, and from June 1985 to December 1987 for the 4 LTER stations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_hydrology_strm_day/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_hydrology_strm_st_120 v1 - The Storm Event Stream Flow Data Set were collected during storm events from five treatment areas within the Konza Prairie Long-Term Ecological Research (LTER) site located within the northwest quadrant of the FIFE study area. These data were recorded so that the hydrology of the streams draining the tallgrass prairie could be studied.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_hydrology_strm_st/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_flux_basel_92_121 v1 - The FIFE Surface Flux Baseline 92 Derived Data Set was compiled from the original surface flux data collected during FIFE (i.e., no measurements were made specifically for this data set). This data set contains data collected from mid-May through mid-October, 1987 at 21 stations located within 19 sitegrids spread throughout the FIFE study area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_flux_basel_92/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_met_ncdc_sur_122 v1 - The NOAA Regional Surface Data - 1989 (NCDC) Data Set contains hourly surface meteorological data for the FIFE area. Though the measurements presented in this data set were not taken precisely at the FIFE study area, it is hypothesized that they present a representative horizontal cross-section of meteorological variables and sky conditions in and around the site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_met_ncdc_sur/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_sur_refl_unl_surf_123 v1 - The Surface Temperatures, Reflected and Emitted Radiation, and PAR from UNL Data Set contains surface temperatures at different view zenith and azimuth angles, net radiation, incoming and reflected photosynthetically active radiation, incoming and reflected shortwave radiation, and reflected and emitted longwave radiation. Surface temperatures were measured at a 30 degree view zenith angle with an Everest infrared thermometer (IRT) Model 112C and at approximately a 60 degree view zenith angle with a Scheduler Plant Stress Monitor at 4 view azimuths (predominantly 90 degree increments from t...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_sur_refl_unl_surf/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_atmos_tempprof_124 v1 - The temperature profile data included in this data set was derived from the FIFE radiosonde data collected during the summer and fall of 1987 and the late summer of 1989 by Dr. Wilfred H.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_atmos_tempprof/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_biology_leaf_h2o_126 v1 - The Total Leaf Tissue Water Potential Data Set was collected during the summer months of 1988 and 1989. The objective of this study was to determine the influence of plant water status on surface reflectance factors.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_biology_leaf_h2o/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_biology_veg_biop_135 v1 - The Biophysical Properties of the Vegetation Data Set were collected as part of the larger FIFE Science effort to characterize the physical and biological properties of the sites within the FIFE study area over the life of the field experiment. These data were collected at 43 locations scattered throughout the FIFE study area between May 1987 and August 1989.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_biology_veg_biop/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_biology_veg_spec_136 v1 - The Vegetation Species and Cover Abundance Data Set documents the species present at the FIFE staff data measurement sites. Percent cover is estimated for each species at approximately the time of the IFC's.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_biology_veg_spec/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_biology_veg_ref_137 v1 - The Konza Natural Research Area is a tallgrass prairie in a biologically heterogeneous environment that is rich in native plant species. Species composition is extremely variable over sites because of the effects of both natural and anthropological factors.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_biology_veg_ref/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_atmos_wind_lid_138 v1 - The aim of this wind profile study was to derive wind profiles and momentum fluxes from the National Oceanic and Atmospheric Administration (NOAA)/Wave Propagation Laboratory (WPL) Doppler LIDAR, and compare LIDAR and airborne measurements of mean wind, turbulent structure, momentum flux, and heat flux. Another objective was to compare profiles of mean wind and temperature obtained from aircraft, balloon sondes, and wind LIDAR.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_atmos_wind_lid/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fife_atmos_wind_son_139 v1 - The wind profile data described in this document were derived from the raw radiosonde data collected during FIFE by Dr. Wilfred H.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fife/fife_atmos_wind_son/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-fife","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA FLASHFLUX Project","Description":"FLASH_SSF_Aqua-FM3-MODIS_Version4A is the Fast Longwave And Shortwave Radiative Fluxes (FLASHFlux) Clouds and Radiative Swath (SSF) Aqua-FM3-MODIS data in HDF Version 4A data product. This product consists of Low latency (< 5 days from observation) Top-of-Atmosphere (TOA) fluxes and parameterized surface radiative fluxes at Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint (SSF) level for quick-look purposes. Data collection for this product is in progress. FLASHFlux data are a product line of the CERES project designed to process and release TOA and surface radiative fluxes for applied sciences and education uses. The FLASHFlux data product is a rapid-release product based on the algorithms developed for and data collected by the CERES project. CERES is currently producing world-class climate data products derived from measurements taken aboard NASA's Terra and Aqua spacecraft. While of exceptional fidelity, these data products require considerable processing time to assure quality, verify accuracy, and assess precision. The result is that CERES data are typically released up to six months after acquiring the initial measurements. Such delays are of little consequence for climate studies, especially considering the improved quality of the released data products. Thus, FLASHFlux products are not intended to achieve climate quality. FLASHFlux data products were envisioned as a resource whereby CERES data could be provided to the community within a few days of the initial measurements, with some calibration accuracy requirements relaxed to gain speed. The SSF TOA/Surface Fluxes and Clouds product contains one hour of instantaneous FLASHFlux data for a single CERES scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager, such as Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites and meteorological and ozone information from The Goddard Earth Observing System GEOS-5 FP-IT Atmospheric Data Assimilation System (GEOS-5 ADAS). Scene identification and cloud properties are defined at the higher image resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains Top-of-Atmosphere fluxes in SW, LW, and NET, surface fluxes using the Langley parameterized shortwave and longwave algorithms, and cloud information. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES mission is a follow-up to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The newest CERES instrument Flight Model 5 (FM5), was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite.\n<br>\n\n#### FLASH_SSF_Terra-FM1-MODIS\nFLASH_SSF_Terra-FM1-MODIS_Version4A is the Fast Longwave And Shortwave Radiative Fluxes (FLASHFlux) Clouds and Radiative Swath (SSF) TERRA-FM1 data in HDF Version 4A data product. This product consists of Low latency (< 5 days from observation) Top-of-Atmosphere (TOA) fluxes and parameterized surface radiative fluxes at Clouds and the Earth's Radiant Energy Systems (CERES) Single Scanner Footprint (SSF) level for quick-look purposes. FLASHFlux data are a product line of the CERES project designed to process and release TOA and surface radiative fluxes for applied sciences and education uses. The FLASHFlux data product is a rapid-release product based on the algorithms developed for and data collected by the CERES project. CERES is currently producing world-class climate data products derived from measurements taken aboard NASA's Terra and Aqua spacecraft. While of exceptional fidelity, these data products require considerable processing time to assure quality, verify accuracy, and assess precision. The result is that CERES data are typically released up to six months after acquiring the initial measurements. Such delays are of little consequence for climate studies, especially considering the improved quality of the released data products. Thus, FLASHFlux products are not intended to achieve climate quality. FLASHFlux data products were envisioned as a resource whereby CERES data could be provided to the community within a few days of the initial measurements, with some calibration accuracy requirements relaxed to gain speed. The SSF TOA/Surface Fluxes and Clouds product contains one hour of instantaneous FLASHFlux data for a single CERES scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager, such as Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites and meteorological and ozone information from The Goddard Earth Observing System (GEOS) GEOS-5 FP-IT Atmospheric Data Assimilation System (GEOS-5 ADAS). Scene identification and cloud properties are defined at the higher image resolution, and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains Top-of-Atmosphere fluxes in SW, LW, and NET, surface fluxes using the Langley parameterized shortwave and longwave algorithms, and cloud information. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES mission is a follow-up to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4, 2002. The newest CERES instrument, Flight Model 5 (FM5), was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite.\n<br>\n\n#### FLASH_TISA_NOAA20\nFLASH_TISA_NOAA20_Version1A is the Fast Longwave And SHortwave Fluxes (FLASHFlux) Daily Gridded Single Satellite Top-of-Atmosphere (TOA) and Surfaces/Clouds Version 1A data product. This product contains low latency (< 7 days from observations) NOAA-20 FLASHFlux Single Scanner Footprint (SSF) globally gridded TOA and parameterized surface radiative fluxes for applied science uses. Data collection for this product is in progress.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br><br>\n","Documentation":"https://ceres.larc.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","hdf","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"FLASH_SSF_Terra-FM1-MODIS vVersion4A - FLASH_SSF_Terra-FM1-MODIS_Version4A is the Fast Longwave And Shortwave Radiative Fluxes (FLASHFlux) Clouds and Radiative Swath (SSF) TERRA-FM1 data in HDF Version 4A data product. This product consists of Low latency (< 5 days from observation) Top-of-Atmosphere (TOA) fluxes and parameterized surface radiative fluxes at Clouds and the Earth's Radiant Energy Systems (CERES) Single Scanner Footprint (SSF) level for quick-look purposes.","ARN":"arn:aws:s3:::asdc-prod-protected/FLASHFLUX/FLASH_SSF_Terra-FM1-MODIS_Version4A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"FLASH_TISA_NOAA20 vVersion1A - FLASH_TISA_NOAA20_Version1A is the Fast Longwave And SHortwave Fluxes (FLASHFlux) Daily Gridded Single Satellite Top-of-Atmosphere (TOA) and Surfaces/Clouds Version 1A data product. This product contains low latency (< 7 days from observations) NOAA-20 FLASHFlux Single Scanner Footprint (SSF) globally gridded TOA and parameterized surface radiative fluxes for applied science uses.","ARN":"arn:aws:s3:::asdc-prod-protected/FLASHFLUX/FLASH_TISA_NOAA20_Version1A","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-flashflux","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA FLDAS Project","Description":"This dataset contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), adapted from Land Information System (LIS7). The dataset contains 28 parameters in a 0.10 degree spatial resolution and from January 2019 to present. The temporal resolution is monthly and the spatial coverage is global (60S, 180W, 90N, 180E). The simulation was forced by a combination of the Global Data Assimilation System (GDAS) data and Climate Hazards Group InfraRed Precipitation with Station Preliminary (CHIRPS-PRELIM) 6-hourly rainfall data that has been downscaled using the NASA Land Data Toolkit, restarted from CHIRPS-FINAL of the previous month. The simulation was initialized on January 1, 2019 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year.\n<br>\n\n#### FLDAS_NOAH01_C_GL_M\nThis dataset contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The data are in 0.10 degree resolution and range from January 1982 to present. The temporal resolution is monthly and the spatial coverage is global (60S, 180W, 90N, 180E). The FLDAS regional monthly datasets will no longer be available and have been superseded by the global monthly dataset. The simulation was forced by a combination of the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) 6-hourly rainfall data that has been downscaled using the NASA Land Data Toolkit. The simulation was initialized on January 1, 1982 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year. In November 2020, all FLDAS data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information on this can be found in the FLDAS README document, and the MOD44W MODIS land mask is available on the FLDAS Project site. If you had downloaded any FLDAS data prior to November 2020, please download the data again to receive the post-processed data.\n<br>\n\n#### FLDAS_NOAH01_C_GL_MA\nThe monthly anomaly data set contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The dataset comprises of monthly files, each representing how the month compares to the 35-year monthly climatology from 1982 to 2016, based on the FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS) V001 (FLDAS_NOAH01_C_GL_M_001) monthly data. The data are in 0.10 degree resolution and the spatial coverage is global (60S, 180W, 90N, 180E). The FLDAS regional monthly anomaly datasets will no longer be available and have been superseded by the global monthly anomaly dataset. More information about the monthly FLDAS can be found from the dataset landing page for FLDAS_NOAH01_C_GL_M_001 and the FLDAS README document. In November 2020, all FLDAS data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information on this can be found in the FLDAS README document, and the MOD44W MODIS land mask is available on the FLDAS Project site. If you had downloaded any FLDAS data prior to November 2020, please download the data again to receive the post-processed data.\n<br>\n\n#### FLDAS_NOAH01_C_GL_MC\nThe monthly climatology dataset contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The dataset comprises of 12 monthly files, each representing the monthly data averaged over 35 years from 1982 to 2016, based on the FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS) V001 (FLDAS_NOAH01_C_GL_M_001) monthly data. The data are in 0.10 degree resolution and the spatial coverage is global (60S, 180W, 90N, 180E). The FLDAS regional monthly climatology datasets will no longer be available and have been superseded by the global monthly climatology dataset. More information about the monthly FLDAS can be found from the dataset landing page for FLDAS_NOAH01_C_GL_M_001 and the FLDAS README document. In November 2020, all FLDAS data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information on this can be found in the FLDAS README document, and the MOD44W MODIS land mask is available on the FLDAS Project site. If you had downloaded any FLDAS data prior to November 2020, please download the data again to receive the post-processed data.\n<br>\n\n#### FLDAS_NOAHMP001_G_CA_D\nThis dataset contains land surface parameters simulated by the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System version 2 (FLDAS2) Central Asia model. The FLDAS2 Central Asia model is a custom instance of the NASA Land Information System that has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing country settings. The data are produced using the Noah Multi-Parameterization (Noah-MP) version 4.0.1 Land Surface Model (LSM) forced by Global Data Assimilation System (GDAS) meteorological data. The FLDAS2 Central Asia dataset is produced daily with a one-day latency. Data are available from October 1, 2000 to present. The dataset contains 27 parameters at a 0.01 degree spatial resolution over the Central Asia region (30-100°E, 21-56°N).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://ldas.gsfc.nasa.gov/FLDAS/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","precipitation","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"FLDAS_NOAH01_CP_GL_M v001 - This dataset contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), adapted from Land Information System (LIS7). The dataset contains 28 parameters in a 0.10 degree spatial resolution and from January 2019 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/FLDAS/FLDAS_NOAH01_CP_GL_M.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"FLDAS_NOAH01_C_GL_M v001 - This dataset contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The data are in 0.10 degree resolution and range from January 1982 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/FLDAS/FLDAS_NOAH01_C_GL_M.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"FLDAS_NOAH01_C_GL_MA v001 - The monthly anomaly data set contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The dataset comprises of monthly files, each representing how the month compares to the 35-year monthly climatology from 1982 to 2016, based on the FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS) V001 (FLDAS_NOAH01_C_GL_M_001) monthly data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/FLDAS/FLDAS_NOAH01_C_GL_MA.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"FLDAS_NOAH01_C_GL_MC v001 - The monthly climatology dataset contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The dataset comprises of 12 monthly files, each representing the monthly data averaged over 35 years from 1982 to 2016, based on the FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS) V001 (FLDAS_NOAH01_C_GL_M_001) monthly data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/FLDAS/FLDAS_NOAH01_C_GL_MC.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"FLDAS_NOAHMP001_G_CA_D v001 - This dataset contains land surface parameters simulated by the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System version 2 (FLDAS2) Central Asia model. The FLDAS2 Central Asia model is a custom instance of the NASA Land Information System that has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing country settings.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/FLDAS/FLDAS_NOAHMP001_G_CA_D.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-fldas","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Fluxnet Project","Description":"CO2 and water vapor fluxes and ecosystem characteristics were measured at 24 sites along a 317-km transect from the Arctic coast to the latitudinal treeline in Alaska during the growing seasons of 1994-1996. The sites were stratified to sample the ranges of climate, physiography, soil moisture, and vegetation type within the region. Our main objective was to understand what factors control variations in CO2 and water vapor exchange across the region. We therefore developed a spatially extensive approach of documenting fluxes for 1-2 weeks at each of the sites in order to study as many sites as possible during the middle of the short arctic growing season, when plant phenology is most comparable among different vegetation types and climatic regions. This allowed us to compare, with some replication, a given vegetation type across different provinces and climatic zones, as well as multiple vegetation types within a given geographic area.\n<br>\n\n#### FLUXNET_Canada_1335\nFLUXNET Canada is a Fluxnet research network comprised of the Fluxnet-Canada Research Network (FCRN) and the Canadian Carbon Program (CCP) operating from 1993 through 2014. It was a national research network of university and government scientists studying the influence of climate and disturbance on carbon cycling along an east-west transect of Canadian forest and peat land ecosystems. The data provided are measured and modeled results as obtained from the site investigators. They were not standardized and quality-controlled. Data include: atmospheric carbon dioxide (CO2) and water vapor fluxes and many ancillary meteorological variables; soil CO2 efflux and soil moisture; stable carbon isotopes; site soil and vegetation characteristics, plus documentation and descriptions for the 32 tower sites across 12 flux research stations. The time period is from 1993 - 2014; most reported data for a site does not cover the entire period.\n<br>\n\n#### gap_filled_marconi_811\nFluxes of carbon dioxide, water vapor, and energy exchange have been measured at 38 forest, grassland, and crop sites as part of the EUROFLUX and AmeriFlux projects. A total of 97 site-years of data were compiled, primarily between 1996 and 1998 but also for 1992-1995 and 1999-2000. Half-hour flux and meteorology measurements are included plus the gap-filled half-hour estimates and aggregations to day and night, weekly, monthly, and annual periods. The FLUXNET 2000 Synthesis Workshop was held at the Marconi Conference Center, Marshall, California, June 11-14, 2000. The Marconi Flux Data Collection was compiled to aid in exploring the interactions between the terrestrial biosphere and the overlying atmosphere through carbon, water, and energy exchanges. The workshop resulted in several studies to synthesize and interpret differences and similarities in long-term measurements of carbon dioxide, water vapor, and energy exchanges between vegetation and the atmosphere for a spectrum of ecosystems. A series of synthesis papers based on these data and studies was published in a special issue of the Agriculture and Forest Meteorology, Volume 113, 2002. The papers are listed in the reference section. This data product is being archived as a record of the data used the AFM special issue. Updates and revisions to the data are available at the FLUXNET web site.The eddy covariance technique is used for long-term continuous measurements of mass and energy fluxes to capture seasonal dynamics and allow for a meaningful scaling with respect to time. The equipment and methodology were standardized among sites by using common software and instrumentation. Comparisons of ecosystem fluxes among sites are usually performed on annual or monthly sums calculated on complete data records; however, the average site data coverage during a year was only 65%. Therefore, development and application of robust and consistent data gap-filling methods was required before fluxes could be calculated. One of the outcomes of the FLUXNET project was computer applications to process the data into complete, consistent, quality assured, and documented data sets (Falge et al. 2001a,b). Gap-filled flux data from four different filling methods are reported. Selected meteorological parameters were also gap filled to support flux estimating methods and are reported along with non-filled meteorological data. Note that the measured/estimated CO2 fluxes and storage fluxes were summed into net ecosystem exchange (NEE), and ONLY NEE data are reported.\n<br>\n\n#### Fluxnet_site_DB_1530\nFLUXNET is a global network of micrometeorological tower sites that use eddy covariance methods to measure the exchanges of carbon dioxide, water vapor, and energy between terrestrial ecosystems and the atmosphere. This dataset provides information from the ORNL DAAC-hosted FLUXNET site database which was discontinued in 2016. The files provided contain a list of investigators associated with each tower site, site locations and environmental data, and a bibliography of papers that used FLUXNET data. For more up to date information on FLUXNET sites, see http://fluxnet.fluxdata.org/.\n<br>\n\n#### Fluxnet_website_archive_copy_1549\nThis dataset contains an archived copy of the fluxnet.ornl.gov website as of September 2017. This archived website is provided for informational purposes only. The last updates to the website and the underlying database were made in October 2016. Support for the Fluxnet project and website was transitioned to http://fluxnet.fluxdata.org in September 2017. Please see http://fluxnet.fluxdata.org/ for information on site locations, data availability, and to add or update a flux tower site.\n<br>\n\n#### net_carbon_flux_662\nThe variability of net surface carbon assimilation (Asmax), net ecosystem surface respiration (Rsmax), and net surface evapotranspiration (Etsmax) among and within vegetation types was examined based on a review of studies performed in either a micrometeorological setting or an enclosure setting. The majority of studies involved forests and C3 crops, particularly in the northern hemisphere; however, studies on tropical forests, C4 grasslands or wetlands were included. Data are presented for 133 published studies, although individual studies may not have measure all variables of interest.Despite large variations within a vegetation type, enclosure studies tended to give highest Asmax rates compared to micrometeorological techniques (Buchmann and Schulze 1999). Excluding enclosure studies, the investigators tested the effect of stand age and leaf area index (LAI) on net ecosystem gas exchange. The results from these analyses illustrate where gaps in scientific knowledge exist and how ecosystem properties affect the capacity of net ecosystem gas exchange.The information was collected from papers with publication dates from 1969-1998. Mean maximum flux rates for the period chosen by the authors were used instead of absolute maximum values for flux rates. Positive values stand for CO2 uptake by the vegetation and negative values represent CO2 release from the ecosystem. More information about the compilation can be found in Buchmann and Schultze (1999) or the individual studies cited in the data table.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Arctic_Flux_629 v1 - CO2 and water vapor fluxes and ecosystem characteristics were measured at 24 sites along a 317-km transect from the Arctic coast to the latitudinal treeline in Alaska during the growing seasons of 1994-1996. The sites were stratified to sample the ranges of climate, physiography, soil moisture, and vegetation type within the region.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fluxnet/Arctic_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"FLUXNET_Canada_1335 v1 - FLUXNET Canada is a Fluxnet research network comprised of the Fluxnet-Canada Research Network (FCRN) and the Canadian Carbon Program (CCP) operating from 1993 through 2014. It was a national research network of university and government scientists studying the influence of climate and disturbance on carbon cycling along an east-west transect of Canadian forest and peat land ecosystems.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fluxnet/FLUXNET_Canada/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"gap_filled_marconi_811 v1 - Fluxes of carbon dioxide, water vapor, and energy exchange have been measured at 38 forest, grassland, and crop sites as part of the EUROFLUX and AmeriFlux projects. A total of 97 site-years of data were compiled, primarily between 1996 and 1998 but also for 1992-1995 and 1999-2000.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fluxnet/gap_filled_marconi/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Fluxnet_site_DB_1530 v1 - FLUXNET is a global network of micrometeorological tower sites that use eddy covariance methods to measure the exchanges of carbon dioxide, water vapor, and energy between terrestrial ecosystems and the atmosphere. This dataset provides information from the ORNL DAAC-hosted FLUXNET site database which was discontinued in 2016.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fluxnet/Fluxnet_site_DB/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Fluxnet_website_archive_copy_1549 v1 - This dataset contains an archived copy of the fluxnet.ornl.gov website as of September 2017. This archived website is provided for informational purposes only.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fluxnet/Fluxnet_website_archive_copy/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"net_carbon_flux_662 v1 - The variability of net surface carbon assimilation (Asmax), net ecosystem surface respiration (Rsmax), and net surface evapotranspiration (Etsmax) among and within vegetation types was examined based on a review of studies performed in either a micrometeorological setting or an enclosure setting. The majority of studies involved forests and C3 crops, particularly in the northern hemisphere; however, studies on tropical forests, C4 grasslands or wetlands were included.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/fluxnet/net_carbon_flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-fluxnet","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA G-LiHT Project","Description":"Goddard’s LiDAR, Hyperspectral, and Thermal Imager ([G-LiHT](https://gliht.gsfc.nasa.gov/)) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Aerial Orthomosaic data product (GLORTHO) is to provide orthorectified high-resolution aerial photography. This data is provided as a supplement to other G-LiHT data products. GLORTHO data are processed as a raster data product (GeoTIFF) at 1 inch spatial resolution over locally defined areas. A low resolution browse is also provided with a color map applied in PNG format. Known Issues: Orthomosaics are automatically generated, and results may not be optimal.\n<br>\n\n#### GLCHMK\nGoddard’s LiDAR, Hyperspectral, and Thermal Imager ([G-LiHT](https://gliht.gsfc.nasa.gov/)) mission utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Canopy Height Model Keyhole Markup Language (KML) data product (GLCHMK) is to provide LiDAR-derived maximum canopy height and canopy variability information to aid in the study and analysis of biodiversity and climate change. Scientists at NASA’s Goddard Space Flight Center began collecting data over locally-defined areas in 2011 and that the collection will continue to grow as aerial campaigns are flown and processed. GLCHMK data are processed as a Google Earth overlay KML file at a nominal 1 meter spatial resolution over locally-defined areas. A low resolution browse is also provided showing the canopy height with a color map applied in JPEG format.\n<br>\n\n#### GLDSMT\nGoddard’s LiDAR, Hyperspectral, and Thermal Imagery ([G-LiHT](https://gliht.gsfc.nasa.gov/)) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Digital Surface Model data product (GLDSMT) is to provide LiDAR-derived visualizations of elevation above bare earth. GLDSMT data is offered in multiple formats, including Digital Surface Model, Mean, Aspect, Rugosity, and Slope. GLDSMT data are processed as multiple raster data products (GeoTIFFs) at a nominal 1 meter spatial resolution over locally defined areas. A low resolution browse is also provided showing the digital surface model with a color map applied in PNG format.\n<br>\n\n#### GLTRAJECTORY\nGoddard’s LiDAR, Hyperspectral, and Thermal Imagery ([G-LiHT](https://gliht.gsfc.nasa.gov/)) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Trajectory data product (GLTRAJECTORY) is to provide aircraft location and orientation to support and supplement other G-LiHT data products. GLTRAJECTORY data are processed as a Google Earth overlay Keyhole Markup Language (KML) file over the extent of an entire flight path. A low resolution browse is also provided to show the flight path.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/centers/lp-daac","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","elevation","lidar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GLORTHO v001 - Goddard’s LiDAR, Hyperspectral, and Thermal Imager ([G-LiHT](https://gliht.gsfc.nasa.gov/)) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.","ARN":"arn:aws:s3:::lp-prod-protected/GLORTHO.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GLCHMK v001 - Goddard’s LiDAR, Hyperspectral, and Thermal Imager ([G-LiHT](https://gliht.gsfc.nasa.gov/)) mission utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.","ARN":"arn:aws:s3:::lp-prod-protected/GLCHMK.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GLDSMT v001 - Goddard’s LiDAR, Hyperspectral, and Thermal Imagery ([G-LiHT](https://gliht.gsfc.nasa.gov/)) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.","ARN":"arn:aws:s3:::lp-prod-protected/GLDSMT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GLTRAJECTORY v001 - Goddard’s LiDAR, Hyperspectral, and Thermal Imagery ([G-LiHT](https://gliht.gsfc.nasa.gov/)) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.","ARN":"arn:aws:s3:::lp-prod-protected/GLTRAJECTORY.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-g-liht","Sources":["awslabs-open-data-registry"]}
{"Name":"Mars Spectrometry 2: Gas Chromatography for the Sample Analysis at Mars Data (SAM) Instrument","Description":"NASA missions like the Curiosity and Perseverance rovers carry a rich array of instruments suited to collect data and build evidence towards answering if Mars ever had livable environmental conditions. These rovers can collect rock and soil samples and can take measurements that can be used to determine their chemical makeup. <br/><br/>\nBecause communication between rovers and Earth is severely constrained, with limited transfer rates and short daily communication windows, scientists have a limited time to analyze the data and make difficult inferences about the chemistry in order to prioritize the next operations and send those instructions back to the rover. <br/><br/>\nThis project aimed at building a model to automatically analyze gas chromatography mass spectrometry (GCMS) data collected for Mars exploration in order to help the scientists in their analysis of understanding the past habitability of Mars. <br/><br/>\nMore information are available at https://mars.nasa.gov/msl/spacecraft/instruments/sam/ and the data from Mars are available and described at https://pds-geosciences.wustl.edu/missions/msl/sam.htm. <br/><br/>\n\nWe request that you cite the following ackowledgement when using the data provided from this NASA Project: \"NASA provided support for the development of SAM. The datasets for these 2 challenges were provided by NASA Goddard Space Flight Center and NASA Johnson Space Center. They have been collected by the Sample Analysis at Mars (SAM) scientists and specifically processed for these challenges with the help of the scientists from NASA: Doug Archer, Charles Malespin, Caroline Freissinet, Stephanie Getty, Luoth Chou, Eric Lyness, and Victoria Da Poian, and the DrivenData team. Data from all SAM experiments are archived in the Planetary Data System (pds.nasa.gov).\"\n","Documentation":"https://pds-geosciences.wustl.edu/missions/msl/sam.htm","Contact":"victoria.dapoian@nasa.gov, eric.i.lyness@nasa.gov","ManagedBy":"NASA","UpdateFrequency":"\"Static dataset for the commercial data. When more data is collected on the SAM instrument on Mars, the data will be available on the Planetary Data System [website](https://pds-geosciences.wustl.edu/missions/msl/sam.htm)\" \n","Tags":["analytics","archives","aws-pds","deep learning","machine learning","NASA SMD AI","planetary"],"License":"There are no restrictions on the use, access, and/or download of data from the NASA project.","Resources":[{"Description":"GCMS SAM Instrument Exploration Dataset","ARN":"arn:aws:s3:::gcms-samdata-mlchallenge","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Publications":[{"Title":"Mars Spectrometry: Mars Spectrometry 2 (Challenge Results)","URL":"https://www.drivendata.org/competitions/97/nasa-mars-gcms/","AuthorName":"DrivenData team and NASA partners"}]},"RegistryEntryAdded":"2023-01-26","RegistryEntryLastModified":"2023-01-31","Slug":"nasa-gcms","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA GCOM-W Project","Description":"AMSR2/GCOM-W1 downscaled surface soil moisture (LPRM) L2B V001 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM). Each swath is packaged with associated geolocation fields. The data set covers the period from May 2012, when the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-1st Water GCOM-W1 satellite was launched, to the present. The spatial resolution of the data is based on a resampling of the nominally 46 and 31 km resolutions, respectively, of AMSR2's C and X bands (6.9/7.3 and 10.7 GHz, respectively) to 25 km by 25 km and then a downscaling, using the smoothing filter-based intensity modulation (SFIM) technique, to 10 km by 10 km grids. The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR2's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the AMSR2 spatial-resolution-matched brightness temperatures (L1SGRTBR) product, archived at JAXA.\n<br>\n\n#### LPRM_AMSR2_SOILM2\nAMSR2/GCOM-W1 surface soil moisture (LPRM) L2B V001 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM). Each swath is packaged with associated geolocation fields. The data set covers the period from May 2012, when the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-1st Water GCOM-W1 satellite was launched, to the present, at a spatial resolution (nominally 46 and 31 km, respectively) of AMSR2's C and X bands (6.9/7.3 and 10.7 GHz, respectively). The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR2's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the AMSR2 spatial-resolution-matched brightness temperatures (L1SGRTBR) product, archived at JAXA.\n<br>\n\n#### LPRM_AMSR2_DS_A_SOILM3\nAMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 10 km x 10 km ascending V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the daytime product. The data set covers the period from May 2012, when the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-1st Water GCOM-W1 satellite was launched, to the present. The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR2's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the AMSR2 spatial-resolution-matched brightness temperatures (L1SGRTBR) product, daytime passes, as processed using LPRM (i.e., LPRM/AMSR2/GCOM-W1 Downscaled Level 2 product, LPRM_AMSR2_DS_SOILM2_V001).\n<br>\n\n#### LPRM_AMSR2_DS_D_SOILM3\nAMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 10 km x 10 km descending V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the nighttime product. The data set covers the period from May 2012, when the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-1st Water GCOM-W1 satellite was launched, to the present. The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR2's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the AMSR2 spatial-resolution-matched brightness temperatures (L1SGRTBR) product, nighttime passes, as processed using LPRM (i.e., LPRM/AMSR2/GCOM-W1 Downscaled Level 2 product, LPRM_AMSR2_DS_SOILM2_V001).\n<br>\n\n#### LPRM_AMSR2_A_SOILM3\nAMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 25 km x 25 km ascending V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the daytime product. The data set covers the period from May 2012, when the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-1st Water GCOM-W1 satellite was launched, to the present. The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR2's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the AMSR2 spatial-resolution-matched brightness temperatures (L1SGRTBR) product, daytime passes, as processed using LPRM (i.e., LPRM/AMSR2/GCOM-W1 Level 2 product, LPRM_AMSR2_SOILM2_V001).\n<br>\n\n#### LPRM_AMSR2_D_SOILM3\nAMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 25 km x 25 km descending V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the nighttime product. The data set covers the period from May 2012, when the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-1st Water GCOM-W1 satellite was launched, to the present. The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR2's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the AMSR2 spatial-resolution-matched brightness temperatures (L1SGRTBR) product, nighttime passes, as processed using LPRM (i.e., LPRM/AMSR2/GCOM-W1 Level 2 product, LPRM_AMSR2_SOILM2_V001).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/LPRM_AMSR2_DS_SOILM2_001.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","earth observation","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"LPRM_AMSR2_DS_SOILM2 v001 - AMSR2/GCOM-W1 downscaled surface soil moisture (LPRM) L2B V001 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_AMSR2_DS_SOILM2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LPRM_AMSR2_SOILM2 v001 - AMSR2/GCOM-W1 surface soil moisture (LPRM) L2B V001 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_AMSR2_SOILM2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LPRM_AMSR2_DS_A_SOILM3 v001 - AMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 10 km x 10 km ascending V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_AMSR2_DS_A_SOILM3.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LPRM_AMSR2_DS_D_SOILM3 v001 - AMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 10 km x 10 km descending V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_AMSR2_DS_D_SOILM3.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LPRM_AMSR2_A_SOILM3 v001 - AMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 25 km x 25 km ascending V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_AMSR2_A_SOILM3.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LPRM_AMSR2_D_SOILM3 v001 - AMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 25 km x 25 km descending V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_AMSR2_D_SOILM3.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-gcom-w","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA GEDI Project","Description":"GEDI Version 1 data products were decommissioned on February 15, 2022. Users are advised to use the improved [GEDI01_B Version 2](https://doi.org/10.5067/GEDI/GEDI01_B.002) data product. The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station and collects data globally between 51.6 degrees N and 51.6 degrees S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. The GEDI Level 1B Geolocated Waveforms product (GEDI01_B) provides geolocated corrected and smoothed waveforms, geolocation parameters, and geophysical corrections for each laser shot for all eight GEDI beams. GEDI01_B data are created by geolocating the GEDI01_A raw waveform data. The GEDI01_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters. The GEDI01 B data product contains 83 variables for each of the eight beams including the geolocated corrected and smoothed waveform datasets and parameters and the accompanying ancillary, geolocation, and geophysical correction. Additional information can be found in the GEDI L1B Product Data Dictionary. Known Issues: Known Issues: Section 6.1 of the User Guide provides additional information on known issues. * Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).\n<br>\n\n#### GEDI01_B\nThe Global Ecosystem Dynamics Investigation ([GEDI](https://gedi.umd.edu/)) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting. The GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024. The GEDI Level 1B Geolocated Waveforms product (GEDI01_B) provides geolocated corrected and smoothed waveforms, geolocation parameters, and geophysical corrections for each laser shot for all eight GEDI beams. GEDI01_B data are created by geolocating the GEDI01_A raw waveform data. The GEDI01_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters. The GEDI01_B data product contains 85 layers for each of the eight beams including the geolocated corrected and smoothed waveform datasets and parameters and the accompanying ancillary, geolocation, and geophysical correction. Additional information can be found in the GEDI L1B Product Data Dictionary. Known Issues: Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8). * Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this [document](https://lpdaac.usgs.gov/documents/2236/GEDI_CORRECTED_RGT_FILENAMES.pptx) for the correct RGT numbers. * Known Issues: Section 8 of the User Guide provides additional information on known issues.\n<br>\n\n#### GEDI02_A\nGEDI Version 1 data products were decommissioned on February 15, 2022. Users are advised to use the improved [GEDI02_A Version 2](https://doi.org/10.5067/GEDI/GEDI02_A.002) data product. The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station and collects data globally between 51.6 degrees N and 51.6 degrees S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. The purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI01_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters. The GEDI02_A data product contains 156 variables for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (describing canopy vertical structure, for example), and many other interpreted products from the return waveforms. Additional information for the variables can be found in the GEDI Level 2A Dictionary. Known Issues: Known Issues: Section 7 of the User Guide provides additional information on known issues. * Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).\n<br>\n\n#### GEDI02_B\nGEDI Version 1 data products were decommissioned on February 15, 2022. Users are advised to use the improved GEDI02_B Version 2 (https://doi.org/10.5067/GEDI/GEDI02_B.002) data product. The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station and collects data globally between 51.6 degrees N and 51.6 degrees S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. The purpose of the GEDI Level 2B Canopy Cover and Vertical Profile Metrics product (GEDI02_B) is to extract biophysical metrics from each GEDI waveform. These metrics are based on the directional gap probability profile derived from the L1B waveform. Metrics provided include canopy cover, Plant Area Index (PAI), Plant Area Volume Density (PAVD), and Foliage Height Diversity (FHD). The GEDI02_B product is provided in HDF-5 format and has a spatial resolution (average footprint) of 25 meters. The GEDI02_B data product contains 96 variables for each of the eight-beam ground transects (or laser footprints located on the land surface). Variables provided include precise latitude, longitude, elevation, height, canopy cover, and vertical profile metrics. Additional information for the variables can be found in the GEDI Level 2B Data Dictionary. Known Issues: Known Issues: Section 7 of the User Guide provides additional information on known issues. * Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).\n<br>\n\n#### GEDI_L3_Land_Surface_Metrics_1865\nThis dataset provides Global Ecosystem Dynamics Investigation (GEDI) Level 3 (L3) gridded mean canopy height, standard deviation of canopy height, mean ground elevation, standard deviation of ground elevation and counts of laser footprints per 1 km x 1 km grid cells globally within -52 and 52 degrees latitude. L3 gridded products can be used to characterize important carbon and water cycling processes, biodiversity, habitat and can also be of immense value for climate modeling, forest management, snow and glacier monitoring, and the generation of digital elevation models. This first release of L3 products are simple averages and standard deviations of the footprint profile metrics within each 1 sq. km cell. Future versions will optimally interpolate data to produce the best estimate of the mean and its error for each grid cell.\n<br>\n\n#### GEDI_L3_LandSurface_Metrics_V2_1952\nThis dataset provides Global Ecosystem Dynamics Investigation (GEDI) Level 3 (L3) gridded mean canopy height, standard deviation of canopy height, mean ground elevation, standard deviation of ground elevation, and counts of laser footprints per 1-km x 1-km grid cells globally within -52 and 52 degrees latitude. These L3 gridded products were derived from Level 2 (L2) geolocated laser footprint return profile metrics from the GEDI instrument onboard the International Space Station (ISS). Canopy height is provided as the mean height (in meters) above the ground of the received waveform signal that was the first reflection off the top of the canopy (RH100). Ground elevation is provided as the mean elevation (in meters) of the center of the lowest waveform mode relative to the WGS84 reference ellipsoid. L3 gridded products can be used to characterize important carbon and water cycling processes, biodiversity, habitat and can also be of immense value for climate modeling, forest management, snow and glacier monitoring, and the generation of digital elevation models. This dataset version uses Version 2 of the input L2 data, which includes improved geolocation of the footprints as well as a modified method to predict an optimum algorithm setting group.\n<br>\n\n#### GEDI_L4A_AGB_Density_GW_2028\nThis dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the mission weeks 19, 32, 34 and 38 (a.k.a. Golden Weeks). These weeks cover the range of instrument operating conditions important for calibration and validation of geolocation algorithms, and also include GEDI orbits that are coincident with underflights acquired by the LVIS (Land, Vegetation, and Ice Sensor) airborne lidar instrument. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint AGBD was derived from parametric models that relate simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to field plot estimates of AGBD. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFT) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands).\n<br>\n\n#### GEDI_L4A_AGB_Density_1907\nThis dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-18 to 2020-09-02. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint AGBD was derived from parametric models that relate simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to field plot estimates of AGBD. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFT) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands).\n<br>\n\n#### GEDI_L4A_AGB_Density_V2_1986\nThis dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits. The algorithm setting group selection used for GEDI02_A Version 2 has been modified for Evergreen Broadleaf Trees in South America to reduce false positive errors resulting from the selection of waveform modes above ground elevation as the lowest mode. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-18 to 2021-08-05. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint AGBD was derived from parametric models that relate simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to field plot estimates of AGBD. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFTs) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands). For each of the eight beams, additional data are reported with the AGBD estimates, including the associated uncertainty metrics, quality flags, model inputs, and other information about the GEDI L2A waveform for this selected algorithm setting group. Model inputs include the scaled and transformed GEDI L2A RH metrics, footprint geolocation variables and land cover input data including PFTs and the world region identifiers. Additional model outputs include the AGBD predictions for each of the six GEDI L2A algorithm setting groups with AGBD in natural and transformed units and associated prediction uncertainty for each GEDI L2A algorithm setting group. Providing these ancillary data products will allow users to evaluate and select alternative algorithm setting groups. Also provided are outputs of parameters and variables from the L4A models used to generate AGBD predictions that are required as input to the GEDI04_B algorithm to generate 1-km gridded products. Note that there are 351 granules in this release affected by duplicate GEDI shots for selected days (2020-297 to 2020-300, 2020-365, and 2021-106).\n<br>\n\n#### GEDI_L4B_Country_Biomass_2321\nThis dataset provides country-level estimates of land surface mean aboveground biomass density (AGBD), total aboveground biomass (AGB) stocks, and the associated standard errors of the mean calculated using different versions of the Global Ecosystem Dynamics Investigation (GEDI) Level-4B (L4B) product. The GEDI L4B product provides gridded (1 km x 1 km) estimates of AGBD within the GEDI orbital extent (between 51.6 degrees N and 51.6 degrees S). For comparison purposes, this dataset also includes national-scale National Forest Inventory (NFI) estimates of AGBD from the 2020 Global Forest Resources Assessment (FRA) published by the Food and Agriculture Organization (FAO, 2020) of the United Nations. The GEDI instrument produces high-resolution laser ranging observations of the 3-dimensional structure of the Earth's surface. GEDI was launched on December 5, 2018, and is attached to the International Space Station (ISS). The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which consist of ~25 m footprint samples spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. The data are provided in comma-separated value (CSV) format.\n<br>\n\n#### GEDI_L4B_Gridded_Biomass_2017\nThis Global Ecosystem Dynamics Investigation (GEDI) L4B product provides 1 km x 1 km (1 km, hereafter) estimates of mean aboveground biomass density (AGBD) based on observations from mission week 19 starting on 2019-04-18 to mission week 138 ending on 2021-08-04. The GEDI L4A Footprint Biomass product converts each high-quality waveform to an AGBD prediction, and the L4B product uses the sample present within the borders of each 1 km cell to statistically infer mean AGBD. The gridding procedure is described in the GEDI L4B Algorithm Theoretical Basis Document (ATBD). Patterson et al. (2019) describes the hybrid model-based mode of inference used in the L4B product. Corresponding 1 km estimates of the standard error of the mean are also provided in the L4B product. Uncertainty is due to both GEDI's sampling of the 1 km area (as opposed to making wall-to-wall observations) and the fact that L4A biomass values are modeled in a process subject to error instead of measured in a process that may be assumed to be error-free.\n<br>\n\n#### GEDI_L4B_Gridded_Biomass_V2_1_2299\nThis Global Ecosystem Dynamics Investigation (GEDI) L4B product provides 1 km x 1 km (1 km, hereafter) estimates of mean aboveground biomass density (AGBD) based on observations from mission week 19 starting on 2019-04-18 to mission week 223 ending on 2023-03-16. The GEDI L4A Footprint Biomass product converts each high-quality waveform to an AGBD prediction, and the L4B product uses the sample present within the borders of each 1 km cell to statistically infer mean AGBD. The gridding procedure is described in the GEDI L4B Algorithm Theoretical Basis Document (ATBD). Patterson et al. (2019) describes the hybrid model-based mode of inference used in the L4B product. Corresponding 1 km estimates of the standard error of the mean are also provided in the L4B product. Uncertainty is due to both GEDI's sampling of the 1 km area (as opposed to making wall-to-wall observations) and the fact that L4A biomass values are modeled in a process subject to error instead of measured in a process that may be assumed to be error-free.\n<br>\n\n#### GEDI_L4C_WSCI_2338\nThis dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4C (L4C) Version 2 predictions of the Waveform Structural Complexity Index (WSCI) and estimates of prediction intervals for each footprint estimate at 95% confidence. In this version, the granules are in sub-orbits. The algorithm setting group selection used for GEDI04_C is the same as in the GEDI02_A product. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-17 to 2025-03-19. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint WSCI was derived from XGBoost regression models relating simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to a 3D structural complexity metric calculated from matched Airborne laser Scanning (ALS) point clouds. Four global WSCI models were trained on a plant functional type (PFT) basis (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, and the combination of grasslands, shrubs, and woodlands). For each of the eight beams, additional data are reported with the WSCI estimates, including the associated uncertainty metrics, quality flags, and other information about the GEDI L2A waveform for this selected algorithm setting group. Additional model outputs include WSCI predictions for each of the six GEDI L2A algorithm setting groups and associated prediction intervals. Providing these ancillary data products will allow users to evaluate and select alternative algorithm setting groups. The data are provided in HDF5 format.\n<br>\n\n#### GEDI_L4C_WSCI_Gridded_2470\nThis dataset consists of gridded average estimates of Waveform Structural Complexity Index (WSCI) at 1-km resolution on a nearly global scale derived from the Global Ecosystem Dynamics Investigation (GEDI) Level 4C (L4C) footprint-level WSCI data covering the observations starting on 2019-04-18 to 2023-03-17. Mean WSCI values were computed from high quality GEDI shots over the land surface, according to the quality flags available in the GEDI L4C dataset. This dataset enables users to assess spatial patterns of forest structural complexity at a coarse scale anywhere within the GEDI domain without the need to acquire and process GEDI footprint data. The data are provided in cloud-optimized GeoTIFF format.\n<br>\n\n#### GEDI_L4D_Imputed_Waveforms_2455\nThis Global Ecosystem Dynamics Investigation (GEDI) L4D product provides a 30-m spatial elaboration of the mission's sample of footprint-level L2A V002, L2B V002, and L4A Version 2.1 products. A nearest neighbor algorithm was used with Landsat time series data to impute high-quality GEDI shots identified using a combination of metrics. The data were collected between mission week 19 starting on 2019-04-18 to mission week 223 ending on 2023-03-16 to every 30-m pixel. A different nearest neighbor model was developed for every 10 x 10-km tile covering land between 51.6 degrees N and 51.6 degrees S, with \"neighbors\" (potentially imputed shots) drawn from a support area of at least 30 x 30 km centered on the tile. Nearest neighbor classifiers use a distance function to assign one or more reference observations (high-quality shots in this case) to each modeling unit based on multivariate similarity. Imputation of shots for the L4D product assigned the single nearest neighbor to Landsat time series imagery centering on the year 2023. The most basic 30-m output is the shot number of the imputed pixel (relayed in fragments across five fields), from which any shot-level quantity may be retrieved. Predictions for the following commonly used waveform attributes are also included: RH (relative height) 10, 20, ..., 90, 95, and 98 from the L2A product; canopy cover from L2B; and aboveground biomass density (AGBD) from L4A. Model diagnostics are delivered for each 10-km model in a separate product, which gives the root mean square error (RMSE) for each 30-m output as well as results of a Kolmogorov-Smirnov test comparing the 10-km distribution of GEDI observations and 30-m predictions.\n<br>\n\n#### GEDI_FIA_LIDAR_Models_Forests_2417\nThis dataset includes interpolated cumulative waveforms, with uncertainties, over national forest inventory (FIA) field plots across the contiguous United States. The predicted waveforms are for the Global Ecosystem Dynamics Investigation (GEDI) instrument, which produces high resolution laser ranging observations of the 3D structure of the Earth. GEDI's data provides precise measurements of forest canopy height, canopy vertical structure, and surface elevation. This dataset also provides R scripts to extract information from user-selected plots and for training linear regression models between GEDI lidar metrics and target forest attributes. The interpolated waveforms are provided in RData and JSON formats. A table of Forest Inventory and Analysis (FIA) plot information is included in comma separated values format\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/centers/lp-daac","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","climate","elevation","hdf","hdf5","ice","land cover","lidar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GEDI01_B v001 - GEDI Version 1 data products were decommissioned on February 15, 2022. Users are advised to use the improved [GEDI01_B Version 2](https://doi.org/10.5067/GEDI/GEDI01_B.002) data product.","ARN":"arn:aws:s3:::lp-prod-protected/GEDI01_B.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GEDI01_B v002 - The Global Ecosystem Dynamics Investigation ([GEDI](https://gedi.umd.edu/)) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth.","ARN":"arn:aws:s3:::lp-prod-protected/GEDI01_B.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GEDI02_A v001 - GEDI Version 1 data products were decommissioned on February 15, 2022. Users are advised to use the improved [GEDI02_A Version 2](https://doi.org/10.5067/GEDI/GEDI02_A.002) data product.","ARN":"arn:aws:s3:::lp-prod-protected/GEDI02_A.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GEDI02_B v001 - GEDI Version 1 data products were decommissioned on February 15, 2022. Users are advised to use the improved GEDI02_B Version 2 (https://doi.org/10.5067/GEDI/GEDI02_B.002) data product.","ARN":"arn:aws:s3:::lp-prod-protected/GEDI02_B.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GEDI_L3_Land_Surface_Metrics_1865 v1 - This dataset provides Global Ecosystem Dynamics Investigation (GEDI) Level 3 (L3) gridded mean canopy height, standard deviation of canopy height, mean ground elevation, standard deviation of ground elevation and counts of laser footprints per 1 km x 1 km grid cells globally within -52 and 52 degrees latitude. L3 gridded products can be used to characterize important carbon and water cycling processes, biodiversity, habitat and can also be of immense value for climate modeling, forest management, snow and glacier monitoring, and the generation of digital elevation models.","ARN":"arn:aws:s3:::gedi-l3-land-surface-metrics-1865","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.uat.earthdata.nasa.gov/s3credentials"},{"Description":"GEDI_L3_LandSurface_Metrics_V2_1952 v2 - This dataset provides Global Ecosystem Dynamics Investigation (GEDI) Level 3 (L3) gridded mean canopy height, standard deviation of canopy height, mean ground elevation, standard deviation of ground elevation, and counts of laser footprints per 1-km x 1-km grid cells globally within -52 and 52 degrees latitude. These L3 gridded products were derived from Level 2 (L2) geolocated laser footprint return profile metrics from the GEDI instrument onboard the International Space Station (ISS).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/gedi/GEDI_L3_LandSurface_Metrics_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GEDI_L4A_AGB_Density_GW_2028 v1.1 - This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the mission weeks 19, 32, 34 and 38 (a.k.a.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density_GW/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GEDI_L4A_AGB_Density_1907 v1 - This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-18 to 2020-09-02.","ARN":"arn:aws:s3:::gedi-l4a-agb-density-1907","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.uat.earthdata.nasa.gov/s3credentials"},{"Description":"GEDI_L4A_AGB_Density_V2_1986 v2 - This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits.","ARN":"arn:aws:s3:::gedi-l4a-agb-density-v2-1986","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.uat.earthdata.nasa.gov/s3credentials"},{"Description":"GEDI_L4B_Country_Biomass_2321 v1 - This dataset provides country-level estimates of land surface mean aboveground biomass density (AGBD), total aboveground biomass (AGB) stocks, and the associated standard errors of the mean calculated using different versions of the Global Ecosystem Dynamics Investigation (GEDI) Level-4B (L4B) product. The GEDI L4B product provides gridded (1 km x 1 km) estimates of AGBD within the GEDI orbital extent (between 51.6 degrees N and 51.6 degrees S).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/gedi/GEDI_L4B_Country_Biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GEDI_L4B_Gridded_Biomass_2017 v2 - This Global Ecosystem Dynamics Investigation (GEDI) L4B product provides 1 km x 1 km (1 km, hereafter) estimates of mean aboveground biomass density (AGBD) based on observations from mission week 19 starting on 2019-04-18 to mission week 138 ending on 2021-08-04. The GEDI L4A Footprint Biomass product converts each high-quality waveform to an AGBD prediction, and the L4B product uses the sample present within the borders of each 1 km cell to statistically infer mean AGBD.","ARN":"arn:aws:s3:::gedi-l4b-gridded-biomass-2017","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.uat.earthdata.nasa.gov/s3credentials"},{"Description":"GEDI_L4B_Gridded_Biomass_V2_1_2299 v2.1 - This Global Ecosystem Dynamics Investigation (GEDI) L4B product provides 1 km x 1 km (1 km, hereafter) estimates of mean aboveground biomass density (AGBD) based on observations from mission week 19 starting on 2019-04-18 to mission week 223 ending on 2023-03-16. The GEDI L4A Footprint Biomass product converts each high-quality waveform to an AGBD prediction, and the L4B product uses the sample present within the borders of each 1 km cell to statistically infer mean AGBD.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/gedi/GEDI_L4B_Gridded_Biomass_V2_1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GEDI_L4C_WSCI_2338 v2 - This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4C (L4C) Version 2 predictions of the Waveform Structural Complexity Index (WSCI) and estimates of prediction intervals for each footprint estimate at 95% confidence. In this version, the granules are in sub-orbits.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/gedi/GEDI_L4C_WSCI/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GEDI_L4C_WSCI_Gridded_2470 v2 - This dataset consists of gridded average estimates of Waveform Structural Complexity Index (WSCI) at 1-km resolution on a nearly global scale derived from the Global Ecosystem Dynamics Investigation (GEDI) Level 4C (L4C) footprint-level WSCI data covering the observations starting on 2019-04-18 to 2023-03-17. Mean WSCI values were computed from high quality GEDI shots over the land surface, according to the quality flags available in the GEDI L4C dataset.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/gedi/GEDI_L4C_WSCI_Gridded/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GEDI_L4D_Imputed_Waveforms_2455 v2 - This Global Ecosystem Dynamics Investigation (GEDI) L4D product provides a 30-m spatial elaboration of the mission's sample of footprint-level L2A V002, L2B V002, and L4A Version 2.1 products. A nearest neighbor algorithm was used with Landsat time series data to impute high-quality GEDI shots identified using a combination of metrics.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/gedi/GEDI_L4D_Imputed_Waveforms/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GEDI_FIA_LIDAR_Models_Forests_2417 v1 - This dataset includes interpolated cumulative waveforms, with uncertainties, over national forest inventory (FIA) field plots across the contiguous United States. The predicted waveforms are for the Global Ecosystem Dynamics Investigation (GEDI) instrument, which produces high resolution laser ranging observations of the 3D structure of the Earth.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/gedi/GEDI_FIA_LIDAR_Models_Forests/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-gedi","Sources":["awslabs-open-data-registry"]}
{"Name":"GEDI L2A Elevation and Height Metrics Data Global Footprint Level V002","Description":"The Global Ecosystem Dynamics Investigation ([GEDI](https://gedi.umd.edu/)) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.\n\nThe GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024.\n\nThe purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI02_A product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.\n\nThe GEDI02_A data product contains 156 layers for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (e.g., canopy vertical structure), and many other interpreted products from the return waveforms. Additional information for the layers can be found in the GEDI Level 2A Dictionary.\n\nKnown Issues\n\n* Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).\n* Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this [document](https://lpdaac.usgs.gov/documents/2236/GEDI_CORRECTED_RGT_FILENAMES.pptx) for the correct RGT numbers.\n* Known Issues: Section 8 of the User Guide provides additional information on known issues.\n\nImprovements/Changes from Previous Versions\n\n* Metadata has been updated to include spatial coordinates.\n* Granule size has been reduced from one full ISS orbit (~5.83 GB) to four segments per orbit (~1.48 GB).\n* Filename has been updated to include segment number and version number.\n* Improved geolocation for an orbital segment.\n* Added elevation from the SRTM digital elevation model for comparison.\n* Modified the method to predict an optimum algorithm setting group per laser shot.\n* Added additional land cover datasets related to phenology, urban infrastructure, and water persistence.\n* Added selected_mode_flag dataset to root beam group using selected algorithm.\n* Removed shots when the laser is not firing.\n* Modified file name to include segment number and dataset version.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/GEDI/GEDI02_A.002","Contact":"User Services: lpdaac@usgs.gov","ManagedBy":"NASA","UpdateFrequency":"From 2019-04-04 to 2023-03-16 (Varies)","Tags":["aws-pds","biodiversity","carbon","datacenter","earth observation","energy","global","hdf","ice","land","land cover","lidar","metadata","orbit","urban","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GEDI L2A Elevation and Height Metrics Data Global Footprint Level V002.","ARN":"arn:aws:s3:::lp-prod-protected/GEDI02_A.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Getting Started with GEDI L2A Version 2 Data in Python","URL":"https://github.com/nasa/GEDI-Data-Resources/blob/main/python/tutorials/GEDI_L2A_V2_Tutorial.ipynb","AuthorName":"Land Processes Distributed Active Archive Center (LP DAAC)","AuthorURL":"https://lpdaac.usgs.gov/"},{"Title":"How to: Find and Access GEDI Data","URL":"https://github.com/nasa/GEDI-Data-Resources/blob/main/python/tutorials/how-to-find-and-access-GEDI-data_earthaccess.ipynb","AuthorName":"Land Processes Distributed Active Archive Center (LP DAAC)","AuthorURL":"https://lpdaac.usgs.gov/"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-gedi02a","Sources":["awslabs-open-data-registry"]}
{"Name":"GEDI L4A Footprint Level Aboveground Biomass Density, Version 2.1","Description":"This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits. The algorithm setting group selection used for GEDI02_A Version 2 has been modified for Evergreen Broadleaf Trees in South America to reduce false positive errors resulting from the selection of waveform modes above ground elevation as the lowest mode. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-18 to 2024-11-27. No acquisitions occurred while the GEDI instrument was in storage on the International Space Station (ISS) from March 2023 to April 2024. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint AGBD was derived from parametric models that relate simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to field plot estimates of AGBD. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFTs) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands). For each of the eight beams, additional data are reported with the AGBD estimates, including the associated uncertainty metrics, quality flags, model inputs, and other information about the GEDI L2A waveform for this selected algorithm setting group. Model inputs include the scaled and transformed GEDI L2A RH metrics, footprint geolocation variables and land cover input data including PFTs and the world region identifiers. Additional model outputs include the AGBD predictions for each of the six GEDI L2A algorithm setting groups with AGBD in natural and transformed units and associated prediction uncertainty for each GEDI L2A algorithm setting group. Providing these ancillary data products will allow users to evaluate and select alternative algorithm setting groups. Also provided are outputs of parameters and variables from the L4A models used to generate AGBD predictions that are required as input to the GEDI04_B algorithm to generate 1-km gridded products.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.3334/ORNLDAAC/2056","Contact":"ORNL DAAC User Services Office: uso@daac.ornl.gov.","ManagedBy":"NASA","UpdateFrequency":"From 2019-04-17 to 2024-11-27","Tags":["aws-pds","earth observation","ecosystems","global","hdf","land","land cover","lidar","opendap"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GEDI L4A Footprint Level Aboveground Biomass Density, Version 2.1.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density_V2_1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Searching and Downloading GEDI L4A Dataset","URL":"https://github.com/ornldaac/gedi_tutorials/blob/main/notebooks/gedi_l4a_search_download.ipynb","AuthorName":"Rupesh Shrestha"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-gedil4aagbdensityv212056","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA GEOS-3 Project","Description":"These data consist of Geos-3 altimeter measurements produced by NOAA/NODC/Laboratory for Satellite Altimetry. The dataset contains 5,006,956 altimetric sea surface heights and supporting information such as sea state, wind speed, Schwiderski ocean tide height, and Cartwright solid-tide height. Corrections for altimeter bias, wet and dry troposheric delays, and electromagnetic bias are not included. The corrections in this dataset (tides and even orbit height) are old and not very accurate. This dataset should only be used by those with an expertise in altimetry. Measurements are compressed to a rate of 1 per second using a trim mean filter. Data values are written in binary format.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GEOS-3_ALT_GDR v1 - These data consist of Geos-3 altimeter measurements produced by NOAA/NODC/Laboratory for Satellite Altimetry. The dataset contains 5,006,956 altimetric sea surface heights and supporting information such as sea state, wind speed, Schwiderski ocean tide height, and Cartwright solid-tide height.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GEOS-3_ALT_GDR","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-geos-3","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA GFSAD Project","Description":"The Landsat-Derived Global Irrigated-Cropland Product Level 1 2020 (LGRIP30_L1_IRRI) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP_L1_IRRI V2 maps agricultural lands by dividing them into 32 irrigated cropland types and calculates applicable cropland areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. Each LGRIP30 L1 V2 Irrigated 30 m resolution GeoTIFF file contains a layer that identifies areas of irrigated cropland (cropland that had at least one irrigation during the crop growing period) divided into 32 types, non-irrigated land (rainfed cropland and areas not classified as cropland), and water bodies over a 10° by 10° area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. Currently, LGRIP30 V2 products contain data only for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date.\n<br>\n\n#### LGRIP30_L2_IRRI\nThe Landsat-Derived Global Irrigated-Cropland Product Level 2 2020 (LGRIP30_L2_IRRI) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP_L2_IRRI V2 maps agricultural lands by dividing them into irrigated single crop, double crop, and continuous croplands, and calculates applicable cropland areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. Each LGRIP L2 Irrigated 30 m resolution GeoTIFF file contains a layer that identifies areas of irrigated cropland (cropland that had at least one irrigation during the crop growing period) divided into single, double, and continuous crop classifications, non-irrigated land (rainfed cropland and areas not classified as cropland), and water bodies over a 10° by 10° area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. Currently, LGRIP30 V2 products only contain data for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date.\n<br>\n\n#### LGRIP30_L3\nThe Landsat-derived Global Rainfed and Irrigated-Cropland Product Level 3 2020 (LGRIP30_L3) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP L3 V2 maps agricultural croplands by dividing them into irrigated and rainfed croplands, and calculates irrigated and rainfed areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. Each LGRIP30 L3 V2 30 m resolution GeoTIFF file contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation without any artificial watering), irrigated cropland (cropland that had at least one irrigation during the crop growing period), non-cropland, and water bodies over a 10° by 10° area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. Currently, LGRIP30 V2 products only contain data for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date.\n<br>\n\n#### LGRIP30_L1_RAIN\nThe Landsat-Derived Global Rainfed-Cropland Product Level 1 2020 (LGRIP30_L1_RAIN) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP30_L1_RAIN V2 maps agricultural lands by dividing them into 24 types of rainfed croplands and calculates applicable cropland areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. Each LGRIP L1 Rainfed 30 m resolution GeoTIFF file contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation without any artificial watering) divided into 24 types, non-rainfed land (irrigated croplands and areas not classified as cropland), and water bodies over a 10° by 10° area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. Currently, LGRIP30 V2 products only contain data for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date.\n<br>\n\n#### LGRIP30_L2_RAIN\nThe Landsat-Derived Global Rainfed-Cropland Product Level 2 2020 (LGRIP30_L2_RAIN) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP_L2_RAIN V2 maps agricultural lands by dividing them into rainfed single croplands and rainfed single croplands mixed with natural vegetation, and calculates applicable cropland areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. Each LGRIP L2 Rainfed 30 m resolution GeoTIFF file contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation without any artificial watering) divided into single crop and single crop that is mixed with natural vegetation, non-rainfed land (irrigated croplands and areas not classified as cropland), and water bodies over a 10° by 10° area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. Currently, LGRIP30 V2 products only contain data for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/centers/lp-daac","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","precipitation","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"LGRIP30_L1_IRRI v002 - The Landsat-Derived Global Irrigated-Cropland Product Level 1 2020 (LGRIP30_L1_IRRI) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP_L1_IRRI V2 maps agricultural lands by dividing them into 32 irrigated cropland types and calculates applicable cropland areas across the globe.","ARN":"arn:aws:s3:::lp-prod-protected/LGRIP30_L1_IRRI.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"LGRIP30_L2_IRRI v002 - The Landsat-Derived Global Irrigated-Cropland Product Level 2 2020 (LGRIP30_L2_IRRI) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP_L2_IRRI V2 maps agricultural lands by dividing them into irrigated single crop, double crop, and continuous croplands, and calculates applicable cropla...","ARN":"arn:aws:s3:::lp-prod-protected/LGRIP_L2_IRRI.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"LGRIP30_L3 v002 - The Landsat-derived Global Rainfed and Irrigated-Cropland Product Level 3 2020 (LGRIP30_L3) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP L3 V2 maps agricultural croplands by dividing them into irrigated and rainfed croplands, and calculates irrigated and rainfed areas across the g...","ARN":"arn:aws:s3:::lp-prod-protected/LGRIP30_L3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"LGRIP30_L1_RAIN v002 - The Landsat-Derived Global Rainfed-Cropland Product Level 1 2020 (LGRIP30_L1_RAIN) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP30_L1_RAIN V2 maps agricultural lands by dividing them into 24 types of rainfed croplands and calculates applicable cropland areas across the globe.","ARN":"arn:aws:s3:::lp-prod-protected/LGRIP_L1_RAIN.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"LGRIP30_L2_RAIN v002 - The Landsat-Derived Global Rainfed-Cropland Product Level 2 2020 (LGRIP30_L2_RAIN) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data ([GFSAD](https://www.usgs.gov/centers/western-geographic-science-center/science/global-food-security-support-analysis-data-30-m)) project, LGRIP_L2_RAIN V2 maps agricultural lands by dividing them into rainfed single croplands and rainfed single croplands mixed with natural vegetation, and calcula...","ARN":"arn:aws:s3:::lp-prod-protected/LGRIP_L2_RAIN.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-gfsad","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA GHISA Project","Description":"The Global Hyperspectral Imaging Spectral-library of Agricultural crops (GHISA) is a comprehensive compilation, collation, harmonization, and standardization of hyperspectral signatures of agricultural crops of the world. This hyperspectral library of agricultural crops is developed for all major world crops and was collected by United States Geological Survey (USGS) and partnering volunteer agencies from around the world. Crops include wheat, rice, barley, corn, soybeans, cotton, sugarcane, potatoes, chickpeas, lentils, and pigeon peas, which together occupy about 65% of all global cropland areas. The GHISA spectral libraries were collected and collated using spaceborne, airborne (e.g., aircrafts and drones), and ground based hyperspectral imaging spectroscopy. The GHISA for Central Asia (GHISACASIA) Version 1 product provides dominant crop data (wheat, rice, corn, alfalfa, and cotton) in different growth stages across the Galaba and Kuva farm fields in the Syr Darya river basin in Central Asia. The GHISA hyperspectral library for the two irrigated areas was developed using Earth Observing-1 (EO-1) Hyperion hyperspectral data acquired in 2007 and ASD (Analytical Spectral Devices, Inc.) Spectroradiometer data acquired in 2006 and 2007. GHISACASIA is extracted from three Hyperion hyperspectral images and several thousands of field ASD Spectroradiometer data. Measurements were taken from 1,232 randomly chosen points scattered across the two farm sites throughout the growing season. All the processing algorithms are coded in Statistical Analysis System (SAS) format and available for download. Provided in the .xlsx files are the spectral library including image information, plot IDs, study area, instrument, Julian or acquisition date, and crop type labels for Central Asia sample fields.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/centers/lp-daac","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GHISACASIA v001 - The Global Hyperspectral Imaging Spectral-library of Agricultural crops (GHISA) is a comprehensive compilation, collation, harmonization, and standardization of hyperspectral signatures of agricultural crops of the world. This hyperspectral library of agricultural crops is developed for all major world crops and was collected by United States Geological Survey (USGS) and partnering volunteer agencies from around the world.","ARN":"arn:aws:s3:::lp-prod-protected/GHISACASIA.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-ghisa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA GHRSST Project","Description":"CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.0625 deg. x 0.0625 deg. horizontal resolution over the Black Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Black sea.\n<br>\n\n#### OISST_UHR_NRT-GOS-L4-BLK-v2.0\nCNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.01 deg. x 0.01 deg. horizontal resolution over the Black Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Black sea.\n<br>\n\n#### G18-ABI-L2P-ACSPO-v2.90\nThe G18-ABI-L2P-ACSPO-v2.90 dataset produced by the NOAA ACSPO system is used to derive Subskin and Depth Sea Surface Temperature (SST) from the ABI onboard the G18 satellite. NOAA’s G18 (aka, GOES-T pre-launch) was launched on March 1, 2022, replacing the G17 as GOES West in Jan'2023. It is the third satellite in the US GOES–R Series, the Western Hemisphere’s most sophisticated weather-observing and environmental-monitoring system. The ABI is the primary instrument on the GOES-R Series for imaging Earth’s weather, oceans, and environment. <br><br> G18/ABI maps SST in a Full Disk (FD) area from 163E-77W and 60S-60N, with a spatial resolution of 2km/nadir to 15km/VZA 67-deg, and 10-min temporal sampling. The 10-min FD data are subsequently collated in time, to produce the 1-hr product, with improved coverage and reduced cloud leakages and image noise. The L2P is produced in netCDF4 GDS2 format, with 24 granules per day, and a total data volume 0.8 GB/day. The near-real time (NRT) data are updated hourly, with several hours latency. The NRT files are replaced with Delayed Mode (DM) files, with a latency of ~2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing). <br><br> Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script available at Documents tab under How-To section. The ACSPO G18 ABI SSTs are validated against quality controlled in situ data from the NOAA iQuam system (Xu and Ignatov, 2014) and continuously monitored in NOAA SQUAM system (Dash et al, 2010). A 0.02-deg equal-angle gridded L3C product 0.7GB/day) is available at https://podaac.jpl.nasa.gov/dataset/G18-ABI-L3C-ACSPO-v2.90\n<br>\n\n#### H09-AHI-L2P-ACSPO-v2.90\nThe H09-AHI-L2P-ACSPO-v2.90 dataset contains the Subskin Sea Surface Temperature (SST) produced by the NOAA ACSPO system from the Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) onboard the Himawari-9 (H09) satellite. The H09 is a Japanese weather satellite, the 9th of the Himawari geostationary weather satellite operated by the Japan Meteorological Agency. It was launched on November 2, 2016 into its nominal position at 140.7-deg E, and declared operational on December 13, 2022, replacing the Himawari-8. The AHI is the primary instrument on the Himawari Series for imaging Earth’s weather, oceans, and environment with high temporal and spatial resolutions. <br><br> The H08/AHI maps SST in a Full Disk (FD) area from 80E-160W and 60S-60N, with spatial resolution 2km at nadir to 15km/VZA (view zenith angle) 67-deg, and 10-min temporal sampling. The 10-min FD data are subsequently collated in time, to produce the 1-hr product, with improved coverage and reduced cloud leakages and image noise. The L2P data is produced in GHRSST compliant netCDF4 GDS2 format, with 24 granules per day, and a total data volume 1.2 GB/day. The near-real time (NRT) data are updated hourly, with several hours latency. The NRT files are replaced with Delayed Mode (DM) files, with a latency of approximately 2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing). <br><br> Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Pixel locations can be obtained using a flat lat/lon file or a Python script available via Documents tab from the dataset landing page. Climate and Forecast (CF) metadata aware software (e.g., Panoply, xarray) can detect and map the data as is via the granule CF projection attributes and variables. The ACSPO H09 HAI SSTs are validated against quality controlled in situ data from the NOAA iQuam system (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). A 0.02-deg equal-angle gridded L3C product 0.7GB/day) is available at https://podaac.jpl.nasa.gov/dataset/H09-AHI-L3C-ACSPO-v2.90\n<br>\n\n#### AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0\nA global Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A) platform (launched 19 Oct 2006). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the AVHRR infrared channels (3.7, 10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. This global L3C product is derived from full resolution AVHRR l1b data that are re-mapped onto a 0.05 degree grid twice daily. The product format is compliant with the GHRSST Data Specification (GDS) version 2.\n<br>\n\n#### AVHRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0\nA global Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) platform (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the AVHRR infrared channels (3.7, 10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. This global L3C product is derived from full resolution AVHRR l1b data that are re-mapped onto a 0.05 degree grid twice daily. The product format is compliant with the GHRSST Data Specification (GDS) version 2.\n<br>\n\n#### GOES16-SST-OSISAF-L3C-v1.0\nThe data is regional and part of the Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset covering the America Region based on retrievals from the Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-16 (GOES-16). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from GOES-16 in the Eastern position. GOES-16 Imager level 1 data are acquired at Météo-France/Centre de Météorologie Spatiale (CMS) through the EUMETSAT/EUMETCast system. The GOES-16 ABI enables daytime SST calculations (whereas, previously, GOES East SST was restricted to nighttime conditions). The L3C SST is derived from a three-band (centered at 8.4, 10.3, and 12.3 um) algorithm. The ABI split-window configuration features three bands instead of the two found in heritage sensors (GOES-13). The 8.5-um is used in conjunction with the 10.3-um and 12.3-um bands for improved thin cirrus detection as well as for better atmospheric moisture correction in relatively dry atmospheres. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Each 10-minute observation interval is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05-degree regular grid (60S-60N and 135W-15W) SST fields obtained by aggregating the available10-minute SST data into hourly files-hour time, with priority being given to the value closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.\n<br>\n\n#### SEVIRI_SST_DR-OSISAF-L3C-v1.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Eastern Atlantic Region from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the MSG satellites (Meteosat-8 and Meteosat-9). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) has reprocessed SST products in (long) delayed-mode from MSG/SEVIRI. SST is retrieved from the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm and the cloud mask (CM) from OSI SAF. Atmospheric profiles of water vapor and temperature from a numerical weather prediction (NWP) model, OSTIA Sea Surface Temperature re-analysis and analysis, together with a radiative transfer model (RTTOV), are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 15-minute slot is processed at full satellite resolution. The products are then produced by remapping over a 0.05-degree regular grid (60S-60N and 60W-60E) SST fields obtained by aggregating all available 15-minute SST data into hourly files with priority being given to the value closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.\n<br>\n\n#### G18-ABI-L3C-ACSPO-v2.90\nThe G18-ABI-L3C-ACSPO-v2.90 dataset produced by the NOAA ACSPO system is used to derive Subskin and Depth Sea Surface Temperature (SST) from the ABI sensor onboard the G18 satellite. NOAA’s G18 (aka GOES-T before launch) was launched on March 1, 2022, replacing G17 as GOES West in Jan'2023. It is the third satellite in the US GOES–R Series, the Western Hemisphere’s most sophisticated weather-observing and environmental-monitoring system. The ABI is the primary instrument on the GOES-R Series for imaging Earth’s weather, oceans, and environment. <br><br> The G18-ABI-L3C-ACSPO-v2.90 dataset is a gridded version of the G18-ABI-L2P-ACSPO-v2.90 dataset (https://podaac.jpl.nasa.gov/dataset/G18-ABI-L2P-ACSPO-v2.90). The L3C (Level 3 Collated) outputs 24 hourly granules per day, with a daily volume of 0.7 GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST. <br><br> The ACSPO G18/ABI L3C product is validated against iQuam in situ data (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). The NRT files are replaced with Delayed Mode (DM) files, with a latency of ~2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing).\n<br>\n\n#### H09-AHI-L3C-ACSPO-v2.90\nThe H09-AHI-L3C-ACSPO-v2.90 dataset contains the Subskin Sea Surface Temperature (SST) produced by the NOAA ACSPO system from the Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) onboard the Himawari-9 (H09) satellite. The H09 is a Japanese weather satellite, the 9th of the Himawari geostationary weather satellite operated by the Japan Meteorological Agency. It was launched on November 2, 2016 into its nominal position at 140.7-deg E, and declared operational on December 13, 2022, replacing the Himawari-8. The AHI is the primary instrument on the Himawari Series for imaging Earth’s weather, oceans, and environment with high temporal and spatial resolutions. <br><br> The H09-AHI-L3C-ACSPO-v2.90 dataset is a gridded version of the ACSPO H09-AHI-L2P-ACSPO-v2.90 dataset (https://podaac.jpl.nasa.gov/dataset/AHI_H09-STAR-L2P-v2.90). The L3C (Level 3 Collated) data is mapped on 0.02-deg lat-lon grid and outputs 24 hourly granules per day, with a daily volume of 0.7 GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST. <br><br> The ACSPO H09/AHI L3C product is validated against iQuam in situ data (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). The NRT files are replaced with Delayed Mode (DM) files, with a latency of approximately 2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA for DM instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing).\n<br>\n\n#### VIIRS_NPP-NAVO-L2P-v1.0\nA global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) satellite launched on 28 October 2011. The VIIRS instrument is a a 22-band, multi-spectral scanning radiometer with a 3040-km swath width that builds on the heritage of the MODIS , AVHRR and SeaWIFS sensors for sea surface temperature (SST) and ocean color. For the infrared bands for SST the effective pixel size is 740 meters at nadir and the pixel size variation across the swath is constrained to no more than 1600 meters at the edge of the swath. However, the processing of this dataset aggregates two pixels into one so the resolution is 1500 meters at nadir. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.\n<br>\n\n#### VIIRS_NPP-NAVO-L2P-v3.0\nA global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Partnership (Suomi_NPP) satellite launched on 28 October 2011. VIIRS is a whiskbroom scanning radiometer which takes measurements in the cross-track direction within a field of regard of 112.56 degrees using 16 detectors and a double-sided mirror assembly. At a nominal altitude of 829 km, the swath width is 3060 km, providing full daily coverage both on the day and night side of the Earth. The VIIRS instrument is a 22-band, multi-spectral scanning radiometer that builds on the heritage of the MODIS , AVHRR and SeaWIFS sensors for sea surface temperature (SST) and ocean color. For the infrared bands for SST the effective pixel size is 750 meters at nadir and the pixel size variation across the swath is constrained to no more than 1600 meters at the edge of the swath. This L2P SST v3.0 is upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades. It contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the GHRSST Data Processing Specification (GDS) version 2 format specifications.\n<br>\n\n#### MSG03-OSPO-L2P-v1.0\nThe Meteosat Second Generation (MSG-3) satellites are spin stabilized geostationary satellites operated by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) to provide accurate weather monitoring data through its primary instrument the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which has the capacity to observe the Earth in 12 spectral channels. Eight of these channels are in the thermal infrared, providing among other information, observations of the temperatures of clouds, land and sea surfaces at approximately 5 km resolution with a 15 minute duty cycle. This Group for High Resolution Sea Surface Temperature (GHRSST) dataset produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) is derived from the SEVIRI instrument on the second MSG satellite (also known as Meteosat-9) that was launched on 22 December 2005. Skin sea surface temperature (SST) data are calculated from the infrared channels of SEVIRI at full resolution every 15 minutes. L2P data products with Single Sensor Error Statistics (SSES) are then derived following the GHRSST-PP Data Processing Specification (GDS) version 2.0.\n<br>\n\n#### GOES15-OSPO-L2P-v1.0\nThe Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-15 launched 4 March 2010. The radiometer aboard the satellite, The GOES N-P Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the far IR channels of GOES-15 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0. The full disk image is subsetted into granules representing distinct northern and southern regions.\n<br>\n\n#### AVHRRMTA_G-NAVO-L2P-v1.0\nA global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A; launched 19 Oct 2006) ) satellite produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European undertaking providing weather data services for monitoring climate and improving weather forecasts. It was jointly established by the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) with a contribution by the US National Oceanic and Atmospheric Administration (NOAA) of an AVHRR sensor identical to those flying on the family of Polar Orbiting Environmental Satellites (POES). AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The MetOp-A platform is sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. This particular dataset is produced from Global Area Coverage (GAC) data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir. Further binning and averaging of these pixels results in a final dataset resolution of 8.8 km.\n<br>\n\n#### AVHRRMTB_G-NAVO-L2P-v1.0\nA global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B; launched 19 Oct 2006) ) satellite produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European undertaking providing weather data services for monitoring climate and improving weather forecasts. It was jointly established by the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) with a contribution by the US National Oceanic and Atmospheric Administration (NOAA) of an AVHRR sensor identical to those flying on the family of Polar Orbiting Environmental Satellites (POES). AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The MetOp-A platform is sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. This particular dataset is produced from Global Area Coverage (GAC) data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir. Further binning and averaging of these pixels results in a final dataset resolution of 8.8 km.\n<br>\n\n#### AVHRR18_G-NAVO-L2P-v1.0\nA global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-18 platform (launched 20 May 2005) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC). HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. This particular dataset is produced from GAC data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir. Further binning and averaging of these pixels results in a final dataset resolution of 8.8 km.\n<br>\n\n#### AVHRR19_G-NAVO-L2P-v1.0\nA global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC). HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. This particular dataset is produced from GAC data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir. Further binning and averaging of these pixels results in a final dataset resolution of 8.8 km.\n<br>\n\n#### AMSR2-REMSS-L2P_RT-v8.2\nThis product provides a near-real-time (NRT) Level-2 Sea Surface Temperature (SST) (identified by \"_rt_\" within the file name) for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, which is derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by Remote Sensing Systems (RSS, or REMSS). AMSR2 was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. The NRT SST is made as available as soon as possible, generally within 3 hours latency. The v8.2 supersedes the previous v8a dataset which can be found at https://www.doi.org/10.5067/GHAM2-2TR8A.\n<br>\n\n#### VIIRS_NPP-JPL-L2P-v2016.2\nThese files contain NASA produced skin sea surface temperature (SST) products from the Infrared (IR) channels of the Visible and Infrared Imager/Radiometer Suite (VIIRS) onboard the Suomi-NPP satellite. VIIRS is a multi-disciplinary instrument that is also being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, of which NOAA-20 is the first. JPSS is a multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). Suomi-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data. VIIRS has 22 spectral bands ranging from 412 nm to 12 micron . There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375 m), and one day-night band (DNB). VIIRS uses on-board pixel aggregation to reduce the growth in size of pixels away from nadir. Two SST products are contained in these files. The first is a skin SST produced separately for day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST products from heritage and current NASA sensors. At night, a second triple channel SST product is generated using the 3.7 , 11 and 12 micron IR channels, identified as SST_triple. Due to the sun glint in the 3.7 micron SST_triple can only be used at night. VIIRS L2P SST data have a 750 spatial resolution at nadir and are stored in ~288 five minute granules per day. Full global coverage is obtained each day. The production of VIIRS NASA L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS were responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of VIIRS ocean products. JPL acquires VIIRS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. In mid-August, 2018, the RSMAS involvement in the VIIRS SST project ceased, and the subsequent fields are not maintained.The R2016.2 supersedes the previous v2016.0 datasets which can be found at https://doi.org/10.5067/GHVRS-2PN16\n<br>\n\n#### MODIS_A-JPL-L2P-v2019.0\nNASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 1:30 pm, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. MODIS L2P SST data have a 1 km spatial resolution at nadir and are stored in 288 five minute granules per day. Full global coverage is obtained every two days, with coverage poleward of 32.3 degree being complete each day. The production of MODIS L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS are responsible for SST algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of daily MODIS ocean products. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and ancillary variables, and distributes the data as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous R2014.0 datasets which can be found at https://doi.org/10.5067/GHMDA-2PJ02\n<br>\n\n#### MODIS_T-JPL-L2P-v2019.0\nNASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. MODIS L2P SST data have a 1 km spatial resolution at nadir and are stored in 288 five minute granules per day. Full global coverage is obtained every two days, with coverage poleward of 32.3 degree being complete each day. The production of MODIS L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project, and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS are responsible for SST algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of daily MODIS ocean products. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and ancillary variables, and distributes the data as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous R2014.0 datasets which can be found at https://doi.org/10.5067/GHMDT-2PJ02\n<br>\n\n#### AVHRRMTA_G-NAVO-L2P-v2.0\nA global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P data set containing multi-channel Sea Surface Temperature (SST) retrievals derived in real-time from the Advanced Very High Resolution Radiometer (AVHRR) level-1B data from the Meteorological Operational-A (MetOp-A) satellite. The SST data in this data set are used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European multi-satellite program to provide weather data services for monitoring climate and improving weather forecasts. MetOp-A, MetOp-B and Metop-C were respectively launched on 19 Oct 2006, 17 September 2012 and 7 November 2018. The program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) with the US National Oceanic and Atmospheric Administration (NOAA) contributing the AVHRR sensor. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micron) and near-infrared (0.9 micron) regions, the third one is located around 4 (3.6) micron, and the last two sample the emitted thermal radiation, around 11 and 12 micron, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micron. Typically, the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The swath of the AVHRR sensor is a relatively large 2400 km. All MetOp platforms are sun synchronous and generally view the same earth location twice a day (latitude dependent). The ground native resolution of the AVHRR instruments is approximately 1.1 km at nadir and degrades off nadir. This particular data set is produced from legacy Global Area Coverage (GAC) data that are derived from a sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km spatial resolution at nadir. The v2.0 is the updated version from current v1.0 with extensive algorithm improvements and upgrades. The major improvements include: 1) Significant changes in contaminant/cloud detection; 2) Increased the spatial resolution from 9 km to 4 km; 3) Updated compliance with GDS2, ACDD 1.3, and CF 1.6; and 4) Removed the dependency on the High-resolution Infrared Radiation Sounder (HIRS) sensor (only available to MetOp-A/B), thus allowing for the consistent inter-calibration and the processing of MetOp-A/B/C data\n<br>\n\n#### AVHRRMTB_G-NAVO-L2P-v2.0\nA global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P data set containing multi-channel Sea Surface Temperature (SST) retrievals derived in real-time from the Advanced Very High Resolution Radiometer (AVHRR) level-1B data from the Meteorological Operational-B (MetOp-B) satellite. The SST data in this data set are used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European multi-satellite program to provide weather data services for monitoring climate and improving weather forecasts. MetOp-A, MetOp-B and Metop-C were respectively launched on 19 Oct 2006, 17 September 2012 and 7 November 2018. The program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) with the US National Oceanic and Atmospheric Administration (NOAA) contributing the AVHRR sensor. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micron) and near-infrared (0.9 micron) regions, the third one is located around 4 (3.6) micron, and the last two sample the emitted thermal radiation, around 11 and 12 micron, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micron. Typically, the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The swath of the AVHRR sensor is a relatively large 2400 km. All MetOp platforms are sun synchronous and generally view the same earth location twice a day (latitude dependent). The ground native resolution of the AVHRR instruments is approximately 1.1 km at nadir and degrades off nadir. This particular data set is produced from legacy Global Area Coverage (GAC) data that are derived from a sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km spatial resolution at nadir. The v2.0 is the updated version from current v1.0 with extensive algorithm improvements and upgrades. The major improvements include: 1) Significant changes in contaminant/cloud detection; 2) Increased the spatial resolution from 9 km to 4 km; 3) Updated compliance with GDS2, ACDD 1.3, and CF 1.6; and 4) Removed the dependency on the High-resolution Infrared Radiation Sounder (HIRS) sensor (only available to MetOp-A/B), thus allowing for the consistent inter-calibration and the processing of MetOp-A/B/C data\n<br>\n\n#### AVHRRMTC_G-NAVO-L2P-v2.0\nA global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P data set containing multi-channel Sea Surface Temperature (SST) retrievals derived in real-time from the Advanced Very High Resolution Radiometer (AVHRR) level-1B data from the Meteorological Operational-C (MetOp-C) satellite. The SST data in this data set are used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European multi-satellite program to provide weather data services for monitoring climate and improving weather forecasts. MetOp-A, MetOp-B and Metop-C were respectively launched on 19 Oct 2006, 17 September 2012 and 7 November 2018. The program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) with the US National Oceanic and Atmospheric Administration (NOAA) contributing the AVHRR sensor. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micron) and near-infrared (0.9 micron) regions, the third one is located around 4 (3.6) micron, and the last two sample the emitted thermal radiation, around 11 and 12 micron, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micron. Typically, the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The swath of the AVHRR sensor is a relatively large 2400 km. All MetOp platforms are sun synchronous and generally view the same earth location twice a day (latitude dependent). The ground native resolution of the AVHRR instruments is approximately 1.1 km at nadir and degrades off nadir. This particular data set is produced from legacy Global Area Coverage (GAC) data that are derived from a sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km spatial resolution at nadir. The v2.0 is the updated version from current v1.0 with extensive algorithm improvements and upgrades. The major improvements include: 1) Significant changes in contaminant/cloud detection; 2) Increased the spatial resolution from 9 km to 4 km; 3) Updated compliance with GDS2, ACDD 1.3, and CF 1.6; and 4) Removed the dependency on the High-resolution Infrared Radiation Sounder (HIRS) sensor (only available to MetOp-A/B), thus allowing for the consistent inter-calibration and the processing of MetOp-A/B/C data\n<br>\n\n#### IASI_SST_METOP_A-OSISAF-L2P-v1.0\nA global 1 km Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Infrared Atmospheric Sounding Interferometer (IASI) on the European Meteorological Operational-A (MetOp-A&#65289;satellite &#65288;launched 19 Oct 2006). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near realtime from METOP/IASI. The Infrared Atmospheric Sounding Interferometer (IASI) measures inthe infrared part of the electromagnetic spectrum at a horizontal resolution of 12 km at nadir up to40km over a swath width of about 2,200 km. With 14 orbits in a sun-synchronous mid-morningorbit (9:30 Local Solar Time equator crossing, descending node) global observations can beprovided twice a day. The SST retrieval is performed and provided by the IASI L2 processor atEUMETSAT headquarters. The product format is compliant with the GHRSST Data Specification(GDS) version 2.\n<br>\n\n#### IASI_SST_METOP_B-OSISAF-L2P-v1.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Infrared Atmospheric Sounding Interferometer (IASI) on the European Meteorological Operational-B (MetOp-B)satellite (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near realtime from METOP/IASI. The Infrared Atmospheric Sounding Interferometer (IASI) measures inthe infrared part of the electromagnetic spectrum at a horizontal resolution of 12 km at nadir up to40km over a swath width of about 2,200 km. With 14 orbits in a sun-synchronous mid-morningorbit (9:30 Local Solar Time equator crossing, descending node) global observations can beprovided twice a day. The SST retrieval is performed and provided by the IASI L2 processor atEUMETSAT headquarters. The product format is compliant with the GHRSST Data Specification(GDS) version 2.\n<br>\n\n#### AMSRE-REMSS-L2P-v7a\nThe Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. Remote Sensing Systems (RSS, or REMSS) is the provider of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"_rt_\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v7\" within the file name) are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.\n<br>\n\n#### TMI-REMSS-L2P-v4\nGDS2 Version -The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is a well calibrated passive microwave radiometer, similar to the Special Sensor Microwave Imager (SSM/I), that contains lower frequency channels required for sea surface temperature (SST) retrievals. The TRMM is part of the NASA's mission to planet Earth, and is a joint venture between NASA and the Japan Aerospace Exploration Agency (JAXA) to measure precipitation, water vapor, SST and wind in the global tropical regions and was launched in 27 November 1997 from the Tanegashima Space Center in Tanegashima, Japan. The TRMM satellite travels west to east in a 402 km altitude semi-equatorial precessing orbit that results in day-to-day changes in the observation time of any given earth location between 38S and 38N. Remote Sensing Systems has produced a Version-4 TMI ocean SST dataset for the Group for High Resolution Sea Surface Temperature (GHRSST) by applying an algorithm to the 10.7 GHz channel through a removal of surface roughness effects. In contrast to infrared SST observations, microwave retrievals can be measured through clouds, which are nearly transparent at 10.7 GHz. Microwave retrievals are also insensitive to water vapor and aerosols. The algorithm for retrieving SSTs from radiometer data is described in \"AMSR Ocean Algorithm.\n<br>\n\n#### AMSR2-REMSS-L2P-v8.2\nThis product provides a “Final” (Refined) Level-2 Sea Surface Temperature (SST) (currently identified by \"v8.2\" within the file name) for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, which is derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by Remote Sensing Systems (RSS, or REMSS). AMSR2 was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. The “Final” SSTs are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The v8.2 supersedes the previous v8a dataset which can be found at https://www.doi.org/10.5067/GHAM2-2PR8A.\n<br>\n\n#### VIIRS_N20-NAVO-L2P-v3.0\nThe VIIRS_N20-NAVO-L2P-v3.0 dataset produced by the Naval Oceanographic Office (NAVO) derives the 1-meter depth Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-1 satellite, renamed as NOAA-20 (N20). N20 was launched on November 18, 2017, the 2nd satellite in the US NOAA JPSS series. <br><br> VIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NAVO's Level-2 SST processor version 3.0 (v3.0). Data contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The data record is available back to Feb. 20 2024. The L2P SST v3.0 is the first release at PO.DAAC derived from the L2P SST processor v3.0, which was upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades.<br><br> The product is comparable with the NPP VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-NAVO-L2P-v3.0) and the N21 VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_N21-NAVO-L2P-v3.0) datasets. It also has similar coverage and quality as the NOAA ACSPO VIIRS L2P SST (https://podaac.jpl.nasa.gov/dataset/N20-VIIRS-L2P-ACSPO-v2.80).\n<br>\n\n#### VIIRS_N21-NAVO-L2P-v3.0\nThe VIIRS_N21-NAVO-L2P-v3.0 dataset produced by the Naval Oceanographic Office (NAVO) derives the 1-meter depth Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on November 10, 2022, the 3rd satellite in the US NOAA JPSS series. <br><br> VIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NAVO's Level-2 SST processor version 3.0 (v3.0). Data contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The data record is available back to Feb. 21 2024. The L2P SST v3.0 is the first release at PO.DAAC derived from the L2P SST processor v3.0, which was upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades.<br><br> The product is comparable with the NPP VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-NAVO-L2P-v3.0) and the N20 VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-NAVO-L2P-v3.0). It also has similar coverage and quality as the NOAA ACSPO VIIRS L2P SST (https://podaac.jpl.nasa.gov/dataset/N21-VIIRS-L2P-ACSPO-v2.80).\n<br>\n\n#### N21-VIIRS-L2P-ACSPO-v2.80\nThe N21-VIIRS-L2P-ACSPO-v2.80 dataset produced by the NOAA ACSPO system derives the Subskin Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the The Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on Nov. 10, 2022, the 3rd satellite in the US NOAA latest JPSS series. <br><br> VIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system (Jonasson et al. 2022). Data are reported in 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The ACSPO N21 VIIRS SST record is available back to 19 Mar 2023. In ACSPO products, SSTs are derived using the Non-Linear SST (NLSST) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Only ACSM confidently clear pixels with quality level QL=5 are recommended. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5. <br><br> The ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM). A reduced size (0.5GB/day), equal-angle gridded (0.02-deg resolution), ACSPO N21 VIIRS L3U product is also available (10.5067/GHV21-3U280) (Ignatov et al., 2017).\n<br>\n\n#### VIIRS_N20-STAR-L2P-v2.80\nNOAA-20 (N20/JPSS-1/J1) is the second satellite in the US NOAA latest generation Joint Polar Satellite System (JPSS), launched on November 18, 2017. NOAA is responsible for all JPSS products, including SST from the Visible Infrared Imaging Radiometer Suite (VIIRS). The L2P SST product is derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system, and reported in 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). SSTs are derived from Brightness Temperatures (BTs) using the Non-Linear SST (NLSST) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Only ACSM confidently clear pixels are recommended (equivalent to GDS2 quality level=5). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5. The ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM). A reduced size (0.5GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3U product is also available at https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-STAR-L3U-v2.80, where gridded L2P SSTs with QL=5 only are reported. The v2.80 is an updated version from the v2.61 with several algorithm improvements including two added thermal front layers, reduced L2P SST data size, mitigated warm biases in the high latitudes, and improved clear-sky mask.\n<br>\n\n#### VIIRS_NPP-STAR-L2P-v2.80\nThe Joint Polar Satellite System (JPSS), starting with S-NPP launched on 28 October 2011, is the new generation of the US Polar Operational Environmental Satellites (POES). NOAA is responsible for all JPSS products, including SST from the Visible Infrared Imaging Radiometer Suite (VIIRS). The L2P SST product is derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system, and reported in 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). SSTs are derived from Brightness Temperatures (BTs) using the Non-Linear SST (NLSST) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Only ACSM confidently clear pixels are recommended (equivalent to GDS2 quality level=5). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5. The ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM). A reduced size (0.5GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3U product is also available at https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-STAR-L3U-v2.80, where gridded L2P SSTs with QL=5 only are reported. The v2.80 is an updated version from the v2.61 with several algorithm improvements including two added thermal front layers, reduced L2P SST data size, mitigated warm biases in the high latitudes, and improved clear-sky mask.\n<br>\n\n#### AVHRR19_L-NAVO-L2P-v1.0\nA regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC). HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. GAC data provide daily subsampled global coverage recorded on tape recorders and then transmitted to a ground station. This particular dataset is derived from LAC data. Further binning and averaging of the 1.1 km LAC pixels results in a final dataset resolution of 2.2 km. The coverage of the LAC data can vary but generally contains scenes over the oceans adjacent to Australia and the North Indian Ocean.\n<br>\n\n#### EWSG1-NAVO-L2P-v01\nA Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P sea surface temperature produced by The Naval Oceanographic Office (NAVO) from the GOES Imager sensor on the Electro-Optical Infrared Weather System – Geostationary satellite (EWS-G1). The EWS-G1, formerly GOES-13, is the first Department of Defense owned geostationary weather satellite, which has been repositioned over Indian Ocean (IO) region at 60.0° West longitude in January 2018 and fully operational since September 8, 2020, providing timely cloud characterization and theater weather imagery to DoD. The EWS-G1 L2P SST product is calculated based on the 4-micron (band 2) and 11-micron (band 4) channels, providing nighttime and daytime SST. However, daytime SSTs are not produced in areas where the 4-micron channel is strongly affected by Solar radiation, which is defined by solar reflection angle > 50 degree. The L2P data are packaged according to the GHRSST Data Specification version 2 (GDS2) in netCDF4 format at 0.04-degree spatial resolution and stored in 48 half-hour granules per day. The data will be continually updated with 24 hours latency.\n<br>\n\n#### EWSG2-NAVO-L2P-v01\nA Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P sea surface temperature dataset produced by the Naval Oceanographic Office (NAVO) from the GOES Imager sensor on the Electro-Optical Infrared Weather System – Geostationary satellite (EWS-G2). The EWS-G2, formerly GOES-15, is the second Department of Defense owned geostationary weather satellite, which has been repositioned over Indian Ocean (IO) region at 60.0° West longitude in September 2023 and fully operational since December 3, 2023, providing timely cloud characterization and theater weather imagery to DoD. The EWS-G2 L2P SST product is calculated based on the 4-micron (band 2) and 11-micron (band 4) channels, providing nighttime and daytime SST. However, daytime SSTs are not produced in areas where the 4-micron channel is strongly affected by Solar radiation, which is defined by solar reflection angle > 50 degrees. The L2P data are packaged according to the GHRSST Data Specification version 2 (GDS2) in netCDF4 format at 0.04-degree spatial resolution and stored in 104 partial disks per day. The data will be continually updated with 24 hours latency.\n<br>\n\n#### AVHRR_SST_METOP_A-OSISAF-L2P-v1.0\nA global 1 km Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A)satellite (launched 19 Oct 2006). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the AVHRR infrared channels (3.7, 10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. This product is delivered at full resolution in satellite projection as metagranule corresponding to 3 minutes of acquisition. The product format is compliant with the GHRSST Data Specification (GDS) version 2.\n<br>\n\n#### AVHRR_SST_METOP_B-OSISAF-L2P-v1.0\nA global 1 km Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) satellite (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the AVHRR infrared channels (3.7, 10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. This product is delivered at full resolution in satellite projection as metagranule corresponding to 3 minutes of acquisition. The product format is compliant with the GHRSST Data Specification (GDS) version 2.\n<br>\n\n#### GOES13-OSPO-L2P-v1.0\nThe Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-13 launched 24 May 2006. The radiometer aboard the satellite, The GOES N-P Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the far IR channels of GOES-13 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0. The full disk image is subsetted into granules representing distinct northern and southern regions.\n<br>\n\n#### MTSAT2-OSPO-L2P-v1.0\nMulti-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilized satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-2 (also known as Himawari 7) and its radiometer (MTSAT-2 Imager) was successfully launched on 18 February 2006. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-2 Imager full resolution data in satellite projection on a hourly basis by using Bayesian Cloud Mask algorithm at the Office of Satellite and Product Operations (OSPO). L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0.\n<br>\n\n#### SEVIRI_SST-OSISAF-L3C-v1.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Eastern Atlantic Region from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the Meteosat Second Generation (MSG-3) satellites (launched 5 July 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from MSG/SEVIRI. SEVIRI level 1.5 data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 15 minutes slot is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05 degree regular grid (60S-60N and 60W-60E) SST fields obtained by aggregating all 15 minute SST data available in one hour time, and the priority being given to the value the closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.\n<br>\n\n#### SEVIRI_IO_SST-OSISAF-L3C-v1.0\nThis dataset is produced by the Ocean and Sea Ice Satellite Application Facility (OSI SAF) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument onboard the Meteosat Second Generation (MSG-1), Meteosat-8 satellite (launched on 28 August 2002). The dataset covers the Indian Ocean region with latitude of 60S-60N and longitude of 101.5E-18.5W. Level-3C SST, in the NetCDF format recommended by Group for High Resolution Sea Surface Temperature (GHRSST), is identical to Level-2P GHRSST products, 3 refers to gridded products and C to the fact that hourly products result from compositing 15 minutes (MSG) or 30 minutes (GOES-E) data. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), OSI SAF is producing SST products in near real time from MSG/SEVIRI. SEVIRI level 1.5 data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 15 minutes slot is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05-degree regular grid (60S-60N and 135W-15W) SST fields obtained by aggregating all 15-minute SST data available in one-hour time, and the priority being given to the value the closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.\n<br>\n\n#### AVHRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) platform (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR and SNPP/VIIRS. Global AVHRR level 1b data are acquired at Meteo- France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. NAR SNPP/VIIRS level 0 data are acquired through direct readout and converted into l1b at CMS. SST is retrieved from the AVHRR and VIIRS infrared channels using a multispectral algorithm. This product is delivered as four six hourly collated files per day on a regular 2km grid. The product format is compliant with the GHRSST Data Specification (GDS) version 2.\n<br>\n\n#### VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0\nA regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset for the North Atlantic Region (NAR) based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR and SNPP/VIIRS. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. NAR SNPP/VIIRS level 0 data are acquired through direct readout and converted into l1b at CMS. SST is retrieved from the AVHRR and VIIRS infrared channels using a multispectral algorithm. This product is delivered as four six hourly collated files per day on a regular 2km grid. The product format is compliant with the GHRSST Data Specification (GDS) version 2.\n<br>\n\n#### AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A) platform (launched 19 Oct 2006). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR and SNPP/VIIRS. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. NAR SNPP/VIIRS level 0 data are acquired through direct readout and converted into l1b at CMS. SST is retrieved from the AVHRR and VIIRS infrared channels using a multispectral algorithm. This product is delivered as four six hourly collated files per day on a regular 2km grid. The product format is compliant with the GHRSST Data Specification (GDS) version 2.\n<br>\n\n#### AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009). This particular dataset is produced by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) in France. The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA-19 platform is sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The NAR products are SST fields derived from 1km AVHRR data that are re-mapped onto a 0.02 degree equal angle grid. In the processing chain, global AVHRR level 1b data are acquired at Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. A cloud mask is applied and SST is retrieved from the AVHRR infrared (IR) channels by using a multispectral technique. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.\n<br>\n\n#### GOES13-OSISAF-L3C-v1.0\nA regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset for the America Region (AMERICAS) based on retrievals from the GOES-13 Imager on board GOES-13 satellite. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from GOES 13 in East position. GOES 13 imager level 1 data are acquired at Meteo- France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the GOES 13 infrared channels (3.9 and 10.8 micrometer) using a multispectral algorithm. Due to the lack of 12 micrometer channel in the GOES 13 imager, SST retrieval is not possible in daytime conditions. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 30 minutes slot is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05 degree regular grid (60S-60N and 135W-15W) SST fields obtained by aggregating 30 minute SST data available in one hour time, and the priority being given to the value the closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.\n<br>\n\n#### AMSR2-REMSS-L3U_RT-v8.2\nThis product contains a near-real-time (NRT) Level-3U Sea Surface Temperature (SST) (identified by \"_rt_\" within the file name) for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, which is derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by Remote Sensing Systems (RSS, or REMSS). AMSR2 was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. The NRT SST is made as available as soon as possible, generally within 3 hours latency. The v8.2 supersedes the previous v8a dataset which can be found at https://www.doi.org/10.5067/GHAM2-3TR8A.\n<br>\n\n#### AMSR2-REMSS-L3U_RT-v8a\nGDS2 Version -The Advanced Microwave Scanning Radiometer 2 (AMSR2) was launched on 18 May 2012, onboard the Golbal Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. From about 700 km above the Earth, AMSR2 will provide us highly accurate measurements of the intensity of microwave emission and scattering. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. Remote Sensing Systems (RSS, or REMSS), providers of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"rt\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v8\" within the file name) are processed when RSS receives the atmospheric mode NCEP FNL analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The final \"v8\" products will continue to accumulate new swaths (half orbits) until the maps are full, generally within 2 days.\n<br>\n\n#### GMI-REMSS-L3U-v8.2a\nThe Global Precipitation Measurement (GPM) satellite was launched on February 27th, 2014 with the GPM Microwave Imager (GMI) instrument on board. The GPM mission is a joint effort between NASA, the Japan Aerospace Exploration Agency (JAXA) and other international partners. In march 2005, NASA has chosen the Ball Aerospace and Technologies Corp., Boulder, Colorado to build the GMI instrument on the continued success of the Tropical Rainfall Measuring Mission (TRMM) satellite by expanding current coverage of precipitation from the tropics to the entire world. GMI is a dual-polarization, multi-channel, conical-scanning, passive microwave radiometer with frequent revisit times. One of the primary differences between GPM and other satellites with microwave radiometers is the orbit, which is inclined 65 degrees, allowing a full sampling of all local Earth times repeated approximately every 2 weeks. The GPM platform undergoes yaw maneuvers approximately every 40 days to compensate for the sun's changing position and prevent the side of the spacecraft facing the sun from overheating. Today, the GMI instrument plays an essential role in the worldwide measurement of precipitation and environmental forecasting. Sea Surface Temperature (SST) is one of its major products. The GMI data from the Remote Sensing System (REMSS) have been produced using an updated RTM, Version-8. The V8 brightness temperatures from GMI are slightly different from the V7 brightness temperatures; The SST datasets are available in near-real time (NRT) as they arrive, with a delay of about 3 to 6 hours, including the Daily, 3-Day, Weekly, and Monthly time series products.\n<br>\n\n#### TMI-REMSS-L3U-v7.1a\nThe Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is a well calibrated passive microwave radiometer, similar to the Special Sensor Microwave Imager (SSM/I), that contains lower frequency channels required for sea surface temperature (SST) retrievals. The TRMM is part of the NASA's mission to planet Earth, and is a joint venture between NASA and the Japan Aerospace Exploration Agency (JAXA) to measure precipitation, water vapor, sea surface temperature (SST) and surface wind in the global tropical regions and was launched in 27 November 1997 from the Tanegashima Space Center in Tanegashima, Japan. The TRMM satellite travels west to east in a 402 km altitude semi-equatorial processing orbit that results in day-to-day changes in the observation time of any given earth location between 38S and 38N. Remote Sensing Systems (REMSS) has produced a Version-7.1a TMI SST dataset for the Group for High Resolution Sea Surface Temperature (GHRSST) by applying an algorithm to the 10.7 GHz channel through a removal of surface roughness effects. In contrast to infrared SST observations, microwave retrievals can be measured through clouds, which are nearly transparent at 10.7 GHz. Microwave retrievals are also insensitive to water vapor and aerosols. The algorithm for retrieving SSTs from radiometer data is described in \"AMSR Ocean Algorithm.\n<br>\n\n#### AMSR2-REMSS-L3U-v8.2\nThis product contains a “Final” (Refined) Level-3U Sea Surface Temperature (SST) (currently identified by \"v8.2\" within the file name) for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, which is derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by Remote Sensing Systems (RSS, or REMSS). AMSR2 was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. The “Final” SSTs are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The final \"v8.2\" products will continue to accumulate new swaths (half orbits) until the maps are full, generally within 2 days. The v8.2 L3U SST supersedes the previous v8a dataset which can be found at https://www.doi.org/10.5067/GHAM2-3UR8A.\n<br>\n\n#### WindSat-REMSS-L3U-v7.0.1a\nThe WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). In addition to wind speed and direction, the instrument can also measure sea surface temperature, soil moisture, ice and snow characteristics, water vapor, cloud liquid water, and rain rate. Unlike previous radiometers, the WindSat sensor takes observations during both the forward and aft looking scans. This makes the WindSat geometry of the earth view swath quite different and significantly more complicated to work with than the other passive microwave sensors. The Remote Sensing Systems (RSS, or REMSS) WindSat products are the only dataset available that uses both the fore and aft look directions. By using both directions, a wider swath and more complicated swath geometry is obtained. RSS providers of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of WindSat instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"rt\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v7.0.1a\" within the file name) are processed when RSS receives the atmospheric mode NCEP FNL analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The final \"v7.0.1a\" products will continue to accumulate new swaths (half orbits) until the maps are full, generally within 7 days. The version with letter \"a\" refers to the file incompliance with GHRSST format.\n<br>\n\n#### N21-VIIRS-L3U-ACSPO-v2.80\nThe N21-VIIRS-L3U-ACSPO-v2.80 dataset produced by the NOAA ACSPO system derives the Subskin Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on Nov. 10, 2022, the 3rd satellite in the US NOAA latest JPSS series. <br><br> The ACSPO N21 VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO N21 VIIRS L2P product, also available at PO.DAAC (10.5067/GHV21-2P280). The L3U output files are 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The ACSPO N21 VIIRS SST record is available back to 19 Mar 2023. There are 144 granules per 24 hour interval, with a total data volume of 0.6GB/day. Fill values are reported at all invalid pixels, including pixels >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SST, a subset of variable l2p_flags (including day/night, land, ice, twilight, and glint flags), wind speed, and the SST minus reference CMC SST (Canadian Met Centre 0.1deg L4 SST, 10.5067/GHCMC-4FM03). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. <br><br> The ACSPO VIIRS L3U product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM).\n<br>\n\n#### VIIRS_N20-STAR-L3U-v2.80\nNOAA-20 (N20/JPSS-1/J1) is the second satellite in the US NOAA latest generation Joint Polar Satellite System (JPSS), launched on November 18, 2017. The ACSPO N20/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO N20/VIIRS L2P product available here https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-STAR-L2P-v2.80. The L3U output files are 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 0.5GB/day. Fill values are reported at all invalid pixels, including pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, a subset of l2p_flags (including day/night, land, ice, twilight, and glint flags), wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://www.doi.org/10.5067/GHCMC-4FM03). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data in SQUAM. The v2.80 is an updated version from the v2.61 with several L2P algorithm improvements including two added thermal front layers, mitigated warm biases in the high latitudes, and improved clear-sky mask.\n<br>\n\n#### VIIRS_NPP-STAR-L3U-v2.80\nThe Joint Polar Satellite System (JPSS), starting with S-NPP launched on 28 October 2011, is the new generation of the US Polar Operational Environmental Satellites (POES). The ACSPO SNPP/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO NPP/VIIRS L2P product available here https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-STAR-L2P-v2.80 . The L3U output files are 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 0.5GB/day. Fill values are reported at all invalid pixels, including pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, a subset of l2p_flags (including day/night, land, ice, twilight, and glint flags), wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://www.doi.org/10.5067/GHCMC-4FM03). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data in SQUAM. The v2.80 is an updated version from the v2.61 with several L2P algorithm improvements including two added thermal front layers, mitigated warm biases in the high latitudes, and improved clear-sky mask.\n<br>\n\n#### AVHRR_OI-NCEI-L4-GLOB-v2.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.25 degree grid at the NOAA National Centers for Environmental Information. This product uses optimal interpolation (OI) by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships and buoys), and the specific datasets employed may change over. At the marginal ice zone, sea ice concentrations are used to generate proxy SSTs. A preliminary version of this file is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. Note that this is the AVHRR-ONLY (AVHRR-OI), available from September 1, 1981, but there is a companion SST product that includes microwave satellite data, available from June 2002.\n<br>\n\n#### AVHRR_OI-NCEI-L4-GLOB-v2.1\nA Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature dataset is produced daily on a 0.25 degree grid at the NOAA National Centers for Environmental Information. This product uses optimal interpolation (OI) by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships, buoys, and Argo floats above 5m depth), and the specific datasets employed may change over time. In the regions with sea-ice concentration higher than 30%, freezing points of seawater are used to generate proxy SSTs. A preliminary version of this dataset is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. The v2.1 (Huang et al. 2021) is updated from the previous AVHRR_OI-NCEI-L4-GLOB-v2.0 data. Major improvements include: 1) In-situ ship and buoy data changed from the NCEP Traditional Alphanumeric Codes (TAC) to the NCEI merged TAC + Binary Universal Form for the Representation (BUFR) data, with large increases of buoy data included to correct satellite SST biases; 2) Addition of Argo float observed SST data as well, for further correction of satellite SST biases; 3) Satellite input from the METOP-A and NOAA-19 to METOP-A and METOP-B, removing degraded satellite data; 4) Revised ship-buoy SST corrections for improved accuracy; and 5) Revised sea-ice-concentration to SST conversion to remove warm biases in the Arctic region. These updates only apply to data after January 1st, 2016. The data pre 2016 are still the same as v2.0 except for metadata upgrades. NCEI has panned to update the entire dataset from 1982 to fix the In-Situ data ingest and bias correction which exist prior 2016.\n<br>\n\n#### CMC0.1deg-CMC-L4-GLOB-v3.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis at the Canadian Meteorological Center. This dataset merges infrared satellite SST at varying points in the time series from the Advanced Very High Resolution Radiometer (AVHRR) from NOAA-18,19, the European Meteorological Operational-A (METOP-A) and Operational-B (METOP-B), and microwave data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite in conjunction with in situ observations of SST from drifting buoys and ships from the ICOADS program. It uses the previous days analysis as the background field for the statistical interpolation used to assimilate the satellite and in situ observations. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.\n<br>\n\n#### CMC0.2deg-CMC-L4-GLOB-v2.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis at the Canadian Meteorological Center. This dataset merges infrared satellite SST at varying points in the time series from the (A)TSR series of radiometers from ERS-1, ERS-2 and Envisat, AVHRR from NOAA-16,17,18,19 and METOP-A, and microwave data from TMI, AMSR-E and Windsat in conjunction with in situ observations of SST from drifting buoys and ships from the ICOADS program. It uses the previous days analysis as the background field for the statistical interpolation used to assimilate the satellite and in situ observations. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.\n<br>\n\n#### DMI_OI-DMI-L4-GLOB-v1.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis by the Danish Meteorological Institute (DMI) using an optimal interpolation (OI) approach on a global 0.05 degree grid. The analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several satellites. The sensors include the Advanced Very High Resolution Radiometer (AVHRR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Visible Infrared Imager Radiometer Suite (VIIRS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua. An ice field from the EUMETSAT OSI-SAF is used to mask out areas with ice. This dataset adheres to the version 2 GHRSST Data Processing Specification (GDS).\n<br>\n\n#### GAMSSA_28km-ABOM-L4-GLOB-v01\nA Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a global 0.25 degree grid. This Global Australian Multi-Sensor SST Analysis (GAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers with in situ data from ships, drifting buoys and moorings from the Global Telecommunications System (GTS). SST observations that have experienced recent surface wind speeds less than 6 m/s during the day or less than 2 m/s during night are rejected from the analysis. The processing results in daily foundation SST estimates that are largely free of nocturnal cooling and diurnal warming effects. Sea ice concentrations are supplied by the NOAA/NCEP 12.7 km sea ice analysis. In the absence of observations, the analysis relaxes to the Reynolds and Smith (1994) Monthly 1 degree SST climatology for 1961 - 1990.\n<br>\n\n#### K10_SST-NAVO-L4-GLOB-v01\nThis is a Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis dataset produced daily on an operational basis by the Naval Oceanographic Office (NAVO) on a global 0.1x0.1 degree grid. The K10 (NAVO 10-km gridded SST analyzed product) L4 analysis uses SST observations from the following instruments: Advanced Very High Resolution Radiometer (AVHRR), Visible Infrared Imaging Radiometer Suite (VIIRS), and Spinning Enhanced Visible and InfraRed Imager (SEVIRI). The AVHRR data for this comes from the MetOp-A, MetOp-B, and NOAA-19 satellites; VIIRS data is sourced from the Suomi_NPP satellite; SEVIRI data comes from the Meteosat-8 and -11 satellites. The age (time-lag), reliability, and resolution of the data are used in the weighted average with the analysis tuned to represent SST at a reference depth of 1-meter. Input data from the AVHRR Pathfinder 9km climatology dataset (1985-1999) is used when no new satellite SST retrievals are available after 34 days. Comparing with its predecessor (DOI: https://doi.org/10.5067/GHK10-L4N01 ), this updated dataset has no major changes in Level-4 interpolated K10 algorithm, except for using different satellite instrument data, and updating metadata and file format. The major updates include: (a) updated and enhanced the granule-level metadata information, (b) converted the SST file from GHRSST Data Specification (GDS) v1.0 to v2.0, (c) added the sea_ice_fraction variable to the product, and (d) updated the filename convention to reflect compliance with GDS v2.0.\n<br>\n\n#### MUR25-JPL-L4-GLOB-v04.2\nA Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.25 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains an additional SST anomaly variable derived from a MUR climatology (average between 2003 and 2014). This dataset was originally funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ) and the NASA CEOS COVERAGE project and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.\n<br>\n\n#### MUR-JPL-L4-GLOB-v4.1\nA Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains additional variables for some granules including the SST anomaly (variable sst_anomaly) derived from a MUR climatology, and the temporal distance in hours to the nearest IR measurement for each pixel (variable dt_1km_data). Variable dt_1km_data first appears in the time series on October 4, 2015, while sst_anomaly starts July 23, 2019. This dataset was originally funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. Use the file global metadata \"history:\" attribute to determine if a granule is near-realtime or retrospective.\n<br>\n\n#### MW_IR_OI-REMSS-L4-GLOB-v5.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.09-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from both microwave (MW) sensors including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite, and WindSat operates on the Coriolis satellite, and infrared (IR) sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platform and the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi-NPP satellite. The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST) while infrared radiometers (i.e., MODIS) have a higher spatial resolution. This analysis does not use any in situ SST data such as drifting buoy SST. Comparing with previous version 4.0 dataset, the version 5.0 has made the updates in several areas, including the diurnal warming model, the sensor-specific error statistics (SSES) for each microwave sensor, the sensor correlation model, and the quality mask.\n<br>\n\n#### MW_IR_OI-REMSS-L4-GLOB-v5.1\nA Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.09-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from microwave (MW) sensors including the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the WindSat on the Coriolis satellite, the Global Precipitation Measurement (GPM) Microwave Imager (GMI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite, as well as infrared (IR) sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms and the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi-NPP and NOAA-20 satellites. These MW sensors are used through the SST production based on the sensor data availability. The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST) while infrared radiometers (i.e., MODIS) have a higher spatial resolution. This analysis does not use any in situ SST data such as drifting buoy SST. Compared with the previous version 5.0 dataset, version 5.1 is processed using updated input files, VIIRS on NOAA-20 is included, the sensor-specific error statistics (SSES) for each microwave sensor are updated, and deficiencies in the OI processing have been addressed.\n<br>\n\n#### MW_OI-REMSS-L4-GLOB-v5.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.25-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from microwave (MW) sensors including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite, and WindSat operates on the Coriolis satellite. The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST). This analysis does not use any in situ SST data such as drifting buoy SST. Comparing with previous version 4.0 dataset, the version 5.0 has made the updates in several areas, including the diurnal warming model, the sensor-specific error statistics (SSES) for each microwave sensor, the sensor correlation model, and the quality mask.\n<br>\n\n#### MW_OI-REMSS-L4-GLOB-v5.1\nA Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.25-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from microwave (MW) sensors including the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the WindSat on the Coriolis satellite, the Global Precipitation Measurement (GPM) Microwave Imager (GMI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite. These MW sensors are used through the SST production based on the sensor data availability. The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST). This analysis does not use any in situ SST data such as drifting buoy SST. Compared with the previous version 5.0 dataset, version 5.1 is processed using updated input files, the sensor-specific error statistics (SSES) for each microwave sensor are updated, and deficiencies in the OI processing have been addressed.\n<br>\n\n#### Geo_Polar_Blended-OSPO-L4-GLOB-v1.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the Office of Satellite and Product Operations (OSPO) using optimal interpolation (OI) on a global 0.054 degree grid. The Geo-Polar Blended Sea Surface Temperature (SST) Analysis combines multi-satellite retrievals of sea surface temperature into a single analysis of SST. This analysis uses both daytime and nighttime data from sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Visible Infrared Imager Radiometer Suite (VIIRS), the Geostationary Operational Environmental Satellite (GOES) imager, the Japanese Advanced Meteorological Imager (JAMI) and in situ data from ships, drifting and moored buoys. This analysis was specifically produced to be used as a lower boundary condition in Numerical Weather Prediction (NWP) models. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.\n<br>\n\n#### OSTIA-UKMO-L4-GLOB-v2.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the UK Met Office using optimal interpolation (OI) on a global 0.05x0.05 degree grid. The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) analysis uses satellite data from over 10 unique sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Geostationary Operational Environmental Satellite (GOES) imager, the Infrared Atmospheric Sounding Interferometer (IASI), the Tropical Rainfall Measuring Mission Microwave Imager (TMI) and in situ data from ships, drifting and moored buoys. This analysis was specifically produced to be used as a lower boundary condition in Numerical Weather Prediction (NWP) models. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications and is updated daily with 24-hours nominal latency in a Near Real Time (NRT) mode. UKMO also produces the higher quality reprocessed OSTIA L4 SST using more sensors and data with a biannual latency (https://podaac.jpl.nasa.gov/dataset/OSTIA-UKMO-L4-GLOB-REP-v2.0).\n<br>\n\n#### OSTIA-UKMO-L4-GLOB-REP-v2.0\nThe Operational Sea Surface Temperature and Sea Ice Analysis Reprocessed (OSTIA-REP) is a GHRSST reprocessed Level-4 sea surface temperature and ice-concentration analysis produced by the UK Met Office (UKMO) using optimal interpolation (OI) on a global 0.05 degree grid. It is a sister product of the Near Real Time version (OSTIA-NRT), but incorporates satellite data from over 25 different SST sensors as well as in situ data from drifting and moored buoys. The OSTIA-REP is produced on a biannual frequency when more satellite and climatology observations are available from existing geostationary IR, and polar orbiting IR and MW satellites in addition to the data used in OSTIA-NRT. <br><br> While OSTIA-NRT is produced to mainly serve as a lower boundary condition in Numerical Weather Prediction (NWP) models, this OSTIA-REP aims to provide a more accurate and consistent record of SST measurements over time, which is crucial for detecting long-term climate trends and variability. Both versions follow GHRSST Data Processing Specification (GDS) version 2 format guidelines.<br><br> Data to June 2022 are also distributed through the E.U. Copernicus Marine Service Information (https://marine.copernicus.eu/, DOI: https://doi.org/10.48670/moi-00168 with the following license). Please refer to the user guide for more information.\n<br>\n\n#### RAMSSA_09km-ABOM-L4-AUS-v01\nA Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a regional 1/12 degree grid over the Australian region (20N - 70S, 60E - 170W). This Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers, with in situ data from ships, Argo floats, XBTs, CTDs, drifting buoys and moorings from the Global Telecommunications System (GTS). SST observations that have experienced recent surface wind speeds less than 6 m/s during the day or less than 2 m/s during night are rejected from the analysis. The processing results in daily foundation SST estimates that are largely free of nocturnal cooling and diurnal warming effects. Sea ice concentrations are supplied by the NOAA/NCEP 12.7 km sea ice analysis. In the absence of observations, the analysis relaxes to the BoM Global Weekly 1 degree OI SST analysis, which relaxes to the Reynolds and Smith (1994) Monthly 1 degree SST climatology for 1961 - 1990.\n<br>\n\n#### REMO_OI_SST_5km-UFRJ-L4-SAMERICA-v1.0\nA Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis by the Oceanographic Modeling and Observation Network (REMO) at Applied Meteorology Laboratory/Federal University of Rio de Janeiro (LMA/UFRJ) using the Barnes sub optimal interpolation (OI) technique on a regional 0.05 degree grid. REMO uses Advanced Very High Resolution Radiometer (AVHRR) data from National Oceanic and Atmospheric Administration (NOAA) satellites series (NOAA 15, NOAA 16, NOAA 17, NOAA 18 and NOAA 19) and Microwave Imager (TMI) data from Tropical Rainfall Measuring Mission (TRMM) which is a joint mission between NASA and the Japan Aerospace Exploration Agency (JAXA) to generate 0.05 degree daily cloud free blended (infrared and microwave) SST products (approximately 5.5 km). The data lies between latitudes 45 S and 15 N and longitudes 70 W and 15 W region and are fully validated by in situ measurements from eleven buoys of Prediction and Research Moored Array in the Tropical Atlantic (PIRATA).AVHRR is a scanning radiometer capable of detecting energy from land, ocean and atmosphere. It operates with six spectral bands arranged in the regions of visible and infrared region. TRMM was launched in December, 1997, having an orbital inclination of 53 degree and altitude 350 km, an equatorial orbit that ranges from 40 N to 40 S and a spatial resolution of 0.25 degree (&#8764;27.75 km). Although infrared AVHRR SST data have high spatial resolution, they are contaminated by cloud cover and aerosols, while lower resolution microvwave TMI data are barely influenced by these.\n<br>\n\n#### SPORT-MSFC-L4-GLOB-v1.0\nThe GHRSST Level 4 SPoRT Global Foundation Sea Surface Temperature Analysis (v1.0) dataset is produced by the by the NASA Short-term Prediction Research and Transition (SPoRT) project to provide continuous high-resolution composited near global (80N – 80S) sea surface temperature fields twice daily at 2 km resolution for regional weather, maritime, and coastal applications. <br><br> It was originally a regional L4 dataset based on MODIS Aqua/Terra Level 2 SST inputs. Currently, the SPoRT SST analysis composites seven-days of Visible Infrared Imaging Radiometer Suite (VIIRS) and Advanced Very High Resolution Radiometer (AVHRR) satellite L2 SST data, with additional data from OSTIA and Geo-Polar Blended L4 SST products into a single analysis. Two types of weighting are used in the compositing process. One weight is for the data latency and the other for the product type and spatial resolution. The VIIRS and AVHRR data, being at 750m and 1km resolution respectively, are given the most weight, while the L4 datasets are given weaker weights. <br><br> This SPoRT SST dataset adheres to the GHRSST Data Specification (GDS) version 2 format specifications with full netCDF-4 and metadata compliance. The SPoRT project is funded by NASA and the data produced at the NASA Marshall Space Flight Center.\n<br>\n\n#### L3S_LEO_AM-STAR-v2.80\nNOAA STAR produces two lines of gridded 0.02 degree super-collated L3S LEO sub-skin Sea Surface Temperature (SST) datasets, one from the NOAA afternoon JPSS (L3S_LEO_PM) satellites and the other from the EUMETSAT mid-morning Metop (L3S_LEO_AM) satellites. The L3S_LEO_AM is derived from three Low Earth Orbiting (LEO) Metop-FG satellites: Metop-A, -B and -C . The Metop-FG satellite program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The US National Oceanic and Atmospheric Administration (NOAA) under the joint NOAA/EUMETSAT Initial Joint Polar System Agreement, has contributed three Advanced Very High Resolution Radiometer (AVHRR) sensors capable of collecting and transmitting data in the Full Resolution Area Coverage (FRAC; 1km/nadir) format. The L3S_LEO_AM dataset is produced by aggregating three L3U datasets from MetOp-FG satellites (MetOp-A, -B and -C; all hosted in PO.DAAC) and covers from Dec 2006-present. The L3S_LEO_AM SST dataset is reported in two files per 24-hour interval, daytime and nighttime (nominal Metop local equator crossing times around 09:30/21:30, respectively), in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The Near Real Time (NRT) L3S-LEO data are archived at PO.DAAC with approximately 6 hours latency, and then replaced by the Re-ANalysis (RAN) files about 2 months later, with identical file names. The dataset is validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014), and monitored in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010). The L3S SST imagery and local coverage are continuously evaluated, and checked for consistency with other Level 2, 3 and 4 datasets in the ACSPO Regional Monitor for SST (ARMS) system. NOAA plans to include data from other mid-morning platforms and sensors, such as MetOp-SG METImage and Terra MODIS, into L3S_LEO_AM. More information about the dataset can be found under the Documentation and Citation tabs.\n<br>\n\n#### L3S_LEO_DY-STAR-v2.81\nThe L3S_LEO_DY-STAR-v2.81 dataset produced by the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system derives the Subskin Sea Surface Temperature (SST) from multiple instruments, including the VIIRS onboard the Suomi-NPP, NOAA-20 and NOAA-21 satellites, AVHRR onboard Metop-A, B , C satellites and MODIS onboard the Terra and Aqua satellites. The L3S-LEO is a family of multi-sensor super-collated (L3S) gridded 0.02º resolution SST products from low earth orbit (LEO) satellites. The L3S-LEO PM ( https://doi.org/10.5067/GHLPM-3S281 ) and AM ( https://doi.org/10.5067/GHLAM-3SS28 ) data include SSTs from afternoon (~1:30 am/pm) and mid-morning (~9:30 am/pm) satellites, respectively. The PM and AM SSTs, for both day (D) and night (N), and Terra MODIS SSTs, are further aggregated into a daily L3S-LEO-DY SST product. <br><br> The L3S-DY-SST combines the both L3S-LEO-PM/AM SSTs into a single daily product. It covers from 2000-02-24 to present and is reported in one file per 24h interval. Data are in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The v2.81 succeeds the v2.80 dataset (not available from the PO.DAAC) with the following improvements: (1) The L3S-LEO-PM input was updated from v2.80 to v2.81; and (2) ACSPO Terra MODIS SST is included from 2000-02-24 to 2021-12-31. The inclusion of Terra extends the availability of L3S-LEO-DY back to 2000-02-24 (from 2006-12-01 in v2.80). The SST diurnal warming effects from different daily observation times across the series of instruments have been corrected and are described in the publications by Jonasson et al., 2022 <br><br> The Near Real Time (NRT) data are available with 6h latency, and replaced by the Re-ANalysis (RAN) files in 2 months, with identical file names. They can be differentiated by the file creation time and ancillary inputs. The data are validated against quality controlled in situ data from the NOAA in situ SST Quality Monitor (iQuam; https://www.star.nesdis.noaa.gov/socd/sst/iquam), and monitored in another NOAA system, SST Quality Monitor (SQUAM; https://www.star.nesdis.noaa.gov/socd/sst/squam)\n<br>\n\n#### L3S_LEO_PM-STAR-v2.81\nThe L3S_LEO_PM-STAR-v2.81 dataset produced by the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system derives the Subskin Sea Surface Temperature (SST) from the VIIRSs (Visible Infrared Imaging Radiometer Suite) onboard the Suomi-NPP, NOAA-20 and NOAA-21 satellites and MODIS (Moderate Resolution Imaging Spectroradiometer) onboard the Aqua satellite. The L3S-LEO is a family of multi-sensor super-collated (L3S) gridded 0.02º resolution SST products from low earth orbit (LEO) satellites. The L3S-LEO-PM ( https://doi.org/10.5067/GHLPM-3S281 ) and AM ( https://doi.org/10.5067/GHLAM-3SS28 ) data include SSTs from afternoon (~1:30 am/pm) and mid-morning (~9:30 am/pm) satellites, respectively. The PM and AM SSTs, for both day (D) and night (N), and Terra MODIS SSTs, are further aggregated into a daily L3S-LEO-DY SST product ( https://doi.org/10.5067/GHLDY-3S281 ). <br><br> This PM SST product is derived by collating individual satellite ACSPO L3U data ( https://doi.org/10.5067/GHVRS-3UO61, https://doi.org/10.5067/GHV20-3UO61 and https://doi.org/10.5067/GHN21-3U280 ). It covers from 2002-07-04 to present and is reported in 2 files daily, day and night, at 1:30am/pm local time. The SST is in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The v2.81 is updated from the previous v2.80 ( https://doi.org/10.5067/GHLPM-3SS28 ): (1) v2.81 includes 3 VIIRSs (NPP, N20, and N21 from 2023-03-19 - on); (2) Aqua MODIS SST included from 2002-07-04 to 2022-12-31; (3) Time series in v2.81 extended back to 2002-07-04 (from 2012-02-01 in v2.80); (4) recently uncovered VIIRS daytime SST drifts in NPP and N20 SSTs of approximately -0.1 K/decade mitigated. <br><br> The Near Real Time (NRT) data are available with 6h latency, and replaced by the Re-ANalysis (RAN) files in 2 months, with identical file names. They can be differentiated by the file creation time and ancillary inputs. The data are validated against quality controlled in situ data from the NOAA in situ SST Quality Monitor (iQuam; https://www.star.nesdis.noaa.gov/socd/sst/iquam), and monitored in another NOAA system, SST Quality Monitor (SQUAM; https://www.star.nesdis.noaa.gov/socd/sst/squam)\n<br>\n\n#### ABI_G16-STAR-L3C-v2.70\nThe ACSPO G16/ABI L3C (Level 3 Collated) product is a gridded version of the ACSPO G16/ABI L2P product available at https://podaac.jpl.nasa.gov/dataset/ABI_G16-STAR-L2P-v2.70. The L3C output files are 1hr granules in netCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 24 granules per 24hr interval, with a total data volume of 0.2GB/day. Fill values are reported at all invalid pixels, including pixels with 5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0). All valid SSTs in L3C are recommended for users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).\n<br>\n\n#### ABI_G16-STAR-L2P-v2.70\nGOES-16 (G16) is the first satellite in the US NOAA third generation of Geostationary Operational Environmental Satellites (GOES), a.k.a. GOES-R series (which will also include -S, -T, and -U). G16 was launched on 19 Nov 2016 and initially placed in an interim position at 89.5-deg W, between GOES-East and -West. Upon completion of Cal/Val in Dec 2018, it was moved to its permanent position at 75.2-deg W, and declared NOAA operational GOES-East on 18 Dec 2018. NOAA is responsible for all GOES-R products, including Sea Surface Temperature (SST) from the Advanced Baseline Imager (ABI). The ABI offers vastly enhanced capabilities for SST retrievals, over the heritage GOES-I/P Imager, including five narrow bands (centered at 3.9, 8.4, 10.3, 11.2, and 12.3 um) out of 16 that can be used for SST, as well as accurate sensor calibration, image navigation and co-registration, spectral fidelity, and sophisticated pre-processing (geo-rectification, radiance equalization, and mapping). From altitude 35,800 km, G16/ABI can accurately map SST in a Full Disk (FD) area from 15-135-deg W and 60S-60N, with spatial resolution 2km at nadir (degrading to 15km at view zenith angle, 67-deg) and temporal sampling of 10min (15min prior to 2 Apr 2019). The Level 2 Preprocessed (L2P) SST product is derived at the native sensor resolution using NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system. ACSPO first processes every 10min FD data SSTs are derived from BTs using the ACSPO clear-sky mask (ACSM; Petrenko et al., 2010) and Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Currently, only 4 longwave bands centered at 8.4, 10.3, 11.2, and 12.3 um are used (the 3.9 microns was initially excluded, to minimize possible discontinuities in the diurnal cycle). The regression is tuned against quality controlled in situ SSTs from drifting and tropical mooring buoys in the NOAA iQuam system (Xu and Ignatov, 2014). The 10-min FD data are subsequently collated in time, to produce 1-hr L2P product, with improved coverage, and reduced cloud leakages and image noise, compared to each individual 10min image. In the collated L2P, SSTs and BTs are only reported in clear-sky water pixels (defined as ocean, sea, lake or river, and up to 5 km inland) and fill values elsewhere. The L2P is reported in netCDF4 GHRSST Data Specification version 2 (GDS2) format, 24 granules per day, with a total data volume of 0.6GB/day. In addition to SST, ACSPO files also include sun-sensor geometry, four BTs in ABI bands 11 (8.4um), 13 (10.3um), 14 (11.2um), and 15 (12.3um) and two reflectances in bands 2 and 3 (0.64um and 0.86um; used for cloud identification). The l2p_flags layer includes day/night, land, ice, twilight, and glint flags. Other variables include NCEP wind speed and ACSPO SST minus reference SST (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0). Pixel-level earth locations are not reported in the granules, as they remain unchanged from granule to granule. To obtain those, user has a choice of using a flat lat-lon file, or a Python script, both available at ftp://ftp.star.nesdis.noaa.gov/pub/socd4/coastwatch/sst/nrt/abi/nav/. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel. The ACSPO VIIRS L2P product is monitored and validated against in situ data (Xu and Ignatov, 2014) using the Satellite Quality Monitor SQUAM (Dash et al, 2010), and BTs are validated against RTM simulation in MICROS (Liang and Ignatov, 2011). A reduced size (0.2GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3C product is also available at https://podaac.jpl.nasa.gov/dataset/ABI_G16-STAR-L3C-v2.70, where gridded L2P SSTs are reported, and BT layers omitted.\n<br>\n\n#### ABI_G17-STAR-L3C-v2.71\nThe ACSPO G17/ABI L3C (Level 3 Collated) product is a gridded version of the ACSPO G17/ABI L2P product available at https://podaac.jpl.nasa.gov/dataset/ABI_G17-STAR-L2P-v2.71. The L3C output files are 1hr granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Due to the loop heat pipe (LHP) issue on G17 ABI, there are only 13 granules available per 24hr interval, from 20UTC to 08UTC, followed by a break from 09UTC to 19UTC, with a total data volume of 0.1GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. The following additional layers are also reported: SST, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0 ). All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).\n<br>\n\n#### ABI_G17-STAR-L2P-v2.71\nGOES-17 (G17) is the second satellite in the US NOAA's GOES-R series. It was launched on 1 Mar 2018 in an interim position at 89.5-deg W for initial Cal/Val, moved to its nominal position at 137.2-deg W in Nov 2018, and declared NOAA operational GOES-West satellite on 12 Feb 2019. Advanced Baseline Imager (ABI) is a 16 channel sensor, of which five (3.9, 8.4, 10.3, 11.2, 12.3 um) are suitable for SST. From altitude 35,800km, G17/ABI maps SST in a Full Disk (FD) area from 163E-77W and 60S-60N, with spatial resolution 2km/nadir to 15km/VZA 67-deg, and 10-min temporal sampling. The ABI L2P SST is derived at the native sensor resolution using NOAA ACSPO system. ACSPO processes every 10-min FD, identifies good-quality ocean pixels (Petrenko et al., 2010) and derives SST using Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Unfortunately, the G17 ABI loop heat pipe (LHP) that should maintain the ABI at its intended temperature, is not operating at its designed capacity, which required mitigations to the ACSPO algorithms and releasing an updated ACSPO version 2.71 (Pennybacker et al, 2019). In particular, band 11.2um, most subject to calibration problems, is not used leading to a 3-band (8.4, 10.3, and 12.3um) NLSST, and increased calibration problems prevent SST retrievals at night. As a result, the G17 SST is only reported for 13 out of 24hrs/day, from 20UTC to 08UTC. The 10-min FD data are subsequently collated in time, to produce 1-hr product, with improved coverage and reduced cloud leakages and image noise. The collation algorithm also reduces G17 excessive sensor noise and striping to levels similar to G16. The collated SSTs are only reported over clear-sky water pixels. All pixels with valid SSTs are recommended for use. The L2P is reported in NetCDF4 GDS2 format, 13 granules per day, with a total data volume 0.3GB/day. ACSPO files also report sun-sensor geometry, wind speed and l2p_flags (day/night, land, ice, twilight, glint flags). Per GDS2 specifications, two Sensor-Specific Error Statistics (bias and standard deviation) are reported in each pixel (Petrenko et al., 2016). Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script (see Documentation page). The ACSPO G17 ABI SSTs are continuously validated in SQUAM (Dash et al, 2010). A reduced size (0.1GB/day), 0.02-deg equal-angle gridded L3C product is available at https://podaac.jpl.nasa.gov/dataset/ABI_G17-STAR-L3C-v2.71.\n<br>\n\n#### AHI_H08-STAR-L2P-v2.70\nHimawari-8 (H08) was launched on 7 October 2014 into its nominal position at 140.7-deg E, and declared operational on 7 July 2015. The Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) is a 16 channel sensor, of which five (3.9, 8.4, 10.3, 11.2, and 12.3 um) are suitable for SST. Accurate sensor calibration, image navigation and (co)registration, high spectral fidelity, and sophisticated pre-processing (geo-rectification, radiance equalization, and mapping) offer vastly enhanced capabilities for SST retrievals, over the heritage GOES-I/P and MTSAT-2 Imagers. From altitude 35,800km, H08/AHI maps SST in a Full Disk (FD) area from 80E-160W and 60S-60N, with spatial resolution 2km at nadir to 15km at view zenith angle 67-deg, with a 10-min temporal sampling. The AHI L2P (swath) SST product is derived at the native sensor resolution using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system. ACSPO processes every 10-min FD data, identifies good quality ocean pixels (Petrenko et al., 2010) and derives SST using the four-band (8.4, 10.3, 11.2 and 12.3um) Non-Linear SST (NLSST) regression algorithm (Petrenko et al., 2014), trained against in situ SSTs from drifting and tropical mooring buoys in the NOAA iQuam system (Xu and Ignatov, 2014). The 10-min data are subsequently collated in time, to produce 1-hr L2P product, with improved coverage, and reduced cloud leakages and image noise. The collated L2P reports SSTs and brightness temperatures (BTs) in clear-sky water pixels (defined as ocean, sea, lake or river), and fill values elsewhere. All pixels with valid SSTs are recommended for use. ACSPO files also include sun-sensor geometry, l2p_flags (day/night, land, ice, twilight, and glint flags), and NCEP wind speed. The L2P is reported in NetCDF4 GHRSST Data Specification version 2 (GDS2) format, 24 granules per day, with a total data volume 0.6GB/day. Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script (see Documentation page). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel (Petrenko et al., 2016). The H08 AHI SSTs and BTs are continuously validated against in situ data in SQUAM (Dash et al, 2010), and RTM simulation in MICROS (Liang and Ignatov, 2011). A reduced size (0.2GB/day), 0.02-deg equal-angle gridded ACSPO L3C product is available at https://podaac.jpl.nasa.gov/dataset/AHI_H08-STAR-L3C-v2.70.\n<br>\n\n#### AHI_H08-STAR-L3C-v2.70\nThe ACSPO H08/AHI L3C (Level 3 Collated) product is a gridded version of the ACSPO H08/AHI L2P product available at https://podaac.jpl.nasa.gov/dataset/AHI_H08-STAR-L2P-v2.70. The L3C output files are 1hr granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 24 granules available per 24hr interval, with a total data volume of 0.2GB/day. Valid SSTs are found over clear-sky oceans, sea, lakes or rivers, with fill values reported elsewhere. The following layers are reported: SST, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0 ). All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST (Petrenko et al., 2016). The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).\n<br>\n\n#### AVHRRF_MA-STAR-L3U-v2.80\nThis L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite A (Metop-A) Advanced Very High Resolution Radiometer 3 (AVHRR/3) (https://podaac.jpl.nasa.gov/dataset/AVHRRF_MA-STAR-L2P-v2.80 ) in Full Resolution Area Coverage (FRAC) mode as input. It is distributed as 10-minute granules in netCDF-4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24-hour interval. Fill values are reported in all invalid pixels, including land pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river), and up to 5 km inland, the following major layers are reported: SSTs and ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags). Only input L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. Ancillary layers include wind speed and ACSPO minus reference Canadian Meteorological Centre (CMC) Level 4 (L4) SST. The ACSPO Metop-A AVHRR FRAC L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in the NOAA SST Quality Monitor (SQUAM) system (Dash et al, 2010). SST imagery and clear-sky mask are evaluated, and checked for consistency with L2P and other satellites/sensors SST products, in the NOAA ACSPO Regional Monitor for SST (ARMS) system. More information about the dataset is found at AVHRRF_MA-STAR-L2P-v2.80 and in (Pryamitsyn et al., 2021).\n<br>\n\n#### AVHRRF_MA-STAR-L2P-v2.80\nThe MetOp First Generation (FG) is a European multi-satellite program jointly established by ESA and EUMETSAT, comprising three satellites, MetOp-A, -B and -C. The primary sensor onboard MetOp-FG, the Advanced Very High Resolution Radiometer/3 (AVHRR/3) contributed by NOAA, measures Earth emissions and reflectances in 5 out of 6 available bands (centered at 0.63, 0.83, 1.61, 3.7, 11 and 12 microns), in a swath of 2,600km from an 817km altitude. These data are collected in a Full Resolution Area Coverage (FRAC) mode, with pixel size of 1.1km at nadir. MetOp-A launched on 19 October 2006 is the first in the MetOp-FG series. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) Level 2 Preprocessed (L2P) SST product is derived at the full AVHRR FRAC resolution and reported in 10 minute granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Subskin SSTs are derived using the regression Nonlinear SST (NLSST) algorithm, which employs three bands (3.7, 11 and 12 microns) at night and two bands (11 and 12 microns) during the day. The ACSPO AVHRR FRAC L2P product is monitored and validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014, https://doi.org/10.1175/JTECH-D-13-00121.1 ), in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010, https://doi.org/10.1175/2010JTECHO756.1 ). SST imagery and clear-sky masking are continuously evaluated, and checked for consistency with other sensors and platforms, in the ACSPO Regional Monitor for SST (ARMS) system. MetOp-A orbital characteristics and AVHRR/3 sensor performance are tracked in the NOAA 3S system (He et al., 2016, https://doi.org/10.3390/rs8040346 ).The L2P Near Real Time (NRT) SST files are archived at PO.DAAC with 3-6 hours latency, and then replaced by the Re-ANalysis (RAN) SST after about 2 months later with identical file names. Two features can be used to identify them: different file name time stamps and netCDF global attribute metadata source=NOAA-NCEP-GFS for NRT and source=MERRA-2 for RAN. A reduced size (0.45GB/day), equal-angle gridded (0.02-deg resolution) ACSPO L3U product is available at https://doi.org/10.5067/GHMTA-3US28\n<br>\n\n#### AVHRRF_MB-STAR-L3U-v2.80\nThis L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite B (Metop-B) Advanced Very High Resolution Radiometer 3 (AVHRR/3) (https://podaac.jpl.nasa.gov/dataset/AVHRRF_MB-STAR-L2P-v2.80 ) in Full Resolution Area Coverage (FRAC) mode as input. It is distributed as 10-minute granules in netCDF-4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24-hour interval. Fill values are reported in all invalid pixels, including land pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river), and up to 5 km inland, the following major layers are reported: SSTs and ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags). Only input L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. Ancillary layers include wind speed and ACSPO minus reference Canadian Meteorological Centre (CMC) Level 4 (L4) SST. The ACSPO Metop-B AVHRR FRAC L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in the NOAA SST Quality Monitor (SQUAM) system (Dash et al, 2010). SST imagery and clear-sky mask are evaluated, and checked for consistency with L2P and other satellites/sensors SST products, in the NOAA ACSPO Regional Monitor for SST (ARMS) system. More information about the dataset is found at AVHRRF_MB-STAR-L2P-v2.80 and in (Pryamitsyn et al., 2021).\n<br>\n\n#### AVHRRF_MC-STAR-L3U-v2.80\nThis L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite C (Metop-C) Advanced Very High Resolution Radiometer 3 (AVHRR/3) (https://podaac.jpl.nasa.gov/dataset/AVHRRF_MC-STAR-L2P-v2.80 ) in Full Resolution Area Coverage (FRAC) mode as input. It is distributed as 10-minute granules in netCDF-4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24-hour interval. Fill values are reported in all invalid pixels, including land pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river), and up to 5 km inland, the following major layers are reported: SSTs and ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags). Only input L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. Ancillary layers include wind speed and ACSPO minus reference Canadian Meteorological Centre (CMC) Level 4 (L4) SST. The ACSPO Metop-C AVHRR FRAC L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in the NOAA SST Quality Monitor (SQUAM) system (Dash et al, 2010). SST imagery and clear-sky mask are evaluated, and checked for consistency with L2P and other satellites/sensors SST products, in the NOAA ACSPO Regional Monitor for SST (ARMS) system. More information about the dataset is found at AVHRRF_MC-STAR-L2P-v2.80 and in (Pryamitsyn et al., 2021).\n<br>\n\n#### OISST_HR_NRT-GOS-L4-MED-v2.0\nCNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.0625deg. x 0.0625deg. horizontal resolution over the Mediterranean Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Mediterranean sea.\n<br>\n\n#### OISST_UHR_NRT-GOS-L4-MED-v2.0\nCNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.01 deg. x 0.01deg. horizontal resolution over the Mediterranean Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Mediterranean sea.\n<br>\n\n#### MSG04-OSPO-L2P-v1.0\nThe GHRSST L2P MSG04 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-11 (MSG4) satellite. It provides the full disk SEVIRI imagery covering the Atlantic Ocean region from its position at 0.0°E longitude. The L2P SST is produced at approximately 3 km resolution with a 15 minute duty cycle. On Feb. 2, 2018 the Meteosat-11 (MSG4) took over the Meteosat-10 (MSG3) (MSG03-OSPO-L2P-v1.0) and produced the L2P SST data from Sept 10. 2018 to March 24, 2023. In March 2023, Meteosat-10 and Meteosat-11 were swapped roles and orbital positions. The MSG03 has started to produce the L2P SST data again over the Atlantic Ocean region. Be aware that the granules before Dec. 1, 2022 contain some uncorrected metadata errors. <br><br> The SST measurements from SEVIRI are parameters in study of the weather, atmosphere, climate and ocean environments. Meteosat satellites have been providing crucial data for weather forecasting since 1977. <br><br> This L2P SST product which includes Single Sensor Error Statistics (i.e., uncertainty statistics) follows the GHRSST Data Processing Specification (GDS) version 2.0 format guidelines. Please refer to the user guide for more information.\n<br>\n\n#### MSG01-OSPO-L2P-v1.0\nThe GHRSST L2P MSG01 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-8 (MSG1) satellite. It provides the full disk SEVIRI imagery covering the Indian Ocean region from its position at 45.5°E longitude. The L2P SST is produced at approximately 3 km resolution with a 15 minute duty cycle. The full data records stretch from Sept. 18, 2018 to June 1, 2022. After June 1, 2022, the Meteosat-9 (MSG2) took over as the prime geostationary satellite for the Indian Ocean region (MSG02-OSPO-L2P-v1.0). Be aware that the granules before Dec. 1, 2022 contain some uncorrected metadata errors. <br><br> The SST measurements from SEVIRI are key parameters in study of the weather, atmosphere, climate and ocean environments. Meteosat satellites have been providing crucial data for weather forecasting since 1977. <br><br> This L2P SST product which includes Single Sensor Error Statistics (i.e., uncertainty statistics) follows the GHRSST Data Processing Specification (GDS) version 2.0 format guidelines. Please refer to the user guide for more information.\n<br>\n\n#### MSG02-OSPO-L2P-v1.0\nThe GHRSST L2P MSG02 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-9 (MSG2) satellite. It provides the full disk SEVIRI imagery covering the Indian Ocean region from its position at 45.5°E longitude. The L2P SST is produced at approximately 3 km resolution with a 15 minute duty cycle. On June 1, 2022, the Meteosat-9 (MSG2) replaced the Meteosat-8 (MSG1) (MSG01-OSPO-L2P-v1.0) and produced the L2P SST data from June 11. 2022 to the present. This dataset will be updated every 15 minutes as a forward data stream with 3-24 hours nominal latency. Be aware that the granules before Dec. 1, 2022 contain some uncorrected metadata errors.<br><br> The SST measurements from SEVIRI are key parameters in study of the weather, atmosphere, climate and ocean environments. Meteosat satellites have been providing crucial data for weather forecasting since 1977. <br><br> This L2P SST product which includes Single Sensor Error Statistics (i.e., uncertainty statistics) follows the GHRSST Data Processing Specification (GDS) version 2.0 format guidelines. Please refer to the user guide for more information.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","earth observation","hydrology","ice","netcdf","oceans","precipitation","satellite imagery","soil moisture","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OISST_HR_NRT-GOS-L4-BLK-v2.0 v2.0 - CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.0625 deg. x 0.0625 deg.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OISST_HR_NRT-GOS-L4-BLK-v2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OISST_UHR_NRT-GOS-L4-BLK-v2.0 v2.0 - CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.01 deg. x 0.01 deg.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OISST_UHR_NRT-GOS-L4-BLK-v2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"G18-ABI-L2P-ACSPO-v2.90 v2.90 - The G18-ABI-L2P-ACSPO-v2.90 dataset produced by the NOAA ACSPO system is used to derive Subskin and Depth Sea Surface Temperature (SST) from the ABI onboard the G18 satellite. NOAA’s G18 (aka, GOES-T pre-launch) was launched on March 1, 2022, replacing the G17 as GOES West in Jan'2023.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/G18-ABI-L2P-ACSPO-v2.90","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"H09-AHI-L2P-ACSPO-v2.90 v2.90 - The H09-AHI-L2P-ACSPO-v2.90 dataset contains the Subskin Sea Surface Temperature (SST) produced by the NOAA ACSPO system from the Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) onboard the Himawari-9 (H09) satellite. The H09 is a Japanese weather satellite, the 9th of the Himawari geostationary weather satellite operated by the Japan Meteorological Agency.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/H09-AHI-L2P-ACSPO-v2.90","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0 v1 - A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A) platform (launched 19 Oct 2006). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0 v1 - A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) platform (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GOES16-SST-OSISAF-L3C-v1.0 v1.0 - The data is regional and part of the Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset covering the America Region based on retrievals from the Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-16 (GOES-16). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from GOES-16 in the Eastern position.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GOES16-OSISAF-L3C-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SEVIRI_SST_DR-OSISAF-L3C-v1.0 v1.0 - A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Eastern Atlantic Region from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the MSG satellites (Meteosat-8 and Meteosat-9). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) has reprocessed SST products in (long) delayed-mode from MSG/SEVIRI.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SEVIRI_SST_DR-OSISAF-L3C-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"G18-ABI-L3C-ACSPO-v2.90 v2.90 - The G18-ABI-L3C-ACSPO-v2.90 dataset produced by the NOAA ACSPO system is used to derive Subskin and Depth Sea Surface Temperature (SST) from the ABI sensor onboard the G18 satellite. NOAA’s G18 (aka GOES-T before launch) was launched on March 1, 2022, replacing G17 as GOES West in Jan'2023.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/G18-ABI-L3C-ACSPO-v2.90","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"H09-AHI-L3C-ACSPO-v2.90 v2.90 - The H09-AHI-L3C-ACSPO-v2.90 dataset contains the Subskin Sea Surface Temperature (SST) produced by the NOAA ACSPO system from the Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) onboard the Himawari-9 (H09) satellite. The H09 is a Japanese weather satellite, the 9th of the Himawari geostationary weather satellite operated by the Japan Meteorological Agency.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/H09-AHI-L3C-ACSPO-v2.90","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"VIIRS_NPP-NAVO-L2P-v1.0 v1.0 - A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) satellite launched on 28 October 2011.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/VIIRS_NPP-NAVO-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"VIIRS_NPP-NAVO-L2P-v3.0 v3.0 - A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Partnership (Suomi_NPP) satellite launched on 28 October 2011.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/VIIRS_NPP-NAVO-L2P-v3.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MSG03-OSPO-L2P-v1.0 v1.0 - The Meteosat Second Generation (MSG-3) satellites are spin stabilized geostationary satellites operated by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) to provide accurate weather monitoring data through its primary instrument the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which has the capacity to observe the Earth in 12 spectral channels. Eight of these channels are in the thermal infrared, providing among other information, observations of the temperatures of clouds, land and sea surfaces at approximately 5 km resolution with a 15 mi...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MSG03-OSPO-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GOES15-OSPO-L2P-v1.0 v1.0 - The Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-15 launched 4 March 2010.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GOES15-OSPO-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRRMTA_G-NAVO-L2P-v1.0 v1.0 - A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A; launched 19 Oct 2006) ) satellite produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European undertaking providing weather data services for monitoring climate and improving weather forecasts.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRRMTA_G-NAVO-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRRMTB_G-NAVO-L2P-v1.0 v1.0 - A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B; launched 19 Oct 2006) ) satellite produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European undertaking providing weather data services for monitoring climate and improving weather forecasts.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRRMTB_G-NAVO-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRR18_G-NAVO-L2P-v1.0 v1.0 - A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-18 platform (launched 20 May 2005) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces b...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRR18_G-NAVO-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRR19_G-NAVO-L2P-v1.0 v1 - A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces ba...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRR19_G-NAVO-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AMSR2-REMSS-L2P_RT-v8.2 v8.2 - This product provides a near-real-time (NRT) Level-2 Sea Surface Temperature (SST) (identified by \"_rt_\" within the file name) for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, which is derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by Remote Sensing Systems (RSS, or REMSS). AMSR2 was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AMSR2-REMSS-L2P_RT-v8.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"VIIRS_NPP-JPL-L2P-v2016.2 v2016.2 - These files contain NASA produced skin sea surface temperature (SST) products from the Infrared (IR) channels of the Visible and Infrared Imager/Radiometer Suite (VIIRS) onboard the Suomi-NPP satellite. VIIRS is a multi-disciplinary instrument that is also being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, of which NOAA-20 is the first.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/VIIRS_NPP-JPL-L2P-v2016.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MODIS_A-JPL-L2P-v2019.0 v2019.0 - NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 1:30 pm, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MODIS_T-JPL-L2P-v2019.0 v2019.0 - NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MODIS_T-JPL-L2P-v2019.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRRMTA_G-NAVO-L2P-v2.0 v2.0 - A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P data set containing multi-channel Sea Surface Temperature (SST) retrievals derived in real-time from the Advanced Very High Resolution Radiometer (AVHRR) level-1B data from the Meteorological Operational-A (MetOp-A) satellite. The SST data in this data set are used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRRMTA_G-NAVO-L2P-v2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRRMTB_G-NAVO-L2P-v2.0 v2.0 - A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P data set containing multi-channel Sea Surface Temperature (SST) retrievals derived in real-time from the Advanced Very High Resolution Radiometer (AVHRR) level-1B data from the Meteorological Operational-B (MetOp-B) satellite. The SST data in this data set are used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRRMTB_G-NAVO-L2P-v2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRRMTC_G-NAVO-L2P-v2.0 v2.0 - A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P data set containing multi-channel Sea Surface Temperature (SST) retrievals derived in real-time from the Advanced Very High Resolution Radiometer (AVHRR) level-1B data from the Meteorological Operational-C (MetOp-C) satellite. The SST data in this data set are used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRRMTC_G-NAVO-L2P-v2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"IASI_SST_METOP_A-OSISAF-L2P-v1.0 v1 - A global 1 km Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Infrared Atmospheric Sounding Interferometer (IASI) on the European Meteorological Operational-A (MetOp-A&#65289;satellite &#65288;launched 19 Oct 2006). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near realtime from METOP/IASI.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/IASI_SST_METOP_A-OSISAF-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"IASI_SST_METOP_B-OSISAF-L2P-v1.0 v1 - A Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Infrared Atmospheric Sounding Interferometer (IASI) on the European Meteorological Operational-B (MetOp-B)satellite (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near realtime from METOP/IASI.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/IASI_SST_METOP_B-OSISAF-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AMSRE-REMSS-L2P-v7a v7a - The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AMSRE-REMSS-L2P-v7a","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"TMI-REMSS-L2P-v4 v4.0 - GDS2 Version -The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is a well calibrated passive microwave radiometer, similar to the Special Sensor Microwave Imager (SSM/I), that contains lower frequency channels required for sea surface temperature (SST) retrievals. The TRMM is part of the NASA's mission to planet Earth, and is a joint venture between NASA and the Japan Aerospace Exploration Agency (JAXA) to measure precipitation, water vapor, SST and wind in the global tropical regions and was launched in 27 November 1997 from the Tanegashima Space Center in Tanegashima, ...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TMI-REMSS-L2P-v4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AMSR2-REMSS-L2P-v8.2 v8.2 - This product provides a “Final” (Refined) Level-2 Sea Surface Temperature (SST) (currently identified by \"v8.2\" within the file name) for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, which is derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by Remote Sensing Systems (RSS, or REMSS). AMSR2 was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AMSR2-REMSS-L2P-v8.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"VIIRS_N20-NAVO-L2P-v3.0 v3.0 - The VIIRS_N20-NAVO-L2P-v3.0 dataset produced by the Naval Oceanographic Office (NAVO) derives the 1-meter depth Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-1 satellite, renamed as NOAA-20 (N20). N20 was launched on November 18, 2017, the 2nd satellite in the US NOAA JPSS series.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/VIIRS_N20-NAVO-L2P-v3.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"VIIRS_N21-NAVO-L2P-v3.0 v3.0 - The VIIRS_N21-NAVO-L2P-v3.0 dataset produced by the Naval Oceanographic Office (NAVO) derives the 1-meter depth Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on November 10, 2022, the 3rd satellite in the US NOAA JPSS series.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/VIIRS_N21-NAVO-L2P-v3.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"N21-VIIRS-L2P-ACSPO-v2.80 v2.80 - The N21-VIIRS-L2P-ACSPO-v2.80 dataset produced by the NOAA ACSPO system derives the Subskin Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the The Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on Nov.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/N21-VIIRS-L2P-ACSPO-v2.80","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"VIIRS_N20-STAR-L2P-v2.80 v2.80 - NOAA-20 (N20/JPSS-1/J1) is the second satellite in the US NOAA latest generation Joint Polar Satellite System (JPSS), launched on November 18, 2017. NOAA is responsible for all JPSS products, including SST from the Visible Infrared Imaging Radiometer Suite (VIIRS).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/VIIRS_N20-STAR-L2P-v2.80","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"VIIRS_NPP-STAR-L2P-v2.80 v2.80 - The Joint Polar Satellite System (JPSS), starting with S-NPP launched on 28 October 2011, is the new generation of the US Polar Operational Environmental Satellites (POES). NOAA is responsible for all JPSS products, including SST from the Visible Infrared Imaging Radiometer Suite (VIIRS).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/VIIRS_NPP-STAR-L2P-v2.80","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRR19_L-NAVO-L2P-v1.0 v1 - A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces ...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRR19_L-NAVO-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"EWSG1-NAVO-L2P-v01 v1.0 - A Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P sea surface temperature produced by The Naval Oceanographic Office (NAVO) from the GOES Imager sensor on the Electro-Optical Infrared Weather System – Geostationary satellite (EWS-G1). The EWS-G1, formerly GOES-13, is the first Department of Defense owned geostationary weather satellite, which has been repositioned over Indian Ocean (IO) region at 60.0° West longitude in January 2018 and fully operational since September 8, 2020, providing timely cloud characterization and theater weather imagery to DoD.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/EWSG1-NAVO-L2P-v01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"EWSG2-NAVO-L2P-v01 v1.0 - A Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P sea surface temperature dataset produced by the Naval Oceanographic Office (NAVO) from the GOES Imager sensor on the Electro-Optical Infrared Weather System – Geostationary satellite (EWS-G2). The EWS-G2, formerly GOES-15, is the second Department of Defense owned geostationary weather satellite, which has been repositioned over Indian Ocean (IO) region at 60.0° West longitude in September 2023 and fully operational since December 3, 2023, providing timely cloud characterization and theater weather imagery to DoD.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/EWSG2-NAVO-L2P-v01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRR_SST_METOP_A-OSISAF-L2P-v1.0 v1 - A global 1 km Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A)satellite (launched 19 Oct 2006). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRR_SST_METOP_A-OSISAF-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRR_SST_METOP_B-OSISAF-L2P-v1.0 v1 - A global 1 km Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) satellite (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRR_SST_METOP_B-OSISAF-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GOES13-OSPO-L2P-v1.0 v1.0 - The Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-13 launched 24 May 2006.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GOES13-OSPO-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MTSAT2-OSPO-L2P-v1.0 v1.0 - Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MTSAT2-OSPO-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SEVIRI_SST-OSISAF-L3C-v1.0 v1 - A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Eastern Atlantic Region from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the Meteosat Second Generation (MSG-3) satellites (launched 5 July 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from MSG/SEVIRI.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SEVIRI_SST-OSISAF-L3C-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SEVIRI_IO_SST-OSISAF-L3C-v1.0 v1.0 - This dataset is produced by the Ocean and Sea Ice Satellite Application Facility (OSI SAF) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument onboard the Meteosat Second Generation (MSG-1), Meteosat-8 satellite (launched on 28 August 2002). The dataset covers the Indian Ocean region with latitude of 60S-60N and longitude of 101.5E-18.5W.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SEVIRI_IO_SST-OSISAF-L3C-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0 v1 - A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) platform (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR and SNPP/VIIRS.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0 v1 - A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset for the North Atlantic Region (NAR) based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR and SNPP/VIIRS.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0 v1 - A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A) platform (launched 19 Oct 2006). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR and SNPP/VIIRS.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0 v1 - A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009). This particular dataset is produced by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) in France.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GOES13-OSISAF-L3C-v1.0 v1 - A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset for the America Region (AMERICAS) based on retrievals from the GOES-13 Imager on board GOES-13 satellite. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from GOES 13 in East position.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GOES13-OSISAF-L3C-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AMSR2-REMSS-L3U_RT-v8.2 v8.2 - This product contains a near-real-time (NRT) Level-3U Sea Surface Temperature (SST) (identified by \"_rt_\" within the file name) for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, which is derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by Remote Sensing Systems (RSS, or REMSS). AMSR2 was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AMSR2-REMSS-L3U_RT-v8.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AMSR2-REMSS-L3U_RT-v8a v8a - GDS2 Version -The Advanced Microwave Scanning Radiometer 2 (AMSR2) was launched on 18 May 2012, onboard the Golbal Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AMSR2-REMSS-L3U_RT-v8a","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GMI-REMSS-L3U-v8.2a v8.2a - The Global Precipitation Measurement (GPM) satellite was launched on February 27th, 2014 with the GPM Microwave Imager (GMI) instrument on board. The GPM mission is a joint effort between NASA, the Japan Aerospace Exploration Agency (JAXA) and other international partners.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GMI-REMSS-L3U-v8.2a","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"TMI-REMSS-L3U-v7.1a v7.1a - The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is a well calibrated passive microwave radiometer, similar to the Special Sensor Microwave Imager (SSM/I), that contains lower frequency channels required for sea surface temperature (SST) retrievals. The TRMM is part of the NASA's mission to planet Earth, and is a joint venture between NASA and the Japan Aerospace Exploration Agency (JAXA) to measure precipitation, water vapor, sea surface temperature (SST) and surface wind in the global tropical regions and was launched in 27 November 1997 from the Tanegashima Space Cen...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TMI-REMSS-L3U-v7.1a","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AMSR2-REMSS-L3U-v8.2 v8.2 - This product contains a “Final” (Refined) Level-3U Sea Surface Temperature (SST) (currently identified by \"v8.2\" within the file name) for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, which is derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by Remote Sensing Systems (RSS, or REMSS). AMSR2 was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AMSR2-REMSS-L3U-v8.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"WindSat-REMSS-L3U-v7.0.1a v7.0.1a - The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/WindSat-REMSS-L3U-v7.0.1a","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"N21-VIIRS-L3U-ACSPO-v2.80 v2.80 - The N21-VIIRS-L3U-ACSPO-v2.80 dataset produced by the NOAA ACSPO system derives the Subskin Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on Nov.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/N21-VIIRS-L3U-ACSPO-v2.80","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"VIIRS_N20-STAR-L3U-v2.80 v2.80 - NOAA-20 (N20/JPSS-1/J1) is the second satellite in the US NOAA latest generation Joint Polar Satellite System (JPSS), launched on November 18, 2017. The ACSPO N20/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO N20/VIIRS L2P product available here https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-STAR-L2P-v2.80.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/VIIRS_N20-STAR-L3U-v2.80","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"VIIRS_NPP-STAR-L3U-v2.80 v2.80 - The Joint Polar Satellite System (JPSS), starting with S-NPP launched on 28 October 2011, is the new generation of the US Polar Operational Environmental Satellites (POES). The ACSPO SNPP/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO NPP/VIIRS L2P product available here https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-STAR-L2P-v2.80.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/VIIRS_NPP-STAR-L3U-v2.80","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRR_OI-NCEI-L4-GLOB-v2.0 v2.0 - A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.25 degree grid at the NOAA National Centers for Environmental Information. This product uses optimal interpolation (OI) by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRR_OI-NCEI-L4-GLOB-v2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRR_OI-NCEI-L4-GLOB-v2.1 v2.1 - A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature dataset is produced daily on a 0.25 degree grid at the NOAA National Centers for Environmental Information. This product uses optimal interpolation (OI) by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRR_OI-NCEI-L4-GLOB-v2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMC0.1deg-CMC-L4-GLOB-v3.0 v3.0 - A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis at the Canadian Meteorological Center. This dataset merges infrared satellite SST at varying points in the time series from the Advanced Very High Resolution Radiometer (AVHRR) from NOAA-18,19, the European Meteorological Operational-A (METOP-A) and Operational-B (METOP-B), and microwave data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite in conjunction with in situ observations of SST from drifting buoys a...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CMC0.1deg-CMC-L4-GLOB-v3.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMC0.2deg-CMC-L4-GLOB-v2.0 v2.0 - A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis at the Canadian Meteorological Center. This dataset merges infrared satellite SST at varying points in the time series from the (A)TSR series of radiometers from ERS-1, ERS-2 and Envisat, AVHRR from NOAA-16,17,18,19 and METOP-A, and microwave data from TMI, AMSR-E and Windsat in conjunction with in situ observations of SST from drifting buoys and ships from the ICOADS program.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CMC0.2deg-CMC-L4-GLOB-v2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"DMI_OI-DMI-L4-GLOB-v1.0 v1.0 - A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis by the Danish Meteorological Institute (DMI) using an optimal interpolation (OI) approach on a global 0.05 degree grid. The analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several satellites.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/DMI_OI-DMI-L4-GLOB-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GAMSSA_28km-ABOM-L4-GLOB-v01 v1.0 - A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a global 0.25 degree grid. This Global Australian Multi-Sensor SST Analysis (GAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers with in situ data from ships, drifting buoys and moorings from the Global Telecommunications System (GTS).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GAMSSA_28km-ABOM-L4-GLOB-v01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"K10_SST-NAVO-L4-GLOB-v01 v1.0 - This is a Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis dataset produced daily on an operational basis by the Naval Oceanographic Office (NAVO) on a global 0.1x0.1 degree grid. The K10 (NAVO 10-km gridded SST analyzed product) L4 analysis uses SST observations from the following instruments: Advanced Very High Resolution Radiometer (AVHRR), Visible Infrared Imaging Radiometer Suite (VIIRS), and Spinning Enhanced Visible and InfraRed Imager (SEVIRI).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/K10_SST-NAVO-L4-GLOB-v01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MUR25-JPL-L4-GLOB-v04.2 v4.2 - A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.25 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imag...","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MUR25-JPL-L4-GLOB-v04.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MUR-JPL-L4-GLOB-v4.1 v4.1 - A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwav...","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MUR-JPL-L4-GLOB-v4.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MW_IR_OI-REMSS-L4-GLOB-v5.0 v5.0 - A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.09-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from both microwave (MW) sensors including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite, and WindSat operates on the Coriolis satellite, and i...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MW_IR_OI-REMSS-L4-GLOB-v5.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MW_IR_OI-REMSS-L4-GLOB-v5.1 v5.1 - A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.09-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from microwave (MW) sensors including the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the WindSat on the Coriolis satellite, the Global Precipitation Measurement (GPM) Microwave Imager (GMI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite, as well as inf...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MW_IR_OI-REMSS-L4-GLOB-v5.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MW_OI-REMSS-L4-GLOB-v5.0 v5.0 - A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.25-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from microwave (MW) sensors including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite, and WindSat operates on the Coriolis satellite.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MW_OI-REMSS-L4-GLOB-v5.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MW_OI-REMSS-L4-GLOB-v5.1 v5.1 - A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.25-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from microwave (MW) sensors including the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the WindSat on the Coriolis satellite, the Global Precipitation Measurement (GPM) Microwave Imager (GMI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MW_OI-REMSS-L4-GLOB-v5.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"Geo_Polar_Blended-OSPO-L4-GLOB-v1.0 v1.0 - A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the Office of Satellite and Product Operations (OSPO) using optimal interpolation (OI) on a global 0.054 degree grid. The Geo-Polar Blended Sea Surface Temperature (SST) Analysis combines multi-satellite retrievals of sea surface temperature into a single analysis of SST.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/Geo_Polar_Blended-OSPO-L4-GLOB-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OSTIA-UKMO-L4-GLOB-v2.0 v2.0 - A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the UK Met Office using optimal interpolation (OI) on a global 0.05x0.05 degree grid. The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) analysis uses satellite data from over 10 unique sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Geostationary Operational Environmental Satellite (GOES) imager, the Infrared Atmospheric Sounding Interferome...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OSTIA-UKMO-L4-GLOB-v2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OSTIA-UKMO-L4-GLOB-REP-v2.0 v2.0 - The Operational Sea Surface Temperature and Sea Ice Analysis Reprocessed (OSTIA-REP) is a GHRSST reprocessed Level-4 sea surface temperature and ice-concentration analysis produced by the UK Met Office (UKMO) using optimal interpolation (OI) on a global 0.05 degree grid. It is a sister product of the Near Real Time version (OSTIA-NRT), but incorporates satellite data from over 25 different SST sensors as well as in situ data from drifting and moored buoys.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OSTIA-UKMO-L4-GLOB-REP-v2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"RAMSSA_09km-ABOM-L4-AUS-v01 v1.0 - A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a regional 1/12 degree grid over the Australian region (20N - 70S, 60E - 170W). This Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers, with in situ data from ships, Argo floats, XBTs, CTDs, drifting buoys and moorings from the Global Telecommunications System (GTS).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/RAMSSA_09km-ABOM-L4-AUS-v01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"REMO_OI_SST_5km-UFRJ-L4-SAMERICA-v1.0 v1.0 - A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis by the Oceanographic Modeling and Observation Network (REMO) at Applied Meteorology Laboratory/Federal University of Rio de Janeiro (LMA/UFRJ) using the Barnes sub optimal interpolation (OI) technique on a regional 0.05 degree grid. REMO uses Advanced Very High Resolution Radiometer (AVHRR) data from National Oceanic and Atmospheric Administration (NOAA) satellites series (NOAA 15, NOAA 16, NOAA 17, NOAA 18 and NOAA 19) and Microwave Imager (TMI)...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/REMO_OI_SST_5km-UFRJ-L4-SAMERICA-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SPORT-MSFC-L4-GLOB-v1.0 v1.0 - The GHRSST Level 4 SPoRT Global Foundation Sea Surface Temperature Analysis (v1.0) dataset is produced by the by the NASA Short-term Prediction Research and Transition (SPoRT) project to provide continuous high-resolution composited near global (80N – 80S) sea surface temperature fields twice daily at 2 km resolution for regional weather, maritime, and coastal applications. <br><br> It was originally a regional L4 dataset based on MODIS Aqua/Terra Level 2 SST inputs.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/SPORT-MSFC-L4-GLOB-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"L3S_LEO_AM-STAR-v2.80 v2.80 - NOAA STAR produces two lines of gridded 0.02 degree super-collated L3S LEO sub-skin Sea Surface Temperature (SST) datasets, one from the NOAA afternoon JPSS (L3S_LEO_PM) satellites and the other from the EUMETSAT mid-morning Metop (L3S_LEO_AM) satellites. The L3S_LEO_AM is derived from three Low Earth Orbiting (LEO) Metop-FG satellites: Metop-A, -B and -C.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/L3S_LEO_AM-STAR-v2.80","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"L3S_LEO_DY-STAR-v2.81 v2.81 - The L3S_LEO_DY-STAR-v2.81 dataset produced by the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system derives the Subskin Sea Surface Temperature (SST) from multiple instruments, including the VIIRS onboard the Suomi-NPP, NOAA-20 and NOAA-21 satellites, AVHRR onboard Metop-A, B , C satellites and MODIS onboard the Terra and Aqua satellites. The L3S-LEO is a family of multi-sensor super-collated (L3S) gridded 0.02º resolution SST products from low earth orbit (LEO) satellites.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/L3S_LEO_DY-STAR-v2.81","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"L3S_LEO_PM-STAR-v2.81 v2.81 - The L3S_LEO_PM-STAR-v2.81 dataset produced by the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system derives the Subskin Sea Surface Temperature (SST) from the VIIRSs (Visible Infrared Imaging Radiometer Suite) onboard the Suomi-NPP, NOAA-20 and NOAA-21 satellites and MODIS (Moderate Resolution Imaging Spectroradiometer) onboard the Aqua satellite. The L3S-LEO is a family of multi-sensor super-collated (L3S) gridded 0.02º resolution SST products from low earth orbit (LEO) satellites.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/L3S_LEO_PM-STAR-v2.81","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABI_G16-STAR-L3C-v2.70 v2.70 - The ACSPO G16/ABI L3C (Level 3 Collated) product is a gridded version of the ACSPO G16/ABI L2P product available at https://podaac.jpl.nasa.gov/dataset/ABI_G16-STAR-L2P-v2.70. The L3C output files are 1hr granules in netCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ABI_G16-STAR-L3C-v2.70","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABI_G16-STAR-L2P-v2.70 v2.70 - GOES-16 (G16) is the first satellite in the US NOAA third generation of Geostationary Operational Environmental Satellites (GOES), a.k.a. GOES-R series (which will also include -S, -T, and -U).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ABI_G16-STAR-L2P-v2.70","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABI_G17-STAR-L3C-v2.71 v2.71 - The ACSPO G17/ABI L3C (Level 3 Collated) product is a gridded version of the ACSPO G17/ABI L2P product available at https://podaac.jpl.nasa.gov/dataset/ABI_G17-STAR-L2P-v2.71. The L3C output files are 1hr granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ABI_G17-STAR-L3C-v2.71","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABI_G17-STAR-L2P-v2.71 v2.71 - GOES-17 (G17) is the second satellite in the US NOAA's GOES-R series. It was launched on 1 Mar 2018 in an interim position at 89.5-deg W for initial Cal/Val, moved to its nominal position at 137.2-deg W in Nov 2018, and declared NOAA operational GOES-West satellite on 12 Feb 2019.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ABI_G17-STAR-L2P-v2.71","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AHI_H08-STAR-L2P-v2.70 v2.70 - Himawari-8 (H08) was launched on 7 October 2014 into its nominal position at 140.7-deg E, and declared operational on 7 July 2015. The Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) is a 16 channel sensor, of which five (3.9, 8.4, 10.3, 11.2, and 12.3 um) are suitable for SST.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AHI_H08-STAR-L2P-v2.70","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AHI_H08-STAR-L3C-v2.70 v2.70 - The ACSPO H08/AHI L3C (Level 3 Collated) product is a gridded version of the ACSPO H08/AHI L2P product available at https://podaac.jpl.nasa.gov/dataset/AHI_H08-STAR-L2P-v2.70. The L3C output files are 1hr granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AHI_H08-STAR-L3C-v2.70","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRRF_MA-STAR-L3U-v2.80 v2.80 - This L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite A (Metop-A) Advanced Very High Resolution Radiometer 3 (AVHRR/3) (https://podaac.jpl.nasa.gov/dataset/AVHRRF_MA-STAR-L2P-v2.80 ) in Full Resolution Area Coverage (FRAC) mode as input.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRRF_MA-STAR-L3U-v2.80","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRRF_MA-STAR-L2P-v2.80 v2.80 - The MetOp First Generation (FG) is a European multi-satellite program jointly established by ESA and EUMETSAT, comprising three satellites, MetOp-A, -B and -C. The primary sensor onboard MetOp-FG, the Advanced Very High Resolution Radiometer/3 (AVHRR/3) contributed by NOAA, measures Earth emissions and reflectances in 5 out of 6 available bands (centered at 0.63, 0.83, 1.61, 3.7, 11 and 12 microns), in a swath of 2,600km from an 817km altitude.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRRF_MA-STAR-L2P-v2.80","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRRF_MB-STAR-L3U-v2.80 v2.80 - This L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite B (Metop-B) Advanced Very High Resolution Radiometer 3 (AVHRR/3) (https://podaac.jpl.nasa.gov/dataset/AVHRRF_MB-STAR-L2P-v2.80 ) in Full Resolution Area Coverage (FRAC) mode as input.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRRF_MB-STAR-L3U-v2.80","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRRF_MC-STAR-L3U-v2.80 v2.80 - This L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite C (Metop-C) Advanced Very High Resolution Radiometer 3 (AVHRR/3) (https://podaac.jpl.nasa.gov/dataset/AVHRRF_MC-STAR-L2P-v2.80 ) in Full Resolution Area Coverage (FRAC) mode as input.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/AVHRRF_MC-STAR-L3U-v2.80","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OISST_HR_NRT-GOS-L4-MED-v2.0 v2.0 - CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.0625deg. x 0.0625deg.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OISST_HR_NRT-GOS-L4-MED-v2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OISST_UHR_NRT-GOS-L4-MED-v2.0 v2.0 - CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.01 deg. x 0.01deg.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OISST_UHR_NRT-GOS-L4-MED-v2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MSG04-OSPO-L2P-v1.0 v1.0 - The GHRSST L2P MSG04 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-11 (MSG4) satellite. It provides the full disk SEVIRI imagery covering the Atlantic Ocean region from its position at 0.0°E longitude.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MSG04-OSPO-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MSG01-OSPO-L2P-v1.0 v1.0 - The GHRSST L2P MSG01 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-8 (MSG1) satellite. It provides the full disk SEVIRI imagery covering the Indian Ocean region from its position at 45.5°E longitude.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MSG01-OSPO-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MSG02-OSPO-L2P-v1.0 v1.0 - The GHRSST L2P MSG02 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-9 (MSG2) satellite. It provides the full disk SEVIRI imagery covering the Indian Ocean region from its position at 45.5°E longitude.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MSG02-OSPO-L2P-v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-ghrsst","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA GLDAS Project","Description":"NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products. GLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. This data product is an Early Product for GLDAS-2.1 Catchment 1.0 degree 3-hourly dataset. The GLDAS-2.1 3 hourly 1.0 degree product was simulated with the Catchment-F2.5 Land Surface Model in Land Information System (LIS) Version 7. The data product contains 34 land surface fields from January 2000 to present. The GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards. The GLDAS-2.1 products supersede their corresponding GLDAS-1 products. The GLDAS-2.1 data are archived and distributed in NetCDF format.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://ldas.gsfc.nasa.gov/gldas/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","netcdf","oceans","precipitation","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GLDAS_CLSM10_3H_EP v2.1 - NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GLDAS/GLDAS_CLSM10_3H_EP.2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-gldas","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA GOES Project","Description":"The ABI G16 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid product, short-name AERDB_D3_ABI_G16, derived from the L2 (AERDB_L2_ABI_G16) input data, each D3 ABI/GOES-16 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element. Another statistic like standard deviation is also provided in some cases. The final retrievals used in the aggregation process are Quality Assurance (QA)-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-16 (GOES-16) Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_D3_ABI_G16 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_ABI_G16 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue\n<br>\n\n#### AERDB_M3_ABI_G16\nThe ABI G16 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid product, short-name AERDB_M3_ABI_G16, derived by aggregating the L3 daily (AERDB_D3_ABI_G16) input data, each M3 ABI/GOES-16 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the short-name as M3) product’s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product. As a mechanism to filter out poorly sampled grid elements, at least three valid days of data in the month are required to populate the monthly grid element. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-16 (GOES-16) Deep Blue Monthly Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO)) instruments. The AERDB_D3_ABI_G16 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_ABI_G16 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue\n<br>\n\n#### AERDB_D3_ABI_G17\nThe ABI G17 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid product, short-name AERDB_D3_ABI_G17, derived from the L2 (AERDB_L2_ABI_G17) input data, each D3 ABI/GOES-17 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element. Another statistic like standard deviation is also provided in some cases. The final retrievals used in the aggregation process are Quality Assurance (QA)-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-17 (GOES-17) Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_D3_ABI_G17 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_ABI_G17 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue\n<br>\n\n#### AERDB_M3_ABI_G17\nThe ABI G17 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid product, short-name AERDB_M3_ABI_G17, derived by aggregating the L3 daily (AERDB_D3_ABI_G17) input data, each M3 ABI/GOES-17 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the shortname as M3) product’s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product. As a mechanism to filter out poorly sampled grid elements, at least three valid days of data in the month are required to populate the monthly grid element. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-17 (GOES-17) Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_M3_ABI_G17 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_ABI_G17 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue\n<br>\n\n#### AERDB_L2G_GEOLEO_Merged\nThe GEO-LEO Merged Deep Blue Aerosol 0.25x0.25 degree Gridded L2 product, short-name AERDB_L2G_GEOLEO_Merged contains gridded Aerosol Optical Thickness (AOT) at 550 nm reference wavelength, derived from seven merged GEO-LEO AOT layers (G16-ABI, G17-ABI, H08-AHI, SNPP-VIIRS, NOAA20-VIIRS, Terra MODIS and Aqua MODIS) and from each of the individual (three GEO and four LEO) instrument sources. Each L2G aggregated datafile is spatially comprised of a 0.25˚ x 0.25˚ horizontal grid that exists for every 30 minutes. This represents a 30-minute Deep Blue best-estimate AOT from each of the seven sources besides an error-weighted merged AOT layer. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-2G (L2G) Geostationary Earth Orbit (GEO)-Low-Earth Orbit (LEO) Merged Deep Blue Aerosol 0.25 x 0.25-degree Gridded dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_L2G_GEOLEO_Merged product, in netCDF4 format, contains 16 GEO-LEO Merged Group Science Data Set (SDS) layers and 15 GEO and LEO SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2G_GEOLEO_Merged Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue\n<br>\n\n#### AERDB_D3_GEOLEO_Merged\nThe GEO-LEO Merged Deep Blue Aerosol Daily 1 x 1 degree Gridded L3 product, short-name AERDB_D3_GEOLEO_Merged contains gridded Aerosol Optical Thickness (AOT) at 550 nm reference wavelength that are composited from the L2G product (AERDB_L2G_GEOLEO_Merged) using best-estimate AOT values. Please note that while the individual standalone gridded data layer for each instrument is calculated as the arithmetic mean, the merged AOT layer is derived via an error-weighted average approach. The final retrievals used in the aggregation process are QA-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. Each L3 daily aggregated datafile is spatially comprised of a 1˚ x 1˚ horizontal grid that exists for every 30 minutes. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Geostationary Earth Orbit (GEO)-Low-Earth Orbit (LEO) Merged Deep Blue Aerosol Daily 1 x 1-degree Gridded dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_D3_GEOLEO_Merged product, in netCDF4 format, contains 16 GEO-LEO Merged Group Science Data Set (SDS) layers and 15 GEO and LEO SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_GEOLEO_Merged Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue\n<br>\n\n#### AERDB_M3_GEOLEO_Merged\nThe GEO-LEO Merged Deep Blue Aerosol Monthly 1 x 1 degree Gridded L3 product, short-name AERDB_M3_GEOLEO_Merged contains gridded Aerosol Optical Thickness (AOT) at 550 nm reference wavelength that are composited from the L3 daily product (AERDB_D3_GEOLEO_Merged). Please note that while the individual standalone gridded data layer for each instrument is calculated as the arithmetic mean, the merged AOT layer is derived via an error-weighted average approach. The final retrievals used in the aggregation process are QA-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. Each L3 daily aggregated datafile is spatially comprised of a 1˚ x 1˚ horizontal grid that exists for every 30 minutes. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Geostationary Earth Orbit (GEO)-Low-Earth Orbit (LEO) Merged Deep Blue Aerosol Daily 1 x 1-degree Gridded dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_M3_GEOLEO_Merged product, in netCDF4 format, contains 16 GEO-LEO Merged Group Science Data Set (SDS) layers and 15 GEO and LEO SDS layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_GEOLEO_Merged Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue\n<br>\n\n#### AERDB_L2_ABI_G16\nThe ABI G16 Deep Blue Aerosol 10-Min L2 Full Disk product, short-name AERDB_L2_ABI_G16 is produced every 30 minutes and contains full-disk observation data. The L2 data products comprise 10 x 10 native GEO pixels. Each spectral band with 0.5 km or 2 km resolution is downscaled or upscaled to a nominal ~1 km horizontal pixel size in the production process. To distinguish them from native instrument pixels, these 10 x 10 aggregated pixels are also called retrieval pixels. Therefore, the L2 products’ image dimensions are roughly 10 km x 10 km at the sub-satellite point and are larger away from that point because of the combined effects of the sensor’s scanning geometry and Earth’s curvature. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-2 (L2) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-16 (GOES-16) Deep Blue Aerosol Full-Disk dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_L2_ABI_G16 product, in netCDF4 format, contains 51 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2_ABI_G16 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue\n<br>\n\n#### AERDB_L2_ABI_G17\nThe ABI G17 Deep Blue Aerosol 10-Min L2 Full Disk product, short-name AERDB_L2_ABI_G17 is produced every 30 minutes and contains full-disk observation data. The L2 data products comprise 10 x 10 native GEO pixels. Each spectral band with 0.5 km or 2 km resolution is downscaled or upscaled to a nominal ~1 km horizontal pixel size in the production process. To distinguish them from native instrument pixels, these 10 x 10 aggregated pixels are also called retrieval pixels. Therefore, the L2 products’ image dimensions are roughly 10 km x 10 km at the sub-satellite point and are larger away from that point because of the combined effects of the sensor’s scanning geometry and Earth’s curvature. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-2 (L2) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-17 (GOES-17) Deep Blue Aerosol Full-Disk dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_L2_ABI_G17 product, in netCDF4 format, contains 51 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2_ABI_G17 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue\n<br>\n\n#### VISSRGOES1IMIR\nVISSRGOES1IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the first Geostationary Operational Environmental Satellite (GOES-1). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification. The GOES-1 satellite was parked over the equator at longitude 115W on Dec 18, 1975 viewing the hemisphere below the satellite. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00247 (old ID 75-100A-01B).\n<br>\n\n#### VISSRGOES1IMVIS\nVISSRGOES1IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the first Geostationary Operational Environmental Satellite (GOES-1). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification. The GOES-1 satellite was parked over the equator at longitude 115W on Dec 18, 1975 viewing the hemisphere below the satellite. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00247 (old ID 75-100A-01B).\n<br>\n\n#### VISSRGOES2IMIR\nVISSRGOES2IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the second Geostationary Operational Environmental Satellite (GOES-2). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification. The GOES-2 satellite was parked over the equator at longitude 75W from 1977 through 1978 viewing the hemisphere below the satellite. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00028 (old ID 77-048A-01C).\n<br>\n\n#### VISSRGOES2IMVIS\nVISSRGOES2IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the second Geostationary Operational Environmental Satellite (GOES-2). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification. The GOES-2 satellite was parked over the equator at longitude 75W from 1977 through 1978 viewing the hemisphere below the satellite. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00087 (old ID 77-048A-01B).\n<br>\n\n#### VISSRGOES3IMIR\nVISSRGOES3IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the third Geostationary Operational Environmental Satellite (GOES-3). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification. The GOES-3 satellite was parked over the equator at longitude 135W from 1978 through 1981 viewing the hemisphere below the satellite. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00105 (old ID 75-100A-01C).\n<br>\n\n#### VISSRGOES3IMVIS\nVISSRGOES3IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the third Geostationary Operational Environmental Satellite (GOES-3). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification. The GOES-3 satellite was parked over the equator at longitude 135W from 1978 through 1981 viewing the hemisphere below the satellite. It was moved to its final operational position at 135W on Dec 19, 1975. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00247 (old ID 75-100A-01B).\n<br>\n\n#### VISSRSMS1IMIR\nVISSRSMS1IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the first Synchronous Meteorological Satellite (SMS-1). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification. The SMS-1 satellite was initially parked over the equator at longitude 45W on June 7, 1974 viewing the hemisphere below the satellite to support the GARP Atlantic Tropical Experiment (GATE). It was moved to its operational position at 75W on Nov 15, 1974 where it remained until GOES-1 was launched, after which SMS-1 was moved to 105W and placed in stand-by-mode as a backup to GOES-1 or SMS-2. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00068 (old ID 74-033A-01C).\n<br>\n\n#### VISSRSMS1L1AOIPS\nVISSRSMS1L1AOIPS is the Visible Infrared Spin-Scan Radiometer (VISSR) Level 1 Atmospheric and Oceanographic Image Processing System (AOIPS) data product from the first Synchronous Meteorological Satellite (SMS-1). There are typically three data files for a scene of the Earth with radiances that were measured in the visible (0.55 to 0.70 micrometer) and/or IR (10.5 to 12.6 micrometer) wavelengths with a spatial resolution of 0.9 and 8 km, respectively. Files also include time, geolocation, orbit, attitude, and telemetry information. There are three types of data files in this product: one contains IR data, one contains the IR grid information (blank before 1974/10/29), and another contains VIS data. Each data file is structured with an AOIPS label, followed by an IPD label, and then an optional 8 telemetry records followed by a set of data records. Visible data are typically 3904 pixels by either 4000 or 2000 scan lines (5 or 2.5 minute scenes respectively). IR data are typically 976 pixels by either 500 or 250 scan lines (5 or 2.5 minute scenes respectively). A full scan of the Earth was made every 20 minutes. The data were used to make 70mm film negatives and 9.5” positive prints on a Dicomed Image Recording System. Data for this product are available from 1974/07/01 through 1979/04/19 (with gaps plus no data between 1975/08/20 and 1979/02/17). The SMS-1 satellite was initially parked over the equator at longitude 45W on June 7, 1974 viewing the hemisphere below the satellite to support the GARP Atlantic Tropical Experiment (GATE). It was moved to its operational position at 75W on Nov 15, 1974 where it remained until GOES-1 was launched, after which SMS 1 was moved to 105W and placed in stand-by-mode as a backup to GOES-1 or SMS-2. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00018 (old ID 74-033A-01D).\n<br>\n\n#### VISSRSMS1L1EHT\nVISSRSMS1L1EHT is the Visible Infrared Spin-Scan Radiometer (VISSR) Level 1 Experimenter History Tape (EHT) data product from the first Synchronous Meteorological Satellite (SMS-1). Each data file contains a segment of the Earth with radiances that were measured in the visible (0.55 to 0.70 micrometer) and/or IR (10.5 to 12.6 micrometer) wavelengths with a spatial resolution of 0.9 and 8 km, respectively. Files also include time, geolocation, orbit, attitude, and telemetry information. A data file is structured with a header, followed by an IR scan line and then 8 visible scan lines (although some files only contain IR scans). Visible scans are at full resolution of 15288 pixels and a file will contain several hundred scan lines. IR scans are at 3822 pixels and up to a hundred scan lines. A full scan of the Earth was made every 20 minutes. Data for this product are only available for 9 days: 1974/08/23 (IR only), 1974/08/27 (IR only), 1974/08/31, 1974/09/01, 1974/09/02, 1974/09/05, 1974/09/24 (IR only), 1975/01/10, and 1975/02/17. The SMS-1 satellite was initially parked over the equator at longitude 45W on June 7, 1974 viewing the hemisphere below the satellite to support the GARP Atlantic Tropical Experiment (GATE). It was moved to its operational position at 75W on Nov 15, 1974 where it remained until GOES-1 was launched, after which SMS 1 was moved to 105W and placed in stand-by-mode as a backup to GOES-1 or SMS-2. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00126 (old ID 74-033A-01A).\n<br>\n\n#### VISSRSMS1IMVIS\nVISSRSMS1IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the first Synchronous Meteorological Satellite (SMS-1). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification. The SMS-1 satellite was initially parked over the equator at longitude 45W on June 7, 1974 viewing the hemisphere below the satellite to support the GARP Atlantic Tropical Experiment (GATE). It was moved to its operational position at 75W on Nov 15, 1974 where it remained until GOES-1 was launched, after which SMS-1 was moved to 105W and placed in stand-by-mode as a backup to GOES-1 or SMS-2. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00040 (old ID 74-033A-01B).\n<br>\n\n#### VISSRSMS2IMIR\nVISSRSMS2IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the second Synchronous Meteorological Satellite (SMS-2). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification. The SMS-2 satellite was initially parked over the equator at longitude 105W on Feb 22, 1975 viewing the hemisphere below the satellite. It was moved to its final operational position at 135W on Dec 19, 1975. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00038 (old ID 75-011A-04C).\n<br>\n\n#### VISSRSMS2L1AOIPS\nVISSRSMS2L1AOIPS is the Visible Infrared Spin-Scan Radiometer (VISSR) Level 1 Atmospheric and Oceanographic Image Processing System (AOIPS) data product from the second Synchronous Meteorological Satellite (SMS-2). There are typically three data files for a scene of the Earth with radiances that were measured in the visible (0.55 to 0.70 micrometer) and/or IR (10.5 to 12.6 micrometer) wavelengths with a spatial resolution of 0.9 and 8 km, respectively. Files also include time, geolocation, orbit, attitude, and telemetry information. There are three types of data files in this product: one contains IR data, one contains the IR grid information, and another contains VIS data. Each data file is structured with an AOIPS label, followed by an IPD label, and then an optional 8 telemetry records followed by a set of data records. Visible data are typically 3904 pixels by either 4000 or 2000 scan lines (5 or 2.5 minute scenes respectively). IR data are typically 976 pixels by either 500 or 250 scan lines (5 or 2.5 minute scenes respectively). A full scan of the Earth was made every 20 minutes. The data were used to make 70mm film negatives and 9.5” positive prints on a Dicomed Image Recording System. Data for this product are available from 1975/04/27 through 1980/08/22 (with gaps plus no data between 1975/07/31 and 1979/05/10). The SMS-2 satellite was initially parked over the equator at longitude 105W on Feb 22, 1975 viewing the hemisphere below the satellite. It was moved to its final operational position at 135W on Dec 19, 1975. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00095 (old ID 75-011A-04D).\n<br>\n\n#### VISSRSMS2L1EHT\nVISSRSMS2L1EHT is the Visible Infrared Spin-Scan Radiometer (VISSR) Level 1 Experimenter History Tape (EHT) data product from the second Synchronous Meteorological Satellite (SMS-2). Each data file contains a segment of the Earth with radiances that were measured in the visible (0.55 to 0.70 micrometer) and/or IR (10.5 to 12.6 micrometer) wavelengths with a spatial resolution of 0.9 and 8 km, respectively. Files also include time, geolocation, orbit, attitude, and telemetry information. A data file is structured with a header, followed by an IR scan line and then 8 visible scan lines (although some files only contain IR scans). Visible scans are at full resolution of 15288 pixels and a file will contain several hundred scan lines. IR scans are at 3822 pixels and up to a hundred scan lines. A full scan of the Earth was made every 20 minutes. Data for this product are only available for 5 days: 1975/02/17, 1975/04/24, 1975/04/25, 1975/05/06, and 1975/08/28. The SMS-2 satellite was initially parked over the equator at longitude 105W on Feb 22, 1975 viewing the hemisphere below the satellite. It was moved to its final operational position at 135W on Dec 19, 1975. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00039 (old ID 75-011A-04A).\n<br>\n\n#### VISSRSMS2IMVIS\nVISSRSMS2IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the second Synchronous Meteorological Satellite (SMS-2). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification. The SMS-2 satellite was initially parked over the equator at longitude 105W on Feb 22, 1975 viewing the hemisphere below the satellite. It was moved to its final operational position at 135W on Dec 19, 1975. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00202 (old ID 75-011A-04B).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","earth observation","ice","netcdf","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AERDB_D3_ABI_G16 v1 - The ABI G16 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid product, short-name AERDB_D3_ABI_G16, derived from the L2 (AERDB_L2_ABI_G16) input data, each D3 ABI/GOES-16 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element.","ARN":"arn:aws:s3:::prod-lads/AERDB_D3_ABI_G16","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_M3_ABI_G16 v1 - The ABI G16 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid product, short-name AERDB_M3_ABI_G16, derived by aggregating the L3 daily (AERDB_D3_ABI_G16) input data, each M3 ABI/GOES-16 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the short-name as M3) product’s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product.","ARN":"arn:aws:s3:::prod-lads/AERDB_M3_ABI_G16","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_D3_ABI_G17 v1 - The ABI G17 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid product, short-name AERDB_D3_ABI_G17, derived from the L2 (AERDB_L2_ABI_G17) input data, each D3 ABI/GOES-17 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element.","ARN":"arn:aws:s3:::prod-lads/AERDB_D3_ABI_G17","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_M3_ABI_G17 v1 - The ABI G17 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid product, short-name AERDB_M3_ABI_G17, derived by aggregating the L3 daily (AERDB_D3_ABI_G17) input data, each M3 ABI/GOES-17 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the shortname as M3) product’s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product.","ARN":"arn:aws:s3:::prod-lads/AERDB_M3_ABI_G17","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_L2G_GEOLEO_Merged v1 - The GEO-LEO Merged Deep Blue Aerosol 0.25x0.25 degree Gridded L2 product, short-name AERDB_L2G_GEOLEO_Merged contains gridded Aerosol Optical Thickness (AOT) at 550 nm reference wavelength, derived from seven merged GEO-LEO AOT layers (G16-ABI, G17-ABI, H08-AHI, SNPP-VIIRS, NOAA20-VIIRS, Terra MODIS and Aqua MODIS) and from each of the individual (three GEO and four LEO) instrument sources. Each L2G aggregated datafile is spatially comprised of a 0.25˚ x 0.25˚ horizontal grid that exists for every 30 minutes.","ARN":"arn:aws:s3:::prod-lads/AERDB_L2G_GEOLEO_Merged","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_D3_GEOLEO_Merged v1 - The GEO-LEO Merged Deep Blue Aerosol Daily 1 x 1 degree Gridded L3 product, short-name AERDB_D3_GEOLEO_Merged contains gridded Aerosol Optical Thickness (AOT) at 550 nm reference wavelength that are composited from the L2G product (AERDB_L2G_GEOLEO_Merged) using best-estimate AOT values. Please note that while the individual standalone gridded data layer for each instrument is calculated as the arithmetic mean, the merged AOT layer is derived via an error-weighted average approach.","ARN":"arn:aws:s3:::prod-lads/AERDB_D3_GEOLEO_Merged","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_M3_GEOLEO_Merged v1 - The GEO-LEO Merged Deep Blue Aerosol Monthly 1 x 1 degree Gridded L3 product, short-name AERDB_M3_GEOLEO_Merged contains gridded Aerosol Optical Thickness (AOT) at 550 nm reference wavelength that are composited from the L3 daily product (AERDB_D3_GEOLEO_Merged). Please note that while the individual standalone gridded data layer for each instrument is calculated as the arithmetic mean, the merged AOT layer is derived via an error-weighted average approach.","ARN":"arn:aws:s3:::prod-lads/AERDB_M3_GEOLEO_Merged","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_L2_ABI_G16 v1 - The ABI G16 Deep Blue Aerosol 10-Min L2 Full Disk product, short-name AERDB_L2_ABI_G16 is produced every 30 minutes and contains full-disk observation data. The L2 data products comprise 10 x 10 native GEO pixels.","ARN":"arn:aws:s3:::prod-lads/AERDB_L2_ABI_G16","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_L2_ABI_G17 v1 - The ABI G17 Deep Blue Aerosol 10-Min L2 Full Disk product, short-name AERDB_L2_ABI_G17 is produced every 30 minutes and contains full-disk observation data. The L2 data products comprise 10 x 10 native GEO pixels.","ARN":"arn:aws:s3:::prod-lads/AERDB_L2_ABI_G17","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VISSRGOES1IMIR v001 - VISSRGOES1IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the first Geostationary Operational Environmental Satellite (GOES-1). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GOES/VISSRGOES1IMIR.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VISSRGOES1IMVIS v001 - VISSRGOES1IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the first Geostationary Operational Environmental Satellite (GOES-1). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GOES/VISSRGOES1IMVIS.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VISSRGOES2IMIR v001 - VISSRGOES2IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the second Geostationary Operational Environmental Satellite (GOES-2). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GOES/VISSRGOES2IMIR.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VISSRGOES2IMVIS v001 - VISSRGOES2IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the second Geostationary Operational Environmental Satellite (GOES-2). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GOES/VISSRGOES2IMVIS.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VISSRGOES3IMIR v001 - VISSRGOES3IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the third Geostationary Operational Environmental Satellite (GOES-3). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GOES/VISSRGOES3IMIR.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VISSRGOES3IMVIS v001 - VISSRGOES3IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the third Geostationary Operational Environmental Satellite (GOES-3). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GOES/VISSRGOES3IMVIS.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VISSRSMS1IMIR v001 - VISSRSMS1IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the first Synchronous Meteorological Satellite (SMS-1). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SMS/VISSRSMS1IMIR.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VISSRSMS1L1AOIPS v001 - VISSRSMS1L1AOIPS is the Visible Infrared Spin-Scan Radiometer (VISSR) Level 1 Atmospheric and Oceanographic Image Processing System (AOIPS) data product from the first Synchronous Meteorological Satellite (SMS-1). There are typically three data files for a scene of the Earth with radiances that were measured in the visible (0.55 to 0.70 micrometer) and/or IR (10.5 to 12.6 micrometer) wavelengths with a spatial resolution of 0.9 and 8 km, respectively.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SMS/VISSRSMS1L1AOIPS.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VISSRSMS1L1EHT v001 - VISSRSMS1L1EHT is the Visible Infrared Spin-Scan Radiometer (VISSR) Level 1 Experimenter History Tape (EHT) data product from the first Synchronous Meteorological Satellite (SMS-1). Each data file contains a segment of the Earth with radiances that were measured in the visible (0.55 to 0.70 micrometer) and/or IR (10.5 to 12.6 micrometer) wavelengths with a spatial resolution of 0.9 and 8 km, respectively.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SMS/VISSRSMS1L1EHT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VISSRSMS1IMVIS v001 - VISSRSMS1IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the first Synchronous Meteorological Satellite (SMS-1). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SMS/VISSRSMS1IMVIS.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VISSRSMS2IMIR v001 - VISSRSMS2IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the second Synchronous Meteorological Satellite (SMS-2). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SMS/VISSRSMS2IMIR.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VISSRSMS2L1AOIPS v001 - VISSRSMS2L1AOIPS is the Visible Infrared Spin-Scan Radiometer (VISSR) Level 1 Atmospheric and Oceanographic Image Processing System (AOIPS) data product from the second Synchronous Meteorological Satellite (SMS-2). There are typically three data files for a scene of the Earth with radiances that were measured in the visible (0.55 to 0.70 micrometer) and/or IR (10.5 to 12.6 micrometer) wavelengths with a spatial resolution of 0.9 and 8 km, respectively.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SMS/VISSRSMS2L1AOIPS.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VISSRSMS2L1EHT v001 - VISSRSMS2L1EHT is the Visible Infrared Spin-Scan Radiometer (VISSR) Level 1 Experimenter History Tape (EHT) data product from the second Synchronous Meteorological Satellite (SMS-2). Each data file contains a segment of the Earth with radiances that were measured in the visible (0.55 to 0.70 micrometer) and/or IR (10.5 to 12.6 micrometer) wavelengths with a spatial resolution of 0.9 and 8 km, respectively.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SMS/VISSRSMS2L1EHT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VISSRSMS2IMVIS v001 - VISSRSMS2IMVIS is the Visible Infrared Spin-Scan Radiometer (VISSR) Visible Imagery on 70mm Film data product from the second Synchronous Meteorological Satellite (SMS-2). This set of visible imagery (0.55 to 0.70 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SMS/VISSRSMS2IMVIS.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-goes","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA GPCP Project","Description":"These data are transitioned to a state of permanent preservation. They are available upon request. More advanced datasets have been developed since. One recommended replacement is the GPCP (doi: 10.5067/DBVUO4KQHXTK) product developed under the MEaSUREs project. The Arkin and Janowiak GPI (GOES Precipitation Index) was the infrared-based monthly rainfall estimate produced by the early GPCP (Global Precipitation Climatology Project) algorithms. The infrared observations from geostationary satellites (GOES, GMS, Meteosat) are used to produce these monthly mean rainfall totals on a 2.5 deg by 2.5 deg grid from 40 N to 40 S for the period Jan 1986 to Dec 1995.\n<br>\n\n#### RAIN_JAEGER\nThe Jaeger Surface Rain Gauge Observations data set consists of gridded mean monthly global precipitation values for 1931 to 1960 over the continents and 1955 to 1965 over the oceans. In order to calculate monthly, seasonal, and annual variations of precipitation over the whole globe, both hemispheres, and various meridional zones, a gridding technique was used on data spanning 1931 to 1960 over the continents, and 1955 to 1965 over the oceans. For the continental regions, the grid point values were obtained as eye estimates from isopleth maps prepared from up-to-date climatic atlases containing annual and monthly rainfall values, supplemented by other data sets. Although it was initially intended to use data for the standard period 1931-1960, this did not prove possible for all regions. Moller's (1951) method for estimating rainfall frequencies was adopted to provide ocean precipitation data. Monthly percentage frequencies were extracted from the mapped isolines of the US Marine Climatic Atlas (US Naval Weather Service 1955-1965) and interpolated to the grid points. After re-expressing the monthly frequencies as annual percentages, the values were scaled to rainfall depth units using Geiger's (1965) precipitation map to yield monthly precipitation means.\n<br>\n\n#### RAIN_LEGATES\nThe Legates Surface and Shipboard Rain Gauge Observations data set consists of a global climatology of monthly mean precipitation values. A global climatology of mean monthly precipitation was developed using traditional land-based gauge measurements as well as extrapolations of oceanic precipitation from coastal and island observations. Data were obtained from a variety of source archives. These data were screened for coding errors, merged, and redundant stations were removed. The resulting data base contains 24,635 independent terrestrial station records and 2223 oceanic gridpoint estimates. Precipitation gauge catches, however, are known to underestimate actual precipitation. Errors in the gauge catch result from wind-field deformation above the orifice of the gauge, wetting losses, and evaporation from the gauge and amount globally to nearly 8, 2, and 1 percent of the catch, respectively. A procedure was developed to estimate these errors and was used to obtain better estimates of global precipitation. Spatial variations in gauge type, air temperature, wind speed, and natural vegetation have been interpolated to the nodes of a 0.5 degrees of latitude by 0.5 degrees of longitude lattice using a spherically-based interpolation algorithm. The data set is used to validate general circulation model simulations of the present-day precipitation climate, for ground-based comparison with satellite-derived precipitation estimates, and as a basis for global water balance studies.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/RAIN_ARKIN_1.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","ice","oceans","precipitation","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"RAIN_JAEGER v1 - The Jaeger Surface Rain Gauge Observations data set consists of gridded mean monthly global precipitation values for 1931 to 1960 over the continents and 1955 to 1965 over the oceans. In order to calculate monthly, seasonal, and annual variations of precipitation over the whole globe, both hemispheres, and various meridional zones, a gridding technique was used on data spanning 1931 to 1960 over the continents, and 1955 to 1965 over the oceans.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/LEGACY/RAIN_JAEGER.1/1931/jaeger.dat.Z","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"RAIN_LEGATES v1 - The Legates Surface and Shipboard Rain Gauge Observations data set consists of a global climatology of monthly mean precipitation values. A global climatology of mean monthly precipitation was developed using traditional land-based gauge measurements as well as extrapolations of oceanic precipitation from coastal and island observations.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/LEGACY/RAIN_LEGATES.1/1920/legates.cor.Z","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-gpcp","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA GPM Project","Description":"Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. The 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals. The 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: + TMI (TRMM) + GMI, (GPM) + SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19) + AMSR2 (GCOM-W1) + MHS (NOAA 18,19) + MHS (METOP A,B) + ATMS (NPP) + SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n<br>\n\n#### GPM_1CAQUAAMSRE\nAll 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent. 1CSSMIS contains common calibrated brightness temperature from the SSMIS passive microwave instruments flown on the DMSP satellites. Swath S1 has 3 low frequency channels (19V 19H 22V). Swath S2 has 2 low frequency channels (37V 37H). Swath S3 has 4 high frequency channels (150H 183+/-1H 183+/-3H 183+/-7H). S4 has 2 high frequency channels (91V 91H). All the above frequencies are in GHz. Earth observations for all four swaths are taken during a 144o segment of the instrument rotation when SSMIS scans in the direction of foreward satellite motion. We define the spacecraft vector (v) at the center of this segment.\n<br>\n\n#### GPM_2AGPROFNOAA15AMSUB_CLIM\nThe 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals. The 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: + TMI (TRMM) + GMI, (GPM) + SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19) + AMSR2 (GCOM-W1) + MHS (NOAA 18,19) + MHS (METOP A,B) + ATMS (NPP) + SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty\n<br>\n\n#### GPM_3GPROFNOAA15AMSUB_DAY_CLIM\nThe \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals. 3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.\n<br>\n\n#### GPM_2AGPROFNOAA20ATMS_CLIM\nVersion 07 is the current version of the data set. The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals. The 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (SNPP and NOAA-20). This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n<br>\n\n#### GPM_3GPROFNOAA20ATMS_DAY_CLIM\nVersion 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals. 3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.\n<br>\n\n#### GPM_2AGPROFNOAA20ATMS\nVersion 07 is the current version of the data set. The 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (SNPP and NOAA-20). This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n<br>\n\n#### GPM_3GPROFNOAA20ATMS\nVersion 07 is the current version of the data set. 3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.\n<br>\n\n#### GPM_2AGPROFNOAA21ATMS\nVersion 07 is the current version of the data set. The 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (SNPP and NOAA-21). This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n<br>\n\n#### GPM_3GPROFNPPATMS\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.\n<br>\n\n#### GPM_3GCSH\nThe Gridded Convective Stratiform Heating (3GCSH) products contain latent heating, Q1-QR and Q2 profiles from DPR raindata. Version 07 is the current version of the data set. Older versions will no longer be available and are superseded by Version 07.\n<br>\n\n#### GPM_3HCSH\nThe Gridded Convective Stratiform Heating (3HCSH) products contain latent heating, Q1-QR and Q2 profiles from DPR raindata. Version 07 is the current version of the data set. Older versions will no longer be available and are superseded by Version 07.\n<br>\n\n#### GPM_2HSLH\nVersion 6B of these data were introduced in July, 2020. Please, see documentation tab for release notes. Latent heating variables are retrieved utilizing two separate algorithms for tropics and for mid-latitudes. First, location of each GPM KuPR pixel is assigned to either tropics or mid-latitudes, depending on monthly maps of precipitation types determined in a similar manner as described in Takayabu (2008). Then, three dimensional convective latent heating are retrieved, Q1-QR (Q1R), and Q2, applying either tropical/mid-latitude algorithms to precipitation data observed from GPM DPR (KuPR). Here, Q1 and Q2 are apparent heat source and apparent moisture sink, respectively, introduced by Yanai et al. (1973), and QR is radiative heating of the atmosphere.\n<br>\n\n#### GPM_BASEGPMGMI\nVersion 07 is the current version of the data set. Older versions are no longer available and have been superseded by the current version. GMI is a multi-channel, conical- scanning, microwave radiometer. The BASEGPMGMI product contains unaltered data directly from the Global Microwave Imager (GMI) aboard the GPM core satellite. It is the standard GMI calibration product with full precision of all physical fields. It contains one full orbit with no overlaps to other orbits in the production, although up to 200 overlap scans may be used for multi-scan calibration in the process. If there is enough bandwidth, the entire circle of GMI samples will be sent down. The BASEGPMGMI product's swaths 4 and 5 contain all of the samples that are sent down. Later products only use the subset of these data that contains the Earth view, hot load, and cold sky samples.\n<br>\n\n#### GPM_3IMERGHHE\nVersion 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07. The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team. The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases. The half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme. The KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR. The IMERG system is run twice in near-real time: \"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and \"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing and once after the monthly gauge analysis is received: \"Final\", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses. In V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users. Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure.\n<br>\n\n#### GPM_2AGPROFMETOPCMHS\nVersion 07 is the current version of the data set. The 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A;B;C), ATMS (NPP), SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty. ABSTRACT\n<br>\n\n#### GPM_1CMETOPCMHS\nVersion 07 is the current version of the data set. 1CAMSR2 contains common calibrated brightness temperature from the AMSR2 passive microwave instrument flown on the GCOMW1 satellite. This products contains 6 swaths. Swath 1 has channels 10.65V 10.65H. Swath 2 has channels 18.7V 18.7H. Swath 3 has channels 23.8V 23.8H. Swath 4 has channels 36.5V 36.5H. Swath S5 has 2 high frequency A-Scan channels (89V 89H). Swath S6 has 2 high frequency B-Scan channels (89V 89H). Data for all six swaths is observed in the same revolution of the instrument. High frequency A and high frequency B data are observed in separate feedhorns. All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc). One or more swaths are included in a product. The radiometer data are recalibrated to a common basis so that precipitation products derived from them are consistent.\n<br>\n\n#### GPM_3CMB_TRMM_DAY\nThis a new (GPM-formated) TRMM product, for the TRMM epoch (December 1997 - April 2015), created using GPM Algorithms. There is no equivalent in the old TRMM suite of products. This is the GPM-like formatted TRMM Combined Precipitation (TRMM Ku radar and microwave radiometer/imager), first released with the \"V8\" TRMM reprocessing. Combined Radar-Radiometer Algorithm performs two basic functions: First, it provides, in principle, the most accurate, high resolution estimates of surface rainfall rate and precipitation vertical distributions that can be achieved from a spaceborne platform, and it is therefore valuable for applications where information regarding instantaneous storm structure are vital. Second, a global, representative collection of combined algorithm estimates will yield a single common reference dataset that can be used to “cross-calibrate” rain rate estimates from all of the passive microwave radiometers in the TRMM and GPM constellations. The cross-calibration of radiometer estimates is crucial for developing a consistent, high time-resolution precipitation record for climate science and prediction model validation applications.\n<br>\n\n#### GPM_3CMB_TRMM\nThis is the new (GPM-formated) TRMM Combined product, using the GPM algorithms, for the TRMM epoch (December 1997 - April 2015). It replaces the old TRMM_3B31 This is the GPM-like formatted TRMM Combined Precipitation (TRMM Ku radar and microwave radiometer/imager), first released with the \"V8\" TRMM reprocessing. The corresponding GPM Combined product is archived under the name GPM_3CMB, with beginning date March 2014. Gombined Radar-Radiometer Algorithm performs two basic functions: First, it provides, in principle, the most accurate, high resolution estimates of surface rainfall rate and precipitation vertical distributions that can be achieved from a spaceborne platform, and it is therefore valuable for applications where information regarding instantaneous storm structure are vital. Second, a global, representative collection of combined algorithm estimates will yield a single common reference dataset that can be used to “cross-calibrate” rain rate estimates from all of the passive microwave radiometers in the TRMM and GPM constellations. The cross-calibration of radiometer estimates is crucial for developing a consistent, high time-resolution precipitation record for climate science and prediction model validation applications.\n<br>\n\n#### GPM_2BCMB_TRMM\nThis is the new (GPM-formated) TRMM Combined product, using the GPM algorithms, for the TRMM epoch (December 1997 - April 2015). It replaces the old TRMM_2B31 This is the GPM-like formatted TRMM Combined Precipitation (TRMM Ku radar and microwave radiometer/imager), first released with the \"V8\" TRMM reprocessing. The corresponding GPM Combined product is archived under the name GPM_2BCMB, with beginning date March 2014. Gombined Radar-Radiometer Algorithm performs two basic functions: First, it provides, in principle, the most accurate, high resolution estimates of surface rainfall rate and precipitation vertical distributions that can be achieved from a spaceborne platform, and it is therefore valuable for applications where information regarding instantaneous storm structure are vital. Second, a global, representative collection of combined algorithm estimates will yield a single common reference dataset that can be used to “cross-calibrate” rain rate estimates from all of the passive microwave radiometers in the TRMM and GPM constellations. The cross-calibration of radiometer estimates is crucial for developing a consistent, high time-resolution precipitation record for climate science and prediction model validation applications.\n<br>\n\n#### GPM_2APRPSMT1SAPHIR_CLIM\nVersion 6 is the current version of this dataset. Older versions are no longer available and have been superseded by Version 6. The Precipitation Retrieval and Profiling Scheme (PRPS)is designed to provide a best estimate of precipitation based upon matched SAPHIR-DPR observations. This fulfils in part the essence of GPM (and its predecessor, TRMM) in which the core observatory acts as a calibrator of precipitation retrievals for the international constellation of passive microwave instruments. In doing so the retrievals from the partner constellation sensors are able to provide greater temporal sampling and great spatial coverage than is possible from the DPR instrument alone. However, the limitations of the DPR instrument are transferred through the retrieval scheme to the resulting precipitation products. Fundamental to the design of the PRPS is the independence from any dynamic ancillary data sets: the retrieval is based solely upon the satellite radiances, a static a priori radiance-rainrate database (and index), and (static) topographical data. Critically, the technique is independent of any model information, unlike the retrievals generated through the Goddard PROFiling (GPROF) scheme: this independence is advantageous when generating products across time scales from near real-time (inaccessibility to model data) to climatological scales (circumventing trends in model data). The algorithm is designed to generate instantaneous estimates of precipitation at a constant resolution (regardless of scan position), for all scan positions and scan lines. In addition to the actual precipitation estimate, an assessment of the error is made, and a measure of the ‘fit’ of the observations to the database provided. A quality flag is also provided, with any bad data generating a ‘missing flag’ in the retrieval.\n<br>\n\n#### GPM_2AGPROFF08SSMI_CLIM\nVersion 7 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version. The 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals. The 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: + TMI (TRMM) + GMI, (GPM) + SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19) + AMSR2 (GCOM-W1) + MHS (NOAA 18,19) + MHS (METOP A,B) + ATMS (NPP) + SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n<br>\n\n#### GPM_2AGPROFTRMMTMI_CLIM\nThis is the new (GPM-formated) TRMM product. It replaces the old TRMM_2A12 Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals. The 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: + TMI (TRMM) + GMI, (GPM) + SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19) + AMSR2 (GCOM-W1) + MHS (NOAA 18,19) + MHS (METOP A,B) + ATMS (NPP) + SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n<br>\n\n#### GPM_3GPROFTRMMTMI_DAY_CLIM\nThis a new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products. Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals. 3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.\n<br>\n\n#### GPM_3GPROFTRMMTMI_CLIM\nThis is the new (GPM-formated) TRMM product. It replaces the old TRMM_3A12,3A11 Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals. 3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.\n<br>\n\n#### GPM_IMERG_LandSeaMask\nVersion 2 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 2. This land sea mask originated from the NOAA group at SSEC in the 1980s. It was originally produced at 1/6 deg resolution, and then regridded for the purposes of GPCP, TMPA, and IMERG precipitation products. NASA code 610.2, Terrestrial Information Systems Laboratory, restructured this land sea mask to match the IMERG grid, and converted the file to CF-compliant netCDF4. Version 2 was created in May, 2019 to resolve detected inaccuracies in coastal regions. Users should be aware that this is a static mask, i.e. there is no seasonal or annual variability, and it is due for update. It is not recommended to be used outside of the aforementioned precipitation data.\n<br>\n\n#### GPM_MERGIR\nThese data originate from NOAA/NCEP. The NOAA Climate Prediction Center/NCEP/NWS is making the data available originally in binary format, in a weekly rotating archive. The NASA GES DISC is acquiring the binary files as they become available, converts them into CF (Climate and Forecast) -convention compliant netCDF-4 format, and stores the product in a permanent archive. The original record started from February, 2000, but in June, 2025 it was extended back to January, 1998. The leading edge of data availability is delayed by about 24 hours from real-time to abide by international data exchange agreements between NOAA and EUMETSAT (the METEOSAT data providers). The data contain globally-merged (60°S-60°N) 4-km pixel-resolution IR brightness temperature data (equivalent blackbody temps), merged from the European, Japanese, and U.S. geostationary satellites over the period of record (GOES-8/9/10/11/12/13/14/15/16/17/18/19, METEOSAT-5/7/8/9/10/11, and GMS-5/MTSat-1R/2/Himawari-8/9). The global geo-IR are dynamically calibrated to GOES East, using a 35 day trailing inter-calibration using time/space-matched IR Tb’s at the mid-point between sub-satellite positions. In the event of duplicate data in a grid box, the value with the smaller zenith angle is taken. The data have been corrected for \"zenith angle dependence\", in which IR temperatures for locations far from satellite nadir are erroneously cold due to a combination of geometric effects and radiometric path extinction effects (Joyce et al. 2001). Finally, the data are re-navigated for parallax, which shifts the geo-location of the GEO-IR footprints to approximately account for the cloud tops that the IR “sees” being displaced away from their actual geographic location when viewed along a slanted path. These corrections allow for the merging of the IR data from the various GEO-satellites with greatly reduced discontinuities at GEO-satellite data boundaries. In the event of duplicate data in a grid box, the value with the smaller zenith angle is taken. The NASA GES DISC is curating these data in a self-documenting, CF-compliant, netCDF-4 format, which allows a broad range of applications to access the data directly, without the need to cope with the original binary data format. In addition to the direct download of netCDF-4 data, the GES DISC provides data download in binary, ASCII, and netCDF-3 formats using the OPeNDAP interface which also provides remote data access. Similarities with the original ----------------------------- As in the original binaries, every netCDF-4 file covers one hour, and contains two half-hourly grids, at 4-km grid cell resolution. Differences from the original ----------------------------- 1. The data in the netCDF-4 files are already converted to physical values of Brightness Temperatures in Kelvin. Because the original data values are round with no decimal precision, the data type in the netCDF-4 files has been changed to 2-byte signed integer, a transition that took place in mid-August, 2025. This reduces the file size and speeds up data download and remote access. There is no need to further scale these data. The netCDF-4 format is machine-independent and users need not worry about the endian-ness of their machines. 2. To meet the requirements of collection spatial metadata, the grid is re-ordered from the original and now goes from -180 (West) to 180 (East). It is also starting from -60 (South). The data and time units are reflected in the corresponding \"units\" attributes, and grid dimensions are described by longitude (\"lon\"), latitude (\"lat\") and \"time\" vectors. Thus, any CF-compliant tool should automatically understand the setup in the data files and the starting time for each half-hourly grid. Even without such tools, simple \"ncdump\" or \"h5dump\" command line tools will easily disclose the netCDF-4 files configuration. Acknowledgements ------------------ The creation of the original data at NOAA/NCEP is supported by funding from the NOAA Office of Global Programs for the Global Precipitation Climatology Project (GPCP) and by NASA via the Tropical Rainfall Measuring Mission (TRMM). The permanent archive at GES DISC is supported by NASA's HQ Earth Science Data Systems (ESDS) Program.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://gpm.nasa.gov","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","earth observation","ice","netcdf","oceans","precipitation","radar","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GPM_2AGPROFGCOMW1AMSR2_CLIM v07 - Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L2/GPM_2AGPROFGCOMW1AMSR2_CLIM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_1CAQUAAMSRE v07 - All 1C products have a common L1C data structure, simple and generic. Each L1C swath includes scan time, latitude and longitude, scan status, quality, incidence angle, Sun glint angle, and the intercalibrated brightness temperature (Tc).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L1C/GPM_1CAQUAAMSRE.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_2AGPROFNOAA15AMSUB_CLIM v07 - The 'CLIM' products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L2/GPM_2AGPROFNOAA15AMSUB_CLIM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3GPROFNOAA15AMSUB_DAY_CLIM v07 - The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3GPROFNOAA15AMSUB_DAY_CLIM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_2AGPROFNOAA20ATMS_CLIM v07 - Version 07 is the current version of the data set. The \"CLIM\" products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L2/GPM_2AGPROFNOAA20ATMS_CLIM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3GPROFNOAA20ATMS_DAY_CLIM v07 - Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3GPROFNOAA20ATMS_DAY_CLIM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_2AGPROFNOAA20ATMS v07 - Version 07 is the current version of the data set. The 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (SNPP and NOAA-20).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L2/GPM_2AGPROFNOAA20ATMS.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3GPROFNOAA20ATMS v07 - Version 07 is the current version of the data set. 3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3GPROFNOAA20ATMS.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_2AGPROFNOAA21ATMS v07 - Version 07 is the current version of the data set. The 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (SNPP and NOAA-21).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L2/GPM_2AGPROFNOAA21ATMS.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3GPROFNPPATMS v07 - 3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3GPROFNPPATMS.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3GCSH v07 - The Gridded Convective Stratiform Heating (3GCSH) products contain latent heating, Q1-QR and Q2 profiles from DPR raindata. Version 07 is the current version of the data set.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3GCSH.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3HCSH v07 - The Gridded Convective Stratiform Heating (3HCSH) products contain latent heating, Q1-QR and Q2 profiles from DPR raindata. Version 07 is the current version of the data set.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3HCSH.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_2HSLH v07 - Version 6B of these data were introduced in July, 2020. Please, see documentation tab for release notes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L2/GPM_2HSLH.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_BASEGPMGMI v07 - Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by the current version.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L1BASE/GPM_BASEGPMGMI.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3IMERGHHE v07 - Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGHHE.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_2AGPROFMETOPCMHS v07 - Version 07 is the current version of the data set. The 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A;B;C), ATMS (NPP), SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L2/GPM_2AGPROFMETOPCMHS.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_1CMETOPCMHS v07 - Version 07 is the current version of the data set. 1CAMSR2 contains common calibrated brightness temperature from the AMSR2 passive microwave instrument flown on the GCOMW1 satellite.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L1C/GPM_1CMETOPCMHS.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3CMB_TRMM_DAY v07 - This a new (GPM-formated) TRMM product, for the TRMM epoch (December 1997 - April 2015), created using GPM Algorithms. There is no equivalent in the old TRMM suite of products.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/GPM_3CMB_TRMM_DAY.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3CMB_TRMM v07 - This is the new (GPM-formated) TRMM Combined product, using the GPM algorithms, for the TRMM epoch (December 1997 - April 2015). It replaces the old TRMM_3B31 This is the GPM-like formatted TRMM Combined Precipitation (TRMM Ku radar and microwave radiometer/imager), first released with the \"V8\" TRMM reprocessing.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/GPM_3CMB_TRMM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_2BCMB_TRMM v07 - This is the new (GPM-formated) TRMM Combined product, using the GPM algorithms, for the TRMM epoch (December 1997 - April 2015). It replaces the old TRMM_2B31 This is the GPM-like formatted TRMM Combined Precipitation (TRMM Ku radar and microwave radiometer/imager), first released with the \"V8\" TRMM reprocessing.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L2/GPM_2BCMB_TRMM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_2APRPSMT1SAPHIR_CLIM v06 - Version 6 is the current version of this dataset. Older versions are no longer available and have been superseded by Version 6.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L2/GPM_2APRPSMT1SAPHIR_CLIM.06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_2AGPROFF08SSMI_CLIM v07 - Version 7 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L2/GPM_2AGPROFF08SSMI_CLIM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_2AGPROFTRMMTMI_CLIM v07 - This is the new (GPM-formated) TRMM product. It replaces the old TRMM_2A12 Version 07 is the current version of the data set.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L2/GPM_2AGPROFTRMMTMI_CLIM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3GPROFTRMMTMI_DAY_CLIM v07 - This a new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/GPM_3GPROFTRMMTMI_DAY_CLIM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3GPROFTRMMTMI_CLIM v07 - This is the new (GPM-formated) TRMM product. It replaces the old TRMM_3A12,3A11 Version 07 is the current version of the data set.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/GPM_3GPROFTRMMTMI_CLIM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_IMERG_LandSeaMask v2 - Version 2 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/AUXILIARY/GPM_IMERG_LandSeaMask.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_MERGIR v1 - These data originate from NOAA/NCEP. The NOAA Climate Prediction Center/NCEP/NWS is making the data available originally in binary format, in a weekly rotating archive.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERGED_IR/GPM_MERGIR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-gpm","Sources":["awslabs-open-data-registry"]}
{"Name":"GPM DPR Precipitation Profile L2A 1.5 hours 5 km V07 (GPM_2ADPR) at GES DISC","Description":"Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n.\n\n2ADPR provides single- and dual-frequency-derived precipitation estimates from the Ku and Ka radars of the Dual-Frequency Precipitation Radar (DPR) on the core GPM spacecraft. The output consists of three main classes of precipitation products: those derived from the Ku-band frequency over a wide swath (245 km), those derived from the Ka-band frequency over a narrow swath (125 km), and those derived from the dual-frequency data over the narrow swath. The Ka-band results are further divided into the standard and high-sensitivity estimates. In the standard sensitivity mode, the fields of view within the inner swath are matched to those of the Ku-band. Data from these matched-beam Ku- and Ka-band fields of view are used to derive the dual-frequency precipitation products. The retrievals are performed at each radar range bin along the slant path of the radar instrument field of view (IFOV).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/GPM/DPR/GPM/2A/07","Contact":"GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/","ManagedBy":"NASA","UpdateFrequency":"From 2014-03-08 to Ongoing","Tags":["atmosphere","aws-pds","contamination","datacenter","earth observation","global","hdf","metadata","opendap","radar","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GPM DPR Precipitation Profile L2A 1.5 hours 5 km V07 (GPM_2ADPR) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L2/GPM_2ADPR.07/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."},{"Title":"How to Read IMERG Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Read_IMERG_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-gpm2adpr","Sources":["awslabs-open-data-registry"]}
{"Name":"GPM IMERG Early Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDE) at GES DISC","Description":"Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000.  It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques.  IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations.  The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill.  IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency).  While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones.  As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.\n\nThis dataset is the GPM Level 3 IMERG  *Early* Daily  10 x 10 km (GPM_3IMERGDE) derived from the half-hourly GPM_3IMERGHHE. The derived result represents an early (expedited) estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24.  This minimizes the possible dry bias in versions before \"07\", in the simple daily totals  for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared (and rain gauge in the final) dataset, variable \"precipitation\",  and appears in higher latitudes. Thus, in most cases users of global \"precipitation\" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable \"MWprecipitation\", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version \"07\", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. \n\nThe latency of the derived Early daily product is a couple of minutes after the last granule of GPM_3IMERGHHE for the UTC data day is received at GES DISC. Since the target latency of GPM_3IMERGHHE is 4 hours, the daily should appear about 4 hours after the closure of the UTC day. For information on the original data (GPM_3IMERGHHE), please see the Documentation (Related URL). \n\nThe daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24.  Thus, for every grid cell we have                \n\nPdaily_mean     = SUM{Pi * 1[Pi valid]} / Pdaily_cnt  * 24, i=[1,Nf]\n\nWhere:\nPdaily_cnt = SUM{1[Pi valid]}\n\nPi              - half-hourly input, in (mm/hr)\nNf              - Number of half-hourly files per day, Nf=48\n1[.]            - Indicator function; 1 when Pi is valid, 0 otherwise\nPdaily_cnt      - Number of valid retrievals in a grid cell per day.\n\nGrid cells for which Pdaily_cnt=0, are set to fill value in the Daily files.\nNote that Pi=0 is a valid value.\n\nPdaily_cnt are provided in the data files as variables \"precipitation_cnt\" and \"MWprecipitation_cnt\", for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. \n\nThere are various ways the daily error could be estimated from the source half-hourly random error (variable \"randomError\"). The daily error provided in the data files is calculated in a fashion similar to the daily mean precipitation rate. First, the mean of the squared half-hourly \"randomError\"  for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result in (mm/day):\n\nPerr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err  * 24}^0.5, i=[1,Nf]\nNcnt_err   = SUM( 1[Perr_i valid] )\n\nwhere:\nPerr_i\t\t- half-hourly input, \"randomError\", (mm/hr)\nPerr_daily\t- Magnitude of the daily error, (mm/day)\nNcnt_err\t\t- Number of valid half-hour error estimates\n\nAgain, the sum of squared \"randomError\" can be reconstructed, and other estimates can be derived using the available counts in the Daily files.\n\n\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/GPM/IMERGDE/DAY/07","Contact":"GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/","ManagedBy":"NASA","UpdateFrequency":"From 1998-01-01 to Ongoing","Tags":["atmosphere","aws-pds","climate","coastal","datacenter","global","hydrology","land","metadata","netcdf","opendap"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GPM IMERG Early Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDE) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDE.07/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations","URL":"https://www.researchgate.net/profile/Udo-Schneider-4/publication/253114707_Calculation_of_Gridded_Precipitation_Data_for_the_Global_Land-Surface_using_in-situ_Gauge_Observations/links/0deec53bbcb3a0e220000000/Calculation-of-Gridded-Precipitation-Data-for-the-Global-Land-Surface-using-in-situ-Gauge-Observations.pdf","AuthorName":"Rudolf, B., and U. Schneider"},{"Title":"Kalman Filter Based CMORPH","URL":"https://doi.org/10.1175/JHM-D-11-022.1","AuthorName":"Joyce, R. J., P. Xie, and J. E. Janowiak"},{"Title":"Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System","URL":"https://doi.org/10.1175/JAM2173.1","AuthorName":"Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao"}],"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-gpm3imergde","Sources":["awslabs-open-data-registry"]}
{"Name":"GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDF) at GES DISC","Description":"Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000.  It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques.  IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations.  The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill.  IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency).  While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones.  As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.\n\nThis dataset is the GPM Level 3 IMERG  *Final* Daily  10 x 10 km (GPM_3IMERGDF) derived from the half-hourly GPM_3IMERGHH.  The derived result represents the Final estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24.  This minimizes the possible dry bias in versions before \"07\", in the simple daily totals  for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared and rain gauge  dataset, variable \"precipitation\",  and appears in higher latitudes. Thus, in most cases users of global \"precipitation\" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable \"MWprecipitation\", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version \"07\", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. \n\nThe latency of the derived *Final* Daily product depends on the delivery of the IMERG *Final* Half-Hourly product GPM_IMERGHH. Since the latter are delivered in a batch, once per month for the entire month, with up to 4 months latency, so will be the latency for the Final Daily, plus about 24 hours. Thus, e.g. the Dailies for January  can be expected to appear no earlier than April 2. \n\nThe daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24.  Thus, for every grid cell we have                \n\nPdaily_mean     = SUM{Pi * 1[Pi valid]} / Pdaily_cnt  * 24, i=[1,Nf]\n\nWhere:\nPdaily_cnt = SUM{1[Pi valid]}\n\nPi              - half-hourly input, in (mm/hr)\nNf              - Number of half-hourly files per day, Nf=48\n1[.]            - Indicator function; 1 when Pi is valid, 0 otherwise\nPdaily_cnt      - Number of valid retrievals in a grid cell per day.\n\nGrid cells for which Pdaily_cnt=0, are set to fill value in the Daily files.\nNote that Pi=0 is a valid value.\n\nPdaily_cnt are provided in the data files as variables \"precipitation_cnt\" and \"MWprecipitation_cnt\", for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. \n\nThere are various ways the daily error could be estimated from the source half-hourly random error (variable \"randomError\"). The daily error provided in the data files is calculated in a fashion similar to the daily mean precipitation rate. First, the mean of the squared half-hourly \"randomError\"  for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result in (mm/day):\n\nPerr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err  * 24}^0.5, i=[1,Nf]\nNcnt_err   = SUM( 1[Perr_i valid] )\n\nwhere:\nPerr_i\t\t- half-hourly input, \"randomError\", (mm/hr)\nPerr_daily\t- Magnitude of the daily error, (mm/day)\nNcnt_err\t\t- Number of valid half-hour error estimates\n\nAgain, the sum of squared \"randomError\" can be reconstructed, and other estimates can be derived using the available counts in the Daily files.\n\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/GPM/IMERGDF/DAY/07","Contact":"Email: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/","ManagedBy":"NASA","UpdateFrequency":"From 1998-01-01 to Ongoing","Tags":["aws-pds","climate","coastal","datacenter","global","hydrology","ice","land","metadata","netcdf","opendap"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDF) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDF.07/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations","URL":"https://www.researchgate.net/profile/Udo-Schneider-4/publication/253114707_Calculation_of_Gridded_Precipitation_Data_for_the_Global_Land-Surface_using_in-situ_Gauge_Observations/links/0deec53bbcb3a0e220000000/Calculation-of-Gridded-Precipitation-Data-for-the-Global-Land-Surface-using-in-situ-Gauge-Observations.pdf","AuthorName":"Rudolf, B., and U. Schneider"},{"Title":"Kalman Filter Based CMORPH","URL":"https://doi.org/10.1175/JHM-D-11-022.1","AuthorName":"Joyce, R. J., P. Xie, and J. E. Janowiak"},{"Title":"Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System","URL":"https://doi.org/10.1175/JAM2173.1","AuthorName":"Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao"}],"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-gpm3imergdf","Sources":["awslabs-open-data-registry"]}
{"Name":"GPM IMERG Late Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDL) at GES DISC","Description":"Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000.  It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques.  IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations.  The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill.  IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency).  While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones.  As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.\n\nThis dataset is the GPM Level 3 IMERG  Late Daily  10 x 10 km (GPM_3IMERGDL) derived from the half-hourly GPM_3IMERGHHL. The derived result represents a Late expedited estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24.  This minimizes the possible dry bias in versions before \"07\", in the simple daily totals  for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared (and rain gauge in the final) dataset, variable \"precipitation\",  and appears in higher latitudes. Thus, in most cases users of global \"precipitation\" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable \"MWprecipitation\", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version \"07\", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. \n\nThe latency of the derived Late daily product is a couple of minutes after the last granule of GPM_3IMERGHHL for the UTC data day is received at GES DISC. Since the target latency of GPM_3IMERGHHL is 14 hours, the daily should appear no earlier than 14 hours after the closure of the UTC day. For information on the original data (GPM_3IMERGHHL), please see the Documentation (Related URL).  \n\nThe daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24.  Thus, for every grid cell we have                \n\nPdaily_mean     = SUM{Pi * 1[Pi valid]} / Pdaily_cnt  * 24, i=[1,Nf]\n\nWhere:\nPdaily_cnt = SUM{1[Pi valid]}\n\nPi              - half-hourly input, in (mm/hr)\nNf              - Number of half-hourly files per day, Nf=48\n1[.]            - Indicator function; 1 when Pi is valid, 0 otherwise\nPdaily_cnt      - Number of valid retrievals in a grid cell per day.\n\nGrid cells for which Pdaily_cnt=0, are set to fill value in the Daily files.\nNote that Pi=0 is a valid value.\n\nPdaily_cnt are provided in the data files as variables \"precipitation_cnt\" and \"MWprecipitation_cnt\", for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. \n\nThere are various ways the daily error could be estimated from the source half-hourly random error (variable \"randomError\"). The daily error provided in the data files is calculated in a fashion similar to the daily mean precipitation rate. First, the mean of the squared half-hourly \"randomError\"  for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result in (mm/day):\n\nPerr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err  * 24}^0.5, i=[1,Nf]\nNcnt_err   = SUM( 1[Perr_i valid] )\n\nwhere:\nPerr_i\t\t- half-hourly input, \"randomError\", (mm/hr)\nPerr_daily\t- Magnitude of the daily error, (mm/day)\nNcnt_err\t\t- Number of valid half-hour error estimates\n\nAgain, the sum of squared \"randomError\" can be reconstructed, and other estimates can be derived using the available counts in the Daily files.\n\n\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/GPM/IMERGDL/DAY/07","Contact":"GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/","ManagedBy":"NASA","UpdateFrequency":"From 1998-01-01 to Ongoing","Tags":["atmosphere","aws-pds","climate","coastal","datacenter","global","hydrology","land","metadata","netcdf","opendap"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GPM IMERG Late Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDL) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDL.07/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations","URL":"https://www.researchgate.net/profile/Udo-Schneider-4/publication/253114707_Calculation_of_Gridded_Precipitation_Data_for_the_Global_Land-Surface_using_in-situ_Gauge_Observations/links/0deec53bbcb3a0e220000000/Calculation-of-Gridded-Precipitation-Data-for-the-Global-Land-Surface-using-in-situ-Gauge-Observations.pdf","AuthorName":"Rudolf, B., and U. Schneider"},{"Title":"Kalman Filter Based CMORPH","URL":"https://doi.org/10.1175/JHM-D-11-022.1","AuthorName":"Joyce, R. J., P. Xie, and J. E. Janowiak"},{"Title":"Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System","URL":"https://doi.org/10.1175/JAM2173.1","AuthorName":"Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao"}],"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-gpm3imergdl","Sources":["awslabs-open-data-registry"]}
{"Name":"GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHH) at GES DISC","Description":"Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.\n\nThe half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.\n\nThe KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and\n\"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.\n\nIn V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.\n\nBriefly describing the Final Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then \"forward/backward morphed\" and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with seasonal GPCP SG surface precipitation data to provide half-hourly and monthly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 4 months).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/GPM/IMERG/3B-HH/07","Contact":"GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov","ManagedBy":"NASA","UpdateFrequency":"From 1998-01-01 to Ongoing","Tags":["atmosphere","aws-pds","climate","datacenter","forecast","global","hdf","hydrology","land","metadata","opendap","radar","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHH) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGHH.07/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations","URL":"https://www.researchgate.net/profile/Udo-Schneider-4/publication/253114707_Calculation_of_Gridded_Precipitation_Data_for_the_Global_Land-Surface_using_in-situ_Gauge_Observations/links/0deec53bbcb3a0e220000000/Calculation-of-Gridded-Precipitation-Data-for-the-Global-Land-Surface-using-in-situ-Gauge-Observations.pdf","AuthorName":"Rudolf, B., and U. Schneider"},{"Title":"Kalman Filter Based CMORPH","URL":"https://doi.org/10.1175/JHM-D-11-022.1","AuthorName":"Joyce, R. J., P. Xie, and J. E. Janowiak"},{"Title":"Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System","URL":"https://doi.org/10.1175/JAM2173.1","AuthorName":"Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao"}],"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."},{"Title":"How to Read IMERG Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Read_IMERG_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-gpm3imerghh","Sources":["awslabs-open-data-registry"]}
{"Name":"GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHE) at GES DISC","Description":"Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.\n\nThe half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.\n\nThe KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and\n\"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.\n\nIn V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.\n\nPrecipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. \nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/GPM/IMERG/3B-HH-E/07","Contact":"Email: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/","ManagedBy":"NASA","UpdateFrequency":"From 1998-01-01 to Ongoing","Tags":["atmosphere","aws-pds","climate","datacenter","forecast","global","hdf","hydrology","land","metadata","opendap","radar","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHE) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGHHE.07/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations","URL":"https://www.researchgate.net/profile/Udo-Schneider-4/publication/253114707_Calculation_of_Gridded_Precipitation_Data_for_the_Global_Land-Surface_using_in-situ_Gauge_Observations/links/0deec53bbcb3a0e220000000/Calculation-of-Gridded-Precipitation-Data-for-the-Global-Land-Surface-using-in-situ-Gauge-Observations.pdf","AuthorName":"Rudolf, B., and U. Schneider"},{"Title":"Kalman Filter Based CMORPH","URL":"https://doi.org/10.1175/JHM-D-11-022.1","AuthorName":"Joyce, R. J., P. Xie, and J. E. Janowiak"},{"Title":"Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System","URL":"https://doi.org/10.1175/JAM2173.1","AuthorName":"Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao"}],"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."},{"Title":"How to Read IMERG Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Read_IMERG_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-gpm3imerghhe","Sources":["awslabs-open-data-registry"]}
{"Name":"GPM IMERG Late Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHL) at GES DISC","Description":"Version 07B is the current version of the IMERG data sets. Older versions\nwill no longer be available and have been superseded by Version 07.\\n\\nThe Integrated\nMulti-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides\nthe multi-satellite precipitation product for the U.S. GPM team.\\n\\nThe precipitation\nestimates from the various precipitation-relevant satellite passive microwave (PMW)\nsensors comprising the GPM constellation are computed using the 2021 version of\nthe Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the\nGPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly\n0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly\nGlobal Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over\nhigh-latitude ocean to correct known biases.\\n\\nThe half-hourly intercalibrated\nmerged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian\ntime interpolation scheme based on work by the Climate Prediction Center (CPC) and\nthe Precipitation Estimation from Remotely Sensed Information using Artificial Neural\nNetworks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In\nparallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR\nfields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported\nby an asynchronous re-calibration cycle) which are then input to the KF morphing\n(quasi-Lagrangian time interpolation) scheme.\\n\\nThe KF morphing (supported by an\nasynchronous KF weights updating cycle) uses the PMW and IR estimates to create\nhalf-hourly estimates. Motion vectors for the morphing are computed by maximizing\nthe pattern correlation of successive hours within each of the precipitation (PRECTOT),\ntotal precipitable liquid water (TQL), and vertically integrated vapor (TQV) data\nfields provided by the Modern-Era Retrospective Analysis for Research and Applications,\nVersion 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5)\nForward Processing (FP) for the post-real-time (Final) Run and the near-real-time\n(Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available,\nelse from TQL, if available, else from TQV. The KF uses the morphed data as the\n“forecast” and the IR estimates as the “observations”, with weighting that depends\non the time interval(s) away from the microwave overpass time. The IR becomes important\nafter about ±90 minutes away from the overpass time. Variable averaging in the KF\nis accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation\nEstimates in the Neighborhood, or SHARPEN) that compares the local histogram of\nKF morphed precipitation to the local histogram of forward- and backward-morphed\nmicrowave data and the IR.\\n\\nThe IMERG system is run twice in near-real time:\\n\\n\\\"Early\\\"\nmulti-satellite product ~4 hr after observation time using only forward morphing\nand\\n\\\"Late\\\" multi-satellite product ~14 hr after observation time, using both\nforward and backward morphing\\nand once after the monthly gauge analysis is received:\\n\\n\\\"Final\\\",\nsatellite-gauge product ~4 months after the observation month, using both forward\nand backward morphing and including monthly gauge analyses.\\n\\nIn V07, the near-real-time\nEarly and Late half-hourly estimates have a monthly climatological concluding calibration\nbased on averaging the concluding calibrations computed in the Final, while in the\npost-real-time Final Run the multi-satellite half-hourly estimates are adjusted\nso that they sum to the Final Run monthly satellite-gauge combination. In all cases\nthe output contains multiple fields that provide information on the input data,\nselected intermediate fields, and estimation quality. In general, the complete calibrated\nprecipitation, precipitation, is the data field of choice for most users.\\n\\nPrecipitation\nphase is a diagnostic variable computed using analyses of surface temperature, humidity,\nand pressure. \\n\\n\\n\\nRead our doc on how to get AWS Credentials to retrieve this\ndata: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/GPM/IMERG/3B-HH-L/07","Contact":"GES DISC HELP DESK SUPPORT GROUP\": gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/","ManagedBy":"NASA","UpdateFrequency":"From 1998-01-01 to Ongoing","Tags":["atmosphere","aws-pds","climate","datacenter","forecast","global","hdf","hydrology","land","metadata","opendap","radar","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GPM IMERG Late Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHL) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGHHL.07/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations","URL":"https://www.researchgate.net/profile/Udo-Schneider-4/publication/253114707_Calculation_of_Gridded_Precipitation_Data_for_the_Global_Land-Surface_using_in-situ_Gauge_Observations/links/0deec53bbcb3a0e220000000/Calculation-of-Gridded-Precipitation-Data-for-the-Global-Land-Surface-using-in-situ-Gauge-Observations.pdf","AuthorName":"Rudolf, B., and U. Schneider"},{"Title":"Kalman Filter Based CMORPH","URL":"https://doi.org/10.1175/JHM-D-11-022.1","AuthorName":"Joyce, R. J., P. Xie, and J. E. Janowiak"},{"Title":"Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System","URL":"https://doi.org/10.1175/JAM2173.1","AuthorName":"Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao"}],"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."},{"Title":"How to Read IMERG Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Read_IMERG_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-gpm3imerghhl","Sources":["awslabs-open-data-registry"]}
{"Name":"GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V07 (GPM_3IMERGM) at GES DISC","Description":"Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.\n\nThe half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.\n\nThe KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and\n\"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.\n\nIn V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.\n\nBriefly describing the Final Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then \"forward/backward morphed\" and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with seasonal GPCP SG surface precipitation data to provide half-hourly and monthly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 4 months).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/GPM/IMERG/3B-MONTH/07","Contact":"GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/","ManagedBy":"NASA","UpdateFrequency":"From 1998-01-01 to Ongoing","Tags":["atmosphere","aws-pds","climate","datacenter","forecast","global","hdf","hydrology","land","metadata","opendap","radar","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V07 (GPM_3IMERGM) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGM.07/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations","URL":"https://www.researchgate.net/profile/Udo-Schneider-4/publication/253114707_Calculation_of_Gridded_Precipitation_Data_for_the_Global_Land-Surface_using_in-situ_Gauge_Observations/links/0deec53bbcb3a0e220000000/Calculation-of-Gridded-Precipitation-Data-for-the-Global-Land-Surface-using-in-situ-Gauge-Observations.pdf","AuthorName":"Rudolf, B., and U. Schneider"},{"Title":"Kalman Filter Based CMORPH","URL":"https://doi.org/10.1175/JHM-D-11-022.1","AuthorName":"Joyce, R. J., P. Xie, and J. E. Janowiak"},{"Title":"Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System","URL":"https://doi.org/10.1175/JAM2173.1","AuthorName":"Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao"}],"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."},{"Title":"How to Read IMERG Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Read_IMERG_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-gpm3imergm","Sources":["awslabs-open-data-registry"]}
{"Name":"Land/Sea static mask relevant to IMERG precipitation 0.1x0.1 degree V2 (GPM_IMERG_LandSeaMask) at GES DISC","Description":"Version 2 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 2.\n\nThis land sea mask originated from the NOAA group at SSEC in the 1980s. It was originally produced at 1/6 deg resolution, and then regridded for the purposes of GPCP, TMPA, and IMERG precipitation products. NASA code 610.2, Terrestrial Information Systems Laboratory, restructured this land sea mask to match the IMERG grid, and converted the file to CF-compliant netCDF4. Version 2 was created in May, 2019 to resolve detected inaccuracies in coastal regions.\n\nUsers should be aware that this is a static mask, i.e. there is no seasonal or annual variability, and it is due for update. It is not recommended to be used outside of the aforementioned precipitation data.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/6P5EM1HPR3VD","Contact":"GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/","ManagedBy":"NASA","UpdateFrequency":"From 1998-01-01 to Ongoing","Tags":["atmosphere","aws-pds","coastal","datacenter","global","land","metadata","netcdf","opendap"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Land/Sea static mask relevant to IMERG precipitation 0.1x0.1 degree V2 (GPM_IMERG_LandSeaMask) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/AUXILIARY/GPM_IMERG_LandSeaMask.2/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-gpmimerglandseamask","Sources":["awslabs-open-data-registry"]}
{"Name":"NCEP/CPC L3 Half Hourly 4km Global (60S - 60N) Merged IR V1 (GPM_MERGIR) at GES DISC","Description":"These data originate from NOAA/NCEP.\n\nThe NOAA Climate Prediction Center/NCEP/NWS is making the data available originally in binary format, in a weekly rotating archive. The NASA GES DISC is acquiring the binary files as they become available, converts them into CF (Climate and Forecast) -convention compliant netCDF-4 format, and stores the product in a permanent archive. The original record started from February, 2000, but in June, 2025 it was extended back to January, 1998.\n\nThe leading edge of data availability is delayed by about 24 hours from real-time to abide by international data exchange agreements between NOAA and EUMETSAT (the METEOSAT data providers).\n\nThe data contain globally-merged (60°S-60°N) 4-km pixel-resolution IR brightness temperature data (equivalent blackbody temps), merged from the European, Japanese, and U.S. geostationary satellites over the period of record (GOES-8/9/10/11/12/13/14/15/16/17/18/19, METEOSAT-5/7/8/9/10/11, and GMS-5/MTSat-1R/2/Himawari-8/9).\n\nThe global geo-IR are dynamically calibrated to GOES East, using a 35 day trailing inter-calibration using time/space-matched IR Tb’s at the mid-point between sub-satellite positions.  In the event of duplicate data in a grid box, the value with the smaller zenith angle is taken. The data have been corrected for \"zenith angle dependence\", in which IR temperatures for locations far from satellite nadir are erroneously cold due to a combination of geometric effects and radiometric path extinction effects (Joyce et al. 2001). Finally, the data are re-navigated for parallax, which shifts the geo-location of the GEO-IR footprints to approximately account for the cloud tops that the IR “sees” being displaced away from their actual geographic location when viewed along a slanted path. These corrections allow for the merging of the IR data from the various GEO-satellites with greatly reduced discontinuities at GEO-satellite data boundaries. In the event of duplicate data in a grid box, the value with the smaller zenith angle is taken.\n\nThe NASA GES DISC is curating these data in a self-documenting, CF-compliant, netCDF-4 format, which allows a broad range of applications to access the data directly, without the need to cope with the original binary data format. In addition to the direct download of netCDF-4 data, the GES DISC provides data download in binary, ASCII, and netCDF-3 formats using the OPeNDAP interface.\n\nSimilarities with the original\n-----------------------------\nAs in the original binaries, every netCDF-4 file covers one hour, and contains two half-hourly grids, at 4-km grid cell resolution. \n\nDifferences from the original\n-----------------------------\n1. The data in the netCDF-4 files are already converted to real (float) values of Brightness Temperatures in Kelvin. There is no need to further scale these data. The netCDF-4 format is machine-independent and users need not worry about the endian-ness of their machines. \n\n2. To meet the requirements of collection spatial metadata, the grid is re-ordered from the original and now goes from -180 (West) to 180 (East). It is also starting from -60 (South).\n\nThe data and time units are reflected in the corresponding \"units\" attributes, and grid dimensions are described by longitude (\"lon\"), latitude (\"lat\") and \"time\" vectors. Thus, any CF-compliant tool should automatically understand the setup in the data files and the starting time for each half-hourly grid. Even without such tools, simple \"ncdump\" or \"h5dump\" command line tools will easily disclose the netCDF-4 files configuration.\n\nAcknowledgements\n------------------\nThe creation of the original data at NOAA/NCEP is supported by funding from the NOAA Office of Global Programs for the Global Precipitation Climatology Project (GPCP) and by NASA via the Tropical Rainfall Measuring Mission (TRMM). \n\nThe permanent archive at GES DISC is supported by NASA's HQ Earth Science Data Systems (ESDS) Program. \n\n\n\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/P4HZB9N27EKU","Contact":"GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/","ManagedBy":"NASA","UpdateFrequency":"From 1998-01-01 to Ongoing","Tags":["atmosphere","aws-pds","climate","datacenter","forecast","global","metadata","netcdf","opendap"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NCEP/CPC L3 Half Hourly 4km Global (60S - 60N) Merged IR V1 (GPM_MERGIR) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERGED_IR/GPM_MERGIR.1/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-gpmmergir","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA GRACE-DA-DM Project","Description":"Scientists at NASA Goddard Space Flight Center generate groundwater and soil moisture drought indicators each week. They are based on terrestrial water storage observations derived from GRACE satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes. This data product is GRACE Data Assimilation for Drought Monitoring (GRACE-DA-DM) U.S. Version 4.0 data product and supersedes the GRACE-DA-DM Version 2.0. The GRACE-DA-DM U.S. V4.0 is based on the Catchment Land Surface Model (CLSM) Fortuna 2.5 version simulation that was created within the Land Information System data assimilation framework (Kumar et al., 2016). This simulation used the latest GRACE RL06 (GRACE; 2002-2017) and GRACE Follow On (GRACE-FO; 2018-present) Mascon solutions version 2, at 0.25 degree resolution, from the University of Texas at Austin (Save et al., 2016; Save, 2020). The CLSM soil parameters were updated to address a soil moisture dry limit issue found near Zapata, Texas. Because the root zone soil moisture frequently reaches the dry limit there, drought conditions are often “normal” when the area should be in drought. The new soil parameters resolved the issue, and the root zone soil moisture now matches closely the in-situ observation near Zapata. In the data assimilation, the baseline for Terrestrial Water Storage anomaly computation was updated to the 2003-2019 mean, whereas previous simulations used the 2003-2016 mean. The percentile computation was switched to a 7-day moving average climatology, instead of monthly, to improve the temporal transition of drought/wetness conditions. The GRACE-DA-DM V1.0 was created by the stand alone CLSM (an older version) using the GRACE-Tellus 1 degree data from the Center for Space Research at University of Texas. The GRACE data assimilation (DA) is executed on a grid-to-grid basis in V2.0, while a basin scale average was used in V1.0 (Zaitchik et al. 2008). The V2.0 data were reprocessed (on June 14, 2017), using the GRACE RL05 Mascon solutions version 1 data set from UT CSR, for the entire period from April 1, 2002 to June 5, 2017. The reprocessing included fixes in the DA and increased the bedrock depth by 3 meters to enhance the drought indicator calculations. The GRACE-DA-DM U.S. V4.0 uses the same configuration as the V2.0 for the DA scheme and increased bedrock depth, with the updates previously mentioned, thus supersedes the previous versions. The GRACE-DA-DM U.S. V4.0 data product contains three drought indicators: Groundwater Percentile, Root Zone Soil Moisture Percentile, and Surface Soil Moisture Percentile. These drought indicators express wet or dry conditions as a percentile, indicating the probability of occurrence within the period of record from 1948 to 2014. The drought indicator data are daily, but available only one day (Monday) per week. The data have a spatial resolution of 0.125 x 0.125 degree over North America and range from April 1, 2002 to present (with a 3-6 months latency). The data are archived in NetCDF format.\n<br>\n\n#### GRACEDADM_CLSM025GL_7D\nScientists at NASA Goddard Space Flight Center generate groundwater and soil moisture drought indicators each week. They are based on terrestrial water storage observations derived from GRACE-FO satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes. This data product is GRACE Data Assimilation for Drought Monitoring (GRACE-DA-DM) Global Version 3.0 from a global GRACE and GRACE-FO data assimilation and drought indicator product generation (Li et al., 2019). It varies from the other GRACE-DA-DM products which are from the U.S. GRACE-based drought indicator product generation (Houborg et al., 2012). The GRACE-DA-DM Global V3.0 is similar to the GRACE-DA-DM U.S. V4.0 product. Both products are based on the Catchment Land Surface Model (CLSM) Fortuna 2.5 version simulation that was created within the Land Information System data assimilation framework (Kumar et al., 2016). GRACE-DA-DM Global V3.0 drought indicator maps are derived from the GLDAS_CLSM025_DA1_D product, at 0.25 degree resolution, forced by ECMWF meteorological data, and assimilated RL06 GRACE and GRACE-FO data from the University of Texas at Austin (Save et al., 2016; Save, 2020). The GRACE-DA-DM U.S. V4.0 is at 0.125 degree, which is based on a model simulation (not published at GES DISC) forced by NLDAS-2 meteorological data and assimilated with RL06 GRACE/GRACE-FO data. More information on GRACE-DA-DM U.S. V4.0 and previous versions of the data can be found in the README. The GRACE-DA-DM Global V3.0 data product contains three drought indicators: Groundwater Percentile, Root Zone Soil Moisture Percentile, and Surface Soil Moisture Percentile. These drought indicators express wet or dry conditions as a percentile, indicating the probability of occurrence within the period of record from 1948 to 2014. The drought indicator data are daily, but available only one day (Monday) per week. The data have a spatial resolution of 0.25 x 0.25 degree with global coverage (60S, 180W, 90N, 180E), and a temporal range from February 2003 to present (with a 3-6 month latency). The data are archived in NetCDF format. The GRACE-DA-DM is an operational project which produces groundwater and soil moisture drought indicators each week. The operational data is available weekly with a 2-9 day latency from the NASA GRACE project home page found under the Documentation tab. The GRACE-DA-DM data distributed here at GESDISC is the final archive version, which is generated after the latest GRACE-FO data are available.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nasagrace.unl.edu/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","netcdf","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GRACEDADM_CLSM0125US_7D v4.0 - Scientists at NASA Goddard Space Flight Center generate groundwater and soil moisture drought indicators each week. They are based on terrestrial water storage observations derived from GRACE satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GRACEDA/GRACEDADM_CLSM0125US_7D.4.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GRACEDADM_CLSM025GL_7D v3.0 - Scientists at NASA Goddard Space Flight Center generate groundwater and soil moisture drought indicators each week. They are based on terrestrial water storage observations derived from GRACE-FO satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GRACEDA/GRACEDADM_CLSM025GL_7D.3.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-grace-da-dm","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA GRACE-FO Project","Description":"This data set is produced by the Center for Space Research (CSR) GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats. <br><br> GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.\n<br>\n\n#### TELLUS_GRFO_L3_CSR_RL06.3_OCN_v04\nThis data set is produced by the Center for Space Research (CSR) GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the ocean bottom pressure (OBP) anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats. <br><br> GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.\n<br>\n\n#### TELLUS_GRFO_L3_GFZ_RL06.3_LND_v04\nThis data set is produced by the German Research Centre for Geosciences (GFZ) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats. <br><br> GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.\n<br>\n\n#### TELLUS_GRFO_L3_GFZ_RL06.3_OCN_v04\nThis data set is produced by the German Research Centre for Geosciences (GFZ) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the ocean bottom pressure (OBP) anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats. <br><br> GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.\n<br>\n\n#### GRACEFO_L1A_ASCII_GRAV_JPL_RL04\nFOR EXPERT USE ONLY. The GRACE-FO Level-1A data contains telemetry data that has been converted to engineering units, from which Level-1B data products are derived. For a detailed description, please see the GRACE-FO Level-1 documentation (https://podaac.jpl.nasa.gov/gravity/gracefo-documentation).\n<br>\n\n#### GRACEFO_L1B_ASCII_GRAV_JPL_RL04\nFOR EXPERT USE ONLY. The GRACE-FO Level-1B data provide all necessary inputs to derive monthly time variations in the Earth gravity field. Level-1B data are also used for GRACE orbit and mean gravity field determination. For a detailed description, please see the GRACE-FO Level-1 documentation (https://podaac.jpl.nasa.gov/gravity/gracefo-documentation).\n<br>\n\n#### GRACEFO_L2_JPL_MONTHLY_0063\nFOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL). The data are provided as spherical harmonic coefficients, averaged over approximately a month, and available from 2018 onward. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) mission. The GRACE-FO mission, a joint partnership between NASA and the German Research Centre for Geosciences (GFZ), launched on 22 May 2018. It uses twin satellites to accurately map variations in the Earth's gravity field and surface mass distribution. It is designed as a successor to the Gravity Recovery and Climate Experiment (GRACE) mission. <br><br> This GRACE-FO RL06.3 data is an updated version of the GRACE-FO RL06.1 Level-2 data products. RL06.3 differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.3 uses ACH1B RL04 that is contained within the ACX2 Level-1 bundle, which replaces ACH1B RL04 contained within the ACX Level-1 bundle that was used for Level-2 RL06.1 (note: ACX2-L1B is only applicable for 01/2023 onwards in wide-pointing operational mode; from 6/2018 through 12/2022, RL06.1 and RL06.3 GRACE-FO data are identical and based on ACX; ACX2 is not available for 03/2023-06/2023 as the satellites were not in wide-pointing mode during that period). All GRACE-FO RL06.3 Level-2 products are fully compatible with the GRACE RL06 level-2 fields. Refer to the mission page for more information.\n<br>\n\n#### GRACEFO_L2_CSR_MONTHLY_0063\nFOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the University of Texas (at Austin) Center for Space Research (CSR). The data are provided as spherical harmonic coefficients, averaged over approximately a month, and available from 2018 onward. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) mission. The GRACE-FO mission, a joint partnership between NASA and the German Research Centre for Geosciences (GFZ), launched on 22 May 2018. It uses twin satellites to accurately map variations in the Earth's gravity field and surface mass distribution. It is designed as a successor to the Gravity Recovery and Climate Experiment (GRACE) mission. <br><br> This GRACE-FO RL06.3 data is an updated version of the GRACE-FO RL06.1 Level-2 data products. RL06.3 differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.3 uses ACH1B RL04 that is contained within the ACX2 Level-1 bundle, which replaces ACH1B RL04 contained within the ACX Level-1 bundle that was used for Level-2 RL06.1 (note: ACX2-L1B is only applicable for 01/2023 onwards in wide-pointing operational mode; from 6/2018 through 12/2022, RL06.1 and RL06.3 GRACE-FO data are identical and based on ACX; ACX2 is not available for 03/2023-06/2023 as the satellites were not in wide-pointing mode during that period). All GRACE-FO RL06.3 Level-2 products are fully compatible with the GRACE RL06 level-2 fields. Refer to the mission page for more information.\n<br>\n\n#### GRACEFO_L2_GFZ_MONTHLY_0063\nFOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the German Research Centre for Geosciences (GFZ). The data are provided as spherical harmonic coefficients, averaged over approximately a month. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO mission. Refer to the mission page for more information. <br><br> This GRACE-FO RL06.3 data is an updated version of the GRACE-FO RL06.1 Level-2 data products. RL06.3 differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.3 uses ACH1B RL04 that is contained within the ACX2 Level-1 bundle, which replaces ACH1B RL04 contained within the ACX Level-1 bundle that was used for Level-2 RL06.1 (note: ACX2-L1B is only applicable for 01/2023 onwards in wide-deadband operational mode; from 6/2018 through 12/2022, RL06.1 and RL06.3 GRACE-FO data are identical). All GRACE-FO RL06.3 Level-2 products are fully compatible with the GRACE RL06 level-2 fields. Refer to the mission page for more information.\n<br>\n\n#### GRACE_ABPR_FO_L2_V1.0\nThis dataset provides hourly ocean bottom pressure measurements at the North Pole from the Arctic Bottom Pressure Recorder - Follow On (ABPR-FO) from August 2022 to August 2023. Based around a Paroscientific Digiquartz pressure sensor, the ABPR-FO was designed and powered to collect and transmit data which was then collected, quality-controlled and updated at PO.DAAC on a yearly basis. The full time series is provided as a single netCDF file, and reports both bottom pressure (reported as liquid water equivalent thickness) and temperature. This dataset aims to serve as in situ validation for GRACE-FO data products in the central Arctic. This project is funded by NASA’s GRACE and GRACE-FO Science Team and supported by PONANT Science.\n<br>\n\n#### TELLUS_GRFO_L3_JPL_RL06.3_LND_v04\nThis data set is produced by the Jet Propulsion Laboratory (JPL) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats. <br><br> GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this RL06.3 is an updated release of the previous RL06.1. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/data/instruments/grace-fo-lri","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","ice","netcdf","oceans","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"TELLUS_GRFO_L3_CSR_RL06.3_LND_v04 vRL06.3v04 - This data set is produced by the Center for Space Research (CSR) GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TELLUS_GRFO_L3_CSR_RL06.3_LND_v04","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"TELLUS_GRFO_L3_CSR_RL06.3_OCN_v04 vRL06.3v04 - This data set is produced by the Center for Space Research (CSR) GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the ocean bottom pressure (OBP) anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TELLUS_GRFO_L3_CSR_RL06.3_OCN_v04","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"TELLUS_GRFO_L3_GFZ_RL06.3_LND_v04 vRL06.3v04 - This data set is produced by the German Research Centre for Geosciences (GFZ) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TELLUS_GRFO_L3_GFZ_RL06.3_LND_v04","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"TELLUS_GRFO_L3_GFZ_RL06.3_OCN_v04 vRL06.3v04 - This data set is produced by the German Research Centre for Geosciences (GFZ) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the ocean bottom pressure (OBP) anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TELLUS_GRFO_L3_JPL_RL06.3_OCN_v04","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACEFO_L1A_ASCII_GRAV_JPL_RL04 v4 - FOR EXPERT USE ONLY. The GRACE-FO Level-1A data contains telemetry data that has been converted to engineering units, from which Level-1B data products are derived.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACEFO_L1A_ASCII_GRAV_JPL_RL04","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACEFO_L1B_ASCII_GRAV_JPL_RL04 v4 - FOR EXPERT USE ONLY. The GRACE-FO Level-1B data provide all necessary inputs to derive monthly time variations in the Earth gravity field.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACEFO_L1B_ASCII_GRAV_JPL_RL04","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACEFO_L2_JPL_MONTHLY_0063 v6.3 - FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACEFO_L2_JPL_MONTHLY_0063","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACEFO_L2_CSR_MONTHLY_0063 v6.3 - FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the University of Texas (at Austin) Center for Space Research (CSR).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACEFO_L2_CSR_MONTHLY_0063","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACEFO_L2_GFZ_MONTHLY_0063 v6.3 - FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the German Research Centre for Geosciences (GFZ).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACEFO_L2_GFZ_MONTHLY_0063","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_ABPR_FO_L2_V1.0 v1.0 - This dataset provides hourly ocean bottom pressure measurements at the North Pole from the Arctic Bottom Pressure Recorder - Follow On (ABPR-FO) from August 2022 to August 2023. Based around a Paroscientific Digiquartz pressure sensor, the ABPR-FO was designed and powered to collect and transmit data which was then collected, quality-controlled and updated at PO.DAAC on a yearly basis.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_ABPR_FO_L2_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"TELLUS_GRFO_L3_JPL_RL06.3_LND_v04 vRL06.3v04 - This data set is produced by the Jet Propulsion Laboratory (JPL) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TELLUS_GRFO_L3_JPL_RL06.3_LND_v04","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-grace-fo","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA GRACE Project","Description":"The monthly land mass grids contain water mass anomalies given as equivalent water thickness derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.\n<br>\n\n#### TELLUS_GRAC_L3_CSR_RL06_OCN_v04\nThe monthly ocean bottom pressure anomaly grids are given as equivalent water thickness changes derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represent sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). The Level-2 GAD product has been added back, a glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters (i.e., de-striping and spatial smoothing) have been applied to reduce correlated errors. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.\n<br>\n\n#### GRACE_AOD1B_GRAV_GFZ_RL06\nThe GRACE Atmosphere and Ocean De-aliasing dataset contains spherical harmonic coefficients of combined barotropic or baroclinic sea level and vertical integrated pressure variations at 6-hour sample rate. It is used as a correction product for the Level 2 GRACE datasets.\n<br>\n\n#### GRACE_GSM_L2_GRAV_CSR_RL06\nFOR EXPERT USE ONLY. This dataset contains estimates of static field geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the Center for Space Research (CSR) at University of Texas at Austin. The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.\n<br>\n\n#### GRACE_GSM_L2_GRAV_GFZ_RL06\nFOR EXPERT USE ONLY. This dataset contains estimates of static field geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the German Research Centre for Geosciences (GFZ). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.\n<br>\n\n#### GRACE_GSM_L2_GRAV_JPL_RL06\nFOR EXPERT USE ONLY. This dataset contains estimates of static field geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.\n<br>\n\n#### GRACE_L1B_GRAV_JPL_RL02\nFOR EXPERT USE ONLY. The GRACE Level 1B data provide all necessary inputs to derive monthly time variations in the Earth's gravity field. Level 1B data are also used for GRACE orbit and mean gravity field determination. It contains K-Band Ranging Data Product (KBR1B), Star Camera Data Product (SCA1B), Accelerometer Data Product (ACC1B), GPS Data Product (GPS1B), Vector Products (VGN1B, VGO1B, VGB1B, VCM1B, VKB1B, VSL1B), Quaternion Products (QSA1B, QSB1B), and Housekeeping Products (AHK1B, IHK1B, THR1B, TNK1B, MAG1B, MAS1B, TIM1B)\n<br>\n\n#### GRACE_GAC_L2_GRAV_CSR_RL06\nFOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic and atmospheric model produced by the Center for Space Research (CSR) at University of Texas at Austin. The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.\n<br>\n\n#### GRACE_GAC_L2_GRAV_GFZ_RL06\nFOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic and atmospheric model produced by the German Research Centre for Geosciences (GFZ). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.\n<br>\n\n#### GRACE_GAC_L2_GRAV_JPL_RL06\nFOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic and atmospheric model produced by the NASA Jet Propulsion Laboratory (JPL). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.\n<br>\n\n#### GRACE_GAA_L2_GRAV_GFZ_RL06\nFOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal atmospheric model produced by the German Research Centre for Geosciences (GFZ). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.\n<br>\n\n#### GRACE_GAA_L2_GRAV_JPL_RL06\nFOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal atmospheric model produced by the NASA Jet Propulsion Laboratory (JPL). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.\n<br>\n\n#### GRACE_GAB_L2_GRAV_GFZ_RL06\nFOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic model produced by the German Research Centre for Geosciences (GFZ). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.\n<br>\n\n#### GRACE_GAB_L2_GRAV_JPL_RL06\nFOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic model produced by the Jet Propulsion Laboratory (JPL). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.\n<br>\n\n#### GRACE_GAD_L2_GRAV_CSR_RL06\nFOR EXPERT USE ONLY. This dataset contains estimates of ocean bottom pressure derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the Center for Space Research (CSR) at University of Texas at Austin. The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.\n<br>\n\n#### GRACE_GAD_L2_GRAV_GFZ_RL06\nFOR EXPERT USE ONLY. This dataset contains estimates of ocean bottom pressure derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the German Research Centre for Geosciences (GFZ). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.\n<br>\n\n#### GRACE_GAD_L2_GRAV_JPL_RL06\nFOR EXPERT USE ONLY. This dataset contains estimates of ocean bottom pressure derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL). The data are in spherical harmonics averaged over approximately a month. The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations. This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system. In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer. The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively. Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently. These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth. This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft. The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals. The so called dual-one-way range change measurements can be reconstructed from these phase measurements. This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.\n<br>\n\n#### TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4\nThis dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (RL06.3Mv04). A Coastal Resolution Improvement (CRI) filter has been applied to this data set to reduce signal leakage errors across coastlines. For most land hydrology, oceanographic as well as land-ice applications this is the recommend data set for the analysis of surface mass changes. The data are provided in a single data file in netCDF format, with water storage/height anomalies in equivalent water thickness units (cm). The data are derived from solving for monthly gravity field variations on geolocated spherical cap mass concentration functions, rather than global spherical harmonic coefficients. Additionally, realistic geophysical information is introduced during the computation to intrinsically remove correlated errors. Thus, these Mascon grids do not need to be de-correlated or smoothed, like traditional spherical harmonic gravity solutions. <br><br> The complete Mascon solution consists of 4,551 independent estimates of surface mass change that have been derived using an equal-area 3-degree grid of individual mascons. A subset of these individual mascons span coastlines, and contain mixed land and ocean mass change signals. In a post-processing step, the CRI filter is applied to those mixed land/ocean Mascons to separate land and ocean mass. The land mask used to perform this separation is provided in the same directory as this dataset, as are uncertainty values, and the gridded mascon-ID number to enable further analysis. Since the individual mascons act as an inherent smoother on the gravity field, a set of optional gain factors (or scale factors) is provided within the netCDF and can be applied to the solution to study mass change signals at sub-mascon resolution (e.g. for continental hydrology applications). For use-case examples and further background on the gain factors, please see Wiese, Landerer & Watkins, 2016, https://doi.org/10.1002/2016WR019344. <br><br> This RL06.3Mv04 is an updated version of the previous Tellus JPL Mascon RL06.1Mv03 (DOI, 10.5067/TEMSC-3JC63). For a detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. For a detailed description of the CRI filter implementation, please see Wiese et al., 2016, doi:10.1002/2016WR019344.\n<br>\n\n#### TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4\nThis dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (RL06.3Mv04). These data are provided in a single data file in netCDF format, and can be used for analysis for ocean, ice, and hydrology phenomena. The data are provided in a single data file in netCDF format, with water storage/height anomalies in equivalent water thickness units (cm). The data are derived from solving for monthly gravity field variations on geolocated spherical cap mass concentration functions, rather than global spherical harmonic coefficients. Additionally, realistic geophysical information is introduced during the computation to intrinsically remove correlated errors. Thus, these Mascon grids do not need to be de-correlated or smoothed, like traditional spherical harmonic gravity solutions. <br><br> The complete Mascon solution consists of 4,551 independent estimates of surface mass change that have been derived using an equal-area 3-degree grid of individual mascons. Please note that this dataset does not correct for leakage errors across coastlines; it is therefore recommended only for users who want to apply their own algorithm to separate between land and ocean mass very near coastlines. <br><br> This RL06.3Mv04 is an updated version of the previous Tellus JPL Mascon RL06.1Mv03. For more information, please visit https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/. For a detailed description on the Mascon processing, including the mathematical derivation, implementation of geophysical constraints, and validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. This product is intended for expert use only; other users are encouraged to use the CRI-filtered Mascon dataset, which is available here: https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4.\n<br>\n\n#### GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06.3\nGRACE non-tidal high-frequency atmospheric and oceanic mass variation models are routinely generated at GFZ as so-called Atmosphere and Ocean De-aliasing Level-1B (AOD1B) products (in terms of corresponding spherical harmonic geopotential coefficients) to be added to the background static gravity model during GRACE monthly gravity field determination. AOD1B products are 3-hourly series of spherical harmonic coefficients up to degree and order 180 which are routinely provided to the GRACE Science Data System and the user community with only a few days time delay. These products reflect spatio-temporal mass variations in the atmosphere and oceans deduced from an operational atmospheric model and corresponding ocean dynamics provided by an ocean model. The variability is derived by subtraction of a long-term mean of vertical integrated atmospheric mass distributions and a corresponding mean of ocean bottom pressure as simulated with the ocean model. <br><br> The Gridded AOD1B data sets provided here contain the monthly mean AOD1B data in geolocated gridded form, smoothed or spatially aggregated to be consistent with the GRACE and GRACE-FO Tellus Level-3 data products of land and/or ocean mass anomalies. With these gridded AOD1B Level-3 products, users can remove or add the effects of the modeled mean monthly atmospheric and ocean bottom pressure change (e.g., to compare different models).\n<br>\n\n#### TELLUS_GLDAS-NOAH-3.3_TWS-ANOMALY_MONTHLY\nThe total land water storage anomalies are aggregated from the Global Land Data Assimilation System (GLDAS) NOAH model. GLDAS outputs land water content by using numerous land surface models and data assimilation. For more information on the GLDAS project and model outputs please visit https://ldas.gsfc.nasa.gov/gldas. The aggregated land water anomalies (sum of soil moisture, snow, canopy water) provided here can be used for comparison against and evaluations of the observations of Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO over land. The monthly anomalies are computed over the same days during each month as GRACE and GRACE-FO data, and are provided on monthly 1 degree lat/lon grids in NetCDF format. Currently, the days included in these monthly anomaly computation are same as GRACE-FO monthly Level-2 RL06.3 JPL solutions.\n<br>\n\n#### TELLUS_GIA_L3_0.5-DEG_V1.0\nGlacial isostatic adjustment (GIA) is an ongoing geophysical process and is measured by gravimetry satellites like GRACE and GRACE-FO. To isolate signals of contemporary surface mass loss in the cumulative satellite gravimetry measurements, contemporary GIA rates are computed and subtracted from the satellite gravimetry observations. The GIA correction models provided here are filtered such that they are compatible with Level-3 post-processing filters applied to GRACE(-FO) data as indicated in the [product_id]. In this way, user can effectively assess the impact of the applied GIA correction, and substitute different GIA models should that be desired. This GIA dataset is mapped into 0.5-degree global grid compatible with the JPL Mascon solution, provided in netCDF format.\n<br>\n\n#### ANTARCTICA_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4\nThis dataset is a time series of mass variability averaged over all of Antarctica. It provides the ice mass changes of Antarctica over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL063Mv04 dataset, which can be found at https://doi.org/10.5067/TEMSC-3JC634. A more detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. The mass variability are provided as an ASCII table.\n<br>\n\n#### GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4\nThis dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL06.3Mv04 dataset, which can be found at https://doi.org/10.5067/TEMSC-3JC634. A more detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. The mass variability is provided as an ASCII table.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/grace-grace-fo","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","hydrology","ice","netcdf","oceans","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"TELLUS_GRAC_L3_CSR_RL06_LND_v04 vRL06v04 - The monthly land mass grids contain water mass anomalies given as equivalent water thickness derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TELLUS_GRAC_L3_CSR_RL06_LND_v04","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"TELLUS_GRAC_L3_CSR_RL06_OCN_v04 vRL06v04 - The monthly ocean bottom pressure anomaly grids are given as equivalent water thickness changes derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represent sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TELLUS_GRAC_L3_CSR_RL06_OCN_v04","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_AOD1B_GRAV_GFZ_RL06 v6.0 - The GRACE Atmosphere and Ocean De-aliasing dataset contains spherical harmonic coefficients of combined barotropic or baroclinic sea level and vertical integrated pressure variations at 6-hour sample rate. It is used as a correction product for the Level 2 GRACE datasets.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_AOD1B_GRAV_GFZ_RL06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_GSM_L2_GRAV_CSR_RL06 v6.0 - FOR EXPERT USE ONLY. This dataset contains estimates of static field geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the Center for Space Research (CSR) at University of Texas at Austin.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_GSM_L2_GRAV_CSR_RL06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_GSM_L2_GRAV_GFZ_RL06 v6.0 - FOR EXPERT USE ONLY. This dataset contains estimates of static field geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the German Research Centre for Geosciences (GFZ).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_GSM_L2_GRAV_GFZ_RL06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_GSM_L2_GRAV_JPL_RL06 v6.0 - FOR EXPERT USE ONLY. This dataset contains estimates of static field geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_GSM_L2_GRAV_JPL_RL06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_L1B_GRAV_JPL_RL02 v2 - FOR EXPERT USE ONLY. The GRACE Level 1B data provide all necessary inputs to derive monthly time variations in the Earth's gravity field.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_L1B_GRAV_JPL_RL02","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_GAC_L2_GRAV_CSR_RL06 v6.0 - FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic and atmospheric model produced by the Center for Space Research (CSR) at University of Texas at Austin.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_GAC_L2_GRAV_CSR_RL06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_GAC_L2_GRAV_GFZ_RL06 v6.0 - FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic and atmospheric model produced by the German Research Centre for Geosciences (GFZ).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_GAC_L2_GRAV_GFZ_RL06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_GAC_L2_GRAV_JPL_RL06 v6.0 - FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic and atmospheric model produced by the NASA Jet Propulsion Laboratory (JPL).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_GAC_L2_GRAV_JPL_RL06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_GAA_L2_GRAV_GFZ_RL06 v6.0 - FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal atmospheric model produced by the German Research Centre for Geosciences (GFZ).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_GAA_L2_GRAV_GFZ_RL06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_GAA_L2_GRAV_JPL_RL06 v6.0 - FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal atmospheric model produced by the NASA Jet Propulsion Laboratory (JPL).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_GAA_L2_GRAV_JPL_RL06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_GAB_L2_GRAV_GFZ_RL06 v6.0 - FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic model produced by the German Research Centre for Geosciences (GFZ).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_GAB_L2_GRAV_GFZ_RL06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_GAB_L2_GRAV_JPL_RL06 v6.0 - FOR EXPERT USE ONLY. This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal oceanic model produced by the Jet Propulsion Laboratory (JPL).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_GAB_L2_GRAV_JPL_RL06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_GAD_L2_GRAV_CSR_RL06 v6.0 - FOR EXPERT USE ONLY. This dataset contains estimates of ocean bottom pressure derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the Center for Space Research (CSR) at University of Texas at Austin.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_GAD_L2_GRAV_CSR_RL06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_GAD_L2_GRAV_GFZ_RL06 v6.0 - FOR EXPERT USE ONLY. This dataset contains estimates of ocean bottom pressure derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the German Research Centre for Geosciences (GFZ).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_GAD_L2_GRAV_GFZ_RL06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRACE_GAD_L2_GRAV_JPL_RL06 v6.0 - FOR EXPERT USE ONLY. This dataset contains estimates of ocean bottom pressure derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRACE_GAD_L2_GRAV_JPL_RL06","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4 vRL06.3Mv04 - This dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (RL06.3Mv04). A Coastal Resolution Improvement (CRI) filter has been applied to this data set to reduce signal leakage errors across coastlines.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4 vRL06.3Mv04 - This dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (RL06.3Mv04). These data are provided in a single data file in netCDF format, and can be used for analysis for ocean, ice, and hydrology phenomena.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06.3 vRL06.3 - GRACE non-tidal high-frequency atmospheric and oceanic mass variation models are routinely generated at GFZ as so-called Atmosphere and Ocean De-aliasing Level-1B (AOD1B) products (in terms of corresponding spherical harmonic geopotential coefficients) to be added to the background static gravity model during GRACE monthly gravity field determination. AOD1B products are 3-hourly series of spherical harmonic coefficients up to degree and order 180 which are routinely provided to the GRACE Science Data System and the user community with only a few days time delay.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"TELLUS_GLDAS-NOAH-3.3_TWS-ANOMALY_MONTHLY v3.3 - The total land water storage anomalies are aggregated from the Global Land Data Assimilation System (GLDAS) NOAH model. GLDAS outputs land water content by using numerous land surface models and data assimilation.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TELLUS_GLDAS-NOAH-3.3_TWS-ANOMALY_MONTHLY","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"TELLUS_GIA_L3_0.5-DEG_V1.0 v1.0 - Glacial isostatic adjustment (GIA) is an ongoing geophysical process and is measured by gravimetry satellites like GRACE and GRACE-FO. To isolate signals of contemporary surface mass loss in the cumulative satellite gravimetry measurements, contemporary GIA rates are computed and subtracted from the satellite gravimetry observations.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TELLUS_GIA_L3_0.5-DEG_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ANTARCTICA_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4 vRL06.3Mv04 - This dataset is a time series of mass variability averaged over all of Antarctica. It provides the ice mass changes of Antarctica over time.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ANTARCTICA_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4 vRL06.3Mv04 - This dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-grace","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA GSESA Project","Description":"The Global Surface Emissivity Spectral Atlas (GSESA) database contains global, monthly climatology infrared emissivity functional Empirical Orthogonal Function (EOF) scores in 0.25 x 0.25 latitude-longitude resolution. An eigenvector file and a reader file allow customers to produce emissivity spectra. The emissivity functional EOF scores were developed using the Infrared Atmospheric Sounding Interferometer (IASI) instrument on the METOP-A, METOP-B, and METOP-C satellites for the period 2007-07-01 to 2025-01-31. An inversion scheme, dealing with cloudy as well as cloud-free radiances observed with ultraspectral infrared (IR) sounders, was developed to simultaneously retrieve atmospheric thermodynamic and surface or cloud microphysical parameters. This inversion scheme was applied to the IASI instrument. Rapidly produced surface spectral emissivity (SSE) is initially evaluated through quality control checks on the retrievals of other impacted surface and atmospheric parameters. The GSESA data are provided in binary format, with sample reader files that can be used in a Fortan IDE to read a functional emissivity EOF compressed file (e.g., MFEMI_MONTH01_A_V5P.bin) and its EOF eigenvector file (IASI_B_EV_FUNC_GLOBAL_V4.bin) to produce spectral emissivity at a certain location (latitude and longitude). A sample reader file can be used in a Matlab IDE is also provided. These data were created with funding from the NASA Internal Scientist Funding Model for the National Airborne Sounder Testbed-Interferometer (NAST-I).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://asdc.larc.nasa.gov/project/GSESA/GSESA_V01","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GSESA vV01 - The Global Surface Emissivity Spectral Atlas (GSESA) database contains global, monthly climatology infrared emissivity functional Empirical Orthogonal Function (EOF) scores in 0.25 x 0.25 latitude-longitude resolution. An eigenvector file and a reader file allow customers to produce emissivity spectra.","ARN":"arn:aws:s3:::asdc-prod-protected/GSESA/GSESA_V01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-gsesa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA HAQAST Project","Description":"Our mission is to put the power of NASA’s satellites down to earth and in your hands. HAQAST is a collaborative team that works in partnership with public health and air quality agencies to use NASA data and tools for the public benefit. Here, you can learn about our team, partnerships, and newsworthy achievements. You…\n<br>\n\n#### ALAN_VIIRS_CONUS\nThis product provides detailed information about the satellite-based data on artificial light at night (ALAN). The Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) nighttime lights (NTL) product (VNP46A4, DOI: 10.5067/VIIRS/VNP46A4.001 ) in NASA’s Black Marble suite is used to derive annual summary of ALAN levels throughout the CONUS at both county and tract level for the period of 2012-2020. The PI Dr. Qian Xiao is a member of NASA Heath and Air Quality Applied Sciences Team (HAQAST).\n<br>\n\n#### HAQ_TROPOMI_NO2_CONUS_A_L3\nThis product provides level 3 annual averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the Continental United States oversampled to a spatial resolution of 0.01˚ x 0.01˚ (~1 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in 2019 and continues to the present. This L3 product was developed by the George Washington University Air, Climate and Health Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST) using Level 2 version 2.4 TROPOMI NO2 files from the ESA. The TROPOMI instrument on Sentinel-5 Precursor acquires tropospheric NO2 column contents from low Earth orbit (~824 km above ground level) once per day globally at approximately 13:30 local time. NO2 is an air pollutant that adversely affects the human respiratory system and leads to premature mortality. NO2 is also an important precursor for ozone and fine particulates, which also have severe health impacts. In urban areas, the majority of NO2 originates from anthropogenic NOx (=NO+NO2; most NOx is emitted as NO, which rapidly cycles to NO2) emissions during high-temperature fossil fuel combustion. Tropospheric NO2 vertical column contents are qualitatively representative of near-surface NO2 concentrations and NOx emissions in urban/polluted locations.\n<br>\n\n#### HAQ_TROPOMI_NO2_CONUS_M_L3\nThis product provides level 3 monthly averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the Continental United States oversampled to a spatial resolution of 0.01˚ x 0.01˚ (~1 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in May 2018 and continues to the present. This L3 product was developed by the George Washington University Air, Climate and Health Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST) using Level 2 version 2.4 TROPOMI NO2 files from the ESA. The TROPOMI instrument on Sentinel-5 Precursor acquires tropospheric NO2 column contents from low Earth orbit (~824 km above ground level) once per day globally at approximately 13:30 local time. NO2 is an air pollutant that adversely affects the human respiratory system and leads to premature mortality. NO2 is also an important precursor for ozone and fine particulates, which also have severe health impacts. In urban areas, the majority of NO2 originates from anthropogenic NOx (=NO+NO2; most NOx is emitted as NO, which rapidly cycles to NO2) emissions during high-temperature fossil fuel combustion. Tropospheric NO2 vertical column contents are qualitatively representative of near-surface NO2 concentrations and NOx emissions in urban/polluted locations.\n<br>\n\n#### HAQ_TROPOMI_NO2_CONUS_S_L3\nThis product provides level 3 seasonal averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the Continental United States oversampled to a spatial resolution of 0.01˚ x 0.01˚ (~1 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in June-August 2018 and continues to the present. This L3 product was developed by the George Washington University Air, Climate and Health Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST) using Level 2 version 2.4 TROPOMI NO2 files from the ESA. The TROPOMI instrument on Sentinel-5 Precursor acquires tropospheric NO2 column contents from low Earth orbit (~824 km above ground level) once per day globally at approximately 13:30 local time. NO2 is an air pollutant that adversely affects the human respiratory system and leads to premature mortality. NO2 is also an important precursor for ozone and fine particulates, which also have severe health impacts. In urban areas, the majority of NO2 originates from anthropogenic NOx (=NO+NO2; most NOx is emitted as NO, which rapidly cycles to NO2) emissions during high-temperature fossil fuel combustion. Tropospheric NO2 vertical column contents are qualitatively representative of near-surface NO2 concentrations and NOx emissions in urban/polluted locations.\n<br>\n\n#### HAQ_TROPOMI_NO2_GLOBAL_A_L3\nThis product provides level 3 annual averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the globe oversampled to a spatial resolution of 0.1˚ x 0.1˚ (~10 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in January 2019 and continues to the present. This L3 product was developed by the George Washington University Air, Climate and Health Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST) using Level 2 version 2.4 TROPOMI NO2 files from the ESA. The TROPOMI instrument on Sentinel-5 Precursor acquires tropospheric NO2 column contents from low Earth orbit (~824 km above ground level) once per day globally at approximately 13:30 local time. NO2 is an air pollutant that adversely affects the human respiratory system and leads to premature mortality. NO2 is also an important precursor for ozone and fine particulates, which also have severe health impacts. In urban areas, the majority of NO2 originates from anthropogenic NOx (=NO+NO2; most NOx is emitted as NO, which rapidly cycles to NO2) emissions during high-temperature fossil fuel combustion. Tropospheric NO2 vertical column contents are qualitatively representative of near-surface NO2 concentrations and NOx emissions in urban/polluted locations.\n<br>\n\n#### HAQ_TROPOMI_NO2_GLOBAL_M_L3\nThis product provides level 3 monthly averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the globe oversampled to a spatial resolution of 0.1˚ x 0.1˚ (~10 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in January 2019 and continues to the present. This L3 product was developed by the George Washington University Air, Climate and Health Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST) using Level 2 version 2.4 TROPOMI NO2 files from the ESA. The TROPOMI instrument on Sentinel-5 Precursor acquires tropospheric NO2 column contents from low Earth orbit (~824 km above ground level) once per day globally at approximately 13:30 local time. NO2 is an air pollutant that adversely affects the human respiratory system and leads to premature mortality. NO2 is also an important precursor for ozone and fine particulates, which also have severe health impacts. In urban areas, the majority of NO2 originates from anthropogenic NOx (=NO+NO2; most NOx is emitted as NO, which rapidly cycles to NO2) emissions during high-temperature fossil fuel combustion. Tropospheric NO2 vertical column contents are qualitatively representative of near-surface NO2 concentrations and NOx emissions in urban/polluted locations.\n<br>\n\n#### HAQES_NA_PM25_BC_CENSUS\nThis product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration at the census level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).\n<br>\n\n#### HAQES_NA_PM25_BC_COUNTY\nThis product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration at the county level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).\n<br>\n\n#### HAQES_NA_PM25_BC\nThis product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration over the continental United States (CONUS) and surrounding regions. The data is mapped on Lambert projection. The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).\n<br>\n\n#### HAQES_NA_PM25_OC_CENSUS\nThis product provides HAQES 3-hourly ensemble mean surface PM2.5 Organic Carbon concentration at the census level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).\n<br>\n\n#### HAQES_NA_PM25_OC_COUNTY\nThis product provides HAQES 3-hourly ensemble mean surface PM2.5 Organic Carbon concentration at the county level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).\n<br>\n\n#### HAQES_NA_PM25_OC\nThis product provides HAQES 3-hourly ensemble mean surface PM2.5 Organic Carbon concentration over the continental United States (CONUS) and surrounding regions. The data is mapped on Lambert projection. The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).\n<br>\n\n#### HAQES_NA_PM25_TOT_CENSUS\nThis product provides HAQES 3-hourly ensemble mean surface total PM2.5 concentration at the census level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).\n<br>\n\n#### HAQES_NA_PM25_TOT_COUNTY\nThis product provides HAQES 3-hourly ensemble mean surface total PM2.5 concentration at the county level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).\n<br>\n\n#### HAQES_NA_PM25_TOT\nThis product provides HAQES 3-hourly ensemble mean surface total PM2.5 concentration over the continental United States (CONUS) and surrounding regions. The data is mapped on Lambert projection. The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).\n<br>\n\n#### MERRA2_CNN_HAQAST_PM25\nThis product provides MERRA-2 bias-corrected global hourly surface total PM2.5 mass concentration with the same horizontal spatial resolution as MERRA-2, covering a temporal range from 2000 to 2024. It is derived using a machine learning (ML) approach with a convolutional neural network (CNN) method and is specifically developed for the NASA Health and Air Quality Applied Sciences Team (HAQAST). The dataset consists of two parameters: MERRA2_CNN_Surface_PM25 and QFLAG. MERRA2_CNN_Surface_PM25, a 3-dimensional variable (time, latitude, longitude), represents the surface PM2.5 concentrations in µg/m³. QFLAG denotes the quality of data at each grid point, where 4 indicates the highest quality and 1 indicates the lowest quality. It is recommended to use QFLAG values of 3 and 4 for quantitative analysis.\n<br>\n\n#### SFC_NITROGEN_DIOXIDE_CONC\nThe Nitrogen Dioxide Surface-Level Annual Average Concentrations Product (SFC_NITROGEN_DIOXIDE_CONC) contains estimated global NO2 surface values derived using a Land Use Regression (LUR) model (based on 5220 NO2 monitors in 58 countries and land use variables) for the years 2010-2012. NO2 column densities from the Ozone Monitoring Instrument and MERRA-2 scale the concentrations to other years between 1990 and 2020. This product is part of NASA's Health and Air Quality Applied Sciences Team (HAQAST) effort. The data are global over land and span the latitude range between 60 south and 75 north, gridded at 0.0083 degree resolution (array size is 43080 x 16200). Data variables include surface NO2, as well as latitude and longitude values. The data are written to files using the new version 4 netCDF format. The average file size is about 150 Megabytes.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://haqast.org/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","netcdf","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ALAN_VIIRS_CONUS v1 - This product provides detailed information about the satellite-based data on artificial light at night (ALAN). The Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) nighttime lights (NTL) product (VNP46A4, DOI: 10.5067/VIIRS/VNP46A4.001 ) in NASA’s Black Marble suite is used to derive annual summary of ALAN levels throughout the CONUS at both county and tract level for the period of 2012-2020.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/ALAN_VIIRS_CONUS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HAQ_TROPOMI_NO2_CONUS_A_L3 v2.4 - This product provides level 3 annual averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the Continental United States oversampled to a spatial resolution of 0.01˚ x 0.01˚ (~1 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in 2019 and continues to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/HAQ_TROPOMI_NO2_CONUS_A_L3.2.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HAQ_TROPOMI_NO2_CONUS_M_L3 v2.4 - This product provides level 3 monthly averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the Continental United States oversampled to a spatial resolution of 0.01˚ x 0.01˚ (~1 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in May 2018 and continues to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/HAQ_TROPOMI_NO2_CONUS_M_L3.2.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HAQ_TROPOMI_NO2_CONUS_S_L3 v2.4 - This product provides level 3 seasonal averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the Continental United States oversampled to a spatial resolution of 0.01˚ x 0.01˚ (~1 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in June-August 2018 and continues to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/HAQ_TROPOMI_NO2_CONUS_S_L3.2.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HAQ_TROPOMI_NO2_GLOBAL_A_L3 v2.4 - This product provides level 3 annual averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the globe oversampled to a spatial resolution of 0.1˚ x 0.1˚ (~10 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in January 2019 and continues to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/HAQ_TROPOMI_NO2_GLOBAL_A_L3.2.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HAQ_TROPOMI_NO2_GLOBAL_M_L3 v2.4 - This product provides level 3 monthly averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the globe oversampled to a spatial resolution of 0.1˚ x 0.1˚ (~10 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in January 2019 and continues to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/HAQ_TROPOMI_NO2_GLOBAL_M_L3.2.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HAQES_NA_PM25_BC_CENSUS v1 - This product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration at the census level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/HAQES_NA_PM25_BC_CENSUS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HAQES_NA_PM25_BC_COUNTY v1 - This product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration at the county level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/HAQES_NA_PM25_BC_COUNTY.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HAQES_NA_PM25_BC v1 - This product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration over the continental United States (CONUS) and surrounding regions. The data is mapped on Lambert projection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/HAQES_NA_PM25_BC.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HAQES_NA_PM25_OC_CENSUS v1 - This product provides HAQES 3-hourly ensemble mean surface PM2.5 Organic Carbon concentration at the census level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/HAQES_NA_PM25_OC_CENSUS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HAQES_NA_PM25_OC_COUNTY v1 - This product provides HAQES 3-hourly ensemble mean surface PM2.5 Organic Carbon concentration at the county level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/HAQES_NA_PM25_OC_COUNTY.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HAQES_NA_PM25_OC v1 - This product provides HAQES 3-hourly ensemble mean surface PM2.5 Organic Carbon concentration over the continental United States (CONUS) and surrounding regions. The data is mapped on Lambert projection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/HAQES_NA_PM25_OC.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HAQES_NA_PM25_TOT_CENSUS v1 - This product provides HAQES 3-hourly ensemble mean surface total PM2.5 concentration at the census level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/HAQES_NA_PM25_TOT_CENSUS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HAQES_NA_PM25_TOT_COUNTY v1 - This product provides HAQES 3-hourly ensemble mean surface total PM2.5 concentration at the county level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQES_NA_PM25_TOT_COUNTY.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HAQES_NA_PM25_TOT v1 - This product provides HAQES 3-hourly ensemble mean surface total PM2.5 concentration over the continental United States (CONUS) and surrounding regions. The data is mapped on Lambert projection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/HAQES_NA_PM25_TOT.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MERRA2_CNN_HAQAST_PM25 v1 - This product provides MERRA-2 bias-corrected global hourly surface total PM2.5 mass concentration with the same horizontal spatial resolution as MERRA-2, covering a temporal range from 2000 to 2024. It is derived using a machine learning (ML) approach with a convolutional neural network (CNN) method and is specifically developed for the NASA Health and Air Quality Applied Sciences Team (HAQAST).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/MERRA2_CNN_HAQAST_PM25.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SFC_NITROGEN_DIOXIDE_CONC v1 - The Nitrogen Dioxide Surface-Level Annual Average Concentrations Product (SFC_NITROGEN_DIOXIDE_CONC) contains estimated global NO2 surface values derived using a Land Use Regression (LUR) model (based on 5220 NO2 monitors in 58 countries and land use variables) for the years 2010-2012. NO2 column densities from the Ozone Monitoring Instrument and MERRA-2 scale the concentrations to other years between 1990 and 2020.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/HAQAST/SFC_NITROGEN_DIOXIDE_CONC.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-haqast","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA High Energy Astrophysics Mission Data","Description":"NASA data for high energy astrophysics (generally x-ray and gamma-ray domains) is made available here by the High Energy Astrophysics Science Archive Research Center. The HEASARC hosts the full data archives of over 30 different missions spanning 50 years. The data archive for each mission will contain a range of data types from spacecraft housekeeping and raw photon event list data up to high level science-ready products such as images, light curves (time series), and energy spectra.\n<br/><br/>\nThis is a relatively modest total data volume but contains significant complexity and heterogeneity among the different missions. Data provided here are stored in the Flexible Image Transport System ([FITS](https://heasarc.gsfc.nasa.gov/docs/heasarc/fits.html)) format common in astronomy. Higher level products are further defined to be consistent between missions following [data model standards](https://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/ofwg_recomm.html) agreed by the community and maintained by the HEASARC. Analysis of these data may require software also provided by HEASARC, the [HEASoft package](https://heasarc.gsfc.nasa.gov/docs/software/lheasoft/), consisting of tools generic to all FITS data, generic to all HEASARC-compliant data, and/or specific to individual missions as appropriate. Some missions provide standard science-ready data products, while others provide low-level data types and software to generate science-ready products from them. See the links for each mission for more information on how to use the data.\n<br/><br/>\nThe [HEASARC Website](https://heasarc.gsfc.nasa.gov/) also has archive browsing tools where you can query for observations corresponding to temporal and spatial constraints among others. These tools will ultimately point to files located on the archive by giving a URL beginning with the path https://heasarc.gsfc.nasa.gov/FTP/. The data that are provided in the ODR follow the same structure, so when our tools give an https access URL, a user can simply swap in s3://nasa-heasarc/ for the first part of that URL and get a cloud URI. Note also that some selections have been made to what has been copied to the ODR, while the HEASARC archive itself remains the definitive and legacy source for the complete datasets.\n<br/><br/>\nThe HEASARC also provides user support, both generic and mission-specific, through a set of email helpdesks available through the website's [Feedback page](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback).\n","Documentation":"The [HEASARC Website](https://heasarc.gsfc.nasa.gov/)","Contact":"The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback)","ManagedBy":"The [HEASARC](https://heasarc.gsfc.nasa.gov/)","UpdateFrequency":"Various.","Tags":["archives","astronomy","aws-pds","datacenter","imaging","satellite imagery","x-ray"],"License":"See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasarc/data_policy.html)","Citation":"See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasarc/data_policy.html)","Resources":[{"Description":"The [Ariel5 Mission](https://heasarc.gsfc.nasa.gov/docs/ariel5/ariel5.html) Data Archive.  Total size < 1 GB.","ARN":"arn:aws:s3:::nasa-heasarc/ariel5/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [ASCA Mission](https://heasarc.gsfc.nasa.gov/docs/asca/asca.html) Data Archive. More information available at [the mission support website](https://heasarc.gsfc.nasa.gov/docs/halosat/halosat.html). Total size of 1.3 TB.","ARN":"arn:aws:s3:::nasa-heasarc/asca/data/rev2","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [BBXRT Mission](https://heasarc.gsfc.nasa.gov/docs/bbxrt/bbxrt.html) Data Archive.  Total size of 1 GB.","ARN":"arn:aws:s3:::nasa-heasarc/bbxrt","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"[Chandra Mission](https://heasarc.gsfc.nasa.gov/docs/chandra/chandra.html) Data Archive supported by the [Chandra X-ray Observatory](https://chandra.harvard.edu/) and the [Chandra X-ray Center](https://cxc.harvard.edu/) in addition to the HEASARC.  Total size > 3.7 TB.","ARN":"arn:aws:s3:::nasa-heasarc/chandra/data/byobsid","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [Compton Mission](https://heasarc.gsfc.nasa.gov/docs/cgro/cgro.html). More information available at [the mission support website](https://heasarc.gsfc.nasa.gov/docs/cgro/). Total size of 207 GB.","ARN":"arn:aws:s3:::nasa-heasarc/compton/data","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [COPERNICUS Mission](https://heasarc.gsfc.nasa.gov/docs/copernicus/copernicus.html) Data Archive.  Total size < 1 GB.","ARN":"arn:aws:s3:::nasa-heasarc/copernicus/data/uclrawfits","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [COS-B](https://heasarc.gsfc.nasa.gov/docs/cosb/cosb.html) Mission Data Archive. Total size  < 1 GB.","ARN":"arn:aws:s3:::nasa-heasarc/cosb/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [DXS Mission](https://heasarc.gsfc.nasa.gov/docs/dxs/dxs.html) Data Archive. Total size  < 1 GB.","ARN":"arn:aws:s3:::nasa-heasarc/dxs/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [The Einstein Observatory (aka HEAO-2)](https://heasarc.gsfc.nasa.gov/docs/einstein/heao2.html) Data Archive. Total size 15 GB.","ARN":"arn:aws:s3:::nasa-heasarc/einstein/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [The EXOSAT Observatory](https://heasarc.gsfc.nasa.gov/docs/exosat/exosat.html) Data Archive. Total size 80 GB.","ARN":"arn:aws:s3:::nasa-heasarc/exosat/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"[Fermi](https://heasarc.gsfc.nasa.gov/docs/heasarc/missions/fermi.html) LAT weekly Data Products as well as GBM bursts and triggers.  (The full mission data are archived at HEASARC, though only the most generically useful subset of them are provided here.)  Total size 2.3 TB.","ARN":"arn:aws:s3:::nasa-heasarc/fermi/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [Ginga Mission](https://heasarc.gsfc.nasa.gov/docs/ginga/ginga.html) Data Archive. Total size 19 GB.","ARN":"arn:aws:s3:::nasa-heasarc/ginga/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [HaloSat Mission](https://heasarc.gsfc.nasa.gov/docs/halosat/halosat.html) Data Archive.  More information available at [the mission support website](https://heasarc.gsfc.nasa.gov/docs/halosat/halosat.html). Total size 3.5 GB.","ARN":"arn:aws:s3:::nasa-heasarc/halosat/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [HEAO-1](https://heasarc.gsfc.nasa.gov/docs/heao1/heao1.html) Mission Data Archive. Total size 8.4 GB.","ARN":"arn:aws:s3:::nasa-heasarc/heao1/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [HETE-2](https://heasarc.gsfc.nasa.gov/docs/hete2/hete2.html) Mission Data Archive. Total size 287 GB.","ARN":"arn:aws:s3:::nasa-heasarc/hete2/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [Hitomi Mission](https://heasarc.gsfc.nasa.gov/docs/hitomi/index.html) Data Archive. Total size 115 GB.","ARN":"arn:aws:s3:::nasa-heasarc/hitomi/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [NICER Mission](https://heasarc.gsfc.nasa.gov/docs/heasarc/missions/nicer.html) Data Archive.  For more information, see the [NICER Science Center](https://heasarc.gsfc.nasa.gov/docs/nicer/). Total size > 17 TB.","ARN":"arn:aws:s3:::nasa-heasarc/nicer/data/obs/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [NuSTAR Mission](https://heasarc.gsfc.nasa.gov/docs/heasarc/missions/nustar.html) Data Archive.  For more information, see the NuSTAR pages [at HEASARC](https://heasarc.gsfc.nasa.gov/docs/nustar/) or [at CalTech](https://www.nustar.caltech.edu/).  Total size > 3 TB.","ARN":"arn:aws:s3:::nasa-heasarc/nustar/data/obs/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [OSO-8 Mission](https://heasarc.gsfc.nasa.gov/docs/oso8/oso8.html) Data Archive. Total size 5 GB.","ARN":"arn:aws:s3:::nasa-heasarc/oso8/data/gcxse/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [ROSAT Mission](https://heasarc.gsfc.nasa.gov/docs/rosat/rosat.html) Data Archive.  For more information, see the website of the [ROSAT Guest Observer Facility](https://heasarc.gsfc.nasa.gov/docs/rosat/rosgof.html). Total size 209 GB.","ARN":"arn:aws:s3:::nasa-heasarc/rosat/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [RXTE Mission](https://heasarc.gsfc.nasa.gov/docs/xte/rxte.html) Data Archive. For more information, see the website of [the RXTE Guest Observer Facility](https://heasarc.gsfc.nasa.gov/docs/xte/xte_1st.html). Total size 5.4 TB.","ARN":"arn:aws:s3:::nasa-heasarc/xte/data/archive/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [SAS-2 Mission](https://heasarc.gsfc.nasa.gov/docs/sas2/sas2.html) Data Archive. Total size  < 1 GB.","ARN":"arn:aws:s3:::nasa-heasarc/sas2/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [BeppoSAX Mission](https://heasarc.gsfc.nasa.gov/docs/sax/sax.html) Data Archive.  More information available at [the mission support website](https://heasarc.gsfc.nasa.gov/docs/sax/saxgof.html). Total size 293 GB.","ARN":"arn:aws:s3:::nasa-heasarc/sax/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [SRG Mission](https://heasarc.gsfc.nasa.gov/docs/heasarc/missions/srg.html) eROSITA Instrument Data Archive. More information available at [the eROSITA support site](https://heasarc.gsfc.nasa.gov/docs/srg/erosita/). Total size > 3 TB.","ARN":"arn:aws:s3:::nasa-heasarc/srg/data/erosita/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [Suzaku Mission](https://heasarc.gsfc.nasa.gov/docs/astroe/astroe2.html) Data Archive.  For more information, see the website of the [Suzaku/Astro-E2 Guest Observer Facility](https://heasarc.gsfc.nasa.gov/docs/suzaku/astroegof.html). Total size 5.4 TB.","ARN":"arn:aws:s3:::nasa-heasarc/suzaku/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"[Swift Mission](https://heasarc.gsfc.nasa.gov/docs/heasarc/missions/swift.html) Data Archive. For more information, see the website of the [Swift Science Center](http://swift.gsfc.nasa.gov/). Total size > 12 TB.","ARN":"arn:aws:s3:::nasa-heasarc/swift/data/obs/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [VELA 5B Mission](https://heasarc.gsfc.nasa.gov/docs/vela5b/vela5b.html) Data Archive.  Total size < 1 GB.","ARN":"arn:aws:s3:::nasa-heasarc/vela5b/data/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [WASS Mission](https://heasarc.gsfc.nasa.gov/docs/rockets/wass.html) Data Archive.  Total size < 1 GB.","ARN":"arn:aws:s3:::nasa-heasarc/wass/data/maps/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"[XMM-Newton  Mission](https://heasarc.gsfc.nasa.gov/docs/xmm/xmm.html) Data Archive.  For more information, see the website of [the XMM-Newton Guest Observer Facility](https://heasarc.gsfc.nasa.gov/docs/xmm/xmmgof.html).  Total size > 10 TB.","ARN":"arn:aws:s3:::nasa-heasarc/xmm/data/rev0/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [XQC Mission](https://heasarc.gsfc.nasa.gov/docs/rockets/xqc.html) Data Archive.  Total size < 1 GB.","ARN":"arn:aws:s3:::nasa-heasarc/xqc/data/spectra/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"The [XRISM Mission](https://heasarc.gsfc.nasa.gov/docs/heasarc/missions/xrism.html) Data Archive.  Total size > 1 TB.","ARN":"arn:aws:s3:::nasa-heasarc/xrism/data/obs/","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"HEASARC Cloud access page","URL":"https://heasarc.gsfc.nasa.gov/docs/archive/cloud.html","AuthorName":"NASA/HEASARC","AuthorURL":"https://heasarc.gsfc.nasa.gov/"}],"Tools & Applications":[{"Title":"HEASoft","URL":"https://heasarc.gsfc.nasa.gov/docs/software/lheasoft/","AuthorName":"NASA/HEASARC","AuthorURL":"https://heasarc.gsfc.nasa.gov/"}],"Publications":[{"Title":"HEASARC Bibliography","URL":"https://heasarc.gsfc.nasa.gov/docs/heasarc/biblio/pubs/","AuthorName":"Various; curated by HEASARC","AuthorURL":"https://heasarc.gsfc.nasa.gov/"}]},"RegistryEntryAdded":"2023-08-04","RegistryEntryLastModified":"2026-07-20","Slug":"nasa-heasarc","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA HLS Project","Description":"The HLSL30 V1.5 data product was decommissioned on January 4, 2022. Users are encouraged to use the improved [HLSL30 V2](https://doi.org/10.5067/HLS/HLSL30.002) data product. The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment. The HLSL30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Landsat 8 OLI data products. The [HLSS30](https://doi.org/10.5067/HLS/HLSS30.015) and HLSL30 products are gridded to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) tiling system and thus are “stackable” for time series analysis. The HLSL30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate file. There are 10 bands included in the HLSL30 product along with one quality assessment (QA) band and four angle bands. For a more detailed description of the individual bands provided in the HLSL30 product, please see the User Guide. The HLS project is funded by NASA’s Satellite Needs Working Group (SNWG) which provides data products developed to meet the needs of stakeholders from US government agencies. Known Issues: HLSL30.015 products are based on input Landsat 8 L1TP (precision terrain corrected) products, which require identification of ground control targets for precision geometric correction. Images where ground control is not available (e.g., very cloudy images) cannot be processed to L1TP and are not included in the HLSL30 dataset. * Interruptions in data service occurred during a restaging of backlogged data between June 1 and June 15, 2021 for both HLSS30 and HLSL30 version 1.5 data products. During this time period increased errors in the processing workflow resulted in a significant number of data ingestion failures and thus, significant gaps in data availability. Given the pending release of the version 2.0, science quality HLS products, these missing data will not be filled for version 1.5. Users of the provisional version 1.5 products should be aware of the significant data gap in this two week window. The version 2.0 products will incorporate these data back into the archive. If you have any feedback or questions on the data please contact [Customer Services](https://www.earthdata.nasa.gov/centers/lp-daac/contact) or join our HLS conversion on the [Earthdata Forum](https://forum.earthdata.nasa.gov/viewtopic.php?f=7&t=618&hilit=hls&sid=95750d868b6448e0f4360a1473def234).\n<br>\n\n#### HLSL30_VI\nThe Harmonized Landsat and Sentinel-2 (HLS) project provides consistent data products from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30 meter (m) spatial resolution. The HLSL30 Vegetation Indices (HLSL30_VI) product is derived from Landsat 8 and Landsat 9 OLI data products. Vegetation indices combine specific bands of satellite data to quantify various aspects of vegetation. Analysis of vegetation indices allows for tracking changes in vegetation over time, identifying areas of stress or deforestation, and assessing crop health. Vegetation indices provide a reliable and efficient means of understanding the complex dynamics of vegetation health. The HLSS30_VI and HLSL30_VI products are gridded to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) tiling system and thus are “stackable” for time series analysis. The HLSL30_VI product is provided in Cloud Optimized GeoTIFF (COG) format, and each variable is distributed as a separate file. Nine indicators of vegetation health are included in the HLSL30_VI product: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Triangular Vegetation Index (TVI). See the User Guide for a more detailed description of the individual vegetation health variables provided in the HLSL30_VI product. The HLS project is funded by NASA’s Satellite Needs Working Group (SNWG) which provides data products developed to meet the needs of stakeholders from US government agencies.\n<br>\n\n#### HLSS30\nThe HLSS30 V1.5 data product was decommissioned on January 4, 2022. Users are encouraged to use the improved [HLSS30 V2](https://doi.org/10.5067/HLS/HLSS30.002) data product. The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard the European Union’s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2-3 days at 30 meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment. The HLSS30 product provides 30 m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A and Sentinel-2B MSI data products. The HLSS30 and [HLSL30](https://doi.org/10.5067/HLS/HLSL30.015) products are gridded to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) tiling system and thus are “stackable” for time series analysis. The HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. For a more detailed description of the individual bands provided in the HLSS30 product, please see the User Guide. The HLS project is funded by NASA’s Satellite Needs Working Group (SNWG) which provides data products developed to meet the needs of stakeholders from US government agencies. Known Issues: Interruptions in data service occurred during a restaging of backlogged data between June 1 and June 15, 2021 for both HLSS30 and HLSL30 version 1.5 data products. During this time period increased errors in the processing workflow resulted in a significant number of data ingestion failures and thus, significant gaps in data availability. Given the pending release of the version 2.0, science quality HLS products, these missing data will not be filled for version 1.5. Users of the provisional version 1.5 products should be aware of the significant data gap in this two week window. The version 2.0 products will incorporate these data back into the archive. If you have any feedback or questions on the data please contact [Customer Services](https://www.earthdata.nasa.gov/centers/lp-daac/contact) or join our HLS conversion on the [Earthdata Forum](https://forum.earthdata.nasa.gov/viewtopic.php?f=7&t=618&hilit=hls&sid=95750d868b6448e0f4360a1473def234).\n<br>\n\n#### HLSS30\nThe Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment. The HLSS30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A, Sentinel-2B, and Sentinel-2C MSI data products. The HLSS30 and [HLSL30](https://doi.org/10.5067/HLS/HLSL30.002) products are gridded to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) tiling system and thus are “stackable” for time series analysis. The HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. See the User Guide for a more detailed description of the individual bands provided in the HLSS30 product. The HLS project is funded by NASA’s Satellite Needs Working Group (SNWG) which provides data products developed to meet the needs of stakeholders from US government agencies. Known Issues: Unrealistically high aerosol and low surface reflectance over bright areas: The atmospheric correction over bright targets occasionally retrieves unrealistically high aerosol and thus makes the surface reflectance too low. High aerosol retrievals, both false high aerosol and realistically high aerosol, are masked when quality bits 6 and 7 are both set to 1 (see Table 9 in the [User Guide](https://lpdaac.usgs.gov/documents/1698/HLS_User_Guide_V2.pdf)); the corresponding spectral data should be discarded from analysis. * Issues over high latitudes: For scenes greater than or equal to 80 degrees north, multiple overpasses can be gridded into a single MGRS tile resulting in an L30 granule with data sensed at two different times. In this same area, it is also possible that Landsat overpasses that should be gridded into a single MGRS tile are actually written as separate data files. Finally, for scenes with a latitude greater than or equal to 65 degrees north, ascending Landsat scenes may have a slightly higher error in the BRDF correction because the algorithm is calibrated using descending scenes. * Fmask omission errors: There are known issues regarding the Fmask band of this data product that impacts HLSL30 data prior to April of 2022. The HLS Fmask data band may have omission errors in water detection for cases where water detection using spectral data alone is difficult, and omission and commission errors in cloud shadow detection for areas with great topographic relief. This issue does not impact other bands in the dataset. * Inconsistent snow surface reflectance between Landsat and Sentinel-2: The HLS snow surface reflectance can be highly inconsistent between Landsat and Sentinel-2. When assessed on same-day acquisitions from Landsat and Sentinel-2, Landsat reflectance is generally higher than Sentinel-2 reflectance in the visible bands. * Unrealistically high snow surface reflectance in the visible bands: By design, the Land Surface Reflectance Code (LaSRC) atmospheric correction does not attempt aerosol retrieval over snow; instead, a default aerosol optical thickness (AOT) is used to drive the snow surface reflectance. If the snow detection fails, the full LaSRC is used in both AOT retrieval and surface reflectance derivation over snow, which produces surface reflectance values as high as 1.6 in the visible bands. This is a common problem for spring images at high latitudes. * Unrealistically low surface reflectance surrounding snow/ice: Related to the above, the AOT retrieval over snow/ice is generally too high. When this artificially high AOT is used to derive the surface reflectance of the neighboring non-snow pixels, very low surface reflectance will result. These pixels will appear very dark in the visible bands. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. In Figure 1, the pixels in front of the glaciers have surface reflectance values that are too low. * Unrealistically low reflectance surrounding clouds: Like for snow, the HLS atmospheric correction does not attempt aerosol retrieval over clouds and a default AOT is used instead. But if the cloud detection fails, an artificially high AOT will be retrieved over clouds. If the high AOT is used to derive the surface reflectance of the neighboring cloud-free pixels, very low surface reflectance values will result. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. * Unusually low reflectance around other bright land targets: While the HLS atmospheric correction retrieves AOT over non-cloud, non-snow bright pixels, the retrieved AOT over bright targets can be unrealistically high in some cases, similar to cloud or snow. If this unrealistically high AOT is used to derive the surface reflectance of the neighboring pixels, very low surface reflectance values can result as shown in Figure 2. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. These types of bright targets are mostly man-made, such as buildings, parking lots, and roads. * Dark plumes over water: The HLS atmospheric correction does not attempt aerosol retrieval over water. For water pixels, the AOT retrieved from the nearest land pixels is used to derive the surface reflectance, but if the retrieval is incorrect, e.g. from a cloud pixel, this high AOT will create dark stripes over water, as shown in Figure 3. This happens more often over large water bodies, such as lakes and bays, than over narrow rivers. * Landsat WRS-2 Path/Row boundary in L30 reflectance: HLS performs atmospheric correction on Landsat Level 1 images in the original Worldwide Reference System 2 (WRS2) path/row before the derived surface reflectance is reprojected into Military Grid Reference System (MGRS) tiles. If a WRS-2 Landsat image is very cloudy, the AOT from a few remaining clear pixels might be used for the atmospheric correction of the entire image. The AOT that is used can be quite different from the value for the adjacent row in the same path, which results in an artificial abrupt change from one row to the next, as shown in Figure 4. This occurrence is very rare. * Landsat WRS2 path/row boundary in cloud masks: The cloud mask algorithm Fmask creates mask labels by applying thresholds to the histograms of some metrics for each path/row independently. If two adjacent rows in the same path have distinct distributions within the metrics, abrupt changes in masking patterns can appear across the row boundary, as shown in Figure 5. This occurrence is very rare. * Fmask configuration was deficient for 2-3 months in 2021: The HLS installation of Fmask failed to include auxiliary digital elevation model (DEM) and European Space Agency (ESA) Global Surface Water Occurrence data for a 2-3 month run in 2021. This impacted the masking results over water and in mountainous regions. * The reflectance “scale_factor” and “offset” for some L30 and S30 bands were not set: The HLS reflectance scaling factor is 0.0001 and offset is 0. However, this information was not set in the Cloud Optimized GeoTIFF (COG) files of some bands for a small number of granules. The lack of this information creates a problem for automatic conversion of the reflectance data, requiring explicit scaling in applications. The problem has been corrected, but the affected granules have not been reprocessed. * Incomplete map projection information: For a time, HLS imagery was produced with an incomplete coordinate reference system (CRS). The metadata contains the Universal Transverse Mercator (UTM) zone and coordinates necessary to geolocate pixels within the image but might not be in a standard form, especially for granules produced early in the HLS mission. As a result, an error will occur in certain image processing packages due to the incomplete CRS. The simplest solution is to update to the latest version of Geospatial Data Abstraction Library (GDAL) and/or rasterio, which use the available information without error. * False northing of 10^7 for the L30 angle data: The L30 and S30 products do not use a false northing for the UTM projection, and the angle data are supposed to follow the same convention. However, the L30 angle data incorrectly uses a false northing of 10^7. There is no problem with the angle data itself, but the false northing needs to be set to 0 for it to be aligned with the reflectance. * L30 from Landsat L1GT scenes: Landsat L1GT scenes were not intended for HLS due to their poor geolocation. However, some scenes made it through screening for a short period of HLS production. L1GT L30 scenes mainly consist of extensive cloud or snow that can be eliminated using the Fmask quality bits layer. Users can also identify an L1GT-originated L30 granule by examining the HLS cmr.xml metadata file. * The UTC dates in the L30/S30 filenames may not be the local dates: UTC dates are used by ESA and the U.S. Geological Survey (USGS) in naming their Level 1 images, and HLS processing retains this information to name the L30 and S30 products. Landsat and Sentinel-2 overpass eastern Australia and New Zealand around 10AM local solar time, but this area is in either UTC+10:00 or +11:00 zone; therefore, the UTC time for some orbits is in fact near the end of the preceding UTC day. For example, HLS.S30.T59HQS.2016117T221552.v2.0 was acquired in the 22nd hour of day 117 of year 2016 in UTC, but the time was 10:15:52 of day 118 locally. Approximately 100 minutes later HLS.S30.T56JML.2016117T235252.v2.0 was acquired in the next orbit in eastern Australia. This issue also occurs for Landsat. For example, HLS.L30.T59HQS.2016117T221209.v2.0 was acquired on the same day as the first S30 example given above, but both on day 118 of 2016 locally. Adding to the confusion for L30, in the same region, Landsat 8 and 9 can each overpass once in one of the two adjacent WRS-2 Paths (91/92/93) over a two-day period on a local calendar, but based on UTC time, the two overpasses can appear to be on the same day. For example, in the following seemingly same-day pair, the second L30 is actually for day 168 locally: HLS.L30.T55GCN.2023167T000407.v2.0 HLS.L30.T55GCN.2023167T235747.v2.0 Bear in mind, the date peculiarity for the data occurs when the overpass time is during the late hours of a UTC day. * The atmospheric ancillary data from the wrong date was used for LaSRC: Related to the above, for eastern Australia and New Zealand, L30 and S30 surface reflectance on certain days was created using the atmospheric ancillary data from a date that was one day too early. The exact geographic extent of the affected HLS products and the impact on the surface reflectance quality are under investigation. Practice caution when using data with overpass times during the late hours of a UTC day. * Duplicates in L30: The Landsat 9 acquisitions from October 2021 to March 2023 in Landsat Collection 2 were reprocessed by USGS in March 2023. This reprocessing updated the overpass time by a fraction of a second for some scenes. Since HLS uses overpass time as part of the L30 filename, the older L30 granules were not automatically overwritten due to the different filenames. For example, the first L30 granule in the following pair originated from an older version of L1TP of Landsat 9 with the second granule originating from the reprocessed version. HLS.L30.T11SLC.2022166T182646.v2.0 HLS.L30.T11SLC.2022166T182645.v2.0 There are other causes of duplicate L30 granules, but the overall number of duplicates is very small. * Poor Geolocation: A large amount of granules that were processed for May through July 2023 were created with L1GT input scenes which were deemed undesirable due to a poor geolocation issue. These granules were removed from the archive. (see the full list of removed [granules](https://lpdaac.usgs.gov/documents/2161/L30_L1GT_granules_May_July_2023.csv))\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/centers/lp-daac","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","cog","elevation","geospatial","ice","satellite imagery","stac"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"HLSL30 v1.5 - The HLSL30 V1.5 data product was decommissioned on January 4, 2022. Users are encouraged to use the improved [HLSL30 V2](https://doi.org/10.5067/HLS/HLSL30.002) data product.","ARN":"arn:aws:s3:::lp-prod-protected/HLSL30.020","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HLSL30_VI v2.0 - The Harmonized Landsat and Sentinel-2 (HLS) project provides consistent data products from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30 meter (m) spatial resolution.","ARN":"arn:aws:s3:::lp-sit-protected/HLSL30_VI.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.sit.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HLSS30 v1.5 - The HLSS30 V1.5 data product was decommissioned on January 4, 2022. Users are encouraged to use the improved [HLSS30 V2](https://doi.org/10.5067/HLS/HLSS30.002) data product.","ARN":"arn:aws:s3:::lp-prod-protected/HLSS30.015","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HLSS30 v2.0 - The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution.","ARN":"arn:aws:s3:::lp-prod-protected/HLSS30.020","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-hls","Sources":["awslabs-open-data-registry"]}
{"Name":"HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0","Description":"The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from a virtual constellation of satellite sensors. The Operational Land Imager (OLI) is housed aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites, while the Multi-Spectral Instrument (MSI) is mounted aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment.\n\nThe HLSL30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Landsat 8/9 OLI data products. The [HLSS30](https://doi.org/10.5067/HLS/HLSS30.002) and HLSL30 products are gridded to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) tiling system and thus are “stackable” for time series analysis.\n\nThe HLSL30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate file. There are 11 bands included in the HLSL30 product along with one quality assessment (QA) band and four angle bands. See the User Guide for a more detailed description of the individual bands provided in the HLSL30 product.\n\nKnown Issues\n\n* Unrealistically high aerosol and low surface reflectance over bright areas: The atmospheric correction over bright targets occasionally retrieves unrealistically high aerosol and thus makes the surface reflectance too low. High aerosol retrievals, both false high aerosol and realistically high aerosol, are masked when quality bits 6 and 7 are both set to 1 (see Table 9 in the [User Guide](https://lpdaac.usgs.gov/documents/1698/HLS_User_Guide_V2.pdf)); the corresponding spectral data should be discarded from analysis.\n\n* Issues over high latitudes: For scenes greater than or equal to 80 degrees north, multiple overpasses can be gridded into a single MGRS tile resulting in an L30 granule with data sensed at two different times. In this same area, it is also possible that Landsat overpasses that should be gridded into a single MGRS tile are actually written as separate data files. Finally, for scenes with a latitude greater than or equal to 65 degrees north, ascending Landsat scenes may have a slightly higher error in the BRDF correction because the algorithm is calibrated using descending scenes.\n\n* Fmask omission errors: There are known issues regarding the Fmask band of this data product that impacts HLSL30 data prior to April of 2022. The HLS Fmask data band may have omission errors in water detection for cases where water detection using spectral data alone is difficult, and omission and commission errors in cloud shadow detection for areas with great topographic relief. This issue does not impact other bands in the dataset.\n\n* Inconsistent snow surface reflectance between Landsat and Sentinel-2: The HLS snow surface reflectance can be highly inconsistent between Landsat and Sentinel-2. When assessed on same-day acquisitions from Landsat and Sentinel-2, Landsat reflectance is generally higher than Sentinel-2 reflectance in the visible bands.\n\n* Unrealistically high snow surface reflectance in the visible bands: By design, the Land Surface Reflectance Code (LaSRC) atmospheric correction does not attempt aerosol retrieval over snow; instead, a default aerosol optical thickness (AOT) is used to drive the snow surface reflectance. If the snow detection fails, the full LaSRC is used in both AOT retrieval and surface reflectance derivation over snow, which produces surface reflectance values as high as 1.6 in the visible bands. This is a common problem for spring images at high latitudes.\n\n* Unrealistically low surface reflectance surrounding snow/ice: Related to the above, the AOT retrieval over snow/ice is generally too high. When this artificially high AOT is used to derive the surface reflectance of the neighboring non-snow pixels, very low surface reflectance will result. These pixels will appear very dark in the visible bands. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used.\n\n* Unrealistically low reflectance surrounding clouds: Like for snow, the HLS atmospheric correction does not attempt aerosol retrieval over clouds and a default AOT is used instead. But if the cloud detection fails, an artificially high AOT will be retrieved over clouds. If the high AOT is used to derive the surface reflectance of the neighboring cloud-free pixels, very low surface reflectance values will result. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. \n\n* Unusually low reflectance around other bright land targets: While the HLS atmospheric correction retrieves AOT over non-cloud, non-snow bright pixels, the retrieved AOT over bright targets can be unrealistically high in some cases, similar to cloud or snow. If this unrealistically high AOT is used to derive the surface reflectance of the neighboring pixels, very low surface reflectance values can result as shown in Figure 2. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. These types of bright targets are mostly man-made, such as buildings, parking lots, and roads.  \n\n* Dark plumes over water: The HLS atmospheric correction does not attempt aerosol retrieval over water. For water pixels, the AOT retrieved from the nearest land pixels is used to derive the surface reflectance, but if the retrieval is incorrect, e.g. from a cloud pixel, this high AOT will create dark stripes over water, as shown in Figure 3. This happens more often over large water bodies, such as lakes and bays, than over narrow rivers.  \n\n* Landsat WRS-2 Path/Row boundary in L30 reflectance: HLS performs atmospheric correction on Landsat Level 1 images in the original Worldwide Reference System 2 (WRS2) path/row before the derived surface reflectance is reprojected into Military Grid Reference System (MGRS) tiles. If a WRS-2 Landsat image is very cloudy, the AOT from a few remaining clear pixels might be used for the atmospheric correction of the entire image. The AOT that is used can be quite different from the value for the adjacent row in the same path, which results in an artificial abrupt change from one row to the next, as shown in Figure 4. This occurrence is very rare.  \n \n* Landsat WRS2 path/row boundary in cloud masks: The cloud mask algorithm Fmask creates mask labels by applying thresholds to the histograms of some metrics for each path/row independently. If two adjacent rows in the same path have distinct distributions within the metrics, abrupt changes in masking patterns can appear across the row boundary, as shown in Figure 5. This occurrence is very rare.  \n\n* Fmask configuration was deficient for 2-3 months in 2021: The HLS installation of Fmask failed to include auxiliary digital elevation model (DEM) and European Space Agency (ESA) Global Surface Water Occurrence data for a 2-3 month run in 2021. This impacted the masking results over water and in mountainous regions. \n\n* The reflectance “scale_factor” and “offset” for some L30 and S30 bands were not set: The HLS reflectance scaling factor is 0.0001 and offset is 0. However, this information was not set in the Cloud Optimized GeoTIFF (COG) files of some bands for a small number of granules. The lack of this information creates a problem for automatic conversion of the reflectance data, requiring explicit scaling in applications. The problem has been corrected, but the affected granules have not been reprocessed. \n\n* Incomplete map projection information: For a time, HLS imagery was produced with an incomplete coordinate reference system (CRS). The metadata contains the Universal Transverse Mercator (UTM) zone and coordinates necessary to geolocate pixels within the image but might not be in a standard form, especially for granules produced early in the HLS mission. As a result, an error will occur in certain image processing packages due to the incomplete CRS. The simplest solution is to update to the latest version of Geospatial Data Abstraction Library (GDAL) and/or rasterio, which use the available information without error. \n\n* False northing of 10^7 for the L30 angle data: The L30 and S30 products do not use a false northing for the UTM projection, and the angle data are supposed to follow the same convention. However, the L30 angle data incorrectly uses a false northing of 10^7. There is no problem with the angle data itself, but the false northing needs to be set to 0 for it to be aligned with the reflectance.\n\n* L30 from Landsat L1GT scenes: Landsat L1GT scenes were not intended for HLS due to their poor geolocation. However, some scenes made it through screening for a short period of HLS production. L1GT L30 scenes mainly consist of extensive cloud or snow that can be eliminated using the Fmask quality bits layer. Users can also identify an L1GT-originated L30 granule by examining the HLS cmr.xml metadata file.\n\n* The UTC dates in the L30/S30 filenames may not be the local dates: UTC dates are used by ESA and the U.S. Geological Survey (USGS) in naming their Level 1 images, and HLS processing retains this information to name the L30 and S30 products. Landsat and Sentinel-2 overpass eastern Australia and New Zealand around 10AM local solar time, but this area is in either UTC+10:00 or +11:00 zone; therefore, the UTC time for some orbits is in fact near the end of the preceding UTC day. For example, HLS.S30.T59HQS.2016117T221552.v2.0 was acquired in the 22nd hour of day 117 of year 2016 in UTC, but the time was 10:15:52 of day 118 locally. Approximately 100 minutes later HLS.S30.T56JML.2016117T235252.v2.0 was acquired in the next orbit in eastern Australia. \n\n     This issue also occurs for Landsat. For example, HLS.L30.T59HQS.2016117T221209.v2.0 was acquired on the same day as the first S30 example given above, but both on day 118 of 2016 locally. Adding to the confusion for L30, in the same region, Landsat 8 and 9 can each overpass once in one of the two adjacent WRS-2 Paths (91/92/93) over a two-day period on a local calendar, but based on UTC time, the two overpasses can appear to be on the same day. For example, in the following seemingly same-day pair, the second L30 is actually for day 168 locally:  \n     HLS.L30.T55GCN.2023167T000407.v2.0  \n     HLS.L30.T55GCN.2023167T235747.v2.0  \n     Bear in mind, the date peculiarity for the data occurs when the overpass time is during the late hours of a UTC day. \n\n* The atmospheric ancillary data from the wrong date was used for LaSRC: Related to the above, for eastern Australia and New Zealand, L30 and S30 surface reflectance on certain days was created using the atmospheric ancillary data from a date that was one day too early. The exact geographic extent of the affected HLS products and the impact on the surface reflectance quality are under investigation. Practice caution when using data with overpass times during the late hours of a UTC day.\n\n* Duplicates in L30: The Landsat 9 acquisitions from October 2021 to March 2023 in Landsat Collection 2 were reprocessed by USGS in March 2023. This reprocessing updated the overpass time by a fraction of a second for some scenes. Since HLS uses overpass time as part of the L30 filename, the older L30 granules were not automatically overwritten due to the different filenames. For example, the first L30 granule in the following pair originated from an older version of L1TP of Landsat 9 with the second granule originating from the reprocessed version.    \nHLS.L30.T11SLC.2022166T182646.v2.0  \nHLS.L30.T11SLC.2022166T182645.v2.0  \nThere are other causes of duplicate L30 granules, but the overall number of duplicates is very small.\n\n* Poor Geolocation: A large amount of granules that were processed for May through July 2023 were created with L1GT input scenes which were deemed undesirable due to a poor geolocation issue. These granules were removed from the archive. (see the full list of removed [granules](https://lpdaac.usgs.gov/documents/2161/L30_L1GT_granules_May_July_2023.csv))\n\nImprovements/Changes from Previous Versions\n\n* Aerosol QA bits from the USGS Land Surface Reflectance Code (LaSRC) model output have been added into the Function of Mask (Fmask) data layer. The added two bits indicate the aerosol levels: high, medium, low, and climatology aerosol.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/HLS/HLSL30.002","Contact":"User Services: lpdaac@usgs.gov","ManagedBy":"NASA","UpdateFrequency":"From 2013-04-11 to Ongoing","Tags":["atmosphere","aws-pds","cog","datacenter","earth observation","geospatial","global","ice","land","metadata","orbit","satellite imagery","stac","surface water","tiles","water","xml"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0.","ARN":"arn:aws:s3:::lp-prod-protected/HLSL30.020","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Getting Started with Cloud-Native HLS Data in Python","URL":"https://github.com/nasa/HLS-Data-Resources/blob/main/python/tutorials/HLS_Tutorial.ipynb","AuthorName":"Mahsa Jami, Erik A. Bolch, Cole K. Krehbiel, Aaron M. Friesz, Brianna M. Lind"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-hlsl30","Sources":["awslabs-open-data-registry"]}
{"Name":"HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0","Description":"The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment. \n\nThe HLSS30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A, Sentinel-2B, and Sentinel-2C MSI data products. The HLSS30 and [HLSL30](https://doi.org/10.5067/HLS/HLSL30.002) products are gridded to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) tiling system and thus are “stackable” for time series analysis.\n\nThe HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. See the User Guide for a more detailed description of the individual bands provided in the HLSS30 product.\n\nKnown Issues\n\n* Unrealistically high aerosol and low surface reflectance over bright areas: The atmospheric correction over bright targets occasionally retrieves unrealistically high aerosol and thus makes the surface reflectance too low. High aerosol retrievals, both false high aerosol and realistically high aerosol, are masked when quality bits 6 and 7 are both set to 1 (see Table 9 in the [User Guide](https://lpdaac.usgs.gov/documents/1698/HLS_User_Guide_V2.pdf)); the corresponding spectral data should be discarded from analysis.\n\n* Issues over high latitudes: For scenes greater than or equal to 80 degrees north, multiple overpasses can be gridded into a single MGRS tile resulting in an L30 granule with data sensed at two different times. In this same area, it is also possible that Landsat overpasses that should be gridded into a single MGRS tile are actually written as separate data files. Finally, for scenes with a latitude greater than or equal to 65 degrees north, ascending Landsat scenes may have a slightly higher error in the BRDF correction because the algorithm is calibrated using descending scenes.\n\n* Fmask omission errors: There are known issues regarding the Fmask band of this data product that impacts HLSL30 data prior to April of 2022. The HLS Fmask data band may have omission errors in water detection for cases where water detection using spectral data alone is difficult, and omission and commission errors in cloud shadow detection for areas with great topographic relief. This issue does not impact other bands in the dataset.\n\n* Inconsistent snow surface reflectance between Landsat and Sentinel-2: The HLS snow surface reflectance can be highly inconsistent between Landsat and Sentinel-2. When assessed on same-day acquisitions from Landsat and Sentinel-2, Landsat reflectance is generally higher than Sentinel-2 reflectance in the visible bands.\n\n* Unrealistically high snow surface reflectance in the visible bands: By design, the Land Surface Reflectance Code (LaSRC) atmospheric correction does not attempt aerosol retrieval over snow; instead, a default aerosol optical thickness (AOT) is used to drive the snow surface reflectance. If the snow detection fails, the full LaSRC is used in both AOT retrieval and surface reflectance derivation over snow, which produces surface reflectance values as high as 1.6 in the visible bands. This is a common problem for spring images at high latitudes.\n\n* Unrealistically low surface reflectance surrounding snow/ice: Related to the above, the AOT retrieval over snow/ice is generally too high. When this artificially high AOT is used to derive the surface reflectance of the neighboring non-snow pixels, very low surface reflectance will result. These pixels will appear very dark in the visible bands. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. In Figure 1, the pixels in front of the glaciers have surface reflectance values that are too low.  \n\n* Unrealistically low reflectance surrounding clouds: Like for snow, the HLS atmospheric correction does not attempt aerosol retrieval over clouds and a default AOT is used instead. But if the cloud detection fails, an artificially high AOT will be retrieved over clouds. If the high AOT is used to derive the surface reflectance of the neighboring cloud-free pixels, very low surface reflectance values will result. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. \n\n* Unusually low reflectance around other bright land targets: While the HLS atmospheric correction retrieves AOT over non-cloud, non-snow bright pixels, the retrieved AOT over bright targets can be unrealistically high in some cases, similar to cloud or snow. If this unrealistically high AOT is used to derive the surface reflectance of the neighboring pixels, very low surface reflectance values can result as shown in Figure 2. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. These types of bright targets are mostly man-made, such as buildings, parking lots, and roads.  \n\n* Dark plumes over water: The HLS atmospheric correction does not attempt aerosol retrieval over water. For water pixels, the AOT retrieved from the nearest land pixels is used to derive the surface reflectance, but if the retrieval is incorrect, e.g. from a cloud pixel, this high AOT will create dark stripes over water, as shown in Figure 3. This happens more often over large water bodies, such as lakes and bays, than over narrow rivers.  \n\n* Landsat WRS-2 Path/Row boundary in L30 reflectance: HLS performs atmospheric correction on Landsat Level 1 images in the original Worldwide Reference System 2 (WRS2) path/row before the derived surface reflectance is reprojected into Military Grid Reference System (MGRS) tiles. If a WRS-2 Landsat image is very cloudy, the AOT from a few remaining clear pixels might be used for the atmospheric correction of the entire image. The AOT that is used can be quite different from the value for the adjacent row in the same path, which results in an artificial abrupt change from one row to the next, as shown in Figure 4. This occurrence is very rare.  \n \n* Landsat WRS2 path/row boundary in cloud masks: The cloud mask algorithm Fmask creates mask labels by applying thresholds to the histograms of some metrics for each path/row independently. If two adjacent rows in the same path have distinct distributions within the metrics, abrupt changes in masking patterns can appear across the row boundary, as shown in Figure 5. This occurrence is very rare.  \n\n* Fmask configuration was deficient for 2-3 months in 2021: The HLS installation of Fmask failed to include auxiliary digital elevation model (DEM) and European Space Agency (ESA) Global Surface Water Occurrence data for a 2-3 month run in 2021. This impacted the masking results over water and in mountainous regions. \n\n* The reflectance “scale_factor” and “offset” for some L30 and S30 bands were not set: The HLS reflectance scaling factor is 0.0001 and offset is 0. However, this information was not set in the Cloud Optimized GeoTIFF (COG) files of some bands for a small number of granules. The lack of this information creates a problem for automatic conversion of the reflectance data, requiring explicit scaling in applications. The problem has been corrected, but the affected granules have not been reprocessed. \n\n* Incomplete map projection information: For a time, HLS imagery was produced with an incomplete coordinate reference system (CRS). The metadata contains the Universal Transverse Mercator (UTM) zone and coordinates necessary to geolocate pixels within the image but might not be in a standard form, especially for granules produced early in the HLS mission. As a result, an error will occur in certain image processing packages due to the incomplete CRS. The simplest solution is to update to the latest version of Geospatial Data Abstraction Library (GDAL) and/or rasterio, which use the available information without error. \n\n* False northing of 10^7 for the L30 angle data: The L30 and S30 products do not use a false northing for the UTM projection, and the angle data are supposed to follow the same convention. However, the L30 angle data incorrectly uses a false northing of 10^7. There is no problem with the angle data itself, but the false northing needs to be set to 0 for it to be aligned with the reflectance.\n\n* L30 from Landsat L1GT scenes: Landsat L1GT scenes were not intended for HLS due to their poor geolocation. However, some scenes made it through screening for a short period of HLS production. L1GT L30 scenes mainly consist of extensive cloud or snow that can be eliminated using the Fmask quality bits layer. Users can also identify an L1GT-originated L30 granule by examining the HLS cmr.xml metadata file.\n\n* The UTC dates in the L30/S30 filenames may not be the local dates: UTC dates are used by ESA and the U.S. Geological Survey (USGS) in naming their Level 1 images, and HLS processing retains this information to name the L30 and S30 products. Landsat and Sentinel-2 overpass eastern Australia and New Zealand around 10AM local solar time, but this area is in either UTC+10:00 or +11:00 zone; therefore, the UTC time for some orbits is in fact near the end of the preceding UTC day. For example, HLS.S30.T59HQS.2016117T221552.v2.0 was acquired in the 22nd hour of day 117 of year 2016 in UTC, but the time was 10:15:52 of day 118 locally. Approximately 100 minutes later HLS.S30.T56JML.2016117T235252.v2.0 was acquired in the next orbit in eastern Australia.  \n\n     This issue also occurs for Landsat. For example, HLS.L30.T59HQS.2016117T221209.v2.0 was acquired on the same day as the first S30 example given above, but both on day 118 of 2016 locally. Adding to the confusion for L30, in the same region, Landsat 8 and 9 can each overpass once in one of the two adjacent WRS-2 Paths (91/92/93) over a two-day period on a local calendar, but based on UTC time, the two overpasses can appear to be on the same day. For example, in the following seemingly same-day pair, the second L30 is actually for day 168 locally:  \n     HLS.L30.T55GCN.2023167T000407.v2.0  \n     HLS.L30.T55GCN.2023167T235747.v2.0  \n     Bear in mind, the date peculiarity for the data occurs when the overpass time is during the late hours of a UTC day.  \n\n* The atmospheric ancillary data from the wrong date was used for LaSRC: Related to the above, for eastern Australia and New Zealand, L30 and S30 surface reflectance on certain days was created using the atmospheric ancillary data from a date that was one day too early. The exact geographic extent of the affected HLS products and the impact on the surface reflectance quality are under investigation. Practice caution when using data with overpass times during the late hours of a UTC day.\n\n* Duplicates in L30: The Landsat 9 acquisitions from October 2021 to March 2023 in Landsat Collection 2 were reprocessed by USGS in March 2023. This reprocessing updated the overpass time by a fraction of a second for some scenes. Since HLS uses overpass time as part of the L30 filename, the older L30 granules were not automatically overwritten due to the different filenames. For example, the first L30 granule in the following pair originated from an older version of L1TP of Landsat 9 with the second granule originating from the reprocessed version.  \nHLS.L30.T11SLC.2022166T182646.v2.0  \nHLS.L30.T11SLC.2022166T182645.v2.0  \nThere are other causes of duplicate L30 granules, but the overall number of duplicates is very small.\n\n* Poor Geolocation: A large amount of granules that were processed for May through July 2023 were created with L1GT input scenes which were deemed undesirable due to a poor geolocation issue. These granules were removed from the archive. (see the full list of removed [granules](https://lpdaac.usgs.gov/documents/2161/L30_L1GT_granules_May_July_2023.csv))\n\nImprovements/Changes from Previous Versions\n\n* Aerosol QA bits from the USGS Land Surface Reflectance Code (LaSRC) model output have been added into the Function of Mask (Fmask) data layer. The added two bits indicate the aerosol levels: high, medium, low, and climatology aerosol.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/HLS/HLSS30.002","Contact":"User Services: lpdaac@usgs.gov","ManagedBy":"NASA","UpdateFrequency":"From 2015-11-28 to Ongoing","Tags":["aws-pds","cog","datacenter","earth observation","geospatial","global","hdf","ice","land","metadata","orbit","satellite imagery","stac","surface water","tiles","water","xml"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0.","ARN":"arn:aws:s3:::lp-prod-protected/HLSS30.020","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Getting Started with Cloud-Native HLS Data in Python","URL":"https://github.com/nasa/HLS-Data-Resources/blob/main/python/tutorials/HLS_Tutorial.ipynb","AuthorName":"Mahsa Jami, Erik A. Bolch, Cole K. Krehbiel, Aaron M. Friesz, Brianna M. Lind"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-hlss30","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA HRAC Project","Description":"This is a dataset that enhances the TMPA monthly product (3B43) in its accuracy and spatial resolution, in hydrometeorological applications. About 9,200 gauge measurement are used to compare with the 3B43 product at 0.25° x 0.25° spatial resolution across the CONUS. Observed is a strong relationship between the bias and land surface elevation, in which 3B43 underestimates the true precipitation at the elevations above 1,500 m amsl. Satellite data is resampled to elevation data at ~1km grid size and applied a correction function to reduce bias in the data. Accordingly, a High-Resolution Altitude-Corrected product is constructed, based on 3B43 and covering the entire CONUS at 1-km resolution. This product is verified against 9,200 gauges across the country. The results showed a substantial improvement in the satellite-gauge data accuracy as well as spatial resolution.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/HRAC_Precip_1.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","precipitation","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"HRAC_Precip v1 - This is a dataset that enhances the TMPA monthly product (3B43) in its accuracy and spatial resolution, in hydrometeorological applications. About 9,200 gauge measurement are used to compare with the 3B43 product at 0.25° x 0.25° spatial resolution across the CONUS.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/LEGACY/HRAC_Precip.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-hrac","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA HWHYP Project","Description":"The Headwall Hyperspectral Reflectance data are for the Long-Term Ecological Research (LTER) at Cedar Creek Ecosystem Science Reserve (CCESR), Minnesota (HWHYPCCMN1MM). The reflectance products are at 1 millimeter (mm) spatial resolution in the 400 to 1,000 nanometer spectral range. This dataset can be used to understand the optical diversity-biodiversity relationship and investigate the spatial sensitivity of the relationship at local scales. A Headwall Series E imaging spectrometer was mounted on a tram system to collect designated plots for the biodiversity (BioDIV) experiment at the LTER Cedar Creek site. The imaging spectrometer, located 3 meters above the ground surface, obtained 1 mm ground pixel size of hyperspectral field image cubes that are among the finest spatial resolution available. The fine-scale images were acquired at a prairie grassland ecosystem during June/July 2014 and July 2015. Provided in the HWHYPCCMN1MM product are 920 to 924 hyperspectral bands in an Environment for Visualizing Images (ENVI) format.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/centers/lp-daac","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"HWHYPCCMN1MM v001 - The Headwall Hyperspectral Reflectance data are for the Long-Term Ecological Research (LTER) at Cedar Creek Ecosystem Science Reserve (CCESR), Minnesota (HWHYPCCMN1MM). The reflectance products are at 1 millimeter (mm) spatial resolution in the 400 to 1,000 nanometer spectral range.","ARN":"arn:aws:s3:::lp-prod-protected/HWHYPCCMN1MM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-hwhyp","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Hydroclimatology Project","Description":"The Global Monthly River Discharge Data Set (RivDIS) contains monthly averaged discharge measurements for 1,018 stations located throughout the world from 1807-1991. The period of record varies widely from station to station with a mean of 21.5 years. The data are derived from the published UNESCO archives for river discharge, and checked against information obtained from the Global Runoff Center in Koblenz, Germany through the U.S. National Geophysical Data Center in Boulder, Colorado.\n<br>\n\n#### Sahel_Water_Bodies_1269\nThis data set provides an estimate of the spatial and temporal extent of surface water at 250-m resolution over nine years (2003-2011) for the African Sahel region (10-20 degrees N) using imagery from the Moderate-resolution Imaging Spectroradiometer (MODIS). Water bodies were identified by a spectral analysis of MODIS vegetation indices with the aim to improve existing regional to global mapping products. This data set can be used to enhance the understanding of Earth system processes, and to support global change studies, agricultural planning, and disease prevention. These data provide a gridded (250-m) estimate of the number of years (during 2003-2011) that a pixel was covered by water. The data are presented in a single netCDF (.nc) file.\n<br>\n\n#### HCDN_810\nTime series of monthly minimum and maximum temperature, precipitation, and potential evapotranspiration were derived for 1,469 watersheds in the conterminous United States for which stream flow measurements were also available from the national streamflow database, termed the Hydro-Climatic Data Network (HCDN), developed by Slack et al. (1993a,b). Monthly climate estimates were derived for the years 1951-1990. The climate characteristic estimates of temperature and precipitation were estimated using the PRISM (Daly et al. 1994, 1997) climate analysis system as described in Vogel, et al. 1999. Estimates of monthly potential evaporation were obtained using a method introduced by Hargreaves and Samani (1982) which is based on monthly time series of average minimum and maximum temperature data along with extraterrestrial solar radiation. Extraterrestrial solar radiation was estimated for each basin by computing the solar radiation over 0.1 degree grids using the method introduced by Duffie and Beckman (1980) and then summing those estimates for each river basin. This process is described in Sankarasubramanian, et al. (2001). Revision Notes: This data set has been revised to update the number of watersheds included in the data set and to updated the units for the potential evapotranspiration variable. Please see the Data Set Revisions section of this document for detailed information.\n<br>\n\n#### PAD_935\nThe Peace-Athabasca Delta (PAD) is a large boreal wetland located in northeastern Alberta, Canada at the confluence of the Peace and Athabasca Rivers with Lake Athabasca (Figures 1 and 2). A Ramsar Convention wetland and UNESCO World Heritage Site, it is among the world's most ecologically significant wetlands. This data set contains four comma-delimited ASCII files, two of which contain water surface elevation site and measurement information and two contain water quality and ancillary parameter location and measurement data for 120 sites within the PAD. Data archived include water surface elevation and water quality parameters measured at points throughout the Delta during summers 2006 and 2007. These data sets were originally collected to improve understanding of hydrologic recharge processes in low-relief environments and to provide ground-based measurements to validate satellite observations of inundation and sediment transport. All work was supported by the NASA Terrestrial Hydrology Program under grant NNG06GE05G to the Department of Geography, University of California-Los Angeles, Los Angeles, California.\n<br>\n\n#### PAD_2011_1133\nThe Peace-Athabasca Delta (PAD) is a hydrologically complex and ecologically diverse freshwater delta formed by the confluence of the Peace, Athabasca, and Birch Rivers near the western end of Lake Athabasca. This data set includes 3 comma-delimited ASCII files: one containing water quality data and site characteristics from June and July 2010, a second containing water quality data and site characteristics for June and July 2011, and a third containing spectral reflectance of the water surface for 2011. The 2010 data file has measurements from 62 unique sites, the majority of which were revisited in 2011. Both of the 2011 data files have measurements from 99 unique sites visited 1-4 times.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","elevation","hydrology","ice","netcdf","precipitation","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"rivdis_199 v1 - The Global Monthly River Discharge Data Set (RivDIS) contains monthly averaged discharge measurements for 1,018 stations located throughout the world from 1807-1991. The period of record varies widely from station to station with a mean of 21.5 years.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_hydroclimatology/rivdis/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Sahel_Water_Bodies_1269 v1 - This data set provides an estimate of the spatial and temporal extent of surface water at 250-m resolution over nine years (2003-2011) for the African Sahel region (10-20 degrees N) using imagery from the Moderate-resolution Imaging Spectroradiometer (MODIS). Water bodies were identified by a spectral analysis of MODIS vegetation indices with the aim to improve existing regional to global mapping products.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_hydroclimatology/Sahel_Water_Bodies/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"HCDN_810 v1 - Time series of monthly minimum and maximum temperature, precipitation, and potential evapotranspiration were derived for 1,469 watersheds in the conterminous United States for which stream flow measurements were also available from the national streamflow database, termed the Hydro-Climatic Data Network (HCDN), developed by Slack et al. (1993a,b).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_hydroclimatology/HCDN/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PAD_935 v1 - The Peace-Athabasca Delta (PAD) is a large boreal wetland located in northeastern Alberta, Canada at the confluence of the Peace and Athabasca Rivers with Lake Athabasca (Figures 1 and 2). A Ramsar Convention wetland and UNESCO World Heritage Site, it is among the world's most ecologically significant wetlands.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_hydroclimatology/PAD/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PAD_2011_1133 v2 - The Peace-Athabasca Delta (PAD) is a hydrologically complex and ecologically diverse freshwater delta formed by the confluence of the Peace, Athabasca, and Birch Rivers near the western end of Lake Athabasca. This data set includes 3 comma-delimited ASCII files: one containing water quality data and site characteristics from June and July 2010, a second containing water quality data and site characteristics for June and July 2011, and a third containing spectral reflectance of the water surface for 2011.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_hydroclimatology/PAD_2011/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-hydroclimatology","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA icesat-2 Project","Description":"These Level 1B time-ordered telemetry data are used for system-level, quality control analysis by the Advanced Topographic Laser Altimeter System (ATLAS) ICESat-2 Science Investigator-led Processing System (SIPS). They also provide source data for the Level 2 products and the Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations.\n<br>\n\n#### ATL03\nATL03 contains height above the WGS 84 ellipsoid, latitude, longitude, and time for each photon downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board ICESat-2. This product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products.\n<br>\n\n#### ATL04\nATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.\n<br>\n\n#### ATL04\nATL04 contains along-track normalized relative backscatter (NRB) profiles of the atmosphere. The product includes full 532 nm (14 km tall) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data between 13.5 and 11 km above the surface are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.\n<br>\n\n#### ATL24\nATL24 provides global along-track coastal and nearshore bathymetry, consisting of refraction-corrected seafloor and sea surface heights (orthometric and ellipsoidal heights and instantaneous depths), as well as associated uncertainties. The data are derived from ATLAS/ICESat-2 L2A Global Geolocated Photon Data (ATL03).\n<br>\n\n#### ATL13\nThis data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit).\n<br>\n\n#### ATL13\nATL13 provides along-track surface water products for inland water bodies, defined as lakes, reservoirs, bays, estuaries, rivers, and a 7 km near-shore buffer. Data parameters include surface water height statistics and related parameters including significant wave height, transect slope, subsurface signal attenuation, and shallow water bathymetry. Water surface heights are provided as both orthometric height and height referencing the WGS84 ellipsoid. ATL13 is also used to produce the ATLAS/ICESat-2 L3B Mean Inland Surface Water Data product (ATL22).\n<br>\n\n#### ATL09\nThis data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.\n<br>\n\n#### ATL09\nATL09 contains calibrated, attenuated backscatter profiles (CAB), layer-integrated attenuated backscatter, blowing snow, and other parameters including cloud and aerosol layer height and atmospheric characteristics. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.\n<br>\n\n#### ATL08\nThis data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.\n<br>\n\n#### ATL08\nATL08 contains along-track estimates of terrain height, canopy height, and canopy cover, as well as beam and reference parameters. Data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.\n<br>\n\n#### ATL06\nThis data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.\n<br>\n\n#### ATL06\nATL06 contains geolocated land-ice surface heights above the WGS84 ellipsoid, plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.\n<br>\n\n#### ATL12\nThis data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.\n<br>\n\n#### ATL12\nATL12 contains along-track sea surface height (SSH) of the global open ocean relative to the WGS84 ellipsoid. Height averages, distributions, and uncertainties are provided over ocean segments 400 to 7,000 m in length. Heights relative to the EGM2008 geoid and variables such as photon return rate are provided in 10 m along-track bins within each ocean segment. Additionally, 10 m bins corresponding to ATL07 bright leads are identified, and heights within the bins are averaged over ocean segments to yield accurate SSHs where ice concentrations are greater than 15%. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.\n<br>\n\n#### ATL10\nThis data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.\n<br>\n\n#### ATL10\nATL10 contains along-track sea ice freeboard calculated for 10 km swath segments. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.\n<br>\n\n#### ATL07\nThe data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.\n<br>\n\n#### ATL07\nATL07 contains along-track sea surface height and sea ice height for segments of variable lengths for all beams as well as fixed 10-meter segments for strong beams. Heights are also computed using the DDA-Bifurcation algorithm for strong beams. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.\n<br>\n\n#### ATL21\nATL21 contains daily and monthly gridded polar sea surface height (SSH) anomalies, derived from the along-track ATLAS/ICESat-2 L3A Sea Ice Height product (ATL10, V6). The ATL10 product identifies leads in sea ice and establishes a reference sea surface used to estimate SSH in 10 km along-track segments. ATL21 aggregates the ATL10 along-track SSH estimates and computes daily and monthly gridded SSH anomaly in NSIDC Polar Stereographic Northern and Southern Hemisphere 25 km grids.\n<br>\n\n#### ATL21\nATL21 contains daily and monthly gridded polar sea surface height anomalies, derived from ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10) along-track freeboard estimates.\n<br>\n\n#### ATL20\nATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection.\n<br>\n\n#### ATL20\nATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10) along-track freeboard estimates.\n<br>\n\n#### ATL15\nThis data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).\n<br>\n\n#### ATL14\nThis data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).\n<br>\n\n#### ATL22\nATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product. Starting from ATL13, ATL22 computes the mean surface water quantities with no additional photon analysis. The two data products, ATL22 and ATL13, can be used in conjunction as they include the same orbit and water body nomenclature independent from version numbers.\n<br>\n\n#### ATL22\nATL22 includes the per-beam mean surface water height, mean surface height standard deviation, and mean ATLAS 532 nm attenuation coefficient over a water body transect, reported at the center of the beam transect. Additional parameters include the beginning and end of the transect, transect length, and center of transect. ATL22 is derived from ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data (ATL13).\n<br>\n\n#### ATL23\nThis data set contains 3-month gridded averages of dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided. Both single beam and all-beam gridded averages are available. Simple averages, degree-of-freedom averages, and averages interpolated to the center of grid cells are included, as well as uncertainty estimates.\n<br>\n\n#### ATL23\nATL23 contains monthly 3-month gridded averages of dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the corresponding weighted average geoid height. Both single beam and all-beam gridded averages are available: single beam averages are useful for identifying potential biases among the beams, and the all-beam averages are useful in physical oceanography. Simple averages, degree-of-freedom averages, and averages interpolated to the center of grid cells are included, as well as uncertainty estimates. Sea surface statistics histograms and wave statistics within grid are also provided.\n<br>\n\n#### ATL17\nThis data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.\n<br>\n\n#### ATL19\nThis data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography.\n<br>\n\n#### ATL19\nATL19 contains monthly gridded dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the monthly gridded DOT and the weighted average geoid height also provided. Both single beam and all-beam gridded averages are available: single beam averages are useful for identifying potential biases among the beams, and the all-beam averages are useful in physical oceanography. Simple averages, degree-of-freedom uncertainty averages, and averages interpolated to the center of grid cells are included, as well as uncertainty estimates.\n<br>\n\n#### ATL11\nThis data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations.\n<br>\n\n#### ATL11\nATL11 provides spatially organized time series of land-ice surface heights and ice-shelf locations where ICESat-2 operates in repeat-track mode (i.e., for polar ice), along with parameters useful in determining whether each height estimate is valid or a result of potential errors. ATL11 is derived from the ATLAS/ICESat-2 L3A Land Ice Height product (ATL06) and intended primarily as an input for higher-level gridded products.\n<br>\n\n#### ATL16\nThis product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.\n<br>\n\n#### ISSITGR4\nThis data set reports seasonal gridded winter sea ice thickness across the Arctic Ocean. Sea ice thickness is estimated using ICESat/GLAS L3A Sea Ice Freeboard data and NASA Eulerian Snow On Sea Ice Model (NESOSIM) Version 1.1 snow loading. This data set is a historical complement to ICESat-2 L4 Monthly Gridded Sea Ice Thickness (IS2SITMOGR4).\n<br>\n\n#### NSIDC-0773\nThis data set comprises monthly estimates of Arctic sea ice snow depth and ice thickness using combined ICESat-2 and CryoSat-2 freeboard measurements.\n<br>\n\n#### IS2OLVIS1BCV\nThis data set contains georeferenced imagery from the NASA Land, Vegetation, and Ice Sensor (LVIS) PhaseOne medium-format camera, which was operated on high-altitude segments of flights during the ICESat-2 2022 Arctic Summer calibration campaign.\n<br>\n\n#### IS2ATBABD\nThis data set provides a quality-filtered set of ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 5 (ATL08) observations of relative canopy heights and aboveground biomass density model results for circumpolar boreal forests. The data were collected at 30 m along-track segment lengths for strong beams only during the 2019–2021 high northern latitude growing seasons. The ATL08 point observations were clipped to the extent of the boreal forest spatial domain.\n<br>\n\n#### IS2CHM\nThis data set provides a regression-based canopy height model of the contiguous United States (CONUS) using data from ATLAS/ICESat-2 L3A Land and Vegetation Height (ATL08), as well as data from Landsat, LANDFIRE, and NASADEM.\n<br>\n\n#### IS2MPDDA\nThis data set provides locations and depths of melt ponds in the Multi-Year Arctic Sea Ice Region, calculated from ATLAS/ICESat-2 L2A Global Geolocated Photon Data, Version 5 (ATL03) using an autoadaptive algorithm.\n<br>\n\n#### IS2SITDAT4\nThis data set reports daily, along-track winter sea ice thickness across the Arctic Ocean. Sea ice thickness is estimated using ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10), Version 5 data and NASA Eulerian Snow On Sea Ice Model (NESOSIM) snow loading.\n<br>\n\n#### IS2GZANT\nThis data set provides an Antarctic ice shelf grounding zone geolocation product, including the landward limit of ice flexure caused by ocean tidal movement (Point F), the seaward limit of ice flexure (Point H), and the break in surface slope (Point Ib) based on the ATLAS/ICESat-2 ATL06 Land Ice Height data set acquired between March 2019 and September 2020. The grounding zone estimates were derived from automated techniques using ICESat-2 repeat tracks.\n<br>\n\n#### IS2SITMOGR4\nThis data set provides monthly, gridded, winter Arctic sea ice thickness, freeboard, ice density, snow depth, and snow density estimates derived from ICESat-2 L4 Along-Track Sea Ice Thickness (IS2SITDAT4).\n<br>\n\n#### NSIDC-0715\nThis data set consists of an enhanced resolution digital elevation model (DEM) for the Greenland Ice Sheet, derived from sub-meter resolution, panchromatic stereoscopic imagery collected by the GeoEye-1, WorldView-1, -2, and -3 satellites operated by Maxar Technologies. The DEM was created from in-track image pairs (i.e., both images collected minutes apart along the same orbital pass) and cross-track images (i.e., from different orbits) within the in-track imaging geometry and maximum time separation criteria. The DEM is registered to ATLAS/ICESat-2 L3A Land Ice Height, Version 5 (ATL06, V5) data collected in the summers of 2019 and 2020. See <a href=\"http://nsidc.org/data/measures/gimp\">Greenland Ice Mapping Project (GrIMP)</a> for related data\n<br>\n\n#### NSIDC-0782\nThis data product contains monthly ice sheet elevation change data for Antarctica derived from five radar altimetry missions (Geosat, ERS-1 and -2, Envisat and CryoSat-2) and two laser altimetry missions (ICESat and ICESat-2). Each time step and grid node includes relative error estimates and a quality flag that can be used to filter the data in space and time. The product is also provided with an estimate of static topography in the form of a digital elevation model (DEM), which was used to estimate monthly ice sheet elevation change. With a temporal coverage of 17 April 1985 to 16 December 2020, this product can be used to determine changes in ice sheet mass balance over time.\n<br>\n\n#### NSIDC-0812\nThis data set provides weekly snow stake height measurements in Summit Station, Greenland, near the ice divide. The array comprises 121 snow stakes and helps understand snowfall processes and contextualizes snow accumulation, which is a major component of the region’s surface mass balance. As a long-term climate record, this data set provides valuable ground validation for ICESat-2 and other satellite and airborne missions.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/atl02/versions/7","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","elevation","ice","oceans","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ATL02 v007 - These Level 1B time-ordered telemetry data are used for system-level, quality control analysis by the Advanced Topographic Laser Altimeter System (ATLAS) ICESat-2 Science Investigator-led Processing System (SIPS). They also provide source data for the Level 2 products and the Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL02/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL03 v007 - ATL03 contains height above the WGS 84 ellipsoid, latitude, longitude, and time for each photon downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board ICESat-2. This product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL03/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL04 v006 - ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL04/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL04 v007 - ATL04 contains along-track normalized relative backscatter (NRB) profiles of the atmosphere. The product includes full 532 nm (14 km tall) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL04/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL24 v001 - ATL24 provides global along-track coastal and nearshore bathymetry, consisting of refraction-corrected seafloor and sea surface heights (orthometric and ellipsoidal heights and instantaneous depths), as well as associated uncertainties. The data are derived from ATLAS/ICESat-2 L2A Global Geolocated Photon Data (ATL03).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL24/001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL13 v006 - This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL13/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL13 v007 - ATL13 provides along-track surface water products for inland water bodies, defined as lakes, reservoirs, bays, estuaries, rivers, and a 7 km near-shore buffer. Data parameters include surface water height statistics and related parameters including significant wave height, transect slope, subsurface signal attenuation, and shallow water bathymetry.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL13/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL09 v006 - This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL09/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL09 v007 - ATL09 contains calibrated, attenuated backscatter profiles (CAB), layer-integrated attenuated backscatter, blowing snow, and other parameters including cloud and aerosol layer height and atmospheric characteristics. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL09/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL08 v006 - This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL08/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL08 v007 - ATL08 contains along-track estimates of terrain height, canopy height, and canopy cover, as well as beam and reference parameters. Data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL08/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL06 v006 - This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL06/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL06 v007 - ATL06 contains geolocated land-ice surface heights above the WGS84 ellipsoid, plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL06/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL12 v006 - This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL12/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL12 v007 - ATL12 contains along-track sea surface height (SSH) of the global open ocean relative to the WGS84 ellipsoid. Height averages, distributions, and uncertainties are provided over ocean segments 400 to 7,000 m in length.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL12/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL10 v006 - This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL10/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL10 v007 - ATL10 contains along-track sea ice freeboard calculated for 10 km swath segments. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL10/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL07 v006 - The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL07/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL07 v007 - ATL07 contains along-track sea surface height and sea ice height for segments of variable lengths for all beams as well as fixed 10-meter segments for strong beams. Heights are also computed using the DDA-Bifurcation algorithm for strong beams.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL07/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL21 v003 - ATL21 contains daily and monthly gridded polar sea surface height (SSH) anomalies, derived from the along-track ATLAS/ICESat-2 L3A Sea Ice Height product (ATL10, V6). The ATL10 product identifies leads in sea ice and establishes a reference sea surface used to estimate SSH in 10 km along-track segments.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL21/003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL21 v004 - ATL21 contains daily and monthly gridded polar sea surface height anomalies, derived from ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10) along-track freeboard estimates.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL21/004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL20 v004 - ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL20/004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL20 v005 - ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10) along-track freeboard estimates.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL20/005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL15 v004 - This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL15/004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL14 v004 - This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL14/004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL22 v003 - ATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL22/003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL22 v004 - ATL22 includes the per-beam mean surface water height, mean surface height standard deviation, and mean ATLAS 532 nm attenuation coefficient over a water body transect, reported at the center of the beam transect. Additional parameters include the beginning and end of the transect, transect length, and center of transect.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL22/004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL23 v001 - This data set contains 3-month gridded averages of dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL23/001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL23 v002 - ATL23 contains monthly 3-month gridded averages of dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the corresponding weighted average geoid height.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL23/002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL17 v005 - This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL17/005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL19 v003 - This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL19/003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL19 v004 - ATL19 contains monthly gridded dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the monthly gridded DOT and the weighted average geoid height also provided.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL19/004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL11 v006 - This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL11/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL11 v007 - ATL11 provides spatially organized time series of land-ice surface heights and ice-shelf locations where ICESat-2 operates in repeat-track mode (i.e., for polar ice), along with parameters useful in determining whether each height estimate is valid or a result of potential errors. ATL11 is derived from the ATLAS/ICESat-2 L3A Land Ice Height product (ATL06) and intended primarily as an input for higher-level gridded products.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL11/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL16 v005 - This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL16/005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ISSITGR4 v1 - This data set reports seasonal gridded winter sea ice thickness across the Arctic Ocean. Sea ice thickness is estimated using ICESat/GLAS L3A Sea Ice Freeboard data and NASA Eulerian Snow On Sea Ice Model (NESOSIM) Version 1.1 snow loading.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/GLAS-Related/ISSITGR4/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0773 v1 - This data set comprises monthly estimates of Arctic sea ice snow depth and ice thickness using combined ICESat-2 and CryoSat-2 freeboard measurements.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICESAT2-Related/NSIDC-0773/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IS2OLVIS1BCV v1 - This data set contains georeferenced imagery from the NASA Land, Vegetation, and Ice Sensor (LVIS) PhaseOne medium-format camera, which was operated on high-altitude segments of flights during the ICESat-2 2022 Arctic Summer calibration campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICESAT2-Related/IS2OLVIS1BCV/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IS2ATBABD v1 - This data set provides a quality-filtered set of ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 5 (ATL08) observations of relative canopy heights and aboveground biomass density model results for circumpolar boreal forests. The data were collected at 30 m along-track segment lengths for strong beams only during the 2019–2021 high northern latitude growing seasons.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICESAT2-Related/IS2ATBABD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IS2CHM v1 - This data set provides a regression-based canopy height model of the contiguous United States (CONUS) using data from ATLAS/ICESat-2 L3A Land and Vegetation Height (ATL08), as well as data from Landsat, LANDFIRE, and NASADEM.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICESAT2-Related/IS2CHM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IS2MPDDA v3 - This data set provides locations and depths of melt ponds in the Multi-Year Arctic Sea Ice Region, calculated from ATLAS/ICESat-2 L2A Global Geolocated Photon Data, Version 5 (ATL03) using an autoadaptive algorithm.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICESAT2-Related/IS2MPDDA/3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IS2SITDAT4 v001 - This data set reports daily, along-track winter sea ice thickness across the Arctic Ocean. Sea ice thickness is estimated using ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10), Version 5 data and NASA Eulerian Snow On Sea Ice Model (NESOSIM) snow loading.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICESAT2-Related/IS2SITDAT4/001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IS2GZANT v1 - This data set provides an Antarctic ice shelf grounding zone geolocation product, including the landward limit of ice flexure caused by ocean tidal movement (Point F), the seaward limit of ice flexure (Point H), and the break in surface slope (Point Ib) based on the ATLAS/ICESat-2 ATL06 Land Ice Height data set acquired between March 2019 and September 2020. The grounding zone estimates were derived from automated techniques using ICESat-2 repeat tracks.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICESAT2-Related/IS2GZANT/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IS2SITMOGR4 v4 - This data set provides monthly, gridded, winter Arctic sea ice thickness, freeboard, ice density, snow depth, and snow density estimates derived from ICESat-2 L4 Along-Track Sea Ice Thickness (IS2SITDAT4).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICESAT2-Related/IS2SITMOGR4/4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0715 v2 - This data set consists of an enhanced resolution digital elevation model (DEM) for the Greenland Ice Sheet, derived from sub-meter resolution, panchromatic stereoscopic imagery collected by the GeoEye-1, WorldView-1, -2, and -3 satellites operated by Maxar Technologies. The DEM was created from in-track image pairs (i.e., both images collected minutes apart along the same orbital pass) and cross-track images (i.e., from different orbits) within the in-track imaging geometry and maximum time separation criteria.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0715/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0782 v1 - This data product contains monthly ice sheet elevation change data for Antarctica derived from five radar altimetry missions (Geosat, ERS-1 and -2, Envisat and CryoSat-2) and two laser altimetry missions (ICESat and ICESat-2). Each time step and grid node includes relative error estimates and a quality flag that can be used to filter the data in space and time.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0782/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0812 v1 - This data set provides weekly snow stake height measurements in Summit Station, Greenland, near the ice divide. The array comprises 121 snow stakes and helps understand snowfall processes and contextualizes snow accumulation, which is a major component of the region’s surface mass balance.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICESAT2-Related/NSIDC-0812/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-icesat-2","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA icesat Project","Description":"Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter. Each data granule has an associated browse product.\n<br>\n\n#### GLAH02\nGLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product.\n<br>\n\n#### GLAH03\nLevel-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02.\n<br>\n\n#### GLAH04\nLevel-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.\n<br>\n\n#### GLAH07\nGLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product.\n<br>\n\n#### GLAH06\nGLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product.\n<br>\n\n#### GLAH05\nGLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product.\n<br>\n\n#### GLAH10\nGLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product.\n<br>\n\n#### GLAH12\nGLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.\n<br>\n\n#### GLAH09\nGLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product.\n<br>\n\n#### GLAH08\nGLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product.\n<br>\n\n#### GLAH11\nGLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product.\n<br>\n\n#### NSIDC-0645\nThis data set consists of an enhanced resolution digital elevation model (DEM) for the Greenland Ice Sheet. It was constructed by combining ASTER and SPOT 5 DEMs over the ice sheet periphery and margin with AVHRR photoclinometry for the interior and far north, and calibrating the data to approximate mean ICESat/GLAS elevations from 2003 to 2009. See <a href=\"http://nsidc.org/data/measures/gimp\">Greenland Ice Mapping Project (GIMP)</a> for related data.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/glah01/versions/33","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","ice","lidar","oceans","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GLAH01 v033 - Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter. Each data granule has an associated browse product.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/GLAS/GLAH01/033","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GLAH02 v033 - GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/GLAS/GLAH02/033","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GLAH03 v033 - Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/GLAS/GLAH03/033","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GLAH04 v033 - Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/GLAS/GLAH04/033","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GLAH07 v033 - GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/GLAS/GLAH07/033","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GLAH06 v034 - GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/GLAS/GLAH06/034","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GLAH05 v034 - GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/GLAS/GLAH05/034","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GLAH10 v033 - GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/GLAS/GLAH10/033","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GLAH12 v034 - GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/GLAS/GLAH12/034","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GLAH09 v033 - GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/GLAS/GLAH09/033","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GLAH08 v033 - GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/GLAS/GLAH08/033","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"GLAH11 v033 - GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/GLAS/GLAH11/033","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0645 v1 - This data set consists of an enhanced resolution digital elevation model (DEM) for the Greenland Ice Sheet. It was constructed by combining ASTER and SPOT 5 DEMs over the ice sheet periphery and margin with AVHRR photoclinometry for the interior and far north, and calibrating the data to approximate mean ICESat/GLAS elevations from 2003 to 2009.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0645/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-icesat","Sources":["awslabs-open-data-registry"]}
{"Name":"ABoVE: Bias-Corrected IMERG Monthly Precipitation for Alaska and Canada, 2000-2020","Description":"This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (*.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global coverage. This dataset bias-corrected IMERG's HQprecipitation precipitation estimates, which are based on passive microwave (PMW)-only retrievals, using a linear regression method. This method utilizes empirical measurements from rain gauge stations from the Global Historical Climatology Network (GHCN) and a digital elevation model. This bias correction approach improves estimates at elevations above 500 m a.s.l., which are typically underestimated.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.3334/ORNLDAAC/2097","Contact":"ORNL DAAC User Services Office: uso@daac.ornl.gov.","ManagedBy":"NASA","UpdateFrequency":"From 2000-06-01 to 2020-12-31","Tags":["atmosphere","aws-pds","cog","cog","earth observation","global","land","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ABoVE: Bias-Corrected IMERG Monthly Precipitation for Alaska and Canada, 2000-2020.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/above/IMERG_Precip_Canada_Alaska/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing Data through ORNL DAAC Web Services","URL":"https://github.com/ornldaac/web_services_data_access/blob/master/web_services_data_access.ipynb","AuthorName":"ORNL DAAC"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-imergprecipcanadaalaska2097","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA INTEXB Project","Description":"INTEX-NA is a two phase experiment that aims to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. The primary constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. The first phase (INTEX-A) was completed in the summer of 2004 and the second phase (INTEX-B) is to be performed in the spring of 2006. This document is intended to provide an update on the goals of INTEX-B and define its implementation strategy. The scientific goals envisioned here are based on the joint implementation of INTEX-B, MIRAGE-Mex and DLR/IMPACT studies and their coordination with satellite observations. In collaboration with these partners, the main goals of INTEX-B are to:- Quantify the transpacific transport and evolution of Asian pollution to North America and assess its implications for regional air quality and climate; - Quantify the outflow and evolution of gases and aerosols from the Mexico City Megaplex; - Investigate the transport of Asian and North America pollution to the eastern Atlantic and assess its implications for European air quality; - Validate and refine satellite observations of tropospheric composition; - Map emissions of trace gases and aerosols and relate atmospheric composition to sources and sinks.The INTEX-B field study is to be performed during an approximate 8-week period from March 1 to April 30, 2006.\n<br>\n\n#### IXBMIGEO\nIXBMIGEO_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Geometric Parameters subset for the INTEX-B region version 2. It contains the Geometric Parameters that measure the sun and view angles at the reference ellipsoid for the region covered by the INTEXB_2006 theme. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### IXBMIB2E\nIXBMIB2E_3 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Product subset for the INTEX-B region V003. It contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected and geometrically corrected by PGE22 for the region covered by the INTEXB_2006 theme. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### IXBMIB2T\nIXBMIB2T_3 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Product subset for the INTEX-B region version 3. It contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, and geometrically corrected by PGE22 for the region covered by the INTEXB_2006 theme. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### IXBMI2AE\nMISR Level 2 Aerosol Product containing aerosol optical depth and particle type, with associated atmospheric data for the INTEXB_2006 theme.\n<br>\n\n#### IXBMI2LS\nIXBMI2LS_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Land Surface Product subset for the INTEX-B region. It contains information on land directional reflectance properties, albedos(spectral and PAR integrated), FPAR, associated radiation parameters, and terrain-referenced geometric parameters for the region covered by the INTEXB_2006 theme. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### IXBMI2ST\nIXBMI2ST_2 is the Multi-angle Imaging SpectroRadiometer (MISR) L2 TOA/Cloud Stereo Product subset for the INTEX-B region V002. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, and Reflecting Level Reference Altitude (RLRA), with associated data for the region covered by the INTEXB_2006 theme. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br><br>\n","Documentation":"https://cloud1.arc.nasa.gov/intex-b/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"IXBMIGEO v002 - IXBMIGEO_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Geometric Parameters subset for the INTEX-B region version 2. It contains the Geometric Parameters that measure the sun and view angles at the reference ellipsoid for the region covered by the INTEXB_2006 theme.","ARN":"arn:aws:s3:::ixbmigeo","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"IXBMIB2E v003 - IXBMIB2E_3 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Product subset for the INTEX-B region V003. It contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected and geometrically corrected by PGE22 for the region covered by the INTEXB_2006 theme.","ARN":"arn:aws:s3:::ixbmib2e","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"IXBMIB2T v003 - IXBMIB2T_3 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Product subset for the INTEX-B region version 3. It contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, and geometrically corrected by PGE22 for the region covered by the INTEXB_2006 theme.","ARN":"arn:aws:s3:::ixbmib2t","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"IXBMI2AE v002 - MISR Level 2 Aerosol Product containing aerosol optical depth and particle type, with associated atmospheric data for the INTEXB_2006 theme.","ARN":"arn:aws:s3:::ixbmi2ae","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"IXBMI2LS v002 - IXBMI2LS_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Land Surface Product subset for the INTEX-B region. It contains information on land directional reflectance properties, albedos(spectral and PAR integrated), FPAR, associated radiation parameters, and terrain-referenced geometric parameters for the region covered by the INTEXB_2006 theme.","ARN":"arn:aws:s3:::ixbmi2ls","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"IXBMI2ST v002 - IXBMI2ST_2 is the Multi-angle Imaging SpectroRadiometer (MISR) L2 TOA/Cloud Stereo Product subset for the INTEX-B region V002. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, and Reflecting Level Reference Altitude (RLRA), with associated data for the region covered by the INTEXB_2006 theme.","ARN":"arn:aws:s3:::ixbmi2st","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-intexb","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ISLSCP II Project","Description":"This data set contains the calculated net ocean-air carbon dioxide (CO2) flux and sea-air CO2 partial pressure (pCO2) difference. The estimates are based on approximately one million measurements made for the pCO2 in surface waters of the global ocean since the International Geophysical Year, 1956-1959. Only the ocean water pCO2 values measured using direct gas-seawater equilibration methods were used. The results represent the climatological distributions under non-El Nino conditions. Since the measurements were made in different years, during which the atmospheric pCO2 was increasing, they were corrected to a single reference year (arbitrarily chosen to be 1995) on the basis of the following assumptions: -Surface waters in subtropical gyres mix vertically at slow rates with subsurface waters due to the presence of strong stratification at the base of the mixed layer. This will allow a long contact time with the atmosphere to exchange CO2. Therefore, their CO2 chemistry tends to follow the atmospheric CO2 increase. Accordingly, the pCO2 measured in a given month and year is corrected to the same month of the reference year 1995 using changes in the atmospheric CO2 concentration occurred during this period. -Oceanic pCO2 measurements made after the beginning of 1979 have been corrected to 1995 using the atmospheric CO2 concentration data from the GLOBALVIEW-CO2 database (2000), in which the zonal mean atmospheric concentrations (for each 0.05 in sine of latitude) within the planetary boundary layer are summarized for each month since 1979 to 2000. -Pre-1979 oceanic pCO2 data were corrected to 1979 using the annual mean trend for the global mean atmospheric CO2 concentration constructed from the Mauna Loa data of Keeling and Whorf (2000), and then from 1979 to 1995 using the GLOBALVIEW-CO2 database. -Measurements for pCO2 made in the following areas have been corrected for the time of observation; 45 degrees N, 50 degrees S, in the Atlantic Ocean, north of 50 degrees S in the Indian Ocean, 40 degrees N, 50 degrees S in the western Pacific west of the date line, and 40 degrees N, 60 degrees S, in the eastern Pacific east of the date line.\n<br>\n\n#### atmos_co2_by_erosion_xdeg_1019\nThe Continental Atmospheric CO2 Consumption data set represents gridded estimates for the riverine export of carbon and of sediments based on empirical models. All data exist for the overall continental area in a spatial resolution of 0.5 x 0.5 degree longitude/ latitude. The units are tC/km2/yr for all carbon species, and t/km2/yr for sediment fluxes. There are two data files (.zip) with this data set which describe the following: dissolved organic carbon (DOC) export, particulate organic carbon (POC) export, bicarbonate export, export of bicarbonate being of atmospheric origin (also called atmospheric CO2 consumption by rock weathering), and sediment export.\n<br>\n\n#### avhrr_albedo_1995_xdeg_928\nThis Albedo and BRDF (Bidirectional Reflectance Distribution Function) data set contains three files containing BRDF parameters, white- sky albedo and black-sky albedo at solar noon for three bands ((350-680nm, 680-3000nm, and 350-30000nm)derived from AVHRR (Advanced Very High Resolution Radiometer). These data are available at spatial resolutions of quarter, half, and one degree. Black-sky albedo (direct beam contribution) and white-sky (Completely diffuse contribution) can be linearly combined as a function of the fraction of diffuse skylight (itself a function of optical depth) to provide an actual or instantaneous albedo at local solar noon.\n<br>\n\n#### c4_percent_1deg_932\nThe photosynthetic composition (C3 or C4) of vegetation on the land surface is essential for accurate simulations of biosphere-atmosphere exchanges of carbon, water, and energy. C3 and C4 plants have different responses to light, temperature, CO2, and nitrogen; they also differ in physiological functions like stomatal conductance and isotope fractionation. A fine-scale distribution of these plant types is essential for earth science modeling. The C4 percentage is determined from datasets that describe the continuous distribution of plant growth forms (i.e., the percent of a grid cell covered by herbaceous or woody vegetation), climate classifications, the fraction of a grid cell covered in croplands, and national crop type harvest area statistics. The staff from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II have made the original data set consistent with the ISLSCP-2 land/water mask. This data set contains a single file in ArcInfo ASCIIGRID format. This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews. ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.\n<br>\n\n#### co2_emissions_1deg_1021\nThis data set contains decadal (1950, 1960, 1970, 1980, 1990 and 1995) estimates of gridded fossil-fuel emissions, expressed in 1,000 metric tons C per year per one degree latitude by one degree longitude. The CO2 emissions are the summed emissions from fossil-fuel burning, hydraulic cement production and gas flaring. The years 1950 to 1990 were developed and compiled using somewhat different procedures and information than the 1995 data. The national annual estimates (Boden et al., 1996) from 1950 to 1990 were allocated to one degree grid cells based on gridded information on national boundaries and political units, and a 1984 gridded human population map (Andres et al., 1996). For the 1995 data, the population data base developed by Li (1996a) and documented by CDIAC (DB1016: Li, 1996b) was used as proxy to grid the 1995 emission estimates. There is one .zip data file with this data set at 1.0 degree spatial resolution.\n<br>\n\n#### fluxnet_point_1029\nThis International Satellite Land Surface Climatology Project (ISLSCP II) data set, ISLSCP II Carbon Dioxide Flux at Harvard Forest and Northern BOREAS Sites, contains gapp-filled flux and meterological data for half-hourly, daily, weekly, monthly, and annual time intervals presented for each site and year. The 1992-1995 Harvard Forest, MA site, and the 1994-95 Old Black Spruce, Alberta, Canada site are members of the FLUXNET global network of micrometeorological towers that use eddy covariance methods to measure the excahanges of carbon dioxide (CO2), water vapor, and energy between terrestrial ecosystem and atmosphere. There are 6 .zip files with this data set.\n<br>\n\n#### cru_monthly_mean_xdeg_1015\nThis data set contains a mean monthly climatology for several climate variables averaged over the period from 1961 to 1990, and constructed from a data set of station 1961-1990 climatological normals, numbering between 19,800 (precipitation) and 3,615 (windspeed; see New et al, 1999 for details). The station data were interpolated as a function of latitude, longitude and elevation using thin-plate splines. The data comprise a suite of climate elements: precipitation, mean, maximum, and minimum temperature, frost frequency, diurnal temperature range, radiation, wet-day frequency, vapor pressure, wind, and cloud cover. There are 23 files in this data set provided at 0.5 and 1.0 degree spatial resolutions.\n<br>\n\n#### srb_clouds_1deg_1073\nThis data set contains cloud and meteorology data on a 1.0 degree x 1.0 degree spatial resolution. There are eight data files (.zip) with this data set for several cloud parameters (monthly only) and meteorological parameters including monthly surface skin temperature, monthly total column ozone, and water vapor burdens for the period 1986-1995. All monthly parameters include files with a monthly mean value, a monthly standard deviation, and monthly minimum and maximum values.\n<br>\n\n#### veg_continuous_fields_xdeg_931\nThe objective of this study was to derive continuous fields of vegetation cover from multi-temporal Advanced Very High Resolution Radiometer (AVHRR) data using all available bands and derived Normalized Difference Vegetation Index (NDVI). The continuous fields describe sub-pixel proportions of cover for tree, herbaceous, bare ground and water cover types. For tree cover, additional fields describing leaf longevity (evergreen and deciduous) and leaf morphology (broadleaf and needleleaf) were also generated. The modeling of carbon dynamics and climate require knowing tree characteristics such as these. These products were resampled and aggregated to 0.25, 0.5 and 1.0 degree grids for the International Satellite Land Surface Climatology Project (ISLSCP) data initiative II. The data set describes the geographic distributions of three fundamental vegetation characteristics: tree, herbaceous and bare ground cover, plus a water layer. For tree cover, leaf longevity and morphology layers were produced. This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews. ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.\n<br>\n\n#### cru_monthly_climate_xdeg_1014\nThis data set contains monthly climate time series data created by the Climatic Research Unit (CRU) at the University of East Anglia, U.K., for every year covering the period 1986 to 1995. This time series is a subset of a larger CRU monthly data set that covers the period of 1901 to 1996. The data comprise a suite of six climate elements: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapor pressure, and cloud cover. There are 13 files in this data set provided at 0.5 and 1.0 degree spatial resolutions.\n<br>\n\n#### erbe_albedo_monthly_xdeg_957\nThis data set, ISLSCP II Earth Radiation Budget Experiment (ERBE) Monthly Albedo, 1986-1990, contains both the original ERBE albedo data at 2.5 degree spatial resolution, and the International Land Surface Climatology Project Initative II (ISLSCP Initiative II) albedo product re-gridded to 1 degree resolution. The goals of the ERBE were (1) to understand the radiation balance between the Sun, Earth, atmosphere, and space and (2) to establish an accurate, long-term baseline data set for detection of climate changes. Earth Radiation Budget (ERB) data are fundamental to the development of realistic climate models and to the understanding of natural and anthropogenic perturbations of the climate system. As part of ERBE, measurements of broadband shortwave radiation reflected from the Earth-atmosphere system were obtained, from which top of atmosphere albedo values were calculated. In addition, values from scenes determined to be free of clouds were analyzed separately and clear-sky albedos were derived. For this study, only the clear-sky albedos are included. The ERBE data sets for ISLSCP Initiative II contain global, top of atmosphere, clear sky albedo data from January 1986 to February 1990.\n<br>\n\n#### ecmwf_met_1deg_1222\nThis data set for the ISLSCP Initiative II data collection provides meteorology data with fixed, monthly, monthly-6-hourly, 6-hourly, and 3-hourly temporal resolutions. The data were derived from the European Centre for Medium-range Weather Forecasts (ECMWF) near-surface meteorology data set, 40-year re-analysis, or ERA-40 (Simmons and Gibson, 2000), which covers the years 1957 to 2001. The data were processed onto the ISLSCP II Earth grid with a spatial resolution of 1-degree in both latitude and longitude, and span the common ISLSCP II period from 1986 to 1995. The ECMWF forecast system is called the Integrated Forecasting System (IFS) and was developed in co-operation with Meteo-France. For ERA40 it is used with 60 levels from the top of the model at 10 Pa to the lowest level at about 10 m above the surface. There are 46 compressed (.tar.gz) data files with this data set. Each uncompressed file contains space-delimited text (.asc) data files.\n<br>\n\n#### ecosystem_roots_1deg_929\nThe goal of this study was to predict the global distribution of plant rooting depths based on data about global aboveground vegetation structure and climate. Vertical root distributions influence the fluxes of water, carbon, and soil nutrients and the distribution and activities of soil fauna. Roots transport nutrients and water upwards, but they are also pathways for carbon and nutrient transport into deeper soil layers and for deep water infiltration. Roots also affect the weathering rates of soil minerals. For calculating such processes on a global scale, data on vertical root distributions are needed as inputs to global biogeochemistry and vegetation models. In the Project for Intercomparison of Land Surface Parameterization Schemes (PILPS), rooting depth and vertical soil characteristics were the most important factors explaining scatter for simulated transpiration among 14 land-surface models. Recently, the Terrestrial Observation Panel for Climate of the Global Climate Observation System (GCOS) identified the 95% rooting depth as a key variable needed to quantify the interactions between the climate, soil, and plants, stating that the main challenge was to find the correlation between rooting depth and soil and climate features (GCOS/GTOS Terrestrial Observation Panel for Climate 1997). In response to this challenge, a data set of vertical rooting depths was collected from the literature in order to construct maps of global ecosystem rooting depths. The parameters included in these data sets are estimates for the soil depths containing 50% and 95% of all roots, termed 50% and 95% rooting depths (D50 and D95, respectively). Together, these variables can be used to calculate estimates for vertical root distributions, using a logistic equation provided in this documentation. The data represent mean ecosystem rooting depths for 1 by 1 degree grid cells. Related data sets: The ORNL DAAC offers related data sets by Jackson et al. (2003), Gordon and Jackson (2003), Schenk and Jackson (2003), and Gill and Jackson (2003). This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews. ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.\n<br>\n\n#### edgar_atmos_emissions_1deg_1022\nThe EDGAR (Emission Database for Global Atmospheric Research) database project is a comprehensive task carried out jointly by the National Institute for Public Health (RIVM) and the Netherlands Organization for Applied Scientific Research (TNO) and stores global emission inventories of direct and indirect greenhouse gases from anthropogenic sources including halocarbons and aerosols both on a per country and region basis as well as on a grid (see http://www.rivm.nl/edgar/). For the ISLSCP Initiative II data collection, gridded global annual anthropogenic emissions for the greenhouse gases CO2, CH4, N2O are provided on a 1.0 degree by 1.0 degree grid for the years 1970, 1980, 1990, and 1995 and for the tropospheric ozone precursor gases CO, NOx, NMVOC (Non-Methane Volatile Organic Compounds) and SO2 for the years 1990 and 1995. There are 2 .zip data files with this data set.\n<br>\n\n#### fasir_biophys_monthly_xdeg_970\nThe Fourier-Adjusted, Sensor and Solar zenith angle corrected, Interpolated, Reconstructed (FASIR) adjusted Normalized Difference Vegetation Index (NDVI) data set and derived biophysical parameter fields were generated to provide a 17-year, satellite record of monthly changes in the photosynthetic activity of terrestrial vegetation. This multiple resolution (1/4, 1/2 and 1 degree in latitude and longitude) biophysical parameter data set contains essential variables for the calculation of photosynthesis, and the energy and water exchange between the Earth's surface (in particular of vegetation) and the lower boundary layer of the atmosphere. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is related to the light absorption and the photosynthetic capacity of vegetation. It also serves as an intermediate variable to calculate vegetation cover fraction (Vcover), total Leaf Area Index (LAI_T), green leaf area index (LAI_G), roughness length (z0), zero plane displacement (d), and snow-free albedo. The biophysical parameters were derived assuming one canopy layer. The production of the FASIR NDVI data set and its associated biophysical parameters was funded by NASA's Land Surface Hydrology program and the Higher Education Funding Council for Wales (HEFCW) as a core component of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II Data Collection.\n<br>\n\n#### fasir_ndvi_monthly_xdeg_972\nThe Fourier-Adjusted, Sensor and Solar zenith angle corrected, Interpolated, Reconstructed (FASIR) adjusted Normalized Difference Vegetation Index (NDVI) data sets were generated to provide a 17-year, satellite record of monthly changes in the photosynthetic activity of terrestrial vegetation. FASIR-NDVI data are also used in climate models and biogeochemical models to calculate photosynthesis, the exchange of CO2 between the atmosphere and the land surface, land-surface evapotranspiration and the absorption and release of energy by the land surface. There are three data files provided at spatial resolutions of 0.25, 0.5 and 1.0 degree in latitude and longitude. FASIR adjustments concentrated on reducing NDVI variations arising from atmospheric, calibration, view and illumination geometries and other effects not related to actual vegetation change. FASIR NDVI was also generated to provide inputs for computing a 17-year time series of associated biophysical parameters, provided as a separate data set in this data collection. The production of the FASIR NDVI data set and its associated biophysical parameters was funded by NASA's Land Surface Hydrology program and the Higher Education Funding Council for Wales (HEFCW) as a core component of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II Data Collection.\n<br>\n\n#### gts_precip_daily_xdeg_1001\nThe objective of this work was to construct a long-term data set of daily precipitation on half degree and one degree latitude/longitude grids over the global land areas. The analyses are defined by interpolating station observations from GTS (Global Telecommunications System) gauges using the algorithm of Shepard (1968). The algorithm of Shepard (1968) has been widely used to interpolate gauge observations of monthly, pentad, and daily precipitation (Rudolf 1993, Xie et al. 1996). This algorithm is used to interpolate the irregularly distributed station observations onto grid points. The weighting coefficients are inversely proportional to the gauge-grid point distance and are adjusted by a cosine function taking into account the directional isolation of each gauge relative to all other nearby gauges. There are 6 data files with this data set.\n<br>\n\n#### gimms_ndvi_monthly_xdeg_973\nThe Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data sets were generated to provide a 22-year satellite record of monthly changes in terrestrial vegetation. This data set contains three data files provided at spatial resolutions of 0.25, 0.5 and 1.0 degree in latitude and longitude with data from July 1981 through December 2002. New features include reduced NDVI variations arising from calibration, view geometry, volcanic aerosols, and other effects not related to actual vegetation change. In particular, NOAA-9 descending node data from September 1994 to January 1995, volcanic stratospheric aerosol correction for 1982-1984 and 1991-1994, and improved NDVI using empirical mode decomposition/reconstruction (EMD) to minimize effects of orbital drift. Global NDVI was generated to provide inputs for computing the time series of biophysical parameters contained in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II collection. NDVI is used in climate models and biogeochemical models to calculate photosynthesis, the exchange of CO2 between the atmosphere and the land surface, land-surface evapotranspiration and the absorption and release of energy by the land surface.\n<br>\n\n#### gdp_xdeg_974\nThe data sets in this directory were provided by Mr. Gregory Yetman and Drs. Stuart Gaffin and Deborah Balk from the Center for International Earth Science Information Network (CIESIN) at Columbia University. There are three data files at three spatial resolutions of 0.25, 0.5 and 1.0 degree in both latitude and longitude and for the reference year of 1990. Estimates of Gross Domestic Product (GDP) are commonly given for nations as a single aggregated number. This data set generates estimates of GDP density distributed subnationally to facilitate the integration of GDP with other data at a sub-national level and to promote interdisciplinary studies that include socioeconomic aspects. This is one of two coarse resolution Socioeconomic data sets included in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data collection, the other being the Gridded Population of the World (GPW), also produced by CIESIN.\n<br>\n\n#### islscp2_soils_1deg_1004\nThis data set provides gridded data for selected soil parameters derived from data and methods developed by the Global Soil Data Task, an international collaborative project with the objective of making accurate and appropriate data relating to soil properties accessible to the global change research community. The task was coordinated by the International Geosphere-Biosphere Programme (IGBP-DIS). The data in this data set were produced by the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) staff from data obtained from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, http://daac.ornl.gov/). See the related data sets section below. Two-dimensional gridded maps of selected soil parameters, including soil texture, at a 1.0 by 1.0 degree spatial resolution and for two soil depths are provided. All data layers have been adjusted to match the ISLSCP II land/water mask. There are 36 data files with this data set.\n<br>\n\n#### global_population_xdeg_975\nGlobal Population of the World (GPW) translates census population data to a latitude-longitude grid so that population data may be used in cross-disciplinary studies. There are three data files with this data set for the reference years 1990 and 1995. Over 127,000 administrative units and population counts were collected and integrated from various sources to create the gridded data. In brief, GPW was created using the following steps: * Population data were estimated for the product reference years, 1990 and 1995, either by the data source or by interpolating or extrapolating the given estimates for other years. * Additional population estimates were created by adjusting the source population data to match UN national population estimates for the reference years. * Borders and coastlines of the spatial data were matched to the Digital Chart of the World where appropriate and lakes from the Digital Chart of the World were added. * The resulting data were then transformed into grids of UN-adjusted and unadjusted population counts for the reference years. * Grids containing the area of administrative boundary data in each cell (net of lakes) were created and used with the count grids to produce population densities. As with any global data set based on multiple data sources, the spatial and attribute precision of GPW is variable. The level of detail and accuracy, both in time and space, vary among the countries for which data were obtained.\n<br>\n\n#### gpcc_precip_monthly_xdeg_995\nThe Global Precipitation Climatology Centre (GPCC), which is operated by the Deutscher Wetterdienst (National Meteorological Service of Germany), is a component of the Global Precipitation Climatology Project (GPCP) with the main emphasis on the treatment of the global in-situ observations. The GPCC simultaneously contributes to the Global Climate Observing System (GCOS) and other international research and climate monitoring projects. This rain gauge-only data set was acquired from GPCC and resampled to 0.5 degree grid boxes for use in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II. The GPCC collects precipitation data which are locally observed at rain gauge stations and distributed as CLIMAT and SYNOP reports via the Global Telecommunication System of the World Weather Watch (GTS) of the World Meteorological Organization (WMO). The Centre acquires additional monthly precipitation data from meteorological and hydrological networks which are operated by national services.\n<br>\n\n#### gpcp_precip_pentad_xdeg_1002\nThe Global Precipitation Climatology Project (GPCP) pentad version 1 precipitation data set includes global precipitation rates for 5-day, or pentad, periods. The data sets are derived from measured rain gauge data and merged with satellite estimates of rainfall. This is a portion of the version 1 GPCP pentad data set and covers the ISLSCP II period from 1986 to 1995. The original precipitation rates at 2.5 degrees were re-gridded to a 1 degree spatial resolution by the ISLSCP II staff.\n<br>\n\n#### gpcp_precip_monthly_xdeg_1003\nThe Global Precipitation Climatology Project (GPCP) Version 2 data set includes global, monthly precipitation rates and associated random errors (RMSE), and a monthly precipitation climatology derived as an average from all GPCP data sets from January 1979 to December 1999. The data are derived from measured gauge data and merged with satellite estimates of rainfall. This is a portion of the version 2 GPCP data and covers the ISLSCP II period from 1986 to 1995. There are six data files included with this data set: the original precipitation rates, errors and climatology at 2.5 degrees spatial resolution, and the same data re-gridded to a 1 degree spatial resolution by the ISLSCP II staff.\n<br>\n\n#### gppdi_npp_gridded_xdeg_1023\nNet Primary Production (NPP) is an important component of the carbon cycle and, among the pools and fluxes that make up the cycle, it is one of the steps that are most accessible to field measurement. Direct measurement of NPP is not practical for large areas and so models are generally used to study the carbon cycle at a global scale. This data set contains 2 .zip files for above ground and total NPP data.\n<br>\n\n#### river_carbon_flux_xdeg_1028\nThe River Carbon Flux data set represents estimates for the riverine export of carbon and of sediments. This data set includes the amounts of carbon and of sediments that are discharged to the oceans by rivers for each coastal grid point which receives river inputs. This data set contains three compressed (.zip) files: the original data at 2.5 x 2.0 degrees, and global maps at spatial resolutions of 0.5 and 1.0 degree which the ISLSCP II staff has created from the original data.\n<br>\n\n#### sea_ice_extent_xdeg_981\nThis International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data set, ISLSCP II Global Sea Ice Concentration, is based on the Goddard Space Flight Center (GSFC) Sea Ice Concentrations from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and the Defense Meteorological Satellites Program (DMSP) Special Sensor Microwave/Imager (SSM/I) Passive Microwave Data. This data set contains four zip files which includes sea ice concentration (in percentage of ocean area covered by sea ice), table data and map data. These original data were re-gridded by the National Snow and Ice Data Center (NSIDC) from their original 25-km spatial resolution and EASE-Grid into equal angle Earth grids with quarter, half and one degree spatial resolutions in latitude/longitude. The ISLSCP II staff have taken the one degree resolution original data provided by the Principal Investigator and created global maps of monthly sea ice concentration on a global one degree grid using the latitude and longitude coordinates that were provided. Individual monthly files were created and written to the ASCII format. The re-gridded one degree original data were also adjusted to match the one degree ISLSCP II land/water mask.\n<br>\n\n#### globalview_co2_point_1111\nThe GlobalView Carbon Dioxide (CO2) data product contains synchronized and smoothed time series of atmospheric CO2 concentrations at selected sites that were created using the data extension and integration techniques described by Masarie and Tans, (1995). The information needed to derive this time series is also in this data set, along with extensive documentation. The longest period of coverage is from 1979 to 2001 with some sites having longer or shorter temporal coverage. Note that the GlobalView CO2 data products are derived from measurements but contain no actual data. To facilitate heterogeneous CO2 data use in carbon cycle modeling studies, the measurements have been processed (smoothed, interpolated, and extrapolated) resulting in extended records that are evenly incremented in time. There are 92 files with this data set which includes 89 .zip data files. The other three files include 2 files with site information, one comma-delimited ASCII file (.csv), and one .dat file, and one .dat file which is a single reference marine boundary layer matrix file which contains CO2 mixing ratios as a function of time and sine of latitude and is a by-product of the data extension procedure.\n<br>\n\n#### globalview_ch4_point_1109\nThe GlobalView Methane (CH4) data product contains synchronized and smoothed time series of atmospheric CH4 concentrations at selected sites that were created using the data extension and integration techniques described by Masarie and Tans (1995). The information needed to derive this time series is also in this data set, along with extensive documentation. The longest period of coverage is from 1984 to 1998 with some sites having shorter or longer temporal coverage. Note that the GlobalView-CH4 data products are derived from measurements but contain no actual data. To facilitate heterogeneous CH4 data use in carbon cycle modeling studies, the measurements have been processed (smoothed, interpolated, and extrapolated) resulting in extended records that are evenly incremented in time. There are 74 files with this data set which includes 71 .zip data files. The other three files include 2 files with site information, one comma-delimited ASCII file (.csv), and one .dat file, and one .dat file which is a single reference marine boundary layer matrix file containing CH4 mixing ratios as a function of time and sine of latitude and is a by-product of the data extension procedure.\n<br>\n\n#### gppdi_npp_point_1033\nThe Global Primary Production Data Initiative (GPPDI) was set up as a Focus 1 activity of the IGBP Data and Information System, a coordinated international program to improve worldwide estimates of terrestrial net primary productivity (NPP) for parameterization, calibration, and validation of NPP models at various scales. The GPPDI data collection contains documented field measurements of NPP for global terrestrial sites compiled from published literature and other extant data sources. The point measurements of NPP were categorized as either Class A, representing intensively studied or well-documented study sites (e.g., with site-specific climate, soils information, etc.), Class B, representing more numerous “extensive” sites with less documentation and site-specific information available, or Class C, representing regional collections of half-degree latitude-longitude grid cells. This data set in the ISLSCP II collection represents the GPPDI Class B NPP data. The Class B NPP data file contains biomass dynamics, climate, and site-characteristics data georeferenced to each site. There is one ASCII data file with this data set.\n<br>\n\n#### historic_cropland_xdeg_966\nThe Historical Croplands Cover data set was developed to understand the consequences of historical changes in land use and land cover for ecosystem goods and services. In particular, this data set can be used to study how global changes in cultivated area has influenced climate, biogeochemical cycles, biodiversity, etc. This data set can be used directly within spatially-explicit climate and biogeochemical models. This is a gridded data set describing the fraction of each grid cell in the globe that is occupied by cultivated land from 1700 to 1992. Data layers are provided for every 50 years from 1700 to 1850, every 10 years from 1850 to 1980, and every year from 1986 to 1992. There are two sources of global land cover/land use data. The most recent estimates are derived from satellite measurements, and are available in a spatially-explicit fashion for roughly the last 30 years. The other estimate is based on ground-based sources such as census statistics, land surveys, estimates by historical geographers, etc. These land inventory data are only available at the scale of political units, but have the advantage of being historical. Ramankutty and Foley (1998) derived a spatially-explicit data set of croplands in 1992 by synthesizing remotely-sensed land cover data with contemporary land inventory data. Furthermore, Ramankutty and Foley (1999) extended this data set into the past (back to 1700) using historical land inventory data. The data set should only be used for continental-to-global scale analysis and modeling. The data set captures the broad patterns of cropland change over history, but not necessarily the fine details at local to regional scales - please check the data quality before using it at fine spatial scales. The quality of historical data for the Russian Federation is poor. The quality of data prior to 1850 is poor -- only continental-scale historical data were used for that period.\n<br>\n\n#### historic_landcover_xdeg_967\nThe Historical Land Cover and Land Use data set was developed to provide the global change community with historical land use estimates. The data set describes historical land use changes over a 300-year historical period (1700-1990). Testing against historical data is an important step for validating integrated models of global environmental change. Owing to long time lags in the climate and biogeochemical systems, these models should aim to simulate the land use dynamics for long periods, i.e., spanning decades to centuries. Developing such models requires an understanding of past and current trends and is therefore strongly data dependent. For this purpose, a historical database of the global environment has been developed: HYDE. Historical statistical inventories on agricultural land (census data, tax records, land surveys, etc) and different spatial analysis techniques were used to create a geographically-explicit data set of land use change, with a regular time interval. The data set can be used to test integrated models of global change. Continental-scale historical data were used for that period.\n<br>\n\n#### hydro1k_elevation_xdeg_1007\nThis data set contains coarse scale elevation and elevation-based parameters at 1.0 and 0.5-degree spatial resolutions that were developed to support a wide variety of global modeling activities through the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data collection. These coarse scale data have sufficient statistical information (up to fourth moment) to allow a good statistical description of the sub-cell distribution of any particular elevation parameter (i.e. elevation, slope and aspect). The database used in the development effort was the HYDRO1k product (http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/HYDRO1K) with a native spatial resolution of 1 km, the highest resolution database of global coverage of standard elevation-based derivatives (slope, aspect, elevation, compound topographic index, etc.).\n<br>\n\n#### edc_landcover_xdeg_930\nThis data set describes the geographic distributions of 17 classes of land cover based on the International Geosphere-Biosphere DISCover land cover legend (Loveland and Belward 1997) and the 15 classes of the SiB model processed at the USGS EROS Data Center (EDC). Specifically, the resampled DISCover datasets were derived from the 1km DISCover data set compiled by the USGS. The 1km data sets for each classification scheme were aggregated to 1, 0.5 and 0.25 degree spatial resolutions for this ISLSCP II data collection. Each layer of the aggregated products corresponds to a single DISCover land cover category and the values represent the percentage of the coarse resolution cell (1 degree, etcâ�¦) occupied by that land cover category. The dominant class data show the land cover category that occupies the majority of the cell and is derived from the percentage files for each cover type. The objective of this study was to create a land cover map derived from 1 kilometer AVHRR data using a full year of data (April 1992-March 1993). This thematic map was resampled to 0.25, 0.5 and 1.0 degree grids for the International Satellite Land Surface Climatology Project (ISLSCP) data initiative II. During this re-processing, the original EDC land cover type and fraction maps were adjusted to match the water/land fraction of the ISLSCP II land/water mask. These maps were generated for use by global modelers and others. This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews. ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.\n<br>\n\n#### model_npp_xdeg_1027\nThis data set contains modeled annual net primary production (NPP) for the land biosphere from seventeen different global models. Annual NPP is defined as the net difference of annual carbon uptake (grams CO2/m2/yr) from the atmosphere through photosynthesis by the land vegetation and that lost back to the atmosphere through autotrophic and maintenance respiration. NPP is also related to the Net Ecosystem Exchange (NEE) of carbon accumulated by or lost from the surface by its vegetation and soils. NPP is NEE plus heterotrophic (decomposition) respiration of the vegetation and soils. Only NPP values are included in this data set as some models did not estimate NEE. Data for the mean, standard deviation and coefficient of variation of NPP for the 17 models are provided at spatial resolutions of 1.0 degree and 0.5 degrees. There are two compressed (.zip) data files with this data set.\n<br>\n\n#### combined_ancillary_xdeg_1200\nThis data set contains the ISLSCP II fixed land/water masks and percentages of land or water in each cell. There are seven zip data files: four produced from a 1-km land/water mask compiled at the Jet Propulsion Laboratory (JPL) in support of NASA's Earth Observing System; two files of a land outline overlay created from the land/water mask files created at NASA's Goddard Space Flight Center; and one file which is a latitude grid coordinate file and longitude grid coordinate file produced by the ISLSCP II staff. All of these data are provided at three spatial resolutions of .25, 0.5 and 1-degree in latitude and longitude and on a common Earth grid.\n<br>\n\n#### ornl_lai_point_971\nLeaf Area Index (LAI) data from the scientific literature, covering the period from 1932-2000, have been compiled at the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) to support model development and validation for products from the MODerate Resolution Imaging Spectroradiometer (MODIS) instrument. There is one data file which consists of a spreadsheet table, together with a bibliography of more than 300 original-source references. Although the majority of measurements are from natural or semi-natural ecosystems, some LAI values have been included from crops (limited to a sub-set representing different crops at different stages of development under a range of treatments). Like Net Primary Productivity (NPP), Leaf Area Index (LAI) is a key parameter for global and regional models of biosphere/atmosphere exchange. Modeling and validation of coarse scale satellite measurements both require field measurements to constrain LAI values for different biomes (typical minimum, maximum values, phenology, etc.). Maximum values for point measurements are unlikely to be approached or exceeded by area-weighted LAI, which is what satellites and true spatial models are measuring or modeling.\n<br>\n\n#### modis_albedo_2002_xdeg_958\nThis International Satellite Land Surface Climatology Project (ISLSCP II) MODerate resolution Image Spectroradiometer (MODIS) dataset, ISLSCP II MODIS (Collection 4) Albedo 2002, provides albedo data for the period January 2002 through December 2002.The MODIS bidirectional reflectance distribution function (BRDF) albedo product (MOD43B) provides measures of clear sky surface albedo every 16 days. Both white-sky albedo (bihemispherical reflectance) and black-sky albedo (directional hemispherical reflectance) at local solar noon are provided for 7 spectral bands and 3 broadbands. Since black-sky albedo represents the direct beam contribution while white-sky represents the completely diffuse contribution, these measures can be linearly combined as a function of the fraction of diffuse skylight (itself a function of optical depth) to provide an actual or instantaneous albedo at local solar noon.\n<br>\n\n#### modis_landcover_xdeg_968\nThis data set, ISLSCP II MODIS (Collection 4) IGBP Land Cover, 2000-2001, contains global land cover classifications (dominant type, classification confidence and fractional cover) generated using a full year of MODerate Resolution Imaging Spectroradiometer (MODIS) data covering the period from October 2000 to October 2001. The objective of the MODIS Land Cover Product is to provide a suite of land cover types useful to global system science modelers by exploiting the information content of MODIS data in the spectral, temporal, spatial, and directional domains. These products describe the geographic distribution of the 17 land cover classification scheme proposed by the International Geosphere-Biosphere Programme (IGBP).\n<br>\n\n#### snowfree_albedo_1deg_956\nThis data set contains monthly average snow-free surface shortwave albedo calculated for the period 1982-1998 and estimates of background soil/litter reflectances in the visible (0.4-0.7 μm) and near-infrared (NIR) (0.7-1.0 μm) wavelengths. Biophysical Parameters derived from the FASIR-NDVI (Fourier Adjusted, Solar zenith angle correction, Interpolation, and Reconstruction of Normalized Difference Vegetation Index) data set developed for the ISLSCP Initiative II data collection for the months of January 1982 through December 1998 were used to calculate monthly mean surface albedos at 1 X 1 degree spatial resolution for vegetated land surfaces (Sellers et al, 1996b) for the wavelength interval from 0.4 to 3.0 µm. The instantaneous albedo is a function of the properties of the land surface and the solar zenith angle. The monthly mean albedo is an average weighted over time weighted by the incident radiation. NDVI data are used to generate the biophysical parameters leaf area index (LAI) and green fraction of vegetation (Greenness) used by the canopy radiative transfer model of the Simple Biosphere (SiB2) model (Sellers et al, 1996a), which computes the instantaneous albedo. This is coupled to the Colorado State University (CSU) General Circulation Model (GCM) (Randall et al, 1989) which integrates the SiB2 radiative transfer through time. The incident radiation for weighting the time-averaged albedo was provided by a previous run of the GCM using the atmospheric radiation parameterization of Harshvardhan et al (1987). The Harshvardhan parameterization models radiative transfer through the atmosphere in both the longwave and shortwave bands, including the effects of cloudiness and water vapor, carbon dioxide and ozone. The shortwave radiation distinguishes between the direct and diffuse components of the solar beam.\n<br>\n\n#### noaa_albedo_5year-av_xdeg_959\nThe objective of this work was to produce a monthly climatology of broadband surface albedos for use in global numerical weather prediction models at the National Centers for Environmental Prediction (NCEP). Monthly means of clear-sky, surface, broadband, snow-free albedos for overhead sun illumination angle were determined using data from a five-year period from April 1985-December 1987 and January 1989-March 1991. The data set is compatible in temporal coverage and spatial resolution with a monthly climatology of green vegetation fraction (Gutman and Ignatov, 1998) delivered earlier and currently in use at NCEP. Files showing the differences between the original data set and the Land/water mask used in the International Satellite land Surface Climatology Project (ISLSCP) Initiative II are also provided.\n<br>\n\n#### snow_cover_xdeg_982\nThis ISLSCP data set is derived from the National Snow and Ice Data Center (NSIDC) Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent product which combines snow cover and sea ice extent at weekly intervals for October 1978 through June 2001, and snow cover alone from 1966 through June 2001. The original data set was the first representation of combined snow and sea ice measurements derived from satellite observations for the period of record. Designed to facilitate study of Northern Hemisphere seasonal fluctuations of snow cover and sea ice extent, the original NSIDC data set also includes monthly climatologies describing average extent, probability of occurrence, and variance. This data set shows the extent of snow on the land at a variety of scales (1.0 degree, 0.5 degree, 0.25 degree). The values represent the percentage of days in each month where snow was present -- 100 means 100% of the month, 80 means 80% of the month, etc. There are 4 .zip files provided. Missing data is represented by -99 for water and -88 for land. The data were originally in a yearly tabular format. The file was converted to multi-scale maps by plotting each point in the tabular data onto a map of -99 (water) and -88 (land) created from the standard ISLSCP II Land/Sea Mask.\n<br>\n\n#### potential_veg_xdeg_961\nThis data set was developed to describe the state of the global land cover in terms of 15 major vegetation types, plus water, before alteration by humans. It forms a complement to the historical croplands data set developed by Ramankutty and Foley (1999). By overlaying the two, one can determine the extent to which natural vegetation has been cleared for cultivation. This data set can be used directly within spatially-explicit climate and biogeochemical models. There are four total files in this data set. Two files contain the land cover types representing potential natural vegetation before human alteration, and two other files contain those points in the original data set submitted by the Principal Investigator that have been modified in order to match the land/water mask of the ISLSCP Initiative II. The geographic distribution of contemporary land cover types can be derived from remotely-sensed data. However, humans now dominate much of the world and there is little evidence of the pre-human-settlement natural vegetation or Potential Natural Vegetation (PNV). PNV, as defined here, does not necessarily represent the world'??s natural pre-human-disturbance vegetation. Rather, our definition of PNV represents the world's vegetation cover that would most likely exist now in equilibrium with present-day climate and natural disturbance, in the absence of human activities.\n<br>\n\n#### ncep_met_1deg_1226\nThis data set for the ISLSCP Initiative II data collection provides near surface meteorological variables, fluxes of heat, moisture and momentum at the surface, and land surface state variables, all with a spatial resolution of 1 degree in both latitude and longitude. There are four temporal categories of data: time invariant and monthly mean annual cycle fields (together referred to as \"fixed\" fields), monthly mean fields, monthly 3-hourly diurnal, and 3-hourly fields. Two types of variables exist in this data; instantaneous fields (primarily state variables), and average fields (primarily flux fields expressed as a rate). The Center for Ocean-Land Atmosphere Studies (COLA) near-surface data set for ISLSCP II was derived from the National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmospheric Model Inter-comparison Project (AMIP-II) reanalysis (http://www.cpc.ncep.noaa.gov/products/wesley/reanalysis2/), covering the years from 1979-2003. The data set for ISLSCP II covers the period from 1986 to 1995. The purpose of the reanalysis was to provide an improved version of the original NCEP/National Center for Atmospheric Research (NCAR) reanalysis for General Circulation Model (GCM) validation. To co-register the NCEP/DOE reanalysis on the ISLSCP 1-degree grid, the reanalysis data set was regridded from its native T62 Gaussian grid) resolution (192 x 94 grid boxes globally) to 1-degree ISLSCP II required resolution. There are 136 compressed (.tar.gz) data files with this data set. When extrapolated, the individual data files are in ASCII (.asc) format.\n<br>\n\n#### river_routing_stn_xdeg_1005\nThe Simulated Topological Network (STN-30p) data set provides the large-scale hydrological modeling community an accurate representation of the global river system at 0.5 degree and 1.0 degree spatial resolutions. STN-30p represents the potential connectivity of the continental land mass by assigning one of eight (E, SE, S, SW, W, NW, N, NE) possible flow directions to each continental grid cell (Jenson 1988, Band 1993). The potentiality of STN-30p reflects the fact that flow direction is assigned to every land cell regardless of the existence of actively flowing rivers. STN-30p can be viewed as a river network which would exist if sufficient surface runoff was available to form river channels everywhere. There are two data files with this data set.\n<br>\n\n#### sea_surface_temp_1deg_980\nSea surface temperature (SST) is an important indicator of the state of the earth climate system as well as a key variable in the coupling between the atmosphere and the ocean. Accurate knowledge of SST is essential for climate monitoring, prediction and research. It is also a key surface boundary condition for numerical weather prediction and for other atmospheric simulations using atmospheric general circulation models and regional models. SST also is important in gas exchange between the ocean and atmosphere, including the air-sea flux of carbon. Gridded SST products have been developed to satisfy these needs. There are 3 .zip files provided with this data set. Gridded monthly and weekly sea surface temperature (SST) and long term SST monthly climatology for the period 1971-2000 are provided here. Weekly normalized error variance fields are also provided with the weekly data. The data are derived using the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation (OI) global sea surface temperature analyses that use seven days of in situ (ship and buoy) and satellite SST observations and SST values derived from sea ice concentration. These analyses are produced weekly using optimum interpolation (OI) on a 1-degree grid. The data sets included in the ISLSCP II data collection are produced using version 2 of the OI analyses, called OIv2. In this data set, the ISLSCP II staff have masked land areas based on the ISLSCP II land/water mask. A file describing the differences between the ISLSCP II mask and the original mask used is provided.\n<br>\n\n#### modis_albedo_2002_filled_xdeg_960\nThis data set, ISLSCP II Snow-Free, Spatially Complete, 16 Day Albedo, 2002, contains 9 files for snow-free, spatially complete 16-day global black-sky albedos at local solar noon, white-sky albedos and quality information based on MODerate Resolution Imaging Spectroradiometer (MODIS) Collection 4 Albedo Products (MOD43B3). Data are provided for 7 spectral bands and 3 broad bands for a full year of MODIS data (2002). An ecosystem-dependent temporal interpolation technique was developed to fill any missing or seasonally snow-covered data in the official MOD43B3 albedo product. The resulting data set maintains the original resolution and data of the MOD43B3 product while replacing fill values to provide snow-free spatially complete maps.\n<br>\n\n#### srb_radiation_1deg_1201\nThis data set contains global Surface Radiation Budget (SRB) and a few top-of-atmosphere (TOA) radiation budget parameters on a 1-degree x 1-degree spatial resolution. These parameters are provided as monthly, monthly-3 hourly (i.e. monthly average for a particular 3 hourly period) and 3-hourly averages. All monthly parameters include files with a monthly mean value, a monthly standard deviation, and monthly minimum and maximum values. The surface and TOA Shortwave (SW) radiative parameters were computed with the Pinker and Laszlo (1992) radiation model. The Longwave (LW) SRB parameters were derived with the Gupta et al. (1992) model. Meteorological inputs for all processing were taken from the Goddard Earth Observing System version 1 (GEOS-1) reanalysis data sets (Schubert et al., 1993) from the Data Assimilation Office (DAO), at NASA Goddard Space Flight Center (GSFC). Required cloud parameters were derived at NASA Langley Research Center (LaRC) from International Satellite Cloud Climatology Project (ISCCP) DX data using the algorithms developed at the NASA Goddard Institute for Space Studies (GISS) (Rossow et al., 1996). Surface albedos are derived internally in the Pinker and Laszlo SW model. There are 30 compressed data files (.zip) with this data set. When the .zip files are expanded, there are 114,912 3-hourly files, 42,064 diurnal files, and 6,254 monthly files.\n<br>\n\n#### root_water_storage_1deg_1006\nThis data set provides two estimates of the geographic distribution of the total plant-available soil water storage capacity of the rooting zone (\"rooting zone water storage size\") on a 1.0 degree global grid. Two inverse modeling methods were used. The first modeling approach (optimization) was based on the assumption that vegetation has adapted to the environment such that it makes optimum use of water (Kleidon and Heimann 1998). The second method (assimilation) was based on the assumption that green vegetation indicates sufficient available water for transpiration (Knorr 1997). The data set was developed to provide alternative means to describe rooting characteristics of the global vegetation cover for land surface and climate models in support of the ISLSCP Initiative II data collection. There are three files in this data set.\n<br>\n\n#### comp_runoff_monthly_xdeg_994\nThe University of New Hampshire (UNH)/Global Runoff Data Centre (GRDC) composite runoff data combines simulated water balance model runoff estimates derived from climate forcing with monitored river discharge. It can be viewed as a data assimilation applied in a water balance model context (conceptually similar to the commonly used 4DDA techniques used in meteorological modeling). Such a data assimilation scheme preserves the spatial specificity of the water balance calculations while constrained by the more accurate discharge measurement. There are 11 data files in this data set and 1 changemap file which shows the differences between the ISLSCP II land/water mask and the original data set.\n<br>\n\n#### umd_landcover_xdeg_969\nThe objective of the International Satellite Land Surface Climatology Project (ISLSCP II) study that produced this data set, ISLSCP II University of Maryland Global Land Cover Classifications 1992-1993, was to create a land cover map derived from 1 kilometer Advanced Very High Resolution Radiometer (AVHRR) data using all available bands, derived Normalized Difference Vegetation Index (NDVI), and a full year of data (April 1992-March 1993). This thematic map was resampled to 0.25, 0.5 and 1.0 degree grids. During this re-processing, the original University of Maryland (UMD) land cover type and fraction maps were adjusted to match the water/land fraction of the ISLSCP II land/water mask. These maps were generated for use by modelers of global biogeochemical cycles and others in need of an internally consistent, global depiction of land cover. This 1km map was also one of the MODerate resolution Imaging Spectroradiometer (MODIS) at-launch land cover maps. This product describes the geographic distributions of 13 classes of vegetation cover (plus water and unclassified classes) based on a modified International Geosphere-Biosphere Programme (IGBP) legend. The data set also provides the fraction of each of the 15 classes within the coarser resolution cells, at three spatial resolutions of 0.25, 0.5 and 1.0 degrees in latitude and longitude.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","elevation","hydrology","ice","land cover","oceans","precipitation","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"air_sea_gas_exchange_xdeg_1208 v1 - This data set contains the calculated net ocean-air carbon dioxide (CO2) flux and sea-air CO2 partial pressure (pCO2) difference. The estimates are based on approximately one million measurements made for the pCO2 in surface waters of the global ocean since the International Geophysical Year, 1956-1959.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/carbon/air_sea_gas_exchange_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"atmos_co2_by_erosion_xdeg_1019 v1 - The Continental Atmospheric CO2 Consumption data set represents gridded estimates for the riverine export of carbon and of sediments based on empirical models. All data exist for the overall continental area in a spatial resolution of 0.5 x 0.5 degree longitude/ latitude.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/carbon/atmos_co2_by_erosion_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"avhrr_albedo_1995_xdeg_928 v1 - This Albedo and BRDF (Bidirectional Reflectance Distribution Function) data set contains three files containing BRDF parameters, white- sky albedo and black-sky albedo at solar noon for three bands ((350-680nm, 680-3000nm, and 350-30000nm)derived from AVHRR (Advanced Very High Resolution Radiometer). These data are available at spatial resolutions of quarter, half, and one degree.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/avhrr_albedo_1995_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"c4_percent_1deg_932 v1 - The photosynthetic composition (C3 or C4) of vegetation on the land surface is essential for accurate simulations of biosphere-atmosphere exchanges of carbon, water, and energy. C3 and C4 plants have different responses to light, temperature, CO2, and nitrogen; they also differ in physiological functions like stomatal conductance and isotope fractionation.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/c4_percent_1deg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"co2_emissions_1deg_1021 v1 - This data set contains decadal (1950, 1960, 1970, 1980, 1990 and 1995) estimates of gridded fossil-fuel emissions, expressed in 1,000 metric tons C per year per one degree latitude by one degree longitude. The CO2 emissions are the summed emissions from fossil-fuel burning, hydraulic cement production and gas flaring.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/carbon/co2_emissions_1deg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fluxnet_point_1029 v1 - This International Satellite Land Surface Climatology Project (ISLSCP II) data set, ISLSCP II Carbon Dioxide Flux at Harvard Forest and Northern BOREAS Sites, contains gapp-filled flux and meterological data for half-hourly, daily, weekly, monthly, and annual time intervals presented for each site and year. The 1992-1995 Harvard Forest, MA site, and the 1994-95 Old Black Spruce, Alberta, Canada site are members of the FLUXNET global network of micrometeorological towers that use eddy covariance methods to measure the excahanges of carbon dioxide (CO2), water vapor, and energy between terres...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/carbon/fluxnet_point/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"cru_monthly_mean_xdeg_1015 v1 - This data set contains a mean monthly climatology for several climate variables averaged over the period from 1961 to 1990, and constructed from a data set of station 1961-1990 climatological normals, numbering between 19,800 (precipitation) and 3,615 (windspeed; see New et al, 1999 for details). The station data were interpolated as a function of latitude, longitude and elevation using thin-plate splines.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/near-surface_met/cru_monthly_mean_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"srb_clouds_1deg_1073 v1 - This data set contains cloud and meteorology data on a 1.0 degree x 1.0 degree spatial resolution. There are eight data files (.zip) with this data set for several cloud parameters (monthly only) and meteorological parameters including monthly surface skin temperature, monthly total column ozone, and water vapor burdens for the period 1986-1995.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/radiation_clouds/srb_clouds_1deg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"veg_continuous_fields_xdeg_931 v1 - The objective of this study was to derive continuous fields of vegetation cover from multi-temporal Advanced Very High Resolution Radiometer (AVHRR) data using all available bands and derived Normalized Difference Vegetation Index (NDVI). The continuous fields describe sub-pixel proportions of cover for tree, herbaceous, bare ground and water cover types.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/veg_continuous_fields_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"cru_monthly_climate_xdeg_1014 v1 - This data set contains monthly climate time series data created by the Climatic Research Unit (CRU) at the University of East Anglia, U.K., for every year covering the period 1986 to 1995. This time series is a subset of a larger CRU monthly data set that covers the period of 1901 to 1996.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/near-surface_met/cru_monthly_climate_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"erbe_albedo_monthly_xdeg_957 v1 - This data set, ISLSCP II Earth Radiation Budget Experiment (ERBE) Monthly Albedo, 1986-1990, contains both the original ERBE albedo data at 2.5 degree spatial resolution, and the International Land Surface Climatology Project Initative II (ISLSCP Initiative II) albedo product re-gridded to 1 degree resolution. The goals of the ERBE were (1) to understand the radiation balance between the Sun, Earth, atmosphere, and space and (2) to establish an accurate, long-term baseline data set for detection of climate changes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/erbe_albedo_monthly_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ecmwf_met_1deg_1222 v1 - This data set for the ISLSCP Initiative II data collection provides meteorology data with fixed, monthly, monthly-6-hourly, 6-hourly, and 3-hourly temporal resolutions. The data were derived from the European Centre for Medium-range Weather Forecasts (ECMWF) near-surface meteorology data set, 40-year re-analysis, or ERA-40 (Simmons and Gibson, 2000), which covers the years 1957 to 2001.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/near-surface_met/ecmwf_met_1deg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ecosystem_roots_1deg_929 v1 - The goal of this study was to predict the global distribution of plant rooting depths based on data about global aboveground vegetation structure and climate. Vertical root distributions influence the fluxes of water, carbon, and soil nutrients and the distribution and activities of soil fauna.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/ecosystem_roots_1deg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"edgar_atmos_emissions_1deg_1022 v1 - The EDGAR (Emission Database for Global Atmospheric Research) database project is a comprehensive task carried out jointly by the National Institute for Public Health (RIVM) and the Netherlands Organization for Applied Scientific Research (TNO) and stores global emission inventories of direct and indirect greenhouse gases from anthropogenic sources including halocarbons and aerosols both on a per country and region basis as well as on a grid (see http://www.rivm.nl/edgar/). For the ISLSCP Initiative II data collection, gridded global annual anthropogenic emissions for the greenhouse gases C...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/carbon/edgar_atmos_emissions_1deg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fasir_biophys_monthly_xdeg_970 v1 - The Fourier-Adjusted, Sensor and Solar zenith angle corrected, Interpolated, Reconstructed (FASIR) adjusted Normalized Difference Vegetation Index (NDVI) data set and derived biophysical parameter fields were generated to provide a 17-year, satellite record of monthly changes in the photosynthetic activity of terrestrial vegetation. This multiple resolution (1/4, 1/2 and 1 degree in latitude and longitude) biophysical parameter data set contains essential variables for the calculation of photosynthesis, and the energy and water exchange between the Earth's surface (in particular of vegetati...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/fasir_biophys_monthly_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fasir_ndvi_monthly_xdeg_972 v1 - The Fourier-Adjusted, Sensor and Solar zenith angle corrected, Interpolated, Reconstructed (FASIR) adjusted Normalized Difference Vegetation Index (NDVI) data sets were generated to provide a 17-year, satellite record of monthly changes in the photosynthetic activity of terrestrial vegetation. FASIR-NDVI data are also used in climate models and biogeochemical models to calculate photosynthesis, the exchange of CO2 between the atmosphere and the land surface, land-surface evapotranspiration and the absorption and release of energy by the land surface.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/fasir_ndvi_monthly_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"gts_precip_daily_xdeg_1001 v1 - The objective of this work was to construct a long-term data set of daily precipitation on half degree and one degree latitude/longitude grids over the global land areas. The analyses are defined by interpolating station observations from GTS (Global Telecommunications System) gauges using the algorithm of Shepard (1968).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/hydrology_soils/gts_precip_daily_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"gimms_ndvi_monthly_xdeg_973 v1 - The Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data sets were generated to provide a 22-year satellite record of monthly changes in terrestrial vegetation. This data set contains three data files provided at spatial resolutions of 0.25, 0.5 and 1.0 degree in latitude and longitude with data from July 1981 through December 2002.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/gimms_ndvi_monthly_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"gdp_xdeg_974 v1 - The data sets in this directory were provided by Mr. Gregory Yetman and Drs.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/socioeconomic/gdp_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"islscp2_soils_1deg_1004 v1 - This data set provides gridded data for selected soil parameters derived from data and methods developed by the Global Soil Data Task, an international collaborative project with the objective of making accurate and appropriate data relating to soil properties accessible to the global change research community. The task was coordinated by the International Geosphere-Biosphere Programme (IGBP-DIS).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/hydrology_soils/islscp2_soils_1deg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"global_population_xdeg_975 v1 - Global Population of the World (GPW) translates census population data to a latitude-longitude grid so that population data may be used in cross-disciplinary studies. There are three data files with this data set for the reference years 1990 and 1995.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/socioeconomic/global_population_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"gpcc_precip_monthly_xdeg_995 v1 - The Global Precipitation Climatology Centre (GPCC), which is operated by the Deutscher Wetterdienst (National Meteorological Service of Germany), is a component of the Global Precipitation Climatology Project (GPCP) with the main emphasis on the treatment of the global in-situ observations. The GPCC simultaneously contributes to the Global Climate Observing System (GCOS) and other international research and climate monitoring projects.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/hydrology_soils/gpcc_precip_monthly_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"gpcp_precip_pentad_xdeg_1002 v1 - The Global Precipitation Climatology Project (GPCP) pentad version 1 precipitation data set includes global precipitation rates for 5-day, or pentad, periods. The data sets are derived from measured rain gauge data and merged with satellite estimates of rainfall.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/hydrology_soils/gpcp_precip_pentad_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"gpcp_precip_monthly_xdeg_1003 v1 - The Global Precipitation Climatology Project (GPCP) Version 2 data set includes global, monthly precipitation rates and associated random errors (RMSE), and a monthly precipitation climatology derived as an average from all GPCP data sets from January 1979 to December 1999. The data are derived from measured gauge data and merged with satellite estimates of rainfall.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/hydrology_soils/gpcp_precip_monthly_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"gppdi_npp_gridded_xdeg_1023 v1 - Net Primary Production (NPP) is an important component of the carbon cycle and, among the pools and fluxes that make up the cycle, it is one of the steps that are most accessible to field measurement. Direct measurement of NPP is not practical for large areas and so models are generally used to study the carbon cycle at a global scale.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/carbon/gppdi_npp_gridded_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"river_carbon_flux_xdeg_1028 v1 - The River Carbon Flux data set represents estimates for the riverine export of carbon and of sediments. This data set includes the amounts of carbon and of sediments that are discharged to the oceans by rivers for each coastal grid point which receives river inputs.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/carbon/river_carbon_flux_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"sea_ice_extent_xdeg_981 v1 - This International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data set, ISLSCP II Global Sea Ice Concentration, is based on the Goddard Space Flight Center (GSFC) Sea Ice Concentrations from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and the Defense Meteorological Satellites Program (DMSP) Special Sensor Microwave/Imager (SSM/I) Passive Microwave Data. This data set contains four zip files which includes sea ice concentration (in percentage of ocean area covered by sea ice), table data and map data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/snow_sea-ice_oceans/sea_ice_extent_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"globalview_co2_point_1111 v1 - The GlobalView Carbon Dioxide (CO2) data product contains synchronized and smoothed time series of atmospheric CO2 concentrations at selected sites that were created using the data extension and integration techniques described by Masarie and Tans, (1995). The information needed to derive this time series is also in this data set, along with extensive documentation.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/carbon/globalview_co2_point/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"globalview_ch4_point_1109 v1 - The GlobalView Methane (CH4) data product contains synchronized and smoothed time series of atmospheric CH4 concentrations at selected sites that were created using the data extension and integration techniques described by Masarie and Tans (1995). The information needed to derive this time series is also in this data set, along with extensive documentation.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/carbon/globalview_ch4_point/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"gppdi_npp_point_1033 v1 - The Global Primary Production Data Initiative (GPPDI) was set up as a Focus 1 activity of the IGBP Data and Information System, a coordinated international program to improve worldwide estimates of terrestrial net primary productivity (NPP) for parameterization, calibration, and validation of NPP models at various scales. The GPPDI data collection contains documented field measurements of NPP for global terrestrial sites compiled from published literature and other extant data sources.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/carbon/gppdi_npp_point/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"historic_cropland_xdeg_966 v1 - The Historical Croplands Cover data set was developed to understand the consequences of historical changes in land use and land cover for ecosystem goods and services. In particular, this data set can be used to study how global changes in cultivated area has influenced climate, biogeochemical cycles, biodiversity, etc.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/historic_cropland_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"historic_landcover_xdeg_967 v1 - The Historical Land Cover and Land Use data set was developed to provide the global change community with historical land use estimates. The data set describes historical land use changes over a 300-year historical period (1700-1990).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/historic_landcover_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"hydro1k_elevation_xdeg_1007 v1 - This data set contains coarse scale elevation and elevation-based parameters at 1.0 and 0.5-degree spatial resolutions that were developed to support a wide variety of global modeling activities through the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data collection. These coarse scale data have sufficient statistical information (up to fourth moment) to allow a good statistical description of the sub-cell distribution of any particular elevation parameter (i.e.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/hydrology_soils/hydro1k_elevation_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"edc_landcover_xdeg_930 v1 - This data set describes the geographic distributions of 17 classes of land cover based on the International Geosphere-Biosphere DISCover land cover legend (Loveland and Belward 1997) and the 15 classes of the SiB model processed at the USGS EROS Data Center (EDC). Specifically, the resampled DISCover datasets were derived from the 1km DISCover data set compiled by the USGS.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/edc_landcover_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"model_npp_xdeg_1027 v1 - This data set contains modeled annual net primary production (NPP) for the land biosphere from seventeen different global models. Annual NPP is defined as the net difference of annual carbon uptake (grams CO2/m2/yr) from the atmosphere through photosynthesis by the land vegetation and that lost back to the atmosphere through autotrophic and maintenance respiration.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/carbon/model_npp_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"combined_ancillary_xdeg_1200 v1 - This data set contains the ISLSCP II fixed land/water masks and percentages of land or water in each cell. There are seven zip data files: four produced from a 1-km land/water mask compiled at the Jet Propulsion Laboratory (JPL) in support of NASA''s Earth Observing System; two files of a land outline overlay created from the land/water mask files created at NASA''s Goddard Space Flight Center; and one file which is a latitude grid coordinate file and longitude grid coordinate file produced by the ISLSCP II staff.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/ancillary/combined_ancillary_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ornl_lai_point_971 v1 - Leaf Area Index (LAI) data from the scientific literature, covering the period from 1932-2000, have been compiled at the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) to support model development and validation for products from the MODerate Resolution Imaging Spectroradiometer (MODIS) instrument. There is one data file which consists of a spreadsheet table, together with a bibliography of more than 300 original-source references.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/ornl_lai_point/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"modis_albedo_2002_xdeg_958 v1 - This International Satellite Land Surface Climatology Project (ISLSCP II) MODerate resolution Image Spectroradiometer (MODIS) dataset, ISLSCP II MODIS (Collection 4) Albedo 2002, provides albedo data for the period January 2002 through December 2002.The MODIS bidirectional reflectance distribution function (BRDF) albedo product (MOD43B) provides measures of clear sky surface albedo every 16 days. Both white-sky albedo (bihemispherical reflectance) and black-sky albedo (directional hemispherical reflectance) at local solar noon are provided for 7 spectral bands and 3 broadbands.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/modis_albedo_2002_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"modis_landcover_xdeg_968 v1 - This data set, ISLSCP II MODIS (Collection 4) IGBP Land Cover, 2000-2001, contains global land cover classifications (dominant type, classification confidence and fractional cover) generated using a full year of MODerate Resolution Imaging Spectroradiometer (MODIS) data covering the period from October 2000 to October 2001. The objective of the MODIS Land Cover Product is to provide a suite of land cover types useful to global system science modelers by exploiting the information content of MODIS data in the spectral, temporal, spatial, and directional domains.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/modis_landcover_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"snowfree_albedo_1deg_956 v1 - This data set contains monthly average snow-free surface shortwave albedo calculated for the period 1982-1998 and estimates of background soil/litter reflectances in the visible (0.4-0.7 μm) and near-infrared (NIR) (0.7-1.0 μm) wavelengths. Biophysical Parameters derived from the FASIR-NDVI (Fourier Adjusted, Solar zenith angle correction, Interpolation, and Reconstruction of Normalized Difference Vegetation Index) data set developed for the ISLSCP Initiative II data collection for the months of January 1982 through December 1998 were used to calculate monthly mean surface albedos at 1 X ...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/snowfree_albedo_1deg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"noaa_albedo_5year-av_xdeg_959 v1 - The objective of this work was to produce a monthly climatology of broadband surface albedos for use in global numerical weather prediction models at the National Centers for Environmental Prediction (NCEP). Monthly means of clear-sky, surface, broadband, snow-free albedos for overhead sun illumination angle were determined using data from a five-year period from April 1985-December 1987 and January 1989-March 1991.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/noaa_albedo_5year-av_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"snow_cover_xdeg_982 v1 - This ISLSCP data set is derived from the National Snow and Ice Data Center (NSIDC) Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent product which combines snow cover and sea ice extent at weekly intervals for October 1978 through June 2001, and snow cover alone from 1966 through June 2001. The original data set was the first representation of combined snow and sea ice measurements derived from satellite observations for the period of record.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/snow_sea-ice_oceans/snow_cover_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"potential_veg_xdeg_961 v1 - This data set was developed to describe the state of the global land cover in terms of 15 major vegetation types, plus water, before alteration by humans. It forms a complement to the historical croplands data set developed by Ramankutty and Foley (1999).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/potential_veg_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ncep_met_1deg_1226 v1 - This data set for the ISLSCP Initiative II data collection provides near surface meteorological variables, fluxes of heat, moisture and momentum at the surface, and land surface state variables, all with a spatial resolution of 1 degree in both latitude and longitude. There are four temporal categories of data: time invariant and monthly mean annual cycle fields (together referred to as \"fixed\" fields), monthly mean fields, monthly 3-hourly diurnal, and 3-hourly fields.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/near-surface_met/ncep_met_1deg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"river_routing_stn_xdeg_1005 v1 - The Simulated Topological Network (STN-30p) data set provides the large-scale hydrological modeling community an accurate representation of the global river system at 0.5 degree and 1.0 degree spatial resolutions. STN-30p represents the potential connectivity of the continental land mass by assigning one of eight (E, SE, S, SW, W, NW, N, NE) possible flow directions to each continental grid cell (Jenson 1988, Band 1993).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/hydrology_soils/river_routing_stn_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"sea_surface_temp_1deg_980 v1 - Sea surface temperature (SST) is an important indicator of the state of the earth climate system as well as a key variable in the coupling between the atmosphere and the ocean. Accurate knowledge of SST is essential for climate monitoring, prediction and research.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/snow_sea-ice_oceans/sea_surface_temp_1deg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"modis_albedo_2002_filled_xdeg_960 v1 - This data set, ISLSCP II Snow-Free, Spatially Complete, 16 Day Albedo, 2002, contains 9 files for snow-free, spatially complete 16-day global black-sky albedos at local solar noon, white-sky albedos and quality information based on MODerate Resolution Imaging Spectroradiometer (MODIS) Collection 4 Albedo Products (MOD43B3). Data are provided for 7 spectral bands and 3 broad bands for a full year of MODIS data (2002).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/modis_albedo_2002_filled_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"srb_radiation_1deg_1201 v1 - This data set contains global Surface Radiation Budget (SRB) and a few top-of-atmosphere (TOA) radiation budget parameters on a 1-degree x 1-degree spatial resolution. These parameters are provided as monthly, monthly-3 hourly (i.e.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/radiation_clouds/srb_radiation_1deg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"root_water_storage_1deg_1006 v1 - This data set provides two estimates of the geographic distribution of the total plant-available soil water storage capacity of the rooting zone (\"rooting zone water storage size\") on a 1.0 degree global grid. Two inverse modeling methods were used.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/hydrology_soils/root_water_storage_1deg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"comp_runoff_monthly_xdeg_994 v1 - The University of New Hampshire (UNH)/Global Runoff Data Centre (GRDC) composite runoff data combines simulated water balance model runoff estimates derived from climate forcing with monitored river discharge. It can be viewed as a data assimilation applied in a water balance model context (conceptually similar to the commonly used 4DDA techniques used in meteorological modeling).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/hydrology_soils/comp_runoff_monthly_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"umd_landcover_xdeg_969 v1 - The objective of the International Satellite Land Surface Climatology Project (ISLSCP II) study that produced this data set, ISLSCP II University of Maryland Global Land Cover Classifications 1992-1993, was to create a land cover map derived from 1 kilometer Advanced Very High Resolution Radiometer (AVHRR) data using all available bands, derived Normalized Difference Vegetation Index (NDVI), and a full year of data (April 1992-March 1993). This thematic map was resampled to 0.25, 0.5 and 1.0 degree grids.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/islscp_ii/vegetation/umd_landcover_xdeg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-islscp-ii","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ISS_RapidScat Project","Description":"This dataset contains the ISS-RapidScat Version 2.0 Level 1B geo-located Sigma-0 measurements and antenna pulse \"egg\" and \"slice\" geometries as derived from ephemeris and the Level 1A dataset. The pulse \"egg\" represents the complete footprint of the pulse, which has a spatial geometry of approximately 25 km by 35 km. There are 8 slices that constitute the range-binned components of a pulse each of which has a spatial geometry of approximately 25 km by 7 km. The orientation of the long dimension of the slices varies with the rotation of the antenna and thus does not align with the along/across track orientation of the wind vector grid in the L2B/L2A products. Version 2.0 represents a complete historical re-processing of the L1B data record and provides a calibration which is consistent across the several signal to noise ratio states experienced by RapidScat throughout its operation period (see the technical note for Version 2.0 under Documentation). The Version 2.0 is also the dataset used to derive the Version 2.0 wind products (L2B). Data are provided in single-orbit files in HDF-4 format. This dataset is intended for expert use only. If you must use RapidScat Sigma-0 data but you are unsure about how to use the L1B data record, please consider using either of the following L2A datasets: 1) https://podaac.jpl.nasa.gov/dataset/RSCAT_L2A_25KM_V2.0 or 2) https://podaac.jpl.nasa.gov/dataset/RSCAT_L2A_12KM_V2.0. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the ISS Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval.\n<br>\n\n#### RSCAT_L2A_12KM_V2.0\nThis dataset contains the Version 2.0 ISS-RapidScat on Level 2A 12.5 km science data record, which provides surface-flagged sigma-0 in 12.5 km Wind Vector Cells processed using the pulse \"slice\" Sigma-0 data provided by the Level 1B dataset. Due to the circular scan of the RapidScat instrument the expected number of Sigma-0 cells per WVC is not constant. To minimize the L2A data volume, the Sigma-0 cell data are stored as \"lists\" for each WVC row, with each list indexed by a \"cell_index\" array to indicate the cross-track WVC membership of the data. Each cell is then checked for land or ice and flagged accordingly. Attenuation corrections for each Sigma-0 measurement are also provided. Version 2.0 represents a complete historical re-processing of the L2A data record and provides a calibration which is consistent across the several signal to noise ratio states experienced by RapidScat throughout its operation period (see the technical note for Version 2.0 under Documentation). It is also derived from the same L1B V2.0 product that was used to generate Version 2.0 wind products (L2B). Data are provided in single-orbit files in HDF-4 format. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval.\n<br>\n\n#### RSCAT_L2A_25KM_V2.0\nThis dataset contains the Version 2.0 ISS-RapidScat Level 2A 25km science data record, which provides surface-flagged sigma-0 in 25km Wind Vector Cells processed using the pulse \"egg\" Sigma-0 data provided by the Level 1B dataset. Due to the circular scan of the SeaWinds instrument the expected number of Sigma-0 cells per WVC is not constant. To minimize the L2A data volume, the Sigma-0 cell data are stored as \"lists\" for each WVC row, with each list indexed by a \"cell_index\" array to indicate the cross-track WVC membership of the data. Each cell is then checked for land or ice and flagged accordingly. Attenuation corrections for each Sigma-0 measurement are also provided. Version 2.0 represents a complete historical re-processing of the L2A data record and provides a calibration which is consistent across the several signal to noise ratio states experienced by RapidScat throughout its operation period (see the technical note for Version 2.0 under Documentation). It is also derived from the same L1B V2.0 product that was used to generate Version 2.0 wind products (L2B). Data are provided in single-orbit files in HDF-4 format. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval.\n<br>\n\n#### RSCAT_LEVEL_2B_OWV_CLIM_12_V1\nThis dataset contains the RapidScat Level 2B 12.5km Version 1.0 Climate quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the using the \"full aperture\" normalized radar cross-section (NRCS, a.k.a. Sigma-0) from the L1B dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via Direct Download and OPeNDAP. For data access, please click on the \"Data Access\" tab above. This climate quality data set differs from the nominal \"slice\" L2B dataset as follows: 1) it uses full antenna footprint measurements (~20-km) without subdividing by range (~7-km) and 2) the absolute calibration has been modified for the two different low signal-to-noise ratio (SNR) mode data sets: LowSNR1 14 August 2015 to 18 September 2015; LowSNR2 6 October 2015 to 7 February 2016. The above enhancements allow this dataset to provide consistent calibration across all SNR states. Low SNR periods and other key quality control (QC) issues are tracked and kept up-to-date in PO.DAAC Drive at https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/rapidscat/open/L1B/docs/revtime.csv. If you have any questions, please visit our user forums: https://podaac.jpl.nasa.gov/forum/.\n<br>\n\n#### RSCAT_LEVEL_2B_OWV_CLIM_12_V2\nThis dataset contains the RapidScat Level 2B 12.5km Version 2.0 Climate quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the using the \"full aperture\" normalized radar cross-section (NRCS, a.k.a. Sigma-0) from the L1B dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. The new version has two important improvements over the previous version 1.0. First, an SST-dependent GMF developed by Lucrezia Ricciardulli of Remote Sensing Systems is used in wind retrieval in order to fix persistent speed biases in Ku-band data over cold ocean. Second, flagging is simplified and extra flags are provided. All the previously existing flags are still there and still reflect the same meaning and purpose. A new single bit wind_retrieval_likely_corrupted_flag specifies the approximately 3% of the data which is known to have suboptimal performance due to rain, ice, or a few other rare anomalous cases. Another bit wind_retrieval_possibly_corrupted_flag specifies the approximately 15% of the data near rain, near ice, or near the coast, that is thought to be high quality but may not match up well with numerical wind models due to either remaining rain/ice/land contamination or variability in the winds near ice, rain, and coasts that are not reflected in the NWPs. In addition to these two new bits, copious quality information is provided in the data to allow users to tailor flags to meet their own needs. There is also an added a global attribute called rev_status that specifies whether the RapidScat Instrument was in the original (highest data quality) high SNR mode, or one of the four low SNR time periods, the latter of which indicates the accuracy of winds below 5 m/s is degraded. This attribute also serves to identify MARGINAL orbits in which there are large gaps in the data record due to suboptimal spacecraft attitude. Other than gaps in the data, the accuracy of the winds in the MARGINAL orbits are similar to other orbits. This dataset is provided in netCDF-4 format and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above.\n<br>\n\n#### RSCAT_LEVEL_2B_OWV_COMP_12_V1.1\nThis dataset contains the RapidScat Level 2B 12.5km Version 1.1 science-quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above. This Version 1.1 dataset differs from the previous Version 1 dataset as follows: 1) A new neural network approach for high wind speeds provided rain corrections for the \"retrieve_wind_speed\" variable for wind speeds in excess of 15 m/s. 2) The data variables containing the number of measurements of each type for each wind vector cell have been corrected; these variables include \"number_in_aft\", \"number_in_fore\", \"number_out_aft\", and \"number_out_fore\". 3) The \"wind_obj\" data variable has been corrected to include the proper data for the conditional probability for the objective DIRTH function values. It is advised for users to avoid using the \"wind_obj\" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the \"ambiguity_obj\" variable. The \"wind_obj\" variable contains DIRTH probabilities (which are derived form the \"ambiguity_obj\" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions, please contact podaac@podaac.jpl.nasa.gov\n<br>\n\n#### RSCAT_LEVEL_2B_OWV_COMP_12_V1.2\nThis dataset contains the RapidScat Level 2B 12.5km Version 1.2 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 data forward from orbital revolution number 5127, corresponding to 19 August 2015; the overlapping time period starting on 19 August 2015 corresponds to the first time period of the recorded low signal-to-noise ratio (SNR). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above. This Version 1.2 dataset differs from the previous Version 1.1 dataset as follows: 1) L1B sigma-0 has been re-calibrated during the periods of low signal-to-noise ratio (SNR) and 2) during low SNR periods the L1B sigma-0 calibration is determined using re-pointed L1B QuikSCAT data. It is advised for users to avoid using the \"wind_obj\" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the \"ambiguity_obj\" variable. The \"wind_obj\" variable contains DIRTH probabilities (which are derived form the \"ambiguity_obj\" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions or concerns, please visit our Forum at https://podaac.jpl.nasa.gov/forum/.\n<br>\n\n#### RSCAT_LEVEL_2B_OWV_COMP_12_V1.3\nThis dataset contains the RapidScat Level 2B 12.5km Version 1.3 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 and 1.2 data forward from orbital revolution number 7873, corresponding to 11 February 2016; on 11 Feb 2016, RapidScat entered it's 3rd low signal to noise ratio (SNR) state and the initial calibration of low SNR 3 was preliminary during the Version 1.2 release. The fundamental difference between Version 1.3 and the previous Version 1.2 datasets is that the L1B sigma-0 has been re-calibrated during the periods of low SNR states 3 and 4 using re-pointed QuikSCAT data. The Version 1.1 should still be considered valid up to the first rev of version 1.2 (5127), and similarly version 1.2 shall be considered valid up to the first rev of version 1.3 (7873). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above. It is advised for users to avoid using the \"wind_obj\" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the \"ambiguity_obj\" variable. The \"wind_obj\" variable contains DIRTH probabilities (which are derived form the \"ambiguity_obj\" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions or concerns, please visit our Forum at https://podaac.jpl.nasa.gov/forum/.\n<br>\n\n#### RSCAT_COLOCATED_RSS_RADIOMETER_LEVEL_2B_V1\nThis dataset contains the multi-sourced microwave radiometer wind speed, rain and cloud liquid water data collocated to RapidScat Level 2B wind vector cell (WVC) locations. The corresponding NASA mission is officially referred to as ISS-RapidScat. This dataset is produced by Remote Sensing Systems (RSS) with direct funding from the JPL RapidScat project. All of the collocated radiometer data is produced by RSS. The co-located radiometer sources include: 1) DMSP SSM/I (F15) and SSMIS (F16/F17), 2) Coriolis WindSat, 3) GCOM-W1 AMSR2 and 4) GPM Core GMI; more details on these radiometer sources and sensors can be extracted by scrolling down to the \"Platform/Sensor\" section below this description. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-4 file format that follows the netCDF \"classic\" model and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","earth observation","hdf","ice","netcdf","oceans","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"RSCAT_L1B_V2.0 v2.0 - This dataset contains the ISS-RapidScat Version 2.0 Level 1B geo-located Sigma-0 measurements and antenna pulse \"egg\" and \"slice\" geometries as derived from ephemeris and the Level 1A dataset. The pulse \"egg\" represents the complete footprint of the pulse, which has a spatial geometry of approximately 25 km by 35 km.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/RSCAT_L1B_V2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"RSCAT_L2A_12KM_V2.0 v2.0 - This dataset contains the Version 2.0 ISS-RapidScat on Level 2A 12.5 km science data record, which provides surface-flagged sigma-0 in 12.5 km Wind Vector Cells processed using the pulse \"slice\" Sigma-0 data provided by the Level 1B dataset. Due to the circular scan of the RapidScat instrument the expected number of Sigma-0 cells per WVC is not constant.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/RSCAT_L2A_12KM_V2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"RSCAT_L2A_25KM_V2.0 v2.0 - This dataset contains the Version 2.0 ISS-RapidScat Level 2A 25km science data record, which provides surface-flagged sigma-0 in 25km Wind Vector Cells processed using the pulse \"egg\" Sigma-0 data provided by the Level 1B dataset. Due to the circular scan of the SeaWinds instrument the expected number of Sigma-0 cells per WVC is not constant.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/RSCAT_L2A_25KM_V2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"RSCAT_LEVEL_2B_OWV_CLIM_12_V1 v1.0 - This dataset contains the RapidScat Level 2B 12.5km Version 1.0 Climate quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the using the \"full aperture\" normalized radar cross-section (NRCS, a.k.a.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/RSCAT_LEVEL_2B_OWV_CLIM_12_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"RSCAT_LEVEL_2B_OWV_CLIM_12_V2 v2.0 - This dataset contains the RapidScat Level 2B 12.5km Version 2.0 Climate quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the using the \"full aperture\" normalized radar cross-section (NRCS, a.k.a.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/RSCAT_LEVEL_2B_OWV_CLIM_12_V2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"RSCAT_LEVEL_2B_OWV_COMP_12_V1.1 v1.1 - This dataset contains the RapidScat Level 2B 12.5km Version 1.1 science-quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/RSCAT_LEVEL_2B_OWV_COMP_12_V1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"RSCAT_LEVEL_2B_OWV_COMP_12_V1.2 v1.2 - This dataset contains the RapidScat Level 2B 12.5km Version 1.2 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 data forward from orbital revolution number 5127, corresponding to 19 August 2015; the overlapping time period starting on 19 August 2015 corresponds to the first time period of the recorded low signal-to-noise ratio (SNR). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/RSCAT_LEVEL_2B_OWV_COMP_12_V1.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"RSCAT_LEVEL_2B_OWV_COMP_12_V1.3 v1.3 - This dataset contains the RapidScat Level 2B 12.5km Version 1.3 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 and 1.2 data forward from orbital revolution number 7873, corresponding to 11 February 2016; on 11 Feb 2016, RapidScat entered it's 3rd low signal to noise ratio (SNR) state and the initial calibration of low SNR 3 was preliminary during the Version 1.2 release. The fundamental difference between Version 1.3 and the previous Version 1.2 datasets is that the L1B sigma-0 has been re-calibrated during the periods of ...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/RSCAT_LEVEL_2B_OWV_COMP_12_V1.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"RSCAT_COLOCATED_RSS_RADIOMETER_LEVEL_2B_V1 v1.0 - This dataset contains the multi-sourced microwave radiometer wind speed, rain and cloud liquid water data collocated to RapidScat Level 2B wind vector cell (WVC) locations. The corresponding NASA mission is officially referred to as ISS-RapidScat.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/RSCAT_COLOCATED_RSS_RADIOMETER_LEVEL_2B_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-iss-rapidscat","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA JASON-1 Project","Description":"The enhanced Jason-1 Microwave Radiometer (JMR) corrections contains better wet tropospheric path delay corrections along with better land, rain and ice flagging for coastal regions than that found in the Jason-1 Geophysical Data Records (GDR). The enhanced corrections can be used in place of the GDR wet troposphere correction to provide more accurate Sea Surface Height Anomalies for coastal regions.\n<br>\n\n#### JASON-1_L2_OST_GPR_E\nThese Sea Surface Height Anomalies (SSHA) are derived from the Jason-1 Geophysical Data Record (GDR). Jason-1 is an altimetric mission whose instruments make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, and position relative to the GPS satellite constellation. Using the various parameter the SSHA can be calculated and are provided in this dataset. The data are in NetCDF format. This dataset only contains the parameters that are directly related to SSHA.\n<br>\n\n#### JASON-1_L2_OST_GPR_E_GEODETIC\nThese Sea Surface Height Anomalies (SSHA) are derived from the Jason-1 Geophysical Data Record (GDR) Geodetic Mission. Jason-1 is an altimetric mission whose instruments make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, and position relative to the GPS satellite constellation. Using the various parameter the SSHA can be calculated and are provided in this dataset. The data are in NetCDF format.\n<br>\n\n#### JASON-1_L2_OST_GPN_E\nThe Jason-1 Geophysical Data Records (GDR) contain full accuracy altimeter data to measure sea surface height, with a high precision orbit (accuracy ~1.5 cm). The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation. The GDR contain all relevant corrections needed to calculate the sea surface height. Sea surface height anomalies calculation and recommended data edit criteria are specified in the Jason-1 GDR User Handbook at https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/jason1/open/L2/gdr_netcdf_e/docs/Handbook_Jason-1_v5.1_April2016.pdf\n<br>\n\n#### JASON-1_L2_OST_GPN_E_GEODETIC\nThe Jason-1 Geophysical Data Records (GDR) Geodetic Mission contain full accuracy altimeter data, with a high precision orbit, provided approximately 35 days after data collection. The data are sorted into cycles that are approximately 11 days long and contain 280 pass files. The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation. The GDR contain all relevant corrections needed to calculate the sea surface height.\n<br>\n\n#### JASON-1_L2_OST_GPS_E\nThe Sensory Geophysical Data Record (SGDR) files contain full accuracy altimeter data, with a high precision orbit (accuracy ~1.5 cm). The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation. The SGDR contain all relevant corrections needed to calculate the sea surface height. It also contains the 20Hz waveforms that are required for retracking. The SGDR is an expert level product, if you do not require the waveforms then the GDR/GPN or GPR will be more suited for your needs.\n<br>\n\n#### JASON-1_L2_OST_GPS_E_GEODETIC\nThe Sensory Geophysical Data Record (SGDR) files from the Geodetic Mission contain full accuracy altimeter data, with a high precision orbit. The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation. The SGDR contain all relevant corrections needed to calculate the sea surface height. It also contains the 20Hz waveforms that are required for retracking. The SGDR is an expert level product, if you do not require the waveforms then the GDR will be more suited for your needs.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","netcdf","oceans","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"JASON-1_JMR_ENH v1 - The enhanced Jason-1 Microwave Radiometer (JMR) corrections contains better wet tropospheric path delay corrections along with better land, rain and ice flagging for coastal regions than that found in the Jason-1 Geophysical Data Records (GDR). The enhanced corrections can be used in place of the GDR wet troposphere correction to provide more accurate Sea Surface Height Anomalies for coastal regions.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON-1_JMR_ENH","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON-1_L2_OST_GPR_E vE - These Sea Surface Height Anomalies (SSHA) are derived from the Jason-1 Geophysical Data Record (GDR). Jason-1 is an altimetric mission whose instruments make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, and position relative to the GPS satellite constellation.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON-1_L2_OST_GPR_E","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON-1_L2_OST_GPR_E_GEODETIC vE - These Sea Surface Height Anomalies (SSHA) are derived from the Jason-1 Geophysical Data Record (GDR) Geodetic Mission. Jason-1 is an altimetric mission whose instruments make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, and position relative to the GPS satellite constellation.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON-1_L2_OST_GPR_E_GEODETIC","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON-1_L2_OST_GPN_E vE - The Jason-1 Geophysical Data Records (GDR) contain full accuracy altimeter data to measure sea surface height, with a high precision orbit (accuracy ~1.5 cm). The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON-1_L2_OST_GPN_E","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON-1_L2_OST_GPN_E_GEODETIC vE - The Jason-1 Geophysical Data Records (GDR) Geodetic Mission contain full accuracy altimeter data, with a high precision orbit, provided approximately 35 days after data collection. The data are sorted into cycles that are approximately 11 days long and contain 280 pass files.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON-1_L2_OST_GPN_E_GEODETIC","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON-1_L2_OST_GPS_E vE - The Sensory Geophysical Data Record (SGDR) files contain full accuracy altimeter data, with a high precision orbit (accuracy ~1.5 cm). The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON-1_L2_OST_GPS_E","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON-1_L2_OST_GPS_E_GEODETIC vE - The Sensory Geophysical Data Record (SGDR) files from the Geodetic Mission contain full accuracy altimeter data, with a high precision orbit. The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON-1_L2_OST_GPS_E_GEODETIC","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-jason-1","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA JASON-3 Project","Description":"This is a near real time dataset that provides a GPS based orbit and Sea Surface Height Anomalies (SSHA) from that orbit. It is similar to the Jason-3 Operation Geophysical Data Record (OGDR) that is distributed at NOAA (http://www.nodc.noaa.gov/sog/jason/), but includes the GPS orbit and SSHA as two additional variables. It has a 5 hour time lag due to the time needed to calculate the GPS orbit and SSHA. The GPS orbits have been shown to be more accurate than the DORIS orbits on a near real time scale and therefore produces a more accurate SSHA.<br> Forward stream transitioned from processing baseline/version \"F\" to \"G\" in January 2025. The change is reflected in the product filename: JA3_GPSOPR_2P<b>f</b>S* -> JA3_GPSOPR_2P<b>g</b>S*\n<br>\n\n#### JASON_3_PD_CORRECTION\nThis dataset provides supplementary wet tropospheric corrections for historical Jason-3 observations (https://www.ncei.noaa.gov/archive/accession/Jason3-xGDR). Recent assessments of the global sea level budget have resulted in increased scrutiny of estimates of global sea level change based on Jason-3. After a careful assessment of the wet tropospheric correction derived from the Advanced Microwave Radiometer (AMR) instrument, it was determined that further improvements to the accuracy of the historical Jason-3 observations could be made. Since this assessment was only completed after Jason-3 data was reprocessed to GDR-F (Geophysical Data Record – Version F) standards, it was not included in the GDR-F product release. For this reason, this supplementary correction product has been created using the method of Brown et al. (2012) to allow users to correct path delay and sea surface height observations, reducing errors in estimates of global sea level change by 2-3 mm over 8 years.<br><br> The correction was computed based on comparison of the AMR-observed brightness temperatures with independent satellite observations from the Special Sensor Microwave Imager Sounder (SSMI), F16, F17 and F18, Fundamental Climate Data Records. SSMI data was obtained from the NOAA Climate Data Record (CDR) of SSMIS Microwave Brightness Temperatures, RSS Version 8 (Wentz et al., 2019, https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc:C01567/html). The method described in Brown et al. (2012) to map SSMI Brightness Temperatures to AMR equivalent brightness temperatures (TBs) was used. Although it was found that it made little difference to the result, a bias was removed between SSMI equivalent AMR TBs and AMR TBs with respect to latitude for all data prior to computing temporal trends. In addition, only rain free, mostly clear data (TB18.7 GHz < 160K) data were considered.<br><br> The correction is supplied on a pass-by-pass basis in a 4-column text file. See the product documentation for guidance on how to apply it to Jason-3 observations.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"JASON_3_L2_OST_OGDR_GPS vG - This is a near real time dataset that provides a GPS based orbit and Sea Surface Height Anomalies (SSHA) from that orbit. It is similar to the Jason-3 Operation Geophysical Data Record (OGDR) that is distributed at NOAA (http://www.nodc.noaa.gov/sog/jason/), but includes the GPS orbit and SSHA as two additional variables.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_3_L2_OST_OGDR_GPS","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_3_PD_CORRECTION vF - This dataset provides supplementary wet tropospheric corrections for historical Jason-3 observations (https://www.ncei.noaa.gov/archive/accession/Jason3-xGDR). Recent assessments of the global sea level budget have resulted in increased scrutiny of estimates of global sea level change based on Jason-3.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_3_PD_CORRECTION","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-jason-3","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA JPSS Project","Description":"This High-Resolution (0.1 x 0.1 degree) Level 3 daily Aerosol Optical Depth (AOD) product is generated by combining two Visible Infrared Imaging Radiometer Suite (VIIRS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the NOAA-20 satellite. This dataset is provided in daily files ranging from 2018-02-17 to the present. The spatial coverage is global and the dataset is gridded at 0.1 x 0.1 degree spatial resolution. The data are generated using Level 2 AOD retrieved using DT and DB algorithms. The product provides multiple options for using data either from DT or DB or combined. Depending on user need and application, they can choose one or more relevant parameter. The pixels with highest quality as recommended by science teams are only considered in these averaging. In addition to averaged AOD at 0.1 x 0.1 degree resolution, standard deviation and number of pixels averaged from each algorithm are also provided. Average sensor zenith angle is also provided for additional filtering of the data. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### AER_DBDT_M10KM_L3_VIIRS_NOAA20\nThis High-Resolution (0.1 x 0.1 degree) Level 3 monthly Aerosol Optical Depth (AOD) product is generated by combining two Visible Infrared Imaging Radiometer Suite (VIIRS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the NOAA-20 satellite. This dataset is provided in monthly files ranging from February 2018 to the present. The spatial coverage is global and the dataset is gridded at 0.1 x 0.1 degree spatial resolution. The data are generated using Level 2 AOD retrieved using DT and DB algorithms. The product provides multiple options for using data either from DT or DB or combined. Depending on user need and application, they can choose one or more relevant parameter. The pixels with highest quality as recommended by science teams are only considered in these averaging. In addition to averaged AOD at 0.1 x 0.1 degree resolution, standard deviation and number of pixels averaged from each algorithm are also provided. Average sensor zenith angle is also provided for additional filtering of the data. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### OMPS_N21_LP_L1G_EV\nThe OMPS-N21 L1G LP Radiance EV Wavelength-Altitude Grid swath orbital 3slit product contains the calibrated earth-viewing radiances measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the NOAA 21 (JPSS-2) satellite. The LP L1G product measures radiances in the wavelength region from 280 nm to 1000 nm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each measuring three limb profiles spaced approximately 250 km in the cross-track direction. The profiles are measured from the ground up to about 80 km with a vertical resolution of the retrieved profiles of approximately 1-2 km. The data are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### SNDRJ1ATMSMAP\nThe Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also known as NOAA-20 (National Oceanic and Atmospheric Administration). The ATMS is a 22-channel mm-wave radiometer. The ATMS will measure upwelling radiances in six frequency bands centered at 23 GHz, 31 GHz, 50-58 GHz, 89 GHz, 66 GHz, and 183 GHz. The ATMS is a total power radiometer, with \"through-the-antenna\" radiometric calibration. Radiometric data is collected by a pair of antenna apertures, scanned by rotating flat plate reflectors. Scanning is performed cross-track to the satellite motion from sun to anti-sun, using the \"integrate-while-scan\" type data collection. The scan period is 8/3 second, synchronized to the Cross-track Infrared Sounder (CrIS) using a spacecraft provided scan synchronization pulse. Since the JPSS-1 satellite is orbiting at an altitude of about 830 km, the instantaneous spatial resolution on the ground at nadir is about 16 km, 32 km, or 75 km depending upon the channel. The brightness temperature data are contained in an array with 135 rows in the along-track direction, 96 columns in the cross-track direction, and a 3rd dimension for each of the 22 channels. The ATMS cross-track scan interval is 0.018 seconds and the along-track scan period is 8/3 seconds.. Data products are constructed on six minute boundaries. The Granule Map Product consists of daily images of granule coverage in PDF format.\n<br>\n\n#### SNDRJ1ML2RMS\nThis level 2 product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the NOAA-20 (National Oceanic and Atmospheric Administration) also know as Joint Polar Satellite System (JPSS-1) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II Level-2 retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties for six minutes of instrument observation at a time. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval A level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### SNDRJ1ML2RMSSUP\nThis level 2 support product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the NOAA-20 (National Oceanic and Atmospheric Administration) also know as Joint Polar Satellite System (JPSS-1) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II Level-2 retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties for six minutes of instrument observation at a time. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval A level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### SNDRJ1ML3DRMS\nThis level 3 daily product is generated from the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the NOAA-20 (National Oceanic and Atmospheric Administration) also know as Joint Polar Satellite System (JPSS-1) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval This daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable.\n<br>\n\n#### SNDRJ1ML3MRMS\nThis level 3 monthly product is generated from the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the NOAA-20 (National Oceanic and Atmospheric Administration) also know as Joint Polar Satellite System (JPSS-1) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval This monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable.\n<br>\n\n#### SNDRJ1IML2CPS\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the NOAA-20 platform, also known as JPSS-1. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR (Full Spectral Resolution) infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2100-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products. A level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### SNDRJ1IML2PLEVCPS\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using data from the JPSS-1 (Joint Polar Satellite System). They include including gas mixing ratio profiles, column totals, surface values, tropopause properties, and relative humidity, together with per-field quality flagging. The profiles are specified at the surface and layer boundaries and are estimated from layer amounts using the L2 algorithm An level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.\n<br>\n\n#### SNDRJ1IML2CCPRET\nWARNING: To users of the derived product “co_mmr_midtrop” (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply “co_mmr_midtrop” by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (“mol_lay/co_mol_lay”) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the NOAA-20 platform, also known as JPSS-1. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR (Full Spectral Resolution) infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2100-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products. A level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### VJ114IMG\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires (VJ114IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor aboard the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as thermal anomalies. The VJ114IMG product includes 26 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., radiance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114IMG product is also used to generate higher-level fire data products. Use of the [VJ103MODLL](https://doi.org/10.5067/viirs/vJ103modll.021) data product is required to apply accurate geolocation information to the VNP14IMG Science Datasets (SDS). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ109\nThe VIIRS/JPSS1 Atmospherically Corrected Surface Reflectance 6-Min L2 Swath 375m, 750m product, with short name VJ109, are estimates of surface reflectance in each of the Visible Infrared Imaging Radiometer Suite (VIIRS) reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. The VJ109 contains approximately 6 minutes' worth of data. Surface reflectance for each moderate-resolution (750m) or imagery-resolution (375m) pixel is retrieved separately for the Level-2 products. Surface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. All surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans.\n<br>\n\n#### VJ143C3\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Albedo Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VJ143C3) is derived from the 30 arc second CMG VJ143D Version 2 product suite. VJ143C3 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VJ143C1](https://doi.org/10.5067/VIIRS/VJ143C1.002) to compute white-sky albedos and the black-sky albedos at local solar noon for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. The quality and inversion status of the majority of the underlying 30 arc second pixels is provided as well as the percentage of the underlying pixels that were present or were snow covered. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VJ143C3 product includes 26 layers containing white-sky albedos and the black-sky albedos for the VIIRS DNB, moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and NIR broadbands. Along with the albedo data for the 13 bands are five ancillary layers for uncertainty, quality, local solar noon, percent finer resolution inputs, and snow cover. A low-resolution image is also available showing retrievals of WSA for the shortwave broadband in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143MA3\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA3 product provides a total of 36 SDS layers including: BSA; WSA; and mandatory quality layers for nine VIIRS moderate bands M1-M5, M7-M8, and M10-M11; as well as near-infrared (NIR); shortwave; and visible broadbands. A low-resolution image is also available showing retrievals of WSA for band M1 in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143IA3\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VJ143IA1](https://doi.org/10.5067/VIIRS/VJ143IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VJ143IA4](https://doi.org/10.5067/VIIRS/VJ143IA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143D54\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M1 (VJ143D54) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D54 is the BSA for VIIRS band M1 (0.412 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D61\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M10 (VJ143D61) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D61 is the BSA for VIIRS band M10 (1.61 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D62\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M11 (VJ143D62) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D62 is the BSA for VIIRS band M11 (2.25 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D55\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M2 (VJ143D55) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D55 is the BSA for VIIRS band M2 (0.445 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D56\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M3 (VJ143D56) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D56 is the BSA for VIIRS band M3 (0.488 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D57\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M4 (VJ143D57) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D57 is the BSA for VIIRS band M4 (0.555 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D58\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M5 (VJ143D58) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D58 is the BSA for VIIRS band M5 (0.672 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D59\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M7 (VJ143D59) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D59 is the BSA for VIIRS band M7 (0.865 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D60\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M8 (VJ143D60) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D60 is the BSA for VIIRS band M8 (1.240 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D63\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band VIS (VJ143D63) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D63 is the BSA for the VIIRS visible broadband (0.64 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D66\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for DNB (VJ143D66) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D66 is the BSA for the VIIRS DNB (0.7 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D64\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for NIR (VJ143D64) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D64 is the BSA for the VIIRS NIR broadband (0.865 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D65\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for ShortWave (VJ143D65) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D65 is the BSA for the VIIRS shortwave broadband (1.61 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D41\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Local Solar Noon product (VJ143D41) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer for each of the parameters included in the [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) product. VJ143D40 through VJ143D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VJ143MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D41 contains the local solar zenith angle at the local solar noon of the representative pixel for the retrieval period. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue). Improvements/Changes from Previous Versions The NOAA-20 VIIRS algorithms include the same improvements as the S-NPP VIIRS V002 * Improved calibration algorithm and better coefficients for entire NOAA-20 mission * Improved geolocation accuracy and updates to fix outliers around maneuver periods and other events * Corrections to the aerosol quantity flag (low, average, high) mainly over brighter surfaces in the mid to high latitudes such as desert and tropical vegetation areas. This has an impact on the retrieval of other downstream data products such as VJ113 Vegetation Indices and VJ143 BRDF/Albedo. * Improved cloud mask input product for corrections along coastlines and artifacts from use of coarse resolution climatology data * Replaced the land/water mask input product with MODIS heritage seven class land/water mask\n<br>\n\n#### VJ143C1\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VJ143C1) is derived from the 30 arc second CMG VJ143D Version 2 product suite. VJ143C1 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. VJ143C1 supplies the weighting parameters associated with the RossThick/Li-Sparse-Reciprocal BRDF model that best describes the anisotropy of each pixel, which is used to produce the [VJ143C3](https://doi.org/10.5067/VIIRS/VJ143C3.002) Albedo and [VJ143C4](https://doi.org/10.5067/VIIRS/VJ143C4.002) Nadir BRDF-Adjusted Reflectance (NBAR) products. The highest quality full inversion values are used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the ancillary layers. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VJ143C1 product includes 39 layers containing the three parameters (fiso, fvol, and fgeo) for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. Along with the parameter data for the 13 bands are five ancillary layers for uncertainty, quality, local solar noon, percent finer resolution inputs, and snow cover. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M4, and M3 as a red, green, blue (RGB) image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143MA1\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VJ143MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M7, and M5 as an RGB image in JPEG format. Known Issues: Known issues for VIIRS BRDF/Albedo data products can be found on the [VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143IA1\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VJ143IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VJ143IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth’s surface. All VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VJ143IA4](https://doi.org/10.5067/VIIRS/VJ143IA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VJ143IA3](https://doi.org/10.5067/VIIRS/VJ143IA3.002)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA1 data product provides a total of six SDS layers including: three mandatory quality layers for bands I1, I2, and I3 and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for each imagery band. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143MA4\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143MA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VJ143 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low-resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143IA4\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VJ143IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VJ143IA1](https://doi.org/10.5067/VIIRS/VJ143IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VJ143IA3](https://doi.org/10.5067/VIIRS/VJ143IA3.002)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143C4\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VJ143C4) is derived from the 30 arc second CMG VJ143D Version 2 product suite. VJ143C3 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VJ143C1](https://doi.org/10.5067/VIIRS/VJ143C1.002) to compute NBAR values for the VIIRS Day/Night band (DNB), and moderate resolution bands M1 through M5, M7, M8, M10, and M11. The algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. The quality and inversion status of the majority of the underlying 30 arc second pixels is provided as well as the percentage of the underlying pixels that were present or were snow covered. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VJ143C4 product includes 10 layers containing NBAR values for VIIRS DNB and moderate resolution bands M1 through M5, M7, M8, M10, and M11. Along with the NBAR data for the 10 bands are five ancillary layers for uncertainty, quality, local solar noon, percent finer resolution inputs, and snow cover. A low-resolution browse image is also available showing NBAR bands M5, M4, and M3 as a red, green, blue (RGB) image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143D80\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M1 (VJ143D80) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D80 through VJ143D89 are the NBAR products of the VJ143D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002) product. In addition to the bands included in VJ143MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VJ143MA4 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D80 is the NBAR for VIIRS band M1 (0.412 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D87\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M10 (VJ143D87) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D80 through VJ143D89 are the NBAR products of the VJ143D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002) product. In addition to the bands included in VJ143MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VJ143MA4 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D87 is the NBAR for VIIRS band M10 (1.61 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D88\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M11 (VJ143D88) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D80 through VJ143D89 are the NBAR products of the VJ143D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002) product. In addition to the bands included in VJ143MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VJ143MA4 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D88 is the NBAR for VIIRS band M11 (2.25 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D81\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M2 (VJ143D81) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D80 through VJ143D89 are the NBAR products of the VJ143D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002) product. In addition to the bands included in VJ143MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VJ143MA4 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D81 is the NBAR for VIIRS band M2 (0.445 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D82\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M3 (VJ143D82) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D80 through VJ143D89 are the NBAR products of the VJ143D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002) product. In addition to the bands included in VJ143MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VJ143MA4 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D82 is the NBAR for VIIRS band M3 (0.488 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D83\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M4 (VJ143D83) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D80 through VJ143D89 are the NBAR products of the VJ143D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002) product. In addition to the bands included in VJ143MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VJ143MA4 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D83 is the NBAR for VIIRS band M4 (0.555 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D84\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M5 (VJ143D84) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D80 through VJ143D89 are the NBAR products of the VJ143D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VJ143MA4 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D84 is the NBAR for VIIRS band M5 (0.672 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D85\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M7 (VJ143D85) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D80 through VJ143D89 are the NBAR products of the VJ143D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002) product. In addition to the bands included in VJ143MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VJ143MA4 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D85 is the NBAR for VIIRS band M7 (0.865 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D86\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M8 (VJ143D86) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D80 through VJ143D89 are the NBAR products of the VJ143D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002) product. In addition to the bands included in VJ143MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VJ143MA4 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D86 is the NBAR for VIIRS band M8 (1.240 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D89\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for DNB (VJ143D89) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D80 through VJ143D89 are the NBAR products of the VJ143D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002) product. In addition to the bands included in VJ143MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VJ143MA4 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D89 is the NBAR for VIIRS DNB (0.7 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D01\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M1 product (VJ143D01) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D01 is the BRDF isotropic parameter for VIIRS band M1 (0.412 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D22\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M10 product (VJ143D22) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D22 is the BRDF isotropic parameter for VIIRS band M10 (1.61 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D25\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M11 product (VJ143D25) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D25 is the BRDF isotropic parameter for VIIRS band M11 (2.25 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue). Improvements/Changes from Previous Versions The NOAA-20 VIIRS algorithms include the same improvements as the S-NPP VIIRS V002 * Improved calibration algorithm and better coefficients for entire NOAA-20 mission * Improved geolocation accuracy and updates to fix outliers around maneuver periods and other events * Corrections to the aerosol quantity flag (low, average, high) mainly over brighter surfaces in the mid to high latitudes such as desert and tropical vegetation areas. This has an impact on the retrieval of other downstream data products such as VJ113 Vegetation Indices and VJ143 BRDF/Albedo. * Improved cloud mask input product for corrections along coastlines and artifacts from use of coarse resolution climatology data * Replaced the land/water mask input product with MODIS heritage seven class land/water mask\n<br>\n\n#### VJ143D04\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M2 product (VJ143D04) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D04 is the BRDF isotropic parameter for VIIRS band M2 (0.445 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D07\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M3 product (VJ143D07) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D07 is the BRDF isotropic parameter for VIIRS band M3 (0.488 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D10\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M4 product (VJ143D10) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D10 is the BRDF isotropic parameter for VIIRS band M4 (0.555 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D13\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M5 product (VJ143D13) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D13 is the BRDF isotropic parameter for VIIRS band M5 (0.672 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D16\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M7 product (VJ143D16) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D16 is the BRDF isotropic parameter for VIIRS band M7 (0.865 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D19\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M8 product (VJ143D19) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D19 is the BRDF isotropic parameter for VIIRS band M8 (1.240 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M8. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D37\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Day/Night Band (DNB) product (VJ143D37) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D37 is the BRDF isotropic parameter for the VIIRS DNB (0.7 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS DNB. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D31\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 NIR product (VJ143D31) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D31 is the BRDF isotropic parameter for the VIIRS NIR broadband (0.865 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS NIR broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D34\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Shortwave product (VJ143D34) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D34 is the BRDF isotropic parameter for the VIIRS shortwave broadband (1.61 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS shortwave broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D28\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 VIS product (VJ143D28) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D28 is the BRDF isotropic parameter for the VIIRS visible broadband (0.64 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS visible broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D02\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M1 product (VJ143D02) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D02 is the BRDF volumetric parameter for VIIRS band M1 (0.412 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D23\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M10 product (VJ143D23) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D23 is the BRDF volumetric parameter for VIIRS band M10 (1.61 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D26\nThe NOAA-20 Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M11 product (VJ143D26) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document [ATBD). VJ143D26 is the BRDF volumetric parameter for VIIRS band M11 (2.25 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D05\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M2 product (VJ143D05) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D05 is the BRDF volumetric parameter for VIIRS band M2 (0.445 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D08\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M3 product (VJ143D08) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document [ATBD). VJ143D08 is the BRDF volumetric parameter for VIIRS band M3 (0.488 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D11\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M4 product (VJ143D11) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D11 is the BRDF volumetric parameter for VIIRS band M4 (0.555 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D14\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M5 product (VJ143D14) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D14 is the BRDF volumetric parameter for VIIRS band M5 (0.672 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D17\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M7 product (VJ143D17) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D17 is the BRDF volumetric parameter for VIIRS band M7 (0.865 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue). Improvements/Changes from Previous Versions The NOAA-20 VIIRS algorithms include the same improvements as the S-NPP VIIRS V002 * Improved calibration algorithm and better coefficients for entire NOAA-20 mission * Improved geolocation accuracy and updates to fix outliers around maneuver periods and other events * Corrections to the aerosol quantity flag (low, average, high) mainly over brighter surfaces in the mid to high latitudes such as desert and tropical vegetation areas. This has an impact on the retrieval of other downstream data products such as VJ113 Vegetation Indices and VJ143 BRDF/Albedo. * Improved cloud mask input product for corrections along coastlines and artifacts from use of coarse resolution climatology data * Replaced the land/water mask input product with MODIS heritage seven class land/water mask\n<br>\n\n#### VJ143D20\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M8 product (VJ143D20) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D20 is the BRDF volumetric parameter for VIIRS band M8 (1.240 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M8. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D38\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Day/Night Band (DNB) product (VJ143D38) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D38 is the BRDF volumetric parameter for the VIIRS DNB (0.7 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS DNB. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D32\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 NIR product (VJ143D32) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D32 is the BRDF volumetric parameter for the VIIRS NIR broadband (0.865 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS NIR broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D35\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Shortwave product (VJ143D35) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D35 is the BRDF volumetric parameter for VIIRS shortwave broadband (1.61 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS shortwave broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D29\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 VIS product (VJ143D29) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D29 is the BRDF volumetric parameter for the VIIRS visible broadband (0.64 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS visible broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D03\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M1 product (VJ143D03) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D03 is the BRDF geometric parameter for VIIRS band M1 (0.412 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D24\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M10 product (VJ143D24) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D24 is the BRDF geometric parameter for VIIRS band M10 (1.61 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D27\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M11 product (VJ143D27) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D27 is the BRDF geometric parameter for VIIRS band M11 (2.25 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D06\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M2 product (VJ143D06) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D06 is the BRDF geometric parameter for VIIRS band M2 (0.445 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D09\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M3 product (VJ143D09) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D09 is the BRDF geometric parameter for VIIRS band M3 (0.488 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D12\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M4 product (VJ143D12) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D12 is the BRDF geometric parameter for VIIRS band M4 (0.555 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D15\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M5 product (VJ143D15) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D15 is the BRDF geometric parameter for VIIRS band M5 (0.672 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D18\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M7 product (VJ143D18) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D18 is the BRDF geometric parameter for VIIRS band M7 (0.865 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D21\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M8 product (VJ143D21) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D21 is the BRDF geometric parameter for VIIRS band M8 (1.240 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M8. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D39\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Day-Night Band (DNB) product (VJ143D39) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D39 is the BRDF geometric parameter for the VIIRS DNB (0.7 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS DNB. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D33\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 NIR product (VJ143D33) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D33 is the BRDF geometric parameter for the VIIRS NIR broadband (0.865 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS NIR broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D36\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Shortwave product (VJ143D36) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D36 is the BRDF geometric parameter for VIIRS shortwave broadband (1.61 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS shortwave broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D30\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 VIS product (VJ143D30) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) product is stored in a separate file as VJ143D01 through VJ143D36. In addition to the bands included in VJ143MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VJ143D37 through VJ143D39. Details regarding methodology are available on the VJ143MA1 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D30 is the BRDF geometric parameter for the VIIRS visible broadband (0.64 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS visible broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143MA2\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VJ143MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11; as well as land water type class flag; snow BRDF albedo class flag; local solar noon; albedo uncertainty and the platform name. Known Issues: Known issues for VIIRS BRDF/Albedo data products can be found on the [VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143D40\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality product (VJ143D40) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer for each of the parameters included in the [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) product. VJ143D40 through VJ143D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VJ143MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D40 consists of BRDF/Albedo quality information representing the overall quality of each pixel for VIIRS moderate resolution bands M1 through M5, M7, M8, M10, M11, and DNB. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143IA2\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VJ143IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VJ143IA1](https://doi.org/10.5067/VIIRS/VJ143IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VJ143IA4](https://doi.org/10.5067/VIIRS/VJ143IA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VJ143IA3](https://doi.org/10.5067/VIIRS/VJ143IA3.002)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143D52\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow Status product (VJ143D52) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer for each of the parameters included in the [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) product. VJ143D40 through VJ143D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VJ143MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D52 contains the snow status quality layer, which identifies each pixel as either “Snow-free Albedo Retrieved” or “Snow Albedo Retrieved” for the acquisition period. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143C2\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow-free Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VJ143C2) is derived from the 30 arc second CMG VJ143D Version 2 product suite. VJ143C2 is generated daily from all available snow-free acquisitions over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. VJ143C2 supplies the weighting parameters associated with the RossThick/Li-Sparse-Reciprocal BRDF model, which is used to produce the [VJ143C3](https://doi.org/10.5067/VIIRS/VJ143C3.002) Albedo and [VJ143C4](https://doi.org/10.5067/VIIRS/VJ143C4.002) Nadir BRDF-Adjusted Reflectance (NBAR) products. The highest quality full inversion values are used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the ancillary layers. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VJ143C2 product includes 39 layers containing the three parameters (fiso, fvol, and fgeo) for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. Along with the parameter data for the 13 bands are four ancillary layers for uncertainty, quality, local solar noon, and percent finer resolution inputs. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143D53\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Uncertainty product (VJ143D53) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer for each of the parameters included in the [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) product. VJ143D40 through VJ143D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VJ143MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D53 contains the uncertainty range of each BRDF/Albedo pixel for the retrieval period. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D42\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M1 product (VJ143D42) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer for each of the parameters included in the [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) product. VJ143D40 through VJ143D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VJ143MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D42 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D49\nThe NOAA-20 Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M10 product (VJ143D49) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer for each of the parameters included in the [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) product. VJ143D40 through VJ143D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VJ143MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D49 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M10. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D50\nThe NOAA-20 Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M11 product (VJ143D50) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer for each of the parameters included in the [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) product. VJ143D40 through VJ143D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VJ143MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D50 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M11. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D43\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M2 product (VJ143D43) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer for each of the parameters included in the [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) product. VJ143D40 through VJ143D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VJ143MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D43 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D44\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M3 product (VJ143D44) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer for each of the parameters included in the [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) product. VJ143D40 through VJ143D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VJ143MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D44 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D45\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M4 product (VJ143D45) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer for each of the parameters included in the [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) product. VJ143D40 through VJ143D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VJ143MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D45 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D46\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M5 product (VJ143D46) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer for each of the parameters included in the [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) product. VJ143D40 through VJ143D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VJ143MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D46 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D47\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M7 product (VJ143D47) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer for each of the parameters included in the [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) product. VJ143D40 through VJ143D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VJ143MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D47 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D48\nThe NOAA-20 Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M8 product (VJ143D48) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer for each of the parameters included in the [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) product. VJ143D40 through VJ143D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VJ143MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D48 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M8. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D51\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Day/Night Band (DNB) product (VJ143D51) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer for each of the parameters included in the [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) product. VJ143D40 through VJ143D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VJ143MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D51 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS DNB. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D67\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M1 (VJ143D67) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D67 is the WSA for VIIRS band M1 (0.412 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D74\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M10 (VJ143D74) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D74 is the WSA for VIIRS band M10 (1.61 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D75\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M11 (VJ143D75) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D75 is the WSA for VIIRS band M11 (2.25 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D68\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M2 (VJ143D68) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D68 is the WSA for VIIRS band M2 (0.445 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D69\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M3 (VJ143D69) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D69 is the WSA for VIIRS band M3 (0.488 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D70\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M4 (VJ143D70) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D70 is the WSA for VIIRS band M4 (0.555 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D71\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M5 (VJ143D71) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D71 is the WSA for VIIRS band M5 (0.672 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D72\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M7 (VJ143D72) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D72 is the WSA for VIIRS band M7 (0.865 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D73\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M8 (VJ143D73) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D73 is the WSA for VIIRS band M8 (1.240 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D76\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band VIS (VJ143D76) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D76 is the WSA for the VIIRS visible broadband (0.64 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D79\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for DNB (VJ143D79) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D79 is the WSA for the VIIRS DNB (0.7 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D77\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band NIR (VJ143D77) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D77 is the WSA for the VIIRS NIR broadband (0.865 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ143D78\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for ShortWave (VJ143D78) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VJ143D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VJ143D product contains just one data layer. VJ143D54 through VJ143D79 are the albedo products of the VJ143D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) product. In addition to the bands included in VJ143MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VJ143MA3 product page and in the Algorithm Theoretical Basis Document (ATBD). VJ143D78 is the WSA for the VIIRS shortwave broadband (1.61 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website]( https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VJ146A1\nThe NOAA-20 VIIRS Daily Gridded Day Night Band 15 arc-second Linear Lat Lon Grid Night product, short-name VJ146A1 is a daily, top-of-atmosphere, at-sensor nighttime radiance product. This product is available at 15 arc-second spatial resolution from January 2018 onward. The VJ146A1 product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. The current v2.0 collection contains several changes and differences relative to the previous v1.0 collection. These include radiance data format change from unsigned integer to floating-point, from exclusively for land surfaces coverage to both land and water surfaces, updated Mandatory_Quality_Flag layer, and others. Consult the v2.0-specific Black Marble User Guide for additional details at: https://landweb.modaps.eosdis.nasa.gov/data/userguide/BlackMarbleUserGuide_Collection2.0_20241203.pdf\n<br>\n\n#### VJ102DNB\nThe VIIRS/JPSS1 Day/Night Band 6-Min L1B Swath 750 m, short-name VJ102DNB is platform-derived single NASA VIIRS panchromatic Day-Night band (DNB) calibrated radiance product. The DNB is one of the M-bands with an at-nadir spatial resolution of 750 meters (across the entire scan). The panchromatic DNB’s spectral wavelength ranges from 0.5 µm to 0.9 µm. It facilitates measuring night lights, reflected solar/lunar lights with a large dynamic range between a low of a quarter moon illumination to the brightest daylight. The spatial resolution of the instrument at viewing nadir is approximately 750 m for the DNB and the Moderate-resolution Bands and 375m for the Imagery bands. The DNB is aggregated to maintain nearly constant horizontal spatial resolution across the swath.\n<br>\n\n#### VJ103DNB\nThe VIIRS/JPSS1 Day/Night Band Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m product contains the derived line-of-sight (LOS) vectors for the single panchromatic Day-Night band (DNB). The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It provides geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ103DNB product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, lunar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, moon illumination fraction and phase angle, and quality flag for every pixel location.\n<br>\n\n#### VJ143DNBA3\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143DNBA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VJ143DNBA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VJ143DNBA1](https://doi.org/10.5067/VIIRS/VJ143DNBA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VJ143DNBA4](https://doi.org/10.5067/VIIRS/VJ143DNBA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143DNBA3 product provides BSA, WSA, and mandatory quality layers for the VIIRS DNB. A low-resolution image is also available showing retrievals of WSA for the shortwave broadband in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143DNBA1\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143DNBA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VJ143DNBA1 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VJ143DNBA4](https://doi.org/10.5067/VIIRS/VJ143DNBA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VJ143DNBA3](https://doi.org/10.5067/VIIRS/VJ143DNBA3.002)). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143DNBA1 data product provides two SDS layers for mandatory quality and model parameters representing fiso, fvol, and fgeo for the VIIRS DNB. A low-resolution browse is also available showing BRDF/Albedo parameters for the DNB as a red, green, blue (RGB) image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143DNBA4\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143DNBA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectance resulting in a stable and consistent NBAR product. The VJ143 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VJ143DNBA1](https://doi.org/10.5067/VIIRS/VJ143DNBA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VJ143DNBA3](https://doi.org/10.5067/VIIRS/VJ143DNBA3.002)). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143DNBA4 product includes BRDF/Albedo mandatory quality and nadir reflectance for the VIIRS DNB. A low-resolution browse image is also available showing NBAR of the DNB as a red, green, blue (RGB) image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ143DNBA2\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143DNBA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VJ143DNBA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143DNBA2 product gives information regarding band quality and days of valid observation within a 16-day period for the VIIRS DNB. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VJ143DNBA1](https://doi.org/10.5067/VIIRS/VJ143DNBA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VJ143DNBA4](https://doi.org/10.5067/VIIRS/VJ143DNBA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VJ143DNBA3](https://doi.org/10.5067/VIIRS/VJ143DNBA3.002)). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143DNBA2 data product provides a total of seven SDS layers, including BRDF/Albedo band quality and days of valid observation within a 16-day period for the VIIRS DNB, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ147IMG\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) FIre Light Detection Algorithm-2 (FILDA-2) Modified Combustion Efficiency (MCE) Version 2 swath product (VJ147IMG) is produced in 6-minute orbit segments 375 meter (m) resolution from the VIIRS sensor aboard the NOAA-20 satellite. The VJ147IMG product takes advantage of the rich information that visible band observation conveys at night to assess fire combustion efficiency and enhance fire detection. It also utilizes improvements made in fire detection algorithms to detect smaller and cooler fires. The VJ147IMG product includes 83 layers containing fire detection and retrievals of Radiative Power (FRP), fire Visible Energy Fraction (VEF), and Modified Combustion Efficiency (MCE). Known Issues: Known issues can be found in Section 5 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS&as=5200).\n<br>\n\n#### VJ146A2\nThe NOAA-20 VIIRS Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 15 arc-second Linear Lat Lon Grid product, short-name VJ146A2 is a daily moonlight- and atmosphere-corrected Nighttime Lights (NTL) product. This product is available at 15 arc-second resolution and contains seven Science Data Sets (SDS) that include DNB BRDF-Corrected NTL, Gap-Filled DNB BRDF-Corrected NTL, DNB Lunar Irradiance, Latest High-Quality Retrieval, Mandatory Quality Flag, Cloud Mask Quality Flag, and Snow Flag. The VJ146A2 product files are provided in standard Hierarchical Data Format–Earth Observing System (HDF-EOS5) format. The current v2.0 collection contains several changes and differences relative to the previous v1.0 collection. These include radiance data format change from unsigned integer to floating-point, from exclusively for land surfaces coverage to both land and water surfaces, updated Mandatory_Quality_Flag layer, and others. Consult the v2.0-specific Black Marble User Guide for additional details at: https://landweb.modaps.eosdis.nasa.gov/data/userguide/BlackMarbleUserGuide_Collection2.0_20241203.pdf\n<br>\n\n#### VJ102IMG\nThe VIIRS/JPSS1 Imagery Resolution 6-Min L1B Swath 375m, short-name VJ102IMG product that comprise five image-resolution or I-bands, which have a 375-meter resolution at nadir. These I-bands comprise three reflective solar bands (RSB) and two thermal emissive bands (TEB). Ranging in wavelengths from 0.6 µm to 12.4 µm, the I-bands are sensitive to visible/reflective, near-, shortwave-, mediumwave-, and longwave-infrared wavelengths. Derived from the NASA VIIRS L1A raw radiances, this product includes calibrated and geolocated radiance and reflectance data, quality flags, and granule- and collection-level metadata. In contrast to a MODIS L1B product, which temporally spans 5 minutes, the VIIRS L1B calibrated radiances product contains a nominal temporal duration of 6 minutes.\n<br>\n\n#### VJ103IMG\nThe VIIRS/JPSS1 Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath 375m product, short-name VJ103IMG, contains the derived line- of-sight (LOS) vectors for each of the 375-m image-resolution or I-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ103IMG product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location.\n<br>\n\n#### VJ121\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VJ121) is produced daily in 6-minute temporal increments of satellite acquisition. The VJ121 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 µm), M15 (10.76 µm), and M16 (12 µm) at a spatial resolution of 750 meters. The VJ121 product is developed developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product ([MOD21](https://doi.org/10.5067/MODIS/MOD21.061)) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD). Provided in the VJ121 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VJ121 granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide.\n<br>\n\n#### VJ121C2\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day Climate Modeling Grid Version 2 product (VJ121C2) combines the daily [VJ121A1D](http://doi.org/10.5067/VIIRS/VJ121A1D.002) and [VJ121A1N](http://doi.org/10.5067/VIIRS/VJ121A1N.002) products over an 8-day compositing period into a single product. The VJ121C2 dataset is an 8-day composite LST&E product at 0.05 degree (~5,600 meter) resolution that uses an algorithm based on a simple-averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud-free VJ121A1D and VJ121A1N daily acquisitions from the 8-day period. Unlike the VJ121A1 datasets where the daytime and nighttime acquisitions are separate products, the VJ121C2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). The VJ121C2 product contains 25 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VJ121C2 granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide.\n<br>\n\n#### VJ121A2\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day product (VJ121A2) combines the daily [VJ121A1D](http://doi.org/10.5067/VIIRS/VJ121A1D.002) and [VJ121A1N](http://doi.org/10.5067/VIIRS/VJ121A1N.002) products over an 8-day compositing period into a single product. The VJ121A2 dataset is an 8-day composite LST&E product at 1 kilometer resolution that uses an algorithm based on a simple-averaging method. The algorithm calculates the average from all the cloud-free VJ121A1D and VJ121A1N daily acquisitions from the 8-day period. Unlike the VJ121A1 datasets where the daytime and nighttime acquisitions are separate products, the VJ121A2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The VJ121A2 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product ([MOD21A2](https://doi.org/10.5067/MODIS/MOD21A2.061)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). The VJ121A2 product contains 11 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VJ121A2 granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide.\n<br>\n\n#### VJ121C1\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Climate Modeling Grid Version 2 product (VJ121C) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily [VJ121](https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VJ121C1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The 0.05 degree (5,600 m) dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). The VJ121C1 product contains 25 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VJ121C1 granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide.\n<br>\n\n#### VJ121A1D\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VJ121A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily [VJ121](https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VJ121A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product ([MOD21A1D](https://doi.org/10.5067/MODIS/MOD21A1D.061)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). The VJ121A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VJ121A1D granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide.\n<br>\n\n#### VJ121A1N\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VJ121A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily [VJ121](https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VJ121A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VJ121A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product ([MOD21A1N](https://doi.org/10.5067/MODIS/MOD21A1N.061)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). The VJ121A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VJ121A1N granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide.\n<br>\n\n#### VJ121C3\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) monthly Climate Modeling Grid Version 2 product (VJ121C3) provides LST&E by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (~5,600 meter) resolution. The VJ121C3 dataset is a monthly composite LST&E product that uses an algorithm based on a simple averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud free [VJ121A1D](http://doi.org/10.5067/VIIRS/VJ121A1D.002) and [VJ121A1N](http://doi.org/10.5067/VIIRS/VJ121A1N.002) daily acquisitions from the monthly period. Unlike the VJ121A1 data sets where the daytime and nighttime acquisitions are separate products, the VJ121C3 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). The VJ121C3 product contains 25 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VJ121C3 granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide.\n<br>\n\n#### VJ115A2H\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) Version 2 data product (VJ115A2H) provides information about the vegetative canopy layer at 500 meter resolution. The VIIRS sensor is located aboard the NOAA-20 satellite. LAI is an index that quantifies the one-sided leaf area of a canopy, while FPAR is the fraction of incoming solar energy absorbed through photosynthesis at 400 to 700 nanometers. This product is intentionally designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR operational algorithm to promote the continuity of the Earth Observation System (EOS) mission. The VJ115A2H product includes six Science Data Set Layers for the analysis of key factors in LAI/FPAR measurements. These include the LAI and FPAR measurements, quality detail for LAI/FPAR, extra quality detail for FPAR, and the standard deviation for LAI and FPAR. Two low resolution browse images are also available for each VJ115A2H granule: LAI and FPAR. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ102MOD\nThe VIIRS/JPSS1 Moderate Resolution 6-Min L1B Swath 750 m, short-name VJ102MOD is VIIRS Level-1B calibrated radiances product that comprise sixteen moderate-resolution or M-bands, which have a spatial resolution of 750-meters at nadir. These M-bands comprise eleven reflective solar bands (RSB) and five thermal emissive bands (TEB). Each of the M-bands has 16 detectors in the along-track direction with 16 rows of pixels per scan that provide a 750-m resolution. Ranging in wavelengths from 0.402 µm to 12.49 µm, the M-bands are sensitive to visible, near-, shortwave-, mediumwave-, and longwave-infrared wavelengths. Derived from the NASA VIIRS L1A raw radiances, this product includes calibrated and geolocated radiance and reflectance data, quality flags, and granule- and collection-level metadata. In contrast to a MODIS L1B product, which temporally spans 5 minutes, the VIIRS L1B calibrated radiances product contains a nominal temporal duration of 6 minutes.\n<br>\n\n#### VJ103MODLL\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) Moderate Resolution Terrain Correction Geolocation (VJ103MODLL) Version 2.1 product from the NOAA-20 VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth’s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60° North to 60° South. VJ103MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). Provided in the VJ103MODLL product are layers for height, latitude, and longitude. These Science Data Sets (SDS) layers are used in conjunction with the ([VJ114](https://doi.org/10.5067/viirs/vj114.002)) swath product for accurate geolocation information. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ103MOD\nThe VIIRS/JPSS1 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m product, short-name VJ103MOD contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ103MOD product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location.\n<br>\n\n#### VJ109A1\nThe 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance (VJ109A1) Version 2 composite product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor for nine moderate resolution bands (M1 - M5, M7, M8, M10, M11) at nominal 1 kilometer resolution (~926 meter). The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the Level 2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period that is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. Included in the product along with the nine reflectance bands are day of year, reflectance band quality, control, reflectance state quality assurance, relative azimuth angle, sensor zenith angle, and solar zenith angle layers. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and in Section 4.0 of the User Guide.\n<br>\n\n#### VJ109H1\nThe 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) Surface Reflectance (VJ109H1) Version 2 composite product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor for three imagery bands (I1, I2, I3) at nominal 500 meter resolution (~463 meter). The 500 meter dataset is derived through resampling the native 375 meter VIIRS resolution in the L2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period, which is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. In addition to the three reflectance bands, this product includes a state quality assurance (QA) layer and a reflectance band quality layer. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and in Section 4.0 of the User Guide.\n<br>\n\n#### VJ109GA\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance (VJ109GA) Version 2 product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands, the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VJ109GA data product are used as input data for many of the VIIRS land products. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and in Section 4.0 of the User Guide.\n<br>\n\n#### VJ109CMG\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance Climate Modeling Grid (VJ109CMG) Version 2 product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. This product uses a weighted average of the best quality observation and is formatted as a CMG for use in climate simulation models. This product includes the twelve reflectance bands, five moderate resolution brightness temperature bands (M12-M16) and information layers representing relative azimuth angle, sensor zenith angle, solar zenith angle, reflectance band quality, time of day, and number mapping. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and in Section 4.0 of the User Guide.\n<br>\n\n#### VJ114A1\nThe daily NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies and Fire (VJ114A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VJ114A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VJ114 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite. The VJ114A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and in Section 5.0 “Frequently Asked Questions” of the User Guide.\n<br>\n\n#### VJ114\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies (VJ114) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. The VJ114 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114 product is also used to generate higher-level fire data products. Use of the [VJ103MODLL](https://doi.org/10.5067/viirs/vj103modll.021) data product is required to apply accurate geolocation information to the VJ114 Science Datasets (SDS). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and in Section 5.0 “Frequently Asked Questions” of the User Guide.\n<br>\n\n#### VJ113C1\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ113C1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 0.05 degree resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VJ113C1 product: EVI and NDVI. Known Issues: Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide and ATBD.\n<br>\n\n#### VJ113A2\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ113A2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 1 kilometer (km) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles, and a quality layer. Two low resolution browse images are also available for each VJ113A2 product: EVI and NDVI. Known Issues: Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide and ATBD.\n<br>\n\n#### VJ113A1\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ113A1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 500 meter (m) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ113 algorithm process produces three vegetation indices: Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VJ113A1 product: EVI and NDVI. Known Issues: Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide and ATBD.\n<br>\n\n#### VJ113C2\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ113C2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VJ113C2 product: EVI and NDVI. Known Issues: Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide and ATBD.\n<br>\n\n#### VJ113A3\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ113A3) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 1 kilometer (km) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; pixel reliability; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VJ113A3 product: EVI and NDVI. Known Issues: Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide and ATBD.\n<br>\n\n#### VJ128C2\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Water Reservoir 8-day Level 3 (L3) Global (VJ128C2) Version 2 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The VJ128C2 data product provides an 8-day time series of surface area, elevation, and water storage. Datasets are combined with pre-established [Area-Elevation (A-E) curves](https://doi.org/10.1016/j.rse.2020.111831) and image classifications of near-infrared (NIR) reflectance from the surface reflectance product acquired by the VIIRS satellite ([VJ109H1](https://lpdaac.usgs.gov/products/vj109h1v002/)). The VJ128C2 data product consists of a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir surface area, elevation, and water storage capacity. Known Issues: Water occurrence images generally show smaller surface area dynamics in high latitude regions, creating pixels with low occurrence values that have relatively large uncertainties. In addition, the quality of raw water area classification can be affected by lake ice coverage typically creating an overestimation of surface area in the enhancement algorithm. This issue will be addressed in a future release of the enhancement algorithm. For additional information about known issues, refer to Section 4 in the User Guide.\n<br>\n\n#### VJ128C3\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Water Reservoir Monthly Level 3 (L3) Global (VJ128C3) Version 2 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The VJ128C3 data product is a composite of the 8-day area classifications from [VJ128C2](https://lpdaac.usgs.gov/products/vj128c2v002/) which is converted to provide monthly elevation and water storage. The Lake Temperature and Evaporation Model ([LTEM](https://doi.org/10.1016/j.rse.2020.112104)) with input from VIIRS Land Surface Temperature and Emissivity ([VJ121A2](https://lpdaac.usgs.gov/products/vj121a2v002/)) and meteorological data from Global Land Data Assimilation System ([GLDAS](https://earth.gsfc.nasa.gov/hydro/data/gldas-global-land-data-assimilation-system-data)) are used to produce monthly evaporation rates and volume losses. The VJ128C3 data product provides a monthly time series that consists of a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir surface area, elevation, water storage capacity, evaporation rate, and evaporation volume. Known Issues: Water occurrence images generally show smaller surface area dynamics in high latitude regions, creating pixels with low occurrence values that have relatively large uncertainties. In addition, the quality of raw water area classification can be affected by lake ice coverage typically creating an overestimation of surface area in the enhancement algorithm. This issue will be addressed in a future release of the enhancement algorithm. For additional information about known issues, refer to Section 4 in the User Guide.\n<br>\n\n#### VJ147MOD\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) FIre Light Detection Algorithm-2 (FILDA-2) Modified Combustion Efficiency (MCE) Version 2 swath product (VJ147MOD) is produced in 6-minute orbit segments 750 meter (m) resolution from the VIIRS sensor aboard the NOAA-20 satellite. The VJ147MOD product takes advantage of the rich information that visible band observation conveys at night to assess fire combustion efficiency and enhance fire detection. It also utilizes improvements made in fire detection algorithms to detect smaller and cooler fires. The VJ147MOD product includes 85 layers containing fire detection and retrievals of Radiative Power (FRP), fire Visible Energy Fraction (VEF), and Modified Combustion Efficiency (MCE). Known Issues: Known issues can be found in Section 5 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS&as=5200).\n<br>\n\n#### VJ202DNB\nThe VIIRS/JPSS2 Day/Night Band 6-Min L1B Swath 750 m, short-name VJ202DNB, of the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21; referred to hereafter as J2) platform-derived single NASA VIIRS panchromatic Day-Night band (DNB) calibrated radiance product. The DNB is one of the M-bands with an at-nadir spatial resolution of 750 meters (across the entire scan). The panchromatic DNB’s spectral wavelength ranges from 0.5 micrometer to 0.9 micrometer. It facilitates measuring night lights, reflected solar/lunar lights with a large dynamic range between a low of a quarter moon illumination to the brightest daylight. The J2 VIIRS radiometric calibration Level-1B reprocessing includes a few calibration updates for the reflective solar bands (RSB), but no significant changes for the day-night band (DNB) or thermal emissive bands (TEB). For more information and for users guide, visit: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ202DNB\n<br>\n\n#### VJ203DNB\nThe VIIRS/JPSS2 Day/Night Band Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750 m, short-name VJ203DNB product is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-based NASA Visible Infrared Imaging Radiometer Suite (VIIRS) L1 terrain-corrected geolocation product, and contains the derived line-of-sight (LOS) vectors for the single panchromatic Day-Night band (DNB). The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It provides geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ203DNB product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, lunar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, moon illumination fraction and phase angle, and quality flag for every pixel location. The J2 VIIRS geolocation underwent an on-orbit validation. Geolocation errors of about 350 m in the along-scan direction and about 165 m in the along-track direction were corrected for the image-resolution bands and moderate-resolution bands. The Day-Night band (DNB) geolocation error of about 2000 m was corrected. For more information and documents, visit LAADS product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ203DNB\n<br>\n\n#### VJ202IMG\nThe VIIRS/JPSS2 Imagery Resolution 6-Min L1B Swath 375m, short-name VJ202IMG is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21; referred to hereafter as J2) platform-derived NASA Visible Infrared Imaging Radiometer Suite (VIIRS) L1B calibrated radiances product that comprise five image-resolution or I-bands, which have a 375-meter resolution at nadir. These I-bands comprise three reflective solar bands (RSB) and two thermal emissive bands (TEB). Each of the I-bands has 32 detectors in the along-track direction with 32 rows of pixels per scan that offer a resolution that is twice finer than that of the moderate (M) and Day-Night bands (DNB). Ranging in wavelengths from 0.6 µm to 12.4 µm, the I-bands are sensitive to visible/reflective, near-, shortwave-, mediumwave-, and longwave-infrared wavelengths. The J2 VIIRS radiometric calibration Level-1B reprocessing includes a few calibration updates for the reflective solar bands (RSB), but no significant changes for the day-night band (DNB) or thermal emissive bands (TEB). For more information and documents, visit LAADS product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ202IMG\n<br>\n\n#### VJ203IMG\nThe VIIRS/JPSS2 Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath 375 m, short-name VJ203IMG is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-derived NASA Visible-Infrared Imaging-Radiometer Suite (VIIRS) L1 terrain-corrected geolocation product and contains the derived line-of-sight (LOS) vectors for each of the 375-m image-resolution or I-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ203IMG product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location. VJ203IMG provides a fundamental input to derive a number of VIIRS I-band higher-level products. The J2 VIIRS geolocation underwent an on-orbit validation. Geolocation errors of about 350 m in the along-scan direction and about 165 m in the along-track direction were corrected for the image-resolution bands and moderate-resolution bands. The Day-Night band (DNB) geolocation error of about 2000 m was corrected. Further, the geolocation biases in the scan profile were also corrected. For more information and documents, visit LAADS product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ203IMG\n<br>\n\n#### VJ202MOD\nThe VIIRS/JPSS2 Moderate Resolution 6-Min L1B Swath 750m, short-name VJ202MOD is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21; referred to hereafter as J2) platform-derived NASA Visible Infrared Imaging Radiometer Suite (VIIRS) L1B calibrated radiances product that comprise sixteen moderate-resolution or M-bands, which have a spatial resolution of 750-meters at nadir. These M-bands comprise eleven reflective solar bands (RSB) and five thermal emissive bands (TEB). Each of the M-bands has 16 detectors in the along-track direction with 16 rows of pixels per scan that provide a 750-m resolution. Ranging in wavelengths from 0.402 µm to 12.49 µm, the M-bands are sensitive to visible, near-, shortwave-, mediumwave-, and longwave-infrared wavelengths. Derived from the NASA VIIRS L1A raw radiances, this product includes calibrated and geolocated radiance and reflectance data, quality flags, and granule- and collection-level metadata. The J2 VIIRS radiometric calibration Level-1B reprocessing includes a few calibration updates for the reflective solar bands (RSB), but no significant changes for the day-night band (DNB) or thermal emissive bands (TEB). For more information and documentation, visit LAADS product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ202MOD\n<br>\n\n#### VJ203MOD\nThe VIIRS/JPSS2 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750 m, short-name VJ203MOD is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-based NASA Visible-Infrared Imaging-Radiometer Suite (VIIRS) L1 terrain-corrected geolocation product, and contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ203MOD product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location. VJ203MOD provides a fundamental input to derive a number of VIIRS M-band higher-level products. The J2 VIIRS geolocation underwent an on-orbit validation. Geolocation errors of about 350 m in the along-scan direction and about 165 m in the along-track direction were corrected for the image-resolution bands and moderate-resolution bands. The Day-Night band (DNB) geolocation error of about 2000 m was corrected. For more information and documents, visit LAADS product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ203MOD\n<br>\n\n#### VJ209A1\nThe 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance (VJ209A1) Version 2 composite product provides an estimate of land surface reflectance from the NOAA-21 VIIRS sensor for nine moderate resolution bands (M1 - M5, M7, M8, M10, M11) at nominal 1 kilometer resolution (~926 meter). The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the Level 2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period that is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. Included in the product along with the nine reflectance bands are day of year, reflectance band quality, control, reflectance state quality assurance, relative azimuth angle, sensor zenith angle, and solar zenith angle layers. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS&sat=J2).\n<br>\n\n#### VJ209H1\nThe 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) Surface Reflectance (VJ209H1) Version 2 composite product provides an estimate of land surface reflectance from the NOAA-21 VIIRS sensor for three imagery bands (I1, I2, I3) at nominal 500 meter resolution (~463 meter). The 500 meter dataset is derived through resampling the native 375 meter VIIRS resolution in the L2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period, which is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. In addition to the three reflectance bands, this product includes a state quality assurance (QA) layer and a reflectance band quality layer. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS&sat=J2).\n<br>\n\n#### VJ209GA\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance (VJ209GA) Version 2 product provides an estimate of land surface reflectance from the NOAA-21 VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands, the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VJ209GA data product are used as input data for many of the VIIRS land products. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS&sat=J2).\n<br>\n\n#### VJ209CMG\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance Climate Modeling Grid (VJ209CMG) Version 2 product provides an estimate of land surface reflectance from the NOAA-21 VIIRS sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. This product uses a weighted average of the best quality observation and is formatted as a CMG for use in climate simulation models. This product includes the twelve reflectance bands, five moderate resolution brightness temperature bands (M12-M16) and information layers representing relative azimuth angle, sensor zenith angle, solar zenith angle, reflectance band quality, time of day, and number mapping. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS&sat=J2).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/AER_DBDT_D10KM_L3_VIIRS_NOAA20_001.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","earth observation","elevation","hdf","hdf5","ice","oceans","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AER_DBDT_D10KM_L3_VIIRS_NOAA20 v001 - This High-Resolution (0.1 x 0.1 degree) Level 3 daily Aerosol Optical Depth (AOD) product is generated by combining two Visible Infrared Imaging Radiometer Suite (VIIRS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the NOAA-20 satellite. This dataset is provided in daily files ranging from 2018-02-17 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TASNPP_AOD_Level3/AER_DBDT_D10KM_L3_VIIRS_NOAA20.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AER_DBDT_M10KM_L3_VIIRS_NOAA20 v001 - This High-Resolution (0.1 x 0.1 degree) Level 3 monthly Aerosol Optical Depth (AOD) product is generated by combining two Visible Infrared Imaging Radiometer Suite (VIIRS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the NOAA-20 satellite. This dataset is provided in monthly files ranging from February 2018 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TASNPP_AOD_Level3/AER_DBDT_M10KM_L3_VIIRS_NOAA20.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_N21_LP_L1G_EV v1.0 - The OMPS-N21 L1G LP Radiance EV Wavelength-Altitude Grid swath orbital 3slit product contains the calibrated earth-viewing radiances measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the NOAA 21 (JPSS-2) satellite. The LP L1G product measures radiances in the wavelength region from 280 nm to 1000 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NOAA21_OMPS_Level1B/OMPS_N21_LP_L1G_EV.1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRJ1ATMSMAP v1 - The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also known as NOAA-20 (National Oceanic and Atmospheric Administration).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Granule_Maps/SNDRJ1ATMSMAP.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRJ1ML2RMS v3 - This level 2 product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the NOAA-20 (National Oceanic and Atmospheric Administration) also know as Joint Polar Satellite System (JPSS-1) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/JPSS1_Sounder_Level2/SNDRJ1ML2RMS.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRJ1ML2RMSSUP v3 - This level 2 support product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the NOAA-20 (National Oceanic and Atmospheric Administration) also know as Joint Polar Satellite System (JPSS-1) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/JPSS1_Sounder_Level2/SNDRJ1ML2RMSSUP.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRJ1ML3DRMS v3 - This level 3 daily product is generated from the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the NOAA-20 (National Oceanic and Atmospheric Administration) also know as Joint Polar Satellite System (JPSS-1) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/JPSS1_Sounder_Level3/SNDRJ1ML3DRMS.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRJ1ML3MRMS v3 - This level 3 monthly product is generated from the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the NOAA-20 (National Oceanic and Atmospheric Administration) also know as Joint Polar Satellite System (JPSS-1) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/JPSS1_Sounder_Level3/SNDRJ1ML3MRMS.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRJ1IML2CPS v2.1 - The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the NOAA-20 platform, also known as JPSS-1.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/JPSS1_Sounder_Level2/SNDRJ1IML2CPS.2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRJ1IML2PLEVCPS v2.1 - The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using data from the JPSS-1 (Joint Polar Satellite System).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/JPSS1_Sounder_Level2/SNDRJ1IML2PLEVCPS.2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRJ1IML2CCPRET v2 - WARNING: To users of the derived product “co_mmr_midtrop” (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/JPSS1_Sounder_Level2/SNDRJ1IML2CCPRET.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VJ114IMG v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires (VJ114IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor aboard the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission.","ARN":"arn:aws:s3:::lp-prod-protected/VJ114IMG.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ109 v2 - The VIIRS/JPSS1 Atmospherically Corrected Surface Reflectance 6-Min L2 Swath 375m, 750m product, with short name VJ109, are estimates of surface reflectance in each of the Visible Infrared Imaging Radiometer Suite (VIIRS) reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. The VJ109 contains approximately 6 minutes' worth of data.","ARN":"arn:aws:s3:::prod-lads/VJ109","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143C3 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Albedo Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VJ143C3) is derived from the 30 arc second CMG VJ143D Version 2 product suite. VJ143C3 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143C3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143MA3 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143MA3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143IA3 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143IA3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D54 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M1 (VJ143D54) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D54.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D61 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M10 (VJ143D61) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D61.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D62 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M11 (VJ143D62) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D62.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D55 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M2 (VJ143D55) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D55.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D56 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M3 (VJ143D56) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D56.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D57 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M4 (VJ143D57) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D57.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D58 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M5 (VJ143D58) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D58.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D59 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M7 (VJ143D59) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D59.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D60 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M8 (VJ143D60) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D60.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D63 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band VIS (VJ143D63) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D63.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D66 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for DNB (VJ143D66) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D66.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D64 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for NIR (VJ143D64) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D64.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D65 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for ShortWave (VJ143D65) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D65.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D41 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Local Solar Noon product (VJ143D41) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D41.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143C1 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VJ143C1) is derived from the 30 arc second CMG VJ143D Version 2 product suite. VJ143C1 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143C1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143MA1 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VJ143MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143MA1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143IA1 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VJ143IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143IA1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143MA4 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143MA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143MA4.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143IA4 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VJ143IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143IA4.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143C4 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VJ143C4) is derived from the 30 arc second CMG VJ143D Version 2 product suite. VJ143C3 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143C4.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D80 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M1 (VJ143D80) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D80.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D87 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M10 (VJ143D87) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D87.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D88 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M11 (VJ143D88) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D88.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D81 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M2 (VJ143D81) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D81.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D82 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M3 (VJ143D82) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D82.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D83 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M4 (VJ143D83) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D83.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D84 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M5 (VJ143D84) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D84.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D85 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M7 (VJ143D85) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D85.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D86 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M8 (VJ143D86) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D86.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D89 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for DNB (VJ143D89) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D89.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D01 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M1 product (VJ143D01) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D01.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D22 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M10 product (VJ143D22) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D22.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D25 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M11 product (VJ143D25) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D25.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D04 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M2 product (VJ143D04) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D04.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D07 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M3 product (VJ143D07) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D07.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D10 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M4 product (VJ143D10) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D10.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D13 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M5 product (VJ143D13) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D13.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D16 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M7 product (VJ143D16) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D16.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D19 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M8 product (VJ143D19) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D19.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D37 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Day/Night Band (DNB) product (VJ143D37) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D37.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D31 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 NIR product (VJ143D31) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D31.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D34 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Shortwave product (VJ143D34) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D34.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D28 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 VIS product (VJ143D28) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D28.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D02 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M1 product (VJ143D02) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D02.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D23 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M10 product (VJ143D23) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D23.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D26 v002 - The NOAA-20 Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M11 product (VJ143D26) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D26.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D05 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M2 product (VJ143D05) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D05.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D08 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M3 product (VJ143D08) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D08.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D11 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M4 product (VJ143D11) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D11.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D14 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M5 product (VJ143D14) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D14.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D17 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M7 product (VJ143D17) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D17.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D20 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M8 product (VJ143D20) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D20.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D38 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Day/Night Band (DNB) product (VJ143D38) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D38.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D32 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 NIR product (VJ143D32) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D32.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D35 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Shortwave product (VJ143D35) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D35.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D29 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 VIS product (VJ143D29) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D29.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D03 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M1 product (VJ143D03) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D03.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D24 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M10 product (VJ143D24) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D24.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D27 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M11 product (VJ143D27) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D27.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D06 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M2 product (VJ143D06) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D06.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D09 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M3 product (VJ143D09) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D09.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D12 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M4 product (VJ143D12) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D12.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D15 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M5 product (VJ143D15) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D15.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D18 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M7 product (VJ143D18) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D18.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D21 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M8 product (VJ143D21) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D21.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D39 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Day-Night Band (DNB) product (VJ143D39) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D39.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D33 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 NIR product (VJ143D33) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D33.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D36 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Shortwave product (VJ143D36) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D36.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D30 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 VIS product (VJ143D30) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D30.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143MA2 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VJ143MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143MA2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D40 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality product (VJ143D40) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D40.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143IA2 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VJ143IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143IA2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D52 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow Status product (VJ143D52) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D52.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143C2 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow-free Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VJ143C2) is derived from the 30 arc second CMG VJ143D Version 2 product suite. VJ143C2 is generated daily from all available snow-free acquisitions over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143C2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D53 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Uncertainty product (VJ143D53) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D53.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D42 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M1 product (VJ143D42) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D42.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D49 v002 - The NOAA-20 Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M10 product (VJ143D49) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D49.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D50 v002 - The NOAA-20 Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M11 product (VJ143D50) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D50.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D43 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M2 product (VJ143D43) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D43.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D44 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M3 product (VJ143D44) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D44.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D45 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M4 product (VJ143D45) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D45.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D46 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M5 product (VJ143D46) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D46.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D47 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M7 product (VJ143D47) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D47.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D48 v002 - The NOAA-20 Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M8 product (VJ143D48) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D48.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D51 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Day/Night Band (DNB) product (VJ143D51) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D51.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D67 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M1 (VJ143D67) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D67.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D74 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M10 (VJ143D74) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D74.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D75 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M11 (VJ143D75) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D75.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D68 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M2 (VJ143D68) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D68.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D69 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M3 (VJ143D69) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D69.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D70 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M4 (VJ143D70) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D70.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D71 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M5 (VJ143D71) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D71.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D72 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M7 (VJ143D72) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D72.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D73 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M8 (VJ143D73) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D73.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D76 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band VIS (VJ143D76) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D76.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D79 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for DNB (VJ143D79) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D79.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D77 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band NIR (VJ143D77) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D77.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143D78 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for ShortWave (VJ143D78) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143D78.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ146A1 v2 - The NOAA-20 VIIRS Daily Gridded Day Night Band 15 arc-second Linear Lat Lon Grid Night product, short-name VJ146A1 is a daily, top-of-atmosphere, at-sensor nighttime radiance product. This product is available at 15 arc-second spatial resolution from January 2018 onward.","ARN":"arn:aws:s3:::prod-lads/VJ146A1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ102DNB v2.1 - The VIIRS/JPSS1 Day/Night Band 6-Min L1B Swath 750 m, short-name VJ102DNB is platform-derived single NASA VIIRS panchromatic Day-Night band (DNB) calibrated radiance product. The DNB is one of the M-bands with an at-nadir spatial resolution of 750 meters (across the entire scan).","ARN":"arn:aws:s3:::prod-lads/VJ102DNB","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ103DNB v2.1 - The VIIRS/JPSS1 Day/Night Band Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m product contains the derived line-of-sight (LOS) vectors for the single panchromatic Day-Night band (DNB). The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry.","ARN":"arn:aws:s3:::prod-lads/VJ103DNB","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143DNBA3 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143DNBA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VJ143DNBA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143DNBA3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143DNBA1 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143DNBA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VJ143DNBA1 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143DNBA1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143DNBA4 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143DNBA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143DNBA4.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ143DNBA2 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143DNBA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VJ143DNBA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VJ143DNBA2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ147IMG v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) FIre Light Detection Algorithm-2 (FILDA-2) Modified Combustion Efficiency (MCE) Version 2 swath product (VJ147IMG) is produced in 6-minute orbit segments 375 meter (m) resolution from the VIIRS sensor aboard the NOAA-20 satellite. The VJ147IMG product takes advantage of the rich information that visible band observation conveys at night to assess fire combustion efficiency and enhance fire detection.","ARN":"arn:aws:s3:::lp-prod-protected/VJ147IMG.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ146A2 v2 - The NOAA-20 VIIRS Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 15 arc-second Linear Lat Lon Grid product, short-name VJ146A2 is a daily moonlight- and atmosphere-corrected Nighttime Lights (NTL) product. This product is available at 15 arc-second resolution and contains seven Science Data Sets (SDS) that include DNB BRDF-Corrected NTL, Gap-Filled DNB BRDF-Corrected NTL, DNB Lunar Irradiance, Latest High-Quality Retrieval, Mandatory Quality Flag, Cloud Mask Quality Flag, and Snow Flag.","ARN":"arn:aws:s3:::prod-lads/VJ146A2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ102IMG v2.1 - The VIIRS/JPSS1 Imagery Resolution 6-Min L1B Swath 375m, short-name VJ102IMG product that comprise five image-resolution or I-bands, which have a 375-meter resolution at nadir. These I-bands comprise three reflective solar bands (RSB) and two thermal emissive bands (TEB).","ARN":"arn:aws:s3:::prod-lads/VJ102IMG","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ103IMG v2.1 - The VIIRS/JPSS1 Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath 375m product, short-name VJ103IMG, contains the derived line- of-sight (LOS) vectors for each of the 375-m image-resolution or I-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry.","ARN":"arn:aws:s3:::prod-lads/VJ103IMG","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ121 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VJ121) is produced daily in 6-minute temporal increments of satellite acquisition. The VJ121 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 µm), M15 (10.76 µm), and M16 (12 µm) at a spatial resolution of 750 meters.","ARN":"arn:aws:s3:::lp-prod-protected/VJ121.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ121C2 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day Climate Modeling Grid Version 2 product (VJ121C2) combines the daily [VJ121A1D](http://doi.org/10.5067/VIIRS/VJ121A1D.002) and [VJ121A1N](http://doi.org/10.5067/VIIRS/VJ121A1N.002) products over an 8-day compositing period into a single product. The VJ121C2 dataset is an 8-day composite LST&E product at 0.05 degree (~5,600 meter) resolution that uses an algorithm based on a simple-averaging method and is formatted as a CMG for use in climate simulation models.","ARN":"arn:aws:s3:::lp-prod-protected/VJ121C2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ121A2 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day product (VJ121A2) combines the daily [VJ121A1D](http://doi.org/10.5067/VIIRS/VJ121A1D.002) and [VJ121A1N](http://doi.org/10.5067/VIIRS/VJ121A1N.002) products over an 8-day compositing period into a single product. The VJ121A2 dataset is an 8-day composite LST&E product at 1 kilometer resolution that uses an algorithm based on a simple-averaging method.","ARN":"arn:aws:s3:::lp-prod-protected/VJ121A2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ121C1 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Climate Modeling Grid Version 2 product (VJ121C) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily [VJ121](https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day.","ARN":"arn:aws:s3:::lp-prod-protected/VJ121C1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ121A1D v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VJ121A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily [VJ121](https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day.","ARN":"arn:aws:s3:::lp-prod-protected/VJ121A1D.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ121A1N v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VJ121A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily [VJ121](https://doi.org/10.5067/VIIRS/VJ121.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night.","ARN":"arn:aws:s3:::lp-prod-protected/VJ121A1N.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ121C3 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) monthly Climate Modeling Grid Version 2 product (VJ121C3) provides LST&E by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (~5,600 meter) resolution. The VJ121C3 dataset is a monthly composite LST&E product that uses an algorithm based on a simple averaging method and is formatted as a CMG for use in climate simulation models.","ARN":"arn:aws:s3:::lp-prod-protected/VJ121C3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ115A2H v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) Version 2 data product (VJ115A2H) provides information about the vegetative canopy layer at 500 meter resolution. The VIIRS sensor is located aboard the NOAA-20 satellite.","ARN":"arn:aws:s3:::lp-prod-protected/VJ115A2H.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ102MOD v2.1 - The VIIRS/JPSS1 Moderate Resolution 6-Min L1B Swath 750 m, short-name VJ102MOD is VIIRS Level-1B calibrated radiances product that comprise sixteen moderate-resolution or M-bands, which have a spatial resolution of 750-meters at nadir. These M-bands comprise eleven reflective solar bands (RSB) and five thermal emissive bands (TEB).","ARN":"arn:aws:s3:::prod-lads/VJ102MOD","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ103MODLL v021 - The Visible Infrared Imaging Radiometer Suite (VIIRS) Moderate Resolution Terrain Correction Geolocation (VJ103MODLL) Version 2.1 product from the NOAA-20 VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth’s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60° North to 60° South.","ARN":"arn:aws:s3:::lp-prod-protected/VJ103MODLL.021","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ103MOD v2.1 - The VIIRS/JPSS1 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m product, short-name VJ103MOD contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry.","ARN":"arn:aws:s3:::prod-lads/VJ103MOD","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ109A1 v002 - The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance (VJ109A1) Version 2 composite product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor for nine moderate resolution bands (M1 - M5, M7, M8, M10, M11) at nominal 1 kilometer resolution (~926 meter). The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the Level 2 input product.","ARN":"arn:aws:s3:::lp-prod-protected/VJ109A1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ109H1 v002 - The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) Surface Reflectance (VJ109H1) Version 2 composite product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor for three imagery bands (I1, I2, I3) at nominal 500 meter resolution (~463 meter). The 500 meter dataset is derived through resampling the native 375 meter VIIRS resolution in the L2 input product.","ARN":"arn:aws:s3:::lp-prod-protected/VJ109H1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ109GA v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance (VJ109GA) Version 2 product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution.","ARN":"arn:aws:s3:::lp-prod-protected/VJ109GA.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ109CMG v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance Climate Modeling Grid (VJ109CMG) Version 2 product provides an estimate of land surface reflectance from the NOAA-20 VIIRS sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution.","ARN":"arn:aws:s3:::lp-prod-protected/VJ109CMG.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ114A1 v002 - The daily NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies and Fire (VJ114A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VJ114A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period.","ARN":"arn:aws:s3:::lp-prod-protected/VJ114A1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ114 v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies (VJ114) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission.","ARN":"arn:aws:s3:::lp-prod-protected/VJ114.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ113C1 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ113C1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 0.05 degree resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.","ARN":"arn:aws:s3:::lp-prod-protected/VJ113C1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ113A2 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ113A2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 1 kilometer (km) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.","ARN":"arn:aws:s3:::lp-prod-protected/VJ113A2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ113A1 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ113A1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 500 meter (m) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.","ARN":"arn:aws:s3:::lp-prod-protected/VJ113A1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ113C2 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ113C2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.","ARN":"arn:aws:s3:::lp-prod-protected/VJ113C2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ113A3 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ113A3) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 1 kilometer (km) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.","ARN":"arn:aws:s3:::lp-prod-protected/VJ113A3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ128C2 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Water Reservoir 8-day Level 3 (L3) Global (VJ128C2) Version 2 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The VJ128C2 data product provides an 8-day time series of surface area, elevation, and water storage.","ARN":"arn:aws:s3:::lp-prod-protected/VJ128C2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ128C3 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Water Reservoir Monthly Level 3 (L3) Global (VJ128C3) Version 2 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The VJ128C3 data product is a composite of the 8-day area classifications from [VJ128C2](https://lpdaac.usgs.gov/products/vj128c2v002/) which is converted to provide monthly elevation and water storage.","ARN":"arn:aws:s3:::lp-prod-protected/VJ128C3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ147MOD v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) FIre Light Detection Algorithm-2 (FILDA-2) Modified Combustion Efficiency (MCE) Version 2 swath product (VJ147MOD) is produced in 6-minute orbit segments 750 meter (m) resolution from the VIIRS sensor aboard the NOAA-20 satellite. The VJ147MOD product takes advantage of the rich information that visible band observation conveys at night to assess fire combustion efficiency and enhance fire detection.","ARN":"arn:aws:s3:::lp-prod-protected/VJ147MOD.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ202DNB v2.1 - The VIIRS/JPSS2 Day/Night Band 6-Min L1B Swath 750 m, short-name VJ202DNB, of the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21; referred to hereafter as J2) platform-derived single NASA VIIRS panchromatic Day-Night band (DNB) calibrated radiance product. The DNB is one of the M-bands with an at-nadir spatial resolution of 750 meters (across the entire scan).","ARN":"arn:aws:s3:::prod-lads/VJ202DNB","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ203DNB v2.1 - The VIIRS/JPSS2 Day/Night Band Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750 m, short-name VJ203DNB product is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-based NASA Visible Infrared Imaging Radiometer Suite (VIIRS) L1 terrain-corrected geolocation product, and contains the derived line-of-sight (LOS) vectors for the single panchromatic Day-Night band (DNB). The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of t...","ARN":"arn:aws:s3:::prod-lads/VJ203DNB","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ202IMG v2.1 - The VIIRS/JPSS2 Imagery Resolution 6-Min L1B Swath 375m, short-name VJ202IMG is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21; referred to hereafter as J2) platform-derived NASA Visible Infrared Imaging Radiometer Suite (VIIRS) L1B calibrated radiances product that comprise five image-resolution or I-bands, which have a 375-meter resolution at nadir. These I-bands comprise three reflective solar bands (RSB) and two thermal emissive bands (TEB).","ARN":"arn:aws:s3:::prod-lads/VJ202IMG","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ203IMG v2.1 - The VIIRS/JPSS2 Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath 375 m, short-name VJ203IMG is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-derived NASA Visible-Infrared Imaging-Radiometer Suite (VIIRS) L1 terrain-corrected geolocation product and contains the derived line-of-sight (LOS) vectors for each of the 375-m image-resolution or I-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sens...","ARN":"arn:aws:s3:::prod-lads/VJ203IMG","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ202MOD v2.1 - The VIIRS/JPSS2 Moderate Resolution 6-Min L1B Swath 750m, short-name VJ202MOD is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21; referred to hereafter as J2) platform-derived NASA Visible Infrared Imaging Radiometer Suite (VIIRS) L1B calibrated radiances product that comprise sixteen moderate-resolution or M-bands, which have a spatial resolution of 750-meters at nadir. These M-bands comprise eleven reflective solar bands (RSB) and five thermal emissive bands (TEB).","ARN":"arn:aws:s3:::prod-lads/VJ202MOD","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ203MOD v2.1 - The VIIRS/JPSS2 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750 m, short-name VJ203MOD is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-based NASA Visible-Infrared Imaging-Radiometer Suite (VIIRS) L1 terrain-corrected geolocation product, and contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS ...","ARN":"arn:aws:s3:::prod-lads/VJ203MOD","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ209A1 v002 - The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance (VJ209A1) Version 2 composite product provides an estimate of land surface reflectance from the NOAA-21 VIIRS sensor for nine moderate resolution bands (M1 - M5, M7, M8, M10, M11) at nominal 1 kilometer resolution (~926 meter). The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the Level 2 input product.","ARN":"arn:aws:s3:::lp-prod-protected/VJ209A1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ209H1 v002 - The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) Surface Reflectance (VJ209H1) Version 2 composite product provides an estimate of land surface reflectance from the NOAA-21 VIIRS sensor for three imagery bands (I1, I2, I3) at nominal 500 meter resolution (~463 meter). The 500 meter dataset is derived through resampling the native 375 meter VIIRS resolution in the L2 input product.","ARN":"arn:aws:s3:::lp-prod-protected/VJ209H1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ209GA v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance (VJ209GA) Version 2 product provides an estimate of land surface reflectance from the NOAA-21 VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution.","ARN":"arn:aws:s3:::lp-prod-protected/VJ209GA.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ209CMG v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance Climate Modeling Grid (VJ209CMG) Version 2 product provides an estimate of land surface reflectance from the NOAA-21 VIIRS sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution.","ARN":"arn:aws:s3:::lp-prod-protected/VJ209CMG.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-jpss","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA)","Description":"NASA data for cosmic microwave background (CMB) analysis is made available here by the Legacy Archive for Microwave Background Data Analysis (LAMBDA), which is a part of NASA's High Energy Astrophysics Science Archive Research Center (HEASARC). LAMBDA hosts the data archives of over 30 different CMB missions spanning 30+ years. The data archive for each mission may contain a range of data types from low-level time-ordered data to high level science-ready products such as sky maps and angular power spectra. Also provided in consistent formats are a variety of full sky maps in complementary wavebands that are useful for CMB analysis, such as maps of radio synchrotron or infrared dust emission.\n<br/><br/>\nData provided here are normally stored in the generic Flexible Image Transport System ([FITS](https://heasarc.gsfc.nasa.gov/docs/heasarc/fits.html)) format common in astronomy. Sky maps are generally provided in the Hierarchical Equal-Area isoLatitude Pixelization ([HEALPix](https://healpix.sourceforge.io/)) scheme, though the older COsmic Background Explorer (COBE) data is stored in COBE Quadrilateralizated Spherical Cube pixelization ([CSC](https://lambda.gsfc.nasa.gov/product/cobe/skymap_info_new.html)). See the links for each mission for more information on the data and how to use it. LAMBDA also provides user support, both generic and mission-specific, through a helpdesk available through the website's [Feedback page](https://lambda.gsfc.nasa.gov/contact/contact.html).\"\n","Documentation":"[The LAMBDA Website](https://lambda.gsfc.nasa.gov/)","Contact":"[LAMBDA Feedback](https://lambda.gsfc.nasa.gov/contact/contact.html)","ManagedBy":"[LAMBDA](https://lambda.gsfc.nasa.gov/)","UpdateFrequency":"Various.","Tags":["archives","astronomy","aws-pds","datacenter","imaging","satellite imagery"],"License":"There are no restrictions on the use of this data.","Citation":null,"Resources":[{"Description":"the [Wilkinson Microwave Anisotropy Probe (WMAP)](https://lambda.gsfc.nasa.gov/product/wmap/current/index.html).otal size of 2 TB.","ARN":"arn:aws:s3:::nasa-lambda/cobe/map/dr5","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"The [COBE/Far-InfraRed Absolute Spectrophotometer (FIRAS)](https://lambda.gsfc.nasa.gov/product/cobe/firas_products.html).  Total size of 9.4 GB.","ARN":"arn:aws:s3:::nasa-lambda/cobe/firas","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"The [COBE/Diffuse Infrared Background Experiment  (DIRBE)](https://lambda.gsfc.nasa.gov/product/cobe/dirbe_overview.html).  Total size of 95 GB.","ARN":"arn:aws:s3:::nasa-lambda/cobe/dirbe","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"The [COBE/Differential Microwave Radiometer  (DMR)](https://lambda.gsfc.nasa.gov/product/cobe/dmr_overview.html).  Total size of 25 GB.","ARN":"arn:aws:s3:::nasa-lambda/cobe/dmr","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"LAMBDA cloud access","URL":"https://lambda.gsfc.nasa.gov/product/aws/","AuthorName":"NASA/LAMBDA","AuthorURL":"https://lambda.gsfc.nasa.gov/"}],"Tools & Applications":[{"Title":"LAMBDA Tools","URL":"https://lambda.gsfc.nasa.gov/toolbox/","AuthorName":"NASA/LAMBDA","AuthorURL":"https://lambda.gsfc.nasa.gov/"}],"Publications":[{"Title":"CMB Experiment Bibliography","URL":"https://lambda.gsfc.nasa.gov/papers/","AuthorName":"Various; curated by LAMBDA","AuthorURL":"https://lambda.gsfc.nasa.gov/"}]},"RegistryEntryAdded":"2023-08-04","RegistryEntryLastModified":"2024-09-03","Slug":"nasa-lambda","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA LANCE Project","Description":"ATL13QL is the quick look version of ATL13 and is based on the same algorithms that generate the ATL13 final data products. Once final ATL13 files are available, the corresponding ATL13QL files are removed. ATL13QL provides along-track surface water products for inland water bodies, defined as lakes, reservoirs, bays, estuaries, rivers, and a 7 km near-shore buffer. Data parameters include surface water height statistics and related parameters including significant wave height, transect slope, subsurface signal attenuation, and shallow water bathymetry. Water surface heights are provided as both orthometric height and height referencing the WGS84 ellipsoid.\n<br>\n\n#### ATL09QL\nATL09QL is the quick look version of ATL09 and is based on the same algorithms that generate the ATL09 final data products. Once final ATL09 files are available, the corresponding ATL09QL files are removed. ATL09QL contains calibrated, attenuated backscatter profiles (CAB), layer-integrated attenuated backscatter, blowing snow, and other parameters including cloud and aerosol layer height and atmospheric characteristics. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.\n<br>\n\n#### ATL08QL\nATL08QL is the quick look version of ATL08 and is based on the same algorithms that generate the ATL08 final data products. Once final ATL08 files are available, the corresponding ATL08QL files are removed. ATL08QL contains along-track estimates of terrain height, canopy height, and canopy cover, as well as beam and reference parameters. Data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.\n<br>\n\n#### ATL10QL\nATL10QL is the quick look version of ATL10 and is based on the same algorithms that generate the ATL10 final data products. Once final ATL10 files are available, the corresponding ATL10QL files are removed. ATL10 contains along-track sea ice freeboard calculated for 10 km swath segments. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.\n<br>\n\n#### ATL07QL\nATL07QL is the quick look version of ATL07 and is based on the same algorithms that generate the ATL07 final data products. Once final ATL07 files are available, the corresponding ATL07QL files are removed. ATL07 contains along-track sea surface height and sea ice height for segments of variable lengths for all beams as well as fixed 10-meter segments for strong beams. Heights are also computed using the DDA-Bifurcation algorithm for strong beams. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.\n<br>\n\n#### SPL1BTB_NRT\nThis Near Real-Time (NRT) data set corresponds to the standard SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures (SPL1BTB) product. The data provide calibrated estimates of time-ordered geolocated brightness temperature data measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product, SPL1BTB (<a href=\"https://doi.org/10.5067/ZHHBN1KQLI20\">https://doi.org/10.5067/ZHHBN1KQLI20</a>).\n<br>\n\n#### SPL2SMP_NRT\nThis Near Real-Time (NRT) data set corresponds to the standard SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture (SPL2SMP) product. The data provide estimates of global land surface conditions measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product SPL2SMP (<a href=\"https://doi.org/10.5067/LPJ8F0TAK6E0\">https://doi.org/10.5067/LPJ8F0TAK6E0</a>).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/data/projects/lance","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ATL13QL v007 - ATL13QL is the quick look version of ATL13 and is based on the same algorithms that generate the ATL13 final data products. Once final ATL13 files are available, the corresponding ATL13QL files are removed.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL13QL/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL09QL v007 - ATL09QL is the quick look version of ATL09 and is based on the same algorithms that generate the ATL09 final data products. Once final ATL09 files are available, the corresponding ATL09QL files are removed.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL09QL/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL08QL v007 - ATL08QL is the quick look version of ATL08 and is based on the same algorithms that generate the ATL08 final data products. Once final ATL08 files are available, the corresponding ATL08QL files are removed.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL08QL/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL10QL v007 - ATL10QL is the quick look version of ATL10 and is based on the same algorithms that generate the ATL10 final data products. Once final ATL10 files are available, the corresponding ATL10QL files are removed.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL10QL/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ATL07QL v007 - ATL07QL is the quick look version of ATL07 and is based on the same algorithms that generate the ATL07 final data products. Once final ATL07 files are available, the corresponding ATL07QL files are removed.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL07QL/007","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL1BTB_NRT v105 - This Near Real-Time (NRT) data set corresponds to the standard SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures (SPL1BTB) product. The data provide calibrated estimates of time-ordered geolocated brightness temperature data measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL1BTB_NRT/105","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL2SMP_NRT v107 - This Near Real-Time (NRT) data set corresponds to the standard SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture (SPL2SMP) product. The data provide estimates of global land surface conditions measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL2SMP_NRT/107","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-lance","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA landsat-7 Project","Description":"This data set, part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides a complete 15 m resolution image mosaic of the Greenland ice sheet, derived from USGS Landsat 7 ETM+ imagery and Canadian Space Agency's (CSA) RADARSAT-1 imagery from the years 1999 to 2002. Additional bands (some at 30 m resolution) are provided for each tile in the mosaic and are useful for understanding surface properties, such as snow grain size, bedrock outcrops, mapping layering in the snow, and blue ice or lake-filled regions, during the spring and summer months. The panchromatic (band 8) mosaic provides the highest-resolution view of the ice sheet surface at 15 m, resolving topographic features, large crevasses, and other geophysical structures. See <a href=\"http://nsidc.org/data/measures/gimp\">Greenland Ice Mapping Project (GIMP)</a> for related data.\n<br>\n\n#### NSIDC-0714\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides a complete land ice and ocean classification mask for the Greenland ice sheet, that was mapped using a combination of USGS Landsat 7 ETM+ panchromatic band imagery, and the Canadian Space Agency's (CSA) RADARSAT-1 Synthetic Aperture Radar (SAR) amplitude images. See <a href=\"http://nsidc.org/data/measures/gimp\">Greenland Ice Mapping Project (GIMP)</a> for related data.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/landsat-7","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","oceans","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NSIDC-0713 v1 - This data set, part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides a complete 15 m resolution image mosaic of the Greenland ice sheet, derived from USGS Landsat 7 ETM+ imagery and Canadian Space Agency's (CSA) RADARSAT-1 imagery from the years 1999 to 2002. Additional bands (some at 30 m resolution) are provided for each tile in the mosaic and are useful for understanding surface properties, such as snow grain size, bedrock outcrops, mapping layering in the snow, and blue ice or lake-filled regions, during the spring and summer m...","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0713/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0714 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides a complete land ice and ocean classification mask for the Greenland ice sheet, that was mapped using a combination of USGS Landsat 7 ETM+ panchromatic band imagery, and the Canadian Space Agency's (CSA) RADARSAT-1 Synthetic Aperture Radar (SAR) amplitude images. See <a href=\"http://nsidc.org/data/measures/gimp\">Greenland Ice Mapping Project (GIMP)</a> for related data.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0714/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-landsat-7","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA landsat-8 Project","Description":"This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of mean monthly velocity maps for selected glacier outlet areas. The maps are generated by tracking visible features between optical image pairs acquired by the Landsat 4 and 5 Thematic Mapper (TM), the Landsat 7 Enhanced Thematic Mapper Plus (ETM+), the Landsat 8 Operational Land Imager (OLI), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). See <a href=\"http://nsidc.org/data/measures/gimp\">Greenland Ice Mapping Project (GIMP)</a> for related data.\n<br>\n\n#### NSIDC-0775\nThis data set, part of NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of ice velocities at 240 m resolution, generated from Landsat 4, 5, 7, and 8 optical image pairs. Velocities were derived using the autonomous Repeat Image Feature Tracking algorithm (autoRIFT) processing chain. Data are available for all land ice areas larger than 5 square km, spanning the period from 1985 to 2018 (subject to image availability and quality). Data scarcity and/or low radiometric quality are significant limiting factors for many regions during the earlier years of the data record. Annual, global coverage is nearly complete after the 2013 launch of Landsat 8.\n<br>\n\n#### NSIDC-0670\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains a multi-year ice-sheet-wide velocity mosaic for Greenland, derived from Interferometric Synthetic Aperture Radar (InSAR), Synthetic Aperture Radar (SAR), and Landsat 8 optical imagery data. See <a href=\"http://nsidc.org/data/measures/gimp\">Greenland Ice Mapping Project (GIMP)</a> for related data.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/landsat-8","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NSIDC-0646 v3 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of mean monthly velocity maps for selected glacier outlet areas. The maps are generated by tracking visible features between optical image pairs acquired by the Landsat 4 and 5 Thematic Mapper (TM), the Landsat 7 Enhanced Thematic Mapper Plus (ETM+), the Landsat 8 Operational Land Imager (OLI), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0646/3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0775 v1 - This data set, part of NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of ice velocities at 240 m resolution, generated from Landsat 4, 5, 7, and 8 optical image pairs. Velocities were derived using the autonomous Repeat Image Feature Tracking algorithm (autoRIFT) processing chain.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0775/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0670 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains a multi-year ice-sheet-wide velocity mosaic for Greenland, derived from Interferometric Synthetic Aperture Radar (InSAR), Synthetic Aperture Radar (SAR), and Landsat 8 optical imagery data. See <a href=\"http://nsidc.org/data/measures/gimp\">Greenland Ice Mapping Project (GIMP)</a> for related data.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0670/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-landsat-8","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA landsat Project","Description":"This data set contains shapefiles of Greenland’s glacial termini and basins for the years 1972 to 2019. These vector data were created from Landsat 1-8 satellite imagery using the Calving Front Machine (CALFIN) an automated processing workflow utilizing neural networks for extracting calving fronts from satellite images of marine-terminating glaciers.\n<br>\n\n#### HMA2_DCG_SGL\nThis data set identifies the extent of supraglacial lakes (SGLs) from 1988 to 2023 on 769 of the largest debris-covered glaciers in the High Mountain Asia region. SGLs were generated via automated processing of 30 m Landsat imagery and are provided at 1-year intervals from 2004-2023, at 2-year intervals from 1992-2003, and as a single 4-year interval from 1988-1991.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/landsat","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NSIDC-0764 v1 - This data set contains shapefiles of Greenland’s glacial termini and basins for the years 1972 to 2019. These vector data were created from Landsat 1-8 satellite imagery using the Calving Front Machine (CALFIN) an automated processing workflow utilizing neural networks for extracting calving fronts from satellite images of marine-terminating glaciers.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0764/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA2_DCG_SGL v1 - This data set identifies the extent of supraglacial lakes (SGLs) from 1988 to 2023 on 769 of the largest debris-covered glaciers in the High Mountain Asia region. SGLs were generated via automated processing of 30 m Landsat imagery and are provided at 1-year intervals from 2004-2023, at 2-year intervals from 1992-2003, and as a single 4-year interval from 1988-1991.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA2_DCG_SGL/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-landsat","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Landslide Project Project","Description":"The Landslide Hazard Assessment for Situational Awareness (LHASA) model identifies locations with high potential for landslide occurrence at a daily temporal resolution. LHASA combines satellite‐based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. When rainfall is considered to be extreme and susceptibility values are moderate to very high, a “nowcast” is issued to indicate the times and places where landslides are more probable. Although the model could be run every half hour, this archive contains a daily record derived from a retrospective model run and spatial coverage is from 60°N to 60°S .\n<br>\n\n#### Global_Landslide_Nowcast\nThe Global Landslide Nowcast addresses the need for real-time situational awareness of landslide hazard. The Landslide Hazard Assessment for Situational Awareness model (LHASA) combines satellite rainfall estimates from the Global Precipitation Measurement mission (GPM) with soil moisture estimates from the Soil Moisture Active Passive (SMAP) satellite and other factors to produce a map of locations where rainfall-triggered landslide activity is probable. Due to the latency of the rainfall data, the nowcast is a near-real time product with a minimum latency of 5 hours. Although the model could be run every half hour, this archive contains a daily record derived from a retrospective model run. The Global Landslide Nowcast version 2.0.0 retains replaces the heuristic decision tree from version 1.0 with a machine learning model. Instead of merging all factors other than precipitation into a susceptibility map, LHASA 2.0 takes in each variable as a separate input layer. The most important change is the replacement of the categorical nowcast with a probabilistic output. This will enable users to adjust the threshold to suit their specific application and geographic location.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://landslides.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","precipitation","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Global_Landslide_Nowcast v1.1 - The Landslide Hazard Assessment for Situational Awareness (LHASA) model identifies locations with high potential for landslide occurrence at a daily temporal resolution. LHASA combines satellite‐based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Landslide/Global_Landslide_Nowcast.1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Landslide_Nowcast v2.0.0 - The Global Landslide Nowcast addresses the need for real-time situational awareness of landslide hazard. The Landslide Hazard Assessment for Situational Awareness model (LHASA) combines satellite rainfall estimates from the Global Precipitation Measurement mission (GPM) with soil moisture estimates from the Soil Moisture Active Passive (SMAP) satellite and other factors to produce a map of locations where rainfall-triggered landslide activity is probable.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Landslide/Global_Landslide_Nowcast.2.0.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-landslide-project","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA LBA-ECO Project","Description":"This data set provides measurements from the Amazonian Aerosol Characterization Experiment (AMAZE-08) carried out during the wet season from February 4 to March 21, 2008 in the central Amazon Basin. Aerosol and atmospheric samples and measurements were collected at Tower TT34 located 60 km NNW of downtown Manaus, and at Tower K34, located 1.6 km from the TT34 site. Physical characterization of aerosols included size, mass, and number distributions and light scattering properties. Chemical characterization included mass concentrations of organics, major anions and cations, and trace metals. Aerosol sources were estimated with measurements of black carbon and biogenic particles. Meteorological and atmospheric conditions including relative humidity, temperature, wind speed and direction, rain, photosynthetically active radiation (PAR), downward and upward solar irradiance, and condensation nuclei were measured. Atmospheric trace gases and volatile organic compounds (VOCs) were sampled and analyzed.\n<br>\n\n#### amazon_precip_228\nThe Amazon River Basin precipitation grids were derived from data which was collected daily by the gauging network operated by the Divisao Nacional de Aguas e Energia Eletrica (DNAEE, SGAN 603 Modulo J, Anexo DNC, CEP 70.830-030 Brasilia DF, Brazil). The DNAEE provided the Earth Observing System (EOS) Regional Amazon Model (EOSRAM) project with this data for cooperative analysis. The project includes both empirical and modeling studies of rainfall and runoff from sample hillslopes to the entire Amazon basin. The precipitation data is 0.2 degree gridded monthly precipitation data. The data spans the period from January 1972 to December 1992.\n<br>\n\n#### JERS-1_SAR_GRFM_Amazon_Mosaics_1280\nThis data set provides ~100-m resolution image mosaics of South America acquired during the low flood season between September and December 1995 and during the high flood season between May and July of 1996. The images cover the same areas during both seasons and were obtained from the Japanese Earth Resources Satellite 1 (JERS-1) Synthetic Aperture Radar (SAR) of the National Space Development Agency of Japan (NASDA). The data were mosaicked into 34 tiles for each season, each consisting of about 50 JERS-1 scenes. This data set constitutes the first-ever high-resolution and single season coverage of the entire Amazon River Basin, made possible by the cloud penetrating properties of the radar sensor. The images are from the original JERS-1 SAR Global Rain Forest Mapping Project. This data set contains 66 files in GeoTIFF (.tiff) format. There are 32 files for the low flood season and 34 files for the high flood season.\n<br>\n\n#### basin_border_670\nThis data set is an expanded version of the Costa et al. (2000) data set and consists of a single grid with values of 1 for cells within the basins and 0 for cells outside. The resolution of the data set is 5 x 5 min (approximately 9 x 9 km). The area of this data set is consistent with the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America. The data file is in ASCII GRID format.\n<br>\n\n#### legal_amazon_mask_671\nThe Legal Amazon of Brazil is defined by law to include the states of Acre, Amapá, Amazonas, Pará, Rondônia, Roraima, Mato Grosso, Maranhão, and Tocantins [Fundãcao Instituto Brasileiro de Geografia e Estatística (IBGE) 1991]. This is the definition used in generating the Legal Amazon mask. The 8-km Legal Amazon mask was generated by Christopher Potter at the Ecosystem Science and Technology Branch of the Earth Science Division at NASA Ames Research Center (Potter and Brooks-Genovese 1999). The mask was generated from the Digital Chart of the World available from Environmental Systems Research Institute, Inc. (ESRI). The mask is available in ASCII GRID format. The README file accompanying the mask has more information regarding data format. More information can be found at ftp://daac.ornl.gov/data/lba/human_dimensions/legal_amazon_mask/comp/legamazon_readme.pdf.\n<br>\n\n#### olson_672\nThis data set is a subset of Olson et al. (1985, 2000) \"Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation.\" This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10° N to 25° S, longitude 30° to 85° W). The data are in ASCII GRID format. \"Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation\" is a computerized database used to generate a global vegetation map of 44 different land ecosystem complexes (mosaics of vegetation or landscapes) comprising seven broad groups. The map is derived from patterns of preagricultural vegetation, modern areal surveys, and intensive biomass data from research sites. Work on the database was begun in 1960 and completed in 1980. Ecosystem complexes are defined for each 0.5-degree grid cell, reflecting the major climatic, topographic, and land-use patterns. Numeric codes are assigned to each vegetation type. Classifications include natural as well as human managed/modified complexes such as mainly cropped, residential, commercial, and park. The complexes are ranked by estimated organic carbon in the mass of live plants given in units of kilograms of carbon per square meter. Counting the cells of each type and adding their areas give total area estimates for the ecosystem complexes. Multiplying by carbon estimates gives corresponding estimates of carbon by ecosystem complex with in the LBA study area. The results help define the role of the terrestrial biosphere in the global carbon cycle. Information about the ecosystem classifications, as well as the procedure used to create the LBA subset can be found at ftp://daac.ornl.gov/data/lba/carbon_dynamics/olson/comp/olson_readme.pdf. LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.\n<br>\n\n#### willmott_673\nThis data set is a subset of a 0.5-degree gridded temperature and precipitation data set for South America (Willmott and Webber 1998). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), defined as 10° N to 25° S, 30° to 85° W. The data are in ASCII GRID format. The data consist of the following: Monthly mean air temperature time series (1960-1990), in degrees C: monthly mean air temperatures for 1960-1990 cross validation errors associated with time series monthly mean air temperatures for 1960-1990, DEM assisted interpolation cross validation errors associated with DEM assisted interpolation time series Monthly mean air temperature climatology, in degrees C: climatic means of monthly and annual air temperatures cross validation errors associated with climatic means climatic means of monthly and annual mean air temperatures, DEM assisted interpolation cross validation errors associated with DEM assisted interpolation climatic means Monthly total precipitation time series (1960-1990), in millimeters: monthly precipitation totals for 1960-1990 cross validation errors associated with time series monthly precipitation totals for 1960-1990, climatologically aided interpolation cross validation errors associated with climatologically aided interpolation time series Monthly total precipitation climatology, in millimeters: climatic means of monthly and annual precipitation totals cross validation errors associated with climatic means More information about the full data set can be found at \"Willmott, Matsuura, and Collaborators' Global Climate Resource Pages\" (http://climate.geog.udel.edu/~climate) at the University of Delaware. To obtain the original documentation and data, follow the link for \"Available Climate Data,\" register or sign in, and follow the link for \"South American Climate Data.\" Information on the LBA subset can be found at ftp://daac.ornl.gov/data/lba/physical_climate/willmott/comp/willmott_readme.pdf.\n<br>\n\n#### lba_isric_wise_701\nThe data set consists of a subset of the ISRIC-WISE global data set of derived soil properties for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 to 30 degrees W, latitude 25 degrees S to 10 degrees N).The World Inventory of Soil Emission Potentials (WISE) database currently contains data for over 4300 soil profiles collected mostly between 1950 and 1995. This database has been used to generate a series of uniform data sets of derived soil properties for each of the 106 soil units considered in the Soil Map of the World (FAO-UNESCO, 1974). These data sets were then linked to a 1/2 degree longitude by 1/2 degree latitude version of the edited and digital Soil Map of the World (FAO, 1995) to generate GIS raster image files for the following variables:Total available water capacity (mm water per 1 m soil depth)Soil organic carbon density (kg C/m**2 for 0-30 cm depth range)Soil organic carbon density (kg C/m**2 for 0-100 cm depth range)Soil carbonate carbon density (kg C/m**2 for 0-100 cm depth range)Soil pH (0-30 cm depth range)Soil pH (30-100 cm depth range)LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. LBA was a cooperative international research initiative led by Brazil and NASA was a lead sponsor for several experiments. More information about LBA and links to other LBA project sites can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.\n<br>\n\n#### soller_wetlands_674\nThis data set consists of a subset of a 1-degree gridded global freshwater wetlands database (Stillwell-Soller et al. 1995). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10° N to 25° S, 30° to 85° W). The data are in ASCII GRID format. The global freshwater wetlands database was assembled from two data sets: Aselman and Crutzen's (1989) wetlands data set and Klinger's political Alaska data set (pers. comm. to L. M. Stillwell-Soller, 1995). The aim of Stillwell-Soller's global data set was to provide an accurate, comprehensive and uniform set of files for convenient specification of wetlands in global climate models. The main source of data was Aselman and Crutzen's global maps of percent cover for a variety of wetlands categories at 2.5-degree latitude by 5-degree longitude resolution. There was some reorganization for seasonally varying categories. Aselman and Crutzen's data were interpolated to a standard 1-degree by 1-degree grid through bilinear interpolation. Their data were geographically complete except for the Alaskan region, for which Klinger's data set provided values. More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/soller_wetlands/comp/soller_readme.pdf. LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.\n<br>\n\n#### lba_ghcn_702\nThis data set consists of a subset of the Global Historical Climatology Network (GHCN) Version 1 database for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 to 30 degrees W, latitude 25 degrees S to 10 degrees N). There are three files available, one each for precipitation, temperature, and pressure data. Within this subset the oldest data date from 1832 and the most recent from 1990.The GHCN V1 database contains monthly temperature, precipitation, sea-level pressure, and station-pressure data for thousands of meteorological stations worldwide. The database was compiled from pre-existing national, regional, and global collections of data as part of the Global Historical Climatology Network (GHCN) project, the goal of which was to produce, maintain and make available a comprehensive global surface baseline climate data set for monitoring climate and detecting climate change. It contains data from roughly 6000 temperature stations, 7500 precipitation stations, 1800 sea-level pressure stations, and 1800 station-pressure stations. Each station has at least 10 years of data; 40% have more than 50 years of data. Spatial coverage is good over most of the globe, particularly for the United States and Europe. Data gaps are evident over the Amazon rainforest, the Sahara desert, Greenland, and Antarctica. The earliest station data are from 1697; the most recent are from 1990. The database was created from 15 source data sets including:The National Climatic Data Center's (NCDC's) World Weather Records,CAC's Climate Anomaly Monitoring System (CAMS),NCAR's World Monthly Surface Station Climatology,CIRES' (Eischeid/Diaz) Global precipitation data set,P. Jones' Temperature data base for the world, andS. Nicholson's African precipitation database. Quality Control of the GHCN V1 database included visual inspection of graphs of all station time series, tests for precipitation digitized 6 months out of phase, tests for different stations having identical data, and other tests. This detailed analysis has revealed that most stations (95% for temperature and precipitation, 75% for pressure) contain high-quality data. However, gross data-processing errors (e.g., keypunch problems) and discontinuous inhomogeneities (e.g., station relocations and instrumentation changes) do characterize a small number of stations. All major data processing problems have been flagged (or corrected, when possible). Similarly, all major inhomogeneities have been flagged, although no homogeneity corrections were applied.LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. LBA was a cooperative international research initiative led by Brazil and NASA was a lead sponsor for several experiments. More information about LBA and links to other LBA project sites can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.\n<br>\n\n#### historical_croplands_675\nThis data set is a subset of a global croplands data set (Ramankutty and Foley 1999a). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10° N to 25° S, 30° to 85° W). The data are in ASCII GRID format at 5-min resolution. Navin Ramankutty and Jonathan Foley, of the Center for Sustainability and the Global Environment (SAGE) at the University of Wisconsin, developed a global, spatially explicit data set of reconstructed historical croplands from 1700 to 1992. The method for historical reconstruction used a simple algorithm that linked contemporary satellite data and historical cropland inventory data. A spatially explicit croplands data set for 1992 was first derived by calibrating a satellite-derived land cover classification data set against cropland inventory data for 1992. This derived data set was then used within a simple land cover change model, along with historical cropland inventory data, to derive spatially explicit maps of historical croplands. The global data set was restricted to a representation of permanent croplands (i.e., excluding shifting cultivation), which follows the Food and Agriculture Organization (FAO) definition of arable lands and permanent crops. Data values represent fraction of grid cell in croplands. Data for the LBA study area are available for the years 1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, and 1992. Although the global croplands data set contains data representing croplands since 1700, essentially no croplands were in the LBA study area until 1900. Data from previous years were excluded at the suggestion of the data originator. More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/historical_croplands/comp/uwcrop_readme.pdf. LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.\n<br>\n\n#### gghydro_676\nThis subset of the Global Hydrographic data set (GGHYDRO) Release 2.2 for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N) is organized into 19 files containing terrain type, stream frequency counts, major drainage basins, main features of the cryosphere surface, and ice/water runoff per year for the entire Earth's surface at a spatial resolution of 1- degree longitude by 1-degree latitude. The data are provided in both ASCII GRID and binary image file formats.More information and selected thumbnails images can be found at: ftp://daac.ornl.gov/data/lba/surf_hydro_and_water_chem/gghydro/comp/README. Exposed land not covered by swamp, intermittent water bodies, glacier ice, sand dunes, saltmarsh or salt flats (LAND)2. Perennial freshwater lakes (FLAK)3. Swamp, marsh and other wetlands(SWMP)4. Saltwater, whether marine or terrestrial (SLTW)5. Intermittent water bodies (ILAK)6. Glacier ice, including shelf ice but excluding pack ice (GLAC)7. Sand dunes (DUNE)8. Saltmarsh (SMRS)9. Salt flats (SFLT)10. Land + Swamp + Sand dunes + Saltmarsh (DSRF)11. Perennial rivers (FRIV)12. Intermittent rivers (IRIV)13. Land mask (MS05)14. Major drainage basins (BAS1)15. Smaller drainage basins (BAS2)16. Main features of the cryosphere (CRYO)17. Surface runoff of water (kg/m**2/yr) (RNOF)18. Estimated root-mean-square error of RNOF (%) (RNER)19. Runoff of ice ( kg/m**2/yr ) (RICE)\n<br>\n\n#### land_cover_data_1deg_677\nThis data set consists of a subset for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N) of the University of Maryland (UMD) 1-degree Global Land Cover product in ASCII GRID and binary image formats.The UMD 1-degree Global Land Cover product was produced by researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at UMD. The product is based on Advanced Very High Resolution Radiometer (AVHRR) maximum monthly composites for 1987 of Normalized Difference Vegetation Index (NDVI) values at approximately 8-km resolution, averaged to one-by-one degree resolution. This coarse- resolution data set was used as the basis for a supervised classification of eleven cover types that broadly represent the major biomes of the world. Because of missing values at high latitudes, the Pathfinder AVHRR data set for 1987 for summer monthly NDVI and red reflectance values were used to distinguish the following cover types: tundra, high latitude deciduous forest and woodland, coniferous evergreen forest and woodland.The 1-degree global land cover product is available for download from the Global Land Cover Facility (GLCF)[http://glcf.umiacs.umd.edu/data/landcover/index.shtml] web site. The data are available as a global coverage in both binary and ASCII format. Additional information and references on this data set can be found at the GLCF web site as well as at the LGRSS web site (link provided at the GLCF web site ) and in the readme file found along with the data [ ftp://daac.ornl.gov/data/lba/land_use_change/land_cover_data_1deg/comp/README].\n<br>\n\n#### land_cover_data_1km_678\nThis data set is a subset of Hansen et al. (1999), \"1 km Global Land Cover Data Set Derived from AVHRR,\" which was developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10 N to 25 S, longitude 30 to 85 W). The data are in ASCII GRID file format. In recent years, researchers have increasingly turned to remotely sensed data to improve the accuracy of data sets that describe the geographic distribution of land cover at regional and global scales. To develop improved methodologies for global land cover classifications as well as to provide global land cover products for immediate use in global change research, LGRSS researchers have employed the NASA/NOAA Pathfinder AVHRR Land (PAL) data set with a spatial resolution of 1 km. The PAL data set has a record length of 14 years (1981-1994), providing the ability to test the stability of classification algorithms. The PAL data set includes red, infrared, and thermal bands in addition to the Normalized Difference Vegetation Index (NDVI). Inclusion of these additional bands improves discrimination between cover types. The LGRSS researchers' aim was to develop and validate global land cover data sets and to develop advanced methodologies for more realistically describing the vegetative land surface based on satellite data. The 1-km global land cover product was created from 1992-1993 local area coverage (LAC) AVHRR data. The global land cover product is available for download from the University of Maryland's Global Land Cover Facility (GLCF) Web site (http://glcf.umiacs.umd.edu/data/landcover/index.shtml). Forty-one metrics were developed to describe global vegetation phenology, and these data were used to make the 1-km land cover map. The final product contains 13 land cover classes. More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/land_cover_data_1km/comp/glcf1km_readme.pdf. LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.\n<br>\n\n#### DISCover_land_cover_679\nThe data set consists of a LBA study area subset of the IGBP DISCover Data Set. The DISCover data set is one data set contained within the Global Land Cover Characteristics Data Base. The U.S. Geological Survey (USGS), the University of Nebraska-Lincoln (UNL), and the European Commission's Joint Research Centre (JRC) have generated a 1-km resolution global land cover characteristics data base for use in a wide range of environmental research and modeling applications. The global land cover characteristics data base was developed on a continent-by-continent basis. All continental data bases share the same map projections (Interrupted Goode Homolosine and Lambert Azimuthal Equal Area), have 1-km nominal spatial resolution, and are based on 1-km Advanced Very High Resolution Radiometer (AVHRR) data spanning April 1992 through March 1993. Each data base contains unique elements based on the geographic aspects of the specific continent. In addition, a core set of derived thematic maps produced through the aggregation of seasonal land cover regions are included in each continental data base. The continental data bases are combined to make six global data sets, each representing a different landscape based on a particular classification legend. The following derived data sets are included in the global land cover data base: * Global Ecosystems (Olson, 1994a, 1994b) * IGBP Land Cover Classification (Belward, 1996) * U.S. Geological Survey Land Use/Land Cover System(Anderson &amp; others, 1976) * Simple Biosphere Model (Sellers and others, 1986) * Simple Biosphere 2 Model (Sellers and others, 1996) * Biosphere-Atmosphere Transfer Scheme (Dickinson and others, 1986) The legends for each of these derived data sets can be found in the documentation accompanying the data. For a description of the methodology for the global data base, see the global readme file found under the EROS Data Center DAAC home page (http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html).\n<br>\n\n#### land_cover_data_8km_680\nThis data set is a subset of an 8-km global land cover product (DeFries et al. 1998). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10° N to 25° S, longitude 30° to 85° W). The data are in ASCII GRID file format. To develop improved methodologies for global land cover classifications as well as to provide global land cover products for immediate use in global change research, researchers at the Laboratory for Global Remote Sensing Studies at the University of Maryland employed the NASA/NOAA Pathfinder AVHRR Land (PAL) data set with a spatial resolution of 8 km. The PAL data set has a length of record of 14 years (1981-1994), providing the ability to test the stability of classification algorithms. Furthermore, the data set includes red, infrared, and thermal bands in addition to the Normalized Difference Vegetation Index (NDVI). Inclusion of these additional bands improves discrimination between cover types. The project's aim was to develop and validate global land cover data sets and to develop advanced methodologies for more realistically describing the vegetative land surface based on satellite data. The global land cover product (Defries et al. 1998) was derived by testing several metrics that describe the temporal dynamics of vegetation over an annual cycle. These metrics were applied to 1984 PAL data at 8-km resolution to derive a global land cover classification product using a decision tree classifier. The final product contains 13 land cover classes. The original 8-km global land cover product is available for download from the University of Maryland's Global Land Cover Facility (GLCF) Web site (http://glcf.umiacs.umd.edu/data/landcover/index.shtml). Additional information and references on this data set can be found at the GLCF Web site, as well as at the LGRSS Web site (http://www.geog.umd.edu/LGRSS/intro.html). More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/comp/land_cover_data_8km/glcf8km_readme.pdf. LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.\n<br>\n\n#### leemans_cramer_681\nThis data set is a subset of Cramer and Leemans' (2001) global database of mean monthly climatology, which contains monthly averages of mean temperature, temperature range, precipitation, rain days, and sunshine hours for terrestrial areas during 1931-1960. This subset was created for the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10° N to 25° S, longitude 30° to 85° W). The data are presented at 0.5-degree latitude/longitude resolution in ASCII GRID file format. Cramer and Leemans (2001, Version 2.1) constituted a major update of an earlier database, Leemans and Cramer (1991). The new version was generated from a larger database by means of the partial thin-plate splining algorithm developed by Michael F. Hutchinson, Canberra (Hutchinson and Bischof 1983). Version 2.1 has been used widely, notably by all groups participating in the International Geosphere-Biosphere Programme's Net Primary Productivity (NPP) model intercomparison (Olsen et al. 2001). More information about the data can be found at ftp://daac.ornl.gov/data/lba/physical_climate/leemans_cramer/comp/cramer_lmns_readme.pdf. LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA be found at http://www.daac.ornl.gov/LBA/misc_amazon.html. tabase is a major update of the Leemans and Cramer database (Leemans and Cramer 1991). It contains long-term monthly averages, for the period 1931-1960, of mean temperature, temperature range, precipitation, rain days and sunshine hours for the terrestrial surface of the globe, gridded at 0.5-degree longitude/latitude resolution. It was generated from a larger database, using the partial thin-plate splining algorithm developed by Michael F. Hutchinson, Canberra (Hutchinson and Bischof 1983). The current version is 2.1--this is the same version that is currently used widely around the globe, notably by all groups participating in the IGBP NPP model intercomparison.More information can be found at: ftp://daac.ornl.gov/data/lba/physical_climate/leemans_cramer/comp/README.\n<br>\n\n#### Zinke_soil_683\nThe data set contains a subset of a global organic soil carbon and nitrogen data set (Zinke et al. 1986). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W). The point data are available in three formats: a comma-delimited ASCII file (.csv), an ESRI shapefile, and an ESRI export file (.e00). The data for the global data set (Zinke et al. 1986) were obtained from soil surveys conducted by Zinke in 1965-1984 and from soil survey literature. The main samples for laboratory analyses were collected at uniform soil increments and included bulk density determinations. Many samples reported in the literature did not have uniform soil increments or bulk density determinations. Only soil profiles that had been sampled either to a meter in depth or to actual depth were included in this database from soil survey literature. When carbon content was known but bulk densities were absent from soil samples reported in the literature, densities were estimated by regression analysis on the basis of the relationship between organic carbon content and measured bulk density in 1800 soil profiles for which bulk densities were known. Further information can be found at ftp://daac.ornl.gov/data/lba/carbon_dynamics/Zinke_soil/comp/zinke_readme.pdf. LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.\n<br>\n\n#### potential_vegetation_684\nThe data set consists of a subset for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N) of the 5-min resolution Global Potential Vegetation data set developed by Navin Ramankutty and Jon Foley at the University of Wisconsin. Data are available in both ASCII GRID and binary image file formats.The original map was derived at a 5-min resolution and contains natural vegetation classified into 15 types. This data set is derived mainly from the DISCover land cover data set, with the regions dominated by land use filled using the vegetation data set of Haxeltine and Prentice (1996). The data set represents the world's potential vegetation (i.e., vegetation that would most likely exist now in the absence of human activities), and not necessarily natural pre-settlement vegetation. This is because human activities such as fire suppression have modified the stages of succession at which vegetation communities exist.More information can be found at: ftp://daac.ornl.gov/data/lba/land_use_change/potential_vegetation/comp/README.\n<br>\n\n#### sage_685\nThis data set is a subset of a global river discharge data set by Coe and Olejniczak (1999). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10° N to 25° S, 30° to 85° W). The global river discharge data set (Coe and Olejniczak 1999), formerly known as the \"Climate, People, and Environment Program (CPEP) Global River Discharge Database,\" is a compilation of monthly mean discharge data for more than 2600 sites worldwide. The data were compiled from RivDIS Version 1.1 (Vorosmarty et al. 1998), the U.S. Geological Survey, and the Brazilian National Department of Water and Electrical Energy. The period of record for the sites varies from 3 years to greater than 100. The purpose of the global compilation is to provide detailed hydrographic information for the climate research community in as general a format as possible. Data are given in units of meters cubed per second (m**3/sec) and are in ASCII format. Data from stations that had less than 3 years of information or that had a basin area less than 5000 square kilometers were excluded from the global data set. Thus, the data sources may include more sites than the data set by Coe and Olejniczak (1999). Users should refer to the data originators for further documentation on the source data. More information, a map of discharge sites, and a clickable site data table can be found at ftp://daac.ornl.gov/data/lba/surf_hydro_and_water_chem/sage/comp/sagedischarge_readme.pdf. LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. Further information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.\n<br>\n\n#### lba_tree_cover-1km_686\nThe data set consists of a subset for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N) of the 1km Global Tree Cover Data Set developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Data are available in both ASCII GRID and binary image files formats.Characterization of terrestrial vegetation from the Advanced Very High Resolution Radiometer (AVHRR) on the global to regional scale has traditionally been accomplished using classification schemes with discrete numbers of vegetation classes. Representation of vegetation into a limited number of homogeneous classes does not account for the variability within land cover, nor does the portrayal recognize transition zones between adjacent cover types. An alternative paradigm to describing land cover as discrete classes is to represent land cover as continuous fields of vegetation characteristics using a linear mixture model approach. This prototype data set, created by researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland, contains 1-km cells estimating: 1) Percent tree cover; 2) Percentage cover for two layers representing leaf longevity (evergreen and deciduous); and 3) Percentage cover for two layers estimating leaf type (broadleaf and needleleaf).Data acquired in 1992-93 from NOAA's AVHRR at a 1-km spatial resolution and processed under the guidance of the International Geosphere Biosphere Programme (IGBP) were used to derive the tree cover, leaf type and leaf longevity maps. Each pixel in the layers has a value between 10 and 80 percent. These layers can be directly used as parameters in models or aggregated into more conventional land cover maps. For the latter, the product offers the flexibility to derive land cover maps based on user's requirements for a particular application. The product is intended for use in terrestrial carbon cycle models, in conjunction with other spatial data sets such as climate and soil type, to obtain more consistent and reliable estimates of carbon stocks.\n<br>\n\n#### wilhend_687\nThis data set is a subset of a global vegetation and soils data set by Wilson and Henderson-Sellers (1985a). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10° N to 25° S, 30° to 85° W). The data are in ASCII GRID format. The original global data set (Wilson and Henderson-Sellers 1985a) is an archive of soil type and land cover data derived for use in general circulation models (GCMs). The data were collated from maps depicting natural vegetation, forestry, agriculture, land use, and soil, and they were archived at a resolution of 1° latitude by 1° longitude. The data set indicates soil type, soil data reliability, primary vegetation, secondary vegetation, and land cover data reliability. Approximately 50 land cover classifications are used, including categories for agricultural and urban uses. The inclusion of secondary vegetation type is particularly useful in areas with cover types that may have a fragmented distribution, such as in areas of urban development. The soil type data are classified according to climatically important properties for GCMs, and they indicate color (light, medium, or dark), texture, and drainage quality of the soil. The land cover data are compatible with the soils data, forming a coherent and consistent data set. The reliability of the land cover data is ranked on a scale of 1 to 5 (high to low). The reliability of the soil data is ranked as high, good, moderate, fair, or poor. Recommendations for the use of these data, as well as more detailed information can be found in Wilson and Henderson-Sellers (1985b). Further data set information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/wilhend/comp/wilhend_readme.pdf. LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.\n<br>\n\n#### lba_gisswetlands_688\nThis database, compiled by Matthews and Fung (1987), provides information on the distribution and environmental characteristics of natural wetlands. The database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources. The original data consists of five global 1-degree latitude by 1-degree longitude arrays. This subset, for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America, retains all five arrays at the 1-degree resolution but only for the area of interest (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N). The arrays are (1) wetland data source, (2) wetland type, (3) fractional inundation, (4) vegetation type, and (5) soil type. The data subsets are in both ASCII GRID and binary image file formats.The data base is the result of the integration of three independent digital sources: (1) vegetation classified according to the United Nations Educational Scientific and Cultural Organization (UNESCO) system (Matthews, 1983), (2) soil properties from the Food and Agriculture Organization (FAO) soil maps (Zobler, 1986), and (3) fractional inundation in each 1-degree cell compiled from a global map survey of Operational Navigation Charts (ONC). With vegetation, soil, and inundation characteristics of each wetland site identified, the data base has been used for a coherent and systematic estimate of methane emissions from wetlands and for an analysis of the causes for uncertainties in the emission estimate.The complete global data base is available from NASA/GISS [http://www.giss.nasa.gov] and NCAR data set ds765.5 [http://www.ncar.ucar.edu]; the global vegetation types data are available from ORNL DAAC [http://www.daac.ornl.gov].\n<br>\n\n#### CD01_CIRSAN_Meteorology_2001_1114\nThis data set contains meteorological data collected around the confluence of the Tapajos River with the Amazon River in the Amazon Basin near Santarem, Brazil, in July and August 2001. Boundary layer and upper air measurements were collected with an acoustic sounder-sodar instrument, pilot balloons with optical theodolites, and radiosondes. Radiosondes also measured pressure, temperature, and relative humidity in addition to wind speed and direction. Measurements were made from five local stations at varying frequencies. There are 41 comma-delimited data files with this data set. Supporting information provided with the data set as companion files include: Weather forecasts: Weather forecasts were used to determine the presence of favorable conditions for the balloon flights during the CIRSAN experiment, as well as to help decide the radiosonde launch frequency. The daily observed and forecast weather descriptions for the study period (Weather_forecasts_Santarem.txt) are included. Satellite images: All the satellite images during the CIRSAN period are provided. This is a compilation of images from various instruments and satellite platforms. (See readme_sat.txt). There are 42 images in .gif format. CPTEC Analysis files: The CIRSAN measurement data were used in the CPTEC Global Analysis modeling activity. Model output results for the Pacific and South American region are provided in GRIB format. (See readme_GPSA.txt)\n<br>\n\n#### CD01_BRAMS_907\nWe have investigated mesoscale variations of atmospheric CO2 over a heterogeneous landscape of forests, pastures, and large rivers during the Santarem Mesoscale Campaign (SMC) of August 2001. The variations of atmospheric CO2 concentration were simulated using the Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS) with four nested grids that included a 1-km finest grid centered on the Flona Tapajos. Surface fluxes of CO2 were prescribed in the model using idealized diurnal cycles over forest and pasture vegetation derived from flux tower observations, and over surface water using a value suggested by in situ measurements in the Amazon region. The distribution of vegetation types was derived from the 1-km International Geosphere-Biosphere Programme (IGBP) land-cover dataset version 2.0. Our simulation ran from the 1st through the 15th of August 2001, which was concurrent with the SMC. Evaluation against flux tower observations and the SMC field measurements shows that, in many respects, the model captures observed meteorological variables and CO2 concentrations reasonably well. The results also suggest that the local topography, differences in roughness length between water and land, the T shape juxtaposition of Amazon and Tapajos Rivers, and the resulting horizontal and vertical wind shears, all facilitated the generation of local mesoscale circulations. Possible mechanisms producing a lower level convergence line near the east bank of the Tapajos River during strong trade-wind conditions are also explored. Our modeling study is helping us to understand observed patterns of CO2 fluxes and concentration distribution obtained from flux towers and light aircraft.\n<br>\n\n#### CD02_C_N_Isotopes_1097\nThis data set reports delta 13C/12C results for leaf tissues and atmospheric carbon dioxide (CO2), 15N/14N ratios for leaf tissue, and leaf carbon and nitrogen concentrations along a topographical gradient in old-growth forests in the ZF2 Reserve (km 34), Instituto Nacional de Pesquisas da Amazonia (INPA), near Manaus, Amazonas, Brazil. During the dry seasons of 2004 and 2006, leaves were sampled at various heights within the canopy and atmospheric air flask samples were also collected at various heights at three locations along this gradient. Also included are coincident meteorological, atmospheric CO2, and CO2 flux measurements from the plateau KM34 tower. There are 3 comma-delimited data files with this data set.\n<br>\n\n#### CD02_Atmosphere_CO2_Isotopes_1011\nThis data set reports carbon and oxygen stable isotope ratios of atmospheric carbon dioxide (CO2) collected at several forest and pasture sites and in the free troposphere over Amazonia. There are three comma-delimited ASCII files with this data set. Atmospheric CO2 concentrations and isotope signatures were measured at ten different forest and pasture canopy sites across the states of Amazonas, Para, and Rondonia within the Brazilian Amazon between March 1999 and March 2004. Both daytime and nighttime profile samples were collected. Samples of CO2 in the troposphere were collected during aircraft flights over the Amazon/Tapajos Rivers, FLONA Tapajos, and pasture/agriculture areas during five days in May 2003 (wet season). Samples were analyzed for carbon and oxygen isotopes of atmospheric CO2. Flights ranged from low altitudes to above the diurnal tropospheric boundary layer. Measurements of carbon and oxygen stable isotope ratios of atmospheric carbon dioxide (CO2) are a powerful indicator of large-scale CO2 exchange on land across multiple spatial scales. Stable carbon isotope composition of leaf tissue and CO2 released by respiration (delta r) can be used as an estimate of changes in ecosystem isotopic discrimination that occur in response to seasonal and interannual changes in environmental conditions, and land-use change (forest-pasture conversion). Understanding of carbon dioxide stable isotope composition can play a central role in influencing our understanding of the extent to which terrestrial ecosystems are carbon sinks.\n<br>\n\n#### CD02_C_N_O_Organic_983\nThis data set reports the measurement of stable carbon, nitrogen, and oxygen isotope ratios in organic material (plant, litter and soil samples) in forest canopy profiles and pasture (grasses and shrubs) as well as corresponding carbon and nitrogen tissue concentrations in a number of different sites across Brazil. The sampling design captured the temporal variation in rainfall over the course of several years. Carbon and nitrogen isotope ratios can act as a proxy for interpreting aspects of the carbon and nitrogen cycles in Amazonian rainforests. Data are in three comma-delimited ASCII files.\n<br>\n\n#### CD02_Forest_Canopy_Structure_1009\nThis data set reports on Leaf Area Index (LAI) and Specific Leaf Area (SLA) measurements collected from forest and pasture sites in or near the Tapajos National Forest (TNF), 80 km south of the city of Santarem, Para, Brazil. The collections were between October 1999 and June 2003 from tower sites accessed via the km 67 forest entrance. There are 2 comma-delimited ASCII data files with this data set, and 1 companion data file which provides site descriptions.\n<br>\n\n#### CD02_Leaf_Level_Gas_Exchange_1010\nThis data set reports leaf gas flux and leaf properties from samples collected from trees, liana, pasture saplings, and pasture grass located at eight different sampling locations in the states of Para (south of Santarem) and Amazonas (near Manaus) from November 1999 through December 2003. Data are reported on photosynthesis measurements, CO2 response curves, light response curves, humidity response curves, and stomatal responses to variations of the leaf-to-air water vapor mole fraction deficit. Leaf weight, carbon and nitrogen concentrations as well as stable isotope signatures for 13C and 15N are reported for a subset of the samples. There is one comma-delimited ASCII data file with this data set.\n<br>\n\n#### CD02_Leaf_Water_Potential_1100\nData are reported for leaf water potential of leaves of seven species of trees and lianas from the primary forest at the km 67 Tower Site, Tapajos National Forest, and measurements of five sapling tree species and the grass Brachiaria brizantha from a pasture site located near the km 77 Pasture Tower Site, approximately 10 km from the primary forest site. The research area is situated within the Tapajos National Forest reserve, south of the city of Santarem, Para, Brazil. Measurements were made quarterly between March 2000 and March 2001. There is one comma-delimited data file with this data set.\n<br>\n\n#### CD02_O_H_Isotopes_1008\nThis data set reports the oxygen isotope signatures of water extracted from plant tissue (xylem from the stems and leaf tissue) and of atmospheric water vapor from twelve different sites (including both pasture and forest) throughout the Amazon region of Brazil. Samples were collected approximately every 4 months between 1999 and 2003 with additional samples collected monthly between January and May of 2003. In 2004 the collection of water samples from plant tissue continued at two sites, though water vapor collections were discontinued, and measurements of deuterium signatures were added to the analyses. In addition, water vapor from the troposphere was collected during a series of aircraft flights over the Tapajos National Forest in May of 2003 and analyzed for oxygen isotopes using the same methodology. There is one comma-delimited ASCII data file with this data set.\n<br>\n\n#### CD03_Ceilometer_Km67_942\nA Vaisala CT-25K ceilometer was installed at an old-growth forest site located at the km 67 Eddy Flux Tower site in the Tapajos National Forest, Para, Brazil, off Kilometer 67 of BR-163 south of Santarem in April 2001 and remained operational through December 2003, with reliable data being collected between May 2001 and June 2003. Annual, 2001 to 2003, 30-minute average cloud base and backscatter profile data and measurement statistics (sample count, variance, skewness, and kurtosis) are presented in 15 ASCII comma-delineated files. In addition, the cloud base values (m) and measurement statistics for the three reported cloud base levels have been consolidated in 3 annual comma-separated files. The ceilometer provides 15-second measurements of cloud base (three levels up to 7500 m), echo intensity, and a 30-m resolution backscatter profile. The ceilometer reports vertical visibility during periods when the sky is obscured but a cloud base is not detectable. The ceilometer was operational for a sufficient amount of time to examine wet-to-dry season variations in cloud cover fraction and cloud base height.\n<br>\n\n#### CD03_Pasture_Flux_962\nEddy correlation and micrometeorological measurements began in 2001 and continued through 2005 at the pasture site at km 77 on BR-163 just south of the city of Santarem, Para, Brazil. Measurements included turbulent fluxes (momentum, heat, water vapor, and CO2) using the eddy covariance (EC) approach. Other measurements included the CO2 profile, air temperature, humidity, wind speed profile, downward and upward solar and terrestrial radiation, downward and upward photosynthetically active radiation (PAR), atmospheric pressure, rainfall, soil temperature, soil moisture, and soil heat flux. Data are presented in 5 comma-separated ASCII value (csv) files each corresponding roughly to one calendar year. At the beginning of the measurements, in September 2000, the field was a pasture. In November 2001, the pasture was burned, plowed, and planted in upland (non-irrigated) rice. Land use practices during the study period were recorded and are included in a table in Section 5 of this guide. The EC system was composed of a 3D sonic anemometer (ATI 3D) and an infrared analyzer (LICOR 6262) installed on a 20m tower in the agricultural field. The methodology to calculate the flux is described in detail in Sakai et al. (2004) and a companion file is included that describes in detail the formulae used to calculate the eddy flux variables (CD03_Pasture_Flux_Calculations.pdf).\n<br>\n\n#### CD03_Mesoscale_Meteorology_944\nWe analyzed rainfall obtained in a network of 38 rain gauges located near the confluence of the Tapajos and Amazon rivers in the eastern Amazon Basin. We found that tipping bucket rain gauges work adequately in the Amazon rainfall regime, but careful field calibration and comparison with collocated conventional rain gauges was essential to incorporate daily totals from operational array into regional maps. Near-river stations miss the afternoon convective rain as expected as the river breeze promotes subsidence over the river, but paradoxically, this deficiency is more than compensated for by additional nocturnal rainfall at these locations. The 0.25-degree CMORPH passive infrared inferred rainfall data do an adequate job of describing medium scale variability in this region, but some localized breeze effects are not resolved. For inland areas away from the breezes, the nocturnal period precipitation contributes less than half of total precipitation. The large-scale rainfall increase just to the west of Santarem manifests itself locally as a 'tongue' of enhanced rain from along the wide area of open water at the Tapajos-Amazon confluence The breeze circulations associated with the Amazon River (which lies parallel to the mean flow) affects rainfall more than does the Tapajos breeze (normal to the predominant wind). The Tapajos breeze influence extends only a few kilometers inland east of the riverbank. Rainfall increases to the north of the Amazon, possibly the result of orographic effects. Dry season rainfall increases by up to 30% going away from the Amazon River, as would be expected given breeze subsidence over the river.\n<br>\n\n#### CD03_Tethered_Balloon_1108\nThis data set contains measurements of nocturnal meteorological profiles collected from tethered balloon platforms during July 2001, October 2001, and November 2003. Measurements were made near the pasture/agricultural tower site at km 77 on BR-163 just south of the city of Santarem, and the near the Tapajos National Forest, km 83 tower site, Santarem, Para, Brazil. Measurements collected include air temperature, wind speed and direction, and specific humidity. The 2003 measurements also included CO2 concentrations. Sites were near enough to allow comparison between sounding profiles and tower data. There are three comma-delimited ASCII files with this data set. Profiles were obtained from sunset until the first hours after sunrise. Each sounding provided information on temperature, humidity, horizontal wind magnitude and direction as the balloon went up and down. Typical soundings went up to 300 to 400 m. During most of the night, soundings were performed hourly. The balloon rose at a rate of 0.5 m per second in the first 100 m, and 2 m per second above it. The time between successive samplings was 10 s. Intensive periods of shallow, successive soundings were performed starting at dawn, to catch the early development of the convective boundary layer (CBL). These early morning soundings went up only to the capping inversion.\n<br>\n\n#### CD04_Biomass_990\nThis data set contains the results of a biometric tree survey of a 19.25 ha area adjacent to the eddy flux tower at the km 83 logged forest tower site in Tapajos National Forest, Para, Brazil. The survey was done in March 2000. All measurements reported here were taken before the logging began. Diameters of all trees > 35 cm DBH within the 19.25 ha survey area were recorded and trees with DBH between 10 and 35 cm DBH were recorded along three transects with a total area of 2.3 ha (Miller et al., 2004). These data were used to calculate net ecosystem productivity (NEP) and the role of this forest as a carbon source or sink. Biometric data are reported in one comma-delimited ASCII file.\n<br>\n\n#### CD04_Tower_Flux_Gap_978\nThis data set reports 30-minute values for above-canopy meteorology and fluxes of momentum, heat, and carbon dioxide, and within-canopy carbon dioxide and water vapor concentrations collected at 12 levels between 10 cm and 64 m at the tower located within a logging gap at km 83 Tower Site in the Tapajos National Forest, Para, Brazil. Data were collected over 1.5 years between June 3, 2002 and January 30, 2004. All of the data are contained in one comma separated file. Two towers are located at the km 83 site. The first tower was installed in an intact forest area at this site in June 2000 (the 'intact' tower). In September 2001, the area adjacent to the tower was selectively logged (Bruno et al., 2006). The second tower (the 'gap tower') was installed and operating in June 2002, 400 m east of the intact tower. The gap tower was installed in the middle of a 50 m x 50 m log landing.\n<br>\n\n#### CD04_CO2_Profiles_947\nWe used two independent approaches, biometry and micrometeorology, to determine the net ecosystem production (NEP) of an old growth forest in Para, Brazil. Biometric inventories indicated that the forest was either a source or, at most, a modest sink of carbon from 1984 to 2000 (+0.8 +/- 2 Mg C(.)ha(-1.)yr(-1); a positive flux indicates carbon loss by the forest, a negative flux indicates carbon gain). Eddy covariance measurements of CO2 exchange were made from July 2000 to July 2001 using both open- and closed-path gas analyzers. The annual eddy covariance flux-calculated without correcting for the underestimation of flux on calm nights indicated that the forest was a large carbon sink (-3.9 Mg C.ha(-1.)yr(-1)). This annual uptake is comparable to past reports from other Amazonian forests, which also were calculated without correcting for calm nights. The magnitude of the annual integral was relatively insensitive to the selection of open- versus closed-path gas analyzer, averaging time, detrending, and high-frequency correction. In contrast, the magnitude of the annual integral was highly sensitive to the treatment of calm nights, changing by over 4 Mg C(.)ha(-1.)yr(-1) when a filter was used to replace the net ecosystem exchange (NEE) during nocturnal periods with u* < 0.2 m/s. Analyses of the relationship between nocturnal NEE and u* confirmed that the annual sum needs to be corrected for the effect of calm nights, which resulted in our best estimate of the annual flux (+0.4 Mg C(.)ha(-1.)yr(-1)). The observed sensitivity of the annual sum to the u* filter is far greater than has been previously reported for temperate and boreal forests. The annual carbon balance determined by eddy covariance is therefore less certain for tropical than temperate forests. Nonetheless, the biometric and micrometeorological measurements in tandem provide strong evidence that the forest was not a strong, persistent carbon sink during the study interval.\n<br>\n\n#### CD04_Dendrometry_989\nA dendrometry study was conducted at the logged forest tower site, km 83 site, Tapajos National Forest, Para, Brazil over a period of 4 years following the implementation of a reduced impact logging management regime. Dendrometer bands were installed to measure diameter growth increments for 234 trees in an 18 ha plot adjacent to the eddy flux tower at the km 83 site. In addition to trees randomly selected for measurements within the plot prior to logging, a set of smaller diameter trees within or adjacent to gaps created during the logging treatment were added to the study in 2002. Selective logging is a major land use in the Amazon Basin. An accurate accounting of the effect of logging on regional carbon balances requires better information on the rates at which the logged forest recovers biomass. There is one comma-delimited data file with this data set.\n<br>\n\n#### CD04_LAI_Estimates_1103\nThis data set contains summary data for monthly leaf area index (LAI) and plant area index (PAI) at the km 83 Tower Site, in the Tapajos National Forest, Para, Brazil. LAI was estimated for hemispherical photographs of leaves collected between 2000 and 2003, using the histogram and gap-fraction analysis methods. There are two data files with this data set: one comma-delimited ASCII data file with this data set which contains the monthly summary LAI and PAI data, and one compressed (.zip) file that contains hemispherical photo images (.bmp) for 2000-2001. The images include those taken pre-logging and post-logging at the measurement site for the purpose of comparing LAI. In addition, there is a companion file containing a program code developed for LAI analysis provided as an ASCII text file.\n<br>\n\n#### CD04_LAI_992\nLeaf area index was estimated in an 18 ha plot at the logged forest tower site, km 83, Tapajos National Forest, Para, Brazil. The plot was adjacent to the eddy flux tower at km 83, Tapajos National Forest, Para, Brazil. Thirty litter traps were placed at 25-m intervals along two east–west transects in the 18 ha block. Litter samples were collected biweekly from the traps and returned to the lab where they were sorted, air dried, and weighed. The leaf area of a subsample of air-dried leaves was determined using a computer scanner and image processing software. The subsample was then dried in an oven and the air-dried weights were corrected to oven-dried weight. The area of leaf litter collected during each sampling was calculated using the relationship between weight and area measured for the subsample (Goulden et al., 2004). There is one comma-delimited data file with this data set.\n<br>\n\n#### CD04_Leaf_Litter_991\nAbove-ground litter productivity was measured in a 18 ha plot adjacent to the eddy flux tower at the logged forest tower site, km 83, Tapajos National Forest, Para, Brazil. Thirty litter baskets distributed within the grid were visited bi-weekly (Goulden et al., 2004). Oven dry mass of leaves, wood, reproductive parts and miscellaneous components of the collected litter was determined for each collection. Collections covered a pre-harvest period (Sept 2000 - July 2001) and a post- harvest period (Aug 2001-Mar 2003). There is one comma-delimited data file with this data set.\n<br>\n\n#### CD04_Leaf_Level_Gas_Exchange_1060\nThis data set reports the results of measurements of (1) leaf-level photosynthesis response curves for the effects of temperature, leaf age, warming, irradiation, and circadian rhythm and (2) leaf-level photorespiration rates at 30 and 37 degrees C. Measurements were made between June 2000 and February 2006 at the km 83 Logged Forest Tower site, the km 67 Primary Forest Tower site, and the control site at Seca Floresta, all in the Tapajos National Forest, Para, Brazil. There are 7 comma delimited ASCII data files with this data set.\n<br>\n\n#### CD04_Logging_Damage_1038\nThis data set contains the results of a survey of logging damage in a 18 ha plot (300 m N-S, 600 m E-W) east (upwind) of the eddy flux tower at km 83, Tapajos National Forest, Para, Brazil. Data collected include type of damage, snap height, and log dimensions, as well as calculated biomass of stems and canopy either damaged or removed in logging. There are two comma-delimited data files with this data set.\n<br>\n\n#### CD04_Soil_Moisture_Km83_979\nThis data set reports continuous high-resolution frequency-domain reflectometry measurements of soil moisture to 10 m depth and precipitation data near each of the two towers located at the km 83 tower site (logged forest site) in the Tapajos National Forest in the state of Para, Brazil. Measurements were made during 2002 and 2003. Soil moisture and precipitation data are provided in two comma-delimited ASCII files. The first tower was installed in an intact forest area at this site in June 2000 (the 'intact' tower) and instrumented for eddy flux and micrometerological measurements and operated 15 months prior to any logging in the area (Goulden et al., 2004; Miller et al., 2004; Rocha et al., 2004). In September 2001, the area adjacent to the tower was selectively logged (Bruno et al., 2006). The second tower (the 'gap tower' tower) was installed and operating in June 2002, 400 m east of the intact tower. The gap tower was installed in the middle of a 50 m x 50 m log landing. Soil moisture measurements were made in 10 m deep vertical pits (1 x 1 m2) approximately 20 m from the micrometerological tower sites in undisturbed forest patches. Reflectometers were inserted horizontally into shaft walls at 0.15, 0.30, 0.60, 1, 2, 3, 4, 6, 8, and 10 meters beneath the surface. These data were used to determine how soil moisture varies on diel, seasonal and multi-year timescales and to better understand the quantitative and mechanistic relationships between soil moisture and forest evapotranspiration.\n<br>\n\n#### CD04_Soil_Respiration_1039\nThis data set reports on the flux of carbon dioxide from logged forest soils near the eddy flux tower at the km 83 site, Para, Brazil. The automated soil respiration measurements were collected using 15 chambers, installed August 2001 in primary forest. Data were collected between December 19, 2001 and March 1, 2002. There is one comma-delimited data file with this data set.\n<br>\n\n#### CD05_Fuel_Loads_1233\nThis data set contains estimates of understory fuel loads (forest litter) at six locations near Paragominas in Northeastern Amazonia. Samples were collected from three different forest conditions: primary forest, logged forest, and burned forest. Volumes and weights are provided by size and condition class based on the planar transect method of estimating understory fuel loads (Brown 1971). Means and standard errors are reported from 3 transects in each forest x condition class. There is one comma-delimited data file (.csv) with this data set. DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products.\n<br>\n\n#### CD05_Micromet_1169\nThis data set reports soil moisture expressed as volumetric water content (VWC), daily precipitation, air temperature, relative humidity, and dew point measurements conducted at the Seca Floresta site, km 67, Tapajos National Forest, Brazil. The measurements were part of the Rainfall Exclusion Experiment (REE) established to study the response of a humid Amazonian forest to severe drought. VWC was measured with continuous high-resolution frequency-domain reflectometry to 11-m depth in two 1-ha plots from 1999 to 2007. One plot was subjected to ~75 percent throughfall exclusion during the rainy season (exclusion) and another monitored under normal conditions (control). Daily precipitation was measured in the control plot and in a nearby clearing between 1999 and 2006 using wedge rain gauges. Air temperature, relative humidity, and dew point were measured along the vertical forest profile of the control and dry plots of the site between 2000 and 2003. There are three comma-delimited data files (.csv) with this data set.\n<br>\n\n#### CD05_REE_Fuel_Sticks_Moisture_1232\nThis data set contains moisture content measurements for fuel sticks located in the forest understory of the rainfall exclusion experimental site, Tapajos National Forest, Para, Brazil. The mean and standard errors are reported for control and treatment plot measurements. The measurements were taken on various dates and times of day between 1998 and 2000 during the dry season. The rainfall exclusion treatment began in late January 2000 and continued through December 2004. About 60% of throughfall (equivalent to approximately half the rainfall) was diverted from a 1-hectare plot (i.e., dry) using plastic panels installed in the understory. The comparable 1-hectare control plot (i.e., wet) was unaltered. The purpose was to observe the potential effects of severe water stress on a humid Amazonian forest (Nepstad et al., 2002). There is one comma-separated (.csv) data file with this data set.\n<br>\n\n#### CD06_Camrex_1086\nThis data set provides high-resolution (~500 m) gridded land and stream drainage direction maps for the Amazon River basin, excluding the Rio Tocantins basin. These maps are the result of a new topography-independent analysis method (Mayorga et al., 2005) using the vector river network from the Digital Chart of the World (DCW, Danko, 1992) to create a high-resolution flow direction map. The data products include (1) a stream network coverage with stream order assigned to each reach; (2) the basin boundaries of the major tributaries to the Amazon mainstem; (3) the mouths; and (4) the source points of these tributaries. There are 7 ESRI ArcGIS shapefiles provided in compressed .zip format and 4 GeoTiff image files with this data set.\n<br>\n\n#### CD06_BGC_JiParana_1227\nThis data set provides spatially extensive and temporally intensive surveys of the river biogeochemistry of the Ji-Parana River Basin in Western Amazonia, Rondonia, Brazil. The concentrations of major nutrient ions, dissolved organic and inorganic carbon, pH, temperature, dissolved oxygen, and conductivity were measured in Ji-Parana River and tributary samples at the defined seasonal or monthly intervals. Dominant landuse/landcover classes, slope, and soil cation exchange capacity are included for each of the extensive sampling locations derived from river basin and sub-basin characteristics. Water samples were collected from 1999 to 2003 along the main stem of the Ji-Parana River as well as from the major tributaries including the Urupa. Shapefiles with the boundaries of the major sub-basins of the study area as well as the location of the sample collection points are included for the intensive and extensive sampling campaigns as well as the Urupa River campaign. There are six comma-separated data files (.csv) and five compressed shapefiles (.zip) with this data set.\n<br>\n\n#### CD06_Carbon_respiration_1125\nThis data set provides measured and calculated variables describing the carbon pools in river waters, CO2 respired from the water and total amount of CO2 evaded, dissolved oxygen isotopes (delta 18O-O2), and concentration of bacterial cells in river water. Samples were collected from 10 white-water rivers, two clear-water streams (one each in Amazonas and Acre), and two black-water rivers in Amazonas from July to September 2005, which coincided with a severe drought in the western and southern regions of the Amazon Basin (Zeng et al. 2008). Eight of these sites were resampled during August through September 2006 of the following year (no drought).\n<br>\n\n#### CD06_C02_Exchange_1136\nThis data set provides measurements of carbon dioxide flux rates (FCO2), gas transfer velocity (k), and partial pressures (pCO2) at 75 sites on rivers and streams of the Amazon River system in South America for the period beginning July 1, 2004, and ending January 23, 2007. Several fieldwork campaigns occurred between June 2004 and January 2007 in the Amazon River basin, with discharge conditions ranging from low to high flow. The sampled areas span the spectrum of chemical characteristics observed across the entire basin, including, for example, both low and high pH values and suspended sediment loads. There is one comma-delimited data file in this data set.\n<br>\n\n#### CD06_Outgassing_1151\nThis data set provides estimates of monthly carbon dioxide (CO2) flux from the Amazon mainstem rivers, tributary stream networks, and their associated varzeas (floodplains). CO2 flux was calculated using two aggregation approaches: for defined river basins (data file #2) and for defined river reaches (figure 2). Flux was calculated from (1) estimated surface water area by month for the Amazon mainstem rivers, associated varzeas, and tributary stream networks, (2) mean daily partial pressures of CO2 (pCO2) concentrations for the mainstem rivers, and (3) calculated mean pCO2 values for the varzea waters. Mean monthly discharge data for 11 mainstem rivers are also included. There are five comma-delimited data files with this data set. Amazon mainstem is a region covering the Amazon/Solimoes River mainstem from 70 degrees W to 54 degrees W. Data from the Japanese Earth Resources Satellite-1 (JERS-1) L-band synthetic aperture radar were used to estimate the areal coverage and inundation status of rivers and floodplains over 100 m in width and compiled into mosaics for periods of high and low water. For each mosaic, the study area was classified into either flooded or non-flooded areas. Data for the seasonal and spatial distributions of pCO2 within each hydrographic region were utilized from over 1,800 samples taken on 13 Carbon in the Amazon River Experiment (CAMREX) expeditions at different water stages throughout a 2,000 km reach of the central Amazon mainstem, tributary, and floodplain waters (Degens et al., 1991, Devol et al., 1995, Richey et al., 1988).\n<br>\n\n#### CD06_C_Isotopes_1120\nThis data set includes measurements of standard geochemical variables, dissolved CO2, dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), fine particulate organic carbon (FPOC), and coarse particulate organic carbon (CPOC) in samples taken from 60 Amazonian river locations across the Amazon Basin from 1991 to 2003 (Mayorga et al., 2005). The 14C and 13C isotopic composition of DIC was measured on samples collected between 1991 and 2003. The 14C composition of organic carbon fractions was measured on samples collected from 1995 through 1996. There are four comma-delimited data files with this data set. Note that site descriptions include a categorization of each site by topography according to the percentage of the drainage area above 1,000 m elevation (Mayorga et al., 2005). Only means of geochemical and carbon-fraction results are provided. Both individual 13C and 14C measurements and mean results are provided.\n<br>\n\n#### CD06_LULC_Map_JiParana_1087\nThis data set provides a land use/land cover map of the Ji-Parana River Basin in the state of Rondonia, Brazil produced from the digital classification of eight Landsat 7-ETM+ scenes from 1999 acquired from the Tropical Rain Forest Information Center (TRFIC) at Michigan State University. Nine land cover classes covering the Ji-Parana Basin were identified. There is one GeoTiff file with this data set.\n<br>\n\n#### CD06_Landuse_Timeseries_JiParana_844\nThis data set contains four land use/land cover maps (1986, 1992, 1996 and 2001) for the Ji-Parana River Basin, derived from the digital classification of 8 Landsat images obtained from The Tropical Rain Forest Information Center (TRFIC).\n<br>\n\n#### CD06_Physical_Template_JiParana_1090\nThis data set contains physical, hydrologic, political, demographic, and societal maps for the Ji-Parana River Basin, in the state of Rondonia, Brazil. These data were used as base information in subsequent investigations of land use/land cover, biogeochemistry, soils, and water balance processes (Ballester et al., 2003). This data set includes a Digital Elevation Model (DEM), river networks and morphometric characteristics of the region (derived from the DEM), and a number of social and demographic vector sets (roads as of 2001, county borders, population change from 1970-2000, and settlement projects). The DEM is provided in GeoTIFF format. Other files are provided as shapefiles.\n<br>\n\n#### CD06_Soils_JiParana_1088\nThis data set provides a digital map of soil orders for the Ji-Parana River Basin, in the state of Rondonia, Brazil (Western Amazonia). Soil orders were manually digitized from a 1:500,000 map from EMBRAPA originally published in 1983. Oxisols and Ultisols are the predominant soil types in the basin, encompassing 47% and 24% of the total drainage area, respectively. Entisols cover 14%, Alfisols 13% and Eptisols 2% of the basin (Ballester et al., 2003). One data file is provided in ESRI ArcGIS Shapefile format compressed into a single zip file (.zip).\n<br>\n\n#### CD06_Water_Balance_JiParana_1132\nThis data set provides simulated minimum, average, and maximum monthly rainfall, potential evapotranspiration, water deficit, and water surplus values for the Ji-Parana River basin, Rondonia, Brazil. The Thornthwaite-??Mather climatological model integrated into a Geographic Information System (GIS) was used to derive the data by utilizing Advanced Very High Resolution Radar (AVHRR) images for temperatures, rainfall amounts from gauges within and around the basin, soil profiles, and land cover maps as model inputs. The monthly water balance for the Ji-Parana river basin is simulated from February 1995 through December 1996 (Victoria et al., 2007). Data are also provided from the Ji-Parana subbasin stations for total basin rainfall, basin discharge and basin evapotranspiration. This data was used to check the results of the water balance model. There are 2 comma-delimited data files with this data set.\n<br>\n\n#### CD07_GOES_L3_Gridded_SRB_831\nHigh resolution downwelling solar, PAR, infrared radiation and rain rates retrieved from GOES-8 imager. The data set covers primarily Amazon watershed area. It has 8km and half hourly resolution. Data covers two periods in 1999: March 1 - April 30 and September 1 - October 31. Files are available in compressed binary format.\n<br>\n\n#### CD08_C_Isotopes_Belowground_1025\nThis data set contains carbon isotope signatures from soil organic matter collected from the following sites: the forests of the ZF-2 INPA reserve approximately 80 km north of the city of Manaus, Amazon; the Tapajos National Forest approximately 83 km south of the city of Santarem, Para; and the Fazenda Vitoria, a ranch near the city of Paragominas, Para. Samples from the Fazenda Vitoria were from degraded and managed pasture sites as well as mature and secondary forests. In addition,carbon isotope signatures from roots sorted by size class, hand-picked from soil pits in the Flona Tapajos and Fazenda Vitoria, are included, as are carbon isotope signatures from soil gases from samples collected at the Fazenda Vitoria. There are 4 ASCII data files with this data set.\n<br>\n\n#### CD08_CWD_Res_and_Decomp_Manaus_911\nRespiration from coarse litter (trunks and large branches > 10 cm diameter) was studied in central Amazon forests. Respiration rates varied over almost two orders of magnitude (1.003-0.014 mug C g(-1) C min(-1), n = 61), and were significantly correlated with wood density (r(adj)(2) = 0.42), and moisture content (r(adj)(2) = 0.39). Additional samples taken from a nearby pasture indicated that wood moisture content was the most important factor controlling respiration rates across sites (r(adj)(2) = 0.65). Based on average coarse litter wood density and moisture content, the mean long-term carbon loss rate due to respiration was estimated to be 0.13 yr(-1) (range of 95% prediction interval (PI) = 0.11-0.15 yr(-1)). Comparing mean respiration rate with mean mass loss (decomposition) rate from a previous study, respiratory emissions to the atmosphere from coarse litter were predicted to be 76% (95% PI = 65-88%) of total carbon loss, or about 1.9 (95% PI = 1.6-2.2) Mg C ha(- 1) yr(-) (1). Optimum respiration activity corresponded to about 2.5 g H2O g(-1) dry wood, and severely restricted respiration to < 0.5 g H2O g(-1) dry wood. Respiration from coarse litter in central Amazon forests is comparable in magnitude to decomposing fine surface litter (e.g. leaves, twigs) and is an important carbon cycling component when characterizing heterotrophic respiration budgets and net ecosystem exchange (NEE).\n<br>\n\n#### CD08_Leaf_Isotopes_Manaus_1245\nThis data set provides measurements for carbon (C), nitrogen (N), leaf area index (LAI), and carbon isotope ratio data (13C and 14C) of leaves sampled at the Manaus ZF2 Jacaranda transect area, Amazonas, Brazil, in 2001. Leaf tips and the petioles from the youngest and oldest leaves from a sampled branch were analyzed for nine different species. There is one comma-delimited data file (.csv) with this data set.\n<br>\n\n#### CD08_Radiocarbon_Dates_Manaus_996\nThis data set reports the ages and growth rates of trees as determined by radiocarbon dating (14C), selected from a logging operation near the city of Itacoatiara, about 250 km east of Manaus, Brazil in 1997. Samples were collected from forty-four trees from 15 species with a basal diameter greater than 100 cm and prepared for radiocarbon dating by Accelerator Mass Spectrometry (AMS) at Lawrence Livermore National Laboratory. There is one comma-separated ASCII data file with this data set.\n<br>\n\n#### CD08_Radiocarbon_Dates_997\nThis data set reports the ages and growth rates of trees determined by radiocarbon dating (14C) in three Amazonia forests. Tree samples were collected from permanent research plots at ZF2 km 34, Manaus, Amazonas, the Catuaba Experimental Farm, Acre, and the km 83 tower site (logged forest site) in the Tapajos National Forest, Para, between 2001-2003. Samples from 97 individual trees were either tree cores (Manaus and Acre) or a combination of tree cores and slabs cut from stems as part of the logging in the Tapajos National Forest (Para). Radiocarbon dating (14C) was used to determine the age and the mean diameter growth increment of samples from individual trees in various diameter size classes. These measurements can be used to verify and extend short-term diameter increment measurements done with dendrometers and to constrain models of tree demography. There is one comma-separated ASCII data file with this data set.\n<br>\n\n#### CD08_Tree_Growth_Manaus_1194\nThis data set provides diameter at breast height (DBH) measurements made of trees in a dense terra-firme tropical moist forest at the ZF-2 Experimental Station, 90 km north of Manaus, Brazil. DBH was measured over two transects (East to West and North to South) which were established in 1996 by the Jacaranda Project (agreement between the National Institute for Research in the Amazon (INPA) and the Japan International Cooperation Agency, JICA). For each tree, a metal dendrometer band was fixed to the trunk and growth in circumference was measured monthly with digital calipers. The transects measured 20-m x 2500-m, and were stratified by plateau, slope, and baixio (lowland areas near small streams). Topography location, distance along the transect, height at which the band was installed, local tree name, and field notes are also provided in the data files. Measurements were taken between June 1999 and December 2001.\n<br>\n\n#### CD08_Tree_Inventory_Ducke_910\nThis data set includes in one data file the common names, base diameters, and calculated tree masses for almost 3,000 trees on a 5 hectare plot (20 x 2,500 m) located in the Ducke Reserve near Manaus, Brazil in the central Amazon. Measurements were taken during October-December 1999. All diameter measurements were taken at 1.3 meters in height (DBH), or above the buttresses or other stem anomalies. Forest structure characteristics such as biomass density, stem density, diameter class distribution, and taxonomic information at the family and perhaps genus level, can be derived from these data.\n<br>\n\n#### CD08_Ecosystem_Resp_Manaus_912\nUnderstanding how tropical forest carbon balance will respond to global change requires knowledge of individual heterotrophic and autotrophic respiratory sources, together with factors that control respiratory variability. We measured leaf, live wood (tree stem), and soil respiration, along with additional environmental factors over a 1-yr period in a Central Amazon terra firme forest. Scaling these fluxes to the ecosystem, and combining our data with results from other studies, we estimated an average total ecosystem respiration (R-eco) of 7.8 mumol(.)m(-2.)s(-1). Average estimates (per unit ground area) for leaf, wood, soil, total heterotrophic, and total autotrophic respiration were 2.6, 1.1, 3.2, 5.6, and 2.2 mumol(.)m(-2.)s(-1), respectively. Comparing autotrophic respiration with net primary production (NPP) estimates indicated that only similar to30% of carbon assimilated in photosynthesis was used to construct new tissues, with the remaining 70% being respired back to the atmosphere as autotrophic respiration. This low ecosystem carbon use efficiency (CUE) differs considerably from the relatively constant CUE of similar to0.5 found for temperate forests. Our R-eco estimate was comparable to the above-canopy flux (F-ac) from eddy covariance during defined sustained high turbulence conditions (when presumably F-ac = R-eco) of 8.4 (95% CI = 7.59.4). Multiple regression analysis demonstrated that similar to50% of the nighttime variability in Fa, was accounted for by friction velocity (u*, a measure of turbulence) variables. After accounting for u* variability, mean F-ac varied significantly with seasonal and daily changes in precipitation. A seasonal increase in precipitation resulted in a decrease in F-ac similar to our soil respiration response to moisture. The effect of daily changes in precipitation was complex: precipitation after a dry period resulted in a large increase in F-ac whereas additional precipitation after a rainy period had little effect. This response was similar to that of surface litter (coarse and fine), where respiration is greatly reduced when moisture is limiting, but increases markedly and quickly saturates with an increase in moisture.\n<br>\n\n#### CD09_Soils_Veg_Tapajos_1104\nThis data set reports the results of soil and vegetation surveys at four distinct areas within the Tapajos National Forest (TNF), 50 to 100 km south of Santarem, Para, Brazil, in November 1999. At 13 individual sites across the four areas, all located in primary forest, core soil samples at 10, 30 and 50 cm depths were collected and analyzed for dry mass, bulk density, texture, percentage carbon (C), percentage organic matter, and percentage nitrogen (N). At these 13 sites, vegetation was characterized for 250 m long by 10 m wide transects. Biomass was estimated for all stems over 10 cm DBH from allometric relationships for species, measured height, canopy dimension, and diameter. LAI was measured along the transect at 26 points with a LICOR LAI-2000. Canopy foliage samples were collected with a shotgun at dawn and leaf water potential was determined with a pressure chamber. Samples of foliage, wood, bark, fine roots, and litter were analyzed for %N, % C, delta 13C, and delta 15N. There are five comma-delimited ASCII data files with this data set.\n<br>\n\n#### CD10_CO_Tapajos_856\nThis data set contains half-hourly average CO mixing ratios measured from 2001/04/18 to 2003/08/29 in the old-growth upland forest at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil.CO concentrations were measured in air drawn from above the canopy top of tower (approx. 64 meters) using a TEI 48CTL instrument modified for increased stability and sensitivity. The sensor was frequently zeroed by passing ambient air over a CO oxidation catalyst. The span was checked 4 times daily by sampling calibration gases at 100 and 500 ppb. Time in the file is given in UTC (decimal date) at the start of each half hour interval.Associated meteorological parameters, CO2 concentrations and micrometerological fluxes are available in LBA-ECO CD-10 CO2 and H2O Eddy Flux Data at km 67 Tower Site, Tapajos National Forest.\n<br>\n\n#### CD10_CO_CO2_Maxaranguape_1012\nThis data set reports the concentrations of carbon monoxide (CO) and carbon dioxide (CO2), wind direction, wind speed, and air temperature measured at the Maxaranguape Atmospheric Observatory in northeast Brazil, January 4, 2003 - December 27, 2006. The data are 30-minute averages. The concentrations observed at Maxaranguape are representative of upstream atmospheric boundary conditions for the Amazon basin and could be used in conjunction with Santarem data and other data sets to estimate regional budgets for these gasses (Kirchhoff et al., 2003). There is one comma-delimited ASCII text file with this data set.\n<br>\n\n#### CD10_EddyFlux_Tapajos_860\nThis data set reports eddy flux measurements of CO2 and H2O exchange and associated meteorological measurements at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil. Measurements extend from January 2002 through January 2006.Eddy fluxes of CO2 and H2O were measured at two levels (58m and 47m) using tower-mounted closed-path Licor 6262 gas analyzers and Campbell CSAT3 sonic anemometers (Figure 1). Eddy-flux measurements were made at a sampling rate of 8 Hz and averaged over a 1 hour interval.. A comprehensive set of meteorological parameters (air temperature, pressure, PAR, net radiation, precipitation, etc) were also measured.Co-located measurements included a third Licor gas analyzer that measured (a) the CO2 and H2O concentration profiles at 8 levels in and above the canopy (1 level every 2 minutes) and (b) the instantaneous integrated canopy storage of CO2 and H2O, using a design that pulled air simultaneously through all 8 inlets (once every 20 minutes). See related data sets.With the permission of the author, Hutyra, L.R. 2007. Carbon and water exchange in Amazonian rainforests. Ph.D. Thesis, Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts., is included as a companion file.\n<br>\n\n#### CD10_CO2_Profiles_Tapajos_855\nEddy fluxes of CO2 and H2O are measured at two levels (58m and 47m) using tower-mounted closed-path Licor 6262 analyzers and Campbell CSAT3 sonic anemometers. A third Licor gas analyzer measures (a) the CO2/H2O concentration profile (1 of 8 levels every 2 minutes) and (b) the instantaneous integrated canopy storage of CO2/H2O, using a design pulling air simultaneously through 8 inlets (once every 20 minutes). Comprehensive meteorological data (air temperature, PAR, net radiation, etc) are also included. Pressure and temperature of the Licor cells are controlled to 500 torr and 48 degrees C. Eddy licors are automatically zeroed every 2 hours and the profile licor every 20 minutes. All Licors are automatically calibrated with span gases (at 325, 400, and 475 ppm) every 6 hours.\n<br>\n\n#### CD10_CWD_Tapajos_858\nThis data sets reports properties of fallen course woody debris in an old-growth upland forest at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil. Measurements extend from April 2001 through July 2001.Standing and Fallen coarse woody debris (CWD), or necromass were measured in a series of ecological plots at the km 67 eddy flux tower site in the Tapajos National Forest (Figure 2). The data set includes different size classes of debris measured in different plot sizes. Size classes were: 2-10cm (in 64 m2 subplots) , 10-30cm (in 1600 m2 subplots), 30cm (in 38400 m2 subplots), standing (in entire 50m by 1000m transects).\n<br>\n\n#### CD10_Litter_Tapajos_862\nThis data set reports litter type and mass in the old-growth upland forest at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil. Measurements extend from July 2000 through June 2005.Litter collection began in July 2000 using 40 circular, mesh screen traps (0.43 m diameter, 0.156 m2) randomly located throughout the 19.75-ha tree-survey area (Rice et al., 2004). Approximately every 14 days, litter was collected, sorted, oven dried at 60 degrees C, and weighed. The litterfall from each trap was sorted into (1) leaves, (2) fruits and flowers, (3) wood , <2 cm diameter, and (4) miscellaneous. Data values reported are the mean and standard error of the 40 mass measurements of each of the litter components and the combined total, that have been converted to the reporting units of Mg/ha/yr.\n<br>\n\n#### CD10_Biometry_Tapajos_854\nThis data sets reports biometry measurements of the old-growth upland forest at the Para¡ Western (Santarrem)km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil. Measurements extend from July 1999 through March 2005.To monitor tree woody increment, metal dendrometer bands (Figure 1) were placed on a sub-sample of 1000 trees in December 1999. The data set contains estimates of tree diameter at breast height (cm) based on caliper measurements made approximately every six weeks. The first column of data refers to the tree identification number. For a more detailed description of the biometry study refer to Rice et al. 2004.The data file contains a time series of DBH (cm) values from July 1999 through March 2005.\n<br>\n\n#### CD10_H2O_Profiles_Tapajos_861\nThis data set reports vertical profiles of H2O vapor concentrations measured at the Para Western (Santarem) - km 67, Primary Forest Tower Site (Figure 1). This site is in the Tapajos National Forest located in north central Brazil. Measurements extend from January 2002 through January 2006.H2O concentrations were measured at 8 levels on the tower (62.2, 50, 39.4, 28.7, 19.6, 10.4, and 0.91 m). Sample air was drawn at 1000 sccm (standard cubic centimeters per minute) through 8 profile inlets in sequence (2 minutes at each level) and then a mixed air sample was simultaneously drawn from all 8 levels to obtain a total column integral (once every 20 minutes) and analyzed with an infrared gas analyzer (IRGA, LI-6262, Licor, Lincoln, NE). Data were averaged over a 1 hour interval. Calibration for H2O used two independent calibrations for the IRGA concentration measurements: (a) the nighttime relationship between ambient temperature measurements and sonic temperature measurements; (b) a chilled mirror dew point hygrometer mounted on the tower. See Appendix A of Hutyra (2007) for addition details about calibration methods. Co-located measurements included eddy fluxes of CO2 and H2O were measured at two levels (58m and 47m) using tower-mounted closed-path Licor 6262 gas analyzers and Campbell CSAT3 sonic anemometers. And a comprehensive set of meteorological parameters (air temperature, pressure, PAR, net radiation, precipitation, etc) were also measured. With the permission of the author, Hutyra, L.R. 2007. Carbon and water exchange in Amazonian rainforests. Ph.D. Thesis, Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts., is included as a companion file.\n<br>\n\n#### CD10_Temperature_Profiles_Tapajos_863\nThis data set reports temperature measurements at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil. Measurements extend from January 2002 through January 2006 (Figure 1).Air temperature measurements were collected at 8 levels on the tower (61.9, 49.8, 39.1, 28.4, 18.3, 10.1, 2.8, and 0.6 m). Temperature measurements were made with aspirated thermistors (Met One 076B-4 aspiration with YSI 44032 thermistors) and averaged over a 1 hour interval.Co-located measurements included a Licor gas analyzer that measured (a) the CO2 and H2O concentration profiles at 8 levels in and above the canopy (1 level every 2 minutes), (b) the instantaneous integrated canopy storage of CO2 and H2O, using a design that pulled air simultaneously through all 8 inlets (once every 20 minutes), and (c) eddy fluxes of CO2 and H2O were measured at two levels (58m and 47m) using tower-mounted closed-path Licor 6262 gas analyzers and Campbell CSAT3 sonic anemometers. A comprehensive set of meteorological parameters (air pressure, PAR, net radiation, precipitation, etc) were also measured. See related data sets.With the permission of the author, Hutyra, L.R. 2007. Carbon and water exchange in Amazonian rainforests. Ph.D. Thesis, Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts., is included as a companion file.\n<br>\n\n#### CD10_DBH_Tapajos_859\nThis data sets reports diameter at breast height (DBH) measurements in the old-growth upland forest at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil. Measurements were made periodically from July 1999 through August 2005.Trees with DBH >35cm were measured for ~2600 trees in four 5ha transects. Trees >10cm were measured in a smaller area (Rice et al., 2004). Measurements were made in 1999, 2001, and 2005. Trees are identified by local common names. A cross reference to scientific names is provided as a companion file.Coarse woody debris and litter samples and measurements were collected same area. See related data sets.\n<br>\n\n#### CD11_Forest_Degradation_1118\nThis data set reports the results of vegetation field surveys that measured tree height and diameter at breast height (DBH) in defined size classes at three study sites -- Santarem, Para; Paragominas, Para; and Alo Brasil, Mato Grosso, Brazil, from 2001-2003. At each site, plots and transects within plots, were defined that represented different types of logging and fire treatments, each including one primary forest plot used as a control. Along each transect all trees with more than 30 cm DBH were measured. Dead standing trees were also measured and classified in three classes of decomposition. A 4 m wide transect was used to measure individuals between 10 and 30 cm DBH. Six small subplots were set along each transect to measure regeneration individuals from 2-10 cm DBH and 0-2 cm DBH. DBH is also provided for stumps found in each of the logged forest plots. There are ten comma-delimited data files with this data set.\n<br>\n\n#### CD15_Productivity_1167\nThis data set provides mean leaf area index (LAI), dendrometry band measurements, and litterfall mass from samples collected at the km 67 research site, Topajos National Forest, Para, Brazil. Litterfall collections were from January 23, 2004 through December 3, 2004, dendrometer measurements were monthly between December 2003 and December 2004, and LAI measurements were collected from January 26, 2004 through November 3, 2004. All measurements were taken at the km 67 site in the Tapajos National Forest. This site is situated in an area of Amazonian primary tropical forest belonging to the municipality of Belterra, Para, Brazil. The forest is mostly evergreen with a few deciduous species. The canopy is characterized by large emergent trees up to 55-m tall, with a closed canopy at approximately 40-m; there are few indications of recent anthropogenic disturbance other than hunting trails. Measurement plots (50) were established along 4 transects at the site and within each plot, 5 subplots were established. The longest transect (25 m x 500 m) was the location of 20 (25 m x 25 m) plots. The other 3 transects (25 m x 250 m) contain 10 plots per transect. Note that the assignment of plots to transects is not provided. There are four comma-delimited data files (.csv) with this data set.\n<br>\n\n#### CD17_Forest_Survey_1254\nThis data set provides measurements for diameter at breast height (DBH), tree height, distance from tree stems to the furthest canopy element, and a species survey of secondary forests in Para and Rondonia, Brazil, from 2002-2003. The forest areas were defined as Type A and Type B stands. Measurements were made in the overstory, understory, and midstory of each stand. Type A stands were sampled intensively, with the goal of providing high-fidelity spatial information about the 3-dimensional structure of the stand. These stands were 60 x 60-m (0.36-ha) areas divided into 10 x 10-m grids of uniform clearing and abandonment history and were identifiable from Landsat images. Type B stands were sampled extensively, with the goal of providing unbiased estimates of biomass, along with some information about the vertical structure of the stand and of spatial variability. These stands were polygons of uniform clearing and afforestation history based on multitemporal Landsat imagery, and varied in size and shape. The Landsat files provide classified land cover for each scene and can be used as a time series to evaluate land cover change over time. Each file is a geolocated land cover map based on 30-m Landsat data. NOTE: There were additional files which could not be archived due to file problems. Data Quality Statement: The Data Center has determined that this data set has missing or incomplete data, metadata, or other documentation resulting in diminished usability of this product. Known Problems: Some unresolved issues remain where data values are inconsistent with the variable descriptions provided with the data set. The site identification and plot identification values are not consistently used in all three data files. The variables are not adequately described.\n<br>\n\n#### CD32_Fluxes_Brazil_1842\nThis dataset is a compilation of carbon and energy eddy covariance flux, meteorology, radiation, canopy temperature, humidity, CO2 profiles and soil moisture and temperature profile data that were collected at nine towers across the Brazilian Amazon. Independent investigators provided the data from a variety of flux tower projects over the period 1999 thru 2006. This is Version 2 of the tower data compilation, where the data have been harmonized across projects, additional quality control checks were performed, and have been aggregated to hourly, daily, 16-day, and monthly timesteps. This integrated dataset is intended to facilitate integrative studies and data-model synthesis from a common reference point.\n<br>\n\n#### CD32_LBA_MIP_Drivers_1177\nThe source meteorological observations for the forcing data, from the nine Brazilian flux towers, were recently published as Saleska, et al. (2013). See related data sets. These source data were gap-filled according to the LBA-MIP standard protocol. Note that the CAX forest tower was not included in the MIP. See the companion file driver_data.pdf for additional gap-filling information. There are 34 data products with this data set and they are provided in both text (.txt) and ALMA-compliant NetCDF (.nc) formats. The files have been compressed into nine .zip files according to site.\n<br>\n\n#### CD34_Amazon_Hyperion_1064\nThis data set contains 20 multispectral surface reflectance images collected by the EO-1 satellite Hyperion sensor at 30-m resolution and covering the entire Amazon Basin for 2002 - 2005. All images were converted to GeoTiff format for distribution. The respective ENVI .hdr files are included as companion files and contain image projection and band information. The selected multispectral images were processed using ENVI software as described in Chambers et al. (2009). Bands with uncalibrated wavelengths and those with low spectral response were removed leaving a spectral subset of generally 196 bands (some images have fewer). A cloud mask was developed using 2-d scatter plots of variable reflectance bands to highlight clouds as regions of interest (ROIs), allowing clouds and cloud edges to be masked. A de-streaking algorithm was then applied to the image to reduce variance in balance between the vertical columns. Apparent surface reflectance was calculated for this balanced image using the atmospheric correction algorithm ACORN in 1.5pb mode (AIG-LLC, Boulder, CO). The images (18 of the 20) were georeferenced using the corresponding Advanced Land Imager (ALI) satellite images.\n<br>\n\n#### CD34_Amazon_Landsat_1176\nThis data set provides the results of fractional land cover analysis for nonphotosynthetic vegetation (NPV) from two Landsat images of Manaus, Brazil, for October 14, 2004, and for July 29, 2005. Both images are from Landsat 5, path 231, row 62. The Manauas area experienced a squall line with intense downbursts from January 16-18, 2005, that resulted in widespread blowdown and tree mortality. The pre- and post-disturbance Landsat images were obtained and processed using spectral mixture analysis (SMA) in order to investigate forest disturbance and tree mortatility resulting from the downburst. SMA was based on scene-derived end-members of green vegetation (GV, photosynthetically active vegetation), NPV ( wood, dead vegetation, and surface litter), soil, and shade obtained using a pixel purity index (PPI) algorithm (Negron-Juarez et al., 2010). Changes in NPV due to disturbance were calculated by subtracting the 2004 NPV image from the 2005 NPV image. This NPV difference image is provided. There are three image files (.tif) with this data set: The two Landsat images that were georectified and converted to reflectance values and the NPV difference image. DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS: Four additional images were needed to make this data set complete but are unavailable. Specifically, the two images resulting from SMA as applied to the Landsat images collected on the 14th of October, 2004 and the 29th of July, 2005 to determine per-pixel fractional abundance of GV, NPV (wood, dead vegetation, and surface litter), soil, and shade and the 2004 NPV and 2005 NPV images that were used to derive the ??NPV changes? image (which we do provide) (Negron-Juarez, et al., 2010).\n<br>\n\n#### CD36_SALDAS_1162\nThis data set provides South American Land Data Assimilation System (SALDAS) forcing data including atmospheric fields necessary for land surface modeling for South America which are derived by combining modeled and observation based sources. The forcing data cover the entire continent of South America at 0.125 degree resolution and are built around the model-calculated values of air temperature, wind speed and specific humidity at two meters, surface pressure, downward shortwave and longwave surface radiation, and precipitation from the South American Regional Reanalysis (SARR). These SARR data (Aravequia et al. 2007), which were released in 2006 by INPE/CPTEC, are a medium-term, dynamically consistent, high-resolution (0.125 degree), high-frequency, atmospheric dataset covering South America. The forcing data are available at a 3-hourly time step for a 5-year period from 2000 to 2004. There are 60 monthly .zip files with each zipped file containing ~240 3-hourly time step data files for that particular month in NetCDF format. Each zipped file is approximately one GB in size.\n<br>\n\n#### CD37_Biomass_Landsat_Glas_1145\nThis data set provides tree age, forest formation, and land cover classification maps, and estimates of landscape-level above-ground live woody biomass (AGLB) for secondary forests in Rondonia, Brazil. The Threshold Age Mapping Algorithm (TAMA) was applied to a densely spaced time series of Landsat images (1975 to 2003) to derive forest type and age classification maps. The AGLB of the secondary forest was estimated by combining the forest classification map with coincident biomass estimates from the Geoscience Laser Altimeter System (GLAS). There are five raster images and three comma-delimited data files with this data set.\n<br>\n\n#### LC01_Cities_Communities_Roads_1058\nThis data set contains the boundaries of the four major cities in the Northern Ecuadorian Amazon, the locations of primary communities in the colonist settlement area, and the locations of the road network, circa 2002. This area in northeastern Ecuador, know as the northern Oriente of Ecuador, borders the Andes Mountains and contains the headwaters of the Amazon River. The road network was originally digitized from 1:50,000 scale topographic maps from 1990. The surface attributes for the majority of the roads have been updated based on later remote sensing and field observations from 1999 and 2002. There are three compressed (.zip) files with this data set.\n<br>\n\n#### LC01_Hydrography_Edaphology_NEC_1059\nThis data set provides map images of hydrographic, morphologic, and edaphic features for the northern Amazon Basin in eastern Ecuador. The hydrographic data are available at two scales based on the 1:50,000 and 1:250,000-scale topographic source maps that were generated in 1990 and 1993, respectively. Morphological and edaphological data were digitized from a 1:500,000 map published in 1983. There are 3 compressed (.zip) data files with this data set.\n<br>\n\n#### LC01_Landsat_1187\nThis data set contains a time series of early Landsat-4 MSS satellite imagery as well as Landsat-5 TM and Landsat-7 ETM+ satellite imagery of the northern Ecuadorian Amazon. Some of the TM and ETM images have been georectified to UTM Zone 18 South, WGS84 Datum. Not all of the images have been georectified.\n<br>\n\n#### LC01_LULC_Classes_Ecuador_ISA_1084\nThis data set contains Landsat TM imagery for the years 1986, 1989, 1996, and 1999, that have been classified into four land use/land cover (LULC) classes: Forest, Non-Forest Vegetation, Urban/Barren, and Water; and a fifth class of Clouds/Shadows. The areas of interest were the four Intensive Study Areas (ISA) of the University of North Carolina's Carolina Population Center (CPC) Ecuador Projects: Eastern Intensive Study Area; Northern Intensive Study Area; Southern Intensive Study Area, and Southwestern Intensive Study Area. These areas are in the Northern Ecuadorian Amazon, in the area known as the northern Oriente of Ecuador. The resolution of the data is 30 meters. There are 12 image files (.tif) with this data set.\n<br>\n\n#### LC01_Boundaries_Ecuador_1057\nThis data set contains the national and provincial boundaries of Ecuador as well as the boundaries of two national parks: the Cuyabeno Wildlife Reserve and the Yasuni National Park. There are four data files in ESRI ARCGIS Shapefile format within this data set. Each shape file has been compressed into a single compressed file (.zip).\n<br>\n\n#### LC01_Households_NEC_1052\nThis data set reports summary statistics from socioeconomic and demographic surveys administered to the male and female heads of household on 767 farm plots. The surveys were performed in the provinces of Sucumbios and Napo/Orellana, in the northern Ecuadorian Amazon colonist settlements (Oriente) in 1999 (Pan and Bilsborrow, 2005). In addition, perception of, and opinions about local climate, soil quality, and environmental contamination were assessed for both the male and female heads of household. There are two comma-delimited (csv) ASCII data files. One file provides summary data from male respondents; the other data file provides summary responses from the female household survey (generally the spousal respondent). The original questionnaire forms are included as companion files (PDF format).\n<br>\n\n#### LC01_SRTM_DEM_90m_NEC_1083\nThis data set provides 90-meter resolution Digital Elevation Model data used in the University of North Carolina's Carolina Population Center (CPC) Ecuador Projects. The topographic data were derived from Shuttle Radar Topography Mission (SRTM) C-band and X-band interferometric synthetic aperture radars (IFSARs) data that were acquired over 80% of Earth's land mass in February 2000. This data set includes one image in GeoTiff format that is a subset for the Northern Ecuadorian Amazon region.\n<br>\n\n#### LC01_Topography_Ecuador_ISA_1082\nThis data set contains topographic/geomorphological data associated with the four Intensive Study Areas (ISAs) in the Northern Ecuadorian Amazon (northern Oriente) that are part of the University of North Carolina's Carolina Population Center (CPC) Ecuador Projects study. Study area boundaries were developed directly from 1:50,000 topographical maps. Point elevation features and 20-meter elevation contours were digitized from these same maps. Digital elevation models (DEMs) were derived from these elevation data and, in turn, terrain aspect and terrain slope were derived from the digital elevation models. Only boundary data were provided for the southwestern ISA. These data are provided in ESRI shapefile format and GeoTiff. There are six compressed (.zip) data files with this data set.\n<br>\n\n#### LC02_PermPlot_Acre_1237\nThis data set provides diameter at breast height (DBH) measurements for 1,063 trees located at the Catuaba Experimental Farm, and 812 trees located in the Humaita Forest Reserve. Both sites are in the state of Acre, southwest Amazonia, Brazil. Measurements were made on individuals with DBH between 10 and 35 cm and individuals with DBH > 35 cm. The Catuaba Experimental Farm is part of a forest fragment of approximately 800 ha. The Humaita Forest Reserve is located in a 1,500-ha forest band with dominant bamboo characteristic. Ten-ha areas were inventoried at both sites. There is one data file in comma-delimited (.csv) format with this data set. There is also one companion data file with supplemental Catuaba site tree height and biomass data.\n<br>\n\n#### LC02_Meteorology_Acre_1091\nThis data set provides meteorological measurements collected from 3 different meteorological stations within a radius of 8 km in Rio Branco, Acre Brazil, for the periods of June of 1970 to 1974, December of 1974 to 1980, and May of 1980 thru May 31, 2001. Daily average values for rainfall, relative humidity, evapotranspiration, maximum and minimum temperature, pressure, wind direction and speed, solar radiation, and cloud cover are reported. There is one comma-delimited data file with this data set.\n<br>\n\n#### LC02_Forest_Flammability_Acre_1089\nThis data set provides the results of controlled burns conducted to assess the flammability of mature forests on the Catuaba Experimental Farm of the Federal University of Acre - Rio Branco, Acre, Brazil. Controlled burns were conducted in 1998, and the rate of fire spread was calculated based on the duration of the fire and the measured extent of the burned area. Environmental variables measured included type of forest, canopy openness, leaf area index, number of days without rainfall, precipitation, height of litter, litter humidity, brushwood humidity, amount of water in the ground, air temperature, and relative humidity. Results from 50 fires set in 1998 are reported. There is one comma-delimited data file with this data set. These data are part of a larger study reported in the thesis by Elsa Renee Huamán Mendoza, Susceptibility of primary forest to fire in 1998 and 1999: A case study in Acre, south-eastern Amazonia, Brazil. The thesis, in Portuguese, is included as a companion file with this data set.\n<br>\n\n#### LC02_GOES8_Hotpixel_Acre_1092\nThis data set provides hot pixel data, as an indicator of fires that were detected by the GOES-8 satellite for the state of Acre, Brazil. Image data were collected for extended periods over the course of 3 years (1998, 2000 and 2001). Data were filtered to select only pixels identified and processed by the GOES-8 Automated Biomass Burning Algorithm (ABBA), where estimates of sub-pixel fire characteristics including size and temperature were able to be determined. There are three comma-delimited ASCII data files with this data set.\n<br>\n\n#### LC02_Water_Table_Acre_1062\nThis data set reports bi-weekly or monthly depth-to-water measurements for three wells located in a ~1,500 ha forest fragment on the Catuaba Experimental Farm, which is the property of the Federal University of Acre, Brazil. Data were collected between February 1999 and December 2004. There is one comma-delimited ASCII data file with this data set. DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS: The depth-to-water measurements for the three wells lack ground surface elevation reference points, therefore, the groundwater table elevation for the site cannot be determined. The depth-to-water measurements are of limited use unless paired with other site data for precipitation, tree growth, etc.\n<br>\n\n#### LC02_MAP_Fire_Indicators_1044\nThis data set provides hot pixel data, as an indicator of fires, that were detected by various satellites in the tri-national MAP region (Madre de Dios-Peru, Acre-Brazil, and Pando-Bolivia) in 2003, 2004, 2005, and 2006. Data from the following satellites/sensors were compiled: NOAA-12, NOAA-14, NOAA-15, and NOAA-16, which transports the AVHRR sensor; GOES-8 and GOES- 12, which transports the GOES Imager; and AQUA and TERRA, both which transport the MODIS sensor. These data were made available by the Centro de Previsão do Tempo e Estudos Climáticos (CPTEC) of the Instituto Nacional de Pesquisas Espaciais (INPE) via the internet (http://sigma.cptec.inpe.br/queimadas/). This data set contains 12 comma-delimited ASCII data files. Hot pixel data from satellites can be used as an indicator of fires and for the understanding of fire frequency in remote areas. The publication by Vasconcelos and Brown, 2007, which has been included as a companion file, describes the application of these data in the MAP region. In addition to the the hot pixel data, each observation has a derived vegetation type, susceptibility to fire, recent and past precipitation amounts, and a calculated fire risk value. These data are described in the Fire Risk Factor companion file, by Alberto W. Setzer and Raffi A. Sismanoglu, Version 5, February 2006.\n<br>\n\n#### LC02_Streams_Acre_1243\nThis data set provides coordinates for points at the mouth of tributaries of the Acre River in the Tri-national River Basin in South America. Three Global Positioning System (GPS) readings were made at the outlet of each tributary and the average of the three readings is reported. The Tri-national River Basin is located in the tri-national frontier region of Madre de Dios, Peru, Acre, Brazil, and Pando, Bolivia (known as the MAP region). The MAP region is approximately 300,000 km2. The Acre River flows through Brazil, Bolivia, and Peru. Data on the basin drainage network from the Digital Elevation Model (DEM) Shuttle Radar Topography Mission (SRTM) was obtained as a source of information for the border areas. The GPS readings were part of an assessment of the reliability of the DEM/SRTM drainage network data (Maldonado and Brown, 2003). There is one data file in comma-delimited (.csv) format and one compamion file (.pdf) with this data set. DATA QUALITY STATEMENT: This data set provides GPS coordinates only and is not associated with any additional measurements. There is no associated research documentation.\n<br>\n\n#### LC03_Hypsography_DEM_1094\nThis data set provides four related spatial data products for four study areas across the Brazilian Amazon: Manaus, Amazonas; Tapajos National Forest, Para Western (Santarem); Rio Branco, Acre; and Rondonia, Rondonia. Products include vector data showing (1) roads, (2) rivers, and (3) hypsography and (4) digital elevation model (DEM) images that were encoded from the hypsography vectors. There are 15 data files with this data set which includes 12 compressed .zip files containing ArcInfo shape files and 3 GeoTIFFS. This data set contains vector data showing roads, rivers, and hypsography for each study area in ESRI ArcGIS shapefile format. The vectors were hand-digitized by the Images Company in Brazil from paper maps produced by the Brazilian government. Depending on the scale of the original maps, the digitization errors vary. For some maps, some vectors are missing. Data were manually checked for duplicate or extra vectors. These data sets were derived from several map sheets produced from aerial coverages dating from 1974 to 1978. The DEM images were encoded from the hypsography vectors and are provided in GeoTIFF format. The attribute value associated with each line and point in the vector segment is encoded into the image channel; the image channel is then filled in by interpolating image data between encoded vector data. For each DEM: 1 image channel with pixel resolution = 25m x 25m. DEM images are provided for Manaus, Tapajos National Forest, and Rondonia. The files for Rio Branco were unusable due to a documentation error. DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS: The data providers note that due to limited resources, these data have been neither validated nor quality-assured for general use. For that reason, extreme caution is advised when considering the use of these data. - Any use of the derived data is not recommended because the results have not been validated. - However, the DEM, vectors, and orthorectified SAR data (related data set) can be used if the user understands how these were produced and accepts the limitations.\n<br>\n\n#### LC03_SAR_LC_Biomass_1093\nThis data set provides three related land cover products for four study areas across the Brazilian Amazon: Manaus, Amazonas; Tapajos National Forest, Para Western (Santarem); Rio Branco, Acre; and Rondonia, Rondonia. Products include (1) orthorectified JERS-1 and RadarSat images, (2) land cover classifications derived from the SAR data, and (3) biomass estimates in tons per hectare based on the land cover classification. There are 12 image files (.tif) with this data set. Orthorectified JERS-1 and RadarSat images are provided as GeoTIFF images - one file for each study area. For the Manaus and Tapajos sites: The images are orthorectified at 12.5-meter resolution and then re-sampled at 25-meter resolution. For the Rondonia and Rio Branco sites: The images from 1978 are orthorectified at 25-meter resolution and then re-sampled at 90-meter resolution. Each GeoTIFF file contains 3 image channels: - 2 L-band JERS-1 data in Fall and Spring seasons and - 1 C-band RadarSat data. Land cover classifications are based on two JERS-1 images and one RadarSat image and provided as GeoTIFFs - one file for each study area. Four major land cover classes are distinguished: (1) Flat surface; (2) Regrowth area; (3) Short vegetation; and (4) Tall vegetation. The biomass estimates in tons per hectare are based on the land cover classification results and are reported in one GeoTIFF file for each study area. DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS: The data providers note that due to limited resources, these data have been neither validated nor quality-assured for general use. For that reason, extreme caution is advised when considering the use of these data. Any use of the derived data is not recommended because the results have not been validated. However, the DEM and vectors (related data set), and orthorectified SAR data can be used if the user understands how these were produced and accepts the limitations.\n<br>\n\n#### LC04_IBIS_Model_1139\nThe provided data were generated by the Integrated BIosphere Simulator (IBIS) terrestrial ecosystem model (Foley et al. 1996, Kucharik et al. 2000) using data from the CRU05 climate record for the years 1921-1998 (New et al. 2000). Data are included for the annual net ecosystem exchange of the surface, microbial respiration, root respiration, total soil respiration, soil moisture, leaf area index, drainage, and surface and subsurface runoff, for the entire Amazon and Tocantins basins. The data files are provided in netCDF format and standard ESRI ARCGIS ARC/INFO ASCIIGRID format. The netCDF files consist of either annual or monthly means from 1921 to 1998. The ASCII files are available only for the annual mean files.\n<br>\n\n#### LC04_Macrohydrology_1048\nThis data set provides continental-scale hydrological river flow routing parameter data for the Amazon and Tocantins River basins at 5 minute (~9 km) resolution (Costa et al., 2002). The data set includes four geospatial data files (in standard ESRI Arc/Info ASCII Grid format): (1) the river network (flow direction); (2) sinuosity of each of the main rivers, measured at 111 river sections in the basins; (3) depth to the water table; and (4) transmissivity of the aquifer. The latter two parameters were derived from measurements taken at 81 wells located throughout the basins. There is also a compressed file (.zip) which contains the time series of monthly mean river discharge and long-term climatology (monthly mean) for the period of record at each of 122 fluviometric stations located throughout the basin. These files are provided in ASCII common-separated (.csv) format. Also included in this data set are two data files in .csv format; one containing river discharge station location and drainage area information and one containing original well data.\n<br>\n\n#### LC04_Land_Use_5min_906\nAmazonia has been under considerable development pressure as croplands and pasture are established in areas formerly occupied by tropical forest and cerrado. Although this region is an important part of several important planetary biogeochemical cycles, the location and impact of human land use are not well understood. In particular, there is no existing satellite-based map of agriculture across the Amazon or Tocantins river drainage basins. Recent efforts have classified land cover across this vast region, although they disagree on the location and amount of cropland and do not directly address pasture, a land use that has grown in importance in the last 2 decades. Here we present an analysis of land cover and land use practices over the Amazon and Tocantins basins of South America. In this study, we demonstrate how satellite imagery and agricultural censuses can be merged in order to provide a geographically explicit, fine- scale description of land cover and land use practices. The result depicts the fraction of each 5-min (9 x 9 km) grid cell that was devoted to agricultural activity during the mid-1990s. The resultant map retains many of the characteristics of the agricultural census data, but with a much finer spatial resolution. During the mid-1990s, cultivated area is estimated to have been 1.7 x 10(7) ha (2.5% of the basin), natural pasture is estimated at 3.3 x 10(7) ha (4.9% of the basin), and planted pasture is estimated to cover 3.3 x 10(7) ha (4.9% of the basin). Perhaps more important than the quantities, however, is that these data sets provide a new blend of ground- based and satellite-based spatially explicit data. This snapshot can be used as a basis to project either forward or backward in time, as a new check of finer scale land use classifications or as a driver of ecosystem models.\n<br>\n\n#### LC04_THMB-HYDRA_Model_1138\nThe model output data provided were generated by the THMB 1.2 (Terrestrial Hydrology Model with Biogeochemistry) model which simulates the flow of water through groundwater systems, rivers, lakes and wetlands. The model operates at a 5-minute latitude-by-longitude grid with a 1-hour time step and requires as boundary conditions: topography, evaporation from water surfaces, surface runoff, base flow, and precipitation. Data are included for the mean monthly simulated water height above flood stage, mean monthly simulated river discharge, and mean monthly inundated area for the period 1939-1998 for the entire Amazon and Tocantins River basins. There are three netCDF files (.nc) with this data set.\n<br>\n\n#### LC05_BDFF_Biomass_Soils_1040\nThis data set reports (1) total aboveground dry biomass based on detailed estimates of all live and dead plant material, (2) results from repeated surveys of aboveground biomass allowing the calculation of above-ground productivity, and (3) soil chemical and physical characteristics for 50 1-ha plots of undisturbed and fragmented central Amazonian rainforest within the Biological Dynamics of Forest Fragments Project (BDFFP) study area. The reported data are plot-level summaries based on plant and soil samples and measurements obtained over the 1997 to 2001 timeframe. The BDFFP study area is an experimentally fragmented landscape spanning 1,000 km2 located 70-90 km north of Manaus, Amazonas, Brazil. For additional information about the BDFFP and research conducted at the site, please visit their web site at http://pdbff.inpa.gov.br/index.html. There are six comma-separated ASCII data files with this data set.\n<br>\n\n#### LC07_Lake_Chlorophyll_MODIS_1000\nThis data set contains chlorophyll concentration maps of the Amazon River floodplain region from Parintins (Amazonas) to Almeirim (Para). These chlorophyll fraction maps were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product (MOD09) for 19 months from April 2002 to December 2003. The study was conducted in a floodplain reach upstream from Santarem, Para, in order to assess seasonal changes in phytoplanktonic chlorophyll-a distributions in the floodplain Lake Curuai. MODIS reflectance data were acquired at four river stages: rising (April), high (June), decreasing (September), and low (November). Chlorophyll maps were derived and used to compute the weighted average of chlorophyll concentration from MODIS images in the region. Field measurements of suspended inorganic matter and chlorophyll-a in Lake Curuai were made almost concurrently with satellite overpasses (Barbosa, 2005). The images and the estimated chlorophyll concentrations were compared to measured chlorophyll concentrations at control points for different hydrological states. This data set may be applied to better understand the seasonal dynamics of primary production of the Amazon floodplains. The maps of chlorophyll-a concentration may be used to model spatial and temporal variations of primary production in this region. The monthly chlorophyll-a maps are provided as GeoTIFF files. There are two formats: (1) color-mapped pixels and (2) pixels as chlorophyll-a concentrations. These latter images are not intended for browsing. These images have pixel values that are the chlorophyll-a concentration in mg/m3 and need to be download and opened in GIS software.\n<br>\n\n#### LC07_Biomass_LGrande_1127\nThis data set reports measurements of aquatic macrophyte biomass, phenology, leaf characteristics, and length and diameter of stems of both submerged and unsubmerged macrophytes. Data were collected from sites in the Monte Alegre Lake region on the eastern Amazon River floodplain in Para, Brazil. Ten field surveys were made at approximately monthly intervals from December 2003 to November 2004. There is one comma-delimited data file with this data set.\n<br>\n\n#### LC07_Bathymetry_Curuai_999\nThe bathymetry data provided represent a continuous surface of interpolated point measurements of depth values of Lago Curuai, an Amazon River floodplain lake, upstream from Santarem, Para, Brazil, from measurements made in June of 2004. The first product contains the actual depth values (in meters) of the interpolated continuous surface saved as real numbers in both ENVI and GeoTIFF formats. Also available is a color scaled depth GeoTIFF image which has an embedded color scale bar. This secondary file is meant only for viewing but has the unique advantage of being a GeoTIFF file. Therefore, this map can be a background image with other projected files of interest in the area. Data provided in this data set were used to develop a methodology for processing and applying high resolution bathymetric data acquired with a Lowrance-480M ecosounder in the Amazon floodplain. This research was supported by the addition of Landast/TM images for planning and executing the survey. 4600 km of transects were processed semi-automatically and integrated into a georeferenced database. A digital elevation model with 15 m horizontal resolution and 1 cm vertical resolution was generated for the floodplain. The changes in inundated area and volume of water on the floodplain were estimated. Regression models were constructed to predict flood area and water stored volume from water level. The results of this research show that water level and flooded area mapped from images are good enough for estimating water stored volume in the Lago Grande de Curuai (Barbosa et al., 2006).\n<br>\n\n#### LC07_Wetlands_fluxes_1209\nThis data set provides estimates of daily and monthly carbon dioxide (CO2) and methane (CH4) diffusive and ebullitive flux for dry and flooded areas from two study sites, Cuini and Itu, in the interfluvial wetlands of the upper Negro River basin, Brazil. CO2 (ebullitive and diffusive) and CH4 diffusive flux measurements were made one day each month from February 2005 through January 2006 in both permanently and seasonally flooded areas. For the remaining days of each month, fluxes were calculated as the mean of the two measurements bracketing that time period, times the area flooded each day. Total site area, dry area, and seasonally varying flooded area estimates for the two wetlands were determined through analysis of synthetic aperture radar data from Radarsat images. From these estimates, the total flux of CO2 and CH4 for the sites was calculated. Values for CH4 ebullitive flux were determined from a constant for each area based on whether the water was rising or falling and the area flooded. Hydrologic measurements were taken from April 2004 through January, 2006. There are three comma-separated (.csv) data files with this data set.\n<br>\n\n#### LC07_Lake_Nutrient_Sediments_1050\nThis data set reports lake sediment texture and porosity, carbon (C), nitrogen (N), and phosphorus (P) content of surficial sediments, 210Pb-derived nutrient accumulation rates in sediments, and burial rates of C, N, and P in sediments at eleven locations in Lake Calado, Amazonas, Brazil. Field samples were collected between February 1982 and August 1984. There are eight comma-delimited ASCII data files with this data set.\n<br>\n\n#### LC07_Reservoir_GHG_1143\nThis data set provides flux measurements of methane (CH4) and carbon dioxide (CO2) from surface waters to the atmosphere. It also provides CH4, CO2, and oxygen (O2) concentrations of surface water, and concentrations measured at several depths of the Balbina Reservoir in the central Amazon Basin, Amazonas, Brazil. The Balbina Reservoir was formed by impounding the Uatuma River in 1987. Reservoir surface water samples, bottom water samples, and gas samples from static flux enclosures were collected at 10 to 14 sites at monthly intervals between April and November of 2005, and 6 times in February, 2006. Water samples to determine the vertical profiles of temperature, dissolved O2, CH4, and CO2 were collected during the rainy and dry seasons immediately above the dam between September 2004 and February 2006. Water samples were collected downstream from the dam from July 2004 - November 2005 for analysis of CH4 and CO2 concentrations. There are three comma-delimited data files with this data set.\n<br>\n\n#### LC07_Reservoir_Methane_Emissions_1047\nThis data set reports methane (CH4) fluxes at the water-air interface and concentrations and isotopic signals of CH4 in the bubbles stirred up from the sediment in Tucurui and Samuel reservoirs in 2000 and 2001. Tucurui (deep) reservoir is located near Belem city in the Tocantins-Araguaia basin in the eastern Amazon. Samuel (shallow) reservoir is situated near Porto Velho city in the Jamari River, a tributary of the Madeira River in the western Amazon. Field samples were collected between June 2000 and September 2001. There are two comma-delimited ASCII data files in this data set. This study was carried out to identify differences in methane cycling between deep and shallow reservoirs (Lima, 2005). Isotopic and concentration analyses of methane in bubbles, dissolved in the water column, and emitted to the atmosphere demonstrate that water depth is critical regarding methane emissions from hydroreservoirs in the Amazon. Methanotrophic activities are greater in Tucurui (deep) while light isotopic methane is directly released from Samuel (shallow). Therefore, the methanotrophic layer of the deep reservoir is more efficient in oxidizing methane before reaching the atmosphere, since the quantity of methane in the sediments of the reservoirs were equivalent.\n<br>\n\n#### LC07_SMMR_Inundated_Area_1051\nThis data set reports the monthly record of inundated area, in square km, for six floodplain and open water regions in South America. The following floodplains were analyzed: (1) mainstem Amazon River floodplain in Brazil; (2) Llanos de Mojos (Beni and Mamore rivers) in Bolivia; (3) Bananal Island (Araguaia River) in Brazil; (4) Roraima savannas (Branco and Rupununi rivers) in Brazil and Guyana; (5) Llanos del Orinoco (Apure and Meta rivers) in Venezuela and Colombia; and (6) Pantanal wetland (Paraguay River) in Brazil. Flooded area was estimated at monthly intervals from December 1978 through August 1987 for the Amazon mainstem region and from January 1979 through August 1987 for the other five regions. Inundated area was determined from SMMR (Scanning Multichannel Microwave Radiometer) passive microwave data. Area estimates include permanent open water as well as land subject to seasonal inundation. This data set contains five data files: two comma-delimited (.csv) ASCII data files providing the monthly inundation area values for six floodplain and open water regions in South America; a compressed (.zip) file providing seventeen ESRI Shape files for the region bounding polygons; and two .csv files providing information about the region bounding polygons and latitude/longitude verticies.\n<br>\n\n#### LC07_Monthly_Inundated_Areas_1049\nThis data set reports monthly mean inundation areas (square kilometers) for four cover classes of Central Amazon wetlands habitat: Open water (OW), river channel (RC) class, macrophyte (MA) class, and a flooded forest (FF) class, which also incorporates a flooded shrub class. The full study area was a 1.77 million km2 quadrant covering the Central Amazon Basin. Inundation was also calculated from three subsets of this area: (1) covering only the Amazon/Solimoes River mainstem and (2) the Eastern and (3) the Western halves of this mainstem area. There is one comma-delimited ASCII data file in this data set.\n<br>\n\n#### LC07_Spectroradiometry_1144\nThis data set includes bidirectional reflectance (BDR) spectra and water-quality data of floodplain lakes of the Solimoes and Negro Rivers in the central Amazon basin, Amazonas, Brazil. Samples and measurements were collected during July 2000 to August 2000. Bidirectional reflectance factors were recorded, at 3 nm intervals from 400 to 900 nm, concurrently with in situ measurements of water temperature and Secchi depth, and collection of samples for analysis of optically active components including total suspended solids, chlorophyll, and dissolved organic carbon (DOC). The lakes sampled were in the low-lying varzea of the Solimoes River (\"varzea\" is the local name for the floodplain formed by the overflow of white-water rivers) and igapo (\"igapo\" is the local name for the floodplain formed by the overflow of black-water rivers) of the Negro River. There are two comma-delimited data files with this data set.\n<br>\n\n#### LC07_Airborne_Rasters_1274\nThis data set includes high-resolution geocoded mosaics derived from the Validation Overflight for Amazon Mosaics (VOAM) aerial video surveys as part of the Large-Scale Biosphere-Atmosphere (LBA) Experiment in the Amazon. The VOAM flights were carried out in the wet-season (June) 1999 in the Brazilian Amazon to provide ground verification for mapping of wetland cover in the Amazon Basin conducted by the Global Rain Forest Mapping (GRFM) Project JERS-1 (Japanese Earth Remote Sensing Satellite). Digital camcorder systems were installed in a Bandeirante survey plane operated by Brazil's National Institute for Space Research. The VOAM99 surveys circumscribed the Brazilian Amazon, documenting ground conditions at resolutions on the order of 1-m resolution for wetlands, forests, savannas, and human-impacted areas. Geocoded mosaics were generated by processing the aerial videography into GeoTIFF format, maximizing its usefulness for environmental monitoring applications. Other applications of the VOAM99 videography include acquisition of ground control points for image geolocation, forest biomass estimation, and rapid assessment of fire damage. Geocoded digital videography provides a cost-effective means of compiling a high-resolution validation data set for land cover mapping in remote, cloud-covered regions.\n<br>\n\n#### LC07_Airborne_Videography_1272\nThis data set presents georeferenced digital video files from Validation Overflight for Amazon Mosaics (VOAM) aerial video surveys as part of the Large-Scale Biosphere-Atmosphere Experiment in the Amazon. The VOAM flights were carried out in the wet-season (June) 1999 in the Brazilian Amazon to provide ground verification for mapping of wetland cover with the Global Rain Forest Mapping (GRFM) Project JERS-1 (Japanese Earth Remote Sensing Satellite) mosaics of the Amazon basin. Digital camcorder systems were installed in a Bandeirante survey plane operated by Brazil's National Institute for Space Research. The VOAM99 surveys circumscribed the Brazilian Amazon, documenting ground conditions at resolutions on the order of 1-m (wide-angle format) and 10-cm (zoom format) for wetlands, forests, savannas, and human-impacted areas. Other applications of the VOAM videography include acquisition of ground control points for image geolocation, creation of a high-resolution geocoded mosaic of a forest study area, forest biomass estimation, and rapid assessment of fire damage. Geocoded digital videography provides a cost-effective means of compiling a high-resolution validation data set for land cover mapping in remote, cloud-covered regions.\n<br>\n\n#### LC07_Curuai_chl_1134\nThis data set reports (1) concentrations of total, organic, and inorganic suspended solids; dissolved inorganic, and organic carbon; chlorophyll-a and (2) measurements of turbidity, ph, temperature, transparency, conductivity, and calculated carbon dioxide (CO2) in water samples collected from Lago Curuai (Lake Curuai), in the floodplain of the Amazon River south of Obidos, Para, Brazil. Approximately 70 stations were sampled during four phases of the hydrological cycle: receding (September 2003), low (November 2003), rising (February 2004), and high water (June 2004). There is one comma-delimited data file with this data set.\n<br>\n\n#### LC07_Amazon_Wetlands_1284\nThis data set provides a map of wetland extent, vegetation type, and dual-season flooding state of the entire lowland Amazon basin. As described in Hess et al. (2015), the classified image was derived from the Global Rain Forest Mapping Project (GRFM) Amazon mosaics (Rosenqvist et al 2000; Siqueira et al. 2002) acquired during Oct.-Nov. 1995 and May-June 1996, corresponding to the low-flood and high-flood seasons for much of the central Amazon. Hess et al. (2003) mapped wetland extent, vegetative cover, and flooding state for an 18 degree × 8 degree portion of the central Amazon using the dual-season GRFM mosaics. This study extends the previous wetlands mapping to report the first validated estimate of wetland extent, cover, and flooding for the lowland Amazon basin. A wetlands mask was created by segmentation of the mosaics and clustering of the resulting polygons; a rules set was then applied to classify wetland areas into five land cover classes and two flooding classes using dual-season backscattering values. The mapped wetland area of 8.4 × 105 km2 is equivalent to 14 % of the total basin area (5.83 × 106 km2) and 17% of the lowland basin (5.06 × 106 km2). The mapped flooding extent is representative of average high and low-flood conditions for latitudes north of 6 degrees S; flooding conditions were less well captured for the southern part of the basin. The wetlands map is provided in GeoTIFF format using two coordinate systems: unprojected (Geographic) with pixel size of 3 arcseconds, and Albers Conical Equal Area with pixel size of 100 m.\n<br>\n\n#### LC08_Ecosystem_Demography_Model_1102\nThis data set provides Ecosystem Demography Model (ED) estimates of potential above-ground net primary production (NPP) (kg C/m2/y), potential average live biomass (kg C/m2), and potential average soil carbon (kg C/m2) for the Brazilian Amazon at 1 degree resolution. Ecosystem Demography Model predicts both ecosystem structure (e.g. above and below-ground biomass, vegetation height and basal area, and soil carbon stocks) and corresponding ecosystem fluxes (e.g. NPP, NEP, and evapotranspiration) from climate, soil, and land-use inputs. Estimates for the Brazilian Amazon include the effects of natural disturbances such as windthrow and fire, but do not include the effects of human land use. To produce these estimates, ED was forced with ISLSCP I data for 1987 and 1988 and averaged into a single average year (Moorcroft et al., 2001). The data are provided for the three estimates in both ASCII text and in NetCDF formatted files.\n<br>\n\n#### LC08_Fire_Observations_1095\nThis data set reports observations of fires in the vicinity of Maraba, Para, Brazil, from November 3-5th, 2001, and in Mato Grosso, Brazil, between Cuiaba and Alta Floresta, for July 12-15th, 2002. These ground-based data were collected by visual inspection from roads primarily during daylight hours. Data include fire position and time, estimates of fire size, and type of vegetation burned. There is one comma-delimited ASCII file with this data set.\n<br>\n\n#### LC08_EOS_Maps_1155\nThis data set provides (1) soil maps for Brazil that are digital versions of the MAPA DE SOLOS DO BRASIL (EMBRAPA, 1981) classified at three levels of detail, 19-class, 70-class and 249-class; (2) vegetation maps for Brazil that are digital versions of the MAPA DE VEGETACAO DO BRASIL (IBGE, 1988) classified at three levels of detail, 13-class, 59-class, and an overprint (combination) class; and (3) a land cover map for all of South America that was derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data over the time period 1987 through 1991 (Stone et al., 1994). The seven soil, vegetation, and general land cover classification maps are provided as GeoTIFF files (.tif) files. There are also three companion files (.pdf), one each, for the soil, vegetation, and land cover maps, with information on map units, class values, codes, and descriptions.\n<br>\n\n#### LC09_Precipitation_940\nThis data set reports daily total precipitation data retrieved from Brazilian National Institute of Meteorology (INMET) network for three stations near two Amazonian research sites: Altamira, and Santarem, from 1961-1998. Daily precipitation totals are provided in one comma-separated ASCII file for three stations in Para, including two sites in Altamira: Altamira City and on the Transamazon Highway at Km 100 near Medicilandia (operated by EMBRAPA); and, one site in Santarem: Taperinha. Data availability varies by station (sublocation): Altamira City from 1961-1990, Transamazon Km 100 from 1982-1998, and Taperinha from 1983-1992.\n<br>\n\n#### LC09_Transition_Matricies_1098\nThis data set includes classified land cover transition maps at 30-m resolution derived from Landsat TM, MSS, ETM+ imagery and aerial photos of Altamira, Santarem, and Ponta de Pedras, in the state of Para, Brazil. The Landsat images were classified into several types of land use (e.g., forest, secondary succession, pasture, annual crops, perennial crops, and water) and subjected to change detection analysis to create transition matrices of land cover change. Dates of acquired images represent the most cloud-free image retrievals from 1970-2001 for each site and are therefore not continuous. There are 3 GeoTIFF files (.tif) with this data set.\n<br>\n\n#### LC09_Landsat_987\nThis data set includes 15 zipped archives of rectified .tif format Landsat 5 TM and Landsat 7 ETM+ scenes from near the study sites of Altamira, Santarem, Ponta de Pedras, and Bragantina in the state of Para, Brazil and Machadinho D'Oeste in Rondonia, Brazil. Dates represent the most cloud-free image retrievals from 1985-2004 and are therefore not continuous. These images may be useful to evaluate potential environmental impacts resulting from the establishment of colonization projects in the Amazon.\n<br>\n\n#### LC09_GIS_Study_Areas_986\nThis data set includes 16 zipped archives of shapefiles of cities, rivers and streams, roads, and study area boundaries of several Amazonian study sites: Altamira, Santarem, Bragantina, and Ponta de Pedras, in the state of Para, and 1 site at Machadinho D'Oeste, in the state of Rondonia. Data from Brazil were digitized from Instituto Nacional de Colonizacao e Reforma Agraria (INCRA) maps and other data from Instituto Brasileiro de Geografia e Estatistica (IBGE). These products were prepared in the 2000-2004 time period. The data of creation for the source material is unknown.\n<br>\n\n#### LC09_Soil_Composition_938\nThis data set reports basic soil structure and composition information for five Amazonian research sites: Altamira, Bragantina, Tome-Acu, and Ponta de Pedras, all four in the state of Para, Brazil; and one site in Yapu, Colombia. Soil characteristics reported for all five study sites include cation information (e.g., H, Al, Mg, K, Na, S), percent of soil C, N, and organic matter, soil texture/composition and color, pH, and land use history. Soil bulk density and tons of carbon/ha are reported for only three of the study sites: Altamira, Bragantina, and Tome-Acu. All of the data are provided in one comma-separated data file. The five study areas represent characteristic differences in soil fertility and a range of land uses typical of the Amazon region. One of these areas, Altamira, is characterized by above average pH, nutrients, and texture. The other four areas are more typical of the 75 percent of the Amazon that is characterized by Oxisols and Ultisols, with well-drained but low pH and low levels of nutrients. Ponta de Pedras in Marajo Island, located in the estuary, is composed of upland Oxisols and floodplain alluvial soils. Igarape-Acu in the Bragantina region is characterized by both nutrient-poor Spodosols and Oxisols. Tome-Acu, south of Igarape-Acu, represents a mosaic of Oxisols and Ultisols. Yapu, in the Colombian Vaupes, is composed of patches of Spodosols and Oxisols. Three of the areas are colonization regions at various degrees of development: Altamira is a colonization front that opened up in 1971, whereas Tome-Acu was settled by a Japanese population in the 1930s, and Bragantina was settled in the early part of the twentieth century. Marajo (Ponta de Pedras) is the home of caboclos, whereas Yapu is home to Tukanoan Native American populations. In these study areas slash-and-burn cultivation as well as plantation agriculture and mechanized agriculture are employed. Length of fallows vary in these communities. The two indigenous areas leave their land in longer fallow than do the three colonization areas, and the proportion of land prepared from secondary forests increases with length of settlement as the stock of mature forest declines over time.\n<br>\n\n#### LC09_Vegetation_Composition_939\nNo abstract available.\n<br>\n\n#### LC10_Landsat_TM_852\nThis data set includes Landsat TM scenes from across the Legal Amazon region. A single image is provided for each spatial tile, representing the most cloud-free retrieval from 9/21/86 to 9/17/94. All files are in a single directory, including one band-sequential (bsq) file and one database (ddr) file for each scene.\n<br>\n\n#### LC10_Landsat_ETM_846\nThis data set includes orthorectified Landsat ETM+ scenes across the Legal Amazon region. At least one scene is provided for each spatial tile, representing the most cloud-free retrievals from mid-1999 through late 2001 (Fig. 1). Dates are therefore not continuous but include scenes from July 8, 1999 to November 13, 2001. Data have been atmospherically corrected and orthorectified. The individual images should be highly useful as they include very little cloud cover, but they should not be mosaicked together since retrieval dates vary.Data files (and format) included for each scene are: six multispectral bands (tif), two thermal bands (tif), one panchromatic band (tif), two preview files (jpg), and one metadata file (txt). The individual Geotiff files have been g-zipped and subsequently all of the files for a scene have been g-zipped together for ordering convenience.\n<br>\n\n#### LC13_GIS_Cauaxi_890\nWe combined a detailed field study of canopy gap fraction with spectral mixture analysis of Landsat ETM+ satellite imagery to assess landscape and regional dynamics of canopy damage following selective logging in an eastern Amazon forest. Our field studies encompassed measurements of ground damage and canopy gap fractions along multi-temporal sequences of post-harvest regrowth of 0.5-3.5 yr. Areas used to stage harvested logs prior to transport, called log decks, had the largest forest gap fractions, but their contribution to the landscape-level gap dynamics was minor. Tree falls were spatially the most extensive form of canopy damage following selective logging, but the canopy gap fractions resulting from them were small. Reduced-impact logging resulted in consistently less damage to the forest canopy than did conventional logging practices. This was true at the level of individual landscape strata such as roads, skids, and tree falls as well as at the area-integrated scale. A spectral mixture model was employed that utilizes bundles of field and image spectral reflectance measurements with a Monte Carlo analysis to estimate high spatial resolution (sub-pixel) cover of forest canopies, exposed non-photosynthetic vegetation, and soils in the Landsat imagery. The method proved highly useful for quantifying forest canopy cover fraction in the log decks, roads, skids, tree fall, and intact forest areas, and it tracked canopy damage up to 3.5 yr post-harvest. Forest canopy cover fractions derived from satellite observations were highly and inversely correlated with field- and satellite-based measurements. A 450-km^2 study of gap fraction showed that approximately one-half of the canopy opening caused by logging is closed within one year of regrowth following timber harvests. This is the first regional-scale study utilizing field measurements, satellite observations, and models to quantify forest canopy damage and recovery following selective logging in the Amazon.\n<br>\n\n#### LC13_GIS_Juruena_888\nWe combined a detailed field study of canopy gap fraction with spectral mixture analysis of Landsat ETM+ satellite imagery to assess landscape and regional dynamics of canopy damage following selective logging in an eastern Amazon forest. Our field studies encompassed measurements of ground damage and canopy gap fractions along multitemporal sequences of post-harvest regrowth of 0.5-3.5 yr. Areas used to stage harvested logs prior to transport, called log decks, had the largest forest gap fractions, but their contribution to the landscape-level gap dynamics was minor. Tree falls were spatially the most extensive form of canopy damage following selective logging, but the canopy gap fractions resulting from them were small. Reduced-impact logging resulted in consistently less damage to the forest canopy than did conventional logging practices. This was true at the level of individual landscape strata such as roads, skids, and tree falls as well as at the area-integrated scale. A spectral mixture model was employed that utilizes bundles of field and image spectral reflectance measurements with a Monte Carlo analysis to estimate high spatial resolution (subpixel) cover of forest canopies, exposed nonphotosynthetic vegetation, and soils in the Landsat imagery. The method proved highly useful for quantifying forest canopy cover fraction in the log decks, roads, skids, tree fall, and intact forest areas, and it tracked caopy damage up to 3.5 yr post-harvest. Forest canopy cover fractions derived from satellite observations were highly and inversely correlated with field- and satellite-based measurements. A 450-km^2 study of gap fraction showed that approximately one-half of the canopy opening caused by logging is closed within one year of regrowth following timber harvests. This is the first regional-scale study utilizing field measurements, satellite observations, and models to quantify forest canopy damage and recovery following selective logging in the Amazon.\n<br>\n\n#### LC14_Aboveground_Prod_1196\nThis data set reports forest biophysical measurements from a rainfall exclusion experiment conducted at the km 67 Seca Floresta site, Tapajos National Forest, Brazil from 1998 to 2006. The purpose was to observe the potential effects of severe water stress on a humid Amazonian forest (Nepstad 2002). Data are reported for stem inventory, tree diameter at breast height (DBH) and height, dendrometer measurements of tree diameter growth increments, canopy density, leaf area index (LAI), and coarse and fine litter mass. The measurements were made monthly from September 28, 1998 through November 10, 2006. There are six comma-delimited data files (.csv), one text file (.txt), and two companion files with this data set.\n<br>\n\n#### LC14_Amazon_Scenarios_1153\nThis data set provides the results of the two modeled scenarios for future patterns of deforestation across the Amazon Basin from 2002 to 2050. This larger defined Amazon Basin (PanAmazon area) includes the Amazon River watershed, the Legal Amazon in Brazil, and the Guiana region. The model SimAmazonia was used to simulate monthly deforestation in the Amazon Basin from 2002 to 2050 for two scenarios: (1) a \"Business-as-Usual\" scenario, which considered the deforestation trends across the basin and projected the rates by using historical images and their variations from 1997 to 2002 and then added to that the effect of paving a set of major roads, and (2) a \"Governance\" scenario, that also considered the current deforestation trends, but assumed a 50% limit imposed for deforested land within each basin's subregion, and that existing and proposed Protected Areas (PAs), play a decisive role in limiting deforestation as well (Soares et al., 2006). The provided data products include one GeoTiff (.tif) for each year (2002 to 2050) for both model scenarios for a total of 98 files. The files have been compressed in two .zip files, one for each model scenario. There is also one comma-delimited file that contains the model input data derived from satellite deforestation maps.\n<br>\n\n#### LC14_RISQUE_1147\nA simple GIS soil-water balance model for the Amazon Basin, called RisQue (Risco de Queimadasa -- Fire Risk), was used to conduct an analysis of spatial and temporal patterns of drought in moist tropical forests and the complex relationships between patterns of drought and forest fire regimes from 1995 through 2001. The provided data products are the model output estimates of maximum plant-available soil water (PAWmax) at 10 m depth at 8 km resolution and model data inputs of monthly precipitation and evapotranspiration. RisQue estimates PAWmax at 10 m depth starting with a map of PAWmax (1-2 m depth) developed using 1,565 RADAMBRASIL soil texture profiles and empirical relationships between soil texture and critical soil water parameters and then interpolated to 8 km resolution. In RisQue, plant-available soil water (PAW) is depleted by monthly evapotranspiration estimated using the Penman Monteith equation and satellite-derived radiation and recharged by monthly precipitation. There are three data files with this data set, two .zip, and one GeoTIFF image (.tif). The .zip files expand to 83 .asc files of evapotranspiration and 89 .asc files for precipitation data. The image (.tif) is a map of maximum percent available water at 10 m depth. All the files in this data set are in standard arc/info asciigrid format at 8 km resolution.\n<br>\n\n#### LC14_Surface_Roots_Phenology_1268\nThis data set contains biomass estimates for coarse roots measured on the forest floor and measurements of fine root growth down to 2-m depth at the km 67 Rainfall Exclusion Experiment site, Tapajos National Forest, Brazil. The study site was part of a rainfall exclusion experiment that was conducted from 1999-2006 to develop an understanding of the physical processes driving the observed soil water dynamics at the site. All surface roots intersected along three 1000-m long x 1-m wide transects were identified to species, measured, and biomass calculated. The collections were made on January 26, 2001 during the experimental rain exclusion period. The fine root growth was measured from 0.5-m to 2-m depth with a rhizotron. The rhizotron tubes were inserted into deep soil pits in the control and treatment plots. Average root growth measurements are provided by depth interval on a monthly basis from July 25, 2000 to December 14, 2003. DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS: There are discrepancies with the documentation, collection dates reported and collection method for fine roots utilizing rhizotrons.\n<br>\n\n#### LC14_REE_SLA_1211\nThis data set provides measurements of specific leaf area and monthly phenological observations for selected tree and vine species at the km 67 Seca Floresta site, Tapajos National Forest, Para, Brazil. The study site was part of a rainfall exclusion experiment that was conducted from 1999-2006 to develop an understanding of the physical processes driving the observed soil water dynamics at the site. Phenological observations were made from 2001-2004 in rainfall exclusion and control plots. In total, 3,224 leaves were observed across 223 individuals and 56 species. The phenological observations included the month and year when a given leaf was first observed fully expanded and last observed alive. Starting in July 2004 and continuing through January 2006, leaves that had been followed in the phenology study were sampled and leaf area and mass were determined and the specific leaf area was calculated. There are two comma-delimited data files with this data set.\n<br>\n\n#### LC15_Roughness_Map_1182\nThis data set provides physical roughness maps of vegetation canopies in the Amazon Basin. The images are estimates of aerodynamic roughness length (Z0) and zero plane displacement height (D0) at 1-km spatial resolution. The aerodynamic roughness length (Z0) is an important parameter to determine the vertical gradients of mean wind speed and the conditions for momentum transfer over a vegetated or bare rough surface. The maps were produced from a multivariate regression model algorithm developed from field-measured vegetation structure and remote-sensing data. The data input sources included Shuttle Radar Topography Mission (SRTM) (Saatchi, 2013), JERS-1, MODIS, and field data from vegetation biomass plots over the Amazon basin, as well as tower-based wind profile measurements, and roughness parameters from LBA tower sites. There are two GeoTIFF (.tif) files with this data set.\n<br>\n\n#### LC15_AGLB_Distribution_Map_908\nThe amount and spatial distribution of forest biomass in the Amazon basin is a major source of uncertainty in estimating the flux of carbon released from land-cover and landuse change. Direct measurements of aboveground live biomass (AGLB) are limited to small areas of forest inventory plots and site-specific allometric equations that cannot be readily generalized for the entire basin. Furthermore, there is no space-borne remote sensing instrument that can measure tropical forest biomass directly. To determine the spatial distribution of forest biomass of the Amazon basin, we report a method based on remote sensing metrics representing various forest structural parameters and environmental variables, and more than 500 plot measurements of forest biomass distributed over the basin. A decision tree approach was used to develop the spatial distribution of AGLB for seven distinct biomass classes of lowland old-growth forests with more than 80% accuracy. AGLB for other vegetation types, such as the woody and herbaceous savanna and secondary forests, was directly estimated with a regression based on satellite data. Results show that AGLB is highest in Central Amazonia and in regions to the east and north, including the Guyanas. Biomass is generally above 300 Mg ha-1 here except in areas of intense logging or open floodplains. In Western Amazonia, from the lowlands of Peru, Ecuador, and Colombia to the Andean mountains, biomass ranges from 150 to 300 Mg ha-1. Most transitional and seasonal forests at the southern and northwestern edges of the basin have biomass ranging from 100 to 200 Mg ha-1. The AGLB distribution has a significant correlation with the length of the dry season. We estimate that the total carbon in forest biomass of the Amazon basin, including the dead and belowground biomass, is 86 Pg C with +/- 20% uncertainty.\n<br>\n\n#### LC15_GRFM_JERS1_Mosaic_1024\nThis data set contains two image mosaics of L-band radar backscatter and two image mosaics of first order texture. The two backscatter images are mosaics of L-band Radar Backscatter at Horizontal-Horizontal (HH) Polarization created from 1,500 images collected by the Japanese Earth Resources Satellite-1 (JERS-1) Synthetic Aperture Radar (SAR) over the Amazon River Basin as part of the Global Rainforest Mapping Project (GRMP). These backscatter image mosaics were developed using data collected over 62 days from August to November of 1995 for the peak of the dry season and for 62 days from May to June of 1996 during the peak of the wet season. The two image mosaics are at 3 arc-sec resolution. Data provided under this project are resampled images at 30 arc-sec resolution (or about 1 km resolution). For each radar backscatter image, first order texture statistical information was derived and is distributed along with the image mosaic. This data set contains four images each in both geotiff and ENVI formats, provided in eight zip files. The four files in ENVI file format contain “_envi” in their file name and when extrapolated contain an envi image (*_envi.dat) and an envi image header file (_envi.hdr). The four files in geotiff format contain “_geotiff” in their file name and when extrapolated contain .tif and .tfw file pairs. See Section 2 for more information about the characteristics of these data files.\n<br>\n\n#### LC15_SPOT_Metrics_1239\nThis data set provides Normalized Difference Vegetation Index (NDVI) composite images of the Amazon Basin for the years 1999-2000 at approximately1-km spatial resolution. The images were from the VEGETATION 1 sensor, aboard the SPOT 4 satellite. Ten day composite images were reprocessed through several filters for cloud removal. Monthly NDVI data were used to create five metrics: maximum NDVI, minimum of 6 greenest months, range of NDVI between min and max, mean NDVI dry months, and mean NDVI wet months. There are five GeoTIFF (.tif) files with this data set.\n<br>\n\n#### LC15_SRTM_Topography_1181\nThis dataset provides a subset of the SRTM30 Digital Elevation Model (DEM) elevation and standard deviation data for the Amazon Basin. SRTM30 is a near-global digital elevation model (DEM) comprising a combination of data from the Shuttle Radar Topography Mission (SRTM), flown in February, 2000, and the earlier U.S. Geological Survey's GTOPO30 data set. The SRTM30 resolution is 30 arc-sec or about 1 km. In processing the SRTM data, to combine with GTOPO30, the data were resampled from 3 arc-sec to 30 arc-sec. Provided here are the mean elevation and the standard deviation (STD) of the data points used in the averaging. The STD is thus an indication of topographic roughness useful in some applications.\n<br>\n\n#### LC15_MODIS_TreeCover_1035\nThis data set contains proportional estimates for the vegetative cover types of woody vegetation, herbaceous vegetation, and bare ground over the Amazon Basin for the period 2000-2001. These products were derived from all seven bands of the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Terra satellite. A set of MODIS 32-day composites were used to create the vegetation cover types using the Vegetation Continuous Fields (VCF) (Hansen et al., 2002) approach which shows how much of a land cover such as \"forest\" or \"grassland\" exists anywhere on the land surface. The VCF product may depict areas of heterogeneous land cover better than traditional discrete classification schemes which shows where land cover types are concentrated. The original MODIS products are 500-m spatial resolution and are derived from 2000-2001 data products. The data were resampled to 1-km resolution for the regional study under this project, and provided as 3 separate cover type files in ENVI and GeoTIFF file formats that are provided in six zipped files. These products are registered to the rest of the regional data sets over the Amazon basin. These data are also available for download from the Global Land Cover Facility Website (http://modis.umiacs.umd.edu/).\n<br>\n\n#### LC18_Hyperion_889\nThis image was collected by the Hyperion sensor on 10-July-2004 at 13:16:16 GMT. It was calibrated to apparent surface reflectance using the ACORN atmospheric model. The Hyperion imager has a spectral range of 400-2500 nm, a spectral resolution of 10 nm, spatial resolution of 30 m, and a swath width of 7.8 km. Sampling is scene based (256 samples, 512 lines) (http://eo1.usgs.gov/sensors.php). Through these large number of spectral bands, complex land ecosystems can be imaged and accurately classified. Data from the EO-1 Hyperion imaging spectrometer may greatly increase our ability to estimate the presence and structural attributes of selective logging in the Amazon Basin using four biogeophysical indicators not yet derived simultaneously from any satellite sensor: 1) green canopy leaf area index; 2) degree of shadowing; 3) presence of exposed soil and; 4) non-photosynthetic vegetation material. Airborne, field and modeling studies have shown that the optical reflectance continuum (400-2500 nm) contains sufficient information to derive estimates of each of these indicators. Our ongoing studies in the eastern Amazon basin also suggest that these four indicators are sensitive to logging intensity. Satellite-based estimates of these indicators should provide a means to quantify both the presence and degree of structural disturbance caused by various logging regimes.\n<br>\n\n#### LC19_Field_2002_1261\nThis data set provides measurements for soil physical and chemical properties, rooting depth and weight, leaf area index (LAI), plant area index (PAI), biomass, fraction of photosynthetically active radiation (fPAR), and ground-based reflectance measurements of soil and litter samples. The samples were collected from 23 areas within the Brazilian research sites of the Brasilia National Park (BNP) and Aguas Emnendadas Ecological Station (AE), Brasilia; Cangacu Research Center, Tocantins; and Tapajos National Forest, Para. The research areas were in the most intensely stressed areas in Brazil, with rapid and aggressive land use conversions in forested and cerrado-transition areas. These field measurements were conducted from June to July 2002. There are 61 comma-delimited (.csv) data files with this data set.\n<br>\n\n#### LC21_Fractional_Cover_1152\nThis data set provides Landsat Enhanced Thematic Mapper Plus (ETM+) imagery, derived classified land cover products, and cloud-water masks for selected Brazilian states (Acre, Amapa, Amazonas, Maranhao, Mato Grosso, Para, Rondonia, and Roraima) for the years 1999-2002. The Landsat ETM+ images were processed to derive fractional land cover types (photosynthetic vegetation [PV], non-photosynthetic vegetation [NPV], and bare substrate) by application of the Carnegie Landsat Analysis System (CLAS) methodology (Asner et al., 2005). CLAS utilizes a quantitative determination of fractional land cover at the subpixel scale (e.g., within each Landsat 30 x 30 m pixel). The resulting images display estimates of subpixel land cover fraction values including free of clouds, cloud shadows, and water. There are 584 .zip files in this data set which when expanded, contain a total of 1,717 (.tif) images files (GeoTiff Standard format).\n<br>\n\n#### LC21_Foliar_Nutrients_1234\nThis data set provides measurements for foliar nutrients from logging blocks in the Tapajos National Forest, Para Western Santarem, Brazil. Data are included for calcium (Ca), phosphorus (P), magnesium (Mg), nitrogen (N), and potassium (K) concentrations. In March 2003 foliar samples were collected from the cover types remaining after selective logging in 2002: forest, tree-fall gaps, skids, roads, and deck areas. Fresh foliage was also collected in March 2003, from 192 upper canopy species at an intact forest site 17 km from the logging area. There are two data files with this data set.\n<br>\n\n#### LC21_Selective_Logging_1172\nThis data set provides the results of analyses of Landsat Enhanced Thematic Mapper Plus (ETM+) images for selective logging activity in the Brazilian states of Para, Mato Gross, Rondonia, Roraima, and Acre over the years 1999 through 2001. Images were analyzed using the Carnegie Landsat Analysis System (CLAS) to detect and to quantify the amount of damage due to selective logging in the major timber-production states of the Brazilian Amazon. This approach provided automated image analysis using atmospheric modeling for detection of forest canopy openings, surface debris, and bare soil exposed by forest disturbances; and pattern-recognition techniques. CLAS provides detailed measurements of forest-canopy damage at a spatial resolution of 30 x 30m. Fifteen GeoTiff format files are included -- one for each of the three years from 1999-2001 for each of the five states. Each GeoTiff is a single band image where each pixel represents if logging activity was or was not detected. A zero (0) value indicates that no logging was detected, while a value of one (1) indicates that damage from logging was detected. The 15 GeoTiff (.tif) files have been compressed into one .zip file.\n<br>\n\n#### LC21_Soil_Characteristics_1236\nThis data set provides measurements for soil nutrients from areas that were selectively logged and from control areas in the Tapajos National Forest, Para Western Santarem, Brazil. Data are included for calcium (Ca), phosphorus (P), magnesium (Mg), nitrogen (N), aluminum (Al), iron (Fe), silicon (Si), carbon 13, nitrogen 15, and potassium (K) concentrations. In addition, data are included for Phosphorus fractionation which was performed on a subset of the soils, and soil bulk density measurements. The samples were from clay-dominated (oxisols) soils.\n<br>\n\n#### LC22_MODIS_Field_Val_2004_1262\nThis data set contains field observations, corresponding GPS points, and point and polygons of deforested areas in the state of Mato Grosso, Brazil, for the period August 2003 to July 2004. The field observations were conducted in the forested areas between Nova Mutum and Sinop, MT. These data were part of a study to validate Moderate Resolution Imaging Spectroradiometer (MODIS) data at 250-m resolution for the detection of deforested areas. There are 16 data files with this data set. This includes 10 shapefile (.shp) and six comma-separated files (.csv).\n<br>\n\n#### LC22_MODIS_Field_Val_2005_1260\nThis data set contains field observations, corresponding GPS points, and point and polygons of deforested areas in the state of Mato Grosso, Brazil, for the period March 17-24,2005. Fieldwork was conducted in the regions surrounding Sinop, Mato Grosso, with specific emphasis on large clearings occurring in the Xingu Basin. The field campaign was designed to validate preliminary MODIS deforestation products designed to detect deforestation during the wet season. There are five data files with this data set: four shapefiles (.shp) and one comma-separated file (.csv).\n<br>\n\n#### LC22_MODIS_Phenology_Mato_Grosso_1185\nThis data set, LBA-ECO LC-22 Land Cover from MODIS Vegetation Indices, Mato Grosso, Brazil, provides land cover classifications for Mato Grosso, Brazil, for the years 2000-2001 and 2003-2004. The classifications were derived from annual vegetation phenology information from a time series of Collection 4, 16-day MODerate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI), and the Enhanced Vegetation Index (EVI) vegetation data, at 250-m resolution. A decision tree classifier was trained using field observations and Landsat TM data of land cover from 2003-2004 to identify seven land-cover classes. The classifier was applied to the 2000-2001 and 2003-2004 MODIS ENVI and EVI data. There are two GeoTIFF (.tif) files with this data set.\n<br>\n\n#### LC22_Post_Deforestation_LULC_1099\nThis data set provides (1) areal estimates of deforestation events (>25 ha) that were identified from 2001-2004 in Mato Grosso by the Brazilian Institute for Space Research (INPE) as part of the Program for the Estimation of Deforestation in the Brazilian Amazon (PRODES) and (2) the classification of the post-deforestation land use as either cropland, cattle pasture, or not in production (deforested areas that were never fully cleared or returned immediately to secondary forest) in the years after the large deforestation events from 2001-2005. Data are provided in ESRI shapefile format. There are five compressed (.zip) data files with this data set. Each shapefile represents one year of post-deforestation land use. Land use in the years following deforestation was estimated using annual time series of MODIS NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index). Metrics of vegetation phenology derived annual time series of MODIS NDVI and EVI data were analyzed using a decision-tree classifier to characterize the major cover type in each area of new deforestation. Post-deforestation land use for each large deforestation event was classified based on the classification of MODIS phenology metrics for all years following deforestation during 2002-2005.\n<br>\n\n#### LC22_MODIS_VCF_Tree_Cover_1112\nThis data set contains proportional estimates for the vegetative cover types of tree cover, herbaceous vegetation, and bare ground over South America for the period 2000-2001. These products were derived from all seven bands of the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Terra satellite. A set of 500-m MOD09A1 Surface Reflectance 8-day minimum blue reflectance composites were used as input data. To reduce the presence of cloud shadows, The data were converted to 40-day composites using a second darkest albedo (sum of blue, green, and red bands), and the Vegetation Continuous Fields (VCF) algorithmn was utilized (Hansen et al., 2002). The VCF shows how much of a land cover such as forest or grassland exists anywhere on the land surface. The VCF product may depict areas of heterogeneous land cover better than traditional discrete classification schemes which shows where land cover types are concentrated. There are three images provided in GeoTIFF format.\n<br>\n\n#### LC23_MODIS_ASTER_Fire_Comparisons_839\nThis data set contains data associated with MODIS fire maps generated using two different algorithms and compared against fire maps produced by ASTER. These data relate to a paper (Morisette et al., 2005) that describes the use of high spatial resolution ASTER data to evaluate the characteristics of two fire detection algorithms, both applied to MODIS-Terra data and both operationally producing publicly available fire locations. The two algorithms are NASA's operational Earth Observing System MODIS fire detection product and Brazil's National Institute for Space Research (INPE) algorithm. These data are the ASCII files used in the logistic regression and error matrices presented in the paper.\n<br>\n\n#### LC23_Vegetation_Fire_Dynamics_843\nSatellite fire detection was determined from two sensors—the Advanced Very High Resolution Radiometer (AVHRR) on NOAA-12 and the Moderate Resolution Imaging Spectroradiometer (MODIS) on both the Terra and Aqua platforms, for 2001- 2003 to characterize fire activity in Brazil, giving special emphasis to the Amazon region. Active fire data for AVHRR/NOAA-12 was produced using a fixed threshold fire detection technique based on the algorithm developed by the Centro de Previsao do Tempo e Estudos Climaticos (CPTEC/INPE) (Setzer and Pereira, 1991; Setzer et al., 1994; Setzer and Malingreau, 1996). Active fire data for MODIS/Terra and MODIS/Aqua was produced using a contextual fire detection technique based on NASA-University of Maryland algorithm (Justice et al., 2003; Giglio et al.2003).Resulting fire counts were compared for major biomes of Brazil (Figure 1), the nine states of the Legal Amazon (e.g., Tocantins, Figure 2), and two important road corridors in the Amazon region (Figure 3). In evaluating the daily fire counts, there is a dependence on variations in satellite viewing geometry, overpass time, atmospheric conditions, and fire characteristics (Schroeder et al., 2005). The data provided are the coordinates of daily active vegetation fires in Brazil for 2001 through 2003 at 1km resolution for both AVHRR and MODIS sensors. Data are provided in both Arcview (shape file format) and ASCII comma separated file formats. Vector files for the major biomes of Brazil, the nine states of the Legal Amazon, and two important road corridors in the Amazon region are also included.\n<br>\n\n#### LC23_Vegetation_Fires_2003_887\nThe ASTER high resolution satellite data are available for visible-near infrared (15m resolution), short wave infrared (30m), and thermal infrared (90m) bands. Two sets of imagery were collected over Roraima state - Brazil covering a strip of approximately 180 X 60 km each on January 19 and 28, 2003. Each date has one prescribed burn and many other opportunistic fires. Data format is ASTER L1B HDF. There is a corresponding .met file (metadata file) for each hdf file. These files can be opened using any standard hdf viewer, ENVI can recognize the bands and georegistration automatically. The companion document, ASTER_GeoRef_FINAL.pdf, gives a good description of how to georeference ASTER L1B data and to open the files in ERDAS Imagine and also older ENVI versions.\n<br>\n\n#### LC24_Basin_Scale_Hot_Pixels_2001_882\nThis data set provides the number of hot spots detected across the legal Amazon Basin at 5- km resolution by the AVHRR (Advanced Very High Resolution Radiometer) on NOAA 12, 14, 15, 16, 17, and 18 satellites for the entirety of 2001 (January 1 - December 31). Only hot spots detected at night are included. This data is useful for modeling fire events and evaluating human impacts on the Amazon Basin using fire as an indicator of anthropogenic disturbance (Arima et al., 2007).\n<br>\n\n#### LC24_Cadastral_Property_Map_Para_1042\nThis data set contains a shapefile of a digitized map of the land parcel information of the original properties of the Uruara colonization site, Para, Brazil, acquired from the Instituto de Colonizacao e Reforma Agraria, or the Colonization and Agrarian Reform Institute (INCRA). The Uruara settlement geometry was initially designed by INCRA, and consists of mostly 100 hectare lots (400 x 2500 meters, and 500 x 2000 meters), running north and south of the Trans-Amazon Highway, as a fine network of small, narrow rectangles. The other parcels in the landscape are the so-called glebas that range up to 3,000 hectares. The map was in the form of a paper map without a projection (a spherical geographic coordinate system) in the South American 1969 datum (SAD 1969). This paper map was digitized in Environmental Science Research Institute (ESRI) ArcInfo 8.1 using a digitizing table, and the digital cadastral data were geo-referenced and projected to match the Universal Transverse Mercator projection (Zone 22 South, World Geodetic System 1984 datum) of Landsat imagery (Landsat.org). There is one compressed (.zip) file with this data set.\n<br>\n\n#### LC24_MODIS_Forest_Cover_500-m_1056\nThis data set, LBA-ECO LC-24 Forest Cover Map from MODIS, 500-m, South America: 2001, contains forest cover information for 2001 for all of South America. The data were collected by the MODerate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Earth Observing System, TERRA (AM-1) satellite platform and released by the MODIS science team as an image showing percent canopy cover. This information was then reclassified so that all pixels with a percent canopy cover greater than 40% (40% after the 1973 UNESCO standard) were classified as forest (a value of 1), and all other pixels were classified as non-forest (a value of 2). Water features were given a value of 3. This data has a pixel resolution of 500 meters and is unprojected with the WGS-1984 datum (Hansen et al. 2006). There is one GeoTIFF data file for this data set.\n<br>\n\n#### LC24_Historical_Roads_Amazon_1043\nUnderstanding the impact of road investments on deforestation is part of a complete evaluation of the expansion of infrastructure for development.We find evidence of spatial spillovers from roads in the Brazilian Amazon: deforestation rises in the census tracts that lack roads but are in the same county as and within 100 km of a tract with a new paved or unpaved road. At greater distances from the new roads the evidence is mixed, including negative coefficients of inconsistent significance between 100 and 300km, and if anything, higher neighbor deforestation at distances over 300 km.\n<br>\n\n#### LC24_Land_Cover_Uruara_Para_1053\nThis data set provides course land cover classifications derived from Landsat TM images for 1986, 1988, and 1991 for the area surrounding the municipality of Uruara, Para, Brazil. Five land cover classes (Water, Clouds/Shadow, Forest, Not Forest, and Background) were derived (Aldrich et al. 2006). The Land Cover is in a compressed (.zip) GeoTIFF file for each year.\n<br>\n\n#### LC24_ETM_Deforestation_Map_Para_1999_1054\nThis data set contains a 1999 Landsat ETM+ mosaic image land of cover classification showing forested and deforestation areas in Uruara, Para, Brazil. This image may be overlain with the cadastral property map of the same area (see related data set LBA-ECO LC-24 Cadastral Property Map of Uruara, Para, Brazil: ca.1975). This data sets contains a single geotiff image distributed as deforested_large.zip.\n<br>\n\n#### LC24_Land_Cover_Southern_Para_1055\nThis data set is a five-class land cover for Southern Para for the years 1984 (Landsat MSS), 1988 (Landsat TM), 1996, and 2003 (Landsat ETM+). The final classification shows five classes derived using visual comparison (Water, Clouds/Shadow, Forest, Not Forest, Background). These data were used in 2007 to illustrate the nature of deforestation in Southern Para, Brazil over the past twenty years (Simmons et al. 2007). There are four annual GeoTIFF files distributed with this data set. Each GeoTIFF file and accompanying .tfw file have been compressed into a single .zip file.\n<br>\n\n#### LC31_AMZ_Historical_LU_1170\nThis data set provides annual spatial patterns of cropland, natural pasture, and planted pasture land uses across Amazonia for the period 1940/1950-1995. Two series of 5-minute grid cell historical maps were generated starting from land use classification products for 1995. Annual data are the fraction of natural pasture, planted pasture, and cropland in each 5-min grid cell. The annual maps are provided in two NetCDF (.nc) format file at 5-minute resolution. The AMZ-C.nc file covers the Brazilian portion of Amazon and Tocantins Rivers basins, and is based on the 1995 land use classification of Cardille et al. (2002), generated through the fusion of remote sensing (AVHRR) and agricultural census data. The second file, AMZ-R.nc, covers the entire Legal Amazon region and adjacent areas and is based on the 1995 land use classification by Ramankutty et al. (2008). The land use classification was generated by the fusion of satellite imagery (MODIS and VEGETATION-SPOT) and data from the agricultural census. A historical land-use reconstruction algorithm was used to generate the annual spatial patterns (based on work from Ramunkutty and Foley, 1999).\n<br>\n\n#### LC31_SITE_1173\nThis model product provides the Fortran source code and input data for the Simple Tropical Ecosystem Model (SITE). SITE is a simplified point model of vegetation dynamics that uses an integration interval of one hour to estimate the fluxes of CO2, water, and energy. Model forcing data are hourly meteorological parameters. SITE is a simplified model of vegetation dynamics for tropical ecosystems developed by Santos and Costa (2004). Model input data measurements of temperature, wind velocity, precipitation, latent heat, sensible heat, downward incident solar flux, and downward incident infrared flux were collected at the km 67 Tapajos National Forest site, Para, Brazil, from 2002 to 2003. SITE is structured with a canopy layer and two soil layers, and incorporates the following processes: *infrared radiation balance in the canopy and balance of solar radiation *aerodynamic processes *plant physiology *transpiration *balance of water intercepted by the canopy *transport of mass and energy fluxes *soil heat flux and soil moisture *carbon balance There are five files provided with this data set: the Fortran source code (version 1.1-0d), one file for the main program that declares variables and input parameters, one file that initializes vegetation parameters, one file used to compile the SITE model, and the km 67 input data file in comma-delimited (.csv) format. The four SITE files are provided in the compressed file SITE_Model.zip. One companion file is also provided that describes the collection and processing of the meteorological and flux measurements at the km 67 Tapajos National Forest site and the use of the data to calibrate SITE.\n<br>\n\n#### LC35_GOES_WF_ABBA_1180\nThis data set is an active fire detection product resulting from the application of The Wildfire Automated Biomass Burning Algorithm (WF_ABBA) to Geostationary Environmental Operational Satellite (GOES) imager data for all of South America from 2000 through 2005. GOES imager data are available at 30 minute intervals with a nominal 4 x 4-km resolution. The data provided are the latitude/longitude, brightness temperature, estimates of sub-pixel fire size and temperature, Global Land Cover Characterization (GLCC) ecosystem type, and a pixel-fire flag (0-5, information regarding the probability of a fire or processing characteristics) for each active fire detected by WF_ABBA for a 30 minute imager interval. Spatial area coverage data files are provided as a complement to individual fire detection data files because the area of the latter varied according to the GOES imager scan mode in use. Versions 5.9 and 6.0 WF_ABBA data are provided. Differences between the two versions are assumed to be small though (typically less than 10%). An in-line temporal filter has been added to the algorithm to screen out false alarms associated with noise in the imagery and cloud edge issues in version 6.0. This is especially important for screening false alarms due to reflection off clouds at extreme view angles and at sunrise and sunset. There are nine compressed (.zip) files with this data set which expand to the filtered ASCII text data files (.filt), and seven coverage files text (.txt).\n<br>\n\n#### LC35_Landsat7_Fire_Masks_1071\nThis data set provides active fire detection images and associated summary information derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images for various locations in Brazilian Amazonia during 2001-2003. There are two image types: (1) GeoTiff images (masks) of active fire pixels, and (2) GeoTiff images (masks) of clustered active fire pixels where a distinct cluster identification number has been assigned to each individual group of contiguous active fire pixels. There are 122 GeoTiff format files of each type of fire mask; a total of 244 images. The spatial resolution of the fire mask images is 30 meters. ETM+ images were selected based on data quality, availability, as well as on the occurrence of vegetation fires. In addition to the two image types, there are also two types of fire pixel summary information provided in text files: (1) one file of active fire pixel summary information derived from the active fire pixel images, and (2) 122 files of clustered active fire pixel information derived from individual clustered fire pixel masks, each of which correspond to a clustered image.\n<br>\n\n#### LC39_DECAF_Model_1190\nThis data set contains modeled estimates of carbon flux, biomass, and annual burning emissions across the Brazilian state of Mato Grosso from 2000-2006. The model, DEforestation CArbon Flux (DECAF), was used to provide annual carbon fluxes from large deforestation events (>25 ha) based on post-deforestation land use, and the frequency and duration of active fires during the deforestation process. Carbon fluxes associated with the conversion of Cerrado to mechanized crop production, fires in Cerrado, and managed pasture cover types were also estimated. Model data outputs provided include: * Estimated aboveground live biomass from DECAF in 2000 and 2004. * Annual biomass burning emissions estimates for 2001-2005 from low, middle, and high emissions scenarios with DECAF. There are 15 GeoTIFF files for annual emissions which represent the carbon emissions per pixel in grams of carbon per m2 (g C m-2). Model data inputs provided include: * Annual burn trajectories for 2001 - 2005, including deforestation, Cerrado land cover conversion, and fires in pasture and Cerrado ecosystems unrelated to agricultural expansion. These data were assembled from three sources: MODIS 500-m burned area maps, annual deforestation based on data from the INPE PRODES program, and the conversion of Cerrado savannah/woodland to cropland estimated from land cover information from MODIS phenology metrics. * Annual land cover data 2001-2004 for the portion of Mato Grosso covered by MODIS phenology metrics, tile h12v10, updated based on annual land cover changes in Amazon forest and Cerrado cover types. * Monthly Normalized Difference Vegetation Index (NDVI) for MODIS tile h12v10 from 10/2000 - 09/2006, based on cloud and gap-filled 16-day NDVI data from MODIS Collection 4 16-day NDVI composites MOD13 product (Huete et al., 2002). There are six compressed (.gz) files with this data set.\n<br>\n\n#### LC39_MODIS_Fire_SA_1186\nThis data set provides active fire locations and estimates of annual fire frequencies for South America from 2000-2007. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra (2000-??2007) and Aqua (2003-2007) satellite platforms were analyzed to determine spatial and temporal patterns in satellite fire detections. The analysis considered a high-confidence subset of all MODIS fire detections to reduce the influence of false fire detections over small forest clearings in Amazonia (Schroeder et al., 2008). The number of unique days on which the active fire detections were recorded within a 1 km radius was estimated from the subset of active fire detections and the ArcGIS neighborhood variety algorithm. There are 14 data files with this data set: 7 GeoTIFF (.tif) files of fire frequency at MODIS 250 m resolution, where each grid cell value represents the number of days in that year on which active fires were detected, and 7 shape files of active fire locations for the years 2001-2007.\n<br>\n\n#### ND01_Pasture_Nutrients_1135\nThis data set provides soil physical and chemical properties, and grass nutrient measurements of samples collected from 17 pasture sites located within the state of Rondonia in the southwestern Brazilian Amazon. Soil data includes bulk density, class, texture, and measurements of carbon (C), phosphorus (P), calcium (Ca), magnesium (mg), and potassium (K) concentrations. Foliar data includes nitrogen (N), P, Ca, Mg, and K concentrations. The 17 pasture sites were cattle ranches selected within the region between Porto Velho and Presidente Medici of Rondonia. Four of the ranches with Ultisols support dairy cattle, and the rest have beef cattle pastures dispersed across three soil orders: Oxisols, Ultisols, and Alfisols. Nearby primary forest sites were also sampled to provide data on the original soil properties for each soil order. Soil samples were collected in May 2003, July through August 2003, and May 2004, which covered the late rainy season (May) and the dry season (July through August). Grass species sampled included Brachiaria brizantha, Brachiaria decumbens, B. brizantha, and Pennestum clandestinum, and represented three phenologically distinct grass materials: wet-season live grass, collected in May 2004, dry-season live grass and dry-season senesced grass, both collected between the end of July and the beginning of August 2003. There are 4 comma-delimited data files with this data set.\n<br>\n\n#### ND01_Spectral_Mixture_Models_1188\nThis data set provides fractional land cover type images for shade, green vegetation (GV), non-photosynthetic vegetation (NPV), and soil for the regions of JiParana, PortoVelho, Luiza, Ariquemes, and Cacoal in the state of Rondonia, Brazil, for the period 1984 to 2000. The images were derived with a spectral mixture analysis (SMA) of Landsat Thematic Mapper (TM) time series scenes for each of these areas. There were 249 TM scenes and one Landsat Multispectral Scanner (MSS) scene acquired for these analyses. The images are 30-m Landsat resolution and were georectified to the Brazilian space agency 1998 and 1999 PRODES imagery. There are 250 GeoTIIF image files (.tif) in this data set. Files are grouped by region and year/month/day scene was taken.\n<br>\n\n#### ND01_Registered_TM_MSS_1197\nThis data set provides a time series of Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) scenes for five (Path/Row) areas in Rondonia, Brazil. The scenes are from the period June 1975 through June 2000, but all areas do not have scenes for all the years. The areas and Landsat Path/Rows included are as follows: Ariquemes (P232,R67), Ji-Parana (P231, R67), Luiza (P231, R68), Cacoal (P230, R68), and Porto Velho (P232, R66). TM images are available for all five areas. Because of a paucity of digital Landsat MSS imagery from the 1970s, only two scenes could be included, a 1975 scene from Ariquemes and a 1978 scene from Ji-Parana. Each of the Landsat scenes has been coregistered to a Path/Row-specific georectified PRODES Landsat file obtained from the Brazilian Government's National Institute for Space Research (INPE) program. For each scene, the coregistration is accurate to within (plus or minus)1 pixel (30-m Landsat resolution) in most places. The five INPE PRODES Landsat scenes used in the georectification process are included with this data set. There are five compressed files (tar.gz format) with this data set. When expanded, each compressed file (which corresponds to one of the five areas) contains a directory for each scene with GeoTIFF files for individual Landsat bands, a text file of tie points, and another text file of slope and intercept values for converting radiance to reflectance. There are two dates for Landsat MSS scenes, 45 dates for TM scenes, and six dates for ETM+ scenes.\n<br>\n\n#### ND01_Land_Cover_Maps_1259\nThis data set provides a time series of land cover classifications for Ariquemes, Ji-Parana, and Luiza, research sites in Rondonia, Brazil. The land cover classifications are derived from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) sensors. The time period ranges from June 1975 through June 2000, but all areas do not have images for all the years. The images were classified into the following categories: 1. Primary upland forest, representing the dominant natural vegetation in the area; 2. Pasture and green pasture; 3. Second growth, dominated by small trees and shrubs with low species diversity and biomass relative to primary forest; 4. Soil/urban; 5. Rock/savanna; 6. Water; and 7. Cloud and smoke obscured. In addition, areas covered by rock and savanna were mapped and all areas outside of the overlap zone between all dates within a scene, and scene edges, were masked. There are 75 GeoTIFF files (.tif) with this data set which includes: classified images (*ful.tif) and a corresponding image mask (*ful_mask .tif) for each date (with the exception of 1978 and 1996 images for Ji-Parana, for which there are only ful_mask.tif files), and three mask files for rock, savannah, and scene edges, for each area. By area, there are 31 images for Ariquemes, 23 images for Ji-Parana, and 21 images for Luiza.\n<br>\n\n#### ND01_Georectified_Products_1165\nThis data set provides a 27-year land cover time series of 28.5-m resolution products derived from Landsat images for 80% of Rondonia, Brazil, for the period 1984 to 2010. Selected Landsat Thematic Mapper (TM) and Landsat Multispectral Scanner (MSS) images from the years 1984 through 2010, for seven path/row scenes (PortoVelho, Ariquemes, Jiparana, Luiza (or Urupa), Cacoal, Chapuingaia, and Vilhena) were mosaicked for each year. Each mosaicked image was georectified and classified into seven land-cover classes--savanna/rock, pasture, secondary forest, primary forest, cloud, urban, or water. This 27-year time series allows the long-term assessment of land-cover variation across the state. There are 27 GeoTIFF image files (.tif) and one accompanying .xml file for each GeoTIFF file, compressed and available as .zip files, one file for each year for the period 1984-2010, with this data set.\n<br>\n\n#### ND01_Age_Maps_1184\nThis data set provides classified land cover transition images (maps) derived from Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS) imagery for Ariquemes, Luiza, and Ji-Parana¡ areas in Rondonia, Brazil, at 30-m resolution. Images depict the age relative to the year 2000, of cleared land from the date the land was cut, to the date when primary forests transitioned into nonforest class (for example, 25 = cut by 1975, or 25 years before the year 2000). Temporal changes in three regions are represented by 31 TM scenes acquired between 1984 and 1999, and a pair of MSS scenes from 1975 and 1978. Data are provided as three GeoTiff (.tif) images, one for each of the three areas.\n<br>\n\n#### ND01_Pasture_Spectra_1154\nThis data set provides the results of spectral reflectance (350 to 2,500 nm at 1-nm increments) and biophysical measurements on grass pastures in eight cattle ranches in the state of Rondonia, located in the southwestern Brazilian Amazon. The ranches are located near the cities of Porto Velho, Ariquemes, Ouro Preto, Ji-Parana, and Presidente Medici. Field measurements were collected in July and August 2003. The primary grass species sampled were Brachiaria brizantha and Brachiaria decumbens. Spectrometer measurements were taken at 5-m intervals along 100 m transects on the pastures - fourteen total transects. Vegetation was sampled at 20-m intervals along the transects. All standing biomass and litter on the soil surface were collected and separated into live and senesced biomass and then dried to calculate water content. Sixty-eight reflectance spectra coincided with grass biophysical samples. Note that the research was done on private lands in Rondonia, and to protect the privacy of those land owners no geographic information is associated with the reported measurements. Three data files are included: an ENVI spectral library file with reflectance data for 484 pasture sampling points, an ASCII comma-separated file with reflectance data for the 484 pasture sampling points, and an ASCII comma-separated file with the biophysical measurements.\n<br>\n\n#### ND01_Stream_Chemistry_1119\nThis data set provides the results of (1) synoptic streamwater sampling and analyses from numerous sites across Rondonia and (2) corresponding watershed characteristics derived from remote sensing and historical/available data sources. Sixty streams, in both forested and non-forested sites, were sampled once during the dry season in August of 1998 and 49 of the same streams were sampled again during the wet season in January-February of 1999. Analyses included sodium (Na), calcium (Ca), magnesium (Mg), potassium (K), silica (Si), chloride (Cl), sulfate, pH, and acid neutralizing capacity. Watershed characteristics, including soil cation content, pH, watershed lithology, area, percent deforested, and urban watershed population density, were derived and calculated from digitized soil maps and available soil profile analyses, digitized topographic maps, land use mosaics from Landsat Thematic Mapper (TM) images, and Brazilian census data. The objective of the study was to determine the relative influence of watershed soil exchangeable cation content, rock type, deforestation, and urban population density on stream concentrations of base cations, dissolved silicon, chloride and sulfate in both the dry and wet seasons in a humid tropical region undergoing regional land use transformation. There are three comma-delimited data files with this data set.\n<br>\n\n#### ND01_Watershed_Defor_1159\nThis data set provides estimates of watershed deforestation, as a proportion of the total area of watersheds, in Rondonia, Brazil for 1999. Deforestation maps were determined for the main agricultural and surrounding forested areas of Rondonia using multiple Landsat TM scenes (Biggs et al. 2008). Cumulative deforestation estimates were derived from this time series of Landsat scenes from 1975 to 1999. To obtain watershed-level estimates of deforestation, watershed boundaries and stream networks were delineated by a flow accumulation algorithm using a 90-m resolution digital elevation model (DEM) from NASA's Shuttle Radar Topography Mission (SRTM). The results were watersheds of seven Strahler stream orders (1-7) with stream networks that closely matched those of the 1:100,000 topographic maps for the area. The watershed boundaries, classified by stream order, were overlain on the time series of deforestation maps to determine the cumulative deforestation extent in 1999. This data set contains six ESRI ArcGIS shapefiles of the watershed boundaries for streams orders 2-7, the smallest watershed (second order) to the largest inclusive watershed (seventh order). The cumulative deforestation estimates, as a proportion of total area for each watershed, are available as a comma-delimited text file that can be related to the individual watershed boundary shapefiles. Cumulative deforestation data are available for first order streams, although not as a shapefile. There are six zipped ESRI ArcGIS shapefiles (.zip) and one ASCII comma separated file with this data set.\n<br>\n\n#### ND02_Mulching_Experiment_950\nFires set for slash-and-burn agriculture contribute to the current unsustainable accumulation of atmospheric greenhouse gases, and they also deplete the soil of essential nutrients, which compromises agricultural sustainability at local scales. Integrated assessments of greenhouse gas emissions have compared intensive cropping systems in industrialized countries, but such assessments have not been applied to common cropping systems of smallholder farmers in developing countries. We report an integrated assessment of greenhouse gas emissions in slash-and-burn agriculture and an alternative chop-and-mulch system in the Amazon Basin. The soil consumed atmospheric methane under slash-and-burn treatment and became a net emitter of methane to the atmosphere under the mulch treatment. Mulching also caused about a 50% increase in soil emissions of nitric oxide and nitrous oxide and required greater use of fertilizer and fuel for farm machinery. Despite these significantly higher emissions of greenhouse gases during the cropping phase under the alternative chop-and-mulch system, calculated pyrogenic emissions in the slash-and-burn system were much larger, especially for methane. The global warming potential CO2-equivalent emissions calculated for the entire crop cycles were at least five times lower in chop-and-mulch compared to slash-and-burn. The crop yields were similar for the two systems. While economic and logistical considerations remain to be worked out for alternatives to slash-and-burn, these results demonstrate a potential win-win strategy for maintaining soil fertility and reducing net greenhouse gas emissions, thus simultaneously contributing to sustainability at both spatial scales.\n<br>\n\n#### ND02_Soil_CO2_Extracts_1074\nThis data set provides a time series of calcium, magnesium, and potassium concentrations extracted from soil samples from a laboratory column extraction study conducted in 2002. Soils used in the columns were originally collected in 1998 in Fazenda Vitoria, a cattle ranch 6 km north of the town of Paragominas, Para, Brazil. The soils were from contrasting land uses of primary forest (mata), secondary forest (capoeira), or pasture (pasto). Water equilibrated with increasing concentrations of CO2 was used to extract cations from the soil columns. Data represent the time series of cation concentrations in the extract solutions as well as the total content of cations removed from the soils. There is one comma-delimited ASCII file with this data set.\n<br>\n\n#### ND02_Soil_CO2_Flux_1066\nThis data set reports soil CO2 flux and results of This data set reports soil CO2 flux and results of physical and chemical characterization of soils from pastures, secondary forests, and mature forests near Rio Branco, Acre, Brazil. CO2 flux measurements were made in the field on a monthly basis at 16 sites from June of 1999 to January 2001. In addition, litter was collected monthly from 2001-2002 at each of the mature forest sites and at 4 of the secondary forest sites, and mean litter mass is reported. Soil samples were collected and analyzed from several land cover types at two sites during this same time period. There are four comma-delimited ASCII data files with this data set. and chemical characterization of soils from pastures, secondary forests, and mature forests near Rio Branco, Acre, Brazil. CO2 flux measurements were made in the field on a monthly basis at 16 sites from June of 1999 to January 2001. In addition, litter was collected monthly from 2001-2002 at each of the mature forest sites and at 4 of the secondary forest sites, and mean litter mass is reported. Soil samples were collected and analyzed from several land cover types at two sites during this same time period. There are four comma-delimited ASCII data files with this data set.\n<br>\n\n#### ND02_Landsat_TM_MSS_Para_1156\nThis data set provides Landsat images of the county of Sao Francisco do Para located in the Bragantina region of Para, Brazil, the oldest agriculture frontier in Amazonia. These images are subsets for the municipio (county) and immediate region. There are seven GeoTIFF files (.tif) with this data set which includes two for July 24, 1984 multispectral scanner (MSS), one for June 21, 1994 thematic mapper Landsat 5 (TM5), three for July 13, 1999 thematic mapper Landsat 7 (TM7), and one TM for June 21, 1994.\n<br>\n\n#### ND02_Soil_Hydraulic_Conductivity_1075\nThis data set reports field estimated saturated hydraulic conductivity measurements from June 12 through June 20, 2001. This study was part of a rainfall exclusion experiment that was conducted from 1999-2001 at the km 67 Seca Floresta site, Tapajos National Forest, Para, Brazil. The objective of this component of the study was to develop an understanding of the physical processes driving the observed soil water dynamics at the site. There is one comma-delimited ASCII data file with this data set.\n<br>\n\n#### ND02_Non_Woody_Biomass_1115\nThis data set reports biomass from small stems and non-woody vegetation measured from 1999 to 2005 in plots of a secondary-growth forest fertilization experiment. The study location was Fazenda Vitoria, 6.5-km northwest of the town of Paragominas, Para, Brazil, in a 6-year old secondary-growth forest. Vegetation life-forms with diameters less than or equal to 2 cm (grasses, herbs, vines and dead material) were destructively sampled in November 1999, June 2000, June 2001, July 2003, July 2004, and July 2005. All data are provided in a single comma-separated file. The site was divided into three blocks with four treatment plots (each 20m x 20m) located in each block (3 reps x 4 treatments = 12 plots). Three of the twelve plots were fertilized with nitrogen (100 kg N/ha as urea), three were fertilized with phosphorus (50 kg P/ha as superphosphate), three were fertilized with both nitrogen and phosphorus. The remaining three plots were not fertilized and served as the experimental control.\n<br>\n\n#### ND02_Tree_Heights_DBH_951\nUnderstanding secondary successional processes in Amazonian terrestrial ecosystems is becoming increasingly important as continued deforestation expands the area that has become secondary forest, or at least has been through a recent phase of secondary forest growth. Most Amazonian soils are highly weathered and relatively nutrient poor, but the role of nutrients as a factor determining successional processes is unclear. Soils testing and chronosequence studies have yielded equivocal results regarding the possible role of nutrient limitation. The objective of this paper is to report the first two years' results of a nitrogen (N) and phosphorus (P) fertilization experiment in a 6-yr-old secondary forest growing on an abandoned cattle pasture on a clayey Oxisol. Growth of remnant grasses responded significantly to the N + P treatment, whereas tree biomass increased significantly following N-only and N + P treatments. The plants took up about 10% of the 50 kg P/ha of the first year's application, and recovery in soil fractions could account for the rest. The trees took up about 20% of the 100 kg N/ha of the first year's application. No changes in soil inorganic N, soil microbial biomass N, or litter decomposition rates have been observed so far, but soil faunal abundances increased in fertilized plots relative to the control in the second year of the study. A pulse of nitric oxide and nitrous oxide emissions was measured in the N-treated plots only shortly after the second year's application. Net N mineralization and net nitrification assays demonstrated strong immobilization potential, indicating that much of the N was probably retained in the large soil organic-N pool. Although P availability is low in these soils and may partially limit biomass growth, the most striking result of this study so far is the significant response of tree growth to N fertilization. Repeated fire and other losses of N from degraded pastures may render tree growth N limited in some young Amazonian forests. Changes in species composition and monitoring of long-term effects on biomass accumulation will be addressed as this experiment is continued.\n<br>\n\n#### ND02_Soil_Gases_REE_1117\nThis data set reports soil carbon dioxide (CO2) and nitrous oxide (N2O) concentrations and soil volumetric water content (VWC) from a rainfall exclusion experiment that was conducted at the km 67 Seca Floresta site, Tapajos National Forest, Brazil. Samples were collected every two to three months. The purpose was to observe the potential effects of severe water stress on a humid Amazonian forest (Nepstad 2002). Data provided are from December 9, 1999, and April 2, 2000-June 14, 2002. There is one comma-delimited data file with this data set.\n<br>\n\n#### ND02_REE_Trace_Gas_Tapajos_955\nMoist tropical forests in Amazonia and elsewhere are subjected to increasingly severe drought episodes through the El Nino-Southern Oscillation (ENSO) and possibly through deforestation-driven reductions in rainfall. The effects of this trend on tropical forest canopy dynamics, emissions of greenhouse gases, and other ecological functions are potentially large but poorly understood. We established a throughfall exclusion experiment in an east-central Amazon forest (Tapajos National Forest, Brazil) to help understand these effects. After 1-year intercalibration period of two 1-ha forest plots, we installed plastic panels and wooden gutters in the understory of one of the plots, thereby excluding similar to 890 mm of throughfall during the exclusion period of 2000 (late January to early August) and similar to680 mm thus far in the exclusion period of 2001 (early January to late May). Average daily throughfall reaching the soil during the exclusion period in 2000 was 4.9 and 8.3 mm in the treatment and control plots and was 4.8 and 8.1 mm in 2001, respectively. During the first exclusion period, surface soil water content (0-2 m) declined by similar to100 mm, while deep soil water (2-11 m) was unaffected. During the second exclusion period, which began shortly after the dry season when soil water content was low, surface and deep soil water content declined by similar to140 and 160 mm, respectively. Although this depletion of soil water provoked no detectable increase in leaf drought stress (i.e., no reduction in predawn leaf water potential), photosynthetic capacity declined for some species, the canopy thinned (greater canopy openness and lower leaf area index) during the second exclusion period, stem radial growth of trees <15 m tall declined, and fine litterfall declined in the treatment plot, as did tree fruiting. Aboveground net primary productivity (NPP) (stemwood increment and fine litter production) declined by one fourth, from 15.1 to 11.4 Mg ha(-1) yr(-1), in the treatment plot and decreased slightly, from 11.9 to 11.5 Mg ha(-1) yr(-1), in the control plot. Stem respiration varied seasonally and was correlated with stem radial growth but showed no treatment response. The fastest response to the throughfall exclusion, and the surface soil moisture deficits that it provoked, was found in the soil itself. The treatment reduced N2O emissions and increased CH4 consumption relative to the control plot, presumably in response to the improved soil aeration that is associated with soil drying. Our hypothesis that NO emissions would increase following exclusion was not supported. The conductivity and alkalinity of water percolating through the litter layer and through the mineral soil to a depth of 200 cm was higher in the treatment plot, perhaps because of the lower volume of water that was moving through these soil layers in this plot. Decomposition of the litter showed no difference between plots. In sum, the small soil water reductions provoked during the first 2 years of partial throughfall exclusion were sufficient to lower aboveground NPP, including the stemwood increment that determines the amount of carbon stored in the forest. These results suggest that the net accumulation of carbon in mature Amazon forests indicated by recent permanent plot and eddy covariance studies may be very sensitive to small reductions in rainfall. The soil water reductions were also sufficient to increase soil emissions of N2O and to increase soil consumption of CH4-both radiatively important gases in the atmosphere.The possible reduction of tree reproductive activity points to potentially important effects of drought on the long-term species composition of Amazon forests.\n<br>\n\n#### ND02_Soil_Gas_Flux_Apeu_953\nChanges in land-use and climate are likely to alter moisture and substrate availability in tropical forest soils, but quantitative assessment of the role of resource constraints as regulators of soil trace gas fluxes is rather limited. The primary objective of this study was to quantify the effects of moisture and substrate availability on soil trace gas fluxes in an Amazonian regrowth forest. We measured the efflux of carbon dioxide (CO2), nitric oxide (NO), nitrous oxide (N2O), and methane (CH4) from soil in response to two experimental manipulations. In the first, we increased soil moisture availability during the dry season by irrigation; in the second, we decreased substrate availability by continuous removal of aboveground litter. In the absence of irrigation, soil CO2 efflux decreased during the dry season while irrigation maintained soil CO2 efflux levels similar to the wet season. Large variations in soil CO2 efflux consistent with a significant moisture constraint on respiration were observed in response to soil wet-up and dry-down events. Annual soil C efflux for irrigated plots was 27 and 13% higher than for control plots in 2001 and 2002, respectively. Litter removal significantly reduced soil CO2 efflux; annual soil C efflux in 2002 was 28% lower for litter removal plots compared to control plots. The annual soil C efflux: litterfall C ratio for the control treatment (4.0-5.2) was consistent with previously reported values for regrowth forests that indicate a relatively large belowground C allocation. In general, fluxes of N2O and CH4 were higher during the wet season and both fluxes increased during dry-season irrigation. There was no seasonal effect on NO fluxes. Litter removal had no significant impact on N oxide or CH4 emissions. Net soil nitrification did not respond to dry-season irrigation, but was somewhat reduced by litter removal. Overall, these results demonstrate significant soil moisture and substrate constraints on soil trace gas emissions, particularly for CO2, and suggest that climate and land-use changes that alter moisture and substrate availability are therefore likely to have an impact on atmosphere chemistry.\n<br>\n\n#### ND02_REE_Soil_VWC_1061\nThis data set reports monthly measured soil volumetric water content (VWC) from a rainfall exclusion experiment that was conducted from 1999-2001 at the km 67 Seca Floresta site, Tapajos National Forest, Brazil. The purpose was to observe the potential effects of severe water stress on a humid Amazonian forest (Nepstad 2002). There are two ASCII comma delimited files with measured VWC, one for the control plot and one for the rainfall exclusion plot. These measured values were used by the authors to develop a model of daily changes in the distribution of water through the soil layers. The simulated daily VWC values are also provided in the file with the measured VWC. For comparison, results of VWC simulation for the control and treatment plots using a STELLA model which incorporates rainfall and plant water uptake are provided. There are two ASCII comma delimited files of simulated results. See Belk et. al., 2007 for details.\n<br>\n\n#### ND02_Water_Chemistry_Paragominas_1067\nThis data set includes measurements of dissolved nutrient and organic carbon concentrations, as well as dissolved oxygen, alkalinity, conductivity, turbidity, pH, and discharge from three streams located in mixed land use (crop fields, pastures, secondary vegetation, and forest) and two streams in entirely forested landscapes near Paragominas in the state of Para, Brazil. Stream water samples were collected during two different periods: 1) weekly from August 1999 to July 2001 at location Igarape 54, Station 5 and 2) monthly from April 2003 through October 2005 at all of the stations. The exact start date and suite of measurements vary by location. In addition, samples from precipitation collectors at the Paragominas Meteorological Station were measured for nutrient concentrations every two weeks from 1999 to 2001. There are two comma delimited ASCII data files with this data set.\n<br>\n\n#### ND03_Flowpath_Chemistry_1076\nThis data set consists of water chemistry data from streams, wells, rainwater, and canopy throughfall samples. The field measurements were carried out at Rancho Grande in the Brazilian state of Rondonia, in the southwestern Brazilian Amazon basin, at two adjacent watersheds, a forest (1.37 ha), and pasture (0.73 ha). Samples were collected during one entire rainy season starting in August 2004 and ending in April 2005. There is one comma-delimited data file with this data set.\n<br>\n\n#### ND03_Streams_Soilwater_1113\nThis data set provides the results of (1) the physical and chemical characterization of streams and (2) comparable chemical analyses of extracted soil water in the Aldeia River basin at Fazenda Nova Vida, a large cattle ranch 50 km from the city of Ariquemes, in central Rondonia, Brazil, from 1994-2001. Data are provided on the stream beds including cross-sectional depth and stream bed surface type. Stream discharge is reported. Streamwater was sampled and analyzed periodically over the eight year duration of the study at numerous steam locations. Soil solution samples were collected at the same frequency with lysimeters placed at 30 cm and 100 cm depths on the floodplain and at upland forest and pasture sites in the Aldeia River watershed. There are five comma-delimited data files in this data set.\n<br>\n\n#### ND04_Soil_H2O_Manaus_1246\nThis data set contains soil water measurements to a depth of 3 meters for the years 1999, 2000, and 2001, and total monthly precipitation data for 1999-2000. The data were collected from a pasture site located at the Embrapa Pasture Research Site, a former cattle research station 54 km north of Manaus on the highway BR 174 Manaus-Boa Vista, Brazil. There are three comma-separated data files (.csv) with this data set. DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS: There is no associated research documentation and the units were not provided with the data.\n<br>\n\n#### ND04_C_Nutrient_Stocks_1069\nThis data set reports the carbon and nutrient stocks of above-ground vegetation and soil pools at three locations where post-pasture secondary forest recovery ranged from 0 to 14 years since abandonment. These sites are located in the state of Amazonas, Brazil, along the road BR-174 north of the city of Manaus within three fazendas (cattle ranches) now in various stages of grazing, pasture abandonment, or pasture reclamation: Fazenda Rodao (km 46), Embrapa-District of SUFRAMA (DAS) pasture research site (km 53) and Fazenda Dimona (km 72). From September 2000 to July 2001, measurements were obtained for aboveground biomass (cite ND-04 Sec For Recovery), foliage and wood samples were collected and analyzed for total nutrient (C, N, P, K, Ca and Mg) concentrations, and soil samples from 0 to 45 cm depth were collected and analyzed for total nutrient (C, N, P, K, Ca and Mg) concentrations. Total carbon (C) and nutrient stocks were calculated for various vegetation and soil pools to gain an understanding of the dynamics of nutrient and C buildup in regenerating secondary forests in central Amazonia (Feldpausch et al., 2004). There are 2 comma-delimited ASCII data files with this data set.\n<br>\n\n#### ND04_Secondary_Forest_Recovery_1068\nThis data set reports measurements of the canopy and structure of secondary forests regenerating from abandoned pastures. These secondary forests are located in the state of Amazonas, Brazil, along the road BR-174 north of the city of Manaus within three fazendas (cattle ranches) now in various stages of grazing, pasture abandonment, or pasture reclamation: Fazenda Rodao (km 46), Embrapa-District of SUFRAMA (DAS) pasture research site (km 53), and Fazenda Dimona (km 72). Ten secondary forest study sites were selected within the three fazendas where post-pasture forest recovery ranged from 0 to 14 years since abandonment. From 2000-2001 estimates of leaf area index (LAI) and canopy cover were derived from hemispherical canopy digital photographs, and estimates of aboveground biomass and basal area were derived utilizing allometric equations from diameter at breast height (DBH) measurements. Estimates were classified by growth-form and diameter class. See Feldpausch et al. (2005) for more information. There are four comma-delimited data files with this data set and one companion file with information regarding the allometric equations relating diameter at breast height (for dbh > 5 cm) to dry weight for biomass calculations.\n<br>\n\n#### ND04_Termite_Mounds_1072\nThis data set reports the results of a comprehensive study of mound building termites at the Embrapa research station in the Distrito Agropecuario da SUFRAMA, located at km 53 of the federal highway BR 174 outside Manaus, Amazonas, Brazil. Study areas included a primary forest site, an adjacent 7-8 year old secondary forest site, and two abandoned pasture sites which were being used for agroforest purposes. Reported are (1) the termite species occurrence and areal abundance of mounds, (2) characterization of the mound soil microbiological community, root biomass, seedling emergence success, soil respiration, nitrogen mineralization, and (3) the characterization of the termite mound soil physical, chemical, and hydraulic properties. Analyses were also performed on samples from adjacent control soils for comparison. This data set contains 15 comma-delimited data files.\n<br>\n\n#### ND06_LandUse_Studies_1130\nThis data set provides measurements of soil properties compiled from 39 studies on nutrient dynamics in natural forests and forest-derived land uses (pasture, shifting cultivation and tree plantations) conducted in Amazonia over the period of 1950-2001. The initial literature survey for the data consisted of more than 100 studies conducted during this period. The objectives of this project were to compare soil data from major land uses across Amazonia and identify gaps in present knowledge that offer direction for future research. Five widely cited hypotheses were tested concerning the effects of land-use change on soil properties by analyzing data compiled from 39 studies in multi-factorial ANOVA models: ?¢ effective cation exchange capacity (ECEC), and exchangeable calcium (Ca) concentrations rise and remain elevated following the slash-and-burn conversion of forest to pasture or crop fields ¢ soil contents of total carbon (C), nitrogen (N), and inorganic readily (i.e., Bray, Mehlich I or resin) extractable phosphorus (Pi) decline following forest-to-pasture conversion ?¢ soil concentrations of total C, N, and Pi increase in secondary forests with time since abandonment from agricultural activities ?¢ soil nutrient conditions under all tree-dominated land-use systems (natural or not) remain the same ?¢ higher efficiencies of nutrient utilization occur where soil nutrient pools are lower There is one comma-delimited ASCII file (.csv) with this data set and a list of the 39 studies used in this data set provided as a companion file in text format.\n<br>\n\n#### ND07_15N_Leaves_Soils_1121\nThis data set provides (1) delta 15N ratios and nitrogen concentrations for foliar samples and (2) delta 13C and delta 15N ratios as well as carbon and nitrogen concentrations for soil samples collected from cerrado sites within the Ecological Reserve of the Brazilian Institute of Geography and Statistic (IBGE), Brasilia, Brazil. Foliar samples, collected from 320 individuals representing 45 woody tree and shrub species, and soil samples were collected from 5 cerrado locations (2 in campo sujo, 2 in cerrado denso and 1 in cerrado). Soil samples were collected to 450 cm depth in the campo sujo and 800 cm depth elsewhere. Samples were collected during the period December 1999 to September 2000. Eiten (1972) described campo sujo as an open savanna with scattered trees and shrubs, cerrado sensu stricto as a savanna woodland with abundant evergreen and deciduous trees and shrubs and an herbaceous understory, and cerrado denso as medium to tall woodlands with closed or semiclosed canopies (Bustamante et al., 2004). There are two comma-delimited data files with this data set.\n<br>\n\n#### ND07_Stream_Chemistry_Brasilia_1018\nThis data set reports on dissolved nutrient concentrations, as well as dissolved oxygen, alkalinity, conductivity, turbidity, and pH measured in water samples collected from nine streams located in the state of Brasilia, Brazil, between September, 2004 and December, 2006. Streams were located in different land cover types including natural (forest), rural (agricultural), and developed landscapes. In addition, water samples from wells, lysimeters, surface runoff, and precipitation were collected from four sites, 2 natural and 2 rural, and analyzed for nutrient concentrations. Streams were sampled every 2-4 weeks; rain water was collected approximately monthly during the wet season and once during a dry season; wells and lysimeters were sampled monthly; and surface runoff collections were event based. There are three comma-delimited data files with this data set.\n<br>\n\n#### ND07_PLFA_Soils_Microbial_Biomass_1017\nThis data set reports the microbial biomass in soil samples collected from the Cerrado, a woodlands-savannah area, in Brasilia, Brazil. Microbial biomass was determined as the total concentration of phospholipid fatty acids (PLFAs). Soil samples (0-5 cm) were collected from June, 2000 to June, 2001 in two native areas of Cerrado that were subjected to a range of fire regimes. Two plots were protected from fire since 1973, another two plots were subjected to prescribed fires every two years since 1992, and a fifth plot was in a 20 year-old active pasture (Brachiaria brizantha). The analyses were conducted to determine the effects of fire regimes and changes in vegetation cover on the microbial communities of Cerrada soils. There is one comma-separated ASCII data file with this data set.\n<br>\n\n#### ND07_NO_Flux_Cerrado_1124\nThis data set reports the results of soil nitric oxide (NO) flux, soil moisture, and soil nitrate (NO3) and ammonium (NH4) concentration measurements on Cerrado soils receiving nitrogen fertilization. Measurements and samples were collected from control and fertillized experimental plots on Cerrado soils within the Ecological Reserve of the Brazilian Institute of Geography and Statistic (IBGE), Brasilia, Brazil. Sampling dates were from March 26, 2004 to November 25, 2004. The soils had received nitrogen and phosphorus fertilization treatments which began in 1998. The objective of this project was to determine the long-term effects of nutrient addition (N and N+P) in native Cerrado area on N oxide fluxes from soil to the atmosphere. There is one comma delimited (.csv) ASCII file with this data set.\n<br>\n\n#### ND07_Trace_Gas_Land_Use_1016\nThis data set reports on soil-atmosphere fluxes of trace carbon dioxide, carbon monoxide, nitrous oxide, and nitric oxide (CO2, CO, N2O, NO) under various natural and manipulated land use conditions. The studies were conducted near Brasilia, Brazil in pastures and agricultural areas under a variety of management regimes and in more natural areas of cerrado (20-50% canopy cover) and campo sujo (open, grass-dominated), which were either burned every 2 years or protected from fire. Results provide data and relationships needed for regional trace gas models. There are nine comma-separated ASCII data files with this data set.\n<br>\n\n#### ND08_Biomass_Jari_1148\nThis data set reports the concentrations of the nutrients nitrogen (N), phosphorus (P), magnesium (Mg), calcium (Ca), and potassium (K) in roots, litterfall, leaves, and twigs, biomass of fine roots and litterfall, and the decomposition of leaves and twigs in samples that were collected on the property of Jari Celulose, Monte Dourado, Para, Brazil, from 1999-2001. Samples were collected from two study sites, a eucalyptus plantation and an adjacent primary forest, during both rainy and dry seasons. Roots were sampled from three depths (0-15 cm, 35-50 cm, and 85-100 cm). There are five comma-delimited data files with this data set. DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS: The data files do not identify the year in which samples were collected. The methods for nutrient, decomposition, and biomass sampling and analyses were not provided. The data file descriptions indicate that samples were collected from two soil types (sandy and clay) but there is no documentation of which data field provides that information. Also, there is no documentation for the Location or Block fields in the data files.\n<br>\n\n#### ND08_Soil_Respiration_1250\nThis data set provides (1) carbon (C) and nitrogen (N) concentration measurements of two soil aggregate fractions (250-2000 micon, small macro-aggregates (SMAG)), and (53-250 micron (micro-aggregates (mico)) and (2) in situ soil respiration measurements (January-March 2003) on sand and clay soils from a Eucalyptus plantation and an adjacent primary forest. The soils for fractionation were sampled in July 2001 from 0-20 cm and 30-50 cm depths. The research site was on the property of Jari Celulose, Monte Dourado, Para, Brazil. There are two files with this data set in comma-delimited (.csv) format.\n<br>\n\n#### ND10_Soil_Chemistry_1171\nThis data set provides the results of soil physical property and chemical measurements of samples collected from two pasture chronosequences (years since conversion from primary forest) located on two ranches south of Santarem, Para, Brazil, and east of the Tapajos River. Soil data includes soil classification, bulk density, texture, and mean concentrations of total nitrogen (N), carbon (C), phosphorus (P), and P fractions. The soils were high clay oxisols and highly sandy entisols. One chronosequence of sites was established on oxisol soils dating 2, 7, and 15 years since conversion from primary forest. A second set of sites, 1, 7, and 15 years old was established on the sandy entisols. Five of the six pasture sites were on a single ranch; the 2-year-old oxisol pasture was the exception. Ten soil samples per site were collected from 0-10 cm depth along random intervals within 100-m transects in August 1997. There are two comma-delimited (.csv) data files with this data set.\n<br>\n\n#### ND11_Veg_Biomass_MT_964\nNot available.\n<br>\n\n#### ND11_Logging_Damage_MT_977\nData were collected in the logging concession at the Fazenda Rohsamar in the municipality of Juruena in northwestern Mato Grosso. Estimates of damage associated with logging operations were made after logging operations were complete in 2003 and 2004. Damage associated with gaps created by felling single trees was estimated in 54 individual gaps. Characteristics of the single harvested tree were recorded and included species, DBH, commercial height, total height, and canopy proportions. Damage to all surrounding trees was recorded. Stratified transects in two logging blocks were used to estimate damage associated with road building and skid trails. Twenty-six transects were established in Block 5 and 21 transects in Block 18 to assess the frequency of damage by log skidders and tree felling. The boundaries between different types of damage were noted along the transect and the length in meters of that damage type along the transect was recorded. From this information, the area of the logging block affected by road building and skid trails was determined. The Gap Survey and the Logging Damage Transects Survey data are provided in comma-separated ASCII files. A third file provides the coordinates of the starting points for the Survey Transects.\n<br>\n\n#### ND11_Soil_Nitrate_Moisture_MT_976\nThis data set reports the results of the analysis of soil samples for Nitrate (NO3) and physical properties that were collected for one year following reduced impact logging in logging concessions at the Fazenda Rohsamar in the municipality of Juruena in northwestern Mato Grosso. Sample locations were randomly selected from stratified regions of the 1,400 ha Block 5 to account for local scale soil variability. Soil samples were collected to 8-m depth in (1) nine gaps formed by single tree removal and (2) nine areas of undisturbed primary forest. Areas of undisturbed forest were confined to patches of forest within Block 5 that were protected from logging. An additional 3 forested areas were sampled to 3-m depth that contained high sand content. These results quantified the effects of reduced impact logging, to test whether nitrogen (N) loss from leaves and coarse woody debris under reduced impact logging results in a significant accumulation of subsoil nitrate (Feldpausch et al., 2009). One comma separated data file contains the soil moisture results and a second file the soil NO3 content and soil physical properties.\n<br>\n\n#### ND11_Nitrogen_Transfer_Leaf_Litter_915\nIt has been proposed that the C/N ratio, or quality, of litter or mulch mixtures affects N release. Although total N release from these mixtures and the effects on soil N are relatively well understood, a mechanistic understanding of the interactions between litter species with respect to their N release is still lacking. This study examines decomposition and N dynamics in mixtures of high-quality leguminous mulch, gliricidia [Gliricidia sepium (Jacq.) Kunth. ex Walp.] with a C/N ratio of 13, and low-quality cupuacu [Theobroma grandiflorum (Wild. ex Spring) Schumann] litter with a C/N ratio of 42, which occur in combination in agroforestry systems. Ratios of 100:0, 80:20, 50:50, 20:80, 0:100 of fresh 15N-enriched gliricidia leaves and senescent cupuacu leaves, totaling the same dry weight of 6.64 t ha-1, were applied to an Oxisol and sampled at 6, 14, 38, and 96 days after application. After more than 40% of the N in the gliricidia leaves had been released and the microbial biomass N reached its peak, a significant increase in available soil N occurred at day 14, which was more pronounced with greater amounts of gliricidia in the leaf mixture. However, relative to the N applied in the leaf mixture, there was no significant difference in available soil N with greater proportions of gliricidia. Total N release from the mixtures corresponded to the total N applied by gliricidia. Until day 38, cupuaçu C mineralization was significantly faster in the presence of the highest proportion of gliricidia compared to lower proportions. This faster C mineralization of more than 0.5% per day, however, did not increase total C loss or N release from cupuaçu leaves after 96 days. The use of 15N tracers identified an N transfer from gliricidia leaves and the soil to cupuaçu leaves and consequently, a lower N release from gliricidia to the soil in the presence of cupuaçu leaves. Though we expected that available N in the soil would also decrease with greater amounts of cupuaçu litter in the mixture, our results indicated an additive effect of the two species on N release and soil mineral N, with gross interactions between them canceling net interactive effects. Therefore, N release of leaf mixtures behaved as predicted from a calculated sum of individual release patterns, in spite of a transfer of N from the high- to the low-quality leaves.\n<br>\n\n#### ND11_Carbon_Export_CPOM_913\nResolving the carbon (C) balance in the Amazonian forest depends on an improved quantification of production and losses of particulate C from forested landscapes via stream export. The main goal of this work was to quantify litterfall, the lateral movement of litter, and the export of coarse organic particulate matter (>2 mm) in four small watersheds (1-2 ha) under native forest in southern Amazonia near Juruena, Mato Grosso, Brazil (10°25 S, 58°46 W). Mean litterfall production was 11.8 Mg ha-1 y-1 (5.7 Mg C ha-1 y-1). Litterfall showed strong seasonality, with the highest deposition in the driest months of the year. About two times more C per month was deposited on the forest floor during the 6-mo dry season (0.65 Mg C ha-1 mo-1) compared to the rainy season (0.3 Mg C ha-1 mo-1). The measured C concentration of the litterfall samples was greater in the dry season than in the rainy season (49% vs. 46%, P < 0.05). The lateral movement of litter increased from the plateau (upper landscape position) towards the riparian zone. However, the trend in C concentration of laterally transported litter samples was the opposite, being highest on the plateau (44%) and lowest in the riparian zone (42%) (P < 0.05). Stream-water exports of particulate C were positively correlated with streamflow, increasing in the rainiest months. The export of particulate C in streamflow was found to be very small (less than 1%) in relation to the amount of litterfall produced.\n<br>\n\n#### ND11_Tree_Vine_Biomass_MT_922\nThe purpose of this study was to determine if spatially-explicit commercial timber inventories (CTI) could be used in conjunction with satellite imagery to improve timber assessments and forest biomass estimates in Amazonia. As part of a CTI, all commercial trees >= 45 cm DBH were measured and georeferenced in 3500 ha of a logging concession in NW Mato Grosso, Brazil. A scientific inventory was conducted of all trees and palms >= 10 cm DBH in 11.1 ha of this area. A total of > 20,000 trees were sampled for both inventories. To characterize vegetation radiance and topographic features, regional LANDSAT TM and ASTER images were obtained. Using a stream network derived from the ASTER-based 30 m digital elevation model (DEM), a procedure was developed to predict areas excluded from logging based on reduced impact logging (RIL) criteria. A topographic index (TI) computed from the DEM was used to identify areas with similar hydrologic regimes and to distinguish upland and lowland areas. Some timber species were associated with convergent landscape positions (i.e., higher TI values). There were significant differences in timber density and aboveground biomass (AGB) in upland (6.0 stems ha(-1), 33 Mg ha(-1)) versus lowland (5.4 stems ha(-1), 29 Mg ha(-1)) areas. Upland and lowland, and timber and non-timber areas could be distinguished through single and principal component analysis of LANDSAT bands. However, radiance differences between areas with and without commercial timber on a sub-hectare scale were small, indicating LANDSAT images would have limited utility for assessing commercial timber distribution at this scale. Assuming a 50 m stream buffer, areas protected from logging ranged from 7% (third order streams and above) to 28% (first order and above) of the total area. There was a strong positive relationship between AGB based on the scientific inventory of all trees and from the commercial timber, indicating that the CTI could be used in conjunction with limited additional sampling to predict total AGB (276 Mg ha(-1)). The methods developed in this study could be useful for facilitating commercial inventory practices, understanding the relationship of tree species distribution to landscape features, and improving the novel use of CTIs to estimate AGB.\n<br>\n\n#### ND11_Soil_Spatial_Variability_914\nThe northwestern region of Mato Grosso State consists of complex landscapes due to ongoing geomorphologic activity that contributes to the occurrence of different soil classes over small distances, which complicates soil sampling strategies. This study was conducted in Juruena (MT), with the objective of identifying pedologic classes in undisturbed forested headwater catchments by examining the spatial variability of soil texture and color, and taking elevation and topographic position into consideration. The spatial variability of soil texture and color were determined for 185 georeferenced sample points from a 20 x 20 m grid over the four headwater catchments. By sampling each location at depths of 0-20 and 40-60 cm it was possible to distinguish and map the principle soil classes found in the study area to the 2nd category level of the Brazilian System of Soil Classification, associated with the topographic relief. A satisfactory relationship between the redness index of the diagnostic horizons and the soil class colors was also found. The small headwater catchments contained the soil classes Plintossolos and Argissolos (of plinthic character) at altitudes below 280 m, and Latossolos at higher elevations. In this way, the use of geostatistics to map soil classes proved effective, as well as to estimate the precision of the resulting maps. Nevertheless, pedologic knowledge and a follow-up field validation of the resulting maps are necessary for an application as well as adjustments to the geostatistical models.\n<br>\n\n#### ND11_Soil_Water_Pressure_851\nThis data set contains information that can be used to examine water fluxes in soils beneath tree crops in an Amazonian agroforest. The data consists of repeated measurements of soil matrix pressure and soil moisture content at several depths. The study was carried out at the Empresa Brasileira de Pesquisa Agropecuaria (Embrapa)-Amazônia Ocidental, 29 km North of Manaus, Brazil (3° 8' S, 59° 52' W, 40 - 50 m above sea level), in 1998 and 1999.Microaggregated tropical soils have shown high water conductivity even under unsaturated conditions in laboratory experiments. It is not clear, however, what depth the infiltrating soil water reaches during storm events under humid tropical conditions. Dynamics and fluxes of water were determined with high temporal resolution to a depth of 5 m in a Xanthic Hapludox of central Amazonia, Brazil. The soil water percolated to a depth of 0.9 m within 2 h of a rainfall event of 48 mm. Water fluxes were significantly slower below 0.9 m (17% of infiltration at 0 - 0.9 m) due to higher bulk densities. Percolation not only started rapidly after a rainfall event when soil water suction reached a certain threshold (ca. 20 - 30 hPa) but was also reduced to background levels less than 1 h after the rain had ended. The demonstrated extremely short-term dynamics of water fluxes have implications for measurement design of water availability and solute leaching in microaggregated tropical soil that require correct time integrals of solution concentrations and soil water dynamics. Measurement intervals of 30 min or less were necessary in our study. Rapid water flows may explain the observed high nutrient losses from the topsoil of microaggregated tropical soil and the large accumulation of nutrients in the deep soil (> 5 m).\n<br>\n\n#### ND11_Stream_Nutrients_921\nThis data set contains baseflow streamwater concentrations of pH, specific conductivity, base cations, carbon (dissolved organic carbon (DOC), particulate organic carbon (POC) and bicarbonate alkalinity) and silica for four headwater streams in the seasonally dry Amazon (Johnson et al. (2006a) and Johnson et al. (2006b). Data are provided in one comma-separated ASCII file. This hydrologic study of four headwater watersheds was conducted in an undisturbed forest near Juruena, Mato Grosso in the seasonally dry, southern Amazon. The small catchments range in size from 0.85 to 1.9 ha. Stream water samples were collected weekly during rainy seasons and biweekly during the dry seasons. Baseflow stream water concentrations of base cations, silica, electrical conductivity, DOC, and alkalinity varied inversely with discharge. While there was variation among the watersheds, the concentration-discharge patterns were consistent for each of the four watersheds. Baseflow discharge data are not included in this data set and will be archived separately.\n<br>\n\n#### ND30_Pasture_Degradation_1164\nThis data set contains images of fractional cover estimates of photosynthetic vegetation (PV) canopy, nonphotosynthetic vegetation (NPV), and exposed soils (S) derived from Landsat images (30-m resolution) obtained for two ranches in the Brazilian Amazon from 1996 to 2002. The Fazenda Vitoria ranch is located in eastern Para near the city of Paragominas and is a mosaic of primary forest, logged forest, secondary forest, and pasture with moderately dissected topography. The Fazenda Nova Vida ranch is located in the state of Rondonia in western Amazonia and is a mosaic of primary forest, logged forest, and pastures. For Fazenda Vitoria, two dry-season Landsat images were obtained, subset, and analyzed. For Nova Vida three dry-season images and one end-of-wet-season image were obtained, subset, and analyzed. Spectral mixture analysis, which decomposes individual satellite pixels into constituent cover fractions of surface materials, was used with a general probabilistic modeling approach to derive subpixel cover fractions of PV, NPV, and S. There are six GeoTIFF (.tif) files with this data set.\n<br>\n\n#### ND30_Litter_Para_1129\nThis data set provides fine litterfall mass and nutrient concentrations from samples collected at chronosequences established at Sao Francisco do Para and Capitao Poco, Para, Brazil. Nitrogen (N) and phosphorus (P) concentrations were determined for litterfall samples from the Sao Francisco do Para, and N, P, potassium (K), calcium (Ca), and magnesium (Mg) concentrations are reported for samples from the Capitao Poco. In addition, carbon (C), N, delta C13, and delta N15 values were determined for leaves from the dominant species of the forests at Sao Francisco do Para; soil physical and chemical characteristics were determined for a subset of the chronosequence plots at the two study sites; and soil trace gas fluxes were determined from the Sao Francisco do Para site. All samples were collected between March 2001-February 2005. Trace gas fluxes were measured 10 times between October 2000 and June 2002 with 5 sample periods in dry season and 5 in wet season months. There are five comma-delimited data files with this data set.\n<br>\n\n#### ND30_REE_Water_Chemistry_1131\nThis data set reports the results of chemical analyses of rainfall, throughfall, litter leachate, and soil water samples collected before, during, and after a rainfall exclusion experiment conducted at the km 67 Seca Floresta site, Tapajos National Forest, Brazil. Samples were collected every two weeks from May 17, 1999 through May 10, 2006. Measurements included alkalinity, conductivity, pH, and selected anions and cations analyzed by ion chromatography. The exclusion treatment, began in late January 2000 and continued through December 2004, involved diverting about 60% of throughfall (equivalent to approximately half the rainfall) from a 1-hectare plot using plastic panels installed in the understory. The comparable 1-hectare control plot was unaltered. The purpose was to observe the potential effects of severe water stress on a humid Amazonian forest (Nepstad et al., 2002 and Nepstad et al., 2007). There are five comma-delimited data files with this data set.\n<br>\n\n#### TG02_Balloon_VOC_1110\nThis data set reports concentrations of biogenic volatile organic compounds (BVOCs) collected from tethered balloon-sampling platforms above selected forest and pasture sites in the Brazilian Amazon in March 1998, February 1999, and February 2000. The air samples were collected from forested sites in Brazil: the Tapajos forest (Para) in the Tapajos/Xingu moist forest; Balbina (Amazonas) in the Uatuma moist forest; and Jaru (Rondonia) in the Purus/Madeira moist forest. Two other sites were also located in Rondonia: at a forest reserve (Rebio Jaru) and a pasture (Fazenda Nossa Senhora Aparecida). The BVOCs measured included isoprene, alpha and beta pinene, camphene, sabinene, myrcene, limonene, and other monoterpenes. Approximately 24 to 40 soundings, including as many as four VOC samples collected simultaneously at various altitudes, were made at each site. There is one comma-delimited data file with this data set.\n<br>\n\n#### TG03_AERONET_AOT_1128\nThis data set includes aerosol optical thickness measurements from the CIMEL sunphotometer for 22 sites in Brazil during the period from 1993-2005. The AERONET (AErosol RObotic NETwork) program is an inclusive federation of ground-based remote sensing aerosol networks established by AERONET and the PHOtométrie pour le Traitement Opérationnel de Normalisation Satellitaire (PHOTONS) and greatly expanded by AEROCAN (the Canadian sunphotometer network) and other agency, institute and university partners. The goal is to assess aerosol optical properties and validate satellite retrievals of aerosol optical properties. The network imposes standardization of instruments, calibration, and processing. Data from this collaboration provides globally distributed observations of spectral aerosol optical depths, inversion products, and precipitable water in geographically diverse aerosol regimes. Three levels of data are available from the AERONET website: Level 1.0 (unscreened), Level 1.5 (cloud-screened), and Level 2.0 (cloud-screened and quality-assured). Data provided here are Level 2.0. There are 22 comma-delimited data files with this data set and one companion text file which contains the latitude, longitude, and elevation of the 22 sites.\n<br>\n\n#### TG03_Aeronet_Solar_Flux_1137\nThis data set includes solar surface irradiance from Kipp and Zonen CM-21 pyranometers, both total unfiltered and filtered (RG695), and photosynthetically active radiation (PAR) from Skye-Probetech SKE-510 PAR sensors. Measurements were made at six sites acrosss the Brazilian Amazon during the period from 1999 to 2004. These sites were co-located with AERONET (AErosol RObotic NETwork) program sites. There are 17 comma-delimited data files (.csv) with this data set. The AERONET program is an inclusive federation of ground-based remote sensing aerosol networks established by AERONET and the PHOtometrie pour le Traitement Operationnel de Normalisation Satellitaire (PHOTONS) and greatly expanded by AEROCAN (the Canadian sunphotometer network) and other agency, institute and university partners. The goal is to assess aerosol optical properties and validate satellite retrievals of those properties. The network imposes standardization of instruments, calibration, and processing.\n<br>\n\n#### TG05_CASA_1199\nThis data set provides maps produced from model output data from the National Aeronautics and Space Administration-Carnegie Ames Stanford Approach (NASA-CASA) model and other modeling approaches. The maps include estimated annual Net Primary Production (ANPP), leaf (live) biomass carbon, wood (live) biomass carbon, fine root (live) biomass carbon, metabolic leaf litter (dead) carbon, structural leaf litter (dead) carbon, woody detritus (dead) carbon, and slow soil carbon, gridded at half-degree spatial resolution for the years 1982-1998, and 2001 (NPP data) for Brazil. Maps are provided at one-degree resolution for monthly soil emissions and soil uptake of N2O, NO, CO, and CH4. In addition, there are maps in 8-km resolution for soil texture, soil carbon, soil pH, soil maximum plant available water (paw), and net primary productivity (NPP). There are three files with this data set in tar.gz format. The files are in half-degree, one-degree, and 8-km resolution. When expanded, the half degree and one degree files contain 83 map files in GeoTIFF (.tif) format. The third file (8-km resolution) contains the soil and productivity maps. When expanded, this file contains 22 files in GeoTIFF (.tif) format.\n<br>\n\n#### TG06_Vertical_Profiles_1175\nThis data set contains measurements of atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), hydrogen (H2), nitrous oxide (N2O), and sulfur hexafluoride (SF6) collected from December 2000-November 2005 as vertical profiles above three sites in Brazil: Fortaleza, Santarem, and Manaus. At Santarem, ascending profiles were made above the Tapajos National Forest, near the km 67 Tower Site. At Manaus, ascending profiles were made above the K34 flux tower (aka, ZF2 km 34 tower) to the northwest of the city of Manaus. Descending profiles were flown nearby, but at locations upwind of population centers to avoid possible pollution. Fortaleza samples were collected off the coast, over the Atlantic Ocean to sample background air before it flows over the Amazon Basin. Air samples were collected as discrete samples aboard light aircraft and shipped to laboratories for analysis relative to internationally accepted calibration standards. There are three comma-delimited (.csv) data files with this data set.\n<br>\n\n#### TG07_Fallen_Standing_Necromass_998\nThis data set reports the characterization of fallen necromass as the volume and density of coarse woody debris (CWD), and standing necromass as the volume and density of standing dead trees. Measurements were made in undisturbed and logged forest areas of the Tapajos National Forest, and Cauaxi Forest, Para, Brazil, and Juruena Forest, Mato Grosso, Brazil from 2002-2004. Fallen and standing necromass were classified into one of five categories according to its state of decomposition. There are two comma-delimited ASCII data files with this data set: two files contain the sampling information, decomposition state, and DBH measurements. There are also two files provided as companion data files which provide sampling transect descriptions.\n<br>\n\n#### TG07_Soil_Nutrients_1085\nThis data set reports phosphorus (P), carbon (C), and nitrogen (N) nutrient pool concentrations for forest soils and roots and P pool concentrations for forest floor litter, soil solutions, and microbial extracts. Soils samples were also extracted using the Hedley sequential fractionation method and the extracts analyzed for P. Nutrient pool concentrations are presented on an areal basis of 1 hectare to a depth of 10 cm, as calculated from soil bulk densities and respective pool biomass quantities. There is one comma-delimited ASCII file with this data set. These measurements were made during a soil P addition fertilization experiment conducted at the km 83 site, Tapajos National Forest, Para, Brazil. Control and fertilized plots were established in both sandy loam and clay soils. Soil cores were collected every 4 months from August 1999 through April 2000 (McGroddy et al. 2008).\n<br>\n\n#### TG07_STM_GLAS_836\nThis data set provides the results of a GLAS (the Geoscience Laser Altimeter System) forest structure validation survey conducted in Santarem and Sao Jorge, Para during November 2004 (Lefsky et al., 2005). DBH, total height, commercial height, canopy width and canopy class description were measured for 11 primary forest sites in Santarem along two 75m transects per GLAS measurement. For 10 secondary forest sites in Sao Jorge, the number of stems 0-2cm, 2-5cm, 5-10cm, and greater than 10cm were measured. For all stems greater than 10cm the DBH was measured, and for all sites, the maximum height was recorded. The basal area was calculated for all trees with DBH greater than 10cm within our transects, and biomass was calculated using the Brown, 1997 formula.Exchange of carbon between forests and the atmosphere is a vital component of the global carbon cycle. Satellite laser altimetry has a unique capability for estimating forest canopy height, which has a direct and increasingly well understood relationship to aboveground carbon storage.\n<br>\n\n#### TG07_FFT_Survey_Km83_923\nChanges in the biomass of Amazon region forests represent an important component of the global carbon cycle but the biomass of these forests remains poorly quantified. We examined forest survey data for trees with a diameter at breast height (DBH) greater than 35 cm from 4 plots with a total area of 392 ha in the Tapajos National Forest near Santarem, Para, Brazil (S 3.04, W 54.95). The average frequency of trees greater than 35 cm DBH was approximately 55 ha^-1. Based on tree diameter data, allometric relations, and published relations for biomass in other compartments besides trees of DBH > 35 cm, we estimated a total biomass density of 372 Mg ha^-1. Trees with diameters greater than 35 cm DBH accounted for about half of the total biomass. This estimate includes all live and dead plant material above and below ground with the exception of soil organic matter. We propagated errors in sampling and those associated with allometric relations and other ratios used to estimate biomass of roots, lianas and epiphytes, and necromass. The major sources of uncertainty in our estimate were found in the allometric relations for trees with DBH greater than 35 cm, in the estimates of numbers of trees with DBH less than 35 cm, and in root biomass. In the worst case, we estimate an uncertainty in this value of about 40%. Simulated sampling based on our full survey, suggests that we could have estimated mean biomass per hectare for trees (DBH greater than 35 cm) to within 20% with 95% confidence by sampling 21 randomly selected 0.25 ha plots in our study area.\n<br>\n\n#### TG07_Litter_Decomposition_925\nOnce the weathering of parent material ceases to supply significant inputs of phosphorus (P), vegetation depends largely on the decomposition of litter and soil organic matter and the associated mineralization of organic P forms to provide an adequate supply of this essential nutrient. At the same time, the decomposition of litter is often characterized by the immobilization of nutrients, suggesting that nutrient availability is a limiting factor for this process. Immobilization temporally decouples nutrient mineralization from decomposition and may play an important role in nutrient retention in low-nutrient ecosystems. In this study, we used a common substrate to study the effects of native soil P availability as well as artificially elevated P availability on litter decomposition rates in a lowland Amazonian rain forest on highly weathered soils. Although both available and total soil P pools varied almost three fold across treatments, there was no significant difference in decomposition rates among treatments. Decomposition was rapid in all treatments, with approximately 50% of the mass lost over the 11-month study period. Carbon (C) and nitrogen (N) remaining and C:N ratios were the most effective predictors of amount of mass remaining at each time point in all treatments. Fertilized treatments showed significant amounts of P immobilization (P < 0.001). By the final collection point, the remaining litter contained a quantity equivalent to two-thirds of the initial P and N, even though only half of the original mass remained. In these soils, immobilization of nutrients in the microbial biomass, late in the decomposition process, effectively prevents the loss of essential nutrients through leaching or occlusion in the mineral soil.\n<br>\n\n#### TG07_Root_Mortality_Longterm_1116\nThis data set reports measurements of trace gas fluxes of methane (CH4), nitric oxide (N2O), nitrous oxide (NO), carbon dioxide (CO2) from soils at a study site in the Tapajos National Forest (TNF), near the km 83 on the Santarem-Cuiaba Highway south of Santarem, Para, Brazil. Data for root mass and carbon content, soil nitrogen (N), nitrification, and moisture content are also provided. There are five comma-delimited data files with this data set. The research was conducted to test the effects of root mortality on the soil-atmosphere trace-gas fluxes over the course of one year. Root mortality was induced by isolating blocks of land to 1 m depth using trenching and root exclusion screening. Gas fluxes were measured weekly for ten weeks following the trenching treatment and monthly for the remainder of the year. Note: The related data set LBA-ECO TG-07 Soil Trace Gas Flux and Root Mortality, Tapajos National Forest contains the same flux data that were measured weekly for ten weeks following the trenching treatment. This data set also provides the monthly data for the remainder of the year.\n<br>\n\n#### TG07_Trace_Gas_Profiles_1107\nThis data set provides concentrations of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from air samples collected at several heights on towers at three locations in upland old growth forests in the Brazilian Amazon during the wet and dry seasons of 2004 and 2005. Towers are located in the Caxiuana National Forest, in the state of Amazonas; the Manaus, Para, site in the Cuieiras Reserve; and the Sinop site, located north of that city in the state of Mato Grosso. Two sampling campaigns were conducted at each location. Samples were collected from each height 3-5 times on several nights and at least once during well-mixed daytime conditions during each campaign for a total of 75 profiles on 19 dates. There is one comma-delimited ASCII file with this data set.\n<br>\n\n#### TG07_Autochamber_Soil_CO2_Flux_Km67_927\nThe net ecosystem exchange of carbon dioxide was measured by eddy covariance methods for 3 years in two old-growth forest sites near Santarem, Brazil. Carbon was lost in the wet season and gained in the dry season, which was opposite to the seasonal cycles of both tree growth and model predictions. The 3-year average carbon loss was 1.3 (confidence interval: 0.0 to 2.0) megagrams of carbon per hectare per year. Biometric observations confirmed the net loss but imply that it is a transient effect of recent disturbance superimposed on long-term balance. Given that episodic disturbances are characteristic of old-growth forests, it is likely that carbon sequestration is lower than has been inferred from recent eddy covariance studies at undisturbed sites.\n<br>\n\n#### TG07_Root_Mortality_Experiment_924\nWe conducted an experiment on sand and clay tropical forest soils to test the short-term effect of root mortality on the soil-atmosphere flux of nitrous oxide, nitric oxide, methane, and carbon dioxide. We induced root mortality by isolating blocks of land to 1 m using trenching and root exclusion screening. Gas fluxes were measured weekly for ten weeks following the trenching treatment. For nitrous oxide there was a highly significant increase in soil-atmosphere flux over the ten weeks following treatment for trenched plots compared to control plots. N2O flux averaged 37.5 and 18.5 ng N cm-2 h-1 from clay trenched and control plots and 4.7 and 1.5 ng N cm-2 h-1 from sand trenched and control plots. In contrast, there was no effect for soil-atmosphere flux of nitric oxide, carbon dioxide, or methane.\n<br>\n\n#### TG07_Manual_Flux_Km67_1026\nTrace gas fluxes of carbon dioxide, methane, nitrous oxide, and nitric oxide (CO2, CH4, N2O, and NO) from surface soil were measured manually in an undisturbed forest at the Tapajos National Forest Seca-Floresta Site, which is within the footprint of the km 67 eddy flux tower. Measurements were made in January 2000 through April 2004, approximately twice per month. On each sampling date, up to four sets of 30-m lines were established off the existing transects at the Seca-Floresta site. Along each line eight chambers were installed for gas collection. In addition soil samples were collected for analysis of soil moisture as water-filled pore space (WFPS). There is one comma-delimited ASCII file with this data set.\n<br>\n\n#### TG07_Soil-Atmosphere_Flux_Km83_926\nSelective logging is an extensive land use in the Brazilian Amazon region. We studied the soil-atmosphere fluxes of nitrous oxide (N2O), nitric oxide (NO), methane (CH4), and carbon dioxide (CO2) on two soil types (clay Oxisol and sandy loam Ultisol) over two years (2000-2001)in both undisturbed forest and forest recently logged using reduced impact forest management in the Tapajos National Forest, near Santarem, Para, Brazil. In undisturbed forest, annual soil-atmosphere fluxes of N2O (mean +/- standard error) were 7.9 +/- 0.7 and 7.0 +/- 0.6 ng N cm-2 h-1 for the Oxisol and 1.7 +/- 0.1 and 1.6 +/- 0.3 ng N cm-2 h-1 for the Ultisol for 2000 and 2001 respectively. The annual fluxes of NO from undisturbed forest soil in 2001 was 9.0 +/- 2.8 ng N cm-2 h-1 for the Oxisol and 8.8 +/- 5.0 ng N cm-2 h-1 for the Ultisol. Consumption of CH4 from the atmosphere dominated over production on undisturbed forest soils. Fluxes averaged -0.3 +/- 0.2 and -0.1 +/- 0.9 mg CH4 m-2 d-1 on the Oxisol and -1.0 +/- 0.2 and -0.9 +/- 0.3 mg CH4 m-2 d-1 on the Ultisol for years 2000 and 2001. For CO2 in 2001, the annual fluxes averaged 3.6 +/- 0.4 :mol m-2 s-1 on the Oxisol and 4.9 +/- 1.1 :mol m-2 s-1 on the Ultisol. We measured fluxes over one year each from two recently logged forests on the Oxisol in 2000 and on the Ultisol in 2001. Sampling in logged areas was stratified from greatest to least ground disturbance covering log decks, skid trails, tree-fall gaps, and forest matrix. Areas of strong soil compaction, especially the skid trails and logging decks were prone to significantly greater emissions of N2O, NO, and especially CH4. In the case of CH4, estimated annual emissions from decks reached extremely high rates of 531 +/- 419 and 98 +/- 41 mg CH4 m-2 d-1, for Oxisol and Ultisol sites respectively, comparable to wetland emissions in the region. We calculated excess fluxes from logged areas by subtraction of a background forest matrix or undisturbed forest flux and adjusted these fluxes for the proportional area of ground disturbance. Our calculations suggest that selective logging increases emissions of N2O and NO from 30% to 350% depending upon conditions. While undisturbed forest was a CH4 sink, logged forest tended to emit methane at moderate rates. Soil-atmosphere CO2 fluxes were only slightly affected by logging. The regional effects of logging cannot be simply extrapolated based upon one site. We studied sites where reduced impact harvest management was used while in typical conventional logging ground damage is twice as great. Even so, our results indicate that for N2O, NO, and CH4, logging disturbance may be as important for regional budgets of these gases as other extensive land use changes in the Amazon such as the conversion of forest to cattle pasture.\n<br>\n\n#### TG07_DBH_Cauaxi_1063\nCanopy measurements in an undisturbed eastern Amazon forest (Cauxi, Para, Brazil. See Figure 1) were derived from a one-time event in 2000 using a hand-held laser range finder, and diameter at breast height (DBH) was determined manually. Parameters reported include: Crown Width, Crown Depth, Tree Height, and DBH. There is one comma-delimited ASCII data file with this data set. In addition, these manually derived measurements were compared to the IKONOS satellite data of crown dimensions that were acquired on 2 November 2000, from an orbital altitude of 680 km. The data from a 600 x 600 m block of undisturbed forest, including the 50 ha area surveyed in the field, were analyzed in a combined image processing and geographic information system environment. DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS: Only the general location for this study was identified -- Cauaxi, Para, Brazil. The tree measurement data are of limited use because coordinates for the study site, coordinates of the beginning and end of the transects, and coordinates of the measured trees were not provided. Also, the area that the IKONOS image captured was not provided and the IKONOS image is not available due to restricted distribution.\n<br>\n\n#### TG08_Soil_Gas_Fertilization_1105\nThis data set provides nitric oxide (NO), nitrous oxide (N2O), carbon dioxide (CO2) flux measurements, nitrogen (N) and phosphorus (P) pools, net N mineralization and nitrification rates, and measurements of soil moisture, in response to nitrogen and phosphorus soil fertilization treatments. The research was conducted in a mature moist tropical forest and an 11-year pasture at Nova Vida in Rondonia, in the Brazilian Amazon, in 1998 and 1999. There is one comma-delimited ASCII data file with this data set.\n<br>\n\n#### TG08_Soil_Gas_Wetting_1101\nThis data set includes the results of measurements of the soil gas fluxes of nitric oxide (NO), nitrous oxide (N2O), and carbon dioxide (CO2), soil moisture, soil temperature, and soil pools of ammonium and nitrate in response to a simulated rain event. Study sites were soils in mature forests and pastures of two ages (11 and 26 yrs old). The study took place during the dry season in August 1998 at Fazenda Nova Vida, Rondonia in the Brazilian Amazon. There is one comma-delimited ASCII file with this data set. This study investigated how changes in soil moisture (i.e., rains at the end of the dry season) affected the fluxes of NO, N20 and CO2 from forest and pasture soils in the southwestern Brazilian Amazon (Garcia-Montiel, et al., 2003). The main objectives were to measure the short-term dynamics of soil emissions of NO, N20, and CO2 in forest and pasture soils associated with soil wetting after prolonged dryness; and quantify the contribution of the pulses of N oxide fluxes resulting from soil wetting to dry season and annual fluxes.\n<br>\n\n#### TG09_N2O_Soils_1013\nThis data set reports the results of carbon, nitrogen, and oxygen isotopic analyses of soil, soil water, and N2O soil gas samples; total soil carbon and nitrogen concentrations; and soil texture and bulk density. Samples were collected from the km 83 Logged Forest Tower Site and the km 67 Seca-Floresta Site in the Tapajos National Forest (TNF) near Santarem, Para, Brazil. Soil samples were collected in July of 2000 and soil gas samples were collected in 2001 and 2002. Soil and gas samples were collected from various soil types at each site and from several depths in specially constructed pits. There is one comma-delimited ASCII data file with this data set.\n<br>\n\n#### TG10_TROFFEE_1195\nThis data set provides derived emission factors (EFs), reported in grams of compound emitted per kilogram of dry fuel (g/kg), for PM10 (particulate matter up to 10 micrometers in size), O3, CO2, CO, NO, NO2, HONO, HCN, NH3, OCS, DMS, CH4, and up to 48 non-methane organic compounds (NMOC) from the Tropical Forest and Fire Emissions Experiment (TROFFEE). TROFFEE used laboratory measurements followed by airborne and ground based field campaigns in Mato Grosso, Para, and Amazonas, Brazil during the 2004 Amazon dry season to quantify the emissions from pristine tropical forest and several plantations as well as the emissions, fuel consumption, and fire ecology of tropical deforestation fires. EFs were determined for 19 tropical deforestation fires in August and September, 2004. The combined output of these fires created a massive megaplume more than 500-km wide and covered a large area in Brazil, Bolivia, and Paraguay for about one month. For the megaplume, the EFs (reported in grams of compound emitted per kilogram of dry fuel (g/kg)) represented the effective emissions factor measured downwind from the source. There are two comma-delimited data files (.csv) and one text file (.txt) with this data set. The text file contains information regarding the fuel/fire sources, latitude and longitudes (also provided in the data files).\n<br>\n\n#### PC06_ECMWF_LBA_1141\nThis data set provides the mean diurnal cycle of precipitation, near-surface thermodynamics, and surface fluxes generated from short-term forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The model outputs were 12- to 36-hour short-range forecasts, run at a triangular truncation of T319 and a vertical resolution of 60 levels, from each daily 1200 (UTC) analysis. The version of the forecast model used to prepare this data product was the operational ECMWF model in fall 2000, which included the tiled land-surface scheme (TESSEL) (Van den Hurk et al., 2000) and recent revisions to the convection, radiation, and cloud schemes described by Gregory et al., (2000). The ECMWF model was run for two Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) campaigns conducted in Rondonia, Brazil, during January and February of 1999: the Wet Season Atmospheric Mesoscale Campaign (WETAMC) and the Tropical Rainfall Measuring Mission (TRMM). See Silva Dias et al.,(2002) for additional information regarding the WETAMAC and TRMM campaigns. There are two comma-delimited data files with this data set: the ECMWF model output data and a file containing the mean hourly precipitation observations used to check the model output for biases.\n<br>\n\n#### ABLE_897\nThe ABLE 2A and 2B (Atmospheric Boundary Layer Experiments) data consists of estimates of the rate of exchange of a wide variety of aerosols and gases between the Amazon Basin and its atmospheric boundary layer, and the processes by which these aerosols and gases are moved between the boundary layer and the free troposphere. The data are presented in gzipped ASCII text files in Global Tropospheric Experiment (GTE) format. The ABLE-2 project consisted of two expeditions: the first in the Amazonian dry season (ABLE-2A, July-August 1985); and the second in the wet season (ABLE-2B, April-May 1987). The ABLE-2 core research data were gathered by NASA Electra aircraft flights that stretched from Belem, at the mouth of the Amazon River, west to Tabatinga, on the Brazil-Colombia border, from a base at Manaus in the heart of the forest. See Figure 1. These observations were supplemented by ground based chemical and meteorological measurements in the dry forest, the Amazon floodplain, and the tributary rivers through use of enclosures, an instrumented tower in the jungle, a large tethered balloon, and weather and ozone sondes. This study showed air above the Amazon jungle to be extremely clean during the wet season but air quality deteriorated dramatically during the dry season as the result of biomass burning, performed mostly at the edges of the forest. Biomass burning is also a source of greenhouse gases carbon dioxide and methane, as well as other pollutants (carbon monoxide and oxides of nitrogen). Amazonian ozone deposition rates were found to be 5 to 50 times higher than those previously measured over pine forests and water surfaces. The Amazon River floodplain is a globally significant source of methane, supplying about 12% of the estimated worldwide total from all wetlands sources. Over Amazonia, carbon monoxide is enhanced by factors ranging from 1.2 to 2.7 by comparison with adjacent regions due to isoprene oxidation and biomass burning. Over the rainforest individual convective storms transport 200 megatons of air per hour, of which 3 megatons is water vapor that releases 100,000 megawatts of energy into the atmosphere through condensation into rain. The ABLE was a collaboration of U.S. and Brazilian scientists sponsored by NASA and Instituto Nacional de Pesquisas Espaciais (INPE) and supported by the Global Tropospheric Experiment (GTE) component of the NASA Tropospheric Chemistry Program.\n<br>\n\n#### ARME_898\nThe Amazonian Region Micrometeorological Experiment (ARME) data contain micrometeorological data (climate, interception of precipitation, mircometeorology and soil moisture) on the elements of the energy balance and evapotranspiration for the Amazonian forest. ASCII text data files for each of the four data types have been zipped toghether. One of the many scientific findings of this experiment was that tropical forest does not experience water stress due to the lack of precipitation, during periods when evapotranspiration is at the potential rate (Shuttleworth, 1988). ARME data types include climate (meteorological), interception of precipitation, micrometeorology, and soil moisture. These data are described in the Data Description section below.\n<br>\n\n#### Pre_LBA_ABRACOS_899\nThe data set presents the principal data from the Anglo-BRazilian Amazonian Climate Observation Study (ABRACOS) (Gash et al, 1996) and provides quality controlled information from five of the study topics considered by the project in five zipped files containing ASCII text data. The five study topics include Micrometeorology, Climate, Carbon Dioxide and Water Vapor, Plant Physiology, and Soil Moisture. The objectives of the ABRACOS were to monitor Amazonian climate and improve the understanding of the consequences of deforestation and to provide data for the calibration and validation of GCMs and GCM sub-models of Amazonian forest and post-deforestation pasture (Shuttleworth et al, 1991). Three areas were instrumented, each with different soils, dry season intensities and deforestation densities (Gash et al, 1996). In each area, an automatic weather station and soil moisture measurement equipment were installed: in a primary forest site and in nearby cattle pasture, for monitoring climate and soil status throughout the year. Additional intensive periods of study (or Missions), of varying duration, were operated at these sites: for calibration purposes, to understand the physical processes relevant to each site, and for detailed comparisons between sites. These data were collected under the ABRACOS project and made available by the UK Institute of Hydrology and the Instituto Nacional de Pesquisas Espaciais (Brazil). ABRACOS is a collaboration between the Agencia Brasileira de Cooperacao and the UK Overseas Development Administration. The processed, quality controlled and integrated data in the documented Pre-LBA data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually.\n<br>\n\n#### CABARE_918\nSurface parameter digital maps of vegetation, soil, and topography were obtained for Rondonia, Brazil, covering the 5x5 degree region bounded by 13-8 degrees S and 65-60 degrees W. Numerical maps of the natural landscape structure were prepared by digitizing existing 1:1,000,000 maps. Satellite data give information about the most recent modifications of the surface due to human activities. This mapping work was the first step of a mesoscale meteorological modeling program (Calvet et al., 1997) in forested and deforested Southwestern Amazonia (Rondonia, Brazil). This work was performed in the framework of a research program (CABARE) supported by the European Union, CEC Environment Program. Data are provided in ArcGIS ArcInfo grid ascii format for the following surface parameters: Elevation of terrain of the Rondonia region (altitude.txt) LANDSAT-derived vegetation classification of the Rondonia region in 1993-1994 (classify.txt) Soil classification of the Rondonia region (soil.txt) Sand and Clay of the Rondonia region (sand.txt and clay.txt) Vegetation classification of the Rondonia region from RADAMBRASIL (Macedo et al., 1979) (vegetation.txt)\n<br>\n\n#### CAMREX_904\nThe objective of CAMREX (Carbon in the Amazon River Experiment) project which was conducted from 1982 through 1991, was been to define by mass balances and direct measurements those processes which control the distribution of bioactive elements (C, N, P and O) in the mainstem of the Amazon River in Brazil. The CAMREX dataset represents a time series unique in its length and detail for very large river systems. The central sampling strategy has been to obtain representative flux-weighted water samples for comprehensive chemical analysis and to make rate measurements over 18 different sites within a 2000 km reach of the Brazilian Amazon mainstem, including major intervening tributaries. Samples have now been collected on 13 different cruises (1982-1991) during contrasting hydrographic stages. Data or images are provided for (1) water chemistry, (2) daily river discharge, (3) monthly estimates for 1989 of some model drivers and structure including NPP, Evapotranspiration, Precipitation, Temperature, and AVHRR data, (4) daily precipitation, and (5) air temperature anomalies. The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually.\n<br>\n\n#### FLOODAMA_903\nThis data set provides a digital mosaic of the Amazon River floodplain that was compiled using Landsat TM images. This mosaic was planned in July 1995 as an activity of the EOS-IDS Project that was developed with cooperation among INPE, CENA, University of Washington in Seattle (UW), University of California in Santa Barbara (UCSB), and NASA. The mosaic is composed by 29 Landsat TM images covering a period from 1986 to 1995 that were selected with minimum cloud cover and within the July to September high water season of the Amazon River. These images were geometrically corrected using ground control points extracted from topographic charts and image charts at the 1:250,000 scale. In addition, these images were radiometrically rectified to 231/062 (Manaus region) TM image using the method developed by Hall et al. (1991). The radiometric rectification applied had a good performance for bands 3, 5, and 7, for most of the scenes. For bands 1 and 2 the radiometric rectification was limited, especially for scenes with intense haze. Nevertheless, the overall performance of radiometric normalization allowed the production of a uniform data set for the entire Brazilian Amazon River mainstem floodplain. The mosaic was then built using the best bands (rectified or non-rectified) of the TM images with 90 meter spatial resolution. The mosaic data are provided in geoTIFF-formatted files, rectified and geocoded, for six TM bands (1 to 5 and 7) with 90-meter spatial resolution. The mosaic is divided in two parts: Part 1, from the mouth of the Amazon river in Brazil to the Brazil/Peru boundary and Part 2, from the Brazil/Peru boundary to its spring. There is also a 500-meter resolution mosaic covering all the Amazon river (from spring to the mouth) with geoTIFF-formatted data files for TM bands 3, 4, and 5. The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually.\n<br>\n\n#### FLUAMAZON_896\nThe FLUAMAZON Experiment data set includes meteorological data collected with radiosondes to examine the moisture flux from the northern coast of South America (near the mouth of the Amazon River) into central Amazonia. The measurements were collected from November 23, to December 21, 1989 during the period of transition between the dry and humid seasons in the region. Some of the studies performed with data from FLUAMAZON were related to the atmospheric thermodynamic structure over Amazonia. During FLUAMAZON, radiosonde measurements were made simultaneously in five different locations: Alcantara, Belem, Oiapoque, Manaus, and Alta Floresta. ASCII text data files for each location have been compiled and compressed into site-specific zipped files.\n<br>\n\n#### ISLSCP_919\nThis data set contains hydrology, soils, radiation, cloud, and vegetation data from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative I. The ISLSCP data sets should provide LBA modelers with many of the fields required to describe boundary conditions, and to initialize and force a wide range of land-biosphere-atmosphere models. All of the data have been processed to the same global spatial resolution (1 deg. x 1 deg.), using the same land/sea mask and steps have been taken to ensure spatial and temporal continuity of the data. The data sets cover the period 1987-1988 at 1-month time resolution for most of the seasonally varying quantities. For this pre-LBA data set, the ISLSCP I data are provided as global coverages. The companion file illustrations were subset over the LBA study area, from 35-85 deg. W longitude and 20 deg. S to 10 deg. N latitude, as shown in Figure 1. The data files and illustrations are organized into the three groups listed below. 1. Hydrology and Soils 2. Radiation and Clouds 3. Vegetation The data within each of these areas were acquired from a variety of sources including model output, satellites, and ground measurements. The individual data sets were provided in a variety of forms. In some cases, this required the data publication team to regrid and reformat data sets and in others to produce monthly averages from finer resolution data. The specific processing for each data set is detailed in the documentation. The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD-ROMs (Marengo and Victoria, 1998) but are now archived individually.\n<br>\n\n#### RADAMBrasil_941\nThe RADAMBRASIL project extensively mapped the Amazon soils using a combination of soil pit information, aerial photography, and geologic maps. During the project, 1,153 soil pits, distributed basin-wide, were described and sampled by horizon and analyzed for texture and chemical composition. This data set, which consists of one file in ASCII comma separated format, contains soil profile descriptions for locations throughout Brazilian Amazonia. These data are based on RADAMBRASIL surveys from the Soil Profiles of Amazonia (Source: IPAM, Brazil/WHRC, USA). See the companion file Soil Profiles of Amazonia.pdf\n<br>\n\n#### RBLE_917\nThe atmospheric boundary layer (ABL) is the layer of air closest to the ground which is directly influenced on a daily basis by the heating and cooling of the earth's surface. The exact depth of the ABL varies according synoptic weather conditions and the time of day. During the daytime it is usually between 1 and 3 km; during the night it is much shallower. The ABL is important because it links the fluxes of heat and water vapor observed at the surface to the general circulation of the atmosphere. To model climate correctly, it is necessary for the ABL to be well understood and represented in the model. Because the air in the ABL is turbulent, small scale variations (about 1 km or less) in evaporation and heat flux at the surface are smoothed, with the temperature, humidity and depth of the ABL being uniform over the entire area. Larger scale variations (on the scale of 10 km or more) may lead to differences in ABL properties between the different surface types. Such differences may cause local atmospheric circulations to develop which may be important for the local climate of an area. During ABRACOS, three ABL measurement campaigns were carried out. These campaigns were called the Rondonia Boundary Layer Experiment (RBLE) 1, 2 and 3 and were held at Ji-Parana where the scale of the forested and deforested areas is large enough for each surface type to develop its own ABL. Refer to the related data set, Pre-LBA Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) Data, for additional information. The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually. The campaigns were held during the dry season when the difference in evaporation between the two surfaces types, forest and pasture, is at its greatest. Measurements were made with both free-flying radiosondes which measure temperature, humidity, and wind up to about 12 km and with a tethered balloon which makes more detailed measurements in the lowest 1 km of the atmosphere. Measurements were made at both the forest and clearing sites. Profiles of potential temperature measured during RBLE2 show that the daytime ABL was deeper over the clearing than the forest. The data have been used to test several models of ABL development. It appears that the ABL over pastures or over clearings grows more rapidly than predicted by the models, possibly because of the increased turbulence generated by the strips of forest typical of this area. The data have also been used to initialize one-dimensional climate models used in experiments to investigate the sensitivity of climate to land surface parameters, and to initialize a mesoscale model which can predict local effects on climate caused by the pattern of deforestation in this area.\n<br>\n\n#### SCAR-B_916\nThis data set contains meteorological data, reanalysis data, remote sensing images, and data on atmospheric composition collected during the Smoke, Clouds, and Radiation - Brazil (SCAR-B) experiment. The SCAR-B examined the effects of biomass burning on atmospheric processes with four primary goals: (1) improving techniques for remote sensing of these process from space, (2) obtain measurements of the rates of emissions of trace gases and particles from biomass burning, (3) observe the influence of atmospheric processes on the emission products was to obtain measurements of the rates of emissions of trace gases and particles from biomass burning, and to observe the influence of atmospheric processes on these emission products, and (4) characterize the physical and radiative properties of smoke particles from biomass burning. SCAR-B was conducted during biomass burning in the cerrado ( dry savannah) and Amazonia rainforest to understand the influence of land cover type on smoke, clouds, and radiation. Selected archived data and images from SCAR-B are described in the Data Description section table. Extensive background information on SCAR-B is provided following the Data Description. The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually.\n<br>\n\n#### TRACE-A_920\nThis data set contains atmospheric chemistry and meteorological data from the NASA Transport and Atmospheric Chemistry near the Equator-Atlantic (TRACE-A) field study. The NASA TRACE-A study took place in August 1992 to determine the cause and source of high concentrations of ozone that accumulate over the Atlantic ocean between southern Africa and South America during the months of August through October. The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD-ROMs (Marengo and Victoria, 1998) but have now been archived individually.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","cog","earth observation","elevation","geospatial","hdf","hydrology","ice","land cover","netcdf","oceans","precipitation","radar","satellite imagery","soil moisture","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AMAZE-08_1308 v1 - This data set provides measurements from the Amazonian Aerosol Characterization Experiment (AMAZE-08) carried out during the wet season from February 4 to March 21, 2008 in the central Amazon Basin. Aerosol and atmospheric samples and measurements were collected at Tower TT34 located 60 km NNW of downtown Manaus, and at Tower K34, located 1.6 km from the TT34 site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/atmos_chemistry/AMAZE-08/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"amazon_precip_228 v1 - The Amazon River Basin precipitation grids were derived from data which was collected daily by the gauging network operated by the Divisao Nacional de Aguas e Energia Eletrica (DNAEE, SGAN 603 Modulo J, Anexo DNC, CEP 70.830-030 Brasilia DF, Brazil). The DNAEE provided the Earth Observing System (EOS) Regional Amazon Model (EOSRAM) project with this data for cooperative analysis.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/physical_climate/amazon_precip/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"JERS-1_SAR_GRFM_Amazon_Mosaics_1280 v2 - This data set provides ~100-m resolution image mosaics of South America acquired during the low flood season between September and December 1995 and during the high flood season between May and July of 1996. The images cover the same areas during both seasons and were obtained from the Japanese Earth Resources Satellite 1 (JERS-1) Synthetic Aperture Radar (SAR) of the National Space Development Agency of Japan (NASDA).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/JERS-1_SAR_GRFM_Amazon_Mosaics/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"basin_border_670 v1 - This data set is an expanded version of the Costa et al. (2000) data set and consists of a single grid with values of 1 for cells within the basins and 0 for cells outside.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/surf_hydro_and_water_chem/basin_border/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"legal_amazon_mask_671 v1 - The Legal Amazon of Brazil is defined by law to include the states of Acre, Amapá, Amazonas, Pará, Rondônia, Roraima, Mato Grosso, Maranhão, and Tocantins [Fundãcao Instituto Brasileiro de Geografia e Estatística (IBGE) 1991]. This is the definition used in generating the Legal Amazon mask.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/human_dimensions/legal_amazon_mask/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"olson_672 v1 - This data set is a subset of Olson et al. (1985, 2000) \"Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation.\" This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10° N to 25° S, longitude 30° to 85° W).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/olson/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"willmott_673 v1 - This data set is a subset of a 0.5-degree gridded temperature and precipitation data set for South America (Willmott and Webber 1998). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), defined as 10° N to 25° S, 30° to 85° W.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/physical_climate/willmott/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"lba_isric_wise_701 v1 - The data set consists of a subset of the ISRIC-WISE global data set of derived soil properties for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 to 30 degrees W, latitude 25 degrees S to 10 degrees N).The World Inventory of Soil Emission Potentials (WISE) database currently contains data for over 4300 soil profiles collected mostly between 1950 and 1995. This database has been used to generate a series of uniform data sets of derived soil properties for each of the 106 soil units considered in the Soil Map of the Wor...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/lba_isric_wise/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"soller_wetlands_674 v1 - This data set consists of a subset of a 1-degree gridded global freshwater wetlands database (Stillwell-Soller et al. 1995).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/soller_wetlands/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"lba_ghcn_702 v1 - This data set consists of a subset of the Global Historical Climatology Network (GHCN) Version 1 database for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 to 30 degrees W, latitude 25 degrees S to 10 degrees N). There are three files available, one each for precipitation, temperature, and pressure data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/physical_climate/lba_ghcn/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"historical_croplands_675 v1 - This data set is a subset of a global croplands data set (Ramankutty and Foley 1999a). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10° N to 25° S, 30° to 85° W).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/historical_croplands/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"gghydro_676 v1 - This subset of the Global Hydrographic data set (GGHYDRO) Release 2.2 for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N) is organized into 19 files containing terrain type, stream frequency counts, major drainage basins, main features of the cryosphere surface, and ice/water runoff per year for the entire Earth's surface at a spatial resolution of 1- degree longitude by 1-degree latitude. The data are provided in both ASCII GRID and binary image file formats.More informa...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/surf_hydro_and_water_chem/gghydro/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"land_cover_data_1deg_677 v1 - This data set consists of a subset for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N) of the University of Maryland (UMD) 1-degree Global Land Cover product in ASCII GRID and binary image formats.The UMD 1-degree Global Land Cover product was produced by researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at UMD. The product is based on Advanced Very High Resolution Radiometer (AVHRR) maximum monthly composites for 1987 of Normalized Difference Vegeta...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/land_cover_data_1deg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"land_cover_data_1km_678 v1 - This data set is a subset of Hansen et al. (1999), \"1 km Global Land Cover Data Set Derived from AVHRR,\" which was developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/land_cover_data_1km/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"DISCover_land_cover_679 v1 - The data set consists of a LBA study area subset of the IGBP DISCover Data Set. The DISCover data set is one data set contained within the Global Land Cover Characteristics Data Base.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/DISCover_land_cover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"land_cover_data_8km_680 v1 - This data set is a subset of an 8-km global land cover product (DeFries et al. 1998).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/land_cover_data_8km/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"leemans_cramer_681 v1 - This data set is a subset of Cramer and Leemans' (2001) global database of mean monthly climatology, which contains monthly averages of mean temperature, temperature range, precipitation, rain days, and sunshine hours for terrestrial areas during 1931-1960. This subset was created for the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10° N to 25° S, longitude 30° to 85° W).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/physical_climate/leemans_cramer/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Zinke_soil_683 v1 - The data set contains a subset of a global organic soil carbon and nitrogen data set (Zinke et al. 1986).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/Zinke_soil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"potential_vegetation_684 v1 - The data set consists of a subset for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N) of the 5-min resolution Global Potential Vegetation data set developed by Navin Ramankutty and Jon Foley at the University of Wisconsin. Data are available in both ASCII GRID and binary image file formats.The original map was derived at a 5-min resolution and contains natural vegetation classified into 15 types.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/potential_vegetation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"sage_685 v1 - This data set is a subset of a global river discharge data set by Coe and Olejniczak (1999). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10° N to 25° S, 30° to 85° W).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/surf_hydro_and_water_chem/sage/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"lba_tree_cover-1km_686 v1 - The data set consists of a subset for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N) of the 1km Global Tree Cover Data Set developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Data are available in both ASCII GRID and binary image files formats.Characterization of terrestrial vegetation from the Advanced Very High Resolution Radiometer (AVHRR) on the global to regional scale has traditionally been accomplished using classif...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/lba_tree_cover-1km/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"wilhend_687 v1 - This data set is a subset of a global vegetation and soils data set by Wilson and Henderson-Sellers (1985a). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10° N to 25° S, 30° to 85° W).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/wilhend/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"lba_gisswetlands_688 v1 - This database, compiled by Matthews and Fung (1987), provides information on the distribution and environmental characteristics of natural wetlands. The database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/lba_gisswetlands/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD01_CIRSAN_Meteorology_2001_1114 v1 - This data set contains meteorological data collected around the confluence of the Tapajos River with the Amazon River in the Amazon Basin near Santarem, Brazil, in July and August 2001. Boundary layer and upper air measurements were collected with an acoustic sounder-sodar instrument, pilot balloons with optical theodolites, and radiosondes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD01_CIRSAN_Meteorology_2001/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD01_BRAMS_907 v1 - We have investigated mesoscale variations of atmospheric CO2 over a heterogeneous landscape of forests, pastures, and large rivers during the Santarem Mesoscale Campaign (SMC) of August 2001. The variations of atmospheric CO2 concentration were simulated using the Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS) with four nested grids that included a 1-km finest grid centered on the Flona Tapajos.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD01_BRAMS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD02_C_N_Isotopes_1097 v1 - This data set reports delta 13C/12C results for leaf tissues and atmospheric carbon dioxide (CO2), 15N/14N ratios for leaf tissue, and leaf carbon and nitrogen concentrations along a topographical gradient in old-growth forests in the ZF2 Reserve (km 34), Instituto Nacional de Pesquisas da Amazonia (INPA), near Manaus, Amazonas, Brazil. During the dry seasons of 2004 and 2006, leaves were sampled at various heights within the canopy and atmospheric air flask samples were also collected at various heights at three locations along this gradient.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD02_C_N_Isotopes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD02_Atmosphere_CO2_Isotopes_1011 v1 - This data set reports carbon and oxygen stable isotope ratios of atmospheric carbon dioxide (CO2) collected at several forest and pasture sites and in the free troposphere over Amazonia. There are three comma-delimited ASCII files with this data set.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD02_Atmosphere_CO2_Isotopes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD02_C_N_O_Organic_983 v1 - This data set reports the measurement of stable carbon, nitrogen, and oxygen isotope ratios in organic material (plant, litter and soil samples) in forest canopy profiles and pasture (grasses and shrubs) as well as corresponding carbon and nitrogen tissue concentrations in a number of different sites across Brazil. The sampling design captured the temporal variation in rainfall over the course of several years.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD02_C_N_O_Organic/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD02_Forest_Canopy_Structure_1009 v1 - This data set reports on Leaf Area Index (LAI) and Specific Leaf Area (SLA) measurements collected from forest and pasture sites in or near the Tapajos National Forest (TNF), 80 km south of the city of Santarem, Para, Brazil. The collections were between October 1999 and June 2003 from tower sites accessed via the km 67 forest entrance.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD02_Forest_Canopy_Structure/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD02_Leaf_Level_Gas_Exchange_1010 v1 - This data set reports leaf gas flux and leaf properties from samples collected from trees, liana, pasture saplings, and pasture grass located at eight different sampling locations in the states of Para (south of Santarem) and Amazonas (near Manaus) from November 1999 through December 2003. Data are reported on photosynthesis measurements, CO2 response curves, light response curves, humidity response curves, and stomatal responses to variations of the leaf-to-air water vapor mole fraction deficit.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD02_Leaf_Level_Gas_Exchange/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD02_Leaf_Water_Potential_1100 v1 - Data are reported for leaf water potential of leaves of seven species of trees and lianas from the primary forest at the km 67 Tower Site, Tapajos National Forest, and measurements of five sapling tree species and the grass Brachiaria brizantha from a pasture site located near the km 77 Pasture Tower Site, approximately 10 km from the primary forest site. The research area is situated within the Tapajos National Forest reserve, south of the city of Santarem, Para, Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD02_Leaf_Water_Potential/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD02_O_H_Isotopes_1008 v1 - This data set reports the oxygen isotope signatures of water extracted from plant tissue (xylem from the stems and leaf tissue) and of atmospheric water vapor from twelve different sites (including both pasture and forest) throughout the Amazon region of Brazil. Samples were collected approximately every 4 months between 1999 and 2003 with additional samples collected monthly between January and May of 2003.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD02_O_H_Isotopes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD03_Ceilometer_Km67_942 v1 - A Vaisala CT-25K ceilometer was installed at an old-growth forest site located at the km 67 Eddy Flux Tower site in the Tapajos National Forest, Para, Brazil, off Kilometer 67 of BR-163 south of Santarem in April 2001 and remained operational through December 2003, with reliable data being collected between May 2001 and June 2003. Annual, 2001 to 2003, 30-minute average cloud base and backscatter profile data and measurement statistics (sample count, variance, skewness, and kurtosis) are presented in 15 ASCII comma-delineated files.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD03_Ceilometer_Km67/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD03_Pasture_Flux_962 v1 - Eddy correlation and micrometeorological measurements began in 2001 and continued through 2005 at the pasture site at km 77 on BR-163 just south of the city of Santarem, Para, Brazil. Measurements included turbulent fluxes (momentum, heat, water vapor, and CO2) using the eddy covariance (EC) approach.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD03_Pasture_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD03_Mesoscale_Meteorology_944 v1 - We analyzed rainfall obtained in a network of 38 rain gauges located near the confluence of the Tapajos and Amazon rivers in the eastern Amazon Basin. We found that tipping bucket rain gauges work adequately in the Amazon rainfall regime, but careful field calibration and comparison with collocated conventional rain gauges was essential to incorporate daily totals from operational array into regional maps.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD03_Mesoscale_Meteorology/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD03_Tethered_Balloon_1108 v1 - This data set contains measurements of nocturnal meteorological profiles collected from tethered balloon platforms during July 2001, October 2001, and November 2003. Measurements were made near the pasture/agricultural tower site at km 77 on BR-163 just south of the city of Santarem, and the near the Tapajos National Forest, km 83 tower site, Santarem, Para, Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD03_Tethered_Balloon/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD04_Biomass_990 v1 - This data set contains the results of a biometric tree survey of a 19.25 ha area adjacent to the eddy flux tower at the km 83 logged forest tower site in Tapajos National Forest, Para, Brazil. The survey was done in March 2000.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD04_Biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD04_Tower_Flux_Gap_978 v1 - This data set reports 30-minute values for above-canopy meteorology and fluxes of momentum, heat, and carbon dioxide, and within-canopy carbon dioxide and water vapor concentrations collected at 12 levels between 10 cm and 64 m at the tower located within a logging gap at km 83 Tower Site in the Tapajos National Forest, Para, Brazil. Data were collected over 1.5 years between June 3, 2002 and January 30, 2004.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD04_Tower_Flux_Gap/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD04_CO2_Profiles_947 v1 - We used two independent approaches, biometry and micrometeorology, to determine the net ecosystem production (NEP) of an old growth forest in Para, Brazil. Biometric inventories indicated that the forest was either a source or, at most, a modest sink of carbon from 1984 to 2000 (+0.8 +/- 2 Mg C(.)ha(-1.)yr(-1); a positive flux indicates carbon loss by the forest, a negative flux indicates carbon gain).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD04_CO2_Profiles/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD04_Dendrometry_989 v1 - A dendrometry study was conducted at the logged forest tower site, km 83 site, Tapajos National Forest, Para, Brazil over a period of 4 years following the implementation of a reduced impact logging management regime. Dendrometer bands were installed to measure diameter growth increments for 234 trees in an 18 ha plot adjacent to the eddy flux tower at the km 83 site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD04_Dendrometry/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD04_LAI_Estimates_1103 v1 - This data set contains summary data for monthly leaf area index (LAI) and plant area index (PAI) at the km 83 Tower Site, in the Tapajos National Forest, Para, Brazil. LAI was estimated for hemispherical photographs of leaves collected between 2000 and 2003, using the histogram and gap-fraction analysis methods.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD04_LAI_Estimates/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD04_LAI_992 v1 - Leaf area index was estimated in an 18 ha plot at the logged forest tower site, km 83, Tapajos National Forest, Para, Brazil. The plot was adjacent to the eddy flux tower at km 83, Tapajos National Forest, Para, Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD04_LAI/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD04_Leaf_Litter_991 v1 - Above-ground litter productivity was measured in a 18 ha plot adjacent to the eddy flux tower at the logged forest tower site, km 83, Tapajos National Forest, Para, Brazil. Thirty litter baskets distributed within the grid were visited bi-weekly (Goulden et al., 2004).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD04_Leaf_Litter/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD04_Leaf_Level_Gas_Exchange_1060 v1 - This data set reports the results of measurements of (1) leaf-level photosynthesis response curves for the effects of temperature, leaf age, warming, irradiation, and circadian rhythm and (2) leaf-level photorespiration rates at 30 and 37 degrees C. Measurements were made between June 2000 and February 2006 at the km 83 Logged Forest Tower site, the km 67 Primary Forest Tower site, and the control site at Seca Floresta, all in the Tapajos National Forest, Para, Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD04_Leaf_Level_Gas_Exchange/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD04_Logging_Damage_1038 v1 - This data set contains the results of a survey of logging damage in a 18 ha plot (300 m N-S, 600 m E-W) east (upwind) of the eddy flux tower at km 83, Tapajos National Forest, Para, Brazil. Data collected include type of damage, snap height, and log dimensions, as well as calculated biomass of stems and canopy either damaged or removed in logging.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD04_Logging_Damage/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD04_Soil_Moisture_Km83_979 v1 - This data set reports continuous high-resolution frequency-domain reflectometry measurements of soil moisture to 10 m depth and precipitation data near each of the two towers located at the km 83 tower site (logged forest site) in the Tapajos National Forest in the state of Para, Brazil. Measurements were made during 2002 and 2003.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD04_Soil_Moisture_Km83/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD04_Soil_Respiration_1039 v1 - This data set reports on the flux of carbon dioxide from logged forest soils near the eddy flux tower at the km 83 site, Para, Brazil. The automated soil respiration measurements were collected using 15 chambers, installed August 2001 in primary forest.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD04_Soil_Respiration/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD05_Fuel_Loads_1233 v1 - This data set contains estimates of understory fuel loads (forest litter) at six locations near Paragominas in Northeastern Amazonia. Samples were collected from three different forest conditions: primary forest, logged forest, and burned forest.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/grab_bag/CD05_Fuel_Loads/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD05_Micromet_1169 v1 - This data set reports soil moisture expressed as volumetric water content (VWC), daily precipitation, air temperature, relative humidity, and dew point measurements conducted at the Seca Floresta site, km 67, Tapajos National Forest, Brazil. The measurements were part of the Rainfall Exclusion Experiment (REE) established to study the response of a humid Amazonian forest to severe drought.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD05_Micromet/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD05_REE_Fuel_Sticks_Moisture_1232 v1 - This data set contains moisture content measurements for fuel sticks located in the forest understory of the rainfall exclusion experimental site, Tapajos National Forest, Para, Brazil. The mean and standard errors are reported for control and treatment plot measurements.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD05_REE_Fuel_Sticks_Moisture/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD06_Camrex_1086 v1 - This data set provides high-resolution (~500 m) gridded land and stream drainage direction maps for the Amazon River basin, excluding the Rio Tocantins basin. These maps are the result of a new topography-independent analysis method (Mayorga et al., 2005) using the vector river network from the Digital Chart of the World (DCW, Danko, 1992) to create a high-resolution flow direction map.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD06_Camrex/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD06_BGC_JiParana_1227 v1 - This data set provides spatially extensive and temporally intensive surveys of the river biogeochemistry of the Ji-Parana River Basin in Western Amazonia, Rondonia, Brazil. The concentrations of major nutrient ions, dissolved organic and inorganic carbon, pH, temperature, dissolved oxygen, and conductivity were measured in Ji-Parana River and tributary samples at the defined seasonal or monthly intervals.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD06_BGC_JiParana/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD06_Carbon_respiration_1125 v1 - This data set provides measured and calculated variables describing the carbon pools in river waters, CO2 respired from the water and total amount of CO2 evaded, dissolved oxygen isotopes (delta 18O-O2), and concentration of bacterial cells in river water. Samples were collected from 10 white-water rivers, two clear-water streams (one each in Amazonas and Acre), and two black-water rivers in Amazonas from July to September 2005, which coincided with a severe drought in the western and southern regions of the Amazon Basin (Zeng et al.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD06_Carbon_respiration/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD06_C02_Exchange_1136 v1 - This data set provides measurements of carbon dioxide flux rates (FCO2), gas transfer velocity (k), and partial pressures (pCO2) at 75 sites on rivers and streams of the Amazon River system in South America for the period beginning July 1, 2004, and ending January 23, 2007. Several fieldwork campaigns occurred between June 2004 and January 2007 in the Amazon River basin, with discharge conditions ranging from low to high flow.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD06_C02_Exchange/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD06_Outgassing_1151 v1 - This data set provides estimates of monthly carbon dioxide (CO2) flux from the Amazon mainstem rivers, tributary stream networks, and their associated varzeas (floodplains). CO2 flux was calculated using two aggregation approaches: for defined river basins (data file #2) and for defined river reaches (figure 2).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD06_Outgassing/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD06_C_Isotopes_1120 v1 - This data set includes measurements of standard geochemical variables, dissolved CO2, dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), fine particulate organic carbon (FPOC), and coarse particulate organic carbon (CPOC) in samples taken from 60 Amazonian river locations across the Amazon Basin from 1991 to 2003 (Mayorga et al., 2005). The 14C and 13C isotopic composition of DIC was measured on samples collected between 1991 and 2003.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD06_C_Isotopes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD06_LULC_Map_JiParana_1087 v1 - This data set provides a land use/land cover map of the Ji-Parana River Basin in the state of Rondonia, Brazil produced from the digital classification of eight Landsat 7-ETM+ scenes from 1999 acquired from the Tropical Rain Forest Information Center (TRFIC) at Michigan State University. Nine land cover classes covering the Ji-Parana Basin were identified.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD06_LULC_Map_JiParana/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD06_Landuse_Timeseries_JiParana_844 v1 - This data set contains four land use/land cover maps (1986, 1992, 1996 and 2001) for the Ji-Parana River Basin, derived from the digital classification of 8 Landsat images obtained from The Tropical Rain Forest Information Center (TRFIC).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD06_Landuse_Timeseries_JiParana/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD06_Physical_Template_JiParana_1090 v1 - This data set contains physical, hydrologic, political, demographic, and societal maps for the Ji-Parana River Basin, in the state of Rondonia, Brazil. These data were used as base information in subsequent investigations of land use/land cover, biogeochemistry, soils, and water balance processes (Ballester et al., 2003).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD06_Physical_Template_JiParana/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD06_Soils_JiParana_1088 v1 - This data set provides a digital map of soil orders for the Ji-Parana River Basin, in the state of Rondonia, Brazil (Western Amazonia). Soil orders were manually digitized from a 1:500,000 map from EMBRAPA originally published in 1983.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD06_Soils_JiParana/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD06_Water_Balance_JiParana_1132 v1 - This data set provides simulated minimum, average, and maximum monthly rainfall, potential evapotranspiration, water deficit, and water surplus values for the Ji-Parana River basin, Rondonia, Brazil. The Thornthwaite-??Mather climatological model integrated into a Geographic Information System (GIS) was used to derive the data by utilizing Advanced Very High Resolution Radar (AVHRR) images for temperatures, rainfall amounts from gauges within and around the basin, soil profiles, and land cover maps as model inputs.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD06_Water_Balance_JiParana/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD07_GOES_L3_Gridded_SRB_831 v1 - High resolution downwelling solar, PAR, infrared radiation and rain rates retrieved from GOES-8 imager. The data set covers primarily Amazon watershed area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD07_GOES_L3_Gridded_SRB/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD08_C_Isotopes_Belowground_1025 v1 - This data set contains carbon isotope signatures from soil organic matter collected from the following sites: the forests of the ZF-2 INPA reserve approximately 80 km north of the city of Manaus, Amazon; the Tapajos National Forest approximately 83 km south of the city of Santarem, Para; and the Fazenda Vitoria, a ranch near the city of Paragominas, Para. Samples from the Fazenda Vitoria were from degraded and managed pasture sites as well as mature and secondary forests.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD08_C_Isotopes_Belowground/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD08_CWD_Res_and_Decomp_Manaus_911 v1 - Respiration from coarse litter (trunks and large branches > 10 cm diameter) was studied in central Amazon forests. Respiration rates varied over almost two orders of magnitude (1.003-0.014 mug C g(-1) C min(-1), n = 61), and were significantly correlated with wood density (r(adj)(2) = 0.42), and moisture content (r(adj)(2) = 0.39).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD08_CWD_Res_and_Decomp_Manaus/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD08_Leaf_Isotopes_Manaus_1245 v1 - This data set provides measurements for carbon (C), nitrogen (N), leaf area index (LAI), and carbon isotope ratio data (13C and 14C) of leaves sampled at the Manaus ZF2 Jacaranda transect area, Amazonas, Brazil, in 2001. Leaf tips and the petioles from the youngest and oldest leaves from a sampled branch were analyzed for nine different species.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD08_Leaf_Isotopes_Manaus/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD08_Radiocarbon_Dates_Manaus_996 v1 - This data set reports the ages and growth rates of trees as determined by radiocarbon dating (14C), selected from a logging operation near the city of Itacoatiara, about 250 km east of Manaus, Brazil in 1997. Samples were collected from forty-four trees from 15 species with a basal diameter greater than 100 cm and prepared for radiocarbon dating by Accelerator Mass Spectrometry (AMS) at Lawrence Livermore National Laboratory.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD08_Radiocarbon_Dates_Manaus/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD08_Radiocarbon_Dates_997 v1 - This data set reports the ages and growth rates of trees determined by radiocarbon dating (14C) in three Amazonia forests. Tree samples were collected from permanent research plots at ZF2 km 34, Manaus, Amazonas, the Catuaba Experimental Farm, Acre, and the km 83 tower site (logged forest site) in the Tapajos National Forest, Para, between 2001-2003.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD08_Radiocarbon_Dates/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD08_Tree_Growth_Manaus_1194 v1 - This data set provides diameter at breast height (DBH) measurements made of trees in a dense terra-firme tropical moist forest at the ZF-2 Experimental Station, 90 km north of Manaus, Brazil. DBH was measured over two transects (East to West and North to South) which were established in 1996 by the Jacaranda Project (agreement between the National Institute for Research in the Amazon (INPA) and the Japan International Cooperation Agency, JICA).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD08_Tree_Growth_Manaus/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD08_Tree_Inventory_Ducke_910 v1 - This data set includes in one data file the common names, base diameters, and calculated tree masses for almost 3,000 trees on a 5 hectare plot (20 x 2,500 m) located in the Ducke Reserve near Manaus, Brazil in the central Amazon. Measurements were taken during October-December 1999.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD08_Tree_Inventory_Ducke/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD08_Ecosystem_Resp_Manaus_912 v1 - Understanding how tropical forest carbon balance will respond to global change requires knowledge of individual heterotrophic and autotrophic respiratory sources, together with factors that control respiratory variability. We measured leaf, live wood (tree stem), and soil respiration, along with additional environmental factors over a 1-yr period in a Central Amazon terra firme forest.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD08_Ecosystem_Resp_Manaus/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD09_Soils_Veg_Tapajos_1104 v1 - This data set reports the results of soil and vegetation surveys at four distinct areas within the Tapajos National Forest (TNF), 50 to 100 km south of Santarem, Para, Brazil, in November 1999. At 13 individual sites across the four areas, all located in primary forest, core soil samples at 10, 30 and 50 cm depths were collected and analyzed for dry mass, bulk density, texture, percentage carbon (C), percentage organic matter, and percentage nitrogen (N).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD09_Soils_Veg_Tapajos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD10_CO_Tapajos_856 v1 - This data set contains half-hourly average CO mixing ratios measured from 2001/04/18 to 2003/08/29 in the old-growth upland forest at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil.CO concentrations were measured in air drawn from above the canopy top of tower (approx.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD10_CO_Tapajos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD10_CO_CO2_Maxaranguape_1012 v1 - This data set reports the concentrations of carbon monoxide (CO) and carbon dioxide (CO2), wind direction, wind speed, and air temperature measured at the Maxaranguape Atmospheric Observatory in northeast Brazil, January 4, 2003 - December 27, 2006. The data are 30-minute averages.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD10_CO_CO2_Maxaranguape/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD10_EddyFlux_Tapajos_860 v1 - This data set reports eddy flux measurements of CO2 and H2O exchange and associated meteorological measurements at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD10_EddyFlux_Tapajos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD10_CO2_Profiles_Tapajos_855 v1 - Eddy fluxes of CO2 and H2O are measured at two levels (58m and 47m) using tower-mounted closed-path Licor 6262 analyzers and Campbell CSAT3 sonic anemometers. A third Licor gas analyzer measures (a) the CO2/H2O concentration profile (1 of 8 levels every 2 minutes) and (b) the instantaneous integrated canopy storage of CO2/H2O, using a design pulling air simultaneously through 8 inlets (once every 20 minutes).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD10_CO2_Profiles_Tapajos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD10_CWD_Tapajos_858 v1 - This data sets reports properties of fallen course woody debris in an old-growth upland forest at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD10_CWD_Tapajos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD10_Litter_Tapajos_862 v1 - This data set reports litter type and mass in the old-growth upland forest at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD10_Litter_Tapajos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD10_Biometry_Tapajos_854 v1 - This data sets reports biometry measurements of the old-growth upland forest at the Para¡ Western (Santarrem)km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD10_Biometry_Tapajos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD10_H2O_Profiles_Tapajos_861 v1 - This data set reports vertical profiles of H2O vapor concentrations measured at the Para Western (Santarem) - km 67, Primary Forest Tower Site (Figure 1). This site is in the Tapajos National Forest located in north central Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD10_H2O_Profiles_Tapajos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD10_Temperature_Profiles_Tapajos_863 v1 - This data set reports temperature measurements at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD10_Temperature_Profiles_Tapajos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD10_DBH_Tapajos_859 v1 - This data sets reports diameter at breast height (DBH) measurements in the old-growth upland forest at the Para Western (Santarem) - km 67, Primary Forest Tower Site. This site is in the Tapajos National Forest located in north central Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD10_DBH_Tapajos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD11_Forest_Degradation_1118 v1 - This data set reports the results of vegetation field surveys that measured tree height and diameter at breast height (DBH) in defined size classes at three study sites -- Santarem, Para; Paragominas, Para; and Alo Brasil, Mato Grosso, Brazil, from 2001-2003. At each site, plots and transects within plots, were defined that represented different types of logging and fire treatments, each including one primary forest plot used as a control.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD11_Forest_Degradation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD15_Productivity_1167 v1 - This data set provides mean leaf area index (LAI), dendrometry band measurements, and litterfall mass from samples collected at the km 67 research site, Topajos National Forest, Para, Brazil. Litterfall collections were from January 23, 2004 through December 3, 2004, dendrometer measurements were monthly between December 2003 and December 2004, and LAI measurements were collected from January 26, 2004 through November 3, 2004.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD15_Productivity/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD17_Forest_Survey_1254 v1 - This data set provides measurements for diameter at breast height (DBH), tree height, distance from tree stems to the furthest canopy element, and a species survey of secondary forests in Para and Rondonia, Brazil, from 2002-2003. The forest areas were defined as Type A and Type B stands.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/grab_bag/CD17_Forest_Survey/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD32_Fluxes_Brazil_1842 v2 - This dataset is a compilation of carbon and energy eddy covariance flux, meteorology, radiation, canopy temperature, humidity, CO2 profiles and soil moisture and temperature profile data that were collected at nine towers across the Brazilian Amazon. Independent investigators provided the data from a variety of flux tower projects over the period 1999 thru 2006.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD32_Fluxes_Brazil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD32_LBA_MIP_Drivers_1177 v1 - The source meteorological observations for the forcing data, from the nine Brazilian flux towers, were recently published as Saleska, et al. (2013).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD32_LBA_MIP_Drivers/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD34_Amazon_Hyperion_1064 v1 - This data set contains 20 multispectral surface reflectance images collected by the EO-1 satellite Hyperion sensor at 30-m resolution and covering the entire Amazon Basin for 2002 - 2005. All images were converted to GeoTiff format for distribution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD34_Amazon_Hyperion/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD34_Amazon_Landsat_1176 v1 - This data set provides the results of fractional land cover analysis for nonphotosynthetic vegetation (NPV) from two Landsat images of Manaus, Brazil, for October 14, 2004, and for July 29, 2005. Both images are from Landsat 5, path 231, row 62.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/grab_bag/CD34_Amazon_Landsat/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD36_SALDAS_1162 v1 - This data set provides South American Land Data Assimilation System (SALDAS) forcing data including atmospheric fields necessary for land surface modeling for South America which are derived by combining modeled and observation based sources. The forcing data cover the entire continent of South America at 0.125 degree resolution and are built around the model-calculated values of air temperature, wind speed and specific humidity at two meters, surface pressure, downward shortwave and longwave surface radiation, and precipitation from the South American Regional Reanalysis (SARR).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD36_SALDAS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CD37_Biomass_Landsat_Glas_1145 v1 - This data set provides tree age, forest formation, and land cover classification maps, and estimates of landscape-level above-ground live woody biomass (AGLB) for secondary forests in Rondonia, Brazil. The Threshold Age Mapping Algorithm (TAMA) was applied to a densely spaced time series of Landsat images (1975 to 2003) to derive forest type and age classification maps.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/CD37_Biomass_Landsat_Glas/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC01_Cities_Communities_Roads_1058 v1 - This data set contains the boundaries of the four major cities in the Northern Ecuadorian Amazon, the locations of primary communities in the colonist settlement area, and the locations of the road network, circa 2002. This area in northeastern Ecuador, know as the northern Oriente of Ecuador, borders the Andes Mountains and contains the headwaters of the Amazon River.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC01_Cities_Communities_Roads/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC01_Hydrography_Edaphology_NEC_1059 v1 - This data set provides map images of hydrographic, morphologic, and edaphic features for the northern Amazon Basin in eastern Ecuador. The hydrographic data are available at two scales based on the 1:50,000 and 1:250,000-scale topographic source maps that were generated in 1990 and 1993, respectively.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC01_Hydrography_Edaphology_NEC/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC01_Landsat_1187 v1 - This data set contains a time series of early Landsat-4 MSS satellite imagery as well as Landsat-5 TM and Landsat-7 ETM+ satellite imagery of the northern Ecuadorian Amazon. Some of the TM and ETM images have been georectified to UTM Zone 18 South, WGS84 Datum.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC01_Landsat/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC01_LULC_Classes_Ecuador_ISA_1084 v1 - This data set contains Landsat TM imagery for the years 1986, 1989, 1996, and 1999, that have been classified into four land use/land cover (LULC) classes: Forest, Non-Forest Vegetation, Urban/Barren, and Water; and a fifth class of Clouds/Shadows. The areas of interest were the four Intensive Study Areas (ISA) of the University of North Carolina''s Carolina Population Center (CPC) Ecuador Projects: Eastern Intensive Study Area; Northern Intensive Study Area; Southern Intensive Study Area, and Southwestern Intensive Study Area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC01_LULC_Classes_Ecuador_ISA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC01_Boundaries_Ecuador_1057 v1 - This data set contains the national and provincial boundaries of Ecuador as well as the boundaries of two national parks: the Cuyabeno Wildlife Reserve and the Yasuni National Park. There are four data files in ESRI ARCGIS Shapefile format within this data set.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC01_Boundaries_Ecuador/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC01_Households_NEC_1052 v1 - This data set reports summary statistics from socioeconomic and demographic surveys administered to the male and female heads of household on 767 farm plots. The surveys were performed in the provinces of Sucumbios and Napo/Orellana, in the northern Ecuadorian Amazon colonist settlements (Oriente) in 1999 (Pan and Bilsborrow, 2005).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC01_Households_NEC/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC01_SRTM_DEM_90m_NEC_1083 v1 - This data set provides 90-meter resolution Digital Elevation Model data used in the University of North Carolina's Carolina Population Center (CPC) Ecuador Projects. The topographic data were derived from Shuttle Radar Topography Mission (SRTM) C-band and X-band interferometric synthetic aperture radars (IFSARs) data that were acquired over 80% of Earth's land mass in February 2000.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC01_SRTM_DEM_90m_NEC/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC01_Topography_Ecuador_ISA_1082 v1 - This data set contains topographic/geomorphological data associated with the four Intensive Study Areas (ISAs) in the Northern Ecuadorian Amazon (northern Oriente) that are part of the University of North Carolina's Carolina Population Center (CPC) Ecuador Projects study. Study area boundaries were developed directly from 1:50,000 topographical maps.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC01_Topography_Ecuador_ISA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC02_PermPlot_Acre_1237 v1 - This data set provides diameter at breast height (DBH) measurements for 1,063 trees located at the Catuaba Experimental Farm, and 812 trees located in the Humaita Forest Reserve. Both sites are in the state of Acre, southwest Amazonia, Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC02_PermPlot_Acre/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC02_Meteorology_Acre_1091 v1 - This data set provides meteorological measurements collected from 3 different meteorological stations within a radius of 8 km in Rio Branco, Acre Brazil, for the periods of June of 1970 to 1974, December of 1974 to 1980, and May of 1980 thru May 31, 2001. Daily average values for rainfall, relative humidity, evapotranspiration, maximum and minimum temperature, pressure, wind direction and speed, solar radiation, and cloud cover are reported.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC02_Meteorology_Acre/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC02_Forest_Flammability_Acre_1089 v1 - This data set provides the results of controlled burns conducted to assess the flammability of mature forests on the Catuaba Experimental Farm of the Federal University of Acre - Rio Branco, Acre, Brazil. Controlled burns were conducted in 1998, and the rate of fire spread was calculated based on the duration of the fire and the measured extent of the burned area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC02_Forest_Flammability_Acre/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC02_GOES8_Hotpixel_Acre_1092 v1 - This data set provides hot pixel data, as an indicator of fires that were detected by the GOES-8 satellite for the state of Acre, Brazil. Image data were collected for extended periods over the course of 3 years (1998, 2000 and 2001).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC02_GOES8_Hotpixel_Acre/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC02_Water_Table_Acre_1062 v1 - This data set reports bi-weekly or monthly depth-to-water measurements for three wells located in a ~1,500 ha forest fragment on the Catuaba Experimental Farm, which is the property of the Federal University of Acre, Brazil. Data were collected between February 1999 and December 2004.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/grab_bag/LC02_Water_Table_Acre/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC02_MAP_Fire_Indicators_1044 v1 - This data set provides hot pixel data, as an indicator of fires, that were detected by various satellites in the tri-national MAP region (Madre de Dios-Peru, Acre-Brazil, and Pando-Bolivia) in 2003, 2004, 2005, and 2006. Data from the following satellites/sensors were compiled: NOAA-12, NOAA-14, NOAA-15, and NOAA-16, which transports the AVHRR sensor; GOES-8 and GOES- 12, which transports the GOES Imager; and AQUA and TERRA, both which transport the MODIS sensor.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC02_MAP_Fire_Indicators/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC02_Streams_Acre_1243 v1 - This data set provides coordinates for points at the mouth of tributaries of the Acre River in the Tri-national River Basin in South America. Three Global Positioning System (GPS) readings were made at the outlet of each tributary and the average of the three readings is reported.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/grab_bag/LC02_Streams_Acre/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC03_Hypsography_DEM_1094 v1 - This data set provides four related spatial data products for four study areas across the Brazilian Amazon: Manaus, Amazonas; Tapajos National Forest, Para Western (Santarem); Rio Branco, Acre; and Rondonia, Rondonia. Products include vector data showing (1) roads, (2) rivers, and (3) hypsography and (4) digital elevation model (DEM) images that were encoded from the hypsography vectors.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/grab_bag/LC03_Hypsography_DEM/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC03_SAR_LC_Biomass_1093 v1 - This data set provides three related land cover products for four study areas across the Brazilian Amazon: Manaus, Amazonas; Tapajos National Forest, Para Western (Santarem); Rio Branco, Acre; and Rondonia, Rondonia. Products include (1) orthorectified JERS-1 and RadarSat images, (2) land cover classifications derived from the SAR data, and (3) biomass estimates in tons per hectare based on the land cover classification.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/grab_bag/LC03_SAR_LC_Biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC04_IBIS_Model_1139 v1 - The provided data were generated by the Integrated BIosphere Simulator (IBIS) terrestrial ecosystem model (Foley et al. 1996, Kucharik et al.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC04_IBIS_Model/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC04_Macrohydrology_1048 v1 - This data set provides continental-scale hydrological river flow routing parameter data for the Amazon and Tocantins River basins at 5 minute (~9 km) resolution (Costa et al., 2002). The data set includes four geospatial data files (in standard ESRI Arc/Info ASCII Grid format): (1) the river network (flow direction); (2) sinuosity of each of the main rivers, measured at 111 river sections in the basins; (3) depth to the water table; and (4) transmissivity of the aquifer.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC04_Macrohydrology/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC04_Land_Use_5min_906 v1 - Amazonia has been under considerable development pressure as croplands and pasture are established in areas formerly occupied by tropical forest and cerrado. Although this region is an important part of several important planetary biogeochemical cycles, the location and impact of human land use are not well understood.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC04_Land_Use_5min/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC04_THMB-HYDRA_Model_1138 v1 - The model output data provided were generated by the THMB 1.2 (Terrestrial Hydrology Model with Biogeochemistry) model which simulates the flow of water through groundwater systems, rivers, lakes and wetlands. The model operates at a 5-minute latitude-by-longitude grid with a 1-hour time step and requires as boundary conditions: topography, evaporation from water surfaces, surface runoff, base flow, and precipitation.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC04_THMB-HYDRA_Model/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC05_BDFF_Biomass_Soils_1040 v1 - This data set reports (1) total aboveground dry biomass based on detailed estimates of all live and dead plant material, (2) results from repeated surveys of aboveground biomass allowing the calculation of above-ground productivity, and (3) soil chemical and physical characteristics for 50 1-ha plots of undisturbed and fragmented central Amazonian rainforest within the Biological Dynamics of Forest Fragments Project (BDFFP) study area. The reported data are plot-level summaries based on plant and soil samples and measurements obtained over the 1997 to 2001 timeframe.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC05_BDFF_Biomass_Soils/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC07_Lake_Chlorophyll_MODIS_1000 v1 - This data set contains chlorophyll concentration maps of the Amazon River floodplain region from Parintins (Amazonas) to Almeirim (Para). These chlorophyll fraction maps were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product (MOD09) for 19 months from April 2002 to December 2003.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC07_Lake_Chlorophyll_MODIS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC07_Biomass_LGrande_1127 v1 - This data set reports measurements of aquatic macrophyte biomass, phenology, leaf characteristics, and length and diameter of stems of both submerged and unsubmerged macrophytes. Data were collected from sites in the Monte Alegre Lake region on the eastern Amazon River floodplain in Para, Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC07_Biomass_LGrande/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC07_Bathymetry_Curuai_999 v1 - The bathymetry data provided represent a continuous surface of interpolated point measurements of depth values of Lago Curuai, an Amazon River floodplain lake, upstream from Santarem, Para, Brazil, from measurements made in June of 2004. The first product contains the actual depth values (in meters) of the interpolated continuous surface saved as real numbers in both ENVI and GeoTIFF formats.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC07_Bathymetry_Curuai/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC07_Wetlands_fluxes_1209 v1 - This data set provides estimates of daily and monthly carbon dioxide (CO2) and methane (CH4) diffusive and ebullitive flux for dry and flooded areas from two study sites, Cuini and Itu, in the interfluvial wetlands of the upper Negro River basin, Brazil. CO2 (ebullitive and diffusive) and CH4 diffusive flux measurements were made one day each month from February 2005 through January 2006 in both permanently and seasonally flooded areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC07_Wetlands_fluxes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC07_Lake_Nutrient_Sediments_1050 v1 - This data set reports lake sediment texture and porosity, carbon (C), nitrogen (N), and phosphorus (P) content of surficial sediments, 210Pb-derived nutrient accumulation rates in sediments, and burial rates of C, N, and P in sediments at eleven locations in Lake Calado, Amazonas, Brazil. Field samples were collected between February 1982 and August 1984.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC07_Lake_Nutrient_Sediments/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC07_Reservoir_GHG_1143 v1 - This data set provides flux measurements of methane (CH4) and carbon dioxide (CO2) from surface waters to the atmosphere. It also provides CH4, CO2, and oxygen (O2) concentrations of surface water, and concentrations measured at several depths of the Balbina Reservoir in the central Amazon Basin, Amazonas, Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC07_Reservoir_GHG/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC07_Reservoir_Methane_Emissions_1047 v1 - This data set reports methane (CH4) fluxes at the water-air interface and concentrations and isotopic signals of CH4 in the bubbles stirred up from the sediment in Tucurui and Samuel reservoirs in 2000 and 2001. Tucurui (deep) reservoir is located near Belem city in the Tocantins-Araguaia basin in the eastern Amazon.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC07_Reservoir_Methane_Emissions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC07_SMMR_Inundated_Area_1051 v1 - This data set reports the monthly record of inundated area, in square km, for six floodplain and open water regions in South America. The following floodplains were analyzed: (1) mainstem Amazon River floodplain in Brazil; (2) Llanos de Mojos (Beni and Mamore rivers) in Bolivia; (3) Bananal Island (Araguaia River) in Brazil; (4) Roraima savannas (Branco and Rupununi rivers) in Brazil and Guyana; (5) Llanos del Orinoco (Apure and Meta rivers) in Venezuela and Colombia; and (6) Pantanal wetland (Paraguay River) in Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC07_SMMR_Inundated_Area/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC07_Monthly_Inundated_Areas_1049 v1 - This data set reports monthly mean inundation areas (square kilometers) for four cover classes of Central Amazon wetlands habitat: Open water (OW), river channel (RC) class, macrophyte (MA) class, and a flooded forest (FF) class, which also incorporates a flooded shrub class. The full study area was a 1.77 million km2 quadrant covering the Central Amazon Basin.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC07_Monthly_Inundated_Areas/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC07_Spectroradiometry_1144 v1 - This data set includes bidirectional reflectance (BDR) spectra and water-quality data of floodplain lakes of the Solimoes and Negro Rivers in the central Amazon basin, Amazonas, Brazil. Samples and measurements were collected during July 2000 to August 2000.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC07_Spectroradiometry/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC07_Airborne_Rasters_1274 v1 - This data set includes high-resolution geocoded mosaics derived from the Validation Overflight for Amazon Mosaics (VOAM) aerial video surveys as part of the Large-Scale Biosphere-Atmosphere (LBA) Experiment in the Amazon. The VOAM flights were carried out in the wet-season (June) 1999 in the Brazilian Amazon to provide ground verification for mapping of wetland cover in the Amazon Basin conducted by the Global Rain Forest Mapping (GRFM) Project JERS-1 (Japanese Earth Remote Sensing Satellite).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC07_Airborne_Rasters/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC07_Airborne_Videography_1272 v1 - This data set presents georeferenced digital video files from Validation Overflight for Amazon Mosaics (VOAM) aerial video surveys as part of the Large-Scale Biosphere-Atmosphere Experiment in the Amazon. The VOAM flights were carried out in the wet-season (June) 1999 in the Brazilian Amazon to provide ground verification for mapping of wetland cover with the Global Rain Forest Mapping (GRFM) Project JERS-1 (Japanese Earth Remote Sensing Satellite) mosaics of the Amazon basin.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC07_Airborne_Videography/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC07_Curuai_chl_1134 v1 - This data set reports (1) concentrations of total, organic, and inorganic suspended solids; dissolved inorganic, and organic carbon; chlorophyll-a and (2) measurements of turbidity, ph, temperature, transparency, conductivity, and calculated carbon dioxide (CO2) in water samples collected from Lago Curuai (Lake Curuai), in the floodplain of the Amazon River south of Obidos, Para, Brazil. Approximately 70 stations were sampled during four phases of the hydrological cycle: receding (September 2003), low (November 2003), rising (February 2004), and high water (June 2004).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC07_Curuai_chl/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC07_Amazon_Wetlands_1284 v2 - This data set provides a map of wetland extent, vegetation type, and dual-season flooding state of the entire lowland Amazon basin. As described in Hess et al.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC07_Amazon_Wetlands/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC08_Ecosystem_Demography_Model_1102 v1 - This data set provides Ecosystem Demography Model (ED) estimates of potential above-ground net primary production (NPP) (kg C/m2/y), potential average live biomass (kg C/m2), and potential average soil carbon (kg C/m2) for the Brazilian Amazon at 1 degree resolution. Ecosystem Demography Model predicts both ecosystem structure (e.g.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC08_Ecosystem_Demography_Model/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC08_Fire_Observations_1095 v1 - This data set reports observations of fires in the vicinity of Maraba, Para, Brazil, from November 3-5th, 2001, and in Mato Grosso, Brazil, between Cuiaba and Alta Floresta, for July 12-15th, 2002. These ground-based data were collected by visual inspection from roads primarily during daylight hours.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC08_Fire_Observations/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC08_EOS_Maps_1155 v1 - This data set provides (1) soil maps for Brazil that are digital versions of the MAPA DE SOLOS DO BRASIL (EMBRAPA, 1981) classified at three levels of detail, 19-class, 70-class and 249-class; (2) vegetation maps for Brazil that are digital versions of the MAPA DE VEGETACAO DO BRASIL (IBGE, 1988) classified at three levels of detail, 13-class, 59-class, and an overprint (combination) class; and (3) a land cover map for all of South America that was derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data over the time period 1...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC08_EOS_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC09_Precipitation_940 v1 - This data set reports daily total precipitation data retrieved from Brazilian National Institute of Meteorology (INMET) network for three stations near two Amazonian research sites: Altamira, and Santarem, from 1961-1998. Daily precipitation totals are provided in one comma-separated ASCII file for three stations in Para, including two sites in Altamira: Altamira City and on the Transamazon Highway at Km 100 near Medicilandia (operated by EMBRAPA); and, one site in Santarem: Taperinha.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC09_Precipitation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC09_Transition_Matricies_1098 v1 - This data set includes classified land cover transition maps at 30-m resolution derived from Landsat TM, MSS, ETM+ imagery and aerial photos of Altamira, Santarem, and Ponta de Pedras, in the state of Para, Brazil. The Landsat images were classified into several types of land use (e.g., forest, secondary succession, pasture, annual crops, perennial crops, and water) and subjected to change detection analysis to create transition matrices of land cover change.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC09_Transition_Matricies/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC09_Landsat_987 v1 - This data set includes 15 zipped archives of rectified .tif format Landsat 5 TM and Landsat 7 ETM+ scenes from near the study sites of Altamira, Santarem, Ponta de Pedras, and Bragantina in the state of Para, Brazil and Machadinho D'Oeste in Rondonia, Brazil. Dates represent the most cloud-free image retrievals from 1985-2004 and are therefore not continuous.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC09_Landsat/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC09_GIS_Study_Areas_986 v1 - This data set includes 16 zipped archives of shapefiles of cities, rivers and streams, roads, and study area boundaries of several Amazonian study sites: Altamira, Santarem, Bragantina, and Ponta de Pedras, in the state of Para, and 1 site at Machadinho D''Oeste, in the state of Rondonia. Data from Brazil were digitized from Instituto Nacional de Colonizacao e Reforma Agraria (INCRA) maps and other data from Instituto Brasileiro de Geografia e Estatistica (IBGE).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC09_GIS_Study_Areas/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC09_Soil_Composition_938 v1 - This data set reports basic soil structure and composition information for five Amazonian research sites: Altamira, Bragantina, Tome-Acu, and Ponta de Pedras, all four in the state of Para, Brazil; and one site in Yapu, Colombia. Soil characteristics reported for all five study sites include cation information (e.g., H, Al, Mg, K, Na, S), percent of soil C, N, and organic matter, soil texture/composition and color, pH, and land use history.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC09_Soil_Composition/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC09_Vegetation_Composition_939 v1 - No abstract available.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC09_Vegetation_Composition/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC10_Landsat_TM_852 v1 - This data set includes Landsat TM scenes from across the Legal Amazon region. A single image is provided for each spatial tile, representing the most cloud-free retrieval from 9/21/86 to 9/17/94.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC10_Landsat_TM/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC10_Landsat_ETM_846 v1 - This data set includes orthorectified Landsat ETM+ scenes across the Legal Amazon region. At least one scene is provided for each spatial tile, representing the most cloud-free retrievals from mid-1999 through late 2001 (Fig.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC10_Landsat_ETM/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC13_GIS_Cauaxi_890 v1 - We combined a detailed field study of canopy gap fraction with spectral mixture analysis of Landsat ETM+ satellite imagery to assess landscape and regional dynamics of canopy damage following selective logging in an eastern Amazon forest. Our field studies encompassed measurements of ground damage and canopy gap fractions along multi-temporal sequences of post-harvest regrowth of 0.5-3.5 yr.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC13_GIS_Cauaxi/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC13_GIS_Juruena_888 v1 - We combined a detailed field study of canopy gap fraction with spectral mixture analysis of Landsat ETM+ satellite imagery to assess landscape and regional dynamics of canopy damage following selective logging in an eastern Amazon forest. Our field studies encompassed measurements of ground damage and canopy gap fractions along multitemporal sequences of post-harvest regrowth of 0.5-3.5 yr.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC13_GIS_Juruena/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC14_Aboveground_Prod_1196 v1 - This data set reports forest biophysical measurements from a rainfall exclusion experiment conducted at the km 67 Seca Floresta site, Tapajos National Forest, Brazil from 1998 to 2006. The purpose was to observe the potential effects of severe water stress on a humid Amazonian forest (Nepstad 2002).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC14_Aboveground_Prod/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC14_Amazon_Scenarios_1153 v1 - This data set provides the results of the two modeled scenarios for future patterns of deforestation across the Amazon Basin from 2002 to 2050. This larger defined Amazon Basin (PanAmazon area) includes the Amazon River watershed, the Legal Amazon in Brazil, and the Guiana region.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC14_Amazon_Scenarios/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC14_RISQUE_1147 v1 - A simple GIS soil-water balance model for the Amazon Basin, called RisQue (Risco de Queimadasa -- Fire Risk), was used to conduct an analysis of spatial and temporal patterns of drought in moist tropical forests and the complex relationships between patterns of drought and forest fire regimes from 1995 through 2001. The provided data products are the model output estimates of maximum plant-available soil water (PAWmax) at 10 m depth at 8 km resolution and model data inputs of monthly precipitation and evapotranspiration.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC14_RISQUE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC14_Surface_Roots_Phenology_1268 v1 - This data set contains biomass estimates for coarse roots measured on the forest floor and measurements of fine root growth down to 2-m depth at the km 67 Rainfall Exclusion Experiment site, Tapajos National Forest, Brazil. The study site was part of a rainfall exclusion experiment that was conducted from 1999-2006 to develop an understanding of the physical processes driving the observed soil water dynamics at the site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/grab_bag/LC14_Surface_Roots_Phenology/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC14_REE_SLA_1211 v1 - This data set provides measurements of specific leaf area and monthly phenological observations for selected tree and vine species at the km 67 Seca Floresta site, Tapajos National Forest, Para, Brazil. The study site was part of a rainfall exclusion experiment that was conducted from 1999-2006 to develop an understanding of the physical processes driving the observed soil water dynamics at the site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC14_REE_SLA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC15_Roughness_Map_1182 v1 - This data set provides physical roughness maps of vegetation canopies in the Amazon Basin. The images are estimates of aerodynamic roughness length (Z0) and zero plane displacement height (D0) at 1-km spatial resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC15_Roughness_Map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC15_AGLB_Distribution_Map_908 v1 - The amount and spatial distribution of forest biomass in the Amazon basin is a major source of uncertainty in estimating the flux of carbon released from land-cover and landuse change. Direct measurements of aboveground live biomass (AGLB) are limited to small areas of forest inventory plots and site-specific allometric equations that cannot be readily generalized for the entire basin.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC15_AGLB_Distribution_Map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC15_GRFM_JERS1_Mosaic_1024 v1 - This data set contains two image mosaics of L-band radar backscatter and two image mosaics of first order texture. The two backscatter images are mosaics of L-band Radar Backscatter at Horizontal-Horizontal (HH) Polarization created from 1,500 images collected by the Japanese Earth Resources Satellite-1 (JERS-1) Synthetic Aperture Radar (SAR) over the Amazon River Basin as part of the Global Rainforest Mapping Project (GRMP).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC15_GRFM_JERS1_Mosaic/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC15_SPOT_Metrics_1239 v1 - This data set provides Normalized Difference Vegetation Index (NDVI) composite images of the Amazon Basin for the years 1999-2000 at approximately1-km spatial resolution. The images were from the VEGETATION 1 sensor, aboard the SPOT 4 satellite.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC15_SPOT_Metrics/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC15_SRTM_Topography_1181 v1.1 - This dataset provides a subset of the SRTM30 Digital Elevation Model (DEM) elevation and standard deviation data for the Amazon Basin. SRTM30 is a near-global digital elevation model (DEM) comprising a combination of data from the Shuttle Radar Topography Mission (SRTM), flown in February, 2000, and the earlier U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC15_SRTM_Topography/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC15_MODIS_TreeCover_1035 v1 - This data set contains proportional estimates for the vegetative cover types of woody vegetation, herbaceous vegetation, and bare ground over the Amazon Basin for the period 2000-2001. These products were derived from all seven bands of the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Terra satellite.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC15_MODIS_TreeCover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC18_Hyperion_889 v1 - This image was collected by the Hyperion sensor on 10-July-2004 at 13:16:16 GMT. It was calibrated to apparent surface reflectance using the ACORN atmospheric model.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC18_Hyperion/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC19_Field_2002_1261 v1 - This data set provides measurements for soil physical and chemical properties, rooting depth and weight, leaf area index (LAI), plant area index (PAI), biomass, fraction of photosynthetically active radiation (fPAR), and ground-based reflectance measurements of soil and litter samples. The samples were collected from 23 areas within the Brazilian research sites of the Brasilia National Park (BNP) and Aguas Emnendadas Ecological Station (AE), Brasilia; Cangacu Research Center, Tocantins; and Tapajos National Forest, Para.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC19_Field_2002/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC21_Fractional_Cover_1152 v1 - This data set provides Landsat Enhanced Thematic Mapper Plus (ETM+) imagery, derived classified land cover products, and cloud-water masks for selected Brazilian states (Acre, Amapa, Amazonas, Maranhao, Mato Grosso, Para, Rondonia, and Roraima) for the years 1999-2002. The Landsat ETM+ images were processed to derive fractional land cover types (photosynthetic vegetation [PV], non-photosynthetic vegetation [NPV], and bare substrate) by application of the Carnegie Landsat Analysis System (CLAS) methodology (Asner et al., 2005).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC21_Fractional_Cover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC21_Foliar_Nutrients_1234 v1 - This data set provides measurements for foliar nutrients from logging blocks in the Tapajos National Forest, Para Western Santarem, Brazil. Data are included for calcium (Ca), phosphorus (P), magnesium (Mg), nitrogen (N), and potassium (K) concentrations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC21_Foliar_Nutrients/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC21_Selective_Logging_1172 v1 - This data set provides the results of analyses of Landsat Enhanced Thematic Mapper Plus (ETM+) images for selective logging activity in the Brazilian states of Para, Mato Gross, Rondonia, Roraima, and Acre over the years 1999 through 2001. Images were analyzed using the Carnegie Landsat Analysis System (CLAS) to detect and to quantify the amount of damage due to selective logging in the major timber-production states of the Brazilian Amazon.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC21_Selective_Logging/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC21_Soil_Characteristics_1236 v1 - This data set provides measurements for soil nutrients from areas that were selectively logged and from control areas in the Tapajos National Forest, Para Western Santarem, Brazil. Data are included for calcium (Ca), phosphorus (P), magnesium (Mg), nitrogen (N), aluminum (Al), iron (Fe), silicon (Si), carbon 13, nitrogen 15, and potassium (K) concentrations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC21_Soil_Characteristics/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC22_MODIS_Field_Val_2004_1262 v1 - This data set contains field observations, corresponding GPS points, and point and polygons of deforested areas in the state of Mato Grosso, Brazil, for the period August 2003 to July 2004. The field observations were conducted in the forested areas between Nova Mutum and Sinop, MT.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/grab_bag/LC22_MODIS_Field_Val_2004/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC22_MODIS_Field_Val_2005_1260 v1 - This data set contains field observations, corresponding GPS points, and point and polygons of deforested areas in the state of Mato Grosso, Brazil, for the period March 17-24,2005. Fieldwork was conducted in the regions surrounding Sinop, Mato Grosso, with specific emphasis on large clearings occurring in the Xingu Basin.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/grab_bag/LC22_MODIS_Field_Val_2005/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC22_MODIS_Phenology_Mato_Grosso_1185 v1 - This data set, LBA-ECO LC-22 Land Cover from MODIS Vegetation Indices, Mato Grosso, Brazil, provides land cover classifications for Mato Grosso, Brazil, for the years 2000-2001 and 2003-2004. The classifications were derived from annual vegetation phenology information from a time series of Collection 4, 16-day MODerate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI), and the Enhanced Vegetation Index (EVI) vegetation data, at 250-m resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC22_MODIS_Phenology_Mato_Grosso/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC22_Post_Deforestation_LULC_1099 v1 - This data set provides (1) areal estimates of deforestation events (>25 ha) that were identified from 2001-2004 in Mato Grosso by the Brazilian Institute for Space Research (INPE) as part of the Program for the Estimation of Deforestation in the Brazilian Amazon (PRODES) and (2) the classification of the post-deforestation land use as either cropland, cattle pasture, or not in production (deforested areas that were never fully cleared or returned immediately to secondary forest) in the years after the large deforestation events from 2001-2005. Data are provided in ESRI shapefile format.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC22_Post_Deforestation_LULC/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC22_MODIS_VCF_Tree_Cover_1112 v1 - This data set contains proportional estimates for the vegetative cover types of tree cover, herbaceous vegetation, and bare ground over South America for the period 2000-2001. These products were derived from all seven bands of the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Terra satellite.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC22_MODIS_VCF_Tree_Cover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC23_MODIS_ASTER_Fire_Comparisons_839 v1 - This data set contains data associated with MODIS fire maps generated using two different algorithms and compared against fire maps produced by ASTER. These data relate to a paper (Morisette et al., 2005) that describes the use of high spatial resolution ASTER data to evaluate the characteristics of two fire detection algorithms, both applied to MODIS-Terra data and both operationally producing publicly available fire locations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC23_MODIS_ASTER_Fire_Comparisons/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC23_Vegetation_Fire_Dynamics_843 v1 - Satellite fire detection was determined from two sensors—the Advanced Very High Resolution Radiometer (AVHRR) on NOAA-12 and the Moderate Resolution Imaging Spectroradiometer (MODIS) on both the Terra and Aqua platforms, for 2001- 2003 to characterize fire activity in Brazil, giving special emphasis to the Amazon region. Active fire data for AVHRR/NOAA-12 was produced using a fixed threshold fire detection technique based on the algorithm developed by the Centro de Previsao do Tempo e Estudos Climaticos (CPTEC/INPE) (Setzer and Pereira, 1991; Setzer et al., 1994; Setzer and Malingreau, 19...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC23_Vegetation_Fire_Dynamics/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC23_Vegetation_Fires_2003_887 v1 - The ASTER high resolution satellite data are available for visible-near infrared (15m resolution), short wave infrared (30m), and thermal infrared (90m) bands. Two sets of imagery were collected over Roraima state - Brazil covering a strip of approximately 180 X 60 km each on January 19 and 28, 2003.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC23_Vegetation_Fires_2003/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC24_Basin_Scale_Hot_Pixels_2001_882 v1 - This data set provides the number of hot spots detected across the legal Amazon Basin at 5- km resolution by the AVHRR (Advanced Very High Resolution Radiometer) on NOAA 12, 14, 15, 16, 17, and 18 satellites for the entirety of 2001 (January 1 - December 31). Only hot spots detected at night are included.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC24_Basin_Scale_Hot_Pixels_2001/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC24_Cadastral_Property_Map_Para_1042 v1 - This data set contains a shapefile of a digitized map of the land parcel information of the original properties of the Uruara colonization site, Para, Brazil, acquired from the Instituto de Colonizacao e Reforma Agraria, or the Colonization and Agrarian Reform Institute (INCRA). The Uruara settlement geometry was initially designed by INCRA, and consists of mostly 100 hectare lots (400 x 2500 meters, and 500 x 2000 meters), running north and south of the Trans-Amazon Highway, as a fine network of small, narrow rectangles.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC24_Cadastral_Property_Map_Para/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC24_MODIS_Forest_Cover_500-m_1056 v2 - This data set, LBA-ECO LC-24 Forest Cover Map from MODIS, 500-m, South America: 2001, contains forest cover information for 2001 for all of South America. The data were collected by the MODerate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Earth Observing System, TERRA (AM-1) satellite platform and released by the MODIS science team as an image showing percent canopy cover.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC24_MODIS_Forest_Cover_500-m/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC24_Historical_Roads_Amazon_1043 v1 - Understanding the impact of road investments on deforestation is part of a complete evaluation of the expansion of infrastructure for development.We find evidence of spatial spillovers from roads in the Brazilian Amazon: deforestation rises in the census tracts that lack roads but are in the same county as and within 100 km of a tract with a new paved or unpaved road. At greater distances from the new roads the evidence is mixed, including negative coefficients of inconsistent significance between 100 and 300km, and if anything, higher neighbor deforestation at distances over 300 km.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC24_Historical_Roads_Amazon/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC24_Land_Cover_Uruara_Para_1053 v1 - This data set provides course land cover classifications derived from Landsat TM images for 1986, 1988, and 1991 for the area surrounding the municipality of Uruara, Para, Brazil. Five land cover classes (Water, Clouds/Shadow, Forest, Not Forest, and Background) were derived (Aldrich et al.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC24_Land_Cover_Uruara_Para/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC24_ETM_Deforestation_Map_Para_1999_1054 v1 - This data set contains a 1999 Landsat ETM+ mosaic image land of cover classification showing forested and deforestation areas in Uruara, Para, Brazil. This image may be overlain with the cadastral property map of the same area (see related data set LBA-ECO LC-24 Cadastral Property Map of Uruara, Para, Brazil: ca.1975).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC24_ETM_Deforestation_Map_Para_1999/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC24_Land_Cover_Southern_Para_1055 v1 - This data set is a five-class land cover for Southern Para for the years 1984 (Landsat MSS), 1988 (Landsat TM), 1996, and 2003 (Landsat ETM+). The final classification shows five classes derived using visual comparison (Water, Clouds/Shadow, Forest, Not Forest, Background).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC24_Land_Cover_Southern_Para/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC31_AMZ_Historical_LU_1170 v1 - This data set provides annual spatial patterns of cropland, natural pasture, and planted pasture land uses across Amazonia for the period 1940/1950-1995. Two series of 5-minute grid cell historical maps were generated starting from land use classification products for 1995.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC31_AMZ_Historical_LU/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC31_SITE_1173 v1 - This model product provides the Fortran source code and input data for the Simple Tropical Ecosystem Model (SITE). SITE is a simplified point model of vegetation dynamics that uses an integration interval of one hour to estimate the fluxes of CO2, water, and energy.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC31_SITE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC35_GOES_WF_ABBA_1180 v1 - This data set is an active fire detection product resulting from the application of The Wildfire Automated Biomass Burning Algorithm (WF_ABBA) to Geostationary Environmental Operational Satellite (GOES) imager data for all of South America from 2000 through 2005. GOES imager data are available at 30 minute intervals with a nominal 4 x 4-km resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC35_GOES_WF_ABBA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC35_Landsat7_Fire_Masks_1071 v1 - This data set provides active fire detection images and associated summary information derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images for various locations in Brazilian Amazonia during 2001-2003. There are two image types: (1) GeoTiff images (masks) of active fire pixels, and (2) GeoTiff images (masks) of clustered active fire pixels where a distinct cluster identification number has been assigned to each individual group of contiguous active fire pixels.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC35_Landsat7_Fire_Masks/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC39_DECAF_Model_1190 v1 - This data set contains modeled estimates of carbon flux, biomass, and annual burning emissions across the Brazilian state of Mato Grosso from 2000-2006. The model, DEforestation CArbon Flux (DECAF), was used to provide annual carbon fluxes from large deforestation events (>25 ha) based on post-deforestation land use, and the frequency and duration of active fires during the deforestation process.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC39_DECAF_Model/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LC39_MODIS_Fire_SA_1186 v1 - This data set provides active fire locations and estimates of annual fire frequencies for South America from 2000-2007. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra (2000-??2007) and Aqua (2003-2007) satellite platforms were analyzed to determine spatial and temporal patterns in satellite fire detections.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/LC39_MODIS_Fire_SA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND01_Pasture_Nutrients_1135 v1 - This data set provides soil physical and chemical properties, and grass nutrient measurements of samples collected from 17 pasture sites located within the state of Rondonia in the southwestern Brazilian Amazon. Soil data includes bulk density, class, texture, and measurements of carbon (C), phosphorus (P), calcium (Ca), magnesium (mg), and potassium (K) concentrations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND01_Pasture_Nutrients/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND01_Spectral_Mixture_Models_1188 v1 - This data set provides fractional land cover type images for shade, green vegetation (GV), non-photosynthetic vegetation (NPV), and soil for the regions of JiParana, PortoVelho, Luiza, Ariquemes, and Cacoal in the state of Rondonia, Brazil, for the period 1984 to 2000. The images were derived with a spectral mixture analysis (SMA) of Landsat Thematic Mapper (TM) time series scenes for each of these areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND01_Spectral_Mixture_Models/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND01_Registered_TM_MSS_1197 v1 - This data set provides a time series of Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) scenes for five (Path/Row) areas in Rondonia, Brazil. The scenes are from the period June 1975 through June 2000, but all areas do not have scenes for all the years.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND01_Registered_TM_MSS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND01_Land_Cover_Maps_1259 v1 - This data set provides a time series of land cover classifications for Ariquemes, Ji-Parana, and Luiza, research sites in Rondonia, Brazil. The land cover classifications are derived from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) sensors.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND01_Land_Cover_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND01_Georectified_Products_1165 v1 - This data set provides a 27-year land cover time series of 28.5-m resolution products derived from Landsat images for 80% of Rondonia, Brazil, for the period 1984 to 2010. Selected Landsat Thematic Mapper (TM) and Landsat Multispectral Scanner (MSS) images from the years 1984 through 2010, for seven path/row scenes (PortoVelho, Ariquemes, Jiparana, Luiza (or Urupa), Cacoal, Chapuingaia, and Vilhena) were mosaicked for each year.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND01_Georectified_Products/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND01_Age_Maps_1184 v1 - This data set provides classified land cover transition images (maps) derived from Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS) imagery for Ariquemes, Luiza, and Ji-Parana¡ areas in Rondonia, Brazil, at 30-m resolution. Images depict the age relative to the year 2000, of cleared land from the date the land was cut, to the date when primary forests transitioned into nonforest class (for example, 25 = cut by 1975, or 25 years before the year 2000).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND01_Age_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND01_Pasture_Spectra_1154 v1 - This data set provides the results of spectral reflectance (350 to 2,500 nm at 1-nm increments) and biophysical measurements on grass pastures in eight cattle ranches in the state of Rondonia, located in the southwestern Brazilian Amazon. The ranches are located near the cities of Porto Velho, Ariquemes, Ouro Preto, Ji-Parana, and Presidente Medici.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND01_Pasture_Spectra/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND01_Stream_Chemistry_1119 v1 - This data set provides the results of (1) synoptic streamwater sampling and analyses from numerous sites across Rondonia and (2) corresponding watershed characteristics derived from remote sensing and historical/available data sources. Sixty streams, in both forested and non-forested sites, were sampled once during the dry season in August of 1998 and 49 of the same streams were sampled again during the wet season in January-February of 1999.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND01_Stream_Chemistry/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND01_Watershed_Defor_1159 v1 - This data set provides estimates of watershed deforestation, as a proportion of the total area of watersheds, in Rondonia, Brazil for 1999. Deforestation maps were determined for the main agricultural and surrounding forested areas of Rondonia using multiple Landsat TM scenes (Biggs et al.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND01_Watershed_Defor/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND02_Mulching_Experiment_950 v1 - Fires set for slash-and-burn agriculture contribute to the current unsustainable accumulation of atmospheric greenhouse gases, and they also deplete the soil of essential nutrients, which compromises agricultural sustainability at local scales. Integrated assessments of greenhouse gas emissions have compared intensive cropping systems in industrialized countries, but such assessments have not been applied to common cropping systems of smallholder farmers in developing countries.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND02_Mulching_Experiment/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND02_Soil_CO2_Extracts_1074 v1 - This data set provides a time series of calcium, magnesium, and potassium concentrations extracted from soil samples from a laboratory column extraction study conducted in 2002. Soils used in the columns were originally collected in 1998 in Fazenda Vitoria, a cattle ranch 6 km north of the town of Paragominas, Para, Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND02_Soil_CO2_Extracts/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND02_Soil_CO2_Flux_1066 v1 - This data set reports soil CO2 flux and results of This data set reports soil CO2 flux and results of physical and chemical characterization of soils from pastures, secondary forests, and mature forests near Rio Branco, Acre, Brazil. CO2 flux measurements were made in the field on a monthly basis at 16 sites from June of 1999 to January 2001.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND02_Soil_CO2_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND02_Landsat_TM_MSS_Para_1156 v1 - This data set provides Landsat images of the county of Sao Francisco do Para located in the Bragantina region of Para, Brazil, the oldest agriculture frontier in Amazonia. These images are subsets for the municipio (county) and immediate region.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND02_Landsat_TM_MSS_Para/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND02_Soil_Hydraulic_Conductivity_1075 v1 - This data set reports field estimated saturated hydraulic conductivity measurements from June 12 through June 20, 2001. This study was part of a rainfall exclusion experiment that was conducted from 1999-2001 at the km 67 Seca Floresta site, Tapajos National Forest, Para, Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND02_Soil_Hydraulic_Conductivity/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND02_Non_Woody_Biomass_1115 v1 - This data set reports biomass from small stems and non-woody vegetation measured from 1999 to 2005 in plots of a secondary-growth forest fertilization experiment. The study location was Fazenda Vitoria, 6.5-km northwest of the town of Paragominas, Para, Brazil, in a 6-year old secondary-growth forest.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND02_Non_Woody_Biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND02_Tree_Heights_DBH_951 v1 - Understanding secondary successional processes in Amazonian terrestrial ecosystems is becoming increasingly important as continued deforestation expands the area that has become secondary forest, or at least has been through a recent phase of secondary forest growth. Most Amazonian soils are highly weathered and relatively nutrient poor, but the role of nutrients as a factor determining successional processes is unclear.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND02_Tree_Heights_DBH/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND02_Soil_Gases_REE_1117 v1 - This data set reports soil carbon dioxide (CO2) and nitrous oxide (N2O) concentrations and soil volumetric water content (VWC) from a rainfall exclusion experiment that was conducted at the km 67 Seca Floresta site, Tapajos National Forest, Brazil. Samples were collected every two to three months.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND02_Soil_Gases_REE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND02_REE_Trace_Gas_Tapajos_955 v1 - Moist tropical forests in Amazonia and elsewhere are subjected to increasingly severe drought episodes through the El Nino-Southern Oscillation (ENSO) and possibly through deforestation-driven reductions in rainfall. The effects of this trend on tropical forest canopy dynamics, emissions of greenhouse gases, and other ecological functions are potentially large but poorly understood.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND02_REE_Trace_Gas_Tapajos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND02_Soil_Gas_Flux_Apeu_953 v1 - Changes in land-use and climate are likely to alter moisture and substrate availability in tropical forest soils, but quantitative assessment of the role of resource constraints as regulators of soil trace gas fluxes is rather limited. The primary objective of this study was to quantify the effects of moisture and substrate availability on soil trace gas fluxes in an Amazonian regrowth forest.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND02_Soil_Gas_Flux_Apeu/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND02_REE_Soil_VWC_1061 v1 - This data set reports monthly measured soil volumetric water content (VWC) from a rainfall exclusion experiment that was conducted from 1999-2001 at the km 67 Seca Floresta site, Tapajos National Forest, Brazil. The purpose was to observe the potential effects of severe water stress on a humid Amazonian forest (Nepstad 2002).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND02_REE_Soil_VWC/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND02_Water_Chemistry_Paragominas_1067 v1 - This data set includes measurements of dissolved nutrient and organic carbon concentrations, as well as dissolved oxygen, alkalinity, conductivity, turbidity, pH, and discharge from three streams located in mixed land use (crop fields, pastures, secondary vegetation, and forest) and two streams in entirely forested landscapes near Paragominas in the state of Para, Brazil. Stream water samples were collected during two different periods: 1) weekly from August 1999 to July 2001 at location Igarape 54, Station 5 and 2) monthly from April 2003 through October 2005 at all of the stations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND02_Water_Chemistry_Paragominas/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND03_Flowpath_Chemistry_1076 v1 - This data set consists of water chemistry data from streams, wells, rainwater, and canopy throughfall samples. The field measurements were carried out at Rancho Grande in the Brazilian state of Rondonia, in the southwestern Brazilian Amazon basin, at two adjacent watersheds, a forest (1.37 ha), and pasture (0.73 ha).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND03_Flowpath_Chemistry/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND03_Streams_Soilwater_1113 v1 - This data set provides the results of (1) the physical and chemical characterization of streams and (2) comparable chemical analyses of extracted soil water in the Aldeia River basin at Fazenda Nova Vida, a large cattle ranch 50 km from the city of Ariquemes, in central Rondonia, Brazil, from 1994-2001. Data are provided on the stream beds including cross-sectional depth and stream bed surface type.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND03_Streams_Soilwater/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND04_Soil_H2O_Manaus_1246 v1 - This data set contains soil water measurements to a depth of 3 meters for the years 1999, 2000, and 2001, and total monthly precipitation data for 1999-2000. The data were collected from a pasture site located at the Embrapa Pasture Research Site, a former cattle research station 54 km north of Manaus on the highway BR 174 Manaus-Boa Vista, Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/grab_bag/ND04_Soil_H2O_Manaus/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND04_C_Nutrient_Stocks_1069 v1 - This data set reports the carbon and nutrient stocks of above-ground vegetation and soil pools at three locations where post-pasture secondary forest recovery ranged from 0 to 14 years since abandonment. These sites are located in the state of Amazonas, Brazil, along the road BR-174 north of the city of Manaus within three fazendas (cattle ranches) now in various stages of grazing, pasture abandonment, or pasture reclamation: Fazenda Rodao (km 46), Embrapa-District of SUFRAMA (DAS) pasture research site (km 53) and Fazenda Dimona (km 72).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND04_C_Nutrient_Stocks/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND04_Secondary_Forest_Recovery_1068 v1 - This data set reports measurements of the canopy and structure of secondary forests regenerating from abandoned pastures. These secondary forests are located in the state of Amazonas, Brazil, along the road BR-174 north of the city of Manaus within three fazendas (cattle ranches) now in various stages of grazing, pasture abandonment, or pasture reclamation: Fazenda Rodao (km 46), Embrapa-District of SUFRAMA (DAS) pasture research site (km 53), and Fazenda Dimona (km 72).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND04_Secondary_Forest_Recovery/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND04_Termite_Mounds_1072 v1 - This data set reports the results of a comprehensive study of mound building termites at the Embrapa research station in the Distrito Agropecuario da SUFRAMA, located at km 53 of the federal highway BR 174 outside Manaus, Amazonas, Brazil. Study areas included a primary forest site, an adjacent 7-8 year old secondary forest site, and two abandoned pasture sites which were being used for agroforest purposes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND04_Termite_Mounds/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND06_LandUse_Studies_1130 v1 - This data set provides measurements of soil properties compiled from 39 studies on nutrient dynamics in natural forests and forest-derived land uses (pasture, shifting cultivation and tree plantations) conducted in Amazonia over the period of 1950-2001. The initial literature survey for the data consisted of more than 100 studies conducted during this period.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND06_LandUse_Studies/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND07_15N_Leaves_Soils_1121 v1 - This data set provides (1) delta 15N ratios and nitrogen concentrations for foliar samples and (2) delta 13C and delta 15N ratios as well as carbon and nitrogen concentrations for soil samples collected from cerrado sites within the Ecological Reserve of the Brazilian Institute of Geography and Statistic (IBGE), Brasilia, Brazil. Foliar samples, collected from 320 individuals representing 45 woody tree and shrub species, and soil samples were collected from 5 cerrado locations (2 in campo sujo, 2 in cerrado denso and 1 in cerrado).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND07_15N_Leaves_Soils/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND07_Stream_Chemistry_Brasilia_1018 v1 - This data set reports on dissolved nutrient concentrations, as well as dissolved oxygen, alkalinity, conductivity, turbidity, and pH measured in water samples collected from nine streams located in the state of Brasilia, Brazil, between September, 2004 and December, 2006. Streams were located in different land cover types including natural (forest), rural (agricultural), and developed landscapes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND07_Stream_Chemistry_Brasilia/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND07_PLFA_Soils_Microbial_Biomass_1017 v1 - This data set reports the microbial biomass in soil samples collected from the Cerrado, a woodlands-savannah area, in Brasilia, Brazil. Microbial biomass was determined as the total concentration of phospholipid fatty acids (PLFAs).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND07_PLFA_Soils_Microbial_Biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND07_NO_Flux_Cerrado_1124 v1 - This data set reports the results of soil nitric oxide (NO) flux, soil moisture, and soil nitrate (NO3) and ammonium (NH4) concentration measurements on Cerrado soils receiving nitrogen fertilization. Measurements and samples were collected from control and fertillized experimental plots on Cerrado soils within the Ecological Reserve of the Brazilian Institute of Geography and Statistic (IBGE), Brasilia, Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND07_NO_Flux_Cerrado/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND07_Trace_Gas_Land_Use_1016 v1 - This data set reports on soil-atmosphere fluxes of trace carbon dioxide, carbon monoxide, nitrous oxide, and nitric oxide (CO2, CO, N2O, NO) under various natural and manipulated land use conditions. The studies were conducted near Brasilia, Brazil in pastures and agricultural areas under a variety of management regimes and in more natural areas of cerrado (20-50% canopy cover) and campo sujo (open, grass-dominated), which were either burned every 2 years or protected from fire.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND07_Trace_Gas_Land_Use/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND08_Biomass_Jari_1148 v1 - This data set reports the concentrations of the nutrients nitrogen (N), phosphorus (P), magnesium (Mg), calcium (Ca), and potassium (K) in roots, litterfall, leaves, and twigs, biomass of fine roots and litterfall, and the decomposition of leaves and twigs in samples that were collected on the property of Jari Celulose, Monte Dourado, Para, Brazil, from 1999-2001. Samples were collected from two study sites, a eucalyptus plantation and an adjacent primary forest, during both rainy and dry seasons.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/grab_bag/ND08_Biomass_Jari/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND08_Soil_Respiration_1250 v1 - This data set provides (1) carbon (C) and nitrogen (N) concentration measurements of two soil aggregate fractions (250-2000 micon, small macro-aggregates (SMAG)), and (53-250 micron (micro-aggregates (mico)) and (2) in situ soil respiration measurements (January-March 2003) on sand and clay soils from a Eucalyptus plantation and an adjacent primary forest. The soils for fractionation were sampled in July 2001 from 0-20 cm and 30-50 cm depths.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/grab_bag/ND08_Soil_Respiration/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND10_Soil_Chemistry_1171 v1 - This data set provides the results of soil physical property and chemical measurements of samples collected from two pasture chronosequences (years since conversion from primary forest) located on two ranches south of Santarem, Para, Brazil, and east of the Tapajos River. Soil data includes soil classification, bulk density, texture, and mean concentrations of total nitrogen (N), carbon (C), phosphorus (P), and P fractions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND10_Soil_Chemistry/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND11_Veg_Biomass_MT_964 v1 - Not available.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND11_Veg_Biomass_MT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND11_Logging_Damage_MT_977 v1 - Data were collected in the logging concession at the Fazenda Rohsamar in the municipality of Juruena in northwestern Mato Grosso. Estimates of damage associated with logging operations were made after logging operations were complete in 2003 and 2004.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND11_Logging_Damage_MT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND11_Soil_Nitrate_Moisture_MT_976 v1 - This data set reports the results of the analysis of soil samples for Nitrate (NO3) and physical properties that were collected for one year following reduced impact logging in logging concessions at the Fazenda Rohsamar in the municipality of Juruena in northwestern Mato Grosso. Sample locations were randomly selected from stratified regions of the 1,400 ha Block 5 to account for local scale soil variability.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND11_Soil_Nitrate_Moisture_MT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND11_Nitrogen_Transfer_Leaf_Litter_915 v1 - It has been proposed that the C/N ratio, or quality, of litter or mulch mixtures affects N release. Although total N release from these mixtures and the effects on soil N are relatively well understood, a mechanistic understanding of the interactions between litter species with respect to their N release is still lacking.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND11_Nitrogen_Transfer_Leaf_Litter/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND11_Carbon_Export_CPOM_913 v1 - Resolving the carbon (C) balance in the Amazonian forest depends on an improved quantification of production and losses of particulate C from forested landscapes via stream export. The main goal of this work was to quantify litterfall, the lateral movement of litter, and the export of coarse organic particulate matter (>2 mm) in four small watersheds (1-2 ha) under native forest in southern Amazonia near Juruena, Mato Grosso, Brazil (10°25 S, 58°46 W).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND11_Carbon_Export_CPOM/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND11_Tree_Vine_Biomass_MT_922 v1 - The purpose of this study was to determine if spatially-explicit commercial timber inventories (CTI) could be used in conjunction with satellite imagery to improve timber assessments and forest biomass estimates in Amazonia. As part of a CTI, all commercial trees >= 45 cm DBH were measured and georeferenced in 3500 ha of a logging concession in NW Mato Grosso, Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND11_Tree_Vine_Biomass_MT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND11_Soil_Spatial_Variability_914 v1 - The northwestern region of Mato Grosso State consists of complex landscapes due to ongoing geomorphologic activity that contributes to the occurrence of different soil classes over small distances, which complicates soil sampling strategies. This study was conducted in Juruena (MT), with the objective of identifying pedologic classes in undisturbed forested headwater catchments by examining the spatial variability of soil texture and color, and taking elevation and topographic position into consideration.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND11_Soil_Spatial_Variability/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND11_Soil_Water_Pressure_851 v1 - This data set contains information that can be used to examine water fluxes in soils beneath tree crops in an Amazonian agroforest. The data consists of repeated measurements of soil matrix pressure and soil moisture content at several depths.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND11_Soil_Water_Pressure/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND11_Stream_Nutrients_921 v1 - This data set contains baseflow streamwater concentrations of pH, specific conductivity, base cations, carbon (dissolved organic carbon (DOC), particulate organic carbon (POC) and bicarbonate alkalinity) and silica for four headwater streams in the seasonally dry Amazon (Johnson et al. (2006a) and Johnson et al.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND11_Stream_Nutrients/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND30_Pasture_Degradation_1164 v1 - This data set contains images of fractional cover estimates of photosynthetic vegetation (PV) canopy, nonphotosynthetic vegetation (NPV), and exposed soils (S) derived from Landsat images (30-m resolution) obtained for two ranches in the Brazilian Amazon from 1996 to 2002. The Fazenda Vitoria ranch is located in eastern Para near the city of Paragominas and is a mosaic of primary forest, logged forest, secondary forest, and pasture with moderately dissected topography.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND30_Pasture_Degradation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND30_Litter_Para_1129 v1 - This data set provides fine litterfall mass and nutrient concentrations from samples collected at chronosequences established at Sao Francisco do Para and Capitao Poco, Para, Brazil. Nitrogen (N) and phosphorus (P) concentrations were determined for litterfall samples from the Sao Francisco do Para, and N, P, potassium (K), calcium (Ca), and magnesium (Mg) concentrations are reported for samples from the Capitao Poco.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND30_Litter_Para/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ND30_REE_Water_Chemistry_1131 v1 - This data set reports the results of chemical analyses of rainfall, throughfall, litter leachate, and soil water samples collected before, during, and after a rainfall exclusion experiment conducted at the km 67 Seca Floresta site, Tapajos National Forest, Brazil. Samples were collected every two weeks from May 17, 1999 through May 10, 2006.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/nutrient_dynamics/ND30_REE_Water_Chemistry/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG02_Balloon_VOC_1110 v1 - This data set reports concentrations of biogenic volatile organic compounds (BVOCs) collected from tethered balloon-sampling platforms above selected forest and pasture sites in the Brazilian Amazon in March 1998, February 1999, and February 2000. The air samples were collected from forested sites in Brazil: the Tapajos forest (Para) in the Tapajos/Xingu moist forest; Balbina (Amazonas) in the Uatuma moist forest; and Jaru (Rondonia) in the Purus/Madeira moist forest.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG02_Balloon_VOC/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG03_AERONET_AOT_1128 v1 - This data set includes aerosol optical thickness measurements from the CIMEL sunphotometer for 22 sites in Brazil during the period from 1993-2005. The AERONET (AErosol RObotic NETwork) program is an inclusive federation of ground-based remote sensing aerosol networks established by AERONET and the PHOtométrie pour le Traitement Opérationnel de Normalisation Satellitaire (PHOTONS) and greatly expanded by AEROCAN (the Canadian sunphotometer network) and other agency, institute and university partners.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG03_AERONET_AOT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG03_Aeronet_Solar_Flux_1137 v1 - This data set includes solar surface irradiance from Kipp and Zonen CM-21 pyranometers, both total unfiltered and filtered (RG695), and photosynthetically active radiation (PAR) from Skye-Probetech SKE-510 PAR sensors. Measurements were made at six sites acrosss the Brazilian Amazon during the period from 1999 to 2004.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG03_Aeronet_Solar_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG05_CASA_1199 v1 - This data set provides maps produced from model output data from the National Aeronautics and Space Administration-Carnegie Ames Stanford Approach (NASA-CASA) model and other modeling approaches. The maps include estimated annual Net Primary Production (ANPP), leaf (live) biomass carbon, wood (live) biomass carbon, fine root (live) biomass carbon, metabolic leaf litter (dead) carbon, structural leaf litter (dead) carbon, woody detritus (dead) carbon, and slow soil carbon, gridded at half-degree spatial resolution for the years 1982-1998, and 2001 (NPP data) for Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG05_CASA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG06_Vertical_Profiles_1175 v1 - This data set contains measurements of atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), hydrogen (H2), nitrous oxide (N2O), and sulfur hexafluoride (SF6) collected from December 2000-November 2005 as vertical profiles above three sites in Brazil: Fortaleza, Santarem, and Manaus. At Santarem, ascending profiles were made above the Tapajos National Forest, near the km 67 Tower Site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG06_Vertical_Profiles/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG07_Fallen_Standing_Necromass_998 v1 - This data set reports the characterization of fallen necromass as the volume and density of coarse woody debris (CWD), and standing necromass as the volume and density of standing dead trees. Measurements were made in undisturbed and logged forest areas of the Tapajos National Forest, and Cauaxi Forest, Para, Brazil, and Juruena Forest, Mato Grosso, Brazil from 2002-2004.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG07_Fallen_Standing_Necromass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG07_Soil_Nutrients_1085 v1 - This data set reports phosphorus (P), carbon (C), and nitrogen (N) nutrient pool concentrations for forest soils and roots and P pool concentrations for forest floor litter, soil solutions, and microbial extracts. Soils samples were also extracted using the Hedley sequential fractionation method and the extracts analyzed for P.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG07_Soil_Nutrients/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG07_STM_GLAS_836 v1 - This data set provides the results of a GLAS (the Geoscience Laser Altimeter System) forest structure validation survey conducted in Santarem and Sao Jorge, Para during November 2004 (Lefsky et al., 2005). DBH, total height, commercial height, canopy width and canopy class description were measured for 11 primary forest sites in Santarem along two 75m transects per GLAS measurement.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG07_STM_GLAS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG07_FFT_Survey_Km83_923 v1 - Changes in the biomass of Amazon region forests represent an important component of the global carbon cycle but the biomass of these forests remains poorly quantified. We examined forest survey data for trees with a diameter at breast height (DBH) greater than 35 cm from 4 plots with a total area of 392 ha in the Tapajos National Forest near Santarem, Para, Brazil (S 3.04, W 54.95).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG07_FFT_Survey_Km83/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG07_Litter_Decomposition_925 v1 - Once the weathering of parent material ceases to supply significant inputs of phosphorus (P), vegetation depends largely on the decomposition of litter and soil organic matter and the associated mineralization of organic P forms to provide an adequate supply of this essential nutrient. At the same time, the decomposition of litter is often characterized by the immobilization of nutrients, suggesting that nutrient availability is a limiting factor for this process.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG07_Litter_Decomposition/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG07_Root_Mortality_Longterm_1116 v1 - This data set reports measurements of trace gas fluxes of methane (CH4), nitric oxide (N2O), nitrous oxide (NO), carbon dioxide (CO2) from soils at a study site in the Tapajos National Forest (TNF), near the km 83 on the Santarem-Cuiaba Highway south of Santarem, Para, Brazil. Data for root mass and carbon content, soil nitrogen (N), nitrification, and moisture content are also provided.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG07_Root_Mortality_Longterm/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG07_Trace_Gas_Profiles_1107 v1 - This data set provides concentrations of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from air samples collected at several heights on towers at three locations in upland old growth forests in the Brazilian Amazon during the wet and dry seasons of 2004 and 2005. Towers are located in the Caxiuana National Forest, in the state of Amazonas; the Manaus, Para, site in the Cuieiras Reserve; and the Sinop site, located north of that city in the state of Mato Grosso.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG07_Trace_Gas_Profiles/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG07_Autochamber_Soil_CO2_Flux_Km67_927 v1 - The net ecosystem exchange of carbon dioxide was measured by eddy covariance methods for 3 years in two old-growth forest sites near Santarem, Brazil. Carbon was lost in the wet season and gained in the dry season, which was opposite to the seasonal cycles of both tree growth and model predictions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG07_Autochamber_Soil_CO2_Flux_Km67/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG07_Root_Mortality_Experiment_924 v1 - We conducted an experiment on sand and clay tropical forest soils to test the short-term effect of root mortality on the soil-atmosphere flux of nitrous oxide, nitric oxide, methane, and carbon dioxide. We induced root mortality by isolating blocks of land to 1 m using trenching and root exclusion screening.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG07_Root_Mortality_Experiment/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG07_Manual_Flux_Km67_1026 v1 - Trace gas fluxes of carbon dioxide, methane, nitrous oxide, and nitric oxide (CO2, CH4, N2O, and NO) from surface soil were measured manually in an undisturbed forest at the Tapajos National Forest Seca-Floresta Site, which is within the footprint of the km 67 eddy flux tower. Measurements were made in January 2000 through April 2004, approximately twice per month.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG07_Manual_Flux_Km67/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG07_Soil-Atmosphere_Flux_Km83_926 v1 - Selective logging is an extensive land use in the Brazilian Amazon region. We studied the soil-atmosphere fluxes of nitrous oxide (N2O), nitric oxide (NO), methane (CH4), and carbon dioxide (CO2) on two soil types (clay Oxisol and sandy loam Ultisol) over two years (2000-2001)in both undisturbed forest and forest recently logged using reduced impact forest management in the Tapajos National Forest, near Santarem, Para, Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG07_Soil-Atmosphere_Flux_Km83/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG07_DBH_Cauaxi_1063 v1 - Canopy measurements in an undisturbed eastern Amazon forest (Cauxi, Para, Brazil. See Figure 1) were derived from a one-time event in 2000 using a hand-held laser range finder, and diameter at breast height (DBH) was determined manually.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/grab_bag/TG07_DBH_Cauaxi/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG08_Soil_Gas_Fertilization_1105 v1 - This data set provides nitric oxide (NO), nitrous oxide (N2O), carbon dioxide (CO2) flux measurements, nitrogen (N) and phosphorus (P) pools, net N mineralization and nitrification rates, and measurements of soil moisture, in response to nitrogen and phosphorus soil fertilization treatments. The research was conducted in a mature moist tropical forest and an 11-year pasture at Nova Vida in Rondonia, in the Brazilian Amazon, in 1998 and 1999.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG08_Soil_Gas_Fertilization/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG08_Soil_Gas_Wetting_1101 v1 - This data set includes the results of measurements of the soil gas fluxes of nitric oxide (NO), nitrous oxide (N2O), and carbon dioxide (CO2), soil moisture, soil temperature, and soil pools of ammonium and nitrate in response to a simulated rain event. Study sites were soils in mature forests and pastures of two ages (11 and 26 yrs old).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG08_Soil_Gas_Wetting/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG09_N2O_Soils_1013 v1 - This data set reports the results of carbon, nitrogen, and oxygen isotopic analyses of soil, soil water, and N2O soil gas samples; total soil carbon and nitrogen concentrations; and soil texture and bulk density. Samples were collected from the km 83 Logged Forest Tower Site and the km 67 Seca-Floresta Site in the Tapajos National Forest (TNF) near Santarem, Para, Brazil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG09_N2O_Soils/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TG10_TROFFEE_1195 v1 - This data set provides derived emission factors (EFs), reported in grams of compound emitted per kilogram of dry fuel (g/kg), for PM10 (particulate matter up to 10 micrometers in size), O3, CO2, CO, NO, NO2, HONO, HCN, NH3, OCS, DMS, CH4, and up to 48 non-methane organic compounds (NMOC) from the Tropical Forest and Fire Emissions Experiment (TROFFEE). TROFFEE used laboratory measurements followed by airborne and ground based field campaigns in Mato Grosso, Para, and Amazonas, Brazil during the 2004 Amazon dry season to quantify the emissions from pristine tropical forest and several planta...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/trace_gases/TG10_TROFFEE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PC06_ECMWF_LBA_1141 v1 - This data set provides the mean diurnal cycle of precipitation, near-surface thermodynamics, and surface fluxes generated from short-term forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The model outputs were 12- to 36-hour short-range forecasts, run at a triangular truncation of T319 and a vertical resolution of 60 levels, from each daily 1200 (UTC) analysis.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/physical_climate/PC06_ECMWF_LBA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ABLE_897 v1 - The ABLE 2A and 2B (Atmospheric Boundary Layer Experiments) data consists of estimates of the rate of exchange of a wide variety of aerosols and gases between the Amazon Basin and its atmospheric boundary layer, and the processes by which these aerosols and gases are moved between the boundary layer and the free troposphere. The data are presented in gzipped ASCII text files in Global Tropospheric Experiment (GTE) format.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/atmos_chemistry/ABLE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ARME_898 v1 - The Amazonian Region Micrometeorological Experiment (ARME) data contain micrometeorological data (climate, interception of precipitation, mircometeorology and soil moisture) on the elements of the energy balance and evapotranspiration for the Amazonian forest. ASCII text data files for each of the four data types have been zipped toghether.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/physical_climate/ARME/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Pre_LBA_ABRACOS_899 v1.1 - The data set presents the principal data from the Anglo-BRazilian Amazonian Climate Observation Study (ABRACOS) (Gash et al, 1996) and provides quality controlled information from five of the study topics considered by the project in five zipped files containing ASCII text data. The five study topics include Micrometeorology, Climate, Carbon Dioxide and Water Vapor, Plant Physiology, and Soil Moisture.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/physical_climate/Pre_LBA_ABRACOS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CABARE_918 v1 - Surface parameter digital maps of vegetation, soil, and topography were obtained for Rondonia, Brazil, covering the 5x5 degree region bounded by 13-8 degrees S and 65-60 degrees W. Numerical maps of the natural landscape structure were prepared by digitizing existing 1:1,000,000 maps.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/CABARE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CAMREX_904 v1 - The objective of CAMREX (Carbon in the Amazon River Experiment) project which was conducted from 1982 through 1991, was been to define by mass balances and direct measurements those processes which control the distribution of bioactive elements (C, N, P and O) in the mainstem of the Amazon River in Brazil. The CAMREX dataset represents a time series unique in its length and detail for very large river systems.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/surf_hydro_and_water_chem/CAMREX/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"FLOODAMA_903 v1 - This data set provides a digital mosaic of the Amazon River floodplain that was compiled using Landsat TM images. This mosaic was planned in July 1995 as an activity of the EOS-IDS Project that was developed with cooperation among INPE, CENA, University of Washington in Seattle (UW), University of California in Santa Barbara (UCSB), and NASA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/FLOODAMA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"FLUAMAZON_896 v1 - The FLUAMAZON Experiment data set includes meteorological data collected with radiosondes to examine the moisture flux from the northern coast of South America (near the mouth of the Amazon River) into central Amazonia. The measurements were collected from November 23, to December 21, 1989 during the period of transition between the dry and humid seasons in the region.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/physical_climate/FLUAMAZON/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ISLSCP_919 v1 - This data set contains hydrology, soils, radiation, cloud, and vegetation data from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative I. The ISLSCP data sets should provide LBA modelers with many of the fields required to describe boundary conditions, and to initialize and force a wide range of land-biosphere-atmosphere models.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/land_use_land_cover_change/ISLSCP/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"RADAMBrasil_941 v1 - The RADAMBRASIL project extensively mapped the Amazon soils using a combination of soil pit information, aerial photography, and geologic maps. During the project, 1,153 soil pits, distributed basin-wide, were described and sampled by horizon and analyzed for texture and chemical composition.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/carbon_dynamics/RADAMBrasil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"RBLE_917 v1 - The atmospheric boundary layer (ABL) is the layer of air closest to the ground which is directly influenced on a daily basis by the heating and cooling of the earth's surface. The exact depth of the ABL varies according synoptic weather conditions and the time of day.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/physical_climate/RBLE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SCAR-B_916 v1 - This data set contains meteorological data, reanalysis data, remote sensing images, and data on atmospheric composition collected during the Smoke, Clouds, and Radiation - Brazil (SCAR-B) experiment. The SCAR-B examined the effects of biomass burning on atmospheric processes with four primary goals: (1) improving techniques for remote sensing of these process from space, (2) obtain measurements of the rates of emissions of trace gases and particles from biomass burning, (3) observe the influence of atmospheric processes on the emission products was to obtain measurements of the rates of emi...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/physical_climate/SCAR-B/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-A_920 v1 - This data set contains atmospheric chemistry and meteorological data from the NASA Transport and Atmospheric Chemistry near the Equator-Atlantic (TRACE-A) field study. The NASA TRACE-A study took place in August 1992 to determine the cause and source of high concentrations of ozone that accumulate over the Atlantic ocean between southern Africa and South America during the months of August through October.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/lba/atmos_chemistry/TRACE-A/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-lba-eco","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Low-Cost Sensor AQ Project","Description":"Low-Cost-Sensors-AQ_AirQino is the ground site data collected by the AirQino sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing. The Low-Cost Sensor AQ Harmonization aims to harmonize sensor networks by amalgamating measurements from a multitude of networks into one open access framework. Currently, data from 10 unique US-based sensor networks have been collected for redistribution in the archive. Data from each network is reformatted in a common data format with metadata embedded to streamline data processing. A key feature is a tier system in which the team critically reviews each sensor network by their calibration efforts and subsequent data quality, providing data quality flags to help end users determine which sensor data best meets their target application. The framework allows users to search based on calibration criteria in addition to location, date range, and pollutant of interest. The sensor database is aimed at scientific end users seeking ground-based validation data for satellites and models alike but is also accessible to community scientists. The team is currently accepting new submissions! If you maintain a sensor network that you would like included, please contact the PI.\n<br>\n\n#### Low-Cost-Sensors-AQ_BEACO2N\nLow-Cost-Sensors-AQ_BEACO2N is the ground site data collected by the Berkeley Environmental Air-quality & CO2 Network (BEACO2N) as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing. The Low-Cost Sensor AQ Harmonization aims to harmonize sensor networks by amalgamating measurements from a multitude of networks into one open access framework. Currently, data from 10 unique US-based sensor networks have been collected for redistribution in the archive. Data from each network is reformatted in a common data format with metadata embedded to streamline data processing. A key feature is a tier system in which the team critically reviews each sensor network by their calibration efforts and subsequent data quality, providing data quality flags to help end users determine which sensor data best meets their target application. The framework allows users to search based on calibration criteria in addition to location, date range, and pollutant of interest. The sensor database is aimed at scientific end users seeking ground-based validation data for satellites and models alike but is also accessible to community scientists. The team is currently accepting new submissions! If you maintain a sensor network that you would like included, please contact the PI.\n<br>\n\n#### Low-Cost-Sensors-AQ_HAQPods\nLow-Cost-Sensors-AQ_HAQPods is the ground site data collected by the Hannigan Air Quality Pods (HAQ Pods) sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing. The Low-Cost Sensor AQ Harmonization aims to harmonize sensor networks by amalgamating measurements from a multitude of networks into one open access framework. Currently, data from 10 unique US-based sensor networks have been collected for redistribution in the archive. Data from each network is reformatted in a common data format with metadata embedded to streamline data processing. A key feature is a tier system in which the team critically reviews each sensor network by their calibration efforts and subsequent data quality, providing data quality flags to help end users determine which sensor data best meets their target application. The framework allows users to search based on calibration criteria in addition to location, date range, and pollutant of interest. The sensor database is aimed at scientific end users seeking ground-based validation data for satellites and models alike but is also accessible to community scientists. The team is currently accepting new submissions! If you maintain a sensor network that you would like included, please contact the PI.\n<br>\n\n#### Low-Cost-Sensors-AQ_INSTEP\nLow-Cost-Sensors-AQ_INSTEP is the ground site data collected by the Inexpensive Network Sensor Technology Exploring Pollution (INSTEP) sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing. The Low-Cost Sensor AQ Harmonization aims to harmonize sensor networks by amalgamating measurements from a multitude of networks into one open access framework. Currently, data from 10 unique US-based sensor networks have been collected for redistribution in the archive. Data from each network is reformatted in a common data format with metadata embedded to streamline data processing. A key feature is a tier system in which the team critically reviews each sensor network by their calibration efforts and subsequent data quality, providing data quality flags to help end users determine which sensor data best meets their target application. The framework allows users to search based on calibration criteria in addition to location, date range, and pollutant of interest. The sensor database is aimed at scientific end users seeking ground-based validation data for satellites and models alike but is also accessible to community scientists. The team is currently accepting new submissions! If you maintain a sensor network that you would like included, please contact the PI.\n<br>\n\n#### Low-Cost-Sensors-AQ_Love-my-Air-Denver\nLow-Cost-Sensors-AQ_Love-my-Air-Denver is the ground site data collected by the Love my Air Denver sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing. The Low-Cost Sensor AQ Harmonization aims to harmonize sensor networks by amalgamating measurements from a multitude of networks into one open access framework. Currently, data from 10 unique US-based sensor networks have been collected for redistribution in the archive. Data from each network is reformatted in a common data format with metadata embedded to streamline data processing. A key feature is a tier system in which the team critically reviews each sensor network by their calibration efforts and subsequent data quality, providing data quality flags to help end users determine which sensor data best meets their target application. The framework allows users to search based on calibration criteria in addition to location, date range, and pollutant of interest. The sensor database is aimed at scientific end users seeking ground-based validation data for satellites and models alike but is also accessible to community scientists. The team is currently accepting new submissions! If you maintain a sensor network that you would like included, please contact the PI.\n<br>\n\n#### Low-Cost-Sensors-AQ_Love-my-Air-Wisconsin\nLow-Cost-Sensors-AQ_Love-my-Air-Wisconsin is the ground site data collected by the Love my Air Wisconsin sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing. The Low-Cost Sensor AQ Harmonization aims to harmonize sensor networks by amalgamating measurements from a multitude of networks into one open access framework. Currently, data from 10 unique US-based sensor networks have been collected for redistribution in the archive. Data from each network is reformatted in a common data format with metadata embedded to streamline data processing. A key feature is a tier system in which the team critically reviews each sensor network by their calibration efforts and subsequent data quality, providing data quality flags to help end users determine which sensor data best meets their target application. The framework allows users to search based on calibration criteria in addition to location, date range, and pollutant of interest. The sensor database is aimed at scientific end users seeking ground-based validation data for satellites and models alike but is also accessible to community scientists. The team is currently accepting new submissions! If you maintain a sensor network that you would like included, please contact the PI.\n<br>\n\n#### Low-Cost-Sensors-AQ_MKE-Fresh-Air\nLow-Cost-Sensors-AQ_MKE-Fresh-Air is the ground site data collected by the MKE Fresh Air Collective sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing. The Low-Cost Sensor AQ Harmonization aims to harmonize sensor networks by amalgamating measurements from a multitude of networks into one open access framework. Currently, data from 10 unique US-based sensor networks have been collected for redistribution in the archive. Data from each network is reformatted in a common data format with metadata embedded to streamline data processing. A key feature is a tier system in which the team critically reviews each sensor network by their calibration efforts and subsequent data quality, providing data quality flags to help end users determine which sensor data best meets their target application. The framework allows users to search based on calibration criteria in addition to location, date range, and pollutant of interest. The sensor database is aimed at scientific end users seeking ground-based validation data for satellites and models alike but is also accessible to community scientists. The team is currently accepting new submissions! If you maintain a sensor network that you would like included, please contact the PI.\n<br>\n\n#### Low-Cost-Sensors-AQ_RAMP\nLow-Cost-Sensors-AQ_RAMP is the ground site data collected by the Real-Time Multi-Pollutant (RAMP) sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing. The Low-Cost Sensor AQ Harmonization aims to harmonize sensor networks by amalgamating measurements from a multitude of networks into one open access framework. Currently, data from 10 unique US-based sensor networks have been collected for redistribution in the archive. Data from each network is reformatted in a common data format with metadata embedded to streamline data processing. A key feature is a tier system in which the team critically reviews each sensor network by their calibration efforts and subsequent data quality, providing data quality flags to help end users determine which sensor data best meets their target application. The framework allows users to search based on calibration criteria in addition to location, date range, and pollutant of interest. The sensor database is aimed at scientific end users seeking ground-based validation data for satellites and models alike but is also accessible to community scientists. The team is currently accepting new submissions! If you maintain a sensor network that you would like included, please contact the PI.\n<br>\n\n#### Low-Cost-Sensors-AQ_STAIR\nLow-Cost-Sensors-AQ_STAIR is the ground site data collected by the Smart and Trustworthy AIR quality network (STAIR) as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing. The Low-Cost Sensor AQ Harmonization aims to harmonize sensor networks by amalgamating measurements from a multitude of networks into one open access framework. Currently, data from 10 unique US-based sensor networks have been collected for redistribution in the archive. Data from each network is reformatted in a common data format with metadata embedded to streamline data processing. A key feature is a tier system in which the team critically reviews each sensor network by their calibration efforts and subsequent data quality, providing data quality flags to help end users determine which sensor data best meets their target application. The framework allows users to search based on calibration criteria in addition to location, date range, and pollutant of interest. The sensor database is aimed at scientific end users seeking ground-based validation data for satellites and models alike but is also accessible to community scientists. The team is currently accepting new submissions! If you maintain a sensor network that you would like included, please contact the PI.\n<br>\n\n#### Low-Cost-Sensors-AQ_UPC\nLow-Cost-Sensors-AQ_UPC is the ground site data collected by the Universitat Politècnica of Catalunya (UPC), Spain sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing. The Low-Cost Sensor AQ Harmonization aims to harmonize sensor networks by amalgamating measurements from a multitude of networks into one open access framework. Currently, data from 10 unique US-based sensor networks have been collected for redistribution in the archive. Data from each network is reformatted in a common data format with metadata embedded to streamline data processing. A key feature is a tier system in which the team critically reviews each sensor network by their calibration efforts and subsequent data quality, providing data quality flags to help end users determine which sensor data best meets their target application. The framework allows users to search based on calibration criteria in addition to location, date range, and pollutant of interest. The sensor database is aimed at scientific end users seeking ground-based validation data for satellites and models alike but is also accessible to community scientists. The team is currently accepting new submissions! If you maintain a sensor network that you would like included, please contact the PI.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.airqino.it/en/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Low-Cost-Sensors-AQ_AirQino v1 - Low-Cost-Sensors-AQ_AirQino is the ground site data collected by the AirQino sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing.","ARN":"arn:aws:s3:::low-cost-sensors-aq-airqino","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"Low-Cost-Sensors-AQ_BEACO2N v1 - Low-Cost-Sensors-AQ_BEACO2N is the ground site data collected by the Berkeley Environmental Air-quality & CO2 Network (BEACO2N) as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing.","ARN":"arn:aws:s3:::low-cost-sensors-aq-beaco2n","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"Low-Cost-Sensors-AQ_HAQPods v1 - Low-Cost-Sensors-AQ_HAQPods is the ground site data collected by the Hannigan Air Quality Pods (HAQ Pods) sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing.","ARN":"arn:aws:s3:::low-cost-sensors-aq-haqpods","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"Low-Cost-Sensors-AQ_INSTEP v1 - Low-Cost-Sensors-AQ_INSTEP is the ground site data collected by the Inexpensive Network Sensor Technology Exploring Pollution (INSTEP) sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing.","ARN":"arn:aws:s3:::low-cost-sensors-aq-instep","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"Low-Cost-Sensors-AQ_Love-my-Air-Denver v1 - Low-Cost-Sensors-AQ_Love-my-Air-Denver is the ground site data collected by the Love my Air Denver sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing.","ARN":"arn:aws:s3:::low-cost-sensors-aq-love-my-air-denver","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"Low-Cost-Sensors-AQ_Love-my-Air-Wisconsin v1 - Low-Cost-Sensors-AQ_Love-my-Air-Wisconsin is the ground site data collected by the Love my Air Wisconsin sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing.","ARN":"arn:aws:s3:::low-cost-sensors-aq-love-my-air-wisconsin","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"Low-Cost-Sensors-AQ_MKE-Fresh-Air v1 - Low-Cost-Sensors-AQ_MKE-Fresh-Air is the ground site data collected by the MKE Fresh Air Collective sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing.","ARN":"arn:aws:s3:::low-cost-sensors-aq-mke-fresh-air","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"Low-Cost-Sensors-AQ_RAMP v1 - Low-Cost-Sensors-AQ_RAMP is the ground site data collected by the Real-Time Multi-Pollutant (RAMP) sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing.","ARN":"arn:aws:s3:::low-cost-sensors-aq-ramp","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"Low-Cost-Sensors-AQ_STAIR v1 - Low-Cost-Sensors-AQ_STAIR is the ground site data collected by the Smart and Trustworthy AIR quality network (STAIR) as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing.","ARN":"arn:aws:s3:::low-cost-sensors-aq-stair","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"Low-Cost-Sensors-AQ_UPC v1 - Low-Cost-Sensors-AQ_UPC is the ground site data collected by the Universitat Politècnica of Catalunya (UPC), Spain sensor network as part of the Low-Cost Air Quality Sensor Harmonization Database. Data collection for this product is ongoing.","ARN":"arn:aws:s3:::low-cost-sensors-aq-upc","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-low-cost-sensor-aq","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA LPJ Project","Description":"Exploring Greenhouse Gas Data; Driving Sustainable Strategies through Powerful Analysis\n<br>\n\n#### LPJ_EOSIM_L2_DCH4E_LL\nThe Lund-Potsdam-Jena Earth Observation SIMulator (LPJ-EOSIM) model estimates global wetland methane (CH4) emissions using simulated wetland extent and characteristics including soil moisture, temperature, and carbon content. For this dataset, wetlands are defined as land areas that are either permanently or seasonally saturated, excluding small ponds, lakes, and coastal wetlands. These wetland CH4 flux data will be used to support the United States Greenhouse Gas Center ([GHGC](https://earth.gov/ghgcenter)) and its mission to study natural GHG fluxes. The model will also be used to facilitate improved rapid detection and attribution of climate-carbon feedback and help with strategic placement of measurement campaigns and monitoring systems as they relate to predicted biogeochemical hotspots. The LPJ-EOSIM Level 2 Global Simulated Daily Wetland Methane Flux Low Latency (LPJ_EOSIM_L2_DCH4E_LL) Version 1 data product provides simulated daily wetland CH4 flux globally at a spatial resolution of 0.5 degrees. The daily data are presented in four Cloud Optimized GeoTIFF (COG) files: two based on the forcing datasets Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), and two containing the mean and standard deviation values. Due to the latency of global carbon dioxide (CO2) concentration estimates required for computation of LPJ-EOSIM simulated daily CH4 flux data products, low latency (LPJ_EOSIM_L2_DCH4E_LL) and high latency ([LPJ_EOSIM_L2_DCH4E](https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_DCH4E.001)) collections are available. Low latency data are delivered on a two-month cadence throughout the year. Granules will also be updated as new CO2 input data become available. Please refer to Section 2.0.1 of the User Guide for a more detailed explanation of CO2 estimate inputs and timing for scheduled updates to the collections.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earth.gov/ghgcenter/data-catalog/lpjeosim-wetlandch4-grid-v2","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","climate","cog","earth observation","soil moisture","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"LPJ_EOSIM_L2_DCH4E_LL v001 - The Lund-Potsdam-Jena Earth Observation SIMulator (LPJ-EOSIM) model estimates global wetland methane (CH4) emissions using simulated wetland extent and characteristics including soil moisture, temperature, and carbon content. For this dataset, wetlands are defined as land areas that are either permanently or seasonally saturated, excluding small ponds, lakes, and coastal wetlands.","ARN":"arn:aws:s3:::lp-prod-protected/LPJ_EOSIM_L2_DCH4E_LL.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-lpj","Sources":["awslabs-open-data-registry"]}
{"Name":"MERRA-2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields","Description":"M2I3NPASM (or  inst3_3d_asm_Np) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2).  This collection consists of assimilations of meteorological parameters at 42 pressure levels, such as  temperature, wind components, vertical pressure velocity, water vapor, ozone mass mixing ratio, and layer height. The data field is available every three hours starting from 00:00 UTC, e.g.:  00:00, 03:00, … , 21:00 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document.  \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4.  The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing:  Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page.  Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\",  “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page.  If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/QBZ6MG944HW0","Contact":"GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov","ManagedBy":"NASA","UpdateFrequency":"From 1980-01-01 to Ongoing","Tags":["agriculture","air temperature","atmosphere","aws-pds","biodiversity","climate","coastal","datacenter","ecosystems","global","hydrology","ice","land","metadata","netcdf","opendap","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MERRA-2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields 0.625 x 0.5 degree V5.12.4 (M2I3NPASM) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I3NPASM.5.12.4/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"2015b: MERRA-2: Initial Evaluation of the Climate","URL":"https://ntrs.nasa.gov/api/citations/20160005045/downloads/20160005045.pdf","AuthorName":"Bosilovich, M. G., S. Akella, L. Coy, R. Cullather, C. Draper, R. Gelaro, R. Kovach, Q.Liu, A. Molod, P. Norris, K. Wargan, W. Chao, R. Reichle, L. Takacs, Y. Vikhliaev, S. Bloom, A. Collow, S. Firth, G. Labow, G. Partyka, S. Pawson, O. Reale, S. D. Schubert, and M. Suarez"},{"Title":"A catchment-based approach to modeling land surface processes in a GCM, Part 1, Model Structure","URL":"https://doi.org/10.1029/2000JD900327","AuthorName":"Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar"},{"Title":"Assessment of MERRA-2 Land Surface Hydrology Estimates.","URL":"https://doi.org/10.1175/JCLI-D-16-0720.1","AuthorName":"Reichle, R. H., C. S. Draper, Q. Liu, M. Girotto, S. P. P. Mahanama, R. D. Koster, and G. J. M. De Lannoy"},{"Title":"Data assimilation using incremental analysis updates","URL":"https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2","AuthorName":"Bloom, S., L. Takacs, A. DaSilva, and D. Ledvina"},{"Title":"Design and implementation of components in the Earth System Modeling Framework","URL":"https://doi.org/10.1177/1094342005056120","AuthorName":"Collins, N., G. Theurich, C. DeLuca, M. Suarez, A. Trayanov, V. Balaji, P. Li, W. Yang, C. Hill, and A. da Silva"},{"Title":"Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2.","URL":"https://doi.org/10.5194/gmd-8-1339-2015","AuthorName":"Molod, A., L. Takacs, M. Suarez, and J. Bacmeister"},{"Title":"Documentation and Validation of the Goddard Earth Observing System (GEOS) Data Assimilation System - Version 4","URL":"https://ntrs.nasa.gov/citations/20050175690","AuthorName":"Bloom, S., A. da Silva, D. Dee, M. Bosilovich, J.-D. Chern, S. Pawson, S. Schubert, M. Sienkiewicz, I. Stajner, W.-W. Tan, M.-L. Wu"},{"Title":"Land Surface Precipitation in MERRA-2.","URL":"https://doi.org/10.1175/JCLI-D-16-0570.1","AuthorName":"Reichle, R.H., Q. Liu, R.D. Koster, C.S. Draper, S.P.P. Mahanama, and G.S. Partyka"},{"Title":"Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances","URL":"https://doi.org/10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2","AuthorName":"Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts"},{"Title":"Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances","URL":"https://doi.org/10.1175//2543.1","AuthorName":"Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts"},{"Title":"The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation.","URL":"https://doi.org/10.1175/JCLI-D-16-0609.1","AuthorName":"Randles, C. A., A. M. da Silva, V. Buchard, P. R. Colarco, A. Darmenov, R. Govindaraju, A. Smirnov, B. Holben, R. Ferrare, J. Hair, Y.Shinozuka, and C.J. Flynn"},{"Title":"The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and case studies.","URL":"https://doi.org/10.1175/JCLI-D-16-0613.1","AuthorName":"Buchard V., C. A. Randles, A. M. da Silva, A. Darmenov, P. R. Colarco, R. Govindaraju, R. Ferrare, J. Hair, A. J. Beyersdorf, L. D. Ziemba, H. Yu"},{"Title":"The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2).","URL":"https://doi.org/10.1175/JCLI-D-16-0758.1","AuthorName":"Gelaro, R., W. McCarty, M. J. Suárez, R. Todling, A. Molod, L. Takacs, C. A. Randles, A. Darmenov, M. G. Bosilovich, R. Reichle, et al."},{"Title":"Three-dimensional variational analysis with spatially inhomogeneous covariances","URL":"https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2","AuthorName":"Wu, W.-S., R.J. Purser and D.F. Parrish"}],"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-m2i3npasm","Sources":["awslabs-open-data-registry"]}
{"Name":"MERRA-2 inst3_3d_aer_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Aerosol Mixing Ratio 0.625 x 0.5 degree","Description":"M2I3NVAER (or  inst3_3d_aer_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2).  This collection consists of assimilations of aerosol mixing ratio parameters at 72 model layers, such as dust, sulphur dioxide, sea salt, black carbon, and organic carbon.  The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4.  The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing:  Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page.  Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\",  “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page.  If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/LTVB4GPCOTK2","Contact":"GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov","ManagedBy":"NASA","UpdateFrequency":"From 1980-01-01 to Ongoing","Tags":["agriculture","air quality","atmosphere","aws-pds","biodiversity","carbon","climate","coastal","datacenter","ecosystems","global","hydrology","ice","land","metadata","netcdf","opendap","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MERRA-2 inst3_3d_aer_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Aerosol Mixing Ratio 0.625 x 0.5 degree V5.12.4 (M2I3NVAER) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I3NVAER.5.12.4/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"2015b: MERRA-2: Initial Evaluation of the Climate","URL":"https://ntrs.nasa.gov/api/citations/20160005045/downloads/20160005045.pdf","AuthorName":"Bosilovich, M. G., S. Akella, L. Coy, R. Cullather, C. Draper, R. Gelaro, R. Kovach, Q.Liu, A. Molod, P. Norris, K. Wargan, W. Chao, R. Reichle, L. Takacs, Y. Vikhliaev, S. Bloom, A. Collow, S. Firth, G. Labow, G. Partyka, S. Pawson, O. Reale, S. D. Schubert, and M. Suarez"},{"Title":"A catchment-based approach to modeling land surface processes in a GCM, Part 1, Model Structure","URL":"https://doi.org/10.1029/2000JD900327","AuthorName":"Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar"},{"Title":"Assessment of MERRA-2 Land Surface Hydrology Estimates.","URL":"https://doi.org/10.1175/JCLI-D-16-0720.1","AuthorName":"Reichle, R. H., C. S. Draper, Q. Liu, M. Girotto, S. P. P. Mahanama, R. D. Koster, and G. J. M. De Lannoy"},{"Title":"Data assimilation using incremental analysis updates","URL":"https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2","AuthorName":"Bloom, S., L. Takacs, A. DaSilva, and D. Ledvina"},{"Title":"Design and implementation of components in the Earth System Modeling Framework","URL":"https://doi.org/10.1177/1094342005056120","AuthorName":"Collins, N., G. Theurich, C. DeLuca, M. Suarez, A. Trayanov, V. Balaji, P. Li, W. Yang, C. Hill, and A. da Silva"},{"Title":"Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2.","URL":"https://doi.org/10.5194/gmd-8-1339-2015","AuthorName":"Molod, A., L. Takacs, M. Suarez, and J. Bacmeister"},{"Title":"Documentation and Validation of the Goddard Earth Observing System (GEOS) Data Assimilation System - Version 4","URL":"https://ntrs.nasa.gov/citations/20050175690","AuthorName":"Bloom, S., A. da Silva, D. Dee, M. Bosilovich, J.-D. Chern, S. Pawson, S. Schubert, M. Sienkiewicz, I. Stajner, W.-W. Tan, M.-L. Wu"},{"Title":"Land Surface Precipitation in MERRA-2.","URL":"https://doi.org/10.1175/JCLI-D-16-0570.1","AuthorName":"Reichle, R.H., Q. Liu, R.D. Koster, C.S. Draper, S.P.P. Mahanama, and G.S. Partyka"},{"Title":"Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances","URL":"https://doi.org/10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2","AuthorName":"Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts"},{"Title":"Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances","URL":"https://doi.org/10.1175//2543.1","AuthorName":"Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts"},{"Title":"The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation.","URL":"https://doi.org/10.1175/JCLI-D-16-0609.1","AuthorName":"Randles, C. A., A. M. da Silva, V. Buchard, P. R. Colarco, A. Darmenov, R. Govindaraju, A. Smirnov, B. Holben, R. Ferrare, J. Hair, Y.Shinozuka, and C.J. Flynn"},{"Title":"The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and case studies.","URL":"https://doi.org/10.1175/JCLI-D-16-0613.1","AuthorName":"Buchard V., C. A. Randles, A. M. da Silva, A. Darmenov, P. R. Colarco, R. Govindaraju, R. Ferrare, J. Hair, A. J. Beyersdorf, L. D. Ziemba, H. Yu"},{"Title":"The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2).","URL":"https://doi.org/10.1175/JCLI-D-16-0758.1","AuthorName":"Gelaro, R., W. McCarty, M. J. Suárez, R. Todling, A. Molod, L. Takacs, C. A. Randles, A. Darmenov, M. G. Bosilovich, R. Reichle, et al."},{"Title":"Three-dimensional variational analysis with spatially inhomogeneous covariances","URL":"https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2","AuthorName":"Wu, W.-S., R.J. Purser and D.F. Parrish"}],"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-m2i3nvaer","Sources":["awslabs-open-data-registry"]}
{"Name":"MERRA-2 inst3_3d_asm_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Assimilated Meteorological Fields 0.625 x 0.5 degree","Description":"M2I3NVASM (or  inst3_3d_asm_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2).  This collection consists of assimilations of meteorological parameters at 72 model layers, such as temperature, wind components, vertical pressure velocity, water vapor, and layer height.  The data field is available every three hour starting from 00:00 UTC, e.g.:  00:00, 03:00, … , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. \n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4.  The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing:  Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page.  Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\",  “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page.  If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/WWQSXQ8IVFW8","Contact":"GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov","ManagedBy":"NASA","UpdateFrequency":"From 1980-01-01 to Ongoing","Tags":["agriculture","air temperature","atmosphere","aws-pds","biodiversity","climate","coastal","datacenter","ecosystems","global","hydrology","ice","land","metadata","netcdf","opendap","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MERRA-2 inst3_3d_asm_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Assimilated Meteorological Fields 0.625 x 0.5 degree V5.12.4 (M2I3NVASM) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I3NVASM.5.12.4/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"2015b: MERRA-2: Initial Evaluation of the Climate","URL":"https://ntrs.nasa.gov/api/citations/20160005045/downloads/20160005045.pdf","AuthorName":"Bosilovich, M. G., S. Akella, L. Coy, R. Cullather, C. Draper, R. Gelaro, R. Kovach, Q.Liu, A. Molod, P. Norris, K. Wargan, W. Chao, R. Reichle, L. Takacs, Y. Vikhliaev, S. Bloom, A. Collow, S. Firth, G. Labow, G. Partyka, S. Pawson, O. Reale, S. D. Schubert, and M. Suarez"},{"Title":"A catchment-based approach to modeling land surface processes in a GCM, Part 1, Model Structure","URL":"https://doi.org/10.1029/2000JD900327","AuthorName":"Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar"},{"Title":"Assessment of MERRA-2 Land Surface Hydrology Estimates.","URL":"https://doi.org/10.1175/JCLI-D-16-0720.1","AuthorName":"Reichle, R. H., C. S. Draper, Q. Liu, M. Girotto, S. P. P. Mahanama, R. D. Koster, and G. J. M. De Lannoy"},{"Title":"Data assimilation using incremental analysis updates","URL":"https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2","AuthorName":"Bloom, S., L. Takacs, A. DaSilva, and D. Ledvina"},{"Title":"Design and implementation of components in the Earth System Modeling Framework","URL":"https://doi.org/10.1177/1094342005056120","AuthorName":"Collins, N., G. Theurich, C. DeLuca, M. Suarez, A. Trayanov, V. Balaji, P. Li, W. Yang, C. Hill, and A. da Silva"},{"Title":"Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2.","URL":"https://doi.org/10.5194/gmd-8-1339-2015","AuthorName":"Molod, A., L. Takacs, M. Suarez, and J. Bacmeister"},{"Title":"Documentation and Validation of the Goddard Earth Observing System (GEOS) Data Assimilation System - Version 4","URL":"https://ntrs.nasa.gov/citations/20050175690","AuthorName":"Bloom, S., A. da Silva, D. Dee, M. Bosilovich, J.-D. Chern, S. Pawson, S. Schubert, M. Sienkiewicz, I. Stajner, W.-W. Tan, M.-L. Wu"},{"Title":"Land Surface Precipitation in MERRA-2.","URL":"https://doi.org/10.1175/JCLI-D-16-0570.1","AuthorName":"Reichle, R.H., Q. Liu, R.D. Koster, C.S. Draper, S.P.P. Mahanama, and G.S. Partyka"},{"Title":"Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances","URL":"https://doi.org/10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2","AuthorName":"Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts"},{"Title":"Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances","URL":"https://doi.org/10.1175//2543.1","AuthorName":"Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts"},{"Title":"The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation.","URL":"https://doi.org/10.1175/JCLI-D-16-0609.1","AuthorName":"Randles, C. A., A. M. da Silva, V. Buchard, P. R. Colarco, A. Darmenov, R. Govindaraju, A. Smirnov, B. Holben, R. Ferrare, J. Hair, Y.Shinozuka, and C.J. Flynn"},{"Title":"The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and case studies.","URL":"https://doi.org/10.1175/JCLI-D-16-0613.1","AuthorName":"Buchard V., C. A. Randles, A. M. da Silva, A. Darmenov, P. R. Colarco, R. Govindaraju, R. Ferrare, J. Hair, A. J. Beyersdorf, L. D. Ziemba, H. Yu"},{"Title":"The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2).","URL":"https://doi.org/10.1175/JCLI-D-16-0758.1","AuthorName":"Gelaro, R., W. McCarty, M. J. Suárez, R. Todling, A. Molod, L. Takacs, C. A. Randles, A. Darmenov, M. G. Bosilovich, R. Reichle, et al."},{"Title":"Three-dimensional variational analysis with spatially inhomogeneous covariances","URL":"https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2","AuthorName":"Wu, W.-S., R.J. Purser and D.F. Parrish"}],"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shre"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-m2i3nvasm","Sources":["awslabs-open-data-registry"]}
{"Name":"MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics 0.625 x 0.5 degree","Description":"M2T1NXSLV (or  tavg1_2d_slv_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2).  This collection consists of meteorology diagnostics at popularly used vertical levels, such as air temperature at 2-meter (or at 10-meter, 850hPa, 500 hPa, 250hPa), wind components at 50-meter (or at 2-meter, 10-meter, 850 hPa, 500hPa, 250 hPa), sea level pressure, surface pressure, and total precipitable water vapor (or ice water, liquid water).  The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4.  The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing:  Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page.  Note that a reprocessed data filename is different from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File Specification Document\",  “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page.  If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/VJAFPLI1CSIV","Contact":"GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/, GLOBAL MODELING AND ASSIMILATION OFFICE: data@gmao.gsfc.nasa.gov","ManagedBy":"NASA","UpdateFrequency":"From 1980-01-01 to Ongoing","Tags":["agriculture","air temperature","atmosphere","aws-pds","biodiversity","climate","coastal","datacenter","ecosystems","global","hydrology","ice","land","metadata","netcdf","oceans","opendap","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics 0.625 x 0.5 degree V5.12.4 (M2T1NXSLV) at GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T1NXSLV.5.12.4/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"2015b: MERRA-2: Initial Evaluation of the Climate","URL":"https://ntrs.nasa.gov/api/citations/20160005045/downloads/20160005045.pdf","AuthorName":"Bosilovich, M. G., S. Akella, L. Coy, R. Cullather, C. Draper, R. Gelaro, R. Kovach, Q.Liu, A. Molod, P. Norris, K. Wargan, W. Chao, R. Reichle, L. Takacs, Y. Vikhliaev, S. Bloom, A. Collow, S. Firth, G. Labow, G. Partyka, S. Pawson, O. Reale, S. D. Schubert, and M. Suarez"},{"Title":"A catchment-based approach to modeling land surface processes in a GCM, Part 1, Model Structure","URL":"https://doi.org/10.1029/2000JD900327","AuthorName":"Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar"},{"Title":"Assessment of MERRA-2 Land Surface Hydrology Estimates.","URL":"https://doi.org/10.1175/JCLI-D-16-0720.1","AuthorName":"Reichle, R. H., C. S. Draper, Q. Liu, M. Girotto, S. P. P. Mahanama, R. D. Koster, and G. J. M. De Lannoy"},{"Title":"Data assimilation using incremental analysis updates","URL":"https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2","AuthorName":"Bloom, S., L. Takacs, A. DaSilva, and D. Ledvina"},{"Title":"Design and implementation of components in the Earth System Modeling Framework","URL":"https://doi.org/10.1177/1094342005056120","AuthorName":"Collins, N., G. Theurich, C. DeLuca, M. Suarez, A. Trayanov, V. Balaji, P. Li, W. Yang, C. Hill, and A. da Silva"},{"Title":"Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2.","URL":"https://doi.org/10.5194/gmd-8-1339-2015","AuthorName":"Molod, A., L. Takacs, M. Suarez, and J. Bacmeister"},{"Title":"Documentation and Validation of the Goddard Earth Observing System (GEOS) Data Assimilation System - Version 4","URL":"https://ntrs.nasa.gov/citations/20050175690","AuthorName":"Bloom, S., A. da Silva, D. Dee, M. Bosilovich, J.-D. Chern, S. Pawson, S. Schubert, M. Sienkiewicz, I. Stajner, W.-W. Tan, M.-L. Wu"},{"Title":"Land Surface Precipitation in MERRA-2.","URL":"https://doi.org/10.1175/JCLI-D-16-0570.1","AuthorName":"Reichle, R.H., Q. Liu, R.D. Koster, C.S. Draper, S.P.P. Mahanama, and G.S. Partyka"},{"Title":"Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances","URL":"https://doi.org/10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2","AuthorName":"Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts"},{"Title":"Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances","URL":"https://doi.org/10.1175//2543.1","AuthorName":"Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts"},{"Title":"The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation.","URL":"https://doi.org/10.1175/JCLI-D-16-0609.1","AuthorName":"Randles, C. A., A. M. da Silva, V. Buchard, P. R. Colarco, A. Darmenov, R. Govindaraju, A. Smirnov, B. Holben, R. Ferrare, J. Hair, Y.Shinozuka, and C.J. Flynn"},{"Title":"The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and case studies.","URL":"https://doi.org/10.1175/JCLI-D-16-0613.1","AuthorName":"Buchard V., C. A. Randles, A. M. da Silva, A. Darmenov, P. R. Colarco, R. Govindaraju, R. Ferrare, J. Hair, A. J. Beyersdorf, L. D. Ziemba, H. Yu"},{"Title":"The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2).","URL":"https://doi.org/10.1175/JCLI-D-16-0758.1","AuthorName":"Gelaro, R., W. McCarty, M. J. Suárez, R. Todling, A. Molod, L. Takacs, C. A. Randles, A. Darmenov, M. G. Bosilovich, R. Reichle, et al."},{"Title":"Three-dimensional variational analysis with spatially inhomogeneous covariances","URL":"https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2","AuthorName":"Wu, W.-S., R.J. Purser and D.F. Parrish"}],"Tutorials":[{"Title":"How to Access GES DISC Data Using Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."},{"Title":"How to Read and Plot NetCDF MERRA-2 Data in Python","URL":"https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Read_and_Plot_NetCDF_MERRA-2_data_in_Python.ipynb","AuthorName":"James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha."}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-m2t1nxslv","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA MAS_eMAS Project","Description":"The Enhanced Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (eMAS)instrument is maintained and operated by the Airborne Sensor Facility at NASA Ames Research Center in Mountain View, California, under the oversight of the EOS Project Science Office at NASA Goddard. Prior to 1995, the MAS was deployed on the NASA's ER-2 and C-130 aircraft platforms using a 12-channel, 8-bit data system that somewhat constrained the full benefit of having a 50-channel scanning spectrometer. Beginning in January 1995, a 50-channel, 16-bit digitizer was used on the ER-2 platform, which greatly enhanced the capability of MAS to simulate MODIS data over a wide range of environmental conditions. Recently, it has undergone extensive upgrades to the optics and other components. New detectors have been installed and the spectral bands have been streamlined. The eMAS instrument is now a 38-channel instrument, sensing in the range from 0.445 to 13.844 um. For more information and for a list of MAS campaign flights visit ladsweb at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/mas/ or, visit the eMAS Homepage at: https://asapdata.arc.nasa.gov/emas/\n<br>\n\n#### eMASL2AER\nThe Enhanced Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (eMAS)instrument is maintained and operated by the Airborne Sensor Facility at NASA Ames Research Center in Mountain View, California, under the oversight of the EOS Project Science Office at NASA Goddard. The eMAS instrument is now a 38-channel instrument, sensing in the range from 0.445 to 13.844 um. The Enhanced MODIS Airborne Simulator (eMAS) L2 Aerosol Data product (eMASL2AER) consists of in-situ measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, follow plumes downwind to understand chemical transformation and air quality impacts, and assess the efficacy of satellite detections for estimating the emissions from sampled fires. These measurements were collected onboard the DC-8 aircraft during FIREX-AQ, during summer 2019. The DC-8 aircraft had a comprehensive instrument payload capable of measuring over 200 trace gases as well as aerosol microphysical, optical, and chemical properties. The eMASL2AER product files are stored in Hierarchical Data Format (HDF-EOS). All gridded cloud parameters are stored as Scientific Data Sets (SDS) within the file. For more information and for a list of MAS campaign flights visit ladsweb at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/mas/ or, visit the eMAS Homepage at: https://asapdata.arc.nasa.gov/emas/\n<br>\n\n#### MASL1B\nThe Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) sensor was developed for NASA's high-altitude ER-2 research aircraft by Daedalus Enterprises, Inc., in support of the MODIS remote sensing algorithm development. The overall goal was to modify the spectral coverage and gains of the MAS to emulate as many of the MODIS spectral channels as possible. With its much higher spatial resolution (50 m vs. 250-1000 m for MODIS), MAS is able to provide unique information on the small-scale distribution of various geophysical parameters. The MAS instrument has been deployed on multiple platforms for many field campaigns since its first mission in 1991, as the prototype Wildfire Spectrometer. For more information and for a list of MAS campaign flights visit ladsweb at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/mas/\n<br>\n\n#### MASL2CLD\nThe MODIS Airborne Simulator (MAS) Level-2 Cloud Data product (MASL2CLD) consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared and near infrared solar reflected radiances. Multispectral images of the reflectance and brightness temperature at 10 wavelengths between 0.66 and 13.98nm were used to derive the probability of clear sky (or cloud), cloud thermodynamic phase, and the optical thickness and effective radius of liquid water and ice clouds. MASL2CLD product files are stored in Hierarchical Data Format (HDF-EOS). All gridded cloud parameters are stored as Scientific Data Sets (SDS) within the file. For more information and for a list of MAS campaign flights visit ladsweb at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/mas/\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://asapdata.arc.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","earth observation","hdf","ice","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"eMASL1B v1 - The Enhanced Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (eMAS)instrument is maintained and operated by the Airborne Sensor Facility at NASA Ames Research Center in Mountain View, California, under the oversight of the EOS Project Science Office at NASA Goddard. Prior to 1995, the MAS was deployed on the NASA's ER-2 and C-130 aircraft platforms using a 12-channel, 8-bit data system that somewhat constrained the full benefit of having a 50-channel scanning spectrometer.","ARN":"arn:aws:s3:::prod-lads/eMASL1B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"eMASL2AER v1 - The Enhanced Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (eMAS)instrument is maintained and operated by the Airborne Sensor Facility at NASA Ames Research Center in Mountain View, California, under the oversight of the EOS Project Science Office at NASA Goddard. The eMAS instrument is now a 38-channel instrument, sensing in the range from 0.445 to 13.844 um.","ARN":"arn:aws:s3:::prod-lads/eMASL2AER","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MASL1B v1 - The Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) sensor was developed for NASA's high-altitude ER-2 research aircraft by Daedalus Enterprises, Inc., in support of the MODIS remote sensing algorithm development. The overall goal was to modify the spectral coverage and gains of the MAS to emulate as many of the MODIS spectral channels as possible.","ARN":"arn:aws:s3:::prod-lads/MASL1B","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MASL2CLD v1 - The MODIS Airborne Simulator (MAS) Level-2 Cloud Data product (MASL2CLD) consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared and near infrared solar reflected radiances.","ARN":"arn:aws:s3:::prod-lads/MASL2CLD","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-mas-emas","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA MASTER Project","Description":"This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 7 flights aboard a DOE B-200 aircraft over Baja California, Mexico, and Nevada, U.S., on 1999-04-23 to 1999-05-05. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_October_2010_2127\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during six flights aboard a DOE B-200 aircraft over California, Arizona, and New Mexico, U.S., from 2010-10-04 to 2010-10-13. Objectives included mapping for California Fire-Burn Area Emergency Response (BAER) and Jornada Experimental Range in southern New Mexico (JORNEX). This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_Houston_2011_1972\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 16 flights aboard a NASA ER-2 aircraft over portions of California, Colorado, Wisconsin, Michigan, Louisiana, Mississippi, the central Mississippi River basin, and the Gulf of Mexico from 2011-07-19 to 2011-08-18. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_August_2007_2020\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a DOE B-200 aircraft over Nevada and California, U.S., from 2007-08-30 to 2007-09-02. This data collection focused on mapping earthquake faults in southern California. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_DFRC_RSL_August_2002_2066\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a NASA ER-2 and two flights on a DOE B-200 aircraft over California and Nevada U.S. from 2002-08-09 to 2002-08-20. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California, and the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at spatial resolution of 10 to 50 m. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_August_2004_2036\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during one flight aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2004-08-18 to 2004-08-29. Objectives of this deployment included mapping geological faults in southern California. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_DFRC_Fall_2005_2030\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2005-10-19 to 2005-12-09. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_June_2001_2090\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2001-06-06 to 2001-06-16. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_March_2000_2094\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2000-03-10 to 2000-03-14. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_March_2002_2068\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during six flights aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2002-03-08 to 2002-04-07. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_DFRC_May-June_2008_2011\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during four flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2008-05-29 to 2008-06-19. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_May_1999_2101\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a DOE B-200 aircraft over California, Nevada, and Arizona, U.S., on 1999-05-11. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_May_2002_V2_2148\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a DOE B-200 aircraft over California, New Mexico, and Nevada, U.S., on 2002-05-14 to 2002-05-24. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_June_2002_2067\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a DOE B-200 aircraft over California, Nevada and Utah, U.S., on 2002-06-07 to 2002-06-18. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_May_2001_V2_2145\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a DOE B-200 aircraft over California and New Mexico, U.S., on 2001-05-11 to 2001-05-12. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_October_2005_2031\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during one flight aboard a DOE B-200 aircraft over Catalina Island, California, U.S., on 2005-10-31. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 7-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_Hawaii_October_2001_V2_2142\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 19 flights aboard a NASA ER-2 aircraft over Hawaii, eastern Pacific Ocean, and western U.S. from 2001-10-14 to 2001-11-14. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_July_2003_2047\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a DOE B-200 aircraft over Nevada, U.S., on 2003-07-14 to 2003-07-22. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_July_2004_2038\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during one functional check flight aboard a DOE B-200 aircraft over Nevada, U.S., on 2004-07-29. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 5-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_May_2006_2029\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a DOE B-200 aircraft over Nevada, U.S., from 2006-05-26 to 2006-06-01. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_Sky_2006_2026\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a Cessna Caravan aircraft over Oregon and Wyoming, U.S., from 2006-08-01 to 2006-08-03. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_August_2006_2027\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 18 flights aboard a DOE B-200 aircraft over Nevada, California and Colorado, U.S., from 2006-08-21 to 2006-09-06. This data collection focused on mapping geological faults. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_DFRC_June_2011_1976\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 6 flights aboard a NASA ER-2 aircraft over southwestern U.S. and northern Mexico, from 2011-06-08 to 2011-06-20. The purposes of these flights include collecting data for wildfire mapping, airborne science initiatives, and calibration data for AVIRIS. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_DFRC_May_2011_1985\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a NASA ER-2 aircraft over southwestern U.S., from 2011-05-15 to 2011-05-23. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_DFRC_November_2011_1973\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a NASA ER-2 aircraft over southwestern U.S. from 2011-11-02 to 2011-11-16. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. The L1B file formats are HDF-4 and KMZ. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_September_2005_2037\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a DOE B-200 aircraft over Arizona, California, and Nevada, U.S., on 2005-09-27 to 2005-09-29. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 5-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_June_2000_2096\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 12 flights aboard a DOE B-200 aircraft over California, Nevada, Arizona, and New Mexico, U.S., on 2000-06-01 to 2000-06-17. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_DFRC_September_2002_2064\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 11 flights aboard a NASA ER-2 aircraft over southwestern U.S. from 2002-09-10 to 2002-09-26. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_DFRC_April_2004_2043\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during four flights aboard a NASA ER-2 aircraft over western U.S. and Pacific Ocean from 2004-04-01 to 2004-04-13. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_August_2001_V2_2143\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 11 flights aboard a DOE B-200 aircraft over California, Nevada, Oregon, and Washington, U.S., on 2001-08-22 to 2001-08-31. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_DFRC_July-Aug_2001_V2_2144\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a NASA ER-2 aircraft over California, Nevada, Oregon, Washington, U.S., and British Columbia, Canada, from 2001-07-20 to 2001-08-18. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_June_1999_2098\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 10 flights aboard a DOE B-200 aircraft over California, Nevada, Arizona, New Mexico, and Texas, U.S., on 1999-05-28 to 1999-06-10. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_May_2004_2041\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a DOE B-200 aircraft over New Mexico, California, Utah, and Colorado, U.S., on 2004-05-20 to 2004-05-25. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 15-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_DFRC_October_2003_2044\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during eight flights aboard a NASA ER-2 aircraft over western U.S. and Pacific Ocean on 2003-10-03 to 2003-11-01. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_Sky_2004_2035\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 11 flights aboard a Cessna Caravan aircraft over California, Oregon, Washington, and Colorado, U.S., from 2004-09-15 to 2004-10-14. A focus of this deployment involved mapping volcanic landforms. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_DFRC_September_2006_2022\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a NASA ER-2 aircraft over California, Nevada, Wyoming, Utah, Colorado, Nebraska, South Dakota, Wisconsin, and Minnesota, U.S., from 2006-09-19 to 2006-10-13. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_B200_Fall_1999_2099\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 18 flights aboard a DOE B-200 aircraft over California, Nevada, Arizona, New Mexico, Washington, Colorado, and Texas, U.S., on 1999-09-13 to 1999-10-06. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_Houston_2010_2126\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The raw data were collected during 9 flights aboard a NASA ER-2 aircraft over the Gulf of America and portions of California, Colorado, Arizona, Utah, Idaho, New Mexico, Texas, Arkansas, Illinois, Wisconsin, Michigan, Louisiana, Mississippi, and Florida from 2010-07-31 to 2010-09-01. A primary purpose of this deployment was to collect imagery related to the Deepwater Horizon-BP Oil Spill that occurred in late April 2010 in the Gulf of America. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_DFRC_June_1999_2100\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a DC-8 aircraft and one flight on a NASA ER-2 aircraft over California, Nevada, and eastern Pacific Ocean on 1999-06-18 to 1999-06-30. The objectives of this deployment included validation and cross-calibration of the instrument on the two airborne platforms. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at spatial resolution of 7 to 50 meters. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_April_2008_2016\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during four flights aboard a DOE B-200 aircraft over California, U.S., 2008-04-14 to 2008-04-26. The locations sampled include areas affected by wildfires in 2007. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_August_2008_2010\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during four flights aboard a DOE B200 aircraft over California, U.S., from 2008-08-20 to 2008-08-27. Objectives included mapping for California Fire-Burn Area Emergency Response (BAER). This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_November_2007_2015\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during six flights aboard a DOE B-200 aircraft over California, U.S., from 2007-11-05 to 2007-11-15. This data collection focused on mapping area affected by wildfires in southern California. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_CLASIC_2007_2019\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a NASA ER-2 aircraft over California, Arizona, New Mexico, Texas, and Oklahoma, U.S., from 2007-09-20 to 2007-09-21. This data collection supported the Cloud And Land Surface Interaction Campaign (CLASIC), a cross-disciplinary interagency research effort to study cumulus convection as an important component in the atmospheric radiation budget and hydrologic cycle of the Southern Great Plains (SGP). Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_CARTA_2005_2034\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 23 flights aboard a NASA WB-57 aircraft over Costa Rica on 2005-03-01 to 2005-04-06. The CARTA-2005 project was a collaborative effort between the Centro Nacional de Alta Tecnologia (CENAT) of Costa Rica and NASA. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_CARTA_2003_2054\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 14 flights aboard a NASA WB-57 aircraft over Texas, U.S., Gulf of Mexico, and Costa Rica on 2003-03-06 to 2003-03-29. The CARTA-2003 project was a collaborative effort between the Centro Nacional de Alta Tecnologia (CENAT) of Costa Rica and NASA. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 30-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file\n<br>\n\n#### MASTER_DFRC_August_2003_2046\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during eight flights aboard a NASA ER-2 aircraft over California, U.S., on 2003-08-05 to 2003-08-11. The objective of this deployment was farmland mapping. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_FireSense_Spring_2025_2439\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the FireSense project during 7 flights aboard a NASA B200 aircraft over California, Alabama, Georgia and Florida, U.S., 2025-03-17 to 2025-04-18. The FireSense project is focused on delivering NASA's unique Earth science and technological capabilities to operational agencies, striving towards measurable improvement in US wildland fire management. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4, and L2 products are provided in HDF-5 and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_FireSense_2023_2330\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the FireSense project during 11 flights aboard a NASA B200 aircraft over California, Nevada, Utah, and Arizona, U.S., 2023-10-16 to 2023-10-26. The FireSense project is focused on delivering NASA's unique Earth science and technological capabilities to operational agencies, striving towards measurable improvement in US wildland fire management. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_FIREX_AQ_JulySept_2019_1941\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) program during 21 flights aboard a NASA DC-8 aircraft over the central and western U.S. from 2019-07-22 to 2019-09-03. The purpose of these flights was to measure emissions and to characterize the aerosols in the smoke plume above and downwind of the fire, and to determine the overall spatial extent of wildfires and prescribed fires. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_Flightline_Locator_2151\nThis dataset provides resources for identifying flight lines of interest for the MODIS/ASTER Airborne Simulator (MASTER) instrument based on spatial and temporal criteria. MASTER first flew in 1998 and has ongoing deployments as a Facility Instrument in the NASA Airborne Science Program (ASP). MASTER is a joint project involving the Airborne Sensor Facility (ASF) at the Ames Research Center, the Jet Propulsion Laboratory (JPL), and the Earth Resources Observation and Science Center (EROS). The primary goal of these airborne campaigns is to demonstrate important science and applications research that is uniquely enabled by the full suite of MASTER thermal infrared bands as well as the contiguous spectroscopic measurements of the AVIRIS (also flown in similar campaigns), or combinations of measurements from both instruments. This dataset includes a table of flight lines with dates, bounding coordinates, site names, investigators involved, flight attributes, and associated campaigns for the MASTER Facility Instrument Collection. A shapefile containing flights for all years, a GeoJSON version of the shapefile, and separate KMZ files for all years allow users to visualize flight line locations using GIS software.\n<br>\n\n#### MASTER_Sky_2003_2052\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a Cessna Caravan aircraft over California and Nevada, U.S., on 2003-05-31. The purpose of this deployment was a functional check flight. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for the flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_GEMx_Summer_2023_2319\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 13 flights aboard a NASA ER-2 aircraft over California, Oregon, Nevada, and Arizona, US, from 2023-04-25 to 2023-09-26. The Geological Earth Mapping Experiment (GEMx) research project used NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), Hyperspectral Thermal Emission Spectrometer (HyTES), and MODIS/ASTER Airborne Simulator (MASTER) instruments to collect the measurements over the country's arid and semi-arid regions, including parts of California, Nevada, Arizona, and New Mexico, to map portions of southwest US for critical minerals. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in five bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_GEMx_Spring_2024_2370\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 26 flights aboard a NASA ER-2 aircraft over California, Oregon, Nevada, and Arizona, US, from 2024-04-02 to 2024-06-24. The Geological Earth Mapping Experiment (GEMx) research project used NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), Hyperspectral Thermal Emission Spectrometer (HyTES), and MODIS/ASTER Airborne Simulator (MASTER) instruments to collect the measurements over the country's arid and semi-arid regions, including parts of California, Nevada, Arizona, and New Mexico, to map portions of southwest US for critical minerals. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in five bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_GEMx_Summer_2025_2472\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 29 flights aboard a NASA ER-2 aircraft over California, Oregon, Nevada, and Arizona, US, from 2025-05-23 to 2025-09-23. The Geological Earth Mapping Experiment (GEMx) research project used NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), Hyperspectral Thermal Emission Spectrometer (HyTES), and MODIS/ASTER Airborne Simulator (MASTER) instruments to collect the measurements over the country's arid and semi-arid regions, including parts of California, Nevada, Arizona, and New Mexico, to map portions of southwest US for critical minerals. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in five bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in HDF-5 and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_October_2003_2048\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during six flights aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2003-10-05 to 2003-10-12. An objective of this deployment was geological fault mapping. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_June_2004_2042\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a DOE B-200 aircraft over Colorado and Utah, U.S., on 2004-07-01. Objectives of this deployment included mapping geological substrates and their mineral content. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSLPhoenix_2011_1975\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a B-200 aircraft over Phoenix, Arizona, and Lake Mead, Nevada from 2011-07-11 to 2011-07-16 as part of a study on urban heat islands. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 5-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_Summer_2016_1913\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 6 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2016-06-09 to 2016-06-21. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_Fall_2015_1956\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during seven flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2015-08-24 to 2015-10-26. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_Summer_2015_1959\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during six flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2015-05-28 to 2015-06-11. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_Summer_2017_1950\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) airborne campaign during 9 flights aboard a NASA ER-2 aircraft over southern California and western Nevada, U.S., from 2017-06-07 to 2017-06-28. Two flights on 2017-06-26 and 2017-06-28 were flown jointly for the Student Airborne Research Program (SARP). SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_EarlyS2013_V2_2146\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 6 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2013-03-26 to 2013-04-19. An additional purpose of this campaign was an underpass of Landsat 8. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_EarlySpring2014_1965\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 10 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2014-03-31 to 2014-05-07. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_Fall_2013_1966\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The raw data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 11 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2013-09-13 to 2013-12-05. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_Fall_2014_1962\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during seven flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2014-09-19 to 2014-11-24. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_LateSpring_2013_1968\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The raw data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 7 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2013-05-02 to 2013-06-26. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_LateSpring_2014_1963\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during seven flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2014-05-28 to 2014-06-13. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_Spring_2015_1957\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during six flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2015-04-16 to 2015-05-05. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_Summer_2014_1964\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during four flights aboard a NASA ER-2 aircraft over California, U.S., from 2014-08-18 to 2014-08-29. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_Hawaii_2017_1951\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) airborne campaign during 18 flights aboard a NASA ER-2 aircraft over Hawaii, California and Nevada, U.S., from 2016-12-14 to 2017-03-03. This deployment includes imagery of Hawaii's volcanoes. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_Hawaii_2018_1945\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) airborne campaign during 12 flights aboard a NASA ER-2 aircraft over Hawaii and southern California, U.S., from 2018-01-11 to 2018-02-20. This campaign includes imagery of Hawaii's volcanoes. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_HyspIRI_Summer_2018_1942\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 15 flights aboard a NASA ER-2 aircraft over California, Arizona, Oregon, and Nevada, U.S., from 2018-06-19 to 2018-09-06. Two flights on 2018-06-20 and 2018-06-27 were flown jointly with Student Airborne Research Program (SARP). SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_January_1999_2103\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a DOE B-200 aircraft over California, U.S., on 1999-01-17. A primary objective of this deployment was instrument validation. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_December_1998_2104\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a DOE B-200 aircraft over California and Nevada, U.S., on 1998-12-02. A primary objective of this deployment was instrument validation. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_Ames_August_2003_2045\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a NASA WB-57 aircraft over California, Nevada, Oregon, and Washington, U.S., on 2003-08-27. This deployment was an instrument validation flight. Imagery was collected over the Cascade Mountains and Lake Tahoe. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 25-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight path, spectral band information, instrument configuration, ancillary notes, and summary information for the flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_Oct_2002_V2_2147\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during nine flights aboard a DOE B-200 aircraft over Arizona, California, Nevada and New Mexico, U.S., on 2002-10-01 to 2002-10-08. Flights included coverage of the Jornada Experimental Range (JORNEX) in New Mexico. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 15-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_April_2003_2053\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a DOE B-200 aircraft over Phoenix, Arizona, and the Jornada Experimental Range (JORNEX) in New Mexico, U.S., on 2003-04-28 to 2003-05-02. These data were used to evaluate the Temperature Emissivity Separation (TES) algorithm for extracting land surface temperature and emissivity data from thermal infrared data from ASTER. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL_October_2007_2013\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during four flights aboard a DOE B-200 aircraft over Nevada, Arizona, and New Mexico, U.S., from 2007-10-01 to 2007-10-04. A focus of this data collection was the USDA Jornada Experimental Range (Jornada) in southern New Mexico. To complement the programs of ground measurements, JORNEX (JORNada EXperiment) began in 1995 to collect remotely sensed data from aircraft and satellite platforms to provide spatial and temporal data on physical and biological states of the Jornada rangeland. JORNEX uses remote sensing techniques to study arid rangeland and the responses of vegetation to changing hydrologic fluxes and atmospheric driving forces. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_RSL-DFRC_October_2008_2014\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during four flights aboard a DOE B-200 and a NASA ER-2 aircraft over California and New Mexico, U.S., 2008-10-20 to 2008-10-29. A focus of this data collection was the USDA Jornada Experimental Range (Jornada) in southern New Mexico. To complement the programs of ground measurements, JORNEX (JORNada EXperiment) began in 1995 to collect remotely sensed data from aircraft and satellite platforms to provide spatial and temporal data on physical and biological states of the Jornada rangeland. JORNEX uses remote sensing techniques to study arid rangeland and the responses of vegetation to changing hydrologic fluxes and atmospheric driving forces. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California, and the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 30-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_PacificRim_2000_2093\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 46 flights aboard a NASA DC-8 aircraft over sites encompassing the Pacific Rim, including Alaska, California, Hawaii, islands of the south and western Pacific Ocean, New Zealand, Australia, Polynesia, southeast Asia, South Korea, and Japan. Flights took place on 2000-07-21 to 2000-10-23. The Pacific Rim 2000 (PacRim II) Campaign gathered geographic and atmospheric data for coastal analysis, oceanography, forestry, geology, hydrology and archaeology of various regions using data from the Airborne Synthetic Aperture Radar (AirSAR) and MODIS/ASTER Airborne Simulator (MASTER) instruments. This was the first campaign to operate both the AIRSAR and MASTER instruments simultaneously, providing scientists with additional insight on how topography affects the vegetation and land surface temperature as seen in the MASTER data. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 25-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_SARP_2009_1990\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2009-07-22 to 2009-07-24 over southern California, U.S., in a NASA DC-8 aircraft. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2009 deployment included two flights with 11 flight tracks. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight.\n<br>\n\n#### MASTER_SARP_2010_1989\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2010-06-28 to 2010-07-01 over southern California, U.S., in a NASA DC-8 aircraft. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2010 deployment included three flights with 21 flight tracks. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_SARP_2011_1974\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2011-06-27 to 2011-06-30 over southern California, U.S., in a NASA DC-8 aircraft. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2011 deployment included five flights with 23 flight tracks. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_SARP_2012_1983\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2012-06-25 to 2012-06-27 over southern California, U.S., in a Lockheed P-3B Orion aircraft. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2012 deployment included five flights with 19 flight tracks. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_SARP_2013_1967\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2013-06-17 to 2013-06-19 over southern California, U.S., in a NASA DC-8 aircraft. The SARP 2013 deployment included four flights with 21 flight tracks. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_SARP_2014_1961\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2014-06-23 to 2014-06-25 over southern California, U.S., in a NASA DC-8 aircraft. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2014 deployment included three flights with 17 flight tracks. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution, and the L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_SARP_2015_1958\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The raw spectral data were collected from flights flown on 2015-06-23 to 2015-06-24 over southern California, U.S., in a NASA DC-8 aircraft. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2015 deployment included three flights with 25 flight tracks. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 20-meter spatial resolution, and the L1B file format is HDF-4. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_SARP_2016_1912\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2016-06-17 in a NASA ER-2 aircraft over Santa Barbara, California. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2016 deployment included one flight with 5 flight tracks. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_SuomiNPP_2013_1969\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected for the Suomi National Polar-orbiting Partnership (Suomi-NPP) instrument validation airborne campaign during 11 flights aboard a NASA ER-2 aircraft over California, Texas, and Oklahoma, U.S.; Baja California, Mexico; and eastern Pacific Ocean from 2013-05-07 to 2013-06-01. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_SuomiNPP_2015_1970\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected for the Suomi National Polar-orbiting Partnership (Suomi-NPP) instrument validation airborne campaign during 10 flights aboard a NASA ER-2 aircraft over Greenland, portions of the conterminous U.S., and Canada from 2015-02-23 to 2015-03-31. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_TC4_2007_2021\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a NASA ER-2 aircraft over California, Nevada, Central America, and eastern Pacific Ocean from 2007-07-29 to 2007-08-18. This deployment supported the Tropical Composition, Cloud and Climate Coupling Campaign (TC4), which investigated the atmospheric structure, properties, and processes in the Eastern Pacific Tropics. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_WH2yMSIE_2024_2402\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Westcoast & Heartland Hyperspectral Microwave Sensor Intensive Experiment (WH2yMSIE) airborne campaign during 10 flights aboard a NASA ER-2 aircraft across the central US from Arkansas to California, U.S., and along the west coast and eastern Pacific Ocean. Flights occurred from 2024-10-18 to 2024-11-13. WH2yMSIE demonstrates the first-of-its-kind hyperspectral microwave airborne measurements and is complemented by other passive (infrared, visible) and active (lidar) sensors onboard the aircraft. It serves as a future NASA planetary boundary-layer (PBL) mission prototype and aims to capture a wide variety of thermodynamic, moisture, and PBL regimes across a variety of surface types. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in five bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in HDF-5 and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_WDTS_SeptOct_2020_1940\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) program during nine flights aboard a NASA ER-2 aircraft over selected areas of California, U.S, from 2020-09-17 to 2020-10-15. The WDTS program will observe California's ecosystems and provide critical information on natural disasters such as volcanoes, wildfires, and drought. MASTER products can identify vegetation type and health and provide a benchmark for the state of the ecosystems against which future changes can be assessed. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each deployment, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_WDTS_Spring_2021_1953\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during nine flights aboard a NASA ER-2 aircraft over selected areas of California and Nevada, U.S., from 2021-02-09 to 2021-04-02. The WDTS campaign will observe California's ecosystems and provide critical information on natural disasters such as volcanoes, wildfires, and drought. MASTER products can identify vegetation type and health and provide a benchmark for the state of the ecosystems against which future changes can be assessed. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_WDTS_Fall_2022_2141\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during five flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2022-09-02 to 2022-09-09. The WDTS campaign will observe California's ecosystems and provide critical information on natural disasters such as volcanoes, wildfires, and drought. MASTER products can identify vegetation type and health and provide a benchmark for the state of the ecosystems against which future changes can be assessed. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_WDTS_Spring_2023_2252\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during 12 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2023-03-31 to 2023-05-02. The WDTS campaign will observe California's ecosystems and provide critical information on natural disasters such as volcanoes, wildfires, and drought. MASTER products can identify vegetation type and health and provide a benchmark for the state of the ecosystems against which future changes can be assessed. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in five bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_WDTS_Spring_2024_2383\nThis dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during four flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2024-06-04 to 2024-06-28. The WDTS campaign will observe California's ecosystems and provide critical information on natural disasters such as volcanoes, wildfires, and drought. MASTER products can identify vegetation type and health and provide a benchmark for the state of the ecosystems against which future changes can be assessed. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in five bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_WDTS_Spring_2025_2471\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during seven flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2025-05-27 to 2025-07-17. The WDTS campaign will observe California's ecosystems and provide critical information on natural disasters such as volcanoes, wildfires, and drought. MASTER products can identify vegetation type and health and provide a benchmark for the state of the ecosystems against which future changes can be assessed. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in five bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in HDF-5 and KMZ formats. In addition, the dataset includes the flight path, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### MASTER_DFRC_July_2004_2039\nThis dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a NASA ER-2 aircraft over California, U.S., from 2004-07-20 to 2004-07-21. The primary objective of this deployment was mapping of farmland and sites of recent wildfires. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 50-meter spatial resolution. Derived L2 data products are emissivity in 5 bands in thermal infrared range (8.58 to 12.13 micrometers) and land surface temperature. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","earth observation","hdf","hydrology","lidar","oceans","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MASTER_Baja_Mexico_1999_2102 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 7 flights aboard a DOE B-200 aircraft over Baja California, Mexico, and Nevada, U.S., on 1999-04-23 to 1999-05-05.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_Baja_Mexico_1999/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_October_2010_2127 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during six flights aboard a DOE B-200 aircraft over California, Arizona, and New Mexico, U.S., from 2010-10-04 to 2010-10-13.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_October_2010/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_Houston_2011_1972 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 16 flights aboard a NASA ER-2 aircraft over portions of California, Colorado, Wisconsin, Michigan, Louisiana, Mississippi, the central Mississippi River basin, and the Gulf of Mexico from 2011-07-19 to 2011-08-18.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_Houston_2011/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_August_2007_2020 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a DOE B-200 aircraft over Nevada and California, U.S., from 2007-08-30 to 2007-09-02.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_August_2007/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_DFRC_RSL_August_2002_2066 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a NASA ER-2 and two flights on a DOE B-200 aircraft over California and Nevada U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_DFRC_RSL_August_2002/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_August_2004_2036 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during one flight aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2004-08-18 to 2004-08-29.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_August_2004/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_DFRC_Fall_2005_2030 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2005-10-19 to 2005-12-09.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_DFRC_Fall_2005/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_June_2001_2090 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2001-06-06 to 2001-06-16.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_June_2001/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_March_2000_2094 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2000-03-10 to 2000-03-14.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_March_2000/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_March_2002_2068 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during six flights aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2002-03-08 to 2002-04-07.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_March_2002/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_DFRC_May-June_2008_2011 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during four flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2008-05-29 to 2008-06-19.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_DFRC_May-June_2008/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_May_1999_2101 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a DOE B-200 aircraft over California, Nevada, and Arizona, U.S., on 1999-05-11.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_May_1999/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_May_2002_V2_2148 v2 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a DOE B-200 aircraft over California, New Mexico, and Nevada, U.S., on 2002-05-14 to 2002-05-24.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_May_2002_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_June_2002_2067 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a DOE B-200 aircraft over California, Nevada and Utah, U.S., on 2002-06-07 to 2002-06-18.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_June_2002/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_May_2001_V2_2145 v2 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a DOE B-200 aircraft over California and New Mexico, U.S., on 2001-05-11 to 2001-05-12.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_May_2001_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_October_2005_2031 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during one flight aboard a DOE B-200 aircraft over Catalina Island, California, U.S., on 2005-10-31.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_October_2005/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_Hawaii_October_2001_V2_2142 v2 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 19 flights aboard a NASA ER-2 aircraft over Hawaii, eastern Pacific Ocean, and western U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_Hawaii_October_2001_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_July_2003_2047 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a DOE B-200 aircraft over Nevada, U.S., on 2003-07-14 to 2003-07-22.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_July_2003/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_July_2004_2038 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during one functional check flight aboard a DOE B-200 aircraft over Nevada, U.S., on 2004-07-29.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_July_2004/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_May_2006_2029 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a DOE B-200 aircraft over Nevada, U.S., from 2006-05-26 to 2006-06-01.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_May_2006/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_Sky_2006_2026 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a Cessna Caravan aircraft over Oregon and Wyoming, U.S., from 2006-08-01 to 2006-08-03.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_Sky_2006/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_August_2006_2027 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 18 flights aboard a DOE B-200 aircraft over Nevada, California and Colorado, U.S., from 2006-08-21 to 2006-09-06.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_August_2006/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_DFRC_June_2011_1976 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 6 flights aboard a NASA ER-2 aircraft over southwestern U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_DFRC_June_2011/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_DFRC_May_2011_1985 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a NASA ER-2 aircraft over southwestern U.S., from 2011-05-15 to 2011-05-23.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_DFRC_May_2011/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_DFRC_November_2011_1973 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a NASA ER-2 aircraft over southwestern U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_DFRC_November_2011/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_September_2005_2037 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a DOE B-200 aircraft over Arizona, California, and Nevada, U.S., on 2005-09-27 to 2005-09-29.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_September_2005/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_June_2000_2096 v1.1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 12 flights aboard a DOE B-200 aircraft over California, Nevada, Arizona, and New Mexico, U.S., on 2000-06-01 to 2000-06-17.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_June_2000/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_DFRC_September_2002_2064 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 11 flights aboard a NASA ER-2 aircraft over southwestern U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_DFRC_September_2002/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_DFRC_April_2004_2043 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during four flights aboard a NASA ER-2 aircraft over western U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_DFRC_April_2004/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_August_2001_V2_2143 v2 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 11 flights aboard a DOE B-200 aircraft over California, Nevada, Oregon, and Washington, U.S., on 2001-08-22 to 2001-08-31.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_August_2001_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_DFRC_July-Aug_2001_V2_2144 v2 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a NASA ER-2 aircraft over California, Nevada, Oregon, Washington, U.S., and British Columbia, Canada, from 2001-07-20 to 2001-08-18.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_DFRC_July-Aug_2001_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_June_1999_2098 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 10 flights aboard a DOE B-200 aircraft over California, Nevada, Arizona, New Mexico, and Texas, U.S., on 1999-05-28 to 1999-06-10.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_June_1999/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_May_2004_2041 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a DOE B-200 aircraft over New Mexico, California, Utah, and Colorado, U.S., on 2004-05-20 to 2004-05-25.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_May_2004/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_DFRC_October_2003_2044 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during eight flights aboard a NASA ER-2 aircraft over western U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_DFRC_October_2003/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_Sky_2004_2035 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 11 flights aboard a Cessna Caravan aircraft over California, Oregon, Washington, and Colorado, U.S., from 2004-09-15 to 2004-10-14.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_Sky_2004/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_DFRC_September_2006_2022 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a NASA ER-2 aircraft over California, Nevada, Wyoming, Utah, Colorado, Nebraska, South Dakota, Wisconsin, and Minnesota, U.S., from 2006-09-19 to 2006-10-13.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_DFRC_September_2006/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_B200_Fall_1999_2099 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 18 flights aboard a DOE B-200 aircraft over California, Nevada, Arizona, New Mexico, Washington, Colorado, and Texas, U.S., on 1999-09-13 to 1999-10-06.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_B200_Fall_1999/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_Houston_2010_2126 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The raw data were collected during 9 flights aboard a NASA ER-2 aircraft over the Gulf of America and portions of California, Colorado, Arizona, Utah, Idaho, New Mexico, Texas, Arkansas, Illinois, Wisconsin, Michigan, Louisiana, Mississippi, and Florida from 2010-07-31 to 2010-09-01.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_Houston_2010/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_DFRC_June_1999_2100 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a DC-8 aircraft and one flight on a NASA ER-2 aircraft over California, Nevada, and eastern Pacific Ocean on 1999-06-18 to 1999-06-30.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_DFRC_June_1999/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_April_2008_2016 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during four flights aboard a DOE B-200 aircraft over California, U.S., 2008-04-14 to 2008-04-26.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_April_2008/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_August_2008_2010 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during four flights aboard a DOE B200 aircraft over California, U.S., from 2008-08-20 to 2008-08-27.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_August_2008/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_November_2007_2015 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during six flights aboard a DOE B-200 aircraft over California, U.S., from 2007-11-05 to 2007-11-15.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_November_2007/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_CLASIC_2007_2019 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a NASA ER-2 aircraft over California, Arizona, New Mexico, Texas, and Oklahoma, U.S., from 2007-09-20 to 2007-09-21.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_CLASIC_2007/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_CARTA_2005_2034 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 23 flights aboard a NASA WB-57 aircraft over Costa Rica on 2005-03-01 to 2005-04-06.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_CARTA_2005/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_CARTA_2003_2054 v1.1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 14 flights aboard a NASA WB-57 aircraft over Texas, U.S., Gulf of Mexico, and Costa Rica on 2003-03-06 to 2003-03-29.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_CARTA_2003/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_DFRC_August_2003_2046 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during eight flights aboard a NASA ER-2 aircraft over California, U.S., on 2003-08-05 to 2003-08-11.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_DFRC_August_2003/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_FireSense_Spring_2025_2439 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the FireSense project during 7 flights aboard a NASA B200 aircraft over California, Alabama, Georgia and Florida, U.S., 2025-03-17 to 2025-04-18.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_FireSense_Spring_2025/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_FireSense_2023_2330 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the FireSense project during 11 flights aboard a NASA B200 aircraft over California, Nevada, Utah, and Arizona, U.S., 2023-10-16 to 2023-10-26.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_FireSense_2023/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_FIREX_AQ_JulySept_2019_1941 v1.2 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) program during 21 flights aboard a NASA DC-8 aircraft over the central and western U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_FIREX_AQ_JulySept_2019/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_Flightline_Locator_2151 v1.0 - This dataset provides resources for identifying flight lines of interest for the MODIS/ASTER Airborne Simulator (MASTER) instrument based on spatial and temporal criteria. MASTER first flew in 1998 and has ongoing deployments as a Facility Instrument in the NASA Airborne Science Program (ASP).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_Flightline_Locator/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_Sky_2003_2052 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a Cessna Caravan aircraft over California and Nevada, U.S., on 2003-05-31.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_Sky_2003/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_GEMx_Summer_2023_2319 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 13 flights aboard a NASA ER-2 aircraft over California, Oregon, Nevada, and Arizona, US, from 2023-04-25 to 2023-09-26.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_GEMx_Summer_2023/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_GEMx_Spring_2024_2370 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 26 flights aboard a NASA ER-2 aircraft over California, Oregon, Nevada, and Arizona, US, from 2024-04-02 to 2024-06-24.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_GEMx_Spring_2024/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_GEMx_Summer_2025_2472 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 29 flights aboard a NASA ER-2 aircraft over California, Oregon, Nevada, and Arizona, US, from 2025-05-23 to 2025-09-23.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_GEMx_Summer_2025/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_October_2003_2048 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during six flights aboard a DOE B-200 aircraft over California and Nevada, U.S., on 2003-10-05 to 2003-10-12.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_October_2003/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_June_2004_2042 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a DOE B-200 aircraft over Colorado and Utah, U.S., on 2004-07-01.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_June_2004/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSLPhoenix_2011_1975 v1.1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during five flights aboard a B-200 aircraft over Phoenix, Arizona, and Lake Mead, Nevada from 2011-07-11 to 2011-07-16 as part of a study on urban heat islands.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSLPhoenix_2011/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_Summer_2016_1913 v1.2 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 6 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2016-06-09 to 2016-06-21.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_Summer_2016/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_Fall_2015_1956 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during seven flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2015-08-24 to 2015-10-26.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_Fall_2015/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_Summer_2015_1959 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during six flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2015-05-28 to 2015-06-11.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_Summer_2015/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_Summer_2017_1950 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) airborne campaign during 9 flights aboard a NASA ER-2 aircraft over southern California and western Nevada, U.S., from 2017-06-07 to 2017-06-28.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_Summer_2017/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_EarlyS2013_V2_2146 v2 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 6 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2013-03-26 to 2013-04-19.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_EarlyS2013_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_EarlySpring2014_1965 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 10 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2014-03-31 to 2014-05-07.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_EarlySpring2014/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_Fall_2013_1966 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The raw data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 11 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2013-09-13 to 2013-12-05.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_Fall_2013/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_Fall_2014_1962 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during seven flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2014-09-19 to 2014-11-24.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_Fall_2014/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_LateSpring_2013_1968 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The raw data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 7 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2013-05-02 to 2013-06-26.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_LateSpring_2013/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_LateSpring_2014_1963 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during seven flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2014-05-28 to 2014-06-13.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_LateSpring_2014/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_Spring_2015_1957 v1.1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during six flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2015-04-16 to 2015-05-05.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_Spring_2015/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_Summer_2014_1964 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during four flights aboard a NASA ER-2 aircraft over California, U.S., from 2014-08-18 to 2014-08-29.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_Summer_2014/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_Hawaii_2017_1951 v1.1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) airborne campaign during 18 flights aboard a NASA ER-2 aircraft over Hawaii, California and Nevada, U.S., from 2016-12-14 to 2017-03-03.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_Hawaii_2017/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_Hawaii_2018_1945 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) airborne campaign during 12 flights aboard a NASA ER-2 aircraft over Hawaii and southern California, U.S., from 2018-01-11 to 2018-02-20.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_Hawaii_2018/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_HyspIRI_Summer_2018_1942 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during 15 flights aboard a NASA ER-2 aircraft over California, Arizona, Oregon, and Nevada, U.S., from 2018-06-19 to 2018-09-06.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_HyspIRI_Summer_2018/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_January_1999_2103 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a DOE B-200 aircraft over California, U.S., on 1999-01-17.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_January_1999/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_December_1998_2104 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a DOE B-200 aircraft over California and Nevada, U.S., on 1998-12-02.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_December_1998/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_Ames_August_2003_2045 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a NASA WB-57 aircraft over California, Nevada, Oregon, and Washington, U.S., on 2003-08-27.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_Ames_August_2003/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_Oct_2002_V2_2147 v2 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during nine flights aboard a DOE B-200 aircraft over Arizona, California, Nevada and New Mexico, U.S., on 2002-10-01 to 2002-10-08.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_Oct_2002_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_April_2003_2053 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during three flights aboard a DOE B-200 aircraft over Phoenix, Arizona, and the Jornada Experimental Range (JORNEX) in New Mexico, U.S., on 2003-04-28 to 2003-05-02.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_April_2003/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL_October_2007_2013 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during four flights aboard a DOE B-200 aircraft over Nevada, Arizona, and New Mexico, U.S., from 2007-10-01 to 2007-10-04.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL_October_2007/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_RSL-DFRC_October_2008_2014 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during four flights aboard a DOE B-200 and a NASA ER-2 aircraft over California and New Mexico, U.S., 2008-10-20 to 2008-10-29.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_RSL-DFRC_October_2008/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_PacificRim_2000_2093 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during 46 flights aboard a NASA DC-8 aircraft over sites encompassing the Pacific Rim, including Alaska, California, Hawaii, islands of the south and western Pacific Ocean, New Zealand, Australia, Polynesia, southeast Asia, South Korea, and Japan.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_PacificRim_2000/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_SARP_2009_1990 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2009-07-22 to 2009-07-24 over southern California, U.S., in a NASA DC-8 aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_SARP_2009/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_SARP_2010_1989 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2010-06-28 to 2010-07-01 over southern California, U.S., in a NASA DC-8 aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_SARP_2010/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_SARP_2011_1974 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2011-06-27 to 2011-06-30 over southern California, U.S., in a NASA DC-8 aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_SARP_2011/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_SARP_2012_1983 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2012-06-25 to 2012-06-27 over southern California, U.S., in a Lockheed P-3B Orion aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_SARP_2012/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_SARP_2013_1967 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2013-06-17 to 2013-06-19 over southern California, U.S., in a NASA DC-8 aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_SARP_2013/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_SARP_2014_1961 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2014-06-23 to 2014-06-25 over southern California, U.S., in a NASA DC-8 aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_SARP_2014/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_SARP_2015_1958 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The raw spectral data were collected from flights flown on 2015-06-23 to 2015-06-24 over southern California, U.S., in a NASA DC-8 aircraft.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_SARP_2015/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_SARP_2016_1912 v1.2 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2016-06-17 in a NASA ER-2 aircraft over Santa Barbara, California.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_SARP_2016/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_SuomiNPP_2013_1969 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected for the Suomi National Polar-orbiting Partnership (Suomi-NPP) instrument validation airborne campaign during 11 flights aboard a NASA ER-2 aircraft over California, Texas, and Oklahoma, U.S.; Baja California, Mexico; and eastern Pacific Ocean from 2013-05-07 to 2013-06-01.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_SuomiNPP_2013/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_SuomiNPP_2015_1970 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected for the Suomi National Polar-orbiting Partnership (Suomi-NPP) instrument validation airborne campaign during 10 flights aboard a NASA ER-2 aircraft over Greenland, portions of the conterminous U.S., and Canada from 2015-02-23 to 2015-03-31.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_SuomiNPP_2015/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_TC4_2007_2021 v1.1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during seven flights aboard a NASA ER-2 aircraft over California, Nevada, Central America, and eastern Pacific Ocean from 2007-07-29 to 2007-08-18.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_TC4_2007/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_WH2yMSIE_2024_2402 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Westcoast & Heartland Hyperspectral Microwave Sensor Intensive Experiment (WH2yMSIE) airborne campaign during 10 flights aboard a NASA ER-2 aircraft across the central US from Arkansas to California, U.S., and along the west coast and eastern Pacific Ocean.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_WH2yMSIE_2024/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_WDTS_SeptOct_2020_1940 v1.2 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) program during nine flights aboard a NASA ER-2 aircraft over selected areas of California, U.S, from 2020-09-17 to 2020-10-15.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_WDTS_SeptOct_2020/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_WDTS_Spring_2021_1953 v1.1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during nine flights aboard a NASA ER-2 aircraft over selected areas of California and Nevada, U.S., from 2021-02-09 to 2021-04-02.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_WDTS_Spring_2021/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_WDTS_Fall_2022_2141 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during five flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2022-09-02 to 2022-09-09.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_WDTS_Fall_2022/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_WDTS_Spring_2023_2252 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during 12 flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2023-03-31 to 2023-05-02.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_WDTS_Spring_2023/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_WDTS_Spring_2024_2383 v1 - This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during four flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2024-06-04 to 2024-06-28.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_WDTS_Spring_2024/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_WDTS_Spring_2025_2471 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during seven flights aboard a NASA ER-2 aircraft over California and Nevada, U.S., from 2025-05-27 to 2025-07-17.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_WDTS_Spring_2025/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MASTER_DFRC_July_2004_2039 v1 - This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during two flights aboard a NASA ER-2 aircraft over California, U.S., from 2004-07-20 to 2004-07-21.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/master/MASTER_DFRC_July_2004/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-master","Sources":["awslabs-open-data-registry"]}
{"Name":"MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global - 500m V061","Description":"The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A1 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. MCD43A1 provides the three model weighting parameters (isotropic, volumetric, and geometric) used to derive the Albedo ([MCD43A3](https://doi.org/10.5067/MODIS/MCD43A3.061)) and Nadir BRDF-Adjusted Reflectance (NBAR) ([MCD43A4](https://doi.org/10.5067/MODIS/MCD43A4.061)) products.\n\nUsers are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43a1-brdfalbedo-model-parameters-product/).\n\nThe MCD43A1 provides the three model weighting parameters for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the three-dimensional parameter layers for these bands are the simplified mandatory quality layers for each of the 10 bands. Essential quality information provided in the corresponding [MCD43A2](https://doi.org/10.5067/MODIS/MCD43A2.061) data file should be consulted when using this product. \n\nKnown Issues\n\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/MODIS/MCD43A1.061","Contact":"User Services: lpdaac@usgs.gov. Home Page: https://www.earthdata.nasa.gov/centers/lp-daac/contact","ManagedBy":"NASA","UpdateFrequency":"From 2000-02-16 to Ongoing (Daily - < Weekly)","Tags":["aws-pds","earth observation","geospatial","global","hdf","land","opendap","tiles"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global - 500m V061.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43A1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Download Files from S3 Using boto3","URL":"https://github.com/nasa/LPDAAC-Data-Resources/blob/7f58ad3abeca7f0d17637ddb812642c0120a57ab/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb","AuthorName":"LPDAAC"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-mcd43a1","Sources":["awslabs-open-data-registry"]}
{"Name":"MODIS/Terra+Aqua BRDF/Albedo Albedo Daily L3 Global - 500m V061","Description":"The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 Version 6.1 Albedo Model dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the 16 day which is reflected in the Julian date in the file name.\n\nUsers are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43a3-albedo-product/).\n\nThe MCD43A3 provides black-sky albedo (directional hemispherical reflectance) and white-sky albedo (bihemispherical reflectance) data at local solar noon for MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. Along with the albedo layers are the simplified mandatory quality layers for each of the 10 bands. Essential quality information provided in the corresponding [MCD43A2](https://doi.org/10.5067/MODIS/MCD43A2.061) data file should be consulted when using this product.\n\nKnown Issues\n\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/MODIS/MCD43A3.061","Contact":"User Services: lpdaac@usgs.gov. Home Page: https://www.earthdata.nasa.gov/centers/lp-daac","ManagedBy":"NASA","UpdateFrequency":"From 2000-02-16 to Ongoing (Daily - < Weekly)","Tags":["aws-pds","earth observation","geospatial","global","hdf","land","opendap","satellite imagery","tiles"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MODIS/Terra+Aqua BRDF/Albedo Albedo Daily L3 Global - 500m V061.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43A3.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Download Files from S3 Using boto3","URL":"https://github.com/nasa/LPDAAC-Data-Resources/blob/7f58ad3abeca7f0d17637ddb812642c0120a57ab/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb","AuthorName":"LPDAAC"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-mcd43a3","Sources":["awslabs-open-data-registry"]}
{"Name":"MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global - 500m V061","Description":"The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.\n\nUsers are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43a4-nbar-product/).\n\nThe MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding [MCD43A2](https://doi.org/10.5067/MODIS/MCD43A2.061) data file should be consulted when using this product.\n\nKnown Issues\n\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n\nImprovements/Changes from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/MODIS/MCD43A4.061","Contact":"User Services: lpdaac@usgs.gov. Home Page: https://www.earthdata.nasa.gov/centers/lp-daac/contact","ManagedBy":"NASA","UpdateFrequency":"From 2000-02-16 to Ongoing (Daily - < Weekly)","Tags":["aws-pds","earth observation","geospatial","global","hdf","land","opendap","satellite imagery","tiles"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global - 500m V061.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43A4.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Download Files from S3 Using boto3","URL":"https://github.com/nasa/LPDAAC-Data-Resources/blob/7f58ad3abeca7f0d17637ddb812642c0120a57ab/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb","AuthorName":"LPDAAC"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-mcd43a4","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA MEaSUREs/HOMaGE Project","Description":"This data set contains the monthly Global Ocean Mass Anomalies (goma) since 04/2002, as measured by the GRACE and GRACE Follow-On (G/GFO) satellite missions. The data are averaged over the global ocean domain, at monthly intervals (note: data gaps exist). This file contains the goma time series based on the spherical harmonic gravity fields provided by the G/GFO SDS centers: JPL, CSR, GFZ. The data are frequently updated as new monthly observations are acquired by the GFO mission. The processing of the spherical harmonics gravity field coefficients is as follows: (1) GAD + GSM: the monthly de-aliasing product GAD is added back to the GSM L2 gravity fields; (2) [GSM + GAD] coefficients are averaged over the global ocean with a coastal buffer of 300 km (to avoid land-ocean leakage); (3) the spatial mean of atmospheric loading of the entire global ocean domain is removed (via the GAA L2 data product). A GIA correction using the ICE-6GD model (Peltier et al., 2018) is applied.\n<br>\n\n#### HOMAGE_GGFO_MSC_CRI_SALGRD_v01\nThis data set contains global gridded, monthly solutions of the sea level equation (gravitational-rotational-deformational GRD components) using surface mass data from the GRACE and GRACE Follow-On (G/GFO) satellite missions, beginning in April 2002. The sea level components are provided for separate land domains to allow users the local contributions from various ice melt sources (e.g., Greenland, Antarctica, mountain glaciers), as well as non-glaciated land regions. The full record for each component is given in a separate netCDF file, and includes gridded data of radial deformation, geoid height and relative sea level, as well as timeseries of global mean barystatic sea level. The data are frequently updated as new monthly observations are acquired by the GFO mission. <br><br> These data are based on the gridded JPL CRI-filtered mascons (e.g., https://doi.org/10.5067/TEMSC-3JC634), and thus inherit all processing steps from those fields. For more information on GRD definitions and related terminology, please refer to Gregory et al., 2019 (https://doi.org/10.1007/s10712-019-09525-z).\n<br>\n\n#### HOMAGE_STERIC_OHC_TIME_SERIES_v01\nThe [HOMAGE_STERIC_OHC_TIME_SERIES_v01] dataset contains monthly global mean ocean heat content (OHC) anomalies as well as thermosteric, halosteric and total steric sea level anomalies computed from various gridded ocean data sets of sub-temperature and salinity profiles as provided by different institutions: Scripps Institution of Oceanography (SIO); Institute of Atmospheric Physics (IAP); Barnes objective analysis (BOA from CSIO, MNR); Jamstec / Ishii et al. 2017 (I17); and Met Office Hadley Centre: EN4_c13, EN4_c14, EN4_g10, and EN4_I09. The data are averaged over the quasi-global ocean domain (i.e., where valid values are defined; note that gaps exist, in particular towards polar latitudes), at monthly intervals. The input profiling data (i.e, temperature and salinity profiles at depth levels), editing, quality flags and processing schemes vary across the different gridded products, please refer to the documentation for each institution’s data product for details. Since 2005, the profiling data are dominated by the observations from the global Argo network (e.g., https://argo.ucsd.edu/), which comprises nearly 4000 active floats (as of 08/2022). Before 2005, non-Argo data such as XBT profilers were used, and the global ocean coverage was significantly more sparse. Data sets from SIO and BOA are Argo-only, while the others also include other observations, such as expendable bathythermographs (XBTs) and Conductivity-Temperature-Depth (CTD) observations. The data are active forward stream data files and will be frequently updated as new observations are acquired by Argo, and processed by the data centers.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","netcdf","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"HOMAGE_GGFO_L4_GOMA_Monthly_v01 v1.0 - This data set contains the monthly Global Ocean Mass Anomalies (goma) since 04/2002, as measured by the GRACE and GRACE Follow-On (G/GFO) satellite missions. The data are averaged over the global ocean domain, at monthly intervals (note: data gaps exist).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/HOMAGE_GGFO_L4_GOMA_Monthly_v01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"HOMAGE_GGFO_MSC_CRI_SALGRD_v01 v1.0 - This data set contains global gridded, monthly solutions of the sea level equation (gravitational-rotational-deformational GRD components) using surface mass data from the GRACE and GRACE Follow-On (G/GFO) satellite missions, beginning in April 2002. The sea level components are provided for separate land domains to allow users the local contributions from various ice melt sources (e.g., Greenland, Antarctica, mountain glaciers), as well as non-glaciated land regions.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/HOMAGE_GGFO_MSC_CRI_SALGRD_v01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"HOMAGE_STERIC_OHC_TIME_SERIES_v01 v1.0 - The [HOMAGE_STERIC_OHC_TIME_SERIES_v01] dataset contains monthly global mean ocean heat content (OHC) anomalies as well as thermosteric, halosteric and total steric sea level anomalies computed from various gridded ocean data sets of sub-temperature and salinity profiles as provided by different institutions: Scripps Institution of Oceanography (SIO); Institute of Atmospheric Physics (IAP); Barnes objective analysis (BOA from CSIO, MNR); Jamstec / Ishii et al. 2017 (I17); and Met Office Hadley Centre: EN4_c13, EN4_c14, EN4_g10, and EN4_I09.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/HOMAGE_STERIC_OHC_TIME_SERIES_v01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-measures-homage","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA MEaSUREs/OSWV Project","Description":"This dataset contains model output interpolated in space and time to observations from the MetOp-A ASCAT (ASCAT-A) instrument (a satellite-based scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. These auxiliary fields are included to complement those scatterometer observations, specifically for the ASCATA_ESDR_L2_WIND_STRESS_V1.1 dataset. Model variables include: i) ocean surface wind fields from ERA-5 short-term forecast (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobCurrent project. The fields are provided on a non-uniform grid within the sampled locations of the ASCAT-A Level 2 product, and at a nominal 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit. The thumbnail shows data for two orbits - using all orbits for a single day will provide global coverage. <br><br> The dataset represents the first science quality release of this product with funding from the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) cleaned up ancillary data points in between the left/right swaths for improved collocation with available satellite data, 2) improved variable metadata, 3) removed the GlobCurrent stokes drift variables, and 4) provided data source metadata including DOIs for the ERA-5, IMERGE, and GlobCurrent data sources. The primary purpose of this Version 1.1 release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).\n<br>\n\n#### ASCATA_ESDR_L2_WSDERIV_V1.0\nThis dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations (the MetOp-A ASCAT scatterometer), representing the first science quality release of these data funded under the MEaSUREs program. This product from MetOp-A ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-B, ScatSat-1, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. These Level 2 data are provided on a non-uniform grid within the satellite swath at ~12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit - the thumbnail preview shows data for all orbits over a day (typically 14 orbits). Estimates for the curls and divergences are computed over several spatial domains with varying radii from the point of interest, and included as separate variables. <br><br> The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). This V1.0 of the data was derived from V1.1 of the L2 wind and stress product.\n<br>\n\n#### ASCATA_ESDR_L2_WIND_STRESS_V1.1\nThis dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations (the MetOp-A ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaSUREs program. This product from MetOp-A ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-B, ScatSat-1, and QuikScat satellites (all of which can be found on the MEaSUREs OSVW Project Page), and if used together create an unbroken record of winds from 1999 to 2022. The wind vector and stress retrievals are provided on a non-uniform grid within the swath (Level 2 products) at 12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit. The thumbnail shows data for two orbits - using all orbits for a single day will provide global coverage. <br><br> The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) increased data coverage, 2) improved quality control, and 3) new global metadata attributes featuring revolution number, equator crossing longitude, and equator crossing time (UTC). The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).\n<br>\n\n#### ASCATA_ESDR_L3_WIND_STRESS_V1.0\nThis dataset contains gridded ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations (the MetOp-A ASCAT scatterometer), representing the first science quality release of these data funded under the MEaSUREs program. This product from MetOp-A ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-B, ScatSat-1, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. The wind vector and stress retrievals are provided on a global grid (one per file) at 12.5 km pixel resolution. Data exist only over areas of the globe that fell within one of the satellite swaths/orbits for that day (see thumbnail). <br><br> The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Note that this is the first version of the Level 3 data (V1.0) but they were derived from V1.1 of the Level 2 product. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).\n<br>\n\n#### ASCATB_ESDR_ANCILLARY_L2_V1.1\nThis dataset contains model output interpolated in space and time to observations from the MetOp-B ASCAT (ASCAT-B) instrument (a satellite-based scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. These auxiliary fields are included to complement those scatterometer observations, specifically for the ASCATB_ESDR_L2_WIND_STRESS_V1.1 dataset. Model variables include: i) ocean surface wind fields from ERA-5 short-term forecast (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobCurrent project. The fields are provided on a non-uniform grid within the sampled locations of the ASCAT-B Level 2 product, and at a nominal 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit. The thumbnail shows data for two orbits - using all orbits for a single day will provide global coverage. <br><br> The dataset represents the first science quality release of this product with funding from the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) cleaned up ancillary data points in between the left/right swaths for improved collocation with available satellite data, 2) improved variable metadata, 3) removed the GlobCurrent stokes drift variables, and 4) provided data source metadata including DOIs for the ERA-5, IMERGE, and GlobCurrent data sources. The primary purpose of this Version 1.1 release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).\n<br>\n\n#### ASCATB_ESDR_L2_WSDERIV_V1.0\nThis dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations (the MetOp-B ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaSUREs program. This product from MetOp-B ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, ScatSat-1, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. These Level 2 data are provided on a non-uniform grid within the satellite swath at ~12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit - the thumbnail preview shows data for all orbits over a day (typically 14 orbits). Estimates for the curls and divergences are computed over several spatial domains with varying radii from the point of interest, and included as separate variables. <br><br> The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). This V1.0 of the data was derived from V1.1 of the L2 wind and stress product.\n<br>\n\n#### ASCATB_ESDR_L2_WIND_STRESS_V1.1\nThis dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations (the MetOp-B ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. This product from MetOp-B ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, ScatSat-1, and QuikScat satellites (all of which can be found on the MEaSUREs OSVW Project Page), and if used together create an unbroken record of winds from 1999 to 2022. The wind vector and stress retrievals are provided on a non-uniform grid within the swath (Level 2 (L2) products) at 12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit. The thumbnail shows data for two orbits - using all orbits for a single day will provide global coverage. <br><br> The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) increased data coverage, 2) improved quality control, and 3) new global metadata attributes featuring revolution number, equator crossing longitude, and equator crossing time (UTC). The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).\n<br>\n\n#### ASCATB_ESDR_L3_WIND_STRESS_V1.0\nThis dataset contains gridded ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations (the MetOp-B ASCAT scatterometer), representing the first science quality release of these data funded under the MEaSUREs program. This product from MetOp-B ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, ScatSat-1, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. The wind vector and stress retrievals are provided on a global grid (one per file) at 12.5 km pixel resolution, but data exist only over areas of the globe that fell within one of the satellite swaths/orbits for that day (see thumbnail). <br><br> The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Note that this is the first version of the Level 3 data (V1.0) but they were derived from V1.1 of the Level 2 product. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).\n<br>\n\n#### QUIKSCAT_ESDR_ANCILLARY_L2_V1.1\nThis dataset contains model output interpolated in space and time to the ESDR product from the scatterometer on the QuikSCAT satellite, representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. These auxiliary fields are included to complement the scatterometer observations, specifically for the QUIKSCAT_ESDR_L2_WIND_STRESS_V1.1 dataset.. Model variables include: i) ocean surface wind fields from ERA-5 short-term forecast (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobCurrent project. The modeled ocean surface auxiliary fields are provided on a non-uniform grid within the native L2 QuikSCAT sampled locations at a nominal 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. The thumbnail shows data for two orbits - using all orbits for a single day will provide global coverage. <br><br> Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) improved variable metadata, 2) removed the GlobCurrent stokes drift variables, and 3) provided data source metadata including DOIs for the ERA-5, IMERGE, and GlobCurrent data sources. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).\n<br>\n\n#### QUIKSCAT_ESDR_L2_WSDERIV_V1.0\nThis dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations aboard QuikScat, representing the first science quality release of these data funded under the MEaSUREs program. This product from QuikScat has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B ASCAT, and ScatSat-1 satellites, all of which can be found on the MEaSUREs OSVW Project Page. These Level 2 data are provided on a non-uniform grid within the satellite swath at ~12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit - the thumbnail preview shows data for all orbits over a day (typically 14 orbits). Estimates for the curls and divergences are computed over several spatial domains with varying radii from the point of interest, and included as separate variables. <br><br> The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). This V1.0 of the data was derived from V1.1 of the L2 wind and stress product.\n<br>\n\n#### QUIKSCAT_ESDR_L2_WIND_STRESS_V1.1\nThis dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations aboard QuikScat, representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. This product from QuikScat has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and ScatSat-1 satellites (all of which can be found on the MEaSUREs OSVW Project Page), and if used together create an unbroken record of winds from 1999 to 2022. The wind vector and stress retrievals are provided on a non-uniform grid within the swath (Level 2 (L2) products) at 12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit. The thumbnail shows data for two orbits - using all orbits for a single day will provide global coverage. <br><br> The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) increased data coverage, 2) improved quality control, and 3) new global metadata attributes featuring revolution number, equator crossing longitude, and equator crossing time (UTC). The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).\n<br>\n\n#### QUIKSCAT_ESDR_L3_WIND_STRESS_V1.0\nThis dataset contains gridded ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations aboard QuikScat, representing the first science quality release of these data funded under the MEaSUREs program. This product from QuikScat has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and ScatSat-1, all of which can be found on the MEaSUREs OSVW Project Page. The wind vector and stress retrievals are provided on a global grid (one per file) at 12.5 km pixel resolution, but data exist only over areas of the globe that fell within one of the satellite swaths/orbits for that day (see thumbnail). <br><br> The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Note that this is the first version of the Level 3 data (V1.0) but they were derived from V1.1 of the Level 2 product. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).\n<br>\n\n#### SCATSAT1_ESDR_ANCILLARY_L2_V1.1\nThis dataset contains the first science quality release (post-provisional after v1.0) of the MEaSUREs-funded Earth Science Data Record (ESDR) of ancillary data corresponding to the SCATSAT-1 Level 2 (L2) data products, interpolated in space and time to the scatterometer observations. These auxiliary fields are included to complement those scatterometer observations, specifically for the SCATSAT_ESDR_L2_WIND_STRESS_V1.1 dataset. The fields include: i) ocean surface wind fields from ERA-5 short-term forecast (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) collocated in space and time estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobCurrent project. These auxiliary fields are included to complement the scatterometer observation fields and to help in the evaluation process. They are provided on a non-uniform grid within the native L2 SCATSAT-1 sampled locations at a nominal 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. The thumbnail shows data for two orbits - using all orbits for a single day will provide global coverage. <br><br> The dataset represents the first science quality release of this product with funding from the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) improved variable metadata, 2) removed the GlobCurrent stokes drift variables, and 3) provided data source metadata including DOIs for the ERA-5, IMERGE, and GlobCurrent data sources. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).\n<br>\n\n#### SCATSAT1_ESDR_L2_WSDERIV_V1.0\nThis dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations aboard SCATSAT-1, representing the first science quality release of these data funded under the MEaSUREs program. This product from SCATSAT-1 has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. These Level 2 data are provided on a non-uniform grid within the satellite swath at ~12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit. There are typically 14 orbits per day, and the thumbnail preview shows coverage for the first ten orbits in an example day. Estimates for the curls and divergences are computed over several spatial domains with varying radii from the point of interest, and included as separate variables. <br><br> The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). This V1.0 of the data was derived from V1.1 of the L2 wind and stress product.\n<br>\n\n#### SCATSAT1_ESDR_L2_WIND_STRESS_V1.1\nThis dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations aboard ScatSat-1, representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. This product from ScatSat-1 has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and QuikScat satellites (all of which can be found on the MEaSUREs OSVW Project Page), and if used together create an unbroken record of winds from 1999 to 2022. The wind vector and stress retrievals are provided on a non-uniform grid within the swath (Level 2 (L2) products) at 12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit. The thumbnail shows data for two orbits - using all orbits for a single day will provide global coverage.<br><br> The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) increased data coverage, 2) improved quality control, and 3) new global metadata attributes featuring revolution number, equator crossing longitude, and equator crossing time (UTC). The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).\n<br>\n\n#### SCATSAT1_ESDR_L3_WIND_STRESS_V1.0\nThis dataset contains gridded ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations aboard SCATSAT-1, representing the first science quality release of these data funded under the MEaSUREs program. This product from SCATSAT-1 has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. The wind vector and stress retrievals are provided on a global grid (one per file) at 12.5 km pixel resolution, but data exist only over areas of the globe that fell within one of the satellite swaths/orbits for that day (see thumbnail). <br><br> The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Note that this is the first version of the Level 3 data (V1.0) but they were derived from V1.1 of the Level 2 product. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","oceans","precipitation","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ASCATA_ESDR_ANCILLARY_L2_V1.1 v1.1 - This dataset contains model output interpolated in space and time to observations from the MetOp-A ASCAT (ASCAT-A) instrument (a satellite-based scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. These auxiliary fields are included to complement those scatterometer observations, specifically for the ASCATA_ESDR_L2_WIND_STRESS_V1.1 dataset.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATA_ESDR_ANCILLARY_L2_V1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ASCATA_ESDR_L2_WSDERIV_V1.0 v1.0 - This dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations (the MetOp-A ASCAT scatterometer), representing the first science quality release of these data funded under the MEaSUREs program. This product from MetOp-A ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-B, ScatSat-1, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATA_ESDR_L2_WSDERIV_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ASCATA_ESDR_L2_WIND_STRESS_V1.1 v1.1 - This dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations (the MetOp-A ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaSUREs program. This product from MetOp-A ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-B, ScatSat-1, and QuikScat satellites (all of which can be found on the MEaSUREs OSVW Project Page), and if used together create an unbroken recor...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATA_ESDR_L2_WIND_STRESS_V1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ASCATA_ESDR_L3_WIND_STRESS_V1.0 v1.0 - This dataset contains gridded ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations (the MetOp-A ASCAT scatterometer), representing the first science quality release of these data funded under the MEaSUREs program. This product from MetOp-A ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-B, ScatSat-1, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATA_ESDR_L3_WIND_STRESS_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ASCATB_ESDR_ANCILLARY_L2_V1.1 v1.1 - This dataset contains model output interpolated in space and time to observations from the MetOp-B ASCAT (ASCAT-B) instrument (a satellite-based scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. These auxiliary fields are included to complement those scatterometer observations, specifically for the ASCATB_ESDR_L2_WIND_STRESS_V1.1 dataset.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATB_ESDR_ANCILLARY_L2_V1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ASCATB_ESDR_L2_WSDERIV_V1.0 v1.0 - This dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations (the MetOp-B ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaSUREs program. This product from MetOp-B ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, ScatSat-1, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATB_ESDR_L2_WSDERIV_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ASCATB_ESDR_L2_WIND_STRESS_V1.1 v1.1 - This dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations (the MetOp-B ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. This product from MetOp-B ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, ScatSat-1, and QuikScat satellites (all of which can be found on the MEaSUREs OSVW Project Page), and if used together create an unbroken recor...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATB_ESDR_L2_WIND_STRESS_V1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ASCATB_ESDR_L3_WIND_STRESS_V1.0 v1.0 - This dataset contains gridded ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations (the MetOp-B ASCAT scatterometer), representing the first science quality release of these data funded under the MEaSUREs program. This product from MetOp-B ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, ScatSat-1, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATB_ESDR_L3_WIND_STRESS_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"QUIKSCAT_ESDR_ANCILLARY_L2_V1.1 v1.1 - This dataset contains model output interpolated in space and time to the ESDR product from the scatterometer on the QuikSCAT satellite, representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. These auxiliary fields are included to complement the scatterometer observations, specifically for the QUIKSCAT_ESDR_L2_WIND_STRESS_V1.1 dataset.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/QUIKSCAT_ESDR_ANCILLARY_L2_V1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"QUIKSCAT_ESDR_L2_WSDERIV_V1.0 v1.0 - This dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations aboard QuikScat, representing the first science quality release of these data funded under the MEaSUREs program. This product from QuikScat has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B ASCAT, and ScatSat-1 satellites, all of which can be found on the MEaSUREs OSVW Project Page.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/QUIKSCAT_ESDR_L2_WSDERIV_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"QUIKSCAT_ESDR_L2_WIND_STRESS_V1.1 v1.1 - This dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations aboard QuikScat, representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. This product from QuikScat has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and ScatSat-1 satellites (all of which can be found on the MEaSUREs OSVW Project Page), and if used together create an unbroken record of winds from 1999 to ...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/QUIKSCAT_ESDR_L2_WIND_STRESS_V1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"QUIKSCAT_ESDR_L3_WIND_STRESS_V1.0 v1.0 - This dataset contains gridded ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations aboard QuikScat, representing the first science quality release of these data funded under the MEaSUREs program. This product from QuikScat has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and ScatSat-1, all of which can be found on the MEaSUREs OSVW Project Page.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/QUIKSCAT_ESDR_L3_WIND_STRESS_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SCATSAT1_ESDR_ANCILLARY_L2_V1.1 v1.1 - This dataset contains the first science quality release (post-provisional after v1.0) of the MEaSUREs-funded Earth Science Data Record (ESDR) of ancillary data corresponding to the SCATSAT-1 Level 2 (L2) data products, interpolated in space and time to the scatterometer observations. These auxiliary fields are included to complement those scatterometer observations, specifically for the SCATSAT_ESDR_L2_WIND_STRESS_V1.1 dataset.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SCATSAT1_ESDR_ANCILLARY_L2_V1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SCATSAT1_ESDR_L2_WSDERIV_V1.0 v1.0 - This dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations aboard SCATSAT-1, representing the first science quality release of these data funded under the MEaSUREs program. This product from SCATSAT-1 has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SCATSAT1_ESDR_L2_WSDERIV_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SCATSAT1_ESDR_L2_WIND_STRESS_V1.1 v1.1 - This dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations aboard ScatSat-1, representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. This product from ScatSat-1 has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and QuikScat satellites (all of which can be found on the MEaSUREs OSVW Project Page), and if used together create an unbroken record of winds from 1999 to...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SCATSAT1_ESDR_L2_WIND_STRESS_V1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SCATSAT1_ESDR_L3_WIND_STRESS_V1.0 v1.0 - This dataset contains gridded ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations aboard SCATSAT-1, representing the first science quality release of these data funded under the MEaSUREs program. This product from SCATSAT-1 has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SCATSAT1_ESDR_L3_WIND_STRESS_V1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-measures-oswv","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA measures Project","Description":"This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides a daily record of Arctic sea ice characteristics for the years 1979 through 2012 derived from passive microwave brightness temperatures. Characteristics include the location of sea ice cover, sea ice age, day of melt onset, and status of melt onset. Data are gridded in the 25 km Equal-Area Scalable Earth Grid (EASE-Grid) 2.0 and provided as netCDF files.\n<br>\n\n#### NSIDC-0756\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains a bed topography/bathymetry map of Antarctica based on mass conservation, streamline diffusion, and other methods. The data set also includes ice thickness, surface elevation, an ice/ocean/land mask, ice thickness estimation errors, and a map showing where each method was utilized.\n<br>\n\n#### NSIDC-0533\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, offers users a 25 km daily record of surface/near-surface melting on the Greenland Ice Sheet. The presence of melting is determined from brightness temperature data acquired during a 34 year span by three satellite-borne microwave radiometers: the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS).\n<br>\n\n#### NSIDC-0484\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, provides the first comprehensive, high-resolution, digital mosaics of ice motion in Antarctica assembled from multiple satellite interferometric, synthetic-aperture radar systems. Data were largely acquired during the International Polar Years 2007 to 2009, as well as between 2013 and 2016. Additional data acquired between 1996 and 2016 were used as needed to maximize coverage. See <a href=\"https://nsidc.org/data/measures/aiv\">Antarctic Ice Sheet Velocity and Mapping Data</a> for related data.\n<br>\n\n#### NSIDC-0525\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of two high-resolution digital mosaics of ice motion in Central Antarctica. The mosaics were assembled from satellite interferometric synthetic-aperture radar (InSAR) data acquired by RADARSAT-1 in 1997 and by RADARSAT-2 in 2009. See <a href=\"https://nsidc.org/data/measures/aiv\">Antarctic Ice Sheet Velocity and Mapping Data</a> for related data.\n<br>\n\n#### NSIDC-0794\nThis ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of 240 m Antarctic Ice Sheet extent masks at roughly annual resolution from 1997 through 2021. The ice masks were generated by combining data acquired by multiple satellite-borne optical, thermal, and radar sensors. The ice thickness and velocity data used to determine the presence of ice are also provided.\n<br>\n\n#### NSIDC-0534\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, reports the location of Northern Hemisphere snow cover and sea ice extent, the status of melt onset across Greenland and Arctic sea ice, and the level of agreement between three different snow cover data sources.\n<br>\n\n#### NSIDC-0535\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, reports the location of Northern Hemisphere snow cover and sea ice extent, the status of melt onset across Greenland and Arctic sea ice, and the level of agreement between snow cover maps derived from two different sources.\n<br>\n\n#### NSIDC-0531\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, offers users weekly 100 km Northern Hemisphere snow cover extent represented by three different variables. Two of the variables are derived from individual source products: the NOAA/NCDC Northern Hemisphere Snow Cover Extent Climate Data Record and Defense Meteorological Satellite Program (DMSP) passive microwave brightness temperatures, respectively. The third variable merges the other two into a single representation of snow cover.\n<br>\n\n#### NSIDC-0754\nThis data set, as part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, combines interferometric phases from multiple satellite interferometric synthetic-aperture radar systems to derive the first comprehensive phase-based map of Antarctic ice velocity. The precision in ice speed and flow direction over 80% of Antarctica is better than prior mappings based on feature and speckle tracking by a factor of 10. Phase-derived velocity mostly covers the years between 2007 and 2018, while tracking-derived velocity (for regions along the coasts) is mostly found in the years from 2013 to 2017. Additional data acquired between 1996 and 2018 were used as needed to maximize coverage. See <a href=\"https://nsidc.org/data/measures/aiv\">Antarctic Ice Sheet Velocity and Mapping Data</a> for related data.\n<br>\n\n#### SNEX17_CAR\nThis data set contains measurements of the bidirectional reflectance distribution function (BRDF) for two locations in Colorado, USA: Grand Mesa, a snow-covered, forested study site about 40 miles east of Grand Junction; and Senator Beck Basin approximately 80 miles to the SSE of Grand Mesa. Measurements were acquired using the NASA Cloud Absorption Radiometer (CAR), an airborne multi-angular, multi-wavelength scanning radiometer. The CAR instrument measures scattered light in 14 spectral bands between 0.34 μm and 2.30 μm, which lie in the UV, visible, and near-infrared atmospheric windows. Data were obtained for a variety of conditions including snow grain size (or age), snow liquid water content, solar zenith angle, cloud cover, and snowpack thickness. The data set can be used to assess the accuracy of satellite reflectance and albedo products in snow-covered, forested landscapes.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/about/competitive-programs/measures","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","elevation","ice","netcdf","oceans","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NSIDC-0532 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides a daily record of Arctic sea ice characteristics for the years 1979 through 2012 derived from passive microwave brightness temperatures. Characteristics include the location of sea ice cover, sea ice age, day of melt onset, and status of melt onset.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0532/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0756 v3 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains a bed topography/bathymetry map of Antarctica based on mass conservation, streamline diffusion, and other methods. The data set also includes ice thickness, surface elevation, an ice/ocean/land mask, ice thickness estimation errors, and a map showing where each method was utilized.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0756/3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0533 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, offers users a 25 km daily record of surface/near-surface melting on the Greenland Ice Sheet. The presence of melting is determined from brightness temperature data acquired during a 34 year span by three satellite-borne microwave radiometers: the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0533/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0484 v2 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, provides the first comprehensive, high-resolution, digital mosaics of ice motion in Antarctica assembled from multiple satellite interferometric, synthetic-aperture radar systems. Data were largely acquired during the International Polar Years 2007 to 2009, as well as between 2013 and 2016.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0484/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0525 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of two high-resolution digital mosaics of ice motion in Central Antarctica. The mosaics were assembled from satellite interferometric synthetic-aperture radar (InSAR) data acquired by RADARSAT-1 in 1997 and by RADARSAT-2 in 2009.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0525/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0794 v1 - This ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of 240 m Antarctic Ice Sheet extent masks at roughly annual resolution from 1997 through 2021. The ice masks were generated by combining data acquired by multiple satellite-borne optical, thermal, and radar sensors.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0794/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0534 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, reports the location of Northern Hemisphere snow cover and sea ice extent, the status of melt onset across Greenland and Arctic sea ice, and the level of agreement between three different snow cover data sources.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0534/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0535 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, reports the location of Northern Hemisphere snow cover and sea ice extent, the status of melt onset across Greenland and Arctic sea ice, and the level of agreement between snow cover maps derived from two different sources.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0535/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0531 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, offers users weekly 100 km Northern Hemisphere snow cover extent represented by three different variables. Two of the variables are derived from individual source products: the NOAA/NCDC Northern Hemisphere Snow Cover Extent Climate Data Record and Defense Meteorological Satellite Program (DMSP) passive microwave brightness temperatures, respectively.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0531/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0754 v1 - This data set, as part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, combines interferometric phases from multiple satellite interferometric synthetic-aperture radar systems to derive the first comprehensive phase-based map of Antarctic ice velocity. The precision in ice speed and flow direction over 80% of Antarctica is better than prior mappings based on feature and speckle tracking by a factor of 10.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0754/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX17_CAR v1 - This data set contains measurements of the bidirectional reflectance distribution function (BRDF) for two locations in Colorado, USA: Grand Mesa, a snow-covered, forested study site about 40 miles east of Grand Junction; and Senator Beck Basin approximately 80 miles to the SSE of Grand Mesa. Measurements were acquired using the NASA Cloud Absorption Radiometer (CAR), an airborne multi-angular, multi-wavelength scanning radiometer.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX17_CAR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-measures","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA MERRA-2 Observation Project","Description":"Global Modeling and Assimilation Office Research\n<br>\n\n#### GMAO_M2SCREAM_INST3_CHEM\nThe MERRA-2 Stratospheric Composition Reanalysis of Aura MLS (M2-SCREAM) products produced at NASA’s Global Modeling and Assimilation Office (GMAO) are generated by assimilating MLS and OMI retrievals into the GEOS Constituent Data Assimilation System (CoDAS) driven by meteorological fields from MERRA-2. M2-SCREAM assimilates hydrochloric acid (HCl), nitric acid (HNO3), stratospheric water vapor (H2O), nitrous oxide (N2O) and ozone with a system equipped with a version of the GEOS general circulation model and a stratospheric chemistry model, StratChem. Assimilated fields are provided globally at 0.5° by 0.625° resolution at three-hourly frequencies from 2004/09/01 to 2024/09/30. Assimilation uncertainties for each of the assimilated constituents are calculated from the CoDAS statistical output (Wargan et al., 2022) and provided as global full-resolution three-dimensional monthly files. Data product updates in March 2024, as a result of Aura MLS “duty cycle” of 190-GHz measurements, include reduced availability of H2O, N2O and HNO3 retrievals resulting in expected M2-SCREAM data quality degradation. However, preliminary analysis shows that the GEOS CoDAS handles the reduced temporal data coverage well, indicating that the GEOS model accurately propagates information from past observations. Data product updates in June 2024 resulting from MLS version upgrade to v5.0 include discontinuities in assimilated H2O (throughout the stratosphere) and N2O (in the lower stratosphere). To note: MLS water vapor is about 0.5 ppmv lower in v5.0, and the vertical range of assimilated N2O data is 100 hPa, extended down from 68 hPa. GMAO is not aware of discontinuities in HCl, HNO3, and ozone related to the version switch.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://gmao.gsfc.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GMAO_M2SCREAM_INST3_CHEM v1 - The MERRA-2 Stratospheric Composition Reanalysis of Aura MLS (M2-SCREAM) products produced at NASA’s Global Modeling and Assimilation Office (GMAO) are generated by assimilating MLS and OMI retrievals into the GEOS Constituent Data Assimilation System (CoDAS) driven by meteorological fields from MERRA-2. M2-SCREAM assimilates hydrochloric acid (HCl), nitric acid (HNO3), stratospheric water vapor (H2O), nitrous oxide (N2O) and ozone with a system equipped with a version of the GEOS general circulation model and a stratospheric chemistry model, StratChem.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/M2SCREAM/GMAO_M2SCREAM_INST3_CHEM.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-merra-2-observation","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA MERRA-2 Project","Description":"Global Modeling and Assimilation Office Research\n<br>\n\n#### M2_TMAX_PM25\nM2_TMAX_PM25 is a value-added product derived from the MERRA-2 aerosol monthly product M2TMNXAER_5.12.4 (or tavgM_2d_aer_Nx). The surface concentration of fine particulate matter (PM2.5) is calculated as the sum of individual aerosol components (organic carbon, black carbon, sulfate, sea salt, and dust) (Buchard et al., 2017) and is recast from the native MERRA-2 model grid. This data collection includes separate files for country-level (and territories) PM2.5 concentrations with and without population weighting applied. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2C0NXASM\nM2C0NXASM (or const_2d_asm_Nx) is a data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of 2-dimensional constant model parameters, such as the fraction of lake, land, and ocean within a model grid cell. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2C0NXCTM\nM2C0NXCTM (or const_2d_ctm_Nx) is a data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of 2-dimensional constant model parameters for usage by the chemistry transport model (CTM), such as the fraction of lake, land, ice, or ocean within a model grid cell. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2C0NXLND\nM2C0NXLND (or const_2d_lnd_Nx) is a data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of 2-dimensional constant land surface parameters, such as thickness of the predefined soil layers, soil porosity, and soil wilting point. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2I1NXASM\nM2I1NXASM (or inst1_2d_asm_Nx) is an instantaneous 2-dimensional hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological diagnostic parameters at the single levels, such as temperature at 2-meter and 10-meter; wind components at 2-meter, 10-meter, and 50-meter; surface pressure, and total precipitable water. The timestamp of a data field is on each hour starting from 00:00 UTC, e.g.: 00:00, 01:00, … , 23:00 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2I1NXINT\nM2I1NXINT (or inst1_2d_int_Nx) is an instantaneous 2-dimensional hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertically integrated diagnostics, such as kinetic energy, virtual potential temperature, and total precipitable water (or ice, liquid, and vapor). The timestamp of a data field is on each hour starting from 00:00 UTC, e.g.: 00:00, 01:00, … , 23:00 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2I1NXLFO\nM2I1NXLFO (or inst1_2d_lfo_Nx) is an instantaneous 2-dimensional hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as height, specific humidity, wind, and air temperature of the model surface layer. The timestamp of a data field is on each hour starting from 00:00 UTC, e.g.: 00:00, 01:00, … , 23:00 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2I3NXGAS\nM2I3NXGAS (or inst3_3d_gas_Nx) is an instantaneous 2-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilation of aerosol optical depth analysis and aerosol optical depth analysis increment. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2I3NVAER\nM2I3NVAER (or inst3_3d_aer_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of aerosol mixing ratio parameters at 72 model layers, such as dust, sulphur dioxide, sea salt, black carbon, and organic carbon. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2I3NPASM\nM2I3NPASM (or inst3_3d_asm_Np) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 42 pressure levels, such as temperature, wind components, vertical pressure velocity, water vapor, ozone mass mixing ratio, and layer height. The data field is available every three hours starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2I3NVASM\nM2I3NVASM (or inst3_3d_asm_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 72 model layers, such as temperature, wind components, vertical pressure velocity, water vapor, and layer height. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2I3NVCHM\nM2I3NVCHM (or inst3_3d_chm_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of carbon monoxide and ozone mixing ratio at 72 model layers. The data is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2I3NVGAS\nM2I3NVGAS (or inst3_3d_gas_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of aerosol mixing ratio analysis increments at 72 model layers, such as mixing ratio analysis increments of black carbon, dust, organic carbon, sea salt, and sulfate. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2I6NPANA\nM2I6NPANA (or inst6_3d_ana_Np) is an instantaneous 3-dimensional 6-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of analyzed meteorological fields at 42 pressure levels, such as temperature, wind components, specific humidity, ozone mixing ratio, and geopotential height. The data field is available every six hour starting from 00:00 UTC, e.g.: 00:00, 06:00, … , 18:00 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2I6NVANA\nM2I6NVANA (or inst6_3d_ana_Nv) is an instantaneous 3-dimensional 6-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of analized meteorological fields at 72 model layers, such as temperature, wind components,specific humidity, and layer pressure thickness. The data field is available every six hour starting from 00:00 UTC, e.g.: 00:00, 06:00, … , 18:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2IMNXASM\nM2IMNXASM (or instM_2d_asm_Nx) is an instantaneous 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological diagnostic parameters at the single levels, such as temperature at 2-meter and 10-meter; wind components at 2-meter, 10-meter, and 50-meter; surface pressure, and total precipitable water. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2IMNXGAS\nM2IMNXGAS (or instM_3d_gas_Nx) is an instantaneous 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilation of aerosol optical depth analysis and aerosol optical depth analysis increment. The collection also includes the variance of parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2IMNXINT\nM2IMNXINT (or instM_2d_int_Nx) is an instantaneous 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertically integrated diagnostics, such as kinetic energy, virtual potential temperature, and total precipitable water (or ice, liquid, and vapor). The collection also includes variance of certain variables. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2IMNXLFO\nM2IMNXLFO (or instM_2d_lfo_Nx) is an instantaneous 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as height, specific humidity, wind, and air temperature of the model surface layer. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2IMNPANA\nM2IMNPANA (or instM_3d_ana_Np) is an instantaneous 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of analyzed meteorological fields at 42 pressure levels, such as temperature, wind components, specific humidity, ozone mixing ratio, and geopotential height. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes certain quadratic information (such as the variance and covariance of certain parameters). MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2IMNPASM\nM2IMNPASM (or instM_3d_asm_Np) is an instantaneous 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 42 pressure levels, such as temperature, wind components, vertical pressure velocity, water vapor, ozone mass mixing ratio, and layer height. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes certain quadratic information (such as the variance and covariance of certain parameters). MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2IUNXASM\nM2IUNXASM (or instU_2d_asm_Nx) is an instantaneous 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological diagnostic parameters at the single levels, such as temperature at 2-meter and 10-meter; wind components at 2-meter, 10-meter, and 50-meter; surface pressure, and total precipitable water. The data consists of the monthly mean of the data field at each hour of a day, e.g., 00:00, 01:00, …, 23:00 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2IUNXGAS\nM2IUNXGAS (or instU_3d_gas_Nx) is an instantaneous 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilation of aerosol optical depth analysis and aerosol optical depth analysis increment. It consists of the monthly mean of the data fields every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2IUNXINT\nM2IUNXINT (or instU_2d_int_Nx) is an instantaneous 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertically integrated diagnostics, such as kinetic energy, virtual potential temperature, and total precipitable water (or ice, liquid, and vapor). It consists of the monthly mean of the data fields on each hour starting from 00:00 UTC, e.g.: 00:00, 01:00, … , 23:00 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2IUNXLFO\nM2IUNXLFO (or instU_2d_lfo_Nx) is an instantaneous 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as height, specific humidity, wind, and air temperature of the model surface layer. It consists of the monthly mean of the data fields on each hour starting from 00:00 UTC, e.g.: 00:00, 01:00, … , 23:00 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2IUNPANA\nM2IUNPANA (or instU_3d_ana_Np) is an instantaneous 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of analyzed meteorological fields at 42 pressure levels, such as temperature, wind components, specific humidity, ozone mixing ratio, and geopotential height. It is the monthly mean of data fields every six hour starting from 00:00 UTC, e.g.: 00:00, 06:00, … , 18:00 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2IUNPASM\nM2IUNPASM (or instU_3d_asm_Np) is an instantaneous 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 42 pressure levels, such as temperature, wind components, vertical pressure velocity, water vapor, ozone mass mixing ratio, and layer height. The data collection is the monthly mean of data fields every three hours starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2SDNXSLV\nM2SDNXSLV (or statD_2d_slv_Nx) is a 2-dimensional daily data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of daily statistics, such as daily mean (or daily minimum and maximum) air temperature at 2-meter, and maximum precipitation rate during the period. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2SMNXEDI\nM2SMNXEDI (or statM_2d_edi_Nx) is a 2-dimensional monthly data collection for extreme detection indices derived from daily Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets within each month. V2 of this extreme detection indices data collection is computed based on the 1991-2020 climatology, covering the time period from January 1980 to present. In contrast, V1, the original version, is computed based on an earlier 30-year climatology (1981-2010). This collection consists of indices used to identify or characterize extreme weather events associated with temperature, such as heatwaves and cold spells (e.g., their frequency, duration, and intensity), as well as events associated with precipitation, such as dry days and wet days (e.g., their frequency, duration, and intensity). MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes”, linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original filename. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read the \"MERRA-2 File Specification Document', “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2SMNXEDI\nM2SMNXEDI (or statM_2d_edi_Nx) is a 2-dimensional monthly data collection for extreme detection indices derived from daily Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets within each month. V1, the original version of this extreme detection indices data collection, is computed based on the 1981-2010 climatology, covering the period from January 1980 to December 2022. In contrast, V2, the second version, is calculated based on a 30-year climatology (1991-2020), covering the period from January 1980 to the present. This collection consists of indices used to identify or characterize extreme weather events associated with temperature, such as heatwaves and cold spells (e.g., their frequency, duration, and intensity), as well as events associated with precipitation, such as dry days and wet days (e.g., their frequency, duration, and intensity). MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes”, linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original filename. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read the \"MERRA-2 File Specification Document', “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2SMNXPCT\nM2SMNXPCT (or statM_2d_pct_Nx) is a 2-dimensional monthly data collection for percentile statistics derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V2 of this percentile data collection is computed based on the 1991-2020 climatology, covering the time period from January 1980 to present. In contrast, V1, the original version, is computed based on an earlier 30-year climatology (1981-2010). This collection consists of percentiles used to identify or characterize extreme weather events associated with temperature (maximum, mean, and minimum 2-m air temperature), as well as with precipitation (total precipitation). MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes”, linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original filename. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read the \"MERRA-2 File Specification Document', “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2SMNXPCT\nM2SMNXPCT (or statM_2d_pct_Nx) is a 2-dimensional monthly data collection for percentile statistics derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V1, the original version of this percentile data collection, is computed based on the 1981-2010 climatology, covering the period from January 1980 to December 2022. In contrast, V2, the second version, is calculated based on a 30-year climatology (1991-2020), covering the period from January 1980 to the present. This collection consists of percentiles used to identify or characterize extreme weather events associated with temperature (maximum, mean, and minimum 2-m air temperature), as well as with precipitation (total precipitation). MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes”, linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original filename. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read the \"MERRA-2 File Specification Document', “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2SMNXSLV\nM2SMNXSLV (or statM_2d_slv_Nx) is a 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of monthly mean of daily statistics, such as daily mean (or daily minimum and maximum) air temperature at 2-meter, and maximum precipitation rate during the period. The collection also includes the variance of parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T1NXADG\nM2T1NXADG (or tavg1_2d_adg_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics (extended), such as dry and wet deposition of each aerosol component, dust emission and sedimentation for each sized bin, and organic carbon convective scavenging. The data fields are time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T1NXAER\nM2T1NXAER (or tavg1_2d_aer_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics, such as column mass density of aerosol components (black carbon, dust, sea salt, sulfate, and organic carbon), surface mass concentration of aerosol components, and total extinction (and scattering ) aerosol optical thickness (AOT) at 550 nm. The total PM1.0, PM2.5, and PM10 may be derived with the formula described in the FAQs under the Documentation tab of this page. The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T1NXCHM\nM2T1NXCHM (or tavg1_2d_chm_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated carbon monoxide and ozone diagnostics, such as properties of carbon monoxide (column burden, emission, chemical production, and surface concentration), and total column ozone. The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T1NXCSP\nM2T1NXCSP (or tavg1_2d_csp_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of parameters from CFMIP Observations Simulator Package(COSP), such as ISCCP total cloud area fraction, MODIS cloud fraction water (ice) mean, MODIS cloud fraction low (mid,high) mean, modis cloud particle size water (ice) mean. CFMIP is the abbreviation of Cloud Feedback Model Intercomparison Project. The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T1NXFLX\nM2T1NXFLX (or tavg1_2d_flx_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated surface flux diagnostics, such as total precipitation, bias corrected total precipitation, surface air temperature, surface specific humidity, surface wind speed, and evaporation from turbulence. The “surface” in this data collection is the model surface layer. The heights of the model surface layer (HLML) vary with time and location, with the value of ~60 meter above ground. The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T1NXINT\nM2T1NXINT (or tavg1_2d_int_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertically Integrated diagnostics of water and energy, such as autoconversion loss of cloud water, convective source of cloud ice (water), eastward (nothward) flux of atmospheric ice (liquid, vapor), total potential energy tendency, vertically integrated potential energy tendency, and vertically integrated kinetic energy tendency. The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T1NXLFO\nM2T1NXLFO (or tavg1_2d_lfo_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as bias corrected precipitation, shortwave and longwave radiation at surface. The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T1NXLND\nM2T1NXLND (or tavg1_2d_lnd_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface diagnostics, such as baseflow flux, runoff, surface soil wetness, root zone soil wetness, water at surface layer, water at root zone layer, and soil temperature at six layers. The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T1NXOCN\nM2T1NXOCN (or tavg1_2d_ocn_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of ocean surface diagnostics, such as open water skin temperature (sea surface temperature), open water latent energy flux, open water upward sensible heat flux, and open water net downward longwave ( or shortwave ) flux . The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T1NXRAD\nM2T1NXRAD (or tavg1_2d_rad_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of radiation diagnostics, such as surface albedo, cloud area fraction, in cloud optical thickness, surface incoming shortwave flux (i.e. solar radiation), surface net downward shortwave flux, and upwelling longwave flux at toa (top of atmosphere) (i.e. outgoing longwave radiation (OLR) at toa). The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T1NXSLV\nM2T1NXSLV (or tavg1_2d_slv_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of meteorology diagnostics at popularly used vertical levels, such as air temperature at 2-meter (or at 10-meter, 850hPa, 500 hPa, 250hPa), wind components at 50-meter (or at 2-meter, 10-meter, 850 hPa, 500hPa, 250 hPa), sea level pressure, surface pressure, and total precipitable water vapor (or ice water, liquid water). The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NXGLC\nM2T3NXGLC (or tavg3_2d_glc_Nx) is a 2-dimensional 3-hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated land ice surface diagnostics at the single levels, such as fractional area of glaciated surface snow cover, snow mass over glaciated surface, snow depth over glaciated surface, and total snow mass residual due to densification. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NVASM\nM2T3NVASM (or tavg3_3d_asm_Nv) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 72 model layers, such as air temperature, wind components, vertical pressure velocity, water vapor, and layer height. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NPCLD\nM2T3NPCLD (or tavg3_3d_cld_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of cloud diagnostics on the 42 pressure levels, such as updraft areal fraction, cloud fraction for radiation , in-cloud cloud liquid (or ice) for radiation, and mass fraction of cloud liquid (or ice) water. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NVCLD\nM2T3NVCLD (or tavg3_3d_cld_Nv) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of cloud diagnostics at 72 model layers, such as cloud fraction for radiation, pressure thickness, in cloud cloud ice (or liquid) for radiation, and relative humidity. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NEMST\nM2T3NEMST (or tavg3_3d_mst_Ne) is a 3-dimensional 3-hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moisture processes diagnostics at the 73 model layer edges. The parameters include cumulative mass flux, 3D flux of liquid (or ice) convective (or nonconvective) precipitation, and model layer edge pressure. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=73 is for the bottom (or surface) model layer edge. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NPMST\nM2T3NPMST (or tavg3_3d_mst_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist processes diagnostics on the 42 pressure levels, such as convective rainwater source, 3D flux of ice convective (or nonconvective) precipitation, and 3D flux of liquid convective (or nonconvective) precipitation. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NVMST\nM2T3NVMST (or tavg3_3d_mst_Nv) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated moist processes diagnostics at 72 model layers, such as convective rainwater source, large scale rainwater source, evap subl of convective precipitation, and evap subl of non convective precipitation. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NENAV\nM2T3NENAV (or tavg3_3d_nav_Ne) is a 3-dimensional 3-hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertical coordinates of the 73 model layer edges. The parameters include edge pressure and edge heights. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=73 is for the bottom (or surface) model layer edge. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NPODT\nM2T3NPODT (or tavg3_3d_odt_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of ozone tendencies on the 42 pressure levels, such as total ozone analysis tendency, tendency of odd oxygen mixing ratio due to chemistry, tendency of odd oxygen due to moist processes, and tendency of ozone due to dynamics. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NPQDT\nM2T3NPQDT (or tavg3_3d_qdt_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist tendencies on the 42 pressure levels, such as tendency of ice (or liquid) water due to dynamics, total ice (or liquid) water tendency due to moist, total specific humidity analysis tendency, and specific humidity tendency due to moist. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NPRAD\nM2T3NPRAD (or tavg3_3d_rad_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of radiation diagnostics on 42 pressure levels, such as cloud fraction for radiation, and air temperature tendency due to longwave (or shortwave). The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NVRAD\nM2T3NVRAD (or tavg3_3d_rad_Nv) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated radiation diagnostics at 72 model layers, such as air temperature tendency due to longwave and air temperature tendency due to shortwave. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NPTDT\nM2T3NPTDT (or tavg3_3d_tdt_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of air temperature tendencies on 42 pressure levels, such as total temperature analysis tendency, and tendency of air temperature due to dynamics (or friction, moisture, radiation, and physics). The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NETRB\nM2T3NETRB (or tavg3_3d_trb_Ne) is a 3-dimensional 3-hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated turbulence diagnostics at the 73 model layer edges. The parameters include total scalar diffusivity, total momentum diffusivity, momentum diffusivity from Louis, Richardson number from Louis, and more. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=73 is for the bottom (or surface) model layer edge. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NPTRB\nM2T3NPTRB (or tavg3_3d_trb_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of turbulence diagnostics on 42 pressure levels, such as total scalar diffusivity, total momentum diffusivity, momentum diffusivity from Louis, and Richardson number from Louis. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2T3NPUDT\nM2T3NPUDT (or tavg3_3d_udt_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of wind tendencies on 42 pressure levels, such as total eastward (or northward) wind analysis tendency, tendency of eastward (or northward) wind due to dynamics, and tendency of eastward (or northward) wind due to turbulence. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TCNXLTM\nM2TCNXLTM (or tavgC_2d_ltm_Nx) is a 2-dimensional monthly data collection for climatological long term mean and standard deviation representing the interannual variability on a monthly timescale, derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V2 of this data collection is calculated with data from January 1991 to December 2020. In contrast, V1, the original version, is computed with data from an earlier 30-year time of 1981-2010. This collection consists of meteorological diagnostics, such as air temperature (maximum, mean, and minimum at 2-meter), wind components at different vertical levels (2-meter, 10-meter, 50-meter, 850 hPa, 500hPa, and 250 hPa), sea level pressure, surface pressure, and total precipitation, evaporation, and total precipitable water vapor. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes”, linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original filename. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read the \"MERRA-2 File Specification Document', “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TCNPLTM\nM2TCNPLTM (or tavgC_3d_ltm_Np) is a 3-dimensional monthly data collection for climatological long term mean and standard deviation representing the interannual variability on a monthly timescale, derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V2 of this data collection is calculated with data from January 1991 to December 2020. In contrast, V1, the original version, is computed with data from an earlier 30-year time of 1981-2010. This collection consists of meteorological diagnostics at 12 vertical pressure levels (e.g.,850 hPa, 500hPa, and 200 hPa), such as air temperature, wind components, and both relative and specific humidity. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes”, linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original filename. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read the \"MERRA-2 File Specification Document', “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNXADG\nM2TMNXADG (or tavgM_2d_adg_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics (extended), such as dry and wet deposition of each aerosol component, dust emission and sedimentation for each sized bin, and organic carbon convective scavenging. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNXAER\nM2TMNXAER (or tavgM_2d_aer_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics, such as column mass density of aerosol components (black carbon, dust, sea salt, sulfate, and organic carbon), surface mass concentration of aerosol components, and total extinction (and scattering ) aerosol optical thickness (AOT) at 550 nm. The total PM1.0, PM2.5, and PM10 may be derived with the formula described in the FAQs under the Documentation tab of this page. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNXCHM\nM2TMNXCHM (or tavgM_2d_chm_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated carbon monoxide and ozone diagnostics, such as properties of carbon monoxide (column burden, emission, chemical production, and surface concentration), and total column ozone. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNXCSP\nM2TMNXCSP (or tavgM_2d_csp_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of parameters from CFMIP Observations Simulator Package(COSP), such as ISCCP total cloud area fraction, MODIS cloud fraction water (ice) mean, MODIS cloud fraction low (mid,high) mean, modis cloud particle size water (ice) mean. CFMIP is the abbreviation of Cloud Feedback Model Intercomparison Project. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNXFLX\nM2TMNXFLX (or tavgM_2d_flx_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated surface flux diagnostics, such as total precipitation, bias corrected total precipitation, surface air temperature, surface specific humidity, surface wind speed, and evaporation from turbulence. The “surface” in this data collection is the model surface layer. The heights of the model surface layer (HLML) vary with time and location, with the value of ~60 meter above ground. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNXGLC\nM2TMNXGLC (or tavgM_2d_glc_Nx) is a 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated land ice surface diagnostics at the single levels, such as fractional area of glaciated surface snow cover, snow mass over glaciated surface, snow depth over glaciated surface, and total snow mass residual due to densification. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNXINT\nM2TMNXINT (or tavgM_2d_int_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of water and energy related vertically Integrated diagnostics, such as autoconversion loss of cloud water, convective source of cloud ice (water), eastward (nothward) flux of atmospheric ice (liquid, vapor), total potential energy tendency, vertically integrated potential energy tendency, and vertically integrated kinetic energy tendency. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNXLFO\nM2TMNXLFO (or tavgM_2d_lfo_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as bias corrected precipitation, shortwave and longwave radiation at surface. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNXLND\nM2TMNXLND (or tavgM_2d_lnd_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface diagnostics, such as baseflow flux, runoff, surface soil wetness, root zone soil wetness, water at surface layer, water at root zone layer, and soil temperature at six layers. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNXOCN\nM2TMNXOCN (or tavgM_2d_ocn_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of ocean surface diagnostics, such as open water skin temperature (sea surface temperature), open water latent energy flux, open water upward sensible heat flux, and open water net downward longwave ( or shortwave ) flux . The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNXRAD\nM2TMNXRAD (or tavgM_2d_rad_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of radiation diagnostics, such as surface albedo, cloud area fraction, in cloud optical thickness, surface incoming shortwave flux (i.e. solar radiation), surface net downward shortwave flux, and upwelling longwave flux at toa (top of atmosphere) (i.e. outgoing longwave radiation (OLR) at toa). The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNXSLV\nM2TMNXSLV (or tavgM_2d_slv_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of meteorology diagnostics at popularly used vertical levels, such as air temperature at 2-meter (or at 10-meter, 850hPa, 500 hPa, 250hPa), wind components at 50-meter (or at 2-meter, 10-meter, 850hPa, 500hPa, 250 hPa), sea level pressure, surface pressure, and total precipitable water vapor (or ice water, liquid water). The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNPCLD\nM2TMNPCLD (or tavgM_3d_cld_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of cloud diagnostics on 42 the pressure levels, such as updraft areal fraction, cloud fraction for radiation, in-cloud cloud liquid (or ice) for radiation, and mass fraction of cloud liquid (or ice) water. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNPMST\nM2TMNPMST (or tavgM_3d_mst_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist processes diagnostics on the 42 pressure levels, such as convective rainwater source, 3D flux of ice convective (or nonconvective) precipitation, and 3D flux of liquid convective (or nonconvective) precipitation. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNPODT\nM2TMNPODT (or tavgM_3d_odt_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of ozone tendencies on the 42 pressure levels, such as total ozone analysis tendency, tendency of odd oxygen mixing ratio due to chemistry, tendency of odd oxygen due to moist processes, and tendency of ozone due to dynamics. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNPQDT\nM2TMNPQDT (or tavgM_3d_qdt_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist tendencies on the 42 pressure levels, such as tendency of ice (or liquid) water due to dynamics, total ice (or liquid) water tendency due to moist, total specific humidity analysis tendency, and specific humidity tendency due to moist. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNPRAD\nM2TMNPRAD (or tavgM_3d_rad_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of radiation diagnostics on 42 pressure levels, such as cloud fraction for radiation, and air temperature tendency due to longwave (or shortwave). The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNPTDT\nM2TMNPTDT (or tavgM_3d_tdt_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of air temperature tendencies on 42 pressure levels, such as total temperature analysis tendency, and tendency of air temperature due to dynamics (or friction, moisture, radiation, and physics). The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNPTRB\nM2TMNPTRB (or tavgM_3d_trb_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of turbulence diagnostics on 42 pressure levels, such as total scalar diffusivity, total momentum diffusivity, momentum diffusivity from Louis, and Richardson number from Louis. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TMNPUDT\nM2TMNPUDT (or tavgM_3d_udt_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of wind tendencies on 42 pressure levels, such as total eastward (or northward) wind analysis tendency, tendency of eastward (or northward) wind due to dynamics, and tendency of eastward (or northward) wind due to turbulence. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNXADG\nM2TUNXADG (or tavgU_2d_adg_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics (extended), such as dry and wet deposition of each aerosol component, dust emission and sedimentation for each sized bin, and organic carbon convective scavenging. This data collection is the monthly mean of data fields for each hour and is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNXAER\nM2TUNXAER (or tavgU_2d_aer_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics, such as column mass density of aerosol components (black carbon, dust, sea salt, sulfate, and organic carbon), surface mass concentration of aerosol components, and total extinction (and scattering ) aerosol optical thickness (AOT) at 550 nm. The total PM1.0, PM2.5, and PM10 may be derived with the formula described in the FAQs under the Documentation tab of this page. This data collection is the monthly mean of data fields for each hour and is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNXCHM\nM2TUNXCHM (or tavgU_2d_chm_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated carbon monoxide and ozone diagnostics, such as properties of carbon monoxide (column burden, emission, chemical production, and surface concentration), and total column ozone. This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNXCSP\nM2TUNXCSP (or tavgU_2d_csp_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of parameters from CFMIP Observations Simulator Package(COSP), such as ISCCP total cloud area fraction, MODIS cloud fraction water (ice) mean, MODIS cloud fraction low (mid,high) mean, modis cloud particle size water (ice) mean. CFMIP is the abbreviation of Cloud Feedback Model Intercomparison Project. This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNXFLX\nM2TUNXFLX (or tavgU_2d_flx_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated surface flux diagnostics, such as total precipitation, bias corrected total precipitation, surface air temperature, surface specific humidity, surface wind speed, and evaporation from turbulence. The “surface” in this data collection is the model surface layer. The heights of the model surface layer (HLML) vary with time and location, with the value of ~60 meter above ground. This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNXGLC\nM2TUNXGLC (or tavgU_2d_glc_Nx) is a 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated land ice surface diagnostics at the single levels, such as fractional area of glaciated surface snow cover, snow mass over glaciated surface, snow depth over glaciated surface, and total snow mass residual due to densification. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNXINT\nM2TUNXINT (or tavgU_2d_int_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of water and energy related vertically Integrated diagnostics, such as autoconversion loss of cloud water, convective source of cloud ice (water), eastward (nothward) flux of atmospheric ice (liquid, vapor), total potential energy tendency, vertically integrated potential energy tendency, and vertically integrated kinetic energy tendency. This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNXLFO\nM2TUNXLFO (or tavgU_2d_lfo_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as bias corrected precipitation, shortwave and longwave radiation at surface. This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNXLND\nM2TUNXLND (or tavgU_2d_lnd_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface diagnostics, such as baseflow flux, runoff, surface soil wetness, root zone soil wetness, water at surface layer, water at root zone layer, and soil temperature at six layers. This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNXOCN\nM2TUNXOCN (or tavgU_2d_ocn_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of ocean surface diagnostics, such as open water skin temperature (sea surface temperature), open water latent energy flux, open water upward sensible heat flux, and open water net downward longwave ( or shortwave ) flux . This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNXRAD\nM2TUNXRAD (or tavgU_2d_rad_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of radiation diagnostics, such as surface albedo, cloud area fraction, in cloud optical thickness, surface incoming shortwave flux (i.e. solar radiation), surface net downward shortwave flux, and upwelling longwave flux at toa (top of atmosphere) (i.e. outgoing longwave radiation (OLR) at toa). This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNXSLV\nM2TUNXSLV (or tavgU_2d_slv_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of meteorology diagnostics at popularly used vertical levels, such as air temperature at 2-meter (or at 10-meter, 850 hPa, 500 hPa, 250hPa), wind components at 50-meter (or at 2-meter, 10-meter, 850hPa, 500hPa, 250 hPa), sea level pressure, surface pressure, and total precipitable water vapor (or ice water, liquid water). This data collection is the monthly mean of data fields for each hour and time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNPCLD\nM2TUNPCLD (or tavgU_3d_cld_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of cloud diagnostics on 42 the pressure levels, such as updraft areal fraction, cloud fraction for radiation, in-cloud cloud liquid (or ice) for radiation, and mass fraction of cloud liquid (or ice) water. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNPMST\nM2TUNPMST (or tavgU_3d_mst_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist processes diagnostics on the 42 pressure levels, such as convective rainwater source, 3D flux of ice convective (or nonconvective) precipitation, and 3D flux of liquid convective (or nonconvective) precipitation. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNPODT\nM2TUNPODT (or tavgU_3d_odt_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of ozone tendencies on the 42 pressure levels, such as total ozone analysis tendency, tendency of odd oxygen mixing ratio due to chemistry, tendency of odd oxygen due to moist processes, and tendency of ozone due to dynamics. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNPQDT\nM2TUNPQDT (or tavgU_3d_qdt_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist tendencies on the 42 pressure levels, such as tendency of ice (or liquid) water due to dynamics, total ice (or liquid) water tendency due to moist, total specific humidity analysis tendency, and specific humidity tendency due to moist. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNPRAD\nM2TUNPRAD (or tavgU_3d_rad_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of radiation diagnostics on 42 pressure levels, such as cloud fraction for radiation, and air temperature tendency due to longwave (or shortwave). The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour that is time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNPTDT\nM2TUNPTDT (or tavgU_3d_tdt_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of air temperature tendencies on 42 pressure levels, such as total temperature analysis tendency, and tendency of air temperature due to dynamics (or friction, moisture, radiation, and physics). The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNPTRB\nM2TUNPTRB (or tavgU_3d_trb_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of turbulence diagnostics on 42 pressure levels, such as total scalar diffusivity, total momentum diffusivity, momentum diffusivity from Louis, and Richardson number from Louis. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### M2TUNPUDT\nM2TUNPUDT (or tavgU_3d_udt_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of wind tendencies on 42 pressure levels, such as total eastward (or northward) wind analysis tendency, tendency of eastward (or northward) wind due to dynamics, and tendency of eastward (or northward) wind due to turbulence. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://gmao.gsfc.nasa.gov/gmao-products/merra-2/documentation_merra-2/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","ice","oceans","precipitation","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"M2_TMAX_PM25 v1 - M2_TMAX_PM25 is a value-added product derived from the MERRA-2 aerosol monthly product M2TMNXAER_5.12.4 (or tavgM_2d_aer_Nx). The surface concentration of fine particulate matter (PM2.5) is calculated as the sum of individual aerosol components (organic carbon, black carbon, sulfate, sea salt, and dust) (Buchard et al., 2017) and is recast from the native MERRA-2 model grid.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_CLIM/M2_TMAX_PM25.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2C0NXASM v5.12.4 - M2C0NXASM (or const_2d_asm_Nx) is a data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of 2-dimensional constant model parameters, such as the fraction of lake, land, and ocean within a model grid cell.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2C0NXASM.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2C0NXCTM v5.12.4 - M2C0NXCTM (or const_2d_ctm_Nx) is a data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of 2-dimensional constant model parameters for usage by the chemistry transport model (CTM), such as the fraction of lake, land, ice, or ocean within a model grid cell.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2C0NXCTM.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2C0NXLND v5.12.4 - M2C0NXLND (or const_2d_lnd_Nx) is a data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of 2-dimensional constant land surface parameters, such as thickness of the predefined soil layers, soil porosity, and soil wilting point.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2C0NXLND.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2I1NXASM v5.12.4 - M2I1NXASM (or inst1_2d_asm_Nx) is an instantaneous 2-dimensional hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological diagnostic parameters at the single levels, such as temperature at 2-meter and 10-meter; wind components at 2-meter, 10-meter, and 50-meter; surface pressure, and total precipitable water.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I1NXASM.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2I1NXINT v5.12.4 - M2I1NXINT (or inst1_2d_int_Nx) is an instantaneous 2-dimensional hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertically integrated diagnostics, such as kinetic energy, virtual potential temperature, and total precipitable water (or ice, liquid, and vapor).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I1NXINT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2I1NXLFO v5.12.4 - M2I1NXLFO (or inst1_2d_lfo_Nx) is an instantaneous 2-dimensional hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as height, specific humidity, wind, and air temperature of the model surface layer.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I1NXLFO.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2I3NXGAS v5.12.4 - M2I3NXGAS (or inst3_3d_gas_Nx) is an instantaneous 2-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilation of aerosol optical depth analysis and aerosol optical depth analysis increment.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I3NXGAS.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2I3NVAER v5.12.4 - M2I3NVAER (or inst3_3d_aer_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of aerosol mixing ratio parameters at 72 model layers, such as dust, sulphur dioxide, sea salt, black carbon, and organic carbon.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I3NVAER.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2I3NPASM v5.12.4 - M2I3NPASM (or inst3_3d_asm_Np) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 42 pressure levels, such as temperature, wind components, vertical pressure velocity, water vapor, ozone mass mixing ratio, and layer height.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I3NPASM.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2I3NVASM v5.12.4 - M2I3NVASM (or inst3_3d_asm_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 72 model layers, such as temperature, wind components, vertical pressure velocity, water vapor, and layer height.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I3NVASM.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2I3NVCHM v5.12.4 - M2I3NVCHM (or inst3_3d_chm_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of carbon monoxide and ozone mixing ratio at 72 model layers.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I3NVCHM.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2I3NVGAS v5.12.4 - M2I3NVGAS (or inst3_3d_gas_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of aerosol mixing ratio analysis increments at 72 model layers, such as mixing ratio analysis increments of black carbon, dust, organic carbon, sea salt, and sulfate.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I3NVGAS.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2I6NPANA v5.12.4 - M2I6NPANA (or inst6_3d_ana_Np) is an instantaneous 3-dimensional 6-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of analyzed meteorological fields at 42 pressure levels, such as temperature, wind components, specific humidity, ozone mixing ratio, and geopotential height.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I6NPANA.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2I6NVANA v5.12.4 - M2I6NVANA (or inst6_3d_ana_Nv) is an instantaneous 3-dimensional 6-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of analized meteorological fields at 72 model layers, such as temperature, wind components,specific humidity, and layer pressure thickness.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I6NVANA.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2IMNXASM v5.12.4 - M2IMNXASM (or instM_2d_asm_Nx) is an instantaneous 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological diagnostic parameters at the single levels, such as temperature at 2-meter and 10-meter; wind components at 2-meter, 10-meter, and 50-meter; surface pressure, and total precipitable water.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2IMNXASM.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2IMNXGAS v5.12.4 - M2IMNXGAS (or instM_3d_gas_Nx) is an instantaneous 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilation of aerosol optical depth analysis and aerosol optical depth analysis increment.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2IMNXGAS.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2IMNXINT v5.12.4 - M2IMNXINT (or instM_2d_int_Nx) is an instantaneous 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertically integrated diagnostics, such as kinetic energy, virtual potential temperature, and total precipitable water (or ice, liquid, and vapor).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2IMNXINT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2IMNXLFO v5.12.4 - M2IMNXLFO (or instM_2d_lfo_Nx) is an instantaneous 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as height, specific humidity, wind, and air temperature of the model surface layer.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2IMNXLFO.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2IMNPANA v5.12.4 - M2IMNPANA (or instM_3d_ana_Np) is an instantaneous 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of analyzed meteorological fields at 42 pressure levels, such as temperature, wind components, specific humidity, ozone mixing ratio, and geopotential height.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2IMNPANA.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2IMNPASM v5.12.4 - M2IMNPASM (or instM_3d_asm_Np) is an instantaneous 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 42 pressure levels, such as temperature, wind components, vertical pressure velocity, water vapor, ozone mass mixing ratio, and layer height.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2IMNPASM.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2IUNXASM v5.12.4 - M2IUNXASM (or instU_2d_asm_Nx) is an instantaneous 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological diagnostic parameters at the single levels, such as temperature at 2-meter and 10-meter; wind components at 2-meter, 10-meter, and 50-meter; surface pressure, and total precipitable water.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2IUNXASM.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2IUNXGAS v5.12.4 - M2IUNXGAS (or instU_3d_gas_Nx) is an instantaneous 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilation of aerosol optical depth analysis and aerosol optical depth analysis increment.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2IUNXGAS.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2IUNXINT v5.12.4 - M2IUNXINT (or instU_2d_int_Nx) is an instantaneous 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertically integrated diagnostics, such as kinetic energy, virtual potential temperature, and total precipitable water (or ice, liquid, and vapor).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2IUNXINT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2IUNXLFO v5.12.4 - M2IUNXLFO (or instU_2d_lfo_Nx) is an instantaneous 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as height, specific humidity, wind, and air temperature of the model surface layer.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2IUNXLFO.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2IUNPANA v5.12.4 - M2IUNPANA (or instU_3d_ana_Np) is an instantaneous 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of analyzed meteorological fields at 42 pressure levels, such as temperature, wind components, specific humidity, ozone mixing ratio, and geopotential height.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2IUNPANA.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2IUNPASM v5.12.4 - M2IUNPASM (or instU_3d_asm_Np) is an instantaneous 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 42 pressure levels, such as temperature, wind components, vertical pressure velocity, water vapor, ozone mass mixing ratio, and layer height.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2IUNPASM.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2SDNXSLV v5.12.4 - M2SDNXSLV (or statD_2d_slv_Nx) is a 2-dimensional daily data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of daily statistics, such as daily mean (or daily minimum and maximum) air temperature at 2-meter, and maximum precipitation rate during the period.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2SDNXSLV.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2SMNXEDI v2 - M2SMNXEDI (or statM_2d_edi_Nx) is a 2-dimensional monthly data collection for extreme detection indices derived from daily Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets within each month. V2 of this extreme detection indices data collection is computed based on the 1991-2020 climatology, covering the time period from January 1980 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_CLIM/M2SMNXEDI.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2SMNXEDI v1 - M2SMNXEDI (or statM_2d_edi_Nx) is a 2-dimensional monthly data collection for extreme detection indices derived from daily Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets within each month. V1, the original version of this extreme detection indices data collection, is computed based on the 1981-2010 climatology, covering the period from January 1980 to December 2022.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_CLIM/M2SMNXEDI.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2SMNXPCT v2 - M2SMNXPCT (or statM_2d_pct_Nx) is a 2-dimensional monthly data collection for percentile statistics derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V2 of this percentile data collection is computed based on the 1991-2020 climatology, covering the time period from January 1980 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_CLIM/M2SMNXPCT.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2SMNXPCT v1 - M2SMNXPCT (or statM_2d_pct_Nx) is a 2-dimensional monthly data collection for percentile statistics derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V1, the original version of this percentile data collection, is computed based on the 1981-2010 climatology, covering the period from January 1980 to December 2022.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_CLIM/M2SMNXPCT.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2SMNXSLV v5.12.4 - M2SMNXSLV (or statM_2d_slv_Nx) is a 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of monthly mean of daily statistics, such as daily mean (or daily minimum and maximum) air temperature at 2-meter, and maximum precipitation rate during the period.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2SMNXSLV.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T1NXADG v5.12.4 - M2T1NXADG (or tavg1_2d_adg_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics (extended), such as dry and wet deposition of each aerosol component, dust emission and sedimentation for each sized bin, and organic carbon convective scavenging.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T1NXADG.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T1NXAER v5.12.4 - M2T1NXAER (or tavg1_2d_aer_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics, such as column mass density of aerosol components (black carbon, dust, sea salt, sulfate, and organic carbon), surface mass concentration of aerosol components, and total extinction (and scattering ) aerosol optical thickness (AOT) at 550 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T1NXAER.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T1NXCHM v5.12.4 - M2T1NXCHM (or tavg1_2d_chm_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated carbon monoxide and ozone diagnostics, such as properties of carbon monoxide (column burden, emission, chemical production, and surface concentration), and total column ozone.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T1NXCHM.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T1NXCSP v5.12.4 - M2T1NXCSP (or tavg1_2d_csp_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of parameters from CFMIP Observations Simulator Package(COSP), such as ISCCP total cloud area fraction, MODIS cloud fraction water (ice) mean, MODIS cloud fraction low (mid,high) mean, modis cloud particle size water (ice) mean.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T1NXCSP.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T1NXFLX v5.12.4 - M2T1NXFLX (or tavg1_2d_flx_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated surface flux diagnostics, such as total precipitation, bias corrected total precipitation, surface air temperature, surface specific humidity, surface wind speed, and evaporation from turbulence.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T1NXFLX.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T1NXINT v5.12.4 - M2T1NXINT (or tavg1_2d_int_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertically Integrated diagnostics of water and energy, such as autoconversion loss of cloud water, convective source of cloud ice (water), eastward (nothward) flux of atmospheric ice (liquid, vapor), total potential energy tendency, vertically integrated potential energy tendency, and vertically integrated kinetic energy tendency.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T1NXINT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T1NXLFO v5.12.4 - M2T1NXLFO (or tavg1_2d_lfo_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as bias corrected precipitation, shortwave and longwave radiation at surface.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T1NXLFO.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T1NXLND v5.12.4 - M2T1NXLND (or tavg1_2d_lnd_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface diagnostics, such as baseflow flux, runoff, surface soil wetness, root zone soil wetness, water at surface layer, water at root zone layer, and soil temperature at six layers.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T1NXLND.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T1NXOCN v5.12.4 - M2T1NXOCN (or tavg1_2d_ocn_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of ocean surface diagnostics, such as open water skin temperature (sea surface temperature), open water latent energy flux, open water upward sensible heat flux, and open water net downward longwave ( or shortwave ) flux.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T1NXOCN.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T1NXRAD v5.12.4 - M2T1NXRAD (or tavg1_2d_rad_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of radiation diagnostics, such as surface albedo, cloud area fraction, in cloud optical thickness, surface incoming shortwave flux (i.e.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T1NXRAD.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T1NXSLV v5.12.4 - M2T1NXSLV (or tavg1_2d_slv_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of meteorology diagnostics at popularly used vertical levels, such as air temperature at 2-meter (or at 10-meter, 850hPa, 500 hPa, 250hPa), wind components at 50-meter (or at 2-meter, 10-meter, 850 hPa, 500hPa, 250 hPa), sea level pressure, surface pressure, and total precipitable water vapor (or ice water, liquid water).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T1NXSLV.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NXGLC v5.12.4 - M2T3NXGLC (or tavg3_2d_glc_Nx) is a 2-dimensional 3-hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated land ice surface diagnostics at the single levels, such as fractional area of glaciated surface snow cover, snow mass over glaciated surface, snow depth over glaciated surface, and total snow mass residual due to densification.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NXGLC.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NVASM v5.12.4 - M2T3NVASM (or tavg3_3d_asm_Nv) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 72 model layers, such as air temperature, wind components, vertical pressure velocity, water vapor, and layer height.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NVASM.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NPCLD v5.12.4 - M2T3NPCLD (or tavg3_3d_cld_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of cloud diagnostics on the 42 pressure levels, such as updraft areal fraction, cloud fraction for radiation , in-cloud cloud liquid (or ice) for radiation, and mass fraction of cloud liquid (or ice) water.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NPCLD.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NVCLD v5.12.4 - M2T3NVCLD (or tavg3_3d_cld_Nv) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of cloud diagnostics at 72 model layers, such as cloud fraction for radiation, pressure thickness, in cloud cloud ice (or liquid) for radiation, and relative humidity.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NVCLD.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NEMST v5.12.4 - M2T3NEMST (or tavg3_3d_mst_Ne) is a 3-dimensional 3-hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moisture processes diagnostics at the 73 model layer edges.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NEMST.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NPMST v5.12.4 - M2T3NPMST (or tavg3_3d_mst_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist processes diagnostics on the 42 pressure levels, such as convective rainwater source, 3D flux of ice convective (or nonconvective) precipitation, and 3D flux of liquid convective (or nonconvective) precipitation.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NPMST.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NVMST v5.12.4 - M2T3NVMST (or tavg3_3d_mst_Nv) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated moist processes diagnostics at 72 model layers, such as convective rainwater source, large scale rainwater source, evap subl of convective precipitation, and evap subl of non convective precipitation.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NVMST.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NENAV v5.12.4 - M2T3NENAV (or tavg3_3d_nav_Ne) is a 3-dimensional 3-hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertical coordinates of the 73 model layer edges.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NENAV.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NPODT v5.12.4 - M2T3NPODT (or tavg3_3d_odt_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of ozone tendencies on the 42 pressure levels, such as total ozone analysis tendency, tendency of odd oxygen mixing ratio due to chemistry, tendency of odd oxygen due to moist processes, and tendency of ozone due to dynamics.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NPODT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NPQDT v5.12.4 - M2T3NPQDT (or tavg3_3d_qdt_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist tendencies on the 42 pressure levels, such as tendency of ice (or liquid) water due to dynamics, total ice (or liquid) water tendency due to moist, total specific humidity analysis tendency, and specific humidity tendency due to moist.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NPQDT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NPRAD v5.12.4 - M2T3NPRAD (or tavg3_3d_rad_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of radiation diagnostics on 42 pressure levels, such as cloud fraction for radiation, and air temperature tendency due to longwave (or shortwave).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NPRAD.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NVRAD v5.12.4 - M2T3NVRAD (or tavg3_3d_rad_Nv) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated radiation diagnostics at 72 model layers, such as air temperature tendency due to longwave and air temperature tendency due to shortwave.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NVRAD.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NPTDT v5.12.4 - M2T3NPTDT (or tavg3_3d_tdt_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of air temperature tendencies on 42 pressure levels, such as total temperature analysis tendency, and tendency of air temperature due to dynamics (or friction, moisture, radiation, and physics).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NPTDT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NETRB v5.12.4 - M2T3NETRB (or tavg3_3d_trb_Ne) is a 3-dimensional 3-hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated turbulence diagnostics at the 73 model layer edges.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NETRB.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NPTRB v5.12.4 - M2T3NPTRB (or tavg3_3d_trb_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of turbulence diagnostics on 42 pressure levels, such as total scalar diffusivity, total momentum diffusivity, momentum diffusivity from Louis, and Richardson number from Louis.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NPTRB.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2T3NPUDT v5.12.4 - M2T3NPUDT (or tavg3_3d_udt_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of wind tendencies on 42 pressure levels, such as total eastward (or northward) wind analysis tendency, tendency of eastward (or northward) wind due to dynamics, and tendency of eastward (or northward) wind due to turbulence.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T3NPUDT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TCNXLTM v2 - M2TCNXLTM (or tavgC_2d_ltm_Nx) is a 2-dimensional monthly data collection for climatological long term mean and standard deviation representing the interannual variability on a monthly timescale, derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V2 of this data collection is calculated with data from January 1991 to December 2020.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_CLIM/M2TCNXLTM.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TCNPLTM v2 - M2TCNPLTM (or tavgC_3d_ltm_Np) is a 3-dimensional monthly data collection for climatological long term mean and standard deviation representing the interannual variability on a monthly timescale, derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V2 of this data collection is calculated with data from January 1991 to December 2020.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_CLIM/M2TCNPLTM.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNXADG v5.12.4 - M2TMNXADG (or tavgM_2d_adg_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics (extended), such as dry and wet deposition of each aerosol component, dust emission and sedimentation for each sized bin, and organic carbon convective scavenging.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNXADG.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNXAER v5.12.4 - M2TMNXAER (or tavgM_2d_aer_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics, such as column mass density of aerosol components (black carbon, dust, sea salt, sulfate, and organic carbon), surface mass concentration of aerosol components, and total extinction (and scattering ) aerosol optical thickness (AOT) at 550 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNXAER.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNXCHM v5.12.4 - M2TMNXCHM (or tavgM_2d_chm_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated carbon monoxide and ozone diagnostics, such as properties of carbon monoxide (column burden, emission, chemical production, and surface concentration), and total column ozone.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNXCHM.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNXCSP v5.12.4 - M2TMNXCSP (or tavgM_2d_csp_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of parameters from CFMIP Observations Simulator Package(COSP), such as ISCCP total cloud area fraction, MODIS cloud fraction water (ice) mean, MODIS cloud fraction low (mid,high) mean, modis cloud particle size water (ice) mean.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNXCSP.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNXFLX v5.12.4 - M2TMNXFLX (or tavgM_2d_flx_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated surface flux diagnostics, such as total precipitation, bias corrected total precipitation, surface air temperature, surface specific humidity, surface wind speed, and evaporation from turbulence.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNXFLX.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNXGLC v5.12.4 - M2TMNXGLC (or tavgM_2d_glc_Nx) is a 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated land ice surface diagnostics at the single levels, such as fractional area of glaciated surface snow cover, snow mass over glaciated surface, snow depth over glaciated surface, and total snow mass residual due to densification.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNXGLC.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNXINT v5.12.4 - M2TMNXINT (or tavgM_2d_int_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of water and energy related vertically Integrated diagnostics, such as autoconversion loss of cloud water, convective source of cloud ice (water), eastward (nothward) flux of atmospheric ice (liquid, vapor), total potential energy tendency, vertically integrated potential energy tendency, and vertically integrated kinetic energy tendency.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNXINT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNXLFO v5.12.4 - M2TMNXLFO (or tavgM_2d_lfo_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as bias corrected precipitation, shortwave and longwave radiation at surface.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNXLFO.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNXLND v5.12.4 - M2TMNXLND (or tavgM_2d_lnd_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface diagnostics, such as baseflow flux, runoff, surface soil wetness, root zone soil wetness, water at surface layer, water at root zone layer, and soil temperature at six layers.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNXLND.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNXOCN v5.12.4 - M2TMNXOCN (or tavgM_2d_ocn_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of ocean surface diagnostics, such as open water skin temperature (sea surface temperature), open water latent energy flux, open water upward sensible heat flux, and open water net downward longwave ( or shortwave ) flux.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNXOCN.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNXRAD v5.12.4 - M2TMNXRAD (or tavgM_2d_rad_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of radiation diagnostics, such as surface albedo, cloud area fraction, in cloud optical thickness, surface incoming shortwave flux (i.e.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNXRAD.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNXSLV v5.12.4 - M2TMNXSLV (or tavgM_2d_slv_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of meteorology diagnostics at popularly used vertical levels, such as air temperature at 2-meter (or at 10-meter, 850hPa, 500 hPa, 250hPa), wind components at 50-meter (or at 2-meter, 10-meter, 850hPa, 500hPa, 250 hPa), sea level pressure, surface pressure, and total precipitable water vapor (or ice water, liquid water).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNXSLV.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNPCLD v5.12.4 - M2TMNPCLD (or tavgM_3d_cld_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of cloud diagnostics on 42 the pressure levels, such as updraft areal fraction, cloud fraction for radiation, in-cloud cloud liquid (or ice) for radiation, and mass fraction of cloud liquid (or ice) water.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNPCLD.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNPMST v5.12.4 - M2TMNPMST (or tavgM_3d_mst_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist processes diagnostics on the 42 pressure levels, such as convective rainwater source, 3D flux of ice convective (or nonconvective) precipitation, and 3D flux of liquid convective (or nonconvective) precipitation.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNPMST.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNPODT v5.12.4 - M2TMNPODT (or tavgM_3d_odt_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of ozone tendencies on the 42 pressure levels, such as total ozone analysis tendency, tendency of odd oxygen mixing ratio due to chemistry, tendency of odd oxygen due to moist processes, and tendency of ozone due to dynamics.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNPODT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNPQDT v5.12.4 - M2TMNPQDT (or tavgM_3d_qdt_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist tendencies on the 42 pressure levels, such as tendency of ice (or liquid) water due to dynamics, total ice (or liquid) water tendency due to moist, total specific humidity analysis tendency, and specific humidity tendency due to moist.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNPQDT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNPRAD v5.12.4 - M2TMNPRAD (or tavgM_3d_rad_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of radiation diagnostics on 42 pressure levels, such as cloud fraction for radiation, and air temperature tendency due to longwave (or shortwave).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNPRAD.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNPTDT v5.12.4 - M2TMNPTDT (or tavgM_3d_tdt_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of air temperature tendencies on 42 pressure levels, such as total temperature analysis tendency, and tendency of air temperature due to dynamics (or friction, moisture, radiation, and physics).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNPTDT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNPTRB v5.12.4 - M2TMNPTRB (or tavgM_3d_trb_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of turbulence diagnostics on 42 pressure levels, such as total scalar diffusivity, total momentum diffusivity, momentum diffusivity from Louis, and Richardson number from Louis.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNPTRB.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TMNPUDT v5.12.4 - M2TMNPUDT (or tavgM_3d_udt_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of wind tendencies on 42 pressure levels, such as total eastward (or northward) wind analysis tendency, tendency of eastward (or northward) wind due to dynamics, and tendency of eastward (or northward) wind due to turbulence.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_MONTHLY/M2TMNPUDT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNXADG v5.12.4 - M2TUNXADG (or tavgU_2d_adg_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics (extended), such as dry and wet deposition of each aerosol component, dust emission and sedimentation for each sized bin, and organic carbon convective scavenging.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNXADG.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNXAER v5.12.4 - M2TUNXAER (or tavgU_2d_aer_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics, such as column mass density of aerosol components (black carbon, dust, sea salt, sulfate, and organic carbon), surface mass concentration of aerosol components, and total extinction (and scattering ) aerosol optical thickness (AOT) at 550 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNXAER.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNXCHM v5.12.4 - M2TUNXCHM (or tavgU_2d_chm_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated carbon monoxide and ozone diagnostics, such as properties of carbon monoxide (column burden, emission, chemical production, and surface concentration), and total column ozone.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNXCHM.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNXCSP v5.12.4 - M2TUNXCSP (or tavgU_2d_csp_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of parameters from CFMIP Observations Simulator Package(COSP), such as ISCCP total cloud area fraction, MODIS cloud fraction water (ice) mean, MODIS cloud fraction low (mid,high) mean, modis cloud particle size water (ice) mean.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNXCSP.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNXFLX v5.12.4 - M2TUNXFLX (or tavgU_2d_flx_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated surface flux diagnostics, such as total precipitation, bias corrected total precipitation, surface air temperature, surface specific humidity, surface wind speed, and evaporation from turbulence.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNXFLX.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNXGLC v5.12.4 - M2TUNXGLC (or tavgU_2d_glc_Nx) is a 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated land ice surface diagnostics at the single levels, such as fractional area of glaciated surface snow cover, snow mass over glaciated surface, snow depth over glaciated surface, and total snow mass residual due to densification.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNXGLC.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNXINT v5.12.4 - M2TUNXINT (or tavgU_2d_int_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of water and energy related vertically Integrated diagnostics, such as autoconversion loss of cloud water, convective source of cloud ice (water), eastward (nothward) flux of atmospheric ice (liquid, vapor), total potential energy tendency, vertically integrated potential energy tendency, and vertically integrated kinetic energy tendency.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNXINT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNXLFO v5.12.4 - M2TUNXLFO (or tavgU_2d_lfo_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as bias corrected precipitation, shortwave and longwave radiation at surface.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNXLFO.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNXLND v5.12.4 - M2TUNXLND (or tavgU_2d_lnd_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface diagnostics, such as baseflow flux, runoff, surface soil wetness, root zone soil wetness, water at surface layer, water at root zone layer, and soil temperature at six layers.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNXLND.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNXOCN v5.12.4 - M2TUNXOCN (or tavgU_2d_ocn_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of ocean surface diagnostics, such as open water skin temperature (sea surface temperature), open water latent energy flux, open water upward sensible heat flux, and open water net downward longwave ( or shortwave ) flux.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNXOCN.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNXRAD v5.12.4 - M2TUNXRAD (or tavgU_2d_rad_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of radiation diagnostics, such as surface albedo, cloud area fraction, in cloud optical thickness, surface incoming shortwave flux (i.e.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNXRAD.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNXSLV v5.12.4 - M2TUNXSLV (or tavgU_2d_slv_Nx) is a time-averaged 2-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of meteorology diagnostics at popularly used vertical levels, such as air temperature at 2-meter (or at 10-meter, 850 hPa, 500 hPa, 250hPa), wind components at 50-meter (or at 2-meter, 10-meter, 850hPa, 500hPa, 250 hPa), sea level pressure, surface pressure, and total precipitable water vapor (or ice water, liquid water).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNXSLV.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNPCLD v5.12.4 - M2TUNPCLD (or tavgU_3d_cld_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of cloud diagnostics on 42 the pressure levels, such as updraft areal fraction, cloud fraction for radiation, in-cloud cloud liquid (or ice) for radiation, and mass fraction of cloud liquid (or ice) water.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNPCLD.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNPMST v5.12.4 - M2TUNPMST (or tavgU_3d_mst_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist processes diagnostics on the 42 pressure levels, such as convective rainwater source, 3D flux of ice convective (or nonconvective) precipitation, and 3D flux of liquid convective (or nonconvective) precipitation.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNPMST.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNPODT v5.12.4 - M2TUNPODT (or tavgU_3d_odt_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of ozone tendencies on the 42 pressure levels, such as total ozone analysis tendency, tendency of odd oxygen mixing ratio due to chemistry, tendency of odd oxygen due to moist processes, and tendency of ozone due to dynamics.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNPODT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNPQDT v5.12.4 - M2TUNPQDT (or tavgU_3d_qdt_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist tendencies on the 42 pressure levels, such as tendency of ice (or liquid) water due to dynamics, total ice (or liquid) water tendency due to moist, total specific humidity analysis tendency, and specific humidity tendency due to moist.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNPQDT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNPRAD v5.12.4 - M2TUNPRAD (or tavgU_3d_rad_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of radiation diagnostics on 42 pressure levels, such as cloud fraction for radiation, and air temperature tendency due to longwave (or shortwave).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNPRAD.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNPTDT v5.12.4 - M2TUNPTDT (or tavgU_3d_tdt_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of air temperature tendencies on 42 pressure levels, such as total temperature analysis tendency, and tendency of air temperature due to dynamics (or friction, moisture, radiation, and physics).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNPTDT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNPTRB v5.12.4 - M2TUNPTRB (or tavgU_3d_trb_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of turbulence diagnostics on 42 pressure levels, such as total scalar diffusivity, total momentum diffusivity, momentum diffusivity from Louis, and Richardson number from Louis.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNPTRB.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"M2TUNPUDT v5.12.4 - M2TUNPUDT (or tavgU_3d_udt_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of wind tendencies on 42 pressure levels, such as total eastward (or northward) wind analysis tendency, tendency of eastward (or northward) wind due to dynamics, and tendency of eastward (or northward) wind due to turbulence.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2_DIURNAL/M2TUNPUDT.5.12.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-merra-2","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA MetOp Project","Description":"This dataset represents the first historically reprocessed Level 2 coastal ocean surface wind vector climate data record from the Advanced Scatterometer (ASCAT) on MetOp-A sampled on a 12.5 km grid. This coastal dataset utilizes a spatial box filter to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset and obtains additional winds near the coast. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the vector cell wind using the same CMOD7 geophysical model function as in the operational OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds are computed as close to ~15 km from the coast. Each file corresponds to one complete orbit and is provided in netCDF version 3 format. The beginning of the orbit files is defined near the South Pole. ASCAT is a C-band fan beam radar scatterometer, providing two independent swaths of backscatter retrievals, aboard the MetOp-A platform in sun-synchronous polar orbit. It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For access to more contemporaneous and near-real-time MetOp-A ASCAT 12.5km data, please visit: https://podaac.jpl.nasa.gov/dataset/ASCATA-L2-Coastal. For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used. Use cases and feedback on the products will be much appreciated and in fact helps to sustain the reprocessing capability.\n<br>\n\n#### ASCATA_L2_25KM_CDR\nThis dataset represents the first historically reprocessed Level 2 ocean surface wind vector climate data record from the Advanced Scatterometer (ASCAT) on MetOp-A sampled on a 25 km grid. Products at 25-km sampling are less noisy than 12.5-km products, but also contain less geophysical information on small scales and near the coasts. The wind vector retrievals are currently processed using the CMOD7 geophysical model function using a Hamming filter to spatially average the Level 1 Sigma-0 data over 25 km swath grid cells. Each file corresponds to one complete orbit and is provided in netCDF version 3 format. The beginning of the orbit files is defined near the South Pole. ASCAT is a C-band fan beam radar scatterometer, providing two independent swaths of backscatter retrievals, aboard the MetOp-A platform in sun-synchronous polar orbit. It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For access to more contemporaneous and near-real-time MetOp-A ASCAT 25-km data, please visit: https://podaac.jpl.nasa.gov/dataset/ASCATA-L2-25km. For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used. Use cases and feedback on the products will be much appreciated and in fact helps to sustain the reprocessing capability.\n<br>\n\n#### ASCATA-L2-25km\nThis dataset contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-A at 25 km sampling resolution (note: the effective resolution is 50 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). The wind vector retrievals are currently processed using the CMOD7.n geophysical model function using a Hamming filter to spatially average the Sigma-0 data in the ASCAT L1B data. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-A platform. For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.\n<br>\n\n#### ASCATA-L2-Coastal\nThis dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-A at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). This coastal dataset differs from the standard 25 km datasets in that it utilizes a spatial box filter (rather than the Hamming filter) to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset; all full resolution Sigma-0 retrievals within a 15 km radius of the wind vector cell centroid are used in the averaging. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the wind vector cell using the same CMOD7.n geophysical model function as in the standard OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds can be computed as close to ~15 km from the coast, as compared to the static ~35 km land mask in the standard 12.5 km dataset. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-A platform. For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.\n<br>\n\n#### ASCATB-L2-25km\nThis dataset contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-B at 25 km sampling resolution (note: the effective resolution is 50 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). The wind vector retrievals are currently processed using the CMOD.n geophysical model function using a Hamming filter to spatially average the Sigma-0 data in the ASCAT L1B data. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual swath fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-B platform. For more information on the MetOp-B mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.\n<br>\n\n#### ASCATB-L2-Coastal\nThis dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-B at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). This coastal dataset differs from the standard 12.5 and 25 km datasets in that it utilizes a spatial box filter (rather than the Hamming filter) to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset; all full resolution Sigma-0 retrievals within a 15 km radius of the wind vector cell centroid are used in the averaging. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the wind vector cell using the same CMOD5.n geophysical model function as in the standard OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds can be computed as close to ~15 km from the coast, as compared to the static ~35 km land mask in the standard 12.5 km dataset. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual swath fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-B platform. For more information on the MetOp-B mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.\n<br>\n\n#### M1_AVH13C1\nThe Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B. Currently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product. The METOP-B AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name M1_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (M1_AVH01C1). The M1_AVH13C1 product is available in HDF4 file format.\n<br>\n\n#### ASCATC-L2-25km\nThis dataset contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-C at 25 km sampling resolution (note: the effective resolution is 50 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). The wind vector retrievals are currently processed using the CMOD7.n geophysical model function using a Hamming filter to spatially average the Sigma-0 data in the ASCAT L1B data. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual swath fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-C platform. For more information about the MetOp-C platform and mission, please refer to: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.\n<br>\n\n#### ASCATC-L2-Coastal\nThis dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-C at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). This coastal dataset differs from the standard 12.5 and 25 km datasets in that it utilizes a spatial box filter (rather than the Hamming filter) to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset; all full resolution Sigma-0 retrievals within a 15 km radius of the wind vector cell centroid are used in the averaging. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the wind vector cell using the same CMOD7.n geophysical model function as in the standard OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds can be computed as close to ~15 km from the coast. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual swath fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-C platform. For more information on the MetOp-C mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","hdf","ice","netcdf","oceans","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ASCATA_L2_COASTAL_CDR v1.0 - This dataset represents the first historically reprocessed Level 2 coastal ocean surface wind vector climate data record from the Advanced Scatterometer (ASCAT) on MetOp-A sampled on a 12.5 km grid. This coastal dataset utilizes a spatial box filter to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset and obtains additional winds near the coast.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATA_L2_COASTAL_CDR","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ASCATA_L2_25KM_CDR v1.0 - This dataset represents the first historically reprocessed Level 2 ocean surface wind vector climate data record from the Advanced Scatterometer (ASCAT) on MetOp-A sampled on a 25 km grid. Products at 25-km sampling are less noisy than 12.5-km products, but also contain less geophysical information on small scales and near the coasts.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATA_L2_25KM_CDR","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ASCATA-L2-25km vOperational/Near-Real-Time - This dataset contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-A at 25 km sampling resolution (note: the effective resolution is 50 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATA-L2-25km","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ASCATA-L2-Coastal vOperational/Near-Real-Time - This dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-A at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATA-L2-Coastal","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ASCATB-L2-25km vOperational/Near-Real-Time - This dataset contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-B at 25 km sampling resolution (note: the effective resolution is 50 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATB-L2-25km","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ASCATB-L2-Coastal vOperational/Near-Real-Time - This dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-B at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATB-L2-Coastal","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"M1_AVH13C1 v6 - The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change.","ARN":"arn:aws:s3:::prod-lads/M1_AVH13C1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ASCATC-L2-25km vOperational/Near-Real-Time - This dataset contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-C at 25 km sampling resolution (note: the effective resolution is 50 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATC-L2-25km","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"ASCATC-L2-Coastal vOperational/Near-Real-Time - This dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-C at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ASCATC-L2-Coastal","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-metop","Sources":["awslabs-open-data-registry"]}
{"Name":"MISR Level 1B2 Ellipsoid Data V004","Description":"MI1B2E_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Data Version 4 product. It contains Ellipsoid-projected Top-of-Atmosphere (TOA) Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. Data collection for this product is ongoing.\r\n\r\nMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.asdc.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/TERRA/MISR/MI1B2E_L1.004","Contact":"ASDC USER SERVICES: support-asdc@earthdata.nasa.gov. Home Page: https://asdc.larc.nasa.gov/","ManagedBy":"NASA","UpdateFrequency":"From 1999-12-18 to Ongoing","Tags":["atmosphere","aws-pds","climate","cyclone typhoon hurricane","datacenter","earth observation","global","land","opendap","orbit"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MISR Level 1B2 Ellipsoid Data V004. (Format: netCDF-4)","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI1B2E_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentialsREADME"}],"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-mi1b2e","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA MISR Project","Description":"MIANACP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Aerosol Climatology Product version 1. It is 1) the microphysical and scattering characteristics of pure aerosol upon which routine retrievals are based, 2) mixtures of pure aerosol to be compared with MISR observations, and 3) the likelihood value assigned to each mode geographically. The ACP describes mixtures of up to three component aerosol types from a list of eight components in varying proportions. ACP component aerosol particle data quality depends on the ACP input data, which are based on aerosol particles described in the literature and consider MISR-specific sensitivity to particle size, single-scattering albedo, and shape, and shape - roughly: small, medium, and large; dirty and clean; spherical and nonspherical [Kahn et al., 1998; 2001]. Also reported in the ACP are the mixtures of these components used by the retrieval algorithm. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### MIANCAGP\nMIANCAGP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Geographic Product version 1. It is a set of 233 pre-computed files. Each AGP file pertains to a single Terra orbital path. MISR production software relies on information in the AGP, such as digital terrain elevation, as input to the algorithms that generate MISR products. The AGP contains eleven fields of geographical data. This product consists primarily of geolocation data on a Space Oblique Mercator (SOM) Grid. It has 233 parts, corresponding to the 233 repeat orbits of the EOS-AM1 Spacecraft. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### MIANCARP\nMIANCARP_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Radiometric Product version 2. It is composed of 4 files covering instrument characterization data, pre-flight calibration data, in-flight calibration data, and configuration parameters. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### MISBR\nThis is the browse data associated with a particular granule. MISBR_005 is the Multi-angle Imaging SpectroRadiometer (MISR) Browse data version 5. It consists of Ellipsoid color images obtained by each camera resampled to 2. 2 km resolution. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### MIRCCMF\nMIRCCMF_001 is the Multi-angle Imaging SpectroRadiometer (MISR) FIRSTLOOK radiometric camera-by-camera Cloud Mask (RCCM) version 1 data product. It was produced using ancillary inputs from the previous time period, such as Radiometric Camera-by-camera Cloud mask Threshold (RCCT). It is used to determine whether a scene is clear, cloudy, or dusty (over the ocean). Data collection for this product is ongoing. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### ATSMIGEO\nATSMIGEO_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Geometric Parameters subset for the ARCTAS region version 2. It measures the sun and view angles at the reference ellipsoid. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### GOMIGEO\nGOMIGEO_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Geometric Parameters subset for the GoMACCS region version 2. It contains the Geometric Parameters, which measure the sun and view angles at the reference ellipsoid. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### SAMMIGEO\nSAMMIGEO_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Geometric Parameters subset for the SAMUM region version 2. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, and Reflecting Level Reference Altitude (RLRA), with associated data for the SAMUM_2006 theme. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### VBEMIGEO\nVBEMIGEO_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Geometric Parameters subset for the VBBE region version 2. It contains the Geometric Parameters that measure the sun and view angles at the reference ellipsoid. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIB2GEOP\nMulti-angle Imaging SpectroRadiometer (MISR) is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Geometric Parameters V002 contains the Geometric Parameters which measure the sun and view angles at the reference ellipsoid\n<br>\n\n#### MIB2GEOP\nMIB2GEOP_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Geometric Parameters Version 3 product. It contains the Geometric Parameters which measure the sun and view angles at the reference ellipsoid. Data collection for this product is ongoing. The distribution format of this product is NetCDF-4 which is a migration from the previous version's format of HDF-EOS2. MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### ATSMIB2E\nATSMIB2E_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Product subset for the ARCTAS region. It contains an Ellipsoid-projected TOA Radiance subset for the ARCTAS region, resampled at the surface and topographically corrected and geometrically corrected by PGE22. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### RICMIB2E\nRICMIB2E_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Product subset for the RICO region version 2. It contains the ellipsoid projected TOA Radiance over the RICO region, resampled to WGS84 ellipsoid corrected, and it requires the radiances from all nine cameras of MISR to be projected to a surface defined by the reference WGS84 ellipsoid. On this surface, the camera-to-camera stereo matching will be performed to determine cloud altitude. Topographic distortions are removed. Corrections due to errors in the supplied Navigation and attitude data are obtained during Terrain-projected parameter processing and are applied to these parameters. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### SAMMIB2E\nSAMMIB2E_3 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Product subset for the SAMUM region version 1. It contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected and geometrically corrected by PGE22 for the SAMUM_2006 theme. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### ATSMIB2T\nATSMIB2T_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Product subset for the ARCTAS region version 3. It contains a Terrain-projected TOA Radiance subset for the ARCTAS region, resampled at the surface and topographically corrected and geometrically corrected by PGE22. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### SAMMIB2T\nSAMMIB2T_3 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Product subset for the SAMUM region version 3. It contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22 for the SAMUM_2006 theme. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### RICMIRCM\nRICMIRCM_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B3 Radiometric Camera-by-camera Cloud Mask Product subset for the RICO region version 4. This file contains the Radiometric camera-by-camera Cloud Mask dataset over the RICO region. It is used during geophysical parameter retrievals to determine whether a scene is classified as clear or cloudy. A new parameter has been added to indicate dust over ocean. This version of the ESDT is used by MISR PGE 13. In the TOA/Cloud Product, RCCM is combined with a stereoscopically derived cloud mask to 1) establish values of the Reflecting Level Reference Altitude, 2) determine how a scene is classified for choosing angular integration coefficients for establishing TOA albedos, and 3) calculate regional scene classifiers. Retrieval of Aerosol/Surface Product properties requires the absence of clouds for retrieval assumptions to be valid. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### CMRMIAAE\nCMRMIAAE_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Aerosol Product subset for the C-MARE Region version 2. It contains aerosol optical depth, ancillary meteorological data, and related parameters on a 17.6 km grid for the CMARE_2004 theme. To study the magnitude and natural variability in space and time of sunlight and cloud interactions with aerosols in the earth's atmosphere and to determine their effect on climate; to improve knowledge of sources, sinks, and regional budgets of aerosols; and to provide atmospheric correction inputs for surface imaging data acquired by MISR and other instruments that are simultaneously viewing the same portion of the Earth, to make better quantitative estimates of surface reflectance. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### CARMIAAE\nCARMIAAE_002 is the MISR L2 Aerosol Product subset for the ICARTT region V002 product. It contains aerosol optical depth and particle type, with associated atmospheric data over the region covered by the ICARTT_2004 theme. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### RICMIAAE\nRICMIAAE_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Aerosol Product subset for the RICO region version 2. It contains Aerosol optical depth and particle type, with associated atmospheric data over the RICO region. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### SAMMI2AE\nSAMMI2AE_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Aerosol Product subset for the SAMUM region Version 2. It contains Aerosol optical depth and particle type, with associated atmospheric data for the SAMUM_2006 theme. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### ATSM2AEF\nATSM2AEF_001 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 FIRSTLOOK Aerosol Product subset for the ARCTAS region version 1 data product. It contains Aerosol optical depth and particle type, with associated atmospheric data produced using ancillary inputs from the previous time period. Data collection for this product is complete. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### ATSM2LSF\nATSM2LSF_001 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 FIRSTLOOK Land Surface Product subset for the ARCTAS region version 1. It contains directional reflectance properties, albedo (spectral and PAR integrated), FPAR, radiation parameters, and terrain-referenced geometric parameters produced using ancillary input from the previous time period. Data collection for this product is complete. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### ATSM2STF\nATSM2STF_001 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 FIRSTLOOK TOA/Cloud Stereo Product subset for the ARCTAS region version 1. It contains the Stereoscopically Derived winds, heights, and cloud mask, along with associated data, produced using ancillary inputs (TASC) from the previous time period. Data collection for this product is complete. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### CMRMIGEO\nCMRMIGEO_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Geometric Parameters subset for the C-MARE Region version 2. It contains the geometric parameters which measure the sun and view angles at the reference ellipsoid for the region covered by the CMARE_2004 theme. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### CMRMIALS\nCMRMIALS_2 is the MISR L2 Land Surface Product subset for the C-MARE Region V002. It contains albedo and BRF data for the region covered by the CMARE_2004 theme. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### SAMMI2LS\nSAMMI2LS_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Surface Product subset for the SAMUM region version 2. It contains information about land directional reflectance properties, albedos (spectral and PAR integrated), FPAR, associated radiation parameters, and terrain-referenced geometric parameters for the SAMUM_2006 theme. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### RICMITAL\nRICMITAL_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Albedo Product subset for the RICO region version 2. It contains local, restrictive, and expansive albedo, with associated data. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### RICMITCL\nRICMITCL_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Classifier Product subset for the RICO region, version 2 data product. It contains the Angular Signature Cloud Mask (ASCM), Regional Cloud Classifiers, Cloud Shadow Mask, and Topographic Shadow Mask, with associated data. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### CMRMITST\nCMRMITST_2 is the C-MARE Region's Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Stereo Product subset. It contains a stereoscopically derived cloud mask and cloud height and reflecting level reference altitude for the region covered by the CMARE_2004 theme. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### CARMITST\nCARMITST_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Stereo Product subset for the ICARTT region version 2. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, and Reflecting Level Reference Altitude (RLRA), with associated data over the ICARTT_2004 theme. This data product is intended to be used to study, on a regional basis, the effects of different types of cloud fields on the spectral solar radiance and irradiance reflected in space, including spatial and temporal dependences. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### RICMITST\nRICMITST_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Stereo Product subset for the RICO region version 2. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, Reflecting Level Reference Altitude (RLRA), with associated data. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### SAMMI2ST\nSAMMI2ST_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Stereo Product subset for the SAMUM region Version 2. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, and Reflecting Level Reference Altitude (RLRA), with associated data for the SAMUM_2006 theme. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### MI1AC\nMI1AC_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1A Calibration data in DN. The data numbers have been commuted from 12-bit to 16-bit, byte-aligned half-word version 2. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### MIB1LM\nMIB1LM_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B1 Local Mode Radiance Data version 2. It contains the data numbers (DNs) radiometrically scaled to radiances with no geometric resampling. Multi-angle Imaging SpectroRadiometer (MISR) Level 1B1 Radiance data product contains spectral radiances for all MISR channels. Each value represents the incident radiance averaged over the sensor's total band response. Processing includes both radiance scaling and conditioning steps. Radiance scaling converts the Level 1A data from digital counts to radiances, using coefficients derived from the onboard calibrator (OBC) and vicarious calibrations. The OBC contains Spectralon calibration panels, deployed monthly and reflect sunlight into cameras. The OBC detector standards then measure this reflected light to provide the calibration. No out-of-band correction is done for this product, nor are the data geometrically corrected or resampled. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI1B1\nMI1B1_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B1 Radiance Data version 2. It contains the data numbers (DNs) radiometrically scaled to radiances with no geometric resampling and spectral radiances for all MISR channels. Each value represents the incident radiance averaged over the sensor's total band response. Processing includes both radiance scaling and conditioning steps. Radiance scaling converts the Level 1A data from digital counts to radiances, using coefficients derived with the On-Board Calibrator (OBC) and vicarious calibrations. The OBC contains Spectralon calibration panels, deployed monthly and reflect sunlight into cameras. The OBC detector standards then measure this reflected light to provide the calibration. No out-of-band correction is done for this product, nor are the data geometrically corrected or resampled. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. Data collection for this product is ongoing. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### UAEMIB2E\nUAEMIB2E_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Data subset for the UAE region Version 2 . It contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### UAEMIB2E\nUAEMIB2E_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Data subset for the UAE region version 3. It contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected and geometrically corrected by PGE22. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI1B2E\nMulti-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment, as well as distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Ellipsoid Data V003 contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.\n<br>\n\n#### MI1B2E\nMI1B2E_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Data Version 4 product. It contains Ellipsoid-projected Top-of-Atmosphere (TOA) Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. Data collection for this product is ongoing. MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### GOMIB2E\nGOMIB2E_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Product subset for the GoMACCS region version 3. It contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### VBEMIB2E\nVBEMIB2E_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Product subset for the VBBE region version 3. It contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### UAEM1LMT\nUAEM1LMT_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Local Mode Terrain Radiance Data subset for the UAE region. It contains the terrain-projected TOA radiance for the single local mode scene, resampled at the surface and topographically corrected. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MB2LMT\nMB2LMT_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Local Mode Terrain Radiance Data Version 2 product. It contains the terrain-projected Top-of-Atmosphere (TOA) radiance for the single local mode scene, resampled at the surface and topographically corrected. MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### UAEMIB2T\nUAEMIB2T_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Data subset for the UAE region version 2 data product. It contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. Data collection for this product is complete. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### UAEMIB2T\nUAEMIB2T_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Data subset for the UAE region version 3 data product. It contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. Data collection for this product is complete. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI1B2T\nMulti-angle Imaging SpectroRadiometer (MISR) is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Terrain Data V003 contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected and geometrically corrected by PGE22.\n<br>\n\n#### MI1B2T\nMI1B2T_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Data Version 4 product. It contains Terrain-projected Top-of-Atmosphere (TOA) Radiance, resampled at the surface, and topographically corrected and geometrically corrected by PGE22. Data collection for this product is ongoing. MISR is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### GOMIB2T\nGOMIB2T_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Product subset for the GoMACCS region version 3. It contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### VBEMIB2T\nVBEMIB2T_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Product subset for the VBBE region version 3. It contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### UAEMIAAE\nUAEMIAAE_002 is the Multi-angle Imaging spectro-radiometer (MISR). It contains aerosol optical depth, particle type, and associated atmospheric data. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL2ASAE\nMIL2ASAE_3 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Aerosol parameters Version 3 product. It contains information on retrieved aerosol column amount, aerosol particle properties, and ancillary information based on Level 1B2 geolocated radiances observed by MISR. Data collection for this product is ongoing. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in each of the 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. The entire mission has been reprocessed to version 3. The revision to the aerosol and land surface products includes both product format and significant algorithm changes, which impact the quality and performance of both aerosol and land surface retrievals.\n<br>\n\n#### MIL2ASAF\nThis is the Level 2 FIRSTLOOK Aerosol Product. It contains Aerosol optical depth and particle type, with associated atmospheric data produced using ancillary inputs from the previous time period. MIL2ASAF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 FIRSTLOOK Aerosol parameters version 2. It contains Aerosol optical depth and particle type, with associated atmospheric data produced using ancillary inputs from the previous time period. Data collection for this product is ongoing. Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Aerosol data products contain various information on the Earth's atmosphere. The aerosol data include tropospheric aerosol optical depth on 17. 6-km centers archived with a compositional model identifier and retrieval residuals, ancillary data including relative humidity, ozone optical depth, stratospheric aerosol optical depth, and retrieval flags. MISR multi-angle imagery will be used to monitor global and regional trends radiatively significant to optical properties (optical depth, single scattering albedo, and size distribution) and amounts (mass loading) of natural and anthropogenic aerosols, including those arising from industrial and volcanic emissions, slash-and-burn agriculture, and desertification. Coupled with MISR's determinations of top-of-atmosphere and surface albedos, these data will measure the global aerosol forcing of the shortwave planetary radiation budget. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL2ASLF\nThis Level 2 FIRSTLOOK Land Surface product contains directional reflectance properties, albedo(spectral and PAR integrated), FPAR, radiation parameters, and terrain-referenced geometric parameters produced using ancillary input from the previous time period. MIL2ASLF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 FIRSTLOOK Surface parameters version 2. It contains directional reflectance properties, albedo (spectral and photosynthetically active radiation (PAR) integrated), a fraction of photosynthetically active radiation absorbed by vegetation (FPAR), radiation parameters, and terrain-referenced geometric parameters produced using ancillary input from the previous time period. Data collection for this product is ongoing. This collection contains the Leaf Area Index (LAI). Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Aerosol data products contain information on the Earth's atmosphere. The aerosol data include tropospheric aerosol optical depth on 17. 6-km centers archived with a compositional model identifier and retrieval residuals, ancillary data including relative humidity, ozone optical depth, stratospheric aerosol optical depth, and retrieval flags. MISR multi-angle imagery will be used to monitor global and regional trends radiatively significant to optical properties (optical depth, single scattering albedo, and size distribution) and amounts (mass loading) of natural and anthropogenic aerosols, including those arising from industrial and volcanic emissions, slash-and-burn agriculture, and desertification. Coupled with MISR's determinations of top-of-atmosphere and surface albedos, these data will measure the global aerosol forcing of the shortwave planetary radiation budget. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL2TCCF\nMIL2TCCF_001 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 FIRSTLOOK TOA/Cloud Classifier parameters Version 1 data product. It contains the Angular Signature Cloud Mask (ASCM), Cloud Classifiers, and Support Vector Machine classifiers, produced using ancillary inputs and Terrestrial Atmosphere and Surface Climatology (TASC) from the previous time period. Data collection for this product is complete. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### UAEMIALS\nUAEMIALS_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Land Surface parameters subset for the UAE region version 2. It contains information on land directional reflectance properties, albedos (spectral and photosynthetically active radiation (PAR) integrated), fraction of absorbed photosynthetically active radiation (FPAR), associated radiation parameters, and terrain-referenced geometric parameters. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### GOMI2LS\nGOMI2LS_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Land Surface Product subset for the GoMACCS region Version 2 product. It contains information on land directional reflectance properties, albedos (spectral and photosynthetically active radiation (PAR) integrated), a fraction of absorbed photosynthetically active radiation (FPAR), associated radiation parameters, and terrain-referenced geometric parameters. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### VBEMI2LS\nVBEMI2LS_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Land Surface Product subset for the VBBE region version 2. It contains information on land directional reflectance properties; albedos (spectral and photosynthetically active radiation (PAR) integrated); fraction of absorbed photosynthetically active radiation (FPAR); associated radiation parameters; and terrain-referenced geometric parameters. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL2ASLS\nMIL2ASLS_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Land Surface parameters version 2 data product. It contains a variety of information on the Earth's surface; such ashemispherical directional reflectance factor (HDRF), bihemispherical reflectance (BHR) (i.e., albedo), bidirectional reflectance factor (BRF), directional hemispherical reflectance (DHR), BRF model parameters, Fractional absorbed Photosysenthetically Active Radiation (FPAR), and terrain-referenced view and illumination angles. A surface retrieval is conducted on regions for which valid land aerosol retrieval exists. The retrieval is performed using the corrected equivalent reflectances, retrieved aerosol parameters, and auxiliary information from the Simulated MISR Ancillary Radiative Transfer (SMART) dataset. The spectral and Photosynthetically Active spectral Region (PAR)-integrated BHR and DHR are retrieved, along with the spectral land HDRF and BRF and BRF model parameters, for all valid land and inland water subregions. Subregion surface classification, leaf area index (LAI), and regional FPAR are also determined. Subregion variability is also calculated for land regions. Data collection for this product was completed in June 2017. The entire mission has been reprocessed to version 3. The revision to the aerosol and land surface products includes both product format and significant algorithm changes, which impact the quality and performance of both aerosol and land surface retrievals. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL2ASLS\nMIL2ASLS_3 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Land Surface parameters version 3 data product. It contains a variety of information on the Earth's surface, such as hemispherical directional reflectance factor (HDRF), bihemispheric reflectance (BHR) (i.e., albedo), bidirectional reflectance factor (BRF), directional hemispherical reflectance (DHR), BRF model parameters, Fractional absorbed Photosynthetically Active Radiation (FPAR), and terrain-referenced view and illumination angles. A surface retrieval is conducted on regions where valid land aerosol retrieval exists. The retrieval uses the corrected equivalent reflectances, retrieved aerosol parameters, and auxiliary information from the Simulated MISR Ancillary Radiative Transfer (SMART) dataset. The spectral and Photosynthetically Active spectral Region (PAR)-integrated BHR and DHR are retrieved, along with the spectral land HDRF and BRF and BRF model parameters, for all valid land and inland water subregions. Subregion surface classification leaf area index (LAI) and regional FPAR are also determined. Subregion variability is also calculated for land regions. Data collection for this product is ongoing. This collection contains the Leaf Area Index (LAI). The entire mission has been reprocessed to version 3. The revision to the aerosol and land surface products includes both product format and significant algorithm changes, which impact the quality and performance of both aerosol and land surface retrievals. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### GOMI2AE\nGOMI2AE_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud-Aerosol Product subset for the GoMACCS region version 2. It contains Aerosol optical depth and particle type with associated atmospheric data. MISR is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### VBEMI2AE\nVBEMI2AE_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Aerosol Product subset for the VBBE region version 2. It contains Aerosol optical depth and particle type, with associated atmospheric data. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### UAEMITAL\nUAEMITAL_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Albedo parameters subset for the UAE region version 2. It contains local, restrictive, and expansive albedo with associated data. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### UAEMITCL\nUAEMITCL_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Classifier parameters subset for the UAE region version 3. It contains the Angular Signature Cloud Mask (ASCM), Regional Cloud Classifiers, Cloud Shadow Mask, and Topographic Shadow Mask, with associated data. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL2TCCL\nMIL2TCCL_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Top-of-Atmosphere (TOA)/Cloud Classifier parameters Version 3 data product. It contains the Angular Signature Cloud Mask (ASCM), Regional Cloud Classifiers, Cloud Shadow Mask, and Topographic Shadow Mask, with associated data. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL2TCSP\nThe Multi-angle Imaging SpectroRadiometer (MISR) is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 TOA/Cloud Height and Motion parameters V001 contains the Stereo Heights, Stereoscopically Derived Cloud Mask (SDCM), and Cloud Motion Vectors with associated data.\n<br>\n\n#### UAEMITST\nUAEMITST_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Stereo parameters subset for the UAE region version 2. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, Reflecting Level Reference Altitude (RLRA), with associated data. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### GOMI2ST\nGOMI2ST_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Stereo Product subset for the GoMACCS region version 2. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, and Reflecting Level Reference Altitude (RLRA) with associated data. MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### VBEMI2ST\nVBEMI2ST_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Stereo Product subset for the VBBE region version 2. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, and Reflecting Level Reference Altitude (RLRA), with associated data. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3DCFA\nThis file contains the public MISR Level 3 Cloud Fraction by Altitude Product covering a day. MIL3DCFA_001 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Fraction by Altitude Product covering a day version 1. It provides the frequency of cloud occurrence partitioned into different cloud top height bins at a global and monthly scale with a latitude/longitude resolution of 0.5 degrees by 0.5 degrees and a vertical resolution of 500m. For each height bin, the frequency of cloud occurrence of a region over a time period is represented by the temporal mean of the spatial coverage of cloud tops. The spatial coverage of clouds is called cloud fraction, which is defined as the ratio of the number of cloudy pixels to the total number of cloudy and cloud-free pixels observed by the instrument. Clouds are assigned to height bins based on their top height, as the MISR stereoscopic technique retrieved. Data collection for this product is complete. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3MCFA\nMIL3MCFA_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Fraction by Altitude Product covering a month version 1. It provides the frequency of cloud occurrence partitioned into different cloud top height bins at a global and monthly scale with a latitude/longitude resolution of 0.5 degrees by 0.5 degrees and a vertical resolution of 500m. For each height bin, the frequency of a region's cloud occurrence over time is represented by the temporal mean of the spatial coverage of cloud tops. The spatial coverage of clouds is called cloud fraction, which is defined as the ratio of the number of cloudy pixels to the total number of cloudy and cloud-free pixels observed by the instrument. Clouds are assigned to height bins based on their top height, as the MISR stereoscopic technique retrieved. Data collection for this product is complete. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3QCFA\nMIL3QCFA_001 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Fraction by Altitude Product covering a quarter (seasonal) version 1. It provides the frequency of cloud occurrence partitioned into different cloud top height bins at a global and monthly scale with a latitude/longitude resolution of 0.5 degrees by 0.5 degrees and a vertical resolution of 500m. For each height bin, the frequency of cloud occurrence of a region over a time period is represented by the temporal mean of the spatial coverage of cloud tops. The spatial coverage of clouds is called cloud fraction, which is defined as the ratio of the number of cloudy pixels to the total number of cloudy and cloud-free pixels observed by the instrument. Clouds are assigned to height bins based on their top height, as the MISR stereoscopic technique retrieved. Data collection for this product is complete. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3YCFA\nMIL3YCFA_001 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Fraction by Altitude Product covering a year version 1. It provides the frequency of cloud occurrence partitioned into different cloud top height bins at a global and monthly scale. It has multiple data layers, with a latitude/longitude resolution of 0.5 degrees by 0.5 degrees and a vertical resolution of 500m. For each height bin, the frequency of cloud occurrence of a region over a time period is represented by the temporal mean of the spatial coverage of cloud tops. The spatial coverage of clouds is called cloud fraction, which is defined as the ratio of the number of cloudy pixels to the total number of cloudy and cloud-free pixels observed by the instrument. Clouds are assigned to height bins based on their top height, as the MISR stereoscopic technique retrieved. Data collection for this product is complete. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3MCMVN\nMI3MCMVN_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Motion Vector monthly Product in netCDF format version 2 data product. It contains retrievals of cloud motion determined by geometrically triangulating the position and motion of cloud features observed by MISR from multiple perspectives and times during the overpass of the Terra platform over each cloud scene. Estimates of cloud motion are a valuable proxy observation of the horizontal atmospheric wind field at the retrieved altitude of the cloud. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3QCMVN\nMI3QCMVN_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Motion Vector quarterly Product in netCDF format version 2 data product. It contains retrievals of cloud motion determined by geometrically triangulating the position and motion of cloud features observed by MISR from multiple perspectives and times during the overpass of the Terra platform over each cloud scene. Estimates of cloud motion are a valuable proxy observation of the horizontal atmospheric wind field at the retrieved altitude of the cloud. The seasons are winter (December from the previous year, January, and February), spring (March, April, and May), summer (June, July, and August), and fall (September, October, and November). Data collection for this product is ongoing. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3YCMVN\nMI3YCMVN_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Motion Vector yearly Product in netCDF format version 2. It contains retrievals of cloud motion determined by geometrically triangulating the position and motion of cloud features observed by MISR from multiple perspectives and times during the overpass of the Terra platform over each cloud scene. Estimates of cloud motion are a valuable proxy observation of the horizontal atmospheric wind field at the retrieved altitude of the cloud. Data collection for this product is ongoing. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3YCOD\nMIL3YCOD_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Top Height-Optical Depth Product covering a year version 1. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### UAEMRDAE\nUAEMRDAE_004 is the Multi-angle Imaging SpecrtroRadiometer (MISR) Level 3 Component Global Aerosol Product covering a day subset for the UAE region version 4. It contains a statistical summary of column aerosol 555 nanometer optical depth, and a monthly aerosol compositional type frequency histogram. This data product is a regional summary of the Level 2 aerosol parameters of interest averaged over a day and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3DAE\nMIL3DAE_4 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Product covering a day version 4. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of the Level 2 aerosol parameters of interest averaged over a day and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product was completed in June of 2017. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3MAE\nMIL3MAE_4 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Product covering a month version 4. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of relevant Level 2 aerosol parameters, averaged over a month and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. The collection for this product was completed in May of 2017. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3QAE\nMIL3QAE_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Product covering a quarter (seasonal) version 4. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of the Level 2 aerosol parameters of interest averaged over a quarter (seasonal) and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. The seasons are winter (December from the previous year, January, February), spring (March, April, May), summer (June, July, August), and fall (September, October, November). Data collection for this product was completed in May of 2017. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3YAE\nMIL3YAE_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Product covering a year version 4 data product. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of relevant Level 2 aerosol parameters averaged over a year and reported on a geographic grid with a resolution of 0.5 degree by 0.5 degree. Data collection for this product was completed in November 2016. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3DAEN\nThis file contains the MISR Level 3 Component Global Aerosol product in netCDF format covering a day. MIL3DAEN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol product in netCDF format covering a day version 4 data product. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of the Level 2 aerosol parameters of interest averaged over a day and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3MAEN\nThis file contains the MISR Level 3 Component Global Aerosol product in netCDF format covering a month. MIL3MAEN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol product in netCDF format covering a month version 4. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of relevant Level 2 aerosol parameters, averaged over a month and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3YAEN\nMIL3YAEN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol product in netCDF format covering a year version 4 data product. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of relevant Level 2 aerosol parameters, averaged over a year and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3DAER\nMI3DAER_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Regional public Product covering a day version 2. It contains a statistical summary of column aerosol 555 nanometer optical depth, and a monthly aerosol compositional type frequency histogram. This data product is a global summary of the Level 2 aerosol parameters of interest averaged over a day and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. Data collection for this product is complete. The data are for distinct regions associated with associated field campaigns. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### MI3MAER\nMI3MAER_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Regional public Product covering a month version 2. It contains a monthly statistical summary of aerosol optical depth (AOD) and single scattering albedo (SSA) model parameters. Data collection for this product was complete in August 2007. MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3QAEN\nThis file contains the MISR Level 3 Component Global Aerosol product in netCDF format covering a quarter (seasonal). MIL3QAEN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol seasonal product in netCDF format version 4 data product. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of relevant Level 2 aerosol parameters, averaged over a quarter (seasonal) and reported on a geographic grid, with a resolution of 0.5 degree by 0.5 degree. The seasons are winter (December from the previous year, January, February), spring (March, April, May), summer (June, July, August), and fall (September, October, November). Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3DAL\nMISR Level 3 Component Global Albedo publicly available product covering a day to be used starting with MISR Release V3.2.\n<br>\n\n#### MIL3MAL\nMISR Level 3 Component Global Albedo is a publicly available product covering a month, to be used starting with MISR Release V3.2. MIL3MAL_006 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo product covering a month version 6. It contains a statistical summary of column albedo 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of relevant Level 2 albedo parameters, averaged over a month and reported on a geographic grid, it has multiple data layers, with varying temporal resolutions of 1 degree by 1 degree, and 5 degrees by 5 degrees. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3QAL\nMIL3QAL_006 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo product covering a quarter (seasonal) version 6 data product. It contains a statistical summary of column albedo 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of relevant Level 2 albedo parameters, averaged over a quarter and reported on a geographic grid, it has multiple data layers, with varying temporal resolutions of 1 degree by 1 degree and 5 degrees by 5 degrees. The seasons are winter (December from the previous year, January, February), spring (March, April, May), summer (June, July, August), and fall (September, October, November). Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3YAL\nMIL3YAL_006 Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo product covering a year version 6 data product. It contains a statistical summary of column albedo 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of relevant Level 2 albedo parameters, averaged over a year and reported on a geographic grid, with varying temporal resolutions of 1 degree by 1 degree and 5 degrees by 5 degrees. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3DALN\nMISR Level 3 Component Global Albedo is a publicly available product in netCDF format covering a day. MIL3DALN_006 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo product in netCDF format covering a day version 6 data product. It contains a statistical summary of column albedo 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of relevant Level 2 albedo parameters, averaged over a day and reported on a geographic grid; it has multiple data layers with varying temporal resolutions of 1 degree by 1 degree and 5 degrees by 5 degrees. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3MALN\nMISR Level 3 Component Global Albedo is a publicly available product in netCDF format covering a month. MIL3MALN_006 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo product in netCDF format covering a month version 6. It contains a statistical summary of column albedo 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of relevant Level 2 albedo parameters, averaged over a month and reported on a geographic grid, it has multiple data layers, with varying temporal resolutions of 1 degree by 1 degree, and 5 degrees by 5 degrees. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3YALN\nMIL3YALN_006 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo product in netCDF format covering a year version 6. This data product is a global summary of relevant Level 2 albedo parameters, averaged over a year and reported on a geographic grid; it has multiple data layers with varying temporal resolutions of 1 degree by 1 degree and 5 degrees by 5 degrees. Data collection for this product is complete. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3QALN\nMIL3QALN_006 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo seasonal product in netCDF format version 6 data product. It is a global summary of relevant Level 2 albedo parameters, averaged over a quarter and reported on a geographic grid, it has multiple data layers, with varying temporal resolutions of 1 degree by 1 degree and 5 degrees by 5 degrees. The seasons are winter (December from the previous year, January, February), spring (March, April, May), summer (June, July, August), and fall (September, October, November). Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3DCLD\nThis file contains the public MISR Level 3 Component Global Cloud Product covering a day. MIL3DCLD_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Cloud Product covering a day version 2. It is a global summary of the Level 1 and Level 2 cloud parameters of interest averaged over a day and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3MCLD\nThis file contains the public MISR Level 3 Component Global Cloud Product covering a month. MIL3MCLD_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Cloud Product covering a month version 2. It is a global summary of relevant Level 1 and Level 2 cloud parameters, averaged over a month and reported on a geographic grid with a resolution of 0.5 degree by 0.5 degree. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3QCLD\nThis file contains the public MISR Level 3 Component Global Cloud Product covering a quarter (seasonal). MIL3QCLD_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Cloud Product covering a quarter (seasonal) version 2 data product. It is a global summary of relevant Level 1 and Level 2 cloud parameters, averaged over a quarter (season) and reported on a geographic grid, with a resolution of 0.5 degree by 0.5 degree. The seasons are winter (December from the previous year, January, February), spring (March, April, May), summer (June, July, August), and fall (September, October, November). Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3YCLD\nThis file contains the public MISR Level 3 Component Global Cloud Product covering a year. MIL3YCLD_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Cloud Product covering a year version 2. It is a global summary of the Level 1 and Level 2 cloud parameters of interest averaged over a year and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865nm.\n<br>\n\n#### UAEMRDLS\nUAEMRDLS_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a day subset for the UAE region version 4. It contains a daily statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI), and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a regional summary of the Level 2 land/surface parameters of interest averaged over a day and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3DLS\nMIL3DLS_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a day version 4. It contains a daily statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of the Level 2 land/surface parameters of interest averaged over a day and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. This collection contains the Leaf Area Index (LAI).\n<br>\n\n#### UAEMRMLS\nUAEMRMLS_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a month subset for the UAE region version 4. It contains a statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI), and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a regional summary of the Level 2 land/surface parameters of interest averaged over a month and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3MLS\nMIL3MLS_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a month version 4. It contains a statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI), and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of relevant Level 2 land/surface parameters, averaged over a month and reported on a geographic grid with a resolution of 0.5 degree by 0.5 degree. Data collection for this product was completed in May of 2017. This collection contains the Leaf Area Index (LAI). MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3QLS\nMIL3QLS_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a quarter (seasonal) version 4 data product. It contains a statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI), and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of relevant Level 2 land/surface parameters, averaged over a quarter (seasonal) and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. The seasons are winter (December from the previous year, January, February), spring (March, April, May), summer (June, July, August), and fall (September, October, November). Data collection for this product was completed in May of 2017. This collection contains the Leaf Area Index (LAI). The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3YLS\nMIL3YLS_4 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a year version 4. It contains a statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI), and land surface bidirectional reflectance factor (BRF) model parameters, classified into six vegetated and one non-vegetated types. This data product is a global summary of relevant Level 2 land/surface parameters, averaged over a year and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. Data collection for this product was completed in November of 2016. This collection contains Leaf Area Index (LAI). The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3DLSN\nThis file contains the MISR Level 3 Component Global Land product in netCDF format covering a day. MIL3DLSN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land product in netCDF format covering a day version 4. It contains a daily statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of relevant Level 2 land/surface parameters, averaged over a day and reported on a geographic grid with a resolution of 0.5 degree by 0.5 degree. Data collection for this product is ongoing. This collection contains Leaf Area Index (LAI). The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3MLSN\nThis file contains the MISR Level 3 Component Global Land product in netCDF format covering a month. MIL3MLSN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land product in netCDF format covering a month version 4. It contains a monthly statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of relevant Level 2 land/surface parameters, averaged over a month and reported on a geographic grid with a resolution of 0.5 degree by 0.5 degree. Data collection for this product is ongoing. This collection contains the Leaf Area Index (LAI). The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3YLSN\nMIL3YLSN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land product in netCDF format covering a year version 4. It contains a yearly statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of relevant Level 2 land/surface parameters, averaged over a day and reported on a geographic grid with a resolution of 0.5 degree by 0.5 degree. Data collection for this product is complete. This collection contains Leaf Area Index (LAI). The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3DLSR\nMI3DLSR_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Regional public Product covering a dayversion 2. It contains a daily statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI), and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of the Level 2 land/surface parameters of interest averaged over a day and reported on a geographic grid, with a resolution of 0.5 degree by 0.5 degree. Data collection for this product is complete. The data are for distinct regions associated with associated field campaigns. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### MI3MLSR\nMI3MLSR_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Regional public Product covering a month version 2. It contains a daily statistical summary of average directional hemispherical reflectance (DHR), DHR for photosynthetically active spectral region (DHR-PAR), fractional absorbed photosynthetically active radiation (FPAR), leaf area index (LAI), and normalized difference vegetation index (NDVI) model parameters. Data collection for this product was complete in August 2007. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### MIL3QLSN\nMIL3QLSN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land seasonal product in netCDF format version 4 data product. It contains a statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of relevant Level 2 land/surface parameters, averaged over a quarter (seasonal) and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. The seasons are winter (December from the previous year, January, February), spring (March, April, May), summer (June, July, August), and fall (September, October, November). Data collection for this product is ongoing. This collection contains Leaf Area Index (LAI). The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### UAEMRDRD\nUAEMRDRD_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Product covering a day subset for the UAE region version 4. It contains a statistical summary of spectral top-of-atmosphere Bidirectional Reflectance Factor for various subregion classifications; and a statistical summary of spectral expansive albedos for several sky classifications. This data product is a regional summary of the Level 1 radiance parameters of interest averaged over a day and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3DRD\nThis file contains the MISR Level 3 Component Global Radiance Product covering a day. MIL3DRD_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Product covering a day version 4. It contains a statistical summary of spectral top-of-atmosphere Bidirectional Reflectance Factors for various sub-region classifications and a statistical overview of spectral expansive albedos for several sky classifications. This data product is a global summary of relevant Level 1 radiance parameters, averaged over a day and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### UAEMRMRD\nUAEMRMRD_005 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Product covering a month subset for the UAE region version 5. It contains a summary of spectral top-of-atmosphere Bidirectional Reflectance Factor for various subregion classifications and a summary of spectral expansive albedos for several sky classifications. This data product is a regional summary of the Level 1 radiance parameters of interest averaged over a month and reported on a geographic grid, with a resolution of 0.5 degree by 0.5 degree. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3MRD\nThis file contains the MISR Level 3 Component Global Radiance Product covering a month. MIL3MRD_005 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Product covering a month version 5. It contains a statistical summary of spectral top-of-atmosphere Bidirectional Reflectance Factor for various sub-region classifications and a statistical overview of spectral expansive albedos for several sky classifications. This data product is a global summary of relevant Level 1 radiance parameters, averaged over a month and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3QRD\nMIL3QRD_005 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Product covering a quarter (seasonal) version 5 data product. It contains a statistical summary of spectral top-of-atmosphere Bidirectional Reflectance Factor for various sub-region classifications and a statistical overview of spectral expansive albedos for several sky classifications. This data product is a global summary of relevant Level 1 radiance parameters, averaged over a quarter (seasonal) and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. The seasons are winter (December from the previous year, January, February), spring (March, April, May), summer (June, July, August), and fall (September, October, November). Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3YRD\nMIL3YRD_005 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Product covering a year version 5 data product. It contains a statistical summary of spectral top-of-atmosphere (TOA) Bidirectional Reflectance Factor (BDRF) for various sub-region classifications and a statistical summary of spectral expansive albedos for several sky classifications. This data product is a global summary of related Level 1 radiance parameters averaged over a year, from December of the previous through November of the current year, and is reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3DRDR\nMI3DRDR_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Regional public Product covering a day version 2. It contains a global summary of the Level 1 and Level 2 radiance parameters of interest averaged over a day and reported on a geographic grid, with a resolution of 0.5 degree by 0.5 degree. Data collection for this product is complete. The data are for distinct regions associated with associated field campaigns. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### MI3MRDR\nMI3MRDR_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Regional public Product covering a month version 2. It contains a summary of spectral top-of-atmosphere Bidirectional Reflectance Factor for various sub-region classifications and a summary of spectral expansive albedos for several sky classifications. This data product is a global summary of the Level 1 radiance parameters of interest averaged over a month and reported on a geographic grid, with a resolution of 0.5 degree by 0.5 degree. Data collection for this product is complete. The data are for distinct regions associated with associated field campaigns.\n<br>\n\n#### MI3DAEF\nMI3DAEF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Aerosol Product covering a day version 2 data product. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. Data collection for this product was completed in November 2017. This data product is a global summary of the Level 2 aerosol parameters of interest averaged over a day and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3MAEF\nMI3MAEF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Aerosol Product covering a month version 2 data product. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of the Level 2 aerosol parameters of interest averaged over a month and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product was completed in October 2017. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3DALF\nMI3DALF_002 is the Multiangle Imaging SpectroRadiometer (MISR) Level 3 FIRST LOOK Component Global Albedo product covering a day version 2. It is intended to be used starting with MISR Release version 4.2, a global summary of the Level 2 albedo parameters of interest averaged over a day and reported on a geographic grid. It has multiple data layers, with varying temporal resolutions of 1 degree by 1 degree and 5 degrees by 5 degrees. Data collection for this product is ongoing. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snowice and ocean surface wind speed values that are updated monthly. Therefore, these data sets cannot be generated until the end of the month. The presence of FIRST LOOK in the file names distinguishes the products generated. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is an instrument designed to view Earth with cameras pointed in 9 different directions. As the \"instrument flies overhead, each piece of Earth's \"surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth, as well as distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3MALF\nMISR Level 3 FIRSTLOOK Component Global Albedo publicly available product covering a month to be used starting with MISR Release V4.2.\n<br>\n\n#### MI3DCDF\nThis file contains the public MISR Level 3 FIRSTLOOK Component Global Cloud Product covering a day. MI3DCDF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Cloud Product covering a day version 2. It is a global summary of the Level 1 and Level 2 cloud parameters of interest averaged over a day and reported on a geographic grid; it has multiple data layers with varying temporal resolutions of 0.5 degrees by 0.5 degrees and 2.5 degrees by 2.5 degrees resolution. Data collection for this product is ongoing. \\r\\n\\r\\nFIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3MCDF\nThis file contains the public MISR Level 3 FIRSTLOOK Component Global Cloud Product covering a month. MI3MCDF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Cloud Product covering a month version 2 data product. It is a global summary of the Level 1 and Level 2 cloud parameters of interest averaged over a month and reported on a geographic grid; it has multiple data layers with varying temporal resolutions of 0.5 degrees by 0.5 degrees and 2.5 degrees by 2.5 degrees resolution. Data collection for this product is ongoing. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated monthly. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3DLSF\nMI3DLSF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Land Product covering a day version 2 data product. It contains a daily statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of the Level 2 land/surface parameters of interest averaged over a day and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. The data are for distinct regions associated with associated field campaigns. Data collection for this product was completed in November 2017. This collection contains the Leaf Area Index (LAI). FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3MLSF\nMI3MLSF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Land Product covering a month version 2 data product. It contains a monthly statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of the Level 2 land/surface parameters of interest averaged over a month and reported on a geographic grid, with a resolution of 0.5 degrees by 0.5 degrees. The data are for distinct regions associated with associated field campaigns. Data collection for this product was completed in October 2017. This collection contains the Leaf Area Index (LAI). FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3DRDF\nThis file contains the MISR Level 3 FIRSTLOOK Component Global Radiance Product covering a day. MI3DRDF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Radiance Product covering a day version 2 data product. It is a global summary of the Level 1 and Level 2 radiance parameters of interest averaged over a day and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product is ongoing. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated monthly. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3MRDF\nThis file contains the MISR Level 3 FIRSTLOOK Component Global Radiance Product covering a month. MI3MRDF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Radiance Product covering a month version 2 data product. It contains a summary of spectral top-of-atmosphere Bidirectional Reflectance Factor for various sub-region classifications and a summary of spectral expansive albedos for several sky classifications. This data product is a global summary of the Level 1 radiance parameters of interest averaged over a month and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product is ongoing. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3DAENF\nMI3DAENF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Aerosol product in netCDF format covering a day version 2. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. Data collection for this product is ongoing. This data product is a global summary of the Level 2 aerosol parameters of interest averaged over a day and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated monthly. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3MAENF\nThis file contains the MISR Level 3 FIRSTLOOK Component Global Aerosol product in netCDF format covering a month\n<br>\n\n#### MI3DALNF\nMI3DALNF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Albedo product in netCDF format covering a day version 2. It is a global summary of the Level 2 albedo parameters of interest averaged over a day and reported on a geographic grid; it has multiple data layers with varying temporal resolutions of 1 degree by 1 degree and 5 degrees by 5 degrees. Data collection for this product is ongoing. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth, as well as distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 3 FIRSTLOOK Component Global Albedo is a publicly available product in netCDF format that covers a day.\n<br>\n\n#### MI3MALNF\nMISR Level 3 FIRSTLOOK Component Global Albedo is a publicly available product in netCDF format that covers a month. MI3MALNF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Albedo product in netCDF format covering a month version 2 data product. It contains a statistical summary of column albedo 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram. This data product is a global summary of the Level 2 albedo parameters of interest averaged over a month and reported on a geographic grid, it has multiple data layers, with varying temporal resolutions of 1 degree by 1 degree, and 5 degree by 5 degree. Data collection for this product is ongoing. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated monthly. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3DCDNF\nMI3DCDNF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Cloud public Product in netCDF covering a day version 2. It contains the public MISR Level 3 FIRSTLOOK Global Cloud public product in netCDF format covering a day. It is a global summary of the Level 1 and Level 2 cloud parameters of interest averaged over a day and reported on a geographic grid. It has multiple data layers, with varying temporal resolutions of 0.5 degrees by 0.5 degrees and granules of 2.5 degrees by 2.5 degrees. Data collection for this product is ongoing. FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated monthly. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is an instrument designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. This file contains the public MISR Level 3 FIRSTLOOK Global Cloud public Product in netCDF format covering a day.\n<br>\n\n#### MI3MCDNF\nThis file contains the MISR Level 3 FIRSTLOOK Global Cloud public Product in netCDF format covering a month\n<br>\n\n#### MI3DLSNF\nThis file contains the MISR Level 3 FIRSTLOOK Component Global Land product in netCDF format covering a day. MI3DLSNF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Land product in netCDF format covering a day version 2 data product. It contains a daily statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of the Level 2 land/surface parameters of interest averaged over a day and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product is ongoing. This collection contains Leaf Area Index (LAI). FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated monthly. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth's environment and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3MLSNF\nMI3MLSNF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Land product in netCDF format covering a month version 2 data product. It contains a monthly statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of the Level 2 land/surface parameters of interest averaged over a month and reported on a geographic grid with a resolution of 0.5 degrees by 0.5 degrees. Data collection for this product is ongoing. This collection contains the Leaf Area Index (LAI). FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3DCLDN\nMI3DCLDN_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Global Cloud public Product in netCDF format covering a day version 2. It contains the public MISR Level 3 Global Cloud Product in netCDF format covering a day and is a global summary of the Level 1 and Level 2 cloud parameters of interest averaged over a year and reported on a geographic grid; it has multiple data layers, with varying temporal resolutions of 0.5 degrees by 0.5 degrees as well as 2.5 degrees by 2.5 degrees. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3MCLDN\nThis file contains the MISR Level 3 Global Cloud public Product in netCDF format covering a month. MI3MCLDN_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Global Cloud public Product in netCDF format covering a month version 2 data product. It is a global summary of the Level 1 and Level 2 cloud parameters of interest averaged over a year and reported on a geographic grid; it has multiple data layers with varying temporal resolutions of 0.5 degrees by 0.5 degrees and 2.5 degrees by 2.5 degrees resolution. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3QCLDN\nThis file contains the MISR Level 3 Global Cloud public Product in netCDF format covering a quarter (seasonal). MI3QCLDN_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Global Cloud public Product in netCDF format covering a quarter version 2 data product. It is a global summary of the Level 1 and Level 2 cloud parameters of interest averaged over a quarter (seasonal) and reported on a geographic grid; it has multiple data layers with varying temporal resolutions of 0.5 degrees by 0.5 degrees and 2.5 degrees by 2.5 degrees resolution. The seasons are winter (December from the previous year, January and February), spring (March, April, and May), summer (June, July, and August), and fall (September, October, and November). Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI3YCLDN\nThis file contains the MISR Level 3 Global Cloud public Product in netCDF format covering a year. MI3YCLDN_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Global Cloud Public Product in netCDF format covering a year version 2 data product. It is a global summary of the Level 1 and Level 2 cloud parameters of interest averaged over a year and reported on a geographic grid; it has multiple data layers with varying temporal resolutions of 0.5 degrees by 0.5 degrees and 2.5 degrees by 2.5-degree resolution. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR isdesigned to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIL3MJTA\nMIL3MJTA_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Global Joint Aerosol monthly version 2 data product. It contains global statistical summaries of MISR Level 2 aerosol optical depth on a 5-degree geographic grid. Within each grid cell, optical depth is summarized by a set of representative vectors, each representing a cluster of similar Level 2 aerosol optical depth retrievals. Data is summarized monthly. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MI1AOBC\nMI1AOBC_2 is the Multi-angle Imaging SpectroRadiometer (MISR) OBC Data version 2. This file contains the output for the Level 1A On-board Calibrator Data, and it provides the radiometry from PIN and HQE diodes and goniometer mechanism readings collected during calibration mode operations near the north and south poles and over the dark side of the Earth (or during science mode operations over the sunlit side of the Earth). The diode radiometry acquired during the north and south pole calibration sequences will be used to determine an MISR diffuser panel's brightness and reflective characteristics as observed by each of the nine MISR cameras. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### UAEMIRCM\nUAEMIRCM_003 is the Multi-angle Imaging SpectroRadiometer (MISR) radiometric camera-by-camera Cloud Mask subset for the UAE region version 3. It contains the Radiometric camera-by-camera Cloud Mask dataset. It is used to determine whether a scene is classified as clear or cloudy. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### UAEMIRCM\nUAEMIRCM_004 is the Multi-angle Imaging SpectroRadiometer (MISR) radiometric camera-by-camera Cloud Mask subset for the UAE region version 4. It contains the Radiometric camera-by-camera Cloud Mask dataset. It is used to determine whether a scene is classified as clear or cloudy. A new parameter has been added to indicate dust over the ocean. This version of the ESDT is used by MISR PGE 13. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.\n<br>\n\n#### MIRCCM\nMIRCCM_004 is the Multi-angle Imaging SpectroRadiometer (MISR) radiometric camera-by-camera Cloud Mask version 4 data product. It contains the Radiometric camera-by-camera Cloud Mask dataset. It determines whether a scene is classified as clear or cloudy. A new parameter has been added to indicate dust over the ocean. This version is used by MISR Product Generation Executable (PGE) 13. Data collection for this product is ongoing. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. This version of the ESDT is used by MISR PGE 13.\n<br>\n\n#### MIANTASC\nMIANTASC_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Terrestrial Atmosphere and Surface Climatology (TASC) dataset version 2. It is produced by the MISR Science Computing Facility (SCF) and shipped to the Atmospheric Science Data Center (ASDC) for generating MISR Level 2 products and consists of one HDF file and data set per month in HDF-EOS Grid format with a resolution of 1.0 x 1.0 degrees. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://misr.jpl.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","elevation","hdf","ice","netcdf","oceans"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MIANACP v001 - MIANACP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Aerosol Climatology Product version 1. It is 1) the microphysical and scattering characteristics of pure aerosol upon which routine retrievals are based, 2) mixtures of pure aerosol to be compared with MISR observations, and 3) the likelihood value assigned to each mode geographically.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIANACP_001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIANCAGP v001 - MIANCAGP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Geographic Product version 1. It is a set of 233 pre-computed files.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIANCAGP_001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIANCARP v002 - MIANCARP_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Radiometric Product version 2. It is composed of 4 files covering instrument characterization data, pre-flight calibration data, in-flight calibration data, and configuration parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIANCARP_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MISBR v005 - This is the browse data associated with a particular granule. MISBR_005 is the Multi-angle Imaging SpectroRadiometer (MISR) Browse data version 5.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MISBR_005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIRCCMF v001 - MIRCCMF_001 is the Multi-angle Imaging SpectroRadiometer (MISR) FIRSTLOOK radiometric camera-by-camera Cloud Mask (RCCM) version 1 data product. It was produced using ancillary inputs from the previous time period, such as Radiometric Camera-by-camera Cloud mask Threshold (RCCT).","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIRCCMF_001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ATSMIGEO v002 - ATSMIGEO_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Geometric Parameters subset for the ARCTAS region version 2. It measures the sun and view angles at the reference ellipsoid.","ARN":"arn:aws:s3:::atsmigeo","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"GOMIGEO v002 - GOMIGEO_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Geometric Parameters subset for the GoMACCS region version 2. It contains the Geometric Parameters, which measure the sun and view angles at the reference ellipsoid.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/GOMIGEO_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SAMMIGEO v002 - SAMMIGEO_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Geometric Parameters subset for the SAMUM region version 2. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, and Reflecting Level Reference Altitude (RLRA), with associated data for the SAMUM_2006 theme.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/SAMMIGEO_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"VBEMIGEO v002 - VBEMIGEO_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Geometric Parameters subset for the VBBE region version 2. It contains the Geometric Parameters that measure the sun and view angles at the reference ellipsoid.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/VBEMIGEO_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIB2GEOP v002 - Multi-angle Imaging SpectroRadiometer (MISR) is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successfully imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared).","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIB2GEOP_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIB2GEOP v003 - MIB2GEOP_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Geometric Parameters Version 3 product. It contains the Geometric Parameters which measure the sun and view angles at the reference ellipsoid.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIB2GEOP_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ATSMIB2E v003 - ATSMIB2E_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Product subset for the ARCTAS region. It contains an Ellipsoid-projected TOA Radiance subset for the ARCTAS region, resampled at the surface and topographically corrected and geometrically corrected by PGE22.","ARN":"arn:aws:s3:::atsmib2e","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"RICMIB2E v002 - RICMIB2E_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Product subset for the RICO region version 2. It contains the ellipsoid projected TOA Radiance over the RICO region, resampled to WGS84 ellipsoid corrected, and it requires the radiances from all nine cameras of MISR to be projected to a surface defined by the reference WGS84 ellipsoid.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/RICMIB2E_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SAMMIB2E v003 - SAMMIB2E_3 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Product subset for the SAMUM region version 1. It contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected and geometrically corrected by PGE22 for the SAMUM_2006 theme.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/SAMMIB2E_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ATSMIB2T v003 - ATSMIB2T_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Product subset for the ARCTAS region version 3. It contains a Terrain-projected TOA Radiance subset for the ARCTAS region, resampled at the surface and topographically corrected and geometrically corrected by PGE22.","ARN":"arn:aws:s3:::atsmib2t","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SAMMIB2T v003 - SAMMIB2T_3 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Product subset for the SAMUM region version 3. It contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22 for the SAMUM_2006 theme.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/SAMMIB2T_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"RICMIRCM v004 - RICMIRCM_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B3 Radiometric Camera-by-camera Cloud Mask Product subset for the RICO region version 4. This file contains the Radiometric camera-by-camera Cloud Mask dataset over the RICO region.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/RICMIRCM_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CMRMIAAE v002 - CMRMIAAE_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Aerosol Product subset for the C-MARE Region version 2. It contains aerosol optical depth, ancillary meteorological data, and related parameters on a 17.6 km grid for the CMARE_2004 theme.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/CMRMIAAE_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CARMIAAE v002 - CARMIAAE_002 is the MISR L2 Aerosol Product subset for the ICARTT region V002 product. It contains aerosol optical depth and particle type, with associated atmospheric data over the region covered by the ICARTT_2004 theme.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/CARMIAAE_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"RICMIAAE v002 - RICMIAAE_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Aerosol Product subset for the RICO region version 2. It contains Aerosol optical depth and particle type, with associated atmospheric data over the RICO region.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/RICMIAAE_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SAMMI2AE v002 - SAMMI2AE_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Aerosol Product subset for the SAMUM region Version 2. It contains Aerosol optical depth and particle type, with associated atmospheric data for the SAMUM_2006 theme.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/SAMMI2AE_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ATSM2AEF v001 - ATSM2AEF_001 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 FIRSTLOOK Aerosol Product subset for the ARCTAS region version 1 data product. It contains Aerosol optical depth and particle type, with associated atmospheric data produced using ancillary inputs from the previous time period.","ARN":"arn:aws:s3:::atsm2aef","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ATSM2LSF v001 - ATSM2LSF_001 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 FIRSTLOOK Land Surface Product subset for the ARCTAS region version 1. It contains directional reflectance properties, albedo (spectral and PAR integrated), FPAR, radiation parameters, and terrain-referenced geometric parameters produced using ancillary input from the previous time period.","ARN":"arn:aws:s3:::atsm2lsf","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"ATSM2STF v001 - ATSM2STF_001 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 FIRSTLOOK TOA/Cloud Stereo Product subset for the ARCTAS region version 1. It contains the Stereoscopically Derived winds, heights, and cloud mask, along with associated data, produced using ancillary inputs (TASC) from the previous time period.","ARN":"arn:aws:s3:::atsm2stf","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CMRMIGEO v002 - CMRMIGEO_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Geometric Parameters subset for the C-MARE Region version 2. It contains the geometric parameters which measure the sun and view angles at the reference ellipsoid for the region covered by the CMARE_2004 theme.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/CMRMIGEO_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CMRMIALS v002 - CMRMIALS_2 is the MISR L2 Land Surface Product subset for the C-MARE Region V002. It contains albedo and BRF data for the region covered by the CMARE_2004 theme.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/CMRMIALS_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SAMMI2LS v002 - SAMMI2LS_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Surface Product subset for the SAMUM region version 2. It contains information about land directional reflectance properties, albedos (spectral and PAR integrated), FPAR, associated radiation parameters, and terrain-referenced geometric parameters for the SAMUM_2006 theme.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/SAMMI2LS_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"RICMITAL v002 - RICMITAL_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Albedo Product subset for the RICO region version 2. It contains local, restrictive, and expansive albedo, with associated data.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/RICMITAL_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"RICMITCL v003 - RICMITCL_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Classifier Product subset for the RICO region, version 2 data product. It contains the Angular Signature Cloud Mask (ASCM), Regional Cloud Classifiers, Cloud Shadow Mask, and Topographic Shadow Mask, with associated data.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/RICMITCL_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CMRMITST v002 - CMRMITST_2 is the C-MARE Region's Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Stereo Product subset. It contains a stereoscopically derived cloud mask and cloud height and reflecting level reference altitude for the region covered by the CMARE_2004 theme.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/CMRMITST_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"CARMITST v002 - CARMITST_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Stereo Product subset for the ICARTT region version 2. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, and Reflecting Level Reference Altitude (RLRA), with associated data over the ICARTT_2004 theme.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/CARMITST_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"RICMITST v002 - RICMITST_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Stereo Product subset for the RICO region version 2. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, Reflecting Level Reference Altitude (RLRA), with associated data.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/RICMITST_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SAMMI2ST v002 - SAMMI2ST_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Stereo Product subset for the SAMUM region Version 2. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, and Reflecting Level Reference Altitude (RLRA), with associated data for the SAMUM_2006 theme.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/SAMMI2ST_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI1AC v002 - MI1AC_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1A Calibration data in DN. The data numbers have been commuted from 12-bit to 16-bit, byte-aligned half-word version 2.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI1AC_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIB1LM v002 - MIB1LM_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B1 Local Mode Radiance Data version 2. It contains the data numbers (DNs) radiometrically scaled to radiances with no geometric resampling.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIB1LM_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI1B1 v002 - MI1B1_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B1 Radiance Data version 2. It contains the data numbers (DNs) radiometrically scaled to radiances with no geometric resampling and spectral radiances for all MISR channels.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI1B1_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMIB2E v002 - UAEMIB2E_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Data subset for the UAE region Version 2 . It contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMIB2E_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMIB2E v003 - UAEMIB2E_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Data subset for the UAE region version 3. It contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected and geometrically corrected by PGE22.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMIB2E_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI1B2E v003 - Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared).","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI1B2E_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI1B2E v004 - MI1B2E_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Data Version 4 product. It contains Ellipsoid-projected Top-of-Atmosphere (TOA) Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI1B2E_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"GOMIB2E v003 - GOMIB2E_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Product subset for the GoMACCS region version 3. It contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/GOMIB2E_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"VBEMIB2E v003 - VBEMIB2E_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Product subset for the VBBE region version 3. It contains Ellipsoid-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/VBEMIB2E_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEM1LMT v002 - UAEM1LMT_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Local Mode Terrain Radiance Data subset for the UAE region. It contains the terrain-projected TOA radiance for the single local mode scene, resampled at the surface and topographically corrected.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEM1LMT_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MB2LMT v002 - MB2LMT_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Local Mode Terrain Radiance Data Version 2 product. It contains the terrain-projected Top-of-Atmosphere (TOA) radiance for the single local mode scene, resampled at the surface and topographically corrected.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MB2LMT_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMIB2T v002 - UAEMIB2T_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Data subset for the UAE region version 2 data product. It contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMIB2T_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMIB2T v003 - UAEMIB2T_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Data subset for the UAE region version 3 data product. It contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMIB2T_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI1B2T v003 - Multi-angle Imaging SpectroRadiometer (MISR) is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared).","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI1B2T_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI1B2T v004 - MI1B2T_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Data Version 4 product. It contains Terrain-projected Top-of-Atmosphere (TOA) Radiance, resampled at the surface, and topographically corrected and geometrically corrected by PGE22.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI1B2T_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"GOMIB2T v003 - GOMIB2T_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Product subset for the GoMACCS region version 3. It contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/GOMIB2T_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"VBEMIB2T v003 - VBEMIB2T_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Product subset for the VBBE region version 3. It contains Terrain-projected TOA Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/VBEMIB2T_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMIAAE v002 - UAEMIAAE_002 is the Multi-angle Imaging spectro-radiometer (MISR). It contains aerosol optical depth, particle type, and associated atmospheric data.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMIAAE_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL2ASAE v003 - MIL2ASAE_3 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Aerosol parameters Version 3 product. It contains information on retrieved aerosol column amount, aerosol particle properties, and ancillary information based on Level 1B2 geolocated radiances observed by MISR.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL2ASAE_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL2ASAF v002 - This is the Level 2 FIRSTLOOK Aerosol Product. It contains Aerosol optical depth and particle type, with associated atmospheric data produced using ancillary inputs from the previous time period.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL2ASAF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL2ASLF v002 - This Level 2 FIRSTLOOK Land Surface product contains directional reflectance properties, albedo(spectral and PAR integrated), FPAR, radiation parameters, and terrain-referenced geometric parameters produced using ancillary input from the previous time period. MIL2ASLF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 FIRSTLOOK Surface parameters version 2.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL2ASLF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL2TCCF v001 - MIL2TCCF_001 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 FIRSTLOOK TOA/Cloud Classifier parameters Version 1 data product. It contains the Angular Signature Cloud Mask (ASCM), Cloud Classifiers, and Support Vector Machine classifiers, produced using ancillary inputs and Terrestrial Atmosphere and Surface Climatology (TASC) from the previous time period.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL2TCCF_001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMIALS v002 - UAEMIALS_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Land Surface parameters subset for the UAE region version 2. It contains information on land directional reflectance properties, albedos (spectral and photosynthetically active radiation (PAR) integrated), fraction of absorbed photosynthetically active radiation (FPAR), associated radiation parameters, and terrain-referenced geometric parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMIALS_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"GOMI2LS v002 - GOMI2LS_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Land Surface Product subset for the GoMACCS region Version 2 product. It contains information on land directional reflectance properties, albedos (spectral and photosynthetically active radiation (PAR) integrated), a fraction of absorbed photosynthetically active radiation (FPAR), associated radiation parameters, and terrain-referenced geometric parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/GOMI2LS_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"VBEMI2LS v002 - VBEMI2LS_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Land Surface Product subset for the VBBE region version 2. It contains information on land directional reflectance properties; albedos (spectral and photosynthetically active radiation (PAR) integrated); fraction of absorbed photosynthetically active radiation (FPAR); associated radiation parameters; and terrain-referenced geometric parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/VBEMI2LS_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL2ASLS v002 - MIL2ASLS_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Land Surface parameters version 2 data product. It contains a variety of information on the Earth's surface; such ashemispherical directional reflectance factor (HDRF), bihemispherical reflectance (BHR) (i.e., albedo), bidirectional reflectance factor (BRF), directional hemispherical reflectance (DHR), BRF model parameters, Fractional absorbed Photosysenthetically Active Radiation (FPAR), and terrain-referenced view and illumination angles.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL2ASLS_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL2ASLS v003 - MIL2ASLS_3 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Land Surface parameters version 3 data product. It contains a variety of information on the Earth's surface, such as hemispherical directional reflectance factor (HDRF), bihemispheric reflectance (BHR) (i.e., albedo), bidirectional reflectance factor (BRF), directional hemispherical reflectance (DHR), BRF model parameters, Fractional absorbed Photosynthetically Active Radiation (FPAR), and terrain-referenced view and illumination angles.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL2ASLS_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"GOMI2AE v002 - GOMI2AE_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud-Aerosol Product subset for the GoMACCS region version 2. It contains Aerosol optical depth and particle type with associated atmospheric data.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/GOMI2AE_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"VBEMI2AE v002 - VBEMI2AE_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Aerosol Product subset for the VBBE region version 2. It contains Aerosol optical depth and particle type, with associated atmospheric data.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/VBEMI2AE_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMITAL v002 - UAEMITAL_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Albedo parameters subset for the UAE region version 2. It contains local, restrictive, and expansive albedo with associated data.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMITAL_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMITCL v003 - UAEMITCL_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Classifier parameters subset for the UAE region version 3. It contains the Angular Signature Cloud Mask (ASCM), Regional Cloud Classifiers, Cloud Shadow Mask, and Topographic Shadow Mask, with associated data.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMITCL_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL2TCCL v003 - MIL2TCCL_003 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Top-of-Atmosphere (TOA)/Cloud Classifier parameters Version 3 data product. It contains the Angular Signature Cloud Mask (ASCM), Regional Cloud Classifiers, Cloud Shadow Mask, and Topographic Shadow Mask, with associated data.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL2TCCL_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL2TCSP v001 - The Multi-angle Imaging SpectroRadiometer (MISR) is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared).","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL2TCSP_001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMITST v002 - UAEMITST_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Stereo parameters subset for the UAE region version 2. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, Reflecting Level Reference Altitude (RLRA), with associated data.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMITST_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"GOMI2ST v002 - GOMI2ST_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Stereo Product subset for the GoMACCS region version 2. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, and Reflecting Level Reference Altitude (RLRA) with associated data.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/GOMI2ST_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"VBEMI2ST v002 - VBEMI2ST_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 TOA/Cloud Stereo Product subset for the VBBE region version 2. It contains the Stereoscopically Derived Cloud Mask (SDCM), cloud winds, and Reflecting Level Reference Altitude (RLRA), with associated data.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/VBEMI2ST_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3DCFA v001 - This file contains the public MISR Level 3 Cloud Fraction by Altitude Product covering a day. MIL3DCFA_001 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Fraction by Altitude Product covering a day version 1.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3DCFA_001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3MCFA v001 - MIL3MCFA_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Fraction by Altitude Product covering a month version 1. It provides the frequency of cloud occurrence partitioned into different cloud top height bins at a global and monthly scale with a latitude/longitude resolution of 0.5 degrees by 0.5 degrees and a vertical resolution of 500m.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3MCFA_001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3QCFA v001 - MIL3QCFA_001 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Fraction by Altitude Product covering a quarter (seasonal) version 1. It provides the frequency of cloud occurrence partitioned into different cloud top height bins at a global and monthly scale with a latitude/longitude resolution of 0.5 degrees by 0.5 degrees and a vertical resolution of 500m.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3QCFA_001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3YCFA v001 - MIL3YCFA_001 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Fraction by Altitude Product covering a year version 1. It provides the frequency of cloud occurrence partitioned into different cloud top height bins at a global and monthly scale.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3YCFA_001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3MCMVN v002 - MI3MCMVN_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Motion Vector monthly Product in netCDF format version 2 data product. It contains retrievals of cloud motion determined by geometrically triangulating the position and motion of cloud features observed by MISR from multiple perspectives and times during the overpass of the Terra platform over each cloud scene.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3MCMVN_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3QCMVN v002 - MI3QCMVN_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Motion Vector quarterly Product in netCDF format version 2 data product. It contains retrievals of cloud motion determined by geometrically triangulating the position and motion of cloud features observed by MISR from multiple perspectives and times during the overpass of the Terra platform over each cloud scene.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3QCMVN_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3YCMVN v002 - MI3YCMVN_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Motion Vector yearly Product in netCDF format version 2. It contains retrievals of cloud motion determined by geometrically triangulating the position and motion of cloud features observed by MISR from multiple perspectives and times during the overpass of the Terra platform over each cloud scene.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3YCMVN_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3YCOD v001 - MIL3YCOD_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Top Height-Optical Depth Product covering a year version 1. MISR is designed to view Earth with cameras pointed in 9 different directions.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3YCOD_001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMRDAE v004 - UAEMRDAE_004 is the Multi-angle Imaging SpecrtroRadiometer (MISR) Level 3 Component Global Aerosol Product covering a day subset for the UAE region version 4. It contains a statistical summary of column aerosol 555 nanometer optical depth, and a monthly aerosol compositional type frequency histogram.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMRDAE_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3DAE v4 - MIL3DAE_4 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Product covering a day version 4. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3DAE_4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3MAE v4 - MIL3MAE_4 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Product covering a month version 4. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3MAE_4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3QAE v004 - MIL3QAE_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Product covering a quarter (seasonal) version 4. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3QAE_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3YAE v004 - MIL3YAE_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Product covering a year version 4 data product. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3YAE_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3DAEN v004 - This file contains the MISR Level 3 Component Global Aerosol product in netCDF format covering a day. MIL3DAEN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol product in netCDF format covering a day version 4 data product.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3DAEN_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3MAEN v004 - This file contains the MISR Level 3 Component Global Aerosol product in netCDF format covering a month. MIL3MAEN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol product in netCDF format covering a month version 4.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3MAEN_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3YAEN v004 - MIL3YAEN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol product in netCDF format covering a year version 4 data product. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3YAEN_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3DAER v002 - MI3DAER_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Regional public Product covering a day version 2. It contains a statistical summary of column aerosol 555 nanometer optical depth, and a monthly aerosol compositional type frequency histogram.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3DAER_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3MAER v002 - MI3MAER_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Regional public Product covering a month version 2. It contains a monthly statistical summary of aerosol optical depth (AOD) and single scattering albedo (SSA) model parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3MAER_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3QAEN v004 - This file contains the MISR Level 3 Component Global Aerosol product in netCDF format covering a quarter (seasonal). MIL3QAEN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol seasonal product in netCDF format version 4 data product.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3QAEN_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3DAL v006 - MISR Level 3 Component Global Albedo publicly available product covering a day to be used starting with MISR Release V3.2.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3DAL_006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3MAL v006 - MISR Level 3 Component Global Albedo is a publicly available product covering a month, to be used starting with MISR Release V3.2. MIL3MAL_006 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo product covering a month version 6.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3MAL_006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3QAL v006 - MIL3QAL_006 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo product covering a quarter (seasonal) version 6 data product. It contains a statistical summary of column albedo 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3QAL_006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3YAL v006 - MIL3YAL_006 Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo product covering a year version 6 data product. It contains a statistical summary of column albedo 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3YAL_006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3DALN v006 - MISR Level 3 Component Global Albedo is a publicly available product in netCDF format covering a day. MIL3DALN_006 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo product in netCDF format covering a day version 6 data product.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3DALN_006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3MALN v006 - MISR Level 3 Component Global Albedo is a publicly available product in netCDF format covering a month. MIL3MALN_006 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo product in netCDF format covering a month version 6.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3MALN_006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3YALN v006 - MIL3YALN_006 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo product in netCDF format covering a year version 6. This data product is a global summary of relevant Level 2 albedo parameters, averaged over a year and reported on a geographic grid; it has multiple data layers with varying temporal resolutions of 1 degree by 1 degree and 5 degrees by 5 degrees.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3YALN_006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3QALN v006 - MIL3QALN_006 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Albedo seasonal product in netCDF format version 6 data product. It is a global summary of relevant Level 2 albedo parameters, averaged over a quarter and reported on a geographic grid, it has multiple data layers, with varying temporal resolutions of 1 degree by 1 degree and 5 degrees by 5 degrees.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3QALN_006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3DCLD v002 - This file contains the public MISR Level 3 Component Global Cloud Product covering a day. MIL3DCLD_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Cloud Product covering a day version 2.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3DCLD_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3MCLD v002 - This file contains the public MISR Level 3 Component Global Cloud Product covering a month. MIL3MCLD_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Cloud Product covering a month version 2.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3MCLD_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3QCLD v002 - This file contains the public MISR Level 3 Component Global Cloud Product covering a quarter (seasonal). MIL3QCLD_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Cloud Product covering a quarter (seasonal) version 2 data product.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3QCLD_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3YCLD v002 - This file contains the public MISR Level 3 Component Global Cloud Product covering a year. MIL3YCLD_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Cloud Product covering a year version 2.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3YCLD_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMRDLS v004 - UAEMRDLS_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a day subset for the UAE region version 4. It contains a daily statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI), and land surface bidirectional reflectance factor (BRF) model parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMRDLS_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3DLS v004 - MIL3DLS_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a day version 4. It contains a daily statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3DLS_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMRMLS v004 - UAEMRMLS_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a month subset for the UAE region version 4. It contains a statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI), and land surface bidirectional reflectance factor (BRF) model parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMRMLS_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3MLS v004 - MIL3MLS_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a month version 4. It contains a statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI), and land surface bidirectional reflectance factor (BRF) model parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3MLS_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3QLS v004 - MIL3QLS_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a quarter (seasonal) version 4 data product. It contains a statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI), and land surface bidirectional reflectance factor (BRF) model parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3QLS_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3YLS v004 - MIL3YLS_4 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Product covering a year version 4. It contains a statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI), and land surface bidirectional reflectance factor (BRF) model parameters, classified into six vegetated and one non-vegetated types.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3YLS_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3DLSN v004 - This file contains the MISR Level 3 Component Global Land product in netCDF format covering a day. MIL3DLSN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land product in netCDF format covering a day version 4.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3DLSN_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3MLSN v004 - This file contains the MISR Level 3 Component Global Land product in netCDF format covering a month. MIL3MLSN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land product in netCDF format covering a month version 4.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3MLSN_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3YLSN v004 - MIL3YLSN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land product in netCDF format covering a year version 4. It contains a yearly statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3YLSN_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3DLSR v002 - MI3DLSR_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Regional public Product covering a dayversion 2. It contains a daily statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI), and land surface bidirectional reflectance factor (BRF) model parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3DLSR_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3MLSR v002 - MI3MLSR_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land Regional public Product covering a month version 2. It contains a daily statistical summary of average directional hemispherical reflectance (DHR), DHR for photosynthetically active spectral region (DHR-PAR), fractional absorbed photosynthetically active radiation (FPAR), leaf area index (LAI), and normalized difference vegetation index (NDVI) model parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3MLSR_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3QLSN v004 - MIL3QLSN_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Land seasonal product in netCDF format version 4 data product. It contains a statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3QLSN_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMRDRD v004 - UAEMRDRD_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Product covering a day subset for the UAE region version 4. It contains a statistical summary of spectral top-of-atmosphere Bidirectional Reflectance Factor for various subregion classifications; and a statistical summary of spectral expansive albedos for several sky classifications.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMRDRD_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3DRD v004 - This file contains the MISR Level 3 Component Global Radiance Product covering a day. MIL3DRD_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Product covering a day version 4.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3DRD_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMRMRD v005 - UAEMRMRD_005 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Product covering a month subset for the UAE region version 5. It contains a summary of spectral top-of-atmosphere Bidirectional Reflectance Factor for various subregion classifications and a summary of spectral expansive albedos for several sky classifications.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMRMRD_005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3MRD v005 - This file contains the MISR Level 3 Component Global Radiance Product covering a month. MIL3MRD_005 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Product covering a month version 5.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3MRD_005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3QRD v005 - MIL3QRD_005 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Product covering a quarter (seasonal) version 5 data product. It contains a statistical summary of spectral top-of-atmosphere Bidirectional Reflectance Factor for various sub-region classifications and a statistical overview of spectral expansive albedos for several sky classifications.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3QRD_005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3YRD v005 - MIL3YRD_005 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Product covering a year version 5 data product. It contains a statistical summary of spectral top-of-atmosphere (TOA) Bidirectional Reflectance Factor (BDRF) for various sub-region classifications and a statistical summary of spectral expansive albedos for several sky classifications.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3YRD_005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3DRDR v002 - MI3DRDR_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Regional public Product covering a day version 2. It contains a global summary of the Level 1 and Level 2 radiance parameters of interest averaged over a day and reported on a geographic grid, with a resolution of 0.5 degree by 0.5 degree.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3DRDR_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3MRDR v002 - MI3MRDR_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Radiance Regional public Product covering a month version 2. It contains a summary of spectral top-of-atmosphere Bidirectional Reflectance Factor for various sub-region classifications and a summary of spectral expansive albedos for several sky classifications.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3MRDR_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3DAEF v002 - MI3DAEF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Aerosol Product covering a day version 2 data product. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3DAEF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3MAEF v002 - MI3MAEF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Aerosol Product covering a month version 2 data product. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3MAEF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3DALF v002 - MI3DALF_002 is the Multiangle Imaging SpectroRadiometer (MISR) Level 3 FIRST LOOK Component Global Albedo product covering a day version 2. It is intended to be used starting with MISR Release version 4.2, a global summary of the Level 2 albedo parameters of interest averaged over a day and reported on a geographic grid.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3DALF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3MALF v002 - MISR Level 3 FIRSTLOOK Component Global Albedo publicly available product covering a month to be used starting with MISR Release V4.2.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3MALF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3DCDF v002 - This file contains the public MISR Level 3 FIRSTLOOK Component Global Cloud Product covering a day. MI3DCDF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Cloud Product covering a day version 2.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3DCDF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3MCDF v002 - This file contains the public MISR Level 3 FIRSTLOOK Component Global Cloud Product covering a month. MI3MCDF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Cloud Product covering a month version 2 data product.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3MCDF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3DLSF v002 - MI3DLSF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Land Product covering a day version 2 data product. It contains a daily statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3DLSF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3MLSF v002 - MI3MLSF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Land Product covering a month version 2 data product. It contains a monthly statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3MLSF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3DRDF v002 - This file contains the MISR Level 3 FIRSTLOOK Component Global Radiance Product covering a day. MI3DRDF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Radiance Product covering a day version 2 data product.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3DRDF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3MRDF v002 - This file contains the MISR Level 3 FIRSTLOOK Component Global Radiance Product covering a month. MI3MRDF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Component Global Radiance Product covering a month version 2 data product.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3MRDF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3DAENF v002 - MI3DAENF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Aerosol product in netCDF format covering a day version 2. It contains a statistical summary of column aerosol 555-nanometer optical depth and a monthly aerosol compositional type frequency histogram.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3DAENF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3MAENF v002 - This file contains the MISR Level 3 FIRSTLOOK Component Global Aerosol product in netCDF format covering a month.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3MAENF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3DALNF v002 - MI3DALNF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Albedo product in netCDF format covering a day version 2. It is a global summary of the Level 2 albedo parameters of interest averaged over a day and reported on a geographic grid; it has multiple data layers with varying temporal resolutions of 1 degree by 1 degree and 5 degrees by 5 degrees.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3DALNF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3MALNF v002 - MISR Level 3 FIRSTLOOK Component Global Albedo is a publicly available product in netCDF format that covers a month. MI3MALNF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Albedo product in netCDF format covering a month version 2 data product.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3MALNF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3DCDNF v002 - MI3DCDNF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Cloud public Product in netCDF covering a day version 2. It contains the public MISR Level 3 FIRSTLOOK Global Cloud public product in netCDF format covering a day.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3DCDNF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3MCDNF v002 - This file contains the MISR Level 3 FIRSTLOOK Global Cloud public Product in netCDF format covering a month.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3MCDNF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3DLSNF v002 - This file contains the MISR Level 3 FIRSTLOOK Component Global Land product in netCDF format covering a day. MI3DLSNF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Land product in netCDF format covering a day version 2 data product.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3DLSNF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3MLSNF v002 - MI3MLSNF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Land product in netCDF format covering a month version 2 data product. It contains a monthly statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3MLSNF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3DCLDN v002 - MI3DCLDN_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Global Cloud public Product in netCDF format covering a day version 2. It contains the public MISR Level 3 Global Cloud Product in netCDF format covering a day and is a global summary of the Level 1 and Level 2 cloud parameters of interest averaged over a year and reported on a geographic grid; it has multiple data layers, with varying temporal resolutions of 0.5 degrees by 0.5 degrees as well as 2.5 degrees by 2.5 degrees.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3DCLDN_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3MCLDN v002 - This file contains the MISR Level 3 Global Cloud public Product in netCDF format covering a month. MI3MCLDN_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Global Cloud public Product in netCDF format covering a month version 2 data product.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3MCLDN_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3QCLDN v002 - This file contains the MISR Level 3 Global Cloud public Product in netCDF format covering a quarter (seasonal). MI3QCLDN_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Global Cloud public Product in netCDF format covering a quarter version 2 data product.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3QCLDN_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI3YCLDN v002 - This file contains the MISR Level 3 Global Cloud public Product in netCDF format covering a year. MI3YCLDN_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Global Cloud Public Product in netCDF format covering a year version 2 data product.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI3YCLDN_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL3MJTA v002 - MIL3MJTA_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Global Joint Aerosol monthly version 2 data product. It contains global statistical summaries of MISR Level 2 aerosol optical depth on a 5-degree geographic grid.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL3MJTA_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MI1AOBC v002 - MI1AOBC_2 is the Multi-angle Imaging SpectroRadiometer (MISR) OBC Data version 2. This file contains the output for the Level 1A On-board Calibrator Data, and it provides the radiometry from PIN and HQE diodes and goniometer mechanism readings collected during calibration mode operations near the north and south poles and over the dark side of the Earth (or during science mode operations over the sunlit side of the Earth).","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MI1AOBC_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMIRCM v003 - UAEMIRCM_003 is the Multi-angle Imaging SpectroRadiometer (MISR) radiometric camera-by-camera Cloud Mask subset for the UAE region version 3. It contains the Radiometric camera-by-camera Cloud Mask dataset.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMIRCM_003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"UAEMIRCM v004 - UAEMIRCM_004 is the Multi-angle Imaging SpectroRadiometer (MISR) radiometric camera-by-camera Cloud Mask subset for the UAE region version 4. It contains the Radiometric camera-by-camera Cloud Mask dataset.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/UAEMIRCM_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIRCCM v004 - MIRCCM_004 is the Multi-angle Imaging SpectroRadiometer (MISR) radiometric camera-by-camera Cloud Mask version 4 data product. It contains the Radiometric camera-by-camera Cloud Mask dataset.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIRCCM_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIANTASC v002 - MIANTASC_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Terrestrial Atmosphere and Surface Climatology (TASC) dataset version 2. It is produced by the MISR Science Computing Facility (SCF) and shipped to the Atmospheric Science Data Center (ASDC) for generating MISR Level 2 products and consists of one HDF file and data set per month in HDF-EOS Grid format with a resolution of 1.0 x 1.0 degrees.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIANTASC_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-misr","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA MISST Project","Description":"The Saildrone Arctic 2021 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land. The 2021 Saildrone Arctic campaign featured two Saildrone USVs deployed during a 76-day cruise in the Bering and Chukchi Seas, spanning from 6 July 2021 to 20 September 2021. The primary mission objective for 2021 was to gather comprehensive atmospheric and oceanographic data in Alaskan arctic waters, with special emphasis on better understanding the spatial/temporal scales of air-sea covariance in the Chukchi Sea, which was accomplished by running a series of parallel tracks using the two Saildrones at varying horizontal offsets. Please see the cruise report: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/insitu/open/L2/saildrone/docs/2021_Saildrone_Arctic_Cruise_Report.pdf\n\nDuring the Arctic campaign, Saildrones SD-1057 and SD-1058 ran transects in the Chukchi Sea, approaching the sea ice edge (up to 50 km away) to measure air-sea heat and momentum fluxes in the ocean near sea ice and to validate satellite sea-surface temperature measurements in the Arctic. Each Saildrone was equipped with a suite of instruments to measure various parameters, including air temperature, relative humidity, barometric pressure, surface skin temperature, wind speed and direction, wave height and period, seawater temperature and salinity, chlorophyll fluorescence, and dissolved oxygen. Additionally, both vehicles utilized 300 kHz acoustic Doppler current profilers (ADCP) to measure near-surface currents.\n\nThe Saildrone Arctic 2021 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses two netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies.\n\nThe Saildrone Arctic 2021 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses two netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies.\n<br>\n\n#### SAILDRONE_ARCTIC_2022\nThe Saildrone Arctic 2022 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land. The 2022 Saildrone Arctic campaign featured two Saildrone USVs deployed during a 60-day cruise in the Bering and Chukchi Seas, spanning from 18 June 2022 to 17 August 2022. The primary mission objective for 2022 was to gather comprehensive atmospheric and oceanographic data in Alaskan arctic waters, specifically in collaboration with the Distributed Biological Observatory (DBO; https://www.pmel.noaa.gov/dbo/; https://dbo.cbl.umces.edu/). Please see the cruise report: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/insitu/open/L2/saildrone/docs/Saildrone_2022_Arctic_Cruise_Report.pdf\n\nDuring the Arctic campaign, Saildrones SD-1041 and SD-1046 undertook distinct trajectories to cover designated areas. SD-1041 traversed repeat transects from Point Hope, AK southwestward to near the International Date Line, following DBO line #3 (https://dbo.cbl.umces.edu/images/Frey_DBOmap_IceEdge2022.png). In contrast, SD-1046 ventured northward to DBO line #4 and, upon sea ice retreat, proceeded further north to DBO line #5. Each Saildrone was equipped with a suite of instruments to measure various parameters, including air temperature, relative humidity, barometric pressure, surface skin temperature, wind speed and direction, wave height and period, seawater temperature and salinity, chlorophyll fluorescence, and dissolved oxygen. Additionally, both vehicles utilized 300 kHz acoustic Doppler current profilers (ADCP) to measure near-surface currents. Seven temperature data loggers positioned vertically along the hull enhanced understanding of thermal variability near the ocean surface.\n\nThe Saildrone Arctic 2022 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses three netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. The third file includes temperature logger measurements at various depths at 1-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","netcdf","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SAILDRONE_ARCTIC_2021 v1 - The Saildrone Arctic 2021 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SAILDRONE_ARCTIC_2021","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SAILDRONE_ARCTIC_2022 v1 - The Saildrone Arctic 2022 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SAILDRONE_ARCTIC_2022","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-07-09","Slug":"nasa-misst","Sources":["awslabs-open-data-registry"]}
{"Name":"MODIS/Terra Calibrated Radiances 5-Min L1B Swath 500m","Description":"The MODIS/Terra Calibrated Radiances 5Min L1B Swath 500m data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data.\r\n\r\nVisible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously.\r\n\r\nChannels 1 and 2 have 250 m resolution, channels 3 through 7 have 500 m resolution. However, for the MODIS L1B 500 m product, the 250 m band radiance data and their associated uncertainties have been aggregated to 500m resolution. Thus the entire channel data set has been co-registered to the same spatial scale in the 500 m product. Separate L1B products are available for the 250 m resolution channels (MOD02QKM) and 1 km resolution channels (MOD021KM). For the latter product, the 250 m and 500 m channel data (bands 1 through 7) have been aggregated into equivalent 1 km pixel values.\r\n            \r\nSpatial resolution for pixels at nadir is 500 km, degrading to 2.4 km in the along-scan direction at the scan extremes. However, thanks to the overlapping of consecutive swaths and respectively pixels there, the resulting resolution at the scan extremes is about 1 km. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 500 m granule will contain a scene built from 203 scans sampled 2708 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 20 along-track spatial elements for the 500 m channels, the scene will be composed of (2708 x 4060) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 20 degrees scan angle.      \r\n\r\nTo summarize, the MODIS L1B 500 m data product consists of:\r\n            \r\n1. Calibrated radiances, uncertainties and number of samples for (2) 250 m reflected solar bands aggregated to 500 m resolution\r\n            \r\n2. Calibrated radiances and uncertainties for (5) 500 m reflected solar bands\r\n            \r\n3. Geolocation for 1km pixels, that must be interpolated to get 500 m pixel locations. For the relationship of 1km pixels to 500m pixels, see the Geolocation ATBD https://modis.gsfc.nasa.gov/data/atbd/atbd_mod28_v3.pdf.\r\n            \r\n4. Calibration data for all channels (scale and offset) \r\n            \r\n5. Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization users requiring all geolocation and solar/satellite geometry fields at 1km resolution can obtain the separate MODIS Level 1 Geolocation product (MOD03) from LAADS  https://ladsweb.modaps.eosdis.nasa.gov/ . \r\n            \r\nThe shortname for this product is MOD02HKM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical MOD02HKM file size is approximately 135 MB.\r\n            \r\nEnvironmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world.\r\n\r\nSee the MODIS Characterization Support Team webpage for more C6 product information at:\r\n\r\nhttps://mcst.gsfc.nasa.gov/l1b/product-information\r\n\r\n\r\nor visit Science Team homepage at:\r\nhttps://modis.gsfc.nasa.gov/data/dataprod/\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/MODIS/MOD02HKM.061","Contact":"MODAPS USER SUPPORT TEAM: MODAPSUSO@lists.nasa.gov. Home Page: https://modaps.modaps.eosdis.nasa.gov/, MODIS Characterization Support Team (MCST): https://mcst.gsfc.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"From 2000-02-24 to Ongoing","Tags":["atmosphere","aws-pds","datacenter","earth observation","environmental","global","hdf","metadata","opendap","orbit"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MODIS/Terra Calibrated Radiances 5-Min L1B Swath 500m.","ARN":"arn:aws:s3:::prod-lads/MOD02HKM","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"MODIS Level 1B - Calibrated Radiances - Natural Color RGB - 500m","URL":"https://fire.trainhub.eumetsat.int/docs/figure1_MODIS_L1B.html","AuthorName":"EUMETSAT"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-mod02hkm","Sources":["awslabs-open-data-registry"]}
{"Name":"MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V061","Description":"The  Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. \n\nKnown Issues\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/MODIS/MOD09A1.061","Contact":"User Services: lpdaac@usgs.gov. Home Page: https://www.earthdata.nasa.gov/centers/lp-daac/contact","ManagedBy":"NASA","UpdateFrequency":"From 2000-02-18 to Ongoing (Weekly - < Monthly)","Tags":["aws-pds","earth observation","geospatial","global","hdf","land","opendap","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V061.","ARN":"arn:aws:s3:::lp-prod-protected/MOD09A1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Download Files from S3 Using boto3","URL":"https://github.com/nasa/LPDAAC-Data-Resources/blob/7f58ad3abeca7f0d17637ddb812642c0120a57ab/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb","AuthorName":"LPDAAC"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-mod09a1","Sources":["awslabs-open-data-registry"]}
{"Name":"MODIS/Terra Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061","Description":"The MOD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer (km) observation bands and geolocation flags. The reflectance layers from the MOD09GA are used as the source data for many of the MODIS land products. \n\nKnown Issues\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/MODIS/MOD09GA.061","Contact":"User Services: lpdaac@usgs.gov","ManagedBy":"NASA","UpdateFrequency":"From 2000-02-24 to Ongoing","Tags":["aws-pds","datacenter","earth observation","geospatial","global","hdf","ice","land","opendap","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MODIS/Terra Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061.","ARN":"arn:aws:s3:::lp-prod-protected/MOD09GA.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Download Files from S3 Using boto3","URL":"https://github.com/nasa/LPDAAC-Data-Resources/blob/main/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb","AuthorName":"LPDAAC","AuthorURL":"https://www.earthdata.nasa.gov/centers/lp-daac"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-mod09ga","Sources":["awslabs-open-data-registry"]}
{"Name":"MODIS/Terra Surface Reflectance Daily L2G Global 250m SIN Grid V061","Description":"The MOD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m surface reflectance bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MOD09GA). \n\nKnown Issues\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/MODIS/MOD09GQ.061","Contact":"User Services: lpdaac@usgs.gov","ManagedBy":"NASA","UpdateFrequency":"From 2000-02-24 to Ongoing (Daily - < Weekly)","Tags":["aws-pds","datacenter","earth observation","geospatial","global","hdf","ice","land","opendap"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MODIS/Terra Surface Reflectance Daily L2G Global 250m SIN Grid V061.","ARN":"arn:aws:s3:::lp-prod-protected/MOD09GQ.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Download Files from S3 Using boto3","URL":"https://github.com/nasa/LPDAAC-Data-Resources/blob/main/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb","AuthorName":"LPDAAC","AuthorURL":"https://www.earthdata.nasa.gov/centers/lp-daac"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-mod09gq","Sources":["awslabs-open-data-registry"]}
{"Name":"MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061","Description":"The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.\n\nAlong with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. \n\nKnown Issues\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/MODIS/MOD13Q1.061","Contact":"User Services: lpdaac@usgs.gov","ManagedBy":"NASA","UpdateFrequency":"From 2000-02-18 to Ongoing (Weekly - < Monthly)","Tags":["aws-pds","datacenter","earth observation","geospatial","global","hdf","ice","land","opendap","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061.","ARN":"arn:aws:s3:::lp-prod-protected/MOD13Q1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Download Files from S3 Using boto3","URL":"https://github.com/nasa/LPDAAC-Data-Resources/blob/main/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb","AuthorName":"LPDAAC","AuthorURL":"https://www.earthdata.nasa.gov/centers/lp-daac"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-mod13q1","Sources":["awslabs-open-data-registry"]}
{"Name":"MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V061","Description":"The MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. \n\nProvided in the MOD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2 granule.\n\nThe pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year.\n\nKnown Issues\n* Operational and uncertainty issues are provided under Section 3 in the User Guide.\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n\nImprovements/Changes from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/MODIS/MOD16A2.061","Contact":"User Services: lpdaac@usgs.gov. Home Page: https://www.earthdata.nasa.gov/centers/lp-daac/contact","ManagedBy":"NASA","UpdateFrequency":"From 2021-01-01 to Ongoing (Weekly - < Monthly)","Tags":["atmosphere","aws-pds","earth observation","evapotranspiration","geospatial","global","hdf","land","land cover","opendap","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V061.","ARN":"arn:aws:s3:::lp-prod-protected/MOD16A2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Download Files from S3 Using boto3","URL":"https://github.com/nasa/LPDAAC-Data-Resources/blob/7f58ad3abeca7f0d17637ddb812642c0120a57ab/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb","AuthorName":"LPDAAC"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-mod16a2","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Model Archive Project","Description":"This model product provides: (1) the source code for the updated Berkeley-Dalhousie Soil Nitric Oxide (NO) Parameterization module (BDSNP, Version 1.0) as implemented with the Community Multi-scale Air Quality model (CMAQ, Version 5.0.2), (2) module input data from historical and new sources of maps for soil biome type, fertilizer, and arid and non-arid climates, and (3) sample CMAQ simulation outputs for three BDSNP module NO parameterizations (standard, historical, and newer inputs). The simulations use a 12-km spatial grid resolution for CMAQ modeling covering the conterminous United States for July 2011.\n<br>\n\n#### BIOME_BGC_m2_4_1_2_809\nThis archived model product contains the directions, executables, and procedures for running Biome-BGC, Version 4.1.2, to recreate the results of Law BE, Sun OJ, Campbell J, Van Tuyl S, Thornton PE, 2003. Changes in carbon storage and fluxes in a chronosequence of ponderosa pine. Global Change Biology, 9(4), 510-514.Law et al. 2003 excerpt: Abstract Forest development following stand-replacing disturbance influences a variety of ecosystem processes including carbon exchange with the atmosphere. On a series of ponderosa pine (Pinius ponderosa var. Laws.) stands ranging from 9 to > 300 years in central Oregon, USA, we used biological measurements to estimate carbon storage in vegetation and soil pools, net primary productivity (NPP) and net ecosystem productivity (NEP) to examine variation with stand age. Measurements were made in 2000 on a chronosequence of 12 ponderosa pine stands. Total ecosystem carbon storage and the fraction of ecosystem carbon in aboveground wood mass increased rapidly until 150-200 years, and did not decline in older stands. Forest inventory data on 950 ponderosa pine plots in Oregon show that the greatest proportion of plots exist in stands ~ 100 years old, indicating that a majority of stands are approaching maximum carbon storage and net carbon uptake. Our data suggests that NEP averages ~ 70 g C m-2 year-1 for ponderosa pine forests in Oregon. About 85% of the total carbon storage in biomass on the survey plots exists in stands greater than 100 years, which has implications for managing forests for carbon sequestration. To investigate variation in carbon storage and fluxes with disturbance, simulation with process models requires a dynamic parameterization for biomass allocation that depends on stand age, and should include a representation of competition between multiple plant functional types for space, water, and nutrients.\n<br>\n\n#### BIOME_BGC_m_4_1_1_806\nThis archived model product contains the directions, executables, and procedures for running Biome-BGC, Version 4.1.1, to recreate the results of: Thornton, P.E., Law, B.E., Gholz, H.L., Clark, K.L., Falge, E., Ellsworth, D.S., Goldstein, A.H., Monson, R.K., Hollinger, D., Falk, M., Chen, J. and Sparks, J.P. 2002. Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology 113:185-222.Thornton et al., 2002 excerpt: AbstractThe effects of disturbance history, climate, and changes in atmospheric carbon dioxide (CO2) concentration and nitrogen deposition (Ndep) on carbon and water fluxes in seven North American evergreen forests are assessed using a coupled water, carbon, nitrogen model, canopy-scale flux observations, and descriptions of the vegetation type, management practices, and disturbance histories at each site. The effects of interannual climate variability, disturbance history, and vegetation ecophysiology on carbon and water fluxes and storage are integrated by the ecosystem process model Biome-BGC, with results compared to site biometric analyses and eddy covariance observations aggregated by month and year. The model produced good estimates of between-site variation in leaf area index, with mixed performance for between- and within-site variation in evapotranspiration. There is a model bias toward smaller annual carbon sinks at five sites, with a seasonal model bias toward smaller warm-season sink strength at all sites.\n<br>\n\n#### BIOME_BGC_4_1_1_805\nBiome-BGC is a computer program that estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems. The primary model purpose is to study global and regional interactions between climate, disturbance, and biogeochemical cycles.Biome-BGC represents physical and biological processes that control fluxes of energy and mass. These processes include: New leaf growth and old leaf litterfall Sunlight interception by leaves and penetration to the ground Precipitation routing to leaves and soil Snow accumulation and melting Drainage and runoff of soil water Evaporation of water from soil and wet leaves Transpiration of soil water through leaf stomata Photosynthetic fixation of carbon from CO2 in the air Uptake of nitrogen from the soil Distribution of carbon and nitrogen to growing plant parts Decomposition of fresh plant litter and old soil organic matter Plant mortality Fire The model uses a daily time-step. This means that each flux is estimated for a one-day period. Between days, the program updates its memory of the mass stored in different components of the vegetation, litter, and soil. Weather is the most important control on vegetation processes. Flux estimates in Biome-BGC depend strongly on daily weather conditions. Model behavior over time depends on climate--the history of these weather conditions.A companion file with more information about Biome-BGC and its components is available at ftp://daac.ornl.gov/data/model_archive/BIOME_BGC/biome_bgc_4.1.1/comp/BiomeBGC_v411_release.pdf .Biome-BGC, Version 4.1.1 was developed and is maintained by the Numerical Terradynamic Simulation Group, School of Forestry, The University of Montana, Missoula, Montana, USA. Additional information can be found on there web site at: http://www.ntsg.umt.edu/.\n<br>\n\n#### century_vemap_m4_820\nThe CENTURY model, Version 4, is a general model of plant-soil nutrient cycling that is being used to simulate carbon and nutrient dynamics for different types of ecosystems including grasslands, agricultural lands, forests and savannas. CENTURY is composed of a soil organic matter/ decomposition submodel, a water budget model, a grassland/crop submodel, a forest production submodel, and management and events scheduling functions. It computes the flow of carbon, nitrogen, phosphorus, and sulfur through the model's compartments.\n<br>\n\n#### EDM_SA_Vegetation_1149\nThis model product contains the source code for the Ecosystem Demography Model (ED version 1.0) as well as model input and output data for a portion of South America including the Brazilian Amazon. The model output data are estimates of potential average live biomass (kg C/m2), potential average soil carbon (kg C/m2), and potential above-ground net primary production (NPP) (kg C/m2/yr) at 1.0 degree resolution. To produce these estimates, ED was forced with ISLSCP I data for 1987 and 1988, averaged into a single year (Moorcroft et al., 2001). Data for the three estimates are provided in both ASCII text and in NetCDF formatted files. ED is an individual-based terrestrial ecosystem model that predicts both ecosystem structure (e.g. above and below-ground biomass, vegetation height and basal area, and soil carbon stocks) and corresponding ecosystem fluxes (e.g. NPP, NEP and evapotranspiration) from climate, soil, and land-use inputs. The model consists of integrated sub-models governing processes such as leaf-level physiology, plant allocation, allometry, phenology, dispersal, the effects of fire disturbances, and below-ground sub-models for soil carbon dynamics and hydrology. Using a new method for scaling-up it is possible to predict ED's large-scale behavior without simulating the fate of every plant individually. ED is used to examine how climate and edaphic factors, natural disturbances, and human land-use practices affect ecosystem structure and fluxes. This data set contains six zip files which each uncompress into six unique subdirectories. Each subdirectory is described in detail in the Model Product Description section of this document. Installation and execution instructions are provided in the Model Documentation and User's Guide section of this document.\n<br>\n\n#### EDM_US_Carbon_1160\nThis model product contains the source code for the Ecosystem Demography Model (ED version 1.0) as well as model input and output data files for the conterminous United States. The ED is a mechanistic ecosystem model built around established sub-models of leaf level physiology, organic matter decomposition, hydrology, and functional biodiversity. It was used herein to estimate ecosystem carbon stocks and fluxes in the conterminous U.S. at 1.0 degree resolution from 1700 to 1990. Output data of carbon stocks and fluxes are stored in NetCDF format. To produce the U.S. scenario, ED was run from an estimated state of ecosystems in the year 1700 to an estimated state of ecosystems in the year 1990 for each 1 degree by 1 degree grid cell through time using ISLSCP Initiative I climate and soil data and a gridded land-use history reconstruction as inputs (Hurtt et al., 2002). The land-use history was based on several sources including: spatial distribution of potential vegetation in 1700, spatial patterns of cropland from 1700 to 1990, regional estimates of land use and logging from 1700 to 1990, and U.S. Forest Inventory and Analysis (FIA) data on the current age distribution of forest stands. The Miami Land Use History Model (Miami-LU), a far simpler empirically-based ecosystem model, was used to track the history of disturbance, land use, fire, and ecosystem recovery. The effects of fire suppression were also included. Atmospheric CO2 concentrations and climatic conditions were held constant throughout the runs to focus on the consequences of land-use and fire-management changes on carbon stocks and fluxes.\n<br>\n\n#### ibis_2_5_808\nThe Integrated Biosphere Simulator (or IBIS) is designed to be a comprehensive model of the terrestrial biosphere; the model represents a wide range of processes, including land surface physics, canopy physiology, plant phenology, vegetation dynamics and competition, and carbon and nutrient cycling. The model generates global simulations of the surface water balance (e.g., runoff), the terrestrial carbon balance (e.g., net primary production, net ecosystem exchange, soil carbon, aboveground and belowground litter, and soil CO2 fluxes), and vegetation structure (e.g., biomass, leaf area index, and vegetation composition). IBIS was developed by Center for Sustainability and the Global Environment (SAGE) researchers as a first step toward gaining an improved understanding of global biospheric processes and studying their potential response to human activity [Foley et al., 1996]. IBIS was constructed to explicitly link land surface and hydrological processes, terrestrial biogeochemical cycles, and vegetation dynamics within a single, physically consistent framework. Furthermore, IBIS was one of a new generation of global biosphere models, termed Dynamic Global Vegetation Models (or DGVMs), that consider transient changes in vegetation composition and structure in response to environmental change. Previous global ecosystem models have typically focused on the equilibrium state of vegetation and could not allow vegetation patterns to change over time.Version 2.5 of IBIS includes several major improvements and additions [Kucharik et al. 2000]. SAGE continues to test the performance of the model, assembling a wide range of continental- and global-scale data, including measurements of river discharge, net primary production, vegetation structure, root biomass, soil carbon, litter carbon, and soil CO2 flux. Using these field data and model results for the contemporary biosphere (1965-1994), their evaluation shows that simulated patterns of runoff, NPP, biomass, leaf area index, soil carbon, and total soil CO2 flux agreed reasonably well with measurements that have been compiled from numerous ecosystems. These results also compare favorably to other global model results [Kucharik et al. 2000].\n<br>\n\n#### LSM_807\nThe NCAR LSM 1.0 is a land surface model developed by Gordon Bonan to examine biogeophysical and biogeochemical land-atmosphere interactions, especially the effects of land surfaces on climate and atmospheric chemistry. It can be run coupled to an atmospheric model or uncoupled, in a stand-alone mode, if an atmospheric forcing is provided. The model runs on a spatial grid that can range from one point to global. The model was designed for coupling to atmospheric numerical models. Consequently, there is a compromise between computational efficiency and the complexity with which the necessary atmospheric, ecological, and hydrologic processes are parameterized. The model is not meant to be a detailed micrometeorological model, but rather a simplified treatment of surface fluxes that reproduces at minimal computational cost the essential characteristics of land-atmosphere interactions important for climate simulations. The model is a complete executable code with its own time-stepping driver, initialization (subroutine lsmini), and main calling routine (subroutine lsmdrv). When coupled to an atmospheric model, the atmospheric model is the time-stepping driver. There is one call to subroutine lsmini during initialization to initialize all land points in the domain; there is one call per time step to subroutine lsmdrv to calculate surface fluxes and update the ecological, hydrological, and thermal state for all land points in the domain. The model writes its own restart and history files. These can be turned off if appropriate.Available for downloading from the ORNL DAAC are the LMS Model Documentation and User's Guide (ftp://daac.ornl.gov/data/model_archive/LSM/lsm_1.0/comp/NCAR_LSM_Users_Guide.pdf ), the model source code, input data set, and scripts for running the model. Applications of the model are described in two additional companion files (ftp://daac.ornl.gov/data/model_archive/LSM/lsm_1.0/comp/NCAR_LSM_Bckgrnd_Application_Info.pdf and ftp://daac.ornl.gov/data/model_archive/LSM/lsm_1.0/comp/NCAR_LSM_Analyzed-Data.pdf.\n<br>\n\n#### LINKAGES_1166\nThis model product contains the source codes for version 1 of the individual-based forest ecosystem biogeochemistry model LINKAGES and two subsequent versions as well as example input and output data. LINKAGES predicts long-term structure and dynamics of forest ecosystems as constrained by nitrogen availability, climate, and soil moisture. Model simulations compare favorably to field data from different geographic areas worldwide. LINKAGES, written in FORTRAN and provided in ASCII format, simulates birth, growth, and death of all trees greater than 1.43-cm dbh. Litter fall and decomposition are also simulated. Sunlight is the driving variable. Growing season degree days, soil water availability, and AET are calculated from precipitation, temperature, soil field moisture capacity, and wilting point. Decomposition and soil N availability are calculated from organic matter quantity and carbon chemistry, evapotranspiration, and degree of canopy closure. Light availability to each tree is a function of leaf biomass of taller trees. Degree days and availabilities of light and water constrain species reproduction. These variables plus soil N constrain tree growth and carbon accumulation in biomass. Tree death probability increases with age and slow growth. Leaf, root, and woody litter are returned to the soil at the end of each year to decay the following year. Climatic and forest data for eastern North America and New South Wales are provided as example model inputs. Modelers may use their own site data within any version of LINKAGES. Example model output is also provided.\n<br>\n\n#### LPJ-WHyMe_v1-3-1_1150\nThis model product provides the Fortran 77 source code for the Lund-Potsdam-Jena (LPJ) Wetland Hydrology and Methane Dynamic Global Vegetation Model (LPJ-WHyMe v1.3.1), auxiliary C++ routines, ASCII and NetCDF input data, and NetCDF example output data. LPJ-WHyMe v1.3.1 simulates peatland hydrology, permafrost dynamics, peatland vegetation, and methane emissions. The model processes can be simulated on an area-averaged 0.5 or 1.0 degree grid cell basis at global, regional, or site scales and on a daily, monthly, or annual time step as appropriate. Input driver data are monthly mean air temperature, total precipitation, percentage of full sunshine, annual atmospheric CO2 concentration, and soil texture class. The simulation for each grid cell begins from \"bare ground\", requiring a \"spin up\" (under non-transient climate) of ca. 1,000 years to develop equilibrium vegetation, carbon, and soil structure. Model simulations compare favorably, with some exceptions, to field observations collected from peatland sites (e.g., Degero, Sweden; Lakkasuo, Finland; BOREAS Northern Study Area, Canada; and others) and non-peatland sites (e.g., Point Barrow, Alaska, and Spasskaya, Siberia). LPJ-WHyMe is a further development of LPJ-WHy, which dealt with the introduction of permafrost and peatlands into LPJ. Implementing peatlands in LPJ required the addition of two new plant functional types (PFTs) (flood tolerant C3 graminoids and Sphagnum mosses) to the already existing ten PFTs, the introduction of inundation stress for non-peatland PFTs, a slow-down in decomposition under inundation, and the addition of a root exudates pool. LPJ-WHyMe v1.3.1 adds a methane model subroutine. This model product has one compressed data file (.zip) and seven companion files.\n<br>\n\n#### MAPSS_853\nMAPSS (Mapped Atmosphere-Plant-Soil System) is a landscape to global vegetation distribution model that was developed to simulate the potential biosphere impacts and biosphere-atmosphere feedbacks from climatic change. Model output from MAPSS has been used extensively in the Intergovernmental Panel on Climate Change's (IPCC) regional and global assessments of climate change impacts on vegetation and in several other projects.\n<br>\n\n#### CMAQ-N_Module_1661\nThis model product provides source code, input data files, and example model outputs for a new mechanistic soil nitrogen (N) module in-line with the Community Multiscale Air Quality (CMAQ) model 5.1 to simulate nitric oxide (NO), nitrous acid (HONO), nitrous oxide (N2O), and ammonia (NH3) soil emissions. The modeling domain covers the continental USA plus portions of northern Mexico and southern Canada, extending from 25 degrees north to 52 degrees north.The simulations use a 12-km spatial grid resolution. Input data are from high-quality reference sources for year 2011. Example model output data are provided for one day, April 21, 2011.\n<br>\n\n#### pnet_4_and_5_817\nPnET (Photosynthetic / EvapoTranspiration model) is a nested series of models of carbon, water, and nitrogen dynamics in forest ecosystems. The models can be used to predict transient responses in net primary production (NPP), carbon and water balances, net nitrogen (N) mineralization and nitrification and N leaching losses, resulting from changes in climate, N deposition, tropospheric ozone and land use as well as variation in species composition. The models have been developed and validated in the Northeastern U.S. at both the site and grid level (to 1-km resolution) at the Complex Systems Research Center, University of New Hampshire, by John Aber and colleagues.\n<br>\n\n#### pnet_m_bgc_818\nThis archived model product contains the directions, executables, and procedures for running PnET-BGC to recreate the results of Gbondo-Tugbawa, S.S., C.T. Driscoll , J.D. Aber and G.E. Likens. 2001. The evaluation of an integrated biogeochemical model (PnET-BGC) at a northern hardwood forest ecosystem. Water Resources Research 37:1057-1070. Gbondo-Tugbawa et al,. 2001 Excerptfrom Abstract: An integrated biogeochemical model (PnET-BGC) was formulated to simulate chemical transformations of vegetation, soil, and drainage water in northern forest ecosystems. The model operates on a monthly time step and depicts the major biogeochemical processes, such as forest canopy element transformations, hydrology, soil organic matter dynamics, nitrogen cycling, geochemical weathering, and chemical equilibrium reactions involving solid and solution phases. The model was evaluated against soil and stream data at the Hubbard Brook Experimental Forest, New Hampshire. Model predictions of concentrations and fluxes of major elements generally agreed reasonably well with measured values, as estimated by normalized mean error and normalized mean absolute error. Model output of soil base saturation and stream acid neutralizing capacity were sensitive to parameter values of soil partial pressure of carbon dioxide, soil mass, soil cation exchange capacity, and soil selectivity coefficients of calcium and aluminum. PnET-BGC can be used as a tool to evaluate the response of soil and water chemistry of forest ecosystems to disturbances such as clear-cutting, climatic events, and atmospheric deposition.PnET-BGC, was used to investigate inputs and dynamics of S in a northern hardwood forest at the Hubbard Brook Experimental Forest (HBEF) (Gbondo-Tugbawa et al., 2002). The changes in soil S pools and stream-water were simulated to assess the response 22 SO4 to both atmospheric S deposition and forest clear-cutting disturbances. Watershed studies across the northeastern United States have shown that stream losses of exceed atmospheric sulfur (S) deposition. Understanding the processes responsible for this additional source of S is critical to quantifying ecosystem response to ongoing and potential future controls on SO2 emission.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","hydrology","ice","netcdf","precipitation","soil moisture","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"BDSNP_CMAQ_Model_1351 v1 - This model product provides: (1) the source code for the updated Berkeley-Dalhousie Soil Nitric Oxide (NO) Parameterization module (BDSNP, Version 1.0) as implemented with the Community Multi-scale Air Quality model (CMAQ, Version 5.0.2), (2) module input data from historical and new sources of maps for soil biome type, fertilizer, and arid and non-arid climates, and (3) sample CMAQ simulation outputs for three BDSNP module NO parameterizations (standard, historical, and newer inputs). The simulations use a 12-km spatial grid resolution for CMAQ modeling covering the conterminous United Sta...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/BDSNP_CMAQ_Model/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BIOME_BGC_m2_4_1_2_809 v1 - This archived model product contains the directions, executables, and procedures for running Biome-BGC, Version 4.1.2, to recreate the results of Law BE, Sun OJ, Campbell J, Van Tuyl S, Thornton PE, 2003. Changes in carbon storage and fluxes in a chronosequence of ponderosa pine.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/BIOME_BGC_m2_4_1_2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BIOME_BGC_m_4_1_1_806 v1 - This archived model product contains the directions, executables, and procedures for running Biome-BGC, Version 4.1.1, to recreate the results of: Thornton, P.E., Law, B.E., Gholz, H.L., Clark, K.L., Falge, E., Ellsworth, D.S., Goldstein, A.H., Monson, R.K., Hollinger, D., Falk, M., Chen, J. and Sparks, J.P.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/BIOME_BGC_m_4_1_1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BIOME_BGC_4_1_1_805 v1 - Biome-BGC is a computer program that estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems. The primary model purpose is to study global and regional interactions between climate, disturbance, and biogeochemical cycles.Biome-BGC represents physical and biological processes that control fluxes of energy and mass.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/BIOME_BGC_4_1_1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"century_vemap_m4_820 v1 - The CENTURY model, Version 4, is a general model of plant-soil nutrient cycling that is being used to simulate carbon and nutrient dynamics for different types of ecosystems including grasslands, agricultural lands, forests and savannas. CENTURY is composed of a soil organic matter/ decomposition submodel, a water budget model, a grassland/crop submodel, a forest production submodel, and management and events scheduling functions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/century_vemap_m4/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"EDM_SA_Vegetation_1149 v1 - This model product contains the source code for the Ecosystem Demography Model (ED version 1.0) as well as model input and output data for a portion of South America including the Brazilian Amazon. The model output data are estimates of potential average live biomass (kg C/m2), potential average soil carbon (kg C/m2), and potential above-ground net primary production (NPP) (kg C/m2/yr) at 1.0 degree resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/EDM_SA_Vegetation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"EDM_US_Carbon_1160 v1 - This model product contains the source code for the Ecosystem Demography Model (ED version 1.0) as well as model input and output data files for the conterminous United States. The ED is a mechanistic ecosystem model built around established sub-models of leaf level physiology, organic matter decomposition, hydrology, and functional biodiversity.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/EDM_US_Carbon/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ibis_2_5_808 v1 - The Integrated Biosphere Simulator (or IBIS) is designed to be a comprehensive model of the terrestrial biosphere; the model represents a wide range of processes, including land surface physics, canopy physiology, plant phenology, vegetation dynamics and competition, and carbon and nutrient cycling. The model generates global simulations of the surface water balance (e.g., runoff), the terrestrial carbon balance (e.g., net primary production, net ecosystem exchange, soil carbon, aboveground and belowground litter, and soil CO2 fluxes), and vegetation structure (e.g., biomass, leaf area inde...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/ibis_2_5/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LSM_807 v1 - The NCAR LSM 1.0 is a land surface model developed by Gordon Bonan to examine biogeophysical and biogeochemical land-atmosphere interactions, especially the effects of land surfaces on climate and atmospheric chemistry. It can be run coupled to an atmospheric model or uncoupled, in a stand-alone mode, if an atmospheric forcing is provided.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/LSM/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LINKAGES_1166 v1 - This model product contains the source codes for version 1 of the individual-based forest ecosystem biogeochemistry model LINKAGES and two subsequent versions as well as example input and output data. LINKAGES predicts long-term structure and dynamics of forest ecosystems as constrained by nitrogen availability, climate, and soil moisture.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/LINKAGES/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LPJ-WHyMe_v1-3-1_1150 v1 - This model product provides the Fortran 77 source code for the Lund-Potsdam-Jena (LPJ) Wetland Hydrology and Methane Dynamic Global Vegetation Model (LPJ-WHyMe v1.3.1), auxiliary C++ routines, ASCII and NetCDF input data, and NetCDF example output data. LPJ-WHyMe v1.3.1 simulates peatland hydrology, permafrost dynamics, peatland vegetation, and methane emissions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/LPJ-WHyMe_v1-3-1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MAPSS_853 v1 - MAPSS (Mapped Atmosphere-Plant-Soil System) is a landscape to global vegetation distribution model that was developed to simulate the potential biosphere impacts and biosphere-atmosphere feedbacks from climatic change. Model output from MAPSS has been used extensively in the Intergovernmental Panel on Climate Change's (IPCC) regional and global assessments of climate change impacts on vegetation and in several other projects.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/MAPSS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CMAQ-N_Module_1661 v1 - This model product provides source code, input data files, and example model outputs for a new mechanistic soil nitrogen (N) module in-line with the Community Multiscale Air Quality (CMAQ) model 5.1 to simulate nitric oxide (NO), nitrous acid (HONO), nitrous oxide (N2O), and ammonia (NH3) soil emissions. The modeling domain covers the continental USA plus portions of northern Mexico and southern Canada, extending from 25 degrees north to 52 degrees north.The simulations use a 12-km spatial grid resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/CMAQ-N_Module/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"pnet_4_and_5_817 v1 - PnET (Photosynthetic / EvapoTranspiration model) is a nested series of models of carbon, water, and nitrogen dynamics in forest ecosystems. The models can be used to predict transient responses in net primary production (NPP), carbon and water balances, net nitrogen (N) mineralization and nitrification and N leaching losses, resulting from changes in climate, N deposition, tropospheric ozone and land use as well as variation in species composition.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/pnet_4_and_5/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"pnet_m_bgc_818 v1 - This archived model product contains the directions, executables, and procedures for running PnET-BGC to recreate the results of Gbondo-Tugbawa, S.S., C.T. Driscoll , J.D.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/model_archive/pnet_m_bgc/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-model-archive","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA modis-aqua Project","Description":"This global Level-3 data set (MYD10A1F) provides daily cloud-free snow cover derived from the MODIS/Aqua Snow Cover Daily L3 Global 500m SIN Grid data set (MYD10A1). Grid cells in MYD10A1 which are obscured by cloud cover are filled by retaining clear-sky views of the surface from previous days. A separate parameter is provided which tracks the number of days in each cell since the last clear-sky observation. Each data granule contains a 10° x 10° tile projected to the 500 m sinusoidal grid. The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MYD29\nThis global Level-2 (L2) product provides daily sea ice extent and ice surface temperature. The data are derived from Level-1B radiances acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite. Each data granule contains 5 minutes of swath data observed at a resolution of 1000 m. The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MYD29E1D\nThis global Level-3 (L3) data set provides Northern and Southern Hemisphere maps of sea ice extent and ice surface temperature. The maps are generated by compositing 1 km observations from the 'MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Day’ (https://doi.org/10.5067/MODIS/MYD29P1D.061) product. These data are provided daily in the EASE-Grid polar projection at a resolution of approximately 4 km. The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MYD29P1D\nThis global Level-3 (L3) data set provides daily daytime sea ice extent and ice surface temperature derived from the 'MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km' (https://doi.org/10.5067/MODIS/MYD29.061) product. Each data granule is a tile consisting of 10° x 10° of data gridded to the Lambert Azimuthal Equal Area Scalable Earth Grid (EASE-Grid). The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MYD29P1N\nThis global Level-3 (L3) data set provides daily nighttime ice surface temperature derived from the 'MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km' (https://doi.org/10.5067/MODIS/MYD29.061) product. Each data granule is a tile consisting of 10 x 10 degrees of data gridded to the Lambert Azimuthal Equal Area Scalable Earth Grid (EASE-Grid). The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MYD10_L2\nThis global Level-2 (L2) data set provides daily snow cover detected using Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections. The NDSI is derived from radiance data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite: DOI:10.5067/MODIS/MYD02HKM.061 and DOI:10.5067/MODIS/MYD021KM.061. Each data granule contains 5 minutes of swath data observed at a resolution of 500 m. The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MYD10C2\nThis global Level-3 (L3) data set provides the maximum percentage of snow-covered land and persistent cloud-covered land observed over eight-days, within 0.05° (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'MODIS/Aqua Snow Cover 8-Day L3 Global 500m SIN Grid' data set (DOI:10.5067/MODIS/MYD10A2.061). The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MYD10A2\nThis global Level-3 (L3) data set provides the maximum snow cover extent (SNE) observed over an eight-day period within 10° x 10° MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS/Aqua Snow Cover Daily L3 Global 500m Grid' data set (DOI:10.5067/MODIS/MYD10A1.061). A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell. The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MYD10C1\nThis global Level-3 (L3) data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05° (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'MODIS/Aqua Snow Cover Daily L3 Global 500m Grid' data set (DOI:10.5067/MODIS/MYD10A1.061). The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MYD10A1\nThis global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS/Aqua Snow Cover 5-Min L2 Swath 500m' data set (DOI:10.5067/MODIS/MYD10_L2.061). Each data granule is a 10°x10° tile projected to a 500 m sinusoidal grid. The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MYD10CM\nThis global Level-3 (L3) data set provides monthly average snow cover within 0.05° (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. Monthly averages are computed from daily snow cover observations in the MODIS/Aqua Snow Cover Daily L3 Global 0.05Deg CMG (https://doi.org/10.5067/MODIS/MYD10CM.061) data set. The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/myd10a1f/versions/61","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","ice","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MYD10A1F v61 - This global Level-3 data set (MYD10A1F) provides daily cloud-free snow cover derived from the MODIS/Aqua Snow Cover Daily L3 Global 500m SIN Grid data set (MYD10A1). Grid cells in MYD10A1 which are obscured by cloud cover are filled by retaining clear-sky views of the surface from previous days.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MYD10A1F/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD29 v61 - This global Level-2 (L2) product provides daily sea ice extent and ice surface temperature. The data are derived from Level-1B radiances acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MYD29/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD29E1D v61 - This global Level-3 (L3) data set provides Northern and Southern Hemisphere maps of sea ice extent and ice surface temperature. The maps are generated by compositing 1 km observations from the 'MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Day’ (https://doi.org/10.5067/MODIS/MYD29P1D.061) product.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MYD29E1D/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD29P1D v61 - This global Level-3 (L3) data set provides daily daytime sea ice extent and ice surface temperature derived from the 'MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km' (https://doi.org/10.5067/MODIS/MYD29.061) product. Each data granule is a tile consisting of 10° x 10° of data gridded to the Lambert Azimuthal Equal Area Scalable Earth Grid (EASE-Grid).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MYD29P1D/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD29P1N v61 - This global Level-3 (L3) data set provides daily nighttime ice surface temperature derived from the 'MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km' (https://doi.org/10.5067/MODIS/MYD29.061) product. Each data granule is a tile consisting of 10 x 10 degrees of data gridded to the Lambert Azimuthal Equal Area Scalable Earth Grid (EASE-Grid).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MYD29P1N/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD10_L2 v61 - This global Level-2 (L2) data set provides daily snow cover detected using Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections. The NDSI is derived from radiance data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite: DOI:10.5067/MODIS/MYD02HKM.061 and DOI:10.5067/MODIS/MYD021KM.061.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MYD10_L2/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD10C2 v61 - This global Level-3 (L3) data set provides the maximum percentage of snow-covered land and persistent cloud-covered land observed over eight-days, within 0.05° (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MYD10C2/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD10A2 v61 - This global Level-3 (L3) data set provides the maximum snow cover extent (SNE) observed over an eight-day period within 10° x 10° MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS/Aqua Snow Cover Daily L3 Global 500m Grid' data set (DOI:10.5067/MODIS/MYD10A1.061).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MYD10A2/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD10C1 v61 - This global Level-3 (L3) data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05° (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MYD10C1/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD10A1 v61 - This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS/Aqua Snow Cover 5-Min L2 Swath 500m' data set (DOI:10.5067/MODIS/MYD10_L2.061). Each data granule is a 10°x10° tile projected to a 500 m sinusoidal grid.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MYD10A1/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MYD10CM v61 - This global Level-3 (L3) data set provides monthly average snow cover within 0.05° (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MYD10CM/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-modis-aqua","Sources":["awslabs-open-data-registry"]}
{"Name":"GHRSST Level 2P Global Sea Surface Skin Temperature from the MODIS on the NASA Terra satellite (GDS2)","Description":"NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions.  Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. MODIS L2P SST data have a 1 km spatial resolution at nadir and are stored in 288 five minute granules per day. Full global coverage is obtained every two days, with coverage poleward of 32.3 degree being complete each day. The production of MODIS L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project, and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS are responsible for SST algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of daily MODIS ocean products. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and ancillary variables, and distributes the data as the official Physical Oceanography Data Archive (PO.DAAC) for SST.  The R2019.0 supersedes the previous R2014.0 datasets which can be found at  https://doi.org/10.5067/GHMDT-2PJ02\nRead our doc on how to get AWS Credentials to retrieve this data: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/GHMDT-2PJ19","Contact":"Help Desk: podaac@podaac.jpl.nasa.gov. Home Page: https://podaac.jpl.nasa.gov/","ManagedBy":"NASA","UpdateFrequency":"From 2000-02-24 to Ongoing (Hourly - < Daily)","Tags":["atmosphere","aws-pds","datacenter","earth observation","global","land","marine","metadata","netcdf","oceans","orbit"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GHRSST Level 2P Global Sea Surface Skin Temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Terra satellite (GDS2).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MODIS_T-JPL-L2P-v2019.0/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"A decade of sea surface temperature from MODIS","URL":"http://dx.doi.org/10.1016/j.rse.2015.04.023","AuthorName":"Kilpatrick, K.A., Podestá, G., Walsh, S., Williams, E., Halliwell, V., Szczodrak, M., Brown, O.B., Minnett, P.J., & Evans, R."}],"Tutorials":[{"Title":"Direct S3 Access tutorial","URL":"https://github.com/podaac/tutorials/blob/3d2ac9cb3626b656802638f864631a68beb11823/notebooks/s3/S3-Access.ipynb#L33","AuthorName":"PODAAC"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-modis-t-jpl-l2p-v2019-0","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA modis-terra Project","Description":"Mission Objectives: The Surface Water and Ocean Topography (SWOT) mission aims to provide valuable data and information about the world's oceans and its terrestrial surface water such as lakes, rivers, and wetlands. SWOT is being developed jointly by NASA and Centre National D'Etudes Spatiales (CNES), with contributions from the Canadian Space Agency (CSA) and United Kingdom Space Agency (UKSA).\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_BassStrait_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Bass Strait region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_Boknis_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Baltic Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### NSIDC-0630\nThe Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR, Version 2 data set is a multi-sensor Level 3 Earth Science Data Record (ESDR) with improvements upon Version 1 in cross-sensor calibration and quality checking, modern file formats, better quality control, improved projection grids, and local time-of-day (LTOD) processing. These data are gridded to three EASE-Grid 2.0 projections (North Azimuthal, South Azimuthal, and Cylindrical) and include enhanced-resolution imagery, as well as coarse-resolution, averaged imagery. Inputs include brightness temperature data from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), Special Sensor Microwave Imager/Sounder (SSMIS), Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2).\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_CapeBasin_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Cape Basin region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### NSIDC-0796\nThis data set provides spatial distributions of fast ice and glacial ice in eight fjords spanning the Southeast Greenland coast: Nansen, Kangerlusruaq, Ikertivaq, Skjoldungen, Tingmiarmiut, Napasorsvaq, Anoritup, and Kangerlluluk. Temporal coverage is discontinuous, depending on the availability and quality of images. Fjord data were sourced from USGS EarthExplorer, Copernicus Open Access Hub, and the NSIDC. Landsat-8 and MODIS imagery for ice identification were collected from NASA Worldview and USGS EarthExplorer. Fjord, fast ice, and glacial ice boundaries were manually delineated using ArcGIS. Glacial ice was further categorized as dense glacial melange (Type 3), substantial glacial ice with large icebergs (Type 2), low-density glacial ice with large icebergs (Type 1), consistent small ice surface without large icebergs (Type 0), or glacier surface (Type 99).\n<br>\n\n#### HMA2_GGP\nThis data set comprises results from a hybrid glacier evolution model that uses the mass balance module of the Python Glacier Evolution Model (PyGEM) and the glacier dynamics module of the Open Global Glacier Model (OGGM). Output parameters include projections of glacier mass change, fixed runoff, and various mass balance components at regionally aggregated and glacier scales.\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_GotlandBasin_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Gotland Basin region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### AERDB_D3_AHI_H08\nThe H08 Deep Blue Level 3 daily aerosol data, 1x1 degree grid product, short-name AERDB_D3_AHI_H08, derived from the L2 (AERDB_L2_AHI_H08) input data, each D3 AHI/Himawari-8 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element. Another statistic like standard deviation is also provided in some cases. The final retrievals used in the aggregation process are Quality Assurance (QA)-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Himawari Imager (AHI) Himawari-8 Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_D3_AHI_H08 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_AHI_H08 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue\n<br>\n\n#### AERDB_M3_AHI_H08\nThe H08 Deep Blue Level 3 Monthly aerosol data, 1x1 degree grid product, short-name AERDB_M3_AHI_H08, derived by aggregating the L3 daily (AERDB_D3_AHI_H08) input data, each M3 AHI/ Himawari-8 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the shortname as M3) product’s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product. As a mechanism to filter out poorly sampled grid elements, at least three valid days of data in the month are required to populate the monthly grid element. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Himawari Imager (AHI) Himawari-8 Deep Blue Aerosol Deep Blue Monthly Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_M3_AHI_H08 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_AHI_H08 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue\n<br>\n\n#### BAROCLINIC_HRET14\nThis dataset of Harmonic Constants for Baroclinic Tide Prediction was produced by Edward Zaron (Oregon State University) and Shane Elipot (University of Miami). It provides sea surface height and ocean surface currents associated with the predictable astronomical tide at the M2, S2, N2, K1, and O1 frequencies. The tidal harmonic constants, in-phase and quadrature with respect to the equilibrium potential, are provided on a latitude/longitude at 1/20-deg resolution. Using the software available at the Github repository, the dataset can be used to predict baroclinic tidal sea surface height and surface ocean currents at arbitrary time and location throughout the world oceans. <br> The harmonic constants were estimated within the time period from 1993 to 2021 and incorporate roughly 30 years of multi-satellite altimeter data and 20 years of data from drifting buoys. The observations were combined with a kinematic wave model and the internal wave polarization relations to prepare uniformly gridded estimates from the sparse and irregular data sampling. These files may be used by the altimeter community to compute corrections intended to remove baroclinic tidal variability from sea level anomaly observations. Researchers with an interest in ocean surface currents may also use these data to predict baroclinic tidal surface currents. Such information may be used to plan observational campaigns or optimize the design of future surface current mapping satellite missions. <br> This dataset is funded by NASA SWOT Science Team award #80NSSC21K0346 and NSF Physical Oceanography Program award #1850961. The software to make baroclinic tidal calculations using this dataset is regularly updated at the provided Github link, and an archived snapshot of the software is also provided in the documentation. The harmonic constants and prediction software may be updated every few years as additional data for mapping the tides becomes available.\n<br>\n\n#### HMA2_GFTP\nThis data set consists of 1 km resolution monthly land surface temperatures (MLSTs); mean annual ground temperatures (MAGTs); and estimates of permafrost extent (PE) in the High Mountain Asia region from 1 Jan 2003 – 31 Dec 2016. The data were generated by gap-filling daily MODIS Terra/Aqua Land surface temperatures (LSTs) with downscaled Atmospheric Infra-Red Sounder (AIRS) skin surface temperatures.\n<br>\n\n#### HMA2_MATCHA\nThis data set contains a 12 km resolution, simulated reanalysis of aerosol transport, chemistry, and deposition over the High Mountain Asia (HMA) region for 1 January 2003 through 31 August 2019. Two-dimensional surface data are provided at one hour intervals. Three-dimensional atmospheric data are provided at three-hour intervals for 35 sigma levels extending from the surface to 50 hPa. Also known as the Model for Atmospheric Transport and Chemistry in Asia (MATCHA), the data comprise a wide range of variables intended to help assess the impacts of aerosols on the cryosphere in the HMA region, including: concentrations of black/brown carbon and other light absorbing particles (LAPs), broken out by source region; longwave/shortwave heating rates due to LAPs; wet/dry deposition of LAPs; precipitation and hydrological data; and meteorological state variables. The simulation was generated using a fully coupled, regional chemistry-climate model (WRF-Chem-CLM-SNICAR), constrained by aerosol optical depth (AOD) and carbon monoxide (CO) satellite observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Measurements Of Pollution In The Troposphere (MOPITT) instruments, respectively.\n<br>\n\n#### HMA2_DCG_SMB\nThis High Mountain Asia data set contains 2 m resolution digital elevation models (DEMs), surface velocities, surface mass balance (SMB) rates, and SMB uncertainties for six debris-covered glaciers in Nepal. SMB rate is estimated by applying a Lagrangian specification to DEMs derived from very-high-resolution optical stereo imagery acquired by Maxar Technologies satellites WorldView-1, WorldView-2, WorldView-3, and GeoEye-1. This data set was granted permission for public release on 1 March 2024 under the National Reconnaissance Office (NRO) Electro-Optical Commercial Layer (EOCL) program.\n<br>\n\n#### HMA2_FGP\nThis data set contains Flood Geomorphic Potential (FGP) at 30 m resolution for the High Mountain Asia region and 8 m resolution over Nepal. FGP is a digital elevation model-derived index that provides high-resolution flood mapping based on bankfull elevations, defined in terms of river widths, and elevation differences between points under examination and the closest bankfull elevations in the river network.\n<br>\n\n#### HMA2_DDSMET\nThis High Mountain Asia (HMA) data set contains simulated meteorological data for the Indus Basin from 2000 through 2015, at three horizontal resolutions – 36 km, 12 km, and 4 km – and 9 pressure levels spanning 1000 hPa – 200 hPa. The data were produced by using the Advanced Research Weather Research & Forecasting (ARW-WRF) model to dynamically downscale Climate Forecast System Reanalysis (CFSR) data into three nested domains with increasing horizontal resolution.\n<br>\n\n#### HMA2_HFD\nThis High Mountain Asia (HMA) data set contains hydrological flow directions at 5 arc-minute resolution for the headwaters of the Amu Darya and Indus River basins. The domain spans parts of Afghanistan, Tajikistan, Kyrgyzstan, and Pakistan. Flow directions are reported in deterministic eight (D8) format. The data were developed to support the University of New Hampshire Water Balance Model and the \"High Mountain Asia CMIP6 Monthly and Yearly Water Balance Projections, 2016-2099 for Parts of Afghanistan, Tajikistan, Kyrgyzstan, and Pakistan, Version 1\" data set.\n<br>\n\n#### HMA_DEM8m_MOS\nThis data set contains 8-meter Digital Elevation Model (DEM) mosaics of high mountain Asia glacier and snow regions generated from very-high-resolution (VHR) commercial satellite imagery.\n<br>\n\n#### HMA_DEM8m_AT\nThis data set contains 8-meter Digital Elevation Models (DEMs) of high mountain Asia glacier and snow regions generated from very-high-resolution commercial stereoscopic satellite imagery.\n<br>\n\n#### HMA_DEM8m_CT\nThis data set contains 8-meter Digital Elevation Model (DEM) mosaics of high mountain Asia glacier and snow regions generated from from very-high-resolution commercial stereo satellite imagery.\n<br>\n\n#### HMA_GlacierAvg_dH\nThis data set contains average thickness changes for approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from HEXAGON KH-9 and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.\n<br>\n\n#### HMA2_WBP\nThis High Mountain Asia (HMA) data set comprises a suite of monthly and yearly water balance model (WBM) projections for the years 2016 – 2099, covering parts of Afghanistan, Tajikistan, Kyrgyzstan, and Pakistan (primarily the headwaters of the Amu Darya and Indus River basins). Projections are available for 12 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5). The data were generated using the University of New Hampshire WBM. A historical run is also available for the years 1980 through 2018, using as input ERA5 reanalysis temperature data and ensemble precipitation estimates.\n<br>\n\n#### HMA_RCMO_6H\nThis data product contains either 6-hourly accumulated or 6-hourly snapshots of modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.\n<br>\n\n#### HMA_RCMO_D\nThis data product contains either daily averaged or daily accumulated modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.\n<br>\n\n#### HMA_RCMO_1H\nThis data product contains either hourly accumulated or hourly snapshots of modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.\n<br>\n\n#### HMA_RCMO_M\nThis data product contains either monthly averaged or monthly accumulated modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.\n<br>\n\n#### HMA2_NLSMR\nThis data set consists of a water budget reanalysis for the High Mountain Asia (HMA) region spanning the years 2003 through 2020. Estimates are provided for more than 30 parameters, including storages; fluxes; snow depth, extent, and snow water equivalent; temperature (land surface, soil, snow, and ice); surface albedo; soil moisture; evapotranspiration; and streamflow. The data were generated using the Noah Multi-Parameterization (Noah-MP) land surface model (Version 4.0.1), driven by precipitation estimates and hydrological inputs developed specifically for HMA.\n<br>\n\n#### HMA2_DSPAT\nThis data set consists of daily, 5 km resolution precipitation and mean, near-surface air temperature projections from 2015 through 2100 for the High Mountain Asia (HMA) region. The data were generated by statistically downscaling 0.5° resolution model data from the Geophysical Fluid Dynamic Laboratory (GFDL) Seamless System for Prediction and EArth System Research (SPEAR) 30-member ensemble climate model. Projections are provided for two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5 8.5. A historical model run from 1990 through 2014 is also available.\n<br>\n\n#### HMA2_LHI\nThis data set projects the daily hazard of rainfall-triggered landslides in the High Mountain Asia region from 2015 through 2100, at 5 km resolution. Projections are provided for two Shared Socioeconomic Pathways (SSPs)—SSP2-4.5 and SSP5 8.5—based on downscaled temperature and precipitation projections from a 30-member ensemble climate model. Landslide hazard is represented by a landslide hazard indicator (LHI), computed with a machine learning (ML) model trained on historical temperatures and precipitation from 1990 through 2019 and a catalog of documented landslides. Two historical LHI data sets are also available: the ML model LHIs generated for 1990 through 2019; and retrodicted LHIs computed by inputting downscaled temperatures and precipitation for 1990 through 2014 to the ensemble climate model.\n<br>\n\n#### HMA2_RSRD\nThis data set reports daily, reach-scale river discharge for 114,147 river reaches in the High Mountain Asia region from 1 January 2004 through 31 December 2019. The data were generated by combining ensemble hydrologic modeling with data assimilation of remotely sensed discharge from Landsat and PlanetScope satellite imagery.\n<br>\n\n#### HMA_EAPrecip_FLOR\nThis data set includes three climate simulations of daily precipitation over the Himalayan region for summer and winter, covering different time periods: two 30-member ensemble simulations spanning 40-year time periods in the 20th century (1961-2000) and 21st century (2061-2100), and a present-day climate simulation from 1982 to 2017 nudged to reanalysis winds. These precipitation estimates were simulated by the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low Ocean Resolution version of the CM2.5 model (GFDL FLOR).\n<br>\n\n#### HMA_Precip_FLOR\nThis data set features seven standard annual mean extreme precipitation indices: Rx1day, Rx5day, CWD, R10mm, R20mm, R95pTOT, and R99pTOT. They were selected on the basis of potential relevance to landslide activity from the 27 indices established by the joint CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI). The seven indices were simulated by the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low Ocean Resolution version of CM2.5 (GFDL FLOR).\n<br>\n\n#### HMA_Glacier_dH_Mosaics\nThis data set contains thickness change mosaics that include approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from HEXAGON KH-9 and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.\n<br>\n\n#### HMA_Glacier_dH\nThis data set contains gridded thickness changes for approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from KH-9 HEXAGON and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.\n<br>\n\n#### HMA_AWS\nThis data set contains meteorological data, such as air temperature, pressure, rainfall intensity, relative humidity, and wind direction/speed measured by the International Centre for Integrated Mountain Development (ICIMOD).\n<br>\n\n#### HMA_SDI\nThis data set contains thermal-dome-corrected downward shortwave irradiance at the bottom of atmosphere, measured by the Shared Mobile Atmospheric Research and Teaching Radar (SMART-R) and collected by the International Centre for Integrated Mountain Development (ICIMOD).\n<br>\n\n#### HMA_SBRF\nThis data set contains snow bidirectional reflectance factor (BRF) between 350 and 2500 nm collected on the Yala Glacier on 23 April and 24 April 2018 by the International Centre for Integrated Mountain Development (ICIMOD).\n<br>\n\n#### HMA_Snowfield\nThis data set contains measurements of several different snow properties, including reflectance at 1310 nm, specific surface area, and optical mean radius, collected on the Yala Glacier, Nepal. These data were collected on 23 April and 24 April 2018 by the International Centre for Integrated Mountain Development (ICIMOD) using the IceCube instrument.\n<br>\n\n#### HMA_STParams\nThis data set provides daily-averaged NASA Land Information System (LIS) output at a spatial resolution of 1 km. LIS was driven by uncorrected Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) data, using the Noah Multiparameterization Land Surface Model (Noah-MP). Modeled parameters include snow water equivalent (SWE), snow depth, surface temperature, and soil temperature profile.\n<br>\n\n#### HMA_LIS_LandSurfaceHydro\nThe data provided in this data set are simulated using the Noah-Multiparameterization Land Surface Model (Noah-MP LSM) Version 3.6 within the NASA Land Information System (LIS) Version 7.2. The data files contain estimates of water, energy fluxes, and land surface states for the High Mountain Asia (HMA) region.\n<br>\n\n#### HMA_MAR3_5\nThis data set provides modeled surface and atmospheric fields from the Modèle Atmosphérique Régionale (MAR) regional climate model (version 3.5) over the Himalayan region at 10 km spatial resolution. Modeled parameters include surface mass and energy balance components, near-surface atmospheric properties, and snowpack properties.\n<br>\n\n#### HMA_OptDepth\nThis data set contains monthly mean MODIS Level 3 data from aboard the Aqua and Terra satellites. The parameters provided in this data set are aerosol optical depth (AOD) and Angstrom exponent (AE) at a spatial resolution of 1º by 1º.\n<br>\n\n#### HMA_GL_RCP\nThis data set comprises results from the Python Glacier Evolution Model (PyGEM) that include projections of glacier mass change, glacier runoff, and the various components associated with changes in mass and runoff.\n<br>\n\n#### HMA_GL_RCPR\nThis data set comprises a rasterized (gridded) version of the of glacier point data from the Python Glacier Evolution Model (PyGEM) that include projections of glacier mass change, glacier runoff, and the various components associated with changes in mass and runoff.\n<br>\n\n#### AERDB_L2_AHI_H08\nThe Himawari-08 AHI Deep Blue Aerosol L2 Full Disk product, short-name AERDB_L2_AHI_H08 is produced every 30 minutes and contains full-disk observation data. The L2 data products comprise 10 x 10 native GEO pixels. Each spectral band with 0.5 km or 2 km resolution is downscaled or upscaled to a nominal ~1 km horizontal pixel size in the production process. To distinguish them from native instrument pixels, these 10 x 10 aggregated pixels are also called retrieval pixels. Therefore, the L2 products’ image dimensions are roughly 10 km x 10 km at the sub-satellite point and are larger away from that point because of the combined effects of the sensor’s scanning geometry and Earth’s curvature. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-2 (L2) Advanced Himawari Imager (AHI) Himawari-8 Deep Blue Aerosol Full-Disk dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_L2_AHI_H08 product, in netCDF4 format, contains 51 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2_AHI_H08 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_LabradorSea_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Labrador Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_MarmaraSea_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Marmara Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### NSIDC-0756\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains a bed topography and bathymetry map of Antarctica. Bed topography is deduced by subtracting ice thickness from the surface elevation; using Ice Flow Perturbation Analysis (IFPA); and by other methods. Surface elevations are obtained from the Reference Elevation Model of Antarctica (REMA) and high-resolution satellite maps.\n<br>\n\n#### NSIDC-0724\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of monthly image mosaics of the Greenland coastline and ice sheet periphery constructed from composited MODIS imagery. See <a href=\"http://nsidc.org/data/measures/gimp\">Greenland Ice Mapping Project (GIMP)</a> for related data.\n<br>\n\n#### NSIDC-0792\nThis ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, includes quarterly estimates of Antarctic ice shelf surface elevation, thickness, basal melt rate, surface mass balance, firn air content, and associated errors, from 17 March 1992 through 16 December 2017 at 1920 m resolution. The data were generated from four European Space Agency (ESA) satellite radar altimetry missions—ERS-1, ERS-2, Envisat, and CryoSat-2—using a novel data fusion approach and the Glacier Energy and Mass Balance model (GEMB).\n<br>\n\n#### NSIDC-0547\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of two digital Greenland image maps each for the 2005, 2010, and 2015 measurement periods: the MOG Surface Morphology Image Map and the MOG Grain Size Image Map. The image maps are constructed from MODIS imagery acquired during 2005, 2010, and 2015 and provide nearly cloud-free views of all land areas and islands larger than a few hundred meters, including the ice caps on Baffin Island, Devon Island, Axel Heiberg Island, and Ellesmere Island.\n<br>\n\n#### NSIDC-0530\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, offers users 25 km Northern Hemisphere snow cover extent represented by four different variables. Three of the snow cover variables are derived from the Interactive Multisensor Snow and Ice Mapping System, MODIS Cloud Gap Filled Snow Cover, and passive microwave brightness temperatures, respectively. The fourth variable merges the three source products into a single representation of snow cover.\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_WesternMed_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the western Mediterranean Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### MOD10A1F\nThis global Level-3 data set (MOD10A1F) provides daily cloud-free snow cover derived from the MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid data set (MOD10A1). Grid cells in MOD10A1 which are obscured by cloud cover are filled by retaining clear-sky views of the surface from previous days. A separate parameter is provided which tracks the number of days in each cell since the last clear-sky observation. Each data granule contains a 10° x 10° tile projected to the 500 m sinusoidal grid. The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### NSIDC-0791\nThis data set presents new global snow cover classification regimes derived from the MODIS Terra cloud gap-filled NDSI data (<a href=\"https://nsidc.org/data/mod10a1f/versions/61\">MOD10A1F</a>), elevation, and temperature climatology inputs. The six data granules are available as NetCDF (.nc) files, with each containing a unique snow cover classification spanning 2001 to 2023. The six classifications included in this data set are: (1) snow class climatology (SSC), (2) core snow season length (CSS), (3) snow cover duration (SCD), (4) full snow season length (FSS), (5) snow persistence (SP), and (6) snow season persistence (SSP).\n<br>\n\n#### MOD29\nThis global Level-2 (L2) product provides daily sea ice extent and ice surface temperature. The data are derived from Level-1B radiances acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite. Each data granule contains 5 minutes of swath data observed at a resolution of 1000 m. The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MOD29E1D\nThis global Level-3 (L3) data set provides Northern and Southern Hemisphere maps of sea ice extent and ice surface temperature. The maps are generated by compositing 1 km observations from the 'MODIS/Terra Sea Ice Extent Daily L3 Global 1km EASE-Grid Day’ (https://doi.org/10.5067/MODIS/MOD29P1D.061) product. These data are provided daily in the EASE-Grid polar projection at a resolution of approximately 4 km. The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MOD29P1D\nThis global Level-3 (L3) data set provides daily daytime sea ice extent and ice surface temperature derived from the 'MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km' (https://doi.org/10.5067/MODIS/MOD29.061) product. Each data granule is a tile consisting of 10 x 10 degrees of data gridded to the Lambert Azimuthal Equal Area Scalable Earth Grid (EASE-Grid). The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MOD29P1N\nThis global Level-3 (L3) data set provides daily nighttime ice surface temperature derived from the 'MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km' (https://doi.org/10.5067/MODIS/MOD29.061) product. Each data granule is a tile consisting of 10 x 10 degrees of data gridded to the Lambert Azimuthal Equal Area Scalable Earth Grid (EASE-Grid). The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MOD10_L2\nThis global Level-2 (L2) data set provides daily snow cover detected using the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections. The NDSI is derived from radiance data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite: DOI:10.5067/MODIS/MOD02HKM.061 and DOI:10.5067/MODIS/MOD021KM.061. Each data granule contains 5 minutes of swath data observed at a resolution of 500 m. The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MOD10C2\nThis global level-3 (L3) data set provides the maximum percentage of snow-covered land and persistent cloud-covered land observed over eight-days, within 0.05° (approx. 5 km) MODIS climate modeling grid (CMG) cells. Percentages are computed from snow cover observations in the 'MODIS/Terra Snow Cover 8-Day L3 Global 500m SIN Grid' data set (DOI:10.5067/MODIS/MOD10A2.061). The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MOD10A2\nThis global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10° x 10° MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS/Terra Snow Cover Daily L3 Global 500m Grid' data set (DOI:10.5067/MODIS/MOD10A1.061). A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell. The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MOD10C1\nThis global Level-3 (L3) data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05° (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'MODIS/Terra Snow Cover Daily L3 Global 500m Grid' data set (DOI:10.5067/MODIS/MOD10A1.061). The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MOD10A1\nThis global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS/Terra Snow Cover 5-Min L2 Swath 500m' data set (DOI:10.5067/MODIS/MOD10_L2.061). Each data granule is a 10°x10° tile projected to a 500 m sinusoidal grid. The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MOD10CM\nThis global Level-3 (L3) data set provides monthly mean snow cover extent within 0.05° (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. This data set is derived from snow cover observations in the 'MODIS/Terra Snow Cover Daily L3 Global 0.05Deg CMG’ data set (DOI:10.5067/MODIS/MOD10C1.061). The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.\n<br>\n\n#### MODGRNLD\nThis multilayer data set includes standard MODIS Collection 6.1 ice surface temperature (IST) and derived melt map, as well as MODIS Collection 6.0 albedo and water vapor for Greenland, at a spatial resolution of 0.78 km. These fields enable the relationship between IST and surface melt to be evaluated by researchers studying surface changes on the Greenland ice sheet. Water vapor is included to assist with evaluating the accuracy of the IST data and the model output. Also included is an ice mask and a basins mask for delineating drainage basins in Greenland. Surface temperature is a fundamental input for dynamical ice sheet models because it is a component of the ice sheet radiation budget and mass balance. Surface temperature also influences ice sheet processes, such as surface melt. This data set may be used as a resource for model-validation studies such as comparing MERRA-2 surface temperature with MODIS IST, and for comparing MODIS IST, albedo and water vapor with products from sensors on other satellites such as VIIRS and AIRS The temporal coverage for this data set spans 1 March 2000 through 31 December 2019, with the exception of the IST data, which has been extended through 31 Aug 2021.\n<br>\n\n#### MSLSP30NA\nMSLSP V1 data was decommissioned on December 14, 2021. Users are encouraged to use the improved [MSLSP V1.1](https://doi.org/10.5067/Community/MuSLI/MSLSP30NA.011) data product. NASA’s Multi-Source Land Imaging (MuSLI) Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel 2A and 2B Multispectral Instrument (MSI) provide the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy. MSLSP30NA is aligned with the Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system)) at 30 m spatial resolution. These datasets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, land cover, land use, and land cover change mapping. Provided in the MSLSP product are variables for percent greenness, onset greenness dates, Enhanced Vegetative Index (EVI2) amplitude, maximum EVI2, and data quality information for up to two phenological cycles per year. For areas where the data values are missing due to cloud cover or other reasons, the data gaps are filled with good quality values from the year directly preceding or following the product year. A low-resolution browse image representing maximum EVI is also available for each MSLSP30NA granule. Known Issues: Data are sparse in 2016 and early 2017, as Sentinel-2B was not yet launched, and Sentinel-2A was not fully operational, leading to poorer quality retrievals of phenology in 2016 and 2017. However, poor quality pixels can be masked with Quality Assurance (QA) flags. * Disturbance has not been explicitly accounted for or mapped, which can lead to premature detections of senescence and dormancy when sharp spectral changes occur. * Pixels with more than two growth cycles per year (e.g., alfalfa fields) may not be accurately characterized, especially if they occur in rapid succession.\n<br>\n\n#### MSLSP30NA\nThe Multi-Source Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1.1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel-2A and 2B Multispectral Instrument (MSI) provides the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy at 30m spatial resolution. These data sets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and land cover, land use, and land cover change mapping. Provided in the MSLSP product are layers for percent greenness, onset greenness dates, Enhanced Vegetative Index (EVI2) amplitude, and maximum EVI2, and data quality information for up to two phenological cycles per year. For areas where the data values are missing due to cloud cover or other reasons, the data gaps are filled with good quality values from the year directly preceding or following the product year. A low resolution browse image representing maximum EVI is also available for each MSLSP30NA granule. Known Issues: Data are sparse in 2016 and early 2017, as Sentinel-2B was not yet launched, and Sentinel-2A was not fully operational, leading to poorer quality retrievals of phenology in 2016 and 2017. However, poor quality pixels can be masked with Quality Assurance (QA) flags. * Disturbance has not been explicitly accounted for or mapped, which can lead to premature detections of senescence and dormancy when sharp spectral changes occur. * Pixels with more than two growth cycles per year (e.g., alfalfa fields) may not be accurately characterized, especially if they occur in rapid succession.\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_NewCaledonia_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the New Caledonia region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_ROAM_MIZ_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Northeast Weddell Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWAustralia_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Northwest Australian Shelf region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWPacific_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Northwest Pacific Ocean region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### EN1_MDSI_MER_RR__1P\nThe Medium Resolution Imaging Spectrometer (MERIS) is one of 10 sensors deployed in March of 2002 on board the polar-orbiting Envisat-1 environmental research satellite by the European Space Agency (ESA). The MERIS instrument is a moderate-resolution wide field-of-view push-broom imaging spectroradiometer capable of sensing in the 390 nm to 1040 nm spectral range. Being a programmable instrument, it had the unique capability of selectively adjusting the width and location of its 15 bands through ground command. The instrument has a 68.5-degree field of view and a swath width of 1150 meters, providing a global coverage every 3 days at 300 m resolution. Communication with the Envisat-1 satellite was lost suddenly on the 8th of April, 2012, just weeks after celebrating its 10th year in orbit. All attempts to re-establish contact were unsuccessful, and the end of the mission was declared on May 9th, 2012. The 4th reprocessing cycle, in 2020, has produced both the full-resolution and reduced-resolution L1 and L2 MERIS products. EN1_MDSI_MER_RR__1P is the short-name for the MERIS Level-1 reduced resolution, geolocated and calibrated top-of-atmosphere (TOA) radiance product. This product contains the TOA upwelling spectral radiance measurements at reduced resolution. The in-band reference irradiances for the 15 MERIS bands are computed by averaging the in-band solar irradiance for each pixel. Each pixel’s in-band solar irradiance is computed by integrating the reference solar spectrum with the band-pass of each pixel. The Level-1 product contains 22 data files: 15 files contain radiances for each band (one band per file) along with associated error estimates, and 7 annotation data files. It also includes a Manifest file that provides metadata information describing the product.\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_RockallTrough_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Rockall Trough region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### WAF_DEALIASED_SASS_L2\nContains Seasat-A Scatterometer (SASS) wind vector measurements for the entire Seasat mission, from July 1978 until October 1978. The data are global and presented chronologically in by swath. Each record contains data binned in 100 km cells. No wind vectors are computed for the cells along the left and right edges of the swath. Wind direction ambiguities are resolved using a global weather prediction model. This complete dataset is the result of the reprocessing efforts on behalf of Frank Wentz, Robert Atlas, and Michael Freilich.\n<br>\n\n#### CHELTON_SEASAT_SASS_L3\nContains monthly averaged ocean surface wind stress derived from Seasat-A Scatterometer (SASS) wind retrievals, from July 1978 until October 1978, gridded on a 2.5-degree by 2.5 degree global grid. The vector average wind stress is stored in units of dynes per centimeter squared (dyn/cm^2). Data is provided in formatted ASCII text. The primary data set used to construct these wind stress fields consists of 96 days of SASS vector winds supplied by Robert Atlas at GSFC. The directional ambiguities in the raw SASS data had been objectively removed using the GSFC Laboratory for Atmospheric Sciences atmospheric general circulation model.\n<br>\n\n#### SPL4SMAU\nSMAP Level-4 (L4) surface and root zone soil moisture (L4_SM) data are provided in three products. The SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/T5RUATAQREF8) product is a series of 3-hourly time average geophysical land surface fields that are output by the L4_SM algorithm. It is likely of primary interest to most users. The SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/02LGW4DGJYRX) product provides diagnostics from the land surface analysis updates. It consists of a series of 3-hourly instantaneous (or snapshot) files that contain the assimilated SMAP observations, the corresponding land model predictions and analysis estimates, and additional data assimilation diagnostics. Lastly, the SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/PXQIBL2ALDZD) product provides static (time-invariant) land surface model constants that will be needed by some users for further interpretation of the geophysical land surface fields. This product consists of only one granule (file) per L4_SM data product version (as defined by a distinct Science Version ID). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.\n<br>\n\n#### SPL4CMDL\nThe Level-4 (L4) carbon product (SPL4CMDL) provides global gridded daily estimates of net ecosystem carbon (CO2) exchange derived using a satellite data based terrestrial carbon flux model informed by the following: Soil Moisture Active Passive (SMAP) L-band microwave observations, land cover and vegetation inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Goddard Earth Observing System Model, Version 5 (GEOS-5) land model assimilation system. Parameters are computed using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.\n<br>\n\n#### NSIDC-0779\nThis data product contains global daily 1 km resolution surface soil moisture derived from the SMAP L-band radiometer. Specifically, MODIS land surface temperature data is used with the SMAP Enhanced L2radiometer Half-Orbit 9 km EASE-Grid Soil Moisture product in a downscaling algorithm to estimate soil moisture. The data set is validated by in situ soil moisture measurements from dense soil moisture networks representing different global land cover types.\n<br>\n\n#### NSIDC-0797\nThis data set is derived by downscaling Soil Moisture Active Passive (SMAP) enhanced Level-3 9 km brightness temperatures (TB) using Cyclone Global Navigation Satellite System (CYGNSS) reflectivity data and employing a slightly modified version of the SMAP baseline active-passive TB algorithm. The SMAP Single Channel Algorithm – Vertical polarization (SCA-V) is then used to derive soil moisture from SMAP/CYGNSS Tb data. The main parameter of this data set is surface soil moisture presented on the Global EASE-Grid 2.0 projection, with each data point representing the top 5 cm of the soil column. For SMAP-derived data, see <a href=\"https://doi.org/10.5067/4DQ54OUIJ9DL\">SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture, Version 5</a>; for CYGNSS-derived data, see <a href=\"https://doi.org/10.5067/CYGNS-L1X21\">CYGNSS Level 1, Version 2.1</a>.\n<br>\n\n#### SV19MA_DEM\nThese digital elevation model (DEM) data consist of ground surface elevations derived from source lidar measurements collected in April and August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods occurred to characterize differences during \"leaf-off” and \"leaf-on\" conditions.\n<br>\n\n#### SV19MA_DSM\nThese digital surface model (DSM) data consist of surface elevations derived from source lidar measurements collected in August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. The location was selected due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The August collection period was selected to characterize ‘leaf-on’ conditions. DSM data represents the highest elevation of features on the Earth’s surface, which may include bare-earth, vegetation, and human-made objects.\n<br>\n\n#### SV19MB_DEM\nThese digital elevation model (DEM) data consist of ground surface elevations derived from source lidar measurements collected in April and August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods occurred to characterize differences during \"leaf-off\" and \"leaf-on\" conditions.\n<br>\n\n#### SV19MB_DSM\nThese digital surface model (DSM) data consist of surface elevations derived from source lidar measurements collected in August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. The location was selected due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The August collection period was selected to characterize ‘leaf-on’ conditions. DSM data represents the highest elevation of features on the Earth’s surface, which may include bare-earth, vegetation, and human-made objects.\n<br>\n\n#### SNEX_Met_Raw\nThis dataset contains raw meteorological data collected as part of the ongoing the NASA SnowEx mission, from five meteorological stations installed between 2016-2017 in Grand Mesa, Colorado, to provide supporting data for SnowEx field campaigns and forcing data for modeling\n<br>\n\n#### SNEX_Met\nThis dataset contains meteorological data collected as part of the ongoing the NASA SnowEx mission, from five meteorological stations installed between 2016-2017 in Grand Mesa, Colorado, to provide supporting data for SnowEx field campaigns and forcing data for modeling. Each station collects a suite of meteorological data at a fixed geographic point, from varying heights above and below the surface elevation. Measured data include: air temperature, relative humidity, long and shortwave solar radiation, barometric pressure, soil moisture and temperature, and derived snow depth. The temporal data coverage varies between each station, but spans October 2016 to August 2022. The dataset(s) contain air temperature and relative humidity (10ft and 20ft levels), 4-component radiation (shortwave, longwave), barometric pressure, soil-moisture and temperature (three depths), and a snow-depth product. Data coverage varies for each met station, but spans the time period of October 2016 – August, 2022. The data frequency is hourly and times are in UTC. The data is monotonic (no duplicate or mis-orderd timestamps) and steps have been taken to remove erroneous data. Periods of missing data are filled with NaN values. The scripts used to process raw data into the current format are available on the github page (https://github.com/wrudisill/GrandMesaMetData/blob/main/process_data_initial.py).\n<br>\n\n#### SNEX17_UWScat\nThis data set consists of ground-based scatterometer data acquired during the SnowEx 2017 campaign at Grand Mesa, Colorado, USA, a snow-covered, forested study site about 40 miles east of the city of Grand Junction, CO. The data comprise operational parameters used during data acquisition, Mueller matrices for each acquisition, and the nearfield-corrected normalized radar cross section (NRCS) in VV, VH, HV, and HH polarizations. Range profile data are also provided for each scan which report the raw power returned as a function of range from the antenna.\n<br>\n\n#### SNEX17_SSD\nThis data set contains 15-min snow depth observations for two study sites on Grand Mesa, CO, USA, acquired as part of NASA's 2017 SnowEx campaign. The data were recorded using two arrays of Judd Communications Ultrasonic Depth Sensors, configured as a TLS K footprint on the west side of the mesa and a TLS N footprint in the east. The sensors were positioned to represent three primary vegetation conditions: open-canopy; canopy-edge; and closed-canopy. A total of 10 and 7 sensors recorded usable data at the west and east sites, respectively, from the beginning of the snow season in November 2016 through the end in June 2017. These data can be used for a variety of purposes, including: model forcing, calibration, and validation; evaluation of airborne and satellite remote sensing data; to analyze how vegetation affects snow accumulation and ablation.\n<br>\n\n#### SMAP_L1_L3_ANC_GEOS\nThis ancillary SMAP product contains three dynamic GMAO GEOS-5 modeled data sets. Each data set contains surface and atmospheric parameters pertinent to SMAP provided in 1) hourly, 2) 3-hour, and 3) averaged over 3-hour intervals.\n<br>\n\n#### SMAP_L1_L3_ANC_NOAA\nThis ancillary SMAP product contains six dynamic data sets originally produced by NOAA or NOAA-affiliated organizations. 1) NCEP Geophysical Forecast System modeled data provided in 6-hour time steps 2) Daily Reynolds Sea Surface Temperature data 3) Snow Cover data from NOAA Interactive Multisensor Snow and Ice Mapping System 4) NOAA Solar Radio Flux 5) GPS-derived total electron content used to compute the Faraday rotation correction for the SMAP radar 6) Instantaneous wave height measures\n<br>\n\n#### SMAP_L1_L3_ANC_STATIC\nThis ancillary SMAP product contains more than 50 data sets. These data sets contain the inputs necessary to create SMAP products from raw instrument counts, such as permanent masks (land, water, forest, urban, mountain, etc.), the grid cell average elevation and slope derived from a Digital Elevation Model (DEM), permanent open water fraction, soils information (primarily sand and clay fraction), vegetation parameters, and surface roughness parameters.\n<br>\n\n#### SMAP_L4_C_ANC_BPLUT\nThis ancillary SMAP product contains biophysical characteristics (biome parameters) used to estimate carbon fluxes and soil organic carbon in the SMAP L4 Carbon algorithm. Biophysical characteristics were established from previous studies and the parameters defined for the MODIS MOD17 operation GPP algorithm. This data set was refined through regional and global comparisons and calibration of prototype SMAP L4 Carbon calculations.\n<br>\n\n#### SMAP_L4_C_ANC_FPAR_CLIM\nThis ancillary SMAP product contains a static climatology data set. The climatology data is derived from MODIS Fractional Photosynthetically Active Radiation (FPAR) models and represents a global 8-day average.\n<br>\n\n#### SMAP_L4_C_ANC_MET_LOG\nThis ancillary SMAP product contains daily meteorological model log files, including model outputs. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm.\n<br>\n\n#### SMAP_L4_C_ANC_MET_RIP\nThis ancillary SMAP product contains meteorological model configurations, including model inputs. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm.\n<br>\n\n#### SMAP_L4_C_ANC_MDL_LOG\nThis ancillary SMAP product contains SMAP L4 Carbon model log files, including model outputs.\n<br>\n\n#### SMAP_L4_C_ANC_MDL_RIP\nThis ancillary SMAP product contains SMAP L4 Carbon model configurations, including model inputs.\n<br>\n\n#### SMAP_L4_C_ANC_MOD_LOG\nThis ancillary SMAP product contains MODIS Fractional Photosynthetically Active Radiation (FPAR) model log files, including model outputs.\n<br>\n\n#### SMAP_L4_C_ANC_MOD_RIP\nThis ancillary SMAP product contains MODIS Fractional Photosynthetically Active Radiation (FPAR) model configurations, including model inputs.\n<br>\n\n#### SMAP_L4_C_ANC_MET\nThis ancillary SMAP product contains dynamic surface meteorological forcing data. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm. The forcing data is processed from hourly GEOS-5 files into daily values. Daily files are processed every eight days.\n<br>\n\n#### SMAP_L4_SM_ANC_CAT_TILE\nThis ancillary SMAP product contains tile information for the NASA Land Data Assimilation System (LDAS) Catchment model, including center-of-mass latitude/longitude, minimum/maximum latitude/longitude, and the land area fraction of tiles.\n<br>\n\n#### SMAP_L4_SM_ANC_PARAM\nThis ancillary SMAP product contains three dynamic Land Data Assimilation Systems (LDAS) data sets. These data sets include Brightness Temperature (TB) scaling parameters; catchment model parameters such as topographic statistics, soil texture, and soil hydraulic parameters; and LDAS L-band microwave radiative transfer model parameters.\n<br>\n\n#### SMAP_L4_SM_ANC_LOG\nThis ancillary SMAP product includes Land Data Assimilation Systems (LDAS) Catchment model log files, including model outputs.\n<br>\n\n#### SMAP_L4_SM_ANC_RST\nThis ancillary SMAP product contains static restart files for the Land Data Assimilation Systems (LDAS) Catchment model. This product includes prognostic variables for both the catchment model and perturbations model.\n<br>\n\n#### SMAP_L4_SM_ANC_RIP\nThis ancillary SMAP product contains Land Data Assimilation Systems (LDAS) model configurations, including model inputs.\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Southern Ocean region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_CALVAL_V1\nThis dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) SSH data from KaRIn. The simulated data were generated by the \"ECCO LLC4320\" global ocean simulation. ECCO, which means \"Estimating the Circulation and Climate of the Ocean\", is a data assimilation and model (and the international consortium of scientists who maintains it) based on the MIT general circulation model (MITgcm) that assimilates and constrains observational data from numerous sources to estimate the ocean state. The model operates on the Lat-Lon-Cap (LLC) grid with a nominal horizontal resolution of 1/48-degrees (when approximated over the entire model domain, corresponding to ~2-km cell size at the equator). SSH data produced by ECCO LLC4320 were rendered from the native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.\n<br>\n\n#### SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_CALVAL_V1\nThis dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) altimetry data stream from the Poseidon 3C nadir altimeter. The simulated data were generated by the \"ECCO LLC4320\" global ocean simulation. ECCO, which means \"Estimating the Circulation and Climate of the Ocean\", is a data assimilation and model (and the international consortium of scientists who maintains it) based on the MIT general circulation model (MITgcm) that assimilates and constrains observational data from numerous sources to estimate the ocean state. The model operates on the Lat-Lon-Cap (LLC) grid with a nominal horizontal resolution of 1/48-degrees (when approximated over the entire model domain, corresponding to ~2-km cell size at the equator). SSH data produced by ECCO LLC4320 were rendered from the native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.\n<br>\n\n#### VJ130\nThis data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's first satellite (JPSS-1). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.\n<br>\n\n#### VJ130P1D\nThis data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique. VIIRS flies on board the Joint Polar Satellite System 1 (JPSS-1), also known as NOAA-20.\n<br>\n\n#### VJ129\nThis data set reports the location of sea ice cover derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's first satellite (JPSS-1). Following the approach used by MODIS, sea ice is detected using the Normalized Difference Snow Index.\n<br>\n\n#### VJ129P1D\nThis data set reports sea ice cover/extent derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes sea ice cover using Normalized Difference Snow Index (NDSI). VIIRS flies on board the Joint Polar Satellite System 1 (JPSS-1), also known as NOAA-20.\n<br>\n\n#### VJ110C1\nThis global Level-3 data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05° (approx. 5 km at the equator) MODIS/VIIRS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'VIIRS/JPSS1 Snow Cover Daily L3 Global 375m SIN Grid' data set (DOI:10.5067/UAJGR7WVWDDI).\n<br>\n\n#### VJ230\nThis data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's second satellite (JPSS-2). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.\n<br>\n\n#### VJ229\nThis data set reports the location of sea ice cover derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's second satellite (JPSS-2). Following the approach used by MODIS, sea ice is detected using the Normalized Difference Snow Index.\n<br>\n\n#### VNP30\nThis data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.\n<br>\n\n#### VNP30P1D\nThis data set estimates of sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique. VIIRS flies on board the Suomi National Polar-orbiting Partnership (NPP) satellite.\n<br>\n\n#### VNP29\nThis data set reports the location of sea ice cover derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. Following the approach used by MODIS, Sea Ice is detected using the Normalized Difference Snow Index.\n<br>\n\n#### VNP10C1\nThis global Level-3 data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05° (approx. 5 km) MODIS/VIIRS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'VIIRS/NPP Snow Cover Daily L3 Global 375m SIN Grid' data set (DOI:10.5067/45VDCKJBXWEE).\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_WestAtlantic_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the West Atlantic region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### MITgcm_LLC4320_Pre-SWOT_JPL_L4_Yongala_v1.0\nThis dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Yongala region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://podaac.jpl.nasa.gov/SWOT","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","earth observation","elevation","ice","land cover","lidar","netcdf","oceans","precipitation","radar","satellite imagery","soil moisture","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_BassStrait_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Bass Strait region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MITgcm_LLC4320_Pre-SWOT_JPL_L4_BassStrait_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_Boknis_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Baltic Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MITgcm_LLC4320_Pre-SWOT_JPL_L4_Boknis_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NSIDC-0630 v2 - The Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR, Version 2 data set is a multi-sensor Level 3 Earth Science Data Record (ESDR) with improvements upon Version 1 in cross-sensor calibration and quality checking, modern file formats, better quality control, improved projection grids, and local time-of-day (LTOD) processing. These data are gridded to three EASE-Grid 2.0 projections (North Azimuthal, South Azimuthal, and Cylindrical) and include enhanced-resolution imagery, as well as coarse-resolution, averaged imagery.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/PM/NSIDC-0630/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_CapeBasin_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Cape Basin region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MITgcm_LLC4320_Pre-SWOT_JPL_L4_CapeBasin_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NSIDC-0796 v1 - This data set provides spatial distributions of fast ice and glacial ice in eight fjords spanning the Southeast Greenland coast: Nansen, Kangerlusruaq, Ikertivaq, Skjoldungen, Tingmiarmiut, Napasorsvaq, Anoritup, and Kangerlluluk. Temporal coverage is discontinuous, depending on the availability and quality of images.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/OTHR/NSIDC-0796/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA2_GGP v1 - This data set comprises results from a hybrid glacier evolution model that uses the mass balance module of the Python Glacier Evolution Model (PyGEM) and the glacier dynamics module of the Open Global Glacier Model (OGGM). Output parameters include projections of glacier mass change, fixed runoff, and various mass balance components at regionally aggregated and glacier scales.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA2_GGP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_GotlandBasin_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Gotland Basin region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MITgcm_LLC4320_Pre-SWOT_JPL_L4_GotlandBasin_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"AERDB_D3_AHI_H08 v1 - The H08 Deep Blue Level 3 daily aerosol data, 1x1 degree grid product, short-name AERDB_D3_AHI_H08, derived from the L2 (AERDB_L2_AHI_H08) input data, each D3 AHI/Himawari-8 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element.","ARN":"arn:aws:s3:::prod-lads/AERDB_D3_AHI_H08","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_M3_AHI_H08 v1 - The H08 Deep Blue Level 3 Monthly aerosol data, 1x1 degree grid product, short-name AERDB_M3_AHI_H08, derived by aggregating the L3 daily (AERDB_D3_AHI_H08) input data, each M3 AHI/ Himawari-8 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the shortname as M3) product’s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product.","ARN":"arn:aws:s3:::prod-lads/AERDB_M3_AHI_H08","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"BAROCLINIC_HRET14 v14 - This dataset of Harmonic Constants for Baroclinic Tide Prediction was produced by Edward Zaron (Oregon State University) and Shane Elipot (University of Miami). It provides sea surface height and ocean surface currents associated with the predictable astronomical tide at the M2, S2, N2, K1, and O1 frequencies.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/BAROCLINIC_HRET14","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"HMA2_GFTP v1 - This data set consists of 1 km resolution monthly land surface temperatures (MLSTs); mean annual ground temperatures (MAGTs); and estimates of permafrost extent (PE) in the High Mountain Asia region from 1 Jan 2003 – 31 Dec 2016. The data were generated by gap-filling daily MODIS Terra/Aqua Land surface temperatures (LSTs) with downscaled Atmospheric Infra-Red Sounder (AIRS) skin surface temperatures.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA2_GFTP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA2_MATCHA v1 - This data set contains a 12 km resolution, simulated reanalysis of aerosol transport, chemistry, and deposition over the High Mountain Asia (HMA) region for 1 January 2003 through 31 August 2019. Two-dimensional surface data are provided at one hour intervals.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA2_MATCHA/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA2_DCG_SMB v1 - This High Mountain Asia data set contains 2 m resolution digital elevation models (DEMs), surface velocities, surface mass balance (SMB) rates, and SMB uncertainties for six debris-covered glaciers in Nepal. SMB rate is estimated by applying a Lagrangian specification to DEMs derived from very-high-resolution optical stereo imagery acquired by Maxar Technologies satellites WorldView-1, WorldView-2, WorldView-3, and GeoEye-1.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA2_DCG_SMB/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA2_FGP v1 - This data set contains Flood Geomorphic Potential (FGP) at 30 m resolution for the High Mountain Asia region and 8 m resolution over Nepal. FGP is a digital elevation model-derived index that provides high-resolution flood mapping based on bankfull elevations, defined in terms of river widths, and elevation differences between points under examination and the closest bankfull elevations in the river network.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA2_FGP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA2_DDSMET v1 - This High Mountain Asia (HMA) data set contains simulated meteorological data for the Indus Basin from 2000 through 2015, at three horizontal resolutions – 36 km, 12 km, and 4 km – and 9 pressure levels spanning 1000 hPa – 200 hPa. The data were produced by using the Advanced Research Weather Research & Forecasting (ARW-WRF) model to dynamically downscale Climate Forecast System Reanalysis (CFSR) data into three nested domains with increasing horizontal resolution.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA2_DDSMET/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA2_HFD v1 - This High Mountain Asia (HMA) data set contains hydrological flow directions at 5 arc-minute resolution for the headwaters of the Amu Darya and Indus River basins. The domain spans parts of Afghanistan, Tajikistan, Kyrgyzstan, and Pakistan.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA2_HFD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_DEM8m_MOS v1 - This data set contains 8-meter Digital Elevation Model (DEM) mosaics of high mountain Asia glacier and snow regions generated from very-high-resolution (VHR) commercial satellite imagery.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_DEM8m_MOS/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_DEM8m_AT v1 - This data set contains 8-meter Digital Elevation Models (DEMs) of high mountain Asia glacier and snow regions generated from very-high-resolution commercial stereoscopic satellite imagery.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_DEM8m_AT/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_DEM8m_CT v1 - This data set contains 8-meter Digital Elevation Model (DEM) mosaics of high mountain Asia glacier and snow regions generated from from very-high-resolution commercial stereo satellite imagery.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_DEM8m_CT/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_GlacierAvg_dH v1 - This data set contains average thickness changes for approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from HEXAGON KH-9 and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_GlacierAvg_dH/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA2_WBP v1 - This High Mountain Asia (HMA) data set comprises a suite of monthly and yearly water balance model (WBM) projections for the years 2016 – 2099, covering parts of Afghanistan, Tajikistan, Kyrgyzstan, and Pakistan (primarily the headwaters of the Amu Darya and Indus River basins). Projections are available for 12 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA2_WBP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_RCMO_6H v1 - This data product contains either 6-hourly accumulated or 6-hourly snapshots of modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_RCMO_6H/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_RCMO_D v1 - This data product contains either daily averaged or daily accumulated modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_RCMO_D/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_RCMO_1H v1 - This data product contains either hourly accumulated or hourly snapshots of modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_RCMO_1H/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_RCMO_M v1 - This data product contains either monthly averaged or monthly accumulated modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_RCMO_M/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA2_NLSMR v1 - This data set consists of a water budget reanalysis for the High Mountain Asia (HMA) region spanning the years 2003 through 2020. Estimates are provided for more than 30 parameters, including storages; fluxes; snow depth, extent, and snow water equivalent; temperature (land surface, soil, snow, and ice); surface albedo; soil moisture; evapotranspiration; and streamflow.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA2_NLSMR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA2_DSPAT v1 - This data set consists of daily, 5 km resolution precipitation and mean, near-surface air temperature projections from 2015 through 2100 for the High Mountain Asia (HMA) region. The data were generated by statistically downscaling 0.5° resolution model data from the Geophysical Fluid Dynamic Laboratory (GFDL) Seamless System for Prediction and EArth System Research (SPEAR) 30-member ensemble climate model.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA2_DSPAT/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA2_LHI v1 - This data set projects the daily hazard of rainfall-triggered landslides in the High Mountain Asia region from 2015 through 2100, at 5 km resolution. Projections are provided for two Shared Socioeconomic Pathways (SSPs)—SSP2-4.5 and SSP5 8.5—based on downscaled temperature and precipitation projections from a 30-member ensemble climate model.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA2_LHI/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA2_RSRD v1 - This data set reports daily, reach-scale river discharge for 114,147 river reaches in the High Mountain Asia region from 1 January 2004 through 31 December 2019. The data were generated by combining ensemble hydrologic modeling with data assimilation of remotely sensed discharge from Landsat and PlanetScope satellite imagery.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA2_RSRD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_EAPrecip_FLOR v1 - This data set includes three climate simulations of daily precipitation over the Himalayan region for summer and winter, covering different time periods: two 30-member ensemble simulations spanning 40-year time periods in the 20th century (1961-2000) and 21st century (2061-2100), and a present-day climate simulation from 1982 to 2017 nudged to reanalysis winds. These precipitation estimates were simulated by the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low Ocean Resolution version of the CM2.5 model (GFDL FLOR).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_EAPrecip_FLOR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_Precip_FLOR v1 - This data set features seven standard annual mean extreme precipitation indices: Rx1day, Rx5day, CWD, R10mm, R20mm, R95pTOT, and R99pTOT. They were selected on the basis of potential relevance to landslide activity from the 27 indices established by the joint CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_Precip_FLOR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_Glacier_dH_Mosaics v1 - This data set contains thickness change mosaics that include approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from HEXAGON KH-9 and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_Glacier_dH_Mosaics/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_Glacier_dH v1 - This data set contains gridded thickness changes for approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from KH-9 HEXAGON and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_Glacier_dH/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_AWS v1 - This data set contains meteorological data, such as air temperature, pressure, rainfall intensity, relative humidity, and wind direction/speed measured by the International Centre for Integrated Mountain Development (ICIMOD).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_AWS/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_SDI v1 - This data set contains thermal-dome-corrected downward shortwave irradiance at the bottom of atmosphere, measured by the Shared Mobile Atmospheric Research and Teaching Radar (SMART-R) and collected by the International Centre for Integrated Mountain Development (ICIMOD).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_SDI/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_SBRF v1 - This data set contains snow bidirectional reflectance factor (BRF) between 350 and 2500 nm collected on the Yala Glacier on 23 April and 24 April 2018 by the International Centre for Integrated Mountain Development (ICIMOD).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_SBRF/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_Snowfield v1 - This data set contains measurements of several different snow properties, including reflectance at 1310 nm, specific surface area, and optical mean radius, collected on the Yala Glacier, Nepal. These data were collected on 23 April and 24 April 2018 by the International Centre for Integrated Mountain Development (ICIMOD) using the IceCube instrument.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_Snowfield/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_STParams v1 - This data set provides daily-averaged NASA Land Information System (LIS) output at a spatial resolution of 1 km. LIS was driven by uncorrected Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) data, using the Noah Multiparameterization Land Surface Model (Noah-MP).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_STParams/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_LIS_LandSurfaceHydro v1 - The data provided in this data set are simulated using the Noah-Multiparameterization Land Surface Model (Noah-MP LSM) Version 3.6 within the NASA Land Information System (LIS) Version 7.2. The data files contain estimates of water, energy fluxes, and land surface states for the High Mountain Asia (HMA) region.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_LIS_LandSurfaceHydro/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_MAR3_5 v1 - This data set provides modeled surface and atmospheric fields from the Modèle Atmosphérique Régionale (MAR) regional climate model (version 3.5) over the Himalayan region at 10 km spatial resolution. Modeled parameters include surface mass and energy balance components, near-surface atmospheric properties, and snowpack properties.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_MAR3_5/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_OptDepth v1 - This data set contains monthly mean MODIS Level 3 data from aboard the Aqua and Terra satellites. The parameters provided in this data set are aerosol optical depth (AOD) and Angstrom exponent (AE) at a spatial resolution of 1º by 1º.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_OptDepth/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_GL_RCP v1 - This data set comprises results from the Python Glacier Evolution Model (PyGEM) that include projections of glacier mass change, glacier runoff, and the various components associated with changes in mass and runoff.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_GL_RCP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_GL_RCPR v1 - This data set comprises a rasterized (gridded) version of the of glacier point data from the Python Glacier Evolution Model (PyGEM) that include projections of glacier mass change, glacier runoff, and the various components associated with changes in mass and runoff.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_GL_RCPR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_L2_AHI_H08 v1 - The Himawari-08 AHI Deep Blue Aerosol L2 Full Disk product, short-name AERDB_L2_AHI_H08 is produced every 30 minutes and contains full-disk observation data. The L2 data products comprise 10 x 10 native GEO pixels.","ARN":"arn:aws:s3:::prod-lads/AERDB_L2_AHI_H08","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_LabradorSea_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Labrador Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MITgcm_LLC4320_Pre-SWOT_JPL_L4_LabradorSea_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_MarmaraSea_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Marmara Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MITgcm_LLC4320_Pre-SWOT_JPL_L4_MarmaraSea_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NSIDC-0756 v4 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains a bed topography and bathymetry map of Antarctica. Bed topography is deduced by subtracting ice thickness from the surface elevation; using Ice Flow Perturbation Analysis (IFPA); and by other methods.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0756/4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0724 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of monthly image mosaics of the Greenland coastline and ice sheet periphery constructed from composited MODIS imagery. See <a href=\"http://nsidc.org/data/measures/gimp\">Greenland Ice Mapping Project (GIMP)</a> for related data.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0724/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0792 v1 - This ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, includes quarterly estimates of Antarctic ice shelf surface elevation, thickness, basal melt rate, surface mass balance, firn air content, and associated errors, from 17 March 1992 through 16 December 2017 at 1920 m resolution. The data were generated from four European Space Agency (ESA) satellite radar altimetry missions—ERS-1, ERS-2, Envisat, and CryoSat-2—using a novel data fusion approach and the Glacier Energy and Mass Balance model (GEMB).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0792/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0547 v2 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of two digital Greenland image maps each for the 2005, 2010, and 2015 measurement periods: the MOG Surface Morphology Image Map and the MOG Grain Size Image Map. The image maps are constructed from MODIS imagery acquired during 2005, 2010, and 2015 and provide nearly cloud-free views of all land areas and islands larger than a few hundred meters, including the ice caps on Baffin Island, Devon Island, Axel Heiberg Island, and Ellesmere Island.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0547/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0530 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, offers users 25 km Northern Hemisphere snow cover extent represented by four different variables. Three of the snow cover variables are derived from the Interactive Multisensor Snow and Ice Mapping System, MODIS Cloud Gap Filled Snow Cover, and passive microwave brightness temperatures, respectively.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0530/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_WesternMed_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the western Mediterranean Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MITgcm_LLC4320_Pre-SWOT_JPL_L4_WesternMed_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MOD10A1F v61 - This global Level-3 data set (MOD10A1F) provides daily cloud-free snow cover derived from the MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid data set (MOD10A1). Grid cells in MOD10A1 which are obscured by cloud cover are filled by retaining clear-sky views of the surface from previous days.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MOD10A1F/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0791 v1 - This data set presents new global snow cover classification regimes derived from the MODIS Terra cloud gap-filled NDSI data (<a href=\"https://nsidc.org/data/mod10a1f/versions/61\">MOD10A1F</a>), elevation, and temperature climatology inputs. The six data granules are available as NetCDF (.nc) files, with each containing a unique snow cover classification spanning 2001 to 2023.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS-Related/NSIDC-0791/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD29 v61 - This global Level-2 (L2) product provides daily sea ice extent and ice surface temperature. The data are derived from Level-1B radiances acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MOD29/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD29E1D v61 - This global Level-3 (L3) data set provides Northern and Southern Hemisphere maps of sea ice extent and ice surface temperature. The maps are generated by compositing 1 km observations from the 'MODIS/Terra Sea Ice Extent Daily L3 Global 1km EASE-Grid Day’ (https://doi.org/10.5067/MODIS/MOD29P1D.061) product.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MOD29E1D/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD29P1D v61 - This global Level-3 (L3) data set provides daily daytime sea ice extent and ice surface temperature derived from the 'MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km' (https://doi.org/10.5067/MODIS/MOD29.061) product. Each data granule is a tile consisting of 10 x 10 degrees of data gridded to the Lambert Azimuthal Equal Area Scalable Earth Grid (EASE-Grid).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MOD29P1D/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD29P1N v61 - This global Level-3 (L3) data set provides daily nighttime ice surface temperature derived from the 'MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km' (https://doi.org/10.5067/MODIS/MOD29.061) product. Each data granule is a tile consisting of 10 x 10 degrees of data gridded to the Lambert Azimuthal Equal Area Scalable Earth Grid (EASE-Grid).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MOD29P1N/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD10_L2 v61 - This global Level-2 (L2) data set provides daily snow cover detected using the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections. The NDSI is derived from radiance data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite: DOI:10.5067/MODIS/MOD02HKM.061 and DOI:10.5067/MODIS/MOD021KM.061.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MOD10_L2/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD10C2 v61 - This global level-3 (L3) data set provides the maximum percentage of snow-covered land and persistent cloud-covered land observed over eight-days, within 0.05° (approx. 5 km) MODIS climate modeling grid (CMG) cells.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MOD10C2/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD10A2 v61 - This global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10° x 10° MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS/Terra Snow Cover Daily L3 Global 500m Grid' data set (DOI:10.5067/MODIS/MOD10A1.061).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MOD10A2/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD10C1 v61 - This global Level-3 (L3) data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05° (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MOD10C1/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD10A1 v61 - This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS/Terra Snow Cover 5-Min L2 Swath 500m' data set (DOI:10.5067/MODIS/MOD10_L2.061). Each data granule is a 10°x10° tile projected to a 500 m sinusoidal grid.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MOD10A1/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD10CM v61 - This global Level-3 (L3) data set provides monthly mean snow cover extent within 0.05° (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS/MOD10CM/61","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MODGRNLD v1 - This multilayer data set includes standard MODIS Collection 6.1 ice surface temperature (IST) and derived melt map, as well as MODIS Collection 6.0 albedo and water vapor for Greenland, at a spatial resolution of 0.78 km. These fields enable the relationship between IST and surface melt to be evaluated by researchers studying surface changes on the Greenland ice sheet.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MODIS-Related/MODGRNLD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MSLSP30NA v001 - MSLSP V1 data was decommissioned on December 14, 2021. Users are encouraged to use the improved [MSLSP V1.1](https://doi.org/10.5067/Community/MuSLI/MSLSP30NA.011) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MSLSP30NA.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MSLSP30NA v011 - The Multi-Source Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1.1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel-2A and 2B Multispectral Instrument (MSI) provides the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy at 30m spatial resolution.","ARN":"arn:aws:s3:::lp-prod-protected/MSLSP30NA.011","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_NewCaledonia_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the New Caledonia region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NewCaledonia_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_ROAM_MIZ_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Northeast Weddell Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MITgcm_LLC4320_Pre-SWOT_JPL_L4_ROAM_MIZ_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWAustralia_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Northwest Australian Shelf region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWAustralia_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWPacific_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Northwest Pacific Ocean region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWPacific_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"EN1_MDSI_MER_RR__1P v4 - The Medium Resolution Imaging Spectrometer (MERIS) is one of 10 sensors deployed in March of 2002 on board the polar-orbiting Envisat-1 environmental research satellite by the European Space Agency (ESA). The MERIS instrument is a moderate-resolution wide field-of-view push-broom imaging spectroradiometer capable of sensing in the 390 nm to 1040 nm spectral range.","ARN":"arn:aws:s3:::prod-lads/EN1_MDSI_MER_RR__1P","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_RockallTrough_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Rockall Trough region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MITgcm_LLC4320_Pre-SWOT_JPL_L4_RockallTrough_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"WAF_DEALIASED_SASS_L2 v1 - Contains Seasat-A Scatterometer (SASS) wind vector measurements for the entire Seasat mission, from July 1978 until October 1978. The data are global and presented chronologically in by swath.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/WAF_DEALIASED_SASS_L2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"CHELTON_SEASAT_SASS_L3 v1 - Contains monthly averaged ocean surface wind stress derived from Seasat-A Scatterometer (SASS) wind retrievals, from July 1978 until October 1978, gridded on a 2.5-degree by 2.5 degree global grid. The vector average wind stress is stored in units of dynes per centimeter squared (dyn/cm^2).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/CHELTON_SEASAT_SASS_L3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SPL4SMAU v008 - SMAP Level-4 (L4) surface and root zone soil moisture (L4_SM) data are provided in three products. The SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/T5RUATAQREF8) product is a series of 3-hourly time average geophysical land surface fields that are output by the L4_SM algorithm.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL4SMAU/008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL4CMDL v008 - The Level-4 (L4) carbon product (SPL4CMDL) provides global gridded daily estimates of net ecosystem carbon (CO2) exchange derived using a satellite data based terrestrial carbon flux model informed by the following: Soil Moisture Active Passive (SMAP) L-band microwave observations, land cover and vegetation inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Goddard Earth Observing System Model, Version 5 (GEOS-5) land model assimilation system. Parameters are computed using an Earth-fixed, global cylindrical 9 km...","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL4CMDL/008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0779 v1 - This data product contains global daily 1 km resolution surface soil moisture derived from the SMAP L-band radiometer. Specifically, MODIS land surface temperature data is used with the SMAP Enhanced L2radiometer Half-Orbit 9 km EASE-Grid Soil Moisture product in a downscaling algorithm to estimate soil moisture.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-Related/NSIDC-0779/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0797 v1 - This data set is derived by downscaling Soil Moisture Active Passive (SMAP) enhanced Level-3 9 km brightness temperatures (TB) using Cyclone Global Navigation Satellite System (CYGNSS) reflectivity data and employing a slightly modified version of the SMAP baseline active-passive TB algorithm. The SMAP Single Channel Algorithm – Vertical polarization (SCA-V) is then used to derive soil moisture from SMAP/CYGNSS Tb data.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-Related/NSIDC-0797/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MA_DEM v1 - These digital elevation model (DEM) data consist of ground surface elevations derived from source lidar measurements collected in April and August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MA_DEM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MA_DSM v1 - These digital surface model (DSM) data consist of surface elevations derived from source lidar measurements collected in August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. The location was selected due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MA_DSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MB_DEM v1 - These digital elevation model (DEM) data consist of ground surface elevations derived from source lidar measurements collected in April and August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MB_DEM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MB_DSM v1 - These digital surface model (DSM) data consist of surface elevations derived from source lidar measurements collected in August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. The location was selected due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MB_DSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX_Met_Raw v1 - This dataset contains raw meteorological data collected as part of the ongoing the NASA SnowEx mission, from five meteorological stations installed between 2016-2017 in Grand Mesa, Colorado, to provide supporting data for SnowEx field campaigns and forcing data for modeling.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX_Met_Raw/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX_Met v1 - This dataset contains meteorological data collected as part of the ongoing the NASA SnowEx mission, from five meteorological stations installed between 2016-2017 in Grand Mesa, Colorado, to provide supporting data for SnowEx field campaigns and forcing data for modeling. Each station collects a suite of meteorological data at a fixed geographic point, from varying heights above and below the surface elevation.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX_Met/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX17_UWScat v1 - This data set consists of ground-based scatterometer data acquired during the SnowEx 2017 campaign at Grand Mesa, Colorado, USA, a snow-covered, forested study site about 40 miles east of the city of Grand Junction, CO. The data comprise operational parameters used during data acquisition, Mueller matrices for each acquisition, and the nearfield-corrected normalized radar cross section (NRCS) in VV, VH, HV, and HH polarizations.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX17_UWScat/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX17_SSD v1 - This data set contains 15-min snow depth observations for two study sites on Grand Mesa, CO, USA, acquired as part of NASA's 2017 SnowEx campaign. The data were recorded using two arrays of Judd Communications Ultrasonic Depth Sensors, configured as a TLS K footprint on the west side of the mesa and a TLS N footprint in the east.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX17_SSD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L1_L3_ANC_GEOS v1 - This ancillary SMAP product contains three dynamic GMAO GEOS-5 modeled data sets. Each data set contains surface and atmospheric parameters pertinent to SMAP provided in 1) hourly, 2) 3-hour, and 3) averaged over 3-hour intervals.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L1_L3_ANC_GEOS/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L1_L3_ANC_NOAA v1 - This ancillary SMAP product contains six dynamic data sets originally produced by NOAA or NOAA-affiliated organizations. 1) NCEP Geophysical Forecast System modeled data provided in 6-hour time steps 2) Daily Reynolds Sea Surface Temperature data 3) Snow Cover data from NOAA Interactive Multisensor Snow and Ice Mapping System 4) NOAA Solar Radio Flux 5) GPS-derived total electron content used to compute the Faraday rotation correction for the SMAP radar 6) Instantaneous wave height measures.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L1_L3_ANC_NOAA/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L1_L3_ANC_STATIC v1 - This ancillary SMAP product contains more than 50 data sets. These data sets contain the inputs necessary to create SMAP products from raw instrument counts, such as permanent masks (land, water, forest, urban, mountain, etc.), the grid cell average elevation and slope derived from a Digital Elevation Model (DEM), permanent open water fraction, soils information (primarily sand and clay fraction), vegetation parameters, and surface roughness parameters.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L1_L3_ANC_STATIC/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_C_ANC_BPLUT v1 - This ancillary SMAP product contains biophysical characteristics (biome parameters) used to estimate carbon fluxes and soil organic carbon in the SMAP L4 Carbon algorithm. Biophysical characteristics were established from previous studies and the parameters defined for the MODIS MOD17 operation GPP algorithm.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_C_ANC_BPLUT/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_C_ANC_FPAR_CLIM v1 - This ancillary SMAP product contains a static climatology data set. The climatology data is derived from MODIS Fractional Photosynthetically Active Radiation (FPAR) models and represents a global 8-day average.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_C_ANC_FPAR_CLIM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_C_ANC_MET_LOG v1 - This ancillary SMAP product contains daily meteorological model log files, including model outputs. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_C_ANC_MET_LOG/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_C_ANC_MET_RIP v1 - This ancillary SMAP product contains meteorological model configurations, including model inputs. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_C_ANC_MET_RIP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_C_ANC_MDL_LOG v1 - This ancillary SMAP product contains SMAP L4 Carbon model log files, including model outputs.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_C_ANC_MDL_LOG/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_C_ANC_MDL_RIP v1 - This ancillary SMAP product contains SMAP L4 Carbon model configurations, including model inputs.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_C_ANC_MDL_RIP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_C_ANC_MOD_LOG v1 - This ancillary SMAP product contains MODIS Fractional Photosynthetically Active Radiation (FPAR) model log files, including model outputs.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_C_ANC_MOD_LOG/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_C_ANC_MOD_RIP v1 - This ancillary SMAP product contains MODIS Fractional Photosynthetically Active Radiation (FPAR) model configurations, including model inputs.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_C_ANC_MOD_RIP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_C_ANC_MET v1 - This ancillary SMAP product contains dynamic surface meteorological forcing data. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_C_ANC_MET/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_SM_ANC_CAT_TILE v1 - This ancillary SMAP product contains tile information for the NASA Land Data Assimilation System (LDAS) Catchment model, including center-of-mass latitude/longitude, minimum/maximum latitude/longitude, and the land area fraction of tiles.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_SM_ANC_CAT_TILE/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_SM_ANC_PARAM v1 - This ancillary SMAP product contains three dynamic Land Data Assimilation Systems (LDAS) data sets. These data sets include Brightness Temperature (TB) scaling parameters; catchment model parameters such as topographic statistics, soil texture, and soil hydraulic parameters; and LDAS L-band microwave radiative transfer model parameters.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_SM_ANC_PARAM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_SM_ANC_LOG v1 - This ancillary SMAP product includes Land Data Assimilation Systems (LDAS) Catchment model log files, including model outputs.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_SM_ANC_LOG/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_SM_ANC_RST v1 - This ancillary SMAP product contains static restart files for the Land Data Assimilation Systems (LDAS) Catchment model. This product includes prognostic variables for both the catchment model and perturbations model.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_SM_ANC_RST/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_SM_ANC_RIP v1 - This ancillary SMAP product contains Land Data Assimilation Systems (LDAS) model configurations, including model inputs.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_SM_ANC_RIP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Southern Ocean region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_CALVAL_V1 v1 - This dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) SSH data from KaRIn. The simulated data were generated by the \"ECCO LLC4320\" global ocean simulation.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_CALVAL_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_CALVAL_V1 v1 - This dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) altimetry data stream from the Poseidon 3C nadir altimeter. The simulated data were generated by the \"ECCO LLC4320\" global ocean simulation.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_CALVAL_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"VJ130 v2 - This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's first satellite (JPSS-1). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ130/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ130P1D v2 - This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ130P1D/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ129 v2 - This data set reports the location of sea ice cover derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's first satellite (JPSS-1). Following the approach used by MODIS, sea ice is detected using the Normalized Difference Snow Index.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ129/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ129P1D v2 - This data set reports sea ice cover/extent derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes sea ice cover using Normalized Difference Snow Index (NDSI).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ129P1D/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ110C1 v2 - This global Level-3 data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05° (approx. 5 km at the equator) MODIS/VIIRS Climate Modeling Grid (CMG) cells.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ110C1/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ230 v2 - This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's second satellite (JPSS-2). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ230/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ229 v2 - This data set reports the location of sea ice cover derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's second satellite (JPSS-2). Following the approach used by MODIS, sea ice is detected using the Normalized Difference Snow Index.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ229/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP30 v2 - This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VNP30/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP30P1D v2 - This data set estimates of sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VNP30P1D/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP29 v2 - This data set reports the location of sea ice cover derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. Following the approach used by MODIS, Sea Ice is detected using the Normalized Difference Snow Index.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VNP29/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP10C1 v2 - This global Level-3 data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05° (approx. 5 km) MODIS/VIIRS Climate Modeling Grid (CMG) cells.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VNP10C1/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_WestAtlantic_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the West Atlantic region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MITgcm_LLC4320_Pre-SWOT_JPL_L4_WestAtlantic_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MITgcm_LLC4320_Pre-SWOT_JPL_L4_Yongala_v1.0 v1.0 - This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Yongala region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-public/MITgcm_LLC4320_Pre-SWOT_JPL_L4_Yongala_v1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-modis-terra","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA MOPITT Project","Description":"MOP03J_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded daily means (Near and Thermal Infrared Radiances) version 109 product is an unvalidated beta product subject to recalibration, contains daily mean gridded versions of the daily Level 2 CO profile and total column retrievals. The averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. Data collection for this product is ongoing. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. [From the MOPITT version 3 Level 3 Data Quality Summary] MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP03N\nMOP03N_109 is the Measurements of Pollution in the Troposphere (MOPITT) Beta CO gridded daily means (Near Infrared Radiances) version 109 product. It is a non-validated beta product subject to recalibration and contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals. The averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. Version 109 products are beta versions of version 9 products; they are unvalidated beta products subject to recalibration. Data collection for this product is ongoing. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. [From the MOPITT version 3 Level 3 Data Quality Summary] MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP03T\nMOP03T_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded daily means (Thermal Infrared Radiances) version 109 product. It is an unvalidated beta product subject to recalibration and contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals. The averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. Version 109 products are beta versions of version 9 products; they are unvalidated beta products subject to recalibration. Data collection for this version is ongoing. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. [From the MOPITT version 3 Level 3 Data Quality Summary] MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP03JM\nMOP03JM_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded monthly means (Near and Thermal Infrared Radiances) version 109 product. It contains monthly mean-gridded daily L2 CO profile versions and total column retrievals. The averaging kernels associated with each retrieval are also gridded and included in the L3 files. Version 109 products are beta versions of version 9 products; they are unvalidated beta products subject to recalibration. Data collection for this product is ongoing. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. [From the MOPITT version 3 Level 3 Data Quality Summary] MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP03NM\nMOP03NM_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded monthly means (Near Infrared Radiances) version 109 product. This product contains monthly mean-gridded daily Level 2 CO profile versions and total column retrievals. The averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. Version 109 products are beta versions of version 9 products; they are unvalidated beta products subject to recalibration. Data collection for this product is ongoing. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. [From the MOPITT version 3 Level 3 Data Quality Summary] MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP03TM\nMOP03TM_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded monthly means (Thermal Infrared Radiances) version 109 product. It is an unvalidated beta product subject to recalibration and contains monthly mean-gridded versions of the daily Level 2 CO profile and total column retrievals. The averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. Version 109 products are beta versions for version 9 products, they are unvalidated beta products subject to recalibration. Data collection for this product is ongoing. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. [From the MOPITT version 3 Level 3 Data Quality Summary] MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP02J\nMOP02J_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta Derived Carbon Monoxide (CO) (Near and Thermal Infrared Radiances) version 109 product. It consists of the geo-located, retrieved carbon monoxide profiles and total column amounts for carbon monoxide. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. An estimated error accompanies each retrieval. Version 109 products are beta versions of version 9 products; they are unvalidated beta products subject to recalibration. Data collection for this product is ongoing. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP02N\nMOP02N_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta Derived CO (Near Infrared Radiances) version 109 product. It is an unvalidated beta product subject to recalibration, and consists of the geo-located, retrieved carbon monoxide profiles and total column amounts for carbon monoxide. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. Each retrieval is accompanied by an estimated error. Version 109 products are beta versions for version 9 products, they are unvalidated beta products subject to recalibration. Data collection for this version is ongoing. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP02T\nMOP02T_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta Derived CO (Thermal Infrared Radiances) version 109 product. It is an unvalidated beta product subject to recalibration and consists of the geo-located, retrieved carbon monoxide profiles and total column amounts for carbon monoxide. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. An estimated error accompanies each retrieval. Version 109 products are beta versions of version 9 products; they are unvalidated beta products subject to recalibration. Data collection for this version is ongoing. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft,, on December 18, 1999. The instrument was constructed by a consortium of Canadian companies and funded by the Canadian Space Agency's Space Science Division.\n<br>\n\n#### MOP03J\nMOP03J_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded daily means (Near and Thermal Infrared Radiances) version 9 data product. The \"J\" refers to the Joint use of the Thermal (T) and NIR (N) channels in the retrieval. It contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP03N\nMOP03N_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded daily means (Near Infrared Radiances) version 9 data product. The \"N\" refers to the use of the NIR (N) channels in the retrieval. It contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP03T\nMOP03T_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded daily means (Thermal Infrared Radiances) version 9 data product. The \"T\" refers to the use of the Thermal (T) channels in the retrieval. It contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP03JM\nMOP03JM_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded monthly means (Near and Thermal Infrared Radiances) version 9 data product. The \"J\" refers to the Joint use of the Thermal (T) and NIR (N) channels in the retrieval. It contains monthly mean-gridded daily Level 2 CO profile versions and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP03NM\nMOP03NM_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded monthly means (Near Infrared Radiances) version 9 data product. The \"N\" refers to the use of the NIR (N) channels in the retrieval. It contains monthly mean-gridded daily Level 2 CO profile versions and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP03TM\nMOP03TM_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded monthly means (Thermal Infrared Radiances) version 9 data product. The \"T\" refers to the use of the Thermal (T) channels in the retrieval. It contains monthly mean-gridded daily Level 2 CO profile versions and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. V9 is an improvement over V8 because of several scientific enhancements. These include a revision of the cloud filter to allow through a much higher number of pixels that were previously considered cloudy, a minor correction to the Forward Model to account for the long-term drift of the pressure in the gas cell, and a careful analysis of the NIR calibration process which reduces discontinuities associated with calibration events.\n<br>\n\n#### MOP02J\nMOP02J_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Derived Carbon Monoxide (CO) (Near and Thermal Infrared Radiances) version 9 product. The \"J\" refers to the Joint use of the Thermal (T) and NIR (N) channels in the retrieval. It consists of geo-located, retrieved CO profiles and total column amounts for CO. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. An estimated error accompanies each retrieval. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP02N\nMOP02N_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Derived carbon monoxide (CO) (Near Infrared Radiances) version 9 dataset. The \"N\" refers to the use of the NIR (N) channels in the retrieval. It consists of geo-located, retrieved CO profiles and total column amounts for CO. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. An estimated error accompanies each retrieval. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### MOP02T\nMOP02T_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Derived Carbon Monoxide (CO) (Thermal Infrared Radiances) version 9 product. The \"T\" refers to the use of the Thermal (T) channels in the retrieval. It consists of geo-located, retrieved CO profiles and total column amounts for CO. Ancillary data concerning surface properties and cloud conditions at the locations of the retrieved parameters are also included. Each retrieval is accompanied by an estimated error. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www2.acom.ucar.edu/mopitt/overview","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MOP03J v109 - MOP03J_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded daily means (Near and Thermal Infrared Radiances) version 109 product is an unvalidated beta product subject to recalibration, contains daily mean gridded versions of the daily Level 2 CO profile and total column retrievals. The averaging kernels associated with each retrieval are also gridded and included in the Level 3 files.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP03J_109","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP03N v109 - MOP03N_109 is the Measurements of Pollution in the Troposphere (MOPITT) Beta CO gridded daily means (Near Infrared Radiances) version 109 product. It is a non-validated beta product subject to recalibration and contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP03N_109","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP03T v109 - MOP03T_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded daily means (Thermal Infrared Radiances) version 109 product. It is an unvalidated beta product subject to recalibration and contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP03T_109","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP03JM v109 - MOP03JM_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded monthly means (Near and Thermal Infrared Radiances) version 109 product. It contains monthly mean-gridded daily L2 CO profile versions and total column retrievals.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP03JM_109","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP03NM v109 - MOP03NM_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded monthly means (Near Infrared Radiances) version 109 product. This product contains monthly mean-gridded daily Level 2 CO profile versions and total column retrievals.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP03NM_109","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP03TM v109 - MOP03TM_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded monthly means (Thermal Infrared Radiances) version 109 product. It is an unvalidated beta product subject to recalibration and contains monthly mean-gridded versions of the daily Level 2 CO profile and total column retrievals.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP03TM_109","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP02J v109 - MOP02J_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta Derived Carbon Monoxide (CO) (Near and Thermal Infrared Radiances) version 109 product. It consists of the geo-located, retrieved carbon monoxide profiles and total column amounts for carbon monoxide.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP02J_109","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP02N v109 - MOP02N_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta Derived CO (Near Infrared Radiances) version 109 product. It is an unvalidated beta product subject to recalibration, and consists of the geo-located, retrieved carbon monoxide profiles and total column amounts for carbon monoxide.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP02N_109","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP02T v109 - MOP02T_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta Derived CO (Thermal Infrared Radiances) version 109 product. It is an unvalidated beta product subject to recalibration and consists of the geo-located, retrieved carbon monoxide profiles and total column amounts for carbon monoxide.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP02T_109","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP03J v9 - MOP03J_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded daily means (Near and Thermal Infrared Radiances) version 9 data product. The \"J\" refers to the Joint use of the Thermal (T) and NIR (N) channels in the retrieval.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP03J_9","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP03N v9 - MOP03N_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded daily means (Near Infrared Radiances) version 9 data product. The \"N\" refers to the use of the NIR (N) channels in the retrieval.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP03N_9","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP03T v9 - MOP03T_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded daily means (Thermal Infrared Radiances) version 9 data product. The \"T\" refers to the use of the Thermal (T) channels in the retrieval.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP03T_9","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP03JM v9 - MOP03JM_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded monthly means (Near and Thermal Infrared Radiances) version 9 data product. The \"J\" refers to the Joint use of the Thermal (T) and NIR (N) channels in the retrieval.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP03JM_9","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP03NM v9 - MOP03NM_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded monthly means (Near Infrared Radiances) version 9 data product. The \"N\" refers to the use of the NIR (N) channels in the retrieval.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP03NM_9","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP03TM v9 - MOP03TM_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded monthly means (Thermal Infrared Radiances) version 9 data product. The \"T\" refers to the use of the Thermal (T) channels in the retrieval.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP03TM_9","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP02J v9 - MOP02J_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Derived Carbon Monoxide (CO) (Near and Thermal Infrared Radiances) version 9 product. The \"J\" refers to the Joint use of the Thermal (T) and NIR (N) channels in the retrieval.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP02J_9","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP02N v9 - MOP02N_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Derived carbon monoxide (CO) (Near Infrared Radiances) version 9 dataset. The \"N\" refers to the use of the NIR (N) channels in the retrieval.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP02N_9","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MOP02T v9 - MOP02T_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Derived Carbon Monoxide (CO) (Thermal Infrared Radiances) version 9 product. The \"T\" refers to the use of the Thermal (T) channels in the retrieval.","ARN":"arn:aws:s3:::asdc-prod-protected/MOPITT/MOP02T_9","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-mopitt","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA MULTI_NASA Project","Description":"This data set contains surface elevations from retracked CryoSat-2 waveforms, as well as model fitting parameters used to retrack the waveform. The primary data set used in the production of these data come from the ESA CryoSat-2 satellite.\n<br>\n\n#### RDEFT4\nThis data set contains estimates of Arctic sea ice thickness and concentration, ice freeboard and surface roughness, as well as snow density and depth, derived from the ESA CryoSat-2 Synthetic Aperture Interferometric Radar Altimeter (SIRAL). The data are provided daily on a 25 km grid as 30-day averages for the months between September and May.\n<br>\n\n#### IDBMG4\nThis data set contains a bed topography and bathymetry map of Greenland based on mass conservation, multi-beam data, and other techniques. It also includes surface elevation and ice thickness data, as well as an ice/ocean/land mask.\n<br>\n\n#### IRMCR3\nThis data set contains products from depth sounder measurements over Greenland and Antarctica taken from the Multichannel Coherent Radar Depth Sounder (MCoRDS). The data were collected as part of NASA Operation IceBridge funded campaigns.\n<br>\n\n#### IGBTH4\nThis data set contains bathymetry of Arctic fjords and Antarctic ice shelves based on measurements from the Sander Geophysics Airborne Inertially Referenced Gravimeter (AIRGrav) system. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.\n<br>\n\n#### RDGBV4\nThis data set contains calculated balance velocity of the Greenland Ice Sheet during the last three quarters of the Holocene epoch (9ka).\n<br>\n\n#### RRRAG4\nThis data set presents the gridded radiostratigraphy of the Greenland Ice Sheet using very high frequency (VHF) radar sounding data, collected by The Center for Remote Sensing and Integrated Systems (CReSIS) at The University of Kansas.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/rdwes1b/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","earth observation","elevation","ice","oceans","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"RDWES1B v1 - This data set contains surface elevations from retracked CryoSat-2 waveforms, as well as model fitting parameters used to retrack the waveform. The primary data set used in the production of these data come from the ESA CryoSat-2 satellite.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE-Related/RDWES1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"RDEFT4 v1 - This data set contains estimates of Arctic sea ice thickness and concentration, ice freeboard and surface roughness, as well as snow density and depth, derived from the ESA CryoSat-2 Synthetic Aperture Interferometric Radar Altimeter (SIRAL). The data are provided daily on a 25 km grid as 30-day averages for the months between September and May.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE-Related/RDEFT4/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IDBMG4 v6 - This data set contains a bed topography and bathymetry map of Greenland based on mass conservation, multi-beam data, and other techniques. It also includes surface elevation and ice thickness data, as well as an ice/ocean/land mask.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IDBMG4/6","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IRMCR3 v2 - This data set contains products from depth sounder measurements over Greenland and Antarctica taken from the Multichannel Coherent Radar Depth Sounder (MCoRDS). The data were collected as part of NASA Operation IceBridge funded campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IRMCR3/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IGBTH4 v1 - This data set contains bathymetry of Arctic fjords and Antarctic ice shelves based on measurements from the Sander Geophysics Airborne Inertially Referenced Gravimeter (AIRGrav) system. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IGBTH4/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"RDGBV4 v1 - This data set contains calculated balance velocity of the Greenland Ice Sheet during the last three quarters of the Holocene epoch (9ka).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE-Related/RDGBV4/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"RRRAG4 v2 - This data set presents the gridded radiostratigraphy of the Greenland Ice Sheet using very high frequency (VHF) radar sounding data, collected by The Center for Remote Sensing and Integrated Systems (CReSIS) at The University of Kansas.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE-Related/RRRAG4/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-multi-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA MULTI-TASTE Project","Description":"The Medium Resolution Imaging Spectrometer (MERIS) is one of 10 sensors deployed in March of 2002 on board the polar-orbiting Envisat-1 environmental research satellite by the European Space Agency (ESA). The MERIS instrument is a moderate-resolution wide field-of-view push-broom imaging spectroradiometer capable of sensing in the 390 nm to 1040 nm spectral range. Being a programmable instrument, it had the unique capability of selectively adjusting the width and location of its 15 bands through ground command. The instrument has a 68.5-degree field of view and a swath width of 1150 meters, providing a global coverage every 3 days at 300 m resolution. Communication with the Envisat-1 satellite was lost suddenly on the 8th of April, 2012, just weeks after celebrating its 10th year in orbit. All attempts to re-establish contact were unsuccessful, and the end of the mission was declared on May 9th, 2012. The 4th reprocessing cycle, in 2020, has produced both the full-resolution and reduced-resolution L1 and L2 MERIS products. EN1_MDSI_MER_FRS_1P is the short-name for the MERIS Level-1 full resolution, full swath, geolocated and calibrated top-of-atmosphere (TOA) radiance product. This product contains the TOA upwelling spectral radiance measurements. The in-band reference irradiances for the 15 MERIS bands are computed by averaging the in-band solar irradiance for each pixel. Each pixel’s in-band solar irradiance is computed by integrating the reference solar spectrum with the band-pass of each pixel. The Level-1 product contains 22 data files: 15 files contain radiances for each band (one band per file) along with associated error estimates, and 7 annotation data files. It also includes a Manifest file that provides metadata information describing the product.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"EN1_MDSI_MER_FRS_1P v4 - The Medium Resolution Imaging Spectrometer (MERIS) is one of 10 sensors deployed in March of 2002 on board the polar-orbiting Envisat-1 environmental research satellite by the European Space Agency (ESA). The MERIS instrument is a moderate-resolution wide field-of-view push-broom imaging spectroradiometer capable of sensing in the 390 nm to 1040 nm spectral range.","ARN":"arn:aws:s3:::prod-lads/EN1_MDSI_MER_FRS_1P","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-multi-taste","Sources":["awslabs-open-data-registry"]}
{"Name":"GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1)","Description":"A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes.  The dataset also contains additional variables for some granules including a SST anomaly derived from a MUR climatology and the temporal distance to the nearest IR measurement for each pixel.This dataset is funded by the NASA MEaSUREs program ( http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ), and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. Use the file global metadata \"history:\" attribute to determine if a granule is near-realtime or retrospective.\nRead our doc on how to get AWS Credentials to retrieve this data: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/GHGMR-4FJ04","Contact":"Help Desk: podaac@podaac.jpl.nasa.gov","ManagedBy":"NASA","UpdateFrequency":"From 2002-05-31 to Ongoing (Hourly - < Daily)","Tags":["aws-pds","datacenter","earth observation","global","ice","metadata","oceans","parquet","us","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). (Format: netCDF-4)","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME"}],"DataAtWork":{"Publications":[{"Title":"A multi-scale high-resolution analysis of global sea surface temperature","URL":"https://doi.org/10.1016/j.rse.2017.07.029","AuthorName":"Chin, T.M,  J. Vazquez-Cuervo, and E.M. Armstrong"}],"Tutorials":[{"Title":"MUR Sea Surface Temperature Analysis of Washington State","URL":"https://podaac.github.io/tutorials/notebooks/datasets/MUR_SST_Washington_Comparison.html","AuthorName":"Zoë Walschots","NotebookURL":"https://github.com/podaac/tutorials/blob/master/notebooks/datasets/MUR_SST_Washington_Comparison.ipynb"},{"Title":"Using Sea Surface Temperature and Sea Surface Height Data for Hurricane Helene","URL":"https://podaac.github.io/tutorials/notebooks/DataStories/HurricaneHelene_SST_SSH_Notebook.html","AuthorName":"Julie Sanchez","NotebookURL":"https://github.com/podaac/tutorials/blob/4466294936f7c992a78bd2954a9f4909784bf0ba/notebooks/DataStories/HurricaneHelene_SST_SSH_Notebook.ipynb"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-mur-jpl-l4-glob-v41","Sources":["awslabs-open-data-registry"]}
{"Name":"MODIS/Aqua Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061","Description":"The MYD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer observation bands and geolocation flags. The reflectance layers from the MYD09GA are used as the source data for many of the MODIS land products. \n\nKnown Issues\n* Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded seriously after launch and presently a majority of the Band 6 detectors are non-functional. Science users should read and use the non-functional detector flags and decide for themselves the optimum manner to handle non-functional detector \"gaps\" for their products. For complete information please refer to the [MODIS Characterization Support Team (MCST) website](https://mcst.gsfc.nasa.gov/time-dependent-list-non-functional-or-noisy-detector).\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n\nImprovments/Changes from Previous Version\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/MODIS/MYD09GA.061","Contact":"User Services: lpdaac@usgs.gov","ManagedBy":"NASA","UpdateFrequency":"From 2002-07-04 to Ongoing (Daily - < Weekly)","Tags":["aws-pds","datacenter","earth observation","geospatial","global","hdf","ice","land","opendap","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MODIS/Aqua Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061.","ARN":"arn:aws:s3:::lp-prod-protected/MYD09GA.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Download Files from S3 Using boto3","URL":"https://github.com/nasa/LPDAAC-Data-Resources/blob/main/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb","AuthorName":"LPDAAC","AuthorURL":"https://www.earthdata.nasa.gov/centers/lp-daac"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-myd09ga","Sources":["awslabs-open-data-registry"]}
{"Name":"MODIS/Aqua Surface Reflectance Daily L2G Global 250m SIN Grid V061","Description":"The MYD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MYD09GA). \n\nKnown Issues\n* Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded seriously after launch and presently a majority of the Band 6 detectors are non-functional. Science users should read and use the non-functional detector flags and decide for themselves the optimum manner to handle non-functional detector \"gaps\" for their products. For complete information please refer to the [MODIS Characterization Support Team (MCST) website](https://mcst.gsfc.nasa.gov/time-dependent-list-non-functional-or-noisy-detector).\n* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n\nImprovments/Changes from Previous Version\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/MODIS/MYD09GQ.061","Contact":"User Services: lpdaac@usgs.gov","ManagedBy":"NASA","UpdateFrequency":"From 2002-07-04 to Ongoing (Daily - < Weekly)","Tags":["aws-pds","datacenter","earth observation","geospatial","global","hdf","ice","land","opendap"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MODIS/Aqua Surface Reflectance Daily L2G Global 250m SIN Grid V061.","ARN":"arn:aws:s3:::lp-prod-protected/MYD09GQ.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Download Files from S3 Using boto3","URL":"https://github.com/nasa/LPDAAC-Data-Resources/blob/main/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb","AuthorName":"LPDAAC","AuthorURL":"https://www.earthdata.nasa.gov/centers/lp-daac"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-myd09gq","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NACP Project","Description":"This dataset provides estimates of hourly carbon dioxide (CO2) emissions from the combustion of fossil fuels at 1-km resolution for the coterminous United States (CONUS) covering the years 2012 through 2017. Emissions from the ACES model are reported for ten distinct emissions source sectors: Airports and Aircraft, Commercial Buildings, Electric Power Generation facilities, Industrial point and non-point sources, Commercial Marine Vessels, Nonroad vehicles and equipment, Oil and Gas wells and facilities, Onroad vehicles, Railway engines and yards, and Residential buildings. All emissions are reported hourly on a 1-km x 1-km spatial grid. The data are provided in NetCDF version 4 format.\n<br>\n\n#### FireBoundaries_CircPolarRegion_2477\nThis dataset contains polygons of retrievable fire boundaries across the circumpolar region attributed with pre-fire and post-fire normalized burn ratio (NBR), differenced normalized burn ratio (dNBR), relative differenced normalized burn ratio (RdNBR), and terrestrial ecoregions. The dataset is separated into three regions: North America, Europe, and Asia; each saved in an appropriate projection and with the years of data available from that region. Historical fire perimeters were available from 1986 to 2020 for North America. Fires from Europe and Asia were available from 2003 to 2016. Polygon geometries were simplified and processed to represent each fire with a single polygon. Google Earth Engine was used to calculate the mean pre-fire and post-fire NBR both within the polygon and in a buffer around the fire. The buffer values were used to adjust NBR values to account for differences in reflectance between image acquisition dates not related to the fire. The data are provided in GeoPackage format.\n<br>\n\n#### NACP_NAM_HYSPLIT_Footprints_1586\nThis dataset reports continuous atmospheric measurements of CO2 from two receptor sites and three boundary sites in and around Boston, Massachusetts, USA, that were combined with high-resolution CO2 emissions estimates and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to estimate regional CO2 emissions from September 2013 to December 2014. The HYSPLIT model followed an ensemble of 1,000 particles released at the urban CO2 measurement sites backward in time based on wind fields and turbulence from the North American Mesoscale Forecast System (NAM) at 12-km resolution to the boundary CO2 measurement sites to derive footprint values and CO2 enhancements expected from the prior emissions based on the Anthropogenic Carbon Emissions System (ACES) inventory and the urban-Vegetation Photosynthesis Respiration Model (urbanVPRM). This dataset contains three sets of data products: (1) observed hourly mean CO2 observations for two urban receptor sites in Boston, MA (Boston University (BU) and Copley Square (COP)), (2) observed hourly mean CO2 and calculated vertical profiles (50 - 5000 m) for three boundary sites around Boston including Harvard Forest at Petersham, MA (HF), Canaan, NH (CA), and Martha's Vineyard, MA (MVY), and modeled mean boundary CO2 concentrations for particles released from BU and COP, and (3) particle trajectory files including footprint values and CO2 enhancements above boundary CO2 concentrations from the HYSPLIT model.\n<br>\n\n#### echidna_1045\nThis data set contains forest canopy scan data from the Echidna® Validation Instrument (EVI) and field measurements data from three campaigns conducted in the United States: 2007 New England Campaign; 2008 Sierra National Forest Campaign; and 2009 New England Campaign. The New England field sites were located in Harvard Forest (Massachusetts), Howland Research Forest (Maine), and the Bartlett Experimental Forest (New Hampshire). The objective of the research was to evaluate the ability of the EVI ground-based, scanning near-infrared lidar to retrieve stem diameter, stem count density, stand height, leaf area index, foliage profile, foliage area volume density, and other useful forest structural parameters rapidly and accurately. The EVI scan data are Andrieu Transpose (AT) Projection images in ENVI .img and .hdr file pairs. There are 28 images from the 2007 New England Campaign, 30 images from the 2008 Sierra National Forest Campaign, and 54 images from the 2009 New England Campaign. There are range-weighted mean preview image files (.jpg format) for each AT Projection image. Manual measurements of tree structural properties were made during each campaign at EVI scan locations. The field measurements are provided in one file for each campaign (.csv format). Parameters include species identification, DBH, tree height, crown base, etc. organized by field plot. There is also a data file (.csv format) which compares EVI derived measurements to the field measured data (DBH, stem density, basal area, biomass, and LAI) from the 2007 New England Campaign.\n<br>\n\n#### MISR_Forest_AGB_SW_US_1978\nThis dataset provides estimates of forest aboveground biomass (AGB; in Mg ha-1) at a resolution of 250 m for the southwestern United States over the time period 2000-2021. The AGB estimates were derived from the Jet Propulsion Laboratory Multiangle Imaging Spectro-Radiometer (MISR) Level 1B2 Terrain radiance data and a multi-angle approach that exploits the relationship between forest AGB and a suite of red band reflectance values modeled at viewing angles with respect to the direction of illumination. The year 2000 National Biomass and Carbon Dataset (NBCD 2000) AGB estimates were used to fit a model coefficient for the MISR-derived AGB estimates for the year 2000, with AGB estimates for all subsequent years dependent on both this coefficient and MISR red band bidirectional reflectance factors (BRFs). Quality assurance (QA) files are also provided that allow users to impose criteria of varying stringency. The bidirectional reflectance distribution function (BRDF) model-fitting root mean square error (RMSE) value was used as one of the criteria to determine if the AGB estimates were reasonable. This dataset is the first example of forest AGB estimation based on a multi-angle index applied using MISR data.\n<br>\n\n#### Forest_Carbon_Priority_1803\nThis dataset provides related gridded outputs of future modeled forest carbon sequestration priority and related species richness and habitat suitability for the western United States. The primary dataset is of the ranking of forest lands in the western U.S. for preservation based on the ability of these lands to sequester carbon over the coming century. The preservation ranking was derived from the results of simulations of future potential forest net ecosystem productivity (NEP) and vulnerability to drought and wildfire, as modeled from 2020 to 2099 at 4 km x 4 km resolution using a modified version of the Community Land Model (CLM 4.5). In addition, data files of potential forest NEP ranking and the forest vulnerability ranking are also provided. Co-located data of species richness for amphibians, birds, mammals, and reptiles are included to illustrate habitat suitability in relation to forest carbon preservation rankings. There are two files for each vertebrate class, one reflecting all western U.S. species included in the USGS GAP Analysis Project and a second for the subset of species listed as threatened or endangered by the U.S. Fish and Wildlife Service. Establishing this forest carbon preservation priority ranking for forest lands in the western U.S. will help guide the conservation of land for climate change mitigation activities and improved harvest management in the region.\n<br>\n\n#### Global_Reservoirs_Methane_1918\nThis dataset includes global maps of methane (CH4) emissions from inland dam-reservoir systems at 0.25-degree spatial resolution. Daily emission rates (as grams of CH4 per day per total area of grid cell) were estimated for boreal, temperate, and subtropical-tropical eco-climatic domains and total emissions. The annual duration of the emission season is based on freeze-thaw cycles of these water bodies as applicable. In addition, the dataset includes the total fractional area of reservoirs in each grid cell. These estimates will promote understanding of the current and future role of reservoirs in the global CH4 budget and guide efforts to mitigate reservoir-related CH4 emissions. These emission estimates are climatological; one daily value for each day of year (n=365) is provided for each grid cell. Modeled estimates were based on daily mean inputs, averaged over 2002 to 2015.\n<br>\n\n#### Wildfire_Impacts_Boreal_Ecosys_2359\nThis dataset contains simulations of net primary production (NPP), heterotrophic respiration (RH), net ecosystem production (NEP), and soil temperature data in North American boreal forests for the period 1986-2020. Data sources included historical fire sources and Landsat data. The delta Normalized Burn Ratio (dNBR), which can be used to represent burn severity for a fire, was calculated for each individual fire over the time period. The interactions between canopy, fire and soil thermal dynamics were modelled using a soil surface energy balance model incorporated into a previous Terrestrial Ecosystem Model (TEM). Using the revised TEM, two regional simulations were conducted with and without fire disturbance. Fire polygons were dissected into each unit with unique fire history and then intersected with each grid cell to measure fire impacts. The output values for each grid cell are the area-weighted mean of each fire polygon and unburned area within the cell. Two extra simulations without a canopy energy balance scheme were also conducted to quantify the impact of the canopy. Soil temperature was simulated with and without the canopy energy balance scheme in the model in addition to considering fire impacts. The data are provided in comma separated values (CSV) format.\n<br>\n\n#### Dendrophenology_Eastern_US_1369\nThis data set provides a 30-year record of Landsat TM and ETM+ derived forest phenology and the results of tree ring analyses for annual wood production and nitrogen and carbon isotopic composition at 113 selected forested sites in the eastern United States. The sites are located in four national parks: Prince William Forest Park (PRWI), Harpers Ferry National Historical Park (HAFE), Catoctin Mountain Park (CATO), and Great Smoky Mountains National Park (GRSM). Phenology and tree ring data cover 1984-2013.\n<br>\n\n#### Coastal_Carbon_Budget_NE_US_1594\nThis dataset contains best estimates and uncertainties for mean annual fluxes of inorganic, organic, and total (organic + inorganic) carbon in tidal wetlands, estuaries and shelf waters of eastern North America, which is defined by the coastline running between the tip of the Scotian Peninsula (Canada) and the southern tip of Florida (USA). The data are provided on a per-unit-area basis and as spatially integrated values for each of the three ecosystem types (tidal wetlands, estuaries, and shelf waters) and the entire coastal ecosystem (tidal wetlands + estuaries + shelf waters) as well as for three geographic subregions (the Gulf of Maine, the Mid-Atlantic Bight, and the South Atlantic Bight) and the entire Eastern North America domain (Gulf of Maine + Mid-Atlantic Bight + South Atlantic Bight). The data include the net uptake from the atmosphere by the three ecosystems; burial in tidal wetland soils, estuarine sediments, and continental shelf sediments; riverine input from land to estuaries; and the net lateral advective transports from ecosystem to ecosystem. In addition, heterotrophic respiration (HR), net primary production (NPP), and net ecosystem production (NEP) estimates were computed for each ecosystem. The fluxes were derived using a variety of sources and are estimates for average conditions over the past decades from data covering roughly the period 1976-01-01 to 2017-12-31.\n<br>\n\n#### Dairy_Methane_CA_V1-2_1902\nThis dataset provides estimates of methane (CH4) emissions from dairies in California at a resolution of 0.1 degrees (~ 10 km x 10 km) for the year 2019. The mapped sources of dairy CH4 emissions are enteric fermentation and manure management reported in gigagrams per square km per year (Gg km-2 y-1). The sum of the two sources is also provided. These data are in the succession of Vista California (Vista-CA) spatial datasets that have identified and classified potential methane source emitters in California and were created utilizing an assortment of publicly available data sources from local, state, and federal agencies. This dataset can serve as a planning tool for mitigation, a prior for atmospheric observation-based emissions estimates, attribution of emissions to a specific facility, and to validate CH4 emissions reductions from management changes.\n<br>\n\n#### US_MODIS_Veg_Parameters_1539\nThis dataset provides MODIS-derived leaf area index (LAI), stem area index (SAI), vegetation area fraction, dominant landcover category, and albedo parameters for the continental US (CONUS), parts of southern Canada, and Mexico at 30 km resolution. The data cover the period 2003-2010 and were developed to be used as surface input data for regional agroecosystem-climate models. MODIS Collection 5 products used to derive these parameters included the Terra yearly water mask, vegetation continuous field products, the combined Terra and Aqua yearly land-cover category (LCC) (MCD12Q1), 8-day composites for LAI (MCD15A2), and albedo parameter (MCD43B1) products. Please note that the MODIS Version 5 land data products used in this dataset have been superseded by Version 6 data products.\n<br>\n\n#### NBCD2000_V2_1161\nThe NBCD 2000 (National Biomass and Carbon Dataset for the Year 2000) data set provides a high-resolution (30 m) map of year-2000 baseline estimates of basal area-weighted canopy height, aboveground live dry biomass, and standing carbon stock for the conterminous United States. This data set distributes, for each of 66 map zones, a set of six raster files in GeoTIFF format. There is a detailed README companion file for each map zone. There is also an ArcGIS shapefile (mapping_zone_shapefile.shp) with the boundaries of all the map zones. A mosaic image of biomass at 240 m resolution for the whole conterminous U.S. is also included. Please read this important note regarding the differences of Version 2 from Version 1 of the NBCD 2000 data. With Version 1, in some mapping zones, certain land cover types (in particular Shrubs, NLCD Type 52) were missing from and unaccounted for in modeled estimates because of a lack of reference data. In Version 1, when landcover types were missing in the models, the model for the deciduous tree cover type was applied. While more woody vegetation was mapped, the authors think this had little effect on model performance as in most cases NLCD version 1 cover type was not a strong predictor of modeled estimates (See companion Mapping Zone Readme files). In Version 2, after renewed modeling efforts and user feedback, these previously unaccounted for cover types are now included in modeled estimates. All 66 mapping zones were updated with the previously unmapped land cover types now mapped. The authors recommend use of the new version for all analyses and will only support the updated version. Development of the data set used an empirical modeling approach that combined USDA Forest Service Forest Inventory and Analysis (FIA) data with high-resolution InSAR data acquired from the 2000 Shuttle Radar Topography Mission (SRTM) and optical remote sensing data acquired from the Landsat ETM+ sensor. Three-season Landsat ETM+ data were systematically compiled by the Multi-Resolution Land Characteristics Consortium (MRLC) between 1999 and 2002 for the entire U.S. and were the foundation for development of both the USGS National Land Cover Dataset 2001 (NLCD 2001) and the Landscape Fire and Resource Management Planning Tools Project (LANDFIRE). Products from both the NLCD 2001 (landcover and canopy density) and LANDFIRE (existing vegetation type) projects as well as topographic information from the USGS National Elevation Dataset (NED) were used within the NBCD 2000 project as spatial predictor layers for canopy height and biomass estimation. Forest survey data provided by the USDA Forest Service FIA program were made available to the project under a national Memorandum of Understanding. The response variables (canopy height and biomass) used in model development and validation were derived from the FIA database (FIADB). Production of the NLCD 2001 and LANDFIRE projects was based on a mapping zone approach in which the conterminous U.S. was split into 66 ecoregionally distinct mapping zones. This mapping zone approach was also adopted by the NBCD 2000 project.\n<br>\n\n#### NACP_PNW_Carbon_Balance_1317\nThis data set provides Biome-BGC modeled estimates of carbon stocks and fluxes in the U.S. Pacific Northwest for the years 1986-2010. Fluxes include net ecosystem production (NEP), and net aboveground wood growth. Stocks include aboveground wood mass. Also present are mapped distributions of associated forest disturbances, distinguished by disturbance type (harvest, fire, pest/pathogen). The data are presented in a mapped form as well as in tabular summaries broken out by ownership and ecoregion. Maps of annual precipitation and temperature data are included for the years 1980-2010.\n<br>\n\n#### NA_TreeAge_1096\nThis data set provides forest age map products at 1-km resolution for Canada and the United States (U.S.A.). These continental forest age maps were compiled from forest inventory data, historical fire data, optical satellite data, and the images from NASA’s Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) project. These input data products have various sources and creation dates as described in the source paper by Pan et al. (2011). Canadian maps were produced with data available through 2004 and U.S.A. maps with data available through 2006. A supplementary map of the standard deviations for age estimates was developed for quantifying uncertainty. Note that the Pan et al. (2011) paper is included as a companion file with this data set and was the source of descriptions in the guide. Forest age, implicitly reflecting the past disturbance legacy, is a simple and direct surrogate for the time since disturbance and may be used in various forest carbon analyses that concern the impact of disturbances. By combining geographic information about forest age with estimated carbon dynamics by forest type, it is possible to conduct a simple but powerful analysis of the net CO2 uptake by forests, and the potential for increasing (or decreasing) this rate as a result of direct human intervention in the disturbance/age status.\n<br>\n\n#### NACP_GHG_Data_Compilation_1206\nThis data set is a collection of measurements of carbon dioxide (CO2) and non-CO2 greenhouse gases made across North America by nine independent atmospheric monitoring networks from 2000 - 2009. During this North American Carbon Program (NACP) sponsored activity, data were compiled from the following networks: AGAGE, COBRA, CSIRO, INTEX-A, INTEX B, Irvine Latitude Network, NOAA CMDL, SCRIPPS, and Stanley Tyler-UC Irvine. The files presented here are the products of merging multiple original measurement results files for selected sites across North America from each monitoring network. The primary focus of this effort was the compilation of non-CO2 greenhouse gases over North America, but numerous CO2 observations are also included. The data files for each network are accompanied by detailed readme documentation files prepared by the respective network investigators. Project descriptions, objectives, references, sampling and analysis methods, and data file descriptions are included in these READMEs. Table 1 in the documentation displays the monitoring network sites, sample types, analytes, and links to the detailed network README files. Network- and laboratory-specific data citations are included in the README documentation and should be used to acknowledge the use of these data as appropriate. The data files for each monitoring network and each sampling type (continuous or flasks) have been combined into one compressed (.zip) file along with the detailed README document. There are 17 compressed files that when expanded contain data files which represent one yearâ�™s data for that specific campaign and sampling method. The number of annual files that were compiled from a network into this collection varies.\n<br>\n\n#### NACP_Wild_Cropland_Fuel_Map_1163\nThe data set provides a 30-m comprehensive fuelbed characteristics map for both the wildland and cropland areas of the conterminous United States (CONUS) for 2010. This integrated product is the result of combining the spatially discrete Fuel Characteristic Classification System (FCCS) data of the US Forest Service (USFS) with the crop-and grassland-specific information of the US Department of Agriculture?'s (USDA's) Cropland Data Layer (CDL). By combining the spatially discrete details of the FCCS data set with the crop-and grassland-specific information of the CDL, a more robust map of fuelbed characteristics is available. The merged product has an advantage over the original FCCS map for estimating emissions from burned areas due to the integration of the fuelbed characteristics for agricultural areas from the CDL. There are three GeoTIFF format files and three comma-separated companion files distributed with this data set. The three tif files provided are very large and exceed the size limits of a standard GeoTIFF file format (4 GB). File sizes range from 20 to 30 GB. Compressed file sizes range from 2 to 3 GB. They are in a format that is called a BigTIFF file. ArcGIS 10.0 and ERDAS Imagine are able to read these files.\n<br>\n\n#### NACP_Boreal_Biome_Biomass_1273\nThis data set provides estimates of aboveground biomass (AGB) for defined land cover types within World Wildlife Fund (WWF) ecoregions across the boreal biome of Alaska and western and eastern Canada, roughly between 45 and 70 degrees N. The study focused on within-growing-season data, i.e. leaf-on conditions. The AGB estimates were derived from a series of models that first related ground-based measured biomass to Portable Airborne Laser System (PALS) LiDAR measurements, and a second set of models that related the airborne estimates of biomass to Geoscience Laser Altimeter System (GLAS) LiDAR canopy structure measurements. The GLAS LiDAR biomass estimates were extrapolated by land cover types and ecoregions across the entire biome area. The study compiled remotely sensed forest structure data collected in June of 2005 and 2006 from the GLAS LiDAR instrument aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite and from the PALS airborne instrument flown at various times from 2005-2009 over both the ground plots and the ICESat GLAS flight path. For a consistent biome-level analysis, ecoregions contained within the boreal forest biome were identified by the World Wildlife Fund's (WWF) ecoregion map of the world (Olson et al., 2001). Land cover maps were used to identify land cover types for stratification purposes within eco-regions. Land cover data for Canada were provided by the Earth Observations for Sustainable Development (EOSD) project centered on year 2000, with images from 1999 to 2002. The National Land Cover Data (NLCD) 2001 classification was used for Alaska based on data collected between 1999 and 2004. The ground-based measurements are not provided with this data set.\n<br>\n\n#### NACP_MCI_CO2_Inventory_1205\nThis data set provides a bottom-up CO2 emissions inventory for the mid-continent region of the United States for the year 2007. The study was undertaken as part of the North American Carbon Program (NACP) Mid-Continent Intensive (MCI) campaign. Emissions for the MCI region were compiled from these resources into nine inventory sources (Table 1):(1) forest biomass and soil carbon, harvested woody products carbon, and agricultural soil carbon from the U.S. Greenhouse Gas (GHG) Inventory (EPA, 2010; Heath et al., 2011); (2) high resolution data on fossil and biofuel CO2 emissions from Vulcan (Gurney et al,. 2009); (3) CO2 uptake by agricultural crops, lateral transport in crop biomass harvest, and livestock CO2 emissions using USDA statistics (West et al., 2011); (4) agricultural residue burning (McCarty et al., 2011); (5) CO2 emissions from landfills (EPA, 2012); (6) and CO2 losses from human respiration using U.S. Census data (West et al., 2009). The CO2 inventory in the MCI region was dominated by fossil fuel combustion, carbon uptake during crop production, carbon export in biomass (commodities) from the region, and to a lesser extent, carbon sinks in forest growth and incorporation of carbon into timber products.\n<br>\n\n#### NACP_MCI_CO2_Inversions_1204\nThis data set provides estimates of Net Ecosystem Exchange (NEE) flux for the U.S. Upper Midwest at 0.5-degree resolution for the year 2007. Estimates were produced by two atmospheric CO2 inversion systems (\"??top-down\"?), referenced as the continental Colorado State University (CSU) inversion and the mesoscale Pennsylvania State University (PSU) inversion. This modeling work was performed in support of the North American Carbon Program (NACP) Mid-Continent Intensive (MCI) experimental campaign in the U.S. Upper Midwest designed to evaluate innovative methods for CO2 flux inversion and data assimilation. The experiment was performed over a relatively flat, heavily managed agricultural landscape which features a high density of atmospheric CO2 observation measurements. Among the CO2 observations used by the inversion systems were results from a network of instrumented tall towers in the region. The NEE estimates were produced for comparison with CO2 fluxes derived from bottom-up inventory estimates. There are five data files with this data set. The NEE estimates are provided in two NetCDF files, one for each inversion system. Boundary CO2 inflow data used by each inversion system are provided in three comma-separated-format files (.csv).\n<br>\n\n#### NACP_MCI_Crop_GPP_1217\nThis data set provides an integrated collection of (1) ground-based meteorological, radiometric, and vegetation measurements, (2) flux-based estimates of gross primary production (GPP), and (3) numerous vegetation indices derived from satellite imagery for three eddy covariance flux tower locations near Lincoln, Nebraska, USA. Land use surrounding the towers is cropland with corn and soybeans. Data are reported for selected days during the growing seasons of 2001 through 2008 only when ground-based crop canopy reflectance was measured. Algorithms developed to relate ground-based and satellite spectral information to GPP of the cropland adjacent to the towers are provided. AmeriFlux tower-based Level 2 measurements included photosynthetically active radiation (PAR), heat flux, and GPP estimates; see Section 2 for specific towers. Ground-based measurements on the corn and soybean vegetation surrounding the towers included total chlorophyll content (Chl) and leaf area index (LAI). Ground-based crop canopy reflectance was measured at 5.4 m above the corn and soybean canopy using hyperspectral radiometers (range 400 to 1100 nm) during the growing season from May to October in eight different years (2001-2008). This resulted in 173 measurement campaigns (18 in 2001, 31 in 2002, 34 in 2003, 31 in 2004, 21 in 2005, 15 in 2006, 14 in 2007, and 9 in 2008). Spectral bands from Landsat TM and ETM+, MERIS , and MODIS instruments were used to calculate vegetation indices. Vegetation indices related to chlorophyll can be used as a proxy for GPP because of the observed close relationship between GPP and Chl content in crops. Algorithms developed to relate spectral information to the GPP of the cropland adjacent to the towers are provided as companion files.\n<br>\n\n#### NACP_MCI_CO2_Measurements_1202\nThis data set provides high precision and high accuracy atmospheric CO2 data from seven instrumented communication towers located in the U.S. Upper Midwest. The overall sampling period was from January 2007 through December 2009 although actual sampling dates vary within this time period for individual towers and sampling heights above ground level. The measurements were obtained in support of the North American Carbon Program (NACP) Mid-Continent Intensive (MCI) campaign. The sampling network included: the five Ring 2 towers (Centerville (Iowa), Galesville (Wisconsin), Kewanee (Illinois), Mead (Nebraska), and Round Lake (Minnesota)) deployed and operated by PSU; the Missouri Ozarks (Missouri) co-located AmeriFlux site (PSU/Oak Ridge National Laboratory (ORNL)); and the Rosemount (Minnesota) tall tower trace gas observatory (University of Minnesota, Rosemount Research and Outreach Center (RROC)). Hourly CO2 dry mole fractions (in ppm) were averaged from measurements made at different above-ground levels on the towers and are reported in Coordinated Universal Time (UTC). For the five Ring 2 sites, daily daytime average CO2 dry mole fractions were also calculated, from hourly values between 12:00-17:00 local standard time and reported in UTC. There are seven compressed (.zip) data files and one comma-separated (.csv) file with this data set. Data quality flags are provided in each file.\n<br>\n\n#### NACP_MsTMIP_Model_Structure_1228\nThis data set provides a summary of the model structure and characteristics of participating models in the North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP), a formal model intercomparison and evaluation effort focused on improving the diagnosis and attribution of carbon exchange at regional and global scales. Model structure refers to the types of processes considered (e.g. nutrient cycling, disturbance, lateral transport of carbon), and the specific ways these processes are represented (e.g. photosynthetic formulation, temperature sensitivity, respiration) in the models. These data are the result of a comprehensive survey of investigators responsible for each MsTMIP participating model. For a given characteristic (i.e., process/attribute), a model was assigned a binary value (0 or 1) indicating whether it included a particular characteristic; a value of one (1) was given if it considered or included that process, or a zero (0) if it did not. MsTMIP builds upon current and past synthesis activities, and has a unique framework designed to isolate, interpret, and inform understanding of how model structural differences impact estimates of carbon uptake and release. There is one data file with this data set in .csv format.\n<br>\n\n#### NACP_MsTMIP_TBMO_1225\nThis data set provides global gridded estimates of carbon, energy, and hydrologic fluxes between the land and atmosphere from 15 Terrestrial Biosphere Models (TBMs) in a standard format. Model estimates are at monthly and yearly time steps for the period 1900 to 2010, with a spatial resolution of 0.5 degree x 0.5 degree globally, excluding Antarctica.\n<br>\n\n#### NACP_MsTMIP_Model_Driver_1220\nThis data set provides environmental data that have been standardized and aggregated for use as input to carbon cycle models at global (0.5-degree resolution) and regional (North America at 0.25-degree resolution) scales. The data were compiled from selected sources (Table 2) and integrated into gridded global and regional collections of climatology variables (precipitation, air temperature, air specific humidity, air relative humidity (NA only), pressure, downward longwave radiation, downward shortwave radiation, and wind speed), time-varying atmospheric CO2 concentrations, time-varying nitrogen deposition, biome fraction and type, land-use and land-cover change, C3/C4 grasses fractions, major crop distribution, phenology, multiple soil characteristics, and a land-water mask. The temporal ranges of the data are sufficient for carbon cycle model simulations from 1801 to 2010. These data were compiled specifically for the North American Carbon Program (NACP) Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as the prescribed model input driver data (Huntzinger et al., 2013). The driver data were used by 22 terrestrial biosphere models to run baseline and sensitivity simulations. The standardized data provided consistent model inputs to minimize the inter-model variability caused by differences in environmental drivers and initial conditions. Together with the sensitivity simulations, the standardized input data enable better interpretation and quantification of structural and parameter uncertainties of model estimates. Data are provided in Climate and Forecast (CF) metadata convention compliant (version 1.4) netCDF-4 file formats. There are 3,152 .nc4 data files with this data set.\n<br>\n\n#### NACP_MsTMIP_Unified_NA_SoilMap_1242\nThis data set provides soil maps for the United States (US) (including Alaska), Canada, Mexico, and a part of Guatemala. The map information content includes maximum soil depth and eight soil attributes including sand, silt, and clay content, gravel content, organic carbon content, pH, cation exchange capacity, and bulk density for the topsoil layer (0-30 cm) and the subsoil layer (30-100 cm). The spatial resolution is 0.25 degree. The Unified North American Soil Map (UNASM) combined information from the state-of-the-art US General Soil Map (STATSGO2) and Soil Landscape of Canada (SLCs) databases. The area not covered by these data sets was filled by using the Harmonized World Soil Database version 1.21 (HWSD1.21). The Northern Circumpolar Soil Carbon (NCSCD) database was used to provide more accurate and up-to-date soil organic carbon information for the high-latitude permafrost region and was combined with soil organic carbon content derived from the UNASM (Liu et al., 2013). The UNASM data were utilized in the North American Carbon Program (NACP) Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as model input driver data (Huntzinger et al., 2013). The driver data were used by 22 terrestrial biosphere models to run baseline and sensitivity simulations. The compilation of these data was facilitated by the NACP Modeling and Synthesis Thematic Data Center (MAST-DC). MAST-DC was a component of the NACP (www.nacarbon.org) designed to support NACP by providing data products and data management services needed for modeling and synthesis activities.\n<br>\n\n#### NAFD-NEX_Forest_Disturbance_1290\nThe North American Forest Dynamics (NAFD) products provided in this data set consist of 25 annual and two time-integrated forest disturbance maps for the conterminous United States (CONUS) derived from Landsat images for the period 1986-2010. Each annual map has classified pixels showing water, no forest cover, forest cover, no data available (data gaps) in present year, and forest disturbances that occurred in that year. The time-integrated maps are similarly classified, but over the entire 1986-2010 period with the first and last forest disturbance years identified and provided as separate maps.\n<br>\n\n#### Forest_Disturbance_Intensity_2059\nThis dataset contains estimates of forest disturbance intensity for the conterminous United States from 1986 to 2015. It quantifies the severity/intensity of forest disturbances at 30 m resolution using time series Landsat observations and the vegetation change tracker algorithm. For each disturbance event mapped at a pixel location, the percentage of basal area removal (PBAR) after the disturbance event was estimated based on spectral changes derived from Landsat data and Random Forest models calibrated using field measurements collected by the US Forest Service Forest Inventory and Analysis (FIA) Program. This dataset complements and extends the North American Forest Dynamics-NASA Earth eXchange (NAFD-NEX) US Forest Disturbance History dataset and the NAFD-ATT Forest Canopy Cover Loss dataset from 1986-2010 to 1986-2015. Data are provided in Cloud Optimized GeoTIFF (.tif) format.\n<br>\n\n#### NACP_Forest_Biophysical_1046\nThis data set includes biophysical measurements collected in 2009 from five New England experimental forest stations: Bartlett Experimental Forest, Harvard Forest, Howland Research Forest, Hubbard Brook Experimental Forest, and the Penobscot Experimental Forest. Howland measurements were repeated in 2010 and one site in the Sierra National Forest, California, was surveyed in 2008. Biomass in respective measurement plots was calculated with allometric equations using measured diameter at breast height (DBH) for trees greater than 10 cm and species identification. Within selected subplots, the number of stems with diameters less than 10 cm were counted and classified to allow for an estimate of biomass for these stems. There are 16 data files provided that present the biophysical measurement results and the biomass estimates in ASCII comma-separated format. For a subset of sites and plots (Bartlett Experimental Forest, Harvard Forest and Howland Research Forest), more intensive inventories were done in coordination with Echidna lidar imaging (Strahler et al., 2008). In these intensive collections, the stem location, species, DBH and live/dead status were recorded for all stems with total stem height and canopy dimensions recorded for every tenth stem. In addition, for stems below 10 cm DBH, species and count were recorded in a subplot of each intensive inventory plot. See the related data set Strahler et al., 2011. Investigators from Federal and university laboratories conducted these field campaign to make estimates of forest biophysical attributes that will prove useful in comparisons with airborne lidar (LVIS) and UAVSAR remote sensing acquisitions. The North American Carbon Program (NACP) is a multi-disciplinary research program designed to obtain scientific understanding of North America's carbon sources and sinks and of the changes in carbon stocks needed to meet societal concerns, and to provide tools for decision makers. NACP began in 2002 and continues to date. The NACP data collection contains continental carbon budgets, dynamics, processes, and management of the sources and sinks of carbon dioxide, methane, and carbon monoxide in North America and in adjacent ocean regions.\n<br>\n\n#### NACP_Modeled_NEE_NEP_Fluxes_1203\nThis data set provides modeled carbon flux estimates at 8-km spatial resolution over North America for the year 2004 of (1) net ecosystem exchange (NEE) of carbon dioxide (CO2), (2) net ecosystem production (NEP, the balance of net primary production and heterotrophic respiration), (3) stream evasion (CO2 emitted from streams and rivers), (4) emissions from harvested forest and agricultural products, and (5) emissions from biomass burning. Annual estimates, in g C/m2/year, are provided for all five fluxes. Daily estimates, in g C/m2/day, are provided for NEP and stream evasion fluxes. Fluxes for fire emissions, harvest decomposition/respiration, stream evasion, and NEP were derived as described in Section 5. NEE fluxes were estimated using a full bottom-up accounting of NEE produced by integrating emissions from harvested forest and agricultural products, CO2 emitted from streams and rivers, and biomass burning in the CarbonTracker (version 2011_oi) modeling system. NEE estimates were run in the forward mode through the CarbonTracker inversion setup that calculates CO2 uptake and release at the Earth's surface over time. Refer to Turner et al.(2013) for details. There are seven data files in NetCDF (.nc) format with this data set, including: five annual files for fire emissions, harvest decomposition/respiration, stream evasion, NEP, and NEE fluxes; and two daily files for NEP and stream evasion fluxes.\n<br>\n\n#### NAFD_Disturbance_1077\nThis data set provides the results of time-series analyses of Landsat imagery for 55 selected forested sites across the conterminous U.S.A. The output is a pair of disturbance data products for each site, one showing the first year of disturbance in the time series, the other showing the last year of disturbance. Each data pixel is labeled as either a static land class (persistent non-forest, persistent forest, or persistent water) or with the year of change for disturbed forest pixels. The time period analyzed is approximately 1984-2009. These forest disturbance data are distributed as a single band GeoTiff, with appropriate projection information defined within the file. The analyses were performed in three phases: 5 sites during the Prototype/Focal phase; 23 sites in Phase I; and 27 sites in Phase II. The spatial resolution of the Prototype/Focal and Phase I data is 28.5 meters. The spatial resolution of the Phase II data is 30 meters. The temporal resolution is nominally biennial. The mapped area for each forested site is approximately 185 km x 185 km. There are a total of 110 GeoTiff files â?? a first year and a last year of disturbance file for each of the 55 sites.\n<br>\n\n#### NACP_Peatland_Land_Cover_MI_1513\nThis dataset provides a land cover map focused on peatland ecosystems in the upper peninsula of Michigan. The map was produced at 12.5-m resolution using a multi-sensor fusion (optical and L-band SAR) approach with imagery from Landsat-5 TM and ALOS PALSAR collected between 2007 and 2011. A random forest classifier trained with polygons delineated from field data and aerial photography was used to determine pixel classes. Accuracy assessment based on field-sampled sites show high overall map accuracy (92%).\n<br>\n\n#### NACP_Peatland_Burn-Severity_1283\nThis data set provides landcover maps of (1) peatland type (bog, fen, marsh, swamp) with levels of biomass (open, forested) and (2) Burn Severity Index (BSI) (Dyrness and Norum, 1983) for four wildfire areas in northern Alberta, Canada. The four wildfire sites include the Utikuma fire site of 2011, Kidney Lake fire site of 2011, Fort McMurray west fire site of 2009, and Fort McMurray east fire site of 2009. The peatland classification at 12.5-m resolution (fen vs. bog including treed vs. open vs. shrubby) at each wildfire site was based on a pre-burn 2007 multi-date, multi-sensor fusion (Optical-IR, C-band and L-band SAR) approach. Over 350 field locations were sampled in central Alberta to train and validate the peatland type maps. The additional site, Wabasca, was an unburned site. Burn severity was measured in the field using the Burn Severity Index (BSI) (Dyrness and Norum 1987), a qualitative assessment of burnt moss that uses a 1-5 scale, with 1 being unburnt and 5 being severely burnt. The field data of ground consumption were correlated with Landsat pre- and post-burn imagery, specific to peatlands, to develop multivariate models for calculating burn severity and %-not-sphagnum-moss. These models were used to generate the Burn Severity Maps at 30-m resolution (percent unburned moss, and the burn severity index (BSI)). All sites were visited in 2013 for field measurements and the Utikuma site was also visited in 2012 for field measurements. Additional biophysical data for the various peatlands (aboveground biomass – tree and shrub, plant heights, density, etc. were collected and will be provided in another data set.\n<br>\n\n#### NACP_Regional_Obs_Model_Grid_1157\nThis data set contains standardized gridded observation data, terrestrial biosphere model output data, and inverse model simulations of carbon flux parameters that were used in the North American Carbon Program (NACP) Regional Synthesis activities. The data set provides five observation data files (MODIS GPP, MODIS NPP, FIA forest biomass/forest area, NASS crop NPP, and NASS agricultural land fraction) and simulation results from 18 terrestrial biosphere models (TBM) (28 variables; 114 files) and seven inverse models (IM) (two variables; 8 files). To produce this data set, the NACP Modeling and Synthesis Thematic Data Center (MAST-DC) resampled original model simulation results and observation measurement data to 1-degree spatial resolution for North American region, interpolated into monthly or yearly temporal resolution, and reformatted into Climate and Forecast (CF) convention compatible netCDF format. This data set is related to two other processed regional data sets (i.e., NACP Regional: Supplemental Gridded Observations, Biosphere and Inverse Model Outputs; and NACP Regional: National Greenhouse Gas Inventories and Aggregated Gridded Model Data) and the originally-submitted NACP Regional: Original Observation Data and Biosphere and Inverse Model Outputs.\n<br>\n\n#### NACP_Regional_Model_GHG_Aggr_1179\nThis data set provides two products that were derived from the recently published North American Carbon Program (NACP) Regional Synthesis 1-degree terrestrial biosphere model (TBM) and inverse model (IM) outputs (Gridded 1-deg Observation Data and Biosphere and Inverse Model Outputs, Wei et al., 2013). The first product is the aggregation of the standardized gridded 1-degree TBM and IM outputs to the Greenhouse Gas (GHG) inventory zones as defined for North America (United States, Canada, and Mexico). Depending on the data availability, the monthly/yearly Net Ecosystem Exchange (NEE), Net Primary Production (NPP), Total Vegetation Carbon (VegC), Heterotrophic Respiration (Rh), and Fire Emissions (FE) outputs from the 22 TBM and 7 IM models were aggregated from the 1-degree resolution gridded format to the inventory zones and then, further divided into Forest Lands, Crop Lands, and Other Lands sectors within each inventory zone based on the 1-km resolution GLC2000 land cover map (GLC2000, 2003). The second product is the North American national GHG inventories on the scale of inventory zones which contain estimated land-atmosphere exchange of CO2 (NEE) in forest lands, crop lands, and other lands sectors. NEE estimates were synthesized from inventory-based data on productivity, ecosystem carbon stock change, and harvested product stock change, and additional information from national-level GHG inventories of the United States, Canada, and Mexico including EPA (2011) and Environment Canada (2011). An additional summary file of annual mean NEE (2000-2006)is provided for both land sectors and reporting zones in North America and was created by combining the aggregated model output and the national GHG database and is provided. The aggregated monthly and yearly model output data and the national GHG inventories data are available in comma separated value (.csv) format files. Also provided are detailed inventory zone spatial data as an ESRI Shapefile. Included are zone names, boundaries, and zone and land cover type area attributes. For mapping convenience, the inventory zones shapefile was merged with 1-km forest, crop, and other lands masks to create a 1-km resolution reference data file that was converted to GeoTIFF format. The GeoTIFF defines to which inventory zone and land cover type each 1-km grid cell belongs. This document provides detailed information about the content, format, and processing procedures of these two data products. Detailed descriptions of the TBMs and IMs can be found in a separate companion document: NACP Regional Synthesis - Description of Observations and Models.\n<br>\n\n#### NACP_Regional_Obs_Model_Orig_1193\nThis data set contains the originally-submitted observation measurement data, terrestrial biosphere model output data, and inverse model simulations that various investigator teams contributed to the North American Carbon Program (NACP) Regional Synthesis activities. The data set provides nine (9) data packages of remote sensing and ground observation measurements (OM) (MODIS gross primary productivity (GPP), MODIS net primary production (NPP), MODIS fraction of photosynthetically active radiation (fPar), MODIS leaf area index (LAI), MODIS enhanced vegetation index (EVI), MODIS normalize difference vegetation index (NDVI), Forest Inventory and Analysis (FIA) forest biomass, National Agricultural Statistics Service (NASS) crop NPP, and Flux Anomaly). The data set also provides data packages of simulation results from 19 terrestrial biosphere models (TBM) and eight (8) inverse models (IM). The data packages are respectively OM, TBM, and IM data files listed in Tables 4-6. Each OM, TBM, and IM data package contains all of the original data (and documentation, if any) that the NACP Modeling and Synthesis Thematic Data Center (MAST-DC) acquired or received. These originally-submitted data were processed by the MAST-DC to produce the three standardized gridded data sets of carbon flux for inter-comparison purposes (see Related Data Products below). These original data and documentation are provided to allow users of the standardized gridded data products to be able to trace back to the data origins when needed. The Data Center (ORNL DAAC) transformed some of the originally-submitted data files to file formats that are more suitable for long-term archiving. For example, .xlsx files were saved as .csv, ERDAS Imagine files were converted to GeoTIFFs, and MATLAB files were converted to GeoTIFF and NetCDF formats as appropriate. Files received in NetCDF, GeoTIFF, and HDF formats were not transformed.\n<br>\n\n#### NACP_Regional_Obs_Model_Suppl_1158\nThis data set contains standardized gridded observation data, terrestrial biospheric model output, and inverse model simulations that were compiled but not used in the North American Carbon Program (NACP) Regional Synthesis activities, thus the supplemental designation. The data set provides six (6) observation data packages (9 variables - MODIS LAI, MODIS FPAR, MODIS NDVI, MODIS EVI, FIA forest biomass, forest area, GPP Anomaly, NEE Anomaly, Reco Anomaly; 8 data files), output results from three terrestrial biosphere models (TBM) (14 variables; 214 files), and simulations from one inverse model (IM) (one variable; 1 file). To produce this data set, the NACP Modeling and Synthesis Thematic Data Center (MAST-DC) original data files were resampled to 1-degree spatial resolution for North American region (except for FIA Forest Biomass which was resampled to 0.5-degree resolution), interpolated into monthly or yearly temporal resolution, and reformatted into Climate and Forecast (CF) convention compatible netCDF format.\n<br>\n\n#### NACP_Site_Model_Flux_Std_Fmt_1183\nThis data set provides standardized output variables for gross primary productivity (GPP), net ecosystem exchange (NEE), leaf area index (LAI), ecosystem respiration (Re), latent heat flux (LE), and sensible heat flux (H) from 24 terrestrial biosphere models for 47 eddy covariance flux tower sites in North America. Each model used standardized input data for each flux tower site (i.e., gap-filled, locally observed weather; land use history; and other site specific data) and followed standard model setup and spinup procedures. The files also contain gap-filled observations and total uncertainty estimates. The data set was compiled for the North American Carbon Program (NACP) Site-Level Synthesis for use in model inter-comparison and assessment of how well the models simulate carbon processes across vegetation types and environmental conditions in North America. There is one compressed (.zip) file with this data set. When expanded, the .zip file contains model output data for one variable at one site. The model output and observations are available at the native half-hourly time step, or in daily, monthly, and annual aggregations, in comma-separated text (.csv) format.\n<br>\n\n#### NACP_Site_Model_Data_Orig_Fmt_1192\nThis data set contains the original model output data submissions from the 24 terrestrial biosphere models (TBM) that participated in the North American Carbon Program (NACP) Site-Level Synthesis. The model teams generated estimates for, but not limited to, a minimum of six variables, including gross primary productivity (GPP), net ecosystem exchange (NEE), leaf area index (LAI), ecosystem respiration (Re), latent heat flux (LE), and sensible heat flux (H) for each of 47 selected eddy covariance flux tower sites across North America. Participating modeling teams followed the NACP Site Synthesis Protocol (site_synthesis_protocol_v7.pdf), which covers procedures, plans, and infrastructure for the site-level analyses. File format and units conversions of several data submissions were made by the MAST-DC to produce NetCDF files of consistent content and structure for all 24 TBM outputs. The model outputs are structured as described in Appendix A: Model Output Variables, of the Site Synthesis Protocol. In addition, MAST-DC processed these original model submissions to derive uniquely processed and formatted data files for model inter-comparison and evaluation (NACP Site: Terrestrial Biosphere Model and Aggregated Flux Data in Standard Format). This related data set provides GPP, NEE, LAI, Re, LE, and sensible heat (H) model output variables at the native half-hourly time step, and in daily, monthly, and annual aggregations. The related data set also contains gap-filled observations and total uncertainty estimates at the same time steps. There are 24 compressed (.zip) files with this data set -- one file for each model. When expanded, the .zip files contain model output data files for flux tower sites in NetCDF and some in text formats.\n<br>\n\n#### NACP_Site_Tower_Met_and_Flux_1178\nThis data set contains meteorological, carbon cycle flux, phenology, and ancillary data measured at 47 eddy covariance flux tower sites across North America. The data were used by North American Carbon Program (NACP) Site-Level Synthesis as model driver data and for assessing how well 24 Terrestrial Biosphere Models simulated carbon processes across vegetation types and environmental conditions. *The meteorology data include eight variables: air temperature (K), specific humidity (kg/kg), wind speed (m/s), precipitation (kg/m2/s), surface pressure (Pa), surface incident shortwave radiation (W/m2), surface incident longwave radiation (W/m2), and CO2 concentration (ppm). Gap-filled data were used by modeling teams as input model driver data. *Measured fluxes of net ecosystem exchange (NEE) and derived gross primary productivity (GPP) and respiration (R) and respective calculated uncertainty estimates are provided for each tower site at the native time resolution of the observations (30 or 60-minute) as well as the diurnal, seasonal, and annual time scales. The data were gap-filled following a standard protocol. Components of uncertainty include uncertainties resulting from turbulence, gap-filling, flux partitioning, and u* threshold determination. Flux observations and uncertainty data were used to assess how well models simulated carbon processes. *Remotely sensed NDVI, LAI, and fPAR phenology data were derived from the GIMMS version g NDVI data set for each flux tower site. Phenology data were used by some modeling teams as input model driver data. *Ancillary data and information describe tower location and physical characteristics, disturbance history, and biological and ecological attributes of the vegetation, litter, and soil. These ancillary data were used by modeling teams as input model driver data. The data files are in both ASCII text and NetCDF formats (ALMA standard). The compilation of these data was facilitated by the NACP Modeling and Synthesis Thematic Data Center (MAST-DC).\n<br>\n\n#### NACP_BlackSpruce_Burn-Severity_1331\nThis data set provides organic soil layer characteristics, estimated carbon content, and soil depth measurements made at four black spruce stands in interior Alaska that had burned twice in the last 37-52 years (intermediate-interval fire events). The most recent fires occurred in 2004, 2005, and 2010. Measurements of soil depth and distance from the adventitious roots to the soil, and total organic matter are also included for unburned black spruce sites adjacent to the burned sites dominated by live, intermediate-aged (~37-52 years) black spruce trees.\n<br>\n\n#### NACP_TERRA-PNW_1292\nThis data set contains measurements and estimates of leaf, tree, and soil data from six projects conducted by the Terrestrial Ecosystem Research and Regional Analysis- Pacific Northwest (TERRA-PNW) research group between 1999 and 2014 across forests in Oregon and Northern California. Included are standardized, integrated measurements and estimates of specific leaf area, leaf longevity, leaf carbon and nitrogen for 35 tree and shrub species derived from more than 1,200 branch samples collected from over 200 forest plots, including several AmeriFlux sites. Plot-level measurements of forest composition, structure (e.g. tree biomass), and productivity estimates, as well as measurements of soil structure (e.g. bulk density) and chemistry (e.g. carbon) are also included.\n<br>\n\n#### VPRM_North_America_Parameters_1349\nThis data set provides Vegetation Photosynthesis Respiration Model (VPRM) net ecosystem exchange (NEE) parameter values optimized to 65 flux tower sites across North America. The parameters include the basal rate of ecosystem respiration (beta), the slope of respiration with respect to temperature (alpha), light-use efficiency (LUE) (lambda), and LUE curve half-saturation (PAR_0). Observed eddy covariance data from the 65 tower sites, locally observed temperature and photosynthetically active radiation (PAR) along with satellite-derived phenology and moisture were used as input data to optimize the VPRM parameters for the 65 sites. The data are provided by individual site, plant functional types (PFTs), and all sites together, and as monthly, annual, and all available data. The data are for the conterminous USA, Alaska, and Canada for the period 2000 to 2006.\n<br>\n\n#### NACP_Woody_Veg_N_Slope_AK_V2_1365\nThis data set provides the results of (1) field measurements of woody vegetation (shrubs) at 26 diverse sites across the North Slope of Alaska during 2010 and 2011, (2) field-based statistical estimates of site shrub structural characteristics, (3) high-resolution panchromatic satellite imagery-based estimates of field site shrub characteristics using the Canopy Analysis with Panchromatic Imagery (CANAPI) model, and (4) adjusted CANAPI estimates of shrub characteristics at 1,013 selected sites widely distributed across the North Slope.\n<br>\n\n#### HighRes_ClimateData_Western_US_1682\nThis dataset provides sub-daily, high-resolution, climate data inputs including temperature, precipitation, near surface specific humidity, incoming short-wave radiation, and near-surface wind speed over 11 states of the western USA. States included are Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming. These data were derived for use in the Community Land Model (CLM v4.5) and are at 3-hourly temporal and 4 x 4 km spatial resolutions for the 1979 through 2015 time period. The source for observational data was METDATA (now called GRIDMET), at a daily resolution. Modeling efforts using these data estimated annual carbon stocks, fluxes, and productivity across the western United States.\n<br>\n\n#### NACP_Forest_Conservation_1662\nThis dataset contains annual estimates of carbon stocks, fluxes, and productivity over forested land in 11 states of the western USA (Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming). The estimates were produced from multiple simulations using the Community Land Model (v4.5) with different climate forcing data and prescribed harvest rates. Business as usual (BAU) scenarios were run to ensure that the simulations represented present-day stand ages. The estimates span two modeled time periods, 1979-2014 and 2015-2099, at 1/24-degree (4 km x 4 km) resolution. Variables included are gross primary production (GPP), net ecosystem exchange (NEE), net ecosystem productivity (NEP), net primary production (NPP), autotrophic respiration (RA), heterotrophic respiration (RH), transpiration factor, aboveground live tree carbon, carbon loss from fire, allocation to stem carbon, and burned area fraction over forested areas of the western USA.\n<br>\n\n#### MODIS_PAR_1140\nThis data set contains daily Moderate Resolution Imaging Spectroradiometer (MODIS) land incident photosynthetically active radiation (PAR) images over North America for the years 2003 - 2005 and was created to fill the need for daily PAR estimates. Incident PAR is the solar radiation in the range of 400 to 700 nm reaching the earth's surface and plays an important role in modeling terrestrial ecosystem productivity. The daily images were derived by integrating MODIS/Terra and MODIS/Aqua instantaneous PAR data where the instantaneous PAR data is estimated directly from Terra or Aqua MODIS 5-min L1b swath data (Liang et al., 2006 and Wang et al., 2010). The spatial distribution of this data set includes the MODIS tile subsets covering North America, Central America, portions of South America, and Greenland, available for the years 2003 - 2005. There are 45,376 .hdf files with a spatial resolution of 4 km x 4 km in sinusoidal projection distributed by year in three compressed data files: 2003.zip, 2004.zip, and 2005.zip. Contained within each daily file are 4 separate image files: DirectPar, DiffusePAR, TotalPAR, and Observation Count. There are 46 MODIS tiles that cover the study area extent.\n<br>\n\n#### HourlyUrban_GreenhouseGases_US_1916\nThis dataset provides hourly urban greenhouse gas measurements for cities in the CO2 Urban Synthesis and Analysis (CO2-USA) Data Synthesis Network for 2000 to 2019. Measurements include carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) concentrations measured at hourly intervals at multiple sites within the U.S. cities of Boston, Indianapolis, Los Angeles, Portland, Salt Lake City, San Francisco, and Washington DC/Baltimore, and Toronto, Canada.\n<br>\n\n#### NAFD-NEX_Attribution_1799\nCharacterizing the cause of forest canopy changes through time is fundamental to understanding current and future forest functions. A better understanding of forest dynamics can help build linkages between patterns and processes. The North American Forest Dynamics (NAFD) products provided in this dataset predict characteristics related to the cause of forest canopy cover losses for the conterminous United States (CONUS) derived from Landsat images for the period 1986-2010. The characteristics are summarized in four separate data layers. The first layer labels the type of change event (stable-no change, removals, fire, stress, wind, conversion, other), the second labels the year of the event, the third and fourth layers measure dominance and diversity, measures of qualitative confidence metrics derived from the model predictions. For each pixel the maps depict the greatest magnitude event occurring between 1986-2010.\n<br>\n\n#### NACP_PalEON_MIP_1779\nThis dataset from the PalEON Ecosystem Model Intercomparison Project (PEMIP) provides harmonized regional environmental and meteorological drivers at a resolution of 0.5 degrees for the North-central and Northeastern U.S. over the time period 0850-01-01 to 2010-12-31. This dataset consists of the regional environmental and meteorological drivers. The environmental drivers include (1) dominant biome type, (2) plant functional type, (3) annual carbon dioxide concentration, (4) monthly carbon dioxide concentration, (5) land use-land cover change, (6) nitrogen concentrations, and (7) soil measurements. The meteorological drivers include (1) incident longwave radiation, (2) incident shortwave radiation, (3) precipitation, (4) surface pressure, (5) specific humidity, (6) air temperature, and (7) wind speed. The PEMIP is a coordinated effort to develop a set of terrestrial ecosystem model simulations with the ability to evaluate high-resolution ecophysiological causes and consequences of forest responses to climatic variability and change over the past millennium.\n<br>\n\n#### SOC_3M_Maps_NE_TidalWetlands_1905\nThis dataset provides estimates of soil organic carbon (SOC) in tidal wetlands for the northeastern United States. The data cover the period 1998-2018. Northeastern U.S. tidal wetlands and bordering areas were harmonized from government agencies [U.S. Department of Agriculture - Natural Resources Conservation Service (USDA-NRCS), National Cooperative Soil Survey (NCSS), USDA-NRCS - Rapid Carbon Assessment (RaCA), U.S. Environmental Protection Agency - National Wetland Condition and Assessment (EPA-NWCA)] and published studies. Point data for carbon stocks (in kg m-2) at four soil depths (0-5, 0-30, 0-100, and 0-200 cm) are included. SOC for the four depths was predicted for eight regional zones using regression models driven by environmental covariates. Two methods were used to estimate parameters for these models, a Random Forest (RF) Ranger method and a Quantile Regression Forest (QRF) model. The distribution of SOC was predicted for tidal wetland cover types mapped by Correll et al. (2019). Predictions and uncertainties are available at a 3 m resolution.\n<br>\n\n#### NACP_Vista_CA_CH4_Inventory_1726\nThis dataset provides spatial data products with identified and organized locations of potential methane (CH4) emitting facilities and infrastructure spanning the State of California. These data products form a GIS-based mapping database designed to address shortcomings in current CH4 source inventories and is known as Vista California (Vista-CA). Vista-CA consists of detailed spatial maps for facilities and infrastructure in California that are known or expected sources of CH4 emissions and illustrates the spatial distribution of potential CH4 sources. Vista-CA spatial data sets were created utilizing an assortment of publicly available data sources from local, state, and federal agencies for the years 2005 to 2019. The final Vista-CA database contains over 230,000 entries, which are presented as fifteen CH4 emitting infrastructure maps. The database was used to support flight planning and source attribution for the California Methane Survey project.\n<br>\n\n#### NACP_Vista_LA_CH4_Inventory_1525\nThis data set provides spatial data products with identified and classified locations of potential methane (CH4) emitting facilities and infrastructure in the South Coast Air Basin (SoCAB). These data products form a GIS-based mapping database designed to address shortcomings in current urban CH4 source inventories and is known as Vista Los Angeles (Vista-LA). SoCAB is the air shed for the greater Los Angeles urban area, which includes urbanized portions of the Los Angeles, Orange, Riverside, and San Bernardino Counties, California, USA. Vista-LA consists of detailed spatial maps for facilities and infrastructure in the SoCAB that are known or expected sources of CH4 emissions and illustrates the spatial distribution of potential CH4 sources, representing a first step towards developing an urban-scale CH4 emissions gridded inventory for the SoCAB. Vista-LA spatial data sets were created utilizing an assortment of publicly available data sources from local, state, and federal agencies for the years 2012 to 2017. The final Vista-LA database contains over 33,000 entries, which are presented as thirteen CH4 emitting infrastructure maps.\n<br>\n\n#### SOC_Stocks_Great_Plains_1603\nThis dataset provides estimates of total organic soil carbon (SOC), pyrogenic (PyC), particulate (POC), and other organic soil carbon (OOC) fractions in 473 surface layer soil samples collected from stratified-sampling locations in Colorado, Kansas, New Mexico, and Wyoming, USA. Terrain, climate, soil, fire, and land cover data used to predict and map SOC, PyC, POC, and OOC at 1 km resolution throughout the study region are also included. The estimates were derived using a best random forest regression model and cover the period 2007-05-01 to 2010-10-01.\n<br>\n\n#### Tidal_Wetland_Estuaries_Data_1742\nThis dataset provides a synthesis of soil organic carbon (SOC) estimates and a variety of other environmental information from tidal wetlands within estuaries in the conterminous United States for the period 1972-2015. The data were compiled from several existing data resources and include the following: soil organic carbon stock estimates, the proportion of the catchment area containing the wetlands that is barren, tidal wetland area, nontidal wetland land, open water, saltwater zone, mixed zone, agricultural, urban, forest, and wetland areas, land elevation, ocean salinity, sea surface temperature, ocean dissolved inorganic phosphorus, estuary latitude, longitude, depth, perimeter, salinity, and estuary volume, river flow, carbon, nitrogen, and phosphorus river flux, sediment organic carbon content, windspeed, mean temperature, daily and mean precipitation, frost days, and the population within each catchment. Estuaries were also classified to one of six typological categories. Coastal locations were determined by natural environmental and political divisions within the US. The data were used to investigate how tidal wetland soil organic carbon density is distributed across the continental US among various coastal locations, estuarine typologies, vegetation types, water regimes, and management regimes, and to identify whether SOC density is correlated with different environmental variables. The analytical results are not included with this dataset.\n<br>\n\n#### CH4_Modeled_EmissionSources_2411\nThis dataset holds estimates of methane emissions derived from a dual tracer inversion of atmospheric measurements of CH4 mole fractions and d13CH4 isotopic values. The measurements were assimilated in the TM5 4-Dimensional Variational (TM5-4DVAR) source-sink inversion system to estimate methane emissions from fossil fuel, microbial, and pyrogenic sources. These estimates include monthly means of methane emissions from each source and all three sources combined at 1-degree longitude x 1-degree latitude spatial resolution globally and monthly totals across all global grid cells from each source and all three sources combined from 1999 to 2016. The data are provided in netCDF version 4 format.\n<br>\n\n#### TillageErosion_SOCRedistribute_1944\nThis dataset contains model predictions of soil erosion and soil organic carbon (SOC) redistribution caused by agricultural practices such as tillage erosion. Soil erosion diminishes agricultural productivity by driving the loss of SOC. This model addresses a growing need to predict soil organic carbon transport, loss, and deposition. The model was applied to three sites containing paired prairie grassland and field plots in Iowa, and predicts SOC redistribution between 1859 to 2019. The model was developed by incorporating a SOC mixing model with a landscape evolution model that simulates tillage erosion.\n<br>\n\n#### Biogenic_CO2flux_SIF_SMUrF_1899\nThis dataset contains estimates of biogenic CO2 flux components at 0.05 degree resolution from the Solar-Induced Fluorescence (SIF) for Modeling Urban biogenic Fluxes (SMUrF) model. Estimates were produced for the following regions and periods: eastern and western CONUS (2010-2019), western Europe (2010-2014 and 2017-2018), eastern Asia, eastern China, eastern Australia, South America, and Central Africa (2017-2018). Modeled CO2 flux components include gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange (NEE). Four-day means of GPP are estimated from solar-induced fluorescence (SIF) and biome-specific GPP-SIF relationships. Daily estimates of Reco are included. In addition, GPP and Reco were downscaled to hourly estimates and used to generate hourly NEE. Uncertainties for 4-day GPP and daily Reco estimates are provided. The input data streams included 500 m MODIS-based annual land cover classification, 0.05 degree spatiotemporally contiguous SIF, above-ground biomass (AGB) from GlobBiomass, eddy-covariance (EC) flux measurements, and gridded products of air and soil temperatures.\n<br>\n\n#### Vulcan_V3_Annual_Emissions_1741\nThe Vulcan version 3.0 annual dataset provides estimates of annual carbon dioxide (CO2) emissions from the combustion of fossil fuels (FF) and CO2 emissions from cement production for the conterminous United States and the State of Alaska. Referred to as FFCO2, the emissions from Vulcan are categorized into 10 source sectors including; residential, commercial, industrial, electricity production, onroad, nonroad, commercial marine vessel, airport, rail, and cement. Data are gridded annually on a 1-km grid for the years 2010 to 2015. These data are annual sums of hourly estimates. Also provided are estimates of the upper 95% confidence interval and the lower 95% confidence interval boundaries for each emission estimate. For each uncertainty level, there are 10 individual sector files and one total file. These data are designed to be used as emission estimates in atmospheric transport modeling, policy, mapping, and other data analyses and applications.\n<br>\n\n#### Vulcan_V3_Hourly_Emissions_1810\nThe Vulcan version 3.0 hourly dataset quantifies hourly emissions at a 1-km resolution for the 2010-2015 time period. Estimates are provided of hourly carbon dioxide (CO2) emissions from the combustion of fossil fuels (FF) and CO2 emissions from cement production for the conterminous United States and the state of Alaska. Referred to as FFCO2, the emissions from Vulcan are categorized into 10 source sectors including; residential, commercial, industrial, electricity production, onroad, nonroad, commercial marine vessel, airport, rail, and cement. Files for hourly total emissions are also available. Data are represented in space on a 1 km x 1 km grid as hourly totals for 2010-2015. This dataset provides the first bottom-up U.S.-wide FFCO2 emissions data product at 1 km2/hourly for multiple years and is designed to be used as emission estimates in atmospheric transport modeling, policy, mapping, and other data analyses and applications.\n<br>\n\n#### WRF_STILT_Footprints_Boston_1572\nThis dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) footprint data products for two receptors located in Boston, Massachusetts, USA, for July 2013 - December 2014. The data are gridded footprints on a 1-km grid congruent with the ACES emissions inventory. Meteorological fields from version 3.5.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the \"receptor\" location), to create the adjoint of the transport model in the form of a \"footprint\" field. The footprint, with units of mixing ratio, quantifies the influence of upwind surface fluxes on CO2 and CH4 concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume.\n<br>\n\n#### WRF_STILT_Particles_Boston_1596\nThis dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data and footprint products for two receptors located in Boston, Massachusetts, USA, for July 2013 - December 2014. Meteorological fields from version 3.6.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the \"receptor\" location), to create the adjoint of the transport model in the form of a \"footprint\" field. The footprint, with units of mixing ratio (ppm) per surface flux (umol m-2 s-1), quantifies the influence of upwind surface fluxes on CO2 and CH4 concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. Footprints are provided for the two receptors at two temporal and spatial scales: three days of surface influence over the whole North American coverage area at 1-degree resolution and 24 hours of surface influence within a smaller region close to the measurement locations ('near field') at 0.1-degree resolution.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","earth observation","elevation","hdf","ice","land cover","lidar","netcdf","oceans","precipitation","radar","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NACP_ACES_V2_1943 v2 - This dataset provides estimates of hourly carbon dioxide (CO2) emissions from the combustion of fossil fuels at 1-km resolution for the coterminous United States (CONUS) covering the years 2012 through 2017. Emissions from the ACES model are reported for ten distinct emissions source sectors: Airports and Aircraft, Commercial Buildings, Electric Power Generation facilities, Industrial point and non-point sources, Commercial Marine Vessels, Nonroad vehicles and equipment, Oil and Gas wells and facilities, Onroad vehicles, Railway engines and yards, and Residential buildings.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_ACES_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"FireBoundaries_CircPolarRegion_2477 v1 - This dataset contains polygons of retrievable fire boundaries across the circumpolar region attributed with pre-fire and post-fire normalized burn ratio (NBR), differenced normalized burn ratio (dNBR), relative differenced normalized burn ratio (RdNBR), and terrestrial ecoregions. The dataset is separated into three regions: North America, Europe, and Asia; each saved in an appropriate projection and with the years of data available from that region.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/FireBoundaries_CircPolarRegion/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_NAM_HYSPLIT_Footprints_1586 v1 - This dataset reports continuous atmospheric measurements of CO2 from two receptor sites and three boundary sites in and around Boston, Massachusetts, USA, that were combined with high-resolution CO2 emissions estimates and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to estimate regional CO2 emissions from September 2013 to December 2014. The HYSPLIT model followed an ensemble of 1,000 particles released at the urban CO2 measurement sites backward in time based on wind fields and turbulence from the North American Mesoscale Forecast System (NAM) at 12-km resol...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_NAM_HYSPLIT_Footprints/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"echidna_1045 v1 - This data set contains forest canopy scan data from the Echidna® Validation Instrument (EVI) and field measurements data from three campaigns conducted in the United States: 2007 New England Campaign; 2008 Sierra National Forest Campaign; and 2009 New England Campaign. The New England field sites were located in Harvard Forest (Massachusetts), Howland Research Forest (Maine), and the Bartlett Experimental Forest (New Hampshire).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/echidna/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MISR_Forest_AGB_SW_US_1978 v1 - This dataset provides estimates of forest aboveground biomass (AGB; in Mg ha-1) at a resolution of 250 m for the southwestern United States over the time period 2000-2021. The AGB estimates were derived from the Jet Propulsion Laboratory Multiangle Imaging Spectro-Radiometer (MISR) Level 1B2 Terrain radiance data and a multi-angle approach that exploits the relationship between forest AGB and a suite of red band reflectance values modeled at viewing angles with respect to the direction of illumination.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/MISR_Forest_AGB_SW_US/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Forest_Carbon_Priority_1803 v1 - This dataset provides related gridded outputs of future modeled forest carbon sequestration priority and related species richness and habitat suitability for the western United States. The primary dataset is of the ranking of forest lands in the western U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/Forest_Carbon_Priority/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Reservoirs_Methane_1918 v1 - This dataset includes global maps of methane (CH4) emissions from inland dam-reservoir systems at 0.25-degree spatial resolution. Daily emission rates (as grams of CH4 per day per total area of grid cell) were estimated for boreal, temperate, and subtropical-tropical eco-climatic domains and total emissions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/Global_Reservoirs_Methane/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Wildfire_Impacts_Boreal_Ecosys_2359 v1 - This dataset contains simulations of net primary production (NPP), heterotrophic respiration (RH), net ecosystem production (NEP), and soil temperature data in North American boreal forests for the period 1986-2020. Data sources included historical fire sources and Landsat data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/Wildfire_Impacts_Boreal_Ecosys/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Dendrophenology_Eastern_US_1369 v1 - This data set provides a 30-year record of Landsat TM and ETM+ derived forest phenology and the results of tree ring analyses for annual wood production and nitrogen and carbon isotopic composition at 113 selected forested sites in the eastern United States. The sites are located in four national parks: Prince William Forest Park (PRWI), Harpers Ferry National Historical Park (HAFE), Catoctin Mountain Park (CATO), and Great Smoky Mountains National Park (GRSM).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/Dendrophenology_Eastern_US/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Coastal_Carbon_Budget_NE_US_1594 v1 - This dataset contains best estimates and uncertainties for mean annual fluxes of inorganic, organic, and total (organic + inorganic) carbon in tidal wetlands, estuaries and shelf waters of eastern North America, which is defined by the coastline running between the tip of the Scotian Peninsula (Canada) and the southern tip of Florida (USA). The data are provided on a per-unit-area basis and as spatially integrated values for each of the three ecosystem types (tidal wetlands, estuaries, and shelf waters) and the entire coastal ecosystem (tidal wetlands + estuaries + shelf waters) as well as ...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/Coastal_Carbon_Budget_NE_US/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Dairy_Methane_CA_V1-2_1902 v1.2 - This dataset provides estimates of methane (CH4) emissions from dairies in California at a resolution of 0.1 degrees (~ 10 km x 10 km) for the year 2019. The mapped sources of dairy CH4 emissions are enteric fermentation and manure management reported in gigagrams per square km per year (Gg km-2 y-1).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/Dairy_Methane_CA_V1-2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"US_MODIS_Veg_Parameters_1539 v1 - This dataset provides MODIS-derived leaf area index (LAI), stem area index (SAI), vegetation area fraction, dominant landcover category, and albedo parameters for the continental US (CONUS), parts of southern Canada, and Mexico at 30 km resolution. The data cover the period 2003-2010 and were developed to be used as surface input data for regional agroecosystem-climate models.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/US_MODIS_Veg_Parameters/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NBCD2000_V2_1161 v2 - The NBCD 2000 (National Biomass and Carbon Dataset for the Year 2000) data set provides a high-resolution (30 m) map of year-2000 baseline estimates of basal area-weighted canopy height, aboveground live dry biomass, and standing carbon stock for the conterminous United States. This data set distributes, for each of 66 map zones, a set of six raster files in GeoTIFF format.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NBCD2000_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_PNW_Carbon_Balance_1317 v1 - This data set provides Biome-BGC modeled estimates of carbon stocks and fluxes in the U.S. Pacific Northwest for the years 1986-2010.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_PNW_Carbon_Balance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NA_TreeAge_1096 v1 - This data set provides forest age map products at 1-km resolution for Canada and the United States (U.S.A.). These continental forest age maps were compiled from forest inventory data, historical fire data, optical satellite data, and the images from NASA’s Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) project.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NA_TreeAge/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_GHG_Data_Compilation_1206 v1 - This data set is a collection of measurements of carbon dioxide (CO2) and non-CO2 greenhouse gases made across North America by nine independent atmospheric monitoring networks from 2000 - 2009. During this North American Carbon Program (NACP) sponsored activity, data were compiled from the following networks: AGAGE, COBRA, CSIRO, INTEX-A, INTEX B, Irvine Latitude Network, NOAA CMDL, SCRIPPS, and Stanley Tyler-UC Irvine.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_GHG_Data_Compilation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Wild_Cropland_Fuel_Map_1163 v1 - The data set provides a 30-m comprehensive fuelbed characteristics map for both the wildland and cropland areas of the conterminous United States (CONUS) for 2010. This integrated product is the result of combining the spatially discrete Fuel Characteristic Classification System (FCCS) data of the US Forest Service (USFS) with the crop-and grassland-specific information of the US Department of Agriculture?'s (USDA's) Cropland Data Layer (CDL).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Wild_Cropland_Fuel_Map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Boreal_Biome_Biomass_1273 v1 - This data set provides estimates of aboveground biomass (AGB) for defined land cover types within World Wildlife Fund (WWF) ecoregions across the boreal biome of Alaska and western and eastern Canada, roughly between 45 and 70 degrees N. The study focused on within-growing-season data, i.e.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Boreal_Biome_Biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_MCI_CO2_Inventory_1205 v1 - This data set provides a bottom-up CO2 emissions inventory for the mid-continent region of the United States for the year 2007. The study was undertaken as part of the North American Carbon Program (NACP) Mid-Continent Intensive (MCI) campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_MCI_CO2_Inventory/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_MCI_CO2_Inversions_1204 v1 - This data set provides estimates of Net Ecosystem Exchange (NEE) flux for the U.S. Upper Midwest at 0.5-degree resolution for the year 2007.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_MCI_CO2_Inversions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_MCI_Crop_GPP_1217 v1 - This data set provides an integrated collection of (1) ground-based meteorological, radiometric, and vegetation measurements, (2) flux-based estimates of gross primary production (GPP), and (3) numerous vegetation indices derived from satellite imagery for three eddy covariance flux tower locations near Lincoln, Nebraska, USA. Land use surrounding the towers is cropland with corn and soybeans.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_MCI_Crop_GPP/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_MCI_CO2_Measurements_1202 v1 - This data set provides high precision and high accuracy atmospheric CO2 data from seven instrumented communication towers located in the U.S. Upper Midwest.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_MCI_CO2_Measurements/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_MsTMIP_Model_Structure_1228 v1 - This data set provides a summary of the model structure and characteristics of participating models in the North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP), a formal model intercomparison and evaluation effort focused on improving the diagnosis and attribution of carbon exchange at regional and global scales. Model structure refers to the types of processes considered (e.g.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_MsTMIP_Model_Structure/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_MsTMIP_TBMO_1225 v1 - This data set provides global gridded estimates of carbon, energy, and hydrologic fluxes between the land and atmosphere from 15 Terrestrial Biosphere Models (TBMs) in a standard format. Model estimates are at monthly and yearly time steps for the period 1900 to 2010, with a spatial resolution of 0.5 degree x 0.5 degree globally, excluding Antarctica.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_MsTMIP_TBMO/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_MsTMIP_Model_Driver_1220 v1 - This data set provides environmental data that have been standardized and aggregated for use as input to carbon cycle models at global (0.5-degree resolution) and regional (North America at 0.25-degree resolution) scales. The data were compiled from selected sources (Table 2) and integrated into gridded global and regional collections of climatology variables (precipitation, air temperature, air specific humidity, air relative humidity (NA only), pressure, downward longwave radiation, downward shortwave radiation, and wind speed), time-varying atmospheric CO2 concentrations, time-varying ni...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_MsTMIP_Model_Driver/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_MsTMIP_Unified_NA_SoilMap_1242 v1 - This data set provides soil maps for the United States (US) (including Alaska), Canada, Mexico, and a part of Guatemala. The map information content includes maximum soil depth and eight soil attributes including sand, silt, and clay content, gravel content, organic carbon content, pH, cation exchange capacity, and bulk density for the topsoil layer (0-30 cm) and the subsoil layer (30-100 cm).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_MsTMIP_Unified_NA_SoilMap/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NAFD-NEX_Forest_Disturbance_1290 v1 - The North American Forest Dynamics (NAFD) products provided in this data set consist of 25 annual and two time-integrated forest disturbance maps for the conterminous United States (CONUS) derived from Landsat images for the period 1986-2010. Each annual map has classified pixels showing water, no forest cover, forest cover, no data available (data gaps) in present year, and forest disturbances that occurred in that year.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NAFD-NEX_Forest_Disturbance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Forest_Disturbance_Intensity_2059 v1 - This dataset contains estimates of forest disturbance intensity for the conterminous United States from 1986 to 2015. It quantifies the severity/intensity of forest disturbances at 30 m resolution using time series Landsat observations and the vegetation change tracker algorithm.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/Forest_Disturbance_Intensity/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Forest_Biophysical_1046 v1 - This data set includes biophysical measurements collected in 2009 from five New England experimental forest stations: Bartlett Experimental Forest, Harvard Forest, Howland Research Forest, Hubbard Brook Experimental Forest, and the Penobscot Experimental Forest. Howland measurements were repeated in 2010 and one site in the Sierra National Forest, California, was surveyed in 2008.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Forest_Biophysical/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Modeled_NEE_NEP_Fluxes_1203 v1 - This data set provides modeled carbon flux estimates at 8-km spatial resolution over North America for the year 2004 of (1) net ecosystem exchange (NEE) of carbon dioxide (CO2), (2) net ecosystem production (NEP, the balance of net primary production and heterotrophic respiration), (3) stream evasion (CO2 emitted from streams and rivers), (4) emissions from harvested forest and agricultural products, and (5) emissions from biomass burning. Annual estimates, in g C/m2/year, are provided for all five fluxes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Modeled_NEE_NEP_Fluxes/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NAFD_Disturbance_1077 v1 - This data set provides the results of time-series analyses of Landsat imagery for 55 selected forested sites across the conterminous U.S.A. The output is a pair of disturbance data products for each site, one showing the first year of disturbance in the time series, the other showing the last year of disturbance.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NAFD_Disturbance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Peatland_Land_Cover_MI_1513 v1 - This dataset provides a land cover map focused on peatland ecosystems in the upper peninsula of Michigan. The map was produced at 12.5-m resolution using a multi-sensor fusion (optical and L-band SAR) approach with imagery from Landsat-5 TM and ALOS PALSAR collected between 2007 and 2011.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Peatland_Land_Cover_MI/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Peatland_Burn-Severity_1283 v1 - This data set provides landcover maps of (1) peatland type (bog, fen, marsh, swamp) with levels of biomass (open, forested) and (2) Burn Severity Index (BSI) (Dyrness and Norum, 1983) for four wildfire areas in northern Alberta, Canada. The four wildfire sites include the Utikuma fire site of 2011, Kidney Lake fire site of 2011, Fort McMurray west fire site of 2009, and Fort McMurray east fire site of 2009.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Peatland_Burn-Severity/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Regional_Obs_Model_Grid_1157 v1 - This data set contains standardized gridded observation data, terrestrial biosphere model output data, and inverse model simulations of carbon flux parameters that were used in the North American Carbon Program (NACP) Regional Synthesis activities. The data set provides five observation data files (MODIS GPP, MODIS NPP, FIA forest biomass/forest area, NASS crop NPP, and NASS agricultural land fraction) and simulation results from 18 terrestrial biosphere models (TBM) (28 variables; 114 files) and seven inverse models (IM) (two variables; 8 files).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Regional_Obs_Model_Grid/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Regional_Model_GHG_Aggr_1179 v1 - This data set provides two products that were derived from the recently published North American Carbon Program (NACP) Regional Synthesis 1-degree terrestrial biosphere model (TBM) and inverse model (IM) outputs (Gridded 1-deg Observation Data and Biosphere and Inverse Model Outputs, Wei et al., 2013). The first product is the aggregation of the standardized gridded 1-degree TBM and IM outputs to the Greenhouse Gas (GHG) inventory zones as defined for North America (United States, Canada, and Mexico).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Regional_Model_GHG_Aggr/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Regional_Obs_Model_Orig_1193 v1 - This data set contains the originally-submitted observation measurement data, terrestrial biosphere model output data, and inverse model simulations that various investigator teams contributed to the North American Carbon Program (NACP) Regional Synthesis activities. The data set provides nine (9) data packages of remote sensing and ground observation measurements (OM) (MODIS gross primary productivity (GPP), MODIS net primary production (NPP), MODIS fraction of photosynthetically active radiation (fPar), MODIS leaf area index (LAI), MODIS enhanced vegetation index (EVI), MODIS normalize di...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Regional_Obs_Model_Orig/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Regional_Obs_Model_Suppl_1158 v1 - This data set contains standardized gridded observation data, terrestrial biospheric model output, and inverse model simulations that were compiled but not used in the North American Carbon Program (NACP) Regional Synthesis activities, thus the supplemental designation. The data set provides six (6) observation data packages (9 variables - MODIS LAI, MODIS FPAR, MODIS NDVI, MODIS EVI, FIA forest biomass, forest area, GPP Anomaly, NEE Anomaly, Reco Anomaly; 8 data files), output results from three terrestrial biosphere models (TBM) (14 variables; 214 files), and simulations from one inverse ...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Regional_Obs_Model_Suppl/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Site_Model_Flux_Std_Fmt_1183 v1 - This data set provides standardized output variables for gross primary productivity (GPP), net ecosystem exchange (NEE), leaf area index (LAI), ecosystem respiration (Re), latent heat flux (LE), and sensible heat flux (H) from 24 terrestrial biosphere models for 47 eddy covariance flux tower sites in North America. Each model used standardized input data for each flux tower site (i.e., gap-filled, locally observed weather; land use history; and other site specific data) and followed standard model setup and spinup procedures.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Site_Model_Flux_Std_Fmt/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Site_Model_Data_Orig_Fmt_1192 v1 - This data set contains the original model output data submissions from the 24 terrestrial biosphere models (TBM) that participated in the North American Carbon Program (NACP) Site-Level Synthesis. The model teams generated estimates for, but not limited to, a minimum of six variables, including gross primary productivity (GPP), net ecosystem exchange (NEE), leaf area index (LAI), ecosystem respiration (Re), latent heat flux (LE), and sensible heat flux (H) for each of 47 selected eddy covariance flux tower sites across North America.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Site_Model_Data_Orig_Fmt/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Site_Tower_Met_and_Flux_1178 v1 - This data set contains meteorological, carbon cycle flux, phenology, and ancillary data measured at 47 eddy covariance flux tower sites across North America. The data were used by North American Carbon Program (NACP) Site-Level Synthesis as model driver data and for assessing how well 24 Terrestrial Biosphere Models simulated carbon processes across vegetation types and environmental conditions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Site_Tower_Met_and_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_BlackSpruce_Burn-Severity_1331 v1 - This data set provides organic soil layer characteristics, estimated carbon content, and soil depth measurements made at four black spruce stands in interior Alaska that had burned twice in the last 37-52 years (intermediate-interval fire events). The most recent fires occurred in 2004, 2005, and 2010.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_BlackSpruce_Burn-Severity/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_TERRA-PNW_1292 v1 - This data set contains measurements and estimates of leaf, tree, and soil data from six projects conducted by the Terrestrial Ecosystem Research and Regional Analysis- Pacific Northwest (TERRA-PNW) research group between 1999 and 2014 across forests in Oregon and Northern California. Included are standardized, integrated measurements and estimates of specific leaf area, leaf longevity, leaf carbon and nitrogen for 35 tree and shrub species derived from more than 1,200 branch samples collected from over 200 forest plots, including several AmeriFlux sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_TERRA-PNW/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"VPRM_North_America_Parameters_1349 v1 - This data set provides Vegetation Photosynthesis Respiration Model (VPRM) net ecosystem exchange (NEE) parameter values optimized to 65 flux tower sites across North America. The parameters include the basal rate of ecosystem respiration (beta), the slope of respiration with respect to temperature (alpha), light-use efficiency (LUE) (lambda), and LUE curve half-saturation (PAR_0).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/VPRM_North_America_Parameters/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Woody_Veg_N_Slope_AK_V2_1365 v2 - This data set provides the results of (1) field measurements of woody vegetation (shrubs) at 26 diverse sites across the North Slope of Alaska during 2010 and 2011, (2) field-based statistical estimates of site shrub structural characteristics, (3) high-resolution panchromatic satellite imagery-based estimates of field site shrub characteristics using the Canopy Analysis with Panchromatic Imagery (CANAPI) model, and (4) adjusted CANAPI estimates of shrub characteristics at 1,013 selected sites widely distributed across the North Slope.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Woody_Veg_N_Slope_AK_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"HighRes_ClimateData_Western_US_1682 v1 - This dataset provides sub-daily, high-resolution, climate data inputs including temperature, precipitation, near surface specific humidity, incoming short-wave radiation, and near-surface wind speed over 11 states of the western USA. States included are Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/HighRes_ClimateData_Western_US/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Forest_Conservation_1662 v1 - This dataset contains annual estimates of carbon stocks, fluxes, and productivity over forested land in 11 states of the western USA (Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming). The estimates were produced from multiple simulations using the Community Land Model (v4.5) with different climate forcing data and prescribed harvest rates.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Forest_Conservation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MODIS_PAR_1140 v1 - This data set contains daily Moderate Resolution Imaging Spectroradiometer (MODIS) land incident photosynthetically active radiation (PAR) images over North America for the years 2003 - 2005 and was created to fill the need for daily PAR estimates. Incident PAR is the solar radiation in the range of 400 to 700 nm reaching the earth's surface and plays an important role in modeling terrestrial ecosystem productivity.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/MODIS_PAR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"HourlyUrban_GreenhouseGases_US_1916 v2 - This dataset provides hourly urban greenhouse gas measurements for cities in the CO2 Urban Synthesis and Analysis (CO2-USA) Data Synthesis Network for 2000 to 2019. Measurements include carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) concentrations measured at hourly intervals at multiple sites within the U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/HourlyUrban_GreenhouseGases_US/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NAFD-NEX_Attribution_1799 v1 - Characterizing the cause of forest canopy changes through time is fundamental to understanding current and future forest functions. A better understanding of forest dynamics can help build linkages between patterns and processes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NAFD-NEX_Attribution/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_PalEON_MIP_1779 v1 - This dataset from the PalEON Ecosystem Model Intercomparison Project (PEMIP) provides harmonized regional environmental and meteorological drivers at a resolution of 0.5 degrees for the North-central and Northeastern U.S. over the time period 0850-01-01 to 2010-12-31.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_PalEON_MIP/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SOC_3M_Maps_NE_TidalWetlands_1905 v1 - This dataset provides estimates of soil organic carbon (SOC) in tidal wetlands for the northeastern United States. The data cover the period 1998-2018.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/SOC_3M_Maps_NE_TidalWetlands/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Vista_CA_CH4_Inventory_1726 v1 - This dataset provides spatial data products with identified and organized locations of potential methane (CH4) emitting facilities and infrastructure spanning the State of California. These data products form a GIS-based mapping database designed to address shortcomings in current CH4 source inventories and is known as Vista California (Vista-CA).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Vista_CA_CH4_Inventory/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NACP_Vista_LA_CH4_Inventory_1525 v1 - This data set provides spatial data products with identified and classified locations of potential methane (CH4) emitting facilities and infrastructure in the South Coast Air Basin (SoCAB). These data products form a GIS-based mapping database designed to address shortcomings in current urban CH4 source inventories and is known as Vista Los Angeles (Vista-LA).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/NACP_Vista_LA_CH4_Inventory/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SOC_Stocks_Great_Plains_1603 v1 - This dataset provides estimates of total organic soil carbon (SOC), pyrogenic (PyC), particulate (POC), and other organic soil carbon (OOC) fractions in 473 surface layer soil samples collected from stratified-sampling locations in Colorado, Kansas, New Mexico, and Wyoming, USA. Terrain, climate, soil, fire, and land cover data used to predict and map SOC, PyC, POC, and OOC at 1 km resolution throughout the study region are also included.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/SOC_Stocks_Great_Plains/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Tidal_Wetland_Estuaries_Data_1742 v1 - This dataset provides a synthesis of soil organic carbon (SOC) estimates and a variety of other environmental information from tidal wetlands within estuaries in the conterminous United States for the period 1972-2015. The data were compiled from several existing data resources and include the following: soil organic carbon stock estimates, the proportion of the catchment area containing the wetlands that is barren, tidal wetland area, nontidal wetland land, open water, saltwater zone, mixed zone, agricultural, urban, forest, and wetland areas, land elevation, ocean salinity, sea surface te...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/Tidal_Wetland_Estuaries_Data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CH4_Modeled_EmissionSources_2411 v1 - This dataset holds estimates of methane emissions derived from a dual tracer inversion of atmospheric measurements of CH4 mole fractions and d13CH4 isotopic values. The measurements were assimilated in the TM5 4-Dimensional Variational (TM5-4DVAR) source-sink inversion system to estimate methane emissions from fossil fuel, microbial, and pyrogenic sources.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/CH4_Modeled_EmissionSources/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TillageErosion_SOCRedistribute_1944 v1 - This dataset contains model predictions of soil erosion and soil organic carbon (SOC) redistribution caused by agricultural practices such as tillage erosion. Soil erosion diminishes agricultural productivity by driving the loss of SOC.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/TillageErosion_SOCRedistribute/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Biogenic_CO2flux_SIF_SMUrF_1899 v1 - This dataset contains estimates of biogenic CO2 flux components at 0.05 degree resolution from the Solar-Induced Fluorescence (SIF) for Modeling Urban biogenic Fluxes (SMUrF) model. Estimates were produced for the following regions and periods: eastern and western CONUS (2010-2019), western Europe (2010-2014 and 2017-2018), eastern Asia, eastern China, eastern Australia, South America, and Central Africa (2017-2018).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/Biogenic_CO2flux_SIF_SMUrF/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Vulcan_V3_Annual_Emissions_1741 v1 - The Vulcan version 3.0 annual dataset provides estimates of annual carbon dioxide (CO2) emissions from the combustion of fossil fuels (FF) and CO2 emissions from cement production for the conterminous United States and the State of Alaska. Referred to as FFCO2, the emissions from Vulcan are categorized into 10 source sectors including; residential, commercial, industrial, electricity production, onroad, nonroad, commercial marine vessel, airport, rail, and cement.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/Vulcan_V3_Annual_Emissions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Vulcan_V3_Hourly_Emissions_1810 v1 - The Vulcan version 3.0 hourly dataset quantifies hourly emissions at a 1-km resolution for the 2010-2015 time period. Estimates are provided of hourly carbon dioxide (CO2) emissions from the combustion of fossil fuels (FF) and CO2 emissions from cement production for the conterminous United States and the state of Alaska.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/Vulcan_V3_Hourly_Emissions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"WRF_STILT_Footprints_Boston_1572 v1 - This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) footprint data products for two receptors located in Boston, Massachusetts, USA, for July 2013 - December 2014. The data are gridded footprints on a 1-km grid congruent with the ACES emissions inventory.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/WRF_STILT_Footprints_Boston/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"WRF_STILT_Particles_Boston_1596 v1 - This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data and footprint products for two receptors located in Boston, Massachusetts, USA, for July 2013 - December 2014. Meteorological fields from version 3.6.1 of the Weather Research and Forecasting model are used to drive STILT.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/nacp/WRF_STILT_Particles_Boston/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-nacp","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NASA-SSH Project","Description":"This dataset contains the Global Mean Sea Level (GMSL) trend generated from the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.2. The GMSL trend is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6A that covers September 1992 to present with a lag of up to 4 months. The data are reported as variations relative to a 20-year TOPEX/Jason collinear mean. Bias adjustments and cross-calibrations were applied to ensure SSHA data are consistent across the missions; Glacial Isostatic Adjustment (GIA) was also applied. The data are available as a table in ASCII format. Changes between the version 5.1 and version 5.2 releases are described in detail in the user handbook.\n<br>\n\n#### NASA_SSH_REF_ALONGTRACK_V1\nThe NASA-SSH Along-Track Sea Surface Height from Standardized Reference Missions Version 1 dataset produced by NASA provide observations of sea surface height, or sea level, anomaly measured using radar altimeter satellites in the reference mission orbit. These include TOPEX/Poseidon, the Jason series, and Sentinel-6. The data begin in Oct 1992, with data from TOPEX/Poseidon, and continues to the present. In this data set all missions have been referenced to a common baseline, additional quality control has been performed, and errors with wavelengths around one orbital cycle have been reduced. <br> The data consist of along-track observations of sea surface height, collected approximately once per second (1 Hz), and are parsed into files containing one day’s worth of data per file. A flag variable is included to allow users to easily select only valid observations, and a variable containing sea surface height with the flag applied and a small amount along track smoothing (~20 km), is suggested for most users. <br> Additionally, a “basin” flag variable is provided, along with a table defining it. This allows users to easily select all observations from a specific body of water. The basin flag assigns a number to each point corresponding to a specific ocean basin or lake. A table is included with a text description of each basin number. A text version of that table is available (https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/basin_name_table.txt). The basin definitions can be downloaded as a shape file from https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/basin_polygon_files.tar.gz, or as a kml file https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/NASA-SSH_Basins.kmz. <br> New data will be released approximately once per week, with a latency of a few weeks.\n<br>\n\n#### NASA_SSH_ENSO_INDICATOR\nThis file contains an indicator for the El Nino-Southern Oscillation (ENSO), based on satellite observations of sea surface height anomaly, measured by radar altimeter missions such as TOPEX/Poseidon, the Jason series, and Sentinel-6. The indicator values were calculated using NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Only Version 1 https://podaac.jpl.nasa.gov/dataset/NASA_SSH_REF_SIMPLE_GRID_V1 . <br><br> Indicator values were calculated using cyclostationary empirical orthogonal functions (CSEOFs; Kim et al., 2015) computed by decomposing the gridded sea surface height anomalies over the time period from 1993 to 2019. After removing the linear trend from each individual gridded location, three sets of regional CSEOFs were generated, one each for ENSO, the Pacific Decadal Oscillation (PDO) and Indian Ocean Diode (IOD). In each case, the dominant statistical mode represents the seasonal cycle. The second most dominant mode represents the variability explained by each respective indicator and is referred to as the “indicator mode”. The seasonal mode and indicator mode are then projected onto the along-track sea surface height anomalies to produce the indicator time series through the most current date. In this case, the ENSO mode was used to produce the time series contained in this file.<br><br> The file with the filename \"NASA_SSH_ENSO_INDICATOR.txt\" is always the most up-to-date time series containing the most recent data.\n<br>\n\n#### NASA_SSH_GMSL_INDICATOR\nThis file contains a time series of globally-averaged sea level change, or \"global mean sea level\" (GMSL) in units of centimeters. The estimate is based on satellite observations of sea surface height anomaly, measured by reference radar altimeter missions such as TOPEX/Poseidon, the Jason series, and Sentinel-6. The indicator values were calculated using NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Only Version 1 https://podaac.jpl.nasa.gov/dataset/NASA_SSH_REF_SIMPLE_GRID_V1 . <br><br> GMSL was calculated as the area-weighted average over each map in the time series of Simple Gridded Sea Surface Height. Because maps are computed using 10-days of observations, but are computed once every 7 days, there is a small amount of overlap between data used to compute successive time steps. A version of the estimate smoothed over 60 days is also provided. <br><br> Expert users, please note that this estimate has NOT been adjusted for Glacial Isostatic Adjustment, to account for the slight long-term depression of the sea floor. Users who study the sea level budget will need to account for this effect in order to properly evaluate closure of the budget. <br><br> Note that occasional improvements to data quality can result in mm level change in the historical portions of this estimate. However, such changes remain small relative to the 3-5 mm uncertainty in global sea level estimates over the course of the 30+ year record (https://essd.copernicus.org/articles/11/1189/2019/) <br><br> The file with the filename \"NASA_SSH_GMSL_INDICATOR.txt\" is always the most up-to-date time series containing the most recent data.\n<br>\n\n#### NASA_SSH_IOD_INDICATOR\nThis file contains an indicator for the Indian Ocean Dipole (IOD), based on satellite observations of sea surface height anomaly, measured by radar altimeter missions such as TOPEX/Poseidon, the Jason series, and Sentinel-6. The indicator values were calculated using NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Only Version 1 https://podaac.jpl.nasa.gov/dataset/NASA_SSH_REF_SIMPLE_GRID_V1 . <br><br> Indicator values were calculated using cyclostationary empirical orthogonal functions (CSEOFs; Kim et al., 2015) computed by decomposing the gridded sea surface height anomalies over the time period from 1993 to 2019. After removing the linear trend from each individual gridded location, three sets of regional CSEOFs were generated, one each for the El Nino-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) and (IOD). In each case, the dominant statistical mode represents the seasonal cycle. The second most dominant mode represents the variability explained by each respective indicator and is referred to as the “indicator mode”. The seasonal mode and indicator mode are then projected onto the along-track sea surface height anomalies to produce the indicator time series through the most current date. In this case, the IOD mode was used to produce the time series contained in this file. <br><br> The file with the filename \"NASA_SSH_IOD_INDICATOR.txt\" is always the most up-to-date time series containing the most recent data.\n<br>\n\n#### NASA_SSH_PDO_INDICATOR\nThis file contains an indicator for the Pacific Decadal Oscillation (PDO), based on satellite observations of sea surface height anomaly, measured by radar altimeter missions such as TOPEX/Poseidon, the Jason series, and Sentinel-6. The indicator values were calculated using NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Only Version 1 https://podaac.jpl.nasa.gov/dataset/NASA_SSH_REF_SIMPLE_GRID_V1 . <br><br> Indicator values were calculated using cyclostationary empirical orthogonal functions (CSEOFs; Kim et al., 2015) computed by decomposing the gridded sea surface height anomalies over the time period from 1993 to 2019. After removing the linear trend from each individual gridded location, three sets of regional CSEOFs were generated, one each for El Nino-Southern Oscillation (ENSO), the PDO and Indian Ocean Diode (IOD). In each case, the dominant statistical mode represents the seasonal cycle. The second most dominant mode represents the variability explained by each respective indicator and is referred to as the “indicator mode”. The seasonal mode and indicator mode are then projected onto the along-track sea surface height anomalies to produce the indicator time series through the most current date. In this case, the PDO mode was used to produce the time series contained in this file.<br><br> The file with the filename \"NASA_SSH_PDO_INDICATOR.txt\" is always the most up-to-date time series containing the most recent data.\n<br>\n\n#### NASA_SSH_REF_SIMPLE_GRID_V1\nThe NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Only Version 1 dataset produced by NASA provides 2-D maps of sea surface height, or sea level, anomaly once every 7 days. The grids are based on observations of sea surface height from the radar altimeter satellites in the reference mission orbits, including TOPEX/Poseidon, the Jason series, and Sentinel-6. The data begin in Oct 1992 and continue through the present. They are created using the NASA-SSH Along-Track Sea Surface Height from Standardized Reference Missions Version 1 dataset. <br> The grids consist of 10-days worth of observations, which covers approximately 1 complete repeat cycle of observations from the reference missions. The grids are produced on a 0.5-degree latitude and longitude grid, by taking a simple gaussian weighted spatial average with a width of 100 km. The grids are produced every 7 days to allow for easy interpolation in time. However, since they are created using 10-days of data, there is some overlap of information between adjacent time steps. The grids are also created using the basin flags to avoid mixing data from distinct ocean basins (for example, to avoid mixing observations from the Caribbean Sea with observations from the Pacific across the Isthmus of Panama). Connected basins are allowed to share data, however. This is accomplished by using a table of connections between basins. The basin connection table is available (https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/basin_connection_table.txt). The basin definitions can be downloaded as a shape file from https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/basin_polygon_files.tar.gz, or as a kml file https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/NASA-SSH_Basins.kmz. <br> A new grid will be released approximately once per week, with a latency of a few weeks.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","oceans","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MERGED_TP_J1_OSTM_OST_GMSL_ASCII_V52 v5.2 - This dataset contains the Global Mean Sea Level (GMSL) trend generated from the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.2. The GMSL trend is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6A that covers September 1992 to present with a lag of up to 4 months.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MERGED_TP_J1_OSTM_OST_GMSL_ASCII_V52","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NASA_SSH_REF_ALONGTRACK_V1 v1 - The NASA-SSH Along-Track Sea Surface Height from Standardized Reference Missions Version 1 dataset produced by NASA provide observations of sea surface height, or sea level, anomaly measured using radar altimeter satellites in the reference mission orbit. These include TOPEX/Poseidon, the Jason series, and Sentinel-6.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/NASA_SSH_REF_ALONGTRACK_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NASA_SSH_ENSO_INDICATOR v1 - This file contains an indicator for the El Nino-Southern Oscillation (ENSO), based on satellite observations of sea surface height anomaly, measured by radar altimeter missions such as TOPEX/Poseidon, the Jason series, and Sentinel-6. The indicator values were calculated using NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Only Version 1 https://podaac.jpl.nasa.gov/dataset/NASA_SSH_REF_SIMPLE_GRID_V1.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/NASA_SSH_ENSO_INDICATOR","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NASA_SSH_GMSL_INDICATOR v1 - This file contains a time series of globally-averaged sea level change, or \"global mean sea level\" (GMSL) in units of centimeters. The estimate is based on satellite observations of sea surface height anomaly, measured by reference radar altimeter missions such as TOPEX/Poseidon, the Jason series, and Sentinel-6.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/NASA_SSH_GMSL_INDICATOR","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NASA_SSH_IOD_INDICATOR v1 - This file contains an indicator for the Indian Ocean Dipole (IOD), based on satellite observations of sea surface height anomaly, measured by radar altimeter missions such as TOPEX/Poseidon, the Jason series, and Sentinel-6. The indicator values were calculated using NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Only Version 1 https://podaac.jpl.nasa.gov/dataset/NASA_SSH_REF_SIMPLE_GRID_V1.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/NASA_SSH_IOD_INDICATOR","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NASA_SSH_PDO_INDICATOR v1 - This file contains an indicator for the Pacific Decadal Oscillation (PDO), based on satellite observations of sea surface height anomaly, measured by radar altimeter missions such as TOPEX/Poseidon, the Jason series, and Sentinel-6. The indicator values were calculated using NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Only Version 1 https://podaac.jpl.nasa.gov/dataset/NASA_SSH_REF_SIMPLE_GRID_V1.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/NASA_SSH_PDO_INDICATOR","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NASA_SSH_REF_SIMPLE_GRID_V1 v1 - The NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Only Version 1 dataset produced by NASA provides 2-D maps of sea surface height, or sea level, anomaly once every 7 days. The grids are based on observations of sea surface height from the radar altimeter satellites in the reference mission orbits, including TOPEX/Poseidon, the Jason series, and Sentinel-6.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/NASA_SSH_REF_SIMPLE_GRID_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-nasa-ssh","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NCA-LDAS Project","Description":"The National Climate Assessment - Land Data Assimilation System, or NCA-LDAS, is a terrestrial water reanalysis in support of the United States Global Change Research Program's NCA activities. NCA-LDAS features high resolution, gridded, daily time series data products of terrestrial water and energy balance stores, states, and fluxes over the continental U.S., derived from land surface hydrologic modeling with multivariate assimilation of satellite Environmental Data Records (EDRs). The overall goal is to provide the highest quality terrestrial hydrology products that enable improved scientific understanding, adaptation, and management of water and related energy resources during a changing climate. An overview of NCA-LDAS and its capability for developing climate change indicators are provided in Jasinski et al. (2019). Details on the data assimilation used in NCA-LDAS are described in Kumar et al. (2019). Sample mean annual trends are provided in the NCA-LDAS V2.0 README document. This NCA-LDAS version 2.0 data product was simulated for the continental United States for the satellite era from January 1979 to December 2016. The core of NCA-LDAS is the multivariate assimilation of past and current satellite based data records within the Noah Version 3.3 land-surface model (LSM) at 1/8th degree resolution using NASA's Land Information System (LIS; Kumar et al. 2006) software framework during the Earth observing satellite era. The temporal resolution is daily. NCA-LDAS V001 data will no longer be available and have been superseded by V2.0. NCA-LDAS includes 42 variables including land-surface fluxes (e.g. precipitation, radiation and latent and sensible heat, etc.), stores (e.g. soil moisture and snow), states (e.g., surface temperature), and routing variables (e.g., runoff, streamflow, flooded area, etc.), driven by the atmospheric forcing data from North American Land Data Assimilation System Phase 2 (NLDAS-2; Xia et al., 2012). NCA-LDAS builds upon NLDAS through the addition of multivariate assimilation of earth observations such as soil moisture (Kumar et al, 2014), snow (Liu et al, 2015; Kumar et al, 2015a) and irrigation (Ozdagon et al, 2010; Kumar et al, 2015b). The EDRs that have been assimilated into the NCA-LDAS include soil moisture and snow depth from principally microwave sensors including SMMR, SSM/I, AMSR-E, ASCAT, AMSR-2, SMOS, and SMAP, irrigation intensity estimates from MODIS, and snow covered area from MODIS and from the multisensor IMS snow product.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://ldas.gsfc.nasa.gov/NCA-LDAS/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","earth observation","hydrology","ice","precipitation","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NCALDAS_NOAH0125_D v2.0 - The National Climate Assessment - Land Data Assimilation System, or NCA-LDAS, is a terrestrial water reanalysis in support of the United States Global Change Research Program's NCA activities. NCA-LDAS features high resolution, gridded, daily time series data products of terrestrial water and energy balance stores, states, and fluxes over the continental U.S., derived from land surface hydrologic modeling with multivariate assimilation of satellite Environmental Data Records (EDRs).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NCALDAS/NCALDAS_NOAH0125_D.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-nca-ldas","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NEESPI NASA Project","Description":"The dataset contains global monthly-mean soil moisture statistics (average values) for 1 by 1 degree grid cells. The source for the data is AMSR-E daily estimates of soil moisture (AE_Land3.002: AMSR-E/Aqua Daily L3 Surface Soil Moisture, Interpretive Parameters, QC EASE-Grids. Version 2 ). The dataset covers the time period from 2002-10-01 to 2011-09-30.\n<br>\n\n#### AMSRE_STDMO\nThe dataset contains global monthly soil moisture statistics (standard deviation ) for 1 by 1 degree grid cells. The source for the data is AMSR-E daily estimates of soil moisture (AE_Land3.002: AMSR-E/Aqua Daily L3 Surface Soil Moisture, Interpretive Parameters, QC EASE-Grids. Version 2 ). The dataset covers the time period from 2002-10-01 to 2011-09-30.\n<br>\n\n#### MYD14CM1\nThe gridded MODIS active fire products present statistical summaries of fire pixel information (Giglio et al., 2003). The global monthly products are generated at 1x1 degree spatial resolution for time period of one calendar month. These products are derived from MODIS CMG 0.5 degree products (Giglio et al., 2006) for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program in supporting researches on surface processes and climate modeling.\n<br>\n\n#### MYD11CM1D\nThe dataset contains global monthly day-time land surface temperature averaged within 1 by 1 degree grid cells. The source for the data is MODIS/Aqua MYD11C3 Collection 005 product (MODIS/Aqua Monthly mean land surface temperature at 0.05 degree spatial resolution). The dataset covers the time period from 2002-08-01 to 2015-06-30.\n<br>\n\n#### MYD11CM1N\nThe dataset contains global monthly night-time land surface temperature averaged within 1 by 1 degree grid cells. The source for the data is MODIS/Aqua MYD11C3 Collection 005 product (MODIS/Aqua Monthly mean land surface temperature at 0.05 degree spatial resolution). The dataset covers the time period from 2002-08-01 to 2015-06-30.\n<br>\n\n#### MYDVI\nThe global monthly gridded MODIS vegetation indices product is derived from the standard 0.05 CMG MODIS Aqua Vegetation Indices Monthly product MYD13C2 (Huete et al, 2002) collection-5. The product is generated for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program in supporting researches on the surface processes and climate modeling. The vegetation indices product is generated at 1x1 degree spatial resolution starting from July 2002.\n<br>\n\n#### MOD11CM1D\nThe dataset contains global monthly day-time land surface temperature averaged within 1 by 1 degree grid cells. The source for the data is MODIS/Terra MOD11C3 Collection 005 product (MODIS/Terra Monthly mean land surface temperature at 0.05 degree spatial resolution). The dataset covers the time period from 2000-03-01 to 2015-06-30.\n<br>\n\n#### MOD11CM1N\nThe dataset contains global monthly night-time land surface temperature averaged within 1 by 1 degree grid cells. The source for the data is MODIS/Terra MOD11C3 Collection 005 product (MODIS/Terra Monthly mean land surface temperature at 0.05 degree spatial resolution). The dataset covers the time period from 2000-03-01 to 2015-06-30.\n<br>\n\n#### MODVI\nThe global monthly gridded MODIS vegetation indices product is derived from the standard 0.05 CMG MODIS Terra Vegetation Indices Monthly product MOD13C2 (Huete et al, 2002) collection-5. The product is generated for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program in supporting researches on the surface processes and climate modeling. The vegetation indices product is generated at 1x1 degree spatial resolution starting from 2000.\n<br>\n\n#### NHICEM\nThis product is monthly Ice Cover Statistics. The dataset was prepared by Dr. Peter Romanov at Cooperative Institute for Climate Studies(CICS) of the University of Maryland for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program. The product includes the monthly ice statistics (frequency of occurrence) for Northern Hemisphere at 1x1 degree spatial resolution. The dataset covers the time period starting January 2000 to November 2014. The data was derived from daily ice cover charts produced at NOAA/NESDIS within Interactive Multisensor Ice Mapping System (IMS).\n<br>\n\n#### NHSNOWM\nThis product is Snow Cover Statistics. The dataset was prepared by Dr. Peter Romanov at Cooperative Institute for Climate Studies(CICS) of the University of Maryland for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program. The product includes the monthly snow statistics (frequency of occurrence) for Northern Hemisphere at 1x1 degree spatial resolution. The dataset covers the time period from January 2000 to November 2014. Monthly data were derived from daily snow cover charts produced at NOAA/NESDIS within Interactive Multisensor Ice Mapping System (IMS).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/AMSRE_AVRMO_005.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","ice","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AMSRE_AVRMO v005 - The dataset contains global monthly-mean soil moisture statistics (average values) for 1 by 1 degree grid cells. The source for the data is AMSR-E daily estimates of soil moisture (AE_Land3.002: AMSR-E/Aqua Daily L3 Surface Soil Moisture, Interpretive Parameters, QC EASE-Grids.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Soil_Moisture/AMSRE_AVRMO.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AMSRE_STDMO v005 - The dataset contains global monthly soil moisture statistics (standard deviation ) for 1 by 1 degree grid cells. The source for the data is AMSR-E daily estimates of soil moisture (AE_Land3.002: AMSR-E/Aqua Daily L3 Surface Soil Moisture, Interpretive Parameters, QC EASE-Grids.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Soil_Moisture/AMSRE_STDMO.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MYD14CM1 v005 - The gridded MODIS active fire products present statistical summaries of fire pixel information (Giglio et al., 2003). The global monthly products are generated at 1x1 degree spatial resolution for time period of one calendar month.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Fire/MYD14CM1.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MYD11CM1D v005 - The dataset contains global monthly day-time land surface temperature averaged within 1 by 1 degree grid cells. The source for the data is MODIS/Aqua MYD11C3 Collection 005 product (MODIS/Aqua Monthly mean land surface temperature at 0.05 degree spatial resolution).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Land_Surface_Temperature/MYD11CM1D.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MYD11CM1N v005 - The dataset contains global monthly night-time land surface temperature averaged within 1 by 1 degree grid cells. The source for the data is MODIS/Aqua MYD11C3 Collection 005 product (MODIS/Aqua Monthly mean land surface temperature at 0.05 degree spatial resolution).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Land_Surface_Temperature/MYD11CM1N.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MYDVI v005 - The global monthly gridded MODIS vegetation indices product is derived from the standard 0.05 CMG MODIS Aqua Vegetation Indices Monthly product MYD13C2 (Huete et al, 2002) collection-5. The product is generated for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program in supporting researches on the surface processes and climate modeling.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Vegetation_Indices/MYDVI.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MOD11CM1D v005 - The dataset contains global monthly day-time land surface temperature averaged within 1 by 1 degree grid cells. The source for the data is MODIS/Terra MOD11C3 Collection 005 product (MODIS/Terra Monthly mean land surface temperature at 0.05 degree spatial resolution).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Land_Surface_Temperature/MOD11CM1D.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MOD11CM1N v005 - The dataset contains global monthly night-time land surface temperature averaged within 1 by 1 degree grid cells. The source for the data is MODIS/Terra MOD11C3 Collection 005 product (MODIS/Terra Monthly mean land surface temperature at 0.05 degree spatial resolution).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Land_Surface_Temperature/MOD11CM1N.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MODVI v005 - The global monthly gridded MODIS vegetation indices product is derived from the standard 0.05 CMG MODIS Terra Vegetation Indices Monthly product MOD13C2 (Huete et al, 2002) collection-5. The product is generated for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program in supporting researches on the surface processes and climate modeling.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Vegetation_Indices/MODVI.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NHICEM v001 - This product is monthly Ice Cover Statistics. The dataset was prepared by Dr.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Snow_Ice/NHICEM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NHSNOWM v001 - This product is Snow Cover Statistics. The dataset was prepared by Dr.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Snow_Ice/NHSNOWM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-neespi-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NEWS Project","Description":"The world's premier catalyst for understanding Earth as a unified and dynamic system, empowering humanity through transformative insights into Earth system science.\n<br>\n\n#### NEWS_WEB_ACLIM\nNASA Energy and Water cycle Study (NEWS) Climatology of the 1st decade of the 21st Century Dataset summarizes the original observationally-based mean fluxes of water and energy budget components during the first decade of the 21st Century, for each continent and ocean basin on monthly and annual scales as well as means over all oceans, all continents, and the globe. A careful accounting of uncertainty in the estimates is included. Also, it includes optimized versions of all component fluxes that simultaneously satisfy energy and water cycle balance constraints. The NEWS Climatology contains two data products: an annual climatology data product and a monthly climatology data product. This data product is the annual climatology product. The climatology base period is roughly 1998-2010, where individual datasets cover various periods starting as early as 1998 and as late as 2002, not all extending to 2010. The continents and ocean basins boundaries map is used in this study to compute regional means. The ocean basin data was provided by Kyle Hilburn and Chelle Gentemann at Remote Sensing Systems. The land portion and some inland water bodies of the data are delineated into continents according to general definitions found in Wikipedia and relevant past studies. The data are distributed with four different units (1000 km^3/year, W/m^2, cm/year, and mm/day), in three formats (NetCDF, xlsx, and csv).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://science.nasa.gov/earth-science/focus-areas/water-and-energy-cycle","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","earth observation","netcdf","oceans"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NEWS_WEB_ACLIM v1.0 - NASA Energy and Water cycle Study (NEWS) Climatology of the 1st decade of the 21st Century Dataset summarizes the original observationally-based mean fluxes of water and energy budget components during the first decade of the 21st Century, for each continent and ocean basin on monthly and annual scales as well as means over all oceans, all continents, and the globe. A careful accounting of uncertainty in the estimates is included.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NEWS/NEWS_WEB_ACLIM.1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-news","Sources":["awslabs-open-data-registry"]}
{"Name":"Downscaled CMIP6 projections over CONUS at 30-arcsecond horizontal resolution","Description":"The NEX-DCP30-CMIP6 dataset contains daily, monthly, and annual downscaled CMIP6 projections over CONUS at 30-arcsecond hoirzontal resolution for maximum temperature, minimum temperature, precipitation, and vapor pressure.  \n","Documentation":"https://doi.org/10.25966/427f-8370","Contact":"Bridget Thrasher (bridget@climateanalyticsgroup.org)","ManagedBy":"National Aeronautics and Space Administration (NASA) Ames Research Center","UpdateFrequency":"N/A","Tags":["aws-pds","CMIP6","precipitation"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"Downscaled Climate Projections over CONUS","ARN":"arn:aws:s3:::nasa-nex-dcp30-cmip6","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://data.nas.nasa.gov/nex-dcp30-cmip6)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Daily Bias Correction/Spatial Disaggregation (BCSD) Statistical Downscaling Method","URL":"https://doi.org/10.5281/zenodo.18525436","AuthorName":"Bridget Thrasher","AuthorURL":"https://www.climateanalyticsgroup.org"},{"Title":"NCAR Command Language","URL":"http://dx.doi.org/10.5065/D6WD3XH5","AuthorName":"National Center for Atmospheric Research","AuthorURL":"https://ncar.ucar.edu"}]},"DeprecatedNotice":null,"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-05-18","RegistryEntryLastModified":"2026-06-02","Slug":"nasa-nex-dcp30-cmip6","Sources":["awslabs-open-data-registry"]}
{"Name":"Hawai'i Downscaled Climate Projections CMIP6","Description":"The NEX-HIDCP-CMIP6 dataset contains high-resolution daily downscaled CMIP6 projections over Hawai'i for maximum temperature, minimum temperature, and precipitation. This dataset compliments the NEX-DCP30-CMIP6 collection of downscaled projections over CONUS.\n","Documentation":"https://doi.org/10.25966/nex-hidcp-cmip6","Contact":"Bridget Thrasher (bridget@climateanalyticsgroup.org)","ManagedBy":"National Aeronautics and Space Administration (NASA) Ames Research Center","UpdateFrequency":"N/A","Tags":["aws-pds","CMIP6","precipitation"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"Downscaled Climate Projections over Hawai'i","ARN":"arn:aws:s3:::nasa-nex-hidcp-cmip6","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://data.nas.nasa.gov/index.php?portal=nex-hidcp-cmip6)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"NCAR Command Language","URL":"http://dx.doi.org/10.5065/D6WD3XH5","AuthorName":"National Center for Atmospheric Research","AuthorURL":"https://ncar.ucar.edu"}]},"DeprecatedNotice":null,"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2026-05-18","RegistryEntryLastModified":"2026-06-02","Slug":"nasa-nex-hidcp-cmip6","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NIMBUS-7 Project","Description":"NIMBUS7_NFOV_MLCE data are Nimbus 7 Narrow Field of View (NFOV) Maximum Likelihood Cloud Estimation (MLCE) Data in Native Format.The NIMBUS7_NFOV_MLCE data set uses the Nimbus-7 measurements and the MLCE algorithm for better regional and temporal resolution. The Earth Radiation Budget (ERB) parameters, derived from the Nimbus-7 scanner measurements, were rederived in 1990 using a Maximum Likelihood Cloud Estimation (MLCE) algorithm similar, but not identical, to the Earth Radiation Budget Experiment (ERBE) algorithm. Daily and monthly means are presented on two commensurate equal area world grids: (167 km by 167 km) and (500 km by 500 km). The MLCE procedure also yielded a rough estimate of the regional cloud cover.The scanner took measurements from November 16, 1978 through June 20, 1980; however, only 13 months (May 1979 through May 1980) of data sampling were reprocessed using the Sorting into Angular Bins and MLCE algorithms. There was poorer temporal sampling during the first five months of the experiment.The Nimbus 7 research-and-development satellite served as a stabilized, earth-oriented platform for the testing of advanced systems for sensing and collecting data in the pollution, oceanographic and meteorological disciplines. The polar-orbiting spacecraft consisted of three major structures: (1) a hollow torus-shaped sensor mount, (2) solar paddles, and (3) a control housing unit that was connected to the sensor mount by a tripod truss structure.\n<br>\n\n#### WENTZ_NIMBUS-7_SMMR_L2\nContains three parameters: ocean near-surface wind speed, columnar water vapor, and columnar liquid water. Product is produced by Frank Wentz at Remote Sensing Systems using data obtained from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR). Observations within 100 km of land are excluded; ice flags are also utilized. Data is obtained from all 10 individual SMMR channels, which closely correspond to the SMM/I channels and utilizing the same processing algorithms that were used to produce similar products derived from SSM/I observations (see PO.DAAC products 33 and 34).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br><br>\n","Documentation":"https://asdc.larc.nasa.gov/project/NIMBUS-7","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","earth observation","ice","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"WENTZ_NIMBUS-7_SMMR_L2 v1 - Contains three parameters: ocean near-surface wind speed, columnar water vapor, and columnar liquid water. Product is produced by Frank Wentz at Remote Sensing Systems using data obtained from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/WENTZ_NIMBUS-7_SMMR_L2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-nimbus-7","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Nimbus Project","Description":"The Nimbus-4 BUV Level-1 Dark Current Study Master Data is derived from the BUV Level 1 Radiance (RUT) product and contains the geophysical indices and classification, geographic and geomagnetic coordinates, solar magnetic parameters and angles; monochromator and photometer pulse count and analog data, and energetic trapped particles. There is one-to-one correspondence between this product and the dark current working data files, the difference is the working product data have been filtered. The data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains about one orbit of data from the nighttime descending node. The data files consist of 140 4-byte word records which are blocked with up to 25 records. The average size of an orbit file is 480 kB. This product was previously available from the NSSDC with the identifier ESAC-00045 (old ID 70-025A-05H).\n<br>\n\n#### BUVN4L1DCW\nThe Nimbus-4 BUV Level-1 Dark Current Study Working Data is derived from the BUV Level 1 Radiance (RUT) product and contains the geophysical indices and classification, geographic and geomagnetic coordinates, solar magnetic parameters and angles; monochromator and photometer pulse count and analog data, and energetic trapped particles. There is one-to-one correspondence between this product and the dark current master data files, the difference is the working product data have been filtered. The data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains about one orbit of data from the nighttime descending node. The data files consist of 140 4-byte word records which are blocked with up to 25 records. The average size of an orbit file is 375 kB. This product was previously available from the NSSDC with the identifier ESAC-00054 (old ID 70-025A-05I).\n<br>\n\n#### BUVN4L1PDB\nThe Nimbus-4 BUV Level-1 Radiance and Housekeeping Data in Telemetry Units collection contains the raw counts measured by the Backscatter Ultraviolet Spectrometer every 32 seconds at 12 wavelengths between 250 and 340 nm during the daylit orbit portion. The data collection also contains ephemeris data, experiment subsystem status information, and spacecraft housekeeping and orbit data. This data collection was used to create the Level 1 Radiance U-Tape or RUT product. The data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains about one orbit of data. The data files consist of 850 2-byte word records which are blocked in up to ten records. The first record in the file is the header record, followed by a series of data records, and ends with a trailer record. A typical orbit file is about 260 kB in size. The BUV instrument was operational from April 10, 1970 until May 6, 1977. In July 1972 the Nimbus-4 solar power array partially failed such that BUV operations were curtailed. Thus data collected in the later years was increasingly sparse, particularly in the equatorial region. This product was previously available from the NSSDC with the identifier ESAC-00024 (old ID 70-025A-05E).\n<br>\n\n#### BUVN4L1RUT\nThe Nimbus-4 BUV Level-1 Radiance data collection was derived from the Primary Data Base (PDB) product and contains the calibrated and geolocated backscattered ultraviolet radiances measured every 32 seconds at 12 wavelengths between 250 and 340 nm during the daylit orbit portion. The data collection also contains quality flags, dark current analyses of the data, orbital information, and housekeeping data. The data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains about one orbit of data. The data files consist of 100 4-byte word records which are blocked with up to 25 records. The first record is the header record, followed by a series of data records, and ended with two trailer records. A typical orbit file is about 70 kB in size. The BUV instrument was operational from April 10, 1970 until May 6, 1977. In July 1972 the Nimbus-4 solar power array partially failed such that BUV operations were curtailed. Thus data collected in the later years was increasingly sparse, particularly in the equatorial region. This product was previously available from the NSSDC with the identifier ESAC-00055 (old ID 70-025A-05B).\n<br>\n\n#### BUVN4L2CPOZ\nThe Nimbus-4 BUV Level 2 Compressed Ozone Profile Data collection or CPOZ contains total ozone, reflectivities, ozone mixing ratios and layer ozone amounts measured every 32 seconds during the daylit portion of an orbit. Mixing ratios are given at 19 levels: 0.3, 0.4, 0.5, 0.7, 1, 1.5, 2, 3, 4, 5, 7, 10, 15, 20, 30, 40, 50, 70 and 100 mbar. Layer ozone amounts are provided at 12 layers: 0.24, 0.49, 0.99, 1.98, 3.96, 7.92, 15.8, 31.7, 63.3, 127, 253, and 1013 mbar (bottom of layer value). This product is a condensed version of the BUV High-Density Ozone Data Product or (HDBUV). The data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains about one day of data (14 orbits). The files consist of data records each with seventy-two 4-byte words. The first record is the header record, followed by a series of data records, and ends with several trailer records that pad out the original blocked records. A typical daily file is about 100 kB in size. The BUV instrument was operational from April 10, 1970 until May 6, 1977. In July 1972 the Nimbus-4 solar power array partially failed such that BUV operations were curtailed. Thus data collected in the later years was increasingly sparse, particularly in the equatorial region. This product was previously available from the NSSDC as the Compressed Ozone Profile (CPOZ) Data with the identifier ESAC-00010 (old ID 70-025A-05P).\n<br>\n\n#### BUVN4L2HDBUV\nThe Nimbus-4 BUV Level 2 High-Density Ozone Data collection contains the vertical distribtuion and total column amount of ozone, as well as the full set of ancillary information. Each file contains total ozone, reflectivities, ozone mixing ratios and layer ozone amounts measured every 32 seconds during the daylit portion of an orbit. Mixing ratios are given at 19 levels: 0.3, 0.4, 0.5, 0.7, 1, 1.5, 2, 3, 4, 5, 7, 10, 15, 20, 30, 40, 50, 70 and 100 mbar. Layer ozone amounts are provided at 12 layers: 0.24, 0.49, 0.99, 1.98, 3.96, 7.92, 15.8, 31.7, 63.3, 127, 253, and 1013 mbar (bottom of layer value). The data collection also contains quality flags, orbital information, and housekeeping data. The data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains about one orbit of data. The files consist of data records each with two hundred and seven 4-byte words. The first record is the header record, followed by a series of data records, and ends with several trailer records that pad out the original blocked records. A typical orbit file is about 96 kB in size. The BUV instrument was operational from April 10, 1970 until May 6, 1977. In July 1972 the Nimbus-4 solar power array partially failed such that BUV operations were curtailed. Thus data collected in the later years was increasingly sparse, particularly in the equatorial region. This product was previously available from the NSSDC as the Total and Profile Ozone Data (HDBUV) with the identifier ESAC-00030 (old ID 70-025A-05Q).\n<br>\n\n#### BUVN4L3ZMT\nThe Nimbus-4 BUV Level 3 Ozone Zonal Means collection or ZMT contains total ozone, reflectivities, and ozone mixing ratios averaged in 10 degree latitude zones centered from 80 to -80 degrees. Mixing ratios are given at 19 levels: 0.3, 0.4, 0.5, 0.7, 1, 1.5, 2, 3, 4, 5, 7, 10, 15, 20, 30, 40, 50, 70 and 100 mbar. In addition to the means, files also include the standard deviation, minimum and maximum values, as well as sample size. The data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains monthly, weekly and daily zonal means, as well as quarterly means if it is the last month of the quarter. The files consist of data records each with one-hundred-eighty 4-byte words. Monthly, weekly, daily and quarterly means are distinguished by the seventh 4-byte word in the records. A typical file is about 380 kB in size. The BUV instrument was operational from April 10, 1970 until May 6, 1977. In July 1972 the Nimbus-4 solar power array partially failed such that BUV operations were curtailed. Thus data collected in the later years was increasingly sparse, particularly in the equatorial region. This product was previously available from the NSSDC as the Zonal Means File (ZMT) with the identifier ESAC-00039 (old ID 70-025A-05O).\n<br>\n\n#### ESMRN5IM\nESMRN5IM is the Nimbus-5 Electrically Scanning Microwave Radiometer (ESMR) data product containing daily brightness temperature images from 70-mm photofacsimile film strips. Each frame contains a geographic grid and two groups of three parallel strips of imagery, each containing one-half the orbital data. The spatial coverage is identical in each group, but each strip has a different dynamic range for its gray scale: 100-200 K, 190-270 K, and 250-300 K, respectively. The spatial resolution is 25 x 25 km near nadir, degrading to 160 km cross-track by 45 km down-track at the ends of the scan. The images are saved as JPEG 2000 digital files. About 2 weeks of images are archived into a TAR file. Additional information can be found in \"The Nimbus 5 User's Guide.\" The primary objectives of the ESMR experiment were: (1) to derive the liquid water content of clouds from brightness temperatures over oceans, (2) to observe differences between sea ice and the open sea over the polar caps, and (3) to test the feasibility of inferring surface composition and soil moisture. To accomplish these objectives, the ESMR was capable of continuous global mapping of the 1.55-cm (19.36 GHz) microwave radiation emitted by the earth/atmosphere system, and could function even in the presence of cloud conditions that block conventional satellite infrared sensors. The ESMR instrument made measurements from Dec. 11, 1972 until May 16, 1977. This product was previously available from the NSSDC with the identifier ESAD-00192 (old ID 72-097A-04C).\n<br>\n\n#### ESMRN5L1\nESMRN5L1 is the Nimbus-5 Electrically Scanning Microwave Radiometer (ESMR) Level 1 Calibrated Brightness Temperature product and contains calibrated radiances expressed in units of brightness temperature measured at 19.35 GHz. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as the Calibrated Brightness Temperature Tapes (CBTT). The data are archived in their original IBM binary proprietary format, also referred to as a binary TAP file. The Nimbus-5 satellite was successfully launched on December 11, 1972. The ESMR experiment on Nimbus-5 continued the measurements made by its predecessor flown on Nimbus-4. The ESMR instrument objectives were (1) to derive the liquid water content of clouds from brightness temperatures over oceans, (2) to observe differences between sea ice and the open sea over the polar caps, and (3) to test the feasibility of inferring surface composition and soil moisture. The ESMR Principal Investigator was Dr. Thomas T. Wilheit, Jr. from NASA Goddard Space Flight Center. The Nimbus-5 ESMR data are available from December 11, 1972 (day of year 346) through May 16, 1977 (day of year 136) This product was previously available from the NSSDC with the identifier ESAD-00219 (old ID 72-097A-04A).\n<br>\n\n#### ESMRN6IM\nESMRN6IM is the Nimbus-6 Electrically Scanning Microwave Radiometer (ESMR) data product containing daily brightness temperature images from 70-mm photofacsimile film strips (both positives and negatives). Each frame contains two sets with a geographic grid and either 10 (F = full scale) or 5 (P1,P2 = partial scale) parallel strips of imagery, each containing one-half of an orbit swath (ascending on left, descending on right). The spatial coverage is identical in each set, but each swath strip has a different dynamic range and polarization. The spatial resolution is about 20 x 45 km near nadir. The images are saved as TIFF digital files. About 5-10 months worth of images are archived into a ZIP file. Additional information about ESMR can be found in \"The Nimbus 6 User's Guide.\" The primary objectives of the ESMR experiment were: (1) to derive the liquid water content of clouds from brightness temperatures over oceans, (2) to observe differences between sea ice and the open sea over the polar caps, and (3) to test the feasibility of inferring surface composition and soil moisture. To accomplish these objectives, the ESMR was capable of continuous global mapping of the 0.81 cm (37.0 GHz) microwave radiation emitted by the earth/atmosphere system, using both horizontal and vertical polarized components. The ESMR instrument performance was satisfactory until September 15, 1976, when the horizontal channel failed. Another ESMR instrument was flown on Nimbus 5. This product was previously available from the NSSDC with the identifier ESAD-00201 (old ID 75-052A-03B).\n<br>\n\n#### HIRSSCAMSN6L2GARP\nThe Nimbus-6 High Resolution Infrared Radiometer (HIRS) and (SCAMS) Merged Level 2 Radiation, Temperature and Humidity Sounding Data for the Global Atmospheric Research Program (GARP) data product contains measurements at standard pressure levels from 1000 to 1 mbar. This product was generated by the former Goddard Applications Directorate on the Atmospheric and Oceanographic Information Processing System (AOIPS) as a data system test in support of the Global Atmospheric Research Program (GARP). The data, originally written on IBM 360 machines, were recovered from 9-track magnetic tapes. The data are archived in their original IBM 32-bit word binary record format, also referred to as a binary TAP file, and contain one orbit of measurements. The HIRS experiment on Nimbus-6 was a follow on to the successful Nimbus-5 ITPR experiment. HIRS was a multi-channel filter radiometer with a Cassegrain telescope before the chopper assembly. The instrument scanned in the cross track direction with 21 scans on each side of the subtrack point with about 30 km x 55 km resolution at nadir. HIRS measured radiances primarily in five spectral regions: (1) seven channels near the 15-micrometer CO2 absorption band, (2) two channels (11.1 and 3.7 micrometers) in the IR window, (3) two channels (8.2 and 6.7 micrometers) in the water vapor absorption band, (4) five channels in the 4.3-micrometer band, and (5) one channel in the visible 0.69-micrometer region. The SCAMS experiment on Nimbus-6 was a follow on to the successful Nimbus-5 NEMS experiment. SCAMS continuously monitored emitted microwave radiation at frequencies of 22.235, 31.65, 52.85, 53.85 and 55.45 GHz. The three channels near the 5.0-mm oxygen absorption band were used primarily to deduce atmospheric temperature profiles. The two channels near 10 mm permitted water vapor and cloud water content over calm oceans to be estimated separately. The instrument, a Dicke-superheterodyne type, scanned +/- 45 degrees normal to the orbital plane with a 10 degree field of view. The three oxygen channels shared common signal and reference antennas. Both water vapor channels had their own signals and reference antennas. The absolute rms accuracy of the oxygen channels was better than 2 Kelvin and that of the water vapor channels better than 1 Kelvin. The HIRS Principal Investigator was Mr. W. L. Smith from the NOAA National Environmental Satellite Service. The SCAMS Principal Investigator was Prof. David H. Staelin from MIT. The Nimbus-6 HIRS and SCAMS merged data are available from August 17, 1975 through March 4, 1976. These data were previously archived at NASA NSSDC under the entry ID ESAD-00017 together with the merged retrieval data set.\n<br>\n\n#### HIRSN6IM\nThe HIRSN6IM data product consists of images of brightness temperatures on 70 mm film strips from the Nimbus-6 High Resolution Infrared Radiation Sounder. Each display contains black and white images at either full vertical scale (F) or partial vertical scale (P). A full scale mode image will contain one orbit of data or 125 minutes of data, while a partial scale mode image will contain twice the vertical scale and thus requires two images for an orbit of data (the last 60 minutes on the first image, P1 and the remaining data on the second image, P2). There are 10 channels (swaths) for an orbit on each image, with a header identifying the channel (1-1, 6-6, etc.). An 18-step gray scale is found at the bottom. Time and geographic information is encoded in the center of the image. Conversion from the 18-step gray scale to brightness temperatures can be found in a table in each of the first six volumes of \"The Nimbus 6 Data Catalog.\" The HIRS experiment on Nimbus-6 is a follow on to the successful Nimbus-5 ITPR experiment. HIRS was a multi-channel filter radiometer with a Cassegrain telescope before the chopper assembly. The instrument scans in the cross track direction with 21 scans on each side of the subtrack point with about 30 km x 55 km resolution at nadir. HIRS measured radiances primarily in five spectral regions: (1) seven channels near the 15-micrometer CO2 absorption band, (2) two channels (11.1 and 3.7 micrometers) in the IR window, (3) two channels (8.2 and 6.7 micrometers) in the water vapor absorption band, (4) five channels in the 4.3-micrometer band, and (5) one channel in the visible 0.69-micrometer region. The HIRS Principal Investigator was Mr. W. L. Smith from the NOAA National Environmental Satellite Service. The Nimbus-6 HIRS data are available from August 17, 1975 (day of year 229) through March 4, 1976 (day of year 238). These data were previously archived at NASA NSSDC under the entry ID ESAD-00094 (old id 75-052A-02A).\n<br>\n\n#### HIRSN6L1GARP\nThe Nimbus-6 High Resolution Infrared Radiometer (HIRS) Level 1 Calibrated Radiances for the Global Atmospheric Research Program (GARP) data product contains daily infrared radiances. The HIRS was designed to measure surface temperature and albedo, temperature and H2O profiles, cloud liquid water content, cloud amount and outgoing longwave fluxes in the infrared. The data, originally written on IBM 360 machines, were recovered from 9-track magnetic tapes. The data are archived in their original IBM 32-bit word binary record format, also referred to as a binary TAP file, and contain one orbit of measurements. The HIRS experiment on Nimbus-6 is a follow on to the successful Nimbus-5 ITPR experiment. HIRS was a multi-channel filter radiometer with a Cassegrain telescope before the chopper assembly. The instrument scans in the cross track direction with 21 scans on each side of the subtrack point with about 30 km x 55 km resolution at nadir. HIRS measured radiances primarily in five spectral regions: (1) seven channels near the 15-micrometer CO2 absorption band, (2) two channels (11.1 and 3.7 micrometers) in the IR window, (3) two channels (8.2 and 6.7 micrometers) in the water vapor absorption band, (4) five channels in the 4.3-micrometer band, and (5) one channel in the visible 0.69-micrometer region. The HIRS Principal Investigator was Mr. W. L. Smith from the NOAA National Environmental Satellite Service. The Nimbus-6 HIRS data are available from August 17, 1975 (day of year 229) through March 4, 1976 (day of year 238). These data were previously archived at NASA NSSDC under the entry ID ESAD-00017 together with the merged retrieval data set).\n<br>\n\n#### HRIRN1IM\nHRIRN1IM is the Nimbus-1 High-Resolution Infrared Radiometer (HRIR) data product containing scanned negatives of photofacsimile 70mm film strips. The images contain orbital nighttime (3.5 to 4.1 microns) brightness temperature values showing cloud cover and the Earth's surface temperature. Each orbital swath picture is gridded with geographic coordinates and covers a distance approximately from the north pole to the south pole. The images are saved as JPEG 2000 digital files. About 7 days of images are archived into a TAR file. The processing techniques used to produce the data set and a full description of the data are contained in section 3.4.1 of the \"Nimbus I Users' Guide.\" The HRIR instrument was designed to perform two major functions: first to map the Earth's cloud cover at night to complement the television coverage during the daytime portion of the orbit, and second to measure the temperature of cloud tops and terrain features. The HRIR instrument was launched on the Nimbus-1 satellite and was operational from August 28, 1964 through September 22, 1964. Dr. L. L. Foshee of the US Army Electronics Command was the Principal Investigator. This product was previously available from the NSSDC with the identifier ESAD-00135 (old ID 64-052A-03B).\n<br>\n\n#### HRIRN1L1\nHRIRN1L1 is the High Resolution Infrared Radiometer (HRIR) Nimbus-1 Level 1 Meteorological Radiance Data (NMRT) product and contains infrared radiances converted to equivalent black-body temperature or \"brightness\" temperature values. he data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The HRIR instrument was designed to perform two major functions: first to map the Earth's cloud cover at night to complement the television coverage during the daytime portion of the orbit, and second to measure the temperature of cloud tops and terrain features. The HRIR instrument was launched on the Nimbus-1 satellite and was operational from August 28, 1964 through September 22, 1964 when the spacecraft malfunctioned. Dr. L. L. Foshee of the US Army Electronics Command was the Principal Investigator. Measurements taken during daytime do not reveal true surface temperaturessince the radiometer operates in the 3.5 to 4.1 micron region, and reflectedsolar radiation is added to emitted surface radiation. However, reflected sunlight in this spectral region does not saturate the radiometer output and usable pictures can be made. This product was previously available from the NSSDC with the identifier ESAD-00209 (old ID 64-052A-03A).\n<br>\n\n#### HRIRN2IM\nHRIRN2IM is the Nimbus-2 High-Resolution Infrared Radiometer (HRIR) data product containing scanned negatives of photofacsimile 70mm film strips. The images contain orbital nighttime (3.5 to 4.1 microns) brightness temperature values showing cloud cover and the Earth's surface temperature. Each orbital swath picture is gridded with geographic coordinates and covers a distance approximately from the north pole to the south pole. The images are saved as JPEG 2000 digital files. About 7 days of images are archived into a TAR file. The processing techniques used to produce the data set and a full description of the data are contained in section 3.4.1 of the \"Nimbus II Users' Guide. \"The HRIR instrument was designed to perform two major functions: first to map the Earth's cloud cover at night to complement the television coverage during the daytime portion of the orbit, and second to measure the temperature of cloud tops and terrain features. This HRIR instrument was launched on the Nimbus-2 satellite and was operational from May 15, 1966 through November 15, 1966. Dr. L. L. Foshee of the US Army Electronics Command was the Principal Investigator. This product was previously available from the NSSDC with the identifier ESAD-00226 (old ID 66-040A-03B).\n<br>\n\n#### HRIRN2L1\nHRIRN2L1 is the High Resolution Infrared Radiometer (HRIR) Nimbus-2 Level 1 Meteorological Radiance Data (NMRT) product and contains infrared radiances converted to equivalent black-body temperature or \"brightness\" temperature values. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The HRIR instrument was designed to perform two major functions: first to map the Earth's cloud cover at night to complement the television coverage during the daytime portion of the orbit, and second to measure the temperature of cloud tops and terrain features. The HRIR instrument was launched on the Nimbus-2 satellite and was operational from May 16, 1966 through November 15, 1966. Dr. L. L. Foshee of the US Army Electronics Command was the Principal Investigator. Measurements taken during daytime do not reveal true surface temperatures since the radiometer operates in the 3.5 to 4.1 micron region, and reflected solar radiation is added to emitted surface radiation. However, reflected sunlight in this spectral region does not saturate the radiometer output and usable pictures can be made. This product was previously available from the NSSDC with the identifier ESAD-00108 (old ID 66-040A-03A).\n<br>\n\n#### HRIRN3IM\nHRIRN3IM is the Nimbus-3 High-Resolution Infrared Radiometer (HRIR) data product containing scanned negatives of photofacsimile 70mm film strips. The images contain orbital daytime (0.7 to 1.3 microns) and nighttime (3.4 to 4.2 microns) brightness temperature values showing cloud cover and the Earth's surface temperature. Each orbital swath picture is gridded with geographic coordinates and covers a distance approximately from the south pole to the north pole (day) and the north pole to the south pole (night). The images are saved as JPEG 2000 digital files. About 7 days of images are archived into a TAR file. The processing techniques used to produce the data set and a full description of the data are contained in section 3.4.1 of the \"Nimbus III Users' Guide.\" The HRIR instrument was designed to perform two major functions: first to map the Earth's cloud cover at night to complement the television coverage during the daytime portion of the orbit, and second to measure the temperature of cloud tops and terrain features. The HRIR instrument was launched on the Nimbus-3 satellite and was operational from April 22, 1969 through January 31, 1970. Mr. G. Thomas Cherrix from Goddard Space Flight Center was the Principal Investigator. This product was previously available from the NSSDC with the identifier ESAD-00223 (old ID 69-037A-02B).\n<br>\n\n#### HRIRN3L1\nHRIRN3L1 is the High Resolution Infrared Radiometer (HRIR) Nimbus-3 Level 1 Meteorological Radiance Data (NMRT) product and contains infrared radiances converted to equivalent black-body temperature or \"brightness\" temperature values. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The HRIR instrument was designed to perform two major functions: first to map the Earth's cloud cover at night to complement the television coverage during the daytime portion of the orbit, and second to measure the temperature of cloud tops and terrain features. The HRIR flown on Nimbus-3 was modified to allow nighttime and daytime cloud cover mapping by use of dual band-pass filter which transmits 0.7 to 1.3 micron, and 3.4 to 4.2 micron radiation. The HRIR instrument was launched on the Nimbus-3 satellite and was operational from April 14, 1966 through July 22, 1969. Nighttime operation was made in the 3.4 to 4.2 micron near infrared region. Daytime operation was based on the predominance of reflected solar energy in the 0.7 to 1.3 micron region. Change-over from nighttime to daytime operation was accomplished automatically (or by ground station command), by actuating a relay in the early stages of the radiometer electronics. The system gain was reduced in the daytime mode to compensate for the higher energy levels. Mr. G. Thomas Cherrix from Goddard Space Flight Center was the Principal Investigator. This product was previously available from the NSSDC with the identifier ESAD-00222 (old ID 69-037A-02C).\n<br>\n\n#### IRISN4RAD\nThe Nimbus-4 Infrared Interferometer Spectrometer (IRIS) Level 1 Radiance Data contain thermal emissions of the Earth's atmosphere at wave numbers between 400 and 1600 cm**-1, with a nominal resolution of 2.8 cm**-1. The data also contain documentation information, reference calibration, average instrument temperatures, and a summary for each orbital pass. The data, originally written on IBM 360 machines, were recovered from 9-track magnetic tapes. The data are archived in their original IBM 32-bit word binary record format, and each file contains an entire day of measurements. The product contains data from April 9, 1970 (day of year 99) through Jan 31, 1971 (day of year 31). The IRIS instrument was designed to provide information on the vertical structure of the atmosphere and on the emissive properties of the earth's surface by measuring the surface and atmospheric radiation in the 6.25 to 25 micrometer range using a modified Michelson interferometer. IRIS viewed along the satellite track direction with a spatial resolution of 94 km at nadir. A Fourier transform was applied to the interferograms to produce thermal emmision spectra of the Earth which could be used to derive vertical profiles of temperature, water vapor, and ozone, as well as other parameters of meteorological interest. The Infrared Interferometer Spectrometer (IRIS) experiment on Nimbus-4 is a follow on experiment to the Nimbus-3 IRIS experiment. The IRIS Principal Investigator was Dr. Rudolf A. Hanel. These data were previously archived at NASA NSSDC under the entry ID ESAD-00093 (originally 70-025A-03A).\n<br>\n\n#### ITPRN5L1\nITPRN5L1 is the Nimbus-5 Infrared Temperature Profile Radiometer (ITPR) Level-1 Calibrated Radiances data product which contains radiances at 7 infrared spectral regions (2683.0, 899.0, 747.0, 713.8, 689.5, 668.3, and 507.4 cm-1) in a single binary data file. Four are centered near the 15 micron CO2 band, one interval in the water vapor rotational band near 20 microns and two spectral intervals in the atmospheric window regions near 3.7 and 11 microns. The instrument scan sequence consists of three separate grid matrices, to the right, center and left of nadir. Each matrix consists of 10 scan lines with 14 scenes per scan. Each scan footprint is 32 km wide. Due to problems with the instrument, data are limited to three time periods from 14 February 1975 to 1 March 1975 covering East Asia, from 10 May 1976 to 4 June 1976 covering the United States and the Gulf, and from 1 September 1976 to 30 September 1976 covering southern Australia and New Zealand. The principal investigator for the ITPR experiment was William L. Smith from NOAA.\n<br>\n\n#### LIMSN7L1PROFILER\nLIMSN7L1PROFILER is the Nimbus-7 Limb Infrared Monitor of the Stratosphere (LIMS) Level-1 Profiles of Radiance Data product and contains selected daily vertical profiles across the earth’s atmospheric limb derived from the LIMS Level-1 Radiance Archival Tape (RAT) data product. Measurements are obtained, as a function of tangent height (or scan angle), once every 12 seconds in each of the six spectral bands (two 15-micrometer CO2 bands (narrow and wide), an 11.3-micrometer HNO3 band, a 9.6-micrometer O3 band, a 6.9-micrometer H2O band, and a 6.2-micrometer NO2 band) from the highest pressure level to the lowest in steps of 0.1 km Each file contains one days worth of data (~14 orbits per day). LIMS is a limb profiler and spatial coverage is near global between latitude -64 and +84 degrees. Vertical coverage is from about 10 to 50 km (O3 channel to 65 km), with vertical resolution of about 1.5 km. The data are available from 25 October 1978 through 30 May 1979. The principal investigators for the LIMS experiment were Dr. James M. Russell, III from NASA Langley and Dr. John Gille from NCAR. This product was previously available from the NASA National Space Science Data Center (NSSDC) under the name LIMS Radiance Archival Data with the identifier ESAC-00032 (old id 78-098A-01B).\n<br>\n\n#### LIMSN7L1RAT\nLIMSN7L1RAT is the Nimbus-7 Limb Infrared Monitor of the Stratosphere (LIMS) Level-1 Radiance Data product. It contains calibrated, earth-located radiances, as well as housekeeping information, instrument status, and data quality information. Radiances of the Earth limb were measured both day and night in six spectral bands (6.2, 6.3, 9.6, 11.3, and two at 15 micrometers). Though calibrated, the radiances are not corrected for instrument effects such as field-of-view, electronic delay, and spacecraft motion. Each file contains one orbit of data (~14 orbits per day). LIMS is a limb profiler and spatial coverage is near global between latitude -64 and +84 degrees. Vertical coverage is from about 10 to 50 km (O3 channel to 65 km), with vertical resolution of about 1.5 km. The data are available from 25 October 1978 through 30 May 1979. The principal investigators for the LIMS experiment were Dr. James M. Russell, III from NASA Langley and Dr. John Gille from NCAR. This product was previously available from the NASA National Space Science Data Center (NSSDC) under the name LIMS Radiance Archival Data with the identifier ESAC-00032 (old id 78-098A-01B).\n<br>\n\n#### LIMSN7L2\nThe Limb Infrared Monitor of the Stratosphere (LIMS) version 6 Level-2 data product consists of daily, geolocated, vertical profiles of temperature, geopotential height, and mixing ratios of ozone (O3), nitrogen dioxide (NO2), water vapor (H2O), and nitric acid (HNO3). Version 6 LIMS data have improved spatial resolution in both the vertical and along the orbital track, as well as improved accuracy and precision of measured geophysical parameters. The data files are in an ASCII text format and each data file is accompanied by three data screening files. The LIMS instrument was launched on the Nimbus-7 satellite and was operational from 25 October 1978 until May 28, 1979. These data supersede the previous version 5 product, known as the LIMS Inverted Profile Archival Tape (LAIPAT).\n<br>\n\n#### LIMSN7L3\nThe Limb Infrared Monitor of the Stratosphere (LIMS) version 6 Level-3 data product consists of daily, 2 degree zonal Fourier coefficients, of vertical profiles of temperature, geopotential height, and mixing ratios of ozone (O3), nitrogen dioxide (NO2), water vapor (H2O), and nitric acid (HNO3). The data are on 28 pressure levels, equally spaced logarithmically, between 316 hPa and 0.01 hPa. Version 6 LIMS data have improved accuracy and precision of measured geophysical parameters. The data files are in an ASCII text format compressed using gzip. The LIMS instrument was launched on the Nimbus-7 satellite and was operational from 25 October 1978 until May 28, 1979. These data supercede the previous version 5 products, known as the LIMS Map Archival Tape (LAMAT) and the LIMS Seasonal Map Archival Tape (LASMAT).\n<br>\n\n#### LRIRN6L2IPAT\nLRIRN6L2IPAT is the Nimbus-6 Limb Radiance Inversion Radiometer (LRIR) Level 2 Inverted Profiles of Temperature and Ozone data product. The product contains daily profiles of temperature and ozone concentration profiles that were inverted from radiances measured in four spectral regions: two in the 15 micron carbon dioxide band; one in the 9.7 micron ozone band; and one located in the rotational water vapor band (23 to 27 microns). The calibrated radiances are also included in this product. There are a maximum of 13 orbits per day each with up to 115 profiles per orbit. LRIR is a limb profiler with spatial coverage from latitude -64 to +84 degrees. Vertical profiles are provided at 17 standard pressure levels (from 100 to 0.1 mbar, i.e., from 15 to 64 km) with about 1.5 km vertical resolution. The instrument operated successfully and data are available from 20 June 1975 to 6 January 1976. After this, the detector temperature began to rise rapidly, and the instrument was turned off. The principal investigator for the LRIR experiment was Dr. John Gille from NCAR. This product was previously available from the NSSDC with the identifier ESAD-00037 (old ID 75-052A-04A).\n<br>\n\n#### MRIRN2IM\nMRIRN2IM is the Nimbus-2 Medium Resolution Infrared Radiometer (MRIR) data product consisting of 4 x 5 inch photographic film sheets. Each film sheet contains an entire orbit (daylight portion) of brightness temperatures measured at five wavelength bands: 6.4-6.9, 10-11, 14-16, 5-30, and 0.2-4.0 micrometers. There are also associated latitude grids, time, and gray scales representing different temperatures. The images are saved as JPEG 2000 digital files. About 3 weeks of images are archived into a TAR file. The processing techniques used to produce the data set and a full description of the data set are contained in section 4.3.4 of the \"Nimbus II Users' Guide.\" The MRIR experiment measured the intensity and distribution of electromagnetic radiation emitted by and reflected from the earth and its atmosphere in five selected wavelength intervals from 0.2 to 30 micrometers. Data for heat balance of the earth-atmosphere system were obtained, as well as measurements of water vapor distribution, surface or near-surface temperatures, and seasonal changes of stratospheric temperature distribution. The MRIR experiment was successful, and good data were obtained from launch on May 15 1966 until the recorder failed on July 29, 1966. This product was previously available from the NSSDC with the identifier ESAD-00003 (old ID 66-040A-04B).\n<br>\n\n#### MRIRN2L1\nThe Nimbus 2 Medium Resolution Infrared Radiometer (MRIR) was designed to measure electromagnetic radiation emitted and reflected from the earth and its atmosphere at 5 wavelengths. The five wavelengths regions are as follows: * 6.7 to 6.9 microns: This channel covers the 6.7 micron water vapor absorption band. Its purpose is to provide information on water vapor distribution in the upper troposphere and, in conjunction with the other channels to provide relative humidities at these altitudes * 10 to 11 microns: This channel measures surface or near surface temperatures over clear portions of the atmosphere. It also provides cloud cover and cloud height information (day and night). * 14 to 16 microns: This channel, centered about the strong absorption band of C02 at 15 microns, measures radiation which emanates primarily from the stratosphere. * 5 to 30 microns: This channel measures the emitted long wavelength infrared energy and, in conjunction with the reflected solar radiation channel furnishes data on the heat budget of the planet. * 0.2 to 4.0 microns: This channel covers more than 99% of the solar spectrum and yields information on the intensity of the reflected solar energy from the earth and its atmosphere. The Nimbus 2 HRIR data are stored in a binary TAP format (proprietary Tape emulated format) .The MRIR instrument was launched on the Nimbus-2 satellite and was operational from May 15, 1966 through July 28, 1966.\n<br>\n\n#### MRIRN3IM\nMRIRN3IM is the Nimbus-3 Medium Resolution Infrared Radiometer (MRIR) data product consisting of 4 x 5 inch photographic film sheets. Each film sheet contains an entire orbit (daylight portion) of brightness temperatures measured at five wavelength bands: 6.5-7.0, 10-11, 14.5-15.5, 5-30, and 0.2-4.0 micrometers. There are also associated latitude grids, time, and gray scales representing different temperatures. The images are saved as JPEG 2000 digital files. About 3 weeks of images are archived into a TAR file. The processing techniques used to produce the data set and a full description of the data set are contained in section 4 of the \"Nimbus III Users' Guide.\" The MRIR experiment measured the intensity and distribution of electromagnetic radiation emitted by and reflected from the earth and its atmosphere in five selected wavelength intervals from 0.2 to 30 micrometers. Data for heat balance of the earth-atmosphere system were obtained, as well as measurements of water vapor distribution, surface or near-surface temperatures, and seasonal changes of stratospheric temperature distribution. The MRIR experiment obtained data from April 15 1969 until February 4, 1970. This product was previously available from the NSSDC with the identifier ESAD-00184 (old ID 69-037A-05A).\n<br>\n\n#### MRIRN3L1\nMRIRN3L1 is the Nimbus-3 Medium-Resolution Infrared Radiometer (MRIR) Level 1 Meteorological Radiance Data product and contain radiances expressed as equivalent blackbody temperature or \"brightness\" temperature, along with geolocation, time and other housekeeping information. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The MRIR instrument was designed to measure infrared electromagnetic radiation emitted and reflected from the Earth and its atmosphere at 5 wavelengths. The five wavelengths regions are as follows: (1) 6.5 to 7.0 microns - This channel covers the 6.7 micron water vapor absorption band. Its purpose is to provide information on water vapor distribution in the upper troposphere and, in conjunction with the other channels to provide data concerning relative humidities at these altitudes. (2) 10 to 11 microns - Operating in an atmospheric \"window,\" this channel measures surface or near-surface temperatures over clear portions of the atmosphere. It also provides cloud cover and cloud height information (day and night). (3) 14.5 to 15.5 microns - This channel, centered about the strong absorption band of C02 at 15 microns, measures radiation which emanates primarily from the stratosphere. The information gained here is of primary importance to in following seasonal stratospheric temperature changes. (4) 20 to 23 microns - This channel yields data from the spectral region containing the broad rotational absorption bands of water vapor. It will provide information similar to that of the 6.5 to 7.0 micron channel except that the flux will largely be radiated from lower in the atmosphere. (5) 0.2 to 4.0 microns - This channel covers more than 99% of the solar spectrum and yields information on the intensity of the reflected solar energy from the earth and its atmosphere. These data were previously archived at NASA NSSDC as product NMRT-MRIR under the entry ID ESAD-00183 (originally 69-037A-05B).\n<br>\n\n#### NEMSN5L2\nNEMSN5L2 is the Nimbus-5 or Nimbus-E Microwave Spectrometer (NEMS) Level-2 Output Data product and contains surface reflectivity, water vapor, liquid water, layer thickness, temperature at standard pressure levels, surface brightness temperature, and surface type information, as well as the input antenna and brightness temperatures at 5 microwave channels (H2O channels 22.235 and 31.4 GHz, and O2 channels 53.65, 54.9 and 58.8 GHz). The NEMS instrument views the nadir with a footprint is a 180-km diameter circle on the earth's surface. Data are available for the time period from 1972-12-17 to 1973-10-31 with data for about five days stored in a single binary data file. The principal investigator for the NEMS experiment was David H. Staelin from MIT. An advanced version of this instrument, the Scanning Microwave Spectrometer (SCAMS) was flown on the subsequent Nimbus-6 satellite.\n<br>\n\n#### NMCN6L2GARP\nThe NMCN6L2GARP is the Nimbus-6 Level 2 NMC Data Systems Test for the Global Atmospheric Research Program (GARP) product. The product consists of meterological parameters, such as relative humidity, temperature, wind speed, etc., reported every 6 hours. Data Systems Tests (DST) 5 and 6 were conducted to assess the NMC's numerical analysis and forecasting, and to determine the impact of meteorological satellite data from Nimbus-6 HRIR and SCAMS. DST-5 ran for the period of 30 August to 4 September 1975, and DST-6 was conducted from 29 February to 3 March 1976. The data were recovered from the original magnetic tapes, and are now stored online as orbit files in their original proprietary binary format each with a days worth of data (14 orbits). These data were previously archived at NASA NSSDC under the entry ID ESAD-00094 (old id 775-052A-02B).\n<br>\n\n#### NMCN6L3GARP\nThe NMCN6L3GARP is the Nimbus-6 Level 3 NMC Data Systems Test for the Global Atmospheric Research Program (GARP) product. The product consists of meterological parameters, such as relative humidity, temperature, wind speed, etc. an a regular lat/lon grid reported every 12 hours. Data Systems Tests (DST) 5 and 6 were conducted to assess the NMC's numerical analysis and forecasting, and to determine the impact of meteorological satellite data from Nimbus-6 HRIR and SCAMS. DST-5 ran for the period of 30 August to 4 September 1975, and DST-6 was conducted from 29 February to 3 March 1976. The data were recovered from the original magnetic tapes, and are now stored online as orbit files in their original proprietary binary format with a days worth of data. These data were previously archived at NASA NSSDC under the entry ID ESAD-00094 (old id 775-052A-02B).\n<br>\n\n#### PMRN6L1RAD_CDROM\nPMRN6L1RAD_CDROM is the gridded Nimbus-6 Pressure Modulated Radiometer (PMR) Level 1 Radiance Data Product. The radiances are measured at CO2 lines in the 15 micron band. The purpose of the PMR experiment is to measure the temperature of the upper stratosphere and mesosphere from 40 to 90 km with a vertical resolution of about 10 km, and 500 km horizontal resolution. This product contains radiances in a daily 4 degree latitude x 10 degree longitude grid format, as well as copies of the original tapes. The data for this product are available from 16 June 1975 to 24 June 1978. The principal investigator for the PMR experiment was Dr. John T. Houghton from Oxford University. This product was created by the Oxford University's Atmospheric, Oceanic and Planetary Physics (AOPP) group. The data are stored on two CD-ROMs in ASCII files of hexadecimal characters, and are available in gzipped Unix tar archive files. The first CD-ROM contains the gridded radiance data and a few original tape data files, the second CD-ROM contains the remaining compressed copies of the original data tapes. The byte-ordering in the data files follows the DEC convention for 16-bit integers of less significant byte first. Normal 2's complement integer storage is assumed.\n<br>\n\n#### SAMSN7L1RAD_CDROM\nSAMSN7L1RAD_CDROM is the gridded Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 1 Radiance Data Product. The radiances were selected to derive gas concentrations at the wavelength bands 15 (CO2), 25-100 (H2O) 4-5 (CO and NO), and 7.7 (N2O and CH4) microns in the stratosphere and mesosphere, with a resolution of 100 km in the horizontal by 10 km in the vertical at the limb. This product contains radiances in a daily 2.5 degree latitude x 10 degree longitude grid format, gridded temperature profiles at 100, 30, 10, 3, 1, 0.3, 0.1, 0.03, 0.01 and 0.003 hPa, as well as the calibration, apriori and reformatted copies of the original tapes. The data for this product are available from 22 October 1978 through June 9 1983, with a few additional raw radiances to 16 April 1984. The principal investigators for the SAMS experiment were Prof. John T. Houghton and Dr. Fredric W. Taylor from Oxford University. This product was created by the Oxford University's Atmospheric, Oceanic and Planetary Physics (AOPP) group. The data are stored on a set of 53 CD-ROMs in ASCII files of hexadecimal characters, and are available in gzipped Unix tar archive files. The first CD-ROM contains the a-priori temperature profile, monthly mean retrieved temperature profile, pre-launch calibration, housekeeping and instrument subsystem status files. CD-ROMs 2-5 contain the gridded temperature data. CD-ROMs 6-22 contain the radiances from the C-series and G-series tapes, and CD-ROMs 34-53 contain the raw radiance values from the R-series tape. The byte-ordering in the data files follows the DEC convention for 16-bit integers of less significant byte first. Normal 2's complement integer storage is assumed.\n<br>\n\n#### SAMSN7L1RAT\nSAMSN7L1RAT is the gridded Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 1 Radiance Data Product. The radiances were selected to derive gas concentrations at the wavelength bands 15 (CO2), 25-100 (H2O) 4-5 (CO and NO), and 7.7 (N2O and CH4) microns in the stratosphere and mesosphere, with a vertical resolution of 10 km. The instrument scanned the vertical from about 15 km to 140 km. The data were recovered from the original magnetic tapes, and are now stored online as orbit files in their original proprietary binary format each with about 14 orbits per day. The data for this product are available from 26 October 1978 through June 9 1983. The principal investigators for the SAMS experiment were Prof. John T. Houghton and Dr. Fredric W. Taylor from Oxford University. This product was subsequently used to create the SAMS/Nimbus-7 Level 1 Radiance Data from CD-ROM product (SAMSN7L1RAD_CDROM), a set of 53 CD-ROMs.\n<br>\n\n#### SAMSN7L3GRIDT\nSAMSN7L3GRIDT is the Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 3 Gridded Retrieval Temperature Data Product. The Earth's surface is divided into 2.5 deg latitude by 10 deg longitude grids that extend from 50 deg South to 67.5 deg North. The data are stored in two different record types. The first contains temperatures at all 62 retrieved pressure levels between 246 and 0.0012 mbar, and the second contains temperature and error values at 10 standard pressure levels: 100, 30, 10, 3, 1, 0.3, 0.1, 0.03, 0.01 and 0.003 mbar. The data were recovered from the original magnetic tapes, and are now stored online as daily files in their original proprietary binary format. The data for this product are available from 24 December 1978 through 9 June 1983. The principal investigators for the SAMS experiment were Prof. John T. Houghton and Dr. Fredric W. Taylor from Oxford University. This product was previously available from the NSSDC with the identifier ESAD-00016 (old ID 78-098A-02B).\n<br>\n\n#### SAMSN7L3ZMTG\nSAMSN7L3ZMTG is the Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 3 Zonal Means Composition Data Product. The Earth's surface is divided into 2.5-deg latitudinal zones that extend from 50 deg South to 67.5 deg North. Retrieved mixing ratios of nitrous oxide (N2O) and methane (CH4) are averaged over day and night, along with errors, at 31 pressure levels between 50 and 0.125 mbar. Because the N2O and CH4 channels cannot function simultaneously, only one type of measurement is made for any nominal day. The data were recovered from the original magnetic tapes, and are now stored online as one file in its original proprietary binary format. The data for this product are available from 1 January 1979 through 30 December 1981. The principal investigators for the SAMS experiment were Prof. John T. Houghton and Dr. Fredric W. Taylor from Oxford University. This product was previously available from the NSSDC with the identifier ESAD-00180 (old ID 78-098A-02C).\n<br>\n\n#### SBUVN7O3\nThe version 8 SBUV Nimbus-7 ozone data were first released at the 2004 Quadrennial Ozone Symposium on DVD. The DVD contained all of the SBUV/2 data from NOAA-9, NOAA-11 and NOAA-16 satellites as well as SBUV data from the Nimbus-7 satellite. The DVD is no longer available, however all the data are available on-line from the NASA GES DISC. The Nimbus-7 SBUV v8 data are available from 1978-10-31 to 1990-06-21. The instrument spatial resolution is 180 km x 180 km footprint at nadir. The ozone profiles are made at 21 pressure levels between 1000 and 0.1 hPa. Each data file contains a days worth of ozone measurements, and is written in an ASCII text format. The SBUV measures incoming solar irradiance and radiance backscattered by the atmosphere at 12 wavelengths in the UV range [0.25-0.34 micrometer] with a spectral band pass of 0.001 micrometer. The wavelength channels used for ozone retrievals for SBUV were: 256, 273, 283, 288, 292, 298, 302, 306, 312, 318, 331, and 340 nm. Radiation at these wavelengths are absorbed by ozone, such that the difference between the incoming and outgoing radiation can be related to the amount of ozone in the atmosphere. The SBUV consists of a double Ebert-Fastie spectrometer and a filter photometer similar to the BUV on Nimbus 4. The bandwidth of the photometer is about 3 nm and the spectral resolution for SBUV monochromator is 1.1 nm.\n<br>\n\n#### SCAMSN6L2\nThe Nimbus-6 Scanning Microwave Spectrometer (SCAMS) Level 2 data product contains water vapor and temperature profiles, as well as antenna and brightness temperatures. SCAMS was designed to map tropospheric temperature profiles, water vapor abundance, and cloud water content to be used for weather prediction even in the presence of clouds, which block conventional satellite infrared sensors. The data, originally written on IBM 360 machines, were recovered from 9-track magnetic tapes. The data are archived in their original IBM 32-bit word binary record format, also referred to as a binary TAP file, and contain one orbit of measurements. The SCAMS experiment on Nimbus-6 is a follow on to the successful Nimbus-5 NEMS experiment. SCAMS continuously monitored emitted microwave radiation at frequencies of 22.235, 31.65, 52.85, 53.85 and 55.45 GHz. The three channels near the 5.0-mm oxygen absorption band were used primarily to deduce atmospheric temperature profiles. The two channels near 10 mm permitted water vapor and cloud water content over calm oceans to be estimated separately. The instrument, a Dicke-superheterodyne type, scanned +/- 45 degrees normal to the orbital plane with a 10 degree field of view. The three oxygen channels shared common signal and reference antennas. Both water vapor channels had their own signals and reference antennas. The absolute rms accuracy of the oxygen channels was better than 2 Kelvin and that of the water vapor channels better than 1 Kelvin. The SCAMS Principal Investigator was Prof. David H. Staelin from MIT. The Nimbus-6 SCAMS data are available from June 15, 1975 (day of year 166) through May 31, 1976 (day of year 152). This product was previously available from the NSSDC with the identifier ESAD-00093 (old ID 75-052A-10A).\n<br>\n\n#### SCMRN5L1RAD\nSCMRN5L1RAD is the Nimbus-5 Surface Composition Mapping Radiometer (SCMR) Level 1 Calibrated and Geolocated Radiances data product. SCMR measured (1) terrestrial radiation in the 8.3 to 9.3 micron and 10.2 to 11.2 micron intervals and (2) reflected solar radiation in the 0.8 to 1.1 micron range. Surface composition and sea surface temperatures could be obtained from these measurements. The SCMR had an instantaneous field of view (FOV) of 0.6 mrad, equivalent to a ground resolution of 660 m at nadir. The scan mirror rotated at 10 rps to provide scan lines 800 km wide across the spacecraft track. Data are available from December 11, 1972 through December 30, 1972. A modified version of this instrument, the Heat Capacity Mapping Radiometer, was flown on the Heat Capacity Mapping Mission (HCMM) in 1978.\n<br>\n\n#### SCRN4L1RAD\nSCRN4L1RAD is the Nimbus-4 Selective Chopper Radiometer (SCR) Level 1 Calibrated Radiances data product. The calibrated radiances are measured at 6 channels from 2.3 to 15 micrometers with a ground resolution of 25 km, and are \"declouded\" (interpolated and smoothed across regions of cloud). The radiances were used to obtain the temperatures of six successive 10-km layers of the atmosphere from earth or cloudtop level to 60-km height. The data were recovered from the original 9-track tapes, and are now stored online as daily files in their original proprietary binary format with about 14 orbits per day. Spatial coverage is near global from latitude -80 to +80 degrees. The data are available from 27 July 1970 (day of year 208) to 20 February 1973 (day of year 51). The channel 1 temperature monitoring system failed on June 15, 1970, thereby reducing the accuracy of the SCR data. Channels 3 and 4 became noisy and unusable on April 18, 1972. The principal investigator for the SCR experiment was Dr. John T. Houghton from Oxford University. This product was previously available from the NSSDC with the identifier ESAD-00096 (old ID 78-098A-10E).\n<br>\n\n#### SCRN4L1RAD_CDROM\nSCRN4L1RAD_CDROM is the gridded Nimbus-4 Selective Chopper Radiometer (SCR) Level 1 Radiance Data Product. The radiances are measured by 16 channels at 2.3 to 15 micrometers with a ground resolution of 25 km. The CD-ROM contains corrected radiances in a daily 4 degree latitude x 10 degree longitude grid format, as well as the original orbit format. The data for this product are available from 27 July 1970 to 2 November 1972. The principal investigator for the SCR experiment was Dr. John T. Houghton from Oxford University. This product was created by the Oxford University's Atmospheric, Oceanic and Planetary Physics (AOPP) group. The data are stored on a single CD-ROM in ASCII files of hexadecimal characters, and are available in a single gzipped Unix tar archive file. The byte-ordering in the data files follows the DEC convention for 16-bit integers of less significant byte first. Normal 2's complement integer storage is assumed.\n<br>\n\n#### SCRN5L1RAD\nSCRN5L1RAD is the Nimbus-5 Selective Chopper Radiometer (SCR) Level 1 Calibrated Radiances data product. The calibrated radiances are measured at 16 channels from 2.3 to 133 micrometers with a ground resolution of 25 km, and are \"declouded\" (interpolated and smoothed across regions of cloud). The radiances were used to obtain the global temperature structure of the atmosphere up to 50 km altitude, the distribution of water vapor, and the density of ice particles in cirrus clouds. The data were recovered from the original 9-track tapes, and are now stored online as daily files in their original proprietary binary format with about 14 orbits per day. Spatial coverage is near global from latitude -80 to +80 degrees. The data are available from 13 December 1972 (day of year 347) to 26 December 1974 (day of year 360). The principal investigator for the SCR experiment was Dr. John T. Houghton from Oxford University. This product was previously available from the NSSDC with the identifier ESAD-00250 (old ID 72-097A-02A).\n<br>\n\n#### SCRN5L1RAD_CDROM\nSCRN5L1RAD_CDROM is the gridded Nimbus-5 Selective Chopper Radiometer (SCR) Level 1 Radiance Data Product. The radiances are measured by 16 channels at 2.3 to 15 micrometers with a ground resolution of 25 km. This product contains corrected and uncorrected radiances in a daily 4 degree latitude x 10 degree longitude grid format, as well as the original orbit format and reformatted copies of the original tapes. This was the follow-on to the SCR experiment flown on Nimbus-4. The data for this product are available from 13 December 1972 to 20 April 1978. The principal investigator for the SCR experiment was Dr. John T. Houghton from Oxford University. This product was created by the Oxford University's Atmospheric, Oceanic and Planetary Physics (AOPP) group. The data are stored on a set of 28 CD-ROMs in ASCII files of hexadecimal characters, and are available in gzipped Unix tar archive files. The first CD-ROM contains the gridded radiance data and a few original tape data files, the subsequent CD-ROMs contain the remaining compressed copies of the original data tapes. The byte-ordering in the data files follows the DEC convention for 16-bit integers of less significant byte first. Normal 2's complement integer storage is assumed.\n<br>\n\n#### SIRSN3L1\nSIRSN3L1 is the Nimbus-3 Satellite Infrared Spectrometer (SIRS) Level 1 Radiance Data product. SIRS measured infrared radiation (11 to 36 micrometers) emitted from the earth and its atmosphere in 13 selected spectral intervals in the carbon dioxide and water vapor bands plus one channel in the 11-micrometer atmospheric window. The radiances were used to determine the vertical temperature and water vapor profiles of the atmosphere. The data were recovered from the original 6250 tapes, and are now stored online as daily files in their original proprietary binary format each with about 14 orbits per day. The Nimbus-3 SIRS only viewed the nadir of the subsatellite track. Spatial coverage is near global from about latitude -80 to +80 degrees. The data are available from 08 April 1970 (day of year 98) to 08 April 1971. The principal investigator for the SIRS experiment was Dr. David Q. Wark from the NOAA National Environmental Satellite Data and Information Service. This product was previously available from the NSSDC with the identifier ESAD-00130 (old ID 70-025A-04A).\n<br>\n\n#### SIRSN4L1\nSIRSN4L1 is the Nimbus-4 Satellite Infrared Spectrometer (SIRS) Level 1 Radiance Data product. SIRS measured infrared radiation (11 to 36 micrometers) emitted from the earth and its atmosphere in 13 selected spectral intervals in the carbon dioxide and water vapor bands plus one channel in the 11-micrometer atmospheric window. The radiances were used to determine the vertical temperature and water vapor profiles of the atmosphere. The data were recovered from the original 6250 tapes, and are now stored online as daily files in their original proprietary binary format each with about 14 orbits per day. The Nimbus-4 SIRS used a scan mirror to observe 12.5 deg to either side of the subsatellite track. Spatial coverage is near global from latitude -85 to +85 degrees. The data are available from 08 April 1970 (day of year 98) to 08 April 1971. The principal investigator for the SIRS experiment was Dr. David Q. Wark from the NOAA National Environmental Satellite Data and Information Service. This product was previously available from the NSSDC with the identifier ESAD-00130 (old ID 70-025A-04A).\n<br>\n\n#### SMMRN7IM\nSMMRN7IM is the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) Color Image data product scanned from 17\" x 15\" color prints and saved as JPEG-2000 files. Sea surface temperature, sea surface winds, total atmospheric water vapor over oceans, total atmospheric liquid water over oceans, including brightness temperature parameters are available as both 6-day composites and 1-month averages between 64 south and north latitudes in Mercator projection. Sea ice fraction, sea ice and ocean surface temperature, sea ice concentration, including brightness temperature parameters are available as both 3-day and 1-month averages in north and south polar stereographic projections. Images may contain between one and three measured parameters. These SMMR images are available from 30 October 1978 through 2 November 1983. The principal investigator for the SMMR experiment was Dr. Per Gloersen from NASA GSFC. These products were previously available from the NSSDC under the ids ESAD-00007, ESAD-00056, ESAD-00123, ESAD-00124, ESAD-00162, ESAD-00172, ESAD-00173, ESAD-00176 ESAD-00177, ESAD-00178, and ESAD-00241 (old ids 78-098A-08I-S).\n<br>\n\n#### THIRN4IMCH67\nTHIRN4IMCH67 is the Nimbus-4 Temperature-Humidity Infrared Radiometer (THIR) data product consisting of daily montages of brightness temperatures measured at 6.7 microns on 70 mm photofacsimile film strips. Daytime and nighttime orbital swaths are displayed in strips, each corresponding to a distance approximately from pole to pole and a width from horizon to horizon. The ground resolution of 22.6 km at nadir decreases as the horizontal distance from the subsatellite track increases. Each film strip is gridded with geographic coordinates and is identified by orbit number, time, and an indication of whether it is daytime (D) or nighttime (N). The images are saved as JPEG 2000 digital files. About 1 week of images are archived into a TAR file. Additional information can be found in section 3.4.1 of \"The Nimbus IV User's Guide\". The THIR instrument was designed to detect emitted thermal radiation in both the 10.5- to 12.5-micrometer region (IR window) and the 6.5- to 7.0-micrometer region (water vapor). The window channel measured cloudtop temperatures day and night. The other channel operated primarily at night to map the water vapor distribution in the upper troposphere and stratosphere. The THIR experiment made measurements from April 18, 1970 until April 8, 1971. This product was previously available from the NSSDC with the identifier ESAD-00194 (old ID 70-025A-02B).\n<br>\n\n#### THIRN4L1CH115\nTHIRN4L1CH115 is the Nimbus-4 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 11.5 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 10.5 - 12.5 (11.5) micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The Nimbus-4 satellite was successfully launched on December 11, 1972. The THIR experiment on Nimbus-4 replaced the measurements made by the HRIR and MRIR instruments flown on previous Nimbus satellites. The THIR instrument is a two channel high resolution scanning radiometer designed to perform two major functions:* The 11.5 micron channel provides both day and night cloud top or surface temperatures. The ground resolution at the sub-point is 8 km and operates day and night.* The 6.7 micron channel gives information on the moisture content of the upper troposphere and stratosphere and the location of jet streams and frontal systems. The water vapor channel has a resolution of the sub-point is 22 km and operates mostly at night. The THIR Principal Investigator was Andrew W. McCulloch from NASA Goddard Space Flight Center. The Nimbus-4 THIR data are available from April 13, 1970 (day of year 103) through April 1, 1971 (day of year 91). The THIRN4L1CH67 product contains the 6.7 micron channel data. This product was previously available from the NSSDC with the identifier ESAD-00004 (old ID 70-025A-02D).\n<br>\n\n#### THIRN4L1CH67\nTHIRN4L1CH67 is the Nimbus-4 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 6.7 microns product contains radiances expressed in units of equivalent brightness temperature measured in the 6.7 micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The Nimbus-4 satellite was successfully launched on April 8, 1970. The THIR experiment on Nimbus-4 replaced the measurements made the HRIR and MRIR instruments flown on previous Nimbus satellites. The THIR instrument is a two channel high resolution scanning radiometer designed to perform two major functions:* The 11.5 micron channel provides both day and night cloud top or surface temperatures. The ground resolution at the sub-point is 8 km and operates day and night.* The 6.7 micron channel gives information on the moisture content of the upper troposphere and stratosphere and the location of jet streams and frontal systems. The water vapor channel has a resolution of the sub-point is 22 km and operates mostly at night. The THIR Principal Investigator was Andrew W. McCulloch from NASA Goddard Space Flight Center. The Nimbus-4 THIR data are available from April 14, 1970 (day of year 104) through March 25, 1971 (day of year 84). The THIRN4L1CH115 product contains the 11.5 micron channel data. This product was previously available from the NSSDC with the identifier ESAD-00071 (old ID 70-025A-02E).\n<br>\n\n#### THIRN5L1CH115\nTHIRN5L1CH115 is the Nimbus-5 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 11.5 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 10.5 - 12.5 (11.5) micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The Nimbus-5 satellite was successfully launched on December 11, 1972. The THIR experiment on Nimbus-5 continued the measurements made by its predecessor flown on Nimbus-4. The THIR instrument is a two channel high resolution scanning radiometer designed to perform two major functions:* The 11.5 micron channel provides both day and night cloud top or surface temperatures. The ground resolution at the sub-point is 8 km and operates day and night.* The 6.7 micron channel gives information on the moisture content of the upper troposphere and stratosphere and the location of jet streams and frontal systems. The water vapor channel has a resolution of the sub-point is 22 km and operates mostly at night. The THIR Principal Investigator was Andrew W. McCulloch from NASA Goddard Space Flight Center. The Nimbus-5 THIR data are available from December 19, 1972 (day of year 354) through March 1, 1975 (day of year 60). The THIRN5L1CH67 product contains the 6.7 micron channel data. This product was previously available from the NSSDC with the identifier ESAD-00020 (old ID 72-097A-08C).\n<br>\n\n#### THIRN5L1CH67\nTHIRN5L1CH67 is the Nimbus-5 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 6.7 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 6.7 micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The Nimbus-5 satellite was successfully launched on December 11, 1972. The THIR experiment on Nimbus-5 continued the measurements made by its predecessor flown on Nimbus-4. The THIR instrument is a two channel high resolution scanning radiometer designed to perform two major functions:* 11.5 micron channel provides both day and night cloud top or surface temperatures. The ground resolution at the sub-point is 8 km and operates day and night.* 6.7 micron channel gives information on the moisture content of the upper troposphere and stratosphere and the location of jet streams and frontal systems. The water vapor channel has a resolution of the sub-point is 22 km and operates mostly at night. The THIR Principal Investigator was Andrew W. McCulloch from NASA Goddard Space Flight Center. The Nimbus-5 THIR data are available from December 19, 1972 (day of year 354) through August 26, 1974 (day of year 238). The THIRN5L1CH115 product contains the 11.5 micron channel data. This product was previously available from the NSSDC with the identifier ESAD-00167 (old ID 72-097A-08D).\n<br>\n\n#### THIRN6IM\nThe THIRN6IM data product consists of daily montages of brightness temperatures on 70 mm photofacsimile film strips from the Nimbus-6 Temperature-Humidity Infrared Radiometer measured at 6.7 and 11.5 microns. Daytime and nighttime orbital swaths are displayed in strips, each corresponding to a distance approximately from pole to pole and a width from horizon to horizon. The ground resolution of 22.6 km for 6.7 microns and 8.2 km for 11.5 microns at nadir decreases as the horizontal distance from the subsatellite track increases. Each film strip is gridded with geographic coordinates and is identified by orbit number, time, and an indication of whether it is daytime (D) or nighttime (N). The images are saved as JPEG 2000 digital files. About 1 week of images are archived into a TAR file. Additional information can be found in section 2.4.1 of \"The Nimbus 6 User's Guide.\" The Nimbus 6 Temperature-Humidity Infrared Radiometer (THIR) was designed to detect emitted thermal radiation in both the 10.5- to 12.5-micron region (IR window) and the 6.5- to 7.0-micron region (water vapor). The window channel measured cloudtop temperatures day and night. The other channel operated primarily at night to map the water vapor distribution in the upper troposphere and stratosphere. The THIR experiment made measurements from June 18, 1975 until May 6, 1976.\n<br>\n\n#### THIRN6L1CH115\nTHIRN6L1CH115 is the Nimbus-6 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 11.5 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 10.5 - 12.5 (11.5) micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The Nimbus-6 satellite was successfully launched on June 18, 1975. The THIR experiment on Nimbus-6 continued the measurements made by its predecessors flown on Nimbus-4 and Nimbus-5. The THIR instrument is a two channel high resolution scanning radiometer designed to perform two major functions: 1) The 11.5 micron channel provides both day and night cloud top or surface temperatures, with a resolution at nadir of 8 km, and operates day and night. 2) The 6.7 micron channel gives information on the water vapor content of the upper troposphere and stratosphere and the location of jet streams and frontal systems, with a resolution at nadir of 22 km, and operates mostly at night. The THIR Principal Investigator was Andrew W. McCulloch from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00125 (old ID 75-052A-12C).\n<br>\n\n#### THIRN6L1CH67\nTHIRN6L1CH67 is the Nimbus-6 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 6.7 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 6.7 micron (water vapor) channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The Nimbus-6 satellite was successfully launched on June 18, 1975. The Temperature-Humidity Infrared Radiometer (THIR) experiment on Nimbus-6 continues the measurements made by its predecessors flown on Nimbus-4 and Nimbus-5. The THIR instrument is a two channel high resolution scanning radiometer designed to perform two major functions: 1) The 11.5-12.5 micron channel provides both day and night cloud top or surface temperatures, with a resolution at nadir of 8 km, and operates day and night. 2) The 6.7 micron channel gives information on the water vapor content of the upper troposphere and stratosphere and the location of jet streams and frontal systems, with a resolution at nadir of 22 km, and operates mostly at night. The THIR Principal Investigator was Andrew W. McCulloch from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00164 (old ID 75-052A-12D).\n<br>\n\n#### THIRN7IM\nTHIRN7IM is the Nimbus-7 Temperature-Humidity Infrared Radiometer (THIR) data product consisting of daily montages of brightness temperatures measured at 6.7 and 11.5 microns. Each montage contains either a daytime or nighttime assembly of up to 14 individual swaths. Each swath corresponds to a distance approximately from pole to pole and a width from horizon to horizon. The ground resolution is 22.6 km for 6.7 microns and 8.2 km for 11.5 microns at nadir and decreases as the horizontal distance from the subsatellite track increases. Below each swath is information describing the orbit number, and the equator crossing longitude and time. The THIR instrument was designed to detect emitted thermal radiation in both the 10.5 to 12.5 micron region (IR window) and the 6.5 to 7.0 micron region (water vapor). The window channel measured cloudtop temperatures day and night. The other channel operated primarily at night to map the water vapor distribution in the upper troposphere and stratosphere. The THIR experiment made measurements from Oct. 30, 1978 to May 9, 1985 when the instrument was turned off to conserve power. Additional information can be found in section 9 of \"The Nimbus 7 User's Guide,\" and the \"Nimbus 7 Temperature-Humidity Infrared Radiometer (THIR) Data User's Guide.\" This product was previously available from the NSSDC as two products with the identifiers ESAD-00174 and ESAD-00239 (old ID 78-098A-10A and 78-098A-10B).\n<br>\n\n#### THIRN7L1CLDT\nTHIRN7L1CLDT is the Nimbus-7 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Calibrated Located Radiation Data (CLDT) at 6.7 and 11.5 microns product and contains radiances expressed in units of W/m2/sr measured in the 10.5 - 12.5 (11.5) micron and 6.5 - 7.0 (6.7) micron channels. The data, originally written on IBM 360 machines, were recovered from magnetic 9-track tapes. The data are archived in their original proprietary format. The Nimbus-7 satellite was successfully launched on October 28, 1978. The Temperature-Humidity Infrared Radiometer (THIR) experiment on Nimbus-7 is basically identical to its predecessors flown on Nimbus-4, -5 and -6, except that the data were digitized on board. The THIR instrument is a two channel high resolution scanning radiometer designed to perform two major functions:* The 11.5 micron channel provides both day and night cloud top or surface temperatures. The ground resolution at the sub-point is 6.7 km and operates day and night.* The 6.7 micron channel gives information on the moisture content of the upper troposphere and stratosphere and the location of jet streams and frontal systems. The water vapor channel has a resolution of the sub-point is 20 km and operates mostly at night. The THIR Principal Investigator was Dr. Larry L. Stowe from NOAA NESDIS. The Nimbus-7 THIR data are available from October 30, 1979 (day of year 303) through May 13, 1985 (day of year 133). This product was previously available from the NSSDC with the identifier ESAD-00107 (old ID 78-098A-10C).\n<br>\n\n#### THIRN7L1BCLT\nTHIRN7L1BCLT is the Nimbus-7 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Cloud Data for SBUV/TOMS (BCLT) product and contains total cloud amounts; radiances at three cloud altitudes: low (below 2km), middle (2 to 7 km depending on latitude), and height (above the middle cloud layer); cirrus and deep convective clouds; and mean and RMS deviations of cloud and surface radiances. Data are averaged orbit by orbit onto each of the Nimbus 7 TOMS IFOV, which vary from 50 km to 50 km at nadir to 200 km x 200 km at the edges, as well as SBUV 180 km x 180 km IFOVs. The BCLT product includes improved cloud estimation compared to the earlier CLT product. Each file contains one day of data (~14 orbits per day). Spatial coverage is global. The data are available from October 31, 1978 (day of year 304) through October 27, 1984 (day of year 301). This product was previously available from the NSSDC with the identifier ESAD-00196 (old ID 78-098A-10E)\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/BUVN4L1DCM_001.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","ice","oceans","satellite imagery","soil moisture","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"BUVN4L1DCM v001 - The Nimbus-4 BUV Level-1 Dark Current Study Master Data is derived from the BUV Level 1 Radiance (RUT) product and contains the geophysical indices and classification, geographic and geomagnetic coordinates, solar magnetic parameters and angles; monochromator and photometer pulse count and analog data, and energetic trapped particles. There is one-to-one correspondence between this product and the dark current working data files, the difference is the working product data have been filtered.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus4_BUV_Level1/BUVN4L1DCM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"BUVN4L1DCW v001 - The Nimbus-4 BUV Level-1 Dark Current Study Working Data is derived from the BUV Level 1 Radiance (RUT) product and contains the geophysical indices and classification, geographic and geomagnetic coordinates, solar magnetic parameters and angles; monochromator and photometer pulse count and analog data, and energetic trapped particles. There is one-to-one correspondence between this product and the dark current master data files, the difference is the working product data have been filtered.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus4_BUV_Level1/BUVN4L1DCW.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"BUVN4L1PDB v001 - The Nimbus-4 BUV Level-1 Radiance and Housekeeping Data in Telemetry Units collection contains the raw counts measured by the Backscatter Ultraviolet Spectrometer every 32 seconds at 12 wavelengths between 250 and 340 nm during the daylit orbit portion. The data collection also contains ephemeris data, experiment subsystem status information, and spacecraft housekeeping and orbit data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus4_BUV_Level1/BUVN4L1PDB.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"BUVN4L1RUT v001 - The Nimbus-4 BUV Level-1 Radiance data collection was derived from the Primary Data Base (PDB) product and contains the calibrated and geolocated backscattered ultraviolet radiances measured every 32 seconds at 12 wavelengths between 250 and 340 nm during the daylit orbit portion. The data collection also contains quality flags, dark current analyses of the data, orbital information, and housekeeping data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus4_BUV_Level1/BUVN4L1RUT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"BUVN4L2CPOZ v005 - The Nimbus-4 BUV Level 2 Compressed Ozone Profile Data collection or CPOZ contains total ozone, reflectivities, ozone mixing ratios and layer ozone amounts measured every 32 seconds during the daylit portion of an orbit. Mixing ratios are given at 19 levels: 0.3, 0.4, 0.5, 0.7, 1, 1.5, 2, 3, 4, 5, 7, 10, 15, 20, 30, 40, 50, 70 and 100 mbar.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus4_BUV_Level2/BUVN4L2CPOZ.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"BUVN4L2HDBUV v005 - The Nimbus-4 BUV Level 2 High-Density Ozone Data collection contains the vertical distribtuion and total column amount of ozone, as well as the full set of ancillary information. Each file contains total ozone, reflectivities, ozone mixing ratios and layer ozone amounts measured every 32 seconds during the daylit portion of an orbit.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus4_BUV_Level2/BUVN4L2HDBUV.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"BUVN4L3ZMT v005 - The Nimbus-4 BUV Level 3 Ozone Zonal Means collection or ZMT contains total ozone, reflectivities, and ozone mixing ratios averaged in 10 degree latitude zones centered from 80 to -80 degrees. Mixing ratios are given at 19 levels: 0.3, 0.4, 0.5, 0.7, 1, 1.5, 2, 3, 4, 5, 7, 10, 15, 20, 30, 40, 50, 70 and 100 mbar.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus4_BUV_Level3/BUVN4L3ZMT.005","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ESMRN5IM v001 - ESMRN5IM is the Nimbus-5 Electrically Scanning Microwave Radiometer (ESMR) data product containing daily brightness temperature images from 70-mm photofacsimile film strips. Each frame contains a geographic grid and two groups of three parallel strips of imagery, each containing one-half the orbital data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus5_ESMR_Level2/ESMRN5IM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ESMRN5L1 v001 - ESMRN5L1 is the Nimbus-5 Electrically Scanning Microwave Radiometer (ESMR) Level 1 Calibrated Brightness Temperature product and contains calibrated radiances expressed in units of brightness temperature measured at 19.35 GHz. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as the Calibrated Brightness Temperature Tapes (CBTT).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus5_ESMR_Level1/ESMRN5L1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ESMRN6IM v001 - ESMRN6IM is the Nimbus-6 Electrically Scanning Microwave Radiometer (ESMR) data product containing daily brightness temperature images from 70-mm photofacsimile film strips (both positives and negatives). Each frame contains two sets with a geographic grid and either 10 (F = full scale) or 5 (P1,P2 = partial scale) parallel strips of imagery, each containing one-half of an orbit swath (ascending on left, descending on right).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus6_ESMR_Level1/ESMRN6IM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HIRSSCAMSN6L2GARP v001 - The Nimbus-6 High Resolution Infrared Radiometer (HIRS) and (SCAMS) Merged Level 2 Radiation, Temperature and Humidity Sounding Data for the Global Atmospheric Research Program (GARP) data product contains measurements at standard pressure levels from 1000 to 1 mbar. This product was generated by the former Goddard Applications Directorate on the Atmospheric and Oceanographic Information Processing System (AOIPS) as a data system test in support of the Global Atmospheric Research Program (GARP).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus6_HIRS_SCAMS_Level2/HIRSSCAMSN6L2GARP.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HIRSN6IM v001 - The HIRSN6IM data product consists of images of brightness temperatures on 70 mm film strips from the Nimbus-6 High Resolution Infrared Radiation Sounder. Each display contains black and white images at either full vertical scale (F) or partial vertical scale (P).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus6_HIRS_SCAMS_Level1/HIRSN6IM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HIRSN6L1GARP v001 - The Nimbus-6 High Resolution Infrared Radiometer (HIRS) Level 1 Calibrated Radiances for the Global Atmospheric Research Program (GARP) data product contains daily infrared radiances. The HIRS was designed to measure surface temperature and albedo, temperature and H2O profiles, cloud liquid water content, cloud amount and outgoing longwave fluxes in the infrared.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus6_HIRS_SCAMS_Level1/HIRSN6L1GARP.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HRIRN1IM v001 - HRIRN1IM is the Nimbus-1 High-Resolution Infrared Radiometer (HRIR) data product containing scanned negatives of photofacsimile 70mm film strips. The images contain orbital nighttime (3.5 to 4.1 microns) brightness temperature values showing cloud cover and the Earth's surface temperature.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus1_HRIR_Level1/HRIRN1IM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HRIRN1L1 v001 - HRIRN1L1 is the High Resolution Infrared Radiometer (HRIR) Nimbus-1 Level 1 Meteorological Radiance Data (NMRT) product and contains infrared radiances converted to equivalent black-body temperature or \"brightness\" temperature values. he data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus1_HRIR_Level1/HRIRN1L1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HRIRN2IM v001 - HRIRN2IM is the Nimbus-2 High-Resolution Infrared Radiometer (HRIR) data product containing scanned negatives of photofacsimile 70mm film strips. The images contain orbital nighttime (3.5 to 4.1 microns) brightness temperature values showing cloud cover and the Earth's surface temperature.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus2_HRIR_Level1/HRIRN2IM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HRIRN2L1 v001 - HRIRN2L1 is the High Resolution Infrared Radiometer (HRIR) Nimbus-2 Level 1 Meteorological Radiance Data (NMRT) product and contains infrared radiances converted to equivalent black-body temperature or \"brightness\" temperature values. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus2_HRIR_Level1/HRIRN2L1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HRIRN3IM v001 - HRIRN3IM is the Nimbus-3 High-Resolution Infrared Radiometer (HRIR) data product containing scanned negatives of photofacsimile 70mm film strips. The images contain orbital daytime (0.7 to 1.3 microns) and nighttime (3.4 to 4.2 microns) brightness temperature values showing cloud cover and the Earth's surface temperature.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus3_HRIR_Level1/HRIRN3IM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"HRIRN3L1 v001 - HRIRN3L1 is the High Resolution Infrared Radiometer (HRIR) Nimbus-3 Level 1 Meteorological Radiance Data (NMRT) product and contains infrared radiances converted to equivalent black-body temperature or \"brightness\" temperature values. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus3_HRIR_Level1/HRIRN3L1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"IRISN4RAD v001 - The Nimbus-4 Infrared Interferometer Spectrometer (IRIS) Level 1 Radiance Data contain thermal emissions of the Earth's atmosphere at wave numbers between 400 and 1600 cm**-1, with a nominal resolution of 2.8 cm**-1. The data also contain documentation information, reference calibration, average instrument temperatures, and a summary for each orbital pass.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus4_IRIS_Level1B/IRISN4RAD.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"ITPRN5L1 v001 - ITPRN5L1 is the Nimbus-5 Infrared Temperature Profile Radiometer (ITPR) Level-1 Calibrated Radiances data product which contains radiances at 7 infrared spectral regions (2683.0, 899.0, 747.0, 713.8, 689.5, 668.3, and 507.4 cm-1) in a single binary data file. Four are centered near the 15 micron CO2 band, one interval in the water vapor rotational band near 20 microns and two spectral intervals in the atmospheric window regions near 3.7 and 11 microns.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus5_ITPR_Level1/ITPRN5L1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LIMSN7L1PROFILER v001 - LIMSN7L1PROFILER is the Nimbus-7 Limb Infrared Monitor of the Stratosphere (LIMS) Level-1 Profiles of Radiance Data product and contains selected daily vertical profiles across the earth’s atmospheric limb derived from the LIMS Level-1 Radiance Archival Tape (RAT) data product. Measurements are obtained, as a function of tangent height (or scan angle), once every 12 seconds in each of the six spectral bands (two 15-micrometer CO2 bands (narrow and wide), an 11.3-micrometer HNO3 band, a 9.6-micrometer O3 band, a 6.9-micrometer H2O band, and a 6.2-micrometer NO2 band) from the highest pressur...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_LIMS_Level1/LIMSN7L1PROFILER.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LIMSN7L1RAT v001 - LIMSN7L1RAT is the Nimbus-7 Limb Infrared Monitor of the Stratosphere (LIMS) Level-1 Radiance Data product. It contains calibrated, earth-located radiances, as well as housekeeping information, instrument status, and data quality information.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_LIMS_Level1/LIMSN7L1RAT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LIMSN7L2 v006 - The Limb Infrared Monitor of the Stratosphere (LIMS) version 6 Level-2 data product consists of daily, geolocated, vertical profiles of temperature, geopotential height, and mixing ratios of ozone (O3), nitrogen dioxide (NO2), water vapor (H2O), and nitric acid (HNO3). Version 6 LIMS data have improved spatial resolution in both the vertical and along the orbital track, as well as improved accuracy and precision of measured geophysical parameters.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_LIMS_Level2/LIMSN7L2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LIMSN7L3 v006 - The Limb Infrared Monitor of the Stratosphere (LIMS) version 6 Level-3 data product consists of daily, 2 degree zonal Fourier coefficients, of vertical profiles of temperature, geopotential height, and mixing ratios of ozone (O3), nitrogen dioxide (NO2), water vapor (H2O), and nitric acid (HNO3). The data are on 28 pressure levels, equally spaced logarithmically, between 316 hPa and 0.01 hPa.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_LIMS_Level3/LIMSN7L3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LRIRN6L2IPAT v001 - LRIRN6L2IPAT is the Nimbus-6 Limb Radiance Inversion Radiometer (LRIR) Level 2 Inverted Profiles of Temperature and Ozone data product. The product contains daily profiles of temperature and ozone concentration profiles that were inverted from radiances measured in four spectral regions: two in the 15 micron carbon dioxide band; one in the 9.7 micron ozone band; and one located in the rotational water vapor band (23 to 27 microns).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus6_LRIR_Level2/LRIRN6L2IPAT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MRIRN2IM v001 - MRIRN2IM is the Nimbus-2 Medium Resolution Infrared Radiometer (MRIR) data product consisting of 4 x 5 inch photographic film sheets. Each film sheet contains an entire orbit (daylight portion) of brightness temperatures measured at five wavelength bands: 6.4-6.9, 10-11, 14-16, 5-30, and 0.2-4.0 micrometers.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus2_MRIR_Level1/MRIRN2IM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MRIRN2L1 v001 - The Nimbus 2 Medium Resolution Infrared Radiometer (MRIR) was designed to measure electromagnetic radiation emitted and reflected from the earth and its atmosphere at 5 wavelengths. The five wavelengths regions are as follows: * 6.7 to 6.9 microns: This channel covers the 6.7 micron water vapor absorption band.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus2_MRIR_Level1/MRIRN2L1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MRIRN3IM v001 - MRIRN3IM is the Nimbus-3 Medium Resolution Infrared Radiometer (MRIR) data product consisting of 4 x 5 inch photographic film sheets. Each film sheet contains an entire orbit (daylight portion) of brightness temperatures measured at five wavelength bands: 6.5-7.0, 10-11, 14.5-15.5, 5-30, and 0.2-4.0 micrometers.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus3_MRIR_Level1/MRIRN3IM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MRIRN3L1 v001 - MRIRN3L1 is the Nimbus-3 Medium-Resolution Infrared Radiometer (MRIR) Level 1 Meteorological Radiance Data product and contain radiances expressed as equivalent blackbody temperature or \"brightness\" temperature, along with geolocation, time and other housekeeping information. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus3_MRIR_Level1/MRIRN3L1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NEMSN5L2 v001 - NEMSN5L2 is the Nimbus-5 or Nimbus-E Microwave Spectrometer (NEMS) Level-2 Output Data product and contains surface reflectivity, water vapor, liquid water, layer thickness, temperature at standard pressure levels, surface brightness temperature, and surface type information, as well as the input antenna and brightness temperatures at 5 microwave channels (H2O channels 22.235 and 31.4 GHz, and O2 channels 53.65, 54.9 and 58.8 GHz). The NEMS instrument views the nadir with a footprint is a 180-km diameter circle on the earth's surface.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus5_NEMS_Level2/NEMSN5L2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NMCN6L2GARP v001 - The NMCN6L2GARP is the Nimbus-6 Level 2 NMC Data Systems Test for the Global Atmospheric Research Program (GARP) product. The product consists of meterological parameters, such as relative humidity, temperature, wind speed, etc., reported every 6 hours.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus6_HIRS_SCAMS_Level2/NMCN6L2GARP.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NMCN6L3GARP v001 - The NMCN6L3GARP is the Nimbus-6 Level 3 NMC Data Systems Test for the Global Atmospheric Research Program (GARP) product. The product consists of meterological parameters, such as relative humidity, temperature, wind speed, etc.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus6_HIRS_SCAMS_Level3/NMCN6L3GARP.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"PMRN6L1RAD_CDROM v001 - PMRN6L1RAD_CDROM is the gridded Nimbus-6 Pressure Modulated Radiometer (PMR) Level 1 Radiance Data Product. The radiances are measured at CO2 lines in the 15 micron band.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus6_PMR_Level1/PMRN6L1RAD_CDROM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SAMSN7L1RAD_CDROM v001 - SAMSN7L1RAD_CDROM is the gridded Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 1 Radiance Data Product. The radiances were selected to derive gas concentrations at the wavelength bands 15 (CO2), 25-100 (H2O) 4-5 (CO and NO), and 7.7 (N2O and CH4) microns in the stratosphere and mesosphere, with a resolution of 100 km in the horizontal by 10 km in the vertical at the limb.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_SAMS_Level1/SAMSN7L1RAD_CDROM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SAMSN7L1RAT v001 - SAMSN7L1RAT is the gridded Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 1 Radiance Data Product. The radiances were selected to derive gas concentrations at the wavelength bands 15 (CO2), 25-100 (H2O) 4-5 (CO and NO), and 7.7 (N2O and CH4) microns in the stratosphere and mesosphere, with a vertical resolution of 10 km.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_SAMS_Level1/SAMSN7L1RAT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SAMSN7L3GRIDT v001 - SAMSN7L3GRIDT is the Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 3 Gridded Retrieval Temperature Data Product. The Earth's surface is divided into 2.5 deg latitude by 10 deg longitude grids that extend from 50 deg South to 67.5 deg North.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_SAMS_Level3/SAMSN7L3GRIDT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SAMSN7L3ZMTG v001 - SAMSN7L3ZMTG is the Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 3 Zonal Means Composition Data Product. The Earth's surface is divided into 2.5-deg latitudinal zones that extend from 50 deg South to 67.5 deg North.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_SAMS_Level3/SAMSN7L3ZMTG.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SBUVN7O3 v008 - The version 8 SBUV Nimbus-7 ozone data were first released at the 2004 Quadrennial Ozone Symposium on DVD. The DVD contained all of the SBUV/2 data from NOAA-9, NOAA-11 and NOAA-16 satellites as well as SBUV data from the Nimbus-7 satellite.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_SBUV_Level2/SBUVN7O3.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SCAMSN6L2 v001 - The Nimbus-6 Scanning Microwave Spectrometer (SCAMS) Level 2 data product contains water vapor and temperature profiles, as well as antenna and brightness temperatures. SCAMS was designed to map tropospheric temperature profiles, water vapor abundance, and cloud water content to be used for weather prediction even in the presence of clouds, which block conventional satellite infrared sensors.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus6_SCAMS_Level2/SCAMSN6L2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SCMRN5L1RAD v001 - SCMRN5L1RAD is the Nimbus-5 Surface Composition Mapping Radiometer (SCMR) Level 1 Calibrated and Geolocated Radiances data product. SCMR measured (1) terrestrial radiation in the 8.3 to 9.3 micron and 10.2 to 11.2 micron intervals and (2) reflected solar radiation in the 0.8 to 1.1 micron range.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus5_SCMR_Level1/SCMRN5L1RAD.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SCRN4L1RAD v001 - SCRN4L1RAD is the Nimbus-4 Selective Chopper Radiometer (SCR) Level 1 Calibrated Radiances data product. The calibrated radiances are measured at 6 channels from 2.3 to 15 micrometers with a ground resolution of 25 km, and are \"declouded\" (interpolated and smoothed across regions of cloud).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus4_SCR_Level1/SCRN4L1RAD.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SCRN4L1RAD_CDROM v001 - SCRN4L1RAD_CDROM is the gridded Nimbus-4 Selective Chopper Radiometer (SCR) Level 1 Radiance Data Product. The radiances are measured by 16 channels at 2.3 to 15 micrometers with a ground resolution of 25 km.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus4_SCR_Level1/SCRN4L1RAD_CDROM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SCRN5L1RAD v001 - SCRN5L1RAD is the Nimbus-5 Selective Chopper Radiometer (SCR) Level 1 Calibrated Radiances data product. The calibrated radiances are measured at 16 channels from 2.3 to 133 micrometers with a ground resolution of 25 km, and are \"declouded\" (interpolated and smoothed across regions of cloud).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus5_SCR_Level1/SCRN5L1RAD.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SCRN5L1RAD_CDROM v001 - SCRN5L1RAD_CDROM is the gridded Nimbus-5 Selective Chopper Radiometer (SCR) Level 1 Radiance Data Product. The radiances are measured by 16 channels at 2.3 to 15 micrometers with a ground resolution of 25 km.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus5_SCR_Level1/SCRN5L1RAD_CDROM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SIRSN3L1 v001 - SIRSN3L1 is the Nimbus-3 Satellite Infrared Spectrometer (SIRS) Level 1 Radiance Data product. SIRS measured infrared radiation (11 to 36 micrometers) emitted from the earth and its atmosphere in 13 selected spectral intervals in the carbon dioxide and water vapor bands plus one channel in the 11-micrometer atmospheric window.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus3_SIRS_Level1/SIRSN3L1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SIRSN4L1 v001 - SIRSN4L1 is the Nimbus-4 Satellite Infrared Spectrometer (SIRS) Level 1 Radiance Data product. SIRS measured infrared radiation (11 to 36 micrometers) emitted from the earth and its atmosphere in 13 selected spectral intervals in the carbon dioxide and water vapor bands plus one channel in the 11-micrometer atmospheric window.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus4_SIRS_Level1/SIRSN4L1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SMMRN7IM v001 - SMMRN7IM is the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) Color Image data product scanned from 17\" x 15\" color prints and saved as JPEG-2000 files. Sea surface temperature, sea surface winds, total atmospheric water vapor over oceans, total atmospheric liquid water over oceans, including brightness temperature parameters are available as both 6-day composites and 1-month averages between 64 south and north latitudes in Mercator projection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_SMMR_Level1/SMMRN7IM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"THIRN4IMCH67 v001 - THIRN4IMCH67 is the Nimbus-4 Temperature-Humidity Infrared Radiometer (THIR) data product consisting of daily montages of brightness temperatures measured at 6.7 microns on 70 mm photofacsimile film strips. Daytime and nighttime orbital swaths are displayed in strips, each corresponding to a distance approximately from pole to pole and a width from horizon to horizon.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus4_THIR_Level1/THIRN4IMCH67.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"THIRN4L1CH115 v001 - THIRN4L1CH115 is the Nimbus-4 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 11.5 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 10.5 - 12.5 (11.5) micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus4_THIR_Level1/THIRN4L1CH115.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"THIRN4L1CH67 v001 - THIRN4L1CH67 is the Nimbus-4 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 6.7 microns product contains radiances expressed in units of equivalent brightness temperature measured in the 6.7 micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus4_THIR_Level1/THIRN4L1CH67.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"THIRN5L1CH115 v001 - THIRN5L1CH115 is the Nimbus-5 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 11.5 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 10.5 - 12.5 (11.5) micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus5_THIR_Level1/THIRN5L1CH115.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"THIRN5L1CH67 v001 - THIRN5L1CH67 is the Nimbus-5 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 6.7 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 6.7 micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus5_THIR_Level1/THIRN5L1CH67.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"THIRN6IM v001 - The THIRN6IM data product consists of daily montages of brightness temperatures on 70 mm photofacsimile film strips from the Nimbus-6 Temperature-Humidity Infrared Radiometer measured at 6.7 and 11.5 microns. Daytime and nighttime orbital swaths are displayed in strips, each corresponding to a distance approximately from pole to pole and a width from horizon to horizon.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus6_THIR_Level1/THIRN6IM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"THIRN6L1CH115 v001 - THIRN6L1CH115 is the Nimbus-6 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 11.5 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 10.5 - 12.5 (11.5) micron channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus6_THIR_Level1/THIRN6L1CH115.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"THIRN6L1CH67 v001 - THIRN6L1CH67 is the Nimbus-6 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Meteorological Radiation Data at 6.7 microns product and contains radiances expressed in units of equivalent brightness temperature measured in the 6.7 micron (water vapor) channel. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT-THIR).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus6_THIR_Level1/THIRN6L1CH67.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"THIRN7IM v001 - THIRN7IM is the Nimbus-7 Temperature-Humidity Infrared Radiometer (THIR) data product consisting of daily montages of brightness temperatures measured at 6.7 and 11.5 microns. Each montage contains either a daytime or nighttime assembly of up to 14 individual swaths.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_THIR_Level1/THIRN7IM.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"THIRN7L1CLDT v001 - THIRN7L1CLDT is the Nimbus-7 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Calibrated Located Radiation Data (CLDT) at 6.7 and 11.5 microns product and contains radiances expressed in units of W/m2/sr measured in the 10.5 - 12.5 (11.5) micron and 6.5 - 7.0 (6.7) micron channels. The data, originally written on IBM 360 machines, were recovered from magnetic 9-track tapes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_THIR_Level1/THIRN7L1CLDT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"THIRN7L1BCLT v001 - THIRN7L1BCLT is the Nimbus-7 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Cloud Data for SBUV/TOMS (BCLT) product and contains total cloud amounts; radiances at three cloud altitudes: low (below 2km), middle (2 to 7 km depending on latitude), and height (above the middle cloud layer); cirrus and deep convective clouds; and mean and RMS deviations of cloud and surface radiances. Data are averaged orbit by orbit onto each of the Nimbus 7 TOMS IFOV, which vary from 50 km to 50 km at nadir to 200 km x 200 km at the edges, as well as SBUV 180 km x 180 km IFOVs.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_THIR_Level1/THIRN7L1BCLT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-nimbus","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NLDAS Project","Description":"This data set contains thirty-eight fields simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is netCDF (converted from the GRIB format). Mosaic was developed by Koster and Suarez (1994, 1996) to account for subgrid vegetation variability with a tile approach. Each vegetation tile carries its own energy and water balance and soil moisture and temperature. Each tile has three soil layers, with the first two in the root zone. In NLDAS, Mosaic is configured to support a maximum of 10 tiles per grid cell with a 5% cutoff that ignores vegetation classes covering less than 5% of the cell. Additionally in NLDAS, all tiles of Mosaic in a grid cell have a predominant soil type and three soil layers with fixed thickness values of 10, 30, and 160 cm (hence constant rooting depth of 40 cm and constant total column depth of 200 cm). Details about the NLDAS-2 configuration of the Mosaic LSM can be found in Xia et al. (2012).\n<br>\n\n#### NLDAS_MOS0125_M\nThis data set contains thirty-seven fields simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is monthly. The file format is netCDF (converted from the GRIB format). The NLDAS-2 monthly Mosaic model data, containing thirty-seven fields, were generated from the NLDAS-2 hourly Mosaic model data, as monthly accumulation for rainfall, snowfall, subsurface runoff, surface runoff, total evapotranspiration, and snow melt, and monthly average for other variables. Monthly period of each month is from 00Z at start of the month to 23:59Z at end of the month, except the first month (Jan. 1979) that starts from 00Z 02 Jan 1979. Details about the NLDAS-2 configuration of the Mosaic LSM can be found in Xia et al. (2012).\n<br>\n\n#### NLDAS_MOS0125_MC\nThis monthly climatology data set contains a series of land surface parameters simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the monthly data averaged over forty years (1981 - 2020). The file format is netCDF. The previous version of this dataset (NLDAS_MC 002) was a 30-year average and was stored in GRIB file format. A brief description about the NLDAS-2 hourly and monthly Mosaic LSM data can be found from the NLDAS_MOS0125_H_2.0 and NLDAS_MOS0125_M_2.0 landing pages. Details about the NLDAS-2 configuration of the Mosaic LSM can be found in Xia et al. (2012). For more information, please see the README Document.\n<br>\n\n#### NLDAS_NOAH0125_H\nThis data set contains fifty-three fields simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is netCDF (converted from the GRIB format). The Noah model was developed as the land component of the NOAA NCEP mesoscale Eta model [Betts et al. (1997); Chen et al. (1997); Ek et al. (2003)]. As used in NLDAS-2, recent modifications were made to Noah's cold-season [Livneh et al. (2010)] and warm-season [Wei et al. (2012)] parameterizations. Noah serves as the land component in the evolving Weather Research and Forecasting (WRF) regional atmospheric model, the NOAA NCEP coupled Climate Forecast System (CFS), and the Global Forecast System (GFS). The model simulates the soil freeze-thaw process and its impact on soil heating/cooling and transpiration, following Koren et al. (1999). The model has four soil layers with spatially invariant thicknesses of 10, 30, 60, and 100 cm. The first three layers form the root zone in non-forested regions, with the fourth layer included in forested regions. Details about the NLDAS-2 configuration of the Noah LSM can be found in Xia et al. (2012).\n<br>\n\n#### NLDAS_NOAH0125_M\nThis data set contains fifty-two fields simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is monthly. The file format is netCDF (converted from the GRIB format). The NLDAS-2 monthly Noah model data, containing fifty-two fields, were generated from the NLDAS-2 hourly Noah model data, as monthly accumulation for rainfall, snowfall, subsurface runoff, surface runoff, total evapotranspiration, snow melt, and monthly averages for other variables. The monthly period of each month is from 00Z at start of the month to 23:59Z at end of the month, with the exception of the very first month in the data set (Jan 1979) which starts at 00Z 02 Jan 1979. Also for the first month (Jan 1979), because the variables listed as instantaneous in the README file do not have valid data exactly on 00Z 02 Jan 1979, and this one hour is not included in the average for this month only. Details about the NLDAS-2 configuration of the Noah LSM can be found in Xia et al. (2012).\n<br>\n\n#### NLDAS_NOAH0125_MC\nThis monthly climatology data set contains a series of land surface parameters simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the monthly data averaged over forty years (1981 - 2020). The file format is netCDF. The previous version of this dataset (NLDAS_MC 002) was a 30-year average and was stored in GRIB file format. A brief description about the NLDAS-2 hourly and monthly Noah LSM data can be found from the dataset landing pages for NLDAS_NOAH0125_H_2.0 and NLDAS_NOAH0125_M_2.0. Details about the NLDAS-2 configuration of the Noah LSM can be found in Xia et al. (2012). For more information, please see the README Document.\n<br>\n\n#### NLDAS_FORA0125_H\nThis data set contains the primary forcing hourly data \"File A\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is netCDF (converted from the GRIB data files). The non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are 32-km spatial resolution and 3-hourly temporal frequency. Those NARR fields that are utilized to generate NLDAS-2 forcing fields are spatially interpolated to the finer resolution of the NLDAS 1/8th-degree grid and then temporally disaggregated to the NLDAS hourly frequency. Additionally, the fields of surface pressure, surface downward longwave radiation, near-surface air temperature, and near-surface specific humidity are adjusted vertically to account for the vertical difference between the NARR and NLDAS fields of terrain height. This vertical adjustment applies the traditional vertical lapse rate of 6.5 K/km for air temperature. The details of the spatial interpolation, temporal disaggregation, and vertical adjustment are presented by Cosgrove et al. (2003). The surface downward shortwave radiation field in \"File A\" is a bias-corrected field wherein a bias-correction algorithm was applied to the NARR surface downward shortwave radiation. This bias correction utilizes five years (1996-2000) of the hourly 1/8th-degree GOES-based surface downward shortwave radiation fields derived by Pinker et al. (2003). The potential evaporation field in \"File A\" is that computed in NARR using the modified Penman scheme of Mahrt and Ek (1984). The precipitation field in \"File A\" is not the NARR precipitation forcing, but is rather a product of a temporal disaggregation of a gauge-only CPC analysis of daily precipitation, performed directly on the NLDAS grid and including an orographic adjustment based on the widely-applied PRISM climatology. The precipitation is temporally disaggregated into hourly fields by deriving hourly disaggregation weights from either WSR-88D Doppler radar-based precipitation estimates, 8-km CMORPH hourly precipitation analyses, or NARR-simulated precipitation (based on availability, in order). The latter fields from radar, CMORPH, and NARR are used only to derive disaggregation weights and do not change the daily total precipitation. The field in \"File A\" that gives the fraction of total precipitation that is convective is an estimate derived from the following two NARR precipitation fields (which are provided in \"File B\"): NARR total precipitation and NARR convective precipitation (the latter is less than or equal to the NARR total precipitation and can be zero). The Convective Available Potential Energy (CAPE) is the final variable in the forcing data set, also interpolated from NARR. The hourly land surface forcing fields for NLDAS-2 are grouped into two files, \"File A\" and \"File B\". \"File A\" is the primary (default) forcing file and contains eleven meteorological forcing fields. Details about the generation of the NLDAS-2.0 forcing datasets can be found in Xia et al. (2012).\n<br>\n\n#### NLDAS_FORA0125_M\nThis data product contains the monthly primary forcing data \"File A\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is monthly. The file format is netCDF (converted from the GRIB data files). The NLDAS-2 monthly primary forcing data were generated from the NLDAS-2 hourly primary forcing data, as monthly accumulation for total precipitation, convective precipitation, and potential evaporation, and monthly average for other variables. The convective precipitation monthly total is the hourly convective fraction multiplied by the hourly precipitation (both from the NLDAS-2 \"File A\" files), and then summed over all hours of the month. Monthly period of each month is from 00Z at start of the month to 23:59Z at end of the month. The one exception to this is the first month (Jan. 1979) that starts from 00Z 02 Jan 1979, except for the monthly accumulated precipitation and convective precipitation that both start from 12Z 01 Jan 1979. The monthly land surface forcing fields for NLDAS-2 are grouped into two files, \"File A\" and \"File B\". \"File A\" is the primary (default) forcing file and contains eleven meteorological forcing fields. Details about the generation of the NLDAS-2.0 forcing datasets can be found in Xia et al. (2012).\n<br>\n\n#### NLDAS_FORA0125_MC\nThis data set contains the monthly climatology data of the primary forcing data for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the NLDAS-2 monthly data averaged over forty years (1981 - 2020). The file format is netCDF. The previous version of this dataset (NLDAS_MC 002) was a 30-year average and was stored in GRIB file format. A brief description about the NLDAS-2 hourly and monthly primary forcing data can be found from the NLDAS_FORA0125_H_2.0 and NLDAS_FORA0125_M_2.0 landing pages. Details about the generation of the NLDAS-2 forcing datasets can be found in Xia et al. (2012). For more information, please see the README Document.\n<br>\n\n#### NLDAS_FORB0125_H\nThis data set contains the secondary forcing hourly data \"File B\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format isnetCDF (converted from the GRIB data files). The non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are 32-km spatial resolution and 3-hourly temporal frequency. Those NARR fields that are utilized to generate NLDAS-2 forcing fields are spatially interpolated to the finer resolution of the NLDAS 1/8th-degree grid and then temporally disaggregated to the NLDAS hourly frequency. NLDAS-2 is providing a second forcing file, \"File B\", in which the surface temperature, humidity, and wind fields are represented not at 2-meters and 10-meters above the height of the NLDAS terrain, but rather at the same height above the NLDAS terrain as the height above the NARR terrain of the lowest prognostic level of the NARR assimilation system (namely, the same height above the model terrain as the lowest prognostic level of the mesoscale Eta model, which is the assimilating model in NARR). The surface downward surface radiation field in \"File B\" is taken directly from NARR, without any bias correction. The precipitation and convective precipitation fields in \"File B\" are also taken directly from NARR, and are used to calculate the convective fraction provided in \"File A\". The aerodynamic conductance is \"File B\" is also taken from NARR. The hourly land surface forcing fields for NLDAS-2 are grouped into two files, \"File A\" and \"File B\". \"File B\" is the secondary (optional) forcing file and contains ten meteorological forcing fields. Details about the generation of the NLDAS-2 forcing datasets can be found in Xia et al. (2012).\n<br>\n\n#### NLDAS_FORB0125_M\nThis data set contains the monthly secondary forcing data \"File B\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is monthly. The file format is netCDF (converted from the GRIB format). The NLDAS-2 monthly secondary forcing data were generated from the NLDAS-2 hourly secondary forcing data, as monthly accumulation for precipitation and convective precipitation and monthly average for other variables. Monthly period of each month is from 00Z at start of the month to 23:59Z at end of the month. The one exception to this is the first month (Jan. 1979) that starts from 00Z 02 Jan 1979, except for the monthly accumulated precipitation and convective precipitation that both start from 12Z 01 Jan 1979. The monthly land surface forcing fields for NLDAS-2 are grouped into two files, \"File A\" and \"File B\". \"File B\" is the secondary (optional) forcing file and contains ten meteorological forcing fields. Details about the generation of the NLDAS-2.0 forcing datasets can be found in Xia et al. (2012).\n<br>\n\n#### NLDAS_FORB0125_MC\nThis data set contains the monthly climatology (MC) data of the secondary forcing data for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the NLDAS-2 monthly data averaged over forty years (1981 - 2020). The file format is netCDF. The previous version of this dataset (NLDAS_MC 002) was a 30-year average and was stored in GRIB file format. A brief description about the NLDAS-2 hourly and monthly secondary forcing data can be found from the NLDAS_FORB0125_H_2.0 and NLDAS_FORB0125_M_2.0 landing pages. Details about the generation of the NLDAS-2 forcing datasets can be found in Xia et al. (2012). For more information, please see the README Document.\n<br>\n\n#### NLDAS_VIC0125_H\nThis data set contains forty-four fields simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is netCDF (converted from the GRIB format). The VIC model was developed at the University of Washington and Princeton University as a macroscale, semi-distributed, grid-based, hydrologic model [Liang et al., 1994; Wood et al., 1997]. The full water and energy balance modes of VIC were used for NLDAS-2. VIC uses three soil layers, with thicknesses that vary spatially. The root zone depends on the vegetation type and its root distribution, and can span all three soil layers. The VIC model includes a two-layer energy balance snow model [Cherkauer et al., 2003]. Details about the NLDAS-2 configuration of the VIC LSM can be found in Xia et al. (2012). The version of the VIC model for the NLDAS-2 VIC data available from the NASA GES DISC is VIC-4.0.3; this version of the VIC model is the same as used in Sheffield et al. (2003).\n<br>\n\n#### NLDAS_VIC0125_M\nThis data set contains forty-three fields simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from January 1979 to present. The temporal resolution is monthly. The file format is netCDF (converted from GRIB format). The NLDAS-2 monthly Noah model data were generated from the NLDAS-2 hourly Noah model data, as monthly accumulation for rainfall, snowfall, subsurface runoff, surface runoff, total evapotranspiration, snow melt, and monthly averages for other variables. Each monthly period is from 00Z at start of the month to 23:59Z at end of the month, with the exception of the very first month in the data set (January 1979), which starts at 00Z 02 January 1979. Also, for the first month (January 1979), since the variables listed as instantaneous in the README file do not have valid data exactly on 00Z 02 January 1979, this one hour is not included in the average for this one month. Details about the NLDAS-2 configuration of the VIC LSM can be found in Xia et al. (2012).\n<br>\n\n#### NLDAS_VIC0125_MC\nThis data set contains a series of land surface parameters simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the NLDAS-2 monthly data averaged over forty years (1981 - 2020). The file format is netCDF. The previous version of this dataset (NLDAS_MC 002) was a 30-year average and was stored in GRIB file format. A brief description about the NLDAS-2 hourly and monthly VIC LSM data can be found from the NLDAS_VIC0125_H_2.0 and NLDAS_VIC0125_M_2.0 landing pages. Details about the NLDAS-2 configuration of the VIC LSM can be found in Xia et al. (2012). The version of the VIC model for the NLDAS-2 VIC data available from the NASA GES DISC is VIC-4.0.3; this version of the VIC model is the same as used in Sheffield et al. (2003). For more information, please see the README Document.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://ldas.gsfc.nasa.gov/nldas/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","ice","netcdf","precipitation","radar","satellite imagery","soil moisture","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NLDAS_MOS0125_H v2.0 - This data set contains thirty-eight fields simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_MOS0125_H.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NLDAS_MOS0125_M v2.0 - This data set contains thirty-seven fields simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_MOS0125_M.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NLDAS_MOS0125_MC v2.0 - This monthly climatology data set contains a series of land surface parameters simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_MOS0125_MC.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NLDAS_NOAH0125_H v2.0 - This data set contains fifty-three fields simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_NOAH0125_H.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NLDAS_NOAH0125_M v2.0 - This data set contains fifty-two fields simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_NOAH0125_M.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NLDAS_NOAH0125_MC v2.0 - This monthly climatology data set contains a series of land surface parameters simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_NOAH0125_MC.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NLDAS_FORA0125_H v2.0 - This data set contains the primary forcing hourly data \"File A\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_FORA0125_H.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NLDAS_FORA0125_M v2.0 - This data product contains the monthly primary forcing data \"File A\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_FORA0125_M.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NLDAS_FORA0125_MC v2.0 - This data set contains the monthly climatology data of the primary forcing data for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_FORA0125_MC.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NLDAS_FORB0125_H v2.0 - This data set contains the secondary forcing hourly data \"File B\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_FORB0125_H.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NLDAS_FORB0125_M v2.0 - This data set contains the monthly secondary forcing data \"File B\" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_FORB0125_M.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NLDAS_FORB0125_MC v2.0 - This data set contains the monthly climatology (MC) data of the secondary forcing data for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_FORB0125_MC.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NLDAS_VIC0125_H v2.0 - This data set contains forty-four fields simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_VIC0125_H.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NLDAS_VIC0125_M v2.0 - This data set contains forty-three fields simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from January 1979 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_VIC0125_M.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NLDAS_VIC0125_MC v2.0 - This data set contains a series of land surface parameters simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NLDAS/NLDAS_VIC0125_MC.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-nldas","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NOAA - SPACE WEATHER PROGRAM Project","Description":"The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B. Currently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product. The NOAA-07 AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name N07_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (N07_AVH01C1). The N07_AVH13C1 product is available in HDF4 file format.\n<br>\n\n#### AERDT_L2_VIIRS_NOAA20\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua missions’ Moderate Imaging Spectroradiometer (MODIS) instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible wavelengths). This orbit-level product (Short-name: AERDT_L2_VIIRS_NOAA20) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor’s scanning geometry and Earth’s curvature. Viewed differently, this product’s resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. Hence, the Level-2 Dark Target Aerosol Optical Thickness data product incorporates 64 (750 m) pixels over a 6-minute acquisition. Version 2.1 constitutes the latest collection of the NOAA-20 VIIRS L2 Dark Target Aerosol product and contains improvements over its previous collection (v2.0). For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDT_L2_VIIRS_NOAA20 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/\n<br>\n\n#### FSNRAD_L2_VIIRS_CRIS_NOAA20\nThe VIIRS-CrIS Data Fusion Level-2 Product is designed to facilitate improved continuity in derived cloud and moisture products realized with the High Resolution Infrared Radiation Sounder (HIRS) and the Moderate resolution Imaging Spectroradiometer (MODIS) and to continue other applications that require IR absorption spectral bands. Based on data fusion with VIIRS (Visible Infrared Imaging Radiometer Suite) and CrIS (Cross-track Infrared Sounder), infrared (IR) absorption band radiances for VIIRS are constructed at 750m spatial resolution (i.e., M-band resolution). These spectral bands are similar to the MODIS spectral bands. The fusion radiances, and look-up tables required to compute the related brightness temperatures, are stored in compressed NetCDF4 files of 6-minutes duration. This L2 NOAA-20 VIIRS+CrIS product release relates to Version-2.0 (v2.0) collection, which has undergone some improvements over its previous version. In the v2.0 Fusion product, scanlines are checked for quality instead of the entire input granule as was done in the previous version of this product. Such a process has helped salvage and use granules with continuous blocks of good data with good calibration. The v2.0 product also includes a couple of improvements to the VIIRS-CrIS collocation. The first relates to how VIIRS scan sync loss events are addressed while the other correctly characterizes VIIRS pixels that should have been identified as falling within a CrIS Field-of-View. A final improvement in the v2.0 product attempts to correct an artifact detected over warm, dry surfaces in the water vapor channels that are derived using the MODIS Band-27 and -28 response functions that apparently manifest signs of surface features that should not exist for these channels. Check the User Guide for further details on these improvements. The L2 VIIRS+CrIS Fusion product has a horizontal pixel size of 750 m, which is the native VIIRS moderate-resolution (M) band pixel-size. Consult the VIIRS+CrIS Fusion product User Guide (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/VIIRSCrISFusionUserGuidev1.11.pdf) for additional information regarding this product’s algorithm, file format, global and data-field attributes, quality control flags, etc.\n<br>\n\n#### AERDT_L2_VIIRS_NOAA21\nThe NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua missions’ Moderate Imaging Spectroradiometer (MODIS) instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible wavelengths). This orbit-level product (Short-name: AERDT_L2_VIIRS_NOAA21) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor’s scanning geometry and Earth’s curvature. Viewed differently, this product’s resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. Hence, the Level-2 Dark Target Aerosol Optical Thickness data product incorporates 64 (750 m) pixels over a 6-minute acquisition. Version 2.1 constitutes the first collection of the L2 Dark Target Aerosol product derived from the NOAA-21 VIIRS observations. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDT_L2_VIIRS_NOAA21 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/\n<br>\n\n#### FSNRAD_L2_VIIRS_CRIS_SNPP\nThe VIIRS-CrIS Data Fusion Level-2 Product is designed to facilitate improved continuity in derived cloud and moisture products realized with HIRS and MODIS and to continue other applications that require IR absorption spectral bands. Based on data fusion with VIIRS (Visible Infrared Imaging Radiometer Suite) and CrIS (Cross-track Infrared Sounder), infrared (IR) absorption band radiances for VIIRS are constructed at 750m spatial resolution (i.e., M-band resolution). These spectral bands are similar to the MODIS spectral bands. This L2 SNPP VIIRS+CrIS product release relates to Version-2.0 (v2.0) collection, which has undergone some improvements over its previous version. In the v2.0 Fusion product, scanlines are checked for quality instead of the entire input granule as was done in the previous version of this product. Such a process has helped salvage and use granules with continuous blocks of good data with good calibration. The v2.0 product also includes a couple of improvements to the VIIRS-CrIS collocation. The first relates to how VIIRS scan sync loss events are addressed while the other correctly characterizes VIIRS pixels that should have been identified as falling within a CrIS Field-of-View. A final improvement in the v2.0 product attempts to correct an artifact detected over warm, dry surfaces in the water vapor channels that are derived using the MODIS Band-27 and -28 response functions that apparently manifest signs of surface features that should not exist for these channels. Check the User Guide for further details on these improvements. The fusion radiances, and look-up tables required to compute the related brightness temperatures, are stored in compressed NetCDF4 files of 6-minutes duration. The L2 VIIRS+CRiS Fusion product has a horizontal pixel size of 750m, which is the native VIIRS moderate-resolution (M) band pixel-size. Consult the VIIRS+CRiS Fusion product User Guide for additional information regarding this product’s algorithm, file format, global and data-field attributes, quality control flags, etc.\n<br>\n\n#### VJ104ANC\nThe VIIRS/JPSS1 Aerosol Daily L3 Global 0.05 Deg CMG, Short-name VJ104ANC product provides VIIRS aerosol, air temperature, ozone and water vapor data at 0.05-degree resolution. This product is based on a Climate Modeling Grid (CMG) for the purpose of being used in climate simulation models. This is derived by using a weighted average of the best quality observation form only daytime data from Level 2 product, VJ104_L2. This daily product record starts from January 5th, 2018. This L3 daily product, in HDF5 file format , contains 5 Science Data Set (SDS) layers.\n<br>\n\n#### CLDMSK_L2_VIIRS_NOAA20\nThe VIIRS/NOAA20 Cloud Mask and Spectral Test Results 6-Min L2 Swath 750m product Mask is one of three continuity products designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS heritages. CLDMSK_L2_VIIRS_NOAA20 is the shortname for the NOAA-20 VIIRS incarnation of the Cloud Mask continuity product derived from the MODIS-VIIRS cloud mask (MVCM) algorithm, which itself is based on the MODIS (MOD35) algorithm. MVCM describes a continuity algorithm that is central to both MODIS data (from Terra and Aqua missions) and VIIRS data (from Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System missions). Please bear in mind that the term MVCM does not appear as an attribute within the product’s metadata. Implemented to consistently handle MODIS and VIIRS inputs, the NOAA-20 VIIRS collection-1 products use calibration-adjusted NASA VIIRS L1B as inputs. The nominal spatial resolution of the NOAA-20 VIIRS L2 Cloud mask is 750 meters. For more information consult Product Page at: https://cimss.ssec.wisc.edu/MVCM/\n<br>\n\n#### CLDPROP_L2_VIIRS_NOAA20\nThe VIIRS/NOAA20 Cloud Properties 6-min L2 Swath 750m product is a continuity product similar to its counterpart product from the Suomi National Polar-orbiting Partnership (SNPP) VIIRS. Judiciously leveraging a common set of spectral channels, they help sustain the long-term records of both MODIS and VIIRS heritages. A commonly applicable algorithm to both MODIS and VIIRS inputs is the hallmark of this continuity approach. CLDPROP_L2_VIIRS_NOAA20 is the shortname for the NOAA20 VIIRS incarnation of the orbital swath-based Cloud Properties continuity product. For more information, visit product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/CLDPROP_L2_VIIRS_NOAA20\n<br>\n\n#### CLDPROPCOSP_D3_VIIRS_NOAA20\nThe VIIRS/NOAA20 Cloud Properties COSP Level 3 daily, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_D3_VIIRS_NOAA20. It contains VIIRS NOAA-20 cloud mask, cloud top, and cloud optical retrieval data over daily timeframe. It provides a set of custom cloud-related parameters for better comparison with climate model output. The “COSP” acronym in the short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. Provided in netCDF4 format, it contains 32 aggregated science data sets (SDS/parameters). Consult the CLDPROPCOSP User Guide for details regarding how the L3 daily statistics are computed, and to learn more about the gridding and sampling protocols specific to this product and a number of other topics germane to the user community. The collection of this product starts from February 17, 2018 and includes 365 granules each calendar year.\n<br>\n\n#### CLDPROPCOSP_M3_VIIRS_NOAA20\nThe VIIRS/NOAA20 Cloud Properties COSP Level 3 monthly, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_M3_VIIRS_NOAA20. It contains VIIRS SNPP cloud mask, cloud top, and cloud optical retrieval data over monthly timeframe. It provides a set of custom cloud-related parameters for better comparison with climate model output. The “COSP” acronym in the short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. Provided in netCDF4 format, it contains 32 aggregated science data sets (SDS/parameters). Consult the CLDPROPCOSP User Guide for details regarding how the L3 monthly statistics are computed, and to learn more about the gridding and sampling protocols specific to this product and a number of other topics germane to the user community. The collection of this product starts from March 1, 2018 and includes 12 granules each calendar year.\n<br>\n\n#### CLDPROP_M3_VIIRS_NOAA20\nThe VIIRS/NOAA20 Cloud Properties Level 3 monthly, 1x1 degree grid product, shortname CLDPROP_M3_VIIRS_NOAA20, is a continuity product designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS heritages. CLDPROP is used to represent Cloud Properties, which includes both Cloud-Optical Property (COP) and Cloud-Top Property parameters. This product ensures continuity of approach through a common algorithm that is applicable to both MODIS and VIIRS data by leveraging only those spectral channels that are common to both instruments. For more information, visit product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/CLDPROP_M3_VIIRS_NOAA20\n<br>\n\n#### AERDB_L2_VIIRS_NOAA20\nThe VIIRS/NOAA20 Deep Blue Aerosol L2 6-Min Swath 6 km product from the Visible Infrared Imaging Radiometer Suite (VIIRS) determines atmospheric aerosol loading for daytime cloud-free snow-free scenes. This orbit-level product (Short-name: AERDB_L2_VIIRS_ NOAA20) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor’s scanning geometry and Earth’s curvature. Viewed differently, this product’s resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. The L2 Deep Blue AOT data products, at 550 nm reference wavelengths, are derived from particular VIIRS bands using two primary AOT retrieval algorithms: Deep Blue algorithm over land, and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over ocean. Although this product is called Deep Blue based on retrievals for the land algorithm, the data includes over-water retrievals as well. The Deep Blue algorithm draws its heritage from previous applications to retrieve AOT from Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) over both land and ocean and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements over land. This L2 description pertains to the VIIRS Deep Blue Aerosol Version 2.0 (V2.0) product, whose record starts from February 17, 2018. The L2 netCDF product, acquired every 6 minutes, contains 55 Science Data Set (SDS) layers. The V2.0 VIIRS deep blue aerosol products are available from both NOAA20 and SNPP platforms. Significant changes have been made to the V2.0 Deep Blue/SOAR algorithms to further improve the data quality. A number of other improvements and changes have been added that can be found from Product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2_VIIRS_NOAA20 Consult the VIIR Deep Blue product user guide for additional information regarding the global attributes, data field attributes, quality flags, software to handle and use these data products, etc., at: https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/VIIRS_Deep_Blue_Aerosol_User_Guide_v2.pdf\n<br>\n\n#### AERDB_D3_VIIRS_NOAA20\nThe VIIRS/NOAA20 Deep Blue Level 3 daily aerosol data, 1x1 degree grid, Short-name AERDB_D3_VIIRS_NOAA20 product is derived from the Version-2.0 (V2.0) L2 6-minute swath-based products (AERDB_L2_VIIRS_NOAA20), and is provided in a 1x1 degree horizontal resolution grid. Each data field, in most cases, represents the arithmetic mean of all the cells whose latitude and longitude coordinates positions them within each grid element’s bounding limits. Other measures like standard deviation are also provided. This aggregated product is derived only using the best-estimate, QA-filtered retrievals. Using only cells that were measured on the day of interest, the algorithm requires at least three retrieved measurements to render a given grid as valid on any given day. This daily product record starts from January 5th, 2018. This L3 daily product, in netCDF, contains 45 Science Data Set (SDS) layers. For more information about the product and Science Data Set (SDS) layers, consult product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_VIIRS_NOAA20 Or Consult Deep Blue aerosol team Page at: https://deepblue.gsfc.nasa.gov\n<br>\n\n#### AERDB_M3_VIIRS_NOAA20\nThe VIIRS/NOAA20 Deep Blue Level 3 monthly aerosol data, 1x1 degree grid, Short-name AERDB_M3_VIIRS_NOAA20 product is derived from the Version-2.0 (V2.0) daily L3 gridded products (AERDB_D3_VIIRS_NOAA20), and is provided in a 1 x 1 degree horizontal resolution grid. Arithmetic mean values from the daily L3 gridded products also provide the basis to derive a complement of statistics for the monthly aggregated products. To exclude poorly sampled grid elements, the algorithm requires at least 3 valid days’ worth of data to render a given monthly grid element as valid. This monthly product record starts from January 5th, 2018. This L3 monthly product, in netCDF format, contains 45 Science Data Set (SDS) layers that are named identical to the SDSs in the daily L3 product. For more information about the product and Science Data Set (SDS) layers, consult product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_VIIRS_NOAA20 Or Consult Deep Blue aerosol team Page at: https://deepblue.gsfc.nasa.gov\n<br>\n\n#### CLDMSK_L2_VIIRS_NOAA21\nThe National Oceanic and Atmospheric Administration (NOAA-21) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA Level-2 (L2) Cloud Mask (VIIRS/NOAA21 Cloud Mask and Spectral Test Results 6-Min L2 Swath 750m) product is one of four continuity products designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS heritages. CLDMSK_L2_VIIRS_NOAA21 is the short-name for the NOAA-21 VIIRS incarnation of the Cloud Mask continuity product derived from the MODIS-VIIRS cloud mask (MVCM) algorithm, which itself is based on the MODIS (MOD35) algorithm. The MVCM describes a continuity algorithm that is central to both MODIS data (from Terra and Aqua missions) and VIIRS data (from Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System missions). Please bear in mind that the term MVCM does not appear as an attribute within the product’s metadata. Implemented to consistently handle MODIS and VIIRS inputs, the NOAA-21 VIIRS collection-1 products use calibration-adjusted NASA VIIRS L1B as inputs. The nominal spatial resolution of the NOAA-21 VIIRS L2 Cloud mask is 750 meters. The L2 netCDF product is acquired and processed every 6 minutes. For more information consult Product Page at: https://cimss.ssec.wisc.edu/MVCM/\n<br>\n\n#### CLDPROP_D3_VIIRS_NOAA20\nThe VIIRS/NOAA20 Cloud Properties Level 3 daily, 1x1 degree grid product, shortname CLDPROP_D3_VIIRS_NOAA20, is a continuity product designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS heritages. The Cloud Properties in this product includes both Cloud-Optical Property (COP) and Cloud-Top Property parameters. This product ensures continuity of approach through a common algorithm that is applicable to both MODIS and VIIRS data by leveraging only those spectral channels that are common to both instruments. For more information, visit product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/CLDPROP_D3_VIIRS_NOAA20\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","hdf","hdf5","ice","netcdf","oceans","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"N07_AVH13C1 v6 - The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change.","ARN":"arn:aws:s3:::prod-lads/N07_AVH13C1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDT_L2_VIIRS_NOAA20 v2.1 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua missions’ Moderate Imaging Spectroradiometer (MODIS) instruments.","ARN":"arn:aws:s3:::prod-lads/AERDT_L2_VIIRS_NOAA20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"FSNRAD_L2_VIIRS_CRIS_NOAA20 v2 - The VIIRS-CrIS Data Fusion Level-2 Product is designed to facilitate improved continuity in derived cloud and moisture products realized with the High Resolution Infrared Radiation Sounder (HIRS) and the Moderate resolution Imaging Spectroradiometer (MODIS) and to continue other applications that require IR absorption spectral bands. Based on data fusion with VIIRS (Visible Infrared Imaging Radiometer Suite) and CrIS (Cross-track Infrared Sounder), infrared (IR) absorption band radiances for VIIRS are constructed at 750m spatial resolution (i.e., M-band resolution).","ARN":"arn:aws:s3:::prod-lads/FSNRAD_L2_VIIRS_CRIS_NOAA20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDT_L2_VIIRS_NOAA21 v2.1 - The NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua missions’ Moderate Imaging Spectroradiometer (MODIS) instruments.","ARN":"arn:aws:s3:::prod-lads/AERDT_L2_VIIRS_NOAA21","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"FSNRAD_L2_VIIRS_CRIS_SNPP v2 - The VIIRS-CrIS Data Fusion Level-2 Product is designed to facilitate improved continuity in derived cloud and moisture products realized with HIRS and MODIS and to continue other applications that require IR absorption spectral bands. Based on data fusion with VIIRS (Visible Infrared Imaging Radiometer Suite) and CrIS (Cross-track Infrared Sounder), infrared (IR) absorption band radiances for VIIRS are constructed at 750m spatial resolution (i.e., M-band resolution).","ARN":"arn:aws:s3:::prod-lads/FSNRAD_L2_VIIRS_CRIS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ104ANC v2 - The VIIRS/JPSS1 Aerosol Daily L3 Global 0.05 Deg CMG, Short-name VJ104ANC product provides VIIRS aerosol, air temperature, ozone and water vapor data at 0.05-degree resolution. This product is based on a Climate Modeling Grid (CMG) for the purpose of being used in climate simulation models.","ARN":"arn:aws:s3:::prod-lads/VJ104ANC","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDMSK_L2_VIIRS_NOAA20 v1 - The VIIRS/NOAA20 Cloud Mask and Spectral Test Results 6-Min L2 Swath 750m product Mask is one of three continuity products designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS heritages. CLDMSK_L2_VIIRS_NOAA20 is the shortname for the NOAA-20 VIIRS incarnation of the Cloud Mask continuity product derived from the MODIS-VIIRS cloud mask (MVCM) algorithm, which itself is based on the MODIS (MOD35) algorithm.","ARN":"arn:aws:s3:::prod-lads/CLDMSK_L2_VIIRS_NOAA20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDPROP_L2_VIIRS_NOAA20 v1.1 - The VIIRS/NOAA20 Cloud Properties 6-min L2 Swath 750m product is a continuity product similar to its counterpart product from the Suomi National Polar-orbiting Partnership (SNPP) VIIRS. Judiciously leveraging a common set of spectral channels, they help sustain the long-term records of both MODIS and VIIRS heritages.","ARN":"arn:aws:s3:::prod-lads/CLDPROP_L2_VIIRS_NOAA20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDPROPCOSP_D3_VIIRS_NOAA20 v1.1 - The VIIRS/NOAA20 Cloud Properties COSP Level 3 daily, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_D3_VIIRS_NOAA20. It contains VIIRS NOAA-20 cloud mask, cloud top, and cloud optical retrieval data over daily timeframe.","ARN":"arn:aws:s3:::prod-lads/CLDPROPCOSP_D3_VIIRS_NOAA20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDPROPCOSP_M3_VIIRS_NOAA20 v1.1 - The VIIRS/NOAA20 Cloud Properties COSP Level 3 monthly, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_M3_VIIRS_NOAA20. It contains VIIRS SNPP cloud mask, cloud top, and cloud optical retrieval data over monthly timeframe.","ARN":"arn:aws:s3:::prod-lads/CLDPROPCOSP_M3_VIIRS_NOAA20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDPROP_M3_VIIRS_NOAA20 v1.1 - The VIIRS/NOAA20 Cloud Properties Level 3 monthly, 1x1 degree grid product, shortname CLDPROP_M3_VIIRS_NOAA20, is a continuity product designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS heritages. CLDPROP is used to represent Cloud Properties, which includes both Cloud-Optical Property (COP) and Cloud-Top Property parameters.","ARN":"arn:aws:s3:::prod-lads/CLDPROP_M3_VIIRS_NOAA20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_L2_VIIRS_NOAA20 v2 - The VIIRS/NOAA20 Deep Blue Aerosol L2 6-Min Swath 6 km product from the Visible Infrared Imaging Radiometer Suite (VIIRS) determines atmospheric aerosol loading for daytime cloud-free snow-free scenes. This orbit-level product (Short-name: AERDB_L2_VIIRS_ NOAA20) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor’s scanning geometry and Earth’s curvature.","ARN":"arn:aws:s3:::prod-lads/AERDB_L2_VIIRS_NOAA20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_D3_VIIRS_NOAA20 v2 - The VIIRS/NOAA20 Deep Blue Level 3 daily aerosol data, 1x1 degree grid, Short-name AERDB_D3_VIIRS_NOAA20 product is derived from the Version-2.0 (V2.0) L2 6-minute swath-based products (AERDB_L2_VIIRS_NOAA20), and is provided in a 1x1 degree horizontal resolution grid. Each data field, in most cases, represents the arithmetic mean of all the cells whose latitude and longitude coordinates positions them within each grid element’s bounding limits.","ARN":"arn:aws:s3:::prod-lads/AERDB_D3_VIIRS_NOAA20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_M3_VIIRS_NOAA20 v2 - The VIIRS/NOAA20 Deep Blue Level 3 monthly aerosol data, 1x1 degree grid, Short-name AERDB_M3_VIIRS_NOAA20 product is derived from the Version-2.0 (V2.0) daily L3 gridded products (AERDB_D3_VIIRS_NOAA20), and is provided in a 1 x 1 degree horizontal resolution grid. Arithmetic mean values from the daily L3 gridded products also provide the basis to derive a complement of statistics for the monthly aggregated products.","ARN":"arn:aws:s3:::prod-lads/AERDB_M3_VIIRS_NOAA20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDMSK_L2_VIIRS_NOAA21 v1 - The National Oceanic and Atmospheric Administration (NOAA-21) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA Level-2 (L2) Cloud Mask (VIIRS/NOAA21 Cloud Mask and Spectral Test Results 6-Min L2 Swath 750m) product is one of four continuity products designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS heritages. CLDMSK_L2_VIIRS_NOAA21 is the short-name for the NOAA-21 VIIRS incarnation of the Cloud Mask continuity product derived from the MODIS-VIIRS cloud mask (MVCM) algorithm, which itself is based on the MODIS (MOD35) alg...","ARN":"arn:aws:s3:::prod-lads/CLDMSK_L2_VIIRS_NOAA21","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDPROP_D3_VIIRS_NOAA20 v1.1 - The VIIRS/NOAA20 Cloud Properties Level 3 daily, 1x1 degree grid product, shortname CLDPROP_D3_VIIRS_NOAA20, is a continuity product designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS heritages. The Cloud Properties in this product includes both Cloud-Optical Property (COP) and Cloud-Top Property parameters.","ARN":"arn:aws:s3:::prod-lads/CLDPROP_D3_VIIRS_NOAA20","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-noaa---space-weather-program","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NOAA CLIMATE DATA RECORD (CDR) PROGRAM Project","Description":"The Smith & Reynolds Extended Reconstructed Sea Surface Temperature (ERSST) Level 4 dataset provides a historical reconstruction of monthly global ocean surface temperatures and temperature anomalies over a 2 degree spatial grid since 1854 from in-situ observations based on a consistent statistical methodology that accounts for uneven sampling distributions over time and related observational biases. Version 5 of this dataset implements release 3.0 of ICOADS (International Comprehensive Ocean-Atmosphere Data Set) and is supplemented by monthly GTS (Global Telecommunications Ship and buoy) system data. As for the prior ERSST version, v5 implements Empirical Orthogonal Teleconnection analysis (EOT) but with an improved tuning method for sparsely sampled regions and periods. ERSST anomalies are computed with respect to a 1971-2000 monthly climatology. The version 5 has been improved from previous version 4. Major improvements in v5 include: 1) Inclusion and use of new sources and new versions of input datasets, such as data from Argo floats (new source), ICOADS R3.0 (from R2.5), HadISST2 (from HadISST1) sea ice concentration, and 2) Improved methodologies, such as inclusion of additional statistical modes, less spatial-temporal smoothing, better quality control method, and bias correction with baseline to modern buoy observations. The new version improves the spatial structures and magnitudes of El Nino and La Nina events. The ERSST v5 in netCDF format contains extended reconstructed sea surface temperature, SST anomaly, and associated estimated SST error standard deviation fields, in compliance with CF1.6 standard metadata.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","ice","netcdf","oceans"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"REYNOLDS_NCDC_L4_MONTHLY_V5 v5 - The Smith & Reynolds Extended Reconstructed Sea Surface Temperature (ERSST) Level 4 dataset provides a historical reconstruction of monthly global ocean surface temperatures and temperature anomalies over a 2 degree spatial grid since 1854 from in-situ observations based on a consistent statistical methodology that accounts for uneven sampling distributions over time and related observational biases. Version 5 of this dataset implements release 3.0 of ICOADS (International Comprehensive Ocean-Atmosphere Data Set) and is supplemented by monthly GTS (Global Telecommunications Ship and buoy) s...","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/REYNOLDS_NCDC_L4_MONTHLY_V5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-noaa-climate-data-record-cdr-program","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NOBM Project","Description":"Global Modeling and Assimilation Office Research\n<br>\n\n#### NOBM_DAY\nThis is the assimilated daily data from NASA Ocean Biogeochemical Model (NOBM). The NOBM is a comprehensive, interactive ocean biogeochemical model coupled with a circulation and radiative model in the global oceans (Gregg and Casey, 2007). It spans the domain from -84 to 72 degree latitude in increments of 1.25 degree longitude by 2/3 degree latitude, including only open ocean areas where bottom depth > 200m. NOBM contains 4 phytoplankton groups, 4 nutrient groups, a single herbivore group, and 3 detrital pools, and the major ocean carbon components, dissolved organic and inorganic carbon (DOC and DIC).\n<br>\n\n#### NOBM_MON\nThis is the assimilated monthly data from NASA Ocean Biogeochemical Model (NOBM). The NOBM is a comprehensive, interactive ocean biogeochemical model coupled with a circulation and radiative model in the global oceans (Gregg and Casey, 2007). It spans the domain from -84 to 72 degree latitude in increments of 1.25 degree longitude by 2/3 degree latitude, including only open ocean areas where bottom depth >200m. NOBM contains 4 phytoplankton groups, 4 nutrient groups, a single herbivore group, and 3 detrital pools, and the major ocean carbon components, dissolved organic and inorganic carbon (DOC and DIC).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://gmao.gsfc.nasa.gov/gmao-products/merra-nobm/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","ice","oceans"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NOBM_DAY vR2017 - This is the assimilated daily data from NASA Ocean Biogeochemical Model (NOBM). The NOBM is a comprehensive, interactive ocean biogeochemical model coupled with a circulation and radiative model in the global oceans (Gregg and Casey, 2007).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/nobm/NOBM_DAY.R2017","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"NOBM_MON vR2017 - This is the assimilated monthly data from NASA Ocean Biogeochemical Model (NOBM). The NOBM is a comprehensive, interactive ocean biogeochemical model coupled with a circulation and radiative model in the global oceans (Gregg and Casey, 2007).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/nobm/NOBM_MON.R2017","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-nobm","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NOPP_MISST Project","Description":"The Saildrone Arctic 2019 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land. The 2019 Saildrone Arctic campaign featured six Saildrone USVs (jointly funded by NOAA and NASA) deployed during a 150-day cruise in the Bering and Chukchi Seas, spanning from 14 May 2019 to 11 October 2019. The primary mission objective for 2019 was to gather comprehensive atmospheric and oceanographic data in Alaskan arctic waters, which could lead to significant improvements in modeling of diurnal warming and understanding of the marginal ice zones. Additionally, these new data will provide additional Arctic SST observations to benefit SST algorithm development and validation, and for studies of air- sea-ice interactions. Please see the cruise report: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/insitu/open/L2/saildrone/docs/Saildrone_2019_Arctic_Cruise_Report.pdf\n\nDuring the Arctic campaign, NASA-funded Saildrones SD-1036 and SD-1037 undertook transects in the Chukchi Sea, approaching the sea ice edge to measure air-sea heat and momentum fluxes in the ocean near sea ice and to validate satellite sea-surface temperature measurements in the Arctic. Each Saildrone was equipped with a suite of instruments to measure various parameters, including air temperature, relative humidity, barometric pressure, surface skin temperature, wind speed and direction, wave height and period, seawater temperature and salinity, chlorophyll fluorescence, and dissolved oxygen. Additionally, both vehicles utilized 300 kHz acoustic Doppler current profilers (ADCP) to measure near-surface currents. Seven temperature data loggers positioned vertically along the hull enhanced understanding of thermal variability near the ocean surface.\n\nThe Saildrone Arctic 2019 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses three netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. The third file includes temperature logger measurements at various depths at 1-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","netcdf","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SAILDRONE_ARCTIC v1.0 - The Saildrone Arctic 2019 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SAILDRONE_ARCTIC","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-07-09","Slug":"nasa-nopp-misst","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NPP-JPSS Project","Description":"The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also known as NOAA-20 (National Oceanic and Atmospheric Administration). The ATMS is a 22-channel mm-wave radiometer. The ATMS will measure upwelling radiances in six frequency bands centered at 23 GHz, 31 GHz, 50-58 GHz, 89 GHz, 66 GHz, and 183 GHz. The ATMS is a total power radiometer, with &#8220;through-the-antenna&#8221; radiometric calibration. Radiometric data is collected by a pair of antenna apertures, scanned by rotating flat plate reflectors. Scanning is performed cross-track to the satellite motion from sun to anti-sun, using the \"integrate-while-scan\" type data collection. The scan period is 8/3 second, synchronized to the Cross-track Infrared Sounder (CrIS) using a spacecraft provided scan synchronization pulse. Since the JPSS-1 satellite is orbiting at an altitude of about 830 km, the instantaneous spatial resolution on the ground at nadir is about 16 km, 32 km, or 75 km depending upon the channel. The brightness temperature data are contained in an array with 135 rows in the along-track direction, 96 columns in the cross-track direction, and a 3rd dimension for each of the 22 channels. The ATMS cross-track scan interval is 0.018 seconds and the along-track scan period is 8/3 seconds. Data products are constructed on six minute boundaries. If you were redirected to this page from a DOI from an older version, please note this is the current version of the product. Please contact the GES DISC user support if you need information about previous data collections.\n<br>\n\n#### SNDRJ1CrISL1BIMG\nThe Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also know as NOAA-20 (National Oceanic and Atmospheric Administration). The JPSS-1 mission with CrIS instrumentation is a follow-on to the Suomi National Polar-orbiting Partnership (SNPP) mission. The CrIS instrumentation and data processing system is nearly identical to that of the SNPP satellite. / The FSR files have 2,223 channels (*2211 apodized channels): 637 (*633) shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 (*865) midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 (*713)longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries. The Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. It provides the sensor data records for clouds, sea surface temperature, ocean color, and others. This IMG product primarily contains statistics of the VIIRS cloud mask and VIIRS L1B data within each CrIS footprint.\n<br>\n\n#### SNDRJ1CrISL1BPCARED\nThis sample data collection contains L1B radiance values that are compressed and denoised via Principal Component Analysis (PCA). Additionally it contains a new Rapid Event Detection (RED) product, which for multiple spectral regions identify where rare signals are observed, providing an efficient and useful way to locate and study interesting phenomena. Some of the potential uses for these RED components are the detection of atmospheric gases where fires and volcanoes occur. PCA/RED might be a desirable alternative to the existing L1B product because (1) it is many times smaller in data volume, (2) about 80% of the random noise is removed, and (3) it comes with the new RED component. The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also know as NOAA-20 (National Oceanic and Atmospheric Administration).\n<br>\n\n#### SNDRJ2CrISL1BIMG\nThe Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-2 (JPSS-2) platform. This platform is also know as NOAA-21 (National Oceanic and Atmospheric Administration). The IMG product supplements the CrIS Level 1B (L1B) hyperspectral radiance product by providing collocated high-spatial resolution data from the Visible Infrared Imaging Radiometer Suite (VIIRS) imager located on the same platform. VIIRS radiance and cloud mask values are grouped and aggregated for every CrIS field of view (FOV) and made available in a format intended for use alongside the CrIS L1B data. The collocated VIIRS level 1 / cloud mask statistical summary is the main product and consists of collocated CrIS field of views with the VIIRS cloud mask and radiances/reflectances. This can be thought of as a match-up between CrIS and VIIRS. The supplementary product, array indices for collocated VIIRS observations (collection name SNDRJ2CrISL1BIMGC), consists of array indices for collocated VIIRS observations and provides the collocated indices of the VIIRS pixels within each CrIS footprint. The FSR files have 2,223 channels (*2211 apodized channels): 637 (*633) shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 (*865) midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 (*713)longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries. The Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. It provides the sensor data records for clouds, sea surface temperature, ocean color, and others. This IMG product primarily contains statistics of the VIIRS cloud mask and VIIRS L1B data within each CrIS footprint.\n<br>\n\n#### SNDRJ2CrISL1B\nThe goal of creating this product is to allow users to begin working with data from the CrIS instrument on-board the recently launched NOAA-21 / JPSS-2 (J2) satellite. The J2 beta product was generated using an updated version of the software that is used to generate the Version 3 SNPP and JPSS-1 (J1) products, which are currently available from GES DISC. Changes were made to allow J2 processing, but the underlying algorithm theoretical basis remains the same. It should be noted that work on refining the J2 calibration parameters is ongoing, and the parameters used in this release are preliminary. A future Version 4 set of products is planned for release after J2 has reached validated status, which will include optimized J2 parameters and will use a single software version for all three instruments incorporating various algorithm improvements. The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-2 (JPSS-2) platform. This platform is also know as NOAA-21 (National Oceanic and Atmospheric Administration). The JPSS-2 mission with CrIS instrumentation is a follow-on to the Suomi National Polar-orbiting Partnership (SNPP) mission. The CrIS instrumentation and data processing system is nearly identical to that of the SNPP satellite. CrIS is designed to be used with the ATMS (Advanced Technology Microwave Sounder) instrument. Processing the data from both of these instruments together is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite). The FSR files have 2,223 channels (*2211 apodized channels): 637 (*633) shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 (*865) midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 (*713)longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries. An issue has been observed in J2 CrIS data that impacts calibration of observations for a period of several minutes during each orbit. The issue occurs in the southern hemisphere during the descending part of the orbit, near a satellite solar zenith angle of -118 degrees. There is significant variability in affected latitudes throughout the year. The issue is indicated by non-zero quality flags in the Level 1B product. The root cause is believed to be variation in calibration views due to rapid instrument temperature changes after the J2 satellite passes from the Earth's shadow into direct sunlight. Potential solutions are being evaluated for implementation in a future product version. If you were redirected to this page from a DOI from an older version, please note this is the current version of the product. Please contact the GES DISC user support if you need information about previous data collections.\n<br>\n\n#### OMPS_N20_NMSO2_PCA_L2_Step1\nThe OMPS-N20 NM PCA SO2 Step1 Total Column 1-Orbit L2 Swath 17x13km collection 1 product contains the retrieved sulfur dioxide (SO2) measured by the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) sensor on the NOAA-20 (JPSS-1) satellite. The product is based on the NASA Goddard Space Flight Center principal component analysis (PCA) spectral fitting algorithm (Li et al., 2013, 2017) used to retrieve the SO2 total column amounts assuming different SO2 plume heights, including the boundary layer (lowest 1 km of the atmosphere), the lower (centered at 3 km), middle (centered at 8 km) and upper (centered at 13 km) troposphere, as well as the lower stratosphere (centered at 18 km). Each granule contains data from the daylight portion for a single orbit or about 50 minutes. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14 orbits per day each with a swath width of 2600 km. There are 104 pixels in the cross-track direction before February 13, 2019 with a pixel resolution of about 17 km x 17 km at nadir. Since then, the pixel resolution has been enhanced to 17 km x 13 km at nadir, with 140 pixels in the cross-track direction. The files are written using netCDF version 4.\n<br>\n\n#### OMPS_N21_LP_L2_AER_DAILY\nThe OMPS-N21 L2 LP Aerosol Extinction Vertical Profile swath daily 3slit (AER) product contains the retrieved aerosol extinction coefficients measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the NOAA-21 satellite. The AER product measures stratospheric aerosol abundance and evolution at 6 wavelengths (510, 600, 675, 745, 869 and 997 nm) to complement the OMPS LP measurements of stratospheric and mesospheric profile ozone. This product replaces the previous single wavelength 675 nm (AER675) product. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each measuring three limb profiles spaced approximately 250 km in the cross-track direction. The profiles are measured from the ground up to about 80 km with a vertical resolution of the retrieved profiles of approximately 1.8 km. The files are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_N21_LP_L2_O3_DAILY\nThe OMPS-N21 L2 LP Ozone (O3) Vertical Profile swath daily 3slit collection contains ozone measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the NOAA-21 satellite. The LP ozone product measures the vertical distribution of ozone in the stratosphere and lower mesosphere. The algorithm derives ozone profile values along with errors in the UV from 29.5 km and 52.5 km, and in the visible from cloud top to 37.5 km (when there are no clouds the lower limit is 12.5 km). Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, the data from the center of the LP three slits are used to make a vertical profile. The profile is measured from the ground up to about 60 km with a vertical resolution of the retrieved profiles of approximately 1.8 km. The data are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_NPP_LP_L1G_EV\nThe OMPS-NPP L1G LP Radiance EV Wavelength-Altitude Grid swath orbital 3slit product contains the calibrated earth-viewing radiances measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The LP L1G product measures radiances in the wavelength region from 280 nm to 1000 nm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each measuring three limb profiles spaced approximately 250 km in the cross-track direction. The profiles are measured from the ground up to about 80 km with a vertical resolution of the retrieved profiles of approximately 1-2 km. The data are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_NPP_LP_L2_AER_DAILY\nThe OMPS-NPP L2 LP Aerosol Extinction Vertical Profile swath daily 3slit (AER) product contains the retrieved aerosol extinction coefficients measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The AER product measures stratospheric aerosol abundance and evolution at 6 wavelengths (510, 600, 675, 745, 869 and 997 nm) to complement the OMPS LP measurements of stratospheric and mesospheric profile ozone. This product replaces the previous single wavelength 675 nm (AER675) product. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each measuring three limb profiles spaced approximately 250 km in the cross-track direction. The profiles are measured from the ground up to about 80 km with a vertical resolution of the retrieved profiles of approximately 1.8 km. The files are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_NPP_LP_L2_O3_DAILY\nThe OMPS-NPP L2 LP Ozone (O3) Vertical Profile swath daily 3slit collection contains ozone measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The LP ozone product measures the vertical distribution of ozone in the stratosphere and lower mesosphere. The algorithm derives ozone profile values along with errors in the UV from 29.5 km and 52.5 km, and in the visible from cloud top to 37.5 km (when there are no clouds the lower limit is 12.5 km). Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, the data from the center of the LP three slits are used to make a vertical profile. The profile is measured from the ground up to about 60 km with a vertical resolution of the retrieved profiles of approximately 1.8 km. The data are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_NPP_LP_L2_O3_DAILY\nVersion 2.6 is the current version of this data product, and supersedes all previous versions. The OMPS-NPP L2 LP Ozone (O3) Vertical Profile swath daily Center slit collection contains ozone measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The LP ozone product measures the vertical distribution of ozone in the stratosphere and lower mesosphere. The algorithm derives ozone profile values along with errors in the UV from 29.5 km and 52.5 km, and in the visible from cloud top to 37.5 km (when there are no clouds the lower limit is 12.5 km). See the README for full description of the product and updated retrieval algorithm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, the data from the center of the LP three slits are used to make a vertical profile. The profile is measured from the ground up to about 60 km with a vertical resolution of the retrieved profiles of approximately 1.8 km. The data are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_NPP_NMMIEAI_L2\nThe OMPS-NPP L2 NM Aerosol Index swath orbital product provides aerosol index values from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. This is now the official NASA aerosol index product, replacing the aerosol index found in the OMPS-NPP L2 NM Total Ozone product. The aerosol index is derived from normalized radiances using 2 wavelength pairs at 340 and 378.5 nm. Additionally, this data product contains measurements of normalized radiances, reflectivity, cloud fraction, reflectivity, and other ancillary variables. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, each has typically 400 swaths. The swath width of the NM is about 2800 km with 36 scenes, or pixels, with a footprint size of 50 km x 50 km at nadir. The data are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_NPP_NMCLDRR_L2\nThe OMPS-NPP L2 NM Cloud Pressure and Fraction swath orbital product provides effective cloud fraction and effective cloud pressure retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. The cloud pressure algorithm retrieves effective cloud pressus, a.k.a optical centroid pressure (OCP) and effective cloud fraction (ECF) using a concept of Mixed Lambert Equivalent Reflectivity (MLER) at 354.1 nm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each measuring three limb profiles spaced approximately 250 km in the cross-track direction. The profiles are measured from the ground up to about 80 km with a vertical resolution of the retrieved profiles of approximately 1.8 km. The files are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_NPP_NMNO2_L2\nThe OMPS-NPP L2 NM Nitrogen Dioxide (NO2) Total and Tropospheric Column swath orbital collection 2 version 2.0 product contains the retrieved nitrogen dioxide (NO2) measured by the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) sensor on the Suomi-NPP satellite. A direct vertical column fitting (DVCF) algorithm is used to retrieve the NO2 total column amount and a new spatial technique is applied to separate the stratospheric and tropospheric amounts. Each granule contains data from the daylight portion for a single orbit or about 50 minutes. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14 orbits per day each with a swath width of 2600 km. There are 35 pixels in the cross-track direction, with a pixel resolution of about 50 km x 50 km at nadir. The files are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_NPP_NMTO3_L2\nThe OMPS-NPP L2 NM Ozone (O3) Total Column swath orbital product provides total ozone measurements from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. The total column ozone amount is derived from normalized radiances using 2 wavelength pairs 317.5 and 331.2 nm under most conditions, and 331.2 and 360 nm for high ozone and high solar zenith angle conditions. Additionally, this data product contains measurements of UV aerosol index and reflectivity at 331 nm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, each has typically 400 swaths. The swath width of the NM is about 2800 km with 36 scenes, or pixels, with a footprint size of 50 km x 50 km at nadir. The files are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_NPP_NMSO2_L2\nThe OMPS-NPP L2 NM Sulfur Dioxide (SO2) Total and Tropospheric Column swath orbital collection 2 version 2.0 product contains the retrieved sulfur dioxide (SO2) measured by the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) sensor on the Suomi-NPP satellite. A direct vertical column fitting (DVCF) algorithm is used to retrieve the SO2 total column amount and column amounts in the lower (centered at 2.5 km), middle (centered at 7.5 km) and upper (centered at 11 km) troposphere, as well as the lower stratosphere (centered at 16 km). Each granule contains data from the daylight portion for a single orbit or about 50 minutes. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14 orbits per day each with a swath width of 2600 km. There are 35 pixels in the cross-track direction, with a pixel resolution of about 50 km x 50 km at nadir. The files are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_NPP_NPBUVO3_L2\nThe OMPS-NPP L2 NP Ozone (O3) Total Column swath orbital product provides ozone profile retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Profiler (NP) instrument on the Suomi-NPP satellite. The V8 ozone profile algorithm relies on nadir profiler measurements made in the 250 to 310 nm range, as well as from measurements from the nadir mapper in the 300 to 380 nm range. Ozone mixing ratios are reported at 15 pressure levels between 50 and 0.5 hPa. Additionally, this data product contains measurements of total ozone, UV aerosol index and reflectivities at 331 and 380 nm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 80 profiles. The NP footprint size is 250 km x 250 km. The files are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_NPP_LP_L3_AER_MONTHLY\nThe OMPS-NPP L3 LP Aerosol Extinction Vertical Profile 5 x 15 deg lat-lon grid multi-wavelength monthly (AER) product contains the retrieved aerosol extinction coefficients measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The AER product measures stratospheric aerosol abundance and evolution at 6 wavelengths (510, 600, 675, 745, 869 and 997 nm) to complement the OMPS LP measurements of stratospheric and mesospheric profile ozone. Each granule contains data from the daylight portion of each orbit averaged over an entire month in 5 x 15 deg lat-lon grids. Spatial coverage is global (-90 to 90 degrees latitude). The profiles are gridded from the ground up to about 41 km with a vertical resolution of the 1.0 km. The files are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_NPP_NMTO3_L3_DAILY\nThe OMPS-NPP L3 NM Ozone (O3) Total Column 1.0 deg grid daily product provides total ozone measurements from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. The level-3 gridding algorithm is used to combine the orbital OMPS cross track measurements into a daily map product with a fixed global grid. Grid cells are computed as weighted averages of a given parameter derived for the field-of-views that overlay the given cell. The current version of this product includes UV aerosol index and reflectivity at 331 nm retrievals as well. Each granule contains data for a full day. Spatial coverage is global (-90 to 90 degrees latitude), with a resolution of 1.0 degree in longitude and 1.0 degree in latitude, and array size of 360 by 180. The files are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_NPP_LP_L2_Temp_DAILY\nThe OMPS-NPP LP L2 Temperature Vertical Profile swath daily 3-slit collection contains the temperature profile measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The product measures the temperature in the stratosphere and lower mesosphere. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, the data from the center of the LP three slits are used to make a vertical profile. The profile is measured from the ground up to about 60 km with a vertical resolution of the retrieved profiles of approximately 1.8 km. The data are written using the Hierarchical Data Format Version 5 or HDF5.\n<br>\n\n#### OMPS_NPP_NMEV_L1B\nThe OMPS-NPP L1B NM Radiance EV Calibrated Geolocated Swath Orbital collection contains calibrated and geolocated radiances from 300 to 380 nm measured by the OMPS Nadir-Mapper sensor on the Suomi-NPP satellite. Each granule contains data from the daylight portion of a single orbit (about 50 minutes). Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day each with a swath width of 2600 km.\n<br>\n\n#### OMPS_NPP_NPEV_L1B\nThe OMPS-NPP L1B NP Radiance EV Calibrated Geolocated Swath Orbital collection contains calibrated and geolocated radiances from 300 to 380 nm measured by the OMPS Nadir-Profiler sensor on the Suomi-NPP satellite. Each granule typically contains data from the daylight portion of a single orbit (about 50 minutes). Spatial coverage is nearly global (-82 to 82 degrees latitude), and there are about 14.5 orbits per day each with a single nadir measurement along the satellite track.\n<br>\n\n#### OMPS_NPP_NMSO2_PCA_L3_DAILY\nThe OMPS-NPP L3 NM PCA Sulfur Dioxide (SO2) Total Column Daily Best Pixel Global Gridded 0.25 x 0.25 degree product contains sulfur dioxide amounts gridded at 0.25 x 0.25 degree resolution using the 'best' pixel method. Data are measured by the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) sensor on the Suomi-NPP satellite. The product is based on the NASA Goddard Space Flight Center principal component analysis (PCA) spectral fitting algorithm (Li et al., 2013, 2017). Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and the spatial resolution is 0.25 x 0.25 degrees. The files are written using netCDF version 4.\n<br>\n\n#### VJ116A2\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Actual and Potential Evapotranspiration product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The VJ116A2 algorithm is based on the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with VIIRS remotely sensed data products such as 8-day vegetation property dynamics and daily surface albedo. Provided in the VJ116A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each VJ116A2 granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the summation of 8-day total water loss within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average total energy over a unit area for a day during the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year. Known Issues: Please refer to the [VIIRS Land Products website](https://viirsland.gsfc.nasa.gov/Products/NASA/ET_ESDR.html) and [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) for information about VJ116A2 known issues.\n<br>\n\n#### VJ116A2GF\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Actual and Potential Evapotranspiration product is a gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The VIIRS ET and PET algorithm is based on the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with VIIRS remotely sensed data products such as 8-day vegetation property dynamics and daily surface albedo. The VJ116A2GF will be generated at the end of each year when the entire yearly 8-day VJ115A2H is available. Hence, the gap-filled VJ116A2GF is the improved VJ116A2, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get VJ116A2GF in near-real time because it will be generated only at the end of a given year. Provided in the VJ116A2GF product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each VJ116A2GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the summation of 8-day total water loss within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average total energy over a unit area for a day during the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year. Known Issues: Please refer to the [VIIRS Land Products website](https://viirsland.gsfc.nasa.gov/Products/NASA/ET_ESDR.html) and [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) for information about VJ116A2GF known issues.\n<br>\n\n#### VJ116A3GF\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Actual and Potential Evapotranspiration product is a gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The VIIRS ET and PET algorithm is based on the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with VIIRS remotely sensed data products such as 8-day vegetation property dynamics and daily surface albedo. The VJ116A3GF will be generated at the end of each year when the entire yearly 8-day VJ115A2H is available. Hence, the gap-filled VJ116A3GF is the improved VJ116A2, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get VJ116A3GF in near-real time because it will be generated only at the end of a given year. Provided in the VJ116A3GF product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each VJ116A3GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year. Known Issues: Please refer to the [VIIRS Land Products website](https://viirsland.gsfc.nasa.gov/Products/NASA/ET_ESDR.html) and [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) for information about VJ116A3GF known issues.\n<br>\n\n#### VJ118C1\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Downward Shortwave Radiation (DSR) Level 3 Climate Modeling Grid (CMG) product produced at 0.05 degree pixel resolution at daily intervals. DSR is the incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is a significant variable in land surface models (LSMs), receiving increased attention due to its importance for the management and operation of solar energy systems. The VJ118 algorithm adapts the narrowband look-up table (LUT) approach to estimate DSR. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. The Algorithm Theoretical Basis Document (ATBD) is forthcoming and will provide additional details regarding the methodology used to create the data. The VJ118C1 product is aggregated from VJ118A1 files and includes one set of global 3-hour DSR variables and one set of daily mean DSR.\n<br>\n\n#### VJ118A1\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Downward Shortwave Radiation (DSR) gridded Level 3 product is produced at 1 kilometer pixel resolution at daily intervals. DSR is the incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is an important variable in land-surface models that address a variety of scientific and is a significant variable in land surface models (LSMs), receiving increased attention due to its importance for the management and operation of solar energy systems. The VJ118 algorithm adapts the narrowband look-up table (LUT) approach to estimate DSR. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. The Algorithm Theoretical Basis Document (ATBD) is forthcoming and will provide additional details regarding the methodology used to create the data. Provided in the VJ118A1 product are variables for instantaneous total, direct, and diffuse DSR, as well as 3-hour DSR, daily mean DSR, and a quality variable.\n<br>\n\n#### VJ117A3GF\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Gross and Net Primary Production (NPP) Gap-Filled (VJ117A3GF) Version 2 data product is a cumulative composite of Gross Primary Productivity (GPP) values based on the radiation-use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VJ117A3GF is a yearly composite at 500 meter spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection. The VJ117A3GF will be generated at the end of each year when the entire yearly 8-day VJ115A2H is available. Hence, the gap-filled VJ117A3GF is the improved VJ117A2, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get VJ117A3GF in near-real time because it will be generated only at the end of a given year. Provided in the VJ117A3GF product are layers for GPP, NPP, along with a quality control layer. A low resolution browse image for GPP is also available for each VJ117A3GF granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ117A2\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) (VJ117A2) Version 2 data product is a cumulative composite of GPP values based on the radiation use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VJ117A2 is an 8-day composite at 500 meter spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection. Provided in the VJ117A2 product are layers for GPP, PSN, along with a quality control layer. A low resolution browse image for GPP is also available for each VJ117A2 granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ117A2GF\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) Gap-Filled (VJ117A2GF) Version 2 data product is a cumulative composite of GPP values based on the radiation-use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VJ117A2GF is an 8-day composite at 500 meter spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection. The VJ117A2GF will be generated at the end of each year when the entire yearly 8-day VJ115A2H is available. Hence, the gap-filled VJ117A2GF is the improved VJ117A2, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get VJ117A2GF in near-real time because it will be generated only at the end of a given year. Provided in the VJ117A2GF product are layers for GPP, PSN, along with a quality control layer. A low resolution browse image for GPP is also available for each VJ117A2GF granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VJ119A2\nThe VJ119A2 Version 2 data product is a NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 750 meter (m) pixel resolution using the Rotated Sinusoidal (RSIN) projection. The VJ119A2 product provides the atmospheric properties and view geometry used to calculate the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, VJ119A1 product. Provided in the VJ119A2 product are layers for AOD, AOD uncertainty and QA, injection height of smoke plume, AOD outside of glint area, fine mode fraction, and cosines of solar and view zenith angles, relative azimuth, scattering angle, glint angle, and Angstrom Exponent over the ocean. Known Issues • The spatial resolution and projection of this product are listed incorrectly in the VJ119A2 long name. VJ119A2 uses the RSIN projection and is at 750m resolution. The long name should read “VIIRS/JPSS1 Land Aerosol Optical Depth Daily L2G Global 750m RSIN Grid V002”. This will be rectified in future versions. • MAIAC Lookup Tables (LUTs) are built assuming pseudo-spherical correction in single scattering which has a reduced accuracy for high sun/view zenith angles. A reduced MAIAC performance is expected at solar zenith angles > 78°. • MAIAC may be missing bright salt pans in several world deserts. In such cases, it may generate a persistent high Aerosol Optical Depth (AOD) resulting in missing surface retrievals. • Because of inherent uncertainties of gridding on the coastline, the area of 1-3 pixels from the coastline may contain frequent artifacts in cloud mask (usually over-detection), AOD (higher values) and surface BRF. Users should exercise caution near the coastline as represented by the AOD QA bit 1010 (See Table 5.4 bits 8-11, 1010 -- AOD within +-2km from the coastline is replaced by nearby AOD). • Atmospheric Correction (AC) over detected snow: as MAIAC does not retrieve AOD over snow, it assumes a low climatology AOD=0.05 globally and 0.02 at high elevations (H>4.2km). Over north-central China, which is often heavily polluted and low AOD assumption can lead to a significant bias, we use AOD averaged over mesoscale area of 150km using reliable AOD retrievals over snow-free pixels. Such approach does improve quality of AC as compared to low-AOD assumption as judged by the reduced boundaries and color artefacts, but it does not account for the aerosol variability inside 150km area. • Ice mask is currently unreliable.\n<br>\n\n#### VJ119A1\nThe VJ119A1 Version 2 data product is a NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 375 meter (m) and 750 m pixel resolutions using the Rotated Sinusoidal (RSIN) projection. The VJ119A1 product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The VIIRS MAIAC Land Surface BRF products provide an estimate of the surface spectral reflectance as it would be measured at ground-level in the absence of atmospheric scattering or absorption. Provided in the VJ119A1 product are layers for surface reflectance, cosines of solar and view zenith angles, relative azimuth, sun azimuth (SAZ), sensor view azimuth (VAZ), scattering angle and glint angle. Volumetric (Fv) and geometric-optics (Fg) kernels of the Ross-Thick Li-Sparce (RTLS) model for the observation geometry are also included. The 750 m surface reflectance layers are produced in cloud-free conditions from bands M1-M5, M7, M8, M10, and M11, whereas the 375 m layers are produced from bands I1-I3. Known Issues • The spatial resolution and projection of this product are listed incorrectly in the VJ119A1 long name. VJ119A1 uses the RSIN projection and is at 375m and 750m resolution. The long name should read “VIIRS/JPSS1 Land Surface BRF Daily L2G Global 375m and 750m RSIN Grid V002”. This will be rectified in future versions. • MAIAC Lookup Tables (LUTs) are built assuming pseudo-spherical correction in single scattering which has a reduced accuracy for high sun/view zenith angles. A reduced MAIAC performance is expected at solar zenith angles > 78°. • MAIAC may be missing bright salt pans in several world deserts. In such cases, it may generate a persistent high Aerosol Optical Depth (AOD) resulting in missing surface retrievals. • Because of inherent uncertainties of gridding on the coastline, the area of 1-3 pixels from the coastline may contain frequent artifacts in cloud mask (usually over-detection), AOD (higher values) and surface BRF. Users should exercise caution near the coastline as represented by the AOD QA bit 1010 (See Table 5.4 bits 8-11, 1010 -- AOD within +-2km from the coastline is replaced by nearby AOD). • Atmospheric Correction (AC) over detected snow: as MAIAC does not retrieve AOD over snow, it assumes a low climatology AOD=0.05 globally and 0.02 at high elevations (H>4.2km). Over north central China, which is often heavily polluted and low AOD assumption can lead to a significant bias, we use AOD averaged over mesoscale area of 150km using reliable AOD retrievals over snow-free pixels. Such approach does improve quality of AC as compared to low-AOD assumption as judged by the reduced boundaries and color artefacts, but it does not account for the aerosol variability inside 150km area. • Ice mask is currently unreliable.\n<br>\n\n#### VJ118C2\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Photosynthetically Active Radiation (PAR) Level 3 Climate Modeling Grid (CMG) product produced at 0.05 degree pixel resolution at daily intervals. PAR is the incident solar radiation in the visible spectrum (400-700 nanometers) and is a significant variable in land surface models (LSMs), receiving increased attention due to its importance for the management and operation of solar energy systems. The VJ118 algorithm adapts the narrowband look-up table (LUT) approach to estimate PAR. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. The Algorithm Theoretical Basis Document (ATBD) is forthcoming and will provide additional details regarding the methodology used to create the data. The VJ118C2 product is aggregated from VJ118A2 files and includes one set of global 3-hour PAR variables and one set of daily mean PAR.\n<br>\n\n#### VJ118A2\nThe NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Photosynthetically Active Radiation (PAR) gridded Level 3 product is produced at 1 kilometer pixel resolution at daily intervals. PAR is the incident solar radiation in the visible spectrum (400-700 nanometers) and is a significant variable in land surface models (LSMs), receiving increased attention due to its importance for the management and operation of solar energy systems. The VJ118 algorithm adapts the narrowband look-up table (LUT) approach to estimate PAR. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. The Algorithm Theoretical Basis Document (ATBD) is forthcoming and will provide additional details regarding the methodology used to create the data. Provided in the VJ118A2 product are variables for instantaneous total, direct, and diffuse PAR, as well as 3-hour PAR, daily mean PAR, and a quality variable.\n<br>\n\n#### VNP14IMG\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires (VNP14IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This Level 2 product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events. Due to its higher spatial resolution, the VNP14IMG active fire product provides greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters in comparison to the VNP14 fire data product. The VNP14IMG product includes 26 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., radiance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14IMG product is also used to generate higher-level fire data products. Use of the (VNP03MODLL) data product is required to apply accurate geolocation information to the VNP14IMG Science Datasets (SDS) / Variables. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP16A2\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Actual and Potential Evapotranspiration (VNP16A2) data product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The VNP16A2 algorithm is based on the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with VIIRS remotely sensed data products such as 8-day vegetation property dynamics and daily surface albedo. Provided in the VNP16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each VNP16A2 granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the summation of 8-day total water loss within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average total energy over a unit area for a day during the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year. Known Issues: Please refer to the [VIIRS Land Products website](https://viirsland.gsfc.nasa.gov/Products/NASA/ET_ESDR.html) and [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) for information about VNP16A2 known issues.\n<br>\n\n#### VNP16A2GF\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Actual and Potential Evapotranspiration product is a gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The VIIRS ET and PET algorithm is based on the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with VIIRS remotely sensed data products such as 8-day vegetation property dynamics and daily surface albedo. The VNP16A2GF will be generated at the end of each year when the entire yearly 8-day VNP15A2H is available. Hence, the gap-filled VNP16A2GF is the improved VNP16A2, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get VNP16A2GF in near-real time because it will be generated only at the end of a given year. Provided in the VNP16A2GF product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each VNP16A2GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the summation of 8-day total water loss within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average total energy over a unit area for a day during the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year. Known Issues: Please refer to the [VIIRS Land Products website](https://viirsland.gsfc.nasa.gov/Products/NASA/ET_ESDR.html) and [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) for information about VNP16A2GF known issues.\n<br>\n\n#### VNP16A3GF\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Actual and Potential Evapotranspiration product is a gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The VIIRS ET and PET algorithm is based on the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with VIIRS remotely sensed data products such as 8-day vegetation property dynamics and daily surface albedo. The VNP16A3GF will be generated at the end of each year when the entire yearly 8-day VNP15A2H is available. Hence, the gap-filled VNP16A3GF is the improved VNP16A2, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get VNP16A3GF in near-real time because it will be generated only at the end of a given year. Provided in the VNP16A3GF product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each VNP16A3GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year. Known Issues: Please refer to the [VIIRS Land Products website](https://viirsland.gsfc.nasa.gov/Products/NASA/ET_ESDR.html) and [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) for information about VNP16A3GF known issues.\n<br>\n\n#### VNP43C3\nThe VNP43C3 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43C3](https://doi.org/10.5067/VIIRS/VNP43C3.002) and [VJ143C3](https://doi.org/10.5067/VIIRS/VJ143C3.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Albedo Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 1 product (VNP43C3) is derived from the 30 arc second CMG VNP43D Version 1 product suite. VNP43C3 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43C1](https://doi.org/10.5067/VIIRS/VNP43C1.001) to compute white-sky albedos and the black-sky albedos at local solar noon for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. The quality and inversion status of the majority of the underlying 30 arc second pixels is provided as well as the percentage of the underlying pixels that were present or were snow covered. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VNP43C3 product includes 26 layers containing white-sky albedos and the black-sky albedos for the VIIRS DNB, moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and NIR broadbands. Along with the albedo data for the 13 bands are five ancillary layers for uncertainty, quality, local solar noon, percent finer resolution inputs, and snow cover. A low-resolution image is also available showing retrievals of WSA for the shortwave broadband in JPEG format. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect [most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43C3\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Albedo Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VNP43C3) is derived from the 30 arc second CMG VNP43D Version 2 product suite. VNP43C3 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43C1](https://doi.org/10.5067/VIIRS/VNP43C1.002) to compute white-sky albedos and the black-sky albedos at local solar noon for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. The quality and inversion status of the majority of the underlying 30 arc second pixels is provided as well as the percentage of the underlying pixels that were present or were snow covered. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VNP43C3 product includes 26 layers containing white-sky albedos and the black-sky albedos for the VIIRS DNB, moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and NIR broadbands. Along with the albedo data for the 13 bands are five ancillary layers for uncertainty, quality, local solar noon, percent finer resolution inputs, and snow cover. A low-resolution image is also available showing retrievals of WSA for the shortwave broadband in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43MA3\nThe VNP43MA3 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) and [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43MA3) Version 1 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA3 product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave, and visible. A low-resolution image is also available showing retrievals of WSA for the shortwave broadband in JPEG format. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces) results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43MA3\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA3 product provides a total of 36 SDS layers including: BSA; WSA; and mandatory quality layers for nine VIIRS moderate bands M1-M5, M7-M8, and M10-M11; as well as near-infrared (NIR); shortwave; and visible broadbands. A low-resolution image is also available showing retrievals of WSA for band M1 in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43IA3\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43IA3) Version 1 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43IA1](https://doi.org/10.5067/VIIRS/VNP43IA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43IA4](https://doi.org/10.5067/VIIRS/VNP43IA4.001)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands: I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces) results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43IA3\nThe VNP43IA3 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43IA3](https://doi.org/10.5067/VIIRS/VNP43IA3.002) and [VJ143IA3](https://doi.org/10.5067/VIIRS/VJ143IA3.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43IA1](https://doi.org/10.5067/VIIRS/VNP43IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43IA4](https://doi.org/10.5067/VIIRS/VNP43IA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D54\nThe VNP43D54 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D54](https://doi.org/10.5067/VIIRS/VNP43D54.002) and [VJ143D54](https://doi.org/10.5067/VIIRS/VJ143D54.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M1 (VNP43D54) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D54 is the BSA for VIIRS band M1 (0.412 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D54\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M1 (VNP43D54) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D54 is the BSA for VIIRS band M1 (0.412 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D61\nThe VNP43D61 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D61](https://doi.org/10.5067/VIIRS/VNP43D61.002) and [VJ143D61](https://doi.org/10.5067/VIIRS/VJ143D61.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M10 (VNP43D61) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D61 is the BSA for VIIRS band M10 (1.61 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D61\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M10 (VNP43D61) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D61 is the BSA for VIIRS band M10 (1.61 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D62\nThe VNP43D62 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D62](https://doi.org/10.5067/VIIRS/VNP43D62.002) and [VJ143D62](https://doi.org/10.5067/VIIRS/VJ143D62.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M11 (VNP43D62) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D62 is the BSA for VIIRS band M11 (2.25 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D62\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M11 (VNP43D62) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D62 is the BSA for VIIRS band M11 (2.25 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D55\nThe VNP43D55 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D55](https://doi.org/10.5067/VIIRS/VNP43D55.002) and [VJ143D55](https://doi.org/10.5067/VIIRS/VJ143D55.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M2 (VNP43D55) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D55 is the BSA for VIIRS band M2 (0.445 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D55\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M2 (VNP43D55) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D55 is the BSA for VIIRS band M2 (0.445 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue). Improvements/Changes from Previous Version * Improved calibration algorithm and better coefficients for entire Suomi NPP mission. * Improved geolocation accuracy and updates to fix outliers around maneuver periods and other events. * Corrections to the aerosol quantity flag (low, average, high) mainly over brighter surfaces in the mid to high latitudes such as desert and tropical vegetation areas. This has an impact on the retrieval of other downstream data products such as VNP13 Vegetation Indices and VNP43 BRDF/Albedo. * Improved cloud mask input product for corrections along coastlines and artifacts from use of coarse resolution climatology data. * Replaced the land/water mask input product with MODIS heritage seven class land/water mask.\n<br>\n\n#### VNP43D56\nThe VNP43D56 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D56](https://doi.org/10.5067/VIIRS/VNP43D56.002) and [VJ143D56](https://doi.org/10.5067/VIIRS/VJ143D56.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M3 (VNP43D56) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D56 is the BSA for VIIRS band M3 (0.488 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D56\nThe NASA/NOAA Suomi National Polar-Orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M3 (VNP43D56) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D56 is the BSA for VIIRS band M3 (0.488 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D57\nThe VNP43D57 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D57](https://doi.org/10.5067/VIIRS/VNP43D57.002) and [VJ143D57](https://doi.org/10.5067/VIIRS/VJ143D57.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M4 (VNP43D57) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D57 is the BSA for VIIRS band M4 (0.555 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D57\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M4 (VNP43D57) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D57 is the BSA for VIIRS band M4 (0.555 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D58\nThe VNP43D58 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D58](https://doi.org/10.5067/VIIRS/VNP43D58.002) and [VJ143D58](https://doi.org/10.5067/VIIRS/VJ143D58.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M5 (VNP43D58) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D58 is the BSA for VIIRS band M5 (0.672 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D58\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M5 (VNP43D58) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D58 is the BSA for VIIRS band M5 (0.672 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D59\nThe VNP43D59 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D59](https://doi.org/10.5067/VIIRS/VNP43D59.002) and [VJ143D59](https://doi.org/10.5067/VIIRS/VJ143D59.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M7 (VNP43D59) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D59 is the BSA for VIIRS band M7 (0.865 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D59\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M7 (VNP43D59) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D59 is the BSA for VIIRS band M7 (0.865 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D60\nThe VNP43D60 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D60](https://doi.org/10.5067/VIIRS/VNP43D60.002) and [VJ143D60](https://doi.org/10.5067/VIIRS/VJ143D60.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M8 (VNP43D60) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D60 is the BSA for VIIRS band M8 (1.240 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D60\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M8 (VNP43D60) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D60 is the BSA for VIIRS band M8 (1.240 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D63\nThe VNP43D63 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D63](https://doi.org/10.5067/VIIRS/VNP43D63.002) and [VJ143D63](https://doi.org/10.5067/VIIRS/VJ143D63.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band VIS (VNP43D63) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D63 is the BSA for the VIIRS visible broadband (0.64 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D63\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band VIS (VNP43D63) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D63 is the BSA for the VIIRS visible broadband (0.64 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D66\nThe VNP43D66 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D66](https://doi.org/10.5067/VIIRS/VNP43D66.002) and [VJ143D66](https://doi.org/10.5067/VIIRS/VJ143D66.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for DNB (VNP43D66) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D66 is the BSA for the VIIRS DNB (0.7 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D66\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Day/Night Band (DNB) product (VNP43D66) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D66 is the BSA for the VIIRS DNB (0.7 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D64\nThe VNP43D64 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D64](https://doi.org/10.5067/VIIRS/VNP43D64.002) and [VJ143D64](https://doi.org/10.5067/VIIRS/VJ143D64.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for NIR (VNP43D64) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D64 is the BSA for the VIIRS NIR broadband (0.865 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D64\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for NIR (VNP43D64) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D64 is the BSA for the VIIRS NIR broadband (0.865 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D65\nThe VNP43D65 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D65](https://doi.org/10.5067/VIIRS/VNP43D65.002) and [VJ143D65](https://doi.org/10.5067/VIIRS/VJ143D65.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for ShortWave (VNP43D65) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D65 is the BSA for the VIIRS shortwave broadband (1.61 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D65\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for ShortWave (VNP43D65) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D65 is the BSA for the VIIRS shortwave broadband (1.61 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D41\nThe VNP43D41 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D41](https://doi.org/10.5067/VIIRS/VNP43D41.002) and [VJ143D41](https://doi.org/10.5067/VIIRS/VJ143D41.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Local Solar Noon product (VNP43D41) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D41 contains the local solar zenith angle at the local solar noon of the representative pixel for the retrieval period. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D41\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Local Solar Noon product (VNP43D41) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product page and in the Algorithm Theoretical Basis Document ([ATBD](https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf)).. VNP43D41 contains the local solar zenith angle at the local solar noon of the representative pixel for the retrieval period. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43C1\nThe VNP43C1 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43C1](https://doi.org/10.5067/VIIRS/VNP43C1.002) and [VJ143C1](https://doi.org/10.5067/VIIRS/VJ143C1.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 1 product (VNP43C1) is derived from the 30 arc second CMG VNP43D Version 1 product suite. VNP43C1 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. VNP43C1 supplies the weighting parameters associated with the RossThick/Li-Sparse-Reciprocal BRDF model that best describes the anisotropy of each pixel, which is used to produce the [VNP43C3](https://doi.org/10.5067/VIIRS/VNP43C3.001) Albedo and [VNP43C4](https://doi.org/10.5067/VIIRS/VNP43C4.001) Nadir BRDF-Adjusted Reflectance (NBAR) products. The highest quality full inversion values are used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the ancillary layers. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VNP43C1 product includes 39 layers containing the three parameters (fiso, fvol, and fgeo) for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. Along with the parameter data for the 13 bands are five ancillary layers for uncertainty, quality, local solar noon, percent finer resolution inputs, and snow cover. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M4, and M3 as a red, green, blue (RGB) image in JPEG format. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect [most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43C1\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VNP43C1) is derived from the 30 arc second CMG VNP43D Version 2 product suite. VNP43C1 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. VNP43C1 supplies the weighting parameters associated with the RossThick/Li-Sparse-Reciprocal BRDF model that best describes the anisotropy of each pixel, which is used to produce the [VNP43C3](https://doi.org/10.5067/VIIRS/VNP43C3.002) Albedo and [VNP43C4](https://doi.org/10.5067/VIIRS/VNP43C4.002) Nadir BRDF-Adjusted Reflectance (NBAR) products. The highest quality full inversion values are used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the ancillary layers. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VNP43C1 product includes 39 layers containing the three parameters (fiso, fvol, and fgeo) for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. Along with the parameter data for the 13 bands are five ancillary layers for uncertainty, quality, local solar noon, percent finer resolution inputs, and snow cover. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M4, and M3 as a red, green, blue (RGB) image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43MA1\nThe VNP43MA1 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) and [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43MA1) Version 1 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43MA1 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M4, and M3 as a red, green, blue (RGB) image in JPEG format. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces) results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43MA1\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M7, and M5 as an RGB image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43IA1\nThe VNP43IA1 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43IA1](https://doi.org/10.5067/VIIRS/VNP43IA1.002) and [VJ143IA1](https://doi.org/10.5067/VIIRS/VJ143IA1.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version 1 product provides kernel weights (parameters) at 500 meter (m) resolution. The VNP43IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VNP43IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth's surface. All VNP43 data products are designed to promote the continuity of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43IA4](https://doi.org/10.5067/VIIRS/VNP43IA4.001)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43IA3](https://doi.org/10.5067/VIIRS/VNP43IA3.001)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA1 data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. Known Issues: The abnormally high activation of the [high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces) results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43IA1\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VNP43IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VNP43IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth’s surface. All VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43IA4](https://doi.org/10.5067/VIIRS/VNP43IA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43IA3](https://doi.org/10.5067/VIIRS/VNP43IA3.002)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA1 data product provides a total of six SDS layers including: three mandatory quality layers for bands I1, I2, and I3 and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for each imagery band. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43MA4\nThe VNP43MA4 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) and [VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low-resolution browse image is also available showing NBAR bands M5, M4, and M3 as a red, green, blue (RGB) image in JPEG format. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces) results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43MA4\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low-resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43IA4\nThe VNP43IA4 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43IA4](https://doi.org/10.5067/VIIRS/VNP43IA4.002) and [VJ143IA4](https://doi.org/10.5067/VIIRS/VJ143IA4.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43IA1](https://doi.org/10.5067/VIIRS/VNP43IA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43IA3](https://doi.org/10.5067/VIIRS/VNP43IA3.001)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces) results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43IA4\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43IA1](https://doi.org/10.5067/VIIRS/VNP43IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) [(VNP43IA3)](https://doi.org/10.5067/VIIRS/VNP43IA3.002). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43C4\nThe VNP43C4 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43C4](https://doi.org/10.5067/VIIRS/VNP43C4.002) and [VJ143C4](https://doi.org/10.5067/VIIRS/VJ143C4.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 1 product (VNP43C4) is derived from the 30 arc second CMG VNP43D Version 1 product suite. VNP43C3 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43C1](https://doi.org/10.5067/VIIRS/VNP43C1.001) to compute NBAR values for the VIIRS Day/Night band (DNB), and moderate resolution bands M1 through M5, M7, M8, M10, and M11. The algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. The quality and inversion status of the majority of the underlying 30 arc second pixels is provided as well as the percentage of the underlying pixels that were present or were snow covered. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VNP43C4 product includes 10 layers containing NBAR values for VIIRS DNB and moderate resolution bands M1 through M5, M7, M8, M10, and M11. Along with the NBAR data for the 10 bands are five ancillary layers for uncertainty, quality, local solar noon, percent finer resolution inputs, and snow cover. A low-resolution browse image is also available showing NBAR bands M5, M4, and M3 as a red, green, blue (RGB) image in JPEG format. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect [most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43C4\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VNP43C4) is derived from the 30 arc second CMG VNP43D Version 2 product suite. [VNP43C3](https://doi.org/10.5067/VIIRS/VNP43C3.002) is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43C1 to compute NBAR values for the VIIRS Day/Night band (DNB), and moderate resolution bands M1 through M5, M7, M8, M10, and M11. The algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. The quality and inversion status of the majority of the underlying 30 arc second pixels is provided as well as the percentage of the underlying pixels that were present or were snow covered. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VNP43C4 product includes 10 layers containing NBAR values for VIIRS DNB and moderate resolution bands M1 through M5, M7, M8, M10, and M11. Along with the NBAR data for the 10 bands are five ancillary layers for uncertainty, quality, local solar noon, percent finer resolution inputs, and snow cover. A low-resolution browse image is also available showing NBAR bands M5, M4, and M3 as a red, green, blue (RGB) image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D80\nThe VNP43D80 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D80](https://doi.org/10.5067/VIIRS/VNP43D80.002) and [VJ143D80](https://doi.org/10.5067/VIIRS/VJ143D80.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M1 (VNP43D80) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D80 is the NBAR for VIIRS band M1 (0.412 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D80\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M1 (VNP43D80) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D80 is the NBAR for VIIRS band M1 (0.412 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D87\nThe VNP43D87 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D87](https://doi.org/10.5067/VIIRS/VNP43D87.002) and [VJ143D87](https://doi.org/10.5067/VIIRS/VJ143D87.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M10 (VNP43D87) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D87 is the NBAR for VIIRS band M10 (1.61 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D87\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M10 (VNP43D87) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D87 is the NBAR for VIIRS band M10 (1.61 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D88\nThe VNP43D88 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D88](https://doi.org/10.5067/VIIRS/VNP43D88.002) and [VJ143D88](https://doi.org/10.5067/VIIRS/VJ143D88.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M11 (VNP43D88) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D88 is the NBAR for VIIRS band M11 (2.25 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D88\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M11 (VNP43D88) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D88 is the NBAR for VIIRS band M11 (2.25 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D81\nThe VNP43D81 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D81](https://doi.org/10.5067/VIIRS/VNP43D81.002) and [VJ143D81](https://doi.org/10.5067/VIIRS/VJ143D81.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M2 (VNP43D81) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D81 is the NBAR for VIIRS band M2 (0.445 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D81\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M2 (VNP43D81) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D81 is the NBAR for VIIRS band M2 (0.445 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D82\nThe VNP43D82 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D82](https://doi.org/10.5067/VIIRS/VNP43D82.002) and [VJ143D82](https://doi.org/10.5067/VIIRS/VJ143D82.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M3 (VNP43D82) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D82 is the NBAR for VIIRS band M3 (0.488 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D82\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M3 (VNP43D82) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D82 is the NBAR for VIIRS band M3 (0.488 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D83\nThe VNP43D83 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D83](https://doi.org/10.5067/VIIRS/VNP43D83.002) and [VJ143D83](https://doi.org/10.5067/VIIRS/VJ143D83.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M4 (VNP43D83) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D83 is the NBAR for VIIRS band M4 (0.555 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D83\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M4 (VNP43D83) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D83 is the NBAR for VIIRS band M4 (0.555 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D84\nThe VNP43D84 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D84](https://doi.org/10.5067/VIIRS/VNP43D84.002) and [VJ143D84](https://doi.org/10.5067/VIIRS/VJ143D84.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M5 (VNP43D84) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D84 is the NBAR for VIIRS band M5 (0.672 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D84\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M5 (VNP43D84) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D84 is the NBAR for VIIRS band M5 (0.672 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D85\nThe VNP43D85 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D85](https://doi.org/10.5067/VIIRS/VNP43D85.002) and [VJ143D85](https://doi.org/10.5067/VIIRS/VJ143D85.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M7 (VNP43D85) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D85 is the NBAR for VIIRS band M7 (0.865 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D85\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M7 (VNP43D85) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D85 is the NBAR for VIIRS band M7 (0.865 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D86\nThe VNP43D86 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D86](https://doi.org/10.5067/VIIRS/VNP43D86.002) and [VJ143D86](https://doi.org/10.5067/VIIRS/VJ143D86.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M8 (VNP43D86) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D86 is the NBAR for VIIRS band M8 (1.240 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D86\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M8 (VNP43D86) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D86 is the NBAR for VIIRS band M8 (1.240 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D89\nThe VNP43D89 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D89](https://doi.org/10.5067/VIIRS/VNP43D89.002) and [VJ143D89](https://doi.org/10.5067/VIIRS/VJ143D89.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Day/Night Band (DNB) product (VNP43D89) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D89 is the NBAR for VIIRS DNB (0.7 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D89\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Day/Night Band (DNB) product (VNP43D89) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D89 is the NBAR for VIIRS DNB (0.7 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D01\nThe VNP43D01 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D01](https://doi.org/10.5067/VIIRS/VNP43D01.002) and [VJ143D01](https://doi.org/10.5067/VIIRS/VJ143D01.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M1 product (VNP43D01) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D01 is the BRDF isotropic parameter for VIIRS band M1 (0.412 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D01\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M1 product (VNP43D01) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D01 is the BRDF isotropic parameter for VIIRS band M1 (0.412 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D22\nThe VNP43D22 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D22](https://doi.org/10.5067/VIIRS/VNP43D22.002) and [VJ143D22](https://doi.org/10.5067/VIIRS/VJ143D22.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M10 product (VNP43D22) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D22 is the BRDF isotropic parameter for VIIRS band M10 (1.61 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D22\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M10 product (VNP43D22) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D22 is the BRDF isotropic parameter for VIIRS band M10 (1.61 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D25\nThe VNP43D25 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D25](https://doi.org/10.5067/VIIRS/VNP43D25.002) and [VJ143D25](https://doi.org/10.5067/VIIRS/VJ143D25.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M11 product (VNP43D25) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D25 is the BRDF isotropic parameter for VIIRS band M11 (2.25 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D25\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M11 product (VNP43D25) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D25 is the BRDF isotropic parameter for VIIRS band M11 (2.25 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D04\nThe VNP43D04 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D04](https://doi.org/10.5067/VIIRS/VNP43D04.002) and [VJ143D04](https://doi.org/10.5067/VIIRS/VJ143D04.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M2 product (VNP43D04) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D04 is the BRDF isotropic parameter for VIIRS band M2 (0.445 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M2. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D04\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M2 product (VNP43D04) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D04 is the BRDF isotropic parameter for VIIRS band M2 (0.445 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D07\nThe VNP43D07 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D07](https://doi.org/10.5067/VIIRS/VNP43D07.002) and [VJ143D07](https://doi.org/10.5067/VIIRS/VJ143D07.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M3 product (VNP43D07) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D07 is the BRDF isotropic parameter for VIIRS band M3 (0.488 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M3. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D07\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M3 product (VNP43D07) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D07 is the BRDF isotropic parameter for VIIRS band M3 (0.488 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D10\nThe VNP43D10 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D10](https://doi.org/10.5067/VIIRS/VNP43D10.002) and [VJ143D10](https://doi.org/10.5067/VIIRS/VJ143D10.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M4 product (VNP43D10) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D10 is the BRDF isotropic parameter for VIIRS band M4 (0.555 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M4. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D10\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M4 product (VNP43D10) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D10 is the BRDF isotropic parameter for VIIRS band M4 (0.555 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D13\nThe VNP43D13 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D13](https://doi.org/10.5067/VIIRS/VNP43D13.002) and [VJ143D13](https://doi.org/10.5067/VIIRS/VJ143D13.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M5 product (VNP43D13) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D13 is the BRDF isotropic parameter for VIIRS band M5 (0.672 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M5. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D13\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M5 product (VNP43D13) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D13 is the BRDF isotropic parameter for VIIRS band M5 (0.672 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D16\nThe VNP43D16 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D16](https://doi.org/10.5067/VIIRS/VNP43D16.002) and [VJ143D16](https://doi.org/10.5067/VIIRS/VJ143D16.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M7 product (VNP43D16) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D16 is the BRDF isotropic parameter for VIIRS band M7 (0.865 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M7. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D16\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M7 product (VNP43D16) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D16 is the BRDF isotropic parameter for VIIRS band M7 (0.865 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D19\nThe VNP43D19 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D19](https://doi.org/10.5067/VIIRS/VNP43D19.002) and [VJ143D19](https://doi.org/10.5067/VIIRS/VJ143D19.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M8 product (VNP43D19) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D19 is the BRDF isotropic parameter for VIIRS band M8 (1.240 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M8. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D19\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M8 product (VNP43D19) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D19 is the BRDF isotropic parameter for VIIRS band M8 (1.240 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M8. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D37\nThe VNP43D37 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D37](https://doi.org/10.5067/VIIRS/VNP43D37.002) and [VJ143D37](https://doi.org/10.5067/VIIRS/VJ143D37.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Day/Night Band (DNB) product (VNP43D37) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D37 is the BRDF isotropic parameter for the VIIRS DNB (0.7 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS DNB. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D37\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Day/Night Band (DNB) product (VNP43D37) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D37 is the BRDF isotropic parameter for the VIIRS DNB (0.7 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS DNB. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D31\nThe VNP43D31 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D31](https://doi.org/10.5067/VIIRS/VNP43D31.002) and [VJ143D31](https://doi.org/10.5067/VIIRS/VJ143D31.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Near-Infrared (NIR) broadband product (VNP43D31) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, NIR, and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D31 is the BRDF isotropic parameter for the VIIRS NIR broadband (0.865 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS NIR broadband. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D31\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 NIR product (VNP43D31) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D31 is the BRDF isotropic parameter for the VIIRS NIR broadband (0.865 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS NIR broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D34\nThe VNP43D34 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D34](https://doi.org/10.5067/VIIRS/VNP43D34.002) and [VJ143D34](https://doi.org/10.5067/VIIRS/VJ143D34.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Shortwave product (VNP43D34) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D34 is the BRDF isotropic parameter for the VIIRS shortwave broadband (1.61 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS shortwave broadband. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D34\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Shortwave product (VNP43D34) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D34 is the BRDF isotropic parameter for the VIIRS shortwave broadband (1.61 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS shortwave broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D28\nThe VNP43D28 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D28](https://doi.org/10.5067/VIIRS/VNP43D28.002) and [VJ143D28](https://doi.org/10.5067/VIIRS/VJ143D28.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Visible (VIS) broadband product (VNP43D28) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the VIS, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D28 is the BRDF isotropic parameter for the VIIRS VIS broadband (0.64 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS VIS broadband. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D28\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 VIS product (VNP43D28) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D28 is the BRDF isotropic parameter for the VIIRS visible broadband (0.64 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS visible broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D02\nThe VNP43D02 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D02](https://doi.org/10.5067/VIIRS/VNP43D02.002) and [VJ143D02](https://doi.org/10.5067/VIIRS/VJ143D02.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M1 product (VNP43D02) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D02 is the BRDF volumetric parameter for VIIRS band M1 (0.412 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D02\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M1 product (VNP43D02) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D02 is the BRDF volumetric parameter for VIIRS band M1 (0.412 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D23\nThe VNP43D23 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D23](https://doi.org/10.5067/VIIRS/VNP43D23.002) and [VJ143D23](https://doi.org/10.5067/VIIRS/VJ143D23.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M10 product (VNP43D23) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D23 is the BRDF volumetric parameter for VIIRS band M10 (1.61 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D23\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M10 product (VNP43D23) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D23 is the BRDF volumetric parameter for VIIRS band M10 (1.61 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D26\nThe VNP43D26 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D26](https://doi.org/10.5067/VIIRS/VNP43D26.002) and [VJ143D26](https://doi.org/10.5067/VIIRS/VJ143D26.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M11 product (VNP43D26) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D26 is the BRDF volumetric parameter for VIIRS band M11 (2.25 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D26\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M11 product (VNP43D26) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D26 is the BRDF volumetric parameter for VIIRS band M11 (2.25 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D05\nThe VNP43D05 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D05](https://doi.org/10.5067/VIIRS/VNP43D05.002) and [VJ143D05](https://doi.org/10.5067/VIIRS/VJ143D05.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M2 product (VNP43D05) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D05 is the BRDF volumetric parameter for VIIRS band M2 (0.445 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M2. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D05\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M2 product (VNP43D05) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D05 is the BRDF volumetric parameter for VIIRS band M2 (0.445 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D08\nThe VNP43D08 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D08](https://doi.org/10.5067/VIIRS/VNP43D08.002) and [VJ143D08](https://doi.org/10.5067/VIIRS/VJ143D08.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M3 product (VNP43D08) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D08 is the BRDF volumetric parameter for VIIRS band M3 (0.488 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M3. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D08\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M3 product (VNP43D08) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D08 is the BRDF volumetric parameter for VIIRS band M3 (0.488 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D11\nThe VNP43D11 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D11](https://doi.org/10.5067/VIIRS/VNP43D11.002) and [VJ143D11](https://doi.org/10.5067/VIIRS/VJ143D11.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M4 product (VNP43D11) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D11 is the BRDF volumetric parameter for VIIRS band M4 (0.555 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M4. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D11\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M4 product (VNP43D11) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D11 is the BRDF volumetric parameter for VIIRS band M4 (0.555 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D14\nThe VNP43D14 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D14](https://doi.org/10.5067/VIIRS/VNP43D14.002) and [VJ143D14](https://doi.org/10.5067/VIIRS/VJ143D14.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M5 product (VNP43D14) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D14 is the BRDF volumetric parameter for VIIRS band M5 (0.672 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M5. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D14\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M5 product (VNP43D14) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the product page [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) and in the Algorithm Theoretical Basis Document (ATBD). VNP43D14 is the BRDF volumetric parameter for VIIRS band M5 (0.672 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D17\nThe VNP43D17 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D17](https://doi.org/10.5067/VIIRS/VNP43D17.002) and [VJ143D17](https://doi.org/10.5067/VIIRS/VJ143D17.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M7 product (VNP43D17) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D17 is the BRDF volumetric parameter for VIIRS band M7 (0.865 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M7. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D17\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M7 product (VNP43D17) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D17 is the BRDF volumetric parameter for VIIRS band M7 (0.865 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D20\nThe VNP43D20 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D20](https://doi.org/10.5067/VIIRS/VNP43D20.002) and [VJ143D20](https://doi.org/10.5067/VIIRS/VJ143D20.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M8 product (VNP43D20) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D20 is the BRDF volumetric parameter for VIIRS band M8 (1.240 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M8. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D20\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M8 product (VNP43D20) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D20 is the BRDF volumetric parameter for VIIRS band M8 (1.240 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M8. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D38\nThe VNP43D38 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D38](https://doi.org/10.5067/VIIRS/VNP43D38.002) and [VJ143D38](https://doi.org/10.5067/VIIRS/VJ143D38.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Day/Night Band (DNB) product (VNP43D38) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D38 is the BRDF volumetric parameter for the VIIRS DNB (0.7 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS DNB. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D38\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Day/Night Band (DNB) product (VNP43D38) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D38 is the BRDF volumetric parameter for the VIIRS DNB (0.7 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS DNB. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D32\nThe VNP43D32 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D32](https://doi.org/10.5067/VIIRS/VNP43D32.002) and [VJ143D32](https://doi.org/10.5067/VIIRS/VJ143D32.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Near-Infrared (NIR) broadband product (VNP43D32) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, NIR, and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D32 is the BRDF volumetric parameter for the VIIRS NIR broadband (0.865 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS NIR broadband. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D32\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 NIR product (VNP43D32) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D32 is the BRDF volumetric parameter for the VIIRS NIR broadband (0.865 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS NIR broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D35\nThe VNP43D35 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D35](https://doi.org/10.5067/VIIRS/VNP43D35.002) and [VJ143D35](https://doi.org/10.5067/VIIRS/VJ143D35.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Shortwave product (VNP43D35) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D35 is the BRDF volumetric parameter for VIIRS shortwave broadband (1.61 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS shortwave broadband. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D35\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Shortwave product (VNP43D35) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D35 is the BRDF volumetric parameter for VIIRS shortwave broadband (1.61 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS shortwave broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D29\nThe VNP43D29 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D29](https://doi.org/10.5067/VIIRS/VNP43D29.002) and [VJ143D29](https://doi.org/10.5067/VIIRS/VJ143D29.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Visible (VIS) broadband product (VNP43D29) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the VIS, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D29 is the BRDF volumetric parameter for the VIIRS VIS broadband (0.64 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS VIS broadband. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D29\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 VIS product (VNP43D29) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D29 is the BRDF volumetric parameter for the VIIRS visible broadband (0.64 μm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS visible broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D03\nThe VNP43D03 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D03](https://doi.org/10.5067/VIIRS/VNP43D03.002) and [VJ143D03](https://doi.org/10.5067/VIIRS/VJ143D03.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M1 product (VNP43D03) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D03 is the BRDF geometric parameter for VIIRS band M1 (0.412 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D03\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M1 product (VNP43D03) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D03 is the BRDF geometric parameter for VIIRS band M1 (0.412 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D24\nThe VNP43D24 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D24](https://doi.org/10.5067/VIIRS/VNP43D24.002) and [VJ143D24](https://doi.org/10.5067/VIIRS/VJ143D24.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M10 product (VNP43D24) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D24 is the BRDF geometric parameter for VIIRS band M10 (1.61 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D24\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M10 product (VNP43D24) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D24 is the BRDF geometric parameter for VIIRS band M10 (1.61 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D27\nThe VNP43D27 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D27](https://doi.org/10.5067/VIIRS/VNP43D27.002) and [VJ143D27](https://doi.org/10.5067/VIIRS/VJ143D27.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M11 product (VNP43D27) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D27 is the BRDF geometric parameter for VIIRS band M11 (2.25 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D27\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M11 product (VNP43D27) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D27 is the BRDF geometric parameter for VIIRS band M11 (2.25 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D06\nThe VNP43D06 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D06](https://doi.org/10.5067/VIIRS/VNP43D06.002) and [VJ143D06](https://doi.org/10.5067/VIIRS/VJ143D06.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M2 product (VNP43D06) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D06 is the BRDF geometric parameter for VIIRS band M2 (0.445 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M2. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D06\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M2 product (VNP43D06) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D06 is the BRDF geometric parameter for VIIRS band M2 (0.445 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D09\nThe VNP43D09 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D09](https://doi.org/10.5067/VIIRS/VNP43D09.002) and [VJ143D09](https://doi.org/10.5067/VIIRS/VJ143D09.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M3 product (VNP43D09) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D09 is the BRDF geometric parameter for VIIRS band M3 (0.488 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M3. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D09\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M3 product (VNP43D09) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D09 is the BRDF geometric parameter for VIIRS band M3 (0.488 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D12\nThe VNP43D12 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D12](https://doi.org/10.5067/VIIRS/VNP43D12.002) and [VJ143D12](https://doi.org/10.5067/VIIRS/VJ143D12.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M4 product (VNP43D12) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D12 is the BRDF geometric parameter for VIIRS band M4 (0.555 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M4. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D12\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M4 product (VNP43D12) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D12 is the BRDF geometric parameter for VIIRS band M4 (0.555 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D15\nThe VNP43D15 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D15](https://doi.org/10.5067/VIIRS/VNP43D15.002) and [VJ143D15](https://doi.org/10.5067/VIIRS/VJ143D15.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M5 product (VNP43D15) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D15 is the BRDF geometric parameter for VIIRS band M5 (0.672 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M5. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D15\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M5 product (VNP43D15) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D15 is the BRDF geometric parameter for VIIRS band M5 (0.672 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D18\nThe VNP43D18 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D18](https://doi.org/10.5067/VIIRS/VNP43D18.002) and [VJ143D18](https://doi.org/10.5067/VIIRS/VJ143D18.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M7 product (VNP43D18) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D18 is the BRDF geometric parameter for VIIRS band M7 (0.865 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M7. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D18\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M7 product (VNP43D18) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D18 is the BRDF geometric parameter for VIIRS band M7 (0.865 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D21\nThe VNP43D21 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D21](https://doi.org/10.5067/VIIRS/VNP43D21.002) and [VJ143D21](https://doi.org/10.5067/VIIRS/VJ143D21.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M8 product (VNP43D21) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D21 is the BRDF geometric parameter for VIIRS band M8 (1.240 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M8. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D21\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M8 product (VNP43D21) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D21 is the BRDF geometric parameter for VIIRS band M8 (1.240 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M8. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D39\nThe VNP43D39 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D39](https://doi.org/10.5067/VIIRS/VNP43D39.002) and [VJ143D39](https://doi.org/10.5067/VIIRS/VJ143D39.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Day-Night Band (DNB) product (VNP43D39) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D39 is the BRDF geometric parameter for the VIIRS DNB (0.7 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS DNB. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D39\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Day/Night Band (DNB) product (VNP43D39) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D39 is the BRDF geometric parameter for the VIIRS DNB (0.7 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS DNB. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D33\nThe VNP43D33 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D33](https://doi.org/10.5067/VIIRS/VNP43D33.002) and [VJ143D33](https://doi.org/10.5067/VIIRS/VJ143D33.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Near-Infrared (NIR) broadband product (VNP43D33) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, NIR, and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D33 is the BRDF geometric parameter for the VIIRS NIR broadband (0.865 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS NIR broadband. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D33\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 NIR product (VNP43D33) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D33 is the BRDF geometric parameter for the VIIRS NIR broadband (0.865 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS NIR broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D36\nThe VNP43D36 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D36](https://doi.org/10.5067/VIIRS/VNP43D36.002) and [VJ143D36](https://doi.org/10.5067/VIIRS/VJ143D36.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Shortwave product (VNP43D36) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D36 is the BRDF geometric parameter for VIIRS shortwave broadband (1.61 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS shortwave broadband. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D36\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Shortwave product (VNP43D36) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D36 is the BRDF geometric parameter for VIIRS shortwave broadband (1.61 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS shortwave broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D30\nThe VNP43D30 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D30](https://doi.org/10.5067/VIIRS/VNP43D30.002) and [VJ143D30](https://doi.org/10.5067/VIIRS/VJ143D30.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Visible (VIS) broadband product (VNP43D30) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the VIS, near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D30 is the BRDF geometric parameter for the VIIRS VIS broadband (0.64 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS VIS broadband. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D30\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 VIS product (VNP43D30) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D30 is the BRDF geometric parameter for the VIIRS visible broadband (0.64 μm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS visible broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43MA2\nThe VNP43MA2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) and [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43MA2) Version 1 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VNP43 data products are designed to promote the continuity of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.001)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces) results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43MA2\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11; as well as land water type class flag; snow BRDF albedo class flag; local solar noon; albedo uncertainty and the platform name. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D40\nThe VNP43D40 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D40](https://doi.org/10.5067/VIIRS/VNP43D40.002) and [VJ143D40](https://doi.org/10.5067/VIIRS/VJ143D40.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality product (VNP43D40) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D40 consists of BRDF/Albedo quality information representing the overall quality of each pixel for VIIRS moderate resolution bands M1 through M5, M7, M8, M10, M11, and DNB. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D40\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality product (VNP43D40) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D40 consists of BRDF/Albedo quality information representing the overall quality of each pixel for VIIRS moderate resolution bands M1 through M5, M7, M8, M10, M11, and DNB. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43IA2\nThe VNP43IA2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43IA2](https://doi.org/10.5067/VIIRS/VNP43IA2.002) and [VJ143IA2](https://doi.org/10.5067/VIIRS/VJ143IA2.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 1 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VNP43 data products are designed to promote the continuity of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43IA1](https://doi.org/10.5067/VIIRS/VNP43IA2.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43IA4](https://doi.org/10.5067/VIIRS/VNP43IA4.001)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43IA3](https://doi.org/10.5067/VIIRS/VNP43IA3.001)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces) results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43IA2\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43IA1](https://doi.org/10.5067/VIIRS/VNP43IA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43IA4](https://doi.org/10.5067/VIIRS/VNP43IA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43IA3](https://doi.org/10.5067/VIIRS/VNP43IA3.002)). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D52\nThe VNP43D52 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D52](https://doi.org/10.5067/VIIRS/VNP43D52.002) and [VJ143D52](https://doi.org/10.5067/VIIRS/VJ143D52.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow Status product (VNP43D52) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D52 contains the snow status quality layer, which identifies each pixel as either Snow-free Albedo Retrieved or Snow Albedo Retrieved for the acquisition period. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D52\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow Status product (VNP43D52) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D52 contains the snow status quality layer, which identifies each pixel as either “Snow-free Albedo Retrieved” or “Snow Albedo Retrieved” for the acquisition period. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43C2\nThe VNP43C2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43C2](https://doi.org/10.5067/VIIRS/VNP43C2.002) and [VJ143C1](https://doi.org/10.5067/VIIRS/VJ143C2.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow-free Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 1 product (VNP43C2) is derived from the 30 arc second CMG VNP43D Version 1 product suite. VNP43C2 is generated daily from all available snow-free acquisitions over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. VNP43C2 supplies the weighting parameters associated with the RossThick/Li-Sparse-Reciprocal BRDF model, which is used to produce the [VNP43C3](https://doi.org/10.5067/VIIRS/VNP43C3.001) Albedo and [VNP43C4](https://doi.org/10.5067/VIIRS/VNP43C4.001) Nadir BRDF-Adjusted Reflectance (NBAR) products. The highest quality full inversion values are used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the ancillary layers. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VNP43C2 product includes 39 layers containing the three parameters (fiso, fvol, and fgeo) for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. Along with the parameter data for the 13 bands are four ancillary layers for uncertainty, quality, local solar noon, and percent finer resolution inputs. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect [most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43C2\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow-free Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VNP43C2) is derived from the 30 arc second CMG VNP43D Version 2 product suite. VNP43C2 is generated daily from all available snow-free acquisitions over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. VNP43C2 supplies the weighting parameters associated with the RossThick/Li-Sparse-Reciprocal BRDF model, which is used to produce the [VNP43C3](https://doi.org/10.5067/VIIRS/VNP43C3.002) Albedo and [VNP43C4](https://doi.org/10.5067/VIIRS/VNP43C4.002) Nadir BRDF-Adjusted Reflectance (NBAR) products. The highest quality full inversion values are used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the ancillary layers. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VNP43C2 product includes 39 layers containing the three parameters (fiso, fvol, and fgeo) for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. Along with the parameter data for the 13 bands are four ancillary layers for uncertainty, quality, local solar noon, and percent finer resolution inputs. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D53\nThe VNP43D53 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D53](https://doi.org/10.5067/VIIRS/VNP43D53.002) and [VJ143D53](https://doi.org/10.5067/VIIRS/VJ143D53.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Uncertainty product (VNP43D53) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D53 contains the uncertainty range of each BRDF/Albedo pixel for the retrieval period. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D53\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Uncertainty product (VNP43D53) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D53 contains the uncertainty range of each BRDF/Albedo pixel for the retrieval period. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D42\nThe VNP43D42 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D42](https://doi.org/10.5067/VIIRS/VNP43D42.002) and [VJ143D42](https://doi.org/10.5067/VIIRS/VJ143D42.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M1 product (VNP43D42) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D42 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M1. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D42\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M1 product (VNP43D42) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D42 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D49\nThe VNP43D49 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D49](https://doi.org/10.5067/VIIRS/VNP43D49.002) and [VJ143D49](https://doi.org/10.5067/VIIRS/VJ143D49.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M10 product (VNP43D49) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D49 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M10. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D49\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M10 product (VNP43D49) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D49 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M10. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D50\nThe VNP43D50 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D50](https://doi.org/10.5067/VIIRS/VNP43D50.002) and [VJ143D50](https://doi.org/10.5067/VIIRS/VJ143D50.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M11 product (VNP43D50) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D50 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M11. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D50\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M11 product (VNP43D50) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D50 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M11. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D43\nThe VNP43D43 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D43](https://doi.org/10.5067/VIIRS/VNP43D43.002) and [VJ143D43](https://doi.org/10.5067/VIIRS/VJ143D43.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M2 product (VNP43D43) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D43 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M2. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D43\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M2 product (VNP43D43) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D43 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D44\nThe VNP43D44 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D44](https://doi.org/10.5067/VIIRS/VNP43D44.002) and [VJ143D44](https://doi.org/10.5067/VIIRS/VJ143D44.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M3 product (VNP43D44) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D44 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M3. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D44\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M3 product (VNP43D44) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D44 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D45\nThe VNP43D45 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D45](https://doi.org/10.5067/VIIRS/VNP43D45.002) and [VJ143D45](https://doi.org/10.5067/VIIRS/VJ143D45.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M4 product (VNP43D45) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D45 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M4. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D45\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M4 product (VNP43D45) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D45 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D46\nThe VNP43D46 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D46](https://doi.org/10.5067/VIIRS/VNP43D46.002) and [VJ143D46](https://doi.org/10.5067/VIIRS/VJ143D46.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M5 product (VNP43D46) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D46 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M5. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D46\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M5 product (VNP43D46) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D46 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D47\nThe VNP43D47 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D47](https://doi.org/10.5067/VIIRS/VNP43D47.002) and [VJ143D47](https://doi.org/10.5067/VIIRS/VJ143D47.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M7 product (VNP43D47) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D47 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M7. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D47\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M7 product (VNP43D47) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D47 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D48\nThe VNP43D48 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D48](https://doi.org/10.5067/VIIRS/VNP43D48.002) and [VJ143D48](https://doi.org/10.5067/VIIRS/VJ143D48.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M8 product (VNP43D48) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D48 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M8. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D48\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M8 product (VNP43D48) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D48 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M8.\n<br>\n\n#### VNP43D51\nThe VNP43D51 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D51](https://doi.org/10.5067/VIIRS/VNP43D51.002) and [VJ143D51](https://doi.org/10.5067/VIIRS/VJ143D51.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Day/Night Band (DNB) product (VNP43D51) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D51 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS DNB. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D51\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Day/Night Band (DNB) product (VNP43D51) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D51 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS DNB. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D67\nThe VNP43D67 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D67](https://doi.org/10.5067/VIIRS/VNP43D67.002) and [VJ143D67](https://doi.org/10.5067/VIIRS/VJ143D67.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M1 (VNP43D67) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D67 is the WSA for VIIRS band M1 (0.412 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D67\nThe Suomi National Polar-Orbiting Partnership (Suomi NPP) NASA Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M1 (VNP43D67) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D67 is the WSA for VIIRS band M1 (0.412 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D74\nThe VNP43D74 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D74](https://doi.org/10.5067/VIIRS/VNP43D74.002) and [VJ143D74](https://doi.org/10.5067/VIIRS/VJ143D74.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M10 (VNP43D74) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D74 is the WSA for VIIRS band M10 (1.61 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D74\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M10 (VNP43D74) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D74 is the WSA for VIIRS band M10 (1.61 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D75\nThe VNP43D75 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D75](https://doi.org/10.5067/VIIRS/VNP43D75.002) and [VJ143D75](https://doi.org/10.5067/VIIRS/VJ143D75.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M11 (VNP43D75) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D75 is the WSA for VIIRS band M11 (2.25 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D75\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M11 (VNP43D75) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D75 is the WSA for VIIRS band M11 (2.25 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D68\nThe VNP43D68 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D68](https://doi.org/10.5067/VIIRS/VNP43D68.002) and [VJ143D68](https://doi.org/10.5067/VIIRS/VJ143D68.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M2 (VNP43D68) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D68 is the WSA for VIIRS band M2 (0.445 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D68\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M2 (VNP43D68) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D68 is the WSA for VIIRS band M2 (0.445 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D69\nThe VNP43D69 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D69](https://doi.org/10.5067/VIIRS/VNP43D69.002) and [VJ143D69](https://doi.org/10.5067/VIIRS/VJ143D69.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M3 (VNP43D69) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D69 is the WSA for VIIRS band M3 (0.488 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D69\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M3 (VNP43D69) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D69 is the WSA for VIIRS band M3 (0.488 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D70\nThe VNP43D70 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D70](https://doi.org/10.5067/VIIRS/VNP43D70.002) and [VJ143D70](https://doi.org/10.5067/VIIRS/VJ143D70.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M4 (VNP43D70) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D70 is the WSA for VIIRS band M4 (0.555 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D70\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M4 (VNP43D70) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D70 is the WSA for VIIRS band M4 (0.555 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D71\nThe VNP43D71 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D71](https://doi.org/10.5067/VIIRS/VNP43D71.002) and [VJ143D71](https://doi.org/10.5067/VIIRS/VJ143D71.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M5 (VNP43D71) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D71 is the WSA for VIIRS band M5 (0.672 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D71\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M5 (VNP43D71) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D71 is the WSA for VIIRS band M5 (0.672 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D72\nThe VNP43D72 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D72](https://doi.org/10.5067/VIIRS/VNP43D72.002) and [VJ143D72](https://doi.org/10.5067/VIIRS/VJ143D72.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M7 (VNP43D72) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D72 is the WSA for VIIRS band M7 (0.865 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D72\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M7 (VNP43D72) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D72 is the WSA for VIIRS band M7 (0.865 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D73\nThe VNP43D73 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D73](https://doi.org/10.5067/VIIRS/VNP43D73.002) and [VJ143D73](https://doi.org/10.5067/VIIRS/VJ143D73.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M8 (VNP43D73) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D73 is the WSA for VIIRS band M8 (1.240 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D73\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M8 (VNP43D73) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D73 is the WSA for VIIRS band M8 (1.240 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D76\nThe VNP43D76 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D76](https://doi.org/10.5067/VIIRS/VNP43D76.002) and [VJ143D76](https://doi.org/10.5067/VIIRS/VJ143D76.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band VIS (VNP43D76) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D76 is the WSA for the VIIRS visible broadband (0.64 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D76\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band VIS (VNP43D76) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D76 is the WSA for the VIIRS visible broadband (0.64 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D79\nThe VNP43D79 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D79](https://doi.org/10.5067/VIIRS/VNP43D79.002) and [VJ143D79](https://doi.org/10.5067/VIIRS/VJ143D79.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for Day/Night Band (DNB) product (VNP43D79) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D79 is the WSA for the VIIRS DNB (0.7 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D79\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for Day/Night Band (DNB) product (VNP43D79) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D79 is the WSA for the VIIRS DNB (0.7 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D77\nThe VNP43D77 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D77](https://doi.org/10.5067/VIIRS/VNP43D77.002) and [VJ143D77](https://doi.org/10.5067/VIIRS/VJ143D77.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band NIR (VNP43D77) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D77 is the WSA for the VIIRS NIR broadband (0.865 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D77\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band NIR (VNP43D77) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D77 is the WSA for the VIIRS NIR broadband (0.865 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP43D78\nThe VNP43D78 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D78](https://doi.org/10.5067/VIIRS/VNP43D78.002) and [VJ143D78](https://doi.org/10.5067/VIIRS/VJ143D78.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for ShortWave (VNP43D78) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.001) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D78 is the WSA for the VIIRS shortwave broadband (1.61 μm). Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are **always strongly encouraged** to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43D78\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for ShortWave (VNP43D78) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D78 is the WSA for the VIIRS shortwave broadband (1.61 μm). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue).\n<br>\n\n#### VNP64A1\nThe VNP64A1 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP64A1](https://doi.org/10.5067/VIIRS/VNP64A1.002) Version 2 data product. The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 1 data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1-366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission. VNP64A1 has been released on a limited basis due to concerns over the quality of the data along the edges of inland water bodies and at high latitudes. These regions contain grid cells falsely identified as burned as a result of coarse resolution inputs to the cloud mask used in the generation of the 750 m VIIRS active fire observations. Users are urged to exercise caution when using this [provisional](https://viirsland.gsfc.nasa.gov/Val_overview.html) data in research. The Version 2 burned area product generated with an improved cloud mask was released on October 22, 2024. Users are encouraged to use the improved V002 burned area product. The data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS). Additional information on falsely identified burn areas related to cloud mask artifacts and cropland burning is provided in section 6 of the User Guide.\n<br>\n\n#### VNP64A1\nThe daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 2 data product is a monthly, global gridded 500-meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1–366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission. The data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, and Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format. Known Issues: Additional information on known issues is provided in Section 8 of the User Guide and on the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43DNBA3\nThe VNP43DNBA3 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43DNBA3](https://doi.org/10.5067/VIIRS/VNP43DNBA3.002) and [VJ143DNBA3](https://doi.org/10.5067/VIIRS/VJ143DNBA3.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43DNBA3) Version 1 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43DNBA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43DNBA1](https://doi.org/10.5067/VIIRS/VNP43DNBA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43DNBA4](https://doi.org/10.5067/VIIRS/VNP43DNBA4.001)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43DNBA3](https://doi.org/10.5067/VIIRS/VNP43DNBA3.001)). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43DNBA3 product provides BSA, WSA, and mandatory quality layers for the VIIRS DNB. A low-resolution image is also available showing retrievals of WSA for the shortwave broadband in JPEG format. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces) results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43DNBA3\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43DNBA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43DNBA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43DNBA1](https://doi.org/10.5067/VIIRS/VNP43DNBA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43DNBA4](https://doi.org/10.5067/VIIRS/VNP43DNBA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43DNBA3 product provides BSA, WSA, and mandatory quality layers for the VIIRS DNB. A low-resolution image is also available showing retrievals of WSA for the shortwave broadband in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43DNBA1\nThe VNP43DNBA1 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43DNBA1](https://doi.org/10.5067/VIIRS/VNP43DNBA1.002) and [VJ143DNBA1](https://doi.org/10.5067/VIIRS/VJ143DNBA1.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43DNBA1) Version 1 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43DNBA1 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43DNBA4](https://doi.org/10.5067/VIIRS/VNP43DNBA4.001)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43DNBA3](https://doi.org/10.5067/VIIRS/VNP43DNBA3.001)). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43DNBA1 data product provides two SDS layers for mandatory quality and model parameters representing fiso, fvol, and fgeo for the VIIRS DNB. A low-resolution browse is also available showing BRDF/Albedo parameters for the DNB as a red, green, blue (RGB) image in JPEG format. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces) results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43DNBA1\nThe Suomi National Polar-Orbiting Partnership (Suomi NPP) NASA Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43DNBA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43DNBA1 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43DNBA4](https://doi.org/10.5067/VIIRS/VNP43DNBA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43DNBA3](https://doi.org/10.5067/VIIRS/VNP43DNBA3.002)). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43DNBA1 data product provides two SDS layers for mandatory quality and model parameters representing fiso, fvol, and fgeo for the VIIRS DNB. A low-resolution browse is also available showing BRDF/Albedo parameters for the DNB as a red, green, blue (RGB) image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43DNBA4\nThe VNP43DNBA4 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43DNBA4](https://doi.org/10.5067/VIIRS/VNP43DNBA4.002) and [VJ143DNBA4](https://doi.org/10.5067/VIIRS/VJ143DNBA4.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43DNB4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43DNBA1](https://doi.org/10.5067/VIIRS/VNP43DNBA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry [VNP43DNBA4](https://doi.org/10.5067/VIIRS/VNP43DNBA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43DNBA3](https://doi.org/10.5067/VIIRS/VNP43DNBA3.001)). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43DNBA4 product includes BRDF/Albedo mandatory quality and nadir reflectance for the VIIRS DNB. A low-resolution browse image is also available showing NBAR of the DNB as a red, green, blue (RGB) image in JPEG format. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces) results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43DNBA4\nTThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43DNBA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43DNBA1](https://doi.org/10.5067/VIIRS/VNP43DNBA1.002) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43DNBA3](https://doi.org/10.5067/VIIRS/VNP43DNBA3.002)). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43DNBA4 product includes BRDF/Albedo mandatory quality and nadir reflectance for the VIIRS DNB. A low-resolution browse image is also available showing NBAR of the DNB as a red, green, blue (RGB) image in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43DNBA2\nThe VNP43DNBA2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43DNBA2](https://doi.org/10.5067/VIIRS/VNP43DNBA2.002) and [VJ143DNBA2](https://doi.org/10.5067/VIIRS/VJ143DNBA2.002) Version 2 data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43DNBA2) Version 1 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43DNBA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43DNBA2 product gives information regarding band quality and days of valid observation within a 16-day period for the VIIRS DNB. The VNP43 data products are designed to promote the continuity of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from [VNP43DNBA1](https://doi.org/10.5067/VIIRS/VNP43DNBA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43DNBA4](https://doi.org/10.5067/VIIRS/VNP43DNBA4.001)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43DNBA3](https://doi.org/10.5067/VIIRS/VNP43DNBA3.001)). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43DNBA2 data product provides a total of seven SDS layers, including BRDF/Albedo band quality and days of valid observation within a 16-day period for the VIIRS DNB, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name. Known Issues: The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect (most obvious over arid bright surfaces) results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP43DNBA2\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43DNBA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43DNBA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43DNBA2 product gives information regarding band quality and days of valid observation within a 16-day period for the VIIRS DNB. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry ([VNP43DNBA4](https://doi.org/10.5067/VIIRS/VNP43DNBA4.002)), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) ([VNP43DNBA3](https://doi.org/10.5067/VIIRS/VNP43DNBA3.002)). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43DNBA2 data product provides a total of seven SDS layers, including BRDF/Albedo band quality and days of valid observation within a 16-day period for the VIIRS DNB, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP18C1\nThe VNP18C1 Version 2 is a NASA/NOAA Suomi National Polar-Orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Downward Shortwave Radiation (DSR) Level 3 Climate Modeling Grid (CMG) product. It is produced at 0.05 degree pixel resolution at daily intervals. DSR is the incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is a significant variable in land surface models (LSMs), receiving increased attention due to its importance for the management and operation of solar energy systems. The VNP18 algorithm adapts the narrowband look-up table (LUT) approach to estimate DSR. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination and viewing geometry. The Algorithm Theoretical Basis Document (ATBD) is forthcoming and will provide additional details regarding the methodology used to create the data. The VNP18C1 product is aggregated from VNP18A1 files and includes one set of global 3-hour DSR variables and daily mean DSR.\n<br>\n\n#### VNP18A1\nThe VNP18A1 Version 2 is a NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Downward Shortwave Radiation (DSR) gridded Level 3 product. It is produced at 1 kilometer pixel resolution at daily intervals. DSR is the incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is a significant variable in land surface models (LSMs), receiving increased attention due to its importance for the management and operation of solar energy systems. The VNP18 algorithm adapts the narrowband look-up table (LUT) approach to estimate DSR. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination and viewing geometry. The Algorithm Theoretical Basis Document (ATBD) is forthcoming and will provide additional details regarding the methodology used to create the data. Provided in the VNP18A1 product are variables for instantaneous total, direct, and diffuse DSR, as well as 3-hour DSR, daily mean DSR, and a quality variable.\n<br>\n\n#### VNP47IMG\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) FIre Light Detection Algorithm-2 (FILDA-2) Modified Combustion Efficiency (MCE) Version 2 swath product (VNP47IMG) is produced in 6-minute orbit segments at 375 meter (m) resolution. The VNP47IMG product takes advantage of the rich information that visible band observation conveys at night to assess fire combustion efficiency and enhance fire detection. It also utilizes improvements made in fire detection algorithms to detect smaller and cooler fires. The VNP47IMG product includes 83 layers containing fire detection and retrievals of Radiative Power (FRP), fire Visible Energy Fraction (VEF), and Modified Combustion Efficiency (MCE). Known Issues: Known issues can be found in Section 5 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS&as=5200).\n<br>\n\n#### VNP47MOD\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) FIre Light Detection Algorithm-2 (FILDA-2) Modified Combustion Efficiency (MCE) swath product (VNP47MOD) is produced in 6-minute orbit segments at 750 meter (m) resolution. The VNP47MOD product takes advantage of the rich information that visible band observation conveys at night to assess fire combustion efficiency and enhance fire detection. It also utilizes improvements made in fire detection algorithms to detect smaller and cooler fires. The VNP47MOD product includes 85 layers containing fire detection and retrievals of Radiative Power (FRP), fire Visible Energy Fraction (VEF), and Modified Combustion Efficiency (MCE). Known Issues: Known issues can be found in Section 5 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS&as=5200).\n<br>\n\n#### VNP17A3GF\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Gross and Net Primary Production (NPP) Gap-Filled (VNP17A3GF) Version 2 product is a cumulative composite of Gross Primary Productivity (GPP) values based on the radiation use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VNP17A3GF is a yearly composite at 500 meter (m) spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection. The VNP17A3GF will be generated at the end of each year when the entire yearly 8-day VNP15A2H is available. Hence, the gap-filled VNP17A3GF is the improved VNP17A2, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get VNP17A3GF in near-real time because it will be generated only at the end of a given year. Provided in the VNP17A3GF product are layers for GPP, NPP, along with a quality control layer. A low resolution browse image for GPP is also available for each VNP17A3GF granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP17A2\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) (VNP17A2) Version 2 data product is a cumulative composite of GPP values based on the radiation use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VNP17A2 is an 8-day composite at 500 meter (m) spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection. Provided in the VNP17A2 product are layers for GPP, PSN, along with a quality control layer. A low resolution browse image for GPP is also available for each VNP17A2 granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP17A2GF\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) Gap-Filled (VNP17A2GF) Version 2 product is a cumulative composite of GPP values based on the radiation use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VNP17A2GF is an 8-day composite at 500 meters (m) spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection. The VNP17A2GF will be generated at the end of each year when the entire yearly 8-day VNP15A2H is available. Hence, the gap-filled VNP17A2GF is the improved VNP17A2, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get VNP17A2GF in near-real time because it will be generated only at the end of a given year. Provided in the VNP17A2GF product are layers for GPP, PSN, along with a quality control layer. A low resolution browse image for GPP is also available for each VNP17A2GF granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP19A2\nThe VNP19A2 Version 2 data product is a NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 750 meter (m) pixel using the Rotated Sinusoidal (RSIN) projection. The VNP19A2 product provides the atmospheric properties and view geometry used to calculate the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, VNP19A1 product. Provided in the VNP19A2 product are layers for AOD, AOD uncertainty and QA, injection height of smoke plume, AOD outside of glint area, fine mode fraction, and cosines of solar and view zenith angles, relative azimuth, scattering angle, glint angle, and Angstrom Exponent over the ocean. Known Issues • The spatial resolution and projection of this product are listed incorrectly in the VNP19A2 long name. VNP19A2 uses the RSIN projection and is at 750m resolution. The long name should read “VIIRS/NPP Land Aerosol Optical Depth Daily L2G Global 750m RSIN Grid V002”. This will be rectified in future versions. • MAIAC Lookup Tables (LUTs) are built assuming pseudo-spherical correction in single scattering which has a reduced accuracy for high sun/view zenith angles. A reduced MAIAC performance is expected at solar zenith angles > 78°. • MAIAC may be missing bright salt pans in several world deserts. In such cases, it may generate a persistent high Aerosol Optical Depth (AOD) resulting in missing surface retrievals. • Because of inherent uncertainties of gridding on the coastline, the area of 1-3 pixels from the coastline may contain frequent artifacts in cloud mask (usually over-detection), AOD (higher values) and surface BRF. Users should exercise caution near the coastline as represented by the AOD QA bit 1010 (See Table 5.4 bits 8-11, 1010 -- AOD within +-2km from the coastline is replaced by nearby AOD). • Atmospheric Correction (AC) over detected snow: as MAIAC does not retrieve AOD over snow, it assumes a low climatology AOD=0.05 globally and 0.02 at high elevations (H>4.2km). Over north-central China, which is often heavily polluted and low AOD assumption can lead to a significant bias, we use AOD averaged over mesoscale area of 150km using reliable AOD retrievals over snow-free pixels. Such approach does improve quality of AC as compared to low-AOD assumption as judged by the reduced boundaries and color artefacts, but it does not account for the aerosol variability inside 150km area. • Ice mask is currently unreliable.\n<br>\n\n#### VNP22C2\nThe VNP22C2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP22C2](https://doi.org/10.5067/VIIRS/VNP22C2.002) Version 2 data product. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22C2 data product is derived from time series of the two-band Enhanced Vegetation Index (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 0.05 degree (~5,600 meters) spatial resolution are identified for up to two detected growing cycles per year. Provided in each VNP22C2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22C2 granule. Important information is provided in Section 5 of the User Guide when comparing VNP22C2 with the MCD12C2 data product. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP22Q2\nThe VNP22Q2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP22Q2](https://doi.org/10.5067/VIIRS/VNP22Q2.002) Version 2 data product. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22Q2 data product is derived from time series of the two-band Enhanced Vegetation Index (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year. Provided in each VNP22Q2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22Q2 granule. Important information is provided in Sections 5 of the User Guide when comparing VNP22Q2 with the MCD12Q2 data product. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP19A1\nThe VNP19A1 Version 2 data product is a NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 375 meter (m) and 750 m pixel resolution using the Rotated Sinusoidal (RSIN) projection. The VNP19A1 product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The VIIRS MAIAC Land Surface BRF products provide an estimate of the surface spectral reflectance as it would be measured at ground-level in the absence of atmospheric scattering or absorption. Provided in the VNP19A1 product are layers for surface reflectance, cosines of solar and view zenith angles, relative azimuth, sun azimuth (SAZ), sensor view azimuth (VAZ), scattering angle and glint angle. Volumetric (Fv) and geometric-optics (Fg) kernels of the Ross-Thick Li-Sparse (RTLS) model for the observation geometry are also included. The 750 m surface reflectance layers are produced in cloud-free conditions from bands M1-M5, M7, M8, M10, and M11, whereas the 375 m layers are produced from bands I1-I3. Known Issues • The spatial resolution and projection of this product are listed incorrectly in the VNP19A1 long name. VNP19A1 uses the RSIN projection and is at 375m and 750m resolution. The long name should read “VIIRS/JPSS1 Land Surface BRF Daily L2G Global 375m and 750m RSIN Grid V002”. This will be rectified in future versions. • MAIAC Lookup Tables (LUTs) are built assuming pseudo-spherical correction in single scattering which has a reduced accuracy for high sun/view zenith angles. A reduced MAIAC performance is expected at solar zenith angles > 78°. • MAIAC may be missing bright salt pans in several world deserts. In such cases, it may generate a persistent high Aerosol Optical Depth (AOD) resulting in missing surface retrievals. • Because of inherent uncertainties of gridding on the coastline, the area of 1-3 pixels from the coastline may contain frequent artifacts in cloud mask (usually over-detection), AOD (higher values) and surface BRF. Users should exercise caution near the coastline as represented by the AOD QA bit 1010 (See Table 5.4 bits 8-11, 1010 -- AOD within +-2km from the coastline is replaced by nearby AOD). • Atmospheric Correction (AC) over detected snow: as MAIAC does not retrieve AOD over snow, it assumes a low climatology AOD=0.05 globally and 0.02 at high elevations (H>4.2km). Over north central China, which is often heavily polluted and low AOD assumption can lead to a significant bias, we use AOD averaged over mesoscale area of 150km using reliable AOD retrievals over snow-free pixels. Such approach does improve quality of AC as compared to low-AOD assumption as judged by the reduced boundaries and color artifacts, but it does not account for the aerosol variability inside 150km area. • Ice mask is currently unreliable.\n<br>\n\n#### VNP22C2\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Phenology data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22C2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 0.05 degree (~5,600 meters) spatial resolution are identified for up to two detected growing cycles per year. Provided in each VNP22C2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22C2 granule. Important information is provided in Section 5 of the User Guide when comparing VNP22C2 with the MCD12C2 data product. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP22Q2\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Phenology data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22Q2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year. Provided in each VNP22Q2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22Q2 granule. Important information is provided in Section 5 of the User Guide when comparing VNP22Q2 with the MCD12Q2 data product. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP21\nThe VNP21 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP21](https://doi.org/10.5067/VIIRS/VNP21.002)) and NOAA-20 ([VJ121]( https://doi.org/10.5067/VIIRS/VJ121.002)) satellites. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 1 swath product (VNP21) is produced daily in 6-minute temporal increments of satellite acquisition. The VNP21 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 µm), M15 (10.76 µm), and M16 (12 µm) at a spatial resolution of 750 meters. The VNP21 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD). Provided in the VNP21 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VNP21 granule. Known Issues: Users of VIIRS and MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=707) in the unfiltered VNP21 and MxD21 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact MxD11, MxD21, and VNP21 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the VNP21 and MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in VNP21 and MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the VNP21 and MxD21 LST products. Future versions of the VNP21 and MxD21 products will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * To mitigate the impact of dust in the VNP21 and MxD21 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/QA_WWW/forPage/MOD21_dust_QC_examples.pdf). * VNP21 v001 products utilize the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) as an input. This may result in product latency of a month or more. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP21\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VNP21) is produced daily in 6-minute temporal increments of satellite acquisition. The VNP21 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 µm), M15 (10.76 µm), and M16 (12 µm) at a spatial resolution of 750 meters. The VNP21 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product ([MOD21](https://doi.org/10.5067/MODIS/MOD21.061)) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD). Provided in the VNP21 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VNP21 granule. Known Issues: Users of VIIRS and MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered VNP21 and MxD21 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact MxD11, MxD21, and VNP21 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the VNP21 and MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in VNP21 and MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the VNP21 and MxD21 LST products. Future versions of the VNP21 and MxD21 products will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * To mitigate the impact of dust in the VNP21 and MxD21 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/QA_WWW/forPage/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP21C2\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day Climate Modeling Grid Version 2 product (VNP21C2) combines the daily [VNP21A1D](http://doi.org/10.5067/VIIRS/VNP21A1D.002) and [VNP21A1N](http://doi.org/10.5067/VIIRS/VNP21A1N.002) products over an 8-day compositing period into a single product. The VNP21C2 dataset is an 8-day composite LST&E product at 0.05 degree (~5,600 meter) resolution that uses an algorithm based on a simple-averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud-free VNP21A1D and VNP21A1N daily acquisitions from the 8-day period. Unlike the VNP21A1 datasets where the daytime and nighttime acquisitions are separate products, the VNP21C2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). The VNP21C2 product contains 25 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VNP21C2 granule. Known Issues: Users of VIIRS and MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered VNP21 and MxD21 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact MxD11, MxD21, and VNP21 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the VNP21 and MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in VNP21 and MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the VNP21 and MxD21 LST products. Future versions of the VNP21 and MxD21 products will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * To mitigate the impact of dust in the VNP21 and MxD21 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP21A2\nThe VNP21A2 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP21A2](https://doi.org/10.5067/VIIRS/VNP21A2.002)) and NOAA-20 ([VJ121A2]( https://doi.org/10.5067/VIIRS/VJ121A2.002)) satellites. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day product (VNP21A2) combines the daily [VNP21A1D](http://doi.org/10.5067/VIIRS/VNP21A1D.001) and [VNP21A1N](http://doi.org/10.5067/VIIRS/VNP21A1N.001) products over an 8-day compositing period into a single product. The VNP21A2 dataset is an 8-day composite LST&E product at 1 kilometer resolution that uses an algorithm based on a simple-averaging method. The algorithm calculates the average from all the cloud-free VNP21A1D and VNP21A1N daily acquisitions from the 8-day period. Unlike the VNP21A1 datasets where the daytime and nighttime acquisitions are separate products, the VNP21A2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The VNP21A2 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A2) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VNP21A2 product contains 11 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VNP21A2 granule. Known Issues: Users of VIIRS and MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=707) in the unfiltered VNP21 and MxD21 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact MxD11, MxD21, and VNP21 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the VNP21 and MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in VNP21 and MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the VNP21 and MxD21 LST products. Future versions of the VNP21 and MxD21 products will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * To mitigate the impact of dust in the VNP21 and MxD21 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/QA_WWW/forPage/MOD21_dust_QC_examples.pdf). * VNP21 v001 products utilize the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) as an input. This may result in product latency of a month or more. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP21A2\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day product (VNP21A2) combines the daily [VNP21A1D](http://doi.org/10.5067/VIIRS/VNP21A1D.002) and [VNP21A1N](http://doi.org/10.5067/VIIRS/VNP21A1N.002) products over an 8-day compositing period into a single product. The VNP21A2 dataset is an 8-day composite LST&E product at 1 kilometer resolution that uses an algorithm based on a simple-averaging method. The algorithm calculates the average from all the cloud-free VNP21A1D and VNP21A1N daily acquisitions from the 8-day period. Unlike the VNP21A1 datasets where the daytime and nighttime acquisitions are separate products, the VNP21A2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The VNP21A2 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product ([MOD21A2](https://doi.org/10.5067/MODIS/MOD21A2.061)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). The VNP21A2 product contains 11 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VNP21A2 granule. Known Issues: Users of VIIRS and MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered VNP21 and MxD21 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact MxD11, MxD21, and VNP21 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the VNP21 and MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in VNP21 and MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the VNP21 and MxD21 LST products. Future versions of the VNP21 and MxD21 products will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * To mitigate the impact of dust in the VNP21 and MxD21 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP21C1\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Climate Modeling Grid Version 2 product (VNP21C) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily [VNP21](https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The 0.05 degree (5600 m) dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). The VNP21C1 product contains 25 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VNP21C1 granule. Known Issues: Users of VIIRS and MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered VNP21 and MxD21 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact MxD11, MxD21, and VNP21 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the VNP21 and MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in VNP21 and MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the VNP21 and MxD21 LST products. Future versions of the VNP21 and MxD21 products will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * To mitigate the impact of dust in the VNP21 and MxD21 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP21A1D\nThe VNP21A1D VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP21A1D](https://doi.org/10.5067/VIIRS/VNP21A1D.002)) and NOAA-20 ([VJ121A1D]( https://doi.org/10.5067/VIIRS/VJ121A1D.002)) satellites. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 1 product (VNP21A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily [VNP21](https://doi.org/10.5067/VIIRS/VNP21.001) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VNP21A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1D) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VNP21A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1D granule. Known Issues: Users of VIIRS and MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=707) in the unfiltered VNP21 and MxD21 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact MxD11, MxD21, and VNP21 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the VNP21 and MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in VNP21 and MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the VNP21 and MxD21 LST products. Future versions of the VNP21 and MxD21 products will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * To mitigate the impact of dust in the VNP21 and MxD21 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/QA_WWW/forPage/MOD21_dust_QC_examples.pdf). * VNP21 v001 products utilize the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) as an input. This may result in product latency of a month or more. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP21A1D\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VNP21A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily [VNP21](https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VNP21A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product [(MOD21A1D)](https://doi.org/10.5067/MODIS/MOD21A1D.061) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). The VNP21A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1D granule. Known Issues: Users of VIIRS and MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered VNP21 and MxD21 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact MxD11, MxD21, and VNP21 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the VNP21 and MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in VNP21 and MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the VNP21 and MxD21 LST products. Future versions of the VNP21 and MxD21 products will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * To mitigate the impact of dust in the VNP21 and MxD21 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP21A1N\nThe VNP21A1N VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP21A1N](https://doi.org/10.5067/VIIRS/VNP21A1N.002)) and NOAA-20 ([VJ121A1N]( https://doi.org/10.5067/VIIRS/VJ121A1N.002)) satellites. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 1 product (VNP21A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily [VNP21](https://doi.org/10.5067/VIIRS/VNP21.001) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VNP21A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1N) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs. The VNP21A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1N granule. Known Issues: Users of VIIRS and MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=707) in the unfiltered VNP21 and MxD21 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact MxD11, MxD21, and VNP21 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the VNP21 and MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in VNP21 and MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the VNP21 and MxD21 LST products. Future versions of the VNP21 and MxD21 products will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * To mitigate the impact of dust in the VNP21 and MxD21 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/QA_WWW/forPage/MOD21_dust_QC_examples.pdf). * VNP21 v001 products utilize the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) as an input. This may result in product latency of a month or more. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP21A1N\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VNP21A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily [VNP21](https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The VNP21A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product ([MOD21A1N](https://doi.org/10.5067/MODIS/MOD21A1N.061)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). The VNP21A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1N granule. Known Issues: Users of VIIRS and MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered VNP21 and MxD21 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact MxD11, MxD21, and VNP21 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the VNP21 and MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in VNP21 and MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the VNP21 and MxD21 LST products. Future versions of the VNP21 and MxD21 products will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * To mitigate the impact of dust in the VNP21 and MxD21 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP21C3\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) monthly Climate Modeling Grid Version 2 product (VNP21C3) provides LST&E by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (~5,600 meter) resolution. The VNP21C3 dataset is a monthly composite LST&E product that uses an algorithm based on a simple averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud free [VNP21A1D](http://doi.org/10.5067/VIIRS/VNP21A1D.002) and [VNP21A1N](http://doi.org/10.5067/VIIRS/VNP21A1N.002) daily acquisitions from the monthly period. Unlike the VNP21A1 data sets where the daytime and nighttime acquisitions are separate products, the VNP21C3 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). The VNP21C3 product contains 25 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VNP21C3 granule. Known Issues: Users of VIIRS and MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered VNP21 and MxD21 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact MxD11, MxD21, and VNP21 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the VNP21 and MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in VNP21 and MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the VNP21 and MxD21 LST products. Future versions of the VNP21 and MxD21 products will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * To mitigate the impact of dust in the VNP21 and MxD21 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP15A2H\nThe VNP15A2H VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP15A2H](https://doi.org/10.5067/VIIRS/VNP15A2H.002)) and NOAA-20 ([VJ115A2H]( https://doi.org/10.5067/VIIRS/VJ115A2H.002)) satellites. The Visible Infrared Imaging Radiometer Suite (VIIRS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) Version 1 data product provides information about the vegetative canopy layer at 500 meter resolution (VNP15A2H). The VIIRS sensor is located aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. LAI is an index that quantifies the one-sided leaf area of a canopy, while FPAR is the fraction of incoming solar energy absorbed through photosynthesis at 400 to 700 nanometers. This product is intentionally designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR operational algorithm to promote the continuity of the Earth Observation System (EOS) mission. The VNP15A2H product includes six Science Data Set Layers for the analysis of key factors in LAI/FPAR measurements. These include the LAI and FPAR measurements, quality detail for LAI/FPAR, extra quality detail for FPAR, and the standard deviation for LAI and FPAR. Two low resolution browse images are also available for each VNP15A2H granule: LAI and FPAR. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP15A2H\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) Version 2 data product (VNP15A2H) provides information about the vegetative canopy layer at 500 meter resolution. The VIIRS sensor is located aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. LAI is an index that quantifies the one-sided leaf area of a canopy, while FPAR is the fraction of incoming solar energy absorbed through photosynthesis at 400 to 700 nanometers. This product is intentionally designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR operational algorithm to promote the continuity of the Earth Observation System (EOS) mission. The VNP15A2H product includes six Science Data Set Layers for the analysis of key factors in LAI/FPAR measurements. These include the LAI and FPAR measurements, quality detail for LAI/FPAR, extra quality detail for FPAR, and the standard deviation for LAI and FPAR. Two low resolution browse images are also available for each VNP15A2H granule: LAI and FPAR. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP03MODLL\nThe VNP03MODLL Version 1 data product was decommissioned on July 31, 2025. Forward processing of VNP03MODLL Version 1 data product was discontinued on June 17, 2024. Users are encouraged to use the [VNP03MODLL](https://doi.org/10.5067/VIIRS/VNP03MODLL.002) Version 2 and [VJ103MODLL](https://doi.org/10.5067/VIIRS/VJ103MODLL.021) Version 2.1 data products. The Visible Infrared Imaging Radiometer Suite (VIIRS) Moderate Resolution Terrain Correction Geolocation (VNP03MODLL) Version 1 product from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth's geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60° North to 60° South. VNP03MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). Provided in the VNP03MODLL product are layers for height, latitude, and longitude. These Science Data Sets (SDS) layers are used in conjunction with the [VNP14](https://doi.org/10.5067/viirs/vnp14.001) swath product for accurate geolocation information. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QS/new/pages.cgi?name=known_issues).\n<br>\n\n#### VNP03MODLL\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) Moderate Resolution Terrain Correction Geolocation (VNP03MODLL) Version 2 product from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth’s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60° North to 60° South. VNP03MODLL is a terrain correction geolocation product that provides the spatial location for various VIIRS data products. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). Provided in the VNP03MODLL product are layers for height, latitude, and longitude. These Science Data Sets (SDS) layers / Variables are used in conjunction with the [VNP14](https://doi.org/10.5067/viirs/vnp14.002) swath product for accurate geolocation information. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP18C2\nThe VNP18C2 Version 2 is a NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Photosynthetically Active Radiation (PAR) Level 3 CMG product produced at 0.05 degree pixel resolution at daily intervals. PAR is incident solar radiation in the visible spectrum (400-700 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The VNP18 algorithm adapts the narrowband look-up table (LUT) approach to estimate DSR. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD). The VNP18C2 product is aggregated from VNP18A2 files and includes one set of global 3-hour PAR variables and one set of daily mean PAR.\n<br>\n\n#### VNP18A2\nThe VNP18A2 Version 2 is a NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Photosynthetically Active Radiation (PAR) gridded Level 3 product. It is produced at 1 kilometer pixel resolution at daily intervals. PAR is the incident solar radiation in the visible spectrum (400-700 nanometers) and is a significant variable in land surface models (LSMs), receiving increased attention due to its importance for the management and operation of solar energy systems. The VNP18 algorithm adapts the narrowband look-up table (LUT) approach to estimate PAR. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination and viewing geometry. The Algorithm Theoretical Basis Document (ATBD) is forthcoming and will provide additional details regarding the methodology used to create the data. Provided in the VNP18A2 product are variables for instantaneous total, direct, and diffuse PAR, as well as 3-hour PAR, daily mean PAR, and a quality variable.\n<br>\n\n#### VNP09A1\nThe VNP09A1 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP09A1](https://doi.org/10.5067/VIIRS/VNP09A1.002)) and NOAA-20 ([VJ109A1]( https://doi.org/10.5067/VIIRS/VJ109A1.002)) satellites. The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance (VNP09A1) Version 1 composite product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor for nine moderate resolution bands (M1 - M5, M7, M8, M10, M11) at nominal 1 kilometer resolution (~926 meter). The 1 kiliometer dataset is derived through resampling the native 750 meter VIIRS resolution in the Level 2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period that is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. Included in the product along with the nine reflectance bands are day of year, reflectance band quality, control, reflectance state quality assurance, relative azimuth angle, sensor zenith angle, and solar zenith angle layers. Known Issues: The VIIRS Land Surface Reflectance product (VNP09) may incorrectly flag retrievals as \"High Aerosol\" over brighter surfaces and at higher view angles. This will impact the downstream VIIRS BRDF/Albedo (VNP43) and Vegetation Index (VNP13) data products which use the aerosol quantity flag to screen out high aerosol values. * Corrections will be implemented in Collection 2 reprocessing in 2022. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and in Section 4.0 \"Caveats and Known Problems\" of the User Guide.\n<br>\n\n#### VNP09A1\nThe 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance (VNP09A1) Version 2 composite product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor for nine moderate resolution bands (M1 - M5, M7, M8, M10, M11) at nominal 1 kilometer resolution (~926 meter). The 1 kiliometer dataset is derived through resampling the native 750 meter VIIRS resolution in the Level 2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period that is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. Included in the product along with the nine reflectance bands are day of year, reflectance band quality, control, reflectance state quality assurance, relative azimuth angle, sensor zenith angle, and solar zenith angle layers. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and in Section 4.0 of the User Guide.\n<br>\n\n#### VNP09H1\nThe VNP09H1 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP09H1](https://doi.org/10.5067/VIIRS/VNP09H1.002)) and NOAA-20 ([VJ109H1]( https://doi.org/10.5067/VIIRS/VJ109H1.002)) satellites. The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) Surface Reflectance (VNP09H1) Version 1 composite product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor for three imagery bands (I1, I2, I3) at nominal 500 meter resolution (~463 meter). The 500 meter dataset is derived through resampling the native 375 meter VIIRS resolution in the L2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period, which is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. The three reflectance bands, this product includes a state quality assurance (QA) layer and a reflectance band quality layer. Known Issues: The VIIRS Land Surface Reflectance product (VNP09) may incorrectly flag retrievals as 'High Aerosol' over brighter surfaces and at higher view angles. This will impact the downstream VIIRS BRDF/Albedo (VNP43) and Vegetation Index (VNP13) data products which use the aerosol quantity flag to screen out high aerosol values. * Corrections will be implemented in Collection 2 reprocessing in 2022. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and in Section 4.0 \"Caveats and Known Problems\" of the User Guide.\n<br>\n\n#### VNP09H1\nThe 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) Surface Reflectance (VNP09H1) Version 2 composite product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor for three imagery bands (I1, I2, I3) at nominal 500 meter resolution (~463 meter). The 500 meter dataset is derived through resampling the native 375 meter VIIRS resolution in the L2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period, which is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. This product includes three reflectance bands, a state quality assurance (QA) layer, and a reflectance band quality layer. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and in Section 4.0 of the User Guide.\n<br>\n\n#### VNP09GA\nThe VNP09GA VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP09GA](https://doi.org/10.5067/VIIRS/VNP09GA.002)) and NOAA-20 ([VJ109GA]( https://doi.org/10.5067/VIIRS/VJ109GA.002)) satellites. The Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance (VNP09GA) Version 1 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~ 463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands,the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle,solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. Known Issues: The VIIRS Land Surface Reflectance product (VNP09) may incorrectly flag retrievals as 'High Aerosol' over brighter surfaces and at higher view angles. This will impact the downstream VIIRS BRDF/Albedo (VNP43) and Vegetation Index (VNP13) data products which use the aerosol quantity flag to screen out high aerosol values. * Corrections will be implemented in Collection 2 reprocessing in 2022. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and in Section 4.0 \"Caveats and Known Problems\" of the User Guide.\n<br>\n\n#### VNP09GA\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance (VNP09GA) Version 2 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets are derived through resampling the native 375 meter and 750 meter VIIRS resolutions, respectively, in the Level 2 input product. These bands are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm are top-of-atmosphere reflectance for the VIIRS visible bands, the VIIRS cloud mask and aerosol product, aerosol optical thickness and atmospheric data obtained from the NOAA National Centers for Environmental Prediction (NCEP) reanalysis system. Along with the twelve reflectance bands are reflectance band quality, sensor azimuth angle, solar azimuth angle, sensor zenith angle, solar zenith angle, and observations layers. The reflectance layers from the VNP09GA data product are used as input data for many of the VIIRS land products. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and in Section 4.0 of the User Guide.\n<br>\n\n#### VNP09CMG\nThe VNP09CMG VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP09CMG](https://doi.org/10.5067/VIIRS/VNP09CMG.002)) and NOAA-20 ([VJ109CMG]( https://doi.org/10.5067/VIIRS/VJ109CMG.002)) satellites. The Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance Climate Modeling Grid (VNP09CMG) Version 1 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. This product uses a weighted average of the best quality observation and is formatted as a CMG for use in climate simulation models. This product includes the twelve reflectance bands, five moderate resolution brightness temperature bands (M12-M16) and information layers representing relative azimuth angle, sensor zenith angle, solar zenith angle, reflectance band quality, time of day, and number mapping. Known Issues: The VIIRS Land Surface Reflectance product (VNP09) may incorrectly flag retrievals as 'High Aerosol' over brighter surfaces and at higher view angles. This will impact the downstream VIIRS BRDF/Albedo (VNP43) and Vegetation Index (VNP13) data products which use the aerosol quantity flag to screen out high aerosol values. * Corrections will be implemented in Collection 2 reprocessing in 2022. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and in Section 4.0 \"Caveats and Known Problems\" of the User Guide.\n<br>\n\n#### VNP09CMG\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance Climate Modeling Grid (VNP09CMG) Version 2 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. This product uses a weighted average of the best quality observation and is formatted as a CMG for use in climate simulation models. This product includes the twelve reflectance bands, five moderate resolution brightness temperature bands (M12-M16) and information layers representing relative azimuth angle, sensor zenith angle, solar zenith angle, reflectance band quality, time of day, and number mapping. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and in Section 4.0 of the User Guide.\n<br>\n\n#### VNP14A1\nThe VNP14A1 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP14A1](https://doi.org/10.5067/VIIRS/VNP14A1.002)) and NOAA-20 ([VJ114A1]( https://doi.org/10.5067/VIIRS/VJ114A1.002)) satellites. The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies/Fire (VNP14A1) Version 1 data product provides daily information about active fires and other thermal anomalies. The VNP14A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VNP14 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite. The VNP14A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP14A1\nThe daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies and Fire (VNP14A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VNP14A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period. The VNP14 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire product suite. The VNP14A1 product provides a total of four Science Dataset (SDS) layers for the confidence of fire, maximum fire radiative power (FRP), quality assessment (QA), and position of fire within scan. Each data product file is provided in HDF-EOS5 format. A low resolution browse is also provided showing the fire mask layer with a color map applied in JPEG format. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP14\nThe VNP14 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP14](https://doi.org/10.5067/VIIRS/VNP14.002)) and NOAA-20 ([VJ114]( https://doi.org/10.5067/VIIRS/VJ114.002)) satellites. The Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies (VNP14) Version 1 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. The VNP14 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g. atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14 product is also used to generate higher-level fire data products. Use of the [VNP03MODLL](https://doi.org/10.5067/viirs/vnp03modll.001) data product is required to apply accurate geolocation information to the VNP14 Science Datasets (SDS). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP14\nThe Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies (VNP14) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies. The VNP14 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VNP14 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16. Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VNP14 product is also used to generate higher-level fire data products. Use of the [VNP03MODLL](https://doi.org/10.5067/viirs/vnp03modll.002) data product is required to apply accurate geolocation information to the VNP14 Science Datasets (SDS) / Variables. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP13C1\nThe VNP13C1 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP13C1](https://doi.org/10.5067/VIIRS/VNP13C1.002)) and NOAA-20 ([VJ113C1]( https://doi.org/10.5067/VIIRS/VJ113C1.002)) satellites. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13C1) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VNP13C1 product: EVI and NDVI. Known Issues: The incorrect representation of the aerosol quantities (low, average, high) in the VIIRS Land Surface Reflectance product (VNP09) may have impacted VNP13 Vegetation Index data products particularly over arid bright surfaces. * Corrections were implemented in 2023 with the release of the Version 2 Vegetation Index data products. Users are encouraged to use the improved Version 2 data. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP13C1\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13C1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VNP13C1 product: EVI and NDVI. Known Issues: Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide and ATBD.\n<br>\n\n#### VNP13A2\nThe VNP13A2 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP13A2](https://doi.org/10.5067/VIIRS/VNP13A2.002)) and NOAA-20 ([VJ113A2]( https://doi.org/10.5067/VIIRS/VJ113A2.002)) satellites. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A2) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles, and a quality layer. Two low resolution browse images are also available for each VNP13A2 product: EVI and NDVI. Known Issues: The incorrect representation of the aerosol quantities (low, average, high) in the VIIRS Land Surface Reflectance product (VNP09) may have impacted VNP13 Vegetation Index data products particularly over arid bright surfaces. * Corrections were implemented in 2023 with the release of the Version 2 Vegetation Index data products. Users are encouraged to use the improved Version 2 data. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP13A2\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles, and a quality layer. Two low resolution browse images are also available for each VNP13A2 product: EVI and NDVI. Known Issues: Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide and ATBD.\n<br>\n\n#### VNP13A1\nThe VNP13A1 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP13A1](https://doi.org/10.5067/VIIRS/VNP13A1.002)) and NOAA-20 ([VJ113A1]( https://doi.org/10.5067/VIIRS/VJ113A1.002)) satellites. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A1) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 500 meter (m) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: Normalized Difference Vegetation Index (NDVI),the Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VNP13A1 product: EVI and NDVI. Known Issues: The incorrect representation of the aerosol quantities (low, average, high) in the VIIRS Land Surface Reflectance product (VNP09) may have impacted VNP13 Vegetation Index data products particularly over arid bright surfaces. * Corrections were implemented in 2023 with the release of the Version 2 Vegetation Index data products. Users are encouraged to use the improved Version 2 data. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP13A1\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 500 meter (m) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VNP13A1 product: EVI and NDVI. Known Issues: Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide and ATBD.\n<br>\n\n#### VNP13C2\nThe VNP13C2 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP13C2](https://doi.org/10.5067/VIIRS/VNP13C2.002)) and NOAA-20 ([VJ113C2]( https://doi.org/10.5067/VIIRS/VJ113C2.002)) satellites. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13C2) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VNP13C2 product: EVI and NDVI. Known Issues: The incorrect representation of the aerosol quantities (low, average, high) in the VIIRS Land Surface Reflectance product (VNP09) may have impacted VNP13 Vegetation Index data products particularly over arid bright surfaces. * Corrections were implemented in 2023 with the release of the Version 2 Vegetation Index data products. Users are encouraged to use the improved Version 2 data. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP13C2\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13C2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VNP13C2 product: EVI and NDVI. Known Issues: Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide and ATBD.\n<br>\n\n#### VNP13A3\nThe VNP13A3 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user. VIIRS Version 2 data products are available from both the SNPP ([VNP13A3](https://doi.org/10.5067/VIIRS/VNP13A3.002)) and NOAA-20 ([VJ113A3]( https://doi.org/10.5067/VIIRS/VJ113A3.002)) satellites. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A3) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; pixel reliability; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VNP13A3 product: EVI and NDVI. Known Issues: The incorrect representation of the aerosol quantities (low, average, high) in the VIIRS Land Surface Reflectance product (VNP09) may have impacted VNP13 Vegetation Index data products particularly over arid bright surfaces. * Corrections were implemented in 2023 with the release of the Version 2 Vegetation Index data products. Users are encouraged to use the improved Version 2 data. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).\n<br>\n\n#### VNP13A3\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A3) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; pixel reliability; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VNP13A3 product: EVI and NDVI. Known Issues: Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS) and the User Guide and ATBD.\n<br>\n\n#### VNP28C2\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Water Reservoir 8-day Level 3 (L3) Global (VNP28C2) Version 2 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The VNP28C2 data product provides an 8-day time series of surface area, elevation, and water storage. Datasets are combined with pre-established [Area-Elevation (A-E) curves](https://doi.org/10.1016/j.rse.2020.111831) and image classifications of near-infrared (NIR) reflectance from the surface reflectance product acquired by the VIIRS satellite ([VNP09H1](https://doi.org/10.5067/VIIRS/VNP09H1.002)). The VNP28C2 data product consists of a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir surface area, elevation, and water storage capacity. Known Issues: Water occurrence images generally show smaller surface area dynamics in high latitude regions, creating pixels with low occurrence values that have relatively large uncertainties. In addition, the quality of raw water area classification can be affected by lake ice coverage typically creating an overestimation of surface area in the enhancement algorithm. This issue will be addressed in a future release of the enhancement algorithm. For additional information about known issues, refer to the User Guide.\n<br>\n\n#### VNP28C3\nThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Water Reservoir Product Monthly Level 3 (L3) Global (VNP28C3) Version 2 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The VNP28C3 data product is a composite of the 8-day area classifications from [VNP28C2](https://doi.org/10.5067/VIIRS/VNP28C2.002) which is converted to provide monthly elevation and water storage. The Lake Temperature and Evaporation Model ([LTEM](https://doi.org/10.1016/j.rse.2020.112104)) with input from VIIRS Land Surface Temperature and Emissivity ([VNP21A2](https://doi.org/10.5067/VIIRS/VNP21A2.002)) and meteorological data from Global Land Data Assimilation System ([GLDAS](https://earth.gsfc.nasa.gov/hydro/data/gldas-global-land-data-assimilation-system-data)) are used to produce monthly evaporation rates and volume losses. The VNP28C3 data product provides a monthly time series that consists of a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir surface area, elevation, water storage capacity, evaporation rate, and evaporation volume. Known Issues: Water occurrence images generally show smaller surface area dynamics in high latitude regions, creating pixels with low occurrence values that have relatively large uncertainties. In addition, the quality of raw water area classification can be affected by lake ice coverage typically creating an overestimation of surface area in the enhancement algorithm. This issue will be addressed in a future release of the enhancement algorithm. For additional information about known issues, refer to the User Guide.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/SNDRJ1ATMSL1B_2.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","earth observation","elevation","hdf","hdf5","ice","land cover","netcdf","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SNDRJ1ATMSL1B v2 - The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also known as NOAA-20 (National Oceanic and Atmospheric Administration).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/JPSS1_Sounder_Level1/SNDRJ1ATMSL1B.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRJ1CrISL1BIMG v2 - The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also know as NOAA-20 (National Oceanic and Atmospheric Administration).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/JPSS1_Sounder_Level1/SNDRJ1CrISL1BIMG.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRJ1CrISL1BPCARED v3.0 - This sample data collection contains L1B radiance values that are compressed and denoised via Principal Component Analysis (PCA). Additionally it contains a new Rapid Event Detection (RED) product, which for multiple spectral regions identify where rare signals are observed, providing an efficient and useful way to locate and study interesting phenomena.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/JPSS1_Sounder_Level1/SNDRJ1CrISL1BPCARED.3.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRJ2CrISL1BIMG v3.0 - The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-2 (JPSS-2) platform. This platform is also know as NOAA-21 (National Oceanic and Atmospheric Administration).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/JPSS2_Sounder_Level1/SNDRJ2CrISL1BIMG.3.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRJ2CrISL1B v3 - The goal of creating this product is to allow users to begin working with data from the CrIS instrument on-board the recently launched NOAA-21 / JPSS-2 (J2) satellite. The J2 beta product was generated using an updated version of the software that is used to generate the Version 3 SNPP and JPSS-1 (J1) products, which are currently available from GES DISC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/JPSS2_Sounder_Level1/SNDRJ2CrISL1B.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_N20_NMSO2_PCA_L2_Step1 v1 - The OMPS-N20 NM PCA SO2 Step1 Total Column 1-Orbit L2 Swath 17x13km collection 1 product contains the retrieved sulfur dioxide (SO2) measured by the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) sensor on the NOAA-20 (JPSS-1) satellite. The product is based on the NASA Goddard Space Flight Center principal component analysis (PCA) spectral fitting algorithm (Li et al., 2013, 2017) used to retrieve the SO2 total column amounts assuming different SO2 plume heights, including the boundary layer (lowest 1 km of the atmosphere), the lower (centered at 3 km), middle (centered at 8 km...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NOAA20_OMPS_Level2/OMPS_N20_NMSO2_PCA_L2_Step1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_N21_LP_L2_AER_DAILY v1.0 - The OMPS-N21 L2 LP Aerosol Extinction Vertical Profile swath daily 3slit (AER) product contains the retrieved aerosol extinction coefficients measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the NOAA-21 satellite. The AER product measures stratospheric aerosol abundance and evolution at 6 wavelengths (510, 600, 675, 745, 869 and 997 nm) to complement the OMPS LP measurements of stratospheric and mesospheric profile ozone.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NOAA21_OMPS_Level2/OMPS_N21_LP_L2_AER_DAILY.1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_N21_LP_L2_O3_DAILY v1.0 - The OMPS-N21 L2 LP Ozone (O3) Vertical Profile swath daily 3slit collection contains ozone measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the NOAA-21 satellite. The LP ozone product measures the vertical distribution of ozone in the stratosphere and lower mesosphere.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NOAA21_OMPS_Level2/OMPS_N21_LP_L2_O3_DAILY.1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_LP_L1G_EV v2 - The OMPS-NPP L1G LP Radiance EV Wavelength-Altitude Grid swath orbital 3slit product contains the calibrated earth-viewing radiances measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The LP L1G product measures radiances in the wavelength region from 280 nm to 1000 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level1B/OMPS_NPP_LP_L1G_EV.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_LP_L2_AER_DAILY v2 - The OMPS-NPP L2 LP Aerosol Extinction Vertical Profile swath daily 3slit (AER) product contains the retrieved aerosol extinction coefficients measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The AER product measures stratospheric aerosol abundance and evolution at 6 wavelengths (510, 600, 675, 745, 869 and 997 nm) to complement the OMPS LP measurements of stratospheric and mesospheric profile ozone.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level2/OMPS_NPP_LP_L2_AER_DAILY.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_LP_L2_O3_DAILY v2 - The OMPS-NPP L2 LP Ozone (O3) Vertical Profile swath daily 3slit collection contains ozone measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The LP ozone product measures the vertical distribution of ozone in the stratosphere and lower mesosphere.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level2/OMPS_NPP_LP_L2_O3_DAILY.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_LP_L2_O3_DAILY v2.6 - Version 2.6 is the current version of this data product, and supersedes all previous versions. The OMPS-NPP L2 LP Ozone (O3) Vertical Profile swath daily Center slit collection contains ozone measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level2/OMPS_NPP_LP_L2_O3_DAILY.2.6","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_NMMIEAI_L2 v2 - The OMPS-NPP L2 NM Aerosol Index swath orbital product provides aerosol index values from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. This is now the official NASA aerosol index product, replacing the aerosol index found in the OMPS-NPP L2 NM Total Ozone product.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level2/OMPS_NPP_NMMIEAI_L2.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_NMCLDRR_L2 v2 - The OMPS-NPP L2 NM Cloud Pressure and Fraction swath orbital product provides effective cloud fraction and effective cloud pressure retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. The cloud pressure algorithm retrieves effective cloud pressus, a.k.a optical centroid pressure (OCP) and effective cloud fraction (ECF) using a concept of Mixed Lambert Equivalent Reflectivity (MLER) at 354.1 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level2/OMPS_NPP_NMCLDRR_L2.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_NMNO2_L2 v2 - The OMPS-NPP L2 NM Nitrogen Dioxide (NO2) Total and Tropospheric Column swath orbital collection 2 version 2.0 product contains the retrieved nitrogen dioxide (NO2) measured by the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) sensor on the Suomi-NPP satellite. A direct vertical column fitting (DVCF) algorithm is used to retrieve the NO2 total column amount and a new spatial technique is applied to separate the stratospheric and tropospheric amounts.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level2/OMPS_NPP_NMNO2_L2.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_NMTO3_L2 v2 - The OMPS-NPP L2 NM Ozone (O3) Total Column swath orbital product provides total ozone measurements from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. The total column ozone amount is derived from normalized radiances using 2 wavelength pairs 317.5 and 331.2 nm under most conditions, and 331.2 and 360 nm for high ozone and high solar zenith angle conditions.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level2/OMPS_NPP_NMTO3_L2.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_NMSO2_L2 v2 - The OMPS-NPP L2 NM Sulfur Dioxide (SO2) Total and Tropospheric Column swath orbital collection 2 version 2.0 product contains the retrieved sulfur dioxide (SO2) measured by the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) sensor on the Suomi-NPP satellite. A direct vertical column fitting (DVCF) algorithm is used to retrieve the SO2 total column amount and column amounts in the lower (centered at 2.5 km), middle (centered at 7.5 km) and upper (centered at 11 km) troposphere, as well as the lower stratosphere (centered at 16 km).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level2/OMPS_NPP_NMSO2_L2.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_NPBUVO3_L2 v2 - The OMPS-NPP L2 NP Ozone (O3) Total Column swath orbital product provides ozone profile retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Profiler (NP) instrument on the Suomi-NPP satellite. The V8 ozone profile algorithm relies on nadir profiler measurements made in the 250 to 310 nm range, as well as from measurements from the nadir mapper in the 300 to 380 nm range.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level2/OMPS_NPP_NPBUVO3_L2.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_LP_L3_AER_MONTHLY v1 - The OMPS-NPP L3 LP Aerosol Extinction Vertical Profile 5 x 15 deg lat-lon grid multi-wavelength monthly (AER) product contains the retrieved aerosol extinction coefficients measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The AER product measures stratospheric aerosol abundance and evolution at 6 wavelengths (510, 600, 675, 745, 869 and 997 nm) to complement the OMPS LP measurements of stratospheric and mesospheric profile ozone.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NPP_OMPS_Level3/OMPS_NPP_LP_L3_AER_MONTHLY.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_NMTO3_L3_DAILY v2 - The OMPS-NPP L3 NM Ozone (O3) Total Column 1.0 deg grid daily product provides total ozone measurements from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. The level-3 gridding algorithm is used to combine the orbital OMPS cross track measurements into a daily map product with a fixed global grid.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level3/OMPS_NPP_NMTO3_L3_DAILY.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_LP_L2_Temp_DAILY v1.0 - The OMPS-NPP LP L2 Temperature Vertical Profile swath daily 3-slit collection contains the temperature profile measured by the Ozone Mapping and Profiling Suite (OMPS) Limb-Profiler (LP) sensor on the Suomi-NPP satellite. The product measures the temperature in the stratosphere and lower mesosphere.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level2/OMPS_NPP_LP_L2_Temp_DAILY.1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_NMEV_L1B v2 - The OMPS-NPP L1B NM Radiance EV Calibrated Geolocated Swath Orbital collection contains calibrated and geolocated radiances from 300 to 380 nm measured by the OMPS Nadir-Mapper sensor on the Suomi-NPP satellite. Each granule contains data from the daylight portion of a single orbit (about 50 minutes).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level1B/OMPS_NPP_NMEV_L1B.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_NPEV_L1B v2 - The OMPS-NPP L1B NP Radiance EV Calibrated Geolocated Swath Orbital collection contains calibrated and geolocated radiances from 300 to 380 nm measured by the OMPS Nadir-Profiler sensor on the Suomi-NPP satellite. Each granule typically contains data from the daylight portion of a single orbit (about 50 minutes).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level1B/OMPS_NPP_NPEV_L1B.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OMPS_NPP_NMSO2_PCA_L3_DAILY v1 - The OMPS-NPP L3 NM PCA Sulfur Dioxide (SO2) Total Column Daily Best Pixel Global Gridded 0.25 x 0.25 degree product contains sulfur dioxide amounts gridded at 0.25 x 0.25 degree resolution using the 'best' pixel method. Data are measured by the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) sensor on the Suomi-NPP satellite.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_OMPS_Level3/OMPS_NPP_NMSO2_PCA_L3_DAILY.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VJ116A2 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Actual and Potential Evapotranspiration product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The VJ116A2 algorithm is based on the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with VIIRS remotely sensed data products such as 8-day vegetation property dynamics and daily surface albedo.","ARN":"arn:aws:s3:::lp-prod-protected/VJ116A2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ116A2GF v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Actual and Potential Evapotranspiration product is a gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The VIIRS ET and PET algorithm is based on the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with VIIRS remotely sensed data products such as 8-day vegetation property dynamics and daily surface albedo.","ARN":"arn:aws:s3:::lp-prod-protected/VJ116A2GF.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ116A3GF v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Actual and Potential Evapotranspiration product is a gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The VIIRS ET and PET algorithm is based on the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with VIIRS remotely sensed data products such as 8-day vegetation property dynamics and daily surface albedo.","ARN":"arn:aws:s3:::lp-prod-protected/VJ116A3GF.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ118C1 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Downward Shortwave Radiation (DSR) Level 3 Climate Modeling Grid (CMG) product produced at 0.05 degree pixel resolution at daily intervals. DSR is the incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is a significant variable in land surface models (LSMs), receiving increased attention due to its importance for the management and operation of solar energy systems.","ARN":"arn:aws:s3:::lp-prod-protected/VJ118C1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ118A1 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Downward Shortwave Radiation (DSR) gridded Level 3 product is produced at 1 kilometer pixel resolution at daily intervals. DSR is the incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is an important variable in land-surface models that address a variety of scientific and is a significant variable in land surface models (LSMs), receiving increased attention due to its importance for the management and operation of solar energy systems.","ARN":"arn:aws:s3:::lp-prod-protected/VJ118A1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ117A3GF v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Gross and Net Primary Production (NPP) Gap-Filled (VJ117A3GF) Version 2 data product is a cumulative composite of Gross Primary Productivity (GPP) values based on the radiation-use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VJ117A3GF is a yearly composite at 500 meter spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection.","ARN":"arn:aws:s3:::lp-prod-protected/VJ117A3GF.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ117A2 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) (VJ117A2) Version 2 data product is a cumulative composite of GPP values based on the radiation use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VJ117A2 is an 8-day composite at 500 meter spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection.","ARN":"arn:aws:s3:::lp-prod-protected/VJ117A2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ117A2GF v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) Gap-Filled (VJ117A2GF) Version 2 data product is a cumulative composite of GPP values based on the radiation-use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VJ117A2GF is an 8-day composite at 500 meter spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection.","ARN":"arn:aws:s3:::lp-prod-protected/VJ117A2GF.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ119A2 v002 - The VJ119A2 Version 2 data product is a NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 750 meter (m) pixel resolution using the Rotated Sinusoidal (RSIN) projection. The VJ119A2 product provides the atmospheric properties and view geometry used to calculate the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, VJ119A1 product.","ARN":"arn:aws:s3:::lp-prod-protected/VJ119A2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ119A1 v002 - The VJ119A1 Version 2 data product is a NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 375 meter (m) and 750 m pixel resolutions using the Rotated Sinusoidal (RSIN) projection. The VJ119A1 product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that is based on a time series analysis and a combination of pixel- and image-based processing.","ARN":"arn:aws:s3:::lp-prod-protected/VJ119A1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ118C2 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Photosynthetically Active Radiation (PAR) Level 3 Climate Modeling Grid (CMG) product produced at 0.05 degree pixel resolution at daily intervals. PAR is the incident solar radiation in the visible spectrum (400-700 nanometers) and is a significant variable in land surface models (LSMs), receiving increased attention due to its importance for the management and operation of solar energy systems.","ARN":"arn:aws:s3:::lp-prod-protected/VJ118C2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ118A2 v002 - The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Photosynthetically Active Radiation (PAR) gridded Level 3 product is produced at 1 kilometer pixel resolution at daily intervals. PAR is the incident solar radiation in the visible spectrum (400-700 nanometers) and is a significant variable in land surface models (LSMs), receiving increased attention due to its importance for the management and operation of solar energy systems.","ARN":"arn:aws:s3:::lp-prod-protected/VJ118A2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP14IMG v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires (VNP14IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This Level 2 product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission.","ARN":"arn:aws:s3:::lp-prod-protected/VNP14IMG.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP16A2 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Actual and Potential Evapotranspiration (VNP16A2) data product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The VNP16A2 algorithm is based on the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with VIIRS remotely sensed data products such as 8-day vegetation property dynamics and daily surface albedo.","ARN":"arn:aws:s3:::lp-prod-protected/VNP16A2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP16A2GF v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Actual and Potential Evapotranspiration product is a gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The VIIRS ET and PET algorithm is based on the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with VIIRS remotely sensed data products such as 8-day vegetation property dynamics and daily surface albedo.","ARN":"arn:aws:s3:::lp-prod-protected/VNP16A2GF.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP16A3GF v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Actual and Potential Evapotranspiration product is a gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The VIIRS ET and PET algorithm is based on the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with VIIRS remotely sensed data products such as 8-day vegetation property dynamics and daily surface albedo.","ARN":"arn:aws:s3:::lp-prod-protected/VNP16A3GF.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43C3 v001 - The VNP43C3 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43C3](https://doi.org/10.5067/VIIRS/VNP43C3.002) and [VJ143C3](https://doi.org/10.5067/VIIRS/VJ143C3.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43C3.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43C3 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Albedo Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VNP43C3) is derived from the 30 arc second CMG VNP43D Version 2 product suite. VNP43C3 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43C3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43MA3 v001 - The VNP43MA3 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43MA3](https://doi.org/10.5067/VIIRS/VNP43MA3.002) and [VJ143MA3](https://doi.org/10.5067/VIIRS/VJ143MA3.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43MA3.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43MA3 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43MA3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43IA3 v001 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43IA3) Version 1 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43IA3.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43IA3 v002 - The VNP43IA3 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43IA3](https://doi.org/10.5067/VIIRS/VNP43IA3.002) and [VJ143IA3](https://doi.org/10.5067/VIIRS/VJ143IA3.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43IA3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D54 v001 - The VNP43D54 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D54](https://doi.org/10.5067/VIIRS/VNP43D54.002) and [VJ143D54](https://doi.org/10.5067/VIIRS/VJ143D54.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D54.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D54 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M1 (VNP43D54) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D54.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D61 v001 - The VNP43D61 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D61](https://doi.org/10.5067/VIIRS/VNP43D61.002) and [VJ143D61](https://doi.org/10.5067/VIIRS/VJ143D61.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D61.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D61 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M10 (VNP43D61) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D61.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D62 v001 - The VNP43D62 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D62](https://doi.org/10.5067/VIIRS/VNP43D62.002) and [VJ143D62](https://doi.org/10.5067/VIIRS/VJ143D62.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D62.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D62 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M11 (VNP43D62) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D62.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D55 v001 - The VNP43D55 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D55](https://doi.org/10.5067/VIIRS/VNP43D55.002) and [VJ143D55](https://doi.org/10.5067/VIIRS/VJ143D55.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D55.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D55 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M2 (VNP43D55) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D55.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D56 v001 - The VNP43D56 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D56](https://doi.org/10.5067/VIIRS/VNP43D56.002) and [VJ143D56](https://doi.org/10.5067/VIIRS/VJ143D56.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D56.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D56 v002 - The NASA/NOAA Suomi National Polar-Orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M3 (VNP43D56) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D56.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D57 v001 - The VNP43D57 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D57](https://doi.org/10.5067/VIIRS/VNP43D57.002) and [VJ143D57](https://doi.org/10.5067/VIIRS/VJ143D57.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D57.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D57 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M4 (VNP43D57) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D57.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D58 v001 - The VNP43D58 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D58](https://doi.org/10.5067/VIIRS/VNP43D58.002) and [VJ143D58](https://doi.org/10.5067/VIIRS/VJ143D58.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D58.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D58 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M5 (VNP43D58) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D58.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D59 v001 - The VNP43D59 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D59](https://doi.org/10.5067/VIIRS/VNP43D59.002) and [VJ143D59](https://doi.org/10.5067/VIIRS/VJ143D59.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D59.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D59 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M7 (VNP43D59) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D59.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D60 v001 - The VNP43D60 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D60](https://doi.org/10.5067/VIIRS/VNP43D60.002) and [VJ143D60](https://doi.org/10.5067/VIIRS/VJ143D60.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D60.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D60 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M8 (VNP43D60) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D60.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D63 v001 - The VNP43D63 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D63](https://doi.org/10.5067/VIIRS/VNP43D63.002) and [VJ143D63](https://doi.org/10.5067/VIIRS/VJ143D63.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D63.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D63 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band VIS (VNP43D63) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D63.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D66 v001 - The VNP43D66 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D66](https://doi.org/10.5067/VIIRS/VNP43D66.002) and [VJ143D66](https://doi.org/10.5067/VIIRS/VJ143D66.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D66.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D66 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Day/Night Band (DNB) product (VNP43D66) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D66.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D64 v001 - The VNP43D64 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D64](https://doi.org/10.5067/VIIRS/VNP43D64.002) and [VJ143D64](https://doi.org/10.5067/VIIRS/VJ143D64.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D64.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D64 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for NIR (VNP43D64) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D64.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D65 v001 - The VNP43D65 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D65](https://doi.org/10.5067/VIIRS/VNP43D65.002) and [VJ143D65](https://doi.org/10.5067/VIIRS/VJ143D65.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D65.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D65 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for ShortWave (VNP43D65) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D65.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D41 v001 - The VNP43D41 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D41](https://doi.org/10.5067/VIIRS/VNP43D41.002) and [VJ143D41](https://doi.org/10.5067/VIIRS/VJ143D41.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D41.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D41 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Local Solar Noon product (VNP43D41) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D41.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43C1 v001 - The VNP43C1 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43C1](https://doi.org/10.5067/VIIRS/VNP43C1.002) and [VJ143C1](https://doi.org/10.5067/VIIRS/VJ143C1.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43C1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43C1 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VNP43C1) is derived from the 30 arc second CMG VNP43D Version 2 product suite. VNP43C1 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43C1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43MA1 v001 - The VNP43MA1 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43MA1](https://doi.org/10.5067/VIIRS/VNP43MA1.002) and [VJ143MA1](https://doi.org/10.5067/VIIRS/VJ143MA1.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43MA1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43MA1 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43MA1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43IA1 v001 - The VNP43IA1 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43IA1](https://doi.org/10.5067/VIIRS/VNP43IA1.002) and [VJ143IA1](https://doi.org/10.5067/VIIRS/VJ143IA1.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43IA1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43IA1 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VNP43IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43IA1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43MA4 v001 - The VNP43MA4 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43MA4](https://doi.org/10.5067/VIIRS/VNP43MA4.002) and [VJ143MA4](https://doi.org/10.5067/VIIRS/VJ143MA4.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43MA4.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43MA4 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43MA4.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43IA4 v001 - The VNP43IA4 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43IA4](https://doi.org/10.5067/VIIRS/VNP43IA4.002) and [VJ143IA4](https://doi.org/10.5067/VIIRS/VJ143IA4.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43IA4.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43IA4 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43IA4.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43C4 v001 - The VNP43C4 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43C4](https://doi.org/10.5067/VIIRS/VNP43C4.002) and [VJ143C4](https://doi.org/10.5067/VIIRS/VJ143C4.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43C4.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43C4 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VNP43C4) is derived from the 30 arc second CMG VNP43D Version 2 product suite. [VNP43C3](https://doi.org/10.5067/VIIRS/VNP43C3.002) is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Juli...","ARN":"arn:aws:s3:::lp-prod-protected/VNP43C4.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D80 v001 - The VNP43D80 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D80](https://doi.org/10.5067/VIIRS/VNP43D80.002) and [VJ143D80](https://doi.org/10.5067/VIIRS/VJ143D80.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D80.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D80 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M1 (VNP43D80) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D80.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D87 v001 - The VNP43D87 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D87](https://doi.org/10.5067/VIIRS/VNP43D87.002) and [VJ143D87](https://doi.org/10.5067/VIIRS/VJ143D87.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D87.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D87 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M10 (VNP43D87) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D87.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D88 v001 - The VNP43D88 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D88](https://doi.org/10.5067/VIIRS/VNP43D88.002) and [VJ143D88](https://doi.org/10.5067/VIIRS/VJ143D88.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D88.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D88 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M11 (VNP43D88) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D88.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D81 v001 - The VNP43D81 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D81](https://doi.org/10.5067/VIIRS/VNP43D81.002) and [VJ143D81](https://doi.org/10.5067/VIIRS/VJ143D81.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D81.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D81 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M2 (VNP43D81) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D81.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D82 v001 - The VNP43D82 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D82](https://doi.org/10.5067/VIIRS/VNP43D82.002) and [VJ143D82](https://doi.org/10.5067/VIIRS/VJ143D82.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D82.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D82 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M3 (VNP43D82) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D82.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D83 v001 - The VNP43D83 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D83](https://doi.org/10.5067/VIIRS/VNP43D83.002) and [VJ143D83](https://doi.org/10.5067/VIIRS/VJ143D83.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D83.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D83 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M4 (VNP43D83) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D83.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D84 v001 - The VNP43D84 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D84](https://doi.org/10.5067/VIIRS/VNP43D84.002) and [VJ143D84](https://doi.org/10.5067/VIIRS/VJ143D84.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D84.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D84 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M5 (VNP43D84) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D84.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D85 v001 - The VNP43D85 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D85](https://doi.org/10.5067/VIIRS/VNP43D85.002) and [VJ143D85](https://doi.org/10.5067/VIIRS/VJ143D85.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D85.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D85 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M7 (VNP43D85) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D85.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D86 v001 - The VNP43D86 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D86](https://doi.org/10.5067/VIIRS/VNP43D86.002) and [VJ143D86](https://doi.org/10.5067/VIIRS/VJ143D86.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D86.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D86 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M8 (VNP43D86) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D86.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D89 v001 - The VNP43D89 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D89](https://doi.org/10.5067/VIIRS/VNP43D89.002) and [VJ143D89](https://doi.org/10.5067/VIIRS/VJ143D89.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D89.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D89 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Day/Night Band (DNB) product (VNP43D89) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D89.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D01 v001 - The VNP43D01 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D01](https://doi.org/10.5067/VIIRS/VNP43D01.002) and [VJ143D01](https://doi.org/10.5067/VIIRS/VJ143D01.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D01.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D01 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M1 product (VNP43D01) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D01.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D22 v001 - The VNP43D22 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D22](https://doi.org/10.5067/VIIRS/VNP43D22.002) and [VJ143D22](https://doi.org/10.5067/VIIRS/VJ143D22.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D22.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D22 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M10 product (VNP43D22) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D22.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D25 v001 - The VNP43D25 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D25](https://doi.org/10.5067/VIIRS/VNP43D25.002) and [VJ143D25](https://doi.org/10.5067/VIIRS/VJ143D25.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D25.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D25 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M11 product (VNP43D25) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D25.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D04 v001 - The VNP43D04 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D04](https://doi.org/10.5067/VIIRS/VNP43D04.002) and [VJ143D04](https://doi.org/10.5067/VIIRS/VJ143D04.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D04.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D04 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M2 product (VNP43D04) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D04.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D07 v001 - The VNP43D07 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D07](https://doi.org/10.5067/VIIRS/VNP43D07.002) and [VJ143D07](https://doi.org/10.5067/VIIRS/VJ143D07.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D07.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D07 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M3 product (VNP43D07) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D07.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D10 v001 - The VNP43D10 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D10](https://doi.org/10.5067/VIIRS/VNP43D10.002) and [VJ143D10](https://doi.org/10.5067/VIIRS/VJ143D10.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D10.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D10 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M4 product (VNP43D10) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D10.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D13 v001 - The VNP43D13 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D13](https://doi.org/10.5067/VIIRS/VNP43D13.002) and [VJ143D13](https://doi.org/10.5067/VIIRS/VJ143D13.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D13.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D13 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M5 product (VNP43D13) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D13.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D16 v001 - The VNP43D16 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D16](https://doi.org/10.5067/VIIRS/VNP43D16.002) and [VJ143D16](https://doi.org/10.5067/VIIRS/VJ143D16.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D16.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D16 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M7 product (VNP43D16) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D16.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D19 v001 - The VNP43D19 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D19](https://doi.org/10.5067/VIIRS/VNP43D19.002) and [VJ143D19](https://doi.org/10.5067/VIIRS/VJ143D19.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D19.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D19 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M8 product (VNP43D19) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D19.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D37 v001 - The VNP43D37 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D37](https://doi.org/10.5067/VIIRS/VNP43D37.002) and [VJ143D37](https://doi.org/10.5067/VIIRS/VJ143D37.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D37.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D37 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Day/Night Band (DNB) product (VNP43D37) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D37.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D31 v001 - The VNP43D31 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D31](https://doi.org/10.5067/VIIRS/VNP43D31.002) and [VJ143D31](https://doi.org/10.5067/VIIRS/VJ143D31.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D31.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D31 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 NIR product (VNP43D31) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D31.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D34 v001 - The VNP43D34 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D34](https://doi.org/10.5067/VIIRS/VNP43D34.002) and [VJ143D34](https://doi.org/10.5067/VIIRS/VJ143D34.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D34.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D34 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Shortwave product (VNP43D34) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D34.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D28 v001 - The VNP43D28 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D28](https://doi.org/10.5067/VIIRS/VNP43D28.002) and [VJ143D28](https://doi.org/10.5067/VIIRS/VJ143D28.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D28.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D28 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 VIS product (VNP43D28) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D28.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D02 v001 - The VNP43D02 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D02](https://doi.org/10.5067/VIIRS/VNP43D02.002) and [VJ143D02](https://doi.org/10.5067/VIIRS/VJ143D02.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D02.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D02 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M1 product (VNP43D02) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D02.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D23 v001 - The VNP43D23 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D23](https://doi.org/10.5067/VIIRS/VNP43D23.002) and [VJ143D23](https://doi.org/10.5067/VIIRS/VJ143D23.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D23.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D23 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M10 product (VNP43D23) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D23.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D26 v001 - The VNP43D26 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D26](https://doi.org/10.5067/VIIRS/VNP43D26.002) and [VJ143D26](https://doi.org/10.5067/VIIRS/VJ143D26.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D26.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D26 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M11 product (VNP43D26) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D26.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D05 v001 - The VNP43D05 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D05](https://doi.org/10.5067/VIIRS/VNP43D05.002) and [VJ143D05](https://doi.org/10.5067/VIIRS/VJ143D05.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D05.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D05 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M2 product (VNP43D05) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D05.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D08 v001 - The VNP43D08 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D08](https://doi.org/10.5067/VIIRS/VNP43D08.002) and [VJ143D08](https://doi.org/10.5067/VIIRS/VJ143D08.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D08.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D08 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M3 product (VNP43D08) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D08.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D11 v001 - The VNP43D11 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D11](https://doi.org/10.5067/VIIRS/VNP43D11.002) and [VJ143D11](https://doi.org/10.5067/VIIRS/VJ143D11.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D11.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D11 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M4 product (VNP43D11) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D11.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D14 v001 - The VNP43D14 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D14](https://doi.org/10.5067/VIIRS/VNP43D14.002) and [VJ143D14](https://doi.org/10.5067/VIIRS/VJ143D14.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D14.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D14 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M5 product (VNP43D14) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D14.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D17 v001 - The VNP43D17 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D17](https://doi.org/10.5067/VIIRS/VNP43D17.002) and [VJ143D17](https://doi.org/10.5067/VIIRS/VJ143D17.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D17.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D17 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M7 product (VNP43D17) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D17.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D20 v001 - The VNP43D20 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D20](https://doi.org/10.5067/VIIRS/VNP43D20.002) and [VJ143D20](https://doi.org/10.5067/VIIRS/VJ143D20.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D20.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D20 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M8 product (VNP43D20) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D20.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D38 v001 - The VNP43D38 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D38](https://doi.org/10.5067/VIIRS/VNP43D38.002) and [VJ143D38](https://doi.org/10.5067/VIIRS/VJ143D38.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D38.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D38 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Day/Night Band (DNB) product (VNP43D38) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D38.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D32 v001 - The VNP43D32 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D32](https://doi.org/10.5067/VIIRS/VNP43D32.002) and [VJ143D32](https://doi.org/10.5067/VIIRS/VJ143D32.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D32.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D32 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 NIR product (VNP43D32) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D32.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D35 v001 - The VNP43D35 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D35](https://doi.org/10.5067/VIIRS/VNP43D35.002) and [VJ143D35](https://doi.org/10.5067/VIIRS/VJ143D35.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D35.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D35 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Shortwave product (VNP43D35) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D35.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D29 v001 - The VNP43D29 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D29](https://doi.org/10.5067/VIIRS/VNP43D29.002) and [VJ143D29](https://doi.org/10.5067/VIIRS/VJ143D29.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D29.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D29 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 VIS product (VNP43D29) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D29.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D03 v001 - The VNP43D03 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D03](https://doi.org/10.5067/VIIRS/VNP43D03.002) and [VJ143D03](https://doi.org/10.5067/VIIRS/VJ143D03.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D03.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D03 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M1 product (VNP43D03) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D03.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D24 v001 - The VNP43D24 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D24](https://doi.org/10.5067/VIIRS/VNP43D24.002) and [VJ143D24](https://doi.org/10.5067/VIIRS/VJ143D24.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D24.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D24 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M10 product (VNP43D24) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D24.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D27 v001 - The VNP43D27 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D27](https://doi.org/10.5067/VIIRS/VNP43D27.002) and [VJ143D27](https://doi.org/10.5067/VIIRS/VJ143D27.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D27.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D27 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M11 product (VNP43D27) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D27.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D06 v001 - The VNP43D06 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D06](https://doi.org/10.5067/VIIRS/VNP43D06.002) and [VJ143D06](https://doi.org/10.5067/VIIRS/VJ143D06.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D06.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D06 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M2 product (VNP43D06) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D06.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D09 v001 - The VNP43D09 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D09](https://doi.org/10.5067/VIIRS/VNP43D09.002) and [VJ143D09](https://doi.org/10.5067/VIIRS/VJ143D09.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D09.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D09 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M3 product (VNP43D09) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D09.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D12 v001 - The VNP43D12 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D12](https://doi.org/10.5067/VIIRS/VNP43D12.002) and [VJ143D12](https://doi.org/10.5067/VIIRS/VJ143D12.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D12.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D12 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M4 product (VNP43D12) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D12.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D15 v001 - The VNP43D15 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D15](https://doi.org/10.5067/VIIRS/VNP43D15.002) and [VJ143D15](https://doi.org/10.5067/VIIRS/VJ143D15.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D15.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D15 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M5 product (VNP43D15) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D15.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D18 v001 - The VNP43D18 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D18](https://doi.org/10.5067/VIIRS/VNP43D18.002) and [VJ143D18](https://doi.org/10.5067/VIIRS/VJ143D18.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D18.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D18 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M7 product (VNP43D18) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D18.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D21 v001 - The VNP43D21 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D21](https://doi.org/10.5067/VIIRS/VNP43D21.002) and [VJ143D21](https://doi.org/10.5067/VIIRS/VJ143D21.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D21.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D21 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M8 product (VNP43D21) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D21.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D39 v001 - The VNP43D39 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D39](https://doi.org/10.5067/VIIRS/VNP43D39.002) and [VJ143D39](https://doi.org/10.5067/VIIRS/VJ143D39.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D39.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D39 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Day/Night Band (DNB) product (VNP43D39) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D39.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D33 v001 - The VNP43D33 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D33](https://doi.org/10.5067/VIIRS/VNP43D33.002) and [VJ143D33](https://doi.org/10.5067/VIIRS/VJ143D33.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D33.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D33 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 NIR product (VNP43D33) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D33.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D36 v001 - The VNP43D36 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D36](https://doi.org/10.5067/VIIRS/VNP43D36.002) and [VJ143D36](https://doi.org/10.5067/VIIRS/VJ143D36.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D36.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D36 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Shortwave product (VNP43D36) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D36.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D30 v001 - The VNP43D30 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D30](https://doi.org/10.5067/VIIRS/VNP43D30.002) and [VJ143D30](https://doi.org/10.5067/VIIRS/VJ143D30.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D30.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D30 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 VIS product (VNP43D30) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D30.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43MA2 v001 - The VNP43MA2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.002) and [VJ143MA2](https://doi.org/10.5067/VIIRS/VJ143MA2.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43MA2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43MA2 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43MA2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D40 v001 - The VNP43D40 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D40](https://doi.org/10.5067/VIIRS/VNP43D40.002) and [VJ143D40](https://doi.org/10.5067/VIIRS/VJ143D40.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D40.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D40 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality product (VNP43D40) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D40.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43IA2 v001 - The VNP43IA2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43IA2](https://doi.org/10.5067/VIIRS/VNP43IA2.002) and [VJ143IA2](https://doi.org/10.5067/VIIRS/VJ143IA2.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43IA2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43IA2 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43IA2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D52 v001 - The VNP43D52 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D52](https://doi.org/10.5067/VIIRS/VNP43D52.002) and [VJ143D52](https://doi.org/10.5067/VIIRS/VJ143D52.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D52.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D52 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow Status product (VNP43D52) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D52.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43C2 v001 - The VNP43C2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43C2](https://doi.org/10.5067/VIIRS/VNP43C2.002) and [VJ143C1](https://doi.org/10.5067/VIIRS/VJ143C2.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43C2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43C2 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow-free Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VNP43C2) is derived from the 30 arc second CMG VNP43D Version 2 product suite. VNP43C2 is generated daily from all available snow-free acquisitions over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43C2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D53 v001 - The VNP43D53 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D53](https://doi.org/10.5067/VIIRS/VNP43D53.002) and [VJ143D53](https://doi.org/10.5067/VIIRS/VJ143D53.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D53.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D53 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Uncertainty product (VNP43D53) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D53.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D42 v001 - The VNP43D42 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D42](https://doi.org/10.5067/VIIRS/VNP43D42.002) and [VJ143D42](https://doi.org/10.5067/VIIRS/VJ143D42.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D42.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D42 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M1 product (VNP43D42) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D42.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D49 v001 - The VNP43D49 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D49](https://doi.org/10.5067/VIIRS/VNP43D49.002) and [VJ143D49](https://doi.org/10.5067/VIIRS/VJ143D49.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D49.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D49 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M10 product (VNP43D49) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D49.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D50 v001 - The VNP43D50 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D50](https://doi.org/10.5067/VIIRS/VNP43D50.002) and [VJ143D50](https://doi.org/10.5067/VIIRS/VJ143D50.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D50.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D50 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M11 product (VNP43D50) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D50.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D43 v001 - The VNP43D43 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D43](https://doi.org/10.5067/VIIRS/VNP43D43.002) and [VJ143D43](https://doi.org/10.5067/VIIRS/VJ143D43.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D43.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D43 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M2 product (VNP43D43) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D43.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D44 v001 - The VNP43D44 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D44](https://doi.org/10.5067/VIIRS/VNP43D44.002) and [VJ143D44](https://doi.org/10.5067/VIIRS/VJ143D44.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D44.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D44 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M3 product (VNP43D44) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D44.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D45 v001 - The VNP43D45 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D45](https://doi.org/10.5067/VIIRS/VNP43D45.002) and [VJ143D45](https://doi.org/10.5067/VIIRS/VJ143D45.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D45.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D45 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M4 product (VNP43D45) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D45.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D46 v001 - The VNP43D46 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D46](https://doi.org/10.5067/VIIRS/VNP43D46.002) and [VJ143D46](https://doi.org/10.5067/VIIRS/VJ143D46.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D46.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D46 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M5 product (VNP43D46) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D46.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D47 v001 - The VNP43D47 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D47](https://doi.org/10.5067/VIIRS/VNP43D47.002) and [VJ143D47](https://doi.org/10.5067/VIIRS/VJ143D47.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D47.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D47 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M7 product (VNP43D47) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D47.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D48 v001 - The VNP43D48 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D48](https://doi.org/10.5067/VIIRS/VNP43D48.002) and [VJ143D48](https://doi.org/10.5067/VIIRS/VJ143D48.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D48.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D48 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M8 product (VNP43D48) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D48.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D51 v001 - The VNP43D51 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D51](https://doi.org/10.5067/VIIRS/VNP43D51.002) and [VJ143D51](https://doi.org/10.5067/VIIRS/VJ143D51.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D51.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D51 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Day/Night Band (DNB) product (VNP43D51) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D51.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D67 v001 - The VNP43D67 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D67](https://doi.org/10.5067/VIIRS/VNP43D67.002) and [VJ143D67](https://doi.org/10.5067/VIIRS/VJ143D67.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D67.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D67 v002 - The Suomi National Polar-Orbiting Partnership (Suomi NPP) NASA Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M1 (VNP43D67) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D67.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D74 v001 - The VNP43D74 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D74](https://doi.org/10.5067/VIIRS/VNP43D74.002) and [VJ143D74](https://doi.org/10.5067/VIIRS/VJ143D74.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D74.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D74 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M10 (VNP43D74) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D74.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D75 v001 - The VNP43D75 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D75](https://doi.org/10.5067/VIIRS/VNP43D75.002) and [VJ143D75](https://doi.org/10.5067/VIIRS/VJ143D75.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D75.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D75 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M11 (VNP43D75) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D75.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D68 v001 - The VNP43D68 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D68](https://doi.org/10.5067/VIIRS/VNP43D68.002) and [VJ143D68](https://doi.org/10.5067/VIIRS/VJ143D68.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D68.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D68 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M2 (VNP43D68) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D68.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D69 v001 - The VNP43D69 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D69](https://doi.org/10.5067/VIIRS/VNP43D69.002) and [VJ143D69](https://doi.org/10.5067/VIIRS/VJ143D69.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D69.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D69 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M3 (VNP43D69) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D69.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D70 v001 - The VNP43D70 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D70](https://doi.org/10.5067/VIIRS/VNP43D70.002) and [VJ143D70](https://doi.org/10.5067/VIIRS/VJ143D70.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D70.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D70 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M4 (VNP43D70) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D70.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D71 v001 - The VNP43D71 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D71](https://doi.org/10.5067/VIIRS/VNP43D71.002) and [VJ143D71](https://doi.org/10.5067/VIIRS/VJ143D71.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D71.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D71 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M5 (VNP43D71) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D71.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D72 v001 - The VNP43D72 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D72](https://doi.org/10.5067/VIIRS/VNP43D72.002) and [VJ143D72](https://doi.org/10.5067/VIIRS/VJ143D72.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D72.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D72 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M7 (VNP43D72) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D72.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D73 v001 - The VNP43D73 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D73](https://doi.org/10.5067/VIIRS/VNP43D73.002) and [VJ143D73](https://doi.org/10.5067/VIIRS/VJ143D73.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D73.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D73 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M8 (VNP43D73) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D73.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D76 v001 - The VNP43D76 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D76](https://doi.org/10.5067/VIIRS/VNP43D76.002) and [VJ143D76](https://doi.org/10.5067/VIIRS/VJ143D76.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D76.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D76 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band VIS (VNP43D76) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D76.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D79 v001 - The VNP43D79 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D79](https://doi.org/10.5067/VIIRS/VNP43D79.002) and [VJ143D79](https://doi.org/10.5067/VIIRS/VJ143D79.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D79.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D79 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for Day/Night Band (DNB) product (VNP43D79) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D79.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D77 v001 - The VNP43D77 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D77](https://doi.org/10.5067/VIIRS/VNP43D77.002) and [VJ143D77](https://doi.org/10.5067/VIIRS/VJ143D77.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D77.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D77 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band NIR (VNP43D77) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D77.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D78 v001 - The VNP43D78 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43D78](https://doi.org/10.5067/VIIRS/VNP43D78.002) and [VJ143D78](https://doi.org/10.5067/VIIRS/VJ143D78.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D78.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43D78 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for ShortWave (VNP43D78) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43D78.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP64A1 v001 - The VNP64A1 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP64A1](https://doi.org/10.5067/VIIRS/VNP64A1.002) Version 2 data product.","ARN":"arn:aws:s3:::lp-prod-protected/VNP64A1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP64A1 v002 - The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 2 data product is a monthly, global gridded 500-meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations.","ARN":"arn:aws:s3:::lp-prod-protected/VNP64A1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43DNBA3 v001 - The VNP43DNBA3 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43DNBA3](https://doi.org/10.5067/VIIRS/VNP43DNBA3.002) and [VJ143DNBA3](https://doi.org/10.5067/VIIRS/VJ143DNBA3.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43DNBA3.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43DNBA3 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43DNBA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43DNBA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43DNBA3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43DNBA1 v001 - The VNP43DNBA1 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43DNBA1](https://doi.org/10.5067/VIIRS/VNP43DNBA1.002) and [VJ143DNBA1](https://doi.org/10.5067/VIIRS/VJ143DNBA1.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43DNBA1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43DNBA1 v002 - The Suomi National Polar-Orbiting Partnership (Suomi NPP) NASA Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43DNBA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43DNBA1 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43DNBA1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43DNBA4 v001 - The VNP43DNBA4 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43DNBA4](https://doi.org/10.5067/VIIRS/VNP43DNBA4.002) and [VJ143DNBA4](https://doi.org/10.5067/VIIRS/VJ143DNBA4.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43DNBA4.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43DNBA4 v002 - TThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43DNBA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43DNBA4.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43DNBA2 v001 - The VNP43DNBA2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43DNBA2](https://doi.org/10.5067/VIIRS/VNP43DNBA2.002) and [VJ143DNBA2](https://doi.org/10.5067/VIIRS/VJ143DNBA2.002) Version 2 data products.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43DNBA2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP43DNBA2 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43DNBA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43DNBA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/VNP43DNBA2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP18C1 v002 - The VNP18C1 Version 2 is a NASA/NOAA Suomi National Polar-Orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Downward Shortwave Radiation (DSR) Level 3 Climate Modeling Grid (CMG) product. It is produced at 0.05 degree pixel resolution at daily intervals.","ARN":"arn:aws:s3:::lp-prod-protected/VNP18C1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP18A1 v002 - The VNP18A1 Version 2 is a NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Downward Shortwave Radiation (DSR) gridded Level 3 product. It is produced at 1 kilometer pixel resolution at daily intervals.","ARN":"arn:aws:s3:::lp-prod-protected/VNP18A1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP47IMG v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) FIre Light Detection Algorithm-2 (FILDA-2) Modified Combustion Efficiency (MCE) Version 2 swath product (VNP47IMG) is produced in 6-minute orbit segments at 375 meter (m) resolution. The VNP47IMG product takes advantage of the rich information that visible band observation conveys at night to assess fire combustion efficiency and enhance fire detection.","ARN":"arn:aws:s3:::lp-prod-protected/VNP47IMG.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP47MOD v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) FIre Light Detection Algorithm-2 (FILDA-2) Modified Combustion Efficiency (MCE) swath product (VNP47MOD) is produced in 6-minute orbit segments at 750 meter (m) resolution. The VNP47MOD product takes advantage of the rich information that visible band observation conveys at night to assess fire combustion efficiency and enhance fire detection.","ARN":"arn:aws:s3:::lp-prod-protected/VNP47MOD.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP17A3GF v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Gross and Net Primary Production (NPP) Gap-Filled (VNP17A3GF) Version 2 product is a cumulative composite of Gross Primary Productivity (GPP) values based on the radiation use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VNP17A3GF is a yearly composite at 500 meter (m) spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection.","ARN":"arn:aws:s3:::lp-prod-protected/VNP17A3GF.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP17A2 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) (VNP17A2) Version 2 data product is a cumulative composite of GPP values based on the radiation use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VNP17A2 is an 8-day composite at 500 meter (m) spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection.","ARN":"arn:aws:s3:::lp-prod-protected/VNP17A2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP17A2GF v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) Gap-Filled (VNP17A2GF) Version 2 product is a cumulative composite of GPP values based on the radiation use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VNP17A2GF is an 8-day composite at 500 meters (m) spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection.","ARN":"arn:aws:s3:::lp-prod-protected/VNP17A2GF.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP19A2 v002 - The VNP19A2 Version 2 data product is a NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 750 meter (m) pixel using the Rotated Sinusoidal (RSIN) projection. The VNP19A2 product provides the atmospheric properties and view geometry used to calculate the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, VNP19A1 product.","ARN":"arn:aws:s3:::lp-prod-protected/VNP19A2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP22C2 v001 - The VNP22C2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP22C2](https://doi.org/10.5067/VIIRS/VNP22C2.002) Version 2 data product.","ARN":"arn:aws:s3:::lp-prod-protected/VNP22C2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP22Q2 v001 - The VNP22Q2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP22Q2](https://doi.org/10.5067/VIIRS/VNP22Q2.002) Version 2 data product.","ARN":"arn:aws:s3:::lp-prod-protected/VNP22Q2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP19A1 v002 - The VNP19A1 Version 2 data product is a NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 375 meter (m) and 750 m pixel resolution using the Rotated Sinusoidal (RSIN) projection. The VNP19A1 product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that is based on a time series analysis and a combination of pixel- and image-based processing.","ARN":"arn:aws:s3:::lp-prod-protected/VNP19A1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP22C2 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Phenology data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22C2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR).","ARN":"arn:aws:s3:::lp-prod-protected/VNP22C2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP22Q2 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Phenology data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22Q2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR).","ARN":"arn:aws:s3:::lp-prod-protected/VNP22Q2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP21 v001 - The VNP21 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP21.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP21 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VNP21) is produced daily in 6-minute temporal increments of satellite acquisition. The VNP21 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 µm), M15 (10.76 µm), and M16 (12 µm) at a spatial resolution of 750 meters.","ARN":"arn:aws:s3:::lp-prod-protected/VNP21.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP21C2 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day Climate Modeling Grid Version 2 product (VNP21C2) combines the daily [VNP21A1D](http://doi.org/10.5067/VIIRS/VNP21A1D.002) and [VNP21A1N](http://doi.org/10.5067/VIIRS/VNP21A1N.002) products over an 8-day compositing period into a single product. The VNP21C2 dataset is an 8-day composite LST&E product at 0.05 degree (~5,600 meter) resolution that uses an algorithm based on a simple-averaging method and is formatted as a CM...","ARN":"arn:aws:s3:::lp-prod-protected/VNP21C2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP21A2 v001 - The VNP21A2 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP21A2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP21A2 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day product (VNP21A2) combines the daily [VNP21A1D](http://doi.org/10.5067/VIIRS/VNP21A1D.002) and [VNP21A1N](http://doi.org/10.5067/VIIRS/VNP21A1N.002) products over an 8-day compositing period into a single product. The VNP21A2 dataset is an 8-day composite LST&E product at 1 kilometer resolution that uses an algorithm based on a simple-averaging method.","ARN":"arn:aws:s3:::lp-prod-protected/VNP21A2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP21C1 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Climate Modeling Grid Version 2 product (VNP21C) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily [VNP21](https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day.","ARN":"arn:aws:s3:::lp-prod-protected/VNP21C1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP21A1D v001 - The VNP21A1D VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP21A1D.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP21A1D v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 2 product (VNP21A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily [VNP21](https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day.","ARN":"arn:aws:s3:::lp-prod-protected/VNP21A1D.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP21A1N v001 - The VNP21A1N VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP21A1N.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP21A1N v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VNP21A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily [VNP21](https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night.","ARN":"arn:aws:s3:::lp-prod-protected/VNP21A1N.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP21C3 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) monthly Climate Modeling Grid Version 2 product (VNP21C3) provides LST&E by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (~5,600 meter) resolution. The VNP21C3 dataset is a monthly composite LST&E product that uses an algorithm based on a simple averaging method and is formatted as a CMG for use in climate simulation models.","ARN":"arn:aws:s3:::lp-prod-protected/VNP21C3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP15A2H v001 - The VNP15A2H VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP15A2H.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP15A2H v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) Version 2 data product (VNP15A2H) provides information about the vegetative canopy layer at 500 meter resolution. The VIIRS sensor is located aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite.","ARN":"arn:aws:s3:::lp-prod-protected/VNP15A2H.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP03MODLL v001 - The VNP03MODLL Version 1 data product was decommissioned on July 31, 2025. Forward processing of VNP03MODLL Version 1 data product was discontinued on June 17, 2024.","ARN":"arn:aws:s3:::lp-prod-protected/VNP03MODLL.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP03MODLL v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) Moderate Resolution Terrain Correction Geolocation (VNP03MODLL) Version 2 product from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor is produced in 6 minute temporal satellite increments (swaths) at 750 meter resolution. Intersecting the VIIRS line of sight vector with Earth’s geoid and the World Geodetic System (WGS) ellipsoid, this product is based on the SRTM30 Version 2 digital elevation model (DEM), which uses GTOPO30 data for areas from 60° North to 60° South.","ARN":"arn:aws:s3:::lp-prod-protected/VNP03MODLL.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP18C2 v002 - The VNP18C2 Version 2 is a NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Photosynthetically Active Radiation (PAR) Level 3 CMG product produced at 0.05 degree pixel resolution at daily intervals. PAR is incident solar radiation in the visible spectrum (400-700 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues.","ARN":"arn:aws:s3:::lp-prod-protected/VNP18C2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP18A2 v002 - The VNP18A2 Version 2 is a NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Photosynthetically Active Radiation (PAR) gridded Level 3 product. It is produced at 1 kilometer pixel resolution at daily intervals.","ARN":"arn:aws:s3:::lp-prod-protected/VNP18A2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP09A1 v001 - The VNP09A1 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP09A1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP09A1 v002 - The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance (VNP09A1) Version 2 composite product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor for nine moderate resolution bands (M1 - M5, M7, M8, M10, M11) at nominal 1 kilometer resolution (~926 meter). The 1 kiliometer dataset is derived through resampling the native 750 meter VIIRS resolution in the Level 2 input product.","ARN":"arn:aws:s3:::lp-prod-protected/VNP09A1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP09H1 v001 - The VNP09H1 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP09H1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP09H1 v002 - The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) Surface Reflectance (VNP09H1) Version 2 composite product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor for three imagery bands (I1, I2, I3) at nominal 500 meter resolution (~463 meter). The 500 meter dataset is derived through resampling the native 375 meter VIIRS resolution in the L2 input product.","ARN":"arn:aws:s3:::lp-prod-protected/VNP09H1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP09GA v001 - The VNP09GA VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP09GA.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP09GA v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance (VNP09GA) Version 2 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) at nominal 500 meter resolution (~463 meter) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at nominal 1 kilometer (~926 meter) resolution.","ARN":"arn:aws:s3:::lp-prod-protected/VNP09GA.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP09CMG v001 - The VNP09CMG VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP09CMG.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP09CMG v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance Climate Modeling Grid (VNP09CMG) Version 2 product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor. Data are provided for three imagery bands (I1-I3) and nine moderate resolution bands (M1-M5, M7, M8, M10, M11) at 0.05 degree (~5,600 meter) resolution.","ARN":"arn:aws:s3:::lp-prod-protected/VNP09CMG.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP14A1 v001 - The VNP14A1 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP14A1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP14A1 v002 - The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies and Fire (VNP14A1) Version 2 data product provides daily information about active fires and other thermal anomalies. The VNP14A1 data product is a global, 1 kilometer (km) gridded composite of fire pixels detected from VIIRS 750 meter (m) bands over a daily (24-hour) period.","ARN":"arn:aws:s3:::lp-prod-protected/VNP14A1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP14 v001 - The VNP14 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP14.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP14 v002 - The Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Anomalies (VNP14) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission.","ARN":"arn:aws:s3:::lp-prod-protected/VNP14.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP13C1 v001 - The VNP13C1 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP13C1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP13C1 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13C1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.","ARN":"arn:aws:s3:::lp-prod-protected/VNP13C1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP13A2 v001 - The VNP13A2 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP13A2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP13A2 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.","ARN":"arn:aws:s3:::lp-prod-protected/VNP13A2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP13A1 v001 - The VNP13A1 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP13A1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP13A1 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 500 meter (m) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.","ARN":"arn:aws:s3:::lp-prod-protected/VNP13A1.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP13C2 v001 - The VNP13C2 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP13C2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP13C2 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13C2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.","ARN":"arn:aws:s3:::lp-prod-protected/VNP13C2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP13A3 v001 - The VNP13A3 VIIRS Version 1 data product was decommissioned on April 8th, 2025. Users are encouraged to use Version 2 data products, which provide [better calibration and consistency](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf) for the end user.","ARN":"arn:aws:s3:::lp-prod-protected/VNP13A3.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP13A3 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A3) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.","ARN":"arn:aws:s3:::lp-prod-protected/VNP13A3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP28C2 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Water Reservoir 8-day Level 3 (L3) Global (VNP28C2) Version 2 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The VNP28C2 data product provides an 8-day time series of surface area, elevation, and water storage.","ARN":"arn:aws:s3:::lp-prod-protected/VNP28C2.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP28C3 v002 - The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Water Reservoir Product Monthly Level 3 (L3) Global (VNP28C3) Version 2 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The VNP28C3 data product is a composite of the 8-day area classifications from [VNP28C2](https://doi.org/10.5067/VIIRS/VNP28C2.002) which is converted to provide monthly elevation and water storage.","ARN":"arn:aws:s3:::lp-prod-protected/VNP28C3.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-npp-jpss","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NPP Project","Description":"This data set contains two files (.txt). One file contains stand characteristics, soil characteristics, biomass distribution, and production allocation data measured during the 1984 growing season in four lodgepole pine stands (Pinus contorta var. latifolia) located near Canal Flats, British Columbia, Canada (50.2 N -115.5 W Elevation 1,300-1,380 m). The second file contains climate data from a nearby weather station at Kananaskis Boundary, Alberta (50.98 N -115.12 W Elevation 1,463 m). Two lodgepole pine stands were growing on xeric sites and two stands were growing on mesic sites. The stands were 70-78 years old, were unmanaged, and had regenerated naturally following wildfire. They were studied to determine the influence of soil water content on resource allocation to above-ground versus below-ground plant components. Above-ground NPP of the two xeric stands was 350 and 330 g/m2/yr, and below-ground NPP was 430 and 630 g/m2/yr, respectively, giving a range of total NPP from 780 to 960 g/m2/yr. ANPP of the two mesic stands was 640 and 740 g/m2/yr, and BNPP was 550 and 450 g/m2/yr, respectively, giving total NPP of 1,190 g/m2/yr in each case. Although the ANPP of the mesic stands was approximately double that of the two xeric stands, total NPP was only 36% greater for the mesic stands than for the xeric stands. Production allocation was in the following order: fine and small roots > stems > foliage > coarse roots > branches, for all but the wettest site, where stem production exceeded fine and small root production. Revision Notes: This data set has been revised to correct the sampling date (month) when above-ground biomass samples were collected. Please see the Data Set Revisions section of this document for detailed information.\n<br>\n\n#### NPP_BOREAL_611\nThis data set provides estimates of above- and below-ground biomass, above- and below-ground NPP (ANPP and BNPP), and total NPP(TNPP) for selected North American and Eurasian boreal forests located between 66.37 degrees N and 47.5 degrees N. Each stand was selected through a review of published literature and classified into one of three classes, depending upon completeness of NPP budget, ancillary site data, and stand information. Within the overall 1965-1995 temporal range, data available for individual sites varies widely. There are two ASCII files (comma-separated-value format) of NPP data. • The first file provides carbon distribution in above- and below-ground vegetation biomass, above- and below-ground net primary production, and mean annual biomass increment for twenty-four (24) Class I sites which have complete NPP budgets (ANPP + BNPP). Information about site characteristics and NPP measurement approaches are also provided. • The second file provides stand information, carbon distribution in above-ground vegetation biomass, and ANPP data for forty-five (45) Class II boreal forest stands that have incomplete NPP budgets. Revision Notes:Above- and below-ground biomass, ANPP, and TNPP values for several sites have been corrected to agree with primary published sources and related data sets. The temporal coverage for both has been corrected to agree with primary published sources. Please see the Data Set Revision section of this document for detailed information.\n<br>\n\n#### NPP_FLK_201\nThis data set contains three files (.txt format) for an established 8.25 ha boreal forest dominated by Norway spruce, Picea abies, at Flakaliden (64.12 N 19.45 E) in northern Sweden. Two data files contain stand characteristics, above- and below-ground biomass, and Net Primary Productivity (NPP) allocation data (one file for plots fertilized and irrigated during the growing season and one file for control plots). The third file provides climate data for the period 1991-1995 from a weather station established at the study site. The experimental forest was established in 1963 by planting 4-year-old P. abies seedlings after clear-felling, burning, and soil scarification. A yield optimization study was started in 1986 to compare the productivity of the boreal forest under four types of treatment (only the results of fertilization/irrigation and no treatment are presented herein). Treatments began in 1987 and continued through the 1996 growing season. Field measurements were made by inventory and harvest methods. After three years of treatment, height and diameter growth in the fertilized/irrigated stands were double that of the control stand. After 10 years, volume growth of fertilized/irrigated stands were almost four times that of the control. Total net primary production (TNPP) of the 36-year-old untreated stand in 1995 was 291 g/m2/year. TNPP in the fertilized/irrigated stand (902 g/m2/year) was more than three times that of the control, confirming earlier findings that nutrient availability is a major constraint on forest production in Sweden.\n<br>\n\n#### NPP_JDR_202\nThis data set contains three files (.txt format). Two of the files contain stand characteristics, above- and below-ground biomass, and above- and below-ground production allocation data for Scots pine (Pinus sylvestris) forests near Jädraås, Sweden. One file is for a young regenerating forest (14-20 years old) and the other is for an old-growth forest (120-125 years old). The field measurements were made by destructive and non-destructive methods between 1973 and 1983. The third file contains climate data recorded at the Jädraås site from 1974 through 1990. The research was conducted under the auspices of SWECON (Swedish Coniferous Forest Biome) Project to enhance understanding of plant biomass dynamics and factors regulating plant growth. Most of the research concerned plant and vegetation processes, but particular interest was also given to soil processes, consumption processes, and energy and water exchange in the canopy and the soil. Total net primary production (NPP) in the young forest was estimated at 860 g/m2/year (above-ground = 372 g/m2/year; below-ground = 488 g/m2/year). NPP for the old-growth forest was not calculated; however, the data set contains estimates of branch and trunk growth (43 g/m2/year and 106 g/m2/year, respectively), annual litterfall (135-162 g/m2/year), needle grazing loss (1.55 g/m2/year), fine tree root production (188 g/m2/year), and understory root production (30 g/m2/year). Revision Notes: The NPP data file has been split into two files, one for the young regenerating forest and one for old-growth forest. The data files have been revised to correct previously reported data and information and to add new data from published sources. Please see the ORNL DAAC Data Set Change Information file for more information.\n<br>\n\n#### NPP_KSM_466\nThis data set contains three files (.txt format). One file provides stand characteristics, biomass, and production allocation data for an old-growth boreal forest near Kuusamo, Finland. The research was conducted during the 1967-1972 growing seasons. The other two files provide climate data from a weather station about 60 km south of the forest. One record contains precipitation and mean average temperature data for the 1961-1994 period (excluding 1971-1980) and the other contains precipitation data for 1908-1994. The Kuusamo research site is located just south of the Arctic Circle (66.37 N 29.32 E) and belongs to the northern boreal zone of taiga forests. The forest is an old Hylocomium-Myrtillus type spruce forest which has remained in a natural state and reached climatic climax long ago. The average age of the dominant spruces (Picea abies) is about 260 years. There is a well-developed ground layer of vegetation, chiefly dwarf scrub and mosses (dominant species: Vaccinium myrtillus, V. vitis-idaea, Hylocomium splendens, and Pleurozium schreberi). The northerly location of the forest and the age of its trees are the main factors responsible for low biomass and net production figures in comparison with spruce forests further south. Total above-ground biomass (including tree, understory, and moss layers) was determined by harvest methods and estimated to be 10,194 g/m2. Below-ground tree biomass estimates, also determined by harvest methods, are less reliable, at 3,753 g/m2. Total net primary productivity (NPP) for this site was estimated to be 441 g/m2/yr (421 g/m2/yr above-ground, 20 g/m2/yr below-ground).\n<br>\n\n#### NPP_MSS_572\nThis data set contains two files (.txt format) for study sites in the Mississagi River area of Ontario, Canada (46.35 N -83.38 W elevation 860 m). One file summarizes the results of a series of investigations on the nutrition of jack pine ecosystems, including stand characteristics, above-ground biomass, and where available, litterfall amount and nutrient content of vegetation, litterfall, and soil horizons. Field data were collected in three jack pine (Pinus banksiana) study plots between 1969 and 1973. The stands were 20, 30, and 65 years of age. The second file contains climate data recorded at two weather stations near the Mississagi sites. Precipitation data were obtained from Saulte Sainte Marie, Michigan, USA (46.5 N -84.4 W elevation 218 m) and temperature data were obtained from Saulte Sainte Marie, Ontario, Canada (46.5 N -84.5 W elevation 188 m). Net primary productivity (NPP) was not directly measured, but is estimated based on above-ground tree growth and litterfall. For the different aged stands, above-ground tree growth was estimated at 262 g/m2/yr (0-20 years), 289 g/m2/yr (20-30 years), and 93 g/m2/yr (30-65 years). Annual tree litter production for the 30-year-old stand averaged 372.9 g/m2/yr over the course of 3 years. Understory litterfall production for the 30-year-old stand in one year was 33.1 g/m2/yr. Revision Notes: The NPP and climate data files for Mississagi have been revised to correct previously reported temporal coverage and litterfall data. Please see the Data Set Revisions section of this document for detailed information.\n<br>\n\n#### NPP_SCH_573\nThis data set contains two files (.txt format). One file provides above- and below-ground biomass, soil, and nutrient data for a mature boreal ecosystem (subarctic Picea mariana/lichen woodland) near Schefferville, Canada (54.72 N, -67.70) for the 1974 growing season. The second data file contains climate data (precipitation amount and maximum/minimum temperature) from a weather station located 22 km northeast of the study site for the 1948-1990 period. The black spruce/lichen woodland is a vegetation type found in the transitional zone between boreal forest and tundra on well-drained, nutrient-poor podzolic soils. These spruce/lichen woodlands are generally not subject to attack by herbivory, but natural fires are common. The study forest was estimated to be 110 years old, based on annual tree ring data which showed the number of years since it was last burned. Biomass estimates were determined by harvesting trees, shrubs, and ground cover in the 0.2 ha study plot. To confirm the \"typical\" nature of the site, species composition and density were evaluated for the principal plot and compared to that of fifteen other plots. Organic and mineral soils were also extracted. The plant and soil samples were evaluated for nutrient and mineral content. Living tree, shrub, and lichen components contributed a total biomass of 2,636, 833, and 939 g/m2, respectively. NPP was estimated by the Terrestrial Ecosystem Model (TEM) to be about 340 g/m2/yr. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2001.\n<br>\n\n#### NPP_SSP_467\nThis data set contains two files (comma-separated-value format). One file provides components of net primary productivity, standing biomass, age and stand structure, and litterfall data for 11 stands of Scots pine (Pinus sylvestris) in the Tomsk Region of Russia (approx. 58 N 83 E). The second file contains data for the same types of variables for three stands of Scots pine in the Irkutsk Region of Siberia (approx. 53 N 103 E). Field measurements were made in 0.3-0.4 ha forest plots between 1968 and 1974. The forest plots range in age from 25 to 122 years old. Tree biomass was determined from volume and density measurements and selective harvest. Understory and ground cover was harvested in 0.25m2 plots. Root mass has determined from harvested trees and soil monoliths. Wood increment was measured from annual rings. Root production was based on species-specific turnover rates. Leaf litterfall was measured in 0.5-1.0 m2 traps, and branch litterfall was estimated from 4.0 m2 plots. Revision Notes: The NPP data file has been split into two files, one for the Tomask forests and one for the Irkutsk forests. The data files have been revised to rearrange columns, add a total ANPP column, and correct previously reported data, where needed. Please see the Data Set Revisions section of this document for detailed information. The Net Primary Productivity (NPP) data collection contains field measurements of biomass, estimated NPP, and climate data for terrestrial grassland, tropical forest, temperate forest, boreal forest, and tundra sites worldwide. Data were compiled from the published literature for intensively studied and well-documented individual field sites and from a number of previously compiled multi-site, multi-biome data sets of georeferenced NPP estimates. The principal compilation effort (Olson et al., 2001) was sponsored by the NASA Terrestrial Ecology Program. For more information, please visit the NPP web site at http://daac.ornl.gov/NPP/npp_home.html.\n<br>\n\n#### NPP_SNF_190\nThis data set contains two files (.txt format). One file provides ground-based biophysical measurements and above-ground net primary productivity (ANPP) estimates for 31 black spruce (Picea mariana) and 30 quaking aspen (Populus tremuloides) stands in Superior National Forest (SNF) in northeastern Minnesota, U.S.A. (-92 W 48 N). The measurements were obtained during a 1983-1984 intensive field campaign. Non-destructive measurements were made in over 100 forest plots covering a 50 x 50 km area. Trees sacrificed for biomass and annual increment measurements were taken outside the plots. The second file provides climate data from nearby weather stations for the period 1976-1986. The data set provides stand structural measurements (diameter at breast height, tree height, crown depth, and stem density), above-ground biomass, leaf area index, bark area index, and ANPP estimates. ANPP data are based on a combination of allometric relationships and annual tree-ring (radial) increments for the 5-year period 1979-1983. In the spruce stands, above-ground biomass ranged from 700-15,100 g/m2, LAI varied between 0.5-4.3, and ANPP ranged from 39-572 g/m2/yr. In comparison, above-ground biomass among aspen stands ranged from 600-22,000 g/m2, LAI varied between 1.3-4.0, and ANPP ranged from 213-1,199 g/m2/yr. The purpose of the SNF campaign was to investigate the ability of remote sensing to provide estimates of ecosystem biophysical properties. In addition to the results presented herein, satellite, aircraft, and helicopter observations and other ground measurements for the study sites are available from the ORNL DAAC Superior National Forest (SNF) Project web site [http://daac.ornl.gov/SNF/snf.shtml]. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1997.\n<br>\n\n#### cropland_612\nThis data set contains a single data file (.csv format) that provides gridded values of net primary productivity (NPP) for cropland in eight counties in the central United States for the year 1992 and estimates of interannual cropland NPP in Iowa for years from 1982 through 1996. The data file also includes climate, soil texture, and land cover data for each 0.5 degree grid cell. The magnitude and interannual variation in NPP was estimated using crop area and yield data from the U.S. Department of Agriculture, National Agricultural Statistics Service (NASS). The major harvested commodities were corn, soybean, sorghum, sunflower, oats, barley, wheat, and hay. Total NPP estimates include both above- and below-ground components. County-level NPP in 1992 ranged from 195 to 760 gC/m2/year. The area of highest NPP, ranging from 650 to 760 gC/m2/year, was found in a band extending across Iowa, through northern Illinois, Indiana, and southwestern Ohio. Areas of moderate NPP, from 550 to 650 gC/m2/year, occurred mostly in Michigan and Wisconsin, while large areas of low NPP, from 200 to 550 gC/m2/year, occurred in North Dakota, southern Illinois, and Minnesota. The area of highest production was also the area with the largest proportion of land sown with corn and soybean. NPP for counties in Iowa varied among years (1982-1996) by a factor of 2, with the lowest NPP in 1983 (which had an unusually wet spring), in 1988 (which was a drought year), and in 1993 (which experienced floods). Revision Notes: The documentation for this data set has been modified, and the data files have been reformatted. The data files have been checked for accuracy and the contents are identical to those originally published in 2001.\n<br>\n\n#### NPP_BDK_203\nThis data set provides two data files in text format (.txt). One file contains estimates of above-ground live phytomass for an ephemeral desert steppe located in the Badkhyz Nature Reserve in southern Turkmenistan. Monthly measurements of plant biomass were made by harvest during the growing season (January-May) from 1948 through 1963. Afterwards, an annual measurement of peak live biomass was made in May from 1964 through 1982, with gaps for years 1973-1976. The second file contains monthly and annual climate data recorded at the study site from 1941 through 1982. Above-ground net production (ANPP) for the Badkhyz site is the lowest among the eight Eurasian grassland sites, at 100 g/m2/yr. Below-ground NPP for Badkhyz (1,745 g/m2/yr) was conservatively estimated from a similar dry, semiarid continental steppe in Northern Kazakhstan (i.e., Shortandy). Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998.\n<br>\n\n#### NPP_BCN_204\nThis data set contains two ASCII text files, one providing productivity measurements for a chalk grassland on Beacon Hill, West Sussex, U.K. (50.92 N, -0.85 W) and the other containing climate data from a weather station at the former King's College London, Rogate Field Centre, 6 km distant (51.01 N, -0.85 W). Measurements of above-ground live biomass and total dead matter were made by harvesting 0.25 m2 quadrats in the 20 x 20-m study area at eight to ten week intervals from March 1972 to April 1973. Precipitation amount and minimum/maximum temperature were recorded from 1969 through 1993. Above-ground net primary production (ANPP) was estimated by several methods: 332 g/m2/year (annual increase in living biomass, sum of species); 355 g/m2/year (peak or maximum live biomass, plant dry matter weight); 773 g/m2/year (maximum live + dead biomass); 310 g/m2/year (annual increase in living biomass carbon by summing positive increments in biomass); and 691 g/m2/year (annual net production accounting for leaf turnover). The carbon content of ANPP (accounting for leaf turnover) was estimated to be 310 gC/m2/year using a conversion factor of 0.45. Below-ground production was not measured. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998.\n<br>\n\n#### NPP_BRD_205\nThis data set contains three ASCII text files for the Bridger grassland study site in the Rocky Mountains (45.78 N, -110.78 W, Elevation 2,340 m). Two files contain above- and below-ground biomass data, one for each treatment (ungrazed and moderately grazed). The third file contains climate data from the Big Timber weather station (45.80 N, -110.00 W, Elevation 1,249 m) near Bridger. Dynamics of above- and below-ground plant biomass were monitored by harvest technique at roughly 2-week intervals during the growing season for the years 1970, 1972, and 1973. Data on above-ground live biomass, standing dead matter, and litter are provided for each sampling date. Below-ground biomass (roots and crowns) are provided for 0-30 cm and 0-50 cm depths. Data were collected as part of a coordinated study over 1-3 years at ten grassland sites of the central and western United States under the U.S. Grassland Biome Project of the International Biological Program (IBP). Above-ground net primary production (ANPP) was estimated, conservatively, by summing peak biomass of individual species. Below-ground net primary production (BNPP) was estimated as the sum of positive increments in total root biomass (including root crowns). Values varied according to treatment. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998.\n<br>\n\n#### NPP_CLB_206\nThis data set contains four ASCII text files for a 260-hectare humid Trachypogon savanna at the Estacion Biologica de Los Llanos, Calabozo, Venezuela (8.93 N, -67.42 W, Elevation 98 m). Two data files contain monthly estimates of above-ground biomass for the period January 1969 to October 1969, one file for each treatment (burned and unburned plots). These files also provide one estimate of below-ground biomass. Another NPP data file contains monthly estimates of above- and below-ground biomass, LAI, and nitrogen content of living and dead leaves and stems and below-ground biomass for an unburned area of the Calabozo savanna for March 1986 to April 1987. The fourth data file contains precipitation and maximum/minimum temperature data from a nearby weather station at Estacion Biologica de Los Llanos (8.88 N, -67.32 W, Elevation 86 m) for the period 1968-1986. Harvest methods were used to estimate above- and below-ground biomass. Total NPP (above- plus below-ground productivity) was estimated at 682 g/m2/yr for unburned and 755 g/m2/yr for burned grassland plots in 1969. Later TNPP estimates (1986/87) for the unburned grassland ranged from 823 to 1,310 g/m2/yr. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998.\n<br>\n\n#### NPP_CNS_191\nThis data set contains two ASCII text files; one providing above-ground biomass, productivity, and bioelement concentration data for a derived savanna at Cañas (10.4 N 85.1 W Elevation 45 m) in northwestern Costa Rica, and the other providing climate data from the La Pacifica weather station near Cañas and rom other sources. Monthly dynamics of above-ground plant matter were monitored from July 1969 to June 1970 using harvest procedures within an exclosure to restrict grazing. The climate data are available from three time periods: 1951-1960, 1963, and 1969-1970. The climate is characterized by a dry season from late November to April, with little seasonal differences in temperature. The Cañas study site is dominated almost exclusively by Hyparrhenia rufa, a perennial grass of African origin introduced extensively throughout the tropics. The original vegetation cover was closed semideciduous forest. The study area was deforested and converted to a grass savanna around January 1947, 22.5 years before the start of the present study. Light grazing by cattle and horses is accompanied by burning of the Cañas savanna annually between December and April. Above-ground net primary productivity (ANPP) was estimated by two methods: maximum standing crop of herbaceous shoot tissue (peak live + dead matter) (968 g/m2/year); and the sum of monthly estimates of shoot production (including estimated mortality) (1,387 g/m2/year). End-of-season (November, 1969) live root biomass estimates of 1,220 g/m2 at 0-20 cm depth and 2,254 g/m2 at 0-100 cm depth are available in the literature.\n<br>\n\n#### NPP_CPR_145\nThis data set records the productivity of a semiarid shortgrass prairie steppe located in the Central Plains Experimental Reserve (CPER)/Pawnee National Grassland in north-central Colorado. There are nine data files (.txt). Four files contain measurements of monthly dynamics of harvested above-ground plant biomass, one file each for untreated, irrigated, fertilized, and irrigated + fertilized plots for the period 1970 to 1975. The fifth file contains annual above-ground NPP estimates for the untreated plot for the period 1970-1974. The sixth file contains long-term ANPP estimated from field harvest measurements made between 1970 and 1990 and by correlation with forage production measurements made between 1939 and 1990. Two additional files provide estimates of above- and below-ground NPP based on peak growing season harvests; one record covers 1970-1972 from the Pawnee site and the other covers 1985-1988 from CPER. The ninth file contains climate data for 1912-1990 from a weather station located at CPER.Revision Notes: This data set has been revised to correct the study site elevation, extend the temporal coverage, and add four data files containing estimates of NPP. Please see the Data Set Revisions section of this document for detailed information.\n<br>\n\n#### NPP_CHR_468\nThis data set contains three ASCII files (.txt format). One file provides above- and below-ground biomass, productivity, litterfall, and bioelement data for a native C3 grassland near Charleville (-26.40 S, 146.27 E, Elevation 304 m) in southern Queensland, northeast Australia. The second file provides above- and below-ground biomass and productivity estimates for an introduced C4 grassland near Charleville. The third file contains climate data (precipitation and maximum/minimum temperature) recorded a weather station located at the Charleville Airport for the period 1942-1994. The NPP studies were carried out over a 12-month period from 1973 to 1974 using harvest techniques with a view to parameterizing a simulation model of primary production and livestock carrying capacity. Peak above-ground standing crop at the end of the summer season was 122 g/m2 and 154 g/m2 for the native and introduced grasslands, respectively. Maximum below-ground standing crop was markedly different, at 110 g/m2 and 400 g/m2, respectively, suggesting a significant difference in shoot/root allocation. Annual net primary production was estimated as the sum of above-ground peak standing crop (live + dead) and root increment. These values were 182 and 319 g/m2/yr for the native and introduced grasslands, respectively. Additional data on litter production and nutrient dynamics are available for the native grassland site. Data on soil moisture, determined gravimetrically with each biomass harvest, are available in the literature.\n<br>\n\n#### NPP_Grassland_613\nThe NPP Database contains documented field measurements of NPP for global terrestrial sites compiled from published literature and other extant data sources. The NPP Database contains biomass dynamics, climate, and site-characteristics data georeferenced to each intensive site. A major goal of the data compilation is to use consistent and standard well-documented methods to estimate NPP from the field data. Other important components of the database include a summary, investigator contact information, and a list of key references for each site. As far as possible, the original principal investigator or his/her successor has been contacted to review the data and documentation. The NPP Database currently contains detailed data for over 60 intensive study sites. A majority of these sites are grasslands, the remainder being tropical forests, boreal forests, and tundra study sites. Some combination of above-ground annual peak live biomass data and/or seasonal biomass dynamics data are available for all sites. Many sites also have data on below-ground biomass and/or turnover. Estimates of net primary productivity are included, where available, for individual sites, and as part of the NPP Summary tables. Climate and soils data are available for all sites in varying degrees of detail. The sites have been grouped according to vegetation maps based upon Bailey ecoregions, Holdridge Life-Zones, Matthews vegetation classes, and Olson World Ecosystem Complexes. Previously compiled multi-site data sets of georeferenced NPP estimates are also provided. NPP estimates are available from a number of different collections, containing more than 1700 sites but with less information available for each individual site as compared to the intensive sites. Records for these sites typically include an NPP value, latitude and longitude, original source of the data, and sometimes information on vegetation type, management, soils, and local climate. More information on the entire Net Primary Productivity Project can be found at the NPP home page, with links to further details on individual study sites or multi-site collections. Users are encouraged to browse these Web pages to find details of original studies, methodologies, and original research contacts. NPP data are available on-line from the ORNL Distributed Active Archive Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee, U.S.A. Data Citation: Cite the data sets using the following reference format: Author, P. A., and M. B. Author. Year. Data Set Title. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. For example: Williamson, P., and J. Pitman. 1999. NPP Grassland: Beacon Hill, U.K. 1972-1973. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.\n<br>\n\n#### NPP_DCK_207\nThis data set contains three ASCII files (.txt format). Two files contain above- and below-ground biomass and productivity data for a northern mixed prairie grassland, one file for an ungrazed treatment and the other for a heavily grazed treatment. The study site (46.90 N, -102.82 W, Elevation 784 m) is located in the northern Great Plains, near the city of Dickinson, about 160-km west of Bismarck, North Dakota. The third file contains climate data for the period 1891-1994 obtained from a weather station near Dickinson (46.88 N, -102.80 W, Elevation 750 m). Dynamics of above-ground living and dead plant biomass were monitored by the harvest technique at roughly 2-week intervals during the growing season of 1970. Total below-ground biomass was sampled at the same intervals by manual coring within the harvested plots to a depth sufficient to include at least 90% of the root mass. Data were collected as part of a coordinated study over 1-3 years at ten grassland sites of the central and western United States, under the US Grassland Biome Project of the International Biological Program (IBP). Above-ground net primary productivity (ANPP) was estimated conservatively by summing peak biomass of individual species. These values were 351 g/m2/year for ungrazed and 302 g/m2/year for grazed grassland plots. Below-ground net primary productivity (BNPP) was estimated as the sum of positive increments in total root biomass (including root crowns); 932 g/m2/year for ungrazed and 958 g/m2/year for grazed grassland plots. Revision Notes: Only the documentation for this data set has been modified. The files have been checked for accuracy and are identical to those originally published in 1998.\n<br>\n\n#### NPP_DZH_192\nThis data set provides two data files in text format (.txt). One file contains a long time series of biomass measurements made between 1955 and 1989 on a semi-desert steppe at the Dzhanybek Research Station in Kazakhstan (49.33 N 46.78 E Elevation 20 m). The second file contains monthly and annual climate data for the study site for the period 1953-1989. Peak live biomass measurements were made from 1955 to 1989 (excluding 1976) and additional measurements of above-ground live biomass and dead matter were made seasonally from 1985 to 1989. Averaged over the time series, above-ground live phytomass and standing dead were estimated to be 137 g/m2 and 32 g/m2 (dry matter weight), respectively, while below-ground phytomass dry weight was 1,750 g/m2. ANPP was estimated to be 201 g/m2/yr. BNPP and TNPP were not estimated. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1997.\n<br>\n\n#### NPP_HYS_208\nThis data set contains three ASCII files (.txt format). Two files contain above- and below-ground biomass and productivity data for a mixed prairie grassland, one file for an ungrazed treatment and the other for a moderately grazed treatment. The study site (38.87 N, - 99.38 W, Elevation 714 m) is located in the central Great Plains near the city of Hays, Kansas, about 400-km west of Kansas City. The third file contains monthly and annual climate data for the period 1891-1994 obtained from a weather station (38.87 N, -99.38 W, Elevation 613 m) located at the Hays grassland study site. Dynamics of above-ground living and dead plant biomass were monitored by the harvest technique at roughly 2-week intervals during the growing season of 1970. Total below-ground biomass was sampled at the same intervals by manual coring within the harvested plots to a depth sufficient to include at least 90% of the root mass. Data were collected as part of a coordinated study over 1-3 years at ten grassland sites of the central and western United States, under the US Grassland Biome Project of the International Biological Program (IBP). Annual above-ground net primary production (ANPP) was estimated conservatively by summing peak biomass of individual species. These values were 363 g/m2/year for ungrazed and 372 g/m2/year for grazed grassland plots. Annual below-ground net primary production (BNPP) was estimated as the sum of positive increments in total root biomass (including root crowns); 1,062 g/m2/year for ungrazed and 855 g/m2/year for grazed grassland plots.\n<br>\n\n#### NPP_JRN_209\nThis data set contains three ASCII files (.txt format). Two files contain above- and below-ground biomass and productivity data for a desert grassland in the Jornada Experimental Range, New Mexico, one file for an ungrazed treatment and the other for a light to moderately grazed treatment. The study site (32.60 N, -106.85 W, Elevation 1,350 m) is located in the Basin and Range geomorphic province at the northernmost extent of the Chihuahuan Desert, near the city of Las Cruces, New Mexico, about 60-km northwest of El Paso, Texas. The third file contains climate data for the period 1954-1992 obtained from a weather station located near the study site (32.62 N, -106.73 W, Elevation 1,300 m). Dynamics of above-and below-ground plant biomass were monitored at roughly 2-week intervals during the growing season from 1970 to 1972. Data on above-ground live biomass, recent and old dead matter, and root-crown biomass are available for one to two replications of grazed and \"ungrazed\" (relatively undisturbed) treatments. Total below-ground biomass was also sampled. Data were collected as part of a coordinated study over 1-3 years at ten grassland sites of the central and western United States, under the US GrasslandBiome Project of the International Biological Program (IBP). Annual above-ground net primary production (ANPP) was estimated, conservatively, by summing peak biomass of individual species, and annual below-ground net primary production (BNPP) estimated as the sum of positive increments in total root biomass.\n<br>\n\n#### NPP_KHM_146\nThis data set provides two data files in text format (.txt). One file contains biomass measurements made in 1948 and between 1967 and 1970 for a humid temperate steppe in the Khomutovskaya Steppe Nature Reserve in the Donezk Region of Ukraine. The second file contains monthly and annual climate data for the study site for the period 1955-1972. Biomass measurements were made once in July of 1948 and biweekly to monthly during the growing season (April-August/September) from 1967 to 1970 at the permanent Khomutovskaya research station. ANPP was calculated for each sampling date and cumulatively over the four years, 1967-1970. Averaged over the time series, above-ground live phytomass, standing dead, and litter biomass were estimated to be 340, 90, and 240 g/m2 (dry matter weight), respectively, while below-ground phytomass and mortmass were estimated to be 1,675 and 792 g/m2 (dry matter weight), respectively. ANPP was estimated to be 460 g/m2/yr. BNPP was estimated to be 1,340 g/m2/yr.\n<br>\n\n#### NPP_KLN_147\nThis data set provides three data files in text format (.txt). One file contains monthly biomass measurements and Net Primary Productivity (NPP) estimates made between April 1984 and July 1990 on a semi-natural tropical monsoon grassland in southern Thailand. The second file contains a year-long record of monthly biomass measurements and NPP estimates made on a portion of the same grassland that was accidentally burned in January 1985. The third file contains monthly and annual climate data for the study site for the period 1973-1989. Two accidental fires occurred during the course of the study; the one in January 1985 burnt 40% of the study area and the other in February 1986 burnt most of the site. During 1985, sampling occurred simultaneously on unburned primary and burned secondary portions of the grassland; afterwards sampling continued on the primary site. Measurements of above- and below-ground live and dead biomass were made on the 15th day of each month. NPP estimates were calculated from changes in biomass and decomposition rates in unburned areas from 1984 through 1986. From April 1984 to April 1985, NPP was 2,036 g/m2/yr, with below-ground organs contributing 23% of this total. Production in the unburned area in 1985 was substantially lower (1,677 g/m2/yr) which coincided with a 35% decrease in mean leaf area index. NPP in the 1985 burned area was only slightly lower (1,524 g/m2/yr); however, NPP was substantially lower after the second fire in 1986 (134 g/m2/yr). Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996.\n<br>\n\n#### NPP_KNZ_148\nThis data set contains three ASCII files (.txt format). Two files contain above-ground biomass and productivity data for a humid temperate tall-grass prairie grassland located in the Konza Prairie Natural Research Area, Kansas. One file provides data for an unburned treatment and the other for a burned treatment for 1975 to 1990. The third file contains climate data for the period 1891-1988 obtained from a weather station at Konza. The above-ground net primary productivity measurement presented here (394 g/m2/year) is a 10-year average (1975-1984) based on peak seasonal live biomass values averaged for burned and unburned lowland and upland grasslands. The Konza study site (39.10 N, - 96.61 W, Elevation 400 m) is situated near the town of Manhattan in north-eastern Kansas, about 170-km west of Kansas City. The Konza research program is built upon a long-term database on ecological pattern and process data derived from a fully replicated watershed-level experimental design, in place at the Konza Prairie Biological Station since 1977. This design includes replicate watersheds subject to different fire and grazing treatments. Within the watersheds, permanent sampling transects are replicated at various topographic positions, where plant species composition, plant and consumer populations, above-ground net primary production (ANPP), soil properties, and other key above- and below-ground processes are measured. In addition to these watershed-level studies, the Konza Long Term Ecological Research (LTER) program includes a number of long-term plot-level experiments.\n<br>\n\n#### NPP_KRS_149\nThis data set provides two data files in text format (.txt). One file contains a long time series of biomass measurements made between 1954 and 1983 on a virgin meadow steppe in the Central-Chernozem V.V. Alyekhin Natural Reserve, Kursk Region, Russia. The second file contains monthly and annual climate data for the study site for the period 1947-1983. Above-ground live biomass measurements were made at biweekly to monthly intervals over the entire 30-year time series. Discontinuous measurements of above-ground standing dead matter and litter biomass (1956-1983) and below-ground live and dead biomass (1972-1973 and 1981-1983) were also made. Cumulative ANPP was estimated at the end of the growing season (1956-1963 and 1972-1973) and monthly (1982-1983). Averaged over the time series, above-ground live phytomass, standing dead, and litter biomass were estimated to be 362, 344, and 424 g/m2 (dry matter weight), respectively, while below-ground phytomass and mortmass were 910 and 1,370 g/m2 (dry matter weight), respectively. ANPP was estimated to be 774 g/m2/yr and BNPP was estimated to be 1,700 g/m2/yr for a TNPP estimate of 2,474 g/m2/yr. The study site is one of eight major grassland types of Eurasia which encompass an extremely wide climatic gradient in the direction of increasing maximum summer temperatures and continentality and decreasing precipitation in a north-west to the south-east band of steppes in the European and Asian parts of the former USSR (Commonwealth of Independent States). Kurst, on rich loamy chernozem soil, is one of the most productive upland grassland ecosystems of Russia with annual mean maximum/minimum temperatures of 24.8/-14.4 C and annual mean precipitation of 582.7 mm for the period 1947-1983. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996.\n<br>\n\n#### NPP_KRK_193\nThis data set contains two ASCII files (.txt format). One file contains above- and below-ground biomass (including standing dead material and litter) and productivity data for a tropical grassland at Kurukshetra University (29.97 N, 76.85 E, Elevation 247 m) in northern India, about 150-km north-northwest of Delhi. The second file contains climate data from a weather station located at the study site. Biomass measurements were made monthly by harvest methods from mid-May 1970 to mid-May 1971. Annual net primary productivity (NPP) was calculated for the grassland according to several methods, with preference shown for the estimate given by summing positive increases in biomass and accounting for mortality. Total NPP was estimated at 3,538 g/m2/yr, with above-ground net primary productivity (ANPP) of 2,407 g/m2/yr and below-ground net primary productivity (BNPP) of 1,131 g/m2/yr. Seasonal changes in the vegetation were studied through tiller analysis. Examination of vertical distribution of above-ground biomass showed that different layers of vegetation were dominated by different species in different months. ANPP was maximum during the rainy season (1,706 g/m2) and BNPP was maximum during the dry winter season (785 g/m2). Production was more directed above ground during the rainy season and below ground during the dry season. Apparent efficiency of energy conversion was calculated at 1.66% on the basis of 50% total solar radiation.\n<br>\n\n#### NPP_LMT_150\nThis data set provides three data files in text format (.txt). One file contains monthly above-ground biomass measurements made in 1965 in a humid grass savanna at the Lamto Research Station, Cote Ivoire, Africa. The second file contains monthly above- and below-ground biomass measurements and calculations of carbon/nitrogen ratio of above-ground live and dead biomass and below-ground biomass from the same site for 1969-1987. The third file contains monthly and annual climate data for the study site for the period 1969-1990. Total net primary production (NPP) of the Loudetia simplex grass savanna was estimated at 2,150 g/m2/yr, of which 1,320 g/m2/yr (61%) was below-ground production. Normally 50-90% of above-ground grass biomass is burned annually, Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996.\n<br>\n\n#### NPP_MTD_469\nThis data set contains five ASCII files (.txt format). Three files contain productivity data for a mixed prairie at the Matador Field Station, Canada, and two files contain climate data. The 8-km2 Matador Field Station (50.70 N, -107.72 W, Elevation 676 m) is located approximately 47-km north of the city of Swift Current in southern Saskatchewan Province at the northern limit of the \"mixed prairie\" portion of the North American Great Plains. The study area is located on the bed of a former glacial lake. One NPP file contains monthly measurements of above-ground standing live and dead biomass and litter made in \"Section 16\" by harvest methods during the growing season from March or April 1968 to October or November 1972. The second NPP file contains monthly measurements of root biomass made at different depths in \"Section 16\" during the growing season from April 1968 to July 1971. The third NPP file contains monthly above-ground biomass estimates for a secondary study area during the growing season from May 1970 to August 1972. The climate data are reported from two locations, one file from a weather station at Swift Current (1938-1990) and the other from a weather station at the Field Station during the study period (1968-1972). The above- and below-ground net primary productivity (ANPP and BNPP, respectively) were calculated for \"Section 16\" only. ANPP (363 g/m2/yr) was sum of the increase in green biomass over the growing season, plus increase in dead biomass and litter. BNPP (600 g/m2/yr) was the increase in root biomass over the growing season.\n<br>\n\n#### NPP_MDL_210\nThis data set contains two ASCII files (.txt format). One file provides monthly above-ground live biomass, dead matter, and litter data and daily above-ground net primary productivity (ANPP) data for a temperate grassland steppe at the Media Luna Ranch in Patagonia, Argentina (45.60 S, 71.42 W, Elevation 630 m) for the period May 1981 to March 1983. The second file contains climate data recorded at Media Luna Ranch from 1981 through 1985. Productivity of the steppe was monitored at monthly or bi-monthly intervals during two growing seasons. The ANPP measurement presented here is the sum of the increase in above-ground live biomass, dead matter, and litter. ANPP was estimated to be 0.00-0.15 g/m2/day in winter months (May-September) and 0.22-0.94 g/m2/day in the warmer months. Annual ANNP was estimated to be 35 g/m2/year. The study site is a 2.5-hectare exclosure situated on the Rio Mayo terraces. The steepe represents one of the most important grassland areas of Patagonia which occupy a narrow belt in the foothills of the Andes. The region has been over-grazed by introduced livestock since the early 1900s, and is currently used for sheep production.\n<br>\n\n#### NPP_MNT_413\nThis data set provides three data files in text format (.txt). One file contains monthly biomass measurements and net primary productivity (NPP) estimates made between June 1984 and December 1994 on an ungrazed saline grassland (MNT1) that was accidently burned in February 1986 at the Colegio de Postgraduados field station site, Montecillo, Mexico. The second file contains a shorter time series of monthly biomass measurements and NPP estimates on an adjacent saline grassland (MNT2) that was burned in May 1989, probably by local farmers following normal burning practices. Both files also contain above-and below-ground dead matter decomposition rates. There are data gaps in both files. The third file contains monthly and annual climate data for the period 1963-1989 from the Chapingo meteorological station located 5 km northeast for the study area. Annual NPP at MNT1 was calculated for 1985-1987 to determine impacts of the unexpected fire. Above-ground NPP (NPP) averaged 669.2 g/m2/yr despite post-fire decline and recovery and variable rainfall over the period. Below-ground NPP (BNPP) was higher, averaging 1,007 g/m2/yr, but with a similar post-fire decline and recovery pattern. Total NPP reflected the yearly variation in ANPP and BNPP, averaging 1,676 g/m2/yr over the 3 year period. Monthly ANPP and BNPP values for MNT2 were lower overall but were not analyzed to determine annual trends. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996.\n<br>\n\n#### NPP_NRB_151\nThis data set provides three data files in text format (.txt). One file contains a long time series of monthly biomass measurements and net primary productivity (NPP) estimates made between July 1984 and November 1994 on an ungrazed tropical dry savanna grassland in the Nairobi National Park, Kenya. The second file contains a shorter time series (October 1989-June 1991) of monthly biomass measurements made on an adjacent savanna that had been clipped in September 1989. The third file contains monthly and annual climate data for the study site for the period 1969-1989. Measurements of above-ground live phytomass, standing dead, and litter biomass were made on the 15th day of each month on the ungrazed grassland over the time series (except during 1987 and at a few other times). The time series for below-ground biomass (BNPP) measurements (live and dead) is more discontinuous with large gaps in 1987-1989. NPP estimates are only available for part of 1984 through 1986 on the ungrazed savanna. The above-ground NPP (ANPP) values averaged 1,004 g/m2/yr and BNPP averaged 875 g/m2/yr. The record of above- and below-ground biomass measurements for the clipped savanna is complete except for a few months in 1990. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published.\n<br>\n\n#### NPP_Grassland_31_654\nThis data set includes monthly grassland biomass data, net primary productivity (NPP) estimates, and climate (rainfall amounts and temperature) data for multiple study sites in major grassland types worldwide. Field measurements of biomass and associated environmental data were compiled for the multiple grassland study sites. When sufficient biomass data were available, NPP was estimated by six different algorithms for 31 grassland sites to examine potential bias associated with the algorithms (Scurlock et al. 2002). The data consisted of monthly measurements of biomass components including aboveground live material, standing dead, litter, belowground biomass, and belowground dead material. However, many of the sites did not collect all of the components. There are 1,477 field measurements of some component of NPP, all sites having at least aboveground biomass measurements. Of the 31 sites, 20 also measured standing dead and litter or total live plus dead material. In addition, 17 sites measured total belowground biomass, and six of these sites provided separate measurements of live and dead root components. The study sites had from 1 to 29 years of biomass data with an average of three years per site. Five ecoregions were represented, including cold desert steppe, temperate dry steppe, humid savanna, humid temperate, and savanna. The selection of study sites was based on the availability of complete and consistent information on NPP or at least partial NPP, together with the dynamics of live biomass and dead matter for at least the growing season (Scurlock et al. 2002). Site-description metadata, such as latitude, longitude, elevation, and information on vegetation type (biome), soil type, and land-use history were also desirable for inclusion for study sites in the compilation. Study sites were included that had at least one reference from the peer-reviewed literature. There are two data files in comma-separated (.csv) format with this data set. Revision Notes: Only the documentation for this data set has been modified from the original data set publication.\n<br>\n\n#### NPP_NLS_194\nThis data set contains five data files in text format (.txt). Three files contain biomass dynamics data for a broad-leaved savanna located in the 800-hectare Nylsvley study site 200 km north of Johannesburg, South Africa. One net primary productivity (NPP) file contains monthly above-ground biomass data from harvests made between mid-October 1974 and mid-September 1977. A second NPP file contains three-year mean monthly values for above-ground, standing dead, and litter biomass. The third NPP file contains monthly below-ground living and dead biomass data from excavations made from August 1988 to November 1989. Climate data are provided in the other two files. One file contains air temperature data measured at the study site (1975-1983). The other file contains rainfall data measured at a nearby farmhouse (1917-1995). Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published.\n<br>\n\n#### NPP_OLK_195\nThis data set contains two ASCII files (.txt format). One file contains monthly above-ground biomass data (total live biomass plus dead matter) for May 1956 to February 1958 for an annually burned, humid derived savanna in the Olokemeji Forest Reserve, Nigeria (7.42 N, 3.55 E) . This file also contains single measurements of above-ground biomass for years 1963 and 1964, single measurements of above-ground biomass at a nearby area for years 1960 and 1964, a single measurement of peak herbaceous leaf area index (LAI) for 1963, and a single measurement of peak tree/shrub LAI for 1964. Harvest procedures were used to measure biomass. LAI was determined by direct measurements. The second file contains climate data (precipitation amount and maximum/minimum temperature) from a weather station at the study site for the period 1956/01/01 through 1964/12/31. Annual above-ground net primary production (ANPP) estimates presented here are the sum of the increase in above-ground plant matter accumulation (total live biomass plus dead matter). ANPP of the herbaceous layer was estimated in 1957 to be around 680 g/m2/yr based on peak total clipped matter.\n<br>\n\n#### NPP_OSG_211\nThis data set contains three ASCII files (.txt format). Two files contain above- and below-ground biomass and productivity data for the Osage tallgrass prairie study site (36.95 N, -96.55 W, Elevation 392 m) in the U.S. Central Lowlands. There is one file for each treatment area (ungrazed and lightly grazed). The third file contains climate data from weather station at Pawhuska, Oklahoma (36.67 N, -96.35 W, Elevation of 255 m) near Osage. Dynamics of above- and below-ground plant biomass were monitored by harvest technique at roughly 2-week intervals during the growing season for the years 1970-1972. Data on above-ground live biomass, standing dead matter, and litter are available for two replications each at recently grazed and an \"ungrazed\" (relatively undisturbed) grassland sites at Osage. Below-ground biomass was sampled at 0-30 cm and 0-90 cm depths. The data were collected as part of a coordinated study over 1-3 years at ten grassland sites of the central and western United States, under the US Grassland Biome Project of the International Biological Program (IBP). Annual above-ground net primary production (ANPP) was estimated conservatively by summing peak biomass of individual species (346 g/m2/yr), and annual below-ground net primary production (BNPP) was estimated as the sum of positive increments in root biomass (including crown biomass) (542 g/m2/yr). Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998.\n<br>\n\n#### NPP_OTR_152\nThis data set provides three data files in text format (.txt). Two files contain biomass and above-ground net primary production (ANPP) estimates for two upland meadows with contrasting soil types at the Otradnoe research station of the V.L. Komarov Botanical Institute of the Russian Academy of Sciences located on the Karelian peninsula 100-km to the north of St. Petersburg, Russia. The third file contains monthly and annual climate data recorded at the study site for the period 1968-1973. Measurements of above- and below-ground live and dead biomass were made at a sandy meadow (OTRS) from 1969 to 1972 and at a loamy meadow (OTRL) from 1969 to 1973. Additional biomass measurements were made at OTRS in June 1972 and at OTRL in May 1973. Monthly N, P, and S content of above-ground live biomass were measured 1969-1971 at OTRS. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996.\n<br>\n\n#### NPP_PMP_212\nThis data set contains two ASCII files (.txt format). One file contains monthly productivity data measured on an arid dwarf-shrub steppe in northern Patagonia, Argentina from August 1980 to March 1982. The second file contains climate data recorded at a weather station set up onsite for the duration of the NPP study. Dynamics of above-ground biomass, dead matter, and litter were monitored at Pampa de Leman (-45.43 S, -69.83 W, Elevation 400 m) at monthly or bi-monthly intervals in a 1.5-hectare exclosure area protected from sheep grazing. The vegetative community is dominated by the dwarf shrub Nassauvia glomerulosa, with two grasses (Poa dusenii and Hordeum comosum). Annual above-ground net primary productivity (ANPP) of 78 g/m2/yr was estimated from the sum of the increase in above-ground biomass, dead matter, and litter.\n<br>\n\n#### NPP_RMY_574\nThis data set contains three data files in text format (.txt) for a temperate dry steppe at Rio Mayo, Argentia. One file contains quarterly above-ground biomass data for grasses on the steepe (May 1984-May 1985). The second file contains average annual above-ground primary production (ANPP) data for grasses and shrubs for years 1972-1997 based upon peak above-ground biomass estimates. The third file contains precipitation and maximum/minimum temperature data for the Rio Mayo site for the period 1968 through 1990. Rio Mayo is located in the Patagonia region of Argentina. The vegetation is chiefly composed of grasses and shrubs. Harvest methods were used to estimate grass and shrub production. Between 1972 and 1997, peak annual ANPP of grasses plus shrubs ranged from 21 to 75 g/m2/yr, with an average of about 60 g/m2/yr. Grasses accounted for about two-thirds of the productivity. ANPP was reduced in a drought year, but did not increase in relatively wet years, suggesting that it may not be linearly related to precipitation. ANPP for 1984-1985 was slightly higher (79 g/m2/yr) when a different algorithm was used for estimation. Revision Notes: The original npp data file (rmy_npp.txt) has been split into two files, one file containing seasonal biomass and the other containing annual ANPP estimates. The data file containing annual ANPP estimates has been revised to extend temporage coverage and add additional annual ANPP data. See the Revisions section in this document for details.\n<br>\n\n#### NPP_SHR_153\nThis data set provides two data files in text format (.txt). One file contains biomass measurements and cumulative ANPP calculations made between 1977 and 1980 on a dry continental steppe at Shortandy Biological Station in Kazakhstan. The second file contains monthly and annual climate data for the study site for the period 1976-1980. Measurements of above- and below-ground live and dead matter were made at biweekly to monthly intervals during the growing season at Shortandy from 1977 to 1980. Cumulative ANPP estimates are calculated from these measurements. The study site is one of eight major grassland types of Eurasia which encompass an extremely wide climatic gradient in the direction of increasing maximum summer temperatures and continentality and decreasing precipitation in a north-west to the south-east band of steppes in the European and Asian parts of the former USSR (Commonwealth of Independent States). Shortandy represents the semiarid continental grass-forb steppe found on the southern chernozem soils of northern Kazakhstan. The site had annual mean maximum/minimum temperatures of 27.7/-24.6 degrees C and annual mean precipitation of 349.8 mm for the period 1976-1980. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996.\n<br>\n\n#### NPP_TWM_213\nThis data set provides seven data files in text format (.txt). The files provide biomass estimates, soil carbon (C), nitrogen (N), and phosphorus (P) measurements made at an artificially-established grassland savanna study site in Towoomba, South Africa. The study site was part of a long-term experiment to test the effect of fertilizer application. Biomass data are available for the years 1950-1981 (data are not available for 1976 or 1977); soil C, N, and P data are available for the years 1949, 1962, 1980, and 1990. The 1949 estimates were inferred in 1990 from undisturbed savanna adjacent to the experiment. The savanna-fertilizer experiment was several hectares in extent, with five levels of nitrogen and three levels of phosphorus laid out in a randomized block design on an area from which all trees were removed. The ammonium sulphate and superphosphate fertilizers were added during November (50% of total), January (25%) and February (25%). The response to N fertilizer saturated at higher levels, so data from only six (3 x 2) of the 15 possible treatment combinations are provided. Above-ground biomass was sampled by mowing to 5-cm height. It was assumed that 50 g/m2 was left in the field, plus an additional 5% of the mowed dry weight. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published.\n<br>\n\n#### NPP_TLL_196\nThis data set contains three ACSII files (.txt format). Two files contain above-ground biomass data for two ungrazed seashore meadow plots dominated by the saltmeadow rush Juncus gerardii at Tullgarnsnaset, near Stockholm, Sweden (approximately 59.20 N, 17.50 E). There is one file for each plot. The third data file contains monthly and annual climate data from weather station near Stockholm (59.4 N, 18.0 E) for the period 1951-1990. Measurements of above-ground live biomass and total dead matter were made approximately monthly from April 1968 to April 1969. Below-ground biomass was also measured, but the data are not reported in this data set. Annual above-ground net primary production (ANPP) was estimated by several calculation methods, including peak total live plus current dead matter; sum of species maxima (biomass + dead material); single square clippings; and variations of these equations. The rate of disappearance of dead material and mortality were also determined. Mean ANPP estimates ranged from 324 g/m2/yr (max live + dead) to 430 g/m2/yr (taken as the mean of the two sites accounting for disappearance of dead matter).\n<br>\n\n#### NPP_TMN_154\nThis data set provides two data files in text format (.txt). One file contains tri-monthly measurements of above-ground biomass made during the growing season between July 1982 and August 1990 on a dry, cold Eurasian steppe dominated by Stipa grandis at the Tumentsogt Research Station in Mongolia. The second file contains monthly and annual climate data recorded at the study site from 1963 through 1983. Mongolian steppes occupy a major part of eastern Mongolia and northern China and are characterized climatically by low mean annual rainfall and temperature, with a highly seasonal pattern in both. The beginning of spring rainfall and warming are strongly correlated, and the onset of the growing season rainfall triggers the green-up in the region. Land use is dominated by grazing, historically by nomadic pastoralists and more recently for cooperative livestock production. Privatization of grazing land and cropland conversions have been increasing since 1990. Ecosystem degradation such as deterioration of vegetation (e.g., vegetation removal and replacement) and soil (e.g., erosion) is becoming widespread. Peak above-ground biomass at Tumentsogt occurs during a short rainy season (June-August). The amount of biomass fluctuates from year-to-year coherently with rainfall variation. Above-ground net primaryp roductivity (ANPP) estimates are relatively low in comparison to other temperate grasslands, ranging from 72 to 160 g/m2/yr. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996.\n<br>\n\n#### NPP_TMG_470\nThis data set contains four ASCII files (.txt format). Three files contain monthly above- and below-ground biomass data, one data file for each cold meadow steppe studied from 1981 to 1990 at Tumugi, Xingan League, in eastern Inner Mongolia, China (approximately 46.10 N 123.00 E Elevation 191 m). The fourth file contains climate data recorded at a weather station located in the study area for the length of the study. The Tumugi study sites consist of three different natural steppes dominated by Filifolium sibiricum, Stipa baicalensis, and Leymus chinense, respectively. Measurements of above- and below-ground live biomass were made monthly throughout the growing season (March to November) by clipping 1.0 m2 quadrats and sampling 1.0 m2 soil pits to a depth of 1.0 m, respectively. The study areas had been protected from grazing since 1976. Above-ground net primary productivity (ANPP) was estimated at 155 g/m2/year (average for the three steppes, based on peak above-ground living biomass). Peak live below-ground biomass was used to estimate below-ground net primary productivity (BNPP): 968 g/m2/year for the F. sibiricum steppe; 983 g/m2/year the Stipa baicalensis steppe; and 1,022 g/m2/year for the L. chinense steppe. Above- and below-ground biomass data were compared with simulation results from the CENTURY model. Simulated data agreed reasonably well with the observed data (within +25%).\n<br>\n\n#### NPP_TVA_155\nThis data set provides two data files in text format (.txt). One file contains biomass measurements and cumulative ANPP estimates made between 1978 and 1985 at an ultracontinental steppe at the Tuva Research Station in Russia. The second file contains monthly and annual climate data for the study site for 1976-1985. Monthly measurements of above-ground live phytomass, standing dead, and litter biomass were made during each growing season (May-August) of the eight-year study period. Harvests of below-ground biomass were made at the end of the growing season in some years. A year-end measurement of above-ground biomass (particularly standing dead and litter) was also made in 1980. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996.\n<br>\n\n#### NPP_VND_197\nThis data set contains four data files in text format (.txt). Three files provide above- and below-ground productivity data for three derived savannas on the Vindhyan plateau in northern India from 1986 to 1989, one file for each of three treatments. Each study site (Ranitali, Hathinala, Telburva) contains three treatment areas: ungrazed; grazed annually for 30-40 years; and grazed but temporarily fenced for 2-6 years prior to the study. The fourth file provides climate data from a weather station at Daltonganj, India, for the period 1893-1990. Monthly dynamics of above- and below-ground biomass were measured by harvest methods in each treatment area at each site for two annual cycles (1986/1987 and 1987/1988). Additional above-ground peak biomass data (live shoot + dead shoot) for October 1988 from ungrazed and grazed plots represent the 1988/1989 annual cycle. Annual above-ground net primary production (ANPP) was estimated using trough-peak analysis of increments in live biomass, standing dead matter, and litter. Annual below-ground production (BNPP) was estimated from biomass increments combined with root in-growth studies. Mean ANPP for the ungrazed treatment areas on the three sites ranged from 377 to 664 g/m2/yr over the 1986 to 1989 period. Mean BNPP in the same areas was estimated at 510 and 727 g/m2/yr for 1986/1987 and 1987/1988, respectively. Mean total ungrazed NPP (ANPP + BNPP) was estimated at 1,082 and 1,391 g/m2/yr for 1986/1987 and 1987/1988, respectively. Revision Notes: The NPP data for the temporarily fenced grassland sites for the 1987/1988 annual cycle have been revised to correct previously reported BNPP estimates. Please see the Data Set Revisions section of this document for detailed information.\n<br>\n\n#### NPP_XLN_156\nThis data set provides two data files in text format (.txt). One file contains bi-weekly measurements of above-ground live biomass recorded during the growing season (early May to early October) from 1980 through 1989 on a cold desert steppe at the Inner Mongolia Grassland Research Station of the Chinese Academy of Sciences within the Xilingol Biosphere Reserve. The second file contains monthly and annual climate data recorded at the study site from 1978 through 1989. The study site contains grassland steppes of Leymus chinense and Stipa grandis which are the dominant vegetation types, respectively, in the Eastern Eurasian steppe zone (semi-arid and sub-humid) and the middle Eurasian steppe zone (semi-arid). Both steppes provide good livestock forage and are used mainly as natural grazing lands. Above-ground net primary production (ANPP) was estimated by summing peak live biomass of each of 5 species categories. Peak live biomass of L. chinense steppe occurred between late July and late August and averaged 182.68 g/m2 between 1980 and 1988 while that of S. grandis steppe occurred in mid August to early September and averaged 144.43 g/m2 over the same time period. Mean ANPP for L. chinense steppe during 1980-1989 was 248.63 g/m2/yr. ANPP for S. grandis steppe was not calculated. Data are only provided for the Leymus chinense steppe in this data set. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996.\n<br>\n\n#### NPP_IBP_198\nThis data set provides four data files containing net primary productivity (NPP) data, edaphic characteristics, average climatic conditions, and basic descriptive and quantitative information on vegetation for 117 globally-distributed terrestrial forest sites. The data set was derived from the IBP (International Biological Programme) Woodlands Data Set of DeAngelis et al. (1981). The data were collected from the mid 1950s to the early 1970s and were compiled into an electronic data set at the Oak Ridge National Laboratory to facilitate comparisons involving the diverse woodland ecosystems. One data file provides a complete synthesis of NPP, vegetation, edaphic, and climate data and data-source references for each of the 117 sites as published in DeAngelis et al. (1981) for a total of 5,887 records. The second file provides site location, biome, and selected forest productivity and soils data for the 117 sites. The third file provides summary climate data (temperature, precipitation, radiation, growing season length) for each site, and the fourth file provides forest type, investigator(s), and years of the study for each site. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally archived in 1997 (DeAngelis, et al, 1997.)\n<br>\n\n#### NPP_ODS_214\nThis data set contains three files. The first file provides net primary productivity (NPP) estimates, vegetation characteristics, and summary climate data for 720 globally-distributed terrestrial sites. Each site is geographically referenced (latitude/longitude) and classified according to biome (i.e., cropland, desert, forest, grassland, Mediterranean, pasture, plantation, savanna, tundra, and wetlands), where known. The data were extracted and synthesized from scientific literature dating from 1869 to 1982. The majority of references were published in the 1960s and 1970s. The second file provides a summary of climate, vegetation type, species, and type for the 720 records. The third file provides the bibliography of 858 original-source references of data on NPP from Esser et al. (1997). Literature that is not directly cross-referenced to the NPP data set records is marked with an asterisk (*). Of the 720 unique NPP records, about two-thirds have above-ground net primary production (ANPP) estimates that range between 1 and 8,530 g/m2/year dry matter, one-fourth of the sites have estimates for below-ground NPP that range between 0 and 5,828 g/m2/year, and more than half of the sites have total NPP estimates that range from 3 to 9,320 g/m2/year dry matter. The high-range estimates are lower when doubtful values, wetlands estimates, and estimates for crops/pastures and other managed systems are excluded from calculations. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998.\n<br>\n\n#### NPP_GPPDI_617\nNet primary productivity (NPP) estimates were compiled by the Global Primary Production Data Initiative (GPPDI). The database covers 2,523 individual sites and 5,164 half-degree grid cells and underwent extensive review under the Ecosystem Model-Data Intercomparison (EMDI) process. The GPPDI database includes NPP measurements that were collected over a long time period by many investigators using a variety of methods. The measurements are categorized as either Class A, from intensively studied sites; Class B, from extensive sites; or reported as Class C, 0.5 latitude-longitude grid cells. The data set contains six comma-separated files (.csv format). There are two files for each class. One file for each class contains site locations, elevation, NPP estimates, climate data, biome and dominant species information, and references. The other file for each class contains model validation outlier flags derived from site-specific reviews. This document and a companion file (Olson et al., 2001) describe the compilation of NPP estimates under the GPPDI. The results of the EMDI review and outlier analysis produced a refined set of NPP estimates and model driver data (the EMDI database; Olson et al., 2001; 2013). Another ORNL DAAC data set (Zheng et al., 2013) contributed to the compilation of GPPDI. Revision Notes: This data set has been revised to correct previously reported ANPP, BNPP, and TNPP estimates for three OTTER Transect sites, USA, in the Class A NPP data file and BNPP, and TNPP estimates for Vindhyan, India, in the Class B NPP data file. Please see the Data Set Revisions section of this document for detailed information.\n<br>\n\n#### NPP_MULTIBIOME_653\nThis data set contains one data file (.csv format) that provides net primary productivity (NPP) estimates for 34 grasslands, 14 tropical forests, and 5 boreal forest sites distributed worldwide. The NPP data were compiled from published literature. In addition to above- and below-ground NPP, and total NPP estimates, the file includes site name and location, biome type, mean annual precipitation, and mean annual temperature, where available. Aboveground net primary production (ANPP), ranged from 35 to 2,320 g/m2/year, belowground net primary production (BNPP) ranged from 20 to 1,832 g/m2/year, and total net primary production (TNPP) ranged from 182 to 3,538 g/m2/year. Revision Notes: This data file has been revised to add a negative sign to south latitude and west longitude decimal degree coordinates, and the compass direction (N, S, E, W) for coordinates has been removed. NPP data for Vindhyan, India; Atherton, Australia; John Crow Ridge, Jamaica; and La Selva, Costa Rica, have been revised to correct previously reported values. Additional data references for Kuusamo, Finland, and La Selva, Costa Rica, have been added. Please see the Data Set Revisions section of this document for detailed information.\n<br>\n\n#### NPP_GRIDDED_GPPDI_614\nThis data set provides two data files (.csv format) containing gridded (0.5-degree) estimates of net primary productivity (NPP), elevation, temperature, precipitation, NDVI, and biome type for selected terrestrial regions of the world. The field data used to develop NPP estimates came from 15 worldwide data sources in several different biomes covering the period 1954-1998. NPP values were developed from natural resource field inventories (e.g., forest, rangeland, crop) at different scales, from plot to county; from data compiled from published literature and high resolution maps; from simulation models using key independent variables; from regression analyses with environmental variables; and by using relationships between remotely-sensed spectral vegetation indices and field observations. The first file, NPP_Gridded_3654_cells_R3.csv with 3,654 0.5-degree grid cells, is suitable for biome level and overall analyses because of a larger sample size. In this file, 36 cells have above-ground net primary production (ANPP) only, 320 cells have total net promary production (TNPP) only, and 3,298 cells have both TNPP and ANPP. The second file, NPP_Gridded_2335_unique_cell_R3s.csv, was derived from the larger file and contains 2,335 0.5-degree grid cells after outliers were excluded, replicate measurements were averaged out for each unique geographic location, and cells classified as water, bare ground, and urban were excluded. This smaller data file is more appropriate for model/data inter-comparisons. Overall, gridded ANPP values ranged from 3 to 890 gC/m2/yr, and gridded TNPP values ranged from 3 to 1,235 gC/m2/yr. The lowest values are for sparsely vegetated ground (e.g., open shrublands) and the highest values are for forests. Revision Notes: This data set has been revised to correct the previously reported temporal coverage of field data measurements that were used to develop NPP estimates and the data set title was also corrected to reflect the date change. Missing value codes were added. Please see the Data Set Revisions section of this document for detailed information.\n<br>\n\n#### NPP_EMDI_615\nThis data set represents a refined set of global net primary productivity (NPP) estimates and model driver data that are the results of the Ecosystem Model-Data Intercomparison (EMDI) workshop review and outlier analyses undertaken to assess the accuracy of global model forecasts of terrestrial carbon cycling. EMDI builds upon the accomplishments of the original worldwide synthesis of NPP measurements and associated model driver data prepared by the Global Primary Production Data Initiative (GPPDI) (Olson et al., 2001; 2013). The EMDI review and analyses produced NPP, climate, NDVI, land cover, vegetation, and soil data for a sub-set of GPPDI data: 81 Class A sites, 933 Class B sites, and 3,855 Class C 0.5-degree cell grids. Class A sites represent well-documented study sites that have complete above- and below-ground NPP measurements. Class B sites represent more numerous extensive sites with less documentation and site-specific information available. Class C cells represent estimates of NPP for 0.5-degree grid cells for which inventory, modeling, or remote-sensing tools were used to scale up the point measurements. The data files are in comma-separated-value (.csv) format: • 18 data files for Class A sites which includes 12 comma-separated files (.csv) and six compressed files (.zip) • 11 data files for Class B sites in comma-separated format (.csv). •9 data files for Class C grid cells in comma-separated format (.csv). This document and a companion file (Olson et al., 2001) describe the compilation of NPP estimates under the GPPDI and the EMDI review and outlier analyses that produced this refined set of NPP estimates and model driver data. Revision Notes: This data set has been revised to correct previously reported NPP estimates for three OTTER Transect sites, USA, in the Class A NPP data file. Please see the Data Set Revisions section of this document for detailed information.\n<br>\n\n#### NPP_PIK_575\nThere is one comma-separated (.csv) data file and one text (.txt) file (bibliographic information) with this data set. This data set provides above-ground net primary production (ANPP) and total net primary producitivity (NPP) [expressed in grams of carbon per square meter per year (gC/m2/year)], and the C fraction used to convert dry biomass weight to carbon content, for 127 unique study sites in northern Eurasia. The sites are classified by ecozone (i.e., tundra, forest-tundra, taiga, mixed forest, broadleaf forest, small-leaved secondary forests, forest bogs, meadows, steppe, semi-desert, and polar desert) and plant community (phytocoenosis). Each study location is georeferenced (latitude/longitude) with elevation and zonal/interzonal information. References to original author, year of publication, and table/record in Bazilevich (1993) are also included. The data set also provides a bibliography of 274 original-source references (in Russian) to accompany the 127 data records on NPP from Bazilevich (1993). The data are a subset of data adapted from Bazilevich, N.I. 1993. Biological Productivity of Ecosystems of Northern Eurasia. Nauka Publishers, Moscow. 293 pp. (in Russian). The data set originated from field measurements of primary productivity collected between 1940 and 1988 for most of the terrestrial vegetation types in northern Eurasia. The NPP data collection contains field measurements of biomass, estimated NPP, and climate data for terrestrial grassland, tropical forest, temperate forest, boreal forest, and tundra sites worldwide. Data were compiled from the published literature for intensively studied and well-documented individual field sites and from a number of previously compiled multi-site, multi-biome data sets of georeferenced NPP estimates. The principal compilation effort (Olson et al., 2001) was sponsored by the NASA Terrestrial Ecology Program. For more information, please visit the NPP web site at http://daac.ornl.gov/NPP/npp_home.html.\n<br>\n\n#### NPP_WOODY_655\nThere are two data files (tab-delimited .txt format) with this data set that provide estimates of above-ground biomass per county; county-level annual above-ground biomass growth, removals (harvest), and mortality of woody biomass per hectare; county-level total annual above-ground woody biomass production per hectare; forest area per county; mortality (%) in forests within each county; and total annual production and mortality per county. The data provide annual mean above-ground wood increments for temperate forests in 1,956 counties of the 28 eastern US states. The data are derived from forest inventory data from 1960s to 1990s that were collected from an extensive network of permanent inventory plots as part of the US Department of Agriculture Forest Service Forest Inventory and Analysis (FIA). Based on the analysis of the above-ground production data (Brown and Schroeder, 1999), above-ground production of woody biomass (APWB) for hardwood forests ranged from 0.6 to 28 Mg/ha/yr and averaged 5.2 Mg/ha/yr. For softwood forests, APWB ranged from 0.2 to 31 Mg/ha/yr and averaged 4.9 Mg/ha/yr. APWB was generally highest in southeastern and southern counties, mostly along an arc from southern Virginia to Louisiana and eastern Texas. No clear spatial pattern of mortality of woody biomass (MWB) existed, except for a distinct area of high mortality in South Carolina as a result of Hurricane Hugo in 1989. For hardwood forests, MWB ranged from 0 to 15 Mg/ha/yr and averaged 1.1 Mg/ha/yr. The average MWB for softwood forests was 0.6 Mg/ha/yr with a range of 0 to 10 Mg/ha/yr. The rate of above-ground MWB averaged <1%/yr for both hardwood and softwood forests. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2003.\n<br>\n\n#### NPP_Multi-Biome_125_Sites_1352\nThis data set, NPP Multi-Biome: Summary Data from Intensive Studies at 125 Sites, 1936-2006, contains a single shapefile that provides site-level summary statistics from 125 sites in five biomes: boreal forest, grassland, temperate forest, tropical forest, and tundra. The spatial coverage is global and spans the time period from 1936 through 2006. Study periods, and both spatial and temporal resolution vary by site. Data include georeferenced location, elevation, mean annual precipitation, mean annual minimum and maximum air temperature, dominant soil type, ecoregion type, dominant plant species, general vegetation types, annual mean or peak living above- and below-ground biomass, average annual above- and below-ground Net Primary Productivity (NPP), and reference information. Additionally study sampling period and intervals, plot management, and long-term site management history are also provided.\n<br>\n\n#### NPP_TEM_471\nThis data set contains one data file (.csv format) that is known as the Terrestrial Ecosystem Model (TEM) data set. The data provide pool sizes and fluxes of carbon (C) and nitrogen (N) for 16 globally distributed field sites that represent a wide range of terrestrial biomes, tundra to tropical forest, but exclude wetlands. The net primary productivity (NPP) data were extracted from the literature. They were not previously widely available to the ecosystem modeling community in electronic form until this data set and additional NPP data sets were published by the ORNL DAAC. The data were used to calibrate the ecosystem process-based TEM. The TEM was developed by staff at the Ecosystem Center, Marine Biological Laboratory, Woods Hole, Massachusetts, USA, to estimate the spatial and temporal distribution of major carbon (C) and nitrogen (N) fluxes and pool sizes at continental to global scales (resolution: 0.5 degrees latitude x 0.5 degrees longitude). Eight of the TEM calibration sites are also included in the ORNL DAAC NPP data collection as individual site NPP data sets. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1999.\n<br>\n\n#### NPP_VAST_576\nThis data set contains one data file in comma-delimited (.csv format) that provides observations from Australia for use in parameterizing the Vegetation and Soil-carbon Transfer (VAST) Model (version 1.1). The observations include net primary productivity (NPP), biomass, litter mass, surface horizon soil carbon concentration (i.e., mass fraction) and bulk density, and soil carbon and bulk density measurements at various depths. The data consist of 33 estimates of above-ground NPP based on cut grass swards and visual assessment of growth, 150 measurements of litterfall (leaf and fine twig), 76 measurements of above-ground biomass (phytomass), 91 determinations of fine litter mass, 341 measurements of soil carbon concentration in surface layers (to 15 cm depth), and 50 determinations of soil bulk density (to 15 cm depth). All these data were derived from 174 original literature references describing study sites throughout Australia. VAST is a conceptual carbon (C) cycle model that depicts large scale dynamics of terrestrial C pools and the net exchange of C between the land surface and the atmosphere at a resolution of 0.05 degrees. The model consists of 10 C pools comprising two above-ground biomass pools, two litter pools, and three pools each of below-ground biomass and soil C. Below-ground pools are distributed among three soil layers (0–20, 20–50, and 50–100 cm). Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2001.\n<br>\n\n#### NPP_GSM_804\nThis data set contains two data files (.csv format). One file contains site characteristics, stand descriptors, and above-ground biomass and ANPP data for seven old-growth temperate forest stands and one young cove forest stand in the Great Smoky Mountains of Tennessee. The old-growth stands (> 200 years old) span several watersheds on the north slope of the mountains at elevations ranging from 720 to 1,140 m. The younger stand (48-63 years old, elevation 910 m) developed after agricultural abandonment. The second file contains monthly mean climate data averaged over four years (1947-1950) from four climate stations located along an elevational gradient (445-1,920 m) in Great Smoky Mountains National Park. DBH measurements were made at the beginning of the study and biomass increment was measured from a subset of trees. ANPP was estimated using regional species-specific allometric relationships for tree mass. Biomass, volume, and annual input of coarse woody detritus are also reported. Live biomass in the old-growth stands (32,600-47,100 g/m2) is among the highest reported for temperate forests of eastern North America while ANPP is moderate (630-1,010 g/m2/yr). ANPP in the younger stand was higher (1,180-1,310 g/m2/yr). In comparison with forests worldwide, inputs of coarse woody debris is moderate. Revision Notes: Previously reported field collection dates have been corrected in the NPP file. Biomass and ANPP values were converted from Mg/ha and Mg/ha/yr to g/m2 and g/m2/yr, respectively, consistent with units in other files in the NPP collection. Please see the Data Set Revisions section of this document for detailed information.\n<br>\n\n#### NPP_REDWOOD_803\nThis data set contains site characteristics, stand descriptors, and measured and calculated above-ground biomass, above-ground net primary production (ANPP), and woody detritus input data for an old Sequoia sempervirens stand at Bull Creek in Humboldt Redwoods State Park, California. There is one data file (.csv format) with this data set. Productivity of the Sequoia stand was studied via tree re-measurement (1972 and 2001) and allometric relationships. Measurements of tree circumference at 1.7 m above ground were made at the beginning and the end of the study. A 1972 stem map of the stand allowed the investigators to identify and re-measure trees >10 cm in diameter. ANPP was estimated using a range of specific gravities and several allometric relationships for tree volume. Estimation procedures were outlined by Busing and Fujimori (2005). Tree loss to mortality over the study interval was included in the analysis. Estimates of total tree ANPP ranged from 600 to 1,400 g/m2/yr. However, ANPP values in the range of 700-1,000 g/m2/yr were considered to be the most reasonable estimate because of the accuracy of the particular equations, specific gravities, and assumptions used to obtain them (Busing and Fujimori, 2005). Above-ground total tree biomass was extremely high (> 300,000 g/m2). Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2005.\n<br>\n\n#### NPP_OTTER_472\nThis data set provides net primary productivity (NPP) estimates and associated field measurements for six sites located along the 250-km, west-east transect of the Oregon Transect Ecosystem Research Project (OTTER) in the Pacific Northwest. Leaf area indices, biomass, and NPP vary about 10-fold across the OTTER transect. Leaf area index (LAI) ranges from 0.4 m2/m2 at the Juniper/Sisters site to 8.6 m2/m2 at the Scio western Cascade site. Total NPP follows a similar trend with the Juniper/Sisters site having the lowest NPP value (300 g/m2/yr) and the Scio site having the highest (2,250-2,570 g/m2/yr). Total tree biomass across the transect ranges from to 1,080 g/m2 at Juniper/Sisters to 71,080 g/m2 at Cascade Head. Vegetation intercepts 22% to 99.5% of incident photosynthetically active radiation along the transect. There is one data file (.csv format) with this data set. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1999.\n<br>\n\n#### NPP_ATH_577\nThis data set contains eight data files (.txt format): three net primary productivity (NPP) data files and five climate data files. The NPP estimates are based on field measurements of litterfall accumulation at three tropical rainforest sub-sites near Atherton, Queensland, in northeast Australia. Additional NPP component data include standing litter biomass, leaf decomposition rates, and nutrient concentrations in litter, where available. Precipitation and temperature data are provided from measurements at two of the study sites, nearby weather stations, and from published literature sources. Annual litterfall estimates are based on weekly or bi-weekly measurements for 3-4 years (1974/5-1978) at the Wongabel (17.32 S 145.50 E) and the Gadgarra (17.30 S 145.72 E) State Forest Reserves, and weekly measurements for 5 years (1980-1985) at Tableland (17.28 S 145.57 E) near Atherton. Additional measurements were made at Wongabel and Gadgarra including branchfall (monthly); litter on the forest floor (quarterly); rainfall and throughfall (weekly); and trace element concentrations in litterfall (weekly or bi-weekly). In a related study at Gadgarra, litterfall and standing litter crop were sampled at approximately 19-day intervals from 1974 to 1976 and at approximately 3-month intervals 1976 to 1978. Total litterfall averaged 905 g/m2/year at Wongabel, 987 g/m2/year at Gadgarra, and 1,103 g/m2/year at Tableland, giving minimum estimates of above-ground NPP for this region of Australia and an indication of tropical forest productivity south of the Equator. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2001.\n<br>\n\n#### NPP_BRR_157\nThis data set contains three ASCII files (.txt format). One file provides net primary productivity (NPP) data for the moist lowland tropical forest on Barro Colorado Island, Panama. NPP estimates are based on field measurements of litterfall accumulation, tree growth and mortality, and herbivory. Above-ground biomass and LAI are also reported. The other two files provide climate data recorded onsite. Annual litterfall accumulation (leaf + twig + other litterfall) averaged 1,064 g/m2/year, excluding losses to herbivory, on the central plateau of the island and in the Lutz catchment (1969-1979) and 1,246 g/m2/year at Poacher's Peninsula (1986-1990). Herbivory due to insects (about 50 g/m2/year) was estimated from leaf litterfall (1974-1977) by measuring holes and gaps in fallen leaves. An additional 30 g/m2/year may be lost to vertebrate herbivores which leave no identifiable traces in litter traps. Coarse wood litterfall due to tree damage may represent an additional 46 g/m2/year. Above-ground biomass averaged 27,425 g/m2 based on inventory data collected every 5 years from 1985 to 2000 and allometric regression equations. Tree growth of 554 g/m2/year was based on above-ground biomass changes during the three census intervals. Tree mortality of 2-3% was estimated by recording dead or missing trees (1982-1990). LAI of 7.3 was based on the average area of leaves that fell per area of ground per year. Overall, above-ground NPP for Barro Colorado Island was estimated at 1,800 g/m2/year.\n<br>\n\n#### NPP_CHM_578\nThis data set contains five data files (.txt format). Three data files provide net primary productivity (NPP) estimates for a tropical dry deciduous forest within the 3,300-ha Chamela Biological Station, Mexico. There is one file for each of the three permanent watershed plots located along an elevational gradient from 60 to 160-m above sea level. NPP was estimated from field measurements obtained during wet and dry seasons between 1982 and 1995. A fourth NPP data file provides average nutrient fluxes into and out of five watersheds. The fifth file provides precipitation and minimum/maximum temperature data from measurements obtained onsite. Detailed data are available for above-ground NPP (ANPP) (fine litterfall, wood increment, and leaf herbivory plus an estimation of understory production), and below-ground NPP (BNPP) (fine root production and root increment). Biomass data and nutrient inputs/outputs (P, K, Ca, Mg) averaged from five watersheds are also included in the data set. Estimated ANPP ranged from 611 to 808 g/m2/year between the three sub-sites (average 682 g/m2/year), and total NPP ranged from 1,119 to 1,353 g/m2/year (average 1,206 g/m2/year). These estimates are thought to represent the lower bounds of NPP because root and stem herbivory have not been taken into account, although leaf herbivory is included. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2001.\n<br>\n\n#### NPP_CBAY_473\nThis data set contains three ASCII files (.txt format). One data file contains above-ground biomass, litter, litterfall, herbivory, biomass change, and above-ground net primary productivity (ANPP) estimates for a late secondary moist subtropical forest based on measurements from 16 permanent study plots located along an elevational (60-290 m) and topological gradient within the 132-ha Cinnamon Bay watershed on St. John, U.S. Virgin Islands. The purpose of the study was to provide information on forest structure, species composition, and forest productivity along environmental gradients, including the effects of hurricanes and drought. The other two files provide climate records from nearby weather stations (1917-1981). Above-ground biomass was measured every 5 years (1983-2003). Litterfall accumulation was determined in 1992-1993. In 1983, total above-ground biomass on all plots combined averaged 13,870 g/m2; by 2003 during a post-hurricane recovery period, it had declined by nearly 7 percent. In 1983, biomass was greatest on the summit, intermediate on slopes and valleys, and least on ridges; by 2003, the quantities for all sites had converged except on the summit plot. In 1992, ANPP was estimated based on annual litterfall accumulation (897 g/m2/year) plus biomass change due to delayed mortality (142 g/m2/year) plus estimated herbivory (25 g/m2/year), giving a total ANPP of 1,064 g/m2/year. Periodic storms and drought appear to maintain the forest in a disturbed state.\n<br>\n\n#### NPP_TROPICAL_616\nThis data set contains documented field measurements of NPP components for 39 old-growth tropical forests distributed worldwide between latitudes 23.58 N and 23.58 S. The data were compiled from published literature and other extant sources. The data are georeferenced to each intensive study site and include above- and below-ground biomass, fine root biomass, litterfall, branchfall, above-ground biomass increment, and herbivory estimates, where available. Other site characteristics are included, such as elevation, forest type and age, soil type, and climate summaries. Key references are provided. Estimates of above-ground net primary productivity (ANPP) for the 39 sites were made based on the sum of (1) measured or estimated above-ground biomass increment, (2) measured or estimated fine litterfall accumulation, (3) estimated losses to consumers, and (4) estimated biogenic volatile organic compound emissions. Estimates of below-ground NPP were made based on professional judgment that below-ground production is 0.2 x ANPP (lower bounds) or 1.2 x ANPP (upper bounds). TNPP was calculated as the range between the low and high values of ANPP + BNPP. Average BNPP and TNPP estimates were also calculated. Across the broad spectrum of the tropical forests studied (dry to wet, lowland to montane, nutrient-rich to nutrient-poor soils), the estimates of total NPP range from 3.4 to 34.4 Mg/ha/yr (lower bounds) and from 6.2 to 63.0 Mg/ha/yr (upper bounds). There is one comma-separated data file (.csv) with this data set. The ORNL DAAC [http://daac.ornl.gov] NPP Collection for tropical forests contains additional biomass and NPP component estimates and climate data for 28 of the intensive study sites in this data set. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2001.\n<br>\n\n#### NPP_DRN_215\nThis NPP data set contains one ASCII file (.txt format). The data file contains above- and below-ground biomass, litterfall, LAI, vegetation/soil micro-nutrient content (P, K, Ca, Mg, etc.), and above-ground net primary productivity (ANPP) estimates for transitional moist/dry tropical forests at Rio Lara (wet season site) and Rio Sabana (dry season site) in Darien Province, Panama. Field measurements were made in 1967 and 1968. No climate data are provided. Apart from litter quantity, most of the differences between these data reflect variations between the two plots sampled, rather than seasonal changes. Both plots were considered representative of the surrounding forest. The area was thought to have been forested for the previous 400 years, following abandonment of open savanna lands maintained by the Precolumbian Indians. Total annual leaf and branch fall averaged for the two sites was 1,137 g/m2/yr, representing a minimum ANPP estimate. Litter decomposition over the 9-month wet season was around 90%. LAI at both sites was high; however, the index for the dry site (10.6 m2/m2) was only half that of the wet season site (22.4 m2/m2).\n<br>\n\n#### NPP_GNN_474\nThis data set contains seven ASCII data files (.txt format). Four files provide NPP data for contrasting lowland rainforests within Gunung Mulu National Park on the island of Borneo, Malaysia. Three files provide climate data from weather stations near Gunung Mulu. The study areas are located along an environmental gradient of varying soil types at elevations ranging from 50 to 300 m within the 544 km2 Park. The study sites are primary lowland evergreen rainforests, each about 1.0 hectare in size. They include: an alluvial forest (at 50 m elevation and often inundated during the wet season); a heath forest (on a sandy terrace at about 170 m elevation); a mixed dipterocarp forest (on ridge slopes at 200-250 m elevation); and a limestone forest (on shallow soils over limestone at the base of cliffs and ravines at 300 m elevation). The NPP files contain estimates of above-ground biomass, annual litterfall accumulation, standing litter crop, and nutrient content of different vegetation components and soils. The scientific expedition was carried out between June 1977 and September 1978. Estimates of litterfall, ranging from 886 g/m2/year to 1,203 g/m2/year, give a minimum estimate of above-ground production. The climate record for the study sites extends from 1915 through 1990. The area's climate is controlled largely by the Indo-Australian monsoon system with wet northeast monsoon from December to March and slightly drier southwest monsoon from May to October. Mean annual precipitation is around 3,000 mm and mean average temperature is about 27 C.\n<br>\n\n#### NPP_JHN_475\nThis data set contains five NPP data files and three climate data files (ASCII .txt format). There is one NPP file for each of the five sub-types of upper montane tropical forest located along John Crow Ridge in the Blue Mountains of Jamaica. Biomass, productivity, tree mortality, standing crop of litter, LAI, and nutrient content of leaf fall were measured from 1974 to 1978. Long-term climate data are available from Cinchona Botanic Gardens, approximately 3 km south of the John Crow Ridge study area and at similar elevation. The John Crow Ridge study areas (18.08 N 76.65 W) consist of 8 to 10 contiguous 10 m x 10 m permanent plots. The forests are of low stature and appear to be completely undisturbed. The forest floor at one sub-site (Mor Ridge) was overlain by a 30-50 cm layer of mor humus with a high C/N ratio. ANPP was estimated to be between 654-997 g/m2/year (sum of litterfall and trunk/branch increment), or 854-1,057 g/m2/year (including tree mortality). These figures are lower than for lowland tropical forests with greater leaf turnover and reflect the relatively low stature of the upper montane forest plots. Above-ground biomass was estimated by both destructive methods and nondestructive regression analysis. Values ranged from 23,000 to 34,000 g/m2. Below-ground biomass (5,370 g/m2) was determined for one site by harvesting roots to a depth of 50 cm. LAI for trees plus understory, where measured, was 5.5-5.7 m2/m2.\n<br>\n\n#### NPP_KDE_216\nThis data set contains one NPP data file and two climate data files (ASCII .txt format). The NPP file contains above- and below- ground biomass, litterfall, standing litter crop, and nutrient content data for a moist semi-deciduous secondary tropical forest at the Kade Agricultural Research Station (6.15 N 0.92 W), Ghana, spanning several collections periods between 1957 and 1972. Climate data come from weather stations at Kade near the study site (1958-1997) and at Kumasi near Kade (1945-1990). The Kade study site is typical of an old secondary forest which has probably not been cultivated or harvested since around 1915-1925. Tree basal area measured in 1957 was quite high at 33.7 m2/ha; measurements in 1968 of a plot a few hundred meters away gave 30.6 m2/ha. Detailed above- and below-ground biomass data are provided from a single clear-felling made in 1957. Nutrient content for lianas, leaves and twigs, branches, large wood, standing dead wood, stumps, litter, and roots is also provided. Total live + dead biomass was 36,102 g/m2, of which 5,414 g/m2 (15%) was below-ground live biomass and 23,568 g/m2 was above-ground live biomass. Monthly litterfall is available for 26 months (1970-72). Total annual NPP was estimated in the late 1950s at about 2,400 g/m2/year based on litterfall of 1,054 g/m2/year plus rough estimates of timber fall (1,070-1,121 g/m2/year) and root production (258 g/m2/year). In the 1970s, NPP was recalculated at 2,200-2,500 g/m2/year based on additional measurements of litter, wood fall, and decomposition.\n<br>\n\n#### NPP_KHC_217\nThis data set contains one net primary productivity (NPP) data file and three climate data files (.txt format) for a fully closed tropical rainforest in the Khao Chong Reserve (7.58 N 99.8 E) in southern Thailand. The Reserve comprises 500 ha of well-preserved rainforest considered typical of the region, although maximum tree height (36 m) and biodiversity were less than in Malaysian forests. Net primary productivity (NPP) was estimated as the sum of annual net above-and below-ground biomass increase plus extrapolated annual litterfall and tree mortality. Biomass increment for trees > 4.5 cm diameter at breast height (DBH) was monitored between 1962 to 1965, and daily litterfall was measured for one month in 1962. Total NPP was estimated at 2,860 g/m2/year. This value includes a possible over-estimate of litterfall (2,330 g/m2/year) plus above-ground woody biomass increment and turnover as mortality combined (489 g/m2/year) plus below-ground woody biomass increment (41 g/m2/year). Fine root turnover and herbivory were not included in these estimates. Allometric relationships for estimating above-ground biomass were checked by destructive harvest. Leaf area index was relatively high at 11.4-12.3 m2/m2. Long-term climate data for Khao Chong are available from weather stations at Songkhla, Thailand (7.2 N 100.6 E) and Trang, Thailand (7.52 N 99.62 E). Depending on station location and temporal coverage, mean annual temperature is 27.2-27.4 C and mean annual precipitation is between 1,928 and 2,696 mm. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998.\n<br>\n\n#### NPP_SLV_218\nThere are two data files with this data set in (.txt) format. The files contain net primary productivity (NPP) data and climate data for a mature tropical lowland rainforest at the La Selva Biological Station, Costa Rica. The La Selva forest reserve (10.43 N, 83.98 W) covers over 1,500 ha, of which 53% is primary forest and the rest is in various types of secondary forest and abandoned land. Above- and below-ground biomass, litterfall, root production, and nutrient content of different vegetation components and soils were determined in different areas of the biological station on different occasions between 1975 and 1994. Work has continued to the present day. Precipitation (1984-1997) and maximum/minimum temperature (1992-1997) were measured at the forest study site. NPP has not been completely estimated although detailed data on forest dynamics are available from a variety of published and unpublished sources. A crude minimum estimate of NPP for La Selva was obtained by summing the estimates of litterfall [850 g/m2/year predicted for average annual temperature and precipitation conditions at La Selva by the equation of Brown and Lugo (1982)] and unpublished data on root production (550-1,250 g/m2/year), giving a NPP range of about 1,400-2,100 g/m2/year. Revision Notes: The NPP data file has been revised to add additional root biomass estimates, correct temporal coverage of data series, correct parameter label for potassium concentration in leaf litterfall, and add additional References/Comments. Please see the Data Set Revisions section of this document for detailed information.\n<br>\n\n#### NPP_LQL_476\nThis data set contains ten ASCII files (.txt format), one NPP file for each of the nine different montane tropical rainforest sites within the Luquillo Experimental Forest (LEF) of Puerto Rico and one file containing climate data. The NPP study sites are located along an environmental gradient of different soils, elevation (100-1,000 m), develop stage, and mean annual rainfall. Field measurements were carried out from 1946 through 1994. Estimates of above-ground net primary productivity (ANPP) in LEF are based on summation of litterfall accumulation, biomass increment, and herbivory estimates. ANPP values range from 370-1,950 g/m2/year, with ANPP decreasing with elevation. The lowest ANPP was in the Dwarf cloud rainforest. The Palm floodplain and Bisley (tabonuco) forests have the highest ANPP (1,950 g/m2/year and 1,630 g/m2/year, respectively). Below-ground NPP was measured at only two of the sites (Guzman and Bisley) and was estimated for the Dwarf site. TNPP estimates for these sites are 1,945, 2,160, and 383 g/m2/year, respectively. Climate data are available from a weather station at the El Verde forest study site.\n<br>\n\n#### NPP_MGD_477\nThis data set contains two NPP data files and one climate data file (ASCI .txt format). The NPP files contain data for above-ground biomass, litterfall, and nutrient content of above-ground vegetation, organic surface layer, and soils measured during an 18-month period in 1970 and 1971 at two contrasting tropical seasonal evergreen forests in Magdalena Valley, Colombia. The climate record provides mean monthly and annual precipitation (1951-1992) and mean monthly and annual average temperature (1970-1997) from Barranca Bermeja (7.00 N 73.80 W) near the Magdalena Valley sites. One forest stand sits atop a perched water table on a typical valley terrace. It is low in height, basal area (22 m2/ha), and above-ground biomass (18,109 g/m2), but rich in palms with a simple two-layered structure, frequent windthrows and mortality, and few older trees. The contrasting forest stand developed on a lower slope site under more advantageous soil and water conditions. Although number of stems/ha in this forest are smaller and palms less conspicuous, the slope forest shares dominant species of both forests but with taller trees and greater basal area (32 m2/ha) and above-ground biomass (32,581 g/m2). The above-ground biomass and the vegetative bioelement stores were estimated by harvesting sample trees and palms and allometric regressions. Above-ground net primary production (ANPP) is based on total litterfall accumulation, measured over a 12-15 month period (1,202 g/m2/yr for the terrace forest and 873 g/m2/yr for the slope forest), giving minimum estimates of NPP.\n<br>\n\n#### NPP_MNS_579\nThis data set includes six ASCII files (.txt format). Five files contain productivity values for several types of tropical Amazon rainforest near Manaus, Brazil studied between 1963 and 1990, and one file contains monthly and annual climate data for the period 1910-1993. The Manaus NPP study sites occur along a hydrologic gradient and include several stands of terra firme (dry land) forest located in and adjacent to the Walter Egler Forest Reserve, a riverine forest in the Ducke Forest Reserve (intermittent wet and in small terra firme valleys), and a varzea forest subject to seasonal inundation adjacent to Rio Tiruma Mirim (a small tributary of the Rio Negro). Additional sites include a primary terra firme forest on a plateau in the Bacia Modelo Reserve and a mixed-species lowland terra firme forest at Highway 174 south of Fazendo Dimona. The net primary productivity (NPP) measurements for the Manaus sites are based on total annual litterfall, giving a minimum estimate of above-ground NPP ranging from 640 to 800 g/m2/year. These estimates are within the recorded values for litterfall in other tropical rainforests. The seasonality of leaf litterfall for several of the sites is also provided. Total above- and below-ground biomass and vegetation bioelement concentrations were also determined for one of the terra firme sites (\"km64\") from destructive sampling. Above-ground biomass and LAI estimates for the Highway 17 FD site are provided as well.\n<br>\n\n#### NPP_MRF_478\nThis data set contains two ASCII files (.txt format), one providing net primary production (NPP) component data for a lower montane rainforest and the other providing climate data. The NPP studies were conducted at Marafunga (6.00 S 145.18 E) in the highlands of Papua New Guinea to the east of Mount Kerigomna, about 25 km west of the town of Goroka. LAI, litterfall, litter standing crop and decomposition, and nutrient content of different vegetation components were measured from November 1970 through December 1971 at four representative forest stands: Ridge Top; Ridge Gap; Valley; and Slope. Forest inventories and field measurements of above- and below-ground biomass were made by destructive harvest at a fifth stand (Ridge Top) during October-December 1970 and April-August 1971. The results of these studies are given for the forest at large. The only component of NPP determined at Marafunga was litterfall (755 g/m2/year). The climate data in this data set are available from a weather station at the Marafunga sawmill, about 2.5 km from the Marafunga study sites, and from a weather station in a clearing in the primary forest. A rainfall record for the period 1969-1971 was made daily at the sawmill. Records of maximum and minimum temperatures were made every two weeks for the period December 1970-August 1971 at litter level at the four nondestructive study sites.\n<br>\n\n#### NPP_MMG_802\nThis data set contains one data file (.csv format) that quantifies net primary productivity (NPP) as a function of rainfall in mesic to wet montane rainforests on the island of Maui, Hawaii, U.S.A. The NPP data were collected at six mature forests stands that comprise the Maui Moisture Gradient, a sequence of sites located on Maui where mean annual precipitation ranges from 2,200 mm to 5,050 mm while temperature and all other state factors (parent material, substrate age, organisms, and topography) that control NPP remain relatively constant. Estimates are given for above-ground and below-ground productivity, and total NPP based on measurements made in 1996 and 1997. These data are part of a larger study that focused on the dynamics of carbon cycling and storage in everwet rainforest as a function of changes in rainfall regime. The Hawaiian Islands flora and fauna are relatively species-poor, thus a few species and genera occupy a broad range of environmental conditions. As a result, the forest canopy at all sites was consistently dominated by the native evergreen tree Metrosideros polymorpha (Myrtaceae) which comprises 80% to 100% of basal area in these forests. The understory vegetation was dominated by a variety of ferns and other herbaceous species at all sites, but the dominance of particular understory species shifted among sites. This watershed area has never been cleared by humans.\n<br>\n\n#### NPP_PSH_219\nThis data set contains four ASCII data files (.txt format), one providing net primary production (NPP) component data and three providing climate data. The NPP studies were conducted in a lowland tropical rainforest in the Pasoh Forest Reserve, Malaysia (2.98 N 102.31 E) from 1971 through 1973. Precipitation and temperature data are available from weather stations located about 25 km from the study sites. The main part of the 2,450 ha Pasoh Forest Reserve is covered by lowland dipterocarp forest, with a core area of about 600 ha of undisturbed forest surrounded by a buffer zone of regenerating logged lowland forest. Annual rainfall ranges from 1,728 to 3,112 mm (mean 2,054 mm), which is relatively low for Malaysia, but the fairly even distribution of rain throughout the year permits the development of a typical lowland rainforest. Annual average litterfall data (1,055 g/m2/year) are available for several sub-sites based on bi-weekly collections. An additional 300 g/m2/year of leaf production was estimated to have been consumed by insects, and large wood fall/mortality was estimated to be 370 g/m2/year. Annual tree biomass increment was determined to be 640 g/m2/year, and a further 60 g/m2/year was allowed for root increments and 400 g/m2/year for root turnover. Including additional corrections to account for wood decay before measurement, total NPP was estimated to be 2,780 g/m2/year.\n<br>\n\n#### NPP_SCR_479\nThis data set includes five ASCII files (.txt format). Three files contain above- and below-ground biomass and net primary productivity (NPP) data, one file for each tropical forest study site near San Carlos de Rio Negro, Venezuela. The study sites are located along an ecosystem gradient from riverine to lateritic hill: Tall Amazon Caatinga forest on coarse sandy spodosols close to river level; Bana vegetation on sandy soils less prone to flooding; and Tierra Firme mixed forest on clay oxisols of higher ground. Bioelement concentrations are also provided. The other two files contain climate data from a weather station in San Carlos village. ANPP for the Tierra Firme forest is estimated at 1,590 g/m2/year, including woody biomass increment of 600 g/m2/year, and BNPP values in the range of 201-1,117 g/m2/year, suggesting a minimum estimate of TNPP of 1,800-2,700 g/m2/year. ANPP of a nearby cut-and-burned oxisol plot attained 1,940 g/m2 in the fifth year following clearing. ANPP of Tall Amazon Caatinga is estimated at 1,150 g/m2/year, with root turnover of 120 g/m2/year, giving a minimum estimate of TNPP of 1,270 g/m2/year. TNPP estimate for Bana vegetation, based on annual litterfall accumulation plus root production, is 478 g/m2/year.\n<br>\n\n#### NPP_SES_480\nThis data set contains three ASCII data files (.txt format), one for net primary production (NPP) component data and two for climate data. The NPP studies were conducted in a tropical montane forest in the Sierra de Merida at San Eusebio (8.62 N 71.35 W) in northwestern Venezuela. The forest is mostly primary in character, with some selective logging having taken place in the past. Biomass, litterfall, and nutrient content of above- and below-ground vegetation and soil were determined in 1973-1974. Standing stocks of biomass and nutrients are reported as the mean of 13 plots with contrasting soil types, slope, and relief. Litterfall is reported as the mean determined for 3 of these plots, measured bi-weekly over the course of one year. Root turnover was estimated by correlation with leaf litter turnover (13%), and woody turnover was estimated by assuming the system to be in steady-state. A minimum estimate of total NPP (1,497 g/m2/year) may be obtained by summing total litterfall (697 g/m2/year), woody turnover (480 g/m2/year), and fine root turnover (320 g/m2/year). The forest contains high mineral stores, accumulated especially in the woody compartment. N and P values are very high in the soil compartment. Long-term climate data are available for Merida (8.60 N 71.18 W), about 30 km southeast of the study site. Mean annual temperature is 18.9 C and mean annual precipitation is 1,752 mm, giving a humid/sub-humid climate. Short-term weather observation recorded at the study site are also provided in the data set.\n<br>\n\n#### NPP_BRW_580\nThis data set contains three data files. One file (.csv format) provides above- and below-ground biomass and leaf area index (LAI) data for a wet arctic tundra meadow (Biome research site 2, Dupontia meadow, vegetation type V) studied from 1970 to 1971 at Point Barrow, Alaska, USA, (71.30 N -156.67 W Elevation 5 m). The second file, also in .csv format, provides net primary productivity (NPP) estimates for different plant growth forms for eight vegetation types recognized in the coastal tundra at Barrow. The third file (.txt format) provides climate data from the weather station at Barrow, Alaska (71.30 N -156.78 W Elevation 31 m). Measurements of above- and below-ground living and dead biomass were made at 10-day intervals during the growing season (mid June to end of August) by harvest methods in 6 x 6 m study plots of undisturbed vegetation. LAI was estimated at 10-day intervals with inclined point quadrats and other methods. NPP estimates are based on harvest at the period of peak above-ground vascular biomass and seasonal CO2 gas exchange estimates in 1972. The studies were conducted as part of the International Biological Program (IBP) U.S. Tundra Biome program. Average total NPP for the eight vegetation communities recognized for the coastal tundra at Barrow was 230 g/m2/year (110 g/m2/year ANPP plus 120 g/m2/year BNPP). Values varied by vegetation community type. Revision Notes: This data set has been revised to correct several values of average below-ground plant standing crop. A second NPP data file has been added to provide NPP estimates for the different vegetation types at the coastal tundra study site from measurements made in 1972. Please see the Data Set Revisions section of this document for detailed information.\n<br>\n\n#### NPP_TLK_581\nThis data set contains one text file (.csv format) that provides productivity data for four contrasting tundra vegetation types studied during 1982 near Toolik Lake in the northern foothills of the Brooks Range on the North Slope of Alaska (68.63 N 149.72 W). The vegetation types include a \"tussock\" tundra containing graminoids, deciduous shrubs, and evergreen shrubs; a \"shrub\" tundra dominated by deciduous willow shrubs; a \"heath\" tundra of evergreen shrubs; and a \"wet\" tundra site containing rhizomatous graminoids. The study sites were selected to represent extreme examples of the wide range of local plant growth forms in the region. Living above- and below-ground biomass were sampled on three occasions during the growing season using randomly located quadrats ranging in size from 10 cm x 20 cm to 50 cm x 50 cm. Production and biomass of most tissues were determined by harvest methods, with additional separate determinations of stem secondary growth and below-ground rhizome growth as components of net primary production (NPP). Elemental content of above-ground samples was analyzed. Production, biomass, and elemental content of roots were not determined. Leaf area index (LAI) was measured using a LI-COR leaf area meter.\n<br>\n\n#### NPP_WBW_819\nThis data set contains five data files, in comma-separated format (.csv), derived from the Walker Branch Watershed (WBW) vegetation inventory in eastern Tennessee. Field studies of permanent vegetation plots were conducted using one sampling design over a 40-year period (1967 to 2006). The data set contains long-term measurements of diameter at breast height (DBH) determined on stratified randomly-located inventory plots within the 4 different vegetation types (oak-hickory, pine-oak-hickory, pine, and mesophytic hardwoods) located in the WBW in 1967. The WBW plot-level vegetation DBH data are provided in four files. One file contains the complete set of inventory records (139,806 observations). To accommodate spreadsheet use, the complete inventory is split into three files, one containing 52,110 observations and the other two containing 48,231 and 39,465 observations, respectively. The fifth file contains the WBW vegetation species inventory with species names, the numeric species code for each species, a species group designation, the scientific name for each species, and the literature-derived ratio of g lignin/g N for leaves of each species. NPP values have been reported for various forest stands at different locations within the WBW by Olson et al. (2012a, b; DeAngelis et al. (1997); and Esser (1998). Total NPP values range from 380 gC/m2/yr for forest stands dominated by yellow poplar to 790 gC/m2/yr for forest stands dominated by oaks. Revision Notes: This updated vegetation inventory data set includes results of the 2006 survey and updates to previous results based on the latest survey. The 1967-2006 data set completely supersedes the 1967-1997 data set. If you downloaded the 1967-1997 data set before September 3, 2013, you should download the 1967-2006 version at your earliest convenience.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","cog","earth observation","elevation","ice","land cover","precipitation","satellite imagery","soil moisture","stac","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NPP_CNL_465 v2 - This data set contains two files (.txt). One file contains stand characteristics, soil characteristics, biomass distribution, and production allocation data measured during the 1984 growing season in four lodgepole pine stands (Pinus contorta var.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/boreal_forest/NPP_CNL/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_BOREAL_611 v2 - This data set provides estimates of above- and below-ground biomass, above- and below-ground NPP (ANPP and BNPP), and total NPP(TNPP) for selected North American and Eurasian boreal forests located between 66.37 degrees N and 47.5 degrees N. Each stand was selected through a review of published literature and classified into one of three classes, depending upon completeness of NPP budget, ancillary site data, and stand information.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/boreal_forest/NPP_BOREAL/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_FLK_201 v2 - This data set contains three files (.txt format) for an established 8.25 ha boreal forest dominated by Norway spruce, Picea abies, at Flakaliden (64.12 N 19.45 E) in northern Sweden. Two data files contain stand characteristics, above- and below-ground biomass, and Net Primary Productivity (NPP) allocation data (one file for plots fertilized and irrigated during the growing season and one file for control plots).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/boreal_forest/NPP_FLK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_JDR_202 v2 - This data set contains three files (.txt format). Two of the files contain stand characteristics, above- and below-ground biomass, and above- and below-ground production allocation data for Scots pine (Pinus sylvestris) forests near Jädraås, Sweden.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/boreal_forest/NPP_JDR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_KSM_466 v2 - This data set contains three files (.txt format). One file provides stand characteristics, biomass, and production allocation data for an old-growth boreal forest near Kuusamo, Finland.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/boreal_forest/NPP_KSM/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_MSS_572 v2 - This data set contains two files (.txt format) for study sites in the Mississagi River area of Ontario, Canada (46.35 N -83.38 W elevation 860 m). One file summarizes the results of a series of investigations on the nutrition of jack pine ecosystems, including stand characteristics, above-ground biomass, and where available, litterfall amount and nutrient content of vegetation, litterfall, and soil horizons.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/boreal_forest/NPP_MSS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_SCH_573 v2 - This data set contains two files (.txt format). One file provides above- and below-ground biomass, soil, and nutrient data for a mature boreal ecosystem (subarctic Picea mariana/lichen woodland) near Schefferville, Canada (54.72 N, -67.70) for the 1974 growing season.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/boreal_forest/NPP_SCH/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_SSP_467 v2 - This data set contains two files (comma-separated-value format). One file provides components of net primary productivity, standing biomass, age and stand structure, and litterfall data for 11 stands of Scots pine (Pinus sylvestris) in the Tomsk Region of Russia (approx.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/boreal_forest/NPP_SSP/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_SNF_190 v2 - This data set contains two files (.txt format). One file provides ground-based biophysical measurements and above-ground net primary productivity (ANPP) estimates for 31 black spruce (Picea mariana) and 30 quaking aspen (Populus tremuloides) stands in Superior National Forest (SNF) in northeastern Minnesota, U.S.A.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/boreal_forest/NPP_SNF/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"cropland_612 v2 - This data set contains a single data file (.csv format) that provides gridded values of net primary productivity (NPP) for cropland in eight counties in the central United States for the year 1992 and estimates of interannual cropland NPP in Iowa for years from 1982 through 1996. The data file also includes climate, soil texture, and land cover data for each 0.5 degree grid cell.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/cropland/cropland/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_BDK_203 v2 - This data set provides two data files in text format (.txt). One file contains estimates of above-ground live phytomass for an ephemeral desert steppe located in the Badkhyz Nature Reserve in southern Turkmenistan.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_BDK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_BCN_204 v2 - This data set contains two ASCII text files, one providing productivity measurements for a chalk grassland on Beacon Hill, West Sussex, U.K. (50.92 N, -0.85 W) and the other containing climate data from a weather station at the former King's College London, Rogate Field Centre, 6 km distant (51.01 N, -0.85 W).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_BCN/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_BRD_205 v2 - This data set contains three ASCII text files for the Bridger grassland study site in the Rocky Mountains (45.78 N, -110.78 W, Elevation 2,340 m). Two files contain above- and below-ground biomass data, one for each treatment (ungrazed and moderately grazed).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_BRD/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_CLB_206 v2 - This data set contains four ASCII text files for a 260-hectare humid Trachypogon savanna at the Estacion Biologica de Los Llanos, Calabozo, Venezuela (8.93 N, -67.42 W, Elevation 98 m). Two data files contain monthly estimates of above-ground biomass for the period January 1969 to October 1969, one file for each treatment (burned and unburned plots).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_CLB/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_CNS_191 v2 - This data set contains two ASCII text files; one providing above-ground biomass, productivity, and bioelement concentration data for a derived savanna at Cañas (10.4 N 85.1 W Elevation 45 m) in northwestern Costa Rica, and the other providing climate data from the La Pacifica weather station near Cañas and rom other sources. Monthly dynamics of above-ground plant matter were monitored from July 1969 to June 1970 using harvest procedures within an exclosure to restrict grazing.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_CNS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_CPR_145 v2 - This data set records the productivity of a semiarid shortgrass prairie steppe located in the Central Plains Experimental Reserve (CPER)/Pawnee National Grassland in north-central Colorado. There are nine data files (.txt).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_CPR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_CHR_468 v2 - This data set contains three ASCII files (.txt format). One file provides above- and below-ground biomass, productivity, litterfall, and bioelement data for a native C3 grassland near Charleville (-26.40 S, 146.27 E, Elevation 304 m) in southern Queensland, northeast Australia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_CHR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_Grassland_613 v2 - The NPP Database contains documented field measurements of NPP for global terrestrial sites compiled from published literature and other extant data sources. The NPP Database contains biomass dynamics, climate, and site-characteristics data georeferenced to each intensive site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_Grassland/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_DCK_207 v2 - This data set contains three ASCII files (.txt format). Two files contain above- and below-ground biomass and productivity data for a northern mixed prairie grassland, one file for an ungrazed treatment and the other for a heavily grazed treatment.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_DCK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_DZH_192 v2 - This data set provides two data files in text format (.txt). One file contains a long time series of biomass measurements made between 1955 and 1989 on a semi-desert steppe at the Dzhanybek Research Station in Kazakhstan (49.33 N 46.78 E Elevation 20 m).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_DZH/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_HYS_208 v2 - This data set contains three ASCII files (.txt format). Two files contain above- and below-ground biomass and productivity data for a mixed prairie grassland, one file for an ungrazed treatment and the other for a moderately grazed treatment.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_HYS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_JRN_209 v2 - This data set contains three ASCII files (.txt format). Two files contain above- and below-ground biomass and productivity data for a desert grassland in the Jornada Experimental Range, New Mexico, one file for an ungrazed treatment and the other for a light to moderately grazed treatment.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_JRN/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_KHM_146 v2 - This data set provides two data files in text format (.txt). One file contains biomass measurements made in 1948 and between 1967 and 1970 for a humid temperate steppe in the Khomutovskaya Steppe Nature Reserve in the Donezk Region of Ukraine.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_KHM/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_KLN_147 v2 - This data set provides three data files in text format (.txt). One file contains monthly biomass measurements and Net Primary Productivity (NPP) estimates made between April 1984 and July 1990 on a semi-natural tropical monsoon grassland in southern Thailand.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_KLN/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_KNZ_148 v2 - This data set contains three ASCII files (.txt format). Two files contain above-ground biomass and productivity data for a humid temperate tall-grass prairie grassland located in the Konza Prairie Natural Research Area, Kansas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_KNZ/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_KRS_149 v2 - This data set provides two data files in text format (.txt). One file contains a long time series of biomass measurements made between 1954 and 1983 on a virgin meadow steppe in the Central-Chernozem V.V.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_KRS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_KRK_193 v2 - This data set contains two ASCII files (.txt format). One file contains above- and below-ground biomass (including standing dead material and litter) and productivity data for a tropical grassland at Kurukshetra University (29.97 N, 76.85 E, Elevation 247 m) in northern India, about 150-km north-northwest of Delhi.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_KRK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_LMT_150 v2 - This data set provides three data files in text format (.txt). One file contains monthly above-ground biomass measurements made in 1965 in a humid grass savanna at the Lamto Research Station, Cote Ivoire, Africa.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_LMT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_MTD_469 v2 - This data set contains five ASCII files (.txt format). Three files contain productivity data for a mixed prairie at the Matador Field Station, Canada, and two files contain climate data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_MTD/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_MDL_210 v2 - This data set contains two ASCII files (.txt format). One file provides monthly above-ground live biomass, dead matter, and litter data and daily above-ground net primary productivity (ANPP) data for a temperate grassland steppe at the Media Luna Ranch in Patagonia, Argentina (45.60 S, 71.42 W, Elevation 630 m) for the period May 1981 to March 1983.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_MDL/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_MNT_413 v2 - This data set provides three data files in text format (.txt). One file contains monthly biomass measurements and net primary productivity (NPP) estimates made between June 1984 and December 1994 on an ungrazed saline grassland (MNT1) that was accidently burned in February 1986 at the Colegio de Postgraduados field station site, Montecillo, Mexico.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_MNT/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_NRB_151 v2 - This data set provides three data files in text format (.txt). One file contains a long time series of monthly biomass measurements and net primary productivity (NPP) estimates made between July 1984 and November 1994 on an ungrazed tropical dry savanna grassland in the Nairobi National Park, Kenya.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_NRB/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_Grassland_31_654 v2 - This data set includes monthly grassland biomass data, net primary productivity (NPP) estimates, and climate (rainfall amounts and temperature) data for multiple study sites in major grassland types worldwide. Field measurements of biomass and associated environmental data were compiled for the multiple grassland study sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_Grassland_31/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_NLS_194 v2 - This data set contains five data files in text format (.txt). Three files contain biomass dynamics data for a broad-leaved savanna located in the 800-hectare Nylsvley study site 200 km north of Johannesburg, South Africa.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_NLS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_OLK_195 v2 - This data set contains two ASCII files (.txt format). One file contains monthly above-ground biomass data (total live biomass plus dead matter) for May 1956 to February 1958 for an annually burned, humid derived savanna in the Olokemeji Forest Reserve, Nigeria (7.42 N, 3.55 E).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_OLK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_OSG_211 v2 - This data set contains three ASCII files (.txt format). Two files contain above- and below-ground biomass and productivity data for the Osage tallgrass prairie study site (36.95 N, -96.55 W, Elevation 392 m) in the U.S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_OSG/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_OTR_152 v2 - This data set provides three data files in text format (.txt). Two files contain biomass and above-ground net primary production (ANPP) estimates for two upland meadows with contrasting soil types at the Otradnoe research station of the V.L.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_OTR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_PMP_212 v2 - This data set contains two ASCII files (.txt format). One file contains monthly productivity data measured on an arid dwarf-shrub steppe in northern Patagonia, Argentina from August 1980 to March 1982.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_PMP/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_RMY_574 v2 - This data set contains three data files in text format (.txt) for a temperate dry steppe at Rio Mayo, Argentia. One file contains quarterly above-ground biomass data for grasses on the steepe (May 1984-May 1985).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_RMY/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_SHR_153 v2 - This data set provides two data files in text format (.txt). One file contains biomass measurements and cumulative ANPP calculations made between 1977 and 1980 on a dry continental steppe at Shortandy Biological Station in Kazakhstan.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_SHR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_TWM_213 v2 - This data set provides seven data files in text format (.txt). The files provide biomass estimates, soil carbon (C), nitrogen (N), and phosphorus (P) measurements made at an artificially-established grassland savanna study site in Towoomba, South Africa.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_TWM/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_TLL_196 v2 - This data set contains three ACSII files (.txt format). Two files contain above-ground biomass data for two ungrazed seashore meadow plots dominated by the saltmeadow rush Juncus gerardii at Tullgarnsnaset, near Stockholm, Sweden (approximately 59.20 N, 17.50 E).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_TLL/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_TMN_154 v2 - This data set provides two data files in text format (.txt). One file contains tri-monthly measurements of above-ground biomass made during the growing season between July 1982 and August 1990 on a dry, cold Eurasian steppe dominated by Stipa grandis at the Tumentsogt Research Station in Mongolia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_TMN/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_TMG_470 v2 - This data set contains four ASCII files (.txt format). Three files contain monthly above- and below-ground biomass data, one data file for each cold meadow steppe studied from 1981 to 1990 at Tumugi, Xingan League, in eastern Inner Mongolia, China (approximately 46.10 N 123.00 E Elevation 191 m).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_TMG/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_TVA_155 v2 - This data set provides two data files in text format (.txt). One file contains biomass measurements and cumulative ANPP estimates made between 1978 and 1985 at an ultracontinental steppe at the Tuva Research Station in Russia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_TVA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_VND_197 v2 - This data set contains four data files in text format (.txt). Three files provide above- and below-ground productivity data for three derived savannas on the Vindhyan plateau in northern India from 1986 to 1989, one file for each of three treatments.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_VND/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_XLN_156 v2 - This data set provides two data files in text format (.txt). One file contains bi-weekly measurements of above-ground live biomass recorded during the growing season (early May to early October) from 1980 through 1989 on a cold desert steppe at the Inner Mongolia Grassland Research Station of the Chinese Academy of Sciences within the Xilingol Biosphere Reserve.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/grassland/NPP_XLN/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_IBP_198 v2 - This data set provides four data files containing net primary productivity (NPP) data, edaphic characteristics, average climatic conditions, and basic descriptive and quantitative information on vegetation for 117 globally-distributed terrestrial forest sites. The data set was derived from the IBP (International Biological Programme) Woodlands Data Set of DeAngelis et al.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/multi_biome/NPP_IBP/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_ODS_214 v2 - This data set contains three files. The first file provides net primary productivity (NPP) estimates, vegetation characteristics, and summary climate data for 720 globally-distributed terrestrial sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/multi_biome/NPP_ODS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_GPPDI_617 v3 - Net primary productivity (NPP) estimates were compiled by the Global Primary Production Data Initiative (GPPDI). The database covers 2,523 individual sites and 5,164 half-degree grid cells and underwent extensive review under the Ecosystem Model-Data Intercomparison (EMDI) process.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/multi_biome/NPP_GPPDI/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_MULTIBIOME_653 v2 - This data set contains one data file (.csv format) that provides net primary productivity (NPP) estimates for 34 grasslands, 14 tropical forests, and 5 boreal forest sites distributed worldwide. The NPP data were compiled from published literature.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/multi_biome/NPP_MULTIBIOME/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_GRIDDED_GPPDI_614 v4 - This data set provides two data files (.csv format) containing gridded (0.5-degree) estimates of net primary productivity (NPP), elevation, temperature, precipitation, NDVI, and biome type for selected terrestrial regions of the world. The field data used to develop NPP estimates came from 15 worldwide data sources in several different biomes covering the period 1954-1998.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/multi_biome/NPP_GRIDDED_GPPDI/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_EMDI_615 v3 - This data set represents a refined set of global net primary productivity (NPP) estimates and model driver data that are the results of the Ecosystem Model-Data Intercomparison (EMDI) workshop review and outlier analyses undertaken to assess the accuracy of global model forecasts of terrestrial carbon cycling. EMDI builds upon the accomplishments of the original worldwide synthesis of NPP measurements and associated model driver data prepared by the Global Primary Production Data Initiative (GPPDI) (Olson et al., 2001; 2013).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/multi_biome/NPP_EMDI/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_PIK_575 v2 - There is one comma-separated (.csv) data file and one text (.txt) file (bibliographic information) with this data set. This data set provides above-ground net primary production (ANPP) and total net primary producitivity (NPP) [expressed in grams of carbon per square meter per year (gC/m2/year)], and the C fraction used to convert dry biomass weight to carbon content, for 127 unique study sites in northern Eurasia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/multi_biome/NPP_PIK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_WOODY_655 v2 - There are two data files (tab-delimited .txt format) with this data set that provide estimates of above-ground biomass per county; county-level annual above-ground biomass growth, removals (harvest), and mortality of woody biomass per hectare; county-level total annual above-ground woody biomass production per hectare; forest area per county; mortality (%) in forests within each county; and total annual production and mortality per county. The data provide annual mean above-ground wood increments for temperate forests in 1,956 counties of the 28 eastern US states.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/multi_biome/NPP_WOODY/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_Multi-Biome_125_Sites_1352 v1 - This data set, NPP Multi-Biome: Summary Data from Intensive Studies at 125 Sites, 1936-2006, contains a single shapefile that provides site-level summary statistics from 125 sites in five biomes: boreal forest, grassland, temperate forest, tropical forest, and tundra. The spatial coverage is global and spans the time period from 1936 through 2006.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/multi_biome/NPP_Multi-Biome_125_Sites/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_TEM_471 v2 - This data set contains one data file (.csv format) that is known as the Terrestrial Ecosystem Model (TEM) data set. The data provide pool sizes and fluxes of carbon (C) and nitrogen (N) for 16 globally distributed field sites that represent a wide range of terrestrial biomes, tundra to tropical forest, but exclude wetlands.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/multi_biome/NPP_TEM/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_VAST_576 v2 - This data set contains one data file in comma-delimited (.csv format) that provides observations from Australia for use in parameterizing the Vegetation and Soil-carbon Transfer (VAST) Model (version 1.1). The observations include net primary productivity (NPP), biomass, litter mass, surface horizon soil carbon concentration (i.e., mass fraction) and bulk density, and soil carbon and bulk density measurements at various depths.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/multi_biome/NPP_VAST/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_GSM_804 v2 - This data set contains two data files (.csv format). One file contains site characteristics, stand descriptors, and above-ground biomass and ANPP data for seven old-growth temperate forest stands and one young cove forest stand in the Great Smoky Mountains of Tennessee.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/temperate_forest/NPP_GSM/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_REDWOOD_803 v2 - This data set contains site characteristics, stand descriptors, and measured and calculated above-ground biomass, above-ground net primary production (ANPP), and woody detritus input data for an old Sequoia sempervirens stand at Bull Creek in Humboldt Redwoods State Park, California. There is one data file (.csv format) with this data set.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/temperate_forest/NPP_REDWOOD/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_OTTER_472 v2 - This data set provides net primary productivity (NPP) estimates and associated field measurements for six sites located along the 250-km, west-east transect of the Oregon Transect Ecosystem Research Project (OTTER) in the Pacific Northwest. Leaf area indices, biomass, and NPP vary about 10-fold across the OTTER transect.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/temperate_forest/NPP_OTTER/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_ATH_577 v2 - This data set contains eight data files (.txt format): three net primary productivity (NPP) data files and five climate data files. The NPP estimates are based on field measurements of litterfall accumulation at three tropical rainforest sub-sites near Atherton, Queensland, in northeast Australia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_ATH/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_BRR_157 v2 - This data set contains three ASCII files (.txt format). One file provides net primary productivity (NPP) data for the moist lowland tropical forest on Barro Colorado Island, Panama.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_BRR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_CHM_578 v2 - This data set contains five data files (.txt format). Three data files provide net primary productivity (NPP) estimates for a tropical dry deciduous forest within the 3,300-ha Chamela Biological Station, Mexico.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_CHM/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_CBAY_473 v2 - This data set contains three ASCII files (.txt format). One data file contains above-ground biomass, litter, litterfall, herbivory, biomass change, and above-ground net primary productivity (ANPP) estimates for a late secondary moist subtropical forest based on measurements from 16 permanent study plots located along an elevational (60-290 m) and topological gradient within the 132-ha Cinnamon Bay watershed on St.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_CBAY/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_TROPICAL_616 v2 - This data set contains documented field measurements of NPP components for 39 old-growth tropical forests distributed worldwide between latitudes 23.58 N and 23.58 S. The data were compiled from published literature and other extant sources.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_TROPICAL/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_DRN_215 v2 - This NPP data set contains one ASCII file (.txt format). The data file contains above- and below-ground biomass, litterfall, LAI, vegetation/soil micro-nutrient content (P, K, Ca, Mg, etc.), and above-ground net primary productivity (ANPP) estimates for transitional moist/dry tropical forests at Rio Lara (wet season site) and Rio Sabana (dry season site) in Darien Province, Panama.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_DRN/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_GNN_474 v2 - This data set contains seven ASCII data files (.txt format). Four files provide NPP data for contrasting lowland rainforests within Gunung Mulu National Park on the island of Borneo, Malaysia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_GNN/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_JHN_475 v2 - This data set contains five NPP data files and three climate data files (ASCII .txt format). There is one NPP file for each of the five sub-types of upper montane tropical forest located along John Crow Ridge in the Blue Mountains of Jamaica.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_JHN/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_KDE_216 v2 - This data set contains one NPP data file and two climate data files (ASCII .txt format). The NPP file contains above- and below- ground biomass, litterfall, standing litter crop, and nutrient content data for a moist semi-deciduous secondary tropical forest at the Kade Agricultural Research Station (6.15 N 0.92 W), Ghana, spanning several collections periods between 1957 and 1972.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_KDE/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_KHC_217 v2 - This data set contains one net primary productivity (NPP) data file and three climate data files (.txt format) for a fully closed tropical rainforest in the Khao Chong Reserve (7.58 N 99.8 E) in southern Thailand. The Reserve comprises 500 ha of well-preserved rainforest considered typical of the region, although maximum tree height (36 m) and biodiversity were less than in Malaysian forests.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_KHC/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_SLV_218 v2 - There are two data files with this data set in (.txt) format. The files contain net primary productivity (NPP) data and climate data for a mature tropical lowland rainforest at the La Selva Biological Station, Costa Rica.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_SLV/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_LQL_476 v2 - This data set contains ten ASCII files (.txt format), one NPP file for each of the nine different montane tropical rainforest sites within the Luquillo Experimental Forest (LEF) of Puerto Rico and one file containing climate data. The NPP study sites are located along an environmental gradient of different soils, elevation (100-1,000 m), develop stage, and mean annual rainfall.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_LQL/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_MGD_477 v2 - This data set contains two NPP data files and one climate data file (ASCI .txt format). The NPP files contain data for above-ground biomass, litterfall, and nutrient content of above-ground vegetation, organic surface layer, and soils measured during an 18-month period in 1970 and 1971 at two contrasting tropical seasonal evergreen forests in Magdalena Valley, Colombia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_MGD/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_MNS_579 v2 - This data set includes six ASCII files (.txt format). Five files contain productivity values for several types of tropical Amazon rainforest near Manaus, Brazil studied between 1963 and 1990, and one file contains monthly and annual climate data for the period 1910-1993.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_MNS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_MRF_478 v2 - This data set contains two ASCII files (.txt format), one providing net primary production (NPP) component data for a lower montane rainforest and the other providing climate data. The NPP studies were conducted at Marafunga (6.00 S 145.18 E) in the highlands of Papua New Guinea to the east of Mount Kerigomna, about 25 km west of the town of Goroka.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_MRF/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_MMG_802 v2 - This data set contains one data file (.csv format) that quantifies net primary productivity (NPP) as a function of rainfall in mesic to wet montane rainforests on the island of Maui, Hawaii, U.S.A. The NPP data were collected at six mature forests stands that comprise the Maui Moisture Gradient, a sequence of sites located on Maui where mean annual precipitation ranges from 2,200 mm to 5,050 mm while temperature and all other state factors (parent material, substrate age, organisms, and topography) that control NPP remain relatively constant.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_MMG/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_PSH_219 v2 - This data set contains four ASCII data files (.txt format), one providing net primary production (NPP) component data and three providing climate data. The NPP studies were conducted in a lowland tropical rainforest in the Pasoh Forest Reserve, Malaysia (2.98 N 102.31 E) from 1971 through 1973.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_PSH/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_SCR_479 v2 - This data set includes five ASCII files (.txt format). Three files contain above- and below-ground biomass and net primary productivity (NPP) data, one file for each tropical forest study site near San Carlos de Rio Negro, Venezuela.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_SCR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_SES_480 v2 - This data set contains three ASCII data files (.txt format), one for net primary production (NPP) component data and two for climate data. The NPP studies were conducted in a tropical montane forest in the Sierra de Merida at San Eusebio (8.62 N 71.35 W) in northwestern Venezuela.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tropical_forest/NPP_SES/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_BRW_580 v2 - This data set contains three data files. One file (.csv format) provides above- and below-ground biomass and leaf area index (LAI) data for a wet arctic tundra meadow (Biome research site 2, Dupontia meadow, vegetation type V) studied from 1970 to 1971 at Point Barrow, Alaska, USA, (71.30 N -156.67 W Elevation 5 m).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tundra/NPP_BRW/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_TLK_581 v2 - This data set contains one text file (.csv format) that provides productivity data for four contrasting tundra vegetation types studied during 1982 near Toolik Lake in the northern foothills of the Brooks Range on the North Slope of Alaska (68.63 N 149.72 W). The vegetation types include a \"tussock\" tundra containing graminoids, deciduous shrubs, and evergreen shrubs; a \"shrub\" tundra dominated by deciduous willow shrubs; a \"heath\" tundra of evergreen shrubs; and a \"wet\" tundra site containing rhizomatous graminoids.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/tundra/NPP_TLK/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NPP_WBW_819 v2 - This data set contains five data files, in comma-separated format (.csv), derived from the Walker Branch Watershed (WBW) vegetation inventory in eastern Tennessee. Field studies of permanent vegetation plots were conducted using one sampling design over a 40-year period (1967 to 2006).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/npp/temperate_forest/NPP_WBW/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-npp","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NRL Coriolis Project","Description":"WindSat/Coriolis surface soil moisture (LPRM) L2 V001 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from polarimetric microwave radiometer data from WindSat, onboard the Naval Research Laboratory's Coriolis satellite, using the Land Parameter Retrieval Model (LPRM). Each swath is packaged with associated geolocation fields. The data set covers the period from February 2003 to July 2012. The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the WindSat's Ka-band (37.0 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the WindSat brightness temperatures (sdrLowRes) product, archived at the Goddard Earth Sciences Data and Information Services Center (GES DISC).\n<br>\n\n#### LPRM_WINDSAT_DY_SOILM3\nWindSat/Coriolis surface soil moisture (LPRM) L3 1 day 25 km x 25 km daytime V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from polarimetric microwave radiometer data from WindSat, onboard the Naval Research Laboratory's Coriolis satellite, using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the daytime product. The data set covers the period from February 2003 to July 2012. The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the WindSat's Ka-band (37.0 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the WindSat brightness temperatures (sdrLowRes) product, daytime passes, as processed using LPRM (i.e., LPRM/WindSat/Coriolis L2 product, LPRM_WINDSAT_SOILM2_V001).\n<br>\n\n#### LPRM_WINDSAT_NT_SOILM3\nWindSat/Coriolis surface soil moisture (LPRM) L3 1 day 25 km x 25 km nighttime V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from polarimetric microwave radiometer data from WindSat, onboard the Naval Research Laboratory's Coriolis satellite, using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the nighttime product. The data set covers the period from February 2003 to July 2012. The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the WindSat's Ka-band (37.0 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the WindSat brightness temperatures (sdrLowRes) product, nighttime passes, as processed using LPRM (i.e., LPRM/WindSat/Coriolis L2 product, LPRM_WINDSAT_SOILM2_V001).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/LPRM_WINDSAT_SOILM2_001.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"LPRM_WINDSAT_SOILM2 v001 - WindSat/Coriolis surface soil moisture (LPRM) L2 V001 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from polarimetric microwave radiometer data from WindSat, onboard the Naval Research Laboratory's Coriolis satellite, using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_WINDSAT_SOILM2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LPRM_WINDSAT_DY_SOILM3 v001 - WindSat/Coriolis surface soil moisture (LPRM) L3 1 day 25 km x 25 km daytime V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from polarimetric microwave radiometer data from WindSat, onboard the Naval Research Laboratory's Coriolis satellite, using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_WINDSAT_DY_SOILM3.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LPRM_WINDSAT_NT_SOILM3 v001 - WindSat/Coriolis surface soil moisture (LPRM) L3 1 day 25 km x 25 km nighttime V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from polarimetric microwave radiometer data from WindSat, onboard the Naval Research Laboratory's Coriolis satellite, using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_WINDSAT_NT_SOILM3.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-nrl-coriolis","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA NSCAT Project","Description":"The NASA Scatterometer (NSCAT) Level 2.5 high-resolution reduced MGDR contains only wind vector data (sigma-0 is excluded) in 25 km wind vector cell (WVC) swaths which contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction). Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing or ambiguous. In the presence of land or sea ice winds values are set to 0. Wind vectors are processed using the NSCAT-2 geophysical model function.\n<br>\n\n#### NSCAT_25KM_MGDR_V2\nThe NASA Scatterometer (NSCAT) Level 2.5 high-resolution merged ocean wind vectors and sigma-0 in 25 km wind vector cell (WVC) swaths contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction). Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing or ambiguous. In the presence of land or sea ice winds values are set to 0, and sigma-0 values are preserved where measurements are available. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function.\n<br>\n\n#### NSCAT_LEVEL_1.7_V2\nThe NASA Scatterometer (NSCAT) Level 1.7 ocean sigma-0 referenced to 50 km wind vector cells (WVC) contains daily backscatter (sigma-0) data from ascending and descending passes. Rain flagging information is not included. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea ice. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; re-processing had only minor impacts on the Level 1.7 data.\n<br>\n\n#### NSCAT_AER_HOFFMAN_L2_OW_WIND_VECTOR_AMBIGUITY_REMOVAL\nThis dataset contains the NASA Scatterometer (NSCAT) Level 2 ocean wind vector ambiguity overlay files for the NSCAT MGDR version 2 dataset, referenced for 25 km wind vector cells (WVC). The dataset is derived from the results of a study which used a 2-D variational analysis method (VAM) to select a wind solution from the NSCAT ambiguous winds (Hoffman et al. 2003). Hoffman et al. chose the ambiguity closest in direction to the VAM surface wind analysis. No ambiguity was chosen for poor quality wind vector cells (WVCs). ECMWF analyses were used as the background field for the VAM. Their choice of ambiguity selection is compared with that of JPL, which used a median filter initialized with NCEP analysis fields. Ambiguity selection is changed in ~5% of the dataset, often improving the depiction of meteorological features where the surface wind is strongly curved or sheared. See Hoffman et al. (2003) for more on the method and results. Additional work by Henderson et al. (2003) compares the results of median filtering (JPL) vs. the 2d-VAR method (Hoffman et al., 2003) using 51 days of NSCAT data, supplemented by the NCEP 1000 hPa wind analyses as background fields.\n<br>\n\n#### NSCAT_LEVEL_2_V2\nThe NASA Scatterometer (NSCAT) Level 2 ocean wind vectors in 50 km wind vector cell (WVC) swaths contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction). Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea ice. Winds are calculated using the NSCAT-2 model function. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function.\n<br>\n\n#### NSCAT_LEVEL_3_BROWSE_IMAGES\nThis dataset provides browse images of the NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors, which are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function. Information and access to the Level 3 source data used to generate these browse images may be accessed at: http://podaac.jpl.nasa.gov/dataset/NSCAT%20LEVEL%203.\n<br>\n\n#### NSCAT_LEVEL_3_V2\nThe NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea-ice. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","oceans","precipitation"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NSCAT_W25_RMGDR_V2 v2 - The NASA Scatterometer (NSCAT) Level 2.5 high-resolution reduced MGDR contains only wind vector data (sigma-0 is excluded) in 25 km wind vector cell (WVC) swaths which contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/NSCAT_W25_RMGDR_V2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NSCAT_25KM_MGDR_V2 v2 - The NASA Scatterometer (NSCAT) Level 2.5 high-resolution merged ocean wind vectors and sigma-0 in 25 km wind vector cell (WVC) swaths contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/NSCAT_25KM_MGDR_V2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NSCAT_LEVEL_1.7_V2 v2 - The NASA Scatterometer (NSCAT) Level 1.7 ocean sigma-0 referenced to 50 km wind vector cells (WVC) contains daily backscatter (sigma-0) data from ascending and descending passes. Rain flagging information is not included.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/NSCAT_LEVEL_1.7_V2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NSCAT_AER_HOFFMAN_L2_OW_WIND_VECTOR_AMBIGUITY_REMOVAL v2 - This dataset contains the NASA Scatterometer (NSCAT) Level 2 ocean wind vector ambiguity overlay files for the NSCAT MGDR version 2 dataset, referenced for 25 km wind vector cells (WVC). The dataset is derived from the results of a study which used a 2-D variational analysis method (VAM) to select a wind solution from the NSCAT ambiguous winds (Hoffman et al.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/NSCAT_AER_HOFFMAN_L2_OW_WIND_VECTOR_AMBIGUITY_REMOVAL","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NSCAT_LEVEL_2_V2 v2 - The NASA Scatterometer (NSCAT) Level 2 ocean wind vectors in 50 km wind vector cell (WVC) swaths contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/NSCAT_LEVEL_2_V2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NSCAT_LEVEL_3_BROWSE_IMAGES v2 - This dataset provides browse images of the NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors, which are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/NSCAT_LEVEL_3_BROWSE_IMAGES","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NSCAT_LEVEL_3_V2 v2 - The NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/NSCAT_LEVEL_3_V2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-nscat","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA OCO-2 Project","Description":"This is the Gridded Daily OCO-2 Carbon Dioxide assimilated dataset. The OCO-2 mission provides the highest quality space-based XCO2 retrievals to date. However, the instrument data are characterized by large gaps in coverage due to OCO-2’s narrow 10-km ground track and an inability to see through clouds and thick aerosols. This global gridded dataset is produced using a data assimilation technique commonly referred to as state estimation within the geophysical literature. Data assimilation synthesizes simulations and observations, adjusting the state of atmospheric constituents like CO2 to reflect observed values, thus gap-filling observations when and where they are unavailable based on previous observations and short transport simulations by GEOS. Compared to other methods, data assimilation has the advantage that it makes estimates based on our collective scientific understanding, notably of the Earth’s carbon cycle and atmospheric transport. OCO-2 GEOS (Goddard Earth Observing System) Level 3 data are produced by ingesting OCO-2 L2 retrievals every 6 hours with GEOS CoDAS, a modeling and data assimilation system maintained by NASA’s Global Modeling and Assimilation Office (GMAO). GEOS CoDAS uses a high-performance computing implementation of the Gridpoint Statistical Interpolation approach for solving the state estimation problem. GSI finds the analyzed state that minimizes the three-dimensional variational (3D-Var) cost function formulation of the state estimation problem.\n<br>\n\n#### OCO2_GEOS_L3CO2_MONTH\nThis is the Gridded Monthly OCO-2 Carbon Dioxide assimilated dataset. The OCO-2 mission provides the highest quality space-based XCO2 retrievals to date. However, the instrument data are characterized by large gaps in coverage due to OCO-2’s narrow 10-km ground track and an inability to see through clouds and thick aerosols. This global gridded dataset is produced using a data assimilation technique commonly referred to as state estimation within the geophysical literature. Data assimilation synthesizes simulations and observations, adjusting the state of atmospheric constituents like CO2 to reflect observed values, thus gap-filling observations when and where they are unavailable based on previous observations and short transport simulations by GEOS. Compared to other methods, data assimilation has the advantage that it makes estimates based on our collective scientific understanding, notably of the Earth’s carbon cycle and atmospheric transport. OCO-2 GEOS (Goddard Earth Observing System) Level 3 data are produced by ingesting OCO-2 L2 retrievals every 6 hours with GEOS CoDAS, a modeling and data assimilation system maintained by NASA’s Global Modeling and Assimilation Office (GMAO). GEOS CoDAS uses a high-performance computing implementation of the Gridpoint Statistical Interpolation approach for solving the state estimation problem. GSI finds the analyzed state that minimizes the three-dimensional variational (3D-Var) cost function formulation of the state estimation problem.\n<br>\n\n#### OCO2_Att\nVersion 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2 The Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and inmolecular oxygen (O2) A-Band at 0.76 micrometers . Each band has 1016 spectralelements.This product contains pointing angles of the spacecraft for each orbit.It is generated using the following input data:+ APID 20 telemetry+ Orbit Boundary File.It is essential in generating the Geolocations of the science data.\n<br>\n\n#### OCO2_Att\nVersion 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r. The Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and inmolecular oxygen (O2) A-Band at 0.76 micrometers . Each band has 1016 spectralelements.This product contains pointing angles of the spacecraft for each orbit.It is generated using the following input data:+ APID 20 telemetry+ Orbit Boundary File.It is essential in generating the Geolocations of the science data.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.\n<br>\n\n#### OCO2_Eph\nVersion 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers . Each band has 1016 spectral elements.This product contains the position and velocity of the spacecraft for each orbit. It is generated using the following input data:+ APID 20 telemetry+ Orbit Boundary File.It is essential in generating the Geolocations of the science data.\n<br>\n\n#### OCO2_L1B_Calibration\nVersion 11.2r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2r. The Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and inmolecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time tothe Level 1B process as Level 1A products. Each band has 1016 spectralelements, although some are masked out in the L2 retrieval.This L1B product results from calibration mode measurements (e.g., Lunar,Solar, Dark observations), and thus it differs from the OCO2_L1B_Science(L1bSc) product. The differences in the product formats are only in the geolocation information provided. Whereas the L1bSc products report geolocation data for each sounding, calibration products report the directionof the boresight vector.\n<br>\n\n#### OCO2_L1B_Calibration\nVersion 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r. Version 8r is the current version of the data set. Version 7r has been superseded by Version 8r. The Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and inmolecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time tothe Level 1B process as Level 1A products. Each band has 1016 spectralelements, although some are masked out in the L2 retrieval.This L1B product results from calibration mode measurements (e.g., Lunar,Solar, Dark observations), and thus it differs from the OCO2_L1B_Science(L1bSc) product. The differences in the product formats are only in the geolocation information provided. Whereas the L1bSc products report geolocation data for each sounding, calibration products report the directionof the boresight vector.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.\n<br>\n\n#### OCO2_L2_Lite_SIF\nVersion 11.2r is the current version of the data set. The OCO-2 SIF Lite files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.\n<br>\n\n#### OCO2_L2_Fwd_SIF\nVersion 11.2 is the current version of the data set. The OCO-2 Fwd SIF files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.\n<br>\n\n#### OCO2_L2_Lite_FP\nVersion 11.2r is the current version of the data set. The OCO-2 Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.\n<br>\n\n#### OCO2_L2_Fwd_FP\nVersion 11.2 is the current version of the data set. The OCO-2 Full-physics Forward processing files contain bias-corrected XCO2 along with other select fields aggregated as daily files. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://ocov2.jpl.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","ice"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OCO2_GEOS_L3CO2_DAY v10r - This is the Gridded Daily OCO-2 Carbon Dioxide assimilated dataset. The OCO-2 mission provides the highest quality space-based XCO2 retrievals to date.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO2_DATA/OCO2_GEOS_L3CO2_DAY.10r","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCO2_GEOS_L3CO2_MONTH v10r - This is the Gridded Monthly OCO-2 Carbon Dioxide assimilated dataset. The OCO-2 mission provides the highest quality space-based XCO2 retrievals to date.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO2_DATA/OCO2_GEOS_L3CO2_MONTH.10r","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCO2_Att v11.2 - Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2 The Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO2_DATA/OCO2_Att.11.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCO2_Att v11r - Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO2_DATA/OCO2_Att.11r","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCO2_Eph v11.2 - Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO2_DATA/OCO2_Eph.11.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCO2_L1B_Calibration v11.2r - Version 11.2r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2r.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO2_DATA/OCO2_L1B_Calibration.11.2r","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCO2_L1B_Calibration v10r - Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO2_DATA/OCO2_L1B_Calibration.10r","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCO2_L2_Lite_SIF v11.2r - Version 11.2r is the current version of the data set. The OCO-2 SIF Lite files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO2_DATA/OCO2_L2_Lite_SIF.11.2r","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCO2_L2_Fwd_SIF v11.2 - Version 11.2 is the current version of the data set. The OCO-2 Fwd SIF files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO2_DATA/OCO2_L2_Fwd_SIF.11.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCO2_L2_Lite_FP v11.2r - Version 11.2r is the current version of the data set. The OCO-2 Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO2_DATA/OCO2_L2_Lite_FP.11.2r","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCO2_L2_Fwd_FP v11.2 - Version 11.2 is the current version of the data set. The OCO-2 Full-physics Forward processing files contain bias-corrected XCO2 along with other select fields aggregated as daily files.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO2_DATA/OCO2_L2_Fwd_FP.11.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-oco-2","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA OCO-3 Project","Description":"The ECOSTRESS/OCO-3 (“ECOCO3”) data set consists of spatially and temporally co-located observations from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the Orbiting Carbon Observatory 3 (OCO-3) instruments currently operating on the International Space Station. Land Surface Temperature, Evapotranspiration, and Water Use Efficiency data products from ECOSTRESS, and Solar-Induced Chlorophyll Fluorescence and Dry-Air Column Mole Fraction CO2 from OCO-3 are matched in space and time over 3°x3° areas across the globe where OCO-3 performs Snapshot Area Map and Target mode observations. The original, native resolution data products are filtered for data quality and spatially averaged onto a 0.005°x0.005° uniform grid, i.e., 557m x 557m or 0.3km2 at the Equator. The aim of the ECOCO3 product is to provide users with synergistic, high-quality data of terrestrial vegetation and atmospheric parameters, for easy and ready use in scientific analysis and modeling. The initial V1.0 release is based on ECOSTRESS Collection 1 and OCO-3 Version 11 data products, for the common measurement period of August 2019 through January 2023, from the start of OCO-3 observations through the end of ECOSTRESS Collection 1.\n<br>\n\n#### OCO3_L1aAE\nVersion 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-3 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.\n<br>\n\n#### OCO3_L1aAE\nVersion 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-3 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.\n<br>\n\n#### OCO3_L1B_Calibration\nVersion 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r. The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-3 incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectralelements, although some are masked out in the L2 retrieval. This L1B product results from calibration mode measurements (e.g., Lunar,Solar, Dark observations), and thus it differs from the OCO3_L1B_Science(L1bSc) product. The differences in the product formats are only in the geolocation information provided. Whereas the L1bSc products report geolocation data for each sounding, calibration products report the directionof the boresight vector.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality. This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.\n<br>\n\n#### OCO3_L2_Lite_FP\nVersion 10.4r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.4r. The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations. The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://ocov3.jpl.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ECOCO3 v1.0 - The ECOSTRESS/OCO-3 (“ECOCO3”) data set consists of spatially and temporally co-located observations from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the Orbiting Carbon Observatory 3 (OCO-3) instruments currently operating on the International Space Station. Land Surface Temperature, Evapotranspiration, and Water Use Efficiency data products from ECOSTRESS, and Solar-Induced Chlorophyll Fluorescence and Dry-Air Column Mole Fraction CO2 from OCO-3 are matched in space and time over 3°x3° areas across the globe where OCO-3 performs Snapshot Area Ma...","ARN":"arn:aws:s3:::ecoco3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCO3_L1aAE v11 - Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO3_DATA/OCO3_L1aAE.11","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCO3_L1aAE v11r - Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO3_DATA/OCO3_L1aAE.11r","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCO3_L1B_Calibration v10r - Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO3_DATA/OCO3_L1B_Calibration.10r","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"OCO3_L2_Lite_FP v10.4r - Version 10.4r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.4r.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/OCO3_DATA/OCO3_L2_Lite_FP.10.4r","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-oco-3","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA OCO Project","Description":"Version 9r is the current version of the data set. Older versions will no longer be available and are superseded by Version 9r. The ACOS Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files. Orbital granules of the ACOS Level 2 standard product (ACOS_L2S) are used as input. The \"ACOS\" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the \"ACOS\" Level 2 production process.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/ACOS_L2_Lite_FP_9r.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ACOS_L2_Lite_FP v9r - Version 9r is the current version of the data set. Older versions will no longer be available and are superseded by Version 9r.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/GOSAT_TANSO_Level2/ACOS_L2_Lite_FP.9r","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-oco","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA OMG Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### OMG_L2_AXCTD\nThis dataset contains in situ profile measurements from Airborne eXpendable Conductivity Temperature Depth (AXCTD) probes. It provides salinity, density, temperature and sound velocity as a function of depth in the water column. The AXCTDs were jettisoned from a plane to collect temperature and salinity readings around Greenland, where a ship would have had difficulties maneuvering. After landing in the water, the AXCTDs drop a weighted sensor from the surface that falls at a well-calibrated rate, measuring water temperature and conductivity as it falls. An equation is used to determine the depth of the measurements as the probe falls, and another equation is used to convert temperature, depth and conductivity into salinity. These probes provided measurements of the ocean's physical characteristics around Greenland, where a ship would have had difficulties maneuvering. The AXCTDs are part of the Oceans Melting Greenland (OMG) mission. The AXCTDs were deployed in the fall from 2016 through 2021, covering the entire continental shelf surrounding Greenland as part of a once-per-year survey. The goal of the mission is to find out what contributions the ocean has on Greenland's melting glaciers.\n<br>\n\n#### OMG_L1B_AIRGRAV\nThis dataset provides level 1B gravity data products from airborne gravity surveys for the NASA Oceans Melting Greenland (OMG) mission.\n<br>\n\n#### OMG_L2_CTD\nThis dataset contains in situ measurements from Conductivity Temperature Depth (CTD) casts and tows. It provides salinity, density, temperature and sound velocity of the water column. The CTDs were deployed from a ship either as single profile casts or towed yo-yo behind the ship to measure the physical properties of the water. This provided measurements of the ocean's physical characteristics around Greenland. The CTDs are part of the Oceans Melting Greenland (OMG) project. The goal of the project is to find out what contributions the ocean has on Greenland's melting glaciers.\n<br>\n\n#### OMG_L2_AXBT\nThis dataset contains Level 2 in situ temperature profile measurements from the Airborne eXpendable BathyThermograph (AXBT) probes. It provides science quality temperature measurements as a function of depth in the water column. The AXBTs were jettisoned from a plane to collect temperature readings around Greenland, where a ship would have had difficulties maneuvering. After landing in the water, the AXBTs drop a weighted sensor from the surface that falls at a well-calibrated rate, measuring water temperature as it falls. An equation is used to determine the depth of the measurements as the probe falls. The AXBTs are part of the Oceans Melting Greenland (OMG) mission. In the fall of 2020 and 2021, the AXBT probes were used to supplement the ocean-per-year survey of ocean properties on the continental shelf surrounding Greenland. The goal of the mission is to find out what contributions the ocean has on Greenland's melting glaciers.\n<br>\n\n#### OMG_NARWHALS_MOORING_TEMP_CTD_1.0\nThis OMG Narwhals L3 dataset contains daily-averaged temperature and salinity measurements from CTD and temperature loggers from the same mooring. <br><br> NASA’s Oceans Melting Greenland (OMG) campaign obtained oceanographic observations around Greenland at an unprecedented spatial scale and confirmed that the ocean plays a key role in Greenland glacier acceleration and retreat. Yet, ocean observations along Greenland’s margins are biased toward summer months with relatively few year-round measurements. OMG Narwhals, a project coupled with NASA’s OMG mission, seeks to understand the ecological importance of glacial habitats to narwhals. Narwhals return to glacial outlets and fjords each summer with high site fidelity but what attracts them to specific glacier fronts remains unclear. Between 2018 and 2020, five bottom-mounted moorings with marine mammal acoustic recorders and oceanographic instruments were deployed year-round near three glacier fronts: Sverdrup Glacier, Kong Oscar Glacier, and Rink Glacier.\n<br>\n\n#### OMG_L1_FLOAT_ALAMO\nThis dataset contains level 1 in situ measurements of temperature and salinity from several autonomous, profiling Alamo floats. These floats change their buoyancy by inflating an external bladder with oil, allowing them to dive and surface regularly. Conductivity, Temperature and Depth sensors (CTDs) allow them to collect vertical profiles of temperature and salinity. This provided measurements of the ocean's physical characteristics around Greenland. The floats wer deployed as part of the Oceans Melting Greenland (OMG) project. The goal of the project is to find out what contributions the ocean has on Greenland's melting glaciers.\n<br>\n\n#### OMG_L1_FLOAT_APEX\nThis dataset contains level 1 in situ measurements of temperature and salinity from several autonomous, profiling APEX floats. These floats change their buoyancy by inflating an external bladder with oil, allowing them to dive and surface regularly. Conductivity, Temperature and Depth sensors (CTDs) allow them to collect vertical profiles of temperature and salinity. This provided measurements of the ocean's physical characteristics around Greenland. The floats wer deployed as part of the Oceans Melting Greenland (OMG) project. The goal of the project is to find out what contributions the ocean has on Greenland's melting glaciers.\n<br>\n\n#### OMG_L2_Bathy_MBES_Gridded\nThis dataset contains level 2 in situ depth measurements from Multibeam Echo Sounder System (MBES) instruments. These depths were used to map the bathymetry around ocean terminating glaciers of Greenland. The bathymetry mapping is part of the Oceans Melting Greenland (OMG) project. The goal of the project is to find out what contributions the ocean has on Greenland's melting glaciers. The MBES was onboard a ship so the tracks are not of a swath, but less regularly patterned as the ship is limited as to where it can traverse due to floating glaciers, ice cover and general weather conditions. Bathymetry was also measured with the Singlebeam Echo Sounder System (SBES) in areas where the MBES could not go, but has less spatial coverage.\n<br>\n\n#### OMG_L2_Bathy_SBES_Gridded\nThis dataset contains in situ depth measurements from Singlebeam Echo Sounder System (SBES) instruments. These depths were used to map the bathymetry around ocean terminating glaciers of Greenland. The bathymetry mapping is part of the Oceans Melting Greenland (OMG) project. The goal of the project is to find out what contributions the ocean has on Greenland's melting glaciers. The SBES was onboard a ship so the tracks are not of a swath, but less regularly patterned as the ship is limited as to where it can traverse due to floating glaciers, ice cover and general weather conditions.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/OMG/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","oceans","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OMG_L2_AXCTD v1 - This dataset contains in situ profile measurements from Airborne eXpendable Conductivity Temperature Depth (AXCTD) probes. It provides salinity, density, temperature and sound velocity as a function of depth in the water column.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OMG_L2_AXCTD","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OMG_L1B_AIRGRAV v1 - This dataset provides level 1B gravity data products from airborne gravity surveys for the NASA Oceans Melting Greenland (OMG) mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OMG_L1B_AIRGRAV","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OMG_L2_CTD v1 - This dataset contains in situ measurements from Conductivity Temperature Depth (CTD) casts and tows. It provides salinity, density, temperature and sound velocity of the water column.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OMG_L2_CTD","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OMG_L2_AXBT v1 - This dataset contains Level 2 in situ temperature profile measurements from the Airborne eXpendable BathyThermograph (AXBT) probes. It provides science quality temperature measurements as a function of depth in the water column.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OMG_L2_AXBT","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OMG_NARWHALS_MOORING_TEMP_CTD_1.0 v1.0 - This OMG Narwhals L3 dataset contains daily-averaged temperature and salinity measurements from CTD and temperature loggers from the same mooring. <br><br> NASA’s Oceans Melting Greenland (OMG) campaign obtained oceanographic observations around Greenland at an unprecedented spatial scale and confirmed that the ocean plays a key role in Greenland glacier acceleration and retreat.","ARN":"arn:aws:s3:::podaac-ops-cumulus-docs/web-misc/omg","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OMG_L1_FLOAT_ALAMO v1 - This dataset contains level 1 in situ measurements of temperature and salinity from several autonomous, profiling Alamo floats. These floats change their buoyancy by inflating an external bladder with oil, allowing them to dive and surface regularly.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OMG_L1_FLOAT_ALAMO","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OMG_L1_FLOAT_APEX v1 - This dataset contains level 1 in situ measurements of temperature and salinity from several autonomous, profiling APEX floats. These floats change their buoyancy by inflating an external bladder with oil, allowing them to dive and surface regularly.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OMG_L1_FLOAT_APEX","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OMG_L2_Bathy_MBES_Gridded v1 - This dataset contains level 2 in situ depth measurements from Multibeam Echo Sounder System (MBES) instruments. These depths were used to map the bathymetry around ocean terminating glaciers of Greenland.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OMG_L2_Bathy_MBES_Gridded","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OMG_L2_Bathy_SBES_Gridded v1 - This dataset contains in situ depth measurements from Singlebeam Echo Sounder System (SBES) instruments. These depths were used to map the bathymetry around ocean terminating glaciers of Greenland.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OMG_L2_Bathy_SBES_Gridded","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-omg","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA omi Project","Description":"This dataset was produced by the Adaptive Sampling of Rain and Ocean Salinity from Autonomous Seagliders (NASA grant NNX17AK07G) project, an investigation to develop tools and strategies to better measure the structure and variability of upper-ocean salinity in rain-dominated environments. From October 2019 to January 2020, three Seagliders were deployed near Guam (14°N 144°E). The Seaglider is an autonomous profiler measuring salinity and temperature in the upper ocean. The three gliders sampled in an adaptive formation to capture the patchiness of the rain and the corresponding oceanic response in real time. The location was chosen because of the likelihood of intense tropical rain events and the availability of a NEXRAD (S-band) rain radar at the Guam Airport. Spacing between gliders varies from 1 to 60 km. Data samples are gridded by profile and on regular depth bins from 0 to 1000 m. The time interval between profiles was about 3 hours, and they are typically about 1.5 km apart. These profiles are available at Level 2 (basic gridding) and Level 3 (despiked and interpolated). All Seaglider data files are in netCDF format with standards compliant metadata. The project was led by a team from the Applied Physics Laboratory at the University of Washington.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","netcdf","oceans","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SEAGLIDER_GUAM_2019 vV1 - This dataset was produced by the Adaptive Sampling of Rain and Ocean Salinity from Autonomous Seagliders (NASA grant NNX17AK07G) project, an investigation to develop tools and strategies to better measure the structure and variability of upper-ocean salinity in rain-dominated environments. From October 2019 to January 2020, three Seagliders were deployed near Guam (14°N 144°E).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SEAGLIDER_GUAM_2019","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-omi","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA opera Project","Description":"This data set is an inventory of some 2800 landslides that occurred in the High Mountain Asia (HMA) study area between 5 January 2007 and 31 December 2018 (plus one event from 28 January 1990). The catalog includes dates and locations of landslides, plus additional characteristics such as event triggers, country, length and area of the slide, and the number of injuries and fatalities. The events in this catalog represent an HMA-specific subset of the Cooperative Open Online Landslide Repository (COOLR), a project that was created to build a more robust, publicly available inventory of landslides by supplementing data in the NASA Global Landslide Catalog with citizen science reports.\n<br>\n\n#### HMA2_MTLI\nThe transboundary Pumpqu/Arun River basin spreads across Nepal and Tibet. Nearly 95% of the basin lies in Tibet through which the Pumpqu River flows. The river is named the Arun River once it enters Nepal. Five large hydropower projects (in total about 3,163 MW) are currently under construction or are planned for the Arun River valley. Rainfall and earthquake-induced landslides, landslide dammed lakes, and landslide-induced glacial lake outburst floods pose major risks to the smooth operation of these projects. This data set is a multitemporal landslide inventory covering the whole Pumpqu/Arun River basin. It was generated in support of the World Bank’s Risk Assessment of Landslides in the Upper Arun Hydropower Project.\n<br>\n\n#### IPFLT1B\nThis data set contains simplified, or thinned, flight lines from NASA Operation IceBridge Greenland, Arctic, Antarctic, and Alaska missions. The thinning was performed using a Python library called Shapely. The full resolution flight line data were collected as part of Operation IceBridge funded aircraft survey campaigns. The corresponding flight reports can be found in the IceBridge L1B Flight Reports (IPFLR1B) data set.\n<br>\n\n#### NISE\nThe Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation. This NISE Version 2 product contains SSMIS-derived sea ice concentrations and snow extents derived from the Special Sensor Microwave Imager (SSM/I) aboard the Defense Meteorological Satellite Program (DMSP) F13 satellite. For DMSP-F16, SSMIS-derived data, see <a href=\"https://doi.org/10.5067/JAQDJKPX0S60\"> NISE Version 3</a>. For DMSP-F17, SSMIS-derived data, see <a href=\"https://nsidc.org/data/nise/versions/4\"> NISE Version 4</a>. For DMSP-F18, SSMIS-derived data, see <a href=\"https://doi.org/10.5067/3KB2JPLFPK3R\"> NISE Version 5</a>.\n<br>\n\n#### SV19MA_TNET\nThese data consist of ground-based, soil moisture, soil temperature, and air temperature measurements recorded by twenty-five temporary stations located in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. The stations were installed across an area of approximately 23 km by 36 km in May 2019 and operated through 2022. Note that the product is named SMAPVEX19-22 because, although the current coverage is through 2021, it is projected to include 2022 data in the future.\n<br>\n\n#### SV19MB_TNET\nThese data consist of soil moisture sensor, soil temperature and air temperature measurements recorded by 25 temporary stations in the Millbrook (MB) experiment location during SMAPVEX19-22. The stations were spread out over the experiment domain of about 30 by 40 km located around Millbrook, NY. The stations were installed in May 2019 and are to continue operation until 2022. Note that the product is named SMAPVEX19-22 because, although the current coverage is through 2021, it is projected to include 2022 data in the future.\n<br>\n\n#### SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1\nThis dataset provides simulated sea surface height data product that resembles data which will be collected by KaRIn. Swaths span 60 km on both sides of nadir with a nadir gap. Product provides sea surface height, sea surface height anomaly, wind speed, significant waveheight, on a geographically fixed, swath-aligned 2x2 km2 grid, as well as sea surface height on a 250x250 m2 native grid. SSH data produced by GLORYS were rendered from their native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.\n<br>\n\n#### SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_CALVAL_V1\nThis dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) altimetry data from the Poseidon 3C nadir altimeter. The simulated data are from the Global Ocean Reanalysis and Simulations (GLORYS). SSH data from GLORYS were rendered from their native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/hma_ls_cat/versions/2","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","oceans","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"HMA_LS_Cat v2 - This data set is an inventory of some 2800 landslides that occurred in the High Mountain Asia (HMA) study area between 5 January 2007 and 31 December 2018 (plus one event from 28 January 1990). The catalog includes dates and locations of landslides, plus additional characteristics such as event triggers, country, length and area of the slide, and the number of injuries and fatalities.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_LS_Cat/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA2_MTLI v1 - The transboundary Pumpqu/Arun River basin spreads across Nepal and Tibet. Nearly 95% of the basin lies in Tibet through which the Pumpqu River flows.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA2_MTLI/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"IPFLT1B v1 - This data set contains simplified, or thinned, flight lines from NASA Operation IceBridge Greenland, Arctic, Antarctic, and Alaska missions. The thinning was performed using a Python library called Shapely.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ICEBRIDGE/IPFLT1B/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NISE v2 - The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/PM/NISE/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MA_TNET v1 - These data consist of ground-based, soil moisture, soil temperature, and air temperature measurements recorded by twenty-five temporary stations located in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. The stations were installed across an area of approximately 23 km by 36 km in May 2019 and operated through 2022.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MA_TNET/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MB_TNET v1 - These data consist of soil moisture sensor, soil temperature and air temperature measurements recorded by 25 temporary stations in the Millbrook (MB) experiment location during SMAPVEX19-22. The stations were spread out over the experiment domain of about 30 by 40 km located around Millbrook, NY.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MB_TNET/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1 v1 - This dataset provides simulated sea surface height data product that resembles data which will be collected by KaRIn. Swaths span 60 km on both sides of nadir with a nadir gap.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_CALVAL_V1 v1 - This dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) altimetry data from the Poseidon 3C nadir altimeter. The simulated data are from the Global Ocean Reanalysis and Simulations (GLORYS).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_NADIR_SSH_GLORYS_CALVAL_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-opera","Sources":["awslabs-open-data-registry"]}
{"Name":"OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1)","Description":"The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) Static Layers (CSLC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) validated product. Due to the S1 mission’s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA CSLC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static layers generation algorithm. Each OPERA CSLC-S1-STATIC product is distributed as a Hierarchical Data Format version 5 (HDF5) file following the CF-1.8 convention containing both data raster layers and product metadata and corresponds to matching CSLC-S1 products with the same burst ID. OPERA CSLC-S1 products are available over North America which includes the USA and U.S. Territories, Canada within 200 km of the U.S. border, and all mainland countries from the southern U.S. border down to and including Panama. The CSLC-S1 products are available in the associated OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1) dataset.\nRead our doc on how to get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME","Documentation":"https://doi.org/10.5067/SNWG/OPERA_L2_CSLC-S1-STATIC_V1","Contact":"Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/","ManagedBy":"NASA","UpdateFrequency":"From 2014-04-03 to Ongoing","Tags":["aws-pds","coastal","earth observation","hdf","ice","land","metadata","oceans","orbit","radar","sentinel-1","synthetic aperture radar","xml"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1).","ARN":"arn:aws:s3:::asf-cumulus-prod-opera-products/OPERA_L2_CSLC-S1_STATIC/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://cumulus.asf.alaska.edu/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Generate inteferograms without the need to download OPERA CSLC-S1 products locally","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/CSLC/Discover/Create_Interferogram_by_Streaming_CSLC-S1.ipynb","AuthorName":"M. Grace Bato and K. Devlin"},{"Title":"Generate interferograms and map the lava flow emplacement using OPERA CSLC-S1","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/CSLC/Volcano/Map_Deformation_LavaFlow_using_CSLC-S1.ipynb","AuthorName":"M. Grace Bato"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-operal2cslc-s1-staticv1","Sources":["awslabs-open-data-registry"]}
{"Name":"OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1)","Description":"The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 validated product consists of Single Look Complex (SLC) images which contain both amplitude and phase information of the complex radar return. The amplitude is primarily determined by ground surface properties (e.g., terrain slope, surface roughness, and physical properties), and phase primarily represents the distance between the radar and ground targets corrected for the geometrical distance between the two based on the knowledge from Digital Elevation Model and platform’s position, i.e., the CSLC phase represents residual geometrical distance between the sensor and target, the atmospheric propagation delay and the target movements. The CSLC-S1 product is derived from Copernicus Sentinel-1A and Sentinel-1B Interferometric Wide (IW) SLC data.  \n\nThe CSLC images are precisely aligned or “coregistered” to a pre-defined UTM/Polar stereographic map projection systems and posted at 5x10 m spacing in east and north direction, respectively.  Each CSLC-S1 product corresponds to a single S1 burst and is distributed as a Hierarchical Data Format version 5 (HDF5) file following the CF-1.8 convention containing both data raster layers (e.g., geocoded complex backscatter, low-resolution correction look-up tables) and product metadata. OPERA CSLC-S1 products are available over North America which includes the USA and U.S. Territories, Canada within 200 km of the U.S. border, and all mainland countries from the southern U.S. border down to and including Panama.  The OPERA CSLC-S1 product contains modified Copernicus Sentinel data (2016-2025).\n\nDue to the S1 mission’s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA CLSLC-S1 product, as they are produced only once or a limited number of times. The static layers are available in the associated OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1).\nRead our doc on how to get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME","Documentation":"https://doi.org/10.5067/SNWG/OPERA_L2_CSLC-S1_V1","Contact":"Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/","ManagedBy":"NASA","UpdateFrequency":"From 2014-06-15 to Ongoing","Tags":["aws-pds","coastal","earth observation","hdf","ice","land","metadata","oceans","orbit","radar","sentinel-1","synthetic aperture radar","xml"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1).","ARN":"arn:aws:s3:::asf-cumulus-prod-opera-products/OPERA_L2_CSLC-S1/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://cumulus.asf.alaska.edu/s3credentialsREADME"}],"DataAtWork":{"Tutorials":[{"Title":"Generate inteferograms without the need to download OPERA CSLC-S1 products locally","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/CSLC/Discover/Create_Interferogram_by_Streaming_CSLC-S1.ipynb","AuthorName":"M. Grace Bato and K. Devlin"},{"Title":"Generate interferograms and map the lava flow emplacement using OPERA CSLC-S1","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/CSLC/Volcano/Map_Deformation_LavaFlow_using_CSLC-S1.ipynb","AuthorName":"M. Grace Bato"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-operal2cslc-s1v1","Sources":["awslabs-open-data-registry"]}
{"Name":"OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1)","Description":"The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) Static Layers (RTC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) (RTC-S1) validated product.  Due to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA RTC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm.  Static layers are provided as single-band cloud-optimized GeoTIFF (COG) files, with map grid matching RTC-S1 products with the same burst ID.  The standard OPERA RTC-S1 product is derived from the original Copernicus Sentinel-1 (S1) interferometric wide (IW) single-look complex (SLC) data, provided by the European Space Agency, with a temporal sampling coincident with the availability of Sentinel-1A and Sentinel-1B SLC data. The OPERA RTC-S1-STATIC and RTC-S1 products are provided at a near global scope (land masses excluding Antarctica).  The RTC-S1 products are available in the associated OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1) dataset.\nRead our doc on how to get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME","Documentation":"https://doi.org/10.5067/SNWG/OPERA_L2_RTC-S1-STATIC_V1","Contact":"Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/","ManagedBy":"NASA","UpdateFrequency":"From 2014-04-03 to Ongoing","Tags":["aws-pds","coastal","cog","earth observation","geoscience","global","ice","land","metadata","oceans","orbit","radar","sentinel-1","synthetic aperture radar","tiff","xml"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1).","ARN":"arn:aws:s3:::asf-cumulus-prod-opera-products/OPERA_L2_RTC-S1_STATIC/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://cumulus.asf.alaska.edu/s3credentialsREADME"}],"DataAtWork":{"Publications":[{"Title":"An Area-Based Projection Algorithm for SAR Radiometric Terrain Correction and Geocoding","URL":"https://doi.org/10.1109/TGRS.2022.3147472","AuthorName":"Gustavo H. X. Shiroma, Marco Lavalle, and Sean M. Buckley"}],"Tutorials":[{"Title":"Load, Mosaic, and Visualize OPERA RTC-S1 Data","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/RTC/notebooks/RTC_notebook.ipynb","AuthorName":"K. Venkataramani"},{"Title":"RTC Landslide Example","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/RTC/notebooks/RTC_landslide_example.ipynb","AuthorName":"K. Venkataramani"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-operal2rtc-s1-staticv1","Sources":["awslabs-open-data-registry"]}
{"Name":"OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1)","Description":"The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) validated product consists of radar backscatter normalized with respect to the topography. The product maps signals related to the physical properties of ground scattering objects, such as surface roughness and soil moisture and/or vegetation. The OPERA RTC-S1 product is derived from Copernicus Sentinel-1 Interferometric Wide (IW) Single Look Complex (SLC) data with a near global scope and temporal sampling coincident with the availability of S1 SLC data. \n\nEach OPERA RTC-S1 product corresponds to a single S1 burst projected onto a pre-defined UTM/Polar stereographic map projection system map grid with a 30-meter spacing. The Copernicus global 30 m (GLO-30) Digital Elevation Model (DEM) is the reference DEM used to correct for the impacts of topography and to geocode the product.  The OPERA RTC-S1 product is normalized to the backscatter coefficient gamma-naught, ɣ0,  obtained from the original radar brightness beta-naught, β0,  through radiometric terrain correction.  The RTC-S1 product is distributed as cloud optimized GeoTIFFs with one GeoTIFF file per processed polarization. The RTC-S1 product metadata is provided in the Hierarchical Data Format version 5 (HDF5) format. The OPERA RTC-S1 product contains modified Copernicus Sentinel data (2016-2025).\n\nDue to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA RTC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm. The static layers are available in the associated OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1) dataset.\nRead our doc on how to get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME","Documentation":"https://doi.org/10.5067/SNWG/OPERA_L2_RTC-S1_V1","Contact":"Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/","ManagedBy":"NASA","UpdateFrequency":"From 2016-04-14 to Ongoing","Tags":["aws-pds","coastal","earth observation","geoscience","global","hdf","ice","land","metadata","oceans","orbit","radar","sentinel-1","soil moisture","synthetic aperture radar","tiff","xml"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1).","ARN":"arn:aws:s3:::asf-cumulus-prod-opera-products/OPERA_L2_RTC-S1/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://cumulus.asf.alaska.edu/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"An Area-Based Projection Algorithm for SAR Radiometric Terrain Correction and Geocoding","URL":"https://doi.org/10.1109/TGRS.2022.3147472","AuthorName":"Gustavo H. X. Shiroma, Marco Lavalle, and Sean M. Buckley"},{"Title":"Thermal Denoising of Products Generated by the S-1 IPF","URL":"https://sentinels.copernicus.eu/documents/247904/2142675/Thermal-Denoising-of-Products-Generated-by-Sentinel-1-IPF.pdf","AuthorName":"Riccardo Piantanida"}],"Tutorials":[{"Title":"Load, Mosaic, and Visualize OPERA RTC-S1 Data","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/RTC/notebooks/RTC_notebook.ipynb","AuthorName":"K. Venkataramani"},{"Title":"RTC Landslide Example","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/RTC/notebooks/RTC_landslide_example.ipynb","AuthorName":"K. Venkataramani"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-10-31","Slug":"nasa-operal2rtc-s1v1","Sources":["awslabs-open-data-registry"]}
{"Name":"OPERA Surface Displacement from Sentinel-1 validated product (Version 1)","Description":"The Level-3 OPERA Sentinel-1 Surface Displacement (DISP) product is generated through interferometric time-series analysis of Level-2 Coregistered Sentinel-1 Single Look Complex (CSLC) datasets. Using a hybrid Persistent Scatterer (PS) and Distributed Scatterer (DS) approach, this product quantifies Earth's surface displacement in the radar line-of-sight. The DISP products enable the detection of anthropogenic and natural surface changes, including subsidence, tectonic deformation, and landslides. \n\nThe OPERA DISP suite comprises complementary datasets derived from Sentinel-1 and NISAR inputs, designated as DISP-S1 and DISP-NI, respectively. Each product, created per acquisition, adheres to a consistent structure, HDF5 file format, file-naming convention, and a 30 m spatial posting. This collection specifically includes DISP-S1 products, derived from Sentinel-1 data. \n\nDISP-S1 products provide spatial coverage across North America, encompassing the United States, U.S. territories within 200 km of the U.S. border, Canada, and mainland countries from the southern U.S. border to Panama. These products are generated from Sentinel-1 Interferometric Wide (IW) swath mode acquisitions starting in mid-2016. \n\nThe OPERA DISP-S1 product contains modified Copernicus Sentinel data (2016-2025).\nRead our doc on how to get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME","Documentation":"https://doi.org/10.5067/SNWG/OPL3DISPS1-V1","Contact":"Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/","ManagedBy":"NASA","UpdateFrequency":"From 2016-07-01 to Ongoing","Tags":["aws-pds","earth observation","land","metadata","netcdf","orbit","radar","sentinel-1","synthetic aperture radar","xml","zarr"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OPERA Surface Displacement from Sentinel-1 validated product (Version 1).","ARN":"arn:aws:s3:::asf-cumulus-prod-opera-products/OPERA_L3_DISP-S1_V1/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://cumulus.asf.alaska.edu/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Inspect DISP-S1 Layers","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DISP/Timeseries/opera_disp/inspect_DISP-S1_layers.ipynb","AuthorName":"M. Grace Bato"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-operal3disp-s1v1","Sources":["awslabs-open-data-registry"]}
{"Name":"OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 provisional product (Version 0)","Description":"The OPERA_L3_DIST-ALERT-HLS Version 0 data product was decommissioned on April 25, 2025. Users are encouraged to use the [OPERA_L3_DIST-ALERT-HLS V1](https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001) data product which was released on March 14, 2024, and has achieved stage 1 validation.\n\nThe Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) provisional data product Version 0 maps vegetation disturbance alerts from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). Vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes to provide information about more general disturbance trends. HLS data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration, or small magnitude/long duration. \n\nThe OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within in the DIST-ALERT product: vegetation disturbance status, current vegetation cover indicator, current vegetation anomaly value, historical vegetation cover indicator, max vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, and vegetation disturbance duration. See the Product Specification for a more detailed description of the individual layers provided in the DIST-ALERT product. \n\nKnown Issues\n* Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_PROVISIONAL_V0.000","Contact":"Email: lpdaac@usgs.gov. Home Page: https://lpdaac.usgs.gov/","ManagedBy":"NASA","UpdateFrequency":"From 2022-01-01 to 2024-02-26","Tags":["aws-pds","cog","earth observation","environmental","global","land","land cover","land use"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 provisional product (Version 0).","ARN":"arn:aws:s3:::lp-protected/OPERA_DIST-ALERT-HLS_PROVISIONAL_V0.000","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Getting Started with OPERA DIST Product","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DIST/DIST_ALERT/Wildfire/Intro_To_DIST.ipynb","AuthorName":"M. Grace Bato and R. Dhillon"},{"Title":"Getting Started with OPERA DIST-ALERT-HLS Products","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DIST/DIST_ALERT/Discover/Stream_and_Viz_DIST-ALERT-folium.ipynb","AuthorName":"R. Dhillon and M. Grace Bato"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-operal3dist-alert-hlsprovisionalv0","Sources":["awslabs-open-data-registry"]}
{"Name":"OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 product (Version 1)","Description":"The Observational Products for End-Users from Remote Sensing Analysis ([OPERA](https://www.jpl.nasa.gov/go/opera)) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) product Version 1 maps vegetation disturbance alerts that are derived from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). A vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The Level-3 data product also provides additional information about more general disturbance trends and auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes. [HLS](https://lpdaac.usgs.gov/product_search/?collections=HLS&status=Operational&view=list) data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration or small magnitude/long duration.\n\nThe OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within the DIST-ALERT product. The layers for both vegetation and generic disturbance include disturbance status, loss or anomaly, maximum loss anomaly, disturbance confidence layer, date of disturbance, count of observations with loss anomalies, days of ongoing anomalies, and day of last disturbance detection. Additional layers are vegetation cover percent, historical percent vegetation cover, and data mask. See the Product Specification Document (PSD) for a more detailed description of the individual layers provided in the DIST-ALERT product.\n\nThe OPERA_L3_DIST-ALERT-HLS product contains modified Copernicus Sentinel data (2020-2025).\n\nKnown Issues\n* Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001","Contact":"Email: lpdaac@usgs.gov. Home Page: https://lpdaac.usgs.gov/","ManagedBy":"NASA","UpdateFrequency":"From 2022-01-01 to Ongoing (Daily - < Weekly)","Tags":["aws-pds","cog","earth observation","environmental","global","land","land cover","land use","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 product (Version 1).","ARN":"arn:aws:s3:::lp-protected/OPERA_L3_DIST-ALERT-HLS_V1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Getting Started with OPERA DIST Product","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DIST/DIST_ALERT/Wildfire/Intro_To_DIST.ipynb","AuthorName":"M. Grace Bato and R. Dhillon"},{"Title":"Getting Started with OPERA DIST-ALERT-HLS Products","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DIST/DIST_ALERT/Discover/Stream_and_Viz_DIST-ALERT-folium.ipynb","AuthorName":"R. Dhillon and M. Grace Bato"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-13","Slug":"nasa-operal3dist-alert-hlsv1","Sources":["awslabs-open-data-registry"]}
{"Name":"OPERA Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 product (Version 1)","Description":"The Observational Products for End-Users from Remote Sensing Analysis ([OPERA](https://www.jpl.nasa.gov/go/opera)) Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 (HLS) product Version 1 summarizes the [DIST-ALERT](https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001) data product into an annual vegetation disturbance data product. Vegetation disturbance is mapped when there is an indicated decrease in vegetation cover within an HLS Version 2 pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the DIST-ALERT scenes to provide information about more general disturbance trends. The DIST-ANN product tracks changes at the annual scale, aggregating changes identified in the DIST-ALERT product. Only confirmed disturbances from the associated year are reported together with the date of initial disturbance. As confirmed disturbances are determined using subsequent cloud-free observations to determine if the loss detections persist, the required number of HLS scenes depends on visibility of the target. Due to this dependency, summarizing the DIST-ALERT in the DIST-ANN product will have some latency contingent on the algorithmic calibration and is detailed in the Algorithm Theoretical Basis Document (ATBD).\n\nThe OPERA_L3_DIST-ANN-HLS (or DIST-ANN) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate COG. There are 21 layers contained within the DIST-ANN product: vegetation disturbance status, historical vegetation cover indicator, maximum vegetation cover indicator, maximum vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, vegetation disturbance duration, date of last observation assessed for vegetation disturbance, and several generic disturbance layers. Each product layer is gridded to the same resolution and tiling system as HLS V2: 30 meter (m) and Military Grid Reference System (MGRS). See the Product Specification Document (PSD) for a more detailed description of the individual layers provided in the DIST-ANN product. \n\nThe OPERA_L3_DIST-ANN-HLS product contains modified Copernicus Sentinel data (2020-2025).\n\nKnown Issues\n* Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD).\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ANN-HLS_V1.001","Contact":"Email: lpdaac@usgs.gov. Home Page: https://lpdaac.usgs.gov/","ManagedBy":"NASA","UpdateFrequency":"From 2022-01-01 to Ongoing (Annual)","Tags":["aws-pds","cog","earth observation","environmental","global","land","land cover","land use"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OPERA Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 product (Version 1).","ARN":"arn:aws:s3:::lp-protected/OPERA_L3_DIST-ANN-HLS_V1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Visualization and Exploration of OPERA DIST-ANN-HLS Product Layers","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DIST/DIST_ANN/Intro_To_DIST_ANN.ipynb","AuthorName":"C. Speed and M. Grace Bato"},{"Title":"Visualizing and Analyzing the OPERA DIST-ANN-HLS Product to Explore Land-Use Change in Brazil","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DIST/DIST_ANN/Land_Use_Change_DIST_ANN.ipynb","AuthorName":"C. Speed and M. Grace Bato"},{"Title":"Visualizing and Analyzing the OPERA DIST-ANN-HLS Product to Visualize Wildfire Impact in Northern Quebec","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DIST/DIST_ANN/Wildfires_DIST_ANN.ipynb","AuthorName":"C. Speed and M. Grace Bato"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-operal3dist-ann-hlsv1","Sources":["awslabs-open-data-registry"]}
{"Name":"OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 product (Version 1)","Description":"This dataset contains Level-3 Dynamic OPERA surface water extent product version 1.  The data are validated surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B/C (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B/C satellite. The surface water extent products are distributed over projected map coordinates using the Universal Transverse Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km × 109.8 km. This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) files including water classification, associated confidence, land cover classification, terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model (DEM), and Diagnostic layer.\n<br><br>\nThe digital elevation model (DEM) provided as a layer of the DSWx-HLS product (band 10) was generated using the Copernicus DEM 30-m and Copernicus DEM 90-m models provided by the European Space Agency. The Copernicus DEM 30-m and Copernicus DEM 90-m were produced using Copernicus WorldDEM-30 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. The organizations in charge of the OPERA project, the Copernicus programme, and Airbus Defence and Space GmbH by law or by delegation do not assume any legal responsibility or liability, whether express or implied, arising from the use of this DEM.\n<br><br>\nThe OPERA DSWx-HLS product contains modified Copernicus Sentinel data (2023-2025).\n<br><br>\nTo access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov \nRead our doc on how to get AWS Credentials to retrieve this data: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/OPDSW-PL3V1","Contact":"Help Desk: podaac@podaac.jpl.nasa.gov. Home Page: https://podaac.jpl.nasa.gov/","ManagedBy":"NASA","UpdateFrequency":"From 2023-04-04 to Ongoing (Daily - < Weekly)","Tags":["aws-pds","cog","datacenter","earth observation","ice","land","land cover","metadata","surface water","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 product (Version 1).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Publications":[{"Title":"Improved Automated Detection of Subpixel-Scale Inundation—Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests","URL":"https://doi.org/10.3390/rs11040374","AuthorName":"Jones, John W"}],"Tutorials":[{"Title":"Access DSWx-HLS S3","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DSWx/Discover/Access_DSWx-HLS_S3.ipynb","AuthorName":"M. Grace Bato"},{"Title":"Getting Started with OPERA DSWx Product","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DSWx/Reservoir/Intro_to_DSWx.ipynb","AuthorName":"K. Devlin and M. Grace Bato"},{"Title":"Stream and Viz DSWx-HLS via Direct HTTPS","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DSWx/Discover/Stream_and_Viz_DSWx-HLS_viaDirectHTTPS.ipynb","AuthorName":"M. Grace Bato"},{"Title":"Working with OPERA Dynamic Surface Water Extent (DSWx) Data","URL":"https://podaac.github.io/tutorials/notebooks/datasets/OPERA_GIS_Cloud.html","AuthorName":"Nicholas Tarpinian","NotebookURL":"https://github.com/podaac/tutorials/blob/master/notebooks/datasets/OPERA_GIS_Cloud.ipynb"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-12","Slug":"nasa-operal3dswx-hlsv1","Sources":["awslabs-open-data-registry"]}
{"Name":"OPERA Dynamic Surface Water Extent from Sentinel-1 (Version 1)","Description":"This dataset contains Level-3 Dynamic OPERA Surface Water Extent from Sentinel-1 (DSWx-S1) product version 1.  DSWx-S1 provides near-global geographical mapping of surface water extent over land at a spatial resolution of 30 meters over the Military Grid reference System (MGRS) grid system, with a temporal revisit frequency between 6-12 days. Using Sentinel-1 radar observations, DSWx-S1 maps open inland water bodies greater than 3 hectares and 200 meters in width, irrespective of cloud conditions and daylight illumination that often pose challenges to optical sensors. Forward production of the DSWx-S1 data record began in Sept 2024.  Each product is distributed as a set of 3 GeoTIFF (Geographic Tagged Image File Format) files including water classification and associated confidence layers.\n<br><br>\nThe OPERA DSWx-S1 product contains modified Copernicus Sentinel data (2024-2025).\n<br><br>\nTo access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov \nRead our doc on how to get AWS Credentials to retrieve this data: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME","Documentation":"https://doi.org/10.5067/OPDSWS1-L3V1","Contact":"Help Desk: podaac@podaac.jpl.nasa.gov. Home Page: https://podaac.jpl.nasa.gov/","ManagedBy":"NASA","UpdateFrequency":"From 2024-08-01 to Ongoing (Daily - < Weekly)","Tags":["aws-pds","cog","datacenter","earth observation","global","land","orbit","radar","sentinel-1","surface water","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OPERA Dynamic Surface Water Extent from Sentinel-1 (Version 1).","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OPERA_L3_DSWX-S1_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Generate Flood Maps without downloading OPERA DSWx-S1 products locally","URL":"https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DSWx/Flood/Brazil_DSWx-S1_FloodProduct.ipynb","AuthorName":"S. Sangha"},{"Title":"Working with OPERA Dynamic Surface Water Extent (DSWx) Data","URL":"https://podaac.github.io/tutorials/notebooks/datasets/OPERA_GIS_Cloud.html","AuthorName":"Nicholas Tarpinian","NotebookURL":"https://github.com/podaac/tutorials/blob/master/notebooks/datasets/OPERA_GIS_Cloud.ipynb"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-operal3dswx-s1v1","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA OSCAR Project","Description":"Ocean Surface Current Analyses Real-time (OSCAR) is a global surface current database and NASA funded research project. OSCAR ocean mixed layer velocities are calculated from satellite-sensed sea surface height gradients, ocean vector winds, and sea surface temperature gradients using a simplified physical model for geostrophy, Ekman, and thermal wind dynamics. Daily averaged surface currents are provided on a global 0.25 x 0.25 degree grid as an average over an assumed well-mixed top 30 m of the ocean from 1993 to present day. OSCAR currents are provided at three quality levels: final, interim and nrt with a respective latency of each of approximately 1 year, 1 month, and 2 days. OSCAR is generated by Earth & Space Research (ESR) https://www.esr.org/research/oscar/. More details on the source datasets, file structure, and methodology can be found in oscarv2guide.pdf.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OSCAR_L4_OC_FINAL_V2.0 v2.0 - Ocean Surface Current Analyses Real-time (OSCAR) is a global surface current database and NASA funded research project. OSCAR ocean mixed layer velocities are calculated from satellite-sensed sea surface height gradients, ocean vector winds, and sea surface temperature gradients using a simplified physical model for geostrophy, Ekman, and thermal wind dynamics.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OSCAR_L4_OC_FINAL_V2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-oscar","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Space Biology Open Science Data Repository (OSDR)","Description":"NASA’s Space Biology Open Science Data Repository (OSDR) introduces a one-stop site where users can explore and contribute a variety of NASA open science biological data. This site consolidates data from the Ames Life Sciences Data Archive (ALSDA) and GeneLab and includes information about the broader NASA Open Science and Open Data initiatives, all at one centralized location. Our mission is to maximize the utilization of the valuable biological research resources and enable new discoveries.<br/><br/>\nOSDR introduces access to data generated from spaceflight and space relevant experiments that explore the biological response of terrestrial biology through the AWS Open Data Registry page. The ALSDA is the official repository of non-human science data spanning a broad range of biological levels involving data from tissues, organs, whole organisms, physiology, and behavior. GeneLab is an open science repository hosting multiple types of ‘omics including transcriptomics, metagenomics, epigenomics, proteomics, and metabolomics data. Studies comprise of data from model organisms including microbes, plants, fruit flies, rodents, as well as human cell culture, ground study, and commercial astronaut data. In addition, the data repository includes metadata searches across several external omics database.<br/><br/>\n","Documentation":"https://osdr.nasa.gov/bio/repo","Contact":"https://osdr.nasa.gov/bio/help/contact.html","ManagedBy":"NASA","UpdateFrequency":"New research data is added as soon as it is available.","Tags":["aws-pds","bioinformatics","biology","GeneLab","genomic","imaging","life sciences","space biology"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"Biological research data from spaceflight and space relevant experiments","ARN":"arn:aws:s3:::nasa-osdr","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](http://nasa-osdr.s3-website-us-west-2.amazonaws.com)"]}],"DataAtWork":{"Publications":[{"Title":"Advancing the Integration of Biosciences Data Sharing to Further Enable Space Exploration","URL":"https://www.sciencedirect.com/science/article/pii/S2211124720314303","AuthorName":"Ryan T. Scott, Kirill Grigorev, Graham Mackintosh, Samrawit G. Gebre, Christopher E. Mason, Martha E. Del Alto, Sylvain V. Costes"},{"Title":"GeneLab: Omics database for spaceflight experiments","URL":"https://academic.oup.com/bioinformatics/article/35/10/1753/513406","AuthorName":"Shayoni Ray, Samrawit Gebre, Homer Fogle, Daniel C Berrios, Peter B Tran, Jonathan M Galazka, Sylvain V Costes"},{"Title":"NASA GeneLab: interfaces for the exploration of space omics data","URL":"https://academic.oup.com/nar/article/49/D1/D1515/5932845","AuthorName":"Daniel C Berrios, Jonathan Galazka, Kirill Grigorev, Samrawit Gebre, Sylvain V Costes"}]},"RegistryEntryAdded":"2023-04-06","RegistryEntryLastModified":"2023-07-12","Slug":"nasa-osdr","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA OTTER Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### chem_26\nCanopy characteristics: leaf chemistry, specific leaf area, LAI, PAR, IPAR, NPP, and standing biomass.\n<br>\n\n#### model_36\nSteve Running's Forest-BGC Model\n<br>\n\n#### lai_45\nLAI estimates computed from unweighted openness by the canopy program from digitized canopy photographs.\n<br>\n\n#### spectra_licor_47\nThe variability of bi-directional spectral reflectance of cut conifer foilage between age classes, species and sites measured by LICOR.\n<br>\n\n#### spectra_perkin_48\nAbsolute reflectance of leaves measured in the lab by Perkin-Elmer spectrophotometer to aid in understanding remotely sensed spectral data.\n<br>\n\n#### meteo_50\nMeteorology data collected on an hourly basis from stations located near the OTTER sites in 1990 and summarized to monthly data.\n<br>\n\n#### airsunp_61\nAirborne sun photometer data collected from 1990 through 1991 used to provide quantitative atmospheric correction to remotely sensed data of forest reflectance and radiance.\n<br>\n\n#### fieldsunp_65\nField sun photometer data collected from 1990 through 1991 is used to provide quantitative atmospheric correction to remotely sensed data of forest reflectance and radiance.\n<br>\n\n#### spectra_target_74\nSpectral reflectance measurements of flat field targets as reference points representative of psuedo-invariant targets as measured by the Spectron SE590 spectrophotometer.\n<br>\n\n#### spectra_se590_field_80\nSpectral reflectance measurements made by Spectron SE590 instruments in the context of validation of geometric-optical BRDF models.\n<br>\n\n#### spectra_se590_lab_83\nLaboratory hemispherical reflectance spectra measurements taken to eliminate the effects of atmosphere, understory, exposed soils, mixed species and canopy architecture.\n<br>\n\n#### spectra_se590_landscape_84\nBidirectional spectral reflectance factors of landscape elements (litter, scrubs and grasses, leaves) measured by the Spectron SE590 spectroradiometer.\n<br>\n\n#### spectra_se590_altitude_86\nLow altitude (Ultralight) spectral reflectances of OTTER research sites measured by Spectron SE590 spectrophotometer.\n<br>\n\n#### timber_125\nHeight, crown width, DBH, and height-to-crown distance collected using variable-radius plot sampling with a steel tape and a hand held compass to locate points along a transect.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/OTTER/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"chem_26 v1 - Canopy characteristics: leaf chemistry, specific leaf area, LAI, PAR, IPAR, NPP, and standing biomass.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/otter/chem/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"model_36 v1 - Steve Running's Forest-BGC Model.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/otter/model/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"lai_45 v1 - LAI estimates computed from unweighted openness by the canopy program from digitized canopy photographs.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/otter/lai/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"spectra_licor_47 v1 - The variability of bi-directional spectral reflectance of cut conifer foilage between age classes, species and sites measured by LICOR.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/otter/spectra_licor/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"spectra_perkin_48 v1 - Absolute reflectance of leaves measured in the lab by Perkin-Elmer spectrophotometer to aid in understanding remotely sensed spectral data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/otter/spectra_perkin/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"meteo_50 v1 - Meteorology data collected on an hourly basis from stations located near the OTTER sites in 1990 and summarized to monthly data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/otter/meteo/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"airsunp_61 v1 - Airborne sun photometer data collected from 1990 through 1991 used to provide quantitative atmospheric correction to remotely sensed data of forest reflectance and radiance.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/otter/airsunp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fieldsunp_65 v1 - Field sun photometer data collected from 1990 through 1991 is used to provide quantitative atmospheric correction to remotely sensed data of forest reflectance and radiance.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/otter/fieldsunp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"spectra_target_74 v1 - Spectral reflectance measurements of flat field targets as reference points representative of psuedo-invariant targets as measured by the Spectron SE590 spectrophotometer.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/otter/spectra_target/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"spectra_se590_field_80 v1 - Spectral reflectance measurements made by Spectron SE590 instruments in the context of validation of geometric-optical BRDF models.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/otter/spectra_se590_field/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"spectra_se590_lab_83 v1 - Laboratory hemispherical reflectance spectra measurements taken to eliminate the effects of atmosphere, understory, exposed soils, mixed species and canopy architecture.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/otter/spectra_se590_lab/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"spectra_se590_landscape_84 v1 - Bidirectional spectral reflectance factors of landscape elements (litter, scrubs and grasses, leaves) measured by the Spectron SE590 spectroradiometer.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/otter/spectra_se590_landscape/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"spectra_se590_altitude_86 v1 - Low altitude (Ultralight) spectral reflectances of OTTER research sites measured by Spectron SE590 spectrophotometer.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/otter/spectra_se590_altitude/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"timber_125 v1 - Height, crown width, DBH, and height-to-crown distance collected using variable-radius plot sampling with a steel tape and a hand held compass to locate points along a transect.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/otter/timber/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-otter","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA PACE-PAX Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### PACE-PAX_Analysis_CIRPAS-TO_Data\nPACE-PAX_Analysis_CIRPAS-TO_Data is the analysis data collected onboard the Twin Otter aircraft flying the CIRPAS suite of instruments during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### PACE-PAX_CIRPAS-Facility_AircraftInSitu_CIRPAS-TO_Data\nPACE-PAX_CIRPAS-Facility_AircraftInSitu_CIRPAS-TO_Data is the facility instrument data collected onboard the Twin Otter aircraft flying the CIRPAS suite of instruments during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data from the Cloud and Aerosol Spectrometer (CAS), Cloud Imaging Probe (CIP), and the Passive-Cavity Aerosol Spectrometer Probe (PCASP) instruments are featured in this collection. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### PACE-PAX_LARGE-Aerosol_AircraftInSitu_CIRPAS-TO_Data\nPACE-PAX_LARGE-Aerosol_AircraftInSitu_CIRPAS-TO_Data is the in-situ aerosol data collected onboard the Twin Otter aircraft flying the CIRPAS suite of instruments during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data from the Aerodynamic Particle Sizer (APS), Ultra-High Sensitivity Aerosol Spectrometer (UHSAS), TSI-3563 Nephelometer, and Printed Optical Particle Spectrometer (POPS) instruments are featured in this collection. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### PACE-PAX_Analysis_ISARA_Data\nPACE-PAX_Analysis_ISARA_Data is the In-Situ Aerosol Retrieval Algorithm (ISARA) analysis data collected onboard the Twin Otter aircraft flying the CIRPAS suite of instruments during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### PACE-PAX_LiNeph_AircraftInSitu_CIRPAS-TO_Data\nPACE-PAX_LiNeph_AircraftInSitu_CIRPAS-TO_Data is the Laser Imaging Nephelometer (LI Neph) data collected onboard the Twin Otter aircraft flying the CIRPAS suite of instruments during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### PACE-PAX_AircraftRemoteSensing_ER2_AirHARP2-MAP_Data\nPACE-PAX_AircraftRemoteSensing_ER2_AirHARP2-MAP_Data is the Airborne Hyper-Angular Rainbow Polarimeter - 2 (AirHARP2) data collected onboard the ER-2 aircraft during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### PACE-PAX_Analysis_ER2_Data\nPACE-PAX_Analysis_ER2_Data is the analysis data collected onboard the ER-2 aircraft during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### PACE-PAX_AircraftRemoteSensing_ER2_HSRL2_Data\nPACE-PAX_AircraftRemoteSensing_ER2_HSRL2_Data is the High Spectral Resolution Lidar - 2 (HSRL-2) data collected onboard the ER-2 aircraft during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### PACE-PAX_MetNav_AircraftInSitu_ER2_Data\nPACE-PAX_MetNav_AircraftInSitu_ER2_Data is the in-situ meteorology and navigation data collected onboard the ER-2 aircraft during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data from the N806NA Meteorological and Navigation Facility Instrumentation is featured in this collection. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### PACE-PAX_AircraftRemoteSensing_ER2_PRISM-PICARD-L1C_Data\nPACE-PAX_AircraftRemoteSensing_ER2_PRISM-PICARD-L1C_Data is the merged Level-1C data from the Portable Remote Imaging Spectrometer (PRISM) and Pushbroom Imager for Cloud and Aerosol Research and Development (PICARD) instruments collected onboard the ER-2 aircraft during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### PACE-PAX_AircraftRemoteSensing_ER2_RSP_Data\nPACE-PAX_AircraftRemoteSensing_ER2_RSP_Data is the Research Scanning Polarimeter (RSP) data collected onboard the ER-2 aircraft during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### PACE-PAX_AircraftRemoteSensing_ER2_SPEXAIRBORNE_Data\nPACE-PAX_AircraftRemoteSensing_ER2_SPEXAIRBORNE_Data is the Spectro-Polarimeter for Exploration - Airborne (SPEX Airborne) data collected onboard the ER-2 aircraft during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### PACE-PAX_Ivanpah-Playa_Data\nPACE-PAX_Ivanpah-Playa_Data is the Ivanpah Playa ground site data collected during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### PACE-PAX_LARGE-Aerosol_Marina-Tower_Data\nPACE-PAX_LARGE-Aerosol_Marina-Tower_Data is the in-situ aerosol data collected at the Marina Tower ground site during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data from the Aerodynamic Particle Sizer (APS), Ultra-High Sensitivity Aerosol Spectrometer (UHSAS), TSI-3563 Nephelometer, and Printed Optical Particle Spectrometer (POPS) instruments are featured in this collection. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### PACE-PAX_RV-Shearwater_Pandora_Data\nPACE-PAX_RV-Shearwater_Pandora_Data is the Pandora spectrometer data collected onboard the Research Vessel (R/V) Shearwater during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete. The PACE-PAX campaign was conducted in September 2024 to gather data for the validation and refinement of data products generated by the PACE satellite mission. PACE-PAX obtained measurements over Southern and Central California and nearby coastal regions. Scheduled for roughly 9 months after the launch of PACE, sixty flight hours were planned for each of the two aircraft, NASA’s ER-2 high-altitude aircraft, and the CIRPAS Twin Otter aircraft (in situ sampling). Based out of their respective home airports (NASA Armstrong Flight Research Center for the ER-2 and Marina Municipal Airport for the Twin Otter), flights were coordinated between the two aircraft, PACE satellite overpasses, and ground and ocean-based observations. With a robust suite of in situ and remote sensing instruments, and ground-based observations, PACE-PAX validated and refined PACE data products and identified any potential errors or biases in the measurements, which is essential for ensuring the accuracy and reliability of the PACE data products and maximizing the scientific value of the mission. The following were validation objectives for PACE-PAX: Validate new retrieval parameters; Assess spatial and temporal scale impact on validation; Validate within the instrument swath of all PACE instruments; Validate radiometric and polarimetric properties; Target specific geometries, season, and time of day; and focus on specific processes or phenomena (e.g., high-aerosol loads over land and ocean, multiple aerosol layers).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/PACE-PAX/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","earth observation","lidar","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"PACE-PAX_Analysis_CIRPAS-TO_Data v1 - PACE-PAX_Analysis_CIRPAS-TO_Data is the analysis data collected onboard the Twin Otter aircraft flying the CIRPAS suite of instruments during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::pace-pax-analysis-cirpas-to-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PACE-PAX_CIRPAS-Facility_AircraftInSitu_CIRPAS-TO_Data v1 - PACE-PAX_CIRPAS-Facility_AircraftInSitu_CIRPAS-TO_Data is the facility instrument data collected onboard the Twin Otter aircraft flying the CIRPAS suite of instruments during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data from the Cloud and Aerosol Spectrometer (CAS), Cloud Imaging Probe (CIP), and the Passive-Cavity Aerosol Spectrometer Probe (PCASP) instruments are featured in this collection.","ARN":"arn:aws:s3:::pace-pax-cirpas-facility-aircraftinsitu-cirpas-to-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PACE-PAX_LARGE-Aerosol_AircraftInSitu_CIRPAS-TO_Data v1 - PACE-PAX_LARGE-Aerosol_AircraftInSitu_CIRPAS-TO_Data is the in-situ aerosol data collected onboard the Twin Otter aircraft flying the CIRPAS suite of instruments during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data from the Aerodynamic Particle Sizer (APS), Ultra-High Sensitivity Aerosol Spectrometer (UHSAS), TSI-3563 Nephelometer, and Printed Optical Particle Spectrometer (POPS) instruments are featured in this collection.","ARN":"arn:aws:s3:::pace-pax-large-aerosol-aircraftinsitu-cirpas-to-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PACE-PAX_Analysis_ISARA_Data v1 - PACE-PAX_Analysis_ISARA_Data is the In-Situ Aerosol Retrieval Algorithm (ISARA) analysis data collected onboard the Twin Otter aircraft flying the CIRPAS suite of instruments during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::pace-pax-analysis-isara-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PACE-PAX_LiNeph_AircraftInSitu_CIRPAS-TO_Data v1 - PACE-PAX_LiNeph_AircraftInSitu_CIRPAS-TO_Data is the Laser Imaging Nephelometer (LI Neph) data collected onboard the Twin Otter aircraft flying the CIRPAS suite of instruments during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::pace-pax-lineph-aircraftinsitu-cirpas-to-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PACE-PAX_AircraftRemoteSensing_ER2_AirHARP2-MAP_Data v1 - PACE-PAX_AircraftRemoteSensing_ER2_AirHARP2-MAP_Data is the Airborne Hyper-Angular Rainbow Polarimeter - 2 (AirHARP2) data collected onboard the ER-2 aircraft during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::pace-pax-aircraftremotesensing-er2-airharp2-map-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PACE-PAX_Analysis_ER2_Data v1 - PACE-PAX_Analysis_ER2_Data is the analysis data collected onboard the ER-2 aircraft during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::pace-pax-analysis-er2-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PACE-PAX_AircraftRemoteSensing_ER2_HSRL2_Data v1 - PACE-PAX_AircraftRemoteSensing_ER2_HSRL2_Data is the High Spectral Resolution Lidar - 2 (HSRL-2) data collected onboard the ER-2 aircraft during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::pace-pax-aircraftremotesensing-er2-hsrl2-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PACE-PAX_MetNav_AircraftInSitu_ER2_Data v1 - PACE-PAX_MetNav_AircraftInSitu_ER2_Data is the in-situ meteorology and navigation data collected onboard the ER-2 aircraft during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data from the N806NA Meteorological and Navigation Facility Instrumentation is featured in this collection.","ARN":"arn:aws:s3:::pace-pax-metnav-aircraftinsitu-er2-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PACE-PAX_AircraftRemoteSensing_ER2_PRISM-PICARD-L1C_Data v1 - PACE-PAX_AircraftRemoteSensing_ER2_PRISM-PICARD-L1C_Data is the merged Level-1C data from the Portable Remote Imaging Spectrometer (PRISM) and Pushbroom Imager for Cloud and Aerosol Research and Development (PICARD) instruments collected onboard the ER-2 aircraft during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::pace-pax-aircraftremotesensing-er2-prism-picard-l1c-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PACE-PAX_AircraftRemoteSensing_ER2_RSP_Data v1 - PACE-PAX_AircraftRemoteSensing_ER2_RSP_Data is the Research Scanning Polarimeter (RSP) data collected onboard the ER-2 aircraft during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::pace-pax-aircraftremotesensing-er2-rsp-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PACE-PAX_AircraftRemoteSensing_ER2_SPEXAIRBORNE_Data v1 - PACE-PAX_AircraftRemoteSensing_ER2_SPEXAIRBORNE_Data is the Spectro-Polarimeter for Exploration - Airborne (SPEX Airborne) data collected onboard the ER-2 aircraft during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::pace-pax-aircraftremotesensing-er2-spexairborne-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PACE-PAX_Ivanpah-Playa_Data v1 - PACE-PAX_Ivanpah-Playa_Data is the Ivanpah Playa ground site data collected during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::pace-pax-ivanpah-playa-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PACE-PAX_LARGE-Aerosol_Marina-Tower_Data v1 - PACE-PAX_LARGE-Aerosol_Marina-Tower_Data is the in-situ aerosol data collected at the Marina Tower ground site during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data from the Aerodynamic Particle Sizer (APS), Ultra-High Sensitivity Aerosol Spectrometer (UHSAS), TSI-3563 Nephelometer, and Printed Optical Particle Spectrometer (POPS) instruments are featured in this collection.","ARN":"arn:aws:s3:::pace-pax-large-aerosol-marina-tower-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PACE-PAX_RV-Shearwater_Pandora_Data v1 - PACE-PAX_RV-Shearwater_Pandora_Data is the Pandora spectrometer data collected onboard the Research Vessel (R/V) Shearwater during the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::pace-pax-rv-shearwater-pandora-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-pace-pax","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA pace Project","Description":"The data set consists of weekly gridded Level-3 products of Aquarius L-band radiometer brightness temperature (TB) observations and Sea Surface Salinity (SSS) retrievals from the Aquarius/Satélite de Aplicaciones Científicas (SAC-D) mission, developed collaboratively between the U.S. National Aeronautics and Space Administration (NASA) and Argentina's space agency, Comisión Nacional de Actividades Espaciales (CONAE).\n<br>\n\n#### AQ3_FT\nThis Level-3 (L3) data set includes weekly Northern Hemisphere landscape freeze/thaw (FT) data derived from L-band radiometer brightness temperature observations, acquired by the Aquarius sensor on board Argentina's Satélite de Aplicaciones Científicas (SAC-D) satellite. The data are distributed on the Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0), with a spatial resolution of 36 km. The SAC-D satellite was developed collaboratively between NASA and Argentina's space agency, Comisión Nacional de Actividades Espaciales (CONAE).\n<br>\n\n#### AQ3_NRCS\nThe data set consists of weekly polar-gridded Level-3 products of Aquarius L-band Normalized Radar Cross Section (NRCS) retrievals from the Aquarius/Satélite de Aplicaciones Científicas (SAC-D) mission, developed collaboratively between the U.S. National Aeronautics and Space Administration (NASA) and Argentina's space agency, Comisión Nacional de Actividades Espaciales (CONAE).\n<br>\n\n#### AQ3_SSS\nThis data set consists of weekly gridded Level-3 products of Aquarius L-band radiometer Sea Surface Salinity (SSS) retrievals from the Aquarius/Satélite de Aplicaciones Científicas (SAC-D) mission, developed collaboratively between the U.S. National Aeronautics and Space Administration (NASA) and Argentina's space agency, Comisión Nacional de Actividades Espaciales (CONAE).\n<br>\n\n#### NSIDC-0709\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides maps of Antarctic ice shelves, Antarctic basins, and the Antarctic coastline. The maps are assembled from 2008-2009 ice-front data from the Japan Aerospace Exploration Agency's (JAXA) ALOS PALSAR and European Space Agency's ENVISAT ASAR data, acquired during International Polar Years 2007-2009 (IPY); the InSAR-based grounding line data (MEaSUREs Antarctic Grounding Line from Differential Satellite Radar Interferometry), augmented with other grounding line sources; the Antarctic ice velocity map (MEaSUREs InSAR-Based Antarctica Ice Velocity Map); and the Bedmap-2 DEM. See <a href=\"https://nsidc.org/data/measures/aiv\">Antarctic Ice Sheet Velocity and Mapping Data</a> for related data.\n<br>\n\n#### NSIDC-0498\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides comprehensive high-resolution mapping of grounding lines in Antarctica from February 1992 through November 2025. The data were derived by applying differential satellite synthetic aperture radar (SAR) interferometry (DInSAR) to SAR data acquired by various space-based platforms. See <a href=\"https://nsidc.org/data/measures/aiv\">Antarctic Ice Sheet Velocity and Mapping Data</a> for related data.\n<br>\n\n#### NSIDC-0481\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides velocity estimates determined from Interferometric Synthetic Aperture Radar (InSAR) data for major glacier outlet areas in Greenland, some of which have shown profound velocity changes over the MEaSUREs observation period. The InSAR Selected Glacier Site Velocity Maps are produced from image pairs measured by the German Aerospace Center's (DLR) twin satellites TerraSAR-X / TanDEM-X (TSX / TDX). The measurements in this data set are provided in addition to the ice sheet-wide data from the related data set, <a href=\"https://nsidc.org/data/nsidc-0478\">MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data</a>. See <a href=\"https://nsidc.org/grimp\">Greenland Ice Mapping Project (GrIMP)</a> for more related data.\n<br>\n\n#### NSIDC-0786\nThis data set provides twice-daily enhanced-resolution radar backscatter images from 14 GHz NASA Scatterometer (NSCAT) observations by applying the Scatterometer Image Reconstruction with Filtering (SIRF) algorithm. The algorithm incorporates a median filter and a simplified spatial response function. Multiple passes of the spacecraft are combined to produce a higher spatial resolution and fill in coverage gaps between the individual measurement footprints, which are not contiguous and have six-sided shapes. Overlapping imaging periods start each day and extend through 4- and 8-day periods. Data are gridded to Northern Hemisphere, Southern Hemisphere, and Temperate EASE-Grid 2.0 projections at 3.125 km and 25 km resolutions.\n<br>\n\n#### SPL1AP\nEach Level-1A (L1A) granule incorporates all radiometer data downlinked from the Soil Moisture Active Passive (SMAP) spacecraft for one specific half orbit. The data are scaled instrument counts consisting of the first four raw moments of the fullband channel (vertical and horizontal polarizations); the complex cross-correlations of the fullband channel; and the 16 subband channels (vertical and horizontal polarizations).\n<br>\n\n#### SMAP_L1_L3_ANC_SATELLITE\nThis ancillary SMAP product contains two dynamic data sets describing 1) the attitude and 2) the trajectory of the SMAP satellite. The data files are generated using quaternions from the SMAP spacecraft and inputs from earth receiving stations, respectively.\n<br>\n\n#### SWOT_POSTLAUNCH_L3_MOORING\nThe SWOT Postlaunch Oceanography Field Campaign L3 Moorings collection provides the surface air pressure, GPS, current velocity, and conductivity, temperature and depth (CTD) measurements from moorings at the L3 processing level. The SWOT satellite mission launched in late 2022 and underwent a calibration and validation (cal/val) phase in 2023. As part of cal/val, an array of oceanographic instruments was deployed at a site 300 km offshore of California, including moorings at 11 distinct sites. The S2 mooring site had additional re-deployments from an otherwise independent project to study internal waves, the National Oceanographic Partnership Program – Global Internal Waves (NOPP-GIW), beyond the cal/val phase from September 2023 to October 2024. Data from NOPP-GIW are included in this collection, and described here, because they are closely related to the SWOT cal/val efforts. <br><br> L3 data are gridded data on a consistent depth-time grid, whereby “depth” is given as pressure. Multiple versions, or layouts, of L3 data are disseminated for slightly different use cases as described in the user guide. L3 data are meant as the primary end-user datasets, and users should determine which version is most suitable for them. A user should consider what time resolution is needed, whether constantly spaced time steps are needed, and if data from more than one mooring at a time are needed.\n<br>\n\n#### SWOT_POSTLAUNCH_L2_MOORING\nThe SWOT Postlaunch Oceanography Field Campaign L2 Moorings collection provides the surface air pressure, GPS, current velocity, and conductivity, temperature and depth (CTD) measurements from moorings at the L2 processing level. The SWOT satellite mission launched in late 2022 and underwent a calibration and validation (cal/val) phase in 2023. As part of cal/val, an array of oceanographic instruments was deployed at a site 300 km offshore of California, including moorings at 11 distinct sites. The S2 mooring site had additional re-deployments from an otherwise independent project to study internal waves, the National Oceanographic Partnership Program – Global Internal Waves (NOPP-GIW), beyond the cal/val phase from September 2023 to October 2024. Data from NOPP-GIW are included in this collection, and described here, because they are closely related to the SWOT cal/val efforts. <br><br> L2 data are still at the native instrumental resolution in time and space. Data from multiple instruments may be merged into a single file at L2 if the instruments sampled at the same times. Otherwise, they will be in separate files. Data calibration adjustments based on ship-based CTD cast data and water samples, as well as quality control, have been applied.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/aq3_tb/versions/5","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","oceans","radar","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AQ3_TB v5 - The data set consists of weekly gridded Level-3 products of Aquarius L-band radiometer brightness temperature (TB) observations and Sea Surface Salinity (SSS) retrievals from the Aquarius/Satélite de Aplicaciones Científicas (SAC-D) mission, developed collaboratively between the U.S. National Aeronautics and Space Administration (NASA) and Argentina's space agency, Comisión Nacional de Actividades Espaciales (CONAE).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AQUARIUS/AQ3_TB/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AQ3_FT v5 - This Level-3 (L3) data set includes weekly Northern Hemisphere landscape freeze/thaw (FT) data derived from L-band radiometer brightness temperature observations, acquired by the Aquarius sensor on board Argentina's Satélite de Aplicaciones Científicas (SAC-D) satellite. The data are distributed on the Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0), with a spatial resolution of 36 km.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AQUARIUS/AQ3_FT/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AQ3_NRCS v5 - The data set consists of weekly polar-gridded Level-3 products of Aquarius L-band Normalized Radar Cross Section (NRCS) retrievals from the Aquarius/Satélite de Aplicaciones Científicas (SAC-D) mission, developed collaboratively between the U.S. National Aeronautics and Space Administration (NASA) and Argentina's space agency, Comisión Nacional de Actividades Espaciales (CONAE).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AQUARIUS/AQ3_NRCS/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AQ3_SSS v5 - This data set consists of weekly gridded Level-3 products of Aquarius L-band radiometer Sea Surface Salinity (SSS) retrievals from the Aquarius/Satélite de Aplicaciones Científicas (SAC-D) mission, developed collaboratively between the U.S. National Aeronautics and Space Administration (NASA) and Argentina's space agency, Comisión Nacional de Actividades Espaciales (CONAE).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AQUARIUS/AQ3_SSS/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0709 v2 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides maps of Antarctic ice shelves, Antarctic basins, and the Antarctic coastline. The maps are assembled from 2008-2009 ice-front data from the Japan Aerospace Exploration Agency's (JAXA) ALOS PALSAR and European Space Agency's ENVISAT ASAR data, acquired during International Polar Years 2007-2009 (IPY); the InSAR-based grounding line data (MEaSUREs Antarctic Grounding Line from Differential Satellite Radar Interferometry), augmented with other grounding line sources; t...","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0709/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0498 v2 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides comprehensive high-resolution mapping of grounding lines in Antarctica from February 1992 through November 2025. The data were derived by applying differential satellite synthetic aperture radar (SAR) interferometry (DInSAR) to SAR data acquired by various space-based platforms.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0498/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0481 v4 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides velocity estimates determined from Interferometric Synthetic Aperture Radar (InSAR) data for major glacier outlet areas in Greenland, some of which have shown profound velocity changes over the MEaSUREs observation period. The InSAR Selected Glacier Site Velocity Maps are produced from image pairs measured by the German Aerospace Center's (DLR) twin satellites TerraSAR-X / TanDEM-X (TSX / TDX).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0481/4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0786 v1 - This data set provides twice-daily enhanced-resolution radar backscatter images from 14 GHz NASA Scatterometer (NSCAT) observations by applying the Scatterometer Image Reconstruction with Filtering (SIRF) algorithm. The algorithm incorporates a median filter and a simplified spatial response function.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-Related/NSIDC-0786/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL1AP v002 - Each Level-1A (L1A) granule incorporates all radiometer data downlinked from the Soil Moisture Active Passive (SMAP) spacecraft for one specific half orbit. The data are scaled instrument counts consisting of the first four raw moments of the fullband channel (vertical and horizontal polarizations); the complex cross-correlations of the fullband channel; and the 16 subband channels (vertical and horizontal polarizations).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL1AP/002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L1_L3_ANC_SATELLITE v1 - This ancillary SMAP product contains two dynamic data sets describing 1) the attitude and 2) the trajectory of the SMAP satellite. The data files are generated using quaternions from the SMAP spacecraft and inputs from earth receiving stations, respectively.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L1_L3_ANC_SATELLITE/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SWOT_POSTLAUNCH_L3_MOORING v1 - The SWOT Postlaunch Oceanography Field Campaign L3 Moorings collection provides the surface air pressure, GPS, current velocity, and conductivity, temperature and depth (CTD) measurements from moorings at the L3 processing level. The SWOT satellite mission launched in late 2022 and underwent a calibration and validation (cal/val) phase in 2023.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SWOT_POSTLAUNCH_L3_MOORING","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SWOT_POSTLAUNCH_L2_MOORING v1 - The SWOT Postlaunch Oceanography Field Campaign L2 Moorings collection provides the surface air pressure, GPS, current velocity, and conductivity, temperature and depth (CTD) measurements from moorings at the L2 processing level. The SWOT satellite mission launched in late 2022 and underwent a calibration and validation (cal/val) phase in 2023.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SWOT_POSTLAUNCH_L2_MOORING","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-pace","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA PEM-Tropics Project","Description":"PEM-Tropics-A_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data utilizing condensation nuclei counters (CNC) is featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-A_TraceGas_AircraftInSitu_DC8_Data\nPEM-Tropics-A_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data from the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instrument, laser induced fluorescence, grab samples, chemiluminescence, and gas chromatography are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-A_jValue_AircraftInSitu_DC8_Data\nPEM-Tropics-A_jValue_AircraftInSitu_DC8_Data is the photolysis frequencies (j-values) measured along the DC-8 flight during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-A_AircraftRemoteSensing_DC8_DIAL_Data\nPEM-Tropics-A_AircraftRemoteSensing_DC8_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-A_Trajectory_DC8_Data\nPEM-Tropics-A_Trajectory_DC8_Data is the trajectory data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-A_MetNav_AircraftInSitu_DC8_Data\nPEM-Tropics-A_MetNav_AircraftInSitu_DC8_Data is the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data from the DC-8 Data Acquisition and Distribution System (DADS) and the Diode Laser Hygrometer (DLH) are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-A_MetNav_AircraftInSitu_P3B_Data\nPEM-Tropics-A_MetNav_AircraftInSitu_P3B_Data is the in-situ meteorology and navigation data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data from the Turbulent Air Motion Measurement System (TAMMS) is featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-A_Merge_Data\nPEM-Tropics-A_Merge_Data is the merge data collected during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-A_Aerosol_AircraftInSitu_P3B_Data\nPEM-Tropics-A_Aerosol_AircraftInSitu_P3B_Data is the in-situ aerosol data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data from condensation nuclei counters (CNC) are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-A_TraceGas_AircraftInSitu_P3B_Data\nPEM-Tropics-A_TraceGas_AircraftInSitu_P3B_Data is the in-situ trace gas data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data from the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instrument, grab samples, chemiluminescence, and gas chromatography are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-A_AircraftRemoteSensing_P3B_Data\nPEM-Tropics-A_AircraftRemoteSensing_P3B_Data is the remote sensing data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data from actinometers is featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-A_Trajectory_P3B_Data\nPEM-Tropics-A_Trajectory_P3B_Data is the trajectory data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-A_Satellite_Data\nPEM-Tropics-A_Satellite_Data is the satellite data collected during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data from the Advanced Very High Resolution Radiometer (AVHRR), Geostationary Operational Environmental Satellite (GOES) - 8 and 9, TIROS Operational Vertical Sounder, Special Sensor Microwave Imager/Sounder (SSMIS), and NOAA-14 satellites are featuredin this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-A_Sondes_Data\nPEM-Tropics-A_Sondes_Data is the ozonesonde and radiosonde data collected during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_AircraftRemoteSensing_DC8_LASE_Data\nPEM-Tropics-B_AircraftRemoteSensing_DC8_LASE_Data is the remotely sensed Lidar Atmospheric Sensing Experiment (LASE) data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_Aerosol_AircraftInSitu_DC8_Data\nPEM-Tropics-B_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the Forward Scattering Spectrometer Probe (FSSP) and Condensation Nuclei Counters (CNC) are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_TraceGas_AircraftInSitu_DC8_Data\nPEM-Tropics-B_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instrument, Laser Induced Fluorescence (LIF), grab samples, chemiluminescence, and gas chromatography are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_jValue_AircraftInSitu_DC8_Data\nPEM-Tropics-B_jValue_AircraftInSitu_DC8_Data is the photolysis frequencies (j-values) collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_AircraftRemoteSensing_DC8_Data\nPEM-Tropics-B_AircraftRemoteSensing_DC8_Data is the remote sensing data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from actinometers is featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_AircraftRemoteSensing_DC8_DIAL_Data\nPEM-Tropics-B_AircraftRemoteSensing_DC8_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_Trajectory_DC8_Data\nPEM-Tropics-B_Trajectory_DC8_Data is the trajectory data collected along the DC-8 aircraft flight track during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_MetNav_AircraftInSitu_DC8_Data\nPEM-Tropics-B_MetNav_AircraftInSitu_DC8_Data is the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the DC-8 Data Acquisition and Distribution System (DADS), Microwave Temperature Profiler (MTP), and the Diode Laser Hygrometer (DLH) are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_MetNav_AircraftInSitu_P3B_Data\nPEM-Tropics-B_MetNav_AircraftInSitu_P3B_Data is the in-situ meteorology and navigation data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the Turbulent Air Motion Measurement System (TAMMS) is featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_Lightning_Data\nPEM-Tropics-B_Lightning_Data is the lightning data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_Merge_Data\nPEM-Tropics-B_Merge_Data is the merge data collected onboard during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_Model_Data\nPEM-Tropics-B_Model_Data is the model data collected during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_Aerosol_AircraftInSitu_P3B_Data\nPEM-Tropics-B_Aerosol_AircraftInSitu_P3B_Data is the in-situ aerosol data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the Condensation Nuclei Counters (CNC) is featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_TraceGas_AircraftInSitu_P3B_Data\nPEM-Tropics-B_TraceGas_AircraftInSitu_P3B_Data is the in-situ trace gas data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instrument, Chemical Ionization Mass Spectrometer (CIMS), grab samples, chemiluminescence, and gas chromatography are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_jValue_AircraftInSitu_P3B_Data\nPEM-Tropics-B_jValue_AircraftInSitu_P3B_Data is the photolysis frequencies (j-values) collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the Scanning Actinic Flux Spectroradiometer (SAFS) is featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_AircraftRemoteSensing_P3B_Data\nPEM-Tropics-B_AircraftRemoteSensing_P3B_Data is the remote sensing data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from actinometers and the Scanning Actinic Flux Spectroradiometer (SAFS) are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_Trajectory_P3B_Data\nPEM-Tropics-B_Trajectory_P3B_Data is the trajectory data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_Satellite_Data\nPEM-Tropics-B_Satellite_Data is the satellite data collected during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the Geostationary Operational Environmental Satellite - 10 (GOES-10) and the Measurements of Pollution In The Troposphere (MOPITT) instrument are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### PEM-Tropics-B_Sondes_Data\nPEM-Tropics-B_Sondes_Data is the ozonesonde data collected during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www-gte.larc.nasa.gov/gte_hmpg.htm#table","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","climate","cog","earth observation","lidar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"PEM-Tropics-A_Aerosol_AircraftInSitu_DC8_Data v1 - PEM-Tropics-A_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data utilizing condensation nuclei counters (CNC) is featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-A_Aerosol_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-A_TraceGas_AircraftInSitu_DC8_Data v1 - PEM-Tropics-A_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data from the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instrument, laser induced fluorescence, grab samples, chemiluminescence, and gas chromatography are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-A_TraceGas_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-A_jValue_AircraftInSitu_DC8_Data v1 - PEM-Tropics-A_jValue_AircraftInSitu_DC8_Data is the photolysis frequencies (j-values) measured along the DC-8 flight during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-A_jValue_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-A_AircraftRemoteSensing_DC8_DIAL_Data v1 - PEM-Tropics-A_AircraftRemoteSensing_DC8_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-A_AircraftRemoteSensing_DC8_DIAL_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-A_Trajectory_DC8_Data v1 - PEM-Tropics-A_Trajectory_DC8_Data is the trajectory data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-A_Trajectory_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-A_MetNav_AircraftInSitu_DC8_Data v1 - PEM-Tropics-A_MetNav_AircraftInSitu_DC8_Data is the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data from the DC-8 Data Acquisition and Distribution System (DADS) and the Diode Laser Hygrometer (DLH) are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-A_MetNav_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-A_MetNav_AircraftInSitu_P3B_Data v1 - PEM-Tropics-A_MetNav_AircraftInSitu_P3B_Data is the in-situ meteorology and navigation data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data from the Turbulent Air Motion Measurement System (TAMMS) is featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-A_MetNav_AircraftInSitu_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-A_Merge_Data v1 - PEM-Tropics-A_Merge_Data is the merge data collected during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-A_Merge_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-A_Aerosol_AircraftInSitu_P3B_Data v1 - PEM-Tropics-A_Aerosol_AircraftInSitu_P3B_Data is the in-situ aerosol data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data from condensation nuclei counters (CNC) are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-A_Aerosol_AircraftInSitu_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-A_TraceGas_AircraftInSitu_P3B_Data v1 - PEM-Tropics-A_TraceGas_AircraftInSitu_P3B_Data is the in-situ trace gas data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data from the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instrument, grab samples, chemiluminescence, and gas chromatography are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-A_TraceGas_AircraftInSitu_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-A_AircraftRemoteSensing_P3B_Data v1 - PEM-Tropics-A_AircraftRemoteSensing_P3B_Data is the remote sensing data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data from actinometers is featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-A_AircraftRemoteSensing_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-A_Trajectory_P3B_Data v1 - PEM-Tropics-A_Trajectory_P3B_Data is the trajectory data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-A_Trajectory_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-A_Satellite_Data v1 - PEM-Tropics-A_Satellite_Data is the satellite data collected during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data from the Advanced Very High Resolution Radiometer (AVHRR), Geostationary Operational Environmental Satellite (GOES) - 8 and 9, TIROS Operational Vertical Sounder, Special Sensor Microwave Imager/Sounder (SSMIS), and NOAA-14 satellites are featuredin this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-A_Satellite_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-A_Sondes_Data v1 - PEM-Tropics-A_Sondes_Data is the ozonesonde and radiosonde data collected during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-A_Sondes_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_AircraftRemoteSensing_DC8_LASE_Data v1 - PEM-Tropics-B_AircraftRemoteSensing_DC8_LASE_Data is the remotely sensed Lidar Atmospheric Sensing Experiment (LASE) data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_AircraftRemoteSensing_DC8_LASE_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_Aerosol_AircraftInSitu_DC8_Data v1 - PEM-Tropics-B_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the Forward Scattering Spectrometer Probe (FSSP) and Condensation Nuclei Counters (CNC) are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_Aerosol_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_TraceGas_AircraftInSitu_DC8_Data v1 - PEM-Tropics-B_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instrument, Laser Induced Fluorescence (LIF), grab samples, chemiluminescence, and gas chromatography are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_TraceGas_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_jValue_AircraftInSitu_DC8_Data v1 - PEM-Tropics-B_jValue_AircraftInSitu_DC8_Data is the photolysis frequencies (j-values) collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_jValue_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_AircraftRemoteSensing_DC8_Data v1 - PEM-Tropics-B_AircraftRemoteSensing_DC8_Data is the remote sensing data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from actinometers is featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_AircraftRemoteSensing_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_AircraftRemoteSensing_DC8_DIAL_Data v1 - PEM-Tropics-B_AircraftRemoteSensing_DC8_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_AircraftRemoteSensing_DC8_DIAL_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_Trajectory_DC8_Data v1 - PEM-Tropics-B_Trajectory_DC8_Data is the trajectory data collected along the DC-8 aircraft flight track during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_Trajectory_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_MetNav_AircraftInSitu_DC8_Data v1 - PEM-Tropics-B_MetNav_AircraftInSitu_DC8_Data is the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the DC-8 Data Acquisition and Distribution System (DADS), Microwave Temperature Profiler (MTP), and the Diode Laser Hygrometer (DLH) are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_MetNav_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_MetNav_AircraftInSitu_P3B_Data v1 - PEM-Tropics-B_MetNav_AircraftInSitu_P3B_Data is the in-situ meteorology and navigation data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the Turbulent Air Motion Measurement System (TAMMS) is featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_MetNav_AircraftInSitu_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_Lightning_Data v1 - PEM-Tropics-B_Lightning_Data is the lightning data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_Lightning_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_Merge_Data v1 - PEM-Tropics-B_Merge_Data is the merge data collected onboard during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_Merge_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_Model_Data v1 - PEM-Tropics-B_Model_Data is the model data collected during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_Model_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_Aerosol_AircraftInSitu_P3B_Data v1 - PEM-Tropics-B_Aerosol_AircraftInSitu_P3B_Data is the in-situ aerosol data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the Condensation Nuclei Counters (CNC) is featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_Aerosol_AircraftInSitu_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_TraceGas_AircraftInSitu_P3B_Data v1 - PEM-Tropics-B_TraceGas_AircraftInSitu_P3B_Data is the in-situ trace gas data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instrument, Chemical Ionization Mass Spectrometer (CIMS), grab samples, chemiluminescence, and gas chromatography are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_TraceGas_AircraftInSitu_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_jValue_AircraftInSitu_P3B_Data v1 - PEM-Tropics-B_jValue_AircraftInSitu_P3B_Data is the photolysis frequencies (j-values) collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the Scanning Actinic Flux Spectroradiometer (SAFS) is featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_jValue_AircraftInSitu_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_AircraftRemoteSensing_P3B_Data v1 - PEM-Tropics-B_AircraftRemoteSensing_P3B_Data is the remote sensing data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from actinometers and the Scanning Actinic Flux Spectroradiometer (SAFS) are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_AircraftRemoteSensing_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_Trajectory_P3B_Data v1 - PEM-Tropics-B_Trajectory_P3B_Data is the trajectory data collected onboard the P-3B aircraft during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_Trajectory_P3B_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_Satellite_Data v1 - PEM-Tropics-B_Satellite_Data is the satellite data collected during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data from the Geostationary Operational Environmental Satellite - 10 (GOES-10) and the Measurements of Pollution In The Troposphere (MOPITT) instrument are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_Satellite_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-Tropics-B_Sondes_Data v1 - PEM-Tropics-B_Sondes_Data is the ozonesonde data collected during the Pacific Exploratory Mission (PEM) Tropics B suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-Tropics-B_Sondes_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-pem-tropics","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA PEM-West Project","Description":"PEM-West-A_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data utilizing Optical Particle Counters (OPC) and ion chromatography are featured in this collection. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-A_TraceGas_AircraftInSitu_DC8_Data\nPEM-West-A_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data from the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instrument, laser induced fluorescence, grab samples, chemiluminescence, and gas chromatography are featured in this collection. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-A_AircraftRemoteSensing_DC8_DIAL_Data\nPEM-West-A_AircraftRemoteSensing_DC8_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-A_Ground_Data\nPEM-West-A_Ground_Data is the ground site data collected during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data utilizing the Nondispersive Infrared Gas Analyzer (NDIR) and chemiluminescence technique are featured in this collection. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-A_MetNav_AircraftInSitu_DC8_Data\nPEM-West-A_MetNav_AircraftInSitu_DC8_Data is the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data from the DC-8 Data Acquisition and Distribution System is featured in this collection. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-A_Merge_Data\nPEM-West-A_Merge_Data is the merge data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data from the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instrument, laser induced fluorescence, grab samples, chemiluminescence, and gas chromatography are featured in this collection. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-A_Model_Data\nPEM-West-A_Model_Data is the model data collected during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-A_Trajectory_Data\nPEM-West-A_Trajectory_Data is the trajectory data collected during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-B_Aerosol_AircraftInSitu_DC8_Data\nPEM-West-B_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data from the Forward Scattering Spectrometer Probe (FSSP) and the Passive-Cavity Aerosol Spectrometer Probe (PCASP) are featured in this collection. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-B_TraceGas_AircraftInSitu_DC8_Data\nPEM-West-B_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data from the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instrument, laser induced fluorescence, grab samples, chemiluminescence, and gas chromatography are featured in this collection. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-B_AircraftRemoteSensing_DC8_DIAL_Data\nPEM-West-B_AircraftRemoteSensing_DC8_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data collection for this product is complete During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-B_Ground_Data\nPEM-West-B_Ground_Data is the ground site data collected during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data from the Nondispersive Infrared Gas Analyzer (NDIR) instrument, rawinsondes, chemiluminescence, and gas chromatography techniques are featured in this collection. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-B_MetNav_AircraftInSitu_DC8_Data\nPEM-West-B_MetNav_AircraftInSitu_DC8_Data is the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data from the DC-8 Data Acquisition and Distribution System is featured in this collection. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-B_Lightning_Data\nPEM-West-B_Lightning_Data is the lightning data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-B_Merge_Data\nPEM-West-B_Merge_Data is the merge data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-B_Model_Data\nPEM-West-B_Model_Data is the model data collected during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-B_Satellite_Data\nPEM-West-B_Satellite_Data is the satellite data collected during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data from Himawari-4 satellite is featured in this collection. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### PEM-West-B_Trajectory_Data\nPEM-West-B_Trajectory_Data is the trajectory data collected during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data collection for this product is complete. During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution. Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns (PEM-West) were conducted over the northwestern Pacific region, considered the only major region in the northern hemisphere that is “relatively” free from direct anthropogenic influences. PEM-West was a part of the East Asian/North Pacific Regional Study (APARE). The overarching objectives of PEM-West were 1) to investigate the atmospheric chemistry of ozone (O3) and its precursors over the northwestern Pacific, including the examination of their natural budgets as well as the impact of anthropogenic sources; and 2) to investigate the atmospheric sulfur cycle over the region with emphasis on the relative importance and influence of continental vs marine sulfur sources. The two phases of PEM-West were conducted during differing seasons due to contrasting tropospheric outflow from Asia. The first phase, PEM-West A, was conducted over the western Pacific region off the eastern coast of Asia from September-October 1991, a season characterized by the predominance of flow from mid-Pacific regions. The second phase, PEM-West B, was conducted from February-March 1994, a period characterized by maximum air mass outflow. To accomplish its objectives, the PEM-West campaign deployed the NASA DC-8 aircraft across the northwestern Pacific to gather latitudinal, longitudinal, and vertical profile sampling, as well as extensive sampling in both the marine boundary layer and free troposphere. The aircraft was equipped with a comprehensive suite of in-situ instrument packages for characterization of photochemical precursors, intermediate products, and airmass tracers, including O3, nitric oxide (NO), peroxyacetyl nitrate (PAN), nitrogen oxides (NOy), nonmethane hydrocarbons (NMHCs), hydrogen peroxide (H2O2), acetic acid (CH3OOH), carbon monoxide (CO), and formaldehyde (CH2O). Collectively, these measurements enabled the analyses of the photochemical production/destruction of O3 and the distribution of precursor species. In addition, the DC-8 was equipped with instruments for collecting sulfur measurements, including dimethyl sulfide (DMS), carbonyl sulfide (COS), sulfur dioxide (SO2), and carbon disulfide (CS2). Instruments that collected aerosol composition and microphysical properties were also aboard the DC-8. Both missions deployed a Differential Absorption Lidar (DIAL) system for measurements of O3 vertical profiles above and below the aircraft. One highlight of the project was that flight nine of PEM-West A flew over Typhoon Mireille while it made landfall on the coast of Japan. This allowed for a flight by the DC-8 to study the role of typhoons in the transport of trace gases. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the PEM-West A overview paper and the PEM-West B overview paper. A collection of the publications based on PEM-West A and B observation are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-West Phase A and Pacific Exploratory Mission-West, Phase B (PEM-West B).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www-gte.larc.nasa.gov/gte_hmpg.htm#table","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","lidar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"PEM-West-A_Aerosol_AircraftInSitu_DC8_Data v1 - PEM-West-A_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data utilizing Optical Particle Counters (OPC) and ion chromatography are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-A_Aerosol_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-A_TraceGas_AircraftInSitu_DC8_Data v1 - PEM-West-A_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data from the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instrument, laser induced fluorescence, grab samples, chemiluminescence, and gas chromatography are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-A_TraceGas_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-A_AircraftRemoteSensing_DC8_DIAL_Data v1 - PEM-West-A_AircraftRemoteSensing_DC8_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-A_AircraftRemoteSensing_DC8_DIAL_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-A_Ground_Data v1 - PEM-West-A_Ground_Data is the ground site data collected during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data utilizing the Nondispersive Infrared Gas Analyzer (NDIR) and chemiluminescence technique are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-A_Ground_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-A_MetNav_AircraftInSitu_DC8_Data v1 - PEM-West-A_MetNav_AircraftInSitu_DC8_Data is the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data from the DC-8 Data Acquisition and Distribution System is featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-A_MetNav_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-A_Merge_Data v1 - PEM-West-A_Merge_Data is the merge data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data from the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instrument, laser induced fluorescence, grab samples, chemiluminescence, and gas chromatography are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-A_Merge_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-A_Model_Data v1 - PEM-West-A_Model_Data is the model data collected during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-A_Model_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-A_Trajectory_Data v1 - PEM-West-A_Trajectory_Data is the trajectory data collected during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-A_Trajectory_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-B_Aerosol_AircraftInSitu_DC8_Data v1 - PEM-West-B_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data from the Forward Scattering Spectrometer Probe (FSSP) and the Passive-Cavity Aerosol Spectrometer Probe (PCASP) are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-B_Aerosol_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-B_TraceGas_AircraftInSitu_DC8_Data v1 - PEM-West-B_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data from the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instrument, laser induced fluorescence, grab samples, chemiluminescence, and gas chromatography are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-B_TraceGas_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-B_AircraftRemoteSensing_DC8_DIAL_Data v1 - PEM-West-B_AircraftRemoteSensing_DC8_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West A suborbital campaign. Data collection for this product is complete During 1983-2001, NASA conducted a collection of field campaigns as a part of the Global Tropospheric Experiment (GTE) for developing advanced instrumentation to quantify atmospheric trace gases’ sources, sinks, and distribution.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-B_AircraftRemoteSensing_DC8_DIAL_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-B_Ground_Data v1 - PEM-West-B_Ground_Data is the ground site data collected during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data from the Nondispersive Infrared Gas Analyzer (NDIR) instrument, rawinsondes, chemiluminescence, and gas chromatography techniques are featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-B_Ground_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-B_MetNav_AircraftInSitu_DC8_Data v1 - PEM-West-B_MetNav_AircraftInSitu_DC8_Data is the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data from the DC-8 Data Acquisition and Distribution System is featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-B_MetNav_AircraftInSitu_DC8_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-B_Lightning_Data v1 - PEM-West-B_Lightning_Data is the lightning data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-B_Lightning_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-B_Merge_Data v1 - PEM-West-B_Merge_Data is the merge data collected onboard the DC-8 aircraft during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-B_Merge_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-B_Model_Data v1 - PEM-West-B_Model_Data is the model data collected during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-B_Model_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-B_Satellite_Data v1 - PEM-West-B_Satellite_Data is the satellite data collected during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data from Himawari-4 satellite is featured in this collection.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-B_Satellite_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PEM-West-B_Trajectory_Data v1 - PEM-West-B_Trajectory_Data is the trajectory data collected during the Pacific Exploratory Mission (PEM) West B suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/PEM/PEM-West-B_Trajectory_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-pem-west","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA POES Project","Description":"The version 8 SBUV/2 NOAA-11 ozone data were first released at the 2004 Quadrennial Ozone Symposium on DVD. The DVD contained all of the SBUV/2 data from NOAA-9, NOAA-11 and NOAA-16 satellites as well as SBUV data from the Nimbus-7 satellite. The DVD is no longer available, however all the data are available on-line from the NASA GES DISC. The NOAA-11 SBUV/2 v8 data are available from 1988-12-01 to 2001-03-27. The instrument spatial resolution is 180 km x 180 km footprint at nadir. The ozone profiles are made at 21 pressure levels between 1000 and 0.1 hPa. Each data file contains a days worth of ozone measurements, and is written in an ASCII text format. The SBUV/2 is a scanning double monochromator and a cloud cover radiometer (CCR) designed to measure ultraviolet (UV) spectral intensities. In its primary mode of operation, the monochromator measures solar radiation backscattered by the atmosphere in 12 discrete wavelength bands in the near-UV, ranging from 252.0 to 339.8 nanometers, each with a bandpass of 1.1 nm. The total-ozone algorithm uses the four longest wavelength bands (312.5, 317.5, 331.2 and 339.8 nm), whereas the profiling algorithm uses the shorter wavelengths. The cloud cover radiometer operates at 379 nm (i.e., outside the ozone absorption band) with a 3.0 nm bandpass and was designed to measure the reflectivity of the surface in the IFOV. The SBUV/2 also makes periodic measurements of the solar flux by deploying a diffuser plate into the FOV to reflect sunlight into the measurement.\n<br>\n\n#### SBUV2N16O3\nThe version 8 SBUV/2 NOAA-16 ozone data were first released at the 2004 Quadrennial Ozone Symposium on DVD. The DVD contained all of the SBUV/2 data from NOAA-9, NOAA-11 and NOAA-16 satellites as well as SBUV data from the Nimbus-7 satellite. The DVD is no longer available, however all the data are available on-line from the NASA GES DISC. The NOAA-16 SBUV/2 v8 data are available from 2000-10-03 to 2003-12-31. The instrument spatial resolution is 180 km x 180 km footprint at nadir. The ozone profiles are made at 21 pressure levels between 1000 and 0.1 hPa. Each data file contains a days worth of ozone measurements, and is written in an ASCII text format. The SBUV/2 is a scanning double monochromator and a cloud cover radiometer (CCR) designed to measure ultraviolet (UV) spectral intensities. In its primary mode of operation, the monochromator measures solar radiation backscattered by the atmosphere in 12 discrete wavelength bands in the near-UV, ranging from 252.0 to 339.8 nanometers, each with a bandpass of 1.1 nm. The total-ozone algorithm uses the four longest wavelength bands (312.5, 317.5, 331.2 and 339.8 nm), whereas the profiling algorithm uses the shorter wavelengths. The cloud cover radiometer operates at 379 nm (i.e., outside the ozone absorption band) with a 3.0 nm bandpass and was designed to measure the reflectivity of the surface in the IFOV. The SBUV/2 also makes periodic measurements of the solar flux by deploying a diffuser plate into the FOV to reflect sunlight into the measurement.\n<br>\n\n#### SBUV2N09O3\nThe version 8 SBUV/2 NOAA-9 ozone data were first released at the 2004 Quadrennial Ozone Symposium on DVD. The DVD contained all of the SBUV/2 data from NOAA-9, NOAA-11 and NOAA-16 satellites as well as SBUV data from the Nimbus-7 satellite. The DVD is no longer available, however all the data are available on-line from the NASA GES DISC. The NOAA-9 SBUV/2 v8 data are available in two time periods from 1985-02-02 to 1989-12-31 (ascending orbits) and again from 1992-01-01 to 1998-02-19 (descending orbits) due to the drift of the NOAA-9 satellite. The instrument spatial resolution is 180 km x 180 km footprint at nadir. The ozone profiles are made at 21 pressure levels between 1000 and 0.1 hPa. Each data file contains a days worth of ozone measurements, and is written in an ASCII text format. The SBUV/2 is a scanning double monochromator and a cloud cover radiometer (CCR) designed to measure ultraviolet (UV) spectral intensities. In its primary mode of operation, the monochromator measures solar radiation backscattered by the atmosphere in 12 discrete wavelength bands in the near-UV, ranging from 252.0 to 339.8 nanometers, each with a bandpass of 1.1 nm. The total-ozone algorithm uses the four longest wavelength bands (312.5, 317.5, 331.2 and 339.8 nm), whereas the profiling algorithm uses the shorter wavelengths. The cloud cover radiometer operates at 379 nm (i.e., outside the ozone absorption band) with a 3.0 nm bandpass and was designed to measure the reflectivity of the surface in the IFOV. The SBUV/2 also makes periodic measurements of the solar flux by deploying a diffuser plate into the FOV to reflect sunlight into the measurement.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.star.nesdis.noaa.gov/smcd/spb/ozone/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SBUV2N11O3 v008 - The version 8 SBUV/2 NOAA-11 ozone data were first released at the 2004 Quadrennial Ozone Symposium on DVD. The DVD contained all of the SBUV/2 data from NOAA-9, NOAA-11 and NOAA-16 satellites as well as SBUV data from the Nimbus-7 satellite.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NOAA_SBUV2_Level2/SBUV2N11O3.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SBUV2N16O3 v008 - The version 8 SBUV/2 NOAA-16 ozone data were first released at the 2004 Quadrennial Ozone Symposium on DVD. The DVD contained all of the SBUV/2 data from NOAA-9, NOAA-11 and NOAA-16 satellites as well as SBUV data from the Nimbus-7 satellite.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NOAA_SBUV2_Level2/SBUV2N16O3.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SBUV2N09O3 v008 - The version 8 SBUV/2 NOAA-9 ozone data were first released at the 2004 Quadrennial Ozone Symposium on DVD. The DVD contained all of the SBUV/2 data from NOAA-9, NOAA-11 and NOAA-16 satellites as well as SBUV data from the Nimbus-7 satellite.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/NOAA_SBUV2_Level2/SBUV2N09O3.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-poes","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Prediction of Worldwide Energy Resources (POWER)","Description":"NASA's goal in Earth science is to observe, understand, and model the Earth system to discover how it is changing, to better predict change, and to understand the consequences for life on Earth. The Applied Sciences Program, within the Earth Science Division of the NASA Science Mission Directorate, serves individuals and organizations around the globe by expanding and accelerating societal and economic benefits derived from Earth science, information, and technology research and development.<br/><br/>\nThe [Prediction Of Worldwide Energy Resources (POWER)](https://power.larc.nasa.gov/) Project, funded through the Applied Sciences Program at NASA Langley Research Center, gathers NASA Earth observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in energy development, building energy efficiency, and supporting agriculture projects.<br/><br/>\nThe POWER project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly, and climatology. The POWER data archive provides data at the native resolution of the source products. The data is updated nightly to maintain near real time availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER services catalog consists of a series of RESTful Application Programming Interfaces, geospatial enabled image services, and web mapping Data Access Viewer. These three service offerings support data discovery, access, and distribution to the project’s user base as ARD and as direct application inputs to decision support tools.<br/><br/>\nThe latest data version update includes hourly-based source ARD, in addition to enhanced daily, monthly, annual, and climatology data. The daily time series for meteorology is available from 1981, while solar-based parameters start in 1984. The hourly source data are from Clouds and the Earth's Radiant Energy System (CERES) and Global Modeling and Assimilation Office (GMAO), spanning from 1984 for meteorology and from 2001 for solar-based parameters. The hourly data equips users with the ARD needed to model building system energy performance, providing information directly amenable to decision support tools introducing the industry standard EnergyPlus Weather file format.<br/><br/>\n","Documentation":"https://power.larc.nasa.gov/docs/","Contact":"larc-power-project@mail.nasa.gov","ManagedBy":"NASA","UpdateFrequency":"Near Real Time (NRT); as soon as source data becomes available from our source data providers. <br/><br/>\n- Meteorological: ~2-3 days <br/>\n- Solar ~5-7 days <br/><br/>\nFor more information see the [NASA POWER Availability Dashboards](https://power.larc.nasa.gov/dashboard/availability/)\n","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["agriculture","air quality","analytics","archives","atmosphere","aws-pds","climate","climate model","data assimilation","deep learning","earth observation","energy","environmental","forecast","geoscience","geospatial","global","history","imaging","industry","machine learning","machine translation","metadata","meteorological","model","netcdf","opendap","radiation","satellite imagery","solar","statistics","sustainability","time series forecasting","water","weather","zarr"],"License":"There are no restrictions on the use, access, and/or download of data from the NASA POWER Project. We request that you cite the NASA POWER Project when using the data provided from NASA POWER Project. <br/><br/> [Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"POWER's Zarr Analysis Ready Data (ARD) Datasets","ARN":"arn:aws:s3:::nasa-power","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://nasa-power.s3.us-west-2.amazonaws.com/index.html)"]},{"Description":"POWER's NetCDF Datastore","ARN":"arn:aws:s3:::power-datastore","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://power-datastore.s3.us-west-2.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"About the Prediction Of Worldwide Energy Resources (POWER) Project ArcGIS StoryMap.","URL":"https://arcg.is/1PG5jH","AuthorName":"The POWER Project"},{"Title":"Accessing and Subsetting POWER data, from AWS S3 using Python.","URL":"https://power.larc.nasa.gov/docs/tutorials/service-data-request/aws/","AuthorName":"The POWER Project"}],"Tools & Applications":[{"Title":"NASA POWER NetCDF Data Download","URL":"https://power.larc.nasa.gov/data/","AuthorName":"The POWER Project","AuthorURL":"https://power.larc.nasa.gov/"},{"Title":"POWER Application Programming Interface (API)","URL":"https://power.larc.nasa.gov/api/pages/","AuthorName":"The POWER Project","AuthorURL":"https://power.larc.nasa.gov/"},{"Title":"POWER Data Access Viewer (DAV)","URL":"https://power.larc.nasa.gov/data-access-viewer/","AuthorName":"The POWER Project","AuthorURL":"https://power.larc.nasa.gov/"}],"Publications":[{"Title":"A solar azimuth formula that renders circumstantial treatment unnecessary without compromising mathematical rigor: Mathematical setup, application and extension of a formula based on the subsolar point and atan2 function","URL":"https://doi.org/10.1016/j.renene.2021.03.047","AuthorName":"Zhang, T., P. W. Stackhouse, B. Macpherson, and J. C. Mikovitz"},{"Title":"Application of a global-to-beam irradiance model to the NASA GEWEX SRB dataset: An extension of the NASA Surface meteorology and Solar Energy datasets","URL":"https://doi.org/10.1016/j.solener.2014.09.006","AuthorName":"Zhang, T., P. W. Stackhouse, W. S. Chandler, and D. J. Westberg"},{"Title":"Application of Satellite Sensor Data and Models for Energy Management","URL":"https://doi.org/10.1109/JSTARS.2008.2001142","AuthorName":"Zell, E., J. Engel-Cox, R. Eckman, and P. Stackhouse"},{"Title":"Association between solar insolation and a history of suicide attempts in bipolar I disorder","URL":"https://doi.org/10.1016/j.jpsychires.2019.03.001","AuthorName":"Bauer M, et al., Stackhouse PW Jr., et al."},{"Title":"CEOS contributions to informing energy management and policy decision making using space-based Earth observations","URL":"https://doi.org/10.1016/j.apenergy.2011.03.001","AuthorName":"Eckman, R. S., and P. W. Stackhouse"},{"Title":"Enhancing Climate Resilience at NASA Centers: A Collaboration between Science and Stewardship","URL":"https://doi.org/10.1175/BAMS-D-12-00169.1","AuthorName":"Rosenzweig, C., and Coauthors"},{"Title":"Evaluation of NASA satellite- and assimilation model-derived long-term daily temperature data over the continental US","URL":"https://doi.org/10.1016/j.agrformet.2008.05.017","AuthorName":"White, J. W., G. Hoogenboom, P. W. Stackhouse, and J. M. Hoell"},{"Title":"Evaluation of Satellite-Based, Modeled-Derived Daily Solar Radiation Data for the Continental United States","URL":"https://doi.org/10.2134/agronj2011.0038","AuthorName":"White, J. W., G. Hoogenboom, P. W. Wilkens, P. W. Stackhouse, and J. M. Hoel"},{"Title":"Global Surface Solar Energy Anomalies Including El Nino and La Nina Years.","URL":"https://doi.org/10.1115/1.1384570","AuthorName":"C. H., and Coauthors"},{"Title":"Modeling the potential for thermal concentrating solar power technologies","URL":"https://doi.org/10.1016/j.enpol.2010.09.008","AuthorName":"Zhang, Y., S. J. Smith, G. P. Kyle, and P. W. Stackhouse"},{"Title":"NASA Climatological Data for Renewable Energy Assessment","URL":"https://doi.org/10.1115/1.1748466","AuthorName":"Chandler, W. S., C. H. Whitlock, and P. W. Stackhouse"},{"Title":"The Contribution of Solar Brightening to the US Maize Yield Trend","URL":"https://doi.org/10.1038/nclimate3234","AuthorName":"Tollenaar, T., J. Fridgen, P. Tyagi, P. W. Stackhouse Jr., and S. Kumudini"},{"Title":"Variations in seasonal solar insolation are associated with a history of suicide attempts in bipolar I disorder","URL":"https://doi.org/10.1186/s40345-021-00231-7","AuthorName":"Bauer, M., and Coauthors"}]},"RegistryEntryAdded":"2022-06-01","RegistryEntryLastModified":"2025-01-02","Slug":"nasa-power","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA PREFIRE Project","Description":"Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Atmospheric Properties from PREFIRE Satellite 1 COG (PREFIRE_SAT1_2B-ATM_COG) is retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately. PREFIRE_SAT1_2B-ATM_COG contains Cloud-Optimized GeoTIFF (COG) files that each contain a georeferenced rendering of retrieved column water vapor (CWV) values. Those CWV data values are computed from a single granule of PREFIRE Spectral Radiance (PREFIRE_SAT1_1B-RAD) data using an Optimal Estimation retrieval via a radiative transfer model with inputs from PREFIRE_SAT1_2B-MSK (cloud mask), PREFIRE_SAT1_AUX-MET, and PREFIRE_SAT1_2B-SFC. The retrieved CWV values are only available for clear-sky conditions. As part of the rendering process, mean retrieved CWV values are computed for a global grid of raster elements (each approximately 2.23 km in width), based on which raster elements each PREFIRE ground footprint overlaps. This procedure is done in order to better visualize the PREFIRE data despite substantial overlap of adjacent along-track ground footprints – but it is important to remember that the individual PREFIRE ground footprints are much bigger (by about 80x, in terms of area) than each of the GeoTIFF raster elements. These cloud-optimized GeoTIFF images may be viewed using many geographic information systems (GIS), such as the freely available QGIS. Science data retrieval started July 24, 2024 and is ongoing. Currently, the geographic coverage is for latitudes between approximately 60° to 84° in both polar regions, although future releases may include data for all latitudes equatorward of about 84°. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each TIRS-PREFIRE. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was about 264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint and swath sizes will slowly become smaller as the orbit altitude decreases with time. Similar CWV GeoTIFF renderings for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_2B-ATM_COG collection.\n<br>\n\n#### PREFIRE_SAT1_2B-ATM\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Atmospheric Properties from PREFIRE Satellite 1 (PREFIRE_SAT1_2B-ATM) contains several geophysical variables, such as atmospheric temperature and water content, retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately. PREFIRE_SAT1_2B-ATM contains column water vapor, temperature, pressure, and water vapor profiles, surface temperature, and surface pressure as derived from PREFIRE Spectral Radiance (PREFIRE_SAT1_1B-RAD) data using an Optimal Estimation retrieval via a Radiative Transfer Model with inputs from PREFIRE_SAT1_2B-MSK (cloud mask), PREFIRE_SAT1_AUX-MET, and PREFIRE_SAT1_SFC. Science data retrieval started July 24, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4. The atmospheric properties data for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_2B-ATM collection.\n<br>\n\n#### PREFIRE_SAT2_2B-ATM_COG\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Atmospheric Properties from PREFIRE Satellite 2 COG (PREFIRE_SAT2_2B-ATM_COG) is derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual Cs each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT2_2B-ATM_COG contains Cloud-Optimized GeoTIFF (COG) files that each contain a georeferenced rendering of retrieved column water vapor (CWV) values. Those CWV data values are computed from a single granule of PREFIRE Spectral Radiance (PREFIRE_SAT2_1B-RAD) data using an Optimal Estimation retrieval via a radiative transfer model with inputs from PREFIRE_SAT2_2B-MSK (cloud mask), PREFIRE_SAT2_AUX-MET, and PREFIRE_SAT2_2B-SFC. The retrieved CWV values are only available for clear-sky conditions. As part of the rendering process, mean retrieved CWV values are computed for a global grid of raster elements (each approximately 2.23 km in width), based on which raster elements each PREFIRE ground footprint overlaps. This procedure is done in order to better visualize the PREFIRE data despite substantial overlap of adjacent along-track ground footprints – but it is important to remember that the individual PREFIRE ground footprints are much bigger (by about 80x, in terms of area) than each of the GeoTIFF raster elements. These cloud-optimized GeoTIFF images may be viewed using many geographic information systems (GIS), such as the freely available QGIS.Science data retrieval started June 29, 2024 and is ongoing. Currently, the geographic coverage is for latitudes between approximately 60° to 84° in both polar regions, although future releases may include data for all latitudes equatorward of about 84°. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each TIRS-PREFIRE. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was about 264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint and swath sizes will slowly become smaller as the orbit altitude decreases with time.Similar CWV GeoTIFF renderings for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_2B-ATM_COG collection.\n<br>\n\n#### PREFIRE_SAT2_2B-ATM\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Atmospheric Properties from PREFIRE Satellite 2 (PREFIRE_SAT2_2B-ATM) contains several geophysical variables, such as atmospheric temperature and water content, derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT2_2B-ATM contains column water vapor, temperature, pressure, and water vapor profiles, surface temperature, and surface pressure as derived from PREFIRE Spectral Radiance (PREFIRE_SAT2_1B-RAD) data using an Optimal Estimation retrieval via a Radiative Transfer Model with inputs from PREFIRE_SAT2_2B-MSK (cloud mask), PREFIRE_SAT2_AUX-MET, and PREFIRE_SAT2_SFC. Science data retrieval started June 29, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4.The atmospheric properties data for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_2B-ATM collection.\n<br>\n\n#### PREFIRE_SAT1_2B-CLD\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Cloud Properties (PREFIRE_SAT1_2B-CLD) contains cloud properties retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately. PREFIRE_SAT1_2B-CLD contains cloud top pressure, cloud optical depth, and cloud particle diameter as derived from PREFIRE Spectral Radiance (PREFIRE_SAT1_1B-RAD) data using an Optimal Estimation retrieval via a radiative transfer model with inputs from PREFIRE_SAT1_2B-MSK (cloud mask) and PREFIRE_SAT1_AUX-MET (Auxiliary Meteorlogy). The primary purpose of PREFIRE_SAT1_2B-CLD is to provide cloud properties information for the PREFIRE_SAT1_2B-FLX (spectral flux) product, which requires only enough accuracy for a coarse look-up table. This data collection is not suitable for an analysis of specific cloud properties. Science data retrieval started July 24, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4. The cloud properties data for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_2B-CLD collection.\n<br>\n\n#### PREFIRE_SAT2_2B-CLD\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Cloud Properties (PREFIRE_SAT2_2B-CLD) contains cloud properties derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT2_2B-CLD contains cloud top pressure, cloud optical depth, and cloud particle diameter as derived from PREFIRE Spectral Radiance (PREFIRE_SAT2_1B-RAD) data using an Optimal Estimation retrieval via a radiative transfer model with inputs from PREFIRE_SAT2_2B-MSK (cloud mask) and PREFIRE_SAT2_AUX-MET (Auxiliary Meteorlogy). The primary purpose of PREFIRE_SAT2_2B-CLD is to provide cloud properties information for the PREFIRE_SAT2_2B-FLX (spectral flux) product, which requires only enough accuracy for a coarse look-up table. This data collection is not suitable for an analysis of specific cloud properties.Science data retrieval started June 29, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4.The cloud properties data for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_2B-CLD collection.\n<br>\n\n#### PREFIRE_SAT1_2B-MSK\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Cloud Mask (PREFIRE_SAT1_2B-MSK) contains a binary clear/cloud indicator and probabilities retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately. PREFIRE_SAT1_2B-MSK is derived from mid-infrared and far-infrared measurements collected globally by TIRS-PREFIRE aboard PREFIRE-SAT1 using a Machine Learning technique. Specifically, inputs are PREFIRE Spectral Radiance (PREFIRE_SAT1_1B-RAD) from PREFIRE Satellite 1 (PREFIRE_SAT1_2B-RAD) and PREFIRE Auxiliary Meteorology Data created for PREFIRE Satellite 1 (PREFIRE_SAT1_AUX-MET). PREFIRE_SAT1_2B-MSK is used to identify cloudy and clear scenes in TIRS-PREFIRE data, which supports the production of other Level 2 PREFIRE products. These include variables such as top of atmosphere spectral flux, surface spectral emissivity, and atmospheric properties such as temperature and water vapor profiles. Science data retrieval started July 24, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4. The cloud identification data for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_2B-MSK collection.\n<br>\n\n#### PREFIRE_SAT1_0-PAYLOAD-TLM\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Raw Curated Payload (PREFIRE_SAT1_0-PAYLOAD-TLM) contains the curated raw measurements from one of two PREFIRE Thermal Infrared Spectrometers (TIRS-PREFIRE), which is a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will be assimilated into global circulation and other models to predict future conditions more accurately. This collection contains the raw, curated digital number counts for TIRS-PREFIRE aboard PREFIRE Satellite 1 (PREFIRE-SAT1). Data retrieval started in July , 2024, and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. This data has a temporal resolution of 0.707 seconds and is available in binary format. The PREFIRE_SAT2_0-PAYLOAD-TLM collection contains raw, curated digital number counts for the sister instrument aboard PREFIRE-SAT2.\n<br>\n\n#### PREFIRE_SAT1_0-PAYLOAD\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Raw Payload (PREFIRE_SAT1_0-PAYLOAD) contains the uncurated raw measurements from one of two PREFIRE Thermal Infrared Spectrometers (TIRS-PREFIRE), which is a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will be assimilated into global circulation and other models to predict future conditions more accurately. This collection contains the raw and uncurated digital number counts for TIRS-PREFIRE aboard PREFIRE Satellite 1 (PREFIRE-SAT1). Data retrieval started in July 24, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. This data has a temporal resolution of 0.707 seconds and is available in binary format. Raw, Uncurated digital number counts for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_0-PAYLOAD collection.\n<br>\n\n#### PREFIRE_SAT1_0-BUS-TLM\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Telemetry (PREFIRE_SAT1_0-BUS-TLM) contains positioning and pointing information for one of two PREFIRE polar orbiting CubeSats. Both CubeSats carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will be assimilated into global circulation and other models to predict future conditions more accurately. This collection contains the time, beta angle, orbit position and velocity, and the quaternion of PREFIRE Satellite 1 (PREFIRE-SAT1). Combined with a Digital Elevation Map, these telemeters geolocate PREFIRE data on the Earth’s surface. Data retrieval started June 29, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. This data is retrieved at a frequency of 1Hz and is available in CSV format. Positioning and pointing information for the sister satellite, PREFIRE-SAT2, can be found in the PREFIRE_SAT2_0-BUS-TLM collection.\n<br>\n\n#### PREFIRE_SAT2_2B-MSK\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Cloud Mask (PREFIRE_SAT2_2B-MSK) contains a binary clear/cloud indicator and probabilities derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT2_2B-MSK is derived from mid-infrared and far-infrared measurements collected globally by TIRS-PREFIRE aboard PREFIRE-SAT2 using a Machine Learning technique. Specifically, inputs are PREFIRE Spectral Radiance (PREFIRE_SAT2_1B-RAD) from PREFIRE Satellite 2 (PREFIRE_SAT2_2B-RAD) and PREFIRE Auxiliary Meteorology Data created for PREFIRE Satellite 2 (PREFIRE_SAT2_AUX-MET). PREFIRE_SAT2_2B-MSK is used to identify cloudy and clear scenes in TIRS-PREFIRE data, which supports the production of other Level 2 PREFIRE products. These include variables such as top of atmosphere spectral flux, surface spectral emissivity, and atmospheric properties such as temperature and water vapor profiles. Science data retrieval started June 29, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4.The cloud identification data for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_2B-MSK collection.\n<br>\n\n#### PREFIRE_SAT2_0-PAYLOAD-TLM\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Raw Curated Radiance (PREFIRE_SAT2_0-PAYLOAD-TLM) contains the curated raw measurements from one of two PREFIRE Thermal Infrared Spectrometers (TIRS-PREFIRE), which is a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will be assimilated into global circulation and other models to predict future conditions more accurately. This collection contains the raw, curated digital number counts for TIRS-PREFIRE aboard PREFIRE Satellite 2 (PREFIRE-SAT2). Data retrieval started June 29, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. This data has a temporal resolution of 0.707 seconds and is available in binary format. Raw, curated digital number counts for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_0-PAYLOAD-TLM collection.\n<br>\n\n#### PREFIRE_SAT2_0-PAYLOAD\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Raw Payload (PREFIRE_SAT2_0-PAYLOAD) contains the uncurated raw measurements from one of two PREFIRE Thermal Infrared Spectrometers (TIRS-PREFIRE), which is a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will be assimilated into global circulation and other models to predict future conditions more accurately. This collection contains the raw and uncurated digital number counts for TIRS-PREFIRE aboard PREFIRE Satellite 2 (PREFIRE-SAT2). Data retrieval started June 29, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. This data has a temporal resolution of 0.707 seconds and is available in binary format. Raw, uncurated digital number counts for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_0-PAYLOAD collection.\n<br>\n\n#### PREFIRE_SAT2_0-BUS-TLM\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Telemetry (PREFIRE_SAT2_0-BUS-TLM) contains positioning and pointing information for one of two PREFIRE polar orbiting CubeSats. Both CubeSats carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will be assimilated into global circulation and other models to predict future conditions more accurately. This collection contains the time, beta angle, orbit position and velocity, and the quaternion of PREFIRE Satellite 2 (PREFIRE-SAT2). Combined with a Digital Elevation Map, these telemeters geolocate PREFIRE data on the Earth’s surface. Data retrieval started June 29, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. This data is retrieved at a frequency of 1Hz and is available in CSV format. Positioning and pointing information for the sister satellite, PREFIRE-SAT1, can be found in the PREFIRE_SAT1_0-BUS-TLM collection.\n<br>\n\n#### PREFIRE_SAT1_2B-FLX_COG\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Spectral Flux from PREFIRE Satellite 1 COG (PREFIRE_SAT1_2B-FLX_COG) is retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately. PREFIRE_SAT1_2B-FLX_COG contains Cloud-Optimized GeoTIFF (COG) files that each contain a georeferenced rendering of derived outgoing longwave radiation (OLR) values. Those OLR data values are computed from a single granule of PREFIRE Spectral Radiance (PREFIRE_SAT1_1B-RAD) data, along with inputs from PREFIRE_SAT1_2B-MSK (cloud mask), PREFIRE_SAT1_AUX-MET, PREFIRE_SAT1_AUX-SAT, and PREFIRE_SAT1_2B-CLD. As part of the rendering process, mean derived OLR values are computed for a global grid of raster elements (each approximately 2.23 km in width), based on which raster elements each PREFIRE ground footprint overlaps. This procedure is done in order to better visualize the PREFIRE data despite substantial overlap of adjacent along-track ground footprints – but it is important to remember that the individual PREFIRE ground footprints are much bigger (by about 80x, in terms of area) than each of the GeoTIFF raster elements. These cloud-optimized GeoTIFF images may be viewed using many geographic information systems (GIS), such as the freely available QGIS. Science data retrieval started July 24, 2024 and is ongoing. Currently, the geographic coverage is for latitudes between approximately 60° to 84° in both polar regions, although future releases may include data for all latitudes equatorward of about 84°. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each TIRS-PREFIRE. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was about 264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint and swath sizes will slowly become smaller as the orbit altitude decreases with time. Similar OLR GeoTIFF renderings for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_2B-FLX_COG collection.\n<br>\n\n#### PREFIRE_SAT1_2B-FLX\nSatellite 1 (PREFIRE_SAT1_2B-FLX) contains surface emissivity retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately. PREFIRE_SAT1_2B-FLX contains spectral flux at the top of atmosphere for all available TIRS-PREFIRE channels as derived from PREFIRE Spectral Radiance (PREFIRE_SAT1_1B-RAD) data using an Optimal Estimation retrieval with inputs from the PREFIRE_SAT1_2B-MSK (cloud mask), PREFIRE_SAT1_AUX-MET (Auxiliary Meteorology), and PREFIRE_SAT1_AUX-SAT collections. PREFIRE_SAT1_2B-FLX also contains the top-of-atmosphere Outgoing Longwave Radiation (OLR) broadband flux. The primary purpose of this collection is to assess the radiative activities over the polar regions to better quantify the energy budget there. Science data retrieval started July 24, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4. The spectral flux data for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_2B-FLX collection.\n<br>\n\n#### PREFIRE_SAT2_2B-FLX_COG\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Spectral Flux from PREFIRE Satellite 2 COG (PREFIRE_SAT2_2B-FLX_COG) is derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT2_2B-FLX_COG contains Cloud-Optimized GeoTIFF (COG) files that each contain a georeferenced rendering of derived outgoing longwave radiation (OLR) values. Those OLR data values are computed from a single granule of PREFIRE Spectral Radiance (PREFIRE_SAT2_1B-RAD) data, along with inputs from PREFIRE_SAT2_2B-MSK (cloud mask), PREFIRE_SAT2_AUX-MET, PREFIRE_SAT2_AUX-SAT, and PREFIRE_SAT2_2B-CLD. As part of the rendering process, mean derived OLR values are computed for a global grid of raster elements (each approximately 2.23 km in width), based on which raster elements each PREFIRE ground footprint overlaps. This procedure is done in order to better visualize the PREFIRE data despite substantial overlap of adjacent along-track ground footprints – but it is important to remember that the individual PREFIRE ground footprints are much bigger (by about 80x, in terms of area) than each of the GeoTIFF raster elements. These cloud-optimized GeoTIFF images may be viewed using many geographic information systems (GIS), such as the freely available QGIS.Science data retrieval started June 29, 2024 and is ongoing. Currently, the geographic coverage is for latitudes between approximately 60° to 84° in both polar regions, although future releases may include data for all latitudes equatorward of about 84°. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each TIRS-PREFIRE. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was about 264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint and swath sizes will slowly become smaller as the orbit altitude decreases with time.Similar OLR GeoTIFF renderings for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_2B-FLX_COG collection.\n<br>\n\n#### PREFIRE_SAT2_2B-FLX\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Spectral Flux from PREFIRE Satellite 2 (PREFIRE_SAT2_2B-FLX) contains surface emissivity derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT2_2B-FLX contains spectral flux at the top of atmosphere for all available TIRS-PREFIRE channels as derived from PREFIRE Spectral Radiance (PREFIRE_SAT2_1B-RAD) data using an Optimal Estimation retrieval with inputs from the PREFIRE_SAT2_2B-MSK (cloud mask), PREFIRE_SAT2_AUX-MET (Auxiliary Meteorology), and PREFIRE_SAT2_AUX-SAT collections. PREFIRE_SAT2_2B-FLX also contains the top-of-atmosphere Outgoing Longwave Radiation (OLR) broadband flux. The primary purpose of this collection is to assess the radiative activities over the polar regions to better quantify the energy budget there.Science data retrieval started June 29, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4.The spectral flux data for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_2B-FLX collection.\n<br>\n\n#### PREFIRE_SAT1_2B-SFC_COG11um\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity from PREFIRE Satellite 1 COG11um (PREFIRE_SAT1_2B-SFC_COG11um) is retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately. PREFIRE_SAT1_2B-SFC_COG11um contains Cloud-Optimized GeoTIFF (COG) files that each contain a georeferenced rendering of retrieved surface emissivity values for channel 14 (centered at about 11.4 µm). Those surface emissivity data values are computed from a single granule of PREFIRE Spectral Radiance (PREFIRE_SAT1_1B-RAD) data using an Optimal Estimation retrieval via a radiative transfer model with inputs from PREFIRE_SAT1_2B-MSK (cloud mask), and PREFIRE_SAT1_AUX-MET. The retrieved surface emissivity values are only available for clear-sky conditions. As part of the rendering process, mean retrieved surface emissivity values are computed for a global grid of raster elements (each approximately 2.23 km in width), based on which raster elements each PREFIRE ground footprint overlaps. This procedure is done in order to better visualize the PREFIRE data despite substantial overlap of adjacent along-track ground footprints – but it is important to remember that the individual PREFIRE ground footprints are much bigger (by about 80x, in terms of area) than each of the GeoTIFF raster elements. These cloud-optimized GeoTIFF images may be viewed using many geographic information systems (GIS), such as the freely available QGIS. Science data retrieval started July 24, 2024 and is ongoing. Currently, the geographic coverage is for latitudes between approximately 60° to 84° in both polar regions, although future releases may include data for all latitudes equatorward of about 84°. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each TIRS-PREFIRE. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was about 264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint and swath sizes will slowly become smaller as the orbit altitude decreases with time. Similar surface emissivity GeoTIFF renderings for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_2B-SFC_COG11um collection.\n<br>\n\n#### PREFIRE_SAT1_2B-SFC_COG19um\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity from PREFIRE Satellite 1 COG19um (PREFIRE_SAT1_2B-SFC_COG19um) is retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately. PREFIRE_SAT1_2B-SFC_COG19um contains Cloud-Optimized GeoTIFF (COG) files that each contain a georeferenced rendering of retrieved surface emissivity values for channel 22 (centered at about 18.2 µm). Those surface emissivity data values are computed from a single granule of PREFIRE Spectral Radiance (PREFIRE_SAT1_1B-RAD) data using an Optimal Estimation retrieval via a radiative transfer model with inputs from PREFIRE_SAT1_2B-MSK (cloud mask), and PREFIRE_SAT1_AUX-MET. The retrieved surface emissivity values are only available for clear-sky conditions. As part of the rendering process, mean retrieved surface emissivity values are computed for a global grid of raster elements (each approximately 2.23 km in width), based on which raster elements each PREFIRE ground footprint overlaps. This procedure is done in order to better visualize the PREFIRE data despite substantial overlap of adjacent along-track ground footprints – but it is important to remember that the individual PREFIRE ground footprints are much bigger (by about 80x, in terms of area) than each of the GeoTIFF raster elements. These cloud-optimized GeoTIFF images may be viewed using many geographic information systems (GIS), such as the freely available QGIS. Science data retrieval started July 24, 2024 and is ongoing. Currently, the geographic coverage is for latitudes between approximately 60° to 84° in both polar regions, although future releases may include data for all latitudes equatorward of about 84°. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each TIRS-PREFIRE. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was about 264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint and swath sizes will slowly become smaller as the orbit altitude decreases with time. Similar surface emissivity GeoTIFF renderings for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_2B-SFC_COG19um collection.\n<br>\n\n#### PREFIRE_SAT1_2B-SFC\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity from PREFIRE Satellite 1 (PREFIRE_SAT1_2B-SFC) contains surface emissivity derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT1_2B-SFC contains surface spectral emissivity for all available TIRS-PREFIRE channels as derived from PREFIRE Spectral Radiance (PREFIRE_SAT1_1B-RAD) data using an Optimal Estimation retrieval via a radiative transfer model with inputs from the PREFIRE_SAT1_AUX-MET (Auxiliary Meteorology) and PREFIRE_SAT1_2B-MSK (cloud mask) collections. The purpose of this collection is to assess surface emissivity over the polar regions to better quantify the polar radiative budget in those regions. Science data retrieval started July 24, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4.The surface emissivity data for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_2B-SFC collection.\n<br>\n\n#### PREFIRE_SAT2_2B-SFC_COG11um\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity from PREFIRE Satellite 2 COG11um (PREFIRE_SAT2_2B-SFC_COG11um) is derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT2_2B-SFC_COG11um contains Cloud-Optimized GeoTIFF (COG) files that each contain a georeferenced rendering of retrieved surface emissivity values for channel 12 (centered at about 11.0 µm). Those surface emissivity data values are computed from a single granule of PREFIRE Spectral Radiance (PREFIRE_SAT2_1B-RAD) data using an Optimal Estimation retrieval via a radiative transfer model with inputs from PREFIRE_SAT2_2B-MSK (cloud mask), and PREFIRE_SAT2_AUX-MET. The retrieved surface emissivity values are only available for clear-sky conditions. As part of the rendering process, mean retrieved surface emissivity values are computed for a global grid of raster elements (each approximately 2.23 km in width), based on which raster elements each PREFIRE ground footprint overlaps. This procedure is done in order to better visualize the PREFIRE data despite substantial overlap of adjacent along-track ground footprints – but it is important to remember that the individual PREFIRE ground footprints are much bigger (by about 80x, in terms of area) than each of the GeoTIFF raster elements. These cloud-optimized GeoTIFF images may be viewed using many geographic information systems (GIS), such as the freely available QGIS.Science data retrieval started June 29, 2024 and is ongoing. Currently, the geographic coverage is for latitudes between approximately 60° to 84° in both polar regions, although future releases may include data for all latitudes equatorward of about 84°. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each TIRS-PREFIRE. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was about 264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint and swath sizes will slowly become smaller as the orbit altitude decreases with time.Similar surface emissivity GeoTIFF renderings for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_2B-SFC_COG11um collection.\n<br>\n\n#### PREFIRE_SAT2_2B-SFC_COG19um\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity from PREFIRE Satellite 2 COG19um (PREFIRE_SAT2_2B-SFC_COG19um) is derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT2_2B-SFC_COG19um contains Cloud-Optimized GeoTIFF (COG) files that each contain a georeferenced rendering of retrieved surface emissivity values for channel 21 (centered at about 18.6 µm). Those surface emissivity data values are computed from a single granule of PREFIRE Spectral Radiance (PREFIRE_SAT2_1B-RAD) data using an Optimal Estimation retrieval via a radiative transfer model with inputs from PREFIRE_SAT2_2B-MSK (cloud mask), and PREFIRE_SAT2_AUX-MET. The retrieved surface emissivity values are only available for clear-sky conditions. As part of the rendering process, mean retrieved surface emissivity values are computed for a global grid of raster elements (each approximately 2.23 km in width), based on which raster elements each PREFIRE ground footprint overlaps. This procedure is done in order to better visualize the PREFIRE data despite substantial overlap of adjacent along-track ground footprints – but it is important to remember that the individual PREFIRE ground footprints are much bigger (by about 80x, in terms of area) than each of the GeoTIFF raster elements. These cloud-optimized GeoTIFF images may be viewed using many geographic information systems (GIS), such as the freely available QGIS.Science data retrieval started June 29, 2024 and is ongoing. Currently, the geographic coverage is for latitudes between approximately 60° to 84° in both polar regions, although future releases may include data for all latitudes equatorward of about 84°. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each TIRS-PREFIRE. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was about 264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint and swath sizes will slowly become smaller as the orbit altitude decreases with time.Similar surface emissivity GeoTIFF renderings for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_2B-SFC_COG19um collection.\n<br>\n\n#### PREFIRE_SAT2_2B-SFC\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity from PREFIRE Satellite 1 (PREFIRE_SAT1_2B-SFC) contains surface emissivity retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately. PREFIRE_SAT1_2B-SFC contains surface spectral emissivity for all available TIRS-PREFIRE channels as derived from PREFIRE Spectral Radiance (PREFIRE_SAT1_1B-RAD) data using an Optimal Estimation retrieval via a radiative transfer model with inputs from the PREFIRE_SAT1_AUX-MET (Auxiliary Meteorology) and PREFIRE_SAT1_2B-MSK (cloud mask) collections. The purpose of this collection is to assess surface emissivity over the polar regions to better quantify the polar radiative budget in those regions. Science data retrieval started July 24, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4. The surface emissivity data for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_2B-SFC collection.\n<br>\n\n#### PREFIRE_SAT1_3-SFC-SORTED-ALLSKY\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity Sorted All-sky Climatology from PREFIRE Satellite 1 (PREFIRE_SAT1_3-SFC-SORTED=ALLSKY) contains monthly climatologies of the Level 2 PREFIRE Surface Emissivity from the PREFIRE Satellite 1 collection (PREFIRE_SAT1_2B_SFC), which is derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT1_3-SFC-SORTED-ALLSKY climatologies include 1) gridded, time-averaged spectral emissivity sorted by surface type, and 2) gridded standard deviations of time-averaged spectral emissivity sorted by surface type. The grids are 1°x1° and time averaging is currently monthly. Spatially, the cross-track dimension is retained, which consists of 8 distinct tracks. Full swath climatologies will be generated in addition to ascending- and descending-only subsets to account for diurnal variability. The purpose of this collection is to identify surface emissivity behaviors based on surface type and to assimilate PREFIRE surface emissivity data into climate models to further understand and more accurately predict future climates.The data format is NetCDF4.The sorted surface emissivity climatologies for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_3-SFC-SORTED-ALLSKY collection.\n<br>\n\n#### PREFIRE_SAT2_3-SFC-SORTED-ALLSKY\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity Sorted All-sky Climatology from PREFIRE Satellite 2 (PREFIRE_SAT2_3-SFC-SORTED=ALLSKY) contains monthly climatologies of the Level 2 PREFIRE Surface Emissivity from the PREFIRE Satellite 2 collection (PREFIRE_SAT2_2B_SFC), which is derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT1_3-SFC-SORTED-ALLSKY climatologies include 1) gridded, time-averaged spectral emissivity sorted by surface type, and 2) gridded standard deviations of time-averaged spectral emissivity sorted by surface type. The grids are 1°x1° and time averaging is currently monthly. Spatially, the cross-track dimension is retained, which consists of 8 distinct tracks. Full swath climatologies will be generated in addition to ascending- and descending-only subsets to account for diurnal variability. The purpose of this collection is to identify surface emissivity behaviors based on surface type and to assimilate PREFIRE surface emissivity data into climate models to further understand and more accurately predict future climates.The data format is NetCDF4.The sorted surface emissivity climatologies for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_3-SFC-SORTED-ALLSKY collection.\n<br>\n\n#### PREFIRE_SAT1_AUX-MET\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Auxiliary Meteorology Data for PREFIRE Satellite 1 (PREFIRE_SAT1_AUX-MET) contains GEOS-IT analyses and VIIRS satellite data that are subsets and interpolations corresponding to data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual PREFIRE CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT1_AUX-MET contains surface and skin temperatures, land fraction, sea ice concentration, snow cover, surface pressure, temperature profiles, pressure profiles, O3 profiles, wind velocity profiles, and surface type. Science data retrieval started July 24, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4.The auxiliary meteorology data for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_AUX-MET collection.\n<br>\n\n#### PREFIRE_SAT2_AUX-MET\nPolar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Auxiliary Meteorology Data for PREFIRE Satellite 2 (PREFIRE_SAT2_AUX-MET) contains GEOS-IT analyses and VIIRS satellite data that are subsets and interpolations corresponding to data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual PREFIRE CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT2_AUX-MET contains surface and skin temperatures, land fraction, sea ice concentration, snow cover, surface pressure, temperature profiles, pressure profiles, O3 profiles, wind velocity profiles, and surface type. Science data retrieval started June 29, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4.The auxiliary meteorology data for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_AUX-MET collection.\n<br>\n\n#### PREFIRE_SAT1_AUX-SAT\nPREFIRE (Polar Radiant Energy in the Far InfraRed Experiment) Auxiliary Satellite Data for PREFIRE Satellite 2 contains data fields extracted from external satellite datasets for each coincident PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) field of view, which is then used to process PREFIRE Level 2 and 3 products. Dual CubeSats each carry a TIRS-PREFIRE, a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.This collection contains surface-type information from VIIRS, Aqua, and NISE (SSMIS). Its primary purpose is to provide surface type information for the PREFIRE Level 2 spectral flux product (PREFIRE_SAT1_2B-FLX) and the Level 3 surface emissivity product (PREFIRE_SAT1_3-SFC-SORTED-ALLSKY), which is sorted by surface type. Science data retrieval started July 24, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4.The auxiliary satellite data for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_AUX-SAT collection.\n<br>\n\n#### PREFIRE_SAT1_1A-RAD\nPREFIRE (Polar Radiant Energy in the Far InfraRed Experiment) Level 1A Calibrated Spectral Radiance from PREFIRE Satellite 1 (PREFIRE_SAT1_1A-RAD) contains calibrated radiance data retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a TIRS-PREFIRE, a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will be assimilated into global circulation and other models to predict future conditions more accurately. Digital number counts from the PREFIRE_SAT1_0-PAYLOAD-TLM collection are converted to radiance and calibrated using an internal blackbody target and space-look data. The PREFIRE_SAT1_1A-RAD data is used as input for PREFIRE_SAT1_1B-RAD, which is geolocated radiance. Given that it is not geolocated, PREFIRE_SAT1_1A-RAD is not useful for direct scientific study. Data retrieval started July 24, 2024, and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4. Calibrated radiance data for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_1A-RAD collection.\n<br>\n\n#### PREFIRE_SAT2_1A-RAD\nPREFIRE (Polar Radiant Energy in the Far InfraRed Experiment) Level 1A Calibrated Spectral Radiance from PREFIRE Satellite 2 (PREFIRE_SAT2_1A-RAD) contains calibrated radiance data retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a TIRS-PREFIRE, a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.Digital number counts from the PREFIRE_SAT2_0-PAYLOAD-TLM collection are converted to radiance and calibrated using an internal blackbody target and space-look data.The PREFIRE_SAT2_1A-RAD data are used as input for PREFIRE_SAT2_1B-RAD, which is geolocated radiance. Given that it is not geolocated, PREFIRE_SAT2_1A-RAD is not useful for direct scientific study.Science data retrieval for this collection started on June 29, 2024, and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. These data have a temporal resolution of 0.707 seconds and is available in NetCDF-4.Calibrated radiance data for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_1A-RAD collection.\n<br>\n\n#### PREFIRE_SAT1_1B-RAD\nPREFIRE (Polar Radiant Energy in the Far InfraRed Experiment) Spectral Radiance from PREFIRE Satellite 1 (PREFIRE_SAT1_1B-RAD) contains calibrated and geolocated radiance data retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a TIRS-PREFIRE, a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will be assimilated into global circulation and other models to predict future conditions more accurately. PREFIRE_SAT1_1B-RAD data is processed by geolocating the calibrated radiance data in PREFIRE_SAT1_1A-RAD using the pointing and position telemeters in PREFIRE_SAT1_0-BUS-TLM and a Digital Elevation Map. The PREFIRE_SAT1_1A-RAD data is used to analyze the radiative budget in polar regions and produce 3 products: top-of-atmosphere spectral flux, surface spectral emissivity, and atmospheric properties such as cloud cover, temperature profiles, and water vapor profiles. Science data retrieval started July 24, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4. Calibrated and geolocated radiance data for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_1B-RAD collection.\n<br>\n\n#### PREFIRE_SAT2_1B-RAD\nPREFIRE (Polar Radiant Energy in the Far InfraRed Experiment) Spectral Radiance from PREFIRE Satellite 2 (PREFIRE_SAT2_1B-RAD) contains calibrated and geolocated radiance data retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a TIRS-PREFIRE, a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other climate models to predict future climates more accurately.PREFIRE_SAT2_1B-RAD data are processed by geolocating the calibrated radiance data in PREFIRE_SAT2_1A-RAD using the pointing and position telemeters in PREFIRE_SAT2_0-BUS-TLM and a Digital Elevation Map.The PREFIRE_SAT2_1B-RAD data are used to analyze the radiative budget in polar regions and as an input for Level 2 products: top-of-atmosphere spectral flux, surface spectral emissivity, and atmospheric properties such as cloud cover, temperature profiles, and water vapor profiles.Science data retrieval started June 29, 2024, and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4.Calibrated and geolocated radiance data for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_1B-RAD collection.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://science.jpl.nasa.gov/projects/prefire/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","cog","elevation","ice","netcdf","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"PREFIRE_SAT1_2B-ATM_COG vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Atmospheric Properties from PREFIRE Satellite 1 COG (PREFIRE_SAT1_2B-ATM_COG) is retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_2B-ATM_COG_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_2B-ATM vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Atmospheric Properties from PREFIRE Satellite 1 (PREFIRE_SAT1_2B-ATM) contains several geophysical variables, such as atmospheric temperature and water content, retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_2B-ATM_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_2B-ATM_COG vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Atmospheric Properties from PREFIRE Satellite 2 COG (PREFIRE_SAT2_2B-ATM_COG) is derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual Cs each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_2B-ATM_COG_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_2B-ATM vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Atmospheric Properties from PREFIRE Satellite 2 (PREFIRE_SAT2_2B-ATM) contains several geophysical variables, such as atmospheric temperature and water content, derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_2B-ATM_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_2B-CLD vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Cloud Properties (PREFIRE_SAT1_2B-CLD) contains cloud properties retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_2B-CLD_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_2B-CLD vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Cloud Properties (PREFIRE_SAT2_2B-CLD) contains cloud properties derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_2B-CLD_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_2B-MSK vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Cloud Mask (PREFIRE_SAT1_2B-MSK) contains a binary clear/cloud indicator and probabilities retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_2B-MSK_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_0-PAYLOAD-TLM vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Raw Curated Payload (PREFIRE_SAT1_0-PAYLOAD-TLM) contains the curated raw measurements from one of two PREFIRE Thermal Infrared Spectrometers (TIRS-PREFIRE), which is a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_0-PAYLOAD-TLM_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_0-PAYLOAD vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Raw Payload (PREFIRE_SAT1_0-PAYLOAD) contains the uncurated raw measurements from one of two PREFIRE Thermal Infrared Spectrometers (TIRS-PREFIRE), which is a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_0-PAYLOAD_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_0-BUS-TLM vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Telemetry (PREFIRE_SAT1_0-BUS-TLM) contains positioning and pointing information for one of two PREFIRE polar orbiting CubeSats. Both CubeSats carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_0-BUS-TLM_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_2B-MSK vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Cloud Mask (PREFIRE_SAT2_2B-MSK) contains a binary clear/cloud indicator and probabilities derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_2B-MSK_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_0-PAYLOAD-TLM vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Raw Curated Radiance (PREFIRE_SAT2_0-PAYLOAD-TLM) contains the curated raw measurements from one of two PREFIRE Thermal Infrared Spectrometers (TIRS-PREFIRE), which is a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_0-PAYLOAD-TLM_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_0-PAYLOAD vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Raw Payload (PREFIRE_SAT2_0-PAYLOAD) contains the uncurated raw measurements from one of two PREFIRE Thermal Infrared Spectrometers (TIRS-PREFIRE), which is a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_0-PAYLOAD_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_0-BUS-TLM vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Telemetry (PREFIRE_SAT2_0-BUS-TLM) contains positioning and pointing information for one of two PREFIRE polar orbiting CubeSats. Both CubeSats carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_0-BUS-TLM_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_2B-FLX_COG vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Spectral Flux from PREFIRE Satellite 1 COG (PREFIRE_SAT1_2B-FLX_COG) is retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_2B-FLX_COG_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_2B-FLX vR01 - Satellite 1 (PREFIRE_SAT1_2B-FLX) contains surface emissivity retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_2B-FLX_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_2B-FLX_COG vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Spectral Flux from PREFIRE Satellite 2 COG (PREFIRE_SAT2_2B-FLX_COG) is derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_2B-FLX_COG_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_2B-FLX vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Spectral Flux from PREFIRE Satellite 2 (PREFIRE_SAT2_2B-FLX) contains surface emissivity derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_2B-FLX_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_2B-SFC_COG11um vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity from PREFIRE Satellite 1 COG11um (PREFIRE_SAT1_2B-SFC_COG11um) is retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_2B-SFC_COG11um_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_2B-SFC_COG19um vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity from PREFIRE Satellite 1 COG19um (PREFIRE_SAT1_2B-SFC_COG19um) is retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_2B-SFC_COG19um_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_2B-SFC vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity from PREFIRE Satellite 1 (PREFIRE_SAT1_2B-SFC) contains surface emissivity derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_2B-SFC_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_2B-SFC_COG11um vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity from PREFIRE Satellite 2 COG11um (PREFIRE_SAT2_2B-SFC_COG11um) is derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_2B-SFC_COG11um_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_2B-SFC_COG19um vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity from PREFIRE Satellite 2 COG19um (PREFIRE_SAT2_2B-SFC_COG19um) is derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_2B-SFC_COG19um_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_2B-SFC vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity from PREFIRE Satellite 1 (PREFIRE_SAT1_2B-SFC) contains surface emissivity retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_2B-SFC_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_3-SFC-SORTED-ALLSKY vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity Sorted All-sky Climatology from PREFIRE Satellite 1 (PREFIRE_SAT1_3-SFC-SORTED=ALLSKY) contains monthly climatologies of the Level 2 PREFIRE Surface Emissivity from the PREFIRE Satellite 1 collection (PREFIRE_SAT1_2B_SFC), which is derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation fro...","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_3-SFC-SORTED-ALLSKY_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_3-SFC-SORTED-ALLSKY vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity Sorted All-sky Climatology from PREFIRE Satellite 2 (PREFIRE_SAT2_3-SFC-SORTED=ALLSKY) contains monthly climatologies of the Level 2 PREFIRE Surface Emissivity from the PREFIRE Satellite 2 collection (PREFIRE_SAT2_2B_SFC), which is derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation fro...","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_3-SFC-SORTED-ALLSKY_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_AUX-MET vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Auxiliary Meteorology Data for PREFIRE Satellite 1 (PREFIRE_SAT1_AUX-MET) contains GEOS-IT analyses and VIIRS satellite data that are subsets and interpolations corresponding to data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual PREFIRE CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_AUX-MET_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_AUX-MET vR01 - Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Auxiliary Meteorology Data for PREFIRE Satellite 2 (PREFIRE_SAT2_AUX-MET) contains GEOS-IT analyses and VIIRS satellite data that are subsets and interpolations corresponding to data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual PREFIRE CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_AUX-MET_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_AUX-SAT vR01 - PREFIRE (Polar Radiant Energy in the Far InfraRed Experiment) Auxiliary Satellite Data for PREFIRE Satellite 2 contains data fields extracted from external satellite datasets for each coincident PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) field of view, which is then used to process PREFIRE Level 2 and 3 products. Dual CubeSats each carry a TIRS-PREFIRE, a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_AUX-SAT_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_1A-RAD vR01 - PREFIRE (Polar Radiant Energy in the Far InfraRed Experiment) Level 1A Calibrated Spectral Radiance from PREFIRE Satellite 1 (PREFIRE_SAT1_1A-RAD) contains calibrated radiance data retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a TIRS-PREFIRE, a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_1A-RAD_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_1A-RAD vR01 - PREFIRE (Polar Radiant Energy in the Far InfraRed Experiment) Level 1A Calibrated Spectral Radiance from PREFIRE Satellite 2 (PREFIRE_SAT2_1A-RAD) contains calibrated radiance data retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a TIRS-PREFIRE, a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_1A-RAD_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT1_1B-RAD vR01 - PREFIRE (Polar Radiant Energy in the Far InfraRed Experiment) Spectral Radiance from PREFIRE Satellite 1 (PREFIRE_SAT1_1B-RAD) contains calibrated and geolocated radiance data retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a TIRS-PREFIRE, a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT1_1B-RAD_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"PREFIRE_SAT2_1B-RAD vR01 - PREFIRE (Polar Radiant Energy in the Far InfraRed Experiment) Spectral Radiance from PREFIRE Satellite 2 (PREFIRE_SAT2_1B-RAD) contains calibrated and geolocated radiance data retrieved from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a TIRS-PREFIRE, a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm.","ARN":"arn:aws:s3:::asdc-prod-protected/PREFIRE/PREFIRE_SAT2_1B-RAD_R01","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-prefire","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA PROVE Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### jornada_landcover_lai_665\nField measurement of shrubland ecological properties is important for both site monitoring and validation of remote-sensing information. During the NASA Earth Observing System Prototype Validation Exercise (PROVE) at the Jornada Experimental Range, New Mexico, on May 20-30, 1997, we calculated plot-level plant area index, leaf area index, total fractional cover, and green fractional cover with data from four instruments: (1) a Dycam Agricultural Digital Camera (ADC), (2) a LI-COR LAI-2000 plant canopy analyzer, (3) a Decagon sunfleck ceptometer, and (4) a laser altimeter. Estimates from the LAI-2000 and ceptometer were very similar (plant area index 0.3, leaf area index 0.22, total fractional cover 0.19, green fractional cover 0.14), but the ADC produced values 5 to 10% higher. Laser altimeter values, depending on the height cutoff used to establish total fractional cover, were either higher or lower than the other instruments' values: a 10-cm cutoff produced values approximately 80% higher, whereas a 20-cm cutoff produced values approximately 30% lower.\n<br>\n\n#### jornada_mquals_666\nThis study utilized low flying, aircraft-based radiometers for optical characterization of top-of-the-canopy reflectance at Jornada Experimental Range in New Mexico during the Prototype Validation Experiment (PROVE) in May 1997. MQUALS refers to a light aircraft radiometric package for MODLAND Quick Airborne Looks (MQUALS). The objective was to examine the usefulness of low-flying aircraft for Moderate Resolution Imaging Spectroradiometer (MODIS) validation of land products. This project was the responsibility of the MODIS Land (MODLAND) validation group. The descriptive article by Huete et al. (1999) is included as a companion file to this data set. Multiband radiometers (Exotech) were flown at 100 m above ground level along transects encompassing several land cover types within this semi-arid biome validation site. Pointable radiometers acquired data at nadir (i.e., 0 degree), as well as at 15-degree, 30-degree, and 45-degree viewing angles along the principal and orthogonal planes. The sequence of measurements involved two separate sun angle-based overflights.\n<br>\n\n#### jornada_albedo_667\nThe objective of this study was to determine the spatial variations in field measurements of broadband albedo as related to the ground cover and under a range of solar conditions during the Prototype Validation Exercise (PROVE) at the Jornada Experimental Range in New Mexico on May 20-30, 1997. The surface albedo was sampled as described in Barnsley et al. (2000) at intervals along transects radiating north, south, east, and west from the main instrumentation tower at the transitional vegetation site. Several of these transects were sampled on more than one occasion, under a range of solar illumination conditions. At the grassland site, albedo measurements were taken along a single transect, sampled at 10-m intervals, at three different times of day (i.e., at different solar zenith angles). In addition to the albedo data, hemispherical photographs were taken at each of the sample sites to provide a visual record, and the images were classified to estimate the proportion of bare soil, live vegetation, and woody ground cover.\n<br>\n\n#### jornada_canopy_brf_668\nDirectional reflected radiation was measured over plots representing selected canopy components (shrubs and individual plants, bare sand, and background) at the Jornada Experiment Range site near Las Cruces, New Mexico, during the Prototype Validation Experiment (PROVE) in May 1997.At each plot, directional reflected radiation was measured in the solar principal plane; directional reflected radiation in the plane perpendicular to the solar principle plane were measured next (by rotating the mast around the plot so that the pivoting point is 90 degrees from the initial pivoting location). View zenith angles of 60, 45, 30 and 15 degrees in the back and forward scatter directions of nadir were measured in each plane. Nadir-viewed reflected radiation was measured for each plane as well.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/PROVE/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","land cover","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"jornada_landcover_lai_665 v1 - Field measurement of shrubland ecological properties is important for both site monitoring and validation of remote-sensing information. During the NASA Earth Observing System Prototype Validation Exercise (PROVE) at the Jornada Experimental Range, New Mexico, on May 20-30, 1997, we calculated plot-level plant area index, leaf area index, total fractional cover, and green fractional cover with data from four instruments: (1) a Dycam Agricultural Digital Camera (ADC), (2) a LI-COR LAI-2000 plant canopy analyzer, (3) a Decagon sunfleck ceptometer, and (4) a laser altimeter.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/prove/jornada_landcover_lai/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"jornada_mquals_666 v1 - This study utilized low flying, aircraft-based radiometers for optical characterization of top-of-the-canopy reflectance at Jornada Experimental Range in New Mexico during the Prototype Validation Experiment (PROVE) in May 1997. MQUALS refers to a light aircraft radiometric package for MODLAND Quick Airborne Looks (MQUALS).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/prove/jornada_mquals/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"jornada_albedo_667 v1 - The objective of this study was to determine the spatial variations in field measurements of broadband albedo as related to the ground cover and under a range of solar conditions during the Prototype Validation Exercise (PROVE) at the Jornada Experimental Range in New Mexico on May 20-30, 1997. The surface albedo was sampled as described in Barnsley et al.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/prove/jornada_albedo/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"jornada_canopy_brf_668 v1 - Directional reflected radiation was measured over plots representing selected canopy components (shrubs and individual plants, bare sand, and background) at the Jornada Experiment Range site near Las Cruces, New Mexico, during the Prototype Validation Experiment (PROVE) in May 1997.At each plot, directional reflected radiation was measured in the solar principal plane; directional reflected radiation in the plane perpendicular to the solar principle plane were measured next (by rotating the mast around the plot so that the pivoting point is 90 degrees from the initial pivoting location). Vi...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/prove/jornada_canopy_brf/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-prove","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Physical Sciences Informatics (PSI)","Description":"NASA's Physical Sciences Research Program, along with its predecessors, has conducted significant fundamental and applied research in the physical sciences. The International Space Station (ISS) is an orbiting laboratory that provides an ideal facility to conduct long-duration experiments in the near absence of gravity and allows continuous and interactive research similar to Earth-based laboratories. This enables scientists to pursue innovations and discoveries not currently achievable by other means. NASA's Physical Sciences Research Program also benefits from collaborations with several of the ISS international partners—Europe, Russia, Japan, and Canada—and foreign governments with space programs, such as France, Germany and Italy. <br/><br/> In fulfillment of the Open Science model, NASA's Physical Sciences Research Program is pleased to offer the PSI data repository for physical science experiments performed in reduced-gravity environments such as the ISS, Space Shuttle flights, and Free-flyers. PSI also includes data from some related ground-based studies. The PSI system is accessible and open to the public. This provides the opportunity for researchers to data mine results from prior flight investigations, expanding on the research performed. This approach will allow numerous ground-based investigations to be conducted from one flight experiment’s data, exponentially increasing our body of knowledge. PSI is an Open Data initiative.","Documentation":"https://www.nasa.gov/PSI","Contact":"https://www.nasa.gov/content/psi-contacts-tutorials-and-related-links","ManagedBy":"NASA","UpdateFrequency":"New research data is added as soon as it is available.","Tags":["aws-pds","chemistry","fluid dynamics","materials science","physics","space biology"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"Directional Solidification Insert (DSI) and Observation and Analysis of Smectic Islands In Space (OASIS) data","ARN":"arn:aws:s3:::nasa-psi","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://nasa-psi.s3.us-west-2.amazonaws.com/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"PSI System Demonstration","URL":"https://youtu.be/9uXgDUuWxTo","AuthorName":"Cheryl Payne"},{"Title":"Researcher's Guide to Physical Sciences Informatics System","URL":"https://www.nasa.gov/sites/default/files/atoms/files/psi_researchers_guide-tagged.pdf","AuthorName":"NASA ISS Program Science Office"}]},"RegistryEntryAdded":"2023-02-28","RegistryEntryLastModified":"2023-03-01","Slug":"nasa-psi","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA QUIKSCAT Project","Description":"This dataset consists of the version 2 Level 2B science-quality ocean surface wind vector retrievals from the Oceansat-2 scatterometer (OSCAT), which was designed and launched by the Indian Space Research Organization (ISRO) 23 September 2009. This Level 2B dataset is produced by the Jet Propulsion Laboratory (JPL) QuikSCAT Project in cooperation with ISRO. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. This resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. This newest version contains an improved geophysical model function (GMF), known as QSCAT12, consistent with the Remote Sensing Systems Ku2011 GMF, and an improved rain detection and flagging algorithm; these algorithms are consistent with the latest reprocessed version 3 QuikSCAT L2B dataset. Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. As a Ku-band dual pencil-beam rotating scatterometer, OSCAT design specs bear a strong resemblence of the Ku-band SeaWinds scatterometers on QuikSCAT and Midori-II (ADEOS-II). The primary difference between OSCAT and SeaWinds is the ~4 degree increase in the OSCAT incidence angle, which acts as an offset to the relatively lower altitude of Oceansat-2 to help provide a nearly identical swath width to SeaWinds. In the early phase of cal/val, the JPL QuikSCAT Project identified several problems, most of which have been corrected in this latest L2B version. This dataset release is expected to have an accuracy similar to that of the version 3 L2B QuikSCAT product, with minor caveats, all of which are described by Jaruwatanadilok et al. (2014) and summarized in the User Guide document which is made available in PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/oceansat2/L2B/oscat/jpl/docs/ . Read software is made available in MATLAB, Python, R, and IDL and is accessible in PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/oceansat2/L2B/oscat/jpl/sw/ .\n<br>\n\n#### QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2\nThis dataset is Version 2 of the geo-located and averaged Level 1B Sigma-0 measurements and wind retrievals from the SeaWinds on QuikSCAT platform, initiated in the months following the failure of the rotating antenna motor on 22 November 2009, using the various incidence angles at which QuikSCAT was pointed during the time period from November 2009 until present. Incidence angles were varied in order to cross-calibrate the Oceansat-2 and RapidScat scatterometers and to extend the known Ku-band geophysical model function. The averaging of the L1B input data combined with the wind vector processing results are both contained in this product referred to hereafter as Level 1C (L1C). The fixed and repointed beam processing is relative to either the one corresponding to the vertically polarized \"outer\" beam or the other corresponding to the horizontally polarized \"inner\" beam. The Sigma-0 values from the fixed operating beam for each frame are averaged to a single value representing approximately 50 samples. The data points are land flagged, collocated with ECMWF surface winds, and have climatological nadir attenuations provided for the location and time of the data (not applied to the sigma0). The following enhancements have been applied in the Version 2 re-processing: 1) the GMF has been updated (QNS2016a) to make use of ECMWF nowcast 1x1 degree resolution wind direction information for the entire historical data record; 2) the new QNS2016a GMF leverages a calibration adjustment from Remote Sensing Systems (RSS) resulting in a consistently lower Normalized Radar Cross Section (NRCS or Sigma-0) measurements that establishes a Sigma-0 bias of -0.25 dB (-5.9% linear scale) compared to the L1C Version 1 data; 3) the new QNS2016a GMF also applies an azimuthal modulation that is decreased by several tenths of a dB (for Sigma-0) in variation with wind speed; this results in a more consistent wind speed retrieval comparison between \"non-spinning\" and \"spinning\" modes of the QuikSCAT instrument; 4) spacecraft attitude was re-estimated using slice data over multiple orbits as a replacement for lost echo-tracking capability during the \"non-spinning\" mode of the instrument; this new attitude estimation follows an unpublished manual technique that leverages the echo power of individual slice observations; since only a small subset of slice observations are analyzed, rapid variations in attitude are not captured; 5) continues data production beyond October 2016 through the end of mission on 30 August 2018. Retrieved wind directions are only slightly different from ECMWF values and should not be considered an independent measurement of wind direction. Retrieved wind speeds do not depend significantly on ECMWF speeds as evidenced by the fact that they agree closely with WindSAT polarimetric radiometer speeds whenever WindSAT and ECMWF disagree. The Sigma0 values have also been corrected for scan loss (due to the fact that the antenna does not scan) and for X-factor changes due to repointing.\n<br>\n\n#### QSCAT_LEVEL_2B_OWV_COMP_12\nThis dataset contains the latest reprocessed version 3 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Version 3 processing begins with the same L1B (time-ordered backscatter) data as used in the previous processing. Version 3 has several improvements over the previous JPL processing of the QuikSCAT L2B winds: 1) changes to measurement binning, which was done in order to decrease noise and reduce gaps in the 12.5 km L2B wind retrievals, 2) an improved geophysical model function (GMF) to model the effect of wind on backscatter, 3) a neural network approach to correct rain contaminated winds speeds, 4) cross-track dependent wind speed biases were estimated and removed from the wind retrievals. The 12.5 km binning resolution enables users to obtain wind vector retrievals 10 km closer to shore when compared to the 25 km L2B dataset (only available in versions 1 and 2). More details to the processing changes and improvements are noted by Fore et al. (2014): PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/quikscat/L2B12/docs/fore_et_al_ieee_2014.pdf . Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is the official dataset produced by the QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). The Version 3 User Guide document is accessible from https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/quikscat/open/L2B12/docs/qscat_l2b_v3_ug_v1_0.pdf.\n<br>\n\n#### QSCAT_LEVEL_2B_OWV_COMP_12_LCR_3.1\nThis dataset contains the latest reprocessed version 3.1 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Version 3.1 processing begins with the same L1B (time-ordered backscatter) data as used in the previous Version 3.0 processing. Version 3.1 improves upon the previous Version 3.0 processing by incorporating enhanced coastal processing using a Land Contamination Ratio (LCR) method with a fixed threshold. The 12.5 km binning resolution combined with the LCR processing enables this dataset to provide wind vector retrievals with approximately half the coastal gap as compared to the Version 3.0 12.5 km L2B dataset. The geophysical model function used to produce the wind vector cell retrievals remains unchanged between Version 3.0 and 3.1. Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is the official dataset produced by the NASA QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). More details to the processing changes and improvements are to be published in the near future, but for now can be referenced by the following presentation: https://mdc.coaps.fsu.edu/scatterometry/meeting/docs/2016/Thu_AM/coastal-poster.pdf .\n<br>\n\n#### QSCAT_LEVEL_2B_OWV_COMP_12_KUSST_LCRES_4.1\nThis dataset contains the latest reprocessed version 4.1 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is an official dataset produced by the NASA QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). Version 4.1 processing begins with the same L1B (time-ordered backscatter) data as used in the previous Version 4.0 processing. This new version has a number of key improvements and changes over the previous version 4.0, including: 1) winds are now retrieved to within 5-km and 10-km of the coast within oceans/seas and lakes respectively; 2) coastal winds are now flagged as poor coastal quality and likely corrupted in orbits with estimated spacecraft pitch error greater than 0.04 degrees, which affects 150 orbits of data where coastal winds are severely contaminated by land due to poor attitude knowledge (note: attitude error tracking can identify pitch error but not yaw error, so when estimated pitch error is far from zero, it implies yaw error is large and uncorrected); 3) coastal winds are flagged based upon the long term mean wind speed and standard deviation of wind speed for each place on the ground; 4) four quantities, means and standard deviations computed with and without the land contamination correction algorithm applied (note: higher mean and smaller standard deviation are evidence of land contamination), are used to estimate the expected wind speed bias with respect to neighboring wind vector cells over open water; 5) wind vector cells with estimated speed bias greater than 0.4 m/s are flagged as poor coastal quality and likely corrupted; 6) winds within 5-km of the coast of an ocean/sea and 10-km of the coast of a lake are flagged as poor coastal quality and likely corrupted; the larger distance threshold for lakes is due to higher variability in lake water levels.\n<br>\n\n#### QSCAT_LEVEL_1B_V2\nThe SeaWinds on QuikSCAT Level 1B dataset contains the geo-located Sigma-0 measurements and antenna pulse \"egg\" and \"slice\" geometries as derived from ephemeris and the Level 1A dataset. The pulse \"egg\" represents the complete footprint of the pulse, which has a spatial geometry of approximately 25 km by 35 km. There are 8 slices that constitute the range-binned components of a pulse each of which has a spatial geometry of approximately 25 km by 7 km. The orientation of the long dimension of the slices varies with the rotation of the antenna and thus does not align with the along/across track orientation of the wind vector grid in the L2B/L2A products. This dataset represents the second reprocessed version of the Level 1B release. Special note: QuikSCAT went into a \"non-spinning\" mode on 22 November 2009. The final rev number in the nominal Operational \"spinning\" mode is 54296; the \"non-spinning\" mode of the instrument continued predominantly until the end of the time series. There were some brief periods of \"spinning\" in between, which include the following days and rev numbers (identified in parenthesis): 1) 29 January 2013 to 5 February 2013 (7909-71011), 2) 14 March 2013 (71536-71549), 3) 18 March 2013 to 21 March 2013 (71590-71634), and 4) 28 March 2013 to 31 March 2013 (71735-71769). Data during the \"non-spinning\" mode is not consistently calibrated with data from the \"spinning\" mode. Furthermore, incidence angles change periodically during the \"non-spinning\" mode. It is therefore advised that only \"expert\" users attempt using the data during the \"non-spinning\" mode. For standard L1B data users who wish to access consistently calibrated L1B data during the \"non-spinning\" mode, please consider using the L1B Averaged Sigma-0 dataset as alternative, which may be accessed by contacting podaac@podaac.jpl.nasa.gov\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","earth observation","ice","oceans","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OS2_OSCAT_LEVEL_2B_OWV_COMP_12_V2 v2 - This dataset consists of the version 2 Level 2B science-quality ocean surface wind vector retrievals from the Oceansat-2 scatterometer (OSCAT), which was designed and launched by the Indian Space Research Organization (ISRO) 23 September 2009. This Level 2B dataset is produced by the Jet Propulsion Laboratory (JPL) QuikSCAT Project in cooperation with ISRO.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OS2_OSCAT_LEVEL_2B_OWV_COMP_12_V2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2 v2.0 - This dataset is Version 2 of the geo-located and averaged Level 1B Sigma-0 measurements and wind retrievals from the SeaWinds on QuikSCAT platform, initiated in the months following the failure of the rotating antenna motor on 22 November 2009, using the various incidence angles at which QuikSCAT was pointed during the time period from November 2009 until present. Incidence angles were varied in order to cross-calibrate the Oceansat-2 and RapidScat scatterometers and to extend the known Ku-band geophysical model function.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"QSCAT_LEVEL_2B_OWV_COMP_12 v3 - This dataset contains the latest reprocessed version 3 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/QSCAT_LEVEL_2B_OWV_COMP_12","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"QSCAT_LEVEL_2B_OWV_COMP_12_LCR_3.1 v3.1 - This dataset contains the latest reprocessed version 3.1 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/QSCAT_LEVEL_2B_OWV_COMP_12_LCR_3.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"QSCAT_LEVEL_2B_OWV_COMP_12_KUSST_LCRES_4.1 v4.1 - This dataset contains the latest reprocessed version 4.1 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/QSCAT_LEVEL_2B_OWV_COMP_12_KUSST_LCRES_4.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"QSCAT_LEVEL_1B_V2 v2 - The SeaWinds on QuikSCAT Level 1B dataset contains the geo-located Sigma-0 measurements and antenna pulse \"egg\" and \"slice\" geometries as derived from ephemeris and the Level 1A dataset. The pulse \"egg\" represents the complete footprint of the pulse, which has a spatial geometry of approximately 25 km by 35 km.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/QSCAT_LEVEL_1B_V2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-quikscat","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA ROSES Project","Description":"The Reconstructed Sea Level dataset contains sea level anomalies derived from satellite altimetry and tide gauges. The satellite altimetric record provides accurate measurements of sea level with near-global coverage, but it has a relatively short time span, since 1993. Tide gauges have measured sea level over the last 200 years, with some records extending back to 1807, but they only provide regional coverage, not global. Combining satellite altimetry with tide gauges, using a technique known as sea level reconstruction, results in a dataset with the record length of the tide gauges and the near-global coverage of satellite altimetry. Cyclostationary empirical orthogonal functions (CSEOFs), derived from satellite altimetry, are combined with historical sea level measurements from tide gauges to create the Reconstructed Sea Level dataset spanning from 1950 through 2009. Combining the altimetric and tide gauge records alleviates the difficulties caused by the short record length and poor spatial distribution of the satellite altimetry and tide gauges, respectively. Previous sea level reconstructions have utilized empirical orthogonal functions (EOFs) as basis functions, but by using CSEOFs and by addressing other aspects of the reconstruction procedure, an alternative sea level reconstruction can be computed. The resulting reconstructed sea level dataset has weekly temporal resolution and half-degree spatial resolution. For specific information on the algorithm and how the CSEOFs are applied to the tide gauge data please see Hamlington et al. 2011.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"RECON_SEA_LEVEL_OST_L4_V1 v1 - The Reconstructed Sea Level dataset contains sea level anomalies derived from satellite altimetry and tide gauges. The satellite altimetric record provides accurate measurements of sea level with near-global coverage, but it has a relatively short time span, since 1993.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/RECON_SEA_LEVEL_OST_L4_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-roses","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA S-MODE Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### SMODE_L1_DOPPLERSCATT_V1\nThis dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. DopplerScatt is a Ka-band (35.75 GHz) scatterometer with a swath width of 24 km that records Doppler measurements of the relative velocity between the platform and the surface. It is mounted on a B200 aircraft which flies daily surveys of the field domain during deployments, and data is used to give larger scale context, and also to compare with in-situ measurements of velocities and divergence. Level 1 data includes geolocated physical measurements for a measurement footprint, which are the basis for the DopplerScatt L2 surface winds and currents estimates. Data are available in netCDF format and are ordered by measurement acquisition time and radar range, and are not on a geospatial grid.\n<br>\n\n#### SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V2\nThis dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE). S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Data were collected approximately 300 km offshore of San Fransisco during a pilot campaign in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. DopplerScatt is a Ka-band (35.75 GHz) scatterometer with a swath width of 24 km that records Doppler measurements of the relative velocity between the platform and the surface. It is mounted on a B200 aircraft which flies daily surveys of the field domain during deployments, and data is used to give larger scale context, and also to compare with in-situ measurements of velocities and divergence. Level 2 data includes estimates of surface winds and currents. The V2 data have been cross-calibrated against ADCPs, surface drifters, and the SIO-DopVis instrument collected during the Pilot and IOP1 campaigns. Additional DopVis data collected during IOP1 and IOP2, in addition to IOP2 ADCP and surface drifter data will lead to a reprocessing of this dataset, and it should be regarded as provisional. Data are available in netCDF format.\n<br>\n\n#### SMODE_L2_DRIFTER_POSITIONS_V1\nThis dataset contains in-situ position data from surface drifters from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Drifting buoys were deployed from the research vessels and configured to nominally report positions every five minutes. Drifters deployed were a mixture of CARTHE and Microstar types. CARTHE drifters are drogued at 40 cm depth and measure the average horizontal velocity of currents in the upper 60 cm of the ocean (Novelli et al., 2017). Microstar drifters are drogued at 1 m depth and measure the average horizontal velocity of ocean currents between 0.4 m and 1.6 m depth (Ohlmann et al., 2005). See the S-MODE Data Submission Report sections 2.3.2.2, 2.4.2.2 and 2.5.2.3 for more information. Tracking and telemetry of the drifters is done by Pacific Gyre, Inc. The data are available in netCDF format with a dimension of time. <br><br> Novelli, G., C. M. Guigand, C. Cousin, E. H. Ryan, N. J. M. Laxague, H. Dai, B. K. Haus, and T. M. Özgökmen, 2017: A Biodegradable Surface Drifter for Ocean Sampling on a Massive Scale. J. Atmos. Oceanic Technol., 34, 2509–2532, https://doi.org/10.1175/JTECH-D-17-0055.1. <br><br> Ohlmann, J. C., P. F. White, A. L. Sybrandy, and P. P. Niiler, 2005: GPS–Cellular Drifter Technology for Coastal Ocean Observing Systems. J. Atmos. Oceanic Technol., 22, 1381–1388, https://doi.org/10.1175/JTECH1786.1. <br><br> Westbrook, E., Bingham, F. M., Brodnitz, S., Farrar, J. T., Rodriguez, E., & Zappa, C., (2024). Submesoscale Ocean Dynamics Experiment (S-MODE) Data Submission Report. Technical Report. Woods Hole Oceanographic Institution, WHOI-2024-03, https://doi.org/10.1575/1912/69362\n<br>\n\n#### SMODE_L2_SHIPBOARD_ADCP_V1\nThis dataset contains shipboard Acoustic Doppler Current Profiler (ADCP) measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign and two intensive operating periods (IOPs) conducted approximately 300 km offshore of San Francisco during Fall 2021, 2022, and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The ADCP was mounted to the bottom of the hulls of the research vessels deployed during each campaign, measuring horizontal and vertical currents, as well as acoustic backscatter from approximately 3 m to 50 m depth along the ship’s track. The data are available in netCDF format with dimensions of time and depth.\n<br>\n\n#### SMODE_L2_SHIPBOARD_BOTTLES_V1\nThis dataset contains in-situ seawater samples taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Water samples collected in Niskin bottles mounted on the ship’s rosette sampler were taken of chlorophyll (µg/L), phaeopigments (µg/L), and nutrient concentrations (µM or µmol/L) of particulate organic carbon, particulate organic nitrogen, silicate, nitrate, nitrite, and phosphate. Samples analyzed with fluorometry contain chlorophyll concentrations in µg/L and phaeopigment concentrations in µg/L. Samples analyzed with elemental analysis contain POC molarity in µM and PON molarity in µM. Samples analyzed via ion analysis contain silicate concentrations in µM, total nitrate+nitrite in µM, phosphate in µM, nitrite in µM, and nitrate in µM. These data are mainly used by S-MODE for validating the PRISM-derived products and calibrating the in-situ sensors on the autonomous platforms. Data are available in netCDF format.\n<br>\n\n#### SMODE_L2_SHIPBOARD_CTD_V1\nThis dataset contains shipboard conductivity, temperature, and depth (CTD) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The shipboard CTD rosette is cast from the R/V Oceanus where it records ocean temperature, conductivity, and pressure as it descends to depth and then returns to the surface. IOP1 and IOP2 measurements also contain biological data. Data are available in netCDF format.\n<br>\n\n#### SMODE_L2_SHIPBOARD_RADIOSONDES_METEOROLOGY_V1\nThis dataset contains atmospheric sounding measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Sounding profiles were collected using shipboard Windsond S1H3-S radiosondes launched from the R/V Oceanus cruise OC2108A, to a maximum elevation of at least 5 km above ground level (ABL). These measurements are used to understand the vertical structure of atmospheric temperature, winds, and moisture. The original 1Hz observations were gridded onto a uniform 20 m vertical grid. The data are available in netCDF format with dimensions of altitude and profile number.\n<br>\n\n#### SMODE_L2_SHIPBOARD_SUNA_V1\nThis dataset contains Submersible Ultraviolet Nitrate Analyzer (SUNA) nitrate measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. SUNA is a standalone optical nitrate sensor that mounts onto the shipboard CTD rosette cast from the R/V Oceanus. The SUNA measurements are calibrated against bottle nutrient samples taken from the underway flow-through system on the ship and later analyzed with a Lachat Nutrient Analyzer. From the Lachat data, the average concentration of nitrate+nitrite are used for each sample. Data are available in netCDF format.\n<br>\n\n#### SMODE_L2_SHIPBOARD_TSG_V1\nThis dataset contains shipboard thermosalinograph (TSG), meteorology, and bio-optics measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The TSG instrument measures the temperature and conductivity of seawater passing through a port in the hull of the ship. TSG data is calibrated using water samples compared to standard seawater and a laboratory salinometer onboard the ship. This dataset also contains chlorophyll and meteorology measurements including air temperature, barometric pressure, wind speed and direction, relative humidity, and radiative fluxes. Data are available in netCDF format, with separate dimensions for time, time of bio-optics measurements, and time of radiometer measurements.\n<br>\n\n#### SMODE_L2_SAILDRONES_V1\nThis dataset contains Saildrone in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Saildrones are wind-and-solar-powered unmanned surface vehicles rigged with atmospheric and oceanic sensors that measure upper ocean horizontal velocities, near-surface temperature and salinity, Chlorophyll-a fluorescence, dissolved oxygen concentration, 5-m winds, air temperature, and surface radiation. Acoustic Doppler Current Profiler (ADCP) data samples originally measured at 1 Hz frequency are averaged into 5 minute bins, along with navigation data. Non-ADCP data from IOP1 contain additional bio-optical measurements. All data are available in netCDF format.\n<br>\n\n#### SMODE_L2_LAGRANGIAN_FLOATS_V1\nThis dataset contains in-situ measurements of temperature, salinity, and velocity from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco, during an intensive observation period in the fall of 2022. The data are available in netCDF format with a dimension of time. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The target in-situ quantities were measured by Lagrangian floats, which were deployed from research vessels and retrieved 3-5 days later. The floats follow the 3D motion of water parcels at depths within or just below the mixed layer and carried a CTD instrument to measure temperature, salinity, and pressure, in addition to an ADCP instrument to measure velocity.\n<br>\n\n#### SMODE_L1_MASS_DOPPVIS_V1\nThis dataset contains airborne DoppVis imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a Nikon D850 camera with a 14mm lens mounted with a 90 degree rotation and a 30 degree positive pitch angle during flight. The camera was synchronized to a coupled GPS/IMU system with images taken at 2hz. Raw images were calibrated for lens distortion and boresight misalignment with the GPS/IMU. Images were georeferenced to the processed aircraft trajectory and exported with reference to WGS84 datum with a UTM zone 10 projection (EPSG 32610) at 50cm resolution. Level 1 DoppVis images are available as GZIP flightlines containing individual TIFF images.\n<br>\n\n#### SMODE_L3_SEAGLIDERS_TEMP_SALINITY_V1\nThis dataset contains profiles of temperature, dissolved oxygen, salinity, and other observations collected by Seagliders during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Seagliders are autonomous underwater vehicles (AUVs) designed to glide from the ocean surface to as deep as 1000 m and back while collecting profiles of oceanic variables. Data are available in netCDF format.\n<br>\n\n#### SMODE_L3_SHIPBOARD_UCTD_ECOCTD_V1\nThis dataset contains shipboard Underway conductivity, temperature, and depth (UCDT) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Underway CTD system contains a standard UCDT probe measuring conductivity, temperature, and pressure, as well as an augmented EcoCDT probe that concurrently measures both hydrographic and bio-optical data including conductivity, temperature, pressure, dissolved oxygen concentration, chlorophyll-fluorescence, and particulate backscatter at two different wavelengths. Level 3 data are available in netCDF format with dimensions of profile number and depth.\n<br>\n\n#### SMODE_L4_WAVEGLIDERS_V1\nThis dataset contains waveglider observations from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Three wave gliders were deployed as part of the S-MODE pilot campaign, equipped with a suite of sensors including sonic anemometers, shortwave and longwave radiometers, CTD profilers, and ADCPs. All wave gliders include an IMU that records platform orientation and motion at 20Hz. Data are available in netCDF format.\n<br>\n\n#### SMODE_L1_MASS_HYPERSPECTRAL_V1\nThis dataset contains airborne hyperspectral imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a hyperspectral camera operating in the visible to near-IR range (400-990 nm). Hyperspectral data are used by S-MODE to provide visible imagery of the kinematics of whitecaps and ocean color measurements. Level 1 data are available as zip files containing data in ENVI format and text files containing location and timing information.\n<br>\n\n#### SMODE_L1_MASS_LIDAR_V1\nThis dataset contains geolocated airborne LiDAR point cloud measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a high resolution LiDAR, used to characterize the properties of ocean surface topography. The sensor has a maximum pulse repetition rate of 400 kHz, with a +/- 30° cross-heading raster scan rate of 200 Hz. Level 1 LiDAR point clouds are available in .laz format.\n<br>\n\n#### SMODE_L1_MASS_LWIR_V1\nNOTICE: This dataset is currently undergoing maintenance to be repackaged as zip files of flight lines. The file count will decrease dramatically when new zip files are available. <br> This dataset contains airborne longwave infrared (LWIR) imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a FLIR SC6700 camera with 13mm lens was mounted nadir in the aircraft in an orientation so that the short edge of the image was parallel with the flight track. The camera was synchronized to a coupled GPS/IMU system with images collected at 50hz. Raw images were calibrated for lens distortion, vignetting, and boresight misalignment with the GPS/IMU. Images were georeferenced to the processed aircraft trajectory and exported with reference to the WGS84 datum with a UTM zone 10 projection (EPSG 32610) at an altitude-dependent resolution. Level 1 images are available in TIFF format.\n<br>\n\n#### SMODE_L1_MASS_VISIBLE_V1\nThis dataset contains airborne visible imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes an IO Industries Flare 12M125-CL camera with 14mm lens mounted nadir in the aircraft in an orientation so that the short edge of the image was parallel with the aircraft heading. The camera was synchronized to a coupled GPS/IMU system with images taken at 5hz. Raw images were calibrated for lens distortion and boresight misalignment with the GPS/IMU. Images were georeferenced to the post-processed aircraft trajectory and exported with reference to WGS84 datum with a UTM zone 10 projection (EPSG 32610) at an altitude-dependent spatial resolution. Level 1 images are available in TIFF format.\n<br>\n\n#### SMODE_L2_MOSES_LWIR_SST_V1\nThis dataset contains airborne sea surface temperature (SST) measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE). Data were collected approximately 300 km offshore of San Fransisco during a pilot campaign in October 2021, and an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Multiscale Observing System of the Ocean Surface (MOSES) is an aerial observing system that primarily uses a longwave infrared (LWIR) camera to record SST at a resolution of several meters. Individual images are mosaiced together to provide a synoptic map of the sample domain covering approximately 200 km. MOSES is mounted on the B200 aircraft which flies daily surveys of the field domain during deployments. Data are available in netCDF format.\n<br>\n\n#### SMODE_L4_NCOM_V1\nThis dataset contains model output from the Navy Coastal Ocean Model (NCOM) run during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. NCOM model output consists of daily files during the deployment dates of the pilot campaign in Fall 2021, IOP1 in Fall 2022, and IOP2 in Spring 2023. Data consists of ocean variables such as salinity, sea water temperature, water depth, and surface wind stress, and are available in netCDF format.\n<br>\n\n#### SMODE_L1_ASIT_KABODS_V1\nThis data set includes tower-based Ka-band ocean surface backscatter measurements (cross section, incidence angle, radial velocity from radar, pulse-pair correlation) located offshore of Martha’s Vineyard (41°19.5′N, 70°34′W), Massachusetts (USA) over a period of three months, from October 2019 to January 2020. Data from the Ka-band radar are collected at multiple distances from the tower (up to ~32 m) at several incidence angles and at sub-second resolution. The measurements are provided as hourly files in netCDF format. <br> <br> Ka-band backscatter data are often utilized to derived ocean surface vector winds. The instrument used for this dataset was a Ka-Band Ocean continuous wave Doppler Scatterometer (KaBODS) built by the University of Massachusetts, Amherst, which was installed on the Woods Hole Oceanographic Institution Air-Sea Interaction Tower (ASIT). The tower is located in 15 m deep water and extends 76 feet into the marine atmosphere. Data were collected as part of a pre-pilot campaign for the S-MODE (Submesoscale Ocean Dynamics Experiment) project. The measurements provided the opportunity to develop Ka-band backscatter models as well as study backscattering mechanisms under different wind, wave, and weather conditions in order to support operation of the airborne Ka-band Doppler scatterometer used during the main S-MODE intensive observation periods.\n<br>\n\n#### SMODE_L1_ASIT_SLOPEFIELDS_V1\nThese wave slope data from polarimetry described below are considered preliminary and should not be used for any purpose without consulting Chris Zappa (zappa@ldeo.columbia.edu). <br> <br> This data set includes tower-based measurements of ocean wave slope fields from visible-band polarimetry using a Polaris Pyxis Mono VIS polarimetric camera. The data here include wave slope fields at 30 frames per second temporal resolution and mm-scale spatial resolution over a ~2 m x 2 m area of ocean surface observed off the Air-Sea Interaction Tower (ASIT; 41°20.1950'N, 70°33.3865'W). Measurements were taken over the period from October 2019 through January 2020. Surface slopes are along two dimensions: along-look and cross-look orientations of the camera. Data was acquired for 10 minutes per hour, 8 hours per day, and each data file (netCDF-4) captures one of the 10-minute segments. Note that data files are large, 142 GB each. <br> <br> Data were collected as part of a pre-pilot campaign for the S-MODE (Submesoscale Ocean Dynamics Experiment) project. The polarimetric slope sensing (PSS) technique of Zappa et al. [2008] allows one to reconstruct the water surface slope field by measuring the polarization state of reflected light at each image pixel, allowing for surface resolutions of order 1 mm with no in-water measurement component. From these data one is able to compute water surface slope variance, wave directional spreading, and the near-surface current profile.\n<br>\n\n#### SMODE_L1_PRISM_V1\nThis dataset contains PRISM data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Portable Remote Imaging Spectrometer (PRISM) is an airborne instrument package that is mounted on the GIII aircraft which flies long duration detailed surveys of the field domain during deployments. PRISM contains a pushbroom imaging spectrometer operating at near-UV to near-IR wavelengths (350-1050 nm), which will produce high temporal resolution and resolve spatial features as small as 30 cm. PRISM also has a two-channel spot radiometer at short-wave infrared (SWIR) band (1240 nm and 1640 nm), that is co-aligned with the spectrometer and will be used to provide accurate atmospheric correction of the ocean color measurements. Level 1 data is available in netCDF format.\n<br>\n\n#### SMODE_L2_PRISM_CHLA_V1\nThis dataset contains estimated chlorophyll-a and particulate organic carbon concentration data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Portable Remote Imaging Spectrometer (PRISM) is an airborne instrument package that is mounted on the GIII aircraft which flies long duration detailed surveys of the field domain during deployments. PRISM contains a pushbroom imaging spectrometer operating at near-UV to near-IR wavelengths (350-1050 nm), which produced high temporal resolution and resolve spatial features as small as 30 cm. PRISM also has a two-channel spot radiometer at short-wave infrared (SWIR) band (1240 nm and 1640 nm), that is co-aligned with the spectrometer and is used to provide accurate atmospheric correction of the ocean color measurements. Level 2 chlorophyll-a data are available in netCDF format.\n<br>\n\n#### SMODE_L2a_PRISM_REFL_V1\nThis dataset contains orthocorrected and atmospherically corrected water-leaving reflectance data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Portable Remote Imaging Spectrometer (PRISM) is an airborne instrument package that is mounted on the GIII aircraft which flies long duration detailed surveys of the field domain during deployments. PRISM contains a pushbroom imaging spectrometer operating at near-UV to near-IR wavelengths (350-1050 nm), which produced high temporal resolution and resolve spatial features as small as 30 cm. PRISM also has a two-channel spot radiometer at short-wave infrared (SWIR) band (1240 nm and 1640 nm), that is co-aligned with the spectrometer and is used to provide accurate atmospheric correction of the ocean color measurements. Level 2a reflectance data is available in ENVI format.\n<br>\n\n#### SMODE_L1_SAILDRONES_V1\nThis dataset contains a suite of Saildrone in-situ measurements (including but not limited to temperature, salinity, currents, biochemistry, and meteorology) taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign spanning two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Saildrones are wind-and-solar-powered unmanned surface vehicles rigged with atmospheric and oceanic sensors that measure upper ocean horizontal velocities, near-surface temperature and salinity, Chlorophyll-a fluorescence, dissolved oxygen concentration, 5-m winds, air temperature, and surface radiation. Acoustic Doppler Current Profiler (ADCP) data samples are available in their raw 1 Hz sampling frequency as well as 5 minute averages, the latter available with navigation data. Other measurements are available as raw files (1Hz or 20 Hz where applicable), as well as 1 minute averages. L1 data are available as a zip file.\n<br>\n\n#### SMODE_L2_SHIPBOARD_BIO_V1\nThis dataset contains shipboard bio-optical measurements collected during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Data are available in netCDF format.\n<br>\n\n#### SMODE_L2_SHIPBOARD_RADIOMETER_METEOROLOGY_V1\nThis dataset contains shipboard radiometer measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Air-Sea Interaction METeorology (ASIMET) sensors mounted onboard the R/V Oceanus record shortwave and longwave radiation fluxes. These are used by S-MODE to compare with DopplerScatt retrievals. Data are available in netCDF format.\n<br>\n\n#### SMODE_L2_SHIPBOARD_UCTD_ECOCTD_V1\nThis dataset contains shipboard Underway conductivity, temperature, and depth (UCDT) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Underway CTD system contains a standard UCDT probe measuring conductivity, temperature, and pressure, as well as an augmented EcoCDT probe that concurrently measures both hydrographic and bio-optical data including conductivity, temperature, pressure, dissolved oxygen concentration, chlorophyll-fluorescence, and particulate backscatter at two different wavelengths. The level 2 data herein combines measurements from both the UCDT and EcoCDT into a single dataset, where for each variable, all profiles are binned onto a 5m vertical grid and merged into a 2-D matrix. Additional computed variables include backscatter baseline signal and backscatter spike signal. Data are available in netCDF format.\n<br>\n\n#### SMODE_L2_APEX_FLOAT_V1\nThis dataset contains APEX float in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. Data was collected approximately 300 km offshore of San Francisco, during Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. US Naval Oceanographic Office (NAVO) APEX floats measure subsurface properties including temperature and salinity. Data are available in netCDF format.\n<br>\n\n#### SMODE_L2_SLOCUM_GLIDERS_V1\nThis dataset contains Slocum glider in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. US Naval Oceanographic Office (NAVOCEANO) Slocum gliders measure subsurface properties including temperature and salinity by profiling to a depth of 1000m at a fixed location every 4 hours. Data are available in netCDF format.\n<br>\n\n#### SMODE_L4_ROMS_ANCILLARY_V1\nThis dataset provides ancillary data for the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) Regional Ocean Modelling System (ROMS) model runs, and is intended for expert users. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The ancillary data include files required to initialize the model, forcing fields, grid files, and configuration files. ROMS ancillary files are provided in NetCDF format.\n<br>\n\n#### SMODE_L4_ROMS_V1\nThis dataset contains output data files for the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) Regional Ocean Modelling System (ROMS) model runs. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Model output is limited to the SMODE field campaign region and uses 1hr ERA5 atmospheric forcings. ROMS model output files are provided in NetCDF format. Users should note that these files are very large and are encouraged to do analysis in the cloud rather than downloading.\n<br>\n\n#### SMODE_L2_SHIPBOARD_WINDCUBE_LIDAR_V1\nThis dataset contains shipboard windcube data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The data are available in netCDF format.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/S-MODE/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","elevation","geospatial","ice","lidar","netcdf","oceans","radar","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SMODE_L1_DOPPLERSCATT_V1 v1 - This dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L1_DOPPLERSCATT_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V2 v2 - This dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE). S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_DRIFTER_POSITIONS_V1 v1 - This dataset contains in-situ position data from surface drifters from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L2_DRIFTER_POSITIONS_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_SHIPBOARD_ADCP_V1 v1 - This dataset contains shipboard Acoustic Doppler Current Profiler (ADCP) measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign and two intensive operating periods (IOPs) conducted approximately 300 km offshore of San Francisco during Fall 2021, 2022, and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_LX_SHIPBOARD_ADCP_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_SHIPBOARD_BOTTLES_V1 v1 - This dataset contains in-situ seawater samples taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_LX_SHIPBOARD_BOTTLES_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_SHIPBOARD_CTD_V1 v1 - This dataset contains shipboard conductivity, temperature, and depth (CTD) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L2_SHIPBOARD_CTD_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_SHIPBOARD_RADIOSONDES_METEOROLOGY_V1 v1 - This dataset contains atmospheric sounding measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_LX_SHIPBOARD_RADIOSONDES_METEOROLOGY_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_SHIPBOARD_SUNA_V1 v1 - This dataset contains Submersible Ultraviolet Nitrate Analyzer (SUNA) nitrate measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_LX_SHIPBOARD_SUNA_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_SHIPBOARD_TSG_V1 v1 - This dataset contains shipboard thermosalinograph (TSG), meteorology, and bio-optics measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_LX_SHIPBOARD_TSG_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_SAILDRONES_V1 v1 - This dataset contains Saildrone in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L2_SAILDRONES_V2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_LAGRANGIAN_FLOATS_V1 v1 - This dataset contains in-situ measurements of temperature, salinity, and velocity from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco, during an intensive observation period in the fall of 2022. The data are available in netCDF format with a dimension of time.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L2_LAGRANGIAN_FLOATS_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L1_MASS_DOPPVIS_V1 v1 - This dataset contains airborne DoppVis imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L1_MASS_DOPPVIS_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L3_SEAGLIDERS_TEMP_SALINITY_V1 v1 - This dataset contains profiles of temperature, dissolved oxygen, salinity, and other observations collected by Seagliders during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during two intensive operating periods in Fall 2022 and Spring 2023.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L3_SEAGLIDER_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L3_SHIPBOARD_UCTD_ECOCTD_V1 v1 - This dataset contains shipboard Underway conductivity, temperature, and depth (UCDT) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L3_SHIPBOARD_UCTD_ECOCTD_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L4_WAVEGLIDERS_V1 v1 - This dataset contains waveglider observations from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L4_WAVEGLIDERS_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L1_MASS_HYPERSPECTRAL_V1 v1 - This dataset contains airborne hyperspectral imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L1_MASS_HYPERSPECTRAL_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L1_MASS_LIDAR_V1 v1 - This dataset contains geolocated airborne LiDAR point cloud measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L1_MASS_LIDAR_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L1_MASS_LWIR_V1 v1 - NOTICE: This dataset is currently undergoing maintenance to be repackaged as zip files of flight lines. The file count will decrease dramatically when new zip files are available.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L1_MASS_LWIR_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L1_MASS_VISIBLE_V1 v1 - This dataset contains airborne visible imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L1_MASS_VISIBLE_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_MOSES_LWIR_SST_V1 v1 - This dataset contains airborne sea surface temperature (SST) measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE). Data were collected approximately 300 km offshore of San Fransisco during a pilot campaign in October 2021, and an intensive operating period (IOP) in Fall 2022.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L2_MOSES_LWIR_SST_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L4_NCOM_V1 v1 - This dataset contains model output from the Navy Coastal Ocean Model (NCOM) run during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L4_NCOM_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L1_ASIT_KABODS_V1 v1 - This data set includes tower-based Ka-band ocean surface backscatter measurements (cross section, incidence angle, radial velocity from radar, pulse-pair correlation) located offshore of Martha’s Vineyard (41°19.5′N, 70°34′W), Massachusetts (USA) over a period of three months, from October 2019 to January 2020. Data from the Ka-band radar are collected at multiple distances from the tower (up to ~32 m) at several incidence angles and at sub-second resolution.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L1_ASIT_KABODS_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L1_ASIT_SLOPEFIELDS_V1 v1 - These wave slope data from polarimetry described below are considered preliminary and should not be used for any purpose without consulting Chris Zappa (zappa@ldeo.columbia.edu). <br> <br> This data set includes tower-based measurements of ocean wave slope fields from visible-band polarimetry using a Polaris Pyxis Mono VIS polarimetric camera.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L1_ASIT_WAVE_SLOPE_POLARIMETRY_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L1_PRISM_V1 v1 - This dataset contains PRISM data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L1_PRISM_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_PRISM_CHLA_V1 v1 - This dataset contains estimated chlorophyll-a and particulate organic carbon concentration data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L2_PRISM_CHLA_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2a_PRISM_REFL_V1 v1 - This dataset contains orthocorrected and atmospherically corrected water-leaving reflectance data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L2a_PRISM_REFL_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L1_SAILDRONES_V1 v1 - This dataset contains a suite of Saildrone in-situ measurements (including but not limited to temperature, salinity, currents, biochemistry, and meteorology) taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign spanning two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_LX_SAILDRONES_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_SHIPBOARD_BIO_V1 v1 - This dataset contains shipboard bio-optical measurements collected during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L2_SHIPBOARD_BIO_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_SHIPBOARD_RADIOMETER_METEOROLOGY_V1 v1 - This dataset contains shipboard radiometer measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_LX_SHIPBOARD_RADIOMETER_METEOROLOGY_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_SHIPBOARD_UCTD_ECOCTD_V1 v1 - This dataset contains shipboard Underway conductivity, temperature, and depth (UCDT) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L2_SHIPBOARD_UCTD_ECOCTD_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_APEX_FLOAT_V1 v1 - This dataset contains APEX float in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. Data was collected approximately 300 km offshore of San Francisco, during Spring 2023.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L2_APEX_FLOATS_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_SLOCUM_GLIDERS_V1 v1 - This dataset contains Slocum glider in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L2_SLOCUM_GLIDERS_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L4_ROMS_ANCILLARY_V1 v1 - This dataset provides ancillary data for the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) Regional Ocean Modelling System (ROMS) model runs, and is intended for expert users. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L4_ROMS_ANCILLARY_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L4_ROMS_V1 v1 - This dataset contains output data files for the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) Regional Ocean Modelling System (ROMS) model runs. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L4_ROMS_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SMODE_L2_SHIPBOARD_WINDCUBE_LIDAR_V1 v1 - This dataset contains shipboard windcube data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SMODE_L2_SHIPBOARD_WINDCUBE_LIDAR_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-s-mode","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SAFARI 2000 Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### Historic_S2K_Website_1765\nThis dataset contains an archived copy of the Safari 2000 Project website as of October 2008. This archived website is provided for informational purposes only. No updates to the website and associated content have been made since January of 2008. The database that once provided content for this website was transitioned to text and is included herein. SAFARI 2000 was an international regional science initiative developed for southern Africa to explore, study and address linkages between land-atmosphere processes and the relationship of biogenic, pyrogenic or anthropogenic emissions and the consequences of their deposition to the functioning of the biogeophysical and biogeochemical systems of southern Africa. This initiative was built around a number of on-going, already funded activities by NASA, the international community and African nations in the southern African region.\n<br>\n\n#### one_deg_biomass_754\nA new method is used to generate spatial estimates of monthly averaged biomass burned area and spatial and temporal estimates of trace gas and aerosol emissions from open fires in southern Africa.Global burned area data for the year 2000 (GBA2000) supplemented with the Along Track Scanning Radiometer (ATSR) fire count data are employed to quantify the area burned at 1-km resolution by using a fractional vegetation cover map derived from satellite observations. To account for the spatial heterogeneity in main types of vegetation within each 1-km grid cell, fuel load maps were developed from biomass density data sets for herbaceous and tree covered land together with fractional tree and vegetation cover maps. To represent the fire characteristics, land cover is classified into forests, woodlands, and grasslands, based on satellite-derived data sets for fractional tree cover. Combustion factors for each land cover type takes into account fuel composition, tree cover density, and the dryness of herbaceous vegetation. In addition, the emission factors (EF) are calculated; EF for grassland fires represent spatial and seasonal variations in fuel conditions using a modified combustion efficiency calculation. This data set contains spatial and temporal estimates of trace gas and aerosol emissions from open fires in southern Africa for the following variables:acetic acid (CH3COOH); methanol (CH3OH); methane (CH4); carbon dioxide (CO2); carbon monoxide (CO); formaldehyde (HCHO); non-methane hydrocarbons (NMHC); and particulate matter less than 2.5 um in diameter (PM2.5). Annual amounts of total area burned and CO2 emissions for southern Africa are compared to those from other authors.\n<br>\n\n#### AERONET_aerosol_706\nAERONET (AErosol RObotic NETwork) is an optical ground-based aerosol monitoring network and data archive system. AERONET measurements of the column-integrated aerosol optical properties in the southern Africa region were made by sun-sky radiometers at several sites in August-September 2000 as a part of the SAFARI 2000 dry season aircraft campaign.AERONET is supported by NASA's Earth Observing System and expanded by federation with many non-NASA institutions. The network hardware consists of identical automatic sun-sky scanning spectral radiometers owned by national agencies and universities. Data from this collaboration provides globally-distributed near-real-time observations of aerosol spectral optical depths, aerosol size distributions, and precipitable water in diverse aerosol regimes.The AERONET (AErosol RObotic NETwork) program is an inclusive federation of ground-based remote sensing aerosol networks established by AERONET and PHOTON and greatly expanded by AEROCAN (the Canadian sun-photometer network) and other agency, institute and university partners. The goal is to assess aerosol optical properties and validate satellite retrievals of aerosol optical properties. The network imposes standardization of instruments, calibration, and processing. Data from this collaboration provides globally distributed observations of spectral aerosol optical depths, inversion products, and precipitable water in geographically diverse aerosol regimes. Three levels of data are available from the AERONET website: Level 1.0 (unscreened), Level 1.5 (cloud-screened), and Level 2.0 (Cloud-screened and quality-assured). (CAUTION: Data presented in the real time data version is unscreened and may not have final calibration reprocessing.) For each site there is a Principal Investigator (PI), the person responsible for deployment, maintenance and data collection. The PI is entitled to be informed of any use of that site data.NOTICE TO NON-AERONET INVESTIGATORS: To maintain the integrity of the data base and fairness to the individuals who have contributed, use of these data for publication requires an offer of authorship to the AERONET PI(s).\n<br>\n\n#### diffuse_irradiance_791\nThis data set contains monthly mean values of diffuse irradiance fraction [f(Ediff), or ratio of diffuse-to-total irradiance] at ground level for a 30-degree solar zenith angle and in seven spectral bands (MODIS bands 1-7) as well broadband visible (400-700 nm), near-infrared (700-3000 nm) and shortwave (400-3000 nm). Values are provided for eight SAFARI 2000 core sites, including Ghanzi/Okwa River Crossing, Maun (Main and Floodplain Towers), Pandamatenga, and Tshane, Botswana; Skukuza, South Africa; Etosha National Park, Namibia; and Mongu, Zambia. The fractions were estimated with the 6S radiative transfer model, given the mean aerosol optical depth (AOT) values from AERONET sunphotometer measurements. Where sunphotometers were not deployed at a SAFARI 2000 core site, the nearest neighbor sunphotometer data were used. A rough estimate of the likely spatial extrapolation error is provided. These data can be used to estimate typical surface albedo (blue sky conditions) from the theoretical black-sky and white-sky albedo values provided in the MODIS albedo product (MOD43), as well as in other applications.Data for all eight sites are contained in one ASCII file, in csv format. The data file provides the ratio of diffuse (atmospherically-scattered) irradiance to total irradiance, both at ground level, for the eight sites in southern Africa. Mean values are provided for each of 12 months in 10 spectral bands between 0.4 and 4.0 microns, computed for a 30-degree solar zenith angle. The native resolution of the AERONET sunphotometer data varies, but is typically less than 1 hour. Information about the site location, IGBP classification, and nearest AERONET sunphotometer site is also provided.\n<br>\n\n#### billmark_828\nThe Southern African Regional Science Initiative (SAFARI 2000) was conducted in part to investigate the impacts of the large-scale transport and deposition of increasingly anthropogenic emissions on southern African biogeochemical cycling. Aerosol samples from the Mongu site in eastern Zambia were collected and analyzed to identify chemical biomarkers during the SAFARI 2000 dry season field campaign. Total suspended particulate aerosol samples were collected diurnally for a period of two weeks during August and September of 2000.These data include bulk organic carbon, nitrogen and sulfur stable isotopic measurements of total suspended particulate aerosols and gas chromatography/mass spectrometry (GC/MS) analysis of fatty acids extracted from collected aerosols. These data were used to chemically describe temporal variability in aerosol compositions.\n<br>\n\n#### ames_sunphotometer_643\nThe NASA Ames Airborne Tracking 14-channel Sunphotometer (AATS-14) was operated successfully aboard the University of Washington CV-580 for 24 data flights during the dry-season airborne campaign from August 13 to September 25, 2000. Flights originated from Pietersburg, South Africa; Kasane, Botswana; and Walvis Bay, Namibia. The AATS-14 instrument measures the transmission of the direct solar beam at 14 discrete wavelengths (350-1558 nm) from which we derived spectral aerosol optical depths (AOD) and columnar water vapor (CWV). Flying at different altitudes over a fixed location allows derivation of layer AOD and CWV. Data taken during vertical profiles allows derivation of aerosol extinction and water vapor density.\n<br>\n\n#### ali_etm_tandem_821\nA tandem pair of Advanced Land Imager (ALI) and Landsat Enhanced Thematic Mapper Plus (ETM+) scenes covering the same part of Kruger National Park (KNP), South Africa (including the Skukuza tower site and rest camp), were acquired about a minute apart on May 30, 2001. The ALI is one of three instruments aboard NASA's first New Millennium Program Earth Observing 1 (EO-1) satellite. ALI is a technology validation testbed that employs novel wide-angle optics and a highly integrated multispectral and panchromatic spectroradiometer.The tandem pair was produced to evaluate the differences between ALI and ETM+ and determine if technology similar to that of the ALI is suitable for future land imaging that will continue the observations begun by the Landsat satellites in 1972.The ALI and ETM+ images are false color composites combining shortwave infrared, near infrared, and visible wavelengths, displayed as red, green, and blue, respectively. Dense vegetation appears green. The similarity of the images demonstrates the ability of the ALI to produce data comparable to ETM+. Several SAFARI 2000 field campaigns conducted in KNP provided ground-based data needed to evaluate measurements from the satellite sensors.Each band is stored as an individual binary file. A metadata file accompanies each set of ALI and ETM+ band files to document the path and row number, sample and line counts, band file names, and sun azimuth and elevation angles. There is also a calibration parameter file that was used for 1R processing.\n<br>\n\n#### soil_respiration_point_645\nThe data set consists of a southern African subset of the \"Global Annual Soil Respiration Data (Raich and Schlesinger 1992)\" data set. The data file is in ASCII text format and contains four observations. This data set is a compilation of soil respiration rates (g C m -2 yr -1) from terrestrial and wetland ecosystems reported in the literature prior to 1992. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes. In this summary, only those data based on most or all of one full year of measurements were used so that annual rates of soil respiration could be estimated. Data from soil cores were excluded because the sample coring modifies root respiration. Also included in the data set are biome type, vegetation type, locality, and geographic coordinates, based on information from the original paper. Mean annual temperature and precipitation were based on the original paper; where those data were not included, they were estimated from a gridded global climate database (0.5-degree resolution; Legates, D. R., and C. J. Willmott. 1988. Global Air Temperature and Precipitation Data Archive. Department of Geography, University of Delaware, Newark, Delaware, U.S.A.).\n<br>\n\n#### kalahari_aot_h2o_vapor_719\nThe data presented here include the aerosol optical thickness (AOT) and column water vapor measurements taken at sites along the Kalahari Transect using a Microtops sunphotometer. Data were collected every 30 minutes at 4 sites that were visited during the SAFARI 2000 Kalahari Wet Season Campaign between March 3, 2000 and March 18, 2000. AOT values are provided at 340, 440, 675, 870, and 936 nm wavelengths. An estimate of the Angstrom Coefficient is also provided to allow the estimation of AOT at other wavelengths.The purpose of this data collection was primarily for documentation of the conditions at each site and to aid in the correction of remote sensing data, for validation of Earth Observation System (EOS) products such as MODIS and MISR aerosol products, and for modeling of canopy productivity.\n<br>\n\n#### modis_aster_fire_707\nThese data relate to a paper (Morisette et al., 2005) that describes the use of high spatial resolution ASTER data to determine the accuracy of the moderate resolution MODIS active fire product. Our main objective was to develop a methodology to use ASTER data for quantitative evaluation of the MODIS active fire product and to apply it to fires in Southern Africa during the 2001 burning season. We utilize 18 ASTER scenes distributed throughout Southern Africa covering the time period 5 August 2001 to 6 October 2001. The MODIS fire product is characterized through the use of logistic regression models to establish a relationship between the binary MODIS fire/no fire product and summary statistics derived from ASTER data over the coincident MODIS pixel. Probabilities of detection are determined as a function of the total number of ASTER fires and Moran's I, a measure of the spatial heterogeneity of fires within the MODIS pixel. The statistical analysis is done for versions 3 and 4 of the MODIS fire detection algorithm. It is shown that the algorithm changes have a positive effect on the fire product accuracy. References:Morisette, J. T., L. Giglio, I. Csiszar, C. O. Justice. 2005. Validation of the MODIS active fire product over Southern Africa with ASTER data. International Journal of Remote Sensing 26: 4239-4264.\n<br>\n\n#### handheld_haze_708\nData presented in this database are the result of Hand Held 4-band Hazemeters and do not always satisfy to the high standards of quality associated with Automatic Sunphotometers.SAFARI 2000 data are available for the following local measurement sites: Alene_High_School; Chililabombwe; Forestry_Kaoma; Itezhi_Tezhi_Basic; Kafue_Hydrologic; Kalongola; Kananja_Chilanda; Kangaya_Basic; Kapiri_Basic; Kasalu_Basic_School; Kasapa_Basic_School; Kashinakaji_School;Kisasa_Basic; Kitima_Basic_School; Litoya; Livingstone_Met_Dpt; Lubu_Basic_Middle; Lukulu_basic; Lusaka_Met_HQ; Makotolo_School; Met_Kaoma; Miombe; Misamfu_Research; Mongu; Mwayasunka; Mwinilunga; Nalusanga_School; Ndola_Meteorology; Saluzhinka_Basic; Senanga; Shamputa_School; Shikoswe_Basic; Sichili_Primary; Sikufele_School; Sioma_Basic_School; Sitaka; St_Marys; St_Patrics; and Zambezi_Met_Office.Example of data file format:Site: Alene High School, Lat=-11.173000, Long=24.190000PI: Brent HolbenE-mail: brent@aeronet.gsfc.nasa.govt0: 09:08:2000, 00:00:00 Date, Time, Time_offset (days), AOT_1020, AOT_880, AOT_765, AOT_680, AOT_530, AOT_440, AOT_380, Water(cm), Air_Mass09:08:2000, 15:15:12, 0.635556, -100.000000, 1.621565, -100.000000, 1.606205, 1.528689, -100.000000, 0.234460, 0.000000, 3.99342109:08:2000, 15:17:04, 0.636863, -100.000000, 1.589651, -100.000000, 1.565860, 1.501361, -100.000000, 0.369358, 0.000000, 4.110902\n<br>\n\n#### avhrr_822\nThe Global Inventory Mapping and Modeling (GIMMS) group at NASA/GSFC provided SAFARI 2000 with remotely sensed satellite data products at the site and regional level. These AVHRR data contain two main sets of data: site extracts of SAFARI core sites (Mongu, Etosha, Kasungu, Maun, Skukuza, and Tshane), and regional 15-day composites from sets of single-day images. These AVHRR data contain four main sets of data:1.5 km daily site extracts of SAFARI core sites (2000)1.5 km 15-day composites of SAFARI core sites (1998-2000)1.5 km 15-day composites of the southern African region (Mar, Sept 2000)6 km 15-day composites of the southern African region (1998-2000)The primary data layers for site extracts and regional composites are fire pixel counts and maximum NDVI. The fire product is different for the daily and for the composited products (see readme file) and a fire product is not included in the 1.5 km regional data set. NDVI composite-associated data layers for the regional data sets include land surface temperature, reflectance, solar zenith angle, view zenith angle, and relative azimuth angle. NDVI composite-associated data layers for the site extracts include these same variables as well as brightness temperature, fire mask composite, latitude, and longitude. The data are stored in binary image format files. There is a metadata file for each site and date/compositing period, in ASCII format.\n<br>\n\n#### AVHRR_LST_826\nLand Surface Temperature (LST) is a key indicator of land surface states, and can provide information on surface-atmosphere heat and mass fluxes, vegetation water stress, and soil moisture. A daily, day and night, LST data set for continental Africa, including Madagascar, was derived from Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km resolution) data for the 6-year lifetime of the NOAA-14 satellite (from 1995 to 2000) using a modified version of the Global Inventory Mapping and Monitoring System (GIMMS) (Tucker et al., 1994). The data were projected into Albers Equal Area and aggregated to 8 km spatial resolution. The data were cloud-filtered with CLAVR-1 algorithm (Stowe et al., 1999). The LST values were estimated with a split-window technique (Ulivieri et al., 1994) that takes advantage of differential absorption of the thermal infrared signal in bands 4 and 5. The emissivity of the surface was generated using a land cover classification map (Hansen et al., 2000) combined with the FAO soil map of Africa (FAO-UNESCO, 1977) and additional maps of tree, herbaceous, and bare soil percent cover (DeFries et al., 2000). Collateral products include cloud mask, time-of-scan, latitude and longitude, and land/water mask files.The data are in flat binary files. Each data file contains 1152 columns and 1152 rows, in signed integer format (2 bytes), with 8 km by 8 km spatial resolution. A unique map exists for each day and each night of the 6-year NOAA-14 lifetime. The data are best used to infer broad temporal and spatial trends rather than pixel-by-pixel values.\n<br>\n\n#### burning_emissions_752\nBiomass burning is a major source for gaseous and particulate atmospheric pollution over southern Africa and globally. The purpose of this study was to quantify biomass burning emissions in an attempt to better understand and predict associated environmental impacts. Sixty biomass burning experiments were carried out November 2000-January 2001 in three regions of southern Africa that are representative of major regional ecosystem types: Etosha National Park (Namibia), Kruger National Park (South Africa), and woodland sites in Zambia and Malawi. Organic halogen-containing gases were measured in the exhaust of these fires as well as the smoke mixing ratios of CO, CO2, NOx, CH4 and N2O. Fuel and ash samples were analyzed for elemental composition. These data allow investigators to assess the elemental mass balance for each experimental burn as well as the corresponding emission factors for individual compounds as functions of the biofuel composition.This data set contains four types of data:Baseline data for individual fires (time-series engineering data and mixing ratios and mass fluxes of CO, CO2 and NOx);Elemental analyses of fuel and ash (C, N, S, P, Cl, Br, I, K, Ca, Na, and Mg content of biofuel samples and ash);Flux data (integrated mass fluxes of CO, CO2, NOx and total mass of burned elements relative to the fuel element and the above fuel analyses);Emissions data (emissions of NH3, SO2, CH3COOH, HCOOH, HCl, HONO, and HNO3 and inorganic Cl and Br; ionic and elemental compositions of particles; and emissions of CO2, CO and NOx for different types of biofuel).\n<br>\n\n#### sua_pan_skukuza_brdf_779\nThe Jet Propulsion Laboratory's (JPL) Portable Apparatus for Rapid Acquisition of Bidirectional Observation of the Land and Atmosphere (PARABOLA), version III, instrument collected radiance data covering both the upwelling and downwelling hemispheres at the Sua Pan salt playa in the Magkadigkadi region of Botswana and at the Skukuza tower site in the Kruger National Park, South Africa between August 25 and October 2, 2000 during in the SAFARI 2000 Dry Season Aircraft Campaign. PARABOLA III is a sphere-scanning radiometer that provides multi-angle measurements of sky and ground radiances on a spherical grid of 5 degrees in the zenith-to-nadir and azimuthal planes in eight spectral channels (444, 551, 581, 650, 860, 944, 1028, and 1650 nm). The experiment was designed to collect data necessary for multi-angle top-of-atmosphere radiance predictions for a vicarious calibration of the Multi-angle Imaging SpectroRadiometer (MISR) instrument aboard the Terra satellite. Four of the PARABOLA channels (444, 551, 650, and 860 nm) are similar to those of the MISR sensor. Measurements were made on cloud-free days of Terra satellite overpasses.Each data file contains radiance counts (ASCII integer values) for 8 bands for each 3-minute data collection. Zenith and azimuth angles are implied by radiance count positions in file. Additional files contain average dark current readings, empirically determined by covering the detectors. The data can be processed to radiance using a series of second order polynomial fit coefficients, provided in the documentation file, and dark current offset. Site-specific auxiliary information is also provided, for each date of PARABOLA data collection.\n<br>\n\n#### bvoc_flux_759\nBiogenic volatile organic compound (BVOC) emissions were measured in a Colophospermum mopane woodland near Maun, Botswana, and in a Combretum-Acacia savanna in Kruger National Park, 13 km from Skukuza, Republic of South Africa (RSA) during the 2001 wet season campaign of SAFARI 2000. In addition, relaxed eddy accumulation (REA) measurements of BVOC fluxes were made on flux towers at these sites, where net CO2 emissions were also measured simultaneously. The investigators also took advantage of a wide variety of easily accessible plant specimens growing in a nursery in the Kruger National Park to screen an additional 95 species of African plants for their ability to emit isoprene, providing species level isoprene emission information which can be used to further initial estimates of isoprene emissions from additional ecosystems of southern Africa. A leaf cuvette technique was used to determine the emission capacities of the nursery plants and the temperature and light dependence of the emissions. This research on BVOC emissions contributes to the development of a regional scale BVOC emissions model for southern Africa.Data files contain records of BVOC measurements from the Maun and Skukuza flux tower sites; species level isoprene emissions and physiological data; and site meteorological data. The data files are stored as ASCII text files, in comma-delimited format, with column headers.More information can be found in the companion file.\n<br>\n\n#### uk_met_c-130_720\nThe Met Office C-130 research aircraft was based at Windhoek, Namibia between September 5-16, 2000, where it conducted a series of flights over Namibia as part of the SAFARI 2000 Dry Season Aircraft Campaign.The effects of atmospheric aerosols on the Earth's radiation balance are known to be very important, but for many types of aerosols these effects are still poorly understood. In particular, aerosol particles produced from biomass burning (associated with Savanna burning, domestic fuel wood consumption, and certain agricultural practices) are thought to be particularly important because they can both absorb and scatter solar radiation. Understanding the correct balance between absorption and scattering is fundamental to the accurate calculation of aerosol effects on local and global climate.The atmosphere of southern Africa is experiencing significant changes at present, due to the emission of trace gases and aerosols from three distinct sources: the burning of fossil fuels and other industrial activities; biomass burning; and natural processes in the terrestrial and aquatic ecosystems of the regions. Specifically, the burning of fossil fuels in mining, industrial and domestic activities are in part responsible for rising levels of atmospheric aerosols and trace gases (Held et al., 1996), and these emissions, augmented by those from extensive biomass burning areas (Crutzen and Andreae, 1990) and from biogenic sources (Harris et al., 1996), may be altering the biogeochemical cycling of essential nutrients in the region (Garstang et al., 1998).For these reasons, it is important to quantify the relative contributions from the different types of emissions, understand their transport and transformations in the atmosphere, and to determine their influence on regional climate and weather, together with knowledge of the deposition processes and the effects of this deposition on the local ecosystems. SAFARI 2000 was developed as part of an international programme aimed at developing a better understanding of the southern African earth-atmosphere-human system. The aims of the Met Office's research were:1. In-situ measurements of the physical, chemical and optical properties of the aerosol. Size distributions to be measured using PCASP, FSSP and CVI instrumentation. Chemical composition inferred from filter measurements. Optical properties from PSAP and nephelometer data. The data set includes aerosol samples ranging from near source regions to aged plumes several hundreds of kilometres from source, some of which have been cloud processed.2. Investigate the direct radiative impact of aerosol over sea, land and low-level cloud. High-altitude radiation measurements taken from SAFIRE, ARIES, SWS and the broad-band radiometers will be used to infer the direct radiative impact of the aerosol. Low-level orbits and into- and down-sun runs will be used in an attempt to obtain radiative closure. Over land, the ground-based sun photometers may also prove useful for constraining the data.3. In-situ measurements of aerosol properties in conjunction with ground-based sites, in order to validate the ground-based retrievals of, for example, aerosol size distributions.4. In-situ measurements of aerosol properties in conjunction with TERRA overpasses, in order to validate the satellite-based retrievals of aerosol properties.5. In-situ measurements of stratus/stratocumulus cloud of Namibia/Angola in conjunction with TERRA overpasses, in order to validate satellite-based retrievals of cloud properties.\n<br>\n\n#### kt_canopy_structure_768\nThis data set contains leaf area index (LAI), leaf inclination angle, and canopy dimension data from study sites along the Kalahari Transect in southwest Botswana. The data were collected during the 2001 wet season field campaign of the SAFARI 2000 at a total of seven plots of 200 x 150 meter dimensions; two plots each at Tshane and Mabuasehube, and three plots at Tsabong. The data set consists of measurements of leaf angle for plot dominant woody species, LAI calculated from overstory and understory photosynthetically active radiation (PAR) measurements, and canopy dimension data (i. e., crown height, crown width, and height to crown) for grass and woody vegetation, for use in the parameterization of plant canopy reflectance models.Measurements of LAI were made with a plant canopy analyzer which records direct and diffuse PAR at the top of the canopy using a beam fraction sensor. PAR beneath the canopy was measured simultaneously with a one-meter long probe held horizontally beneath the canopy. Leaf inclination angle measurements were made using a clinometer. Measurements of tree height, crown height, height-to-crown, and crown width were made for 10 to 20 individual trees of the dominant species at each field plot using a tape measure or simple trigonometry, and calculations based on these data.The data files are stored as ASCII table files, in comma-separated-value (.csv) format, with column headers. Photographs (.jpg) are provided of each plot to provide an idea of site conditions. The photographs can be viewed on the S2K Photo Gallery pages.\n<br>\n\n#### s2k_olson_633\nThis data set is a subset of Olson's Major World Ecosystem Complexes for southern Africa in ASCII GRID and binary image files formats. Olson's Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation is a computerized database, used to generate a global vegetation map of 44 different land ecosystem complexes (mosaics of vegetation or landscapes) comprising seven broad groups. The map is derived from patterns of preagricultural vegetation, modern areal surveys, and intensive biomass data from research sites. Work on the database was begun in 1960 and completed in 1980. Ecosystem complexes are defined for each 0.5-degree grid cell, reflecting the major climatic, topographic, and land-use patterns. Numeric codes are assigned to each vegetation type. Classifications include natural as well as human managed/modified complexes such as mainly cropped, residential, commercial, and park. The complexes are ranked by estimated organic carbon in the mass of live plants given in units of kilograms of carbon per square meter. Counting the cells of each type and adding their areas give total area estimates for the ecosystem complexes. Multiplying by carbon estimates gives corresponding estimates of global carbon by ecosystem complex. The results help define the role of the terrestrial biosphere in the global carbon cycle. Information about the ecosystem classifications, as well as the procedure used to create the southern Africa subset can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/olson/comp/olson_README.pdf.\n<br>\n\n#### CAR_BRDF_709\nThis record links to the web page for the Cloud Absorption Radiometer (CAR) data for the SAFARI 2000 project. Twenty-one flights were undertaken over Southern Africa during the study. Flight track maps, browse images, and Convair CV-580 flight logs are available on this web site.The Cloud Absorption Radiometer (CAR) is an airborne multi-wavelength scanning radiometer that can perform several functions including: determining the single scattering albedo of clouds at selected wavelengths in the visible and near-infrared; measuring the angular distribution of scattered radiation; measuring bidirectional reflectance of various surface types; and acquiring imagery of cloud and Earth surface features. The CAR instrument was developed at the NASA Goddard Space Flight Center by Dr. Michael King. The CAR now operates from a position mounted in the improved nose cone on a Convair CV-580. In addition to its traditional starboard viewing mode, the CAR instrument can be operated in zenith viewing, nadir viewing, and bidirectional reflectance distribution function (BRDF) mode; and can be switched between each of these four modes during flight.The CAR has been deployed on a regular basis in field campaigns around the world including deployments to Portugal (Azores), Brazil, Kuwait, the conterminous United States, Alaska, and various countries in southern Africa. During typical research field campaigns, the CAR is flown in concert with an array of cloud microphysics, aerosol, atmospheric chemistry, and general meteorological instruments under the direction of the Department of Atmospheric Sciences at the University of Washington.\n<br>\n\n#### cp_lidar_images_721\nThe effect of clouds and aerosols on regional and global climate is of great importance. Two longstanding elements of the NASA climate and radiation science program are field studies incorporating airborne remote sensing and in-situ measurements of clouds and aerosols. These projects involve coordination of ground based and satellite measurements with the airborne observations. The goals of the experiments include testing satellite remote sensing retrievals, development of advanced remote sensing techniques and fundamental advances in knowledge of cloud radiation and microphysical properties. Active lidar profiling is especially valuable because the cloud height structure is measured unambiguously, up to the limit of signal attenuation.The Cloud Physics Lidar (successor to the Cloud Lidar System) is an airborne lidar system designed specifically for studying clouds and aerosols using the NASA ER-2 High Altitude Aircraft. Because the ER-2 typically flies at an altitude of 65,000 feet (20 km), its instruments are above 94% of the Earth's atmosphere, thereby allowing ER-2 instruments to function as spaceborne instrument simulators. The Cloud Physics Lidar provides a unique tool for atmospheric profiling and is sufficiently small and low cost to include in multiple instrument missions.The Cloud Physics Lidar provides a complete battery of cloud physics information. Data products include: (1) Cloud profiling with 30 m vertical and 200 m horizontal resolution at 1064 nm, 532 nm, and 355 nm;(2) Aerosol, boundary layer, and smoke plume profiling;(3) Optical depth estimates (column and by layer); and(4) Extinction profiles. The CPL provides information to permit a comprehensive analysis of radiative and optical properties of optically thin clouds. To determine the effects of particulate layers on the radiative budget of the earth-atmosphere system, certain information about the details of the layer and its constituents is required. The effect of clouds is often referred to as cloud radiative forcing. Cloud radiative forcing, in general, is the alteration that the presence of clouds has on the energy budget. The information required to compute the radiative forcing includes the vertical distribution of short wave cross section, a parameter that the CPL provides up to the limits of optical signal attenuation.Using optical depth measurements determined from attenuation of Rayleigh and aerosol scattering, and using the integrated backscatter, the extinction-to-backscatter parameter can be derived. This permits rapid analysis of cloud optical depth since only lidar data is required; there is no need to use other instrumentation. Using the derived extinction-to-backscatter ratio, the internal cloud extinction profile can then be obtained.The CPL uses photon-counting detectors with a high repetition rate laser to maintain a large signal dynamic range. This dramatically reduces the time required to produce reliable and complete data sets.ORNL DAAC has archived CPL quicklook data samples from the SAFARI 2000 Field Campaign. The samples were provided by the CPL group at the NASA Goddard Space Flight Center (GSFC). The actual Cloud Physics Lidar data are stored at the CPL Web Site at NASA GSFC and can be accessed at [http://virl.gsfc.nasa.gov/cpl/safari2000_pass.htm]. Data users are asked to read and abide by the CPL data usage policy found at [http://virl.gsfc.nasa.gov/cpl/cpl_register.htm]. For systems specifications and other information regarding Cloud Physics Lidar, please visit the Cloud Physics Lidar Home Page at [http://cpl.gsfc.nasa.gov/].\n<br>\n\n#### CV-580_710\nThe Cloud and Aerosol Research Group (CARG) of the University of Washington participated in the SAFARI-2000 Dry Season Aircraft campaign with their Convair-580 research aircraft. This campaign covered five countries in southern Africa from 10 August through 18 September, 2000. Various types of measurements were obtained on the thirty-one research flights of the Convair-580 in SAFARI-2000, to study their relationships to simultaneous measurements from satellites (particularly Terra), other research aircraft, and SAFARI-2000 ground-based measurements and activities. The main goals of the University of Washington's Convair-580 research aircraft were to: * Measure the physical and chemical properties of aerosols and trace gases in ambient air, and from various sources, in southern Africa. * Obtain measurements on aerosols, trace gases, clouds, and surface properties for comparisons with simultaneous remote sensing measurements from the NASA ER-2 aircraft and Terra satellite and from SAFARI-2000 ground stations. * Carry out closure studies using in situ and remote sensing measurements made aboard the Convair-580. * Compare aerosol and trace gas measurements aloft at various locations in Southern Africa. * Measure the nature and concentrations of aerosols and trace gases, and their emission factors, in smoke from prescribed fires and non-prescribed fires of biomass in southern Africa. * Measure the spectral albedo and bidirectional reflection distribution function (BRDF) of various surfaces and clouds in southern Africa. * Measure the microstructures of clouds off the Atlantic Coast of southern Africa. * Investigate aerosol-cloud interactions. For a complete detailed guide to the extensive measurements obtained aboard the UW Convair-580 aircraft in support of SAFARI 2000, see the UW Technical Report for the SAFARI 2000 Project [PDF format]. The latest version of this document can be found at the UW SAFARI 2000 Web site [Internet Link], listed in the CARG Publications on SAFARI 2000 section.\n<br>\n\n#### daily_precip_est_793\nThe Microwave InfraRed Algorithm (MIRA) is used to produce an imagery data set of daily mean rain rates at 0.1 degree spatial resolution over southern Africa for the period 1993-2001. MIRA combines passive microwave (PMW) from the Special Sensor Microwave/Imager (SSM/I) on board the DMSP F10 and F14 satellites at a resolution of 0.5 degrees and infrared (IR) data from the Meteosat 4, 5, 6, and 7 satellites in 2-hour slots at a resolution of 5 km. This approach accounts for the limitations of both data types in estimating precipitation. Rainfall estimates are produced at the high spatial and temporal frequency of the IR data using rainfall information from the PMW data. An IR/rain rate relationship, variable in space and time, is derived from coincident observations of IR and PMW rain rate (accumulated over a calibration domain) using the probability matching method. The IR/rain rate relationship is then applied to IR imagery at full temporal resolution. The results presented here are the daily means of those derived rain rates at 0.1 degree spatial resolution.The rainfall data sets are flat binary images with no headers. They are compressed band sequential (bsq) files that contain all of the daily images for the given year. Each image is an array of 341 lines, each with 401 binary floating-point numbers, containing rainfall at 0.1 degree resolution for the area 10 to 50 degrees longitude and 0 to -34 degrees latitude. The number of band sequential images in each annual file and the associated dates can be found in the file MIRA_data_dates.csv.\n<br>\n\n#### mongu_daily_rainfall_785\nThis data set contains daily rainfall totals (mm) from Mongu, in the Western Province of Zambia. The data were collected with a British standard 5 inch diameter rain gauge in a yard 30 m away from the Meteorological Department building near the Mongu Airport (north of downtown and approximately 20 km from the Kataba Local Forest where the permanent 30 m Mongu tower site is located). Rainfall readings were taken by ZMD staff each morning at 06:00 GMT. These data form the official government rainfall record for Mongu.The data files consist of 3 files, one for each year (July to Jun). Each files contains monthly columns with totals for each day of the month as well as a monthly total. The data files are stored as ASCII text files in comma-separated-value (csv) format.\n<br>\n\n#### s2k_ISRIC-WISE_soil_properties_634\nThe data set consists of a southern Africa subset of the ISRIC-WISE global data set of derived soil properties. The World Inventory of Soil Emission Potentials (WISE) database currently contains data for over 4300 soil profiles collected mostly between 1950 and 1995. This database has been used to generate a series of uniform data sets of derived soil properties for each of the 106 soil units considered in the Soil Map of the World (FAO-UNESCO, 1974). These data sets were then linked to a 1/2 degree longitude by 1/2 degree latitude version of the edited and digital Soil Map of the World (FAO, 1995) to generate GIS raster image files for the following variables: total available water capacity (mm water per 1 m soil depth); soil organic carbon density (kg C/m**2 for 0-30 cm depth range); soil organic carbon density (kg C/m**2 for 0-100 cm depth range); soil carbonate carbon density (kg C/m**2 for 0-100 cm depth range); soil pH (0-30 cm depth range); and soil pH (30-100 cm depth range). More information can be found at: ftp://daac.ornl.gov/data/safari2k/soils/ISRIC-WISE_soil_properties/comp/isric_readme.pdf.\n<br>\n\n#### globe_dem_630\nThis data set consists of a southern African subset of the Global Land One-Kilometer Base Elevation (GLOBE) digital elevation model (DEM) data in both ASCII GRID and binary image file formats. The Global Land One-Kilometer Base Elevation (GLOBE) digital elevation model (DEM) is a global data set with horizontal grid spacing of 30 arc-seconds (0.008333... degrees) in latitude and longitude, resulting in dimensions of 21,600 rows and 43,200 columns. At the Equator a degree of latitude is about 111 kilometers. GLOBE has 120 values per degree, giving GLOBE slightly better than 1-km gridding at the Equator, with progressively finer gridding longitudinally toward the Poles. The horizontal coordinate system is seconds of latitude and longitude referenced to World Geodetic System 84 (WGS84). The vertical units represent elevation in meters above Mean Sea Level. The elevation values range from -407 to 8,752 meters on land. In GLOBE Version 1.0, ocean areas have been masked as no data and have been assigned a value of -500. Because of the nature of the raster structure of the DEM, small islands in the ocean less than approximately 1 square kilometer (specifically, those that are not characterized by at least one 30 grid cell and/or do not have coastlines digitized into Digital Chart of the World or World Vector Shoreline) may not be represented. More information about the procedure used to create the southern African subset is described in the accompanying file ftp://daac.ornl.gov/data/safari2k/almanac/globe_dem/comp/so_africa_dem_readme.pdf.\n<br>\n\n#### MODIS_emissions_758\nThe recently generated MODIS burned area product over southern Africa for the month of September 2000 was used to calculate regional biomass burning emissions from grassland and woodland fires for a number of trace gases and particulates at 1 km spatial resolution. A dynamic regional fuel load model developed for southern Africa in support of SAFARI 2000 fire emissions modeling is used to compute spatially explicit southern Africa fuel load data. Regional grassland and woodland emissions are estimated using ecosystem-specific emission factor algorithms for carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), non-methane hydrocarbons (NMHC), and particulate matter with diameter less than 2.5 um (PM2.5) for southern African savanna fires (Korontzi et al., 2004). In addition, the emissions database for this region is expanded by incorporating emission factors for a variety of compounds, including oxygenated volatile organic compounds (OVOC), halocarbons, nitrogen oxides (NOx), ammonia (NH3), sulfur dioxide (SO2), hydrogen cyanide (HCN), and particulate ionic components measured during the SAFARI 2000 dry season field campaign.The data set archived by ORNL DAAC provides a MODIS-based estimate of CO (carbon monoxide) pyrogenic emissions (in kg) for southern Africa in September 2000 at 1 km. The data image file is stored in GeoTIFF binary format. Pyrogenic emissions of the other trace gases and particulates estimated by this study will be archived by ORNL DAAC when the data sets are finalized.\n<br>\n\n#### er2_aerial_photos_722\nAerial photography from the NASA ER-2 high altitude aircraft was collected to provide detailed and spatially extensive documentation over parts of the SAFARI study area. The ER-2 aerial photography consists of 3,046 color-infrared (IR) transparencies collected during the SAFARI 2000 Dry Season Aircraft Campaign in August and September of 2000. ORNL DAAC has archived scanned subsets of the ER-2 aerial photography.The original color-IR aerial photography the SAFARI 2000 Dry Season Aircraft Campaign is located at NASA Ames Research Center (ARC) Aircraft Data and Sensor Facilities. Copies can be ordered from the U.S. Geological Survey's EROS Data Center (EDC) in Sioux Falls, South Dakota, USA. In addition, 515 image frames have been scanned from copies of the original level-0 ER-2 aerial photography by the University of the Witwatersrand (Wits), in Pretoria, South Africa. ORNL DAAC has archived subsets of the available imagery from ARC and Wits. Information about the imagery subsets that are available from ORNL DAAC, as well as information about the original color-IR aerial photography at ARC and scanned imagery at Wits can be found at: ftp://daac.ornl.gov/data/safari2k/atmospheric/remote_sensing/er2_aerial_photos/comp/er2_aerial_photos_readme.pdf.\n<br>\n\n#### landcover_bvoc_est_764\nImproved vegetation distribution and emission data for Africa south of the equator were developed for the Southern African Regional Science Initiative (SAFARI 2000) and combined with biogenic volatile organic compound (BVOC) emission measurements to estimate BVOC emissions for the southern African region. BVOC emissions were estimated for southern Africa on a monthly basis over a one-year period by combining GIS layers of vegetation, LAI, and climate with a biogenic emissions model, GLOBEIS (Guenther et al, 1993; Guenther, 1999). Model input data included: vegetation data (Rutherford et al., 2000); species emission capacity data (Greenberg et al., 2003; Guenther et al., 1996; Harley et al., 2003; Klinger et al., 1998; Otter et al., 2002; Serca et al., 2001; Wiedinmyer et al., 2004); LAI data (1987-88 ISLSCP LAI; Sellers et al., 1994); cloud cover (MODIS LAI cloud mask); and temperature data (NOAA NCDC data).Model output includes emissions estimates for isoprene, light-dependent monoterpene, stored monoterpene, and other volatile organic compounds by land cover category and by vegetation type (g C m-2 mo-1). Emissions were modeled for a summer (January) and a winter (July) month in 2001. Monthly and annual total emissions per constituent for the year 2001 were also calculated. The data files containing the model outputs are ASCII comma-delimited files. Graphics (.jpgs) included with this data set show the distribution of light dependent monoterpene emissions across southern Africa during January, the average monthly isoprene emissions over southern Africa in January and in July, and the average monthly stored monoterpene emissions over southern Africa in January and in July.\n<br>\n\n#### eta_model_723\nWith modern computer power now capable of making mesoscale model output available in real time in the operational environment, increased attention has been given to utilizing these models in order to improve the forecasting ability of meteorologists. The National Centers for Environmental Prediction (NCEP) has developed a step-mountain eta coordinate model generally known as the ETA Model.This NCEP ETA data assimilation and prediction system (see Mesinger et al., 1988; Black, 1994) has been used by the South African Weather Bureau/Service (SAWS) to provide operational regional forecast guidance since November 1993. SAWS used this model to produce the basic meteorological data for the SAFARI project. The SAWS ETA model is a hydrostatic model with a horizontal grid spacing of approximately 48 km and 38 vertical levels, with layer depths that range from 20 m in the planetary boundary layer to 2 km at 50 mb. There have been several major ETA Model upgrades at SAWS: in March 1996, August 1998, November 1999, and August 2001.\n<br>\n\n#### FEWS_precip_711\nThe U.S. Agency for International Development (USAID) Famine Early Warning System (FEWS) has been supporting the production of 10-day Rainfall Estimate (RFE) data for Africa since 1995. The FEWSNET project was established with the goal of reducing the incidence of drought- or flood-induced famine by providing decision makers with timely and accurate information on conditions that may require intervention. RFE data for continental Africa for 1999, 2000, and 2001 were downloaded the from the African Data Dissemination Service (ADDS) site at [http://edcsnw4.cr.usgs.gov/adds/index.php], and were subset for southern Africa by the SAFARI 2000 data group. The RFE 1.0 algorithm, implemented from 1995 to 2000, uses an interpolation method to combine Meteosat and Global Telecommunication System (GTS) data, and warm cloud information for the 10-day estimations. The 30-minute geostationary Meteosat-7 satellite infrared data are used to estimate convective rainfall from areas where cloud top temperatures are less than 235K. The RFE 2.0 algorithm, implemented as of January 1, 2001, uses additional techniques to better estimate precipitation while continuing the use of cold cloud duration and station rainfall data. The 2.0 algorithm also incorporates two additional satellites, the Special Sensor Microwave/Imager (SSM/I) and the Advanced Microwave Sounding Unit (AMSU) to further aid the estimation. Rain gauge data from around 1000 World Meteorological Organization (WMO) GTS stations that pass quality control procedures are weighted more heavily toward the final rainfall estimate as the distance to the station decreases. Thus, at a distance far from the rain gauge, satellite estimates data dominate the final output result.The RFE subsets are flat binary images, with no headers. The data are limited to the range 0-250 and the rainfall units are millimeters. The data are in an Albers projection, and the pixels are 8 km square. Each single-byte image is 928 samples by 711 lines. There are 3 images per month, thus 36 per year, for a total of 108 10-day rainfall images for the period 1999-2001.\n<br>\n\n#### fire_emissions_724\nAs part of the Southern Africa Regional Science Initiative (SAFARI 2000), the University of Montana participated in both ground-based and airborne campaigns during the southern African dry season of 2000 to measure trace gas emissions from biofuel production and use and savanna fires, respectively.During the airborne campaign, stable and reactive trace gases were measured over southern Africa with an airborne Fourier transform infrared spectroscopy (AFTIR) onboard the University of Washington Convair-580 research aircraft in August-September of 2000. The measurements included vertical profiles of CO2, CO, H2O, and CH4 up to 5.5 km on 6 occasions above instrumented ground sites and below the TERRA satellite and ER-2 high-flying research aircraft as well as trace gas emissions from ten African savanna fires. These measurements are the first broad characterization of the most abundant trace gases in nascent smoke from African savanna fires (i.e., including oxygen- and nitrogen-containing species). The measurements are used to calculate emission factors for savanna fires that can be used for regional emissions estimates.This data set provides emission ratios and emission factors from the aircraft sampling effort for the following trace gases: carbon dioxide (CO2); carbon monoxide (CO); methane (CH4); acetylene (C2H2); ethene (C2H4); acetic acid (CH3COOH); methanol (CH3OH); formaldehyde (HCHO); hydrogen cyanide (HCN); formic acid (HCOOH); ammonia (NH3); nitric oxide (NO); and nitrogen dioxide (NO2). The file is an ASCII text file in comma-separated-value format. All emission factor units are grams of compound emitted per kilogram of fuel burned (g/kg) for the savanna fires investigated. Emission ratios are dimensionless.\n<br>\n\n#### mongu_fpar_trac_784\nData from the Tracing Architecture and Radiation of Canopies (TRAC) instrument were processed to determine the fraction of intercepted photosynthetically active radiation (FPAR) at the EOS Validation Core Site in Kataba Local Forest, approximately 20 km south of Mongu, Zambia. Measurements began in 1999 and continued into 2002, with measurements collected about every month. TRAC contains three pyranometers sensitive to PAR wavelengths, with two sensors upward looking and one downward looking. The TRAC instrument was carried along three parallel transects, each 750 m long and spaced 250 m apart, about 0.7 m off the ground on clear days near midday. The sensors measured PAR at 32 Hz, resulting in a horizontal sampling interval of about 1.7 cm (Privette et al., 2002). Each transect was divided into 25 m segments, and Fpar values, with date/time stamp, are reported for each segment. The length and spacing of the transects were chosen to sample an area large enough to be representative of a 1 km MODIS pixel. PAR transmittance values were determined from the upward viewing pyranometers on the TRAC instrument. Due to the large gaps in the canopy, incident PAR was estimated from the TRAC data as 95% of the maximum PAR transmittance value for each transect. The FPAR values of all the observations were averaged to give segment-average FPAR, and segment average FPAR values were averaged to give transect-average FPAR.The data file is stored as an ASCII text file, in comma-separated-value (csv) format, with column headers.\n<br>\n\n#### s2k_soller_wetlands_635\nThe data set consists of a southern Africa subset of the Global Distribution of Freshwater Wetlands database 1-degree data and are available in ASCII GRID and binary image files formats. The Global Distribution of Freshwater Wetlands database has been assembled from two data sets: Aselman and Crutzen's (AC) (1989) wetlands data set and Klinger's (pers. comm., 1995) Political Alaska data set. The aim is to provide an accurate, comprehensive and uniform set of files for convenient specification of wetlands in global climate models. The main source of data is AC global maps of percent cover for a variety of wetlands categories at 2.5-deg latitude by 5-deg longitude resolution. There is some reorganization for seasonally varying categories. Using bilinear interpolation, the AC data was interpolated to a standard 1-deg by 1-deg grid. The AC data set is geographically complete except for the Alaska region. More information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/soller_wetlands/comp/sollerwetlands_readme.pdf.\n<br>\n\n#### biofuel_emissions_753\nDomestic biomass fuels (biofuels) are estimated to be the second largest source of carbon emissions from global biomass burning. Wood and charcoal provide approximately 90% and 10% of domestic energy in tropical Africa, respectively. As part of the Southern Africa Regional Science Initiative (SAFARI 2000), the University of Montana participated in both ground-based and airborne campaigns during the southern African dry season of 2000 to measure trace gas emissions from biofuel production and use and savanna fires, respectively.In September of 2000, ground-based, open-path Fourier transform infrared spectroscopy (OP-FTIR) was used to quantify 18 of the most abundant trace gases emitted by wood and charcoal cooking fires and an earthen, charcoal-making kiln in Zambia. These are the first, in-situ measurements of an extensive suite of trace gases emitted by tropical biofuel burning.This data set provides biofuel burning emission ratios and emission factors from field measurements for the following trace gases (in order of abundance): carbon dioxide (CO2); carbon monoxide (CO); methane (CH4); acetic acid (CH3COOH); methanol (CH3OH); formaldehyde (HCHO); ethene (C2H4); ammonia (NH3); acetylene (C2H2); nitric oxide (NO); ethane (C2H6); phenol (C6H5OH); propene (C3H6); formic acid (HCOOH); nitrogen dioxide (NO2), hydroxyacetaldehyde (HOCH2COH); and furan (C4H4O). The files are ASCII text files in comma-separated-value format. All emission factors units are grams of compound emitted per kilogram of dry fuel. Emission ratios are dimensionless.\n<br>\n\n#### spot_veg_burned_790\nThe Global Burned Area 2000 initiative (GBA2000) was launched by the Global Vegetation Mapping Unit of the Joint Research Centre of the European Commission, in partnership with several other institutions, to develop reliable and quantitative information on the global magnitude and spatial distribution of biomass burning. The objective of GBA2000 was to produce a map of the areas burned globally for the year 2000, using the medium resolution satellite imagery provided by the SPOT-VEGETATION (VGT) system and to derive statistics of area burned per type of vegetation cover. A subset of the global GBA20000 map was prepared for SAFARI 2000 to map the area burned in sub-Saharan Africa during 2000 on a monthly basis using VGT imagery at 1 km spatial resolution. Burned areas were identified with a classification tree, relying only on the near-infrared channel of VGT. The data used in this work are in the S1 daily synthesis format, i.e. the data are radiometrically calibrated, precisely geo-located, and corrected for atmospheric effects.The data are binary image files of area burned, BSQ format in geographic projection. There is one file for each month of 2000 and one file for all of the year 2000. There is also a comma-delimited ASCII text file that provides geographic coordinates (latitude and longitude) of the center of each pixel indicated as a burned area for all of 2000.\n<br>\n\n#### ghcn_631\nThis data set consists of a southern African subset of the Global Historical Climatology Network (GHCN) Version 1 database. All stations with the following bounding coordinates are included in this subset: 5W - 60E and 5N - 35S. There are three files available, one each for precipitation, temperature, and pressure data. Within this subset the oldest data date from 1874 and the most recent from 1990. The GHCN V1 database contains monthly temperature, precipitation, sea-level pressure, and station-pressure data for thousands of meteorological stations worldwide. The database was compiled from pre-existing national, regional, and global collections of data as part of the Global Historical Climatology Network (GHCN) project, the goal of which is to produce, maintain and make available a comprehensive global surface baseline climate data set for monitoring climate and detecting climate change. It contains data from roughly 6000 temperature stations, 7500 precipitation stations, 1800 sea-level pressure stations, and 1800 station-pressure stations. Each station has at least 10 years of data; 40% have more than 50 years of data. Spatial coverage is good over most of the globe, particularly for the United States and Europe. Data gaps are evident over the Amazon rainforest, the Sahara desert, Greenland, and Antarctica. The earliest station data are from 1697; the most recent are from 1990. The database was created from 15 source data sets including: The National Climatic Data Center's (NCDC's) World Weather Records, CAC's Climate Anomaly Monitoring System (CAMS), NCAR's World Monthly Surface Station Climatology, CIRES' (Eischeid/Diaz) Global precipitation data set, P. Jones' Temperature data base for the world, and S. Nicholson's African precipitation database. Quality Control of the database included visual inspection of graphs of all station time series, tests for precipitation digitized 6 months out of phase, tests for different stations having identical data, and other tests. This detailed analysis has revealed that most stations (95% for temperature and precipitation, 75% for pressure) contain high-quality data. However, gross data-processing errors (e.g., keypunch problems) and discontinuous inhomogeneities (e.g., station relocations and instrumentation changes) do characterize a small number of stations. All major data processing problems have been flagged (or corrected, when possible). Similarly, all major inhomogeneities have been flagged, although no homogeneity corrections were applied. More information can be found at: ftp://daac.ornl.gov/data/safari2k/climate_meteorology/ghcn/comp/ghcn_v1_readme.pdf.\n<br>\n\n#### GPCP_precip_712\nThe Global Precipitation Climatology Project (GPCP) is an international project designed to provide improved long-record estimates of precipitation over the globe. The general approach is to combine the precipitation information available from several sources into a final merged product that takes advantage of the strengths of each data type. The GPCP has promoted the development of an analysis procedure for blending the various estimates together to produce the necessary global gridded precipitation fields. The currently operational procedure is based on Huffman et al. (1995) and has been used to produce the GPCP Version 2 Combined Precipitation Data Set, covering the period January 1979 through the present. The primary product in the Version 2 data set is a combined observation-only data set, that is, a gridded analysis based on gauge measurements and satellite estimates of rainfall. Beginning in October of 1996, the GPCP began producing 3-hourly merged global infrared (IR) brightness temperature (Tb) histograms on a 1 degree by 1 degree grid, which became the impetus for this product, also known as the 1 degree daily (1DD) product. The data set prepared for SAFARI 2000 has been extracted from the 1DD data set for the years 1999, 2000, and 2001.The Global Precipitation Climatology Project (GPCP) is an element of the Global Energy and Water Cycle Experiment (GEWEX) of the World Climate Research program (WCRP). The 1DD is produced by the GPCP Merge Development Centre (GMDC), located at NASA's Goddard Space Flight Center in the Laboratory for Atmospheres.\n<br>\n\n#### knp_fire_maps_756\nKruger National Park (KNP) was established in 1898 to protect wildlife on nearly 2 million hectares of the South African Lowveld. The climate in this region of South Africa is subtropical, with hot summers and warm, dry winters. Summer rains fall between October and March.Savanna fires are common in the South African Lowveld during the dry season. The two primary sources of fire ignition, historically and currently, are human activity and lightning.This data set contains a record of all fires observed in the Kruger National Park for each of the 10 fire years from 1992 to 2001. The data were compiled from various sources, including old fire records in hardcopy and vector form, as well as satellite imagery. The data product consists of a polygon map (GIS shapefile format) of Kruger National Park and a cross-referenced database of fire information associated with all mapped units. The fire information includes the date and time when the fire started (if known); the cause of the fire; the person, group or phenomenon (e.g. lightning) that started the fire; the purpose for which the fire was lit (if relevant); an estimate of the fire intensity aggregated over the whole fire; the extent of the area burnt in hectares; and the perimeter of the area burnt in kilometers. The database file can be viewed with a text editor and can also be imported into common spreadsheet applications.\n<br>\n\n#### hydrographic_gghydro_636\nThis southern African subset of the Global Hydrographic data set (GGHYDRO) Release 2.2 is organized into 19 files containing terrain type, stream frequency counts, major drainage basins, main features of the cryosphere surface, and ice/water runoff per year for the entire Earth's surface at a spatial resolution of 1 degree longitude by 1 degree latitude. This southern African subset of the Global Hydrographic data is provided in both ASCII GRID and binary image files formats. More information and selected thumbnails images can be found at: ftp://daac.ornl.gov/data/safari2k/hydrology/hydrographic_gghydro/comp/gghydro_readme.pdf . GGHYDRO Data Set Categories (Data File): 1. Exposed land not covered by swamp, intermittent water bodies, glacier ice, sand dunes, saltmarsh or salt flats (LAND); 2. Perennial freshwater lakes (FLAK); 3. Swamp, marsh and other wetlands(SWMP); 4. Saltwater, whether marine or terrestrial (SLTW); 5. Intermittent water bodies (ILAK); 6. Glacier ice, including shelf ice but excluding pack ice (GLAC); 7. Sand dunes (DUNE); 8. Saltmarsh (SMRS); 9. Salt flats (SFLT); 10. Land + Swamp + Sand dunes + Saltmarsh (DSRF); 11. Perennial rivers (FRIV); 12. Intermittent rivers (IRIV); 13. Land mask (MS05); 14. Major drainage basins (BAS1); 15. Smaller drainage basins (BAS2); 16. Main features of the cryosphere (CRYO); 17. Surface runoff of water (kg/m**2/yr) (RNOF); 18. Estimated root-mean-square error of RNOF (%) (RNER); and 19. Runoff of ice ( kg/m**2/yr ) (RICE).\n<br>\n\n#### SAWB_JRA_713\nAs part of the 3rd Intensive Campaign of SAFARI 2000, the South African Weather Bureau Aerocommander, JRA, flew 19 missions, for a total of 28 separate flights conducted between August 15th and September 7th, 2000. JRA worked closely with the other Aerocommander, JRA, and was dedicated to the measurement of trace gas and aerosol properties. A suite of trace analyzers (for O3, SO2, CO and NO), laser aerosol probes and atmospheric probes were present for all flights. Other instruments and sampling units present for some of the flights included, a nephelometer (Elias), CO flasks (Novelli) for MOPITT validation purposes, and VOC canisters for the collection and characterization of volatile organic compounds present over various land surface types.\n<br>\n\n#### SAWB_JRB_714\nAs part of the 3rd Intensive Campaign of SAFARI 2000, the South African Weather Bureau Aerocommander, JRB, flew 19 missions, for a total of 28 separate flights conducted between August 15th and September 7th, 2000. JRB worked closely with the other Aerocommander, JRA, and was dedicated to the measurement of trace gas and aerosol properties. A suite of trace analyzers (for O3, SO2, CO and NO), laser aerosol probes and atmospheric probes were present for all flights. Other instruments and sampling units present for some of the flights included, a nephelometer (Elias), CO flasks (Novelli) for MOPITT validation purposes, and VOC canisters for the collection and characterization of volatile organic compounds present over various land surface types.\n<br>\n\n#### kalahari_co2_heat_flux_765\nShort-term measurements of carbon dioxide, water, and energy fluxes were collected at four locations along a mean annual precipitation gradient in southern Africa during the SAFARI 2000 wet (growing) season campaign of 2000. The purpose of this research was to determine how observed vegetation-atmosphere exchange properties are functionally related to long-term climatic conditions. This research was conducted along the Kalahari Transect (KT), one in the global set of International Geosphere-Biosphere Programme (IGBP) transects, which covers a north-south aridity gradient, all on a homogenous sand formation. Eddy covariance instruments were deployed on a permanent tower in Mongu, Zambia (879 mm of rainfall per year), as well as on a portable tower in Maun (460 mm/yr), Okwa River Crossing (407 mm/yr), and Tshane (365 mm/yr), Botswana for several days at each site.The data files are stored as ASCII text files, one file per site, in comma-separated-value (csv) format, with column headers. Each file contains measurements of the following parameters: air temperature; CO2 concentration; CO2 flux; friction velocity; heat flux; infrared surface skin temperature; longwave radiation; net radiation; photosynthetic photon flux density; photosynthesis energy; relative humidity; shortwave radiation; soil moisture; soil temperature; vapor pressure deficit; wind speed and angle; water use efficiency; and water vapor. Additional information can be found in the companion file: ftp://daac.ornl.gov/data/safari2k/climate_meteorology/kalahari_co2_heat_flux/comp/ kt_co2_heat_flux_readme.pdf. Detailed photographs (.jpg) that complement the experimental descriptions for this data set are available on the S2K Photo Gallery pages.\n<br>\n\n#### sua_pan_lai_fpar_778\nThe Multi-angle Imaging SpectroRadiometer (MISR) Validation team was deployed to the Sua Pan salt playa in the Magkadigkadi region of Botswana during the SAFARI 2000 Dry Season Aircraft Campaign to collect various data sets for validating the MISR LAI/FPAR algorithm. Ground measurements of leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) were made using the LAI-2000 plant canopy analyzer and Sunfleck PAR ceptometer, respectively, during focused periods from August 20 to August 28, 2000 at a dry grassland site adjacent to the Sua Pan. The 1 km by 1 km sampling grid was a homogeneous, relatively dense grassland, with a height of 20-100 cm and two prevalent grass types, Odyssea paucinervis and Sporobolus spicatus. Associated reflectance measurements were made with the PARABOLA and ASD instruments (Helmlinger et al., 2004a; 2004b).The data files contain measurements of LAI and PAR reflectance and transmission and a description of sky conditions during the sampling periods. With one exception, all measurements were made under clear sky conditions. PAR data were measured only on the transect scale while LAI are provided at both pixel and transect scales. PAR readings were performed at 93 transect sample points, and LAI readings were performed at 135 (93 transect and 42 subgrid) sample points. Each file also contains mean LAI and PAR values. The data files are ASCII tables, in comma-separated-value format.\n<br>\n\n#### kt_lai_770\nThe Boston University team collected several data sets along the Kalahari Transect during the SAFARI 2000 wet season field campaign from March 3rd to March 18th, 2000 to support the validation of the MODIS LAI/FPAR algorithm. Ground measurements of LAI, FPAR, leaf hemispherical reflectance, leaf hemispherical transmittance, and canopy transmittance were made using a LAI-2000 plant canopy analyzer, an AccuPAR ceptometer, a LiCor 1800-12S External Integrating Sphere (LI-1800) portable spectroradiometer, and an ASD handheld spectroradiometer. LAI data are provided in this data set. LAI was intensively measured at 4 different sites in Botswana -- Pandamatenga, Maun, Okwa River, and Tshane (from north to south) -- where vegetation types range from moist closed woodlands to arid sparsely-shrub-covered grasslands. LAI measurements were mostly made during dusk, shortly before and after sunset at ground level in Maun, Okwa, and Tshane and at two horizontal levels in Pandamatenga -- one 2.0 m above the ground to measure the LAI of trees and the other was at ground level for total LAI.This data set contains reduced LAI measurements made at two scales: a 1 km x 1 km transect scale and a 250 m x 300 m pixel scale. The transect LAI data are stored in one ASCII file containing all measurements along the three transects at each site. The pixel or subgrid LAI are stored in separate ASCII files for each site.\n<br>\n\n#### s2k_glcf1deg_626\nThis data set consists of a southern African subset of the University of Maryland (UMD) 1-degree Global Land Cover product in ASCII GRID and binary image formats. The UMD 1-degree Global Land Cover product was produced by researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at UMD. The product is based on Advanced Very High Resolution Radiometer (AVHRR) maximum monthly composites for 1987 of Normalized Difference Vegetation Index (NDVI) values at approximately 8-km resolution, averaged to one-by-one degree resolution. This coarse- resolution data set was used as the basis for a supervised classification of eleven cover types that broadly represent the major biomes of the world. Because of missing values at high latitudes, the Pathfinder AVHRR data set for 1987 for summer monthly NDVI and red reflectance values were used to distinguish the following cover types: tundra, high latitude deciduous forest and woodland, coniferous evergreen forest and woodland. The 1-degree global land cover product is available for download from the Global Land Cover Facility (GLCF) web site. The data are available as a global coverage in both binary and ASCII format. Additional information and references on this data set can be found at the GLCF web site as well as at the LGRSS web site (link provided at the GLCF web site ) and in the readme file found along with the data [ ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/land_cover_data_1deg/comp/glcf1deg_readme.pdf].\n<br>\n\n#### land_cover_data-1km_627\nThe data set consists of a southern African subset of the 1-km Global Land Cover Data Set Derived from AVHRR developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Both ASCII data and binary image files are available. Over the past several years, researchers have increasingly turned to remotely sensed data to improve the accuracy of data sets that describe the geographic distribution of land cover at regional and global scales. To develop improved methodologies for global land cover classifications as well as to provide global land cover products for immediate use in global change research, researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland have employed the NASA/NOAA Pathfinder Land (PAL) data set with a spatial resolution of 1 km. This data set has a record length of 14 years (1981-1994), providing the ability to test the stability of classification algorithms. Furthermore, this data set includes red, infrared, and thermal bands in addition to the Normalized Difference Vegetation Index (NDVI). Inclusion of these additional bands improves discrimination between cover types. The project aim is to develop and validate global land cover data sets and to develop advanced methodologies for more realistically describing the vegetative land surface based on satellite data. The 1-km global land cover product was created from 1992-93 LAC AVHRR data. The full 1-km global land cover product is available for download from the Global Land Cover Facility (GLCF) web site. Forty-one (41) metrics were developed to describe global vegetation phenology, and these data were used to make the 1-km land cover map. The final product contains 13 land cover classes. More information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/land_cover_data-1km/comp/glcf1km_readme.pdf.\n<br>\n\n#### s2k_land_cover_data_8km_628\nThis data set consists of a southern African subset of the University of Maryland (UMD) 8-km Global Land Cover product in ASCII GRID and binary image files formats. Over the past several years, researchers have increasingly turned to remotely sensed data to improve the accuracy of data sets that describe the geographic distribution of land cover at regional and global scales. To develop improved methodologies for global land cover classifications as well as to provide global land cover products for immediate use in global change research, researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland (UMD) have employed the NASA/NOAA Pathfinder Land (PAL) data set with a spatial resolution of 8 km. This data set has a length of record of 14 years (1981-1994), providing the ability to test the stability of classification algorithms. Furthermore, this data set includes red, infrared, and thermal bands in addition to the Normalized Difference Vegetation Index (NDVI). Inclusion of these additional bands improves discrimination between cover types. The project aim is to develop and validate global land cover data sets and to develop advanced methodologies for more realistically describing the vegetative land surface based on satellite data. The 8-km global land cover product was derived by testing several metrics that describe the temporal dynamics of vegetation over an annual cycle. These metrics were applied to 1984 PAL data at 8-km resolution to derive a global land cover classification product using a decision tree classifier. The final product contains 13 land cover classes. The original 8-km global land cover product is available for download from the Global Land Cover Facility (GLCF) web site. Additional information and references on this data set can be found at the GLCF web site as well as at the LGRSS web site (link provided at the GLCF web site ). More information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/land_cover_data_8km/comp/glcf8km_readme.pdf.\n<br>\n\n#### kt_gps_photos_769\nThis data set contains Global Positioning System (GPS) imprinted landscape photographs at 100 m intervals along the Large Grid Transects at Kalahari Transect sites in Botswana and at measurement sites in Kataba Forest, Mongu, Zambia and in the vicinity of the Skukuza flux tower site in Kruger National Park, South Africa. The Kalahari sites visited were Pandamatenga, Maun, Okwa Valley, and Tshane. There are about 30 pictures per site. In a related study, vegetation cover and composition were measured at various locations along the Kalahari Transect and trends in major vegetative cover, including species types and richness, were recorded (Ringrose and Matheson, 2004). The sites visited showed interesting degrees of variability despite the apparent homogeneity of the Kalahari sands and predominantly semi-arid savanna shrub-woodland vegetation cover. In this data set, the photography provides a visual characterization of species composition and vegetation cover along the Kalahari Transect. The photographs are intended to aid in the interpretation of other data sets, and can be used to suggest canopy height, gap fraction, grass, soil, and sky conditions.The photographs are provided as JPEG images. The following information is imprinted across the top of each photograph: camera number; date mode (G=UTC time) and date (YYMMDD format); time (HHMMSS format); locational mode (1 char) and DOP value (A < 1, J > 9.1); longitude (N, S), followed by DDMM.MMM format; latitude (E, W) followed by DDDMM.MMM format; and bearing (degrees) from magnetic north (indicates direction of shot).\n<br>\n\n#### kt_lai_trac_771\nData from the Tracing Radiation and Architecture of Canopies (TRAC) instrument were collected at five sites along the International Geosphere-Biosphere Programme (IGBP) Kalahari Transect, including Mongu in Zambia and Pandamatenga, Maun, Okwa River Crossing, and Tshane in Botswana, during the 2000 wet season field campaign (March-April) of SAFARI 2000. At the Mongu site, TRAC measurements began in August of 1999 and continued beyond the 2000 wet season field campaign, about every month for the rest of 2000.The TRAC instrument contains pyranometers that are sensitive to photosynthetically active radiation (PAR) at 400-700 nm. The TRAC measures the PAR flux transmitted through the overstory canopy continuously at 32 Hz. The parameters derived from the TRAC instrument include estimates of plant or leaf area index (PAI, LAI), overstory gap fraction, and clumping index.At each site, the TRAC instrument was carried along three parallel transects, each 750 m long and spaced 250 m apart. Measurements were made every 25 m along the transects. The length and spacing of the transects were chosen to sample an area large enough to be representative of a 1 km MODIS pixel.The data files are stored as ASCII text files, one file per transect, site, and date, with column headers. There are also summary files (ASCII text) containing mean values of each derived parameter over an entire transect for each collection date.\n<br>\n\n#### mongu_lai-2000_781\nData from the LAI-2000 instrument were processed to determine the leaf area index (LAI) at the EOS Validation Core Site in Kataba Local Forest, approximately 20 km south of Mongu, Zambia. Measurements began in 2000 and continued into 2002, with measurements collected about every month throughout the growing season to examine the phenology of LAI for the site. The LAI-2000 measures the intensity of blue light in five upward-looking concentric conical rings. Measurements are made under the forest canopy and compared with open-sky measurements to determine transmittance for each of the five viewing angles. The sensor head was placed at ground level while the sensors measured light levels in conical scans. Effective leaf area was calculated from the transmittance in the different view angles based on the assumption of a random distribution of leaves (Welles and Norman, 1991).The LAI-2000 was carried along three parallel transects, each 750 m long and spaced 250 m apart. Each transect was divided into 25 m segments, and measurements were collected at the endpoints of each segment. Data from all transects were combined to provide site-average LAI for each sampling date. The length and spacing of the transects were chosen to sample an area large enough to be representative of a 1 km MODIS pixel. Ground observations of LAI from this study compared with MODIS LAI products were found to be in close agreement.The data are stored in an ASCII text file, in comma-separated-value (csv) format, with column headers.\n<br>\n\n#### kt_leaf_meas_772\nData presented in this data set were collected during an intensive field campaign in Botswana between February 28 and March 18, 2000 along the Kalahari Transect as part of the SAFARI 2000 wet season field campaign. The sites visited were Pandamatenga, Maun, Okwa River Crossing, and Tshane (north to south). Individual leaf blade measurements were made on replicate samples from selected dominant and subdominant tree species using an optical lens and graticule. Leaves used in the study had recently-matured new growth, and were fully exposed to the sun for a significant part of the day. The data set is comprised of individual leaf blade dimensions along the length and width of each leaf by tree species as well as the mean of the replicate leaf length and width samples. The data are in comma-delimited ASCII format (kt_leaf_dimensions.csv).\n<br>\n\n#### kt_leaf_spectra_773\nThe Boston University team collected several data sets along the Kalahari Transect during the SAFARI 2000 wet season field campaign between March 3 and March 18, 2000 to support the validation of the MODIS LAI/FPAR algorithm. Ground measurements of LAI, FPAR, leaf hemispherical reflectance and transmittance, and canopy transmittance were made using a LAI-2000 plant canopy analyzer, an AccuPAR ceptometer, a LiCor 1800-12S External Integrating Sphere (LI-1800) portable spectroradiometer, and an ASD handheld spectroradiometer. Leaf spectral data are provided in this data set. Leaf spectral measurements were made on samples from dominant tree, shrub, and grass species at 5 different Kalahari Transect sites - Mongu in Zambia and Pandamatenga, Maun, Okwa River, and Tshane in Botswana (from north to south) - where vegetation ranges from moist closed woodlands to arid sparsely-shrub-covered grasslands. Measurements were made on site with a LI-1800 portable spectroradiometer right after the leaves were cut from the trees or shrubs. Three or four sample leaves of each dominant species were measured.The data files, in ASCII comma-delimited (.csv) format, contain the wavelength of the measurement (from 400 nm to 1100 nm, at an interval of 1 nm) and the corresponding fraction of leaf reflectance, transmittance, and albedo (reflectance+transmittance). There is a separate data file for each tree and shrub species sampled at each site and a single file containing unidentified grass species collected from all of the sites. Average values for combined samples of trees and of shrubs at different sites are also provided.\n<br>\n\n#### leaf_voc_emissions_763\nBiogenic volatile organic compounds (VOCs) comprise a significant proportion of trace gases in the atmospheric environment and play an important role in the formation of secondary air pollutants. Emissions of monoterpenes from vegetation were studied at adjacent sites in Botswana as part of the SAFARI 2000 (Southern African Regional Science Initiative). Using a LI-COR leaf cuvette, VOC emissions were measured from the dominant tree species (Colophospermum mopane) and other vegetation near Maun, Botswana. The aims of this work were to: (1) determine the VOC emission potential of C. mopane; (2) investigate any differences in VOC emission potential between the tall and short C. mopane morphology types; (3) investigate environmental controls of VOC emissions from C. mopane; and (4) screen other non-dominant vegetation for high VOC emission potential. The data are contained in an ASCII text file (maun_leaf-level_voc.csv) in comma-delimited format with column headers. The data file contains leaf-level VOC emission rates for C. mopane and other plant species growing near Maun, Botswana recorded under different types of experiments associated with the measurements (e.g., preliminary light dependency, emission potential, tall/short and water potential, light dependency, screening). In addition, the data file contains physical measurements, such as leaf area and dry biomass, which are used in calculating leaf-level emissions rates. Environmental parameters in the leaf cuvette (PAR, temperature, relative humidity, and CO2 concentration) are also recorded. All measurements were made during the wet season campaign (February) of 2001.\n<br>\n\n#### mapss_modis_aerosol_814\nThe Moderate Resolution Imaging Spectroradiometer (MODIS)Atmosphere Group develops remote sensing algorithms for deriving sets of atmospheric parameters from MODIS radiance data. These parameters can be integrated into conceptual and predictive global models. MODIS Atmosphere Products Subset Statistics (MAPSS) are generated over important locations around the world, as one of the ways to increase the scope of application of the MODIS atmospheric parameters. This MAPSS data set contains daily time series of the MODIS MOD04_L2 aerosol product over seventeen (17) AERONET sunphotometer measurement sites in southern Africa for the period February 26, 2000, through December 31, 2001. The process of generating the statistics involves identifying these locations on the MODIS MOD04_L2 product, extracting the values of the pixel corresponding to each coordinate point as well as surrounding pixels falling within a 50 x 50 km box centered on the coordinate point. The data files are stored as ASCII tables in comma-separated-value (.csv) format. There is one file per site per year for each of the following variables: cloud fraction (land); cloud fraction (ocean); particle effective radius (ocean); optical depth (land and ocean); optical depth (land, corrected); optical depth (ocean, effective average); and optical depth ratio (small ocean).\n<br>\n\n#### mapss_modis_watervapor_815\nThe Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere Group develops remote-sensing algorithms for deriving sets of atmospheric parameters from MODIS radiance data. These parameters can be integrated into conceptual and predictive global models. MODIS Atmosphere Products Subset Statistics (MAPSS) are generated over important locations around the world, as one of the ways to increase the scope of application of the MODIS atmospheric parameters. This MAPSS data set contains daily time series of the MODIS MOD05_L2 water vapor product over seventeen (17) AERONET sunphotometer measurement sites in southern Africa for the period February 24, 2000, through March 4, 2002. The process of generating the statistics involves identifying these locations on the MODIS MOD05_L2 product, extracting the values of the pixel corresponding to each coordinate point as well as surrounding pixels falling within a 50 x 50 km box centered on the coordinate point. The data product consists of column water-vapor amounts. During the daytime, a near-infrared algorithm is applied over clear land areas of the globe and above clouds over both land and ocean. Over clear ocean areas, water-vapor estimates are provided over the extended glint area. An infrared algorithm for deriving atmospheric profiles is also applied both day and night for Level 2. The data files are stored as ASCII tables in comma-separated-value (.csv) format. There is one file per site per year for each of the following two variables: total column precipitable water vapor (infrared retrieved) and total column precipitable water vapor (near-infrared retrieved).\n<br>\n\n#### cramer_leemans_637\nThis data set is a subset of the global mean monthly climatology (Cramer and Leemans 1999). The subset is for the area of southern Africa within the following bounds: 5 N to 35 S and 5 E to 60 E. The data are available in ASCII grid and binary image formats. The parent database is a major update of the Leemans and Cramer database (Leemans and Cramer 1991). It contains long-term monthly averages, for the period 1931-1960, of mean temperature, temperature range, precipitation, rain days and sunshine hours for the terrestrial surface of the globe, gridded at 0.5-degree longitude/latitude resolution. It was generated from a larger database, using the partial thin-plate splining algorithm developed by Michael F. Hutchinson, Canberra (Hutchinson and Bischof 1983). The current version is 2.1--this is the same version that is currently used widely around the globe, notably by all groups participating in the IGBP NPP model intercomparison. More information can be found at: ftp://daac.ornl.gov/data/safari2k/climate_meteorology/cramer_leemans/comp/cramer_leemans_readme.pdf.\n<br>\n\n#### maun_met_flux_760\nTo investigate potential contributions of savanna ecosystems to the Earth s carbon balance, an eddy covariance system was used to measure the seasonal variation in carbon dioxide, water vapor, and energy flux at the Maun micrometerological tower site in a broadleaf semi-arid savanna in Southern Africa, approximately 20 km east of Maun in northeastern Botswana. The open woodland studied consists of an overstory dominated by Colophospermum mopane with a sparse understory of grasses and herbs. Measurements presented in this data set cover the wet and dry season periods that coincided with field activities of the SAFARI 2000 project in February-April and August-September, 2000. The data files contain numeric values that represent 30-minute averages from instrumentation mounted on the Maun tower. The parameters measured, or derived from measurements, include: air pressure, air temperature, albedo, carbon dioxide flux, dewpoint temperature, friction velocity, latent heat flux, longwave radiation, net radiation, precipitation, relative humidity, sensible heat flux, shortwave radiation, soil moisture, soil temperature, specific humidity, vapor pressure, and wind speed. The data files are stored as ASCII table files, one file per month, in comma-separated-value (.csv) format, with column headers.Long term climatic data of the Maun area (monthly rainfall, maximum recorded monthly rainfall, mean maximum daily temperature, mean minimum daily temperature, and potential evapotranspiration) are provided, along with data set documentation, in the companion file: ftp://daac.ornl.gov/data/safari2k/climate_meteorology/maun_met_flux/comp/maun_met_flux_readme.pdf.\n<br>\n\n#### knp_met_761\nAn eddy covariance system mounted on a tower near the Skukuza Camp in Kruger National Park, South Africa, has been operating continuously since early 2000. Meteorological measurements started in February 2000, and the first flux measurements were made in April 2000. The site is unique in that the micrometeorological instruments are positioned on a tower located between two distinct savanna types, a broad-leafed Combretum savanna and a fine-leafed Acacia savanna. These contrasting savanna types are found on soils of differing texture, water holding capacity, and nutrient status, and are characterized by different physical structure, physiology, and phenology. It was expected that the tower placement and prevailing wind directions would allow for the sampling of net ecosystem fluxes from both of these ecosystems. Measurements on the main eddy covariance tower include net ecosystem exchange of CO2, water, and energy, and measurements of a range of meteorological variables with 30-minute averaging period. Additional continuous measurements in both adjacent savanna systems include soil heat flux, soil moisture and soil temperature profiles (from the soil surface to bedrock), and canopy profiles of CO2 concentration, humidity, and air temperature. Intermittent measurements include soil surface CO2 flux and spatially-distributed soil moisture and soil temperature.The data files contain 30-minute averages from instrumentation mounted on the Skukuza tower from 2000-2002. The data files are stored as ASCII table files, one file per year, in comma-separated-value (.csv) format, with column headers.Additional information about the meteorological and flux measurements can be found at: http://www.nrel.colostate.edu/projects/kruger/ and in the companion file at: ftp://daac.ornl.gov/data/safari2k/climate_meteorology/knp_met/comp/knp_met_readme.pdf.\n<br>\n\n#### micro_pulse_lidar_715\nTwo Micro-Pulse Lidar (MPL) systems were deployed to Africa for the SAFARI 2000 experiment. One MPL was setup in Mongu, Zambia and the other was setup in Skukuza, South Africa. The primary focus of MPL work during SAFARI was to study the vertical distribution and optical properties of smoke from biomass burning in the region.\n<br>\n\n#### MISR_885\nMISR (Multi-angle Imaging SpectroRadiometer) views the sunlit Earth simultaneously at nine widely spaced and collects global images with high spatial detail in four colors at every angle. These images are carefully calibrated to provide accurate measures of the brightness, contrast, and color of reflected sunlight. The change in reflection at different view angles affords the means to distinguish different types of atmospheric particles (aerosols), cloud forms, and land surface covers. Combined with stereoscopic techniques, this enables construction of 3-dimensional models and more accurate estimates of the total amount of sunlight reflected by Earth's diverse environments.MISR was built for NASA by the Jet Propulsion Laboratory. It is part of NASA's Terra spacecraft, launched into a polar orbit around the Earth on December 18, 1999.The Southern African Fire Atmosphere Research Initiative (SAFARI) 2000 field campaign focused on the smoke and gases released into the environment of southern Africa by industrial, biological, and man-made sources such as biomass burning. The area of study and MISR path numbers include Botswana, Lesotho, Malawi, Mozambique, Namibia, South Africa, Swaziland, Zambia, and Zimbabwe. These MISR data cover the period August 12 through September 28, 2000.\n<br>\n\n#### fuel_load_755\nThis data set contains global, spatially explicit (1 km2 grid cells) and temporally explicit (semi-monthly) modeled output of fuel loads over southern Africa. The fuel types considered in the data set are litter (dead tree leaves), dead grass, green grass, and small-diameter twigs. The Production Efficiency Model (PEM) was used to produce the estimated fuel loads for southern Africa for the 1999-2000 growing seasons. In order to accommodate the southern African growing season, which spans October to May, the PEM was run on twenty-four 15-day intervals from September 1, 1999 to August 31, 2000. The fuel load model was calibrated from measurements recorded in 1996 along the Kalahari Transect, and has been verified from independent site measurements of fuel loads recorded during the SAFARI 2000 dry season field campaign in August and September of 2000 (Alleaume et al., 2003, Hely et al., 2003a) as well as other past campaigns.This data set consists of gridded binary image files. The single-byte images have a 1 km resolution and the grid is 3900 columns by 4800 rows in size. There are 24 files each for litter, dead grass, and green grass fuel loads, and a single file for the twig fuel loads. For the latter, small twigs (less than 1cm in diameter) are calculated from the tree cover percentage (UMD Tree Cover from Hansen et al., 2000) using an empirical relationship (Alleaume et al., 2003). Therefore, this fuel type is a constant throughout the year.\n<br>\n\n#### trajectory_images_792\nThe ETA Forecast Trajectory Model was used to produce forecasts of air-parcel trajectories twice a day at three pressure levels over seven sites in Southern Africa for the period August 14, 2000 to September 23, 2000. These sites are Durban, Middleburg, Pietersburg, and Springbok, South Africa; Maun, Botswana; Mongu, Zambia; and Windhoek, Namibia. The twice daily three-dimensional wind field (at 0000 and 1200 UTC) was used as input to the trajectory model. By integrating the vertical motion of the air parcels over a period of time, the trajectory model was able to forecast the net vertical displacement of air parcels during 12-hour periods. The resulting trajectory plots represent the three-dimensional transport of air in time and can be used to examine what is happening in the low-to-mid troposphere during flight and ground-based observations. These levels are most significant in terms of the thermodynamic structure of the troposphere, especially the stable layers and accumulation of material between and below them, as well containing the major levels of subsidence over the subcontinent. The trajectory model output and thermodynamic profiles of the troposphere were used to position aircraft for sampling trace gases, aerosols and other species during the SAFARI 2000 field campaign and to predict regions of high aerosol and trace gas concentrations downwind.The model output data are daily forward and backward trajectory plots at 850 hPa, 700 hPa, and 500 hPa pressure levels for each location. The plots are provided as JPEG images with coordinate, date, and time stamps.\n<br>\n\n#### modis_burned_area_796\nThe SAFARI 2000 project was selected as the first regional test for a prototype regional 500 m MODIS burned area product. The MODIS burned area product maps the 500 m location and approximate day of burning using a change detection algorithm based on a bi-directional reflectance model-based expectation method applied to the MODIS near-infrared and shortwave infrared bands (Roy et al., 2002). The algorithm was applied to recently reprocessed 500 m daily MODIS land surface reflectance data to produce burned area data sets for all of southern Africa for 2000 forward. This archived data set contains MODIS 500 m burned area products for two dry season months (July and September 2000).Burned area products are spatially explicit data sets that describe the approximate day of burning at 500 m resolution for all of southern Africa south of the Equator, including Madagascar. The burned area maps are compressed GeoTiff files. Several text files are included in the compressed files to aid ENVI (Research Systems, Inc.) users, including ENVI header files and ENVI density slice files. The data set also includes a projection parameters file.The MODIS burned area data set was validated using a methodology based upon the interpretation of multitemporal Landsat Enhanced Thematic Mapper plus (ETM+) data as described in Roy et al. (in press).\n<br>\n\n#### MAS_832\nThis dataset contains the Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) multispectral data collected during the SAFARI 2000 project. The flights were undertaken over Southern Africa by the NASA ER-2 aircraft during August and September, 2000.\n<br>\n\n#### modis_l3_albedo_840\nThe Filled Land Surface Albedo Product for Southern Africa, which is generated from MOD43B3 Product (the official Terra/MODIS-derived Land Surface Albedo - http://geography.bu.edu/brdf/userguide/albedo.html ), is a subset of the global data set of spatially complete albedo maps computed for both white-sky and black-sky at 10 wavelengths. The data spatial extent is from approximately 5 degrees N to -30 degrees S latitude and 5 minutes E to 60 degrees E longitude and covers 7 sixteen day periods starting on July 11 through October 15, 2000.Map Products, containing spatially complete land surface albedo data, are generated at 1-minute resolution on an equal-angle grid. The maps are stored in separate HDF files for each wavelength, each 16-day period and each albedo type (white- and black-sky). Data belonging to black sky and white sky albedo have been zipped separately. This format allows the user to have flexibility to download and store only the data absolutely needed.The One-Minute Land Ecosystem Classification Product is a global (static map) data set of the International Geosphere-Biosphere Programme (IGBP) classification scheme stored on an equal-angle rectangular grid at 1-minute resolution. The dataset is generated from the official MODIS land ecosystem classification dataset, MOD12Q1 for year 2000, day 289 data (October 15, 2000). This dataset is used in generating the spatially complete albedo maps, but is also a stand-alone product designed for use by the user community. The Land Ecosystem Classification Map File product file is stored in Hierarchical Data Format (HDF).\n<br>\n\n#### modis_MOD04_aerosol_813\nThe subset of the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD04_L2 aerosol product provided in this data set represents the swaths that coincide with known times of the South African Weather Bureau/Service (SAWS) Aerocommanders JRA and JRB research aircraft missions to support aerosol research and validation activities for the SAFARI 2000 region. The MODIS aerosol product monitors the ambient aerosol optical thickness over the oceans globally and over a portion of the continents. Further, the aerosol size distribution is derived over the oceans, and the aerosol type is derived over the continents. Daily Level 2 data are produced at the spatial resolution of a 10 x 10 1 km (at nadir) pixel array. The daily data files cover the period August 21, 2000, through September 26, 2000. For some data collection dates, there are two or more data files.The MOD04_L2 swath data files included in this data set are from the GSFC DAAC (V4.1.0, Collection 004). The MODIS Level 2 data files were converted from Hierarchical Data Format (HDF) to granule tables (GRANT) format. The GRANT format provides the extracted Scientific Data Set (SDS) in ASCII table form where each pixel (x,y) is represented as a row of data with georeferencing information and each SDS is provided as a separate column in the table. The ASCII tables are in comma-delimited format.\n<br>\n\n#### modis_MOD05_watervapor_812\nThe Moderate Resolution Imaging Spectroradiometer (MODIS) precipitable water product consists of vertical column water-vapor amounts in centimeters (cm) at 1-km spatial resolution. The SAFARI 2000 product, provided in flat binary data files, is a subset of the official MODIS Level 2 MOD05 product in EOS Hierarchical Data Format (HDF) format. Specifically, the SAFARI product contains data from daytime-only MODIS granules over southern Africa for the period August 21, 2000, through September 20, 2000. A granule is the data collected over the full MODIS swath in a five-minute period. Further, the SAFARI product contains values generated by the MODIS near-infrared algorithm applied over clear land areas only (determined via the QA bit field). All values were derived from MODIS on the morning-pass Terra satellite. The product is very sensitive to boundary-layer water vapor since it is derived from attenuation of reflected solar light from the surface. This data product is essential to understanding the hydrological cycle, aerosol properties, aerosol-cloud interactions, energy budget, and climate.The MOD05 water vapor data files were converted from their original HDF format to flat binary files for this SAFARI 2000 data set. The conversion was performed using code developed in the Interactive Data Language (IDL) Version 5.5. The following Scientific Data Sets (SDS) are provided in this data set: Latitude; Longitude; Sensor_Zenith; and Water_Vapor_Near_Infrared.\n<br>\n\n#### modis_h2o_heat_flux_762\nA physically-based model, Energy: Surface Towards Atmosphere (ESTA), was used to model and map the energy and water balances of a heterogeneous land surface in a savanna environment on the southern fringe of the Okavango Delta, near Maun, Botswana. ESTA is governed by remotely sensed values of surface temperature, reflection, and vegetation density.Surface reflectance data from the MODIS sensor aboard the Terra satellite were obtained for the Okavango Delta region for September of 2001. Use was made of the visible, near infrared, and thermal bands of MOD09, a configuration that allows discrimination of broad-band surface albedo, vegetation indices, and surface temperature. A crude land cover classification image was created to provide vegetation canopy heights for the estimation of aerodynamic resistance. The original spatial resolution of the visible and near infrared imagery of 250 and 500 meters, respectively, was reduced to 1 km to be compatible with the resolution of the thermal imagery. Input to the model also includes near-surface meteorological observations from the Maun flux tower.Net radiometer data from the Maun station were used for crude atmospheric corrections and for validating net radiation estimates. Soil heat flux plates, also in place at the tower site, in combination with an eddy-correlation system, were used to validate the model output for soil and turbulent heat fluxes. This data set consists of 3 separate gridded binary image files representing daily evapotranspiration (mm/day) for the Okavango Delta region for September 2, September 13, and September 29, 2001.\n<br>\n\n#### s2k_soil_respiration_gridded_644\nThe data set provides estimated monthly and annual soil CO2 emissions for southern Africa (the SAFARI 2000 project region). The calculated emissions are from the respiration of both soil organisms and plant roots and are provided on a 0.5-degree grid cell basis. The data are a subset of a global data set (Raich and Potter, CDIAC 1996), reformatted and subsetted to the SAFARI region.\n<br>\n\n#### S2K_EACM_Subset_623\nThis is a data set of mean monthly surface climate data over Southern Africa for nearly all of the twentieth century. The data set is gridded at 0.5 degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency. All variables have mean monthly values for the period 1901-1995, several have data as recent as 1998, and more data will be added by the data originators. In constructing the monthly grids the authors used an anomaly approach which attempts to maximize station data in space and time (New et al., 2000). In this technique, grids of monthly historic anomalies are derived relative to a standard normal period. Station measurement data for the years 1961-1990, extracted from the monthly data holdings of the Climatic Research Unit and the Global Historic Climatology Network (GHCN), served as the normal period (New et al., 1999). The anomaly grids were then combined with high-resolution mean monthly climatology to arrive at fields of estimated historical monthly surface climate. Data users are encouraged to see the companion file New et al. (2000) for a complete description of this technique and potential applications and limitations of the data set. For additional information, refer to the IPCC Data Distribution Centre. Access to the complete year-by-year monthly data set or to data more recent than posted here can be achieved by making a request with the Climate Impacts LINK Project at the Climatic Research Unit (email: d.viner@uea.ac.uk, web site: www.cru.uea.ac.uk/link ).\n<br>\n\n#### MOPITT_co_835\nThe MOPITT (Measurements Of Pollution In The Troposphere) instrument on the NASA Terra Satellite makes measurements of infrared radiation originating from the surface of the planet and isolates the energy being radiated from carbon monoxide (CO). By using appropriate data analysis techniques, concentration profiles of CO (Level-2 (L2) data) can be obtained on a global basis at a reasonably high horizontal (~22km) and vertical resolution (~3km).The MOPITT Level-3 (L3) data products provided in this data set are a subset of the daily averages from the L2 data. This subset was produced by overlaying a global 1x1-degree grid onto the L2 data, and then clipping the data to this southern Africa subset which originates at 5 degrees longitude and -35 degrees latitude and extends to 60 degrees longitude and 35 degrees latitude. Data are reported for 2 heights, 700 and 350 hPa, from daytime swaths for the period August 1-September 30, 2000, the SAFARI 2000 Dry Season Campaign.\n<br>\n\n#### nbi_veg_maps_787\nThe National Botanical Institute (NBI) has mapped woody plant species distribution to provide estimates of individual species contribution to peak leaf area index for designated vegetation types in southern Africa (Rutherford et al., 2000). The target was to account for 80% of the woody vegetation leaf area in terms of named species, for 80% of the surface area of Africa south of the equator. The data sources include published and unpublished species lists for vegetation types and individual sample plots, with the species contribution estimated by local experts in terms of dominants and subdominants. Source maps include: Low and Rebelo (1998); Giess (1971); Wild and Barbosa (1968); Barbosa (1970); and White (1983). Each source map delineates a wide variety of land cover categories that differ from region to region. Because vegetation discontinuities exist along some of the regional borders and a perfectly continuous regional map could not be achieved within the timeframe and budget of the project, the final map is made up of six independent sub-regional maps. A cross-referenced database of woody plant species, in order of species dominance, associated with all mapped units is provided.The data set contains six GIS shapefile archives, each containing a shape file for a given region in southern Africa on a 5 x 5 degree grid. An accompanying ASCII file contains the species list associated with the map files. The regional NBI Vegetation Map (a compilation of the 6 independent sub-regional coverages) is provided as a JPEG image.\n<br>\n\n#### s2k_zinke_soil_638\nThe data set contains a subset of the Worldwide Organic Soil Carbon and Nitrogen (Zinke et al. 1986) data set for southern Africa. The data were obtained from soil surveys by Zinke and soil survey literature. The main samples for laboratory analyses were collected at uniform soil increments and included bulk density determinations. Many samples reported in the literature did not have uniform soil increments or bulk density determinations. Only soil profiles that had been sampled either to a meter in depth or to actual depth were included in this data base from soil survey literature. In literature where bulk densities were absent, densities were estimated by regressions based on organic carbon content of the soil samples associated with the profile using 1800 soil profiles for which bulk densities were known. More information can be found at: ftp://daac.ornl.gov/data/safari2k/soils/Zinke_soil/comp/zinke_readme.pdf.\n<br>\n\n#### kt_pai_estimates_774\nThis data set was collected during February-March 2000 wet season and September 2000 dry season field campaigns of SAFARI 2000. Mongu in Zambia and Pandematenga (aka Kasane) and Tshane in Botswana were visited during the wet season campaign. Dry season data are for Mongu only. Hemispherical photographs, from which Plant Area Index (PAI) estimates are derived, were obtained at the field sites to characterize vegetation structural changes along the Kalahari Transect. At each site, photographs were collected using a Nikon Coolpix 950 digital camera with a fish-eye lens mounted on a tripod, every 25 m along three transects, each 750 m in length. The methodology of van Gardingen et al. (1999) was applied to process the data. The photographs are classified into sky and vegetation (trunk, green and senescent leaves, and branches) using an unsupervised classification scheme. Derivation of gap fraction estimates was carried out by dividing the azimuth sector for each view zenith into 72 equal area segments with the gap fraction estimated for each segment. The average azimuth segmented gap fraction was used to characterize the gap fraction at the current view zenith angle. This approach attempts to reduce the underestimation introduced by assuming a random distribution in clumped canopies. PAI estimates were derived from the gap fraction data using Miller's (1967) formula for average foliage density.Data are in comma-delimited ASCII files, one for each transect at each site, containing a 5-line header, then a blank line, column names, and the two-column data (sample number and PAI).\n<br>\n\n#### kt_par_794\nCeptometer data from a Decagon AccuPAR (Model PAR-80) were collected at four sites in Botswana during the SAFARI 2000 Kalahari Transect Wet Season Campaign (March, 2000). These sites include Maun, Pandamentanga, Ghanzi/Okwa River Crossing, and Tshane. The measurements were taken near stake flags placed at 25 m intervals along three parallel 750 m transects located 250 m apart. The ceptometer contains 80 photosynthetically active radiation (PAR) sensors fixed at 1 cm intervals along a wand and connected to a control box. The sampling protocol followed in general was to first measure above canopy incident PAR, then canopy reflected PAR, then above canopy incident PAR again, and finally, canopy transmitted PAR. The data can be used to compute fraction of photosynthetically active radiation (FPAR), intercepted PAR, leaf area index (LAI), and gap fraction. These data currently exist in raw format, but can be processed using manufacturer-provided software to estimate the derived products.The data are stored as ASCII files, in csv format, organized by site, with one file per transect. Incident, transmitted, and reflected PAR radiation values for a transect and site are in the same file. The type of measurement for each data point is known due to comments in the data files. For the Maun and Pandamatenga sites, there is an additional file containing above canopy PAR irradiance. The PAR data units are micromols m-2 s-1, and the time is in Local Time. There is also a readme file, in txt format, for each site.\n<br>\n\n#### aerosol_properties_725\nThe Southern African Regional Science Initiative 2000 (SAFARI 2000) provided an opportunity to study aerosol particles produced by savanna burning. We used analytical transmission electron microscopy (TEM), including energy-dispersive X-ray spectrometry (EDS) and electron energy-loss spectroscopy (EELS), to study aerosol particles from several smoke and haze samples and from a set of cloud samples. These aerosol particle samples were collected using the University of Washington Convair CV-580 research aircraft (Posfai et al., 2003).Individual aerosol particles in smoke plumes from biomass fires and in regional hazes in southern Africa were studied using analytical transmission electron microscopy, which allowed detailed characterization of carbonaceous particle types in smoke and determination of changes in particle properties and concentrations during smoke aging. Based on composition, morphology, and microstructure, three distinct types of carbonaceous particles were present in the smoke: organic particles with inorganic (K-salt) inclusions; 'tar ball' particles; and soot. The relative number concentrations of organic particles were largest in young smoke, whereas tar balls were dominant in a slightly aged (~1 hour) smoke from a smoldering fire. Flaming fires emitted relatively more soot particles than smoldering fires, but soot was a minor constituent of all studied plumes. Further aging caused the accumulation of sulfate on organic and soot particles, as indicated by the large number of internally mixed organic/sulfate and soot/sulfate particles in the regional haze. Externally mixed ammonium sulfate particles dominated in the boundary layer hazes, whereas organic/sulfate particles were the most abundant type in the upper hazes. Apparently, elevated haze layers were more strongly affected by biomass smoke than those within the boundary layer. Based on size distributions and the observed patterns of internal mixing, we hypothesize that organic and soot particles are the cloud-nucleating constituents of biomass smoke aerosols. Sea-salt particles dominated in the samples taken in stratus clouds over the Atlantic Ocean, off the coast of Namibia, whereas a distinct haze layer above the clouds consisted of aged biomass smoke particles.\n<br>\n\n#### plant_soil_c_n_783\nThis data set contains measurements of the concentration and stable carbon (13C / 12C) and nitrogen (15N / 14N) isotope ratios of plant (leaves, roots and fungi) and soil samples from southern Africa. The study sites in Zambia, Botswana, Namibia, and South Africa are located along the Kalahari Transect precipitation gradient. Some of the sites were relatively undisturbed while others had different intensities of cultivation, domestic grazing, and fires. The data were collected to detect patterns of N cycling along precipitation and grazing gradients, including N2 fixation by legumes. Data from different multiple projects are included. The plants and soils were sampled mainly in the wet season of years 1995, 1999, and 2000, with most of the data collected during the SAFARI 2000 Kalahari Wet Season Field Campaign in February and March of 2000. Some grass samples were collected in the dry season of year 2000 (from Mongu-dambo and Sua Pan grassland sites). Soil and plant samples were analyzed in a laboratory for %C, %N, d13C, and d15N with an Optima isotope ratio mass spectrometer coupled to an elemental analyzer. The stable isotope ratios are expressed using standard delta notation in units per mil. The isotope ratios are expressed relative to the international standard PDB (Pee Dee Belemnite) for carbon and atmospheric N2 for nitrogen samples. The carbon and nitrogen contents are expressed in percentage weight of the dry sample.The data files contain numerical and character fields of varying length separated by commas (.csv format).\n<br>\n\n#### s2k_potential_vegetation_639\nThe data set consists of a southern Africa subset of the 5-min resolution Global Potential Vegetation data set developed by Navin Ramankutty and Jon Foley at the University of Wisconsin. Data are available in both ASCII GRID and binary image file formats. The original map was derived at a 5-min resolution and contains natural vegetation classified into 15 types. This data set is derived mainly from the DISCover land cover data set, with the regions dominated by land use filled using the vegetation data set of Haxeltine and Prentice (1996). The data set represents the world's potential vegetation (i.e., vegetation that would most likely exist now in the absence of human activities), and not necessarily natural pre-settlement vegetation. This is because human activities such as fire suppression have modified the stages of succession at which vegetation communities exist. More information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/potential_vegetation/comp/Potential_Veg_readme.pdf.\n<br>\n\n#### pre_post_fire_refl_757\nThe main goal of this study was to analyze the possibility of estimating combustion completeness based on fire-induced spectral reflectance changes of surface features by the development of relationships between combustion completeness and pre-fire to post-fire spectral reflectance changes, in the green, red, and near-infrared spectral domains (equivalent to Landsat ETM+ channels 2, 3, and 4). Experimental burns were carried out in the Western Province of Zambia in dambos (hydromorphic grasslands) and miombo woodlands during the SAFARI 2000 Third Intensive Field Campaign in August and September of 2000.This data set contains three sets of measurements: reflectance measurements from a FieldSpec UV/VNIR Spectroradiometer along burned and unburned woodland transects, in pre- and post-fire woodland and dambo plots, and for pure spectra of soil, vegetation, and charcoal; reflectance and transmittance measurements of both upper and lower leaf faces for three tree species (Julbernardia globiflora, Isoberlinia angolensis, and Brachystegia spiciformis) using an External Integrating Sphere connected to the FieldSpec Spectroradiometer; and calculations that integrate pre- and post-fire biomass measurements with pre- and post-fire reflectance in ETM+ channel 2, 3, and 4 wavelengths to estimate combustion completeness. The data files are stored as Excel spreadsheets (.csv format). Additional information about the data format, study area, instrumentation, and results is found in the companion file: ftp://daac.ornl.gov/data/safari2k/remote_sensing/pre_post_fire_refl/comp/pre_post_fire_refl_readme.pdf.\n<br>\n\n#### knp_fire_residue_751\nThe goal of this study was to understand the change in reflectance caused by the action of fire and the heterogeneity of fire effects (i.e., the fraction of the observation that burned and the combustion completeness of that observation). A spectral mixture model and field and satellite observations were used to compare changes in Landsat reflectance associated with fire and combustion completeness derived from field measurements at prescribed fire sites in South Africa and to substantiate and illustrate the model findings.Fire residue samples were collected from experimental burn plots in Kruger National Park during the SAFARI 2000 Dry Season Campaign in August-September of 2000. The residues include 3 different ash types: pure white ash; black ash; and residual non-photosynthetic fuel biomass (senescent grass, twigs, leaves, and bark). The samples were analyzed in the laboratory to determine their multi-spectral reflectance using an Analytic Spectral Devices (ASD) radiometer, which measures spectral reflectance in the range 0.45-2.2 um at intervals of 0.01 um. The ASD measurements were made under diffuse illumination conditions in the spectral range of 350 nm to 2500 nm, with the ASD radiometer aligned perpendicular to the samples to simulate nadir remote sensing conditions.The data file contains reflectance measurements (fraction, 0-1) for replicate samples of 3 different ash types (white ash, black ash, and non-photosynthetic vegetation) recorded at 10 nm intervals over the wavelength spectral range of 350 nm to 2500 nm. The data are stored in a single ASCII file in comma-separate-value format (.csv).\n<br>\n\n#### river_discharge_cpep_640\nThe data set consists of southern Africa subset of the CPEP Global River Discharge Data Set. The Climate, People, and Environment Program (CPEP) global river discharge data set is a compilation of monthly mean discharge data for over 2600 sites worldwide. The data sources are RivDis 2.0, the United States Geological Survey, and Brazilian National Department of Water and Electrical Energy. The period of record is variable, from 3 years to greater than 100. The purpose of this compilation is to provide detailed hydrographic information to the climate research community in as general a format as possible. Data is provided in units of meters cubed per second (m**3/sec) in ASCII format. Data from stations with less than 3 years of information or with basin area less than 5000 km2 were excluded from this compilation. Therefore the original sources may have more sites available. No further documentation is available on this data set. Users should refer to the data originators for documentation. More information can be found at: ftp://daac.ornl.gov/data/safari2k/hydrology/river_discharge_cpep/comp/cpep_discharge.pdf.\n<br>\n\n#### seawifs_624\nThis data set contains Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery for the eight core study sites of Mongu, Etosha, Kasangu, Skukuza, Mutoko, Mzola, Nampula, and Ndola. There are two main sets of local area coverage (LAC) data, Level-1 and Level-2 200-km x 200-km image subsets for seven of the sites and 400-km x 400-km image subsets for the Etosha site. The data files presented here were selected from a large collection of SeaWiFS images based on image clarity and lack of clouds. Several hazy days were included as a contrast. The data are provided in HDF format files. The SeaWiFS sensor, launched in late 1997, collects data in 8 bands ranging from 402 nm to 885 nm. SeaWiFS data are available at two spatial resolutions, local area coverage (LAC) at 1.1 km and global area coverage (GAC) at 4.5-km resolution. There are many additional images available at the SeaWiFS Web site. More information about this dataset can be found in the companion document, SAFARI 2000 SeaWiFS Site and Regional Imagery Descriptions. ORNL DAAC maintains information on the entire SAFARI 2000 Project.\n<br>\n\n#### seawifs_region_625\nThis data set contains Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery for the southern African region. These images are Level-1a swaths of the southern Africa region selected from global area coverage (GAC) at 4.5-km resolution. The data files presented here were selected from a large collection of SeaWiFS images based on image clarity and lack of clouds. Several hazy days were included as a contrast. The data are provided in HDF formatted files. The SeaWiFS sensor, launched in late 1997, collects data in 8 bands ranging from 402 nm to 885 nm. SeaWiFS data are available at two spatial resolutions, local area coverage (LAC), at 1.1 km, and global area coverage (GAC) at 4.5-km resolution. There are many additional images available at the SeaWiFS Web site. More information about this dataset can be found in the companion document, SAFARI 2000 SeaWiFS Site and Regional Imagery Descriptions. ORNL DAAC maintains information on the entire SAFARI 2000 Project.\n<br>\n\n#### s2k_IGBP-DIS_Soil_Surfaces_647\nThe data set consists of a southern African subset of the \"Global Gridded Surfaces of Selected Soil Characteristics (IGBP-DIS)\" produced by the International Geosphere-Biosphere Programme - Data and Information Services. Data files are provided in ASCII GRID format. The \"Global Gridded Surfaces of Selected Soil Characteristics (IGBP-DIS)\" data set contains seven data surfaces: soil-carbon density, total nitrogen density, field capacity, wilting point, profile available water capacity, thermal capacity, and bulk density. All the surfaces are global, at a resolution of 5 x 5 arc-minutes, in ASCII GRID format for ARC INFO. Each file contains a single ASCII array in a geographic (lat/long) projection. The ASCII files consist of header information containing a set of keywords, followed by cell values in row-major order.\n<br>\n\n#### shadoz_ozonesonde_726\nOzonesonde launches were made by the Southern Hemisphere ADditional OZonesondes (SHADOZ) group as part of the SAFARI 2000 Dry Season Campaign in September 2000 (Thompson et al., 2002). Ozonesondes are balloon-borne instruments measuring profile ozone, as well as temperature and pressure from an attached radiosonde, up to 35 km in height (around 5 hPa in pressure coordinates) capturing the troposphere and lower stratospheric portion of the atmosphere. During the campaign, ozonesondes were launched daily during the height of the burning season and in a region of active biomass burning activity.\n<br>\n\n#### s2k_ISRIC_Wise_profiles_648\nThe data set consists of a southern African subset of the \"Global Soil Profile Data (ISRIC-WISE)\" data set. Data files are provided in comma-delimited ASCII format. The International Soil Reference and Information Centre - World Inventory of Soil Emission Potentials (ISRIC-WISE) international soil profile data set consists of a homogenized, global set of 1,125 soil profiles for use by global modelers. These profiles provided the basis for the Global Pedon Database (GPDB) of the International Geosphere-Biosphere Programme - Data and Information System (IGBP-DIS). The data set consists of a selection of 665 profiles originating from the Natural Resources Conservation Service (NRCS, Lincoln, Nebraska, U.S.A.), 250 profiles obtained from the Food and Agriculture Organization (FAO, Rome, Italy), and 210 profiles from the reference collection of the International Soil Reference and Information Centre (ISRIC, Wageningen, The Netherlands). All profiles are georeferenced and classified according to the 1974 Legend of the FAO-UNESCO Soil Map of the World (FAO-UNESCO 1974), as well as the 1988 Revised Legend of FAO-UNESCO (FAO 1990). The data set includes information on soil classification, site data, soil horizon data, source of data, and methods used for determining analytical data.\n<br>\n\n#### mongu_skukuza_soil_prop_789\nSoil moisture and temperature profile sensors were deployed at flux tower sites in Mongu, Zambia and Skukuza, South Africa. In addition, thermal infrared sensors were deployed to monitor surface temperature at the sites, and soil samples were collected for physical property analysis. A heat-flux plate was also installed at 10 cm depth at the Mongu site. The data cover the period variously from August, 1999 to December, 2001.At the Mongu site, three profiles of soil moisture and temperature were obtained to a maximum depth of 125 cm. These profiles were located approximately 30 meters north of the Mongu flux tower, within the Kataba Local Forest. Surface radiometric temperature was measured by thermal infrared sensors deployed on top of the 30-meter tower and on a tree. At the Skukuza site, two profiles of soil moisture and temperature were obtained to a maximum depth of 40 cm in a Combretum stand. The radiometric temperature of the tree crown and the background surface were monitored by infrared thermocouple sensors deployed on a pole at 2.5 m and 5 m heights. Soil samples were collected at different depths in the vicinity of the soil profiles at each site and were analyzed at CSIR in Pretoria to determine bulk density, texture, and particle size distribution. The data files are stored as ASCII text files, in comma-separated-value (.csv) format. Associated with each data file is a metadata (.txt ) file. Among other information, the metadata files indicate periods of missing data.\n<br>\n\n#### Zobler_Soil_649\nThe data set consists of a southern African subset of the \"Global Soil Types, 0.5-Degree Grid (Modified Zobler)\" data set. Data files are provided in ASCII GRID format. A global data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler?s (1986) assessment of the FAO/UNESCO Soil Map of the World. This data set is a conversion of the Zobler 1-degree resolution version to a 0.5-degree resolution. The resolution of the data set was not actually increased. Rather, the 1-degree squares were divided into four 0.5-degree squares with the necessary adjustment of continental boundaries and islands.\n<br>\n\n#### SSFR_irradiance_841\nThe Solar Spectral Flux Radiometer (SSFR) was deployed on the University of Washington CV-580 during the dry season component of the Southern African Regional Science Initiative, August 1 - September 20, 2000. The SSFR made simultaneous measurements of both downwelling and upwelling net solar spectral irradiance at varying flight levels. Data have been provided for twenty flights in netcdf format for the period August 17 - September 16, 2000.For a complete detailed guide to the extensive measurements obtained aboard the UW Convair-580 aircraft in support of SAFARI 2000, see the UW Technical Report for the SAFARI 2000 Project.\n<br>\n\n#### GPROF_precip_716\nThe GPROF 6.0 Pentads data set contains 5-day (pentad) averages of the GPROF 6.0 Gridded Orbits. The GPROF(Goddard Profiling Algorithm) data set contains a suite of 9 products providing instantaneous, gridded values of precipitation totals for each granule of the SSM/I (Special Sensor Microwave/Imager) data over the roughly 14-year period July 1987 through the present. Even though there have been at least two satellites for the entire period, sampling is sufficiently sparse that the data are averaged for pentads, then the pentads are smoothed with a 1-2-3-2-1 time-weighting. The last two pentads are unevenly weighted since the last (or last two) pentads in the average are not yet available. Consequently, the last two pentads must be recomputed when the next pentad becomes available.The data set prepared for SAFARI cover the years 1999, 2000, and 2001.The main refereed citations for the data set are Kummerow et al. (1996)and Olson et al. (1999)\n<br>\n\n#### kt_stem_map_775\nThis data set provides species distribution, basal area, height, and crown cover of woody stems at 10 sites along the Kalahari Transect where a large gradient in both the mean and variation of annual rainfall results in dramatic changes in vegetation structure. Some of the data were collected during earlier Kalahari Transect projects in 1995 and 1997 at Vastrap, South Africa; Sandveld and Sachinga, Namibia; and Maziba, Senanga, and Lukulu, Zambia. The rest of the data were collected at Mongu, Zambia; and Pandamatenga, Maun, and Tshane, Botswana during the February-March 2000 wet season field campaign of SAFARI 2000. Stem maps were generated at each site using a variable-width belt-transect approach. Tree location, species, diameter, height, and major and minor axis of crown dimensions were measured for each individual taller than 1.5 meters. For multi-stemmed individuals, the diameter of each stem was recorded separately. Canopy area was calculated to be an ellipse defined by the two major axes of measurement. Canopy height was estimated using a clinometer. Biomass was calculated following Goodman (1990) as modified by Dowty (1999).There are two ASCII data files, in comma-delimited format. The stem map file contains records of living, dead, and cut stem allometry, canopy geometry, and biomass at the SAFARI sites. The species list file provides plant family, genus, and species names, numerical codes that correspond to the stem map file, and species common names in English and AFRICAANS.\n<br>\n\n#### mongu_skukuza_albedo_786\nTop-of-the-canopy broadband albedo and radiation fluxes are calculated from measurements at the Mongu and Skukuza flux tower sites in southern Africa from March 2000 through December 2002. Data were collected by instrumentation deployed at the top of the 30 m tower in the Kataba Local Forest near Mongu, Zambia, and atop the 20 m tower at the Skukuza tower site in Kruger National Park, South Africa. At the Mongu site, Kipp and Zonen albedometers housing both upward- and downward-looking pyranometers were outfitted with clear and red domes to collect broadband albedo and radiation fluxes in the shortwave (SW) and near-infrared (NIR) wavebands, respectively. The data are mean values provided at 15-minute intervals for 2000-2002. At the Skukuza tower, Kipp and Zonen albedometers (also outfitted with clear and red domes) collected broadband albedo and radiation fluxes in the SW and NIR wavebands. In addition, a pyrgeometer was used to collect longwave radiation flux in thermal infrared (TIR) wavebands. The data at Skukuza are mean values provided at 30-minute intervals for 2000-2002, except for the TIR data, which are provided for 2001 and 2002 only. For both sites, photosynthetically active radiation can be calculated from measurements.The data are provided in comma-delimited ASCII files, with column headers. The SW and NIR data for both sites are provided in one file per year per site for 2000-2002. The Skukuza longwave data are provided separately, and for years 2001 and 2002 only.\n<br>\n\n#### smart_radiometers_727\nSurface-sensing Measurements for Radiative Transfer (SMART) and Chemical, Optical, and Microphysical Measurements of In-situ Troposphere (COMMIT) consist of a suite of instruments that measure (both in-situ and by remote sensing) parameters that help to characterize, as completely as possible, constituents of the atmosphere at a given location. SMART and COMMIT are mobile systems that can be deployed to locations that exhibit interesting atmospheric phenomena. This allows investigators to participate in coordinated measurement campaigns, such as SAFARI 2000.The SMART instruments were deployed to the Skukuza Airport from August 15 to September 17, 2000 to take part in the SAFARI 2000 Dry Season Aircraft Campaign. The SMART-COMMIT mission is designed to pursue the following goals: Earth Observing System (EOS) validation; innovative investigations; and long-term atmospheric monitoring.The results reported in this data set are for the following instruments deployed and measurements recorded at the Skukuza Airport site within the Kruger National Park:several broadband radiometers, for global, diffuse, direct downward solar irradiance and global infrared downward irradiance; meteorological sensors, for surface air temperature, pressure, relative humidity, and wind; anda Solar Spectral Flux Radiometer (NASA Ames), for spectral solar downward irradiance.\n<br>\n\n#### mongu_irradiance_782\nThis data set contains the top-of-canopy irradiance in the shortwave (0.3-2.8 micron) and photosynthetically active radiation (PAR; 0.4-0.7 micron) wavebands collected with an Eppley Precision Spectral Pyranometer (PSP) and a Skye SKE510 pyranometer, respectively. The instruments were deployed at the top of the 30-m tower in the Kataba Local Forest approximately 20 km south of Mongu in Western Province, Zambia. The data include the hourly mean and maximum values from 0500-1600 GMT (7 a.m. - 6 p.m. local time) and cover the period from September 4, 2000 to December 31, 2002. The data were obtained primarily for EOS validation and energy budget modeling.The Skye SKE510 uses a blue enhanced planar diffused silicon detector and has a fairly even response from 400 to 700 nm. The Eppley PSP is a World Meteorological Organization First Class Radiometer designed for the measurement of sun and sky radiation, totally or in defined broad wavelength bands. It comprises a circular multi-junction wire-wound thermopile. A data logger sampled the sensors at 60-second intervals and recorded the maximum and mean values every 60 minutes throughout the day.The data are contained within a single ASCII text file, in comma-separated-value format, with associated date, time, and QA information.\n<br>\n\n#### sua_pan_surface_spectra_780\nThe Multi-angle Imaging SpectroRadiometer (MISR) Validation team was deployed to Sua Pan, a salt playa in the Magkadigkadi region of Botswana, from August 18 to September 4, 2000, during the SAFARI 2000 Dry Season Aircraft Campaign. The experiment was designed to collect data necessary for multi-angle top-of-atmosphere radiance predictions in order to provide a vicarious calibration of the MISR instrument aboard the Terra satellite. Reported here are ground-based reflectance measurements collected using an Analytical Spectral Devices (ASD) spectroradiometer at Sua Pan and adjacent grassland targets. The grasslands provided large homogeneous areas for comparison of scale between ground measurements and remote sensing results.Data files contain numeric values that represent mean reflectance over space (grassland) or wavelength range (pan), stored as ASCII files, one file per site, in comma-separated-value (.csv) format, with column headers. The Sua Pan data, collected over a 1 km x 2 km area, are presented as rows of mean reflectance (every 10 nm) for 20 points, where the mean represents the average local reflectance spectra collected within 150 m of the given latitude and longitude. The grassland data cover a 1 km2 area and are provided every nm from 1 to 2500. Each row of data contains a mean and standard deviation at a given wavelength, where the mean represents the average of 570 measurements taken over the 1 km2 area.Related data sets from Sua Pan provide ground measurements of BRDF, and LAI and FPAR (Helmlinger et al., 2004 and Buermann and Helmlinger, 2004, respectively).\n<br>\n\n#### TOMS_aerosol_823\nDaily Aerosol Index (AI) data from Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) for the period of August 12-September 25, 2000 were processed and provided by the Atmospheric Chemistry and Dynamics Branch at NASA/GSFC for the SAFARI 2000 Dry Season Aircraft Campaign.The TOMS AI is formed directly from measured TOMS radiances in two channels. It is a measure of how much the wavelength dependence of backscattered UV radiation from an atmosphere containing aerosols (Mie scattering, Rayleigh scattering, and absorption) differs from that of a pure molecular atmosphere (pure Rayleigh scattering). Quantitatively, the AI is defined at http://toms.gsfc.nasa.gov/aerosols/AI_definition/ai_ep_definition.pdf. Positive values represent absorbing aerosols (dust and smoke); negative values represent non-absorbing aerosols. The identification is not perfect because of geophysical reasons (e.g., when aerosols are too low to the ground).The data from TOMS records have been used increasingly to understand the behavior of aerosols within the atmosphere. The TOMS is the first instrument to allow observation of aerosols as the particles cross the land/sea boundary. Using these data it is possible to observe a wide range of phenomena such as desert dust storms, forest fires, and biomass burning.The TOMS AI data are a daily gridded Level-3 product (ASCII .dat format) that covers the area of 40 deg. S to the Equator and 40 deg. W to 80 deg. E. There is also a JPEG image of each data file.\n<br>\n\n#### TOMS_ozone_824\nTropical Tropospheric Ozone (TTO) data from Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) for the period of August 8-September 29, 2000 were processed and provided by the Atmospheric Chemistry and Dynamics Branch at NASA/GSFC for the SAFARI 2000 Dry Season Aircraft Campaign.The TTO measurement is derived from TOMS total ozone (Thompson and Hudson, 1999; Thompson et al., 2001) using the modified-residual method to separate stratospheric ozone from tropospheric ozone. The tropospheric ozone column thickness is reported in Dobson Units (DU).EP TOMS is currently the only NASA spacecraft on orbit specializing in ozone retrieval. EP TOMS was launched in 1996 into an orbit 500 km rather than the 950 km that was originally planned. The Earth Probe satellite was boosted to 740 km in 1997 when the ADEOS satellite failed. The lower orbit of EP TOMS decreased the size of the footprint of each measurement, which increased the resolution and also increased the ability to make measurements over cloudless scenes. This orbit was chosen to improve the ability of the TOMS instrument to make measurements of UV-absorbing aerosols in the troposphere and enhanced the capability of converting the TOMS aerosol measurements into geophysical quantities such as optical depth. Tropospheric aerosols play a major role in the Earth's climate and the capability to measure them from a TOMS instrument had recently been developed using data from Nimbus-7 and Meteor-3 TOMS.The TOMS Tropospheric Ozone data are 9-day averaged, gridded (1-degree by 2-degree) ASCII products. There is also a GIF image of each data file.\n<br>\n\n#### TOVS_717\nNOAA's TIROS (Television Infrared Observation Satellite) Operational Vertical Sounder (TOVS) is a suite of three sensors: the Microwave Sounding Unit (MSU), the High resolution Infrared Radiation Sounder (HIRS), and the Stratospheric Sounding Unit (SSU) aboard the NOAA series of polar-orbiting operational meteorological satellites. TOVS-derived data provide a means to investigate long-term climate change and interannual variability and study local and periodic phenomena such as El Nino and stratospheric warmings. A set of the derived meteorological parameters for southern Africa have been selected by SAFARI 2000. The data were extracted from 1 degree x 1 degree global fields, with data from each satellite's local AM and PM orbits provided separately.Selected parameters for SAFARI 2000 data set:Layer mean temperature at 4 coarse layers Total effective cloud fraction Cloud fractions at 7 pressure layers Longwave cloud radiative forcing Outgoing long-wave radiation Cloud top pressure Precipitation estimate Precipitable water vapor above the surface and four pressure levels Surface pressure Cloud top temperature Cloud fraction at 7 pressure layers Surface skin temperature Specific humidity at surface and at 5 pressure layers Temperature at multiple levels Retrieved virtual temperature between levelsThe data files are stored as annual files of pentad (5-day) images. The pentads are stored in band sequential [BSQ] format (one image after the other) within the yearly files, 73 pentads per file. There is one file for each of the AM and the PM satellite overpasses for each parameter provided.\n<br>\n\n#### tree_cover-1km_641\nThe data set consists of a southern Africa subset of the 1km Global Tree Cover Data Set developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Data are available in both ASCII GRID and binary image files formats. Characterization of terrestrial vegetation from the Advanced Very High Resolution Radiometer (AVHRR) on the global to regional scale has traditionally been accomplished using classification schemes with discrete numbers of vegetation classes. Representation of vegetation into a limited number of homogeneous classes does not account for the variability within land cover, nor does the portrayal recognize transition zones between adjacent cover types. An alternative paradigm to describing land cover as discrete classes is to represent land cover as continuous fields of vegetation characteristics using a linear mixture model approach. This prototype data set, created by researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland, contains 1-km cells estimating: 1) Percent tree cover; 2) Percentage cover for two layers representing leaf longevity (evergreen and deciduous); and 3) Percentage cover for two layers estimating leaf type (broadleaf and needleleaf). Data acquired in 1992-93 from NOAA's AVHRR at a 1-km spatial resolution and processed under the guidance of the International Geosphere Biosphere Programme (IGBP) were used to derive the tree cover, leaf type and leaf longevity maps. Each pixel in the layers has a value between 10 and 80 percent. These layers can be directly used as parameters in models or aggregated into more conventional land cover maps. For the latter, the product offers the flexibility to derive land cover maps based on user's requirements for a particular application. The product is intended for use in terrestrial carbon cycle models, in conjunction with other spatial data sets such as climate and soil type, to obtain more consistent and reliable estimates of carbon stocks. More information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/tree_cover-1km/comp/glcftree_readme.pdf.\n<br>\n\n#### mongu_tree_rings_788\nThis data set contains tree ring data from three sites located about 25 km of the meteorological station at Mongu, Zambia. Data from about 50 individual trees are reported. In addition, chronologies (or site mean curves) that better represent common influences (e.g., in this study, the climatic signal) were developed for each site based on the individual data (Trouet, 2004; Trouet et al., 2001). The series covers a maximum of 46 years, although most series do not extend longer than 30 years. The data were collected during the SAFARI 2000 Dry Season Field Campaign of August 2000.Ten to 23 samples were taken at each site. Brachystegia bakeriana was sampled at site 1, and Brachystegia spiciformis at sites 2 and 3. The vegetation at all sites underwent primitive harvesting for subsistence earlier the same year, thus samples could be taken from freshly cut trees and no living trees were cut. At all sites, samples consisted of full stem discs. Where possible, samples were taken at breast height (1.3 m) or slightly lower. Growth ring widths were measured to the nearest 0.01 mm using LINTAB equipment and TSAP software (Rinn and Jakel, 1997). Four radii per sample disc were measured. Cross-dating and response function analyses were performed by routine dendrochronological techniques. There are two files for each site, one containing integer values representing tree ring widths (raw data), and the other containing standardized values (chronologies), for each year. The data are stored as ASCII table files in comma-separated-value (.csv) format, with column headers.\n<br>\n\n#### TRMM_precip_718\nThe purpose of the Tropical Rainfall Measuring Mission (TRMM) and TRMM Product 3B-43 is to provide a monthly, best-estimate precipitation rate and root-mean-square (RMS) precipitation error. These gridded estimates are on a one-calendar month temporal resolution and a 1-degree by 1-degree spatial resolution for the global band extending from 40 degrees south to 40 degrees north in latitude. Product 3B-43 combines two independent precipitation estimates, the daily-average adjusted GOES Precipitation Index (GPI) based on merged-infrared (IR) estimates (TRMM Product 3B-42) and the monthly accumulated Climate Assessment and Monitoring System (CAMS) or Global Precipitation Climatology Centre (GPCC) rain gauge analysis (TRMM Products 3A-45A and 3B-45B, respectively). During initial processing the CAMS data is used. During reprocessing, the GPCC data is used.The data set prepared for SAFARI covers the years 1999, 2000, and 2001. The data files are stored as yearly files of monthly images. The monthly images are stored in a band sequential [BSQ] format (one image after the other) within the yearly files. The files for each year thus contain 12 monthly images per file.\n<br>\n\n#### met_profile_skukuza_728\nVaisala RS80 sondes were deployed from Skukuza Airport, South Africa, to collect atmospheric sounding profiles of temperature and moisture data from the surface to 30 km. These sonde launches were coordinated to augment the regional sounding network in the region during the SAFARI 2000 Dry Season Campaigns of 1999 and 2000. The radiosondes were launched from Skukuza Airport between August 14-September 3, 1999 and between August 24-September 23, 2000. The radiosonde instrument package RS80 measured the following meteorological parameters: pressure in hecto-Pascals (P), ambient temperature in degrees Celsius (T), and relative humidity in percentage (RH). A hydrostatic equation was applied to the recorded data, after error-checking, to calculate the output parameters: height above sea level in meters, dew point temperature in degrees Celsius, and q (g/kg) which is specific humidity in grams per kilogram.\n<br>\n\n#### met_profile_SA_729\nThe University of Wyoming has a series of balloonborne radiosonde measurements from all around the world, from the surface to 30 km. This data set contains upper air meteorological profiles from 594 radiosonde launches deployed from sites in South Africa. These sonde launches were made to augment the regional sounding network in the region during the SAFARI 2000 Dry Season Campaign of 2000.Vaisala RS80 sondes were launched from nine sites in South Africa between August 1, 2000 and September 30, 2000. The launch sites were Pietersburg (changed to Polokwane after 2000), Pretoria (Irene), Bethlehem, Springbok, De Aar, Durban, Cape Town, Port Elizabeth, and Gough Island. The parameters measured by the radiosonde instruments include: pressure, air temperature, relative humidity, wind speed, and wind direction.\n<br>\n\n#### vegsoils_wilhend_642\nThis data set contains a subset for southern Africa of Wilson and Henderson-Sellers' Global Vegetation & Soils 1-degree data. The data are available in both ASCII GRID and binary image files formats. The Wilson, Henderson-Sellers' Global Vegetation and Soils data set is an archive of soil type and land cover data derived for use in general circulation models (GCMs). The data were collated from natural vegetation, forestry, agriculture, land use, and soil maps. The data are archived at 1 degree latitude x 1 degree longitude resolution and include data for soil, soil reliability, primary vegetation, secondary vegetation, and land cover reliability. There are approximately fifty land cover classifications which include categories for agricultural and urban uses. The inclusion of secondary vegetation type is particularly useful is areas with cover types which may have a fragmented distribution, such as urban development. The soil type data are classified using climatically important properties for CGMs and provide color (light, medium, or dark), texture, and drainage quality of the soil. The land cover data are compatible with the soils data forming a coherent and consistent data set. Reliability data rank the land cover data on a 1 to 5 scale from high to low reliability. The soil reliability is ranked as one of the following: high, good, moderate, fair, or poor. Recommendations for the use of these data as well as more detailed information can be found in: Wilson, M.F. and A. Henderson-Sellers, 1985. A Global Archive of Land Cover and Soils Data for Use in General Circulation Climate Models. Journal of Climatology, Vol.5, 119-143. More data set information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/vegsoils_wilhend/comp/wilhend_readme.pdf.\n<br>\n\n#### kt_veg_inventory_776\nVegetation cover and composition, including species types and richness assessments, were measured at four locations along the Kalahari Transect in Botswana (Pandamatenga, Maun, Okwa River, and Tshane) during the SAFARI 2000 wet season field campaign. The sites visited showed interesting degrees of variability despite the apparent homogeneity of the Kalahari sands and predominantly semi-arid savanna shrub-woodlands vegetation cover (Ringrose et al., 2003).At each site, twelve individual locations were chosen by random stratified techniques within a 30-km radius at each location, based on differences in topography, soils, and known disturbance, to help determine local variability (Huennecke et al., 2001). Data collection methods were identical at each location (Ringrose et al., 1996; 1998): (1) identification and enumeration of all species along 3 x 90-m transects, spaced 45-m apart; (2) visual estimation (tape measure and pacing) of canopy diameter along each transect; and (3) visual estimation of percent live and dead herbaceous cover, litter, and bare soil using 3 x 50 m2 quadrats spaced at 30-m intervals along each transect. In addition, vegetation components were calculated for each site comprising woody vegetation cover, green herbaceous cover in terms of grass and forbs, dead herbaceous cover, plant litter, and bare soil. Species richness was calculated as the actual number of species per three transects (270 m2) at each site (Kent and Coker, 1996).The data set consists of two data files (ASCII tables) in comma-delimited format (.csv) with descriptive header records.\n<br>\n\n#### mongu_veg_structure_795\nTree basal area, percent tree canopy cover, and proportional contribution of main species to canopy cover were measured at 60 sampling points at 50 m intervals along six transects in the vicinity of the MODIS validation site tower in Kataba Forest, near Mongu, Zambia, in late February to early March 2000 as part of the SAFARI 2000 Wet Season Campaign. The aim of the study was to provide a broad description of the tree canopy layer around the tower.Tree and shrub species composition was recorded for each grid and measurements of canopy cover (% and rank) and frequency of occurrence (%) were made. Basal area was estimated at each grid site in a single 360 degree sweep using a basal area prism. Four estimates of canopy cover, oriented north, south, east, and west around the sample point, were taken at each grid site using a spherical densiometer and the data were averaged to give a single value for each grid. Only the canopies of trees and shrubs above 1.5 m height were measured.The data are stored in an ASCII file, in csv format. The file lists all tree and shrub species recorded and provides the proportional contribution of these species to canopy cover in each grid. Total tree basal area (m2 ha-1) and overall tree canopy cover (%) in each grid is also provided. The companion file provides additional vegetation data, graphics, long-term meteorological data, a discussion of the study results, and photographs of the study site.\n<br>\n\n#### giss_wetlands_632\nThis database, compiled by Matthews and Fung (1987), provides information on the distribution and environmental characteristics of natural wetlands. The database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources. The original data consists of five global 1-degree latitude by 1-degree longitude arrays. The subset retains all five arrays at the 1-degree resolution but only for the area of interest. The arrays are (1) wetland data source, (2) wetland type, (3) fractional inundation, (4) vegetation type, and (5) soil type. The data subsets are in both ASCII GRID and binary image file formats. The data base is the result of the integration of three independent digital sources: (1) vegetation classified according to the United Nations Educational Scientific and Cultural Organization (UNESCO) system (Matthews, 1983), (2) soil properties from the Food and Agriculture Organization (FAO) soil maps (Zobler, 1986), and (3) fractional inundation in each 1-degree cell compiled from a global map survey of Operational Navigation Charts (ONC). With vegetation, soil, and inundation characteristics of each wetland site identified, the data base has been used for a coherent and systematic estimate of methane emissions from wetlands and for an analysis of the causes for uncertainties in the emission estimate. The complete global data base is available from NASA/GISS [http://www.giss.nasa.gov] and NCAR data set ds765.5 [http://www.ncar.ucar.edu]; the global vegetation types data are available from ORNL DAAC [http://www.daac.ornl.gov]. More information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/giss_wetlands/comp/matt_fung_readme.pdf.\n<br>\n\n#### kt_woody_veg_777\nThis data set contains species composition, basal area, height, and crown cover of all woody plants at six sites along the Kalahari Transect visited in February-March of 2000 as part of SAFARI 2000. Similar measurements on woody and herbaceous vegetation at the Skukuza Flux Tower site in Kruger National Park, South Africa, were made in June of 2000. Leaf area index was derived from measurements made using PAR sensors at each site.Sampling protocol was the same at each site, with a slight variation at Skukuza. A grid of 42 points, 6 rows of 7 columns, each 50 m apart, was laid down in an area 300 m x 350 m for the Kalahari Transect sites. At Skukuza, the grid was 7x7, or 350 m x 350 m, centered on the tower site, yielding 49 points. At each grid point, all woody plants within a circular plot of a fixed radius were identified and measured. Stem circumference was measured on all stems and basal area per stem was derived. Basal area for the circular plots, per species, was calculated and extrapolated to hectares. Tree and stem densities were determined from the number of trees and stems in subplots and extrapolated to hectares. Woody plant height and canopy cover were determined, and aboveground woody biomass and peak leaf area index were estimated. The files are in comma-delimited ASCII format, with the first line listing the data set, author, and date, followed by the data records.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/SAFARI 2000/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","cog","earth observation","elevation","hdf","hydrology","ice","land cover","lidar","netcdf","oceans","precipitation","satellite imagery","soil moisture","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Historic_S2K_Website_1765 v1 - This dataset contains an archived copy of the Safari 2000 Project website as of October 2008. This archived website is provided for informational purposes only.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/ancillary_data/Historic_S2K_Website/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"one_deg_biomass_754 v1 - A new method is used to generate spatial estimates of monthly averaged biomass burned area and spatial and temporal estimates of trace gas and aerosol emissions from open fires in southern Africa.Global burned area data for the year 2000 (GBA2000) supplemented with the Along Track Scanning Radiometer (ATSR) fire count data are employed to quantify the area burned at 1-km resolution by using a fractional vegetation cover map derived from satellite observations. To account for the spatial heterogeneity in main types of vegetation within each 1-km grid cell, fuel load maps were developed from ...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/regional/one_deg_biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AERONET_aerosol_706 v1 - AERONET (AErosol RObotic NETwork) is an optical ground-based aerosol monitoring network and data archive system. AERONET measurements of the column-integrated aerosol optical properties in the southern Africa region were made by sun-sky radiometers at several sites in August-September 2000 as a part of the SAFARI 2000 dry season aircraft campaign.AERONET is supported by NASA's Earth Observing System and expanded by federation with many non-NASA institutions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/AERONET_aerosol/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"diffuse_irradiance_791 v1 - This data set contains monthly mean values of diffuse irradiance fraction [f(Ediff), or ratio of diffuse-to-total irradiance] at ground level for a 30-degree solar zenith angle and in seven spectral bands (MODIS bands 1-7) as well broadband visible (400-700 nm), near-infrared (700-3000 nm) and shortwave (400-3000 nm). Values are provided for eight SAFARI 2000 core sites, including Ghanzi/Okwa River Crossing, Maun (Main and Floodplain Towers), Pandamatenga, and Tshane, Botswana; Skukuza, South Africa; Etosha National Park, Namibia; and Mongu, Zambia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/diffuse_irradiance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"billmark_828 v1 - The Southern African Regional Science Initiative (SAFARI 2000) was conducted in part to investigate the impacts of the large-scale transport and deposition of increasingly anthropogenic emissions on southern African biogeochemical cycling. Aerosol samples from the Mongu site in eastern Zambia were collected and analyzed to identify chemical biomarkers during the SAFARI 2000 dry season field campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/billmark/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ames_sunphotometer_643 v1 - The NASA Ames Airborne Tracking 14-channel Sunphotometer (AATS-14) was operated successfully aboard the University of Washington CV-580 for 24 data flights during the dry-season airborne campaign from August 13 to September 25, 2000. Flights originated from Pietersburg, South Africa; Kasane, Botswana; and Walvis Bay, Namibia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/ames_sunphotometer/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ali_etm_tandem_821 v1 - A tandem pair of Advanced Land Imager (ALI) and Landsat Enhanced Thematic Mapper Plus (ETM+) scenes covering the same part of Kruger National Park (KNP), South Africa (including the Skukuza tower site and rest camp), were acquired about a minute apart on May 30, 2001. The ALI is one of three instruments aboard NASA's first New Millennium Program Earth Observing 1 (EO-1) satellite.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/ali_etm_tandem/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"soil_respiration_point_645 v1 - The data set consists of a southern African subset of the \"Global Annual Soil Respiration Data (Raich and Schlesinger 1992)\" data set. The data file is in ASCII text format and contains four observations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/soils/soil_respiration_point/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"kalahari_aot_h2o_vapor_719 v1 - The data presented here include the aerosol optical thickness (AOT) and column water vapor measurements taken at sites along the Kalahari Transect using a Microtops sunphotometer. Data were collected every 30 minutes at 4 sites that were visited during the SAFARI 2000 Kalahari Wet Season Campaign between March 3, 2000 and March 18, 2000.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/kalahari_aot_h2o_vapor/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"modis_aster_fire_707 v1 - These data relate to a paper (Morisette et al., 2005) that describes the use of high spatial resolution ASTER data to determine the accuracy of the moderate resolution MODIS active fire product. Our main objective was to develop a methodology to use ASTER data for quantitative evaluation of the MODIS active fire product and to apply it to fires in Southern Africa during the 2001 burning season.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/modis_aster_fire/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"handheld_haze_708 v1 - Data presented in this database are the result of Hand Held 4-band Hazemeters and do not always satisfy to the high standards of quality associated with Automatic Sunphotometers.SAFARI 2000 data are available for the following local measurement sites: Alene_High_School; Chililabombwe; Forestry_Kaoma; Itezhi_Tezhi_Basic; Kafue_Hydrologic; Kalongola; Kananja_Chilanda; Kangaya_Basic; Kapiri_Basic; Kasalu_Basic_School; Kasapa_Basic_School; Kashinakaji_School;Kisasa_Basic; Kitima_Basic_School; Litoya; Livingstone_Met_Dpt; Lubu_Basic_Middle; Lukulu_basic; Lusaka_Met_HQ; Makotolo_School; Met_Kaoma...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/handheld_haze/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"avhrr_822 v1 - The Global Inventory Mapping and Modeling (GIMMS) group at NASA/GSFC provided SAFARI 2000 with remotely sensed satellite data products at the site and regional level. These AVHRR data contain two main sets of data: site extracts of SAFARI core sites (Mongu, Etosha, Kasungu, Maun, Skukuza, and Tshane), and regional 15-day composites from sets of single-day images.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/avhrr/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVHRR_LST_826 v1 - Land Surface Temperature (LST) is a key indicator of land surface states, and can provide information on surface-atmosphere heat and mass fluxes, vegetation water stress, and soil moisture. A daily, day and night, LST data set for continental Africa, including Madagascar, was derived from Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km resolution) data for the 6-year lifetime of the NOAA-14 satellite (from 1995 to 2000) using a modified version of the Global Inventory Mapping and Monitoring System (GIMMS) (Tucker et al., 1994).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/AVHRR_LST/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"burning_emissions_752 v1 - Biomass burning is a major source for gaseous and particulate atmospheric pollution over southern Africa and globally. The purpose of this study was to quantify biomass burning emissions in an attempt to better understand and predict associated environmental impacts.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/burning_emissions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"sua_pan_skukuza_brdf_779 v1 - The Jet Propulsion Laboratory's (JPL) Portable Apparatus for Rapid Acquisition of Bidirectional Observation of the Land and Atmosphere (PARABOLA), version III, instrument collected radiance data covering both the upwelling and downwelling hemispheres at the Sua Pan salt playa in the Magkadigkadi region of Botswana and at the Skukuza tower site in the Kruger National Park, South Africa between August 25 and October 2, 2000 during in the SAFARI 2000 Dry Season Aircraft Campaign. PARABOLA III is a sphere-scanning radiometer that provides multi-angle measurements of sky and ground radiances on ...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/sua_pan_skukuza_brdf/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"bvoc_flux_759 v1 - Biogenic volatile organic compound (BVOC) emissions were measured in a Colophospermum mopane woodland near Maun, Botswana, and in a Combretum-Acacia savanna in Kruger National Park, 13 km from Skukuza, Republic of South Africa (RSA) during the 2001 wet season campaign of SAFARI 2000. In addition, relaxed eddy accumulation (REA) measurements of BVOC fluxes were made on flux towers at these sites, where net CO2 emissions were also measured simultaneously.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/climate_meteorology/bvoc_flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"uk_met_c-130_720 v1 - The Met Office C-130 research aircraft was based at Windhoek, Namibia between September 5-16, 2000, where it conducted a series of flights over Namibia as part of the SAFARI 2000 Dry Season Aircraft Campaign.The effects of atmospheric aerosols on the Earth's radiation balance are known to be very important, but for many types of aerosols these effects are still poorly understood. In particular, aerosol particles produced from biomass burning (associated with Savanna burning, domestic fuel wood consumption, and certain agricultural practices) are thought to be particularly important because ...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/uk_met_c-130/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"kt_canopy_structure_768 v1 - This data set contains leaf area index (LAI), leaf inclination angle, and canopy dimension data from study sites along the Kalahari Transect in southwest Botswana. The data were collected during the 2001 wet season field campaign of the SAFARI 2000 at a total of seven plots of 200 x 150 meter dimensions; two plots each at Tshane and Mabuasehube, and three plots at Tsabong.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/kt_canopy_structure/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"s2k_olson_633 v1 - This data set is a subset of Olson's Major World Ecosystem Complexes for southern Africa in ASCII GRID and binary image files formats. Olson's Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation is a computerized database, used to generate a global vegetation map of 44 different land ecosystem complexes (mosaics of vegetation or landscapes) comprising seven broad groups.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/vegetation_wetlands/s2k_olson/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CAR_BRDF_709 v1 - This record links to the web page for the Cloud Absorption Radiometer (CAR) data for the SAFARI 2000 project. Twenty-one flights were undertaken over Southern Africa during the study.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/CAR_BRDF/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"cp_lidar_images_721 v1 - The effect of clouds and aerosols on regional and global climate is of great importance. Two longstanding elements of the NASA climate and radiation science program are field studies incorporating airborne remote sensing and in-situ measurements of clouds and aerosols.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/cp_lidar_images/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"CV-580_710 v1 - The Cloud and Aerosol Research Group (CARG) of the University of Washington participated in the SAFARI-2000 Dry Season Aircraft campaign with their Convair-580 research aircraft. This campaign covered five countries in southern Africa from 10 August through 18 September, 2000.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/CV-580/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"daily_precip_est_793 v1 - The Microwave InfraRed Algorithm (MIRA) is used to produce an imagery data set of daily mean rain rates at 0.1 degree spatial resolution over southern Africa for the period 1993-2001. MIRA combines passive microwave (PMW) from the Special Sensor Microwave/Imager (SSM/I) on board the DMSP F10 and F14 satellites at a resolution of 0.5 degrees and infrared (IR) data from the Meteosat 4, 5, 6, and 7 satellites in 2-hour slots at a resolution of 5 km.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/climate_meteorology/daily_precip_est/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"mongu_daily_rainfall_785 v1 - This data set contains daily rainfall totals (mm) from Mongu, in the Western Province of Zambia. The data were collected with a British standard 5 inch diameter rain gauge in a yard 30 m away from the Meteorological Department building near the Mongu Airport (north of downtown and approximately 20 km from the Kataba Local Forest where the permanent 30 m Mongu tower site is located).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/mongu_daily_rainfall/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"s2k_ISRIC-WISE_soil_properties_634 v1 - The data set consists of a southern Africa subset of the ISRIC-WISE global data set of derived soil properties. The World Inventory of Soil Emission Potentials (WISE) database currently contains data for over 4300 soil profiles collected mostly between 1950 and 1995.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/soils/s2k_ISRIC-WISE_soil_properties/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"globe_dem_630 v1 - This data set consists of a southern African subset of the Global Land One-Kilometer Base Elevation (GLOBE) digital elevation model (DEM) data in both ASCII GRID and binary image file formats. The Global Land One-Kilometer Base Elevation (GLOBE) digital elevation model (DEM) is a global data set with horizontal grid spacing of 30 arc-seconds (0.008333..","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/vegetation_wetlands/globe_dem/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MODIS_emissions_758 v1 - The recently generated MODIS burned area product over southern Africa for the month of September 2000 was used to calculate regional biomass burning emissions from grassland and woodland fires for a number of trace gases and particulates at 1 km spatial resolution. A dynamic regional fuel load model developed for southern Africa in support of SAFARI 2000 fire emissions modeling is used to compute spatially explicit southern Africa fuel load data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/MODIS_emissions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"er2_aerial_photos_722 v1 - Aerial photography from the NASA ER-2 high altitude aircraft was collected to provide detailed and spatially extensive documentation over parts of the SAFARI study area. The ER-2 aerial photography consists of 3,046 color-infrared (IR) transparencies collected during the SAFARI 2000 Dry Season Aircraft Campaign in August and September of 2000.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/er2_aerial_photos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"landcover_bvoc_est_764 v1 - Improved vegetation distribution and emission data for Africa south of the equator were developed for the Southern African Regional Science Initiative (SAFARI 2000) and combined with biogenic volatile organic compound (BVOC) emission measurements to estimate BVOC emissions for the southern African region. BVOC emissions were estimated for southern Africa on a monthly basis over a one-year period by combining GIS layers of vegetation, LAI, and climate with a biogenic emissions model, GLOBEIS (Guenther et al, 1993; Guenther, 1999).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/regional/landcover_bvoc_est/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"eta_model_723 v1 - With modern computer power now capable of making mesoscale model output available in real time in the operational environment, increased attention has been given to utilizing these models in order to improve the forecasting ability of meteorologists. The National Centers for Environmental Prediction (NCEP) has developed a step-mountain eta coordinate model generally known as the ETA Model.This NCEP ETA data assimilation and prediction system (see Mesinger et al., 1988; Black, 1994) has been used by the South African Weather Bureau/Service (SAWS) to provide operational regional forecast guid...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/eta_model/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"FEWS_precip_711 v1 - The U.S. Agency for International Development (USAID) Famine Early Warning System (FEWS) has been supporting the production of 10-day Rainfall Estimate (RFE) data for Africa since 1995.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/climate_meteorology/FEWS_precip/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fire_emissions_724 v1 - As part of the Southern Africa Regional Science Initiative (SAFARI 2000), the University of Montana participated in both ground-based and airborne campaigns during the southern African dry season of 2000 to measure trace gas emissions from biofuel production and use and savanna fires, respectively.During the airborne campaign, stable and reactive trace gases were measured over southern Africa with an airborne Fourier transform infrared spectroscopy (AFTIR) onboard the University of Washington Convair-580 research aircraft in August-September of 2000. The measurements included vertical profi...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/fire_emissions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"mongu_fpar_trac_784 v1 - Data from the Tracing Architecture and Radiation of Canopies (TRAC) instrument were processed to determine the fraction of intercepted photosynthetically active radiation (FPAR) at the EOS Validation Core Site in Kataba Local Forest, approximately 20 km south of Mongu, Zambia. Measurements began in 1999 and continued into 2002, with measurements collected about every month.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/mongu_fpar_trac/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"s2k_soller_wetlands_635 v1 - The data set consists of a southern Africa subset of the Global Distribution of Freshwater Wetlands database 1-degree data and are available in ASCII GRID and binary image files formats. The Global Distribution of Freshwater Wetlands database has been assembled from two data sets: Aselman and Crutzen''s (AC) (1989) wetlands data set and Klinger''s (pers.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/vegetation_wetlands/s2k_soller_wetlands/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"biofuel_emissions_753 v1 - Domestic biomass fuels (biofuels) are estimated to be the second largest source of carbon emissions from global biomass burning. Wood and charcoal provide approximately 90% and 10% of domestic energy in tropical Africa, respectively.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/biofuel_emissions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"spot_veg_burned_790 v1 - The Global Burned Area 2000 initiative (GBA2000) was launched by the Global Vegetation Mapping Unit of the Joint Research Centre of the European Commission, in partnership with several other institutions, to develop reliable and quantitative information on the global magnitude and spatial distribution of biomass burning. The objective of GBA2000 was to produce a map of the areas burned globally for the year 2000, using the medium resolution satellite imagery provided by the SPOT-VEGETATION (VGT) system and to derive statistics of area burned per type of vegetation cover.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/spot_veg_burned/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ghcn_631 v1 - This data set consists of a southern African subset of the Global Historical Climatology Network (GHCN) Version 1 database. All stations with the following bounding coordinates are included in this subset: 5W - 60E and 5N - 35S.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/climate_meteorology/ghcn/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GPCP_precip_712 v1 - The Global Precipitation Climatology Project (GPCP) is an international project designed to provide improved long-record estimates of precipitation over the globe. The general approach is to combine the precipitation information available from several sources into a final merged product that takes advantage of the strengths of each data type.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/climate_meteorology/GPCP_precip/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"knp_fire_maps_756 v1 - Kruger National Park (KNP) was established in 1898 to protect wildlife on nearly 2 million hectares of the South African Lowveld. The climate in this region of South Africa is subtropical, with hot summers and warm, dry winters.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/regional/knp_fire_maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"hydrographic_gghydro_636 v1 - This southern African subset of the Global Hydrographic data set (GGHYDRO) Release 2.2 is organized into 19 files containing terrain type, stream frequency counts, major drainage basins, main features of the cryosphere surface, and ice/water runoff per year for the entire Earth's surface at a spatial resolution of 1 degree longitude by 1 degree latitude. This southern African subset of the Global Hydrographic data is provided in both ASCII GRID and binary image files formats.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/hydrology/hydrographic_gghydro/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SAWB_JRA_713 v1 - As part of the 3rd Intensive Campaign of SAFARI 2000, the South African Weather Bureau Aerocommander, JRA, flew 19 missions, for a total of 28 separate flights conducted between August 15th and September 7th, 2000. JRA worked closely with the other Aerocommander, JRA, and was dedicated to the measurement of trace gas and aerosol properties.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/SAWB_JRA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SAWB_JRB_714 v1 - As part of the 3rd Intensive Campaign of SAFARI 2000, the South African Weather Bureau Aerocommander, JRB, flew 19 missions, for a total of 28 separate flights conducted between August 15th and September 7th, 2000. JRB worked closely with the other Aerocommander, JRA, and was dedicated to the measurement of trace gas and aerosol properties.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/SAWB_JRB/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"kalahari_co2_heat_flux_765 v1 - Short-term measurements of carbon dioxide, water, and energy fluxes were collected at four locations along a mean annual precipitation gradient in southern Africa during the SAFARI 2000 wet (growing) season campaign of 2000. The purpose of this research was to determine how observed vegetation-atmosphere exchange properties are functionally related to long-term climatic conditions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/climate_meteorology/kalahari_co2_heat_flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"sua_pan_lai_fpar_778 v1 - The Multi-angle Imaging SpectroRadiometer (MISR) Validation team was deployed to the Sua Pan salt playa in the Magkadigkadi region of Botswana during the SAFARI 2000 Dry Season Aircraft Campaign to collect various data sets for validating the MISR LAI/FPAR algorithm. Ground measurements of leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) were made using the LAI-2000 plant canopy analyzer and Sunfleck PAR ceptometer, respectively, during focused periods from August 20 to August 28, 2000 at a dry grassland site adjacent to the Sua Pan.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/sua_pan_lai_fpar/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"kt_lai_770 v1 - The Boston University team collected several data sets along the Kalahari Transect during the SAFARI 2000 wet season field campaign from March 3rd to March 18th, 2000 to support the validation of the MODIS LAI/FPAR algorithm. Ground measurements of LAI, FPAR, leaf hemispherical reflectance, leaf hemispherical transmittance, and canopy transmittance were made using a LAI-2000 plant canopy analyzer, an AccuPAR ceptometer, a LiCor 1800-12S External Integrating Sphere (LI-1800) portable spectroradiometer, and an ASD handheld spectroradiometer.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/kt_lai/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"s2k_glcf1deg_626 v1 - This data set consists of a southern African subset of the University of Maryland (UMD) 1-degree Global Land Cover product in ASCII GRID and binary image formats. The UMD 1-degree Global Land Cover product was produced by researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at UMD.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/vegetation_wetlands/s2k_glcf1deg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"land_cover_data-1km_627 v1 - The data set consists of a southern African subset of the 1-km Global Land Cover Data Set Derived from AVHRR developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Both ASCII data and binary image files are available.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/vegetation_wetlands/land_cover_data-1km/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"s2k_land_cover_data_8km_628 v1 - This data set consists of a southern African subset of the University of Maryland (UMD) 8-km Global Land Cover product in ASCII GRID and binary image files formats. Over the past several years, researchers have increasingly turned to remotely sensed data to improve the accuracy of data sets that describe the geographic distribution of land cover at regional and global scales.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/vegetation_wetlands/s2k_land_cover_data_8km/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"kt_gps_photos_769 v1 - This data set contains Global Positioning System (GPS) imprinted landscape photographs at 100 m intervals along the Large Grid Transects at Kalahari Transect sites in Botswana and at measurement sites in Kataba Forest, Mongu, Zambia and in the vicinity of the Skukuza flux tower site in Kruger National Park, South Africa. The Kalahari sites visited were Pandamatenga, Maun, Okwa Valley, and Tshane.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/kt_gps_photos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"kt_lai_trac_771 v1 - Data from the Tracing Radiation and Architecture of Canopies (TRAC) instrument were collected at five sites along the International Geosphere-Biosphere Programme (IGBP) Kalahari Transect, including Mongu in Zambia and Pandamatenga, Maun, Okwa River Crossing, and Tshane in Botswana, during the 2000 wet season field campaign (March-April) of SAFARI 2000. At the Mongu site, TRAC measurements began in August of 1999 and continued beyond the 2000 wet season field campaign, about every month for the rest of 2000.The TRAC instrument contains pyranometers that are sensitive to photosynthetically ac...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/kt_lai_trac/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"mongu_lai-2000_781 v1 - Data from the LAI-2000 instrument were processed to determine the leaf area index (LAI) at the EOS Validation Core Site in Kataba Local Forest, approximately 20 km south of Mongu, Zambia. Measurements began in 2000 and continued into 2002, with measurements collected about every month throughout the growing season to examine the phenology of LAI for the site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/mongu_lai-2000/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"kt_leaf_meas_772 v1 - Data presented in this data set were collected during an intensive field campaign in Botswana between February 28 and March 18, 2000 along the Kalahari Transect as part of the SAFARI 2000 wet season field campaign. The sites visited were Pandamatenga, Maun, Okwa River Crossing, and Tshane (north to south).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/kt_leaf_meas/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"kt_leaf_spectra_773 v1 - The Boston University team collected several data sets along the Kalahari Transect during the SAFARI 2000 wet season field campaign between March 3 and March 18, 2000 to support the validation of the MODIS LAI/FPAR algorithm. Ground measurements of LAI, FPAR, leaf hemispherical reflectance and transmittance, and canopy transmittance were made using a LAI-2000 plant canopy analyzer, an AccuPAR ceptometer, a LiCor 1800-12S External Integrating Sphere (LI-1800) portable spectroradiometer, and an ASD handheld spectroradiometer.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/kt_leaf_spectra/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"leaf_voc_emissions_763 v1 - Biogenic volatile organic compounds (VOCs) comprise a significant proportion of trace gases in the atmospheric environment and play an important role in the formation of secondary air pollutants. Emissions of monoterpenes from vegetation were studied at adjacent sites in Botswana as part of the SAFARI 2000 (Southern African Regional Science Initiative).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/leaf_voc_emissions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"mapss_modis_aerosol_814 v1 - The Moderate Resolution Imaging Spectroradiometer (MODIS)Atmosphere Group develops remote sensing algorithms for deriving sets of atmospheric parameters from MODIS radiance data. These parameters can be integrated into conceptual and predictive global models.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/mapss_modis_aerosol/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"mapss_modis_watervapor_815 v1 - The Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere Group develops remote-sensing algorithms for deriving sets of atmospheric parameters from MODIS radiance data. These parameters can be integrated into conceptual and predictive global models.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/mapss_modis_watervapor/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"cramer_leemans_637 v1 - This data set is a subset of the global mean monthly climatology (Cramer and Leemans 1999). The subset is for the area of southern Africa within the following bounds: 5 N to 35 S and 5 E to 60 E.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/climate_meteorology/cramer_leemans/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"maun_met_flux_760 v1 - To investigate potential contributions of savanna ecosystems to the Earth s carbon balance, an eddy covariance system was used to measure the seasonal variation in carbon dioxide, water vapor, and energy flux at the Maun micrometerological tower site in a broadleaf semi-arid savanna in Southern Africa, approximately 20 km east of Maun in northeastern Botswana. The open woodland studied consists of an overstory dominated by Colophospermum mopane with a sparse understory of grasses and herbs.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/climate_meteorology/maun_met_flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"knp_met_761 v1 - An eddy covariance system mounted on a tower near the Skukuza Camp in Kruger National Park, South Africa, has been operating continuously since early 2000. Meteorological measurements started in February 2000, and the first flux measurements were made in April 2000.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/climate_meteorology/knp_met/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"micro_pulse_lidar_715 v1 - Two Micro-Pulse Lidar (MPL) systems were deployed to Africa for the SAFARI 2000 experiment. One MPL was setup in Mongu, Zambia and the other was setup in Skukuza, South Africa.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/micro_pulse_lidar/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MISR_885 v1 - MISR (Multi-angle Imaging SpectroRadiometer) views the sunlit Earth simultaneously at nine widely spaced and collects global images with high spatial detail in four colors at every angle. These images are carefully calibrated to provide accurate measures of the brightness, contrast, and color of reflected sunlight.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/MISR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fuel_load_755 v1 - This data set contains global, spatially explicit (1 km2 grid cells) and temporally explicit (semi-monthly) modeled output of fuel loads over southern Africa. The fuel types considered in the data set are litter (dead tree leaves), dead grass, green grass, and small-diameter twigs.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/regional/fuel_load/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"trajectory_images_792 v1 - The ETA Forecast Trajectory Model was used to produce forecasts of air-parcel trajectories twice a day at three pressure levels over seven sites in Southern Africa for the period August 14, 2000 to September 23, 2000. These sites are Durban, Middleburg, Pietersburg, and Springbok, South Africa; Maun, Botswana; Mongu, Zambia; and Windhoek, Namibia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/climate_meteorology/trajectory_images/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"modis_burned_area_796 v1 - The SAFARI 2000 project was selected as the first regional test for a prototype regional 500 m MODIS burned area product. The MODIS burned area product maps the 500 m location and approximate day of burning using a change detection algorithm based on a bi-directional reflectance model-based expectation method applied to the MODIS near-infrared and shortwave infrared bands (Roy et al., 2002).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/modis_burned_area/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MAS_832 v2 - This dataset contains the Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) multispectral data collected during the SAFARI 2000 project. The flights were undertaken over Southern Africa by the NASA ER-2 aircraft during August and September, 2000.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/MAS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"modis_l3_albedo_840 v1 - The Filled Land Surface Albedo Product for Southern Africa, which is generated from MOD43B3 Product (the official Terra/MODIS-derived Land Surface Albedo - http://geography.bu.edu/brdf/userguide/albedo.html ), is a subset of the global data set of spatially complete albedo maps computed for both white-sky and black-sky at 10 wavelengths. The data spatial extent is from approximately 5 degrees N to -30 degrees S latitude and 5 minutes E to 60 degrees E longitude and covers 7 sixteen day periods starting on July 11 through October 15, 2000.Map Products, containing spatially complete land surf...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/modis_l3_albedo/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"modis_MOD04_aerosol_813 v1 - The subset of the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD04_L2 aerosol product provided in this data set represents the swaths that coincide with known times of the South African Weather Bureau/Service (SAWS) Aerocommanders JRA and JRB research aircraft missions to support aerosol research and validation activities for the SAFARI 2000 region. The MODIS aerosol product monitors the ambient aerosol optical thickness over the oceans globally and over a portion of the continents.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/modis_MOD04_aerosol/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"modis_MOD05_watervapor_812 v1 - The Moderate Resolution Imaging Spectroradiometer (MODIS) precipitable water product consists of vertical column water-vapor amounts in centimeters (cm) at 1-km spatial resolution. The SAFARI 2000 product, provided in flat binary data files, is a subset of the official MODIS Level 2 MOD05 product in EOS Hierarchical Data Format (HDF) format.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/modis_MOD05_watervapor/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"modis_h2o_heat_flux_762 v1 - A physically-based model, Energy: Surface Towards Atmosphere (ESTA), was used to model and map the energy and water balances of a heterogeneous land surface in a savanna environment on the southern fringe of the Okavango Delta, near Maun, Botswana. ESTA is governed by remotely sensed values of surface temperature, reflection, and vegetation density.Surface reflectance data from the MODIS sensor aboard the Terra satellite were obtained for the Okavango Delta region for September of 2001.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/modis_h2o_heat_flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"s2k_soil_respiration_gridded_644 v1 - The data set provides estimated monthly and annual soil CO2 emissions for southern Africa (the SAFARI 2000 project region). The calculated emissions are from the respiration of both soil organisms and plant roots and are provided on a 0.5-degree grid cell basis.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/soils/s2k_soil_respiration_gridded/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"S2K_EACM_Subset_623 v1 - This is a data set of mean monthly surface climate data over Southern Africa for nearly all of the twentieth century. The data set is gridded at 0.5 degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/climate_meteorology/S2K_EACM_Subset/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MOPITT_co_835 v1 - The MOPITT (Measurements Of Pollution In The Troposphere) instrument on the NASA Terra Satellite makes measurements of infrared radiation originating from the surface of the planet and isolates the energy being radiated from carbon monoxide (CO). By using appropriate data analysis techniques, concentration profiles of CO (Level-2 (L2) data) can be obtained on a global basis at a reasonably high horizontal (~22km) and vertical resolution (~3km).The MOPITT Level-3 (L3) data products provided in this data set are a subset of the daily averages from the L2 data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/MOPITT_co/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"nbi_veg_maps_787 v1 - The National Botanical Institute (NBI) has mapped woody plant species distribution to provide estimates of individual species contribution to peak leaf area index for designated vegetation types in southern Africa (Rutherford et al., 2000). The target was to account for 80% of the woody vegetation leaf area in terms of named species, for 80% of the surface area of Africa south of the equator.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/vegetation_wetlands/nbi_veg_maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"s2k_zinke_soil_638 v1 - The data set contains a subset of the Worldwide Organic Soil Carbon and Nitrogen (Zinke et al. 1986) data set for southern Africa.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/soils/s2k_zinke_soil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"kt_pai_estimates_774 v1 - This data set was collected during February-March 2000 wet season and September 2000 dry season field campaigns of SAFARI 2000. Mongu in Zambia and Pandematenga (aka Kasane) and Tshane in Botswana were visited during the wet season campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/kt_pai_estimates/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"kt_par_794 v1 - Ceptometer data from a Decagon AccuPAR (Model PAR-80) were collected at four sites in Botswana during the SAFARI 2000 Kalahari Transect Wet Season Campaign (March, 2000). These sites include Maun, Pandamentanga, Ghanzi/Okwa River Crossing, and Tshane.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/kt_par/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"aerosol_properties_725 v1 - The Southern African Regional Science Initiative 2000 (SAFARI 2000) provided an opportunity to study aerosol particles produced by savanna burning. We used analytical transmission electron microscopy (TEM), including energy-dispersive X-ray spectrometry (EDS) and electron energy-loss spectroscopy (EELS), to study aerosol particles from several smoke and haze samples and from a set of cloud samples.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/aerosol_properties/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"plant_soil_c_n_783 v1 - This data set contains measurements of the concentration and stable carbon (13C / 12C) and nitrogen (15N / 14N) isotope ratios of plant (leaves, roots and fungi) and soil samples from southern Africa. The study sites in Zambia, Botswana, Namibia, and South Africa are located along the Kalahari Transect precipitation gradient.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/plant_soil_c_n/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"s2k_potential_vegetation_639 v1 - The data set consists of a southern Africa subset of the 5-min resolution Global Potential Vegetation data set developed by Navin Ramankutty and Jon Foley at the University of Wisconsin. Data are available in both ASCII GRID and binary image file formats.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/vegetation_wetlands/s2k_potential_vegetation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"pre_post_fire_refl_757 v1 - The main goal of this study was to analyze the possibility of estimating combustion completeness based on fire-induced spectral reflectance changes of surface features by the development of relationships between combustion completeness and pre-fire to post-fire spectral reflectance changes, in the green, red, and near-infrared spectral domains (equivalent to Landsat ETM+ channels 2, 3, and 4). Experimental burns were carried out in the Western Province of Zambia in dambos (hydromorphic grasslands) and miombo woodlands during the SAFARI 2000 Third Intensive Field Campaign in August and Septe...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/pre_post_fire_refl/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"knp_fire_residue_751 v1 - The goal of this study was to understand the change in reflectance caused by the action of fire and the heterogeneity of fire effects (i.e., the fraction of the observation that burned and the combustion completeness of that observation). A spectral mixture model and field and satellite observations were used to compare changes in Landsat reflectance associated with fire and combustion completeness derived from field measurements at prescribed fire sites in South Africa and to substantiate and illustrate the model findings.Fire residue samples were collected from experimental burn plots in ...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/knp_fire_residue/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"river_discharge_cpep_640 v1 - The data set consists of southern Africa subset of the CPEP Global River Discharge Data Set. The Climate, People, and Environment Program (CPEP) global river discharge data set is a compilation of monthly mean discharge data for over 2600 sites worldwide.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/hydrology/river_discharge_cpep/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"seawifs_624 v1 - This data set contains Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery for the eight core study sites of Mongu, Etosha, Kasangu, Skukuza, Mutoko, Mzola, Nampula, and Ndola. There are two main sets of local area coverage (LAC) data, Level-1 and Level-2 200-km x 200-km image subsets for seven of the sites and 400-km x 400-km image subsets for the Etosha site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/seawifs/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"seawifs_region_625 v1 - This data set contains Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery for the southern African region. These images are Level-1a swaths of the southern Africa region selected from global area coverage (GAC) at 4.5-km resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/seawifs_region/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"s2k_IGBP-DIS_Soil_Surfaces_647 v1 - The data set consists of a southern African subset of the \"Global Gridded Surfaces of Selected Soil Characteristics (IGBP-DIS)\" produced by the International Geosphere-Biosphere Programme - Data and Information Services. Data files are provided in ASCII GRID format.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/soils/s2k_IGBP-DIS_Soil_Surfaces/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"shadoz_ozonesonde_726 v1 - Ozonesonde launches were made by the Southern Hemisphere ADditional OZonesondes (SHADOZ) group as part of the SAFARI 2000 Dry Season Campaign in September 2000 (Thompson et al., 2002). Ozonesondes are balloon-borne instruments measuring profile ozone, as well as temperature and pressure from an attached radiosonde, up to 35 km in height (around 5 hPa in pressure coordinates) capturing the troposphere and lower stratospheric portion of the atmosphere.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/shadoz_ozonesonde/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"s2k_ISRIC_Wise_profiles_648 v1 - The data set consists of a southern African subset of the \"Global Soil Profile Data (ISRIC-WISE)\" data set. Data files are provided in comma-delimited ASCII format.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/soils/s2k_ISRIC_Wise_profiles/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"mongu_skukuza_soil_prop_789 v1 - Soil moisture and temperature profile sensors were deployed at flux tower sites in Mongu, Zambia and Skukuza, South Africa. In addition, thermal infrared sensors were deployed to monitor surface temperature at the sites, and soil samples were collected for physical property analysis.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/mongu_skukuza_soil_prop/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Zobler_Soil_649 v1 - The data set consists of a southern African subset of the \"Global Soil Types, 0.5-Degree Grid (Modified Zobler)\" data set. Data files are provided in ASCII GRID format.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/soils/Zobler_Soil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SSFR_irradiance_841 v1 - The Solar Spectral Flux Radiometer (SSFR) was deployed on the University of Washington CV-580 during the dry season component of the Southern African Regional Science Initiative, August 1 - September 20, 2000. The SSFR made simultaneous measurements of both downwelling and upwelling net solar spectral irradiance at varying flight levels.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/SSFR_irradiance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GPROF_precip_716 v1 - The GPROF 6.0 Pentads data set contains 5-day (pentad) averages of the GPROF 6.0 Gridded Orbits. The GPROF(Goddard Profiling Algorithm) data set contains a suite of 9 products providing instantaneous, gridded values of precipitation totals for each granule of the SSM/I (Special Sensor Microwave/Imager) data over the roughly 14-year period July 1987 through the present.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/climate_meteorology/GPROF_precip/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"kt_stem_map_775 v1 - This data set provides species distribution, basal area, height, and crown cover of woody stems at 10 sites along the Kalahari Transect where a large gradient in both the mean and variation of annual rainfall results in dramatic changes in vegetation structure. Some of the data were collected during earlier Kalahari Transect projects in 1995 and 1997 at Vastrap, South Africa; Sandveld and Sachinga, Namibia; and Maziba, Senanga, and Lukulu, Zambia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/kt_stem_map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"mongu_skukuza_albedo_786 v1 - Top-of-the-canopy broadband albedo and radiation fluxes are calculated from measurements at the Mongu and Skukuza flux tower sites in southern Africa from March 2000 through December 2002. Data were collected by instrumentation deployed at the top of the 30 m tower in the Kataba Local Forest near Mongu, Zambia, and atop the 20 m tower at the Skukuza tower site in Kruger National Park, South Africa.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/mongu_skukuza_albedo/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"smart_radiometers_727 v1 - Surface-sensing Measurements for Radiative Transfer (SMART) and Chemical, Optical, and Microphysical Measurements of In-situ Troposphere (COMMIT) consist of a suite of instruments that measure (both in-situ and by remote sensing) parameters that help to characterize, as completely as possible, constituents of the atmosphere at a given location. SMART and COMMIT are mobile systems that can be deployed to locations that exhibit interesting atmospheric phenomena.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/smart_radiometers/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"mongu_irradiance_782 v1 - This data set contains the top-of-canopy irradiance in the shortwave (0.3-2.8 micron) and photosynthetically active radiation (PAR; 0.4-0.7 micron) wavebands collected with an Eppley Precision Spectral Pyranometer (PSP) and a Skye SKE510 pyranometer, respectively. The instruments were deployed at the top of the 30-m tower in the Kataba Local Forest approximately 20 km south of Mongu in Western Province, Zambia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/mongu_irradiance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"sua_pan_surface_spectra_780 v1 - The Multi-angle Imaging SpectroRadiometer (MISR) Validation team was deployed to Sua Pan, a salt playa in the Magkadigkadi region of Botswana, from August 18 to September 4, 2000, during the SAFARI 2000 Dry Season Aircraft Campaign. The experiment was designed to collect data necessary for multi-angle top-of-atmosphere radiance predictions in order to provide a vicarious calibration of the MISR instrument aboard the Terra satellite.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/sua_pan_surface_spectra/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TOMS_aerosol_823 v1 - Daily Aerosol Index (AI) data from Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) for the period of August 12-September 25, 2000 were processed and provided by the Atmospheric Chemistry and Dynamics Branch at NASA/GSFC for the SAFARI 2000 Dry Season Aircraft Campaign.The TOMS AI is formed directly from measured TOMS radiances in two channels. It is a measure of how much the wavelength dependence of backscattered UV radiation from an atmosphere containing aerosols (Mie scattering, Rayleigh scattering, and absorption) differs from that of a pure molecular atmosphere (pure Rayleigh s...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/TOMS_aerosol/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TOMS_ozone_824 v1 - Tropical Tropospheric Ozone (TTO) data from Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) for the period of August 8-September 29, 2000 were processed and provided by the Atmospheric Chemistry and Dynamics Branch at NASA/GSFC for the SAFARI 2000 Dry Season Aircraft Campaign.The TTO measurement is derived from TOMS total ozone (Thompson and Hudson, 1999; Thompson et al., 2001) using the modified-residual method to separate stratospheric ozone from tropospheric ozone. The tropospheric ozone column thickness is reported in Dobson Units (DU).EP TOMS is currently the only NASA spacecr...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/TOMS_ozone/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TOVS_717 v1 - NOAA''s TIROS (Television Infrared Observation Satellite) Operational Vertical Sounder (TOVS) is a suite of three sensors: the Microwave Sounding Unit (MSU), the High resolution Infrared Radiation Sounder (HIRS), and the Stratospheric Sounding Unit (SSU) aboard the NOAA series of polar-orbiting operational meteorological satellites. TOVS-derived data provide a means to investigate long-term climate change and interannual variability and study local and periodic phenomena such as El Nino and stratospheric warmings.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/remote_sensing/TOVS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"tree_cover-1km_641 v1 - The data set consists of a southern Africa subset of the 1km Global Tree Cover Data Set developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Data are available in both ASCII GRID and binary image files formats.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/vegetation_wetlands/tree_cover-1km/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"mongu_tree_rings_788 v1 - This data set contains tree ring data from three sites located about 25 km of the meteorological station at Mongu, Zambia. Data from about 50 individual trees are reported.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/mongu_tree_rings/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_precip_718 v1 - The purpose of the Tropical Rainfall Measuring Mission (TRMM) and TRMM Product 3B-43 is to provide a monthly, best-estimate precipitation rate and root-mean-square (RMS) precipitation error. These gridded estimates are on a one-calendar month temporal resolution and a 1-degree by 1-degree spatial resolution for the global band extending from 40 degrees south to 40 degrees north in latitude.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/climate_meteorology/TRMM_precip/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"met_profile_skukuza_728 v1 - Vaisala RS80 sondes were deployed from Skukuza Airport, South Africa, to collect atmospheric sounding profiles of temperature and moisture data from the surface to 30 km. These sonde launches were coordinated to augment the regional sounding network in the region during the SAFARI 2000 Dry Season Campaigns of 1999 and 2000.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/met_profile_skukuza/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"met_profile_SA_729 v1 - The University of Wyoming has a series of balloonborne radiosonde measurements from all around the world, from the surface to 30 km. This data set contains upper air meteorological profiles from 594 radiosonde launches deployed from sites in South Africa.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/atmospheric/met_profile_SA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"vegsoils_wilhend_642 v1 - This data set contains a subset for southern Africa of Wilson and Henderson-Sellers' Global Vegetation & Soils 1-degree data. The data are available in both ASCII GRID and binary image files formats.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/vegetation_wetlands/vegsoils_wilhend/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"kt_veg_inventory_776 v1 - Vegetation cover and composition, including species types and richness assessments, were measured at four locations along the Kalahari Transect in Botswana (Pandamatenga, Maun, Okwa River, and Tshane) during the SAFARI 2000 wet season field campaign. The sites visited showed interesting degrees of variability despite the apparent homogeneity of the Kalahari sands and predominantly semi-arid savanna shrub-woodlands vegetation cover (Ringrose et al., 2003).At each site, twelve individual locations were chosen by random stratified techniques within a 30-km radius at each location, based on dif...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/kt_veg_inventory/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"mongu_veg_structure_795 v1 - Tree basal area, percent tree canopy cover, and proportional contribution of main species to canopy cover were measured at 60 sampling points at 50 m intervals along six transects in the vicinity of the MODIS validation site tower in Kataba Forest, near Mongu, Zambia, in late February to early March 2000 as part of the SAFARI 2000 Wet Season Campaign. The aim of the study was to provide a broad description of the tree canopy layer around the tower.Tree and shrub species composition was recorded for each grid and measurements of canopy cover (% and rank) and frequency of occurrence (%) were ...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/mongu_veg_structure/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"giss_wetlands_632 v1 - This database, compiled by Matthews and Fung (1987), provides information on the distribution and environmental characteristics of natural wetlands. The database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/vegetation_wetlands/giss_wetlands/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"kt_woody_veg_777 v1 - This data set contains species composition, basal area, height, and crown cover of all woody plants at six sites along the Kalahari Transect visited in February-March of 2000 as part of SAFARI 2000. Similar measurements on woody and herbaceous vegetation at the Skukuza Flux Tower site in Kruger National Park, South Africa, were made in June of 2000.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/safari2k/field_campaign/kt_woody_veg/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-safari-2000","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SAGE III-ISS Project","Description":"g3btmnc_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 1 Monthly Solar Event Species Profiles (NetCDF) V6 data product. It contains pixel group transmission profiles for a month of solar events (the last day of each month is omitted). SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.\n<br>\n\n#### g3btmnc\nData quality notice: The SAGE III/ISS team recommends against using data from events 2024030913SS, 2024030915SS, and 2024030917SS. These events were affected by line-of-sight blockage from a docked spacecraft which undermined the data quality. Typically, such events are withheld by a quality assurance process. g3btmnc_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 1 Monthly Solar Event Species Profiles (NetCDF) V053 data product. It contains pixel group transmission profiles for a month of solar events. Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth’s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere. Several improvements predominately the ozone, nitrogen dioxide and aerosol products are realized in the version 6.0 data product and cause it to be recommended over version 5.3. These improvements include: filling transmission gaps (caused mainly by sunspots) leading to several hundred more events, additional term in L1 uncertainty, updated ozone absorption cross-section database, more robust covariance computation of aerosol uncertainty. Version 6.0 also has incorporated aerosol flags and particle size distribution information which were previously available in separate files. Further details can be found in the version 6.0 release notes.\n<br>\n\n#### g3bt\ng3bt_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 1B Solar Event Transmission Data (HDF5) V6 data product. It contains pixel group transmission profiles for a single solar event. SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.\n<br>\n\n#### g3btb\ng3btb_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 1B Solar Event Transmission Data (Native) V6 data product. It contains pixel group transmission profiles for a single solar event. SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. Data collection for this product is ongoing. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.\n<br>\n\n#### g3blsp\ng3blsp_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Lunar Event Species Profiles (HDF5) V6 data product. It contains all the species products for a single lunar event. SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.\n<br>\n\n#### g3blsp\ng3blsp_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Lunar Event Species Profiles (HDF5) V053 data product. It contains all the species products for a single lunar event. Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth’s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere. Several improvements predominately the ozone, nitrogen dioxide and aerosol products are realized in the version 6.0 data product and cause it to be recommended over version 5.3. These improvements include: filling transmission gaps (caused mainly by sunspots) leading to several hundred more events, additional term in L1 uncertainty, updated ozone absorption cross-section database, more robust covariance computation of aerosol uncertainty. Version 6.0 also has incorporated aerosol flags and particle size distribution information which were previously available in separate files. Further details can be found in the version 6.0 release notes.\n<br>\n\n#### g3blspb\ng3blspb_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Lunar Event Species Profiles (Native) V06 data product. It contains all the species products for a single lunar event. SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.\n<br>\n\n#### g3blspb\ng3blspb_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Lunar Event Species Profiles (Native) V053 data product. It contains all the species products for a single lunar event. Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth’s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere. Several improvements predominately the ozone, nitrogen dioxide and aerosol products are realized in the version 6.0 data product and cause it to be recommended over version 5.3. These improvements include: filling transmission gaps (caused mainly by sunspots) leading to several hundred more events, additional term in L1 uncertainty, updated ozone absorption cross-section database, more robust covariance computation of aerosol uncertainty. Version 6.0 also has incorporated aerosol flags and particle size distribution information which were previously available in separate files. Further details can be found in the version 6.0 release notes.\n<br>\n\n#### g3blmnc\ng3blmnc_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Monthly Lunar Event Species Profiles (NetCDF) V6 data product. It contains all the species products for a month of lunar events (the last day of each month is omitted). SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.\n<br>\n\n#### g3blmnc\ng3blmnc_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Monthly Lunar Event Species Profiles (NetCDF) V053 data product. It contains all the species products for a month of lunar events. Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth’s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere. Several improvements predominately the ozone, nitrogen dioxide and aerosol products are realized in the version 6.0 data product and cause it to be recommended over version 5.3. These improvements include: filling transmission gaps (caused mainly by sunspots) leading to several hundred more events, additional term in L1 uncertainty, updated ozone absorption cross-section database, more robust covariance computation of aerosol uncertainty. Version 6.0 also has incorporated aerosol flags and particle size distribution information which were previously available in separate files. Further details can be found in the version 6.0 release notes.\n<br>\n\n#### g3bsmnc\ng3bsmnc_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Monthly Solar Event Species Profiles (NetCDF) V06 data product. It contains all of the species products for a month of solar events (the last day of each month is omitted). SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.\n<br>\n\n#### g3bssp\ng3bssp_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Solar Event Species Profiles (HDF5) V06 data product. It contains all the species products for a single solar event. SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.\n<br>\n\n#### g3bsspb\ng3bsspb_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Solar Event Species Profiles (Native) V06 data product. It contains all the species products for a single solar event. SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.\n<br>\n\n#### g3bssmc\nSAGE III/ISS, Raw Level 0 Data V5.3 consists of Raw Level 0 data received from Mission Operations Center. Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III-ISS) is the second instrument from the SAGE III project, externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth’s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://sage.larc.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","hdf","hdf5","ice","netcdf"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"g3btmnc v6 - g3btmnc_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 1 Monthly Solar Event Species Profiles (NetCDF) V6 data product. It contains pixel group transmission profiles for a month of solar events (the last day of each month is omitted).","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III_ISS/g3btmnc_6","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3btmnc v53 - Data quality notice: The SAGE III/ISS team recommends against using data from events 2024030913SS, 2024030915SS, and 2024030917SS. These events were affected by line-of-sight blockage from a docked spacecraft which undermined the data quality.","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III_ISS/g3btmnc_53","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3bt v6 - g3bt_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 1B Solar Event Transmission Data (HDF5) V6 data product. It contains pixel group transmission profiles for a single solar event.","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III_ISS/g3bt_6","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3btb v6 - g3btb_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 1B Solar Event Transmission Data (Native) V6 data product. It contains pixel group transmission profiles for a single solar event.","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III_ISS/g3btb_6","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3blsp v6 - g3blsp_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Lunar Event Species Profiles (HDF5) V6 data product. It contains all the species products for a single lunar event.","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III_ISS/g3blsp_6","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3blsp v53 - g3blsp_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Lunar Event Species Profiles (HDF5) V053 data product. It contains all the species products for a single lunar event.","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III_ISS/g3blsp_53","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3blspb v6 - g3blspb_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Lunar Event Species Profiles (Native) V06 data product. It contains all the species products for a single lunar event.","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III_ISS/g3blspb_6","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3blspb v53 - g3blspb_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Lunar Event Species Profiles (Native) V053 data product. It contains all the species products for a single lunar event.","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III_ISS/g3blspb_53","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3blmnc v6 - g3blmnc_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Monthly Lunar Event Species Profiles (NetCDF) V6 data product. It contains all the species products for a month of lunar events (the last day of each month is omitted).","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III_ISS/g3blmnc_6","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3blmnc v53 - g3blmnc_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Monthly Lunar Event Species Profiles (NetCDF) V053 data product. It contains all the species products for a month of lunar events.","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III_ISS/g3blmnc_53","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3bsmnc v6 - g3bsmnc_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Monthly Solar Event Species Profiles (NetCDF) V06 data product. It contains all of the species products for a month of solar events (the last day of each month is omitted).","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III_ISS/g3bsmnc_6","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3bssp v6 - g3bssp_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Solar Event Species Profiles (HDF5) V06 data product. It contains all the species products for a single solar event.","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III_ISS/g3bssp_6","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3bsspb v6 - g3bsspb_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Solar Event Species Profiles (Native) V06 data product. It contains all the species products for a single solar event.","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III_ISS/g3bsspb_6","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3bssmc v53 - SAGE III/ISS, Raw Level 0 Data V5.3 consists of Raw Level 0 data received from Mission Operations Center. Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III-ISS) is the second instrument from the SAGE III project, externally mounted on the International Space Station (ISS).","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III_ISS/g3bssmc_53","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-sage-iii-iss","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SAGE III-M3M Project","Description":"Level 1B pixel group transmission profiles for a single solar event.\n<br>\n\n#### g3acld\ng3acld_004 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the Meteor-3M (M-3M) platform (SAGE III/M3M) Level 2 Monthly Cloud Presence Data (HDF-EOS) Version 4 product. It is a monthly compilation of polar cloud presence from SAGE III coincident with solar event granules, that provides information about cloud presence during data capture of the granules. SAGE III obtains profile measurements of aerosol extinction, ozone, water vapor, nitrogen dioxide, nitrogen trioxide, chlorine dioxide, and cloud formations throughout the mesosphere, stratosphere, and upper troposphere with a vertical resolution of 0.5 - 1 km resolution. SAGE III was a fourth generation, satellite-borne instrument and a crucial element in NASA's Earth Observing System (EOS) . The instrument was launched on the Russian M-3M spacecraft in December 2001. The M-3M and SAGE III missions were terminated on March 6, 2006, because of a power supply system failure resulting in loss of communication with the satellite. The newest SAGE mission, SAGE III on ISS, was launched in 2017. SAGE III M-3M L2 Monthly Cloud Presence Data are monthly data files coincident with solar event granules that provides information about cloud presence during data capture of the granules.\n<br>\n\n#### g3acldb\ng3acldb_004 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the Meteor-3M (M-3M) platform (SAGE III/M3M) SAGE III Meteor-3M L2 Monthly Cloud Presence Data (Native) Version 4 data product. It is monthly data file coincident with solar event granules, that provides a monthly compilation of polar cloud presence from SAGE III during data capture of the granules. SAGE III obtains profile measurements of aerosol extinction, ozone, water vapor, nitrogen dioxide, nitrogen trioxide, chlorine dioxide and cloud formations throughout the mesosphere, stratosphere, and upper troposphere with a vertical resolution of 0.5 - 1 km resolution. SAGE III was a fourth generation, satellite-borne instrument and a crucial element in NASA's Earth Observing System (EOS) . The instrument was launched on the Russian M-3M spacecraft in December 2001. The M-3M and SAGE III missions were terminated on March 6, 2006, because of a power supply system failure resulting in loss of communication with the satellite. The newest SAGE mission, SAGE III on ISS, was launched in 2017.\n<br>\n\n#### g3assp\nA Level 2 data file containing all the species products for a single solar event\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://sage.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","hdf","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"g3at v004 - Level 1B pixel group transmission profiles for a single solar event.","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III/g3at_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3acld v004 - g3acld_004 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the Meteor-3M (M-3M) platform (SAGE III/M3M) Level 2 Monthly Cloud Presence Data (HDF-EOS) Version 4 product. It is a monthly compilation of polar cloud presence from SAGE III coincident with solar event granules, that provides information about cloud presence during data capture of the granules.","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III/g3acld_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3acldb v004 - g3acldb_004 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the Meteor-3M (M-3M) platform (SAGE III/M3M) SAGE III Meteor-3M L2 Monthly Cloud Presence Data (Native) Version 4 data product. It is monthly data file coincident with solar event granules, that provides a monthly compilation of polar cloud presence from SAGE III during data capture of the granules.","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III/g3acldb_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"g3assp v004 - A Level 2 data file containing all the species products for a single solar event.","ARN":"arn:aws:s3:::asdc-prod-protected/SAGE_III/g3assp_004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-sage-iii-m3m","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Saildrone Baja Project","Description":"Saildrone is a wind and solar powered unmanned surface vehicle (USV) capable of long distance deployments lasting up to 12 months and providing high quality, near real-time, multivariate surface ocean and atmospheric observations while transiting at typical speeds of 3-5 knots. The drone is autonomous in that it may be guided remotely from land while being completely wind driven. The saildrone Baja campaign was a 60-day cruise from San Francisco Bay, down along the US/Mexico coast to Guadalupe Island and back again over the period 11 April 2018 to 11 June 2018. Repeat surveys were taken around NDBC moored buoys, and during the final week of the cruise a targeted front was sampled. Scientific objectives included studies of upwelling and frontal region dynamics, air-sea interactions, and diurnal warming effects, while its validation objectives included establishing the utility of data from the saildrone platform for assessment of satellite data accuracy and model assimilation. During the Baja campaign, the single deployed saildrone was equipped with a suite of instruments that included a CTD, IR pyrometer, fluorometer, dissolved oxygen sensor, anemometer, barometer, and Acoustic Doppler Current Profiler (ADCP). Additionally, four temperature data loggers were positioned vertically along hull to provide further information on thermal variability near the ocean surface. This Saildrone Baja dataset is comprised of one data file with the saildrone platform telemetry and near-surface observational data (air temperature, sea surface skin and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, wind speed and direction) for the entire cruise at 1 minute temporal resolution. A second file contains the ADCP current vector data that is depth-resolved to 100m at 2m intervals and binned temporally at 5 minute resolution. All data files are in netCDF format and CF/ACDD compliant consistent with the NOAA/NCEI specification.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","netcdf","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SAILDRONE_BAJA_SURFACE v1.0 - Saildrone is a wind and solar powered unmanned surface vehicle (USV) capable of long distance deployments lasting up to 12 months and providing high quality, near real-time, multivariate surface ocean and atmospheric observations while transiting at typical speeds of 3-5 knots. The drone is autonomous in that it may be guided remotely from land while being completely wind driven.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SAILDRONE_BAJA_SURFACE","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-saildrone-baja","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SARAL Project","Description":"These data are near-real-time (NRT) (within 7-9 hours of measurement) sea surface height anomalies (SSHA) from the AltiKa altimeter onboard the Satellite with ARgos and ALtiKa (SARAL). SARAL is a French(CNES)/Indian(SARAL) collaborative mission to measure sea surface height using the Ka-band AltiKa altimeter and was launched February 25, 2013. The major difference between these data and the Operational Geophysical Data Record (OGDR) data produced by the project is that the orbit from SARAL has been adjusted using SSHA differences with those from the OSTM/Jason-2 GPS-OGDR-SSHA product at inter-satellite crossover locations. This produces a more accurate NRT orbit altitude for SARAL with accuracy of 1.5 cm (RMS), taking advantage of the 1 cm (radial RMS) accuracy of the GPS-based orbit used for the OSTM/Jason-2 GPS-OGDR-SSHA product. This dataset also contains all data from the project (reduced) OGDR, and improved altimeter wind speeds and sea state bias correction. More information on the SARAL mission can be found at: http://www.aviso.oceanobs.com/en/missions/current-missions/saral.html\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ALTIKA_SARAL_L2_OST_XOGDR vf - These data are near-real-time (NRT) (within 7-9 hours of measurement) sea surface height anomalies (SSHA) from the AltiKa altimeter onboard the Satellite with ARgos and ALtiKa (SARAL). SARAL is a French(CNES)/Indian(SARAL) collaborative mission to measure sea surface height using the Ka-band AltiKa altimeter and was launched February 25, 2013.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ALTIKA_SARAL_L2_OST_XOGDR","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-saral","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SARP Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### SARP_2009_Data\nSARP_2009_Data is the data collected onboard the NASA DC-8 aircraft during the 2009 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### SARP_2010_Data\nSARP_2010_Data is the data collected onboard the NASA DC-8 aircraft during the 2010 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### SARP_2011_Data\nSARP_2011_Data is the data collected onboard the NASA DC-8 aircraft during the 2011 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### SARP_2012_Data\nSARP_2012_Data is the data collected onboard the NASA P-3B aircraft during the 2012 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### SARP_2013_Data\nSARP_2013_Data is the data collected onboard the NASA DC-8 aircraft during the 2013 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### SARP_2014_Data\nSARP_2014_Data is the data collected onboard the NASA DC-8 aircraft during the 2014 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### SARP_2015_Data\nSARP_2015_Data is the data collected onboard the NASA DC-8 aircraft during the 2015 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### SARP_2016_Data\nSARP_2016_Data is the data collected onboard the NASA DC-8 aircraft during the 2016 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### SARP_2017_Data\nSARP_2017_Data is the data collected onboard the Wallops C-23 aircraft during the 2017 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### SARP_2018_Data\nSARP_2018_Data is the data collected onboard the NASA DC-8 aircraft during the 2018 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### SARP_2019_Data\nSARP_2019_Data is the data collected onboard the NASA DC-8 aircraft during the 2019 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### SARP_2021_Data\nSARP_2021_Data is the data collected onboard the NASA DC-8 aircraft during the 2021 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### SARP_2022_Data\nSARP_2022_Data is the data collected onboard the NASA DC-8 aircraft and using ozonesondes during the 2022 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### SARP_2024_Data\nSARP_2024_Data is the data collected onboard the NASA P-3B and the Dynamic Aviation B200 aircrafts during the 2024 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### SARP_2023_East_Data\nSARP_2023_East_Data is the ground site data and data collected onboard the Dynamic Aviation B200 aircraft during the 2023 deployment of the Student Airborne Research Program - East (SARP-East) mission. Data collection for this product is complete. NASA’s SARP is an eight-week summer internship program for rising senior undergraduate students to acquire hands-on research experience in all aspects of a scientific field campaign. Active since 2009, SARP utilizes one of the major NASA Airborne Science Program flying science laboratories that are used to study Earth system processes, calibration/validation of space-borne observations, and prototyping of instruments for possible satellite missions. Participants assist in the operation of instruments onboard the aircraft to sample atmospheric gases and/or to image land and water surfaces in multiple spectral bands. Students also participate in collecting measurements at field sites. Research areas for SARP include atmospheric chemistry, forest ecology, air quality, and ocean biology.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/SARP/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SARP_2009_Data v1 - SARP_2009_Data is the data collected onboard the NASA DC-8 aircraft during the 2009 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2009_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SARP_2010_Data v1 - SARP_2010_Data is the data collected onboard the NASA DC-8 aircraft during the 2010 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2010_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SARP_2011_Data v1 - SARP_2011_Data is the data collected onboard the NASA DC-8 aircraft during the 2011 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2011_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SARP_2012_Data v1 - SARP_2012_Data is the data collected onboard the NASA P-3B aircraft during the 2012 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2012_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SARP_2013_Data v1 - SARP_2013_Data is the data collected onboard the NASA DC-8 aircraft during the 2013 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2013_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SARP_2014_Data v1 - SARP_2014_Data is the data collected onboard the NASA DC-8 aircraft during the 2014 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2014_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SARP_2015_Data v1 - SARP_2015_Data is the data collected onboard the NASA DC-8 aircraft during the 2015 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2015_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SARP_2016_Data v1 - SARP_2016_Data is the data collected onboard the NASA DC-8 aircraft during the 2016 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2016_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SARP_2017_Data v1 - SARP_2017_Data is the data collected onboard the Wallops C-23 aircraft during the 2017 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2017_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SARP_2018_Data v1 - SARP_2018_Data is the data collected onboard the NASA DC-8 aircraft during the 2018 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2018_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SARP_2019_Data v1 - SARP_2019_Data is the data collected onboard the NASA DC-8 aircraft during the 2019 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2019_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SARP_2021_Data v1 - SARP_2021_Data is the data collected onboard the NASA DC-8 aircraft during the 2021 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2021_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SARP_2022_Data v1 - SARP_2022_Data is the data collected onboard the NASA DC-8 aircraft and using ozonesondes during the 2022 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2022_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SARP_2024_Data v1 - SARP_2024_Data is the data collected onboard the NASA P-3B and the Dynamic Aviation B200 aircrafts during the 2024 deployment of the Student Airborne Research Program (SARP) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2024_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"SARP_2023_East_Data v1 - SARP_2023_East_Data is the ground site data and data collected onboard the Dynamic Aviation B200 aircraft during the 2023 deployment of the Student Airborne Research Program - East (SARP-East) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/SARP/SARP_2023_East_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-sarp","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SASSIE Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### SASSIE_L2_ALTO_ALAMO_FLOATS_V1\nThe Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge. This dataset contains temperature and salinity measurements collected by ALTO and Air Launched Autonomous Micro Observer (ALAMO) profiling floats deployed in the Beaufort Sea. ALTO floats had ice-avoidance firmware, meaning that they stopped surfacing and transmitting data once surface temperatures dropped to near-freezing values (indicating the presence of sea ice). They will hopefully reappear in summer 2023 to report data from the previous ice-covered season. ALAMO floats did not have ice-avoidance, in order to ensure that they reported data as long as possible during ice freeze-up. As a result, they will likely not survive over the winter. Future versions if this dataset may include data collected after Fall 2022. Data are available in netCDF format.\n<br>\n\n#### SASSIE_L1_SWIFT_V1\nThe Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. The Surface Wave Instrument Float with Tracking (SWIFT) drifter is a passive Lagrangian wave-following sensor platform. During the SASSIE deployment, five SWIFT drifters were deployed in September 2022, collecting measurements of salinity, sea surface temperature, waves, and meteorological data. SWIFT drifter buoys contain GPS, a pulse-coherent Doppler velocity profiler, an autonomous meteorological station, and a digital video recorder. Level 1 data are available as compressed files containing graphics of the measurements alongside MATLAB and NetCDF files.\n<br>\n\n#### SASSIE_L2_SHIPBOARD_ADCP_V1\nThis dataset contains measurements of shipboard ocean current speed vertical shear from an acoustic doppler current profiler (ADCP) during the Salinity and Stratification at the Sea Ice Edge (SASSIE) field campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge.\n<br>\n\n#### SASSIE_L3_SHIPBOARD_SBAND_V1\nThis dataset contains images from the S-BAND marine navigation radar collected during the 2022 Salinity and Stratification at the Sea Ice Edge (SASSIE) campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. Images from the radar were digitized and saved every 10-60 seconds on days that the R/V Wolstad was in or around ice. Images were then georeferenced based on range, heading, image orientation, and ship's GPS position. L3 images are available in GeoTIFF format.\n<br>\n\n#### SASSIE_L2_SBAND_ML_V1\nThis dataset contains ice concentration rankings of S-band images from the S-BAND marine navigation radar collected during the 2022 Salinity and Stratification at the Sea Ice Edge (SASSIE) campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. Images from the radar were digitized and saved every 10-60 seconds on days that the R/V Wolstad was in or around ice. Ice concentration rankings ranging from 0 to 3 determined by VGG19 Machine Learning Model. L2 summary data are available in NetCDF format.\n<br>\n\n#### SASSIE_ECCO_L4_ATM_STATE_LLC1080GRID_DAILY_V1R1\nThis dataset provides model-simulated, daily-averaged atmosphere surface temperature, humidity, and winds from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_GEOMETRY_LLC1080GRID_V1R1\nThis dataset provides geometric parameters for the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. Parameters include areas and lengths of grid cell sides; horizontal and vertical coordinates of grid cell centers and corners; grid rotation angles; and global domain geometry including bathymetry and land/ocean masks. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Ocean density and pressure are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_KPP_BOUNDARY_LAYER_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged model-simulated KPP boundary layer depths from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Ocean density and pressure are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_KPP_DIAGS_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged model-simulated KPP diagnostic terms and related coefficients from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. Model ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Data are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_FRESH_FLUX_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged ocean and sea-ice surface freshwater fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. Model ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_HEAT_FLUX_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged ocean and sea-ice surface heat fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. Model ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_STRESS_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged model-simulated ocean and sea-ice surface stress from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Ocean density and pressure are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_OBP_SSH_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged ocean bottom pressure anomaly and sea surface height anomaly from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. Model ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_OBP_SSH_LLC1080GRID_SNAPSHOT_V1R1\nThis dataset provides instantaneous model ocean bottom pressure anomaly and sea surface height anomaly from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. Model ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Monthly files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_DENS_PRESS_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged model-simulated ocean density and hydrostatic pressure from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Ocean density and pressure are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_TEMP_SALINITY_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged model-simulated ocean potential temperature and salinity from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Data are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_TEMP_SALINITY_LLC1080GRID_SNAPSHOT_V1R1\nThis dataset provides model-simulated instantaneous ocean potential temperature and salinity from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Data are provided at 90 depth levels from 0.5 meters to 6760 meters. Monthly files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_OCN_3D_TEMP_ADV_FLUX_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged ocean three-dimensional potential temperature advective fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. Model ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Data are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_OCN_3D_TEMP_DIFF_FLUX_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged ocean three-dimensional potential temperature diffusive fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. Model ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Data are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_OCN_3D_SALINITY_ADV_FLUX_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged ocean three-dimensional salinity advective fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. Model ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Data are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_OCN_3D_SALINITY_DIFF_FLUX_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged ocean three-dimensional salinity diffusive fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. Model ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Data are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_OCN_3D_VOL_FLUX_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged model-simulated ocean three-dimensional volume fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Data are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_OCN_VEL_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged ocean velocity from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. Model ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Data are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_SEA_ICE_CONC_THICK_VEL_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged model-simulated sea-ice and snow concentration, thickness, pressure loading, and velocity from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Ocean density and pressure are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_SEA_ICE_CONC_THICK_VEL_LLC1080GRID_SNAPSHOT_V1R1\nThis dataset provides instantaneous sea-ice and snow concentration, thickness, pressure loading, and velocity from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Ocean density and pressure are provided at 90 depth levels from 0.5 meters to 6760 meters. Monthly files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_SEA_ICE_VOL_AREA_FLUX_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged model-simulated sea-ice and snow horizontal volume advective fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Ocean density and pressure are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### SASSIE_ECCO_L4_SEA_ICE_VOL_AREA_TEND_LLC1080GRID_DAILY_V1R1\nThis dataset provides daily-averaged model-simulated sea-ice and snow horizontal volume tendencies from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean. In this project, it was run over the Arctic polar region in support of the Salinity and Stratification at the Sea Ice Edge (SASSIE) field experiment - a NASA experiment focused on salinity anomalies in the upper ocean generated by melting sea ice. The SASSIE ECCO simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells, where the global solution provided initial and boundary conditions and atmospheric forcing. The output fields for this dataset cover the period 2014-01-15T12:00:00 to 2021-02-07T12:00:00 and are consolidated onto a single curvilinear grid face focusing on the Arctic domain, using the 5 faces of the lat-lon-cap 1080 (llc1080) native grid from the original simulation. Ocean density and pressure are provided at 90 depth levels from 0.5 meters to 6760 meters. Daily files are available in netCDF-4. <br><br> This dataset is one of 22 produced by SASSIE ECCO - the full list can be found in the user guide. To cite all 22 datasets with a single DOI, please cite the user guide (citation details and DOI can be found within the user guide).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/SASSIE/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","earth observation","ice","netcdf","oceans","radar"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SASSIE_L2_ALTO_ALAMO_FLOATS_V1 v1 - The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_L2_ALTO_ALAMO_FLOATS_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_L1_SWIFT_V1 v1 - The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_L1_SWIFT_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_L2_SHIPBOARD_ADCP_V1 v1 - This dataset contains measurements of shipboard ocean current speed vertical shear from an acoustic doppler current profiler (ADCP) during the Salinity and Stratification at the Sea Ice Edge (SASSIE) field campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_L2_SHIPBOARD_ADCP_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_L3_SHIPBOARD_SBAND_V1 v1 - This dataset contains images from the S-BAND marine navigation radar collected during the 2022 Salinity and Stratification at the Sea Ice Edge (SASSIE) campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_L3_SHIPBOARD_SBAND_RADAR_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_L2_SBAND_ML_V1 v1 - This dataset contains ice concentration rankings of S-band images from the S-BAND marine navigation radar collected during the 2022 Salinity and Stratification at the Sea Ice Edge (SASSIE) campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_L2_SBAND_ML_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_ATM_STATE_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides model-simulated, daily-averaged atmosphere surface temperature, humidity, and winds from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_ATM_STATE_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_GEOMETRY_LLC1080GRID_V1R1 vV1R1 - This dataset provides geometric parameters for the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. Parameters include areas and lengths of grid cell sides; horizontal and vertical coordinates of grid cell centers and corners; grid rotation angles; and global domain geometry including bathymetry and land/ocean masks.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_GEOMETRY_LLC1080GRID_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_KPP_BOUNDARY_LAYER_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged model-simulated KPP boundary layer depths from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_KPP_BOUNDARY_LAYER_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_KPP_DIAGS_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged model-simulated KPP diagnostic terms and related coefficients from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_KPP_DIAGS_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_FRESH_FLUX_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged ocean and sea-ice surface freshwater fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_FRESH_FLUX_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_HEAT_FLUX_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged ocean and sea-ice surface heat fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_HEAT_FLUX_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_STRESS_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged model-simulated ocean and sea-ice surface stress from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_STRESS_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_OBP_SSH_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged ocean bottom pressure anomaly and sea surface height anomaly from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_OBP_SSH_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_OBP_SSH_LLC1080GRID_SNAPSHOT_V1R1 vV1R1 - This dataset provides instantaneous model ocean bottom pressure anomaly and sea surface height anomaly from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_OBP_SSH_LLC1080GRID_SNAPSHOT_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_DENS_PRESS_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged model-simulated ocean density and hydrostatic pressure from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_DENS_PRESS_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_TEMP_SALINITY_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged model-simulated ocean potential temperature and salinity from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_TEMP_SALINITY_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_TEMP_SALINITY_LLC1080GRID_SNAPSHOT_V1R1 vV1R1 - This dataset provides model-simulated instantaneous ocean potential temperature and salinity from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_TEMP_SALINITY_LLC1080GRID_SNAPSHOT_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_OCN_3D_TEMP_ADV_FLUX_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged ocean three-dimensional potential temperature advective fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_OCN_3D_TEMP_ADV_FLUX_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_OCN_3D_TEMP_DIFF_FLUX_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged ocean three-dimensional potential temperature diffusive fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_OCN_3D_TEMP_DIFF_FLUX_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_OCN_3D_SALINITY_ADV_FLUX_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged ocean three-dimensional salinity advective fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_OCN_3D_SALINITY_ADV_FLUX_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_OCN_3D_SALINITY_DIFF_FLUX_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged ocean three-dimensional salinity diffusive fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_OCN_3D_SALINITY_DIFF_FLUX_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_OCN_3D_VOL_FLUX_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged model-simulated ocean three-dimensional volume fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_OCN_3D_VOL_FLUX_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_OCN_VEL_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged ocean velocity from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_OCN_VEL_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_SEA_ICE_CONC_THICK_VEL_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged model-simulated sea-ice and snow concentration, thickness, pressure loading, and velocity from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_SEA_ICE_CONC_THICK_VEL_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_SEA_ICE_CONC_THICK_VEL_LLC1080GRID_SNAPSHOT_V1R1 vV1R1 - This dataset provides instantaneous sea-ice and snow concentration, thickness, pressure loading, and velocity from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_SEA_ICE_CONC_THICK_VEL_LLC1080GRID_SNAPSHOT_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_SEA_ICE_VOL_AREA_FLUX_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged model-simulated sea-ice and snow horizontal volume advective fluxes from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_SEA_ICE_VOL_AREA_FLUX_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SASSIE_ECCO_L4_SEA_ICE_VOL_AREA_TEND_LLC1080GRID_DAILY_V1R1 vV1R1 - This dataset provides daily-averaged model-simulated sea-ice and snow horizontal volume tendencies from the SASSIE ECCO Version 1 Release 1 (V1R1) ocean and sea-ice state estimate. ECCO (Estimating the Circulation and Climate of the Ocean) is a 4D ocean circulation model combining observations with a general circulation model (GCM) to estimate the complete time-evolving state of the global ocean.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SASSIE_ECCO_L4_SEA_ICE_VOL_AREA_TEND_LLC1080GRID_DAILY_V1R1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-sassie","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SBG Project","Description":"This report summarizes the community discussions at the 2024 NASA Surface Biology and Geology (SBG) Technical Interchange Meeting (TIM) from May 29 to 31, 2024, in Washington, D.C., US. The report provides the broader science community with information on the current state of the SBG mission, SBG-relevant science and applications, and future work needed. The SBG mission includes a visible to shortwave infrared (VSWIR) spectrometer and a thermal infrared (TIR) radiometer as outlined in the 2017-2027 National Academies' Decadal Survey. Over 150 in-person and 200 online community members from across research and applications areas gathered to learn about the current state of the mission, discuss needed science and applications developments, provide input on SBG- TIR and SBG- VSWIR acquisition decisions, and learn about how to access precursor data from Earth Surface Mineral Dust Source InvesTigation (EMIT), ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), and relevant airborne instruments. This paper discusses the current state of the SBG mission and the SBG-TIR and SBG-VSWIR instruments, including algorithm development plans, open science approach, and ongoing collaborations with international partners. It presents information on precursor data from the EMIT and ECOSTRESS instruments that can be used to develop and test algorithms for SBG as well as synergistic data from relevant airborne campaigns. These precursor data are also part of a NASA Distributed Active Archive Centers (DAACs) effort to provide tools and tutorials for easier data access and lower the barrier of entry for the use of SBG-like data. The report is provided in Portable Document Format (PDF).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SBG_TIM_2024_Report_2433 v1 - This report summarizes the community discussions at the 2024 NASA Surface Biology and Geology (SBG) Technical Interchange Meeting (TIM) from May 29 to 31, 2024, in Washington, D.C., US. The report provides the broader science community with information on the current state of the SBG mission, SBG-relevant science and applications, and future work needed.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/sbg/SBG_TIM_2024_Report/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-sbg","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SCP Project","Description":"This European Remote Sensing (ERS) Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long at BYU. The dataset provides SIR processed Sigma-0 data from the ERS-1 C-band scatterometer, which is also known as the Active Microwave Instrument (AMI). AMI is a multimode radar operating at a frequency of 5.3 GHz (C-band), using vertically polarized antennas for both transmission and reception. The SIR technique results in an enhanced resolution image reconstruction and gridded on an equal-area grid (for non-polar regions) at 8.9 km pixel resolution stored in SIR files; polar regions are gridded at the same resolution using a polar-stereographic technique. A non-enhanced version is provided at 44.5 km pixel resolution in a format known as GRD (i.e., gridded) files. All files are produced in IEEE formatted binary. All data files are separated and organized by region, parameter, and sampling technique (i.e., SIR vs. GRD). The regions of China and Japan are combined into a single region. In addition to Sigma-0, various statistical parameters are provided for added guidance, including but not limited to: standard deviation, measurement counts, pixel time, Sigma-0 error, and average incidence angle. This dataset was once distributed on tape, but has been made available on FTP thanks to the BYU SCP.\n<br>\n\n#### NSCAT_BYU_L3_OW_SIGMA0_ENHANCED\nThis NASA Scatterometer (NSCAT) satellite Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long. The SIR technique results in an enhanced resolution image reconstruction and gridded on an equal-area grid (for non-polar regions) at 4.45 km pixel resolution stored in SIR files; polar regions are gridded using a polar-stereographic technique. A non-enhanced version is provided at 22.25 km pixel resolution in a format known as GRD files. All files are produced in IEEE formatted binary. All data files are separated and organized by region, polarization, parameter, and sampling technique (i.e., SIR vs. GRD). The regions of China and Japan are combined into a single region. In additional to Sigma-0, various statistical parameters are provided for added guidance, including but not limited to: standard deviation, measurement counts, pixel time, Sigma-0 error, and average incidence angle. For more information, please visti: http://www.scp.byu.edu/docs/NSCAT_user_notes.html\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","earth observation","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ERS-1_BYU_L3_OW_SIGMA0_ENHANCED v1 - This European Remote Sensing (ERS) Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long at BYU.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/ERS-1_BYU_L3_OW_SIGMA0_ENHANCED","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NSCAT_BYU_L3_OW_SIGMA0_ENHANCED v1 - This NASA Scatterometer (NSCAT) satellite Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/NSCAT_BYU_L3_OW_SIGMA0_ENHANCED","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-scp","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SEAWINDS Project","Description":"The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). The dataset contains the Level 1C WindSat Top of the Atmosphere (TOA) TB processed by RSS. The WindSat radiances are turned into TOA TB after correction for hot and cold calibration anomalies, receiver non-linearities, sensor pointing errors, antenna cross-polarization contamination, spillover, Faraday rotation and polarization alignment. The data are resampled on a fixed regular 0.125 deg Earth grid using Backus-Gilbert Optimum Interpolation. The sampling is done separately for fore and aft looks. The 10.7, 18.7, 23.8, 37.0 GHz channels are resampled to the 10.7 GHz spatial resolution. The 6.8 GHz channels are given at their native spatial resolution. The 10.7, 18.7, 23.8, 37.0 GHz channels are absolutely calibrated using the GMI sensor as calibration reference. The 6.8 GHz channels are calibrated using the open ocean with the RSS ocean emission model and the Amazon rain forest as calibration targets. The Faraday rotation angle (FRA) and geometric polarization basis rotation angle (PRA) were added in the last run.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","earth observation","ice","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"RSS_WindSat_L1C_TB_V08.0 v8.0 - The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/RSS_WindSat_L1C_TB_V08.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-seawinds","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA sentinel-1 Project","Description":"This data set contains annual surface melt onset and freeze onset dates across all glaciers in the Hindu Kush Himalayas (HKH) retrieved from time series synthetic aperture radar (SAR) imagery. The data set was based on analysis of C-band Sentinel-1 A/B SAR time series, comprising 32,741 Sentinel-1 A/B SAR images. The duration of annual glacier surface melt was determined for 105,432 mapped glaciers (83,102 km2 glacierized area) during the calendar years 2017-2020.\n<br>\n\n#### NSIDC-0720\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides annual maps of Antarctic ice velocity. The maps are assembled using SAR data from the Japanese Space Agency's (JAXA) ALOS PALSAR, the European Space Agency's (ESA) ENVISAT ASAR and Copernicus Sentinel-1, the Canadian Space Agency's (CSA) RADARSAT-1, RADARSAT-2, the German Aerospace Agency's (DLR) TerraSAR-X (TSX) and TanDEM –X (TDX), and the U.S. Geological Survey's (USGS) Landsat-8 optical imagery.. See <a href=\"https://nsidc.org/data/measures/aiv\">Antarctic Ice Sheet Velocity and Mapping Data</a> for related data.\n<br>\n\n#### NSIDC-0642\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of annual, digitized (polyline) ice front positions for 239 outlet glaciers in Greenland. Ice front positions are derived from Sentinel-1A, Sentinel-1B, and RADARSAT-1 synthetic aperture radar (SAR) mosaics, plus imagery from Landsat 1 through Landsat 5 and Landsat 7 and Landsat 8. Although temporal coverage varies by glacier, data are available for the winter seasons 1972–1973 through 2020–2021. Data are provided as shapefiles. See <a href=\"http://nsidc.org/data/measures/gimp\">Greenland Ice Mapping Project (GrIMP)</a> for related data.\n<br>\n\n#### NSIDC-0766\nThis data set contains 6 and 12 day surface velocity estimates for the Greenland Ice Sheet and periphery derived from images acquired between 2015–2021 by the European Space Agency (ESA) Copernicus Sentinel-1A and Sentinel-1B satellites.\n<br>\n\n#### NSIDC-0766\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains 6 and 12 day surface velocity estimates for the Greenland Ice Sheet and periphery. Velocities are derived from images acquired by the European Space Agency (ESA) Copernicus Sentinel-1A and Sentinel-1B satellites. See <a href=\"http://nsidc.org/grimp\">Greenland Ice Mapping Project (GrIMP)</a> for related data sets.\n<br>\n\n#### NSIDC-0725\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains annual ice velocity mosaics for the Greenland Ice Sheet. Velocities are derived from synthetic aperture radar (SAR) data, obtained by TerraSAR-X/TanDEM-X and Sentinel-1A and -1B, and from optical imagery acquired by Landsat 8 and Landsat 9. See <a href=\"http://nsidc.org/data/measures/gimp\">Greenland Ice Mapping Project (GrIMP)</a> for related data.\n<br>\n\n#### NSIDC-0478\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains seasonal (winter) ice sheet-wide velocity maps for Greenland. The maps are derived from Interferometric Synthetic Aperture Radar (InSAR) data obtained by the Canadian Space Agency's (CSA) RADARSAT-1, the Japan Aerospace Exploration Agency's (JAXA) Advanced Land Observation Satellite (ALOS), and the German Aerospace Center's (DLR) TerraSAR-X/TanDEM-X (TSX/TDX) satellites, as well as from the European Space Agency's (ESA) C-band Synthetic Aperture Radar data from Copernicus Sentinel-1A and -1B. See <a href=\"http://nsidc.org/data/measures/gimp\">Greenland Ice Mapping Project (GIMP)</a> for related data.\n<br>\n\n#### NSIDC-0723\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of 6-day and 12-day 50 m resolution image mosaics of the Greenland coastline and ice sheet periphery. The mosaics are derived from C-band Synthetic Aperture Radar (C-SAR) acquired by the Copernicus Sentinel-1A, -1B, and -1C satellites. See <a href=\"http://nsidc.org/grimp\">Greenland Ice Mapping Project (GrIMP)</a> for related data sets.\n<br>\n\n#### NSIDC-0731\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains monthly ice velocity mosaics for the Greenland Ice Sheet. The data are derived from Synthetic Aperture Radar (SAR) data, obtained by TerraSAR-X/TanDEM-X and Sentinel-1A and -1B, and from optical imagery acquired by Landsat 8 and Landsat 9. See <a href=\"http://nsidc.org/grimp\">Greenland Ice sheet Mapping Project (GrIMP)</a> for related data.\n<br>\n\n#### NSIDC-0727\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains quarterly (three-month interval) ice velocity mosaics for the Greenland Ice Sheet. This data set is derived from Synthetic Aperture Radar (SAR) data, obtained by TerraSAR-X/TanDEM-X and Sentinel-1A and -1B, and from optical imagery acquired by Landsat 8 and Landsat 9. See <a href=\"https://nsidc.org/grimp\">Greenland Ice sheet Mapping Project (GrIMP)</a> for related data.\n<br>\n\n#### NSIDC-0778\nThis data set provides a comprehensive map for the Antarctic Ice Sheet of the short-term zone of migration of the ice grounding line (i.e., the transition boundary between grounded ice and ice floating in the ocean waters) for the given period, due to changes in oceanic tide. This variation is represented by polylines that indicate the upstream and downstream bounds in the grounding line variation for a given year. The data are derived using an automated machine learning algorithm to delineate thousands of grounding lines in interferometric synthetic aperture radar (InSAR) data acquired by Sentinel-1 A/B, supplemented by data from COSMO SkyMed, RADARSAT-2, and European Remote Sensing (ERS) satellites.\n<br>\n\n#### NSIDC-0776\nThis data set from the Inter-Mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project, part of NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of regionally compiled, mean annual surface velocities, plus climatological means, for major glacier-covered regions. Velocities are derived from imagery acquired by the Sentinel-1 and Sentinel-2 and Landsat 4, 5, 7, 8, and 9 satellites. Data are available from 1985 to 2022, with nearly complete annual coverage provided after 2013.\n<br>\n\n#### NSIDC-0781\nThis data set consists of sub-seasonal, digitized (polyline) ice front positions for 219 outlet glaciers in Greenland. For 199 glaciers, ice front positions are digitized at a monthly resolution. For 20 glaciers in northwestern Greenland, ice front positions are digitized at a 6-12 day resolution, depending on the availability of satellite imagery. Ice front positions are derived from Sentinel-1A and Sentinel-1B synthetic aperture radar (SAR) mosaics.\n<br>\n\n#### SPL2SMAP_S\nThis Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/sentinel-1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","earth observation","elevation","ice","oceans","radar","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"HMA_GSM v1 - This data set contains annual surface melt onset and freeze onset dates across all glaciers in the Hindu Kush Himalayas (HKH) retrieved from time series synthetic aperture radar (SAR) imagery. The data set was based on analysis of C-band Sentinel-1 A/B SAR time series, comprising 32,741 Sentinel-1 A/B SAR images.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_GSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0720 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides annual maps of Antarctic ice velocity. The maps are assembled using SAR data from the Japanese Space Agency's (JAXA) ALOS PALSAR, the European Space Agency's (ESA) ENVISAT ASAR and Copernicus Sentinel-1, the Canadian Space Agency's (CSA) RADARSAT-1, RADARSAT-2, the German Aerospace Agency's (DLR) TerraSAR-X (TSX) and TanDEM –X (TDX), and the U.S.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0720/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0642 v2 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of annual, digitized (polyline) ice front positions for 239 outlet glaciers in Greenland. Ice front positions are derived from Sentinel-1A, Sentinel-1B, and RADARSAT-1 synthetic aperture radar (SAR) mosaics, plus imagery from Landsat 1 through Landsat 5 and Landsat 7 and Landsat 8.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0642/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0766 v1 - This data set contains 6 and 12 day surface velocity estimates for the Greenland Ice Sheet and periphery derived from images acquired between 2015–2021 by the European Space Agency (ESA) Copernicus Sentinel-1A and Sentinel-1B satellites.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0766/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0766 v2 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains 6 and 12 day surface velocity estimates for the Greenland Ice Sheet and periphery. Velocities are derived from images acquired by the European Space Agency (ESA) Copernicus Sentinel-1A and Sentinel-1B satellites.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0766/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0725 v5 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains annual ice velocity mosaics for the Greenland Ice Sheet. Velocities are derived from synthetic aperture radar (SAR) data, obtained by TerraSAR-X/TanDEM-X and Sentinel-1A and -1B, and from optical imagery acquired by Landsat 8 and Landsat 9.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0725/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0478 v2 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains seasonal (winter) ice sheet-wide velocity maps for Greenland. The maps are derived from Interferometric Synthetic Aperture Radar (InSAR) data obtained by the Canadian Space Agency's (CSA) RADARSAT-1, the Japan Aerospace Exploration Agency's (JAXA) Advanced Land Observation Satellite (ALOS), and the German Aerospace Center's (DLR) TerraSAR-X/TanDEM-X (TSX/TDX) satellites, as well as from the European Space Agency's (ESA) C-band Synthetic Aperture Radar data from Cope...","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0478/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0723 v4 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of 6-day and 12-day 50 m resolution image mosaics of the Greenland coastline and ice sheet periphery. The mosaics are derived from C-band Synthetic Aperture Radar (C-SAR) acquired by the Copernicus Sentinel-1A, -1B, and -1C satellites.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0723/4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0731 v5 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains monthly ice velocity mosaics for the Greenland Ice Sheet. The data are derived from Synthetic Aperture Radar (SAR) data, obtained by TerraSAR-X/TanDEM-X and Sentinel-1A and -1B, and from optical imagery acquired by Landsat 8 and Landsat 9.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0731/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0727 v5 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains quarterly (three-month interval) ice velocity mosaics for the Greenland Ice Sheet. This data set is derived from Synthetic Aperture Radar (SAR) data, obtained by TerraSAR-X/TanDEM-X and Sentinel-1A and -1B, and from optical imagery acquired by Landsat 8 and Landsat 9.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0727/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0778 v1 - This data set provides a comprehensive map for the Antarctic Ice Sheet of the short-term zone of migration of the ice grounding line (i.e., the transition boundary between grounded ice and ice floating in the ocean waters) for the given period, due to changes in oceanic tide. This variation is represented by polylines that indicate the upstream and downstream bounds in the grounding line variation for a given year.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0778/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0776 v2 - This data set from the Inter-Mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project, part of NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of regionally compiled, mean annual surface velocities, plus climatological means, for major glacier-covered regions. Velocities are derived from imagery acquired by the Sentinel-1 and Sentinel-2 and Landsat 4, 5, 7, 8, and 9 satellites.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0776/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0781 v1 - This data set consists of sub-seasonal, digitized (polyline) ice front positions for 219 outlet glaciers in Greenland. For 199 glaciers, ice front positions are digitized at a monthly resolution.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0781/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL2SMAP_S v003 - This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL2SMAP_S/003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-sentinel-1","Sources":["awslabs-open-data-registry"]}
{"Name":"SENTINEL-1A_DUAL_POL_GRD_HIGH_RES","Description":"Sentinel-1A Dual-pol ground projected high and full resolution images\nRead our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME","Documentation":"https://webdocs.asf.alaska.edu/Sentinel-1/Sentinel-1-User-Guide.pdf","Contact":"Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/","ManagedBy":"NASA","UpdateFrequency":"From 2014-04-03 to Ongoing","Tags":["agriculture","aws-pds","coastal","earth observation","earthquakes","ecosystems","ice","land","land cover","land use","metadata","oceans","radar","sentinel-1","stac","surface water","synthetic aperture radar","tiff","urban","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SENTINEL-1A_DUAL_POL_GRD_HIGH_RES.","ARN":"arn:aws:s3:::asf-ngap2w-p-s1-grd-7d1b4348","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://sentinel1.asf.alaska.edu/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Interferometric Synthetic Aperture Radar Tutorial","URL":"https://github.com/live-eo/sentinel1-slc/blob/main/docs/tutorial_InSAR.md","AuthorName":"LiveEO","AuthorURL":"https://live-eo.com/"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-sentinel-1adpgrdhigh","Sources":["awslabs-open-data-registry"]}
{"Name":"SENTINEL-1A_SLC","Description":"Sentinel-1A slant-range product\nRead our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME","Documentation":"https://webdocs.asf.alaska.edu/Sentinel-1/Sentinel-1-User-Guide.pdf","Contact":"Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/","ManagedBy":"NASA","UpdateFrequency":"From 2014-04-03 to Ongoing","Tags":["aws-pds","coastal","earthquakes","ecosystems","ice","land","land cover","land use","metadata","oceans","orbit","radar","sentinel-1","stac","surface water","synthetic aperture radar","tiff","urban","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SENTINEL-1A_SLC.","ARN":"arn:aws:s3:::asf-ngap2w-p-s1-slc-7b420b89","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://sentinel1.asf.alaska.edu/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Interferometric Synthetic Aperture Radar Tutorial","URL":"https://github.com/live-eo/sentinel1-slc/blob/main/docs/tutorial_InSAR.md","AuthorName":"LiveEO","AuthorURL":"https://live-eo.com/"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-sentinel-1aslc","Sources":["awslabs-open-data-registry"]}
{"Name":"SENTINEL-1B_DUAL_POL_GRD_HIGH_RES","Description":"Sentinel-1B Dual-pol ground projected high and full resolution images\nRead our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME","Documentation":"https://webdocs.asf.alaska.edu/Sentinel-1/Sentinel-1-User-Guide.pdf","Contact":"Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/","ManagedBy":"NASA","UpdateFrequency":"From 2016-04-25 to 2021-12-24","Tags":["agriculture","aws-pds","coastal","earthquakes","ecosystems","ice","land","land cover","land use","metadata","oceans","radar","sentinel-1","stac","surface water","synthetic aperture radar","tiff","urban","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SENTINEL-1B_DUAL_POL_GRD_HIGH_RES.","ARN":"arn:aws:s3:::asf-ngap2w-p-s1-grd-7d1b4348","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://sentinel1.asf.alaska.edu/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Interferometric Synthetic Aperture Radar Tutorial","URL":"https://github.com/live-eo/sentinel1-slc/blob/main/docs/tutorial_InSAR.md","AuthorName":"LiveEO","AuthorURL":"https://live-eo.com/"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-sentinel-1bdpgrdhigh","Sources":["awslabs-open-data-registry"]}
{"Name":"SENTINEL-1B_SLC","Description":"Sentinel-1B slant-range product\nRead our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME","Documentation":"https://webdocs.asf.alaska.edu/Sentinel-1/Sentinel-1-User-Guide.pdf","Contact":"Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/","ManagedBy":"NASA","UpdateFrequency":"From 2016-04-25 to 2021-12-24","Tags":["agriculture","aws-pds","coastal","earthquakes","ecosystems","ice","land","land cover","land use","metadata","oceans","orbit","radar","sentinel-1","stac","surface water","synthetic aperture radar","tiff","urban","water"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SENTINEL-1B_SLC.","ARN":"arn:aws:s3:::asf-ngap2w-p-s1-slc-7b420b89","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://sentinel1.asf.alaska.edu/s3credentials"}],"DataAtWork":{"Tutorials":[{"Title":"Interferometric Synthetic Aperture Radar Tutorial","URL":"https://github.com/live-eo/sentinel1-slc/blob/main/docs/tutorial_InSAR.md","AuthorName":"LiveEO","AuthorURL":"https://live-eo.com/"}]},"RegistryEntryAdded":"2025-08-08","RegistryEntryLastModified":"2025-08-08","Slug":"nasa-sentinel-1bslc","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA sentinel-2 Project","Description":"This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of surface velocity estimates for selected Greenland Ice Sheet outlet glaciers. Velocity fields were generated by tracking visible features in optical images acquired by the U.S. Geological Survey (USGS) Landsat 8 Operational Land Imager (OLI) and the European Space Agency (ESA) Copernicus Sentinel-2A and Sentinel-2B satellites.\n<br>\n\n#### NSIDC-0801\nThis data set consists of velocity maps and error estimates for five surge-type glaciers: Nàłùdäy/Lowell Glacier, Canada; Medvezhiy Glacier, the Pamir mountains; South Rimo Glacier, the Karakoram; Sít' Kusá Glacier, Alaska; and Aavatsmarkbreen Glacier, Spitsbergen, Svalbard. The maps are generated using CautoRIFT, a pipeline that improves temporal coverage during periods of rapid glacier motion by adding pre-processed, daily high-resolution PlanetScope images and customized feature tracking parameters for each glacier to NASA's autonomous Repeat Image Feature Tracking (autoRIFT) algorithm for Sentinel-2 and Landsat imagery. Stable surface masks, area of interest (AOI) and glacier outlines, and ancillary data are also available for each glacier site.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/nsidc-0777/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NSIDC-0777 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of surface velocity estimates for selected Greenland Ice Sheet outlet glaciers. Velocity fields were generated by tracking visible features in optical images acquired by the U.S.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0777/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0801 v1 - This data set consists of velocity maps and error estimates for five surge-type glaciers: Nàłùdäy/Lowell Glacier, Canada; Medvezhiy Glacier, the Pamir mountains; South Rimo Glacier, the Karakoram; Sít'' Kusá Glacier, Alaska; and Aavatsmarkbreen Glacier, Spitsbergen, Svalbard. The maps are generated using CautoRIFT, a pipeline that improves temporal coverage during periods of rapid glacier motion by adding pre-processed, daily high-resolution PlanetScope images and customized feature tracking parameters for each glacier to NASA''s autonomous Repeat Image Feature Tracking (autoRIFT) algorithm f...","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/OTHR/NSIDC-0801/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-sentinel-2","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Sentinel-3A Project","Description":"The OLCI+SLSTR/Sentinel-3A L2 Surface Reflectance and Aerosol parameters over Land product with shortname S3A_SY_2_SYN, is generated by combining data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR), on-board SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands whereas the SLSTR is a dual scan temperature radiometer. The principal objective of SLSTR products is to provide global and regional Sea and Land Surface Temperature (SST, LST) to a very high level of accuracy (better than 0.3 K) for both climatological and meteorological applications. For more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. The S3A_SY_2_SYN is a Level 2 product which consist of surface reflectances for all SYN channels and aerosol parameters over Land. There are 29 Measurement Data Files and 9 Annotation Data Files included in this product. All measurement datasets are provided on the OLCI image grid, similar to the one included in the OLCI L1b product. For more information about the product, read the SENTINEL-3 Synergy User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-synergy\n<br>\n\n#### S3A_OL_1_EFR\nThe OLCI/Sentinel-3A L1 Full Resolution Top of Atmosphere Reflectance product, S3A_OL_1_EFR is generated from the data aquired by the Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands. OLCI is based on the imaging design of ENVISAT's Medium Resolution Imaging Spectrometer (MERIS). It has a 1270km wide swath. For more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. The S3A_OL_1_EFR is a Level-1B product. This is composed of an information package map, called a manifest, 22 measurement data files, and seven annotation data files. The 21 measurement data files (one for each band) consist of Top Of Atmosphere (TOA) radiances, calibrated to geophysical units (W.m-2. sr-1 Micro meter-1), georeferenced onto the Earth's surface, and spatially resampled onto an evenly spaced grid. Seven annotation files provide information on illumination and observation geometry, environment data (meteorological data) and quality and classification flags. Both measurement data files and annotation data files are written in netCDF 4 format. The manifest file is in XML format and contains metadata associated with the instrument and the processing. The S3A_OL_1_EFR is generated in Earth Observation (EO) processing mode and all parameters in this product are provided for each re-gridded pixel on the product image and for each removed pixel. The OL_1_EFR product package is described below: Element name Description Manifest.safe SENTINEL-SAFE product manifest Oa##_radiance.nc Radiance for OLCI acquisition bands 01 to 21 Removed_pixels.nc Removed pixels information needed for Level-1C generation Time_coordinates.nc Time stamp annotations Geo_coordinates.nc High resolution georeferencing data Quality_flags.nc Classification and quality flags Tie_geo_coordinates.nc Low resolution georeferencing data Tie_geometries.nc Sun and view angles Tie_meteo.nc ECMWF meteorology data Instrument_data.nc Instrument data note: Oa## represents all the OLCI channels (Oa1 to Oa21). For more information about the product, read the SENTINEL-3 OLCI User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci.\n<br>\n\n#### S3A_OL_1_ERR\nThe OLCI/Sentinel-3A L1 Reduced Resolution Top of Atmosphere Reflectance, S3A_OL_1_ERR is generated from the data aquired by the Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands. OLCI is based on the imaging design of ENVISAT's Medium Resolution Imaging Spectrometer (MERIS). It has a 1270km wide swath. For more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. The S3A_OL_1_ERR is a Level-1B product. This is composed of an information package map, called a manifest, 22 measurement data files, and seven annotation data files. The 21 measurement data files (one for each band) consist of Top Of Atmosphere (TOA) radiances, calibrated to geophysical units (W.m-2. sr-1 Micro meter-1), georeferenced onto the Earth's surface, and spatially resampled onto an evenly spaced grid. Seven annotation files provide information on illumination and observation geometry, environment data (meteorological data) and quality and classification flags. Both measurement data files and annotation data files are written in netCDF 4 format. The manifest file is in XML format and contains metadata associated with the instrument and the processing. The S3A_OL_1_EFR is generated in Earth Observation (EO) processing mode and all parameters in this product are provided for each re-gridded pixel on the product image and for each removed pixel. The OL_1_EFR product package is described below: Element name Description Manifest.safe SENTINEL-SAFE product manifest Oa##_radiance.nc Radiance for OLCI acquisition bands 01 to 21 Time_coordinates.nc Time stamp annotations Geo_coordinates.nc High resolution georeferencing data Quality_flags.nc Classification and quality flags Tie_geo_coordinates.nc Low resolution georeferencing data Tie_geometries.nc Sun and view angles Tie_meteo.nc ECMWF meteorology data Instrument_data.nc Instrument data note: Oa## represents all the OLCI channels (Oa1 to Oa21). For more information about the product, read the SENTINEL-3 OLCI User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci\n<br>\n\n#### S3A_SL_1_RBT\nThe SLSTR/Sentinel-3A L1 Full Resolution Top of Atmosphere Radiances and Brightness Temperature product with shortname S3A_SL_1_RBT, is generated from the data aquired by the Sea and Land Surface Temperature Radiometer (SLSTR), on-board SENTINEL-3, is a dual scan temperature radiometer. The principal aim of the SLSTR instrument is to maintain continuity with the AATSR series of instruments. The SLSTR instrument design incorporates the basic functionality of AATSR in addition to new, more advanced features including a wider swath, new channels (including two channels dedicated to fire detection), and higher resolution in some channels. The principal objective of SLSTR products is to provide global and regional Sea and Land Surface Temperature (SST, LST) to a very high level of accuracy (better than 0.3 K) for both climatological and meteorological applications. For more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. The S3A_SL_1_RBT is a Level 1B product which consist of full resolution, geolocated, co-located nadir and along track view, Top of Atmosphere (TOA) brightness temperatures (in the case of thermal IR channels) or radiances (in the case of visible, NIR and SWIR channels) from all SLSTR channels. It also contains quality flags, pixel classification information and meteorological annotations. Based on components activated by configuration which are not part of the operational production baseline, the S3A_SL_1_RBT may contain 77 or 111 files. Out of the these files, 22 or 34 files contain the actual measurements, where the other 54 or 76 files contain the annotations data. For more information about the product, read the SENTINEL-3 SLSTR User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","earth observation","netcdf","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"S3A_SY_2_SYN v1 - The OLCI+SLSTR/Sentinel-3A L2 Surface Reflectance and Aerosol parameters over Land product with shortname S3A_SY_2_SYN, is generated by combining data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR), on-board SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands whereas the SLSTR is a dual scan temperature radiometer.","ARN":"arn:aws:s3:::prod-lads/S3A_SY_2_SYN","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"S3A_OL_1_EFR v1 - The OLCI/Sentinel-3A L1 Full Resolution Top of Atmosphere Reflectance product, S3A_OL_1_EFR is generated from the data aquired by the Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands.","ARN":"arn:aws:s3:::prod-lads/S3A_OL_1_EFR","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"S3A_OL_1_ERR v1 - The OLCI/Sentinel-3A L1 Reduced Resolution Top of Atmosphere Reflectance, S3A_OL_1_ERR is generated from the data aquired by the Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands.","ARN":"arn:aws:s3:::prod-lads/S3A_OL_1_ERR","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"S3A_SL_1_RBT v1 - The SLSTR/Sentinel-3A L1 Full Resolution Top of Atmosphere Radiances and Brightness Temperature product with shortname S3A_SL_1_RBT, is generated from the data aquired by the Sea and Land Surface Temperature Radiometer (SLSTR), on-board SENTINEL-3, is a dual scan temperature radiometer. The principal aim of the SLSTR instrument is to maintain continuity with the AATSR series of instruments.","ARN":"arn:aws:s3:::prod-lads/S3A_SL_1_RBT","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-sentinel-3a","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Sentinel-3B Project","Description":"The OLCI+SLSTR/Sentinel-3B L2 Surface Reflectance and Aerosol parameters over Land product with shortname S3A_SY_2_SYN, is generated by combining data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR), on-board SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands whereas the SLSTR is a dual scan temperature radiometer. The principal objective of SLSTR products is to provide global and regional Sea and Land Surface Temperature (SST, LST) to a very high level of accuracy (better than 0.3 K) for both climatological and meteorological applications. For more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. The S3B_SY_2_SYN is a Level 2 product which consist of surface reflectances for all SYN channels and aerosol parameters over Land. There are 29 Measurement Data Files and 9 Annotation Data Files included in this product. All measurement datasets are provided on the OLCI image grid, similar to the one included in the OLCI L1b product. For more information about the product, read the SENTINEL-3 Synergy User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-synergy\n<br>\n\n#### S3B_OL_1_EFR\nThe OLCI/Sentinel-3B L1 Full Resolution Top of Atmosphere Reflectance product, S3B_OL_1_EFR is generated from the data aquired by the Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands. OLCI is based on the imaging design of ENVISAT's Medium Resolution Imaging Spectrometer (MERIS). It has a 1270km wide swath. For more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. The S3B_OL_1_EFR is a Level-1B product. This is composed of an information package map, called a manifest, 22 measurement data files, and seven annotation data files. The 21 measurement data files (one for each band) for Top Of Atmosphere (TOA) radiances, calibrated to geophysical units (W.m-2. sr-1 Micro meter-1), georeferenced onto the Earth's surface, and spatially resampled onto an evenly spaced grid. Seven annotation files provide information pertaining to illumination and observation geometry, environmental data (meteorological data), and quality and classification flags. Both measurement data files and annotation data files are written in netCDF 4 format. The manifest file is in XML format and contains metadata associated with the instrument and the processing. The S3A_OL_1_EFR is generated in Earth Observation (EO) processing mode and all parameters in this product are provided for each re-gridded pixel on the product image and for each removed pixel. The OL_1_EFR product package is described below: Element name Description xfdumanifest.xml SENTINEL-SAFE product manifest Oa##_radiance.nc Radiance for OLCI acquisition bands 01 to 21 removed_pixels.nc Removed pixels information needed for Level-1C generation time_coordinates.nc Time stamp annotations geo_coordinates.nc High resolution georeferencing data qualityFlags.nc Classification and quality flags tie_geo_coordinates.nc Low resolution georeferencing data tie_geometries.nc Sun and view angles tie_meteo.nc ECMWF meteorology data instrument_data.nc Instrument data note: Oa## represents all the OLCI channels (Oa1 to Oa21). For more information about the product, read the SENTINEL-3 OLCI User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci.\n<br>\n\n#### S3B_OL_1_ERR\nThe OLCI/Sentinel-3B L1 Reduced Resolution Top of Atmosphere Reflectance product, S3B_OL_1_ERR is generated from the data acquired by the Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands. OLCI is based on the imaging design of ENVISAT's Medium Resolution Imaging Spectrometer (MERIS). It has a 1270km wide swath. For more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. The S3B_OL_1_ERR is a Level-1B product. This is composed of an information package map, called a manifest, 22 measurement data files, and seven annotation data files. The 21 measurement data files (one for each band) consist of Top Of Atmosphere (TOA) radiances, calibrated to geophysical units (W.m-2. sr-1 Micro meter-1), georeferenced onto the Earth's surface, and spatially resampled onto an evenly spaced grid. Seven annotation files provide information on illumination and observation geometry, environment data (meteorological data) and quality and classification flags. Both measurement data files and annotation data files are written in netCDF 4 format. The manifest file is in XML format and contains metadata associated with the instrument and the processing. The S3A_OL_1_EFR is generated in Earth Observation (EO) processing mode and all parameters in this product are provided for each re-gridded pixel on the product image and for each removed pixel. The OL_1_EFR product package is described below: Element name Description xfdumanifest.xml SENTINEL-SAFE product manifest Oa##_radiance.nc Radiance for OLCI acquisition bands 01 to 21 Time_coordinates.nc Time stamp annotations Geo_coordinates.nc High resolution georeferencing data Quality_flags.nc Classification and quality flags Tie_geo_coordinates.nc Low resolution georeferencing data Tie_geometries.nc Sun and view angles Tie_meteo.nc ECMWF meteorology data Instrument_data.nc Instrument data note: Oa## represents all the OLCI channels (Oa1 to Oa21). For more information about the product, read the SENTINEL-3 OLCI User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci\n<br>\n\n#### S3B_SL_1_RBT\nThe SLSTR/Sentinel-3B L1 Full Resolution Top of Atmosphere Radiances and Brightness Temperature product with shortname S3B_SL_1_RBT, is generated from the data acquired by the Sea and Land Surface Temperature Radiometer (SLSTR), on-board SENTINEL-3, is a dual scan temperature radiometer. The principal aim of the SLSTR instrument is to maintain continuity with the AATSR series of instruments. The SLSTR instrument design incorporates the basic functionality of AATSR in addition to new, more advanced features including a wider swath, new channels (including two channels dedicated to fire detection), and higher resolution in some channels. The principal objective of SLSTR products is to provide global and regional Sea and Land Surface Temperature (SST, LST) to a very high level of accuracy (better than 0.3 K) for both climatological and meteorological applications. For more information about the instrument and the mission, visit \"Sentinel Online\" at https://sentinel.esa.int/web/sentinel/home. The S3B_SL_1_RBT is a Level 1B product which consist of full resolution, geolocated, co-located nadir and along track view, Top of Atmosphere (TOA) brightness temperatures (in the case of thermal IR channels) or radiances (in the case of visible, NIR and SWIR channels) from all SLSTR channels. It also contains quality flags, pixel classification information and meteorological annotations. Based on components activated by configuration which are not part of the operational production baseline, the S3B_SL_1_RBT may contain 77 or 111 files. Out of the these files, 22 or 34 files contain the actual measurements, where the other 54 or 76 files contain the annotations data. For more information about the product, read the SENTINEL-3 SLSTR User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","earth observation","netcdf","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"S3B_SY_2_SYN v1 - The OLCI+SLSTR/Sentinel-3B L2 Surface Reflectance and Aerosol parameters over Land product with shortname S3A_SY_2_SYN, is generated by combining data acquired by the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR), on-board SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands whereas the SLSTR is a dual scan temperature radiometer.","ARN":"arn:aws:s3:::prod-lads/S3B_SY_2_SYN","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"S3B_OL_1_EFR v1 - The OLCI/Sentinel-3B L1 Full Resolution Top of Atmosphere Reflectance product, S3B_OL_1_EFR is generated from the data aquired by the Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands.","ARN":"arn:aws:s3:::prod-lads/S3B_OL_1_EFR","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"S3B_OL_1_ERR v1 - The OLCI/Sentinel-3B L1 Reduced Resolution Top of Atmosphere Reflectance product, S3B_OL_1_ERR is generated from the data acquired by the Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands.","ARN":"arn:aws:s3:::prod-lads/S3B_OL_1_ERR","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"S3B_SL_1_RBT v1 - The SLSTR/Sentinel-3B L1 Full Resolution Top of Atmosphere Radiances and Brightness Temperature product with shortname S3B_SL_1_RBT, is generated from the data acquired by the Sea and Land Surface Temperature Radiometer (SLSTR), on-board SENTINEL-3, is a dual scan temperature radiometer. The principal aim of the SLSTR instrument is to maintain continuity with the AATSR series of instruments.","ARN":"arn:aws:s3:::prod-lads/S3B_SL_1_RBT","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-sentinel-3b","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Sentinel-5P Project","Description":"Sentinel-5P: Unveiling mission goals, applications, sensor insights, and product details, including advanced processing algorithms.\n<br>\n\n#### S5P_L2__AER_AI_HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__AER_AI_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The TROPOMI UV Aerosol Index has been calculated based on wavelength dependent changes in Rayleigh scattering in the UV spectral range where ozone absorption is weak. A residual value is calculated from measured top-of-atmosphere (TOA) reflectance and per-calculated theoretical reflectance for a Rayleigh scattering-only atmosphere given a wavelength pair. TROPOMI UVAI products use the classical wavelength pair of 340/380 nm, and the OMI chosen 354/388 nm pair for the long-term continuous AI record. Figure 1 shows TROPOMI UVAI of orbit# 4060.\n<br>\n\n#### S5P_L2__AER_AI\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__AER_AI data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The TROPOMI UV Aerosol Index has been calculated based on wavelength dependent changes in Rayleigh scattering in the UV spectral range where ozone absorption is weak. A residual value is calculated from measured top-of-atmosphere (TOA) reflectance and per-calculated theoretical reflectance for a Rayleigh scattering-only atmosphere given a wavelength pair. TROPOMI UVAI products use the classical wavelength pair of 340/380 nm, and the OMI chosen 354/388 nm pair for the long-term continuous AI record. Figure 1 shows TROPOMI UVAI of orbit# 4060.\n<br>\n\n#### S5P_L2__AER_LH_HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__AER_LH_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. Copernicus Sentinel-5P TROPOMI aerosol layer height algorithm applies a forward model, which includes DISAMAR (Determining Instrument Specifications and Analyzing Methods for Atmospheric Retrieval, a Layer-Based Orders of Scattering algorithm) to calculate monochromatic reflectance, and also employs a neural network scheme for speedy processor performance. Data retrieval uses the Optimal Estimation Method (OEM) for spectral fitting with various aerosol layer pressures and aerosol optical thicknesses in the Oxygen-A band (758 -770nm). The aerosol baseline model assumes a single uniformly distributed aerosol layer with a fixed pressure thickness and a constant aerosol volume extinction coefficient and single scattering albedo. The aerosol parameterization is particularly suitable for elevated non-scattering aerosols such as volcanic ash, desert dust and biomass burning plume. The product main outputs include the mid-pressure and mid-altitude of aerosol layers, aerosol optical thickness at 760nm, error estimates, and other relevant diagnostics. Starting from orbit #12432 on March 7th of 2020, the S-NPP auxiliary cloud data source used in the aerosol layer height product data processing has been transitioned from the VIIRS Cloud Mask (VCM) into the Enterprise Cloud Mask (ECM).\n<br>\n\n#### S5P_L2__AER_LH\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__AER_LH data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. Copernicus Sentinel-5P TROPOMI aerosol layer height algorithm applies a forward model, which includes DISAMAR (Determining Instrument Specifications and Analyzing Methods for Atmospheric Retrieval, a Layer-Based Orders of Scattering algorithm) to calculate monochromatic reflectance, and also employs a neural network scheme for speedy processor performance. Data retrieval uses the Optimal Estimation Method (OEM) for spectral fitting with various aerosol layer pressures and aerosol optical thicknesses in the Oxygen-A band (758 -770nm). The aerosol baseline model assumes a single uniformly distributed aerosol layer with a fixed pressure thickness and a constant aerosol volume extinction coefficient and single scattering albedo. The aerosol parameterization is particularly suitable for elevated non-scattering aerosols such as volcanic ash, desert dust and biomass burning plume. The product main outputs include the mid-pressure and mid-altitude of aerosol layers, aerosol optical thickness at 760nm, error estimates, and other relevant diagnostics.\n<br>\n\n#### S5P_L2__CO_____HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__CO_____1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The TROPOMI Carbon Monoxide total column applies a modified SWIR CO retrieval (SICOR) algorithm based on the CO absorption band between 2305 nm and 2385 nm. The S5P SICOR algorithm accounts for cloudy and aerosol loaded atmosphere conditions and is suitable for clear sky observations over land and cloudy observations over both land and oceans.\n<br>\n\n#### S5P_L2__CO____\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__CO____ data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The TROPOMI Carbon Monoxide total column applies a modified SWIR CO retrieval (SICOR) algorithm based on the CO absorption band between 2305 nm and 2385 nm. The S5P SICOR algorithm accounts for cloudy and aerosol loaded atmosphere conditions and is suitable for clear sky observations over land and cloudy observations over both land and oceans.\n<br>\n\n#### S5P_L2__CLOUD__HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. Starting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2. For data before August 6th of 2019, please check S5P_L2__CLOUD__1 data collection. For data between August 6th of 2019 and July 13th of 2020, please check S5P_L2__CLOUD__HiR_1 data collection. For data after July 13th of 2020, please check S5P_L2__CLOUD__HiR_2 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The TROPOMI Cloud products are retrieved by two algorithms. The S5P_CLOUD_OCRA is based on the Optical Cloud Recognition Algorithm which generates the fractional cloud cover. The S5P_CLOUD_ROCINN (the Retrieval Of Cloud Information through Neural Networks algorithm) is built on measurements in and around the O2 A-band, which produces the cloud optical thickness and the cloud top height.\n<br>\n\n#### S5P_L1B_IR_SIR\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_IR_SIR\nStarting with version 3, Calibration Key Data (CKD) have been updated. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L2__CH4____HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__CH4____1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The retrieval algorithm for Sentinel-5P TROPOMI methane product is a physics based method that uses the Oxygen-A band (760 nm) and absorption bands in shortwave infrared spectrum. In Sentinel-5P/TROPOMI methane algorithm, the atmosphere forward model simulates the absorbing gas (oxygen, methane, water vapor, and carbon monoxide) optical properties, as well as aerosol optical properties with size distribution, refractive index, and number concentration. The inversion is performed based on the forward calculation, the measurement, and the prior information. Cloud filtering is critical in methane retrieval, S5P methane algorithm applies re-gridded Visible Infrared Imaging Radiometer Suite (VIIRS) cloud mask data. Additional cloud filters based on S5P/TROPOMI measurements and the FRESCO apparent surface pressure will be applied when VIIRS cloud data are not available. Other current data filters in the retrieval algorithm include land-only pixels (excluding mountainous areas), spectrum intensity, solar zenith angle and instrument zenith angle. The S5P/TROPOMI methane retrieval is for non-time-critical (NTC) data stream only, and its main outputs include the column averaged dry air mixing ratio of methane, the random error, and the biased corrected dry air methane fraction data based on the retrieved surface albedo. Starting from orbit #12432 on March 7th of 2020, the S-NPP auxiliary cloud data source used in the methane product data processing has been transitioned from the VIIRS Cloud Mask (VCM) into the Enterprise Cloud Mask (ECM). The data are stored in an enhanced netCDF-4 format.\n<br>\n\n#### S5P_L2__CH4___\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__CH4___ data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The retrieval algorithm for Sentinel-5P TROPOMI methane product is a physics based method that uses the Oxygen-A band (760 nm) and absorption bands in shortwave infrared spectrum. In Sentinel-5P/TROPOMI methane algorithm, the atmosphere forward model simulates the absorbing gas (oxygen, methane, water vapor, and carbon monoxide) optical properties, as well as aerosol optical properties with size distribution, refractive index, and number concentration. The inversion is performed based on the forward calculation, the measurement, and the prior information. Cloud filtering is critical in methane retrieval, S5P methane algorithm applies re-gridded Visible Infrared Imaging Radiometer Suite (VIIRS) cloud mask data. Additional cloud filters based on S5P/TROPOMI measurements and the FRESCO apparent surface pressure will be applied when VIIRS cloud data are not available. Other current data filters in the retrieval algorithm include land-only pixels (excluding mountainous areas), spectrum intensity, solar zenith angle and instrument zenith angle. The S5P/TROPOMI methane retrieval is for non-time-critical (NTC) data stream only, and its main outputs include the column averaged dry air mixing ratio of methane, the random error, and the biased corrected dry air methane fraction data based on the retrieved surface albedo. The data are stored in an enhanced netCDF-4 format.\n<br>\n\n#### S5P_L2__AER_AI_HiR_NRT\nThe Sentinel-5P TROPOMI Near Real Time (NRT) Aerosol Index 1-Orbit L2 5.5km x 3.5km V2 (S5P_L2__AER_AI_HiR_NRT) at GES DISC is the near real time version of the offline S5P_L2__AER_AI_HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE). These data are intended for a rapid turnaround assessment and are only archived for up to ten days. Users who require a longer data record, or wish to conduct rigorous analysis should use the offline version of this product S5P_L2__AER_AI_HiR. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The TROPOMI UV Aerosol Index has been calculated based on wavelength dependent changes in Rayleigh scattering in the UV spectral range where ozone absorption is weak. A residual value is calculated from measured top-of-atmosphere (TOA) reflectance and per-calculated theoretical reflectance for a Rayleigh scattering-only atmosphere given a wavelength pair. TROPOMI UVAI products use the classical wavelength pair of 340/380 nm, and the OMI chosen 354/388 nm pair for the long-term continuous AI record. Figure 1 shows TROPOMI UVAI of orbit# 4060. Data are stored in individual files, or granules, that contain one orbit of information. The data are stored in netCDF4 data format and files and complete files are ~20 MB.\n<br>\n\n#### S5P_L2__CO_____HiR_NRT\nThe Sentinel-5P TROPOMI Near Real Time (NRT) Carbon Monoxide CO Column 1-Orbit L2 5.5km x 7km V2 (S5P_L2__CO_____HiR_NRT) at GES DISC is the near real time version of the offline S5P_L2__CO_____HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE). These data are intended for a rapid turnaround assessment and are only archived for up to ten days. Users who require a longer data record, or wish to conduct rigorous analysis should use the offline version of this product S5P_L2__CO_____HiR. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The TROPOMI Carbon Monoxide total column applies a modified SWIR CO retrieval (SICOR) algorithm based on the CO absorption band between 2305 nm and 2385 nm. The S5P SICOR algorithm accounts for cloudy and aerosol loaded atmosphere conditions and is suitable for clear sky observations over land and cloudy observations over both land and oceans. Data are stored in individual files, or granules, that contain one orbit of information. The data are stored in netCDF4 data format and files and complete files are ~20 MB.\n<br>\n\n#### S5P_L2__CH4____HiR_NRT\nThe Sentinel-5P TROPOMI Near Real Time (NRT) Methane CH4 1-Orbit L2 5.5km x 7km V2 (S5P_L2__CH4____HiR_NRT) at GES DISC is the near real time version of the offline S5P_L2__CH4____HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE). These data are intended for a rapid turnaround assessment and are only archived for up to ten days. Users who require a longer data record, or wish to conduct rigorous analysis should use the offline version of this product S5P_L2__CH4____HiR. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The S5P/TROPOMI methane retrieval is for non-time-critical (NTC) data stream only, and its main outputs include the column averaged dry air mixing ratio of methane, the random error, and the biased corrected dry air methane fraction data based on the retrieved surface albedo. Data are stored in individual files, or granules, that contain one orbit of information. The data are stored in netCDF4 data format and files and complete files are ~82 MB.\n<br>\n\n#### S5P_L1B_RA_BD3_HiR_NRT\nThe Sentinel-5P TROPOMI Near Real Time (NRT) Radiance product band 3 (UVIS detector) L1B 5.5km x 21km V3 (S5P_L1B_RA_BD3_HiR_NRT) product at GES DISC is the near real time version of the offline S5P_L1B_RA_BD3_HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE). These data are intended for a rapid turnaround assessment and are only archived for up to ten days. Users who require a longer data record, or wish to conduct rigorous analysis should use the offline version of this product S5P_L1B_RA_BD3_HiR. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products. Data are stored in individual files, or granules, that contain one orbit of information. The data are stored in netCDF4 data format and files and complete files are ~0.650 GB.\n<br>\n\n#### S5P_L1B_RA_BD4_HiR_NRT\nThe Sentinel-5P TROPOMI Near Real Time (NRT) Radiance product band 4 (UVIS detector) L1B 5.5km x 21km V3 (S5P_L1B_RA_BD4_HiR_NRT) product at GES DISC is the near real time version of the offline S5P_L1B_RA_BD4_HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE). These data are intended for a rapid turnaround assessment and are only archived for up to ten days. Users who require a longer data record, or wish to conduct rigorous analysis should use the offline version of this product S5P_L1B_RA_BD4_HiR. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products. Data are stored in individual files, or granules, that contain one orbit of information. The data are stored in netCDF4 data format and files and complete files are 0.650 GB.\n<br>\n\n#### S5P_L1B_RA_BD5_HiR_NRT\nThe Sentinel-5P TROPOMI Near Real Time (NRT) Radiance product band 5 (NIR detector) L1B 5.5km x 21km V3 (S5P_L1B_RA_BD5_HiR_NRT) product at GES DISC is the near real time version of the offline S5P_L1B_RA_BD4_HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE). These data are intended for a rapid turnaround assessment and are only archived for up to ten days. Users who require a longer data record, or wish to conduct rigorous analysis should use the offline version of this product S5P_L1B_RA_BD5_HiR. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products. Data are stored in individual files, or granules, that contain one orbit of information. The data are stored in netCDF4 data format and files and complete files are ~0.650 GB.\n<br>\n\n#### S5P_L1B_RA_BD6_HiR_NRT\nThe Sentinel-5P TROPOMI Near Real Time (NRT) Radiance product band 6 (NIR detector) L1B 5.5km x 21km V3 (S5P_L1B_RA_BD6_HiR_NRT) product at GES DISC is the near real time version of the offline S5P_L1B_RA_BD1_HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE). These data are intended for a rapid turnaround assessment and are only archived for up to ten days. Users who require a longer data record, or wish to conduct rigorous analysis should use the offline version of this product S5P_L1B_RA_BD6_HiR. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products. Data are stored in individual files, or granules, that contain one orbit of information. The data are stored in netCDF4 data format and files and complete files are ~0.650 GB.\n<br>\n\n#### S5P_L1B_RA_BD7_HiR_NRT\nThe Sentinel-5P TROPOMI Near Real Time (NRT) Radiance product band 7 (SWIR detector) L1B 5.5km x 21km V3 (S5P_L1B_RA_BD7_HiR_NRT) product at GES DISC is the near real time version of the offline S5P_L1B_RA_BD7_HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE). These data are intended for a rapid turnaround assessment and are only archived for up to ten days. Users who require a longer data record, or wish to conduct rigorous analysis should use the offline version of this product S5P_L1B_RA_BD7_HiR. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products. Data are stored in individual files, or granules, that contain one orbit of information. The data are stored in netCDF4 data format and files and complete files are ~0.305 GB.\n<br>\n\n#### S5P_L2__SO2____HiR_NRT\nThe Sentinel-5P TROPOMI Near Real Time (NRT) Sulphur Dioxide SO2 1-Orbit L2 5.5km x 3.5km V2 (S5P_L2__SO2____HiR_NRT) at GES DISC is the near real time version of the offline S5P_L2__SO2____HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE). These data are intended for a rapid turnaround assessment and are only archived for up to ten days. Users who require a longer data record, or wish to conduct rigorous analysis should use the offline version of this product S5P_L2__SO2____HiR. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The retrieval algorithm for Sentinel-5P TROPOMI SO2 from ultraviolet spectral measurements is the Differential Optical Absorption Spectroscopy (DOAS) method. The relevant information of absorption cross section, instrument characteristics, cloud cover, and geolocation are utilized to derive SO2 slant column density (SCD). A sensitive spectral window of 312 to 326 nm is set as the baseline for the slant column fit with another two spectral windows (325 to 335 nm, 360 to 390 nm) to account for the non-linear effects in those high column amount cases. The SCD is then corrected with the empirical offsets to the systematic biases. The air mass factor (AMF) Look-up table has been created with the LIDORT radiative transfer model. The outputs of the DOAS algorithm are SO2 vertical column density (VCD), SCD, AMF, the DOAS-type averaging kernels (AK), and error estimates. Data are stored in individual files, or granules, that contain one orbit of information. The data are stored in netCDF4 data format and files and complete files are ~100 MB.\n<br>\n\n#### S5P_L2__O3_TOT_HiR_NRT\nThe Sentinel-5P TROPOMI Near Real Time (NRT) Total Ozone Column 1-Orbit L2 5.5km x 3.5km V2 (S5P_L2__O3_TOT_HiR_NRT) at GES DISC is the near real time version of the offline S5P_L2__O3_TOT_HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE). These data are intended for a rapid turnaround assessment and are only archived for up to ten days. Users who require a longer data record, or wish to conduct rigorous analysis should use the offline version of this product S5P_L2__O3_TOT_HiR. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. This total column ozone product (S5P_L2__O3_TOT, aka ESA's S5P_L2__O3____) applies the the Direct-fitting algorithm (S5P_TO3_GODFIT), comprising a non-linear least squares inversion by comparing the simulated and measured backscattered radiances. The main products include total vertical column ozone, ozone effective temperature, and the error information in NetCDF-4 format. Data are stored in individual files, or granules, that contain one orbit of information. The data are stored in netCDF4 data format and files and complete files are ~40 MB.\n<br>\n\n#### S5P_L2__NO2____HiR_NRT\nThe Sentinel-5P TROPOMI Near Real Time (NRT) Tropospheric NO2 1-Orbit L2 5.5km x 3.5km V2 (S5P_L2__NO2____HiR_NRT) at GES DISC is the near real time version of the offline S5P_L2__NO2____HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE). These data are intended for a rapid turnaround assessment and are only archived for up to ten days. Users who require a longer data record, or wish to conduct rigorous analysis should use the offline version of this product S5P_L2__NO2____HiR. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The TROPOMI retrieval of total and tropospheric NO2, is based on the DOMINO approach, a DOAS retrieval, a pre-calculated air-mass factor (AMF) look-up table, and a data assimilation/chemistry transport model for the separation of the stratospheric and tropospheric contributions to the NO2 column. It also include many retrieval developments of the European Quality Assurance for Essential Climate Variables (QA4ECV) project. Data are stored in individual files, or granules, that contain one orbit of information. The data are stored in netCDF4 data format and files and complete files are ~60 MB.\n<br>\n\n#### S5P_L2__O3_TCL_NRT\nThe Sentinel-5P TROPOMI Near Real Time (NRT) Tropospheric Ozone Column V2 (S5P_L2__O3_TCL_NRT) at GES DISC is the near real time version of the offline S5P_L2__O3_TCL product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE). These data are intended for a rapid turnaround assessment and are only archived for up to ten days. Users who require a longer data record, or wish to conduct rigorous analysis should use the offline version of this product S5P_L2__O3_TCL. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. Copernicus Sentinel-5P tropospheric ozone data products are retrieved by the convective-cloud-differential (CCD) algorithm to derive the tropospheric ozone columns and by the cloud slicing algorithm (CSA) to derive mean upper tropospheric ozone volume mixing ratios above the clouds. The S5P_TROPOZ_CCD algorithm uses TROPOMI Level-2 ozone column measurements and the cloud parameters provided by the S5P_CLOUD_OCRA and S5P_CLOUD_ROCINN, the average values of the tropospheric ozone columns below 270 hpa can be determined. The S5P_TROPOZ_CSA algorithm uses the correlation between could top pressure and the ozone column above the cloud. The retrieval depends on the amount of measurements with a high cloud cover. The products are restricted in the tropical region (-20 degrees to 20 degrees of latitude). The main outputs of the Copernicus S5P/TROPOMI tropospheric ozone product include the tropospheric ozone column and corresponding errors, upper tropospheric ozone and corresponding errors, stratospheric ozone column and corresponding errors, and the retrieval quality flags. The data are stored in an enhanced netCDF-4 format. Data are stored in individual files, or granules, that contain one orbit of information. The data are stored in netCDF4 data format and files and complete files are ~20 MB.\n<br>\n\n#### S5P_L2__O3__PR_HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. S5P_L2__O3__PR_HiR data collection contains the high spatial resolution products. The Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. This product contains ozone profile with a vertical resolution of 6 km and a horizontal resolution of 30x30km2 observed at about 13:30 local solar time from spectra measured by TROPOMI\n<br>\n\n#### S5P_L1B_RA_BD1_HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD1_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD1_HiR\nStarting with version 3, dynamic straylight correction has been implemented, residual background correction has been applied, and the Calibration Key Data (CKD) have been updated. Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD1_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD1\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD2_HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD2_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD2\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD2 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD3_HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD3_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD3_HiR\nStarting with version 3, residual background correction has been applied, a correction has been introduced for a sharp detector feature, and the Calibration Key Data (CKD) have been updated. Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD3_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD3\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD3 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD4_HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD4_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD4_HiR\nStarting with version 3, residual background correction has been applied and the Calibration Key Data (CKD) have been updated. Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD4_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD4\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD4 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD5_HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD5_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD5\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD5 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD6_HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD6_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD6\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD6 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD7_HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD7_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD7_HiR\nStarting with version 3, Calibration Key Data (CKD) have been updated. Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD7_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD7\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD7 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD8_HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD8_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L1B_RA_BD8\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD8 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.\n<br>\n\n#### S5P_L2__NP_BD7_HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__NP_BD7_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. Copernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.\n<br>\n\n#### S5P_L2__NP_BD7\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__NP_BD7 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. Copernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.\n<br>\n\n#### S5P_L2__NP_BD3_HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__NP_BD3_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. Copernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.\n<br>\n\n#### S5P_L2__NP_BD3\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__NP_BD3 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. Copernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.\n<br>\n\n#### S5P_L2__NP_BD6_HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__NP_BD6_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. Copernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.\n<br>\n\n#### S5P_L2__NP_BD6\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__NP_BD6 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. Copernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.\n<br>\n\n#### S5P_L2__NO2____HiR\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__NO2____1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The TROPOMI retrieval of total and tropospheric NO2, is based on the DOMINO approach, a DOAS retrieval, a pre-calculated air-mass factor (AMF) look-up table, and a data assimilation/chemistry transport model for the separation of the stratospheric and tropospheric contributions to the NO2 column. It also include many retrieval developments of the European Quality Assurance for Essential Climate Variables (QA4ECV) project.\n<br>\n\n#### S5P_L2__NO2___\nStarting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__NO2___ data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The TROPOMI retrieval of total and tropospheric NO2, is based on the DOMINO approach, a DOAS retrieval, a pre-calculated air-mass factor (AMF) look-up table, and a data assimilation/chemistry transport model for the separation of the stratospheric and tropospheric contributions to the NO2 column. It also include many retrieval developments of the European Quality Assurance for Essential Climate Variables (QA4ECV) project.\n<br>\n\n#### S5P_L2__O3_TCL\nStarting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2. For data after July 2nd of 2020, please check S5P_L2__O3_TCL_2 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. Copernicus Sentinel-5P tropospheric ozone data products are retrieved by the convective-cloud-differential (CCD) algorithm to derive the tropospheric ozone columns and by the cloud slicing algorithm (CSA) to derive mean upper tropospheric ozone volume mixing ratios above the clouds. The S5P_TROPOZ_CCD algorithm uses TROPOMI Level-2 ozone column measurements and the cloud parameters provided by the S5P_CLOUD_OCRA and S5P_CLOUD_ROCINN, the average values of the tropospheric ozone columns below 270 hpa can be determined. The S5P_TROPOZ_CSA algorithm uses the correlation between could top pressure and the ozone column above the cloud. The retrieval depends on the amount of measurements with a high cloud cover. The products are restricted in the tropical region (-20 degrees to 20 degrees of latitude). The main outputs of the Copernicus S5P/TROPOMI tropospheric ozone product include the tropospheric ozone column and corresponding errors, upper tropospheric ozone and corresponding errors, stratospheric ozone column and corresponding errors, and the retrieval quality flags. The data are stored in an enhanced netCDF-4 format.\n<br>\n\n#### S5P_L2__O3_TCL\nStarting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2. For data before July 13 of 2020, please check S5P_L2__O3_TCL_1 data collection. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. Copernicus Sentinel-5P tropospheric ozone data products are retrieved by the convective-cloud-differential (CCD) algorithm to derive the tropospheric ozone columns and by the cloud slicing algorithm (CSA) to derive mean upper tropospheric ozone volume mixing ratios above the clouds. The S5P_TROPOZ_CCD algorithm uses TROPOMI Level-2 ozone column measurements and the cloud parameters provided by the S5P_CLOUD_OCRA and S5P_CLOUD_ROCINN, the average values of the tropospheric ozone columns below 270 hpa can be determined. The S5P_TROPOZ_CSA algorithm uses the correlation between could top pressure and the ozone column above the cloud. The retrieval depends on the amount of measurements with a high cloud cover. The products are restricted in the tropical region (-20 degrees to 20 degrees of latitude). The main outputs of the Copernicus S5P/TROPOMI tropospheric ozone product include the tropospheric ozone column and corresponding errors, upper tropospheric ozone and corresponding errors, stratospheric ozone column and corresponding errors, and the retrieval quality flags. The data are stored in an enhanced netCDF-4 format.\n<br>\n\n#### TROPOMAER\nAs part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, this projects describes a multi-decadal Fundamental Climate Data Record (FCDR) of calibrated radiances as well as an Earth System Data Record (ESDR) of aerosol properties over the continents derived from a 32-year record of satellite near-UV observations by three sensors. The Corpenicus Sentinel-5P TROPOMI Near UV (version 1) Aerosol Optical Depth and Single Scattering Albedo data product consists of aerosol absorption optical depth, aerosol total optical depth, aerosol layer height, aerosol UV index, and aerosol single scattering albedo at approximately 7.5kmx3km. This product also contains ancillary data for ocean corrected surface albedo and terrain pressure. Data since July 19, 2022 (orbit 24688) has been processed with the most recent calibrated L1B radiance data (version 2.0.1). Data prior to July 19, 2022, data was processed with version 2.0.0 L1B radiances. Soon, the entire record will be reprocessed using version 2.0.1 L1B data. Any upgrades will be posted here. These Level-2 data are stored in the NetCDF-4 format and are available from the Goddard Earth Sciences (GES) Data and Information Services Center (DISC).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://sentinels.copernicus.eu/web/sentinel/copernicus/sentinel-5p","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","cog","ice","netcdf","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"S5P_L2__AER_AI_HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__AER_AI_1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__AER_AI_HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__AER_AI v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__AER_AI data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__AER_AI.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__AER_LH_HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__AER_LH_1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__AER_LH_HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__AER_LH v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__AER_LH data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__AER_LH.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__CO_____HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__CO_____1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__CO_____HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__CO____ v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__CO____ data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__CO____.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__CLOUD__HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. Starting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__CLOUD__HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_IR_SIR v1 - The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_IR_SIR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_IR_SIR v3 - Starting with version 3, Calibration Key Data (CKD) have been updated. The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_IR_SIR.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__CH4____HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__CH4____1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__CH4____HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__CH4___ v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__CH4___ data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__CH4___.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__AER_AI_HiR_NRT v2 - The Sentinel-5P TROPOMI Near Real Time (NRT) Aerosol Index 1-Orbit L2 5.5km x 3.5km V2 (S5P_L2__AER_AI_HiR_NRT) at GES DISC is the near real time version of the offline S5P_L2__AER_AI_HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_NRT/S5P_L2__AER_AI_HiR_NRT.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__CO_____HiR_NRT v2 - The Sentinel-5P TROPOMI Near Real Time (NRT) Carbon Monoxide CO Column 1-Orbit L2 5.5km x 7km V2 (S5P_L2__CO_____HiR_NRT) at GES DISC is the near real time version of the offline S5P_L2__CO_____HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_NRT/S5P_L2__CO_____HiR_NRT.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__CH4____HiR_NRT v2 - The Sentinel-5P TROPOMI Near Real Time (NRT) Methane CH4 1-Orbit L2 5.5km x 7km V2 (S5P_L2__CH4____HiR_NRT) at GES DISC is the near real time version of the offline S5P_L2__CH4____HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_NRT/S5P_L2__CH4____HiR_NRT.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD3_HiR_NRT v3 - The Sentinel-5P TROPOMI Near Real Time (NRT) Radiance product band 3 (UVIS detector) L1B 5.5km x 21km V3 (S5P_L1B_RA_BD3_HiR_NRT) product at GES DISC is the near real time version of the offline S5P_L1B_RA_BD3_HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_NRT/S5P_L1B_RA_BD3_HiR_NRT.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD4_HiR_NRT v3 - The Sentinel-5P TROPOMI Near Real Time (NRT) Radiance product band 4 (UVIS detector) L1B 5.5km x 21km V3 (S5P_L1B_RA_BD4_HiR_NRT) product at GES DISC is the near real time version of the offline S5P_L1B_RA_BD4_HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_NRT/S5P_L1B_RA_BD4_HiR_NRT.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD5_HiR_NRT v3 - The Sentinel-5P TROPOMI Near Real Time (NRT) Radiance product band 5 (NIR detector) L1B 5.5km x 21km V3 (S5P_L1B_RA_BD5_HiR_NRT) product at GES DISC is the near real time version of the offline S5P_L1B_RA_BD4_HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_NRT/S5P_L1B_RA_BD5_HiR_NRT.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD6_HiR_NRT v3 - The Sentinel-5P TROPOMI Near Real Time (NRT) Radiance product band 6 (NIR detector) L1B 5.5km x 21km V3 (S5P_L1B_RA_BD6_HiR_NRT) product at GES DISC is the near real time version of the offline S5P_L1B_RA_BD1_HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_NRT/S5P_L1B_RA_BD6_HiR_NRT.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD7_HiR_NRT v3 - The Sentinel-5P TROPOMI Near Real Time (NRT) Radiance product band 7 (SWIR detector) L1B 5.5km x 21km V3 (S5P_L1B_RA_BD7_HiR_NRT) product at GES DISC is the near real time version of the offline S5P_L1B_RA_BD7_HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_NRT/S5P_L1B_RA_BD7_HiR_NRT.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__SO2____HiR_NRT v2 - The Sentinel-5P TROPOMI Near Real Time (NRT) Sulphur Dioxide SO2 1-Orbit L2 5.5km x 3.5km V2 (S5P_L2__SO2____HiR_NRT) at GES DISC is the near real time version of the offline S5P_L2__SO2____HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_NRT/S5P_L2__SO2____HiR_NRT.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__O3_TOT_HiR_NRT v2 - The Sentinel-5P TROPOMI Near Real Time (NRT) Total Ozone Column 1-Orbit L2 5.5km x 3.5km V2 (S5P_L2__O3_TOT_HiR_NRT) at GES DISC is the near real time version of the offline S5P_L2__O3_TOT_HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_NRT/S5P_L2__O3_TOT_HiR_NRT.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__NO2____HiR_NRT v2 - The Sentinel-5P TROPOMI Near Real Time (NRT) Tropospheric NO2 1-Orbit L2 5.5km x 3.5km V2 (S5P_L2__NO2____HiR_NRT) at GES DISC is the near real time version of the offline S5P_L2__NO2____HiR data product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_NRT/S5P_L2__NO2____HiR_NRT.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__O3_TCL_NRT v2 - The Sentinel-5P TROPOMI Near Real Time (NRT) Tropospheric Ozone Column V2 (S5P_L2__O3_TCL_NRT) at GES DISC is the near real time version of the offline S5P_L2__O3_TCL product. These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_NRT/S5P_L2__O3_TCL_NRT.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__O3__PR_HiR v2 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. S5P_L2__O3__PR_HiR data collection contains the high spatial resolution products.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__O3__PR_HiR.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD1_HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD1_1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD1_HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD1_HiR v3 - Starting with version 3, dynamic straylight correction has been implemented, residual background correction has been applied, and the Calibration Key Data (CKD) have been updated. Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD1_HiR.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD1 v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD2_HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD2_1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD2_HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD2 v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD2 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD3_HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD3_1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD3_HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD3_HiR v3 - Starting with version 3, residual background correction has been applied, a correction has been introduced for a sharp detector feature, and the Calibration Key Data (CKD) have been updated. Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD3_HiR.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD3 v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD3 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD3.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD4_HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD4_1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD4_HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD4_HiR v3 - Starting with version 3, residual background correction has been applied and the Calibration Key Data (CKD) have been updated. Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD4_HiR.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD4 v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD4 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD4.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD5_HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD5_1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD5_HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD5 v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD5 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD5.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD6_HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD6_1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD6_HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD6 v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD6 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD6.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD7_HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD7_1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD7_HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD7_HiR v3 - Starting with version 3, Calibration Key Data (CKD) have been updated. Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD7_HiR.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD7 v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD7 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD7.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD8_HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L1B_RA_BD8_1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD8_HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L1B_RA_BD8 v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L1B_RA_BD8 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level1B/S5P_L1B_RA_BD8.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__NP_BD7_HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__NP_BD7_1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__NP_BD7_HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__NP_BD7 v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__NP_BD7 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__NP_BD7.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__NP_BD3_HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__NP_BD3_1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__NP_BD3_HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__NP_BD3 v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__NP_BD3 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__NP_BD3.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__NP_BD6_HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__NP_BD6_1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__NP_BD6_HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__NP_BD6 v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__NP_BD6 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__NP_BD6.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__NO2____HiR v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data before August 6th of 2019, please check S5P_L2__NO2____1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__NO2____HiR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__NO2___ v1 - Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented. For data after August 6th of 2019, please check S5P_L2__NO2___ data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__NO2___.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__O3_TCL v1 - Starting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2. For data after July 2nd of 2020, please check S5P_L2__O3_TCL_2 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__O3_TCL.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"S5P_L2__O3_TCL v2 - Starting from July 13th in 2020, five Sentinel-5P TROPOMI level-2 products including total and tropospheric column ozone, sulfur dioxide, CLOUD, and formaldehyde have been generated in processor version 2. For data before July 13 of 2020, please check S5P_L2__O3_TCL_1 data collection.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/S5P_TROPOMI_Level2/S5P_L2__O3_TCL.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TROPOMAER v1 - As part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, this projects describes a multi-decadal Fundamental Climate Data Record (FCDR) of calibrated radiances as well as an Earth System Data Record (ESDR) of aerosol properties over the continents derived from a 32-year record of satellite near-UV observations by three sensors. The Corpenicus Sentinel-5P TROPOMI Near UV (version 1) Aerosol Optical Depth and Single Scattering Albedo data product consists of aerosol absorption optical depth, aerosol total optical depth, aerosol layer height, ...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/AER/TROPOMAER.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-sentinel-5p","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Sentinel-6 Project","Description":"Provides reprocessed L1A high resolution (HR) non-time critical (NTC; 60-day latency) altimetry intermediate outputs from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, which are geo-located bursts of Ku-band echoes (at ~140 Hz) with all instrument calibrations applied and full rate complex waveforms for delay/Doppler or HR processing. The S6A NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L1A_ALT_HR_STC_F\nProvides L1A high resolution (HR) short time critical (STC; 36-hour latency) altimetry intermediate outputs from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, which are geo-located bursts of Ku-band echoes (at ~140 Hz) with all instrument calibrations applied and full rate complex waveforms for delay/Doppler or HR processing. The S6A STC product is analogous to the Jason-3 IGDR product.\n<br>\n\n#### JASON_CS_S6A_L1B_GNSS_POD_DAILY\nProvides L1B daily GNSS-POD tracking data for the Sentinel-6A radar altimetry mission.\n<br>\n\n#### JASON_CS_S6A_L1B_GNSS_POD_HOURLY\nProvides L1B hourly GNSS-POD tracking data for the Sentinel-6A radar altimetry mission.\n<br>\n\n#### JASON_CS_S6A_L1B_GNSS_RO_POD_HOURLY\nProvides L1B GNSS-RO-POD tracking data for the Sentinel-6A radar altimetry mission.\n<br>\n\n#### JASON_CS_S6A_L1B_ALT_HR_NTC_F08\nProvides reprocessed L1B high resolution (HR) non-time critical (NTC; 60-day latency) altimetry data from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft which include the geolocated, fully SAR processed and calibrated multi-looked HR Ku-band waveforms. The S6A NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L1B_ALT_HR_STC_F\nProvides L1B high resolution (HR) short time critical (STC; 36-hour latency) altimetry data from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft which include the geolocated, fully SAR processed and calibrated multi-looked HR Ku-band waveforms. The S6A STC product is analogous to the Jason-3 IGDR product.\n<br>\n\n#### JASON_CS_S6A_L1B_ALT_LR_NTC_F08\nProvides reprocessed L1B low resolution (LR) non-time critical (NTC; 60-day latency) altimetry data from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft which include the geolocated, fully-calibrated pulse-limited LR power echoes. The S6A NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L1B_ALT_LR_STC_F\nProvides L1B low resolution (LR) short time critical (STC; 36-hour latency) altimetry data from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft which include the geolocated, fully-calibrated pulse-limited LR power echoes. The S6A STC product is analogous to the Jason-3 IGDR product.\n<br>\n\n#### JASON_CS_S6A_L2_AMR_RAD_NRT\nProvides L2 near real time (NRT; 3-hour latency) geophysical information from the Advanced Microwave Radiometer on the Sentinel-6A Michael Freilich spacecraft including surface type, wind speed, water vapor, brightness temperature, sigma0, wet troposphere, and associated quality flags. The data are interpolated to intervals that correspond to altimetry measurements from the Poseidon-4 SAR to supply the geophysical and environmental corrections for altimetry. The S6A NRT product is analogous to the Jason-3 OGDR product.\n<br>\n\n#### JASON_CS_S6A_L2_AMR_RAD_NTC_F08_UNVALIDATED\nProvides reprocessed L2 non-time critical (NTC; 60-day latency) geophysical information from the Advanced Microwave Radiometer on the Sentinel-6A Michael Freilich spacecraft including surface type, wind speed, water vapor, brightness temperature, sigma0, wet troposphere, and associated quality flags. The data are interpolated to intervals that correspond to altimetry measurements from the Poseidon-4 SAR to supply the geophysical and environmental corrections for altimetry. The S6A NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L2_AMR_RAD_NTC_F08\nProvides L2 non-time critical (NTC; 60-day latency) validated geophysical information from the Advanced Microwave Radiometer on the Sentinel-6A Michael Freilich spacecraft including surface type, wind speed, water vapor, brightness temperature, sigma0, wet troposphere, and associated quality flags. The data are interpolated to intervals that correspond to altimetry measurements from the Poseidon-4 SAR to supply the geophysical and environmental corrections for altimetry. The S6A NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L2_AMR_RAD_STC\nProvides L2 short time critical (STC; 36-hour latency) geophysical information from the Advanced Microwave Radiometer on the Sentinel-6A Michael Freilich spacecraft including surface type, wind speed, water vapor, brightness temperature, sigma0, wet troposphere, and associated quality flags. The data are interpolated to intervals that correspond to altimetry measurements from the Poseidon-4 SAR to supply the geophysical and environmental corrections for altimetry. The S6A STC product is analogous to the Jason-3 IGDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F\nProvides L2 high resolution (HR) near real time (NRT; 3-hour latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This standard product release provides the geophysical parameters at both 1 and 20 Hz. The S6A NRT product is analogous to the Jason-3 OGDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_HR_RED_OST_NRT_F\nProvides L2 high resolution (HR) near real time (NRT; 3-hour latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This release is reduced to exclude the 20 Hz observations that are included in the standard product. The S6A NRT product is analogous to the Jason-3 OGDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_UNVALIDATED\nProvides reprocessed L2 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This standard product release provides the geophysical parameters at both 1 and 20 Hz. The S6A NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08\nProvides L2 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This standard product release provides the geophysical parameters at both 1 and 20 Hz. The S6A NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_UNVALIDATED\nProvides reprocessed L2 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This release is reduced to exclude the 20 Hz observations that are included in the standard product. The S6A NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08\nProvides L2 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This release is reduced to exclude the 20 Hz observations that are included in the standard product. The S6A NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_HR_STD_OST_STC_F\nProvides L2 high resolution (HR) short time critical (STC; 36-hour latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This standard product release provides the geophysical parameters at both 1 and 20 Hz. The S6A STC product is analogous to the Jason-3 IGDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_HR_RED_OST_STC_F\nProvides L2 high resolution (HR) short time critical (STC; 36-hour latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This release is reduced to exclude the 20 Hz observations that are included in the standard product. The S6A STC product is analogous to the Jason-3 IGDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F\nProvides low resolution (LR) near real time (NRT; 3-hour latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed, along with 1 Hz and 20 Hz measurements from the radar altimeter, orbit altitude, environmental range corrections, instrument corrections, and geophysical models. The NRT product is analogous to the Jason-3 OGDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F\nProvides low resolution (LR) near real time (NRT; 3-hour latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed. The NRT product is analogous to the Jason-3 OGDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_UNVALIDATED\nProvides reprocessed L2 low resolution (LR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour. It also includes altimetry corrections, significant wave height and wind-speed from the AMR-C. This standard product release provides the geophysical parameters at both 1 and 20 Hz. The S6A NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08\nProvides low resolution (LR) non-time critical (NTC; 60-day latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed, along with 1 Hz and 20 Hz measurements from the radar altimeter, orbit altitude, environmental range corrections, instrument corrections, and geophysical models. The NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_UNVALIDATED\nProvides low resolution (LR) non-time critical (NTC; 60-day latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed. The NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_LR_STD_OST_STC_F\nProvides low resolution (LR) short time critical (STC; 36-hour latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed, along with 1 Hz and 20 Hz measurements from the radar altimeter, orbit altitude, environmental range corrections, instrument corrections, and geophysical models. The STC product is analogous to the Jason-3 IGDR product.\n<br>\n\n#### JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F\nProvides low resolution (LR) short time critical (STC; 36-hour latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed. The STC product is analogous to the Jason-3 IGDR product.\n<br>\n\n#### JASON_CS_S6A_L2P_ALT_HR_OST_NTC_F08\nProvides L2P high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, and contains L2-equivalent geophysical sea-state data at a slightly different latency than the other L2 NRT products. The sea-state data were derived from L1B altimetry, and include range, orbital altitude, time, and water vapour. Environmental and geophysical corrections, significant wave height, and wind-speed information are supplied by the AMR-C. The S6A NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L2P_ALT_LR_OST_NTC_F08\nProvides L2P low resolution (LR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, and contains L2-equivalent geophysical sea-state data at a slightly different latency than the other L2 NRT products. The sea-state data were derived from L1B altimetry, and include range, orbital altitude, time, and water vapour. Environmental and geophysical corrections, significant wave height, and wind-speed information are supplied by the AMR-C. The S6A NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L3_ALT_HR_OST_NTC_F08\nProvides L3 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, which includes the unfiltered geophysical sea-state parameters that have been spatially and/or temporally resampled or corrected, including potential averaging over multiple orbits. The S6A NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### JASON_CS_S6A_L3_ALT_LR_OST_NTC_F08\nProvides L3 low resolution (LR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, which includes the unfiltered geophysical sea-state parameters that have been spatially and/or temporally resampled or corrected, including potential averaging over multiple orbits. The S6A NTC product is analogous to the Jason-3 GDR product.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"JASON_CS_S6A_L1A_ALT_HR_NTC_F08 vF08 - Provides reprocessed L1A high resolution (HR) non-time critical (NTC; 60-day latency) altimetry intermediate outputs from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, which are geo-located bursts of Ku-band echoes (at ~140 Hz) with all instrument calibrations applied and full rate complex waveforms for delay/Doppler or HR processing. The S6A NTC product is analogous to the Jason-3 GDR product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L1A_ALT_HR_NTC_F08","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L1A_ALT_HR_STC_F vF - Provides L1A high resolution (HR) short time critical (STC; 36-hour latency) altimetry intermediate outputs from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, which are geo-located bursts of Ku-band echoes (at ~140 Hz) with all instrument calibrations applied and full rate complex waveforms for delay/Doppler or HR processing. The S6A STC product is analogous to the Jason-3 IGDR product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L1A_ALT_HR_STC_F","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L1B_GNSS_POD_DAILY vF - Provides L1B daily GNSS-POD tracking data for the Sentinel-6A radar altimetry mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L1B_GNSS_POD_DAILY","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L1B_GNSS_POD_HOURLY vF - Provides L1B hourly GNSS-POD tracking data for the Sentinel-6A radar altimetry mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L1B_GNSS_POD_HOURLY","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L1B_GNSS_RO_POD_HOURLY vF - Provides L1B GNSS-RO-POD tracking data for the Sentinel-6A radar altimetry mission.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L1B_GNSS_RO_POD_HOURLY","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L1B_ALT_HR_NTC_F08 vF08 - Provides reprocessed L1B high resolution (HR) non-time critical (NTC; 60-day latency) altimetry data from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft which include the geolocated, fully SAR processed and calibrated multi-looked HR Ku-band waveforms. The S6A NTC product is analogous to the Jason-3 GDR product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L1B_ALT_HR_NTC_F08","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L1B_ALT_HR_STC_F vF - Provides L1B high resolution (HR) short time critical (STC; 36-hour latency) altimetry data from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft which include the geolocated, fully SAR processed and calibrated multi-looked HR Ku-band waveforms. The S6A STC product is analogous to the Jason-3 IGDR product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L1B_ALT_HR_STC_F","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L1B_ALT_LR_NTC_F08 vF08 - Provides reprocessed L1B low resolution (LR) non-time critical (NTC; 60-day latency) altimetry data from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft which include the geolocated, fully-calibrated pulse-limited LR power echoes. The S6A NTC product is analogous to the Jason-3 GDR product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L1B_ALT_LR_NTC_F08","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L1B_ALT_LR_STC_F vF - Provides L1B low resolution (LR) short time critical (STC; 36-hour latency) altimetry data from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft which include the geolocated, fully-calibrated pulse-limited LR power echoes. The S6A STC product is analogous to the Jason-3 IGDR product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L1B_ALT_LR_STC_F","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_AMR_RAD_NRT vF - Provides L2 near real time (NRT; 3-hour latency) geophysical information from the Advanced Microwave Radiometer on the Sentinel-6A Michael Freilich spacecraft including surface type, wind speed, water vapor, brightness temperature, sigma0, wet troposphere, and associated quality flags. The data are interpolated to intervals that correspond to altimetry measurements from the Poseidon-4 SAR to supply the geophysical and environmental corrections for altimetry.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_AMR_RAD_NRT","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_AMR_RAD_NTC_F08_UNVALIDATED vF08 - Provides reprocessed L2 non-time critical (NTC; 60-day latency) geophysical information from the Advanced Microwave Radiometer on the Sentinel-6A Michael Freilich spacecraft including surface type, wind speed, water vapor, brightness temperature, sigma0, wet troposphere, and associated quality flags. The data are interpolated to intervals that correspond to altimetry measurements from the Poseidon-4 SAR to supply the geophysical and environmental corrections for altimetry.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_AMR_RAD_NTC_F08_UNVALIDATED","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_AMR_RAD_NTC_F08 vF08 - Provides L2 non-time critical (NTC; 60-day latency) validated geophysical information from the Advanced Microwave Radiometer on the Sentinel-6A Michael Freilich spacecraft including surface type, wind speed, water vapor, brightness temperature, sigma0, wet troposphere, and associated quality flags. The data are interpolated to intervals that correspond to altimetry measurements from the Poseidon-4 SAR to supply the geophysical and environmental corrections for altimetry.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_AMR_RAD_NTC_F08","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_AMR_RAD_STC vF - Provides L2 short time critical (STC; 36-hour latency) geophysical information from the Advanced Microwave Radiometer on the Sentinel-6A Michael Freilich spacecraft including surface type, wind speed, water vapor, brightness temperature, sigma0, wet troposphere, and associated quality flags. The data are interpolated to intervals that correspond to altimetry measurements from the Poseidon-4 SAR to supply the geophysical and environmental corrections for altimetry.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_AMR_RAD_STC","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F vF - Provides L2 high resolution (HR) near real time (NRT; 3-hour latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_HR_RED_OST_NRT_F vF - Provides L2 high resolution (HR) near real time (NRT; 3-hour latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_HR_RED_OST_NRT_F","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_UNVALIDATED vF08 - Provides reprocessed L2 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08_UNVALIDATED","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08 vF08 - Provides L2 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_HR_STD_OST_NTC_F08","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_UNVALIDATED vF08 - Provides reprocessed L2 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08_UNVALIDATED","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08 vF08 - Provides L2 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_HR_RED_OST_NTC_F08","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_HR_STD_OST_STC_F vF - Provides L2 high resolution (HR) short time critical (STC; 36-hour latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_HR_STD_OST_STC_F","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_HR_RED_OST_STC_F vF - Provides L2 high resolution (HR) short time critical (STC; 36-hour latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_HR_RED_OST_STC_F","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F vF - Provides low resolution (LR) near real time (NRT; 3-hour latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed, along with 1 Hz and 20 Hz measurements from the radar altimeter, orbit altitude, environmental range corrections, instrument corrections, and geophysical models. The NRT product is analogous to the Jason-3 OGDR product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F vF - Provides low resolution (LR) near real time (NRT; 3-hour latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed. The NRT product is analogous to the Jason-3 OGDR product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_UNVALIDATED vF08 - Provides reprocessed L2 low resolution (LR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft. It contains Sea Surface Height (SSH), Sea Surface Height Anomalies (SSHA) and Significant Wave Height (SWH), along with 1 Hz and 20 Hz Ku-band measurements processed from L1B altimetry including the range, orbital altitude, time, and water vapour.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_UNVALIDATED","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08 vF08 - Provides low resolution (LR) non-time critical (NTC; 60-day latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed, along with 1 Hz and 20 Hz measurements from the radar altimeter, orbit altitude, environmental range corrections, instrument corrections, and geophysical models. The NTC product is analogous to the Jason-3 GDR product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_UNVALIDATED vF08 - Provides low resolution (LR) non-time critical (NTC; 60-day latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed. The NTC product is analogous to the Jason-3 GDR product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_NTC_F08_UNVALIDATED","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_LR_STD_OST_STC_F vF - Provides low resolution (LR) short time critical (STC; 36-hour latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed, along with 1 Hz and 20 Hz measurements from the radar altimeter, orbit altitude, environmental range corrections, instrument corrections, and geophysical models. The STC product is analogous to the Jason-3 IGDR product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_STD_OST_STC_F","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F vF - Provides low resolution (LR) short time critical (STC; 36-hour latency) measurements of sea surface height anomaly (SSHA), Significant Wave Height (SWH), and Wind Speed. The STC product is analogous to the Jason-3 IGDR product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2P_ALT_HR_OST_NTC_F08 vF08 - Provides L2P high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, and contains L2-equivalent geophysical sea-state data at a slightly different latency than the other L2 NRT products. The sea-state data were derived from L1B altimetry, and include range, orbital altitude, time, and water vapour.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2P_ALT_HR_OST_NTC_F08","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L2P_ALT_LR_OST_NTC_F08 vF08 - Provides L2P low resolution (LR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, and contains L2-equivalent geophysical sea-state data at a slightly different latency than the other L2 NRT products. The sea-state data were derived from L1B altimetry, and include range, orbital altitude, time, and water vapour.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L2P_ALT_LR_OST_NTC_F08","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L3_ALT_HR_OST_NTC_F08 vF08 - Provides L3 high resolution (HR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, which includes the unfiltered geophysical sea-state parameters that have been spatially and/or temporally resampled or corrected, including potential averaging over multiple orbits. The S6A NTC product is analogous to the Jason-3 GDR product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L3_ALT_HR_OST_NTC_F08","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"JASON_CS_S6A_L3_ALT_LR_OST_NTC_F08 vF08 - Provides L3 low resolution (LR) non-time critical (NTC; 60-day latency) altimetry from the Poseidon-4 SAR altimeter on the Sentinel-6A Michael Freilich spacecraft, which includes the unfiltered geophysical sea-state parameters that have been spatially and/or temporally resampled or corrected, including potential averaging over multiple orbits. The S6A NTC product is analogous to the Jason-3 GDR product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/JASON_CS_S6A_L3_ALT_LR_OST_NTC_F08","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-sentinel-6","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SHIFT Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### SHIFT_AVNG_Canopy_WaterContent_2242\nThis dataset provides per-pixel vegetation canopy water content (CWC) derived from surface reflectance measured by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument from 2022-02-24 to 2022-05-17. This imagery was acquired as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign. AVIRIS-NG measures reflected radiance in 425 bands at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Measurements were radiometrically and geometrically calibrated as well as atmospherically corrected, and are provided at approximately 5-meter spatial resolution. These data include mosaics of several flight lines that were flown on a weekly basis covering a 640-square-mile (1,656-square-kilometer) study area, which stretches from Los Padres National Forest in the east to the Central California coast and into the coastal ocean in the west, including Dangermond Preserve and UCSB's Sedgwick Reserve. These data will help track changes in vegetation characteristics from late winter through early summer by providing insights into health and resilience of ecosystems as California's climate grows drier. In particular, temporal and spatial patterns of CWC will indicate drought stress and increased wildfire risk. The CWC files in this publication were processed by applying a simple fitting of spectral absorption features of liquid water and are distributed in cloud-optimized GeoTIFF (COG) format.\n<br>\n\n#### SHIFT_AVNG_Plant_Trait_Mosaics_2453\nThis dataset provides maps of 20 plant functional traits across a broad region of Santa Barbara County, California, USA. The maps were derived from AVIRIS-NG imagery collected during the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign between February and May 2022. Foliar traits were derived from the surface reflectance product by applying partial least squares regression models. These traits include foliar concentrations of cellulose, lignin, sugar, hemicellulose, aluminum, calcium, manganese, nitrogen, magnesium, potassium, phosphorus, and sulfur. Area content of chlorophylls, leaf dry mass, and leaf water content were estimated. Thirteen maps of surface reflectance (7 bands) by image acquisition date and a mask for ocean pixels are included. With near-weekly temporal resolution, the dataset captures phenological trajectories of plant traits across diverse ecosystems in the region. The data are provided in cloud optimized GeoTIFF format.\n<br>\n\n#### SHIFT_AVNG_FullRes_QkLook_2189\nThis dataset holds full-resolution 3-band (true color) imagery acquired by NASA's Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This imagery was collected as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign which occurred during February to May, 2022, with a follow up activity for one week in September. The SHIFT campaign leveraged NASA's AVIRIS-NG facility instrument to collect VSWIR data at approximately a weekly cadence across a broad study area, enabling traceability analyses related to the science value of VSWIR revisits. AVIRIS-NG is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-NG measures radiance at approximately 5-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 380-2510 nm. The images in this dataset are true color (RGB) images from the wavelengths centered at approximately 808, 658, and 563 nm, subset from the full spectrum collected by AVIRIS-NG. The spatial resolution matches the native observed resolution (variable depending on the flightline, generally finer than 5 m and down to 2 m). There are two files for each flight line, one in PNG and one in georeferenced cloud-optimized GeoTIFF format; the GeoTIFF contains radiance floating point values while the PNG has been scaled and converted to integers.\n<br>\n\n#### SHIFT_AVNG_L1A_RDN_unrec_2184\nThis dataset contains Level 1A (L1A) unrectified surface radiance image files as well as files of observational geometry and illumination parameters and supporting sensor band information from NASA's Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This imagery was collected as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign. The SHIFT campaign leveraged NASA's AVIRIS-NG facility instrument to collect VSWIR data at approximately a weekly cadence across a broad study area, enabling traceability analyses related to the science value of VSWIR revisits. This campaign will generate precise, high-frequency data on plant communities collected over nearly 1,656 square kilometers across Santa Barbara County, California, US, and nearby coastal Pacific waters. AVIRIS-NG is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-NG measures reflected radiance at 5-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 380-2510 nm. The AVIRIS-NG sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub-meter range. In this dataset, for each flight line, four file types are included: unrectified measured surface radiance image (rdn) files, geometric lookup table (glt), gridded files of input geometry (igm), and parameters relating to the geometry of observation and illumination rendered using the geometric lookup table (obs). The AVIRIS-NG L1B data are provided in ENVI binary format, which includes a flat binary file (bin) accompanied by a header (hdr) file holding metadata in text format.\n<br>\n\n#### SHIFT_AVNG_L1B_RDN_V2_2432\nThis dataset contains Level 1B (L1B) unrectified calibrated surface radiance images as well as files of observational geometry and illumination parameters and supporting sensor band information from NASA's Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This imagery was collected at regular (approximately weekly) intervals in 2022 between February and May, as well as in September as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign. The SHIFT campaign leveraged NASA's AVIRIS-NG facility instrument to collect VSWIR data at approximately a weekly cadence across a broad study area, enabling traceability analyses related to the science value of VSWIR revisits. This campaign will generate precise, high-frequency data on plant communities collected over nearly 1,656 square kilometers across Santa Barbara County, California, US, and nearby coastal Pacific waters. AVIRIS-NG is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-NG measures reflected radiance at 5-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 380-2510 nm. The AVIRIS-NG sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub-meter range. In this dataset, for each flight line, the data include: orthocorrected calibrated radiance imagery, geometric lookup table, observation geometry and illumination information, input geometry, and orthocorrected locations of each pixel.These AVIRIS-NG L1B data are provided in netCDF format along with RGB quicklook images in TIFF image format.\n<br>\n\n#### SHIFT_AVNG_L2A_RFL_V2_2431\nThis dataset contains Level 2A (L2A) orthorectified surface reflectance images from NASA's Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This imagery was collected approximately weekly in 2022 between February and May, as well as in September as part of the Surface Biology and Geology High Frequency Time Series (SHIFT) campaign. The SHIFT campaign leveraged NASA's AVIRIS-NG facility instrument to collect VSWIR data at approximately a weekly cadence across a broad study area, enabling traceability analyses related to the science value of VSWIR revisits. This campaign will generate precise, high-frequency data on plant communities collected over nearly 1,656 square kilometers across Santa Barbara County, California, US, and nearby coastal Pacific waters. AVIRIS-NG is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-NG measures reflected radiance at 5-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from ~380-2500 nm. The AVIRIS-NG sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub-meter range. For this collection, most data is around 5 m, with some flight lines at finer spatial resolution. For each AVIRIS-NG flight line, two file types are included: orthocorrected surface reflectance (RFL_ORT) and orthocorrected reflectance uncertainty (UNC_ORT) in netCDF format. Both file types include data projected in a UTM coordinate system. In addition, ancillary files for each flight line are provided, including a quick look image in GeoTIFF format and text files in YAML format that document processing algorithms and parameters used during production.\n<br>\n\n#### SHIFT_AVIRISNG_L2A_refl_2376\nThis dataset contains Level 2A (L2A) unrectified surface reflectance images from NASA's Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This imagery was collected as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign which occurred during February to May, 2022, with a follow up activity for one week in September. The SHIFT campaign leveraged NASA's AVIRIS-NG facility instrument to collect VSWIR data at approximately a weekly cadence across a broad study area, enabling traceability analyses related to the science value of VSWIR revisits. This campaign will generate precise, high-frequency data on plant communities collected over nearly 1,656 square kilometers across Santa Barbara County, California, US, and nearby coastal Pacific waters. AVIRIS-NG is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-NG measures reflected radiance at 5-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 380-2510 nm. The AVIRIS-NG sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub-meter range. The AVIRIS-NG L2A data are provided in ENVI binary format, which includes a flat binary file accompanied by a header (.hdr) file holding metadata in text format.\n<br>\n\n#### SHIFT_HyTES_L1_2022_2245\nThis dataset holds Level 1 (L1) brightness temperature data collected by the Hyperspectral Thermal Emission Spectrometer (HyTES) instrument. This imagery was acquired as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign on March 23, 2022. The SHIFT campaign generated precise, high-frequency data on plant communities for nearly 1,656 square kilometers across Santa Barbara County, California, US, and the nearby ocean. HyTES is a compact image spectrometer that acquires data in 256 spectral bands between 7.5 and 12 micrometers; it was deployed on a Twin Otter aircraft. The SHIFT campaign sought to demonstrate the joint use of both VSWIR and thermal infrared (TIR) data. TIR data are used to measure land surface temperature (LST), which informs models of water flux from land surface through processes such as evapotranspiration. LST is sensitive to solar heat gains and local cooling effects due to evaporative cooling. The HyTES instrument measures TIR radiances that can be used to derive LST, emissivity and Level 3 products such as latent heat flux and detection of air pollution sources. The HyTES data are provided in HDF5 format and include 91 flight scenes. The data are not projected, but georeferencing information for each pixel are provided in the HDF5 and a separate ENVI file for each flight scene. In addition, the flight scene boundaries and an overlay image are provided in Keyhole Markup Language (KML) along with a quicklook image and spectral response data.\n<br>\n\n#### SHIFT_HyTES_L2_2022_2246\nThis dataset holds Level 2 (L2) data for surface emissivity and land surface temperature (LST) collected by the Hyperspectral Thermal Emission Spectrometer (HyTES) instrument. This imagery was acquired as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign on March 23, 2022. The SHIFT campaign generated precise, high-frequency data on plant communities for nearly 1,656 square kilometers across Santa Barbara County, California, US, and the nearby ocean. HyTES is a compact image spectrometer that acquires data in 256 spectral bands between 7.5 and 12 micrometers; it was deployed on a Twin Otter aircraft. The SHIFT campaign sought to demonstrate the joint use of both VSWIR and thermal infrared (TIR) data. TIR data are used to measure land surface temperature (LST), which informs models of water flux from land surface through processes such as evapotranspiration. LST is sensitive to solar heat gains and local cooling effects due to evaporative cooling. The HyTES instrument measures TIR radiances that can be used to derive LST, emissivity and Level 3 products such as latent heat flux and detection of air pollution sources. The HyTES data are provided in HDF5 format and include 91 flight scenes. In addition, overlay images for emissivity and LST are provided in compressed Keyhole Markup Language (KMZ) along with a quicklook image.\n<br>\n\n#### SHIFT_Foliar_Chemical_Analysis_2337\nThis dataset holds laboratory foliar chemical analyses results for field samples collected during the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign in Santa Barbara County, California, USA. Leaf samples were collected from plots within the Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt Marsh Reserve during the period of 2022-02-23 to 2022-09-27 and dried for later analysis. This project collected field data contemporaneously with weekly flights of the NASA's Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) facility instrument over the study areas. Sixteen chemical traits from two different lab analyses are provided. (a) Elemental analysis: foliar nitrogen (%), phosphorus (%), magnesium (%), potassium (%), calcium (%), sulfur (%), boron (ppm), iron (ppm), manganese (ppm), copper (ppm), zinc (ppm), aluminum (ppm), and sodium (ppm). (b) AnkomFiber analysis: foliar hemicellulose and bound protein (%), cellulose (%), and lignin (%). Related data packages contain additional plot-level characterization, biogeochemical, reflectance, and foliar data. These data are provided in comma separated values (CSV) format.\n<br>\n\n#### SHIFT_Leaf_Traits_Chl_SB_CA_2233\nThis dataset provides leaf images and measurements of leaf traits (area, wet weight, dry weight, leaf mass per area, leaf water content) and leaf pigments (chlorophyll) and species information as sampled from meadow, shrub, and tree from Santa Barbara California, USA. Samples were collected from plots within the Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt March Reserve during the period of February 23, 2022 to September 27, 2022 for the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign. The associated data package contains image scans used for the leaf area calculations as well as python processing code used to calculate the area. A comma-separated value (CSV) formatted file includes plot-level leaf area (cm2), wet weight (g), leaf mass area (LMA, g leaf dry mass per meter square), leaf water content (LWC, (wet weight - dry weight/wet weight, %)), chlorophyll fluorescence ratio (CFR), and chlorophyll content (CHL).\n<br>\n\n#### SHIFT_ASDSpec_PseudoInvaTarget_2446\nThis dataset provides biweekly surface reflectance measurements of pseudo-invariant sites at Sedgwick Reserve, Santa Barbara, California, collected with an ASD FieldSpec Pro field spectrometer during the SBG High-Frequency Time-Series (SHIFT) campaign in Spring and Fall 2022. The dataset includes measurements of seven primary spatially-homogenous, temporally pseudo-invariant sites (three areas of gravel road, two dirt paddocks, two serpentinite outcrops); one spatially-homogenous, temporally variable site (vegetated meadow); component measurements of three pseudo-invariant sites with high spatial resolution features (a tennis court, a road and its surroundings, and a Y-intersection and its surroundings); and additional temporally transient targets (a car roof). These measurements were taken every two weeks and were made on the same day as the AVIRIS-NG overflights. Atmospheric measurements were also taken with a MicroTops sun photometer at the serpentinite outcrop site. The data are provided in comma separated values (CSV), JPEG image, and GeoJSON formats along with Python processing code.\n<br>\n\n#### SHIFT_RamsesTriOS_Rrs_SB_CA_2234\nThis dataset provides calculated remote sensing reflectance (Rrs) from measurements collected with a Ramses TriOS radiometer deployed on the Santa Barbara Museum of Natural History Sea Center at Stearns Wharf, Santa Barbara, California, U.S. All measurements were taken over a fixed position at (34.41037665, -119.68557147). Three sensors are used to collect solar downwelling irradiance (Ed), sky radiance (Ls) and water-leaving radiance (Lw). These data have been processed to Rrs at 10 second intervals and are either concurrent or taken within 2.5 hours of SHIFT campaign flights. The data collected by the three Ramses TriOS sensors for eight days during the period 2022-04-05 to 2022-05-29 are also included. The data were translated from the proprietary format output by the Ramses TriOS instrument and saved in comma-separated values (CSV) format.\n<br>\n\n#### SHIFT_DriedGround_Leaf_Reflec_2244\nThis dataset provides full-spectrum (350-2500 nm) reflectance measurements of dried ground leaf samples from meadow, shrub, and tree sites. Samples were collected during the period of February 23, 2022 to September 27th, 2022 for the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign in Santa Barbara County, California, USA. The primary goal of the SHIFT campaign was to collect a repeated dense time series of airborne Visible to ShortWave Infrared (VSWIR) airborne imaging spectroscopy data with coincident field measurements in both inland terrestrial and coastal aquatic areas. Reflectance measurements were collected using a ASD FieldSpec 3 spectrometer following Serbin et al. (2014) and Wang et al. (2020). After sample collection, each leaf sample was divided into two portions: one portion with ~10 g fresh weight was oven dried and another portion with ~5 g fresh weight was flash frozen. Both samples were ground and homogenized (20-mesh, 833 micrometers). The oven dried samples were then re-dried in oven at 70 degrees C for 24 h and the flash frozen samples were dried using a Virtis Model 24DX48 specimen freeze dryer before the reflectance measurement. Data are in a comma-separated values (.csv) format.\n<br>\n\n#### SHIFT_Field_Survey_Metadata_2295\nThis dataset contains vegetation plot locations, descriptions, fractional cover, and sample identifier information from surveys conducted as part of the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign. Surveys took place from 2022-02-23 to 2022-09-27 at the Jack and Laura Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt Marsh Reserve, which are located in Santa Barbara County, California, USA. This project collected field data contemporaneously with weekly flights of the NASA Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) facility instrument over the study areas. Plot information includes: plot tree subform, species lists, plot description, plot samples characterization, and plot location and contextual information. Related data packages contain additional biogeochemical, reflectance, and foliar data. Survey data and metadata are presented in comma-separated values (.csv) format along with survey plot polygons in GeoJSON (.geojson) format.\n<br>\n\n#### SHIFT_Plot_Level_Photos_2334\nThis dataset contains photographs of the plots where field vegetation sampling was conducted during the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign. Sampling occurred at the Jack and Laura Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt Marsh Reserve, which are located in Santa Barbara County, California, USA. Photographs were taken from 2022-02-23 to 2022-09-18. This project collected field data contemporaneously with weekly flights of Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over the study areas. Related SHIFT data packages contain additional biogeochemical, reflectance, and foliar data.\n<br>\n\n#### SHIFT_Wetland_Salinity_2436\nThis dataset includes field data, analysis code, and corresponding airborne imagery collected and generated during the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign within Devereux Slough estuary in Santa Barbara County, California, USA. This campaign collected field data contemporaneously with weekly flights of the NASA's Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) facility instrument over the study areas. Soil samples were collected in North Campus Open Space and Coal Oil Point Reserve between 2022-02-24 to 2022-09-16 and analyzed for salinity (electric conductivity). Soil samples were paired with corresponding AVIRIS-NG flights, and AVIRIS-NG Level 2A Unrectified Reflectance data were used to derive fractional cover and vegetation and soil indices across the study area. Multiple endmember spectral mixture analysis (MESMA) was used to estimate fractional cover for four cover classes. Additionally, canopy response stress index, modified anthocyanin reflectance index, normalized difference vegetation index, and vegetation soil salinity index were derived. The data are provided in cloud-optimized GeoTIFF, ENVI, comma separated values (CSV) , and shapefile formats.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/SHIFT/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","cog","earth observation","hdf","hdf5","ice","netcdf","oceans"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SHIFT_AVNG_Canopy_WaterContent_2242 v1 - This dataset provides per-pixel vegetation canopy water content (CWC) derived from surface reflectance measured by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument from 2022-02-24 to 2022-05-17. This imagery was acquired as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_AVNG_Canopy_WaterContent/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_AVNG_Plant_Trait_Mosaics_2453 v1 - This dataset provides maps of 20 plant functional traits across a broad region of Santa Barbara County, California, USA. The maps were derived from AVIRIS-NG imagery collected during the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign between February and May 2022.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_AVNG_Plant_Trait_Mosaics/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_AVNG_FullRes_QkLook_2189 v1 - This dataset holds full-resolution 3-band (true color) imagery acquired by NASA's Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This imagery was collected as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign which occurred during February to May, 2022, with a follow up activity for one week in September.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_AVNG_FullRes_QkLook/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_AVNG_L1A_RDN_unrec_2184 v1 - This dataset contains Level 1A (L1A) unrectified surface radiance image files as well as files of observational geometry and illumination parameters and supporting sensor band information from NASA's Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This imagery was collected as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_AVNG_L1A_RDN_unrec/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_AVNG_L1B_RDN_V2_2432 v2 - This dataset contains Level 1B (L1B) unrectified calibrated surface radiance images as well as files of observational geometry and illumination parameters and supporting sensor band information from NASA's Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This imagery was collected at regular (approximately weekly) intervals in 2022 between February and May, as well as in September as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_AVNG_L1B_RDN_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_AVNG_L2A_RFL_V2_2431 v2 - This dataset contains Level 2A (L2A) orthorectified surface reflectance images from NASA's Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This imagery was collected approximately weekly in 2022 between February and May, as well as in September as part of the Surface Biology and Geology High Frequency Time Series (SHIFT) campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_AVNG_L2A_RFL_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_AVIRISNG_L2A_refl_2376 v1 - This dataset contains Level 2A (L2A) unrectified surface reflectance images from NASA's Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This imagery was collected as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign which occurred during February to May, 2022, with a follow up activity for one week in September.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_AVIRISNG_L2A_refl/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_HyTES_L1_2022_2245 v1 - This dataset holds Level 1 (L1) brightness temperature data collected by the Hyperspectral Thermal Emission Spectrometer (HyTES) instrument. This imagery was acquired as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign on March 23, 2022.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_HyTES_L1_2022/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_HyTES_L2_2022_2246 v1 - This dataset holds Level 2 (L2) data for surface emissivity and land surface temperature (LST) collected by the Hyperspectral Thermal Emission Spectrometer (HyTES) instrument. This imagery was acquired as part of the Surface Biology and Geology High-Frequency Time Series (SHIFT) campaign on March 23, 2022.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_HyTES_L2_2022/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_Foliar_Chemical_Analysis_2337 v1 - This dataset holds laboratory foliar chemical analyses results for field samples collected during the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign in Santa Barbara County, California, USA. Leaf samples were collected from plots within the Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt Marsh Reserve during the period of 2022-02-23 to 2022-09-27 and dried for later analysis.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_Foliar_Chemical_Analysis/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_Leaf_Traits_Chl_SB_CA_2233 v1 - This dataset provides leaf images and measurements of leaf traits (area, wet weight, dry weight, leaf mass per area, leaf water content) and leaf pigments (chlorophyll) and species information as sampled from meadow, shrub, and tree from Santa Barbara California, USA. Samples were collected from plots within the Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt March Reserve during the period of February 23, 2022 to September 27, 2022 for the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_Leaf_Traits_Chl_SB_CA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_ASDSpec_PseudoInvaTarget_2446 v1 - This dataset provides biweekly surface reflectance measurements of pseudo-invariant sites at Sedgwick Reserve, Santa Barbara, California, collected with an ASD FieldSpec Pro field spectrometer during the SBG High-Frequency Time-Series (SHIFT) campaign in Spring and Fall 2022. The dataset includes measurements of seven primary spatially-homogenous, temporally pseudo-invariant sites (three areas of gravel road, two dirt paddocks, two serpentinite outcrops); one spatially-homogenous, temporally variable site (vegetated meadow); component measurements of three pseudo-invariant sites with high s...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_ASDSpec_PseudoInvaTarget/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_RamsesTriOS_Rrs_SB_CA_2234 v1 - This dataset provides calculated remote sensing reflectance (Rrs) from measurements collected with a Ramses TriOS radiometer deployed on the Santa Barbara Museum of Natural History Sea Center at Stearns Wharf, Santa Barbara, California, U.S. All measurements were taken over a fixed position at (34.41037665, -119.68557147).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_RamsesTriOS_Rrs_SB_CA/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_DriedGround_Leaf_Reflec_2244 v1 - This dataset provides full-spectrum (350-2500 nm) reflectance measurements of dried ground leaf samples from meadow, shrub, and tree sites. Samples were collected during the period of February 23, 2022 to September 27th, 2022 for the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign in Santa Barbara County, California, USA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_DriedGround_Leaf_Reflec/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_Field_Survey_Metadata_2295 v1 - This dataset contains vegetation plot locations, descriptions, fractional cover, and sample identifier information from surveys conducted as part of the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign. Surveys took place from 2022-02-23 to 2022-09-27 at the Jack and Laura Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt Marsh Reserve, which are located in Santa Barbara County, California, USA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_Field_Survey_Metadata/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_Plot_Level_Photos_2334 v1 - This dataset contains photographs of the plots where field vegetation sampling was conducted during the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign. Sampling occurred at the Jack and Laura Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt Marsh Reserve, which are located in Santa Barbara County, California, USA.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_Plot_Level_Photos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SHIFT_Wetland_Salinity_2436 v1 - This dataset includes field data, analysis code, and corresponding airborne imagery collected and generated during the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign within Devereux Slough estuary in Santa Barbara County, California, USA. This campaign collected field data contemporaneously with weekly flights of the NASA's Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) facility instrument over the study areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/shift/SHIFT_Wetland_Salinity/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-shift","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SIF-ESDR Project","Description":"This dataset provides global solar-induced chlorophyll fluorescence (SIF) estimates at a 0.05-degree resolution (approximately 5 km at the equator) for each month from January 2003 through December 2017. SIF data (740 nm) was retrieved from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) and Global Ozone Monitoring Experiment 2 (GOME-2) instruments onboard the MetOp-A satellite. The data were downscaled to 0.05 degrees using the Random Forest algorithm and predictors from Moderate Resolution Imaging Spectroradiometer (MODIS) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis, and then harmonized with the cumulative distribution function (CDF) matching technique. The uncertainty of the harmonized SIF estimates was also quantified and provided. Validation of the harmonized product showed that it retained high spatial and temporal consistency with the original SCIAMACHY and GOME-2 SIF retrievals and had good correlations with independent airborne and ground-based SIF measurements. The dataset can inform on the synergy between satellite SIF and photosynthesis and research on drought, yield estimation, and land degradation evaluation. The data are provided in netCDF format.\n<br>\n\n#### Global_SIF_OCO2_MODIS_1863\nThis dataset provides spatially-contiguous global mean daily solar-induced chlorophyll fluorescence (SIF) estimates at 0.05 degree (approximately 5 km at the equator) spatial and 16-day temporal resolution from September 2014 through July 2020. This product was derived from Orbiting Carbon Observatory-2 (OCO-2) SIF observations and produced by training an artificial neural network (ANN) on the native OCO-2 SIF observations and MODIS BRDF-corrected seven-band surface reflectance along OCO-2's orbits. The trained ANN model was then applied to predict mean daily SIF (mW/m2/nm/sr) in OCO-2's gap regions based on MODIS reflectance and landcover. This framework was stratified by biomes and 16-day time steps. This dataset's high resolution and global contiguous coverage will greatly enhance the synergy between satellite SIF and photosynthesis measured on the ground at consistent spatial scales. Potential applications of this dataset include advancing dynamic drought monitoring and mitigation, informing agricultural planning and yield estimation, and providing a benchmark for upcoming satellite missions with SIF capabilities at higher spatial resolutions.\n<br>\n\n#### ERS2_GOME_SIF_1758\nThis dataset provides Level 2 Solar-Induced Fluorescence (SIF) of Chlorophyll estimates derived from the Global Ozone Monitoring Experiment (GOME) instrument on the European Space Agency's (ESA's) European Remote-Sensing 2 (ERS-2) satellite. Each file contains daily raw and bias-adjusted solar-induced fluorescence on an orbital basis (land pixels only), at a resolution of 40 km x 320 km, along with quality control information and ancillary data. Data is provided for the period from 1995-07-01 to 2003-06-22. The GOME SIF product is inherently noisy due to low signal levels and has undergone only a limited amount of validation. This dataset includes both Version 1 and Version 2 files. Version 2 includes new fields such as SIF uncertainties, longitude-latitude corners, and data coverage over ocean. In addition, the SIF bias adjustment differs between Versions 1 and 2. The data are provided in NetCDF format.\n<br>\n\n#### MetOpA_GOME2_SIF_V2_2292\nThis dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the Global Ozone Monitoring Experiment 2 (GOME-2) instrument on the European Meteorological Satellite (EUMETSAT) MetOp-A with ~0.5 nm spectral resolution and wavelengths between 734 and 758 nm. GOME-2 covers global land on an orbital basis at a resolution of approximately 40 km x 80 km (before 15 July 2013) or 40 km x 40 km (since 15 July 2013). Data are provided for the period from 2007-02-01 to 2018-02-01. Each file contains daily raw and bias-adjusted solar-induced fluorescence, quality control information, and ancillary data. SIF measurements can provide information on vegetation's functional status, including light-use efficiency and global primary productivity, which can be used for global carbon cycle modeling and agricultural applications. The GOME-2 SIF product is inherently noisy due to low signal levels and has undergone only a limited amount of validation. The data are provided in netCDF format.\n<br>\n\n#### MetOpB_GOME2_SIF_2182\nThis dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the Global Ozone Monitoring Experiment 2 (GOME-2) instrument on the European Meteorological Satellite (EUMETSAT) MetOp-B with ~0.5 nm spectral resolution and wavelengths between 734 and 758 nm. GOME-2 covers global land (observations up to 75-degree solar zenith angle) at a resolution of approximately 40 km x 80. Data are provided for the period from 2013-04-01 to 2021-06-07. Each file contains daily raw and bias-adjusted solar-induced fluorescence along with quality control information and ancillary data. SIF measurements can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. The GOME-2 SIF product is inherently noisy owing to low signal levels and has undergone only a limited amount of validation. The data are provided in netCDF (.nc) format.\n<br>\n\n#### ENVISAT_SCIAMACHY_SIF_1871\nThis dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument on the European Space Agency's (ESA's) Environmental satellite (Envisat) with ~0.5 nm spectral resolution and wavelengths between 734 and 758 nm. SCIAMACHY covers global land between approximately 70 and -57 degrees latitude on an orbital basis at a resolution of approximately 30 km x 240 km. Data are provided for the period from 2003-01-01 to 2012-04-08. Each file contains daily raw and bias-adjusted solar-induced fluorescence along with quality control information and ancillary data.\n<br>\n\n#### TROPOMI_SIF_Arctic_Ocean_2378\nThis dataset provides solar-induced chlorophyll fluorescence (SIF) estimates over the Arctic Ocean at a 0.05-degree resolution for each month from January 2004 through December 2020. Red SIF data from TROPOspheric Monitoring Instrument (TROPOMI) (2018 to 2021) were extended over the study period using a random forest machine learning model trained using TROPOMI SIF climatological records. These data are useful for monitoring the physiological responses of phytoplankton to ongoing climate change over this ocean region. The data are provided in cloud optimized GeoTIFF format.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://science.jpl.nasa.gov/projects/measures/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","climate","netcdf","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SIF_SCIAMACHY_GOME2_Harmonized_2317 v2 - This dataset provides global solar-induced chlorophyll fluorescence (SIF) estimates at a 0.05-degree resolution (approximately 5 km at the equator) for each month from January 2003 through December 2017. SIF data (740 nm) was retrieved from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) and Global Ozone Monitoring Experiment 2 (GOME-2) instruments onboard the MetOp-A satellite.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/sif-esdr/17-MEASURES-0032/SIF_SCIAMACHY_GOME2_Harmonized/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_SIF_OCO2_MODIS_1863 v2 - This dataset provides spatially-contiguous global mean daily solar-induced chlorophyll fluorescence (SIF) estimates at 0.05 degree (approximately 5 km at the equator) spatial and 16-day temporal resolution from September 2014 through July 2020. This product was derived from Orbiting Carbon Observatory-2 (OCO-2) SIF observations and produced by training an artificial neural network (ANN) on the native OCO-2 SIF observations and MODIS BRDF-corrected seven-band surface reflectance along OCO-2's orbits.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/sif-esdr/17-MEASURES-0032/Global_SIF_OCO2_MODIS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ERS2_GOME_SIF_1758 v1 - This dataset provides Level 2 Solar-Induced Fluorescence (SIF) of Chlorophyll estimates derived from the Global Ozone Monitoring Experiment (GOME) instrument on the European Space Agency's (ESA's) European Remote-Sensing 2 (ERS-2) satellite. Each file contains daily raw and bias-adjusted solar-induced fluorescence on an orbital basis (land pixels only), at a resolution of 40 km x 320 km, along with quality control information and ancillary data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/sif-esdr/17-MEASURES-0032/ERS2_GOME_SIF/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MetOpA_GOME2_SIF_V2_2292 v2 - This dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the Global Ozone Monitoring Experiment 2 (GOME-2) instrument on the European Meteorological Satellite (EUMETSAT) MetOp-A with ~0.5 nm spectral resolution and wavelengths between 734 and 758 nm. GOME-2 covers global land on an orbital basis at a resolution of approximately 40 km x 80 km (before 15 July 2013) or 40 km x 40 km (since 15 July 2013).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/sif-esdr/17-MEASURES-0032/MetOpA_GOME2_SIF_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MetOpB_GOME2_SIF_2182 v1 - This dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the Global Ozone Monitoring Experiment 2 (GOME-2) instrument on the European Meteorological Satellite (EUMETSAT) MetOp-B with ~0.5 nm spectral resolution and wavelengths between 734 and 758 nm. GOME-2 covers global land (observations up to 75-degree solar zenith angle) at a resolution of approximately 40 km x 80.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/sif-esdr/17-MEASURES-0032/MetOpB_GOME2_SIF/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ENVISAT_SCIAMACHY_SIF_1871 v1 - This dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument on the European Space Agency's (ESA's) Environmental satellite (Envisat) with ~0.5 nm spectral resolution and wavelengths between 734 and 758 nm. SCIAMACHY covers global land between approximately 70 and -57 degrees latitude on an orbital basis at a resolution of approximately 30 km x 240 km.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/sif-esdr/17-MEASURES-0032/ENVISAT_SCIAMACHY_SIF/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TROPOMI_SIF_Arctic_Ocean_2378 v1 - This dataset provides solar-induced chlorophyll fluorescence (SIF) estimates over the Arctic Ocean at a 0.05-degree resolution for each month from January 2004 through December 2020. Red SIF data from TROPOspheric Monitoring Instrument (TROPOMI) (2018 to 2021) were extended over the study period using a random forest machine learning model trained using TROPOMI SIF climatological records.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/sif-esdr/17-MEASURES-0032/TROPOMI_SIF_Arctic_Ocean/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-sif-esdr","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SISTER Project","Description":"The Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) activity originated in support of the NASA Earth System Observatory's Surface Biology and Geology (SBG) mission to develop prototype workflows with community algorithms and generate prototype data products envisioned for SBG. SISTER focused on developing a data system that is open, portable, scalable, standards-compliant, and reproducible. This collection contains EXPERIMENTAL workflows and sample data products, including (a) the Common Workflow Language (CWL) process file and a Jupyter Notebook that run the entire SISTER workflow capable of generating experimental sample data products spanning terrestrial ecosystems, inland and coastal aquatic ecosystems, and snow, (b) the archived algorithm steps (as OGC Application Packages) used to generate products at each step of the workflow, (c) a small number of experimental sample data products produced by the workflow which are based on the Airborne Visible/Infrared Imaging Spectrometer-Classic (AVIRIS or AVIRIS-CL) instrument, and (d) instructions for reproducing the sample products included in this dataset. DISCLAIMER: This collection contains experimental workflows, experimental community algorithms, and experimental sample data products to demonstrate the capabilities of an end-to-end processing system. The experimental sample data products provided have not been fully validated and are not intended for scientific use. The community algorithms provided are placeholders which can be replaced by any user's algorithms for their own science and application interests. These algorithms should not in any capacity be considered the algorithms that will be implemented in the upcoming Surface Biology and Geology mission.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SISTER_Workflow_V004_2335 v4 - The Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) activity originated in support of the NASA Earth System Observatory's Surface Biology and Geology (SBG) mission to develop prototype workflows with community algorithms and generate prototype data products envisioned for SBG. SISTER focused on developing a data system that is open, portable, scalable, standards-compliant, and reproducible.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/sister/SISTER_Workflow_V004/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-sister","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA smap Project","Description":"This data set contains data obtained by the Passive Active L- and S-band (PALS) microwave aircraft instrument that are matched up with a variety of soil moisture campaign data. The data were collected as part of four different campaigns: Southern Great Plains 1999 (SGP99), Cloud and Land Surface Interaction Campaign 2007 (CLASIC07), Soil Moisture Experiment 2002 (SMEX02), and the SMAP Validation Experiment 2008 (SMAPVEX08).\n<br>\n\n#### SPL1CTB_E\nThis enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract enhanced information from SMAP antenna temperatures before they are converted to brightness temperatures. The resulting brightness temperatures are posted to an Earth-fixed, 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal.\n<br>\n\n#### SPL2SMP_E\nThis enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product].\n<br>\n\n#### SPL3FTP_E\nThis enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal.\n<br>\n\n#### SPL1CTB\nThis Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP L-band Level-1B time-ordered brightness temperatures resampled to an Earth-fixed, 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. This L1C product is a gridded version of the SMAP time-ordered Level-1B radiometer brightness temperature product.\n<br>\n\n#### SPL3FTA\nThis Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).\n<br>\n\n#### SPL3SMAP\nThis Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).\n<br>\n\n#### SPL3FTP\nThis Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0.\n<br>\n\n#### NSIDC-0774\nThis data set contains twice-daily synthetic aperture radar (SAR) and enhanced-resolution scatterometer radar backscatter derived from SMAP radar data. Data are available on the Northern Hemisphere, Southern Hemisphere, Temperate, and Mid-Latitude (sub-set of Global) EASE-Grid 2.0 projections and as either 25 km or 3.125 km resolution grids. This new product uses the drop-in-the bucket gridding (GRD) algorithm and Scatterometer Image Reconstruction (SIR) algorithm to process the individual swath-based data from the input data sets into twice-daily images, producing SAR images (high-resolution), slice and footprint images (both lower-resolution), respectively. Note, that as a companion product, the radiometer form of the SIR algorithm (rSIR) was used to derive brightness temperatures from SMAP radiometer data for the <i>SMAP Radiometer Twice-Daily rSIR-Enhanced EASE-Grid 2.0 Brightness Temperatures, Version 2</i>, data set (<a href=\"https://nsidc.org/data/nsidc-0738\">NSIDC-0738</a>).\n<br>\n\n#### SV12PLSM\nThis data set contains soil moisture data obtained by the Passive Active L-band System (PALS) aircraft instrument. The data were collected as part of SMAPVEX12, the Soil Moisture Active Passive Validation Experiment 2012.\n<br>\n\n#### SV19MA_VOD\nAs part of the SMAPVEX19-22 campaign, an L-band radiometer was deployed on top of a tower at Harvard Forest,Massachusetts, looking down at a stand of red oak forest. The radiometer collected data in V-polarization from late April to mid October 2019. Over 4 days in early July 2019, the water potential and L-band complex dielectric constant of canopy leaves were measured at various times of day. Other instruments were installed within the radiometer's field of view to measure soil moisture and temperature, air temperature, tree xylem apparent dielectric permittivity at 70 MHz, tree xylem water potential, and canopy wetness. The goal of this experiment was to study the sensitivity of L-band vegetation optical depth (VOD) to changing vegetation water potential over a growing season.\n<br>\n\n#### SMAP_L4_C_ANC_PARAM\nThis ancillary SMAP product contains assorted static ancillary parameters, such as reference grids and land cover classifications, also referred to as Plant Function Type (PFT) maps.\n<br>\n\n#### SMAP_L4_SM_ANC_CLIM\nThis ancillary SMAP product contains a static soil moisture climatology data set. Specifically, this data set includes root zone and profile soil moisture climatology files for percentile conversion and post-processing of Land Data Assimilation Systems (LDAS) output.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/nsidc-0666/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","land cover","radar","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"NSIDC-0666 v1 - This data set contains data obtained by the Passive Active L- and S-band (PALS) microwave aircraft instrument that are matched up with a variety of soil moisture campaign data. The data were collected as part of four different campaigns: Southern Great Plains 1999 (SGP99), Cloud and Land Surface Interaction Campaign 2007 (CLASIC07), Soil Moisture Experiment 2002 (SMEX02), and the SMAP Validation Experiment 2008 (SMAPVEX08).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/NSIDC-0666/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL1CTB_E v004 - This enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL1CTB_E/004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL2SMP_E v006 - This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL2SMP_E/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL3FTP_E v004 - This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL3FTP_E/004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL1CTB v006 - This Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL1CTB/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL3FTA v003 - This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL3FTA/003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL3SMAP v003 - This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL3SMAP/003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL3FTP v004 - This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL3FTP/004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0774 v1 - This data set contains twice-daily synthetic aperture radar (SAR) and enhanced-resolution scatterometer radar backscatter derived from SMAP radar data. Data are available on the Northern Hemisphere, Southern Hemisphere, Temperate, and Mid-Latitude (sub-set of Global) EASE-Grid 2.0 projections and as either 25 km or 3.125 km resolution grids.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-Related/NSIDC-0774/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV12PLSM v1 - This data set contains soil moisture data obtained by the Passive Active L-band System (PALS) aircraft instrument. The data were collected as part of SMAPVEX12, the Soil Moisture Active Passive Validation Experiment 2012.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12PLSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MA_VOD v1 - As part of the SMAPVEX19-22 campaign, an L-band radiometer was deployed on top of a tower at Harvard Forest,Massachusetts, looking down at a stand of red oak forest. The radiometer collected data in V-polarization from late April to mid October 2019.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MA_VOD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_C_ANC_PARAM v1 - This ancillary SMAP product contains assorted static ancillary parameters, such as reference grids and land cover classifications, also referred to as Plant Function Type (PFT) maps.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_C_ANC_PARAM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_SM_ANC_CLIM v1 - This ancillary SMAP product contains a static soil moisture climatology data set. Specifically, this data set includes root zone and profile soil moisture climatology files for percentile conversion and post-processing of Land Data Assimilation Systems (LDAS) output.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_SM_ANC_CLIM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-smap","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SMAPVEX08 Project","Description":"This data set includes several parameters that were obtained from field surveys as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).\n<br>\n\n#### SV08V\nThis data set includes in situ vegetation data collected during the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign. Sampling was designed to coincide with satellite overpasses, such as Landsat's Thematic Mapper (TM) 5 and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Terra satellite (MODIS/Terra), which can be then used to estimate vegetation water content on the regional scale.\n<br>\n\n#### SV08LC\nThis data set consists of land cover classification data derived from satellite imagery and of data obtained in the field as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).\n<br>\n\n#### SV08PLBK\nThis data set contains backscatter data obtained by the Passive Active L-band System (PALS) microwave aircraft radar instrument as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).\n<br>\n\n#### SV08PLTB\nThis data set contains brightness temperatures obtained by the Passive Active L-band System (PALS) microwave aircraft radiometer instrument as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).\n<br>\n\n#### SV08ST\nThis data set contains soil texture data that were extracted from a multi-layer soil characteristics database for the conterminous United States and generated for each regional study area. Data are representative of the conditions present in the regional study areas during the general timeline of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign.\n<br>\n\n#### SV08SR\nSurface roughness data for this data set were collected at several field sites as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign.\n<br>\n\n#### SV08VWC\nThe Vegetation Water Content (VWC) map for the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) was derived by calculating Normalized Difference Water Index (NDWI) from Satellite Pour l'Observation de la Terre-4 (SPOT-4) overpasses on 11 October 2008. In addition, samples from a range of vegetation types were used to compare VWC and NDWI to the satellite imagery.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/sv08sm/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","land cover","radar","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SV08SM v1 - This data set includes several parameters that were obtained from field surveys as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV08SM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV08V v1 - This data set includes in situ vegetation data collected during the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign. Sampling was designed to coincide with satellite overpasses, such as Landsat's Thematic Mapper (TM) 5 and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Terra satellite (MODIS/Terra), which can be then used to estimate vegetation water content on the regional scale.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV08V/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV08LC v1 - This data set consists of land cover classification data derived from satellite imagery and of data obtained in the field as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV08LC/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV08PLBK v1 - This data set contains backscatter data obtained by the Passive Active L-band System (PALS) microwave aircraft radar instrument as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV08PLBK/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV08PLTB v1 - This data set contains brightness temperatures obtained by the Passive Active L-band System (PALS) microwave aircraft radiometer instrument as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV08PLTB/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV08ST v1 - This data set contains soil texture data that were extracted from a multi-layer soil characteristics database for the conterminous United States and generated for each regional study area. Data are representative of the conditions present in the regional study areas during the general timeline of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV08ST/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV08SR v1 - Surface roughness data for this data set were collected at several field sites as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV08SR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV08VWC v1 - The Vegetation Water Content (VWC) map for the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) was derived by calculating Normalized Difference Water Index (NDWI) from Satellite Pour l'Observation de la Terre-4 (SPOT-4) overpasses on 11 October 2008. In addition, samples from a range of vegetation types were used to compare VWC and NDWI to the satellite imagery.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV08VWC/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-smapvex08","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SMAPVEX12 Project","Description":"This data set contains in situ soil moisture data collected with coring devices at several agricultural sites as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).\n<br>\n\n#### SV12CST\nThis data set contains in situ soil texture data collected with coring devices at several sites as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).\n<br>\n\n#### SV12VA\nThis data set contains in situ vegetation data collected at several agricultural sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).\n<br>\n\n#### SV12VF\nThis data set contains in situ vegetation data collected at several forest sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).\n<br>\n\n#### SV12LC\nThis data set consists of land cover classification data derived from satellite imagery as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). Images from the RADARSAT-2, Système Pour l'Observation de la Terre (SPOT-4), and DMC International Imaging Ltd (DMCii) of the study area were retrieved for the summer of 2012. The land use classification image provides information about vegetation present in the study area at a resolution of 20 meters.\n<br>\n\n#### SV12PLBK\nThis data set contains backscatter data obtained by the Passive Active L-band System (PALS) microwave aircraft instrument as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).\n<br>\n\n#### SV12PLTB\nThis data set contains brightness temperatures obtained by the Passive Active L-band System (PALS) aircraft instrument. The data were collected as part of SMAPVEX12, the Soil Moisture Active Passive Validation Experiment 2012.\n<br>\n\n#### SV12PSMA\nThis data set contains in situ soil moisture data collected at several agricultural sites as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).\n<br>\n\n#### SV12PSMF\nThis data set contains in situ soil moisture data collected at several forested sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).\n<br>\n\n#### SV12STM\nThis data set consists of soil texture classification data derived from field surveys as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). The soil texture classification map provides information about vegetation present in the study area.\n<br>\n\n#### SV12SRA\nThis data set contains surface roughness data collected at several agricultural sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).\n<br>\n\n#### SV12SRF\nThis data set contains surface roughness data collected at several forested sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).\n<br>\n\n#### SV12UBK\nThis data set contains backscatter data obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument. The data were collected as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).\n<br>\n\n#### SV12VWC\nThe daily Vegetation Water Content (VWC) maps for the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) were derived by calculating Normalized Difference Vegetation Index (NDVI) from SPOT and RapidEye satellite overpasses and then interpolating it for each day of the campaign. In addition, samples from a range of vegetation types were used to compare ground-based measurements to the satellite-based estimates.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/sv12csma/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","land cover","radar","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SV12CSMA v1 - This data set contains in situ soil moisture data collected with coring devices at several agricultural sites as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12CSMA/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV12CST v1 - This data set contains in situ soil texture data collected with coring devices at several sites as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12CST/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV12VA v1 - This data set contains in situ vegetation data collected at several agricultural sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12VA/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV12VF v1 - This data set contains in situ vegetation data collected at several forest sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12VF/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV12LC v1 - This data set consists of land cover classification data derived from satellite imagery as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). Images from the RADARSAT-2, Système Pour l'Observation de la Terre (SPOT-4), and DMC International Imaging Ltd (DMCii) of the study area were retrieved for the summer of 2012.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12LC/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV12PLBK v1 - This data set contains backscatter data obtained by the Passive Active L-band System (PALS) microwave aircraft instrument as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12PLBK/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV12PLTB v1 - This data set contains brightness temperatures obtained by the Passive Active L-band System (PALS) aircraft instrument. The data were collected as part of SMAPVEX12, the Soil Moisture Active Passive Validation Experiment 2012.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12PLTB/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV12PSMA v1 - This data set contains in situ soil moisture data collected at several agricultural sites as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12PSMA/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV12PSMF v1 - This data set contains in situ soil moisture data collected at several forested sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12PSMF/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV12STM v1 - This data set consists of soil texture classification data derived from field surveys as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). The soil texture classification map provides information about vegetation present in the study area.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12STM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV12SRA v1 - This data set contains surface roughness data collected at several agricultural sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12SRA/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV12SRF v1 - This data set contains surface roughness data collected at several forested sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12SRF/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV12UBK v1 - This data set contains backscatter data obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument. The data were collected as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12UBK/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV12VWC v1 - The daily Vegetation Water Content (VWC) maps for the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) were derived by calculating Normalized Difference Vegetation Index (NDVI) from SPOT and RapidEye satellite overpasses and then interpolating it for each day of the campaign. In addition, samples from a range of vegetation types were used to compare ground-based measurements to the satellite-based estimates.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV12VWC/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-smapvex12","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SMAPVEX15 Project","Description":"This data set contains brightness temperatures obtained by the Passive Active L-band System (PALS) aircraft instrument. The data were collected as part of SMAPVEX15, the Soil Moisture Active Passive Validation Experiment 2015.\n<br>\n\n#### SV15PLSM\nThis data set contains soil moisture data derived from the brightness temperatures measured by the Passive Active L-band System (PALS) microwave aircraft instrument. The data were collected as part of the Soil Moisture Active Passive Validation Experiment 2015 (SMAPVEX15).\n<br>\n\n#### SV15PSM\nThis data set contains in situ gravimetric and volumetric soil moisture, bulk density, and soil temperature measurements collected for the Soil Moisture Active Passive Validation Experiment 2015 (SMAPVEX15). Sampling was performed at field sites approximately 1 m apart.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/sv15pltb/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SV15PLTB v1 - This data set contains brightness temperatures obtained by the Passive Active L-band System (PALS) aircraft instrument. The data were collected as part of SMAPVEX15, the Soil Moisture Active Passive Validation Experiment 2015.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV15PLTB/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV15PLSM v1 - This data set contains soil moisture data derived from the brightness temperatures measured by the Passive Active L-band System (PALS) microwave aircraft instrument. The data were collected as part of the Soil Moisture Active Passive Validation Experiment 2015 (SMAPVEX15).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV15PLSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV15PSM v1 - This data set contains in situ gravimetric and volumetric soil moisture, bulk density, and soil temperature measurements collected for the Soil Moisture Active Passive Validation Experiment 2015 (SMAPVEX15). Sampling was performed at field sites approximately 1 m apart.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV15PSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-smapvex15","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SMAPVEX16 Manitoba Project","Description":"This data set contains in situ measurements of soil moisture and bulk density collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.\n<br>\n\n#### SV16M_CRS\nThis data set contains CropScan observations (solar irradiance and incidence angle) collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.\n<br>\n\n#### SV16M_V\nThis data set contains in situ measurements of crop density, height, and biomass collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.\n<br>\n\n#### SV16M_LC\nThis data set contains land cover classification data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.\n<br>\n\n#### SV16M_LAI\nThis data set contains in situ Leaf Area Index (LAI) data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.\n<br>\n\n#### SV16M_MET\nThis data set contains meteorological data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.\n<br>\n\n#### SV16M_PSM\nThis data set contains in situ measurements of soil moisture, soil and vegetation temperature, and real dielectric constant collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.\n<br>\n\n#### SV16M_TB\nThis data set contains brightness temperatures obtained by in situ L-band radiometers. The data were collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.\n<br>\n\n#### SV16M_ST\nThis data set contains soil texture data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.\n<br>\n\n#### SV16M_SDB\nThis data set contains detailed soil survey data used for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign. Data are provided in a relational geodatabase.\n<br>\n\n#### SV16M_SSM\nThis data set contains soil moisture, temperature, and precipitation data collected at temporary soil stations and the Real-time In-Situ Soil Monitoring for Agriculture (RISMA) station network for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.\n<br>\n\n#### SV16M_SR\nThis data set contains surface soil roughness data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/sv16m_csm/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","land cover","precipitation","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SV16M_CSM v1 - This data set contains in situ measurements of soil moisture and bulk density collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16M_CSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16M_CRS v1 - This data set contains CropScan observations (solar irradiance and incidence angle) collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16M_CRS/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16M_V v1 - This data set contains in situ measurements of crop density, height, and biomass collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16M_V/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16M_LC v1 - This data set contains land cover classification data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16M_LC/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16M_LAI v1 - This data set contains in situ Leaf Area Index (LAI) data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16M_LAI/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16M_MET v1 - This data set contains meteorological data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16M_MET/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16M_PSM v1 - This data set contains in situ measurements of soil moisture, soil and vegetation temperature, and real dielectric constant collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16M_PSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16M_TB v1 - This data set contains brightness temperatures obtained by in situ L-band radiometers. The data were collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16M_TB/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16M_ST v1 - This data set contains soil texture data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16M_ST/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16M_SDB v1 - This data set contains detailed soil survey data used for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign. Data are provided in a relational geodatabase.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16M_SDB/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16M_SSM v1 - This data set contains soil moisture, temperature, and precipitation data collected at temporary soil stations and the Real-time In-Situ Soil Monitoring for Agriculture (RISMA) station network for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16M_SSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16M_SR v1 - This data set contains surface soil roughness data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16M_SR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-smapvex16-manitoba","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SMERGE Project","Description":"Smerge-Noah-CCI root zone soil moisture 0-40 cm L4 daily 0.125 x 0.125 degree V2.0 is a multi-decadal root-zone soil moisture product. Smerge is developed by merging the North American Land Data Assimilation System (NLDAS) land surface model output with surface satellite retrievals from the European Space Agency Climate Change Initiative. The data have a 0.125 degree resolution at a daily time-step, covering the entire continental United States and spanning nearly four decades (January 1979 to May 2019). This data product contains root-zone soil moisture of 0 - 40 cm layer, Climate Change Initiative (CCI) derived soil moisture anomalies of 0-40 cm layer, and a soil moisture data estimation flag. This data product is the recommended replacement for the AMSR-E/Aqua root zone soil moisture L3 1 day 25 km x 25 km descending and 2-Layer Palmer Water Balance Model V001 product (LPRM_AMSRE_D_RZSM3), which will be removed from archive on June 27, 2022. Smerge provides a better root zone soil moisture estimation because it has higher data quality and longer temporal coverage.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.tamiu.edu/cees/smerge/overview.shtml","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","climate","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SMERGE_RZSM0_40CM v2.0 - Smerge-Noah-CCI root zone soil moisture 0-40 cm L4 daily 0.125 x 0.125 degree V2.0 is a multi-decadal root-zone soil moisture product. Smerge is developed by merging the North American Land Data Assimilation System (NLDAS) land surface model output with surface satellite retrievals from the European Space Agency Climate Change Initiative.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SMERGE/SMERGE_RZSM0_40CM.2.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-smerge","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SNF Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### SNF_ASP_CVR_140\nThe purpose of the Superior National Forest (SNF) study was to improve our understanding of the relationship between remotely sensed observations and important biophysical parameters in the boreal forest. A key element of the experiment was the development of methodologies to measure forest stand characteristics to determine values of importance to both remote sensing and ecology. Parameters studied were biomass, leaf area index, above ground net primary productivity, bark area index and ground coverage by vegetation. Thirty two quaking aspen and thirty one black spruce sites were studied. For the aspen sites, in each plot a visual estimation of the percent coverage of the canopy, subcanopy and understory vegetation was made. The site averages of these coverage estimates are presented in this data set.\n<br>\n\n#### SNF_BIOMASS_141\nThe purpose of the SNF study was to improve our understanding of the relationship between remotely sensed observations and important biophysical parameters in the boreal forest. A key element of the experiment was the development of methodologies to measure forest stand characteristics to determine values of importance to both remote sensing and ecology. Parameters studied were biomass, leaf area index, above ground net primary productivity, bark area index and ground coverage by vegetation. Thirty two quaking aspen and thirty one black spruce sites were studied. Aspen is an early successional, shade intolerant species. Aspen stands are essentially even aged, and stand age appears to be the most significant difference among sites in determining stand density, average diameter, and biomass density. Biomass density was highest in stands of older, larger trees and decreased in younger stands with smaller, denser stems. Since all aspen stands had closed canopies, the inverse relationship between biomass density and stem density suggests a series of stands in various stages of self thinning. Biomass density and projected LAI were much more variable for spruce than aspen. Spruce LAI and biomass density have a tight, nearly linear relationship. Stand attributes are often determined by site characteristics. Net primary productivity was estimated from the average radial growth over five years measured from the segments cut from the boles and the terminal growth measured as the height increase of the tree. Allometric equations were used to find the height and radial increment as a function of crown height and diameter at breast height. Spruce used an additional parameter of stem density. The models were used to back project five years and determine biomass at that time. The change in biomass over that time was used to determine the productivity. Measurements of sacrificed trees were used to develop relationships between the biophysical parameters (biomass, leaf area index, bark area index and net primary productivity) and the measurements made at each site (diameter at breast height, tree height, crown depth and stem density). These relationships were then used to estimate biophysical characteristics for the aspen and spruce study sites that are provided in the Forest Biophysical Parameters (SNF) data set.\n<br>\n\n#### SNF_HELO_MMR_144\nA major aspect of the ground data collection effort in the SNF during the summers of 1983 and 1984 was the acquisition of helicopter canopy reflectance measurements. Canopy measurements were made at numerous sites with a helicopter-mounted Barnes multiband radiometer (MMR). MMR data were collected on ten dates in 1983 and eight dates in 1984. An additional Barnes radiometer was used to make simultaneous reference panel measurements. The canopy reflectance was derived from the canopy and reference panel measurements. All canopy and reference panel measurements were made under clear sky conditions. A majority of the helicopter measurements were taken at nadir view, although some off-nadir view angle measurements were taken primarily over black spruce and aspen sites. The reflectance factor is the ratio of radiant flux of the canopy measurement to that of the reference or calibration panel. Another component to be considered is atmospheric scatter, especially for aircraft measurements taken at higher altitudes. The amount of atmospheric scattering can be determined by using reflectance measurements of water targets. Reflectance measurements over water targets are included for all acquisitions in 1983. No water target measurements were taken during the 1984 field campaign. The summarized MMR data for both years, 1983 and 1984, are included in this data set. Fields include site ID number, number of observations averaged, code for altitude of instrument above the canopy, the time at which observations begin, the time at which observations end, sun zenith angle, sun azimuth angle, and reflectance for each of the bands (with standard deviations included within parenthesis). All measurements were taken at nadir, except where otherwise indicated. In 1984, MMR data were collected using off nadir view angles to measure the bi-directional reflectance characteristics of the forests.\n<br>\n\n#### SNF_MET_1972_158\nWeather data were collected by the National Weather Service in International Falls, Minnesota. International Falls is about 80 miles from the SNF, but the weather data is representative of the area. Total solar insolation measurements were made at Fall Lake Dam in Winton, Minn. by Prof. Donald Baker of the Department of Soil Science at the University of Minnesota, St. Paul. Insolation values were measured using a Yellow Springs solar cell calibrated against an Eppley Pyranometer.\n<br>\n\n#### SNF_LEAF_EXP_180\nThe purpose of the SNF study was to improve understanding of the relationship between remotely sensed observations and important biophysical parameters in the boreal forest. A key element of the experiment was the development of methodologies to measure forest stand characteristics to determine values of importance to both remote sensing and ecology. Parameters studied were biomass, leaf area index, above-ground net primary productivity, bark area index and ground coverage by vegetation. Thirty two quaking aspen and thirty one black spruce sites were studied. Sites were chosen in uniform stands of aspen or spruce. Use of multiple plots within each site allowed estimation of the importance of spatial variation in stand parameters. Deciduous vegetation undergoes dramatic changes over the seasonal cycle. The varying amount of green foliage in the canopy effects the transpiration and productivity of the forest. Measurements of changes in the canopy and subcanopy green foliage amount over the spring of 1984 have been made. From above the subcanopy, photographs of the aspen canopy were taken, pointing vertically up. The photographs were taken at two locations in sites 16 and 93 on several different days. Foliage coverage was determined by overlaying grids with 200 points onto the photos of the canopy. The number of points obscured by vegetation were counted. These counts were adjusted for the area of the branches, which had been determined by photos taken before leaf out. The number of foliage points were then scaled between zero, for no leaves, to one, for maximum coverage. Subcanopy leaf extension was measured for beaked hazelnut and mountain maple, the two most common understory shrubs. For selected branches on trees in sites 16 and 93, the length and width of all leaves were measured on several days. These measurements were used to calculate a total leaf area which was scaled between 0 and 1 as with the aspen. The aspen canopy measurements have been combined with the subcanopy measurements and are available in this data set (i.e., SNF Forest Phenology/Leaf Expansion Data). These measurements of leafout show that the subcanopy leaf expansion lags behind that of the canopy. Subcanopy leaf expansion only begins in earnest after the canopy has reached nearly full coverage.\n<br>\n\n#### SNF_LEAFCARY_183\nKnowledge of the optical properties of the components of the forest canopy is important to the understanding of how plants interact with their environment and how this information may be used to determine vegetation characteristics using remote sensing. During the summers of 1983 and 1984, samples of the major components of the boreal forest canopy (needles, leaves, branches, moss, litter) were collected in the Superior National Forest (SNF) of Minnesota and sent to the Johnson Space Center (JSC). At JSC, the spectral reflectance and transmittance characteristics of the samples were determined for wavelengths between .35 and 2.1 micrometers using the Cary-14 radiometer. This report presents plots of these data as well as averages to the Thematic Mapper Simulator (TMS) bands. There were two main thrusts to the SNF optical properties study. The first was to collect the optical properties of many of the components of the boreal forest canopy. The second goal of the study was to investigate the variability of optical properties within a species. The results of these studies allow a comparison of the optical properties of a variety of different species and a measure of the variability within species. These data provide basic information necessary to model canopy reflectance patterns.\n<br>\n\n#### SNF_NS001_185\nThe NS001 Thematic Mapper Simulator (TMS) was flown on the NASA C-130 aircraft over the Superior National Forest study area. TMS data were collected and processed for three days: July 13 and August 6, 1983; and June 28, 1984. The TMS was a scanning radiometer with eight wavelength bands. Band 8 was a thermal band and not processed in this study. The C-130 flew a \"crisscross\" pattern over the SNF, which provided a variety of sun and view angles. The TMS data were processed to provide reflectance values of study sites. These data are useful in the analysis of the bi-directional reflectance function of forest canopies. The TMS radiance values were converted to reflectance using values for insolation, atmospheric transmittance, and path scattered radiance for the appropriate solar and view angles. No measurement of these values were made, so the LOWTRAN6 atmosphere model was used to generate them. Scattering contributions calculated from the path between the canopy and the sensor were subtracted from the sensor detected radiances and divided by the incident flux to generate reflectance factors. Corrected canopy reflectance values for three days are included in the data set. Standard spherical polar coordinates, with zero degree azimuth due north, are given. Errors in the determination of these angles are possible due to the lack of precise aircraft position. The sensor zenith angles were determined from the sensor scan angle and should be accurate to within a degree. The sensor azimuth angles were determined from plotting the center points of a nadir view camera on an air photo of the area and connecting them to determine the aircraft heading. Because of the errors in this method, view azimuth accuracy is probably no more than two to three degrees. Solar zenith and azimuth were determined computationally from the time at the beginning of each flight line and should be within a degree. Sites referred to as 0 and 99 in the tables are observations of water.\n<br>\n\n#### SNF_SAT_INV_186\nThe purpose of the SNF Study was to develop the techniques to make the link from biophysical measurements made on the ground to aircraft radiometric measurements and then to scale up to satellite observations. Therefore, satellite image data were acquired for the Superior National Forest study site. These data were selected from all the scenes available from Landsat 1 through 5 and SPOT platforms. Image data substantially contaminated by cloud cover or of poor radiometric quality was not acquired. Of the Landsat scenes, only one Thematic Mapper (TM) scene was acquired, the remainder were Multispectral Scanner (MSS) images. Some of the acquired image data had cloud cover in portions of the scene or other problems with the data. These problems and other comments about the images are summarized in the data set. This data set contains a listing of the scenes that passed inspection and were acquired and archived by Goddard Space Flight Center. Though these image data are no longer available from either the Goddard Space Flight Center or the ORNL DAAC, this data set has been included in the Superior National Forest data collection in order to document which satellite images were used during the project.\n<br>\n\n#### SNF_CJ_SITES_187\nThis data set documentation is currently in work. In the interim, an abstract of the entire Superior National Forest (SNF) data collection activity from which the SNF Site Characterization Data: C.Jarvis data set is a product is being provided. During the summers of 1983 and 1984, the National Aeronautics and Space Administration (NASA) conducted an intensive experiment in a portion of the Superior National Forest (SNF) near Ely, Minnesota, USA. The purpose of the experiment was to investigate the ability of remote sensing to provide estimates of biophysical properties of ecosystems, such as leaf area index (LAI), biomass and net primary productivity (NPP). The study area covered a 50 x 50 km area centered at approximately 48 degrees North latitude and 92 degrees West longitude in northeastern Minnesota at the southern edge of the North American boreal forest. The SNF is mostly covered by boreal forest. Boreal forests were chosen for this project because of their relative taxonomic simplicity, their great extent, and their potential sensitivity to climatic change. Satellite, aircraft, helicopter and ground observations were obtained for the study area. These data comprise a unique dataset for the investigation of the relationships between the radiometric and biophysical properties of vegetated canopies. This is perhaps the most complete dataset of its type ever collected over a forested region. A key goal of the experiment was to use the aircraft measurements to scale up to satellite observations for the remote sensing of biophysical parameters.\n<br>\n\n#### SNF_SITE_86_188\nThis data set documentation is currently in work. In the interim, an abstract of the entire Superior National Forest (SNF) data collection activity from which the SNF Site Characterization Validation Data Set is a product is being provided. During the summers of 1983 and 1984, the National Aeronautics and Space Administration (NASA) conducted an intensive experiment in a portion of the Superior National Forest (SNF) near Ely, Minnesota, USA. The purpose of the experiment was to investigate the ability of remote sensing to provide estimates of biophysical properties of ecosystems, such as leaf area index (LAI), biomass and net primary productivity (NPP). The study area covered a 50 x 50 km area centered at approximately 48 degrees North latitude and 92 degrees West longitude in northeastern Minnesota at the southern edge of the North American boreal forest. The SNF is mostly covered by boreal forest. Boreal forests were chosen for this project because of their relative taxonomic simplicity, their great extent, and their potential sensitivity to climatic change. Satellite, aircraft, helicopter and ground observations were obtained for the study area. These data comprise a unique dataset for the investigation of the relationships between the radiometric and biophysical properties of vegetated canopies. This is perhaps the most complete dataset of its type ever collected over a forested region. A key goal of the experiment was to use the aircraft measurements to scale up to satellite observations for the remote sensing of biophysical parameters.\n<br>\n\n#### SNF_CJ_VEG_189\nThis data set documentation is currently in work. In the interim, an abstract of the entire Superior National Forest (SNF) data collection activity from which the SNF Vegetation Cover Data: C. Jarvis Data Set is a product is being provided. During the summers of 1983 and 1984, the National Aeronautics and Space Administration (NASA) conducted an intensive experiment in a portion of the Superior National Forest (SNF) near Ely, Minnesota, USA. The purpose of the experiment was to investigate the ability of remote sensing to provide estimates of biophysical properties of ecosystems, such as leaf area index (LAI), biomass and net primary productivity (NPP). The study area covered a 50 x 50 km area centered at approximately 48 degrees North latitude and 92 degrees West longitude in northeastern Minnesota at the southern edge of the North American boreal forest. The SNF is mostly covered by boreal forest. Boreal forests were chosen for this project because of their relative taxonomic simplicity, their great extent, and their potential sensitivity to climatic change. Satellite, aircraft, helicopter and ground observations were obtained for the study area. These data comprise a unique dataset for the investigation of the relationships between the radiometric and biophysical properties of vegetated canopies. This is perhaps the most complete dataset of its type ever collected over a forested region.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/SNF/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","earth observation","ice","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SNF_ASP_CVR_140 v1 - The purpose of the Superior National Forest (SNF) study was to improve our understanding of the relationship between remotely sensed observations and important biophysical parameters in the boreal forest. A key element of the experiment was the development of methodologies to measure forest stand characteristics to determine values of importance to both remote sensing and ecology.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/snf/SNF_ASP_CVR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SNF_BIOMASS_141 v1 - The purpose of the SNF study was to improve our understanding of the relationship between remotely sensed observations and important biophysical parameters in the boreal forest. A key element of the experiment was the development of methodologies to measure forest stand characteristics to determine values of importance to both remote sensing and ecology.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/snf/SNF_BIOMASS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SNF_HELO_MMR_144 v1 - A major aspect of the ground data collection effort in the SNF during the summers of 1983 and 1984 was the acquisition of helicopter canopy reflectance measurements. Canopy measurements were made at numerous sites with a helicopter-mounted Barnes multiband radiometer (MMR).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/snf/SNF_HELO_MMR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SNF_MET_1972_158 v1 - Weather data were collected by the National Weather Service in International Falls, Minnesota. International Falls is about 80 miles from the SNF, but the weather data is representative of the area.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/snf/SNF_MET_1972/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SNF_LEAF_EXP_180 v1 - The purpose of the SNF study was to improve understanding of the relationship between remotely sensed observations and important biophysical parameters in the boreal forest. A key element of the experiment was the development of methodologies to measure forest stand characteristics to determine values of importance to both remote sensing and ecology.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/snf/SNF_LEAF_EXP/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SNF_LEAFCARY_183 v1 - Knowledge of the optical properties of the components of the forest canopy is important to the understanding of how plants interact with their environment and how this information may be used to determine vegetation characteristics using remote sensing. During the summers of 1983 and 1984, samples of the major components of the boreal forest canopy (needles, leaves, branches, moss, litter) were collected in the Superior National Forest (SNF) of Minnesota and sent to the Johnson Space Center (JSC).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/snf/SNF_LEAFCARY/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SNF_NS001_185 v1 - The NS001 Thematic Mapper Simulator (TMS) was flown on the NASA C-130 aircraft over the Superior National Forest study area. TMS data were collected and processed for three days: July 13 and August 6, 1983; and June 28, 1984.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/snf/SNF_NS001/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SNF_SAT_INV_186 v1 - The purpose of the SNF Study was to develop the techniques to make the link from biophysical measurements made on the ground to aircraft radiometric measurements and then to scale up to satellite observations. Therefore, satellite image data were acquired for the Superior National Forest study site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/snf/SNF_SAT_INV/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SNF_CJ_SITES_187 v1 - This data set documentation is currently in work. In the interim, an abstract of the entire Superior National Forest (SNF) data collection activity from which the SNF Site Characterization Data: C.Jarvis data set is a product is being provided.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/snf/SNF_CJ_SITES/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SNF_SITE_86_188 v1 - This data set documentation is currently in work. In the interim, an abstract of the entire Superior National Forest (SNF) data collection activity from which the SNF Site Characterization Validation Data Set is a product is being provided.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/snf/SNF_SITE_86/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SNF_CJ_VEG_189 v1 - This data set documentation is currently in work. In the interim, an abstract of the entire Superior National Forest (SNF) data collection activity from which the SNF Vegetation Cover Data: C.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/snf/SNF_CJ_VEG/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-snf","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SnowEx Project","Description":"This data set provides 3 m gridded, bare-earth elevations (excluding trees) that are used as the baseline for the Airborne Snow Observatory (ASO) snow-on products. The data were collected during snow-free conditions as part of the NASA/JPL ASO aircraft survey campaigns.\n<br>\n\n#### ASO_3M_SD\nThis data set contains 3 m gridded snow depths derived from airborne light detection and ranging, or lidar, measurements of surface elevations. The data were collected as part of the NASA/JPL Airborne Snow Observatory (ASO) aircraft survey campaigns.\n<br>\n\n#### ASO_50M_SD\nThis data set contains 50 m gridded snow depths derived from airborne light detection and ranging, or lidar, measurements of surface elevations. The data were collected as part of the NASA/JPL Airborne Snow Observatory (ASO) aircraft survey campaigns.\n<br>\n\n#### ASO_50M_SWE\nThis data set contains 50 m gridded snow water equivalent (SWE) values collected as part of the NASA/JPL Airborne Snow Observatory (ASO) aircraft survey campaigns. The data were derived from the [ASO L4 Lidar Snow Depth 50m UTM Grid](https://nsidc.org/data/aso_50m_sd) data product and from modeled snow density.\n<br>\n\n#### WV_LCC_SC_FSCA\nThis data set includes: (1) fine-scale snow and land cover maps from two mountainous study sites in the Western U.S., produced using machine-learning models trained to extract land cover data from WorldView-2 and WorldView-3 stereo panchromatic and multispectral images; (2) binary snow maps derived from the land cover maps; and (3) 30 m and 465 m fractional snow-covered area (fSCA) maps, produced via downsampling of the binary snow maps. The land cover classification maps feature between three and six classes common to mountainous regions and integral for accurate stereo snow depth mapping: illuminated snow, shaded snow, vegetation, exposed surfaces, surface water, and clouds. Also included are Landsat and MODSCAG fSCA map products. The source imagery for these data are the Maxar WorldView-2 and Maxar WorldView-3 Level-1B 8-band multispectral images, orthorectified and converted to top-of-atmosphere reflectance. These Level-1B images are available under the NGA NextView/EnhancedView license.\n<br>\n\n#### SNEX_HRSI_SD_DEM_CO\nThis data set contains a time series of snow depth maps and related intermediary snow-on and snow-off DEMs for Grand Mesa and the Banded Peak Ranch areas of Colorado derived from very-high-resolution (VHR) satellite stereo images and lidar point cloud data. Two of the snow depth maps coincide temporally with the 2017 NASA SnowEx Grand Mesa field campaign, providing a comparison between the satellite derived snow depth and in-situ snow depth measurements. The VHR stereo images were acquired each year between 2016 and 2022 during the approximate timing of peak snow depth by the Maxar WorldView-2, WorldView-3, and CNES/Airbus Pléiades-HR 1A and 1B satellites, while lidar data was sourced from the USGS 3D Elevation Program.\n<br>\n\n#### SNEX_MCS_Lidar_Raw\nThe data set described here provides raw lidar data collected as part of a multi-year effort to monitor monthly snow distribution over a 35 km² region of the Mores Creek Headwaters in the Boise Mountains of central Idaho between 2021 and 2025. Data acquisition in 2021 overlapped temporally with the NASA SnowEx 2021 field campaign. Digital terrain models (DTM), digital surface models (DSM) snow depth models, and canopy height models (CHM) derived from these point cloud data are available as [SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey, Version 1](https://nsidc.org/data/SNEX_MCS_Lidar).\n<br>\n\n#### SNEX_MCS_Lidar\nThe data set provides digital terrain models (DTM), digital surface models (DSM) snow depth models, and canopy height models (CHM), derived from point cloud data (available as [SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey Raw, Version 1](https://nsidc.org/data/SNEX_MCS_Lidar_Raw)) acquired by airborne lidar scanning. Data were collected as part of a multi-year effort to monitor monthly snow distribution over a 35 km² region of the Mores Creek Headwaters in the Boise Mountains of central Idaho between 2021 and 2025. Data acquisition in 2021 overlapped temporally with the NASA SnowEx 2021 field campaign.\n\nIn October 2025, corrupted data files were updated for the following acquisition dates: 8 December 2022 and 9 February 2023.\n<br>\n\n#### SNEX17_SD_Perm\nThis data set contains snowpack relative permittivities and densities derived from Ground Penetrating Radar (GPR) surveys and airborne lidar observations of snow depths. The results of the original GPR surveys are published in the [SnowEx17 Ground Penetrating Radar, Version 2](https://doi.org/10.5067/G21LGCNLFSC5) data set, while the lidar snow depths were sourced from [ASO L4 Lidar Snow Depth 3m UTM Grid, Version 1](https://doi.org/10.5067/KIE9QNVG7HP0). Data are available between 08 Feb 2017 to 25 Feb 2017 from Grand Mesa, a snow-covered, forested area about 40 miles east of Grand Junction. Parameters include two-way travel (TWT) time, lidar-measured snow depth, calculated snow water equivalent (SWE), calculated snow density, and calculated relative permittivity.\n<br>\n\n#### SNEX17_KT15_TB\nThis data set contains brightness temperature readings. Brightness temperatures were measured using a KT-15 pyrometer flown on a P-3 aircraft over 2017 SnowEx campaign study areas in Colorado, USA: Grand Mesa, a snow-covered, forested study site about 40 miles east of Grand Junction; and Senator Beck Basin, approximately 80 miles to the SSE of Grand Mesa. Data represents the nadir brightness temperature below the aircraft.\n<br>\n\n#### SNEX17_SD_TLI\nThe dataset comprises snow depth measurements extracted from snow poles in time-lapse images near Grand Mesa, CO during the SnowEx 2016-2017 field campaign. Snow poles were 3.048 m (10 ft) vertical poles painted orange. Collection occurred at 18 sites. At each site, five snow depth measurements were extracted every two hours, beginning at 8:00 AM daily. The time-lapse images are archived separately at NSIDC ([SNEX17_TLI](https://doi.org/10.5067/WYRNU50R9L5R)).\n<br>\n\n#### SNEX20_SWESARR_TB\nThis data set contains airborne microwave brightness temperature observations from the Goddard Space Flight Center SWESARR (Snow Water Equivalent Synthetic Aperture Radar and Radiometer) instrument during the winter (10-12 February 2020) NASA SnowEx 2020 campaign at Grand Mesa, CO. Observations were made at three frequencies (10.65, 18.7, and 36.5 GHz; referred to as X, K, and Ka bands, respectively), at horizontal polarization with a nominal 45-degree look angle.\n<br>\n\n#### SNEX20_CASIE_TIR\nThis data set contains long-wave infrared radiometer data, thermal infrared (TIR) imagery, and visible-band (RGB) imagery from the February 2020 NASA SnowEx field campaign flights over the Grand Mesa, CO. These data are from the center, nadir-viewing radiometer; center, nadir-viewing, TIR camera; or nadir-viewing, visible-band camera. Data were collected from the NASA SWESARR flights onboard the Navy CIRPAS Twin Otter. The measurements were to document the snow, forest, and land surface temperatures over targeted points of the mesa coincident with ground survey stations and can be used to study snow remote-sensing of signatures, small-scale temperature variability (sub-satellite resolution), diurnal snow temperature variability, effects of forest cover, and thermal snow budgets.\n<br>\n\n#### SNEX20_BR_BSU_SMP\nThis data set contains raw penetration force profiles from the SnowEx 2020 Time Series campaign in the Boise River Basin in Idaho. Measurements were taken using the SnowMicroPen (SMP), a digital snow penetrometer, from three snow pits. The data files contain force measurements of the snowpack from the top of the snow surface to the ground. Measurements took place approximately weekly between 18 December 2019 and 24 January 2020.\n<br>\n\n#### SNEX20_TLS_PC_BSU_RAW\nThis data set contains raw, unprocessed terrestrial laser scanner (TLS) point cloud data collected as part of the 2020 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2019) and snow-on (February 2020) conditions, at both open and forested locations. Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i). A processed version of this data set can be found here: https://doi.org/10.5067/F0M99WK7JW4X.\n<br>\n\n#### SNEX20_TLS_PC_BSU\nThis data set contains terrestrial laser scanner (TLS) point cloud data collected as part of the 2020 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2019) and snow-on (February 2020) conditions, at both open and forested locations. Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i). These TLS data can be used to determine snow depth and explore the interactions between snow and vegetation.\n<br>\n\n#### SNEX20_COCP_GPR_Raw\nThis data set contains the raw files from ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx20 campaign. Data were collected between 18 December 2019 and 12 March 2020.\n<br>\n\n#### SNEX20_COCP_GPR\nThis data set contains the results of ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx20 campaign. Data include two-way travel time, pit-measured snow density, calculated snow depth, and calculated snow water equivalent. Data were collected between 18 December 2019 and 12 March 2020\n<br>\n\n#### SNEX20_SD\nThis data set, collected during the SnowEx 2020 Intensive Operation Period (IOP) in Grand Mesa, Colorado, contains in situ snow depth measurements. Snow depth was measured using one of three instruments - Magnaprobe, Mesa 2, or pit ruler. Pit ruler data were collected from 150 snow pits identified for the Grand Mesa IOP. Magnaprobe and Mesa 2 data were collected along spiral tracks moving outwards from snow pit locations. This data set has a temporal coverage from 28 January to 12 February 2020.\n<br>\n\n#### SNEX20_CRSM\nThis data set contains the raw and processed data files from a COSMOS Rover soil moisture probe. The COSMOS Rover uses fast neutron counting to estimate soil moisture over a region up to 300m from the vehicle. Data were collected in one-minute intervals over four days (04 November to 07 November 2019) of driving around Grand Mesa, Colorado. Raw data parameters include atmospheric pressure, temperature, and relative humidity. These raw observations were converted to volumetric soil moisture in the processed data files.\n<br>\n\n#### SNEX20_CSSM\nThese data contain raw and processed hourly observations from a Hydroinnova COSMOS Stationary sensor probe. Parameters in the raw files include atmospheric pressure, temperature, and relative humidity. These observations were converted to volumetric soil moisture in the processed data files. Data were collected between 26 August 2019 and 31 May 2020 at Grand Mesa, Colorado and represent a 200 m to 300 m area around the instrument.\n<br>\n\n#### SNEX20_TLS_PC_CRREL\nThis data set contains terrestrial LIDAR survey (TLS) point cloud data collected at Grand Mesa, Colorado as part of the 2020 SnowEx campaign. Data were collected in fall 2019 (September) and winter 2020 (January and February). Each data file contains X, Y, and Z coordinates (Easting, Northing, and Elevation), along with ancillary information, such as intensity (i) and color (R,G,B), where available.\n<br>\n\n#### SNEX20_A19_GSM\nGravimetric soil moisture data measured from collected soil samples in autumn 2019 as part of the NASA SnowEx 2020 campaign at Grand Mesa, CO. A total of 77 soil samples were taken at snow pit locations, soil moisture stations, and other areas where both the mobile and stationary COsmic-ray Soil Moisture Observing Systems (COSMOS) operated.\n<br>\n\n#### SNEX20_A19_SD\nSnow depth was measured in autumn 2019 as part of the NASA SnowEx 2020 campaign at Grand Mesa, CO. Measurements were taken in the vicinity of snow pit locations and between pit locations using a Magnaprobe or manual depth probes.\n<br>\n\n#### SNEX20_A19_SP\nThis data set contains snow pit measurements obtained as part of the SnowEx 2020 campaign at the Grand Mesa, Colorado, USA site in Autumn 2019. 21 locations were visited for snow pit observations. Some snow pit measurements are incomplete due to shallow or discontinuous snow cover.\n<br>\n\n#### SNEX20_A19_SWE\nSnow water equivalent (SWE) measurements of the autumn shallow snow were obtained using SWE samplers (also referred to as SWE tubes) during the NASA SnowEx 2020 campaign (03-06 Nov. 2019) at Grand Mesa, CO. 221 measurements were taken in the vicinity of snow pit locations.\n<br>\n\n#### SNEX20_DSM\nThis data set consists of soil moisture and soil temperature measurements taken during the SnowEx 2020 field campaign. Soil moisture probes were deployed at 10 stations within the Colorado Grand Mesa study area and monitored soil properties at three different depths (5, 10, and 20 cm).\n<br>\n\n#### SNEX20_GM_SP\nThis data set contains snow pit observations from the SnowEx 2020 Grand Mesa Intensive Observation Period. Data were collected from 154 snow pits on Grand Mesa, Colorado between 27 January and 12 February 2020. The main parameters for this data set are snow temperature, snow depth, snow density, snow stratigraphy, snow grain size, liquid water content, and snow water equivalent. In addition to data files, this data set also contains site photos from each snow pit.\n<br>\n\n#### SNEX20_BSU_CMP_SWE\nThis data set was collected during the SnowEx 2020 Intensive Observation Period (IOP) in Grand Mesa, Colorado. These data contain snow water equivalent (SWE) estimates. SWE is derived from Sensors & Software pulseEKKO PRO 1 GHz multi-polarization ground penetrating radar (GPR) two-way travel times. Data were collected at three locations around Grand Mesa IOP snow pits 2N12 and 1S8 (see DOI: 10.5067/DUD2VZEVBJ7S for more details on Grand Mesa IOP snow pits). Data at snow pit 2N12 were acquired on the groomed snowmobile road (CMP1), in the fresh snow behind the snow pit wall (CMP2), and in the right rut of the SUSV track (CMP3). Data at snow pit 1S8 were acquired in the right rut of the SUSV track (CMP1), in the left rut of the SUSV track (CMP2), and in the fresh snow behind the snow pit wall (CMP3). The raw version of these data (DOI: 10.5067/CL5ZRBCEF8G3) are also archived at NSIDC.\n<br>\n\n#### SNEX20_BSU_GPR_Raw\nThis data set contains the geolocated, unprocessed results of a ground penetrating radar survey conducted as part of the 2020 SnowEx campaign. Data were collected at Grand Mesa, Colorado (a snow-covered, forested study site) between 28 January 2020 and 04 February 2020.\n<br>\n\n#### SNEX20_BSU_GPR\nThis data set contains snow water equivalent (SWE) and snow depth estimates derived from Ground Penetrating Radar (GPR) measurements. GPR measurements were collected during the SnowEx 2020 Grand Mesa Intensive Observation Period (IOP) between 28 January and 04 February 2020. Snow depth was estimated from GPR two-way travel times and average radar velocity; SWE was estimated from snow depth and snow density. These data are derived from the SnowEx20 Grand Mesa IOP BSU 1 GHz Multi-polarization GPR Raw, Version 1 data set (DOI: 10.5067/CJNEM8UDNXKA)\n<br>\n\n#### SNEX20_BSU_CMP_Raw\nThis data set was collected during the SnowEx 2020 Intensive Observation Period (IOP) in Grand Mesa, Colorado. These data contain the geolocated, unprocessed, common midpoint (CMP) gathers from a Sensors & Software pulseEKKO PRO 1 GHz multi-polarization ground penetrating radar (GPR). Multi-offset gathers were collected by placing antennas on the snow surface and expanding the antenna separation about a fixed midpoint out to a 2 m offset. CMP gathers were collected in HH and HV polarizations. Data were collected at three locations around Grand Mesa IOP snow pits 2N12 and 1S8 (see DOI: 10.5067/DUD2VZEVBJ7S for more details on Grand Mesa IOP snow pits). Data at snow pit 2N12 were acquired on the groomed snowmobile road (CMP1), in the fresh snow behind the snow pit wall (CMP2), and in the right rut of the SUSV track (CMP3). Data at snow pit 1S8 were acquired in the right rut of the SUSV track (CMP1), in the left rut of the SUSV track (CMP2), and in the fresh snow behind the snow pit wall (CMP3). These data can be used to estimate snow depth, snow density, and snow water equivalent (SWE).\n<br>\n\n#### SNEX20_BSU_CMP_TT\nThis data set was collected during the SnowEx 2020 Intensive Observation Period (IOP) in Grand Mesa, Colorado. These data contain the radar two-way travel times from a Sensors & Software pulseEKKO PRO 1 GHz multi-polarization ground penetrating radar (GPR). Data were collected at three locations around Grand Mesa IOP snow pits 2N12 and 1S8 (see DOI: 10.5067/DUD2VZEVBJ7S for more details on Grand Mesa IOP snow pits). Data at snow pit 2N12 were acquired on the groomed snowmobile road (CMP1), in the fresh snow behind the snow pit wall (CMP2), and in the right rut of the SUSV track (CMP3). Data at snow pit 1S8 were acquired in the right rut of the SUSV track (CMP1), in the left rut of the SUSV track (CMP2), and in the fresh snow behind the snow pit wall (CMP3). The raw version of these data (DOI: 10.5067/CL5ZRBCEF8G3) are also archived at NSIDC.\n<br>\n\n#### SNEX20_GM_CTSM\nThis data set characterizes snow microstructure for 6 snow pits from the SnowEx 2020 Grand Mesa Intensive Observation Period (February 2020) using microcomputed tomography (micro-CT). Included with this data set are two- and three-dimensional microstructural analysis of DMP/DEP casted and un-casted snow samples, available as .xlsx and .txt file, and visual representations of the three-dimensional snow structure, available is .bmp image files. Containers of snow were collected at 6 snow pits in approximately 17 cm intervals. There were approximately 5-8 discrete containers per pit and each container had an ~2 cm overlap with the sample below. A 6-cm section of snow was dissected from each container of snow, which were analyzed in ~2 cm sub-sections. Each sub-section was scanned in three intervals using a micro-CT instrument. The three interval scans comprise multiple slices, and were combined into the reconstructed final scan used for calculating the snow microstructural data.\n<br>\n\n#### SNEX20_GM_CSU_GPR_RAW\nThis data set the raw files collected with a Sensors & Software 1GHz ground penetrating radar (GPR) as part of the SnowEx 2020 Intensive Observation Period (IOP) at Grand Mesa, Colorado between 06 February 2020 and 09 February 2020. The more processed data are published in SnowEx20 Grand Mesa IOP CSU 1GHz GPR, Version 1 (DOI: 10.5067/S5EGFLCIAB18)\n<br>\n\n#### SNEX20_GM_CSU_GPR\nThis data set contains two-way travel times, snow depth and snow water equivalent (SWE) collected with a Sensors & Software 1GHz ground penetrating radar (GPR) as part of the SnowEx 2020 Intensive Observation Period (IOP) at Grand Mesa, Colorado between 06 February 2020 and 09 February 2020. These data are derived from the SnowEx20 Grand Mesa IOP CSU 1GHz GPR Raw, Version 1 data set (DOI: 10.5067/CT6NS2LIASRS)\n<br>\n\n#### SNEX20_GM_GBPM\nThe dataset consists of passive microwave measurements collected between 4 February and 12 February 2020 as part of the NASA SnowEx 2020 field campaign at the Grand Mesa, Colorado IOP study site. The measurements were taken at v- and h-polarization at both 19 and 37 GHz using two radiometers mounted on a sled. The radiometers measure emission of passive microwave radiation and record voltage for each radiometer. The voltage is converted to microwave brightness temperature using calibrations based on measurements of absorbers of known brightness temperature.\n<br>\n\n#### SNEX20_GM_SWE_SD\nThis data set contains snow water equivalent (SWE) and snow density raster data derived from a lidar-based snow depth data set ([SnowEx20 Grand Mesa IOP QSI Lidar Snow Depth Data, Version 1](https://doi.org/10.5067/M9TPF6NWL53K)), a lidar-based digital terrain model ([ASO L4 Lidar Point Cloud Digital Terrain Model 3m UTM Grid, Version 1](https://doi.org/10.5067/2EHMWG4IT76O)), and two GPR data sets ([SnowEx20 Grand Mesa IOP BSU 1 GHz Multi-polarization GPR, Version 1](https://doi.org/10.5067/Q2LFK0QSVGS2) and [Mesa IOP UNM 800 and 1600 MHz MALA GPR, Version 1](https://doi.org/10.5067/WE9GI1GVMQF6)). Data was collected during the NASA SnowEx 2020 field campaign in Grand Mesa, Colorado.\n<br>\n\n#### SNEX20_GM_Lidar\nThis data set contains rasterized snow depth maps derived from lidar point cloud data collected from Grand Mesa, Colorado during the SnowEx20 campaign. The subset data file was used as input data to derive SWE and snow density data, which is available as [SnowEx20 Grand Mesa IOP Lidar and GPR-Derived Snow Water Equivalent and Snow Density, Version 1](https://doi.org/10.5067/LANQ53RTJ2DR).\n<br>\n\n#### SNEX20_UNM_GPR\nThis data set contains two-way travel times, snow depth and now water equivalent from a ground penetrating radar survey conducted at Grand Mesa, Colorado. Data were collected between 28 January 2020 and 06 February 2020 as part of the SnowEx 2020 campaign.\n<br>\n\n#### SNEX20_GIS_REF\nThis data set contains geolocation information of the infrastructure locations for the SnowEx20 Intensive Observation Period (IOP) and Time Series (TS) campaigns. Available scientific infrastructure locations in this data set are tower and sensor locations, aircraft flight lines, planned and actual snow pit locations, and time-lapse camera locations. Additionally, this data set contains areal snow depth and tree density classification matrix over the Grand Mesa, CO study area.\n<br>\n\n#### SNEX20_SD_TLI\nThis data contains snow depth measurements derived from time-lapse images collected by cameras placed around Grand Mesa, CO at 29 sites coincident with other SnowEx 2020 measurements. The field view of all cameras includes a 3.049 m, (10 ft) vertical pole that was painted red with a yellow top to serve as a reference for quantifying snow depth. The time-lapse images are archived separately at NSIDC (SNEX20_TLI).\n<br>\n\n#### SNEX20_J_UNM_GPR\nThis data set contains two-way travel times from a ground penetrating radar survey conducted at Jemez, New Mexico. Data were collected between 12 February 2020 and 04 March 2020 as part of the SnowEx 2020 campaign.\n<br>\n\n#### SNEX20_SSA\nThis data set contains vertical profiles of snow reflectance, specific surface area (SSA), and spherical equivalent snow grain diameter. Data were collected during the SnowEx 2020 Grand Mesa, Colorado Intensive Observation Period between 27 January and 12 February 2020. Reflectance was measured at snow pits using one of two integrating sphere laser devices, IRIS (InfraRed IntegraXng Sphere) or IceCube. SSA and spherical equivalent diameter were then derived from reflectance.\n<br>\n\n#### SNEX20_VPTS_Raw\nThis data set contains an eight-day time series of vertical temperature profile measurements. Measurements were collected using a thermoprobe to a depth of 30 cm at two site locations. These data were collected as part of the SnowEx 2020 Intensive Observation Period in Grand Mesa, Colorado. Alongside temperature profiles, this data set contains snow depth measurements, manual temperature measurements, and thermal infrared camera images. These data are published without QA/QC or calibration with manual measurements.\n<br>\n\n#### SNEX20_SB_GST\nThis data set contains hourly ground surface temperature measurements collected between 20 October 2019 and 18 July 2020. Data were collected at 7 points along two plant monitoring transects in the Upper Basin of the Senator Beck Study Basin. Temperatures were measured using iButton temperature sensors.\n<br>\n\n#### SNEX20_SMP\nThis data set contains raw penetration force profiles from the SnowEx 2020 Intensive Observation Period in Grand Mesa, Colorado. Measurements were taken using the SnowMicroPen (SMP), a digital snow penetrometer. The data files contain force measurements (in Newtons) at various snow depths. Data are available from 28 January 2020 through 12 February 2020.\n<br>\n\n#### SNEX20_TS_IB\nThis data set contains snow change measurements from interval boards collected as part of the NASA SnowEx20 time series field campaign. Surveys were conducted at field sites across the Western United States 9 December 2019 and 22 April 2020. Snow change measurements from interval boards record the height of new snow (HN) and snow water equivalent (SWE) over weekly, and in some cases biweekly, intervals. Measurements were taken using a 30 cm Snowmetrics Snow Tube and spring scale. Interval boards were positioned nearby snow pit study areas and were checked during the collection of the [SNEX20 Time Series Snow Pit data](https://doi.org/10.5067/KZ43HVLZV6G4). During each visit, if new snow had accumulated on the board, three samples were collected before the board was cleared and placed at the top of the snow surface. Height of new snow was measured to the nearest 0.5 cm and SWE to the nearest mm. Observers also recorded a qualitative Yes/No regarding evidence of melt on the board. These data were collected to provide ground validation for the [airborne UAVSAR L-band flights](https://nsidc.org/data/snex_uavsar/versions/1). Although the variable is referred to as \"height of new snow\", it represents the change in snow height and SWE depth over a given interval and is therefore subject to snow settlement and/or melt between visits.\n<br>\n\n#### SNEX20_TS_SP\nThe data set is a time-series of snow pit measurements obtained by the SnowEx community during the 2020 campaign. Between October 2019 and May 2020, data were collected from 454 snow pits at 12 regional locations throughout California, Colorado, Idaho, New Mexico, and Utah, USA. At each of the locations, between 1 and 11 sites covering a range of conditions (terrains, snow depths, etc.) were chosen for weekly snow pit observations. Also available are photos of the field notes and snow pit sites.\n<br>\n\n#### SNEX20_TLI\nThis data set contains sub-daily time-lapse images collected by cameras placed around Grand Mesa, CO at 29 sites coincident with other SnowEx 2020 measurements. The field view of all cameras includes a 3.049 m, (10 ft) vertical pole that was painted red with a yellow top to serve as a reference for quantifying snow depth. Snow depth data derived from these time-lapse images will be published separately at NSIDC.\n<br>\n\n#### SNEX20_COCP_SPD\nThis data set contains the results of ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx20 campaign. Data include two-way travel time, lidar-measured snow depth, derived snow water equivalent, derived snow density, and derived relative permittivity. Ground-penetrating radar two-way travel times were sourced from two previously published data sets: [SnowEx20 Cameron Pass Ground Penetrating Radar, Version 1](https://doi.org/10.5067/U4Q3X27BMRR4) and [SnowEx21 Cameron Pass Ground Penetrating Radar, Version 1](https://doi.org/10.5067/SRWGLYCB6ZC4). The new data presented here represents a recalculation of the snow water equivalent (SWE), snow density, and relative permittivity using terrestrial lidar scan (TLS) data.\n<br>\n\n#### SNEX20_QSI_DEM\nThis data set is part of the SnowEx 2021 campaign and provides bare Earth digital elevation models (DEM) acquired by a scanning lidar system at a 0.5 m spatial resolution, and derived from point cloud digital terrain models. DEMs are available for September 2021 and from multiple areas in Colorado, Idaho, and Utah. These data were produced alongside [Vegetation Height](https://doi.org/10.5067/8RBIUIPEUJ7Z) and [Snow Depth](https://doi.org/10.5067/VBUN16K365DG) data sets.\n<br>\n\n#### SNEX20_QSI_DEM_3m\nThis data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides bare Earth digital elevation models (DEM) acquired by a scanning lidar system at a 3.0 m spatial resolution, and derived from point cloud digital terrain models. DEMs are available for September 2021 and from multiple areas in Colorado, Idaho, and Utah. These data were produced alongside [Vegetation Height](https://doi.org/10.5067/K67GKZQ9WUEI) and [Snow Depth](https://doi.org/10.5067/4VO7438DP332) data sets.\n<br>\n\n#### SNEX20_QSI_SD\nThis data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides snow depth values at a 0.5 m spatial resolution, derived from point cloud digital terrain models. Snow depths are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah. These data were produced alongside [DEM](https://doi.org/10.5067/YO583L7ZOLOO) and [Vegetation Height](https://doi.org/10.5067/8RBIUIPEUJ7Z) data sets.\n<br>\n\n#### SNEX20_QSI_SD_3m\nThis data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides snow depth values at a 3.0 m spatial resolution, derived from point cloud digital terrain models. Snow depths are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah. These data were produced alongside [DEM](https://doi.org/10.5067/VV382L4MQI7V) and [Vegetation Height](https://10.5067/K67GKZQ9WUEI) data sets.\n<br>\n\n#### SNEX20_QSI_VH\nThis data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides vegetation height values at a 0.5 m spatial resolution, derived from point cloud digital terrain models. Vegetation heights are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah. These data were produced alongside [DEM](https://doi.org/10.5067/YO583L7ZOLOO) and [Snow Depth](https://doi.org/10.5067/VBUN16K365DG) data sets.\n<br>\n\n#### SNEX20_QSI_VH_3m\nThis data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides vegetation height values at a 3.0 m spatial resolution, derived from point cloud digital terrain models. Vegetation heights are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah. These data were produced alongside [DEM](https://doi.org/10.5067/VV382L4MQI7V) and [Snow Depth](https://doi.org/10.5067/4VO7438DP332) data sets.\n<br>\n\n#### SNEX21_BR_GPR\nThis data set contains two-way travel time data calculated using the results of 1 GHz ground-penetrating radar surveys conducted at Banner Summit, as part of the SNEX21 Time Series campaign in the Boise River Basin, Idaho.\n<br>\n\n#### SNEX21_COCP_GPR_Raw\nThis data set contains the raw files from ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx21 campaign. Data were collected between 13 January 2021 and 27 May 2021.\n<br>\n\n#### SNEX21_COCP_GPR\nThis data set contains the results of 1 GHz ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx21 campaign. Data include two-way travel time, pit-measured snow density, calculated snow depth, and calculated snow water equivalent. Data were collected between 13 January 2021 and 27 May 2021.\n<br>\n\n#### SNEX21_CRNS_SWE\nThese data contain raw and processed hourly observations from a stationary Hydroinnova cosmic ray neutron sensor (CRNS). Data were collected between 22 November 2020 and 08 May 2021 at the SnowEx Prairie site at the Central Agricultural Research Center (CARC) in Moccasin, Montana and represent a footprint area of approximately 171 meters radius around the instrument. Observations in the raw files include neutron counts, atmospheric pressure, temperature, and relative humidity and related correction factors. These observations were converted to snow water equivalent (SWE) and soil moisture in the processed data files using Desilets, 2017, methodology from Schrön et al., 2017, and in-situ soil moisture samples collected on 06 May 2021 (available as [SnowEx21 Prairie Station in Situ Dielectric Soil Moisture and Soil Temperature, Version 1](https://nsidc.org/data/snex21_dsm/versions/1)).\n<br>\n\n#### SNEX21_PS_DSM\nThis dataset includes Digital Surface Models (DSMs) derived from UAV-LiDAR imagery acquired during the SnowEx 2021 field campaign at the Central Agricultural Research Center in central Montana (USA). The DSMs are at 0.3 m resolution and consist of one snow-off and seven snow-on flights. Also available is a 0.25 m resolution orthomosaic of the study area collected via UAV on 21 January 2021.\n<br>\n\n#### SNEX21_PS_MET\nThis data set contains observations from a meteorological station installed at the Central Agricultural Research Center in Moccasin Montana as part of the NASA SnowEx 2021 Prairie Snow field campaign. Parameters include: air temperature, wind speed and direction, and barometric pressure. Data are available from 12 November 2020 through 2 March 2021.\n<br>\n\n#### SNEX21_DSM\nThis data set consists of soil moisture and soil temperature measurements taken during the SnowEx 2021 field campaign. Soil moisture probes were deployed at 9 locations within the Montana Prairie Station study area and monitored soil properties at four different depths (5, 10, 20 and 50 cm).\n<br>\n\n#### SNEX21_SSR\nThis data set provides apparent surface spectral reflectance imagery which demonstrates snow albedo and snow optical property evolution across two distinct snow-covered environments in Colorado. Data collection occurred in the spring of 2021 as part of the NASA SnowEx mission. The two study sites (Senator Beck Basin and Grand Mesa) were chosen for their contrasting terrain and vegetation characteristics. Data collection occurred over three days (19 March, 1 April, and 29 April) to produce a time series data set across varying snow conditions.\n<br>\n\n#### SNEX21_TS_IB\nThis data set contains snow change measurements from interval boards collected as part of the NASA SnowEx21 time series field campaign. Surveys were conducted at field sites across the Western United States 18 November 2020 and 12 May 2021. Snow change measurements from interval boards record the height of new snow (HN) and snow water equivalent (SWE) over weekly, and in some cases biweekly, intervals. Measurements were taken using a 30 cm Snowmetrics Snow Tube and spring scale. The interval board network was expanded from the [2020 Time Series campaign](https://nsidc.org/data/snex20_ts_ib/versions/1) to capture a broader spatial domain than previously done. A network of 4-6 interval boards accompanied a single snow pit study area varying in elevation and other physiographic characteristics. During each visit, if new snow had accumulated on the board, three samples were collected before the board was cleared and placed at the top of the snow surface. Height of new snow was measured to the nearest 0.5 cm and SWE to the nearest mm. Observers also recorded a qualitative Yes/No regarding evidence of melt on the board. These data were collected to provide ground validation for the [airborne UAVSAR L-band flights](https://nsidc.org/data/snex_uavsar/versions/1). Although the variable is referred to as \"height of new snow\", it represents the change in snow height and SWE depth over a given interval and is therefore subject to snow settlement and/or melt between visits.\n<br>\n\n#### SNEX21_TS_SD\nThe data set contains manual snow depth measurements collected as part of the NASA SnowEx 2021 Time Series campaign. Data were collected at five out of seven Time Series study sites: Cameron Pass, Fraser Experimental Forest, and Senator Beck, Colorado; Boise River Basin, Idaho; and the Central Agriculture Research Center (CARC), Montana. Snow depth measurements were collected with either an avalanche probe or a SnowMetrics depth probe, with location data recorded using either a Mesa2 tablet with a geode GPS or handheld Garmin GPS unit.\n<br>\n\n#### SNEX23_Lidar_Raw\nThis data set provides raw lidar data from two regions of Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and a coastal tundra environment in the North Slope region of the northern Alaska coastal plain (Arctic coastal plain and Upper Kuparuk Toolik). Processed data, including digital terrain models, snow depth, and canopy height derived from Point Cloud Digital Terrain Models (PCDTMs) are available as [SnowEx23 Airborne Lidar-Derived 0.5M Snow Depth and Canopy Height, Version 1](https://nsidc.org/data/SNEX23_Lidar).\n<br>\n\n#### SNEX23_Lidar\nThis data set provides digital terrain models, snow depth, and canopy height, acquired by a scanning lidar system and derived from Point Cloud Digital Terrain Models (PCDTMs) from two regions of Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and a coastal tundra environment in the North Slope region of the northern Alaska coastal plain (Arctic coastal plain and Upper Kuparuk Toolik). The raw data from which these data are derived are available as [SnowEx23 Airborne Lidar Scans Raw, Version 1](https://nsidc.org/data/SNEX23_Lidar_Raw).\n<br>\n\n#### SNEX23_Apr23_AVIRISNG\nThis data set contains surface reflectance data collected from three boreal forest study sites (Delta Junction, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) around Fairbanks, Alaska as part of the NASA SnowEx 2023 field campaign. Data was acquired using the NASA Jet Propulsion Laboratory (JPL) Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) sensor mounted on a King Air B-200 aircraft. Data collection occurred during April and May 2023.\n<br>\n\n#### SNEX23_Apr_HyLab\nThis dataset provides surface spectral reflectance imagery which demonstrates snow albedo and snow optical properties during the NASA SnowEx 2023 field campaign that took place in Alaska between 7 April 2023 and 5 May 2023. Imagery was collected across several snow-covered boreal forest environments on April 20th, 2023, near Fairbanks, Alaska, including Farmers Loop Creamers Field (FLCF), and Caribou Poker Creek Research Watershed (CPCRW).\n<br>\n\n#### SNEX23_APR_SP\nThis data set contains snow pit data collected from 13 snow pits excavated during the NASA SnowEx23 field campaign near Fairbanks, Alaska. The snow pits were located across three field sites: the Caribou/Poker Creek Research Watershed, Farmer’s Loop/Creamers Field, and Delta Junction. The data was collected during April 2023 concurrent with the data published as [SnowEx23 Apr23 IOP Snow Surface Imagery, Version 1](https://doi.org/10.5067/MNJQKI39VJHG), and coincident with hyperspectral measurements from NEON towers, drone, NASA's AVIRIS-NG instrument aboard an aerial platform, and other satellite-based hyperspectral measurements. Snow pit measurements were collected adjacent to spectrometer transects established throughout boreal forests, burned forests, and open meadows, with the goal of supporting features of snow spectra such as snow microstructure and water and ice content. Snow pit data was collected, processed, and formatted in accordance with [standard NASA SnowEx methodology](https://snow.nasa.gov/sites/default/files/users/user354/SNEX-Campaigns/2023/NASA_SnowEx_Experiment_Plan_2023_draft_20June2024.pdf) featured in other [SnowEx 2023 snow pit datasets](https://doi.org/10.5067/SJZ90KNPKCYR).\n<br>\n\n#### SNEX23_SSI\nThis data set contains 54 photographs of snow surfaces collected during the NASA SnowEx23 field campaign near Fairbanks, Alaska. Photos were acquired in April and May 2023 at three study sites that represent a range of landcover conditions, including intact boreal forest, recently and historically burned forest, and open meadows. Snow surface photographs were collected concurrently with ground-based hyperspectral spectrometer measurements, snow surface samples, and alongside coincident observations from NEON towers, uncrewed aerial vehicles (UAVs), NASA’s AVIRIS-NG airborne sensor, and hyperspectral satellite instruments. Each photo was taken at the same location as field snow surface reflectance and albedo measurements and snow surface samples, providing visual context for interpreting spectral variability.\n<br>\n\n#### SNEX23_Albedo\nThis data set contains a comprehensive set of field-based measurements of snow surface reflectance and broadband snow albedo collected during the NASA SnowEx 2023 field campaign in interior Alaska. Data were acquired in April and May 2023 across three study sites near Fairbanks, Alaska, selected to represent the dominant boreal landcover types: closed-canopy forests, recently and historically burned forests, and open meadows. Together, these environments capture the heterogeneity of snow–vegetation interactions that influence snow albedo in high-latitude ecosystems.\n<br>\n\n#### SNEX23_BCEF_TLS_Raw\nThis data set contains unprocessed point cloud data created from terrestrial lidar scans (TLS) collected during the SnowEx 2023 campaign from the Bonanza Creek Experimental Forest near Fairbanks, Alaska. Data were collected in October 2022 (snow-off) and March 2023 (snow-on). Digital terrain models (DTMs) derived from the raw point cloud data are available as the [SnowEx23 Bonanza Creek Experimental Forest Terrestrial Lidar Scans, Version 1 (SNEX23_BCEF_TLS) data set](https://doi.org/10.5067/R466GRXNA61S)\n<br>\n\n#### SNEX23_BCEF_TLS\nThis data set contains digital terrain models (DTMs) derived from terrestrial lidar scans (TLS) collected as part of the SnowEx 2023 campaign. Data were collected at the Bonanza Creek Experimental Forest near Fairbanks, Alaska in October 2022 (snow-off conditions) and March 2023 (snow-on conditions). The DTMs are provided as Geographic Tagged Image (GeoTIFF) files, where each file corresponds to a unique survey site. Unprocessed point cloud data from which these DTMs were derived are available as the [SnowEx23 Bonanza Creek Experimental Forest Terrestrial Lidar Scans Raw, Version 1 (SNEX23_BCEF_TLS_Raw) data set](https://doi.org/10.5067/OIBBFR49P0Q6)\n<br>\n\n#### SNEX23_CSU_GPR_Raw\nThis data set contains the raw results of 1 GHz ground-penetrating radar surveys conducted as part of the NASA SnowEx23 field campaign in Alaska, USA. Surveys were conducted at three different field sites between 07 March 2023 and 16 March 2023: 1) Farmers Loop/Creamers Field, 2) the Bonanza Creek Experimental Forest, and 3) the Caribou/Poker Creek Research Watershed.\n<br>\n\n#### SNEX23_CR\nThis data set contains an inventory of corner reflectors installed in Northern Alaska, USA, as part of the NASA SnowEx 2023 field campaign (Vuyovich et al., 2024). Corner Reflectors (CRs) are objects with known response characteristics used to calibrate or study radar returns from airborne and spaceborne imagery. The SnowEx Alaska CRs were installed in situ between October 2022 and October 2023 for the following SnowEx Alaska intensive observation periods (IOPs): October 2022, March 2023, and October 2023. During the SnowEx Alaska field campaigns, CRs served two primary purposes. First, “engineering corner reflectors” were deployed to calibrate radar imagery and ensure precise backscatter measurements. Second, an array of “science corner reflectors” was installed to investigate the interaction between radar and forests. CRs were deployed at 32 locations across two SnowEx Alaska study areas: 9 in the Arctic tundra at the Upper Kuparuk Toolik (UKT) study area and 23 in the boreal forest at the Bonanza Creek Experimental Forest (BCEF) study area.\n<br>\n\n#### SNEX23_CRREL_GPR_Raw\nThis data set contains the results of 1 GHz ground-penetrating radar surveys conducted at the Upper Kuparuk/Toolik (UKT) site in northern Alaska, USA as part of the NASA SnowEx 2023 field campaign. Data were collected between 08 Mar 2023 to 15 Mar 2023, spatially coinciding with snow pit locations and along transects between snow pits. Data include georeferenced multichannel ground-penetrating radargrams stored within .nc files. PRocessed GPR data are available as [SnowEx23 CRREL Ground Penetrating Radar, Version 1](https://nsidc.org/data/SNEX23_CRREL_GPR/versions/1).\n<br>\n\n#### SNEX23_GIS_Ref\nThis dataset contains geolocation information for ground-based and airborne measurements collected in Alaska, USA as part of the NASA SnowEx 2023 campaign (Vuyovich et al., 2024). The purpose of this compilation is to facilitate the use of SnowEx 2023 Alaska data by providing Geographical Information System (GIS) files in one location.\n<br>\n\n#### SNEX23_SSA\nThis data set contains vertical profiles of snow reflectance and specific surface area (SSA) from the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, the Caribou Poker Creek watershed and Farmers Loop/Creamer’s Field), and a coastal tundra environment in the North Slope region of northern Alaska (the Arctic coastal plain and Upper Kuparuk Toolik), collected as part of the NASA SnowEx 2023 field campaign in March 2023. Reflectance was measured in snow pits using three different integrating sphere laser devices: an A2 Photonic Sensor IceCube (1310 nm), an IRIS (InfraRed Integrating Sphere) system (1310 nm), and an InfraSnow SSA sensor (945 nm). Measured reflectance values were converted to SSA during data processing. It is recommended that data users work with either the IceCube or IRIS data, as the InfraSnow data was collected primarily for testing of the instrument’s capabilities. Snow-off SSA data from these same study sites are available as [SnowEx23 Laser Snow Microstructure Specific Surface Area Snow-off Data, Version 1](https://nsidc.org/data/snex23_ssa_so/versions/1).\n<br>\n\n#### SNEX23_SSA_SO\nThis data set reports vertical profiles of snow reflectance and specific surface area (SSA) from two study sites in Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, the Caribou Poker Creek watershed and Farmers Loop/Creamer’s Field), and a coastal tundra environment in the North Slope region of northern Alaska (the Arctic coastal plain and Upper Kuparuk Toolik). Reflectance was measured in situ using an A2 Photonic Sensor IceCube (1310 nm). Measured reflectance values were converted to SSA during data processing following the methods of Gallet et al., (2009). Snow-on SSA data from these same study sites were collected in March 2023 and are available as [SnowEx23 Laser Snow Microstructure Specific Surface Area Data, Version 1](https://nsidc.org/data/snex23_ssa/versions/1).\n<br>\n\n#### SNEX23_MAR22_SD\nThe data set contains snow depth measurements from two regions of Alaska, USA collected during the March 2022 intensive observation period (IOP) as part of the NASA SnowEx 2023 field campaign. The study sites include three boreal forest sites in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and a coastal tundra site in the North Slope region (Arctic coastal plain). Snow depth measurements collected from the study sampling sites during the subsequent field season are available as [SnowEx23 Mar23 IOP Snow Depth Measurements, Version 1](https://nsidc.org/data/snex23_mar23_sd/versions/1).\n<br>\n\n#### SNEX23_MAR23_SD\nThe data set contains snow depth measurements from five study sites in Alaska, USA; data were collected during the March 2023 intensive observation period (IOP) as part of the NASA SnowEx 2023 field campaign. The study sites include three boreal forest sites in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and two coastal tundra sites in the North Slope region (Arctic coastal plain and Upper Kuparuk Toolik). Snow depth measurements collected from the study sampling sites during the previous field season are available as [SnowEx23 Mar22 IOP Snow Depth Measurements, Version 1](https://nsidc.org/data/snex23_mar22_sd/versions/1).\n<br>\n\n#### SNEX23_CSU_GPR\nThis data set contains the results of 1 GHz ground-penetrating radar surveys conducted as part of the NASA SnowEx23 field campaign in Alaska, USA. Surveys were conducted at three different field sites between 07 March 2023 and 16 March 2023: 1) Farmers Loop/Creamers Field (FLCF), 2) the Bonanza Creek Experimental Forest (BCEF), and 3) the Caribou/Poker Creek Research Watershed (CPCW). Parameters in this data set include: two-way travel (TWT) time, snow density, calculated snow depth, and calculated snow water equivalent (SWE).\n<br>\n\n#### SNEX23_MAR23_Profiles\nSnow depth profiles were measured from 7-16 March 2023 as part of the NASA SnowEx 2023 March 2023 Intensive Observation Period. Ground-based snow depth profile measurements were taken in the vicinity of 170 snow pits distributed across five study areas in Northern Alaska. Three study areas represented boreal forest snow conditions near Fairbanks, AK: Farmers Loop Creamers Field (FLCF), Caribou Poker Creeks Research Watershed (CPCRW), and Bonanza Creek Experimental Forest (BCEF). Two study areas represented Arctic tundra snow conditions: Arctic Coastal Plain (ACP) and Upper Kuparuk Toolik (UKT).\n<br>\n\n#### SNEX23_CBand\nThis data set contains C-band radar data collected during the NASA SnowEx 2023 Alaska field campaign between 08 March 2023 to 15 March 2023. Data was acquired from two study areas near Fairbanks, Alaska using a multi-polarization radar affixed to sled-mounted tower. The study sites (Caribou Poker Creek watershed and Farmer’s Loop/Creamer’s Field) are boreal forest and wetland environments. Data was also collected from a school adjacent to Farmer’s Loop, to record data from man-made surfaces (i.e., concrete and cultivated grass.)\n<br>\n\n#### SNEX23_MAR23_SP\nThe data set presents snow pit measurements collected during the NASA SnowEx March 2023 Intensive Observation Period (IOP) in Alaska, USA to use for calibration and validation with coincident airborne SWESARR and lidar measurements as part of the strategy focused on snow water equivalence (SWE) and snow depth (HS). In total, 170 snow pits were excavated between the five sites at locations representing a range of snow depth, vegetation, and topographic conditions. Three study areas represented boreal forest snow near Fairbanks, AK: Farmers Loop Creamers Field (FLCF), Caribou Poker Creek Research Watershed (CPCRW), and Bonanza Creek Experimental Forest (BCEF). Two study areas represented Arctic tundra snow: Arctic Coastal Plain (ACP) and Upper Kuparuk Toolik (UKT).\n<br>\n\n#### SNEX23_OCT22_GSR_Raw\nThis data set presents photographs of snow pit ground surface collected using a digital camera during the NASA SnowEx 2023 field campaign between 23 and 25 October 2022. The images were collected from 13 snow pits located at the Upper Kuparuk and Toolik (UKT) study site, an arctic tundra environment in Northern Alaska. These photographs were used to derive point cloud data representative of ground surface roughness, which are available as [SnowEx23 Oct22 Ground Surface Roughness Reconstruction, Version 1](https://doi.org/10.5067/KCXOEYAY6PA9).\n<br>\n\n#### SNEX23_OCT22_GSR\nThis data set presents ground surface roughness data collected during the NASA SnowEx 2023 field campaign between 23 and 25 October 2022. The data are formatted as point clouds, compiled from images acquired using a digital camera. Images were collected from 13 snow pits located at the Upper Kuparuk and Toolik (UKT) study site, an arctic tundra environment in Northern Alaska. The raw imagery from which these data are derived are available as [SnowEx23 Oct22 Ground Surface Roughness Imagery, Version 1](https://doi.org/10.5067/PIW7DPSU3SOA).\n<br>\n\n#### SNEX23_Oct22_SP\nThe data set presents snow pit measurements collected during the NASA SnowEx October 2022 Intensive Observation Period (IOP) in Alaska, USA. In total, 186 snow pits were excavated between the five sites at locations representing a range of snow depth, vegetation, and topographic conditions. Three study areas represented boreal forest snow near Fairbanks, AK: Farmers Loop Creamers Field (FLCF), Caribou Poker Creek Research Watershed (CPCRW), and Bonanza Creek Experimental Forest (BCEF). Two study areas represented Arctic tundra snow: Arctic Coastal Plain (ACP) and Upper Kuparuk Toolik (UKT).\n<br>\n\n#### SNEX23_OCT22_SSA\nThis data set reports vertical profiles of snow reflectance and specific surface area (SSA) collected in October 2022 as part of the NASA SnowEx 2023 Alaska field campaign. Measurements were taken from three snow pits located within the Upper Kuparuk Toolik (UKT) field site, a coastal tundra environment in the North Slope region of the northern Alaska coastal plain. Reflectance was measured in situ using a 1310 nm integrating sphere laser device (IceCube) and converted to SSA during data processing (Gallet et al., 2009). SSA data collected from the same study sites in October 2023 and March 2023 are available as [SnowEx23 Laser Snow Microstructure Specific Surface Area Data, Version 1](https://doi.org/10.5067/BSEP59ADC6XN) and [SnowEx23 Laser Snow Microstructure Specific Surface Area Snow-off Data, Version 1](https://nsidc.org/data/snex23_ssa_so/versions/1).\n<br>\n\n#### SNEX23_OCT23_GSR_Raw\nThis data set presents photographs of snow pit ground surface collected using a digital camera during the NASA SnowEx 2023 field campaign between 17 and 28 October 2023. The images were collected from 22 snow pits located across three study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Caribou Poker Creek watershed (CPCW) and Farmers Loop Creamers Field (FLCF), two boreal forest sites near Fairbanks, Alaska. These photographs were used to derive point cloud data representative of ground surface roughness, which are available as [SnowEx23 Oct23 Ground Surface Roughness Reconstruction, Version 1](https://doi.org/10.5067/9BTG86R3ISRB).\n<br>\n\n#### SNEX23_OCT23_GSR\nThis data set presents ground surface roughness data collected during the NASA SnowEx 2023 field campaign between 17 and 28 October 2023. The data are formatted as point clouds, compiled from images acquired using a digital camera. Images were collected from 22 snow pits located across three study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Caribou Poker Creek watershed (CPCW) and Farmers Loop Creamers Field (FLCF), two boreal forest sites near Fairbanks, Alaska. The raw imagery from which these data are derived are available as [SnowEx23 Oct23 Ground Surface Roughness Imagery, Version 1](https://doi.org/10.5067/FZ3KL32LBPA2).\n<br>\n\n#### SNEX23_SD_TLI\nThe dataset comprises snow depth measurements extracted from snow poles in time-lapse images near Fairbanks, AK during the SnowEx 2022-2023 field campaign. Snow poles were 1.52 m (5 ft) vertical poles painted red with a 10.16-cm (4 in) yellow top. Collection occurred at 18 sites: 9 at the Bonanza Creek LTER and 9 at the Caribou-Poker Creeks Research Watershed. At each site, three snow depth measurements were extracted, beginning at 11:00 AM daily. The time-lapse images are archived separately at NSIDC ([SNEX23_TLI](https://doi.org/10.5067/UB3A44RTR6JD)).\n<br>\n\n#### SNEX23_SWE\nThis data set presents snow depth, snow water equivalent (SWE), and bulk snow density data collected during the NASA SnowEx 2023 field campaign between March 13-16 2023. Samples were collected using an Adirondack snow sampler (SWE tube) from two study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Farmers Loop Creamers Field (FLCF), a boreal forest near Fairbanks, Alaska.\n<br>\n\n#### SNEX23_Tree_SM\nThis dataset contains soil sensor measurements collected as part of the SnowEx Alaska 2023 field campaign. Time series measurements occurred at two locations within the Bonanza Creek Experimental Forest (BCEF), Alaska, from sensors inserted into the soil and into a tree. Variables measured include soil volumetric water content, soil and tree temperature, soil electrical conductivity, and tree apparent dielectric permittivity. Soil measurements were obtained using a Campbell Scientific CS650 water content reflectometer, and tree-mounted measurements were obtained using a METER Group TEROS 12 sensor.\n<br>\n\n#### SNEX23_TLI\nThe dataset comprises time-lapse images captured by cameras placed near Fairbanks, AK as part of the NASA SnowEx 2022-2023 field campaign. Nine cameras were installed in the Bonanza Creek Experimental Forest (BCEF) and eight cameras were installed in the Caribou-Poker Creek Research Watershed (CPCW). The time-lapse images were collected using a WingscapesTimeLapsePro camera in conjunction with a custom-made snow pole. The camera captured one image per hour between 1100 and 1300 local standard time for a total of three images a day, documenting the fluctuations in snow levels throughout the observation period.\n<br>\n\n#### SNEX23_UW_GPR\nThis data set contains the results of 1 GHz ground-penetrating radar surveys conducted at the Arctic Coastal Plain (ACP) site and the Upper Kuparuk/Toolik (UKT) site in northern Alaska during the SnowEx23 field campaign. Data include two-way travel time, derived snow depth, and derived snow water equivalent. Data were collected between 8 - 14 March 2023.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/aso_3m_pcdtm/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","elevation","ice","land cover","lidar","radar","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"ASO_3M_PCDTM v1 - This data set provides 3 m gridded, bare-earth elevations (excluding trees) that are used as the baseline for the Airborne Snow Observatory (ASO) snow-on products. The data were collected during snow-free conditions as part of the NASA/JPL ASO aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ASO/ASO_3M_PCDTM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ASO_3M_SD v1 - This data set contains 3 m gridded snow depths derived from airborne light detection and ranging, or lidar, measurements of surface elevations. The data were collected as part of the NASA/JPL Airborne Snow Observatory (ASO) aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ASO/ASO_3M_SD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ASO_50M_SD v1 - This data set contains 50 m gridded snow depths derived from airborne light detection and ranging, or lidar, measurements of surface elevations. The data were collected as part of the NASA/JPL Airborne Snow Observatory (ASO) aircraft survey campaigns.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ASO/ASO_50M_SD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ASO_50M_SWE v1 - This data set contains 50 m gridded snow water equivalent (SWE) values collected as part of the NASA/JPL Airborne Snow Observatory (ASO) aircraft survey campaigns. The data were derived from the [ASO L4 Lidar Snow Depth 50m UTM Grid](https://nsidc.org/data/aso_50m_sd) data product and from modeled snow density.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/ASO/ASO_50M_SWE/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WV_LCC_SC_FSCA v1 - This data set includes: (1) fine-scale snow and land cover maps from two mountainous study sites in the Western U.S., produced using machine-learning models trained to extract land cover data from WorldView-2 and WorldView-3 stereo panchromatic and multispectral images; (2) binary snow maps derived from the land cover maps; and (3) 30 m and 465 m fractional snow-covered area (fSCA) maps, produced via downsampling of the binary snow maps. The land cover classification maps feature between three and six classes common to mountainous regions and integral for accurate stereo snow depth mapping:...","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/WV_LCC_SC_FSCA/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX_HRSI_SD_DEM_CO v1 - This data set contains a time series of snow depth maps and related intermediary snow-on and snow-off DEMs for Grand Mesa and the Banded Peak Ranch areas of Colorado derived from very-high-resolution (VHR) satellite stereo images and lidar point cloud data. Two of the snow depth maps coincide temporally with the 2017 NASA SnowEx Grand Mesa field campaign, providing a comparison between the satellite derived snow depth and in-situ snow depth measurements.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX_HRSI_SD_DEM_CO/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX_MCS_Lidar_Raw v1 - The data set described here provides raw lidar data collected as part of a multi-year effort to monitor monthly snow distribution over a 35 km² region of the Mores Creek Headwaters in the Boise Mountains of central Idaho between 2021 and 2025. Data acquisition in 2021 overlapped temporally with the NASA SnowEx 2021 field campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX_MCS_Lidar_Raw/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX_MCS_Lidar v1 - The data set provides digital terrain models (DTM), digital surface models (DSM) snow depth models, and canopy height models (CHM), derived from point cloud data (available as [SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey Raw, Version 1](https://nsidc.org/data/SNEX_MCS_Lidar_Raw)) acquired by airborne lidar scanning. Data were collected as part of a multi-year effort to monitor monthly snow distribution over a 35 km² region of the Mores Creek Headwaters in the Boise Mountains of central Idaho between 2021 and 2025.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX_MCS_Lidar/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX17_SD_Perm v1 - This data set contains snowpack relative permittivities and densities derived from Ground Penetrating Radar (GPR) surveys and airborne lidar observations of snow depths. The results of the original GPR surveys are published in the [SnowEx17 Ground Penetrating Radar, Version 2](https://doi.org/10.5067/G21LGCNLFSC5) data set, while the lidar snow depths were sourced from [ASO L4 Lidar Snow Depth 3m UTM Grid, Version 1](https://doi.org/10.5067/KIE9QNVG7HP0).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX17_SD_Perm/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX17_KT15_TB v1 - This data set contains brightness temperature readings. Brightness temperatures were measured using a KT-15 pyrometer flown on a P-3 aircraft over 2017 SnowEx campaign study areas in Colorado, USA: Grand Mesa, a snow-covered, forested study site about 40 miles east of Grand Junction; and Senator Beck Basin, approximately 80 miles to the SSE of Grand Mesa.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX17_KT15_TB/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX17_SD_TLI v1 - The dataset comprises snow depth measurements extracted from snow poles in time-lapse images near Grand Mesa, CO during the SnowEx 2016-2017 field campaign. Snow poles were 3.048 m (10 ft) vertical poles painted orange.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX17_SD_TLI/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_SWESARR_TB v1 - This data set contains airborne microwave brightness temperature observations from the Goddard Space Flight Center SWESARR (Snow Water Equivalent Synthetic Aperture Radar and Radiometer) instrument during the winter (10-12 February 2020) NASA SnowEx 2020 campaign at Grand Mesa, CO. Observations were made at three frequencies (10.65, 18.7, and 36.5 GHz; referred to as X, K, and Ka bands, respectively), at horizontal polarization with a nominal 45-degree look angle.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_SWESARR_TB/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_CASIE_TIR v1 - This data set contains long-wave infrared radiometer data, thermal infrared (TIR) imagery, and visible-band (RGB) imagery from the February 2020 NASA SnowEx field campaign flights over the Grand Mesa, CO. These data are from the center, nadir-viewing radiometer; center, nadir-viewing, TIR camera; or nadir-viewing, visible-band camera.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_CASIE_TIR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_BR_BSU_SMP v1 - This data set contains raw penetration force profiles from the SnowEx 2020 Time Series campaign in the Boise River Basin in Idaho. Measurements were taken using the SnowMicroPen (SMP), a digital snow penetrometer, from three snow pits.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_BR_BSU_SMP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_TLS_PC_BSU_RAW v1 - This data set contains raw, unprocessed terrestrial laser scanner (TLS) point cloud data collected as part of the 2020 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2019) and snow-on (February 2020) conditions, at both open and forested locations.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_TLS_PC_BSU_RAW/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_TLS_PC_BSU v1 - This data set contains terrestrial laser scanner (TLS) point cloud data collected as part of the 2020 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2019) and snow-on (February 2020) conditions, at both open and forested locations.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_TLS_PC_BSU/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_COCP_GPR_Raw v1 - This data set contains the raw files from ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx20 campaign. Data were collected between 18 December 2019 and 12 March 2020.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_COCP_GPR_Raw/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_COCP_GPR v1 - This data set contains the results of ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx20 campaign. Data include two-way travel time, pit-measured snow density, calculated snow depth, and calculated snow water equivalent.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_COCP_GPR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_SD v1 - This data set, collected during the SnowEx 2020 Intensive Operation Period (IOP) in Grand Mesa, Colorado, contains in situ snow depth measurements. Snow depth was measured using one of three instruments - Magnaprobe, Mesa 2, or pit ruler.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_SD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_CRSM v1 - This data set contains the raw and processed data files from a COSMOS Rover soil moisture probe. The COSMOS Rover uses fast neutron counting to estimate soil moisture over a region up to 300m from the vehicle.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_CRSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_CSSM v1 - These data contain raw and processed hourly observations from a Hydroinnova COSMOS Stationary sensor probe. Parameters in the raw files include atmospheric pressure, temperature, and relative humidity.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_CSSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_TLS_PC_CRREL v1 - This data set contains terrestrial LIDAR survey (TLS) point cloud data collected at Grand Mesa, Colorado as part of the 2020 SnowEx campaign. Data were collected in fall 2019 (September) and winter 2020 (January and February).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_TLS_PC_CRREL/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_A19_GSM v1 - Gravimetric soil moisture data measured from collected soil samples in autumn 2019 as part of the NASA SnowEx 2020 campaign at Grand Mesa, CO. A total of 77 soil samples were taken at snow pit locations, soil moisture stations, and other areas where both the mobile and stationary COsmic-ray Soil Moisture Observing Systems (COSMOS) operated.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_A19_GSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_A19_SD v1 - Snow depth was measured in autumn 2019 as part of the NASA SnowEx 2020 campaign at Grand Mesa, CO. Measurements were taken in the vicinity of snow pit locations and between pit locations using a Magnaprobe or manual depth probes.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_A19_SD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_A19_SP v1 - This data set contains snow pit measurements obtained as part of the SnowEx 2020 campaign at the Grand Mesa, Colorado, USA site in Autumn 2019. 21 locations were visited for snow pit observations.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_A19_SP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_A19_SWE v1 - Snow water equivalent (SWE) measurements of the autumn shallow snow were obtained using SWE samplers (also referred to as SWE tubes) during the NASA SnowEx 2020 campaign (03-06 Nov. 2019) at Grand Mesa, CO.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_A19_SWE/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_DSM v1 - This data set consists of soil moisture and soil temperature measurements taken during the SnowEx 2020 field campaign. Soil moisture probes were deployed at 10 stations within the Colorado Grand Mesa study area and monitored soil properties at three different depths (5, 10, and 20 cm).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_DSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_GM_SP v1 - This data set contains snow pit observations from the SnowEx 2020 Grand Mesa Intensive Observation Period. Data were collected from 154 snow pits on Grand Mesa, Colorado between 27 January and 12 February 2020.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_GM_SP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_BSU_CMP_SWE v1 - This data set was collected during the SnowEx 2020 Intensive Observation Period (IOP) in Grand Mesa, Colorado. These data contain snow water equivalent (SWE) estimates.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_BSU_CMP_SWE/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_BSU_GPR_Raw v1 - This data set contains the geolocated, unprocessed results of a ground penetrating radar survey conducted as part of the 2020 SnowEx campaign. Data were collected at Grand Mesa, Colorado (a snow-covered, forested study site) between 28 January 2020 and 04 February 2020.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_BSU_GPR_Raw/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_BSU_GPR v1 - This data set contains snow water equivalent (SWE) and snow depth estimates derived from Ground Penetrating Radar (GPR) measurements. GPR measurements were collected during the SnowEx 2020 Grand Mesa Intensive Observation Period (IOP) between 28 January and 04 February 2020.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_BSU_GPR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_BSU_CMP_Raw v1 - This data set was collected during the SnowEx 2020 Intensive Observation Period (IOP) in Grand Mesa, Colorado. These data contain the geolocated, unprocessed, common midpoint (CMP) gathers from a Sensors & Software pulseEKKO PRO 1 GHz multi-polarization ground penetrating radar (GPR).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_BSU_CMP_Raw/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_BSU_CMP_TT v1 - This data set was collected during the SnowEx 2020 Intensive Observation Period (IOP) in Grand Mesa, Colorado. These data contain the radar two-way travel times from a Sensors & Software pulseEKKO PRO 1 GHz multi-polarization ground penetrating radar (GPR).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_BSU_CMP_TT/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_GM_CTSM v1 - This data set characterizes snow microstructure for 6 snow pits from the SnowEx 2020 Grand Mesa Intensive Observation Period (February 2020) using microcomputed tomography (micro-CT). Included with this data set are two- and three-dimensional microstructural analysis of DMP/DEP casted and un-casted snow samples, available as .xlsx and .txt file, and visual representations of the three-dimensional snow structure, available is .bmp image files.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_GM_CTSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_GM_CSU_GPR_RAW v1 - This data set the raw files collected with a Sensors & Software 1GHz ground penetrating radar (GPR) as part of the SnowEx 2020 Intensive Observation Period (IOP) at Grand Mesa, Colorado between 06 February 2020 and 09 February 2020. The more processed data are published in SnowEx20 Grand Mesa IOP CSU 1GHz GPR, Version 1 (DOI: 10.5067/S5EGFLCIAB18).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_GM_CSU_GPR_RAW/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_GM_CSU_GPR v1 - This data set contains two-way travel times, snow depth and snow water equivalent (SWE) collected with a Sensors & Software 1GHz ground penetrating radar (GPR) as part of the SnowEx 2020 Intensive Observation Period (IOP) at Grand Mesa, Colorado between 06 February 2020 and 09 February 2020. These data are derived from the SnowEx20 Grand Mesa IOP CSU 1GHz GPR Raw, Version 1 data set (DOI: 10.5067/CT6NS2LIASRS).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_GM_CSU_GPR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_GM_GBPM v1 - The dataset consists of passive microwave measurements collected between 4 February and 12 February 2020 as part of the NASA SnowEx 2020 field campaign at the Grand Mesa, Colorado IOP study site. The measurements were taken at v- and h-polarization at both 19 and 37 GHz using two radiometers mounted on a sled.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_GM_GBPM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_GM_SWE_SD v1 - This data set contains snow water equivalent (SWE) and snow density raster data derived from a lidar-based snow depth data set ([SnowEx20 Grand Mesa IOP QSI Lidar Snow Depth Data, Version 1](https://doi.org/10.5067/M9TPF6NWL53K)), a lidar-based digital terrain model ([ASO L4 Lidar Point Cloud Digital Terrain Model 3m UTM Grid, Version 1](https://doi.org/10.5067/2EHMWG4IT76O)), and two GPR data sets ([SnowEx20 Grand Mesa IOP BSU 1 GHz Multi-polarization GPR, Version 1](https://doi.org/10.5067/Q2LFK0QSVGS2) and <a href=\"https://doi.org/10.5067/WE9GI1GVMQF6\">Me...","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_GM_SWE_SD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_GM_Lidar v1 - This data set contains rasterized snow depth maps derived from lidar point cloud data collected from Grand Mesa, Colorado during the SnowEx20 campaign. The subset data file was used as input data to derive SWE and snow density data, which is available as [SnowEx20 Grand Mesa IOP Lidar and GPR-Derived Snow Water Equivalent and Snow Density, Version 1](https://doi.org/10.5067/LANQ53RTJ2DR).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_GM_Lidar/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_UNM_GPR v1 - This data set contains two-way travel times, snow depth and now water equivalent from a ground penetrating radar survey conducted at Grand Mesa, Colorado. Data were collected between 28 January 2020 and 06 February 2020 as part of the SnowEx 2020 campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_UNM_GPR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_GIS_REF v1 - This data set contains geolocation information of the infrastructure locations for the SnowEx20 Intensive Observation Period (IOP) and Time Series (TS) campaigns. Available scientific infrastructure locations in this data set are tower and sensor locations, aircraft flight lines, planned and actual snow pit locations, and time-lapse camera locations.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_GIS_REF/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_SD_TLI v1 - This data contains snow depth measurements derived from time-lapse images collected by cameras placed around Grand Mesa, CO at 29 sites coincident with other SnowEx 2020 measurements. The field view of all cameras includes a 3.049 m, (10 ft) vertical pole that was painted red with a yellow top to serve as a reference for quantifying snow depth.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_SD_TLI/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_J_UNM_GPR v1 - This data set contains two-way travel times from a ground penetrating radar survey conducted at Jemez, New Mexico. Data were collected between 12 February 2020 and 04 March 2020 as part of the SnowEx 2020 campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_J_UNM_GPR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_SSA v1 - This data set contains vertical profiles of snow reflectance, specific surface area (SSA), and spherical equivalent snow grain diameter. Data were collected during the SnowEx 2020 Grand Mesa, Colorado Intensive Observation Period between 27 January and 12 February 2020.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_SSA/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_VPTS_Raw v1 - This data set contains an eight-day time series of vertical temperature profile measurements. Measurements were collected using a thermoprobe to a depth of 30 cm at two site locations.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_VPTS_Raw/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_SB_GST v1 - This data set contains hourly ground surface temperature measurements collected between 20 October 2019 and 18 July 2020. Data were collected at 7 points along two plant monitoring transects in the Upper Basin of the Senator Beck Study Basin.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_SB_GST/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_SMP v1 - This data set contains raw penetration force profiles from the SnowEx 2020 Intensive Observation Period in Grand Mesa, Colorado. Measurements were taken using the SnowMicroPen (SMP), a digital snow penetrometer.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_SMP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_TS_IB v1 - This data set contains snow change measurements from interval boards collected as part of the NASA SnowEx20 time series field campaign. Surveys were conducted at field sites across the Western United States 9 December 2019 and 22 April 2020.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_TS_IB/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_TS_SP v2 - The data set is a time-series of snow pit measurements obtained by the SnowEx community during the 2020 campaign. Between October 2019 and May 2020, data were collected from 454 snow pits at 12 regional locations throughout California, Colorado, Idaho, New Mexico, and Utah, USA.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_TS_SP/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_TLI v1 - This data set contains sub-daily time-lapse images collected by cameras placed around Grand Mesa, CO at 29 sites coincident with other SnowEx 2020 measurements. The field view of all cameras includes a 3.049 m, (10 ft) vertical pole that was painted red with a yellow top to serve as a reference for quantifying snow depth.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_TLI/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_COCP_SPD v1 - This data set contains the results of ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx20 campaign. Data include two-way travel time, lidar-measured snow depth, derived snow water equivalent, derived snow density, and derived relative permittivity.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_COCP_SPD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_QSI_DEM v1 - This data set is part of the SnowEx 2021 campaign and provides bare Earth digital elevation models (DEM) acquired by a scanning lidar system at a 0.5 m spatial resolution, and derived from point cloud digital terrain models. DEMs are available for September 2021 and from multiple areas in Colorado, Idaho, and Utah.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_QSI_DEM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_QSI_DEM_3m v1 - This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides bare Earth digital elevation models (DEM) acquired by a scanning lidar system at a 3.0 m spatial resolution, and derived from point cloud digital terrain models. DEMs are available for September 2021 and from multiple areas in Colorado, Idaho, and Utah.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_QSI_DEM_3m/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_QSI_SD v1 - This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides snow depth values at a 0.5 m spatial resolution, derived from point cloud digital terrain models. Snow depths are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_QSI_SD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_QSI_SD_3m v1 - This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides snow depth values at a 3.0 m spatial resolution, derived from point cloud digital terrain models. Snow depths are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_QSI_SD_3m/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_QSI_VH v1 - This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides vegetation height values at a 0.5 m spatial resolution, derived from point cloud digital terrain models. Vegetation heights are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_QSI_VH/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX20_QSI_VH_3m v1 - This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides vegetation height values at a 3.0 m spatial resolution, derived from point cloud digital terrain models. Vegetation heights are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX20_QSI_VH_3m/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX21_BR_GPR v1 - This data set contains two-way travel time data calculated using the results of 1 GHz ground-penetrating radar surveys conducted at Banner Summit, as part of the SNEX21 Time Series campaign in the Boise River Basin, Idaho.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX21_BR_GPR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX21_COCP_GPR_Raw v1 - This data set contains the raw files from ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx21 campaign. Data were collected between 13 January 2021 and 27 May 2021.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX21_COCP_GPR_Raw/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX21_COCP_GPR v1 - This data set contains the results of 1 GHz ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx21 campaign. Data include two-way travel time, pit-measured snow density, calculated snow depth, and calculated snow water equivalent.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX21_COCP_GPR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX21_CRNS_SWE v1 - These data contain raw and processed hourly observations from a stationary Hydroinnova cosmic ray neutron sensor (CRNS). Data were collected between 22 November 2020 and 08 May 2021 at the SnowEx Prairie site at the Central Agricultural Research Center (CARC) in Moccasin, Montana and represent a footprint area of approximately 171 meters radius around the instrument.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX21_CRNS_SWE/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX21_PS_DSM v1 - This dataset includes Digital Surface Models (DSMs) derived from UAV-LiDAR imagery acquired during the SnowEx 2021 field campaign at the Central Agricultural Research Center in central Montana (USA). The DSMs are at 0.3 m resolution and consist of one snow-off and seven snow-on flights.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX21_PS_DSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX21_PS_MET v1 - This data set contains observations from a meteorological station installed at the Central Agricultural Research Center in Moccasin Montana as part of the NASA SnowEx 2021 Prairie Snow field campaign. Parameters include: air temperature, wind speed and direction, and barometric pressure.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX21_PS_MET/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX21_DSM v1 - This data set consists of soil moisture and soil temperature measurements taken during the SnowEx 2021 field campaign. Soil moisture probes were deployed at 9 locations within the Montana Prairie Station study area and monitored soil properties at four different depths (5, 10, 20 and 50 cm).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX21_DSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX21_SSR v1 - This data set provides apparent surface spectral reflectance imagery which demonstrates snow albedo and snow optical property evolution across two distinct snow-covered environments in Colorado. Data collection occurred in the spring of 2021 as part of the NASA SnowEx mission.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX21_SSR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX21_TS_IB v1 - This data set contains snow change measurements from interval boards collected as part of the NASA SnowEx21 time series field campaign. Surveys were conducted at field sites across the Western United States 18 November 2020 and 12 May 2021.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX21_TS_IB/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX21_TS_SD v1 - The data set contains manual snow depth measurements collected as part of the NASA SnowEx 2021 Time Series campaign. Data were collected at five out of seven Time Series study sites: Cameron Pass, Fraser Experimental Forest, and Senator Beck, Colorado; Boise River Basin, Idaho; and the Central Agriculture Research Center (CARC), Montana.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX21_TS_SD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_Lidar_Raw v1 - This data set provides raw lidar data from two regions of Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and a coastal tundra environment in the North Slope region of the northern Alaska coastal plain (Arctic coastal plain and Upper Kuparuk Toolik).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_Lidar_Raw/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_Lidar v1 - This data set provides digital terrain models, snow depth, and canopy height, acquired by a scanning lidar system and derived from Point Cloud Digital Terrain Models (PCDTMs) from two regions of Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and a coastal tundra environment in the North Slope region of the northern Alaska coastal plain (Arctic coastal plain and Upper Kupa...","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_Lidar/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_Apr23_AVIRISNG v1 - This data set contains surface reflectance data collected from three boreal forest study sites (Delta Junction, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) around Fairbanks, Alaska as part of the NASA SnowEx 2023 field campaign. Data was acquired using the NASA Jet Propulsion Laboratory (JPL) Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) sensor mounted on a King Air B-200 aircraft.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_Apr23_AVIRISNG/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_Apr_HyLab v1 - This dataset provides surface spectral reflectance imagery which demonstrates snow albedo and snow optical properties during the NASA SnowEx 2023 field campaign that took place in Alaska between 7 April 2023 and 5 May 2023. Imagery was collected across several snow-covered boreal forest environments on April 20th, 2023, near Fairbanks, Alaska, including Farmers Loop Creamers Field (FLCF), and Caribou Poker Creek Research Watershed (CPCRW).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_Apr_HyLab/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_APR_SP v1 - This data set contains snow pit data collected from 13 snow pits excavated during the NASA SnowEx23 field campaign near Fairbanks, Alaska. The snow pits were located across three field sites: the Caribou/Poker Creek Research Watershed, Farmer’s Loop/Creamers Field, and Delta Junction.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_APR_SP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_SSI v1 - This data set contains 54 photographs of snow surfaces collected during the NASA SnowEx23 field campaign near Fairbanks, Alaska. Photos were acquired in April and May 2023 at three study sites that represent a range of landcover conditions, including intact boreal forest, recently and historically burned forest, and open meadows.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_SSI/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_Albedo v1 - This data set contains a comprehensive set of field-based measurements of snow surface reflectance and broadband snow albedo collected during the NASA SnowEx 2023 field campaign in interior Alaska. Data were acquired in April and May 2023 across three study sites near Fairbanks, Alaska, selected to represent the dominant boreal landcover types: closed-canopy forests, recently and historically burned forests, and open meadows.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_Albedo/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_BCEF_TLS_Raw v1 - This data set contains unprocessed point cloud data created from terrestrial lidar scans (TLS) collected during the SnowEx 2023 campaign from the Bonanza Creek Experimental Forest near Fairbanks, Alaska. Data were collected in October 2022 (snow-off) and March 2023 (snow-on).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_BCEF_TLS_Raw/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_BCEF_TLS v1 - This data set contains digital terrain models (DTMs) derived from terrestrial lidar scans (TLS) collected as part of the SnowEx 2023 campaign. Data were collected at the Bonanza Creek Experimental Forest near Fairbanks, Alaska in October 2022 (snow-off conditions) and March 2023 (snow-on conditions).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_BCEF_TLS/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_CSU_GPR_Raw v1 - This data set contains the raw results of 1 GHz ground-penetrating radar surveys conducted as part of the NASA SnowEx23 field campaign in Alaska, USA. Surveys were conducted at three different field sites between 07 March 2023 and 16 March 2023: 1) Farmers Loop/Creamers Field, 2) the Bonanza Creek Experimental Forest, and 3) the Caribou/Poker Creek Research Watershed.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_CSU_GPR_Raw/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_CR v1 - This data set contains an inventory of corner reflectors installed in Northern Alaska, USA, as part of the NASA SnowEx 2023 field campaign (Vuyovich et al., 2024). Corner Reflectors (CRs) are objects with known response characteristics used to calibrate or study radar returns from airborne and spaceborne imagery.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_CR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_CRREL_GPR_Raw v1 - This data set contains the results of 1 GHz ground-penetrating radar surveys conducted at the Upper Kuparuk/Toolik (UKT) site in northern Alaska, USA as part of the NASA SnowEx 2023 field campaign. Data were collected between 08 Mar 2023 to 15 Mar 2023, spatially coinciding with snow pit locations and along transects between snow pits.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_CRREL_GPR_Raw/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_GIS_Ref v1 - This dataset contains geolocation information for ground-based and airborne measurements collected in Alaska, USA as part of the NASA SnowEx 2023 campaign (Vuyovich et al., 2024). The purpose of this compilation is to facilitate the use of SnowEx 2023 Alaska data by providing Geographical Information System (GIS) files in one location.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_GIS_Ref/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_SSA v1 - This data set contains vertical profiles of snow reflectance and specific surface area (SSA) from the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, the Caribou Poker Creek watershed and Farmers Loop/Creamer’s Field), and a coastal tundra environment in the North Slope region of northern Alaska (the Arctic coastal plain and Upper Kuparuk Toolik), collected as part of the NASA SnowEx 2023 field campaign in March 2023. Reflectance was measured in snow pits using three different integrating sphere laser devices: an A2 Photonic Sensor IceCube (1310 nm), an IRIS (Infr...","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_SSA/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_SSA_SO v1 - This data set reports vertical profiles of snow reflectance and specific surface area (SSA) from two study sites in Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, the Caribou Poker Creek watershed and Farmers Loop/Creamer’s Field), and a coastal tundra environment in the North Slope region of northern Alaska (the Arctic coastal plain and Upper Kuparuk Toolik).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_SSA_SO/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_MAR22_SD v1 - The data set contains snow depth measurements from two regions of Alaska, USA collected during the March 2022 intensive observation period (IOP) as part of the NASA SnowEx 2023 field campaign. The study sites include three boreal forest sites in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and a coastal tundra site in the North Slope region (Arctic coastal plain).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_MAR22_SD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_MAR23_SD v1 - The data set contains snow depth measurements from five study sites in Alaska, USA; data were collected during the March 2023 intensive observation period (IOP) as part of the NASA SnowEx 2023 field campaign. The study sites include three boreal forest sites in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and two coastal tundra sites in the North Slope region (Arctic coastal plain and Upper Kuparuk Toolik).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_MAR23_SD/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_CSU_GPR v1 - This data set contains the results of 1 GHz ground-penetrating radar surveys conducted as part of the NASA SnowEx23 field campaign in Alaska, USA. Surveys were conducted at three different field sites between 07 March 2023 and 16 March 2023: 1) Farmers Loop/Creamers Field (FLCF), 2) the Bonanza Creek Experimental Forest (BCEF), and 3) the Caribou/Poker Creek Research Watershed (CPCW).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_CSU_GPR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_MAR23_Profiles v1 - Snow depth profiles were measured from 7-16 March 2023 as part of the NASA SnowEx 2023 March 2023 Intensive Observation Period. Ground-based snow depth profile measurements were taken in the vicinity of 170 snow pits distributed across five study areas in Northern Alaska.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_MAR23_Profiles/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_CBand v1 - This data set contains C-band radar data collected during the NASA SnowEx 2023 Alaska field campaign between 08 March 2023 to 15 March 2023. Data was acquired from two study areas near Fairbanks, Alaska using a multi-polarization radar affixed to sled-mounted tower.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_CBand/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_MAR23_SP v1 - The data set presents snow pit measurements collected during the NASA SnowEx March 2023 Intensive Observation Period (IOP) in Alaska, USA to use for calibration and validation with coincident airborne SWESARR and lidar measurements as part of the strategy focused on snow water equivalence (SWE) and snow depth (HS). In total, 170 snow pits were excavated between the five sites at locations representing a range of snow depth, vegetation, and topographic conditions.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_MAR23_SP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_OCT22_GSR_Raw v1 - This data set presents photographs of snow pit ground surface collected using a digital camera during the NASA SnowEx 2023 field campaign between 23 and 25 October 2022. The images were collected from 13 snow pits located at the Upper Kuparuk and Toolik (UKT) study site, an arctic tundra environment in Northern Alaska.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_OCT22_GSR_Raw/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_OCT22_GSR v1 - This data set presents ground surface roughness data collected during the NASA SnowEx 2023 field campaign between 23 and 25 October 2022. The data are formatted as point clouds, compiled from images acquired using a digital camera.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_OCT22_GSR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_Oct22_SP v1 - The data set presents snow pit measurements collected during the NASA SnowEx October 2022 Intensive Observation Period (IOP) in Alaska, USA. In total, 186 snow pits were excavated between the five sites at locations representing a range of snow depth, vegetation, and topographic conditions.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_OCT22_SP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_OCT22_SSA v1 - This data set reports vertical profiles of snow reflectance and specific surface area (SSA) collected in October 2022 as part of the NASA SnowEx 2023 Alaska field campaign. Measurements were taken from three snow pits located within the Upper Kuparuk Toolik (UKT) field site, a coastal tundra environment in the North Slope region of the northern Alaska coastal plain.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_OCT22_SSA/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_OCT23_GSR_Raw v1 - This data set presents photographs of snow pit ground surface collected using a digital camera during the NASA SnowEx 2023 field campaign between 17 and 28 October 2023. The images were collected from 22 snow pits located across three study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Caribou Poker Creek watershed (CPCW) and Farmers Loop Creamers Field (FLCF), two boreal forest sites near Fairbanks, Alaska.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_OCT23_GSR_Raw/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_OCT23_GSR v1 - This data set presents ground surface roughness data collected during the NASA SnowEx 2023 field campaign between 17 and 28 October 2023. The data are formatted as point clouds, compiled from images acquired using a digital camera.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_OCT23_GSR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_SD_TLI v1 - The dataset comprises snow depth measurements extracted from snow poles in time-lapse images near Fairbanks, AK during the SnowEx 2022-2023 field campaign. Snow poles were 1.52 m (5 ft) vertical poles painted red with a 10.16-cm (4 in) yellow top.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_SD_TLI/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_SWE v1 - This data set presents snow depth, snow water equivalent (SWE), and bulk snow density data collected during the NASA SnowEx 2023 field campaign between March 13-16 2023. Samples were collected using an Adirondack snow sampler (SWE tube) from two study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Farmers Loop Creamers Field (FLCF), a boreal forest near Fairbanks, Alaska.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_SWE/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_Tree_SM v1 - This dataset contains soil sensor measurements collected as part of the SnowEx Alaska 2023 field campaign. Time series measurements occurred at two locations within the Bonanza Creek Experimental Forest (BCEF), Alaska, from sensors inserted into the soil and into a tree.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_Tree_SM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_TLI v1 - The dataset comprises time-lapse images captured by cameras placed near Fairbanks, AK as part of the NASA SnowEx 2022-2023 field campaign. Nine cameras were installed in the Bonanza Creek Experimental Forest (BCEF) and eight cameras were installed in the Caribou-Poker Creek Research Watershed (CPCW).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_TLI/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX23_UW_GPR v1 - This data set contains the results of 1 GHz ground-penetrating radar surveys conducted at the Arctic Coastal Plain (ACP) site and the Upper Kuparuk/Toolik (UKT) site in northern Alaska during the SnowEx23 field campaign. Data include two-way travel time, derived snow depth, and derived snow water equivalent.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX23_UW_GPR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-07-09","Slug":"nasa-snowex","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SNWG/OPERA Project","Description":"This dataset contains Level-3 Dynamic OPERA surface water extent product version 1. The data are validated surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B/C (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B/C satellite. The surface water extent products are distributed over projected map coordinates using the Universal Transverse Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km × 109.8 km. This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) files including water classification, associated confidence, land cover classification, terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model (DEM), and Diagnostic layer. <br><br> The digital elevation model (DEM) provided as a layer of the DSWx-HLS product (band 10) was generated using the Copernicus DEM 30-m and Copernicus DEM 90-m models provided by the European Space Agency. The Copernicus DEM 30-m and Copernicus DEM 90-m were produced using Copernicus WorldDEM-30 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. The organizations in charge of the OPERA project, the Copernicus programme, and Airbus Defence and Space GmbH by law or by delegation do not assume any legal responsibility or liability, whether express or implied, arising from the use of this DEM. <br><br> The OPERA DSWx-HLS product contains modified Copernicus Sentinel data (2023-2025). <br><br> To access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov <br><br> The OPERA Project is funded by NASA’s Satellite Needs Working Group (SNWG) which provides data products developed to meet the needs of stakeholders from US government agencies.\n<br>\n\n#### OPERA_L3_DSWX-S1_V1\nThis dataset contains Level-3 Dynamic OPERA Surface Water Extent from Sentinel-1 (DSWx-S1) product version 1. DSWx-S1 provides near-global geographical mapping of surface water extent over land at a spatial resolution of 30 meters over the Military Grid reference System (MGRS) grid system, with a temporal revisit frequency between 6-12 days. Using Sentinel-1 radar observations, DSWx-S1 maps open inland water bodies greater than 3 hectares and 200 meters in width, irrespective of cloud conditions and daylight illumination that often pose challenges to optical sensors. Forward production of the DSWx-S1 data record began in Sept 2024. Each product is distributed as a set of 3 GeoTIFF (Geographic Tagged Image File Format) files including water classification and associated confidence layers. <br><br> The OPERA DSWx-S1 product contains modified Copernicus Sentinel data (2024-2025). <br><br> To access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov <br><br> The OPERA Project is funded by NASA’s Satellite Needs Working Group (SNWG) which provides data products developed to meet the needs of stakeholders from US government agencies.\n<br>\n\n#### OPERA_DSWX-S1_CALVAL_V1\nThis dataset contains the calibration/validation (CalVal) database for the OPERA DSWx-S1 product. The CalVal database is a zip file of an Amazon Web Services S3 bucket containing classification items that enable algorithm calibration and validation of OPERA products. The CalVal database contains a Reference Document that further describes the structure and usage of the database, as well as a Validation Results document. Example notebooks demonstrating how to read the database tables, query for specific items, and download corresponding data files are available through the CalVal GitHub repository here: https://github.com/OPERA-Cal-Val/calval-database\n<br>\n\n#### OPERA_L3_DIST-ALERT-HLS_V1\nThe Observational Products for End-Users from Remote Sensing Analysis ([OPERA](https://www.jpl.nasa.gov/go/opera)) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) product Version 1 maps vegetation disturbance alerts that are derived from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). A vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The Level-3 data product also provides additional information about more general disturbance trends and auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes. [HLS](https://lpdaac.usgs.gov/product_search/?collections=HLS&status=Operational&view=list) data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration or small magnitude/long duration. The OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within the DIST-ALERT product. The layers for both vegetation and generic disturbance include disturbance status, loss or anomaly, maximum loss anomaly, disturbance confidence layer, date of disturbance, count of observations with loss anomalies, days of ongoing anomalies, and day of last disturbance detection. Additional layers are vegetation cover percent, historical percent vegetation cover, and data mask. See the Product Specification Document (PSD) for a more detailed description of the individual layers provided in the DIST-ALERT product. The OPERA project is funded by NASA’s Satellite Needs Working Group (SNWG) which provides data products developed to meet the needs of stakeholders from US government agencies. The OPERA_L3_DIST-ALERT-HLS product contains modified Copernicus Sentinel data (2020-2025). Known Issues: Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD).\n<br>\n\n#### OPERA_L3_DIST-ALERT-HLS_PROVISIONAL_V0\nThe OPERA_L3_DIST-ALERT-HLS Version 0 data product was decommissioned on April 25, 2025. Users are encouraged to use the [OPERA_L3_DIST-ALERT-HLS V1](https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001) data product which was released on March 14, 2024, and has achieved stage 1 validation. The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) provisional data product Version 0 maps vegetation disturbance alerts from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). Vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes to provide information about more general disturbance trends. HLS data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration, or small magnitude/long duration. The OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within in the DIST-ALERT product: vegetation disturbance status, current vegetation cover indicator, current vegetation anomaly value, historical vegetation cover indicator, max vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, and vegetation disturbance duration. See the Product Specification for a more detailed description of the individual layers provided in the DIST-ALERT product. The OPERA project is funded by NASA’s Satellite Needs Working Group (SNWG) which provides data products developed to meet the needs of stakeholders from US government agencies. Known Issues: Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD).\n<br>\n\n#### OPERA_L3_DIST-ANN-HLS_V1\nThe Observational Products for End-Users from Remote Sensing Analysis ([OPERA](https://www.jpl.nasa.gov/go/opera)) Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 (HLS) product Version 1 summarizes the [DIST-ALERT](https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001) data product into an annual vegetation disturbance data product. Vegetation disturbance is mapped when there is an indicated decrease in vegetation cover within an HLS Version 2 pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the DIST-ALERT scenes to provide information about more general disturbance trends. The DIST-ANN product tracks changes at the annual scale, aggregating changes identified in the DIST-ALERT product. Only confirmed disturbances from the associated year are reported together with the date of initial disturbance. As confirmed disturbances are determined using subsequent cloud-free observations to determine if the loss detections persist, the required number of HLS scenes depends on visibility of the target. Due to this dependency, summarizing the DIST-ALERT in the DIST-ANN product will have some latency contingent on the algorithmic calibration and is detailed in the Algorithm Theoretical Basis Document (ATBD). The OPERA_L3_DIST-ANN-HLS (or DIST-ANN) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate COG. There are 21 layers contained within the DIST-ANN product: vegetation disturbance status, historical vegetation cover indicator, maximum vegetation cover indicator, maximum vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, vegetation disturbance duration, date of last observation assessed for vegetation disturbance, and several generic disturbance layers. Each product layer is gridded to the same resolution and tiling system as HLS V2: 30 meter (m) and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)). See the Product Specification Document (PSD) for a more detailed description of the individual layers provided in the DIST-ANN product. The OPERA project is funded by NASA’s Satellite Needs Working Group (SNWG) which provides data products developed to meet the needs of stakeholders from US government agencies. The OPERA_L3_DIST-ANN-HLS product contains modified Copernicus Sentinel data (2020-2025). Known Issues: Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","cog","earth observation","elevation","ice","land cover","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"OPERA_L3_DSWX-HLS_V1 v1.0 - This dataset contains Level-3 Dynamic OPERA surface water extent product version 1. The data are validated surface water extent observations beginning April 2023.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OPERA_L3_DSWX-S1_V1 v1.0 - This dataset contains Level-3 Dynamic OPERA Surface Water Extent from Sentinel-1 (DSWx-S1) product version 1. DSWx-S1 provides near-global geographical mapping of surface water extent over land at a spatial resolution of 30 meters over the Military Grid reference System (MGRS) grid system, with a temporal revisit frequency between 6-12 days.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OPERA_L3_DSWX-S1_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OPERA_DSWX-S1_CALVAL_V1 v1 - This dataset contains the calibration/validation (CalVal) database for the OPERA DSWx-S1 product. The CalVal database is a zip file of an Amazon Web Services S3 bucket containing classification items that enable algorithm calibration and validation of OPERA products.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/OPERA_DSWX-S1_CALVAL_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"OPERA_L3_DIST-ALERT-HLS_V1 v1 - The Observational Products for End-Users from Remote Sensing Analysis ([OPERA](https://www.jpl.nasa.gov/go/opera)) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) product Version 1 maps vegetation disturbance alerts that are derived from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). A vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel.","ARN":"arn:aws:s3:::lp-protected/OPERA_L3_DIST-ALERT-HLS_V1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"OPERA_L3_DIST-ALERT-HLS_PROVISIONAL_V0 v0 - The OPERA_L3_DIST-ALERT-HLS Version 0 data product was decommissioned on April 25, 2025. Users are encouraged to use the [OPERA_L3_DIST-ALERT-HLS V1](https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001) data product which was released on March 14, 2024, and has achieved stage 1 validation.","ARN":"arn:aws:s3:::lp-protected/OPERA_DIST-ALERT-HLS_PROVISIONAL_V0.000","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"OPERA_L3_DIST-ANN-HLS_V1 v1 - The Observational Products for End-Users from Remote Sensing Analysis ([OPERA](https://www.jpl.nasa.gov/go/opera)) Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 (HLS) product Version 1 summarizes the [DIST-ALERT](https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001) data product into an annual vegetation disturbance data product. Vegetation disturbance is mapped when there is an indicated decrease in vegetation cover within an HLS Version 2 pixel.","ARN":"arn:aws:s3:::lp-protected/OPERA_L3_DIST-ANN-HLS_V1.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-snwg-opera","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SOHO/LASCO2 comet challenge on AWS","Description":"The SOHO/LASCO data set (prepared for the challenge hosted in Topcoder) provided here comes from the instrument’s C2 telescope and comprises approximately 36,000 images spread across 2,950 comet observations. The human eye is a very sensitive tool and it is the only tool currently used to reliably detect new comets in SOHO data - particularly comets that are very faint and embedded in the instrument background noise. Bright comets can be easily detected in the LASCO data by relatively simple automated algorithms, but the majority of comets observed by the instrument are extremely faint, noise-level observations. Comets in SOHO/LASCO data are dynamic and morphologically diverse objects, and thus computationally highly complex to detect and track.\n","Contact":"ekaterina.m.verner@nasa.gov","ManagedBy":"[NASA](http://www.nasa.gov/)","Documentation":"https://ssa.esac.esa.int/ssa/#/pages/home","UpdateFrequency":"No updates","Tags":["astronomy","aws-pds","machine learning","NASA SMD AI"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"AI/ML ready data set created using NASA/SOHO/LASCO2 archive data","ARN":"arn:aws:s3:::nasa-comets-training-data","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Topcoder Challenge Finds Two New Comets For NASA","URL":"https://www.topcoder.com/blog/topcoder-challenge-finds-two-new-comets-for-nasa/","AuthorName":"Annika Nagy"},{"Title":"Topcoder NASA Comet Discovery: A Recap","URL":"https://www.youtube.com/watch?v=E4OxTaqTP6E","AuthorName":"TopCoder"},{"Title":"Winners Selected for the NASA SOHO Comet Search with Artificial Intelligence Open-Science Challenge","URL":"https://science.nasa.gov/science-news/winners-selected-for-the-nasa-soho-comet-search-with-artificial-intelligence-open-science-challenge","AuthorName":"Denise Hill"}]},"RegistryEntryAdded":"2022-10-25","RegistryEntryLastModified":"2023-01-31","Slug":"nasa-soho-comet-challenge-on-aws","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Soil Project","Description":"This data set provides the concentrations of soil microbial biomass carbon (C), nitrogen (N) and phosphorus (P), soil organic carbon, total nitrogen, and total phosphorus at biome and global scales. The data were compiled from a comprehensive survey of publications from the late 1970s to 2012 and include 3,422 data points from 315 papers. These data are from soil samples collected primarily at 0-15 cm depth with some from 0-30 cm. In addition, data were compiled for soil microbial biomass concentrations from soil profile samples to depths of 100 cm. Sampling site latitude and longitude were available for the majority of the samples that enabled assembling additional soil properties, site characteristics, vegetation distributions, biomes, and long-term climate data from several global sources of soil, land-cover, and climate data. These site attributes are included with the microbial biomass data. This data set contains two .csv files of the soil microbial biomass C, N, P data. The first provides all compiled results emphasizing the full spatial extent of the data, while the second is a subset that provides only data from a series of profile samples emphasizing the vertical distribution of microbial biomass concentrations. There is a companion file, also in .csv format, of the references for the surveyed publications. A reference_number relates the data to the respective publication. The concentrations of soil microbial biomass, in combination with other soil databases, were used to estimate the global storage of soil microbial biomass C and N in 0-30 cm and 0-100 cm soil profiles. These storage estimates were combined with a spatial map of 12 major biomes (boreal forest, temperate coniferous forest, temperate broadleaf forest, tropical and subtropical forests, mixed forest, grassland, shrub, tundra, desert, natural wetland, cropland, and pasture) at 0.05-degree by 0.5-degree spatial resolution. The biome map and six estimates of C and N storage and C:N ration in soil microbial biomass are provided in a single netCDF format file.\n<br>\n\n#### Global_RTSG_Flux_1078\nThis database contains information compiled from published studies on gas flux from soil following rewetting or thawing. The resulting database includes 222 field and laboratory observations focused on rewetting of dry soils, and 116 field laboratory observations focused on thawing of frozen soils studies conducted from 1956 to 2010. Fluxes of carbon dioxide, methane, nitrous oxide, nitrogen oxide, and ammonia (CO2, CH4, N2O, NO and NH3) were compiled from the literature and the flux rates were normalized for ease of comparison. Field observations of gas flux following rewetting of dry soils include events caused by natural rainfall, simulated rainfall in natural ecosystems, and irrigation in agricultural lands. Similarly, thawing of frozen soils include field observations of natural thawing, simulated freezing-thawing events (i.e., thawing of simulated frozen soil by snow removal), and thawing of seasonal ice in temperate and high latitude regions (Kim et al., 2012). Reported parameters include experiment type, location, site type, vegetation, climate, soil properties, rainfall, soil moisture, soil gas flux after wetting and thawing, peak soil gas flux properties, and the corresponding study references. There is one comma-delimited data file.\n<br>\n\n#### Global_Phosphorus_Hedley_Fract_1230\nThis data set provides concentrations of soil phosphorus (P) compiled from the peer-reviewed literature that cited the Hedley fractionation method (Hedley and Stewart, 1982). This database contains estimates of different forms of naturally occurring soil phosphorus, including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P, based on the analyses of the various Hedley soil fractions. The recent literature survey (Yang and Post, 2011) was restricted to studies of natural, unfertilized, and uncultivated soils since 1995. Ninety measurements of soil P fractions were identified. These were added to the 88 values from soils in natural ecosystems that Cross and Schlesinger (1995) had compiled. Cross and Schlesinger provided a comprehensive survey on Hedley P data prior to 1995. Measurement data are provided for studies published from 1985 through 2010. In addition to the Hedley P fraction measurement data Yang and Post (2011) also compiled information on soil order, soil pH, organic carbon and nitrogen content, as well as the geographic location (longitude and latitude) of the measurement sites.\n<br>\n\n#### SRDB_V5_1827\nThe Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C. The SRDB's orientation is to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. Version 5 (V5) is the compilation of 2,266 published studies with measurements taken between 1961-2017. V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. The database is also restructured to have better interoperability with other datasets related to carbon-cycle science.\n<br>\n\n#### Thermokarst_Circumpolar_Map_1332\nThis data set provides the distribution of thermokarst landscapes in the boreal and tundra ecoregions within the northern circumpolar permafrost zones. This dataset provides an areal estimate of wetland, lake, and hillslope thermokarst landscapes as of 2015. Estimates of soil organic carbon (SOC) content associated with thermokarst and non-thermokarst landscapes were based on available circumpolar 0 to 3 meter SOC storage data.\n<br>\n\n#### Global_Soil_Regolith_Sediment_1304\nThis data set provides high-resolution estimates of the thickness of the permeable layers above bedrock (soil, regolith, and sedimentary deposits) within a global 30-arcsecond (~1-km) grid using the best available data for topography, climate, and geology as input. These data are modeled to represent estimated thicknesses by landform type for the geological present.\n<br>\n\n#### Respiration_622\nThis data set is a compilation of soil respiration rates (g C m-2 yr-1) from terrestrial and wetland ecosystems reported in the literature prior to 1992. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes. In this summary, only those data based on most or all of one full year of measurements were used so that annual rates of soil respiration could be estimated. Data from soil cores were excluded because the sample coring modifies root respiration. Also included in the data set are biome type, vegetation type, locality, and geographic coordinates, based on information from the original paper. Mean annual temperature and precipitation were based on the original paper; where those data were not included, they were estimated from a gridded global climate database [0.5 degree resolution; Legates D.R. and C.J. Willmott. 1988. Global Air Temperature and Precipitation Data Archive. Department of Geography, University of Delaware, Newark, Delaware, USA).\n<br>\n\n#### IsricWiseGrids_546\nThe World Inventory of Soil Emission Potentials (WISE) database currently contains data for over 4300 soil profiles collected mostly between 1950 and 1995. This database has been used to generate a series of uniform data sets of derived soil properties for each of the 106 soil units considered in the Soil Map of the World (FAO-UNESCO, 1974). These data sets were then linked to a 1/2 degree longitude by 1/2 degree latitude version of the edited and digital Soil Map of the World (FAO, 1995) to generate GIS raster image files for the following variables: Total available water capacity (mm water per 1 m soil depth) soil organic carbon density (kg C/m**2 for 0-30cm depth range) soil organic carbon density (kg C/m**2 for 0-100cm depth range) soil carbonate carbon density (kg C/m**2 for 0-100cm depth range) soil pH (0-30 cm depth range) soil pH (30-100 cm depth range) Data Citation: The data set should be cited as follows: Batjes, N. H. (ed). 2000. Global Data Set of Derived Soil Properties, 0.5-Degree Grid (ISRIC-WISE). Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.\n<br>\n\n#### Dunne_545\nPlant-extractable water capacity of soil is the amount of water that can be extracted from the soil to fulfill evapotranspiration demands. It is often assumed to be spatially invariant in large-scale computations of the soil-water balance. Empirical evidence, however, suggests that this assumption is incorrect. This data set provides an estimate of the global distribution of plant-extractable water capacity of soil. A representative soil profile, characterized by horizon (layer) particle size data and thickness, was created for each soil unit mapped by FAO (Food and Agriculture Organization of the United Nations)/Unesco. Soil organic matter was estimated empirically from climate data. Plant rooting depths and ground coverages were obtained from a vegetation characteristic data set. At each 0.5 x 0.5 degree grid cell where vegetation is present, unit available water capacity (cm water per cm soil) was estimated from the sand, clay, and organic content of each profile horizon, and integrated over horizon thickness. Summation of the integrated values over the lesser of profile depth and root depth produced an estimate of the plant-extractable water capacity of soil. The global average of the estimated plant-extractable water capacities of soil is 8.6 cm (Greenland, Antarctica and bare soil areas excluded). Estimates are less than 5, 10 and 15 cm - over approximately 30, 60, and 89 per cent of the area, respectively. Estimates reflect the combined effects of soil texture, soil organic content, and plant root depth or profile depth. The most influential and uncertain parameter is the depth over which the plant-extractable water capacity of soil is computed, which is usually limited by root depth. Soil texture exerts a lesser, but still substantial, influence. Organic content, except where concentrations are very high, has relatively little effect. The file is available in an ascii array format. The format is such that j=1 corresponds to the grid cell bounded by 90.0 and 89.5 degrees south latitude (centered on 89.75) and i=1 corresponds to the grid cell bounded by 0.0 and 0.5 degrees east longitude (centered on 0.25). No data are given for land ice grid cells, most of which occur in Antarctica and Greenland, or for other unvegetated areas. A value of -99.0 indicates either a water grid cell or a land ice grid cell. A value of -1.0 indicates that vegetation is absent (and the plant-extractable water capacity of soil is undefined). Units are cm. The data file may be read as follows: dimension whcdat(720,360) do j=1,360 read(iunit,'(36f5.1)') (whcdat(i,j),i=1,720) enddo Data Citation The data set should be cited as follows: Dunne, K. A., and Cort J. Willmott. 2000. Global Distribution of Plant-extractable Water Capacity of Soil (Dunne). Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.\n<br>\n\n#### Global_Phosphorus_Dist_Map_1223\nThis data set provides estimates of different forms of naturally occurring soil phosphorus (P) including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P on a global scale at 0.5-degree resolution. The data were assembled from chronosequence information and global spatial databases to develop a map of total soil P and the distribution among mineral bound, labile, organic, occluded, and secondary P forms in soils. Uncertainty was calculated for the different forms. The data set has no explicit temporal component -- data were nominally for the pre-industrial period ca. 1850. The estimated global spatial variation and distribution of different soil P forms presented in this study will be useful for global biogeochemistry models that include P as a limiting element in biological production by providing initial estimates of the available soil P for plant uptake and microbial utilization (Yang et al., 2013). There is one netCDF data file (.nc) with this data set.\n<br>\n\n#### IGBP-SurfaceProducts_569\nThe Global Gridded Surfaces of Selected Soil Characteristics (IGBP-DIS) data set contains 7 data surfaces: soil-carbon density, total nitrogen density, field capacity, wilting point, profile available water capacity, thermal capacity, and bulk density. All the surfaces are global, at a resolution of 5x5 arc-minutes, in ASCIIGRID format for ARC INFO. Each file contains a single ASCII array in a geographic (lat/long) projection. The ascii files consist of header information containing a set of keywords, followed by cell values in row-major order. These data surfaces were generated by the SoilData System, which was developed by the Global Soil Data Task of the International Geosphere-Biosphere Programme (IGBP) Data and Information Services (DIS). The SoilData System generates soil information and maps for geographic regions at soil depths and resolutions selected by the user. Derived surfaces of selected soil characteristics are suitable for modeling and inventory purposes. The data surfaces are also distributed as part of the Global Soil Data Products CD-ROM. The SoilData System uses a statistical bootstrapping approach to link the pedon records in the Global Pedon Database to the FAO/UNESCO Digital Soil Map of the World. It can generate maps and output data sets for a range of original and derived soil parameters, such as carbon and nitrogen density, thermal conductivity, and water-holding capacity, for any part of the world at user-selected depth ranges. The digital output can be at any resolution (in increments of 5').\n<br>\n\n#### Global_Hydrologic_Soil_Group_1566\nThis dataset - HYSOGs250m - represents a globally consistent, gridded dataset of hydrologic soil groups (HSGs) with a geographical resolution of 1/480 decimal degrees, corresponding to a projected resolution of approximately 250-m. These data were developed to support USDA-based curve-number runoff modeling at regional and continental scales. Classification of HSGs was derived from soil texture classes and depth to bedrock provided by the Food and Agriculture Organization soilGrids250m system.\n<br>\n\n#### ZinkeSoil_221\nThis package contains worldwide soil carbon and nitrogen data for more than 3,500 soil profiles. The database was begun about 40 years ago with the collection and analyses by Zinke of soil samples from California. Additional data came from soil surveys of California, Italy and Greece, Iran, Thailand, Vietnam, various tropical Amazonian areas, U.S. forest soils,and from the soil survey literature. The main samples for laboratory analyses were collected at uniform soil depth increments and included bulk density determinations, but samples reported in the literature did not always have this uniformity. For the latter group of samples, only profiles that were samples to a meter depth or to actual depth were used; if bulk densities were not reported, then estimates were made from regressions based on organic carbon content of the soil samples associated with the profile. Methods used for analytical carbon determinations were dry combustion, 'wet combustion', or loss on ignition with adjustments made to the values obtained with the last two methods. Nitrogen was determined by the Kjeldahl method on the soil fine earth fraction and reported as total organic nitrogen. The data can be used to estimate the size of the soil's organic carbon and nitrogen pools at equilibrium with natural soil-forming factors. Most of the data are from profiles associated with natural vegetation so they constitute a baseline for evaluation of the effects that disturbance or modification to natural vegetation has on soil carbon equilibrium at either a global or regional scale. The data can also be used for understanding the range and viability of soil carbon and nitrogen pools for specific ecosystems or climatic regimes.\n<br>\n\n#### Peatland_carbon_balance_1382\nThis data set provides a time series of global peatland carbon balance and carbon dioxide emissions from land use change throughout the Holocene (the past 11,000 yrs). Global peatland carbon balance was quantified using a) a continuous net carbon balance history throughout the Holocene derived from a data set of 64 dated peat cores, and b) global model simulations with the LPX-Bern model hindcasting the dynamics of past peatland distribution and carbon balance. CO2 emissions from land-use change are based on published scenarios for anthropogenic land use change (HYDE 3.1, HYDE 3.2, KK10) covering the last 10,000 years. This combination of model estimates with CO2 budget constraints narrows the range of past anthropogenic land use change emissions and their contribution to past carbon cycle changes.\n<br>\n\n#### IGBP-DIS_565\nThis dataset contains global data on soil properties, global maps of soil distributions, and the SoilData System developed by the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS). These data were originally distributed on CD-ROM, but are provided here as a single zip file. The SoilData System allows users to generate soil information and maps for geographic regions at soil depths and resolutions selected by the user. Derived surfaces of carbon density, nutrient status, water-holding capacity, and heat capacity are provided for modeling and inventory purposes.\n<br>\n\n#### IsricWise_547\nThe International Soil Reference and Information Centre-World Inventory of Soil Emission Potentials (ISRIC-WISE) international soil profile data set consists of a homogenized, global set of 1,125 soil profiles for use by global modelers. These profiles provided the basis for the Global Pedon Database (GPDB) of the International Geosphere-Biosphere Programme (IGBP) - Data and Information System (DIS). The data set consists of a selection of 665 profiles originating from the Natural Resources Conservation Service (NRCS, Lincoln), 250 profiles obtained from the Food and Agriculture Organization (FAO, Rome), and 210 profiles from the reference collection of the International Soil Reference and Information Centre (ISRIC, Wageningen). All profiles are georeferenced and classified according to the 1974 Legend of the FAO-UNESCO Soil Map (FAC-UNESCO, 1974) of the World, as well as the 1988 Revised Legend of FAO-UNESCO (FAO, 1990). The data set includes information on soil classification, site data, soil horizon data, source of data, and methods used for determining analytical data. The data files are in a comma-delimited format. Data Citation: The data set should be cited as follows: Batjes, N. H. (ed). 2000. Global Soil Profile Data (ISRIC-WISE). Available on-line from the ORNL Distributed Active Archive Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee, U.S.A.\n<br>\n\n#### WebbRosenzweig_548\nA standardized global data set of soil horizon thicknesses and textures (particle size distributions) was compiled by Webb et al. This data set will be used for the improved ground hydrology parameterization design for the Goddard Institute for Space Studies General Circulation Model (GISS GCM) Model III. The data set specifies the top and bottom depths and the percent abundance of sand, silt, and clay of individual soil horizons in each of the 106 soil types cataloged for nine continental divisions. When combined with the World Soil Data File (Zobler, 1986), the result is a global data set of variations in physical properties throughout the soil profile. These properties are important in the determination of water storage in individual soil horizons and exchange of water with the lower atmosphere. The incorporation of this data set into the GISS GCM should improve model performance by including more realistic variability in land-surface properties. All data are global at a 1 degree resolution and are provided in ASCII format. The primary data consist of 2 files. One file contains the depth and particle size (percent sand, silt, and clay) information for each major continent, soil type, and soil horizon. The other file contains ocean/continental coding (corresponding to FAO/UNESCO Soil Map of the World) (FAO/UNESCO, 1971-1981) and Zobler soil type classifications (Zobler, 1986). A fortran code for reading these data files is provided. In addition to the primary data files, there are also 5 derived data sets available for download: 1) soil profile thickness, 2) potential storage of water in the soil profile, 3) potential storage of water in the root zone, 4) potential storage of water derived from soil texture, 5) data set used to prescribe water-holding capacity in the GISS GCM (Model II). Data Citation The data set should be cited as follows: Webb, Robert W., Cynthia E. Rosenzweig, and Elissa R. Levine. 2000. Global Soil Texture and Derived Water-Holding Capacities (Webb et al.). Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.\n<br>\n\n#### ZoblerSoilDerived_540\nA global data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler?s (1986) assessment of the FAO/UNESCO Soil Map of the World. This data set is a conversion of the Zobler 1-degree resolution version to a 0.5-degree resolution. The resolution of the data set was not actually increased. Rather, the 1-degree squares were divided into four 0.5-degree squares with the necessary adjustment of continental boundaries and islands. The computer code used to convert the original 1-degree data to 0.5-degree is provided as a companion file. A JPG image of the data is provided in this document. The Zobler data (1-degree resolution) as distributed by Webb et al. (1993) [http://www.ngdc.noaa.gov/seg/eco/cdroms/gedii_a/datasets/a12/wr.htm#top] contains two columns, one column for continent and one column for soil type. The Soil Map of the World consists of 9 maps that represent parts of the world. The texture data that Webb et al.(1993) provided allowed for the fact that a soil type in one part of the world may have different properties than the same soil in a different part of the world. This continent-specific information is retained in this 0.5-degree resolution data set, as well as the soil type information which is the second column. A code was written (one2half.c) to take the file CONTIZOB.LER distributed by Webb et al. (1993) [http://www.ngdc.noaa.gov/seg/eco/cdroms/gedii_a/datasets/a12/wr.htm#top] and simply divide the 1-degree cells into quarters. This code also reads in a land/water file (land.wave) that specifies the cells that are land at 0.5 degrees. The code checks for consistency between the newly quartered map and the land/water map to which the quartered map is to be registered. If there is a discrepancy between the two, an attempt was made to make the two consistent using the following logic. If the cell is supposed to be water, it is forced to be water. If it is supposed to be land but was resolved to water at 1 degree, the code looks at the surrounding 8 cells and picks the most frequent soil type and assigns it to the cell. If there are no surrounding land cells then it is kept as water in the hopes that on the next pass one or more of the surrounding cells might be converted from water to a soil type. The whole map is iterated 5 times. The remaining cells that should be land but couldn't be determined from surrounding cells (mostly islands that are resolved at 0.5 degree but not at 1 degree) are printed out with coordinate information. A temporary map is output with -9 indicating where data is required. This is repeated for the continent code in CONTIZOB.LER as well. A separate map of the temporary continent codes is produced with -9 indicating required data. A nearly identical code (one2half.c) does the same for the continent codes. The printout allows one to consult the printed versions of the soil map and look up the soil type with the largest coverage in the 0.5-degree cell. The program manfix.c then will go through the temporary map and prompt for input to correct both the soil codes and the continent codes for the map. This can be done manually or by preparing a file of changes (new_fix.dat) and redirecting stdin. A new complete version of the map is outputted. This is in the form of the original CONTIZOB.LER file (contizob.half) but four times larger. Original documentation and computer codes prepared by Post et al. (1996) are provided as companion files with this data set. Image of 106 global soil types available at 0.5-degree by 0.5-degree resolution. Additional documentation from Zobler?s assessment of FAO soil units is available from the NASA Center for Scientific Information.\n<br>\n\n#### ZoblerSoil_418\nA global data set of soil types is available at 1-degree latitude by 1-degree longitude resolution. There are 26 soil units based on Zobler?s assessment of FAO Soil Units (Zobler, 1986). The data set was compiled as part of an effort to improve modeling of the hydrologic cycle portion of global climate models. A more extensive version of these data, including 106 soil units as well as soil texture and slope, is available from NCAR, Scientific Computing Division, Data Support Section; the more extensive data set is entitled \"Staub and Rosenweig's GISS Soil & Sfc Slope, 1-Deg\" [http://www.dss.ucar.edu/datasets/ds770.0/]. A help file prepared by Matthews and Fung (1987) (soil1x1.help) is provided as a companion file. Image of 26 soil types available at 1-degree by 1-degree resolution. Additional documentation from Zobler?s assessment of FAO soil units is available from the NASA Center for Scientific Information\n<br>\n\n#### Soil_Moisture_Alaska_Alberta_2123\nThis dataset includes hourly in-situ soil moisture measurements from data loggers in predominantly organic soils (very low bulk density) at two locations: 1) along the Sag River in Alaska, U.S., and 2) near Red Earth Creek in Alberta, Canada. The dataset also provides soil moisture probe periods, temperature probe readings, as well as calibration coefficients and soil profile measurements used to create per probe calibrations for derived volumetric moisture content. The Campbell Scientific CR200 data loggers used CS625 water content reflectometers and temperature probe 109. Further details to the derivation of the calibrations are provided in a supplementary document. The purpose of the dataset is to provide field measurements that can be used for calibration/validation for satellite-based soil moisture retrieval algorithms. With some interruptions, the dataset exists from July 2017 to July 2021. The data are provided in comma-separated values (CSV) format.\n<br>\n\n#### Mid-latitude_soils_705\nThe U.S. Department of Agriculture, Agriculture and Agri-Food Canada, the Russian Academy of Agricultural Sciences, the University of Copenhagen Institute of Geography, the European Soil Bureau, the University of Manchester Institute of Landscape Ecology, MTT Agrifood Research Finland, and the Agricultural Research Institute Iceland have shared data and expertise in order to develop the Northern and Mid Latitude Soil Database (Cryosol Working Group, 2001). This database was the source of data for the current product. The spatial coverage of the Northern and Mid Latitude Soil Database is the polar and mid-latitude regions of the northern hemisphere: Alaska, Canada, Conterminous United States, Eurasia (except Italy), Greenland, Iceland, Kazakstan, Mexico, Mongolia, Italy, and Svalbard. The Northern and Mid Latitude Soil Database represents the proportion (percentage) of polygon encompassed by the dominant soil or nonsoil. Soils include turbels, orthels, histels, histosols, mollisols, vertisols, aridisols, andisols, entisols, spodosols, inceptisols (and hapludolls), alfisols (cryalf and udalf), natric great groups, aqu-suborders, glaciers, and rocklands. Also included are data on the circumpolar distribution of gelisols (turbels, orthels, and histels), and the ice content (low, medium, or high) of circumpolar soil materials (from the International Permafrost Association, 1997). The resulting maps show the dominant soil of the spatial polygon unless the polygon is over 90 percent rock or ice. Data are in the U.S. soil classification system and includes the distribution of soil types (%) within a map unit (polygon). Data are available in ESRI shapefile format and include the same attribute values with the exception of Italy, which does not contain distribution values.\n<br>\n\n#### HWSD_1247\nThis data set describes select global soil parameters from the Harmonized World Soil Database (HWSD) v1.2, including additional calculated parameters such as area weighted soil organic carbon (kg C per m2), as high resolution NetCDF files. These data were regridded and upscaled from the Harmonized World Soil Database v1.2 The HWSD provides information for addressing emerging problems of land competition for food production, bio-energy demand and threats to biodiversity and can be used as input to model global carbon cycles. The data are presented as a series of 27 NetCDF v3/v4 (.nc4) files at 0.05-degree spatial resolution, and one NetCDF file regridded to the Community Land Model (CLM) grid cell resolution (0.9 degree x 1.25 degree) for the nominal year of 2000.\n<br>\n\n#### TopSoil_Erosion_MidWest_US_1774\nThis dataset provides estimates of topsoil loss and economic loss associated with decreased crop productivity resulting from topsoil loss at county- and state-levels across the Corn Belt region of the Midwestern USA. Intermediate products used to derive topsoil loss are provided and include 4 m gridded estimates of study sites elevation, curvature, slope, soil organic carbon index (SOCI), and the probability of exposed B-horizon soil. Topsoil loss at the county- and state-levels was derived from analyses of agricultural land at selected sites across the study area. From WorldView imagery, 759 fields were identified that had exposed bare soil (210 km2) and were grouped into 28 sites. Gridded estimates of the SOCI and of the probability of exposed B-horizon soil were determined for each field within the sites. Topography measures, including elevation (m), curvature (m-1), and slope (deg), were extracted over the entire study area from LiDAR-derived digital elevation models at a 4 m resolution acquired from 2003-2018. Within each of the 28 study sites, the SOCI and topographic curvature values were extracted from co-located pixels. Topsoil loss was estimated from the relationship between subsoil exposure and topography and averaged across each site.The relationship between topsoil loss and topographic curvature was used to up-scale and predict topsoil and economic losses at the county and state-levels across the entire 375,000 km2 study area. The data have been used to demonstrate a robust and scalable method for estimating the magnitude of erosion in agricultural landscapes.\n<br>\n\n#### West_Soil_Carbon_1238\nThis data set provides a soil map with estimates of soil carbon (C) in g C/m2 for 20-cm layers from the surface to one meter depth for the conterminous United States.STATSGO v.1 (State Soil Geographic Database, Soil Survey Staff, 1994) data were used to estimate by 20-cm intervals to a 1-m depth the mean soil carbon for each of the STATSGO-delineated soil map units. These map units are the polygons represented in the provided Shapefile data product.\n<br>\n\n#### SOIL_CO2_Flux_Costa_Rica_1373\nThis data set provides measurements of soil carbon dioxide (CO2) emission rates, soil moisture, relative humidity (RH), temperature, and litterfall from six types of tree plantations at the La Selva Biological Station, Costa Rica. Soil CO2 flux and related measurements were made 1) hourly during 2-day diel field campaigns and 2) as single daytime measurements during multiple survey campaigns, over the period 2004-2010. All measurements were made at the same sites to compare hourly, monthly, and inter-annual variations. Most of the emissions data represent a single soil CO2 flux measurement, with three to five measurements per plot. Litterfall was collected monthly from 2003-2009 and was sorted into fractions prior to drying.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","elevation","hydrology","ice","lidar","netcdf","oceans","precipitation","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Global_Microbial_Biomass_C_N_P_1264 v1 - This data set provides the concentrations of soil microbial biomass carbon (C), nitrogen (N) and phosphorus (P), soil organic carbon, total nitrogen, and total phosphorus at biome and global scales. The data were compiled from a comprehensive survey of publications from the late 1970s to 2012 and include 3,422 data points from 315 papers.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/Global_Microbial_Biomass_C_N_P/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_RTSG_Flux_1078 v1 - This database contains information compiled from published studies on gas flux from soil following rewetting or thawing. The resulting database includes 222 field and laboratory observations focused on rewetting of dry soils, and 116 field laboratory observations focused on thawing of frozen soils studies conducted from 1956 to 2010.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/Global_RTSG_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Phosphorus_Hedley_Fract_1230 v1 - This data set provides concentrations of soil phosphorus (P) compiled from the peer-reviewed literature that cited the Hedley fractionation method (Hedley and Stewart, 1982). This database contains estimates of different forms of naturally occurring soil phosphorus, including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P, based on the analyses of the various Hedley soil fractions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/Global_Phosphorus_Hedley_Fract/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SRDB_V5_1827 v5 - The Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/SRDB_V5/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Thermokarst_Circumpolar_Map_1332 v1 - This data set provides the distribution of thermokarst landscapes in the boreal and tundra ecoregions within the northern circumpolar permafrost zones. This dataset provides an areal estimate of wetland, lake, and hillslope thermokarst landscapes as of 2015.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/Thermokarst_Circumpolar_Map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Soil_Regolith_Sediment_1304 v1 - This data set provides high-resolution estimates of the thickness of the permeable layers above bedrock (soil, regolith, and sedimentary deposits) within a global 30-arcsecond (~1-km) grid using the best available data for topography, climate, and geology as input. These data are modeled to represent estimated thicknesses by landform type for the geological present.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/Global_Soil_Regolith_Sediment/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Respiration_622 v1 - This data set is a compilation of soil respiration rates (g C m-2 yr-1) from terrestrial and wetland ecosystems reported in the literature prior to 1992. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/Respiration/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"IsricWiseGrids_546 v1 - The World Inventory of Soil Emission Potentials (WISE) database currently contains data for over 4300 soil profiles collected mostly between 1950 and 1995. This database has been used to generate a series of uniform data sets of derived soil properties for each of the 106 soil units considered in the Soil Map of the World (FAO-UNESCO, 1974).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/IsricWiseGrids/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Dunne_545 v1 - Plant-extractable water capacity of soil is the amount of water that can be extracted from the soil to fulfill evapotranspiration demands. It is often assumed to be spatially invariant in large-scale computations of the soil-water balance.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/Dunne/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Phosphorus_Dist_Map_1223 v1 - This data set provides estimates of different forms of naturally occurring soil phosphorus (P) including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P on a global scale at 0.5-degree resolution. The data were assembled from chronosequence information and global spatial databases to develop a map of total soil P and the distribution among mineral bound, labile, organic, occluded, and secondary P forms in soils.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/Global_Phosphorus_Dist_Map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"IGBP-SurfaceProducts_569 v1 - The Global Gridded Surfaces of Selected Soil Characteristics (IGBP-DIS) data set contains 7 data surfaces: soil-carbon density, total nitrogen density, field capacity, wilting point, profile available water capacity, thermal capacity, and bulk density. All the surfaces are global, at a resolution of 5x5 arc-minutes, in ASCIIGRID format for ARC INFO.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/IGBP-SurfaceProducts/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Hydrologic_Soil_Group_1566 v1 - This dataset - HYSOGs250m - represents a globally consistent, gridded dataset of hydrologic soil groups (HSGs) with a geographical resolution of 1/480 decimal degrees, corresponding to a projected resolution of approximately 250-m. These data were developed to support USDA-based curve-number runoff modeling at regional and continental scales.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/Global_Hydrologic_Soil_Group/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ZinkeSoil_221 v1 - This package contains worldwide soil carbon and nitrogen data for more than 3,500 soil profiles. The database was begun about 40 years ago with the collection and analyses by Zinke of soil samples from California.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/ZinkeSoil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Peatland_carbon_balance_1382 v1 - This data set provides a time series of global peatland carbon balance and carbon dioxide emissions from land use change throughout the Holocene (the past 11,000 yrs). Global peatland carbon balance was quantified using a) a continuous net carbon balance history throughout the Holocene derived from a data set of 64 dated peat cores, and b) global model simulations with the LPX-Bern model hindcasting the dynamics of past peatland distribution and carbon balance.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/Peatland_carbon_balance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"IGBP-DIS_565 v1 - This dataset contains global data on soil properties, global maps of soil distributions, and the SoilData System developed by the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS). These data were originally distributed on CD-ROM, but are provided here as a single zip file.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/IGBP-DIS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"IsricWise_547 v1 - The International Soil Reference and Information Centre-World Inventory of Soil Emission Potentials (ISRIC-WISE) international soil profile data set consists of a homogenized, global set of 1,125 soil profiles for use by global modelers. These profiles provided the basis for the Global Pedon Database (GPDB) of the International Geosphere-Biosphere Programme (IGBP) - Data and Information System (DIS).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/IsricWise/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"WebbRosenzweig_548 v1 - A standardized global data set of soil horizon thicknesses and textures (particle size distributions) was compiled by Webb et al. This data set will be used for the improved ground hydrology parameterization design for the Goddard Institute for Space Studies General Circulation Model (GISS GCM) Model III.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/WebbRosenzweig/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ZoblerSoilDerived_540 v1 - A global data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler?s (1986) assessment of the FAO/UNESCO Soil Map of the World.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/ZoblerSoilDerived/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ZoblerSoil_418 v1 - A global data set of soil types is available at 1-degree latitude by 1-degree longitude resolution. There are 26 soil units based on Zobler?s assessment of FAO Soil Units (Zobler, 1986).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/ZoblerSoil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Soil_Moisture_Alaska_Alberta_2123 v1 - This dataset includes hourly in-situ soil moisture measurements from data loggers in predominantly organic soils (very low bulk density) at two locations: 1) along the Sag River in Alaska, U.S., and 2) near Red Earth Creek in Alberta, Canada. The dataset also provides soil moisture probe periods, temperature probe readings, as well as calibration coefficients and soil profile measurements used to create per probe calibrations for derived volumetric moisture content.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/Soil_Moisture_Alaska_Alberta/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Mid-latitude_soils_705 v2 - The U.S. Department of Agriculture, Agriculture and Agri-Food Canada, the Russian Academy of Agricultural Sciences, the University of Copenhagen Institute of Geography, the European Soil Bureau, the University of Manchester Institute of Landscape Ecology, MTT Agrifood Research Finland, and the Agricultural Research Institute Iceland have shared data and expertise in order to develop the Northern and Mid Latitude Soil Database (Cryosol Working Group, 2001).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/Mid-latitude_soils/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"HWSD_1247 v1 - This data set describes select global soil parameters from the Harmonized World Soil Database (HWSD) v1.2, including additional calculated parameters such as area weighted soil organic carbon (kg C per m2), as high resolution NetCDF files. These data were regridded and upscaled from the Harmonized World Soil Database v1.2 The HWSD provides information for addressing emerging problems of land competition for food production, bio-energy demand and threats to biodiversity and can be used as input to model global carbon cycles.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/HWSD/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"TopSoil_Erosion_MidWest_US_1774 v1 - This dataset provides estimates of topsoil loss and economic loss associated with decreased crop productivity resulting from topsoil loss at county- and state-levels across the Corn Belt region of the Midwestern USA. Intermediate products used to derive topsoil loss are provided and include 4 m gridded estimates of study sites elevation, curvature, slope, soil organic carbon index (SOCI), and the probability of exposed B-horizon soil.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/TopSoil_Erosion_MidWest_US/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"West_Soil_Carbon_1238 v1 - This data set provides a soil map with estimates of soil carbon (C) in g C/m2 for 20-cm layers from the surface to one meter depth for the conterminous United States.STATSGO v.1 (State Soil Geographic Database, Soil Survey Staff, 1994) data were used to estimate by 20-cm intervals to a 1-m depth the mean soil carbon for each of the STATSGO-delineated soil map units. These map units are the polygons represented in the provided Shapefile data product.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/West_Soil_Carbon/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SOIL_CO2_Flux_Costa_Rica_1373 v1 - This data set provides measurements of soil carbon dioxide (CO2) emission rates, soil moisture, relative humidity (RH), temperature, and litterfall from six types of tree plantations at the La Selva Biological Station, Costa Rica. Soil CO2 flux and related measurements were made 1) hourly during 2-day diel field campaigns and 2) as single daytime measurements during multiple survey campaigns, over the period 2004-2010.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_soil/SOIL_CO2_Flux_Costa_Rica/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-soil","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SORCE Project","Description":"Welcome to the Home Page of the SOlar Radiation and Climate Experiment (SORCE) The Solar Radiation and Climate Experiment (SORCE) […]\n<br>\n\n#### SOR3D_COMBINED\nThe SORCE Combined XPS, SOLSTICE, and SIM Solar Spectral Irradiance 24-Hour Means product consists of daily averages of the solar spetra from 0.1 to 2412 nm. The SORCE instruments make measurements during each daytime orbit portion, 15 orbits per day. This product combines data from the XPS, SOLSTICE and SIM instruments and merges them into a daily averaged solar spectra. The spectral resolution of SIM varies between 1-34 nm, SOLSTICE is 1 nm, and XPS is 7 nm. The SORCE combined data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date (calendar and Julian Day), min wavelength, max wavelength, instrument mode, input data version, spectral irradiance, irradiance uncertainty, and a data quality flag. Each row represents a separate day and wavelength.\n<br>\n\n#### SOR3TSI6\nSOR3TSI6 Version 019 is the final version of this data product, and supersedes all previous versions. The Total Solar Irradiance (TSI) data set SOR3TSI6 contains the total solar irradiance (a.k.a solar constant) data collected by the Total Irradiance Monitor (TIM) instrument covering the full wavelength spectrum averaged at 6-hour intervals. The data are normalized to one astronomical unit (1 AU). The TIM instrument measures the Total Solar Irradiance (TSI), monitoring changes in incident sunlight to the Earth's atmosphere using an ambient temperature active cavity radiometer to a designed absolute accuracy of 100 parts per million (ppm, 1 ppm=0.0001% at 1-sigma) and a precision and long-term relative accuracy of 10 ppm per year. Due to the small size these data and to maximize ease of use to end-users, each delivered TSI product contains science results for the entire mission. Updates to Level 3 TSI data occur monthly in order to reduce repeated delivery of data.\n<br>\n\n#### SOR3TSID\nSOR3TSID Version 019 is the final version of this data product, and supersedes all previous versions. The Total Solar Irradiance (TSI) data set SOR3TSID contains the total solar irradiance (a.k.a solar constant) data collected by the Total Irradiance Monitor (TIM) instrument covering the full wavelength spectrum averaged at daily intervals. The data are normalized to one astronomical unit (1 AU). The TIM instrument measures the Total Solar Irradiance (TSI), monitoring changes in incident sunlight to the Earth's atmosphere using an ambient temperature active cavity radiometer to a designed absolute accuracy of 100 parts per million (ppm, 1 ppm=0.0001% at 1-sigma) and a precision and long-term relative accuracy of 10 ppm per year. Due to the small size these data and to maximize ease of use to end-users, each delivered TSI product contains science results for the entire mission. Updates to Level 3 TSI data occur monthly in order to reduce repeated delivery of data.\n<br>\n\n#### SOR3SIMD\nVersion 027 is the final version of this data product, and supersedes all previous versions. The SORCE SIM Solar Spectral Irradiance (SSI) data product SOR3SIMD is constructed using measurements from the SIM instruments, which are combined into merged daily solar spectra over the spectral range from 240 to 2416 nm at a spectral resolution ranging from 1 to 27 nm. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The SIM absolute uncertainty is about 2%. All of the SOR3SIMD data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date, Julian day, minimum wavelength, maximum wavelength, instrument mode, data version number, irradiance value, irradiance uncertainty, and data quality. The rows are arranged with data at each wavelength over the full SIM wavelength range, repeating for each day for the length of the measurement period.\n<br>\n\n#### SOR3SIMD_TAV\nVersion 002 is the most recent version of this data product, and supersedes all previous versions. The SORCE SIM Level 3 TSIS-Adjusted Values Solar Spectral Irradiance 24-Hour Means data product (SOR3SIMD_TAV) uses the temporal overlap of the Solar Radiation and Climate Experiment (SORCE) and the Total and Spectral Solar Irradiance Sensor (TSIS-1) Spectral Irradiance Monitor (SIM) instruments to create an alternate SORCE-SIM irradiance calibration, known as the TSIS1 Adjusted Values (TAV). The SORCE-SIM Solar Spectral Irradiance (SSI) data products are provided on a fixed wavelength scale which varies in spectral resolution from 1-34 nm over the spectral range from 240 to 2401 nm. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The TAV data is on the SORCE-SIM wavelength scale, with the exception that the longest TAV wavelength is 2401.4 nm. All of the SOR3SIMD_TAV data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date, Julian day, minimum wavelength, maximum wavelength, instrument mode, data version number, irradiance value, irradiance uncertainty, and data quality. The rows are arranged with data at each wavelength over the full SIM wavelength range, repeating for each day for the length of the measurement period.\n<br>\n\n#### SOR3SOLD_HIGH\nSORCE SOLSTICE FUV and MUV Level 3 Solar Spectral Irradiance 0.1nm Res 24-Hour Means data product (SOR3SOLD_HIGH) is constructed using measurements from the SOLSTICE FUV and MUV instrument, which is combined into merged daily solar spectra over the spectral range from 115 to 310 nm at a high spectral resolution of 0.1 nm. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The SOLSTICE absolute uncertainty better than 5%. The SOR3SOLD_HIGH data are stored in netCDF files containing a full year of data. Each file contains variables with the date, Julian day, wavelength, irradiance value, irradiance uncertainty, and irradiance repeatability.\n<br>\n\n#### SOR3SOLFUVD\nVersion 018 is the final version of this data product, and supersedes all previous versions. The SORCE SOLSTICE Far-UV Solar Spectral Irradiance (SSI) data product SOR3SOLFUVD is constructed using measurements from the SOLSTICE FUV instrument, which are combined into merged daily solar spectra over the spectral range from 115 to 180 nm at a spectral resolution of 1 nm. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The SOLSTICE absolute uncertainty better than 5%. All of the SOR3SOLFUVD data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date, Julian day, minimum wavelength, maximum wavelength, instrument mode, data version number, irradiance value, irradiance uncertainty, and data quality. The rows are arranged with data at each wavelength over the full SOLSTICE FUV wavelength range, repeating for each day for the length of the measurement period.\n<br>\n\n#### SOR3SOLS_LA\nThe SORCE SOLSTICE Level 3 Lyman-alpha Irradiance As-Measured Cadence product consists of all measurements of the Lyman-alpha irradiance from the SOLSTICE instrument. The SOLSTICE instrument makes measurements during each daytime orbit portion, 15 orbits per day. A complete solar spectrum is made in about 30 minutes, a quick scan mode in about 5 minutes. The spectral resolution of SOLSTICE is 0.1 nm. The Lyman-alpha data are stored in netCDF files containing a full year of. Each days measurements are in a separate netCDF group. Each group contains variables for irradiance, uncertainty, repeatability, long x-ray flux, short x-ray flux, spacecraft height, latitude, longitude, time, wavelength and target zenith angle, with overr 3000 measurements.\n<br>\n\n#### SOR3SOLD_MGII\nThe SORCE SOLSTICE Level 3 MgII Core to Wing Ratio 24 Hour Means product consists of daily averages of the magnesium II core-to-wing index from the SOLSTICE instrument. The SOLSTICE instrument makes measurements during each daytime orbit portion, 15 orbits per day. This product has solar spectra averaged for a day. The spectral resolution of SOLSTICE is 0.1 nm, allowing the Mg-II doublet to be fully resolved and modeled with Gaussians. The Mg-II core-to-wing ratio is used as a measurement of solar activity. The Mg-II data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date (calendar and Julian Day), the core/wing ratio, and the absolute uncertainty. The rows are arranged with one daily average measurment, repeating for each day for the length of the measurement period.\n<br>\n\n#### SOR3SOLS_MGII\nThe SORCE SOLSTICE Level 3 MgII Core to Wing Ratio As-Measured Cadence product consists of all measurements of the magnesium II core-to-wing index from the SOLSTICE instrument. The SOLSTICE instrument makes measurements during each daytime orbit portion, 15 orbits per day. A complete solar spectrum is made in about 30 minutes, a quick scan mode in about 5 minutes. The spectral resolution of SOLSTICE is 0.1 nm, allowing the Mg-II doublet to be fully resolved and modeled with Gaussians. The Mg-II core-to-wing ratio is used as a measurement of solar activity. The Mg-II data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date (calendar and Julian Day), the core/wing ratio, and the absolute uncertainty. The rows are arranged with one daily average measurment, repeating for each day for the length of the measurement period.\n<br>\n\n#### SOR3SOLMUVD\nVersion 018 is the final version of this data product, and supersedes all previous versions. The SORCE SOLSTICE Mid-UV Solar Spectral Irradiance (SSI) data product SOR3SOLMUVD is constructed using measurements from the SOLSTICE MUV instrument, which are combined into merged daily solar spectra over the spectral range from 180 to 310 nm at a spectral resolution of 1 nm. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The SOLSTICE absolute uncertainty better than 5%. All of the SOR3SOLMUVD data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date, Julian day, minimum wavelength, maximum wavelength, instrument mode, data version number, irradiance value, irradiance uncertainty, and data quality. The rows are arranged with data at each wavelength over the full SOLSTICE FUV wavelength range, repeating for each day for the length of the measurement period.\n<br>\n\n#### SOR3XPS6\nThe SORCE XUV Photometer System (XPS) Solar Spectral Irradiance (SSI) 6-Hour Data Product SOR3XPS6 contains solar extreme ultraviolet irradiances in the 0.1 to 27 nm range, as well as Lyman-alpha, in broad bands (7-10 nm resolution) as listed below: * Diode 1: 0.1 - 7.0 nm * Diode 2: 0.1 - 7.0 nm * Diode 3: 17.0 - 23.0 nm * Diode 6: 0.1 - 11.0 nm * Diode 7: 0.1 - 7.0 nm * Diode 9: 0.1 - 7.0 nm * Diode 11: 121.0 - 122.0 nm The data are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The accuracy for the XPS Level 3 irradiance is 12-30%, photometer dependent, and the long-term repeatability is 1% per year. The SOR3XPS6 data are arranged in a single tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date, Julian day, average measurement date, standard deviation of measurement date, diode number, minimum wavelength, maximum wavelength, instrument mode, data version number, median irradiance, mean irradiance, absolute uncertainty, measurement precision, calculation precision, degradation model, degradation version, and number of data points. The rows are arranged with data for each 6 hour time increment, repeating for each diode wavelength.\n<br>\n\n#### SOR3XPSD\nThe SORCE XUV Photometer System (XPS) Solar Spectral Irradiance (SSI) Daily Data Product SOR3XPSD contains solar extreme ultraviolet irradiances in the 0.1 to 27 nm range, as well as Lyman-alpha, in broad bands (7-10 nm resolution) as listed below: * Diode 1: 0.1 - 7.0 nm * Diode 2: 0.1 - 7.0 nm * Diode 3: 17.0 - 23.0 nm * Diode 6: 0.1 - 11.0 nm * Diode 7: 0.1 - 7.0 nm * Diode 9: 0.1 - 7.0 nm * Diode 11: 121.0 - 122.0 nm The data are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The accuracy for the XPS Level 3 irradiance is 12-30%, photometer dependent, and the long-term repeatability is 1% per year. The SOR3XPSD data are arranged in a single tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date, Julian day, average measurement date, standard deviation of measurement date, diode number, minimum wavelength, maximum wavelength, instrument mode, data version number, median irradiance, mean irradiance, absolute uncertainty, measurement precision, calculation precision, degradation model, degradation version, and number of data points. The rows are arranged with data for each day, repeating for each diode wavelength.\n<br>\n\n#### SOR4XPSD_HIGH\nThe SORCE XPS Level 4 Solar Spectral Irradiance 0.1nm Res 24-Hour Means product (SOR4XPSD_HIGH) contains modelled spectral extreme ultraviolet (XUV) irradiances based on calibrated SORCE XUV Photometer System (XPS) data and flare plasma temperature derived from the GOES XRS instrument. The model data are reported in the spectral range from 0-40 nm at a resolution of 0.1 nm (high res) and averaged over a day. The XPS Level 2 data products, along with CHIANTI reference spectra, are used to construct the XPS Level 4 data products. The SOR4XPSD_HIGH data are stored in netCDF files containing data for the entire mission. Data variables stored in the file include: MODELFLUX: Array of solar irradiances [W/m2/nm] in 0.1 nm bins from 0.1 to 39.9 nm ERR_ABS: Total combined standard uncertainty of the model flux irradiance results (accuracy) ERR_MEAS: Count-rate based combined standard uncertainty of the XPS-1,-2 measurement (precision) DATE: Year and day-of-year of observation (YYDDD) TIME: Seconds of day for middle of observation XPS_QS: Daily quiet-sun scale factor derived from XPS-1,-2 daily minimum XPS_AR: Daily active-region scale factor derived from XPS-1,-2 daily minimum XPS_FLARE: Flare scale factor for each XPS-1,-2 GOES_QS: Daily quiet-sun scale factor derived from GOES XRS-B daily minimum GOES_AR: Daily active-region scale factor derived from GOES XRS-B daily minimum GOES_FLARE: Flare scale factor for each GOES XRS-B integration FMTEMP: Flare model temperature [log10(K)] derived from ratio of GOES XRS-B to XRS-A FMINDEX: Index into flare model table FMWEIGHT: Weight parameter for the flare model\n<br>\n\n#### SOR4XPS5\nThe SORCE XPS Level 4 Solar Spectral Irradiance 0.1nm Res 5-Minute product (SOR4XPS5) contains modelled spectral extreme ultraviolet (XUV) irradiances based on calibrated SORCE XUV Photometer System (XPS) data and flare plasma temperature derived from the GOES XRS instrument. The model data are reported in the spectral range from 0-40 nm at a resolution of 0.1 nm about every 5 minutes. The XPS Level 2 data products, along with CHIANTI reference spectra, are used to construct the XPS Level 4 data products. The SOR4XPS5 data are stored in netCDF files containing one year of data. Data variables stored in each file include: MODELFLUX: Array of solar irradiances [W/m2/nm] in 0.1 nm bins from 0.1 to 39.9 nm ERR_ABS: Total combined standard uncertainty of the model flux irradiance results (accuracy) ERR_MEAS: Count-rate based combined standard uncertainty of the XPS-1,-2 measurement (precision) DATE: Year and day-of-year of observation (YYDDD) TIME: Seconds of day for middle of observation XPS_QS: Daily quiet-sun scale factor derived from XPS-1,-2 daily minimum XPS_AR: Daily active-region scale factor derived from XPS-1,-2 daily minimum XPS_FLARE: Flare scale factor for each XPS-1,-2 GOES_QS: Daily quiet-sun scale factor derived from GOES XRS-B daily minimum GOES_AR: Daily active-region scale factor derived from GOES XRS-B daily minimum GOES_FLARE: Flare scale factor for each GOES XRS-B integration FMTEMP: Flare model temperature [log10(K)] derived from ratio of GOES XRS-B to XRS-A FMINDEX: Index into flare model table FMWEIGHT: Weight parameter for the flare model\n<br>\n\n#### SOR4XPSD_LOW\nThe SORCE XPS Level 4 Solar Spectral Irradiance 1.0nm Res 24-Hour Means product (SOR4XPSD_LOW) contains modelled spectral extreme ultraviolet (XUV) irradiances based on calibrated SORCE XUV Photometer System (XPS) data and flare plasma temperature derived from the GOES XRS instrument. The model data are reported in the spectral range from 0-40 nm at a resolution of 1.0 nm (low res) and averaged over a day. The XPS Level 2 data products, along with CHIANTI reference spectra, are used to construct the XPS Level 4 data products. The SOR4XPS5 data are stored in a single netCDF files containing data for the entire mission. Data variables stored in the file include: MODELFLUX: Array of solar irradiances [W/m2/nm] in 1.0 nm bins from 0 to 40 nm ERR_ABS: Total combined standard uncertainty of the model flux irradiance results (accuracy) ERR_MEAS: Count-rate based combined standard uncertainty of the XPS-1,-2 measurement (precision) DATE: Year and day-of-year of observation (YYDDD) TIME: Seconds of day for middle of observation XPS_QS: Daily quiet-sun scale factor derived from XPS-1,-2 daily minimum XPS_AR: Daily active-region scale factor derived from XPS-1,-2 daily minimum XPS_FLARE: Flare scale factor for each XPS-1,-2 GOES_QS: Daily quiet-sun scale factor derived from GOES XRS-B daily minimum GOES_AR: Daily active-region scale factor derived from GOES XRS-B daily minimum GOES_FLARE: Flare scale factor for each GOES XRS-B integration FMTEMP: Flare model temperature [log10(K)] derived from ratio of GOES XRS-B to XRS-A FMINDEX: Index into flare model table FMWEIGHT: Weight parameter for the flare model\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"http://lasp.colorado.edu/home/sorce/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","climate","ice","netcdf","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SOR3D_COMBINED v001 - The SORCE Combined XPS, SOLSTICE, and SIM Solar Spectral Irradiance 24-Hour Means product consists of daily averages of the solar spetra from 0.1 to 2412 nm. The SORCE instruments make measurements during each daytime orbit portion, 15 orbits per day.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR3D_COMBINED.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR3TSI6 v019 - SOR3TSI6 Version 019 is the final version of this data product, and supersedes all previous versions. The Total Solar Irradiance (TSI) data set SOR3TSI6 contains the total solar irradiance (a.k.a solar constant) data collected by the Total Irradiance Monitor (TIM) instrument covering the full wavelength spectrum averaged at 6-hour intervals.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR3TSI6.019","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR3TSID v019 - SOR3TSID Version 019 is the final version of this data product, and supersedes all previous versions. The Total Solar Irradiance (TSI) data set SOR3TSID contains the total solar irradiance (a.k.a solar constant) data collected by the Total Irradiance Monitor (TIM) instrument covering the full wavelength spectrum averaged at daily intervals.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR3TSID.019","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR3SIMD v027 - Version 027 is the final version of this data product, and supersedes all previous versions. The SORCE SIM Solar Spectral Irradiance (SSI) data product SOR3SIMD is constructed using measurements from the SIM instruments, which are combined into merged daily solar spectra over the spectral range from 240 to 2416 nm at a spectral resolution ranging from 1 to 27 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR3SIMD.027","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR3SIMD_TAV v002 - Version 002 is the most recent version of this data product, and supersedes all previous versions. The SORCE SIM Level 3 TSIS-Adjusted Values Solar Spectral Irradiance 24-Hour Means data product (SOR3SIMD_TAV) uses the temporal overlap of the Solar Radiation and Climate Experiment (SORCE) and the Total and Spectral Solar Irradiance Sensor (TSIS-1) Spectral Irradiance Monitor (SIM) instruments to create an alternate SORCE-SIM irradiance calibration, known as the TSIS1 Adjusted Values (TAV).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR3SIMD_TAV.002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR3SOLD_HIGH v018 - SORCE SOLSTICE FUV and MUV Level 3 Solar Spectral Irradiance 0.1nm Res 24-Hour Means data product (SOR3SOLD_HIGH) is constructed using measurements from the SOLSTICE FUV and MUV instrument, which is combined into merged daily solar spectra over the spectral range from 115 to 310 nm at a high spectral resolution of 0.1 nm. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR3SOLD_HIGH.018","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR3SOLFUVD v018 - Version 018 is the final version of this data product, and supersedes all previous versions. The SORCE SOLSTICE Far-UV Solar Spectral Irradiance (SSI) data product SOR3SOLFUVD is constructed using measurements from the SOLSTICE FUV instrument, which are combined into merged daily solar spectra over the spectral range from 115 to 180 nm at a spectral resolution of 1 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR3SOLFUVD.018","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR3SOLS_LA v018 - The SORCE SOLSTICE Level 3 Lyman-alpha Irradiance As-Measured Cadence product consists of all measurements of the Lyman-alpha irradiance from the SOLSTICE instrument. The SOLSTICE instrument makes measurements during each daytime orbit portion, 15 orbits per day.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR3SOLS_LA.018","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR3SOLD_MGII v018 - The SORCE SOLSTICE Level 3 MgII Core to Wing Ratio 24 Hour Means product consists of daily averages of the magnesium II core-to-wing index from the SOLSTICE instrument. The SOLSTICE instrument makes measurements during each daytime orbit portion, 15 orbits per day.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR3SOLD_MGII.018","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR3SOLS_MGII v018 - The SORCE SOLSTICE Level 3 MgII Core to Wing Ratio As-Measured Cadence product consists of all measurements of the magnesium II core-to-wing index from the SOLSTICE instrument. The SOLSTICE instrument makes measurements during each daytime orbit portion, 15 orbits per day.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR3SOLS_MGII.018","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR3SOLMUVD v018 - Version 018 is the final version of this data product, and supersedes all previous versions. The SORCE SOLSTICE Mid-UV Solar Spectral Irradiance (SSI) data product SOR3SOLMUVD is constructed using measurements from the SOLSTICE MUV instrument, which are combined into merged daily solar spectra over the spectral range from 180 to 310 nm at a spectral resolution of 1 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR3SOLMUVD.018","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR3XPS6 v012 - The SORCE XUV Photometer System (XPS) Solar Spectral Irradiance (SSI) 6-Hour Data Product SOR3XPS6 contains solar extreme ultraviolet irradiances in the 0.1 to 27 nm range, as well as Lyman-alpha, in broad bands (7-10 nm resolution) as listed below: * Diode 1: 0.1 - 7.0 nm * Diode 2: 0.1 - 7.0 nm * Diode 3: 17.0 - 23.0 nm * Diode 6: 0.1 - 11.0 nm * Diode 7: 0.1 - 7.0 nm * Diode 9: 0.1 - 7.0 nm * Diode 11: 121.0 - 122.0 nm The data are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The accuracy for the XPS Level 3 irradiance is 12-...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR3XPS6.012","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR3XPSD v012 - The SORCE XUV Photometer System (XPS) Solar Spectral Irradiance (SSI) Daily Data Product SOR3XPSD contains solar extreme ultraviolet irradiances in the 0.1 to 27 nm range, as well as Lyman-alpha, in broad bands (7-10 nm resolution) as listed below: * Diode 1: 0.1 - 7.0 nm * Diode 2: 0.1 - 7.0 nm * Diode 3: 17.0 - 23.0 nm * Diode 6: 0.1 - 11.0 nm * Diode 7: 0.1 - 7.0 nm * Diode 9: 0.1 - 7.0 nm * Diode 11: 121.0 - 122.0 nm The data are reported at a mean solar distance of 1 AU and zero relative line-of-sight velocity with respect to the Sun. The accuracy for the XPS Level 3 irradiance is 12-3...","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR3XPSD.012","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR4XPSD_HIGH v012 - The SORCE XPS Level 4 Solar Spectral Irradiance 0.1nm Res 24-Hour Means product (SOR4XPSD_HIGH) contains modelled spectral extreme ultraviolet (XUV) irradiances based on calibrated SORCE XUV Photometer System (XPS) data and flare plasma temperature derived from the GOES XRS instrument. The model data are reported in the spectral range from 0-40 nm at a resolution of 0.1 nm (high res) and averaged over a day.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR4XPSD_HIGH.012","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR4XPS5 v012 - The SORCE XPS Level 4 Solar Spectral Irradiance 0.1nm Res 5-Minute product (SOR4XPS5) contains modelled spectral extreme ultraviolet (XUV) irradiances based on calibrated SORCE XUV Photometer System (XPS) data and flare plasma temperature derived from the GOES XRS instrument. The model data are reported in the spectral range from 0-40 nm at a resolution of 0.1 nm about every 5 minutes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR4XPS5.012","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SOR4XPSD_LOW v012 - The SORCE XPS Level 4 Solar Spectral Irradiance 1.0nm Res 24-Hour Means product (SOR4XPSD_LOW) contains modelled spectral extreme ultraviolet (XUV) irradiances based on calibrated SORCE XUV Photometer System (XPS) data and flare plasma temperature derived from the GOES XRS instrument. The model data are reported in the spectral range from 0-40 nm at a resolution of 1.0 nm (low res) and averaged over a day.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SORCE_Level3/SOR4XPSD_LOW.012","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-sorce","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SOTERIA Simulation Testbed Data","Description":"Commercial pilot simulation data during safety-of-flight scenarios.","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/","Contact":"chad.l.stephens@nasa.gov; tyler.fettrow@nasa.gov","ManagedBy":"[NASA](http://www.nasa.gov/)","UpdateFrequency":"As required","Tags":["life sciences","neuroimaging","transportation","workload analysis"],"License":"[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SOTERIA Testbed Data","ARN":"arn:aws:s3:::nasa-soteria-data","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"SOTERIA Simulation - Experimental Methods, Data Processing, and Data Quality","URL":"https://ntrs.nasa.gov/citations/20230008151/","AuthorName":"Tyler Fettrow, Chad Stephens, Lance Prinzel, Jon Holbrook, Sepher Bastami, Michael Stewart, Kathryn Ballard, Daniel Kiggins"}],"Tools & Applications":[{"Title":"Python Processing Code","URL":"https://github.com/tylerfettrow/soteria-processing-python/","AuthorName":"Tyler Fettrow"}]},"RegistryEntryAdded":"2024-09-18","RegistryEntryLastModified":"2024-09-23","Slug":"nasa-soteria-data","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA SPURS Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### SPURS1_ARGO\nThe SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. Part of the Argo global network of autonomous, self-reporting samplers, Argo floats drift horizontally and move vertically through the water column generally on 10 day cycles, collecting high-quality temperature, conductivity and salinity depth profiles from the upper 2000m. Approximately 24 floats were deployed during SPURS-1 within the campaign domain, mainly during the Knorr cruise (6 Sept-9 Oct,2012). These were standard Argo floats with the addition of surface temperature and salinity (STS) sensors and acoustic rain gauges (PAL). Data accessible here only include the standard ARGO profiles, not the STS or PAL data. SPURS-1 ARGO data files are oganized per float and each contain profile trajectory series of conductivity, salinity, temperature, pressure, depth observations.\n<br>\n\n#### SPURS2_ARGO\nThe SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Part of the Argo global network of autonomous, self-reporting samplers, Argo floats drift horizontally and move vertically through the water column generally on 10 day cycles, collecting high-quality temperature, conductivity and salinity depth (CTD) profiles from the upper 2000m. Twenty five floats were deployed during SPURS-2 within the campaign spatial domain and time period, yielding approximately 1,893 profiles. These were standard Argo floats with the addition of acoustic rain gauges (PAL) in some cases. SPURS-2 ARGO data files are organized per float and profile with the vertical conductivity, salinity, temperature, pressure, depth observations per the netCDF ARGO file specification with some augmented global metadata attributes.\n<br>\n\n#### SPURS2_MOORING_CENTRAL\nThe SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and countercurrent. The SPURS central mooring consisted of a surface meteorological package, surface oceanographic instruments, and subsurface, non-real time oceanographic instruments including CTD, ADCP sensors and point current meters providing continuous series of temperature, salinity and current profile data. Meteorological observations included wind speed, air temperature, precipitation, and radiative flux. The mooring was deployed in 4769 m depth of water on 24 August 2016, at N10:03.0481, W125:01.939, and was recovered on November 11, 2017. WHOI mooring netCDF data files include surface and subsurface time series of sea temperature, skin temperature, salinity, conductivity, wind velocity, air temperature, relative humidity, precipitation rate, barometric pressure, shortwave and longwave radiation, short/longwave flux, heat Flux, wind Speed and direction.\n<br>\n\n#### SPURS2_DISDR\nThe SPURS-2 raindrop ODM-470 disdrometer dataset was collected from the ship during both the 2016 and 2017 cruises. Please see file global attributes and Klepp et al. (2015, 2018) for information on the disdrometer: http://dx.doi.org/10.1016/j.atmosres.2014.12.014, https://doi.org/10.1038/sdata.2018.122 . As explained in the references and global attributes, small drops that cause voltage drops < 0.12 V (i.e. drops with diameters < 0.44 mm) cannot be distinguished from noise by this instrument, and are thus missed. This undercounting of small drops cannot be corrected, and prevents accurate estimation of DSD parameters such as Nw, D0, Dm with any confidence or precision since the minimum detectable drop size is close to the median drop size of tropical oceanic rain (Thompson et al. 2015, https://doi.org/10.1175/JAS-D-14-0206.1). Nonetheless, this dataset provides estimates of drop counts as a function of drop size for the remaining rain drops > 0.44 mm in diameter, and their associated rain rates and liquid water contents. The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans.\n<br>\n\n#### SPURS2_XBAND\nThe SPURS-2 X-band marine navigation radar image dataset was collected from the ship during both the 2016 and 2017 cruises. The dataset consists of screenshots of rain echoes captured directly from the science-use X-band marine navigation radar. Raw data could not be saved. The screenshots show qualitative (uncalibrated) echoes of backscatter from rain. For full details on the screenshots, how they should be used, and what they show about rainfall, please refer to our publication: Thompson, E.J., W.E. Asher, A.T. Jessup, and K. Drushka. 2019. High-Resolution Rain Maps from an X-band Marine Radar and Their Use in Understanding Ocean Freshening. Oceanography 32(2):58–65, https://doi.org/10.5670/oceanog.2019.213 . The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/SPURS/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","netcdf","oceans","precipitation","radar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SPURS1_ARGO v1.0 - The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SPURS1_ARGO","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SPURS2_ARGO v1.0 - The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SPURS2_ARGO","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SPURS2_MOORING_CENTRAL v1.0 - The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SPURS2_MOORING_CENTRAL","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SPURS2_DISDR v1.0 - The SPURS-2 raindrop ODM-470 disdrometer dataset was collected from the ship during both the 2016 and 2017 cruises. Please see file global attributes and Klepp et al.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SPURS2_DISDR","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SPURS2_XBAND v1.0 - The SPURS-2 X-band marine navigation radar image dataset was collected from the ship during both the 2016 and 2017 cruises. The dataset consists of screenshots of rain echoes captured directly from the science-use X-band marine navigation radar.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SPURS2_XBAND","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-spurs","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA STAQS Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### STAQS_Sondes_Data\nSTAQS_Sondes_Data is the balloonsonde and ozonesonde data collected during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete. Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.\n<br>\n\n#### STAQS_Chiwaukee-Prairie_Data\nSTAQS_Chiwaukee-Prairie_Data is the data collected at the Chiwaukee Prairie site during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete. Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.\n<br>\n\n#### STAQS_Drone_Data\nSTAQS_Drone_Data is the PM 2.5 data collected by the BlueHalo E900 UAV during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete. Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.\n<br>\n\n#### STAQS_Ground_Data\nSTAQS_Ground_Data is the ground site data collected during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete. Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.\n<br>\n\n#### STAQS_INSTEP_Data\nSTAQS_INSTEP_Data is the trace gas data collected by the Inexpensive Network Sensor Technology Exploring Pollution (INSTEP) instrument during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete. Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.\n<br>\n\n#### STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data\nSTAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data is the remotely sensed trace gas data for the JSC Gulfstream V aircraft taken by the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) instrument as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete. Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.\n<br>\n\n#### STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data\nSTAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data is the remotely sensed trace gas data for the JSC Gulfstream V aircraft taken by the High Spectral Resolution Lidar-2 (HSRL-2) as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete. Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.\n<br>\n\n#### STAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data\nSTAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data is the remotely sensed trace gas data for the NASA Gulfstream III aircraft taken by the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) instrument as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete. Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.\n<br>\n\n#### STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data\nSTAQS_AircraftRemoteSensing_NASA-G3_HALO_Data is the remotely sensed trace gas data for the NASA Gulfstream III aircraft taken by the High Altitude Lidar Observatory (HALO) instrument as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete. Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.\n<br>\n\n#### STAQS_SeaRey_Data\nSTAQS_SeaRey_Data is the data collected onboard the Progressive Aerodyne SeaRey aircraft during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete. Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/STAQS/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","earth observation","lidar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"STAQS_Sondes_Data v1 - STAQS_Sondes_Data is the balloonsonde and ozonesonde data collected during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/STAQS/STAQS_Sondes_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"STAQS_Chiwaukee-Prairie_Data v1 - STAQS_Chiwaukee-Prairie_Data is the data collected at the Chiwaukee Prairie site during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/STAQS/STAQS_Chiwaukee-Prairie_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"STAQS_Drone_Data v1 - STAQS_Drone_Data is the PM 2.5 data collected by the BlueHalo E900 UAV during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/STAQS/STAQS_Drone_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"STAQS_Ground_Data v1 - STAQS_Ground_Data is the ground site data collected during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/STAQS/STAQS_Ground_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"STAQS_INSTEP_Data v1 - STAQS_INSTEP_Data is the trace gas data collected by the Inexpensive Network Sensor Technology Exploring Pollution (INSTEP) instrument during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/STAQS/STAQS_INSTEP_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data v1 - STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data is the remotely sensed trace gas data for the JSC Gulfstream V aircraft taken by the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) instrument as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/STAQS/STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data v1 - STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data is the remotely sensed trace gas data for the JSC Gulfstream V aircraft taken by the High Spectral Resolution Lidar-2 (HSRL-2) as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/STAQS/STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"STAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data v1 - STAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data is the remotely sensed trace gas data for the NASA Gulfstream III aircraft taken by the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) instrument as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/STAQS/STAQS_AircraftRemoteSensing_NASA-G3_GCAS_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data v1 - STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data is the remotely sensed trace gas data for the NASA Gulfstream III aircraft taken by the High Altitude Lidar Observatory (HALO) instrument as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/STAQS/STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"STAQS_SeaRey_Data v1 - STAQS_SeaRey_Data is the data collected onboard the Progressive Aerodyne SeaRey aircraft during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.","ARN":"arn:aws:s3:::asdc-prod-protected/FIELDCAMPAIGN/STAQS/STAQS_SeaRey_Data_1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-staqs","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Suomi-NPP Project","Description":"The objective of this limited edition data collection is to examine the ammonia products generated by the ESSPA (Earth System Science Profiling Algorithm) algorithm from the Cross-track Infrared Sounder (CrIS) instruments. The CrIS instrument used for this product is deployed on board the Suomi National Polar-orbiting Partnership (SNPP) platform and uses the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The NH3 L2 ammonia algorithm is based on an AER (Atmospheric and Environmental Research, Inc.) program initially developed to process TES (Tropospheric Emissions Spectrometer) trace gas products. This version runs within the ESSPA software framework: it uses the AER OSS forward model and an optimal estimation approach with a Levenberg-Marquardt algorithm. Temperature and water profiles are obtained from the CLIMCAPS Field of Regard (FOR) products, as are initial guesses for surface temperature and emissivity. The algorithm consists of a two-step sequential retrieval the first step retrieves surface temperature and emissivity and the second step an ammonia profile. The algorithm produces ammonia retrievals on every field of view (FOV) in each FOR. A level 2 granule has been set as 6 minutes of data, 30 footprints crosstrack by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### SNDRSNCrISL1BIMG\nThe Cross-track Infrared Sounder (CrIS) Level 1B Normal Spectral Resolution (NSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP). In December 2014, the CrIS instrument on the SNPP satellite doubled the spectral resolution of shortwave infrared data being transmitted to the ground. In November 2015, additional points were included at the ends of the longwave and shortwave interferograms to improve the quality of the calibration. Prior to November 2, 2015 the data are only available in Normal Spectral Resolution, after November 2, 2015 at 16:06 UTC, the data are available in both NSR and Full Spectral Resolution (FSR). The NSR files have 1,317 channels: 163 shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 437 midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries. The Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. It provides the sensor data records for clouds, sea surface temperature, ocean color, and others. This IMG product primarily contains statistics of the VIIRS cloud mask and VIIRS L1B data within each CrIS footprint..\n<br>\n\n#### AERDT_L2_VIIRS_SNPP\nThe SNPP Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua missions’ Moderate Imaging Spectroradiometer (MODIS) instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible wavelengths). This orbit-level product (Short-name: AERDT_L2_VIIRS_SNPP) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor’s scanning geometry and Earth’s curvature. Viewed differently, this product’s resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. Hence, the Level-2 Dark Target Aerosol Optical Thickness data product incorporates 64 (750 m) pixels over a 6-minute acquisition. Version 2.1 constitutes the latest collection of the SNPP VIIRS L2 Dark Target Aerosol product and contains improvements over its previous collection (v2.0). For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDT_L2_VIIRS_SNPP Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/\n<br>\n\n#### AER_DBDT_D10KM_L3_VIIRS_SNPP\nThis High-Resolution (0.1 x 0.1 degree) Level 3 daily Aerosol Optical Depth (AOD) product is generated by combining two Visible Infrared Imaging Radiometer Suite (VIIRS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. This dataset is provided in daily files ranging from 2012-03-04 to the present. The spatial coverage is global and the dataset is gridded at 0.1 x 0.1 degree spatial resolution. The data are generated using Level 2 AOD retrieved using DT and DB algorithms. The product provides multiple options for using data either from DT or DB or combined. Depending on user need and application, they can choose one or more relevant parameter. The pixels with highest quality as recommended by science teams are only considered in these averaging. In addition to averaged AOD at 0.1 x 0.1 degree resolution, standard deviation and number of pixels averaged from each algorithm are also provided. Average sensor zenith angle is also provided for additional filtering of the data. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### AER_DBDT_M10KM_L3_VIIRS_SNPP\nThis High-Resolution (0.1 x 0.1 degree) Level 3 monthly Aerosol Optical Depth (AOD) product is generated by combining two Visible Infrared Imaging Radiometer Suite (VIIRS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. This dataset is provided in monthly files ranging from March 2012 to the present. The spatial coverage is global and the dataset is gridded at 0.1 x 0.1 degree spatial resolution. The data are generated using Level 2 AOD retrieved using DT and DB algorithms. The product provides multiple options for using data either from DT or DB or combined. Depending on user need and application, they can choose one or more relevant parameter. The pixels with highest quality as recommended by science teams are only considered in these averaging. In addition to averaged AOD at 0.1 x 0.1 degree resolution, standard deviation and number of pixels averaged from each algorithm are also provided. Average sensor zenith angle is also provided for additional filtering of the data. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### SNDRSNML2RMS\nThis SNDRSNM2RMS level 2 product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm. Different from the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm, the RAMSES II algorithm is a microwave only retrieval algorithm. While the CLIMCAPS uses measurements from both Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite), the RAMSES II only uses observations from ATMS. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz on board the Suomi National Polar-orbiting Partnership (SNPP) platform. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval. A level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. For the initial release of version 1 RAMSES II, a test data set of 8 months is provided. This includes the months of January, April, July and October of the years 2013 and 2015. The dataset is designed to allow research and comparisons over a full seasonal cycle and comparisons of different phases of the ENSO cycle\n<br>\n\n#### SNDRSNML2RMS\nThis level 2 product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the Suomi-National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II Level-2 retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties for six minutes of instrument observation at a time. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval A level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### SNDRSNML2RMSSUP\nThis level 2 support product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the Suomi-National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II Level-2 retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties for six minutes of instrument observation at a time. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval A level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### SNDRSNML3DRMS\nThis level 3 daily product is generated from the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the Suomi-National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval This daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable.\n<br>\n\n#### SNDRSNML3MRMS\nThis level 3 monthly product is generated from the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the Suomi-National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The RAMSES II retrieval products contain a variety of geophysical parameters retrieved from ATMS measurements, including profiles of temperature, water in all phases as well as surface properties. The RAMSES II algorithm doesn't have a cloud clearing process and produces all weather retrieval This monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use) which are provided for each variable.\n<br>\n\n#### SNDRSNIML2CHTRETN\nThe objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The CHART algorithm is uses the basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms. As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; outgoing longwave radiation (OLR); and an infrared-based precipitation estimate. The CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. For comparison, the AIRS/AMSU data collection AIRX2SUP contains similar meteorological information to this CHART data collection and the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) data collection SNDRSNIML2CCPRETN contains CRIMSS data processed with an analogous algorithm. A level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### SNDRSNIML2CCPRET\nWARNING: To users of the derived product “co_mmr_midtrop” (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply “co_mmr_midtrop” by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (“mol_lay/co_mol_lay”) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Full Spectral Resolution (FSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 FSR infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2000-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. The CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid. A level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.\n<br>\n\n#### SNDRSNIML2PLEVCPS\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using full spectral resolution inputs. They include including gas mixing ratio profiles, column totals, surface values, tropopause properties, and relative humidity, together with per-field quality flagging. The profiles are specified at the surface and layer boundaries and are estimated from layer amounts using the L2 algorithm An level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.\n<br>\n\n#### SNDRSNIML2CCPCCR\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Full Spectral Resolution (FSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 2211 NSR infrared sounding channels covering the longwave (645-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2000-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. Cloud clearing is the process of computing the clear column radiance for a given channel n, and represents what the channel would have observed if the entire scene were cloud free. The entire scene is defined as the ATMS field of regard (FOR) which includes and array of 3x3 CrIS field of views (FOV). The basic assumption of cloud-clearing is that if the observed radiances in each field-of-view are different, the differences in the observed radiances are solely attributed to the differences in the fractional cloudiness in each field of view while everything else (surface properties and atmospheric state) is uniform across the field of regard. A level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.\n<br>\n\n#### SNDRSNIML2PLEVCPSN\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using Normal Spectral Resolution data from the Suomi-NPP satellite. They include including gas mixing ratio profiles, column totals, surface values, tropopause properties, and relative humidity, together with per-field quality flagging. The profiles are specified at the surface and layer boundaries and are estimated from layer amounts using the L2 algorithm An level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.\n<br>\n\n#### SNDRSNIML2CCPCCRN\nThe CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. Cloud clearing is the process of computing the clear column radiance for a given channel n, and represents what the channel would have observed if the entire scene were cloud free. The entire scene is defined as the ATMS field of regard (FOR) which includes and array of 3x3 CrIS field of views (FOV). The basic assumption of cloud-clearing is that if the observed radiances in each field-of-view are different, the differences in the observed radiances are solely attributed to the differences in the fractional cloudiness in each field of view while everything else (surface properties and atmospheric state) is uniform across the field of regard. A level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.\n<br>\n\n#### SNDRSNIML2SFSPRET\nThis level 2 standard product is generated by the SiFSAP (Single Field-of-View Sounder Atmospheric Products) algorithm. The SIFSAP algorithm provides retrieval for each sounder Field of View (FOV), therefore, it has 3-times higher horizontal spatial resolution and 9-time denser products compared to other current IR sounder products. Since SiFSAP is an FOV-based algorithm, its product variables have an additional dimension which represents the number of FOVs. For CrIS instrument, there are 9 FOVs for each Field of Regard (FOR). The SiFSAP Level-2 retrieval products contain a variety of geophysical parameters retrieved from IR/MW sounder suites measurements, including profiles of temperature, water vapor and trace gas species as well as clouds and surface properties. This standard product provides retrievals on a reduced vertical profile (11 levels for water profiles and up to 27 for other profile variables). A level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### SNDRSNIML2SFSPSUP\nThis level 2 support product is generated by the SiFSAP (Single Field-of-View Sounder Atmospheric Products) algorithm. The SIFSAP algorithm provides retrieval for each sounder Field of View (FOV), therefore, it has 3-times higher horizontal spatial resolution and 9-time denser products compared to other current IR sounder products. Since SiFSAP is an FOV-based algorithm, its product variables have an additional dimension which represents the number of FOVs. For CrIS instrument, there are 9 FOVs for each Field of Regard (FOR). The SiFSAP Level-2 retrieval products contain a variety of geophysical parameters retrieved from IR/MW sounder suites measurements, including profiles of temperature, water vapor and trace gas species as well as clouds and surface properties. This support product provides height vertical sampling (up to 98 levels) and also includes more detailed Empirical Orthogonal Function (EOF) information like averaging kernels. A level 2 granule has been set as 6 minutes of data. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.\n<br>\n\n#### SNDRSNIML3SDSFSP\nThe SIFSAP (Single Field-of-View Sounder Atmospheric Products) algorithm provides retrieval for each sounder Field of View (FOV), therefore, it has 3-times higher horizontal spatial resolution and 9-time denser products compared to other current IR sounder products. Since SiFSAP is an FOV-based algorithm, its product variables have an additional dimension which represents the number of FOVs. For CrIS instrument, there are 9 FOVs for each Field of Regard (FOR). The SiFSAP L3 products contain a variety of geophysical parameters retrieved from IR/MW sounder suites measurements, including atmospheric temperature profiles, water vapor, ozone profiles, clouds, and surface properties. This daily one half degree latitude by one half degree longitude level-3 product starts with level-2 retrieval products with QC values of 0 (best), 1 (good), and 2 (don't use). The Level-3 products maximize the yield of each individual variable and level by collecting all cases where the corresponding Level 2 quality control from the top of the atmosphere down to the level where the QC determines that the value is 0 (best) or 1 (good). Below this level, the data is rejected. This gives the greatest possible yield for any given level and variable.\n<br>\n\n#### SNDRSNIL1BCALSUBRNDN\nThe CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz. Infrared temperature sounders generate a large amount of Level-1B spectral data. The purpose of the Calibration Data Subsets is extract key information from these data into a few daily files to: 1. Facilitate a quick evaluation of the absolute calibration of the instruments. 2. Facilitate an assessment of the instrument performance under clear, cloudy, and extreme hot and cold conditions. 3. Facilitate the evaluation of instrument trends and their significance relative to climate trends. 4. Facilitate the comparison of AIRS with CrIS using their equivalent data subsets. The output files are constructed from Level-1B and MW brightness or antenna temperatures. Each file contains selected observations taken from a nominal 24-hour period. The S-NPP CrIS summary subset contains Level-1B BTs for all selection types but only for selected channels, while the S-NPP CrIS random calibration subset contains full Level-1B spectra for only the randomly selected observations.\n<br>\n\n#### SNDRSNATMSMAP\nThe Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Suomi National Polar-orbiting Partnership Project (S-NPP). The ATMS instrument is a cross-track scanner with 22 microwave channels in the range 23.8-183.31 Gigahertz (GHz). The beam width is 1.1 degrees for the channels in the 160-183 GHz range, 2.2 degrees for the 80 GHz and 50-60 GHz channels, and 5.2 degrees for the 23.8 and 31.4 GHz channels. Since the SNPP satellite is orbiting at an altitude of about 830 km, the instantaneous spatial resolution on the ground at nadir is about 16 km, 32 km, or 75 km depending upon the channel. The brightness temperature data are contained in an array with 135 rows in the along-track direction, 96 columns in the cross-track direction, and a 3rd dimension for each of the 22 channels. The ATMS cross-track scan interval is 0.018 seconds and the along-track scan period is 8.3 seconds. Data products are constructed on six minute boundaries. The Granule Map Product consists of daily images of granule coverage in PDF format.\n<br>\n\n#### SNPPATMSL1B\nThe Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP). The ATMS instrument is a cross-track scanner with 22 microwave channels in the range 23.8-183.31 Gigahertz (GHz). The beam width is 1.1 degrees for the channels in the 160-183 GHz range, 2.2 degrees for the 80 GHz and 50-60 GHz channels, and 5.2 degrees for the 23.8 and 31.4 GHz channels. Since the SNPP satellite is orbiting at an altitude of about 830 km, the instantaneous spatial resolution on the ground at nadir is about 16 km, 32 km, or 75 km depending upon the channel. The brightness temperature data are contained in an array with 135 rows in the along-track direction, 96 columns in the cross-track direction, and a 3rd dimension for each of the 22 channels. The ATMS cross-track scan interval is 0.018 seconds and the along-track scan period is 8/3 seconds. Data products are constructed on six minute boundaries. The ATMS (Advanced Technology Microwave Sounder) and CrIS (Crosstrack InfraRed Sounder) instruments are meant to operate together as a system, thus providing coverage of a much broader range of atmospheric conditions. The ATMS-CrIS system is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite). If you were redirected to this page from a DOI from an older version, please note this is the current version of the product. Please contact the GES DISC user support if you need information about previous data collections.\n<br>\n\n#### SNPPCrISL1B\nThe Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Suomi National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (SNPP). In December 2014, the CrIS instrument on the SNPP satellite doubled the spectral resolution of shortwave infrared data being transmitted to the ground. In November 2015, additional points were included at the ends of the longwave and shortwave interferograms to improve the quality of the calibration. Prior to November 2, 2015 the data are only available in Normal Spectral Resolution (NSR), after November 2, 2015 at 16:06 UTC, the data are available in both NSR and Full Spectral Resolution. The FSR files have 2,223 channels: 637 shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries. CrIS is designed to be used with the ATMS (Advanced Technology Microwave Sounder) instrument. Processing the data from both of these instruments together is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite). If you were redirected to this page from a DOI from an older version, the \"Version History\" tab will provide additional content including information about the latest version (v3) of this product. Please contact the GES DISC user support if you need additional information.\n<br>\n\n#### SNPPCrISL1B\nVersion 4 is the current version of this dataset and supersedes previous versions (Version 1, 2, 3). Please see Version Description for more information about these changes. The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data for the CrIS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP) satellite. In December 2014, the spectral resolution of data transmitted to the ground from the SNPP CrIS instrument was increased by a factor of two in the midwave infrared band, and by a factor of four in the shortwave infrared band. In November 2015, additional points were included at the ends of the longwave and shortwave SNPP CrIS interferograms to improve calibration quality. Prior to November 2, 2015 at 16:06 UTC, SNPP CrIS data are only available in Normal Spectral Resolution (NSR). After that time, the SNPP CrIS data are available in both NSR and FSR. JPSS-1 and JPSS-2 CrIS data are only provided in FSR. The FSR files have 2,223 channels: 637 shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross-track direction and 45 in the along-track direction. Data products are constructed on six-minute boundaries. CrIS data are designed to be used with the ATMS (Advanced Technology Microwave Sounder) instrument data. These instruments together are referred to as CrIMSS (Cross-track Infrared and Microwave Sounder Suite).\n<br>\n\n#### VNP04ANC\nThe VIIRS/NPP Aerosol Daily L3 Global 0.05 Deg CMG, Short-name VNP04ANC product provides VIIRS aerosol, air temperature, ozone and water vapor data at 0.05-degree resolution. This product is based on a Climate Modeling Grid (CMG) for the purpose of being used in climate simulation models. This is derived by using a weighted average of the best quality observation form only daytime data from Level 2 product, VNP04_L2. This daily product record starts from January 5th, 2018. This L3 daily product, in HDF5 file format , contains 5 Science Data Set (SDS) layers.\n<br>\n\n#### VNP09\nThe VIIRS/NPP Atmospherically Corrected Surface Reflectance 6-Min L2 Swath 375m, 750m product, with short name VNP09, are estimates of surface reflectance in each of the VIIRS reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. The VNP09 contains approximately 6 minutes' worth of data. Surface reflectance for each moderate-resolution (750m) or imagery-resolution (375m) pixel is retrieved separately for the Level-2 products. Surface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. All surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans.\n<br>\n\n#### VNP46A1\nThe VIIRS/NPP Daily Gridded Day Night Band 15 arc-second Linear Lat Lon Grid Night product, short-name VNP46A1 is a daily, top-of-atmosphere, at-sensor nighttime radiance product. This product is available at 15 arc-second spatial resolution from January 2012 onward. The VNP46A1/VJ146A1 product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. The current v2.0 collection contains several changes and differences relative to the previous v1.0 collection. These include radiance data format change from unsigned integer to floating-point, from exclusively for land surfaces coverage to both land and water surfaces, updated Mandatory_Quality_Flag layer, and others. Consult the v2.0-specific Black Marble User Guide for additional details at: https://landweb.modaps.eosdis.nasa.gov/data/userguide/BlackMarbleUserGuide_Collection2.0_20241203.pdf\n<br>\n\n#### VNP02DNB\nThe VIIRS/NPP Day/Night Band 6-Min L1B Swath 750 m product, short-name VNP02DNB, is a panchromatic Day-Night band (DNB) calibrated radiance product. The DNB is one of the M-bands with an at-nadir spatial resolution of 750 meters (across the entire scan). The panchromatic DNB’s spectral wavelength ranges from 0.5 µm to 0.9 µm. It facilitates measuring night lights, reflected solar/lunar lights with a large dynamic range between a low of a quarter moon illumination to the brightest daylight. More information is available at product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VNP02DNB/\n<br>\n\n#### VNP03DNB\nThe VIIRS/NPP Day/Night Band Terrain Corrected Geolocation 6-Min L1 Swath 750m product, short-name VNP03DNB includes the geolocation fields that are calculated for Visible-Infrared Imaging-Radiometer Suite (VIIRS) day-night band (DNB) Line of sight (LOS) for all orbits at the nominal resolution of 750 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 16 detectors in the DNB on the Earth's surface. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It provides geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VNP03DNB product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, lunar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, moon illumination fraction and phase angle, and quality flag for every pixel location.\n<br>\n\n#### VNP46A2\nThe SNPP VIIRS Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 15 arc-second Linear Lat Lon Grid product, short-name VNP46A2 is a daily moonlight- and atmosphere-corrected Nighttime Lights (NTL) product. This product is available at 15 arc-second resolution and contains seven Science Data Sets (SDS) that include DNB BRDF-Corrected NTL, Gap-Filled DNB BRDF-Corrected NTL, DNB Lunar Irradiance, Latest High-Quality Retrieval, Mandatory Quality Flag, Cloud Mask Quality Flag, and Snow Flag. The VNP46A2 product files are provided in standard Hierarchical Data Format–Earth Observing System (HDF-EOS5) format. The current v2.0 collection contains several changes and differences relative to the previous v1.0 collection. These include radiance data format change from unsigned integer to floating-point, from exclusively for land surfaces coverage to both land and water surfaces, updated Mandatory_Quality_Flag layer, and others. Consult the v2.0-specific Black Marble User Guide for additional details at: https://landweb.modaps.eosdis.nasa.gov/data/userguide/BlackMarbleUserGuide_Collection2.0_20241203.pdf\n<br>\n\n#### VNP02IMG\nThe VIIRS/NPP Imagery Resolution 6-Min L1B Swath 375 m product, short-name VNP02IMG is derived from the five image-resolution or I-bands, which have a 375-meter resolution at nadir. Ranging in wavelengths from 0.6 µm to 12.4 µm, the I-bands are sensitive to visible/reflective, near-, shortwave-, mediumwave-, and longwave-infrared wavelengths. Derived from the NASA VIIRS L1A raw radiances, this product includes calibrated and geolocated radiance and reflectance data, quality flags, and granule- and collection-level metadata. In contrast to a MODIS L1B product, which temporally spans 5 minutes, the VIIRS L1B calibrated radiances product contains a nominal temporal duration of 6 minutes. The image dimensions of the 375-m swath product measure 6464 lines by 6400 pixels.\n<br>\n\n#### VNP03IMG\nThe VIIRS/NPP Imagery Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 375m, short-name VNP03IMG, product contains the derived line-of-sight (LOS) vectors for each of the 375-m image-resolution or I-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VNP03IMG product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location.\n<br>\n\n#### VNP46A3\nThe Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) supports a Day-Night Band (DNB) sensor that provides global daily measurements of nocturnal visible and near-infrared (NIR) light that are suitable for Earth system science and applications. The VIIRS DNB’s ultra-sensitivity in lowlight conditions enable us to generate a new set of science-quality nighttime products that manifest substantial improvements in sensor resolution and calibration when compared to the previous era of Defense Meteorological Satellite Program/Operational Linescan System’s (DMSP/OLS) nighttime lights image products. Such improvements allow the VIIRS DNB products to better monitor both the magnitude and signature of nighttime phenomena, and anthropogenic sources of light emissions. VNP46A3 is the short-name for the monthly composited, moonlight- and atmosphere-corrected Nighttime Lights (NTL) product whose long-name is VIIRS/NPP Lunar BRDF-Adjusted Nighttime Lights Monthly L3 Global 15 arc second Linear Lat Lon Grid. It contains 28 Science Data Sets (SDS). This v2.0 VNP46A3 collection is provided in standard Hierarchical Data Format–Earth Observing System (HDF-EOS5) format, and starts from January 1, 2012. This product has been validated through various field campaigns of opportunity supported under the Group on Earth obs. (GEO) Human Planet Initiative’s Nighttime Product Validation (NPV) task. Additionally, the VNP46 product suite comes with ancillary and quality assessment layers indicating sources of error from the calibration, cloud mask, snow flag, and lunar irradiance, etc.\n<br>\n\n#### VNP46A4\nVIIRS/NPP Lunar BRDF-Adjusted Nighttime Lights Yearly L3 Global 15 arc-second Linear Lat Lon Grid, with short-name VNP46A4, is the third nighttime lights (NTL) product in the Black Marble suite, which provides monthly composites generated from daily atmospherically- and lunar-BRDF-corrected NTL radiance to remove the influence of extraneous artifacts and biases. The VNP46A4 product contains 28 layers. They provide information on the NTL composite, the number of observations, quality, and standard deviation for multi-view zenith angle categories (near-nadir, off-nadir, and all angles), their snow-covered and snow-free statuses besides land-water mask, latitude and longitude coordinate information. They also include detailed information and description of the quality flags. This yearly Lunar BRDF-Adjusted NTL collection record starts from January 1st 2012.\n<br>\n\n#### VNP03MOD\nThe VIIRS/NPP Moderate Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 750 m product, short-name VNP03MOD, contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VNP03MOD product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location.\n<br>\n\n#### CLDCR_L2_VIIRS_SNPP\nThe VIIRS/Suomi-NPP Cirrus Reflectance 6-Min Swath 750m product is a Level-2 product generated at 750-m (at nadir) spatial resolutions. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence that an unobstructed view of the Earth's surface has been observed. An indication of shadows affecting the scene is also provided. Radiometrically-accurate radiances are required, thus holes in the Cloud Mask will appear wherever the input radiances are incomplete or of poor quality assurance. For more information consult Product Page at: https://cimss.ssec.wisc.edu/MVCM/\n<br>\n\n#### CLDPROP_L2_VIIRS_SNPP\nThe VIIRS/SNPP Cloud Properties 6-min L2 Swath 750m product is designed to facilitate continuity in cloud properties between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Suomi NPP spacecraft. The VIIRS Cloud Properties product consists of cloud optical and physical parameters. These parameters are derived using observations in visible through infrared spectral channels. VIIRS infrared channel radiances are primarily used to derive cloud top temperature, cloud top height, effective emissivity, an infrared cloud phase product (ice vs. water, opaque vs. non-opaque) and cloud fraction under both daytime and nighttime conditions. The VIIRS solar reflectance channels are primarily used to derive cloud optical thickness, particle effective radius, water path, and to inform the phase used in the optical retrievals. The VIIRS Cloud Properties product is a Level-2 product generated at 750 m (at nadir) spatial resolution. The current version-1.1 of the Level-2 CLDPROP product collection is corrected to address an issue with the cloud optical properties’ thermodynamic phase that caused erroneous liquid water cloud phase results.\n<br>\n\n#### CLDPROPCOSP_D3_VIIRS_SNPP\nThe VIIRS/SNPP Cloud Properties COSP Level 3 daily, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_D3_VIIRS_SNPP. It contains VIIRS SNPP cloud mask, cloud top, and cloud optical retrieval data over daily timeframe. It provides a set of custom cloud-related parameters for better comparison with climate model output. The “COSP” acronym in the short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. Provided in netCDF4 format, it contains 32 aggregated science data sets (SDS/parameters). Consult the CLDPROPCOSP User Guide for details regarding how the L3 daily statistics are computed, and to learn more about the gridding and sampling protocols specific to this product and a number of other topics germane to the user community. The collection of this product starts from March 1, 2012 and includes 365 granules each calendar year.\n<br>\n\n#### CLDPROPCOSP_M3_VIIRS_SNPP\nThe VIIRS/SNPP Cloud Properties COSP Level 3 monthly, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_M3_VIIRS_SNPP. It contains VIIRS SNPP cloud mask, cloud top, and cloud optical retrieval data over monthly timeframe. It provides a set of custom cloud-related parameters for better comparison with climate model output. The “COSP” acronym in the short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. Provided in netCDF4 format, it contains 32 aggregated science data sets (SDS/parameters). Consult the CLDPROPCOSP User Guide for details regarding how the L3 monthly statistics are computed, and to learn more about the gridding and sampling protocols specific to this product and a number of other topics germane to the user community. The collection of this product starts from March 1, 2012 and includes 12 granules each calendar year.\n<br>\n\n#### CLDPROP_D3_VIIRS_SNPP\nThe VIIRS/SNPP Cloud Properties Level 3 daily, 1x1 degree grid product is designed to facilitate continuity in cloud property statistics between the MODIS on the Aqua and Terra platforms and the common continuity products generated for the VIIRS (Visible Infrared Imaging Radiometer Suite) and the MODIS Aqua instruments. CLDPROP Level-3 statistical routines include scalar and histograms (1-D and 2-D) that are calculated identically to statistical datasets in the MODIS standard Level-3 product (MOD08 and MYD08 for MODIS Terra and Aqua, respectively). In addition, the same dataset names are used for all common datasets provided in both the continuity and standard Level-3 files.\n<br>\n\n#### CLDPROP_M3_VIIRS_SNPP\nThe VIIRS/SNPP Cloud Properties Level 3 monthly, 1x1 degree grid product is designed to facilitate continuity in cloud property statistics between the MODIS on the Aqua and Terra platforms and the common continuity products generated for the VIIRS (Visible Infrared Imaging Radiometer Suite) and the MODIS Aqua instruments. CLDPROP Level-3 statistical routines include scalar and histograms (1-D and 2-D) that are calculated identically to statistical datasets in the MODIS standard Level-3 product (MOD08 and MYD08 for MODIS Terra and Aqua, respectively). In addition, the same dataset names are used for all common datasets provided in both the continuity and standard Level-3 files.\n<br>\n\n#### AERDB_L2_VIIRS_SNPP\nThe VIIRS/SNPP Deep Blue Aerosol L2 6-Min Swath 6 km product from the Visible Infrared Imaging Radiometer Suite (VIIRS) determines atmospheric aerosol loading for daytime cloud-free snow-free scenes. This orbit-level product (Short-name: AERDB_L2_VIIRS_SNPP) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor’s scanning geometry and Earth’s curvature. Viewed differently, this product’s resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. The L2 Deep Blue AOT data products, at 550 nanometers reference wavelengths, are derived from particular VIIRS bands using two primary AOT retrieval algorithms: Deep Blue algorithm over land, and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over ocean. Although this product is called Deep Blue based on retrievals for the land algorithm, the data includes over-water retrievals as well. This L2 description pertains to the VIIRS Deep Blue Aerosol collection version 2.0 (C2) product. Significant changes have been made to the V2.0 Deep Blue/SOAR algorithms to further improve the data quality. For C2.0, the aerosol products are available for NOAA20 VIIRS in addition to SNPP. Some of changes in the retrieval algorithms and data products include, new SDS suite for prognostic uncertainties of 550 nm AOT over both land and ocean is added, surface pressure is better accounted for for both over-land and over-ocean retrievals by adding surface pressure nodes in the aerosol lookup table, and a number of other improvements which can be found from Product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2_VIIRS_SNPP For more information consult Deep Blue aerosol team Page at: https://deepblue.gsfc.nasa.gov\n<br>\n\n#### AERDB_D3_VIIRS_SNPP\nThe VIIRS/SNPP Deep Blue Level 3 daily aerosol data, 1x1 degree grid, Short-name AERDB_D3_VIIRS_SNPP product is derived from the Version-2.0 (V2.0) L2 6-minute swath-based products (AERDB_L2_VIIRS_SNPP), and is provided in a 1 x 1 degree horizontal resolution grid. Each data field, in most cases, represents the arithmetic mean of all the cells whose latitude and longitude coordinates positions them within each grid element’s bounding limits. Other measures like standard deviation are also provided. This aggregated product is derived only using the best-estimate, QA-filtered retrievals. Using only cells that were measured on the day of interest, the algorithm requires at least three retrieved measurements to render a given grid as valid on any given day. This daily product record starts from March 1st, 2012 . This L3 daily product, in netCDF, contains 45 Science Data Set (SDS) layers. For more information about the product and Science Data Set (SDS) layers, consult product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_VIIRS_SNPP Or Consult Deep Blue aerosol team Page at: https://deepblue.gsfc.nasa.gov\n<br>\n\n#### AERDB_M3_VIIRS_SNPP\nThe VIIRS/SNPP Deep Blue Level 3 monthly aerosol data, 1x1 degree grid, Short-name AERDB_M3_VIIRS_SNPP product is derived from the Version-2.0 (V2.0) daily L3 gridded products (AERDB_D3_VIIRS_SNPP), and is provided in a 1 x 1 degree horizontal resolution grid. Arithmetic mean values from the daily L3 gridded products also provide the basis to derive a complement of statistics for the monthly aggregated products. To exclude poorly sampled grid elements, the algorithm requires at least 3 valid days’ worth of data to render a given monthly grid element as valid. This monthly product record starts from March1st, 2012. This L3 monthly product, in netCDF format, contains 45 Science Data Set (SDS) layers that are named identical to the SDSs in the daily L3 product. For more information about the product and Science Data Set (SDS) layers, consult product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_VIIRS_SNPP Or Consult Deep Blue aerosol team Page at: https://deepblue.gsfc.nasa.gov\n<br>\n\n#### WATVP_L2_VIIRS_SNPP\nThe Suomi NPP VIIRS Water Vapor Products provide total column water vapor (TPW) properties from merged VIIRS infrared measurements and CrIS plus ATMS water vapor soundings to continue the depiction of global moisture at high spatial resolution started with MODIS on the Terra and Aqua platforms. While MODIS has two water vapor channels within the 6.5 &#956;m H2O absorption band and four channels within the 15 micrometer CO2 absorption band, VIIRS has no channels in either IR absorption band. The VNPWATVP algorithm is similar to the MODIS MOD07 synthetic regression algorithm. It uses the three VIIRS longwave IR window bands in a regression relation and adds the NUCAPS (CrIS+ATMS) water vapor product to compensate for the absence of VIIRS water vapor channels. The Level-2 6-minute granule and 750 m spatial resolution VIIRS TPW product file includes the collocated NUCAPS background TPW, the VIIRS-only TPW, and VIIRS+NUCAPS TPW retrievals with quality flags.\n<br>\n\n#### WATVP_D3_VIIRS_SNPP\nThe VIIRS/SNPP Water Vapor Level-3 daily 0.5 x 0.5 degree grid Product provide total column water vapor (TPW) properties from merged VIIRS infrared measurements and Cross-track Infrared Sounder (CrIS) plus Advanced Technology Microwave Sounder (ATMS) water vapor soundings to continue the depiction of global moisture at a higher spatial resolution started with MODIS on the Terra and Aqua platforms. Level-3 global 0.5 degree by 0.5 degree spatial resolution daily mean data products (called WATVP_D3_VIIRS_SNPP) is derived by using a gridding software (called Yori) developed at the University of Wisconsin, Madison, Space Science and Engineering Center (Veglio et al., 2018), and implemented by the NASA VIIRS Atmosphere Science Investigator-led Processing System (SIPS). The Yori has been adapted for the VIIRS TPW products and is processed using the VIIRS Level-2 Water Vapor products (WATVP_L2_VIIRS_SNPP) separated by day and night. The mean and the standard deviation of each Level-2 water vapor product are calculated for each grid cell. The sum, the square of the sum of each product, and the number of pixels in the cells are also stored in the Level-3 (daily) output files for further aggregation purposes.\n<br>\n\n#### WATVP_M3_VIIRS_SNPP\nThe VIIRS/SNPP Water Vapor Level-3 monthly 0.5 x 0.5 degree grid Product provide total column water vapor (TPW) properties from merged VIIRS infrared measurements and Cross-track Infrared Sounder (CrIS) plus Advanced Technology Microwave Sounder (ATMS) water vapor soundings to continue the depiction of global moisture at a higher spatial resolution started with MODIS on the Terra and Aqua platforms. Level-3 global 0.5 degree by 0.5 degree spatial resolution daily mean data products (called WATVP_M3_VIIRS_SNPP) is derived by using a gridding software (called Yori) developed at the University of Wisconsin, Madison, Space Science and Engineering Center (Veglio et al., 2018), and implemented by the NASA VIIRS Atmosphere Science Investigator-led Processing System (SIPS). The Yori has been adapted for the VIIRS TPW products and is processed using the VIIRS Level-2 Water Vapor products (WATVP_L2_VIIRS_SNPP) separated by day and night. The mean and the standard deviation of each Level-2 water vapor product are calculated for each grid cell. The sum, the square of the sum of each product, and the number of pixels in the cells are also stored in the Level-3 (monthly) output files for further aggregation purposes.\n<br>\n\n#### CLDMSK_L2_VIIRS_SNPP\nThe VIIRS/Suomi-NPP Cloud Mask 6-Min Swath 750m product is a Level-2 product generated at 750-m (at nadir) spatial resolutions. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence that an unobstructed view of the Earth's surface has been observed. An indication of shadows affecting the scene is also provided. Radiometrically-accurate radiances are required, thus holes in the Cloud Mask will appear wherever the input radiances are incomplete or of poor quality assurance. For more information consult Product Page at: https://cimss.ssec.wisc.edu/MVCM/\n<br>\n\n#### VNP02MOD\nThe VNP02MOD | VIIRS/NPP Moderate Resolution 6-Min L1B Swath 750m, short-name VNP02MOD contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VNP03MOD product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/SNDRSNIL2ESPNH3_1.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","hdf","hdf5","ice","netcdf","oceans","precipitation","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SNDRSNIL2ESPNH3 v1 - The objective of this limited edition data collection is to examine the ammonia products generated by the ESSPA (Earth System Science Profiling Algorithm) algorithm from the Cross-track Infrared Sounder (CrIS) instruments. The CrIS instrument used for this product is deployed on board the Suomi National Polar-orbiting Partnership (SNPP) platform and uses the Normal Spectral Resolution (NSR) data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/limited_editions/SNPP_ESSPA/SNDRSNIL2ESPNH3.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNCrISL1BIMG v2 - The Cross-track Infrared Sounder (CrIS) Level 1B Normal Spectral Resolution (NSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP). In December 2014, the CrIS instrument on the SNPP satellite doubled the spectral resolution of shortwave infrared data being transmitted to the ground.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_Sounder_Level1/SNDRSNCrISL1BIMG.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AERDT_L2_VIIRS_SNPP v2.1 - The SNPP Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua missions’ Moderate Imaging Spectroradiometer (MODIS) instruments.","ARN":"arn:aws:s3:::prod-lads/AERDT_L2_VIIRS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AER_DBDT_D10KM_L3_VIIRS_SNPP v001 - This High-Resolution (0.1 x 0.1 degree) Level 3 daily Aerosol Optical Depth (AOD) product is generated by combining two Visible Infrared Imaging Radiometer Suite (VIIRS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. This dataset is provided in daily files ranging from 2012-03-04 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TASNPP_AOD_Level3/AER_DBDT_D10KM_L3_VIIRS_SNPP.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AER_DBDT_M10KM_L3_VIIRS_SNPP v001 - This High-Resolution (0.1 x 0.1 degree) Level 3 monthly Aerosol Optical Depth (AOD) product is generated by combining two Visible Infrared Imaging Radiometer Suite (VIIRS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. This dataset is provided in monthly files ranging from March 2012 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TASNPP_AOD_Level3/AER_DBDT_M10KM_L3_VIIRS_SNPP.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNML2RMS v1 - This SNDRSNM2RMS level 2 product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm. Different from the CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm, the RAMSES II algorithm is a microwave only retrieval algorithm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/limited_editions/SNPP_RAMSES/SNDRSNML2RMS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNML2RMS v3 - This level 2 product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the Suomi-National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_Sounder_Level2/SNDRSNML2RMS.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNML2RMSSUP v3 - This level 2 support product is generated by the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the Suomi-National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_Sounder_Level2/SNDRSNML2RMSSUP.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNML3DRMS v3 - This level 3 daily product is generated from the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the Suomi-National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_Sounder_Level3/SNDRSNML3DRMS.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNML3MRMS v3 - This level 3 monthly product is generated from the RAMSES (Retrieval Algorithm for Microwave Sounders in Earth Science) II algorithm.The RAMSES II algorithm is a microwave only retrieval algorithm using observations from the Suomi-National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS) instrument. The ATMS instrument used for this product is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_Sounder_Level3/SNDRSNML3MRMS.3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNIML2CHTRETN v1 - The objective of this limited edition data collection is to examine products generated by the Climate Heritage AIRS Retrieval Technique (CHART) algorithm to analyze Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/limited_editions/SNPP_CHART/SNDRSNIML2CHTRETN.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNIML2CCPRET v2 - WARNING: To users of the derived product “co_mmr_midtrop” (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_Sounder_Level2/SNDRSNIML2CCPRET.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNIML2PLEVCPS v2.1 - The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using full spectral resolution inputs.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_Sounder_Level2/SNDRSNIML2PLEVCPS.2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNIML2CCPCCR v2 - The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Full Spectral Resolution (FSR) data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_Sounder_Level2/SNDRSNIML2CCPCCR.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNIML2PLEVCPSN v2.1 - The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using Normal Spectral Resolution data from the Suomi-NPP satellite.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_Sounder_Level2/SNDRSNIML2PLEVCPSN.2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNIML2CCPCCRN v2 - The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS) instruments, also known as CrIMSS (Cross-track Infrared and Microwave Sounding Suite). The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_Sounder_Level2/SNDRSNIML2CCPCCRN.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNIML2SFSPRET v2 - This level 2 standard product is generated by the SiFSAP (Single Field-of-View Sounder Atmospheric Products) algorithm. The SIFSAP algorithm provides retrieval for each sounder Field of View (FOV), therefore, it has 3-times higher horizontal spatial resolution and 9-time denser products compared to other current IR sounder products.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SiFSAP/SNDRSNIML2SFSPRET.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNIML2SFSPSUP v2 - This level 2 support product is generated by the SiFSAP (Single Field-of-View Sounder Atmospheric Products) algorithm. The SIFSAP algorithm provides retrieval for each sounder Field of View (FOV), therefore, it has 3-times higher horizontal spatial resolution and 9-time denser products compared to other current IR sounder products.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SiFSAP/SNDRSNIML2SFSPSUP.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNIML3SDSFSP v2 - The SIFSAP (Single Field-of-View Sounder Atmospheric Products) algorithm provides retrieval for each sounder Field of View (FOV), therefore, it has 3-times higher horizontal spatial resolution and 9-time denser products compared to other current IR sounder products. Since SiFSAP is an FOV-based algorithm, its product variables have an additional dimension which represents the number of FOVs.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SiFSAP/SNDRSNIML3SDSFSP.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNIL1BCALSUBRNDN v2 - The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Calibration_Subsets/SNDRSNIL1BCALSUBRNDN.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNDRSNATMSMAP v1 - The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Suomi National Polar-orbiting Partnership Project (S-NPP). The ATMS instrument is a cross-track scanner with 22 microwave channels in the range 23.8-183.31 Gigahertz (GHz).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Granule_Maps/SNDRSNATMSMAP.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNPPATMSL1B v2 - The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP). The ATMS instrument is a cross-track scanner with 22 microwave channels in the range 23.8-183.31 Gigahertz (GHz).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_Sounder_Level1/SNPPATMSL1B.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNPPCrISL1B v2 - The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Suomi National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (SNPP). In December 2014, the CrIS instrument on the SNPP satellite doubled the spectral resolution of shortwave infrared data being transmitted to the ground.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_Sounder_Level1/SNPPCrISL1B.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"SNPPCrISL1B v4 - Version 4 is the current version of this dataset and supersedes previous versions (Version 1, 2, 3). Please see Version Description for more information about these changes.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/SNPP_Sounder_Level1/SNPPCrISL1B.4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"VNP04ANC v2 - The VIIRS/NPP Aerosol Daily L3 Global 0.05 Deg CMG, Short-name VNP04ANC product provides VIIRS aerosol, air temperature, ozone and water vapor data at 0.05-degree resolution. This product is based on a Climate Modeling Grid (CMG) for the purpose of being used in climate simulation models.","ARN":"arn:aws:s3:::prod-lads/VNP04ANC","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP09 v2 - The VIIRS/NPP Atmospherically Corrected Surface Reflectance 6-Min L2 Swath 375m, 750m product, with short name VNP09, are estimates of surface reflectance in each of the VIIRS reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. The VNP09 contains approximately 6 minutes' worth of data.","ARN":"arn:aws:s3:::prod-lads/VNP09","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP46A1 v2 - The VIIRS/NPP Daily Gridded Day Night Band 15 arc-second Linear Lat Lon Grid Night product, short-name VNP46A1 is a daily, top-of-atmosphere, at-sensor nighttime radiance product. This product is available at 15 arc-second spatial resolution from January 2012 onward.","ARN":"arn:aws:s3:::prod-lads/VNP46A1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP02DNB v2 - The VIIRS/NPP Day/Night Band 6-Min L1B Swath 750 m product, short-name VNP02DNB, is a panchromatic Day-Night band (DNB) calibrated radiance product. The DNB is one of the M-bands with an at-nadir spatial resolution of 750 meters (across the entire scan).","ARN":"arn:aws:s3:::prod-lads/VNP02DNB","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP03DNB v2 - The VIIRS/NPP Day/Night Band Terrain Corrected Geolocation 6-Min L1 Swath 750m product, short-name VNP03DNB includes the geolocation fields that are calculated for Visible-Infrared Imaging-Radiometer Suite (VIIRS) day-night band (DNB) Line of sight (LOS) for all orbits at the nominal resolution of 750 m. The locations and ancillary information correspond to the intersection of the centers of each Field of View (FOV) from 16 detectors in the DNB on the Earth's surface.","ARN":"arn:aws:s3:::prod-lads/VNP03DNB","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP46A2 v2 - The SNPP VIIRS Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 15 arc-second Linear Lat Lon Grid product, short-name VNP46A2 is a daily moonlight- and atmosphere-corrected Nighttime Lights (NTL) product. This product is available at 15 arc-second resolution and contains seven Science Data Sets (SDS) that include DNB BRDF-Corrected NTL, Gap-Filled DNB BRDF-Corrected NTL, DNB Lunar Irradiance, Latest High-Quality Retrieval, Mandatory Quality Flag, Cloud Mask Quality Flag, and Snow Flag.","ARN":"arn:aws:s3:::prod-lads/VNP46A2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP02IMG v2 - The VIIRS/NPP Imagery Resolution 6-Min L1B Swath 375 m product, short-name VNP02IMG is derived from the five image-resolution or I-bands, which have a 375-meter resolution at nadir. Ranging in wavelengths from 0.6 µm to 12.4 µm, the I-bands are sensitive to visible/reflective, near-, shortwave-, mediumwave-, and longwave-infrared wavelengths.","ARN":"arn:aws:s3:::prod-lads/VNP02IMG","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP03IMG v2 - The VIIRS/NPP Imagery Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 375m, short-name VNP03IMG, product contains the derived line-of-sight (LOS) vectors for each of the 375-m image-resolution or I-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry.","ARN":"arn:aws:s3:::prod-lads/VNP03IMG","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP46A3 v2 - The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) supports a Day-Night Band (DNB) sensor that provides global daily measurements of nocturnal visible and near-infrared (NIR) light that are suitable for Earth system science and applications. The VIIRS DNB’s ultra-sensitivity in lowlight conditions enable us to generate a new set of science-quality nighttime products that manifest substantial improvements in sensor resolution and calibration when compared to the previous era of Defense Meteorological Satellite Program/Operational Linescan S...","ARN":"arn:aws:s3:::prod-lads/VNP46A3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP46A4 v1 - VIIRS/NPP Lunar BRDF-Adjusted Nighttime Lights Yearly L3 Global 15 arc-second Linear Lat Lon Grid, with short-name VNP46A4, is the third nighttime lights (NTL) product in the Black Marble suite, which provides monthly composites generated from daily atmospherically- and lunar-BRDF-corrected NTL radiance to remove the influence of extraneous artifacts and biases. The VNP46A4 product contains 28 layers.","ARN":"arn:aws:s3:::prod-lads/VNP46A4","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP03MOD v2 - The VIIRS/NPP Moderate Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 750 m product, short-name VNP03MOD, contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry.","ARN":"arn:aws:s3:::prod-lads/VNP03MOD","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDCR_L2_VIIRS_SNPP v1 - The VIIRS/Suomi-NPP Cirrus Reflectance 6-Min Swath 750m product is a Level-2 product generated at 750-m (at nadir) spatial resolutions. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence that an unobstructed view of the Earth's surface has been observed.","ARN":"arn:aws:s3:::prod-lads/CLDCR_L2_VIIRS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDPROP_L2_VIIRS_SNPP v1.1 - The VIIRS/SNPP Cloud Properties 6-min L2 Swath 750m product is designed to facilitate continuity in cloud properties between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Suomi NPP spacecraft. The VIIRS Cloud Properties product consists of cloud optical and physical parameters.","ARN":"arn:aws:s3:::prod-lads/CLDPROP_L2_VIIRS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDPROPCOSP_D3_VIIRS_SNPP v1.1 - The VIIRS/SNPP Cloud Properties COSP Level 3 daily, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_D3_VIIRS_SNPP. It contains VIIRS SNPP cloud mask, cloud top, and cloud optical retrieval data over daily timeframe.","ARN":"arn:aws:s3:::prod-lads/CLDPROPCOSP_D3_VIIRS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDPROPCOSP_M3_VIIRS_SNPP v1.1 - The VIIRS/SNPP Cloud Properties COSP Level 3 monthly, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_M3_VIIRS_SNPP. It contains VIIRS SNPP cloud mask, cloud top, and cloud optical retrieval data over monthly timeframe.","ARN":"arn:aws:s3:::prod-lads/CLDPROPCOSP_M3_VIIRS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDPROP_D3_VIIRS_SNPP v1.1 - The VIIRS/SNPP Cloud Properties Level 3 daily, 1x1 degree grid product is designed to facilitate continuity in cloud property statistics between the MODIS on the Aqua and Terra platforms and the common continuity products generated for the VIIRS (Visible Infrared Imaging Radiometer Suite) and the MODIS Aqua instruments. CLDPROP Level-3 statistical routines include scalar and histograms (1-D and 2-D) that are calculated identically to statistical datasets in the MODIS standard Level-3 product (MOD08 and MYD08 for MODIS Terra and Aqua, respectively).","ARN":"arn:aws:s3:::prod-lads/CLDPROP_D3_VIIRS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDPROP_M3_VIIRS_SNPP v1.1 - The VIIRS/SNPP Cloud Properties Level 3 monthly, 1x1 degree grid product is designed to facilitate continuity in cloud property statistics between the MODIS on the Aqua and Terra platforms and the common continuity products generated for the VIIRS (Visible Infrared Imaging Radiometer Suite) and the MODIS Aqua instruments. CLDPROP Level-3 statistical routines include scalar and histograms (1-D and 2-D) that are calculated identically to statistical datasets in the MODIS standard Level-3 product (MOD08 and MYD08 for MODIS Terra and Aqua, respectively).","ARN":"arn:aws:s3:::prod-lads/CLDPROP_M3_VIIRS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_L2_VIIRS_SNPP v2 - The VIIRS/SNPP Deep Blue Aerosol L2 6-Min Swath 6 km product from the Visible Infrared Imaging Radiometer Suite (VIIRS) determines atmospheric aerosol loading for daytime cloud-free snow-free scenes. This orbit-level product (Short-name: AERDB_L2_VIIRS_SNPP) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor’s scanning geometry and Earth’s curvature.","ARN":"arn:aws:s3:::prod-lads/AERDB_L2_VIIRS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_D3_VIIRS_SNPP v2 - The VIIRS/SNPP Deep Blue Level 3 daily aerosol data, 1x1 degree grid, Short-name AERDB_D3_VIIRS_SNPP product is derived from the Version-2.0 (V2.0) L2 6-minute swath-based products (AERDB_L2_VIIRS_SNPP), and is provided in a 1 x 1 degree horizontal resolution grid. Each data field, in most cases, represents the arithmetic mean of all the cells whose latitude and longitude coordinates positions them within each grid element’s bounding limits.","ARN":"arn:aws:s3:::prod-lads/AERDB_D3_VIIRS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AERDB_M3_VIIRS_SNPP v2 - The VIIRS/SNPP Deep Blue Level 3 monthly aerosol data, 1x1 degree grid, Short-name AERDB_M3_VIIRS_SNPP product is derived from the Version-2.0 (V2.0) daily L3 gridded products (AERDB_D3_VIIRS_SNPP), and is provided in a 1 x 1 degree horizontal resolution grid. Arithmetic mean values from the daily L3 gridded products also provide the basis to derive a complement of statistics for the monthly aggregated products.","ARN":"arn:aws:s3:::prod-lads/AERDB_M3_VIIRS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WATVP_L2_VIIRS_SNPP v1 - The Suomi NPP VIIRS Water Vapor Products provide total column water vapor (TPW) properties from merged VIIRS infrared measurements and CrIS plus ATMS water vapor soundings to continue the depiction of global moisture at high spatial resolution started with MODIS on the Terra and Aqua platforms. While MODIS has two water vapor channels within the 6.5 &#956;m H2O absorption band and four channels within the 15 micrometer CO2 absorption band, VIIRS has no channels in either IR absorption band.","ARN":"arn:aws:s3:::prod-lads/WATVP_L2_VIIRS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WATVP_D3_VIIRS_SNPP v1 - The VIIRS/SNPP Water Vapor Level-3 daily 0.5 x 0.5 degree grid Product provide total column water vapor (TPW) properties from merged VIIRS infrared measurements and Cross-track Infrared Sounder (CrIS) plus Advanced Technology Microwave Sounder (ATMS) water vapor soundings to continue the depiction of global moisture at a higher spatial resolution started with MODIS on the Terra and Aqua platforms. Level-3 global 0.5 degree by 0.5 degree spatial resolution daily mean data products (called WATVP_D3_VIIRS_SNPP) is derived by using a gridding software (called Yori) developed at the University o...","ARN":"arn:aws:s3:::prod-lads/WATVP_D3_VIIRS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WATVP_M3_VIIRS_SNPP v1 - The VIIRS/SNPP Water Vapor Level-3 monthly 0.5 x 0.5 degree grid Product provide total column water vapor (TPW) properties from merged VIIRS infrared measurements and Cross-track Infrared Sounder (CrIS) plus Advanced Technology Microwave Sounder (ATMS) water vapor soundings to continue the depiction of global moisture at a higher spatial resolution started with MODIS on the Terra and Aqua platforms. Level-3 global 0.5 degree by 0.5 degree spatial resolution daily mean data products (called WATVP_M3_VIIRS_SNPP) is derived by using a gridding software (called Yori) developed at the University...","ARN":"arn:aws:s3:::prod-lads/WATVP_M3_VIIRS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"CLDMSK_L2_VIIRS_SNPP v1 - The VIIRS/Suomi-NPP Cloud Mask 6-Min Swath 750m product is a Level-2 product generated at 750-m (at nadir) spatial resolutions. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence that an unobstructed view of the Earth's surface has been observed.","ARN":"arn:aws:s3:::prod-lads/CLDMSK_L2_VIIRS_SNPP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP02MOD v2 - The VNP02MOD | VIIRS/NPP Moderate Resolution 6-Min L1B Swath 750m, short-name VNP02MOD contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform’s ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry.","ARN":"arn:aws:s3:::prod-lads/VNP02MOD","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-suomi-npp","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA swot Project","Description":"This dataset provides a simulated water surface elevation product that resembles the Ka-band Interferometer (KaRIn) measurements by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent. The simulated SWOT KaRIN swaths span 128 km in the cross-swath direction with a 20-km nadir gap. This product is complementary to the L2_HR_PIXC_V1 product. It provides a less noisy, height-constrained geolocation (latitude, longitude, and height) of the L2_HR_PIXC_V1 pixels. In addition, this product provides an identifier associated with each pixel. The identifier contains the information of the river and/or lake features pulled from the Prior River Database (PRD) or in the Prior Lake Database (PLD). Note that this is a simulated SWOT product and not suited for any scientific exploration.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","oceans"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1 v1.0 - This dataset provides a simulated water surface elevation product that resembles the Ka-band Interferometer (KaRIn) measurements by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-swot","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA TCTE Project","Description":"TSI Calibration Transfer Experiment (TCTE) Launch November 19, 2013 The TCTE includes a Total Irradiance Monitor to measure total solar irradiance (TSI). This new instrument is similar to that providing data from NASA’s SORCE mission since 2003, and will overlap TSI measurements with the SORCE mission. TCTE was launched on Nov. 19, 2013 from NASA’s Wallops Flight Facility as […]\n<br>\n\n#### TCTE3TSI6\nTCTE3TSI6 Version 004 is the final version of this data product, and supersedes all previous versions. The Total Solar Irradiance (TSI) Calibration Transfer Experiment (TCTE) data set TCTE3TSI6 contains 6-hour averaged total solar irradiance (a.k.a solar constant) data collected by the Total Irradiance Monitor (TIM) instrument covering the full wavelength spectrum. The data are normalized to one astronomical unit (1 AU). The TCTE/TIM instrument measures the Total Solar Irradiance (TSI), monitoring changes in incident sunlight to the Earth's atmosphere using an ambient temperature active cavity radiometer to a designed absolute accuracy of 350 parts per million (ppm, 1 ppm=0.0001% at 1-sigma), and a precision and long-term relative accuracy of 10 ppm per year. Due to the small size of these data and to maximize ease of use to end-users, each delivered TSI product contains science results for the entire mission in an ASCII column formatted file. Early in the mission, between Dec 2013 and May 2014, TCTE acquired daily measurements to establish good overlap with the SORCE TIM. From May 2014 to Dec 2014, the TCTE measurements were reduced to weekly, which greatly subsample the true solar variability, and thus have little value for solar research. Beginning in Jan 2015, daily obervations were resumed. The mission ended June 30, 2019.\n<br>\n\n#### TCTE3TSID\nTCTE3TSID Version 004 is the final version of this data product, and supersedes all previous versions. The Total Solar Irradiance (TSI) Calibration Transfer Experiment (TCTE) data set TCTE3TSID contains daily averaged total solar irradiance (a.k.a solar constant) data collected by the Total Irradiance Monitor (TIM) instrument covering the full wavelength spectrum. The data are normalized to one astronomical unit (1 AU). The TCTE/TIM instrument measures the Total Solar Irradiance (TSI), monitoring changes in incident sunlight to the Earth's atmosphere using an ambient temperature active cavity radiometer to a designed absolute accuracy of 350 parts per million (ppm, 1 ppm=0.0001% at 1-sigma), and a precision and long-term relative accuracy of 10 ppm per year. Due to the small size of these data and to maximize ease of use to end-users, each delivered TSI product contains science results for the entire mission in an ASCII column formatted file. Early in the mission, between Dec 2013 and May 2014, TCTE acquired daily measurements to establish good overlap with the SORCE TIM. From May 2014 to Dec 2014, the TCTE measurements were reduced to weekly, which greatly subsample the true solar variability, and thus have little value for solar research. Beginning in Jan 2015, daily obervations were resumed. The mission ended June 30, 2019.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"http://lasp.colorado.edu/home/tcte/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"TCTE3TSI6 v004 - TCTE3TSI6 Version 004 is the final version of this data product, and supersedes all previous versions. The Total Solar Irradiance (TSI) Calibration Transfer Experiment (TCTE) data set TCTE3TSI6 contains 6-hour averaged total solar irradiance (a.k.a solar constant) data collected by the Total Irradiance Monitor (TIM) instrument covering the full wavelength spectrum.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCTE_Level3/TCTE3TSI6.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TCTE3TSID v004 - TCTE3TSID Version 004 is the final version of this data product, and supersedes all previous versions. The Total Solar Irradiance (TSI) Calibration Transfer Experiment (TCTE) data set TCTE3TSID contains daily averaged total solar irradiance (a.k.a solar constant) data collected by the Total Irradiance Monitor (TIM) instrument covering the full wavelength spectrum.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCTE_Level3/TCTE3TSID.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-tcte","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA tempo Project","Description":"The mountains of Nepal are one of the most hazardous environments in the world, with frequent landslides caused by tectonic activity, extreme rainfall and infrastructure development. As a landlocked country, Nepal relies on proper functioning of major transportation networks such as the highways to sustain and improve the livelihoods of the population. Every year there are reports of landslides blocking the highways, especially during the rainy season; however, the frequency and location of landslides along the highway corridors are not well reported. RapidEye satellite imagery was used to create annual landslide initiation point inventories along three important highways in Nepal: the Arniko, Karnali, and Pasang Lhamu highway.\n<br>\n\n#### HMA_SR_D\nSnowpack plays a significant role in the hydrologic cycle over High Mountain Asia (HMA). As a vital water resource, the distribution of snowpack volume also impacts the water availability for downstream populations. To assess the regional water balance, it is important to characterize the spatio-temporal distribution of water storage in the HMA snowpack. This HMA snow reanalysis data set contains daily estimates of posterior snow water equivalent (SWE), fractional snow covered area (fSCA), snow depth (SD), etc.\n<br>\n\n#### NmHRIR1T\nThis data set consists of daily, global grayscale TIFF images derived from radiative temperatures measured in the 3.4 to 4.2 µm window. These data were detected by the High Resolution Infrared Radiometer (HRIR) on board the Nimbus 1, Nimbus 2, and Nimbus 3 satellites during 1964, 1966, and 1969-1970. The Nimbus HRIR sensor was used to map the earth's nighttime cloud cover and to measure cloud top temperatures or surface temperatures. Note: This data set is not georeferenced and contains some gaps in temporal coverage because of missing data.\n<br>\n\n#### NmTHIR115-1T\nThis data set consists of daily, global grayscale TIFF images derived from radiative temperatures measured in the 11.5 µm window (10.5 µm - 12.5 µm). These data were detected by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4, Nimbus 5, and Nimbus 6 satellites, respectively, during 1970-1971, 1973-1975 and 1975. The Nimbus satellites used the THIR 11.5 µm window to measure cloud top or surface temperatures. Note: This data set is not georeferenced and contains some gaps in temporal coverage because of missing data.\n<br>\n\n#### NmTHIR67-1T\nThis data set consists of daily, global grayscale TIFF images derived from radiative temperatures measured in the 6.7 µm window (6.5 µm - 7.0 µm). These data were detected by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4, Nimbus 5, and Nimbus 6 satellites, respectively, during 1970-1971, 1973-1975, and 1975. The Nimbus satellites used the THIR 6.7 µm window to map the water vapor distribution in the upper troposphere and stratosphere. Note: This data set is not georeferenced and there are some gaps in temporal coverage because of missing data.\n<br>\n\n#### NmTHIRmtg-1T\nThis data set consists of daily, global grayscale TIFF images measured in the 6.7 µm window (6.5 µm - 7.0 µm) and the 11.5 µm window (10.5 µm - 12.5 µm) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 7 satellite. Each data granule is a daytime or nighttime global composite of all the swaths in a day. Note: This data set is not georeferenced and there are some gaps in the temporal coverage because of missing data.\n<br>\n\n#### SV16I_TNET\nThese data consist of soil moisture and temperature measurements recorded by the temporary soil moisture network deployed to SMAPVEX16-Iowa for the summer season of 2016. The sites were spread out over an experiment domain of about 30km by 40 km located about 30 km north of Ames, Iowa, USA. The data file contains the soil moisture and temperature measurements for each station located at the site.\n<br>\n\n#### WUS_UCLA_SR\nThis Western United States snow reanalysis data set contains daily estimates of posterior snow water equivalent (SWE), fractional snow-covered area (fSCA) and snow depth (SD) at 16 arc-second (~500 m) resolution from water years 1985 to 2021. This data set was developed to be compared to SnowEx data sets but its utility reaches beyond that since its spatial and temporal bounds extend over the entire Western U.S. and over several decades.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/hma_mtli/versions/1","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","satellite imagery","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"HMA_MTLI v1 - The mountains of Nepal are one of the most hazardous environments in the world, with frequent landslides caused by tectonic activity, extreme rainfall and infrastructure development. As a landlocked country, Nepal relies on proper functioning of major transportation networks such as the highways to sustain and improve the livelihoods of the population.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_MTLI/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_SR_D v1 - Snowpack plays a significant role in the hydrologic cycle over High Mountain Asia (HMA). As a vital water resource, the distribution of snowpack volume also impacts the water availability for downstream populations.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_SR_D/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmHRIR1T v1 - This data set consists of daily, global grayscale TIFF images derived from radiative temperatures measured in the 3.4 to 4.2 µm window. These data were detected by the High Resolution Infrared Radiometer (HRIR) on board the Nimbus 1, Nimbus 2, and Nimbus 3 satellites during 1964, 1966, and 1969-1970.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmHRIR1T/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmTHIR115-1T v1 - This data set consists of daily, global grayscale TIFF images derived from radiative temperatures measured in the 11.5 µm window (10.5 µm - 12.5 µm). These data were detected by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4, Nimbus 5, and Nimbus 6 satellites, respectively, during 1970-1971, 1973-1975 and 1975.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmTHIR115-1T/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmTHIR67-1T v1 - This data set consists of daily, global grayscale TIFF images derived from radiative temperatures measured in the 6.7 µm window (6.5 µm - 7.0 µm). These data were detected by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4, Nimbus 5, and Nimbus 6 satellites, respectively, during 1970-1971, 1973-1975, and 1975.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmTHIR67-1T/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmTHIRmtg-1T v1 - This data set consists of daily, global grayscale TIFF images measured in the 6.7 µm window (6.5 µm - 7.0 µm) and the 11.5 µm window (10.5 µm - 12.5 µm) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 7 satellite. Each data granule is a daytime or nighttime global composite of all the swaths in a day.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmTHIRmtg-1T/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16I_TNET v1 - These data consist of soil moisture and temperature measurements recorded by the temporary soil moisture network deployed to SMAPVEX16-Iowa for the summer season of 2016. The sites were spread out over an experiment domain of about 30km by 40 km located about 30 km north of Ames, Iowa, USA.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16I_TNET/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WUS_UCLA_SR v1 - This Western United States snow reanalysis data set contains daily estimates of posterior snow water equivalent (SWE), fractional snow-covered area (fSCA) and snow depth (SD) at 16 arc-second (~500 m) resolution from water years 1985 to 2021. This data set was developed to be compared to SnowEx data sets but its utility reaches beyond that since its spatial and temporal bounds extend over the entire Western U.S.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/WUS_UCLA_SR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-tempo","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Terra Project","Description":"The AST14DEM Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST14DEM Version 4 data product](https://doi.org/10.5067/ASTER/AST14DEM.004). The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (AST14DEM) product is generated using bands 3N (nadir-viewing) and 3B (backward-viewing) of an [ASTER Level 1A](https://doi.org/10.5067/ASTER/AST_L1A.003) image acquired by the Visible and Near Infrared (VNIR) sensor. The VNIR subsystem includes two independent telescope assemblies that facilitate the generation of stereoscopic data. The band 3 stereo pair is acquired in the spectral range of 0.78 and 0.86 microns with a base-to-height ratio of 0.6 and an intersection angle of 27.7 degrees. There is a time lag of approximately one minute between the acquisition of the nadir and backward images.For a better understanding, refer to the [ASTER Along Track Imaging Geometry diagram](https://lpdaac.usgs.gov/documents/301/ASTER_Along_Track_Imaging_Geometry.png) depicting the along-track imaging geometry of the ASTER VNIR nadir and backward-viewing sensors. The accuracy of the new LP DAAC produced DEMs will meet or exceed accuracy specifications set for the ASTER relative DEMs by the Algorithm Theoretical Basis Document (ATBD). Users likely will find that the DEMs produced by the new LP DAAC system have accuracies approaching those specified in the ATBD for absolute DEMs. Validation testing has shown that DEMs produced by the new system frequently are more accurate than 25 meters root mean square error (RMSE) in xyz dimensions. The ASTER Digital Elevation Model data product is only available through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C1299783579-LPDAAC_ECS). The ASTER Order Instructions provide step-by-step directions for ordering this product. Known Issues: Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions. Improvements/Changes from Previous Versions * As of January 2021, the LP DAAC has implemented version 3.0 of the Sensor Information Laboratory Corporation ASTER DEM/Ortho (SILCAST) software, which is used to generate the Level 2 on-demand ASTER Orthorectified and Digital Elevation Model (DEM) products (AST14). The updated software provides digital elevation extraction and orthorectification from ASTER L1A input data without needing to enter ground control points or depending on external global DEMs at 30-arc-second resolution (GTOPO30). It utilizes the ephemeris and attitude data derived from both the ASTER instrument and the Terra spacecraft platform. The outputs are geoid height-corrected and waterbodies are automatically detected in this version. Users will notice differences between AST14DEM, AST14DMO, and AST14OTH products ordered before January 2021 (generated with SILCAST V1) and those generated with the updated version of the production software (version 3.0). Differences may include slight elevation changes over different surface types, including waterbodies. Differences have also been observed over cloudy portions of ASTER scenes. Additional information on SILCAST version 3.0 can be found on the [SILCAST website](http://www.silc.co.jp/en/products.html). * Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\n<br>\n\n#### AST14DEM\nThe Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (AST14DEM) product is generated using bands 3N (nadir-viewing) and 3B (backward-viewing) of an ASTER [Level 1A](https://doi.org/10.5067/ASTER/AST_L1A.004) image acquired by the Visible and Near Infrared (VNIR) sensor. The VNIR subsystem includes two independent telescope assemblies that facilitate the generation of stereoscopic data. The band 3 stereo pair is acquired in the spectral range of 0.78 and 0.86 microns with a base-to-height ratio of 0.6 and an intersection angle of 27.7 degrees. There is a time lag of approximately one minute between the acquisition of the nadir and backward images. For a better understanding, refer to the [diagram](https://lpdaac.usgs.gov/documents/301/ASTER_Along_Track_Imaging_Geometry.png) depicting the along-track imaging geometry diagram of the ASTER VNIR nadir and backward-viewing sensors.\n<br>\n\n#### ASTGTM_NUMNC\nThe Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan's Ministry of Economy, Trade, and Industry (METI). The ASTER GDEM data products are created by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was created from the automated processing of the entire [ASTER Level 1A](https://doi.org/10.5067/ASTER/AST_L1A.003) archive of scenes acquired between March 1, 2000, and November 30, 2013. Stereo correlation was used to produce over one million individual scene based ASTER DEMs, to which cloud masking was applied. All cloud screened DEMs and non-cloud screened DEMs were stacked. Residual bad values and outliers were removed. In areas with limited data stacking, several existing reference DEMs were used to supplement ASTER data to correct for residual anomalies. Selected data were averaged to create final pixel values before partitioning the data into 1 degree latitude by 1 degree longitude tiles with a one pixel overlap. To correct elevation values of water body surfaces, the ASTER Global Water Bodies Database ([ASTWBD](https://doi.org/10.5067/ASTER/ASTWBD.001)) Version 1 data product was also generated. The geographic coverage of the ASTER GDEM extends from 83° North to 83° South. Each tile is distributed in both a Cloud Optimized GeoTIFF (COG) and NetCDF4 format through NASA Earthdata Search. Data are projected on the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each of the 22,912 tiles in the collection contain at least 0.01% land area. Provided in the ASTER GDEM product are layers for DEM and number of scenes (NUM). The NUM layer indicates the number of scenes that were processed for each pixel and the source of the data. While the ASTER GDEM Version 3 data products offer substantial improvements over Version 2, users are advised that the products still may contain anomalies and artifacts that will reduce its usability for certain applications. Known Issues: ASTER GDEM Version 3 tiles overlap by one pixel to the north, south, east, and west of the tile perimeter. In most cases the overlapping edge pixels have identical pixel values, but it is possible that in some instances values will differ. * ASTER GDEM Version 3 is considered to be void free except for Greenland and Antarctica. * Users are reminded that because there are known inaccuracies and artifacts in the dataset, to use the product with awareness of these limitations. The data are provided \"as is\" and neither NASA nor METI/Earth Resources Satellite Data Analysis Center (ERSDAC) will be responsible for any damages resulting from use of the data.\n<br>\n\n#### ASTWBD_ATTNC\nThe Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Water Bodies Database (ASTWBD) Version 1 data product provides global coverage of water bodies larger than 0.2 square kilometers at a spatial resolution of 1 arc second (approximately 30 meters) at the equator, along with associated elevation information. The ASTWBD data product was created in conjunction with the ASTER Global Digital Elevation Model (ASTER GDEM) Version 3 data product by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was generated using [ASTER Level 1A](https://doi.org/10.5067/ASTER/AST_L1A.003) scenes acquired between March 1, 2000, and November 30, 2013. The ASTWBD data product was then generated to correct elevation values of water body surfaces. To generate the ASTWBD data product, water bodies were separated from land areas and then classified into three categories: ocean, river, or lake. Oceans and lakes have a flattened, constant elevation value. The effects of sea ice were manually removed from areas classified as oceans to better delineate ocean shorelines in high latitude areas. For lake water bodies, the elevation for each lake was calculated from the perimeter elevation data using the mosaic image that covers the entire area of the lake. Rivers presented a unique challenge given that their elevations gradually step down from upstream to downstream; therefore, visual inspection and other manual detection methods were required. The geographic coverage of the ASTWBD extends from 83°N to 83°S. Each tile is distributed in GeoTIFF format and referenced to the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each data product is provided as a zipped file that contains an attribute file with the water body classification information and a DEM file, which provides elevation information in meters.\n<br>\n\n#### AST_L1A\nThe AST_L1A Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_L1A Version 4 data product](https://doi.org/10.5067/ASTER/AST_L1A.004). The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1A (AST_L1A) contains reconstructed, instrument digital numbers (DNs) derived from the acquired telemetry streams of the telescopes: Visible and Near Infrared (VNIR), Shortwave Infrared (SWIR), and Thermal Infrared (TIR). Additionally, geometric correction coefficients and radiometric calibration coefficients are calculated and appended to the metadata but not applied. The AST_L1A product has a spatial resolution of 15 meters (m) for the VNIR bands, 30 m for the SWIR bands, and 90 m for the TIR bands. Known Issues: Users are advised that ASTER SWIR data acquired from April 2008 to the present exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details. * Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions. Improvements/Changes from Previous Version * Geometric correction to account for Earth's rotation angle and nutation. * Geometric correction for geolocation errors associated with nighttime TIR scenes. * Radiometric correction to account for degradation of onboard calibration lamps. * Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\n<br>\n\n#### AST_L1A\nThe Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1A (AST_L1A) contains reconstructed, instrument digital numbers (DNs) derived from the acquired telemetry streams of the telescopes: Visible and Near Infrared (VNIR), Shortwave Infrared (SWIR), and Thermal Infrared (TIR). Additionally, geometric correction coefficients and radiometric calibration coefficients are calculated and appended to the metadata but not applied. The AST_L1A product has a spatial resolution of 15 meters (m) for the VNIR bands, 30 m for the SWIR bands, and 90 m for the TIR bands. Know Issues * The QAPERCENTINTERPOLATEDDATA field in .met files was found to be inconsistent due to a software upgrade. Following evaluation by the ASTER Science Team, it was determined to have no impact on science data and has been set to 0 and nullified. * SWIR data acquired after April 2008 may exhibit anomalous saturation and striping. Users should consult the ASTER SWIR User Advisory for additional details.\n<br>\n\n#### AST_L1B\nThe AST_L1B Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_LB1 Version 4 of this product](https://doi.org/10.5067/ASTER/AST_L1B.004) The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1B (AST_L1B) Registered Radiance at the Sensor data product is radiometrically calibrated and geometrically co-registered. Application of intra-telescope and inter-telescope registration corrections for all bands are relative to the reference band for each telescope: Visible and Near Infrared (VNIR) Band 2, Shortwave Infrared (SWIR) Band 6, and Thermal Infrared (TIR) Band 11. The AST_L1B product has a spatial resolution of 15 meters (m) for the VNIR bands, 30 m for the SWIR bands, and 90 m for the TIR bands. The ASTER L1B Registered Radiance at the Sensor data product is only available through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C190733714-LPDAAC_ECS). The ASTER Order Instructions provide step-by-step directions for ordering this product. Known Issues: Users are advised that ASTER SWIR data acquired from April 2008 to the present exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details. * Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions. Improvements/Changed from Previous Version * Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\n<br>\n\n#### AST_L1B\nThe Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1B (AST_L1B) Registered Radiance at the Sensor data product is radiometrically calibrated and geometrically co-registered. Application of intra-telescope and inter-telescope registration corrections for all bands are relative to the reference band for each telescope: Visible and Near Infrared (VNIR) Band 2, Shortwave Infrared (SWIR) Band 6, and Thermal Infrared (TIR) Band 11. The AST_L1B product has a spatial resolution of 15 meters (m) for the VNIR bands, 30 m for the SWIR bands, and 90 m for the TIR bands. Known Issues: The QAPERCENTINTERPOLATEDDATA field in .met files was found to be inconsistent due to a software upgrade. Following evaluation by the ASTER Science Team, it was determined to have no impact on science data and has been set to 0 and nullified. * SWIR data acquired after April 2008 may exhibit anomalous saturation and striping. Users should consult the ASTER SWIR User Advisory for additional details.\n<br>\n\n#### AST_05\nThe AST_05 Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_05 Version 4 data product](https://doi.org/10.5067/ASTER/AST_05.004). The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Emissivity (AST_05) is produced using the Temperature/Emissivity Separation (TES) algorithm for the five Thermal Infrared (TIR) 90 meter bands, acquired during the day or night. The product is comprised of per-pixel emissivity measurements generated over land in addition to embedded metadata and quality assurance data planes. The ASTER L2 Surface Emissivity data product is only available through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C1299783607-LPDAAC_ECS). The ASTER Order Instructions provide step-by-step directions for ordering this product. Known Issues: Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions. Improvements/Changes from Previous Version * The Science Scalable Scripts-based Science Processor for Missions (S4PM) Version 3.4 algorithm, which is used to generate L2 Product Generation Executables (PGEs), is relying on a new ancillary input for atmospheric parameters. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) is global atmospheric reanalysis that combines remote sensing observations and interactions with the climate system. It will be one of the primary ozone and water vapor, pressure, and temperature inputs for L2 PGEs. MERRA-2 will provide a finer geographic resolution grid since it is a 3-dimensional, 3-hourly data collection with 50-km (latitudinal direction) spatial resolution. * The fallback options for L2 PGEs are as follows: * Ozone: [TOVS Ozone (OZ_DLY ) > AURA Ozone Monitoring Instrument (AURAOMI) > Total Ozone Analysis from Stratospheric and Tropospheric (TOAST) > Earth Probe-Total Ozone Mapping Spectrometer (EPTOMS)] or [MERRA-2] > National Centers for Environmental Prediction (NCEP)/Global Data Assimilation System (GDAS) > Climatology * Water Vapor, Pressure, and Temperature: [MOD07_L2] or [MERRA-2] > NCEP/GDAS > Climatology * Caveat: The temporal range for MERRA-2 covers 1980 to present; however, there is latency of ~3 weeks after the end of a month. Hence, NCEP/GDAS > Climatology fallback sequence will be applied for on-demand requests that fall outside of MERRA-2's temporal range or if the data is not science grade. * Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427. * As of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include: * Aura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same. * Toolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data. * Additionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied.\n<br>\n\n#### AST_05\nThe Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Emissivity (AST_05) is produced using the Temperature/Emissivity Separation (TES) algorithm for the five Thermal Infrared (TIR) 90 meter bands, acquired during the day or night. The product comprises per-pixel emissivity measurements generated over land in addition to embedded metadata and quality assurance data planes. Known Issues: Level 2 products that are on the international date line/anti-meridian have incorrect bounding coordinates for the Universal Transverse Mercator (UTM) zone defined in the file metadata.\n<br>\n\n#### AST_08\nThe Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Kinetic Temperature (AST_08) V004 product delivers surface kinetic temperature data derived from the five Thermal Infrared (TIR) bands (Bands 10–14), collected during both daytime and nighttime observations. The AST_08 product has a spatial resolution of 90 meters (m) for the TIR bands. This Level 2 (L2) product is produced using the Temperature/Emissivity Separation (TES) algorithm, which processes atmospherically corrected ASTER surface radiance (TIR) data to estimate emissivity and compute temperature. In Version 4, atmospheric correction has been significantly improved by integrating Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data, enhancing the accuracy of temperature retrievals. Known Issues: Level 2 products that are on the international date line/anti-meridian have incorrect bounding coordinates for the UTM zone defined in the file metadata.\n<br>\n\n#### AST_09XT\nThe AST_09XT Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_09XT Version 4 data product](https://doi.org/10.5067/ASTER/AST_09XT.004). The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Radiance VNIR and Crosstalk Corrected SWIR (AST_09XT) is a multi-file product that contains atmospherically corrected data for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. The crosstalk phenomenon was discovered during the nascent stage of the Terra Mission. It is whereby the incident light with band 4 caused multiple reflections for the SWIR bands, which resulted in blurred images. This has been corrected with the ASTER L2 Surface Radiance VNIR and Crosstalk Corrected SWIR data product. The AST_09XT product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands. Each product delivery includes two Hierarchical Data Format - Earth Observing System (HDF-EOS) files: one for the VNIR, and the other for the SWIR. Both the VNIR and the SWIR data are atmospherically corrected using the corresponding bands from an [ASTER Level 1B]( https://doi.org/10.5067/ASTER/AST_L1B.003) image. The ASTER L2 Surface Radiance VNIR and Crosstalk Corrected SWIR data product is only available through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C1299783631-LPDAAC_ECS). The ASTER Order Instructions provide step-by-step directions for ordering this product. Known Issues: SWIR Crosstalk Correction: The ASTER SWIR sensor is affected by a crosstalk signal scattering problem, a phenomenon discovered after the launch of ASTER aboard the Terra platform in December 1999. The SWIR detector contains 2048 Pt-Si (platinum-silicide) arrays for each of its six spectral bands. There are six pairs of staggered linear CCD arrays for each band that are spaced 1.33 µm apart in the band order 7, 8, 9, 4, 5, and 6. In front of each CCD array pair, there are interference filters that spectrally separate the radiation reflected from the Earth. * Data Anomalies: Users are advised that ASTER SWIR data acquired from April 2008 to the present exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details. * Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions. Improvements/Changes from Previous Version * The Science Scalable Scripts-based Science Processor for Missions (S4PM) Version 3.4 algorithm, which is used to generate L2 Product Generation Executables (PGEs), is relying on a new ancillary input for atmospheric parameters. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) is global atmospheric reanalysis that combines remote sensing observations and interactions with the climate system. It will be one of the primary ozone and water vapor, pressure, and temperature inputs for L2 PGEs. MERRA-2 will provide a finer geographic resolution grid since it is a 3-dimensional, 3-hourly data collection with 50-km (latitudinal direction) spatial resolution. * The fallback options for L2 PGEs are as follows: * Ozone: [TOVS Ozone (OZ_DLY ) > AURA Ozone Monitoring Instrument (AURAOMI) > Total Ozone Analysis from Stratospheric and Tropospheric (TOAST) > Earth Probe-Total Ozone Mapping Spectrometer (EPTOMS)] or [MERRA-2] > National Centers for Environmental Prediction (NCEP)/Global Data Assimilation System (GDAS) > Climatology * Water Vapor, Pressure, and Temperature: [MOD07_L2] or [MERRA-2] > NCEP/GDAS > Climatology * Caveat: The temporal range for MERRA-2 covers 1980 to present; however, there is latency of ~3 weeks after the end of a month. Hence, NCEP/GDAS > Climatology fallback sequence will be applied for on-demand requests that fall outside of MERRA-2's temporal range or if the data is not science grade. * Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at [https://doi.org/10.3390/rs12030427](https://doi.org/10.3390/rs12030427). * As of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include: * Aura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same. * Toolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data. * Additionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied.\n<br>\n\n#### AST_09T\nThe AST_09T Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_09T Version 4 data product](https://doi.org/10.5067/ASTER/AST_09T.004). The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Radiance TIR (AST_09T) is generated using the five Thermal Infrared (TIR) bands (acquired either during the day or night time) between 8 and 12 µm spectral range. It provides surface-leaving radiance for the TIR bands at a spatial resolution of 90 meters, which includes both surface-emitted and surface-reflected components. It also provides the downwelling sky irradiance values (in W/m2/µm) for each of the TIR bands. This product is atmospherically corrected, and the surface-leaving radiance is of known accuracy and valid only for clear-sky scenes (cloud-free pixels). This atmospherically corrected product provides the input for generating two other higher-level products: surface spectral emissivity and surface kinetic temperature. The algorithm to correct atmospheric effects involves two elements: 1) it uses a radiative transfer model which is capable of estimating the magnitude of atmospheric emission, absorption, and scattering. It uses the Moderate Resolution Transmittance Code (MODTRAN) radiative transfer model, which calculates atmospheric transmittance and radiance for frequencies from 0 to 50,000 cmˉ¹ at moderate spectral resolution. 2) It identifies and incorporates all the necessary atmospheric parameters applicable to the location and time for which the measurements require correction. These include temperature, water vapor, elevation, ozone, and aerosols. The ASTER L2 Surface Radiance TIR data product is only available through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C1299783609-LPDAAC_ECS). The ASTER Order Instructions provide step-by-step directions for ordering this product. Known Issues: Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions. Improvements/Changes from Previous Version * The Science Scalable Scripts-based Science Processor for Missions (S4PM) Version 3.4 algorithm, which is used to generate L2 Product Generation Executables (PGEs), is relying on a new ancillary input for atmospheric parameters. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) is global atmospheric reanalysis that combines remote sensing observations and interactions with the climate system. It will be one of the primary ozone and water vapor, pressure, and temperature inputs for L2 PGEs. MERRA-2 will provide a finer geographic resolution grid since it is a 3-dimensional, 3-hourly data collection with 50-km (latitudinal direction) spatial resolution. * The fallback options for L2 PGEs are as follows: * Ozone: [TOVS Ozone (OZ_DLY ) > AURA Ozone Monitoring Instrument (AURAOMI) > Total Ozone Analysis from Stratospheric and Tropospheric (TOAST) > Earth Probe-Total Ozone Mapping Spectrometer (EPTOMS)] or [MERRA-2] > National Centers for Environmental Prediction (NCEP)/Global Data Assimilation System (GDAS) > Climatology * Water Vapor, Pressure, and Temperature: [MOD07_L2] or [MERRA-2] > NCEP/GDAS > Climatology * Caveat: The temporal range for MERRA-2 covers 1980 to present; however, there is latency of ~3 weeks after the end of a month. Hence, NCEP/GDAS > Climatology fallback sequence will be applied for on-demand requests that fall outside of MERRA-2's temporal range or if the data is not science grade. * Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427. * As of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include: * Aura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same. * Toolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data. * Additionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied.\n<br>\n\n#### AST_09T\nThe Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Radiance TIR (AST_09T) is generated using the five Thermal Infrared (TIR) bands (acquired either during the day or nighttime) between 8 and 12 µm spectral range. It provides surface-leaving radiance for the TIR bands at a spatial resolution of 90 meters, which includes both surface-emitted and surface-reflected components. It also provides the downwelling sky irradiance values (in W/m2/µm) for each of the TIR bands. This product is atmospherically corrected, and the surface-leaving radiance is of known accuracy and valid only for clear-sky scenes (cloud-free pixels). This atmospherically corrected product provides the input for generating two other higher level products: surface spectral emissivity and surface kinetic temperature. The algorithm to correct atmospheric effects involves two elements. First, it uses a radiative transfer model—the Moderate Resolution Transmittance Code (MODTRAN)—which can estimate the magnitude of atmospheric emission, absorption, and scattering. MODTRAN calculates atmospheric transmittance and radiance for frequencies from 0 to 50,000 cmˉ¹ at moderate spectral resolution. Second, it identifies and incorporates all the necessary atmospheric parameters applicable to the location and time for which the measurements require correction, including temperature, water vapor, elevation, ozone, and aerosols. Known Issues: Level 2 products that are on the international date line/anti-meridian have incorrect bounding coordinates for the UTM zone defined in the file metadata.\n<br>\n\n#### AST_09XT\nThe Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Radiance VNIR and Crosstalk Corrected SWIR (AST_09XT) contains atmospherically corrected data for the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. The AST_09XT product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands. Both the VNIR and the SWIR data are atmospherically corrected using the corresponding bands from an ASTER [Level 1B](https://doi.org/10.5067/ASTER/AST_L1B.004) image. Known Issues: Level 2 products that are on the international date line/anti-meridian have incorrect bounding coordinates for the UTM zone defined in the file metadata. * SWIR data acquired after April 2008 may exhibit anomalous saturation and striping. Users should consult the ASTER SWIR User Advisory for additional details.\n<br>\n\n#### AST_09\nThe AST_09 Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_09 Version 4 data product](https://doi.org/10.5067/ASTER/AST_09.004). The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Radiance VNIR and SWIR (AST_09) is a multi-file product that contains atmospherically corrected data for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. The AST_09 product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands. Each product delivery includes two Hierarchical Data Format - Earth Observing System (HDF-EOS) files: one for the VNIR, and the other for the SWIR. They are distinguished from one another by a one-second difference in the production time that appears as part of the file name. The more obvious distinguishing feature is the file size; the VNIR file is always the larger of the two. Both the VNIR and SWIR data are atmospherically corrected and are generated using the bands of the corresponding [ASTER Level 1B](https://doi.org/10.5067/ASTER/AST_L1B.003) image. The ASTER L2 Surface Radiance VNIR and SWIR data product is only available through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C1299783574-LPDAAC_ECS). The ASTER Order Instructions provide step-by-step directions for ordering this product. Known Issues: Data Anomalies: Users are advised that ASTER SWIR data acquired from April 2008 to the present exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details. * Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions. Improvements/Changes from Previous Version * The Science Scalable Scripts-based Science Processor for Missions (S4PM) Version 3.4 algorithm, which is used to generate L2 Product Generation Executables (PGEs), is relying on a new ancillary input for atmospheric parameters. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) is global atmospheric reanalysis that combines remote sensing observations and interactions with the climate system. It will be one of the primary ozone and water vapor, pressure, and temperature inputs for L2 PGEs. MERRA-2 will provide a finer geographic resolution grid since it is a 3-dimensional, 3-hourly data collection with 50-km (latitudinal direction) spatial resolution. * The fallback options for L2 PGEs are as follows: * Ozone: [TOVS Ozone (OZ_DLY ) > AURA Ozone Monitoring Instrument (AURAOMI) > Total Ozone Analysis from Stratospheric and Tropospheric (TOAST) > Earth Probe-Total Ozone Mapping Spectrometer (EPTOMS)] or [MERRA-2] > National Centers for Environmental Prediction (NCEP)/Global Data Assimilation System (GDAS) > Climatology * Water Vapor, Pressure, and Temperature: [MOD07_L2] or [MERRA-2] > NCEP/GDAS > Climatology * Caveat: The temporal range for MERRA-2 covers 1980 to present; however, there is latency of ~3 weeks after the end of a month. Hence, NCEP/GDAS > Climatology fallback sequence will be applied for on-demand requests that fall outside of MERRA-2's temporal range or if the data is not science grade. * Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427. * As of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include: * Aura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same. * Toolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data. * Additionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied.\n<br>\n\n#### AST_09\nThe Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Radiance VNIR and SWIR (AST_09) contains atmospherically corrected data for the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. Both the VNIR and SWIR data are atmospherically corrected and are generated using the bands of the corresponding ASTER [Level 1B](https://doi.org/10.5067/ASTER/AST_L1B.004) image. The AST_09 product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands. Known Issues: Level 2 products that are on the international date line/anti-meridian have incorrect bounding coordinates for the UTM zone defined in the file metadata. * SWIR data acquired after April 2008 may exhibit anomalous saturation and striping. Users should consult the ASTER SWIR User Advisory for additional details.\n<br>\n\n#### AST_07\nThe AST_07 Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_07 Version 4 data product](https://doi.org/10.5067/ASTER/AST_07.004). The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Reflectance VNIR and SWIR (AST_07) data product contains measures of the fraction of incoming solar radiation reflected from the Earth's surface to the ASTER instrument corrected for atmospheric effects and viewing geometry for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. The AST_07 product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands. Each product delivery includes two Hierarchical Data Format - Earth Observing System (HDF-EOS) files: one for the VNIR, and the other for the SWIR. They are distinguished from one another by a one-second difference in the production time that appears as part of the file name. The ASTER L2 Surface Reflectance VNIR and SWIR data product is only available through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C1299783800-LPDAAC_ECS). The ASTER Order Instructions provide step-by-step directions for ordering this product. Known Issues: Data Anomalies: Users are advised that ASTER SWIR data acquired from April 2008 to the present exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details. * Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions. Improvements/Changes from Previous Version * The Science Scalable Scripts-based Science Processor for Missions (S4PM) Version 3.4 algorithm, which is used to generate L2 Product Generation Executables (PGEs), is relying on a new ancillary input for atmospheric parameters. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) is global atmospheric reanalysis that combines remote sensing observations and interactions with the climate system. It will be one of the primary ozone and water vapor, pressure, and temperature inputs for L2 PGEs. MERRA-2 will provide a finer geographic resolution grid since it is a 3-dimensional, 3-hourly data collection with 50-km (latitudinal direction) spatial resolution. * The fallback options for L2 PGEs are as follows: * Ozone: [TOVS Ozone (OZ_DLY ) > AURA Ozone Monitoring Instrument (AURAOMI) > Total Ozone Analysis from Stratospheric and Tropospheric (TOAST) > Earth Probe-Total Ozone Mapping Spectrometer (EPTOMS)] or [MERRA-2] > National Centers for Environmental Prediction (NCEP)/Global Data Assimilation System (GDAS) > Climatology * Water Vapor, Pressure, and Temperature: [MOD07_L2] or [MERRA-2] > NCEP/GDAS > Climatology * Caveat: The temporal range for MERRA-2 covers 1980 to present; however, there is latency of ~3 weeks after the end of a month. Hence, NCEP/GDAS > Climatology fallback sequence will be applied for on-demand requests that fall outside of MERRA-2's temporal range or if the data is not science grade. * Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at [https://doi.org/10.3390/rs12030427](https://doi.org/10.3390/rs12030427). * As of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include: * Aura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same. * Toolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data. *Additionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied.\n<br>\n\n#### AST_07XT\nThe AST_07XT Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_07XT Version 4 data product](https://doi.org/10.5067/ASTER/AST_07XT.004). The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Reflectance VNIR and Crosstalk Corrected SWIR (AST_07XT) dataset contains measures of the fraction of incoming solar radiation reflected from the Earth's surface to the ASTER instrument corrected for atmospheric effects and viewing geometry for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. The AST_07XT product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands. Each product delivery includes two Hierarchical Data Format - Earth Observing System (HDF-EOS) files: one for the VNIR, and the other for the SWIR. They are distinguished from one another by a one-second difference in the production time that appears as part of the file name. Both the VNIR and SWIR data are atmospherically corrected and are generated using the bands of the corresponding [ASTER L1B](https://doi.org/10.5067/ASTER/AST_L1B.003) image. AST_07XT is a multi-file product that contains atmospherically corrected data for both the VNIR and SWIR sensors. The crosstalk corrected product no longer displays blurred images initiated by stray light that caused multiple reflections with the SWIR bands. The ASTER L2 Surface Reflectance VNIR and Crosstalk Corrected SWIR data product is only available through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C1299783608-LPDAAC_ECS). The ASTER Order Instructions provide step-by-step directions for ordering this product. Known Issues: SWIR Crosstalk Correction: The ASTER SWIR sensor is affected by a crosstalk signal scattering problem, a phenomenon discovered after the launch of ASTER aboard the Terra platform in December 1999. The SWIR detector contains 2048 Pt-Si (platinum-silicide) arrays for each of its six spectral bands. There are six pairs of staggered linear CCD arrays for each band that are spaced 1.33 µm apart in the band order 7, 8, 9, 4, 5, and 6. In front of each CCD array pair, there are interference filters that spectrally separate the radiation reflected from the Earth. * Data Anomalies: Users are advised that ASTER SWIR data acquired from April 2008 to the present exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details. * Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions. Improvements/Changes from Previous Version * The Science Scalable Scripts-based Science Processor for Missions (S4PM) Version 3.4 algorithm, which is used to generate L2 Product Generation Executables (PGEs), is relying on a new ancillary input for atmospheric parameters. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) is global atmospheric reanalysis that combines remote sensing observations and interactions with the climate system. It will be one of the primary ozone and water vapor, pressure, and temperature inputs for L2 PGEs. MERRA-2 will provide a finer geographic resolution grid since it is a 3-dimensional, 3-hourly data collection with 50-km (latitudinal direction) spatial resolution. * The fallback options for L2 PGEs are as follows: * Ozone: [TOVS Ozone (OZ_DLY ) > AURA Ozone Monitoring Instrument (AURAOMI) > Total Ozone Analysis from Stratospheric and Tropospheric (TOAST) > Earth Probe-Total Ozone Mapping Spectrometer (EPTOMS)] or [MERRA-2] > National Centers for Environmental Prediction (NCEP)/Global Data Assimilation System (GDAS) > Climatology * Water Vapor, Pressure, and Temperature: [MOD07_L2] or [MERRA-2] > NCEP/GDAS > Climatology * Caveat: The temporal range for MERRA-2 covers 1980 to present; however, there is latency of ~3 weeks after the end of a month. Hence, NCEP/GDAS > Climatology fallback sequence will be applied for on-demand requests that fall outside of MERRA-2's temporal range or if the data is not science grade. * Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427. * As of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include: * Aura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same. * Toolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data. * Additionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied.\n<br>\n\n#### AST_07XT\nThe Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Reflectance VNIR and Crosstalk Corrected SWIR (AST_07XT) dataset contains measures of the fraction of incoming solar radiation reflected from the Earth’s surface to the ASTER instrument corrected for atmospheric effects and viewing geometry for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. Both the VNIR and SWIR data are atmospherically corrected and are generated using the bands of the corresponding ASTER [L1B](https://doi.org/10.5067/ASTER/AST_L1B.004) image. The AST_07XT product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands. Known Issues: Level 2 products that are on the international date line/anti-meridian have incorrect bounding coordinates for the UTM zone defined in the file metadata. * SWIR data acquired after April 2008 may exhibit anomalous saturation and striping. Users should consult the ASTER SWIR User Advisory for additional details.\n<br>\n\n#### AST_07\nThe Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Reflectance VNIR and SWIR (AST_07) data product contains measures of the fraction of incoming solar radiation reflected from the Earth’s surface to the ASTER instrument corrected for atmospheric effects and viewing geometry for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. The AST_07 product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands. Known Issues: Level 2 products that are on the international date line/anti-meridian have incorrect bounding coordinates for the Universal Transverse Mercator (UTM) zone defined in the file metadata. * SWIR data acquired after April 2008 may exhibit anomalous saturation and striping. Users should consult the ASTER SWIR User Advisory for additional details.\n<br>\n\n#### AST_08\nThe AST_08 Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_08 Version 4 data product](https://doi.org/10.5067/ASTER/AST_08.004). The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Kinetic Temperature (AST_08) is generated using the five Thermal Infrared (TIR) bands (acquired either during the day or night time) between 8 and 12 µm spectral range. It contains surface temperatures at 90 m spatial resolution for the land areas only. Surface kinetic temperature provides a vital input to studies of volcanism, thermal inertia, surface energy, and high-resolution mapping of fires. This product is derived using the same algorithm as the ASTER Surface Emissivity [AST_05](http://doi.org/10.5067/ASTER/AST_05.003) Product. Surface kinetic temperature is determined by applying Planck's Law using the emissivity values from the Temperature/Emissivity Separation (TES) algorithm, which uses atmospherically corrected ASTER surface radiance (TIR) data. The TES algorithm first estimates emissivity in the TIR channels using the Normalized Emissivity Method (NEM). These estimates are used along with Kirchoff's Law to account for the land-leaving TIR radiance that is due to sky irradiance. That figure is subtracted from TIR radiance iteratively to estimate the emitted radiance from which temperature is calculated using the NEM module. The ASTER L2 Surface Kinetic Temperature data product is only available through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C1299783630-LPDAAC_ECS). The ASTER Order Instructions provide step-by-step directions for ordering this product. Known Issues: Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions. Improvements/Changes from Previous Version * The Science Scalable Scripts-based Science Processor for Missions (S4PM) Version 3.4 algorithm, which is used to generate L2 Product Generation Executables (PGEs), is relying on a new ancillary input for atmospheric parameters. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) is global atmospheric reanalysis that combines remote sensing observations and interactions with the climate system. It will be one of the primary ozone and water vapor, pressure, and temperature inputs for L2 PGEs. MERRA-2 will provide a finer geographic resolution grid since it is a 3-dimensional, 3-hourly data collection with 50-km (latitudinal direction) spatial resolution. * The fallback options for L2 PGEs are as follows: * Ozone: [TOVS Ozone (OZ_DLY ) > AURA Ozone Monitoring Instrument (AURAOMI) > Total Ozone Analysis from Stratospheric and Tropospheric (TOAST) > Earth Probe-Total Ozone Mapping Spectrometer (EPTOMS)] or [MERRA-2] > National Centers for Environmental Prediction (NCEP)/Global Data Assimilation System (GDAS) > Climatology * Water Vapor, Pressure, and Temperature: [MOD07_L2] or [MERRA-2] > NCEP/GDAS > Climatology * Caveat: The temporal range for MERRA-2 covers 1980 to present; however, there is latency of ~3 weeks after the end of a month. Hence, NCEP/GDAS > Climatology fallback sequence will be applied for on-demand requests that fall outside of MERRA-2's temporal range or if the data is not science grade. * Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427. * As of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include: * Aura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same. * Toolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data. * Additionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied.\n<br>\n\n#### AST_L1T\nThe AST_L1T Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_L1T Version 4 data product](https://doi.org/10.5067/ASTER/AST_L1T.004). The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B ([AST_L1B](https://doi.org/10.5067/ASTER/AST_L1B.003)), that has been geometrically corrected and rotated to a north-up UTM projection. The AST_L1T is created from a single resampling of the corresponding ASTER L1A ([AST_L1A](https://doi.org/10.5067/ASTER/AST_L1A.003)) product. The bands available in the AST_L1T depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T dataset does not include the aft-looking VNIR band 3. The AST_L1T product has a spatial resolution of 15 meters (m) for the VNIR bands, 30 m for the SWIR bands, and 90 m for the TIR bands. The precision terrain correction process incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover). For daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution in both as a text file and a single band browse images with the valid GCPs overlaid. This multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depends on the bands available in the corresponding [AST_L1A](https://doi.org/10.5067/ASTER/AST_L1A.003) dataset. AST_L1T V3 will continue forward processing using radiometric calibration coefficients (RCC) Version 4 and will remain available for direct download through the [LP DAAC Data Pool](https://e4ftl01.cr.usgs.gov/ASTT/AST_L1T.003/) and [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C1000000320-LPDAAC_ECS). The new [AST_L1T V3.1](https://doi.org/10.5067/ASTER/AST_L1T.031) data product will be processed with RCC V5 and can be ordered through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C2052604735-LPDAAC_ECS). Known Issues: A modification has been incorporated within the processing of the AST_L1T data product for correcting zero-filled scans that appear in the processing of low-latitude, ascending orbit (night) thermal infrared (TIR) data acquisitions. This correction has been implemented for the historical archive of the ASTER L1T data product and for newly processed scenes as of October 1, 2017. Additional information can be found in the ASTER L1T User Advisory document for more details. * Users are advised that ASTER SWIR data acquired from April 2008 to the present exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details. * Since April 1, 2008, when the anomalies in the SWIR data rendered it unusable, the SWIR band data has not been included in the AST_L1T product. * Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions.\n<br>\n\n#### AST_L1T\nThe AST_L1T.031 Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_L1T.031 Version 4 data product](https://doi.org/10.5067/ASTER/AST_L1T.004) The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) Version 3.1 data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B ([AST_L1B](https://doi.org/10.5067/ASTER/AST_L1B.003)), that has been geometrically corrected and rotated to a north-up UTM projection. The AST_L1T V3.1 is created from a single resampling of the corresponding ASTER Level 1A ([AST_L1A](https://doi.org/10.5067/ASTER/AST_L1A.003)) product. Radiometric calibration coefficients Version 5 (RCC V5) are applied to this product to improve the degradation curve derived from vicarious and lunar calibrations. The bands available in the AST_L1T V3.1 depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T V3.1 dataset does not include the aft-looking VNIR band 3. The AST_L1T product has a spatial resolution of 15 meters (m) for the VNIR bands, 30 m for the SWIR bands, and 90 m for the TIR bands. The 3.1 version uses a precision terrain correction process that incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover). For daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution in both a text file and a single band browse image with the valid GCPs overlaid. This multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depend on the bands available in the corresponding AST_L1A dataset. The AST_L1T V3.1 data product is only available through NASA's Earthdata Search. The [ASTER L1T Earthdata Search Order Instructions](https://lpdaac.usgs.gov/documents/1045/ASTER_L1T_Earthdata_Search_Order_Instructions.pdf) provide step-by-step directions for ordering this product. Known Issues: A modification has been incorporated within the processing of the AST_L1T data product for correcting zero-filled scans that appear in the processing of low-latitude, ascending orbit (night) thermal infrared (TIR) data acquisitions. This correction has been implemented for the historical archive of the ASTER L1T data product and for newly processed scenes as of October 1, 2017. Additional information can be found in the ASTER L1T User Advisory document. * Users are advised that ASTER SWIR data acquired, from April 2008 to the present, exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details. * Since April 1, 2008, when the anomalies in the SWIR data rendered it unusable, the SWIR band data has not been included in the AST_L1T product. * Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions. Improvements/Changes from Previous Version * Available via on-demand processing through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C2052604735-LPDAAC_ECS). * Utilizes new radiometric calibration coefficients. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\n<br>\n\n#### AST_L1T\nThe Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B ([AST_L1B](https://doi.org/10.5067/ASTER/AST_L1B.004)) that has been geometrically corrected and rotated to a north-up UTM projection. The AST_L1T is created from a single resampling of the corresponding ASTER L1A ([AST_L1A](https://doi.org/10.5067/ASTER/AST_L1A.004)) product. The bands available in the AST_L1T depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T dataset does not include the aft-looking VNIR band 3. The AST_L1T product has a spatial resolution of 15 meters (m) for the VNIR bands, 30 m for the SWIR bands, and 90 m for the TIR bands. The precision terrain correction process incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover). For daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution as both a text file and single band browse images with the valid GCPs overlaid. This multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depends on the bands available in the corresponding [AST_L1A](https://doi.org/10.5067/ASTER/AST_L1A.004) dataset. Known Issues: Since October 1, 2017, a correction addresses zero-filled scans in low-latitude, ascending orbit (nighttime) TIR data. Additional details are available in the ASTER L1T User Advisory. * Data from the SWIR bands collected after April 2008 may show anomalous saturation and striping. See the ASTER SWIR User Advisory for further information.\n<br>\n\n#### AST14DMO\nThe AST14DMO Version 3 data product was decommissioned on December 15, 2025. The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model and Orthorectified Registered Radiance at the Sensor (AST14DMO) product form a multi-file product. The product contains both a Digital Elevation Model (DEM) and up to 15 orthorectified images representing Visible and Near Infrared (VNIR), Shortwave Infrared (SWIR), and Thermal Infrared (TIR) data layers, if acquired. For more information, see the links below: [AST14DEM](https://doi.org/10.5067/ASTER/AST14DEM.003) [AST14OTH](https://doi.org/10.5067/ASTER/AST14OTH.003) The ASTER Digital Elevation Model and Orthorectified Registered Radiance at the Sensor data product is only available through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C1299783651-LPDAAC_ECS). The ASTER Order Instructions provide step-by-step directions for ordering this product. Known Issues: Users are advised that ASTER SWIR data acquired from April 2008 to the present exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details. * Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions. Improvements/Changes from Previous Version * As of January 2021, the LP DAAC has implemented version 3.0 of the Sensor Information Laboratory Corporation ASTER DEM/Ortho (SILCAST) software, which is used to generate the Level 2 on-demand ASTER Orthorectified and Digital Elevation Model (DEM) products (AST14). The updated software provides digital elevation extraction and orthorectification from ASTER L1A input data without needing to enter ground control points or depending on external global DEMs at 30-arc-second resolution (GTOPO30). It utilizes the ephemeris and attitude data derived from both the ASTER instrument and the Terra spacecraft platform. The outputs are geoid height-corrected and waterbodies are automatically detected in this version. Users will notice differences between AST14DEM, AST14DMO, and AST14OTH products ordered before January 2021 (generated with SILCAST V1) and those generated with the updated version of the production software (version 3.0). Differences may include slight elevation changes over different surface types, including waterbodies. Differences have also been observed over cloudy portions of ASTER scenes. Additional information on SILCAST version 3.0 can be found on the [SILCAST website](http://www.silc.co.jp/en/products.html). * Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\n<br>\n\n#### AST14OTH\nThe AST14OTH Version 3 data product was decommissioned on December 15, 2025. The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Orthorectified Registered Radiance at the Sensor (AST14OTH) products contain imagery transformed from a perspective projection to an orthogonal one. An orthorectified image possesses the geometric characteristics of a map with near-vertical views for every location. These products are terrain corrected, provide radiometrically calibrated radiance, and are mapped to the Universal Transverse Mercator (UTM) coordinate system. The inputs include the following: an ASTER Level 1A Reconstructed Unprocessed Instrument dataset; georeferencing information from the ASTER instrument's and Terra platform's ephemeris and attitude data; and an ASTER-derived digital elevation model (DEM). The output product includes fifteen orthorectified ASTER Level 1B calibrated radiance images, one per band, as listed below. The ASTER Orthorectified Registered Radiance at the Sensor data product is only available through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C1299783684-LPDAAC_ECS). The ASTER Order Instructions provide step-by-step directions for ordering this product. Known Issues: Users are advised that ASTER SWIR data acquired from April 2008 to the present exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details. * Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions. Improvements/Changes from Previous Version * As of January 2021, the LP DAAC has implemented version 3.0 of the Sensor Information Laboratory Corporation ASTER DEM/Ortho (SILCAST) software, which is used to generate the Level 2 on-demand ASTER Orthorectified and Digital Elevation Model (DEM) products (AST14). The updated software provides digital elevation extraction and orthorectification from ASTER L1A input data without needing to enter ground control points or depending on external global DEMs at 30-arc-second resolution (GTOPO30). It utilizes the ephemeris and attitude data derived from both the ASTER instrument and the Terra spacecraft platform. The outputs are geoid height-corrected and waterbodies are automatically detected in this version. Users will notice differences between AST14DEM, AST14DMO, and AST14OTH products ordered before January 2021 (generated with SILCAST V1) and those generated with the updated version of the production software (version 3.0). Differences may include slight elevation changes over different surface types, including waterbodies. Differences have also been observed over cloudy portions of ASTER scenes. Additional information on SILCAST version 3.0 can be found on the [SILCAST website](http://www.silc.co.jp/en/products.html). * In addition to the recent SILCAST upgrade from Version 1.0 to 3.0, on March 18, 2021, the LP DAAC updated the Interactive Data Language (IDL) software used in the processing system from 8.7.0 to 8.8.0. As a result, users may notice minor and isolated differences in the output files produced for this product in bands 1 – 3(B/N) when comparing output files produced before and after the updates. * Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.\n<br>\n\n#### MODFDS_CLM_GLB_L3\nVersion 1 is the current version of the dataset. This collection MODFDS_CLM_GLB_L3 provides level 3 climatological monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚. It is derived from Level 2, the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products Collection 6.1 from Terra (MOD04_L2). The dataset is the climatological monthly mean for each month averaged over 2000 to 2022. The FDS is calculated as the number of days per month when the daily dust optical depth is greater than a threshold optical depth (e.g., 0.025) with two quality flags: the lowest (1) and highest (3). It is advised to use flag 1, which is of lower quality, over dust source regions, and flag 3 over remote areas or polluted regions. Eight thresholds (0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 2) are saved separately in eight files. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### FluxSatMGPP_L3_Daily_p05deg\nThis dataset provides global gridded daily estimates of gross primary production (GPP) and uncertainties at 0.05 deg latitude by 0.05 deg longitude resolution for the period March 2000 to present. The GPP is derived from MODerate-resolution Imaging Spectroradiometer (MODIS) instruments on NASA Terra and Aqua satellites. GPP is derived using the MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectances (NBAR) product, which is used as input to neural network models to globally upscale GPP estimated from selected collocated FLUXNET 2015 and OneFlux eddy covariance tower sites used for model training. Version 2.2 is the current version of the dataset. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### FluxSatMGPP_L3_Daily_p5deg\nThis dataset provides global gridded daily estimates of gross primary production (GPP) and uncertainties at 0.5 deg latitude by 0.625 deg longitude resolution for the period March 2000 to present. The GPP is derived from MODerate-resolution Imaging Spectroradiometer (MODIS) instruments on NASA Terra and Aqua satellites. GPP is derived using the MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectances (NBAR) product, which is used as input to neural network models to globally upscale GPP estimated from selected collocated FLUXNET 2015 and OneFlux eddy covariance tower sites used for model training. Version 2.2 is the current version of the dataset. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### MIRCCMF\nThis is the FIRSTLOOK Radiometric Camera-by-camera Cloud Mask (RCCM) product. It contains initial estimated classifications of pixels/regions as clear or cloudy. It also has masks for the presence of glitter or dust. The FIRSTLOOK RCCM product is superceded by the final RCCM product following seasonal calibration.\n<br>\n\n#### MIL2TCCF\nMulti-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 2 FIRSTLOOK TOA/Cloud Classifier parameters V002 contains the Angular Signature Cloud Mask (ASCM), Cloud Classifiers, and Support Vector Machine classifiers, produced using ancillary inputs and Terrestrial Atmosphere and Surface Climatology (TASC) from the previous time period.\n<br>\n\n#### MCD06COSP_D3_MODIS\nThe combined MODIS (Aqua/Terra) Cloud Properties Level 3 daily, 1x1 degree grid product represents a new addition that is especially geared to facilitate climate scientists who deal with both models and observations. MCD06COSP_D3_MODIS represents the daily product’s short-name. The “COSP” acronym in its short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. This product is an aggregation of combined MODIS Level-2 inputs from both the Terra and Aqua incarnations (MOD35/MOD06 and MYD35/MYD06, respectively), and employs an aggregation methodology consistent with the MOD08 and MYD08 products. Provided in netCDF4 format, it contains 23 aggregated science data sets (SDS/parameters). The Collection 6.2 (C6.2) is an improved version from the previous version (C6.1) because a number of bugs detected in Collection 6.1 are fixed.\n<br>\n\n#### MCD06COSP_M3_MODIS\nThe combined MODIS (Aqua/Terra) Cloud Properties Level 3 monthly, 1x1 degree grid product represents a new addition that is especially geared to facilitate climate scientists who deal with both models and observations. MCD06COSP_D3_MODIS represents the daily product’s short-name. The “COSP” acronym in its short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. The L3 monthly product is derived by aggregating the daily-produced Aqua+Terra/MODIS D3 Cloud Properties product (MCD06COSP_D3_MODIS). Provided in netCDF4 format, it contains 23 aggregated science data sets (SDS/parameters). The Collection 6.2 (C6.2) is an improved version from the previous version (C6.1) because a number of bugs detected in Collection 6.1 are fixed.\n<br>\n\n#### MOD04_L2\nThe MODIS/Terra Aerosol 5-Min L2 Swath 10km (MOD04_L2) product provides full global coverage of aerosol properties from the Dark Target (DT) and Deep Blue (DB) algorithms. The DT algorithm is applied over ocean and dark land (e.g., vegetation), while the DB algorithm now covers the entire land areas including both dark and bright surfaces. Both results are provided on a 10x10 pixel scale (10 km at nadir). Each MOD04_L2 product file covers a five-minute time interval. The output grid is 135 pixels in width by 203 pixels in length. Every tenth file has an output grid size of 135 by 204 pixels. MOD04_L2 product files are stored in Hierarchical Data Format (HDF-EOS). The new Collection 6.1 (C61) MOD04_L2 product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals. The MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land. In Collection 5 and in earlier collections, there was only one aerosol product (MOD04_L2) at 10km (at nadir) spatial resolution. Starting from C6, the Dark Target (DT) Aerosol algorithm team provided a new 3 km spatial resolution product (MOD04_3k) intended for the air quality community. For more information visit the MODIS Atmosphere website at: https://modis-atmos.gsfc.nasa.gov/products/aerosol And, for C6.1 changes and updates, visit: https://modis-atmosphere.gsfc.nasa.gov/documentation/collection-61\n<br>\n\n#### MOD04_3K\nThe new Collection 6.1 (C61) MODIS/Terra Aerosol 5 Min L2 Swath 3km (MOD04_3K) product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals. The MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land. In Collection 5, and earlier collections, there was only one aerosol product (MOD04_L2) at 10km (at nadir) spatial resolution. Starting from C6, the Dark Target (DT) Aerosol algorithm team provided a new 3 km spatial resolution product (MOD04_3k) intended for the air quality community. The MOD04_3K product is based on the same algorithm and Look up Tables as the standard Dark Target aerosol product. Because of finer resolution, subtle differences are made in selecting pixels for retrieval and in determining QA. The only differences between the existing 10km algorithm and the new 3km algorithm are: 1) the size of the pixel-arrays defining each retrieval box ( 6x6 retrieval boxes of 36 pixels at 0.5km resolution for 3km algorithm as oppose to 20x20 retrieval boxes of 400 pixels at 0.5km resolution for 10km product); 2) the minimum percentage of \"good\" pixels required for a retrieval (a minimum of 5 pixels over ocean and 6 pixels over land instead of a minimum of 10 pixels over ocean or 12 pixels over land for 10km product retrieval); 3) the 10km algorithm attemptes a \"poor quality\" retrieval while 3km algorithm does not. Everything else is the same between two products. For more information on C6.1 changes and updates, visit the MODIS Atmosphere website at: https://modis-atmosphere.gsfc.nasa.gov/documentation/collection-61\n<br>\n\n#### MOD08_E3\nThe MODIS/Terra Aerosol Cloud Water Vapor Ozone 8-Day L3 Global 1Deg CMG product (MOD08_E3) contains 8-Day 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. The MOD08_E3 contains nearly 1000 statistical datasets (SDS's) that are derived from the Level-3 MODIS Atmosphere Daily Global Product. Statistics are computed over a 1 degree equal-angle lat-lon grid that spans an 8-Day interval. Since the grid cells are 1 degree by 1 degree, the output grid is always 360 pixels in width and 180 pixels in length. MOD08_E3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. The MODIS 8-Day Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution. For more information about the MOD08_E3 product, please visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/eight-day\n<br>\n\n#### MOD08_D3\nThe MODIS/Terra Aerosol Cloud Water Vapor Ozone Daily L3 Global 1Deg CMG product (MOD08_D3) contains daily 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. The MOD08_D3 contains roughly 600 statistical datasets that are derived from approximately 80 scientific parameters from four Level-2 MODIS Atmosphere Products: MOD04_L2, MOD05_L2, MOD06_L2, and MOD07_L2. Statistics are computed over a 1 degree equal-angle lat-lon grid that spans a 24-hour (0000 to 2400 Greenwich Mean Time) interval. Since the grid cells are 1 degree by 1 degree, the output grid is always 360 pixels in width and 180 pixels in length. MOD08_D3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. The MODIS Daily Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution. For more information about the MOD08_D3 product, please visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/daily\n<br>\n\n#### MOD08_M3\nThe MODIS/Terra Aerosol Cloud Water Vapor Ozone Monthly L3 Global 1Deg CMG product (MOD08_M3) contains monthly 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. The MOD08_M3 contains roughly 800 statistical datasets that are derived from the Level-3 MODIS Atmosphere Daily Global Product. Statistics are sorted into 1x1 degree cells on an equal-angle grid that spans a (calendar) monthly interval and then summarized over the globe. MOD08_M3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. The MODIS monthly Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution. For more information about the MOD08_M3 product, please visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/monthly\n<br>\n\n#### MODATML2\nThe MODIS/Terra Aerosol, Cloud and Water Vapor Subset 5-Min L2 Swath 5km and 10km (MODATML2) product contains a combination of key high interest science parameters. The ATML2 product provides a subset of datasets from the suite of atmosphere team products on both a 10 km scale (aerosols) and 5km scale (native 5 km cloud properties and a 5x5 pixel sample of the 1km cloud datasets). The ATML2 product employs the same 5x5 pixel sampling scheme for the 1km native resolution Level 2 products as is used in the MOD08 Level 3 global aggregated product, an approach that has been shown to retain statistical integrity for multi-day aggregations. The C6 significantly increases the number of datasets included in the ATML2 product, including the full suite of QA datasets. Since the ATML2 data granule file size is significantly smaller than the combined size of the individual L2 products, and because the 1 km pixel sampling is consistent with the L3 algorithm, the ATML2 product is a more practical means for the user community to develop research L3 algorithms for their own specific purposes. For more information, visit the MODIS Atmosphere website at: https://modis-atmos.gsfc.nasa.gov/products/joint-atm\n<br>\n\n#### MOD09\nThe MODIS/Terra Atmospherically Corrected Surface Reflectance 5-Min L2 Swath 250m, 500m, 1km (MOD09) product is computed from the MODIS Level 1B land bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648 nm, 858 nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm, respectively). The product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption. The surface-reflectance product is the input for product generation for several land products: vegetation Indices (VIs), Bidirectional Reflectance Distribution Function (BRDF), thermal anomaly, snow/ice, and Fraction of Photosynthetically Active Radiation/Leaf Area Index (FPAR/LAI).\n<br>\n\n#### MOD021KM\nThe MODIS/Terra Calibrated Radiances 5Min L1B Swath 1km data set contains calibrated and geolocated at-aperture radiances for 36 discrete bands located in the 0.4 to 14.4 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which during processing are converted to geophysical units of W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously. The shortname for this product is MOD021KM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical file size is approximately 110 MB (a day granule around 150MB and a night granule around 70 MB). In this new version (Collection 6.1) of MOD021KM, an advanced technique is introduced to mitigate the crosstalk contamination issue among the LWIR PV bands using data from lunar calibration events. The electronic crosstalk contamination issue in the Terra long-wave infrared photovoltaic (LWIR PV) bands (27 -30), has existed since the beginning of the mission but become more noticeable during later half of the mission. The electronic crosstalk is where signal from each of the detectors among the LWIR PV bands can leak to the other detectors, producing a false signal contribution. This contamination has had a noticeable effect on the MODIS science products since 2010 for band 27, and since 2012 for bands 28 and 29. Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world. See the MODIS Characterization Support Team webpage for more C6 product information at: https://mcst.gsfc.nasa.gov/l1b/product-information or visit the Science Team homepage at: https://modis.gsfc.nasa.gov/data/dataprod/\n<br>\n\n#### MOD02QKM\nThe MODIS/Terra Calibrated Radiances 5Min L1B Swath 250m data set contains calibrated and geolocated at-aperture radiances for 2 discrete bands located in the 0.62 to 0.88 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which have been processed to geophysical units of W / (m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. Separate L1B products are available for the five 500m resolution channels (MOD02HKM) and the twenty-nine 1km resolution channels (MOD021KM). For the 500m product, there are actually seven channels available since the data from the two 250 m channels have been aggregated to 500m resolution. Similarly, for the 1km product, all 36 MODIS channels are available since the data from the two 250m and five 500m channels have been aggregated into equivalent 1km pixel values. Spatial resolution for pixels at nadir is 250 m, degrading to 1.2 km in the along-scan direction and 0.5 km in the along-track direction for pixels located at the scan extremes. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 250 m granule will contain a scene built from 203 scans sampled 5416 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 40 along-track spatial elements for the 250 m channels, the scene will be composed of (5416 x 8120) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 17 degrees scan angle. The shortname for this product is MOD02QKM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical file size will be approximately 140 MB and the total daily volume is around 22GB. Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world. See the MODIS Characterization Support Team webpage for more C6 product information at: https://mcst.gsfc.nasa.gov/l1b/product-information or visit the Science Team homepage at: https://modis.gsfc.nasa.gov/data/dataprod/\n<br>\n\n#### MOD02HKM\nThe MODIS/Terra Calibrated Radiances 5Min L1B Swath 500m data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously. Channels 1 and 2 have 250 m resolution, channels 3 through 7 have 500 m resolution. However, for the MODIS L1B 500 m product, the 250 m band radiance data and their associated uncertainties have been aggregated to 500m resolution. Thus the entire channel data set has been co-registered to the same spatial scale in the 500 m product. Separate L1B products are available for the 250 m resolution channels (MOD02QKM) and 1 km resolution channels (MOD021KM). For the latter product, the 250 m and 500 m channel data (bands 1 through 7) have been aggregated into equivalent 1 km pixel values. Spatial resolution for pixels at nadir is 500 km, degrading to 2.4 km in the along-scan direction at the scan extremes. However, thanks to the overlapping of consecutive swaths and respectively pixels there, the resulting resolution at the scan extremes is about 1 km. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 500 m granule will contain a scene built from 203 scans sampled 2708 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 20 along-track spatial elements for the 500 m channels, the scene will be composed of (2708 x 4060) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 20 degrees scan angle. To summarize, the MODIS L1B 500 m data product consists of: 1. Calibrated radiances, uncertainties and number of samples for (2) 250 m reflected solar bands aggregated to 500 m resolution 2. Calibrated radiances and uncertainties for (5) 500 m reflected solar bands 3. Geolocation for 1km pixels, that must be interpolated to get 500 m pixel locations. For the relationship of 1km pixels to 500m pixels, see the Geolocation ATBD https://modis.gsfc.nasa.gov/data/atbd/atbd_mod28_v3.pdf. 4. Calibration data for all channels (scale and offset) 5. Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization users requiring all geolocation and solar/satellite geometry fields at 1km resolution can obtain the separate MODIS Level 1 Geolocation product (MOD03) from LAADS https://ladsweb.modaps.eosdis.nasa.gov/ . The shortname for this product is MOD02HKM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical MOD02HKM file size is approximately 135 MB. Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world. See the MODIS Characterization Support Team webpage for more C6 product information at: https://mcst.gsfc.nasa.gov/l1b/product-information or visit Science Team homepage at: https://modis.gsfc.nasa.gov/data/dataprod/\n<br>\n\n#### MOD35_L2\nThe MODIS/Terra Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km product consists of global cloud mask quality assurance and other ancillary parameters. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence levels that an unobstructed view of the Earth's surface is observed. An indication of shadows affecting the scene is also provided. The 250-m cloud mask flags are based on the visible channel data only. Radiometrically accurate radiances are required, so holes in the cloud mask will appear wherever the input radiances are incomplete or of poor quality. The shortname for this Level-2 MODIS cloud mask product is MOD35_L2. The MOD35_L2 product files are stored in Hierarchical Data Format (HDF-EOS). This product consists of 9 parameters and each of these parameters are stored as a Scientific Data Set (SDS) within the HDF-EOS file. The Cloud Mask and Quality Assurance SDS's are stored at 1 kilometer pixel resolution. All other SDS's (those relating to time, geolocation, and viewing geometry) are stored at 5 kilometer pixel resolution. For more information about the MOD35_L2 product, visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/cloud-mask\n<br>\n\n#### MOD06_L2\nThe MODIS/Terra Clouds 5-Min L2 Swath 1km and 5km product (MOD06_L2) consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared, visible and near infrared solar reflected radiances. MODIS infrared channel radiances are used to derive cloud top temperature, cloud top height, effective emissivity, cloud phase (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. MODIS visible radiances are used to derive cloud optical thickness and effective particle radius and cloud shadow effects. Near infrared solar reflected radiance provides additional information in the retrieval of cloud particle phase (ice vs. water, clouds vs. snow). The shortname for this level-2 MODIS cloud product is MOD06_L2. MOD06_L2 consists of parameters at a spatial resolution of either 1- km or 5-km (at nadir). Each MOD06_L2 product file covers a five-minute time interval. This means that for 5-km resolution parameters, the output grid is 270 pixels in width by 406 pixels in length. C6.1 changes for the cloud optical property retrievals are low-impact, and are limited primarily to ancillary product usage, the Quality Assurance (QA), and handling of cloud top (CT) properties fill values; no updates to retrieval science are implemented. The MODIS Cloud Product is used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial (1 kilometer) resolution. For more information about the MOD06_L2 product, visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/cloud\n<br>\n\n#### MOD03\nThe MODIS/Terra Geolocation Fields 5Min L1A Swath 1km are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily (in Collection 6 and later, information is provided to claculate 500m geolocation fields). The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit, the instrument telemetry and the digital elevation model. The geolocation fields include geodetic Latitude, Longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1 km sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors of any of the 36 MODIS bands. This product is used as input by a large number of subsequent MODIS products, particularly the products produced by the Land team. The short name for this product is MOD03. Each file is roughly 30 MB in size, and the total data volume is approximately 8 GB/day. See the MODIS Science Team homepage for more data set information: https://modis.gsfc.nasa.gov/data/dataprod/\n<br>\n\n#### MOD17A2H\nThe MOD17A2H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD17A2H Version 6.1](https://doi.org/10.5067/MODIS/MOD17A2H.061) data product. The MOD17A2H Version 6 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD17A2H\nThe MOD17A2H Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD17A2HGF\nThe MOD17A2HGF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD17A2HGF Version 6.1](https://doi.org/10.5067/MODIS/MOD17A2HGF.061) data product. The MOD17A2HGF Version 6 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MOD17A2HGF will be generated at the end of each year when the entire yearly 8-day [MOD15A2H](https://doi.org/10.5067/modis/mod15a2h.006) is available. Hence, the gap-filled MOD17A2HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A2HGF in near-real time because it will be generated only at the end of a given year. Known Issues: Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD17A2HGF\nThe MOD17A2HGF Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MOD17A2HGF will be generated at the end of each year when the entire yearly 8-day [MOD15A2H](https://doi.org/10.5067/modis/mod15a2h.061) is available. Hence, the gap-filled MOD17A2HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A2HGF in near-real time because it will be generated only at the end of a given year. Known Issues: Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD21\nThe MOD21 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD21 Version 6.1](https://doi.org/10.5067/MODIS/MOD21.061) data product. The MOD21 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MOD21 Land Surface Temperature (LST) algorithm differs from the [MOD11](https://doi.org/10.5067/modis/mod11_l2.006) algorithm in that the MOD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. MOD21 products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Forward processing of Terra MODIS LST&E Version 6 data products was discontinued on December 31, 2005. Users are encouraged to use the [MOD21 Version 6.1](https://doi.org/10.5067/MODIS/MOD21.061) data product. * Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD21\nThe MOD21 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MOD21 Land Surface Temperature (LST) algorithm differs from the [MOD11](https://doi.org/10.5067/modis/mod11_l2.061) algorithm in that the MOD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD21C2\nA suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the [MOD11](https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21C2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free [MOD21A1D](http://doi.org/10.5067/MODIS/MOD21A1D.061) and [MOD21A1N](http://doi.org/10.5067/MODIS/MOD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MOD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD21A2\nThe MOD21A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD21A2 Version 6.1](https://doi.org/10.5067/MODIS/MOD21A2.061) data product. A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6. The MOD21 Land Surface Temperatuer (LST) algorithm differs from the algorithm of the [MOD11](https://doi.org/10.5067/modis/mod11_l2.006) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21A2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free [MOD21A1D](http://doi.org/10.5067/MODIS/MOD21A1D.006) and [MOD21A1N](http://doi.org/10.5067/MODIS/MOD21A1N.006) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MOD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. MOD21A2 products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Forward processing of Terra MODIS LST&E Version 6 data products was discontinued on December 31, 2005. Users are encouraged to use the [MOD21A1D Version 6.1](https://doi.org/10.5067/MODIS/MOD21A1D.061) data product. * Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD21A2\nA suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperatuer (LST) algorithm differs from the algorithm of the [MOD11](https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21A2 dataset is an 8-day composite LST product at 1,000 meter spatial resolution that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free [MOD21A1D](http://doi.org/10.5067/MODIS/MOD21A1D.061) and [MOD21A1N](http://doi.org/10.5067/MODIS/MOD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MOD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD21A1D\nThe MOD21A1D Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD21A1D Version 6.1](https://doi.org/10.5067/MODIS/MOD21A1D.061) data product. A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the [MOD11](https://doi.org/10.5067/modis/mod11_l2.006) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21A1D dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily [MOD21](http://doi.org/10.5067/MODIS/MOD21.006) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21A1D product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. MOD21A1D products are available two months after acquisition due to latency of data inputs. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Forward processing of Terra MODIS LST&E Version 6 data products was discontinued on December 31, 2005. Users are encouraged to use the [MOD21A1D Version 6.1](https://doi.org/10.5067/MODIS/MOD21A1D.061) data product. * Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD21A1N\nThe MOD21A1N Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD21A1N Version 6.1](https://doi.org/10.5067/MODIS/MOD21A1N.061) data product. A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the [MOD11](https://doi.org/10.5067/modis/mod11_l2.006) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily [MOD21](http://doi.org/10.5067/MODIS/MOD21.006) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. MOD21A1N products are available two months after acquisition due to latency of data inputs. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Forward processing of Terra MODIS LST&E Version 6 data products was discontinued on December 31, 2005. Users are encouraged to use the [MOD21A1N Version 6.1](https://doi.org/10.5067/MODIS/MOD21A1N.061) data product. * Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD11_L2\nThe MOD11_L2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11_L2 Version 6.1](https://doi.org/10.5067/MODIS/MOD11_L2.061) data product. The MOD11_L2 Version 6 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MOD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MOD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples. Known Issues: Production of V6 Terra MODIS Land Surface Temperature and Emissivity (LST&E) data products was discontinued on November 16, 2022, due to [significant loss of data retrieval](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=98) following the Constellation Exit Maneuvers. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD11_L2\nThe MOD11_L2 Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MOD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MOD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD11C2\nThe MOD11C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11C2 Version 6.1](https://doi.org/10.5067/MODIS/MOD11C2.061) data product. The MOD11C2 Version 6 product provides Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe. The LST&E values in the MOD11C2 product are derived by compositing and averaging the values from the corresponding eight [MOD11C1](https://doi.org/10.5067/MODIS/MOD11C1.006) daily files. The MOD11C2 granule consists of 17 layers. Each MOD11C2 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Known Issues: Production of V6 Terra MODIS Land Surface Temperature and Emissivity (LST&E) data products was discontinued on November 16, 2022, due to [significant loss of data retrieval](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=98) following the Constellation Exit Maneuvers. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD11C2\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MOD11C2) Version 6.1 product provides Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe. The LST&E values in the MOD11C2 product are derived by compositing and averaging the values from the corresponding eight [MOD11C1](https://doi.org/10.5067/MODIS/MOD11C1.061) daily files. The MOD11C2 granule consists of 17 layers. Each MOD11C2 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD11A2\nThe MOD11A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11A2 Version 6.1](https://doi.org/10.5067/MODIS/MOD11A2.061) data product. The MOD11A2 Version 6 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding [MOD11A1](https://doi.org/10.5067/MODIS/MOD11A1.006) LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. Known Issues: Production of V6 Terra MODIS Land Surface Temperature and Emissivity (LST&E) data products was discontinued on November 16, 2022, due to [significant loss of data retrieval](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=98) following the Constellation Exit Maneuvers. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD11A2\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MOD11A2) Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding [MOD11A1](https://doi.org/10.5067/MODIS/MOD11A1.061) LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD11B2\nThe MOD11B2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11B2 Version 6.1](https://doi.org/10.5067/MODIS/MOD11B2.061) data product. The MOD11B2 Version 6 product provides an average 8-day per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each temperature and emissivity pixel value in the MOD11B2 is a simple average of all the corresponding values from the LST&E values from the [MOD11B1](https://doi.org/10.5067/MODIS/MOD11B1.006) product collected during that 8-day period. Each MOD11B2 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km [MOD11_L2](https://doi.org/10.5067/MODIS/MOD11_L2.006) swath product aggregated to the 6 km grid. Known Issues: Production of V6 Terra MODIS Land Surface Temperature and Emissivity (LST&E) data products was discontinued on November 16, 2022, due to [significant loss of data retrieval](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=98) following the Constellation Exit Maneuvers. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD11B2\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MOD11B2) Version 6.1 product provides an average 8-day per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each temperature and emissivity pixel value in the MOD11B2 is a simple average of all the corresponding values from the LST&E values from the [MOD11B1](https://doi.org/10.5067/MODIS/MOD11B1.061) product collected during that 8-day period. Each MOD11B2 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km [MOD11_L2](https://doi.org/10.5067/MODIS/MOD11_L2.061) swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD11C1\nThe MOD11C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11C1 Version 6.1](https://doi.org/10.5067/MODIS/MOD11C1.061) data product. The MOD11C1 Version 6 product provides daily Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). The MOD11C1 product is directly derived from the [MOD11B1](https://doi.org/10.5067/MODIS/MOD11B1.006) product. A CMG granule follows a Geographic grid, having 7,200 columns and 3,600 rows, which represent the entire globe. Each MOD11C1 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the grid. Known Issues: Production of V6 Terra MODIS Land Surface Temperature and Emissivity (LST&E) data products was discontinued on November 16, 2022, due to [significant loss of data retrieval](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=98) following the Constellation Exit Maneuvers. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD11C1\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MOD11C1) Version 6.1 product provides daily Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). The MOD11C1 product is directly derived from the [MOD11B1](https://doi.org/10.5067/MODIS/MOD11B1.061) product. A CMG granule follows a Geographic grid, having 7,200 columns and 3,600 rows, which represent the entire globe. Each MOD11C1 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the grid. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD11A1\nThe MOD11A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11A1 Version 6.1](https://doi.org/10.5067/MODIS/MOD11A1.061) data product. The MOD11A1 Version 6 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the [MOD11_L2](https://doi.org/10.5067/MODIS/MOD11_L2.006) swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. Known Issues: Production of V6 Terra MODIS Land Surface Temperature and Emissivity (LST&E) data products was discontinued on November 16, 2022, due to [significant loss of data retrieval](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=98) following the Constellation Exit Maneuvers. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD11A1\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MOD11A1) Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the [MOD11_L2](https://doi.org/10.5067/MODIS/MOD11_L2.061) swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD11B1\nThe MOD11B1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11B1 Version 6.1](https://doi.org/10.5067/MODIS/MOD11B1.061) data product. The MOD11B1 Version 6 product provides daily per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each MOD11B1 granule consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the tile. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km [MOD11_L2](https://doi.org/10.5067/MODIS/MOD11_L2.006) swath product aggregated to the 6 km grid. Known Issues: Production of V6 Terra MODIS Land Surface Temperature and Emissivity (LST&E) data products was discontinued on November 16, 2022, due to [significant loss of data retrieval](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=98) following the Constellation Exit Maneuvers. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD11B1\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily(MOD11B1) Version 6.1 product provides daily per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each MOD11B1 granule consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the tile. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km [MOD11_L2](https://doi.org/10.5067/MODIS/MOD11_L2.061) swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD11C3\nThe MOD11C3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11C3 Version 6.1](https://doi.org/10.5067/MODIS/MOD11C3.061) data product. The MOD11C3 Version 6 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe. The LST&E values in the MOD11C3 product are derived by compositing and averaging the values from the corresponding month of [MOD11C1](https://doi.org/10.5067/MODIS/MOD11C1.006) daily files. Each MOD11C3 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Known Issues: Production of V6 Terra MODIS Land Surface Temperature and Emissivity (LST&E) data products was discontinued on November 16, 2022, due to [significant loss of data retrieval](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=98) following the Constellation Exit Maneuvers. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD11C3\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Monthly(MOD11C3) Version 6.1 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe. The LST&E values in the MOD11C3 product are derived by compositing and averaging the values from the corresponding month of [MOD11C1](https://doi.org/10.5067/MODIS/MOD11C1.061) daily files. Each MOD11C3 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD11B3\nThe MOD11B3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11B3 Version 6.1](https://doi.org/10.5067/MODIS/MOD11B3.061) data product. The MOD11B3 Version 6 product provides average monthly per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each LST&E pixel value in the MOD11B3 is a simple average of all the corresponding values from the [MOD11B1](https://doi.org/10.5067/MODIS/MOD11B1.006) collected during the month period. Each MOD11B3 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km [MOD11_L2](https://doi.org/10.5067/MODIS/MOD11_L2.006) swath product aggregated to the 6 km grid. Known Issues: Production of V6 Terra MODIS Land Surface Temperature and Emissivity (LST&E) data products was discontinued on November 16, 2022, due to [significant loss of data retrieval](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=98) following the Constellation Exit Maneuvers. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD11B3\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Monthly (MOD11B3) Version 6.1 product provides average monthly per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each LST&E pixel value in the MOD11B3 is a simple average of all the corresponding values from the [MOD11B1](https://doi.org/10.5067/MODIS/MOD11B1.061) collected during the month period. Each MOD11B3 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km [MOD11_L2](https://doi.org/10.5067/MODIS/MOD11_L2.061) swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD44W\nThe MOD44W Version 6 data product was decommissioned on July 18, 2024. Users are encouraged to use the [MOD44W Version 6.1](https://doi.org/10.5067/MODIS/MOD44W.061) data product. The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Water Mask (MOD44W) Version 6 data product provides a global map of surface water at 250 meter (m) spatial resolution. The data are available annually from 2000 to 2015. MOD44W Version 6 is derived using a decision tree classifier trained with MODIS data and validated with the Version 5 MOD44W data product. A series of masks are applied to address known issues caused by terrain shadow, burn scars, cloudiness, or ice cover in oceans. A primary improvement in Version 6 is the generation of time series data rather than a simple static representation of water, given that water bodies fluctuate in size and location over time due to both natural and anthropogenic causes. Provided in each MOD44W Version 6 Hierarchical Data Format 4 (HDF4) file are layers for land, water, no data, and an associated per pixel quality assurance (QA) layer that provides users with information on the determination of water. Known Issues: The mask area for pixels outside of the Sinusoidal Grid for tile h27v14 should be labeled as 250 (Outside of Projection) instead of 0 (Land) and the QA values should be 10 (Fill) instead of 3 (Lower confidence land) for years 2000 through 2015. * Additional known issues are described in the User Guide and ATBD. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD44W\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Water Mask (MOD44W) Version 6.1 data product provides a global map of surface water at 250 meter (m) spatial resolution. The data are available annually from 2000 to present. MOD44W Version 6.1 is derived using a decision tree classifier trained with MODIS data and validated with the Version 5 MOD44W data product. A series of masks are applied to address known issues caused by terrain shadow, burn scars, cloudiness, or ice cover in oceans. Version 6.1 is the generation of a time series rather than a simple static representation of water, given that water bodies fluctuate in size and location over time due to both natural and anthropogenic causes. Provided in each MOD44W Version 6.1 Hierarchical Data Format 4 (HDF4) file are layers for land water mask and water body classification. A quality assurance (QA) layer provides users with information on the determination of water. The new seven class water classification layer provides values for shallow ocean, land, shoreline, inland water, ephemeral water, deep inland water, moderate ocean, deep ocean, and a classification deemed to fall outside of the projection. Known Issues: The seven_class SDS layer legend for classes 4 and 5 are [incorrectly listed](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=208) as Deep Inland Water and Ephemeral Water in the data for the entire mission period. Users are requested to follow the legend provided in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD15A2H\nThe MOD15A2H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD15A2H Version 6.1](https://doi.org/10.5067/MODIS/MOD15A2H.061) data product. The MOD15A2H Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the “best” pixel available from all the acquisitions of the Terra sensor from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation, 400-700 nanometers (nm), absorbed by the green elements of a vegetation canopy. Science Datasets (SDSs) in the Level 4 (L4) MOD15A2H product include LAI, FPAR, two quality layers, and standard deviation for LAI and FPAR. Two low resolution browse images, LAI and FPAR, are also available for each MOD15A2H granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD15A2H\nThe MOD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the “best” pixel available from all the acquisitions of the Terra sensor from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation, 400-700 nanometers (nm), absorbed by the green elements of a vegetation canopy. Science Datasets (SDSs) in the Level 4 (L4) MOD15A2H product include LAI, FPAR, two quality layers, and standard deviation for LAI and FPAR. Two low resolution browse images, LAI and FPAR, are also available for each MOD15A2H granule. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD16A2\nThe MOD16A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD16A2 Version 6.1](https://doi.org/10.5067/MODIS/MOD16A2.061) data product. The MOD16A2 Version 6 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MOD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2 granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD16A2\nThe MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MOD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2 granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD16A2GF\nThe MOD16A2GF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD16A2GF Version 6.1](https://doi.org/10.5067/MODIS/MOD16A2GF.061) data product. The MOD16A2GF Version 6 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MOD16A2GF will be generated at the end of each year when the entire yearly 8-day [MOD15A2H](https://doi.org/10.5067/MODIS/MOD15A2H.006) is available. Hence, the gap-filled MOD16A2GF is the improved MOD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD16A2GF in near-real time because it will be generated only at the end of a given year. Provided in the MOD16A2GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. The last acquisition period of each year is a 5- or 6-day composite period, depending on the year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD16A2GF\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A2GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MOD16A2GF will be generated at the end of each year when the entire yearly 8-day [MOD15A2H](https://doi.org/10.5067/MODIS/MOD15A2H.061) is available. Hence, the gap-filled MOD16A2GF is the improved MOD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD16A2GF in near-real time because it will be generated only at the end of a given year. Provided in the MOD16A2GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. The last acquisition period of each year is a 5 or 6-day composite period, depending on the year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD16A3GF\nThe MOD16A3GF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD16A3GF Version 6.1](https://doi.org/10.5067/MODIS/MOD16A3GF.061) data product. The MOD16A3GF Version 6 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MOD16A3GF will be generated at the end of each year when the entire yearly 8-day [MOD15A2H](https://doi.org/10.5067/MODIS/MOD15A2H.006) is available. Hence, the gap-filled MOD16A3GF is the improved MOD16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD16A3GF in near-real time because it will be generated only at the end of a given year. Provided in the MOD16A3GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A3GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD16A3GF\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MODIS MOD16A3GF will be generated at the end of each year when the entire yearly 8-day [MOD15A2H](https://doi.org/10.5067/MODIS/MOD15A2H.061) is available. Hence, the gap-filled MOD16A3GF is the improved MOD16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD16A3GF in near-real time because it will be generated only at the end of a given year. Provided in the MOD16A3GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A3GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD16A3\nThe MOD16A3 Version 6 data product was decommissioned due to unexpected errors in the input data. Users are encouraged to use the [MOD16A3GF Version 6.1 data product](https://doi.org/10.5067/MODIS/MOD16A3GF.061). The MOD16A3 Version 6 Evapotranspiration/Latent Heat Flux product is a yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MOD16A3 product are layers for yearly Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A3 granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD17A3HGF\nThe MOD17A3HGF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD17A3HGF Version 6.1](https://doi.org/10.5067/MODIS/MOD17A3HGF.061) data product. The MOD17A3HGF Version 6 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products ([MOD17A2H](https://doi.org/10.5067/MODIS/MOD17A2H.006)) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). The MOD17A3HGF will be generated at the end of each year when the entire yearly 8-day [MOD15A2H](https://doi.org/10.5067/modis/mod15a2h.006) is available. Hence, the gap-filled MOD17A3HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A3HGF in near-real time because it will be generated only at the end of a given year. Known Issues: Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD17A3HGF\nThe MOD17A3HGF Version 6.1 product provides information about annual Gross and Net Primary Production (GPP and NPP) at 500 meter (m) pixel resolution. Annual Terra Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and NPP is derived from the sum of all 8-day GPP Net Photosynthesis (PSN) products ([MOD17A2H](https://doi.org/10.5067/MODIS/MOD17A2H.061)) from the given year. The PSN value is the difference of the GPP and the Maintenance Respiration (MR). The MOD17A3HGF will be generated at the end of each year when the entire yearly 8-day [MOD15A2H](https://doi.org/10.5067/modis/mod15a2h.061) is available. Hence, the gap-filled MOD17A3HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A3HGF in near-real time because it will be generated only at the end of a given year. Known Issues: Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD17A3H\nThe MOD17A3H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD17A3HGF Version 6.1](https://doi.org/10.5067/MODIS/MOD17A3HGF.061) data product. The MOD17A3H Version 6 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products ([MOD17A2H](https://doi.org/10.5067/MODIS/MOD17A2H.006)) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). Known Issues: Forward processing of MOD17A3H Version 6 data product was discontinued on January 3, 2004, due to unexpected errors in the input data. Users are encouraged to use the [MOD17A3HGF Version 6.1](https://doi.org/10.5067/MODIS/MOD17A3HGF.061) data product. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MODOCGA\nThe MODOCGA Version 6 data product was decommissioned on July 31, 2023. The MODOCGA Version 6 Level 2 Gridded Lite (L2G-lite) Ocean Reflectance product provides an estimate of the surface spectral reflectance data from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) bands 8 through 16. Data have been corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. MODOCGA is a daily land product with a pixel size of 1 kilometer (km). The product is referred to as ocean reflectance because bands 8 through 16 are used primarily to produce ocean products. The MODOCGA, as with other L2G data sets, stores the “best available pixel” from all the qualifying observations in the first layer and any subsequent observations are stored in either a full or compact format layer within the Hierarchical Data Format (HDF) file. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6). Improvements/Changes from Previous Versions * New product for MODIS Version 6.\n<br>\n\n#### MOD09Q1\nThe MOD09Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD09Q1 Version 6.1](https://doi.org/10.5067/MODIS/MOD09Q1.061) data product. The MOD09Q1 Version 6 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. Known Issues: Striping due to a dead detector is noticeable for bands 5, 6, and 7 in scenes acquired February 24 through October 31, 2000. Corrections were implemented to reduce the striping in data acquired after November 1, 2000. Users should always check the band quality for dead detectors even though reflectance values may be in the valid range. * The Collection 6 MODIS Land Surface Reflectance product (MOD09) may [incorrectly flag retrievals as ‘High Aerosol’](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD09Q1\nThe MOD09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD09A1\nThe MOD09A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD09A1 Version 6.1](https://doi.org/10.5067/MODIS/MOD09A1.061) data product. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. Known Issues: Striping due to a dead detector is noticeable for bands 5, 6, and 7 in scenes acquired February 24 through October 31, 2000. Corrections were implemented to reduce the striping in data acquired after November 1, 2000. Users should always check the band quality for dead detectors even though reflectance values may be in the valid range. * The Collection 6 MODIS Land Surface Reflectance product (MOD09) may [incorrectly flag retrievals as ‘High Aerosol’](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD09A1\nThe Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD09GA\nThe MOD09GA Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD09GA Version 6.1](https://doi.org/10.5067/MODIS/MOD09GA.061) data product. The MOD09GA Version 6 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer (km) observation bands and geolocation flags. The reflectance layers from the MOD09GA are used as the source data for many of the MODIS land products. Known Issues: Striping due to a dead detector is noticeable for bands 5, 6, and 7 in scenes acquired February 24 through October 31, 2000. Corrections were implemented to reduce the striping in data acquired after November 1, 2000. Users should always check the band quality for dead detectors even though reflectance values may be in the valid range. * The Collection 6 MODIS Land Surface Reflectance product (MOD09) may [incorrectly flag retrievals as ‘High Aerosol’](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD09GA\nThe MOD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer (km) observation bands and geolocation flags. The reflectance layers from the MOD09GA are used as the source data for many of the MODIS land products. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD09GQ\nThe MOD09GQ Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD09GQ Version 6.1](https://doi.org/10.5067/MODIS/MOD09GQ.061) data product. The MOD09GQ Version 6 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m surface reflectance bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MOD09GA). Known Issues: Striping due to a dead detector is noticeable for bands 5, 6, and 7 in scenes acquired February 24 through October 31, 2000. Corrections were implemented to reduce the striping in data acquired after November 1, 2000. Users should always check the band quality for dead detectors even though reflectance values may be in the valid range. * The Collection 6 MODIS Land Surface Reflectance product (MOD09) may [incorrectly flag retrievals as ‘High Aerosol’](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD09GQ\nThe MOD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m surface reflectance bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MOD09GA). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD09CMG\nThe MOD09CMG Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD09CMG Version 6.1](https://doi.org/10.5067/MODIS/MOD09CMG.061) data product. The MOD09CMG Version 6 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, resampled to 5600 meter (m) pixel resolution and corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. The MOD09CMG data product provides 25 layers including MODIS bands 1 through 7; Brightness Temperature data from thermal bands 20, 21, 31, and 32; along with Quality Assurance (QA) and observation bands. This product is based on a Climate Modeling Grid (CMG) for use in climate simulation models. Known Issues: Striping due to a dead detector is noticeable for bands 5, 6, and 7 in scenes acquired February 24 through October 31, 2000. Corrections were implemented to reduce the striping in data acquired after November 1, 2000. Users should always check the band quality for dead detectors even though reflectance values may be in the valid range. * The Collection 6 MODIS Land Surface Reflectance product (MOD09) may [incorrectly flag retrievals as ‘High Aerosol’](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD09CMG\nThe MOD09CMG Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, resampled to 5600 meter (m) pixel resolution and corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. The MOD09CMG data product provides 25 layers including MODIS bands 1 through 7; Brightness Temperature data from thermal bands 20, 21, 31, and 32; along with Quality Assurance (QA) and observation bands. This product is based on a Climate Modeling Grid (CMG) for use in climate simulation models. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD07_L2\nThe MODIS/Terra Temperature and Water Vapor Profiles 5-Min L2 Swath 5km (MOD07_L2) product consists of a numbers of parameters related to atmospheric stability, atmospheric temperature and moisture profiles, total atmospheric water vapor, and total ozone. All of these parameters are produced for both daytime and nighttime conditions at 5-km pixel resolution when at least 9 Field Of View (FOV) are cloud free. The MODIS total-ozone burden is an estimate of the total-column tropospheric and stratospheric ozone content. The MODIS atmospheric stability consists of three daily Level 2 atmospheric stability indices. The Total Totals (TT), the Lifted Index (LI), and the K index (K) are each computed using the infrared temperature- and moisture-profile data, also derived as part of MOD07. The MODIS temperature and moisture profiles are produced at 20 vertical levels. The MODIS atmospheric water-vapor product is an estimate of the total tropospheric column water vapor made from integrated MODIS infrared retrievals of atmospheric moisture profiles in clear scenes. Additional information is available at: https://modis-atmos.gsfc.nasa.gov/products/atm-profile.\n<br>\n\n#### MOD14\nThe MOD14 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD14 Version 6.1](https://doi.org/10.5067/MODIS/MOD14.061) data product. The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire MOD14 Version 6 product is produced daily in 5-minute temporal satellite increments (swaths). The MOD14 product is used to generate all of the higher level fire products, but can also be used to identify fires and other thermal anomalies, such as volcanoes. Each swath of data is approximately 2,030 kilometers along track (long), and 2,300 kilometers across track (wide). Known Issues: Known issues are described on the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6) and in Section 7.2 of the User Guide which covers Pre-November 2000 Data Quality, Detection Confidence, Flagging of Static Sources, and the August 2020 MODIS Aqua Outage\n<br>\n\n#### MOD14\nThe Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire MOD14 Version 6.1 product is produced daily in 5-minute temporal satellite increments (swaths) at 1 kilometer (km) spatial resolution. The MOD14 product is used to generate all of the higher level fire products, but can also be used to identify fires and other thermal anomalies, such as volcanoes. Each swath of data is approximately 2,030 kilometers along track (long), and 2,300 kilometers across track (wide). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD14A2\nThe MOD14A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD14A2 Version 6.1](https://doi.org/10.5067/MODIS/MOD14A2.061) data product. The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day (MOD14A2) Version 6 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MOD14A2 gridded composite contains the maximum value of the individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators. Known Issues: Known issues are described on the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6) and in Section 7.2 of the User Guide which covers Pre-November 2000 Data Quality, Detection Confidence, Flagging of Static Sources, and the August 2020 MODIS Aqua Outage\n<br>\n\n#### MOD14A2\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day (MOD14A2) Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MOD14A2 gridded composite contains the maximum value of the individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD14A1\nThe MOD14A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD14A1 Version 6.1](https://doi.org/10.5067/MODIS/MOD14A1.061) data product. The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily (MOD14A1) Version 6 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition. Known Issues: Known issues are described on the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6) and in Section 7.2 of the User Guide which covers Pre-November 2000 Data Quality, Detection Confidence, Flagging of Static Sources, and the August 2020 MODIS Aqua Outage\n<br>\n\n#### MOD14A1\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily (MOD14A1) Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MODTBGA\nThe MODTBGA Version 6 data product was decommissioned on July 31, 2023. The MODTBGA Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 Gridded Lite (L2G-lite) Thermal Band product consists of brightness temperature data from MODIS bands 20, 31, and 32 and albedo data from band 20 along with the orbit and granule pointer fields. MODTBGA is a daily product with a pixel size of 1 kilometer (km). The MODTBGA, as with other L2G data sets, stores the “best available pixel” from all the qualifying observations in the first layer and any subsequent observations are stored in either full or a compact layers within the Hierarchical Data Format (HDF) file. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6). Improvements/Changes from Previous Versions * New product for MODIS Version 6.\n<br>\n\n#### MOD05_L2\nThe MODIS/Terra Total Precipitable Water Vapor 5-Min L2 Swath 1km and 5km (MOD05_L2) product consists of atmospheric column water-vapor amounts. This product is derived from data collected by the Moderate-Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite. There are two different algorithms used to derive total precipitable water vapor in this data product: a near-infrared algorithm and an infrared algorithm. The near-infrared algorithm relies on observations of reflected solar radiation in MODIS's near-infrared channels, thus, the near-infrared retrievals are only produced during the daytime over surfaces that reflect near-infrared energy. As a result, the near-infrared algorithm is only applied over clear, cloud free land areas of the globe and above clouds over both the land and ocean. Over clear ocean areas, water-vapor estimates are provided over extended sun glint areas. Data produced by the near-infrared algorithm are generated at a 1-km spatial resolution. The other algorithm is the infrared algorithm which can be used to derive atmospheric precipitable water vapor profiles during both day and night. Data from the infrared algorithm are generated at a 5-km spatial resolution when at least nine field of views (FOVs) are cloud free. The infrared-derived precipitable water vapor is generated as a component of product MOD07, and is simply added to product MOD05 for convenience. There are two MODIS Precipitable Water Vapor products: MOD05_L2, containing data collected from the Terra platform; and MYD05_L2, containing data collected from the Aqua platform. This dataset has a short name of MOD05_L2 and provides data from the Terra platform only. The MODIS Adaptive Processing System (MODAPS) is currently generating an improved version 6.1 (061) for all MODIS Level-1 (L1) and higher-level Level-2 (L2) & Level-3 (L3) Atmosphere Team products. The decision to create a new improved Collection 6.1 (061) was driven by the need to address a number of issues in the current Collection 6 (006) Level-1B (L1B) data, which have a negative impact in varying degrees in downstream products. It should be noted that the near-infrared algorithm refinement for this product is no longer being supported by NASA and as such there has been no update to this algorithm for Collection 6.1. For more information, visit the MODIS Atmosphere website at: https://modis-atmos.gsfc.nasa.gov/products/water-vapor\n<br>\n\n#### MOD44B\nThe MOD44B Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD44B Version 6.1](https://doi.org/10.5067/MODIS/MOD44B.061) data product. The MOD44B Version 6 Vegetation Continuous Fields (VCF) yearly product is a global representation of surface vegetation cover as gradations of three ground cover components: percent tree cover, percent non-tree cover, and percent non-vegetated (bare). VCF products provide a continuous, quantitative portrayal of land surface cover at 250 meter (m) pixel resolution, with a sub-pixel depiction of percent cover in reference to the three ground cover components. The sub-pixel mixture of ground cover estimates represents a revolutionary approach to the characterization of vegetative land cover that can be used to enhance inputs to environmental modeling and monitoring applications. The MOD44B data product layers include percent tree cover, percent non-tree cover, percent non-vegetated, cloud cover, and quality indicators. The start date of the annual period for this product begins with day of year (DOY) 65 (March 6 except for leap year which corresponds to March 5). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD44B\nThe MOD44B Version 6.1 Vegetation Continuous Fields (VCF) yearly product is a global representation of surface vegetation cover as gradations of three ground cover components: percent tree cover, percent non-tree cover, and percent non-vegetated (bare). VCF products provide a continuous, quantitative portrayal of land surface cover at 250 meter (m) pixel resolution, with a sub-pixel depiction of percent cover in reference to the three ground cover components. The sub-pixel mixture of ground cover estimates represents a revolutionary approach to the characterization of vegetative land cover that can be used to enhance inputs to environmental modeling and monitoring applications. The MOD44B data product layers include percent tree cover, percent non-tree cover, percent non-vegetated, cloud cover, and quality indicators. The start date of the annual period for this product begins with day of year (DOY) 65 (March 6 except for leap year which corresponds to March 5). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD13C1\nThe MOD13C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13C1 Version 6.1](https://doi.org/10.5067/MODIS/MOD13C1.061) data product. The MOD13C1 Version 6 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The Climate Modeling Grid (CMG) consists 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MOD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer [MOD13A2](https://doi.org/10.5067/MODIS/MOD13A2.006) data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MOD13C1 has data fields for NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues: The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MOD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) MOD13 Vegetation Index data products particularly over arid bright surfaces. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6). Improvements/Changes from Previous Versions * The 16-day composite VI is generated using the two 8-day composite surface reflectance granules ([MOD09A1](https://doi.org/10.5067/MODIS/MOD09A1.006)) in the 16-day period. * This surface reflectance input is based on the minimum blue compositing approach used to generate the 8-day surface reflectance product. * The product format is consistent with the Version 5 product generated using the Level 2 gridded daily surface reflectance product. * A frequently updated long-term global CMG Average Vegetation Index product database is used to fill the gaps in the CMG product suite.\n<br>\n\n#### MOD13C1\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13C1) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The Climate Modeling Grid (CMG) consists 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MOD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer [MOD13A2](https://doi.org/10.5067/MODIS/MOD13A2.061) data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MOD13C1 has data fields for NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD13A2\nThe MOD13A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13A2 Version 6.1](https://doi.org/10.5067/MODIS/MOD13A2.061) data product. The MOD13A2 Version 6 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: The following issues have been detected: * Unexpected missing data in the last cycles of each year. * Incorrect instances of \"NoData\" and spikes in NDVI values. * VI Usefulness Bits are not correctly assigned. * For instances where the VI Quality (bits 0-1) is flagged as good and the VI Usefulness (bits 2-5) indicates the same pixels have the lowest usefulness score, users are advised to disregard the usefulness score. * The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MOD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) MOD13 Vegetation Index data products particularly over arid bright surfaces. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD13A2\nThe MOD13A2 Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD13Q1\nThe MOD13Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13Q1 Version 6.1](https://doi.org/10.5067/MODIS/MOD13Q1.061) data product. The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) Version 6 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: The following issues have been detected: * Unexpected missing data in the last cycles of each year. * Incorrect instances of \"NoData\" and spikes in NDVI values. * VI Usefulness Bits are not correctly assigned. * For instances where the VI Quality (bits 0-1) is flagged as good and the VI Usefulness (bits 2-5) indicates the same pixels have the lowest usefulness score, users are advised to disregard the usefulness score. * The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MOD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) MOD13 Vegetation Index data products particularly over arid bright surfaces. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD13Q1\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD13A1\nThe MOD13A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13A1 Version 6.1](https://doi.org/10.5067/MODIS/MOD13A1.061) data product. The MOD13A1 Version 6 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: The following issues have been detected: * Unexpected missing data in the last cycles of each year. * Incorrect instances of \"NoData\" and spikes in NDVI values. * VI Usefulness Bits are not correctly assigned. * For instances where the VI Quality (bits 0-1) is flagged as good and the VI Usefulness (bits 2-5) indicates the same pixels have the lowest usefulness score, users are advised to disregard the usefulness score. * The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MOD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) MOD13 Vegetation Index data products particularly over arid bright surfaces. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD13A1\nThe MOD13A1 Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD13C2\nThe MOD13C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13C2 Version 6.1](https://doi.org/10.5067/MODIS/MOD13C2.061) data product. The MOD13C2 Version 6 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the [MOD13A2](https://doi.org/10.5067/MODIS/MOD13A2.006) products that overlap the month and employs a weighted temporal average. Global MOD13C1 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MOD13C2 has data fields for the NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues: The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MOD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) MOD13 Vegetation Index data products particularly over arid bright surfaces. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6). Improvements/Changes from Previous Versions * The 16-day composite VI is generated using the two 8-day composite surface reflectance granules ([MOD09A1](https://doi.org/10.5067/MODIS/MOD09A1.006)) in the 16-day period. * This surface reflectance input is based on the minimum blue compositing approach used to generate the 8-day surface reflectance product. * The product format is consistent with the Version 5 product generated using the Level 2 gridded daily surface reflectance product. * A frequently updated long-term global CMG Average Vegetation Index product database is used to fill the gaps in the CMG product suite.\n<br>\n\n#### MOD13C2\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Monthly (MOD13C2) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the [MOD13A2](https://doi.org/10.5067/MODIS/MOD13A2.061) products that overlap the month and employs a weighted temporal average. Global MOD13C2 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MOD13C2 has data fields for the NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD13A3\nThe MOD13A3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13A3 Version 6.1](https://doi.org/10.5067/MODIS/MOD13A3.061) data product. The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13A3) Version 6 data are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the [MOD13A2](https://doi.org/10.5067/MODIS/MOD13A2.006) products that overlap the month and employs a weighted temporal average. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes an Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin clouds. The MODIS NDVI and EVI products are computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes as well as global and regional climate. Additional applications include characterizing land surface biophysical properties and processes, such as primary production and land cover conversion. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as three observation layers. Known Issues: The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MOD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) MOD13 Vegetation Index data products particularly over arid bright surfaces. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).\n<br>\n\n#### MOD13A3\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Monthly (MOD13A3) Version 6.1 data are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the [MOD13A2](https://doi.org/10.5067/MODIS/MOD13A2.061) products that overlap the month and employs a weighted temporal average. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes an Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin clouds. The MODIS NDVI and EVI products are computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes as well as global and regional climate. Additional applications include characterizing land surface biophysical properties and processes, such as primary production and land cover conversion. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as three observation layers. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD28C2\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir 8-Day Level 3 (L3) Global (MOD28C2) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MOD28C2 Version 6.1 data product provides an 8-day time series of surface area, elevation, and water storage. Datasets are combined with pre-established [Area-Elevation (A-E) curves](https://doi.org/10.1016/j.rse.2020.111831) and image classifications of near-infrared (NIR) reflectance from the surface reflectance product acquired by the Terra satellite ([MOD09Q1](https://lpdaac.usgs.gov/products/mod09q1v061/)). The MOD28C2 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir area, elevation, and storage capacity. Known Issues: Water occurrence images generally show smaller surface area dynamics in high latitude regions, creating pixels with low occurrence values that have relatively large uncertainties. In addition, the quality of raw water area classification can be affected by lake ice coverage typically creating an overestimation of surface area in the enhancement algorithm. This issue will be addressed in a future release of the enhancement algorithm. For additional information about known issues, refer to Section 4 in the User Guide and [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MOD28C3\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir Monthly Level 3 (L3) Global (MOD28C3) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MOD28C3 Version 6.1 data product is a composite of the 8-day area classifications from [MOD28C2](https://lpdaac.usgs.gov/products/mod28c2v061/), which is converted to provide monthly elevation and water storage. [Lake Temperature and Evaporation Model (LTEM)](https://doi.org/10.1016/j.rse.2020.112104) via MODIS Land Surface Temperature (LST) ([MOD21](https://lpdaac.usgs.gov/products/mod21v061/)) and meteorological data from [Global Land Data Assimilation System (GLDAS)](https://earth.gsfc.nasa.gov/hydro/data/gldas-global-land-data-assimilation-system-data) are used to produce monthly evaporation rates and volume losses. The MOD28C3 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), monthly reservoir area, elevation, storage capacity, evaporation rate, and evaporation volume. Known Issues: Water occurrence images generally show smaller surface area dynamics in high latitude regions, creating pixels with low occurrence values that have relatively large uncertainties. In addition, the quality of raw water area classification can be affected by lake ice coverage typically creating an overestimation of surface area in the enhancement algorithm. This issue will be addressed in a future release of the enhancement algorithm. For additional information about known issues, refer to Section 4 in the User Guide and [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n<br>\n\n#### MCD19A2CMG\nThe MCD19A2CMG Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) and Water Vapor Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A2CMG product provides the atmospheric properties and view geometry used to calculate the MAIAC Surface Reflectance data products ([MCD19A1CMGL](https://doi.org/10.5067/MODIS/MCD19A1CMGL.061) and [MCD19A1CMGO](https://doi.org/10.5067/MODIS/MCD19A1CMGO.061)). The MCD19A2CMG AOD data product contains the following Science Dataset (SDS) layers: blue band AOD at 0.47 µm, green band AOD at 0.55 µm, AOD uncertainty, column water vapor for Terra, column water vapor for Aqua, average cloud fraction, available AOD, satellite overpass times, line and sample number, offset, and number of AOD records. A low-resolution browse image is also included showing AOD of the blue band at 0.47 µm created using a composite of all available orbits. Known Issues: Known issues are described in Section 6 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD19A3\nThe MCD19A3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD19A3 Version 6.1](https://doi.org/10.5067/MODIS/MCD19A3.061) data product. The MCD19A3 Version 6 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle Implementation of Atmospheric Correction (MAIAC) Bidirectional Reflectance Distribution Function (BRDF) Model Parameters gridded Level 3 product which is output every 8 days at 1 kilometer resolution. The MCD19A3 product provides three coefficients (weights) of the RossThick/Li-Sparse (RTLS) BRDF model that can be used to describe the anisotropy of each pixel. The retrievals represent cloud-free and low aerosol conditions. The MCD19A3 BRDF Model Parameters product contains the following Science Dataset (SDS) layers: RTLS isotropic kernel parameter (Kiso) for bands 1-8, the RTLS volumetric kernel parameter (Kvol) for bands 1-8, RTLS geometric kernel parameter (Kgeo) for bands 1-8, surface albedo for bands 1-8, and the number of days since last update to current day. Three low-resolution browse images are also included showing the BRDF coefficients for the red band: (1) Kiso, (2) Kvol, and (3) Kgeo. Known Issues: Due to Terra MODIS calibration degradation, BRDF in MODIS Band 8 may contain artifacts (e.g., 10 km striping), particularly after 2015. * Known issues are described on page 14 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6).\n<br>\n\n#### MCD19A3D\nThe MCD19A3D Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Bidirectional Reflectance Distribution Function (BRDF) Model Parameters gridded Level 3 product. Output daily at 1 kilometer (km) resolution, the Multi-angle Implementation of Atmospheric Correction (MAIAC) MCD19A3D product provides three coefficients (weights) of the RossThick/Li-Sparse (RTLS) BRDF model that can be used to describe the anisotropy of each pixel. The retrievals represent cloud-free and low aerosol conditions. When snow is detected, gap-filled snow grain size and sub-pixel snow fraction are computed. The gap-filling process retains the parameter in MAIAC’s memory for each grid cell until updated with the latest cloud-free observation. The number of days since the last update is provided in a separate layer. Over snow-free land, MAIAC also reports gap-filled Normalized Difference Vegetation Index (NDVI) at 1 km resolution and gap-filled Nadir BRDF-Adjusted Reflectance (NBAR) at 250 m resolution in the red and near-infrared (NIR) bands. The MCD19A3 BRDF Model Parameters product contains the following Science Dataset (SDS) layers: RTLS isotropic kernel parameter (Kiso) for bands 1-8, the RTLS volumetric kernel parameter (Kvol) for bands 1-8, RTLS geometric kernel parameter (Kgeo) for bands 1-8, three snow parameters, NDVI, NBAR, and three separate layers for the number of days since last update to current day. Known Issues: Known issues are described in Section 6 of the User Guide. * An artifact was discovered in the NDVI1km and NBAR_250m layers over the southern polar region. The affected region shows false retrieval of data during the summer months. The artifacts occurred over permanently snow-covered areas where no meaningful NDVI retrieval is expected. A software fix was implemented for forward processing in June 2023. Existing data products will not be reprocessed. Affected dates are listed on the [Land Data Operational Products Evaluation Quality Assessment note](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=187). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43A3\nThe MCD43A3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43A3 Version 6.1](https://doi.org/10.5067/MODIS/MCD43A3.061) data product. The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 Version 6 Albedo Model dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the 16 day which is reflected in the Julian date in the file name. The MCD43A3 provides black-sky albedo (directional hemispherical reflectance) and white-sky albedo (bihemispherical reflectance) data at local solar noon for MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. Along with the albedo layers are the simplified mandatory quality layers for each of the 10 bands. Essential quality information provided in the corresponding [MCD43A2](https://doi.org/10.5067/MODIS/MCD43A2.006) data file should be consulted when using this product. Users are also urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43A3 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43A3\nThe Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 Version 6.1 Albedo Model dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the 16 day which is reflected in the Julian date in the file name. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43a3-albedo-product/). The MCD43A3 provides black-sky albedo (directional hemispherical reflectance) and white-sky albedo (bihemispherical reflectance) data at local solar noon for MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. Along with the albedo layers are the simplified mandatory quality layers for each of the 10 bands. Essential quality information provided in the corresponding [MCD43A2](https://doi.org/10.5067/MODIS/MCD43A2.061) data file should be consulted when using this product. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43C3\nThe MCD43C3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43C3 Version 6.1](https://doi.org/10.5067/MODIS/MCD43C3.061) data product. The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C3 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Albedo dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. MCD43C3 provides black-sky albedo (directional hemispherical reflectance) and white-sky albedo (bihemispherical reflectance) at local solar noon. Black-sky albedo and white-sky albedo values are available as a separate layer for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the 20 albedo layers are ancillary layers for quality, local solar noon, percent finer resolution inputs, snow cover, and uncertainty. Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43C3 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43C3\nThe Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C3 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Albedo dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43c3-cmg-albedo-product/). MCD43C3 provides black-sky albedo (directional hemispherical reflectance) and white-sky albedo (bihemispherical reflectance) at local solar noon. Black-sky albedo and white-sky albedo values are available as a separate layer for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the 20 albedo layers are ancillary layers for quality, local solar noon, percent finer resolution inputs, snow cover, and uncertainty. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D42\nThe MCD43D42 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D42 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D42.061) data product. The MCD43D42 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D42 is the black-sky albedo for MODIS band 1. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D42 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D42\nThe MCD43D42 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D42 is the black-sky albedo for MODIS band 1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D43\nThe MCD43D43 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D43 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D43.061) data product. The MCD43D43 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D43 is the black-sky albedo for MODIS band 2. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D43 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D43\nThe MCD43D43 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D43 is the black-sky albedo for MODIS band 2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D44\nThe MCD43D44 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D44 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D44.061) data product. The MCD43D44 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D44 is the black-sky albedo for MODIS band 3. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D44 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D44\nThe MCD43D44 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D44 is the black-sky albedo for MODIS band 3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D45\nThe MCD43D45 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D45 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D45.061) data product. The MCD43D45 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D45 is the black-sky albedo for MODIS band 4. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D45 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D45\nThe MCD43D45 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D45 is the black-sky albedo for MODIS band 4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D46\nThe MCD43D46 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D46 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D46.061) data product. The MCD43D46 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D46 is the black-sky albedo for MODIS band 5. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D46 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D46\nThe MCD43D46 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D46 is the black-sky albedo for MODIS band 5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D47\nThe MCD43D47 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D47 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D47.061) data product. The MCD43D47 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D47 is the black-sky albedo for MODIS band 6. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D47 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D47\nThe MCD43D47 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D47 is the black-sky albedo for MODIS band 6. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D48\nThe MCD43D48 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D48 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D48.061) data product. The MCD43D48 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D48 is the black-sky albedo for MODIS band 7. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D48 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D48\nThe MCD43D48 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D48 is the black-sky albedo for MODIS band 7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D50\nThe MCD43D50 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D50 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D50.061) data product. The MCD43D50 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D50 is the black-sky albedo for the MODIS NIR broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D50 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D50\nThe MCD43D50 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D50 is the black-sky albedo for the MODIS NIR broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D51\nThe MCD43D51 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D51 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D51.061) data product. The MCD43D51 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D51 is the black-sky albedo for the MODIS shortwave broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D51 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D51\nThe MCD43D51 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D51 is the black-sky albedo for the MODIS shortwave broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D49\nThe MCD43D49 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D49 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D49.061) data product. The MCD43D49 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D49 is the black-sky albedo for the MODIS visible broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D49 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D49\nThe MCD43D49 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D49 is the black-sky albedo for the MODIS visible broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43GF\nThe Daily Moderate Resolution Imaging Spectroradiometer (MODIS) (Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6.1 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6.1 product suite, with additional processing to provide a gap-filled, snow-free product. The highest quality full inversion values were used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the quality layer for each band. To generate a snow-free product, pixels with ephemeral snow were removed using the [MCD43D41](https://doi.org/10.5067/MODIS/MCD43D41.061) snow flags. The underlying MCD43D utilizes a BRDF model derived from all available high quality cloud clear reflectance data over a 16 day moving window centered on and emphasizing the daily day of interest (the ninth day of each retrieval period as reflected in the Julian date in the filename). This 30 arc second BRDF model is then used to produce the Albedo and NBAR products (MCD43D). These BRDF model parameters are computed for MODIS spectral bands 1 through 7 (0.47 um, 0.55 um, 0.67 um, 0.86 um, 1.24 um, 1.64 um, 2.1 um), as well as the shortwave infrared band (0.3-5.0 um), visible band (0.3-0.7 um), and near-infrared (0.7-5.0 um) broad bands. The MCD43GF product includes 67 layers containing black-sky albedo (BSA) at local solar noon, isotropic model parameter (ISO), volumetric model parameter (VOL), geometric model parameter (GEO), quality (QA), Nadir BRDF-Adjusted Reflectance (NBAR) at local solar noon, and white-sky albedo (WSA). Due to the large file size, each data layer is distributed as a separate HDF file. Users are encouraged to download the quality layers for each of the 10 bands to check quality assessment information before using the BRDF/Albedo data. The MCD43 product is not recommended for solar zenith angles beyond 70 degrees. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43gf-cmg-gap-filled-snow-free-products/). Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sat=TerraAqua&as=61) and the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43gf-cmg-gap-filled-snow-free-products/).\n<br>\n\n#### MCD43A1\nThe MCD43A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43A1 Version 6.1](https://doi.org/10.5067/MODIS/MCD43A1.061) data product. The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A1 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. MCD43A1 provides the three model weighting parameters (isotropic, volumetric, and geometric) used to derive the Albedo ([MCD43A3](https://doi.org/10.5067/MODIS/MCD43A3.006)) and Nadir BRDF-Adjusted Reflectance (NBAR) ([MCD43A4](https://doi.org/10.5067/MODIS/MCD43A4.006)) products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. The MCD43A1 provides the three model weighting parameters for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the three-dimensional parameter layers for these bands are the simplified mandatory quality layers for each of the 10 bands. Essential quality information provided in the corresponding [MCD43A2](https://doi.org/10.5067/MODIS/MCD43A2.006) data file should be consulted when using this product. Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43A1 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43A1\nThe Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A1 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. MCD43A1 provides the three model weighting parameters (isotropic, volumetric, and geometric) used to derive the Albedo ([MCD43A3](https://doi.org/10.5067/MODIS/MCD43A3.061)) and Nadir BRDF-Adjusted Reflectance (NBAR) ([MCD43A4](https://doi.org/10.5067/MODIS/MCD43A4.061)) products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43a1-brdfalbedo-model-parameters-product/). The MCD43A1 provides the three model weighting parameters for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the three-dimensional parameter layers for these bands are the simplified mandatory quality layers for each of the 10 bands. Essential quality information provided in the corresponding [MCD43A2](https://doi.org/10.5067/MODIS/MCD43A2.061) data file should be consulted when using this product. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43C1\nThe MCD43C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43C1 Version 6.1](https://doi.org/10.5067/MODIS/MCD43C1.061) data product. The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C1 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. MCD43C1 provides the three model weighting parameters (isotropic, volumetric, and geometric) used to derive the Albedo ([MCD43C3](https://doi.org/10.5067/MODIS/MCD43C3.006)) and BRDF ([MCD43C4](https://doi.org/10.5067/MODIS/MCD43C4.006)) products. Each model parameter is available as a separate layer for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the 30 parameter layers there are ancillary layers for quality, local solar noon, percent finer resolution inputs, snow cover, and uncertainty. Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43C1 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43C1\nThe Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C1 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43c1-cmg-brdfalbedo-model-parameters-product/). MCD43C1 provides the three model weighting parameters (isotropic, volumetric, and geometric) used to derive the Albedo ([MCD43C3](https://doi.org/10.5067/MODIS/MCD43C3.061)) and BRDF ([MCD43C4](https://doi.org/10.5067/MODIS/MCD43C4.061)) products. Each model parameter is available as a separate layer for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the 30 parameter layers there are ancillary layers for quality, local solar noon, percent finer resolution inputs, snow cover, and uncertainty. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D62\nThe MCD43D62 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D62 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D62.061) data product. The MCD43D62 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. MCD43D62 is the NBAR for MODIS band 1. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D62 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D62\nThe MCD43D62 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D62 is the NBAR for MODIS band 1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D63\nThe MCD43D63 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D63 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D63.061) data product. The MCD43D63 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. MCD43D63 is the NBAR for MODIS band 2. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D63 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D63\nThe MCD43D63 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D63 is the NBAR for MODIS band 2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D64\nThe MCD43D64 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D64 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D64.061) data product. The MCD43D64 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. MCD43D64 is the NBAR for MODIS band 3. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D64 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D64\nThe MCD43D64 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D64 is the NBAR for MODIS band 3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D65\nThe MCD43D65 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D65 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D65.061) data product. The MCD43D65 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. MCD43D65 is the NBAR for MODIS band 4. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D65 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D65\nThe MCD43D65 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D65 is the NBAR for MODIS band 4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D66\nThe MCD43D66 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D66 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D66.061) data product. The MCD43D66 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. MCD43D66 is the NBAR for MODIS band 5. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D66 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D66\nThe MCD43D66 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D66 is the NBAR for MODIS band 5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D67\nThe MCD43D67 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D67 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D67.061) data product. The MCD43D67 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. MCD43D67 is the NBAR for MODIS band 6. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D67 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D67\nThe MCD43D67 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D67 is the NBAR for MODIS band 6. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D68\nThe MCD43D68 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D68 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D68.061) data product. The MCD43D68 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. MCD43D68 is the NBAR for MODIS band 7. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D68 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D68\nThe MCD43D68 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D68 is the NBAR for MODIS band 7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43A4\nThe MCD43A4 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43A4 Version 6.1](https://doi.org/10.5067/MODIS/MCD43A4.061) data product. The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding [MCD43A2](https://doi.org/10.5067/MODIS/MCD43A2.006) data file should be consulted when using this product. Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43A4 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43A4\nThe Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43a4-nbar-product/). The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding [MCD43A2](https://doi.org/10.5067/MODIS/MCD43A2.061) data file should be consulted when using this product. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43C4\nThe MCD43C4 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43C4 Version 6.1](https://doi.org/10.5067/MODIS/MCD43C4.061) data product. The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C4 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. MCD43C4 removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. These NBAR values are calculated from [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006). The product includes separate NBAR layers for MODIS spectral bands 1 through 7 as well as ancillary layers for quality, local solar noon, percent finer resolution inputs, snow cover, and uncertainty. Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43C4 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43C4\nThe Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C4 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43c4-cmg-nbar-product/). MCD43C4 removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. These NBAR values are calculated from [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061). The product includes separate NBAR layers for MODIS spectral bands 1 through 7 as well as ancillary layers for quality, local solar noon, percent finer resolution inputs, snow cover, and uncertainty. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D01\nThe MCD43D01 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D01 is the BRDF isotropic parameter for MODIS band 1. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D04\nThe MCD43D04 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D04 is the BRDF isotropic parameter for MODIS band 2. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D02\nThe MCD43D02 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D02 is the BRDF volumetric parameter for MODIS band 1. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D05\nThe MCD43D05 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D05 is the BRDF volumetric parameter for MODIS band 2. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D03\nThe MCD43D03 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D03 is the BRDF geometric parameter for MODIS band 1. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D06\nThe MCD43D06 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D06 is the BRDF geometric parameter for MODIS band 2. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D01\nThe MCD43D01 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D01 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D01.061) data product. The MCD43D01 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D01 is the BRDF isotropic parameter for MODIS band 1. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 1. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D01 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D04\nThe MCD43D04 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D04 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D04.061) data product. The MCD43D04 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D04 is the BRDF isotropic parameter for MODIS band 2. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 2. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D04 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D07\nThe MCD43D07 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D07 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D07.061) data product. The MCD43D07 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D07 is the BRDF isotropic parameter for MODIS band 3. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 3. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D07 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D07\nThe MCD43D07 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D07 is the BRDF isotropic parameter for MODIS band 3. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D10\nThe MCD43D10 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D10 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D10.061) data product. The MCD43D10 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D10 is the BRDF isotropic parameter for MODIS band 4. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 4. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D10 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D10\nThe MCD43D10 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D10 is the BRDF isotropic parameter for MODIS band 4. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D13\nThe MCD43D13 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D13 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D13.061) data product. The MCD43D13 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D13 is the BRDF isotropic parameter for MODIS band 5. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 5. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D13 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D13\nThe MCD43D13 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D13 is the BRDF isotropic parameter for MODIS band 5. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D16\nThe MCD43D16 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D16 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D16.061) data product. The MCD43D16 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D16 is the BRDF isotropic parameter for MODIS band 6. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 6. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D16 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D16\nThe MCD43D16 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D16 is the BRDF isotropic parameter for MODIS band 6. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 6. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D19\nThe MCD43D19 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D19 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D19.061) data product. The MCD43D19 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D19 is the BRDF isotropic parameter for MODIS band 7. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 7. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D19 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D19\nThe MCD43D19 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D19 is the BRDF isotropic parameter for MODIS band 7. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D25\nThe MCD43D25 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D25 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D25.061) data product. The MCD43D25 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D25 is the BRDF isotropic parameter for the MODIS NIR broadband. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS NIR broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D25 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D25\nThe MCD43D25 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D25 is the BRDF isotropic parameter for the MODIS NIR broadband. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS NIR broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D28\nThe MCD43D28 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D28 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D28.061) data product. The MCD43D28 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D28 is the BRDF isotropic parameter for the MODIS shortwave broadband. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS shortwave broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D28 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D28\nThe MCD43D28 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D28 is the BRDF isotropic parameter for the MODIS shortwave broadband. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS shortwave broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D22\nThe MCD43D22 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D22 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D22.061) data product. The MCD43D22 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D22 is the BRDF isotropic parameter for the MODIS visible broadband. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS visible broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D22 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D22\nThe MCD43D22 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D22 is the BRDF isotropic parameter for the MODIS visible broadband. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS visible broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D02\nThe MCD43D02 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D02 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D02.061) data product. The MCD43D02 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D02 is the BRDF volumetric parameter for MODIS band 1. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 1. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D02 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D05\nThe MCD43D05 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D05 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D05.061) data product. The MCD43D05 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D05 is the BRDF volumetric parameter for MODIS band 2. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 2. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D05 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D08\nThe MCD43D08 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D08 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D08.061) data product. The MCD43D08 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D08 is the BRDF volumetric parameter for MODIS band 3. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 3. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D08 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D08\nThe MCD43D08 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D08 is the BRDF volumetric parameter for MODIS band 3. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D11\nThe MCD43D11 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D11 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D11.061) data product. The MCD43D11 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D11 is the BRDF volumetric parameter for MODIS band 4. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 4. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D11 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D11\nThe MCD43D11 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D11 is the BRDF volumetric parameter for MODIS band 4. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D14\nThe MCD43D14 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D14 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D14.061) data product. The MCD43D14 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D14 is the BRDF volumetric parameter for MODIS band 5. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 5. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D14 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D14\nThe MCD43D14 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D14 is the BRDF volumetric parameter for MODIS band 5. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D17\nThe MCD43D17 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D17 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D17.061) data product. The MCD43D17 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectrometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D17 is the BRDF volumetric parameter for MODIS band 6. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 6. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D17 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D17\nThe MCD43D17 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectrometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D17 is the BRDF volumetric parameter for MODIS band 6. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 6. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D20\nThe MCD43D20 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D20 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D20.061) data product. The MCD43D20 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D20 is the BRDF volumetric parameter for MODIS band 7. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 7. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D20 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D20\nThe MCD43D20 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D20 is the BRDF volumetric parameter for MODIS band 7. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for MODIS band 7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D26\nThe MCD43D26 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D26 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D26.061) data product. The MCD43D26 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D26 is the BRDF volumetric parameter for the MODIS NIR broadband. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS NIR broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D26 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D26\nThe MCD43D26 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D26 is the BRDF volumetric parameter for the MODIS NIR broadband. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS NIR broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D29\nThe MCD43D29 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D29 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D29.061) data product. The MCD43D29 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D29 is the BRDF volumetric parameter for the MODIS shortwave broadband. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS shortwave broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D29 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D29\nThe MCD43D29 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D29 is the BRDF volumetric parameter for the MODIS shortwave broadband. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS shortwave broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D23\nThe MCD43D23 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D23 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D23.061) data product. The MCD43D23 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D23 is the BRDF volumetric parameter for the MODIS visible broadband. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS visible broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D23 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D23\nThe MCD43D23 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D23 is the BRDF volumetric parameter for the MODIS visible broadband. The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS visible broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D03\nThe MCD43D03 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D03 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D03.061) data product. The MCD43D03 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D03 is the BRDF geometric parameter for MODIS band 1. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 1. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D03 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D06\nThe MCD43D06 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D06 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D06.061) data product. The MCD43D06 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D06 is the BRDF geometric parameter for MODIS band 2. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 2. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D06 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D09\nThe MCD43D09 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D09 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D09.061) data product. The MCD43D09 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D09 is the BRDF geometric parameter for MODIS band 3. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 3. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D09 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D09\nThe MCD43D09 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D09 is the BRDF geometric parameter for MODIS band 3. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D12\nThe MCD43D12 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D12 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D12.061) data product. The MCD43D12 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D12 is the BRDF geometric parameter for MODIS band 4. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 4. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D12 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D12\nThe MCD43D12 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D12 is the BRDF geometric parameter for MODIS band 4. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D15\nThe MCD43D15 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D15 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D15.061) data product. The MCD43D15 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Sprectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D15 is the BRDF geometric parameter for MODIS band 5. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 5. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D15 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D15\nThe MCD43D15 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Sprectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D15 is the BRDF geometric parameter for MODIS band 5. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D18\nThe MCD43D18 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D18 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D18.061) data product. The MCD43D18 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D18 is the BRDF geometric parameter for MODIS band 6. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 6. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D18 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D18\nThe MCD43D18 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D18 is the BRDF geometric parameter for MODIS band 6. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 6. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D21\nThe MCD43D21 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D21 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D21.061) data product. The MCD43D21 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D21 is the BRDF geometric parameter for MODIS band 7. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 7. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D21 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D21\nThe MCD43D21 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D21 is the BRDF geometric parameter for MODIS band 7. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for MODIS band 7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D27\nThe MCD43D27 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D27 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D27.061) data product. The MCD43D27 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D27 is the BRDF geometric parameter for the MODIS NIR broadband. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the MODIS NIR broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D27 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D27\nThe MCD43D27 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D27 is the BRDF geometric parameter for the MODIS NIR broadband. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the MODIS NIR broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D30\nThe MCD43D30 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D30 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D30.061) data product. The MCD43D30 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Sprectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D30 is the BRDF geometric parameter for the MODIS shortwave broadband. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the MODIS shortwave broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D30 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D30\nThe MCD43D30 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Sprectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D30 is the BRDF geometric parameter for the MODIS shortwave broadband. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the MODIS shortwave broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D24\nThe MCD43D24 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D24 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D24.061) data product. The MCD43D24 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.006) are stored in a separate file as MCD43D01 through MCD43D30. MCD43D24 is the BRDF geometric parameter for the MODIS visible broadband. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the MODIS visible broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D24 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D24\nThe MCD43D24 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands included in [MCD43C1](https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D24 is the BRDF geometric parameter for the MODIS visible broadband. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the MODIS visible broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D31\nThe MCD43D31 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D31 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D31.061) data product. The MCD43D31 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA BRDF Quality dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. MCD43D31 provides BRDF/Albedo quality information for the MCD43D products. MCD43D31 consists of a BRDF quality layer representing the overall quality of each pixel along with an individual BRDF/Albedo quality layer for MODIS spectral bands 1 through 7. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D31 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D31\nThe MCD43D31 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA BRDF Quality dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. MCD43D31 provides BRDF/Albedo quality information for the MCD43D products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D31 consists of a BRDF quality layer representing the overall quality of each pixel along with an individual BRDF/Albedo quality layer for MODIS spectral bands 1 through 7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D32\nThe MCD43D32 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D32 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D32.061) data product. The MCD43D32 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA Local Solar Noon dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D32 provides local solar noon information for the MCD43D products. The MCD43D32 layer contains the local solar zenith angle at the local solar noon of the representative pixel for the retrieval period. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D32 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D32\nThe MCD43D32 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA Local Solar Noon dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D32 provides local solar noon information for the MCD43D products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). The MCD43D32 layer contains the local solar zenith angle at the local solar noon of the representative pixel for the retrieval period. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D40\nThe MCD43D40 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D40 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D40.061) data product. The MCD43D40 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA Snow Status dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D project contains just one data layer. MCD43D40 provides snow cover information for the MCD43D products. MCD43D40 contains the snow status quality layer which identifies each pixel as either \"Snow-free Albedo Retrieved\" or \"Snow Albedo Retrieved\" for the acquisition period. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D40 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D40\nThe MCD43D40 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA Snow Status dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D project contains just one data layer. MCD43D40 provides snow cover information for the MCD43D products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D40 contains the snow status quality layer which identifies each pixel as either “Snow-free Albedo Retrieved” or “Snow Albedo Retrieved” for the acquisition period. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D41\nThe MCD43D41 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D41 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D41.061) data product. The MCD43D41 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA Uncertainty dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D41 provides BRDF inversion information for the MCD43D products. MCD43D41 layer contains the uncertainty range of each BRDF/Albedo pixel for the retrieval period. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D41 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D41\nThe MCD43D41 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA Uncertainty dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D41 provides BRDF inversion information for the MCD43D products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D41 layer contains the uncertainty range of each BRDF/Albedo pixel for the retrieval period. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D33\nThe MCD43D33 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D33 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D33.061) data product. The MCD43D33 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 1 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D33 provides MODIS band 1 valid observation quality information for the MCD43D products. MCD43D33 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 1. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D33 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D33\nThe MCD43D33 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 1 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D33 provides MODIS band 1 valid observation quality information for the MCD43D products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D33 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D34\nThe MCD43D34 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D34 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D34.061) data product. The MCD43D34 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 2 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D34 provides MODIS band 2 valid observation quality information for the MCD43D products. MCD43D34 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 2. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D34 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D34\nThe MCD43D34 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 2 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D34 provides MODIS band 2 valid observation quality information for the MCD43D products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D34 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D35\nThe MCD43D35 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D35 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D35.061) data product. The MCD43D35 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 3 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D35 provides MODIS band 3 valid observation quality information for the MCD43D products. MCD43D35 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 3. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D35 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D35\nThe MCD43D35 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 3 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D35 provides MODIS band 3 valid observation quality information for the MCD43D products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D35 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D36\nThe MCD43D36 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D36 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D36.061) data product. The MCD43D36 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 4 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D36 provides MODIS band 4 valid observation quality information for the MCD43D products. MCD43D36 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 4. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D36 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D36\nThe MCD43D36 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 4 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D36 provides MODIS band 4 valid observation quality information for the MCD43D products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D36 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D37\nThe MCD43D37 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D37 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D37.061) data product. The MCD43D37 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 5 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D37 provides MODIS band 5 valid observation quality information for the MCD43D products. MCD43D37 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 5. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D37 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D37\nThe MCD43D37 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 5 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D37 provides MODIS band 5 valid observation quality information for the MCD43D products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D37 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D38\nThe MCD43D38 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D38 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D38.061) data product. The MCD43D38 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 6 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D38 provides MODIS band 6 valid observation quality information for the MCD43D products. MCD43D38 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 6. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D38 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D38\nThe MCD43D38 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 6 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D38 provides MODIS band 6 valid observation quality information for the MCD43D products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D38 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 6. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D39\nThe MCD43D39 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D39 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D39.061) data product. The MCD43D39 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 7 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, MCD43D product contains just one data layer. MCD43D39 provides MODIS band 7 valid observation quality information for the MCD43D products. MCD43D39 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 7. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D39 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D39\nThe MCD43D39 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 7 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, MCD43D product contains just one data layer. MCD43D39 provides MODIS band 7 valid observation quality information for the MCD43D products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D39 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43A2\nThe MCD43A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43A2 Version 6.1](https://doi.org/10.5067/MODIS/MCD43A2.061) data product. The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A2 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Quality dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the 16-day retrieval period which is reflected in the Julian date in the file name. MCD43A2 contains the quality information for the corresponding [MCD43A3](https://doi.org/10.5067/MODIS/MCD43A3.006) Albedo and [MCD43A4](https://doi.org/10.5067/MODIS/MCD43A4.006) Nadir BRDF-Adjusted Reflectance (NBAR) products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. The MCD43A2 contains BRDF/Albedo band quality (inversion information) and days of valid observation within the 16-day period for MODIS bands 1 through 7 along with land water type, snow BRDF albedo, local solar noon, platform, and BRDF/Albedo uncertainty. Known Issues: The metadata information within the HDF file of the Version 6 MCD43A2 gives incorrect SDS layer parameters for BRDF_Albedo_ValidObs_Band1 through 7. The metadata states that the Valid Range is 0 to 32767 with a scaling factor of 0.001, which is incorrect. The Data Layers Characteristics section of this product page is accurate, the correct Valid Range is 0 to 65535 with no scaling factor. * The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43A2 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43A2\nThe Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A2 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Quality dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the 16-day retrieval period which is reflected in the Julian date in the file name. MCD43A2 contains the quality information for the corresponding [MCD43A3](https://doi.org/10.5067/MODIS/MCD43A3.061) Albedo and [MCD43A4](https://doi.org/10.5067/MODIS/MCD43A4.061) Nadir BRDF-Adjusted Reflectance (NBAR) products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43a2-brdfalbedo-quality-product/). The MCD43A2 contains BRDF/Albedo band quality (inversion information) and days of valid observation within the 16-day period for MODIS bands 1 through 7 along with land water type, snow BRDF albedo, local solar noon, platform, and BRDF/Albedo uncertainty. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43C2\nThe MCD43C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43C2 Version 6.1](https://doi.org/10.5067/MODIS/MCD43C2.061) data product. The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C2 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Snow-free Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. MCD43C2 provides the three model weighting parameters (isotropic, volumetric, and geometric) computed from snow-free retrievals. Each model parameter is available as a separate layer for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the 30 parameter layers there are ancillary layers for quality, local solar noon, percent finer resolution inputs, and uncertainty. Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43C2 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43C2\nThe Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C2 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Snow-free Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43c2-cmg-brdfalbedo-model-snow-free-parameters-product/). MCD43C2 provides the three model weighting parameters (isotropic, volumetric, and geometric) computed from snow-free retrievals. Each model parameter is available as a separate layer for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the 30 parameter layers there are ancillary layers for quality, local solar noon, percent finer resolution inputs, and uncertainty. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D52\nThe MCD43D52 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D52 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D52.061) data product. The MCD43D52 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D52 is the white-sky albedo for MODIS band 1. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D52 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D52\nThe MCD43D52 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D52 is the white-sky albedo for MODIS band 1. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D53\nThe MCD43D53 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D53 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D53.061) data product. The MCD43D53 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D53 is the white-sky albedo for MODIS band 2. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about MCD43D53 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D53\nThe MCD43D53 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D53 is the white-sky albedo for MODIS band 2. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D54\nThe MCD43D54 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D54 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D54.061) data product. The MCD43D54 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D54 is the white-sky albedo for MODIS band 3. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D54 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D54\nThe MCD43D54 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D54 is the white-sky albedo for MODIS band 3. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D55\nThe MCD43D55 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D55 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D55.061) data product. The MCD43D55 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D55 is the white-sky albedo for MODIS band 4. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D55 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D55\nThe MCD43D55 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D55 is the white-sky albedo for MODIS band 4. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D56\nThe MCD43D56 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D56 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D56.061) data product. The MCD43D56 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D56 is the white-sky albedo for MODIS band 5. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D56 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D56\nThe MCD43D56 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D56 is the white-sky albedo for MODIS band 5. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D57\nThe MCD43D57 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D57 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D57.061) data product. The MCD43D57 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter(m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D57 is the white-sky albedo for MODIS band 6. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D57 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D57\nThe MCD43D57 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter(m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D57 is the white-sky albedo for MODIS band 6. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D58\nThe MCD43D58 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D58 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D58.061) data product. The MCD43D58 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D58 is the white-sky albedo for MODIS band 7. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D58 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D58\nThe MCD43D58 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D58 is the white-sky albedo for MODIS band 7. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D60\nThe MCD43D60 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D60 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D60.061) data product. The MCD43D60 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D60 is the white-sky albedo for the MODIS NIR broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D60 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D60\nThe MCD43D60 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D60 is the white-sky albedo for the MODIS NIR broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D61\nThe MCD43D61 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D61 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D61.061) data product. The MCD43D61 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D61 is the white-sky albedo for the MODIS shortwave broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D61 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D61\nThe MCD43D61 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D61 is the white-sky albedo for the MODIS shortwave broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD43D59\nThe MCD43D59 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D59 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D59.061) data product. The MCD43D59 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. MCD43D59 is the white-sky albedo for the MODIS visible broadband. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues: The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. * [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing. * For complete information about the MCD43D59 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).\n<br>\n\n#### MCD43D59\nThe MCD43D59 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near-infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). MCD43D59 is the white-sky albedo for the MODIS visible broadband. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD64A1\nThe MCD64A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD64A1 Version 6.1](https://doi.org/10.5067/MODIS/MCD64A1.061) data product. The Terra and Aqua combined MCD64A1 Version 6 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year. Known Issues: Known issues are described on the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6) and in Section 8 of the User Guide which covers Cropland Burning, the August 2000 and June 2001 Data Outages, and the August 2020 Aqua Outage.\n<br>\n\n#### MCD64A1\nThe Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD18C1\nThe MCD18C1 Version 6.1 dataset was decommissioned on June 1, 2024. Users are encouraged to use the [MCD18C1 Version 6.2 data product](https://doi.org/10.5067/MODIS/MCD18C1.062). The MCD18C1 Version 6.1 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Downward Shortwave Radiation (DSR) Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG) with estimates of DSR every 3 hours. DSR is incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The MCD18 products are based on a prototyping algorithm that uses multi-temporal signatures of MODIS data to derive surface reflectance and then calculate incident DSR using the look-up table (LUT) approach. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Global DSR products are generated from MODIS and geostationary satellite data. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD). Provided in the MOD18C1 product are layers for instantaneous DSR array for each individual MODIS overpass and 3-hour DSR array along with a View Zenith Angle layer. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD18C1\nThe MCD18C1 Version 6.2 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Downward Shortwave Radiation (DSR) Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG) with estimates of DSR every 3 hours. DSR is incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The MCD18 products are based on a prototyping algorithm that uses multi-temporal signatures of MODIS data to derive surface reflectance and then calculate incident DSR using the look-up table (LUT) approach. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Global DSR products are generated from MODIS and geostationary satellite data. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD). Provided in the MOD18C1 product are layers for instantaneous DSR array for each individual MODIS overpass and 3-hour DSR array along with a View Zenith Angle layer. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=62).\n<br>\n\n#### MCD19A2\nThe MCD19A2 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 1 kilometer (km) pixel resolution. The MCD19A2 product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1 product. The MCD19A2 AOD data product contains the following Science Dataset (SDS) layers: blue band AOD at 0.47 µm, green band AOD at 0.55 µm, AOD uncertainty, fine mode fraction over water, column water vapor over land and clouds (in cm), smoke injection height (m above ground), AOD QA, AOD model at 1km, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, and glint angle at 5km. A low-resolution browse image is also included showing AOD of the blue band at 0.47 µm created using a composite of all available orbits. Each SDS layer within each MCD19A2 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer. Known Issues: Known issues are described in Section 6 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61). * Users should be aware that they may see a dip in AOD values for the first 5 months of 2022. A fix is planned to be implemented in Collection 7.\n<br>\n\n#### MCD19A2\nThe MCD19A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD19A2 Version 6.1](https://doi.org/10.5067/MODIS/MCD19A2.061) data product. The MCD19A2 Version 6 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 1 kilometer (km) pixel resolution. The MCD19A2 product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1 product. The MCD19A2 AOD data product contains the following Science Dataset (SDS) layers: blue band AOD at 0.47 µm, green band AOD at 0.55 µm, AOD uncertainty, fine mode fraction over water, column water vapor over land and clouds (in cm), smoke injection height (m above ground), AOD QA, AOD model at 1km, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, and glint angle at 5km. A low-resolution browse image is also included showing AOD of the blue band at 0.47 µm created using a composite of all available orbits. Each SDS layer within each MCD19A2 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer. Known Issues: The longname in the internal metadata is provided incorrectly. The correct longname is \"MODIS/Terra and Aqua MAIAC Land Aerosol Optical Depth Daily L2G 1 km SIN Grid.\" * Additional known issues are described on page 14 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6).\n<br>\n\n#### MCD12Q2\nThe MCD12Q2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD12Q2 Version 6.1](https://doi.org/10.5067/MODIS/MCD12Q2.061) data product. The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics (MCD12Q2) Version 6 data product provides global land surface phenology metrics at yearly intervals from 2001 to 2019. The MCD12Q2 Version 6 data product is derived from time series of the 2-band Enhanced Vegetation Index (EVI2) calculated from MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year. For pixels with more than two valid vegetation cycles, the data represent the two cycles with the largest NBAR-EVI2 amplitudes. Provided in each MCD12Q2 Version 6 Hierarchical Data Format 4 (HDF4) file are layers for the total number of vegetation cycles detected for the product year, the onset of greenness, greenup midpoint, maturity, peak greenness, senescence, greendown midpoint, dormancy, EVI2 minimum, EVI2 amplitude, integrated EVI2 over a vegetation cycle, as well as overall and phenology metric-specific quality information. A low-resolution browse image showing greenup is also available when viewing each MCD12Q2 granule. SDS layers may be multi-dimensional with up to two valid vegetation cycles. For areas where the NBAR-EVI2 values are missing due to cloud cover or other reasons, the data gaps are filled with good quality NBAR-EVI2 values from the year directly preceding or following the product year. Known Issues: Known issues are described on pages 5 and 6 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6).\n<br>\n\n#### MCD12Q2\nThe Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics (MCD12Q2) Version 6.1 data product provides global land surface phenology metrics at yearly intervals. The MCD12Q2 Version 6.1 data product is derived from time series of the 2-band Enhanced Vegetation Index (EVI2) calculated from MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year. For pixels with more than two valid vegetation cycles, the data represent the two cycles with the largest NBAR-EVI2 amplitudes. Provided in each MCD12Q2 Version 6.1 Hierarchical Data Format 4 (HDF4) file are layers for the total number of vegetation cycles detected for the product year, the onset of greenness, greenup midpoint, maturity, peak greenness, senescence, greendown midpoint, dormancy, EVI2 minimum, EVI2 amplitude, integrated EVI2 over a vegetation cycle, as well as overall and phenology metric-specific quality information. SDS layers may be multi-dimensional with up to two valid vegetation cycles. For areas where the NBAR-EVI2 values are missing due to cloud cover or other reasons, the data gaps are filled with good quality NBAR-EVI2 values from the year directly preceding or following the product year. Known Issues: Known issues are described in Section 3.2 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD12C1\nThe MCD12C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD12C1 Version 6.1](https://doi.org/10.5067/MODIS/MCD12C1.061) data product. The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) (MCD12C1) Version 6 data product provides a spatially aggregated and reprojected version of the tiled [MCD12Q1 Version 6](https://doi.org/10.5067/MODIS/MCD12Q1.006) data product. Maps of the International Geosphere-Biosphere Programme (IGBP), University of Maryland (UMD), and Leaf Area Index (LAI) classification schemes are provided at yearly intervals at 0.05 degree (5,600 meter) spatial resolution for the entire globe from 2001 to 2020. Additionally, sub-pixel proportions of each land cover class in each 0.05 degree pixel is provided along with the aggregated quality assessment information for each of the three land classification schemes. Provided in each MCD12C1 Version 6 Hierarchical Data Format 4 (HDF4) file are layers for Majority Land Cover Type 1-3, Majority Land Cover Type 1-3 Assessment, and Majority Land Cover Type 1-3 Percent. Known Issues: Known issues are described on pages 3 and 4 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6).\n<br>\n\n#### MCD12C1\nThe Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) (MCD12C1) Version 6.1 data product provides a spatially aggregated and reprojected version of the tiled [MCD12Q1 Version 6.1](https://doi.org/10.5067/MODIS/MCD12Q1.061) data product. Maps of the International Geosphere-Biosphere Programme (IGBP), University of Maryland (UMD), and Leaf Area Index (LAI) classification schemes are provided at yearly intervals at 0.05 degree (5,600 meter) spatial resolution for the entire globe. Additionally, sub-pixel proportions of each land cover class in each 0.05 degree pixel is provided along with the aggregated quality assessment information for each of the three land classification schemes. Provided in each MCD12C1 Version 6.1 Hierarchical Data Format 4 (HDF4) file are layers for Majority Land Cover Type 1-3, Majority Land Cover Type 1-3 Assessment, and Majority Land Cover Type 1-3 Percent. Known Issues: Known issues are described in Section 2.2 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=61).\n<br>\n\n#### MCD12Q1\nThe MCD12Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD12Q1 Version 6.1](https://doi.org/10.5067/MODIS/MCD12Q1.061) data product. The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) Version 6 data product provides global land cover types at yearly intervals (2001-2020), derived from six different classification schemes listed in the User Guide. The MCD12Q1 Version 6 data product is derived using supervised classifications of MODIS Terra and Aqua reflectance data. The supervised classifications then undergo additional post-processing that incorporate prior knowledge and ancillary information to further refine specific classes. Layers for Land Cover Type 1-5, Land Cover Property 1-3, Land Cover Property Assessment 1-3, Land Cover Quality Control (QC), and a Land Water Mask are provided in each MCD12Q1 Version 6 Hierarchical Data Format 4 (HDF4) file. Known Issues: The \"units\" field is missing in the metadata, however, this information can be found in the table above or on page 5 of the User Guide. * Known issues are described on pages 3 and 4 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6).\n<br>\n\n#### MCD12Q1\nThe Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) Version 6.1 data product provides global land cover types at yearly intervals. The MCD12Q1 Version 6.1 data product is derived using supervised classifications of MODIS Terra and Aqua reflectance data. Land cover types are derived from the International Geosphere-Biosphere Programme (IGBP), University of Maryland (UMD), Leaf Area Index (LAI), BIOME-Biogeochemical Cycles (BGC), and Plant Functional Types (PFT) classification schemes. The supervised classifications then undergo additional post-processing that incorporate prior knowledge and ancillary information to further refine specific classes. Additional land cover property assessment layers are provided by the Food and Agriculture Organization (FAO) Land Cover Classification System (LCCS) for land cover, land use, and surface hydrology. Layers for Land Cover Type 1-5, Land Cover Property 1-3, Land Cover Property Assessment 1-3, Land Cover Quality Control (QC), and a Land Water Mask are provided in each MCD12Q1 Version 6.1 Hierarchical Data Format 4 (HDF4) file. Known Issues: The \"units\" field is missing in the metadata, however, this information can be found in Table 1 of the User Guide. * The MCD12Q1.061 land cover data product is derived using supervised classification of MODIS Terra and Aqua reflectance data. The classification algorithm uses labeled training samples selected globally to represent land cover categories derived under the IGBP, UMD, LAI, BIOME-BGC, and PFT classification schemes. These training samples were selected to represent land classes, best suited over a certain period. However, due to lack of funding, the science team was not able to keep the training database updated over the course of the years, and some of these training sites could well have undergone changes in their land cover characteristics, especially after 2021. Hence users are urged to maintain caution while using the V6.1/MCD12Q1 land cover layers for 2021 and beyond. More information on this known issue can be found in the [Land Data Products Operational Products (LDOPE) Quality Assessment](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=781). * Known issues are described in Section 2.2 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD19A1\nThe MCD19A1 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 500 meter (m) and 1 kilometer (km) pixel resolutions. The MCD19A1 product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC Land Surface BRF products provide an estimate of the surface spectral reflectance as it would be measured at ground-level in the absence of atmospheric scattering or absorption. The MCD19A1 MAIAC Surface Reflectance data product includes 31 Science Dataset (SDS) layers: surface reflectance for bands 1-12, BRF uncertainty for bands 1-2, Quality Assessment (QA) bits at 1 km, surface reflectance for bands 1-7 at 500 m, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, solar azimuth angle, view azimuth angle, glint angle, RossThick/Li-Sparse (RTLS) volumetric kernel, and RTLS geometric kernel at 5 km. A low-resolution browse image is also included showing surface reflectance band combination 1, 4, 3 created using a composite of all available orbits. Each SDS layer within each MCD19A1 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer. Known Issues: Known issues are described in Section 6 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD19A1\nThe MCD19A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD19A1 Version 6.1](https://doi.org/10.5067/MODIS/MCD19A1.061) data product. The MCD19A1 Version 6 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 500 meter (m) and 1 kilometer (km) pixel resolutions. The MCD19A1 product is corrected for atmospheric gases and aerosols using a new MAIAC algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC Land Surface BRF products provide an estimate of the surface spectral reflectance as it would be measured at ground-level in the absence of atmospheric scattering or absorption. The MCD19A1 MAIAC Surface Reflectance data product includes 34 Science Dataset (SDS) layers: surface reflectance for bands 1-12, BRF uncertainty for bands 1-2, snow fraction, snow grain size, snow fit, Quality Assessment (QA) bits at 1 km, surface reflectance for bands 1-7 at 500 m, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, solar azimuth angle, view azimuth angle, glint angle, RossThick/Li-Sparse (RTLS) volumetric kernel, and RTLS geometric kernel at 5 km. A low-resolution browse image is also included showing surface reflectance band combination 1, 4, 3 created using a composite of all available orbits. Each SDS layer within each MCD19A1 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer. Known Issues: The longname in the internal metadata is provided incorrectly. The correct longname is \"MODIS/Terra and Aqua MAIAC Land Surface BRF Daily L2G Global 500 m and 1 km SIN Grid.\" * Known issues are described on page 14 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6).\n<br>\n\n#### MCD15A3H\nThe MCD15A3H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD15A3H Version 6.1](https://doi.org/10.5067/MODIS/MCD15A3H.061) data product. The MCD15A3H Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA's Terra and Aqua satellites from within the 4-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6).\n<br>\n\n#### MCD15A3H\nThe MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4 combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA’s Terra and Aqua satellites from within the 4-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD15A2H\nThe MCD15A2H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD15A2H Version 6.1](https://doi.org/10.5067/MODIS/MCD15A2H.061) data product. The MCD15A2H Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA's Terra and Aqua satellites from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6).\n<br>\n\n#### MCD15A2H\nThe MCD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4 combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA’s Terra and Aqua satellites from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD18C2\nThe MCD18C2 Version 6.1 dataset was decommissioned on June 1, 2024. Users are encouraged to use the [MCD18C2 Version 6.2 data product](https://doi.org/10.5067/MODIS/MCD18C2.062). The MCD18C2 Version 6.1 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Photosynthetically Active Radiation (PAR) gridded Level 3 product produced daily at 0.05 degree (5,600 meters at the equator) resolution with estimates of PAR every 3 hours. PAR is incident solar radiation in the visible spectrum (400-700 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The MCD18 products are based on a prototyping algorithm that uses multi-temporal signatures of MODIS data to derive surface reflectance and then calculate incident PAR using the look-up table (LUT) approach. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Global PAR products are generated from MODIS and geostationary satellite data. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD). Provided in the MOD18C2 product are layers for instantaneous PAR array for each individual MODIS overpass and 3-hour PAR array along with a View Zenith Angle layer. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD18C2\nThe MCD18C2 Version 6.2 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Photosynthetically Active Radiation (PAR) gridded Level 3 product produced daily at 0.05 degree (5,600 meters at the equator) resolution with estimates of PAR every 3 hours. PAR is incident solar radiation in the visible spectrum (400-700 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The MCD18 products are based on a prototyping algorithm that uses multi-temporal signatures of MODIS data to derive surface reflectance and then calculate incident PAR using the look-up table (LUT) approach. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Global PAR products are generated from MODIS and geostationary satellite data. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD). Provided in the MOD18C2 product are layers for instantaneous PAR array for each individual MODIS overpass and 3-hour PAR array along with a View Zenith Angle layer. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=62).\n<br>\n\n#### MCD18A2\nThe MCD18A2 Version 6.1 dataset was decommissioned on June 1, 2024. Users are encouraged to use the [MCD18A2 Version 6.2 data product](https://doi.org/10.5067/MODIS/MCD18A2.062). The MCD18A2 Version 6.1 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Photosynthetically Active Radiation (PAR) gridded Level 3 product produced daily at 1 kilometer pixel resolution with estimates of PAR every 3 hours. PAR is incident solar radiation in the visible spectrum (400-700 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The MCD18 products are based on a prototyping algorithm that uses multi-temporal signatures of MODIS data to derive surface reflectance and then calculate incident PAR using the look-up table (LUT) approach. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Global PAR products are generated from MODIS and geostationary satellite data. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD). Provided in the MOD18A2 product are layers for instantaneous PAR array for each individual MODIS overpass and 3-hour PAR array along with a View Zenith Angle layer. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD18A2\nThe MCD18A2 Version 6.2 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Photosynthetically Active Radiation (PAR) gridded Level 3 product produced daily at 1 kilometer pixel resolution with estimates of PAR every 3 hours. PAR is incident solar radiation in the visible spectrum (400-700 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The MCD18 products are based on a prototyping algorithm that uses multi-temporal signatures of MODIS data to derive surface reflectance and then calculate incident PAR using the look-up table (LUT) approach. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Global PAR products are generated from MODIS and geostationary satellite data. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD). Provided in the MOD18A2 product are layers for instantaneous PAR array for each individual MODIS overpass and 3-hour PAR array along with a View Zenith Angle layer. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=62).\n<br>\n\n#### MCD18A2\nMCD18A2 Version 6 was decommissioned on October 28, 2022. Users are encouraged to use the [MCD18A2 Version 6.2 data product](https://doi.org/10.5067/MODIS/MCD18A2.062). The MCD18A2 Version 6 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Photosynthetically Active Radiation (PAR) gridded Level 3 product produced daily at 5 kilometer pixel resolution with estimates of PAR every 3 hours. PAR is incident solar radiation in the visible spectrum (400-700 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The MCD18 products are based on a prototyping algorithm that uses multi-temporal signatures of MODIS data to derive surface reflectance and then calculate incident PAR using the look-up table (LUT) approach. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Global PAR products are generated from MODIS and geostationary satellite data. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD). Provided in the MOD18A2 product are layers for instantaneous PAR array for each individual MODIS overpass and 3-hour PAR array along with a View Zenith Angle layer. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6).\n<br>\n\n#### MCD18A1\nThe MCD18A1 Version 6.1 dataset was decommissioned on June 1, 2024. Users are encouraged to use the [MCD18A1 Version 6.2 data product](https://doi.org/10.5067/MODIS/MCD18A1.062). The MCD18A1 Version 6.1 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Downward Shortwave Radiation (DSR) gridded Level 3 product produced daily at 1 kilometer pixel resolution with estimates of DSR every 3 hours. DSR is incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The MCD18 products are based on a prototyping algorithm that uses multi-temporal signatures of MODIS data to derive surface reflectance and then calculate incident DSR using the look-up table (LUT) approach. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Global DSR products are generated from MODIS and geostationary satellite data. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD). Provided in the MOD18A1 product are layers for instantaneous DSR array for each individual MODIS overpass and 3-hour DSR array along with a View Zenith Angle layer. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD18A1\nThe MCD18A1 Version 6.2 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Downward Shortwave Radiation (DSR) gridded Level 3 product produced daily at 1 kilometer pixel resolution with estimates of DSR every 3 hours. DSR is incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The MCD18 products are based on a prototyping algorithm that uses multi-temporal signatures of MODIS data to derive surface reflectance and then calculate incident DSR using the look-up table (LUT) approach. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Global DSR products are generated from MODIS and geostationary satellite data. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD). Provided in the MOD18A1 product are layers for instantaneous DSR array for each individual MODIS overpass and 3-hour DSR array along with a View Zenith Angle layer. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=62).\n<br>\n\n#### MCD18A1\nMCD18A1 Version 6 was decommissioned on October 28, 2022. Users are encouraged to use the [MCD18A1 Version 6.2 data product](https://doi.org/10.5067/MODIS/MCD18A1.062). The MCD18A1 Version 6 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Downward Shortwave Radiation (DSR) gridded Level 3 product produced daily at 5 kilometer pixel resolution with estimates of DSR every 3 hours. DSR is incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues. The MCD18 products are based on a prototyping algorithm that uses multi-temporal signatures of MODIS data to derive surface reflectance and then calculate incident DSR using the look-up table (LUT) approach. The LUTs consider different types of loadings of aerosols and clouds at a variety of illumination/viewing geometry. Global DSR products are generated from MODIS and geostationary satellite data. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD). Provided in the MOD18A1 product are layers for instantaneous DSR array for each individual MODIS overpass and 3-hour DSR array along with a View Zenith Angle layer. Known Issues: For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6).\n<br>\n\n#### MCD19A1CMGL\nThe MCD19A1CMGL Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Surface Reflectance Level 3 (Bands 1-7) product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A1CMGL product is corrected for atmospheric gases and aerosols using a new MAIAC algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC products provide an estimate of the surface spectral reflectance, also referred to as Bidirectional Reflectance Factor (BRF), as it would be measured at ground-level in the absence of atmospheric scattering or absorption. The Surface Reflectance dataset includes BRF, BRF normalized to a fixed geometry of solar zenith angle at 45° and nadir view, and Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) normalized to the nadir view and local sun angle at 1:30 pm. The MCD19A1CMGL MAIAC Surface Reflectance product for land bands includes 25 Science Dataset (SDS) layers: BRF for bands 1-7, BRF normalized to a fixed geometry for bands 1-7, NBAR for bands 1-7, Quality Assessment (QA) bits, cosine of solar zenith angle, cosine of view zenith angle, and relative azimuth angle. A low-resolution browse is also included. Known Issues: Known issues are described in Section 6 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD19A1CMGO\nThe MCD19A1CMGO Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Surface Reflectance Level 3 (Bands 8-12) product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A1CMGO product is corrected for atmospheric gases and aerosols using a new MAIAC algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC products provide an estimate of the surface spectral reflectance, also referred to as Bidirectional Reflectance Factor (BRF), as it would be measured at ground-level in the absence of atmospheric scattering or absorption. The Surface Reflectance dataset includes BRF, BRF normalized to a fixed geometry of solar zenith angle at 45° and nadir view, and Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) normalized to the nadir view and local sun angle at 1:30 pm. The MCD19A1CMGO MAIAC Surface Reflectance product for ocean bands includes 19 Science Dataset (SDS) layers: BRF for bands 8-12, BRF normalized to a fixed geometry for bands 8-12, NBAR for bands 8-12, Quality Assessment (QA) bits, cosine of solar zenith angle, cosine of view zenith angle, and relative azimuth angle. A low-resolution browse is also included. Known Issues: Known issues are described in Section 6 of the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MCD19A3CMG\nThe MCD19A3CMG Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Vegetation Index Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A3CMG product provides Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) at ground level in the absence of atmospheric scattering or absorption. The MCD19A3CMG Vegetation Index data product contains the following Science Dataset (SDS) layers: NDVI, NDVI normalized to a fixed geometry of solar zenith angle at 45° and nadir view, gap-filled NDVI, EVI, and EVI normalized to a fixed geometry of solar zenith angle at 45° and nadir view. A low-resolution browse image is also included showing NDVI created using a composite of all available orbits. Known Issues: Known issues are described in Section 6 of the User Guide. * An artifact was discovered in the EVI, NDVI, and NDVI_gapfill layers over the entire swath of southern polar region. The affected region shows false retrieval of data during the summer months. The artifacts occurred over permanently snow-covered areas where no meaningful NDVI retrieval is expected. A software fix was implemented for forward processing in June 2023. Existing data products will not be reprocessed. Affected dates are listed on the [Land Data Operational Products Evaluation Quality Assessment note](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=187). * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n<br>\n\n#### MODFDS_MON_GLB_L3\nVersion 1 is the current version of the dataset. This collection MODFDS_MON_GLB_L3 provides level 3 monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚. It is derived from Level 2, the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products Collection 6.1 from Terra (MOD04_L2). The dataset covers the monthly mean from 2000 to 2022. The FDS is calculated as the number of days per month when the daily dust optical depth is greater than a threshold optical depth (e.g., 0.025) with two quality flags: the lowest (1) and highest (3). It is advised to use flag 1, which is of lower quality, over dust source regions, and flag 3 over remote areas or polluted regions. Eight thresholds (0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 2) are saved separately in eight files. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### MODFDS_SDV_GLB_L3\nVersion 1 is the current version of the dataset. This collection MODFDS_SDV_GLB_L3 provides level 3 standard deviation of climatological monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚. It is derived from Level 2, the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products Collection 6.1 from Terra (MOD04_L2). The dataset is the standard deviation of climatological monthly mean for each month over 2000 to 2022. The FDS is calculated as the number of days per month when the daily dust optical depth is greater than a threshold optical depth (e.g., 0.025) with two quality flags: the lowest (1) and highest (3). It is advised to use flag 1, which is of lower quality, over dust source regions, and flag 3 over remote areas or polluted regions. Eight thresholds (0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 2) are saved separately in eight files. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### AER_DBDT_D10KM_L3_MODIS_TERRA\nThis High-Resolution (0.1 x 0.1 degree) Level 3 daily Aerosol Optical Depth (AOD) product is generated by combining two Moderate Resolution Imaging Spectroradiometer (MODIS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the TERRA satellite. This dataset is provided in daily files ranging from 2000-02-24 to the present. The spatial coverage is global and the dataset is gridded at 0.1 x 0.1 degree spatial resolution. The data are generated using Level 2 AOD retrieved using DT and DB algorithms. The product provides multiple options for using data either from DT or DB or combined. Depending on user need and application, they can choose one or more relevant parameter. The pixels with highest quality as recommended by science teams are only considered in these averaging. In addition to averaged AOD at 0.1 x 0.1 degree resolution, standard deviation and number of pixels averaged from each algorithm are also provided. Average sensor zenith angle is also provided for additional filtering of the data. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### AER_DBDT_M10KM_L3_MODIS_TERRA\nThis High-Resolution (0.1 x 0.1 degree) Level 3 monthly Aerosol Optical Depth (AOD) product is generated by combining two Moderate Resolution Imaging Spectroradiometer (MODIS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the TERRA satellite. This dataset is provided in monthly files ranging from February 2000 to the present. The spatial coverage is global and the dataset is gridded at 0.1 x 0.1 degree spatial resolution. The data are generated using Level 2 AOD retrieved using DT and DB algorithms. The product provides multiple options for using data either from DT or DB or combined. Depending on user need and application, they can choose one or more relevant parameter. The pixels with highest quality as recommended by science teams are only considered in these averaging. In addition to averaged AOD at 0.1 x 0.1 degree resolution, standard deviation and number of pixels averaged from each algorithm are also provided. Average sensor zenith angle is also provided for additional filtering of the data. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/centers/lp-daac","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","cog","earth observation","elevation","hdf","hydrology","ice","land cover","netcdf","oceans","satellite imagery","stac"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AST14DEM v003 - The AST14DEM Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST14DEM Version 4 data product](https://doi.org/10.5067/ASTER/AST14DEM.004).","ARN":"arn:aws:s3:::lp-prod-protected/AST14DEM.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST14DEM v004 - The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (AST14DEM) product is generated using bands 3N (nadir-viewing) and 3B (backward-viewing) of an ASTER [Level 1A](https://doi.org/10.5067/ASTER/AST_L1A.004) image acquired by the Visible and Near Infrared (VNIR) sensor. The VNIR subsystem includes two independent telescope assemblies that facilitate the generation of stereoscopic data.","ARN":"arn:aws:s3:::lp-prod-protected/AST14DEM.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ASTGTM_NUMNC v003 - The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 (ASTGTM) provides a global digital elevation model (DEM) of land areas on Earth at a spatial resolution of 1 arc second (approximately 30 meter horizontal posting at the equator). The development of the ASTER GDEM data products is a collaborative effort between National Aeronautics and Space Administration (NASA) and Japan's Ministry of Economy, Trade, and Industry (METI).","ARN":"arn:aws:s3:::lp-prod-protected/ASTGTM_NUMNC.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"ASTWBD_ATTNC v001 - The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Water Bodies Database (ASTWBD) Version 1 data product provides global coverage of water bodies larger than 0.2 square kilometers at a spatial resolution of 1 arc second (approximately 30 meters) at the equator, along with associated elevation information. The ASTWBD data product was created in conjunction with the ASTER Global Digital Elevation Model (ASTER GDEM) Version 3 data product by the Sensor Information Laboratory Corporation (SILC) in Tokyo.","ARN":"arn:aws:s3:::lp-prod-protected/ASTWBD_ATTNC.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_L1A v003 - The AST_L1A Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_L1A Version 4 data product](https://doi.org/10.5067/ASTER/AST_L1A.004).","ARN":"arn:aws:s3:::lp-prod-protected/AST_L1A.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_L1A v004 - The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1A (AST_L1A) contains reconstructed, instrument digital numbers (DNs) derived from the acquired telemetry streams of the telescopes: Visible and Near Infrared (VNIR), Shortwave Infrared (SWIR), and Thermal Infrared (TIR). Additionally, geometric correction coefficients and radiometric calibration coefficients are calculated and appended to the metadata but not applied.","ARN":"arn:aws:s3:::lp-prod-protected/AST_L1A.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_L1B v003 - The AST_L1B Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_LB1 Version 4 of this product](https://doi.org/10.5067/ASTER/AST_L1B.004) The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1B (AST_L1B) Registered Radiance at the Sensor data product is radiometrically calibrated and geometrically co-registered.","ARN":"arn:aws:s3:::lp-prod-protected/AST_L1B.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_L1B v004 - The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1B (AST_L1B) Registered Radiance at the Sensor data product is radiometrically calibrated and geometrically co-registered. Application of intra-telescope and inter-telescope registration corrections for all bands are relative to the reference band for each telescope: Visible and Near Infrared (VNIR) Band 2, Shortwave Infrared (SWIR) Band 6, and Thermal Infrared (TIR) Band 11.","ARN":"arn:aws:s3:::lp-prod-protected/AST_L1B.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_05 v003 - The AST_05 Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_05 Version 4 data product](https://doi.org/10.5067/ASTER/AST_05.004).","ARN":"arn:aws:s3:::lp-prod-protected/AST_05.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_05 v004 - The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Emissivity (AST_05) is produced using the Temperature/Emissivity Separation (TES) algorithm for the five Thermal Infrared (TIR) 90 meter bands, acquired during the day or night. The product comprises per-pixel emissivity measurements generated over land in addition to embedded metadata and quality assurance data planes.","ARN":"arn:aws:s3:::lp-prod-protected/AST_05.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_08 v004 - The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Kinetic Temperature (AST_08) V004 product delivers surface kinetic temperature data derived from the five Thermal Infrared (TIR) bands (Bands 10–14), collected during both daytime and nighttime observations. The AST_08 product has a spatial resolution of 90 meters (m) for the TIR bands.","ARN":"arn:aws:s3:::lp-prod-protected/AST_08.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_09XT v003 - The AST_09XT Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_09XT Version 4 data product](https://doi.org/10.5067/ASTER/AST_09XT.004).","ARN":"arn:aws:s3:::lp-prod-protected/AST_09XT.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_09T v003 - The AST_09T Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_09T Version 4 data product](https://doi.org/10.5067/ASTER/AST_09T.004).","ARN":"arn:aws:s3:::lp-prod-protected/AST_09T.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_09T v004 - The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Radiance TIR (AST_09T) is generated using the five Thermal Infrared (TIR) bands (acquired either during the day or nighttime) between 8 and 12 µm spectral range. It provides surface-leaving radiance for the TIR bands at a spatial resolution of 90 meters, which includes both surface-emitted and surface-reflected components.","ARN":"arn:aws:s3:::lp-prod-protected/AST_09T.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_09XT v004 - The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Radiance VNIR and Crosstalk Corrected SWIR (AST_09XT) contains atmospherically corrected data for the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. The AST_09XT product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands.","ARN":"arn:aws:s3:::lp-prod-protected/AST_09XT.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_09 v003 - The AST_09 Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_09 Version 4 data product](https://doi.org/10.5067/ASTER/AST_09.004).","ARN":"arn:aws:s3:::lp-prod-protected/AST_09.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_09 v004 - The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Radiance VNIR and SWIR (AST_09) contains atmospherically corrected data for the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. Both the VNIR and SWIR data are atmospherically corrected and are generated using the bands of the corresponding ASTER [Level 1B](https://doi.org/10.5067/ASTER/AST_L1B.004) image.","ARN":"arn:aws:s3:::lp-prod-protected/AST_09.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_07 v003 - The AST_07 Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_07 Version 4 data product](https://doi.org/10.5067/ASTER/AST_07.004).","ARN":"arn:aws:s3:::lp-prod-protected/AST_07.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_07XT v003 - The AST_07XT Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_07XT Version 4 data product](https://doi.org/10.5067/ASTER/AST_07XT.004).","ARN":"arn:aws:s3:::lp-prod-protected/AST_07XT.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_07XT v004 - The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Reflectance VNIR and Crosstalk Corrected SWIR (AST_07XT) dataset contains measures of the fraction of incoming solar radiation reflected from the Earth’s surface to the ASTER instrument corrected for atmospheric effects and viewing geometry for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. Both the VNIR and SWIR data are atmospherically corrected and are generated using the bands of the corresponding ASTER [L1B](https://doi.org/10.5067/ASTER/AST_L1B.004) image.","ARN":"arn:aws:s3:::lp-prod-protected/AST_07XT.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_07 v004 - The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Reflectance VNIR and SWIR (AST_07) data product contains measures of the fraction of incoming solar radiation reflected from the Earth’s surface to the ASTER instrument corrected for atmospheric effects and viewing geometry for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. The AST_07 product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands.","ARN":"arn:aws:s3:::lp-prod-protected/AST_07.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_08 v003 - The AST_08 Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_08 Version 4 data product](https://doi.org/10.5067/ASTER/AST_08.004).","ARN":"arn:aws:s3:::lp-prod-protected/AST_08.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_L1T v003 - The AST_L1T Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_L1T Version 4 data product](https://doi.org/10.5067/ASTER/AST_L1T.004).","ARN":"arn:aws:s3:::lp-prod-protected/AST_L1T.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_L1T v031 - The AST_L1T.031 Version 3 data product was decommissioned on December 15, 2025. Users are encouraged to use the [AST_L1T.031 Version 4 data product](https://doi.org/10.5067/ASTER/AST_L1T.004) The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) Version 3.1 data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B ([AST_L1B](https://doi.org/10.5067/ASTER/AST_L1B.003)), that has been geometrically corrected and rotated to a north-up UTM projection.","ARN":"arn:aws:s3:::lp-prod-protected/AST_L1T.031","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST_L1T v004 - The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B ([AST_L1B](https://doi.org/10.5067/ASTER/AST_L1B.004)) that has been geometrically corrected and rotated to a north-up UTM projection. The AST_L1T is created from a single resampling of the corresponding ASTER L1A ([AST_L1A](https://doi.org/10.5067/ASTER/AST_L1A.004)) product.","ARN":"arn:aws:s3:::lp-prod-protected/AST_L1T.004","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST14DMO v003 - The AST14DMO Version 3 data product was decommissioned on December 15, 2025. The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model and Orthorectified Registered Radiance at the Sensor (AST14DMO) product form a multi-file product.","ARN":"arn:aws:s3:::lp-prod-protected/AST14DMO.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AST14OTH v003 - The AST14OTH Version 3 data product was decommissioned on December 15, 2025. The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Orthorectified Registered Radiance at the Sensor (AST14OTH) products contain imagery transformed from a perspective projection to an orthogonal one.","ARN":"arn:aws:s3:::lp-prod-protected/AST14OTH.003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MODFDS_CLM_GLB_L3 v1 - Version 1 is the current version of the dataset. This collection MODFDS_CLM_GLB_L3 provides level 3 climatological monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/DustStorm/MODFDS_CLM_GLB_L3.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"FluxSatMGPP_L3_Daily_p05deg v2.2 - This dataset provides global gridded daily estimates of gross primary production (GPP) and uncertainties at 0.05 deg latitude by 0.05 deg longitude resolution for the period March 2000 to present. The GPP is derived from MODerate-resolution Imaging Spectroradiometer (MODIS) instruments on NASA Terra and Aqua satellites.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/FLUXSAT/FluxSatMGPP_L3_Daily_p05deg.2.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"FluxSatMGPP_L3_Daily_p5deg v2.2 - This dataset provides global gridded daily estimates of gross primary production (GPP) and uncertainties at 0.5 deg latitude by 0.625 deg longitude resolution for the period March 2000 to present. The GPP is derived from MODerate-resolution Imaging Spectroradiometer (MODIS) instruments on NASA Terra and Aqua satellites.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/FLUXSAT/FluxSatMGPP_L3_Daily_p5deg.2.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MIRCCMF v002 - This is the FIRSTLOOK Radiometric Camera-by-camera Cloud Mask (RCCM) product. It contains initial estimated classifications of pixels/regions as clear or cloudy.","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIRCCMF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MIL2TCCF v002 - Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared).","ARN":"arn:aws:s3:::asdc-prod-protected/MISR/MIL2TCCF_002","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"MCD06COSP_D3_MODIS v6.2 - The combined MODIS (Aqua/Terra) Cloud Properties Level 3 daily, 1x1 degree grid product represents a new addition that is especially geared to facilitate climate scientists who deal with both models and observations. MCD06COSP_D3_MODIS represents the daily product’s short-name.","ARN":"arn:aws:s3:::prod-lads/MCD06COSP_D3_MODIS","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD06COSP_M3_MODIS v6.2 - The combined MODIS (Aqua/Terra) Cloud Properties Level 3 monthly, 1x1 degree grid product represents a new addition that is especially geared to facilitate climate scientists who deal with both models and observations. MCD06COSP_D3_MODIS represents the daily product’s short-name.","ARN":"arn:aws:s3:::prod-lads/MCD06COSP_M3_MODIS","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD04_L2 v6.1 - The MODIS/Terra Aerosol 5-Min L2 Swath 10km (MOD04_L2) product provides full global coverage of aerosol properties from the Dark Target (DT) and Deep Blue (DB) algorithms. The DT algorithm is applied over ocean and dark land (e.g., vegetation), while the DB algorithm now covers the entire land areas including both dark and bright surfaces.","ARN":"arn:aws:s3:::prod-lads/MOD04_L2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD04_3K v6.1 - The new Collection 6.1 (C61) MODIS/Terra Aerosol 5 Min L2 Swath 3km (MOD04_3K) product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals. The MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land.","ARN":"arn:aws:s3:::prod-lads/MOD04_3K","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD08_E3 v6.1 - The MODIS/Terra Aerosol Cloud Water Vapor Ozone 8-Day L3 Global 1Deg CMG product (MOD08_E3) contains 8-Day 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter.","ARN":"arn:aws:s3:::prod-lads/MOD08_E3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD08_D3 v6.1 - The MODIS/Terra Aerosol Cloud Water Vapor Ozone Daily L3 Global 1Deg CMG product (MOD08_D3) contains daily 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter.","ARN":"arn:aws:s3:::prod-lads/MOD08_D3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD08_M3 v6.1 - The MODIS/Terra Aerosol Cloud Water Vapor Ozone Monthly L3 Global 1Deg CMG product (MOD08_M3) contains monthly 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter.","ARN":"arn:aws:s3:::prod-lads/MOD08_M3","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MODATML2 v6.1 - The MODIS/Terra Aerosol, Cloud and Water Vapor Subset 5-Min L2 Swath 5km and 10km (MODATML2) product contains a combination of key high interest science parameters. The ATML2 product provides a subset of datasets from the suite of atmosphere team products on both a 10 km scale (aerosols) and 5km scale (native 5 km cloud properties and a 5x5 pixel sample of the 1km cloud datasets).","ARN":"arn:aws:s3:::prod-lads/MODATML2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD09 v6.1 - The MODIS/Terra Atmospherically Corrected Surface Reflectance 5-Min L2 Swath 250m, 500m, 1km (MOD09) product is computed from the MODIS Level 1B land bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648 nm, 858 nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm, respectively). The product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption.","ARN":"arn:aws:s3:::prod-lads/MOD09","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD021KM v6.1 - The MODIS/Terra Calibrated Radiances 5Min L1B Swath 1km data set contains calibrated and geolocated at-aperture radiances for 36 discrete bands located in the 0.4 to 14.4 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which during processing are converted to geophysical units of W/(m^2 um sr).","ARN":"arn:aws:s3:::prod-lads/MOD021KM","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD02QKM v6.1 - The MODIS/Terra Calibrated Radiances 5Min L1B Swath 250m data set contains calibrated and geolocated at-aperture radiances for 2 discrete bands located in the 0.62 to 0.88 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which have been processed to geophysical units of W / (m^2 um sr).","ARN":"arn:aws:s3:::prod-lads/MOD02QKM","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD02HKM v6.1 - The MODIS/Terra Calibrated Radiances 5Min L1B Swath 500m data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr).","ARN":"arn:aws:s3:::prod-lads/MOD02HKM","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD35_L2 v6.1 - The MODIS/Terra Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km product consists of global cloud mask quality assurance and other ancillary parameters. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence levels that an unobstructed view of the Earth's surface is observed.","ARN":"arn:aws:s3:::prod-lads/MOD35_L2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD06_L2 v6.1 - The MODIS/Terra Clouds 5-Min L2 Swath 1km and 5km product (MOD06_L2) consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared, visible and near infrared solar reflected radiances.","ARN":"arn:aws:s3:::prod-lads/MOD06_L2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD03 v6.1 - The MODIS/Terra Geolocation Fields 5Min L1A Swath 1km are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily (in Collection 6 and later, information is provided to claculate 500m geolocation fields). The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface.","ARN":"arn:aws:s3:::prod-lads/MOD03","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD17A2H v006 - The MOD17A2H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD17A2H Version 6.1](https://doi.org/10.5067/MODIS/MOD17A2H.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD17A2H.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD17A2H v061 - The MOD17A2H Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN).","ARN":"arn:aws:s3:::lp-prod-protected/MOD17A2H.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD17A2HGF v006 - The MOD17A2HGF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD17A2HGF Version 6.1](https://doi.org/10.5067/MODIS/MOD17A2HGF.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD17A2HGF.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD17A2HGF v061 - The MOD17A2HGF Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN).","ARN":"arn:aws:s3:::lp-prod-protected/MOD17A2HGF.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD21 v006 - The MOD21 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD21 Version 6.1](https://doi.org/10.5067/MODIS/MOD21.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD21.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD21 v061 - The MOD21 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters.","ARN":"arn:aws:s3:::lp-prod-protected/MOD21.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD21C2 v061 - A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the [MOD11](https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique.","ARN":"arn:aws:s3:::lp-prod-protected/MOD21C2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD21A2 v006 - The MOD21A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD21A2 Version 6.1](https://doi.org/10.5067/MODIS/MOD21A2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD21A2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD21A2 v061 - A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperatuer (LST) algorithm differs from the algorithm of the [MOD11](https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique.","ARN":"arn:aws:s3:::lp-prod-protected/MOD21A2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD21A1D v006 - The MOD21A1D Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD21A1D Version 6.1](https://doi.org/10.5067/MODIS/MOD21A1D.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD21A1D.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD21A1N v006 - The MOD21A1N Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD21A1N Version 6.1](https://doi.org/10.5067/MODIS/MOD21A1N.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD21A1N.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11_L2 v006 - The MOD11_L2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11_L2 Version 6.1](https://doi.org/10.5067/MODIS/MOD11_L2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11_L2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11_L2 v061 - The MOD11_L2 Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11_L2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11C2 v006 - The MOD11C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11C2 Version 6.1](https://doi.org/10.5067/MODIS/MOD11C2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11C2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11C2 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MOD11C2) Version 6.1 product provides Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11C2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11A2 v006 - The MOD11A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11A2 Version 6.1](https://doi.org/10.5067/MODIS/MOD11A2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11A2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11A2 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MOD11A2) Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding [MOD11A1](https://doi.org/10.5067/MODIS/MOD11A1.061) LST pixels collected within that 8-day period.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11A2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11B2 v006 - The MOD11B2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11B2 Version 6.1](https://doi.org/10.5067/MODIS/MOD11B2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11B2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11B2 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MOD11B2) Version 6.1 product provides an average 8-day per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each temperature and emissivity pixel value in the MOD11B2 is a simple average of all the corresponding values from the LST&E values from the [MOD11B1](https://doi.org/10.5067/MODIS/MOD11B1.061) product collected during that 8-day period.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11B2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11C1 v006 - The MOD11C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11C1 Version 6.1](https://doi.org/10.5067/MODIS/MOD11C1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11C1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11C1 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MOD11C1) Version 6.1 product provides daily Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). The MOD11C1 product is directly derived from the [MOD11B1](https://doi.org/10.5067/MODIS/MOD11B1.061) product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11C1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11A1 v006 - The MOD11A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11A1 Version 6.1](https://doi.org/10.5067/MODIS/MOD11A1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11A1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11A1 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MOD11A1) Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the [MOD11_L2](https://doi.org/10.5067/MODIS/MOD11_L2.061) swath product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11A1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11B1 v006 - The MOD11B1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11B1 Version 6.1](https://doi.org/10.5067/MODIS/MOD11B1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11B1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11B1 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily(MOD11B1) Version 6.1 product provides daily per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each MOD11B1 granule consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the per...","ARN":"arn:aws:s3:::lp-prod-protected/MOD11B1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11C3 v006 - The MOD11C3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11C3 Version 6.1](https://doi.org/10.5067/MODIS/MOD11C3.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11C3.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11C3 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Monthly(MOD11C3) Version 6.1 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11C3.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11B3 v006 - The MOD11B3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11B3 Version 6.1](https://doi.org/10.5067/MODIS/MOD11B3.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11B3.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD11B3 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Monthly (MOD11B3) Version 6.1 product provides average monthly per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each LST&E pixel value in the MOD11B3 is a simple average of all the corresponding values from the [MOD11B1](https://doi.org/10.5067/MODIS/MOD11B1.061) collected during the month period.","ARN":"arn:aws:s3:::lp-prod-protected/MOD11B3.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD44W v006 - The MOD44W Version 6 data product was decommissioned on July 18, 2024. Users are encouraged to use the [MOD44W Version 6.1](https://doi.org/10.5067/MODIS/MOD44W.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD44W.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD44W v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Water Mask (MOD44W) Version 6.1 data product provides a global map of surface water at 250 meter (m) spatial resolution. The data are available annually from 2000 to present.","ARN":"arn:aws:s3:::lp-prod-protected/MOD44W.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD15A2H v006 - The MOD15A2H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD15A2H Version 6.1](https://doi.org/10.5067/MODIS/MOD15A2H.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD15A2H.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD15A2H v061 - The MOD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the “best” pixel available from all the acquisitions of the Terra sensor from within the 8-day period.","ARN":"arn:aws:s3:::lp-prod-protected/MOD15A2H.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD16A2 v006 - The MOD16A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD16A2 Version 6.1](https://doi.org/10.5067/MODIS/MOD16A2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD16A2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD16A2 v061 - The MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover.","ARN":"arn:aws:s3:::lp-prod-protected/MOD16A2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD16A2GF v006 - The MOD16A2GF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD16A2GF Version 6.1](https://doi.org/10.5067/MODIS/MOD16A2GF.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD16A2GF.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD16A2GF v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A2GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover.","ARN":"arn:aws:s3:::lp-prod-protected/MOD16A2GF.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD16A3GF v006 - The MOD16A3GF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD16A3GF Version 6.1](https://doi.org/10.5067/MODIS/MOD16A3GF.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD16A3GF.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD16A3GF v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover.","ARN":"arn:aws:s3:::lp-prod-protected/MOD16A3GF.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD16A3 v006 - The MOD16A3 Version 6 data product was decommissioned due to unexpected errors in the input data. Users are encouraged to use the [MOD16A3GF Version 6.1 data product](https://doi.org/10.5067/MODIS/MOD16A3GF.061).","ARN":"arn:aws:s3:::lp-prod-protected/MOD16A3.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD17A3HGF v006 - The MOD17A3HGF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD17A3HGF Version 6.1](https://doi.org/10.5067/MODIS/MOD17A3HGF.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD17A3HGF.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD17A3HGF v061 - The MOD17A3HGF Version 6.1 product provides information about annual Gross and Net Primary Production (GPP and NPP) at 500 meter (m) pixel resolution. Annual Terra Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and NPP is derived from the sum of all 8-day GPP Net Photosynthesis (PSN) products ([MOD17A2H](https://doi.org/10.5067/MODIS/MOD17A2H.061)) from the given year.","ARN":"arn:aws:s3:::lp-prod-protected/MOD17A3HGF.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD17A3H v006 - The MOD17A3H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD17A3HGF Version 6.1](https://doi.org/10.5067/MODIS/MOD17A3HGF.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD17A3H.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MODOCGA v006 - The MODOCGA Version 6 data product was decommissioned on July 31, 2023. The MODOCGA Version 6 Level 2 Gridded Lite (L2G-lite) Ocean Reflectance product provides an estimate of the surface spectral reflectance data from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) bands 8 through 16.","ARN":"arn:aws:s3:::lp-prod-protected/MODOCGA.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD09Q1 v006 - The MOD09Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD09Q1 Version 6.1](https://doi.org/10.5067/MODIS/MOD09Q1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD09Q1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD09Q1 v061 - The MOD09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers.","ARN":"arn:aws:s3:::lp-prod-protected/MOD09Q1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD09A1 v006 - The MOD09A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD09A1 Version 6.1](https://doi.org/10.5067/MODIS/MOD09A1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD09A1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD09A1 v061 - The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands.","ARN":"arn:aws:s3:::lp-prod-protected/MOD09A1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD09GA v006 - The MOD09GA Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD09GA Version 6.1](https://doi.org/10.5067/MODIS/MOD09GA.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD09GA.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD09GA v061 - The MOD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer (km) observation bands and geolocation flags.","ARN":"arn:aws:s3:::lp-prod-protected/MOD09GA.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD09GQ v006 - The MOD09GQ Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD09GQ Version 6.1](https://doi.org/10.5067/MODIS/MOD09GQ.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD09GQ.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD09GQ v061 - The MOD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m surface reflectance bands are the Quality Assurance (QA) layer and five observation layers.","ARN":"arn:aws:s3:::lp-prod-protected/MOD09GQ.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD09CMG v006 - The MOD09CMG Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD09CMG Version 6.1](https://doi.org/10.5067/MODIS/MOD09CMG.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD09CMG.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD09CMG v061 - The MOD09CMG Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, resampled to 5600 meter (m) pixel resolution and corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. The MOD09CMG data product provides 25 layers including MODIS bands 1 through 7; Brightness Temperature data from thermal bands 20, 21, 31, and 32; along with Quality Assurance (QA) and observation bands.","ARN":"arn:aws:s3:::lp-prod-protected/MOD09CMG.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD07_L2 v6.1 - The MODIS/Terra Temperature and Water Vapor Profiles 5-Min L2 Swath 5km (MOD07_L2) product consists of a numbers of parameters related to atmospheric stability, atmospheric temperature and moisture profiles, total atmospheric water vapor, and total ozone. All of these parameters are produced for both daytime and nighttime conditions at 5-km pixel resolution when at least 9 Field Of View (FOV) are cloud free.","ARN":"arn:aws:s3:::prod-lads/MOD07_L2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD14 v006 - The MOD14 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD14 Version 6.1](https://doi.org/10.5067/MODIS/MOD14.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD14.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD14 v061 - The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire MOD14 Version 6.1 product is produced daily in 5-minute temporal satellite increments (swaths) at 1 kilometer (km) spatial resolution. The MOD14 product is used to generate all of the higher level fire products, but can also be used to identify fires and other thermal anomalies, such as volcanoes.","ARN":"arn:aws:s3:::lp-prod-protected/MOD14.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD14A2 v006 - The MOD14A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD14A2 Version 6.1](https://doi.org/10.5067/MODIS/MOD14A2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD14A2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD14A2 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day (MOD14A2) Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MOD14A2 gridded composite contains the maximum value of the individual fire pixel classes detected during the eight days of acquisition.","ARN":"arn:aws:s3:::lp-prod-protected/MOD14A2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD14A1 v006 - The MOD14A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD14A1 Version 6.1](https://doi.org/10.5067/MODIS/MOD14A1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD14A1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD14A1 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily (MOD14A1) Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file.","ARN":"arn:aws:s3:::lp-prod-protected/MOD14A1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MODTBGA v006 - The MODTBGA Version 6 data product was decommissioned on July 31, 2023. The MODTBGA Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 Gridded Lite (L2G-lite) Thermal Band product consists of brightness temperature data from MODIS bands 20, 31, and 32 and albedo data from band 20 along with the orbit and granule pointer fields.","ARN":"arn:aws:s3:::lp-prod-protected/MODTBGA.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD05_L2 v6.1 - The MODIS/Terra Total Precipitable Water Vapor 5-Min L2 Swath 1km and 5km (MOD05_L2) product consists of atmospheric column water-vapor amounts. This product is derived from data collected by the Moderate-Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite.","ARN":"arn:aws:s3:::prod-lads/MOD05_L2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD44B v006 - The MOD44B Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD44B Version 6.1](https://doi.org/10.5067/MODIS/MOD44B.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD44B.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD44B v061 - The MOD44B Version 6.1 Vegetation Continuous Fields (VCF) yearly product is a global representation of surface vegetation cover as gradations of three ground cover components: percent tree cover, percent non-tree cover, and percent non-vegetated (bare). VCF products provide a continuous, quantitative portrayal of land surface cover at 250 meter (m) pixel resolution, with a sub-pixel depiction of percent cover in reference to the three ground cover components.","ARN":"arn:aws:s3:::lp-prod-protected/MOD44B.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD13C1 v006 - The MOD13C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13C1 Version 6.1](https://doi.org/10.5067/MODIS/MOD13C1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD13C1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD13C1 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13C1) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers.","ARN":"arn:aws:s3:::lp-prod-protected/MOD13C1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD13A2 v006 - The MOD13A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13A2 Version 6.1](https://doi.org/10.5067/MODIS/MOD13A2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD13A2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD13A2 v061 - The MOD13A2 Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers.","ARN":"arn:aws:s3:::lp-prod-protected/MOD13A2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD13Q1 v006 - The MOD13Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13Q1 Version 6.1](https://doi.org/10.5067/MODIS/MOD13Q1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD13Q1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD13Q1 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers.","ARN":"arn:aws:s3:::lp-prod-protected/MOD13Q1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD13A1 v006 - The MOD13A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13A1 Version 6.1](https://doi.org/10.5067/MODIS/MOD13A1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD13A1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD13A1 v061 - The MOD13A1 Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers.","ARN":"arn:aws:s3:::lp-prod-protected/MOD13A1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD13C2 v006 - The MOD13C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13C2 Version 6.1](https://doi.org/10.5067/MODIS/MOD13C2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD13C2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD13C2 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Monthly (MOD13C2) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers.","ARN":"arn:aws:s3:::lp-prod-protected/MOD13C2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD13A3 v006 - The MOD13A3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD13A3 Version 6.1](https://doi.org/10.5067/MODIS/MOD13A3.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MOD13A3.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD13A3 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Monthly (MOD13A3) Version 6.1 data are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the [MOD13A2](https://doi.org/10.5067/MODIS/MOD13A2.061) products that overlap the month and employs a weighted temporal average.","ARN":"arn:aws:s3:::lp-prod-protected/MOD13A3.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD28C2 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir 8-Day Level 3 (L3) Global (MOD28C2) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MOD28C2 Version 6.1 data product provides an 8-day time series of surface area, elevation, and water storage.","ARN":"arn:aws:s3:::lp-prod-protected/MOD28C2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MOD28C3 v061 - The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir Monthly Level 3 (L3) Global (MOD28C3) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MOD28C3 Version 6.1 data product is a composite of the 8-day area classifications from [MOD28C2](https://lpdaac.usgs.gov/products/mod28c2v061/), which is converted to provide monthly elevation and water storage.","ARN":"arn:aws:s3:::lp-prod-protected/MOD28C3.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD19A2CMG v061 - The MCD19A2CMG Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) and Water Vapor Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A2CMG product provides the atmospheric properties and view geometry used to calculate the MAIAC Surface Reflectance data products ([MCD19A1CMGL](https://doi.org/10.5067/MODIS/MCD19A1CMGL.061) and [MCD19A1CMGO](https://doi.org/10.5067/MODIS/MCD19A1C...","ARN":"arn:aws:s3:::lp-prod-protected/MCD19A2CMG.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD19A3 v006 - The MCD19A3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD19A3 Version 6.1](https://doi.org/10.5067/MODIS/MCD19A3.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD19A3.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD19A3D v061 - The MCD19A3D Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Bidirectional Reflectance Distribution Function (BRDF) Model Parameters gridded Level 3 product. Output daily at 1 kilometer (km) resolution, the Multi-angle Implementation of Atmospheric Correction (MAIAC) MCD19A3D product provides three coefficients (weights) of the RossThick/Li-Sparse (RTLS) BRDF model that can be used to describe the anisotropy of each pixel.","ARN":"arn:aws:s3:::lp-prod-protected/MCD19A3D.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43A3 v006 - The MCD43A3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43A3 Version 6.1](https://doi.org/10.5067/MODIS/MCD43A3.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43A3.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43A3 v061 - The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 Version 6.1 Albedo Model dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the 16 day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43A3.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43C3 v006 - The MCD43C3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43C3 Version 6.1](https://doi.org/10.5067/MODIS/MCD43C3.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43C3.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43C3 v061 - The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C3 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Albedo dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43C3.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D42 v006 - The MCD43D42 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D42 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D42.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D42.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D42 v061 - The MCD43D42 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D42.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D43 v006 - The MCD43D43 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D43 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D43.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D43.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D43 v061 - The MCD43D43 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D43.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D44 v006 - The MCD43D44 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D44 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D44.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D44.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D44 v061 - The MCD43D44 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D44.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D45 v006 - The MCD43D45 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D45 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D45.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D45.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D45 v061 - The MCD43D45 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D45.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D46 v006 - The MCD43D46 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D46 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D46.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D46.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D46 v061 - The MCD43D46 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D46.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D47 v006 - The MCD43D47 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D47 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D47.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D47.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D47 v061 - The MCD43D47 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D47.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D48 v006 - The MCD43D48 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D48 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D48.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D48.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D48 v061 - The MCD43D48 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D48.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D50 v006 - The MCD43D50 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D50 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D50.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D50.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D50 v061 - The MCD43D50 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D50.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D51 v006 - The MCD43D51 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D51 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D51.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D51.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D51 v061 - The MCD43D51 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D51.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D49 v006 - The MCD43D49 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D49 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D49.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D49.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D49 v061 - The MCD43D49 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D49.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43GF v061 - The Daily Moderate Resolution Imaging Spectroradiometer (MODIS) (Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6.1 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6.1 product suite, with additional processing to provide a gap-filled, snow-free product. The highest quality full inversion values were used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43GF.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43A1 v006 - The MCD43A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43A1 Version 6.1](https://doi.org/10.5067/MODIS/MCD43A1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43A1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43A1 v061 - The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A1 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43A1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43C1 v006 - The MCD43C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43C1 Version 6.1](https://doi.org/10.5067/MODIS/MCD43C1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43C1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43C1 v061 - The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C1 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43C1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D62 v006 - The MCD43D62 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D62 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D62.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D62.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D62 v061 - The MCD43D62 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D62.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D63 v006 - The MCD43D63 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D63 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D63.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D63.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D63 v061 - The MCD43D63 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D63.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D64 v006 - The MCD43D64 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D64 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D64.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D64.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D64 v061 - The MCD43D64 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D64.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D65 v006 - The MCD43D65 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D65 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D65.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D65.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D65 v061 - The MCD43D65 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D65.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D66 v006 - The MCD43D66 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D66 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D66.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D66.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D66 v061 - The MCD43D66 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D66.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D67 v006 - The MCD43D67 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D67 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D67.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D67.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D67 v061 - The MCD43D67 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D67.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D68 v006 - The MCD43D68 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D68 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D68.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D68.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D68 v061 - The MCD43D68 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D68.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43A4 v006 - The MCD43A4 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43A4 Version 6.1](https://doi.org/10.5067/MODIS/MCD43A4.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43A4.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43A4 v061 - The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43A4.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43C4 v006 - The MCD43C4 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43C4 Version 6.1](https://doi.org/10.5067/MODIS/MCD43C4.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43C4.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43C4 v061 - The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C4 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43C4.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D01 v061 - The MCD43D01 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D01.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D04 v061 - The MCD43D04 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D04.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D02 v061 - The MCD43D02 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D02.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D05 v061 - The MCD43D05 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D05.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D03 v061 - The MCD43D03 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D03.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D06 v061 - The MCD43D06 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D06.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D01 v006 - The MCD43D01 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D01 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D01.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D01.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D04 v006 - The MCD43D04 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D04 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D04.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D04.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D07 v006 - The MCD43D07 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D07 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D07.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D07.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D07 v061 - The MCD43D07 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D07.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D10 v006 - The MCD43D10 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D10 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D10.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D10.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D10 v061 - The MCD43D10 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D10.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D13 v006 - The MCD43D13 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D13 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D13.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D13.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D13 v061 - The MCD43D13 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D13.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D16 v006 - The MCD43D16 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D16 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D16.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D16.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D16 v061 - The MCD43D16 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D16.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D19 v006 - The MCD43D19 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D19 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D19.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D19.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D19 v061 - The MCD43D19 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D19.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D25 v006 - The MCD43D25 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D25 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D25.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D25.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D25 v061 - The MCD43D25 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D25.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D28 v006 - The MCD43D28 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D28 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D28.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D28.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D28 v061 - The MCD43D28 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D28.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D22 v006 - The MCD43D22 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D22 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D22.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D22.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D22 v061 - The MCD43D22 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D22.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D02 v006 - The MCD43D02 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D02 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D02.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D02.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D05 v006 - The MCD43D05 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D05 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D05.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D05.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D08 v006 - The MCD43D08 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D08 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D08.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D08.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D08 v061 - The MCD43D08 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D08.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D11 v006 - The MCD43D11 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D11 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D11.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D11.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D11 v061 - The MCD43D11 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D11.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D14 v006 - The MCD43D14 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D14 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D14.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D14.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D14 v061 - The MCD43D14 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D14.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D17 v006 - The MCD43D17 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D17 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D17.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D17.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D17 v061 - The MCD43D17 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectrometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D17.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D20 v006 - The MCD43D20 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D20 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D20.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D20.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D20 v061 - The MCD43D20 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D20.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D26 v006 - The MCD43D26 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D26 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D26.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D26.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D26 v061 - The MCD43D26 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D26.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D29 v006 - The MCD43D29 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D29 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D29.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D29.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D29 v061 - The MCD43D29 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D29.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D23 v006 - The MCD43D23 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D23 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D23.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D23.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D23 v061 - The MCD43D23 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D23.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D03 v006 - The MCD43D03 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D03 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D03.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D03.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D06 v006 - The MCD43D06 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D06 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D06.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D06.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D09 v006 - The MCD43D09 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D09 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D09.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D09.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D09 v061 - The MCD43D09 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D09.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D12 v006 - The MCD43D12 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D12 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D12.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D12.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D12 v061 - The MCD43D12 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D12.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D15 v006 - The MCD43D15 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D15 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D15.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D15.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D15 v061 - The MCD43D15 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Sprectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D15.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D18 v006 - The MCD43D18 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D18 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D18.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D18.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D18 v061 - The MCD43D18 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D18.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D21 v006 - The MCD43D21 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D21 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D21.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D21.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D21 v061 - The MCD43D21 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D21.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D27 v006 - The MCD43D27 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D27 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D27.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D27.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D27 v061 - The MCD43D27 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D27.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D30 v006 - The MCD43D30 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D30 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D30.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D30.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D30 v061 - The MCD43D30 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Sprectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D30.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D24 v006 - The MCD43D24 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D24 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D24.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D24.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D24 v061 - The MCD43D24 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D24.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D31 v006 - The MCD43D31 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D31 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D31.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D31.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D31 v061 - The MCD43D31 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA BRDF Quality dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D31.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D32 v006 - The MCD43D32 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D32 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D32.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D32.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D32 v061 - The MCD43D32 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA Local Solar Noon dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D32.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D40 v006 - The MCD43D40 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D40 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D40.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D40.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D40 v061 - The MCD43D40 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA Snow Status dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D40.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D41 v006 - The MCD43D41 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D41 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D41.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D41.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D41 v061 - The MCD43D41 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA Uncertainty dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D41.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D33 v006 - The MCD43D33 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D33 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D33.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D33.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D33 v061 - The MCD43D33 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 1 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D33.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D34 v006 - The MCD43D34 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D34 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D34.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D34.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D34 v061 - The MCD43D34 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 2 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D34.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D35 v006 - The MCD43D35 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D35 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D35.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D35.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D35 v061 - The MCD43D35 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 3 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D35.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D36 v006 - The MCD43D36 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D36 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D36.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D36.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D36 v061 - The MCD43D36 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 4 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D36.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D37 v006 - The MCD43D37 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D37 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D37.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D37.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D37 v061 - The MCD43D37 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 5 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D37.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D38 v006 - The MCD43D38 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D38 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D38.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D38.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D38 v061 - The MCD43D38 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 6 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D38.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D39 v006 - The MCD43D39 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D39 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D39.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D39.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D39 v061 - The MCD43D39 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 7 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D39.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43A2 v006 - The MCD43A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43A2 Version 6.1](https://doi.org/10.5067/MODIS/MCD43A2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43A2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43A2 v061 - The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A2 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Quality dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the 16-day retrieval period which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43A2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43C2 v006 - The MCD43C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43C2 Version 6.1](https://doi.org/10.5067/MODIS/MCD43C2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43C2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43C2 v061 - The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C2 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Snow-free Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43C2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D52 v006 - The MCD43D52 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D52 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D52.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D52.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D52 v061 - The MCD43D52 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D52.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D53 v006 - The MCD43D53 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D53 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D53.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D53.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D53 v061 - The MCD43D53 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D53.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D54 v006 - The MCD43D54 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D54 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D54.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D54.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D54 v061 - The MCD43D54 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D54.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D55 v006 - The MCD43D55 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D55 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D55.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D55.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D55 v061 - The MCD43D55 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D55.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D56 v006 - The MCD43D56 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D56 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D56.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D56.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D56 v061 - The MCD43D56 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D56.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D57 v006 - The MCD43D57 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D57 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D57.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D57.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D57 v061 - The MCD43D57 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter(m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D57.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D58 v006 - The MCD43D58 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D58 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D58.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D58.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D58 v061 - The MCD43D58 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D58.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D60 v006 - The MCD43D60 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D60 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D60.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D60.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D60 v061 - The MCD43D60 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D60.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D61 v006 - The MCD43D61 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D61 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D61.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D61.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D61 v061 - The MCD43D61 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D61.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D59 v006 - The MCD43D59 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D59 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D59.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D59.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD43D59 v061 - The MCD43D59 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) White-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.","ARN":"arn:aws:s3:::lp-prod-protected/MCD43D59.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD64A1 v006 - The MCD64A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD64A1 Version 6.1](https://doi.org/10.5067/MODIS/MCD64A1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD64A1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD64A1 v061 - The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations.","ARN":"arn:aws:s3:::lp-prod-protected/MCD64A1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD18C1 v061 - The MCD18C1 Version 6.1 dataset was decommissioned on June 1, 2024. Users are encouraged to use the [MCD18C1 Version 6.2 data product](https://doi.org/10.5067/MODIS/MCD18C1.062).","ARN":"arn:aws:s3:::lp-prod-protected/MCD18C1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD18C1 v062 - The MCD18C1 Version 6.2 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Downward Shortwave Radiation (DSR) Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG) with estimates of DSR every 3 hours. DSR is incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues.","ARN":"arn:aws:s3:::lp-prod-protected/MCD18C1.062","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD19A2 v061 - The MCD19A2 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 1 kilometer (km) pixel resolution. The MCD19A2 product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1 product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD19A2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD19A2 v006 - The MCD19A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD19A2 Version 6.1](https://doi.org/10.5067/MODIS/MCD19A2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD19A2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD12Q2 v006 - The MCD12Q2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD12Q2 Version 6.1](https://doi.org/10.5067/MODIS/MCD12Q2.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD12Q2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD12Q2 v061 - The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics (MCD12Q2) Version 6.1 data product provides global land surface phenology metrics at yearly intervals. The MCD12Q2 Version 6.1 data product is derived from time series of the 2-band Enhanced Vegetation Index (EVI2) calculated from MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR).","ARN":"arn:aws:s3:::lp-prod-protected/MCD12Q2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD12C1 v006 - The MCD12C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD12C1 Version 6.1](https://doi.org/10.5067/MODIS/MCD12C1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD12C1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD12C1 v061 - The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) (MCD12C1) Version 6.1 data product provides a spatially aggregated and reprojected version of the tiled [MCD12Q1 Version 6.1](https://doi.org/10.5067/MODIS/MCD12Q1.061) data product. Maps of the International Geosphere-Biosphere Programme (IGBP), University of Maryland (UMD), and Leaf Area Index (LAI) classification schemes are provided at yearly intervals at 0.05 degree (5,600 meter) spatial resolution for the entire globe.","ARN":"arn:aws:s3:::lp-prod-protected/MCD12C1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD12Q1 v006 - The MCD12Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD12Q1 Version 6.1](https://doi.org/10.5067/MODIS/MCD12Q1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD12Q1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD12Q1 v061 - The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) Version 6.1 data product provides global land cover types at yearly intervals. The MCD12Q1 Version 6.1 data product is derived using supervised classifications of MODIS Terra and Aqua reflectance data.","ARN":"arn:aws:s3:::lp-prod-protected/MCD12Q1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD19A1 v061 - The MCD19A1 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 500 meter (m) and 1 kilometer (km) pixel resolutions. The MCD19A1 product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that is based on a time series analysis and a combination of pixel- and image-based processing.","ARN":"arn:aws:s3:::lp-prod-protected/MCD19A1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD19A1 v006 - The MCD19A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD19A1 Version 6.1](https://doi.org/10.5067/MODIS/MCD19A1.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD19A1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD15A3H v006 - The MCD15A3H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD15A3H Version 6.1](https://doi.org/10.5067/MODIS/MCD15A3H.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD15A3H.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD15A3H v061 - The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4 combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA’s Terra and Aqua satellites from within the 4-day period.","ARN":"arn:aws:s3:::lp-prod-protected/MCD15A3H.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD15A2H v006 - The MCD15A2H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD15A2H Version 6.1](https://doi.org/10.5067/MODIS/MCD15A2H.061) data product.","ARN":"arn:aws:s3:::lp-prod-protected/MCD15A2H.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD15A2H v061 - The MCD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4 combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA’s Terra and Aqua satellites from within the 8-day period.","ARN":"arn:aws:s3:::lp-prod-protected/MCD15A2H.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD18C2 v061 - The MCD18C2 Version 6.1 dataset was decommissioned on June 1, 2024. Users are encouraged to use the [MCD18C2 Version 6.2 data product](https://doi.org/10.5067/MODIS/MCD18C2.062).","ARN":"arn:aws:s3:::lp-prod-protected/MCD18C2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD18C2 v062 - The MCD18C2 Version 6.2 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Photosynthetically Active Radiation (PAR) gridded Level 3 product produced daily at 0.05 degree (5,600 meters at the equator) resolution with estimates of PAR every 3 hours. PAR is incident solar radiation in the visible spectrum (400-700 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues.","ARN":"arn:aws:s3:::lp-prod-protected/MCD18C2.062","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD18A2 v061 - The MCD18A2 Version 6.1 dataset was decommissioned on June 1, 2024. Users are encouraged to use the [MCD18A2 Version 6.2 data product](https://doi.org/10.5067/MODIS/MCD18A2.062).","ARN":"arn:aws:s3:::lp-prod-protected/MCD18A2.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD18A2 v062 - The MCD18A2 Version 6.2 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Photosynthetically Active Radiation (PAR) gridded Level 3 product produced daily at 1 kilometer pixel resolution with estimates of PAR every 3 hours. PAR is incident solar radiation in the visible spectrum (400-700 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues.","ARN":"arn:aws:s3:::lp-prod-protected/MCD18A2.062","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD18A2 v006 - MCD18A2 Version 6 was decommissioned on October 28, 2022. Users are encouraged to use the [MCD18A2 Version 6.2 data product](https://doi.org/10.5067/MODIS/MCD18A2.062).","ARN":"arn:aws:s3:::lp-prod-protected/MCD18A2.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD18A1 v061 - The MCD18A1 Version 6.1 dataset was decommissioned on June 1, 2024. Users are encouraged to use the [MCD18A1 Version 6.2 data product](https://doi.org/10.5067/MODIS/MCD18A1.062).","ARN":"arn:aws:s3:::lp-prod-protected/MCD18A1.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD18A1 v062 - The MCD18A1 Version 6.2 is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Downward Shortwave Radiation (DSR) gridded Level 3 product produced daily at 1 kilometer pixel resolution with estimates of DSR every 3 hours. DSR is incident solar radiation over land surfaces in the shortwave spectrum (300-4,000 nanometers) and is an important variable in land-surface models that address a variety of scientific and application issues.","ARN":"arn:aws:s3:::lp-prod-protected/MCD18A1.062","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD18A1 v006 - MCD18A1 Version 6 was decommissioned on October 28, 2022. Users are encouraged to use the [MCD18A1 Version 6.2 data product](https://doi.org/10.5067/MODIS/MCD18A1.062).","ARN":"arn:aws:s3:::lp-prod-protected/MCD18A1.006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD19A1CMGL v061 - The MCD19A1CMGL Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Surface Reflectance Level 3 (Bands 1-7) product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A1CMGL product is corrected for atmospheric gases and aerosols using a new MAIAC algorithm that is based on a time series analysis and a combination of pixel- and image-based processing.","ARN":"arn:aws:s3:::lp-prod-protected/MCD19A1CMGL.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD19A1CMGO v061 - The MCD19A1CMGO Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Surface Reflectance Level 3 (Bands 8-12) product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A1CMGO product is corrected for atmospheric gases and aerosols using a new MAIAC algorithm that is based on a time series analysis and a combination of pixel- and image-based processing.","ARN":"arn:aws:s3:::lp-prod-protected/MCD19A1CMGO.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MCD19A3CMG v061 - The MCD19A3CMG Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Vegetation Index Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A3CMG product provides Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) at ground level in the absence of atmospheric scattering or absorption.","ARN":"arn:aws:s3:::lp-prod-protected/MCD19A3CMG.061","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MODFDS_MON_GLB_L3 v1 - Version 1 is the current version of the dataset. This collection MODFDS_MON_GLB_L3 provides level 3 monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/DustStorm/MODFDS_MON_GLB_L3.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MODFDS_SDV_GLB_L3 v1 - Version 1 is the current version of the dataset. This collection MODFDS_SDV_GLB_L3 provides level 3 standard deviation of climatological monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/DustStorm/MODFDS_SDV_GLB_L3.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AER_DBDT_D10KM_L3_MODIS_TERRA v001 - This High-Resolution (0.1 x 0.1 degree) Level 3 daily Aerosol Optical Depth (AOD) product is generated by combining two Moderate Resolution Imaging Spectroradiometer (MODIS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the TERRA satellite. This dataset is provided in daily files ranging from 2000-02-24 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TASNPP_AOD_Level3/AER_DBDT_D10KM_L3_MODIS_TERRA.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"AER_DBDT_M10KM_L3_MODIS_TERRA v001 - This High-Resolution (0.1 x 0.1 degree) Level 3 monthly Aerosol Optical Depth (AOD) product is generated by combining two Moderate Resolution Imaging Spectroradiometer (MODIS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the TERRA satellite. This dataset is provided in monthly files ranging from February 2000 to the present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TASNPP_AOD_Level3/AER_DBDT_M10KM_L3_MODIS_TERRA.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-terra","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA tes Project","Description":"This data set contains Level-2 global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).\n<br>\n\n#### AQ3_ANSM\nThis data set contains Level-3 gridded annual global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).\n<br>\n\n#### AQ3_DYSM\nThis data set contains Level-3 gridded daily global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).\n<br>\n\n#### AQ3_MCSM\nThis data set contains Level-3 gridded monthly global soil moisture climatology estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).\n<br>\n\n#### AQ3_MOSM\nThis data set contains Level-3 gridded monthly global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).\n<br>\n\n#### AQ3_SCSM\nThis data set contains Level-3 gridded seasonal global soil moisture climatology estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).\n<br>\n\n#### AQ3_SNSM\nThis data set contains Level-3 gridded seasonal global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).\n<br>\n\n#### AQ3_WKSM\nThis data set contains Level-3 gridded weekly global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).\n<br>\n\n#### HMA_DTE\nThis data set includes spatially distributed estimates of the debris thickness and sub-debris melt enhancement factors for every debris-covered glacier in the Randolph Glacier Inventory Version 6, excluding the ice sheets and Antarctic Periphery. The debris thickness estimates are derived using a novel approach that uses a combination of sub-debris melt inversion and surface temperature inversion methods. The sub-debris melt enhancement factors are estimated from the debris thickness using debris thickness-melt curves normalized by estimates of the clean-ice melt.\n<br>\n\n#### HMA_FreezeThawMelt_ASCAT\nThis data set contains bulk landscape frozen or thawed status over seasonally frozen land, as well as snowmelt status over glacierized areas for the High Mountain Asia region. Daily Freeze/Thaw/Melt (F/T/M) status is derived from vertically polarized (V-pol) C-band (5.255 GHz) backscatter measurements that were acquired by the Advanced Scatterometer (ASCAT) on EUMETSAT Metop-A and Metop-B satellites. Swath-ordered observations are spatially enhanced using the Scatterometer Image Reconstruction (SIR) algorithm, posted on Earth-fixed 4.45 km grids, and interpolated to a daily product from the original 3-day A.M. overpasses.\n<br>\n\n#### HMA_Precip_3B42\nThis data set features seven standard annual mean extreme precipitation indices: Rx1day, Rx5day, CWD, R10mm, R20mm, R95pTOT, and R99pTOT. They were selected on the basis of potential relevance to landslide activity from the 27 indices established by the joint CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI). The seven indices were calculated from 3B42 version 7 daily satellite precipitation estimates.\n<br>\n\n#### NSIDC-0545\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, provides high-resolution, digital mosaics of ice motion in the Amundsen Sea Embayment (ASE) and West Antarctica, including the Pine Island, Thwaites, Haynes, Pope, Smith, and Kohler glaciers. The mosaics were assembled from interferometric synthetic-aperture radar (InSAR) data acquired in 1996, 2000, 2002, and 2006-2012 by various satellites. See <a href=\"https://nsidc.org/data/measures/aiv\">Antarctic Ice Sheet Velocity and Mapping Data</a> for related data.\n<br>\n\n#### NSIDC-0793\nThis ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains monthly, 120 m resolution ice masks for the Greenland Ice Sheet from 1972 to 2022. The presence of ice was determined from 237,556 manually and AI-derived terminus positions acquired by satellite optical and radar observations. Months with no observations have been gap-filled using past and future observations of terminus positions and advance rates constrained by the average flow speed of the glacier. Animations are also available for 206 catchments that show how the ice front positions have changed over the course of the time series and can be used as a quality control check.\n<br>\n\n#### NSIDC-0761\nThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of three as-complete-as-possible mosaic maps of velocities on the Antarctic ice sheet for the time periods 1995–2001, 2007–2009, and 2014–2017. The maps are posted at 450 m in the WGS 84/Antarctic Polar Stereographic projection. In addition to ice velocity, the data set provides maps of velocity error and standard deviation; counts of velocity estimates used per pixel; date ranges; and masks that delineate the ice fronts and grounding lines for the each period.\n<br>\n\n#### MWOW_L3_EPI_V0.1\nThis data set contains 6-hourly, global gridded wind speed and direction at 0.125 degree horizontal resolution from the MWOW (Multi-sensor Worldwide Ocean Winds) Project. It is a harmonized wind product combining retrievals from a variety of satellite-based instruments, including five scatterometers, one radiometer, and one synthetic aperture radar (SAR) for the Version 0.1 product. Data are available in netCDF format, with one file for each 6-hour timestep. Note that data from the instruments are not aggregated together to create a composite wind map for each timestep. Rather, observations from each instrument are kept separate, split over the “time” dimension. Further, any observations from the same instrument that overlap spatially in the 6-hour window are also reported as separate observations along this time dimension. This “Expanded Platform Input” product is similar to the MWOW_L3_V0.1 product (DOI 10.5067/MWOW-6H-V01) with additional data inputs from scatterometers aboard the Hai Yang 2B and 2C satellites. <br><br> Version 0 of these data include an initial bulk ingest of data in January 2026 with forward streaming planned for later in the year. Version 0 is primarily meant for assessment by stakeholders, with future versions likely to incorporate feedback from the stakeholders. No User Guide is planned for this version, although the data are considered science quality. Interested users looking for more information are encouraged to contact the data producer. <br><br> The MWOW Project is funded by NASA’s Satellite Needs Working Group (SNWG) which provides data products developed to meet the needs of stakeholders from US government agencies.\n<br>\n\n#### MWOW_L3_HIGHRES_EPI_V0.1\nThis data set contains 6-hourly, global gridded wind speed and direction at 1/64th degree horizontal resolution from the MWOW (Multi-sensor Worldwide Ocean Winds) Project. It is a harmonized wind product combining retrievals from the KaRIn satellite-based SAR (synthetic aperture radar) with interpolated data from five scatterometers and one radiometer for comparison where SAR data is available. Data are available in netCDF format, with one file for each 6-hour timestep. Note that data from the instruments are not aggregated together to create a composite wind map for each timestep. Rather, observations from each instrument are kept separate, split over the “time” dimension. Further, any observations from the same instrument that overlap spatially in the 6-hour window are also reported as separate observations along this time dimension. This “Expanded Platform Input” product is similar to the MWOW_L3_HIRES_V0.1 product (DOI 10.5067/MWOW-6HHR-V01) with additional data inputs from scatterometers aboard the Hai Yang 2B and 2C satellites. <br><br> Version 0 of these data include an initial bulk ingest of data in January 2026 with forward streaming planned for later in the year. Version 0 is primarily meant for assessment by stakeholders, with future versions likely to incorporate feedback from the stakeholders. No User Guide is planned for this version, although the data are considered science quality. Interested users looking for more information are encouraged to contact the data producer. <br><br> The MWOW Project is funded by NASA’s Satellite Needs Working Group (SNWG) which provides data products developed to meet the needs of stakeholders from US government agencies.\n<br>\n\n#### NmAVCS3G\nThis data set (NmAVCS3G) consists of daily image composites constructed from Nimbus 1 (1964) and Nimbus 2 (1966) Advanced Vidicon Camera System (AVCS) imagery for the region between 60 N and 60 S. Data are provided as GeoTIFFs. For the HDF5 formatted version of these data, see <a href=\"http://dx.doi.org/10.5067/NIMBUS/NmAVCS3H\">Nimbus Advanced Vidicon Camera System Remapped Visible Imagery Daily L3, HDF5</a>.\n<br>\n\n#### NmAVCS3H\nThis data set (NmAVCS3H) consists of daily, global image composites constructed from Nimbus 1 (1964) and Nimbus 2 (1966) Advanced Vidicon Camera System (AVCS) imagery. Each composite is provided as a set of three HDF5-formatted files: separate North and South Polar projections in the 5 km Equal-Area Scalable Earth Grid (EASE-Grid) and an equatorial projection in a 10 km equidistant grid for the region between 60 N and 60 S.\n<br>\n\n#### NmAVCS1H\nThe Nimbus Advanced Vidicon Camera System Visible Imagery L1, HDF5 (NmAVCS1H) data set consists of black-and-white images captured by the Advanced Vidicon Camera Systems onboard the Nimbus 1 (1964) and Nimbus 2 (1966) satellites. Data are provided as HDF5-formatted files. Browse images are also available.\n<br>\n\n#### NmHRIR1H\nThe Nimbus High Resolution Infrared Radiometer Digital Swath Data L1, HDF data set (NmHRIR1H) consists of High Resolution Infrared Radiometer (HRIR) brightness temperatures obtained by the Nimbus 1, Nimbus 2, and Nimbus 3 satellites during 1964, 1966, and 1969. A correction has been applied to minimize seemingly random alignment errors that caused clouds edges and land features to appear jagged in the original 1960s data.\n<br>\n\n#### NmHRIR3G\nThis data set (NmHRIR3G) consists of daily composites constructed from Nimbus 1, Nimbus 2, and Nimbus 3 satellites High Resolution Infrared Radiometer (HRIR) data for the region between 60 N and 60 S. Measurements were obtained during 1964, 1966, and 1969. Data are available as GeoTIFFs and browse images. For the HDF5 formatted version of these data, see the <a href=\"http://dx.doi.org/10.5067/NIMBUS/NmHRIR3H\">Nimbus High Resolution Infrared Radiometer Remapped Digital Data Daily L3, HDF5</a> data set.\n<br>\n\n#### NmHRIR3H\nThis data set (NmHRIR3H) consists of daily, global composites of High Resolution Infrared Radiometer (HRIR) data obtained by the Nimbus 1, Nimbus 2, and Nimbus 3 satellites during 1964, 1966, and 1969. Each composite is provided as a set of three HDF5-formatted files: separate North and South Polar projections in the 10 km Equal-Area Scalable Earth Grid (EASE-Grid) and an equatorial projection in a 20 km equidistant grid for the region between 60 N and 60 S. Browse images are also available.\n<br>\n\n#### NmIcEdg2\nThis data set (NmIcEdg2) estimates the location of the North and South Pole sea ice edges at various times during the mid to late 1960s, based on recovered Nimbus 1 (1964), Nimbus 2 (1966), and Nimbus 3 (1969) visible imagery.\n<br>\n\n#### NmIDCS3G\nThis data set (NmIDCS3G) consists of daily, global image composites constructed from Nimbus 3 and Nimbus 4 Image Dissector Camera System (IDCS) imagery for the region between 60 N and 60 S. Images were acquired between 23 April, 1969 - 04 January, 1971. Data are available as GeoTIFFs and browse images. For HDF5 formatted version of these data, see <a href=\"http://dx.doi.org/10.5067/NIMBUS/NmIDCS3H\">Nimbus Image Dissector Camera System Remapped Visible Imagery Daily L3, HDF5</a>.\n<br>\n\n#### NmIDCS3H\nThis data set (NmIDCS3H) consists of daily, global image composites constructed from Nimbus 3 and Nimbus 4 Image Dissector Camera System (IDCS) imagery captured from 23 April, 1969 - 04 April, 1971.\n<br>\n\n#### NmIDCS1H\nThe Nimbus Image Dissector Camera System Visible Imagery L1, HDF5 (NmIDCS1H) data set consists of black-and-white images captured by the Image Dissector Camera Systems (IDCSs) onboard the Nimbus 3 and Nimbus 4 satellites. Data are provided as HDF5-formatted files. Browse images are also available.\n<br>\n\n#### NmTHIR115-3G\nThis data set (NmTHIR115-3G) consists of daily, global composites of radiative temperatures obtained in the 11.5 µm window (10.5 µm - 12.5 µm) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4 satellite. This window was used to measure cloud top or surface temperatures. Data files are GeoTIFF versions of the HDF-formatted equatorial projection file <em>only</em> from the Nimbus Temperature-Humidity Infrared Radiometer 11.5 µm Remapped Digital Data Daily L3, HDF5 (<a href=\"http://dx.doi.org/10.5067/NIMBUS/NmTHIR115-3H\">NmTHIR115-3H</a>) data set.\n<br>\n\n#### NmTHIR115-3H\nThis data set (NmTHIR115-3H) consists of daily, global composites of radiative temperatures obtained in the 11.5 µm window (10.5 µm - 12.5 µm) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4 satellite. The composites were constructed from Nimbus 4 THIR <a href=\"http://nsidc.org/data/nmthir115-1h/\">swath data</a> and show cloud top or surface temperatures.\n<br>\n\n#### NmTHIR67-3G\nThis data set (NmTHIR67-3G) consists of daily, global composites of radiative temperatures obtained in the 6.7 µm water vapor window (6.5 µm - 7.0 µm) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4 satellite. The THIR 6.7 µm window was used to map the water vapor distribution in the upper troposphere and stratosphere. Data files are GeoTIFF versions of the HDF-formatted equatorial projection file only from the Nimbus Temperature-Humidity Infrared Radiometer 6.7 µm Water Vapor Remapped Digital Data Daily, HDF5 (<a href=\"http://dx.doi.org/10.5067/NIMBUS/NmTHIR67-3H\">NmTHIR67-3H</a>) data set.\n<br>\n\n#### NmTHIR67-3H\nThis data set (NmTHIR67-3H) consists of daily, global composites of radiative temperatures measured in the 6.7 µm water vapor window (6.5 µm - 7.0 µm) by the Temperature-Humidity Infrared Radiometer (THIR) onboard the Nimbus 4 satellite. The composites were constructed from Nimbus 4 THIR <a href=\"http://nsidc.org/data/nmthir67-1h/\">swath data</a> and show water vapor distribution in the upper troposphere and stratosphere.\n<br>\n\n#### SPL3SMP_E\nThis enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection.\n<br>\n\n#### SPL1BTB\nThis Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed.\n<br>\n\n#### SPL2SMA\nThis Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).\n<br>\n\n#### SPL2SMAP\nThis Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer during 6:00 a.m. descending half-orbit passes. SMAP L-band backscatter and brightness temperatures are used to derive soil moisture data, which are then resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).\n<br>\n\n#### SPL2SMP\nThis Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data.\n<br>\n\n#### SPL3SMA\nThis Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).\n<br>\n\n#### SPL3SMP\nThis Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).\n<br>\n\n#### NSIDC-0712\nSMAP radiometer and radar soil moisture data products are matched with in situ-based soil moisture estimates from core validation sites to produce this data set. These data provide performance assessments of various SMAP soil moisture products.\n<br>\n\n#### NSIDC-0800\nThe Soil Moisture Active Passive (SMAP, launched in 2015) and the Soil Moisture and Ocean Salinity (SMOS, launched in 2009) missions are each L-band satellites that provide brightness temperature and soil moisture estimates and vegetation optical depth approximately every 2-3 days with a spatial resolution of ~40 km. By integrating brightness temperature observations from both satellites, these data products reduce the revisit time to about one day, improving the ability to monitor fast-response processes such as groundwater drainage and recharge, early dry-down after storms, and pre-storm soil moisture conditions for runoff determination. The Integrated SMAP and SMOS Soil Moisture Data is available in two products. The SMOS-Based SMAP L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture (NSIDC-0799) product is generated by processing half-orbit granules (either 6 AM ascending or 6 PM descending) from the pre-processed SMOS product. The L2 product consists solely of SMOS-based data, incorporating information after SMOS brightness temperatures (TBs) at a 40° incidence angle were inter-calibrated and the soil moisture (SM) and vegetation optical depth (VOD) were retrieved using the SMAP Dual Channel Algorithm (DCA). The SMAP/SMOS L3 Radiometer 9 km EASE-Grid Soil Moisture (NSIDC-0800) product is generated by the processing software ingesting one day’s worth of granules from both the SMAP SPL2SMP_E product and the SMOS-derived L2 data. The L3 product contains both SMAP and SMOS descending (6:00 AM/PM) and ascending (6:00 PM/AM) data stored in separate arrays.\n<br>\n\n#### SV16I_TPSM\nThese data consist of calibrated soil moisture sensor measurements recorded by manual sampling teams at the field sites of SMAPVEX16-Iowa during two intensive observation periods in June and August of 2016. The sites were located throughout an experiment domain of about 30 km by 40 km approximately 30 km north of Ames, Iowa.\n<br>\n\n#### SV16I_PLTBSM\nThis product contains data derived from permanent in situ soil stations and observations by the Passive Active L-band System (PALS) microwave aircraft instrument. The PALS instrument was mounted to a DC-3 aircraft, which flew six parallel flight lines at an altitude of 3000 m in order to map a study area in South Fork, Iowa, United States. A total of 20 soil stations were distributed throughout this same area. The soil characteristics included in this data set are volumetric soil moisture, vertically and horizontally polarized brightness temperature, effective soil temperature, effective vegetation temperature, vegetation water content, land cover classification, sand and clay fraction, and volumetric soil moisture uncertainty estimates.\n<br>\n\n#### SV16I_PNET\nThese data consist of soil moisture, soil temperature and precipitation measurements recorded in 2016 by the permanent soil moisture network; SMAPVEX16-Iowa. The sites were spread out over the experiment domain of about 30km by 40 km located about 30 km north of Ames, Iowa, USA. The data file contains the soil moisture, soil temperature and precipitation measurements for each station located at the site.\n<br>\n\n#### SV16M_PLTBSM\nThis product contains data derived from permanent in situ soil stations and observations by the Passive Active L-band System (PALS) microwave aircraft instrument. The PALS instrument was mounted to a DC-3 aircraft, which flew six parallel flight lines at an altitude of 3000 m in order to map a 26 km x 48 km domain in Manitoba, Canada. Nine permanent soil stations were distributed throughout this same area. The soil characteristics included in this data set are volumetric soil moisture, vertically and horizontally polarized brightness temperature, effective soil temperature, effective vegetation temperature, vegetation water content, land cover classification, sand and clay fraction, and volumetric soil moisture uncertainty estimates.\n<br>\n\n#### SV19MA_LID\nThese lidar measurements were collected in April and August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods were selected to characterize differences during \"leaf-off” and \"leaf-on\" conditions.\n<br>\n\n#### SV19MA_FOR\nThe SMAPVEX19-22 Massachusetts Field data (SV19MA_FOR) consists of forest tree inventory, biophysical, leaf area index (LAI), and estimated biomass data as well as photos collected between April and August 2022 in the vicinity of Petersham, Massachusetts during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. Over 500 trees of approximately 45 different species were sampled.\n<br>\n\n#### SV19MA_PAI\nThese data consist of plant area index (PAI) and the RGB images from which the PAI values were derived. Data image files were taken between April 2019 to December 2022 in the vicinity of Petersham, Massachusetts during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.\n<br>\n\n#### SV19MA_SAR\nThis data set consists of mosaicked Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images corrected for terrain-flattened gamma. Data image files at three different polarization configurations were composited daily between April to July 2022 in the vicinity of Petersham, Massachusetts during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.\n<br>\n\n#### SV19MB_LID\nThese lidar measurements were collected in April and August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods were selected to characterize differences during \"leaf-off\" and \"leaf-on\" conditions.\n<br>\n\n#### SV19MB_FOR\nThe SMAPVEX19-22 Millbrook Field data (SV19MB_FOR) consists of forest tree inventory, biophysical, leaf area index (LAI), and estimated biomass data as well as photos collected between April and August 2022 in the vicinity of Millbrook, New York during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. Over 500 trees of approximately 45 different species were sampled.\n<br>\n\n#### SV19MB_PAI\nThese data consist of plant area index (PAI) and the RGB images from which the PAI values were derived. Data image files were taken between April 2019 to December 2022 in the vicinity of Millbrook, New York during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.\n<br>\n\n#### SV19MB_SAR\nThis data set consists of mosaicked Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images corrected for terrain-flattened gamma. Data image files at three different polarization configurations were composited daily between April to July 2022 in the vicinity of Millbrook, New York during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.\n<br>\n\n#### SNEX17_TLS_PC_BSU_Raw\nThis data set contains raw, unprocessed terrestrial laser scanner (TLS) point cloud data collected as part of the 2017 SnowEx campaign in Grand Mesa, Colorado. Data were collected in the fall (September 2016) and winter (February 2017). Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i).\n<br>\n\n#### SNEX17_TLS_PC_BSU\nThis data set contains terrestrial laser scanner (TLS) point cloud data collected as part of the 2017 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2016) and snow-on (February 2017) conditions, at both open and forested locations. Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i). These TLS data can be used to determine snow depth and explore the interactions between snow and vegetation.\n<br>\n\n#### SNEX17_SnowPits\nThis data set contains measurements of snow temperature, density, stratigraphy, grain size, wetness, depth, and snow water equivalent (SWE) for snow pits at two study sites in Colorado, USA: Grand Mesa and Senator Beck Basin. Data were collected during the NASA SnowEx 2017 campaign.\n<br>\n\n#### SNEX17_DGNSS1\nThis data set contains the coordinates of SnowEx infrastructure in Grand Mesa, Colorado, collected through a differential GNSS real-time kinematic (RTK) survey. The surveys were conducted at 244 stakes along 90 transects, 31 snow pits, 24 time-lapse cameras, and 15 reference poles used to estimate snow depth from camera images. Data files report the name, location, elevation, horizontal and vertical precision, date and time, original easting and northing, and any relevant notes for each survey point. Readings were collected using a Trimble R8 GNSS base station and two rovers: a Trimble R8 (Hiemstra) and a Trimble R10 (Gelvin). Both rovers were deployed within approximately 6km of the base station and equipped with a GNSS antennae and a base-station radio antennae from which to receive corrections.\n<br>\n\n#### SNEX17_TLS_PC_CRREL\nThis data set contains terrestrial LIDAR survey (TLS) point cloud data collected at Grand Mesa, Colorado as part of the 2017 SnowEx campaign. Data were collected in the fall (September and October) and winter (February) seasons. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), along with ancillary information, such as intensity (i) and color (R,G,B), where available. This is unprocessed data which has not been classified by land use (e.g. bare earth, low vegetation, trees).\n<br>\n\n#### SNEX17_SBR\nThis data set consists of surface-based radiometer (SBR) brightness temperatures of the snow surface and vegetation at Grand Mesa, CO, USA, acquired during NASA's 2017 SnowEx campaign. Four SBRs were deployed in the field and obtained measurements at 89, 37, 19, and 10.67 GHz in both vertical (V-pol) and horizontal (H-pol) polarizations. Data are available for 37 locations across Grand Mesa collected over five days in February, 2017. They include both 2-4 minute observations and 20 minute, continuous measurements during surface melt, with the latter designed to capture the brightness temperature increase caused by the presence of liquid water in the snow. This data set also contains snowfork measurements of snow wetness for 13 sites.\n<br>\n\n#### SNEX17_TLI\nThis data set contains time-lapse images. Cameras were placed around Grand Mesa, CO at 34 sites and around Senator Beck Basin, CO at one site, coincident with other SnowEx 2017 measurements, including the TLS scans, sonic snow depth arrays, weather stations, and local scale observation sites.\n<br>\n\n#### SNEX21_TS_SP\nThe data set is a time-series of snow pit measurements obtained by the SnowEx community during the 2021 field campaign. Between November 2020 and May 2021 data from 247 snow pits were collected at 24 unique sites distributed over 4 states (CO, ID, MT, UT) throughout the Western United States. Five of the unique sites had a single visit to establish baseline conditions, while the remaining 19 sites had 3 or more repeat visits throughout the season, with a median visit count of 11.5. On a weekly interval, a snow pit was dug approximately 1 m away from the previous week’s snow pit. Available measured parameters are: snow depth, snow temperature, snow density, stratigraphy, grain size, manual wetness, liquid water content (LWC), and snow water equivalent (SWE). Also available are photos of the field notes and snow pit sites.\n<br>\n\n#### SMAP_L4_C_ANC_SOC_RST\nThis ancillary SMAP product contains the yearly soil organic carbon (SOC) restart file. This file contains the area density of SOC at the start of the year, which is used to calculate daily SOC based on defined deposition and decay rates.\n<br>\n\n#### SWOT_POSTLAUNCH_L1_MOORING\nThe SWOT Postlaunch Oceanography Field Campaign L1 Moorings collection provides the surface air pressure, GPS, and conductivity, temperature and depth (CTD) measurements from moorings at the L1 processing level. The SWOT satellite mission launched in late 2022 and underwent a calibration and validation (cal/val) phase in 2023. As part of cal/val, an array of oceanographic instruments was deployed at a site 300 km offshore of California, including moorings at 11 distinct sites from February to October 2023. <br><br> L1 data are instrument data converted to physical units and merged with the relevant metadata. L1 data do not have adjustments to calibrations or scientific quality control applied. L1 data are meant for data provenance only, and are likely only to be useful to users involved in calibration issues.\n<br>\n\n#### SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1\nThis dataset includes simulated water surface elevations that resemble the Ka-band Interferometer (KaRIn) measurements by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent. The simulated SWOT KaRIN swaths span 128 km in the cross-swath direction with a 20-km nadir gap. The primary product contains the following: 1. Geolocated elevations (latitude, longitude, and height) 2. Classification mask (water/land flags, and water fraction) 3. Surface areas (projected pixel area on the ground) 4. Relevant data needed to compute and aggregate height and area uncertainties. Additional information includes: 1. Meta data (global instrument parameters) 2. Time varying parameters (TVP), which include sensor position, velocity, altitude, and time 3. Noise power estimates 4. Quality flags 5. Interferogram measurements (power and phase) and range and azimuth indices 6. Geophysical and crossover-calibration correction values. These additional fields are provided to improve the utility of the product and to facilitate generation of downstream products. Note that this is a simulated SWOT product and not suited for any scientific exploration.\n<br>\n\n#### SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1\nThis dataset contains a simulated lake product to be provided by the Surface Water and Ocean Topography (SWOT) mission with a focus on the North America continent. The product is derived from the high-rate (HR) measurements produced by the SWOT main instrument, a Ka-band Radar Interferometer. These data are produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. This product consists of three shapefiles: 1) an observation-oriented shapefile of lakes identified in the Prior Lake Database (PLD); 2) a PLD-oriented shapefile of lakes identified in the PLD; 3) a shapefile of unassigned features that have not been identified as a lake in the PLD nor as a river in the Prior River Database (PRD). Lake attributes include water surface elevation, area, and uncertainty estimates. The identified lake shapes inherit the SWOT swath width that is approximately 128 km wide in the cross-track direction with a 20-km nadir gap. Note that this is a simulated SWOT product and not suited for any scientific exploration.\n<br>\n\n#### SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1\nThis dataset contains a simulated river data product to be provided by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this dataset is a subset for the North America continent. This product is derived from the measurements produced by the main SWOT instrument, the Ka-band Interferometer. They are produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. This product contains two shapefiles: 1) river reaches (approximately 10 km long) identified in the prior river database (PRD); and 2) river nodes (approximately 200 m spacing) identified in prior river database (PRD). Each river reach is divided into a number of nodes. Attributes include water surface elevation, slope, width, and uncertainty estimates. As they are derived from SWOT KaRIn measurements, each granule covers an area that is approximately 128 km wide in the cross-track direction with a 20-km nadir gap. Note that this is a simulated SWOT product and not suited for any scientific exploration.\n<br>\n\n#### LOCSS_L1_V1\nThis dataset contains data from the Lake Observations by Citizen Science and Satellites project, LOCSS which is a lake monitoring network. The data represent the location and main descriptors of the lake gauges and their readings. LOCSS project aims to collaborate with local citizens to monitor small and medium sized lakes (i.e., lakes with an average surface area less than 100 km2). At each location, a lake gauge is installed and provided with a cellphone number. Local citizens read the water level at each lake gauge and sent it in a text message. Data can also be manually collected and uploaded later from the website in remote places where cellphone signal is challenged. The readings are specified in cm, m, or ft, according to the local unit system. This version of the dataset has lakes located in seven (7) countries: Bangladesh, India, Canada, the United States, Pakistan, and Nepal. This product consists of two files in comma-separated values (csv) format : 1) the list of gauges whose attributes include gauge coordinates, installation dates, the height of the gauge, reading units, city, time zone, and installation notes; 2) list of readings by each gauge specified in the local time. To discover more details about LOCSS, please visit https://www.locss.org/.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/aq2_sm/versions/5","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","climate","elevation","hdf","hdf5","hydrology","ice","land cover","lidar","netcdf","oceans","precipitation","radar","satellite imagery","soil moisture","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"AQ2_SM v5 - This data set contains Level-2 global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AQUARIUS/AQ2_SM/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AQ3_ANSM v5 - This data set contains Level-3 gridded annual global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AQUARIUS/AQ3_ANSM/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AQ3_DYSM v5 - This data set contains Level-3 gridded daily global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AQUARIUS/AQ3_DYSM/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AQ3_MCSM v5 - This data set contains Level-3 gridded monthly global soil moisture climatology estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AQUARIUS/AQ3_MCSM/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AQ3_MOSM v5 - This data set contains Level-3 gridded monthly global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AQUARIUS/AQ3_MOSM/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AQ3_SCSM v5 - This data set contains Level-3 gridded seasonal global soil moisture climatology estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AQUARIUS/AQ3_SCSM/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AQ3_SNSM v5 - This data set contains Level-3 gridded seasonal global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AQUARIUS/AQ3_SNSM/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"AQ3_WKSM v5 - This data set contains Level-3 gridded weekly global soil moisture estimates derived from the NASA Aquarius passive microwave radiometer on the Sat&eacute;lite de Aplicaciones Cient&iacute;ficas (SAC-D).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/AQUARIUS/AQ3_WKSM/5","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_DTE v1 - This data set includes spatially distributed estimates of the debris thickness and sub-debris melt enhancement factors for every debris-covered glacier in the Randolph Glacier Inventory Version 6, excluding the ice sheets and Antarctic Periphery. The debris thickness estimates are derived using a novel approach that uses a combination of sub-debris melt inversion and surface temperature inversion methods.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_DTE/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_FreezeThawMelt_ASCAT v1 - This data set contains bulk landscape frozen or thawed status over seasonally frozen land, as well as snowmelt status over glacierized areas for the High Mountain Asia region. Daily Freeze/Thaw/Melt (F/T/M) status is derived from vertically polarized (V-pol) C-band (5.255 GHz) backscatter measurements that were acquired by the Advanced Scatterometer (ASCAT) on EUMETSAT Metop-A and Metop-B satellites.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_FreezeThawMelt_ASCAT/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"HMA_Precip_3B42 v1 - This data set features seven standard annual mean extreme precipitation indices: Rx1day, Rx5day, CWD, R10mm, R20mm, R95pTOT, and R99pTOT. They were selected on the basis of potential relevance to landslide activity from the 27 indices established by the joint CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/HMA/HMA_Precip_3B42/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0545 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, provides high-resolution, digital mosaics of ice motion in the Amundsen Sea Embayment (ASE) and West Antarctica, including the Pine Island, Thwaites, Haynes, Pope, Smith, and Kohler glaciers. The mosaics were assembled from interferometric synthetic-aperture radar (InSAR) data acquired in 1996, 2000, 2002, and 2006-2012 by various satellites.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0545/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0793 v1 - This ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, contains monthly, 120 m resolution ice masks for the Greenland Ice Sheet from 1972 to 2022. The presence of ice was determined from 237,556 manually and AI-derived terminus positions acquired by satellite optical and radar observations.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0793/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0761 v1 - This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of three as-complete-as-possible mosaic maps of velocities on the Antarctic ice sheet for the time periods 1995–2001, 2007–2009, and 2014–2017. The maps are posted at 450 m in the WGS 84/Antarctic Polar Stereographic projection.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/MEASURES/NSIDC-0761/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"MWOW_L3_EPI_V0.1 v0.1 - This data set contains 6-hourly, global gridded wind speed and direction at 0.125 degree horizontal resolution from the MWOW (Multi-sensor Worldwide Ocean Winds) Project. It is a harmonized wind product combining retrievals from a variety of satellite-based instruments, including five scatterometers, one radiometer, and one synthetic aperture radar (SAR) for the Version 0.1 product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MWOW_L3_EPI_V0.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"MWOW_L3_HIGHRES_EPI_V0.1 v0.1 - This data set contains 6-hourly, global gridded wind speed and direction at 1/64th degree horizontal resolution from the MWOW (Multi-sensor Worldwide Ocean Winds) Project. It is a harmonized wind product combining retrievals from the KaRIn satellite-based SAR (synthetic aperture radar) with interpolated data from five scatterometers and one radiometer for comparison where SAR data is available.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/MWOW_L3_HIGHRES_EPI_V0.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"NmAVCS3G v1 - This data set (NmAVCS3G) consists of daily image composites constructed from Nimbus 1 (1964) and Nimbus 2 (1966) Advanced Vidicon Camera System (AVCS) imagery for the region between 60 N and 60 S. Data are provided as GeoTIFFs.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmAVCS3G/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmAVCS3H v1 - This data set (NmAVCS3H) consists of daily, global image composites constructed from Nimbus 1 (1964) and Nimbus 2 (1966) Advanced Vidicon Camera System (AVCS) imagery. Each composite is provided as a set of three HDF5-formatted files: separate North and South Polar projections in the 5 km Equal-Area Scalable Earth Grid (EASE-Grid) and an equatorial projection in a 10 km equidistant grid for the region between 60 N and 60 S.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmAVCS3H/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmAVCS1H v1 - The Nimbus Advanced Vidicon Camera System Visible Imagery L1, HDF5 (NmAVCS1H) data set consists of black-and-white images captured by the Advanced Vidicon Camera Systems onboard the Nimbus 1 (1964) and Nimbus 2 (1966) satellites. Data are provided as HDF5-formatted files.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmAVCS1H/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmHRIR1H v1 - The Nimbus High Resolution Infrared Radiometer Digital Swath Data L1, HDF data set (NmHRIR1H) consists of High Resolution Infrared Radiometer (HRIR) brightness temperatures obtained by the Nimbus 1, Nimbus 2, and Nimbus 3 satellites during 1964, 1966, and 1969. A correction has been applied to minimize seemingly random alignment errors that caused clouds edges and land features to appear jagged in the original 1960s data.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmHRIR1H/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmHRIR3G v1 - This data set (NmHRIR3G) consists of daily composites constructed from Nimbus 1, Nimbus 2, and Nimbus 3 satellites High Resolution Infrared Radiometer (HRIR) data for the region between 60 N and 60 S. Measurements were obtained during 1964, 1966, and 1969.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmHRIR3G/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmHRIR3H v1 - This data set (NmHRIR3H) consists of daily, global composites of High Resolution Infrared Radiometer (HRIR) data obtained by the Nimbus 1, Nimbus 2, and Nimbus 3 satellites during 1964, 1966, and 1969. Each composite is provided as a set of three HDF5-formatted files: separate North and South Polar projections in the 10 km Equal-Area Scalable Earth Grid (EASE-Grid) and an equatorial projection in a 20 km equidistant grid for the region between 60 N and 60 S.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmHRIR3H/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmIcEdg2 v1 - This data set (NmIcEdg2) estimates the location of the North and South Pole sea ice edges at various times during the mid to late 1960s, based on recovered Nimbus 1 (1964), Nimbus 2 (1966), and Nimbus 3 (1969) visible imagery.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmIcEdg2/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmIDCS3G v1 - This data set (NmIDCS3G) consists of daily, global image composites constructed from Nimbus 3 and Nimbus 4 Image Dissector Camera System (IDCS) imagery for the region between 60 N and 60 S. Images were acquired between 23 April, 1969 - 04 January, 1971.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmIDCS3G/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmIDCS3H v1 - This data set (NmIDCS3H) consists of daily, global image composites constructed from Nimbus 3 and Nimbus 4 Image Dissector Camera System (IDCS) imagery captured from 23 April, 1969 - 04 April, 1971.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmIDCS3H/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmIDCS1H v1 - The Nimbus Image Dissector Camera System Visible Imagery L1, HDF5 (NmIDCS1H) data set consists of black-and-white images captured by the Image Dissector Camera Systems (IDCSs) onboard the Nimbus 3 and Nimbus 4 satellites. Data are provided as HDF5-formatted files.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmIDCS1H/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmTHIR115-3G v1 - This data set (NmTHIR115-3G) consists of daily, global composites of radiative temperatures obtained in the 11.5 µm window (10.5 µm - 12.5 µm) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4 satellite. This window was used to measure cloud top or surface temperatures.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmTHIR115-3G/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmTHIR115-3H v1 - This data set (NmTHIR115-3H) consists of daily, global composites of radiative temperatures obtained in the 11.5 µm window (10.5 µm - 12.5 µm) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4 satellite. The composites were constructed from Nimbus 4 THIR <a href=\"http://nsidc.org/data/nmthir115-1h/\">swath data</a> and show cloud top or surface temperatures.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmTHIR115-3H/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmTHIR67-3G v1 - This data set (NmTHIR67-3G) consists of daily, global composites of radiative temperatures obtained in the 6.7 µm water vapor window (6.5 µm - 7.0 µm) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4 satellite. The THIR 6.7 µm window was used to map the water vapor distribution in the upper troposphere and stratosphere.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmTHIR67-3G/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NmTHIR67-3H v1 - This data set (NmTHIR67-3H) consists of daily, global composites of radiative temperatures measured in the 6.7 µm water vapor window (6.5 µm - 7.0 µm) by the Temperature-Humidity Infrared Radiometer (THIR) onboard the Nimbus 4 satellite. The composites were constructed from Nimbus 4 THIR <a href=\"http://nsidc.org/data/nmthir67-1h/\">swath data</a> and show water vapor distribution in the upper troposphere and stratosphere.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/NIMBUS/NmTHIR67-3H/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL3SMP_E v006 - This enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL3SMP_E/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL1BTB v006 - This Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL1BTB/006","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL2SMA v003 - This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL2SMA/003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL2SMAP v003 - This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer during 6:00 a.m. descending half-orbit passes.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL2SMAP/003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL2SMP v009 - This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL2SMP/009","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL3SMA v003 - This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL3SMA/003","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SPL3SMP v009 - This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP/SPL3SMP/009","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0712 v1 - SMAP radiometer and radar soil moisture data products are matched with in situ-based soil moisture estimates from core validation sites to produce this data set. These data provide performance assessments of various SMAP soil moisture products.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/NSIDC-0712/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"NSIDC-0800 v1 - The Soil Moisture Active Passive (SMAP, launched in 2015) and the Soil Moisture and Ocean Salinity (SMOS, launched in 2009) missions are each L-band satellites that provide brightness temperature and soil moisture estimates and vegetation optical depth approximately every 2-3 days with a spatial resolution of ~40 km. By integrating brightness temperature observations from both satellites, these data products reduce the revisit time to about one day, improving the ability to monitor fast-response processes such as groundwater drainage and recharge, early dry-down after storms, and pre-storm ...","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-Related/NSIDC-0800/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16I_TPSM v1 - These data consist of calibrated soil moisture sensor measurements recorded by manual sampling teams at the field sites of SMAPVEX16-Iowa during two intensive observation periods in June and August of 2016. The sites were located throughout an experiment domain of about 30 km by 40 km approximately 30 km north of Ames, Iowa.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16I_TPSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16I_PLTBSM v1 - This product contains data derived from permanent in situ soil stations and observations by the Passive Active L-band System (PALS) microwave aircraft instrument. The PALS instrument was mounted to a DC-3 aircraft, which flew six parallel flight lines at an altitude of 3000 m in order to map a study area in South Fork, Iowa, United States.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16I_PLTBSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16I_PNET v1 - These data consist of soil moisture, soil temperature and precipitation measurements recorded in 2016 by the permanent soil moisture network; SMAPVEX16-Iowa. The sites were spread out over the experiment domain of about 30km by 40 km located about 30 km north of Ames, Iowa, USA.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16I_PNET/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV16M_PLTBSM v1 - This product contains data derived from permanent in situ soil stations and observations by the Passive Active L-band System (PALS) microwave aircraft instrument. The PALS instrument was mounted to a DC-3 aircraft, which flew six parallel flight lines at an altitude of 3000 m in order to map a 26 km x 48 km domain in Manitoba, Canada.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV16M_PLTBSM/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MA_LID v1 - These lidar measurements were collected in April and August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MA_LID/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MA_FOR v1 - The SMAPVEX19-22 Massachusetts Field data (SV19MA_FOR) consists of forest tree inventory, biophysical, leaf area index (LAI), and estimated biomass data as well as photos collected between April and August 2022 in the vicinity of Petersham, Massachusetts during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MA_FOR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MA_PAI v1 - These data consist of plant area index (PAI) and the RGB images from which the PAI values were derived. Data image files were taken between April 2019 to December 2022 in the vicinity of Petersham, Massachusetts during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MA_PAI/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MA_SAR v1 - This data set consists of mosaicked Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images corrected for terrain-flattened gamma. Data image files at three different polarization configurations were composited daily between April to July 2022 in the vicinity of Petersham, Massachusetts during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MA_SAR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MB_LID v1 - These lidar measurements were collected in April and August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MB_LID/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MB_FOR v1 - The SMAPVEX19-22 Millbrook Field data (SV19MB_FOR) consists of forest tree inventory, biophysical, leaf area index (LAI), and estimated biomass data as well as photos collected between April and August 2022 in the vicinity of Millbrook, New York during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MB_FOR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MB_PAI v1 - These data consist of plant area index (PAI) and the RGB images from which the PAI values were derived. Data image files were taken between April 2019 to December 2022 in the vicinity of Millbrook, New York during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MB_PAI/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SV19MB_SAR v1 - This data set consists of mosaicked Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images corrected for terrain-flattened gamma. Data image files at three different polarization configurations were composited daily between April to July 2022 in the vicinity of Millbrook, New York during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-VAL/SV19MB_SAR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX17_TLS_PC_BSU_Raw v1 - This data set contains raw, unprocessed terrestrial laser scanner (TLS) point cloud data collected as part of the 2017 SnowEx campaign in Grand Mesa, Colorado. Data were collected in the fall (September 2016) and winter (February 2017).","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX17_TLS_PC_BSU_Raw/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX17_TLS_PC_BSU v1 - This data set contains terrestrial laser scanner (TLS) point cloud data collected as part of the 2017 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2016) and snow-on (February 2017) conditions, at both open and forested locations.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX17_TLS_PC_BSU/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX17_SnowPits v1 - This data set contains measurements of snow temperature, density, stratigraphy, grain size, wetness, depth, and snow water equivalent (SWE) for snow pits at two study sites in Colorado, USA: Grand Mesa and Senator Beck Basin. Data were collected during the NASA SnowEx 2017 campaign.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX17_SnowPits/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX17_DGNSS1 v1 - This data set contains the coordinates of SnowEx infrastructure in Grand Mesa, Colorado, collected through a differential GNSS real-time kinematic (RTK) survey. The surveys were conducted at 244 stakes along 90 transects, 31 snow pits, 24 time-lapse cameras, and 15 reference poles used to estimate snow depth from camera images.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX17_DGNSS1/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX17_TLS_PC_CRREL v1 - This data set contains terrestrial LIDAR survey (TLS) point cloud data collected at Grand Mesa, Colorado as part of the 2017 SnowEx campaign. Data were collected in the fall (September and October) and winter (February) seasons.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX17_TLS_PC_CRREL/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX17_SBR v1 - This data set consists of surface-based radiometer (SBR) brightness temperatures of the snow surface and vegetation at Grand Mesa, CO, USA, acquired during NASA's 2017 SnowEx campaign. Four SBRs were deployed in the field and obtained measurements at 89, 37, 19, and 10.67 GHz in both vertical (V-pol) and horizontal (H-pol) polarizations.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX17_SBR/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX17_TLI v1 - This data set contains time-lapse images. Cameras were placed around Grand Mesa, CO at 34 sites and around Senator Beck Basin, CO at one site, coincident with other SnowEx 2017 measurements, including the TLS scans, sonic snow depth arrays, weather stations, and local scale observation sites.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX17_TLI/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SNEX21_TS_SP v1 - The data set is a time-series of snow pit measurements obtained by the SnowEx community during the 2021 field campaign. Between November 2020 and May 2021 data from 247 snow pits were collected at 24 unique sites distributed over 4 states (CO, ID, MT, UT) throughout the Western United States.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SNOWEX/SNEX21_TS_SP/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SMAP_L4_C_ANC_SOC_RST v1 - This ancillary SMAP product contains the yearly soil organic carbon (SOC) restart file. This file contains the area density of SOC at the start of the year, which is used to calculate daily SOC based on defined deposition and decay rates.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/SMAP-ANC/SMAP_L4_C_ANC_SOC_RST/1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"SWOT_POSTLAUNCH_L1_MOORING v1 - The SWOT Postlaunch Oceanography Field Campaign L1 Moorings collection provides the surface air pressure, GPS, and conductivity, temperature and depth (CTD) measurements from moorings at the L1 processing level. The SWOT satellite mission launched in late 2022 and underwent a calibration and validation (cal/val) phase in 2023.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SWOT_POSTLAUNCH_L1_MOORING","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1 v1.0 - This dataset includes simulated water surface elevations that resemble the Ka-band Interferometer (KaRIn) measurements by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1 v1.0 - This dataset contains a simulated lake product to be provided by the Surface Water and Ocean Topography (SWOT) mission with a focus on the North America continent. The product is derived from the high-rate (HR) measurements produced by the SWOT main instrument, a Ka-band Radar Interferometer.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1 v1.0 - This dataset contains a simulated river data product to be provided by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this dataset is a subset for the North America continent.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"LOCSS_L1_V1 v1.0 - This dataset contains data from the Lake Observations by Citizen Science and Satellites project, LOCSS which is a lake monitoring network. The data represent the location and main descriptors of the lake gauges and their readings.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/LOCSS_L1_V1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-tes","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA TIROS Project","Description":"TIROS-2 Medium-Resolution Scanning Radiometer Level 1 Final Meteorological Radiation Data (FMRT) product contains radiances expressed in five infrared/visible wavelength regions, expressed in either equivalent blackbody temperature (IR channels 1 and 2) or effective radiant emmitance (visible channels 3 and 5). The data will trace an elliptical, parabolic, or hyperbolic pattern on the ground due to the rotating of the instrument about the satellite spin axis. There is one orbit per file. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Final Meteorological Radiation Tapes (FMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The TIROS-2 satellite was successfully launched on November 23, 1960. The Medium-Resolution Scanning Radiometer experiment successfully returned data for about five months, becoming the first radiometer to make meteorlogical measurements from space. Three follow-on instruments were flown on TIROS-3, -4 and -7. Initially, all channels performed normally. However, channels 1 and 4 gradually deteriorated and by January 1961 were useless. The signal to noise ratio of channels 3 and 5 was extremely low, and the output was highly questionable. The instrument is a five channel radiometer with a 55 km footprint at nadir with the following characteristics: Channel 1: 6.0 to 6.5 microns - water vapor absorption Channel 2: 8.0 to 12.0 microns - atmospheric window Channel 3: 0.2 to 6.0 microns - reflected solar radiation Channel 4: 8.0 to 30 microns - terrestial radiation Channel 5: 0.55 to 0.75 microns - response to the TV system The Principal Investigator for these data was Joseph D. Barksdale from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00113 (old ID 60-016A-02A).\n<br>\n\n#### TIROS3L1ORT\nThe TIROS-3 Low-Resolution Omnidirectional Radiometer Level 1 Temperature Data product contains the black and white sensor temperature values in degrees Celsius. The experiment consisted of two sets of bolometers in the form of hollow aluminum hemispheres, mounted on opposite sides of the spacecraft, and whose optical axes were parallel to the spin axis. The bolometers were thermally isolated from but in close proximity to reflecting mirrors so that the hemispheres behaved like isolated spheres in space. The experiment was designed to measure the amount of solar energy absorbed, reflected, and emitted by the earth and its atmosphere in order to calculate the Earth's radiation budget. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Omnidirectional Radiometer Temperature (ORT) tapes. The data are archived in their original text format. The TIROS-3 satellite was successfully launched on July 12, 1961. The Low-Resolution Omnidirectional Radiometer experiment returned data for about three months. Two follow-on instruments were flown on TIROS-4 and -7, while a similar instrument flew on Explorer-7. The Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00187 (old id 61-017A-01A).\n<br>\n\n#### TIROS3L1FMRT\nTIROS-3 Medium-Resolution Scanning Radiometer Level 1 Final Meteorological Radiation Data (FMRT) product contains radiances expressed in five infrared/visible wavelength regions, expressed in either equivalent blackbody temperature (IR channels 1, 2 and 4) or effective radiant emmitance (visible channels 3 and 5). The data will trace an elliptical, parabolic, or hyperbolic pattern on the ground due to the rotating of the instrument about the satellite spin axis. There is one orbit per file. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Final Meteorological Radiation Tapes (FMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The TIROS-3 satellite was successfully launched on July 12, 1961. The Medium-Resolution Scanning Radiometer experiment returned data for about two and a half months. A previous instrument flew on TIROS-2 and two follow-on instruments were flown on TIROS-4 and -7. Response characteristics of all channels degraded rapidly after launch. The greatest uncertainty in the radiation measurements is due to the apparent shift in the zero radiation level. Data are usable for channels 1, 2, 3, 4, and 5 up to orbits 118, 875, 875, 130 and 300, respectively. The instrument is a five channel radiometer with a 55 km footprint at nadir with the following characteristics: Channel 1: 6.0 to 6.5 microns - water vapor absorption Channel 2: 8.0 to 12.0 microns - atmospheric window Channel 3: 0.2 to 6.0 microns - reflected solar radiation Channel 4: 8.0 to 30 - terrestial radiation Channel 5: 0.55 to 0.75 microns - response to the TV system The Principal Investigator for these data was Joseph D. Barksdale from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00141 (old id 61-017A-03A).\n<br>\n\n#### TIROS4L1ORR\nThe TIROS-4 Low-Resolution Omnidirectional Radiometer Level 1 Radiance Data product contains the longwave radiation values in Langleys/min derived from the black and white sensors. The experiment consisted of two sets of bolometers in the form of hollow aluminum hemispheres, mounted on opposite sides of the spacecraft, and whose optical axes were parallel to the spin axis. The bolometers were thermally isolated from but in close proximity to reflecting mirrors so that the hemispheres behaved like isolated spheres in space. The experiment was designed to measure the amount of solar energy absorbed, reflected, and emitted by the earth and its atmosphere in order to calculate the Earth's radiation budget. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Omnidirectional Radiometer Radiance (ORR) tapes. The data are archived in their text format. The TIROS-4 satellite was successfully launched on February 8, 1962. The Low-Resolution Omnidirectional Radiometer experiment returned data for about five months. A previous instrument flew on TIROS-3 and a follow-on instrument was flown on TIROS-7, while a similar instrument flew on Explorer-7. The Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00152 (old id 62-002A-01B).\n<br>\n\n#### TIROS4L1ORT\nThe TIROS-4 Low-Resolution Omnidirectional Radiometer Level 1 Temperature Data product contains the black and white sensor temperature values in degrees Celsius. The experiment consisted of two sets of bolometers in the form of hollow aluminum hemispheres, mounted on opposite sides of the spacecraft, and whose optical axes were parallel to the spin axis. The bolometers were thermally isolated from but in close proximity to reflecting mirrors so that the hemispheres behaved like isolated spheres in space. The experiment was designed to measure the amount of solar energy absorbed, reflected, and emitted by the earth and its atmosphere in order to calculate the Earth's radiation budget. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Omnidirectional Radiometer Temperature (ORT) tapes. The data are archived in their text format. The TIROS-4 satellite was successfully launched on February 8, 1962. The Low-Resolution Omnidirectional Radiometer experiment returned data for about five months. A previous instrument flew on TIROS-3 and a follow-on instrument was flown on TIROS-7, while a similar instrument flew on Explorer-7. The Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00252 (old id 62-002A-01A).\n<br>\n\n#### TIROS4L1FMRT\nTIROS-4 Medium-Resolution Scanning Radiometer Level 1 Final Meteorological Radiation Data (FMRT) product contains radiances expressed in five infrared/visible wavelength regions, expressed in either equivalent blackbody temperature (IR channels 1 and 2) or effective radiant emmitance (visible channels 3 and 5). The data will trace an elliptical, parabolic, or hyperbolic pattern on the ground due to the rotating of the instrument about the satellite spin axis. There is one orbit per file. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Final Meteorological Radiation Tapes (FMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The TIROS-4 satellite was successfully launched on February 8, 1962. The Medium-Resolution Scanning Radiometer experiment successfully returned data for about five months, continuing the measurements made by its predecessors flown on TIROS-2, and -3. A follow-on instrument was flown on TIROS-7. Degradation of the instrument after launch results in a departure of the data from the pre-launch calibration. The instrument is a five channel radiometer with a 55 km footprint at nadir with the following characteristics: Channel 1: 6.0 to 6.5 microns - water vapor absorption Channel 2: 8.0 to 12.0 microns - atmospheric window Channel 3: 0.2 to 6.0 microns - reflected solar radiation Channel 4: unused - transmitted redundant time reference signals Channel 5: 0.55 to 0.75 microns - response to the TV system The Principal Investigator for these data was Joseph D. Barksdale from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00139 (old ID 62-002A-03A).\n<br>\n\n#### TIROS7L1FMRT\nTIROS-7 Medium-Resolution Scanning Radiometer Level 1 Final Meteorological Radiation Data (FMRT) product contains radiances expressed in five infrared/visible wavelength regions, expressed in either equivalent blackbody temperature (IR channels 1,2 and 4) or effective radiant emmitance (visible channels 3 and 5). The data will trace an elliptical, parabolic, or hyperbolic pattern on the ground due to the rotating of the instrument about the satellite spin axis. There is one orbit per file. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Final Meteorological Radiation Tapes (FMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The TIROS-7 satellite was successfully launched on June 19, 1963. The Medium-Resolution Scanning Radiometer experiment successfully returned data for two years, continuing the measurements made by its predecessors flown on TIROS-2, -3 and -4. The instrument is a five channel radiometer with a 55 km footprint at nadir with the following characteristics: Channel 1: 14.8 to 15.5 microns - carbon dioxide absorption Channel 2: 8.0 to 12.0 microns - atmospheric window Channel 3: 0.2 to 6.0 microns - reflected solar radiation Channel 4: 8.0 to 30 microns - thermal radiation from the earth and atmosphere Channel 5: 0.55 to 0.75 microns - response to the TV system The Principal Investigator for these data was Joseph D. Barksdale from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00217 (old ID 63-024A-02A).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/TIROS2L1FMRT_001.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"TIROS2L1FMRT v001 - TIROS-2 Medium-Resolution Scanning Radiometer Level 1 Final Meteorological Radiation Data (FMRT) product contains radiances expressed in five infrared/visible wavelength regions, expressed in either equivalent blackbody temperature (IR channels 1 and 2) or effective radiant emmitance (visible channels 3 and 5). The data will trace an elliptical, parabolic, or hyperbolic pattern on the ground due to the rotating of the instrument about the satellite spin axis.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TIROS/TIROS2L1FMRT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TIROS3L1ORT v001 - The TIROS-3 Low-Resolution Omnidirectional Radiometer Level 1 Temperature Data product contains the black and white sensor temperature values in degrees Celsius. The experiment consisted of two sets of bolometers in the form of hollow aluminum hemispheres, mounted on opposite sides of the spacecraft, and whose optical axes were parallel to the spin axis.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TIROS/TIROS3L1ORT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TIROS3L1FMRT v001 - TIROS-3 Medium-Resolution Scanning Radiometer Level 1 Final Meteorological Radiation Data (FMRT) product contains radiances expressed in five infrared/visible wavelength regions, expressed in either equivalent blackbody temperature (IR channels 1, 2 and 4) or effective radiant emmitance (visible channels 3 and 5). The data will trace an elliptical, parabolic, or hyperbolic pattern on the ground due to the rotating of the instrument about the satellite spin axis.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TIROS/TIROS3L1FMRT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TIROS4L1ORR v001 - The TIROS-4 Low-Resolution Omnidirectional Radiometer Level 1 Radiance Data product contains the longwave radiation values in Langleys/min derived from the black and white sensors. The experiment consisted of two sets of bolometers in the form of hollow aluminum hemispheres, mounted on opposite sides of the spacecraft, and whose optical axes were parallel to the spin axis.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TIROS/TIROS4L1ORR.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TIROS4L1ORT v001 - The TIROS-4 Low-Resolution Omnidirectional Radiometer Level 1 Temperature Data product contains the black and white sensor temperature values in degrees Celsius. The experiment consisted of two sets of bolometers in the form of hollow aluminum hemispheres, mounted on opposite sides of the spacecraft, and whose optical axes were parallel to the spin axis.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TIROS/TIROS4L1ORT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TIROS4L1FMRT v001 - TIROS-4 Medium-Resolution Scanning Radiometer Level 1 Final Meteorological Radiation Data (FMRT) product contains radiances expressed in five infrared/visible wavelength regions, expressed in either equivalent blackbody temperature (IR channels 1 and 2) or effective radiant emmitance (visible channels 3 and 5). The data will trace an elliptical, parabolic, or hyperbolic pattern on the ground due to the rotating of the instrument about the satellite spin axis.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TIROS/TIROS4L1FMRT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TIROS7L1FMRT v001 - TIROS-7 Medium-Resolution Scanning Radiometer Level 1 Final Meteorological Radiation Data (FMRT) product contains radiances expressed in five infrared/visible wavelength regions, expressed in either equivalent blackbody temperature (IR channels 1,2 and 4) or effective radiant emmitance (visible channels 3 and 5). The data will trace an elliptical, parabolic, or hyperbolic pattern on the ground due to the rotating of the instrument about the satellite spin axis.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TIROS/TIROS7L1FMRT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-tiros","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA TOMS Project","Description":"The Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily ground station overpass data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), UV aerosol index and sulfur dioxide index values. The overpass data files contain the data derived from the best-matched TOMS field-of-view (FOV) to a site for every day the TOMS instrument was operational. The data are stored in an ASCII format. TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSEPL3dtoz\nThis Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSEPL3mtoz\nThis Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains total column ozone values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSEPL3daer\nThis Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains UV aerosol index values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSEPL3maer\nThis Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains UV aerosol index values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSEPL3dref\nThis Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSEPL3mref\nThis Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSEPL3dery\nThis Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains UV-B erythemal local noon irradiance values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSEPL3mery\nThis Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains UV-B erythemal local noon irradiance values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSEPL2\nThe Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 orbital swath data product contains total column ozone, UV aerosol index, and Lambertian effective surface reflectivity (Rayleigh corrected) at approximately 50x50 km2 resolution (at nadir). This product also contains some auxiliary derived and ancillary parameters including N-values, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and data quality flags. These data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSEPL3\nThe Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), and UV-B erythemal local noon irradiances. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in the EOS Hierarchical Data Format (HDF-EOS). The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSEPL3ztoz\nThis Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains total column ozone values. The data are averaged in 5 degree latitude bands. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSEPL3zaer\nThis Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains UV aerosol index values. The data are averaged in 5 degree latitude bands. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSEPL3zref\nThis Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are averaged in 5 degree latitude bands. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSM3OVP\nThe Meteor-3 Total Ozone Mapping Spectrometer (TOMS) version 8 daily ground station overpass data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), UV aerosol index and sulfur dioxide index values. The overpass data files contain the data derived from the best-matched TOMS field-of-view (FOV) to a site for every day the TOMS instrument was operational. The data are stored in an ASCII format. TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSM3L3dtoz\nThis Meteor-3 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSM3L3\nThe Meteor-3 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), and UV-B erythemal local noon irradiances. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in the EOS Hierarchical Data Format (HDF-EOS). The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSM3L3dref\nThis Meteor-3 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSN7OVP\nThe Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily ground station overpass data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), UV aerosol index and sulfur dioxide index values. The overpass data files contain the data derived from the best-matched TOMS field-of-view (FOV) to a site for every day the TOMS instrument was operational. The data are stored in an ASCII format. TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSN7L2\nThe Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 orbital swath data product contains total column ozone, UV aerosol index, and Lambertian effective surface reflectivity (Rayleigh corrected) at approximately 50x50 km2 resolution (at nadir). This product also contains some auxiliary derived and ancillary parameters including N-values, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and data quality flags. These data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSN7L3ztoz\nThis Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains total column ozone values. The data are averaged in 5 degree latitude bands. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSN7L3dtoz\nThis Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSN7L3mtoz\nThis Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains total column ozone values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSN7L3\nThe Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), and UV-B erythemal local noon irradiances. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in the EOS Hierarchical Data Format (HDF-EOS). The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSN7L3zaer\nThis Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains UV aerosol index values. The data are averaged in 5 degree latitude bands. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSN7L3daer\nThis Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains UV aerosol index values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSN7L3maer\nThis Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains UV aerosol index values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSN7L3zref\nThis Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are averaged in 5 degree latitude bands. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSN7L3dref\nThis Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSN7L3mref\nThis Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSN7L3dery\nThis Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains UV-B erythemal local noon irradiance values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### TOMSN7L3mery\nThis Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains UV-B erythemal local noon irradiance values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees. These data are stored in an ASCII format. The TOMS data were produced by the Laboratory for Atmospheres at NASA Goddard Space Flight Center (Code 614).\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/TOMSEPOVP_008.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","hdf","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"TOMSEPOVP v008 - The Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily ground station overpass data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), UV aerosol index and sulfur dioxide index values. The overpass data files contain the data derived from the best-matched TOMS field-of-view (FOV) to a site for every day the TOMS instrument was operational.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/EarthProbe_TOMS_Level3/TOMSEPOVP.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSEPL3dtoz v008 - This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone values. 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The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/EarthProbe_TOMS_Level3/TOMSEPL3mref.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSEPL3dery v008 - This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains UV-B erythemal local noon irradiance values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/EarthProbe_TOMS_Level3/TOMSEPL3dery.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSEPL3mery v008 - This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains UV-B erythemal local noon irradiance values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/EarthProbe_TOMS_Level3/TOMSEPL3mery.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSEPL2 v008 - The Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 orbital swath data product contains total column ozone, UV aerosol index, and Lambertian effective surface reflectivity (Rayleigh corrected) at approximately 50x50 km2 resolution (at nadir). This product also contains some auxiliary derived and ancillary parameters including N-values, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and data quality flags.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/EarthProbe_TOMS_Level2/TOMSEPL2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSEPL3 v008 - The Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), and UV-B erythemal local noon irradiances. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/EarthProbe_TOMS_Level3/TOMSEPL3.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSEPL3ztoz v008 - This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains total column ozone values. The data are averaged in 5 degree latitude bands.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/EarthProbe_TOMS_Level3/TOMSEPL3ztoz.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSEPL3zaer v008 - This Earth Probe (EP) Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains UV aerosol index values. 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The data are averaged in 5 degree latitude bands.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/EarthProbe_TOMS_Level3/TOMSEPL3zref.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSM3OVP v008 - The Meteor-3 Total Ozone Mapping Spectrometer (TOMS) version 8 daily ground station overpass data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), UV aerosol index and sulfur dioxide index values. The overpass data files contain the data derived from the best-matched TOMS field-of-view (FOV) to a site for every day the TOMS instrument was operational.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Meteor3_TOMS_Level3/TOMSM3OVP.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSM3L3dtoz v008 - This Meteor-3 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Meteor3_TOMS_Level3/TOMSM3L3dtoz.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSM3L3 v008 - The Meteor-3 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), and UV-B erythemal local noon irradiances. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Meteor3_TOMS_Level3/TOMSM3L3.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSM3L3dref v008 - This Meteor-3 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains Lambertian effective surface reflectivity values (Rayleigh corrected). The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Meteor3_TOMS_Level3/TOMSM3L3dref.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSN7OVP v008 - The Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily ground station overpass data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), UV aerosol index and sulfur dioxide index values. The overpass data files contain the data derived from the best-matched TOMS field-of-view (FOV) to a site for every day the TOMS instrument was operational.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_TOMS_Level3/TOMSN7OVP.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSN7L2 v008 - The Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 orbital swath data product contains total column ozone, UV aerosol index, and Lambertian effective surface reflectivity (Rayleigh corrected) at approximately 50x50 km2 resolution (at nadir). This product also contains some auxiliary derived and ancillary parameters including N-values, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and data quality flags.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_TOMS_Level2/TOMSN7L2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSN7L3ztoz v008 - This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains total column ozone values. The data are averaged in 5 degree latitude bands.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_TOMS_Level3/TOMSN7L3ztoz.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSN7L3dtoz v008 - This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_TOMS_Level3/TOMSN7L3dtoz.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSN7L3mtoz v008 - This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains total column ozone values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_TOMS_Level3/TOMSN7L3mtoz.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSN7L3 v008 - The Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains total column ozone, UV aerosol index, Lambertian effective surface reflectivity (Rayleigh corrected), and UV-B erythemal local noon irradiances. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_TOMS_Level3/TOMSN7L3.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSN7L3zaer v008 - This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily zonal means data product contains UV aerosol index values. The data are averaged in 5 degree latitude bands.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_TOMS_Level3/TOMSN7L3zaer.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSN7L3daer v008 - This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains UV aerosol index values. 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The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_TOMS_Level3/TOMSN7L3mref.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSN7L3dery v008 - This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 daily global gridded data product contains UV-B erythemal local noon irradiance values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_TOMS_Level3/TOMSN7L3dery.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOMSN7L3mery v008 - This Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) version 8 monthly averaged global gridded data product contains UV-B erythemal local noon irradiance values. The data are mapped to a global grid of size 180 x 288 with a lat-long resolution of 1.00 x 1.25 degrees.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/Nimbus7_TOMS_Level3/TOMSN7L3mery.008","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-toms","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA TOPEX/POSEIDON Project","Description":"The Sensory Data Record (SDR) is similar to the GDR product except that it also contains waveforms, which are required for retracking. This is an expert level product. If you do not need the waveforms then the GDR should suit your needs.\n<br>\n\n#### TOPEX_POSEIDON_GDR_F\nThe TOPEX/POSEIDON Geophysical Data Record (GDR) contains global coverage altimeter data. The objective of the TOPEX/POSEIDON mission, launched in August 1992, is to determine ocean topography with a sea surface height measurement precision of 3 cm and a sealevel measurement accuracy of 13 cm. The dataset contains measurements from two altimeters, a NASA dual frequency (Ku and C band) instrument similar to the Geosat altimeter, and a French space agency (CNES) instrument which is a proof-of-concept solid-state altimeter (Ku band). It also contains Sea Surface Height (SSH), significant wave height, ionospheric correction, tides and other geophysical corrections. It is emphasized that this product is considered research data because of the form and content of the data. The data consist entirely of files comprising headers and data records which contain over a hundred parameters for each second. It is swath data and there are no images. Analysis software is the responsibility of the user. Calculation of sea surface height anomalies from the altimeter range and environmental corrections is the responsibility of the user. The data are arranged in 10 day cycles that are separated into 254 passes, each about 56 minutes.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","oceans"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"TOPEX_ALTSDR vA - The Sensory Data Record (SDR) is similar to the GDR product except that it also contains waveforms, which are required for retracking. This is an expert level product.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TOPEX_ALTSDR","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"},{"Description":"TOPEX_POSEIDON_GDR_F vF - The TOPEX/POSEIDON Geophysical Data Record (GDR) contains global coverage altimeter data. The objective of the TOPEX/POSEIDON mission, launched in August 1992, is to determine ocean topography with a sea surface height measurement precision of 3 cm and a sealevel measurement accuracy of 13 cm.","ARN":"arn:aws:s3:::podaac-ops-cumulus-protected/TOPEX_POSEIDON_GDR_F","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://archive.podaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-topex-poseidon","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA TOVS Pathfinder Project","Description":"The Microwave Sounding Unit (MSU) Lower Troposphere Deep Layer Temperature product (MSULTT) provides gridded lower tropospheric temperatures derived from MSU instruments on several different platforms. The temperatures are derived using a combination of MSU channels 2 and 3 which has an averaging kernel that peaks near 500 hecto Pascals. The algorithm is based on Spencer and Christy (1990) with the LIMB 93 limb correction based on latitude, longitude, month, and scan angle. The MSU instruments measure the thermal emission of radiation by molecular oxygen at four frequencies near 60 GHz. North (south) of 66.7N (S) the footprint data are assigned to grid boxes in a weighted method depending on footprint latitude. Horizontal averaging is used\n<br>\n\n#### MSUUTT\nThe Microwave Sounding Unit (MSU) Upper Troposphere Temperature product (MSUUTT) provides gridded upper tropospheric temperatures derived from MSU instruments on several different platforms. The temperatures are derived using a combination of MSU channels 3 and 4 which has an averaging kernel that peaks near 250 hecto Pascals. The algorithm is based on Spencer and Christy (1990) with the LIMB 93 limb correction based on latitude, longitude, month, and scan angle. The MSU instruments measure the thermal emission of radiation by molecular oxygen at four frequencies near 60 GHz. North (south) of 66.7N (S) the footprint data are assigned to grid boxes in a weighted method depending on footprint latitude. Horizontal averaging is used to fill some of the empty grid boxes.\n<br>\n\n#### MSULST\nThe Microwave Sounding Unit (MSU) Lower Stratosphere Deep Layer Mean Temperature product (MSULST) provides gridded lower stratospheric temperatures for each day derived from MSU instruments on several different platforms. The temperatures are derived from MSU channel 4 using the method of Spencer and Christy (1990) with the LIMB 93 limb correction based on latitude, longitude, month, and scan angle. The MSU instruments measure the thermal emission of radiation by molecular oxygen at four frequencies near 60 GHz. North (south) of 66.7N (S) the footprint data are assigned to grid boxes in a weighted method depending on footprint latitude. Horizontal averaging is used to fill some of the empty grid boxes.\n<br>\n\n#### MSUOP\nThe Microwave Sounding Unit (MSU) Ocean Precipitation product (MSUOP) provides gridded upper tropospheric temperatures derived from MSU instruments on several different platforms. The precipitation estimates follow the method of Spencer (1993). Oceanic precipitation is estimated by increased warming in MSU channel 1 over a threshold. The increased warming is attributable to emission by liquid water in the lower troposphere. MSU channels 2 and 3 are used to remove warming due to air mass differences. The MSU instruments measure the thermal emission of radiation by molecular oxygen at four frequencies near 60 GHz.\n<br>\n\n#### TOVSA5NG\nThis dataset (TOVSA5NG) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-10 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). There are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid. These data products are 5 day averages.\n<br>\n\n#### TOVSA5NH\nThis dataset (TOVSA5NH) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-11 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). There are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid. This collection contain 5-day averages.\n<br>\n\n#### TOVSA5ND\nThis dataset (TOVSA5ND) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-12 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). There are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid. This collection contains 5-day averages.\n<br>\n\n#### TOVSA5NF\nThis dataset (TOVSA5NF) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-9 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). There are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. This collection contains a 5 day average.\n<br>\n\n#### TOVSA5TN\nThis dataset (TOVSA5TN) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from TIROS-N and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). There are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid. This collection contains 5-day averages.\n<br>\n\n#### TOVSADNG\nThis dataset (TOVSADNG) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-10 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). There are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.\n<br>\n\n#### TOVSADNH\nThis dataset (TOVSADNH) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-11 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). There are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.\n<br>\n\n#### TOVSADND\nThis dataset (TOVSADND) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-12 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). There are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.\n<br>\n\n#### TOVSADNF\nThis dataset (TOVSADNF) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-9 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). There are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.\n<br>\n\n#### TOVSADTN\nThis dataset (TOVSADTN) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from TIROS-N and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). There are level 3 data product files for five TOVS satellites, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.\n<br>\n\n#### TOVSAMNG\nThis dataset (TOVSAMNG) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-10 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). These Level 3 monthly mean products are in the netCDF format. Each data set is representative of a different monthly average time period and for one of nine satellites. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.\n<br>\n\n#### TOVSAMNH\nThis dataset (TOVSAMNH) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-11 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). These Level 3 monthly mean products are in the netCDF format. Each data set is representative of a different monthly average time period and for one of nine satellites. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid. For this NOAA-11 satellite the downlink of data was turned off in mid-1994 then reactivated in 1997. Therefore there is a gap in the monthly data products.\n<br>\n\n#### TOVSAMND\nThis dataset (TOVSAMND) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-12 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). These Level 3 monthly mean products in the netCDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.\n<br>\n\n#### TOVSAMNJ\nThis dataset (TOVSAMNJ) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-14 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). These Level 3 monthly mean products are in the netCDF format. Each data set is representative of a different monthly average time period and for one of nine satellites. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.\n<br>\n\n#### TOVSAMNA\nThis dataset (TOVSAMNA) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-6 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). These Level 3 monthly mean products are in the netCDF format. Each data set is representative of a different monthly average time period and for one of nine satellites. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.\n<br>\n\n#### TOVSAMNC\nThis dataset (TOVSAMNC) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-7 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). These Level 3 monthly mean products are in netCDF format. Each data set is representative of a different monthly average time period and for one of nine satellites. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.\n<br>\n\n#### TOVSAMNE\nThis dataset (TOVSAMNE) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-8 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). These Level 3 monthly mean products are in the netCDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.\n<br>\n\n#### TOVSAMNF\nThis dataset (TOVSAMNF) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-9 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). These Level 3 monthly mean products are in the netCDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.\n<br>\n\n#### TOVSAMTN\nThis dataset (TOVSAMTN) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from TIROS-N and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloud top height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). These Level 3 monthly mean products are in the netCDF format. Each data set is representative of a different monthly average time period and for one of nine satellites. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.\n<br>\n\n#### TOVSAMTN\nThis dataset (TOVSAMTN) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from TIROSN and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates. The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occuring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle. The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984). There are 6 level 3 data product files, each of which is in the HDF format and each representative of a different averaging time period. All files contain the same number of geophysical parameter arrays stored as HDF Scientific Data Sets (SDSs). The time periods include daily, 5 day (pentad) and monthly, with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/MSULTT_001.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","hdf","ice","netcdf","oceans","precipitation","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"MSULTT v001 - The Microwave Sounding Unit (MSU) Lower Troposphere Deep Layer Temperature product (MSULTT) provides gridded lower tropospheric temperatures derived from MSU instruments on several different platforms. The temperatures are derived using a combination of MSU channels 2 and 3 which has an averaging kernel that peaks near 500 hecto Pascals.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/msu/MSULTT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MSUUTT v001 - The Microwave Sounding Unit (MSU) Upper Troposphere Temperature product (MSUUTT) provides gridded upper tropospheric temperatures derived from MSU instruments on several different platforms. The temperatures are derived using a combination of MSU channels 3 and 4 which has an averaging kernel that peaks near 250 hecto Pascals.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/msu/MSUUTT.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MSULST v001 - The Microwave Sounding Unit (MSU) Lower Stratosphere Deep Layer Mean Temperature product (MSULST) provides gridded lower stratospheric temperatures for each day derived from MSU instruments on several different platforms. The temperatures are derived from MSU channel 4 using the method of Spencer and Christy (1990) with the LIMB 93 limb correction based on latitude, longitude, month, and scan angle.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/msu/MSULST.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"MSUOP v001 - The Microwave Sounding Unit (MSU) Ocean Precipitation product (MSUOP) provides gridded upper tropospheric temperatures derived from MSU instruments on several different platforms. The precipitation estimates follow the method of Spencer (1993).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/msu/MSUOP.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSA5NG v01 - This dataset (TOVSA5NG) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-10 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/tovs/TOVSA5NG","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSA5NH v01 - This dataset (TOVSA5NH) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-11 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/tovs/TOVSA5NH","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSA5ND v01 - This dataset (TOVSA5ND) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-12 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/tovs/TOVSA5ND","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSA5NF v01 - This dataset (TOVSA5NF) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-9 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/tovs/TOVSA5NF","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSA5TN v01 - This dataset (TOVSA5TN) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from TIROS-N and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/tovs/TOVSA5TN","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSADNG v01 - This dataset (TOVSADNG) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-10 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/tovs/TOVSADNG","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSADNH v01 - This dataset (TOVSADNH) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-11 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/tovs/TOVSADNH","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSADND v01 - This dataset (TOVSADND) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-12 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/tovs/TOVSADND","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSADNF v01 - This dataset (TOVSADNF) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-9 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/tovs/TOVSADNF","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSADTN v01 - This dataset (TOVSADTN) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from TIROS-N and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/tovs/TOVSADTN","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSAMNG v02 - This dataset (TOVSAMNG) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-10 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TOVS/TOVSAMNG.02","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSAMNH v02 - This dataset (TOVSAMNH) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-11 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TOVS/TOVSAMNH.02","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSAMND v02 - This dataset (TOVSAMND) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-12 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TOVS/TOVSAMND.02","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSAMNJ v02 - This dataset (TOVSAMNJ) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-14 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TOVS/TOVSAMNJ.02","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSAMNA v02 - This dataset (TOVSAMNA) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-6 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TOVS/TOVSAMNA.02","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSAMNC v02 - This dataset (TOVSAMNC) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-7 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TOVS/TOVSAMNC.02","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSAMNE v02 - This dataset (TOVSAMNE) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-8 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TOVS/TOVSAMNE.02","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSAMNF v02 - This dataset (TOVSAMNF) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-9 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TOVS/TOVSAMNF.02","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSAMTN v02 - This dataset (TOVSAMTN) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from TIROS-N and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TOVS/TOVSAMTN.02","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TOVSAMTN v01 - This dataset (TOVSAMTN) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from TIROSN and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/tovs/TOVSAMTN","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-tovs-pathfinder","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA TRACE-A Project","Description":"TRACE-A_Sondes_Data is the balloonsonde and ozonesonde data collected during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data collection for this product is complete. The TRACE-A mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October. NASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal. The NASA DC-8 aircraft and ozonesondes were utilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few instruments on the DC-8 include the Differential Absorption Lidar (DIAL), the Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified Licor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone, and ozone column. The Laser-Induced Fluorescence instrument collected measurements on NxOy in the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified Licor recorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2. Ozonesondes played a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\n<br>\n\n#### TRACE-A_Brazil_Data\nTRACE-A_Brazil_Data is the aircraft and rawinsonde data collected in Brazil during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data from the Advanced Very High Resolution Radiometer (AVHRR) is featured in this collection. Data collection for this product is complete. The TRACE-A mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October. NASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal. The NASA DC-8 aircraft and ozonesondes were utilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few instruments on the DC-8 include the Differential Absorption Lidar (DIAL), the Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified Licor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone, and ozone column. The Laser-Induced Fluorescence instrument collected measurements on NxOy in the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified Licor recorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2. Ozonesondes played a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\n<br>\n\n#### TRACE-A_TraceGas_AircraftInSitu_DC8_Data\nTRACE-A_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data from the Two Photon - Laser Induced Fluorescence (TP-LIF) and Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection. Data collection for this product is complete. The TRACE-A mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October. NASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal. The NASA DC-8 aircraft and ozonesondes were utilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few instruments on the DC-8 include the Differential Absorption Lidar (DIAL), the Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified Licor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone, and ozone column. The Laser-Induced Fluorescence instrument collected measurements on NxOy in the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified Licor recorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2. Ozonesondes played a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\n<br>\n\n#### TRACE-A_AircraftRemoteSensing_DC8_DIAL_Data\nTRACE-A_AircraftRemoteSensing_DC8_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data collection for this product is complete. The TRACE-A mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October. NASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal. The NASA DC-8 aircraft and ozonesondes were utilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few instruments on the DC-8 include the Differential Absorption Lidar (DIAL), the Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified Licor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone, and ozone column. The Laser-Induced Fluorescence instrument collected measurements on NxOy in the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified Licor recorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2. Ozonesondes played a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\n<br>\n\n#### TRACE-A_MetNav_AircraftInSitu_DC8_Data\nTRACE-A_MetNav_AircraftInSitu_DC8_Data is the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data collection for this product is complete. TRACE-A_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data from the Two Photon - Laser Induced Fluorescence (TP-LIF) and Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection. Data collection for this product is complete. The TRACE-A mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October. NASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal. The NASA DC-8 aircraft and ozonesondes were utilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few instruments on the DC-8 include the Differential Absorption Lidar (DIAL), the Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified Licor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone, and ozone column. The Laser-Induced Fluorescence instrument collected measurements on NxOy in the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified Licor recorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2. Ozonesondes played a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\n<br>\n\n#### TRACE-A_Trajectory_Data\nTRACE-A_Trajectory_Data is the kinematic trajectory data collected during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data from the Two Photon - Laser Induced Fluorescence (TP-LIF) and Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection. Data collection for this product is complete. The TRACE-A mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October. NASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal. The NASA DC-8 aircraft and ozonesondes were utilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few instruments on the DC-8 include the Differential Absorption Lidar (DIAL), the Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified Licor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone, and ozone column. The Laser-Induced Fluorescence instrument collected measurements on NxOy in the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified Licor recorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2. Ozonesondes played a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\n<br>\n\n#### TRACE-A_Merge_Data\nTRACE-A_Merge_Data is merge data files created from data collected onboard the DC-8 aircraft during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data collection for this product is complete. The TRACE-A mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October. NASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal. The NASA DC-8 aircraft and ozonesondes were utilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few instruments on the DC-8 include the Differential Absorption Lidar (DIAL), the Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified Licor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone, and ozone column. The Laser-Induced Fluorescence instrument collected measurements on NxOy in the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified Licor recorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2. Ozonesondes played a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\n<br>\n\n#### TRACE-A_Satellite_Data\nTRACE-A_Satellite_Data is the supplementary satellite data collected during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data from the NOAA 10, 11, and 12 satellites and the Total Ozone Mapping Spectrometer (TOMS) satellite instrument are featured in this collection. Data collection for this product is complete. The TRACE-A mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-A was conducted in the Atlantic from September 21 to October 24, 1992. TRACE-A had the objective of determining the cause and source of the high concentrations of ozone that accumulated over the Atlantic Ocean between southern Africa and South America from August to October. NASA partnered with the Brazilian Space Agency (INPE) to accomplish this goal. The NASA DC-8 aircraft and ozonesondes were utilized during TRACE-A to collect the necessary data. The DC-8 was equipped with 19 instruments. A few instruments on the DC-8 include the Differential Absorption Lidar (DIAL), the Laser-Induced Fluorescence, the O3-NO Ethylene/Forward Scattering Spectrometer, the Modified Licor, and the DACOM IR Laser Spectrometer. The DIAL was responsible for a variety of measurements, which include Nadir IR aerosols, Nadir UV aerosols, Zenith IR aerosols, Zenith VS aerosols, ozone, and ozone column. The Laser-Induced Fluorescence instrument collected measurements on NxOy in the atmosphere. Measurements of ozone were recorded by the O3-NO Ethylene/Forward Scattering Spectrometer while the Modified Licor recorded CO2. Finally, the DACOM IR Laser Spectrometer gathered an assortment of data points, including CO, O3, N2O, CH4, and CO2. Ozonesondes played a role in data collection for TRACE-A along with the DC-8 aircraft. The sondes were dropped from the DC-8 aircraft in order to gather data on ozone, temperature, and atmospheric pressure.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www-gte.larc.nasa.gov/trace/tra_hmpg.htm","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","lidar","oceans","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"TRACE-A_Sondes_Data v1 - TRACE-A_Sondes_Data is the balloonsonde and ozonesonde data collected during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::trace-a-sondes-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-A_Brazil_Data v1 - TRACE-A_Brazil_Data is the aircraft and rawinsonde data collected in Brazil during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data from the Advanced Very High Resolution Radiometer (AVHRR) is featured in this collection.","ARN":"arn:aws:s3:::trace-a-brazil-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-A_TraceGas_AircraftInSitu_DC8_Data v1 - TRACE-A_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data from the Two Photon - Laser Induced Fluorescence (TP-LIF) and Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection.","ARN":"arn:aws:s3:::trace-a-tracegas-aircraftinsitu-dc8-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-A_AircraftRemoteSensing_DC8_DIAL_Data v1 - TRACE-A_AircraftRemoteSensing_DC8_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::trace-a-aircraftremotesensing-dc8-dial-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-A_MetNav_AircraftInSitu_DC8_Data v1 - TRACE-A_MetNav_AircraftInSitu_DC8_Data is the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::trace-a-metnav-aircraftinsitu-dc8-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-A_Trajectory_Data v1 - TRACE-A_Trajectory_Data is the kinematic trajectory data collected during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data from the Two Photon - Laser Induced Fluorescence (TP-LIF) and Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection.","ARN":"arn:aws:s3:::trace-a-trajectory-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-A_Merge_Data v1 - TRACE-A_Merge_Data is merge data files created from data collected onboard the DC-8 aircraft during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::trace-a-merge-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-A_Satellite_Data v1 - TRACE-A_Satellite_Data is the supplementary satellite data collected during the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE-A) suborbital campaign. Data from the NOAA 10, 11, and 12 satellites and the Total Ozone Mapping Spectrometer (TOMS) satellite instrument are featured in this collection.","ARN":"arn:aws:s3:::trace-a-satellite-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-trace-a","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA TRACE-P Project","Description":"TRACE-P_Sondes_Data is the balloonsonde and ozonesonde data collected during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_Cloud_AircraftInSitu_DC8_Data\nTRACE-P_Cloud_AircraftInSitu_DC8_Data is the in-situ cloud data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_Aerosol_AircraftInSitu_DC8_Data\nTRACE-P_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_TraceGas_AircraftInSitu_DC8_Data\nTRACE-P_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Two Photon - Laser Induced Fluorescence (TP-LIF) and Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_jValue_AircraftInSitu_DC8_Data\nTRACE-P_jValue_AircraftInSitu_DC8_Data is the photolysis frequencies (j-values) measured along the DC-8 flight during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_AircraftRemoteSensing_DC8_DIAL_Data\nTRACE-P_AircraftRemoteSensing_DC8_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_Trajectory_DC8_Data\nTRACE-P_Trajectory_DC8_Data is the trajectory data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_Ground_Data\nTRACE-P_Ground_Data is the ground site data collected during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_MetNav_AircraftInSitu_DC8_Data\nTRACE-P_MetNav_AircraftInSitu_DC8_Data is the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Diode Laser Hygrometer (DLH) instrument is featured in this collection. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_MetNav_Aircraft_InSitu_P3B_Data\nTRACE-P_MetNav_Aircraft_InSitu_P3B_Data is the in situ meteorology and navigation data collected onboard the P-3B aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the P-3B Turbulent Air Motion Measurement System (TAMMS) is featured in this collection. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_Merge_Data\nTRACE-P_Merge_Data is the merge data files created from data collected during during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_Model_Data\nTRACE-P_Model_Data is the model data collected during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_Aerosol_AircraftInSitu_P3B_Data\nTRACE-P_Aerosol_AircraftInSitu_P3B_Data is the in-situ aerosol data collected onboard the P-3B aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Chemical Ionization Mass Spectrometer (CIMS) and the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_TraceGas_InSitu_P3B_Data\nTRACE-P_TraceGas_InSitu_P3B_Data is the in-situ trace gas data collected onboard the P-3B aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Chemical Ionization Mass Spectrometer (CIMS) and the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_jValue_AircraftInSitu_P3B_Data\nTRACE-P_jValue_AircraftInSitu_P3B_Data is the photolysis frequencies (j-values) measured along the P-3B flight aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_Trajectory_P3B_Data\nTRACE-P_Trajectory_P3B_Data is the trajectory data collected onboard the P-3B aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Chemical Ionization Mass Spectrometer (CIMS) and the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### TRACE-P_Satellite_Data\nTRACE-P_Satellite_Data is the supplementary satellite data collected during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Multi-Angle Imaging SpectroRadiometer (MISR) and the Measurements of Pollution in the Troposphere (MOPITT) satellite instruments are featured in this collection. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.asdc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www-air.larc.nasa.gov/missions/tracep/tracep.htm","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","lidar","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"TRACE-P_Sondes_Data v1 - TRACE-P_Sondes_Data is the balloonsonde and ozonesonde data collected during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::trace-p-sondes-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_Cloud_AircraftInSitu_DC8_Data v1 - TRACE-P_Cloud_AircraftInSitu_DC8_Data is the in-situ cloud data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::trace-p-cloud-aircraftinsitu-dc8-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_Aerosol_AircraftInSitu_DC8_Data v1 - TRACE-P_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::trace-p-aerosol-aircraftinsitu-dc8-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_TraceGas_AircraftInSitu_DC8_Data v1 - TRACE-P_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Two Photon - Laser Induced Fluorescence (TP-LIF) and Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection.","ARN":"arn:aws:s3:::trace-p-tracegas-aircraftinsitu-dc8-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_jValue_AircraftInSitu_DC8_Data v1 - TRACE-P_jValue_AircraftInSitu_DC8_Data is the photolysis frequencies (j-values) measured along the DC-8 flight during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::trace-p-jvalue-aircraftinsitu-dc8-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_AircraftRemoteSensing_DC8_DIAL_Data v1 - TRACE-P_AircraftRemoteSensing_DC8_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::trace-p-aircraftremotesensing-dc8-dial-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_Trajectory_DC8_Data v1 - TRACE-P_Trajectory_DC8_Data is the trajectory data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::trace-p-trajectory-dc8-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_Ground_Data v1 - TRACE-P_Ground_Data is the ground site data collected during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::trace-p-ground-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_MetNav_AircraftInSitu_DC8_Data v1 - TRACE-P_MetNav_AircraftInSitu_DC8_Data is the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Diode Laser Hygrometer (DLH) instrument is featured in this collection.","ARN":"arn:aws:s3:::trace-p-metnav-aircraftinsitu-dc8-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_MetNav_Aircraft_InSitu_P3B_Data v1 - TRACE-P_MetNav_Aircraft_InSitu_P3B_Data is the in situ meteorology and navigation data collected onboard the P-3B aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the P-3B Turbulent Air Motion Measurement System (TAMMS) is featured in this collection.","ARN":"arn:aws:s3:::trace-p-metnav-aircraft-insitu-p3b-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_Merge_Data v1 - TRACE-P_Merge_Data is the merge data files created from data collected during during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::trace-p-merge-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_Model_Data v1 - TRACE-P_Model_Data is the model data collected during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::trace-p-model-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_Aerosol_AircraftInSitu_P3B_Data v1 - TRACE-P_Aerosol_AircraftInSitu_P3B_Data is the in-situ aerosol data collected onboard the P-3B aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Chemical Ionization Mass Spectrometer (CIMS) and the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection.","ARN":"arn:aws:s3:::trace-p-aerosol-aircraftinsitu-p3b-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_TraceGas_InSitu_P3B_Data v1 - TRACE-P_TraceGas_InSitu_P3B_Data is the in-situ trace gas data collected onboard the P-3B aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Chemical Ionization Mass Spectrometer (CIMS) and the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection.","ARN":"arn:aws:s3:::trace-p-tracegas-insitu-p3b-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_jValue_AircraftInSitu_P3B_Data v1 - TRACE-P_jValue_AircraftInSitu_P3B_Data is the photolysis frequencies (j-values) measured along the P-3B flight aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data collection for this product is complete.","ARN":"arn:aws:s3:::trace-p-jvalue-aircraftinsitu-p3b-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_Trajectory_P3B_Data v1 - TRACE-P_Trajectory_P3B_Data is the trajectory data collected onboard the P-3B aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Chemical Ionization Mass Spectrometer (CIMS) and the Differential Absorption of CO, CH4, N2O Measurements (DACOM) instruments are featured in this collection.","ARN":"arn:aws:s3:::trace-p-trajectory-p3b-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"},{"Description":"TRACE-P_Satellite_Data v1 - TRACE-P_Satellite_Data is the supplementary satellite data collected during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Multi-Angle Imaging SpectroRadiometer (MISR) and the Measurements of Pollution in the Troposphere (MOPITT) satellite instruments are featured in this collection.","ARN":"arn:aws:s3:::trace-p-satellite-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.asdc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-trace-p","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA TransCom Project","Description":"The Atmospheric Tracer Transport Model Intercomparison Project (TransCom) was created to quantify and diagnose the uncertainty in inversion calculations of the global carbon budget that results from errors in simulated atmospheric transport, the choice of measured atmospheric carbon dioxide data used, and the inversion methodology employed. Under the third phase of TransCom (TransCom 3), surface-atmosphere CO2 fluxes were estimated from an intercomparison of 16 different atmospheric tracer transport models and model variants in order to assess the contribution of uncertainties in transport to the uncertainties in flux estimates for annual mean, seasonal cycle, and interannual inversions (referred to as Level 1, 2, and 3 experiments, respectively). This data set provides the model output and inversion results for the TransCom 3, Level I annual mean inversion experiments. Annual mean CO2 concentration data (GLOBALVIEW-CO2, 2000) were used to estimate CO2 sources. The annual average fluxes were estimated for the 1992-1996 period using each of the 16 transport models and a common inversion set-up (Gurney et al., 2002). Methodological choices for this control inversion were selected on the basis of knowledge gained from a wide range of sensitivity tests (Law et al., 2003). Gurney et al. (2003) present results from the control inversion for individual models as well as results from a number of sensitivity tests related to the specification of prior flux information. Additional information about the experimental protocol and results is provided in the companion files and the TransCom project web site (http://www.purdue.edu/transcom/index.php). The results of the Level 1 experiments presented here are grouped into two broad categories: forward simulation fields and response functions (model output) and estimated fluxes (inversion results).\n<br>\n\n#### level_2_seasonal_co2_1198\nThis data set provides model outputs and seasonal mean CO2 fluxes from the Atmospheric Carbon Cycle Inversion Intercomparison (TransCom 3), Level 2 inversion experiment. Inversion methods can be used to estimate surface CO2 fluxes from atmospheric CO2 concentration measurements, given an atmospheric transport model to relate the two. This Level 2 experiment inverted for the spatial and temporal pattern of the residual CO2 sources and sinks. There were 12 atmospheric tracer transport models utilized in this experiment. The data inverted were mean CO2 concentration data from 75 sites from the GLOBALVIEW-CO2 2000 data set for the period 1990-1996. The seasonal inversion consists of a 3 year forward simulation (365 days per year) containing 4 presubtracted tracers, 11 SF6 tracers, and 22 CO2 tracers (11 terrestrial, 11 oceanic) (Gurney et al., 2000). Carbon fluxes were estimated for each month of an average year determined as the mean of the 1992-1996 time period from an intercomparison of 12 different atmospheric tracer transport models. This data set provides input data, model output data,the cyclo inversion code, a basis function map, and estimated fluxes.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","ice","oceans"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"level_1_annual_co2_895 v1 - The Atmospheric Tracer Transport Model Intercomparison Project (TransCom) was created to quantify and diagnose the uncertainty in inversion calculations of the global carbon budget that results from errors in simulated atmospheric transport, the choice of measured atmospheric carbon dioxide data used, and the inversion methodology employed. Under the third phase of TransCom (TransCom 3), surface-atmosphere CO2 fluxes were estimated from an intercomparison of 16 different atmospheric tracer transport models and model variants in order to assess the contribution of uncertainties in transport ...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/transcom/level_1_annual_co2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"level_2_seasonal_co2_1198 v1 - This data set provides model outputs and seasonal mean CO2 fluxes from the Atmospheric Carbon Cycle Inversion Intercomparison (TransCom 3), Level 2 inversion experiment. Inversion methods can be used to estimate surface CO2 fluxes from atmospheric CO2 concentration measurements, given an atmospheric transport model to relate the two.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/transcom/level_2_seasonal_co2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-transcom","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA TRMM Project","Description":"This is a new (GPM-formated) TRMM product. The equivalent old TRMM legacy product is TRMM_2H31. Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. Estimating vertical profiles of latent heating released by precipitating cloud systems is one of the key objectives of TRMM, together with accurately measuring the horizontal distribution of tropical rainfall. The method uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The CSH algorithm is severely limited by the inherent sensitivity of the TRMM PR. For latent heating, the quantity required is actually cloud top, but the PR can detect only precipitation-sized particles. Because observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the CSH algorithm in comparison with the CSH algorithm.\n<br>\n\n#### GPM_3HCSH_TRMM\nThis is the new (GPM-formated) TRMM product. It replaces the old TRMM legacy TRMM_3H31 Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. Estimating vertical profiles of latent heating released by precipitating cloud systems is one of the key objectives of TRMM, together with accurately measuring the horizontal distribution of tropical rainfall. The method uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The CSH algorithm is severely limited by the inherent sensitivity of the TRMM PR. For latent heating, the quantity required is actually cloud top, but the PR can detect only precipitation-sized particles. Because observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the CSH algorithm in comparison with the CSH algorithm. Monthly Spectral Latent Heating produces 0.25x0.25 degree grid of latent heating profiles from the TRMM PR rain. The grids are in the Planetary Grid 2 structure matching the Dual-frequency PR on the core GPM observatory that covers 67S to 67N degrees of latitudes. Areas beyond the ±40 degrees of latitudes are padded with empty grid cells.\n<br>\n\n#### GPM_3GCSH_TRMM\nThis is the new (GPM-formated) TRMM product. It replaces the old TRMM legacy product TRMM_3G31. Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. Estimating vertical profiles of latent heating released by precipitating cloud systems is one of the key objectives of TRMM, together with accurately measuring the horizontal distribution of tropical rainfall. The method uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The CSH algorithm is severely limited by the inherent sensitivity of the TRMM PR. For latent heating, the quantity required is actually cloud top, but the PR can detect only precipitation-sized particles. Because observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the CSH algorithm in comparison with the CSH algorithm. The Gridded Orbital Spectral Latent Heating is actually one orbit gridded onto a global map with 0.25x0.25 degree grid cell size. These latent heating profiles from the TRMM Precipitation Radar (PR) rain. The granule temporal size is one orbit.\n<br>\n\n#### GPM_1BPR\nThis is the new (GPM-formated) TRMM product. It replaces the old TRMM_1B21,1C21 Version 07 is the current version of the data set. Previous versions have been superseded by Version 07. The TRMM Precipitation Radar (PR), the first of its kind in space, is an electronically scanning radar, operating at 13.8 GHz that measures the 3-D rainfall distribution over both land and ocean, and defines the layer depth of the precipitation. The 1B21 calculates the received power at the PR receiver input point from the Level-0 count value which is linearly proportional to the logarithm of the PR receiver output power. To convert the count value to the input power, extensive internal calibrations are applied, which are mainly based upon the system model, temperature dependence of model parameters and many temperature sensors attached at various locations of the PR. Periodically the input-output characteristics are measured using an internal calibration loop for the IF unit and later receiver stages. To make an absolute calibration, an Active Radar Calibrator (ARC) is placed at Kansai Branch of CRL and overall system gain of the PR is being measured every 2 months. Using the transfer function based on the above internal and external calibrations, the PR received power is obtained. Note that the value assumes that the signal follows the Rayleigh fading, so if the fading characteristics of a scatter is different, a small bias error may occur (within 1 or 2 dB). Changes in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001: Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km\n<br>\n\n#### GPM_3GSLH_TRMM\nThis is the new (GPM-formated) TRMM product. It replaces the old TRMM_3G25 Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. Estimating vertical profiles of latent heating released by precipitating cloud systems is one of the key objectives of TRMM, together with accurately measuring the horizontal distribution of tropical rainfall. The method uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The SLH algorithm is severely limited by the inherent sensitivity of the TRMM PR. For latent heating, the quantity required is actually cloud top, but the PR can detect only precipitation-sized particles. Because observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the SLH algorithm in comparison with the CSH algorithm. The Gridded Orbital Spectral Latent Heating is actually one orbit gridded onto a global map with 0.5 degree x 0.5 degree grid cell size. These latent heating profiles from the TRMM Precipitation Radar (PR) rain. The granule temporal size is one orbit.\n<br>\n\n#### GPM_3PRD\nThis a new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products. Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. This is the GPM-like formatted TRMM Precipitation Radar (PR) daily gridded data, first released with the \"V8\" TRMM reprocessing. The daily radar grid data is new for TRMM nomenclature and is introduced for consistency with the GPM Dual-frequency Precipitation Radar (DPR). The closest ancestor was 3A25 which was a monthly radar statistics. This product consists of daily statistics of the PR measurements at (0.25x0.25) degrees horizontal resolution. The objective of the algorithm is to calculate various daily statistics from the level 2 PR output products. Four types of statistics are calculated: 1. Probabilities of occurrence (count values) 2. Means and standard deviations In all cases, the statistics are conditioned on the presence of rain or some other quantity such as the presence of stratiform rain or the presence of a bright-band. For example, to compute the unconditioned mean rain rate, the conditional mean must be multiplied by the probability of rain which, in turn is calculated from the ratio of rain counts to the total number of observations in the box of interest. The grids are in the Planetary Grid 2 structure matching the Dual-frequency PR on the core GPM observatory that covers 67S to 67N degrees of latitudes. Areas beyond the ±40 degrees of latitudes are padded with empty grid cells.\n<br>\n\n#### GPM_2APR\nThis is the new, GPM-like, format for TRMM Precipitation Radar L2 data that now incorporates what was known as 2A21, 2A23 and 2A25 datasets. The primary purpose of 2A21 is to compute the path-integrated attenuation (PIA) using the surface reference technique (SRT). The surface reference technique rests on the assumption that the difference between the measurements of the normalized surface cross section outside and within the rain provides an estimate of the PIA. The secondary purpose of 2A21 is to compute the normalized radar cross sections (\"sigma-not\" or Normalized Radar Cross-Sectin (NRTS)) of the surface under rain-free conditions. Main objectives of 2A23 are as follows: (a) Detection of bright band (BB) and determination of the height of BB, the strength of BB, and the width (i.e. thickness) of BB when BB exists. (b) Classification of rain type into the following three categories: - stratiform, - convective, - other, where \"other\" means (ice) cloud only and/or maybe noise. (c) Detection of shallow isolated and shallow non-isolated. (d) Output of Rain/No-rain flag. (e) Computation of the estimated height of freezing level. (f) Output of the height of storm top. The objectives of 2A25 are to correct for the rain attenuation in measured radar reflectivity (Zm) and to estimate the instantaneous three-dimensional distribution of rain from the TRMM Precipitation Radar (PR) data. The estimated vertical profiles of attenuation-corrected radar reflectivity factor (Ze) and rainfall rate (R) are given at each resolution cell of the PR. The estimated rainfall rate at the actual surface height and the average rainfall rate between the two predefined altitudes (2 and 4 km) are also calculated for each beam position. Changes in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001: Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km\n<br>\n\n#### GPM_3PR\nThis is the new (GPM-formated) TRMM product. It replaces the old TRMM_3A25,3A26 Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. This is the GPM-like formatted TRMM Precipitation Radar (PR) monthly gridded data, first released with the \"V8\" TRMM reprocessing. The TRMM radar Level 3 grids are now consistent with the GPM Dual-frequency Precipitation Radar (DPR). The closest ancestor of this dataset was the monthly radar statistics 3A25. This product consists of monthly statistics of the PR measurements at 0.25x0.25 degrees, and monthly histograms and statistics at 5x5 degrees, horizontal resolution. The objective of the algorithm is to calculate various daily statistics from the level 2 PR output products. Four types of statistics are calculated: 1. Probabilities of occurrence (count values) 2. Means and standard deviations 3. Histograms 4. Correlation coefficients In all cases, the statistics are conditioned on the presence of rain or some other quantity such as the presence of stratiform rain or the presence of a bright-band. For example, to compute the unconditioned mean rain rate, the conditional mean must be multiplied by the probability of rain which, in turn is calculated from the ratio of rain counts to the total number of observations in the box of interest. The grids are in the Planetary Grid 2 structure matching the Dual-frequency PR on the core GPM observatory that covers 67S to 67N degrees of latitudes. The low resolution 5x5 deg grid covers 70S to 70N. Areas beyond the ±40 degrees of latitudes are padded with empty grid cells.\n<br>\n\n#### GPM_3HSLH_TRMM\nThis is the new (GPM-formated) TRMM product. It replaces the old TRMM_3H25 Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. Estimating vertical profiles of latent heating released by precipitating cloud systems is one of the key objectives of TRMM, together with accurately measuring the horizontal distribution of tropical rainfall. The method uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The SLH algorithm is severely limited by the inherent sensitivity of the TRMM PR. For latent heating, the quantity required is actually cloud top, but the PR can detect only precipitation-sized particles. Because observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the SLH algorithm in comparison with the CSH algorithm. Monthly Spectral Latent Heating produces 0.5 degree x 0.5 degree grid of latent heating profiles from the TRMM PR rain. The grids are in the Planetary Grid 2 structure matching the Dual-frequency PR on the core GPM observatory that covers 67S to 67N degrees of latitudes. Areas beyond the ±40 degrees of latitudes are padded with empty grid cells.\n<br>\n\n#### GPM_1BTMI\nThis is the new (GPM-formated) TRMM product. It replaces the old TRMM_1B11 Version 07 is the current version of the data set. Previous versions have been superseded by Version 07. This dataset contains TRMM Micrwave Imager (TMI) L1B calibrated radiances in terms of Brightness Temperatures. The TMI calibration algorithm (1B11) converts the radiometer counts to antenna temperatures by applying a linear relationship of the form Ta = c1 + c2 x count. The coefficients are provided by the instrument contractor. Antenna temperatures are corrected for cross-polarization and spill over to produce brightness temperatures (Tb), but no antenna beam pattern correction or sample to pixel averaging are performed. Temperatures are provided at 104 scan positions for the low frequency channels and 208 scan positions at 85 GHz. There are four samples per pixel (3 -dB beamwidth) at 10 GHz, two samples at 19, 22, and 37 GHz, and one sample per pixel for the 85 GHz. Data Flow Description Changes in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001: Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 760 km; Horizontal Resolution: ~13 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 878 km; Horizontal Resolution: ~13 km\n<br>\n\n#### GPM_1ATMI\nThis is the new (GPM-formated) TRMM product. It replaces the old TRMM_1A11 Version 07 is the current version of the data set. Previous versions have been superseded by Version 07. The 1ATMI product contains science and housekeeping sensor count data directly from the TRMM Microwave Imager (TMI) Instrument aboard the TRMM satellite. The data has been unpacked from the spacecraft packets and geolocated. A Level 1A file contains data for a single orbit and has a file size of about 33 MB. There are 16 files of TMI 1A data produced per day. Spatial coverage is between 38 degrees North and 38 degrees South owing to the 35 degree inclination of the TRMM satellite. This orbit provides extensive coverage in the tropics and allows each location to be covered at a different local time each day, enabling the analysis of the diurnal cycle of precipitation.\n<br>\n\n#### GPM_1BVIRS\nThis is the new (GPM-formated) TRMM product. It replaces the old TRMM_1B01 Version 07 is the current version of the data set. Previous versions have been superseded by Version 07. This TRMM Visible and Infrared Scanner (VIRS) Level 1B Calibrated Radiance Product (1B01) contains calibrated radiances and auxiliary geolocation information from the five channels of the VIRS instrument, for each pixel of each scan. The data are stored in the Hierarchical Data Format (HDF), which includes both core and product specific metadata applicable to the VIRS measurements. A file contains a single orbit of data with a file size of about 95 MB. The EOSDIS \"swath\" structure is used to accommodate the actual geophysical data arrays. There are 16 files of VIRS 1B01 data produced per day. For channels 1 and 2, Level 1B radiances are derived from the Level 1A (1A01) sensor counts by computing calibration parameters (gain and offset) derived from the counts registered during space and solar and/or lunar views. New calibration parameters are produced every one to four weeks. Channels 3, 4, and 5 are calibrated using the internal blackbody and the space view. These calibration parameters, together with a quadratic term determined pre-launch, are used to generate a counts vs. radiance curve for each band, which is then used to convert the earth-view pixel counts to spectral radiances. Geolocation and channel data are written out for each pixel along the scan, whereas the time stamp, scan status (containing scan quality information), navigation, calibration coefficients, and solar/satellite geometry are specified on a per-scan basis. There are in general 18026 scans along the orbit pre-boost and 18223 post-boost, with each scan consisting of 261 pixels. The scan width is about 720 km pre-boost and 833 km post-boost. Changes in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001: Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 720 km; Horizontal Resolution: 2.2 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 833 km; Horizontal Resolution: 2.4 km\n<br>\n\n#### GPM_1AVIRS\nThis is the new (GPM-formated) TRMM product. It replaces the old TRMM_1A01 Version 07 is the current version of the data set. Previous versions have been superseded by Version 07. The 1AVIRS product contains science and housekeeping sensor count data directly from the Visible and Infrared Scanner (VIRS) aboard the TRMM satellite. The data has been unpacked from the spacecraft packets and geolocated. A Level 1A file contains data for a single orbit and has a file size of about 131 MB. There are 16 files of VIRS 1A data produced per day. The Visible and Infrared Scanner (VIRS) is a five-channel visible/infrared radiometer, which builds on the heritage of the Advanced Very High Resolution Radiometer (AVHRR) instrument flown aboard the NOAA series of Polar-Orbiting Operational Environmental Satellites (POES). The VIRS detects radiation at 1 visible, 2 near infrared and 2 thermal infrared wavelengths, allowing determination of cloud coverage, cloud top height and temperature, and precipitation indices. The central wavelengths for the VIRS channels are 0.63, 1.60,3.75, 10.8, and 12.0 microns. All channels are in operation during the daytime, but only channels 3, 4 and 5 operate during the nighttime. Spatial coverage is between 38 degrees North and 38 degrees South owing to the 35 degree inclination of the TRMM satellite. This orbit provides extensive coverage in the tropics and allows each location to be covered at a different local time each day, enabling the analysis of the diurnal cycle of precipitation\n<br>\n\n#### TRMM_TMPA_LandSeaMask\nVersion 2 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 2. This land sea mask originated from the NOAA group at SSEC in the 1980s. It was originally produced at 1/6 deg resolution, and then regridded for the purposes of GPCP, TMPA, and IMERG precipitation products. NASA code 610.2, Terrestrial Information Systems Laboratory, restructured this TMPA land sea mask to match the TMPA grid, and converted the file to CF-compliant netCDF4. Version 2 was created in May, 2019 to resolve detected inaccuracies in coastal regions. Users should be aware that this is a static mask, i.e. there is no seasonal or annual variability, and it is due for update. It is not recommended to be used outside of the aforementioned precipitation data.\n<br>\n\n#### LPRM_TMI_SOILM2\nTMI/TRMM surface soil moisture (LPRM) L2 V001 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content are derived from passive microwave remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), using the Land Parameter Retrieval Model (LPRM). Each swath is packaged with associated geolocation fields. The data set covers the period from December 1997 to April 2015 (when the instruments on the TRMM satellite were shut down in preparation for its reentry into the earth's atmosphere), at the spatial resolution (nominally 45 km) of TMI's X band (10.7 GHz). The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from TMI's Ka-band (37 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the TMI Brightness Temperatures (1B-11) product, archived at the Goddard Earth Sciences Data and Information Services Center (GES DISC).\n<br>\n\n#### LPRM_TMI_DY_SOILM3\nTMI/TRMM surface soil moisture (LPRM) L3 1 day 25 km x 25 km daytime V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one daytime and one nighttime, archived as two different products. This document is for the daytime product. The data set covers the period from December 1997 to April 2015 (when the instruments on the TRMM satellite were shut down in preparation for its reentry into the earth's atmosphere). The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from TMI's Ka-band (37 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the TMI Brightness Temperatures (1B-11) product, daytime passes, as processed using LPRM (i.e., LPRM/TMI/TRMM Level 2 product, LPRM_TMI_SOILM2_V001).\n<br>\n\n#### LPRM_TMI_NT_SOILM3\nTMI/TRMM surface soil moisture (LPRM) L3 1 day 25 km x 25 km nighttime V001 is Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one daytime and one nighttime, archived as two different products. This document is for the nighttime product. The data set covers the period from December 1997 to April 2015 (when the instruments on the TRMM satellite were shut down in preparation for its reentry into the earth's atmosphere). The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from TMI's Ka-band (37 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the TMI Brightness Temperatures (1B-11) product, nighttime passes, as processed using LPRM (i.e., LPRM/TMI/TRMM Level 2 product, LPRM_TMI_SOILM2_V001).\n<br>\n\n#### TRMM_3B42_Daily\nTMPA (3B42_Daily) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERGDF/DAY/06). This daily accumulated precipitation product is generated from the research-quality 3-hourly TRMM Multi-Satellite Precipitation Analysis TMPA (3B42). It is produced at the NASA GES DISC, as a value added product. Simple summation of valid retrievals in a grid cell is applied for the data day. The result is given in (mm). The beginning and ending time for every daily granule are listed in the file global attributes, and are taken correspondingly from the first and the last 3-hourly granules participating in the aggregation. Thus the time period covered by one daily granule amounts to 24 hours, which can be inspected in the file global attributes. Counts of valid retrievals for the day are provided for every variable, making it possible to compute conditional and unconditional mean precipitation for grid cells where less than 8 retrievals for the day are available. Efforts have been made to make the format of this derived product as similar as possible to the new Global Precipitation Measurement CF-compliant file format. The information provided here on the TRMM mission, and on the original 3-hr 3B42 product, remain relevant for this derived product. Note, however, this product is in netCDF-4 format. The following describes the derivation in more details. The daily accumulation is derived by summing *valid* retrievals in a grid cell for the data day. Since the 3-hourly source data are in mm/hr, a factor of 3 is applied to the sum. Thus, for every grid cell we have Pdaily = 3 * SUM{Pi * 1[Pi valid]}, i=[1,Nf] Pdaily_cnt = SUM{1[Pi valid]} where: Pdaily - Daily accumulation (mm) Pi - 3-hourly input, in (mm/hr) Nf - Number of 3-hourly files per day, Nf=8 1[.] - Indicator function; 1 when Pi is valid, 0 otherwise Pdaily_cnt - Number of valid retrievals in a grid cell per day. Grid cells for which Pdaily_cnt=0, are set to fill value in the Daily files. Note that Pi=0 is a valid value. On occasion, the 3-hourly source data have fill values for Pi in a very few grid cells. The total accumulation for such grid cells is still issued, inspite of the likelihood that thus resulting accumulation has a larger uncertainty in representing the \"true\" daily total. These events are easily detectable using \"counts\" variables that contain Pdaily_cnt, whereby users can screen out any grid cells for which Pdaily_cnt less than Nf. There are various ways the accumulated daily error could be estimated from the source 3-hourly error. In this release, the daily error provided in the data files is calculated as follows. First, squared 3-hourly errors are summed, and then square root of the sum is taken. Similarly to the precipitation, a factor of 3 is finally applied: Perr_daily = 3 * { SUM[ (Perr_i * 1[Perr_i valid])^2 ] }^0.5 , i=[1,Nf] Ncnt_err = SUM( 1[Perr_i valid] ) where: Perr_daily - Magnitude of the daily accumulated error power, (mm) Ncnt_err - The counts for the error variable Thus computed Perr_daily represents the worst case scenario that assumes the error in the 3-hourly source data, which is given in mm/hr, accumulates first within the 3-hour period of the source data, and then continues to accumulate during the day. These values, however, can easily be converted to root mean square error estimate of the rainfall rate: rms_err = { (Perr_daily/3) ^2 / Ncnt_err }^0.5 (mm/hr) This estimate assumes that the error given in the 3-hourly files is representative of the error of the rainfall rate (mm/hr) within the 3-hour window of the files, and it is random throughout the day. Note, this should be interpreted as the error of the rainfall rate (mm/hr) for the day, not the daily accumulation.\n<br>\n\n#### TRMM_3B42\nTMPA (3B42) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERG/3B-HH/06). This dataset was the output from the TMPA (TRMM Multi-satellite Precipitation Analysis) Algorithm. It provides precipitation estimates in the TRMM regions that have the (nearly-zero) bias of the ”TRMM Combined Instrument” precipitation estimate and the dense sampling of high-quality microwave data with fill-in using microwave-calibrated infrared estimates. The granule temporal coverage is 3 hours.\n<br>\n\n#### TRMM_3B41RT\nTMPA (3B41RT) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG datasets (doi: 10.5067/GPM/IMERG/3B-HH-E/06, 10.5067/GPM/IMERG/3B-HH-L/06). These data were output from the TRMM Multi-satellite Precipitation Analysis (TMPA), the Near Real-Time (RT) processing stream. The latency was about seven hours from the observation time, although processing issues may delay or prevent this schedule. Users should be mindful that the price for the short latency of these data is the reduced quality as compared to the research quality product 3B42. This particular dataset is an intermediate variable (VAR) rainrate IR estimate. Data files start with a header consisting of a 2880-byte record containing ASCII characters. The header line makes the file nearly self-documenting, in particular spelling out the variable and version names, and giving the units of the variables. Immediately after the header follow 3 data fields, \"precip\", \"error\",\"# pixels\", with byte count correspondingly 1382400,1382400,691200. First two are 2-byte integers, and the third is 1-byte. All fields are 1440x480 grid boxes (0-360E,60N-S). The first grid box center is at (0.125E,59.875N). The grid increments most rapidly to the east. Grid boxes without valid data are filled with the (2-byte integer) \"missing\" value -31999. Valid estimates are only provided in the band 50N-S. This binary data sets are in IEEE \"big-endian\" floating-point format.\n<br>\n\n#### TRMM_3B40RT\nTMPA (3B40RT) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG datasets (doi: 10.5067/GPM/IMERG/3B-HH-E/06, 10.5067/GPM/IMERG/3B-HH-L/06). These data were output from the TRMM Multi-satellite Precipitation Analysis (TMPA), the Near Real-Time (RT) processing stream. The latency was about seven hours from the observation time, although processing issues may delay or prevent this schedule. Users should be mindful that the price for the short latency of these data is the reduced quality as compared to e.g. research quality product 3B42. This particular dataset is an intermediate high-quality (HQ) estimate from merged Microwave precipitation estimates. Data files start with a header consisteing of a 2880-byte header record containing ASCII characters. The header line makes the file nearly self-documenting, in particular spelling out the variable and version names, and giving the units of the variables. Immediately after the header follow the data fields. All fields are 1440x720 grid boxes (0-360�E,90�N-S). The first grid box center is at (0.125�E,89.875�N). The grid increments most rapidly to the east. Grid boxes without valid data are filled with the (2-byte integer) \"missing\" value -31999. Estimates are only computed for the band 70�N-S. This binary data sets are in IEEE ?big-endian? floating-point format. Files are produced every 3 hours on synoptic observation hours (00, 03, ..., 21 UTC) as an accumulation of all HQ swath data observed within +/-90 minutes of the nominal file time. I.e. Each file is a snapshot considered to represent the three-hour period centered on the \"nominal\" file time. So, e.g., 00 UTC nominally represents the period from 2230 UTC of the previous day to 0130 UTC of the current day.\n<br>\n\n#### TRMM_3B42RT_Daily\nTMPA (3B42RT_Daily) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERGDE/DAY/06; 10.5067/GPM/IMERGDL/DAY/06). This daily accumulated precipitation product is generated from the Near Real-Time 3-hourly TRMM Multi-Satellite Precipitation Analysis TMPA (3B42RT). It is produced at the NASA GES DISC, as a value added product. Simple summation of valid retrievals in a grid cell is applied for the data day. The result is given in (mm). Although the grid is from 60S to 60N, the high latitudes (beyond 50S/N) near real-time retrievals are considered very unreliable and thus are screened out from the daily accumulations. The beginning and ending time for every daily granule are listed in the file global attributes, and are taken correspondingly from the first and the last 3-hourly granules participating in the aggregation. Thus the time period covered by one daily granule amounts to 24 hours, which can be inspected in the file global attributes. Counts of valid retrievals for the day are provided for every variable, making it possible to compute conditional and unconditional mean precipitation for grid cells where less than 8 retrievals for the day are available. Efforts have been made to make the format of this derived product as similar as possible to the new Global Precipitation Measurement CF-compliant file format. The latency of this derived daily product is about 7 hours after the UTC day is closed. Users should be mindful that the price for the short latency of these data is the reduced quality as compared to the research quality product. The information provided here on the TRMM mission, and on the original 3-hr 3B42 product, remain relevant for this derived product. Note, however, this product is in netCDF-4 format. The following describes the derivation in more details. The daily accumulation is derived by summing *valid* retrievals in a grid cell for the data day. Since the 3-hourly source data are in mm/hr, a factor of 3 is applied to the sum. Thus, for every grid cell we have Pdaily = 3 * SUM{Pi * 1[Pi valid]}, i=[1,Nf] Pdaily_cnt = SUM{1[Pi valid]} where: Pdaily - Daily accumulation (mm) Pi - 3-hourly input, in (mm/hr) Nf - Number of 3-hourly files per day, Nf=8 1[.] - Indicator function; 1 when Pi is valid, 0 otherwise Pdaily_cnt - Number of valid retrievals in a grid cell per day. Grid cells for which Pdaily_cnt=0, are set to fill value in the Daily files. Note that Pi=0 is a valid value. On occasion, the 3-hourly source data have fill values for Pi in a very few grid cells. The total accumulation for such grid cells is still issued, inspite of the likelihood that thus resulting accumulation has a larger uncertainty in representing the \"true\" daily total. These events are easily detectable using \"counts\" variables that contain Pdaily_cnt, whereby users can screen out any grid cells for which Pdaily_cnt less than Nf. There are various ways the accumulated daily error could be estimated from the source 3-hourly error. In this release, the daily error provided in the data files is calculated as follows. First, squared 3-hourly errors are summed, and then square root of the sum is taken. Similarly to the precipitation, a factor of 3 is finally applied: Perr_daily = 3 * { SUM[ (Perr_i * 1[Perr_i valid])^2 ] }^0.5 , i=[1,Nf] Ncnt_err = SUM( 1[Perr_i valid] ) where: Perr_daily - Magnitude of the daily accumulated error power, (mm) Ncnt_err - The counts for the error variable Thus computed Perr_daily represents the worst case scenario that assumes the error in the 3-hourly source data, which is given in mm/hr, is accumulating within the 3-hourly period of the source data and then during the day. These values, however, can easily be conveted to root mean square error estimate of the rainfall rate: rms_err = { (Perr_daily/3) ^2 / Ncnt_err }^0.5 (mm/hr) This estimate assumes that the error given in the 3-hourly files is representative of the error of the rainfall rate (mm/hr) within the 3-hour window of the files, and it is random throughout the day. Note, this should be interpreted as the error of the rainfall rate (mm/hr) for the day, not the daily accumulation.\n<br>\n\n#### TRMM_3B42RT\nTMPA (3B42RT) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG datasets (doi: 10.5067/GPM/IMERG/3B-HH-E/06, 10.5067/GPM/IMERG/3B-HH-L/06). These data were output from the TRMM Multi-satellite Precipitation Analysis (TMPA), the Near Real-Time (RT) processing stream. The latency was about seven hours from the observation time, although processing issues may delay or prevent this schedule. Users should be mindful that the price for the short latency of these data is the reduced quality as compared to the research quality product. Each file is a snapshot considered to represent the three-hour period centered on the \"nominal\" file time. So, e.g., 00 UTC nominally represents the period from 2230 UTC of the previous day to 0130 UTC of the current day. Estimates outside the band 50 degree N-S are considered highly experimental. GES DISC initially receives these data from the Precipitation Processing System (PPS) in binary format. However, before archiving, the data are scaled to real numbers, and re-arranged to a standard grid so that the first grid cell is at 180W, 60S. Thus formatted, data are stored into CF-1.6 compliant netCDF-4 files and archived. This format is machine-independent, self-explanatory, provides extremely efficient seamless compression, and gives various options for previewing the data without downloading it. Apart from these technical differences, all other science content details remain the same, and users are strongly encouraged to read the provider's documentation that is linked to from here.\n<br>\n\n#### TRMM_3B43\nTMPA (3B43) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERG/3B-MONTH/06). The 3B43 dataset is the monthly version of the 3B42 dataset. This product was created using TRMM-adjusted merged microwave-infrared precipitation rate (in mm/hr) and root-mean-square (RMS) precipitation-error estimates. It provides a \"best\" precipitation estimate in a latitude band covering 50o N to 50o S, an expansion of the TRMM region, from all global data sources, namely high-quality microwave data, infrared data, and analyses of rain gauges. The granule size is one month.\n<br>\n\n#### TRMM_1A11\nThe Level-1A Product file, \"1A11\", is a concatenation of Header record, Spacecraft Attitude packets, TMI Housekeeping packets, TMI Science Data packets, QACs and an MDUL. As such, it is reversible to Level 0. The header record contains information pertaining to orbit times, orbit number, times of the first scan, and number of scans, among other things. The Level 0 data contain the actual channel data expressed as\"sensor counts\". A Level 1A file contains data for a single orbit and has a file size of about 31 MB (uncompressed). Spatial coverage is between 38 degrees North and 38 degrees South owing to the 35 degree inclination of the TRMM satellite. This orbit provides extensive coverage in the tropics and allows each location to be covered at a different local time each day, enabling the analysis of the diurnal cycle of precipitation.\n<br>\n\n#### TRMM_1A01\nThe Level-1A Product file, \"1A01\", is a concatenation of Header record, Spacecraft Attitude packets, VIRS Housekeeping Data packets, VIRS Science Data packets, QACs, and an MDUL. As such, it is reversible to Level 0. The header record contains information pertaining to orbit times, orbit number, times of the first scan, and number of scans, among other things. The Level 0 data contain the actual channel data expressed as \"sensor counts\". A Level 1A file contains data for a single orbit and has a file size of about 31 MB (uncompressed). There are 16 files of VIRS 1A01 data produced per day. The Visible and Infrared Scanner (VIRS) is a five-channel visible/infrared radiometer, which builds on the heritage of theAdvanced Very High Resolution Radiometer (AVHRR) instrument flown aboard the NOAA series of Polar-Orbiting Operational EnvironmentalSatellites (POES). The VIRS detects radiation at 1 visible, 2 near infrared and 2 thermal infrared wavelengths, allowing determination of cloud coverage, cloud top height and temperature, and precipitation indices. The central wavelengths for the VIRS channels are 0.63, 1.60,3.75, 10.8, and 12.0 microns. All channels are in operation during the daytime, but only channels 3, 4 and 5 operate during the nighttime. Spatial coverage is between 38 degrees North and 38 degrees South owing to the 35 degree inclination of the TRMM satellite. This orbit provides extensive coverage in the tropics and allows each location to be covered at a different local time each day, enabling the analysis of the diurnal cycle of precipitation.\n<br>\n\n#### TRMM_2B31\nThis is the last version of the TRMM-formatted, in HDF4 format, Combined (Radar-Radiometer) product, which is in a state of permanent preservation. New version that is consistently processed with Global precipitation Measurement (GPM) algorithms, and in a consistent with GPM HDF5 format, is available under short name 'GPM_2BCMB_TRMM'. The dataset TRMM_2B31 combines data from the TMI and PR to produce the best rain estimate for TRMM. This combined rainfall product is derived from vertical hydrometeor profiles using data from the PR radar and TMI. It also includes computed correlation-corrected, mass-weighted, mean drop diameter and its standard deviation, and latent heating data. Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km\n<br>\n\n#### TRMM_CSH\nThis is a discontinued TRMM product from the old version '6' suite that is in a state of permament preservation. The most recent replacement can be found by following this DOI: 10.5067/GPM/PRTMI/TRMM/CSH/3B-MONTH/07. The latter should also be better cross-calibrated with the continuation GPM combined Dual-Frequency Precipitation Radar and GPM Microwave Imager latent heating product: 10.5067/GPM/DPRGMI/CSH/3B-MONTH/07. The version '6' dataset is output from the old Goddard Convective-Stratiform Heating (CSH) algorithm. The dataset contains global 0.5 x 0.5 monthly latent heating profiles from surface convective rain rate and surface stratiform rain rate.\n<br>\n\n#### TRMM_1C51\nThe purpose of the 1C51 algorithm is to remove non-meteorological radar echoes that adversely affect the quality of higher level products, such as clutter associated with insects, birds, chaff, wildfires, antenna towers, and anomalous propagation (AP). Eight adjustable parameters, three echo height thresholds and five radar reflectivity thresholds, are used to optimize the performance of the algorithm. Optimum performance is time consuming and requires an analyst to select different sets of parameters on a per volume scan basis, and on occasions performing several iterations while adjusting one or more of the parameters. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_1C51UW\nThis dataset is part of the University of Washington TRMM Ground Validation products. Files are in \"Universal Format\", described in BAMS, Vol 61, No 11, November 1980, pp. 1401-1404. The purpose of the 1C51UW algorithm is to remove non-meteorological radar echoes that adversely affect the quality of higher level products, such as clutter associated with insects, birds, chaff, wildfires, antenna towers, and anomalous propagation (AP). Eight adjustable parameters, three echo height thresholds and five radar reflectivity thresholds, are used to optimize the performance of the algorithm. Optimum performance is time consuming and requires an analyst to select different sets of parameters on a per volume scan basis, and on occasions performing several iterations while adjusting one or more of the parameters. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_1B51\nThe TRMM_1B51 product displays the existence of rain in the radar volume scan. 'Existence' is the fraction of the radar FOV which has measurable precipitation. The GV radar FOV is defined as a base scan (i.e., the lowest level sweep). Each product file has the Existence data of one site (not one radar) for one month. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_baseUW\nThis dataset is part of the University of Washington TRMM Ground Validation products. Data contains single level, cartesian grids containing output of NCAR SPRINT interpolation of lowest sweep of 1C51UW. Horizontal extent is 150x150km and horizontal resolution is 2km. Note that in the netCDF files, \"alt\" (altitude) is assigned the elevation angle of the lowest sweep (which is used to create this product) and \"z_spacing\" has no meaning. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_3A55\nThe 3A55, 'Monthly 3-D Structure', provides radar site monthly 3-D structure information obtained from 2A55. The 2A55 'Radar Site 3-D Reflectivities', is composed of 3 different fields. The first field has an array of 3-D reflectivities in Cartesian coordinates with a 2 km horizontal resolution over an area of 300 km x 300 km for single radar sites, and 724 km x 568 km for Texas multiple radar site, 512 km x 704 km for Florida multiple radar site. It has a vertical resolution of 1.5km and a height range up to 19.5 km. The second field has an array of vertical profiles based on the first field, and the third field has an array of the Contoured Frequency by Altitude Diagram (CFAD) data based on the first and second field. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_2A55\nRadar Site 3-D Reflectivities', is composed of 3 different fields. The first field has an array of 3-D reflectivities in Cartesian coordinates with a 2 km horizontal resolution over an area of 300 km x 300 km for single radar sites, and 724 km x 568 km for Texas multiple radar site, 512 km x 704 km for Florida multiple radar site. It has a vertical resolution of 1.5km and a height range up to 19.5 km. The second field has an array of vertical profiles based on the first field, and the third field has an array of the Contoured Frequency by Altitude Diagram (CFAD) data based on the first and second field. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_2A55UW\nThis dataset is part of the University of Washington TRMM Ground Validation products. Multiple-level cartesian grid containing output of NCAR SPRINT interpolation of CZ and VR fields of 1C51UW. Horizontal extent is 150x150km and resolution is 2km. Vertical range is 2-15km and resolution is 1km. Unlike other products, 2A55UW is not created for every volume but only for volumes of interest. These are defined as volumes within +/-15 minutes of overpass times and volumes with significant rainfall (areal rainfall rate greater or equal 0.15mm/hr). A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_2A53\nRadar Site Rain Map', is an instantaneous surface rain rate map in Cartesian coordinates with a 2 km horizontal resolution. At single radar sites, the map covers an area of 300km x 300km. For the multiple radar site in Texas, the map covers a region of 724 km x 568 km, and in Florida 512 km x 704 km. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_2A53UW\nThis dataset is part of the University of Washington TRMM Ground Validation products. Instantaneous rain rate cartesian grid based on baseUW and 2A54UW. Units are mm/hr. Min range is 17 km, max range is 150 km. Note that in the netCDF files, \"alt\" (altitude) is assigned the elevation angle of the lowest sweep (which is used to create baseUW) and \"z_spacing\" has no meaning. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_3A53\nThis is the 5-day accumulation of the 2A53 product, 'Radar Site Rain Map', which originally is an instantaneous surface rain rate map in Cartesian coordinates with a 2 km horizontal resolution. At single radar sites, the map covers an area of 300km x 300km. For the multiple radar site in Texas, the map covers a region of 724 km x 568 km, and in Florida 512 km x 704 km. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_2A54\nRadar Site Convective/Stratiform Map', is an instantaneous map in Cartesian coordinates with a 2 km resolution. At single radar sites, the map covers an area of 300 km x 300 km. For the multiple radar site in Texas, the map covers a region of 724 km x 568 km, and in Florida 512 km x 704 km. The map identifies the surface precipitation as convective or stratiform. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_2A54UW\nThis dataset is part of the University of Washington TRMM Ground Validation products. Instantaneous convective-stratiform cartesian grid based on baseUW. Values are 0 (no echo), 1 (stratiform), and 2 (convective). Min range is 17 km, max range is 150 km. Note that in the netCDF files, \"alt\" (altitude) is assigned the elevation angle of the lowest sweep (which is used to create baseUW) and \"z_spacing\" has no meaning. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_3A54\nThe 3A54 product, 'Site Rainfall Map', is a map of monthly surface rain totals derived from the instantaneous rain rate maps (2A53). The map is in Cartesian coordinates with a 2 km horizontal resolution and covers an area of 300km x 300km at single radar sites while the covered area varies for multiple radar sites - 724 km x 568 km at Texas site and 512 km x 704 km at Florida site. This monthly rainfall map is not a simple accumulation of the instantaneous maps as gaps in the data must be factored into the calculation. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_3A54UW\nThis dataset is part of the University of Washington TRMM Ground Validation products. Monthly rain accumulation cartesian grid based on 2A53UW. Units are mm. Min range is 17 km, max range is 150 km. Horizontal extent is 150x150km and resolution is 2km. Note that in the netCDF files, \"alt\" (altitude) is assigned the elevation angle of the lowest sweep (which is used to create baseUW) and \"z_spacing\" has no meaning. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_2A52\nThe TRMM_2A52 product displays the existence of rain in the radar volume scan. 'Existence' is the fraction of the radar FOV which has measurable precipitation. The GV radar FOV is defined as a base scan (i.e., the lowest level sweep). Each product file has the Existence data of one site (not one radar) for one month. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_2A56\nThe program rgmin generates 1-minute hourly rain rates from discrete tipping bucket rain gauge data by applying an interpolation algorithm. The interpolating routine is based on the cubic spline routine published in the book \"Numerical Recipes\". The mathematical theory underlying this algorithm is presented in section 3.3 of the book along with a copy of the source code. The mathematics involved will not be described in any detail, rather the spline will be described in context with its application to the broader algorithm being applied in rgmin. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.\n<br>\n\n#### TRMM_1B11\nThe new version of these data is in GPM-like format (consistent with the GPM Microwave Imager data format), and can be found under the name GPM_1BTMI (search by keyword GPM_1BTMI). This dataset contains TRMM Micrwave Imager (TMI) L1B calibrated radiances in terms of Brightness Temperatures. The TMI calibration algorithm (1B11) converts the radiometer counts to antenna temperatures by applying a linear relationship of the form Ta = c1 + c2 x count. The coefficients are provided by the instrument contractor. Antenna temperatures are corrected for cross-polarization and spill over to produce brightness temperatures (Tb), but no antenna beam pattern correction or sample to pixel averaging are performed. Temperatures are provided at 104 scan positions for the low frequency channels and 208 scan positions at 85 GHz. There are four samples per pixel (3 -dB beamwidth) at 10 GHz, two samples at 19, 22, and 37 GHz, and one sample per pixel for the 85 GHz. Data Flow Description Changes in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001: Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 760 km; Horizontal Resolution: 4.4 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 878 km; Horizontal Resolution: 5.1 km\n<br>\n\n#### TRMM_2A12\nThe new version of these data is in GPM-like format and can be found under the name GPM_2AGPROFTRMMTMI_CLIM. This dataset, 2A12, ”TMI Profiling”, generates surface rainfall and vertical hydrometeor profiles on a pixel by pixel basis from the TRMM Microwave Imager (TMI) brightness temperature data using the Goddard Profiling algorithm GPROF2010. Because the vertical information comes from a radiometer, it is not written out in independent vertical layers like the TRMM Precipitation Radar. Instead, the output is referenced to one of 100 typical structures for each hydrometeor or heating profile. These vertical structures are referenced as clusters in the output structure. Vertical hydrometeor profiles can be reconstructed to 28 layers by knowing the cluster number (i.e. shape) of the profile and a scale factor that is written for each pixel. This product contains hydrometeor profiles of cloud liquid water, precipitation water, cloud ice water, precipitation ice, rainfall type, and latent heating in 28 atmospheric layers. Changes in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001: Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 760 km; Horizontal Resolution: 4.4 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 878 km; Horizontal Resolution: 5.1 km\n<br>\n\n#### TRMM_3A11\nThe new equivalent for this dataset should be searched for as \"GPM_3GPROFTRMMTMI_CLIM\". The TMI Gridded Oceanic Rainfall Product, also known as TMI Emission, consists of 5 degree by 5 degree monthly oceanic rainfall maps using TMI Level 1 data as input. Statistics of the monthly rainfall, including number of samples, standard deviation, goodness-of-fit (of the brightness temperature histogram to the lognormal rainfall distribution function) and rainfall probability are also included in the output for each grid box. TMI brightness temperature histograms at 1 degree intervals are generated based on the 19, 21 and 19-21 GHz combination channels obtained from the Level 1B (calibrated brightness temperature) TMI product. Monthly rainfall indices over the ocean are derived by statistically matching monthly histograms of brightness temperatures with model calculated rainfall Probability Distribution Functions (PDF) using the 19-21 GHz combination data. Retrieved monthly rainfall data must pass a quality test based on the quality of the PDF fit.\n<br>\n\n#### TRMM_3A12\nThe new equivalent for this dataset should be searched for as \"GPM_3GPROFTRMMTMI_CLIM\". This product contains global monthly means of surface precipitation rate, rain rate, convective surface precipitation rate and 28 vertical layers of hydrometeor contents (cloud liquid water, rain water, cloud ice liquid water, snow liquid water, graupel liquid water and latent heating) on 0.5 x 0.5 degree grids.\n<br>\n\n#### TRMM_3G25\n3G25, \"Gridded Orbital Spectral Latent Heating\", produces 0.5 degree x 0.5 degree latent heating, Q1-QR, and Q2 profiles from Precipitation Radar (PR) rain. The granule size is one orbit.\n<br>\n\n#### TRMM_1B21\nThe TRMM Precipitation Radar (PR), the first of its kind in space, is an electronically scanning radar, operating at 13.8 GHz that measures the 3-D rainfall distribution over both land and ocean, and defines the layer depth of the precipitation. The 1B21 calculates the received power at the PR receiver input point from the Level-0 count value which is linearly proportional to the logarithm of the PR receiver output power. To convert the count value to the input power, extensive internal calibrations are applied, which are mainly based upon the system model, temperature dependence of model parameters and many temperature sensors attached at various locations of the PR. Periodically the input-output characteristics are measured using an internal calibration loop for the IF unit and later receiver stages. To make an absolute calibration, an Active Radar Calibrator (ARC) is placed at Kansai Branch of CRL and overall system gain of the PR is being measured every 2 months. Using the transfer function based on the above internal and external calibrations, the PR received power is obtained. Note that the value assumes that the signal follows the Rayleigh fading, so if the fading characteristics of a scatter is different, a small bias error may occur (within 1 or 2 dB). Changes in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001: Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km\n<br>\n\n#### TRMM_1C21\nThe TRMM Precipitation Radar (PR), the first of its kind in space, is an electronically scanning radar, operating at 13.8 GHz that measures the 3-D rainfall distribution over both land and ocean, and defines the layer depth of the precipitation. The 1C21 calculates the effective radar reflectivity factor at 13.8 GHz without any propagation loss (due to rain or any other atmospheric gas) correction (Zm). Therefore, the Zm value can be calculated just by applying a radar equation for volume scatter with PR system parameters. The noise-equivalent Zm is about 21 dBZ. Through the subtraction of the system noise, the Zm value as small as 16 or 18 dBZ are still usable although the data quality is marginal. In 1C21, all echoes stored in 1B21 are converted to \"dBZ\" unit. This is not relevant for \"non-rain\" echo; however, this policy is adopted so that the 1B21 and 1C21 product format should be as close as possible except for the following points: - Radar quantity is Zm in dBZ unit instead of received power (dBm). - Data at echo-free range bins judged in 1B21 are replaced with a dummy value. Changes in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001: Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km\n<br>\n\n#### TRMM_2A23\nThe new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_2APR. The TRMM Precipitation Radar (PR), the first of its kind in space, is an electronically scanning single-frequency radar, operating at 13.8 GHz that measures the 3-D rainfall distribution over both land and ocean, and defines the layer depth of the precipitation. PR 2A23 produces a rain/no-rain flag. Its main objectives are (1) to detect bright band (BB), (2) to classify rain type, and (3) to detect warm rain. 2A23 uses two different methods for classifying rain type: (1) vertical profile method (V-method) and (2) horizontal pattern method (H-method). Both methods classify rain into three categories: stratiform, convective, and other. To make the results user-friendly, 2A23 outputs a unified rain type. Further information about 2A23 can be found in Awaka et al. (1998). Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km\n<br>\n\n#### TRMM_2A25\nThe new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_2APR. The TRMM 2A25 data are estimates of the three-dimensional distribution of rain from the TRMM Precipitation Radar. The average rainfall rate between two pre-defined altitudes is calculated for each beam position. Other output data include parameters of Z-R relationships, integrated rain rate of each beam, range bin numbers of rain layer boundaries, and many intermediate parameters. Iguchi and Meneghini (1994) describe this algorithm. Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km\n<br>\n\n#### TRMM_3H25\n3H25, \"Monthly Spectral Latent Heating\", produces 0.5 degree x 0.5 degree latent heating, Q1-QR, and Q2 profiles from PR rain\n<br>\n\n#### TRMM_2A21\nThe new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_2APR. This is the sigma zero algorithm, which inputs the PR power (1B21) and computes estimates of the path attenuation and its reliability by using the surface as a reference target. It also computes the spatial and temporal statistics of the surface scattering cross section and classifies the cross sections into land/ocean and rain/no rain categories. Changes in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001: Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 215 km; Horizontal Resolution: 4.3 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km\n<br>\n\n#### TRMM_3A26\nThe new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_3PR. This dataset contains distributions of monthly surface rainfall. These data were derived from rain rate statistics and include the estimated values of the probability distribution function of the space-time rain rates at four levels (2 km, 4 km, 6 km, and path-averaged) and the mean, standard deviation, and probability of rain derived from these distributions. Three different rain rate estimates are used as input to the algorithm: (1) the standard Z-R (or 0th-order estimate having no attenuation correction); (2) the Hitschfield-Bordan (H-B); and (3) the rain rates taken from 2A25.\n<br>\n\n#### TRMM_3A25\nThe new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_3PR. This product consists of monthly statistics of the PR measurements at both a low (5 degrees x 5 degrees) and a high (0.5 degrees x 0.5 degrees) horizontal resolution. The low resolution grids are in the Planetary Grid 1 structure and include 1) mean and standard deviation of the rain rate, reflectivity, path-integrated attenuation (PIA), storm height, Xi, bright band height and the NUBF (Non-Uniform Beam Filling) correction; 2) rain fractions; 3) histograms of the storm height, bright-band height, snow-ice layer, reflectivity, rain rate, path-attenuation and NUBF correction; 4) correlation coefficients. The high resolution grids are in the Planetary Grid 2 structure and contain mean rain rate along with standard deviation and rain fractions.\n<br>\n\n#### TRMM_3H31\n3H31, \"Monthly Convective Stratiform Heating from Combined\", produces 0.5 deg x 0.5 deg monthly apparent heating profiles from surface convective rainfall rate and surface stratiform rainfall rate. The PI is Dr. Wei-Kuo Tao. The granule size is one month.\n<br>\n\n#### TRMM_3G31\n3G31, Gridded Orbital Convective Stratiform Heating from Combined, produces 0.5 degree x 0.5 degree orbital apparent heating profiles from surface convective rainfall rate and surface stratiform rainfall rate. The granule size is one orbit.\n<br>\n\n#### TRMM_3B31\nThis is the last version of the TRMM-formatted, in HDF4 format, Combined (Radar-Radiometer) product, which is in a state of permanent preservation. New version that is consistently processed with Global precipitation Measurement (GPM) algorithms, and in a concistent with GPM HDF5 format, is available under short name GPM_3CMB_TRMM. Note that new TRMM Daily Combined product has now been made available: GPM_3CMB_TRMM_DAY. The dataset TRMM_3B31 uses the high quality retrievals done for the narrow swath in 2B31 to calibrate the wide swath retrievals generated in 2A12. For each 0.5 degree box and each vertical layer, an adjustment ratio is calculated for the swath overlap region for one month. Only TMI pixels with 2A12 pixelStatus equal to zero are included in monthly averages, which effectively removes sea ice.\n<br>\n\n#### TRMM_1B01\nThis TRMM Visible and Infrared Scanner (VIRS) Level 1B Calibrated Radiance Product (1B01) contains calibrated radiances and auxiliary geolocation information from the five channels of the VIRS instrument, for each pixel of each scan. The data are stored in the Hierarchical Data Format (HDF), which includes both core and product specific metadata applicable to the VIRS measurements. A file contains a single orbit of data with a file size of about 95 MB. The EOSDIS \"swath\" structure is used to accommodate the actual geophysical data arrays. There are 16 files of VIRS 1B01 data produced per day. For channels 1 and 2, Level 1B radiances are derived from the Level 1A (1A01) sensor counts by computing calibration parameters (gain and offset) derived from the counts registered during space and solar and/or lunar views. New calibration parameters are produced every one to four weeks. Channels 3, 4, and 5 are calibrated using the internal blackbody and the space view. These calibration parameters, together with a quadratic term determined pre-launch, are used to generate a counts vs. radiance curve for each band, which is then used to convert the earth-view pixel counts to spectral radiances. Geolocation and channel data are written out for each pixel along the scan, whereas the time stamp, scan status (containing scan quality information), navigation, calibration coefficients, and solar/satellite geometry are specified on a per-scan basis. There are in general 18026 scans along the orbit pre-boost and 18223 post-boost, with each scan consisting of 261 pixels. The scan width is about 720 km pre-boost and 833 km post-boost. Changes in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001: Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 720 km; Horizontal Resolution: 2.2 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 833 km; Horizontal Resolution: 2.4 km\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://gpm.nasa.gov","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","earth observation","elevation","hdf","hdf5","ice","netcdf","oceans","precipitation","radar","satellite imagery","soil moisture","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"GPM_2HCSH_TRMM v07 - This is a new (GPM-formated) TRMM product. The equivalent old TRMM legacy product is TRMM_2H31.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L2/GPM_2HCSH_TRMM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3HCSH_TRMM v07 - This is the new (GPM-formated) TRMM product. It replaces the old TRMM legacy TRMM_3H31 Version 07 is the current version of the data set.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/GPM_3HCSH_TRMM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3GCSH_TRMM v07 - This is the new (GPM-formated) TRMM product. It replaces the old TRMM legacy product TRMM_3G31.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/GPM_3GCSH_TRMM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_1BPR v07 - This is the new (GPM-formated) TRMM product. It replaces the old TRMM_1B21,1C21 Version 07 is the current version of the data set.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L1/GPM_1BPR.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3GSLH_TRMM v07 - This is the new (GPM-formated) TRMM product. It replaces the old TRMM_3G25 Version 07 is the current version of the data set.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/GPM_3GSLH_TRMM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3PRD v07 - This a new (GPM-formated) TRMM product. There is no equivalent in the old TRMM suite of products.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/GPM_3PRD.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_2APR v07 - This is the new, GPM-like, format for TRMM Precipitation Radar L2 data that now incorporates what was known as 2A21, 2A23 and 2A25 datasets. The primary purpose of 2A21 is to compute the path-integrated attenuation (PIA) using the surface reference technique (SRT).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L2/GPM_2APR.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3PR v07 - This is the new (GPM-formated) TRMM product. It replaces the old TRMM_3A25,3A26 Version 07 is the current version of the data set.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/GPM_3PR.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_3HSLH_TRMM v07 - This is the new (GPM-formated) TRMM product. It replaces the old TRMM_3H25 Version 07 is the current version of the data set.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/GPM_3HSLH_TRMM.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_1BTMI v07 - This is the new (GPM-formated) TRMM product. It replaces the old TRMM_1B11 Version 07 is the current version of the data set.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L1/GPM_1BTMI.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_1ATMI v07 - This is the new (GPM-formated) TRMM product. It replaces the old TRMM_1A11 Version 07 is the current version of the data set.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L1A/GPM_1ATMI.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_1BVIRS v07 - This is the new (GPM-formated) TRMM product. It replaces the old TRMM_1B01 Version 07 is the current version of the data set.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L1/GPM_1BVIRS.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"GPM_1AVIRS v07 - This is the new (GPM-formated) TRMM product. It replaces the old TRMM_1A01 Version 07 is the current version of the data set.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L1A/GPM_1AVIRS.07","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_TMPA_LandSeaMask v2 - Version 2 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 2.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/AUXILIARY/TRMM_TMPA_LandSeaMask.2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LPRM_TMI_SOILM2 v001 - TMI/TRMM surface soil moisture (LPRM) L2 V001 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content are derived from passive microwave remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_TMI_SOILM2.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LPRM_TMI_DY_SOILM3 v001 - TMI/TRMM surface soil moisture (LPRM) L3 1 day 25 km x 25 km daytime V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_TMI_DY_SOILM3.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"LPRM_TMI_NT_SOILM3 v001 - TMI/TRMM surface soil moisture (LPRM) L3 1 day 25 km x 25 km nighttime V001 is Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), using the Land Parameter Retrieval Model (LPRM).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WAOB/LPRM_TMI_NT_SOILM3.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3B42_Daily v7 - TMPA (3B42_Daily) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERGDF/DAY/06).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/TRMM_3B42_Daily.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3B42 v7 - TMPA (3B42) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERG/3B-HH/06).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/TRMM_3B42.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3B41RT v7 - TMPA (3B41RT) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG datasets (doi: 10.5067/GPM/IMERG/3B-HH-E/06, 10.5067/GPM/IMERG/3B-HH-L/06).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_RT/TRMM_3B41RT.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3B40RT v7 - TMPA (3B40RT) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG datasets (doi: 10.5067/GPM/IMERG/3B-HH-E/06, 10.5067/GPM/IMERG/3B-HH-L/06).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_RT/TRMM_3B40RT.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3B42RT_Daily v7 - TMPA (3B42RT_Daily) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERGDE/DAY/06; 10.5067/GPM/IMERGDL/DAY/06).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_RT/TRMM_3B42RT_Daily.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3B42RT v7 - TMPA (3B42RT) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG datasets (doi: 10.5067/GPM/IMERG/3B-HH-E/06, 10.5067/GPM/IMERG/3B-HH-L/06).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_RT/TRMM_3B42RT.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3B43 v7 - TMPA (3B43) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERG/3B-MONTH/06).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/TRMM_3B43.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_1A11 v7 - The Level-1A Product file, \"1A11\", is a concatenation of Header record, Spacecraft Attitude packets, TMI Housekeeping packets, TMI Science Data packets, QACs and an MDUL. As such, it is reversible to Level 0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L1A/TRMM_1A11.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_1A01 v7 - The Level-1A Product file, \"1A01\", is a concatenation of Header record, Spacecraft Attitude packets, VIRS Housekeeping Data packets, VIRS Science Data packets, QACs, and an MDUL. As such, it is reversible to Level 0.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L1A/TRMM_1A01.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_2B31 v7 - This is the last version of the TRMM-formatted, in HDF4 format, Combined (Radar-Radiometer) product, which is in a state of permanent preservation. New version that is consistently processed with Global precipitation Measurement (GPM) algorithms, and in a consistent with GPM HDF5 format, is available under short name 'GPM_2BCMB_TRMM'.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L2/TRMM_2B31.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_CSH v6 - This is a discontinued TRMM product from the old version ''6'' suite that is in a state of permament preservation. The most recent replacement can be found by following this DOI: 10.5067/GPM/PRTMI/TRMM/CSH/3B-MONTH/07.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/TRMM_CSH.6","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_1C51 v7 - The purpose of the 1C51 algorithm is to remove non-meteorological radar echoes that adversely affect the quality of higher level products, such as clutter associated with insects, birds, chaff, wildfires, antenna towers, and anomalous propagation (AP). Eight adjustable parameters, three echo height thresholds and five radar reflectivity thresholds, are used to optimize the performance of the algorithm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L1/TRMM_1C51.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_1C51UW v7 - This dataset is part of the University of Washington TRMM Ground Validation products. Files are in \"Universal Format\", described in BAMS, Vol 61, No 11, November 1980, pp.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L1/TRMM_1C51UW.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_1B51 v7 - The TRMM_1B51 product displays the existence of rain in the radar volume scan. 'Existence' is the fraction of the radar FOV which has measurable precipitation.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L1/TRMM_1B51.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_baseUW v7 - This dataset is part of the University of Washington TRMM Ground Validation products. Data contains single level, cartesian grids containing output of NCAR SPRINT interpolation of lowest sweep of 1C51UW.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L1/TRMM_baseUW.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3A55 v7 - The 3A55, 'Monthly 3-D Structure', provides radar site monthly 3-D structure information obtained from 2A55. The 2A55 'Radar Site 3-D Reflectivities', is composed of 3 different fields.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L3/TRMM_3A55.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_2A55 v7 - Radar Site 3-D Reflectivities', is composed of 3 different fields. The first field has an array of 3-D reflectivities in Cartesian coordinates with a 2 km horizontal resolution over an area of 300 km x 300 km for single radar sites, and 724 km x 568 km for Texas multiple radar site, 512 km x 704 km for Florida multiple radar site.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L2/TRMM_2A55.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_2A55UW v7 - This dataset is part of the University of Washington TRMM Ground Validation products. Multiple-level cartesian grid containing output of NCAR SPRINT interpolation of CZ and VR fields of 1C51UW.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L2/TRMM_2A55UW.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_2A53 v7 - Radar Site Rain Map', is an instantaneous surface rain rate map in Cartesian coordinates with a 2 km horizontal resolution. At single radar sites, the map covers an area of 300km x 300km.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L2/TRMM_2A53.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_2A53UW v7 - This dataset is part of the University of Washington TRMM Ground Validation products. Instantaneous rain rate cartesian grid based on baseUW and 2A54UW.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L2/TRMM_2A53UW.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3A53 v7 - This is the 5-day accumulation of the 2A53 product, 'Radar Site Rain Map', which originally is an instantaneous surface rain rate map in Cartesian coordinates with a 2 km horizontal resolution. At single radar sites, the map covers an area of 300km x 300km.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L3/TRMM_3A53.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_2A54 v7 - Radar Site Convective/Stratiform Map', is an instantaneous map in Cartesian coordinates with a 2 km resolution. At single radar sites, the map covers an area of 300 km x 300 km.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L2/TRMM_2A54.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_2A54UW v7 - This dataset is part of the University of Washington TRMM Ground Validation products. Instantaneous convective-stratiform cartesian grid based on baseUW.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L2/TRMM_2A54UW.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3A54 v7 - The 3A54 product, 'Site Rainfall Map', is a map of monthly surface rain totals derived from the instantaneous rain rate maps (2A53). The map is in Cartesian coordinates with a 2 km horizontal resolution and covers an area of 300km x 300km at single radar sites while the covered area varies for multiple radar sites - 724 km x 568 km at Texas site and 512 km x 704 km at Florida site.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L3/TRMM_3A54.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3A54UW v7 - This dataset is part of the University of Washington TRMM Ground Validation products. Monthly rain accumulation cartesian grid based on 2A53UW.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L3/TRMM_3A54UW.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_2A52 v7 - The TRMM_2A52 product displays the existence of rain in the radar volume scan. 'Existence' is the fraction of the radar FOV which has measurable precipitation.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L2/TRMM_2A52.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_2A56 v7 - The program rgmin generates 1-minute hourly rain rates from discrete tipping bucket rain gauge data by applying an interpolation algorithm. The interpolating routine is based on the cubic spline routine published in the book \"Numerical Recipes\".","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_GV_L2/TRMM_2A56.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_1B11 v7 - The new version of these data is in GPM-like format (consistent with the GPM Microwave Imager data format), and can be found under the name GPM_1BTMI (search by keyword GPM_1BTMI). This dataset contains TRMM Micrwave Imager (TMI) L1B calibrated radiances in terms of Brightness Temperatures.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L1/TRMM_1B11.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_2A12 v7 - The new version of these data is in GPM-like format and can be found under the name GPM_2AGPROFTRMMTMI_CLIM. This dataset, 2A12, ”TMI Profiling”, generates surface rainfall and vertical hydrometeor profiles on a pixel by pixel basis from the TRMM Microwave Imager (TMI) brightness temperature data using the Goddard Profiling algorithm GPROF2010.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L2/TRMM_2A12.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3A11 v7 - The new equivalent for this dataset should be searched for as \"GPM_3GPROFTRMMTMI_CLIM\". The TMI Gridded Oceanic Rainfall Product, also known as TMI Emission, consists of 5 degree by 5 degree monthly oceanic rainfall maps using TMI Level 1 data as input.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/TRMM_3A11.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3A12 v7 - The new equivalent for this dataset should be searched for as \"GPM_3GPROFTRMMTMI_CLIM\". This product contains global monthly means of surface precipitation rate, rain rate, convective surface precipitation rate and 28 vertical layers of hydrometeor contents (cloud liquid water, rain water, cloud ice liquid water, snow liquid water, graupel liquid water and latent heating) on 0.5 x 0.5 degree grids.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/TRMM_3A12.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3G25 v7 - 3G25, \"Gridded Orbital Spectral Latent Heating\", produces 0.5 degree x 0.5 degree latent heating, Q1-QR, and Q2 profiles from Precipitation Radar (PR) rain. The granule size is one orbit.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/TRMM_3G25.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_1B21 v7 - The TRMM Precipitation Radar (PR), the first of its kind in space, is an electronically scanning radar, operating at 13.8 GHz that measures the 3-D rainfall distribution over both land and ocean, and defines the layer depth of the precipitation. The 1B21 calculates the received power at the PR receiver input point from the Level-0 count value which is linearly proportional to the logarithm of the PR receiver output power.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L1/TRMM_1B21.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_1C21 v7 - The TRMM Precipitation Radar (PR), the first of its kind in space, is an electronically scanning radar, operating at 13.8 GHz that measures the 3-D rainfall distribution over both land and ocean, and defines the layer depth of the precipitation. The 1C21 calculates the effective radar reflectivity factor at 13.8 GHz without any propagation loss (due to rain or any other atmospheric gas) correction (Zm).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L1/TRMM_1C21.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_2A23 v7 - The new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_2APR. The TRMM Precipitation Radar (PR), the first of its kind in space, is an electronically scanning single-frequency radar, operating at 13.8 GHz that measures the 3-D rainfall distribution over both land and ocean, and defines the layer depth of the precipitation.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L2/TRMM_2A23.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_2A25 v7 - The new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_2APR. The TRMM 2A25 data are estimates of the three-dimensional distribution of rain from the TRMM Precipitation Radar.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L2/TRMM_2A25.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3H25 v7 - 3H25, \"Monthly Spectral Latent Heating\", produces 0.5 degree x 0.5 degree latent heating, Q1-QR, and Q2 profiles from PR rain.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/TRMM_3H25.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_2A21 v7 - The new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_2APR. This is the sigma zero algorithm, which inputs the PR power (1B21) and computes estimates of the path attenuation and its reliability by using the surface as a reference target.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L2/TRMM_2A21.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3A26 v7 - The new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_3PR. This dataset contains distributions of monthly surface rainfall.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/TRMM_3A26.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3A25 v7 - The new version of these data is in GPM-like format (consistent with the GPM Dual-frequency Radar data format), and can be found under the name GPM_3PR. This product consists of monthly statistics of the PR measurements at both a low (5 degrees x 5 degrees) and a high (0.5 degrees x 0.5 degrees) horizontal resolution.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/TRMM_3A25.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3H31 v7 - 3H31, \"Monthly Convective Stratiform Heating from Combined\", produces 0.5 deg x 0.5 deg monthly apparent heating profiles from surface convective rainfall rate and surface stratiform rainfall rate. The PI is Dr.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/TRMM_3H31.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3G31 v7 - 3G31, Gridded Orbital Convective Stratiform Heating from Combined, produces 0.5 degree x 0.5 degree orbital apparent heating profiles from surface convective rainfall rate and surface stratiform rainfall rate. The granule size is one orbit.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/TRMM_3G31.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_3B31 v7 - This is the last version of the TRMM-formatted, in HDF4 format, Combined (Radar-Radiometer) product, which is in a state of permanent preservation. New version that is consistently processed with Global precipitation Measurement (GPM) algorithms, and in a concistent with GPM HDF5 format, is available under short name GPM_3CMB_TRMM.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L3/TRMM_3B31.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRMM_1B01 v7 - This TRMM Visible and Infrared Scanner (VIRS) Level 1B Calibrated Radiance Product (1B01) contains calibrated radiances and auxiliary geolocation information from the five channels of the VIRS instrument, for each pixel of each scan. The data are stored in the Hierarchical Data Format (HDF), which includes both core and product specific metadata applicable to the VIRS measurements.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TRMM_L1/TRMM_1B01.7","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-trmm","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA TROPESS Project","Description":"The TROPESS AIRS-Aqua and OMI-Aura L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite and the OMI instrument on the EOS Aura satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13 km x 24 km (OMI nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2O3AIRSOMIFS\nThe TROPESS AIRS-Aqua and OMI-Aura L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite and the OMI instrument on the EOS Aura satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13 km x 24 km (OMI nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2NH3AIRSFS\nThe TROPESS AIRS-Aqua L2 Ammonia for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2NH3AIRSFS\nThe TROPESS AIRS-Aqua L2 Ammonia for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2TATMAIRSFS\nThe TROPESS AIRS-Aqua L2 Atmospheric Temperature for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of atmospheric temperature (TATM), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (AIRS nadir FOV), and are reported at 31 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2COAIRSFS\nThe TROPESS AIRS-Aqua L2 Carbon Monoxide for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2COAIRSFS\nThe TROPESS AIRS-Aqua L2 Carbon Monoxide for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2COAIRSORS\nThe TROPESS AIRS-Aqua L2 Carbon Monoxide for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The reanalysis stream summary product is global for the time period from 2002-09-01 to 2020-03-31. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2HDOAIRSFS\nThe TROPESS AIRS-Aqua L2 Deuterated Water Vapor for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2HDOAIRSFS\nThe TROPESS AIRS-Aqua L2 Deuterated Water Vapor for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2HDOAIRSORS\nThe TROPESS AIRS-Aqua L2 Deuterated Water Vapor for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The reanalysis stream summary product is global for the time period from 2002-09-01 to 2020-03-31. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2CH4AIRSFS\nThe TROPESS AIRS-Aqua L2 Methane for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2CH4AIRSFS\nThe TROPESS AIRS-Aqua L2 Methane for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2CH4AIRSORS\nThe TROPESS AIRS-Aqua L2 Methane for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the AIRS instrument on the EOS Auqa satellite. The reanalysis stream summary product is global for the time period from 2002-09-01 to 2020-03-31. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2O3AIRSFS\nThe TROPESS AIRS-Aqua L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2O3AIRSFS\nThe TROPESS AIRS-Aqua L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2O3AIRSORS\nThe TROPESS AIRS-Aqua L2 Ozone for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The reanalysis stream summary product is global for the time period from 2002-09-01 to 2020-03-31. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2H2OAIRSFS\nThe TROPESS AIRS-Aqua L2 Water for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of water vapor (H2O), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13.5 km (AIRS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSCRAERNH46H3D\nThe TROPESS Chemical Reanalysis Surface Aerosol NH4 6-Hourly 3-dimensional Product contains vertical concentrations of ammonium aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRAERNH4M3D\nThe TROPESS Chemical Reanalysis Surface Aerosol NH4 Monthly 3-dimensional Product contains vertical concentrations of ammonium aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRAERNO36H3D\nThe TROPESS Chemical Reanalysis Surface Aerosol NO3 6-Hourly 3-dimensional Product contains the volume mixing rations of nitrate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRAERNO3M3D\nThe TROPESS Chemical Reanalysis Surface Aerosol NO3 Monthly 3-dimensional Product contains the volume mixing rations of nitrate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRAERSO46H3D\nThe TROPESS Chemical Reanalysis Surface Aerosol SO4 6-Hourly 3-dimensional Product contains vertical concentrations of sulfate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRAERSO4M3D\nThe TROPESS Chemical Reanalysis Surface Aerosol SO4 Monthly 3-dimensional Product contains vertical concentrations of sulfate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRCH2O6H3D\nThe TROPESS Chemical Reanalysis CH2O 6-Hourly 3-dimensional Product contains vertical concentrations of formaldehyde. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRCH2OM3D\nThe TROPESS Chemical Reanalysis CH2O Monthly 3-dimensional Product contains vertical concentrations of formaldehyde. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRCO6H3D\nThe TROPESS Chemical Reanalysis CO 6-Hourly 3-dimensional Product contains vertical concentrations of carbon monoxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRCOM3D\nThe TROPESS Chemical Reanalysis CO Monthly 3-dimensional Product contains vertical concentrations of carbon monoxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRCOS6H3D\nThe TROPESS Chemical Reanalysis CO Spread 6-Hourly 3-dimensional Product contains the carbon monoxide ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRCOSM3D\nThe TROPESS Chemical Reanalysis CO Spread Monthly 3-dimensional Product contains the carbon monoxide ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRHNO36H3D\nThe TROPESS Chemical Reanalysis HNO3 6-Hourly 3-dimensional Product contains vertical concentrations of nitric acid. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRHNO3M3D\nThe TROPESS Chemical Reanalysis HNO3 Monthly 3-dimensional Product contains vertical concentrations of nitric acid. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRENOXLM2D\nThe TROPESS Chemical Reanalysis Lightning NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from lightning strikes. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRV6H3D\nThe TROPESS Chemical Reanalysis Meridional Wind 6-Hourly 3-dimensional Product contains vertical meridional wind component (v vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRVM3D\nThe TROPESS Chemical Reanalysis Meridional Wind Monthly 3-dimensional Product contains vertical meridional wind component (v vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRNO6H3D\nThe TROPESS Chemical Reanalysis NO 6-Hourly 3-dimensional Product contains vertical concentrations of nitric oxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRNOM3D\nThe TROPESS Chemical Reanalysis NO Monthly 3-dimensional Product contains vertical concentrations of nitric oxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRNO26H3D\nThe TROPESS Chemical Reanalysis NO2 6-Hourly 3-dimensional Product contains vertical concentrations of nitrogen dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRNO2M3D\nThe TROPESS Chemical Reanalysis NO2 Monthly 3-dimensional Product contains vertical concentrations of nitrogen dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRNO2S6H3D\nThe TROPESS Chemical Reanalysis NO2 Spread 6-Hourly 3-dimensional Product contains the nitrogen dioxide ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRNO2SM3D\nThe TROPESS Chemical Reanalysis NO2 Spread Monthly 3-dimensional Product contains the nitrogen dioxide ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRO36H3D\nThe TROPESS Chemical Reanalysis O3 6-Hourly 3-dimensional Product contains vertical concentrations of ozone. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRO3I6H3D\nThe TROPESS Chemical Reanalysis O3 Increment 6-Hourly 3-dimensional Product contains the ozone increment by data assimilation. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRO3IM3D\nThe TROPESS Chemical Reanalysis O3 Increment Monthly 3-dimensional Product contains the ozone increment by data assimilation. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRO3M3D\nThe TROPESS Chemical Reanalysis O3 Monthly 3-dimensional Product contains vertical concentrations of ozone. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRO3S6H3D\nThe TROPESS Chemical Reanalysis O3 Spread 6-Hourly 3-dimensional Product contains the ozone ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRO3SM3D\nThe TROPESS Chemical Reanalysis O3 Spread Monthly 3-dimensional Product contains the ozone ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCROH6H3D\nThe TROPESS Chemical Reanalysis OH 6-Hourly 3-dimensional Product contains vertical concentrations of the hydroxyl radical. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCROHM3D\nThe TROPESS Chemical Reanalysis OH Monthly 3-dimensional Product contains vertical concentrations of the hydroxyl radical. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRPAN6H3D\nThe TROPESS Chemical Reanalysis PAN 6-Hourly 3-dimensional Product contains vertical concentrations of peroxyacetyl nitrate. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRPANM3D\nThe TROPESS Chemical Reanalysis PAN Monthly 3-dimensional Product contains vertical concentrations of peroxyacetyl nitrate. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRSO26H3D\nThe TROPESS Chemical Reanalysis SO2 6-Hourly 3-dimensional Product contains vertical concentrations of sulfur dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRSO2M3D\nThe TROPESS Chemical Reanalysis SO2 Monthly 3-dimensional Product contains vertical concentrations of sulfur dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRQ6H3D\nThe TROPESS Chemical Reanalysis Specific Humidity 6-Hourly 3-dimensional Product contains vertical specific humidity values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRQM3D\nThe TROPESS Chemical Reanalysis Specific Humidity Monthly 3-dimensional Product contains vertical specific humidity values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRAERNH42H2D\nThe TROPESS Chemical Reanalysis Surface Aerosol NH4 2-Hourly 2-dimensional Product contains surface concentrations of ammonium aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRAERNO32H2D\nThe TROPESS Chemical Reanalysis Surface Aerosol NO3 2-Hourly 2-dimensional Product contains surface concentrations of nitrate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRAERSO42H2D\nThe TROPESS Chemical Reanalysis Surface Aerosol SO4 2-Hourly 3-dimensional Product contains surface concentrations of sulfate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRECOAM2D\nThe TROPESS Chemical Reanalysis Surface Anthropogenic CO emissions Monthly 2-dimensional Product contains carbon monoxide emissions from anthropogenic sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRENOXAM2D\nThe TROPESS Chemical Reanalysis Surface Anthropogenic NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from anthropogenic sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRECOBM2D\nThe TROPESS Chemical Reanalysis Surface Biomass Burning CO emissions Monthly 2-dimensional Product contains carbon monoxide emissions from biomass burning sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRENOXBM2D\nThe TROPESS Chemical Reanalysis Surface Biomass Burning NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from biomass burning sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRCH2O2H2D\nThe TROPESS Chemical Reanalysis CH2O 2-Hourly 2-dimensional Product contains surface concentrations of formaldehyde. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRCO2H2D\nThe TROPESS Chemical Reanalysis CO 2-Hourly 2-dimensional Product contains surface concentrations of carbon monoxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRHNO32H2D\nThe TROPESS Chemical Reanalysis HNO3 2-Hourly 2-dimensional Product contains surface concentrations of nitric acid. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRV2H2D\nThe TROPESS Chemical Reanalysis Surface Meridional Wind 2-Hourly 2-dimensional Product contains surface meridional wind component (v vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRNO2H2D\nThe TROPESS Chemical Reanalysis NO 2-Hourly 2-dimensional Product contains surface concentrations of nitric oxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRNO22H2D\nThe TROPESS Chemical Reanalysis NO2 2-Hourly 2-dimensional Product contains surface concentrations of nitrogen dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRO32H2D\nThe TROPESS Chemical Reanalysis Surface O3 2-Hourly 2-dimensional Product contains surface concentrations of ozone. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCROH2H2D\nThe TROPESS Chemical Reanalysis OH 2-Hourly 2-dimensional Product contains surface concentrations of the hydroxyl radical. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRPAN2H2D\nThe TROPESS Chemical Reanalysis PAN 2-Hourly 2-dimensional Product contains surface concentrations of peryoxyacetyl nitrate. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRPS2H2D\nThe TROPESS Chemical Reanalysis Surface Pressure 2-Hourly 2-dimensional Product contains surface pressure values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRPS6H2D\nThe TROPESS Chemical Reanalysis PS 6-Hourly 2-dimensional Product contains surface pressure values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRPSM2D\nThe TROPESS Chemical Reanalysis PS Monthly 2-dimensional Product contains surface pressure values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRSO22H2D\nThe TROPESS Chemical Reanalysis SO2 2-Hourly 2-dimensional Product contains surface concentrations of sulfur dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRENOXSM2D\nThe TROPESS Chemical Reanalysis Surface Soil NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from surface soil sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRQ2H2D\nThe TROPESS Chemical Reanalysis Surface Specific Humidity 2-Hourly 2-dimensional Product contains surface specific humidity values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRT2H2D\nThe TROPESS Chemical Reanalysis Surface Temperature 2-Hourly 2-dimensional Product contains surface temperature values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRECOTM2D\nThe TROPESS Chemical Reanalysis Surface Total CO emissions Monthly 2-dimensional Product contains carbon monoxide emissions from the total of all sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRENOXTM2D\nThe TROPESS Chemical Reanalysis Surface Total NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from the total of all sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRESO2TM2D\nThe TROPESS Chemical Reanalysis Surface Total SO2 emissions Monthly 2-dimensional Product contains sulfur dioxide emissions from the total of all sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRU2H2D\nThe TROPESS Chemical Reanalysis Surface Zonal Wind 2-Hourly 2-dimensional Product contains surface zonal wind component (u vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRT6H3D\nThe TROPESS Chemical Reanalysis Temperature 6-Hourly 3-dimensional Product contains vertical temperature values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRTM3D\nThe TROPESS Chemical Reanalysis Temperature Monthly 3-dimensional Product contains vertical temperature values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRU6H3D\nThe TROPESS Chemical Reanalysis Zonal Wind 6-Hourly 3-dimensional Product contains vertical zonal wind component (u vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSCRUM3D\nThe TROPESS Chemical Reanalysis Zonal Wind Monthly 3-dimensional Product contains vertical zonal wind component (u vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors. The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.\n<br>\n\n#### TRPSDL2NH3CRS1FS\nThe TROPESS CrIS-JPSS1 L2 Ammonia for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2NH3CRS1FS\nThe TROPESS CrIS-JPSS1 L2 Ammonia for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2TATMCRS1FS\nThe TROPESS CrIS-JPSS1 L2 Atmospheric Temperature for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of atmospheric temperature (TATM), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 31 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2COCRS1FS\nThe TROPESS CrIS-JPSS1 L2 Carbon Monoxide for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2COCRS1FS\nThe TROPESS CrIS-JPSS1 L2 Carbon Monoxide for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2HDOCRS1FS\nThe TROPESS CrIS-JPSS1 L2 Deuterated Water Vapor for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2HDOCRS1FS\nThe TROPESS CrIS-JPSS1 L2 Deuterated Water Vapor for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2CH4CRS1FS\nThe TROPESS CrIS-JPSS1 L2 Methane for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2CH4CRS1FS\nThe TROPESS CrIS-JPSS1 L2 Methane for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2O3CRS1FS\nThe TROPESS CrIS-JPSS1 L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2O3CRS1FS\nThe TROPESS CrIS-JPSS1 L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2PANCRS1FS\nThe TROPESS CrIS-JPSS1 L2 Peroxyacetyl Nitrate for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2PANCRS1FS\nThe TROPESS CrIS-JPSS1 L2 Peroxyacetyl Nitrate for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2H2OCRS1FS\nThe TROPESS CrIS-JPSS1 L2 Water for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of water vapor (H2O), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2NH3CRSAUS\nThe TROPESS CrIS-SNPP L2 Ammonia for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2NH3CRSFS\nThe TROPESS CrIS-SNPP L2 Ammonia for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2NH3CRSFS\nThe TROPESS CrIS-SNPP L2 Ammonia for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2NH3CRSRS\nThe TROPESS CrIS-SNPP L2 Ammonia for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), and formal uncertainties measured by the CrIS instruments on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2023-05-18. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2NH3CRSWCFHI\nThe TROPESS CrIS-SNPP L2 Ammonia for West Coast Fires HiRes, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2NH3CRSWCF\nThe TROPESS CrIS-SNPP L2 Ammonia for West Coast Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2TATMCRSAUS\nThe TROPESS CrIS-SNPP L2 Atmospheric Temperature for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of atmospheric temperature (TATM), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 31 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2TATMCRSFS\nThe TROPESS CrIS-SNPP L2 Atmospheric Temperature for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of atmospheric temperature (TATM), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 31 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2COCRSAUS\nThe TROPESS CrIS-SNPP L2 Carbon Monoxide for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2COCRSTS1\nThe TROPESS CrIS-SNPP L2 Carbon Monoxide for Buchholz2021 TS1, Standard Product in support of the IPCC contains contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the mid-latitude and tropical region (between 60S-60N) for the time period from 2015-11-02 to 2019-03-26. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2COCRSTS2\nThe TROPESS CrIS-SNPP L2 Carbon Monoxide for Buchholz2021 TS2, Standard Product in support of the IPCC contains contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the mid-latitude and tropical region (between 60S-60N) for the time period from 2015-11-02 to 2019-03-26. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2COCRSFS\nThe TROPESS CrIS-SNPP L2 Carbon Monoxide for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2COCRSFS\nThe TROPESS CrIS-SNPP L2 Carbon Monoxide for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2COCRSRS\nThe TROPESS CrIS-SNPP L2 Carbon Monoxide for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the CrIS instruments on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2023-05-18. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2COCRSWCFHI\nThe TROPESS CrIS-SNPP L2 Carbon Monoxide for West Coast Fires HiRes, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2COCRSWCF\nThe TROPESS CrIS-SNPP L2 Carbon Monoxide for West Coast Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 14 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2HDOCRSAUS\nThe TROPESS CrIS-SNPP L2 Deuterated Water Vapor for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2HDOCRSFS\nThe TROPESS CrIS-SNPP L2 Deuterated Water Vapor for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2HDOCRSFS\nThe TROPESS CrIS-SNPP L2 Deuterated Water Vapor for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2HDOCRSRS\nThe TROPESS CrIS-SNPP L2 Deuterated Water Vapor for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued for HDO. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2ALLCRSMGBEI\nThe TROPESS CrIS-SNPP L2 for Beijing Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Beijing for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2ALLCRSMGBEI\nThe TROPESS CrIS-SNPP L2 for Beijing Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Beijing for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2ALLCRSMGDEL\nThe TROPESS CrIS-SNPP L2 for Delhi Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Delhi for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2ALLCRSMGDEL\nThe TROPESS CrIS-SNPP L2 for Delhi Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Delhi for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2ALLCRSMGKAR\nThe TROPESS CrIS-SNPP L2 for Karachi Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 2x2 degree region over Karachi for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2ALLCRSMGKAR\nThe TROPESS CrIS-SNPP L2 for Karachi Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 2x2 degree region over Karachi for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2ALLCRSMGLAG\nThe TROPESS CrIS-SNPP L2 for Lagos Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is centered on a 3x3 degree region over Lagos for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2ALLCRSMGLAG\nThe TROPESS CrIS-SNPP L2 for Lagos Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream summary product is centered on a 3x3 degree region over Lagos for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2ALLCRSMGLOS\nThe TROPESS CrIS-SNPP L2 for Los Angeles Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Los Angeles for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2ALLCRSMGLOS\nThe TROPESS CrIS-SNPP L2 for Los Angeles Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Los Angeles for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2ALLCRSMGMEX\nThe TROPESS CrIS-SNPP L2 for Mexico City Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Mexico City for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2ALLCRSMGMEX\nThe TROPESS CrIS-SNPP L2 for Mexico City Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Mexico City for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2ALLCRSMGSAO\nThe TROPESS CrIS-SNPP L2 for Sao Paulo Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is centered on a 3x3 degree region over Sao Paulo for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2ALLCRSMGSAO\nThe TROPESS CrIS-SNPP L2 for Sao Paulo Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream summary product is centered on a 3x3 degree region over Sao Paulo for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2ALLCRSMGTOK\nThe TROPESS CrIS-SNPP L2 for Tokyo Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Tokyo for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2ALLCRSMGTOK\nThe TROPESS CrIS-SNPP L2 for Tokyo Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Tokyo for the time period from 2016-01-02 to 2021-05-21. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2CH4CRSAUS\nThe TROPESS CrIS-SNPP L2 Methane for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2CH4CRSFS\nThe TROPESS CrIS-SNPP L2 Methane for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2CH4CRSFS\nThe TROPESS CrIS-SNPP L2 Methane for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2CH4CRSRS\nThe TROPESS CrIS-SNPP L2 Methane for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued for CH4. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2O3CRSAUS\nThe TROPESS CrIS-SNPP L2 Ozone for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2O3CRSFS\nThe TROPESS CrIS-SNPP L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2O3CRSFS\nThe TROPESS CrIS-SNPP L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2O3CRSRS\nThe TROPESS CrIS-SNPP L2 Ozone for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the CrIS instruments on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2023-05-18. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2O3CRSWCFHI\nThe TROPESS CrIS-SNPP L2 Ozone for West Coast Fires HiRes, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2O3CRSWCF\nThe TROPESS CrIS-SNPP L2 Ozone for West Coast Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2PANCRSAUS\nThe TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2PANCRSFS\nThe TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2PANCRSFS\nThe TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2PANCRSRS\nThe TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), and formal uncertainties measured by the CrIS instruments on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2023-05-18. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2PANCRSWCFHI\nThe TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for West Coast Fires HiRes, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2PANCRSWCF\nThe TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for West Coast Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S. West Coast wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 16 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2H2OCRSAUS\nThe TROPESS CrIS-SNPP L2 Water for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of water vapor (H2O), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2H2OCRSFS\nThe TROPESS CrIS-SNPP L2 Water for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of water vapor (H2O), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 17 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSDL2O3OMIFS\nThe TROPESS OMI-Aura L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the OMI instrument on the EOS Aura satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13 km x 24 km (OMI nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### TRPSYL2O3OMIFS\nThe TROPESS OMI-Aura L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the OMI instrument on the EOS Aura satellite. The forward stream standard product is global for the time period from 2021-02-01 to present. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES). The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 13 km x 24 km (OMI nadir FOV), and are reported at 26 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://disc.gsfc.nasa.gov/datacollection/TRPSDL2O3AIRSOMIFS_1.html","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","carbon","netcdf","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"TRPSDL2O3AIRSOMIFS v1 - The TROPESS AIRS-Aqua and OMI-Aura L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite and the OMI instrument on the EOS Aura satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2O3AIRSOMIFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2O3AIRSOMIFS v1 - The TROPESS AIRS-Aqua and OMI-Aura L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite and the OMI instrument on the EOS Aura satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2O3AIRSOMIFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2NH3AIRSFS v1 - The TROPESS AIRS-Aqua L2 Ammonia for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2NH3AIRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2NH3AIRSFS v1 - The TROPESS AIRS-Aqua L2 Ammonia for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2NH3AIRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2TATMAIRSFS v1 - The TROPESS AIRS-Aqua L2 Atmospheric Temperature for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of atmospheric temperature (TATM), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2TATMAIRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2COAIRSFS v1 - The TROPESS AIRS-Aqua L2 Carbon Monoxide for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2COAIRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2COAIRSFS v1 - The TROPESS AIRS-Aqua L2 Carbon Monoxide for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2COAIRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2COAIRSORS v1 - The TROPESS AIRS-Aqua L2 Carbon Monoxide for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The reanalysis stream summary product is global for the time period from 2002-09-01 to 2020-03-31.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Reanalysis_Summary/TRPSYL2COAIRSORS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2HDOAIRSFS v1 - The TROPESS AIRS-Aqua L2 Deuterated Water Vapor for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2HDOAIRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2HDOAIRSFS v1 - The TROPESS AIRS-Aqua L2 Deuterated Water Vapor for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2HDOAIRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2HDOAIRSORS v1 - The TROPESS AIRS-Aqua L2 Deuterated Water Vapor for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The reanalysis stream summary product is global for the time period from 2002-09-01 to 2020-03-31.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Reanalysis_Summary/TRPSYL2HDOAIRSORS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2CH4AIRSFS v1 - The TROPESS AIRS-Aqua L2 Methane for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2CH4AIRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2CH4AIRSFS v1 - The TROPESS AIRS-Aqua L2 Methane for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2CH4AIRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2CH4AIRSORS v1 - The TROPESS AIRS-Aqua L2 Methane for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the AIRS instrument on the EOS Auqa satellite. The reanalysis stream summary product is global for the time period from 2002-09-01 to 2020-03-31.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Reanalysis_Summary/TRPSYL2CH4AIRSORS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2O3AIRSFS v1 - The TROPESS AIRS-Aqua L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2O3AIRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2O3AIRSFS v1 - The TROPESS AIRS-Aqua L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2O3AIRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2O3AIRSORS v1 - The TROPESS AIRS-Aqua L2 Ozone for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the AIRS instrument on the EOS Aqua satellite. The reanalysis stream summary product is global for the time period from 2002-09-01 to 2020-03-31.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Reanalysis_Summary/TRPSYL2O3AIRSORS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2H2OAIRSFS v1 - The TROPESS AIRS-Aqua L2 Water for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of water vapor (H2O), formal uncertainties, and diagnostic information measured by the AIRS instrument on the EOS Aqua satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2H2OAIRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRAERNH46H3D v1 - The TROPESS Chemical Reanalysis Surface Aerosol NH4 6-Hourly 3-dimensional Product contains vertical concentrations of ammonium aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_VERTCONCS/TRPSCRAERNH46H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRAERNH4M3D v1 - The TROPESS Chemical Reanalysis Surface Aerosol NH4 Monthly 3-dimensional Product contains vertical concentrations of ammonium aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_VERTCONCS/TRPSCRAERNH4M3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRAERNO36H3D v1 - The TROPESS Chemical Reanalysis Surface Aerosol NO3 6-Hourly 3-dimensional Product contains the volume mixing rations of nitrate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_VERTCONCS/TRPSCRAERNO36H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRAERNO3M3D v1 - The TROPESS Chemical Reanalysis Surface Aerosol NO3 Monthly 3-dimensional Product contains the volume mixing rations of nitrate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_VERTCONCS/TRPSCRAERNO3M3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRAERSO46H3D v1 - The TROPESS Chemical Reanalysis Surface Aerosol SO4 6-Hourly 3-dimensional Product contains vertical concentrations of sulfate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_VERTCONCS/TRPSCRAERSO46H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRAERSO4M3D v1 - The TROPESS Chemical Reanalysis Surface Aerosol SO4 Monthly 3-dimensional Product contains vertical concentrations of sulfate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_VERTCONCS/TRPSCRAERSO4M3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRCH2O6H3D v1 - The TROPESS Chemical Reanalysis CH2O 6-Hourly 3-dimensional Product contains vertical concentrations of formaldehyde. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_VERTCONCS/TRPSCRCH2O6H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRCH2OM3D v1 - The TROPESS Chemical Reanalysis CH2O Monthly 3-dimensional Product contains vertical concentrations of formaldehyde. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_VERTCONCS/TRPSCRCH2OM3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRCO6H3D v1 - The TROPESS Chemical Reanalysis CO 6-Hourly 3-dimensional Product contains vertical concentrations of carbon monoxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_VERTCONCS/TRPSCRCO6H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRCOM3D v1 - The TROPESS Chemical Reanalysis CO Monthly 3-dimensional Product contains vertical concentrations of carbon monoxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_VERTCONCS/TRPSCRCOM3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRCOS6H3D v1 - The TROPESS Chemical Reanalysis CO Spread 6-Hourly 3-dimensional Product contains the carbon monoxide ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_DIAGNOSTIC/TRPSCRCOS6H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRCOSM3D v1 - The TROPESS Chemical Reanalysis CO Spread Monthly 3-dimensional Product contains the carbon monoxide ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_DIAGNOSTIC/TRPSCRCOSM3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRHNO36H3D v1 - The TROPESS Chemical Reanalysis HNO3 6-Hourly 3-dimensional Product contains vertical concentrations of nitric acid. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_VERTCONCS/TRPSCRHNO36H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRHNO3M3D v1 - The TROPESS Chemical Reanalysis HNO3 Monthly 3-dimensional Product contains vertical concentrations of nitric acid. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_VERTCONCS/TRPSCRHNO3M3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRENOXLM2D v1 - The TROPESS Chemical Reanalysis Lightning NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from lightning strikes. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_EMISSIONS/TRPSCRENOXLM2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRV6H3D v1 - The TROPESS Chemical Reanalysis Meridional Wind 6-Hourly 3-dimensional Product contains vertical meridional wind component (v vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_METFIELDS/TRPSCRV6H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRVM3D v1 - The TROPESS Chemical Reanalysis Meridional Wind Monthly 3-dimensional Product contains vertical meridional wind component (v vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_METFIELDS/TRPSCRVM3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRNO6H3D v1 - The TROPESS Chemical Reanalysis NO 6-Hourly 3-dimensional Product contains vertical concentrations of nitric oxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_VERTCONCS/TRPSCRNO6H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRNOM3D v1 - The TROPESS Chemical Reanalysis NO Monthly 3-dimensional Product contains vertical concentrations of nitric oxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_VERTCONCS/TRPSCRNOM3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRNO26H3D v1 - The TROPESS Chemical Reanalysis NO2 6-Hourly 3-dimensional Product contains vertical concentrations of nitrogen dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_VERTCONCS/TRPSCRNO26H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRNO2M3D v1 - The TROPESS Chemical Reanalysis NO2 Monthly 3-dimensional Product contains vertical concentrations of nitrogen dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_VERTCONCS/TRPSCRNO2M3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRNO2S6H3D v1 - The TROPESS Chemical Reanalysis NO2 Spread 6-Hourly 3-dimensional Product contains the nitrogen dioxide ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_DIAGNOSTIC/TRPSCRNO2S6H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRNO2SM3D v1 - The TROPESS Chemical Reanalysis NO2 Spread Monthly 3-dimensional Product contains the nitrogen dioxide ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_DIAGNOSTIC/TRPSCRNO2SM3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRO36H3D v1 - The TROPESS Chemical Reanalysis O3 6-Hourly 3-dimensional Product contains vertical concentrations of ozone. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_VERTCONCS/TRPSCRO36H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRO3I6H3D v1 - The TROPESS Chemical Reanalysis O3 Increment 6-Hourly 3-dimensional Product contains the ozone increment by data assimilation. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_DIAGNOSTIC/TRPSCRO3I6H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRO3IM3D v1 - The TROPESS Chemical Reanalysis O3 Increment Monthly 3-dimensional Product contains the ozone increment by data assimilation. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_DIAGNOSTIC/TRPSCRO3IM3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRO3M3D v1 - The TROPESS Chemical Reanalysis O3 Monthly 3-dimensional Product contains vertical concentrations of ozone. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_VERTCONCS/TRPSCRO3M3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRO3S6H3D v1 - The TROPESS Chemical Reanalysis O3 Spread 6-Hourly 3-dimensional Product contains the ozone ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_DIAGNOSTIC/TRPSCRO3S6H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRO3SM3D v1 - The TROPESS Chemical Reanalysis O3 Spread Monthly 3-dimensional Product contains the ozone ensemble spread, a measure of data assimilation analysis uncertainty. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_DIAGNOSTIC/TRPSCRO3SM3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCROH6H3D v1 - The TROPESS Chemical Reanalysis OH 6-Hourly 3-dimensional Product contains vertical concentrations of the hydroxyl radical. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_VERTCONCS/TRPSCROH6H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCROHM3D v1 - The TROPESS Chemical Reanalysis OH Monthly 3-dimensional Product contains vertical concentrations of the hydroxyl radical. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_VERTCONCS/TRPSCROHM3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRPAN6H3D v1 - The TROPESS Chemical Reanalysis PAN 6-Hourly 3-dimensional Product contains vertical concentrations of peroxyacetyl nitrate. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_VERTCONCS/TRPSCRPAN6H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRPANM3D v1 - The TROPESS Chemical Reanalysis PAN Monthly 3-dimensional Product contains vertical concentrations of peroxyacetyl nitrate. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_VERTCONCS/TRPSCRPANM3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRSO26H3D v1 - The TROPESS Chemical Reanalysis SO2 6-Hourly 3-dimensional Product contains vertical concentrations of sulfur dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_VERTCONCS/TRPSCRSO26H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRSO2M3D v1 - The TROPESS Chemical Reanalysis SO2 Monthly 3-dimensional Product contains vertical concentrations of sulfur dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_VERTCONCS/TRPSCRSO2M3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRQ6H3D v1 - The TROPESS Chemical Reanalysis Specific Humidity 6-Hourly 3-dimensional Product contains vertical specific humidity values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_METFIELDS/TRPSCRQ6H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRQM3D v1 - The TROPESS Chemical Reanalysis Specific Humidity Monthly 3-dimensional Product contains vertical specific humidity values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_METFIELDS/TRPSCRQM3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRAERNH42H2D v1 - The TROPESS Chemical Reanalysis Surface Aerosol NH4 2-Hourly 2-dimensional Product contains surface concentrations of ammonium aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_SURFCONCS/TRPSCRAERNH42H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRAERNO32H2D v1 - The TROPESS Chemical Reanalysis Surface Aerosol NO3 2-Hourly 2-dimensional Product contains surface concentrations of nitrate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_SURFCONCS/TRPSCRAERNO32H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRAERSO42H2D v1 - The TROPESS Chemical Reanalysis Surface Aerosol SO4 2-Hourly 3-dimensional Product contains surface concentrations of sulfate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_SURFCONCS/TRPSCRAERSO42H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRECOAM2D v1 - The TROPESS Chemical Reanalysis Surface Anthropogenic CO emissions Monthly 2-dimensional Product contains carbon monoxide emissions from anthropogenic sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_EMISSIONS/TRPSCRECOAM2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRENOXAM2D v1 - The TROPESS Chemical Reanalysis Surface Anthropogenic NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from anthropogenic sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_EMISSIONS/TRPSCRENOXAM2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRECOBM2D v1 - The TROPESS Chemical Reanalysis Surface Biomass Burning CO emissions Monthly 2-dimensional Product contains carbon monoxide emissions from biomass burning sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_EMISSIONS/TRPSCRECOBM2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRENOXBM2D v1 - The TROPESS Chemical Reanalysis Surface Biomass Burning NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from biomass burning sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_EMISSIONS/TRPSCRENOXBM2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRCH2O2H2D v1 - The TROPESS Chemical Reanalysis CH2O 2-Hourly 2-dimensional Product contains surface concentrations of formaldehyde. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_SURFCONCS/TRPSCRCH2O2H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRCO2H2D v1 - The TROPESS Chemical Reanalysis CO 2-Hourly 2-dimensional Product contains surface concentrations of carbon monoxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_SURFCONCS/TRPSCRCO2H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRHNO32H2D v1 - The TROPESS Chemical Reanalysis HNO3 2-Hourly 2-dimensional Product contains surface concentrations of nitric acid. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_SURFCONCS/TRPSCRHNO32H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRV2H2D v1 - The TROPESS Chemical Reanalysis Surface Meridional Wind 2-Hourly 2-dimensional Product contains surface meridional wind component (v vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_METFIELDS/TRPSCRV2H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRNO2H2D v1 - The TROPESS Chemical Reanalysis NO 2-Hourly 2-dimensional Product contains surface concentrations of nitric oxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_SURFCONCS/TRPSCRNO2H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRNO22H2D v1 - The TROPESS Chemical Reanalysis NO2 2-Hourly 2-dimensional Product contains surface concentrations of nitrogen dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_SURFCONCS/TRPSCRNO22H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRO32H2D v1 - The TROPESS Chemical Reanalysis Surface O3 2-Hourly 2-dimensional Product contains surface concentrations of ozone. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_SURFCONCS/TRPSCRO32H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCROH2H2D v1 - The TROPESS Chemical Reanalysis OH 2-Hourly 2-dimensional Product contains surface concentrations of the hydroxyl radical. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_SURFCONCS/TRPSCROH2H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRPAN2H2D v1 - The TROPESS Chemical Reanalysis PAN 2-Hourly 2-dimensional Product contains surface concentrations of peryoxyacetyl nitrate. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_SURFCONCS/TRPSCRPAN2H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRPS2H2D v1 - The TROPESS Chemical Reanalysis Surface Pressure 2-Hourly 2-dimensional Product contains surface pressure values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_METFIELDS/TRPSCRPS2H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRPS6H2D v1 - The TROPESS Chemical Reanalysis PS 6-Hourly 2-dimensional Product contains surface pressure values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_METFIELDS/TRPSCRPS6H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRPSM2D v1 - The TROPESS Chemical Reanalysis PS Monthly 2-dimensional Product contains surface pressure values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_METFIELDS/TRPSCRPSM2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRSO22H2D v1 - The TROPESS Chemical Reanalysis SO2 2-Hourly 2-dimensional Product contains surface concentrations of sulfur dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_SURFCONCS/TRPSCRSO22H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRENOXSM2D v1 - The TROPESS Chemical Reanalysis Surface Soil NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from surface soil sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_EMISSIONS/TRPSCRENOXSM2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRQ2H2D v1 - The TROPESS Chemical Reanalysis Surface Specific Humidity 2-Hourly 2-dimensional Product contains surface specific humidity values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_METFIELDS/TRPSCRQ2H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRT2H2D v1 - The TROPESS Chemical Reanalysis Surface Temperature 2-Hourly 2-dimensional Product contains surface temperature values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_METFIELDS/TRPSCRT2H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRECOTM2D v1 - The TROPESS Chemical Reanalysis Surface Total CO emissions Monthly 2-dimensional Product contains carbon monoxide emissions from the total of all sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_EMISSIONS/TRPSCRECOTM2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRENOXTM2D v1 - The TROPESS Chemical Reanalysis Surface Total NOx emissions Monthly 2-dimensional Product contains nitrogen oxides (NO and NO2) emissions from the total of all sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_EMISSIONS/TRPSCRENOXTM2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRESO2TM2D v1 - The TROPESS Chemical Reanalysis Surface Total SO2 emissions Monthly 2-dimensional Product contains sulfur dioxide emissions from the total of all sources. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_EMISSIONS/TRPSCRESO2TM2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRU2H2D v1 - The TROPESS Chemical Reanalysis Surface Zonal Wind 2-Hourly 2-dimensional Product contains surface zonal wind component (u vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_2HR_METFIELDS/TRPSCRU2H2D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRT6H3D v1 - The TROPESS Chemical Reanalysis Temperature 6-Hourly 3-dimensional Product contains vertical temperature values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_METFIELDS/TRPSCRT6H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRTM3D v1 - The TROPESS Chemical Reanalysis Temperature Monthly 3-dimensional Product contains vertical temperature values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_METFIELDS/TRPSCRTM3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRU6H3D v1 - The TROPESS Chemical Reanalysis Zonal Wind 6-Hourly 3-dimensional Product contains vertical zonal wind component (u vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_6HR_METFIELDS/TRPSCRU6H3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSCRUM3D v1 - The TROPESS Chemical Reanalysis Zonal Wind Monthly 3-dimensional Product contains vertical zonal wind component (u vector) values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TCR2_MON_METFIELDS/TRPSCRUM3D.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2NH3CRS1FS v1 - The TROPESS CrIS-JPSS1 L2 Ammonia for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2NH3CRS1FS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2NH3CRS1FS v1 - The TROPESS CrIS-JPSS1 L2 Ammonia for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2NH3CRS1FS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2TATMCRS1FS v1 - The TROPESS CrIS-JPSS1 L2 Atmospheric Temperature for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of atmospheric temperature (TATM), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2TATMCRS1FS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2COCRS1FS v1 - The TROPESS CrIS-JPSS1 L2 Carbon Monoxide for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2COCRS1FS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2COCRS1FS v1 - The TROPESS CrIS-JPSS1 L2 Carbon Monoxide for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2COCRS1FS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2HDOCRS1FS v1 - The TROPESS CrIS-JPSS1 L2 Deuterated Water Vapor for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2HDOCRS1FS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2HDOCRS1FS v1 - The TROPESS CrIS-JPSS1 L2 Deuterated Water Vapor for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2HDOCRS1FS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2CH4CRS1FS v1 - The TROPESS CrIS-JPSS1 L2 Methane for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2CH4CRS1FS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2CH4CRS1FS v1 - The TROPESS CrIS-JPSS1 L2 Methane for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2CH4CRS1FS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2O3CRS1FS v1 - The TROPESS CrIS-JPSS1 L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2O3CRS1FS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2O3CRS1FS v1 - The TROPESS CrIS-JPSS1 L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2O3CRS1FS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2PANCRS1FS v1 - The TROPESS CrIS-JPSS1 L2 Peroxyacetyl Nitrate for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2PANCRS1FS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2PANCRS1FS v1 - The TROPESS CrIS-JPSS1 L2 Peroxyacetyl Nitrate for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), and formal uncertainties measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2PANCRS1FS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2H2OCRS1FS v1 - The TROPESS CrIS-JPSS1 L2 Water for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of water vapor (H2O), formal uncertainties, and diagnostic information measured by the CrIS instrument on the JPSS-1 (NOAA-20) satellite. The forward stream standard product is global for the time period from 2021-04-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2H2OCRS1FS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2NH3CRSAUS v1 - The TROPESS CrIS-SNPP L2 Ammonia for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2NH3CRSAUS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2NH3CRSFS v1 - The TROPESS CrIS-SNPP L2 Ammonia for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2NH3CRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2NH3CRSFS v1 - The TROPESS CrIS-SNPP L2 Ammonia for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2NH3CRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2NH3CRSRS v1 - The TROPESS CrIS-SNPP L2 Ammonia for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), and formal uncertainties measured by the CrIS instruments on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2023-05-18.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Reanalysis_Summary/TRPSYL2NH3CRSRS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2NH3CRSWCFHI v1 - The TROPESS CrIS-SNPP L2 Ammonia for West Coast Fires HiRes, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2NH3CRSWCFHI.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2NH3CRSWCF v1 - The TROPESS CrIS-SNPP L2 Ammonia for West Coast Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2NH3CRSWCF.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2TATMCRSAUS v1 - The TROPESS CrIS-SNPP L2 Atmospheric Temperature for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of atmospheric temperature (TATM), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2TATMCRSAUS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2TATMCRSFS v1 - The TROPESS CrIS-SNPP L2 Atmospheric Temperature for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of atmospheric temperature (TATM), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2TATMCRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2COCRSAUS v1 - The TROPESS CrIS-SNPP L2 Carbon Monoxide for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2COCRSAUS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2COCRSTS1 v1 - The TROPESS CrIS-SNPP L2 Carbon Monoxide for Buchholz2021 TS1, Standard Product in support of the IPCC contains contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the mid-latitude and tropical region (between 60S-60N) for the time period from 2015-11-02 to 2019-03-26.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2COCRSTS1.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2COCRSTS2 v1 - The TROPESS CrIS-SNPP L2 Carbon Monoxide for Buchholz2021 TS2, Standard Product in support of the IPCC contains contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the mid-latitude and tropical region (between 60S-60N) for the time period from 2015-11-02 to 2019-03-26.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2COCRSTS2.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2COCRSFS v1 - The TROPESS CrIS-SNPP L2 Carbon Monoxide for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2COCRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2COCRSFS v1 - The TROPESS CrIS-SNPP L2 Carbon Monoxide for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2COCRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2COCRSRS v1 - The TROPESS CrIS-SNPP L2 Carbon Monoxide for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), and formal uncertainties measured by the CrIS instruments on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2023-05-18.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Reanalysis_Summary/TRPSYL2COCRSRS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2COCRSWCFHI v1 - The TROPESS CrIS-SNPP L2 Carbon Monoxide for West Coast Fires HiRes, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2COCRSWCFHI.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2COCRSWCF v1 - The TROPESS CrIS-SNPP L2 Carbon Monoxide for West Coast Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of carbon monoxide (CO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2COCRSWCF.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2HDOCRSAUS v1 - The TROPESS CrIS-SNPP L2 Deuterated Water Vapor for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2HDOCRSAUS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2HDOCRSFS v1 - The TROPESS CrIS-SNPP L2 Deuterated Water Vapor for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2HDOCRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2HDOCRSFS v1 - The TROPESS CrIS-SNPP L2 Deuterated Water Vapor for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2HDOCRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2HDOCRSRS v1 - The TROPESS CrIS-SNPP L2 Deuterated Water Vapor for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of semi-heavy water (HDO), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued for HDO.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Reanalysis_Summary/TRPSYL2HDOCRSRS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2ALLCRSMGBEI v1 - The TROPESS CrIS-SNPP L2 for Beijing Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Beijing for the time period from 2016-01-02 to 2021-05-21.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Standard/TRPSDL2ALLCRSMGBEI.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2ALLCRSMGBEI v1 - The TROPESS CrIS-SNPP L2 for Beijing Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Beijing for the time period from 2016-01-02 to 2021-05-21.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Summary/TRPSYL2ALLCRSMGBEI.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2ALLCRSMGDEL v1 - The TROPESS CrIS-SNPP L2 for Delhi Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Delhi for the time period from 2016-01-02 to 2021-05-21.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Standard/TRPSDL2ALLCRSMGDEL.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2ALLCRSMGDEL v1 - The TROPESS CrIS-SNPP L2 for Delhi Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Delhi for the time period from 2016-01-02 to 2021-05-21.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Summary/TRPSYL2ALLCRSMGDEL.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2ALLCRSMGKAR v1 - The TROPESS CrIS-SNPP L2 for Karachi Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 2x2 degree region over Karachi for the time period from 2016-01-02 to 2021-05-21.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Standard/TRPSDL2ALLCRSMGKAR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2ALLCRSMGKAR v1 - The TROPESS CrIS-SNPP L2 for Karachi Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 2x2 degree region over Karachi for the time period from 2016-01-02 to 2021-05-21.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Summary/TRPSYL2ALLCRSMGKAR.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2ALLCRSMGLAG v1 - The TROPESS CrIS-SNPP L2 for Lagos Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is centered on a 3x3 degree region over Lagos for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Standard/TRPSDL2ALLCRSMGLAG.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2ALLCRSMGLAG v1 - The TROPESS CrIS-SNPP L2 for Lagos Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream summary product is centered on a 3x3 degree region over Lagos for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Summary/TRPSYL2ALLCRSMGLAG.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2ALLCRSMGLOS v1 - The TROPESS CrIS-SNPP L2 for Los Angeles Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Los Angeles for the time period from 2016-01-02 to 2021-05-21.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Standard/TRPSDL2ALLCRSMGLOS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2ALLCRSMGLOS v1 - The TROPESS CrIS-SNPP L2 for Los Angeles Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Los Angeles for the time period from 2016-01-02 to 2021-05-21.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Summary/TRPSYL2ALLCRSMGLOS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2ALLCRSMGMEX v1 - The TROPESS CrIS-SNPP L2 for Mexico City Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Mexico City for the time period from 2016-01-02 to 2021-05-21.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Standard/TRPSDL2ALLCRSMGMEX.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2ALLCRSMGMEX v1 - The TROPESS CrIS-SNPP L2 for Mexico City Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Mexico City for the time period from 2016-01-02 to 2021-05-21.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Summary/TRPSYL2ALLCRSMGMEX.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2ALLCRSMGSAO v1 - The TROPESS CrIS-SNPP L2 for Sao Paulo Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is centered on a 3x3 degree region over Sao Paulo for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Standard/TRPSDL2ALLCRSMGSAO.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2ALLCRSMGSAO v1 - The TROPESS CrIS-SNPP L2 for Sao Paulo Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream summary product is centered on a 3x3 degree region over Sao Paulo for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Summary/TRPSYL2ALLCRSMGSAO.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2ALLCRSMGTOK v1 - The TROPESS CrIS-SNPP L2 for Tokyo Megacity, Standard Product contains the vertical distribution of seven retrieved atmospheric gases (CH4, CO, H2O, HDO, NH3, O3 and PAN) and temperature, along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This standard product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Tokyo for the time period from 2016-01-02 to 2021-05-21.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Standard/TRPSDL2ALLCRSMGTOK.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2ALLCRSMGTOK v1 - The TROPESS CrIS-SNPP L2 for Tokyo Megacity, Summary Product contains the vertical distribution of six retrieved atmospheric gases (CH4, CO, HDO, NH3, O3 and PAN), along with formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. This summary product is one of the TROPESS Special Collections, centered on a 3x3 degree region over Tokyo for the time period from 2016-01-02 to 2021-05-21.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_MegaCities_Summary/TRPSYL2ALLCRSMGTOK.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2CH4CRSAUS v1 - The TROPESS CrIS-SNPP L2 Methane for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2CH4CRSAUS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2CH4CRSFS v1 - The TROPESS CrIS-SNPP L2 Methane for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2CH4CRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2CH4CRSFS v1 - The TROPESS CrIS-SNPP L2 Methane for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2CH4CRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2CH4CRSRS v1 - The TROPESS CrIS-SNPP L2 Methane for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of methane (CH4), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued for CH4.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Reanalysis_Summary/TRPSYL2CH4CRSRS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2O3CRSAUS v1 - The TROPESS CrIS-SNPP L2 Ozone for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2O3CRSAUS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2O3CRSFS v1 - The TROPESS CrIS-SNPP L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2O3CRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2O3CRSFS v1 - The TROPESS CrIS-SNPP L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2O3CRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2O3CRSRS v1 - The TROPESS CrIS-SNPP L2 Ozone for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the CrIS instruments on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2023-05-18.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Reanalysis_Summary/TRPSYL2O3CRSRS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2O3CRSWCFHI v1 - The TROPESS CrIS-SNPP L2 Ozone for West Coast Fires HiRes, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2O3CRSWCFHI.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2O3CRSWCF v1 - The TROPESS CrIS-SNPP L2 Ozone for West Coast Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2O3CRSWCF.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2PANCRSAUS v1 - The TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2PANCRSAUS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2PANCRSFS v1 - The TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2PANCRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2PANCRSFS v1 - The TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), and formal uncertainties measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2PANCRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2PANCRSRS v1 - The TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), and formal uncertainties measured by the CrIS instruments on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2023-05-18.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Reanalysis_Summary/TRPSYL2PANCRSRS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2PANCRSWCFHI v1 - The TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for West Coast Fires HiRes, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2PANCRSWCFHI.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2PANCRSWCF v1 - The TROPESS CrIS-SNPP L2 Peroxyacetyl Nitrate for West Coast Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of peroxyacetyl nitrate (PAN), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the CONUS region (20N-60N; 150W-40W) for the time period from 2020-08-01 to 2020-10-31, during the outbreak of U.S.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2PANCRSWCF.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2H2OCRSAUS v1 - The TROPESS CrIS-SNPP L2 Water for Australian Fires, Standard Product contains the vertical distribution of the retrieved atmospheric state of water vapor (H2O), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. This product focuses on the Australia region (60S-0S; 100E-177.5E) for the time period from 2019-11-01 to 2020-01-31, during the outbreak of Austrailan wildfires.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Special/TRPSDL2H2OCRSAUS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2H2OCRSFS v1 - The TROPESS CrIS-SNPP L2 Water for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of water vapor (H2O), formal uncertainties, and diagnostic information measured by the CrIS instrument on the Suomi-NPP satellite. The forward stream standard product is global for the time period from 2021-02-01 to 2021-05-21, when the CrIS-SNPP processing was discontinued.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2H2OCRSFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSDL2O3OMIFS v1 - The TROPESS OMI-Aura L2 Ozone for Forward Stream, Standard Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), formal uncertainties, and diagnostic information measured by the OMI instrument on the EOS Aura satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Standard/TRPSDL2O3OMIFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TRPSYL2O3OMIFS v1 - The TROPESS OMI-Aura L2 Ozone for Forward Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ozone (O3), and formal uncertainties measured by the OMI instrument on the EOS Aura satellite. The forward stream standard product is global for the time period from 2021-02-01 to present.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPESS_Summary/TRPSYL2O3OMIFS.1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-tropess","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA TROPICS (EVI-3) Project","Description":"The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. Each SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles. This dataset is the Level-2b Precipitation Ice Water Path (PIWP). PIWP is the total mass of ice-phase precipitation (like snow or hail) within a vertical atmospheric column, per unit area (measured in kilograms per square meter or kg/m2). PIWP is specifically about the portion that is falling as precipitation while Cloud Ice Water Path (CIWP) measures all ice particles in a cloud. TROPICS channels are not sensitive to small particles required for CIWP estimation. Presently, the algorithm retrieves PIWP over oceans only. Retrievals over land could be investigated in the future as resources and schedule permit. The inputs to the PIWP are the L1b brightness temperatures. Details for the L1b can be found in the L1 native radiance ATBD (TRPCS-ATBD-034). Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.\n<br>\n\n#### TROPICS01DMINL2B\nThe \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. Each SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles. The Deep Multispectral INtensity of TCs estimator with 183 GHz brightness temperatures (D-MINT183), developed at the University of Wisconsin/CIMSS, estimates two primary TC variables: Minimum Sea Level Pressure (MSLP) and Maximum Sustained Winds (MSW). D-MINT183 is a convolutional neural network (CNN) with no inherent physical understanding of TC intensity relationships, which is an approach that differs from the other two TROPICS TC Intensity algorithm (i.e., TCIE and HISA). D-MINT183 is trained using combinations of 183±1 and 183±3 GHz imagery from SSMIS, ATMS, MHS, and AMSU-B, as well as 15 hours of infrared imagery (in 3-h increments) and scalar predictors. TROPICS has 184.41 GHz and 186.51 GHz imagery, which is used as a proxy for the 183±1 GHz and 183±3 GHz imagery.\n<br>\n\n#### TROPICS01ANTTL1A\nThe \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of six identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. This dataset is produced from the Pathfinder satellite, a single 3U small satellite, which has launched previous to the constellation, on a sun-synchronous orbital plane. Each SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br><br>\n","Documentation":"https://tropics.ll.mit.edu/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","netcdf","oceans","precipitation","satellite imagery","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"TROPICS01DMINL2B v1.0 - The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPICS_L2B/TROPICS01DMINL2B.1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"TROPICS01ANTTL1A v1.0 - The \"Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats\" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of six identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/TROPICS_L1A/TROPICS01ANTTL1A.1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-tropics-evi-3","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA UARS Project","Description":"The UARS Correlative assimilation data from NOAA's National Meteorological Center (NMC) consists of daily model runs at 12 GMT as a means of providing an independent analysis for comparison with data from the UARS instruments. The NMC data product includes temperature (Kelvin), humidity (%), geopotential height (m), and zonal and meridional wind components (m/s). Geopotential height and atmospheric temperature data are derived from two analysis systems: 1) tropospheric fields from 1000 to 100 mb, and 2) stratospheric analyses from 70 to 0.4 mb. The tropospheric fields are the 12 GMT gridded fields which are part of the Global Daily Assimilation System (GDAS), where data from radiosondes, aircraft, satellites, ships, bouys, or any other conventional means are assimilated and merged into meteorological fields (heights, temperature, winds). The stratospheric analyses are 12 GMT operational analyses at the 70 - 0.4 mb pressure levels produced from satellite temperature retrievals and RAOBS via a modified Cressman analysis. Tropospheric temperature analyses use combined NOAA-10 and NOAA-11 data. Moisture (northern hemisphere only) and Winds data are obtained from the NMC GDAS. The gridded fields are on the standard 65 x 65 NMC polar stereographic grid oriented 80W (grid increment 381 km at 60N), and 100E (grid increment 381 km at 60S); Pole at (33,33). The NMC uses 18 standard pressure levels for data at 1000, 850, 700, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 10, 5, 2, 1, 0.4 millibars. Height and temperature data are produced at all 18 pressure levels from 1000 mb to 0.4 mb. Moisture data are only produced in the northern hemisphere for the 6 lowest altitude pressure levels. Wind data are produced for the 12 lowest altitude levels in the northern hemisphere, and at the 4 levels 1000, 500, 300, and 250 mb in the southern hemisphere. There are four data files representing each subtype per day. Included in the NMC correlative data set will be a geographical data richness file. This file is used by the NMC access routines, and indicates the radiosonde coverage for each point on the NMC grid. The data files are available in a binary record oriented format.\n<br>\n\n#### UARZCUKM\nThe UARS Correlative assimilation data from the U.K. Meteorological Office (UKMO) consists of daily model runs at 12:00 GMT as a means of providing an independent analysis for comparison with data from the UARS instruments. The UKMO product includes temperature (Kelvin), geopotential height (m), zonal and meridional wind components (m/s), and vertical velocity (Pa/s). Note: vertical velocity (omega) data are available from 26 August 1992 forward. The numerical Unified Model used in the assimilation system is a global primitive equation model, with a split-explicit time integration scheme. It incorporates a comprehensive range of physical parameterization schemes. It uses a hybrid vertical coordinate system, with terrain-following model levels at low levels, gradually changing to pressure levels in the stratosphere. The input to the assimilation system are from the World Weather Watch network of surface and upper air observations and satellite data. The UKMO correlative product consists of a single granule per day containing all the geophysical parameters. Data coverage is global for where the horizontal resolution is 2.5 degrees latitude by 3.75 degrees longitude, using a staggered grid system. For parameters other than wind components, each horizontal field consists of 73 rows of 96 points, starting at 90N, 0E. The wind data are staggered by half a grid, starting at 88.75N, 1.875E (with 72 rows). The UKMO correlative assimilation data are on the UARS standard pressure levels (in mbars) given by: P(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ... The data files are available in a binary record oriented format.\n<br>\n\n#### UARCL3AL\nThe Cryogenic Limb Array Etalon Spectrometer (CLAES) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of temperature and concentrations of O3, H2O, CH4, N2O, NO, NO2, N2O5, HNO3, ClONO2, HCl, CF2Cl2 (CFC-12), CFCl3 (CFC-11), and aerosol absorption coefficients. The instrument measured infrared thermal emissions at wavelengths from 3.5 to 12.7 microns. CLAES was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the chemical composition of the stratosphere and mesosphere, and also investigated the depletion of stratospheric ozone and ozone chemistry. Limb measurements were made in the altitude range between 10 and 60 km at about 2.5 km resolution. Data were collected between latitude 34S and 80N and 80S and 34N, alternating each satellite yaw cycle of about 36 days. The CLAES Level 3AL data were processed with the version 9 algorithm, except H2O which is version 7. The CLAES level 3AL product consists of 20 granules per day. A data granule is one CLAES species or subtype per day. Data are on the UARS standard pressure levels (in mbars) given by: P(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ... The data files are available in a binary record oriented format.\n<br>\n\n#### UARCL3AT\nThe Cryogenic Limb Array Etalon Spectrometer (CLAES) Level 3AT data product consists of daily, 65.536 second interval time-ordered vertical profiles of temperature and concentrations of O3, H2O, CH4, N2O, NO, NO2, N2O5, HNO3, ClONO2, HCl, CF2Cl2 (CFC-12), CFCl3 (CFC-11), and aerosol absorption coefficients. The instrument measured infrared thermal emissions at wavelengths from 3.5 to 12.7 microns. CLAES was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the chemical composition of the stratosphere and mesosphere, and also investigated the depletion of stratospheric ozone and ozone chemistry. Limb measurements were made in the altitude range between 10 and 60 km at about 2.5 km resolution. Data were collected between latitude 34S and 80N and 80S and 34N, alternating each satellite yaw cycle of about 36 days. The CLAES Level 3AT data were processed with the version 9 algorithm, except H2O which is version 7. The CLAES level 3AL product consists of 20 granules per day. A data granule is one CLAES species or subtype per day. Data are on the UARS standard pressure levels (in mbars) given by: P(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ... The data files are available in a binary record oriented format.\n<br>\n\n#### UARHA2FN\nThe Halogen Occultation Experiment (HALOE) Level 2 data product consists of daily vertical profiles of temperature, aerosol extinciton and pressure, as well as concentrations of HCl, HF, CH4, NO by gas filter correlation radiometry and H2O, NO2, O3 and CO2 by broadband filter radiometry. The instrument measured atmospheric infrared absorption at wavelengths from 2.43 to 10.25 microns. HALOE was flown on NASA's Upper Atmosphere Research Satellite (UARS) to investigate upper atmosphere chemistry, dynamics and radiative processes, particlarly stratospheric ozone destruction and chlorofluromethane impact on ozone. HALOE is a solar occultation experiment which typically views 15 sunrise and sunset events each day. Limb measurements were made in the altitude range between 3 km and 130 km (depending on channel) with less than 1 km resolution. For a given day, the profiles were in two distinct latitude bands, for the sunset and sunrise events. Coverage across the full range of latitudes between 80S and 80N was achieved on a time period ranging from about two to six weeks, depending on the time of year. The HALOE Level 2 data were processed with the version 19 algorithm. The HALOE level 2 product consists of a single granule or file per day. A data granule contains all measured HALOE species and subtypes, and ancillary information. Aerosol extinction coefficients are measured in units of 1/km, chemical species are given in mixing ratios, and temperature is in units of degrees Kelvin. HALOE level 2 data are on 491 instrument resolution altitude levels. The data files are available in a binary record oriented format.\n<br>\n\n#### UARHA3AT\nThe Halogen Occultation Experiment (HALOE) Level 3AT data product consists of daily vertical profiles of temperature, aerosol extinction and concentrations of HCl, HF, CH4, NO by gas filter correlation radiometry and H2O, NO2, O3 by broadband filter radiometry. The instrument measured atmospheric infrared absorption at wavelengths from 2.43 to 10.25 microns. HALOE was flown on NASA's Upper Atmosphere Research Satellite (UARS) to investigate upper atmosphere chemistry, dynamics and radiative processes, particlarly stratospheric ozone destruction and chlorofluromethane impact on ozone. HALOE is a solar occultation experiment which typically views 15 sunrise and sunset events each day. Limb measurements were made in the altitude range between 15 km and 60 - 130 km (depending on channel) with about 2.5 km resolution. For a given day, the profiles were in two distinct latitude bands, for the sunset and sunrise events. Coverage across the full range of latitudes between 80S and 80N was achieved on a time period ranging from about two to six weeks, depending on the time of year. The HALOE Level 3AT data were processed with the version 19 algorithm. The HALOE level 3AT product consists of 13 granules per day. A data granule is one HALOE species or subtype per day. HALOE data are on the UARS standard pressure levels (in mbars) given by: P(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ... 54 Each of the 13 HALOE granules is accompanied by 2 additional parameter files, designated as level 3TP. The first parameter file, subtype LAT, contains the latitude values for each pressure level of each event for the day. The second parameter file, subtype LON, contains the longitude values. The level 3AT files only contain the latitude/longitude location corresponding to the 30 km retrieval point. The data files are available in a binary record oriented format.\n<br>\n\n#### UARHR3AL\nThe High Resolution Doppler Imager (HRDI) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of meridional and zonal wind components, temperature and volume emmission rate of O2. The instrument measured Doppler shifts of spectral lines in the visible and near-IR between 400 and 800 nm. HRDI was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure winds and other parameters in the mesosphere and lower thermosphere by primarily observing the Doppler shift of emitted light, and in the stratosphere by observing the Doppler shift of atmospheric absorption features. Measurements were made in the mesosphere between 50 and 115 km, and in the stratosphere between 10 and 40 km at about 2.5 km resolution. Data were collected between latitude 40S and 76N and 76S and 40N, alternating each satellite yaw cycle of about 36 days. The HRDI Level 3AL data were processed with the version 11 algorithm. The HRDI level 3AL product consists of 8 granules per day. A data granule is one HRDI species or subtype per day. Data are on the UARS standard altitude levels (in km) given by: z(i) = 5*i for i<=12 z(i) = 60 + (i-12)*3 for 13 <= i <= 32 z(i) = 120 + (i-32)*5 for 33 <= i <= 88 as well as the UARS standard pressure levels (in mbars) given by: P(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ... The data files are available in a binary record oriented format.\n<br>\n\n#### UARHR3AT\nThe High Resolution Doppler Imager (HRDI) Level 3AT data product consists of daily, 65.536 second interval time-ordered vertical profiles of meridional and zonal wind components, temperature and volume emmission rate of O2. The instrument measured Doppler shifts of spectral lines in the visible and near-IR between 400 and 800 nm. HRDI was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure winds and other parameters in the mesosphere and lower thermosphere by primarily observing the Doppler shift of emitted light, and in the stratosphere by observing the Doppler shift of atmospheric absorption features. Measurements were made in the mesosphere between 50 and 115 km, and in the stratosphere between 10 and 40 km at about 2.5 km resolution. Data were collected between latitude 40S and 76N and 76S and 40N, alternating each satellite yaw cycle of about 36 days. The HRDI Level 3AT data were processed with the version 11 algorithm. The HRDI level 3AT product consists of 8 granules per day. A data granule is one HRDI species or subtype per day. Data are on the UARS standard altitude levels (in km) given by: z(i) = 5*i for i<=12 z(i) = 60 + (i-12)*3 for 13 <= i <= 32 z(i) = 120 + (i-32)*5 for 33 <= i <= 88 as well as the UARS standard pressure levels (in mbars) given by: P(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ... The data files are available in a binary record oriented format.\n<br>\n\n#### UARIS3AL\nThe Improved Stratospheric and Mesospheric Sounder (ISAMS) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of temperature and concentrations of O3, H2O, CH4, CO, N2O, N2O5, NO2, and aerosol absorption coefficients. The insrument measured infrared molecular emmissions in the spectral region from 4.6 to 16.6 microns. ISAMS was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the global temperature and composition profiles in the stratosphere and mesosphere. Limb measurements were made in the altitude range between 15 and 60 km at about 2.5 km resolution. Data were collected between latitude 34S and 80N and 80S and 34N, alternating each satellite yaw cycle of about 36 days. The ISAMS Level 3AL data were processed with the version 10 algorithm, except H2O which is version 9. The ISAMS level 3AL product consists of 10 granules per day. A data granule is one ISAMS species or subtype per day. Data are on the UARS standard pressure levels (in mbars) given by: P(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ... Each of the 10 ISAMS granules is accompanied by its own additional parameter file, designated as level 3LP. The parameter file, contains the additional ancillary and quality information not found in the 3AL file. The data files are available in a binary record oriented format.\n<br>\n\n#### UARIS3AT\nThe Improved Stratospheric and Mesospheric Sounder (ISAMS) Level 3AT data product consists of daily, 65.536 second interval time-ordered vertical profiles of temperature and concentrations of O3, H2O, CH4, CO, N2O, N2O5, NO2, and aerosol absorption coefficients. The insrument measured infrared molecular emmissions in the spectral region from 4.6 to 16.6 microns. ISAMS was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the global temperature and composition profiles in the stratosphere and mesosphere. Limb measurements were made in the altitude range between 15 and 60 km at about 2.5 km resolution. Data were collected between latitude 34S and 80N and 80S and 34N, alternating each satellite yaw cycle of about 36 days. The ISAMS Level 3AT data were processed with the version 10 algorithm, except H2O which is version 9. The ISAMS level 3AT product consists of 10 granules per day. A data granule is one ISAMS species or subtype per day. Data are on the UARS standard pressure levels (in mbars) given by: P(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ... Each of the 10 ISAMS granules is accompanied by its own additional parameter file, designated as level 3TP. The parameter file, contains the additional ancillary and quality information not found in the 3AT file. The data files are available in a binary record oriented format.\n<br>\n\n#### UARML3AL\nThe Microwave Limb Sounder (MLS) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of temperature, geopotential height, concentrations of O3, H2O, CH3CN, ClO, HNO3, and SO2. The insrument measures in the microwave spectral region at frequencies of 63, 183 and 205 GHz. MLS was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the chemical composition of the stratosphere and mesosphere, relationship between chlorine monoxide and ozone destruction. Limb measurements were made in the altitude range between 10 and 85 km at about 2.5 km resolution. Data were collected between latitude 34S and 80N and 80S and 34N, alternating each satellite yaw cycle of about 36 days. The MLS Level 3AL data were processed with the version 5 algorithm, except SO2 which remained version 4. Note: H2O and O3 at 183 GHz data are available only through April 15, 1993 when the 183 GHz radiometer failed. The MLS level 3AL product consists of 9 granules per day. A data granule is one MLS species or subtype per day. Data are on the UARS standard pressure levels (in mbars) given by: P(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ... Each of the 9 MLS granules is accompanied by an additional parameter file, designated as level 3LP. The parameter file, contains the additional ancillary and quality information, as well as total column amounts not found in the 3AL files. The data files are available in a binary record oriented format.\n<br>\n\n#### UARML3AT\nThe Microwave Limb Sounder (MLS) Level 3AT data product consists of daily, 4 degree increment latitude-ordered vertical profiles of temperature, geopotential height, concentrations of O3, H2O, CH3CN, ClO, HNO3, and SO2, as well as upper tropospheric humidity (UTH). The insrument measures in the microwave spectral region at frequencies of 63, 183 and 205 GHz. MLS was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the chemical composition of the stratosphere and mesosphere, relationship between chlorine monoxide and ozone destruction. Limb measurements were made in the altitude range between 10 and 85 km at about 2.5 km resolution. Data were collected between latitude 34S and 80N and 80S and 34N, alternating each satellite yaw cycle of about 36 days. The MLS Level 3AT data were processed with the version 5 algorithm, except H2O and HNO3 which are version 6, and SO2 and UTH which remained version 4. Note: H2O and O3 at 183 GHz data are available only through April 15, 1993 when the 183 GHz radiometer failed. The MLS level 3AT product consists of 10 granules per day. A data granule is one MLS species or subtype per day. Data are on the UARS standard pressure levels (in mbars) given by: P(i) = 1000 * 10**(-i/6) for i = 0, 1, 2, ... Each of the 10 MLS granules is accompanied by an additional parameter file, designated as level 3TP. The parameter file, contains the additional ancillary and quality information, as well as total column amounts not found in the 3AT files. The data files are available in a binary record oriented format.\n<br>\n\n#### UARPE3AT\nThe Particle Environment Monitor (PEM) Level 3AT data product consists of daily, 65.536 second interval time-ordered, vertical profiles of electron, proton and x-ray energy deposition rates. PEM was made up of four instruments: the Atmospheric X-Ray Imaging Spectrometer (AXIS), the High Energy Particle Spectrometer (HEPS), the Medium Energy Spectrometer (MEPS), and the Vector Magnetometer (VMAG). PEM was flown on NASA's Upper Atmosphere Research Satellite (UARS) to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere. The PEM electron and proton data are processed with the version 4 algorithm, while the x-ray data remained version 3. The PEM level 3AT data consist of 18 granules per day. A data granule is defined as one PEM subtype per day. The x-ray pixel subtypes are grouped together in a single tar file for each day, the electron and proton are available as individual files per day. Data are on the UARS standard altitude levels (in km) given by: z(i) = 5 * i, i <= 12 z(i) = 60 + (i - 12) * 3, 13 <= i <= 32 z(i) = 120 + (i - 32) * 10, 33 <= i <= 50 The data files are available in a binary record oriented format.\n<br>\n\n#### UARPE3TP\nThe Particle Environment Monitor (PEM) Level 3TP data product consists of daily, 65.536 second and 2.048 interval time-ordered, vertical profiles of electron and proton rates. PEM was made up of four instruments: the Atmospheric X-Ray Imaging Spectrometer (AXIS), the High Energy Particle Spectrometer (HEPS), the Medium Energy Spectrometer (MEPS), and the Vector Magnetometer (VMAG). PEM was flown on NASA's Upper Atmosphere Research Satellite (UARS) to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere. The PEM Level 3TP data are processed with the version 4 algorithm. The PEM level 3TP data are a special data product, providing higher temporal resolution than the level 3AT data, with the HEPS and MEPS instrument data in separate files. The HEPS profiles are made every 65.536 seconds, and the MEPS profiles are made every 2.048 seconds. Data are on the UARS standard altitude levels (in km) given by: z(i) = 5 * i, i <= 12 z(i) = 60 + (i - 12) * 3, 13 <= i <= 32 z(i) = 120 + (i - 32) * 10, 33 <= i <= 50 The data files are available in a binary record oriented format.\n<br>\n\n#### UARPE2AXIS1\nThe UARS Particle Environment Monitor (PEM) level 2 Atmosphere X-Ray Imaging Spectrometer (AXIS) unit 1 daily product contains the X-ray high-resolution spectral data converted to number intensity units from the AXIS1 pixels mounted on the UARS body. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere. The PEM AXIS1 X-ray data cover roughly the energy range from 2 keV to 300 keV. There are eight AXIS1 pixels mounted in the AXIS1 housing, each viewing different directions. The AXIS1 pixels are aligned on the spacecraft to project about 45 degrees toward the +x-axis direction from the Earthward pointing direction (+z-axis). Each pixel of AXIS1 is staggered about the 45 degree direction in an every-other fashion. Pixel 1 is closest to the center line of the spacecraft and Pixel 8 is furtherest away; however, their ground projection is dependent on the spacecraft orientation. There is one data file per day for the PEM AXIS1 product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the AXIS1 product ranges between -80 and +80 degrees latitude. The AXIS1 data files are written in network binary format. For more information please review the PEM AXIS1 data format guide.\n<br>\n\n#### UARPE2AXIS2\nThe UARS Particle Environment Monitor (PEM) level 2 Atmosphere X-Ray Imaging Spectrometer (AXIS) unit 2 daily product contains the X-ray high-resolution spectral data converted to number intensity units from the AXIS2 pixels mounted on the UARS body. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere. The PEM AXIS2 X-ray data cover roughly the energy range from 2 keV to 300 keV. There are eight AXIS2 pixels mounted in the AXIS2 housing, each viewing different directions. The AXIS2 pixels are aligned on the spacecraft to project about 45 degrees toward the +x axis direction from the Earthward pointing direction (+z axis). Each pixel of AXIS2 is staggered about the 45 degree direction in an every-other fashion. Pixel 1 is closest to the center line of the spacecraft and Pixel 8 is furtherest away; however, their ground projection is dependent on the spacecraft orientation. There is one data file per day for the PEM AXIS2 product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the AXIS2 product ranges between -80 and +80 degrees latitude. The AXIS2 data files are written in network binary format. For more information please review the PEM AXIS2 data format guide.\n<br>\n\n#### UARPE2HEPSA\nThe Particle Environment Monitor (PEM) level 2 High-Energy Particle Spectrometer (HEPS) A daily product contains electron high-resolution spectral data converted to number intensity units from the HEPS telescopes mounted on the zenith UARS boom. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere. The PEM HEPS electron data covers roughly the energy range from 35 keV to 5 MeV. There are four telescopes mounted in different directions. These measure -15 deg, +15 deg, +45 deg, and +90 degrees with respect to the spacecraft -z-axis and along the spacecraft +x-axis. The HEPS electron units accumulate a spectrum in 4.086 seconds. There are two HEPS units in these data product files (labeled HEPS1 and HEPS2), each containing two telescopes (labeled telescope 1 and telescope 2). Each telescope contains a stack of solid state crystals. The signals from each of the crystals are combined by energy processing electronics of the instrument to yield two logical crystals or detectors, called the DE and EE detectors. The DE detector has the rough energy range from 35 keV to 300 keV and the EE detector rough energy range is from 300 keV to 5 MeV. There is one data file per day for the PEM HEPSA product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the HEPSA product ranges between -57 and +57 degrees latitude. The HEPSA data files are written in network binary format. For more information please review the PEM HEPSA data format guide.\n<br>\n\n#### UARPE2HEPSB\nThe Particle Environment Monitor (PEM) level 2 High-Energy Particle Spectrometer (HEPS) B daily product contains the electron high-resolution spectral data converted to number intensity units from the HEPS telescopes mounted on the nadir UARS boom, and the proton high-resolution spectral data converted to number intensity units from the HEPS telescopes mounted on the zenith boom. There are no HEPS proton measuring instruments on the nadir boom. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere. The PEM HEPS nadir boom electron data covers the rough range from 35 keV to 5 MeV. There are two telescopes mounted in different directions. These measure -165 and +165 degrees with respect to the spacecraft -z-axis and along the spacecraft +x-axis. The HEPS electron units accumulate a spectrum in 16.384 sec. The PEM HEPS zenith boom proton data covers the rough range from 70 keV to 150 MeV for mounting angles less than 30 degrees and 500 keV to 150 MeV for mounting angles greater than 30 deg. There are six telescopes mounted in four different directions. These measure -15 deg, +15 deg, +45 deg, and +90 degrees with respect to the spacecraft -z-axis and along the spacecraft +x-axis. The PEM HEPS proton units accumulate a spectrum in 16.384 sec. The nadir HEPS electron unit in this file (labeled HEPS3) contains two telescopes (labeled telescope 1 and telescope 2). Each telescope contains a stack of solid state crystals. The signals from each of the crystals are combined by energy processing electronics of the instrument to yield two logical crystals or detectors, called the DE and EE detectors. The DE detector has the rough energy range from 35 keV to 300 keV and the EE detector rough energy range is from 300 keV to 5 MeV. These designations are used in the description of HEPS electron data. There is one data file per day for the PEM HEPSB product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the HEPSB product ranges between -57 and +57 degrees latitude. The HEPSB data files are written in network binary format. For more information please review the PEM HEPSB data format guide.\n<br>\n\n#### UARPE2MEPS\nThe Particle Environment Monitor (PEM) level 2 Medium-Energy Particle Spectrometer (MEPS) daily product contains the electron and proton high-resolution spectral data converted to number intensity units from the MEPS sensors mounted on both the zenith and nadir UARS booms. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere. The PEM MEPS data covers roughly the energy range from 1 eV - 5 eV to 32 keV, where the lower energy cutoff is determined by internal instrument protection potentials. There are five analyzers mounted in different directions on the zenith boom, each of which contains an electron and ion sensor. These analyzers are mounted at -23.7 deg, +6.3 deg, +21.3 deg, +36.3 deg, and +66.3 degrees with respect to the spacecraft -z axis and along the spacecraft +y axis. There are three analyzers mounted in different directions on the nadir boom, each of which contain only an electron sensor. These analyzers are mounted at -158.7 deg, +156.3 deg, and +126.3 deg, with respect to the spacecraft -z axis and along the spacecraft +y axis. All MEPS analyzers accumulate a spectrum in 2.046 sec. There is one data file per day for the PEM MEPS product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the MEPS product ranges between -57 and +57 degrees latitude. The MEPS data files are written in network binary format. For more information please review the PEM MEPS data format guide.\n<br>\n\n#### UARPE2VMAGAC\nThe Particle Environment Monitor (PEM) level 2 Vector Magnetometer (VMAG) AC daily product contains the Vector Magnetic Field AC component. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere. The VMAG DC magnetic field measurements are limited to frequencies less than 2.5 Hz by an 18 db/octave antialiasing filter. Higher frequencies are measured with a peak detector and are the Vector Magnetic Field AC component. The AC value is derived from each vector field component where the sensor output is pass band filtered from 2.5 to 50 Hz. The resultant signal is then half wave rectified and passed to an RC circuit with a time constant of 4.7 s. The voltage across the RC circuit is the peak detector output which therefore represents the peak positive amplitude of the 2.5-50 Hz filtered sensor output occurring during the previous 5 seconds; however, the peak detector is determined at a rate of about every second (the VMAG AC data has been adjusted for this time offset). The X and Z peak detectors have full scale of 1oo nT peak to peak (= 5 Volts) and the Y peak detector has a full scale of 10 nT peak to peak (= 5 Volts). The analog 0 to 5 Volts peak detector outputs are sent directly to the PEM central electronics package where they are digitized into a 265 binary word. This is the raw value in the data. No in-flight calibration of the AC peak detection electronics is provided. The VMAG unit is located on the zenith boom of UARS. There is one data file per day for the PEM VMAG AC product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the VMAG AC product ranges between -57 and +57 degrees latitude. The VMAG AC data files are written in network binary format. For more information please review the PEM VMAG AC data format guide.\n<br>\n\n#### UARPE2VMAGDC\nThe Particle Environment Monitor (PEM) level 2 Vector Magnetometer (VMAG) DC daily product contains the Vector Magnetic Field component, UARS Aspect Magnetometers component, and UARS Torquer Bars component data converted to nanoTesla and milliAmp units. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere. The VMAG DC data range is from -65000 nT to +65000 nT, with 2 nT resolution. Measurements were from the three vector components, X, Y, and Z with respect to the UARS spacecraft orientation. The VMAG sensor consists of three orthogonal ring-core fluxgate sensors, surrounded by a thermal sphere. Stray magnetic fields from the spacecraft can be accounted for by use of the UARS torquer bar currents. The VMAG unit is located on the zenith boom of UARS. The UARS aspect magnetometers and torquer bars are located on the body of the UARS spacecraft. There is one data file per day for the PEM VMAG DC product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the VMAG DC product ranges between -57 and +57 degrees latitude. The VMAG DC data files are written in network binary format. For more information please review the PEM VMAG DC data format guide.\n<br>\n\n#### UARSU3BS\nThe Solar Ultraviolet Spectral Irradiance Monitor (SUSIM) Level 3BS data product consists of daily, 1 nm resolution, solar spectral irradiances and selected solar parameters. The instrument consisted of two identical double-dispersion scanning spectrometers, seven detectors (5 photodiodes, 2 photon counters), and solar ultraviolet calibration sources measuring in the wavelength range from 110 to 410 nm. One spectrometer measured the solar disk while the other was for calibration using high-precision deuterium lamps calibrated by NIST. SUSIM was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the full disk solar irradiance with high precision and accuracy to follow short-term (minutes to hours), intermediate-term (days to weeks), and long-term (11 year sunspot, and 22 year solar magnetic field cycles) variations in the solar output. The SUSIM Level 3BS data were processed with the version 22 algorithm. The SUSIM Level 3BS data consists of 1 granule per day. The data are normalized to an earth-sun distance of 1 AU, and instrument degradation have been applied to the data. Included with the primary solar spectral irradiance values are the following solar indices: Lyman alpha, O-I (130 nm), C-II (134 nm) and C-IV (155 nm), Al Edge, and core-wing ratios of Mg-I and Mg-II. The data files are available in a binary record oriented format.\n<br>\n\n#### UARSO3BS\nThe Solar-Stellar Irradiance Comparison Experiment (SOLSTICE) Level 3BS data product consists of daily, 1 nm resolution, solar spectral irradiances and selected solar parameters. The instrument was a three-channel ultraviolet spectrometer that measured the magnitude of solar and spectral irradiances in the wavelength range from 119 to 420 nm. The design of the instrument allowed for observations of both the sun and bright blue stars, whose spectra is assumed to be constant. SOLSTICE was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure the full disk solar irradiance with high precision and accuracy to follow short-term (minutes to hours), intermediate-term (days to weeks), and long-term (11 year sunspot, and 22 year solar magnetic field cycles) variations in the solar output. The SORCE Level 3BS data were processed with the version 18 algorithm. The SOLSTICE Level 3BS data consists of 1 granule per day. The data are normalized to an earth-sun distance of 1 AU, and instrument degradation have been applied to the data. Included with the primary solar spectral irradiance values are daily average integrated intensities of Lyman alpha (121.6 nm), O-I (130.4 nm), C-IV (154.8 nm) and C-I (156.1 and 165.6 nm), and core-wing ratios of Mg-II (280.0 nm) and Ca-II (393.3 nm), as well as Carrington latitude and longitudes. The data files are available in a binary record oriented format.\n<br>\n\n#### UARWI3AL\nThe Wind Imaging Interferometer (WINDII) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of meridional and zonal wind components, and temperature. The instrument was a Michelson interferometer designed to measure Doppler shifts of spectral lines in the visible between 550 and 780 nm. WINDII was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure wind, temperature and emission rates in the mesosphere and thermosphere between 80 and 300 km at about 5 km resolution. Data were collected between latitude 40S and 72N and 72S and 40N, alternating each satellite yaw cycle of about 36 days. The WINDII Level 3AL data were processed with the version 11 algorithm. The WINDII level 3AL product consists of 3 granules per day. A data granule is one WINDII species or subtype per day. Data are on the UARS standard altitude levels (in km) given by: z(i) = 5*i for 1 <= i <= 12 z(i) = 60 + (i-12)*3 for 13 <= i <= 32 z(i) = 120 + (i-32)*5 for 33 <= i <= 88 Each of the 3 WINDII granules is accompanied by an additional parameter file, designated as level 3LP. The parameter file, contains additional ancillary and quality information not found in the 3AL files. The data files are available in a binary record oriented format.\n<br>\n\n#### UARWI3AT\nThe Wind Imaging Interferometer (WINDII) Level 3AT data product consists of daily, 65.536 second interval time-ordered vertical profiles of meridional and zonal wind components, and temperature. The instrument was a Michelson interferometer designed to measure Doppler shifts of spectral lines in the visible between 550 and 780 nm. WINDII was flown on NASA's Upper Atmosphere Research Satellite (UARS) and designed to measure wind, temperature and emission rates in the mesosphere and thermosphere between 80 and 300 km at about 5 km resolution. Data were collected between latitude 40S and 72N and 72S and 40N, alternating each satellite yaw cycle of about 36 days. The WINDII Level 3AT data were processed with the version 11 algorithm. The WINDII level 3AT product consists of 3 granules per day. A data granule is one WINDII species or subtype per day. Data are on the UARS standard altitude levels (in km) given by: z(i) = 5*i for 1 <= i <= 12 z(i) = 60 + (i-12)*3 for 13 <= i <= 32 z(i) = 120 + (i-32)*5 for 33 <= i <= 88 Each of the 3 WINDII granules is accompanied by an additional parameter file, designated as level 3TP. The parameter file, contains additional ancillary and quality information not found in the 3AT files. The data files are available in a binary record oriented format.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://uars.gsfc.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","ice","satellite imagery","stac","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"UARZCNMC v001 - The UARS Correlative assimilation data from NOAA's National Meteorological Center (NMC) consists of daily model runs at 12 GMT as a means of providing an independent analysis for comparison with data from the UARS instruments. The NMC data product includes temperature (Kelvin), humidity (%), geopotential height (m), and zonal and meridional wind components (m/s).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_Correlative_Level4/UARZCNMC","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARZCUKM v001 - The UARS Correlative assimilation data from the U.K. Meteorological Office (UKMO) consists of daily model runs at 12:00 GMT as a means of providing an independent analysis for comparison with data from the UARS instruments.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_Correlative_Level4/UARZCUKM","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARCL3AL v009 - The Cryogenic Limb Array Etalon Spectrometer (CLAES) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of temperature and concentrations of O3, H2O, CH4, N2O, NO, NO2, N2O5, HNO3, ClONO2, HCl, CF2Cl2 (CFC-12), CFCl3 (CFC-11), and aerosol absorption coefficients. The instrument measured infrared thermal emissions at wavelengths from 3.5 to 12.7 microns.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_CLAES_Level3/UARCL3AL","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARCL3AT v009 - The Cryogenic Limb Array Etalon Spectrometer (CLAES) Level 3AT data product consists of daily, 65.536 second interval time-ordered vertical profiles of temperature and concentrations of O3, H2O, CH4, N2O, NO, NO2, N2O5, HNO3, ClONO2, HCl, CF2Cl2 (CFC-12), CFCl3 (CFC-11), and aerosol absorption coefficients. The instrument measured infrared thermal emissions at wavelengths from 3.5 to 12.7 microns.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_CLAES_Level3/UARCL3AT","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARHA2FN v019 - The Halogen Occultation Experiment (HALOE) Level 2 data product consists of daily vertical profiles of temperature, aerosol extinciton and pressure, as well as concentrations of HCl, HF, CH4, NO by gas filter correlation radiometry and H2O, NO2, O3 and CO2 by broadband filter radiometry. The instrument measured atmospheric infrared absorption at wavelengths from 2.43 to 10.25 microns.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_HALOE_Level2/UARHA2FN","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARHA3AT v019 - The Halogen Occultation Experiment (HALOE) Level 3AT data product consists of daily vertical profiles of temperature, aerosol extinction and concentrations of HCl, HF, CH4, NO by gas filter correlation radiometry and H2O, NO2, O3 by broadband filter radiometry. The instrument measured atmospheric infrared absorption at wavelengths from 2.43 to 10.25 microns.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_HALOE_Level3/UARHA3AT","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARHR3AL v011 - The High Resolution Doppler Imager (HRDI) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of meridional and zonal wind components, temperature and volume emmission rate of O2. The instrument measured Doppler shifts of spectral lines in the visible and near-IR between 400 and 800 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_HRDI_Level3/UARHR3AL","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARHR3AT v011 - The High Resolution Doppler Imager (HRDI) Level 3AT data product consists of daily, 65.536 second interval time-ordered vertical profiles of meridional and zonal wind components, temperature and volume emmission rate of O2. The instrument measured Doppler shifts of spectral lines in the visible and near-IR between 400 and 800 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_HRDI_Level3/UARHR3AT","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARIS3AL v010 - The Improved Stratospheric and Mesospheric Sounder (ISAMS) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of temperature and concentrations of O3, H2O, CH4, CO, N2O, N2O5, NO2, and aerosol absorption coefficients. The insrument measured infrared molecular emmissions in the spectral region from 4.6 to 16.6 microns.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_ISAMS_Level3/UARIS3AL","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARIS3AT v010 - The Improved Stratospheric and Mesospheric Sounder (ISAMS) Level 3AT data product consists of daily, 65.536 second interval time-ordered vertical profiles of temperature and concentrations of O3, H2O, CH4, CO, N2O, N2O5, NO2, and aerosol absorption coefficients. The insrument measured infrared molecular emmissions in the spectral region from 4.6 to 16.6 microns.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_ISAMS_Level3/UARIS3AT","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARML3AL v005 - The Microwave Limb Sounder (MLS) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of temperature, geopotential height, concentrations of O3, H2O, CH3CN, ClO, HNO3, and SO2. The insrument measures in the microwave spectral region at frequencies of 63, 183 and 205 GHz.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_MLS_Level3/UARML3AL","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARML3AT v005 - The Microwave Limb Sounder (MLS) Level 3AT data product consists of daily, 4 degree increment latitude-ordered vertical profiles of temperature, geopotential height, concentrations of O3, H2O, CH3CN, ClO, HNO3, and SO2, as well as upper tropospheric humidity (UTH). The insrument measures in the microwave spectral region at frequencies of 63, 183 and 205 GHz.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_MLS_Level3/UARML3AT","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARPE3AT v004 - The Particle Environment Monitor (PEM) Level 3AT data product consists of daily, 65.536 second interval time-ordered, vertical profiles of electron, proton and x-ray energy deposition rates. PEM was made up of four instruments: the Atmospheric X-Ray Imaging Spectrometer (AXIS), the High Energy Particle Spectrometer (HEPS), the Medium Energy Spectrometer (MEPS), and the Vector Magnetometer (VMAG).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_PEM_Level3/UARPE3AT","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARPE3TP v004 - The Particle Environment Monitor (PEM) Level 3TP data product consists of daily, 65.536 second and 2.048 interval time-ordered, vertical profiles of electron and proton rates. PEM was made up of four instruments: the Atmospheric X-Ray Imaging Spectrometer (AXIS), the High Energy Particle Spectrometer (HEPS), the Medium Energy Spectrometer (MEPS), and the Vector Magnetometer (VMAG).","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_PEM_Level3/UARPE3TP","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARPE2AXIS1 v001 - The UARS Particle Environment Monitor (PEM) level 2 Atmosphere X-Ray Imaging Spectrometer (AXIS) unit 1 daily product contains the X-ray high-resolution spectral data converted to number intensity units from the AXIS1 pixels mounted on the UARS body. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_PEM_Level2/UARPE2AXIS1","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARPE2AXIS2 v001 - The UARS Particle Environment Monitor (PEM) level 2 Atmosphere X-Ray Imaging Spectrometer (AXIS) unit 2 daily product contains the X-ray high-resolution spectral data converted to number intensity units from the AXIS2 pixels mounted on the UARS body. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_PEM_Level2/UARPE2AXIS2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARPE2HEPSA v002 - The Particle Environment Monitor (PEM) level 2 High-Energy Particle Spectrometer (HEPS) A daily product contains electron high-resolution spectral data converted to number intensity units from the HEPS telescopes mounted on the zenith UARS boom. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_PEM_Level_2/UARPE2HEPSA","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARPE2HEPSB v001 - The Particle Environment Monitor (PEM) level 2 High-Energy Particle Spectrometer (HEPS) B daily product contains the electron high-resolution spectral data converted to number intensity units from the HEPS telescopes mounted on the nadir UARS boom, and the proton high-resolution spectral data converted to number intensity units from the HEPS telescopes mounted on the zenith boom. There are no HEPS proton measuring instruments on the nadir boom.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_PEM_Level2/UARPE2HEPSB","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARPE2MEPS v001 - The Particle Environment Monitor (PEM) level 2 Medium-Energy Particle Spectrometer (MEPS) daily product contains the electron and proton high-resolution spectral data converted to number intensity units from the MEPS sensors mounted on both the zenith and nadir UARS booms. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_PEM_Level_2/UARPE2MEPS","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARPE2VMAGAC v001 - The Particle Environment Monitor (PEM) level 2 Vector Magnetometer (VMAG) AC daily product contains the Vector Magnetic Field AC component. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_PEM_Level_2/UARPE2VMAGAC","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARPE2VMAGDC v001 - The Particle Environment Monitor (PEM) level 2 Vector Magnetometer (VMAG) DC daily product contains the Vector Magnetic Field component, UARS Aspect Magnetometers component, and UARS Torquer Bars component data converted to nanoTesla and milliAmp units. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_PEM_Level_2/UARPE2VMAGDC","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARSU3BS v022 - The Solar Ultraviolet Spectral Irradiance Monitor (SUSIM) Level 3BS data product consists of daily, 1 nm resolution, solar spectral irradiances and selected solar parameters. The instrument consisted of two identical double-dispersion scanning spectrometers, seven detectors (5 photodiodes, 2 photon counters), and solar ultraviolet calibration sources measuring in the wavelength range from 110 to 410 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_SUSIM_Level3/UARSU3BS","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARSO3BS v018 - The Solar-Stellar Irradiance Comparison Experiment (SOLSTICE) Level 3BS data product consists of daily, 1 nm resolution, solar spectral irradiances and selected solar parameters. The instrument was a three-channel ultraviolet spectrometer that measured the magnitude of solar and spectral irradiances in the wavelength range from 119 to 420 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_SOLSTICE_Level3/UARSO3BS","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARWI3AL v011 - The Wind Imaging Interferometer (WINDII) Level 3AL data product consists of daily, 4 degree increment latitude-ordered vertical profiles of meridional and zonal wind components, and temperature. The instrument was a Michelson interferometer designed to measure Doppler shifts of spectral lines in the visible between 550 and 780 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_WINDII_Level3/UARWI3AL","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"},{"Description":"UARWI3AT v011 - The Wind Imaging Interferometer (WINDII) Level 3AT data product consists of daily, 65.536 second interval time-ordered vertical profiles of meridional and zonal wind components, and temperature. The instrument was a Michelson interferometer designed to measure Doppler shifts of spectral lines in the visible between 550 and 780 nm.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/UARS_WINDII_Level3/UARWI3AT","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-uars","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA / USGS Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) Targeted DTMs","Description":"As of March, 2023 the Mars Reconnaissance Orbiter (MRO) High Resolution Science Experiment (HiRISE) sensor has collected more than 5000 targeted stereopairs. During HiRISE acquisition, the Context Camera (CTX) also collects lower resolution, higher spatial extent context images. These CTX acquisitions are also targeted stereopairs. This data set contains targeted CTX DTMs and orthoimages, created using the NASA Ames Stereopipeline. These data have been created using relatively controlled CTX images that have been globally bundle adjusted using the USGS Integrated System for Imagers and Spectrometers (ISIS) jigsaw application. Relative control at global scale reduces common issues such as spacecraft jitter in the resulting DTMs. DTMs were aligned as part of 26 different groupings to the ultimate MOLA product using an iterative pc_align approach. Therefore, all DTMs and orthoimages are absolutely controlled to MOLA, a proxy product for the Mars geodetic coordinate reference frame.\n","Documentation":"https://stac.astrogeology.usgs.gov/docs/data/mars/ctxdtms/","Contact":"https://answers.usgs.gov/","ManagedBy":"[NASA](https://www.nasa.gov)","UpdateFrequency":"Updated as new stereoapirs are processed","Tags":["aws-pds","cog","elevation","planetary","satellite imagery","stac"],"License":"[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)","Citation":"https://doi.org/10.5066/P9JKVWR3","Resources":[{"Description":"DTMs, orthoimages, error images, and quality assurance metrics","ARN":"arn:aws:s3:::astrogeo-ard/mars/mro/ctx/controlled/usgs/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/collections/mro_ctx_controlled_usgs_dtms)"]}],"DataAtWork":{"Tutorials":[{"Title":"Discovering and Downloading Data via the Command Line","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/cli/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Discovering and Downloading Data with Python","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Querying for Data in an ROI and Loading it into QGIS","URL":"https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"}],"Tools & Applications":[{"Title":"PySTAC Client","URL":"https://github.com/stac-utils/pystac-client","AuthorName":"PySTAC-Client Contributors","AuthorURL":"https://github.com/stac-utils/pystac-client/graphs/contributors"}]},"RegistryEntryAdded":"2023-04-20","RegistryEntryLastModified":"2023-04-20","Slug":"nasa-usgs-controlled-mro-ctx-dtms","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA / USGS Controlled Europa DTMs","Description":"Knowledge of a planetary surface’s topography is necessary to understand its geology and enable landed mission operations. The Solid State Imager (SSI) on board NASA’s Galileo spacecraft acquired more than 700 images of Jupiter’s moon Europa. Although moderate- and high-resolution coverage is extremely limited, repeat coverage of a small number of sites enables the creation of digital terrain models (DTMs) via stereophotogrammetry. Here we provide stereo-derived DTMs of five sites on Europa. The sites are the bright band Agenor Linea, the crater Cilix, the crater Pwyll, pits and chaos adjacent to Rhadamanthys Linea, and ridged plains near Yelland Linea. We generated the DTMs using BAE’s SOCET SET® software and each was manually edited to correct identifiable errors from the automated stereo matching process. Additionally, we used the recently updated image pointing information provided by the U.S. Geological Survey (Bland, et al., 2021), which ties the DTMs to an existing horizontal datum and enables the DTMs to easily be used in coordination with that globally controlled image set. The DTMs are of the highest quality achievable with Galileo data and are therefore suitable for most scientific analysis. However, there are inherent uncertainties in the DTMs including horizontal resolutions that are typically 1–2 km (~10x the image pixel scale) and expected vertical precision (the root mean square (RMS) uncertainty in a point elevation) of 10s–100s of m. The DTMs and their uncertainties are discussed in detail in the documentation.\n","Documentation":"https://stac.astrogeology.usgs.gov/docs/data/jupiter/europa/europa_controlled_usgs_dtms/","Contact":"https://answers.usgs.gov/","ManagedBy":"[NASA](https://www.nasa.gov)","UpdateFrequency":"HiRISE data will be updated as new releases are made to the Planetary Data System.","Tags":["aws-pds","cog","planetary","satellite imagery","stac"],"License":"[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)","Citation":"https://doi.org/10.5066/P99HX1H3","Resources":[{"Description":"Scenes and metadata","ARN":"arn:aws:s3:::astrogeo-ard/jupiter/europa/galileo_voyager/usgs_controlled_dtms/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_dtms?.language=en)"]}],"DataAtWork":{"Tutorials":[{"Title":"Discovering and Downloading Data via the Command Line","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/cli/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Discovering and Downloading Data with Python","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Querying for Data in an ROI and Loading it into QGIS","URL":"https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"}],"Tools & Applications":[{"Title":"PySTAC Client","URL":"https://github.com/stac-utils/pystac-client","AuthorName":"PySTAC-Client Contributors","AuthorURL":"https://github.com/stac-utils/pystac-client/graphs/contributors"}]},"RegistryEntryAdded":"2023-06-04","RegistryEntryLastModified":"2023-06-04","Slug":"nasa-usgs-europa-dtms","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA / USGS Europa Controlled Observation Mosaics","Description":"The Solid State Imager (SSI) on NASA's Galileo spacecraft acquired more than 500 images of Jupiter's moon, Europa. These images vary from relatively low-resolution hemispherical imaging, to high-resolution targeted images that cover a small portion of the surface. Here we provide a set of 92 image mosaics generated from minimally processed, projected Galileo images with photogrammetrically improved locations on Europa's surface.<br/><br/>\nThese images provide users with nearly the entire Galileo Europa imaging dataset at its native resolution and with improved relative image locations. The Solid State Imager on NASA's Galileo spacecraft provided the only moderate- to high-resolution images of Jupiter's moon, Europa. Unfortunately, uncertainty in the position and pointing of the spacecraft, as well as the position and orientation of Europa, when the images were acquired resulted in significant errors in image locations on the surface. The result of these errors is that images acquired during different Galileo orbits, or even at different times during the same orbit, are significantly misaligned (errors of up to 100 km on the surface). <br/><br/>\nThe dataset provides a set of individual images that can be used for scientific analysis and mission planning activities.\n","Documentation":"https://stac.astrogeology.usgs.gov/docs/data/jupiter/europa/galileo_sequence_mosaics/","Contact":"https://answers.usgs.gov/","ManagedBy":"[NASA](https://www.nasa.gov)","UpdateFrequency":"No future updates planned.","Tags":["aws-pds","cog","planetary","satellite imagery","stac"],"License":"[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)","Citation":"https://doi.org/10.5066/P9VKKK7C","Resources":[{"Description":"Scenes and metadata","ARN":"arn:aws:s3:::astrogeo-ard/jupiter/europa/galileo_voyager/usgs_controlled_mosaics/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_mosaics)"]}],"DataAtWork":{"Tutorials":[{"Title":"Discovering and Downloading Data via the Command Line","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/cli/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Discovering and Downloading Data with Python","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Querying for Data in an ROI and Loading it into QGIS","URL":"https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"}],"Tools & Applications":[{"Title":"PySTAC Client","URL":"https://github.com/stac-utils/pystac-client","AuthorName":"PySTAC-Client Contributors","AuthorURL":"https://github.com/stac-utils/pystac-client/graphs/contributors"}]},"RegistryEntryAdded":"2022-12-01","RegistryEntryLastModified":"2023-07-24","Slug":"nasa-usgs-europa-mosaics","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA / USGS Europa Controlled Observations","Description":"The Solid State Imager (SSI) on NASA's Galileo spacecraft acquired more than 500 images of Jupiter's moon, Europa. These images vary from relatively low-resolution hemispherical imaging, to high-resolution targeted images that cover a small portion of the surface. Here we provide a set of 481 minimally processed, projected Galileo images with photogrammetrically improved locations on Europa's surface. These individual images were subsequently used as input into a set of 92 observation mosaics. <br/><br/>\nThese images provide users with nearly the entire Galileo Europa imaging dataset at its native resolution and with improved relative image locations. The Solid State Imager on NASA's Galileo spacecraft provided the only moderate- to high-resolution images of Jupiter's moon, Europa. Unfortunately, uncertainty in the position and pointing of the spacecraft, as well as the position and orientation of Europa, when the images were acquired resulted in significant errors in image locations on the surface. The result of these errors is that images acquired during different Galileo orbits, or even at different times during the same orbit, are significantly misaligned (errors of up to 100 km on the surface). <br/><br/>\nThe dataset provides a set of individual images that can be used for scientific analysis and mission planning activities.\n","Documentation":"https://stac.astrogeology.usgs.gov/docs/data/jupiter/europa/galileo_individual_images/","Contact":"https://answers.usgs.gov/","ManagedBy":"[NASA](https://www.nasa.gov)","UpdateFrequency":"No future updates planned.","Tags":["aws-pds","cog","planetary","satellite imagery","stac"],"License":"[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)","Citation":"https://doi.org/10.5066/P9VKKK7C","Resources":[{"Description":"Scenes and metadata","ARN":"arn:aws:s3:::astrogeo-ard/jupiter/europa/galileo_voyager/usgs_controlled_observations/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_observations)"]}],"DataAtWork":{"Tutorials":[{"Title":"Discovering and Downloading Data via the Command Line","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/cli/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Discovering and Downloading Data with Python","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Querying for Data in an ROI and Loading it into QGIS","URL":"https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"}],"Tools & Applications":[{"Title":"PySTAC Client","URL":"https://github.com/stac-utils/pystac-client","AuthorName":"PySTAC-Client Contributors","AuthorURL":"https://github.com/stac-utils/pystac-client/graphs/contributors"}]},"RegistryEntryAdded":"2022-10-06","RegistryEntryLastModified":"2023-07-24","Slug":"nasa-usgs-europa-observations","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA / USGS Lunar Orbiter Laser Altimeter Cloud Optimized Point Cloud","Description":"The lunar orbiter laser altimeter (LOLA) has collected and released almost 7 billion individual laser altimeter returns from the lunar surface. This dataset includes individual altimetry returns scraped from the Planetary Data System (PDS) LOLA Reduced Data Record (RDR) Query Tool, V2.0. Data are organized in 15˚ x 15˚ (longitude/latitude) sections, compressed and encoded into the Cloud Optimized Point Cloud (COPC) file format, and collected into a Spatio-Temporal Asset Catalog (STAC) collection for query and analysis. The data are in latitude, longitude, and radius (X, Y, Z) format with the proper IAU 2015 30100 well-known text projection string. These data are in the -180 to 180, center longitude 0 domain. Users of this data set are encouraged to use the Point Data Abstract Library (PDAL) STAC driver to query at the collection level or the COPC driver to query individual COPC tiles within the dataset. Queries of these data using bounding boxes, buffered points, or other geometries should use the -180 to 180 longitude domain (converting from 0-360, clone 180 as needed).\n","Documentation":"https://stac.astrogeology.usgs.gov/docs/data/moon/lola/","Contact":"https://answers.usgs.gov/","ManagedBy":"[NASA](https://www.nasa.gov)","UpdateFrequency":"Intermittent as new LOLA RDR data are released and processing capabilities become available.","Tags":["aws-pds","elevation","lidar","planetary","stac"],"License":"[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)","Citation":"https://doi.org/10.5066/P9V5JIWH","Resources":[{"Description":"Lunar Orbiter Laser Altimeter (LOLA) Reduced Data Record (RDR) point cloud in Cloud Optimized Point Cloud (COPC) format.","ARN":"arn:aws:s3:::astrogeo-ard/moon/lro/lola/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/lunar_orbiter_laser_altimeter)"]}],"DataAtWork":{"Tutorials":[{"Title":"Discovering and Downloading Data via the Command Line","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/cli/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Discovering and Downloading Data with Python","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Querying for Data in an ROI and Loading it into QGIS","URL":"https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"}],"Tools & Applications":[{"Title":"Point Data Abstraction Library (PDAL)","URL":"https://pdal.io/","AuthorName":"PDAL Contributors","AuthorURL":"https://github.com/PDAL/PDAL"},{"Title":"PySTAC Client","URL":"https://github.com/stac-utils/pystac-client","AuthorName":"PySTAC-Client Contributors","AuthorURL":"https://github.com/stac-utils/pystac-client/graphs/contributors"}]},"RegistryEntryAdded":"2023-09-05","RegistryEntryLastModified":"2023-09-08","Slug":"nasa-usgs-lunar-orbiter-laser-altimeter","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA / USGS Released HiRISE Digital Terrain Models","Description":"These data are digital terrain models (DTMs) created by multiple different institutions and released to the Planetary Data System (PDS) by the University of Arizona. The data are processed from the Planetary Data System (PDS) stored JP2 files, map projected, and converted to Cloud Optimized GeoTiffs (COGs) for efficient remote data access. These data are controlled to the Mars Orbiter Laser Altimeter (MOLA). Therefore, they are a proxy for the geodetic coordinate reference frame. These data are not guaranteed to co-register with an uncontrolled products (e.g., the uncontrolled High Resolution Science Imaging Experiment (HiRISE) Reduced Data Record (RDR) data). Data are released using simple cylindrical (planetocentric positive East, center longitude 0, -180 - 180 longitude domain) or a pole centered polar stereographic projection. Data are projected to the appropriate IAU Well-known Text v2 (WKT2) represented projection.\n","Documentation":"https://stac.astrogeology.usgs.gov/docs/data/mars/hirise_dtms/","Contact":"https://answers.usgs.gov/","ManagedBy":"[NASA](https://www.nasa.gov)","UpdateFrequency":"HiRISE DTMs will be updated as new releases are made by the University of Arizona to the Planetary Data System.","Tags":["aws-pds","cog","planetary","satellite imagery","stac"],"License":"[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)","Citation":"https://doi.org/10.5066/P9VQTS7V","Resources":[{"Description":"Scenes and metadata","ARN":"arn:aws:s3:::astrogeo-ard/mars/mro/hirise/controlled/dtm","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/mro_hirise_socet_dtms)"]}],"DataAtWork":{"Tutorials":[{"Title":"Discovering and Downloading Data via the Command Line","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/cli/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Discovering and Downloading Data with Python","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Querying for Data in an ROI and Loading it into QGIS","URL":"https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"}],"Tools & Applications":[{"Title":"PySTAC Client","URL":"https://github.com/stac-utils/pystac-client","AuthorName":"PySTAC-Client Contributors","AuthorURL":"https://github.com/stac-utils/pystac-client/graphs/contributors"}]},"RegistryEntryAdded":"2023-07-09","RegistryEntryLastModified":"2023-07-09","Slug":"nasa-usgs-mars-hirise-dtms","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA / USGS Uncontrolled HiRISE RDRs","Description":"These data are red and color Reduced Data Record (RDR) observations collected and originally processed by the High Resolution Imaging Science Experiment (HiRISE) team. The mdata are processed from the Planetary Data System (PDS) stored RDRs, map projected, and converted to Cloud Optimized GeoTiffs (COGs) for efficient remote data access. These data are not photogrammetrically controlled and use a priori NAIF SPICE pointing. Therefore, these data will not co-register with controlled data products. Data are released using simple cylindrical (planetocentric positive East, center longitude 0, -180 - 180 longitude domain) or a pole centered polar stereographic projection. Data are projected to the appropriate IAU Well-known Text v2 (WKT2) represented projection.\n","Documentation":"https://stac.astrogeology.usgs.gov/docs/data/mars/uncontrolled_hirise/","Contact":"https://answers.usgs.gov/","ManagedBy":"[NASA](https://www.nasa.gov)","UpdateFrequency":"HiRISE data will be updated as new releases are made to the Planetary Data System.","Tags":["aws-pds","cog","planetary","satellite imagery","stac"],"License":"[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)","Citation":"https://doi.org/10.5066/P944DLP8","Resources":[{"Description":"Scenes and metadata","ARN":"arn:aws:s3:::astrogeo-ard/mars/mro/hirise/uncontrolled_rdr_observations/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/collections/mro_hirise_uncontrolled_observations)"]}],"DataAtWork":{"Tutorials":[{"Title":"Discovering and Downloading Data via the Command Line","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/cli/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Discovering and Downloading Data with Python","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Querying for Data in an ROI and Loading it into QGIS","URL":"https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"}],"Tools & Applications":[{"Title":"PySTAC Client","URL":"https://github.com/stac-utils/pystac-client","AuthorName":"PySTAC-Client Contributors","AuthorURL":"https://github.com/stac-utils/pystac-client/graphs/contributors"}]},"RegistryEntryAdded":"2023-02-02","RegistryEntryLastModified":"2023-04-21","Slug":"nasa-usgs-mars-hirise","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA / USGS Controlled THEMIS Mosaics","Description":"These data are infrared image mosaics, tiled to the Mars quadrangle, generated using Thermal Emission Imaging System (THEMIS) images from the 2001 Mars Odyssey orbiter mission. The mosaic is generated at the full resolution of the THEMIS infrared dataset, which is approximately 100 meters/pixel. The mosaic was absolutely photogrammetrically controlled to an improved Viking MDIM network that was develop by the USGS Astrogeology processing group using the Integrated Software for Imagers and Spectrometers. Image-to-image alignment precision is subpixel (i.e., <100m). These 8-bit, qualitative data are released as losslessly compressed Cloud Optimized GeoTiffs (COGs). Data are released using simple cylindrical (planetocentric positive East, center longitude 0, -180 to 180 longitude domain).\n","Documentation":"https://stac.astrogeology.usgs.gov/docs/data/mars/themis_controlled_mosaics/","Contact":"https://answers.usgs.gov/","ManagedBy":"[NASA](https://www.nasa.gov)","UpdateFrequency":"None planned.","Tags":["aws-pds","cog","planetary","satellite imagery","stac"],"License":"[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)","Citation":"https://doi.org/10.5066/P96FA04Z","Resources":[{"Description":"Scenes and metadata","ARN":"arn:aws:s3:::astrogeo-ard/mars/mo/themis/controlled_mosaics/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/mo_themis_controlled_mosaics)"]}],"DataAtWork":{"Tutorials":[{"Title":"Discovering and Downloading Data via the Command Line","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/cli/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Discovering and Downloading Data with Python","URL":"https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"},{"Title":"Querying for Data in an ROI and Loading it into QGIS","URL":"https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/","AuthorName":"J. Laura","AuthorURL":"https://astrogeology.usgs.gov"}],"Tools & Applications":[{"Title":"PySTAC Client","URL":"https://github.com/stac-utils/pystac-client","AuthorName":"PySTAC-Client Contributors","AuthorURL":"https://github.com/stac-utils/pystac-client/graphs/contributors"}]},"RegistryEntryAdded":"2023-02-28","RegistryEntryLastModified":"2023-07-24","Slug":"nasa-usgs-themis-mosaics","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Vegetation Project","Description":"This global data set of photosynthetic rates and leaf nutrient traits was compiled from a comprehensive literature review. It includes estimates of Vcmax (maximum rate of carboxylation), Jmax (maximum rate of electron transport), leaf nitrogen content (N), leaf phosphorus content (P), and specific leaf area (SLA) data from both experimental and ambient field conditions, for a total of 325 species and treatment combinations. Both the original published Vcmax and Jmax values as well as estimates at standard temperature are reported. The maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax) are primary determinants of photosynthetic rates in plants, and modeled carbon fluxes are highly sensitive to these parameters. Previous studies have shown that Vcmax and Jmax correlate with leaf nitrogen across species and regions, and locally across species with leaf phosphorus and specific leaf area, yet no universal relationship suitable for global-scale models is currently available. These data are suitable for exploring the general relationships of Vcmax and Jmax with each other and with leaf N, P and SLA. This data set contains one .csv file.\n<br>\n\n#### Leaf_Carbon_Nutrients_1106\nThis data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence. The data were compiled from 86 selected studies in 31 countries, and resulted in approximately 1,000 data points for both green and senesced leaves from woody and non-woody vegetation as described in Vergutz et al (2012). The studies were conducted from 1970-2009. There are two comma-delimited data files with this data set.\n<br>\n\n#### LAI_Woody_Plants_1231\nThis data set provides global leaf area index (LAI) values for woody species. The data are a compilation of field-observed data from 1,216 locations obtained from 554 literature sources published between 1932 and 2011. Only site-specific maximum LAI values were included from the sources; values affected by significant artificial treatments (e.g. continuous fertilization and/or irrigation) and LAI values that were low due to drought or disturbance (e.g. intensive thinning, wildfire, or disease), or because vegetation was immature or old/declining, were excluded (Lio et al., 2014). To maximize the generic applicability of the data, original LAI values from source literature and values standardized using the definition of half of total surface area (HSA) are included. Supporting information, such as geographical coordinates of plot, altitude, stand age, name of dominant species, plant functional types, and climate data are also provided in the data file. There is one data file in comma-separated (.csv) format with this data set and one companion file which provides the data sources.\n<br>\n\n#### Global_Litter_Carbon_Nutrients_1244\nMeasurement data of aboveground litterfall and littermass and litter carbon, nitrogen, and nutrient concentrations were extracted from 685 original literature sources and compiled into a comprehensive database to support the analysis of global patterns of carbon and nutrients in litterfall and litter pools. Data are included from sources dating from 1827 to 1997. The reported data include the literature reference, general site information (description, latitude, longitude, and elevation), site climate data (mean annual temperature and precipitation), site vegetation characteristics (management, stand age, ecosystem and vegetation-type codes), annual quantities of litterfall (by class, kg m-2 yr-1), litter pool mass (by class and litter layer, kg m-2), and concentrations of nitrogen (N), phosphorus (P), and base cations for the litterfall (g m-2 yr-1) and litter pool components (g m-2). The investigators intent was to compile a comprehensive data set of individual direct field measurements as reported by researchers. While the primary emphasis was on acquiring C data, measurements of N, P, and base cations were also obtained, although the database is sparse for elements other than C and N. Each of the 1,497 records in the database represents a measurement site. Replicate measurements were averaged according to conventions described in Section 5 and recorded for each site in the database. The sites were at 575 different locations.\n<br>\n\n#### Amazon_ForestStructure_LIDAR_2412\nThis dataset provides initial condition files for initializing the Ecosystem Demography Model (ED2). This dataset holds regional forest structure characteristics across the Brazilian Amazon that were derived from 545 airborne lidar transects (300 x 12500 m each) acquired during the Amazon Biomass Estimation Project (EBA2016) campaign in 2016. These data contain vertical distributions of stem density, carbon storage, and other vegetation traits for over 1,300,000 columns (50 x 50 m each) that were aggregated into 288 grid cells (1 x 1 degree). This dataset also contains soil edaphic characteristics obtained from existing datasets and carbon stored in litter and soil layers estimated from the land use history and limited measurements in different land use types. Three types of files are provided: Site files (.sss) hold soil and terrain characteristics. Patch files (.sss) hold patch location, area, disturbance type, stem density, stem basal area, leaf area index (LAI), aboveground biomass (AGB), along with carbon and nitrogen density in several categories for patches within sites. Cohort files (.css) hold diameter at breast height, plant height, stem density, mass of living and dead biomass, LAI, AGB), and plant functional type for cohorts of stems within patches and sites. The data are provided in text format compatible with the ED2 model.\n<br>\n\n#### Non-Forest_Trees_Sahara_Sahel_1832\nThis dataset provides georeferenced polygon vectors of individual tree canopy geometries for dryland areas in West African Sahara and Sahel that were derived using deep learning applied to 50-cm resolution satellite imagery. More than 1.8 billion non-forest trees (i.e., woody plants with a crown size over 3 m2) over about 1.3 million km2 were identified from panchromatic and pansharpened normalized difference vegetation index (NDVI) images at 0.5-m spatial resolution using an automatic tree detection framework based on supervised deep-learning techniques. Combined with existing and future fieldwork, these data lay the foundation for a comprehensive database that contains information on all individual trees outside of forests and could provide accurate estimates of woody carbon in arid and semi-arid areas throughout the Earth for the first time.\n<br>\n\n#### LULC_Nigeria_Ethiopia_SAfrica_2367\nThis dataset provides a two-tier annual Land Use (LU) and Urban Land Cover (LC) product suite over three African countries, Ethiopia, Nigeria, and South Africa, across a 5-year period of 2016-2020. Remote sensing data sources were used to create 30-m resolution LU maps (Tier-1), which were then utilized to delineate urban boundaries for 10-m resolution LC classes (Tier-2). Random Forest machine learning classifier models were trained on reference data for each tier and country (but one model was trained across all years); models were validated using a separate reference data set for each tier and country. Tier-1 LU maps were based on the 30-m Landsat time series, and Tier-2 urban LC maps were based on the 10-m Sentinel-2 time series. Additional data sources included climate, topography, night-time light, and soils. The overall map accuracy was 65-80% for Tier-1 maps and 60-80% for Tier-2 maps, depending on the year and country. The data are provided in cloud optimized GeoTIFF (COG) format.\n<br>\n\n#### BASIN_TCP_963\nThis data set reports stable isotope ratio data of CO2 (13C/12C and 18O/16O) associated with photosynthetic and respiratory exchanges across the biosphere-atmosphere boundary. Measurements were made at selected AmeriFlux sites including Harvard Forest, Howland Forest, Rannells Flint Hills Prairie, Niwot Ridge Forest, and the Wind River Canopy Crane Site, which span the dominant ecosystem types of the United States. These data were collected periodically from 2001 through 2004 and are available as an ASCII comma separated file. The goal of this Terrestrial Carbon Processes (TCP) project is to better capture isotopic effects of ecosystem-atmosphere interaction at diurnal, seasonal and interannual time scales by long-term monitoring 13C of CO2 exchange with the atmosphere at weekly intervals. Photosynthesis and respiration in terrestrial ecosystems have opposite effects on diurnal and seasonal patterns on atmospheric CO2 concentration and isotope ratios. This isotopic variation contains information about the functioning of different terrestrial ecosystems.\n<br>\n\n#### biomass_allocation_703\nThis data set of leaf, stem, and root biomass for various plant taxa was compiled from the primary literature of the 20th century with a significant portion derived from Cannell (1982). Recent allometric additions include measurements made by Niklas and colleagues (Niklas, 2003). This is a unique data set with which to evaluate allometric patterns of standing biomass within and across the broad spectrum of vascular plant species. Despite its importance to ecology, global climate research, and evolutionary and ecological theory, the general principles underlying how plant metabolic production is allocated to above- and below-ground biomass remain unclear. The resulting uncertainty severely limits the accuracy of models for many ecologically and evolutionarily important phenomena across taxonomically diverse communities. Thus, although quantitative assessments of biomass allocation patterns are central to biology, theoretical or empirical assessments of these patterns remain contentious.\n<br>\n\n#### LaSelva_Land_Use_1312\nThis data set contains land-use, canopy height, and aboveground carbon estimates derived from LiDAR data collected at La Selva Biological Station in Costa Rica in March 1998 and March 2005. The data are provided as GeoTIFFs (.tif) of 100-m (1-ha) resolution. A look-up table is provided that relates modeled changes in height to changes in stand characteristics (including age and carbon content). The data were used to test the accuracy and scale-dependency of high-resolution predictions of vegetation dynamics and carbon flux by the Ecosystem Demography (ED). The ED model is an individual-based terrestrial ecosystem model that predicts both ecosystem structure and corresponding ecosystem fluxes from climate, soil, and land-use inputs.\n<br>\n\n#### Semi-Arid_Tree_Carbon_50cm_2117\nThis dataset provides allometrically-estimated carbon stocks of 9,947,310,221 tree crowns derived from 50-cm resolution satellite images within the 0 to 1000 mm/year precipitation zone of Africa north of the equator and south of the Sahara Desert. These data are presented in GeoPackage (.gpkg) format and are summarized in Cloud-Optimized GeoTIFF (COG) format. An interactive viewer application developed to display these carbon estimates at the individual tree level across the study area is available at: https://trees.pgc.umn.edu/app. The analysis utilized 326,523 Maxar multispectral satellite images collected between 2002 to 2021 for the early dry season months of November to March to identify tree crowns. Metadata from satellite image processing across the study area are presented in Shapefile (.shp) format. Additionally, field measurements from destructive harvests used to derive allometry equations are contained in comma-separated values (.csv) files. These data demonstrate a new tool for studying discrete semi-arid carbon stocks at the tree level with immediate applications provided by the viewer application. Uncertainty of carbon estimates are +/- 19.8%.\n<br>\n\n#### african_woody_savanna_850\nThis data set includes the soil and vegetation characteristics, herbivore estimates, and precipitation measurement data for the 854 sites described and analyzed in Sankaran et al., 2005. Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover but perceptions differ on which of these are the primary drivers of savanna structure. Analyses of data from 854 sites across Africa (Figure 1) showed that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than approximately 650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered stable systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of approximately 650 mm, savannas are unstable systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation may considerably affect their distribution and dynamics (Sankaran et al., 2005).This data set includes the site characteristics and measurement data for the 854 sites described and analyzed in Sankaran et al., 2005. The data are provided in two formats, .xls and .csv. See the data format section below for more information. A companion document composed of the supplemental documentation and figures provided with Sankaran et al., 2005 is also included (ftp://daac.ornl.gov/data/global_vegetation/savanna_woody_cover/comp/Woody_Cover.pdf).\n<br>\n\n#### Decadal_LULC_India_1336\nThis data set provides land use and land cover (LULC) classification products at 100-m resolution for India at decadal intervals for 1985, 1995 and 2005. The data were derived from Landsat 4 and 5 Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Multispectral (MSS) data, India Remote Sensing satellites (IRS) Resourcesat Linear Imaging Self-Scanning Sensor-1 or III (LISS-I, LISS-III) data, ground truth surveys, and visual interpretation. The data were classified according to the International Geosphere-Biosphere Programme (IGBP) classification scheme.\n<br>\n\n#### Siberian_Larch_Stand_Age_1364\nThis data set provides mapped estimates of the stand age of young (less than 25 years old) larch forests across Siberia from 1989-2012 at 30-m resolution. The age estimates were derived from Landsat-based composites and tree cover for years 2000 and 2012 developed by the Global Forest Change (GFC) project and the stand-replacing fire mapping (SRFM) data set. This approach is based on the assumption that the relationship between the spectral signature of a burned or unburned forest stand acquired by Landsat ETM+ and TM sensors and stand age before and after the year 2000 is similar, thus allowing for training an algorithm on the data from the post-2000 era and applying the algorithm to infer stand age for the pre-2000 era. The output map combines the modeled forest disturbances before 2000 and direct observations of forest loss after 2000 to deliver a 24-year stand age distribution map.\n<br>\n\n#### Young_Russian_Forest_Map_1330\nThis data set provides the distribution of young forests (forests less than 27 years of age) and their estimated stand ages across the full extent of Russia at 500-m resolution for the year 2012. The distribution of young forests was modeled with MODIS 500-m records for 12- to 27-year-old forests and augmented with the 0- to 11-year-old forest distribution as aggregated from 30 m resolution contemporary Landsat imagery.\n<br>\n\n#### litter_decomp_651\nThe results of published and unpublished experiments investigating the impacts of elevated carbon dioxide on the chemistry (nitrogen and lignin concentration) of leaf litter and the decomposition of plant tissues are assembled in a format appropriate for statistical meta-analysis of the effect of carbon dioxide. The synthesis originated from a workshop, Litter Quality and Decomposition under Elevated CO2, held in Capri, Italy, September 1998, under the auspices of Global Change and Terrestrial Ecosystems project (GCTE) and the European COST network. Data on litter chemistry and decomposition of plant tissue grown in elevated and ambient carbon dioxide concentrations were gathered from participants of that workshop and from other published and pre-publication sources. The litter chemistry database comprised observations of naturally senesced leaves of herbaceous and woody plants exposed to elevated carbon dioxide (typically 600-700 ppm) in the field or in field chambers in the United States, Europe, New Zealand, and Australia. Data from plants grown in growth chambers with artificial lighting were excluded because of the possible artifacts of unbalanced nutrition or abnormal senescence. Also excluded were data from non-senesecent green leaves. The measures of litter chemistry were nitrogen concentration (mg/g) and lignin concentration (mg/g), with the number of replications and standard deviations included when available. The decomposition database included a wide range of plant tissue from different types of carbon dioxide-enrichment experiments. The measure of decomposition was percentage of initial mass lost over the course of the decomposition trial, which varied in duration.\n<br>\n\n#### Boreal_Fire_Severity_Metrics_1520\nThis data set provides products characterizing immediate and longer-term ecosystem changes from fires in the circumpolar boreal forests of Northern Eurasia and North America. The data include fire intensity (fire radiative power; FRP), increase in spring albedo, decrease in tree cover, normalized burn ratio, normalized difference vegetation index, and land surface temperature, as well as three derived fire metrics: crown scorch, vegetation destruction, and fire-induced tree mortality. Longer-term changes are indicated by mean albedo determined 5-12 years after fires, mean percent decrease in tree cover 5-7 years after fires, and mean annual burned percentage. The data cover the period 2001-2013 and are provided at quarter, half, and one degree resolutions for boreal forests within the 40 to 80 degree North circumpolar region. The data were derived from a variety of sources including MODIS products, climate reanalysis data, and forest inventories. A data file with identified boreal forest area (pixels), as defined by climate and vegetation type, and a file with the defined North American and Eurasian boreal forest study regions are included.\n<br>\n\n#### Forest_Inventory_Acre_Brazil_1654\nThis data set provides measurements of diameter at breast height (DBH) and species identification at four forest sites in the eastern side of Acre, Brazil including Bonal (A), Catuaba (B), Humaita (C) and Transacreana (D). The inventory locations include forests burned in 2005 and 2010 and nearby unburned areas. Inventory surveys were conducted in October and December 2017.\n<br>\n\n#### Forest_Inventory_Data_Brazil_1563\nThis dataset provides measurements for diameter at breast height (DBH) and species identification of trees for inventories taken at five tropical forest sites in Acre state, Brazil, in the southwestern Amazon region. The sites included one in a forest reserve (Reserva Bonal) and four within forest fragments situated on private property. The inventory sites included forests burned in 2005 and 2010 and also unburned forests. Surveys were conducted in July and August 2014.\n<br>\n\n#### Forest_Diversity_CAF_WesternUS_2481\nThis dataset holds maps of forest structure and structural diversity metrics at a range of spatial scales (1, 5, 10, 15, 20 and 25 km) derived from NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar data collected between April 2019 and March 2023. It also holds the airborne laser scanning (ALS) data that provides simulated GEDI waveforms and was used to evaluate the GEDI-derived metrics. Forest structural diversity is a key component of ecosystem diversity and essential for informing conservation and restoration strategies in the face of rapid global climate change and biodiversity loss. Structural metrics include canopy height at 25th and 98th percentiles, plant area index, canopy cover, and foliage height diversity. Structural diversity metrics include richness, evenness, and divergence. Focusing on two biodiversity global hotspots in Central Africa and the western US, GEDI-derived forest structural metrics were validated at 1 km2 resolution over 391 km2 of airborne ALS coverage. GEDI-derived metrics showed robust correlations with ALS data, particularly in dense, flat Central African forests (R2 up to 0.85) compared to more variable terrains like the California Sierra Nevada (R2 up to 0.55). Structural diversity was calculated through probability density-based methods that consider multivariate forest structural metrics. GEDI canopy height (rh98), canopy cover, and foliage height diversity were effective metrics for capturing structural diversity with an R2 of 0.37 when compared to wall-to-wall ALS data at 1-km2 scale. The maps reveal high structural diversity in mid elevation and coastal forests in the western U.S. and in Central African forest-savanna transitions and volcanic ranges, aligning with ecological processes related to disturbance, wildfires and topographic gradients and aridity. The data are provided in GeoTIFF and comma separated values (CSV) formats. A map of the western US study area is provided in a Keyhole Markup Language (KML) file.\n<br>\n\n#### geoecology_R1_656\nThe Geoecology database is a compilation of environmental data for the period 1941 to 1981. The Geoecology database contains selected data on terrain and soils, water resources, forestry, vegetation, agriculture, land use, wildlife, air quality, climate, natural areas, and endangered species. Data on selected human population characteristics are also included to complement the environmental files. Data represent the conterminous United States at the county level. These historical data are provided as a source of 1970s baseline environmental conditions for the United States.\n<br>\n\n#### Global_Biomass_1950-2010_1296\nThis data set provides global forest area, forest growing stock, and forest biomass data at 1-degree resolution for the period 1950-2010. The data set is based on a compilation of forest area and growing stock data reported in international assessments performed by FAO, MCPFE (now Forest Europe), and UNECE. Data of different assessments are to the extent possible harmonized to reflect both forest area and other wooded land, to be comparable between countries and assessments.\n<br>\n\n#### Global_Clumping_Index_1531\nThis dataset provides global clumping index (CI) data for 2006 derived from the MODIS Bidirectional Reflectance Distribution Function (BRDF) data product. Clumping index is a key structural parameter of plant canopies which represents the degree of foliage grouping within distinct canopy structures relative to a random distribution. The data are provided at substantially higher resolution (500-m) than existing clumping index data products.\n<br>\n\n#### Global_Maps_C_Density_2010_1763\nThis dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at a 300-m spatial resolution. The aboveground biomass map integrates land-cover specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree cover and landcover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided.\n<br>\n\n#### root_biomass_658\nA global data set of root biomass, rooting profiles, and concentrations nutrients in roots was compiled from the primary literature and used to study distributions of root properties. This data set consists of estimates of fine root biomass and specific area, site characteristics, and source references associated with two papers (Jackson et al. 1996 and 1997).Understanding and predicting ecosystem functioning (e.g., carbon and water fluxes) and the role of soils in carbon storage requires an accurate assessment of plant rooting distributions.\n<br>\n\n#### root_nutrients_659\nNutrient measurements for fine roots were compiled from 56 published studies providing information on 372 different combinations of species, root diameter, rooting depths, and soils at a variety of locations. The compilation was used to examine dynamics of 14 nutrients, including translocation properties of roots of varying size and status.Fine roots are an important source and sink for nutrients in terrestrial biogeochemistry. The data collected come from 56 published studies that give information on fine root (less than 5mm diameter) nutrient concentrations, root diameters, and retranslocation of nutrients. These studies include diverse vegetation and biomes, including grass, shrub, and tree functional types from temperate, tropical, boreal and tundra systems. The preponderance of data comes from experiments with temperate and coniferous trees.\n<br>\n\n#### root_profiles_660\nRooting depths were estimated from a global database of root profiles that was assembled from the primary literature to study relationships of abiotic and biotic factors associated with belowground vegetation structure. Variables used to characterize belowground vegetation structure include the depths above which 50% of all roots and 95% of all roots are located in the profile. For each root profile, information recorded includes latitude and longitude, elevation, soil texture, depth of organic horizons, type of roots measured (e.g., fine or total, live or dead), sampling methods, units of measurements (root mass, length, number, surface area), and sampling depth.\n<br>\n\n#### root_turnover_661\nEstimates of root turnover rates were calculated from measurements of live root standing crop and belowground net primary production (BNPP) compiled from the primary literature. Vegetation characteristics, soil properties, and climate conditions were associated with turnover rates to examine patterns and controls for biomes worldwide. Building on prior analyses (Jackson et al. 1996, 1997), data were compiled from approximately 190 papers from additional journals, book chapters, technical reports, and unpublished manuscripts that included information on live root standing crop and belowground BNPP. The papers described research on every continent except Antarctica, although the majority were from North America. In the database, the plant functional type and biome coverage were most abundant for grasslands and temperate zones.\n<br>\n\n#### fire_emissions_v4_R1_1293\nThis dataset provides global estimates of monthly burned area, monthly emissions and fractional contributions of different fire types, daily or 3-hourly fields to scale the monthly emissions to higher temporal resolutions, and data for monthly biosphere fluxes. The data are at 0.25-degree latitude by 0.25-degree longitude spatial resolution and are available from June 1995 through 2016, depending on the dataset. Emissions data are available for carbon (C), dry matter (DM), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), hydrogen (H2), nitrous oxide (N2O), nitrogen oxides (NOx), non-methane hydrocarbons (NMHC), organic carbon (OC), black carbon (BC), particulate matter less than 2.5 microns (PM2.5), total particulate matter (TPM), and sulfur dioxide (SO2) among others. These data are yearly totals by region, globally, and by fire source for each region.\n<br>\n\n#### forest_carbon_flux_949\nA comprehensive global database has been assembled to quantify CO2 fluxes and pathways across different levels of integration (from photosynthesis up to net ecosystem production) in forest ecosystems. The database fills an important gap for model calibration, model validation, and hypothesis testing at global and regional scales. The database archive includes: a Microsoft Office Access Database; data files for all tables in the database; query outputs from the database; and SQL script file for re-creating the database from the tables. The database is structured by site (i.e., a forest or stand of known geographical location, biome, species composition, and management regime). It contains carbon budget variables (fluxes and stocks), ecosystem traits (standing biomass, leaf area index, age), and ancillary information (management regime, climate, soil characteristics) for 529 sites from eight forest biomes. Data entries originated from peer-reviewed literature and personal communications with researchers involved in Fluxnet. Flux estimates were included in the database when they were based on direct measurements (e.g., tower-based eddy covariance system measurements), derived from single or multiple direct measurements, or modeled. Stand description was based on observed values, and climatic description was based on the CRU data set and ORCHIDEE model output. Uncertainty for each carbon balance component in the database was estimated in a uniformed way by expert judgment. Robustness of CO2 balances was tested, and closure terms were introduced as a numerical way to approach data quality and flux uncertainty at the biome level.\n<br>\n\n#### HistoricalLai_584\nApproximately 1000 published estimates of leaf area index (LAI) from nearly 400 unique field sites, covering the period 1932-2000, have been compiled into a single data set. LAI is a key parameter for global and regional models of biosphere/atmosphere exchange of carbon dioxide, water vapor, etc. This data set provides a benchmark of typical values and ranges of LAI for a variety of biomes and land cover types, in support of model development and validation of satellite-derived remote sensing estimates of LAI and other vegetation parameters. The LAI data are linked to a bibliography of over 300 original-source references. These historical LAI data are mostly from natural and semi-natural (managed) ecosystems, although some agricultural estimates are also included. Caution is advised in using these data; they were collected using a wide range of methodologies and assumptions and may not be comparable among sites. Some attempts have been made to detect and flag the outliers in this data set, according to different biome/land cover classes. Needleleaf (coniferous) forests are by far the most commonly measured biome/land cover types in this compilation, with 22% of the measurements from temperate evergreen needleleaf forests, and boreal evergreen needleleaf forests and crops the next most common (about 9% each). About 40% of the records in the data set were published in the past 10 years (1991-2000), with a further 20% collected between 1981 and 1990. Mean LAI (+/- standard deviation), distributed between 15 biome/land cover classes, ranged from 1.31 +/- 0.85 for deserts to 8.72 +/- 4.32 for tree plantations, with evergreen forests (needleleaf and broadleaf) displaying the highest LAI among the natural vegetation classes. Further information on this data set is available from the link below: Leaf Area Index Data Citation: Cite this data set as follows: Scurlock, J. M. O., G. P. Asner, and S. T. Gower. 2001. Global Leaf Area Index from Field Measurements, 1932-2000. Available on-line [http://www.daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.\n<br>\n\n#### FluxSat_GPP_FPAR_1835\nThis dataset provides global gridded daily estimates of gross primary production (GPP) and uncertainties at 0.05-degree resolution for the period 2000-03-01 to the recent past. The GPP was derived from the MODerate-resolution Imaging Spectroradiometer (MODIS) instruments on the NASA Terra and Aqua satellites using the MCD43C4v006 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectances (NBAR) product as input to neural networks that were used to globally upscale GPP estimated from selected FLUXNET 2015 eddy covariance tower sites. Additional data will be added periodically.\n<br>\n\n#### Global_Monthly_GPP_1789\nThis dataset provides global monthly average gross primary productivity (GPP; g carbon/m2/d) modeled at 8 km spatial resolution for each of the 35 years from 1982-2016. GPP is based on the well-known Monteith light use efficiency (LUE) equation but was improved with optimized spatially and temporally explicit LUE values derived from selected FLUXNET tower site data. Optimized LUE was extrapolated to a consistent 8 km resolution global grid using multiple explanatory variables representing climatic, landscape, and vegetation factors influencing LUE and GPP. Global gridded long-term daily GPP was derived using the optimized LUE, Global Inventory Modeling and Mapping Studies (GIMMS3g) canopy fraction of photosynthetically active radiation (FPAR), and Modern-Era Retrospective analysis for Research and Applications, Version 2, (MERRA-2) meteorological information. These data will improve satellite-based estimation and understanding of GPP using a refined LUE model framework.\n<br>\n\n#### Mean_Seasonal_LAI_1653\nThis dataset provides a global 0.25 degree x 0.25 degree gridded monthly mean leaf area index (LAI) climatology as averaged over the period from August 1981 to August 2015. The data were derived from the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g version 2, a bi-weekly data product from 1981 to 2015 (GIMMS-LAI3g version 2). The LAI3g version 2 (raw) data were first regridded from 1/12 x 1/12 degree to 0.25 x 0.25 degree resolution, then processed to remove missing and unreasonable values, scaled to obtain LAI values, and the bi-weekly LAI values were averaged for every month. Finally, the monthly long-term mean LAI (1981-2015) was calculated.\n<br>\n\n#### Global_Veg_Greenness_GIMMS_3G_2187\nThis dataset holds the Global Inventory Modeling and Mapping Studies-3rd Generation V1.2 (GIMMS-3G+) data for the Normalized Difference Vegetation Index (NDVI). NDVI was based on corrected and calibrated measurements from Advanced Very High Resolution Radiometer (AVHRR) data with a spatial resolution of 0.0833 degree and global coverage for 1982 to 2022. Maximum NDVI values are reported within twice monthly compositing periods (two values per month). The dataset was assembled from different AVHRR sensors and accounts for various deleterious effects, such as calibration loss, orbital drift, and volcanic eruptions. The data are provided in NetCDF format.\n<br>\n\n#### GEDI_ICESAT2_Global_Veg_Height_2294\nThis dataset provides global rasters of relative height metrics for vegetation from Global Ecosystem Dynamics Investigation (GEDI) L2A data and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) L3A ATL08 data at 100-, 200-, 500-, and 1000-m spatial resolutions. The metrics include the relative heights RH98, RH90, RH75, and RH50, corresponding to the height at which the respective 98th, 90th, 75th, and 50th percentile of returned energy is reached relative to the ground. These metrics provide measures of vegetation canopy height and structure. The different relative height metrics were intercalibrated over the overlap area (50 - 52 degrees N). GEDI data were collected from 2019-2022, and ICESat2 data were from 2019-2021. The data are provided in cloud optimized GeoTIFF format.\n<br>\n\n#### MatthewsVegetation_419\nThe global vegetation type data of 1 x 1 degree latitude and longitude resolution were designed for use in studies of climate and climate change. Vegetation data were compiled in digital form from approximately 100 published sources. The raw data base distinguished about 180 vegetation types that have been collapsed to 32. The vegetation data were encoded using the UNESCO classification system. Additional information about this data set can be found at http://www.giss.nasa.gov/data/landuse/vegeem.html. ORNL DAAC maintains information on related data sets in the Vegetation Collection. Data Citation The data set should be cited as follows: Matthews, E. 1999. Global Vegetation Types, 1971-1982. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.\n<br>\n\n#### Gridded_Biomass_Africa_1777\nThis dataset provides maps of woody (tree and shrub) cover and biomass across Sub-Saharan Africa at a resolution of 1 km for the period 2000-2004. Canopy cover observations and remote-sensing data related to woody vegetation were used to predict woody cover across Africa. Predicted woody cover, canopy height, and tree allometry were used to estimate woody biomass for Sub-Saharan Africa. Canopy cover observations were assembled from field measurements and Google Earth imagery collected from 2000-2004. Remote-sensing data related to the structural attributes of woody vegetation were derived from MODIS optical data and Q-SCAT (Quick Scatterometer) microwave measurements. Canopy height estimates were derived from spaceborne lidar and tree allometry equations were retrieved from GlobAllomeTree.\n<br>\n\n#### GEDI_HighQuality_Shots_Rasters_2339\nThis dataset consists of near-global, analysis-ready, multi-resolution gridded vegetation structure metrics derived from NASA Global Ecosystem Dynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m diameter lidar footprints. This dataset provides a comprehensive representation of near-global vegetation structure that is inclusive of the entire vertical profile, based solely on GEDI lidar, and validated with independent data. The GEDI sensor, mounted on the International Space Station (ISS), uses eight laser beams spaced by 60 m along-track and 600 m across-track on the Earth surface to measure ground elevation and vegetation structure between approximately 52 degrees North and South latitude. Between April 17th 2019 and March 16th 2023, GEDI acquired 11 and 7.7 billion quality waveforms suitable for measuring ground elevation and vegetation structure, respectively. This dataset provides GEDI shot metrics aggregated into raster grids at three spatial resolutions: 1 km, 6 km, and 12 km. In addition to many of the standard L2 and L4A shot metrics, several additional metrics have been derived which may be particularly useful for applications in carbon and water cycling processes in earth system models, as well as forest management, biodiversity modeling, and habitat assessment. Variables include canopy height, canopy cover, plant area index, foliage height diversity, and plant area volume density at 5 m strata. Eight statistics are included for each GEDI shot metric: mean, bootstrapped standard error of the mean, median, standard deviation, interquartile range, 95th percentile, Shannon's diversity index, and shot count. Quality shot filtering methodology that aligns with the GEDI L4B Gridded Aboveground Biomass Density, Version 2.1 was used. In comparison to the current GEDI L3 dataset, this dataset provides additional gridded metrics at multiple spatial resolutions and over several temporal periods (annual and the full mission duration). Files are provided in cloud optimized GeoTIFF format.\n<br>\n\n#### GEDI_Fusion_Structure_2236\nThis dataset provides eight GEDI forest structure metrics relevant to wildlife habitat modeling and biodiversity assessments at 30-m resolutions across Washington, Oregon, Idaho, Montana, Wyoming, and Colorado. The metrics characterize canopy height, strata densities, and canopy cover. The data were derived using random forest modeling and prediction frameworks. The models created were also hindcasted using 2019 and 2020 GEDI footprints back to 2016 on annual time steps leveraging continuous Landsat spectral and disturbance information, Sentinel-1 backscatter metrics and ratios, topographic information, and bioclimatic variables. Machine learning data fusion approaches were used to scale-up structure information provided by the novel space-borne Global Ecosystems Dynamics Investigation (GEDI) waveform lidar sensor to continuous extents using additional satellite-based continuous earth observation data. GEDI provides a consistent sample of forest structure information at 25-m diameter footprints at near-global extents, providing a valuable source of reference information to drive continuous mapping efforts.\n<br>\n\n#### AVIRIS-NG_Data_Idaho_1533\nThis dataset provides surface reflectance measured by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument during flights over research sites in Idaho and California in 2014 and 2015. AVIRIS-NG measures reflected radiance at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Measurements are radiometrically and geometrically calibrated and provided at 1-meter spatial resolution. The data include 72 flight lines covering long-term research sites in the Reynolds Creek Experimental Watershed in southwestern Idaho and Hollister in southeastern Idaho. Several flight lines from a site in the Inyo National Forest near Big Pine, California are included.\n<br>\n\n#### New_Hampshire_Landcover_1305\nThe New Hampshire Geographically Referenced Analysis and Information Transfer System (GRANIT) land cover data set provides a land cover and land use product at 30-m resolution with 23 individual classes across the state. The classification is based largely on the analysis of 12 Landsat Thematic Mapper (TM and ETM+) images. Over 1,400 new classification training site data points were collected to supplement 1,200 archived sites from previous projects. The classification represents a snapshot in time from 1996 to 2001. This time range spans the dates of the most recent acquisitions of a TM scene for each region of the state and the dates of the most recent field data collection.\n<br>\n\n#### Phenology_AmeriFlux_Neon_Sites_2033\nThis land surface phenology (LSP) dataset provides spatially explicit data related to the timing of phenological changes such as the start, peak, and end of vegetation activity, vegetation index metrics and associated quality assurance flags. The data are for the growing seasons of 2017-2021 for 10-km x 10-km windows centered over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. The dataset is derived at 3-m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. These LSP data can be used to assess satellite-based LSP products, to evaluate predictions from land surface models, and to analyze processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes. The data are provided in NetCDF format along with geospatial area-of-interest information and visualizations of the analysis window for each site in GeoJSON and HTML formats.\n<br>\n\n#### Phenology_Deciduous_Forest_1570\nThis dataset provides Landsat phenology algorithm (LPA) derived start and end of growing seasons (SOS and EOS) at 500-m resolution for deciduous and mixed forest areas of 75 selected Landsat sidelap regions across the Eastern United States and Canada. The data are a 30-year time series (1984-2013) of derived spring and autumn phenology for forested areas of the Eastern Temperate Forest, Northern Forest, and Taiga ecoregions.\n<br>\n\n#### Forested_Areas_Amazonas_Brazil_1515\nThis data set provides LiDAR point clouds and digital terrain models (DTM) from surveys over the K34 tower site in the Cuieiras Biological Reserve, over forest inventory plots in the Adolpho Ducke Forest Reserve, and over sites of the Biological Dynamics of Forest Fragments Project (BDFFP) in Rio Preto da Eva municipality near Manaus, Amazonas, Brazil during June 2008. The surveys encompass the K34 eddy flux tower managed through the Large-scale Biosphere-Atmosphere Experiment in Amazonia, forest inventory plots managed by the Programa de Pesquisa em Biodiversidade (PPBio), and sites managed by the BDFFP. The LiDAR data was collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time.\n<br>\n\n#### Forested_Areas_Para_Brazil_1514\nThis data set provides LiDAR point clouds and digital terrain models (DTM) from surveys over the Tapajos National Forest in Belterra municipality, Para, Brazil during late June and early July 2008. The surveys encompass the K67 and K83 eddy flux towers and a deforestation chronosequence managed through the Large-Scale Biosphere-Atmosphere Experiment in Amazonia providing long-term flux measurements of carbon dioxide. The LiDAR data was collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time.\n<br>\n\n#### LiDAR_Veg_Ht_Idaho_1532\nThis dataset provides the point cloud data derived from small footprint waveform LiDAR data collected in August 2014 over Reynolds Creek Experimental Watershed and Hollister in southern Idaho. The LiDAR data have been georeferenced, noise-filtered, and corrected for misalignment for overlapping flight lines and are provided in 1 km tiles. High resolution digital elevation models and maps of maximum vegetation height derived from the LiDAR data are provided for each site.\n<br>\n\n#### Eurasia_Biomass_1278\nThis data set provides estimates of aboveground biomass (AGB) for defined land cover types within World Wildlife Fund (WWF) ecoregions across the boreal biome of eastern and western Eurasia, roughly between 50 and 70 degrees N. The study focused on within-growing-season data, i.e. leaf-on conditions. The AGB estimates were derived from a series of models that first related ground-based measured biomass to airborne data collected with an Optech Airborne Laser Terrain Mapper (ALTM) 3100, and a second set of models that related the airborne estimates of biomass to Geoscience Laser Altimeter System (GLAS) LiDAR canopy structure measurements. The ground, airborne, and GLAS measurements were used to formulate the models needed to generate biomass predictions for western Eurasia. Eastern Eurasia employed a two-phase approach relating field measurements directly to the GLAS measurements without the airborne intermediary. The GLAS LiDAR biomass estimates were extrapolated by land cover types and ecoregions across the entire biome area. The study compiled remotely sensed forest structure data collected in June of 2005 and 2006 from the GLAS LiDAR instrument aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite and from an Optech Airborne Laser Terrain Mapper (ALTM) 3100 airborne instrument flown in Southeast Norway over both the ground plots and the ICESat GLAS flight path. For a consistent biome-level analysis, ecoregions contained within the boreal forest biome were identified by the World Wildlife Fund's (WWF) ecoregion map of the world (Olson et al., 2001). MODIS MOD12Q1 land cover products (2004) were used to identify land cover types for stratification purposes within eco-regions. The ground-based measurements are not provided with this data set.\n<br>\n\n#### GSMNP_Vegetation_Structure_R1_1286\nThis dataset provides multiple-return LiDAR-derived vegetation canopy structure at 30-meter spatial resolution for the Great Smoky Mountains National Park (GSMNP). Canopy characteristics were analyzed using high resolution three-dimensional point cloud measurements gathered between February-April 2011 for Tennessee and during March-April 2005 for North Carolina sections of the park. Vegetation types were mapped by grouping areas of similar canopy structure. The map was compared and validated against existing vegetation maps for the park.\n<br>\n\n#### white_model_parameters_652\nEcosystem simulation models use descriptive input parameters to establish the physiology, biochemistry, structure, and allocation patterns of vegetation functional types, or biomes. For single-stand simulations, it is possible to measure required data, but as spatial resolution increases, data availability decreases. Generalized biome parameterizations are then required. Undocumented parameter selection and unknown model sensitivity to parameter variation for larger-resolution simulations are currently the major limitations to global and regional modeling. We present documented input parameters for process-based ecosystem simulation models (specifically for the BIOME-BGC) for major natural temperate biomes. Parameter groups include the following: turnover and mortality; allocation; carbon to nitrogen ratios (C:N); the percent of plant material in labile, cellulose, and lignin pools; leaf morphology; leaf conductance rates and limitations; canopy water interception and light extinction; and the percent of leaf nitrogen in Rubisco (i.e., ribulose bisphosphate-1,5-carboxylase/oxygenase). Input parameters may also be used for other ecosystem models.\n<br>\n\n#### GIMMS3g_NDVI_Trends_1275\nThis data set provides normalized difference vegetation index (NDVI) data for the arctic growing season derived primarily with data from Advanced Very High Resolution Radiometer (AVHRR) sensors onboard several NOAA satellites over the years 1982 through 2012. The NDVI data, which show vegetation activity, were averaged annually for the arctic growing season (GS; June, July and August). The products include the annual GS-NDVI values and the results of a cumulative GS-NDVI time series trends analysis. The data are circumpolar in coverage at 8-km resolution and limited to greater than 20 degrees N. These normalized difference vegetation index (NDVI) trends were calculated using the third generation data set from the Global Inventory Modeling and Mapping Studies (GIMMS 3g). GIMMS 3g improves on its predecessor (GIMMS g) in three important ways. First, GIMMS 3g integrates data from NOAA-17 and 18 satellites to lengthen its record. Second, it addresses the spatial discontinuity north of 72 degrees N, by using SeaWiFS, in addition to SPOT VGT, to calibrate between the second and third versions of the AVHRR sensor (AVHRR/2 and AVHRR/3). Finally, the GIMMS 3g algorithm incorporates improved snowmelt detection and is calibrated based on data from the shorter, arctic growing season (May-September) rather than the entire year (January-December).\n<br>\n\n#### Land_Use_Harmonization_V1_1248\nThese data represent fractional land use and land cover patterns annually for the years 1500 - 2100 for the globe at 0.5-degree (~50-km) spatial resolution. Land use categories of cropland, pasture, primary land, secondary (recovering) land, and urban land, and underlying annual land-use transitions, are included. Annual data on age and biomass density of secondary land, as well as annual wood harvest, are included for each grid cell. Historical land cover data for the years 1500 - 2005 are based on HYDE 3.1 and future land cover projections for the period 2006 - 2100 came from four Integrated Assessment Model (IAM) scenarios which reach different levels of radiative forcing by year 2100: MESSAGE (8.5 W/m2), AIM (6 W/m2), GCAM (4.5 W/m2), and IMAGE (2.6 W/m2). A key feature of these data is that historical reconstructions of land use were harmonized (computationally adjusted to minimize differences at the transition period) with modeled future scenarios, allowing for a seamless examination of historical and future land use. The output data present a single consistent, spatially gridded set of land-use change scenarios for studies of human impacts on the past, present, and future Earth system. For additional information about the algorithms, inputs, and options used in creating the land use transitions data, please refer to Hurtt et al. (2006) and Hurtt et al. (2011). Data are presented as a series of twenty (20) different data products representing different past and future model scenarios. There are a total of 560 NetCDF v4 files (.nc4), one for each combination of data product and land use variable.\n<br>\n\n#### LUH2_GCB2019_1851\nThis dataset, referred to as LUH2-GCB2019, includes 0.25-degree gridded, global maps of fractional land-use states, transitions, and management practices for the period 0850-2019. The LUH2-GCB2019 dataset is an update to the previous Land-Use Harmonization Version 2 (LUH2-GCB) datasets prepared as required input to land models in the annual Global Carbon Budget (GCB) assessments, including land-use change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, afforestation, and crop rotations. Compared with previous LUH2-GCB datasets, the LUH2-GCB2019 takes advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil, as far back as 1950. LUH2-GCB datasets are used by bookkeeping models and Dynamic Global Vegetation Models (DGVMs) for the GCB.\n<br>\n\n#### Land_Use_Harmonization_V2_1721\nThis dataset provides 0.25-degree gridded, global, annual estimates of fractional land use and land cover patterns for the period 2015-2100, designed to support the ISIMIP2b effort to assess the impacts of 1.5 Deg Celcius global warming. Land use types, land use transitions, and cropland estimates of area fraction are provided and include detailed separation of primary and secondary natural vegetation into forest and non-forest sub-types, pasture into managed pasture and rangeland, and cropland into multiple crop functional types; all transitions between land use states per grid cell per year, including crop rotations, shifting cultivation, and wood harvest; and agriculture management including irrigation, synthetic nitrogen fertilizer, and biofuel management. The LUH2-ISIMIP2b datasets were derived using Land Use Harmonization 2 (LUH2) methodology and are based on land-use scenarios provided by the REMIND-MAgPIE Integrated Assessment Model using an SSP2 storyline along with RCP2.6 and RCP6.0 emissions scenarios. In contrast to the standard SSP scenarios, these land use changes additionally account for climate and atmospheric CO2 fertilization effects on the underlying patterns of potential crop yields, water availability, and terrestrial carbon content. This is achieved by using the LPJmL (Lund-Potsdam-Jena managed land) model forced with atmospheric CO2 concentrations and patterns of climate change generated from 4 different climate models (GFDL, HADGEM, IPSL, and MIROC) consistent with the 2 different RCP scenarios, resulting in a set of 8 different LUH2-ISIMIP2b datasets.\n<br>\n\n#### MangroveExtent_Landsat_MSS_2329\nThis dataset includes a regional map of mangrove extent for Myanmar, Thailand, and Cambodia for the period of 1972-1977. The map was developed from Landsat 1-2 MSS Collection 1 Tier 2 imagery. Mangrove extent was generated using a Random Forest machine learning algorithm that effectively mapped a total of 15,420.51 km2 at the nominal Landsat scale of 30 m. This map of mangrove extent served as a baseline to analyze changes in mangrove distribution in Southeast Asia from 1970s through 2020. Southeast Asia is home to some of the planet's most carbon-dense and biodiverse mangrove ecosystems. There is still much uncertainty with regards to the timing and magnitude of changes in mangrove cover over the past 50 years. While there are several regional to global maps of mangrove extent in Southeast Asia for the early 21st century, data prior to the mid-1990s are limited due to the scarcity of Earth Observation (EO) data of sufficient quality and the historical limitations to publicly available EO data. The data are provided in Cloud optimized GeoTIFF format at 60-m resolution. In addition, a shapefile outlines the region of analysis.\n<br>\n\n#### US_MODIS_NDVI_1299\nThis data set provides Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, smoothed and gap-filled, for the conterminous US for the period 2000-01-01 through 2015-12-31. The data were generated using the NASA Stennis Time Series Product Tool (TSPT) to generate NDVI data streams from the Terra satellite (MODIS MOD13Q1 product) and Aqua satellite (MODIS MYD13Q1 product) instruments. TSPT produces NDVI data that are less affected by clouds and bad pixels.\n<br>\n\n#### LAI_Africa_2325\nThis dataset provides leaf area index (LAI) estimates for Sub-Saharan Africa for woody, herbaceous, and aggregate vegetation types. The estimates were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4 and the native MODIS LAI product (MCD15A2H Version 6.1), which provides LAI measurements every 8 days at 500-m pixel size. Data from the MCD15A2H product were processed further to generate three layers including: a smoothed and gap filled LAI layer referred to as aggregate leaf area index and two additional layers processed to separate woody LAI (tree and shrubs) and herbaceous LAI (grass and forbs). The data include 31 MODIS 10-degree tiles and cover 2002 to 2022. The data are provided in NetCDF format.\n<br>\n\n#### NA_MODIS_Surface_Biophysics_1210\nThis data set provides MODIS-derived surface biophysical climatologies of bidirectional distribution function (BRDF), BDRF/albedo, land surface temperature (LST), leaf area index (LAI), and evapotranspiration (ET) as separate files for each of the MODIS land cover types, and four radiative forcing data files for four scenarios of potential vegetation shifts in North America. Each biophysical variable has temporal periods that represent the average of all 8-day periods from the years 2000 – 2012. The data have a spatial resolution of 0.05 degree (~5 km) and a temporal resolution of eight days. Additionally, a file containing diffuse fraction of surface downward solar radiation (DiffuseFraction) at a monthly scale, and a file containing snow water equivalent (SWE) are provided. The extent of the data covers the land area of North America, from 20 to 60 degrees N. The land-cover map used was synthesized from nine yearly 500-m MODIS land-cover layers (MCD12 Q1 Collection 5) for 2001-2008. These high-resolution land data were originally developed for quantifying biophysical forcing from land-use changes associated with forestry activities, such as radiative forcing from altered surface albedo.\n<br>\n\n#### UAV_Imagery_BigLakeTrail_1834\nThis dataset provides multispectral reflectance imagery (green at 550 nm, red at 660 nm, red edge at 735 nm, and near-infrared at 790 nm), normalized difference vegetation index (NDVI), and digital surface and terrain models for a 0.5 km2 area surrounding Big Trail Lake (BTL) in the Goldstream Creek Valley north of Fairbanks, Alaska. These high spatial resolution maps (13 cm x 13 cm) were generated by unmanned aerial vehicle (UAV) imagery collected on 2019-08-04. Raw images (n=908) were combined into mosaic layers that incorporated ground control points with centimeter accuracy. These layers were then used to generate vegetation, water body, and elevation maps and then combined with in situ measurements of methane flux to improve upscaling models of greenhouse gas emissions.\n<br>\n\n#### Xingu_Albedo_Radiation_1622\nThis dataset provides daily average land surface net radiation (Rnet) as an 8-day time series at approximately 0.5 km resolution for the upper Xingu River Basin in Mato Grosso, Brazil, from 2000-02-18 to 2012-11-16. The parameters needed to calculate Rnet, including albedo, downward shortwave radiation (RSnet), upward longwave radiation (RLnet[up]) and downward longwave radiation (RLnet[down]) were derived from MODIS products (MOD43A3, MOD11A2, MOD08E3) and local weather station data. Parameters were estimated under all sky conditions. These parameters are also provided for the Xingu Basin but at varying spatial and temporal resolutions.\n<br>\n\n#### ForestHt_Biomass_GEDI_TDX_2298\nThis dataset includes maps of canopy height and aboveground biomass at spatial resolutions of 25 m and 100 m for Mexico, Gabon, French Guiana, and the Amazon Basin. The GEDI-TanDEM-X (GTDX) fusion maps were created by combining data from NASA's Global Ecosystem Dynamics Investigation (GEDI) Version 2 footprint data (from 2019-04-18 to 2021-08-18) and TanDEM-X (abbreviated as TDX) Interferometric Synthetic Aperture Radar (InSAR) images (from 2011-01-06 to 2020-12-31). The GTDX canopy height maps were generated by using the TDX coherence maps to invert the TDX height and subsequently using GEDI canopy height as reference data to calibrate the inverted height. The GTDX aboveground biomass maps were produced based on a generalized hierarchical model-based (GHMB) framework that utilizes GEDI biomass as training data to establish models for estimating biomass based on the GTDX canopy height. The dataset also includes maps of canopy height uncertainty, biomass uncertainty, and ancillary data including a regional modeling parameter and forest disturbance. The uncertainty of GTDX canopy height was estimated for each pixel by propagating the GEDI-TDX model error to each GTDX pixel prediction. The uncertainty of GTDX aboveground biomass was estimated by considering the error in both the GEDI footprint biomass data and the GEDI-TDX model, and then applying it to each GTDX biomass pixel prediction. The regional model parameter indicates the size of the analysis window (2 to 50 km or country wide) used for each pixel. The forest disturbance information identifies pixels where disturbance occurred between 2011 and 2020, and provides the year of last disturbance.\n<br>\n\n#### Phenocam_Images_V2_1689\nThis dataset provides a time series of visible-wavelength digital camera imagery collected through the PhenoCam Network at each of 393 sites predominantly in North America from 2000-2018. The raw imagery was used to derive information on phenology, including time series of vegetation color, canopy greenness, and phenology transition dates for the PhenoCam Dataset v2.0.\n<br>\n\n#### PhenoCam_V2_1674\nThis data set provides a time series of vegetation phenological observations for 393 sites across diverse ecosystems of the world (mostly North America) from 2000-2018. The phenology data were derived from conventional visible-wavelength automated digital camera imagery collected through the PhenoCam Network at each site. From each acquired image, RGB (red, green, blue) color channel information was extracted and means and other statistics calculated for a region-of-interest (ROI) that delineates an area of specific vegetation type. From the high-frequency (typically, 30 minute) imagery collected over several years, time series characterizing vegetation color, including canopy greenness, plus greenness rising and greenness falling transition dates, were summarized over 1- and 3-day intervals.\n<br>\n\n#### Phenocam_Images_V3_2364\nThis dataset provides a time series of visible-wavelength digital camera imagery collected through the PhenoCam Network at each of 738 sites across diverse ecosystems of the world (mostly North America) from 2000-2023. Vegetation types such as deciduous broadleaf forests, grasslands, evergreen needleleaf forests, and agriculture are the best-represented. The raw imagery was used to derive information on phenology, including time series of vegetation color, canopy greenness, and phenology transition dates for the PhenoCam Dataset v3.0. The images are provided in JPEG format organized in compressed tar.gz archives by site and date. Site locations and basic metadata are included in a GeoJSON file.\n<br>\n\n#### PhenoCam_V3_2389\nThis dataset provides vegetation phenological observations for 738 sites across diverse ecosystems of the world (mostly North America) from 2000 to 2023. The phenology data were derived from conventional visible-wavelength automated digital camera imagery collected through the PhenoCam Network at each site. From each acquired image, RGB (red, green, blue) color channel information was extracted and summary statistics were calculated for a region-of-interest (ROI) that delineates an area of specific vegetation type. From the high-frequency (typically, 30 minute) imagery collected over several years, time series characterizing vegetation color, including canopy greenness, plus greenness rising and greenness falling transition dates, were summarized over 1- and 3-day intervals. These data products, consisting of 4805.5 site-years of observations, can be used for phenological modeling, to evaluate satellite remote sensing data products, to understand relationships between canopy phenology and ecosystem processes, to study the seasonal changes in leaf-level physiology that are associated with changes in leaf color, for benchmarking earth system models, and for studies of climate change impacts on terrestrial ecosystems. The data are provided in comma separated values (CSV), TIFF image, text, JSON, and GeoJSON formats.\n<br>\n\n#### Landsat8_Sentinel2_Phenocam_2248\nThis dataset provides a reference of land surface phenology (LSP) at 30-m pixels for 78 regions of 10 x 10 km2 across a wide range of ecological and climatic regions in North America during 2019 and 2020. The data were derived by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near- surface PhenoCam time series (hereafter called HP-LSP). The HP-LSP dataset consists of two parts: (1) the 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series and (2) four key phenological transition dates that are greenup onset, maturity onset, senescence onset, and dormancy onset (accuracy less than or equal to five days). The PhenoCam network offers near-surface observations via the RGB (Red, Green, and Blue) imagery every 30 minutes. Each RGB imagery enables us to calculate as many as 100 Green Chromatic Coordinate (GCC) for generating a collection of localized vegetation dynamics. The HLS EVI2 time series with frequent gaps was fused with the most comparable PhenoCam GCC temporal shape selected from the GCC collection using the Spatiotemporal Shape Matching Model (SSMM) to create the synthetic gap-free HLS-PhenoCam EVI2 time series, which was used to establish the physically-based hybrid piecewise logistic model (HPLM) for detecting phenological transition dates (phenometrics).\n<br>\n\n#### WhitePhenoregions_799\nThe overall purpose in this research was to identify the regions of the world best suited for long-term monitoring of biospheric responses to climate change, i.e. monitoring land surface phenology. The user is referred to White et al. [2005] for further details. Using global 8 km 1982 to 1999 Normalized Difference Vegetation Index (NDVI) data and an eight-element monthly climatology, we identified pixels consistently dominated by annual cycles and then created phenologically and climatically self-similar clusters, which we term phenoregions. We then ranked and screened each phenoregion as a function of landcover homogeneity and consistency, evidence of human impacts, and political diversity.This dataset contains material providing users with direct access to data used to construct the figures in White et al. [2005]. Users are referred to this reference for additional information. Data files include ASCII and binary versions of the image files for the 500 elemental phenoregions and the 136 final monitoring phenoregions (shown in figure below) and a corresponding .jpg map. Also included are the classification data in tabular ACSII format for each of the 500 elemental phenoregions.Selected monitoring phenoregions. Phenoregions with fewer than 100 pixels or dominated by crop, urban or barren landcover removed. The 136 remaining phenoregions are those passing the screening factors in Table 1 and are shown with normalized rankings by landcover. (From White et al., 2005)\n<br>\n\n#### King_Rim_Fire_Analysis_1288\nThis data set provides high-resolution surface reflectance, thermal imagery, burn severity metrics, and LiDAR-derived structural measures of forested areas in the Sierra Nevada Mountains, California, USA, collected before and after the August 2013 Rim and September 2014 King mega forest fires. Pre-fire data were paired with post-fire collections to assess pre- and post-fire landscape characteristics and fire severity. Field estimates of fire severity were collected to compare with derived remote sensing indices. Reflectance measurements for the spectroscopic AVIRIS and MASTER sensors are distributed as multi-band geotiffs for each megafire and acquisition date. Derived operational metric products for each sensor are provided in individual GeoTIFFs. GeoTIFFs produced from LiDAR point data depict first order topographic indices and summary statistics of vertical vegetation structure.\n<br>\n\n#### rlc_land_cover_689\nThis dataset is a 15-kilometer resolution land cover map for the land area of the Former Soviet Union. There are sixty land cover classes distinguished in this dataset, of which 38 are forest cover classes. The data set is useful for stratification of the FSU into general sub-regions of land cover for subsequent study using higher resolution satellite data.\n<br>\n\n#### rlc_landcover_far_east_690\nThis data set is a 1-kilometer resolution land cover map for the land area of the Primor'ye and Southern Khabarovsk Regions, in the Russian Far East, based on 1990 NOAA AVHRR data. Labeling of land cover classes depended upon the Russian 1990 Forest Cover Map (Garsia, 1990), the analyst's experience with AVHRR data, and Russian data sources. There are eight classes distinguished in this dataset, of which 5 are forest cover classes.The objective of this work was to create a 1-km resolution land cover map of the region of the Far Eastern Siberia based on NOAA AVHRR data which might be used by World Wildlife Fund researchers to aid in the definition of remaining habitats and range for threatened animal species (Stone and Schlesinger, 1996).\n<br>\n\n#### rlc_forest_map_1990_691\nThis data set is a 1:2.5 million scale forest cover map for the land area of the Former Soviet Union that was completed in 1990 (Garsia 1990). There are forty-five classes distinguished in this data set, of which 38 are forest cover classes. The purpose of this map was to create a generalized and up-to-date map of forest cover for the USSR. This map should not be viewed as a detailed forest cover map but more like an economic forestry map. The most important tree species of a region are highlighted rather than the dominant trees species or tree cover. Very few tree species are defined. In many cases, of course, the dominant and the most important trees species are the same.\n<br>\n\n#### rlc_forest_map_1973_692\nThis data set is a 1:15 million scale forest cover map for the land area of the Former Soviet Union. Twenty-two land cover classes are distinguished, of which 20 are forest cover classes. The source data were acquired by map digitization from the Atlas of Forests of the USSR (Anon. 1973) which was likely based on forestry data from the 1940s, 1950s and 1960s.\n<br>\n\n#### rlc_forest_map_krasnoyarsk_693\nThis dataset is a 1:2 million scale forest cover map for the land area of the Krasnoyarsk Region, Russia. Thirty-two land cover classes are distinguished. These data were digitized from maps of the Atlas of Forests of the USSR (Anon. 1973). This map should not be strictly viewed as a map of actual forest cover, but rather as a map of dominant tree species. Very few tree species are defined, and generally, each polygon and color has only one tree species assigned to it.\n<br>\n\n#### rlc_fire_images_russia_694\nThis data set is made up of images of forest fires in Russia from NOAA's Operational Significant Event Imagery (OSEI) archive (http://www.osei.noaa.gov) for the 1998 and 1999 seasons. OSEI fire products include multichannel color composite imagery of wildfire and controlled burn events. Products in this event group show fire, smoke, and hotspots (FSMHS) from the fires.\n<br>\n\n#### rlc_fire_sumpt_695\nThis dataset is derived from Russian forest fire imagery from the National Forest Fire Center of Russia archive that was collected by the Center of Remote Sensing, Institute of Solar Terrestrial Physics, Irkutsk, Russia for the 1998 and 1999 fire seasons. The data are vector (point) maps of forest fire locations (1998 and 1999) in ArcView shapefile format.\n<br>\n\n#### rlc_forest_carbon_696\nThis dataset is a 1:15 million scale map of forest stand carbon for the land area of Russia (Stone et al., 2000). The objective was to create a first approximation of the forest stand carbon reserves of Russia. Data include continuous estimates of forest stand carbon in units of metric tons/ha of carbon (C) and categorized data depicting rages of forest stand carbon. The resulting maps show forest stand C by region in a spatially explicit form. It is the first map of its type for Russia of which we are aware. The mapped C represents 96% of the total of 26.1 Pg forest tree stand C described by Alexeyev and Birdsey (1994) and Alexeyev et al. (1995). Of the remaining 4%, nearly half was due to bushes, which were assumed not to be mapped in the 1973 forest cover map.The source data for the forest stand carbon map were acquired by map digitization from the Atlas of Forests for the Soviet Union (State Committee on Forests, 1973) and spatial application and arithmetic manipulation of carbon storage data from Alexeyev and Birdsey (1998).\n<br>\n\n#### rlc_world_forest_map_697\nThis data set is the Former Soviet Union (FSU) portion of the Generalized World Forest Map (WCMC, 1998), a 1-kilometer resolution generalized forest cover map for the land area of the Former Soviet Union. There are five forest classes in the original global generalized map. Only two of those classes were distinguished in the geographical portion comprising the FSU.\n<br>\n\n#### rlc_vector_data_698\nThis data set consists of roads, drainage, railroads, utilities, and population center information in readily usable vector format for the land area of the Former Soviet Union. The purpose of this dataset was to create a completely intact vector layer which could be readily used to aid in mapping efforts for the area of the FSU. These five vector data layers were assembled from the Digital Chart of the World (DCW), 1993. Individual record attributes were stored for population centers only. Vector maps for the FSU are in ArcView shapefile format.\n<br>\n\n#### rlc_admin_boundaries_699\nThis data set of state and regional boundaries was derived from the 1:3 million scale administrative boundaries (ESRI, 1998) for the land area of the Former Soviet Union. There are 162 administrative regions distinguished in this data set. The vector map of state and regional boundaries for the FSU is in ArcView shapefile format.\n<br>\n\n#### rlc_vegetation_1990_700\nThis dataset is a 1:4 million scale vegetation map for the land area of the Former Soviet Union. Three hundred seventy-three cover classes are distinguished, of which nearly 145 are forest cover-related classes. Stone and Schlesinger (1993) digitized the map Vegetation of the Soviet Union, 1990 (Institute of Geography, 1990).\n<br>\n\n#### Russian_Forest_Disturbance_1294\nThis data set provides Boreal forest disturbance maps at 30-m resolution for 55 selected sites across Northern Eurasia within the Russian Federation. Disturbance events were derived from selected high-quality multi-year time series of Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images (stacks) over the 1984 to 2000 time period. Forest pixels were classified by year of latest disturbance or as undisturbed.\n<br>\n\n#### Idaho_field_shrub_data_1503\nThis dataset provides the results of the characterization of shrubland vegetation at two study areas in southern Idaho, USA: the Reynolds Creek Experimental Watershed (RCEW) and Hollister. Data were collected in September and October 2014. In each study area, several 10-m x 10-m plots were randomly established that are representative of the local dominant vegetation types. Measurements are reported for both plot and individual shrub attributes. Plot measurements include shrub density and biometric data, percent shrub cover derived from line intercept transects, percent plant species and bare ground cover derived from photo analysis, and average LAI. Measurements for selected individual shrubs include height, width, length, number of stems, and LAI. Leaf samples were collected for determining LAI, specific leaf area (SLA), carbon and nitrogen concentrations, and isotopic nitrogen and carbon.\n<br>\n\n#### SiB3_Carbon_Flux_909\nThe Simple Biosphere Model, Version 3 (SiB3) was used to produce a global data set of hourly carbon fluxes between the atmosphere and the terrestrial biosphere for the years 1998-2006. This data set represents the global net ecosystem exchange (NEE) of carbon between the atmosphere and the terrestrial biosphere; specifically, the flux of CO2 between the planetary boundary layer (PBL) and the surface vegetation layer. Following atmospheric convention, flux is defined as positive into the atmosphere and negative into the surface vegetation.The data reported are 9 years of estimated hourly carbon flux for 14637 land points. Units are moles C/m2/sec.Data are provided in two NetCDF formats: * The NetCDF format provided by the investigators -- format designed specifically to minimize disk storage volume that excludes water grid cells. * A CF Compliant NetCDF format -- generated by the ORNL DAAC that includes both land and water grid cells.The investigator provided NetCDF formatted files can be processed using the provided FORTRAN code (sib_process_flux.f90) and the land mask (sib_mask.nc) into hourly, daily-mean, or monthly-mean fluxes on a global 1x1 degree Cartesian grid. The monthly-mean SiB3 fluxes were compared to TransCom flux data available for years 2000-2005 (Gurney et al., 2008) as a means of evaluating overall behavior of the model. In general, SiB3 fluxes are within the error bars of the TransCom results.The CF compliant NetCDF format files have been processed by the ORNL DAAC and the hourly and summarized daily-mean and monthly-mean flux data files are provided. GeoTIFF format files:In addition, the CF convention NetCDF files were converted to GeoTIFF image files by the ORNL DAAC and are included with the data set. Companion file:Additional information about the data formats, methodology, and data quality is found in the companion file: SiB3_carbon_flux_readme.pdfAccess to GeoTIFF format files via WCS Interface:The ORNL DAAC also provides access to the GeoTIFF files via a Web Coverage Service Interface (WCS). The OpenGIS® Web Coverage Service Interface Standard (WCS) defines a standard interface and operations that enables interoperable access to geospatial coverages.These data are a carbon cycle reanalysis, which may be thought of as analogous to NCEP meteorological reanalysis products. Carbon fluxes have been used by a large community of atmospheric transport modelers to create reanalysis of CO2 concentrations and the results have been evaluated against observations. In addition, the reanalyzed flux and CO2 fields are important for designing future observing strategies for the global carbon cycle.\n<br>\n\n#### Siberian_Biomass_Wildfire_1321\nThis data set provides 30-meter resolution mapped estimates of Cajander larch (Larix cajanderi) aboveground biomass (AGB), circa 2007, and a map of burn perimeters for 116 forest fires that occurred from 1966-2007. The data cover ~100,000 km2 of the Kolyma River Basin in northeastern Siberia, Sakha Republic, Russia.\n<br>\n\n#### ARID_White_Paper_V1.1_2408\nThis dataset provides the final report from the Adaptation and Response in Drylands (ARID) scoping study. ARID is one of the two scoping studies funded by NASA in 2023 to identify the scientific questions and develop the initial study design and implementation concept for a new NASA Terrestrial Ecology field campaign. This report emphasizes a prioritized research agenda and an initial implementation plan, focusing on the western U.S. to deepen our understanding of national dryland processes and resources. ARID is also leveraging an extensive network of international sites and collaborators in Africa, Australia, Mexico, and South America. This global approach facilitates the evaluation, monitoring, and forecasting of drylands worldwide, ensuring a coordinated effort to address and inform solutions for the challenges facing these critical ecosystems. ARID will use cutting-edge approaches to address four Science Themes: 1) Climate Variability and Drought, 2) Ecosystem Structure, Function, and Biodiversity, 3) Carbon Cycle Interannual Variability and Long-Term Trends, and 4) Social-Ecological Systems. The scoping study performed extensive outreach, conducting over 160 meetings and events with hundreds of scientists and decision-makers across six continents, which translated to the ARID science plan being co-created with a wide range of contributors and perspectives, including remote sensing, modeling, and dryland scientists; Tribal Nations; and a range of U.S. federal entities. This report outlines a targeted plan to deploy field and NASA airborne instruments to vastly augment data derived from satellite observations that, when joined, will substantially advance quantification of drylands' large and changing role in the U.S. and in the Earth system.\n<br>\n\n#### PANGEA_White_Paper_V1.1_2405\nThis dataset provides the final report from the PAN tropical investigation of bioGeochemistry and Ecological Adaptation (PANGEA) scoping study. PANGEA is one of the two scoping studies funded by NASA in 2023 to identify the scientific questions and develop the initial study design and implementation concept for a new NASA Terrestrial Ecology field campaign. This report provides 1) the scientific rationale; 2) an initial study design concept; 3) a presentation of science questions, goals, and objectives; 4) the rationale in terms of state-of-the-art, relevance, and expected advances; 5) implementation concepts; and 6) other information to enable NASA to fully evaluate the project. This report outlines the PANGEA concept, including the PANGEA science themes, science questions, the scientific and technical advancement arising from PANGEA, the critical role of NASA remote sensing, PANGEA's research strategy and study design, PANGEA's capacity-building and training priorities, community engagement strategy, ability to enable Earth Action, and technical and logistical feasibility. The PANGEA concept reflects the voices of many and was developed in collaboration with over 800 individuals representing over 300 organizations from 42 countries across five continents. This report is provided in five languages including English, Spanish, French, Portuguese, and Indonesian.\n<br>\n\n#### Taiga_Tundra_Tree_Cover_1218\nThis data set provides a map of selected areas with defined tree canopy cover over the circumpolar taiga-tundra ecotone (TTE). Canopy cover was derived from the 500-meter MODIS Vegetation Continuous Fields (VCF) product as averaged over six years from 2000-2005 and processed as described in Ranson et al. (2011). This process identified patches of low tree canopy cover which are indicative of the transition from forest to tundra and differentiate the circumpolar taiga–tundra ecotone for the 2000–2005 period. The TTE is the Earth's longest vegetation transition zone and stretches for more than 13,400 km around Arctic North America, Scandinavia, and Eurasia. In Eurasia, the map extends from 60 degrees N to 70 degrees N, and in North America from 50 degrees N to 70 degrees N, excluding Baffin Island in northeastern Canada and the Aleutian Peninsula in southwestern Alaska. Note that for this product, taiga is being used one and the same as boreal forest. This circumpolar TTE area was classified according to VCF tree canopy cover.\n<br>\n\n#### Forest_Inventory_Tapajos_1552\nThis dataset provides tree inventory, tree height, diameter at breast height (DBH), and estimated crown measurements from 30 plots located in the Tapajos National Forest, Para, Brazil collected in September 2010. The plots were located in primary forest, primary forest subject to reduced-impact selective logging (PFL) between 1999 and 2003, and secondary forest (SF) with different age and disturbance histories. Plots were centered on GLAS (the Geoscience Laser Altimeter System) LiDAR instrument footprints selected along two sensor acquisition tracks spanning a wide range in vertical structure and aboveground biomass.\n<br>\n\n#### Tree_Mortality_Western_US_1512\nThis dataset provides annual estimates of tree mortality due to fires and bark beetles from 2003 to 2012 on forestland in the continental western United States. Tree mortality was estimated at 1-km spatial resolution by combining tree aboveground carbon (AGC) and disturbance datasets derived largely from remote sensing. Tree mortality is expressed as the amount of AGC stored in trees killed by disturbance (Mg carbon per km2). The dataset also includes annual uncertainty maps that were generated using a Monte Carlo approach in which tree biomass, biomass carbon content, and disturbance severity were iteratively varied by their uncertainty.\n<br>\n\n#### VHR_Urban_Land_Cover_Maps_2413\nThis dataset consists of very high resolution urban land cover maps for two African cities, Mekelle, Ethiopia and Polokwane, South Africa for 2020. Maps were generated from Planet SuperDove satellite imagery at 3.125-m spatial resolution, and Worldview-3 satellite imagery (Maxar Techologies) at two spatial resolutions, 2 m for multispectral imagery and 0.5-m spatial resolution for pansharpened imagery. An object-based image classification approach was used to produce a multi-class land cover product for each image source. The aim of this work was to support fine scale urban land cover analyses and comparative assessments between different high resolution satellite imagery sources. The data are provided in shapefile format.\n<br>\n\n#### WAVeTrends_1738\nThe WAVeTrends dataset is a 0.05 degree (5.55 km) vegetation change product, spanning the West African Sudano-Sahel region. It provides pixel-wise information on concurrent woody and herbaceous vegetation trends over a 32-year period (1982-2013). Change in woody vegetation was derived using long-term rain use efficiency (RUE) sensitivity, i.e., the per-pixel comparison of the difference of mean RUE between the first and last decades of the 32-year time series. Herbaceous vegetation change was defined by short-term RUE sensitivity, i.e., comparing the slope of the RUE relationship (productivity vs. precipitation) between both decades using per-pixel Analysis of Covariance (ANCOVA). Categorical vegetation change was then determined for each pixel using the direction of the change and a significance level of p<0.05. The use of RUE (the amount of biomass produced per unit of precipitation) for vegetation trend analysis in savanna regions relies on the assumption that rainfall is a significant positive driver of net production in drylands. Testing of this long-term productivity-rainfall relationship revealed that the assumption was not always met, therefore, validity flags are included for each pixel location.\n<br>\n\n#### woody_biomass_657\nEstimates of the woody biomass density and pools were derived at the county scale of resolution of all forests of the eastern United States using new approaches for converting inventoried wood volume to estimates of above and belowground biomass. Biomass density and pools were estimated from the US Department of Agriculture Forest Service, Forest Inventory and Analysis database on growing stock volume by forest type and stand size-class. Estimates were compiled for 2,009 counties in the 33 Eastern states based on state-based inventories conducted between 1983 and 1996 (see Brown and Schroeder 1999). Stand volume was converted to aboveground biomass with regression equations for biomass expansion factors (BEF; ratio of aboveground biomass density of all living trees to merchantable volume). Belowground biomass was estimated as a function of aboveground biomass with regression equations. Biomass pools were calculated as the product of biomass density and forest area, summed by stand-size class. Forest area was defined by the Forest Service as land producing or capable of producing in excess of 20 cubic feet per acre per year of industrial roundwood products. Statistics were presented for hardwood and softwood (pine plus spruce-fir) forest categories. The approach accounted for commercial and non commercial tree species with diameters greater than 2.5 cm and included noncommercial tree components (branches, twigs, and leaves). Belowground components include both fine and coarse roots.The estimation methods were based on work by Schroeder et al. 1997 and were also used to estimate aboveground woody production (Brown and Schroeder 1999).Based on the analysis of the biomass data (Brown et al. 1999), total biomass density for hardwood forests ranged from 36 to 344 Mg ha-1, with an area-weighted mean of 159 Mg ha-1. About 50% of all counties had hardwood forests with biomass densities between 125 and 175 Mg ha-1. For softwood forests, biomass density ranged from 2 to 346 Mg ha-1, with an area-weighted mean of 110 Mg ha-1. Biomass densities were generally lower for softwoods than for hardwoods; ca. 40% of all counties had softwood forests with biomass densities between 75 and 125 Mg ha-1. Highest amounts of forest biomass were located in the Northern Lake states, mountain areas of the Mid-Atlantic states, and parts of New England, and lowest amounts in the Midwest states. The total biomass for all eastern forests for the late 1980s was estimated at 20.5 Pg, 80% of which was in hardwood forests. Maps (Brown et al. 1999) provided a visual representation of the pattern of forest biomass densities and pools over space that are useful for forest managers and decision makers, and for verification of vegetation models.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","cog","earth observation","elevation","geospatial","ice","land cover","lidar","netcdf","precipitation","radar","satellite imagery","stac","weather"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Leaf_Photosynthesis_Traits_1224 v1 - This global data set of photosynthetic rates and leaf nutrient traits was compiled from a comprehensive literature review. It includes estimates of Vcmax (maximum rate of carboxylation), Jmax (maximum rate of electron transport), leaf nitrogen content (N), leaf phosphorus content (P), and specific leaf area (SLA) data from both experimental and ambient field conditions, for a total of 325 species and treatment combinations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Leaf_Photosynthesis_Traits/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Leaf_Carbon_Nutrients_1106 v1 - This data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Leaf_Carbon_Nutrients/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LAI_Woody_Plants_1231 v1 - This data set provides global leaf area index (LAI) values for woody species. The data are a compilation of field-observed data from 1,216 locations obtained from 554 literature sources published between 1932 and 2011.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/LAI_Woody_Plants/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Litter_Carbon_Nutrients_1244 v1 - Measurement data of aboveground litterfall and littermass and litter carbon, nitrogen, and nutrient concentrations were extracted from 685 original literature sources and compiled into a comprehensive database to support the analysis of global patterns of carbon and nutrients in litterfall and litter pools. Data are included from sources dating from 1827 to 1997.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Global_Litter_Carbon_Nutrients/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Amazon_ForestStructure_LIDAR_2412 v1 - This dataset provides initial condition files for initializing the Ecosystem Demography Model (ED2). This dataset holds regional forest structure characteristics across the Brazilian Amazon that were derived from 545 airborne lidar transects (300 x 12500 m each) acquired during the Amazon Biomass Estimation Project (EBA2016) campaign in 2016.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Amazon_ForestStructure_LIDAR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Non-Forest_Trees_Sahara_Sahel_1832 v1 - This dataset provides georeferenced polygon vectors of individual tree canopy geometries for dryland areas in West African Sahara and Sahel that were derived using deep learning applied to 50-cm resolution satellite imagery. More than 1.8 billion non-forest trees (i.e., woody plants with a crown size over 3 m2) over about 1.3 million km2 were identified from panchromatic and pansharpened normalized difference vegetation index (NDVI) images at 0.5-m spatial resolution using an automatic tree detection framework based on supervised deep-learning techniques.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Non-Forest_Trees_Sahara_Sahel/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LULC_Nigeria_Ethiopia_SAfrica_2367 v1 - This dataset provides a two-tier annual Land Use (LU) and Urban Land Cover (LC) product suite over three African countries, Ethiopia, Nigeria, and South Africa, across a 5-year period of 2016-2020. Remote sensing data sources were used to create 30-m resolution LU maps (Tier-1), which were then utilized to delineate urban boundaries for 10-m resolution LC classes (Tier-2).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/LULC_Nigeria_Ethiopia_SAfrica/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"BASIN_TCP_963 v1 - This data set reports stable isotope ratio data of CO2 (13C/12C and 18O/16O) associated with photosynthetic and respiratory exchanges across the biosphere-atmosphere boundary. Measurements were made at selected AmeriFlux sites including Harvard Forest, Howland Forest, Rannells Flint Hills Prairie, Niwot Ridge Forest, and the Wind River Canopy Crane Site, which span the dominant ecosystem types of the United States.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/BASIN_TCP/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"biomass_allocation_703 v1 - This data set of leaf, stem, and root biomass for various plant taxa was compiled from the primary literature of the 20th century with a significant portion derived from Cannell (1982). Recent allometric additions include measurements made by Niklas and colleagues (Niklas, 2003).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/biomass_allocation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LaSelva_Land_Use_1312 v1 - This data set contains land-use, canopy height, and aboveground carbon estimates derived from LiDAR data collected at La Selva Biological Station in Costa Rica in March 1998 and March 2005. The data are provided as GeoTIFFs (.tif) of 100-m (1-ha) resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/LaSelva_Land_Use/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Semi-Arid_Tree_Carbon_50cm_2117 v1 - This dataset provides allometrically-estimated carbon stocks of 9,947,310,221 tree crowns derived from 50-cm resolution satellite images within the 0 to 1000 mm/year precipitation zone of Africa north of the equator and south of the Sahara Desert. These data are presented in GeoPackage (.gpkg) format and are summarized in Cloud-Optimized GeoTIFF (COG) format.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Semi-Arid_Tree_Carbon_50cm/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"african_woody_savanna_850 v1 - This data set includes the soil and vegetation characteristics, herbivore estimates, and precipitation measurement data for the 854 sites described and analyzed in Sankaran et al., 2005. Savannas are globally important ecosystems of great significance to human economies.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/african_woody_savanna/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Decadal_LULC_India_1336 v1 - This data set provides land use and land cover (LULC) classification products at 100-m resolution for India at decadal intervals for 1985, 1995 and 2005. The data were derived from Landsat 4 and 5 Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Multispectral (MSS) data, India Remote Sensing satellites (IRS) Resourcesat Linear Imaging Self-Scanning Sensor-1 or III (LISS-I, LISS-III) data, ground truth surveys, and visual interpretation.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Decadal_LULC_India/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Siberian_Larch_Stand_Age_1364 v1 - This data set provides mapped estimates of the stand age of young (less than 25 years old) larch forests across Siberia from 1989-2012 at 30-m resolution. The age estimates were derived from Landsat-based composites and tree cover for years 2000 and 2012 developed by the Global Forest Change (GFC) project and the stand-replacing fire mapping (SRFM) data set.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Siberian_Larch_Stand_Age/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Young_Russian_Forest_Map_1330 v1 - This data set provides the distribution of young forests (forests less than 27 years of age) and their estimated stand ages across the full extent of Russia at 500-m resolution for the year 2012. The distribution of young forests was modeled with MODIS 500-m records for 12- to 27-year-old forests and augmented with the 0- to 11-year-old forest distribution as aggregated from 30 m resolution contemporary Landsat imagery.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Young_Russian_Forest_Map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"litter_decomp_651 v1 - The results of published and unpublished experiments investigating the impacts of elevated carbon dioxide on the chemistry (nitrogen and lignin concentration) of leaf litter and the decomposition of plant tissues are assembled in a format appropriate for statistical meta-analysis of the effect of carbon dioxide. The synthesis originated from a workshop, Litter Quality and Decomposition under Elevated CO2, held in Capri, Italy, September 1998, under the auspices of Global Change and Terrestrial Ecosystems project (GCTE) and the European COST network.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/litter_decomp/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Boreal_Fire_Severity_Metrics_1520 v1 - This data set provides products characterizing immediate and longer-term ecosystem changes from fires in the circumpolar boreal forests of Northern Eurasia and North America. The data include fire intensity (fire radiative power; FRP), increase in spring albedo, decrease in tree cover, normalized burn ratio, normalized difference vegetation index, and land surface temperature, as well as three derived fire metrics: crown scorch, vegetation destruction, and fire-induced tree mortality.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Boreal_Fire_Severity_Metrics/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Forest_Inventory_Acre_Brazil_1654 v1 - This data set provides measurements of diameter at breast height (DBH) and species identification at four forest sites in the eastern side of Acre, Brazil including Bonal (A), Catuaba (B), Humaita (C) and Transacreana (D). The inventory locations include forests burned in 2005 and 2010 and nearby unburned areas.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Forest_Inventory_Acre_Brazil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Forest_Inventory_Data_Brazil_1563 v1 - This dataset provides measurements for diameter at breast height (DBH) and species identification of trees for inventories taken at five tropical forest sites in Acre state, Brazil, in the southwestern Amazon region. The sites included one in a forest reserve (Reserva Bonal) and four within forest fragments situated on private property.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Forest_Inventory_Data_Brazil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Forest_Diversity_CAF_WesternUS_2481 v1 - This dataset holds maps of forest structure and structural diversity metrics at a range of spatial scales (1, 5, 10, 15, 20 and 25 km) derived from NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar data collected between April 2019 and March 2023. It also holds the airborne laser scanning (ALS) data that provides simulated GEDI waveforms and was used to evaluate the GEDI-derived metrics.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Forest_Diversity_CAF_WesternUS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"geoecology_R1_656 v1 - The Geoecology database is a compilation of environmental data for the period 1941 to 1981. The Geoecology database contains selected data on terrain and soils, water resources, forestry, vegetation, agriculture, land use, wildlife, air quality, climate, natural areas, and endangered species.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/geoecology_R1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Biomass_1950-2010_1296 v1 - This data set provides global forest area, forest growing stock, and forest biomass data at 1-degree resolution for the period 1950-2010. The data set is based on a compilation of forest area and growing stock data reported in international assessments performed by FAO, MCPFE (now Forest Europe), and UNECE.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Global_Biomass_1950-2010/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Clumping_Index_1531 v1 - This dataset provides global clumping index (CI) data for 2006 derived from the MODIS Bidirectional Reflectance Distribution Function (BRDF) data product. Clumping index is a key structural parameter of plant canopies which represents the degree of foliage grouping within distinct canopy structures relative to a random distribution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Global_Clumping_Index/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Maps_C_Density_2010_1763 v1 - This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at a 300-m spatial resolution. The aboveground biomass map integrates land-cover specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Global_Maps_C_Density_2010/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"root_biomass_658 v1 - A global data set of root biomass, rooting profiles, and concentrations nutrients in roots was compiled from the primary literature and used to study distributions of root properties. This data set consists of estimates of fine root biomass and specific area, site characteristics, and source references associated with two papers (Jackson et al.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/root_biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"root_nutrients_659 v1 - Nutrient measurements for fine roots were compiled from 56 published studies providing information on 372 different combinations of species, root diameter, rooting depths, and soils at a variety of locations. The compilation was used to examine dynamics of 14 nutrients, including translocation properties of roots of varying size and status.Fine roots are an important source and sink for nutrients in terrestrial biogeochemistry.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/root_nutrients/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"root_profiles_660 v1 - Rooting depths were estimated from a global database of root profiles that was assembled from the primary literature to study relationships of abiotic and biotic factors associated with belowground vegetation structure. Variables used to characterize belowground vegetation structure include the depths above which 50% of all roots and 95% of all roots are located in the profile.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/root_profiles/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"root_turnover_661 v1 - Estimates of root turnover rates were calculated from measurements of live root standing crop and belowground net primary production (BNPP) compiled from the primary literature. Vegetation characteristics, soil properties, and climate conditions were associated with turnover rates to examine patterns and controls for biomes worldwide.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/root_turnover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"fire_emissions_v4_R1_1293 v4.1 - This dataset provides global estimates of monthly burned area, monthly emissions and fractional contributions of different fire types, daily or 3-hourly fields to scale the monthly emissions to higher temporal resolutions, and data for monthly biosphere fluxes. The data are at 0.25-degree latitude by 0.25-degree longitude spatial resolution and are available from June 1995 through 2016, depending on the dataset.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/fire_emissions_v4_R1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"forest_carbon_flux_949 v1 - A comprehensive global database has been assembled to quantify CO2 fluxes and pathways across different levels of integration (from photosynthesis up to net ecosystem production) in forest ecosystems. The database fills an important gap for model calibration, model validation, and hypothesis testing at global and regional scales.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/forest_carbon_flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"HistoricalLai_584 v1 - Approximately 1000 published estimates of leaf area index (LAI) from nearly 400 unique field sites, covering the period 1932-2000, have been compiled into a single data set. LAI is a key parameter for global and regional models of biosphere/atmosphere exchange of carbon dioxide, water vapor, etc.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/HistoricalLai/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"FluxSat_GPP_FPAR_1835 v2 - This dataset provides global gridded daily estimates of gross primary production (GPP) and uncertainties at 0.05-degree resolution for the period 2000-03-01 to the recent past. The GPP was derived from the MODerate-resolution Imaging Spectroradiometer (MODIS) instruments on the NASA Terra and Aqua satellites using the MCD43C4v006 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectances (NBAR) product as input to neural networks that were used to globally upscale GPP estimated from selected FLUXNET 2015 eddy covariance tower sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/FluxSat_GPP_FPAR/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Monthly_GPP_1789 v1 - This dataset provides global monthly average gross primary productivity (GPP; g carbon/m2/d) modeled at 8 km spatial resolution for each of the 35 years from 1982-2016. GPP is based on the well-known Monteith light use efficiency (LUE) equation but was improved with optimized spatially and temporally explicit LUE values derived from selected FLUXNET tower site data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Global_Monthly_GPP/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Mean_Seasonal_LAI_1653 v1 - This dataset provides a global 0.25 degree x 0.25 degree gridded monthly mean leaf area index (LAI) climatology as averaged over the period from August 1981 to August 2015. The data were derived from the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g version 2, a bi-weekly data product from 1981 to 2015 (GIMMS-LAI3g version 2).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Mean_Seasonal_LAI/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Global_Veg_Greenness_GIMMS_3G_2187 v1 - This dataset holds the Global Inventory Modeling and Mapping Studies-3rd Generation V1.2 (GIMMS-3G+) data for the Normalized Difference Vegetation Index (NDVI). NDVI was based on corrected and calibrated measurements from Advanced Very High Resolution Radiometer (AVHRR) data with a spatial resolution of 0.0833 degree and global coverage for 1982 to 2022.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Global_Veg_Greenness_GIMMS_3G/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GEDI_ICESAT2_Global_Veg_Height_2294 v1 - This dataset provides global rasters of relative height metrics for vegetation from Global Ecosystem Dynamics Investigation (GEDI) L2A data and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) L3A ATL08 data at 100-, 200-, 500-, and 1000-m spatial resolutions. The metrics include the relative heights RH98, RH90, RH75, and RH50, corresponding to the height at which the respective 98th, 90th, 75th, and 50th percentile of returned energy is reached relative to the ground.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/GEDI_ICESAT2_Global_Veg_Height/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MatthewsVegetation_419 v1 - The global vegetation type data of 1 x 1 degree latitude and longitude resolution were designed for use in studies of climate and climate change. Vegetation data were compiled in digital form from approximately 100 published sources.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/MatthewsVegetation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Gridded_Biomass_Africa_1777 v1 - This dataset provides maps of woody (tree and shrub) cover and biomass across Sub-Saharan Africa at a resolution of 1 km for the period 2000-2004. Canopy cover observations and remote-sensing data related to woody vegetation were used to predict woody cover across Africa.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Gridded_Biomass_Africa/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GEDI_HighQuality_Shots_Rasters_2339 v1 - This dataset consists of near-global, analysis-ready, multi-resolution gridded vegetation structure metrics derived from NASA Global Ecosystem Dynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m diameter lidar footprints. This dataset provides a comprehensive representation of near-global vegetation structure that is inclusive of the entire vertical profile, based solely on GEDI lidar, and validated with independent data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/GEDI_HighQuality_Shots_Rasters/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GEDI_Fusion_Structure_2236 v1 - This dataset provides eight GEDI forest structure metrics relevant to wildlife habitat modeling and biodiversity assessments at 30-m resolutions across Washington, Oregon, Idaho, Montana, Wyoming, and Colorado. The metrics characterize canopy height, strata densities, and canopy cover.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/GEDI_Fusion_Structure/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"AVIRIS-NG_Data_Idaho_1533 v1 - This dataset provides surface reflectance measured by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument during flights over research sites in Idaho and California in 2014 and 2015. AVIRIS-NG measures reflected radiance at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/AVIRIS-NG_Data_Idaho/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"New_Hampshire_Landcover_1305 v1 - The New Hampshire Geographically Referenced Analysis and Information Transfer System (GRANIT) land cover data set provides a land cover and land use product at 30-m resolution with 23 individual classes across the state. The classification is based largely on the analysis of 12 Landsat Thematic Mapper (TM and ETM+) images.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/New_Hampshire_Landcover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Phenology_AmeriFlux_Neon_Sites_2033 v1 - This land surface phenology (LSP) dataset provides spatially explicit data related to the timing of phenological changes such as the start, peak, and end of vegetation activity, vegetation index metrics and associated quality assurance flags. The data are for the growing seasons of 2017-2021 for 10-km x 10-km windows centered over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Phenology_AmeriFlux_Neon_Sites/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Phenology_Deciduous_Forest_1570 v1 - This dataset provides Landsat phenology algorithm (LPA) derived start and end of growing seasons (SOS and EOS) at 500-m resolution for deciduous and mixed forest areas of 75 selected Landsat sidelap regions across the Eastern United States and Canada. The data are a 30-year time series (1984-2013) of derived spring and autumn phenology for forested areas of the Eastern Temperate Forest, Northern Forest, and Taiga ecoregions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Phenology_Deciduous_Forest/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Forested_Areas_Amazonas_Brazil_1515 v1 - This data set provides LiDAR point clouds and digital terrain models (DTM) from surveys over the K34 tower site in the Cuieiras Biological Reserve, over forest inventory plots in the Adolpho Ducke Forest Reserve, and over sites of the Biological Dynamics of Forest Fragments Project (BDFFP) in Rio Preto da Eva municipality near Manaus, Amazonas, Brazil during June 2008. The surveys encompass the K34 eddy flux tower managed through the Large-scale Biosphere-Atmosphere Experiment in Amazonia, forest inventory plots managed by the Programa de Pesquisa em Biodiversidade (PPBio), and sites manage...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Forested_Areas_Amazonas_Brazil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Forested_Areas_Para_Brazil_1514 v1 - This data set provides LiDAR point clouds and digital terrain models (DTM) from surveys over the Tapajos National Forest in Belterra municipality, Para, Brazil during late June and early July 2008. The surveys encompass the K67 and K83 eddy flux towers and a deforestation chronosequence managed through the Large-Scale Biosphere-Atmosphere Experiment in Amazonia providing long-term flux measurements of carbon dioxide.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Forested_Areas_Para_Brazil/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LiDAR_Veg_Ht_Idaho_1532 v1 - This dataset provides the point cloud data derived from small footprint waveform LiDAR data collected in August 2014 over Reynolds Creek Experimental Watershed and Hollister in southern Idaho. The LiDAR data have been georeferenced, noise-filtered, and corrected for misalignment for overlapping flight lines and are provided in 1 km tiles.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/LiDAR_Veg_Ht_Idaho/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Eurasia_Biomass_1278 v1 - This data set provides estimates of aboveground biomass (AGB) for defined land cover types within World Wildlife Fund (WWF) ecoregions across the boreal biome of eastern and western Eurasia, roughly between 50 and 70 degrees N. The study focused on within-growing-season data, i.e.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Eurasia_Biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GSMNP_Vegetation_Structure_R1_1286 v1.2 - This dataset provides multiple-return LiDAR-derived vegetation canopy structure at 30-meter spatial resolution for the Great Smoky Mountains National Park (GSMNP). Canopy characteristics were analyzed using high resolution three-dimensional point cloud measurements gathered between February-April 2011 for Tennessee and during March-April 2005 for North Carolina sections of the park.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/GSMNP_Vegetation_Structure_R1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"white_model_parameters_652 v1 - Ecosystem simulation models use descriptive input parameters to establish the physiology, biochemistry, structure, and allocation patterns of vegetation functional types, or biomes. For single-stand simulations, it is possible to measure required data, but as spatial resolution increases, data availability decreases.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/white_model_parameters/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"GIMMS3g_NDVI_Trends_1275 v1 - This data set provides normalized difference vegetation index (NDVI) data for the arctic growing season derived primarily with data from Advanced Very High Resolution Radiometer (AVHRR) sensors onboard several NOAA satellites over the years 1982 through 2012. The NDVI data, which show vegetation activity, were averaged annually for the arctic growing season (GS; June, July and August).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/GIMMS3g_NDVI_Trends/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Land_Use_Harmonization_V1_1248 v1 - These data represent fractional land use and land cover patterns annually for the years 1500 - 2100 for the globe at 0.5-degree (~50-km) spatial resolution. Land use categories of cropland, pasture, primary land, secondary (recovering) land, and urban land, and underlying annual land-use transitions, are included.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Land_Use_Harmonization_V1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LUH2_GCB2019_1851 v1 - This dataset, referred to as LUH2-GCB2019, includes 0.25-degree gridded, global maps of fractional land-use states, transitions, and management practices for the period 0850-2019. The LUH2-GCB2019 dataset is an update to the previous Land-Use Harmonization Version 2 (LUH2-GCB) datasets prepared as required input to land models in the annual Global Carbon Budget (GCB) assessments, including land-use change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, afforestation, and crop rotations.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/LUH2_GCB2019/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Land_Use_Harmonization_V2_1721 v1 - This dataset provides 0.25-degree gridded, global, annual estimates of fractional land use and land cover patterns for the period 2015-2100, designed to support the ISIMIP2b effort to assess the impacts of 1.5 Deg Celcius global warming. Land use types, land use transitions, and cropland estimates of area fraction are provided and include detailed separation of primary and secondary natural vegetation into forest and non-forest sub-types, pasture into managed pasture and rangeland, and cropland into multiple crop functional types; all transitions between land use states per grid cell per ye...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Land_Use_Harmonization_V2/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"MangroveExtent_Landsat_MSS_2329 v1 - This dataset includes a regional map of mangrove extent for Myanmar, Thailand, and Cambodia for the period of 1972-1977. The map was developed from Landsat 1-2 MSS Collection 1 Tier 2 imagery.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/MangroveExtent_Landsat_MSS/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"US_MODIS_NDVI_1299 v3 - This data set provides Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, smoothed and gap-filled, for the conterminous US for the period 2000-01-01 through 2015-12-31. The data were generated using the NASA Stennis Time Series Product Tool (TSPT) to generate NDVI data streams from the Terra satellite (MODIS MOD13Q1 product) and Aqua satellite (MODIS MYD13Q1 product) instruments.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/US_MODIS_NDVI/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"LAI_Africa_2325 v1 - This dataset provides leaf area index (LAI) estimates for Sub-Saharan Africa for woody, herbaceous, and aggregate vegetation types. The estimates were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4 and the native MODIS LAI product (MCD15A2H Version 6.1), which provides LAI measurements every 8 days at 500-m pixel size.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/LAI_Africa/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"NA_MODIS_Surface_Biophysics_1210 v1 - This data set provides MODIS-derived surface biophysical climatologies of bidirectional distribution function (BRDF), BDRF/albedo, land surface temperature (LST), leaf area index (LAI), and evapotranspiration (ET) as separate files for each of the MODIS land cover types, and four radiative forcing data files for four scenarios of potential vegetation shifts in North America. Each biophysical variable has temporal periods that represent the average of all 8-day periods from the years 2000 – 2012.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/NA_MODIS_Surface_Biophysics/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"UAV_Imagery_BigLakeTrail_1834 v1 - This dataset provides multispectral reflectance imagery (green at 550 nm, red at 660 nm, red edge at 735 nm, and near-infrared at 790 nm), normalized difference vegetation index (NDVI), and digital surface and terrain models for a 0.5 km2 area surrounding Big Trail Lake (BTL) in the Goldstream Creek Valley north of Fairbanks, Alaska. These high spatial resolution maps (13 cm x 13 cm) were generated by unmanned aerial vehicle (UAV) imagery collected on 2019-08-04.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/UAV_Imagery_BigLakeTrail/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Xingu_Albedo_Radiation_1622 v1 - This dataset provides daily average land surface net radiation (Rnet) as an 8-day time series at approximately 0.5 km resolution for the upper Xingu River Basin in Mato Grosso, Brazil, from 2000-02-18 to 2012-11-16. The parameters needed to calculate Rnet, including albedo, downward shortwave radiation (RSnet), upward longwave radiation (RLnet[up]) and downward longwave radiation (RLnet[down]) were derived from MODIS products (MOD43A3, MOD11A2, MOD08E3) and local weather station data.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Xingu_Albedo_Radiation/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ForestHt_Biomass_GEDI_TDX_2298 v1 - This dataset includes maps of canopy height and aboveground biomass at spatial resolutions of 25 m and 100 m for Mexico, Gabon, French Guiana, and the Amazon Basin. The GEDI-TanDEM-X (GTDX) fusion maps were created by combining data from NASA's Global Ecosystem Dynamics Investigation (GEDI) Version 2 footprint data (from 2019-04-18 to 2021-08-18) and TanDEM-X (abbreviated as TDX) Interferometric Synthetic Aperture Radar (InSAR) images (from 2011-01-06 to 2020-12-31).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/ForestHt_Biomass_GEDI_TDX/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Phenocam_Images_V2_1689 v2 - This dataset provides a time series of visible-wavelength digital camera imagery collected through the PhenoCam Network at each of 393 sites predominantly in North America from 2000-2018. The raw imagery was used to derive information on phenology, including time series of vegetation color, canopy greenness, and phenology transition dates for the PhenoCam Dataset v2.0.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Phenocam_Images_V2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PhenoCam_V2_1674 v2 - This data set provides a time series of vegetation phenological observations for 393 sites across diverse ecosystems of the world (mostly North America) from 2000-2018. The phenology data were derived from conventional visible-wavelength automated digital camera imagery collected through the PhenoCam Network at each site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/PhenoCam_V2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Phenocam_Images_V3_2364 v3 - This dataset provides a time series of visible-wavelength digital camera imagery collected through the PhenoCam Network at each of 738 sites across diverse ecosystems of the world (mostly North America) from 2000-2023. Vegetation types such as deciduous broadleaf forests, grasslands, evergreen needleleaf forests, and agriculture are the best-represented.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Phenocam_Images_V3/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PhenoCam_V3_2389 v3 - This dataset provides vegetation phenological observations for 738 sites across diverse ecosystems of the world (mostly North America) from 2000 to 2023. The phenology data were derived from conventional visible-wavelength automated digital camera imagery collected through the PhenoCam Network at each site.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/PhenoCam_V3/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Landsat8_Sentinel2_Phenocam_2248 v1 - This dataset provides a reference of land surface phenology (LSP) at 30-m pixels for 78 regions of 10 x 10 km2 across a wide range of ecological and climatic regions in North America during 2019 and 2020. The data were derived by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near- surface PhenoCam time series (hereafter called HP-LSP).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Landsat8_Sentinel2_Phenocam/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"WhitePhenoregions_799 v1 - The overall purpose in this research was to identify the regions of the world best suited for long-term monitoring of biospheric responses to climate change, i.e. monitoring land surface phenology.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/WhitePhenoregions/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"King_Rim_Fire_Analysis_1288 v1 - This data set provides high-resolution surface reflectance, thermal imagery, burn severity metrics, and LiDAR-derived structural measures of forested areas in the Sierra Nevada Mountains, California, USA, collected before and after the August 2013 Rim and September 2014 King mega forest fires. Pre-fire data were paired with post-fire collections to assess pre- and post-fire landscape characteristics and fire severity.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/King_Rim_Fire_Analysis/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rlc_land_cover_689 v1 - This dataset is a 15-kilometer resolution land cover map for the land area of the Former Soviet Union. There are sixty land cover classes distinguished in this dataset, of which 38 are forest cover classes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/rlc_land_cover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rlc_landcover_far_east_690 v1 - This data set is a 1-kilometer resolution land cover map for the land area of the Primor'ye and Southern Khabarovsk Regions, in the Russian Far East, based on 1990 NOAA AVHRR data. Labeling of land cover classes depended upon the Russian 1990 Forest Cover Map (Garsia, 1990), the analyst's experience with AVHRR data, and Russian data sources.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/rlc_landcover_far_east/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rlc_forest_map_1990_691 v1 - This data set is a 1:2.5 million scale forest cover map for the land area of the Former Soviet Union that was completed in 1990 (Garsia 1990). There are forty-five classes distinguished in this data set, of which 38 are forest cover classes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/rlc_forest_map_1990/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rlc_forest_map_1973_692 v1 - This data set is a 1:15 million scale forest cover map for the land area of the Former Soviet Union. Twenty-two land cover classes are distinguished, of which 20 are forest cover classes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/rlc_forest_map_1973/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rlc_forest_map_krasnoyarsk_693 v1 - This dataset is a 1:2 million scale forest cover map for the land area of the Krasnoyarsk Region, Russia. Thirty-two land cover classes are distinguished.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/rlc_forest_map_krasnoyarsk/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rlc_fire_images_russia_694 v1 - This data set is made up of images of forest fires in Russia from NOAA's Operational Significant Event Imagery (OSEI) archive (http://www.osei.noaa.gov) for the 1998 and 1999 seasons. OSEI fire products include multichannel color composite imagery of wildfire and controlled burn events.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/rlc_fire_images_russia/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rlc_fire_sumpt_695 v1 - This dataset is derived from Russian forest fire imagery from the National Forest Fire Center of Russia archive that was collected by the Center of Remote Sensing, Institute of Solar Terrestrial Physics, Irkutsk, Russia for the 1998 and 1999 fire seasons. The data are vector (point) maps of forest fire locations (1998 and 1999) in ArcView shapefile format.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/rlc_fire_sumpt/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rlc_forest_carbon_696 v1 - This dataset is a 1:15 million scale map of forest stand carbon for the land area of Russia (Stone et al., 2000). The objective was to create a first approximation of the forest stand carbon reserves of Russia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/rlc_forest_carbon/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rlc_world_forest_map_697 v1 - This data set is the Former Soviet Union (FSU) portion of the Generalized World Forest Map (WCMC, 1998), a 1-kilometer resolution generalized forest cover map for the land area of the Former Soviet Union. There are five forest classes in the original global generalized map.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/rlc_world_forest_map/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rlc_vector_data_698 v1 - This data set consists of roads, drainage, railroads, utilities, and population center information in readily usable vector format for the land area of the Former Soviet Union. The purpose of this dataset was to create a completely intact vector layer which could be readily used to aid in mapping efforts for the area of the FSU.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/rlc_vector_data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rlc_admin_boundaries_699 v1 - This data set of state and regional boundaries was derived from the 1:3 million scale administrative boundaries (ESRI, 1998) for the land area of the Former Soviet Union. There are 162 administrative regions distinguished in this data set.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/rlc_admin_boundaries/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"rlc_vegetation_1990_700 v1 - This dataset is a 1:4 million scale vegetation map for the land area of the Former Soviet Union. Three hundred seventy-three cover classes are distinguished, of which nearly 145 are forest cover-related classes.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/rlc_vegetation_1990/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Russian_Forest_Disturbance_1294 v1 - This data set provides Boreal forest disturbance maps at 30-m resolution for 55 selected sites across Northern Eurasia within the Russian Federation. Disturbance events were derived from selected high-quality multi-year time series of Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images (stacks) over the 1984 to 2000 time period.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Russian_Forest_Disturbance/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Idaho_field_shrub_data_1503 v1 - This dataset provides the results of the characterization of shrubland vegetation at two study areas in southern Idaho, USA: the Reynolds Creek Experimental Watershed (RCEW) and Hollister. Data were collected in September and October 2014.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Idaho_field_shrub_data/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"SiB3_Carbon_Flux_909 v1 - The Simple Biosphere Model, Version 3 (SiB3) was used to produce a global data set of hourly carbon fluxes between the atmosphere and the terrestrial biosphere for the years 1998-2006. This data set represents the global net ecosystem exchange (NEE) of carbon between the atmosphere and the terrestrial biosphere; specifically, the flux of CO2 between the planetary boundary layer (PBL) and the surface vegetation layer.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/SiB3_Carbon_Flux/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Siberian_Biomass_Wildfire_1321 v1 - This data set provides 30-meter resolution mapped estimates of Cajander larch (Larix cajanderi) aboveground biomass (AGB), circa 2007, and a map of burn perimeters for 116 forest fires that occurred from 1966-2007. The data cover ~100,000 km2 of the Kolyma River Basin in northeastern Siberia, Sakha Republic, Russia.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Siberian_Biomass_Wildfire/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"ARID_White_Paper_V1.1_2408 v1.1 - This dataset provides the final report from the Adaptation and Response in Drylands (ARID) scoping study. ARID is one of the two scoping studies funded by NASA in 2023 to identify the scientific questions and develop the initial study design and implementation concept for a new NASA Terrestrial Ecology field campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/ARID_White_Paper_V1.1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"PANGEA_White_Paper_V1.1_2405 v1.1 - This dataset provides the final report from the PAN tropical investigation of bioGeochemistry and Ecological Adaptation (PANGEA) scoping study. PANGEA is one of the two scoping studies funded by NASA in 2023 to identify the scientific questions and develop the initial study design and implementation concept for a new NASA Terrestrial Ecology field campaign.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/PANGEA_White_Paper_V1.1/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Taiga_Tundra_Tree_Cover_1218 v1 - This data set provides a map of selected areas with defined tree canopy cover over the circumpolar taiga-tundra ecotone (TTE). Canopy cover was derived from the 500-meter MODIS Vegetation Continuous Fields (VCF) product as averaged over six years from 2000-2005 and processed as described in Ranson et al.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Taiga_Tundra_Tree_Cover/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Forest_Inventory_Tapajos_1552 v1 - This dataset provides tree inventory, tree height, diameter at breast height (DBH), and estimated crown measurements from 30 plots located in the Tapajos National Forest, Para, Brazil collected in September 2010. The plots were located in primary forest, primary forest subject to reduced-impact selective logging (PFL) between 1999 and 2003, and secondary forest (SF) with different age and disturbance histories.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Forest_Inventory_Tapajos/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"Tree_Mortality_Western_US_1512 v1.1 - This dataset provides annual estimates of tree mortality due to fires and bark beetles from 2003 to 2012 on forestland in the continental western United States. Tree mortality was estimated at 1-km spatial resolution by combining tree aboveground carbon (AGC) and disturbance datasets derived largely from remote sensing.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/Tree_Mortality_Western_US/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"VHR_Urban_Land_Cover_Maps_2413 v1 - This dataset consists of very high resolution urban land cover maps for two African cities, Mekelle, Ethiopia and Polokwane, South Africa for 2020. Maps were generated from Planet SuperDove satellite imagery at 3.125-m spatial resolution, and Worldview-3 satellite imagery (Maxar Techologies) at two spatial resolutions, 2 m for multispectral imagery and 0.5-m spatial resolution for pansharpened imagery.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/VHR_Urban_Land_Cover_Maps/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"WAVeTrends_1738 v1 - The WAVeTrends dataset is a 0.05 degree (5.55 km) vegetation change product, spanning the West African Sudano-Sahel region. It provides pixel-wise information on concurrent woody and herbaceous vegetation trends over a 32-year period (1982-2013).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/WAVeTrends/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"woody_biomass_657 v1 - Estimates of the woody biomass density and pools were derived at the county scale of resolution of all forests of the eastern United States using new approaches for converting inventoried wood volume to estimates of above and belowground biomass. Biomass density and pools were estimated from the US Department of Agriculture Forest Service, Forest Inventory and Analysis database on growing stock volume by forest type and stand size-class.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/global_vegetation/woody_biomass/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-vegetation","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA VEMAP Project","Description":"The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) is an ongoing multiinstitutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetationtype distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing. The VEMAP data set includes three georeferencing and three cell area variables. Data Citation: This data set should be cited as follows: Kittel, T. G. F., N. A. Rosenbloom, T. H. Painter, D. S. Schimel, H. H. Fisher, A. Grimsdell, VEMAP Participants, C. Daly, and E. R. Hunt, Jr. 2002. VEMAP Phase I Database, revised. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.\n<br>\n\n#### vemap-1_VEMAP1_CDROM_566\nThe VEMAP 1: Model Input Database CD-ROM ISO image contains long-term data that were used as input in comparing models during Phase 1 of the Vegetation Ecosystem Modeling and Analysis Project. Compiled and model-generated data sets of long-term mean climate, soils, vegetation, and climate change scenarios for the conterminous United States. Dates of the data sets range between 1895 and 1996. The data are gridded at 0.5 degree latitude by 0.5 degree longitude.\n<br>\n\n#### vemap-1_results_731\nThe Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) was a multi-institutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetation type distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing.Selected variable output results from the VEMAP Phase I modeling exercise are now available for several combinations of biogeochemistry and biogeography models and climate change scenarios through the ORNL DAAC. For a description of the models and climate scenarios employed in the VEMAP 1 project and a discussion of the results please refer to the following publication: VEMAP Members. 1995. Vegetation/Ecosystem Modeling and Analysis Project: Comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate change and CO2 doubling. Global Biogeochem. Cycles 9:407-437.\n<br>\n\n#### vemap-1_scenario_223\nThe Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) is an ongoing multiinstitutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetation type distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing. Climate scenarios from eight climate change experiments are included in the data set. Seven of these experiments are from atmospheric general circulation model (GCM) 1xCO2 and 2xCO2 equilibrium runs. These GCMs were implemented with a simple \"mixed-layer\" ocean representation that includes ocean heat storage and vertical exchange of heat and moisture with the atmosphere, but omits or specifies (rather than calculates) horizontal ocean heat transport. The eighth scenario is from a limited-area nested regional climate model (RegCM) experiment for the U.S. which was supported by the Model Evaluation Consortium for Climate Assessment (MECCA). The CCC and GFDL R30 runs are among the high resolution GCM experiments reported in IPCC (1990). Changes in monthly mean temperature and relative humidity were represented as differences (2xCO2 climate value - 1xCO2 climate value) and those for monthly precipitation, solar radiation, vapor pressure, and horizontal wind speed as change ratios (2xCO2 climate value/1xCO2 climate value). GCM grid point change values were derived from archives at the National Center for Atmospheric Research (NCAR; Jenne 1992) and spatially interpolated to the 0.5 degree VEMAP grid. Wind speed changes are for the lowest model level. For GISS runs, we calculated winds from vector components and then determined the change ratio. Values from the 60-km RegCM grid were reprojected to the 0.5 degree grid. Vapor pressure (and relative humidity) were not available for the CCC run; relative humidity changes were not determined for the RegCM experiment. A key issue in the generation of altered climates based on climate model output is the strong possibility of physical inconsistencies in the new climates. Change ratios from the NCAR archive have an imposed upper limit of 5.0, providing some constraint on these changes. An exception is that the GISS wind speed change ratios do not have this limit imposed (most GISS wind speed change ratios were less than 5). For a discussion of the utility and limitations of using climate model experiment outputs for exploring ecological sensitivity to climate change, see Sulzman et al. (1995). The 8 climate model experiments are: CCC - Canadian Climate Centre (Boer, McFarlane, and Lazare 1992) GISS - Goddard Institute for Space Studies (Hansen et al. 1984) GFDL - Geophysical Fluid Dynamics Laboratory. Three experiments: (1) GFDL R15: R15 (4.5 degree by 7.5 degree grid) runs without Q- flux corrections (Manabe and Wetherald, 1987). (2) GFDL R15 Q-flux: R15 resolution (4.5 degree by 7.5 degree grid) runs with Q-flux corrections (Manabe and Wetherald 1990, Wetherald and Manabe 1990). (3) GFDL R30: R30 (2.22 degree by 3.75 degree grid) run with Q-flux corrections (Manabe and Wetherald 1990, Wetherald and Manabe 1990). OSU - Oregon State University (Schlesinger and Zhao 1989) UKMO - United Kingdom Meteorological Office (Wilson and Mitchell 1987) RegCM (MM4) - National Center for Atmospheric Research (NCAR) nested regional climate model (climate version of the Pennsylvania State University/NCAR mesoscale model MM4; Giorgi, Brodeur and Bates 1994). Conterminous U.S. simulations were on a 60-km interval grid and were driven by 1x and 2xCO2 equilibrium GCM runs (Thompson and Pollard 1995a, 1995b). 1x and 2xCO2 RegCM runs were each 3 years in length. Climate changes were based on averages for these runs. A complete users guide to the VEMAP Phase I database which includes more information about this data set can be found at ftp://daac.ornl.gov/data/vemap-1/comp/Phase_1_User_Guide.pdf. ORNL DAAC maintains additional information associated with the VEMAP Project. Data Citation: This data set should be cited as follows: Kittel, T. G. F., N. A. Rosenbloom, T. H. Painter, D. S. Schimel, H. H. Fisher, A. Grimsdell, VEMAP Participants, C. Daly, and E. R. Hunt, Jr. 2002. VEMAP Phase I Database, revised. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.\n<br>\n\n#### vemap-2_results_annual_766\nThe Vegetation-Ecosystem Modeling and Analysis Project (VEMAP) was a large, collaborative, multi-institutional, international effort whose goal was to evaluate the sensitivity of terrestrial ecosystem and vegetation processes to altered climate forcing and elevated atmospheric CO2. Phase 1 of the VEMAP project developed historical (1895-1993) data sets of observed climate, soils, and vegetation compatible with the requirements of ecosystem models and vegetation distribution models. See the VEMAP Phase 1 User's Guide for more information. Phase 2 developed historical (1895-1993) gridded data sets of climate (temperature, precipitation, solar radiation, humidity, and wind speed) and projected (1994-2100) gridded annual and monthly climate data sets using output from two climate system models (CCCma (Canadian Centre for Climate Modeling and Analysis) and Hadley Centre models). See the VEMAP Phase 2 User's Guide for additional background information.Two Phase 2 model experiments were run. First, a set of selected biogeochemical models and coupled biogeochemical-biogeographical models were run from 1895 to 1993 to compare model responses to the historical time series and current ecosystem biogeochemistry. Second, these same models were run on the projected 1994 to 2100 data to compare their ecological responses to transient scenarios of climate and atmospheric CO2 change. Model runs were performed for daily, monthly, and annual gridded data sets. The output of the annual model runs in VEMAP grid format are contained in this data set.The models investigated included five biogeochemical cycling models, which simulate plant production and nutrient cycles, but rely on a static land-cover type, and two dynamic global vegetation models (DGVMs) that combine biogeochemical cycling processes with dynamic biogeographical processes including succession and fire simulation.Biogeochemical Cycling ModelsBiome-BGC (BioGeochemical Cycles)CenturyCentury rxveg GTEC (Global Terrestrial Ecosystem Carbon Model)TEM (Terrestrial Ecosystem Model)Dynamic Global Vegetation ModelsLPJ (Lund-Potsdam-Jena MC1 (MC 5 modified Century)VEMAP 2 model intercomparison results have been published by Schimel et al.(2000), Bachelet et al. (2003) and Gordon and Famiglietti (2004). Related Data SetsAvailable on-line [http://www.daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive CenterVEMAP 2: U.S. ANNUAL CLIMATE, 1895-1993 VEMAP 2: U.S. MONTHLY CLIMATE, 1895-1993, VERSION 2 VEMAP 2: U.S. DAILY CLIMATE, 1895-1993 VEMAP 2: U.S. ANNUAL CLIMATE CHANGE SCENARIOS VEMAP 2: U.S. MONTHLY CLIMATE CHANGE SCENARIOS, VERSION 2 VEMAP 2: U.S. DAILY CLIMATE CHANGE SCENARIOS VEMAP 2: Annual Ecosystem Model Responses to U.S. Climate Change, 1994-2100\n<br>\n\n#### vemap-1_VEMAP_Alaska_1344\nThis data set provides the results of the development of The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 transient climate change scenarios for the state of Alaska, USA. The data include gridded monthly historical and future estimates of maximum and minimum temperature, solar radiation, vapor pressure, irradiance, relative humidity and potential evapotranspiration at 0.5-degree spatial resolution. Historical data are for the period 1922-1996; future estimates cover the period 1997-2100.\n<br>\n\n#### vemap-2_TSCENARIO_annual_570\nThe Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 has developed a number of transient climate change scenarios based on coupled atmosphere-ocean general circulation model (AOGCM) transient climate experiments. The purpose of these scenarios is to reflect time-dependent changes in surface climate from AOGCMs in terms of both (1) long-term trends and (2) changes in multiyear (3-5 yr) to decadal variability patterns, such as El Nino/Southern Oscillation (ENSO). Development of the data set is reported in Kittel et al. (1997). Scenarios have been derived from transient greenhouse gas experiments with sulfate aerosols from the Canadian Climate Center (CCC) and the Hadley Centre (HADCM2; Mitchell et al. 1995, Johns et al. 1997) accessed via the Climate Impacts LINK Project, Climatic Research Unit, University of East Anglia. Scenarios were developed for the following variables: total incident solar radiation, minimum and maximum temperature, vapor pressure, precipitation, relative humidity and mean monthly irradiance for the time periods January 1994 to approximately 2100. These data and the VEMAP 1 data (Kittel et al. 1995) were used to drive models in VEMAP Phase 2, the objectives of which are to compare time-dependent ecological responses of biogeochemical and coupled biogeochemical-biogeographical models to historical and projected transient forcings across the conterminous U.S. This data set of annual climate change scenarios was designed to be concatenated with the /VEMAP/vemap.html\">VEMAP 2: U.S. Annual Climate, 1895-1993 data set to create a single climate series from 1895 - ~2100. This data set is being made available for the U.S. National Assessment. Users are requested to confer with the NCAR VEMAP Data Group to ensure that the intended application of the data set is constistent with the generation and limiations of the data. For more information, refer to the VEMAP homepage. Data Citation The data set should be cited as follows: Kittel, T. G. F., N. A. Rosenbloom, C. Kaufman, J. A. Royle, C. Daly, H. H. Fisher, W. P. Gibson, S. Aulenbach, D. N. Yates, R. McKeown, D. S. Schimel, and VEMAP 2 Participants. 2001. VEMAP 2: U. S. Annual Climate Change Scenarios. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.\n<br>\n\n#### vemap-2_TCLIMATE_annual_571\nThe Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 has developed a data set of ~100-year gridded annual, monthly, and daily time series of climate for the conterminous United States that includes realistic interannual variability. This data set has been used to compare time-dependent ecological responses of biogeochemical and coupled biogeochemical-biogeographical models to historical time series and projected scenarios of climate, atmospheric CO2, and N-deposition. Development of the data set is reported in Kittel et al. (1997). As in the VEMAP 1 database, the historical data set has (1) annual, daily, and monthly versions; (2) physical consistency among variables on a daily basis; (3) consistency between climate and topography; and (4) needed input variables for VEMAP2 models (minimum and maximum temperature, precipitation, vapor pressure, and solar radiation) (Kittel et al. 1995). This historical annual climate data set was designed to be concatenated with the /VEMAP/vemap.html\">VEMAP 2: U.S. Annual Climate Change Scenarios data set to create a single climate series from 1895 - ~2100. Users are requested to confer with the NCAR VEMAP Data Group to ensure that the intended application of the data set is consistent with the generation and limitations of the data. Data Citation The data set should be cited as follows: Kittel, T. G. F., N. A. Rosenbloom, C. Kaufman, J. A. Royle, C. Daly, H. H. Fisher, W. P. Gibson, S. Aulenbach, D. N. Yates, R. McKeown, D. S. Schimel, and VEMAP 2 Participants. 2001. VEMAP 2: U. S. Annual Climate, 1895-1993. Available on line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.\n<br>\n\n#### vemap-2_TSCENARIO_daily_618\nThe Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 has developed a number of transient climate change scenarios based on coupled atmosphere-ocean general circulation model (AOGCM) transient climate experiments. The purpose of these scenarios is to reflect time-dependent changes in surface climate from AOGCMs in terms of both (1) long-term trends and (2) changes in multiyear (3-5 yr) to decadal variability patterns, such as El Nino/Southern Oscillation(ENSO). Development of the data set is reported in Kittel et al. (1997). Scenarios have been derived from transient greenhouse gas experiments with sulfate aerosols from the Canadian Climate Center (CCC) and the Hadley Centre (HADCM2; Mitchell et al. 1995, Johns et al. 1997) accessed via the Climate Impacts LINK Project, Climatic Research Unit, University of East Anglia. Scenarios were developed for the following variables: total incident solar radiation, minimum and maximum temperature, vapor pressure, precipitation, relative humidity and mean daily irradiance for the time periods January 1994 to approximately 2100. These data and the VEMAP 1 data (Kittel et al. 1995) were used to drive models in VEMAP Phase 2, the objectives of which are to compare time-dependent ecological responses of biogeochemical and coupled biogeochemical-biogeographical models to historical and projected transient forcings across the conterminous U.S. This data set of daily climate change scenarios was designed to be concatenated with the /VEMAP/vemap.html\">VEMAP 2: U.S. Daily Climate, 1895-1993, Version 2 data set to create a single climate series from 1895 - ~2100. This data set is being made available for the U.S. National Assessment. Users are requested to confer with the NCAR VEMAP Data Group to ensure that the intended application of the data set is consistent with the generation and limitations of the data. For more information, refer to the VEMAP homepage. Data Citation The data set should be cited as follows: Kittel, T. G. F., N. A. Rosenbloom, C. Kaufman, J. A. Royle, C. Daly, H. H. Fisher, W. P. Gibson, S. Aulenbach, R. McKeown, D. S. Schimel, and VEMAP 2 Participants. 2000. VEMAP 2: U. S. Daily Climate Change Scenarios. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.\n<br>\n\n#### vemap-2_TCLIMATE_daily_620\nThe Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 has developed a data set of ~100-year gridded monthly and daily time series of climate for the conterminous United States that includes realistic interannual variability. This data set has been used to compare time-dependent ecological responses of biogeochemical and coupled biogeochemical-biogeographical modesl to historical time series and projected scenarios of climate, atmospheric CO2, and N-Deposition, and N-Deposition\n<br>\n\n#### vemap-2_TCLIMATE_monthly_568\nThe Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 has developed a data set of ~100-year gridded monthly and daily time series of climate for the conterminous United States that includes realistic interannual variability. This data set has been used to compare time-dependent ecological responses of biogeochemical and coupled biogeochemical-biogeographical models to historical time series and projected scenarios of climate, atmospheric CO2, and N-deposition. Development of the data set is reported in Kittel et al. (1997). As in the VEMAP 1 database, the historical data set has (1) daily and monthly versions; (2) physical consistency among variables on a daily basis; (3) consistency between climate and topography; and (4) needed input variables for VEMAP2 models (minimum and maximum temperature, precipitation, vapor pressure, and solar radiation) (Kittel et al. 1995). This historical monthly climate data set was designed to be concatenated with the /VEMAP/vemap.html\">VEMAP 2: U.S. Monthly Climate Change Scenarios, Version 2 data set to create a single climate series from 1895 - ~2100. Users are requested to confer with the NCAR VEMAP Data Group to ensure that the intended application of the data set is consistent with the generation and limitations of the data. Data Citation The data set should be cited as follows: Kittel, T. G. F., N. A. Rosenbloom, C. Kaufman, J. A. Royle, C. Daly, H. H. Fisher, W. P. Gibson, S. Aulenbach, D. N. Yates, R. McKeown, D. S. Schimel, and VEMAP 2 Participants. 2000. VEMAP 2: U. S. Monthly Climate, 1895-1993, Version 2. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://earthdata.nasa.gov/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","carbon","climate","ice","oceans","precipitation"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"vemap-1_geog_222 v1 - The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) is an ongoing multiinstitutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetationtype distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/vemap/vemap-1_geog/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"vemap-1_VEMAP1_CDROM_566 v1 - The VEMAP 1: Model Input Database CD-ROM ISO image contains long-term data that were used as input in comparing models during Phase 1 of the Vegetation Ecosystem Modeling and Analysis Project. Compiled and model-generated data sets of long-term mean climate, soils, vegetation, and climate change scenarios for the conterminous United States.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/vemap/vemap-1_VEMAP1_CDROM/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"vemap-1_results_731 v1 - The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) was a multi-institutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetation type distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing.Selected variable output results from the VEMAP Pha...","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/vemap/vemap-1_results/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"vemap-1_scenario_223 v1 - The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) is an ongoing multiinstitutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetation type distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/vemap/vemap-1_scenario/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"vemap-2_results_annual_766 v1 - The Vegetation-Ecosystem Modeling and Analysis Project (VEMAP) was a large, collaborative, multi-institutional, international effort whose goal was to evaluate the sensitivity of terrestrial ecosystem and vegetation processes to altered climate forcing and elevated atmospheric CO2. Phase 1 of the VEMAP project developed historical (1895-1993) data sets of observed climate, soils, and vegetation compatible with the requirements of ecosystem models and vegetation distribution models.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/vemap/vemap-2_results_annual/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"vemap-1_VEMAP_Alaska_1344 v1 - This data set provides the results of the development of The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 transient climate change scenarios for the state of Alaska, USA. The data include gridded monthly historical and future estimates of maximum and minimum temperature, solar radiation, vapor pressure, irradiance, relative humidity and potential evapotranspiration at 0.5-degree spatial resolution.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/vemap/vemap-1_VEMAP_Alaska/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"vemap-2_TSCENARIO_annual_570 v1 - The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 has developed a number of transient climate change scenarios based on coupled atmosphere-ocean general circulation model (AOGCM) transient climate experiments. The purpose of these scenarios is to reflect time-dependent changes in surface climate from AOGCMs in terms of both (1) long-term trends and (2) changes in multiyear (3-5 yr) to decadal variability patterns, such as El Nino/Southern Oscillation (ENSO).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/vemap/vemap-2_TSCENARIO_annual/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"vemap-2_TCLIMATE_annual_571 v1 - The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 has developed a data set of ~100-year gridded annual, monthly, and daily time series of climate for the conterminous United States that includes realistic interannual variability. This data set has been used to compare time-dependent ecological responses of biogeochemical and coupled biogeochemical-biogeographical models to historical time series and projected scenarios of climate, atmospheric CO2, and N-deposition.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/vemap/vemap-2_TCLIMATE_annual/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"vemap-2_TSCENARIO_daily_618 v1 - The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 has developed a number of transient climate change scenarios based on coupled atmosphere-ocean general circulation model (AOGCM) transient climate experiments. The purpose of these scenarios is to reflect time-dependent changes in surface climate from AOGCMs in terms of both (1) long-term trends and (2) changes in multiyear (3-5 yr) to decadal variability patterns, such as El Nino/Southern Oscillation(ENSO).","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/vemap/vemap-2_TSCENARIO_daily/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"vemap-2_TCLIMATE_daily_620 v1 - The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 has developed a data set of ~100-year gridded monthly and daily time series of climate for the conterminous United States that includes realistic interannual variability. This data set has been used to compare time-dependent ecological responses of biogeochemical and coupled biogeochemical-biogeographical modesl to historical time series and projected scenarios of climate, atmospheric CO2, and N-Deposition, and N-Deposition.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/vemap/vemap-2_TCLIMATE_daily/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"vemap-2_TCLIMATE_monthly_568 v1 - The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 has developed a data set of ~100-year gridded monthly and daily time series of climate for the conterminous United States that includes realistic interannual variability. This data set has been used to compare time-dependent ecological responses of biogeochemical and coupled biogeochemical-biogeographical models to historical time series and projected scenarios of climate, atmospheric CO2, and N-deposition.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/vemap/vemap-2_TCLIMATE_monthly/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-vemap","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA viirs-jpss Project","Description":"This data set contains daily 'cloud-free' snow cover produced from the VIIRS/JPSS-2 Snow Cover Daily L3 Global 375m SIN Grid, Version 2 snow cover product. A cloud-gap-filled algorithm is utilized to replace ‘cloud-covered’ pixels with ‘cloud-free pixels’ for the purpose of estimating the snow cover that may exist under current cloud cover. The data are provided daily and mapped to a 375 m sinusoidal grid.\n<br>\n\n#### VJ210\nThis data set reports the location of snow cover using radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Joint Polar Satellite System's second satellite (JPSS-2). Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of quality control screens.\n<br>\n\n#### VJ210A1\nThis data set contains daily snow cover derived from radiance data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Joint Polar Satellite System's second satellite (JPSS-2). The data is a gridded composite, generated from 6 minute swaths, and projected to a 375 m Sinusoidal grid. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/vj210a1f/versions/2","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"VJ210A1F v2 - This data set contains daily 'cloud-free' snow cover produced from the VIIRS/JPSS-2 Snow Cover Daily L3 Global 375m SIN Grid, Version 2 snow cover product. A cloud-gap-filled algorithm is utilized to replace ‘cloud-covered’ pixels with ‘cloud-free pixels’ for the purpose of estimating the snow cover that may exist under current cloud cover.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ210A1F/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ210 v2 - This data set reports the location of snow cover using radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Joint Polar Satellite System's second satellite (JPSS-2). Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of quality control screens.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ210/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ210A1 v2 - This data set contains daily snow cover derived from radiance data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Joint Polar Satellite System's second satellite (JPSS-2). The data is a gridded composite, generated from 6 minute swaths, and projected to a 375 m Sinusoidal grid.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ210A1/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-viirs-jpss","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA viirs-snpp Project","Description":"This data set contains daily 'cloud-free' snow cover produced from the VIIRS/JPSS-1 Snow Cover Daily L3 Global 375m SIN Grid, Version 2 snow cover product. A cloud-gap-filled algorithm is utilized to replace ‘cloud-covered’ pixels with ‘cloud-free pixels’ for the purpose of estimating the snow cover that may exist under current cloud cover. The data are provided daily and mapped to a 375 m sinusoidal grid.\n<br>\n\n#### VJ110\nThis data set reports the location of snow cover using radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Joint Polar Satellite System's first satellite (JPSS-1). Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of quality control screens.\n<br>\n\n#### VJ110D1F\nVJ110D1F is a daily global view of cloud-gap-filled (CGF) snow cover at 375 m resolution. This daily global product is produced by reprojecting all of the <a href=\"https://nsidc.org/data/vj110a1f/versions/2\">VJ110A1F</a> product tiles from each day to a global geographic projection. The data files include the NDSI_Snow_Cover and Cloud_Persistence data layers from <a href=\"https://nsidc.org/data/vJ110a1f/versions/2\">VJ110A1F</a>.\n<br>\n\n#### VJ110A1\nThis data set contains daily snow cover derived from radiance data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Joint Polar Satellite System's first satellite (JPSS-1). The data is a gridded composite, generated from 6 minute swaths, and projected to a 375 m Sinusoidal grid. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections.\n<br>\n\n#### VJ210D1F\nVJ210D1F is a daily global view of snow cover at 375 m resolution. This daily global product is produced by reprojecting the all the VJ110A1 product tiles for a day to a global geographic projection. Only the NDSI_Snow_Cover data layer from the VJ110A1 product is input to produce this global product.\n<br>\n\n#### VNP10A1F\nThis data set contains daily 'cloud-free' snow cover produced from the VIIRS/NPP Snow Cover Daily L3 Global 375m SIN Grid, Version 2 snow cover product. A cloud-gap-filled algorithm is utilized to replace ‘cloud-covered’ pixels with ‘cloud-free pixels’ for the purpose of estimating the snow cover that may exist under current cloud cover. The data are provided daily and mapped to a 375 m sinusoidal grid.\n<br>\n\n#### VNP10\nThis data set reports snow cover using radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of quality control screens.\n<br>\n\n#### VNP10D1F\nVNP10D1F is a daily global view of cloud-gap-filled (CGF) snow cover at 375 m resolution. This daily global product is produced by reprojecting all of the <a href=\"https://nsidc.org/data/vnp10a1f/versions/2\">VNP10A1F</a> product tiles from each day to a global geographic projection. The data files include the NDSI_Snow_Cover and Cloud_Persistence data layers from <a href=\"https://nsidc.org/data/vnp10a1f/versions/2\">VNP10A1F</a>.\n<br>\n\n#### VNP10A1\nThis data set contains daily snow cover derived from radiance data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite. The data is a gridded composite, generated from 6 minute swaths, and projected to a 375 m Sinusoidal grid. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://nsidc.org/data/vj110a1f/versions/2","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","ice","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"VJ110A1F v2 - This data set contains daily 'cloud-free' snow cover produced from the VIIRS/JPSS-1 Snow Cover Daily L3 Global 375m SIN Grid, Version 2 snow cover product. A cloud-gap-filled algorithm is utilized to replace ‘cloud-covered’ pixels with ‘cloud-free pixels’ for the purpose of estimating the snow cover that may exist under current cloud cover.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ110A1F/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ110 v2 - This data set reports the location of snow cover using radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Joint Polar Satellite System's first satellite (JPSS-1). Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of quality control screens.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ110/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ110D1F v2 - VJ110D1F is a daily global view of cloud-gap-filled (CGF) snow cover at 375 m resolution. This daily global product is produced by reprojecting all of the <a href=\"https://nsidc.org/data/vj110a1f/versions/2\">VJ110A1F</a> product tiles from each day to a global geographic projection.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ110D1F/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ110A1 v2 - This data set contains daily snow cover derived from radiance data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Joint Polar Satellite System's first satellite (JPSS-1). The data is a gridded composite, generated from 6 minute swaths, and projected to a 375 m Sinusoidal grid.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ110A1/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VJ210D1F v2 - VJ210D1F is a daily global view of snow cover at 375 m resolution. This daily global product is produced by reprojecting the all the VJ110A1 product tiles for a day to a global geographic projection.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VJ210D1F/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP10A1F v2 - This data set contains daily 'cloud-free' snow cover produced from the VIIRS/NPP Snow Cover Daily L3 Global 375m SIN Grid, Version 2 snow cover product. A cloud-gap-filled algorithm is utilized to replace ‘cloud-covered’ pixels with ‘cloud-free pixels’ for the purpose of estimating the snow cover that may exist under current cloud cover.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VNP10A1F/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP10 v2 - This data set reports snow cover using radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of quality control screens.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VNP10/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP10D1F v2 - VNP10D1F is a daily global view of cloud-gap-filled (CGF) snow cover at 375 m resolution. This daily global product is produced by reprojecting all of the <a href=\"https://nsidc.org/data/vnp10a1f/versions/2\">VNP10A1F</a> product tiles from each day to a global geographic projection.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VNP10D1F/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"},{"Description":"VNP10A1 v2 - This data set contains daily snow cover derived from radiance data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite. The data is a gridded composite, generated from 6 minute swaths, and projected to a 375 m Sinusoidal grid.","ARN":"arn:aws:s3:::nsidc-cumulus-prod-protected/VIIRS/VNP10A1/2","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.nsidc.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-viirs-snpp","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA WDTS Project","Description":"An inventory of NASA's airborne and field campaigns for Earth Science\n<br>\n\n#### WDTS_AVIRIS-C_foliar_traits_2403\nThis dataset holds maps of 14 foliar functional traits derived from AVIRIS-Classic imagery over the western United States for the Western Diversity Time Series (WDTS) project. Imagery was acquired from 2013-04-10 to 2018-08-16 and a georeferenced, corrected surface reflectance product was developed for WDTS for 87 acquisitions. Foliar traits were derived from the reflectance product by applying partial least squares regression models. These traits include foliar concentrations of nitrogen, cellulose, lignin, fiber, sugar, starch, calcium, potassium, phosphorus, sulfur, phenolics, and nonstructural carbohydrates. In addition, area concentration of chlorophyll A+B and leaf dry mass per area were estimated. The WDTS includes airborne imagery collected at least once per year (but often several times) over large areas of California for almost all years from 2013-2024. This dataset will enable multi-temporal analyses of plant functional traits and biodiversity at the ecoregion scale. The data are provided in cloud optimized GeoTIFF format.\n<br>\n\n#### WDTS_AVIRIS-C_L2_corrected_2391\nThis dataset includes Level-2 (L2) surface reflectance imagery from the AVIRIS-Classic instrument collected for NASA's Western Diversity Time Series Project over California and Nevada in 2013 to 2018. AVIRIS-Classic imagery were collected on the NASA ER-2 from altitude of 19,800 m, yielding imagery with 15-20 m ground sample distance. AVIRIS-Classic is a 224-channel whisk-broom instrument with four grating imaging spectrometers covering a spectral range from 400-2500 nm, with spectral sampling every 10 nm. Radiometric calibration accuracy is within 1 nm, and signal to noise ratio is greater than 100:1. Existing L2 imagery were reprocessed co-register pixels with cloud-free panchromatic imagery from Landsat. Then, topographic, FlexBRDF, and glint corrections were applied and the georectified images were resampled to 15-m spatial resolution. The imagery data are provided in ENVI format. Ground control points used in georectification along the BRDF and topographic correction coefficients are provided in JSON files.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://impact.earthdata.nasa.gov/casei/campaign/WDTS/","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"WDTS_AVIRIS-C_foliar_traits_2403 v1 - This dataset holds maps of 14 foliar functional traits derived from AVIRIS-Classic imagery over the western United States for the Western Diversity Time Series (WDTS) project. Imagery was acquired from 2013-04-10 to 2018-08-16 and a georeferenced, corrected surface reflectance product was developed for WDTS for 87 acquisitions.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/wdts/WDTS_AVIRIS-C_foliar_traits/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"},{"Description":"WDTS_AVIRIS-C_L2_corrected_2391 v1 - This dataset includes Level-2 (L2) surface reflectance imagery from the AVIRIS-Classic instrument collected for NASA's Western Diversity Time Series Project over California and Nevada in 2013 to 2018. AVIRIS-Classic imagery were collected on the NASA ER-2 from altitude of 19,800 m, yielding imagery with 15-20 m ground sample distance.","ARN":"arn:aws:s3:::ornl-cumulus-prod-protected/wdts/WDTS_AVIRIS-C_L2_corrected/data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.ornldaac.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-wdts","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA WELD Project","Description":"WELDLCLUC.015 was decommissioned on December 2, 2019. The Web-Enabled Landsat Data (WELD) 5-year Land Cover Land Use Change (LCLUC) is a composite of 30 meter (m) land use land change product for the contiguous United States (CONUS). The data were generated from five years of consecutive growing season WELD weekly composite inputs from April 15, 2006, to November 17, 2010. WELD data are created using Landsat Thematic Mapper Plus (ETM+) Terrain Corrected data. This product includes data about tree cover loss and bare ground gain, which are composited over the five year period. WELD LCLUC is distributed in Hierarchical Data Format 4 (HDF4). The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues: WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.\n<br>\n\n#### WELDAKLL\nWELDAKLL.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets. The NASA Web-Enabled Landsat Data (WELD) Alaska Latitude Longitude (LL) products are defined with 30 meter (m) composite pixel of the center latitude/longitude point. The NASA WELD data contain a range of values for the minimum/maximum latitude and longitude points. The LL products were created to account for the actual center pixel latitude and longitude points. WELD systematically generated 30 m composited Landsat Enhanced Thematic Mapper Plus (ETM+) mosaics of the United States and Alaska from 2002 to 2012 to provide consistent data for deriving land cover as well as geophysical and biophysical products for regional assessments of surface dynamics for effective study of Earth system function. WELDAKLL is distributed in Hierarchical Data Format 4 (HDF4). The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues: WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.\n<br>\n\n#### WELDAKYR\nWELDAKYR.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved yearly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDYR.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDYR.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDYR.032) datasets. NASA’s Web-Enabled Landsat Data (WELD) are generated from composited 30 meter (m) Landsat Enhanced Thematic Mapper Plus (ETM+) mosaics of the United States and Alaska from 2002 to 2012. These products provide consistent data to derive land cover, geophysical, and biophysical products for regional assessments of surface dynamics for effective study of Earth system function. This product includes Top of Atmosphere (TOA) Reflectance and Brightness Temperature, along with the Normalized Difference Vegetation Index (NDVI) generated from Band 3 and Band 4 TOA Reflectance. WELDAKYR is distributed in Hierarchical Data Format 4 (HDF4). The annual products include the preceding year’s December through the current year’s November. The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues: WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.\n<br>\n\n#### WELDAKMO\nWELDAKMO.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets. NASA’s Web-Enabled Landsat Data (WELD) are generated from composited 30 meter (m) Landsat Enhanced Thematic Mapper Plus (ETM+) mosaics of the United States and Alaska from 2002 to 2012. These mosaics provide consistent data for deriving land cover, geophysical, and biophysical products for regional assessments of surface dynamics for effective study of Earth system function. The Alaska Monthly products are defined with respect to each calendar year by the days in each month. This product includes Top of Atmosphere (TOA) Reflectance and Brightness Temperature, along with the Normalized Difference Vegetation Index (NDVI) generated from Band 3 and Band 4 TOA Reflectance. These products are distributed in Hierarchical Data Format 4 (HDF4). The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues: WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide. Improvements/Changes from Previous Version * Version 1.5 is the original version.\n<br>\n\n#### WELDAKSE\nWELDAKSE.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets. NASA’s Web-Enabled Landsat Data (WELD) are generated from composited 30 meter (m) Landsat Enhanced Thematic Mapper Plus (ETM+) mosaics of the United States and Alaska from 2002 to 2012. The mosaics provide consistent data for deriving land cover, geophysical, and biophysical products for regional assessments of surface dynamics for effective study of Earth system function. This product includes Top of Atmosphere (TOA) Reflectance and Brightness Temperature, along with the Normalized Difference Vegetation Index (NDVI) generated from Band 3 and Band 4 TOA Reflectance. WELDAKSE is distributed in Hierarchical Data Format 4 (HDF4). In the Alaska seasonal products, seasons are defined as follows: Winter: December, January, February Spring: March, April, May Summer: June, July, August Autumn: September, October, November The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues: WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.\n<br>\n\n#### WELDAKWK\nWELDAKWK.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets. NASA’s Web-Enabled Landsat Data (WELD) are generated from composited 30 meter (m) Landsat Enhanced Thematic Mapper Plus (ETM+) mosaics of the United States and Alaska from 2003 to 2012. These mosaics provide consistent data to derive land cover, geophysical, and biophysical products for regional assessments of surface dynamics for effective study of Earth system function. This product includes Top of Atmosphere (TOA) Reflectance and Brightness Temperature, along with the Normalized Difference Vegetation Index (NDVI) generated from Band 3 and Band 4 TOA Reflectance. WELDAKWK is distributed in Hierarchical Data Format 4 (HDF4). The WELD weekly products are defined with respect to each calendar year and as consecutive 7 day products with the following examples defined for the weeks: Week01: January 1 to January 7 Week02: January 8 to January 14 Week52: December 24 to December 30 (non-leap years) Week52: December 23 to December 29 (leap years) Week53: December 31 (non-leap years) Week53: December 30 to December 31 (leap years) The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues: WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.\n<br>\n\n#### WELDUSLL\nWELDUSLL.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets. NASA’s Web-Enabled Landsat Data (WELD) are generated from composited 30 meter (m) Landsat Enhanced Thematic Mapper Plus (ETM+) mosaics of the United States and Alaska from 2002 to 2012. These mosaics provide consistent data to derive land cover as well as geophysical and biophysical products for regional assessments of surface dynamics for effective study of Earth system function. The Conterminous (CONUS) United States Latitude/Longitude (WELDUSLL) products provide the geographic latitude and longitude for the center of each 30 meter (m) pixel within a tile of WELD data. WELDUSLL is distributed in Hierarchical Data Format 4 (HDF4). The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues: WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.\n<br>\n\n#### WELDUSYR\nWELDUSYR.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved yearly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDYR.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDYR.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDYR.032) datasets. NASA’s Web-Enabled Landsat Data (WELD) are generated from composited 30 meter (m) Landsat Enhanced Thematic Mapper Plus (ETM+) mosaics of the United States and Alaska from 2002 to 2012. These products provide consistent data to derive land cover, geophysical, and biophysical products for regional assessments of surface dynamics for effective study of Earth system function. The WELD Conterminous (CONUS) United States Annual (WELDUSYR) products include Top of Atmosphere (TOA) Reflectance and Brightness Temperature, along with the Normalized Difference Vegetation Index (NDVI) generated from Band 3 and Band 4 TOA Reflectance. WELDUSYR is distributed in Hierarchical Data Format 4 (HDF4). The WELDUSYR products include the preceding year’s December through the current year’s November. For example, for the year 2007, the product includes data from December 1, 2006, through November 30, 2007. The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues: WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.\n<br>\n\n#### WELDUSMO\nWELDUSMO.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets. NASA’s Web-Enabled Landsat Data (WELD) are generated from composited 30 meter (m) Landsat Enhanced Thematic Mapper Plus (ETM+) mosaics of the United States and Alaska from 2002 to 2012. These mosaics provide consistent data to derive land cover, geophysical, and biophysical products for regional assessments of surface dynamics for effective study of Earth system function. The WELD Conterminous (CONUS) United States Monthly products include Top of Atmosphere (TOA) Reflectance and Brightness Temperature, along with the Normalized Difference Vegetation Index (NDVI) generated from Band 3 and Band 4 TOA Reflectance. These products are distributed in Hierarchical Data Format 4 (HDF4). WELDUSMO products are defined by a calendar month (e.g. January, February, March, etc.) for December 2002 through November 2012. The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues: WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.\n<br>\n\n#### WELDUSSE\nWELDUSSE.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets. NASA’s Web-Enabled Landsat Data (WELD) are generated from composited 30 meter (m) Landsat Enhanced Thematic Mapper Plus (ETM+) mosaics of the United States and Alaska from 2002 to 2012. The mosaics provide consistent data to derive land cover, geophysical, and biophysical products for regional assessments of surface dynamics for effective study of Earth system function. The Conterminous (CONUS) United States Seasonal (WELDUSSE) product include Top of Atmosphere (TOA) Reflectance and Brightness Temperature, along with the Normalized Difference Vegetation Index (NDVI) generated from Band 3 and Band 4 TOA Reflectance. WELDUSSE is distributed in Hierarchical Data Format 4 (HDF4). In the WELDUSSE products, seasons are defined as follows: Winter: December, January, February Spring: March, April, May Summer: June, July, August Autumn: September, October, November The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues: WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.\n<br>\n\n#### WELDUSWK\nWELDUSWK.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets. NASA’s Web-Enabled Landsat Data (WELD) are generated from composited 30 meter (m) Landsat Enhanced Thematic Mapper Plus (ETM+) mosaics of the United States and Alaska from 2003 to 2012. These mosaics provide consistent data to derive land cover, geophysical, and biophysical products for regional assessments of surface dynamics for effective study of Earth system function. The WELD Conterminous (CONUS) United States Weekly (WELDUSWK) products include Top of Atmosphere (TOA) Reflectance and Brightness Temperature, along with the Normalized Difference Vegetation Index (NDVI) generated from Band 3 and Band 4 TOA Reflectance. WELDUSWK is distributed in Hierarchical Data Format 4 (HDF4). WELDUSWK products are consecutive 7 day products for 2003 through 2012. Individual weeks are defined as follows: Week01: January 1 to January 7 Week02: January 8 to January 14 Week52: December 24 to December 30 (non-leap years) Week52: December 23 to December 29 (leap years) Week53: December 31 (non-leap years) Week53: December 30 to December 31 (leap years) The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues: WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://www.earthdata.nasa.gov/centers/lp-daac","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["atmosphere","aws-pds","geospatial","hdf","land cover","satellite imagery"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"WELDLCLUC v001 - WELDLCLUC.015 was decommissioned on December 2, 2019. The Web-Enabled Landsat Data (WELD) 5-year Land Cover Land Use Change (LCLUC) is a composite of 30 meter (m) land use land change product for the contiguous United States (CONUS).","ARN":"arn:aws:s3:::lp-prod-protected/WELDLCLUC.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WELDAKLL v001 - WELDAKLL.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets.","ARN":"arn:aws:s3:::lp-prod-protected/WELDAKLL.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WELDAKYR v001 - WELDAKYR.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved yearly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDYR.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDYR.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDYR.032) datasets.","ARN":"arn:aws:s3:::lp-prod-protected/WELDAKYR.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WELDAKMO v001 - WELDAKMO.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets.","ARN":"arn:aws:s3:::lp-prod-protected/WELDAKMO.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WELDAKSE v001 - WELDAKSE.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets.","ARN":"arn:aws:s3:::lp-prod-protected/WELDAKSE.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WELDAKWK v001 - WELDAKWK.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets.","ARN":"arn:aws:s3:::lp-prod-protected/WELDAKWK.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WELDUSLL v001 - WELDUSLL.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets.","ARN":"arn:aws:s3:::lp-prod-protected/WELDUSLL.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WELDUSYR v001 - WELDUSYR.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved yearly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDYR.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDYR.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDYR.032) datasets.","ARN":"arn:aws:s3:::lp-prod-protected/WELDUSYR.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WELDUSMO v001 - WELDUSMO.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets.","ARN":"arn:aws:s3:::lp-prod-protected/WELDUSMO.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WELDUSSE v001 - WELDUSSE.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets.","ARN":"arn:aws:s3:::lp-prod-protected/WELDUSSE.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"},{"Description":"WELDUSWK v001 - WELDUSWK.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) [Version 3](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.003), [3.1](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.031), and [3.2](https://doi.org/10.5067/MEaSUREs/GWELD/GWELDMO.032) datasets.","ARN":"arn:aws:s3:::lp-prod-protected/WELDUSWK.001","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-weld","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA WLDAS Project","Description":"The Western Land Data Assimilation System (WLDAS), developed at Goddard Space Flight Center (GSFC) and funded by the NASA Western Water Applications Office, provides water managers and stakeholders in the western United States with a long-term record of near-surface hydrology for use in drought assessment and water resources planning. WLDAS leverages advanced capabilities in land surface modeling and data assimilation to furnish a system that is customized for stakeholders’ needs in the region. WLDAS uses NASA’s Land Information System (LIS) to configure and drive the Noah Multiparameterization (Noah-MP) Land Surface Model (LSM) version 3.6 to simulate land surface states and fluxes. WLDAS uses meteorological observables from the North American Land Data Assimilation System (NLDAS-2) including precipitation, incoming shortwave and longwave radiation, near surface air temperature, humidity, wind speed, and surface pressure along with parameters such as vegetation class, soil texture, and elevation as inputs to a model that simulates land surface energy and water budget processes. Outputs of the model include soil moisture, snow depth and snow water equivalent, evapotranspiration, soil temperature, as well as derived quantities such as groundwater recharge and anomalies of the state variables.\n<br>\n\n#### Data Discovery\nExplore this data using NASA's [Earthdata Search](https://search.earthdata.nasa.gov/), a comprehensive tool for discovering and visualizing Earth science datasets.\n<br>\n\n#### Data Access\nAccess requires an [Earthdata Login](https://urs.earthdata.nasa.gov/) account. [Read our guide on obtaining AWS credentials](https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME) to retrieve this data from AWS.\n<br><br>\n","Documentation":"https://ldas.gsfc.nasa.gov/wldas/wldas-project-goals","Contact":"https://earthdata.nasa.gov/contact","ManagedBy":"NASA","UpdateFrequency":"Varies by dataset","Tags":["aws-pds","elevation","hydrology","ice","precipitation","soil moisture"],"License":"[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"WLDAS_NOAHMP001_DA1 vD1.0 - The Western Land Data Assimilation System (WLDAS), developed at Goddard Space Flight Center (GSFC) and funded by the NASA Western Water Applications Office, provides water managers and stakeholders in the western United States with a long-term record of near-surface hydrology for use in drought assessment and water resources planning. WLDAS leverages advanced capabilities in land surface modeling and data assimilation to furnish a system that is customized for stakeholders’ needs in the region.","ARN":"arn:aws:s3:::gesdisc-cumulus-prod-protected/WLDAS/WLDAS_NOAHMP001_DA1.D1.0","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"ControlledAccess":"https://data.gesdisc.earthdata.nasa.gov/s3credentials"}],"RegistryEntryAdded":"2026-04-22","RegistryEntryLastModified":"2026-04-30","Slug":"nasa-wldas","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Earth Exchange (NEX) Data Collection","Description":"A collection of downscaled climate change projections, derived from the\nGeneral Circulation Model (GCM) runs conducted under the Coupled Model\nIntercomparison Project Phase 5 (CMIP5) [Taylor et al. 2012] and across\nthe four greenhouse gas emissions scenarios known as Representative\nConcentration Pathways (RCPs) [Meinshausen et al. 2011]. The NASA Earth\nExchange group maintains the NEX-DCP30 (CMIP5), NEX-GDDP (CMIP5), and\nLOCA (CMIP5).\n\nNOTE: The S3 Bucket location for this dataset changed on 5/6/2026\n","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/nasa-nex","Contact":"https://www.nasa.gov/nasa-earth-exchange-nex/nex-team/","UpdateFrequency":"Infrequently","ManagedBy":"NASA","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","climate","CMIP5","natural resource","sustainability"],"License":"US Government work","Resources":[{"Description":"Downscaled Climate Projections (NEX-DCP30)","ARN":"arn:aws:s3:::nasa-nex/NEX-DCP30","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Global Daily Downscaled Projections (NEX-GDDP)","ARN":"arn:aws:s3:::nasa-nex/NEX-GDDP","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Localized Constructed Analogs (LOCA)","ARN":"arn:aws:s3:::nasa-nex/LOCA","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Climate Downscaling Using YNet: a Deep Convolutional Network with Skip Connections and Fusion","URL":"https://www.kdd.org/kdd2020/accepted-papers/view/climate-downscaling-using-ynet-a-deep-convolutional-network-with-skip-conne","AuthorName":"Yumin Liu, Auroop Ganguly, and Jennifer Dy"},{"Title":"Downscaled Climate Projections Suitable for Resource Management, Eos Trans. AGU, 94(37), 321.","AuthorName":"Thrasher, B., J. Xiong, W. Wang, F. Melton, A. Michaelis and R. Nemani (2013)","URL":"https://doi.org/10.1002/2013EO370002"},{"Title":"Explainable deep learning for insights in El Nino and river flows","URL":"https://arxiv.org/ftp/arxiv/papers/2201/2201.02596.pdf","AuthorName":"Yumin Liu, Kate Duffy, Jennifer G. Dy, and Auroop R. Ganguly"},{"Title":"Potential changes in cooling degree day under different global warming levels and shared socioeconomic pathways in West Africa","AuthorName":"Oluwarotimi Delano Thierry Odou, Heidi Heinrichs Ursula, Rabani Adamou, Thierry Godjo, and Mounkaila S Moussa","URL":"https://iopscience.iop.org/article/10.1088/1748-9326/acbc8f"},{"Title":"Statistical downscaling using Localized Constructed Analogs (LOCA).","AuthorName":"Pierce, D. W., D. R. Cayan, and B. L. Thrasher (2014)","URL":"https://doi.org/10.1175/JHM-D-14-0082.1"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2026-05-06","Slug":"nasanex","Sources":["awslabs-open-data-registry"]}
{"Name":"Natural Earth","Description":"Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.","Documentation":"https://www.naturalearthdata.com/downloads/","Contact":"https://github.com/nvkelso/natural-earth-vector/issues","ManagedBy":"North American Cartographic Information Society (nacis.org)","UpdateFrequency":"As needed","Collabs":{"ASDI":{"Tags":["ecosystems"]}},"Tags":["aws-pds","earth observation","geospatial","global","mapping","population","tiles"],"License":"Public Domain. https://www.naturalearthdata.com/about/terms-of-use/","Resources":[{"Description":"Shapefile downloads in ZIP archives","ARN":"arn:aws:s3:::naturalearth","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Publications":[{"Title":"Natural Earth Vector (2009)","URL":"http://shadedrelief.com/NEV/natural_earth_vector_cp64.pdf","AuthorName":"Nathaniel Vaughn Kelso, Tom Patterson","AuthorURL":"http://shadedrelief.com/"}]},"RegistryEntryAdded":"2021-08-24","RegistryEntryLastModified":"2022-01-11","Slug":"naturalearth","Sources":["awslabs-open-data-registry"]}
{"Name":"Community Earth System Model Large Ensemble (CESM LENS)","Description":"The Community Earth System Model (CESM) Large Ensemble Numerical Simulation (LENS) dataset includes a 40-member ensemble of climate simulations for the period 1920-2100 using historical data (1920-2005) or assuming the RCP8.5 greenhouse gas concentration scenario (2006-2100), as well as longer control runs based on pre-industrial conditions. The data comprise both surface (2D) and volumetric (3D) variables in the atmosphere, ocean, land, and ice domains. The total data volume of the original dataset is ~500TB, which has traditionally been stored as ~150,000 individual CF/NetCDF files on disk or magnetic tape made available through the NCAR Climate Data Gateway for download or via web services.\n\n   NCAR has copied a subset (currently ~70 TB) of CESM LENS data to Amazon S3 as part of the AWS Public Datasets Program. To optimize for large-scale analytics we have represented the data as ~275 Zarr stores format accessible through the Python Xarray library. Each Zarr store contains a single physical variable for a given model run type and temporal frequency (monthly, daily, 6-hourly).\n","Documentation":"https://doi.org/10.26024/wt24-5j82","Contact":"rdahelp@ucar.edu","ManagedBy":"[National Center for Atmospheric Research](https://ncar.ucar.edu/)","UpdateFrequency":"Rare. The LENS experiment is complete, but we may occasionally copy additional fields from NCAR to AWS, or fix problems discovered in the AWS copy.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","climate","climate model","geospatial","ice","land","model","oceans","sustainability","zarr"],"License":"https://www.ucar.edu/terms-of-use/data","Resources":[{"Description":"Project data files","ARN":"arn:aws:s3:::ncar-cesm-lens","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Jupyter Notebook and other documentation and tools for CESM LENS on AWS","URL":"https://github.com/NCAR/cesm-lens-aws","AuthorName":"NCAR Science at Scale team"},{"Title":"Rendered (static) version of Jupyter Notebook","URL":"http://ncar-aws-www.s3-website-us-west-2.amazonaws.com/kay-et-al-2015.v2.html","AuthorName":"Anderson Banihirwe, NCAR"},{"Title":"Urban Climate Explorer","URL":"https://zhonghuazheng.com/UrbanClimateExplorer/pages/about.html","AuthorName":"Zhonghua Zheng"}],"Tutorials":[{"Title":"Analyzing large climate model ensembles in the cloud","URL":"https://medium.com/pangeo/cesm-lens-on-aws-4e2a996397a1","AuthorName":"Joe Hamman, NCAR"}],"Publications":[{"Title":"The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability","URL":"https://doi.org/10.5065/d6j101d1","AuthorName":"Kay et al. (2015), Bull. AMS, 96, 1333-1349"}]},"RegistryEntryAdded":"2019-09-30","RegistryEntryLastModified":"2024-02-28","Slug":"ncar-cesm-lens","Sources":["awslabs-open-data-registry"]}
{"Name":"Community Earth System Model v2 ARISE (CESM2 ARISE)","Description":"Data from ARISE-SAI Experiments with CESM2","Documentation":"(https://github.com/NCAR/CESM2-ARISE)","Contact":"opendata-aws-arise@ucar.edu","ManagedBy":"[National Center for Atmospheric Research](https://ncar.ucar.edu/)","UpdateFrequency":"Rare once complete (August 2022)","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","climate","climate model","geospatial","ice","land","model","oceans","sustainability"],"License":"https://www.ucar.edu/terms-of-use/data","Resources":[{"Description":"Project data files","ARN":"arn:aws:s3:::ncar-cesm2-arise","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://ncar-cesm2-arise.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Coming Soon","URL":"https://github.com/NCAR/CESM2-ARISE","AuthorName":"NCAR"}]},"RegistryEntryAdded":"2022-06-15","RegistryEntryLastModified":"2023-10-23","Slug":"ncar-cesm2-arise","Sources":["awslabs-open-data-registry"]}
{"Name":"Community Earth System Model v2 Large Ensemble (CESM2 LENS)","Description":"The US National Center for Atmospheric Research partnered with the IBS Center for Climate Physics in South Korea to generate the CESM2 Large Ensemble which consists of 100 ensemble members at 1 degree spatial resolution covering the period 1850-2100 under CMIP6 historical and SSP370 future radiative forcing scenarios. Data sets from this ensemble were made downloadable via the Climate Data Gateway on June 14th, 2021.\nNCAR has copied a subset (currently ~500 TB) of CESM2 LENS data to Amazon S3 as part of the AWS Public Datasets Program. To optimize for large-scale analytics we have represented the data as ~275 Zarr stores format accessible through the Python Xarray library. Each Zarr store contains a single physical variable for a given model run type and temporal frequency (monthly, daily).\n","Documentation":"https://doi.org/10.26024/y48t-q717","Contact":"rdahelp@ucar.edu","ManagedBy":"[National Center for Atmospheric Research](https://ncar.ucar.edu/)","UpdateFrequency":"Rare. The LENS experiment is complete, but we may occasionally copy additional fields from NCAR to AWS, or fix problems discovered in the AWS copy.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","climate","climate model","geospatial","ice","land","model","oceans","sustainability","zarr"],"License":"https://www.ucar.edu/terms-of-use/data","Resources":[{"Description":"Project data files","ARN":"arn:aws:s3:::ncar-cesm2-lens","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Jupyter Notebook and other documentation and tools for CESM2-LE on AWS","URL":"https://github.com/NCAR/cesm2-le-aws","AuthorName":"NCAR Science at Scale team"},{"Title":"Rendered (static) version of Jupyter Notebook","URL":"https://ncar.github.io/cesm2-le-aws/kay_et_al_lens2.html","AuthorName":"Maxwell Grover, NCAR"},{"Title":"Urban Climate Explorer","URL":"https://zhonghuazheng.com/UrbanClimateExplorer/pages/about.html","AuthorName":"Zhonghua Zheng"}],"Publications":[{"Title":"Ubiquity of human-induced changes in climate variability","URL":"https://doi.org/10.5194/esd-2021-50","AuthorName":"Rodgers et al. (2021), Earth Syst. Dynam., 12, 1–19, 2021"}]},"RegistryEntryAdded":"2021-12-06","RegistryEntryLastModified":"2024-02-28","Slug":"ncar-cesm2-lens","Sources":["awslabs-open-data-registry"]}
{"Name":"CAM6 Data Assimilation Research Testbed (DART) Reanalysis: Cloud-Optimized Dataset","Description":"This is a cloud-hosted subset of the CAM6+DART (Community Atmosphere Model version 6 Data Assimilation Research Testbed) Reanalysis dataset. These data products are designed to facilitate a broad variety of research using the NCAR CESM 2.1 (National Center for Atmospheric Research's Community Earth System Model version 2.1), including model evaluation, ensemble hindcasting, data assimilation experiments, and sensitivity studies. They come from an 80 member ensemble reanalysis of the global troposphere and stratosphere using DART and CAM6. The data products represent states of the atmosphere consistent with observations from 2011 through 2019 at 1 degree horizontal resolution and weekly frequency. Each ensemble member is an equally likely description of the atmosphere, and is also consistent with dynamics and physics of CAM6. The dataset also contains corresponding land surface values at 6-hourly frequency.  This dataset is a reformatting, with no change to numerical values, of data from the \"CAM6 Data Assimilation Research Testbed (DART) Reanalysis\", [DOI:10.5065/JG1E-8525](https://doi.org/10.5065/JG1E-8525).\n","Documentation":"https://doi.org/10.26024/sprq-2d04","Contact":"rdahelp@ucar.edu","ManagedBy":"[National Center for Atmospheric Research](https://ncar.ucar.edu/)","UpdateFrequency":"Rare.  Additional variables or years outside of 2011-2019 may be added in the future.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","climate","climate model","data assimilation","forecast","geoscience","geospatial","land","meteorological","weather","zarr"],"License":"https://www.ucar.edu/terms-of-use/data","Resources":[{"Description":"Project data files","ARN":"arn:aws:s3:::ncar-dart-cam6","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Intake-ESM Catalog","URL":"https://ncar-dart-cam6.s3-us-west-2.amazonaws.com/catalogs/aws-dart-cam6.json","AuthorName":"Brian Bonnlander, NCAR"},{"Title":"Jupyter Notebook and other documentation and tools for DART Reanalysis on AWS","URL":"https://github.com/NCAR/ncar-dart-cam6","AuthorName":"NCAR Science at Scale team"},{"Title":"Rendered (static) version of Jupyter Notebook","URL":"https://ncar-dart-cam6.s3-us-west-2.amazonaws.com/examples/plot-ensemble-values.html","AuthorName":"Brian Bonnlander, NCAR"}],"Tutorials":[{"Title":"Analyzing large climate model ensembles in the cloud","URL":"https://medium.com/pangeo/cesm-lens-on-aws-4e2a996397a1","AuthorName":"Joe Hamman, NCAR"}],"Publications":[{"Title":"A new CAM6 + DART reanalysis with surface forcing from CAM6 to other CESM models","URL":"https://doi.org/10.1038/s41598-021-92927-0","AuthorName":"Raeder, K., Hoar, T.J., El Gharamti, M. et al (2021)"}]},"RegistryEntryAdded":"2021-11-23","RegistryEntryLastModified":"2024-02-28","Slug":"ncar-dart-cam6","Sources":["awslabs-open-data-registry"]}
{"Name":"NA-CORDEX - North American component of the Coordinated Regional Downscaling Experiment","Description":"The NA-CORDEX dataset contains regional climate change scenario data and guidance for North America, for use in impacts, decision-making, and climate science. The NA-CORDEX data archive contains output from regional climate models (RCMs) run over a domain covering most of North America using boundary conditions from global climate model (GCM) simulations in the CMIP5 archive. These simulations run from 1950–2100 with a spatial resolution of 0.22°/25km or 0.44°/50km.  This AWS S3 version of the data includes selected variables converted to Zarr format from the original NetCDF. Only daily data are currently available; all daily data were mapped to the Gregorian calendar. Sub-daily data may be added later. Both raw and bias-corrected data are available. Further details about this version of the dataset are available at the documentation link below.\n","Documentation":"https://doi.org/10.26024/9xkm-fp81","Contact":"rdahelp@ucar.edu","ManagedBy":"[National Center for Atmospheric Research](https://ncar.ucar.edu/)","UpdateFrequency":"Rare. NA-CORDEX is complete, but we may occasionally copy additional fields from NCAR to AWS, or fix problems discovered in the data.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","climate","climate model","geospatial","land","model","sustainability","zarr"],"License":"https://na-cordex.org/terms-use.html","Resources":[{"Description":"Project data files","ARN":"arn:aws:s3:::ncar-na-cordex","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Intake-ESM Catalog","URL":"https://ncar-na-cordex.s3-us-west-2.amazonaws.com/catalogs/aws-na-cordex.json","AuthorName":"Brian Bonnlander (NCAR)"},{"Title":"Jupyter Notebook and other documentation and tools","URL":"https://github.com/NCAR/na-cordex-aws","AuthorName":"Brian Bonnlander, Seth McGinnis (NCAR)"},{"Title":"Rendered (static) version of Jupyter Notebook","URL":"http://ncar-aws-www.s3-website-us-west-2.amazonaws.com/plot-zarr-diagnostics.html","AuthorName":"Brian Bonnlander (NCAR)"}],"Publications":[{"Title":"The NA-CORDEX dataset, version 1.0. NCAR Climate Data Gateway, Boulder CO (2017)","URL":"https://doi.org/10.5065/D6SJ1JCH","AuthorName":"Mearns, Linda O., et al."}]},"RegistryEntryAdded":"2021-03-31","RegistryEntryLastModified":"2024-02-28","Slug":"ncar-na-cordex","Sources":["awslabs-open-data-registry"]}
{"Name":"Basic Local Alignment Sequences Tool (BLAST) Databases","Description":"A centralized repository of pre-formatted BLAST databases created by the National Center for Biotechnology Information (NCBI).","Documentation":"https://github.com/ncbi/blast_plus_docs","Contact":"https://support.nlm.nih.gov/support/create-case/","ManagedBy":"[National Library of Medicine (NLM)](http://nlm.nih.gov/)","UpdateFrequency":"Periodically","Tags":["aws-pds","bioinformatics","biology","genetic","genomic","health","life sciences","protein","reference index","STRIDES","transcriptomics"],"License":"\"[NIH Genomic Data Sharing Policy](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)\"","Resources":[{"Description":"BLAST databases with associated files in a public S3 bucket","ARN":"arn:aws:s3:::ncbi-blast-databases","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"BLAST on the Cloud with NCBI’s ElasticBLAST","URL":"https://medium.com/codex/blast-on-the-cloud-with-ncbis-elasticblast-3b35b29afde7","AuthorName":"Sixing Huang","AuthorURL":"https://dgg32.medium.com/"},{"Title":"BLAST+ Docker","URL":"https://github.com/ncbi/blast_plus_docs","AuthorName":"NCBI BLAST"}],"Publications":[{"Title":"BLAST+: Architecture and Applications","URL":"https://pubmed.ncbi.nlm.nih.gov/20003500","AuthorName":"Christiam Camacho  1 , George Coulouris, Vahram Avagyan, Ning Ma, Jason Papadopoulos, Kevin Bealer, Thomas L Madden"},{"Title":"Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs","URL":"https://pubmed.ncbi.nlm.nih.gov/9254694/","AuthorName":"S F Altschul, T L Madden, A A Schäffer, J Zhang, Z Zhang, W Miller, D J Lipman"}]},"RegistryEntryAdded":"2020-07-06","RegistryEntryLastModified":"2026-04-28","Slug":"ncbi-blast-databases","Sources":["awslabs-open-data-registry"]}
{"Name":"COVID-19 Genome Sequence Dataset","Description":"This repository within the [ACTIV TRACE initiative](https://www.nih.gov/research-training/medical-research-initiatives/activ/tracking-resistance-coronavirus-evolution-trace) houses a comprehensive collection of datasets related to SARS-CoV-2. The processing of SARS-CoV-2 Sequence Read Archive (SRA) files has been optimized to identify genetic variations in viral samples. This information is then presented in the Variant Call Format (VCF). Each VCF file corresponds to the SRA parent-run's accession ID. Additionally, the data is available in the parquet format, making it easier to search and filter using the Amazon Athena Service. The SARS-CoV-2 Variant Calling Pipeline is designed to handle new data every six hours, with updates to the AWS ODP bucket occurring daily.","Documentation":"https://www.ncbi.nlm.nih.gov/sra/docs/sra-aws-download/","Contact":"https://support.nlm.nih.gov/support/create-case/","ManagedBy":"[National Library of Medicine (NLM)](http://nlm.nih.gov/)","UpdateFrequency":"Daily","Tags":["aws-pds","bam","bioinformatics","biology","coronavirus","COVID-19","cram","fastq","genetic","genomic","health","life sciences","MERS","SARS","STRIDES","transcriptomics","virus","whole genome sequencing"],"License":"[NIH Genomic Data Sharing Policy](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)","Resources":[{"Description":"Genetic variations of SARS-CoV-2 in VCF format, organized by NCBI accession. Each VCF file corresponds to the SRA parent-run's accession ID. Files in the vcf folder are in VCF and can be read by any program that accepts *.vcf files or can read tabular data.","ARN":"arn:aws:s3:::sra-pub-sars-cov2","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Metadata for sra-pub-sars-cov2 in an Athena-queryable format","ARN":"arn:aws:s3:::sra-pub-sars-cov2-metadata-us-east-1","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Download SRA sequence data using Amazon Web Services (AWS)","URL":"https://www.ncbi.nlm.nih.gov/sra/docs/sra-aws-download/","AuthorName":"NCBI SRA"}]},"RegistryEntryAdded":"2020-07-09","RegistryEntryLastModified":"2025-01-22","Slug":"ncbi-covid-19","Sources":["awslabs-open-data-registry"]}
{"Name":"GX database for NCBI Foreign Contamination Screen (FCS) Tool Suite","Description":"Sequence database used by FCS-GX (Foreign Contamination Screen - Genome Cross-species aligner) to detect contamination from foreign organisms in genome sequences.","Documentation":"https://github.com/ncbi/fcs/wiki/FCS-GX","Contact":"https://support.nlm.nih.gov/support/create-case/","ManagedBy":"[National Library of Medicine (NLM)](http://nlm.nih.gov/)","UpdateFrequency":"Yearly","Tags":["assembly","aws-pds","bioinformatics","biology","contamination","fasta","genetic","genome","health","life sciences","STRIDES","whole genome sequencing"],"License":"[NIH Genomic Data Sharing Policy](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)","Resources":[{"Description":"FCS-GX database","ARN":"arn:aws:s3:::ncbi-fcs-gx","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2023-03-13","RegistryEntryLastModified":"2026-04-28","Slug":"ncbi-fcs-gx","Sources":["awslabs-open-data-registry"]}
{"Name":"NIH NLM NCBI PubMed Central (PMC) Article Datasets - Full-Text Biomedical and Life Sciences Journal Articles on AWS","Description":"PubMed Central® (PMC) is a free full-text archive of biomedical and life sciences journal article at the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM). The PubMed Central (PMC) Article Datasets include full-text articles archived in PMC and made available under license terms that allow for text mining and other types of secondary analysis and reuse. The articles are organized on AWS based on PMCID and version number:<br/><br/>\nThe PMC Open Access (OA) Subset, which includes all articles in PMC that are available for reuse based on terms specified by the publisher. The majority of available articles have machine-readable Creative Commons license<br/><br/> The Author Manuscript Dataset, which includes all articles collected under a funder policy in PMC and made available in machine-readable formats for text mining. NOTEL Author manuscripts with Creative Commons licenses are also part of the PMC Open Access Subset.<br/><br/> The Historical OCR Dataset, which includes historical articles, published in the 18th, 19th, and 20th centuries, scanned as part of an NLM digitization project, that have Creative Commons licenses. NOTE: These articles are also part of the PMC Open Access Subset.<br/><br/>\nThese datasets collectively span more than half of PMC's total collection of full-text articles. PMC enables access to these datasets to expand the impact of open access and publicly-funded research; enable greater machine learning across the spectrum of scientific research; reach new audiences; and open new doors for discovery. The bucket in this registry contains individual article versions in NISO Z39.96-2015 JATS XML format as well as in plain text as extracted from the XML and the full article PDF. Media files and supplementary material files are also available for all open access articles. The bucket is updated continuously with new and updated articles. Also includes JSON metadata objects for each article version and a CVS inventory file.","Contact":"pubmedcentral@ncbi.nlm.nih.gov","Documentation":"https://www.ncbi.nlm.nih.gov/pmc/tools/pmcaws","ManagedBy":"[National Library of Medicine (NLM)](http://nlm.nih.gov/)","UpdateFrequency":"Continuous - article versions are updated continuously. Updates include - the addition of new article versions; the update of one or more objects belonging to an existing article version; in rare cases, the removal of an article version","Tags":["aws-pds","csv","life sciences","STRIDES","txt","xml"],"License":"[PMC Copyright](https://www.ncbi.nlm.nih.gov/pmc/about/copyright/)","Resources":[{"Description":".xml, .txt, and PDF files with the full-text of articles; media and supplementary materials files; all located in a public S3 bucket","ARN":"arn:aws:s3:::pmc-oa-opendata","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing PMC Article Datasets Using Amazon Web Services","URL":"https://www.ncbi.nlm.nih.gov/pmc/tools/pmcaws/","AuthorName":"NCBI PMC","AuthorURL":"https://www.ncbi.nlm.nih.gov/pmc/"}],"Tools & Applications":[{"Title":"Extracting insights from PubMed articles using Amazon Q Business","URL":"https://aws.amazon.com/blogs/publicsector/extracting-insights-from-pubmed-articles-using-amazon-q-business/","AuthorName":"Bharath Gunapati and Stefan Mationg"}]},"RegistryEntryAdded":"2021-07-07","RegistryEntryLastModified":"2026-02-13","Slug":"ncbi-pmc","Sources":["awslabs-open-data-registry"]}
{"Name":"NCBI SRA Gene Feature RNA-Seq counts","Description":"The NIH Sequence Read Archive (SRA), hosted by the [National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM) stores sequencing data and alignment information from high-throughput next-generation sequencing platforms. SRA has conducted gene expression analysis of publicly released human and mouse RNA-Seq experiments to process raw RNA-seq reads into concise formats that summarize the expression results. The un-normalized feature counts for each SRA record are available in tab-delimited (*.tsv) format. The tsv files include two columns, the gene id and count. These counts facilitate differential gene expression analyses, particularly across studies and the entirety of the SRA corpus.","Contact":"sra@ncbi.nlm.nih.gov","Documentation":"https://www.ncbi.nlm.nih.gov/geo/info/rnaseqcounts.html","ManagedBy":"NCBI at NLM","UpdateFrequency":"Daily","Tags":["aws-pds","genetic","life sciences","RNA-seq","transcriptomics"],"License":"[NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomic Data Sharing Policy ](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)","Resources":[{"Description":"*.tsv files in a public s3 public. The files contain un-normalized feature counts for the SRA run accession listed in the file name. The scope of these records is human and mouse, bulk RNA-Seq data released in the non-controlled access portion of SRA.","ARN":"arn:aws:s3:::sra-rnaseq-analysis","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"The Sequence Read Archive","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013647/","AuthorName":"Leinonen et al (2011)","AuthorURL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=Leinonen%20R%5BAuthor%5D&cauthor=true&cauthor_uid=21062823"}]},"RegistryEntryAdded":"2026-06-03","RegistryEntryLastModified":"2026-06-03","Slug":"ncbi-sra-rnaseq","Sources":["awslabs-open-data-registry"]}
{"Name":"NIH NCBI Sequence Read Archive (SRA) on AWS","Description":"The Sequence Read Archive (SRA), produced by the [National Center for Biotechnology Information (NCBI)](https://www.ncbi.nlm.nih.gov/) at the [National Library of Medicine (NLM)](http://nlm.nih.gov/) at the [National Institutes of Health (NIH)](http://www.nih.gov/), stores raw DNA sequencing data and alignment information from high-throughput sequencing platforms. The SRA provides open access to these biological sequence data to support the research community's efforts to enhance reproducibility and make new discoveries by comparing data sets. Buckets in this registry contain public SRA data in the original (user submitted) format from select high value and newly-released studies as well as all public-access SRA formatted ETL+BQS data. Also included is all SRA metadata that can be leveraged for attribute-based data discovery.","Contact":"sra@ncbi.nlm.nih.gov","Documentation":"https://www.ncbi.nlm.nih.gov/sra/docs/sra-cloud/","ManagedBy":"[National Library of Medicine (NLM)](http://nlm.nih.gov/)","UpdateFrequency":"Daily","Tags":["aws-pds","bam","cram","fastq","genetic","genomic","life sciences","STRIDES","transcriptomics","whole exome sequencing","whole genome sequencing"],"License":"[NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomic Data Sharing Policy ](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)","Resources":[{"Description":".bam, .cram, and .fastq files in a public S3 bucket. This is the first of two S3 buckets for source submissions from sequencing methodologies such as PacBio, Oxford Nanopore Technologies, and 10X Genomics.","ARN":"arn:aws:s3:::sra-pub-src-1","Region":"us-east-1","Type":"S3 Bucket"},{"Description":".bam, .cram, and .fastq files in a public S3 bucket. This is the second of two S3 buckets for source submissions from sequencing methodologies such as PacBio, Oxford Nanopore Technologies, and 10X Genomics.","ARN":"arn:aws:s3:::sra-pub-src-2","Region":"us-east-1","Type":"S3 Bucket"},{"Description":".sra files in a public S3 bucket. This bucket contains all open access SRA submissions in the SRA Normalized format with full base quality scores. The SRA Toolkit will be required to convert these objects into FASTQ or SAM files. Note that this bucket is updated on a daily basis, however, there may be a one to two day lag between accessions being findable via the [SRA Search](https://www.ncbi.nlm.nih.gov/sra/advanced) and being accessible in this S3 bucket.","ARN":"arn:aws:s3:::sra-pub-run-odp","Region":"us-east-1","Type":"S3 Bucket"},{"Description":".sra files in a controlled-access S3 bucket. This bucket contains controlled-access dbGaP submissions in the SRA Normalized format with full base quality scores. The SRA Toolkit will be required to convert these objects into FASTQ or SAM files. Note that this bucket is updated on a daily basis, however, there may be a one to two day lag between accessions being findable via the [SRA Search](https://www.ncbi.nlm.nih.gov/sra/advanced) and being accessible in this S3 bucket.","ARN":"arn:aws:s3:::sra-ca-run-odp","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://dbgap.ncbi.nlm.nih.gov/aa/wga.cgi?page=login"},{"Description":"Metadata files for the Sequence Read Archive, ready to load into [AWS Glue](https://aws.amazon.com/glue/) and query with [Amazon Athena](https://aws.amazon.com/athena/).","ARN":"arn:aws:s3:::sra-pub-metadata-us-east-1/sra/metadata","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Update notifications for s3://sra-pub-run-odp. Users can subscribe to this SNS topic with [AWS Lambda](https://aws.amazon.com/lambda/) or [AWS Simple Queue Service](https://aws.amazon.com/sqs/).","ARN":"arn:aws:sns:us-east-1:867126678632:sra-pub-run-odp-objects","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Access SRA data using Amazon Web Services (AWS)","URL":"https://www.ncbi.nlm.nih.gov/sra/docs/sra-aws-download/","AuthorName":"NCBI SRA","AuthorURL":"https://www.ncbi.nlm.nih.gov/sra/"},{"Title":"Get started with the SRA and Amazon Athena","URL":"https://www.ncbi.nlm.nih.gov/sra/docs/sra-athena/","AuthorName":"NCBI SRA","AuthorURL":"https://www.ncbi.nlm.nih.gov/sra/"},{"Title":"SRA in the Cloud","URL":"https://www.ncbi.nlm.nih.gov/sra/docs/sra-cloud/","AuthorName":"NCBI SRA","AuthorURL":"https://www.ncbi.nlm.nih.gov/sra/"}],"Tools & Applications":[{"Title":"SRA Toolkit","URL":"https://github.com/ncbi/sra-tools","AuthorName":"NCBI SRA","AuthorURL":"https://www.ncbi.nlm.nih.gov/sra/"}],"Publications":[{"Title":"The Sequence Read Archive","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013647/","AuthorName":"Leinonen et al (2011)","AuthorURL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=Leinonen%20R%5BAuthor%5D&cauthor=true&cauthor_uid=21062823"}]},"RegistryEntryAdded":"2020-01-23","RegistryEntryLastModified":"2022-07-26","Slug":"ncbi-sra","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Water-Column Sonar Data Archive","Description":"Water-column sonar data archived at the NOAA National Centers for Environmental Information.","Documentation":"https://cires.gitbook.io/ncei-wcsd-archive/","Contact":"wcd.info@noaa.gov","ManagedBy":"[NOAA](https://www.ngdc.noaa.gov/mgg/wcd/)","UpdateFrequency":"New water-column sonar data are added regularly as they are provided to the archive.","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","biodiversity","earth observation","ecosystems","environmental","geospatial","mapping","oceans"],"License":"The data may be used and redistributed for free but is not intended for legal use, since it may contain inaccuracies. Neither the data Creator, NOAA, the United States Government, nor any of their employees or contractors, makes any warranty, express or implied, including warranties of merchantability and fitness for a particular purpose, or assumes any legal liability for the accuracy, completeness, or usefulness, of this information.","Resources":[{"Description":"NCEI Water-Column Sonar Data Archive","ARN":"arn:aws:s3:::noaa-wcsd-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-wcsd-pds.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Frequency Differencing with Raw Data","URL":"https://github.com/CI-CMG/pyEcholab/tree/master/examples/cloud%20tutorials/Frequency_Differencing_With_Raw_Data.ipynb","AuthorName":"Carrie Wall"},{"Title":"Plotting Raw EK60 Data","URL":"https://github.com/CI-CMG/pyEcholab/tree/master/examples/cloud%20tutorials/Plotting_Raw_EK60_Data.ipynb","AuthorName":"Carrie Wall"},{"Title":"Reading and Plotting Bottom Data","URL":"https://github.com/CI-CMG/pyEcholab/tree/master/examples/cloud%20tutorials/Reading_And_Plotting_Bottom_Data.ipynb","AuthorName":"Carrie Wall"},{"Title":"Reading and Plotting Processed CSV Data","URL":"https://github.com/CI-CMG/pyEcholab/tree/master/examples/cloud%20tutorials/Reading_And_Plotting_Processed_CSV_Data.ipynb","AuthorName":"Carrie Wall"}],"Tools & Applications":[{"Title":"pyEcholab - an open-source, python-based toolkit for reading, processing, plotting, and exporting fisheries acoustic echosounder data","URL":"https://github.com/CI-CMG/pyEcholab","AuthorName":"Rick Towler, Chuck Anderson, Veronica Martinez, Pamme Crandell"},{"Title":"Water Column Sonar Data Explorer - Map viewer for exploring sonar water column data","URL":"https://www.ngdc.noaa.gov/maps/water_column_sonar/index.html","AuthorName":"NCEI, NOAA"}],"Publications":[{"Title":"Building an Accessible Archive for Water Column Sonar Data","URL":"https://eos.org/science-updates/building-an-accessible-archive-for-water-column-sonar-data","AuthorName":"Carrie Wall"},{"Title":"Increasing the accessibility of acoustic data through global access and imagery","URL":"https://doi.org/10.1093/icesjms/fsw014","AuthorName":"Carrie Wall, Michael Jech, and Susan McLean"}]},"RegistryEntryAdded":"2020-01-22","RegistryEntryLastModified":"2023-02-28","Slug":"ncei-wcsd-archive","Sources":["awslabs-open-data-registry"]}
{"Name":"National Cancer Institute Imaging Data Commons (IDC) Collections","Description":"[Imaging Data Commons (IDC)](https://imaging.datacommons.cancer.gov)  is a repository within the \n[Cancer Research Data Commons (CRDC)](https://datacommons.cancer.gov) that manages imaging data \nand enables its integration with the other components of CRDC. \n\nIDC hosts a growing number of imaging collections that are contributed \nby either funded US National Cancer Institute (NCI)  data collection \nactivities, or by the individual researchers.\n\nImage data hosted by IDC is stored in DICOM  format. \n","Documentation":"https://learn.canceridc.dev/","Contact":"https://discourse.canceridc.dev/","ManagedBy":"Imaging Data Commons (IDC)(https://imaging.datacommons.cancer.gov) team","UpdateFrequency":"Every 1-3 months","Tags":["aws-pds","cancer","digital pathology","fluorescence imaging","image processing","imaging","life sciences","machine learning","medical imaging","microscopy","radiology"],"License":"https://fairsharing.org/FAIRsharing.0b5a1d","Resources":[{"Description":"De-identified imaging data files in DICOM format distributed under CC-BY license. This bucket is updated with each new IDC release, while maintaining the versioning of the previous releases, as described in the [IDC Data versioning](https://learn.canceridc.dev/docs/data-versioning) documentation. The content of the buckets is [organized](https://learn.canceridc.dev/data/organization-of-data/files-and-metadata) into folders corresponding to versioned DICOM series, with different versions of the series located in the same bucket. Please refer to the [IDC Documentation](https://learn.canceridc.dev/) and [tutorials](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/getting_started) to learn how to search and download the data, and how to comply with attribution requirements.","ARN":"arn:aws:s3:::idc-open-data","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Second bucket containing de-identified imaging data files in DICOM format distributed under CC-BY license. This bucket is updated with each new IDC release, while maintaining the versioning of the previous releases, as described in the [IDC Data versioning](https://learn.canceridc.dev/docs/data-versioning) documentation. The content of the buckets is [organized](https://learn.canceridc.dev/data/organization-of-data/files-and-metadata) into folders corresponding to versioned DICOM series, with different versions of the series located in the same bucket. Please refer to the [IDC Documentation](https://learn.canceridc.dev/) and [tutorials](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/getting_started) to learn how to search and download the data, and how to comply with attribution requirements.","ARN":"arn:aws:s3:::idc-open-data-two","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"De-identified imaging data files in DICOM format distributed under CC-NC license. Files in this bucket are covered by This bucket is updated with each new IDC release, while maintaining the versioning of the previous releases, as described in the [IDC Data versioning](https://learn.canceridc.dev/docs/data-versioning) documentation. The content of the buckets is [organized](https://learn.canceridc.dev/data/organization-of-data/files-and-metadata) into folders corresponding to versioned DICOM series, with different versions of the series located in the same bucket. Please refer to the [IDC Documentation](https://learn.canceridc.dev/) and [tutorials](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/getting_started) to learn how to search and download the data, and how to comply with attribution requirements.","ARN":"arn:aws:s3:::idc-open-data-cr","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"IDC Portal","URL":"https://portal.imaging.datacommons.cancer.gov/","AuthorName":"Imaging Data Commons team","AuthorURL":"https://learn.canceridc.dev/idc-team"}],"Tutorials":[{"Title":"IDC \"Getting started\" tutorial series","URL":"https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/getting_started","AuthorName":"Imaging Data Commons team","AuthorURL":"https://learn.canceridc.dev/idc-team"}],"Publications":[{"Title":"NCI Imaging Data Commons","URL":"https://doi.org/10.1158/0008-5472.can-21-0950","AuthorName":"Fedorov A, Longabaugh W, Pot D, Clunie D, Pieper S, Aerts H, et al.","AuthorURL":"https://learn.canceridc.dev/idc-team"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2023-05-10","RegistryEntryLastModified":"2026-02-05","Slug":"nci-imaging-data-commons","Sources":["awslabs-open-data-registry"]}
{"Name":"National Cancer Institute Center for Cancer Research - Diffuse Large B Cell Lymphoma (DLBCL) Genomics and Expression","Description":"The study describes integrative analysis of genetic lesions in 574 diffuse large B cell lymphomas\n(DLBCL) involving exome and transcriptome sequencing, array-based DNA copy number analysis and\ntargeted amplicon resequencing. The dataset contains open RNA-Seq Gene Expression Quantification\ndata.\n","Documentation":"https://gdc.cancer.gov/about-data/publications/DLBCL-2018","Contact":"dcf-support@datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genomic","life sciences"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"RNA-Seq Gene Expression Quantification","ARN":"arn:aws:s3:::gdc-nciccr-phs001444-2-open","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/NCICCR-DLBCL","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"}],"Publications":[{"Title":"Genetics and Pathogenesis of Diffuse Large B Cell Lymphoma","URL":"https://doi.org/10.1056/NEJMoa1801445","AuthorName":"Roland Schmitz, Ph.D., George W. Wright, Ph.D., Da Wei Huang, M.D., et al."}]},"RegistryEntryAdded":"2020-04-08","RegistryEntryLastModified":"2024-12-20","Slug":"nciccr-dlbcl","Sources":["awslabs-open-data-registry"]}
{"Name":"Normalized Difference Urban Index (NDUI)","Description":"NDUI is combined with cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI) composite and DMSP/OLS Night Time Light (NTL) to characterize global urban areas at a 30 m resolution,and it can greatly enhance urban areas, which can then be easily distinguished from bare lands including fallows and deserts. With the capability to delineate urban boundaries and, at the same time, to present sufficient spatial details within urban areas, the  NDUI has the potential for urbanization studies at regional and global scales.","Documentation":"https://github.com/yifwahaha/NDUI_work_flow/blob/master/User's%20Guide%20-%2020200314_new.pdf","Contact":"zhangqling@mail.sysu.edu.cn","ManagedBy":"Remote Sensing Big Data Intelligent Application Laboratory, School of Aeronautics and Astronautics, Sun Yat-sen University","UpdateFrequency":"Next year's NDUI is added as soon as it is available.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","earth observation","geospatial","satellite imagery","urban"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"NDUI-2000 on a global land scale","ARN":"arn:aws:s3:::qinglinglab-ndui-2000","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"An example of using ndui data with AWS sagemaker tools","URL":"https://github.com/yifwahaha/aws-ndui","Services":["Amazon EC2"],"AuthorName":"Yifang Wang","AuthorURL":"https://github.com/yifwahaha"},{"Title":"Automated extraction of urban built-up areas with NDUI using Python and Google Earth Engine","URL":"https://github.com/yifwahaha/NDUI_work_flow","Services":["Amazon EC2"],"AuthorName":"Yifang Wang","AuthorURL":"https://github.com/yifwahaha"}],"Tools & Applications":[{"Title":"Global DMSP images Correction","URL":"https://code.earthengine.google.com/e0488afe77caa345c3217b0ff339ccca","AuthorName":"Yifang Wang","AuthorURL":"https://github.com/yifwahaha"}],"Publications":[{"Title":"A Robust Method to Generate a Consistent Time Series From DMSP/OLS Nighttime Light Data","URL":"https://doi.org/10.1109/TGRS.2016.2572724","AuthorName":"Qingling Zhang and Bhartendu Pandey and Keren C.Seto"},{"Title":"Building a Better Urban Picture:Combining Day and Night Remote Sensing Imagery","URL":"https://www.mdpi.com/2072-4292/7/9/11887","AuthorName":"Qingling Zhang and Bin Li and David Thau & Rebecca Moore"}]},"RegistryEntryAdded":"2021-06-07","RegistryEntryLastModified":"2025-02-04","Slug":"ndui","Sources":["awslabs-open-data-registry"]}
{"Name":"Noise Enhancement of Sensorimotor Function","Description":"This database contains postural sway measurements for 15 healthy young (mean age 23, standard deviation 2), and 12 healthy elderly (mean age 73, standard deviation 3) volunteers. Each subject's postural sway was recorded during a test of 10 minutes for the young subjects, or 5 minutes for the elderly subjects, in all cases with a 2-minute seated break midway through the test. Each test was divided into 30-second trials, and each file of the database contains data for one of these 30-second trials.","Documentation":"https://doi.org/10.13026/C21S3G","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C21S3G","ARN":"arn:aws:s3:::physionet-open/nesfdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"nesfdb","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)","Description":"The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate\nscenarios derived from the General Circulation Model (GCM) runs\nconducted under the Coupled Model Intercomparison Project Phase 6\n(CMIP6) and across two of the four \"Tier 1\" greenhouse gas emissions\nscenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6\nGCM runs were developed in support of the Sixth Assessment Report\nof the Intergovernmental Panel on Climate Change (IPCC AR6). This\ndataset includes downscaled projections from ScenarioMIP model\nruns for which daily scenarios were produced and distributed\nthrough the Earth System Grid Federation. The purpose of this dataset\nis to provide a set of global, high resolution, bias-corrected\nclimate change projections that can be used to evaluate climate\nchange impacts on processes that are sensitive to finer-scale climate\ngradients and the effects of local topography on climate conditions.\n","Documentation":"https://doi.org/10.7917/OFSG3345","Contact":"support@nccs.nasa.gov","ManagedBy":"[NASA](https://www.nasa.gov)","UpdateFrequency":"No future updates planned.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["air temperature","aws-pds","climate","climate model","climate projections","CMIP6","cog","earth observation","environmental","global","model","NASA Center for Climate Simulation (NCCS)","near-surface relative humidity","near-surface specific humidity","netcdf","precipitation"],"License":"As noted in the metadata of each file, the NEX-GDDP-CMIP6 archive was\ninitially made available through a [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/),\nas required by the standard data license of the original CMIP6 data.\nIn June 2022, the CMIP6 community updated their underlying licenses\nhttps://wcrp-cmip.github.io/CMIP6_CVs/docs/CMIP6_source_id_licenses.html, thereby allowing the\nNEX-GDDP-CMIP6 data license to also be updated. As of September 2022, all NEX-GDDP-CMIP6\noutputs are made available under a blanket Creative Commons Zero license (CC0) license.\n","Citation":"NEX-GDDP-CMIP6 data was accessed on [date] from https://registry.opendata.aws/nex-gddp-cmip6","Resources":[{"Description":"The NEX-GDDP-CMIP6 archive. Files are in NetCDF format with the [CF-1.7](https://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/cf-conventions.html) metadata conventions.\n","ARN":"arn:aws:s3:::nex-gddp-cmip6","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://nex-gddp-cmip6.s3.us-west-2.amazonaws.com/index.html)"]},{"Description":"The NEX-GDDP-CMIP6-COG archive. Files are in [Cloud-Optimized GeoTiff](https://www.cogeo.org/) (COG) format. COG is a raster data format optimized for cloud-native processing and visualizations. Internal tiling and overviews allow map clients to progressively load subsections (or tiles) of the raster data at any zoom level very efficiently. Note: since the overviews are generated by downsampling the data from the zoom level above, care should be taken to use data from the native resolution for most analysis purposes.\nThe NEX-GDDP-CMIP-COG archive was generated by re-processing a subset of the NEX-GDDP-CMIP6 archive from NetCDF into COGs. The subset contains 2 models: GFDL-CM4 and GISS-E2-1-G and 2 SSPs for each model: ssp245 and ssp585.\nThe data is divided into 3 sub-products: daily, monthly, crossing. \n  - daily: source data with daily timestamps.\n  - monthly: monthly aggregatations across both models, currently displayed in the https://cmip6.surge.sh/ dashboard. The reduced temporal granularity makes this sub-product a good choice for interactive dashboards. \n  - crossing: a single data product for each ssp, that indicates the number of years until the average daily temperature is expected to pass the 2 degree limit from the Paris Agreement, at each position on earth.\n","ARN":"arn:aws:s3:::nex-gddp-cmip6-cog","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](https://nex-gddp-cmip6-cog.s3.us-west-2.amazonaws.com/index.html)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"NEX-GDDP-CMIP6 Dashboard","URL":"https://cmip6.surge.sh/","AuthorName":"NASA","AuthorURL":"https://www.earthdata.nasa.gov/"}],"Publications":[{"Title":"NASA and ASDI announce no-cost access to important climate dataset on the AWS Cloud","URL":"https://aws.amazon.com/blogs/publicsector/nasa-asdi-announce-no-cost-access-important-climate-dataset-aws-cloud/","AuthorName":"Dr. Manil Maskey and Ana Pinheiro Privette"},{"Title":"NASA Global Daily Downscaled Projections, CMIP6","URL":"https://doi.org/10.1038/s41597-022-01393-4","AuthorName":"Thrasher, B., Wang, W., Michaelis, A., Melton, F., Lee, T. and Nemani, R."}]},"RegistryEntryAdded":"2022-09-23","RegistryEntryLastModified":"2024-03-26","Slug":"nex-gddp-cmip6","Sources":["awslabs-open-data-registry"]}
{"Name":"NEXRAD ARCO - atmoscale.ai Icechunk-Zarr","Description":"NEXRAD Level II weather radar data converted into a FAIR-compliant, analysis-ready, cloud-optimized (ARCO) format using Zarr v3 and Icechunk V2. The dataset is hierarchically organized by Volume Coverage Pattern (VCP) and radar sweep, enabling efficient time-series access to polarimetric radar variables without downloading and processing individual Level II files.\n\nThis dataset has been transformed and optimized for cloud-native analysis by [AtmoScale](https://atmoscale.ai).\n\n- **Temporal coverage:** January 1, 2015 to present.\n- **Radar site:** KLOT, serving the Chicago, Illinois region.\n- **Available variables:** Reflectivity (DBZH), differential reflectivity (ZDR), copolar correlation coefficient (RHOHV), differential phase (PHIDP), radial velocity (VELOCITY), and spectrum width (WRADH).\n","Documentation":"https://atmoscale.github.io/radar-datatree/","Contact":"contact@atmoscale.ai","ManagedBy":"[Atmoscale](https://atmoscale.ai/)","UpdateFrequency":"New volume scans are added continuously as data becomes available.","Tags":["aws-pds","earth observation","meteorological","radar","weather","zarr"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Citation":"Ladino-Rincón, A., Nesbitt, S. W., & AtmoScale (2026). NEXRAD ARCO (KLOT): Analysis-Ready, Cloud-Optimized NEXRAD Level II Weather Radar Data in Icechunk-Zarr [Data set]. Zenodo. https://doi.org/10.5281/zenodo.23404852","Resources":[{"Description":"NEXRAD ARCO data in Zarr v3 / Icechunk V2 format","ARN":"arn:aws:s3:::nexrad-arco","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Notifications for new NEXRAD ARCO objects","ARN":"arn:aws:sns:us-east-1:475554723765:nexrad-arco-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: NEXRAD-ARCO","URL":"https://atmoscale.github.io/radar-datatree/1.NEXRAD-KLOT-Demo.html","NotebookURL":"https://github.com/AtmoScale/radar-datatree/blob/main/notebooks/1.NEXRAD-KLOT-Demo.ipynb","AuthorName":"Alfonso Ladino-Rincon","AuthorURL":"https://github.com/aladinor"}],"Tools & Applications":[{"Title":"radar-datatree - Examples and tutorials for accessing ARCO weather radar data","URL":"https://github.com/AtmoScale/radar-datatree","AuthorName":"Alfonso Ladino-Rincon","AuthorURL":"https://github.com/aladinor"}],"Publications":[{"Title":"Radar DataTree: A FAIR and Cloud-Native Framework for Scalable Weather Radar Archives","URL":"https://doi.org/10.48550/arXiv.2510.24943","AuthorName":"Alfonso Ladino-Rincon and Stephen W. Nesbitt"}]},"RegistryEntryAdded":"2026-02-09","RegistryEntryLastModified":"2026-10-15","Slug":"nexrad-arco","Sources":["awslabs-open-data-registry"]}
{"Name":"Non-Invasive Fetal ECG Arrhythmia Database","Description":"Fetal cardiac arrhythmias are defined as any irregular fetal cardiac rhythm or regular rhythm at a rate outside the reference range of 100 to 200 beat per minute (bpm). Arrhythmias are discovered in about 1% of fetuses with about 10% of these being considered potential sources of morbidity. Although most fetal arrhythmias are benign, some can cause fetal hydrops and lead to fetal death. This means that up to 1 fetus in 100 need their arrhythmias to be closely monitored and if indicated treated in-utero using antiarrhythmic therapy. The Non-Invasive Fetal ECG Arrhythmia Database (NIFEA DB) provides a series of fetal arrhythmias recordings (n=12) and a number of control normal rhythm recordings (n=14) performed using the non-invasive fetal electrocardiography (NI-FECG) technique.","Documentation":"https://doi.org/10.13026/C2CT0S","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2CT0S","ARN":"arn:aws:s3:::physionet-open/nifeadb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"nifeadb","Sources":["awslabs-open-data-registry"]}
{"Name":"Non-Invasive Fetal ECG Database","Description":"This database contains a series of 55 multichannel abdominal non-invasive fetal electrocardiogram (FECG) recordings, taken from a single subject between 21 to 40 weeks of pregnancy. The records have variable durations, and were taken weekly (two or more records were acquired during some weeks). These records may be very useful for testing signal separation algorithms.","Documentation":"https://doi.org/10.13026/C2X30H","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2X30H","ARN":"arn:aws:s3:::physionet-open/nifecgdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"nifecgdb","Sources":["awslabs-open-data-registry"]}
{"Name":"NIFS Large Helical Device (LHD) Experiment","Description":"The Large Helical Device (LHD), owned and operated by the National Institute for Fusion Science (NIFS), is one of the world's largest plasma confinement device which employs a heliotron magnetic configuration generated by the superconducting coils. The objectives are to conduct academic research on the confinement of steady-state, high-temperature, high-density plasmas, core plasma physics, and fusion reactor engineering, which are necessary to develop future fusion reactors. All the archived data of the LHD plasma diagnostics are available since the beginning of the LHD experiment, started on 31st of March, 1998.","Documentation":"https://www-lhd.nifs.ac.jp/pub/Repository_en.html","Contact":"For any questions regarding data delivery or any general questions regarding the LHD Experiment data repository, please send email to the Data Acquisition and Analysis group at Comp_DAE@nifs.ac.jp.","ManagedBy":"[NIFS](https://www.nifs.ac.jp/)","UpdateFrequency":"Archived data files are updated nightly when new or revised data are generated in LHD experiment.","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["analytics","anomaly detection","archives","aws-pds","computed tomography","datacenter","digital assets","electricity","energy","fluid dynamics","image processing","physics","post-processing","radiation","signal processing","source code","turbulence","video","x-ray","x-ray tomography"],"License":"This data is available for anyone to use under the \"[Rights and Terms](https://www-lhd.nifs.ac.jp/pub/RightsTerms.html)\"","Citation":null,"Resources":[{"Description":"LHD Diagnostic data","ARN":"arn:aws:s3:::nifs-lhd","Region":"ap-northeast-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://nifs-lhd.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Data handling in the LHD experiments -- Guide for the collaborator (pdf)","URL":"https://www-lhd.nifs.ac.jp/pub/pdf/LHD_Guide/5_DataLHDv2.pdf","NotebookURL":null,"AuthorName":"Satoshi Ohdachi","AuthorURL":"https://researchmap.jp/ohdachi?lang=en","Services":null}],"Tools & Applications":[{"Title":"How to retrieve diagnostic raw data and/or primarily processed data using \"Retrieve\"","URL":"https://w3.lhd.nifs.ac.jp/en/Service/DataAccess/RetrieveManual-e.htm","AuthorName":"NIFS LABCOM","AuthorURL":"https://w3.lhd.nifs.ac.jp/"}],"Publications":[{"Title":"Data Acquisition and Management System of LHD","URL":"https://doi.org/10.13182/FST10-A10830","AuthorName":"Hideya Nakanishi, et al.","AuthorURL":"https://orcid.org/0000-0001-6388-4489"}]},"DeprecatedNotice":null,"ADXCategories":["Resources Data"],"RegistryEntryAdded":"2023-11-01","RegistryEntryLastModified":"2025-07-09","Slug":"nifs-lhd","Sources":["awslabs-open-data-registry"]}
{"Name":"NInFEA: Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research","Description":"The development of algorithms for the extraction of the foetal ECG (fECG) from non-invasive recordings is hampered by the lack of publicly-available reference datasets, which could be used to benchmark different algorithms while providing a ground truth on the foetal heart activity when an invasive scalp lead is unavailable. By enriching the electrophysiological recordings with simultaneous multimodal signals, these datasets could also help the investigation of the foetal cardiac physiology, providing ground truth for the analysis in early pregnancy, when the fECG is not directly accessible. The Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (NInFEA) is the first open-access dataset featuring simultaneous non-invasive electrophysiological recordings, fetal pulsed-wave Doppler (PWD) and maternal respiration signals. The dataset includes 60 entries from 39 voluntary pregnant women, between the 21st and the 27th week of gestation. Every entry is composed of 27 electrophysiological channels (2048 Hz, 22 bits, acquired by means of the TMSi Porti7 system), maternal respiration signal (through a resistive thoracic belt), synchronised foetal trans-abdominal PWD and clinical annotations provided by expert clinicians at the time of the signal collection.","Documentation":"https://doi.org/10.13026/c4n5-3b04","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/c4n5-3b04","ARN":"arn:aws:s3:::physionet-open/ninfea/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ninfea","Sources":["awslabs-open-data-registry"]}
{"Name":"New Jersey Statewide Digital Aerial Imagery Catalog","Description":"The New Jersey Office of GIS, NJ Office of Information Technology manages a series of 11 digital orthophotography and scanned aerial photo maps collected at various years ranging from 1930 to 2017. Each year’s worth of imagery are available as Cloud Optimized GeoTIFF (COG) files and some years are available as compressed MrSID and/or JP2 files.  Additionally, each year of imagery is organized into a tile grid scheme covering the entire geography of New Jersey.  Many years share the same tiling grid while others have unique grids as defined by the project at the time.","Documentation":"https://njgin.nj.gov/njgin/edata/imagery/index.html","Contact":"njgin@oit.nj.gov","ManagedBy":"The New Jersey Office of GIS, NJ Office of Information Technology","UpdateFrequency":"None","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aerial imagery","aws-pds","cog","earth observation","geospatial","imaging","mapping"],"License":"The State of New Jersey provides the service \"as is\". The State makes no guarantee or warranty concerning the accuracy of information contained in the data. Also, the State makes no warranty, either expressed or implied, regarding the condition of the product or its fitness for any particular purpose. The burden for determining fitness for use lies entirely with the user. Although these data files and service have been processed successfully on computers at the State, no warranty is made by the State regarding the use of the data or service on any other system, nor does the fact of distribution constitute or imply such a warranty.","Resources":[{"Description":"New Jersey digital orthophotography archive.","ARN":"arn:aws:s3:::njogis-imagery","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Visualize Imagery Changes","URL":"https://njogis-imagery.s3-us-west-2.amazonaws.com/NJ+Imagery+Change.pdf","AuthorName":"stephanie.bosits@tech.nj.gov","AuthorURL":"https://njgin.nj.gov/njgin/edata/imagery/index.html"}]},"RegistryEntryAdded":"2020-07-09","RegistryEntryLastModified":"2023-01-06","Slug":"nj-imagery","Sources":["awslabs-open-data-registry"]}
{"Name":"New Jersey Statewide LiDAR","Description":"Elevation datasets in New Jersey have been collected over several years as several\ndiscrete projects.  Each project covers a geographic area, which is a subsection of\nthe entire state, and has differing specifications based on the available technology\nat the time and project budget.  The geographic extent of one project may overlap that\nof a neighboring project. Each of the 18 projects contains deliverable products such\nas LAS (Lidar point cloud) files, unclassified/classified, tiled to cover project area;\nrelevant metadata records or documents, most adhering to the Federal Geographic Data\nCommittee’s (FGDC) Content Standard for Digital Geospatial Metadata (CSDGM); tiling\nindex feature class or shapefile; flights lines feature class or shapefile; Digital\nElevation Model in image format or Esri grid format; other derivative data products\nsuch as contour lines feature class or shapefile.\n","Documentation":"https://njgin.nj.gov/njgin/edata/elevation/index.html","Contact":"njgin@oit.nj.gov","ManagedBy":"The New Jersey Office of GIS, NJ Office of Information Technology","UpdateFrequency":"None","Collabs":{"ASDI":{"Tags":["elevation"]}},"Tags":["aws-pds","elevation","geospatial","lidar","mapping"],"License":"The State of New Jersey provides the service \"as is\". The State makes no guarantee or warranty concerning the accuracy of information contained in the data. Also, the State makes no warranty, either expressed or implied, regarding the condition of the product or its fitness for any particular purpose. The burden for determining fitness for use lies entirely with the user. Although these data files and service have been processed successfully on computers at the State, no warranty is made by the State regarding the use of the data or service on any other system, nor does the fact of distribution constitute or imply such a warranty.","Resources":[{"Description":"New Jersey digital elevation data archive, LiDAR derived products.","ARN":"arn:aws:s3:::njogis-elevation","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"3D Visualization","URL":"https://njogis-elevation.s3-us-west-2.amazonaws.com/NJ+3D+Visualization.pdf","AuthorName":"stephanie.bosits@tech.nj.us","AuthorURL":"https://njgin.nj.gov/njgin/edata/elevation/index.html"}]},"RegistryEntryAdded":"2020-07-09","RegistryEntryLastModified":"2021-12-28","Slug":"nj-lidar","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA HYSPLIT-compatible meteorological data archives","Description":"The HYSPLIT model is a complete system for computing simple air parcel trajectories, as well as complex transport, dispersion, chemical transformation, and deposition simulations. HYSPLIT continues to be one of the most extensively used atmospheric transport and dispersion models in the atmospheric sciences community. A common application is a back trajectory analysis to determine the origin of air masses and establish source-receptor relationships. HYSPLIT has also been used in a variety of simulations describing the atmospheric transport, dispersion, and deposition of pollutants and hazardous materials. Some examples of the applications include tracking and forecasting the release of radioactive material, wildfire smoke, windblown dust, pollutants from various stationary and mobile emission sources, allergens and volcanic ash.\nThe National Weather Service's National Centers for Environmental Prediction (NCEP) runs a series of computer analyses and forecasts operationally. NOAA's Air Resources Laboratory (ARL) routinely uses NCEP model data for use in air quality transport and dispersion modeling calculations. In 1989 ARL began to archive some of these datasets for future research studies. ARL has in the past, or is presently archiving the following NCEP datasets that are compatible with HYSPLIT. A few datasets that are created outside NOAA are also included. HYSPLIT-compatible meteorological datasets are required to run HYSPLIT for trajectory or dispersion simulations. The HYSPLIT-compatible meteorological datasets can also be used with HYSPLIT utilities to display and/or extract meteorological data from the datasets.\n","Documentation":"For the description of each archive listed above, please refer to their respective README file at  https://www.ready.noaa.gov/archives.php. For information on HYSPLIT, please consult https://www.arl.noaa.gov/hysplit/.\n","Contact":"For questions regarding ARL HYSPLIT data content or quality, contact arl.webmaster@noaa.gov. For any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\nWe also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"At present, the following five datasets are updated every day or every week:\n- HRRR 3 km archive (Jun 2019 - present) - daily\n- NAMS Hybrid sigma-pressure archive (2010 - present) - daily\n- NAM 12 km archive (May 2007 - present) - daily\n- GDAS one-degree archive (Dec 2004 - present) - weekly\n- GFS quarter-degree archive (Jun 2019 - present) - daily\n\nThe following two datasets are updated monthly:\n- NCEP/NCAR reanalysis archive (1948 - present) - monthly, typically around the 5th of the following month.\n- Weather research and forecasting model 27 km archive (1980 - present) - monthly, typically with a delay of two to three months.\n\nThe following datasets are no longer updated:\n- HRRR 3 km version 1 archive (Jun 2015 - Jul 2019) - frozen\n- GDAS half-degree archive (Sep 2007 - Jun 2019) - frozen\n- FNL archive (1997 - 2006) - frozen\n- NGM archive (Jan 1991 - Apr 1997) - frozen\n- NAM data assimilation system archive (2004 - 2018) - frozen\n- NAM(Eta) data assimilation system archive (1997 - 2004) - frozen\n- NARR archive (1979 - 2019) - frozen\n","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","disaster response","environmental","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.\n","Resources":[{"Description":"HYSPLIT-compatible meteorological data archives","ARN":"arn:aws:s3:::noaa-oar-arl-hysplit-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-oar-arl-hysplit-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for ARL HYSPLIT, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewHYSPLITObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"HYSPLIT Basic Tutorial","URL":"https://www.ready.noaa.gov/documents/Tutorial/html/index.html","AuthorName":"NOAA ARL HYSPLIT"}]},"RegistryEntryAdded":"2025-06-13","RegistryEntryLastModified":"2025-06-26","Slug":"noaa-arl-hysplit","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA National Bathymetric Source Data","Description":"The National Bathymetric Source (NBS) project creates and maintains \nhigh-resolution bathymetry composed of the best available data. \nThis project enables the creation of next-generation nautical charts \nwhile also providing support for modeling, industry, science, \nregulation, and public curiosity. Primary sources of bathymetry include \nNOAA and U.S. Army Corps of Engineers hydrographic surveys and \ntopographic bathymetric (topo-bathy) lidar (light detection and ranging) \ndata.  Data submitted through the NOAA Office of Coast Survey’s external \nsource data process are also included, with gaps in deep water filled \nthrough Global Multi-Resolution Topography, a merged model of \nbathymetry. Different vertical datums and file formats are made \navailable to meet various uses. The BlueTopo folder includes multilayer \nfloating point GeoTIFFs with associated Raster Attribute Tables (RAT) \ncontaining elevation, vertical uncertainty, with other quality metrics \nand source information. These files are arranged in a spatial tiling and\nresolution scheme corresponding to the Electronic Navigational Chart (ENC)\nbut are not for navigation due to the inclusion of additional \nnon-navigation data and non-navigation vertical datums. For \nnavigational datasets please see the S-102 distribution portal. \n\"[nowCOAST](https://nowcoast.noaa.gov/)\" provides public access to BlueTopo\nthrough the nowCOAST viewer, web map tile services (WMTS), and links to\nindividual datasets.\n","Documentation":"https://nauticalcharts.noaa.gov/data/bluetopo.html","Contact":"For general questions or feedback about the data, please submit inquiries\nthrough the NOAA Office of Coast Survey (OCS) \"[ASSIST Tool](https://www.nauticalcharts.noaa.gov/customer-service/assist/)\". \nFor any data delivery issues, please contact the NOAA Open Data Dissemination Team (NODD) at NODD@noaa.gov.\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Monthly where new data is available.","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","bathymetry","earth observation","marine navigation","model","oceans","oceans"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA National Bathymetric Source Data","ARN":"arn:aws:s3:::noaa-ocs-nationalbathymetry-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ocs-nationalbathymetry-pds.s3.amazonaws.com/index.html)"]},{"Description":"NOAA National Bathymetry New Object Notification","ARN":"arn:aws:sns:us-east-1:709902155096:NewNationalBathymetryObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2021-04-26","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-bathymetry","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Atmospheric Climate Data Records","Description":"NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).<br/><br/>\nClimate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.<br/><br/>\nAtmospheric Climate Data Records are measurements of several global variables to help characterize the atmosphere at or just above the land and ocean surface as well as other upper air composition variables.\n","Documentation":"https://www.ncei.noaa.gov/products/climate-data-records/atmospheric","Contact":"For questions regarding the specific CDR data holdings, please contact NCEI.SAT.INFO@NOAA.GOV.<br />\nFor any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Open Data Dissemination Team (NODD) email us at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NOAA NODD team here: nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Climate Data Records are updated independently. For update frequency for a specific CDR, please refer to the Climate Data Record website.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","climate","meteorological","sustainability","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Aerosol Optical Thickness","ARN":"arn:aws:s3:::noaa-cdr-aerosol-optical-thickness-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-aerosol-optical-thickness-pds.s3.amazonaws.com/index.html)"]},{"Description":"Cloud Properties ISCCP","ARN":"arn:aws:s3:::noaa-cdr-cloud-properties-isccp-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-cloud-properties-isccp-pds.s3.amazonaws.com/index.html)"]},{"Description":"Cloud Properties Polar Orbiter","ARN":"arn:aws:s3:::noaa-cdr-cloud-properties-polar-orbiter-nasa-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-cloud-properties-polar-orbiter-nasa-pds.s3.amazonaws.com/index.html)"]},{"Description":"Hydrological Properties","ARN":"arn:aws:s3:::noaa-cdr-hydrological-properties-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-hydrological-properties-pds.s3.amazonaws.com/index.html)"]},{"Description":"Ocean Heat Content","ARN":"arn:aws:s3:::noaa-cdr-ocean-heat-content-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-ocean-heat-content-pds.s3.amazonaws.com/index.html)"]},{"Description":"Ocean Heatflux","ARN":"arn:aws:s3:::noaa-cdr-ocean-heatflux-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-ocean-heatflux-pds.s3.amazonaws.com/index.html)"]},{"Description":"Ocean Nearsurface Atmos Profiles","ARN":"arn:aws:s3:::noaa-cdr-ocean-nearsurface-atmos-profiles-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-ocean-nearsurface-atmos-profiles-pds.s3.amazonaws.com/index.html)"]},{"Description":"Outgoing Longwave Radiation - Daily","ARN":"arn:aws:s3:::noaa-cdr-outgoing-longwave-radiation-daily-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-outgoing-longwave-radiation-daily-pds.s3.amazonaws.com/index.html)"]},{"Description":"Outgoing Longwave Radiation - Monthly","ARN":"arn:aws:s3:::noaa-cdr-outgoing-longwave-radiation-monthly-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-outgoing-longwave-radiation-monthly-pds.s3.amazonaws.com/index.html)"]},{"Description":"Ozone - ESRL","ARN":"arn:aws:s3:::noaa-cdr-ozone-esrl-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-ozone-esrl-pds.s3.amazonaws.com/index.html)"]},{"Description":"CMORPH Precip","ARN":"arn:aws:s3:::noaa-cdr-precip-cmorph-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-precip-cmorph-pds.s3.amazonaws.com/index.html)"]},{"Description":"GPCP Precip Daily","ARN":"arn:aws:s3:::noaa-cdr-precip-gpcp-daily-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-precip-gpcp-daily-pds.s3.amazonaws.com/index.html)"]},{"Description":"GPCP Precip Monthly","ARN":"arn:aws:s3:::noaa-cdr-precip-gpcp-monthly-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-precip-gpcp-monthly-pds.s3.amazonaws.com/index.html)"]},{"Description":"NEXRAD Precip","ARN":"arn:aws:s3:::noaa-cdr-precip-nexrad-qpe-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-precip-nexrad-qpe-pds.s3.amazonaws.com/index.html)"]},{"Description":"PERSIANN Precip","ARN":"arn:aws:s3:::noaa-cdr-precip-persiann-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-precip-persiann-pds.s3.amazonaws.com/index.html)"]},{"Description":"Solar Spectral Irradiance","ARN":"arn:aws:s3:::noaa-cdr-solar-spectral-irradiance-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-solar-spectral-irradiance-pds.s3.amazonaws.com/index.html)"]},{"Description":"Total Solar Irradiance","ARN":"arn:aws:s3:::noaa-cdr-total-solar-irradiance-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-total-solar-irradiance-pds.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2021-07-15","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-cdr-atmospheric","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Fundamental Climate Data Records (FCDR)","Description":"NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).<br/><br/>\nClimate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.<br/><br/>\nFundamental CDRs are composed of sensor data (e.g. calibrated radiances, brightness temperatures) that have been improved and quality controlled over time, together with ancillary calibration data.\n","Documentation":"https://www.ncdc.noaa.gov/cdr","Contact":"For questions regarding the specific CDR data holdings, please contact NCEI.SAT.INFO@NOAA.GOV.<br />\nFor any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Open Data Dissemination Team (NODD) email us at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team here: nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Climate Data Records are updated independently. For update frequency for a specific CDR, please refer to the Climate Data Record website.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","climate","meteorological","sustainability","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Gridsat B1","ARN":"arn:aws:s3:::noaa-cdr-gridsat-b1-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-gridsat-b1-pds.s3.amazonaws.com/index.html)"]},{"Description":"Water Vapor Brightness Temperature","ARN":"arn:aws:s3:::noaa-cdr-ir-water-vapor-brit-temp-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-ir-water-vapor-brit-temp-pds.s3.amazonaws.com/index.html)"]},{"Description":"Mean Layer Temperature Lower Stratosphere","ARN":"arn:aws:s3:::noaa-cdr-mean-layer-temp-lower-strat-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-mean-layer-temp-lower-strat-pds.s3.amazonaws.com/index.html)"]},{"Description":"Mean Layer Temperature","ARN":"arn:aws:s3:::noaa-cdr-mean-layer-temp-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-mean-layer-temp-pds.s3.amazonaws.com/index.html)"]},{"Description":"Mean Layer Temperature - RSS","ARN":"arn:aws:s3:::noaa-cdr-mean-layer-temp-rss-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-mean-layer-temp-rss-pds.s3.amazonaws.com/index.html)"]},{"Description":"Mean Layer Temperature - UAH","ARN":"arn:aws:s3:::noaa-cdr-mean-layer-temp-uah-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-mean-layer-temp-uah-pds.s3.amazonaws.com/index.html)"]},{"Description":"Mean Layer Temperature Upper Troposphere","ARN":"arn:aws:s3:::noaa-cdr-mean-layer-temp-upper-trop-lower-strat-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-mean-layer-temp-upper-trop-lower-strat-pds.s3.amazonaws.com/index.html)"]},{"Description":"Microwave Brightness Temperature","ARN":"arn:aws:s3:::noaa-cdr-microwave-brit-temp-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-microwave-brit-temp-pds.s3.amazonaws.com/index.html)"]},{"Description":"Microwave Humidity Sounder Brightness Temperature","ARN":"arn:aws:s3:::noaa-cdr-microwave-humidity-sounder-brit-temp-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-microwave-humidity-sounder-brit-temp-pds.s3.amazonaws.com/index.html)"]},{"Description":"Microwave Imager Brightness Temperature - CSU","ARN":"arn:aws:s3:::noaa-cdr-microwave-imager-brit-temp-csu-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-microwave-imager-brit-temp-csu-pds.s3.amazonaws.com/index.html)"]},{"Description":"Microwave Imager Brightness Temperature - RSS","ARN":"arn:aws:s3:::noaa-cdr-microwave-imager-brit-temp-rss-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-microwave-imager-brit-temp-rss-pds.s3.amazonaws.com/index.html)"]},{"Description":"Microwave Temperature Sounder Brightness Temperature","ARN":"arn:aws:s3:::noaa-cdr-microwave-temp-sounder-brit-temp-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-microwave-temp-sounder-brit-temp-pds.s3.amazonaws.com/index.html)"]},{"Description":"MSU Brightness Temperature","ARN":"arn:aws:s3:::noaa-cdr-msu-brit-temp-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-msu-brit-temp-pds.s3.amazonaws.com/index.html)"]},{"Description":"Polar Pathfinder Extended","ARN":"arn:aws:s3:::noaa-cdr-polar-pathfinder-extended-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-polar-pathfinder-extended-pds.s3.amazonaws.com/index.html)"]},{"Description":"Polar Pathfinder","ARN":"arn:aws:s3:::noaa-cdr-polar-pathfinder-fcdr-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-polar-pathfinder-fcdr-pds.s3.amazonaws.com/index.html)"]},{"Description":"Polar Orbiter Radiances and Cloud Properties - NASA","ARN":"arn:aws:s3:::noaa-cdr-radiances-and-cloud-properties-polar-orbiter-nasa-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-radiances-and-cloud-properties-polar-orbiter-nasa-pds.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2021-07-13","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-cdr-fundamental","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Oceanic Climate Data Records","Description":"NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).<br/><br/>\nClimate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.<br/><br/>\nOceanic Climate Data Records are measurements of oceans and seas both surface and subsurface as well as frozen state variables.\n","Documentation":"https://www.ncdc.noaa.gov/cdr","Contact":"For questions regarding the specific CDR data holdings, please contact NCEI.SAT.INFO@NOAA.GOV.<br />\nFor any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Open Data Dissemination Team (NODD) email us at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team here by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Climate Data Records are updated independently. For update frequency for a specific CDR, please refer to the Climate Data Record website.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","climate","meteorological","oceans","sustainability","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Sea Surface Temperature - Optimum Interpolation","ARN":"arn:aws:s3:::noaa-cdr-sea-surface-temp-optimum-interpolation-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-sea-surface-temp-optimum-interpolation-pds.s3.amazonaws.com/index.html)"]},{"Description":"Sea Ice Concentration","ARN":"arn:aws:s3:::noaa-cdr-sea-ice-concentration-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-sea-ice-concentration-pds.s3.amazonaws.com/index.html)"]},{"Description":"Sea Surface Temperature - WHOI","ARN":"arn:aws:s3:::noaa-cdr-sea-surface-temp-whoi-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-sea-surface-temp-whoi-pds.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2021-07-15","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-cdr-oceanic","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Terrestrial Climate Data Records","Description":"NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).<br/><br/>\nClimate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.<br/><br/>\nTerrestrial CDRs are composed of sensor data that have been improved and quality controlled over time, together with ancillary calibration data.\n","Documentation":"https://www.ncdc.noaa.gov/cdr","Contact":"For questions regarding the specific CDR data holdings, please contact NCEI.SAT.INFO@NOAA.GOV.<br />\nFor any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Open Data Dissemination Team (NODD) email us at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Climate Data Records are updated independently. For update frequency for a specific CDR, please refer to the Climate Data Record website.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","climate","meteorological","sustainability","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Leaf Area Index","ARN":"arn:aws:s3:::noaa-cdr-leaf-area-index-fapar-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-leaf-area-index-fapar-pds.s3.amazonaws.com/index.html)"]},{"Description":"NDVI","ARN":"arn:aws:s3:::noaa-cdr-ndvi-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-ndvi-pds.s3.amazonaws.com/index.html)"]},{"Description":"Snow Cover Extent","ARN":"arn:aws:s3:::noaa-cdr-snow-cover-ext-north-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cdr-snow-cover-ext-north-pds.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2021-07-15","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-cdr-terrestrial","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Climate Forecast System (CFS)","Description":"The Climate Forecast System (CFS) is a model representing the global interaction between Earth's oceans, land, and atmosphere. Produced by several dozen scientists under guidance from the National Centers for Environmental Prediction (NCEP), this model offers hourly data with a horizontal resolution down to one-half of a degree (approximately 56 km) around Earth for many variables. CFS uses the latest scientific approaches for taking in, or assimilating, observations from data sources including surface observations, upper air balloon observations, aircraft observations, and satellite observations. <br /> Please note that the data in this bucket are the CFSv2 Operational Forecasts. To obtain other CFSv2 products such as the Operational Analysis, please visit our [website](https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/climate-forecast-system-version2-cfsv2).","Documentation":"https://cfs.ncep.noaa.gov/","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Hourly, 6-Hourly, and Monthly","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Climate Forecast System (CFS) Model Data","ARN":"arn:aws:s3:::noaa-cfs-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cfs-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for CFS, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewCFSObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"The NCEP Climate Forecast System Reanalysis","URL":"https://journals.ametsoc.org/view/journals/bams/91/8/2010bams3001_1.xml","AuthorName":"Saha, Suranjana, and Coauthors"},{"Title":"The NCEP Climate Forecast System Version 2","URL":"https://journals.ametsoc.org/view/journals/clim/27/6/jcli-d-12-00823.1.xml","AuthorName":"Saha, Suranjana, and Coauthors"}]},"RegistryEntryAdded":"2021-03-05","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-cfs","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA U.S. Climate Normals","Description":"The U.S. Climate Normals are a large suite of data products that provide information about typical climate conditions for thousands of locations across the United States. Normals act both as a ruler to compare today’s weather and tomorrow’s forecast, and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables from almost 15,000 U.S. weather stations. <br/><br/>\nNCEI generates the official U.S. normals every 10 years in keeping with the needs of our user community and the requirements of the World Meteorological Organization (WMO) and National Weather Service (NWS). The 1991–2020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. These data allow travelers to pack the right clothes, farmers to plant the best crop varieties, and utilities to plan for seasonal energy usage. Many other important economic decisions that are made beyond the predictive range of standard weather forecasts are either based on or influenced by climate normals.\n","Documentation":"[https://www.ncei.noaa.gov/products/us-climate-normals](https://www.ncei.noaa.gov/products/us-climate-normals); [https://www.ncei.noaa.gov/news/noaa-delivers-new-us-climate-normals](https://www.ncei.noaa.gov/news/noaa-delivers-new-us-climate-normals)","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Data is updated on 10 year cycles or when corrections are implemented.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","climate","meteorological","sustainability","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"U.S. Climate Normals Data","ARN":"arn:aws:s3:::noaa-normals-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-normals-pds.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Investigating environmental characteristics of US cities using publicly available ASDI datasets using SageMaker Studio Lab (SMSL)","URL":"https://github.com/aws-samples/aws-asdi-cities-smsl-notebook/","NotebookURL":"https://github.com/aws-samples/aws-asdi-cities-smsl-notebook/blob/main/ASDI_Cities_Demo.ipynb","AuthorName":"Darren Ko","Services":["Amazon SageMaker Studio Lab"]}]},"RegistryEntryAdded":"2021-08-11","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-climate-normals","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Coastal Lidar Data","Description":"Lidar (light detection and ranging) is a technology that can measure the 3-dimentional location of objects, including the solid earth surface. The data consists of a point cloud of the positions of solid objects that reflected a laser pulse, typically from an airborne platform. In addition to the position, each point may also be attributed by the type of object it reflected from, the intensity of the reflection, and other system dependent metadata. The NOAA Coastal Lidar Data is a collection of lidar projects from many different sources and agencies, geographically focused on the coastal areas of the United States of America. The data is provided in Entwine Point Tiles (EPT; https://entwine.io) format, which is a lossless streamable octree of the point cloud, and in LAZ format. Datasets are maintained in their original projects and care should be taken when merging projects. The coordinate reference system for the data is The NAD83(2011) UTM zone appropriate for the center of each data set for EPT and geographic coordinates for LAZ. Vertically they are in the orthometric datum appropriate for that area (for example, NAVD88 in the mainland United States, PRVD02 in Puerto Rico, or GUVD03 in Guam). The geoid model used is reflected in the data set resource name. <br>The data are organized under directories entwine and laz for the EPT and LAZ versions respectively. Some datasets are not in EPT format, either because the dataset is already in EPT on the USGS public lidar site, they failed to build or their content does not work well in EPT format. Topobathy lidar datasets using the topobathy domain profile do not translate well to EPT format.","Documentation":"https://coast.noaa.gov/digitalcoast/data/coastallidar.html and https://coast.noaa.gov/digitalcoast/data/jalbtcx.html. A table providing the dataset names and links is at https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/index.html","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. For general questions or feedback about the data, please submit inquiries through the NOAA Office for Coastal Management Contact Form https://coast.noaa.gov/contactform/. <br> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov","ManagedBy":"[NOAA](https://www.noaa.gov/)","UpdateFrequency":"Periodically, as new data becomes available","Collabs":{"ASDI":{"Tags":["elevation"]}},"Tags":["aws-pds","climate","disaster response","elevation","geospatial","lidar","stac"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA Coastal Lidar Dataset Entwine Point Tiles","ARN":"arn:aws:s3:::noaa-nos-coastal-lidar-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://noaa-nos-coastal-lidar-pds.s3.us-east-1.amazonaws.com/entwine/stac/catalog.json)"]},{"Description":"NOAA Coastal Lidar Dataset Cloud Optimized Point Cloud (COPC)","ARN":"arn:aws:s3:::noaa-nos-coastal-lidar-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://noaa-nos-coastal-lidar-pds.s3.us-east-1.amazonaws.com/laz/stac/catalog.json)"]},{"Description":"NOAA Coastal Lidar Dataset Raster DEMs","ARN":"arn:aws:s3:::noaa-nos-coastal-lidar-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://noaa-nos-coastal-lidar-pds.s3.us-east-1.amazonaws.com/dem/stac/catalog.json)"]},{"Description":"NOAA Coastal Lidar Dataset New Dataset Notification","ARN":"arn:aws:sns:us-east-1:709902155096:NewCoastalLidarObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"NOAA Digital Coast Data Access Viewer Custom Processing","URL":"https://coast.noaa.gov/dataviewer","AuthorName":"NOAA Office for Coastal Management","AuthorURL":"https://coast.noaa.gov/"},{"Title":"OpenTopography access and processing of NOAA Coastal Lidar Data","URL":"https://portal.opentopography.org/datasets","AuthorName":"OpenTopography","AuthorURL":"https://opentopography.org/"}]},"RegistryEntryAdded":"2021-02-23","RegistryEntryLastModified":"2026-09-01","Slug":"noaa-coastal-lidar","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Climate Resilience Information System CONUS Thresholds","Description":"The Climate Resilience Information System (CRIS) provides a variety of historical observations and future projections of a variety of decision-relevant thresholds of temperature and precipitation. \n<br><br>\nLOCA2 and STAR-ESDM are two downscaled climate projection datasets covering the Contiguous United States (CONUS) chosen for inclusion in the 5th U.S. National Climate Assessment. These datasets include 16 models along with a weighted ensemble for the scenarios SSP2-4.5, SSP3-7.0 and SSP5-8.5. The precipitation and temperature variables were processed into thresholds by the NOAA Technical Support Unit. \n<br><br>\nLivneh and nClimGrid are gridded observed historical climatology data that were used in the LOCA2 and STAR-ESDM downscaling process. The original Livneh and nClimGrid daily temperature and precipitation observations have been converted to the same series of thresholds as LOCA2 and STAR. \n<br><br>\nDetails on thresholds, models, scenarios, time, and processing methodologies can be found at https://cris.climate.gov/pages/about-the-data\n<br><br>\n","Documentation":"For information, please consult https://cris.climate.gov/pages/about-the-data.\n","Contact":"For any questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\nWe also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"None\n","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","climate","environmental","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.\n","Resources":[{"Description":"LOCA2 and STAR-ESDM","ARN":"arn:aws:s3:::noaa-cris-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cris-pds.s3.us-west-2.amazonaws.com/index.html)"]},{"Description":"New data notifications for LOCA2 and STAR-ESDM, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-west-2:123901341784:NewCRISObject","Region":"us-east-1","Type":"SNS Topic"},{"Description":"nClimGrid and Livneh Gridded Historical Observation Thresholds","ARN":"arn:aws:s3:::noaa-cris-hist-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-cris-hist-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for nClimGrid and Livneh Gridded Historical Observation Thresholds, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-west-2:123901341784:NewCRIS-HISTObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"U.S. CRIS Resources","URL":"https://cris.climate.gov/pages/developers","AuthorName":"U.S. CRIS"}]},"RegistryEntryAdded":"2025-11-17","RegistryEntryLastModified":"2026-07-27","Slug":"noaa-cris-hist","Sources":["awslabs-open-data-registry"]}
{"Name":"Crowdsourced Bathymetry","Description":"Community provided bathymetry data collected in collaboration with the International Hydrographic Organization.","Documentation":"https://noaa-dcdb-bathymetry-pds.s3.amazonaws.com/docs/readme.html","Contact":"mb.info@noaa.gov","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"New data is added once a week.","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","earth observation","oceans"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Crowdsourced bathymetry data","ARN":"arn:aws:s3:::noaa-dcdb-bathymetry-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-dcdb-bathymetry-pds.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for CSB data","ARN":"arn:aws:sns:us-east-1:709902155096:NewDCDBBathymetryObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Crowdsourced Bathymetry Data (CSB) Visualization","URL":"https://github.com/dneufeldcu/notebooks/blob/main/esipCSBfinal.ipynb","AuthorName":"David Neufeld"}]},"RegistryEntryAdded":"2023-03-16","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-dcdb-bathymetry-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA's Observations for Global Workflow (GW) Test Cases","Description":"A collection of observations necessary to run National Weather Service’s (NWS) GW test cases. These may include incomplete or otherwise immature data or data formats that are subject to change as development proceeds.\n\n<br><br>\n- dump_nr: non-restricted versions of observations from NWS Observation Processing (ObsProc) in Binary Universal Form for the Representation of meteorological data (BUFR) format\n- dump_ioda_nr: non-restricted observations converted to the Interface for Observation Data Access (IODA) format\n- experimental_obs: Additional atmospheric and marine observations\n- GEFS_ExtData: Assorted aerosol emission data\n- prod_anl_tars: GFS analysis restarts from production in tarball format\n<br><br>\n\nGW is the primary driver for many of the NWS’s global models, including the Global Forecast System (GFS), Global Ensemble Forecast System (GEFS), and the upcoming Climate Forecast System (CFS) successor, the Seasonal Forecast System (SFS) and the upcoming Global Chemistry and Aerosols Forecast System (GCAFS). The workflow is currently managed by National Oceanic and Atmospheric Administration (NOAA) Office of Model Development (OMD), which is also the primary developer. The Earth Prediction Innovation Center (EPIC) is responsible for the public release and user support of the GW.\n","Documentation":"- NWS ObsProc: https://www.nco.ncep.noaa.gov/pmb/products/obsproc/\n- IODA: https://jcsda-jedi-docs.readthedocs-hosted.com/en/latest/inside/jedi-components/ioda/introduction.html\n- Soil Moisture and Ocean Salinity (SMOS) salinity: https://www.esa.int/Applications/Observing_the_Earth/FutureEO/SMOS\n- Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice: https://www.earthdata.nasa.gov/data/instruments/amsr2\n- Advanced Clear‑Sky Processor for Oceans (ACSPO) global Sea Surface Temperature (SST): https://coastwatch.noaa.gov/cwn/processing-algorithms/acspo.html\n- Emissions: https://global-workflow.readthedocs.io/en/latest/User_Guide/gcafs.html#emissions-preprocessing\n- Modern‑Era Retrospective analysis for Research and Applications, version 2 (MERRA2): https://gmao.gsfc.nasa.gov/gmao-products/merra-2/data-access_merra-2/\n- Global Biomass Burning Emissions Product, experimental (GBBEPx): https://www.ospo.noaa.gov/products/land/gbbepx/\n- Quick Fire Emissions Dataset (QFED): https://github.com/GEOS-ESM/QFED\n- Community Emissions Data System (CEDS): https://www.pnnl.gov/projects/ceds\n- Hemispheric Transport of Air Pollution (HTAP): https://edgar.jrc.ec.europa.eu/dataset_htap_v3\n- Copernicus Atmosphere Monitoring Service (CAMS): https://atmosphere.copernicus.eu/\n- Model of Emissions of Gases and Aerosols from Nature (MEGAN): https://www2.acom.ucar.edu/modeling/model-emissions-gases-and-aerosols-nature-megan\n- Global workflow: https://global-workflow.readthedocs.io/en/latest/\n<br><br>\n","Contact":"For any questions about this particular dataset, please contact Walter.Kolczynski@noaa.gov. For any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Irregularly, as required to support GW.\n<br><br>\n","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["aws-pds","environmental","forecast","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.\n<br><br>\n","Resources":[{"Description":"Observations for Global Workflow (GW) Test Cases","ARN":"arn:aws:s3:::noaa-epic-global-release-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-epic-global-release-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for Global Workflow Test Cases, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewEPICGlobalReleaseObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"Running Global Workfow on Supported HPC","URL":"https://global-workflow.readthedocs.io/en/latest/User_Guide/run_hpc.html","AuthorName":"NOAA/NWS/NCEP/EMC"}]},"RegistryEntryAdded":"2026-06-17","RegistryEntryLastModified":"2026-06-17","Slug":"noaa-epic-global-release","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Emergency Response Imagery","Description":"In order to support NOAA's homeland security and emergency response requirements, the National Geodetic Survey Remote Sensing Division (NGS/RSD) has the capability to acquire and rapidly disseminate a variety of spatially-referenced datasets to federal, state, and local government agencies, as well as the general public. Remote sensing technologies used for these projects have included lidar, high-resolution digital cameras, a film-based RC-30 aerial camera system, and hyperspectral imagers. Examples of rapid response initiatives include acquiring high resolution images with the Emerge/Applanix Digital Sensor System (DSS).","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Manually when needed","Documentation":"https://www.ngs.noaa.gov/RSD/erp.shtml","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aerial imagery","aws-pds","climate","cog","disaster response","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"GeoTIFF & COG Imagery (COG Format available for recent events)","ARN":"arn:aws:s3:::noaa-eri-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-eri-pds.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"Open data helps recovery in the aftermath of devastating weather events","URL":"https://aws.amazon.com/blogs/publicsector/open-data-helps-recovery-aftermath-devastating-weather-events/","AuthorName":"Jena Kent"}],"Tutorials":[{"Title":"ERI notebook using SageMaker Studio Lab (SMSL)","URL":"https://gitlab.cicsnc.org/workshop-development/eri_notebook","NotebookURL":"https://gitlab.cicsnc.org/workshop-development/eri_notebook/-/blob/main/ERI_aws.ipynb","AuthorName":"Mya Sears","Services":["Amazon SageMaker Studio Lab"]},{"Title":"Using Emergency and Pre-Event Imagery","URL":"https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/NGS/NOAA_ER_Pre_Event_Data.pdf","AuthorName":"Jon Sellars"}]},"RegistryEntryAdded":"2019-09-16","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-eri","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Global Ensemble Forecast System (GEFS) Re-forecast","Description":"NOAA has generated a multi-decadal reanalysis and reforecast data set to accompany the next-generation version of its ensemble prediction system, the Global Ensemble Forecast System, version 12 (GEFSv12). Accompanying the real-time forecasts are “reforecasts” of the weather, that is, retrospective forecasts spanning the period 2000-2019. These reforecasts are not as numerous as the real-time data; they were generated only once per day, from 00 UTC initial conditions, and only 5 members were provided, with the following exception. Once weekly, an 11-member reforecast was generated, and these extend in lead time to +35 days. \n","Documentation":"https://noaa-gefs-retrospective.s3.amazonaws.com/Description_of_reforecast_data.pdf","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Re-forecasts do not adhere to an update frequency.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"GEFS Re-forecast in Grib2 Format","ARN":"arn:aws:s3:::noaa-gefs-retrospective","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-gefs-retrospective.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Example using GEFS Reforecast Data from AWS","URL":"https://github.com/vgensini/gefs_v12_example/blob/master/GEFS_v12_eample.ipynb","AuthorName":"Victor Gensini","AuthorURL":"https://atlas.niu.edu"},{"Title":"Retrospective Dissertation - Part 3: Reforecast Data","URL":"https://mogismog.github.io/retro-diss-part-3.html#retro-diss-part-3","AuthorName":"Francisco Alvarez","AuthorURL":"https://github.com/mogismog"}],"Publications":[{"Title":"The GEFS v12 and its reanalyses and reforecasts (Slides)","URL":"https://drive.google.com/file/d/1lCIB3qz9S1nu2qAaetnLq0N5ReyTXuHd/view","AuthorName":"Tom Hamill"}]},"RegistryEntryAdded":"2020-08-26","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-gefs-reforecast","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Global Ensemble Forecast System (GEFS)","Description":"The Global Ensemble Forecast System (GEFS), previously known as the GFS Global ENSemble (GENS), is a weather forecast model made up of 21 separate forecasts, or ensemble members.  The National Centers for Environmental Prediction (NCEP) started the GEFS to address the nature of uncertainty in weather observations, which is used to initialize weather forecast models. The GEFS attempts to quantify the amount of uncertainty in a forecast by generating an ensemble of multiple forecasts, each minutely different, or perturbed, from the original observations. With global coverage, GEFS is produced four times a day with weather forecasts going out to 16 days.","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-gefs-pds","Contact":"For questions regarding data content or quality, visit [the NOAA GEFS site](https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gefs.php). <br/>\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"4 times a day, every 6 hours starting at midnight.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Project data files","ARN":"arn:aws:s3:::noaa-gefs-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-gefs-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for GFS, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewGEFSObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2018-10-04","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-gefs","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Global Surge and Tide Operational Forecast System 2-D (STOFS-2D-Global)","Description":"\nNOTICE - The Coast Survey Development Laboratory (CSDL) in NOAA/National Ocean Service (NOS)/Office of Coast Survey has upgraded the Surge and Tide Operational Forecast System (STOFS) to Version 3.1. A Service Change Notice (SCN) has been issued and can be found \"[HERE](https://www.weather.gov/media/notification/pdf_2026/SCN26-51_STOFS.v3.1_Updated.aaa.pdf)\"<br /><br />\n\nNOAA's Global Surge and Tide Operational Forecast System 2-D (STOFS-2D-Global) provides users with nowcasts (analyses of near present conditions) and forecast guidance of water level conditions for the entire globe. STOFS-2D-Global has been developed to serve the marine navigation, weather forecasting, and disaster mitigation user communities. STOFS-2D-Global has been developed in a collaborative effort between the NOAA/National Ocean Service (NOS)/Office of Coast Survey, the NOAA/National Weather Service (NWS) Central Operations (NCO), the University of Notre Dame and the University of North Carolina, and The Water Institute of the Gulf. The model generates forecasts out to 180 hours four times per day; forecast output includes water levels caused by the combined effects of storm surge and tides, by astronomical tides alone, and by sub-tidal water levels (isolated storm surge). <br /><br />\n\nThe hydrodynamic model employed by STOFS-2D-Global is the ADvanced CIRCulation (ADCIRC) finite element model. The model is forced by GFS winds, mean sea level pressure, and sea ice. The unstructured grid used by STOFS-2D-Global consists of 12,785,004 nodes and 24,875,336 triangular elements. Coastal resolution is at least 1.5 km globally, and up to 80 m for the U.S. West Coast and Alaska, up to 90-120 m for the Pacific Islands including Hawaii, Guam, American Samoa, Marianas, Wake Island, Marshall Islands, and Palau, and up to 120 m for the U.S. East Coast and Puerto Rico. The flood plain extends overland to approximately 10 m elevation above Mean Sea Level (MSL) for the U.S. East Coast, and up to 20 m elevation above MSL for the Pacific Islands. STOFS-2D-Global water level forecast guidance is referenced to Local MSL (LMSL) for gridded output and station output (SHEF files are referenced to MLLW). <br /><br />\n\nSTOFS-2D-Global water level forecast guidance output available here include NetCDF, GRIB2, and SHEF files. Please see README documentation for more details. <br /><br />\n","Documentation":"https://noaa-gestofs-pds.s3.amazonaws.com/README.html","Contact":"For questions regarding data content or quality, visit the STOFS site (https://polar.ncep.noaa.gov/estofs/). \n<br /> For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Four times per day, every 6 hours starting at midnight UTC","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","climate","coastal","disaster response","environmental","forecast","global","hydrologic model","marine navigation","meteorological","ocean sea surface height","oceans","sustainability","water","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Citation":"<br /><br />\nWhen citing this dataset in any research, reports, or applications, please use the full citation below:\n<br />\nOffice of Coast Survey; National Centers for Environmental Prediction. (2020). <i>NOAA Global Surge and Tide Operational Forecast System 2-D (STOFS-2D-Global).</i> National Oceanic and Atmospheric Administration. Accessed ```[DATE]```. https://doi.org/10.25923/ng4h-4b85\n","Resources":[{"Description":"NOAA STOFS-2D-Global Water Level Forecast Guidance","ARN":"arn:aws:s3:::noaa-gestofs-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-gestofs-pds.s3.amazonaws.com/index.html)"]},{"Description":"NOAA STOFS-2D-Global Water Level Forecast Guidance New Dataset Notification","ARN":"arn:aws:sns:us-east-1:123901341784:NewGESTOFSObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2021-01-22","RegistryEntryLastModified":"2026-09-22","Slug":"noaa-gestofs","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Global Forecast System (GFS)","Description":"NOTE - Upgrade NCEP Global Forecast System to v16.3.0 - Effective November 29, 2022 See notification [HERE](https://www.weather.gov/media/notification/pdf2/scn22-104_gfs.v16.3.0.pdf)\n<br/>\n<br/>\nThe Global Forecast System (GFS) is a weather forecast model produced\nby the National Centers for Environmental Prediction (NCEP). Dozens of\natmospheric and land-soil variables are available through this dataset,\nfrom temperatures, winds, and precipitation to soil moisture and\natmospheric ozone concentration. The entire globe is covered by the GFS\nat a base horizontal resolution of 18 miles (28 kilometers) between grid\npoints, which is used by the operational forecasters who predict weather\nout to 16 days in the future. Horizontal resolution drops to 44 miles\n(70 kilometers) between grid point for forecasts between one week and two\nweeks.\n<br/>\n<br/>\nThe NOAA Global Forecast Systems (GFS) Warm Start Initial Conditions are \nproduced by the National Centers for Environmental Prediction Center (NCEP) \nto run operational deterministic medium-range numerical weather predictions.   \nThe GFS is built with the GFDL Finite-Volume Cubed-Sphere Dynamical Core (FV3) \nand the Grid-Point Statistical Interpolation (GSI) data assimilation system.  \nPlease visit the links below in the Documentation section to find more details \nabout the model and the data assimilation systems.   The current operational \nGFS is run at 64 layers in the vertical extending from the surface to the upper\nstratosphere and on six cubic-sphere tiles at  the C768 or 13-km horizontal\nresolution.  A new version of the GFS that has 127 layers extending to the\nmesopause will be implemented for operation on February 3, 2021.   These initial\nconditions are made available four times per day for running forecasts at the\n00Z, 06Z, 12Z and 18Z cycles, respectively.   For each cycle, the dataset\ncontains the first guess of the atmosphere states found in the directory\n./gdas.yyyymmdd/hh-6/RESTART, which are  6-hour GDAS forecast from the last\ncycle,  and atmospheric analysis increments and surface analysis for the current\ncycle  found in the directory ./gfs.yyyymmdd/hh, which are produced by the data\nassimilation systems. \n","Documentation":"https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php","Contact":"For questions regarding data content or quality, visit [the NOAA GFS site](https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php). For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. <br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"4 times a day, every 6 hours starting at midnight UTC","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","disaster response","environmental","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"GFS data","ARN":"arn:aws:s3:::noaa-gfs-bdp-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-gfs-bdp-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for GFS, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewGFSObject","Region":"us-east-1","Type":"SNS Topic"},{"Description":"GFS Warm Start Initial Conditions","ARN":"arn:aws:s3:::noaa-gfs-warmstart-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-gfs-warmstart-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for GFS Warm Start IC, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewGfsWarmStartObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"How to Download GFS Wind Data From AWS in Python","URL":"https://www.spatialworkflow.io/gfs-wind-download-python/","NotebookURL":"https://www.spatialworkflow.io/notebooks/gfs-wind-download-python.ipynb","AuthorName":"Spatial Workflow","AuthorURL":"https://www.spatialworkflow.io/"},{"Title":"NOAA Global Forecast System (GFS) quickstart notebook on AWS","URL":"https://github.com/aws-samples/aws-opendata-samples/blob/main/notebooks/noaa-gfs/noaa_gfs_quickstart.ipynb","AuthorName":"Benoit de Chateauvieux"}],"Publications":[{"Title":"GFS Warm Restart Files Additional Information","URL":"https://noaa-gfs-warmstart-pds.s3.amazonaws.com/GFS+Warm+Restart+Files.v1.pdf","AuthorName":"Fanglin Yang"},{"Title":"Toward the Next Generation of Microwave Sounders: Benefits of a Low-Earth Orbit Hyperspectral Microwave Instrument in All- Weather Conditions using AI","URL":"https://doi.org/10.1109/JSTARS.2024.3356858","AuthorName":"Eric S. Maddy"},{"Title":"Validation and Application of the Accu-Waves Operational Platform for Wave Forecasts at Ports","URL":"https://www.mdpi.com/2077-1312/12/2/220/pdf","AuthorName":"Christos Makris et al."}]},"RegistryEntryAdded":"2019-02-11","RegistryEntryLastModified":"2026-10-16","Slug":"noaa-gfs-bdp-pds","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"See <a href=\"https://registry.opendata.aws/noaa-gfs-bdp-pds/\">here</a> for the latest GFS data.","Name":"Unidata NOAA Global Forecast System (GFS) Model","Description":"The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). Dozens of atmospheric and land-soil variables are available through this dataset, from temperatures, winds, and precipitation to soil moisture and atmospheric ozone concentration. The entire globe is covered by the GFS at a base horizontal resolution of 18 miles (28 kilometers) between grid points, which is used by the operational forecasters who predict weather out to 16 days in the future. Horizontal resolution drops to 44 miles (70 kilometers) between grid point for forecasts between one week and two weeks.","Documentation":"https://docs.opendata.aws/noaa-gfs-pds/readme.html","Contact":"https://www.ncdc.noaa.gov/contact","ManagedBy":"[Unidata](https://www.unidata.ucar.edu/)","UpdateFrequency":"Every 6 hours","Tags":["aws-pds","agriculture","climate","weather","environmental","disaster response"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"A rolling four-week archive of 0.25 degree GFS data","ARN":"arn:aws:s3:::noaa-gfs-pds","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"New data notifications","ARN":"arn:aws:sns:us-east-1:104891172899:noaa-gfs-pds","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":null,"Tools & Applications":[{"Title":"PODPAC: Python Library supporting GFS for analysis","URL":"https://podpac.org/datasets.html#gfs","AuthorName":"Creare","AuthorURL":"http://www.creare.com/"}],"Publications":null},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-gfs-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Global Historical Climatology Network Daily (GHCN-D)","Description":"<br /> UPDATE TO GHCN PREFIXES - The NODD team is working on improving performance and access to the GHCNd data and will be implementing an updated prefix structure. For more information on the prefix changes, please see the \"[READ ME on the NODD Github](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/GHCN-D)\". If you have questions, comments, or feedback, please reach out to nodd@noaa.gov with GHCN in the subject line. <br /> <br >\nGlobal Historical Climatology Network - Daily is a dataset from NOAA that contains daily observations over global land areas. It contains station-based measurements from land-based stations worldwide, about two thirds of which are for precipitation measurement only. Other meteorological elements include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, snowfall and snow depth. It is a composite of climate records from numerous sources that were merged together and subjected to a common suite of quality assurance reviews.  Some data are more than 175 years old. The data is in CSV format. Each file corresponds to a year from 1763 to present and is named as such.","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-ghcn","Contact":"For questions regarding data content or quality, visit [the NOAA GHCN site](https://www.ncdc.noaa.gov/ghcn-daily-description).  <br />\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD is made available under the [Creative Commons 1.0 Universal Public Domain Dedication (CC0-1.0) license](https://creativecommons.org/publicdomain/zero/1.0/?ref=chooser-v1\\), which is well-known and internationally recognized. There are no restrictions on the use of the data. The data are open to the public and can be used as desired. <br/> <br/> NOAA has adopted the Creative Commons license to ensure maximum use of our data, to spur and encourage exploration and innovation throughout the industry. This license is applicable to each of the NOAA datasets made available by NODD. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"GHCN Project data files","ARN":"arn:aws:s3:::noaa-ghcn-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ghcn-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for GHCN, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewGHCNObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Calculating growing degree days using AWS Registry of Open Data","URL":"https://aws.amazon.com/blogs/industries/calculating-growing-degree-days-using-aws-registry-of-open-data/","AuthorName":"Karen Hildebrand and Zac Flamig","Services":["Amazon Athena","AWS CloudFormation","AWS Glue"]},{"Title":"Explore & Visualize 200+ Years of Global Temperature","URL":"https://medium.com/swlh/explore-visualize-200-years-of-global-temperature-using-apache-spark-bigquery-and-google-data-699ed8b3c67?sk=3721ca3220a75af80293ac3e4bf3f4d1","AuthorName":"Kapil Sreedharan"},{"Title":"Visualize over 200 years of global climate data using Amazon Athena and Amazon QuickSight","URL":"https://aws.amazon.com/blogs/big-data/visualize-over-200-years-of-global-climate-data-using-amazon-athena-and-amazon-quicksight/","AuthorName":"Conor Delaney","Services":["Amazon Athena","AWS Glue"]}],"Publications":[{"Title":"Natural and Socioeconomic Conditions Influence Tick-Borne Encephalitis Cases in Russia","URL":"http://www.pjoes.com/Natural-and-Socioeconomic-Conditions-Influence-nTick-Borne-Encephalitis-Cases-in,153552,0,2.html","AuthorName":"Lantian Zhang, Linsheng Yang, Li Wang, Lijuan Gu, Hairong Li, Svetlana Malkhazova"}]},"RegistryEntryAdded":"2018-06-26","RegistryEntryLastModified":"2025-03-19","Slug":"noaa-ghcn","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Global Historical Climatology Network- Hourly (GHCN-H)","Description":"The Global Historical Climatology Network hourly (GHCNh) is a next generation hourly/synoptic dataset that replaces the Integrated Surface Dataset (ISD). GHCNh consists of hourly and synoptic surface weather observations from fixed, land-based stations. \n<br /><br />\nThis dataset is compiled from numerous data sources maintained by NOAA, the U.S. Air Force, and many other meteorological agencies (Met Services) around the world. These sources have been reformatted into a common data format and then harmonized into a set of unique period-of-record station files, which are then provided as GHCNh. \n<br /><br />\nGHCNh was developed as part of a collaboration with the Copernicus Programme and partners at Maynooth University with the aim to make historical land and marine surface weather data more easily accessible worldwide. At NCEI, GHCNh was developed to be vertically aligned with its GHCN daily counterpart (GHCNd).\n<br /><br />\n","Documentation":"https://www.ncei.noaa.gov/products/global-historical-climatology-network-hourly\n<br /><br />\nhttps://www.ncei.noaa.gov/oa/global-historical-climatology-network/hourly/doc/ghcnh_DOCUMENTATION.pdf\n<br /><br />\n","Contact":"For questions regarding data content or quality, visit [the NOAA GHCN site](https://www.ncdc.noaa.gov/ghcn-daily-description).  <br />\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD is made available under the [Creative Commons 1.0 Universal Public Domain Dedication (CC0-1.0) license](https://creativecommons.org/publicdomain/zero/1.0/?ref=chooser-v1\\), which is well-known and internationally recognized. There are no restrictions on the use of the data. The data are open to the public and can be used as desired. \n<br/>\n<br/>\nNOAA has adopted the Creative Commons license to ensure maximum use of our data, to spur and encourage exploration and innovation throughout the industry. This license is applicable to each of the NOAA datasets made available by NODD. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.\n","Resources":[{"Description":"GHCN-H files","ARN":"arn:aws:s3:::noaa-ghcnh-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ghcnh-pds.s3.us-east-1.amazonaws.com/index.html)"]},{"Description":"New data notifications for GHCN-H, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewGHCNHObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Calculating growing degree days using AWS Registry of Open Data","URL":"https://aws.amazon.com/blogs/industries/calculating-growing-degree-days-using-aws-registry-of-open-data/","AuthorName":"Karen Hildebrand and Zac Flamig","Services":["Amazon Athena","AWS CloudFormation","AWS Glue"]},{"Title":"Explore & Visualize 200+ Years of Global Temperature","URL":"https://medium.com/swlh/explore-visualize-200-years-of-global-temperature-using-apache-spark-bigquery-and-google-data-699ed8b3c67?sk=3721ca3220a75af80293ac3e4bf3f4d1","AuthorName":"Kapil Sreedharan"},{"Title":"Visualize over 200 years of global climate data using Amazon Athena and Amazon QuickSight","URL":"https://aws.amazon.com/blogs/big-data/visualize-over-200-years-of-global-climate-data-using-amazon-athena-and-amazon-quicksight/","AuthorName":"Conor Delaney","Services":["Amazon Athena","AWS Glue"]}],"Publications":[{"Title":"Natural and Socioeconomic Conditions Influence Tick-Borne Encephalitis Cases in Russia","URL":"http://www.pjoes.com/Natural-and-Socioeconomic-Conditions-Influence-nTick-Borne-Encephalitis-Cases-in,153552,0,2.html","AuthorName":"Lantian Zhang, Linsheng Yang, Li Wang, Lijuan Gu, Hairong Li, Svetlana Malkhazova"}]},"RegistryEntryAdded":"2019-07-10","RegistryEntryLastModified":"2026-08-19","Slug":"noaa-ghcnh","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Global Hydro Estimator (GHE) / Enterprise Rain Rate","Description":"NOTE - The legacy on-premises version of the Global Hydroestimator (GHE) is being retired. It is being replaced by the global Enterprise Rain Rate algorithm. You can find Enterprise Rain Rate products in the new bucket listed under the Resources section. \n<br/>\n<br/>\nGlobal Hydro-Estimator provides a global mosaic imagery of rainfall estimates from multi-geostationary satellites, which currently includes GOES-16, GOES-15, Meteosat-8, Meteosat-11 and Himawari-8. The GHE products include: Instantaneous rain rate, 1 hour, 3 hour, 6 hour, 24 hour and also multi-day rainfall accumulation.\n","Contact":"For questions regarding product content or\nquality, visit https://www.ospo.noaa.gov/Products/atmosphere/ghe/index.html.\n<br />\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","Documentation":"https://www.ospo.noaa.gov/Products/atmosphere/ghe/index.html","UpdateFrequency":"15 minute-instantaneous","Collabs":{"ASDI":{"Tags":["water"]}},"Tags":["agriculture","aws-pds","meteorological","water","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Enterprise Rain Rate Data","ARN":"arn:aws:s3:::noaa-enterprise-rainrate-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-enterprise-rainrate-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for Enterprise Rain , only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewEntRainRateL3Object","Region":"us-east-1","Type":"SNS Topic"},{"Description":"GHE Project data files","ARN":"arn:aws:s3:::noaa-ghe-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ghe-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for GHE, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewGHEObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2019-07-10","RegistryEntryLastModified":"2025-03-31","Slug":"noaa-ghe","Sources":["awslabs-open-data-registry"]}
{"Name":"Korea Meteorological Administration (KMA) GK-2A Satellite Data","Description":"The Geo-KOMPSAT-2A (GK2A) is the new generation geostationary meteorological satellite (located in 128.2°E) of the Korea Meteorological Administration (KMA). The main mission of the GK2A is to observe the atmospheric phenomena over the Asia-Pacific region. The Advance Meteorological Imager (AMI) on GK2A scan the Earth full disk every 10 minutes and the Korean Peninsula area every 2 minutes with a high spatial resolution of 4 visible channels and 12 infrared channels. In addition, the AMI has an ability of flexible target area scanning useful for monitoring severe weather events such as typhoon and volcanic eruption and so on. And for space weather mission, the Korea Space wEather Monitor (KSEM) on the GK2A observes the space environment with the particle detector, magnetometer and charging monitor. For questions regarding GK2A imagery specifications, visit the GK2A site at https://nmsc.kma.go.kr/enhome/html/base/cmm/selectPage.do?page=satellite.gk2a.intro. To view the GK2A Fact Sheet please visit https://nmsc.kma.go.kr/enhome/html/base/cmm/selectPage.do?page=satellite.gk2a.fact.\n<br/>\n<br/>\nNOAA provides access to GK2A data on AWS in coordination with the Korean Meteorlogical Administration.\n<br/>\n","Documentation":"https://nmsc.kma.go.kr/enhome/html/base/cmm/selectPage.do?page=satellite.gk2a.fact","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"10 minutes for Full Disk, 2 minutes for  4 visible channels and 12 infrared channels.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","disaster response","earth observation","geospatial","meteorological","satellite imagery","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"GK2A Imagery","ARN":"arn:aws:s3:::noaa-gk2a-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-gk2a-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for GK2A, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewGK2AObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"GK2A Full Fact Sheet","URL":"https://nmsc.kma.go.kr/enhome/html/base/cmm/selectPage.do?page=satellite.gk2a.fact","AuthorName":"KMA"}]},"RegistryEntryAdded":"2023-04-19","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-gk2a-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Global Mosaic of Geostationary Satellite Imagery (GMGSI)","Description":"NOAA/NESDIS Global Mosaic of Geostationary Satellite Imagery (GMGSI) visible (VIS), shortwave infrared (SIR), longwave infrared (LIR) imagery, and water vapor imagery (WV) are composited from data from several geostationary satellites orbiting the globe, including the GOES-East and GOES-West Satellites operated by U.S. NOAA/NESDIS, the Meteosat-10 and Meteosat-9 satellites from theMeteosat Second Generation (MSG) series of satellites operated by European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the Himawari-9 satellite operated by the Japan Meteorological Agency (JMA). GOES-East is positioned at 75 deg W longitude over the equator. GOES-West is located at 137.2 deg W longitude over the equator. Both satellites cover an area from the eastern Atlantic Ocean to the central Pacific Ocean region. The Meteosat-10 satellite is located at 0 deg E longitude to cover Europe and Africa regions. The Meteosat-9 satellite is located at 45.5 deg E longitude to cover the Indian Ocean region. The Himawari-9 satellite is located at 140.7 deg E longitude to cover the Asia-Oceania region. The visible imagery indicates cloud cover and ice and snow cover. The shortwave, or mid-infrared, indicates cloud cover and fog at night. The longwave, or thermal infrared, depicts cloud cover and land/sea temperature patterns. The water vapor imagery indicates the amount of water vapor contained in the mid to upper levels of the troposphere, with the darker grays indicating drier air and the brighter grays/whites indicating more saturated air. GMGSI composite images have an approximate 8 km (5 mile) horizontal resolution and are updated every hour.\n","Documentation":"[https://www.ospo.noaa.gov/Operations/GOES/index.html](https://www.ospo.noaa.gov/Operations/GOES/index.html); [https://www.ospo.noaa.gov/Products/imagery/index.html](https://www.ospo.noaa.gov/Products/imagery/index.html)","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. For general questions or feedback about the data, please submit inquiries through the NOAA Office of Coast Survey (OCS) ASSIST Tool at https://www.nauticalcharts.noaa.gov/customer-service/assist/.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Hourly","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA Global Mosaic of Geostationary Satellite Imagery (GMGSI)","ARN":"arn:aws:s3:::noaa-gmgsi-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-gmgsi-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for GMGSI, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewGMGSIObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2021-07-15","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-gmgsi","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Geostationary Operational Environmental Satellites (GOES) 16, 17, 18 & 19","Description":"<br/>\n<br/> \nNEW GOES-19 Data!! On April 4, 2025 at 1500 UTC, the GOES-19 satellite will be declared the Operational GOES-East satellite. All products and services, including NODD, for GOES-East will transition to GOES-19 data at that time. GOES-19 will operate out of the GOES-East location of 75.2°W starting on April 1, 2025 and through the operational transition. Until the transition time and during the final stretch of Post Launch Product Testing (PLPT), GOES-19 products are considered non-operational regardless of their validation maturity level. Shortly following the transition of GOES-19 to GOES-East, all data distribution from GOES-16 will be turned off. GOES-16 will drift to the storage location at 104.7°W. GOES-19 data should begin flowing again on April 4th once this maneuver is complete. \n<br/>\n<br/>\nNEW GOES 16 Reprocess Data!! The reprocessed GOES-16 ABI L1b data mitigates systematic data issues (including data gaps and image artifacts) seen in the Operational products, and improves the stability of both the radiometric and geometric calibration over the course of the entire mission life. These data were produced by recomputing the L1b radiance products from input raw L0 data using improved calibration algorithms and look-up tables, derived from data analysis of the NIST-traceable, on-board sources. In addition, the reprocessed data products contain enhancements to the L1b file format, including limb pixels and pixel timestamps, while maintaining compatibility with the operational products. The datasets currently available span the operational life of GOES-16 ABI, from early 2018 through the end of 2024. The Reprocessed L1b dataset shows improvement over the Operational L1b products but may still contain data gaps or discrepancies. Please provide feedback to Dan Lindsey (dan.lindsey@noaa.gov) and Gary Lin (guoqing.lin-1@nasa.gov). More information can be found in the [GOES-R ABI Reprocess User Guide](https://github.com/NOAA-Big-Data-Program/nodd-data-docs/blob/main/GOES/GOES-R_ABI_Reprocessed_L1b_User_Guide-v1.1.pdf).\n<br/>\n<br/>  \nNOTICE: As of January 10th 2023, GOES-18 assumed the GOES-West position and all data files are deemed both operational and provisional, so no ‘preliminary, non-operational’ caveat is needed. GOES-17 is now offline, shifted approximately 105 degree West, where it will be in on-orbit storage. GOES-17 data will no longer flow into the GOES-17 bucket. Operational GOES-West products can be found in the GOES-18 bucket. \n<br/>\n<br/> \nGOES satellites (GOES-16, GOES-17, GOES-18 & GOES-19) provide continuous weather imagery and\nmonitoring of meteorological and space environment data across North America.\nGOES satellites provide the kind of continuous monitoring necessary for\nintensive data analysis. They hover continuously over one position on the surface.\nThe satellites orbit high enough to allow for a full-disc view of the Earth. Because\nthey stay above a fixed spot on the surface, they provide a constant vigil for the\natmospheric \"triggers\" for severe weather conditions such as tornadoes, flash floods,\nhailstorms, and hurricanes. When these conditions develop, the GOES satellites are able\nto monitor storm development and track their movements. SUVI products available in both NetCDF and FITS. \n","Documentation":"https://github.com/NOAA-Big-Data-Program/nodd-data-docs/tree/main/GOES","Contact":"<br/>\nFor questions related to specific GOES Products, please visit the \"[GOES-R website](https://www.ncei.noaa.gov/products/satellite/goes-r-series)\" and navigate to the Help section.\n<br/>\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"New data is added as soon as it's available","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","disaster response","earth observation","geospatial","meteorological","satellite imagery","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"GOES-19 imagery and metadata","ARN":"arn:aws:s3:::noaa-goes19","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-goes19.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for GOES-19, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewGOES19Object","Region":"us-east-1","Type":"SNS Topic"},{"Description":"GOES-18 imagery and metadata","ARN":"arn:aws:s3:::noaa-goes18","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-goes18.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for GOES-18, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewGOES18Object","Region":"us-east-1","Type":"SNS Topic"},{"Description":"GOES-16 imagery and metadata","ARN":"arn:aws:s3:::noaa-goes16","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-goes16.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for GOES-16, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewGOES16Object","Region":"us-east-1","Type":"SNS Topic"},{"Description":"GOES-17 imagery and metadata","ARN":"arn:aws:s3:::noaa-goes17","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-goes17.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for GOES-17, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewGOES17Object","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Comparison reading GOES-R data from AWS S3 in netCDF versus zarr","URL":"https://nbviewer.jupyter.org/github/oceanhackweek/ohw-tutorials/blob/OHW20/10-satellite-data-access/goes-cmp-netcdf-zarr.ipynb","AuthorName":"Chelle Gentemann","AuthorURL":"http://www.faralloninstitute.org/chelle"},{"Title":"Forecasting Hurricane Tracks with TensorFlow and data from AWS S3","URL":"https://github.com/M00NSH0T/Hurricanes/tree/master/2021%20update","AuthorName":"Kyle Archie"},{"Title":"How to Make a GOES Satellite Loop in Python","URL":"https://www.spatialworkflow.io/goes-satellite-loop-python/","NotebookURL":"https://www.spatialworkflow.io/notebooks/goes-satellite-loop-python.ipynb","AuthorName":"Spatial Workflow","AuthorURL":"https://www.spatialworkflow.io/"},{"Title":"NOAA GOES16 Julia Jupyter Notebook Example","URL":"https://github.com/awslabs/amazon-asdi/tree/master/examples/goes16-precip-julia","AuthorName":"Peter Schmiedeskamp","Services":["AWS CloudFormation","Amazon EC2","Amazon SageMaker"]},{"Title":"Processing GOES-16 data with Dask & AWS Fargate","URL":"https://nbviewer.jupyter.org/github/awslabs/amazon-asdi/blob/main/examples/dask/notebooks/goes16_dask.ipynb","AuthorName":"Zac Flamig","Services":["AWS CloudFormation","AWS Fargate","Amazon SageMaker"]},{"Title":"Visualize GOES-16 in Python using Xarray","URL":"https://github.com/HamedAlemo/visualize-goes16","AuthorName":"Hamed Alemohammad","AuthorURL":"https://www.linkedin.com/in/hamedalemohammad/"}],"Tools & Applications":[{"Title":"GOES Data Explorer","URL":"https://coliveir-aer.github.io/goes-data-explorer/","AuthorName":"Craig Oliveira"},{"Title":"GOES-2-go is a python package for downloading and plotting plotting GOES data.","URL":"https://github.com/blaylockbk/goes2go","AuthorName":"Brian K. Blaylock","AuthorURL":"https://www.linkedin.com/in/blaylockbk/"},{"Title":"Modern Data Platform Orchestration with Dagster Software-Defined Assets","URL":"https://medium.com/p/6a7182b0d834","AuthorName":"Adebayo Adejare","AuthorURL":"https://medium.com/@adebayoadejare"}],"Publications":[{"Title":"Beginner’s Guide to GOES-R Series Data","URL":"https://noaa-goes16.s3.amazonaws.com/Beginners_Guide_to_GOES-R_Series_Data.pdf","AuthorName":"Danielle Losos"},{"Title":"Billions of Birds Migrate. Where Do They Go?","URL":"https://www.nationalgeographic.com/magazine/2018/03/bird-migration-interactive-maps/","AuthorName":"National Geographic"},{"Title":"Comparison of Lightning Forecasts from the High-Resolution Rapid Refresh Model to Geostationary Lightning Mapper Observations","URL":"https://doi.org/10.1175/WAF-D-19-0141.1","AuthorName":"Brian K. Blaylock, and John D. Horel"},{"Title":"Embracing the cloud for climate research","URL":"https://aws.amazon.com/blogs/publicsector/embracing-the-cloud-for-climate-research/","AuthorName":"North Carolina State University’s North Carolina Institute for Climate Studies"},{"Title":"GOES Quick Guides (Spanish)","URL":"https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/GOES/QuickGuides/Spanish","AuthorName":"Anthony Segura García"},{"Title":"Identifying the Causes of Pyrocumulonimbus (PyroCb)","URL":"https://arxiv.org/pdf/2211.08883.pdf","AuthorName":"Emiliano Díaz Salas-Porras, Kenza Tazi, Ashwin Braude, Daniel Okoh, Kara D. Lamb, Duncan Watson-Parris, Paula Harder, and Nis Meinert"},{"Title":"Imaging Considerations From a Geostationary Orbit Using the Short Wavelength Side of the Mid-Infrared Water Vapor Absorption Band","URL":"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021EA002080","AuthorName":"N.B. Miller, M.M. Gunshor, A.J. Merrelli, T.S. L'Ecuyer, T.J. Schmit, J.J. Gerth, N.J. Gordillo"},{"Title":"Observations of lightning in relation to transitions in volcanic activity during the 3 June 2018 Fuego Eruption","URL":"https://www.nature.com/articles/s41598-020-74576-x","AuthorName":"Christopher J. Schultz, Virginia P. Andrews, Kimberly D. Genareau, and Aaron R. Naeger"},{"Title":"Solar irradiance forecasting for the solar powered future","URL":"https://aws.amazon.com/blogs/publicsector/solcast-solar-irradiance-forecasting-for-the-solar-powered-future/","AuthorName":"Solcast"},{"Title":"SunCast: Solar Irradiance Nowcasting from Geosynchronous Satellite Data","URL":"https://arxiv.org/pdf/2201.06173.pdf","AuthorName":"Dhileeban Kumaresan, Richard Wang, Ernesto Martinez, Richard Cziva, Alberto Todeschini, Colorado J Reed, Hossein Vahabi"},{"Title":"The Use of Composite GOES-R Satellite Imagery to Evaluate a TC Intensity and Vortex Structure Forecast by an FV3GFS-Based Hurricane Forecast Model","URL":"https://doi.org/10.3390/atmos13010126","AuthorName":"Shaowu Bao, Zhan Zhang, Evan Kalina, and Bin Liu"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2026-10-16","Slug":"noaa-goes","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Global Surface Summary of Day","Description":"Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. The online data files begin with 1929 and are at the time of this writing at the Version 8 software level. Over 9000 stations' data are typically available. The daily elements included in the dataset (as available from each station) are: <br/>\nMean temperature (.1 Fahrenheit) <br/>\nMean dew point (.1 Fahrenheit) <br/>\nMean sea level pressure (.1 mb) <br/>\nMean station pressure (.1 mb) <br/>\nMean visibility (.1 miles) <br/>\nMean wind speed (.1 knots) <br/>\nMaximum sustained wind speed (.1 knots) <br/>\nMaximum wind gust (.1 knots) <br/>\nMaximum temperature (.1 Fahrenheit) <br/>\nMinimum temperature (.1 Fahrenheit) <br/>\nPrecipitation amount (.01 inches) <br/>\nSnow depth (.1 inches) <br/>\nIndicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel Cloud.<br/><br/>\nGlobal summary of day data for 18 surface meteorological elements are derived from the synoptic/hourly observations contained in USAF DATSAV3 Surface data and Federal Climate Complex Integrated Surface Hourly (ISH). Historical data are generally available for 1929 to the present, with data from 1973 to the present being the most complete. For some periods, one or more countries' data may not be available due to data restrictions or communications problems. In deriving the summary of day data, a minimum of 4 observations for the day must be present (allows for stations which report 4 synoptic observations/day). Since the data are converted to constant units (e.g, knots), slight rounding error from the originally reported values may occur (e.g, 9.9 instead of 10.0). The mean daily values described below are based on the hours of operation for the station. For some stations/countries, the visibility will sometimes 'cluster' around a value (such as 10 miles) due to the practice of not reporting visibilities greater than certain distances. The daily extremes and totals--maximum wind gust, precipitation amount, and snow depth--will only appear if the station reports the data sufficiently to provide a valid value. Therefore, these three elements will appear less frequently than other values. Also, these elements are derived from the stations' reports during the day, and may comprise a 24-hour period which includes a portion of the previous day. The data are reported and summarized based on Greenwich Mean Time (GMT, 0000Z - 2359Z) since the original synoptic/hourly data are reported and based on GMT.\n","Documentation":"http://www.ncdc.noaa.gov/","ManagedBy":"[NOAA](http://www.noaa.gov/)","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","UpdateFrequency":"Data is updated as new products are available.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","environmental","natural resource","regulatory","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data. Open Data. There are no restrictions on the use of this data","Resources":[{"Description":"Measurements and metadata","ARN":"arn:aws:s3:::noaa-gsod-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-gsod-pds.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"ML Demo: Predicting Air Quality w/ ASDI NOAA + OpenAQ Datasets in SageMaker Studio Lab (SMSL)","URL":"https://github.com/aws-samples/aws-smsl-predict-airquality-via-weather/","NotebookURL":"https://github.com/aws-samples/aws-smsl-predict-airquality-via-weather/blob/main/aq_by_weather.ipynb","AuthorName":"Aaron Soto","Services":["Amazon SageMaker Studio Lab"]}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-gsod","Sources":["awslabs-open-data-registry"]}
{"Name":"JMA Himawari-8/9","Description":"Himawari-9, stationed at 140.7E, owned and operated by the Japan Meteorological Agency (JMA), is a geostationary meteorological satellite, with Himawari-8 as on-orbit back-up, that provides constant and uniform coverage of east Asia, and the west and central Pacific regions from around 35,800 km above the equator with an orbit corresponding to the period of the earth’s rotation. This allows JMA weather offices to perform uninterrupted observation of environmental phenomena such as typhoons, volcanoes, and general weather systems. Archive data back to July 2015 is available for Full Disk (AHI-L1b-FLDK) products in the bucket. For questions regarding Himawari-9 imagery specifications, visit the JMA site at https://www.data.jma.go.jp/mscweb/en/himawari89/space_segment/spsg_ahi.html.  For examples of Full Disk Himawari-9 imagery coverage, visit the NOAA Himawari-9 data page at https://www.ospo.noaa.gov/Products/imagery/himawari.html.","Documentation":"https://www.data.jma.go.jp/mscweb/en/himawari89/cloud_service/cloud_service.html https://www.data.jma.go.jp/mscweb/en/himawari89/space_segment/spsg_ahi.html","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"10 minutes for Full Disk, 2.5 minutes for Regions 1, 2, and 3, and .5 minutes for Regions 4 and 5.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","disaster response","earth observation","geospatial","meteorological","satellite imagery","weather"],"License":"Himawari data is produced and managed by JMA. NOAA has rights to distribute this data freely and openly to the public. NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA and JMA request attribution for the use or dissemination of unaltered data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA or JMA. If you modify these data, you may not state or imply that it is original, unaltered data.","Resources":[{"Description":"Himawari-8 Imagery","ARN":"arn:aws:s3:::noaa-himawari8","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-himawari8.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for Himawari-8, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewHimawari8Object","Region":"us-east-1","Type":"SNS Topic"},{"Description":"Himawari-9 Imagery","ARN":"arn:aws:s3:::noaa-himawari9","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-himawari9.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for Himawari-9, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewHimawariNineObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"Himawari-8 Advanced Himawari Imager Data on AWS (pdf file)","URL":"https://github.com/awslabs/open-data-docs/blob/main/docs/noaa/noaa-himawari/2020July08_NOAA_Himawari.pdf","AuthorName":"NOAA NESDIS"},{"Title":"Himawari-8 on AWS (pdf file)","URL":"https://github.com/awslabs/open-data-docs/blob/main/docs/noaa/noaa-himawari/2020July08_Amazon_Himawari.pdf","AuthorName":"ASDI"},{"Title":"Himawari-8: Enabling access to key weather data","URL":"https://aws.amazon.com/blogs/publicsector/himawari-8-enabling-access-key-weather-data/","AuthorName":"Manan Dalal, Jena Kent"},{"Title":"Identifying the Causes of Pyrocumulonimbus (PyroCb)","URL":"https://arxiv.org/pdf/2211.08883.pdf","AuthorName":"Emiliano Díaz Salas-Porras, Kenza Tazi, Ashwin Braude, Daniel Okoh, Kara D. Lamb, Duncan Watson-Parris, Paula Harder, and Nis Meinert"},{"Title":"Introduction of Himawari-8/9 (pdf file)","URL":"https://github.com/awslabs/open-data-docs/blob/main/docs/noaa/noaa-himawari/2020July07_JMA_Himawari.pdf","AuthorName":"JMA"}]},"RegistryEntryAdded":"2020-03-30","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-himawari","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Historical Maps and Charts","Description":"Historical Charts are not for Navigation. The collection primarily consists of historic charts and maps produced by NOAA's Coast Survey and its predecessors, especially the U.S. Coast and Geodetic Survey and the U.S. Lake Survey (previously under the Department of War). The collection also includes bathymetric maps, land sketches, Civil War battle maps, aeronautical charting from the 1930s to the 1950s, and other drawings and photographs.","Documentation":"https://historicalcharts.noaa.gov/about.php","Contact":"For any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov.<br/><br/>\nFor general questions or feedback about the data, please submit inquiries through the NOAA Office of Coast Survey (OCS) ASSIST Tool at https://www.nauticalcharts.noaa.gov/customer-service/assist/.\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Periodic manual updates when historic charts are added to the collection.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","coastal","geospatial","history","mapping","survey"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA Historic Chart Datasets","ARN":"arn:aws:s3:::noaa-nos-historicalcharts-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nos-historicalcharts-pds.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2023-11-17","RegistryEntryLastModified":"2025-08-05","Slug":"noaa-historicalcharts","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA High-Resolution Rapid Refresh (HRRR) Model","Description":"The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh. <br /> <br /> The HRRR ZARR formatted data was originally generated by the University of Utah under a grant provided by NOAA. They are are continuing to publish ZARR versions of HRRR data. For information about data in the s3://hrrrzarr/ please contact atmos-mesowest@lists.utah.edu. \n","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-hrrr","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Hourly","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","disaster response","environmental","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Archive of HRRR data since 2014.","ARN":"arn:aws:s3:::noaa-hrrr-bdp-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-hrrr-bdp-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications","ARN":"arn:aws:sns:us-east-1:123901341784:NewHRRRObject","Region":"us-east-1","Type":"SNS Topic"},{"Description":"HRRR Zarr format near-real time data archive managed by the University of Utah","ARN":"arn:aws:s3:::hrrrzarr","Region":"us-west-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://hrrrzarr.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"How to Read HRRR Wind Data in Python","URL":"https://www.spatialworkflow.io/hrrr-wind-python/","NotebookURL":"https://www.spatialworkflow.io/notebooks/hrrr-wind-python.ipynb","AuthorName":"Spatial Workflow","AuthorURL":"https://www.spatialworkflow.io/"},{"Title":"HRRR-B Python package: download and read HRRR grib2 files","URL":"https://github.com/blaylockbk/HRRR_archive_download","AuthorName":"Brian Blaylock"},{"Title":"The HRRR Zarr Archive Managed by MesoWest","URL":"https://mesowest.utah.edu/html/hrrr/","AuthorName":"Taylor Gowan"}],"Publications":[{"Title":"Using Cloud Computing to Analyze Model Output Archived in Zarr Format","URL":"https://doi.org/10.1175/JTECH-D-21-0106.1","AuthorName":"Taylor A. Gowan, John D. Horel, Alexander A. Jacques, and Adair Kovac"},{"Title":"Using the U.S. Climate Reference Network to Identify Biases in Near- and Sub-Surface Meteorological Fields in the High-Resolution Rapid Refresh (HRRR) Weather Prediction Model","URL":"https://journals.ametsoc.org/view/journals/wefo/aop/WAF-D-22-0213.1/WAF-D-22-0213.1.xml","AuthorName":"Temple R. Lee, Ronald D. Leeper, Tim Wilson, Howard Diamond, Tilden P. Meyers, and David D. Turner"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2026-10-16","Slug":"noaa-hrrr-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA IOOS MARACOOS Regional Ocean Modeling System (ROMS) \"Doppio\" Data Assimilative Reanalysis","Description":"This dataset, identified as Doppio Analysis Version 3 Release 3 (DopAnV3R3-ini2007) initialized January 2007, comprises outputs from a Regional Ocean Modeling System (ROMS) data assimilative reanalysis of ocean circulation in the Mid-Atlantic Bight and Gulf of Maine for 2007-2024.\n<br><br>\nA multi-year reanalysis (2007-2024) of circulation in the coastal ocean and adjacent deep sea of the northeast U.S. continental shelf has been computed using the Regional Ocean Modeling System (ROMS) with four-dimensional variational (4D-Var) data assimilation (DA) of observations from satellites, land-based ocean surface current measuring radar, and all available in situ observations from the MARACOOS (maracoos.org) and NERACOOS (neracoos.org) regional associations of the U.S. Integrated Ocean Observing System (IOOS). The reanalysis downscales open boundary information from the Copernicus Marine Service global analysis. The dynamic model is forced by regional meteorological analyses, observed daily river discharges, and harmonic tides that augment the open boundary conditions. The analysis covers the period 2-Jan-2007 to 31-Dec-2024 on a 7-km horizontal grid with 40 vertical terrain-following s-coordinate levels. Ocean state variables computed are sea level, velocity, temperature, and salinity. Air-sea fluxes of heat and momentum are included.\n<br><br>\nThe ROMS 4D-Var DA configuration and its performance in comparison to observations (both assimilated and independent) are described by Wilkin et al. (2022). The underlying ROMS ocean circulation model configuration is described by López et al (2020). Wilkin et al. (2022) also compare this analysis to Copernicus and the U.S. Navy Global Ocean Forecast System (GOFS) HYCOM model.\n<br><br>\nAll ROMS model outputs here are in netCDF format and the data and metadata follow CF-1.4 conventions for the description of coordinates and variables. The reanalysis results are provided on the model’s native 3-D grid in terrain-following coordinates aggregated into three collections with individual files containing 3 days of model output.\n<br><br>\n- His: 1-hourly snapshots of the ocean state “history” (sea level and 3-D velocity, temperature and salinity) for 02-Jan-2007 01:00 through 31-Dec-2024 00:00\n- Flx: 1-hourly snapshots of surface air-sea flux forcing (wind stress and net heat flux) from the posterior reanalysis for 02-Jan-2007 01:00 through 31-Dec-2024 00:00\n- Avg: Daily averages for 02-Jan-2007 12:00 through 30-Dec-2024 12:00\n<br><br>\n\nIn addition to access here in the Registry of Open Data in AWS, Rutgers University operates a THREDDS (Thematic Real-time Environmental Distributed Data Services) web service at https://tds.marine.rutgers.edu/thredds/roms/doppio/catalog.html that supports geospatial and temporal sub-setting. Access protocols supported are OPeNDAP, WMS and netCDF subsetting. That collection includes: \n\n<br><br>\n- monthly averages for 17-Jan-2007 through 15-Dec-2021\n- yearly averages for 2007 through 2021\n- monthly ensemble averages\n<br><br>\n\nThe observations that were assimilated, after quality control and the formation of super-observations as described by Wilkin et al. (2022), can be accessed via an ERDDAP service at https://tds.marine.rutgers.edu/erddap/tabledap/DOPPIO_REANALYSIS_Ver3_OBS.graph. The dataset includes observation type and provenance fields (documented in the ERDDAP metadata) that allow sub-selection by data types (temperate, salinity, velocity, etc.) or observing platform (e.g., Jason altimeter satellite, AVHRR SST, IOOS gliders, etc.). The ERDDAP catalog includes outputs of the DA system in observation space; namely, prior and posterior model errors, and the Copernicus and GOFS values interpolated to the observation position. An observation scale field flags data that were rejected by 4D-Var QC.\n","Documentation":"- López, A. G., J. L. Wilkin and J. C. Levin, (2020) Doppio – a ROMS (v3.6)-based circulation model for the Mid-Atlantic Bight and Gulf of Maine: configuration and comparison to integrated coastal observing network observations, Geosci. Model Dev., 13, 3709–3729, doi: 10.5194/gmd-13-3709-2020\n- Wilkin, J., J. Levin, A. Moore, H. Arango, A. López and E. Hunter, (2022), A data-assimilative model reanalysis of the U.S. Mid Atlantic Bight and Gulf of Maine: Configuration and comparison to observations and global ocean models, Progress in Oceanography, 209, 102919, https://doi.org/10.1016/j.pocean.2022.102919\n- Wilkin, J., and J. Levin (2022), Outputs from a Regional Ocean Modeling System (ROMS) data assimilative reanalysis (version DopAnV3R3-ini2007) of ocean circulation in the Mid-Atlantic Bight and Gulf of Maine for 2007-2021. SEANOE. https://doi.org/10.17882/89673 \n","UpdateFrequency":"This is a static reanalysis data set. A near-real-time version of the Doppio analysis and forecast system is operated by the Rutgers University Ocean Modeling Group for MARACOOS. \n","License":"NOAA data disseminated through NODD are open to the public and can be used as desired.\n\nNOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.\n","ManagedBy":"\"[NOAA](http://www.noaa.gov/)\"\n","Contact":"For enquiries about details of the Doppio V3R3 reanalysis configuration or to express interest in Doppio based applications, contact Dr. John Wilkin, Rutgers University Ocean Modeling Group, by emailing jwilkin@rutgers.edu.\n<br><br>\nFor any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br><br>\nWe also seek to identify case studies on how NOAA data are being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n<br><br>\n","Collabs":{"ASDI":{"Tags":["water"]}},"Tags":["aws-pds","coastal","environmental","marine","oceans"],"Resources":[{"Description":"NOAA’s ROMS 'Doppio' Data Assimilative Reanalysis","ARN":"arn:aws:s3:::noaa-ioos-roms-doppio-v3-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ioos-roms-doppio-v3-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for ROMS 'Doppio', only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewIOOSRomsDoppioV3Object","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"ROMS model outputs in AWS can be readily visualized and analyzed in Python using the xroms tools","URL":"https://github.com/xoceanmodel/xroms","AuthorName":"XROMS"}],"Publications":[{"Title":" Outputs from a Regional Ocean Modeling System (ROMS) data assimilative reanalysis (version DopAnV3R3-ini2007) of ocean circulation in the Mid-Atlantic Bight and Gulf of Maine for 2007-2021","URL":"https://doi.org/10.17882/89673","AuthorName":"Wilkin, J. and Levin, J."},{"Title":"A data-assimilative model reanalysis of the U.S. Mid Atlantic Bight and Gulf of Maine: Configuration and comparison to observations and global ocean models","URL":"https://doi.org/10.1016/j.pocean.2022.102919","AuthorName":"Wilkin, J., Levin, J., Moore, A., Arango, H., López, A., and Hunter, E."}]},"RegistryEntryAdded":"2026-06-08","RegistryEntryLastModified":"2026-06-09","Slug":"noaa-ioos-roms-doppio","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Integrated Surface Database (ISD)","Description":"The Integrated Surface Database (ISD) consists\nof global hourly and synoptic observations\ncompiled from numerous sources into a gzipped\nfixed width format. ISD was developed as a joint\nactivity within Asheville's Federal Climate\nComplex. The database includes over 35,000 stations\nworldwide, with some having data as far back\nas 1901, though the data show a substantial\nincrease in volume in the 1940s and again in\nthe early 1970s. Currently, there are over\n14,000 \"active\" stations updated daily in the\ndatabase. The total uncompressed data volume is\naround 600 gigabytes; however, it continues to\ngrow as more data are added. ISD includes\nnumerous parameters such as wind speed and\ndirection, wind gust, temperature, dew point,\ncloud data, sea level pressure, altimeter setting,\nstation pressure, present weather, visibility,\nprecipitation amounts for various time periods,\nsnow depth, and various other elements as observed\nby each station.\n","Documentation":"https://www.ncei.noaa.gov/data/global-hourly/doc/isd-format-document.pdf","Contact":"For questions regarding data content or quality, visit [the NOAA ISD site](https://www.ncei.noaa.gov/data/global-hourly/doc/isd-format-document.pdf). <br />\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"ISD in original format","ARN":"arn:aws:s3:::noaa-isd-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-isd-pds.s3.amazonaws.com/index.html)"]},{"Description":"ISD in CSV format","ARN":"arn:aws:s3:::noaa-global-hourly-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-global-hourly-pds.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"NOAA Integrated Surface Database (ISD) Example Notebook","URL":"https://github.com/awslabs/amazon-asdi/tree/main/examples/noaa-isd","AuthorName":"Zac Flamig","Services":["Amazon Athena","AWS CloudFormation","AWS Glue","Amazon SageMaker"]}]},"RegistryEntryAdded":"2019-07-10","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-isd","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Joint Polar Satellite System (JPSS)","Description":"#### Update\n\nThe JPSS data on Amazon Web Services (AWS) is managed through the NODD Program, utilizing AWS and NOAA-owned and managed S3 buckets that are continuously updated as soon as new data becomes available. \n<br/><br/>\nStarting September 14, 2026, JPSS data on AWS will maintain an average total storage of 1PB. A subset of products will have a 90-day, rolling basis, retention policy applied while the remainder will be provided continually. The following JPSS data products will be provided without a retention policy:\n- ATMS-SDR\n- ATMS-TDR\n- ATMS-SDR-GEO\n- CrIS-FS-SDR\n- CrIS-SDR-GEO\n- VIIRS-DNB-GEO\n- VIIRS-DNB-SDR\n- VIIRS_EFIRE_VIIRSI_EDR\n- VIIRS-I1-SDR\n- VIIRS-I2-SDR\n- VIIRS-I3-SDR\n- VIIRS-I4-SDR\n- VIIRS-I5-SDR\n- VIIRS-IMG-GEO-TC\n<br/><br/>\nThe list of products may change over time. This page will be updated as any changes are made.\n<br/><br/>\nThe full suite of JPSS data is available via the <a href=\"https://www.google.com/url?q=https://www.class.noaa.gov/data_available/jpss/index.htm&sa=D&source=docs&ust=1783978138931750&usg=AOvVaw2KP9ByvjyQrwSj7n--Kn0z\">NOAA NCEI CLASS FTP</a>. JPSS data can also be accessed via <a href=\"https://console.cloud.google.com/marketplace/product/noaa-public/noaa-jpss?hl=en&project=nmfs-trusted-images\">Google Cloud</a> and Microsoft Azure. Microsoft Azure containers must be accessed via command line or the Azure Portal at this time: <a href=\"https://jpss.blob.core.windows.net/noaa-20\">NOAA-20</a>, <a href=\"https://jpss.blob.core.windows.net/noaa-21\">NOAA-21</a>, and <a href=\"https://jpss.blob.core.windows.net/snpp\">SNPP</a>.\n\n---\n\n<br /> Satellites in the JPSS constellation gather global measurements of atmospheric, terrestrial and oceanic conditions, including sea and land surface temperatures, vegetation, clouds, rainfall, snow and ice cover, fire locations and smoke plumes, atmospheric temperature, water vapor and ozone. JPSS delivers key observations for the Nation's essential products and services, including forecasting severe weather like hurricanes, tornadoes and blizzards days in advance, and assessing environmental hazards such as droughts, forest fires, poor air quality and harmful coastal waters. Further, JPSS will provide continuity of critical, global observations of Earth’s atmosphere, oceans and land through 2038.\n","Documentation":"https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. <br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"New data is added as soon as it's available","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA JPSS NOAA-20 Data","ARN":"arn:aws:s3:::noaa-nesdis-n20-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nesdis-n20-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for JPSS data, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewNOAA20Object","Region":"us-east-1","Type":"SNS Topic"},{"Description":"NOAA JPSS NOAA-21 Data","ARN":"arn:aws:s3:::noaa-nesdis-n21-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nesdis-n21-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for JPSS data, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewNOAA21Object","Region":"us-east-1","Type":"SNS Topic"},{"Description":"NOAA JPSS SNPP (Suomi NPP) Data","ARN":"arn:aws:s3:::noaa-nesdis-snpp-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nesdis-snpp-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for JPSS data, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewSNPPObject","Region":"us-east-1","Type":"SNS Topic"},{"Description":"NOAA JPSS Development Data","ARN":"arn:aws:s3:::noaa-jpss","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-jpss.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for JPSS data, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewJPSSObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"GOES-R/JPSS Short Course on Making Beautiful Images of NOAA Satellite Data using Python from the 2023 Annual Meeting of American Meteorological Society","URL":"https://github.com/modern-tools-workshop/AMS-python-workshop-2023","AuthorName":"Amy Huff and Rebekah Esmaili"},{"Title":"JPSS Satellites (COMET)","URL":"https://www.meted.ucar.edu/training_course.php?id=54","AuthorName":"UCAR"},{"Title":"JPSS Short Course from the 2018 Annual Meeting of American Meteorological Society","URL":"http://rammb.cira.colostate.edu/training/visit/links_and_tutorials/ams_austin_tx_JPSS_workshop_2018.asp","AuthorName":"Colorado State University"},{"Title":"JPSS Training Resources","URL":"https://www.jpss.noaa.gov/training_resources.html","AuthorName":"NOAA"},{"Title":"JPSS_AWS Geolocate","URL":"https://gitlab.cicsnc.org/workshop-development/jpss_aws","NotebookURL":"https://gitlab.cicsnc.org/workshop-development/jpss_aws/-/blob/main/JPSS_notebook_geolocate.ipynb","AuthorName":"Mya Sears","Services":["Amazon SageMaker Studio Lab"]},{"Title":"JPSS_AWS Timeselect","URL":"https://gitlab.cicsnc.org/workshop-development/jpss_aws","NotebookURL":"https://gitlab.cicsnc.org/workshop-development/jpss_aws/-/blob/main/JPSS_notebook_timeselect.ipynb","AuthorName":"Mya Sears","Services":["Amazon SageMaker Studio Lab"]},{"Title":"Python scripts to download and display JPSS NODD datasets","URL":"https://github.com/jpss-nodd/python-scripts","AuthorName":"Amy Huff and Rebekah Esmaili"},{"Title":"VIIRS Active Fire Quick Guide","URL":"https://rapidrefresh.noaa.gov/hrrr/HRRRsmoke/VIIRSActiveFireQuickGuide-FinalForm-.pdf","AuthorName":"NOAA"},{"Title":"VIIRS Products (NASA SPoRT)","URL":"https://weather.msfc.nasa.gov/sport/jpsspg/viirs.html","AuthorName":"NASA"}],"Publications":[{"Title":"JPSS Annual Science Digest 2022","URL":"https://nesdis-prod.s3.amazonaws.com/2023-01/2022_JPSS_Annual_Science_Digest.pdf?_ga=2.218815898.2099792528.1695138068-953930935.1676650601","AuthorName":"NOAA"},{"Title":"JPSS Science Seminar Annual Digest 2020","URL":"https://www.jpss.noaa.gov/assets/pdfs/science_publications/2020_science_seminar_digest.pdf","AuthorName":"NOAA"},{"Title":"JPSS Science Seminar Annual Digest 2021","URL":"https://nesdis-prod.s3.amazonaws.com/2022-03/2021_science_seminar_digest.pdf","AuthorName":"NOAA"},{"Title":"NUCAPS EDR files in the cloud","URL":"https://cimss.ssec.wisc.edu/satellite-blog/archives/59235","AuthorName":"Scott Lindstrom"}],"Tools & Applications":[{"Title":"JPSS Data Explorer","URL":"https://coliveir-aer.github.io/jpss-data-explorer/","AuthorName":"Craig Oliveira"},{"Title":"JPSS Proving Ground","URL":"https://www.jpss.noaa.gov/proving_ground.html","AuthorName":"NOAA"}]},"RegistryEntryAdded":"2021-12-09","RegistryEntryLastModified":"2026-07-17","Slug":"noaa-jpss","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE)","Description":"J-SCOPE (JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem) is funded by NOAA and presented by NANOOS. This project aims to provide experimental seasonal forecasts (six to nine months) of upper ocean properties, based on operational simulations by NOAA's Climate Forecast System (CFS) model, and dynamical downscaling with a high-resolution version of the Regional Ocean Model System (ROMS) that includes a state-of-the-art biogeochemical module. Forecasts of specific oceanic properties crucial to the nearshore and coastal marine ecosystem such as upwelling, pH, mixed layer depth, oxygen concentration and plankton distributions are anticipated with updates on a monthly basis. For more information about the forecast system, please read [Siedlecki et al. 2016](https://www.nature.com/articles/srep27203).The Regional Ocean Modeling System (ROMS; Rutgers version 3) is configured for the Washington and Oregon coasts after [Giddings et al](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013JC009622). More information about ROMS can be found [here](https://www.myroms.org/). The Cascadia domain was developed by the Coastal Modeling Group at the UW is roughly 1.5 km in resolution. More information about the model physics can be found [here](https://scripps.ucsd.edu/iod). Our implementation of ROMS includes 17 rivers forced with daily river discharge and temperature data from the USGS gauging stations and an Environment Canada gauging station for the Fraser River as described by [Giddings et al](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013JC009622). Tides are included. Water entering the domain at the southern and western boundaries is supplied by CFS. Empirical relationships were derived relating nutrients and oxygen to salinity from the observations of Connolly et al (2010) as described by [Davis et al](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014JC010248) and [Siedlecki et al](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014JC010254). The rivers enter the domain with constant saturated values of oxygen and a seasonal cycle for nutrients from a climatology of USGS gauging stations data described by [Davis et al](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014JC010248). In the hindcast simulations, the rivers are based on observed streamflows, but in the re-forecast and forecast runs, the rivers are forced using a climatology of local river discharge data over seven years (2000-2007).\n<br><br>\nThe forecast system predicts the sea-surface temperature (SST), chlorophyll stock, dissolved oxygen concentration, carbon variables, and sardine as well as hake habitat. Each forecast is composed of three model runs that make up an ensemble. Each model run is initialized at a different time within the initialization month (for April, for example, April 6, April 15, April 25), and has complementary forcing files from the large scale model, [CFS](https://www.nanoos.org/products/j-scope/about_the_model.php). J-SCOPE produces January, April, and September initialized forecasts annually.\n<br><br>\nThe details of the wind forcing for each model run can be found on the California Current Indicators tab. For each of the predicted quantities listed above, we report the ensemble average anomaly as well as the relative uncertainty within the ensemble, which is defined as the standard deviation of the ensemble divided by the mean of the ensemble and is reported as a percentage of the mean. All of these quantities are reported as monthly averaged anomalies from our April-initialized reforecast [climatology](https://www.nanoos.org/products/j-scope/climatology.php?climatology=apr_init), which spans 2009-2017. An anomaly is an indication of how different conditions are to what they have been in the past. For more information about anomalies, please see the [NANOOS Climatology App](https://nvs.nanoos.org/AveragesAnomalies). These predicted quantities are key indicators for the [California Current Integrated Ecosystem Assessment report](https://www.integratedecosystemassessment.noaa.gov/regions/california-current).\n<br><br>\nJ-SCOPE has also been run as a re-forecast and a hindcast each spanning 1998 to present and also rely on CFS forcing. The re-forecasts extend to 2013 when the true forecasts began.  The historical output is available daily while the re-forecasts and forecasts are available as monthly averages. The historical simulation is used to evaluate the Year-in Review and is released after the year is over in the early part of the following year. \n<br><br>\n","Documentation":"https://www.nanoos.org/products/j-scope/home.php","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Quarterly. Updated in February, May and October with January, April, and September initialized forecast products respectively. We also do a year in review when the historical fields will be available and this is typically in January or February of the year following its occurrence.","Collabs":{"ASDI":{"Tags":["water"]}},"Tags":["aws-pds","chemistry","climate","coastal","marine","oceans"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE)","ARN":"arn:aws:s3:::noaa-j-scope-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-j-scope-pds.s3.amazonaws.com/index.html)"]},{"Description":"J-SCOPE Dataset Notifications","ARN":"arn:aws:sns:us-east-1:123901341784:NewJSCOPEObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"J-SCOPE About the Model","URL":"https://www.nanoos.org/products/j-scope/about_the_model.php","AuthorName":"NOAA J_SCOPE","AuthorURL":"https://www.nanoos.org/products/j-scope/home.php"}],"Publications":[{"Title":"J-SCOPE Peer Reviewed Publications","URL":"https://www.nanoos.org/products/j-scope/publications.php","AuthorName":"Multiple publications available through the provided link"}]},"RegistryEntryAdded":"2026-04-02","RegistryEntryLastModified":"2026-04-06","Slug":"noaa-jscope","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Multi-Radar/Multi-Sensor System (MRMS)","Description":"The MRMS system was developed to produce severe weather, transportation, and precipitation products for improved decision-making capability to improve hazardous weather forecasts and warnings, along with hydrology, aviation, and numerical weather prediction.\n<br/>\n<br/>\nMRMS is a system with fully-automated algorithms that quickly and intelligently integrate data streams from multiple radars, surface and upper air observations, lightning detection systems, satellite observations, and forecast models. Numerous two-dimensional multiple-sensor products offer assistance for hail, wind, tornado, quantitative precipitation estimations, convection, icing, and turbulence diagnosis.\n<br/>\n<br/>\nMRMS is being used to develop and test new Federal Aviation Administration (FAA) NextGen products in addition to advancing techniques in quality control, icing detection, and turbulence in collaboration with the National Center for Atmospheric Research, the University Corporation for Atmospheric Research, and Lincoln Laboratories.\n<br/>\n<br/>\nMRMS was deployed operationally in 2014 at the National Center for Environmental Prediction (NCEP). All of the 100+ products it produces are available via NCEP to all of the WFOs, RFCs, CWSUs and NCEP service centers. In addition, the MRMS product suite is publicly available to any other entity who wishes to access and use the data. Other federal agencies that use MRMS include FEMA, DOD, FAA, and USDA.\n<br/>\n<br/>\nMRMS was <a href=\"https://mesonet.agron.iastate.edu/wx/afos/p.php?pil=PNSWSH&e=202009101318\">upgraded</a> from version 11 to 12 on October 14, 2020. As a part of this upgrade MRMS introduced new products, discontinued others, and improved the underlying science and technologies across the product suite. For these reasons, we advise caution when using MRMS version 11 products released before October 14, 2020.\n<br/>\n<br/>\nMRMS is the proposed operational version of the WDSS-II and NMQ research systems.\n<br/>\n<br/> \nThe MRMS system was jointly developed in cooperation with the Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO) (formerly CIMMS), and the University of Oklahoma retains the right to commercially license the software. Several leading weather information companies have previously licensed the MRMS system from the University of Oklahoma for commercial use, although the software is available for government at no cost.\n<br/>\n","Documentation":"https://www.nssl.noaa.gov/projects/mrms/","Contact":"For specific MRMS data questions, please reach out to the MRMS Team at mrms@noaa.gov\n<br/>\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Data is delivered in real-time with a 2-minute update cycle.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA Multi-Radar/Multi-Sensor System (MRMS)","ARN":"arn:aws:s3:::noaa-mrms-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-mrms-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for MRMS data, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewMRMSObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"How to Read MRMS Rainfall Estimates from GRIB2 in Python","URL":"https://www.spatialworkflow.io/mrms-qpe-grib-python/","NotebookURL":"https://www.spatialworkflow.io/notebooks/mrms-qpe-grib-python.ipynb","AuthorName":"Spatial Workflow","AuthorURL":"https://www.spatialworkflow.io/"}],"Tools & Applications":[{"Title":"Collection of Jupyter Notebooks using Python for working with MRMS Data","URL":"https://projectpythia.org/mrms-cookbook/","AuthorName":"Project Pythia Community"},{"Title":"PTE Data Tour Notebooks","URL":"https://github.com/NCEI-NOAAGov/data-tour-notebooks/tree/github/pte","AuthorName":"NOAA Industry Proving Grounds"}],"Publications":[{"Title":"Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities","URL":"https://journals.ametsoc.org/view/journals/bams/97/4/bams-d-14-00174.1.xml","AuthorName":"Jian Zhang, Kenneth Howard, Carrie Langston, Brian Kaney, Youcun Qi, Lin Tang, Heather Grams, Yadong Wang, Stephen Cocks, Steven Martinaitis, Ami Arthur, Karen Cooper, Jeff Brogden, David Kitzmiller"},{"Title":"Multi-Radar Multi-Sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities","URL":"https://journals.ametsoc.org/view/journals/bams/97/9/bams-d-14-00173.1.xml","AuthorName":"Travis M. Smith, Valliappa Lakshmanan, Gregory J. Stumpf, Kiel L. Ortega, Kurt Hondl, Karen Cooper, Kristin M. Calhoun, Darrel M. Kingfield, Kevin L. Manross, Robert Toomey, Jeff Brogden"}]},"RegistryEntryAdded":"2023-03-16","RegistryEntryLastModified":"2026-10-16","Slug":"noaa-mrms-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA North American Mesoscale Forecast System (NAM)","Description":"#### Update\n\nEffective October 14, 2026, the NAM will be replaced by the Rapid Refresh Forecast System (RRFS). A full list of the models and products being retired can be found in the following [Service Change Notice](https://www.weather.gov/media/notification/pdf_2026/SCN26-47_Updated_Retire_NAM_SREF_HREF_HiresW_NAM_MOS.aab.pdf). The retirement of NAM and the production implementation of RRFS and the RRFS Ensemble Forecast System (REFS) will occur on the same day.\n<br/><br/>\nProduction files of RRFS and REFS can be accessed through the [operational bucket](https://registry.opendata.aws/noaa-rrfs-ops/) for these models.\n<br/><br/>\nPlease check back here and monitor [NWS's Service Change Notices](https://www.weather.gov/notification/) for the latest updates as the implementation date nears.\n\n---\nThe North American Mesoscale Forecast System (NAM) is one of the National Centers For Environmental Prediction’s (NCEP) major models for producing weather forecasts. NAM generates multiple grids (or domains) of weather forecasts over the North American continent at various horizontal resolutions. Each grid contains data for dozens of weather parameters, including temperature, precipitation, lightning, and turbulent kinetic energy. NAM uses additional numerical weather models to generate high-resolution forecasts over fixed regions, and occasionally to follow significant weather events like hurricanes.\n","Documentation":"https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/nam.php, https://www.ncei.noaa.gov/products/weather-climate-models/north-american-mesoscale","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Four times daily (0000, 0600, 1200, and 1800 UTC)","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"North American Mesoscale Forecast System (NAM) Data","ARN":"arn:aws:s3:::noaa-nam-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nam-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for NAM data, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewNCEPNAMObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2021-09-30","RegistryEntryLastModified":"2026-09-10","Slug":"noaa-nam","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA National Blend of Models (NBM) Parallel","Description":"The National Blend of Models (NBM) is a nationally consistent and skillful suite of calibrated forecast guidance based on a blend of both NWS and non-NWS numerical weather prediction model data and post-processed model guidance. The goal of the NBM is to create a highly accurate, skillful and consistent starting point for the gridded forecast. This dataset contains data from the current parallel version of the NBM which is a test version, featuring many changes, that is a candidate to be implemented into operations following a careful vetting process.\n","Documentation":"https://vlab.noaa.gov/web/mdl/nbm\n","Contact":"For any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\nWe also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Once per hour.\n","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","disaster response","environmental","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.\n","Resources":[{"Description":"National Blend of Models (NBM) Parallel","ARN":"arn:aws:s3:::noaa-nbm-para-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nbm-para-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for NBM Parallel, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewNBMParaObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2025-07-15","RegistryEntryLastModified":"2025-07-15","Slug":"noaa-nbm-parallel","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA National Blend of Models (NBM)","Description":"The National Blend of Models (NBM) is a nationally consistent and skillful suite of calibrated forecast guidance based on a blend of both NWS and non-NWS numerical weather prediction model data and post-processed model guidance. The goal of the NBM is to create a highly accurate, skillful and consistent starting point for the gridded forecast. \n","Documentation":"https://vlab.noaa.gov/web/mdl/nbm","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Once per hour","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","cog","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"National Blend of Models (NBM) COG Format","ARN":"arn:aws:s3:::noaa-nbm-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nbm-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for NBM-COG Format, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewNBMCOGObject","Region":"us-east-1","Type":"SNS Topic"},{"Description":"National Blend of Models (NBM) Grib2 Format","ARN":"arn:aws:s3:::noaa-nbm-grib2-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nbm-grib2-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for NBM-Grib2 Format, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewNBMGRIBObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2020-05-22","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-nbm","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA U.S. Climate Gridded Dataset (NClimGrid)","Description":"The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature, minimum temperature, average temperature and precipitation. Each file provides monthly values in a 5x5 lat/lon grid for the Continental United States. Data is available from 1895 to the present. On an annual basis, approximately one year of \"final\" nClimGrid will be submitted to replace the initially supplied \"preliminary\" data for the same time period. Users should be sure to ascertain which level of data is required for their research.\n<br/>\n<br/>\nEpiNOAA is an analysis ready dataset that consists of a daily time-series of nClimGrid measures (maximum temperature, minimum temperature, average temperature, and precipitation) at the county scale.  Each file provides daily values for the Continental United States. Data are available from 1951 to the present. Daily data are updated every 3 days with a preliminary data file and replaced with the scaled (i.e., quality controlled) data file every three months. This derivative data product is an enhancement from the original daily nClimGrid dataset in that all four weather parameters are now packaged into one file and assembled in a daily time-series format. In addition to a direct download option, an R package and web interface has been developed to streamline access to the final data product. These options allow end users three separate access modes to arrive at a customized dataset unique to each end user’s application. Users should be sure to review the data documentation to inform which level of data is required for their research.\n","Documentation":"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Monthly","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Daily NClimGrid Data","ARN":"arn:aws:s3:::noaa-nclimgrid-daily-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nclimgrid-daily-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for Daily NClimGrid, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewNClimGridDailyObject","Region":"us-east-1","Type":"SNS Topic"},{"Description":"Monthly NClimGrid Data","ARN":"arn:aws:s3:::noaa-nclimgrid-monthly-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nclimgrid-monthly-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for Daily NClimGrid, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewNClimGridMonthlyObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2021-08-11","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-nclimgrid","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Continuously Operating Reference Stations (CORS) Network (NCN)","Description":"The [NOAA Continuously Operating Reference Stations (CORS) Network (NCN)](https://geodesy.noaa.gov/CORS/), managed by NOAA/National Geodetic Survey ([NGS](https://geodesy.noaa.gov/)), provide Global Navigation Satellite System (GNSS) data, supporting three dimensional positioning, meteorology, space weather, and geophysical applications throughout the United States. The NCN is a multi-purpose, multi-agency cooperative endeavor, combining the efforts of hundreds of government, academic, and private organizations. The stations are independently owned and operated. Each agency shares their GNSS/GPS carrier phase and code range measurements and station metadata with NGS, which are analyzed and distributed free of charge.\nNGS provides access to all NCN data collected since 9 February (040) 1994.\n- #### Access to NCN Data and Products\n    - [NOAA-NCN on AWS](https://noaa-cors-pds.s3.amazonaws.com/index.html)\n    - [NGS server: https://geodesy.noaa.gov/corsdata/](https://geodesy.noaa.gov/corsdata/)\n    - [NGS's customized data request service (UFCORS)](https://geodesy.noaa.gov/UFCORS/)\n- #### NCN Data and Products\n    - **RINEX**: The GPS/GNSS data collected at NCN stations are made available to the public by NGS in Receiver INdependent EXchange (RINEX) format. Most data are available within 1 hour (60 minutes) from when they were recorded at the remote site, and a few sites have a delay of 24 hours (1440 minutes).<br/>RINEX data can be found at: *rinex/`YYYY`/`DDD`/`ssss`/*\n    - **Station logs**: \n        - Station log files contain all the historical equipment (receiver/antenna) used at that station, approximate location, owner and operating agency, etc..<br/>Station log files can be found at: *station_log/`ssss`.log.txt*\n        - Historical and current equipment information of all NCN stations, except those that are considered IGS stations.<br/>These data can be found at: *station_log/cumulative.station.info.cors*\n    - **Published Coordinates and Velocities**: NAD83 and ITRF coordinates and velocities of each NCN station. All published coordinates and velocities are given for the Antenna Reference Point (ARP).<br/>Published coordinate and velocity files can be found at: *coord/coord_`YY`/*<br/>In July 2019, NGS published [MYCS2](https://geodesy.noaa.gov/CORS/news/mycs2/mycs2.shtml)!\n    - **Time-series Plots**:\n        - *Short-term* plots show the repeatability of a site for the last 90-days with respect to the current published position, corrected for the effect of the published velocity. These plots are updated daily.<br/>Short-term plots can be found at: */Plots/`ssss`_`YY`.short.png*\n        - *Long-term* plots show the show weekly residual positions with respect to the current published coordinates from our stacked solution. Newer sites may not have a long-term plot if they were added after our Multi-year Solution Processing campaign.<br/>Long-term plots can be found at: */Plots/Longterm/`ssss`_`YY`.long.png*\n    - **Daily Broadcast Ephemeris**:\n        - Daily GPS Broadcast ephemeris can be found at: *rinex/`YYYY`/`DDD`/brdc`DDD`0.`YY`n.gz*\n        - Daily GLONASS-only Broadcast ephemeris can be found at: *rinex/`YYYY`/`DDD`/brdc`DDD`0.`YY`g.gz*\n    - **Daily final, rapid, and hourly ultra-rapid GNSS Orbit** can be found at: \n        - Daily final and rapid GNSS Orbit can be found at: *rinex/`YYYY`/`DDD`/`AAAWWWWD`.sp3.gz*\n        - Hourly ultra-rapid GNSS Orbit can be found at: *rinex/`YYYY`/`DDD`/`AAAWWWWD`_`HH`.sp3.gz*\n    - In which:\n      - `YYYY`: 4-digit year\n      - `YY`: The last 2-digit of year\n      - `DDD`: 3-digit day of year [001,002,..366]\n      - `D`: day of week [Sun=0, Mon=1,..,Fri=6]\n      - `ssss`: 4-char station ID\n      - `h`: 1-char hour of day (a=00, b=01, c=02,..,x=23)\n      - `HH`: 2-digit hour of day (00,01,02,..,23)\n      - `WWWW`: 4-digit GPS week number\n      - `AAA`: 3-char analysis center name/type of solution, such as:\n          - igs: IGS final solution combination\n          - igl: IGS final solution combination (GLONASS-only)\n          - igr: IGS rapid solution combination\n          - igu: IGS ultra-rapid solution combination\n    \n","Documentation":"For more information, visit [NCN Data and Products](https://geodesy.noaa.gov/CORS/data.shtml)","Contact":"- For general inquiries about NCN data and products, email ✉ ngs.cors at noaa.gov\n- For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n- We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Most data are available within 1 hour from when they were recorded at the remote site, and a few sites have a delay of 24 hours.","Collabs":{"ASDI":{"Tags":["elevation"]}},"Tags":["aws-pds","broadcast ephemeris","Continuously Operating Reference Station (CORS)","earth observation","geospatial","GNSS","GPS","mapping","NOAA CORS Network (NCN)","post-processing","RINEX","survey"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"[NCN Data and Products](https://geodesy.noaa.gov/CORS/data.shtml)","ARN":"arn:aws:s3:::noaa-cors-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse NOAA-NCN Bucket](https://noaa-cors-pds.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2021-01-28","RegistryEntryLastModified":"2025-07-18","Slug":"noaa-ncn","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA National Digital Forecast Database (NDFD)","Description":"<br />\n<br />\nThe National Digital Forecast Database (NDFD) is a suite of gridded forecasts of sensible weather elements (e.g., cloud cover, maximum temperature).  Forecasts prepared by NWS field offices working in collaboration with the National Centers for Environmental Prediction (NCEP) are combined in the NDFD to create a seamless mosaic of digital forecasts from which operational NWS products are generated. The most recent data is under the opnl and expr prefixes. A copy is also placed under the wmo prefix. The wmo prefix is structured like so: wmo/&lt;parameter&gt;/&lt;year&gt;/&lt;month&gt;/&lt;day&gt;/&lt;wmo-file-name&gt; The wmo filename codes can be deciphered using the spreadsheet in the root of the bucket.\n","Documentation":"Visit https://vlab.noaa.gov/web/mdl/ndfd for NDFD Product information, instructions, and lookup tables.\n <br />\n <br />\n Please note, when accessing the data, some decoders may incorrectly handle the NDFD grid’s scanning pattern. To ensure compatibility, we recommend using degrib or wgrib2 for Linux applications. For users working in Python, \"[grib2io](https://github.com/NOAA-MDL/grib2io)\" is the recommended library for opening and processing these files correctly.\n","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"As often as once every half hour (varies by forecast element, forecast projection, and domain)","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"National Digital Forecast Database (NDFD) Grib2 Format","ARN":"arn:aws:s3:::noaa-ndfd-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ndfd-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for NDFD, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewNDFDObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"NDFD Product Spreadsheet (excel file)","URL":"https://noaa-ndfd-pds.s3.amazonaws.com/NDFDelem_fullres_202206.xls","AuthorName":"NOAA MDL"}]},"RegistryEntryAdded":"2020-06-05","RegistryEntryLastModified":"2026-05-22","Slug":"noaa-ndfd","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Space Weather Follow-On Mission Geostationary Operational Environmental Satellite (GOES) 19","Description":"<br/>  \nThe National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite 19 (GOES-19) is the fourth and final satellite in the\nGeostationary Operational Environmental Satellites (GOES) – R Series, the Western Hemisphere’s most sophisticated weather-observing and environmental monitoring \nsystem.\n<br/>  \nThe GOES-R Series provides advanced imagery and atmospheric measurements, real-time mapping of lightning activity, and space weather observations. As a part of the\nSpace Weather Follow On (SWFO) Mission, the GOES-19 spacecraft contains a Compact Coronagraph-1 (CCOR-1) instrument that produces 96 files per day per level in the\nFITS format. These data are produced by the CCOR-1 instrument aboard the GOES-19 spacecraft. The instrument observes broadband optical light that is scattered from\ncoronal electrons and heliospheric dust. The spacecraft also produces daily auxiliary (telemetry and attitude) files in NetCDF4 format.\n","Documentation":"https://github.com/NOAA-Big-Data-Program/nodd-data-docs/tree/main/SWFO","Contact":"For questions related to specific CCOR-1 Products, please contact ncei.swx.coronagraph@noaa.gov\n<br/>\nFor questions related to general Space Weather data, please contact ncei.info@noaa.gov\n<br/>\n<br/>\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"New data is added as soon as it's available","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","satellite imagery","space weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"GOES-19 CCOR-1 data","ARN":"arn:aws:s3:::noaa-nesdis-swfo-ccor-1-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nesdis-swfo-ccor-1-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for GOES-19 CCOR-1 data, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewSWFOCCOR1Object","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2025-03-07","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-nesdis-swfo-ccor-1-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED)","Description":"The Tropical Cyclone Precipitation, Infrared, Microwave and Environmental Dataset (TC PRIMED) is a dataset centered around passive microwave observations of global tropical cyclones from low-Earth-orbiting satellites. TC PRIMED is a compilation of tropical cyclone data from various sources, including 1) tropical cyclone information from the National Oceanic and Atmospheric Administration (NOAA) National Weather Service National Hurricane Center (NHC) and Central Pacific Hurricane Center (CPHC) and the U.S. Department of Defense Joint Typhoon Warning Center, 2) low-Earth-orbiting satellite observations and products from the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement (GPM) mission constellation satellites, and 3) environmental fields and diagnostics calculated from the European Center for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5).","Documentation":"https://rammb-data.cira.colostate.edu/tcprimed/TCPRIMED_v01r01_documentation.pdf","Contact":"CIRA_tcprimed [at] colostate [dot] edu","ManagedBy":"[CIRA](https://www.cira.colostate.edu/)","UpdateFrequency":"Annually for the final version, several months after the conclusion of the Northern Hemisphere tropical cyclone season and daily for the preliminary version, several days after the dissipation of a tropical cyclone.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","earth observation","environmental","geophysics","geoscience","global","meteorological","model","netcdf","precipitation","satellite imagery","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Citation":"See citation instructions at https://rammb-data.cira.colostate.edu/tcprimed/publications.html","Resources":[{"Description":"The Tropical Cyclone Precipitation, Infrared, Microwave and Environmental Dataset (TC PRIMED) is a dataset centered around passive microwave observations of global tropical cyclones from low-Earth-orbiting satellites. The files are available in NetCDF format.","ARN":"arn:aws:s3:::noaa-nesdis-tcprimed-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nesdis-tcprimed-pds.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"NOAA Center for Artificial Intelligence (NCAI) TC PRIMED Learning Journey Jupyter Notebooks","URL":"https://rammb-data.cira.colostate.edu/tcprimed/products.html","NotebookURL":"https://github.com/CSU-CIRA/tcprimed_learning_journey/blob/main/TCPRIMED_Chap0_Introduction.ipynb","AuthorName":"Naufal Razin (CSU/CIRA), Kathy Haynes (CSU/CIRA), Chris Slocum (NOAA/STAR)","Services":["Amazon S3"]}],"Tools & Applications":[{"Title":"tcprimedapi","URL":"https://pypi.org/project/tcprimedapi/","AuthorName":"TC PRIMED Development Team"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2023-09-28","RegistryEntryLastModified":"2025-07-09","Slug":"noaa-nesdis-tcprimed-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NEXRAD on AWS","Description":"Real-time and archival data from the Next Generation Weather Radar (NEXRAD) network.\n\n#### Update\n\nThe NEXRAD Level II archive data is moving to a new bucket: `unidata-nexrad-level2`\nand SNS topic: `arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel2Archive`. The old\nbucket and SNS topic are now deprecated and will no longer be available starting September 1, 2025.\n","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad","Contact":"support-level2@unidata.ucar.edu","ManagedBy":"[Unidata](https://www.unidata.ucar.edu/)","UpdateFrequency":"New Level II data is added as soon as it is available.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","earth observation","meteorological","natural resource","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NEXRAD Level II archive data","ARN":"arn:aws:s3:::unidata-nexrad-level2","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://unidata-nexrad-level2.s3.amazonaws.com/index.html)"]},{"Description":"NEXRAD Level II real-time data","ARN":"arn:aws:s3:::unidata-nexrad-level2-chunks","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"NEXRAD Level III real-time select data","ARN":"arn:aws:s3:::unidata-nexrad-level3","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://unidata-nexrad-level3.s3.amazonaws.com/index.html)"]},{"Description":"[Rich notifications](https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad#subscribing-to-nexrad-data-notifications) for real-time data with filterable fields","ARN":"arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel2ObjectFilterable","Region":"us-east-1","Type":"SNS Topic"},{"Description":"Notifications for the new Level II archival bucket","ARN":"arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel2Archive","Region":"us-east-1","Type":"SNS Topic"},{"Description":"Notifications for the Level III bucket","ARN":"arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel3Object","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Explore NEXRAD in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/b2c6e420-9855-55ce-af73-f8e41a24278e/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Get NEXRAD data from AWS, get severe reports from SPC archive, and plot a loop of them","URL":"https://github.com/russ-schumacher/ats641_spring2022/blob/master/example_notebooks/pyart_nexrad_maps_reports.ipynb","AuthorName":"Russ Schumacher","AuthorURL":"https://www.atmos.colostate.edu/people/faculty/schumacher/"},{"Title":"How to Read NEXRAD Level II Radar Data in Python","URL":"https://www.spatialworkflow.io/read-nexrad-level2-python/","NotebookURL":"https://www.spatialworkflow.io/notebooks/read-nexrad-level2-python.ipynb","AuthorName":"Spatial Workflow","AuthorURL":"https://www.spatialworkflow.io/"},{"Title":"Mapping Noaa Nexrad Radar Data With CARTO","URL":"https://carto.com/blog/mapping-nexrad-radar-data/","AuthorName":"Stuart Lynn","AuthorURL":"https://carto.com/blog/author/stuart-lynn/"},{"Title":"NEXRAD on EC2 tutorial","URL":"https://github.com/openradar/AMS_radar_in_the_cloud","AuthorName":"openradar","AuthorURL":"https://github.com/openradar","Services":["Amazon EC2"]},{"Title":"Updated introduction to S3, Boto, and NOAA Nexrad in SageMaker Studio Lab (SMSL)","URL":"https://github.com/aws-samples/aws-nexrad-smsl-notebook/","NotebookURL":"https://github.com/aws-samples/aws-nexrad-smsl-notebook/blob/main/Nexrad_Demo.ipynb","AuthorName":"Chris Stoner","Services":["Amazon SageMaker Studio Lab"]},{"Title":"Using Python to Access NCEI Archived NEXRAD Level 2 Data (Jupyter notebook)","URL":"http://nbviewer.jupyter.org/gist/dopplershift/356f2e14832e9b676207","AuthorName":"Ryan May","AuthorURL":"http://dopplershift.github.io"}],"Tools & Applications":[{"Title":"Into the eye of the storm: NEXRAD Level II open data","URL":"https://blog.mapbox.com/3d-animated-hurricane-map-from-radar-data-affaed8a3d70","AuthorName":"Jonni Walker"},{"Title":"nexradaws on pypi.python.org - python module to query and download Nexrad data from Amazon S3","URL":"https://pypi.org/project/nexradaws/","AuthorName":"Aaron Anderson","AuthorURL":"https://github.com/aarande"},{"Title":"Open NEXRAD Data Access for Level 3 with Lambda@Edge","URL":"https://github.com/zflamig/ondas-pds","AuthorName":"Zac Flamig"},{"Title":"WeatherPipe - Amazon EMR based analysis tool for NEXRAD data stored on Amazon S3","URL":"https://github.com/stephenlienharrell/WeatherPipe","AuthorName":"Stephen Lien Harrell","AuthorURL":"https://github.com/stephenlienharrell"}],"Publications":[{"Title":"Declines in an abundant aquatic insect, the burrowing mayfly, across major North American waterways","URL":"https://www.pnas.org/content/117/6/2987","AuthorName":"Phillip M. Stepanian, Sally A. Entrekin, Charlotte E. Wainwright, Djordje Mirkovic, Jennifer L. Tank, & Jeffrey F. Kelly"},{"Title":"Extreme Pyroconvective Updrafts During a Megafire","URL":"https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL089001","AuthorName":"B. Rodriguez, N. P. Lareau, D. E. Kingsmill, & C. B. Clements"},{"Title":"Level 2 Interface Control Document for Message Data Formats: Build 18","URL":"https://www.roc.noaa.gov/wsr88d/PublicDocs/ICDs/2620002R.pdf","AuthorName":"NOAA ROC"},{"Title":"Level 2 Interface Control Document for Transfer: Build 18","URL":"https://www.roc.noaa.gov/wsr88d/PublicDocs/ICDs/2620010G.pdf","AuthorName":"NOAA ROC"},{"Title":"Level 3 Interface Control Document for Message Data Formats: Build 18","URL":"https://www.roc.noaa.gov/wsr88d/PublicDocs/ICDs/2620001X.pdf","AuthorName":"NOAA ROC"},{"Title":"Observed Concentric Eyewalls of Supertyphoon Hinnamnor","URL":"https://www.jstage.jst.go.jp/article/sola/advpub/0/advpub_2023-010/_pdf","AuthorName":"Sachie Kanada and Akira Nishii"},{"Title":"Seasonal abundance and survival of North America’s migratory avifauna determined by weather radar","URL":"https://www.nature.com/articles/s41559-018-0666-4","AuthorName":"Adriaan M. Dokter, Andrew Farnsworth, Daniel Fink, Viviana Ruiz-Gutierrez, Wesley M. Hochachka, Frank A. La Sorte, Orin J. Robinson, Kenneth V. Rosenberg & Steve Kelling"},{"Title":"Unlocking the Potential of NEXRAD Data through NOAA’s Big Data Partnership","URL":"https://journals.ametsoc.org/doi/full/10.1175/BAMS-D-16-0021.1","AuthorName":"Steve Ansari and Stephen Del Greco"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2026-10-16","Slug":"noaa-nexrad","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA North American Multi-Model Ensemble (NMME)","Description":"The North American Multi-Model Ensemble (NMME) is an experimental multi-model seasonal forecasting system consisting of coupled models from US modeling centers including NOAA/NCEP, NOAA/GFDL, NCAR, NASA, and Canada's ECCC.\n<br><br>\nThe need for the development of NMME operational predictive capability was recommended in US National Academies report \"Assessment of Intraseasonal to Interannual Climate Prediction and Predictability\".  Indeed, the national effort is required to meet the specific tailored regional prediction and decision support needs of a large community. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) than any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, including an operational European system. There are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model.\n<br><br>\n","Documentation":"https://www.cpc.ncep.noaa.gov/products/NMME/about.html","Contact":"For any issues with the NMME data, please submit an issue at https://www.cpc.ncep.noaa.gov/nwscwi/forms/issue-form-nmme.php. For any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Monthly","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data. <br><br> When using the NMME data, please cite the BAMS article describing the project (Kirtman et al. 2014) and in the acknowledgements please note that the NMME project and data dissemination is supported by NOAA, NSF, NASA and DOE. Please also acknowledge the help of NCEP, IRI and NCAR personnel in creating, updating and maintaining the NMME archive. Thank you.","Resources":[{"Description":"North American Multi-Model Ensemble (NMME)","ARN":"arn:aws:s3:::noaa-nmme-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nmme-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for NMME, only Lambda and SQS protocols allowed)","ARN":"arn:aws:sns:us-east-1:123901341784:NewNMMEObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"Real-time Usage","URL":"https://www.cpc.ncep.noaa.gov/products/NMME/","AuthorName":"NWS Climate Prediction Center NMME"}],"Publications":[{"Title":"NMME : Meeting Future Needs Workshop Report","URL":"https://repository.library.noaa.gov/view/noaa/55665","AuthorName":"Christine, Bassett, Jessie, Carman, D.K. Kang, and Mark Olsen"},{"Title":"NMME Articles","URL":"https://www.cpc.ncep.noaa.gov/products/NMME/Articles.pdf","AuthorName":"Multiple publications listed in the provided link"}]},"RegistryEntryAdded":"2026-04-08","RegistryEntryLastModified":"2026-05-18","Slug":"noaa-nmme","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Cloud Optimized Zarr Reference Files (Kerchunk)","Description":"This repository contains references to datasets published to the NOAA Open Data Dissemination Program. These reference datasets serve as index files to the original data by mapping to the [Zarr V2 specification](https://zarr-specs.readthedocs.io/en/latest/v2/v2.0.html). When multidimensional model output is read through zarr, data can be lazily loaded (i.e. retrieving only the data chunks needed for processing) and data reads can be scaled horizontally to optimize object storage read performance. \n<br/>\n<br/>\nThe process used to optimize the data is called [kerchunk](https://fsspec.github.io/kerchunk/). RPS runs [the workflow](https://asascience-open.github.io/nextgen-dmac/ingest/ingest-prototype.html) in their AWS cloud environment every time a new data notification is received from a relevant source data bucket.\n<br/>\n<br/>\nThese are the current datasets being cloud-optimized. Refer to those pages for file naming conventions and other information regarding the specific model implementations:\n<br/>\n[NOAA Operational Forecast System (OFS)](https://registry.opendata.aws/noaa-ofs/)\n<br/>  \n[NOAA Global Real-Time Ocean Forecast System (Global RTOFS)](https://registry.opendata.aws/noaa-rtofs/)\n<br/>  \n[NOAA National Water Model Short-Range Forecast](https://registry.opendata.aws/noaa-nwm-pds/)\n<br/>\n<br/>\nFilenames follow the source dataset’s conventions. For example, if the source file is\n<br/>\nnos.dbofs.fields.f024.20240527.t00z.nc\n<br/>\n<br/>\nThen the cloud-optimized filename is the same, with “.zarr” appended\n<br/>\nnos.dbofs.fields.f024.20240527.t00z.nc.zarr\n<br/>\n<br/>\n**Data Aggregations**\n<br/>\nWe also produce virtual aggregations to group an entire forecast model run, and the “best” available forecast.\n<br/>\nBest Forecast (continuously updated) -  nos.dbofs.fields.best.nc.zarr\nFull Model Run -  nos.dbofs.fields.forecast.[YYYYMMDD].t[CC]z.nc.zarr\n<br/>\n- CC is the model run cycles, 00, 06, 12, 18 , or 03, 09, 15, 21 for nowcast and forecast runs\n<br/>\n- YYYY = year, MM = month, DD = day\n<br/>\n<br/>\nCloud optimization workflows supported by [RPS Group](https://www.rpsgroup.com/services/oceans-and-coastal/metocean-science-and-technology/), a Tetra Tech Company\n","Documentation":"Refer to source datasets documentation","Contact":"For questions regarding data content or quality, visit [Email The Tetra Tech Team](nodd-support@tetratech.com). \n<br />\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA's National Ocean Service, the Integrated Ocean Observing System (IOOS)](https://ioos.noaa.gov/)","UpdateFrequency":"Optimizations run every time new data is uploaded to the source buckets and are available here within minutes.","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","climate","coastal","disaster response","environmental","meteorological","oceans","water","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Cloud-optimized Zarr Reference Files","ARN":"arn:aws:s3:::noaa-nodd-kerchunk-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nodd-kerchunk-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for Cloud-optimized Zarr Reference Files","ARN":"arn:aws:sns:us-east-1:123901341784:NewNODDKerchunkObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2024-06-24","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-nodd-kerchunk","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA's Coastal Ocean Reanalysis (CORA)","Description":"NOAA's Coastal Ocean Reanalysis (CORA) provides re-analyzed wave and water level data and derived products for the U.S. coastlines. The data are produced by coupling [ADCIRC](https://www.erdc.usace.army.mil/Media/Fact-Sheets/Fact-Sheet-Article-View/Article/476698/advanced-circulation-model/) (ADvanced CIRCulation Model) and [SWAN](https://www.tudelft.nl/en/ceg/about-faculty/departments/hydraulic-engineering/sections/environmental-fluid-mechanics/research/swan) (Simulating WAves Nearshore) and assimilating verified hourly water levels from the National Ocean Service’s [Center of Operational Oceanographic Products & Services](https://tidesandcurrents.noaa.gov/) (CO-OPS).","Documentation":"- Known Issues, Change Log, and more: https://noaa-nos-cora-pds.s3.amazonaws.com/README\n- Metadata Catalog: https://noaa-nos-cora-pds.s3.amazonaws.com/metadata.txt\n- Technical Report: https://repository.library.noaa.gov/view/noaa/66833\n","Contact":"CO-OPS.UserServices@noaa.gov","ManagedBy":"[NOAA’s National Ocean Service, The Center for Operational Oceanographic Products and Services (CO-OPS)](https://tidesandcurrents.noaa.gov/about_us.html)","UpdateFrequency":"Intermittent.","Tags":["agriculture","aws-pds","climate","disaster response","environmental","oceans","transportation","weather"],"License":"These data are produced by NOAA and are not subject to copyright protection in the United States. NOAA waives any potential copyright and related rights in these data worldwide through the Creative Commons Universal Public Domain Dedication (CC0-1.0). Pursuant to 17 U.S.C. 403, third parties producing copyrighted (compilation) works consisting predominantly of material created by Federal Government employees are encouraged to provide notice with such work(s) identifying the U.S. Government material incorporated and stating that such material is not subject to copyright protection.","Citation":"NOAA's Coastal Ocean Reanalysis (CORA) Dataset was accessed on DATE from https://registry.opendata.aws/noaa-nos-cora.","Resources":[{"Description":"CORA data","ARN":"arn:aws:s3:::noaa-nos-cora-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nos-cora-pds.s3.amazonaws.com/index.html)"]},{"Description":"NOAA’s Coastal Ocean Reanalysis (CORA) Dataset Notifications","ARN":"arn:aws:sns:us-east-1:709902155096:NewNOSCORAObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing Data","URL":"https://github.com/NOAA-CO-OPS/CORA-Coastal-Ocean-Reanalysis-CORA/blob/main/CORA_Accessing_Data.ipynb","AuthorName":"NOAA's Center for Operational Oceanographic Products and Services","AuthorURL":"https://tidesandcurrents.noaa.gov/"},{"Title":"Bounding Box","URL":"https://github.com/NOAA-CO-OPS/CORA-Coastal-Ocean-Reanalysis-CORA/blob/main/CORA_bounding_box.ipynb","AuthorName":"NOAA's Center for Operational Oceanographic Products and Services","AuthorURL":"https://tidesandcurrents.noaa.gov/"},{"Title":"Compare With Observations","URL":"https://github.com/NOAA-CO-OPS/CORA-Coastal-Ocean-Reanalysis-CORA/blob/main/CORA_compare_with_observations.ipynb","AuthorName":"NOAA's Center for Operational Oceanographic Products and Services","AuthorURL":"https://tidesandcurrents.noaa.gov/"},{"Title":"Convert Datums","URL":"https://github.com/NOAA-CO-OPS/CORA-Coastal-Ocean-Reanalysis-CORA/blob/main/CORA_Convert_Datums.ipynb","AuthorName":"NOAA's Center for Operational Oceanographic Products and Services","AuthorURL":"https://tidesandcurrents.noaa.gov/"},{"Title":"Explore NOAA CORA in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/81a7be6e-6b6a-5a4a-b416-0ca5c7889bb9/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Plot Mesh","URL":"https://github.com/NOAA-CO-OPS/CORA-Coastal-Ocean-Reanalysis-CORA/blob/main/CORA_Plot_Mesh.ipynb","AuthorName":"NOAA's Center for Operational Oceanographic Products and Services","AuthorURL":"https://tidesandcurrents.noaa.gov/"},{"Title":"Visualize Water Level Maxima","URL":"https://github.com/NOAA-CO-OPS/CORA-Coastal-Ocean-Reanalysis-CORA/blob/main/CORA_visualize_water_level_maxima.ipynb","AuthorName":"NOAA's Center for Operational Oceanographic Products and Services","AuthorURL":"https://tidesandcurrents.noaa.gov/"}],"Tools & Applications":[{"Title":"NOAA's Experimental High Resolution Monthly High Tide Flooding Outlook","URL":"https://tidesandcurrents.noaa.gov/high-tide-flooding/experimental/hires-monthly-outlook.html","AuthorName":"NOAA's Center for Operational Oceanographic Products and Services","AuthorURL":"https://tidesandcurrents.noaa.gov/"}],"Publications":[{"Title":"Assessment of water levels from 43 years of NOAA’s Coastal Ocean Reanalysis (CORA) for the Gulf of Mexico and East Coasts","URL":"https://doi.org/10.3389/fmars.2024.1381228","AuthorName":"Rose, Linta; Widlansky, Matthew J.; Feng, Xue; Thompson, Thompson; Asher, Taylor G.; Dusek, Gregory; Blanton, Blanton; Luettich, Richard A. Jr.; Callahan, John; Brooks, William; Keeney, Analise; Haddad, Jana; Sweet, William; Genz, Ayesha; Hovenga, Paige; Marra, John & Tilson, Jeffrey"},{"Title":"Filling the gaps between tide gauges: Demonstrating high-resolution seasonal high tide flooding predictions using NOAA’s Coastal Ocean Reanalysis","URL":"https://doi.org/10.1371/journal.pone.0344695","AuthorName":"Conlin, Matthew; Dusek, Gregory; Ratcliff, John; Callahan, John; Kavanaugh, Karen; Brooks, William; Waring, Blake; Keeney, Analise; Sweet, William; & Widlansky, Matthew"},{"Title":"NOAA Technical Report NOS CO-OPS 108: NOAA’s Coastal Ocean Reanalysis: Gulf of Mexico, Atlantic, and Caribbean (January 2025)","URL":"https://doi.org/10.25923/5ypp-4e84","AuthorName":"Keeney, Analise; Dusek, Gregory; Callahan, John; Ratcliff, John; Jima, Tigist; Brooks, William; Marcy, Doug; Blanton, Brian; Tilson, Jeffrey; Asher, Taylor G.; Leuttich, Richard A.; Widlansky, Matthew J.; Rose, Linta; Morse, Cheryl; Haddad, Jana; & Waring, Blake"}]},"RegistryEntryAdded":"2024-04-19","RegistryEntryLastModified":"2026-11-03","Slug":"noaa-nos-cora","Sources":["awslabs-open-data-registry"]}
{"Name":"East Coast Community Ocean Forecast System (ECCOFS)","Description":"The East Coast Community Ocean Forecast System (ECCOFS) is a data assimilating ocean analysis and forecast system being developed by Rutgers University, the University of California Santa Cruz, Fathom Science Inc., and the National Ocean Service (NOS) of NOAA for transition to operations at NCEP in 2028. The ECCOFS domain spans the eastern seaboard of North America and Intra-Americas Seas from the Grand Banks of Newfoundland in the north to the mouth of the Orinoco River, Venezuela, in the south. ECCOFS will complement the existing WCOFS (West Coast Operational Forecast System) to achieve complete forecast coverage of U.S. territorial seas adjacent to the 48 contiguous states and Puerto Rico. Each day ECCOFS generates an ocean analysis using the Regional Ocean Modeling System (ROMS) 4-Dimensional Variational (4D-Var) data assimilation (DA) system constrained by ocean observations. The forecast resolution is 3 km in the horizontal at 50 vertical terrain-following levels. A more detailed overview is presented [here](https://github.com/myroms/roms_eccofs/wiki/Overview). \n<br><br>\nThe meteorological forcing data are marine boundary layer conditions (air temperature, humidity, pressure), net shortwave radiation, and downward longwave radiation at 1-hourly intervals from the NWS High Resolution Rapid Refresh (HRRR) 4-km, 48-hour forecast for the northwest Atlantic Ocean and western Caribbean Sea. In regions beyond the HRRR domain, and in forecast days 3 to 5, the NWS GFS (Global Forecast System) forecast is adopted.\n<br><br>\nSub-tidal frequency open boundary conditions are drawn from the Copernicus Marine Service Global Ocean Physics Analysis and Forecast (GLOBAL_ANALYSISFORECAST_PHY_001_024). These data are augmented by boundary harmonic barotropic tidal forcing from the Oregon State University TPXO analysis (12 harmonic constituents, adjusted for the 18.6-year nodal cycle). Luni-solar gravitational tide generating forces for 7 constituents are imposed in the ECCOFS domain interior.\n<br><br>\nDaily average forecast river discharge data from the Global Flood Awareness System (GloFAS) river routing model of ECMWF precipitation set freshwaiter inflows at 93 rivers where annual mean discharge exceeds 50 m3/s. More details on the forward model configuration are presented [here](https://github.com/myroms/roms_eccofs/wiki/Modeling-System-Configuration#Forward-Model-and-Forcing).\n<br><br>\nObservations presently assimilated include satellite sea level and temperature, and in situ temperature and salinity from moorings, drifters, profiling floats, autonomous underwater vehicles, ships of opportunity, and sensors affixed to fishing gear. When fully configured, ECCOFS will also assimilate satellite salinity, ocean surface currents measured by land-based high-frequency radars, and temperature data from animal tags.\n<br><br>\nRecognizing that oceanic conditions that can be meaningfully constrained by existing observing networks have length scales much greater than the forecast model resolution of 3 km, ECCOFS employs a Mixed Resolution assimilation approach wherein the ocean forecast is computed on the 3-km grid, but the iterations of the Tangent Linear (TL) and Adjoint (AD) model inner loops of 4D-Var are computed on a lower resolution (6-km) grid that demands less time for execution.  The control variables of the assimilation are the initial conditions (at day 0 minus 3) and time varying boundary conditions (over 3 days). More details on the assimilation methodology are presented [here](https://github.com/myroms/roms_eccofs/wiki/Modeling-System-Configuration#data-assimilation-methodology). \n<br><br>\n","Documentation":"The configuration of ROMS for ECCOFS is documented at (https://github.com/myroms/roms_eccofs).\n<br><br>\nA description of the configuration is detailed at (https://github.com/myroms/roms_eccofs/wiki/Overview).\n<br><br>\nThe latest ECCOFS forecast outputs can be browsed at (https://eccofs.fathomscience.com).\n","Contact":"For enquiries about the ECCOFS system configuration or to express interest in ECCOFS based applications, see the [Contact Us](https://github.com/myroms/roms_eccofs/wiki/Contact) page at the ECCOFS Wiki. For any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"An ECCOFS 4D-Var analysis is conducted every 24 hours, assimilating observations aggregated for the preceding 3 days, and this provides the basis of a new 5-day ocean forecast.\n<br><br>\nOutputs saved in this data archive are daily average sea level, and ocean temperature, salinity and velocity on the full ROMS curvilinear, terrain-following coordinate grid.\n<br><br>\n","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["aws-pds","coastal","environmental","forecast","marine","oceans","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.\n<br><br>\n","Resources":[{"Description":"East Coast Community Ocean Forecast System (ECCOFS)","ARN":"arn:aws:s3:::noaa-nos-eccofs-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nos-eccofs-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for ECCOFS, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewECCOFSObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"ROMS model outputs in AWS can be readily visualized and analyzed in Python using the xroms tools.","URL":"https://github.com/xoceanmodel/xroms","AuthorName":"XROMS"}]},"RegistryEntryAdded":"2026-05-14","RegistryEntryLastModified":"2026-05-20","Slug":"noaa-nos-eccofs","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA / NGA Satellite Computed Bathymetry Assessment-SCuBA","Description":"One of the National Geospatial-Intelligence Agency’s (NGA) and the National Oceanic and Atmospheric Administration’s (NOAA) missions is to ensure the safety of navigation on the seas by \nmaintaining the most current information and the highest quality services for U.S. and global transport networks. To achieve this mission, we need accurate coastal bathymetry over diverse \nenvironmental conditions. The SCuBA program focused on providing critical information to improve existing bathymetry resources and techniques with two specific objectives. The first objective \nwas to validate National Aeronautics and Space Administration’s (NASA) Ice, Cloud and land Elevation SATellite-2 (ICESat-2), an Earth observing, space-based light detection and ranging (LiDAR) \ncapability, as a useful bathymetry tool for nearshore bathymetry information in differing environmental conditions. Upon validating the ICESat-2 bathymetry retrievals relative to sea floor \ntype, water clarity, and water surface dynamics, the next objective is to use ICESat-2 as a calibration tool to improve existing Satellite Derived Bathymetry (SDB) coastal bathymetry products \nwith poor coastal depth information but superior spatial coverage. Current resources that monitor coastal bathymetry can have large vertical depth errors (up to 50 percent) in the nearshore \nregion; however, derived results from ICESat-2 shows promising results for improving the accuracy of the bathymetry information in the nearshore region.\n<br/>\n<br/>\n**Project Overview**\n<br/>\nOne of NGA’s and NOAA’s primary missions is to provide safety of navigation information.  However, coastal depth information is still lacking in some regions—specifically, remote regions. In fact, it has been reported that 80 percent of the entire seafloor has not been mapped. Traditionally, airborne LiDARs and survey boats are used to map the seafloor, but in remote areas, we have to rely on satellite capabilities, which currently lack the vertical accuracy desired to support safety of navigation in shallow water. In 2018, NASA launched a space-based LiDAR system called ICESat-2 that has global coverage and a polar orbit originally designed to monitor the ice elevation in polar regions. Remarkably, because it has a green laser beam, ICESat-2 also happens to collect bathymetry information [ICESat-2](nasa.gov). With algorithm development provided by University of Texas (UT) Austin, NGA Research and Development (R&D) leveraged the ICESat-2 platform to generate SCuBA, an automated depth retrieval algorithm for accurate, global, refraction-corrected underwater depths from 0 m to 30 m, detailed in Figure 1 of the documentation. The key benefit of this product is the vertical depth accuracy of depth retrievals, which is ideal for a calibration tool.  NGA and NOAA National Geodetic Survey (NGS), partnered to make this product available to the public for all US territories.\n[View the SCuBA Info Graphic](https://raw.githubusercontent.com/NOAA-Big-Data-Program/bdp-data-docs/main/SCuBA/SCuBA_Infographic_small.png)\n<br/>\nAll details on how SCuBA was developed, how to access data, and how to use the data, please visit the [DOCUMENTATION](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/SCuBA/NOAA_website_SCUBA_22MAY24_GOLD.docx.pdf) page. \n","Documentation":"https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/SCuBA/NOAA_website_SCUBA_22MAY24_GOLD.docx.pdf","Contact":"For questions regarding data content or quality, email gretchen.imahori@noaa.gov\n<br/>\nThis data is made available to the public through the NOAA Open Data Dissemination (NODD) Program. For questions regarding this program, email nodd@noaa.gov.\n<br/>\nWe also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NOAA NODD team at NODD@NOAA.GOV.\n","ManagedBy":"[NOAA’s National Geodetic Survey](https://geodesy.noaa.gov/)","UpdateFrequency":"Monthly, quarterly, and annually, depending on the dataset.","Collabs":{"ASDI":{"Tags":["water"]}},"Tags":["agriculture","agriculture","aws-pds","bathymetry","climate","disaster response","environmental","oceans","transportation","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA / NGA Satellite Computed Bathymetry Assessment (SCuBA) data","ARN":"arn:aws:s3:::noaa-nos-scuba-icesat2-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nos-scuba-icesat2-pds.s3.amazonaws.com/index.html)"]},{"Description":"NOAA / NGA Satellite Computed Bathymetry Assessment (SCuBA) data notifications","ARN":"arn:aws:sns:us-east-1:709902155096:NewICESATObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2024-07-03","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-nos-scuba-icesat2-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA 3-D Surge and Tide Operational Forecast System for Atlantic Basin (STOFS-3D-Atlantic) and Pacific Basin (STOFS-3D-Pacific)","Description":"\nNOTICE - The Coast Survey Development Laboratory (CSDL) in NOAA/National Ocean Service (NOS)/Office of Coast Survey is upgrading the Surge and Tide Operational Forecast System (STOFS) to Version 3.1. A Service Change Notice (SCN) has been issued and can be found \"[HERE](https://www.weather.gov/media/notification/pdf_2026/SCN26-51_STOFS.v3.1_Updated.aaa.pdf)\"<br /><br />\n\nNOAA's Surge and Tide Operational Forecast System: Three-Dimensional Component for the Atlantic Basin (STOFS-3D-Atlantic) and for the Pacific Basin (STOFS-3D-Pacific). STOFS-3D-Atlantic and STOFS-3D-Pacific run daily (at 12 UTC) to provide users with 24-hour nowcasts (analyses of near present conditions) and up to 96-hour (Atlantic) and 48-hour (Pacific) forecast guidance of water level conditions, and 2- and 3-dimensional fields of water temperature, salinity, and currents. The water level outputs represent the combined tidal and subtidal water surface elevations and are referenced to xGEOID20B (Atlantic) and Local Mean Sea Level (LMSL) (Pacific). <br /><br />\n\nSTOFS-3D-Atlantic and STOFS-3D-Pacific have been developed to serve the marine navigation, weather forecasting, and disaster mitigation user communities. They are developed in a collaborative effort between the NOAA/National Ocean Service (NOS)/Office of Coast Survey, the NOAA/National Weather Service (NWS) Central Operations (NCO), the Virginia Institute of Marine Science, and the Columbia River Inter-Tribal Fish Commission. <br /><br />\n\nSTOFS-3D-Atlantic employs the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM) as the hydrodynamic model core. Its unstructured grid consists of 3,052,121 nodes and 5,872,610 triangular or quadrilateral elements. Grid resolution is 1.5-2 km near the shoreline, ~600 m for the floodplain, down to 8 m for watershed rivers (at least 3 nodes across each river cross-section), and around 2-10 m for levees. Along the U.S. coastline, the land boundary of the domain aligns with the 10-m contour above xGEOID20B, encompassing the coastal transitional zone most vulnerable to coastal and inland flooding. <br /><br />\n\nSTOFS-3D-Pacific employs the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM) as the hydrodynamic model core. Its unstructured grid consists of 2,961,412 nodes and 5,774,631 triangular or quadrilateral elements. Grid resolution is 1.5-2 km near the shoreline, ~600 m for the floodplain, down to 8 m for watershed rivers (at least 3 nodes across each river cross-section). Along the U.S. coastline, the land boundary of the domain aligns with the 10-m contour above xGEOID20B, encompassing the coastal transitional zone most vulnerable to coastal and inland flooding. <br /><br />\n\nSTOFS-3D-Atlantic makes use of outputs from the National Water Model (NWM) and Canadian stream gauge data to include inland hydrology and extreme precipitation effects on coastal flooding; forecast guidance from the NCEP Global Forecast System (GFS) and High-Resolution Rapid Refresh (HRRR) model as the surface meteorological forcing; the tidal water level and currents from the FES2014 tidal database; and the subtidal water level, and subtidal three-dimensional water temperature, salinity, and currents from the NCEP Global Real-Time Ocean Forecast System (G-RTOFS) with the subtidal water level from G-RTOFS and Copernicus Absolute Dynamic Topography (ADT) satellite altimetry observation as the open ocean boundary forcing. <br /><br />\n\nSTOFS-3D-Pacific makes use of outputs from the National Water Model (NWM), Copernicus Global Flood Awareness System (GloFAS), USGS, and Canadian stream gauge data to include inland hydrology and extreme precipitation effects on coastal flooding; forecast guidance from the NCEP Global Forecast System (GFS) and High-Resolution Rapid Refresh (HRRR) model as the surface meteorological forcing; the tidal water level and currents from the TPXO9 tidal database; and the subtidal water level, and subtidal three-dimensional water temperature, salinity, and currents from the NCEP Global Real-Time Ocean Forecast System (G-RTOFS) with the subtidal water level from G-RTOFS and Copernicus Absolute Dynamic Topography (ADT) satellite altimetry observation as the open ocean boundary forcing. <br /><br />\n\nSTOFS-3D-Atlantic and STOFS-3D-Pacific forecast guidance output available here include NetCDF, GRIB2, SHEF, and GPKG files. Please see README documentation for more details. <br /><br />\n","Documentation":"\nhttps://noaa-nos-stofs3d-pds.s3.amazonaws.com/README_Atl.html <br /><br />\n\nhttps://noaa-nos-stofs3d-pds.s3.amazonaws.com/README_Pac.html <br /><br />\n","Contact":"For questions regarding data content or quality, visit the STOFS site (https://polar.ncep.noaa.gov/stofs/). \n<br /> For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"One time per day at 12 UTC","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","climate","coastal","disaster response","environmental","forecast","hydrologic model","marine navigation","meteorological","ocean sea surface height","oceans","sustainability","water","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Citation":"<br /><br />\nData for STOFS-3D regional domains are stored in prefix paths within this bucket. \n<br /><br />\nPlease cite the specific regional domain used in your research or application: \n<br /><br />\nSTOFS-3D Atlantic Basin (Path: s3://noaa-nos-stofs3d/STOFS-3D-Atl/):\nOffice of Coast Survey; National Centers for Environmental Prediction. (2023). \nNOAA Three-dimensional Surge and Tide Operational Forecast System for the Atlantic Basin (STOFS-3D-Atlantic) [Data set]. \nNational Oceanic and Atmospheric Administration. Accessed ```[DATE]```. https://doi.org/10.25923/v4kp-ee47\n<br /><br />\nSTOFS-3D Pacific Basin (Path: s3://noaa-nos-stofs3d/STOFS-3D-Pac/):\nOffice of Coast Survey; National Centers for Environmental Prediction. (2026). \nNOAA Three-dimensional Surge and Tide Operational Forecast System for the Pacific Basin (STOFS-3D-Pacific) [Data set]. \nNational Oceanic and Atmospheric Administration. Accessed ```[DATE]```. https://doi.org/10.25923/89a2-6691\n<br /><br />\n","Resources":[{"Description":"NOAA STOFS-3D-Atlantic Forecast Guidance","ARN":"arn:aws:s3:::noaa-nos-stofs3d-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nos-stofs3d-pds.s3.amazonaws.com/index.html)"]},{"Description":"NOAA STOFS-3D-Atlantic Forecast Guidance New Dataset Notification","ARN":"arn:aws:sns:us-east-1:709902155096:NewICOGS3DObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2023-01-04","RegistryEntryLastModified":"2026-10-07","Slug":"noaa-nos-stofs3d","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA National Water Model Short-Range Forecast","Description":"The National Water Model (NWM) is a water resources model that simulates and forecasts water\nbudget variables, including snowpack, evapotranspiration, soil moisture and streamflow, over\nthe entire continental United States (CONUS). The model, launched in August 2016, is designed\nto improve the ability of NOAA to meet the needs of its stakeholders (forecasters, emergency\nmanagers, reservoir operators, first responders, recreationists, farmers, barge operators, and\necosystem and floodplain managers) by providing expanded accuracy, detail, and frequency of water\ninformation. It is operated by NOAA’s Office of Water Prediction. This bucket contains a four-week\nrollover of the Short Range Forecast model output and the corresponding forcing data for the\nmodel. The model is forced with meteorological data from the High Resolution Rapid Refresh (HRRR)\nand the Rapid Refresh (RAP) models. The Short Range Forecast configuration cycles hourly and produces\nhourly deterministic forecasts of streamflow and hydrologic states out to 18 hours.\n","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nwm-pds","Contact":"For questions regarding data content or quality, go [here](http://water.noaa.gov/about/nwm).\n<br />\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["water"]}},"Tags":["agriculture","agriculture","aws-pds","climate","disaster response","environmental","transportation","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"A rolling four week archive of NWM data.","ARN":"arn:aws:s3:::noaa-nwm-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nwm-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for NWM, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewNWMObject","Region":"us-east-1","Type":"SNS Topic"},{"Description":"Cloud-optimized zarr reference files managed by RPS Tetra Tech","Type":"S3 Bucket","Region":"us-east-1","ARN":"arn:aws:s3:::noaa-nodd-kerchunk-pds","Explore":["[Browse Bucket](https://noaa-nodd-kerchunk-pds.s3.amazonaws.com/index.html#nwm/)"]}],"DataAtWork":{"Publications":[{"Title":"Harmonic Oscillator Seasonal Trend (HOST) Model for Hydrological Drought Pattern Identification and Analysis","URL":"https://doi.org/10.1016/j.jhydrol.2023.129514","AuthorName":"K. Raczyński, J. Dyer"}]},"RegistryEntryAdded":"2018-06-07","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-nwm-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Analysis of Record for Calibration (AORC) Dataset","Description":"#### Announcements:\n\nApril 2, 2026: The Office of Water Prediction (OWP) has identified some brittle components of the AORC data processing pipelines that, by our estimations, have resulted in 0.04% of all available rows being incorrectly masked. We've resolved the issue and we are currently regenerating the Zarr files to ensure a complete record. We will post an updated announcement once the corrected data is posted.\n<br/>\n<br/>\nThe Analysis Of Record for Calibration (AORC) is a gridded record of near-surface  weather conditions covering the continental United States and Alaska and their  hydrologically contributing areas. It is defined on a latitude/longitude spatial grid with a mesh length of 30 arc seconds (~800 m), and a temporal resolution of one hour.  Elements include hourly total precipitation, temperature, specific humidity, terrain-level  pressure, downward longwave and shortwave radiation, and west-east and south-north wind components. It spans the period from 1979 across the Continental U.S. (CONUS) and from 1981 across Alaska, to the near-present (at all locations). This suite of eight variables is sufficient to drive most land-surface and hydrologic models and is used as input to the National Water Model (NWM) retrospective simulation. While the native AORC process generates netCDF output, the data is post-processed to create a cloud optimized Zarr formatted equivalent for dissemination using cloud technology and infrastructure.\n<br/>\n<br/>\n**AORC Version 1.1 dataset creation**\n<br/>\nThe AORC dataset was created after reviewing, identifying, and processing multiple large-scale, observation, and analysis datasets. There are two versions of The Analysis Of Record for Calibration (AORC) data.\n<br/>\n<br/>\nThe initial AORC Version 1.0 dataset was completed in November 2019 and consisted of a grid with 8 elements at a resolution of 30 arc seconds.  The AORC version 1.1 dataset was created to address issues \"[see Table 1 in Fall et al., 2023](https://onlinelibrary.wiley.com/doi/10.1111/1752-1688.13143)\" in the version 1.0 CONUS dataset. Full documentation on version 1.1 of the AORC data and the related journal publication are provided below.\n<br/>\n<br/>\nThe native AORC version 1.1 process creates a dataset that consists of netCDF files with the following dimensions: 1 hour, 4201 latitude values (ranging from 25.0 to 53.0), and 8401 longitude values (ranging from -125.0 to -67).\n<br/>\n<br/>\nThe data creation runs with a 10-day lag to ensure the inclusion of any corrections to the input Stage IV and NLDAS data.\n<br/>\n<br/>\n**Note** - The full extent of the AORC grid as defined in its data files exceed those cited above; those outermost rows and columns of data grids are filled with missing values and are the remnant of an early set of required AORC extents that have since been adjusted inward.\n<br/>\n<br/>\n**AORC Version 1.1 Zarr Conversion**\n<br/>\n<br/>\nThe goal for converting the AORC data from netCDF to Zarr was to allow users to quickly and efficiently load/use the data.  For example, one year of data takes 28 mins to load via NetCDF while only taking 3.2 seconds to load via Zarr (resulting in a substantial increase in speed).  For longer periods of time, the percentage increase in speed using Zarr (vs NetCDF) is even higher.  Using Zarr also leads to less memory and CPU utilization.\n<br/>\n<br/>\nIt was determined that the optimal conversion for the data was 1 year worth of Zarr files with a chunk size of 18MB. The chunking was completed across all 8 variables.  The chunks consist of the following dimensions: 144 time, 128 latitude, and 256 longitude. To create the files in the Zarr format, the NetCDF files were rechunked using chunk() and \"[Xarray](https://docs.xarray.dev/en/stable/)\".  After chunking the files, they were converted to a monthly Zarr file. Then, each monthly Zarr file was combined using \"[to_zarr](https://docs.xarray.dev/en/latest/generated/xarray.Dataset.to_zarr.html)\" to create a Zarr file that represents a full year\n<br/>\n<br/>\nUsers wanting more than 1 year of data will be able to utilize Zarr utilities/libraries to combine multiple years up to the span of the full data set.\n<br/>\n<br/>\nThere are eight variables representing the meteorological conditions\n<br/>\n**Total Precipitaion (APCP_surface)**\n<br/>\n1) Hourly total precipitation (kgm-2 or mm)  for Calibration (AORC) dataset\n<br/>\n**Air Temperature (TMP_2maboveground)**\n1) Temperature (at 2 m above-ground-level (AGL)) (K)\n<br/>\n**Specific Humidity (SPFH_2maboveground)**\n<br/>\n1) Specific humidity (at 2 m AGL) (g g-1) \n<br/>\n**Downward Long-Wave Radiation Flux (DLWRF_surface)**\n<br/>\n1) longwave (infrared)\n2) radiation flux (at the  surface) (W m-2) \n<br/>\n**Downward Short-Wave Radiation Flux (DSWRF_surface)**\n<br/>\n1) Downward shortwave (solar)\n2) radiation flux (at the  surface) (W m-2)\n<br/>\n**Pressure (PRES_surface)**\n<br/>\n1) Air pressure (at the surface) (Pa)\n<br/>\n**U-Component of Wind (UGRD_10maboveground)\"\n<br/>\n1)U (west-east) - components of the wind (at 10 m AGL) (m s-1)\n<br/>\n**V-Component of Wind (VGRD_10maboveground)\"\n<br/>\n1) V (south-north) - components of the wind (at 10 m AGL) (m s-1)\n<br/>\n<br/>\n**Precipitation and Temperature**\n<br/>\n<br/>\nThe gridded AORC precipitation dataset contains one-hour Accumulated Surface Precipitation (APCP) ending at the “top” of each hour, in liquid water-equivalent units  (kg m-2 to the nearest 0.1 kg m-2), while the gridded AORC temperature dataset is comprised of instantaneous, 2 m above-ground-level (AGL) temperatures at the top of each hour (in Kelvin, to the nearest 0.1).\n<br/>\n<br/>\n**Specific Humidity, Pressure, Downward Radiation, Wind**\n<br/>\n<br/>\nThe development process for the six additional dataset components of the Conus AORC [i.e., specific humidity at 2m above ground (kg kg-1); downward  longwave and shortwave radiation fluxes at the surface (W m-2); terrain-level pressure (Pa); and west-east and south-north wind components at 10 m above ground (m s-1)] has  two distinct periods, based on datasets and methodology applied: 1979–2015 and 2016–present. \n","Documentation":"[Analysis of Record for Calibration: Version 1.1 Sources, Methods, and Verification](https://www.weather.gov/media/owp/operations/aorc_v1_1_methods.pdf)","Contact":"For questions regarding data content or quality, email the AORC team at aorc.info@noaa.gov.\n<br/>\nThis data is made available to the public through the NOAA Open Data Dissemination (NODD) Program. For questions regarding this program, email nodd@noaa.gov.\n<br/>\nWe also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NOAA NODD team at NODD@NOAA.GOV.\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"To be determined","Collabs":{"ASDI":{"Tags":["water"]}},"Tags":["agriculture","agriculture","aws-pds","climate","disaster response","environmental","transportation","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"1-km resolution AORC version 1.1","ARN":"arn:aws:s3:::noaa-nws-aorc-v1-1-1km","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nws-aorc-v1-1-1km.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Explore the AORC 1.1 dataset in Zarr","URL":"https://nbviewer.org/github/NOAA-OWP/AORC-jupyter-notebooks/blob/master/jupyter_notebooks/AORC_Zarr_notebook.ipynb","AuthorName":"Michael AuCoin","AuthorURL":null}],"Publications":[{"Title":"The Office of Water Prediction's Analysis of Record for Calibration, version 1.1: Dataset description and precipitation evaluation (09 July 2023). J. Am. Water Resour. Assoc., 59 (6). 1246-1272.","URL":"https://doi.org/10.1111/1752-1688.13143","AuthorName":"Greg Fall, David Kitzmiller, Sandra Pavlovic, Ziya Zhang, Nathan Patrick, Michael St. Laurent, Carl Trypaluk, Wanru Wu, and Dennis Miller"}]},"RegistryEntryAdded":"2024-03-21","RegistryEntryLastModified":"2026-07-09","Slug":"noaa-nws-aorc","Sources":["awslabs-open-data-registry"]}
{"Name":"(EXPERIMENTAL) NOAA FourCastNet Global Forecast System (FourCastNetGFS) (EXPERIMENTAL)","Description":"The FourCastNet Global Forecast System (FourCastNetGFS) is an experimental system set up by the National Centers for Environmental Prediction (NCEP) to produce medium range global forecasts. The model runs on a 0.25 degree latitude-longitude grid (about 28 km) and 13 pressure levels. The model produces forecasts 4 times a day at 00Z, 06Z, 12Z and 18Z cycles. Major atmospheric and surface fields including temperature, wind components, geopotential height, relative humidity and 2 meter temperature and 10 meter winds are available. The products are 6 hourly forecasts up to 10 days. The data format is GRIB2.\n<br/>\n<br/>\nThe FourCastNetGFS system is an experimental weather forecast model built upon the pre-trained Nvidia’s FourCastNet Machine Learning Weather Prediction (MLWP) model version 2. The FourCastNet (Bonev et al, 2023) was developed by Nvidia using Adaptive Fourier Neural Operators. It uses a Fourier transform-based token-mixing scheme with the vision transformer architecture. This model is pre-trained with ECMWF’s ERA5 reanalysis data. The FourCastNetGFS takes one model state as initial condition from NCEP 0.25 degree GDAS analysis data and runs FourCastNet with weights from the pretrained FourCastNet by Nvidia. Unit conversion to the GDAS data is conducted to match the input data required by FourCastNet and to generate forecast products consistent to GFS.\n<br/>\n<br/>\nThe input data generated from the GDAS data as FourCastNet input is provided under the forecast data directory. Example of file names is:\n<br/>\n<br/>\ninput_2024022000.npy\n<br/>\n<br/>\nThere are 40 files under each directory covering a 10 day forecast. An example of file name is listed below\n<br/>\n<br/>\nfcngfs.t00z.pgrb2.0p25.f006\n<br/>\n<br/>\nPlease note that this NOAA GFS machine learning Model was produced using a code package released by ECMWF’s ai_models_fourcastnetv2 plugin. For information on ai_models_fourcastnetv2 plugin, please visit their github page listed in the documentation and license sections of this page.\n","Documentation":"For the NOAA Product, https://fourcastnetgfs.readthedocs.io/en/latest/index.html and for background FourCastNet v2 information, https://github.com/ecmwf-lab/ai-models-fourcastnetv2.","Contact":"For questions regarding data content or quality, visit [the NOAA EMC Github site](https://github.com/noaa-emc/fourcastnet/issues). For any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. <br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"4 times a day at 00Z, 06Z, 12Z and 18Z","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","disaster response","environmental","meteorological","weather"],"License":"NOAA's FourCastNetGFS products are released under CC0 license. The underlying FourCastNet code package follows Apache-2.0 license as specified in the ECMWF’s ai-models-fourcastnetv2 github page. The products in this bucket only contain NOAA produced products and not FourCastNet code or training data.","Resources":[{"Description":"FourCastNet GFS data","ARN":"arn:aws:s3:::noaa-nws-fourcastnetgfs-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nws-fourcastnetgfs-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for FourCastNet GFS, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewNWSFOURCASTNETGFSObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2025-02-20","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-nws-fourcastnetgfs","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA EAGLE (Experimental AI Global and Limited-Area Ensemble) Global Deterministic and Ensemble Forecasts","Description":"#### Update\n\nEffective on December 17, 2025, the [NOAA/NWS National Centers for Environmental Prediction (NCEP) implemented three new models](https://www.weather.gov/media/notification/pdf_2025/scn25-89_AIGFS_AIGEFS_and_HGEFS.pdf): the Artificial Intelligence Global Forecast System (AIGFS), the Artificial Intelligence Global Ensemble Forecast System (AIGEFS), and the Hybrid Global Ensemble Forecast System (HGEFS). The AIGFS/AIGEFS are the operational replacement for EAGLE SOLO/Ensemble. <b>The EAGLE SOLO/Ensemble forecasts hosted here are based on the GraphCastGFS described below</b>. Please note that <b>this</b> bucket for EAGLE SOLO/Ensemble will continue hosting experimental forecasts to support ongoing development of EAGLE <b>global weather, sub-seasonal to seasonal (S2S) forecast models</b> once they are ready.\n<br/>\n<br/>\nThe EAGLE SOLO/ensemble forecasts are generated from GraphCast Global Forecast System (GraphCastGFS). GraphCastGFS is an experimental system set up by the National Centers for Environmental Prediction (NCEP) to produce medium range global forecasts. It is built upon Google DeepMind’s pre-trained GraphCast, a Machine Learning Weather Prediction (MLWP) model. The horizontal resolution is a 0.25 degree latitude-longitude grid (about 28 km). The model runs 4 times a day at 00Z, 06Z, 12Z and 18Z cycles. Major atmospheric and surface fields including temperature, wind components, geopotential height, specific humidity, and vertical velocity, are available. The products are 6 hourly forecasts up to 16 days. The data format is GRIB2.\n<br/>\n<br/>\n<b>Model upgrade information:</b>\n<br/>\n<br/>\n<b>The GraphCastGFS version 1.0</b> takes two model states as initial conditions (current and 6-hr previous states) from NCEP 0.25 degree GDAS analysis data and runs 37-level GraphCast and 13-level GraphCast-operational with a pre-trained model provided by GraphCast. Unit conversion to the GDAS data is conducted to match the input data required by GraphCast and to generate forecast products consistent with GFS from GraphCastGFS’ native forecast data. The forecast has been uploaded to the bucket since 12Z on February 5, 2024. \n<br/>\n<br/>\n<b>The GraphCastGFS version 2.0</b> made several changes from the GraphCastGFS version 1.0, including: 1) The 37-level model is removed due to the storage restriction and limited accuracy. 2) The 13-level GraphCast ML model was fine-tuned with NCEP’s GDAS analysis data as inputs and ECMWF’s ERA5 data as ground truth. The new weights created from the training are used to create global forecasts. For more details, please refer to NOAA NCEP official note 520 and 522. GraphCastGFS version 2.0 is the base of the EAGLE SOLO/ensemble, whose operational counterparts are AIGFS/AIGEFS v1.0. The EAGLE SOLO version 1.0 forecasts started from 06Z April 24, 2024 and ended at 18Z on December 18, 2025. The EAGLE ensemble version 1.0 forecasts started from 00Z April 29, 2025 and ended at 18Z on December 17, 2025.\n<br/>\n<br/>\nInnovations have since been made separately for AIGFS and AIGEFS. To distinguish the two systems, the future development for AIGFS and AIGEFS will be versioned as AIGFSdevX.Y and AIGEFSdevX.Y with experimental forecasts hosted in this bucket.\n<br/>\n<br/>\n<b>AIGFSdev2.1</b> updated loss function in GraphCast from grid-point mean square error to spectral harmonic based mean square error. The loss scaling is also updated for most of the variables. The forecasts started from 00Z Dec 19, 2025 under graphcast.YYYYMMDD/cyc/forecast_13_levels. Additional feature updates will go to EAGLE SOLO test, whose forecast will be under  graphcast.YYYYMMDD/cyc/forecast_13_levels_test. \n<br/>\n<br/>\n<b>Data information:</b>\n<br/>\n<br/>\nThe input data generated from the GDAS data is provided under the input/ directory. An example of file names is shown below:\n<br/>\n<br/>\nsource-gdas_date-2024022000_res-0.25_levels-13_steps-2.nc\n<br/>\n<br/>\nThe EAGLE SOLO forecast files are under forecasts_13_levels/. There are 128 files (including grib2 index files) under each directory covering 16-day forecasts. An example of a file name is listed below:\n<br/>\n<br/>\ngraphcastgfs.t00z.pgrb2.0p25.f006\n<br/>\n<br/>\ngraphcastgfs.t00z.pgrb2.0p25.f006.idx\n<br/>\n<br/>\nThe EAGLE ensemble forecast files are under EAGLE_ensemble/pmlgefs.YYYYMMDD/cyc/forecasts_13_levels_ICmember_model_X. There are 128 files under each directory covering a16-day forecasts. An example of file name is listed below:\n<br/>\n<br/>\npmlgefsc00.t00z.pgrb2.0p25.f006\n<br/>\n<br/>\npmlgefsc00.t00z.pgrb2.0p25.f006.idx\n<br/>\n<br/>\nPlease note that this NOAA GraphCastGFS Model was produced using a code package released by Google DeepMind. For information on Google DeepMind, please visit their github page listed in the documentation and license sections of this page.\n","Documentation":"For the NOAA Product, https://graphcastgfs.readthedocs.io/en/latest/index.html and for background graphcast information, https://github.com/google-deepmind/graphcast/tree/main/graphcast","Contact":"For questions regarding data content or quality, visit [the NOAA EMC Github site](https://github.com/noaa-emc/graphcast/issues). For any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. <br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"4 times a day at 00Z, 06Z, 12Z and 18Z","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","disaster response","environmental","meteorological","weather"],"License":"NOAA's GraphCast GFS products are released under CC0 license. The products in this bucket only contain NOAA produced products and not GraphCast code or training data. For information on the GraphCast code package, please visit the [Google DeepMind GraphCast Github License page](https://github.com/google-deepmind/graphcast?tab=readme-ov-file#license-and-attribution).","Resources":[{"Description":"GraphCast GFS data","ARN":"arn:aws:s3:::noaa-nws-graphcastgfs-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nws-graphcastgfs-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for GraphCast GFS, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewNWSGRAPHCASTGFSObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2024-02-29","RegistryEntryLastModified":"2026-07-09","Slug":"noaa-nws-graphcastgfs-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Hurricane Analysis and Forecast System (HAFS)","Description":"The last several hurricane seasons have been active with records being set for the number of tropical storms and hurricanes in the Atlantic basin.  These record-breaking seasons underscore the importance of accurate hurricane forecasting. Imperative to increased forecasting skill for hurricanes is the development of the Hurricane Forecast Analysis System or HAFS. To accelerate improvements in hurricane forecasting, this project has the following goals:\n<br /> \n<br /> \n1) To improve the HAFS. The HAFS is NOAA’s next-generation multi-scale numerical model, with data assimilation package and ocean coupling, which will provide an operational analysis and forecast out to seven days, with reliable and skillful guidance on hurricane track and intensity (including rapid intensification), storm size, genesis, storm surge, rainfall and tornadoes associated with hurricanes.\n<br /> \n<br /> \n2) To integrate into the Unified Forecasting System(UFS). The UFS is a community-based, coupled comprehensive Earth system modeling system whose numerical applications span local to global domains and predictive time scales from sub-hourly analyses to seasonal predictions. It is designed to support the Weather Enterprise and to be the source system for NOAA’s operational numerical weather prediction applications. The HAFS will be a part of UFS geared for hurricane model applications. HAFS comprises five major components; (a) High-resolution moving nest (b) High-resolution physics (c) Multi-scale data assimilation (DA) (d) 3D ocean coupling, and (e) Observations to support the DA.\n<br /> \n<br /> \n[Read about how the storm-following model improves intensity forecasts](https://www.aoml.noaa.gov/hurricane-model-that-follows-multiple-storms-improves-intensity-forecasts/)\n","Documentation":"https://wpo.noaa.gov/the-hurricane-analysis-and-forecast-system-hafs/","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Event Driven.<br/> <br/>As of August 2025, a few forecast cycles for Hurricane Fiona (07L) and Hurricane Nicole (17L) from the 2022 Atlantic hurricane season have been made available. These files can be found under the subdirectories of hfsa_retro and hfsb_retro. Additional forecast files from the 2022 hurricane season can be made available upon user request.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Hurricane Analysis Forecast System (HAFS) Data","ARN":"arn:aws:s3:::noaa-nws-hafs-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nws-hafs-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for HAFS, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewHAFSObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2023-09-26","RegistryEntryLastModified":"2025-08-13","Slug":"noaa-nws-hafs","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA National Air Quality Forecast Capability (NAQFC) Regional Model Guidance","Description":"The National Air Quality Forecasting Capability (NAQFC) dataset contains model-generated air quality (AQ) forecast guidance from three different prediction systems. The first system is a coupled weather and atmospheric chemistry numerical forecast model, known as the Air Quality Model (AQM). It is used to produce forecast guidance for ozone (O3) and particulate matter that is less than or equal to 2.5 micrometers in diameter (PM2.5). Prior to May 14, 2024, AQM predictions were derived using the EPA’s Community Multiscale Air Quality (CMAQ) model, driven by meteorological fields from NCEP’s operational weather forecast models, specifically the North American Mesoscale Model (NAM; prior to 20 July 2021) and the Global Forecast System (GFS; beginning 20 July 2021). Since May 14, 2024, AQM guidance has been produced by a unique application within the community-based Unified Forecast System (UFS). The core model components in this application are derived directly from the fully online-coupled UFS-based weather and CMAQ-based chemistry models. In addition, it incorporates information related to chemical and particle source emissions as it integrates forward in time, including anthropogenic chemical emissions provided by the EPA, fire emissions from NOAA/NESDIS, and airborne particles generated by human activities and those predicted to be generated by wind-driven erosion and biosphere at ground level. The NCEP NAQFC AQM output fields in this archive include model raw and bias-corrected predictions dating back to 1 January 2020, all generated by the contemporaneous operational AQM, beginning with AQMv5 in 2020, transitioning to AQMv6 on 20 July 2021, and to AQMv7 on 14 May 2024. The length of each forecast was 48 hours prior to the implementation of AQMv6, and has been 72 hours ever since. The history of AQM upgrades is documented [here](https://www.emc.ncep.noaa.gov/mmb/aq/AQChangelog.html)\n<br/>\n<br/>\nThe second prediction is known as the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT). It is a widely used atmospheric transport and dispersion model containing an internal dust-generation module. It provides forecast guidance for atmospheric dust concentration and, prior to 28 June 2022, it also provided the NAQFC forecast guidance for smoke. Starting on that date, the third prediction system, a regional numerical weather prediction (NWP) model known as the Rapid Refresh (RAP) model, subsumed HYSPLIT for operational smoke guidance, simulating the emission, transport, and deposition of smoke particles that originate from biomass burning (fires) and anthropogenic sources.\n<br/>\n<br/>\nThe output from each of these modeling systems is generated over three separate domains, one covering CONUS, another over Alaska, and the other over Hawaii. Currently, for this archive, the O3, PM2.5, and smoke output is available over all three domains, while dust products are available only over the CONUS domain. The predicted concentrations of all species in the lowest model layer (i.e., the layer in contact with the surface) are available, as are vertically integrated values of smoke and dust. The data is gridded horizontally within each domain, with a grid spacing of approximately 5 km over CONUS, 6 km over Alaska, and 2.5 km over Hawaii. O3 concentrations are provided in parts per billion (PPB), while the concentrations of all other species are quantified in units of micrograms per cubic meter (ug/m3), except for the column-integrated smoke values which are expressed in units of milligrams per square meter (mg/m2).\n<br/>\n<br/>\nTemporally, O3 and PM2.5 are available as maximum and/or averaged values over various time periods, selected in part for consistency with the EPA’s National Ambient Air Quality Standards. Specifically, O3 is available in both 1-hour and 8-hour (backward calculated) averages, as well as preceding 1-hour and 8-hour maximum values. Similarly, PM2.5 is available in 1-hour and 24-hour average values and 24-hour maximum values. In addition, all O3 and PM2.5 fields are available with bias-corrected magnitudes, based on derived historical model biases relative to observations.\n<br/>\n<br/>\nThe AQM produces hourly forecast guidance for O3 and PM2.5 up to 72 hours twice per day. Smoke guidance is available up to 51 hours from once-per-day RAP forecasts, while dust guidance from HYSPLIT is available up to 48 hours.\n","Documentation":"https://vlab.noaa.gov/web/osti-modeling/air-quality","Contact":"For questions regarding data content or quality, visit the NCEP AQM Products website. For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. <br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Two times per day, 0600 and 1200 UTC for O3, PM2.5, and dust; once per day, 0300 UTC for smoke","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","disaster response","environmental","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA National Air Quality Forecast Capability (NAQFC) Regional Model Guidance","ARN":"arn:aws:s3:::noaa-nws-naqfc-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nws-naqfc-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for NAQFC, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewNWSAirQualityObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"An empirically derived emission algorithm for wind-blown dust (J.Geophys. Res., 115, D16212)","URL":"https://doi.org/10.1029/2009JD013167","AuthorName":"Draxler, R. R., P. Ginoux, and A. F. Stein"},{"Title":"Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16 (2022, Geosci. Model Dev., 15, 3281–3313)","URL":"https://doi.org/10.5194/gmd-15-3281-2022","AuthorName":"Campbell, P.C., and Coauthors"},{"Title":"Development of the next-generation air quality prediction system in the UFS framework: Enhancing predictability of wildfire air quality impacts (2024)(Bull. Amer. Meteor. Soc. In review)","URL":"https://epic.noaa.gov/","AuthorName":"Huang, J.P., I. Stajner, R. Montuoro, F. Yang, K. Wang, H.-C. Huang, C.-H. Jeon, B. Curtis, J. McQueen, H. Liu, B. Baker, D. Tong , Y. Tang, P. Campbell, G. Grell, G. Frost, R. Schwantes, S. Wang, S. Kondragunta, F. Li, and Y. Jung"},{"Title":"Improving NOAA NAQFC PM2.5 predictions with a bias correction approach (2017, Wea. and Forecasting, 32(2), 407–421)","URL":"https://doi.org/10.1175/WAF-D-16-0118.1","AuthorName":"Huang, J., McQueen, J., Wilczak, J., Djalalova, I., Stajner, I., Shafran, P., Allured, D., Lee, P., Pan, L., Tong, D., Huang, H.-C., DiMego, G., Upadhayay, S., & Delle Monache, L"},{"Title":"Using VIIRS fire radiative power data to simulate biomass burning emissions, plume rise and smoke transport in a real-time air quality modeling system (Proc. 2017 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS0),Fort Worth, TX, IEEE, 2806–2808)","URL":"https://doi.org/10.1109/IGARSS.2017.8127581","AuthorName":"Ahmadov, R., and Coauthors"}]},"RegistryEntryAdded":"2024-09-03","RegistryEntryLastModified":"2025-10-28","Slug":"noaa-nws-naqfc-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Whole Atmosphere Model-Ionosphere Plasmasphere Electrodynamics (WAM-IPE) Forecast System (WFS)","Description":"The coupled Whole Atmosphere Model-Ionosphere Plasmasphere Electrodynamics (WAM-IPE) Forecast System (WFS) is developed and maintained by the NOAA Space Weather Prediction Center (SWPC). The WAM-IPE model provides a specification of ionosphere and thermosphere conditions with real-time nowcasts and forecasts up to two days in advance in response to solar, geomagnetic, and lower atmospheric forcing. The WAM is an extension of the Global Forecast System (GFS) with a spectral hydrostatic dynamical core utilizing an enthalpy thermodynamic variable to 150 vertical levels on a hybrid pressure-sigma grid, with a model top of approximately 3 x 10-7 Pa (typically 400-600km depending on levels of solar activity). Additional upper atmospheric physics and chemistry, including electrodynamics and plasma processes, are included. The IPE model provides the plasma component of the atmosphere. It is a time-dependent, global 3D model of the ionosphere and plasmasphere from 90 km to approximately 10,000 km. WAM fields of winds, temperature, and molecular and atomic atmospheric composition are coupled to IPE to enable the plasma to respond to changes driven by the neutral atmosphere. <br/> <br/>\nThe operational WAM-IPE is currently running in two different Concepts of Operation (CONOPS) to produce results of Nowcast and Forecast. The WAM-IPE real-time nowcast system (WRS) ingests real-time solar wind parameters every 5 minutes from NOAA’s spacecrafts located at Lagrange point 1 (L1) between the Sun and Earth in order to capture rapid changes in the ionosphere and thermosphere due to the sudden onset of geomagnetic storms. The nowcast is reinitialized every six hours to include the latest forcing from the lower atmosphere. The forecast system (WFS) runs four times daily (0, 6, 12, 18 UT), providing two-day forecasts. Observed solar wind parameters are used whenever observational values are available, for the forecast portion, the forecasted 3-hour Kp and daily F10.7 issued by SWFO are ingested into the model to estimate solar wind parameters. Lower atmospheric data assimilation only carries out twice daily at 0 and 12 UT cycles to maintain the stability of the coupled model. Model version v1.2 became available in July 2023, featuring the implementation of the WRS into operations, as well as improvements to the Kp-derived solar wind parameters utilized by WFS forecasts. <br/> <br/> The data files within this bucket are provided strictly on a non-operational basis, with no guarantee of timely delivery or availability. There may exist temporal gaps in coverage. <br/> <br/> The top-level versioned directories (v1.x) include NetCDF files from operational runs providing 3-dimensional gridded neutral density every 10 minutes with an altitude range from 100 to 1000 km. wfs.YYYYMMDD subdirectories contain two-day forecasts, updated four times daily (cycle initialization 00, 06, 12, 18 UT). wrs.YYYYMMDD subdirectories contain real-time nowcast neutral density outputs, reinitialized every six hours up to the most current UT time. <br/> <br/> Additionally, model outputs intended exclusively for research purposes are contained in the top-level NON-OPERATIONAL_RESEARCH directory. Please contact the WAM-IPE team (contact information available below) before accessing such files. <br/> <br/>","Documentation":"https://www.swpc.noaa.gov/products/wam-ipe","Contact":"For any questions regarding WAM-IPE data, please contact Adam Kubaryk (adam.kubaryk@noaa.gov) and/or Tzu-Wei Fang (tzu-wei.fang@noaa.gov) \n<br/>\nFor data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"The update frequencies of the WAM-IPE dataset range from 10 minutes to 6 hours depending on the CONOPS.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","climate","meteorological","solar","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA WAM-IPE Products","ARN":"arn:aws:s3:::noaa-nws-wam-ipe-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nws-wam-ipe-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for NOAA WAM-IPE Products, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewWIFSObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2023-03-07","RegistryEntryLastModified":"2025-07-09","Slug":"noaa-nws-wam-ipe","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Global Forecast System (GFS) netCDF Formatted Data","Description":"The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). Dozens of atmospheric and land-soil variables are available through this dataset, from temperatures, winds, and precipitation to soil moisture and atmospheric ozone concentration. The GFS data files stored here can be immediately used for [OAR/ARL’s NOAA-EPA Atmosphere-Chemistry Coupler Cloud (NACC-Cloud) tool](https://www.arl.noaa.gov/research/surface-atmosphere-exchange-home/tools-and-products/nacc-nacc-cloud/), and are in a Network Common Data Form (netCDF), which is a very common format used across the scientific community. These particular GFS files contain a comprehensive number of global atmosphere/land variables at a relatively high spatiotemporal resolution (approximately 13x13 km horizontal, vertical resolution of 127 levels, and hourly), are not only necessary for the NACC-Cloud tool to adequately drive community air quality applications (e.g., U.S. EPA’s Community Multiscale Air Quality model; https://www.epa.gov/cmaq), but can be very useful for a myriad of other applications in the Earth system modeling communities (e.g., atmosphere, hydrosphere, pedosphere, etc.). While many other data file and record formats are indeed available for Earth system and climate research (e.g., GRIB, HDF, GeoTIFF), the netCDF files here are advantageous to the larger community because of the comprehensive, high spatiotemporal information they contain, and because they are more scalable, appendable, shareable, self-describing, and community-friendly (i.e., many tools available to the community of users).  Out of the four operational GFS forecast cycles per day (at 00Z, 06Z, 12Z and 18Z) this particular netCDF dataset is updated daily (/inputs/yyyymmdd/) for the 12Z cycle and includes 24-hr output for both 2D (gfs.t12z.sfcf$0hh.nc) and 3D variables (gfs.t12z.atmf$0hh.nc).\n<br/>\n<br/>\nAlso available are netCDF formatted Global Land Surface Datasets (GLSDs) developed by [Hung et al](https://repository.library.noaa.gov/view/noaa/65705). (2024).  The GLSDs are based on numerous satellite products, and have been gridded to match the GFS spatial resolution (~13x13 km).  These GLSDs contain vegetation canopy data (e.g., land surface type, vegetation clumping index, leaf area index, vegetative canopy height, and green vegetation fraction) that are supplemental to and can be combined with the GFS meteorological netCDF data for various applications, including [NOAA-ARL's canopy-app](https://github.com/noaa-oar-arl/canopy-app).  The canopy data variables are climatological, based on satellite data from the year 2020, combined with GFS meteorology for the year 2022, and are created at a daily temporal resolution (/inputs/geo-files/gfs.canopy.t12z.2022mmdd.sfcf000.global.nc)\n<br/>\n","Documentation":"[NACC Landing Page at NOAA-ARL](https://www.arl.noaa.gov/research/surface-atmosphere-exchange-home/tools-and-products/nacc-nacc-cloud/) , [NACC GitHub](https://github.com/noaa-oar-arl/NACC), [NACC-Cloud Link](https://nacc.arl.noaa.gov/nacc/login), [Canopy-App](https://github.com/noaa-oar-arl/canopy-app), [NCEI GLSDs](https://doi.org/10.25921/qzm2-zg29), [Hung et al., 2024](https://repository.library.noaa.gov/view/noaa/65705)\n","Contact":"For questions regarding data content or quality, visit [the OAR NACC site](https://www.arl.noaa.gov/research/surface-atmosphere-exchange-home/tools-and-products/nacc-nacc-cloud/) or contact rick.jiang@noaa.gov or patrick.c.campbell@noaa.gov. For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. <br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Out of the four operational GFS forecast cycles per day (at 00Z, 06Z, 12Z and 18Z) this particular netCDF dataset is updated daily (/yyyymmdd) for the 12Z cycle and includes hourly output out to 24 hours for both 2D (gfs.t12z.sfcf$0hh.nc) and 3D variables (gfs.t12z.atmf$0hh.nc).  The data is uploaded daily at ~ 1800 UTC. <br/> <br/> The climatological GLDSs (including vegetative canopy data variables) are updated on an as-needed basis when new sources, methods, and advancements become available.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","disaster response","environmental","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"GFS netCDF Format Data","ARN":"arn:aws:s3:::noaa-oar-arl-nacc-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-oar-arl-nacc-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for GFS netCDF, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewArlNaccObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"NOAA’s Global Forecast System Data in the Cloud for Community Air Quality Modeling. Atmosphere 2023, 14, 1110.","URL":"https://doi.org/10.3390/atmos14071110","AuthorName":"Campbell, P.C.; Jiang, W.; Moon, Z.; Zinn, S.; Tang, Y."},{"Title":"U.S. EPA’s application of NOAA’s GFS netCDF data on AWS and NACC-Cloud: 'Expedited modeling of burn events results (EMBER): A screening-level dataset of 2023 ozone fire impacts in the US.'","URL":"https://doi.org/10.1016/j.dib.2024.111208","AuthorName":"Simon et al."}]},"RegistryEntryAdded":"2025-03-05","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-oar-arl-nacc-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA - hourly position, current, and sea surface temperature from drifters","Description":"This dataset includes *hourly* sea surface temperature and current data collected by satellite-tracked surface drifting buoys (\"drifters\") of the [NOAA Global Drifter Program](https://www.aoml.noaa.gov/phod/gdp). The [Drifter Data Assembly Center (DAC) at NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML)](https://www.aoml.noaa.gov/phod/gdp/data.php) has applied quality control procedures and processing to edit these observational data and obtain estimates at regular hourly intervals. The data include positions (latitude and longitude), sea surface temperatures (total, diurnal, and non-diurnal components) and velocities (eastward, northward) with accompanying uncertainty estimates. Metadata include identification numbers, experiment number, start location and time, end location and time, drogue loss date, death code, manufacturer, and drifter type. <br /> <br /> Please note that data from the Global Drifter Program are also available at [6-hourly intervals](https://doi.org/10.25921/7ntx-z961) but derived via alternative methods. The 6-hourly dataset goes back further in time (1979) and may be more appropriate for studies of long-term, low frequency patterns of the oceanic circulation. Yet, the 6-hourly dataset does not resolve fully high-frequency processes such as tides and inertial oscillations as well as sea surface temperature diurnal variability. <br /> <br /> [CITING NOAA - hourly position, current, and sea surface temperature from drifters data. Citation for this dataset should include the following information below.] <br /> Elipot, Shane; Sykulski, Adam; Lumpkin, Rick; Centurioni, Luca; Pazos, Mayra (2022). Hourly location, current velocity, and temperature collected from Global Drifter Program drifters world-wide. [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/x46c-3620.","Documentation":"https://www.aoml.noaa.gov/phod/gdp/hourly_data.php","Contact":"Please direct scientific inquiries to Dr. Rick Lumpkin (Rick.Lumpkin@noaa.gov) and Dr. Luca Centurioni (lcenturioni@ucsd.edu). Inquiries about the dataset itself may be directed to Dr. Shane Elipot (selipot@miami.edu).\n<br />\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> \nWe also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"New data is added as soon as it's available.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["aws-pds","climate","environmental","meteorological","oceans","sustainability","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Hourly position, current, and sea surface temperature data from drifters","ARN":"arn:aws:s3:::noaa-oar-hourly-gdp-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-oar-hourly-gdp-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for Hourly position, current, and sea surface temperature data from drifters, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewHourlyGDPObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Working with GDP hourly data using python and xarray, a CloudDrift notebook","URL":"https://github.com/Cloud-Drift/gdp-get-started","AuthorName":"Shane Elipot","AuthorURL":"https://people.miami.edu/profile/2c5f7c9fe748639bf50c85194ee4b558"}],"Publications":[{"Title":"A Dataset of Hourly Sea Surface Temperature From Drifting Buoys (2022)","URL":"https://www.nature.com/articles/s41597-022-01670-2","AuthorName":"Elipot, S., A. Sykulski, R. Lumpkin, L. Centurioni, and M. Pazos"},{"Title":"A global surface drifter dataset at hourly resolution (2016)","URL":"https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016JC011716","AuthorName":"Elipot, S., R. Lumpkin, R. C. Perez, J. M. Lilly, J. J. Early, and A. M. Sykulski"}]},"RegistryEntryAdded":"2022-12-19","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-oar-hourly-gdp","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS)","Description":"The Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) consists of radar reflectivity data run through the Multi-Radar, Multi-Sensor (MRMS) framework to create a three-dimensional radar volume on a quasi-Cartesian latitude-longitude grid across the entire contiguous United States. The radar reflectivity grid is also combined with hourly forecast model analyses to produce derived products such as echo top heights and hail size estimates. Radar Doppler velocity data was also processed into two azimuthal shear layer products. The source radar data was from the [NEXRAD Level-II archive](https://registry.opendata.aws/noaa-nexrad/) and the model analyses came from [NOAA's Rapid Update Cycle model](https://www.ncei.noaa.gov/has/HAS.FileAppRouter?datasetname=RUCANL130&subqueryby=STATION&applname=&outdest=FILE). Radar reflectivity was quality controlled to remove non-weather echoes and the data set was manually quality contolled to remove errors as revealed through inspection of daily accumulations of the hail size product and the azimuthal shear products. MYRORSS contains data from April 1998 through December 2011. The horizontal resolution is 0.01<span>&#176;</span> by 0.01<span>&#176;</span> and the vertical spacing is stretched where at the lowest levels the spacing is 250-m and at the top of the domain 1000-m. The radar data was merged at imperfect timesteps, though in general the temporal spacing is around 5-min. \n","Documentation":"https://osf.io/9gzp2/","Contact":"For any data delivery issues or any questions in general, please contact the NOAA Big Data Program Team at: noaa.bdp@noaa.gov.","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"No updates","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","earth observation","meteorological","natural resource","sustainability","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Multi-Year Reanalysis of Remotely Sensed Storms","ARN":"arn:aws:s3:::noaa-oar-myrorss-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-oar-myrorss-pds.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"Comprehensive radar data for the contiguous United States: Multi-year reanalysis of remotely sensed storms.","URL":"https://doi.org/10.15763/DBS.CIMMS.MYRORSS.DATA","AuthorName":"Williams, S. S., K. L. Ortega, T. M. Smith, and A. E. Reinhart"}]},"RegistryEntryAdded":"2024-02-27","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-oar-myrorss-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA/PMEL Ocean Climate Stations Moorings","Description":"The mission of the Ocean Climate Stations (OCS) Project is to make meteorological and \noceanic measurements from autonomous platforms.  Calibrated, quality-controlled, and well-documented \nclimatological measurements are available on the OCS webpage and the OceanSITES Global Data\nAssembly Centers (GDACs), with near-realtime data available prior to release of the complete, \ndownloaded datasets.<br/><br/>\n\nOCS measurements served through the Big Data Program come from OCS high-latitude moored buoys located in the Kuroshio \nExtension (32°N 145°E) and the Gulf of Alaska (50°N 145°W).  Initiated in 2004 and 2007, \nthe respective moored buoys, KEO and Papa, measure a suite of surface and subsurface essential ocean variables.\nThe surface suite includes air temperature, relative humidity, shortwave and longwave radiation, barometric pressure, winds, and rain, \nwhile subsurface instrumentation includes temperature, salinity, and ocean currents.  Individual buoy deployments are stitched together into \na continuous time-series, which is synced to the OceanSITES GDACs, and subsequently, to BDP.\n","Documentation":"https://www.pmel.noaa.gov/ocs/ http://www.oceansites.org https://dods.ndbc.noaa.gov/oceansites/","Contact":"For questions regarding data content or quality, users are directed to the OCS webpage (https://www.pmel.noaa.gov/ocs/). For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. <br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"KEO and Papa data on BDP are synchronized with the OceanSITES Global Data Assembly Center periodically.","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","climate","environmental","oceans","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"OCS moored buoy data","ARN":"arn:aws:s3:::noaa-oar-keo-papa-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-oar-keo-papa-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for OCS moored buoy data, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewKeoPapaObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"OCS publications - All OCS-relevant publications are updated at the URL below.","URL":"https://www.pmel.noaa.gov/ocs/publications","AuthorName":"PMEL"}]},"RegistryEntryAdded":"2021-09-13","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-ocean-climate-stations","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Office of Coast Survey - Hydrographic Survey Data","Description":"Founded in 1807, NOAA’s Office of Coast Survey is the nation’s first scientific agency and today is responsible for supporting nearly $5.4 trillion in economic activity through providing advanced marine navigation services. The Office of Coast Survey collects and qualifies hydrographic, bathymetric, and topographic data, from NOAA platforms and many other data providers. These data and associated deliverables are posted here for various users to access, including but not limited to the \"[National Bathymetric Source Program](https://registry.opendata.aws/noaa-bathymetry/)\" for incorporation into compilations of the best available bathymetry and NOAA’s National Center for Environmental Information (NCEI) for archival. Qualified data is cleared for application to the navigation products and for public distribution without restriction. Unqualified data is either in queue to be qualified or deemed not to be usable for navigation, but cleared for public distribution according to the data license associated with each file.\n","Documentation":"https://nauticalcharts.noaa.gov/","Contact":"For general questions or feedback about the data, please submit inquiries\nthrough the NOAA Office of Coast Survey (OCS) \"[ASSIST Tool](https://www.nauticalcharts.noaa.gov/customer-service/assist/)\". \nFor any data delivery issues, please contact the NOAA Open Data Dissemination Team (NODD) at NODD@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"This bucket is updated as new data source become available","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","bathymetry","earth observation","marine navigation","model","oceans","oceans"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA Office of Coast Survey Hydrographic Survey Data","ARN":"arn:aws:s3:::noaa-ocs-hydrodata-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ocs-hydrodata-pds.s3.amazonaws.com/index.html)","[STAC Catalog](https://noaa-ocs-hydrodata-pds.s3.amazonaws.com/catalog.json)","[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/noaa-ocs-hydrodata-pds.s3.amazonaws.com/catalog.json?.language=en)"]},{"Description":"NOAA Office of Coast Survey Hydrographic Survey Data New Object Notification","ARN":"arn:aws:sns:us-east-1:709902155096:NewOCSHYDROObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2023-07-10","RegistryEntryLastModified":"2025-09-18","Slug":"noaa-ocs-hydrodata","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Operational Forecast System (OFS)","Description":"ANNOUNCEMENTS: [NOS OFS Version Updates and Implementation of Upgraded Oceanographic Forecast Modeling Systems for Lakes Superior and Ontario; Effective October 25, 2022}(https://www.weather.gov/media/notification/pdf2/scn22-91_nos_loofs_lsofs_v3.pdf)\n<br/>\n<br/>\nFor decades, mariners in the United States have depended on NOAA's Tide Tables for the best estimate of expected water levels. These tables provide accurate predictions of the astronomical tide (i.e., the change in water level due to the gravitational effects of the moon and sun and the rotation of the Earth); however, they cannot predict water-level changes due to wind, atmospheric pressure, and river flow, which are often significant.\n<br/>\n<br/>\nThe National Ocean Service (NOS) has the mission and mandate to provide guidance and information to support navigation and coastal needs. To support this mission, NOS has been developing and implementing hydrodynamic model-based [Operational Forecast Systems](https://tidesandcurrents.noaa.gov/forecast_info.html). \n<br/>\n<br/>\nThis forecast guidance provides oceanographic information that helps mariners safely navigate their local waters. This national network of hydrodynamic models provides users with operational nowcast and forecast guidance (out to 48 – 120 hours) on parameters such as water levels, water temperature, salinity, and currents. These forecast systems are implemented in critical ports, harbors, estuaries, Great Lakes and coastal waters of the United States, and form a national backbone of real-time data, tidal predictions, data management and operational modeling.\n<br/>\n<br/>\nNowcasts and forecasts are scientific predictions about the present and future states of water levels (and possibly currents and other relevant oceanographic variables, such as salinity and temperature) in a coastal area. These predictions rely on either observed data or forecasts from a numerical model. A nowcast incorporates recent (and often near real-time) observed meteorological, oceanographic, and/or river flow rate data. A nowcast covers the period from the recent past (e.g., the past few days) to the present, and it can make predictions for locations where observational data are not available. A forecast incorporates meteorological, oceanographic, and/or river flow rate forecasts and makes predictions for times where observational data will not be available. A forecast is usually initiated by the results of a nowcast.\n<br/>\n<br/>\nOFS generally runs four times per day (every 6 hours) on NOAA's Weather and Climate Operational Supercomputing Systems (WCOSS) in a standard Coastal Ocean Modeling Framework (COMF) developed by the [Center for Operational Oceanographic Products and Services (CO-OPS)](https://tidesandcurrents.noaa.gov/). COMF is a set of standards and tools for developing and maintaining NOS’s hydrodynamic model–based operational forecast systems. The goal of COMF is to provide a standard and comprehensive software infrastructure to enhance ease of use, performance, portability, and interoperability of NOS’s operational forecast systems.\n<br/>\n<br/>\n","Documentation":"https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/OFS/README.md","Contact":"For questions regarding data content or quality, visit [the NOAA OFS site](https://tidesandcurrents.noaa.gov/models.html). \n<br />\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"4 times a day, every 6 hours with 6-hour nowcasts (WCOFS is updated once a day with 24-hour nowcast) and 48- to 120-hour forecast guidance.","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","climate","coastal","disaster response","environmental","meteorological","oceans","water","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOMADS Production OFS Data (30 day rolling retention)","ARN":"arn:aws:s3:::noaa-ofs-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ofs-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for OFS, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewOFSObject","Region":"us-east-1","Type":"SNS Topic"},{"Description":"CO-OPS Operational OFS Data (Historical Retention)","ARN":"arn:aws:s3:::noaa-nos-ofs-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nos-ofs-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for NOS OFS Historical Retention, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewNOSOFSObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"OFS Data Aggregation and Sub-Setting","URL":"https://opendap.co-ops.nos.noaa.gov/ofsdataagg/index.html","AuthorName":"NOAA"}],"Publications":[{"Title":"Chesapeake Bay OFS Flyer","URL":"https://tidesandcurrents.noaa.gov/ofs/publications/CBOFS_Onepager.pdf","AuthorName":"NOAA"},{"Title":"Chesapeake Bay OFS: Model Development and Skill Assessment","URL":"https://tidesandcurrents.noaa.gov/ofs/publications/CBOFS_TechReport.pdf","AuthorName":"NOAA"},{"Title":"Delaware Bay and River OFS Flyer","URL":"https://tidesandcurrents.noaa.gov/ofs/publications/DBOFS_Onepager.pdf","AuthorName":"NOAA"},{"Title":"Skill Assessment of the Delaware River and Bay OFS (DBOFS)","URL":"https://tidesandcurrents.noaa.gov/ofs/publications/DBOFS_TechReport_SA.pdf","AuthorName":"NOAA"},{"Title":"Tampa Bay OFS Flyer","URL":"https://tidesandcurrents.noaa.gov/ofs/publications/TBOFS_Onepager.pdf","AuthorName":"NOAA"},{"Title":"Tampa Bay OFS: Model Development and Skill Assessment","URL":"https://tidesandcurrents.noaa.gov/ofs/publications/TBOFS_TechReport.pdf","AuthorName":"NOAA"},{"Title":"Technical Implementation Notice for Chesapeake Bay OFS","URL":"https://tidesandcurrents.noaa.gov/ofs/publications/NOS-TIN-CBOFS.pdf","AuthorName":"NOAA"},{"Title":"Technical Implementation Notice for Delaware River and Bay OFS","URL":"https://tidesandcurrents.noaa.gov/ofs/publications/NOS-TIN-DBOFS.pdf","AuthorName":"NOAA"},{"Title":"Technical Implementation Notice for Tampa Bay OFS","URL":"https://tidesandcurrents.noaa.gov/ofs/publications/NOS-TIN-TBOFS.pdf","AuthorName":"NOAA"},{"Title":"Three-Dimensional Hydrodynamic Model Developments for a Delaware River and Bay Nowcast/Forecast System","URL":"https://tidesandcurrents.noaa.gov/ofs/publications/DBOFS_TechReport.pdf","AuthorName":"NOAA"}]},"RegistryEntryAdded":"2018-10-04","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-ofs","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Rapid Refresh (RAP)","Description":"The Rapid Refresh (RAP) is a NOAA/NCEP operational weather prediction system comprised primarily of a numerical forecast model and analysis/assimilation system to initialize that model. It covers North America and is run with a horizontal resolution of 13 km and 50 vertical layers. The RAP was developed to serve users needing frequently updated short-range weather forecasts, including those in the US aviation community and US severe weather forecasting community. The model is run for every hour of the day; it is integrated to 51 hours for the 03/09/15/21 UTC cycles and to 21 hours for every other cycle. The RAP uses the ARW core of the WRF model and the Gridpoint Statistical Interpolation (GSI) analysis - the analysis is aided with the assimilation of cloud and hydrometeor data to provide more skill in short-range cloud and precipitation forecasts.","Documentation":"https://www.nco.ncep.noaa.gov/pmb/products/rap/","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Hourly","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Rapid Refresh (RAP) Data","ARN":"arn:aws:s3:::noaa-rap-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-rap-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for RAP, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewRAPObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2021-03-05","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-rap","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA NASA Joint Archive (NNJA) of Observations for Earth System Reanalysis","Description":"The NOAA NASA Joint Archive (NNJA) of Observations for Earth System Reanalysis is a curated joint observation archive containing Earth system data from 1979 to present prepared by teams at NOAA's Physical Sciences Laboratory and NASA's Global Modeling and Assimilation Office. The goal is to foster collaboration across organizations and develop the ability for direct comparison of Earth System reanalysis results. Providing a singular dataset for observation input use will allow reanalyses to be compared on their unique development qualities by removing the variation from using different observations. We anticipate that the open dataset will be of value to the wider Earth System community, including future applications outside of the original goal of reanalysis production.\n<br/>\n<br/>\nThe archive is hosted in an Amazon Web Services (AWS) S3 bucket with atmosphere, ice, land and ocean observations in formats including bufr, ioda, and netcdf. The general structure file structure is noaa-reanalyses-pds/observations/reanalysis/{sensor}/{source(s)}/YYYY/MM/{file format}/{data files}. Some data is reprocessed as appropriate. The dataset also includes specification of observational errors and a black/white list for historic observations which can be access via the [NNJA site](https://psl.noaa.gov/data/nnja_obs/).\n","Documentation":"https://psl.noaa.gov/data/nnja_obs/","Contact":"For questions regarding data content or quality, visit [the NOAA PSL NNJA](https://psl.noaa.gov/data/nnja_obs/). Or contact the team at  psl.nnja@noaa.gov<br/>\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"1 time a day","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NNJA Observations for Earth System Reanalysis Data","ARN":"arn:aws:s3:::noaa-reanalyses-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-reanalyses-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for NNJA Observations, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewNOAAReanalysesObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2024-10-08","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-reanalyses-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Rapid Refresh Forecast System (RRFS) and RRFS Ensemble Forecast System (REFS) [Operational]","Description":"#### Update\n\nVersion 1 of the RRFS and REFS are scheduled to be implemented on October 14th, 2026 per the following [Service Change Notice](https://www.weather.gov/media/notification/pdf_2026/scn26-048_Updated_RRFS_and_REFS_Implementation_aad.pdf). Please note that upon the start of the pre-implementation parallel phase, August 12th, 2026, the RRFS/REFS [prototype data feed](https://noaa-rrfs-pds.s3.amazonaws.com/index.html) will no longer be updated.\n<br/><br/>\nUsers will be able to access the operational data through this bucket or through the feeds noted in the Service Change Notice to receive the RRFS and REFS outputs. Note that the files, filenames, and directory structures in this bucket will align with those described in the Service Change Notice.\n<br/><br/>\nPlease check back here and monitor [NWS's Service Change Notices](https://www.weather.gov/notification/) for the latest updates as the implementation date nears.\n\n---\nThe Rapid Refresh Forecast System (RRFS) is the National Oceanic and Atmospheric Administration’s (NOAA) next generation high-resolution, rapidly-updated deterministic (RRFS) and ensemble prediction system (RRFS Ensemble Forecast System; REFS). The RRFSfeatures a 3 km grid covering North America and includes deterministic forecasts every hour out to 18 hours, with deterministic forecasts to 84 hours and ensemble forecasts to 60 hours four times per day at 00, 06, 12, and 18 UTC. The RRFS and REFS provide guidance to support forecast interests including, but not limited to, aviation, severe convective weather, energy, heavy precipitation, and winter weather on timescales where rapidly-updated guidance is particularly useful.\n<br/><br/>\nThe RRFS is underpinned by the [Unified Forecast System (UFS)](https://ufs.epic.noaa.gov/), a community-based Earth modeling initiative, and benefits from collaborative development efforts across NOAA, academia, and research institutions.\n","Documentation":"https://www.nco.ncep.noaa.gov/pmb/products/rrfs/","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD is made available under the [Creative Commons 1.0 Universal Public Domain Dedication (CC0-1.0) license](https://creativecommons.org/publicdomain/zero/1.0/?ref=chooser-v1\\), which is well-known and internationally recognized. There are no restrictions on the use of the data. The data are open to the public and can be used as desired. <br/><br/> NOAA has adopted the Creative Commons license to ensure maximum use of our data, to spur and encourage exploration and innovation throughout the industry. This license is applicable to each of the NOAA datasets made available by NODD. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Rapid Refresh Forecast System (RRFS) Data","ARN":"arn:aws:s3:::noaa-rrfs-ops-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-rrfs-ops-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for RRFS, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewRRFSObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"RRFS Products Inventory","URL":"https://www.nco.ncep.noaa.gov/pmb/products/rrfs/","AuthorName":"NWS Central Operations"}]},"RegistryEntryAdded":"2026-08-13","RegistryEntryLastModified":"2026-09-10","Slug":"noaa-rrfs-ops","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Rapid Refresh Forecast System (RRFS) [Prototype]","Description":"#### Update\n\nVersion 1 of the RRFS and REFS are scheduled to be implemented on October 14th, 2026 per the following [Service Change Notice](https://www.weather.gov/media/notification/pdf_2026/scn26-048_Updated_RRFS_and_REFS_Implementation_aad.pdf). Please note that upon the start of the pre-implementation parallel phase, August 12th, 2026, the RRFS/REFS *prototype* data feed will no longer be updated.\n<br/><br/>\nUsers will be able to access the pre-implementation and operational data through the [new operational bucket](https://registry.opendata.aws/noaa-rrfs-ops/) or through the feeds noted in the Service Change Notice to receive the RRFS and REFS outputs. Note that the files, filenames, and directory structures in this bucket will align with those described in the Service Change Notice.\n<br/><br/>\nPlease check back here and monitor [NWS's Service Change Notices](https://www.weather.gov/notification/) for the latest updates as the implementation date nears.\n\n---\n\nThe Rapid Refresh Forecast System (RRFS) is the National Oceanic and Atmospheric Administration’s (NOAA) next generation convection-allowing, rapidly-updated ensemble prediction system, currently scheduled for operational implementation in 2026. The operational configuration will feature a 3 km grid covering North America and include deterministic forecasts every hour out to 18 hours, with deterministic and ensemble forecasts to 60 hours four times per day at 00, 06, 12, and 18 UTC.The RRFS will provide guidance to support forecast interests including, but not limited to, aviation, severe convective weather, renewable energy, heavy precipitation, and winter weather on timescales where rapidly-updated guidance is particularly useful.<br/><br/>\n\nThe RRFS is underpinned by the [Unified Forecast System (UFS)](https://ufscommunity.org/), a community-based Earth modeling initiative, and benefits from collaborative development efforts across NOAA, academia, and research institutions.<br/><br/>\n\nThis bucket provides access to a real time, pre-implementation RRFS prototype.  It also hosts some final retrospective parallel output used in the evaluation of RRFSv1\n\n---\n\nThe real-time RRFS output is not under 24x7 monitoring and is not operational. Output may be delayed or missing. Outputs will change. When significant changes to output take place, this description will be updated.<br/><br/>\n\nWe currently provide hourly deterministic forecasts at 3 km grid spacing out to 84 hours at 00, 06, 12, and 18 UTC, and out to 18 hours for other cycles. Output is organized by cycle date and cycle hour.\n\nWe are now dividing what is provided in this bucket into a fuller set of products (intended primarily for model developers of RRFSv2 using this data to initialize their forecasts), and a more limited set of products that will approximate what we'll be able to provide operationally over NOMADS.  \n\nThe fuller set of data is under the rrfs_a/ subdirectory:\n\nFor example, `rrfs_a/rrfs.20260114/12` contains the deterministic forecast initialized at 12 UTC on 14 January 2026. Users will find two primary types of output in GRIB2 format. The first is:<br/><br/>\n`rrfs.t12z.natlev.3km.f018.na.grib2` <br/><br/>\n\nMeaning that this is the RRFS initialized at 12 UTC, covers the full North America domain, and is the native level post-processed gridded data at hour 18.  This output is on a rotated latitude-longitude grid at 3 km grid spacing.<br/><br/>\n\nA second output file in grib2 format is:<br/><br/>\n\n`rrfs.t12z.prslev.3km.f018.conus.grib2` <br/><br/>\n\nThe “prslev” descriptor indicates that this post-processed gridded data is output on pressure levels. The “conus” descriptor indicates that it covers the contiguous United States\n\nFor users interested in other domains, output is provided on the full 3-km North American grid and also for subset over Alaska, Hawaii, and Puerto Rico. The files are identified as follows: <br/><br/>\n\nNorth America: `rrfs.t00z.prslev.3km.f002.na.grib2`\nAlaska: `rrfs.t00z.prslev.3km.f002.ak.grib2`\nHawaii: `rrfs.t00z.prslev.2p5km.f002.hi.grib2`\nPuerto Rico: `rrfs.t00z.prslev.2p5km.f002.pr.grib2`\n\n<br/><br/>\n\nWe also provide prototype RRFSv1 ensemble output and products. Output is available for 00, 06, 12, and 18 UTC cycles, and is organized by cycle date and cycle hour. For example, `rrfs_a/rrfsens.20260115/00/m001` contains the forecast from member 1, and `rrfs_a/refs.20260115/00/enspost_timelag` contains the combined ensemble products.\n\nThe set of files that will be available operationally over NOMADS (and which most users should use rather than the rrfs_a/ products) is under the rrfs_public/ subdirectory:\n\nMost naming conventions listed above for rrfs_a apply here as well.  However, it will not include full North America domain output, and only provides hourly output to 60 h (with 3 h resolution beyond that).  Also, only the 00/03/06/09/12/15/18/21 UTC cycles are provided.  Individual ensemble forecast members are not included, but the combined ensemble products are.  However, the combined ensemble products are under rrfs_public/refs.20260304/12/ensprod/ types of directories, which will better match the operational directory structure name.  \n\nFinal retrospective parallel output is under the retro_output_final/ directory, with spring (May 2024), summer (July 2023), and winter (~Jan 8 to Feb 8, 2024) output available.\n","Documentation":"https://vlab.noaa.gov/web/ufs-r2o","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD is made available under the [Creative Commons 1.0 Universal Public Domain Dedication (CC0-1.0) license](https://creativecommons.org/publicdomain/zero/1.0/?ref=chooser-v1\\), which is well-known and internationally recognized. There are no restrictions on the use of the data. The data are open to the public and can be used as desired. <br/><br/> NOAA has adopted the Creative Commons license to ensure maximum use of our data, to spur and encourage exploration and innovation throughout the industry. This license is applicable to each of the NOAA datasets made available by NODD. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Rapid Refresh Forecast System (RRFS) Data","ARN":"arn:aws:s3:::noaa-rrfs-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-rrfs-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for RRFS, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewRRFSObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"A Limited Area Modeling Capability for the Finite-Volume Cubed-Sphere (FV3) Dynamical Core and Comparison With a Global Two-Way Nest","URL":"https://doi.org/10.1029/2021MS002483","AuthorName":"Black, T. L., J. A. Abeles, B. T. Blake, D. Jovic, E. Rogers, X. Zhang, E. A. Aligo, L. C. Dawson, Y. Lin, E. Strobach, P. C. Shafran, and J. R. Carley"},{"Title":"Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study","URL":"https://doi.org/10.5194/gmd-15-6891-2022","AuthorName":"Banos, I. H., W. D. Mayfield, G. Ge, L. F. Sapucci, J. R. Carley, and L. Nance"},{"Title":"Community modeling framework underpinning the RRFS - The UFS Short Range Weather Application","URL":"https://github.com/ufs-community/ufs-srweather-app/","AuthorName":"UFS Community"},{"Title":"Highlights from a Year of Continued Development of the Rapid Refresh Forecast System (RRFS)","URL":"https://ams.confex.com/ams/102ANNUAL/meetingapp.cgi/Paper/391702","AuthorName":"Carley J. R. and C. R. Alexander"},{"Title":"Prototype UFS-Based Rapid Refresh Forecast System (RRFS) on the Cloud","URL":"https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/381608","AuthorName":"Holt, C., D. Abdi, J. A. Abeles, J. R. Carley, C. W. Harrop, R. Panda, S. Trahan, and C. R. Alexander"},{"Title":"Status and Opportunities with the Rapid Refresh Forecast System","URL":"https://epic.noaa.gov/wp-content/uploads/2022/07/1.-SRW-Carley.pdf","AuthorName":"Carley J. R. and C. R. Alexander"}]},"RegistryEntryAdded":"2021-06-22","RegistryEntryLastModified":"2026-09-10","Slug":"noaa-rrfs","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Real-Time Mesoscale Analysis (RTMA) / Unrestricted Mesoscale Analysis (URMA)","Description":"The Real-Time Mesoscale Analysis (RTMA) is a NOAA National Centers For Environmental Prediction (NCEP) high-spatial and temporal resolution analysis/assimilation system for near-surf ace weather conditions. Its main component is the NCEP/EMC Gridpoint Statistical Interpolation (GSI) system applied in two-dimensional variational mode to assimilate conventional and satellite-derived observations.<br/><br/>\nThe RTMA was developed to support NDFD operations and provide field forecasters with high quality analyses for nowcasting, situational awareness, and forecast verification purposes. The system produces hourly analyses at 2. 5 km resolution for the Conus NDFD grid, 3 km for the Alaska NDFD grid and 2.5 km for the Hawaii, Puerto-Rico and Guam NDFD grids.<br/><br/>\nData is available from the start of 2019 until present. URMA is a time lagged version of this product and is updated when new observations come in.\n","Documentation":"https://www.nco.ncep.noaa.gov/pmb/products/rtma/","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Hourly","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Real-Time Mesoscale Analysis (RTMA) Data","ARN":"arn:aws:s3:::noaa-rtma-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-rtma-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for RTMA data, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewNCEPRTMAObject","Region":"us-east-1","Type":"SNS Topic"},{"Description":"Unrestricted Mesoscale Analysis (URMA) Data","ARN":"arn:aws:s3:::noaa-urma-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-urma-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for URMA data, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewNCEPURMAObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2022-03-31","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-rtma","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Global Real-Time Ocean Forecast System (Global RTOFS)","Description":"NOAA's Global Real-Time Ocean Forecast System (Global RTOFS) provides users with nowcasts (analyses of near present conditions) and forecast guidance up to eight days of ocean temperature and salinity, water velocity, sea surface elevation, sea ice coverage and sea ice thickness. <br /><br />\n\nThe Global Operational Real-Time Ocean Forecast System (Global RTOFS) is based on an eddy resolving 1/12° global HYCOM (HYbrid Coordinates Ocean Model) (https://www.hycom.org/), which is coupled to the Community Ice CodE (CICE) Version 4 (https://www.arcus.org/witness-the-arctic/2018/5/highlight/1). The RTOFS grid has a 1/12 degree horizontal resolution and 41 hybrid vertical levels on a global tripolar grid. <br /><br />\n\nSince 2020, the RTOFS system implements a multivariate, multi-scale 3DVar data assimilation algorithm (Cummings and Smedstad, 2014) using a 24-hour update cycle. The data types presently assimilated include <br /><br />\n\n(1) satellite Sea Surface Temperature (SST) from METOP-B, JPSS-VIIRS, and  in-Situ SST, from ships, fixed and drifting buoys <br />\n(2) Sea Surface Salinity (SSS) from SMAP, SMOS, and buoys <br />\n(3) profiles of Temperature and Salinity from Animal-borne, Alamo floats, Argo floats, CTD, fixed buoys, gliders, TESAC, and XBT <br />\n(4) Absolute Dynamic Topography (ADT) from Altika, Cryosat, Jason-3, Sentinel 3a, 3b, 6a <br />\n(5) sea ice concentration from SSMI/S, AMSR2 <br /> <br />\n\nThe system is designed to incorporate new observing systems as the data becomes available. <br /> <br />\n\nOnce the observations go through a fully automated quality control and thinning process, the increments, or corrections, are obtained by executing the 3D variational algorithm. The increments are then added to the 24-hours forecast fields using a 6-hourly incremental analysis update. An earlier version of the system is described in Garraffo et al (2020). <br /> <br />\n\nGarraffo, Z.D., J.A. Cummings, S. Paturi, Y. Hao, D. Iredell, T. Spindler, B. Balasubramanian, I. Rivin, H-C. Kim, A. Mehra, 2020. Real Time Ocean-Sea Ice Coupled Three Dimensional Variational Global Data Assimilative Ocean Forecast System. In Research Activities in Earth System Modeling, edited by E. Astakhova, WMO, World Climate Research Program Report No.6, July 2020. <br /> <br />\n\nCummings, J. A. and O. M. Smedstad. 2013. Variational Data Assimilation for the Global Ocean.\nData Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol II)\nS. Park and L. Xu (eds), Springer, Chapter 13, 303-343. <br /> <br />\n\nGlobal Real Time Ocean Forecasting System RTOFS output available here include NetCDF and Hycom binary a/b files. Please see README documentation for more details. \n","Documentation":"https://github.com/NOAA-EMC/RTOFS_GLO/wiki","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Once a day. The products are available at the following times each day <br /> 00z - Analysis and analysis post-processing <br /> 06z - forecast days 1-4, post-processing for most of these days <br /> 12z - forecast days 5-8, post-processing for these days and some of forecast days 1-4. <br />","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","climate","coastal","disaster response","environmental","global","meteorological","oceans","water","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA Global Real Time Ocean Forecasting System Data","ARN":"arn:aws:s3:::noaa-nws-rtofs-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nws-rtofs-pds.s3.amazonaws.com/index.html)"]},{"Description":"NOAA Global Real Time Ocean Forecasting System New Dataset Notification","ARN":"arn:aws:sns:us-east-1:709902155096:NewRTOFSObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2022-08-04","RegistryEntryLastModified":"2026-10-21","Slug":"noaa-rtofs","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA S-102 Bathymetric Surface Data","Description":"S-102 is a data and metadata encoding specification that is part of the [S-100 Universal Hydrographic Data Model](https://iho.int/en/s100-project), an international standard for hydrographic data exchange. This collection of data contains bathymetric surfaces from [NOAA/NOS/OCS National Bathymetric Source](https://nauticalcharts.noaa.gov/updates/building-the-national-bathymetry/), for various U.S. coastal and offshore waters and the great lakes. These datasets are encoded as HDF5 files conforming to the S-102 specification.","Documentation":"https://noaa-s102-pds.s3.amazonaws.com/README.html","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. For general questions or feedback about the data, please submit inquiries through the NOAA Office of Coast Survey (OCS) ASSIST Tool at https://www.nauticalcharts.noaa.gov/customer-service/assist/. <br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Irregular, updated when new data is available.","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","bathymetry","hydrography","marine navigation","oceans","seafloor","water"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA S-102 Bathymetric Surface Datasets","ARN":"arn:aws:s3:::noaa-s102-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-s102-pds.s3.amazonaws.com/index.html)"]},{"Description":"NOAA S-102 Bathymetric Surface New Object Notification","ARN":"arn:aws:sns:us-east-1:123901341784:NewS102Object","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2022-08-10","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-s102","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA S-104 Water Level Data","Description":"S-104 is a data and metadata encoding specification that is part of the [S-100 Universal Hydrographic Data Model](https://iho.int/en/s-100-universal-hydrographic-data-model), an international standard for hydrographic data. This collection of data contains water level forecast guidance from [NOAA's Global Surge and Tide Operational Forecast System 2-D (STOFS-2D-Global)](https://polar.ncep.noaa.gov/estofs/), an operational hydrodynamic nowcast and forecast modeling system for global water level conditions. These datasets are encoded as HDF-5 files conforming to the S-104 specification, and are geospatially subset into individual tiles conforming to the NOAA/OCS Nautical Product Tiling Scheme, with filenames indicating the corresponding NOAA Electronic Navigational Chart (ENC) Cell Identifier. A set of prototype S-104 tiles has been created for the Charleston, SC area for a select model run cycle. Each individual S-104 (HDF-5) file contains all forecast projections from a single model run for that geographic area. A single S-104 file will contain multiple gridded arrays each containing a forecast valid at a distinct time in the future, out to the forecast horizon of STOFS-2D-Global, which is 180 hours or 7.5 days. The water level forecast guidance includes the combined effects of storm surge (sub-tidal) and tides (astronomical tide predictions).\n","Documentation":"https://noaa-s104-pds.s3.amazonaws.com/README.html\n","UpdateFrequency":"Static\n","License":"NOAA data disseminated through NODD are open to the public and can be used as desired.\nNOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.\n","ManagedBy":"\"[NOAA](http://www.noaa.gov/)\"\n","Contact":"For any data delivery issues, please contact the NOAA Open Data Dissemination Team at: nodd@noaa.gov. For general questions or feedback about the data, please submit inquiries through the NOAA Office of Coast Survey (OCS) ASSIST Tool at https://www.nauticalcharts.noaa.gov/customer-service/assist/.\nWe also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NOAA NODD team at nodd@noaa.gov.\n","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["aws-pds","coastal","hydrography","marine navigation","oceans","water"],"Resources":[{"Description":"NOAA S-104 Water Level for Surface Navigation Datasets","ARN":"arn:aws:s3:::noaa-s104-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-s104-pds.s3.amazonaws.com/index.html)"]},{"Description":"NOAA S-104 Water Level for Surface Navigation Dataset Notifications","ARN":"arn:aws:sns:us-east-1:123901341784:NewS104Object","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"NOAA nowCOAST","URL":"https://nowcoast.noaa.gov/","AuthorName":"NOAA"},{"Title":"NOAA Precision Marine Navigation Program: Developing Next-Gen Data Svcs for the Maritime Community","URL":"https://www.youtube.com/watch?v=laC0Du6-x3k","AuthorName":"NOAA"}]},"RegistryEntryAdded":"2026-01-07","RegistryEntryLastModified":"2026-05-18","Slug":"noaa-s104","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA S-111 Surface Water Currents Data","Description":"S-111 is a data and metadata encoding specification that is part of the [S-100 Universal Hydrographic Data Model](https://iho.int/en/s100-project), an international standard for hydrographic data. This collection of data contains surface water currents forecast guidance from [NOAA/NOS Operational Forecast Systems](https://tidesandcurrents.noaa.gov/models.html), a set of operational hydrodynamic nowcast and forecast modeling systems, for various U.S. coastal waters and the great lakes. The collection also contains surface current forecast guidance output from the [NCEP Global Real-Time Ocean Forecast System (GRTOFS)](https://polar.ncep.noaa.gov/global/) for some offshore areas. These datasets are encoded as HDF-5 files conforming to the S-111 specification, and are geospatially subset into individual tiles conforming to the NOAA/OCS Nautical Product Tiling Scheme, with filenames indicating the corresponding NOAA Electronic Navigational Chart (ENC) Cell Identifier.\nA full set of S-111 tiles is created for each new model run cycle, which occurs four times per day for all models except for RTOFS, which updates only once per day. Files are organized using a path naming convention that includes the OFS identifier (e.g. 'cbofs' corresponding with output from the Chesapeake Bay Operational Forecast System) as well as the year, month, day, and hour corresponding with each model run initialization time. Each individual S-111 (HDF-5) file contains all forecast projections from a single model run for that geographic area. In other words, a single S-111 file will contain multiple gridded arrays each containing a forecast valid at a distinct time in the future, out to the forecast horizon of the underlying modeling system. All surface currents forecasts in this collection are computed at a depth of 4.5 meters below water surface, or half the water column depth, whichever is shallower.\n","Documentation":"https://noaa-s111-pds.s3.amazonaws.com/README.html","Contact":"For general questions or feedback about the data, please submit inquiries through the NOAA Office of Coast Survey (OCS) ASSIST Tool at https://www.nauticalcharts.noaa.gov/customer-service/assist/.\"\n<br />\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Four times per day for each OFS, except Global RTOFS which updates once per day","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","oceans","water"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA S-111 Surface Water Currents Datasets","ARN":"arn:aws:s3:::noaa-s111-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-s111-pds.s3.amazonaws.com/index.html)"]},{"Description":"NOAA S-111 Surface Water Currents New Dataset Notification","ARN":"arn:aws:sns:us-east-1:123901341784:NewS111Object","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"NOAA Precision Marine Navigation Program: Developing Next-Gen Data Svcs for the Maritime Community","URL":"https://www.youtube.com/watch?v=laC0Du6-x3k","AuthorName":"NOAA"}]},"RegistryEntryAdded":"2020-07-28","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-s111","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Space Weather Forecast and Observation Data","Description":"Space weather forecast and observation data is collected and disseminated by NOAA’s Space Weather Prediction Center (SWPC) in Boulder, CO. SWPC produces forecasts for multiple space weather phenomenon types and the resulting impacts to Earth and human activities.  A variety of products are available that provide these forecast expectations, and their respective measurements, in formats that range from detailed technical forecast discussions to NOAA Scale values to simple bulletins that give information in laymen's terms. Forecasting is the prediction of future events, based on analysis and modeling of the past and present conditions of the environment you are interested in. In Space Weather, persistence and recurrence of active regions on the sun over the 27-day solar rotational period play an important role in accurately forecasting the space environment.\n","Documentation":"https://www.swpc.noaa.gov/products-and-data","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"The update frequencies of the space weather dataset range from one minute observations to daily and monthly updates of more slowly-varying indices","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","climate","meteorological","solar","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA Space Weather Prediction Center Forecasts","ARN":"arn:aws:s3:::noaa-swpc-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-swpc-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for NOAA Space Weather Prediction Center Forecasts, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewSWPCObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2020-08-06","RegistryEntryLastModified":"2025-07-09","Slug":"noaa-space-weather","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Severe Weather Data Inventory (SWDI)","Description":"The Storm Events Database is an integrated database of severe weather events across the United States from 1950 to this year, with information about a storm event's location, azimuth, distance, impact, and severity, including the cost of damages to property and crops. It contains data documenting: The occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce. Rare, unusual, weather phenomena that generate media attention, such as snow flurries in South Florida or the San Diego coastal area. Other significant meteorological events, such as record maximum or minimum temperatures or precipitation that occur in connection with another event. Data about a specific event is added to the dataset within 120 days to allow time for damage assessments and other analysis.\n","Documentation":"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00773","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> For general questions or feedback about the data, please submit inquiries through NOAA National Centers for Environmental Information (NCEI) at swdi.ncdc@noaa.gov\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Monthly","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA Severe Weather Data Inventory Dataset (SWDI)","ARN":"arn:aws:s3:::noaa-swdi-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-swdi-pds.s3.amazonaws.com/index.html)"]},{"Description":"NOAA Severe Weather Data Inventory Dataset Notification","ARN":"arn:aws:sns:us-east-1:123901341784:NewSWDIObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2021-02-12","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-swdi","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Unified Forecast System (UFS) Coastal Model","Description":"The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth Modeling System. The [UFS Coastal application](https://github.com/oceanmodeling/ufs-coastal-app) is a project under development by NOAA and NCAR, which supports coastal forecasting requirements based on UFS standards. The coupling infrastructure for UFS Coastal App is currently being developed based on a fork of the ufs-weather-model (UFS-WM), with additional coastal model-components including SCHISM, ADCIRC, ROMS, and FVCOM, as well as additional infrastructure to support coastal coupling of WW3 and CICE. The [model-level repository](https://github.com/oceanmodeling/ufs-weather-model) contains the model code and external submodules needed to build the UFS coastal model executable and the associated model components. \n<br/>\n<br/>\nThe UFS Coastal Regression Test (RT) system is a type of testing built into the software development that ensures that changes to the model-level code and associated model-components do not break the existing functionality of the code. The number and type of tests currently in the RT system suite are evolving along with current dependencies such as UFS-WM and ESMF libraries. Currently, at least one RT case exists for each coastal model. The status and descriptions of the existing RT cases is available via the [UFS Coastal Wiki page](https://github.com/oceanmodeling/ufs-weather-model/wiki/Current-Status-of-UFS%E2%80%90Coastal-Implementation). These are currently regularly tested on NOAA/MSU Hercules platform, and to a lesser frequency on TACC/Frontera.\n<br/>\n<br/>\nEach of the regression tests require a set of input data files and configuration files. The configuration files include namelist and model configuration files which can be found within the [UFS-Coastal model code repository](https://github.com/oceanmodeling/ufs-weather-model). The RT framework also includes baseline data for each configuration. When code changes are not expected to alter baseline results, regression tests will be performed against current baseline and as a result, the regression test log files are created – revealing a summary of no change within the results. If the code changes are expected to alter model results, impact to the regression tests will be specified in the pull request. The code changes and model results will be reviewed and confirmed. Once the model results are confirmed, a new baseline will be generated. In some cases, new input data will need to be added or old data will need to be replaced, these data will be put in the input data location with proper timestamp, and regression tests will be performed with the updated data sets.\n","Documentation":"https://ufs-coastal-application.readthedocs.io/en/latest/index.html","Contact":"For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. For questions regarding data content or quality, visit https://github.com/oceanmodeling/ufs-weather-model/discussions\n<br> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](https://www.noaa.gov/)","UpdateFrequency":"Periodically, as new data becomes available","Collabs":{"ASDI":{"Tags":["elevation"]}},"Tags":["aws-pds","climate","disaster response","elevation","geospatial","lidar","stac"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.\nNOAA data disseminated through NODD is made available under the [Creative Commons 1.0 Universal Public Domain Dedication (CC0-1.0) license](https://creativecommons.org/publicdomain/zero/1.0/?ref=chooser-v1\\), which is well-known and internationally recognized. There are no restrictions on the use of the data. The data are open to the public and can be used as desired. \n<br/>\n<br/>\nNOAA has adopted the Creative Commons license to ensure maximum use of our data, to spur and encourage exploration and innovation throughout the industry. This license is applicable to each of the NOAA datasets made available by NODD. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.\n<br/>\n<br/>\nNOAA has adopted the Creative Commons license to ensure maximum use of our data, to spur and encourage exploration and innovation throughout the industry. This license is applicable to each of the NOAA datasets made available by NODD. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.\n<br/>\n<br/> \nThe UFS Coastal license page is available at: https://github.com/oceanmodeling/ufs-coastal-app/blob/main/LICENSE.md\n","Resources":[{"Description":"NOAA UFS Coastal Data","ARN":"arn:aws:s3:::noaa-ufs-coastal-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ufs-coastal-pds.s3.amazonaws.com/index.html)"]},{"Description":"NOAA Coastal Lidar Dataset New Dataset Notification","ARN":"arn:aws:sns:us-east-1:709902155096:NewUFSCOASTALObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2025-02-25","RegistryEntryLastModified":"2025-03-17","Slug":"noaa-ufs-coastal-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Global Data Assimilation (DA) Test Data","Description":"The [Unified Forecast System (UFS)](https://ufs.epic.noaa.gov/) is a community-based, coupled, comprehensive Earth Modeling System. It supports [multiple applications](https://ufs.epic.noaa.gov/applications/) with different forecast durations and spatial domains. The [Global Data Assimilation System (GDAS)](https://github.com/NOAA-EMC/GDASApp) Application (App) is being used as the basis for uniting the [Global Workflow](http://NOAA-EMC/global-workflow) and Global Forecast System (GFS) model with [Joint Effort for Data assimilation Integration (JEDI)](https://jointcenterforsatellitedataassimilation-jedi-docs.readthedocs-hosted.com/en/latest/) capabilities.\n<br/>\n<br/>\nThe National Centers for Environmental Prediction (NCEP) use GDAS to interpolate data from various observing systems and instruments onto a three-dimensional grid. GDAS obtains its data through several observation types: surface, balloon, wind profilers, aircraft reports, buoys, radar, and satellite. This gridded output data is then used to initialize the GFS model. GDAS can be used in standalone mode to run forward operators and variational data assimilation (DA) for evaluation/development purposes. In the GDAS App, it supports GFS/GDAS cycled forecasts in the Global Workflow. Currently, GDAS primarily uses GSI for its data assimilation and cycling, but efforts are underway to replace GSI with JEDI. \n","Documentation":"https://github.com/NOAA-EMC/GDASApp/wiki","Contact":"For questions regarding data content or quality, post on the ufs-community forum at https://github.com/orgs/ufs-community/discussions or email support.epic@noaa.gov. For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. <br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"These are stable datasets for use with global DA projects. They will be updated as new sample cases are added.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","disaster response","environmental","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Global Data Assimilation (DA) System Test Data","ARN":"arn:aws:s3:::noaa-ufs-gdas-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ufs-gdas-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for Global Data Assimilation (DA) System Test Data, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewNWSUFSGDASObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2024-05-31","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-ufs-gdas-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Unified Forecast System (UFS) Global Ensemble Forecast System (GEFS) Version 13 Replay","Description":"The NOAA Unified Forecast System (UFS) / Global Ensemble Forecast System version 13 (GEFSv13) Replay dataset supports the retrospective forecast archive in preparation for GEFSv13 / GFSv17. It includes a range of atmospheric and oceanic variables—such as temperature, humidity, winds, salinity, and currents—covering global conditions at a nominal horizontal resolution of ¼ degree, enabling detailed weather analysis.\n<br/>\n<br/>\nThe dataset was generated by replaying the coupled UFS model against pre-existing external reanalyses; ERA5 for atmospheric data and ORAS5 for ocean and ice dynamics. Each simulation stream was initialized from these reanalyses, which were pre-processed for the UFS model components, including the GFDL Finite-Volume Cubed-Sphere Dynamical Core (FV3; 25 km, 127 vertical levels) and the Modular Ocean Model (MOM6; ¼ degree tri-polar grid, 72 vertical levels). This replay methodology enforces a predetermined model state while allowing cross-component fluxes and unconstrained processes to be computed.\n<br/>\n<br/>\nFor the land surface, NOAA’s JEDI-based land data assimilation system incorporated snow depth observations from the NCEI Global Historical Climatology Network (GHCN) and satellite-derived snow cover from the U.S. National Ice Center. The JEDI Sea-ice Ocean and Coupled Analysis system (SOCA) adjusted sea-ice thickness and concentration for consistency with ORAS5.\n<br/>\n<br/>\nThe original dataset spanned January 1994 to October 2023, with plans for ongoing updates and a 1-degree version covering 1958 to 2023. The dataset is hosted on AWS and GCP cloud services, courtesy of the NOAA Open Data Dissemination program. Original model outputs are stored in AWS, while a curated version in Zarr format is available on GCP for analysis and AI training pipelines (see https://psl.noaa.gov/data/ufs_replay/ for details).\n","Documentation":"https://psl.noaa.gov/data/ufs_replay/","Contact":"For questions regarding data content or quality, visit [the NOAA GEFS Replay site](https://psl.noaa.gov/data/ufs_replay/). <br/>\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Static","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"UFS / GEFS Replay Data","ARN":"arn:aws:s3:::-noaa-ufs-gefsv13replay-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ufs-gefsv13replay-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for UFS / GEFS Replay Data, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewUFS-GEFSObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2024-10-08","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-ufs-gefsv13replay-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Unified Forecast System (UFS) Hierarchical Testing Framework (HTF)","Description":"The \"[Unified Forecast System](https://ufs.epic.noaa.gov/)\" (UFS) is a community-based, coupled, comprehensive Earth Modeling System. The Hierarchical Testing Framework (HTF) serves as a comprehensive toolkit designed to enhance the testing capabilities within UFS \"[repositories](https://github.com/ufs-community)\". It aims to standardize and simplify the testing process across various \"[UFS Weather Model](https://github.com/ufs-community/ufs-weather-model)\" (WM) components and associated modules, aligning with the Hierarchical System Development (HSD) approach and NOAA baseline operational metrics.\n<br/>\n<br/>\nThe HTF provides a structured methodology for test case design and execution, which enhances code management practices, fosters user accessibility, and promotes adherence to established testing protocols. It enables developers to conduct testing efficiently and consistently, ensuring code integrity and reliability through the use of established technologies such as CMake and CTest. When integrated with containerization techniques, the HTF facilitates portability of test cases and promotes reproducibility across different computing environments. This approach reduces the computational overhead and enhances collaboration within the UFS community by providing a unified testing framework. \n<br/>\n<br/>\nAcknowledgment - The Unified Forecast System (UFS) atmosphere-ocean coupled model experimental version # data used in this study are made available through the UFS Research to Operations (UFS-R2O) project sponsored by the National Weather Service (NWS) Office of Science and Technology Integration (OSTI) Modeling Program Division and the National Oceanic and Atmospheric Administration (NOAA) Oceanic and Atmospheric Research (OAR) Weather Program Office (WPO).\n","Documentation":"https://epic-ufs-htf.readthedocs.io/en/develop/","Contact":"For questions regarding data content or quality, visit https://github.com/NOAA-EPIC/ufs-htf/issues \n<br />\nFor any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NOAA NODD team here: nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"The UFS HTF is in its prototype phase and will be updated as new case studies are added.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","disaster response","environmental","meteorological","oceans","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA Unified Forecast System (UFS) Hierarchical Testing Framework (HTF) Data","ARN":"arn:aws:s3:::noaa-ufs-htf-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ufs-htf-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for UFS HTF, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewNWSUFSHTFObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2024-05-07","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-ufs-htf-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Unified Forecast System (UFS) Land Data Assimilation (DA) System","Description":"The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth modeling system. It supports \"[multiple applications](https://ufscommunity.org/science/aboutapps/)\" covering different forecast durations and spatial domains. The Land Data Assimilation (DA) System is an offline version of the Noah Multi-Physics (Noah-MP) land surface model (LSM) used in the UFS Weather Model (WM). Its data assimilation framework uses \"[Joint Effort for Data assimilation Integration - JEDI] (https://www.jcsda.org/jcsda-project-jedi)\" software. The offline Noah-MP LSM is a stand-alone, uncoupled model used to execute land surface simulations. In this traditional uncoupled mode, near-surface atmospheric forcing data is required as input. Sample forcing and restart data are provided in this data bucket. <br/> <br/> The Noah-MP LSM has evolved through community efforts to pursue and refine a modern-era LSM suitable for use in the National Centers for Environmental Prediction (NCEP) operational weather and climate prediction models. This collaborative effort continues with participation from entities such as NCAR, NCEP, NASA, and university groups. <br/> <br/> For details regarding the physical parameterizations used in Noah-MP, see \"[Niu, et al. (2011)] (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2010JD015139)\". The \"[Land DA User’s Guide] (https://land-da.readthedocs.io/en/latest/)\" provides information on building and running the Land DA System in offline mode. Users can access additional technical support via the \"[UFS GitHub Discussions] (https://github.com/NOAA-EPIC/land-offline_workflow/discussions)\" for the Land DA System. <br/>","Documentation":"https://land-da.readthedocs.io/en/latest/","Contact":"For questions regarding data content or quality, visit https://github.com/NOAA-EPIC/land-offline_workflow/discussions\n<br/>\n<br/>\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"These are stable datasets for use with the Land DA System. They will not be updated frequently. Additional data may be added with each subsequent Land DA release.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Unified Forecast System (UFS) Land Data Assimilation (DA) System","ARN":"arn:aws:s3:::noaa-ufs-land-da-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ufs-land-da-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for UFS Land Data Assimilation (DA) System, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewUFSLANDDAObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2023-03-07","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-ufs-land-da","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Unified Forecast System (UFS) Marine Reanalysis: 1979-2019","Description":"The NOAA UFS Marine Reanalysis is a global sea ice ocean coupled reanalysis product produced by the marine data assimilation team of the UFS Research-to-Operation (R2O) project. Underlying forecast and data assimilation systems are based on the UFS model prototype version-6 and the Next Generation Global Ocean Data Assimilation System (NG-GODAS) release of the Joint Effort for Data assimilation Integration (JEDI) Sea Ice Ocean Coupled Assimilation (SOCA). Covering the 40 year reanalysis time period from 1979 to 2019, the data atmosphere option of the UFS coupled global atmosphere ocean sea ice (DATM-MOM6-CICE6) model was applied with two atmospheric forcing data sets: CFSR from 1979 to 1999 and GEFS from 2000 to 2019. Assimilated observation data sets include extensive space-based marine observations and conventional direct measurements of in situ profile data sets.\n<br/><br/>\nThis first UFS-marine interim reanalysis product is released to the broader weather and earth system modeling and analysis communities to obtain scientific feedback and applications for the development of the next generation operational numerical weather prediction system at the National Weather Service(NWS). The released file sets include two parts 1.) 1979 - 2019 UFS-DATM-MOM6-CICE6 model free runs and 2) 1979-2019 reanalysis cycle outputs (see descriptions embedded in each file set). Analyzed sea ice and ocean variables are ocean temperature, salinity, sea surface height, and sea ice concentration.\n<br/><br/>\nAcknowledgment - The UFS data assimilation and reanalysis and reforecast products produced from the UFS-R2O project are sponsored by the NWS Office of Science and Technology Integration (OSTI) Modeling Program Division and the National Oceanic and Atmospheric Administration’s(NOAA) Oceanic and Atmospheric Research (OAR) Weather Program Office (WPO).\n","Documentation":"https://vlab.noaa.gov/web/ufs-r2o/ NWS Disclaimer https://www.weather.gov/disclaimer","Contact":"For questions regarding data content or quality, visit https://vlab.noaa.gov/web/ufs-r2o/da-rnr.\n<br/><br/>\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br />\nWe also seek to identify case studies on how NOAA data is being used and will be featuring those\nstories in joint publications and in upcoming events. If you are interested in seeing your story\nhighlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"The UFS-DATM-MOM6-CICE6 model free run output contains continuous files from 1979-01-01 to 2019-08-30. Daily MOM6 and CICE6 diagnostic output files were produced for both model states and observations assimilated in the reanalysis experiment. In the reanalysis runs, the MOM6 and CICE6 models were initialized in a 24-hour analysis window centered at 12Z.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA UFS Marine Reanalysis 1979-2019 data","ARN":"arn:aws:s3:::noaa-ufs-rnrmarine-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ufs-rnrmarine-pds.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2021-11-16","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-ufs-marinereanalysis","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Unified Forecast System Weather Model (UFS-WM) Regression Tests","Description":"The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth Modeling System. The ufs-weather-model (UFS-WM) is the model source    of the UFS for NOAA’s operational numerical weather prediction applications. The UFS-WM Regression Test (RT) is the testing software to ensure that previously developed and tested capabilities in UFS-WM still work after code changes are integrated into the system. It is required that UFS-WM RTs are performed successfully on the required Tier-1 platforms whenever code changes are made to the UFS-WM.  The results of the UFS-WM RTs are summarized in log files and these files will be committed to the UFS-WM repository along with the code changes. Currently, the UFS-WM RTs have been developed to support several applications targeted for operational implementations including the global weather forecast, subseasonal to seasonal forecasts, hurricane forecast, regional rapid refresh forecast, and ocean analysis. <br/> <br/> At this time, there are 123 regression tests to support the UFS applications. The tests are evolving along with the development merged to the UFS-WM  code repository. The regression test framework has been developed in the UFS-WM to run these tests on tier-1 supported systems. Each of the regression tests require a set of input data files and configuration files. The configuration files include namelist and model configuration files residing within the UFS-WM code repository. The input data includes initial conditions, climatology data, and fixed data sets such as orographic data and grid specification data. In addition, the regression test framework maintains baseline data created from certain revisions of the UFS-WM code repository. When code changes are not expected to alter baseline results, regression tests will be performed against current baseline and as a result, the regression test log files are created – revealing a summary of no change within the results. If the code changes are expected to alter model results, impact to the regression tests will be specified in the pull request. The code changes and model results will be reviewed and confirmed. Once the model results are confirmed,  a new baseline will be generated. In some cases, new input data will need to be added or old data will need to be replaced, these data will be put in the input data location with proper timestamp, and regression tests will be performed with the updated data sets. <br/> <br/> The regression test framework serves as a test system to maintain the functionalities in the UFS-WM. The input data and the baselines need to be maintained and updated during the code integration to support the regression tests.","Documentation":"https://ufs-weather-model.readthedocs.io/en/ufs-v1.0.0/index.html","Contact":"For questions regarding data content or quality, visit  https://github.com/ufs-community/ufs-weather-model/issues\n<br/>\n<br/>\nFor any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"The input and baseline datasets are updated for the latest two-months of development code commits/revisions made to the UFS-WM’s development branch.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Unified Forecast System Weather Model (UFS-WM) Regression Tests data","ARN":"arn:aws:s3:::noaa-ufs-regtests-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ufs-regtests-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for UFS Weather Model Regression data, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewUFSObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2022-04-04","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-ufs-regtests","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Unified Forecast System Subseasonal to Seasonal Prototypes","Description":"The Unified Forecast System Subseasonal to Seasonal prototypes consist of reforecast data from the UFS atmosphere-ocean coupled model experimental prototype version 5, 6, 7, and 8 produced by the Medium Range and Subseasonal to Seasonal Application team of the UFS-R2O project. The UFS prototypes are the first dataset released to the broader weather community for analysis and feedback as part of the development of the next generation operational numerical weather prediction system from NWS. The datasets includes all the major weather variables for atmosphere, land, ocean, sea ice, and ocean waves.\n<br/>\n<br/>\nAcknowledgment - The Unified Forecast System (UFS) atmosphere-ocean coupled model experimental version # data used in this study are made available through the UFS Research to Operations (UFS-R2O) project sponsored by the National Weather Service (NWS) Office of Science and Technology Integration (OSTI) Modeling Program Division and the National Oceanic and Atmospheric Administration (NOAA) Oceanic and Atmospheric Research (OAR) Weather Program Office (WPO).\n","Documentation":"https://vlab.noaa.gov/web/ufs-r2o/dataproducts NWS Disclaimer https://www.weather.gov/disclaimer","Contact":"For questions regarding data content or quality, visit https://vlab.noaa.gov/web/ufs-r2o/coupled-model-prototype5. \n<br />\nFor any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Big Data Program, email noaa.bdp@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NOAA BDP team here: noaa.bdp@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"A Prototype is a retrospective run for the period from 2011 to 2018. The runs are initialized twice per month (1st and 15th) and the length of the forecast is 35 days with an output frequency of 6 hours.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","disaster response","environmental","meteorological","oceans","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"UFS prototype files (5, 6, 7, & 8)","ARN":"arn:aws:s3:::noaa-ufs-prototypes-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ufs-prototypes-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for UFS Prototype, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:123901341784:NewUfsPrototypesObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"Advances in Seasonal Predictions of Arctic Sea Ice With NOAA UFS","URL":"https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022GL102392","AuthorName":"Jieshun Zhu, Wanqiu Wang, Yanyun Liu, Arun Kumar, and David DeWitt"},{"Title":"The impact of tropical SST biases on the S2S precipitation forecast skill over the Contiguous United States in the UFS global coupled model","URL":"https://doi.org/10.1175/WAF-D-22-0162.1","AuthorName":"Hedanqiu Bai, Bin Li, Avichal Mehra, Jessica Meixner, Shrinivas Moorthi, Sulagna Ray, Lydia Stefanova, Jiande Wang, Jun Wang, Denise Worthen, Fanglin Yang, and Cristiana Stan"}]},"RegistryEntryAdded":"2021-01-08","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-ufs-s2s","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Unified Forecast System Short-Range Weather (UFS SRW) Application","Description":"The \"[Unified Forecast System (UFS)](https://ufscommunity.org/)\" is a community-based, coupled, comprehensive Earth Modeling System. It supports \" [multiple applications](https://ufscommunity.org/science/aboutapps/)\" with different forecast durations and spatial domains. The UFS Short-Range Weather (SRW) Application figures among these applications. It targets predictions of atmospheric behavior on a limited spatial domain and on time scales from minutes to several days. The SRW Application includes a prognostic atmospheric model, pre-processor, post-processor, and community workflow for running the system end-to-end. The \"[SRW Application Users's Guide](https://ufs-srweather-app.readthedocs.io/en/develop/)\" includes information on these components and provides detailed instructions on how to build and run the SRW Application. Users can access additional technical support via the \"[UFS GitHub Discussions](https://github.com/ufs-community/ufs-srweather-app/discussions/categories/q-a)\" <br/> <br/> This data registry contains the data required to run the “out-of-the-box” SRW Application case. The SRW App requires numerous input files to run, including static datasets (fix files containing climatological information, terrain and land use data), initial condition data files, lateral boundary condition data files, and model configuration files (such as namelists). The SRW App experiment generation system also contains a set of workflow end-to-end (WE2E) tests that exercise various configurations of the system (e.g., different grids, physics suites). Data for running a subset of these WE2E tests are also included within this registry. <br/> <br/> Users can generate forecasts for dates not included in this data registry by downloading and manually adding raw model files for the desired dates. Many of these model files are publicly available and can be accessed via links on the \"[Developmental Testbed Center](https://dtcenter.org/nwp-containers-online-tutorial/publicly-available-data-sets)\" website.","Documentation":"https://ufs-srweather-app.readthedocs.io/en/develop/","Contact":"For questions regarding data content or quality, visit  https://github.com/ufs-community/ufs-srweather-app/issues\n<br/>\n<br/>\nFor any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Big Data Program, email noaa.bdp@noaa.gov. We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NOAA BDP team at noaa.bdp@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"These are stable datasets for use with the SRW Application. They will not be updated frequently.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Unified Forecast System Short-Range Weather (UFS SRW) Application data","ARN":"arn:aws:s3:::noaa-ufs-srw-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-ufs-srw-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for UFS Short-Range Weather data, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewUFSSRWObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2022-04-25","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-ufs-shortrangeweather","Sources":["awslabs-open-data-registry"]}
{"Name":"Coupled Model Intercomparison Project Phase 5 (CMIP5) University of Wisconsin-Madison Probabilistic Downscaling Dataset","Description":"The University of Wisconsin Probabilistic Downscaling (UWPD) is a statistically downscaled dataset based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. UWPD consists of three variables, daily precipitation and maximum and minimum temperature. The spatial resolution is 0.1<span>&#176;</span>x0.1<span>&#176;</span> degree resolution for the United States and southern Canada east of the Rocky Mountains.\n<br/>\n<br/>\nThe downscaling methodology is not deterministic. Instead, to properly capture unexplained variability and extreme events, the methodology predicts a spatially and temporally varying Probability Density Function (PDF) for each variable. Statistics such as the mean, mean PDF and annual maximum statistics can be calculated directly from the daily PDF and these statistics are included in the dataset. In addition, “standard”, “raw” data is created by randomly sampling from the PDFs to create a “realization” of the local scale given the large-scale from the climate model. There are 3 realizations for temperature and 14 realizations for precipitation.\n<br/>\n<br/>\nThe directory structure of the data is as follows\n<br/>\n<code>[cmip_version]/[scenario]/[climate_model]/[ensemble_member]/</code>\n<br/>\nThe realizations are as follows\n<br/>\n<code>prcp_[realization_number]_[year].nc</code>\n<code>temp_[realization_number]_[year].nc</code>\n<br/>\nThe time mean files averaged over certain year bounds are as follows\n<br/>\n<code>prcp_mean_[year_bound_1]_[year_bound_2].nc</code>\n<code>temp_mean_[year_bound_1]_[year_bound_2].nc</code>\n<br/>\nThe time-mean Cumulative Distribution Function (CDF) files are as follows\n<br/>\n<code>prcp_cdf_[year_bound_1]_[year_bound_2].nc</code>\n<code>temp_cdf_[year_bound_1]_[year_bound_2].nc</code>\n<br/>\nThe CDF of the annual maximum precipitation is given for each year in the record\n<code>prcp_annual_max_cdf_[start_year_of_scenario]_[end_year_of_scenario].nc</code>\n<br />\n","Documentation":"http://djlorenz.github.io/downscaling2/main.html","Contact":"For questions about data development, quality and content, please contact Dr. David Lorenz at david.lorenz@wisc.edu.\n<br />\nFor general questions or feedback about the data and its usage, please submit the inquiries to Hydrometeorological Design Study Center at hdsc.questions@noaa.gov. \n<br />\nFor any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Big Data Program, email noaa.bdp@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NOAA BDP team here: noaa.bdp@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Periodically, as new data becomes available or when corrections are implemented.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","climate","coastal","disaster response","environmental","meteorological","oceans","sustainability","water","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"Data files","ARN":"arn:aws:s3:::noaa-nws-uwpd-cmip5-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nws-uwpd-cmip5-pds.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"Assessment Report: Analysis of Impact of Nonstationary Climate on NOAA Atlas 14 Estimates","URL":"https://hdsc.nws.noaa.gov/hdsc/files25/NA14_Assessment_report_202201v1.pdf","AuthorName":"NOAA"}]},"RegistryEntryAdded":"2022-03-16","RegistryEntryLastModified":"2025-03-14","Slug":"noaa-uwpd-cmip5","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Wave Ensemble Reforecast","Description":"This is a 20-year global wave reforecast generated by WAVEWATCH III model (https://github.com/NOAA-EMC/WW3) forced by GEFSv12 winds (https://noaa-gefs-retrospective.s3.amazonaws.com/index.html). The wave ensemble was run with one cycle per day (at 03Z), spatial resolution of 0.25°X0.25° and temporal resolution of 3 hours. There are five ensemble members (control plus four perturbed members) and, once a week (Wednesdays), the ensemble is expanded to eleven members. The forecast range is 16 days and, once a week (Wednesdays), it extends to 35 days. More information about the wave modeling, wave grids and calibration can be found in the WAVEWATCH III regtest ww3_ufs1.3 (https://github.com/NOAA-EMC/WW3/tree/develop/regtests/ww3_ufs1.3).\n<br/><br/>\nThe 20 years of reforecast results were analyzed and quality-controlled. Three output types are available\n<br/><br/>\n1) Global wave fields, in grib2 format, with several variables including significant wave height, period, direction, and partitions;\n<br/>\n2) Point output tables, in netcdf format, containing time-series of significant wave height, period and direction, for 658 points (latitude/longitude informed) at the positions of wave buoys;  and,\n<br/>\n3) For the same positions, spectral outputs are available, in netcdf format, containing the full spectra (2D directional spectrum).\n<br/>\nEach file refers to one forecast cycle with date (year, month, day) written in the file name.\n<br/>\n<br/>\nThis effort has been funded by a NOAA OAR/NWS Service Level Agreement (SLA) whereby NOAA/OAR supports R&D and transition needed for mission delivery of climate\nservices within NWS. The SLA project is led by NWS/NCEP/Ocean Prediction Center in collaboration with NWS/NCEP/Environmental Modeling Center and NWS/NCEP/Climate \nPrediction Center. This is a project also in cooperation with NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) and the University of Miami’s \nCooperative Institute for Marine and Atmospheric Studies (CIMAS).\n<br/>\n<br/>\nThe following github repository has examples and scripts to support users to download, visualize, and process the WAVEWATCH III output files\n<br/>\n<br/>\nhttps://github.com/ricampos/gefswaves_reforecast\n<br/>\n<br/>\nThe reforecast simulations were generated on the supercomputer Orion, which is funded via a grant from NOAA to support research activities in environmental  \nmodeling, including weather modeling and simulation\n<br/>\n<br/>\nhttps://www.noaa.gov/organization/information-technology/orion\n","Documentation":"https://github.com/NOAA-EMC/gefswaves_reforecast/wiki <br/> https://github.com/NOAA-EMC/gefswaves_reforecast","Contact":"For questions related to wave modeling and the ensemble reforecast available, please contact Ricardo Martins Campos, email ricardo.campos@noaa.gov.\n<br/><br/>\nFor any questions regarding data delivery not associated with this platform or any general questions\nregarding the NOAA Open Data Dissemination Program, email nodd@noaa.gov.\nWe also seek to identify case studies on how NOAA data is being used and will be featuring those\nstories in joint publications and in upcoming events. If you are interested in seeing your story\nhighlighted, please share it with the NOAA NODD team at nodd@noaa.gov.\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Quarterly","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","climate","meteorological","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA Wave Ensemble Reforecast Data","ARN":"arn:aws:s3:::noaa-nws-gefswaves-reforecast-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nws-gefswaves-reforecast-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for NOAA Wave Ensemble Reforecast, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewWERObject","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Examples and scripts to support users to download, visualize, and process the WAVEWATCH III output files","URL":"https://github.com/ricampos/gefswaves_reforecast","AuthorName":"Ricardo Campos","AuthorURL":"https://github.com/ricampos"}]},"RegistryEntryAdded":"2022-04-11","RegistryEntryLastModified":"2025-03-13","Slug":"noaa-wave-ensemble-reforecast","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA World Ocean Database (WOD)","Description":"The World Ocean Database (WOD) is the largest uniformly formatted, quality-controlled, publicly available historical subsurface ocean profile database. From Captain Cook's second voyage in 1772 to today's automated Argo floats, global aggregation of ocean variable information including temperature, salinity, oxygen, nutrients, and others vs. depth allow for study and understanding of the changing physical, chemical, and to some extent biological state of the World's Oceans. Browse the bucket via the AWS S3 explorer: https://noaa-wod-pds.s3.amazonaws.com/index.html\n","Documentation":"https://www.nodc.noaa.gov/OC5/WOD/pr_wod.html","Contact":"For any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\nWe also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Data is update on a quarterly basis","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","climate","oceans"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired. <br/> <br/> NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"World Ocean Database (WOD) NetCDF Format","ARN":"arn:aws:s3:::noaa-wod-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-wod-pds.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"The World Ocean Database Introduction","URL":"https://data.nodc.noaa.gov/woa/WOD/DOC/wod_intro.pdf","AuthorName":"Tim P. Boyer, Olga K. Baranova, Carla Coleman, Hernan E. Garcia, Alexandra Grodsky, Ricardo A. Locarnini, Alexey V. Mishonov, Christopher R. Paver, James R. Reagan, Dan Seidov, Igor V. Smolyar, Katharine W. Weathers, Melissa M. Zweng"},{"Title":"The World Ocean Database User's Manual","URL":"https://data.nodc.noaa.gov/woa/WOD/DOC/wodreadme.pdf","AuthorName":"Hernan E. Garcia, Tim P. Boyer, Ricardo A. Locarnini, Olga K. Baranova, Melissa M. Zweng"}]},"RegistryEntryAdded":"2020-09-15","RegistryEntryLastModified":"2025-10-03","Slug":"noaa-wod","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA Wang Sheeley Arge (WSA) Enlil","Description":"The WSA-Enlil heliospheric model provides critical information regarding the propagation of solar Coronal Mass Ejections (CMEs) and transient structures within the heliosphere. Two distinct models comprise the WSA-Enlil modeling system; 1) the Wang-Sheeley-Arge (WSA) semi-empirical solar coronal model, and 2) the Enlil magnetohydrodynamic (MHD) heliospheric model. MHD modeling of the full domain (solar photosphere to Earth) is extremely computationally demanding due to the large parameter space and resulting characteristic speeds within the system. To reduce the computational burden and improve the timeliness (and hence the utility in forecasting space weather disturbances) of model results, the domain of the MHD model (Enlil) is limited from 21.5 Solar Radii (R_s) to just beyond the orbit of Earth, while the inner portion, spanning from the solar photosphere to 21.5R_s, is characterized by the WSA model. This coupled modeling system is driven by solar synoptic maps composed of numerous magnetogram observations from the National Solar Observatory’s (NSO) Global Oscillation Network Group (GONG). Such maps provide a full surface description of solar photospheric magnetic flux density, while not accounting for the evolution of surface features for regions outside the view of the observatories. <br/> <br/> In its current configuration (NOAA WSA-Enlil V3.0), the modeling system consists of WSA V5.4 and Enlil V2.9e. The system relies upon the zero point corrected GONG synoptic maps (mrzqs) to define the inner photospheric boundary. <br/> <br/> The full 3D datasets from the operational model are provided here as compressed tar files with naming convention wsa_enlil.mrid########.full3d.tgz. These files consist of the full set of 3D datacubes (tim.*.nc), all time series results stored at predefined observation points (evo.*.nc), and supplemented by the operational CME fits (conefiles) and the operationally employed photospheric boundary synoptic maps (GONG mrzqs files).","Documentation":"https://www.swpc.noaa.gov/products/wsa-enlil-solar-wind-prediction","Contact":"For any questions regarding WSA-Ensil data, please contact Eric Adamson (eric.adamson@noaa.gov) \n<br/>\nFor data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.\n<br /> We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"Only model output from model runs containing CMEs are provided here. As these runs are executed on demand, there is no regular update frequency.","Tags":["aws-pds","climate","meteorological","solar","weather"],"License":"NOAA data disseminated through NODD are open to the public and can be used as desired.<br/> <br/>NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","Resources":[{"Description":"NOAA WSA-Enlil Products","ARN":"arn:aws:s3:::noaa-wsa-enlil-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-wsa-enlil-pds.s3.amazonaws.com/index.html)"]},{"Description":"New data notifications for NOAA WSA-Enlil Products, only Lambda and SQS protocols allowed","ARN":"arn:aws:sns:us-east-1:709902155096:NewSWPCWSAEnlilObject","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2023-04-18","RegistryEntryLastModified":"2026-04-17","Slug":"noaa-wsa-enlil","Sources":["awslabs-open-data-registry"]}
{"Name":"Northern California Earthquake Data","Description":"This dataset contains various types of digital data relating\nto earthquakes in central and northern California. \nTime series data come from broadband, short period, and strong motion\nseismic sensors, GPS, and other geophysical sensors.\n","Documentation":"https://ncedc.org/db/cloud.html","Contact":"stephane@berkeley.edu","ManagedBy":"[Northern California Earthquake Data Center](https://ncedc.org)","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aws-pds","earth observation","earthquakes","seismology"],"License":"NCEDC hereby grants the non-exclusive, royalty free, non-transferable, \nworldwide right and license to use, reproduce and publicly display in all media \npublic data from the Northern California Seismic Network. Please cite \nthe \"NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley\nSeismological Laboratory. Dataset. doi:10.7932/NCEDC.\" for any research\npublications using this data.\n","Resources":[{"Description":"Seismic waveform data (miniSEED format), metadata (FDSNStationXML format) and earthquake catalog (CSV & Hypoinverse formats)","ARN":"arn:aws:s3:::ncedc-pds","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Getting Started with NCEDC AWS Public Dataset","URL":"https://ncedc.org/db/cloud/getstarted-pds.html","AuthorName":"Ellen Yu, Stephane Zuzlewski","AuthorURL":"https://ncedc.org"}],"Publications":[{"Title":"Collaborative Projects at the Northern California Earthquake Data Center (NCEDC)","URL":"https://www.researchgate.net/publication/252418955_Collaborative_Projects_at_the_Northern_California_Earthquake_Data_Center_NCEDC","AuthorName":"Doug Neuhauser; David Oppenheimer; Stephane Zuzlewski; Lind Gee; Mark Murray"},{"Title":"Improved Data Access From the Northern California Earthquake Data Center","URL":"https://www.researchgate.net/publication/253823256_Improved_Data_Access_From_the_Northern_California_Earthquake_Data_Center","AuthorName":"Doug Neuhauser; Fred Klein; Stephane Zuzlewski; David Oppenheimer; Gray Jensen"},{"Title":"New Continuous Timeseries Data at the Northern California Earthquake Data Center","URL":"https://www.researchgate.net/publication/253267871_New_Continuous_Timeseries_Data_at_the_Northern_California_Earthquake_Data_Center","AuthorName":"Doug Neuhauser; Stephane Zuzlewski; Lind Gee; Lynn Dietz; Will Kohler"},{"Title":"Open Access to Decades of NCSN Waveforms at the Northern California Earthquake Data Center","URL":"https://www.researchgate.net/publication/238583165_Open_Access_to_Decades_of_NCSN_Waveforms_at_the_Northern_California_Earthquake_Data_Center","AuthorName":"Doug Neuhauser; Fred Klein; Stephane Zuzlewski; David Oppenheimer; Gray Jensen"},{"Title":"Robust Distributed Earthquake Monitoring with CISN software in Northern California","URL":"https://www.researchgate.net/publication/253381527_Robust_Distributed_Earthquake_Monitoring_with_CISN_software_in_Northern_California","AuthorName":"Doug Neuhauser; Stephane Zuzlewski; Peter Lombard; Lynn Dietz; Jim Luetgert"},{"Title":"The Northern California Earthquake Data Center: Seismic and Geophysical Data for Northern California and Beyond","URL":"https://www.researchgate.net/publication/252836017_The_Northern_California_Earthquake_Data_Center_Seismic_and_Geophysical_Data_for_Northern_California_and_Beyond","AuthorName":"Doug Neuhauser; Fred Klein; Stephane Zuzlewski; Lind Gee; David Oppenheimer"},{"Title":"The Northern California Earthquake Management System: A Unified System From Realtime Monitoring to Data Distribution","URL":"https://www.researchgate.net/publication/252252363_The_Northern_California_Earthquake_Management_System_A_Unified_System_From_Realtime_Monitoring_to_Data_Distribution","AuthorName":"Doug Neuhauser; Fred Klein; Stephane Zuzlewski; Lynn Dietz; Peter Lombard"}]},"RegistryEntryAdded":"2023-11-08","RegistryEntryLastModified":"2023-11-08","Slug":"northern-california-earthquakes","Sources":["awslabs-open-data-registry"]}
{"Name":"Norwegian Endurance Athlete ECG Database","Description":"The Norwegian Endurance Athlete ECG Database contains 12-lead ECG recordings from 28 elite athletes from various sports in Norway. All recordings are 10 seconds resting ECGs recorded with a General Electric (GE) MAC VUE 360 electrocardiograph. All ECGs are interpreted with both the GE Marquette SL12 algorithm (version 23 (v243)) and one cardiologist with training in interpretation of athlete's ECG. The data was collected at the University of Oslo in February and March 2020.","Documentation":"https://doi.org/10.13026/qpjf-gk87","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/qpjf-gk87","ARN":"arn:aws:s3:::physionet-open/norwegian-athlete-ecg/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"norwegian-athlete-ecg","Sources":["awslabs-open-data-registry"]}
{"Name":"NovoMCP Open Corpus (lite)","Description":"122,454,458 PubChem compounds with RDKit physicochemical descriptors, ADMET/toxicity predictions from NovoMCP's own models (most trained on the public Therapeutics Data Commons ADMET benchmark; a few base heads on other public toxicology data such as Tox21), a machine-learned logP, and PAINS structural-alert flags. One row per PubChem CID, stored as Apache Parquet (Snappy) for direct in-place querying (Athena, DuckDB, PyArrow, pandas). An analysis-ready cheminformatics substrate for drug discovery: virtual screening, drug-likeness (QED) filtering, similarity search, chemical-space exploration, and training ADMET models at scale — so you don't recompute descriptors and property predictions across all of PubChem yourself. Per-endpoint model provenance is documented in the dataset card. Computed properties and model predictions for research use — not experimental measurements or regulatory determinations.","Documentation":"https://github.com/NovoMCP/open-data-examples/tree/main/novomcp-open-corpus-lite","Contact":"https://github.com/NovoMCP/novomcp/issues","ManagedBy":"[NovoMCP](https://github.com/NovoMCP)","UpdateFrequency":"Static release; refreshed periodically as models improve (versioned).","Tags":["chemistry","drug discovery","life sciences","machine learning","molecule","parquet","pharmaceutical"],"License":"CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Parquet shards (21 files, 36.8 GB, 122,454,458 rows x 59 columns)","ARN":"arn:aws:s3:::novomcp-open-corpus/novomcp-open-corpus-lite/","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Explore NovoMCP Open Corpus (lite) in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/06626463-1976-5718-8e2a-79ec7523baa3/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Get To Know A Dataset: NovoMCP Open Corpus (lite)","URL":"https://github.com/NovoMCP/open-data-examples/blob/main/novomcp-open-corpus-lite/get-to-know-a-dataset.ipynb","AuthorName":"Ari Harrison, NovoMCP","AuthorURL":"https://github.com/NovoMCP"}],"Tools & Applications":[{"Title":"Kaggle mirror","URL":"https://www.kaggle.com/datasets/novomcp/novomcp-corpus-lite-122m","AuthorName":"Ari Harrison, NovoMCP"},{"Title":"NovoMCP engine (open computational-chemistry engine that produced this corpus)","URL":"https://github.com/NovoMCP/novomcp","AuthorName":"Ari Harrison, NovoMCP"}],"Publications":[{"Title":"NovoMCP Open Corpus (lite): 122M compounds (Zenodo record, DOI)","URL":"https://doi.org/10.5281/zenodo.21710894","AuthorName":"Ari Harrison, NovoMCP"}]},"RegistryEntryAdded":"2026-07-31","RegistryEntryLastModified":"2026-11-03","Slug":"novomcp-open-corpus-lite","Sources":["awslabs-open-data-registry"]}
{"Name":"neuroQWERTY MIT-CSXPD Dataset","Description":"The neuroQWERTY MIT-CSXPD database contains keystroke logs collected from 85 subjects with and without parkinsons disease (PD). This dataset has been collected and analyzed in order to indicate that the routine interaction with computer keyboards can be used to detect motor signs in the early stages of PD.","Documentation":"https://doi.org/10.13026/C2859Q","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2859Q","ARN":"arn:aws:s3:::physionet-open/nqmitcsxpd/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"nqmitcsxpd","Sources":["awslabs-open-data-registry"]}
{"Name":"End-Use Load Profiles for the U.S. Building Stock","Description":"The U.S. Department of Energy (DOE) funded a three-year project, End-Use Load Profiles for the U.S. Building Stock, that culminated in this publicly\navailable dataset of calibrated and validated 15-minute resolution load profiles for all major residential and commercial building types and end uses,\nacross all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models\nusing many different measured datasets, as described [here](https://docs.nlr.gov/docs/fy22osti/80889.pdf).\nThis dataset includes load profiles for both the baseline building stock and the building stock with various energy efficiency, electrification,\nand demand flexibility upgrades applied.\n","Documentation":"https://www.nlr.gov/buildings/end-use-load-profiles.html","Contact":"ComStock@nlr.gov and ResStock@nlr.gov","ManagedBy":"[National Laboratory of the Rockies](https://www.nlr.gov/)","Citation":"National Laboratory of the Rockies. End-Use Load Profiles for the U.S. Building Stock [Data set]. Open Energy Data Initiative (OEDI). [https://doi.org/10.25984/1876417](https://doi.org/10.25984/1876417). The DOI identifies the repository rather than an individual release, so cite the release path used alongside it.","UpdateFrequency":"Approximately twice per year for each of ResStock and ComStock","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["aws-pds","cities","climate","energy","energy modeling","geospatial","metadata","model","open source software","parquet","sustainability","utilities"],"License":"[ComStock License](https://github.com/NatLabRockies/ComStock/blob/main/LICENSE.txt) and [ResStock License](https://github.com/NatLabRockies/resstock/blob/main/LICENSE.md)","Resources":[{"Description":"[End-Use Load Profiles for the U.S. Building Stock](https://data.openei.org/submissions/4520)","ARN":"arn:aws:s3:::oedi-data-lake/nrel-pds-building-stock/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=nrel-pds-building-stock%2Fend-use-load-profiles-for-us-building-stock%2F)","[Repository README](https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock/end-use-load-profiles-for-us-building-stock/README.md)","[Release catalog, machine-readable: status, successor, upgrades and dictionaries for every release](https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock/end-use-load-profiles-for-us-building-stock/catalog.json)","[Known issues, including those that affect published results](https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock/end-use-load-profiles-for-us-building-stock/KNOWN_ISSUES.md)","[Guide for automated consumers: release selection, key construction and silent-failure cases](https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock/end-use-load-profiles-for-us-building-stock/AGENT_GUIDE.md)"]}],"DataAtWork":{"Tutorials":[{"Title":"BuildStockQuery: a python library designed to simplify and streamline the process of querying datasets generated by ResStock and ComStock","URL":"https://github.com/NatLabRockies/buildstock-query","AuthorName":"National Laboratory of the Rockies (NLR)"},{"Title":"Tutorials, how-to guides, explanations, and reference material for users of the ComStock dataset.","URL":"https://natlabrockies.github.io/ComStock.github.io/docs/resources/resources.html","AuthorName":"National Laboratory of the Rockies (NLR)"},{"Title":"Tutorials, how-to guides, explanations, and reference material for users of the ResStock dataset.","URL":"https://natlabrockies.github.io/ResStock.github.io/docs/resources/resources.html","AuthorName":"National Laboratory of the Rockies (NLR)"}],"Tools & Applications":[{"Title":"Web-Based Data Viewer for Commercial Load Profiles","URL":"https://comstock.nlr.gov/page/datasets","AuthorName":"National Laboratory of the Rockies (NLR)"},{"Title":"Web-Based Data Viewer for Residential Load Profiles","URL":"https://resstock.nlr.gov/datasets","AuthorName":"National Laboratory of the Rockies (NLR)"}],"Publications":[{"Title":"End-Use Load Profiles for the U.S. Building Stock","URL":"https://docs.nlr.gov/docs/fy22osti/80889.pdf","AuthorName":"E. Wilson, A. Parker, A. Fontanini, et al."}]},"RegistryEntryAdded":"2021-07-06","RegistryEntryLastModified":"2026-11-04","Slug":"nrel-pds-building-stock","Sources":["awslabs-open-data-registry"]}
{"Name":"Demand-Side Grid (dsgrid) Toolkit","Description":"Projects that use the dsgrid toolkit assemble bottom-up descriptions of electricity \ndemand and related data that are highly resolved geographically, temporally, and sectorally. \nTypically modelers describe multiple scenarios of future energy use at hourly resolution, \nsuitable for inclusion in long-term power system planning models, i.e., capacity expansion and \nproduction cost models. \n","Documentation":"https://www.nrel.gov/analysis/dsgrid.html","Contact":"elaine.hale@nrel.gov","ManagedBy":"[National Laboratory of the Rockies](https://www.nrel.gov/)","UpdateFrequency":"Annually","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["data assimilation","electricity","energy","energy modeling","industrial","meteorological","solar","transportation"],"License":"Creative Commons Attribution 4.0 United States License","Resources":[{"Description":"Demand-side grid (dsgrid) Toolkit Datasets","ARN":"arn:aws:s3:::nrel-pds-dsgrid/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-dsgrid%2F)"]},{"Description":"[Transportation Energy & Mobility Pathway Options (TEMPO) Model Datasets](https://data.openei.org/submissions/5958)","ARN":"arn:aws:s3:::nrel-pds-dsgrid/tempo/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-dsgrid&prefix=tempo%2F)"]},{"Description":"[Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (EFS)](https://data.openei.org/submissions/4130)","ARN":"arn:aws:s3:::oedi-data-lake/dsgrid-2018-efs/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=dsgrid-2018-efs%2F)"]},{"Description":"[Demand-Side Grid Model (dsgrid) Building Load Profiles](https://data.openei.org/submissions/8446)","ARN":"arn:aws:s3:::nrel-pds-dsgrid/building/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-dsgrid&prefix=building%2F)"]}],"DataAtWork":{"Tutorials":[{"Title":"dsgrid Documentation","URL":"https://dsgrid.github.io/dsgrid/","AuthorName":"Elaine Hale"},{"Title":"dsgrid Project Standard Scenarios for the TEMPO Project","URL":"https://github.com/dsgrid/dsgrid-project-StandardScenarios/tree/main/tempo_project","AuthorName":"Elaine Hale"},{"Title":"Python API for Accessing dsgrid Data for the Electrification Futures Study (EFS)","URL":"https://github.com/dsgrid/dsgrid-legacy-efs-api","AuthorName":"Elaine Hale"}],"Tools & Applications":[{"Title":"Demand-Side Grid Toolkit","URL":"https://www.nrel.gov/analysis/dsgrid.html","AuthorName":"Elaine Hale"},{"Title":"GitHub Repository for Working with the dsgrid Projects","URL":"https://github.com/dsgrid/dsgrid","AuthorName":"Elaine Hale"}],"Publications":[{"Title":"Highly Resolved Projections of Passenger Electric Vehicle Charging Loads for the Contiguous United States","URL":"https://www.nrel.gov/docs/fy23osti/83916.pdf","AuthorName":"Arthur Yip, Christopher Hoehne, Paige Jadun, Catherine Ledna, Elaine Hale, and Matteo Muratori"},{"Title":"The Demand-side Grid (dsgrid) Model Documentation","URL":"https://www.nrel.gov/docs/fy18osti/71492.pdf","AuthorName":"Elaine Hale, Henry Horsey, Brandon Johnson, et al."}]},"RegistryEntryAdded":"2024-06-07","RegistryEntryLastModified":"2025-12-08","Slug":"nrel-pds-dsgrid","Sources":["awslabs-open-data-registry"]}
{"Name":"National Climate Database (NCDB)","Description":"The National Climate Database (NCDB) seeks to be the definitive source of climate \ndata for energy applications. The goal of the NCDB is to provide unbiased high \ntemporal and spatial resolution climate data needed for renewable energy modeling. \nThe NCDB seeks to maintain the inherent relationship between the various parameters \nthat are needed to model solar, wind, hydrology and load and provide data for multiple \nimportant climate scenarios.  \n","Documentation":"https://nsrdb.nrel.gov/","Contact":"Manajit.Sengupta@nrel.gov","ManagedBy":"[National Laboratory of the Rockies](https://www.nrel.gov/)","UpdateFrequency":"As needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["aws-pds","climate projections","CMIP5","CMIP6","earth observation","energy","geospatial","meteorological","solar"],"License":"Creative Commons Attribution 3.0 United States License","Resources":[{"Description":"[National Climate Database (NCDB)](https://data.openei.org/submissions/6163)","ARN":"arn:aws:s3:::nrel-pds-ncdb/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-ncdb)"]},{"Description":"NCDB CONUS 4km Hourly CONUS (2006-2100) in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-ncdb/4km-Hourly-CONUS/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-ncdb&prefix=v3%2F4km-Hourly-CONUS%2F)"]},{"Description":"[HSDS](https://github.com/HDFGroup/hsds) NCDB Domains","ARN":"arn:aws:s3:::nrel-pds-hsds/nrel/ncdb/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fncdb%2F)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"NCDB HSDS Examples","URL":"https://github.com/NREL/hsds-examples/blob/master/notebooks/10_NCDB_introduction.ipynb","AuthorName":"Reid Olson"},{"Title":"NCDB Website","URL":"https://climate.nrel.gov","AuthorName":"NREL NCDB Team"}],"Publications":[{"Title":"Regridding uncertainty for statistical downscaling of solar radiation","URL":"https://ascmo.copernicus.org/articles/9/103/2023/","AuthorName":"Maggie D. Bailey, Douglas Nychka, Manajit Sengupta, Aron Habte, Yu Xie, Soutir Bandyopadhyay"}]},"RegistryEntryAdded":"2024-09-04","RegistryEntryLastModified":"2025-12-08","Slug":"nrel-pds-ncdb","Sources":["awslabs-open-data-registry"]}
{"Name":"NREL National Solar Radiation Database","Description":"Released to the public as part of the Department of Energy's Open Energy Data Initiative,\nthe [National Solar Radiation Database (NSRDB)](https://nsrdb.nrel.gov/) is\na serially complete collection of hourly and half-hourly values of the three\nmost common measurements of solar radiation – global horizontal, direct\nnormal, and diffuse horizontal irradiance — and meteorological data. These\ndata have been collected at a sufficient number of locations and temporal and\nspatial scales to accurately represent regional solar radiation climates.\n","Documentation":"https://nsrdb.nrel.gov/","Contact":"nsrdb@nrel.gov","ManagedBy":"[National Laboratory of the Rockies](https://www.nrel.gov/)","UpdateFrequency":"Annually","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["aws-pds","earth observation","energy","geospatial","meteorological","solar"],"License":"Creative Commons Attribution 3.0 United States License","Resources":[{"Description":"[NREL Solar Radiation Datasets](https://nsrdb.nrel.gov/)","ARN":"arn:aws:s3:::nrel-pds-nsrdb/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb)"]},{"Description":"[NSRDB](https://nsrdb.nrel.gov/) v3 4km x 30min data (1998-2018) in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-nsrdb/v3/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2F)"]},{"Description":"[NSRDB](https://nsrdb.nrel.gov/) v3 typical meteorological years (TMY) in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-nsrdb/v3/tmy/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Ftmy%2F)"]},{"Description":"[NSRDB](https://nsrdb.nrel.gov/) v3 typical direct years (TDY) in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-nsrdb/v3/tdy/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Ftdy%2F)"]},{"Description":"[NSRDB](https://nsrdb.nrel.gov/) v3 typical global years (TGY) in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-nsrdb/v3/tgy/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Ftgy%2F)"]},{"Description":"[NSRDB](https://nsrdb.nrel.gov/) synthetically downscaled 2km x 5min for Puerto Rico (1998-2017) in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-nsrdb/v3/puerto_rico/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Fpuerto_rico%2F)"]},{"Description":"[NSRDB](https://nsrdb.nrel.gov/) 2km-5min data for the Contiguous United States (CONUS) starting with 2018 in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-nsrdb/conus/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse 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[HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-nsrdb/himawari/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=himawari%2F)"]},{"Description":"[NSRDB](https://nsrdb.nrel.gov/) Suny-India data (2000-2014) in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-nsrdb/india/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=india%2F)"]},{"Description":"Meteorological Statistical Model 1 (MTS1) data (1961-1990) in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-nsrdb/mts1","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=mts1%2F)"]},{"Description":"Meteorological Statistical Model 2 (MTS2) data (1991-2005) in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-nsrdb/mts2","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=mts2%2F)"]},{"Description":"[HSDS](https://github.com/NREL/hsds-examples) NSRDB domains","ARN":"arn:aws:s3:::nrel-pds-hsds/nrel/nsrdb/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fnsrdb%2F)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"HSDS Examples","URL":"https://github.com/NREL/hsds-examples","AuthorName":"Caleb Phillips, Caroline Draxl, John Readey, Jordan Perr-Sauer, Michael Rossol"},{"Title":"NSRDB Viewer","URL":"https://nsrdb.nrel.gov/nsrdb-viewer","AuthorName":"Manajit Sengupta, Yu Xe, Anthony Lopez, Aron Habte, Galen Maclaurin, James Shelby, Paul Edwards"}],"Publications":[{"Title":"Physics-guided machine learning for improved accuracy of the National Solar Radiation Database","URL":"https://www.sciencedirect.com/science/article/abs/pii/S0038092X22000044","AuthorName":"Grant Buster, Mike Bannister, Aron Habte, Dylan Hettinger, Galen Maclaurin, Michael Rossol, Manajit Sengupta, Yu Xie"},{"Title":"The National Solar Radiation Data Base (NSRDB)","URL":"https://www.sciencedirect.com/science/article/pii/S136403211830087X","AuthorName":"Manajit Sengupta, Yu Xe, Anthony Lopez, Aron Habte, Galen Maclaurin, James Shelby"}]},"RegistryEntryAdded":"2019-04-09","RegistryEntryLastModified":"2025-12-08","Slug":"nrel-pds-nsrdb","Sources":["awslabs-open-data-registry"]}
{"Name":"PoroTomo","Description":"Released to the public as part of the Department of Energy's Open Energy Data\nInitiative, these data represent vertical and horizontal distributed acoustic\nsensing (DAS) data collected as part of the Poroelastic Tomography (PoroTomo)\nproject funded in part by the Office of Energy Efficiency and Renewable\nEnergy (EERE), U.S. Department of Energy.\n","Documentation":"https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md","Contact":"Thomas Coleman (thomas.coleman@silixa.com)","ManagedBy":"[National Laboratory of the Rockies](https://www.nrel.gov/)","UpdateFrequency":"As needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["aws-pds","geospatial","geothermal","image processing","seismology"],"License":"Creative Commons Attribution 3.0 United States License","Resources":[{"Description":"PoroTomo Datasets","ARN":"arn:aws:s3:::nrel-pds-porotomo/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse 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Interpretations","URL":"https://pangea.stanford.edu/ERE/pdf/IGAstandard/SGW/2018/Miller.pdf","AuthorName":"Douglas E. Miller, Thomas Coleman, Xiangfang Zeng, Jeremy R. Patterson , Elena C. Reinnisch, Michael A. Cardiff, Herbert F. Wang, Dante Fratta, Whitney Trainor-Guitton, Clifford H. Thurber, Michelle ROBERTSON, Kurt FEIGL, and The PoroTomo Team"},{"Title":"Ground motion response to an ML 4.3 earthquake using co-located distributed acoustic sensing and seismometer arrays","URL":"https://doi.org/10.1093/gji/ggy182","AuthorName":"Herbert F Wang, Xiangfang Zeng, Douglas E Miller, Dante Fratta, Kurt L Feigl, Clifford H Thurber, Robert J Mellors"},{"Title":"PoroTomo Final Technical Report: Poroelastic Tomography by Adjoint Inverse Modeling of Data from Seismology, Geodesy, and Hydrology","URL":"https://doi.org/10.2172/1499141","AuthorName":"Kurt L. Feigl, Lesley M. Parker, and the PoroTomo Team"}]},"RegistryEntryAdded":"2020-07-29","RegistryEntryLastModified":"2025-12-08","Slug":"nrel-pds-porotomo","Sources":["awslabs-open-data-registry"]}
{"Name":"Sup3rCC","Description":"Released to the public as part of the Department of Energy's Open Energy Data Initiative, \nthese data represent a serially complete collection of hourly 4km wind, solar, temperature, \nhumidity, and pressure fields for the Continental United States under climate change scenarios.\n\nSup3rCC is downscaled Global Climate Model (GCM) data. For example, the initial file set tagged\n\"sup3rcc_conus_mriesm20_ssp585_r1i1p1f1\" is downscaled from MRI ESM 2.0 for climate change \nscenario SSP5 8.5 and variant label r1i1p1f1. The downscaling process is performed using a \ngenerative machine learning approach called sup3r: Super-Resolution for Renewable Energy \nResource Data. The data includes both historical and future weather years, although the \nhistorical years represent the historical average climate, not true historical weather. \n\nThe Sup3rCC data is intended to help researchers study the impact of climate change on energy \nsystems with high levels of wind and solar power generation. Please note that all climate change \ndata is only a representation of the *possible* future climate and contains significant \nuncertainty. Analysis of multiple climate change scenarios and multiple climate models can help \nquantify this uncertainty.\n","Documentation":"https://github.com/NREL/sup3r","Contact":"Grant Buster (grant.buster@nrel.gov)","ManagedBy":"[National Laboratory of the Rockies](https://www.nrel.gov/)","UpdateFrequency":"Annual","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["air temperature","aws-pds","climate model","energy","solar"],"License":"Creative Commons Attribution 4.0 United States License","Resources":[{"Description":"Sup3rCC","ARN":"arn:aws:s3:::nrel-pds-sup3rcc/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc)"]},{"Description":"Sup3rCC Generative Models","ARN":"arn:aws:s3:::nrel-pds-sup3rcc/models/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=models%2F)"]},{"Description":"Sup3rCC - CONUS - EC-Earth3 - SSP585 - r1i1p1f1","ARN":"arn:aws:s3:::nrel-pds-sup3rcc/conus_ecearth3_ssp585_r1i1p1f1%2F/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_ecearth3_ssp585_r1i1p1f1%2F)"]},{"Description":"Sup3rCC - 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CONUS - TaiESM1 - SSP245 - r1i1p1f1","ARN":"arn:aws:s3:::nrel-pds-sup3rcc/conus_taiesm1_ssp245_r1i1p1f1/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_taiesm1_ssp245_r1i1p1f1%2F)"]}],"DataAtWork":{"Tutorials":[{"Title":"Using the Sup3rCC Data","URL":"https://github.com/NREL/sup3r/blob/main/examples/sup3rcc/using_the_data.ipynb","AuthorName":"Grant Buster","AuthorURL":"https://orcid.org/0000-0001-8616-8100"}]},"RegistryEntryAdded":"2023-05-01","RegistryEntryLastModified":"2025-12-08","Slug":"nrel-pds-sup3rcc","Sources":["awslabs-open-data-registry"]}
{"Name":"Wind AI Bench","Description":"This data lake contains multiple datasets related to fundamental problems \nin wind energy research. This includes data for wind plant power production \nfor various layouts/wind flow scenarios, data for two- and three-dimensional \nflow around different wind turbine airfoils/blades, wind turbine noise \nproduction, among others. The purpose of these datasets is to establish a \nstandard benchmark against which new AI/ML methods can be tested, compared, \nand deployed. Details regarding the generation and formatting of the data for \neach dataset is included in the metadata as well as example notebooks and \ndocumentation that show how to access the data for ML modeling.\n","Documentation":"https://github.com/NREL/windAI_bench","Contact":"Ryan King (ryan.king@nrel.gov)","ManagedBy":"[National Laboratory of the Rockies](https://www.nrel.gov/)","UpdateFrequency":"Annually","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["aws-pds","benchmark","energy","machine learning"],"License":"Creative Commons Attribution 4.0 United States License","Resources":[{"Description":"[Wind AI Bench Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets] (https://data.openei.org/submissions/5884)","ARN":"arn:aws:s3:::nrel-pds-windai/wind_plant_power/floris/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=wind_plant_power%2Ffloris%2F)"]},{"Description":"[Wind AI Bench Airfoil Computational Fluid Dynamics (CFD) - 2k Shapes Data Sets](https://data.openei.org/submissions/5970)","ARN":"arn:aws:s3:::nrel-pds-windai/aerodynamic_shapes/2D/2K_airfoils/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F2k_airfoils%2F)"]},{"Description":"[Wind AI Bench Airfoil Computational Fluid Dynamics (CFD) - 9k Shapes Data Sets](https://data.openei.org/submissions/5889)","ARN":"arn:aws:s3:::nrel-pds-windai/aerodynamic_shapes/2D/9K_airfoils/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F9k_airfoils%2F)"]}],"DataAtWork":{"Tutorials":[{"Title":"Airfoil 2k","URL":"https://github.com/NREL/windAI_bench/tree/main/airfoil_2k","AuthorName":"Dakota Ramos, Andrew Glaws"},{"Title":"Airfoil 9k","URL":"https://github.com/NREL/windAI_bench/tree/main/airfoil_9k","AuthorName":"Dakota Ramos, Andrew Glaws"},{"Title":"Wind AI Bench FLORIS PlayGen","URL":"https://github.com/NREL/windAI_bench/tree/main/FLORIS_PLayGen","AuthorName":"Dakota Ramos, Andrew Glaws"}]},"RegistryEntryAdded":"2023-11-28","RegistryEntryLastModified":"2025-12-08","Slug":"nrel-pds-windai","Sources":["awslabs-open-data-registry"]}
{"Name":"NREL Wind Integration National Dataset","Description":"Released to the public as part of the Department of Energy's Open Energy Data Initiative,\nthe [Wind Integration National Dataset (WIND)](https://www.nrel.gov/grid/wind-toolkit.html)\nis an update and expansion of the Eastern Wind Integration Data Set and\nWestern Wind Integration Data Set. It supports the next generation of wind\nintegration studies.\n","Documentation":"https://www.nrel.gov/grid/wind-toolkit.html","Contact":"wind-toolkit@nrel.gov","ManagedBy":"[National Laboratory of the Rockies](https://www.nrel.gov/)","UpdateFrequency":"As Needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["aws-pds","environmental","geospatial","meteorological"],"License":"Creative Commons Attribution 3.0 United States License","Resources":[{"Description":"NREL Wind Resource Datasets","ARN":"arn:aws:s3:::nrel-pds-wtk/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk)"]},{"Description":"Source files for [WIND Toolkit](https://www.nrel.gov/grid/wind-toolkit.html) CONUS (2007-2014) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/conus/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse 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format","ARN":"arn:aws:s3:::nrel-pds-wtk/Offshore_CA/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Offshore_CA%2F)"]},{"Description":"Hawaii Wind Resource data for (2000-2019) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/Hawaii/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Hawaii%2F)"]},{"Description":"NW Pacific Wind Resource data for (2000-2019) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/NW_Pacific/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=NW_Pacific%2F)"]},{"Description":"Mid Atlantic Wind Resource data for (2000-2020) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/Mid_Atlantic/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Mid_Atlantic%2F)"]},{"Description":"Bangladesh wind resource data (2014-2017) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/bangladesh/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=bangladesh%2F)"]},{"Description":"Central Asia wind resource data (2015) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/central_asia/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=central_asia%2F)"]},{"Description":"India wind resource data (2014) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/india/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=india%2F)"]},{"Description":"Kazakhstan wind resource data (2015) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/kazakhstan/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=kazakhstan%2F)"]},{"Description":"Maine wind resource data (2000-2020) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/maine/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=maine%2F)"]},{"Description":"Gulf of Mexico wind resource data (2000-2020) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/gulf_of_mexico/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=gulf_of_mexico%2F)"]},{"Description":"Philippines wind resource data (2017) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/philippines/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=philippines%2F)"]},{"Description":"Vietnam wind resource data (2016-2018) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/vietnam/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=vietnam%2F)"]},{"Description":"[HSDS](https://github.com/NREL/hsds-examples) WIND domains","ARN":"arn:aws:s3:::nrel-pds-hsds/nrel/wtk/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fwtk%2F)"]},{"Description":"Source files for [wtk-us](https://www.nrel.gov/grid/wind-toolkit.html) gridded (2007-2013) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/wtk-us/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=wtk-us%2F)"]},{"Description":"[HSDS](https://github.com/NREL/hsds-examples) gridded WIND Toolkit domain","ARN":"arn:aws:s3:::nrel-pds-hsds/nrel/wtk-us.h5","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fwtk-us.h5%2F)"]},{"Description":"Techno-economic subset of the [WIND Toolkit](https://www.nrel.gov/grid/wind-toolkit.html) by location in [netCDF](https://www.unidata.ucar.edu/software/netcdf) format","ARN":"arn:aws:s3:::nrel-pds-wtk/wtk-techno-economic/pywtk-data/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=wtk-techno-economic%2Fpywtk-data%2F)"]},{"Description":"Data for the [Eastern Wind Integration Study](https://www.nrel.gov/grid/eastern-western-wind-data.html) (2004-2006) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/eastern_wind/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=eastern_wind%2F)"]},{"Description":"Data for the [Western Wind Integration Study](https://www.nrel.gov/grid/eastern-western-wind-data.html) (2004-2006) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/western_wind/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=western_wind%2F)"]},{"Description":"Philippines typical meteorological year data in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/philippines_tmy/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=philippines_tmy%2F)"]},{"Description":"Great Lakes wind resource data (2000-2020) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/Great_Lakes/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Great_Lakes%2F)"]},{"Description":"Puerto Rico wind resource data (2001-2020) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/pr100/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=pr100%2F)"]},{"Description":"Southeast Asia wind resource data (2017-2021) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/seasiawind/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=seasiawind%2F)"]},{"Description":"Southeast Asia wind resource data v2 (2007-2021) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/seasiawind_v2/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=seasiawind_v2%2F)"]},{"Description":"Southeast Asia wind resource data v3 (2007-2021) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/seasiawind_v3/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=seasiawind_v3%2F)"]},{"Description":"Mid Atlantic wind resource data with modeled wakes in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/NOW-WAKES_Mid_Atlantic/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=NOW-WAKES_Mid_Atlantic%2F)"]},{"Description":"California offshore wind resource data (2000-2022) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/now23_california/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=now23_california%2F)"]},{"Description":"South Atlantic offshore wind resource data (2000-2020) in [HDF5](https://portal.hdfgroup.org/display/HDF5) format","ARN":"arn:aws:s3:::nrel-pds-wtk/south_atlantic/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=south_atlantic)"]},{"Description":"Mid Atlantic three-dimensional planetary boundary layer (3D PBL) scheme wind resource data","ARN":"arn:aws:s3:::nrel-pds-wtk/mid_atlantic_3d_pbl/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=mid_atlantic_3d_pbl%2F)"]},{"Description":"Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications","ARN":"arn:aws:s3:::nrel-pds-wtk/bchrrr/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=bchrrr%2F)"]},{"Description":"Bias corrected Indonesia wind resource","ARN":"arn:aws:s3:::nrel-pds-wtk/indonesia/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=indonesia%2F)"]},{"Description":"Long-Term ensemble dataset","ARN":"arn:aws:s3:::nrel-pds-wtk/wtk-led/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=wtk-led%2F)"]},{"Description":"Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis (Sup3rWind)","ARN":"arn:aws:s3:::nrel-pds-wtk/sup3rwind/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=sup3rwind%2F)"]}],"DataAtWork":{"Tutorials":[{"Title":"HSDS Examples","URL":"https://github.com/NREL/hsds-examples","AuthorName":"Caleb Phillips, Caroline Draxl, John Readey, Jordan Perr-Sauer, Michael Rossol","AuthorURL":"https://www.nrel.gov/research/caleb-phillips.html"}],"Tools & Applications":[{"Title":"Wind Prospector","URL":"https://maps.nrel.gov/wind-prospector/","AuthorName":"Paul Edwards"},{"Title":"Wind Visualization","URL":"https://github.com/NREL/hsds-viz","AuthorName":"Jordan Perr-Sauer","AuthorURL":"https://www.nrel.gov/research/jordan-perr-sauer.html"}],"Publications":[{"Title":"A Twenty-Year Analysis of Winds in California for Offshore Wind Energy Production Using WRF v4.1.2","URL":"https://gmd.copernicus.org/preprints/gmd-2021-50/gmd-2021-50.pdf","AuthorName":"Alex Rybchuk, Mike Optis, Julie K. Lundquist, Michael Rossol, Walt Musial"},{"Title":"Overview and Meteorological Validation of the Wind Integration National Dataset Toolkit","URL":"https://www.nrel.gov/docs/fy15osti/61740.pdf","AuthorName":"Caroline Draxl, Bri-Mathias Hodge, Andrew Clifton, Jim McCaa"},{"Title":"Power from wind: Open data on AWS","URL":"https://aws.amazon.com/blogs/big-data/power-from-wind-open-data-on-aws/","AuthorName":"Caleb Phillips, Caroline Draxl, John Readey, Jordan Perr-Sauer","AuthorURL":"https://www.nrel.gov/research/caleb-phillips.html"},{"Title":"The Wind Integration National Dataset (WIND) Toolkit","URL":"https://www.sciencedirect.com/science/article/pii/S0306261915004237?via%3Dihub/","AuthorName":"Caroline Draxl, Andrew Clifton, Bri-Mathias Hodge, Jim McCaa"},{"Title":"Validation of Power Output for the WIND Toolkit","URL":"https://www.nrel.gov/docs/fy14osti/61714.pdf","AuthorName":"J. King, Andrew Clifton, Bri-Mathias Hodge"},{"Title":"WTK-LED: The WIND Toolkit Long-Term Ensemble Dataset","URL":"https://www.osti.gov/servlets/purl/2473210/","AuthorName":"Caroline Draxl, Jiali Wang, Lindsay Sheridan, et al."}]},"RegistryEntryAdded":"2019-04-09","RegistryEntryLastModified":"2025-12-08","Slug":"nrel-pds-wtk","Sources":["awslabs-open-data-registry"]}
{"Name":"Natural Scenes Dataset","Description":"Here, we collected and pre-processed a massive, high-quality 7T fMRI dataset that can be used to advance our understanding of how the brain works. A unique feature of this dataset is the massive amount of data available per individual subject. The data were acquired using ultra-high-field fMRI (7T, whole-brain, 1.8-mm resolution, 1.6-s TR). We measured fMRI responses while each of 8 participants viewed 9,000–10,000 distinct, color natural scenes (22,500–30,000 trials) in 30–40 weekly scan sessions over the course of a year. Additional measures were collected including resting-state data, retinotopy, category localizers, anatomical data (T1, T2, diffusion, venogram, angiogram), physiological data (pulse, respiration), eye-tracking data, and additional behavioral assessments outside the scanner. Because of its unprecedented scale and richness, NSD can be used to explore diverse neuroscientific questions with high power at the level of individual subjects. In particular, the number of images sampled in this dataset is sufficiently large that the dataset may be of high interest for computer vision, machine learning, and other data-driven applications.","Documentation":"To receive access to the NSD Data Manual, please fill out the [NSD Data Access Agreement Form](https://forms.gle/xue2bCdM9LaFNMeb7). The NSD dataset can be browsed via a simple web interface [here](https://natural-scenes-dataset.s3.amazonaws.com/index.html). The NSD dataset is formally described in the following [manuscript](https://doi.org/10.1038/s41593-021-00962-x).","Contact":"info@cvnlab.net","ManagedBy":"[Computational Visual Neuroscience Laboratory](http://cvnlab.net) at the [Center for Magnetic Resonance Research](http://www.cmrr.umn.edu)","UpdateFrequency":"This dataset is now released. A small portion of the data is held out and will be released in the future. Besides that, we do not expect major updates, aside from any corrections or additions that arise (these will be logged on the NSD Data Manual).","Tags":["aws-pds","computer vision","image processing","imaging","life sciences","machine learning","magnetic resonance imaging","neuroimaging","neuroscience","nifti"],"License":"[Terms & Conditions for the NSD dataset](https://cvnlab.slite.com/api/s/note/9dgh5HCqgZYhMoAESZBS86/Terms-and-Conditions)","Resources":[{"Description":"Structural and functional MRI and other data from the Natural Scenes Dataset.","ARN":"arn:aws:s3:::natural-scenes-dataset","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://natural-scenes-dataset.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2021-01-04","RegistryEntryLastModified":"2022-01-10","Slug":"nsd","Sources":["awslabs-open-data-registry"]}
{"Name":"NSF NCAR Curated ECMWF Reanalysis 5 (ERA5)","Description":"NSF NCAR is providing a NetCDF-4 structured version of the 0.25 degree atmospheric ECMWF Reanalysis 5 (ERA5) to the AWS ODSP. ERA5 is produced using high-resolution forecasts (HRES) at 31 kilometer resolution (one fourth the spatial resolution of the operational model) and a 62 kilometer resolution ten member 4D-Var ensemble of data assimilation (EDA) in CY41r2 of ECMWF's Integrated Forecast System (IFS) with 137 hybrid sigma-pressure (model) levels in the vertical, up to a top level of 0.01 hPa. Atmospheric data on these levels are interpolated to 37 pressure levels (the same levels as in ERA-Interim). Surface or single level data are also available, containing 2D parameters such as precipitation, 2 meter temperature, top of atmosphere radiation and vertical integrals over the entire atmosphere. The IFS is coupled to a soil model, the parameters of which are also designated as surface parameters, and an ocean wave model. Generally, the data is available at an hourly frequency and consists of analyses and short (12 hour) forecasts, initialized twice daily from analyses at 06 and 18 UTC. Most analyses parameters are also available from the forecasts. The data are provided in CF-compliant NetCDF4 format files. ERA5 products are used to train ML/AI based weather forecast models and support retrospective climate research use cases, including where to locate solar and wind farms.","Documentation":"https://doi.org/10.5065/BH6N-5N20","Contact":"rdahelp@ucar.edu","ManagedBy":"[NSF National Center for Atmospheric Research](https://ncar.ucar.edu/)","UpdateFrequency":"Monthly, with a 3-4 month lag from realtime","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","climate","data assimilation","forecast","geoscience","geospatial","land","meteorological","model","netcdf","weather"],"License":"https://www.ucar.edu/terms-of-use/data","Citation":"European Centre for Medium-Range Weather Forecasts. 2019, updated monthly. ERA5 Reanalysis (0.25 Degree Latitude-Longitude Grid). Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. https://doi.org/10.5065/BH6N-5N20.","Resources":[{"Description":"ERA5 NetCDF4 Data Files","ARN":"arn:aws:s3:::nsf-ncar-era5","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://nsf-ncar-era5.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for the NSF NCAR ERA5 bucket","ARN":"arn:aws:sns:us-west-2:891377163634:nsf-ncar-era5-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"The ERA5 global reanalysis","URL":"https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803","AuthorName":"Hersbach et al 2020","AuthorURL":"https://orcid.org/0000-0001-5330-7071"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2024-05-14","RegistryEntryLastModified":"2025-07-09","Slug":"nsf-ncar-era5","Sources":["awslabs-open-data-registry"]}
{"Name":"Normal Sinus Rhythm RR Interval Database","Description":"This database includes beat annotation files for 54 long-term ECG recordings of subjects in normal sinus rhythm (30 men, aged 28.5 to 76, and 24 women, aged 58 to 73). The original ECG recordings (not available) were digitized at 128 samples per second, and the beat annotations were obtained by automated analysis with manual review and correction.","Documentation":"https://doi.org/10.13026/C2S881","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2S881","ARN":"arn:aws:s3:::physionet-open/nsr2db/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"nsr2db","Sources":["awslabs-open-data-registry"]}
{"Name":"MIT-BIH Normal Sinus Rhythm Database","Description":"This database includes 18 long-term ECG recordings of subjects referred to the Arrhythmia Laboratory at Boston's Beth Israel Hospital (now the Beth Israel Deaconess Medical Center). Subjects included in this database were found to have had no significant arrhythmias; they include 5 men, aged 26 to 45, and 13 women, aged 20 to 50.","Documentation":"https://doi.org/10.13026/C2NK5R","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2NK5R","ARN":"arn:aws:s3:::physionet-open/nsrdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"nsrdb","Sources":["awslabs-open-data-registry"]}
{"Name":"National Herbarium of NSW","Description":"The National Herbarium of New South Wales is one of the most significant scientific, cultural and historical botanical resources in the Southern hemisphere. The 1.43 million preserved plant specimens have been captured as high-resolution images and the biodiversity metadata associated with each of the images captured in digital form. Botanical specimens date from year 1770 to today, and form voucher collections that document the distribution and diversity of the world's flora through time, particularly that of NSW, Austalia and the Pacific.\n\nThe data is used in biodiversity assessment, systematic botanical research, ecosystem conservation and policy development. The data is used by scientists, students and the public.\n","Documentation":"https://www.rbgsyd.nsw.gov.au/science/national-herbarium-of-new-south-wales","Contact":"Hannah.McPherson@rbgsyd.nsw.gov.au","ManagedBy":"Royal Botanic Gardens and Domain Trust","UpdateFrequency":"Quarterly","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["agriculture","aws-pds","biodiversity","biology","climate","digital preservation","ecosystems","environmental"],"License":"https://creativecommons.org/licenses/by/4.0/legalcode","Resources":[{"Description":"Herbarium Collection Image files","ARN":"arn:aws:s3:::herbariumnsw-pds","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing the National Herbarium of NSW on AWS","URL":"https://www.rbgsyd.nsw.gov.au/science/national-herbarium-of-new-south-wales","AuthorName":"Dr Shelley James","AuthorURL":"https://www.rbgsyd.nsw.gov.au/Science/Our-Science-Staff"}],"Publications":[{"Title":"Atlas of Living Australia","URL":"https://www.ala.org.au/","AuthorName":"CSIRO","AuthorURL":"https://www.ala.org.au/about-ala/"},{"Title":"The Australasian Virtual Herbarium","URL":"https://avh.ala.org.au/occurrences/search?q=collection_uid%3Aco54#tab_mapView","AuthorName":"avh@chah.org.au","AuthorURL":"https://avh.chah.org.au/index.php/about/"}]},"RegistryEntryAdded":"2020-06-09","RegistryEntryLastModified":"2022-01-11","Slug":"nsw-herbarium","Sources":["awslabs-open-data-registry"]}
{"Name":"NUVIEW - Multi-State Geospatial Data","Description":"NUVIEW hosts and manages a unified collection of geospatial datasets from multiple U.S. states and agencies\n(LiDAR, orthophoto imagery, DEM/DSM, and derivative products). Data are organized in a\nsingle S3 bucket with a logical sub-folder hierarchy: `/state_or_agency_product_type/acqusition_project_name/...`. All assets\nare cloud-optimized (COG GeoTIFFs, COPC (Cloud Optimized Point Cloud) LAZ point clouds, etc.) and available under open licenses.\n","Documentation":"Documentation is available for this data at the [s22s/nuview-state-opendata GitHub repository](https://github.com/s22s/nuview-state-opendata) maintained by NUVIEW.","Contact":"support@nuview.space","ManagedBy":"[NUVIEW](https://nuview.space/)","UpdateFrequency":"Project-based updates.","Tags":["aws-pds","dem","disaster response","geospatial","lidar","natural resource","satellite imagery","sustainability"],"License":"CC0 \"Public Domain\" (or state/agency-specific open data licenses)","Resources":[{"Description":"Imagery","ARN":"arn:aws:s3:::nuview-state-opendata","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"New data notifications","ARN":"arn:aws:sns:us-west-2:830737158982:nuview-state-opendata-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get to Know a Dataset - NUVIEW State Open Data","URL":"https://github.com/s22s/nuview-state-opendata/","NotebookURL":"https://github.com/s22s/nuview-state-opendata/blob/main/get-to-know-a-dataset.ipynb","AuthorName":"NUVIEW, Inc.","AuthorURL":"https://nuview.space"}],"Tools & Applications":[{"Title":"NUVIEW Geospatial Platform for Alaska","URL":"https://alaska.nuview.space/","AuthorName":"NUVIEW, Inc.","AuthorURL":"https://nuview.space"}]},"ADXCategories":["Environmental Data","Public Sector Data"],"RegistryEntryAdded":"2025-11-18","RegistryEntryLastModified":"2025-12-11","Slug":"nuview-state","Sources":["awslabs-open-data-registry"]}
{"Name":"NOAA National Water Model CONUS Retrospective Dataset","Description":"The NOAA National Water Model Retrospective dataset contains input and output from multi-decade CONUS retrospective simulations. These simulations used meteorological input fields from meteorological retrospective datasets. The output frequency and fields available in this historical NWM dataset differ from those contained in the real-time operational NWM forecast model. Additionally, note that no streamflow or other data assimilation is performed within any of the NWM retrospective simulations\n<br/>\n<br/>\nOne application of this dataset is to provide historical context to current near real-time streamflow, soil moisture and snowpack conditions. The retrospective data can be used to infer flow frequencies and perform temporal analyses with hourly streamflow output and 3-hourly land surface output. This dataset can also be used in the development of end user applications which require a long baseline of data for system training or verification purposes. <br />\n<br/>\n<br/>\nDetails for Each Version of the NWM Retrospective Output\n<br/>\n<br/>\n**CONUS Domain** - CONUS retrospective output is provided by all four versions of the NWM\n<br/>\n1) Version 3.0 - A 44-year (February 1979 through January 2023) retrospective simulation using version 3.0 of the National Water Model.\n2) Version 2.1 - A 42-year (February 1979 through December 2020) retrospective simulation using version 2.1 of the National Water Model.\n3) Version 2.0 - A 26-year (January 1993 through December 2018) retrospective simulation using version 2.0 of the National Water Model.\n4) Version 1.2 - A 25-year (January 1993 through December 2017) retrospective simulation using version 1.2 of the National Water Model.\n<br/>\n<br/>\n**oCOUNS Domains** - Only v3.0 of the NWM Retrospective data set provides coverage of the NWM Alaska, Hawaii and Puerto Rico / US Virgin Island domains.\n<br/>\n<br/>\n**CONUS Meteorological Forcing**\n1) Versions 3.0 and 2.1:  NWM Retrospective simulations used forcing from the Office of Water Prediction Analysis of Record for Calibration (AORC) dataset.  NWM v2.1 used AORC v1.0 for 1979-2006 and AORC v1.1 for 2007-2020, while NWM v3.0 used AORC v1.1 for the full v3.0  (1979-2023 period)\n**Important Warning** - While the metadata tag in the NWM v3.0 forcing files label the files as “v2.1”, the files are in fact v3.0 forcing files.\n2) Versions 2.0 and 1.2 -  NWM Retrospective simulation uses forcing from the North American Land Data Assimilation System (NLDAS) dataset\n<br/>\n**oCONUS Meteorological Forcing**\n1) Version 3.0 -  AORC Alaska forcing was used to drive the NWM Alaska simulation, while North American Regional Reanalysis (NARR) data along with precipitation from the Alaska Pacific River Forecast Center (APRFC) was used to drive the Hawaii retrospective simulation.  Similarly, the Puerto Rico / US Virgin Island retrospective simulation was driven by NARR data along with precipitation from the Southeast River Forecast Center.\n<br/>\n<br/>\n**Formats** - NWM Retrospective data is available in two formats, NetCDF and Zarr.  The NetCDF files contain the full set of NWM output data, while the Zarr files contain a subset of NWM output fields that vary with model version.\n<br/>\n<br/>\n1) NWM V3.0:  All model output and forcing input fields are available in the NetCDF format.  All model output fields along with the precipitation forcing field are available in the Zarr format.\n2) NWM V2.1:  All model output and forcing input fields are available in the NetCDF format.  Many of the model output fields along with the precipitation forcing field are available in the Zarr format\n3) NWM V2.0:  All model output fields are available in NetCDF format.  Model channel output including streamflow and related fields are available in Zarr format.\n4) NWM V1.2:  All model output fields are available in NetCDF format.\n<br/>\n<br/>\nA table listing the data available within each NetCDF and Zarr file is located in the '[documentation page](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md)'.  This data includes meteorological NWM forcing inputs along with NWM hydrologic and land surface outputs, and varies by version number. \n","Documentation":"https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md","Contact":"For questions regarding data content or quality, email nws.nwc.ops@noaa.gov.\n<br/>\nFor information on the National Water Model (NWM), go \"[here](http://water.noaa.gov/about/nwm)\".\n<br/>\nThis data is made available to the public through the NOAA Open Data Dissemination (NODD) Program. For questions regarding this program, email nodd@noaa.gov.\n<br/>\nWe also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NOAA NODD team at NODD@NOAA.GOV.\n","ManagedBy":"[NOAA](http://www.noaa.gov/)","UpdateFrequency":"No updates","Collabs":{"ASDI":{"Tags":["water"]}},"Tags":["agriculture","agriculture","aws-pds","climate","disaster response","environmental","transportation","weather"],"License":"Open Data. There are no restrictions on the use of this data.","Resources":[{"Description":"The complete archive of NWM input forcing and model output data version 3.0 in NetCDF and Zarr format.","ARN":"arn:aws:s3:::noaa-nwm-retrospective-3-0-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nwm-retrospective-3-0-pds.s3.amazonaws.com/index.html)"]},{"Description":"The complete archive of NWM input forcing and model output data version 2.1 in NetCDF format.","ARN":"arn:aws:s3:::noaa-nwm-retrospective-2-1-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nwm-retrospective-2-1-pds.s3.amazonaws.com/index.html)"]},{"Description":"The complete archive of NWM model output data version 2.1 in Zarr format. The NWM precipitation input forcing in Zarr format.","ARN":"arn:aws:s3:::noaa-nwm-retrospective-2-1-zarr-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nwm-retrospective-2-1-zarr-pds.s3.amazonaws.com/index.html)"]},{"Description":"The complete archive of NWM data version 2.0.","ARN":"arn:aws:s3:::noaa-nwm-retro-v2-0-pds","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nwm-retro-v2-0-pds.s3.amazonaws.com/index.html)"]},{"Description":"The streamflow from the NWM version 2.0 in Zarr format.","ARN":"arn:aws:s3:::noaa-nwm-retro-v2-zarr-pds","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://noaa-nwm-retro-v2-zarr-pds.s3.amazonaws.com/index.html)"]},{"Description":"The complete archive of NWM data version 1.2.","ARN":"arn:aws:s3:::nwm-archive","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://nwm-archive.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Explore Repository of Tutorials on National Water Model V2.1 Retrospective Dataset in Zarr","URL":"https://github.com/NCAR/rechunk_retro_nwm_v21/tree/main/notebooks","AuthorName":"James McCreight","AuthorURL":"https://github.com/jmccreight"},{"Title":"Explore the National Water Model V2.0 Retrospective in Zarr","URL":"https://nbviewer.jupyter.org/gist/rsignell-usgs/d3dfaf3cd3d8b39894a69b22127dfe38","AuthorName":"Rich Signell","AuthorURL":"https://www.usgs.gov/staff-profiles/richard-p-signell"},{"Title":"Explore the National Water Model V2.1 Retrospective Dataset in Zarr","URL":"https://github.com/NCAR/rechunk_retro_nwm_v21","AuthorName":"James McCreight, Ishita Srivastava, Rich Signell","AuthorURL":"https://github.com/jmccreight"},{"Title":"Processing the 250 TB NWM dataset with Coiled, Dask, and Xarray","URL":"https://medium.com/coiled-hq/processing-a-250-tb-dataset-with-coiled-dask-and-xarray-574370ba5bde","AuthorName":"Sarah Johnson (Coiled)","AuthorURL":"https://www.coiled.io/"}],"Publications":[{"Title":"NOAA's National Water Model: Advancing Operational Hydrology Through Continental-scale Modeling.","URL":"https://doi.org/10.1111/1752-1688.13184","AuthorName":"Brian Cosgrove, David Gochis, Trey Flowers, Aubrey Dugger, Fred Ogden, Tom Graziano, Ed Clark, et al; 2024"},{"Title":"On Strictly Enforced Mass Conservation Constraints for Modeling the Rainfall-Runoff Process","URL":"https://doi.org/10.31223/X5BH0P","AuthorName":"Jonathan M. Frame, Frederik Kratzert, Hoshin V. Gupta, Paul Ullrich and Grey S. Nearing"},{"Title":"Simulating storm surge and compound flooding events with a creek-to-ocean model: Importance of baroclinic effects","URL":"https://doi.org/10.1016/j.ocemod.2019.101526","AuthorName":"Fei Ye, et al."}]},"RegistryEntryAdded":"2018-06-05","RegistryEntryLastModified":"2024-03-22","Slug":"nwm-archive","Sources":["awslabs-open-data-registry"]}
{"Name":"New York City Taxi and Limousine Commission (TLC) Trip Record Data","Description":"Data of trips taken by taxis and for-hire vehicles in New York City. Note: access to this dataset is free, however direct S3 access does require an AWS account. Anonymous downloads are accessible from the dataset's documentation webpage listed below.","Documentation":"https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page","Contact":"research@tlc.nyc.gov","ManagedBy":"City of New York Taxi and Limousine Commission","UpdateFrequency":"As soon as new data is available to be shared publicly.","Collabs":{"ASDI":{"Tags":["infrastructure"]}},"Tags":["aws-pds","cities","transportation","urban"],"License":"http://www1.nyc.gov/home/terms-of-use.page","Resources":[{"Description":"PARQUET files containing NYC TLC trip data.","ARN":"arn:aws:s3:::nyc-tlc","Region":"us-east-1","Type":"S3 Bucket","AccountRequired":true}],"DataAtWork":{"Tutorials":[{"Title":"Build a Real-time Stream Processing Pipeline with Apache Flink on AWS","URL":"https://aws.amazon.com/blogs/big-data/build-a-real-time-stream-processing-pipeline-with-apache-flink-on-aws/","AuthorName":"Steffen Hausmann"},{"Title":"Build and run streaming applications with Apache Flink and Amazon Kinesis Data Analytics for Java Applications","URL":"https://aws.amazon.com/blogs/big-data/build-and-run-streaming-applications-with-apache-flink-and-amazon-kinesis-data-analytics-for-java-applications/","AuthorName":"Steffen Hausmann"},{"Title":"Deep Dive on Flink & Spark on Amazon EMR","URL":"https://www.youtube.com/watch?v=MzTZp47Jy7E","AuthorName":"Keith Steward"},{"Title":"Exploring data with Python and Amazon S3 Select","URL":"https://github.com/aws-samples/cloud-experiments/tree/master/experiments/notebooks/exploring-data","AuthorName":"Manav Sehgal"},{"Title":"Machine learning on distributed Dask using Amazon SageMaker and AWS Fargate","URL":"https://aws.amazon.com/blogs/machine-learning/machine-learning-on-distributed-dask-using-amazon-sagemaker-and-aws-fargate/","AuthorName":"Ram Vittal"},{"Title":"Optimizing data for analysis with Amazon Athena and AWS Glue","URL":"https://github.com/aws-samples/cloud-experiments/tree/master/experiments/notebooks/optimizing-data","AuthorName":"Manav Sehgal"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2025-07-09","Slug":"nyc-tlc-trip-records-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"NYU Langone & FAIR FastMRI Dataset","Description":"This dataset contains deidentified raw k-space data and DICOM image files of over 1,500 knees and 6,970 brains.","Contact":"[Florian  Knoll](florian.knoll@nyuangone.org)","Documentation":"https://fastmri.med.nyu.edu/","ManagedBy":"[FastMRI](https://fastmri.med.nyu.edu/)","UpdateFrequency":"The dataset is estimated to grow annually to include MRI raw data and imaging for additional body structures.","Tags":["aws-pds","biology","health","image processing","imaging","life sciences","magnetic resonance imaging","neurobiology","neuroimaging"],"License":"MIT License","Resources":[{"Description":"Raw k-space data and DICOM files for a knee and brain dataset in a public S3 bucket","ARN":"arn:aws:s3:::fastmri-dataset","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://fastmri.med.nyu.edu/"}],"DataAtWork":{"Tutorials":[{"Title":"FastMRI Tutorial (Jupyter Notebook)","URL":"https://github.com/facebookresearch/fastMRI/blob/master/fastMRI_tutorial.ipynb","AuthorName":"Tullie Murrell","AuthorURL":"https://scholar.google.com/citations?user=LjjLn2YAAAAJ&hl=en"},{"Title":"How to Get Started Working with the FastMRI Dataset (and why you should!)","URL":"https://medium.com/@khodges42/how-to-get-started-diy-neurohacking-with-the-fastmri-dataset-and-why-you-should-6a02a6bb896e","AuthorName":"Kevin Hodges"}],"Publications":[{"Title":"Advancing machine learning for MR image reconstruction with an open competition:Overview of the 2019 fastMRI challenge","URL":"https://arxiv.org/abs/2001.02518","AuthorName":"Knoll et al (2020)","AuthorURL":"https://arxiv.org/search/eess?searchtype=author&query=Knoll%2C+F"},{"Title":"Assessment of the generalization of learned image reconstruction and the potential for transfer learning.","URL":"https://www.ncbi.nlm.nih.gov/pubmed/29774597","AuthorName":"Knoll et al (2019)","AuthorURL":"https://arxiv.org/search/eess?searchtype=author&query=Knoll%2C+F"},{"Title":"Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction","URL":"https://arxiv.org/abs/1904.01112","AuthorName":"Knoll et al (2019)","AuthorURL":"https://arxiv.org/search/eess?searchtype=author&query=Knoll%2C+F"},{"Title":"fastMRI:An Open Dataset and Benchmarks for Accelerated MRI","URL":"https://arxiv.org/abs/1811.08839","AuthorName":"Zbontar et al (2019)","AuthorURL":"https://arxiv.org/search/cs?searchtype=author&query=Zbontar%2C+J"}]},"RegistryEntryAdded":"2020-02-18","RegistryEntryLastModified":"2021-10-22","Slug":"nyu-fastmri","Sources":["awslabs-open-data-registry"]}
{"Name":"NYUMets Brain Dataset","Description":"This dataset contains 8,000+ brain MRIs of 2,000+ patients with brain metastases.","Documentation":"https://nyumets.org/","Contact":"[NYUMets Discussion](https://github.com/nyumets/nyumets.github.io/discussions)","ManagedBy":"[NYUMets](https://nyumets.org/)","UpdateFrequency":"This dataset will be updated as new data is added.","Tags":["aws-pds","biology","cancer","computer vision","health","image processing","imaging","life sciences","machine learning","magnetic resonance imaging","medical imaging","medicine","neurobiology","neuroimaging","segmentation"],"License":"[Custom Non-Commercial License](https://signup.nyumets.org/#agreement)","Resources":[{"Description":"NIFTI files for brain MRIs and segmentations, CSV files with metadata","ARN":"arn:aws:s3:us-east-1:230249121367:accesspoint/external-users-read-only-ap","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://nyumets.org/"}],"DataAtWork":{"Publications":[{"Title":"Longitudinal deep neural networks for assessing metastatic brain cancer on a massive open benchmark.","URL":"https://www.researchsquare.com/article/rs-2444113/v1","AuthorName":"Link et al (2023)","AuthorURL":"https://scholar.google.com/citations?user=bIAmWgUAAAAJ&hl=en"}]},"RegistryEntryAdded":"2023-05-31","RegistryEntryLastModified":"2023-05-31","Slug":"nyumets-brain","Sources":["awslabs-open-data-registry"]}
{"Name":"New Zealand Coastal Elevation","Description":"The New Zealand Coastal Elevation dataset consists of New Zealand's publicly owned coastal digital elevation models, which are freely available to use under an open licence. The data consists of bare earth (DEM) data that traverses the coastal zone, including the seabed down to approximately 25m in depth. Data is provided as nationally consistent 1m resolution tiles derived from LiDAR surveys.\n\nAll of the coastal elevation files are [Cloud Optimised GeoTIFFs](https://www.cogeo.org/) using LERC compression for the main grid and LERC compression with lower max_z_error for the overviews. These elevation files are accompanied by [STAC metadata](https://stacspec.org/). The coastal elevation data is organised by region and survey.\n","Documentation":"https://github.com/linz/coastal","Contact":"hydrosurvey@linz.govt.nz","ManagedBy":"[Toitū Te Whenua Land Information New Zealand](https://www.linz.govt.nz)","UpdateFrequency":"New coastal elevation data will regularly be added, as part of being published to the LINZ Data Service and LINZ Basemaps.","Tags":["aws-pds","coastal","cog","earth observation","elevation","geospatial","lidar","stac"],"License":"Toitū Te Whenua Land Information New Zealand is the copyright owner for the coastal elevation data that is available in this bucket. Licensed for re-use under \"[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)\".","Citation":"Licensed by Toitū Te Whenua Land Information New Zealand for re-use under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).","Resources":[{"Description":"New Zealand Coastal Elevation with accompanying metadata","ARN":"arn:aws:s3:::nz-coastal","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Browsing the NZ elevation buckets","URL":"https://github.com/linz/elevation/blob/master/docs/usage.md","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"},{"Title":"Elevation survey naming and path conventions","URL":"https://github.com/linz/elevation/blob/master/docs/naming.md","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"}],"Tools & Applications":[{"Title":"LINZ Basemaps","URL":"https://basemaps.linz.govt.nz","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"},{"Title":"LINZ Data Service","URL":"https://data.linz.govt.nz","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"},{"Title":"LINZ Topographic Workflows - Bulk elevation data processing with Kubernetes and Argo Workflows","URL":"https://github.com/linz/topo-workflows","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"}]},"RegistryEntryAdded":"2025-05-05","RegistryEntryLastModified":"2025-05-20","Slug":"nz-coastal","Sources":["awslabs-open-data-registry"]}
{"Name":"New Zealand Elevation","Description":"The New Zealand Elevation dataset consists of New Zealand's publicly owned digital elevation models and digital surface models, which are freely available to use under an open licence. The dataset contains 1m resolution grids derived from LiDAR data. Point clouds are not included in the initial release.\n\nAll of the elevation files are [Cloud Optimised GeoTIFFs](https://www.cogeo.org/) using LERC compression for the main grid and LERC compression with lower max_z_error for the overviews. These elevation files are accompanied by [STAC metadata](https://stacspec.org/). The elevation data is organised by region and survey.\n","Documentation":"https://github.com/linz/elevation","Contact":"elevation@linz.govt.nz","ManagedBy":"[Toitū Te Whenua Land Information New Zealand](https://www.linz.govt.nz)","UpdateFrequency":"New elevation data will regularly be added, as part of being published to the LINZ Data Service and LINZ Basemaps.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","cog","earth observation","elevation","geospatial","stac"],"License":"Toitū Te Whenua Land Information New Zealand does not own the copyright for all of the elevation data that is available in this bucket. Licensors are contained in the STAC Collection file accompanying each elevation data survey. Licensed for re-use under \"[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)\".","Citation":"Licensed by (insert the licensor) for re-use under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).","Resources":[{"Description":"New Zealand Elevation with accompanying metadata","ARN":"arn:aws:s3:::nz-elevation","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Browsing the s3://nz-elevation bucket","URL":"https://github.com/linz/elevation/blob/master/docs/usage.md","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"},{"Title":"Elevation survey naming and path conventions","URL":"https://github.com/linz/elevation/blob/master/docs/naming.md","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"}],"Tools & Applications":[{"Title":"LINZ Basemaps","URL":"https://basemaps.linz.govt.nz","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"},{"Title":"LINZ Data Service","URL":"https://data.linz.govt.nz","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"},{"Title":"LINZ Topographic Workflows - Bulk elevation data processing with Kubernetes and Argo Workflows","URL":"https://github.com/linz/topo-workflows","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"},{"Title":"NZ Elevation Tools - Contour, Slope, Viewshed and Terrain Analysis from NZ 1m LiDAR","URL":"https://nzelevationtools.com/","AuthorName":"MapScaping","AuthorURL":"https://mapscaping.com"}],"Publications":[{"Title":"Dairy farming exposure and impacts from coastal flooding and sea level rise in Aotearoa-New Zealand","URL":"https://doi.org/10.1016/j.ijdrr.2023.104079","AuthorName":"Heather Craig, Alec Wild and Ryan Paulik"},{"Title":"Stress Detection in New Zealand Kauri Canopies with WorldView-2 Satellite and LiDAR Data","URL":"https://doi.org/10.3390/rs12121906","AuthorName":"Jane J. Meiforth, Henning Buddenbaum, Joachim Hill, James D. Shepherd and John R. Dymond"},{"Title":"Underpinning Terroir with Data: Integrating Vineyard Performance Metrics with Soil and Climate Data to Better Understand Within-Region Variation in Marlborough, New Zealand","URL":"https://doi.org/10.1155/2023/8811402","AuthorName":"R. G. V. Bramley, J. Ouzman, A. P. Sturman, G. J. Grealish, C. E. M. Ratcliff and M. C. T. Trought"}]},"RegistryEntryAdded":"2024-02-27","RegistryEntryLastModified":"2026-11-09","Slug":"nz-elevation","Sources":["awslabs-open-data-registry"]}
{"Name":"New Zealand Imagery","Description":"The New Zealand Imagery dataset consists of New Zealand's publicly owned aerial and satellite imagery, which is freely available to use under an open licence. The dataset ranges from the latest high-resolution aerial imagery down to 5cm in some urban areas to lower resolution satellite imagery that provides full coverage of mainland New Zealand, Chathams and other offshore islands. It also includes historical imagery that has been scanned from film, orthorectified (removing distortions) and georeferenced (correctly positioned) to create a unique and crucial record of changes to the New Zealand landscape. <br/>\nAll of the imagery files are [Cloud Optimised GeoTIFFs](https://www.cogeo.org/) using lossless WEBP compression for the main image and lossy WEBP compression for the overviews. These image files are accompanied by [STAC metadata](https://stacspec.org/). The imagery is organised by region and survey.\n","Documentation":"https://github.com/linz/imagery","Contact":"imagery@linz.govt.nz","ManagedBy":"[Toitū Te Whenua Land Information New Zealand](https://www.linz.govt.nz)","UpdateFrequency":"New imagery will regularly be added, as part of being published to the LINZ Data Service and LINZ Basemaps.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aerial imagery","aws-pds","cog","earth observation","geospatial","satellite imagery","stac"],"License":"Toitū Te Whenua Land Information New Zealand does not own the copyright for all of the imagery that is available in this bucket. Licensors are contained in the STAC Collection file accompanying each imagery survey. Licensed for re-use under \"[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)\".","Citation":"Licensed by (insert the licensor) for re-use under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).","Resources":[{"Description":"New Zealand Imagery with accompanying metadata","ARN":"arn:aws:s3:::nz-imagery","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"ArcGIS Workflows: NZ Imagery from a public AWS S3 bucket","URL":"https://storymaps.arcgis.com/collections/e6d212054d9744f399fcbed00a75ee43","AuthorName":"Eagle Technology","AuthorURL":"https://www.eagle.co.nz/"},{"Title":"Browsing the s3://nz-imagery bucket","URL":"https://github.com/linz/imagery/blob/master/docs/usage.md","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"},{"Title":"Imagery survey naming and path conventions","URL":"https://github.com/linz/imagery/blob/master/docs/naming.md","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"}],"Tools & Applications":[{"Title":"LINZ Basemaps","URL":"https://basemaps.linz.govt.nz","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"},{"Title":"LINZ Data Service","URL":"https://data.linz.govt.nz","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"},{"Title":"LINZ Topographic Workflows - Bulk imagery processing with Kubernetes and Argo Workflows","URL":"https://github.com/linz/topo-workflows","AuthorName":"Toitū Te Whenua Land Information New Zealand","AuthorURL":"https://www.linz.govt.nz"}],"Publications":[{"Title":"A Boundary Regulated Network for Accurate Roof Segmentation and Outline Extraction","URL":"https://doi.org/10.3390/rs10081195","AuthorName":"Guangming Wu, Zhiling Guo, Xiaodan Shi, Qi Chen, Yongwei Xu, Ryosuke Shibasaki and Xiaowei Shao"},{"Title":"A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning","URL":"https://doi.org/10.1007/978-3-319-46487-9_48","AuthorName":"T. Nathan Mundhenk, Goran Konjevod, Wesam A. Sakla & Kofi Boakye"},{"Title":"Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response","URL":"https://doi.org/10.3390/rs13091789","AuthorName":"Grant D. Pearse, Michael S. Watt, Julia Soewarto and Alan Y. S. Tan"},{"Title":"Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network","URL":"https://doi.org/10.3390/rs11151774","AuthorName":"Yaning Yi, Zhijie Zhang, Wanchang Zhang, Chuanrong Zhang, Weidong Li and Tian Zhao"}]},"RegistryEntryAdded":"2023-09-08","RegistryEntryLastModified":"2025-07-09","Slug":"nz-imagery","Sources":["awslabs-open-data-registry"]}
{"Name":"OB-1 Fetal ECG Database","Description":"This project is developing a set of recordings of fetal scalp electrograms and uterine muscular activity, with beat-by-beat annotations of the fetal ECG, to support studies of fetal heart rate variability.","Documentation":"https://doi.org/10.13026/C2D30V","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2D30V","ARN":"arn:aws:s3:::physionet-open/ob1db/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ob1db","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Biodiversity Information System (OBIS) species distribution models","Description":"This dataset provides access to species distribution models (SDMs) for marine species, initially developed as part of the MPA Europe project (https://mpaeurope.eu/). SDMs are statistical models used to predict the potential geographic distribution of a species based on the relationship between observed species occurrence records and environmental predictors. The models were built using marine occurrence data from the Ocean Biodiversity Information System (OBIS) and the Global Biodiversity Information Facility (GBIF), together with environmental data from Bio-ORACLE v3.0 (Assis et al. 2024, DOI:10.1111/geb.13813).","Documentation":"Documentation for this dataset is available at https://github.com/iobis/mpaeu_sdm and https://iobis.github.io/mpaeu_docs. An application to explore the species distribution models is available at https://shiny.obis.org/distmaps/. SDMs have uncertainties and limitations, and users are advised to review the documentation before using the data.","Contact":"helpdesk@obis.org","ManagedBy":"The Ocean Biodiversity Information System (OBIS)","UpdateFrequency":"Annually","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["aws-pds","biodiversity","climate","coastal","conservation","ecosystems","environmental","geospatial","life sciences","oceans","water"],"License":"https://creativecommons.org/licenses/by-nc/4.0/","Citation":"Ocean Biodiversity information System (OBIS). (2024). Species distribution dashboard for MPA Europe. (version 0.1.0). https://shiny.obis.org/distmaps. Zenodo. https://doi.org/10.5281/zenodo.14524781.","Resources":[{"Description":"Marine species distribution models, with predictions for the current and future period (2050 and 2100) across five Shared Socioeconomic Pathways Scenarios (SSP) stored as cloud-optimized GeoTIFFs (COG).","ARN":"arn:aws:s3:::obis-maps","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse bucket](https://obis-maps.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new Marine species distribution models, with predictions for the current and future period (2050 and 2100) across five Shared Socioeconomic Pathways Scenarios (SSP) stored as cloud-optimized GeoTIFFs (COG) data","ARN":"arn:aws:sns:us-east-1:686255951379:map_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing OBIS species distribution models on AWS","URL":"https://github.com/iobis/mpaeu_sdm/blob/main/notebooks/access_sdm_aws.ipynb","AuthorName":"Silas Principe","AuthorURL":"https://github.com/silasprincipe"}]},"RegistryEntryAdded":"2025-09-19","RegistryEntryLastModified":"2026-07-16","Slug":"obis-maps","Sources":["awslabs-open-data-registry"]}
{"Name":"Ocean Biodiversity Information System (OBIS) species occurrence data","Description":"The Ocean Biodiversity Information System (OBIS) was founded in 2000 under the Census of Marine Life. It is now a programme component of the International Oceanographic Data and Information Exchange (IODE) programme of the Intergovernmental Oceanographic Commission (IOC) of UNESCO. OBIS aims to be the most comprehensive data and information gateway on the diversity, distribution and abundance of marine life to support its Member States in achieving a healthy and resilient ocean ecosystem. The OBIS network consists of over 30 regional and thematic nodes, and provides access to more than 5,000 datasets published by hundreds of organizations such as research institutions, government agencies, and citizen science platforms.","Documentation":"Documentation for this dataset is available at https://github.com/iobis/obis-open-data. More information about the OBIS data standards can be found in the OBIS manual at https://manual.obis.org.","Contact":"helpdesk@obis.org","ManagedBy":"The Ocean Biodiversity Information System (OBIS)","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["biodiversity","coastal","conservation","ecosystems","environmental","geospatial","life sciences","oceans","water"],"License":"https://creativecommons.org/licenses/by-nc/4.0/","Citation":"Ocean Biodiversity Information System (OBIS) OBIS Occurrence Data https://doi.org/10.25607/obis.occurrence.b89117cd.","Resources":[{"Description":"Ocean Biodiversity Information System (OBIS) species occurrence data as GeoParquet files.","ARN":"arn:aws:s3:::obis-open-data","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse bucket](https://obis-open-data.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Querying OBIS occurrence data using Amazon Athena","URL":"https://github.com/iobis/obis-open-data/blob/master/README.md#example-querying-the-dataset-using-amazon-athena","AuthorName":"Pieter Provoost","AuthorURL":"https://github.com/pieterprovoost","Services":["Amazon Athena"]}]},"RegistryEntryAdded":"2025-04-18","RegistryEntryLastModified":"2026-09-09","Slug":"obis","Sources":["awslabs-open-data-registry"]}
{"Name":"OceanOmics","Description":"Minderoo Foundation OceanOmics aims to establish environmental DNA (eDNA) as a tool to measure, understand, and protect oceans. OceanOmics mainly generates two types of data: eDNA sequencing data (metabarcoding, metagenomics), and genome assembly data (marine vertebrates).","Documentation":"https://edna.minderoo.org","Contact":"oceanomics@minderoo.org","ManagedBy":"Minderoo Foundation OceanOmics, Dr Shannon Corrigan, Dr Philipp Bayer","UpdateFrequency":"Data will be continually updated as it is generated.","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","biodiversity","bioinformatics","biology","conservation","genetic","genomic","life sciences","metagenomics"],"License":"CC-BY 4.0 https://creativecommons.org/licenses/by/4.0/deed.en","Resources":[{"Description":"https://github.com/Minderoo-OceanOmics-Centre-UWA/OceanOmics-OpenData/blob/master/aws-public-data.md","ARN":"arn:aws:s3:::minderoo-oceanomics","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"AuthorName":"Philipp Bayer","Title":"Case-studies on using OceanOmics genomes and eDNA data","URL":"https://github.com/Minderoo-OceanOmics-Centre-UWA/OceanOmics-OpenData/blob/master/tutorial.md"}]},"RegistryEntryAdded":"2025-01-30","RegistryEntryLastModified":"2026-07-09","Slug":"oceanomics","Sources":["awslabs-open-data-registry"]}
{"Name":"Ohio State Cardiac MRI Raw Data (OCMR)","Description":"OCMR is an open-access repository that provides multi-coil k-space data for cardiac cine.  The fully sampled MRI datasets are intended for quantitative comparison and evaluation of image reconstruction methods. The free-breathing, prospectively undersampled datasets are intended to evaluate their performance and generalizability qualitatively.","Documentation":"https://github.com/MRIOSU/OCMR","Contact":"https://github.com/MRIOSU/OCMR/issues","ManagedBy":"[OCMR](https://www.ocmr.info/)","UpdateFrequency":"Annually","Tags":["aws-pds","Homo sapiens","image processing","imaging","life sciences","magnetic resonance imaging","signal processing"],"License":"Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/)","Resources":[{"Description":"Raw k-space data for cardiac MRI dataset in a public S3 bucket","ARN":"arn:aws:s3:::ocmr","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"OCMR Tutorial","URL":"https://github.com/MRIOSU/OCMR/blob/master/Python/example_ocmr.ipynb","AuthorName":"Chong Chen","AuthorURL":"https://cmr.engineering.osu.edu/people/trainees"}]},"RegistryEntryAdded":"2020-08-05","RegistryEntryLastModified":"2022-05-19","Slug":"ocmr_data","Sources":["awslabs-open-data-registry"]}
{"Name":"Department of Energy's Open Energy Data Initiative (OEDI)","Description":"Data released under the Department of Energy's (DOE) Open Energy Data Initiative\n(OEDI). The Open Energy Data Initiative aims to improve and automate\naccess of high-value energy data sets across the U.S. Department of Energy’s\nprograms, offices, and national laboratories. OEDI aims to make data\nactionable and discoverable by researchers and industry to accelerate\nanalysis and advance innovation.\n","Documentation":"https://github.com/openEDI/documentation/","Contact":"https://github.com/openEDI/documentation/issues","ManagedBy":"[National Laboratory of the Rockies](https://www.nrel.gov/)","UpdateFrequency":"As needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["aws-pds","energy","environmental","geospatial","lidar","model","solar"],"License":"Creative Commons Attribution 3.0 United States License","Resources":[{"Description":"[Data Catalog of the Open Energy Data Initiative (OEDI)](https://data.openei.org/)","ARN":"arn:aws:s3:::oedi-data-lake/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake)"]},{"Description":"[University of Miami Coupled Model (UMCM) for Hurricanes Ike and Sandy](https://data.openei.org/submissions/574)","ARN":"arn:aws:s3:::oedi-data-lake/umcm/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=umcm%2F)"]},{"Description":"[Distributed Generation Market Demand (dGen) model](https://data.openei.org/submissions/1931)","ARN":"arn:aws:s3:::oedi-data-lake/dgen/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=dgen%2F)"]},{"Description":"[Lawrence Berkeley National Laboratory (LBNL) Tracking the Sun](https://data.openei.org/submissions/3)","ARN":"arn:aws:s3:::oedi-data-lake/tracking-the-sun/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=tracking-the-sun%2F)"]},{"Description":"[National Renewable Energy Laboratory (NREL) PV Rooftop Database - (PVRDB)](https://data.openei.org/submissions/4)","ARN":"arn:aws:s3:::oedi-data-lake/pv-rooftop/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=pv-rooftop%2F)"]},{"Description":"[National Renewable Energy Laboratory (NREL) PV Rooftop Database - Puerto Rico (PVRDB-PR)](https://data.openei.org/submissions/2862)","ARN":"arn:aws:s3:::oedi-data-lake/pv-rooftop-pr/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=pv-rooftop-pr%2F)"]},{"Description":"[Synthetic Models for Advanced, Realistic Testing: Distribution systems and Scenarios (SMART-DS) Datasets](https://data.openei.org/submissions/2981)","ARN":"arn:aws:s3:::oedi-data-lake/SMART-DS/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=SMART-DS%2F)"]},{"Description":"[NREL's Annual Technology Baseline (ATB)](https://atb.nrel.gov)","ARN":"arn:aws:s3:::oedi-data-lake/ATB/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=ATB%2F)"]},{"Description":"[End-Use Load Profiles for the U.S. Building Stock](https://data.openei.org/submissions/4520)","ARN":"arn:aws:s3:::oedi-data-lake/nrel-pds-building-stock/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=nrel-pds-building-stock%2F)"]},{"Description":"[Photovoltaic Data Acquisition (PVDAQ) Public Datasets](https://data.openei.org/submissions/4568)","ARN":"arn:aws:s3:::oedi-data-lake/pvdaq/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=pvdaq%2F)"]},{"Description":"[BUTTER - Empirical Deep Learning Dataset](https://data.openei.org/submissions/5708)","ARN":"arn:aws:s3:::oedi-data-lake/butter/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=butter%2F)"]},{"Description":"[AlphaBuilding - Synthetic Buildings Operation Dataset](https://data.openei.org/submissions/2977/)","ARN":"arn:aws:s3:::oedi-data-lake/building_synthetic_dataset/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=building_synthetic_dataset%2F)"]},{"Description":"[PR100: Puerto Rico Grid Resilience and Transition to 100% Renewable Energy](https://data.openei.org/submissions/5749)","ARN":"arn:aws:s3:::oedi-data-lake/PR100/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=PR100%2F)"]},{"Description":"[Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (EFS)](https://data.openei.org/submissions/4130)","ARN":"arn:aws:s3:::oedi-data-lake/dsgrid-2018-efs/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=dsgrid-2018-efs%2F)"]},{"Description":"[Wind and Structural Loads on Parabolic Trough Solar Collectors at Nevada Solar One](https://data.openei.org/submissions/5938)","ARN":"arn:aws:s3:::oedi-data-lake/NSO/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=NSO%2F)"]},{"Description":"[BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting](https://data.openei.org/submissions/5859)","ARN":"arn:aws:s3:::oedi-data-lake/buildings-bench/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=buildings-bench)"]},{"Description":"[Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI)](https://data.openei.org/submissions/6220)","ARN":"arn:aws:s3:::oedi-data-lake/sup3ruhi/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=sup3ruhi%2F)"]},{"Description":"[Buildings Sector Scenarios (BSS)](https://data.openei.org/submissions/8558)","ARN":"arn:aws:s3:::oedi-data-lake/building-sector-scenarios/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=buildings-sector-scenarios%2F)"]},{"Description":"[U.S. Agrivoltaic Irradiance Database](https://data.openei.org/submissions/8568)","ARN":"arn:aws:s3:::oedi-data-lake/inspire/agrivoltaics_irradiance/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=inspire%2Fagrivoltaics_irradiance%2F)"]},{"Description":"[Wind and Structural Loads on Heliostats](https://data.openei.org/submissions/8601)","ARN":"arn:aws:s3:::oedi-data-lake/crescent_dunes/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=crescent_dunes%2F)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"NSRDB Viewer","URL":"https://maps.nrel.gov/nsrdb-viewer/?aL=0&bL=groad&cE=0&lR=0&mC=31.653381399664%2C-3.1640625&zL=2","AuthorName":"National Renewable Energy Laboratory (NREL)","AuthorURL":"https://www.nrel.gov/"},{"Title":"Tracking the Sun Tool","URL":"https://emp.lbl.gov/tracking-sun-tool","AuthorName":"Lawrence Berkeley National Laboratory (LBNL)","AuthorURL":"https://emp.lbl.gov/tracking-the-sun"}],"Publications":[{"Title":"Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling","URL":"https://iopscience.iop.org/article/10.1088/1748-9326/aaa554/pdf","AuthorName":"Pieter Gagnon et al 2018 Environ. Res. Lett. 13 024027"},{"Title":"On the Use of Coupled Wind, Wave, and Current Fields in the Simulation of Loads on BottomSupported Offshore Wind Turbines during Hurricanes","URL":"https://www.nrel.gov/docs/fy16osti/65283.pdf","AuthorName":"E. Kim, L. Manuel, M. Curcic, S. S. Chen, C. Phillips, P. Veers"},{"Title":"Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment","URL":"https://www.nrel.gov/docs/fy16osti/65298.pdf","AuthorName":"Pieter Gagnon, Robert Margolis, Jennifer Melius, Caleb Phillips, and Ryan Elmore"},{"Title":"Rooftop Solar Technical Potential for Low-to-Moderate Income Households in the United States","URL":"https://www.nrel.gov/docs/fy18osti/70901.pdf","AuthorName":"Benjamin Sigrin and Meghan Mooney"},{"Title":"The Distributed Generation Market Demand Model (dGen):Documentation","URL":"https://www.nrel.gov/docs/fy16osti/65231.pdf","AuthorName":"B. Sigrin, M. Gleason, R. Preus, I. Baring-Gould, R. Margolis"},{"Title":"Tracking the Sun Pricing and Design Trends for Distributed Photovoltaic Systems in the United States: 2019 Edition","URL":"https://emp.lbl.gov/sites/default/files/tracking_the_sun_2019_report.pdf","AuthorName":"G. Barbose, N. Darghouth"},{"Title":"Using GIS-based methods and lidar data to estimate rooftop solar technical potential in US cities","URL":"https://iopscience.iop.org/article/10.1088/1748-9326/aa7225/pdf","AuthorName":"Robert Margolis et al 2017 Environ. Res. Lett. 12 074013"}]},"RegistryEntryAdded":"2020-09-24","RegistryEntryLastModified":"2026-01-07","Slug":"oedi-data-lake","Sources":["awslabs-open-data-registry"]}
{"Name":"ARCO-OCEAN","Description":"ARCO-OCEAN is an analysis-ready cloud-optimized dataset providing physical properties of the ocean, waves, and sea ice for a period of about 28 years between the 1st of January 1993 and the 30th of June 2021. The dataset includes also atmospheric and hydrological variables that would be needed as boundary conditions and used to drive a numerical simulation. The dataset is the result of collecting, processing, merging and optimizing for the cloud different data sources, all retrospective analyses (reanalyses) or hindcasts of different Earth system components. The dataset has been designed with machine learning in mind, and takes inspiration from similar datasets derived from ERA5.\n","Documentation":"[ARCO-OCEAN](https://github.com/inogs/arco-ocean)","Contact":"scampanella@ogs.it","ManagedBy":"[OGS](https://www.ogs.it/en/dynamics-ecosystems-and-computational-oceanography)","UpdateFrequency":"Variable (as needed).","Tags":["analysis ready data","atmosphere","aws-pds","climate","hydrology","ice","machine learning","oceans","physics","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Citation":"Campanella, S., Salon, S., Querin, S., Bortolussi, L., and Stock, J.: ARCO-OCEAN: A dataset of physical properties of the ocean, waves, and sea ice, with hydrological and atmospheric forcing, optimized for machine learning, accessed on DD-MM-YYYY.","Resources":[{"Description":"Zarr analysis ready data","ARN":"arn:aws:s3:::ogs-arco-ocean","Region":"eu-south-1","Type":"S3 Bucket"},{"Description":"Notifications for new ARCO-OCEAN data","ARN":"arn:aws:sns:eu-south-1:985149164500:ogs-arco-ocean-object_created","Region":"eu-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Computing the Oceanic El Nino Index (ONI) with Xarray and ARCO-OCEAN","URL":"https://github.com/inogs/arco-ocean/blob/main/tutorials/oni.ipynb","NotebookURL":"https://github.com/inogs/arco-ocean/blob/main/tutorials/oni.ipynb","AuthorName":"OGS","AuthorURL":"https://github.com/inogs/"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-10-22","RegistryEntryLastModified":"2025-11-04","Slug":"ogs-arco-ocean","Sources":["awslabs-open-data-registry"]}
{"Name":"Oregon Health & Science University Chronic Neutrophilic Leukemia Dataset","Description":"The OHSU-CNL study offers the whole exome and RNA-sequencing on a cohort of 100 cases with rare\nhematologic malignancies such as Chronic neutrophilic leukemia (CNL), atypical chronic myeloid\nleukemia (aCML), and unclassified myelodysplastic syndrome/myeloproliferative neoplasms\n(MDS/MPN-U). This dataset contains open RNA-Seq Gene Expression Quantification data.\n","Documentation":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001799.v1.p1\n","Contact":"dcf-support@datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genomic","life sciences"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"RNA-Seq Gene Expression Quantification","ARN":"arn:aws:s3:::gdc-ohsu-cnl-phs001799-2-open","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"}],"Publications":[{"Title":"Genomic landscape of neutrophilic leukemias of ambiguous diagnosis","URL":"https://www.ncbi.nlm.nih.gov/pubmed/31366621","AuthorName":"Zhang H, Wilmot B, Bottomly D et al."}]},"RegistryEntryAdded":"2020-04-09","RegistryEntryLastModified":"2020-10-05","Slug":"ohsu-cnl","Sources":["awslabs-open-data-registry"]}
{"Name":"Opioid Industry Documents Archive (OIDA) Data on AWS","Description":"The OIDA Data on AWS contain the metadata, documents, and extracted text for all of the documents in the UCSF-JHU Opioid Industry Documents Archive, a growing corpus of internal corporate records and other documents arising from the opioid industry.","Documentation":"https://opioid-industry-documents-archive-dataset-bucket.s3.amazonaws.com/index.html","Contact":"opioidarchive@jh.edu","ManagedBy":"Johns Hopkins University","UpdateFrequency":"monthly","Tags":["archives","aws-pds","life sciences","pharmaceutical","text analysis","txt"],"License":"https://www.industrydocuments.ucsf.edu/opioids/help/copyright/","Citation":null,"Resources":[{"Description":"Raw data from the Opioid Industry Documents Archive (OIDA), including documents in various file formats (PDF, Excel, PowerPoint, etc.), OCR or other extracted text from these files, and corresponding metadata for each document.","ARN":"arn:aws:s3:::opioid-industry-documents-archive-dataset-bucket","Region":"us-east-1","Type":"S3 Bucket","Explore":null}],"DeprecatedNotice":null,"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2023-10-23","RegistryEntryLastModified":"2024-12-20","Slug":"oida","Sources":["awslabs-open-data-registry"]}
{"Name":"OME-Zarr Open SciVis Datasets","Description":"This project provides the Open SciVis Datasets in a chunked, highly-compressed, multi-scale format, encodes metadata in JSON according to the OME-Zarr specification, and hosts the datasets on AWS S3 through the AWS Open Data Program, aiming to serve as a web-based resource for the scientific visualization community to enhance reproducibility and facilitate testing and development of OME-Zarr tools.","Documentation":"https://github.com/InsightSoftwareConsortium/OMEZarrOpenSciVisDatasets","Contact":"Matt McCormick <matt@fideus.io>","ManagedBy":"NumFOCUS","UpdateFrequency":"On a biannual basis we update the datasets and sync with OME-Zarr standards.","Tags":["aws-pds","biology","computed tomography","image processing","imaging","life sciences","magnetic resonance imaging","neuroimaging","neuroscience","volumetric imaging","zarr"],"License":"CC-BY-4.0 unless otherwise specified","Citation":"McCormick, M. (2025). OME-Zarr Open SciVis Datasets (v2025.10.31). Zenodo. https://doi.org/10.5281/zenodo.17495294","Resources":[{"Description":"OME-Zarr Open SciVis Datasets","ARN":"arn:aws:s3:::ome-zarr-scivis","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Explore OME-Zarr Open SciVis Datasets in a Browser-Based Notebook on Scigantic","URL":"https://scigantic.com/archive/c7122f2c-4639-5864-b063-61af7d973461/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com/"},{"Title":"Read and Visualize in Python","URL":"https://github.com/InsightSoftwareConsortium/OMEZarrOpenSciVisDatasets?tab=readme-ov-file#usage","AuthorName":"Matt McCormick","AuthorURL":"https://github.com/thewtex"}],"Tools & Applications":[{"Title":"A list of tools and libraries with OME-Zarr support","URL":"https://ngff.openmicroscopy.org/tools/index.html","AuthorName":"NGFF community","AuthorURL":"https://github.com/ome/ngff"}],"Publications":[{"Title":"OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies","URL":"https://www.nature.com/articles/s41592-021-01326-w","AuthorName":"Josh Moore, Chris Allan, Sébastien Besson, Jean-Marie Burel, Erin Diel, David Gault, Kevin Kozlowski, Dominik Lindner, Melissa Linkert, Trevor Manz, Will Moore, Constantin Pape, Christian Tischer & Jason R. Swedlow"},{"Title":"OME-Zarr: a cloud-optimized bioimaging file format with international community support","URL":"https://link.springer.com/article/10.1007/s00418-023-02209-1","AuthorName":"Josh Moore, Daniela Basurto-Lozada, Sébastien Besson, John Bogovic, Jordão Bragantini, Eva M. Brown, Jean-Marie Burel, Xavier Casas Moreno, Gustavo de Medeiros, Erin E. Diel, David Gault, Satrajit S. Ghosh, Ilan Gold, Yaroslav O. Halchenko, Matthew Hartley, Dave Horsfall, Mark S. Keller, Mark Kittisopikul, Gabor Kovacs, Aybüke Küpcü Yoldaş, Koji Kyoda, Albane le Tournoulx de la Villegeorges, Tong Li, Prisca Liberali, Dominik Lindner, Melissa Linkert, Joel Lüthi, Jeremy Maitin-Shepard, Trevor Manz, Luca Marconato, Matthew McCormick, Merlin Lange, Khaled Mohamed, William Moore, Nils Norlin, Wei Ouyang, Bugra Özdemir, Giovanni Palla, Constantin Pape, Lucas Pelkmans, Tobias Pietzsch, Stephan Preibisch, Martin Prete, Norman Rzepka, Sameeul Samee, Nicholas Schaub, Hythem Sidky, Ahmet Can Solak, David R. Stirling, Jonathan Striebel, Christian Tischer, Daniel Toloudis, Isaac Virshup, Petr Walczysko, Alan M. Watson, Erin Weisbart, Frances Wong, Kevin A. Yamauchi, Omer Bayraktar, Beth A. Cimini, Nils Gehlenborg, Muzlifah Haniffa, Nathan Hotaling, Shuichi Onami, Loic A. Royer, Stephan Saalfeld, Oliver Stegle, Fabian J. Theis & Jason R. Swedlow"},{"Title":"Open SciVis Datasets","URL":"http://klacansky.com/open-scivis-datasets/","AuthorName":"Pavol Klacansky"}]},"DeprecatedNotice":null,"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2024-09-18","RegistryEntryLastModified":"2026-06-08","Slug":"ome-zarr-open-scivis","Sources":["awslabs-open-data-registry"]}
{"Name":"Ozone Monitoring Instrument (OMI) / Aura NO2 Tropospheric Column Density","Description":"NO2 tropospheric column density, screened for CloudFraction < 30% global daily\n composite at 0.25 degree resolution for the temporal range of 2004 to May\n2020. Original archive data in HDF5 has been processed into a [Cloud-Optimized\nGeoTiff (COG)](https://www.cogeo.org/) format. Quality Assurance - This data\nhas been validated by the NASA Science Team at Goddard Space Flight Center.\n\nCautionary Note: https://airquality.gsfc.nasa.gov/caution-interpretation.\n","Documentation":"https://disc.gsfc.nasa.gov/datasets/OMNO2d_003/summary","Contact":"binita.kc@nasa.gov","ManagedBy":"NASA","UpdateFrequency":"None","Collabs":{"ASDI":{"Tags":["air quality"]}},"Tags":["air quality","atmosphere","aws-pds","earth observation","environmental","geospatial","satellite imagery"],"License":"There are no restrictions on the use of these data.","Resources":[{"Description":"S3 Bucket for OMI NO2 in Cloud-Optimized GeoTiff format","ARN":"arn:aws:s3:::omi-no2-nasa","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Medium Post: COG Talk — Part 1: What’s new?","URL":"https://medium.com/devseed/cog-talk-part-1-whats-new-941facbcd3d1","AuthorName":"Vincent Sarago","AuthorURL":"https://medium.com/@_VincentS_"}],"Tools & Applications":[{"Title":"COG Application Programming Interface (API)","URL":"https://pfg2k5mjei.execute-api.us-west-2.amazonaws.com/docs","AuthorName":"Development Seed","AuthorURL":"https://developmentseed.org/"},{"Title":"COG metadata example ","URL":"https://pfg2k5mjei.execute-api.us-west-2.amazonaws.com/v1/metadata?url=https%3A%2F%2Fomi-no2-nasa.s3-us-west-2.amazonaws.com%2FOMI-Aura_L3-OMNO2d_2018m0101_v003-2019m1123t055315.tif&pmin=2&pmax=98&max_size=1024&histogram_bins=20","AuthorName":"Development Seed","AuthorURL":"https://developmentseed.org/"},{"Title":"COG Viewer","URL":"https://cogeo.xyz/?url=s3://omi-no2-nasa/OMI-Aura_L3-OMNO2d_2020m0101_v003-2020m0330t173100.tif","AuthorName":"Development Seed","AuthorURL":"https://developmentseed.org/"},{"Title":"Rasterio (Python Library) for access to geospatial raster data","URL":"https://rasterio.readthedocs.io/","AuthorName":"Mapbox","AuthorURL":"https://www.mapbox.com/"}]},"RegistryEntryAdded":"2020-05-26","RegistryEntryLastModified":"2023-02-28","Slug":"omi-no2-nasa","Sources":["awslabs-open-data-registry"]}
{"Name":"ONS Open Data Portal","Description":"The ONS Open Data Portal, produced by the National Operator of the Electric System (ONS), gathers historical data from the Brazilian electricity sector in an easy and democratic way with the main objective to facilitate and improve the access and consumption of this type of content by all users and audiences. The Portal gathers consolidated data on energy generation (hydraulic, thermal, solar and wind), areas of national and international energy exchange and energy load, information about equipments such as transmission lines, generating units, converters and others; and hydrological data obtained from reservoirs and hydrographic basins in Brazil. (PT-BR O portal de Dados Abertos do ONS, produzido pelo Operador Nacional do Sistema Elétrico (ONS) reúne dados históricos do setor elétrico brasileiro de maneira fácil e democrática. O portal tem como principal objetivo facilitar e melhorar o acesso e consumo desse tipo de conteúdo por todos os usuários e públicos. O portal reúne dados consolidados sobre geração de energia, áreas nacionais e internacionais de intercâmbio e carga de energia, informações sobre equipamentos como linhas de transmissão, unidades geradoras, conversores e outros; e dados hidrológicos obtidos dos reservatórios e bacias hidrográficas no Brasil.)","Documentation":"https://dados.ons.org.br/dataset/","Contact":"dadosabertos@ons.org.br","ManagedBy":"[ONS - National Electric System Operator](https://www.ons.org.br/)","UpdateFrequency":"diary","Citation":"Portuguese","Tags":["aws-pds","electricity","energy","hydrography"],"License":"CC-BY 4.0","Resources":[{"Description":"Subsystems energy balance (PT-BR Balanço de energia nos subsistemas)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/balanco-energia-subsistema)"]},{"Description":"Installed generation capacity (PT-BR Capacidade instalada de geração)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/capacidade-geracao)"]},{"Description":"Basic network transformation capacity (PT-BR Capacidade de transformação da rede básica)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/capacidade-transformacao)"]},{"Description":"Daily energy charge (PT-BR Carga de Energia Diária)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/carga-energia)"]},{"Description":"Monthly energy charge (PT-BR Carga de energia mensal)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/carga-mensal)"]},{"Description":"Scheduled energy charge (PT-BR Carga de energia programda)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/carga-energia-programada)"]},{"Description":"Verfied energy charge (PT-BR Carga de energia verificada)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/carga-energia-verificada)"]},{"Description":"Weekly marginal cost of operation (PT-BR Custo Marginal de Operação (CMO) semanal)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/cmo-semanal)"]},{"Description":"Semi-hourly marginal cost of operation (PT-BR Custo Marginal de Operação (CMO) Semi-Horário)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/cmo-semi-horario)"]},{"Description":"Contour of watersheds (PT-BR Contorno das Bacias Hidrográficas)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/bacia_contorno)"]},{"Description":"Hourly charge curve (PT-BR Curva de carga horária)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/curva-carga)"]},{"Description":"Unit Variable Cost of thermal power plants (PT-BR Custo Variável Unitário (CVU) das Usinas Térmicas)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/cvu-usitermica)"]},{"Description":"Reservoir hydrological data - Daily Basis (PT-BR Dados hidrológicos de reservatório - Base Diária)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/dados-hidrologicos-res)"]},{"Description":"Reservoir hydrological data - Hourly Basis (PT-BR Dados hidrológicos de reservatório - Base Horária)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/dados-hidrologicos-res-ho)"]},{"Description":"Power plant availability data (PT-BR Disponibilidade de Usinas)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/disponibilidade_usina)"]},{"Description":"Energy Exchange Data by Modality (PT-BR Dados de Intercâmbio de Energia por Modalidade)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/intercambio_modalidade)"]},{"Description":"Operation restriction data due to constrained-off of wind farms - breakdown by power plant (PT-BR Dados de restrição de operação por constrained-off de usinas eólicas - detalhamento por usina)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/restricao_coff_eolica_detail)"]},{"Description":"Operation restriction data due to constrained-off of wind farms (PT-BR Dados de restrição de operação por constrained-off de usinas eólicas)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/restricao_coff_eolica_usi)"]},{"Description":"Data of the relationship between power plant and group (PT-BR Dados do relacionamento entre conjuntos e usinas)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/usina_conjunto)"]},{"Description":"Data of the relationship between power plant and groups of small power plants (PT-BR Dados do relacionamento entre conjuntos de pequenas usinas e usinas)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/usina_pqu)"]},{"Description":"Daily stored energy per basin (PT-BR Energia Armazenada (EAR) diário por bacia)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ear-diario-por-bacia)"]},{"Description":"Daily stored energy per equivalent energy reservoir (PT-BR Energia Armazenada (EAR) diário por REE - reservatório equivalente de energia)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ear-diario-por-ree-reservatorio-equivalente-de-energia)"]},{"Description":"Daily stored energy per reservoir (PT-BR Energia Armazenada (EAR) diário por reservatório)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ear-diario-por-reservatorio)"]},{"Description":"Daily stored energy per subsystem (PT-BR Energia Armazenada (EAR) diário por subsistema)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ear-diario-por-subsistema)"]},{"Description":"Daily affluent natural energy per basin (PT-BR Energia Natural Afluente (ENA) diário por bacia)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ena-diario-por-bacia)"]},{"Description":"Daily affluent natural energy per equivalent energy reservoir (PT-BR Energia Natural Afluente (ENA) diário por REE - reservatorio equivalente de energia)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ena-diario-por-ree-reservatorio-equivalente-de-energia)"]},{"Description":"Daily affluent natural energy per reservoir (PT-BR Energia Natural Afluente (ENA) diário por reservatório)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ena-diario-por-reservatorio)"]},{"Description":"Daily affluent natural energy per subsystem (PT-BR Energia Natural Afluente (ENA) diário por subsistema)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ena-diario-por-subsistema)"]},{"Description":"Turbinable spilled energy (PT-BR Energia vertida turbinável)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/energia-vertida-turbinavel)"]},{"Description":"Reagents control equipment (PT-BR Equipamentos de controle de reativos da rede de operação)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/equipamento-controle-reativo)"]},{"Description":"Wind and solar generation capacity factor (PT-BR Fator de capacidade de geração eólica e solar)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/fator-capacidade-2)"]},{"Description":"Commercial generation for international export (PT-BR Geração Comercial Para Exportação Internacional)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/geracao-exportacao-internacional)"]},{"Description":"Thermal generation due to dispatch (PT-BR Geração térmica por motivo de despacho)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/geracao-termica-despacho-2)"]},{"Description":"Generation per power plant on an hourly basis (PT-BR Geração por usina em base horária)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/geracao-usina-2)"]},{"Description":"Fluviometric data (PT-BR Grandezas fluviométricas)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/grandezas_fluviometricas)"]},{"Description":"Import of energy in commercial mode per block (PT-BR Importação de energia na modalidade comercial por bloco)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/importacaoenergia-comercial-2)"]},{"Description":"Basic Network Reliability indicator - Electric charge interrupted by disturbances (PT-BR Indicador de Confiabilidade da Rede Básica: CIPER - Carga Interrompida por Perturbações)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ind_confiarb_ciper)"]},{"Description":"Basic Network Reliability indicator - DREQ, FREQ (PT-BR Indicador de Confiabilidade da Rede Básica: DREQ, FREQ)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ind_confiarb_dreq_freq)"]},{"Description":"Basic Network Reliability indicator: Robustness - RMAL, RMCS, RRB and RRBCS (PT-BR Indicador de Confiabilidade da Rede Básica: Robustez - RMAL, RMCS, RRB and RRBCS)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ind_confiarb_robustez)"]},{"Description":"Basic Network Reliability indicator: SM - Severity of Disturbances (PT-BR Indicador de Confiabilidade da Rede Básica: SM - Severidade das Perturbações)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ind_confiarb_severidade)"]},{"Description":"Transmission function availability indicator - converters (PT-BR Indicador de disponibilidade de função transmissão - conversores)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ind_disponibilidade_ft_conversor)"]},{"Description":"Transmission function availability indicator - reagents control equipment (PT-BR Indicador de disponibilidade de função transmissão - equipamentos de controle de reativos)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ind_disponibilidade_ft_reativo)"]},{"Description":"Transmission function availability indicator - transmission lines and transformers (PT-BR Indicador de disponibilidade de função transmissão - linhas de transmissão e transformadores)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ind_disponibilidade_ft_trlt)"]},{"Description":"Performance indicator of generation functions per generating unit on an yearly basis (PT-BR Indicadores de desempenho das funções geração por unidade geradora em base anual)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ind_disponibilidade_fgeracao_uge_anual)"]},{"Description":"Performance indicator of generation functions per generating unit on an monthly basis (PT-BR Indicadores de desempenho das funções geração por unidade geradora em base mensal)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ind_disponibilidade_fgeracao_uge_mensal)"]},{"Description":"Generation function availability indicators for the National Interconnected System - monthly basis (PT-BR Indicadores de disponibilidade de função geração para o Sistema Interligado Nacional (SIN) - base mensal)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ind-disponibilidade-geracao)"]},{"Description":"National Interconnected System exchange with other countries (PT-BR Intercâmbio do Sistema Interligado Nacional (SIN) com outros países)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/intercambio-internacional)"]},{"Description":"Exchange between subsystems (PT-BR Intercâmbio entre subsistemas)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/intercambio-nacional)"]},{"Description":"Operation network transmission lines (PT-BR Linhas de transmissão da rede de operação)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/linha-transmissao)"]},{"Description":"Power Plant Mode of Operation (PT-BR Modalidade de Operação da Usina)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/modalidade-usina)"]},{"Description":"Price offers for import (PT-BR Ofertas de preço para importação)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/ofertapreco-importacao)"]},{"Description":"Reservoirs (PT-BR Reservatórios)","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/reservatorio)"]},{"Description":"TEIFa (Equivalent Rate of Forced Unavailability) and TEIP (Scheduled Unavailability Rate) Rates (PT-BR Taxas TEIFa (Taxa Equivalente de Indisponibilidade Forçada) e TEIP (Taxa de Indisponibilidade Programada))","ARN":"arn:aws:s3:::ons-aws-prod-opendata","Region":"sa-east-1","Type":"S3 Bucket","Explore":["[Browse Dataset](https://dados.ons.org.br/dataset/taxa_teif_teip)"]}],"RegistryEntryAdded":"2023-04-17","RegistryEntryLastModified":"2024-11-18","Slug":"ons-opendata-portal","Sources":["awslabs-open-data-registry"]}
{"Name":"Oxford Nanopore Technologies Benchmark Datasets","Description":"The ont-open-data registry provides reference sequencing data from Oxford Nanopore Technologies to support, 1) Exploration of the characteristics of nanopore sequence data. 2) Assessment and reproduction of performance benchmarks 3) Development of tools and methods. The data deposited showcases DNA sequences from a representative subset of sequencing chemistries. The datasets correspond to publicly-available reference samples (e.g. Genome In A Bottle reference cell lines). Raw data are provided with metadata and scripts to describe sample and data provenance.\n","Documentation":"https://labs.epi2me.io/dataindex/","Contact":"support@nanoporetech.com","ManagedBy":"Oxford Nanopore Technologies","UpdateFrequency":"Additional datasets will be added periodically. Updates and amendents will be made to existing entries when algorithmic advancements are made (e.g. improvements to basecalling algorithms).\n","Tags":["aws-pds","bioinformatics","biology","fast5","fastq","genomic","Homo sapiens","life sciences","whole genome sequencing"],"License":"Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4.0/\nThe following cell lines/DNA samples were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research: GM24385.\n","Resources":[{"Description":"Oxford Nanopore Open Datasets","ARN":"arn:aws:s3:::ont-open-data","Region":"eu-west-1","Type":"S3 Bucket","RequesterPays":false},{"Description":"Nanopore sequencing data of the Genome in a Bottle samples NA24385, NA24149, and NA24143 (HG002-HG004) using the LSK114 sequencing chemistry.       The direct sequencer output is included, raw signal data stored in  .fast5 files and basecalled data in .fastq file. Additional secondary analyses are included, notably alignments of sequence data to the  reference genome and variant calls are provided along with statistics derived from these.\nThe following cell lines/DNA samples were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research: NA24385, NA24149, and NA24143.\n","ARN":"arn:aws:s3:::ont-open-data/giab_lsk114_2022.12","Region":"eu-west-1","Type":"S3 Bucket","RequesterPays":false},{"Description":"Using nanopore sequencing, researchers have directly identified DNA and RNA base modifications at nucleotide resolution, including 5-methylycytosine, 5-hydroxymethylcytosine, N6-methyladenosine, 5-bromodeoxyuridine in DAN; and N6-methyladenosine in RNA, with detection of other natural or synthetic epigenetic modifications possible through training basecalling algorithms. One of the most widespread genomic modifications is 5-methylcytosine (5mC), which most frequently occurs at  dinucleotides. Compared to whole-genome bisulfite sequencing, the traditional method of 5mC detection, nanopore technology can offer many advantages\nThe following cell lines/DNA samples were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research: GM24385.\n","ARN":"arn:aws:s3:::ont-open-data/gm24385_mod_2021.09/extra_analysis/bonito_remora","Region":"eu-west-1","Type":"S3 Bucket","RequesterPays":false},{"Description":"CpG dinucleotides frequently occur in high-density clusters called CpG islands (CGI) and >60% of human genes have their promoters embedded within CGIs. Determining the methylation status of cytosines within CpGs is of substantial biological interest: alterations in methylation patterns within promoters is associated with changes in gene expression and disease states such as cancer. Exploring methylation differences between tumour samples and normal samples can help to elucidate mechanisms associated with tumour formation and development. Nanopore sequencing enables direct detection of methylated cytosines (e.g. at CpG sites), without the need for bisulfite conversion.\nOxford Nanopore’s Adaptive Sampling offers a flexible method to enrich regions of interest (e.g. CGIs) by depleting off-target regions during the sequencing run itself with no upfront sample manipulation. Here we introduce Reduced Representation Methylation Sequencing (RRMS) to target 310 Mb of the human genome including regions which are highly enriched for CpGs including ~28,000 CpG islands, ~50,600 shores and ~42,700 shelves as well as ~21,600 promoter regions.\n","ARN":"arn:aws:s3:::ont-open-data/rrms_2022.07","Region":"eu-west-1","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Tutorials":[{"Title":"ONT Dataset Tutorials","URL":"https://labs.epi2me.io/tutorials/","AuthorName":"EPI2MELabs","AuthorURL":"https://labs.epi2me.io/"}]},"RegistryEntryAdded":"2020-09-29","RegistryEntryLastModified":"2022-11-18","Slug":"ont-open-data","Sources":["awslabs-open-data-registry"]}
{"Name":"ONT Methylation Benchmarking Datasets","Description":"ONT Methylation Benchmarking Datasets are generated to benchmark existing methylation-calling tools on the Oxford Nanopore sequencing platform using their recent R10.4.1 flowcell chemistry. It spans a diverse range of species, including bacteria (E. coli, H. pylori J99, H. pylori 26695, A. variabilis, T. denticola), plants (Rice, Arabidopsis), and mammals (mouse, human).In addition, the dataset includes EMSeq data for E. coli, plant, and mouse samples, which can serve as ground truth for methylation studies. It also provides unmethylated whole-genome amplified (WGA) DNA for H. pylori 26695 and a dam- dcm- double mutant (DM) of E. coli that lacks canonical 5mC and 6mA methylation. These variants, together with their wild-type counterparts, offer value for both training and benchmarking DNA methylation calling models.","Documentation":"https://github.com/SowpatiLab/ont-basemod-benchmark-data/blob/main/documentation.md","Contact":"onkar.ccmb@csir.res.in; tej.ccmb@csir.res.in","ManagedBy":"[CSIR-Centre for Cellular and Molecular Biology](https://www.ccmb.res.in/)","UpdateFrequency":"Datasets will be updated periodically as additional data is generated.","Tags":["aws-pds","bam","benchmark","bioinformatics","epigenomics","genomic","life sciences","long read sequencing"],"License":"[MIT License](https://opensource.org/license/mit)","Citation":"Please cite Kulkarni et al. Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation. bioRxiv (2024). doi: https://doi.org/10.1101/2024.11.09.622763 when referencing the ONT methylation benchmarking datasets in publications.","Resources":[{"Description":"ONT Methylation Benchmarking Datasets","ARN":"arn:aws:s3:::ont-basemod-benchmark-data","Region":"ap-south-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://ont-basemod-benchmark-data.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for object created","ARN":"arn:aws:sns:ap-south-1:767415906609:ont-basemod-benchmark-data-object_created","Region":"ap-south-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Methylation calling using ONT methylation benchmarking dataset","URL":"https://github.com/SowpatiLab/ont-basemod-benchmark-data/blob/main/tutorial.md","AuthorName":"Onkar Kulkarni"},{"Title":"Running Benchmarking Pipeline (Nextflow/Snakemake) on an Example Dataset using AWS","URL":"https://github.com/SowpatiLab/ont-basemod-benchmark-data/blob/main/tutorial_workflow.md","AuthorName":"Onkar Kulkarni"}],"Publications":[{"Title":"Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation","URL":"https://www.biorxiv.org/content/10.1101/2024.11.09.622763v1","AuthorName":"Kulkarni et al."}]},"RegistryEntryAdded":"2025-10-29","RegistryEntryLastModified":"2026-03-28","Slug":"ont_basemod_data","Sources":["awslabs-open-data-registry"]}
{"Name":"Open Observatory of Network Interference (OONI)","Description":"A free software, global observation network for detecting censorship, surveillance and traffic manipulation on the internet.","Documentation":"https://ooni.org/data/","Contact":"https://ooni.org/get-involved/","ManagedBy":"Open Observatory of Network Interference","UpdateFrequency":"Hourly","Tags":["aws-pds","internet"],"License":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International https://github.com/ooni/license/blob/master/data/LICENSE.md","Resources":[{"Description":"New S3 bucket with JSONL files","ARN":"arn:aws:s3:::ooni-data-eu-fra","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"Old S3 bucket with cans for older measurements","ARN":"arn:aws:s3:::ooni-data","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2022-06-15","Slug":"ooni","Sources":["awslabs-open-data-registry"]}
{"Name":"Open Bioinformatics Reference Data for Galaxy","Description":"This dataset provides genomic reference data and software packages for use with [Galaxy](https://galaxyproject.org/) and [Bioconductor](https://bioconductor.org/) applications. The reference data is available for hundreds of reference genomes and has been formatted for use with a variety of tools. The available configuration files make this data easily incorporable with a local Galaxy server without additional data preparation. Additionally, Bioconductor's AnnotationHub and ExperimentHub data are provided for use via R packages through which the data can be downloaded and installed into any R environment.\n","Documentation":"https://github.com/openbiorefdata/documentation/","Contact":"outreach@galaxyproject.org or https://support.bioconductor.org/","ManagedBy":"Galaxy and Bioconductor Projects","UpdateFrequency":"Periodically but not on a set schedule.","Tags":["aws-pds","bioinformatics","biology","genetic","genomic","life sciences","reference index"],"License":"Public Domain","Resources":[{"Description":"The data is organized as versioned objects for reference data, and data packages for use with the R programming language. The reference data is primarily intended for use with the Galaxy application, and has been formatted for easy configuration. In the future, additional formats may be provided. The packages as well as the AnnotationHub and ExperimentHub data are intended for use with R / Bioconductor.\n","ARN":"arn:aws:s3:::biorefdata","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Using Open Bio Ref Data with Galaxy and Bioconductor","URL":"https://github.com/openbiorefdata/tutorials/","AuthorName":"Enis Afgan, Alexandru Mahmoud, Nuwan Goonasekera"}],"Tools & Applications":[{"Title":"Bioconductor","URL":"https://bioconductor.org/","AuthorName":"Bioconductor Project"},{"Title":"Galaxy","URL":"https://usegalaxy.org/","AuthorName":"Galaxy Project"}],"Publications":[{"Title":"Accessible, curated metagenomic data through ExperimentHub","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862039/","AuthorName":"Edoardo Pasolli, Lucas Schiffer, Paolo Manghi, Audrey Renson, Valerie Obenchain, Duy Tin Truong, Francesco Beghini, Faizan Malik, Marcel Ramos, Jennifer B Dowd, Curtis Huttenhower, Martin Morgan, Nicola Segata, and Levi Waldron"},{"Title":"TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5302158/","AuthorName":"Tiago C. Silva, Antonio Colaprico, Catharina Olsen, Fulvio D'Angelo, Gianluca Bontempi, Michele Ceccarelli, Houtan Noushmehr"},{"Title":"Wrangling Galaxy's reference data","URL":"https://doi.org/10.1093/bioinformatics/btu119","AuthorName":"Daniel Blankenberg, James E. Johnson, The Galaxy Team, James Taylor, Anton Nekrutenko"}]},"RegistryEntryAdded":"2022-03-31","RegistryEntryLastModified":"2022-03-31","Slug":"open-bio-ref-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Open CEDA by Watershed","Description":"CEDA is a multi-regional Environmentally-Extended Input-Output (EEIO) model developed to support a wide range of environmental systems analyses—including corporate carbon accounting and sustainable spend analysis. CEDA provides unparalleled global coverage and granularity, representing 95% of the world's GDP across 148 countries and 400 sectors, enabling robust and geographically comprehensive Scope 3 greenhouse gas (GHG) measurement. \nOpen CEDA is the publicly avaialable version of CEDA, now easy to download and available for free for all use cases. For more information please visit our website at openceda.org.\nThis data registry entry contains CEDA 2025 and CEDA 2024 in two separate files. CEDA 2025, the latest version of CEDA, uses 2023 as its base year, ensuring that emissions factors and economic data reflect the most recent global economic landscape available. To maintain accuracy and relevance, CEDA is updated annually with the latest data releases.\nAt its core, CEDA connects economic exchanges to GHG emissions by quantifying the life-cycle emissions of products and services. This is achieved through the integration of input-output tables, which represent the full supply-chain network of the global economy, with GHG emissions data. As a result, CEDA provides users with a powerful tool to assess the environmental impacts embedded in corporate value chains.\n","Documentation":"https://openceda.org/","Contact":"ceda-support@watershed.com","ManagedBy":"[Watershed Technology](https://watershed.com)","UpdateFrequency":"Annual","Collabs":{"ASDI":{"Tags":["sustainability"]}},"Tags":["aws-pds","carbon","climate","EEIO","scope 3","spend-based models","supply chain"],"License":"Creative Commons CC BY-SA","Resources":[{"Description":"An .xlsx file containing the Open CEDA dataset","ARN":"arn:aws:s3:::open-ceda","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Open CEDA](https://open-ceda.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"For a tutoral please download the CEDA Methodology Documentation on the openceda.org website.","URL":"https://openceda.org/","AuthorName":"Watershed Technology"}],"Publications":[{"Title":"A Consumption-Based Greenhouse Gas Inventory of SanFrancisco Bay Area Neighborhoods, Cities and Counties","URL":"https://escholarship.org/content/qt2sn7m83z/qt2sn7m83z.pdf","AuthorName":"Christopher M. Jones &  Daniel M. Kammen, 2015"},{"Title":"Advancing Sustainable Materials Management - 2016 Recycling Economic Information (REI) Report","URL":"https://www.epa.gov/sites/default/files/2016-11/documents/final_2016_rei_report.pdf","AuthorName":"EPA, 2016"},{"Title":"Are services better for climate change?","URL":"https://pubs.acs.org/doi/10.1021/es0609351","AuthorName":"Sangwon, Suh, 2006"},{"Title":"Converting University Spending to Greenhouse Gas Emissions - A Supply Chain Carbon Footprint Analysis of UC Berkeley","URL":"https://nature.berkeley.edu/classes/es196/projects/2012final/DoyleK_2012.pdf","AuthorName":"Kelley L. Doyle, 2012"},{"Title":"Environmental Impacts of Products - Policy Relevant Information and Data Challenges","URL":"https://publications.jrc.ec.europa.eu/repository/handle/JRC34107","AuthorName":"Eder P, Tukker A, Suh S"},{"Title":"Environmentally extended input-output tables and models for Europe","URL":"https://www.eurogypsum.org/wp-content/uploads/2015/05/N0533.pdf","AuthorName":"Arnold Tukker (TNO), Gjalt Huppes, Lauran van Oers, Reinout Heijungs (CML), 2006"},{"Title":"Greening Government Procurement - Turning Uncle Sam Into an Eco-Friendly Consumer","URL":"https://psmag.com/environment/greening-government-procurement-turning-uncle-sam-eco-friendly-consumer-78592/","AuthorName":"James Badham, 2014"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-05-16","RegistryEntryLastModified":"2025-10-14","Slug":"open-ceda","Sources":["awslabs-open-data-registry"]}
{"Name":"OpenCRAVAT","Description":"OpenCRAVAT is a module variant annotation tool developed by KarchinLab at Johns Hopkins.\nThis dataset is a mirror of the OpenCRAVAT store available at https://store.opencravat.org.\nYou can configure OpenCRAVAT to use this mirror by editing the \"cravat-system.yml\" file.\nThe path to this file is in the first output line of the command \"oc config system\". In that file,\nchange the value of \"store_url\" to \"https://opencravat-store-aws.s3.amazonaws.com\".\n","Documentation":"https://open-cravat.readthedocs.io","Contact":"support@opencravat.org","ManagedBy":"KarchinLab, Potomac IT Group","UpdateFrequency":"Data is mirrored daily. Update frequencies of individual annotators depend on the update schedule\nof the source data.\n","Tags":["aws-pds","genetic","genomic","life sciences","sqlite","tertiary analysis","variant annotation"],"License":"License varies per-annotator. Commercial users must check the \"commercial_warning\" field \nin the module_name.yml file of each annotator.\n","Citation":"Pagel KA, Kim R, Moad K, Busby B, Zheng L, Tokheim C, Ryan M, Karchin R. (2020) Integrated informatics analysis of cancer-related variants. JCO CCI Mar;4:310-317. doi: 10.1200/CCI.19.00132.\n","Resources":[{"Description":"OpenCRAVAT Store US","ARN":"arn:aws:s3:::opencravat-store-aws","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"OpenCRAVAT Store EU","ARN":"arn:aws:s3:::opencravat-store-eu-west-2","Region":"eu-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Changing the OpenCRAVAT store url","URL":"https://open-cravat.readthedocs.io/en/latest/AWS-OpenData.html","AuthorName":"Kyle Moad"}],"Tools & Applications":[{"Title":"OpenCRAVAT","URL":"https://opencravat.org","AuthorName":"Karchinlab","AuthorURL":"https://karchinlab.org"}]},"RegistryEntryAdded":"2023-01-12","RegistryEntryLastModified":"2023-01-12","Slug":"open-cravat","Sources":["awslabs-open-data-registry"]}
{"Name":"Collection of open nation-scale LiDAR datasets","Description":"The goal of this project is to collect all publicly available large scale LiDAR datasets and archive them in an\nuniform fashion for easy access and use. Initial efforts to collect the datasets are concentrated on Europe and will\nbe in future expanded to USA and other regions, striving for global coverage. Every dataset includes files in original\ndata format and translated to COPC format. For faster browsing, we include an overview file that includes a small\nsubset of data points from every dataset file in a single COPC file.\n","Documentation":"https://github.com/flai-ai/open-lidar-data","Contact":"info@flai.ai","ManagedBy":"[Flai](https://flai.ai/)","UpdateFrequency":"When new open dataset is published.","Tags":["aws-pds","earth observation","geoscience","geospatial","land cover","lidar","mapping","survey"],"License":"The exact version of the licence depends on LiDAR dataset and is not the same for all datasets. Look for license.pdf file in the used dataset.","Citation":"Original publisher of the data should be cited.","Resources":[{"Description":"Open LiDAR datasets","ARN":"arn:aws:s3:::open-lidar-data","Region":"eu-central-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"How to download data with FlaiHub","URL":"https://github.com/flai-ai/open-lidar-data/blob/main/tutorials%2Fhow-to-download-data-with-flai-hub.md","AuthorName":"Flai","AuthorURL":"https://flai.ai/"}],"Tools & Applications":[{"Title":"FlaiHub - web application for accessing, browsing and processing open LiDAR datasets","URL":"https://hub.flai.ai/","AuthorName":"Flai","AuthorURL":"https://flai.ai/"}]},"ADXCategories":["Public Sector Data","Environmental Data"],"RegistryEntryAdded":"2024-04-16","RegistryEntryLastModified":"2024-04-16","Slug":"open-lidar-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Open-Meteo Weather API Database","Description":"Open-Meteo integrates weather models from reputable national weather services, offering a swift and efficient weather API. Real-time weather forecasts are unified into a time-series database that provides historical and future weather data for any location worldwide.\n\nThrough Open-Meteo on AWS Open Data, you can download the Open-Meteo weather database and analysis weather data locally. Docker images are provided to download data and to expose an HTTP API endpoint. Using Open-Meteo SDKs, you can seamlessly integrate weather data into your Python, Typescript, Swift, Kotlin, or Java applications.\n\nThe entire source code is open-source, and contributions are welcome!\n\nTo get started, familiarize yourself with the [available weather models](https://github.com/open-meteo/open-data) and explore tutorials on downloading 80 years of [historical weather data from ERA5](https://github.com/open-meteo/open-data/tree/main/tutorial_download_era5) or set up your own [real-time weather API](https://github.com/open-meteo/open-data/tree/main/tutorial_weather_api) with high-resolution models.\n\nIf you have any questions, feel free to reach out on [GitHub discussions](https://github.com/open-meteo/open-meteo/discussions).\n","Documentation":"https://github.com/open-meteo/open-data","Contact":"info@open-meteo.com","ManagedBy":"[Open-Meteo](https://www.open-meteo.com/)","UpdateFrequency":"Hourly","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","climate","earth observation","meteorological","weather"],"License":"CC-BY 4.0","Resources":[{"Description":"Open-Meteo Weather API Database","ARN":"arn:aws:s3:::openmeteo","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://openmeteo.s3.amazonaws.com/index.html#data/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Accessing 80 Years Historical Weather Data from ERA5","URL":"https://github.com/open-meteo/open-data/tree/main/tutorial_download_era5","AuthorName":"Open-Meteo","AuthorURL":"https://www.open-meteo.com/"},{"Title":"Run Your Own Weather API","URL":"https://github.com/open-meteo/open-data/tree/main/tutorial_weather_api","AuthorName":"Open-Meteo","AuthorURL":"https://www.open-meteo.com/"}],"Tools & Applications":[{"Title":"Open-Meteo API documentation","URL":"https://www.open-meteo.com/en/docs","AuthorName":"Open-Meteo","AuthorURL":"https://www.open-meteo.com/"},{"Title":"Open-Meteo API source-code","URL":"https://github.com/open-meteo/open-meteo","AuthorName":"Open-Meteo","AuthorURL":"https://www.open-meteo.com/"}],"Publications":[{"Title":"Zippenfenig, P. (2023). Open-Meteo.com Weather API [Computer software]","AuthorName":"Patrick Zippenfenig","URL":"https://doi.org/10.5281/ZENODO.7970649"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2024-01-22","RegistryEntryLastModified":"2025-07-09","Slug":"open-meteo","Sources":["awslabs-open-data-registry"]}
{"Name":"Open NeuroData","Description":"This bucket contains multiple neuroimaging datasets (as Neuroglancer Precomputed Volumes) across multiple modalities and scales, ranging from nanoscale (electron microscopy), to microscale (cleared lightsheet microscopy and array tomography), and mesoscale (structural and functional magnetic resonance imaging). Additionally, many of the datasets include segmentations and meshes.","Contact":"support@neurodata.io","Documentation":"https://neurodata.io/help/download/","ManagedBy":"[NeuroData](https://neurodata.io/ocp)","UpdateFrequency":"The dataset may be updated with additional or corrected data on a need-to-update basis.","Tags":["array tomography","aws-pds","biology","electron microscopy","image processing","life sciences","light-sheet microscopy","magnetic resonance imaging","neuroimaging","neuroscience"],"License":"ODC-By v1.0 unless otherwise specified","Resources":[{"Description":"Neuroglancer precomputed volumes in a public bucket","ARN":"arn:aws:s3:::open-neurodata","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Download","URL":"https://neurodata.io/help/download/","AuthorName":"Benjamin Falk","AuthorURL":"https://github.com/falkben"},{"Title":"Visualization using Neuroglancer","URL":"https://neurodata.io/help/visualization/","AuthorName":"Benjamin Falk","AuthorURL":"https://github.com/falkben"}],"Tools & Applications":[{"Title":"CloudVolume","URL":"https://github.com/seung-lab/cloud-volume","AuthorName":"William Silversmith","AuthorURL":"https://github.com/william-silversmith"},{"Title":"Igneous","URL":"https://github.com/seung-lab/igneous","AuthorName":"William Silversmith","AuthorURL":"https://github.com/william-silversmith"},{"Title":"Neuroglancer","URL":"https://github.com/google/neuroglancer","AuthorName":"Jeremy Maitin-Shepard","AuthorURL":"https://github.com/jbms"}],"Publications":[{"Title":"A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data","URL":"https://www.nature.com/articles/s41592-018-0181-1","AuthorName":"J. T. Vogelstein, E. Perlman, B. Falk, A. Baden, W. Gray Roncal, V. Chandrashekhar, F. Collman, S. Seshamani, J. L. Patsolic, K. Lillaney, M. Kazhdan, R. Hider, D. Pryor, J. Matelsky, T. Gion, P. Manavalan, B. Wester, M. Chevillet, E. T. Trautman, K. Khairy, E. Bridgeford, D. M. Kleissas, D. J. Tward, A. K. Crow, B. Hsueh, M. A. Wright, M. I. Miller, S. J. Smith, R. J. Vogelstein, K. Deisseroth, and R. Burns","AuthorURL":null},{"Title":"From cosmos to connectomes: The evolution of data-intensive science","URL":"https://doi.org/10.1016/j.neuron.2014.08.045","AuthorName":"R. Burns, J. T. Vogelstein, and A. S. Szalay","AuthorURL":null},{"Title":"The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience","URL":"https://arxiv.org/abs/1306.3543","AuthorName":"R. Burns, W. G. Roncal, D. Kleissas, K. Lillaney, P. Manavalan, E. Perlman, D. R. Berger, D. D. Bock, K. Chung, L. Grosenick, N. Kasthuri, N. C. Weiler, K. Deisseroth, M. Kazhdan, J. Lichtman, R. C. Reid, S. J. Smith, A. S. Szalay, J. T. Vogelstein, and R. J. Vogelstein.","AuthorURL":null},{"Title":"To the Cloud! A Grassroots Proposal to Accelerate Brain Science Discovery","URL":"https://doi.org/10.1016/j.neuron.2016.10.033","AuthorName":"J. T. Vogelstein, B. Mensh, M. Häusser, N. Spruston, A. C. Evans, K. Kording, K. Amunts, C. Ebell, J. Muller, M. Telefont, S. Hill, S. P. Koushika, C. Calì, P. A. Valdés-Sosa, P. B. Littlewood, C. Koch, S. Saalfeld, A. Kepecs, H. Peng, Y. O. Halchenko, G. Kiar, M. M. Poo, J. B. Poline, M. P. Milham, A. P. Schaffer, R. Gidron, H. Okano, V. D. Calhoun, M. Chun, D. M. Kleissas, R. J. Vogelstein, E. Perlman, R. Burns, R. Huganir, and M. I. Miller","AuthorURL":null}]},"RegistryEntryAdded":"2019-12-06","RegistryEntryLastModified":"2021-06-24","Slug":"open-neurodata","Sources":["awslabs-open-data-registry"]}
{"Name":"OpenRoboCare Multi-Modal Expert Demonstration Dataset for Robot-Assisted Caregiving","Description":"A comprehensive multimodal dataset capturing real-world caregiving routines from 21 occupational therapists performing 15 daily caregiving tasks. The dataset includes synchronized RGB-D video, tactile sensing, eye-gaze tracking, pose annotations, and action labels across 315 sessions totaling 19.8 hours of expert demonstrations. Data modalities include anonymized RGB images, depth maps, 44-sensor tactile readings, 2D/3D pose tracking, temporal action annotations, and first/third-person videos, enabling research in robot learning from demonstration, multimodal perception, and safe human-robot interaction for caregiving applications.","Documentation":"https://emprise.cs.cornell.edu/robo-care/docs","Contact":"https://emprise.cs.cornell.edu/robo-care/","ManagedBy":"[EmPRISE Lab at Cornell University](https://emprise.cs.cornell.edu/)","UpdateFrequency":"Static dataset - no regular updates planned","Tags":["aws-pds","computer vision","health","life sciences","machine learning","robotics"],"License":"BSD-3-Clause license - Academic and non-commercial use permitted. See documentation for full terms.","Citation":"Liang, X., Liu, Z., Lin, K., Gu, E., Ye, R., Nguyen, T., Hsu, C., Wu, Z., Yang, X., Cheung, C.S.Y., Soh, H., Dimitropoulou, K., & Bhattacharjee, T. (2025). OpenRoboCare: A Multimodal Multi-Task Expert Demonstration Dataset for Robot Caregiving. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).","Resources":[{"Description":"Full Dataset","ARN":"arn:aws:s3:::open-robo-care","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Explore OpenRoboCare in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/8cc6c95c-ab3d-5f38-bff1-41828c3a9580/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Get To Know A Dataset: OpenRoboCare","URL":"https://github.com/empriselab/open-data-examples/blob/main/open-robo-care/get-to-know-a-dataset.ipynb","AuthorName":"Cornell University EmPRISE Lab","AuthorURL":"https://emprise.cs.cornell.edu/"},{"Title":"OpenRoboCare Dataset Viewer","URL":"https://emprise.cs.cornell.edu/robo-care-viewer/","AuthorName":"Cornell University EmPRISE Lab","AuthorURL":"https://emprise.cs.cornell.edu/"}],"Publications":[{"Title":"OpenRoboCare: A Multimodal Multi-Task Expert Demonstration Dataset for Robot Caregiving","URL":"https://emprise.cs.cornell.edu/robo-care/","AuthorName":"Liang X, Liu Z, Lin K, et al."}]},"RegistryEntryAdded":"2025-10-17","RegistryEntryLastModified":"2026-11-03","Slug":"open-robo-care","Sources":["awslabs-open-data-registry"]}
{"Name":"OpenAerialMap on AWS","Description":"OpenAerialMap is a collection of high-resolution openly licensed satellite and aerial imagery.","Documentation":"https://docs.imagery.hotosm.org/","Contact":"info@openaerialmap.org","ManagedBy":"[Humanitarian OpenStreetMap Team](https://www.hotosm.org/)","UpdateFrequency":"New imagery is added as soon as it is uploaded by community contributors.","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aerial imagery","cog","disaster response","earth observation","satellite imagery"],"License":"All imagery is publicly licensed CC-BY 4.0, with attribution as contributors of Open Imagery Network. All imagery is available to be traced in OpenStreetMap.","Resources":[{"Description":"OpenAerialMap files and metadata","ARN":"arn:aws:s3:::oin-hotosm-temp","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://oin-hotosm-temp.s3.amazonaws.com/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Displaying OpenAerialMap Imagery in Simple GIS Client","URL":"https://www.youtube.com/watch?v=woDsyAQQMko","AuthorName":"Simple GIS Software","AuthorURL":"https://www.youtube.com/@Simplegissoftware"},{"Title":"How to Segment Buildings on Drone Imagery with Fast.ai & Cloud-Native GeoData Tools","URL":"https://medium.com/@anthropoco/how-to-segment-buildings-on-drone-imagery-with-fast-ai-cloud-native-geodata-tools-ae249612c321","AuthorName":"Dave Luo","AuthorURL":"https://medium.com/@anthropoco"},{"Title":"Use deep learning to assess palm tree health","URL":"https://learn.arcgis.com/en/projects/use-deep-learning-to-assess-palm-tree-health/","AuthorName":"Julia Lenhardt","AuthorURL":"https://www.esri.com"},{"Title":"Using OpenAerialMap imagery","URL":"https://docs.imagery.hotosm.org/usage/using-imagery/","AuthorName":"Humanitarian OpenStreetMap Team","AuthorURL":"https://www.hotosm.org/"}],"Tools & Applications":[{"Title":"OpenAerialMap for searching, viewing and downloading imagery","URL":"https://imagery.hotosm.org/","AuthorName":"Humanitarian OpenStreetMap Team","AuthorURL":"https://www.hotosm.org/"},{"Title":"OpenAerialMap STAC API for imagery metadata and search","URL":"https://api.imagery.hotosm.org/stac","AuthorName":"Humanitarian OpenStreetMap Team","AuthorURL":"https://www.hotosm.org/"}],"Publications":[{"Title":"Analysing the use of OpenAerialMap images for OpenStreetMap edits.","URL":"https://doi.org/10.1080/10095020.2024.2341747","AuthorName":"Mandourah, Hochmair"},{"Title":"Mapping a response: Using satellite images to aid humanitarian action","URL":"https://library.alnap.org/system/files/content/resource/files/main/alnap-hot-open-aerial-mapping-case-study-2016.pdf","AuthorName":"ALNAP"},{"Title":"OpenAerialMap: A Distributed Commons for Searching and Hosting Free Imagery","URL":"https://www.slideshare.net/slideshow/openaerialmap-a-distributed-commons-for-searching-and-hosting-free-imagery/52880462","AuthorName":"Kate Chapman, Humanitarian OpenStreetMap Team"}]},"RegistryEntryAdded":"2024-10-23","RegistryEntryLastModified":"2026-11-04","Slug":"openaerialmap","Sources":["awslabs-open-data-registry"]}
{"Name":"OpenAlex dataset","Description":"An open, comprehensive index of scolarly papers, citations, authors, institutions, and journals.","Documentation":"https://docs.openalex.org","Contact":"team@ourresearch.org","ManagedBy":"[OurResearch](https://ourresearch.org/)","UpdateFrequency":"Approximately monthly","Tags":["aws-pds","graph","json","metadata","scholarly communication"],"License":"[CC0](https://creativecommons.org/publicdomain/zero/1.0/)","Resources":[{"Description":"OpenAlex Entities in JSON Lines format","ARN":"arn:aws:s3:::openalex","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://openalex.s3.amazonaws.com/browse.html)"]},{"Description":"Openalex Entities decomposed to tab-separated columnar files for backward compatibility with Microsoft Academic Graph","ARN":"arn:aws:s3:::openalex-mag-format","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://openalex-mag-format.s3.amazonaws.com/browse.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Data Analysis and Knowledge Graph with OpenAlex dataset","URL":"https://github.com/aws-samples/aws-openalex-knowledge-graph","NotebookURL":"https://github.com/aws-samples/aws-openalex-knowledge-graph/blob/main/eda-knowledge-graph-with-openalex-dataset.ipynb","AuthorName":"Gabriel Bella Martini","Services":["Amazon SageMaker Studio Lab"]},{"Title":"Download snapshot","URL":"https://docs.openalex.org/download-snapshot","AuthorName":"OurResearch","AuthorURL":"https://ourresearch.org"},{"Title":"Getting citation data from OpenAlex by DOI (Jupyter notebook)","URL":"https://twitter.com/ipoga/status/1521604494592782336","AuthorName":"Jens Peter Anderson","AuthorURL":"https://twitter.com/ipoga"}],"Publications":[{"Title":"OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts","URL":"https://arxiv.org/abs/2205.01833","AuthorName":"Jason Priem, Heather Piwowar, Richard Orr"}]},"RegistryEntryAdded":"2022-05-17","RegistryEntryLastModified":"2025-02-04","Slug":"openalex","Sources":["awslabs-open-data-registry"]}
{"Name":"OpenAQ","Description":"Global, aggregated physical air quality data from public data sources provided by government, research-grade and other sources. These awesome groups do the hard work of measuring these data and publicly sharing them, and our community makes them more universally-accessible to both humans and machines.","Documentation":"https://openaq.org","Contact":"info@openaq.org","ManagedBy":"[OpenAQ](https://openaq.org)","UpdateFrequency":"Hourly","Collabs":{"ASDI":{"Tags":["air quality"]}},"Tags":["air quality","aws-pds","cities","environmental","geospatial"],"License":"Varies, depends on data provider","Resources":[{"Description":"Daily gzipped CSVs of global air quality measurements fetched from sources all over the world","ARN":"arn:aws:s3:::openaq-data-archive","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"SNS topic for new objects in the openaq-data-archive bucket","ARN":"arn:aws:sns:us-east-1:817926761842:openaq-data-archive-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"[OpenAQ API](https://api.openaq.org)","Host":"api.openaq.org","Region":"us-east-1","Type":"CloudFront Distribution"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing data from the OpenAQ archive","URL":"https://docs.openaq.org/docs/accessing-openaq-archive-data","AuthorName":"OpenAQ","AuthorURL":"https://openaq.org","Services":["Amazon S3"]},{"Title":"OpenaQ direct file access ObservableHQ notebook","URL":"https://observablehq.com/@openaq/openaq-direct-file-access","AuthorName":"OpenAQ","AuthorURL":"https://openaq.org","Services":["Amazon S3"]},{"Title":"Querying with AWS Athena","URL":"https://docs.openaq.org/docs/querying-with-aws-athena","AuthorName":"OpenAQ","AuthorURL":"https://openaq.org","Services":["Amazon Athena"]}],"Tools & Applications":[{"Title":"OpenAQ Aggregator","URL":"https://github.com/ksree/world-air-quality","AuthorName":"Kapil Sreedharan","AuthorURL":"https://github.com/ksree"},{"Title":"ropenaq R package","URL":"https://github.com/ropensci/ropenaq","AuthorName":"Maëlle Salmon","AuthorURL":"https://github.com/maelle"},{"Title":"Smokey: Air Quality Bot","URL":"https://www.facebook.com/smokeybot/","AuthorName":"Amrit Sharma","AuthorURL":"https://twitter.com/amrit_sharma"}],"Publications":[{"Title":"Enabling rapid COVID-19 and air pollution analysis across the globe with OpenAQ and AWS","URL":"https://aws.amazon.com/blogs/publicsector/enabling-rapid-covid-19-air-pollution-analysis-across-globe-openaq/","AuthorName":"Christa Hasenkopf","AuthorURL":"https://openaq.org"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2025-02-04","Slug":"openaq","Sources":["awslabs-open-data-registry"]}
{"Name":"Open City Model (OCM)","Description":"Open City Model is an initiative to provide cityGML data for all the buildings in the United States.\nBy using other open datasets in conjunction with our own code and algorithms it is our goal to provide 3D geometries for every US building.\n","Documentation":"https://github.com/opencitymodel/opencitymodel","Contact":"https://github.com/opencitymodel/opencitymodel#contact","UpdateFrequency":"Quarterly","ManagedBy":"BuildZero","Collabs":{"ASDI":{"Tags":["infrastructure"]}},"Tags":["aws-pds","cities","events","geospatial"],"License":"https://github.com/opencitymodel/opencitymodel#license","Resources":[{"Description":"Project data files","ARN":"arn:aws:s3:::opencitymodel","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Investigating environmental characteristics of US cities using publicly available ASDI datasets","URL":"https://github.com/aws-samples/aws-asdi-cities-smsl-notebook","NotebookURL":"https://github.com/aws-samples/aws-asdi-cities-smsl-notebook/blob/main/ASDI_Cities_Demo.ipynb","AuthorName":"Darren Ko","Services":["Amazon SageMaker Studio Lab"]},{"Title":"Running queries on Open City Model using AWS Athena","URL":"https://github.com/opencitymodel/opencitymodel/blob/master/examples/Query-OpenCityModel-using-AWS-Athena.md","AuthorName":"Allen Gilliland","AuthorURL":"https://github.com/agilliland","Services":["Amazon Athena"]},{"Title":"Using Open City Model with the 3dCityDB","URL":"https://github.com/opencitymodel/opencitymodel/blob/master/examples/3dCityDB-to-GoogleEarth.md","AuthorName":"Allen Gilliland","AuthorURL":"https://github.com/agilliland"}]},"RegistryEntryAdded":"2019-01-16","RegistryEntryLastModified":"2025-02-04","Slug":"opencitymodel","Sources":["awslabs-open-data-registry"]}
{"Name":"OpenProteinSet","Description":"Multiple sequence alignments (MSAs) for 140,000 unique [Protein Data Bank](https://www.rcsb.org/) (PDB) chains and 16,000,000 [UniClust30](https://uniclust.mmseqs.com/) clusters. Template hits are also provided for the PDB chains and 270,000 UniClust30 clusters chosen for maximal diversity and MSA depth. MSAs were generated with HHBlits (-n3) and JackHMMER against MGnify, BFD, UniRef90, and UniClust30 while templates were identified from PDB70 with HHSearch, all according to procedures outlined in the supplement to the AlphaFold 2 Nature paper, [Jumper et al. 2021](https://www.nature.com/articles/s41586-021-03819-2). We expect the database to be broadly useful to structural biologists training or validating deep learning models for protein structure prediction and related tasks.","Documentation":"https://docs.google.com/document/d/1R90-VJSLQEbot7tgXF3zb068Y1ZJAmsckQ_t2sJTv2c/edit?usp=sharing","Contact":"https://github.com/aqlaboratory/openfold/issues","ManagedBy":"OpenFold","UpdateFrequency":"Never","Tags":["alphafold","aws-pds","life sciences","msa","open source software","openfold","protein","protein folding","protein template"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Citation":"Additionally, please cite [our manuscript](https://arxiv.org/abs/2308.05326).","Resources":[{"Description":"A repository of MSAs and template hits.","ARN":"arn:aws:s3:::openfold","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization","URL":"https://biorxiv.org/content/10.1101/2022.11.20.517210","AuthorName":"Ahdritz, Gustaf; Bouatta, Nazim; Kadyan, Sachin; Xia, Qinghui; Gerecke, William; O'Donnell, Timothy J, et al"},{"Title":"OpenProteinSet: Training data for structural biology at scale","URL":"https://arxiv.org/abs/2308.05326","AuthorName":"Ahdritz, Gustaf; Bouatta, Nazim; Kadyan, Sachin; Jarosch, Lukas; Berenberg, Daniel; Fisk, Ian, et al"}],"Tutorials":[{"Title":"Explore OpenProteinSet in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/0aa301d4-b724-5011-a397-5d4f776893c6/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Run inference at scale for OpenFold, a PyTorch-based protein folding ML model, using Amazon EKS","URL":"https://aws.amazon.com/blogs/machine-learning/run-inference-at-scale-for-openfold-a-pytorch-based-protein-folding-ml-model-using-amazon-eks/","AuthorName":"Shubha Kumbadakone, Ankur Srivastava, and Sachin Kadyan","Services":["Amazon EKS","Amazon FSx"]}]},"RegistryEntryAdded":"2022-03-25","RegistryEntryLastModified":"2026-11-03","Slug":"openfold","Sources":["awslabs-open-data-registry"]}
{"Name":"OpenFold3 Training Data","Description":"This dataset contains MSAs and predicted structures used to train  OpenFold3 preview, an open-source, all-atom ligand, RNA and protein structure prediction software. This includes - \n  - PDB - 245k structures and alignments from the RCSB Protein Data Bank - https://www.rcsb.org/\n  - Long monomer distillation set - ~13 million long (sequence length >= 200 amino acids) monomers from the MGNIFY database - https://www.ebi.ac.uk/metagenomics/.\n  - Short monomer distillation set - 400k short (sequence length < 200 amino acid) monomers from the MGNIFY database - https://www.ebi.ac.uk/metagenomics/.\n  - Disordered set - AF2-predicted structures for unresolved segments missing from the PDB\n  - RNA - OF3p2-predicted RNA monomer structures generated from a clustered version of RFAM (current version)\nFor the distillation sets MSAs were generated using the AF3 protocol, and were used to predict structures with AlphaFold2, more details can be found in our whitepaper - https://portal.openfold.omsf.io/reports/of3p2_technical_report.pdf\nFor a full description and an interactive data explorer, please visit https://portal.openfold.omsf.io/datasets\n","Documentation":"https://portal.openfold.omsf.io/datasets","Contact":"https://github.com/aqlaboratory/openfold-3/issues","ManagedBy":"OpenFold Consortium","UpdateFrequency":"Never","Tags":["aws-pds","life sciences","msa","open source software","openfold","protein","protein folding","protein template"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Citation":"Additionally, please cite [our repository](https://doi.org/10.5281/zenodo.19001000) and [our prior manuscript](https://www.nature.com/articles/s41592-024-02272-z).","Resources":[{"Description":"A repository of MSAs and 3D protein structural coordinates used to train OpenFold3.","ARN":"arn:aws:s3:::openfold3-data","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Deploying OpenFold3 with NVIDIA NIMs on Brev & AWS EC2","URL":"https://www.youtube.com/watch?v=7fVsDWfbr7w","AuthorName":"Glòria Macià","AuthorURL":"https://www.linkedin.com/in/gloriamacia/"},{"Title":"Looking at an OpenFold3 MSA in a Browser-Based Notebook on Scigantic","URL":"https://scigantic.com/archive/4c0256e9-c213-5aac-957b-bbd5c52439a6/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"}],"Publications":[{"Title":"OpenFold3-preview2 Technical Report","URL":"https://portal.openfold.omsf.io/reports/of3p2_technical_report.pdf","AuthorName":"The OpenFold3 Team"},{"Title":"OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization","URL":"https://www.nature.com/articles/s41592-024-02272-z","AuthorName":"Ahdritz, Gustaf; Bouatta, Nazim; Kadyan, Sachin; Xia, Qinghui; Gerecke, William; O'Donnell, Timothy J, et al"}]},"RegistryEntryAdded":"2026-03-12","RegistryEntryLastModified":"2026-04-19","Slug":"openfold3","Sources":["awslabs-open-data-registry"]}
{"Name":"Open Food Facts Images","Description":"A dataset of all images of Open Food Facts, the biggest open dataset of food products in the world.","Documentation":"https://openfoodfacts.github.io/openfoodfacts-server/api/aws-images-dataset","Contact":"contact@openfoodfacts.org","ManagedBy":"[Open Food Facts](https://world.openfoodfacts.org)","UpdateFrequency":"Monthly","License":"All data contained in this dataset is licenced under the [Creative Commons Attribution ShareAlike licence](https://creativecommons.org/licenses/by-sa/3.0/deed.en)","Collabs":{"ASDI":{"Tags":["agriculture"]}},"Tags":["aws-pds","image processing","machine learning"],"Resources":[{"Description":"Open Food Facts image dataset","ARN":"arn:aws:s3:::openfoodfacts-images","Region":"eu-west-3","Type":"S3 Bucket"}],"RegistryEntryAdded":"2023-05-18","RegistryEntryLastModified":"2025-07-09","Slug":"openfoodfacts-images","Sources":["awslabs-open-data-registry"]}
{"Name":"Open Human Genome Library","Description":"The Open Human Genome Library (OpenHGL) is a collection of high-quality *de novo* human assemblies that are publicly available in genomic databases (e.g. NCBI and CNCB) or from individual research papers. It provides consistent naming and uniform formats across datasets, supporting efficient subsequence retrieval and approximate string search.\n","Documentation":"https://lh3.github.io/OpenHGL/","Contact":"https://github.com/lh3/OpenHGL/issues","ManagedBy":"Heng Li lab at Dana-Farber Cancer Institute and Harvard Medical School","UpdateFrequency":"As new data or new analyses become available","Tags":["aws-pds","bioinformatics","biology","genomic","life sciences"],"License":"Creative Commons Zero (CC0)","Resources":[{"Description":"This bucket contains genomic sequences in the AGC format and the corresponding FM-index in the ropebwt3 format.\n","ARN":"arn:aws:s3:::openhgl","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Notifications for OpenHGL updates","ARN":"arn:aws:sns:us-east-1:104240442756:openhgl-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Using OpenHGL data","URL":"https://lh3.github.io/OpenHGL/","AuthorName":"Heng Li"}],"Publications":[{"Title":"AGC: compact representation of assembled genomes with fast queries and updates","URL":"https://pubmed.ncbi.nlm.nih.gov/36864624/","AuthorName":"Sebastian Deorowicz, Agnieszka Danek, Heng Li"},{"Title":"BWT construction and search at the terabase scale","URL":"https://doi.org/10.1093/bioinformatics/btae717","AuthorName":"Heng Li"}]},"RegistryEntryAdded":"2025-12-13","RegistryEntryLastModified":"2025-12-22","Slug":"openhgl","Sources":["awslabs-open-data-registry"]}
{"Name":"OpenNeuro","Description":"OpenNeuro is a database of openly-available brain imaging data. The data are shared according to a Creative Commons CC0 license, providing a broad range of brain imaging data to researchers and citizen scientists alike. The database primarily focuses on functional magnetic resonance imaging (fMRI) data, but also includes other imaging modalities including structural and diffusion MRI, electroencephalography (EEG), and magnetoencephalograpy (MEG). OpenfMRI is a project of the [Center for Reproducible Neuroscience at Stanford University](http://reproducibility.stanford.edu). Development of the OpenNeuro resource has been funded by the National Science Foundation, National Institute of Mental Health, National Institute on Drug Abuse, and the Laura and John Arnold Foundation.","Documentation":"http://openneuro.org","Contact":"Support form at https://openneuro.org","ManagedBy":"[Stanford University Center for Reproducible Neuroscience](https://reproducibility.stanford.edu/)","UpdateFrequency":"New datasets deposited every 4-6 days","Tags":["aws-pds","biology","imaging","life sciences","neurobiology","neuroimaging","neuroscience"],"License":"CC0","Resources":[{"Description":"MRI, MEG, EEG, iEEG, and ECoG datasets from OpenNeuro","ARN":"arn:aws:s3:::openneuro.org","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing UCLA Consortium for Neuropsychiatric Phenomics from OpenNeuro with Scigantic","URL":"https://scigantic.com/archive/7e4099d0-fde8-5358-875e-76f6dad3b366/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2026-04-17","Slug":"openneuro","Sources":["awslabs-open-data-registry"]}
{"Name":"OpenSurfaces","Description":"A large database of annotated surfaces created from real-world consumer photographs.","Documentation":"http://opensurfaces.cs.cornell.edu/publications/opensurfaces/","Contact":"snavely@cs.cornell.edu","ManagedBy":"Cornell University","UpdateFrequency":"Static dataset (not updated)","Tags":["aws-pds","computer vision"],"License":"The annotations are licensed under a Creative Commons Attribution 4.0 International License. The photos have their own licenses.","Resources":[{"Description":"OpenSurfaces data","ARN":"arn:aws:s3:::labelmaterial","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse data on project webpage](http://opensurfaces.cs.cornell.edu/)"]}],"DataAtWork":{"Publications":[{"Title":"OpenSurfaces: A Richly Annotated Catalog of Surface Appearance","URL":"http://opensurfaces.cs.cornell.edu/publications/opensurfaces/","AuthorName":"Sean Bell, Paul Upchurch, Noah Snavely, Kavita Bala"}]},"RegistryEntryAdded":"2021-04-01","RegistryEntryLastModified":"2022-06-06","Slug":"opensurfaces","Sources":["awslabs-open-data-registry"]}
{"Name":"Open Targets","Description":"The Open Targets Platform is a comprehensive data integration tool that supports systematic identification and prioritisation of potential therapeutic drug targets. By integrating publicly available datasets including data generated by the Open Targets experimental and informatics research programmes, the Platform provides data and services to assist in the task of therapeutic hypothesis building.","Documentation":"https://platform-docs.opentargets.org/","Contact":"outreach@opentargets.org","ManagedBy":"Open Targets","UpdateFrequency":"The data is released every three months.","Tags":["aws-pds","bioinformatics","biology","drug discovery","genetic","genomic","life sciences","protein"],"License":"https://creativecommons.org/publicdomain/zero/1.0/","Resources":[{"Description":"Open Targets Data","ARN":"arn:aws:s3:::open-targets-public-data-releases","Region":"eu-west-1","Type":"S3 Bucket"},{"Description":"Notifications for new Open Targets data releases","ARN":"arn:aws:sns:eu-west-1:674693859687:open-targets-public-data-releases-object_created","Region":"eu-west-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Autoimmune colocalisations","URL":"https://github.com/opentargets/notebooks","NotebookURL":"https://github.com/opentargets/notebooks/blob/main/notebooks/autoimmune_colocalisations.ipynb","AuthorName":"Open Targets Team","AuthorURL":"https://github.com/opentargets"},{"Title":"Autoimmune credible sets","URL":"https://github.com/opentargets/notebooks","NotebookURL":"https://github.com/opentargets/notebooks/blob/main/notebooks/autoimmune_credible_set.ipynb","AuthorName":"Open Targets Team","AuthorURL":"https://github.com/opentargets"},{"Title":"ChEMBL Evidence Data Download","URL":"https://github.com/opentargets/notebooks","NotebookURL":"https://github.com/opentargets/notebooks/blob/main/notebooks/chembl_evidence_download.ipynb","AuthorName":"Open Targets Team","AuthorURL":"https://github.com/opentargets"},{"Title":"Exploration of Open Targets datasets","URL":"https://github.com/opentargets/notebooks","NotebookURL":"https://github.com/opentargets/notebooks/blob/main/notebooks/exploring_ot_datasets.ipynb","AuthorName":"Open Targets Team","AuthorURL":"https://github.com/opentargets"},{"Title":"Getting started with the Open Targets Platform GraphQL API","URL":"https://www.ebi.ac.uk/training/events/getting-started-open-targets-platform-graphql-api/","AuthorName":"Helena Cornu","AuthorURL":"https://www.ebi.ac.uk/people/person/helena-cornu/"},{"Title":"Open Targets informatics tools","URL":"https://www.ebi.ac.uk/training/online/courses/open-targets-quick-tour/","AuthorName":"Helena Cornu","AuthorURL":"https://www.ebi.ac.uk/people/person/helena-cornu/"},{"Title":"Platform datasets on AWS","URL":"https://platform-docs.opentargets.org/data-access/platform-datasets-on-aws","NotebookURL":"https://colab.research.google.com/github/opentargets/notebooks/blob/main/notebooks/reading_data_from_aws.ipynb","AuthorName":"Daniel Suveges","AuthorURL":"https://www.ebi.ac.uk/people/person/daniel-suveges/"}],"Tools & Applications":[{"Title":"Open Targets Platform API","URL":"https://api.platform.opentargets.org/","AuthorName":"Open Targets","AuthorURL":"https://github.com/opentargets/platform-api"},{"Title":"Open Targets Platform MCP","URL":"https://mcp.platform.opentargets.org/mcp/","AuthorName":"Open Targets","AuthorURL":"https://github.com/opentargets/open-targets-platform-mcp"},{"Title":"Open Targets Platform Web Application","URL":"https://platform.opentargets.org/","AuthorName":"Open Targets","AuthorURL":"https://github.com/opentargets/ot-ui-apps"}],"Publications":[{"Title":"Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery","URL":"https://doi.org/10.1093/nar/gkae1128","AuthorName":"Annalisa Buniello","AuthorURL":"https://orcid.org/0000-0002-4623-8642"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2021-01-19","RegistryEntryLastModified":"2026-01-13","Slug":"opentargets","Sources":["awslabs-open-data-registry"]}
{"Name":"OpenUniverse 2024 Simulated Roman & Rubin Images","Description":"This release consists of simulated data products designed to mimic observations of the same region of the sky as seen by two astronomical facilities: the Nancy Grace Roman Telescope and the Vera C. Rubin Observatory.","Documentation":"https://irsa.ipac.caltech.edu/data/theory/openuniverse2024","Contact":"https://irsa.ipac.caltech.edu/docs/help_desk.html","ManagedBy":"NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech","UpdateFrequency":"OpenUniverse 2024 has been finalized and will not be updated.","Tags":["astronomy","aws-pds","imaging","object detection","parquet","satellite imagery","simulations","survey"],"License":"https://irsa.ipac.caltech.edu/data_use_terms.html","Citation":"If you use the OpenUniverse 2024 Data Preview, please cite the Digital Object Identifier (DOI): [10.26131/IRSA569](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA569). If you use the OpenUniverse 2024 Full Release, please cite the Digital Object Identifier (DOI): [10.26131/IRSA596](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA596). In addition, please follow the [OpenUniverse 2024 citation guidelines](https://irsa.ipac.caltech.edu/data/theory/openuniverse2024/overview.html) and the [IRSA acknowledgement guidelines](https://irsa.ipac.caltech.edu/ack.html).","Resources":[{"Description":"The simulated Roman data products include truth files listing the basic physical properties of the simulated astronomical objects; truth images that include the appropriate bandpass and PSF but limited sources of noise; calibrated images that include relevant backgrounds and major sources of noise; and coadded images created using the IMCOM software.","ARN":"arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/roman/","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"The simulated Rubin data products include raw pixel data, calibrated exposures, coadds of the calibrated exposures, and catalogs of photometry measured from the simulated images.","ARN":"arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/rubin/","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false}],"DataAtWork":{"Tutorials":[{"Title":"Notebook Tutorials","URL":"https://irsa.ipac.caltech.edu/docs/notebooks/","AuthorName":"Caltech/IPAC-IRSA","AuthorURL":"https://irsa.ipac.caltech.edu"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2024-06-03","RegistryEntryLastModified":"2025-02-04","Slug":"openuniverse2024","Sources":["awslabs-open-data-registry"]}
{"Name":"OpenWings OpenData","Description":"DNA sequence data of UCE loci collected from the world's bird species (n=10,560).","Documentation":"https://github.com/openwings-project/opendata","Contact":"https://github.com/openwings-project/opendata/issues","ManagedBy":"[OpenWings Project](https://github.com/openwings-project)","UpdateFrequency":"Monthly","Tags":["biodiversity","fastq","genetic","genome","life sciences","museum","wildlife"],"License":"[OpenWings Data Use Policy](https://github.com/openwings-project/opendata?tab=readme-ov-file#data-use-policy)","Resources":[{"Description":"OpenWings UCE Sequence Data","ARN":"arn:aws:s3:::openwings-project","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Tutorial I - UCE Phylogenomics","URL":"https://phyluce.readthedocs.io/en/latest/tutorials/tutorial-1.html","AuthorName":"Brant Faircloth","AuthorURL":"https://www.faircloth-lab.org/"}],"Tools & Applications":[{"Title":"phyluce","URL":"https://github.com/faircloth-lab/phyluce","AuthorName":"Brant Faircloth"}],"Publications":[{"Title":"Ultraconserved elements anchor thousands of genetic markers for target enrichment spanning multiple evolutionary timescales","URL":"http://doi.org/10.1093/sysbio/SYS004","AuthorName":"BC Faircloth, JE McCormack, NG Crawford, MG Harvey, RT Brumfield, TC Glenn"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-05-29","RegistryEntryLastModified":"2025-06-03","Slug":"openwings","Sources":["awslabs-open-data-registry"]}
{"Name":"Orcasound - bioacoustic data for marine conservation","Description":"Live-streamed and archived audio data (~2018-present) from underwater microphones (hydrophones) containing marine biological signals as well as ambient ocean noise. Hydrophone placement and passive acoustic monitoring effort prioritizes detection of orca sounds (calls, clicks, whistles) and potentially harmful noise. Geographic focus is on the US/Canada critical habitat of Southern Resident killer whales (northern CA to central BC) with initial focus on inland waters of WA. In addition to the raw lossy or lossless compressed data, we provide a growing archive of annotated bioacoustic bouts.","Documentation":"https://github.com/orcasound/orcadata/wiki","Contact":"info@orcasound.net","ManagedBy":"Orcasound","UpdateFrequency":"Typical latency is 10-120 seconds","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["aws-pds","biodiversity","biology","coastal","conservation","deep learning","ecosystems","environmental","geospatial","labeled","life sciences","machine learning","mapping","oceans","open source software","signal processing"],"License":"https://creativecommons.org/licenses/by-nc-sa/4.0/","Resources":[{"Description":"Live-streamed orca audio data (HLS)","ARN":"arn:aws:s3:::streaming-orcasound-net","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Archived lossless orca audio data (FLAC)","ARN":"arn:aws:s3:::archive-orcasound-net","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Labeled audio data for ML model development","ARN":"arn:aws:s3:::acoustic-sandbox","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Github for our open source projects","URL":"https://www.github.com/orcasound","AuthorName":"Orcasound open source community","AuthorURL":"https://www.orcasound.net/support/#hack"}]},"RegistryEntryAdded":"2021-11-05","RegistryEntryLastModified":"2026-06-05","Slug":"orcasound","Sources":["awslabs-open-data-registry"]}
{"Name":"Pancreatic Cancer Organoid Profiling","Description":"This study generated a collection of patient-derived pancreatic normal and cancer organoids and it was sequenced using Whole Genome Sequencing (WGS), Whole Exome Sequencing (WXS) and RNA-Seq as well as matched tumor and normal tissue if available. The study provides a valuable resource for pancreatic cancer researchers.\nThe dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification.\n","Documentation":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v1.p1","Contact":"dcf-support@datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genetic","genomic","life sciences","STRIDES","transcriptomics","whole genome sequencing"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"RNA-Seq Gene Expression Quantification","ARN":"arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-open","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation,\nRNA-Seq Splice Junction Quantification\n","ARN":"arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-controlled","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v1.p1"}],"DataAtWork":{"Tools & Applications":[{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/ORGANOID-PANCREATIC","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"}],"Publications":[{"Title":"Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer","URL":"https://www.ncbi.nlm.nih.gov/pubmed?cmd=DetailsSearch&term=29853643[PMID]","AuthorName":"Tiriac H, Belleau P, Engle DD, Plenker D, Deschênes A, Somerville TD, et al."}]},"RegistryEntryAdded":"2020-04-08","RegistryEntryLastModified":"2024-12-20","Slug":"organoid-pancreatic","Sources":["awslabs-open-data-registry"]}
{"Name":"OS-Climate Physrisk","Description":"Collection of adapted and derived hazard indicator datasets optimized for running physical climate risk analyses.","Documentation":"https://physrisk.readthedocs.io/en/latest/","Contact":"https://os-climate.org/contact-us/","ManagedBy":"[OS-Climate](https://os-climate.org/)","UpdateFrequency":"Data is updated as new important public domain datasets become available or if corrections are published.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["climate risk","extreme weather","hazard","hazard indicator","peril","physical"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"Physrisk hazard indicators","ARN":"arn:aws:s3:::os-climate-physical-risk","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://os-climate-physical-risk.s3.amazonaws.com)"]}],"DataAtWork":{"Tutorials":[{"Title":"Getting started with Physrisk hazard indicators","URL":"https://physrisk.readthedocs.io/en/latest/getting_started/hazard_indicators.html","AuthorName":"OS-Climate","AuthorURL":"https://github.com/os-climate/"},{"Title":"Physrisk documentation","URL":"https://physrisk.readthedocs.io/en/latest/","AuthorName":"OS-Climate","AuthorURL":"https://github.com/os-climate"}],"Tools & Applications":[{"Title":"Physrisk - physical climate risk calculation engine.","URL":"https://github.com/os-climate/physrisk/","AuthorName":"OS-Climate","AuthorURL":"https://github.com/os-climate/"},{"Title":"Physrisk Sandbox - UI for visualization of hazard indicator data and example use-case.","URL":"https://physrisk-ui-physrisk.apps.osc-cl1.apps.os-climate.org/","AuthorName":"OS-Climate","AuthorURL":"https://github.com/os-climate/"}],"Publications":[{"Title":"Physical Climate Risk Methodology","URL":"https://physrisk.readthedocs.io/en/latest/methodology.html","AuthorName":"Joe Moorhouse et al."}]},"RegistryEntryAdded":"2024-12-09","RegistryEntryLastModified":"2025-07-09","Slug":"os-climate-physrisk","Sources":["awslabs-open-data-registry"]}
{"Name":"OpenStreetMap on AWS","Description":"OSM is a free, editable map of the world, created and maintained by volunteers. Regular OSM data archives are made available in Amazon S3 in both standard formats (OSM PBF, XML) and cloud-native formats optimized for analytics workloads.","Documentation":"https://github.com/awslabs/open-data-docs/tree/main/docs/osm-pds","Contact":"https://planet.osm.org (OSMF-managed data), https://github.com/mojodna/osm-pds-pipelines/issues (cloud-native versions)","UpdateFrequency":"Data is updated minutely/hourly/daily (changes) and weekly (snapshots)","ManagedBy":"OpenStreetMap Foundation (OSMF) and Pacific Atlas","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aws-pds","disaster response","geospatial","mapping","osm"],"License":"https://www.openstreetmap.org/copyright","Resources":[{"Description":"OSMF-managed planet and history PBFs, changeset and discussion XML, and OsmChange (XML) replication files (primary)","ARN":"arn:aws:s3:::osm-planet-eu-central-1","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"OSMF-managed planet and history PBFs, changeset and discussion XML, and OsmChange (XML) replication files (replicated)","ARN":"arn:aws:s3:::osm-planet-us-west-2","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"SNS topic for OSMF-managed S3 event notifications (eu-central-1 bucket)","ARN":"arn:aws:sns:eu-central-1:630658470130:osm-planet-eu-central-1-notifications","Region":"eu-central-1","Type":"SNS Topic"},{"Description":"SNS topic for OSMF-managed S3 event notifications (us-west-2 bucket)","ARN":"arn:aws:sns:us-west-2:630658470130:osm-planet-us-west-2-notifications","Region":"us-west-2","Type":"SNS Topic"},{"Description":"OSM planet snapshots, history, and changesets in cloud-native formats optimized for analytics workloads","ARN":"arn:aws:s3:::osm-pds","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"SNS topic for cloud-native data notifications","ARN":"arn:aws:sns:us-east-1:800218804198:New_File","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Develop and Extract Value from Open Data","URL":"https://aws.amazon.com/blogs/publicsector/develop-and-extract-value-from-open-data/","AuthorName":"Daniel Bernao"},{"Title":"Querying OpenStreetMap Changesets with Amazon Athena","URL":"https://www.openstreetmap.org/user/Jennings%20Anderson/diary/394762","AuthorName":"Jennings Anderson","AuthorURL":"https://www.openstreetmap.org/user/Jennings%20Anderson/diary","Services":["Amazon Athena"]},{"Title":"Querying OpenStreetMap with Amazon Athena","URL":"https://aws.amazon.com/blogs/big-data/querying-openstreetmap-with-amazon-athena/","AuthorName":"Seth Fitzsimmons","AuthorURL":"https://github.com/mojodna"}],"Tools & Applications":[{"Title":"OSM+Athena (GitHub)","URL":"https://github.com/developmentseed/mrworldwide/","AuthorName":"Development Seed","AuthorURL":"https://developmentseed.org/"},{"Title":"PlanetUtils (GitHub): Scripts and a Docker container to maintain your own OpenStreetMap planet","URL":"https://github.com/interline-io/planetutils","AuthorName":"Interline Technologies","AuthorURL":"https://www.interline.io/"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2025-05-08","Slug":"osm","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"Name":"OpenStreetMap Linear Referencing","Description":"OSMLR a linear referencing system built on top of OpenStreetMap. OSM has great information about roads around the world and their interconnections, but it lacks the means to give a stable identifier to a stretch of roadway. OSMLR provides a stable set of numerical IDs for every 1 kilometer stretch of roadway around the world. In urban areas, OSMLR IDs are attached to each block of roadways between significant intersections.","Documentation":"https://github.com/opentraffic/osmlr/blob/master/docs/intro.md","Contact":"https://github.com/opentraffic/osmlr/issues","UpdateFrequency":"New data is added based on community feedback","ManagedBy":"Unmanaged","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aws-pds","geospatial","traffic","osm","disaster response"],"License":"https://github.com/opentraffic/osmlr/blob/master/LICENSE.md","Resources":[{"Description":"Manifest and data files","ARN":"arn:aws:s3:::osmlr","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":null,"Tools & Applications":null,"Publications":null},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2023-02-28","Slug":"osmlr","Sources":["awslabs-open-data-registry"]}
{"Name":"Pattern Analysis of Oxygen Saturation Variability","Description":"This database contains one hour oxygen saturation measurements of 36 patients, used for the analysis of oxygen saturation variability.","Documentation":"https://doi.org/10.13026/C2TH47","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2TH47","ARN":"arn:aws:s3:::physionet-open/osv/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"osv","Sources":["awslabs-open-data-registry"]}
{"Name":"Overture Maps Foundation Open Map Data","Description":"Overture is a collaboratively built, global, open map data project for developers who build map services or use geospatial data. Overture Open Map Data contains data that are standardized under the themes of Admins, Base, Buildings, Places, and Transportation. Overture also includes a Global Entity Reference System (GERS) which encodes map data to a shared universal reference. Beginning with the Overture 2023-11-14-alpha.0 release, the data is available as cloud-native GeoParquet files. \n","Documentation":"Documentation is available at [docs.overturemaps.org](https://docs.overturemaps.org/)","Contact":"info@overturemaps.org","ManagedBy":"[Overture Maps Foundation](https://overturemaps.org)","UpdateFrequency":"Monthly","Tags":["aws-pds","geospatial","global","mapping","osm","parquet","transportation"],"License":"Overture data is licensed under the Community Database License Agreement Permissive v2 (CDLA) unless derived from a source that requires publishing under a different license, such as data derived from OpenStreetMap, that constitutes a 'Derivative Database' (as defined under ODbL v1.0), which will be licensed under ODbL v1.0.\n","Citation":null,"Resources":[{"Description":"Overture Maps Foundation Data (GeoParquet)","ARN":"arn:aws:s3:::overturemaps-us-west-2/release/","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"New File Notification","ARN":"arn:aws:sns:us-west-2:913550007193:overturemaps-us-west-2","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing Overture Maps Data","URL":"https://github.com/OvertureMaps/data/blob/main/README.md","AuthorName":"Overture Maps Foundation"},{"Title":"Working With Overture Data: A Step-by-Step Guide","URL":"https://overturemaps.org/webinar-recap-working-with-overture-data-a-step-by-step-guide/","AuthorName":"Jennings Anderson","Services":["Amazon Athena"]}],"Tools & Applications":[{"Title":"BlockMap: any real place as a Minecraft world, with Overture buildings where OpenStreetMap has none","URL":"https://blockmap.app/how-worlds-are-made#buildings","AuthorName":"Nikandr Software Limited","AuthorURL":"https://nikandrsoftware.com"}],"Publications":[{"Title":"Building Heights: From open USGS lidar to open Overture maps","URL":"https://project.linuxfoundation.org/hubfs/Overture%20Maps/Building%20Heights%20Whitepaper_041423.pdf","AuthorName":"Overture Maps Foundation"},{"Title":"Global Entity Reference System","URL":"https://docs.overturemaps.org/gers/","AuthorName":"Overture Maps Foundation"},{"Title":"Overture Data Schema","URL":"https://docs.overturemaps.org/","AuthorName":"Overture Maps Foundation"},{"Title":"Turkey Business Digital Presence 2026: 1.79M Overture places, with every listed website crawled","URL":"https://pinlyx.com/research/turkey-business-digital-report","AuthorName":"Pinlyx","AuthorURL":"https://pinlyx.com"}]},"RegistryEntryAdded":"2023-11-20","RegistryEntryLastModified":"2026-11-01","Slug":"overture","Sources":["awslabs-open-data-registry"]}
{"Name":"OVRO LWA Solar Observation","Description":"This dataset contains long-term low-frequency solar radio imaging spectroscopy observations from the Owens Valley Radio Observatory Long Wavelength Array (OVRO-LWA). The data are produced from solar radio interferometric observations and distributed as HDF5 spectral image products and FITS all-day spectrogram products, with metadata describing the observation time, frequency coverage, imaging products, and other relevant parameters. The dataset provides dynamic, imaging-based measurements of solar radio emission at low frequencies, which are closely connected to solar coronal activity, solar radio bursts, and space weather phenomena. These observations support studies of solar eruptions, particle acceleration, coronal plasma properties, radio wave propagation, and machine learning applications for solar radio burst detection, event classification, anomaly detection, and space weather prediction.\n","Documentation":"https://ovsa.njit.edu/lwa/","Contact":"Peijin Zhang <peijin.zhang@njit.edu>","ManagedBy":"New Jersey Institute of Technology","UpdateFrequency":"New observations are added regularly. The dataset currently contains approximately 12 TB and grows by approximately 9 TB per year.\n","Tags":["astronomy","aws-pds","hdf5","machine learning","solar","space weather","weather"],"License":"Creative Commons Attribution 4.0 International, CC BY 4.0","Citation":"Please cite the dataset documentation and associated OVRO-LWA solar observation publications when using this dataset. The dataset was accessed from the Registry of Open Data on AWS at https://registry.opendata.aws/ovrolwasolar.\n","Resources":[{"Description":"Long-term low-frequency solar radio imaging spectroscopy observations. Spectral image products are published under `image_hdf/YYYY/MM/DD/` and all-day spectrogram products under `spec_fits/YYYY/`. The dataset currently contains approximately 12 TB and grows by approximately 9 TB per year.\n","ARN":"arn:aws:s3:::ovro-lwa-solar","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for new OVRO LWA Solar Observation data","ARN":"arn:aws:sns:us-west-2:817294255270:ovro-lwa-solar-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know a Dataset: OVRO LWA Solar Observation","URL":"https://github.com/peijin94/open-data-examples/blob/main/ovro-lwa-solar-observation/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/peijin94/open-data-examples/blob/main/ovro-lwa-solar-observation/get-to-know-a-dataset.ipynb","AuthorName":"Peijin Zhang, New Jersey Institute of Technology","Services":["Amazon S3"]},{"Title":"Reading and Plotting OVRO-LWA Spectral Image HDF Data","URL":"https://github.com/ovro-eovsa/lwa-solar-util/blob/main/notebook/image_plot_hdf.ipynb","NotebookURL":"https://github.com/ovro-eovsa/lwa-solar-util/blob/main/notebook/image_plot_hdf.ipynb","AuthorName":"Peijin Zhang, New Jersey Institute of Technology","Services":["Amazon S3"]}],"Tools & Applications":[{"Title":"OVRO LWA Solar Data Portal","URL":"https://ovsa.njit.edu/lwa/","AuthorName":"CSTR, New Jersey Institute of Technology"}],"Publications":[{"Title":"OVRO-LWA Solar Data Products","URL":"https://www.ovsa.njit.edu/wiki/index.php/OVRO-LWA_Solar_Data_Products","AuthorName":"CSTR, New Jersey Institute of Technology"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2026-06-03","RegistryEntryLastModified":"2026-06-22","Slug":"ovrolwasolar","Sources":["awslabs-open-data-registry"]}
{"Name":"Reference data for HiFi human WGS","Description":"Reference data bundle for analyzing HiFi human whole genome\nsequencing data\n","Documentation":"https://zenodo.org/records/8415406","Contact":"dl_it-awsopendata@pacificbiosciences.com","ManagedBy":"[Pacific Biosciences of California, Inc](https://www.pacb.com/)","UpdateFrequency":"Files are updated to reflect the support for the lastest version of\n[PacBio WGS Variant Pipeline]\n(https://github.com/PacificBiosciences/HiFi-human-WGS-WDL)\n","Tags":["aws-pds","genetic","health","Homo sapiens","life sciences","long read sequencing","mapping","variant annotation","vcf","whole genome sequencing"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Citation":"Concepcion, G. T., Lake, J. A., Wenger, A. M., & Rowell, W. J. (2023). Reference data bundle for PacificBiosciences/HiFi-human-WGS-WDL (1.0.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8415406","Resources":[{"Description":"HiFi Human WGS Reference data","ARN":"arn:aws:s3:::pacbio-hifi-human-wgs-reference","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"PacBio WGS Variant Pipeline README","URL":"https://github.com/PacificBiosciences/HiFi-human-WGS-WDL","AuthorName":"Pacific Biosciences of California, Inc","AuthorURL":"https://www.pacb.com"}],"Tools & Applications":[{"Title":"Consolidated analysis with the PacBio WGS Variant Pipeline","URL":"https://www.pacb.com/wp-content/uploads/Application-note-Consolidated-analysis-tools-with-the-PacBio-WGS-Variant-Pipeline.pdf","AuthorName":"Pacific Biosciences of California, Inc","AuthorURL":"https://www.pacb.com"}]},"RegistryEntryAdded":"2024-04-03","RegistryEntryLastModified":"2024-04-03","Slug":"pacbio-human-wgs-reference","Sources":["awslabs-open-data-registry"]}
{"Name":"Pacific Ocean Sound Recordings","Description":"This project offers passive acoustic data (sound recordings) from a deep-ocean environment off central California.  Recording began in July 2015, has been nearly continuous, and is ongoing.  These resources are intended for applications\nin ocean soundscape research, education, and the arts.\n","Documentation":"https://docs.mbari.org/pacific-sound/","Contact":"dcline@mbari.org","ManagedBy":"[Monterey Bay Aquarium Research Institute](https://www.mbari.org/)","UpdateFrequency":"daily","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["acoustics","aws-pds","biodiversity","biology","climate","coastal","deep learning","ecosystems","environmental","machine learning","marine mammals","oceans","open source software"],"License":"CC-BY 4.0","Resources":[{"Description":"original 256 kHz audio recordings year 2015","ARN":"arn:aws:s3:::pacific-sound-256khz-2015","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"original 256 kHz audio recordings year 2016","ARN":"arn:aws:s3:::pacific-sound-256khz-2016","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"original 256 kHz audio recordings year 2017","ARN":"arn:aws:s3:::pacific-sound-256khz-2017","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"original 256 kHz audio recordings year 2018","ARN":"arn:aws:s3:::pacific-sound-256khz-2018","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"original 256 kHz audio recordings year 2019","ARN":"arn:aws:s3:::pacific-sound-256khz-2019","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"original 256 kHz audio recordings year 2020","ARN":"arn:aws:s3:::pacific-sound-256khz-2020","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"original 256 kHz audio recordings year 2021","ARN":"arn:aws:s3:::pacific-sound-256khz-2021","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"original 256 kHz audio recordings year 2022","ARN":"arn:aws:s3:::pacific-sound-256khz-2022","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"original 256 kHz audio recordings year 2023","ARN":"arn:aws:s3:::pacific-sound-256khz-2023","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"original 256 kHz audio recordings year 2024","ARN":"arn:aws:s3:::pacific-sound-256khz-2024","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"original 256 kHz audio recordings year 2025","ARN":"arn:aws:s3:::pacific-sound-256khz-2025","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"decimated 2 kHz audio recordings","ARN":"arn:aws:s3:::pacific-sound-2khz","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"decimated 16 kHz audio recordings","ARN":"arn:aws:s3:::pacific-sound-16khz","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"machine learning models","ARN":"arn:aws:s3:::pacific-sound-models","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Tutorials on machine learning and signal processing methods for anthropogenic and cetacean study using the pacific-sound archives","URL":"https://docs.mbari.org/pacific-sound/","AuthorName":"MBARI","AuthorURL":"https://www.mbari.org/"}],"Publications":[{"Title":"Animal-borne metrics enable acoustic detection of blue whale migration","URL":"https://doi.org/10.1016/j.cub.2020.08.105","AuthorName":"Oestreich et al. (2020)"},{"Title":"Humpback whale song occurrence reflects ecosystem variability in feeding and migratory habitat of the northeast Pacific","URL":"https://doi.org/10.1371/journal.pone.0222456","AuthorName":"Ryan et al. (2019)"},{"Title":"New Passive Acoustic Monitoring in Monterey Bay National Marine Sanctuary","URL":"http://doi.org/10.1109/OCEANS.2016.7761363","AuthorName":"Ryan et al. (2016)"},{"Title":"Reduction of Low-Frequency Vessel Noise in Monterey Bay National Marine Sanctuary During the COVID-19 Pandemic","URL":"https://www.frontiersin.org/articles/10.3389/fmars.2021.656566","AuthorName":"Ryan et al. (2021)"},{"Title":"Seal bomb noise as a potential threat to Monterey Bay harbor porpoise","URL":"https://doi.org/10.3389/fmars.2020.00142","AuthorName":"Simonis et al. (2020)"}]},"RegistryEntryAdded":"2021-09-23","RegistryEntryLastModified":"2024-01-26","Slug":"pacific-sound","Sources":["awslabs-open-data-registry"]}
{"Name":"PALSAR-2 ScanSAR Tropical Cycolne Mocha (L2.1)","Description":"Tropical Cyclone Mocha began to form in the Bay of Bengal on 11 May 2023 and continues to intensify as it moves towards Myanmar and Bangladesh.Cyclone Mocha is the first storm to form in the Bay of Bengal this year and is expected to hit several coastal areas in Bangladesh on 14 May with wind speeds of up to 175 km/h.After made its landfall in the coast between Cox’s Bazar (Bangladesh) and Kyaukphyu (Myanmar) near Sittwe (Myanmar). At most, Catastrophic Damage-causing winds was possible especially in the areas of Rakhine State and Chin State, and Severe Damage-causing winds is possible in the areas of Rakhine, Chin, Magway, and Sagaing ([source] TAOS Model, DisasterAWARE). Bangladesh were preparing to evacuate over 500,000 people as the World Meteorological Organisation (WMO) has warned of big humanitarian impacts once the storm makes landfall. Due to its intensity, Cyclone Mocha is expected to inundate several low-lying areas of the delta nation of Bangladesh which could consequently cause landslides.576 cyclone shelters are ready to provide refuge to those evacuated however damage to infrastructure and agriculture would be devastating.Myanmar ? POTENTIAL OF A CATASTROPHIC DISASTER. An estimated 8.7 Million people, 1.9M households, and $35.3 Billion (USD) of infrastructure (total replacement value) were potentially exposed to moderate to severe damaging winds in accordance with AHA Centre.JAXA has responded to the Tropical Cyclone MOCHA by conducting emergency disaster observations and providing data as requested through the International Disaster Charter and Sentinel Asia. The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation sigma zero = 10*log10(DN2) - 83.0 dB. Included in this dataset are ALOS-2 PALSAR-2 ScanSAR 2.1 data. Level 2.1 data is orthorectified from level 1.1 data by using digital elevation model. Pixel spacing is selectable depending on observation modes. Image coordinate in map projection is geocoded.","UpdateFrequency":"As available.","License":"Data is available for free under the terms of use.","Documentation":"https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.eorc.jaxa.jp/ALOS/en/dataset/palsar2_l22_e.htm","ManagedBy":"[JAXA](https://www.jaxa.jp/)","Contact":"aproject@jaxa.jp","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","cog","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac","sustainability","synthetic aperture radar"],"DataAtWork":{"Publications":[{"Title":"ALOS series Open and Free Data by JAXA EORC","URL":"https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm","AuthorName":"JAXA EORC"}]},"Resources":[{"Description":"PALSAR-2 ScanSAR L2.2","ARN":"arn:aws:s3:::jaxaalos2/palsar2-scansar/Bangladesh/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false}],"RegistryEntryAdded":"2023-05-26","RegistryEntryLastModified":"2025-07-09","Slug":"palsar-2-scansar-flooding-in-bangladesh","Sources":["awslabs-open-data-registry"]}
{"Name":"PALSAR-2 ScanSAR Flooding in Rwanda (L2.1)","Description":"Torrential rainfall triggered flooding and landslides in many parts of Rwanda. The hardest-hit districts were Ngororero, Rubavu, Nyabihu, Rutsiro and Karongi. According to reports, 14 people have died in Karongi, 26 in Rutsiro, 18 in Rubavu, 19 in Nyabihu and 18 in Ngororero.Rwanda National Police reported that the Mukamira-Ngororero and Rubavu-Rutsiro roads are impassable due to flooding and landslide debris. UNITAR on behalf of United Nations Office for the Coordination of Humanitarian Affairs (OCHA) / Regional Office for Southern & Eastern Africa in cooperation with Rwanda Space Agency (RSA) was activated International Disaster Charter. JAXA has responded to the flood event in Rwanda by conducting emergency disaster observations and providing data as requested by OCHA and RSA through the International Disaster Charter. The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation sigma zero = 10*log10(DN2) - 83.0 dB. Included in this dataset are ALOS-2 PALSAR-2 ScanSAR 2.1 data. Level 2.1 data is orthorectified from level 1.1 data by using digital elevation model. Pixel spacing is selectable depending on observation modes. Image coordinate in map projection is geocoded.","UpdateFrequency":"As available.","License":"Data is available for free under the terms of use.","Documentation":"https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.eorc.jaxa.jp/ALOS/en/dataset/palsar2_l22_e.htm","ManagedBy":"[JAXA](https://www.jaxa.jp/)","Contact":"aproject@jaxa.jp","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac","sustainability","synthetic aperture radar"],"DataAtWork":{"Publications":[{"Title":"ALOS series Open and Free Data by JAXA EORC","URL":"https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm","AuthorName":"JAXA EORC"}]},"Resources":[{"Description":"PALSAR-2 ScanSAR L1.1 & L2.2","ARN":"arn:aws:s3:::jaxaalos2/palsar2-scansar/Rwanda/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false}],"RegistryEntryAdded":"2023-05-19","RegistryEntryLastModified":"2025-07-09","Slug":"palsar-2-scansar-flooding-in-rwanda","Sources":["awslabs-open-data-registry"]}
{"Name":"PALSAR-2 ScanSAR Turkey & Syria Earthquake (L2.1 & L1.1)","Description":"JAXA has responded to the Earthquake events in Turkey and Syria by conducting emergency disaster observations and providing data as requested by the Disaster and Emergency Management Authority (AFAD), Ministry of Interior in Turkey, through Sentinel Asia and the International Disaster Charter. Additional information on the event and dataset can be found [here](https://earth.jaxa.jp/en/earthview/2023/02/14/7381/index.html). The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation: γ0 = 10*log10(DN2) - 83.0 dB. Included in this dataset are ALOS PALSAR Level 1.1 and 2.1 data. Level 1.1 is range and single look azimuth compressed data represented by complex I and Q channels to preserve the magnitude and phase information. Range coordinate is in slant range. In the case of ScanSAR mode, an image file is generated per each scan. Level 2.1 data is orthorectified from level 1.1 data by using digital elevation model. Pixel spacing is selectable depending on observation modes. Image coordinate in map projection is geocoded.\n","Documentation":"https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.eorc.jaxa.jp/ALOS/en/dataset/palsar2_l22_e.htm","Contact":"aproject@jaxa.jp","ManagedBy":"[JAXA](https://www.jaxa.jp/)","UpdateFrequency":"As available.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","deafrica","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac","sustainability","synthetic aperture radar"],"License":"Data is available for free under the [terms of use](https://earth.jaxa.jp/policy/en.html).","Resources":[{"Description":"PALSAR-2 ScanSAR L1.1 & L2.2","ARN":"arn:aws:s3:::jaxaalos2/palsar2-scansar/Turkey-Syria-earthquake/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Publications":[{"Title":"ALOS series Open and Free Data","URL":"https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm","AuthorName":"JAXA EORC"},{"Title":"ALOS-2 observations of earthquakes in southeastern Turkey in 2023","URL":"https://earth.jaxa.jp/en/earthview/2023/02/14/7381/index.html","AuthorName":"JAXA EORC"}]},"RegistryEntryAdded":"2023-03-01","RegistryEntryLastModified":"2023-03-01","Slug":"palsar2-scansar-turkey-syria","Sources":["awslabs-open-data-registry"]}
{"Name":"Provision of Web-Scale Parallel Corpora for Official European Languages (ParaCrawl)","Description":"ParaCrawl is a set of large parallel corpora to/from English for all official EU languages by a broad web crawling effort. State-of-the-art methods are applied for the entire processing chain from identifying web sites with translated text all the way to collecting, cleaning and delivering parallel corpora that are ready as training data for CEF.AT and translation memories for DG Translation.\n","Documentation":"https://paracrawl.eu/releases.html","Contact":"For questions regarding the datasets contact Kenneth Heafield, email kheafiel@inf.ed.ac.uk. For reporting any issues about bitextor pipeline visit https://github.com/bitextor/bitextor/issues.","ManagedBy":"[ParaCrawl](https://paracrawl.eu)","UpdateFrequency":"New data is added according to ParaCrawl release schedule.","Tags":["aws-pds","machine translation","natural language processing"],"License":"Creative Commons CC0 license (\"no rights reserved\").","Resources":[{"Description":"Parallel Corpora to/from English for all official EU languages","ARN":"arn:aws:s3:::web-language-models","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2019-02-14","RegistryEntryLastModified":"2019-08-16","Slug":"paracrawl","Sources":["awslabs-open-data-registry"]}
{"Name":"PADS - Parkinsons Disease Smartwatch dataset","Description":"Parkinson’s disease (PD) is the second-most common neurodegenerative disorder, while incidence and worldwide burden are further increasing. In the era of digital health transformation, smart devices and mobile sensors, including smartphones and smartwatches, can provide an affordable source to capture and analyse digital objective biomarkers. These can aid in early diagnosis and studying phenotypical characteristics. The Parkinson’s Disease Smartwatch (PADS) dataset comprises clinical assessments of a broad spectrum of PD patients, other similar movement disorders and healthy controls. The assessments were recorded using a smart-device-based system consisting of two smartwatches and one smartphone. The two smartwatches were worn on each of the patient’s wrists and their sensors synchronously recorded 11 interactive movement tasks that were designed by expert neurologists to provoke subtle changes in movement pathologies. In total, 5159 measurement steps of 469 individuals were captured. The PADS dataset includes all acceleration and rotation sensor signals, as well as details on movement steps, demographics, medical history, and PD-specific non-motor symptoms. We believe that our extensively annotated dataset provides a well-suited data foundation for the training, validation, and optimisation of future technology- and sensor-driven systems for movement disorders.","Documentation":"https://doi.org/10.13026/m0w9-zx22","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/m0w9-zx22","ARN":"arn:aws:s3:::physionet-open/parkinsons-disease-smartwatch/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"parkinsons-disease-smartwatch","Sources":["awslabs-open-data-registry"]}
{"Name":"PASS: Perturb-and-Select Summarizer for Product Reviews","Description":"A collection of product reviews summaries automatically generated by PASS for 32 Amazon products from the FewSum dataset","Documentation":"https://pass-summary-fewsum.s3.amazonaws.com/README.md","Contact":"noved@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not updated","Tags":["amazon.science","natural language processing","text analysis"],"License":"This data is available for anyone to use under the terms of the CDLA-Sharing license, which is available [here](https://cdla.dev/sharing-1-0/)","Resources":[{"Description":"A collection of summaries generated by PASS for the FewSum Product Reviews dataset","ARN":"arn:aws:s3:::pass-summary-fewsum","Region":"us-east-1","Type":"S3 Bucket","Explore":["[pass_generated_summaries.jsonl](https://pass-summary-fewsum.s3.amazonaws.com/pass_gen_summaries_fewsum_amazon_val_test.jsonl)"]}],"DataAtWork":{"Publications":[{"Title":"PASS: Perturb-and-Select Summarizer for Product Reviews","URL":"https://www.amazon.science/publications/pass-perturb-and-select-summarizer-for-product-reviews","AuthorName":"Nadav Oved and Ran Levy (2021)"}]},"RegistryEntryAdded":"2021-06-15","RegistryEntryLastModified":"2022-06-06","Slug":"pass-summaries-fewsum","Sources":["awslabs-open-data-registry"]}
{"Name":"Logan Unitigs and Contigs of the Sequence Read Archive (SRA) on AWS","Description":"This repository is a re-analysis of the NCBI Sequence Read Archive (SRA), December 2023 freeze, to make it more accessible. The SRA is an open access database of biological sequences, containing raw data from high-throughput DNA and RNA sequencing platforms. It is the largest database of public DNA sequences worldwide, containing a wealth of genomic diversity across all living organisms. This repository contains Logan, a set of compressed FASTA files for all individual SRA accessions, in the form of unitigs and contigs. Borrowing methods from the realm of genome assembly, unitigs preserve nearly all the information present in the original sample, whereas contigs get rid of variations to increase sequence lengths. Altogether, Logan recapitulates the information present in the SRA while making it an order of magnitude more accessible due to 20-100x smaller size and higher quality genomic content.","Documentation":"https://github.com/IndexThePlanet/Logan","Contact":"rayan.chikhi@pasteur.fr","ManagedBy":"Institut Pasteur (https://www.pasteur.fr)","UpdateFrequency":"Occasional","Tags":["aws-pds","fasta","genetic","genomic","life sciences","metagenomics","STRIDES","transcriptomics","whole exome sequencing","whole genome sequencing"],"License":"[NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomic Data Sharing Policy](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)","Resources":[{"Description":"Compressed FASTA files (.fa.zst) in a public S3 bucket: all unitigs and contigs of SRA accessions.","ARN":"arn:aws:s3:::logan-pub","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Downloading unitigs of several accessions","URL":"https://github.com/IndexThePlanet/Logan/blob/main/Accessions.md","AuthorName":"Rayan Chikhi","AuthorURL":"https://github.com/IndexThePlanet"},{"Title":"Downloading, mapping many contigs to a gene of interest","URL":"https://github.com/IndexThePlanet/Logan/blob/main/Chickens.md","AuthorName":"Rayan Chikhi","AuthorURL":"https://github.com/IndexThePlanet"},{"Title":"Search for sequences inside unitigs or contigs","URL":"https://github.com/IndexThePlanet/Logan/blob/main/Sequence_Search.md","AuthorName":"Rayan Chikhi","AuthorURL":"https://github.com/IndexThePlanet"}],"Tools & Applications":[{"Title":"f2sz","URL":"https://github.com/asl/f2sz","AuthorName":"Anton Korobeynikov","AuthorURL":"https://anton.korobeynikov.info/"},{"Title":"Logan Search","URL":"https://logan-search.org/","AuthorName":"Pierre Peterlongo","AuthorURL":"https://people.rennes.inria.fr/Pierre.Peterlongo/"},{"Title":"Minia 3","URL":"https://github.com/GATB/minia","AuthorName":"Rayan Chikhi","AuthorURL":"https://github.com/GATB"},{"Title":"Open Virome","URL":"https://openvirome.com/","AuthorName":"Artem Babaian","AuthorURL":"https://rrna.ca"}],"Publications":[{"Title":"Logan - Planetary-Scale Genome Assembly Surveys Life’s Diversity","URL":"https://www.biorxiv.org/content/10.1101/2024.07.30.605881v2.full","AuthorName":"Chikhi R., Lemane T., Loll-Krippleber R., et al (2025)"}]},"RegistryEntryAdded":"2024-03-03","RegistryEntryLastModified":"2025-09-09","Slug":"pasteur-logan","Sources":["awslabs-open-data-registry"]}
{"Name":"Patient-level dataset to study the effect of COVID-19 in people with Multiple Sclerosis","Description":"Multiple Sclerosis (MS) is an inflammatory autoimmune disease of the central nervous system, causing increased vulnerability to infections and disability among young adults. Ever since the coronavirus disease 2019 (COVID-19) outbreak, caused by severe acute respiratory syndrome coronavirus 2 infections, there have been concerns among people with MS (PwMS) about the potential interactions between various disease-modifying therapies and COVID-19. The COVID-19 in MS Global Data Sharing Initiative (GDSI) was initiated in 2020 to address these concerns. This paper focuses on the anonymisation and open-sourcing of a GDSI sub-dataset, comprising data entered by people with MS and clinicians using a fast data entry tool. The dataset includes demographics, comorbidities, hospital stay, and COVID-19 symptoms of PwMS. The dataset can be used to perform different statistical analyses to improve our understanding of COVID-19 in MS. Furthermore, this dataset can also be used within the context of educational activities to educate different stakeholders on the complex data science topics that were used within the GDSI.","Documentation":"https://doi.org/10.13026/77ta-1866","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/77ta-1866","ARN":"arn:aws:s3:::physionet-open/patient-level-data-covid-ms/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"patient-level-data-covid-ms","Sources":["awslabs-open-data-registry"]}
{"Name":"Phrase Clustering Dataset (PCD)","Description":"This dataset is part of the paper \"McPhraSy: Multi-Context Phrase Similarity and Clustering\" by DN Cohen et al (2022). The purpose of PCD is to evaluate the quality of semantic-based clustering of noun phrases. The phrases were collected from the [Amazon Review Dataset] (https://nijianmo.github.io/amazon/).\n","Documentation":"https://amazon-phrase-clustering.s3.amazonaws.com/readme.md","Contact":"Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6riTWmh-cGw/aws-open-data) and use the `AWS Open Data` tag.","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not updated","Tags":["amazon.science","json","natural language processing"],"License":"This data is available for anyone to use under the terms of the CDLA-permissive license, which is available [here](https://cdla.dev/permissive-1-0/)","Resources":[{"Description":"Phsrase Clustering Dataset (PCD)","ARN":"arn:aws:s3:::amazon-phrase-clustering","Region":"us-west-2","Type":"S3 Bucket","Explore":["[phrase-clustering-dataset.json](https://amazon-phrase-clustering.s3.amazonaws.com/phrase-clustering-dataset.json)"]}],"DataAtWork":{"Publications":[{"Title":"McPhraSy: Multi context phrase similarity and clustering","URL":"https://www.amazon.science/publications/mcphrasy-multi-context-phrase-similarity-and-clustering","AuthorName":"Amir DN Cohen, Hila Gonen, Ori Shapira, Ran Levy, and Yoav Goldberg"}]},"RegistryEntryAdded":"2022-11-21","RegistryEntryLastModified":"2022-11-21","Slug":"pcd","Sources":["awslabs-open-data-registry"]}
{"Name":"PhysioNet Cardiovascular Signal Toolbox","Description":"The PhysioNet Cardiovascular Signal Toolbox is an open-source modular program for calculating heart rate variability (HRV) implemented in Matlab with evidence-based algorithms and output formats. The Toolbox is compatible with 64-bit MATLAB on GNU/Linux, Mac OS X, and MS-Windows.","Documentation":"https://physionet.org/content/pcst/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/pcst/","ARN":"arn:aws:s3:::physionet-open/pcst/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"pcst","Sources":["awslabs-open-data-registry"]}
{"Name":"PD12M","Description":"PD12M is a collection of 12.4 million CC0/PD image-caption pairs for the purpose of training generative image models.","Documentation":"https://huggingface.co/datasets/Spawning/PD12M","Contact":"info@spawning.ai","ManagedBy":"Spawning","UpdateFrequency":"Data will be adjusted as infringing works are discovered, improved provenance is acquired, or infringing captions are discovered.","Tags":["art","deep learning","image processing","labeled","machine learning","media"],"License":"https://cdla.dev/permissive-2-0/","Resources":[{"Description":"Image files","ARN":"arn:aws:s3:::pd12m","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Downloading Images","URL":"https://huggingface.co/datasets/Spawning/PD12M/blob/main/tutorials/images.md","AuthorName":"Spawning"},{"Title":"Working with the Metadata","URL":"https://huggingface.co/datasets/Spawning/PD12M","AuthorName":"Spawning"}],"Tools & Applications":[{"Title":"Hugging Face Dataset","URL":"https://huggingface.co/datasets/Spawning/PD12M","AuthorName":"Spawning"},{"Title":"Source.Plus","URL":"https://source.plus/pd12m","AuthorName":"Spawning"}],"Publications":[{"Title":"Datasheet","URL":"https://huggingface.co/datasets/Spawning/PD12M/blob/main/Datasheet.pdf","AuthorName":"Spawning"},{"Title":"PD12M: A Large-Scale Image Captioning Dataset","URL":"https://arxiv.org/abs/2410.23144","AuthorName":"Jordan Meyer, Nick Padgett, Laura Exline, Cullen Miller"}]},"RegistryEntryAdded":"2024-11-04","RegistryEntryLastModified":"2024-11-04","Slug":"pd12m","Sources":["awslabs-open-data-registry"]}
{"Name":"Protein Data Bank 3D Structural Biology Data","Description":"The \"Protein Data Bank (PDB) archive\" was established in 1971 as the first open-access digital data archive in biology. It is a collection of three-dimensional (3D) atomic-level structures of biological macromolecules (i.e., proteins, DNA, and RNA) and their complexes with one another and various small-molecule ligands (e.g., US FDA approved drugs, enzyme co-factors). For each PDB entry (unique identifier: 1abc or PDB_0000001abc) multiple data files contain information about the 3D atomic coordinates, sequences of biological macromolecules, information about any small molecules/ligands present in the entry, details about the structure-determination experiment, authors and publication information, experimental data, and the wwPDB validation report. Additional content stored in the archive includes documentation, summary reports, and software (among others).\nThe PDB is a jointly-managed core archive of the Worldwide Protein Data Bank partnership [RCSB Protein Data Bank (RCSB PDB, rcsb.org); Protein Data Bank in Europe (PDBe, pdbe.org); Protein Data Bank Japan (PDBj, pdbj.org); Electron Microscopy Data Bank (EMDB, emdb-empiar.org); and Biological Magnetic Resonance Bank (BMRB, bmrb.io)].\nRCSB PDB serves as the wwPDB-designated Archive Keeper for the Protein Data Bank.\nAdditional wwPDB Core Archives are as follows:\nElectron Microscopy Data Bank (wwPDB-designated Archive Keeper: EMDB)\nBiological Magnetic Resonance Bank (wwPDB-designated Archive Keeper: BMRB)\n","Documentation":"https://www.wwpdb.org/documentation/file-format","Contact":"https://www.wwpdb.org/about/contact","ManagedBy":"[Worldwide Protein Data Bank Partnership](wwpdb.org)","UpdateFrequency":"New and updated data files are published weekly and released on Wednesdays 0:00 UTC.\n","Tags":["amino acid","archives","aws-pds","bioinformatics","biomolecular modeling","cell biology","chemical biology","COVID-19","electron microscopy","electron tomography","enzyme","life sciences","molecule","nuclear magnetic resonance","pharmaceutical","protein","protein template","SARS-CoV-2","structural biology","x-ray crystallography"],"License":"https://creativecommons.org/publicdomain/zero/1.0/","Citation":"For instructions on citing specific PDB entry by their Digital Object Identifier (DOI), please see https://www.wwpdb.org/about/cite-us.","Resources":[{"Description":"Globally cached distribution of the dataset. Web frontend also available to browse the dataset and file directory.\n","Region":"us-west-2","Type":"CloudFront Distribution","Explore":["[Browse Dataset](https://s3.rcsb.org)"]},{"Description":"Historical snapshots of archival datasets from 2005 onwards. Snapshots are generated annually and at major milestone.\n","ARN":"arn:aws:s3:::pdbsnapshots","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://pdbsnapshots.s3.us-west-2.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"File Download Services","URL":"https://www.rcsb.org/docs/programmatic-access/file-download-services","AuthorName":"RCSB PDB","AuthorURL":"https://rcsb.org/"},{"Title":"Get to Know a Dataset: Protein Data Bank 3D Structural Biology Data","URL":"https://github.com/rcsb/AWS-Open_Data_Registry/blob/master/PDB_3D_Dataset_Tour.ipynb","AuthorName":"RCSB PDB","AuthorURL":"https://rcsb.org/"},{"Title":"PDB 101","URL":"https://pdb101.rcsb.org/","AuthorName":"RCSB PDB","AuthorURL":"https://rcsb.org/"}],"Publications":[{"Title":"Announcing the worldwide Protein Data Bank","URL":"https://doi.org/10.1038/nsb1203-980","AuthorName":"Berman, H., Henrick, K. & Nakamura, H."},{"Title":"Protein Data Bank: the single global archive for 3D macromolecular structure data","URL":"https://doi.org/10.1093/nar/gky949","AuthorName":"wwPDB consortium"}]},"RegistryEntryAdded":"2022-04-25","RegistryEntryLastModified":"2025-11-13","Slug":"pdb-3d-structural-biology-data","Sources":["awslabs-open-data-registry"]}
{"Name":"A Comprehensive Dataset of Pattern Electroretinograms for Ocular Electrophysiology Research: The PERG-IOBA Dataset","Description":"The pattern electroretinogram (PERG) is a valuable tool in ophthalmic electrophysiology, offering a non-invasive and objective method to evaluate central retinal function. By measuring electrical activity in the macula and retinal ganglion cells, PERG provides essential quantitative insights, especially when distinguishing between macular and optic nerve diseases remains challenging. This dataset comprises 1354 transient PERG responses from 304 subjects in 336 records, representing a comprehensive collection of ocular electrophysiological data. The evaluations took place at the Institute of Applied Ophthalmobiology (IOBA) at the University of Valladolid, Spain, from 2003 to 2022. Detailed clinical information, including age, gender, and diagnoses, is provided in CSV format. Additionally, the dataset, which guarantees the presence of at least one PERG signal for each eye, incorporates visual acuity measurements in logMar scale, facilitating assessments of vision quality. Addressing a significant gap in ocular electrophysiological signal repositories, this dataset serves as a valuable resource for the advancement of ophthalmology research. Researchers can explore a wide range of eye-related conditions and diseases, leading to improved diagnostic techniques, treatment strategies, and a deeper understanding of ocular electrophysiology.","Documentation":"https://doi.org/10.13026/d24m-w054","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/d24m-w054","ARN":"arn:aws:s3:::physionet-open/perg-ioba-dataset/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"perg-ioba-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"PersonPath22","Description":"PersonPath22 is a large-scale multi-person tracking dataset containing 236 videos captured mostly from static-mounted cameras,  collected from sources where we were given the rights to redistribute the content and participants have given explicit consent. Each video has ground-truth annotations including both bounding boxes and tracklet-ids for all the persons in each frame.","Documentation":"https://amazon-science.github.io/tracking-dataset/personpath22.html","Contact":"Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6riTWmh-cGw/aws-open-data) and use the `AWS Open Data` tag.","ManagedBy":"[Amazon Web Services](https://aws.amazon.com/)","UpdateFrequency":"Periodically","Tags":["amazon.science","computer vision"],"License":"Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0)","Resources":[{"Description":"Source data","ARN":"arn:aws:s3:::tracking-dataset-eccv-2022","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Large scale Real-world Multi-Person Tracking","URL":"https://assets.amazon.science/6f/13/e48da0fa4cefb9c81adbad6d93ec/large-scale-real-world-multi-person-tracking.pdf","AuthorName":"Bing Shuai, Alessandro Bergamo, Uta Buechler, Andrew Berneshawi, Alyssa Boden, Joseph Tighe"}]},"RegistryEntryAdded":"2022-09-23","RegistryEntryLastModified":"2024-03-27","Slug":"person-path-22","Sources":["awslabs-open-data-registry"]}
{"Name":"ArcticDEM","Description":"ArcticDEM - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2007 to the present. The ArcticDEM project seeks to fill the need for high-resolution time-series elevation data in the Arctic. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and comprehensive product over large areas. ArcticDEM data is constructed from in-track and cross-track high-resolution (~0.5 meter) imagery acquired by the Maxar constellation of optical imaging satellites.","Documentation":"https://www.pgc.umn.edu/data/arcticdem/","Contact":"pgc-support@umn.edu","ManagedBy":"[Polar Geospatial Center](https://www.pgc.umn.edu/)","UpdateFrequency":"New DEM strips are added twice yearly.  Mosaic products are added as soon as they are available.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","cog","earth observation","elevation","geospatial","mapping","open source software","satellite imagery","stac"],"License":"[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)","Citation":"See PGC's [acknowledgement policy](https://www.pgc.umn.edu/guides/user-services/acknowledgement-policy/)","Resources":[{"Description":"ArcticDEM DEM Mosaics","ARN":"arn:aws:s3:::pgc-opendata-dems/arcticdem/mosaics/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Static STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/mosaics.json)","[Dynamic STAC API Endpoint](https://stac.pgc.umn.edu/api/v1/)"]},{"Description":"ArcticDEM DEM Strips","ARN":"arn:aws:s3:::pgc-opendata-dems/arcticdem/strips/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Static STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/strips.json)","[Dynamic STAC API Guide](https://www.pgc.umn.edu/guides/stereo-derived-elevation-models/stac-access-static-and-dynamic-api/)"]}],"DataAtWork":{"Tutorials":[{"Title":"PGC Dynamic STAC API Tutorial","URL":"https://polargeospatialcenter.github.io/pgc-code-tutorials/dynamic_stac_api/web_files/stac_api_demo_workflow.html","AuthorName":"Polar Geospatial Center"}],"Tools & Applications":[{"Title":"ArcticDEM Explorer","URL":"https://arcticdem.apps.pgc.umn.edu/","AuthorName":"Polar Geospatial Center & ESRI"},{"Title":"OpenTopography access to ArcticDEM","URL":"https://doi.org/10.5069/G96Q1VFK","AuthorName":"OpenTopography","AuthorURL":"https://opentopography.org/"}],"Publications":[{"Title":"Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions","URL":"https://doi.org/10.1080/15481603.2015.1008621","AuthorName":"Myoung-Jong Noh, Ian M. Howat"},{"Title":"Automatic relative RPC image model bias compensation through hierarchical image matching for improving DEM quality","URL":"https://doi.org/10.1016/j.isprsjprs.2017.12.008","AuthorName":"Myoung-Jong Noh, Ian M. Howat"},{"Title":"Dynamic ice loss from the Greenland Ice Sheet driven by sustained glacier retreat","URL":"https://doi.org/10.1038/s43247-020-0001-2","AuthorName":"Michalea D. King, Ian M. Howat, Salvatore G. Candela, Myoung J. Noh, Seongsu Jeong, Brice P. Y. Noël, Michiel R. van den Broeke, Bert Wouters, Adelaide Negrete"},{"Title":"Future Evolution of Greenland's Marine-Terminating Outlet Glaciers","URL":"https://doi.org/10.1029/2018JF004873","AuthorName":"Ginny A. Catania, Leigh A. Stearns, Twila A. Moon, Ellen M. Enderlin, R. H. Jackson"},{"Title":"The surface extraction from TIN based search-space minimization (SETSM) algorithm","URL":"https://doi.org/10.1016/j.isprsjprs.2017.04.019","AuthorName":"Myoung-Jong Noh, Ian M. Howat"}]},"RegistryEntryAdded":"2022-10-03","RegistryEntryLastModified":"2025-03-10","Slug":"pgc-arcticdem","Sources":["awslabs-open-data-registry"]}
{"Name":"EarthDEM","Description":"EarthDEM - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2002 to the present. The EarthDEM project seeks to fill the need for high-resolution time-series elevation data in non-polar regions. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and comprehensive product over large areas. EarthDEM data is constructed from in-track and cross-track high-resolution (~0.5 meter) imagery acquired by the Maxar constellation of optical imaging satellites. Only a portion of the worldwide EarthDEM dataset is available publicly. Federally-funded researchers may access the entire dataset via NASA CSDA's Smallsat Data Explorer (see Data at Work section).","Documentation":"https://www.pgc.umn.edu/data/earthdem/","Contact":"pgc-support@umn.edu","ManagedBy":"[Polar Geospatial Center](https://www.pgc.umn.edu/)","UpdateFrequency":"New DEM strips are added when allowed by licensing restrictions.  Mosaic products are added as soon as they are available.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","cog","earth observation","elevation","geospatial","mapping","open source software","satellite imagery","stac"],"License":"[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)","Citation":"See PGC's [acknowledgement policy](https://www.pgc.umn.edu/guides/user-services/acknowledgement-policy/)","Resources":[{"Description":"EarthDEM DEM Strips","ARN":"arn:aws:s3:::pgc-opendata-dems/earthdem/strips/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Static STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/earthdem/strips.json)","[Dynamic STAC API Guide](https://www.pgc.umn.edu/guides/stereo-derived-elevation-models/stac-access-static-and-dynamic-api/)"]}],"DataAtWork":{"Tutorials":[{"Title":"PGC Dynamic STAC API Tutorial","URL":"https://polargeospatialcenter.github.io/pgc-code-tutorials/dynamic_stac_api/web_files/stac_api_demo_workflow.html","AuthorName":"Polar Geospatial Center"}],"Tools & Applications":[{"Title":"GLARS Data Viewer","URL":"https://glars.org/data/view-and-download/","AuthorName":"Great Lakes Alliance for Remote Sensing"},{"Title":"NASA CSDA SmallSat Data Explorer","URL":"https://csdap.earthdata.nasa.gov/","AuthorName":"NASA Commercial Smallsat Acquisition Program"}],"Publications":[{"Title":"Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions","URL":"https://doi.org/10.1080/15481603.2015.1008621","AuthorName":"Myoung-Jong Noh, Ian M. Howat"},{"Title":"Multi-Source EO for Dynamic Wetland Mapping and Monitoring in the Great Lakes Basin","URL":"https://doi.org/10.3390/rs13040599","AuthorName":"Michael J. Battaglia, Sarah Banks, Amir Behnamian, Laura Bourgeau-Chavez, Brian Brisco, Jennifer Corcoran, Zhaohua Chen, Brian Huberty, James Klassen, Joseph Knight, Paul Morin, Kevin Murnaghan, Keith Pelletier, Lori White"},{"Title":"The surface extraction from TIN based search-space minimization (SETSM) algorithm","URL":"https://doi.org/10.1016/j.isprsjprs.2017.04.019","AuthorName":"Myoung-Jong Noh, Ian M. Howat"}]},"RegistryEntryAdded":"2024-05-31","RegistryEntryLastModified":"2025-03-10","Slug":"pgc-earthdem","Sources":["awslabs-open-data-registry"]}
{"Name":"Reference Elevation Model of Antarctica (REMA)","Description":"The Reference Elevation Model of Antarctica - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2009 to the present. The REMA project seeks to fill the need for high-resolution time-series elevation data in the Antarctic. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and comprehensive product over large areas. REMA data is constructed from in-track and cross-track high-resolution (~0.5 meter) imagery acquired by the Maxar constellation of optical imaging satellites.","Documentation":"https://www.pgc.umn.edu/data/rema/","Contact":"pgc-support@umn.edu","ManagedBy":"[Polar Geospatial Center](https://www.pgc.umn.edu/)","UpdateFrequency":"New DEM strips are added twice yearly.  Mosaic products are added as soon as they are available.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","cog","earth observation","elevation","geospatial","mapping","open source software","satellite imagery","stac"],"License":"[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)","Citation":"See PGC's [acknowledgement policy](https://www.pgc.umn.edu/guides/user-services/acknowledgement-policy/)","Resources":[{"Description":"REMA DEM Mosaics","ARN":"arn:aws:s3:::pgc-opendata-dems/rema/mosaics/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Static STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/mosaics.json)","[Dynamic STAC API Endpoint](https://stac.pgc.umn.edu/api/v1/)"]},{"Description":"REMA DEM Strips","ARN":"arn:aws:s3:::pgc-opendata-dems/rema/strips/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Static STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/strips.json)","[Dynamic STAC API Guide](https://www.pgc.umn.edu/guides/stereo-derived-elevation-models/stac-access-static-and-dynamic-api/)"]}],"DataAtWork":{"Tutorials":[{"Title":"PGC Dynamic STAC API Tutorial","URL":"https://polargeospatialcenter.github.io/pgc-code-tutorials/dynamic_stac_api/web_files/stac_api_demo_workflow.html","AuthorName":"Polar Geospatial Center"}],"Tools & Applications":[{"Title":"OpenTopography access to REMA","URL":"https://doi.org/10.5069/G9X63K5S","AuthorName":"OpenTopography","AuthorURL":"https://opentopography.org/"},{"Title":"REMA Explorer","URL":"https://rema.apps.pgc.umn.edu/","AuthorName":"Polar Geospatial Center & ESRI"}],"Publications":[{"Title":"Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions","URL":"https://doi.org/10.1080/15481603.2015.1008621","AuthorName":"Myoung-Jong Noh, Ian M. Howat"},{"Title":"Automatic relative RPC image model bias compensation through hierarchical image matching for improving DEM quality","URL":"https://doi.org/10.1016/j.isprsjprs.2017.12.008","AuthorName":"Myoung-Jong Noh, Ian M. Howat"},{"Title":"Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet","URL":"https://doi.org/10.1038/s41561-019-0510-8","AuthorName":"Morlighem, M., Rignot, E., Binder, T. et al."},{"Title":"The Reference Elevation Model of Antarctica","URL":"https://doi.org/10.5194/tc-13-665-2019","AuthorName":"Ian M. Howat, Claire Porter, Benjanim E. Smith, Myoung-Jong Noh, Paul Morin"},{"Title":"The surface extraction from TIN based search-space minimization (SETSM) algorithm","URL":"https://doi.org/10.1016/j.isprsjprs.2017.04.019","AuthorName":"Myoung-Jong Noh, Ian M. Howat"}]},"RegistryEntryAdded":"2022-10-03","RegistryEntryLastModified":"2025-03-10","Slug":"pgc-rema","Sources":["awslabs-open-data-registry"]}
{"Name":"Permittivity of Healthy and Diseased Skeletal Muscle","Description":"A better understanding of the permittivity of skeletal muscle is essential for the development of new diagnostic tools and approaches for neuromuscular evaluation. However, there remain important knowledge gaps in our understanding of this property in healthy and diseased skeletal muscle, which hinder its translation into clinical application. The aim of this project is to provide a platform for the researchers to contribute and share the dissemination of this property in healthy and diseased muscle. Ultimately, the normative data reported will offer the scientific community the opportunity to improve the accuracy of existing techniques as well as developing new diagnostic tools and therapies.","Documentation":"https://doi.org/10.130264/c23h3b","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.130264/c23h3b","ARN":"arn:aws:s3:::physionet-open/phdsm/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"phdsm","Sources":["awslabs-open-data-registry"]}
{"DeprecatedNotice":"Physionet dataset is now \"Will Two Do? Varying Dimensions in Electrocardiography The PhysioNet/Computing in Cardiology Challenge 2021\" dataset. Please refer to \"Will Two Do? Varying Dimensions in Electrocardiography The PhysioNet/Computing in Cardiology Challenge 2021\" dataset <a href=\"https://registry.opendata.aws/challenge-2021/\">here</a>","Name":"Physionet","Description":"PhysioNet offers free web access to large collections of recorded physiologic signals (PhysioBank) and related open-source software (PhysioToolkit).","Documentation":"https://physionet.org/","Contact":"contact@physionet.org","ManagedBy":"[MIT Laboratory for Computational Physiology](https://lcp.mit.edu/)","UpdateFrequency":"Not updated","Tags":["aws-pds","biology","life sciences"],"License":"PhysioBank databases are made available under the ODC Public Domain Dedication and License v1.0. https://opendatacommons.org/licenses/pddl/1.0/","Resources":[{"Description":"https://s3.amazonaws.com/physionet-pds/index.html","ARN":"arn:aws:s3:::physionet-pds","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2024-11-22","Slug":"physionet","Sources":["awslabs-open-data-registry"]}
{"Name":"PhysioTag: An Open-Source Platform for Collaborative Annotation of Physiological Waveforms","Description":"To develop robust algorithms for automated diagnosis of medical conditions such as cardiac arrhythmias, researchers require large collections of data with human expert annotations. Currently, there is a lack of accessible, open-source platforms for human experts to collaboratively develop these annotated datasets through a web interface. In this work, we developed a flexible, generalizable, web-based framework to enable multiple users to create and share annotations on multi-channel physiological waveforms. The software is simple to install and offers a range of features, including: user management and task customization; a programmatic interface for data import and export; and a leaderboard for annotation progress tracking.","Documentation":"https://doi.org/10.13026/g06j-3612","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"MIT License","Resources":[{"Description":"https://doi.org/10.13026/g06j-3612","ARN":"arn:aws:s3:::physionet-open/physiotag/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"physiotag","Sources":["awslabs-open-data-registry"]}
{"Name":"PhysioZoo - mammalian NSR databases","Description":"The PhysioZoo database contains electrocardiographic recordings (ECG) taken from multiple types of mammals (dogs, rabbits, mice). Each record is provided in 3 different formats: MATLAB (.mat), simple text (.txt), PhysioNet (.hea,.dat,.qrs). For each record the following data is available: the raw ECG signal, reference (human corrected) R-peak annotations and the signal quality annotations. All of the files formats are compatible with the PhysioZoo open source software available at physiozoo.com. When the recordings were taken, all the mammals were conscious, and no drugs were administered prior to the recordings.","Documentation":"https://doi.org/10.13026/p63q-hq95","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/p63q-hq95","ARN":"arn:aws:s3:::physionet-open/physiozoo/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"physiozoo","Sources":["awslabs-open-data-registry"]}
{"Name":"Planette C3S Seasonal Forecast Data","Description":"The C3S seasonal forecast dataset provides global, daily, probabilistic forecasts of the Earth system, \nenabling users to assess the likelihood of future climate states. These forecasts are particularly \nvaluable for studying slowly evolving climate patterns such as El Niño, La Niña, and the North Atlantic \nOscillation (NAO), which can be predicted with greater skill than the chaotic atmosphere. This dataset \nis derived from the Copernicus Climate Change Service (C3S) archive and includes SEAS5 hindcasts \n(1981-2016) and forecasts (2017-present) at 1°x1° global resolution. More models from the C3S archive will\nbe updated as they are processed into cloud native format. The planette C3S archive stores this data in \ncloud native format for easy access and analysis. \n","Documentation":"https://github.com/PlanetteAI/planette_c3s_archive/blob/main/README.md","Contact":"aodhan.sweeney@planette.ai","ManagedBy":"Planette.ai","UpdateFrequency":"Monthly","Collabs":{"ASDI":{"Tags":["climate","weather","forecast"]}},"Tags":["aws-pds","climate","earth observation","weather"],"License":"Copernicus Licence (similar to CC-BY-4.0): You are free to share and adapt the material \nfor any purpose, even commercially, provided that you give appropriate credit.\nhttps://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf\n","Citation":"Copernicus Climate Change Service (C3S) (2017): C3S seasonal forecast data. \nCopernicus Climate Change Service, Climate Data Store (CDS). \nhttps://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal-original-single-levels\n","Resources":[{"Description":"C3S Seasonal Forecast Hindcasts and Forecasts (Zarr format)","ARN":"arn:aws:s3:::planette-c3s-seasonal-forecasts/seas5_ic/","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Accessing C3S Seasonal Forecast Data with Python","URL":"https://github.com/PlanetteAI/planette_c3s_archive/blob/main/c3s_seasonal_forecast_tutorial.ipynb","AuthorName":"Aodhan Sweeney-Jaramillo","AuthorURL":"https://github.com/AodhanSweeney"}],"Tools & Applications":[{"Title":"icechunk","URL":"https://github.com/earth-mover/icechunk","AuthorName":"earth-mover"},{"Title":"xarray","URL":"https://docs.xarray.dev/","AuthorName":"xarray Developers"},{"Title":"zarr-python","URL":"https://zarr.dev/","AuthorName":"zarr Developers"}],"Publications":[{"Title":"C3S Seasonal Forecasts Documentation","URL":"https://climate.copernicus.eu/seasonal-forecasts","AuthorName":"Copernicus Climate Change Service"},{"Title":"SEAS5: The new ECMWF seasonal forecast system","URL":"https://doi.org/10.5194/gmd-12-1087-2019","AuthorName":"Johnson, S. J., et al."}]},"DeprecatedNotice":null,"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-06-13","RegistryEntryLastModified":"2026-09-11","Slug":"planette_c3s_seasonal_forecast_data","Sources":["awslabs-open-data-registry"]}
{"Name":"Planette ERA5 Archive","Description":"The ERA5 archive provides a comprehensive record of global weather and climate from 1940 to present, \nwith multiple temporal aggregations for flexible analysis. This dataset is derived from \nthe ECMWF/Copernicus ERA5 reanalysis and includes daily means, 7-day rolling means, \nand monthly/seasonal aggregations at 0.25°×0.25° global resolution. The Planette ERA5 archive stores \nthis data in cloud-native format (Zarr with icechunk) for efficient access and analysis.\n\nThe dataset includes essential atmospheric variables at both surface and pressure levels, enabling a \nwide range of climate analyses, from daily weather patterns to long-term climate trends. Daily means \nare computed by averaging hourly ERA5 data, while longer temporal aggregations are derived from these \ndaily means.\n","Documentation":"https://github.com/PlanetteAI/planette_era5_archive/blob/main/README.md","Contact":"aodhan.sweeney@planette.ai","ManagedBy":"Planette.ai","UpdateFrequency":"Monthly","Collabs":{"ASDI":{"Tags":["climate","weather","forecast"]}},"Tags":["aws-pds","climate","earth observation","weather"],"License":"Copernicus Licence (similar to CC-BY-4.0): You are free to share and adapt the material \nfor any purpose, even commercially, provided that you give appropriate credit.\nhttps://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf\n","Citation":"Hersbach, H., Bell, B., Berrisford, P., et al. (2020): The ERA5 global reanalysis. \nQuarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049. \nhttps://doi.org/10.1002/qj.3803\n","Resources":[{"Description":"ERA5 Reanalysis Data with Multiple Temporal Aggregations (Zarr format)","ARN":"arn:aws:s3:::planette-era5/era5/","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://planette-era5.s3.amazonaws.com/index.html#era5/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Accessing ERA5 Data with Python","URL":"https://github.com/PlanetteAI/planette_era5_archive/blob/main/planette_era5_tutorial.ipynb","AuthorName":"Aodhan Sweeney-Jaramillo","AuthorURL":"https://github.com/AodhanSweeney"}],"Tools & Applications":[{"Title":"icechunk","URL":"https://github.com/earth-mover/icechunk","AuthorName":"earth-mover"},{"Title":"xarray","URL":"https://docs.xarray.dev/","AuthorName":"xarray Developers"},{"Title":"zarr-python","URL":"https://zarr.dev/","AuthorName":"zarr Developers"}],"Publications":[{"Title":"ERA5 Documentation","URL":"https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation","AuthorName":"ECMWF"},{"Title":"The ERA5 global reanalysis","URL":"https://doi.org/10.1002/qj.3803","AuthorName":"Hersbach, H., et al."}]},"DeprecatedNotice":null,"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-12-01","RegistryEntryLastModified":"2025-12-01","Slug":"planette_era5_reanalysis","Sources":["awslabs-open-data-registry"]}
{"Name":"Platinum Pedigree","Description":"The Platinum Pedigree Consortium (PCC) is a collaborative project to create a comprehensive reference for human genetic variation using a four-generation, 28-member family (CEPH-1463). We employed five different short and long-read sequencing technologies to generate phased assemblies and characterize both inherited and de novo variation, including at some of the most difficult to genotype genomic regions such as tandem repeats, centromeres, and the Y chromosome. This extensive \"truth set\" is publicly available and can be used to test and benchmark new algorithms and technologies to better understand human genetic variation.","Documentation":"https://github.com/Platinum-Pedigree-Consortium","Contact":"https://github.com/Platinum-Pedigree-Consortium/Platinum-Pedigree-Datasets/issues","ManagedBy":"Platinum Pedigree Consortium","UpdateFrequency":"As needed","Tags":["bioinformatics","genomic","genotyping","Homo sapiens","life sciences","long read sequencing","whole genome sequencing"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"https://github.com/Platinum-Pedigree-Consortium/Platinum-Pedigree-Datasets","ARN":"arn:aws:s3:::platinum-pedigree-data","Region":"us-west-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"A familial, telomere-to-telomere reference for human de novo mutation and recombination from a four-generation pedigree","URL":"https://www.biorxiv.org/content/10.1101/2024.08.05.606142v1","AuthorName":"Porubsky et. al.","AuthorURL":"https://eichlerlab.gs.washington.edu/porubsky.html"},{"Title":"The Platinum Pedigree: A long-read benchmark for genetic variants","URL":"https://www.biorxiv.org/content/10.1101/2024.10.02.616333v1","AuthorName":"Kronenberg et. al."}]},"RegistryEntryAdded":"2024-10-07","RegistryEntryLastModified":"2024-10-07","Slug":"platinum-pedigree","Sources":["awslabs-open-data-registry"]}
{"Name":"plt - Software for 2D Plots","Description":"plt is a non-interactive plotting utility originally written for Unix by Paul Albrecht. plt can produce publication-quality 2D plots in PostScript from easily-produced text or binary data files, and can also create screen plots under the X Window System.","Documentation":"https://doi.org/10.13026/C2T08K","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2T08K","ARN":"arn:aws:s3:::physionet-open/plt/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"plt","Sources":["awslabs-open-data-registry"]}
{"Name":"A Pressure Map Dataset for In-bed Posture Classification","Description":"In this project, we collected in-bed posture pressure data from multiple adult participants using two different types of pressure sensing mats. To the best of our knowledge, our dataset, PmatData is the first publicly-available dataset of pressure sensor data which includes various sleeping postures. PmatData contains pressure data from two separate experiments.","Documentation":"https://doi.org/10.13026/C2W09K","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2W09K","ARN":"arn:aws:s3:::physionet-open/pmd/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"pmd","Sources":["awslabs-open-data-registry"]}
{"Name":"Prefeitura Municipal de São Paulo (PMSP) LiDAR Point Cloud","Description":"The objective of the Mapa 3D Digital da Cidade (M3DC) of the São Paulo City Hall is to publish LiDAR point cloud data. The initial data was acquired in 2017 by aerial surveying and future data will be added. This publicly accessible dataset is provided in the [Entwine Point Tiles](https://entwine.io/entwine-point-tile.html) format as a lossless octree, full density, based on [LASzip](https://laszip.org) (LAZ) encoding.","Documentation":"https://github.com/geoinfo-smdu/M3DC","Contact":"geosampa@prefeitura.sp.gov.br","ManagedBy":"[GeoSampa - o mapa digital da cidade de São Paulo](http://geosampa.prefeitura.sp.gov.br)","UpdateFrequency":"Local survey executed by demand generates new data as local point clouds.","Collabs":{"ASDI":{"Tags":["elevation"]}},"Tags":["aws-pds","cities","elevation","geospatial","land","lidar","mapping","urban"],"License":"[GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html)","Resources":[{"Description":"São Paulo city's 3D LiDAR - [Entwine Point Tiles](https://entwine.io/entwine-point-tile.html).","ARN":"arn:aws:s3:::ept-m3dc-pmsp","Region":"sa-east-1","Type":"S3 Bucket"},{"Description":"São Paulo city's 3D LiDAR - LAZ Files.","ARN":"arn:aws:s3:::laz-m3dc-pmsp","Region":"sa-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Entwine","URL":"https://entwine.io/","AuthorName":"Hobu, Inc.","AuthorURL":"https://hobu.co/"},{"Title":"Fusion","URL":"http://forsys.sefs.uw.edu/fusion/fusionlatest.html","AuthorName":"US Department of Agriculture - Forest Service","AuthorURL":"https://www.usda.gov/"},{"Title":"LAStools","URL":"https://rapidlasso.com/lastools/","AuthorName":"rapidlasso GmbH, GERMANY","AuthorURL":"https://rapidlasso.com"},{"Title":"PDAL - Point Data Abstraction Library","URL":"https://pdal.io/","AuthorName":"PDAL Contributors","AuthorURL":"https://doi.org/10.5281/zenodo.2556738"}],"Publications":[{"Title":"Describing the Vertical Structure of Informal Settlements on the Basis of LiDAR Data – A Case Study for Favelas (Slums) in Sao Paulo City","URL":"https://doi.org/10.5194/isprs-annals-IV-2-W5-437-2019","AuthorName":"S. C. L. Ribeiro, M. Jarzabek-Rychard, J. P. Cintra, H.-G. Maas"}]},"RegistryEntryAdded":"2020-05-05","RegistryEntryLastModified":"2021-12-28","Slug":"pmsp-lidar","Sources":["awslabs-open-data-registry"]}
{"Name":"pNNx - Time Domain Heart Rate Variability Analysis","Description":"The pNNx software package expands the analysis capabilities of the widely used pNN50 measure, providing researchers with the flexibility to test various thresholds for optimal discrimination of HRV in populations of interest.","Documentation":"https://physionet.org/content/pnnx/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/pnnx/","ARN":"arn:aws:s3:::physionet-open/pnnx/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"pnnx","Sources":["awslabs-open-data-registry"]}
{"Name":"Pohang Canal Dataset: A Multimodal Maritime Dataset for Autonomous Navigation in Restricted Waters","Description":"This dataset presents a multi-modal maritime dataset acquired in restricted waters in Pohang, South Korea. The sensor suite is composed of three LiDARs (one 64-channel LiDAR and two 32-channel LiDARs), a marine radar, two visual cameras used as a stereo camera, an infrared camera, an omnidirectional camera with 6 directions, an AHRS, and a GPS with RTK. The dataset includes the sensor calibration parameters and SLAM-based baseline trajectory. It was acquired while navigating a 7.5 km route that includes a narrow canal area, inner and outer port areas, and a near-coastal area. The aim of this dataset is to facilitate research on autonomous surface vehicles.","Documentation":"https://sites.google.com/view/pohang-canal-dataset/home","Contact":"morin-lab@kaist.ac.kr","ManagedBy":"[MORIN](http://morin.kaist.ac.kr)","UpdateFrequency":"Not updated","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["autonomous vehicles","aws-pds","computer vision","lidar","marine navigation","robotics"],"License":"[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)","Citation":null,"Resources":[{"Description":"Pohang Canal dataset","ARN":"arn:aws:s3:::pohang-canal-dataset","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Ros Message Player for Pohang Dataset","URL":"https://github.com/dhchung/rosmsg_player","AuthorName":"Dongha Chung"},{"Title":"ROS package for LiDAR to image of Pohang Canal Dataset","URL":"https://github.com/dhchung/lidar_to_image","AuthorName":"Dongha Chung"}],"Publications":[{"Title":"Pohang Canal Dataset: A Multimodal Maritime Dataset for Autonomous Navigation in Restricted Waters","URL":"https://doi.org/10.1177/02783649231191145","AuthorName":"Dongha Chung, Jonghwi Kim, Changyu Lee, Jinwhan Kim"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2023-06-22","RegistryEntryLastModified":"2025-07-09","Slug":"pohang-canal-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"Physiologic Response to Changes in Posture","Description":"A collection of physiological signals in ten healthy subjects in response to a slow tilt, a fast tilt, and a standing-up maneuver.","Documentation":"https://doi.org/10.13026/C2PC7R","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2PC7R","ARN":"arn:aws:s3:::physionet-open/prcp/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"prcp","Sources":["awslabs-open-data-registry"]}
{"Name":"Pre- and post-purchase product questions","Description":"This dataset provides product related questions, including their textual content and gap, in hours, between purchase and posting time. \nEach question is also associated with related product details, including its id and title.\n","Documentation":"https://pre-post-purchase-questions.s3.amazonaws.com/README.txt","Contact":"litalku@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not currently being updated","Tags":["amazon.science","machine learning","natural language processing"],"License":"[Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)","Resources":[{"Description":"S3 bucket with dataset","ARN":"arn:aws:s3:::pre-post-purchase-questions","Region":"us-east-1","Type":"S3 Bucket","Explore":["[PrePostQuestions.csv](https://pre-post-purchase-questions.s3.amazonaws.com/PrePostQuestions.csv)"]}],"DataAtWork":{"Publications":[{"URL":"https://sigir.org/sigir2021/","AuthorName":"Lital Kuchy, David Carmel, Thomas Huet & Elad Kravi","Title":"\"Did you buy it already?\", Detecting Users Purchase-State From Their Product-Related Questions\n"}]},"RegistryEntryAdded":"2021-05-24","RegistryEntryLastModified":"2022-06-06","Slug":"pre-post-purchase-questions","Sources":["awslabs-open-data-registry"]}
{"Name":"Pressure, flow, and dynamic thoraco-abdominal circumferences data for adults breathing under CPAP therapy","Description":"A respiratory pressure, flow, and dynamic circumference (chest and abdominal) dataset was collected from 30 healthy adults at the University of Canterbury under Human Research Ethics Committee (HREC) ethics. Pressure and flow data was collected using a bi-directional Venturi, with data combined in post-collection processing. Dynamic circumferences were measured using inelastic band tape measures attached to rotary encoders capturing expansion and contraction. Data was collected at zero end-expiratory pressure (ZEEP) and with continuous positive airway pressure (CPAP) ventilation, at positive end-expiratory pressure (PEEP) settings of 4 and 8 cmH2O. Data was also collected over three breath types, in which subjects were instructed to breathe normally, to pant as fast as possible, and to breathe as deeply as possible. Recording times were set to 65 seconds for normal and deep breathing, and 35 seconds for panting trials. Subjects self-determined rest time between trials. A questionnaire was used to collect relevant demographic data (sex, height, age, weight, and history of smoking or asthma). The dataset was collected to inform and validate model-based respiratory monitoring systems, with the ultimate goal of shifting respiratory testing from specialist clinical settings to primary care and home settings","Documentation":"https://doi.org/10.13026/25q9-y416","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/25q9-y416","ARN":"arn:aws:s3:::physionet-open/pressure-flow-circum-cpap/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"pressure-flow-circum-cpap","Sources":["awslabs-open-data-registry"]}
{"Name":"Product Comparison Dataset for Online Shopping","Description":"The Product Comparison dataset for online shopping is a new, manually annotated dataset with about 15K human generated sentences, which compare related products based on one or more of their attributes (the first such data we know of for product comparison). It covers ∼8K product sets, their selected attributes, and comparison texts.","Documentation":"https://prod-comp-shopping-dataset.s3.us-west-2.amazonaws.com/README.md","Contact":"Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6riTWmh-cGw/aws-open-data) and use the `AWS Open Data` tag.","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"None","Tags":["amazon.science","machine learning","natural language processing","online shopping","product comparison"],"License":"[CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)","Resources":[{"Description":"Product Comparison Dataset for Online Shopping","ARN":"arn:aws:s3:::prod-comp-shopping-dataset","Region":"us-west-2","Type":"S3 Bucket","Explore":["[final_prodcomp_dataset_cleaned.tsv](https://prod-comp-shopping-dataset.s3.us-west-2.amazonaws.com/final_prodcomp_dataset_cleaned.tsv)"]}],"DataAtWork":{"Publications":[{"Title":"Generating Explainable Product Comparisons for Online Shopping","URL":"https://dl.acm.org/doi/pdf/10.1145/3539597.3570489","AuthorName":"Nikhita Vedula, Marcus Collins, Eugene Agichtein and Oleg Rokhlenko"}]},"RegistryEntryAdded":"2023-06-20","RegistryEntryLastModified":"2023-06-20","Slug":"prod-comp-shopping","Sources":["awslabs-open-data-registry"]}
{"Name":"PROJ datum grids","Description":"Horizontal and vertical adjustment datasets for coordinate transformation to be used by PROJ 7 or later. PROJ is a generic coordinate transformation software that transforms geospatial coordinates from one coordinate reference system (CRS) to another. This includes cartographic projections as well as geodetic transformations.","Documentation":"https://github.com/OSGeo/proj-datumgrid-geotiff","Contact":"proj@lists.osgeo.org","ManagedBy":"[PROJ](https://proj.org)","UpdateFrequency":"New grids are added when made available","Collabs":{"ASDI":{"Tags":["infrastructure"]}},"Tags":["aws-pds","geospatial","mapping"],"License":"Per file. Under an Open Source Definition compliant license. Consult the READMEs or look at the Copyright TIFF tag of each file.","Resources":[{"Description":"Horizontal and vertical adjustment datasets","Host":"cdn.proj.org","Region":"us-east-1","Type":"CloudFront Distribution"}],"RegistryEntryAdded":"2020-01-20","RegistryEntryLastModified":"2025-07-09","Slug":"proj-datum-grids","Sources":["awslabs-open-data-registry"]}
{"Name":"ProteinGym","Description":"ProteinGym is a benchmark suite for assessing the performance of protein fitness prediction and design models. It comprises a large curated collection of 200+ high-throughput experimental assays (~3M mutated sequences), as well as clinical annotations from experts about the pathogenicity of mutants in over 3k human genes.\n","Documentation":"https://github.com/OATML-Markslab/ProteinGym/blob/main/README.md","Contact":"pascal_notin@hms.harvard.edu","ManagedBy":"Harvard Medical School, University of Oxford","UpdateFrequency":"Quarterly","Tags":["aws-pds","bioinformatics","biology","deep learning","life sciences","machine learning","protein"],"License":"MIT License","Resources":[{"Description":"ProteinGym dataset including all substitution/indel mutations from Deep Mutational Scanning (DMS) experiments (DMS_substitutions.parquet / DMS_indels.parquet), and all substitution/indel mutations from clinical variant databases (clinical_substitutions.parquet / clinical_indels.parquet).","ARN":"arn:aws:s3:::proteingym","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Scoring ProteinGym assays with TranceptEVE","URL":"https://github.com/OATML-Markslab/ProteinGym/blob/main/notebooks/TranceptEVE_example.ipynb","AuthorName":"Daniel Ritter","AuthorURL":"https://danieldritter.github.io/"}],"Tools & Applications":[{"Title":"ProteinGym website","URL":"https://proteingym.org/","AuthorName":"Pascal Notin & Daniel Ritter"}],"Publications":[{"Title":"ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design","URL":"https://papers.nips.cc/paper_files/paper/2023/hash/cac723e5ff29f65e3fcbb0739ae91bee-Abstract-Datasets_and_Benchmarks.html","AuthorName":"Pascal Notin, et al.","AuthorURL":"https://www.pascalnotin.com/"}]},"RegistryEntryAdded":"2025-04-21","RegistryEntryLastModified":"2025-09-26","Slug":"proteingym","Sources":["awslabs-open-data-registry"]}
{"Name":"PSG-IPA: A PolySomnoGraphic Inter-scorer Performance Assessment database","Description":"This dataset, named PolySomnoGraphic Inter-scorer Performance Assessment (PSG-IPA) database, comprises 20 polysomnography (PSG) recordings and related multi-expert scoring annotations. It was utilized in a study to evaluate inter-scorer variability, specifically assessing reliability and scoring times associated with various PSG review tasks. The study also aimed to compare the impact of computer-assisted (semi-automatic) scoring versus unassisted visual analysis on these factors. The dataset includes recordings of common raw PSG signal activity, such as electroencephalography (EEG), electrooculography (EOG), electromyography (EMG) of both the chin and lower limbs, electrocardiography (ECG), and various respiratory derivations. Each recording is accompanied by both manual and computer-assisted scorings, performed by 12 independent expert sleep technologists. This comprehensive resource offers significant value for further research into PSG inter-scorer variability, as well as for the development and performance assessment of automated sleep stage and event detection algorithms.","Documentation":"https://doi.org/10.13026/esx0-nw71","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/esx0-nw71","ARN":"arn:aws:s3:::physionet-open/psg-ipa/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"psg-ipa","Sources":["awslabs-open-data-registry"]}
{"Name":"PTB-XL+, a comprehensive electrocardiographic feature dataset","Description":"The PTB-XL+ dataset is a comprehensive feature dataset that supplements the PTB-XL ECG dataset. It includes ECG features extracted via two commercial and one open-source algorithm in a harmonized format as well as median beats or, where applicable, fiducial points extracted by the respective algorithms. In addition, it provides automatic diagnosis statements from one commercial ECG analysis algorithm at different processing levels that are ready to be used for training and evaluation of machine learning models.","Documentation":"https://doi.org/10.13026/g6h6-7g88","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/g6h6-7g88","ARN":"arn:aws:s3:::physionet-open/ptb-xl-plus/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ptb-xl-plus","Sources":["awslabs-open-data-registry"]}
{"Name":"PTB-XL, a large publicly available electrocardiography dataset","Description":"Electrocardiography (ECG) is a key diagnostic tool to assess the cardiac condition of a patient. Automatic ECG interpretation algorithms as diagnosis support systems promise large reliefs for the medical personnel - only on the basis of the number of ECGs that are routinely taken. However, the development of such algorithms requires large training datasets and clear benchmark procedures. In our opinion, both aspects are not covered satisfactorily by existing freely accessible ECG datasets. The PTB-XL ECG dataset is a large dataset of 21799 clinical 12-lead ECGs from 18869 patients of 10 second length. The raw waveform data was annotated by up to two cardiologists, who assigned potentially multiple ECG statements to each record. The in total 71 different ECG statements conform to the SCP-ECG standard and cover diagnostic, form, and rhythm statements. To ensure comparability of machine learning algorithms trained on the dataset, we provide recommended splits into training and test sets. In combination with the extensive annotation, this turns the dataset into a rich resource for the training and the evaluation of automatic ECG interpretation algorithms. The dataset is complemented by extensive metadata on demographics, infarction characteristics, likelihoods for diagnostic ECG statements as well as annotated signal properties.","Documentation":"https://doi.org/10.13026/kfzx-aw45","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/kfzx-aw45","ARN":"arn:aws:s3:::physionet-open/ptb-xl/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ptb-xl","Sources":["awslabs-open-data-registry"]}
{"Name":"PTB Diagnostic ECG Database","Description":"Physikalisch-Technische Bundesanstalt (PTB), the National Metrology Institute of Germany, has provided this compilation of digitized ECGs for research, algorithmic benchmarking or teaching purposes to the users of PhysioNet. The ECGs were collected from healthy volunteers and patients with different heart diseases by Professor Michael Oeff, M.D.","Documentation":"https://doi.org/10.13026/C28C71","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C28C71","ARN":"arn:aws:s3:::physionet-open/ptbdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ptbdb","Sources":["awslabs-open-data-registry"]}
{"Name":"PubSeq - Public Sequence Resource","Description":"COVID-19 PubSeq is a free and open online bioinformatics public sequence resource with on-the-fly analysis of sequenced SARS-CoV-2 samples that allows for a quick turnaround in identification of new virus strains. PubSeq allows anyone to upload sequence material in the form of FASTA or FASTQ files with accompanying metadata through the web interface or REST API.","Documentation":"https://covid19.genenetwork.org/about","Contact":"https://covid19.genenetwork.org/contact","ManagedBy":"[UTHSC GeneNetwork](https://covid19.genenetwork.org/)","UpdateFrequency":"Rolling dataset.","Tags":["aws-pds","bam","bioinformatics","biology","coronavirus","COVID-19","fast5","fasta","fastq","genetic","genomic","health","json","life sciences","long read sequencing","medicine","MERS","metadata","open source software","RDF","SARS","SARS-CoV-2","SPARQL"],"License":"Creative Commons Attribution 4.0 International (CC BY 4.0) unless otherwise specified.","Resources":[{"Description":"PubSeq submitted datasets (FASTA and JSON metadata)","ARN":"arn:aws:s3:::pubseq-datasets","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://pubseq-datasets.s3.amazonaws.com/)"]},{"Description":"Pubseq output data (Arvados Keep)","ARN":"arn:aws:s3:::pubseq-output-data","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Arvados download](https://covid19.genenetwork.org/download)"]}],"DataAtWork":{"Tutorials":[{"Title":"PubSeq Documentation","URL":"https://covid19.genenetwork.org/blog","AuthorName":"PubSeq development team","AuthorURL":"https://github.com/pubseq"},{"Title":"PubSeq FAQ","URL":"https://covid19.genenetwork.org/about","AuthorName":"PubSeq development team","AuthorURL":"https://github.com/pubseq"},{"Title":"Query metadata","URL":"https://covid19.genenetwork.org/blog?id=using-covid-19-pubseq-part1","AuthorName":"PubSeq development team","AuthorURL":"https://github.com/pubseq"},{"Title":"REST API","URL":"https://covid19.genenetwork.org/apidoc","AuthorName":"PubSeq development team","AuthorURL":"https://github.com/pubseq"},{"Title":"Submitting a sequence","URL":"https://covid19.genenetwork.org/blog?id=using-covid-19-pubseq-part3","AuthorName":"PubSeq development team","AuthorURL":"https://github.com/pubseq"}],"Tools & Applications":[{"Title":"Source code for (meta)data uploader","URL":"https://github.com/pubseq/bh20-seq-resource/tree/master/bh20sequploader","AuthorName":"PubSeq development team","AuthorURL":"https://github.com/pubseq"},{"Title":"Source code for CWL workflows","URL":"https://github.com/pubseq/bh20-seq-resource/tree/master/workflows","AuthorName":"PubSeq development team","AuthorURL":"https://github.com/pubseq"},{"Title":"Source code for website","URL":"https://github.com/pubseq/bh20-seq-resource/tree/master/bh20simplewebuploader","AuthorName":"PubSeq development team","AuthorURL":"https://github.com/pubseq"}],"Publications":[{"Title":"Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 during Border Quarantine and Air Travel, New Zealand (Aotearoa)","URL":"https://wwwnc.cdc.gov/eid/article/27/5/21-0514_article","AuthorName":"Eichler, N. et al."}]},"RegistryEntryAdded":"2021-03-23","RegistryEntryLastModified":"2021-04-20","Slug":"pubseq","Sources":["awslabs-open-data-registry"]}
{"Name":"Puka - Software for Detection of Breaths in Strain Gauge Recordings","Description":"Puka calculates descriptive statistics such as heart rate variability (HRV), peak-valley respiratory sinus arrhythmia (RSA), and respiratory variables from EKG and strain gauge respiration data.","Documentation":"https://physionet.org/content/puka/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/puka/","ARN":"arn:aws:s3:::physionet-open/puka/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"puka","Sources":["awslabs-open-data-registry"]}
{"Name":"Pulse Transit Time PPG Dataset","Description":"We provide an open access, high resolution and time synchronised dataset from multiple sensors worn at different body locations including Photoplethysmogram (PPG), Inertial, Pressure and ECG. The recordings are from 22 healthy subjects performing 3 physical activities. This dataset contains 66 waveform records from multi-site and multi-wavelength PPGs, sensors’ attachment pressures, sensors’ temperatures, inertial data from accelerometer and gyroscope, along with annotated ECG data for a total of 19 channels. Additionally, systolic and diastolic blood pressures, as well as blood oxygenation saturation levels (SpO2) numerics are provided.","Documentation":"https://doi.org/10.13026/jpan-6n92","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Open Database License v1.0","Resources":[{"Description":"https://doi.org/10.13026/jpan-6n92","ARN":"arn:aws:s3:::physionet-open/pulse-transit-time-ppg/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"pulse-transit-time-ppg","Sources":["awslabs-open-data-registry"]}
{"Name":"MIT-BIH Arrhythmia Database P-Wave Annotations","Description":"This database provides manually reviewed P-wave annotations for twelve ECG recordings from the MIT-BIH Arrhythmia Database [1]. The recordings were selected to support evaluation of P-wave detection across a range of rhythms, including pathological conditions that can make detection difficult. One expert annotated the recordings, a second expert reviewed the annotations, and ambiguous segments were discussed jointly. The annotations are supplied in standard WFDB format alongside the corresponding ECG signals and header files. They support the development and evaluation of automated P-wave detection methods, although annotation completeness and accuracy are not guaranteed.","Documentation":"https://doi.org/10.13026/C2108F","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2108F","ARN":"arn:aws:s3:::physionet-open/pwave/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"pwave","Sources":["awslabs-open-data-registry"]}
{"Name":"PyEnvs and CallArgs","Description":"PyEnvs is a collection of 2814 permissively licensed Python packages along with their isolated development environments. Paired with a program analyzer (e.g. Jedi Language Server), it supports querying for project-related information. \n\nCallArgs is a dataset built on top of PyEnvs for function call argument completion. It provides function definition, implementation, and usage information for each function call instance.\n","Documentation":"https://github.com/amazon-research/function-call-argument-completion","Contact":"Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6riTWmh-cGw/aws-open-data) and use the `AWS Open Data` tag.","ManagedBy":"Amazon","UpdateFrequency":"None Planned","Tags":["code completion","machine learning"],"License":"Please note that while we are providing this aggregation of code snippets unlicensed, each package that makes up the broader dataset may have and often does have its own license. Individual licenses can be found in the source code for the respective packages.\n","Resources":[{"Description":"PyEnvs","ARN":"arn:aws:s3:::pyenvs-and-callargs/pyenvs/","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"CallArgs","ARN":"arn:aws:s3:::pyenvs-and-callargs/callargs/","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion","URL":"https://arxiv.org/abs/2306.03438","AuthorName":"Hengzhi Pei, Jinman Zhao, Leonard Lausen, Sheng Zha, George Karypis"}]},"RegistryEntryAdded":"2023-07-14","RegistryEntryLastModified":"2023-07-14","Slug":"pyenvs-and-callargs","Sources":["awslabs-open-data-registry"]}
{"Name":"High-Order Accurate Direct Numerical Simulation of Flow over a MTU-T161 Low Pressure Turbine Blade","Description":"The archive comprises snapshot, point-probe, and time-average data produced via a high-fidelity computational simulation of turbulent air flow over a low pressure turbine blade, which is an important component in a jet engine. The simulation was undertaken using the open source PyFR flow solver on over 5000 Nvidia K20X GPUs of the Titan supercomputer at Oak Ridge National Laboratory under an INCITE award from the US DOE. The data can be used to develop an enhanced understanding of the complex three-dimensional unsteady air flow patterns over turbine blades in jet engines. This could in turn lead to design of greener more fuel efficient aircraft. It could also be used to train a next-generation of Reynolds Averaged Navier-Stokes turbulence models via a machine learning approach, which would have broad applicability to a wide range of science and engineering problems.","Documentation":"http://pyfr-mtu-t161-dns-data.s3-website-us-west-2.amazonaws.com/","Contact":"info@pyfr.org","ManagedBy":"http://www.pyfr.org","UpdateFrequency":"Periodically","Tags":["aws-pds","computational fluid dynamics","green aviation","low-pressure turbine","turbulence"],"License":"CC BY 2.0","Resources":[{"Description":"Data files","ARN":"arn:aws:s3:::pyfr-mtu-t161-dns-data","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"High-Order Accurate Direct Numerical Simulation of Flow over a MTU-T161 Low Pressure Turbine Blade","URL":"https://doi.org/10.1016/j.compfluid.2021.104989","AuthorName":"A. S. Iyer, Y. Abe, B. C. Vermeire, P. Bechlars, R. D. Baier, A. Jameson, F. D. Witherden, and P. E. Vincent"}]},"RegistryEntryAdded":"2021-05-04","RegistryEntryLastModified":"2022-07-06","Slug":"pyfr-mtu-t161-dns-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management","Description":"We introduce Q-Pain, a dataset for assessing bias in medical QA in the context of pain management. We developed 55 medical question-answer pairs across five different types of pain management: each question includes a detailed patient-specific medical scenario (\"vignette\") designed to enable the substitution of multiple different racial and gender \"profiles\" and to evaluate whether bias is present when answering whether or not to prescribe medication. Along with the dataset, we propose a new framework, including a sample experimental design, to measure the potential bias present during medical decision-making for patients with particular demographic profiles. We demonstrate its use in assessing two reference QA systems, GPT-2 and GPT-3, selected for their documented ability to answer questions given only a few examples. We hope that our dataset and framework will be used to assess bias across a wide variety of pain management QA systems.","Documentation":"https://doi.org/10.13026/2tdv-hj07","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution-ShareAlike 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/2tdv-hj07","ARN":"arn:aws:s3:::physionet-open/q-pain/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"q-pain","Sources":["awslabs-open-data-registry"]}
{"Name":"Quantitative Dehydration Estimation","Description":"Quantitative estimation of dehydration (total body water loss) using bioimpedance measurements, temperature measurements, salivary samples, and sweat samples.","Documentation":"https://doi.org/10.13026/C23082","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C23082","ARN":"arn:aws:s3:::physionet-open/qde/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"qde","Sources":["awslabs-open-data-registry"]}
{"Name":"QIIME 2 Tutorial Data","Description":"QIIME 2 (pronounced “chime two”) is a microbiome multi-omics bioinformatics and data science platform that is trusted, free, open source, extensible, and community developed and supported.","Documentation":"https://use.qiime2.org","Contact":"https://forum.qiime2.org","ManagedBy":"Caporaso Lab","UpdateFrequency":"Twice per year","Tags":["aws-pds","bioinformatics","biology","ecosystems","environmental","genetic","genomic","health","life sciences","metagenomics","microbiome"],"License":"BSD 3-Clause License","Resources":[{"Description":"Source for rendered documentation and tutorial datasets for the QIIME 2 project.","ARN":"arn:aws:s3:::qiime2-data","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Developing with QIIME 2","URL":"https://develop.qiime2.org/","AuthorName":"Caporaso and Bolyen","AuthorURL":"https://github.com/caporaso-lab/developing-with-qiime2/graphs/contributors"},{"Title":"QIIME 2 User Documentation (currently being replaced by *Using QIIME 2*)","URL":"https://docs.qiime2.org/","AuthorName":"The QIIME 2 Development Team","AuthorURL":"https://github.com/qiime2/docs/graphs/contributors"},{"Title":"Using QIIME 2","URL":"https://use.qiime2.org/","AuthorName":"Caporaso Lab","AuthorURL":"https://github.com/caporaso-lab/using-qiime2/graphs/contributors"}]},"RegistryEntryAdded":"2019-01-18","RegistryEntryLastModified":"2024-11-06","Slug":"qiime2","Sources":["awslabs-open-data-registry"]}
{"Name":"QT Database","Description":"Over 100 fifteen-minute two-lead ECG recordings (many excerpted from other databases), with onset, peak, and end markers for P, QRS, T, and (where present) U waves of from 30 to 50 selected beats in each recording.","Documentation":"https://doi.org/10.13026/C24K53","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C24K53","ARN":"arn:aws:s3:::physionet-open/qtdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"qtdb","Sources":["awslabs-open-data-registry"]}
{"Name":"R-DECO: An open-source Matlab based graphical user interface for the detection and correction of R-peaks","Description":"Many of the existing ECG toolboxes focus on the derivation of heart rate variability features from RR-intervals. By doing so, they assume correct detection of the QRS-complexes. However, it is highly likely that not all detections are correct. Therefore, it is recommended to visualize the actual R-peak positions in the ECG signal and allow manual adaptations. As a solution, we present R-DECO, an easy-to-use graphical user interface for the detection and correction of R-peaks. Within R-DECO, the R-peaks are detected by an adaptation of the Pan-Tompkins algorithm. Instead of using all the pre-processing steps of the latter algorithm, the proposed algorithm uses only an envelope-based procedure to flatten the ECG and enhance the QRS-complexes. The algorithm obtained an overall sensitivity of 99.60% and positive predictive value of 99.69% on the MIT/BIH arrhythmia database. Additionally, R-DECO includes support for several input data formats for ECG signals, three basic filters, the possibility to load other R-peak locations and intuitive methods to correct ectopic, wrong, or missed heartbeats. All functionalities can be accessed via the graphical user interface and the analysis results can be exported as Matlab or Excel files. Through its easy-to-use-graphical user interface, R-DECO allows both clinicians and researchers to use all functionalities, without previous knowledge. R-DECO has been developed on Windows 10 using Matlab 2018a. GUIDE, MATLAB’s GUI development environment, was used to design the GUI of R-DECO. It has been tested and proven to be functional for older Matlab versions going back to Matlab 2016a.","Documentation":"https://doi.org/10.13026/x6j7-sp58","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"GNU General Public License version 3","Resources":[{"Description":"https://doi.org/10.13026/x6j7-sp58","ARN":"arn:aws:s3:::physionet-open/r-deco/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"r-deco","Sources":["awslabs-open-data-registry"]}
{"Name":"RACECAR Dataset","Description":"The RACECAR dataset is the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data has been collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the Indy Autonomous Challenge during 2021-22 have contributed to this dataset. The dataset spans 11 interesting racing scenarios across two race tracks which include solo laps, multi-agent laps, overtaking situations, high-accelerations, banked tracks, obstacle avoidance, pit entry and exit at different speeds. The data is organized and released in both ROS2 and nuScenes format. We have also developed the ROS2-to-nuScenes conversion library to achieve this. The RACECAR data is unique because of the high-speed environment of autonomous racing and is suitable to explore issues regarding localization, object detection and tracking (LiDAR, Radar, and Camera), and mapping that arise at the limits of operation of the autonomous vehicle.","Documentation":"https://github.com/linklab-uva/RACECAR_DATA","Contact":"Prof. Madhur Behl (madhur.behl@viginia.edu)","ManagedBy":"Amar Kulkarni (ark8su@virginia.edu)","UpdateFrequency":"This dataset was constructed during a single racing season (2021-22). Future seasons may potentially be added.","Collabs":{"ASDI":{"Tags":["infrastructure"]}},"Tags":["autonomous racing","autonomous vehicles","aws-pds","computer vision","GNSS","image processing","lidar","localization","object detection","object tracking","perception","radar","robotics"],"License":"Creative Commons Attribution-NonCommercial 4.0 International Public License [(CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)","Citation":"Amar Kulkarni, John Chrosniak, Emory Ducote, Florian Sauerbeck, Andrew Saba, Utkarsh Chirimar, John Link, Marcello Cellina, and Madhur Behl. \"RACECAR--The Dataset for High-Speed Autonomous Racing.\" International Conference on Intelligent Robots and Systems IROS (2023).","Resources":[{"Description":"The RACECAR dataset is the first open dataset for full-scale and high-speed autonomous racing. The data is organized and released in both ROS2 and nuScenes format.","ARN":"arn:aws:s3:::racecar-dataset","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"RACECAR Tutorials - nuScenes","URL":"https://github.com/linklab-uva/RACECAR_DATA/tree/main#tutorial-3-nuscenes-jupyter-notebook","NotebookURL":"https://github.com/linklab-uva/RACECAR_DATA/blob/main/nuscenes_tutorial.ipynb","AuthorName":"John Chrosniak"},{"Title":"RACECAR Tutorials - ROS2 Localization","URL":"https://github.com/linklab-uva/RACECAR_DATA/tree/main#tutorial-2-ros2-localization","AuthorName":"Amar Kulkarni"},{"Title":"RACECAR Tutorials - ROS2 Visualization","URL":"https://github.com/linklab-uva/RACECAR_DATA/tree/main#tutorial-1-ros2-visualization","AuthorName":"Amar Kulkarni, Utkarsh Chirimar"}],"Tools & Applications":[{"Title":"rosbag2nuscenes conversion library","URL":"https://github.com/linklab-uva/rosbag2nuscenes","AuthorName":"John Chrosniak, Emory Ducote, John Link, Madhur Behl","AuthorURL":"https://github.com/linklab-uva/rosbag2nuscenes"}],"Publications":[{"Title":"RACECAR--The Dataset for High-Speed Autonomous Racing","URL":"https://doi.org/10.48550/arXiv.2306.03252","AuthorName":"Amar Kulkarni, John Chrosniak, Emory Ducote, Florian Sauerbeck, Andrew Saba, Utkarsh Chirimar, John Link, Marcello Cellina, and Madhur Behl"}]},"RegistryEntryAdded":"2023-06-28","RegistryEntryLastModified":"2025-07-09","Slug":"racecar-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"Radiology Report Generation Models Evaluation Dataset For Chest X-rays (RadEvalX)","Description":"The Radiology Report Generation Models Evaluation Dataset For Chest X-rays (RadEvalX) is publicly available and developed similarly to the ReXVal dataset. Just like ReXVal, RadEvalX focuses on radiologist evaluations of errors found in automatically generated radiology reports. The dataset includes annotations from two board-certified radiologists, who identified clinically significant and clinically insignificant errors across eight different categories of errors. Compared to the ground-truth reports from the IU-Xray dataset, the evaluations were done on candidate radiology reports. For every 100 studies and corresponding ground-truth reports, the dataset contains one report generated using the M2Tr model from the corresponding X-ray image. The radiologists then annotated these reports. The primary purpose of this dataset is to assess the correlation between automated metrics and human radiologists' scoring, explore the limitations of automated metrics, and develop a model-based automated metric. This dataset has been created to support further research in medical artificial intelligence (AI), particularly in the field of radiology.","Documentation":"https://doi.org/10.13026/tp88-q278","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/tp88-q278","ARN":"arn:aws:s3:::physionet-open/rad-eval-x/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"rad-eval-x","Sources":["awslabs-open-data-registry"]}
{"Name":"RADARSAT-1","Description":"Developed and operated by the Canadian Space Agency, it is Canada's first commercial Earth observation satellite \n<br/> <br/>\nDéveloppé et exploité par l'Agence spatiale canadienne, il s'agit du premier satellite commercial d'observation de la Terre au Canada.","Documentation":"https://www.asc-csa.gc.ca/eng/satellites/radarsat1/what-is-radarsat1.asp","Contact":"https://www.eodms-sgdot.nrcan-rncan.gc.ca","ManagedBy":"[Natural Resources Canada](https://nrcan.gc.ca/)","UpdateFrequency":"NRCan opened a new [RADARSAT-1 Processing service](https://github.com/eodms-sgdot/radarsat-notebooks/blob/main/examples/radarsat1_l1_processing.ipynb) to the public on December 18, 2025. The processor allows users to produce image products from the original raw data archive free of charge, with the primary goal of making previously unprocessed data accessible for the first time.\n<br/> <br/>\nRNCan a ouvert un nouveau [service de traitement RADARSAT-1](https://github.com/eodms-sgdot/radarsat-notebooks/blob/main/examples/radarsat1_l1_processing.ipynb) au public le 18 décembre 2025. Le processeur permet aux utilisateurs de produire des produits d'imagerie à partir des archives de données brutes originales gratuitement, avec l'objectif principal de rendre accessible pour la première fois les données jamais traitées auparavant.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","disaster response","earth observation","geospatial","global","ice","satellite imagery","synthetic aperture radar"],"License":"[Open Government License (OGL)](https://open.canada.ca/en/open-government-licence-canada)","Resources":[{"Description":"Cloud Optimized GeoTIFF (COG) images","ARN":"arn:aws:s3:::radarsat-r1-l1-cog","Region":"ca-central-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"RADARSAT-1 Processing Service | Service de traitement RADARSAT-1","URL":"https://github.com/eodms-sgdot/radarsat-notebooks/blob/main/examples/radarsat1_l1_processing.ipynb","AuthorName":"Canada Centre for Remote Sensing | Centre canadien de télédétection","AuthorURL":"https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-remote-sensing"}],"Tools & Applications":[{"Title":"ASF Data Recipes (Tutorials)","URL":"https://asf.alaska.edu/how-to/data-basics/data-recipe-tutorials-2","AuthorName":"Alaska Satellite Facility - Distributed Active Archive Center","AuthorURL":"https://asf.alaska.edu/"},{"Title":"Catalyst Professional","URL":"https://catalyst.earth/products/catalyst-pro/","AuthorName":"CATALYST","AuthorURL":"https://catalyst.earth"},{"Title":"ENVI SARscape","URL":"https://www.harrisgeospatial.com/Software-Technology/ENVI-SARscape","AuthorName":"L3Harris Geospatial","AuthorURL":"https://www.l3harrisgeospatial.com"},{"Title":"Gamma","URL":"https://www.gamma-rs.ch/","AuthorName":"Gamme Remote Sensing","AuthorURL":"https://www.gamma-rs.ch/"},{"Title":"GDAL","URL":"https://gdal.org","AuthorName":"GDAL","AuthorURL":"https://gdal.org"},{"Title":"MapReady","URL":"https://www.asf.alaska.edu/data-tools/mapready","AuthorName":"NASA","AuthorURL":"https://www.asf.alaska.edu"},{"Title":"OpenEV","URL":"http://openev.sourceforge.net/index.php","AuthorName":"Frank Warmerdam","AuthorURL":"http://openev.sourceforge.net/index.php"},{"Title":"QGIS","URL":"https://qgis.org","AuthorName":"German QGIS user group","AuthorURL":"https://www.qgis.de/"},{"Title":"RADARSAT-1 Scripts | Scripts RADARSAT-1","URL":"https://github.com/asc-csa/radarsat1-scripts","AuthorName":"Canadian Space Agency - Agence spatiale canadienne","AuthorURL":"https://github.com/asc-csa/radarsat1-scripts"}]},"RegistryEntryAdded":"2021-04-06","RegistryEntryLastModified":"2026-01-09","Slug":"radarsat-1","Sources":["awslabs-open-data-registry"]}
{"Name":"Radiant MLHub","Description":"Radiant MLHub is an open library for geospatial training data that hosts datasets generated by [Radiant Earth Foundation](https://www.radiant.earth/)'s team as well as other training data catalogs contributed by Radiant Earth’s partners. Radiant MLHub is open to anyone to access, store, register and/or share their training datasets for high-quality Earth observations. All of the training datasets are stored using a [SpatioTemporal Asset Catalog (STAC)](https://stacspec.org/) compliant catalog and exposed through a common API. Training datasets include pairs of imagery and labels for different types of machine learning problems including image classification, object detection, and semantic segmentation. Labels are generated from ground reference data and/or image annotation.","Documentation":"http://docs.mlhub.earth/","Contact":"support@radiant.earth","ManagedBy":"[Radiant Earth Foundation](https://www.radiant.earth/)","UpdateFrequency":"New training data catalogs are added on a rolling basis","Tags":["aws-pds","cog","earth observation","environmental","geospatial","labeled","machine learning","satellite imagery","stac"],"License":"Access to Radiant MLHub data is free for everyone. Each dataset has its own license (usually CC-BY or CC-BY-SA). View [Terms of Service](https://www.radiant.earth/terms/).","Resources":[{"Description":"Radiant MLHub Training Data","ARN":"arn:aws:s3:::radiant-mlhub","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Explore Radiant MLHub Data in a Browser-Based Notebook on Scigantic","URL":"https://scigantic.com/archive/4fac9240-fb9e-5a39-a5f5-a6d8bb29d37c/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com/"},{"Title":"Explore wind speed images from Amazon Sustainability Data Initiative (ASDI) hosted on S3 using SageMaker Studio Lab (SMSL)","URL":"https://github.com/aws-samples/asdi-smsl-wind-speed-data/","NotebookURL":"https://github.com/aws-samples/asdi-smsl-wind-speed-data/blob/main/wind-speed-data.ipynb","AuthorName":"Frank Rubino","Services":["Amazon SageMaker Studio Lab"]},{"Title":"How to access Radiant MLHub Data","URL":"https://github.com/radiantearth/mlhub-tutorials/blob/master/RadiantMLHub-intro.pdf","AuthorName":"Radiant Earth","AuthorURL":"https://www.radiant.earth/"},{"Title":"Radiant MLHub Tutorials with Jupyter Notebooks","URL":"https://github.com/radiantearth/mlhub-tutorials/tree/master/notebooks","AuthorName":"Kevin Booth","AuthorURL":"https://www.linkedin.com/in/kbgg/"}],"Tools & Applications":[{"Title":"Challenge on Computer Vision for Crop Detection from Satellite Imagery","URL":"https://zindi.africa/competitions/iclr-workshop-challenge-2-radiant-earth-computer-vision-for-crop-recognition","AuthorName":"Radiant Earth","AuthorURL":"https://www.radiant.earth/"},{"Title":"Radiant MLHub Dataset Registry","URL":"https://registry.mlhub.earth/","AuthorName":"Radiant Earth","AuthorURL":"https://www.radiant.earth/"}],"Publications":[{"Title":"A Guide for Collecting and Sharing Ground Reference Data for Machine Learning Applications","URL":"https://medium.com/radiant-earth-insights/a-guide-for-collecting-and-sharing-ground-reference-data-for-machine-learning-applications-90664930925e","AuthorName":"Yonah Bromberg Gaber","AuthorURL":"https://www.linkedin.com/in/yonahbg/"},{"Title":"Creating a Machine Learning Commons for Global Development","URL":"https://medium.com/radiant-earth-insights/creating-a-machine-learning-commons-for-global-development-256ef3dd46aa","AuthorName":"Hamed Alemohammad","AuthorURL":"https://www.linkedin.com/in/hamedalemohammad/"},{"Title":"Geo-Diverse Open Training Data as a Global Public Good","URL":"https://aws.amazon.com/blogs/publicsector/geo-diverse-open-training-data-as-a-global-public-good/","AuthorName":"Hamed Alemohammad","AuthorURL":"https://www.linkedin.com/in/hamedalemohammad/"}]},"RegistryEntryAdded":"2020-07-16","RegistryEntryLastModified":"2026-06-11","Slug":"radiant-mlhub","Sources":["awslabs-open-data-registry"]}
{"Name":"RADIANT Public Data","Description":"The Real-time Analysis and Discovery in Integrated And Networked Technologies (RADIANT) initiative seeks to develop an extensible, federated framework for rapid exchange of  multimodal clinical and research data on behalf of accelerated discovery and patient impact. Coordination and implementation of initial RADIANT deployments will leverage a network of  more than 35 partnered health care systems and participating patient families within the  Children’s Brain Tumor Network (CBTN) and the Pediatric Neuro-Oncology Consortium (PNOC).  This data set is composed of public multi-modal data provisioned by RADIANT. The initial bolus of data is from CBTN and consists of clinical data extracted/abstracted from  electronic medical records, omic data such as genomics, transcriptomics and proteomics and radiology and pathology imaging data. Data are collected or generated as part of consent-based,  IRB-approved observational or interventional studies with the goal of making it available globally to researchers across a broad number of disciplines. \n","Documentation":"https://cbtn.org/research-resources","Contact":"research@cbtn.org","ManagedBy":"[The Center for Data-Driven Discovery in Biomedicine (D3b) at the Children's Hospital of Philadelphia](https://d3b.center/)","UpdateFrequency":"Data is updated on a regular basis by the RADIANT teams to make data available as\nrapidly as possible. \n","Tags":["aws-pds","cancer","genetic","genomic","Homo sapiens","life sciences","medical imaging","pediatric","radiology","transcriptomics","whole genome sequencing"],"License":"NIH Genomic Data Sharing Policy: https://grants.nih.gov/grants/guide/notice-files/not-od-14-124.html","Resources":[{"Description":"Children's Brain Tumor Network","ARN":"arn:aws:s3:::opendata-chop-study-us-east-1-prd-sd-bhjxbdqk","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://cbtn.org/"}],"DataAtWork":{"Tools & Applications":[{"Title":"CAVATICA","URL":"http://cavatica.org","AuthorName":"Seven Bridges Genomics","AuthorURL":"http://www.sevenbridges.com"},{"Title":"Flywheel (CHOP D3b)","URL":"https://chop.flywheel.io","AuthorName":"Flywheel","AuthorURL":"https://flywheel.io/"},{"Title":"PedcBioPortal","URL":"https://pedcbioportal.kidsfirstdrc.org","AuthorName":"cBioPortal","AuthorURL":"https://www.cbioportal.org/"},{"Title":"RADIANT Source Code","URL":"https://github.com/radiant-network","AuthorName":"RADIANT Team","AuthorURL":"https://github.com/radiant-network"}],"Publications":[{"Title":"A road map for the treatment of pediatric diffuse midline glioma","URL":"https://pubmed.ncbi.nlm.nih.gov/38039965/","AuthorName":"Carl Koschmann, Wajd N Al-Holou, Marta M Alonso, et al."},{"Title":"Generation and multi-dimensional profiling of a childhood cancer cell line atlas defines new therapeutic opportunities","URL":"https://pubmed.ncbi.nlm.nih.gov/37001527/","AuthorName":"Claire Xin Sun, Paul Daniel, Gabrielle Bradshaw et al."},{"Title":"Germline analysis of an international cohort of pediatric diffuse midline glioma patients","URL":"https://pubmed.ncbi.nlm.nih.gov/40072012/","AuthorName":"Marion K Mateos, Pamela Ajuyah, Noemi Fuentes-Bolanos, et al."},{"Title":"Multi-scale signaling and tumor evolution in high-grade gliomas","URL":"https://pubmed.ncbi.nlm.nih.gov/38981438/","AuthorName":"Jingxian Liu, Song Cao, Kathleen J Imback, et al."},{"Title":"Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma","URL":"https://pubmed.ncbi.nlm.nih.gov/39747214/","AuthorName":"Anahita Gathi Kazerooni, Adam Kraya, Komal S Rathi, Meen Chul Kim, et al."},{"Title":"OpenPBTA: The Open Pediatric Brain Tumor Atlas","URL":"https://pubmed.ncbi.nlm.nih.gov/37492101/","AuthorName":"Joshua A Shapiro, Krutika S Gaonkar, Stephanie J Spielman, et al."},{"Title":"The children's brain tumor network (CBTN) - Accelerating research in pediatric central nervous system tumors through collaboration and open science.","URL":"https://pubmed.ncbi.nlm.nih.gov/36335802/","AuthorName":"Jena V Lilly, Jo Lynne Rokita, Jennifer L Mason, et al."},{"Title":"The landscape of primary mismatch repair deficient gliomas in children, adolescents, and young adults: a multi-cohort study","URL":"https://pubmed.ncbi.nlm.nih.gov/39701117/","AuthorName":"Logine Negm, Jiil Chung, Liana Nobre, et al."},{"Title":"Use of External Control Cohorts in Pediatric Brain Tumor Clinical Trials","URL":"https://pubmed.ncbi.nlm.nih.gov/38394473/","AuthorName":"Ashley S Margol, Annette M Molinaro, Arzu Onar-Thomas, et al."}]},"RegistryEntryAdded":"2025-03-18","RegistryEntryLastModified":"2025-10-24","Slug":"radiant","Sources":["awslabs-open-data-registry"]}
{"Name":"Random Search Toolbox","Description":"A major issue with many signal processing and machine learning algorithms is the lack of optimisation methods for determining the numerous hyper-parameters associated with the model as well as the knowledge of which hyper-parameters are relevant. These parameters are usually tunned by trial and error (manual search) or by grid search. In 2012 Bergstra et al. showed empirically that random search was performing similar or better than grid search while reducing the computational cost substantially.","Documentation":"https://physionet.org/content/random-search/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/random-search/","ARN":"arn:aws:s3:::physionet-open/random-search/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"random-search","Sources":["awslabs-open-data-registry"]}
{"Name":"RAPID NRT Flood Maps","Description":"Near Real-time and archival data of High-resolution (10 m) flood inundation dataset over the Contiguous United States, developed based on the Sentinel-1 SAR imagery (2016-current) archive, using an automated Radar Produced Inundation Diary (RAPID) algorithm.","Documentation":"https://github.com/QingYang6/RAPID-NRT-flood-maps-on-AWS/blob/master/README.md","Contact":"xinyi.shen@uconn.edu; qing.yang6@hotmail.com","ManagedBy":"University of Connecticut; Guangxi University","UpdateFrequency":"NRT data will be update as soon as SAR images available and done processed.","Collabs":{"ASDI":{"Tags":["water"]}},"Tags":["agriculture","aws-pds","disaster response","earth observation","environmental","water"],"License":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International","Resources":[{"Description":"RAPID archive flood maps","ARN":"arn:aws:s3:::rapid-nrt-flood-maps","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://rapid-nrt-flood-maps.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"Inundation Extent Mapping by Synthetic Aperture Radar: A Review","URL":"https://doi.org/10.3390/rs11070879","AuthorName":"Xinyi Shen, Dacheng Wang, Kebiao Mao, Emmanouil Anagnostou, and Yang Hong"},{"Title":"Near Real-Time Nonobstructed Flood Inundation Mapping by Synthetic Aperture Radar","URL":"https://doi.org/10.1016/j.rse.2018.11.008","AuthorName":"Xinyi Shen, Emmanouil N. Anagnostou, George H. Allen, G. Robert Brakenridge, Albert J. Kettner"}]},"RegistryEntryAdded":"2020-05-27","RegistryEntryLastModified":"2020-11-20","Slug":"rapid-nrt-flood-maps","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"DeprecatedNotice":"The provider of this dataset will no longer maintain this dataset.  We are open to talking with anyone else who might be willing to provide this dataset to the community.  Contact <a href=\"mailto:opendata@amazon.com\">opendata@amazon.com</a>","Name":"Rapid7 FDNS ANY Dataset","Description":"This dataset has been deprecated. Please see this [Rapid7 blog post](https://www.rapid7.com/blog/post/2022/02/10/evolving-how-we-share-rapid7-research-data-2/) for details.\n","Documentation":"https://opendata.rapid7.com/about/","Contact":"research@rapid7.com\n","ManagedBy":"Not currently managed","UpdateFrequency":"Monthly\n","Tags":["aws-pds","computer security","cyber security","analytics","internet"],"License":"Non-commercial (NC) - Free for non-commercial purposes. For commercial use, contact research@rapid7.com. Full terms: https://opendata.rapid7.com/about/#terms\n","Resources":[{"Description":"FDNS ANY Gzip-compressed parquet files following a HIVE partitioning model, with\na 90-day retention period. This dataset represents the data returned for a name\nwhen an ANY query is issued. Data is partitioned by date=YYYYMM which is the\nyear-month that the FDNS ANY study was launched.\n","ARN":"arn:aws:s3:::rapid7-opendata/fdns/any/v1/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"New FDNS parquet dataset announcements\n","ARN":"arn:aws:sns:us-east-1:740874647437:s3-event-notification-new-aws-pds-dataset","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"How to Conduct DNS Reconnaissance for $.02 Using Rapid7 Open Data and AWS","URL":"https://blog.rapid7.com/2018/10/16/how-to-conduct-dns-reconnaissance-for-02-using-rapid7-open-data-and-aws/","AuthorName":"Shan Sikdar at Rapid7","AuthorURL":"https://blog.rapid7.com/author/shan/"},{"Title":"Creating a Project Sonar FDNS API with AWS","URL":"http://securityriskadvisors.com/blog/creating-a-project-sonar-fdns-api-with-aws/","AuthorName":"Evan Perotti at SecurityRiskAdvisors","AuthorURL":"https://twitter.com/2xxeformyshirt"}],"Tools & Applications":null,"Publications":null},"RegistryEntryAdded":"2018-07-05","RegistryEntryLastModified":"2022-07-07","Slug":"rapid7-fdns-any","Sources":["awslabs-open-data-registry"]}
{"Name":"RarePlanes","Description":"RarePlanes is a unique open-source machine learning dataset from CosmiQ Works and AI.Reverie that incorporates both real and synthetically generated satellite imagery. The RarePlanes dataset specifically focuses on the value of AI.Reverie synthetic data to aid computer vision algorithms in their ability to automatically detect aircraft and their attributes in satellite imagery. Although other synthetic/real combination datasets exist, RarePlanes is the largest openly-available very high resolution dataset built to test the value of synthetic data from an overhead perspective. The real portion of the dataset consists of 253 Maxar WorldView-3 satellite scenes spanning 112 locations and 2,142 km^2 with 14,700 hand-annotated aircraft. The accompanying synthetic dataset is generated via AI.Reverie’s novel simulation platform and features 50,000 synthetic satellite images with ~630,000 aircraft annotations.","Documentation":"www.cosmiqworks.org/RarePlanes","Contact":"jss5102@gmail.com and avanetten@iqt.org","ManagedBy":"In-Q-Tel - CosmiQ Works","UpdateFrequency":"None Planned","Tags":["aws-pds","computer vision","deep learning","earth observation","geospatial","labeled","machine learning","satellite imagery"],"License":"[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)","Resources":[{"Description":"Real and synthetic satellite imagery, annotations, and metadata","ARN":"arn:aws:s3:::rareplanes-public","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Announcing YOLTv4: Improved Satellite Imagery Object Detection","URL":"https://towardsdatascience.com/announcing-yoltv4-improved-satellite-imagery-object-detection-f5091e913fad","AuthorName":"Adam Van Etten","AuthorURL":"https://medium.com/@avanetten"},{"Title":"Automatically compress and archive satellite imagery for Amazon S3","URL":"https://aws.amazon.com/blogs/storage/automatically-compress-and-archive-satellite-imagery-for-amazon-s3/","AuthorName":"Newel Hirst, Joseph Fahimi, and Justin Downes","Services":["Amazon S3","Amazon S3 Glacier","AWS Lambda","Amazon ECR"]},{"Title":"Getting Started with YOLTv4 for Object Detection in Imagery: Getting Training Data","URL":"https://dev.to/spara_50/getting-started-with-yoltv4-for-object-detection-in-imagery-getting-training-data-2055","AuthorName":"Sophia Parafina","AuthorURL":"https://authory.com/sophiaparafina"},{"Title":"Notebook for training and testing YOLYv4","URL":"https://github.com/avanetten/yoltv4/blob/master/notebooks/train_test_pipeline.ipynb","AuthorName":"Adam Van Etten"}],"Tools & Applications":[{"Title":"RarePlanes Codebase","URL":"https://github.com/aireveries/RarePlanes","AuthorName":"Thomas Hossler and Jacob Shermeyer"}],"Publications":[{"Title":"RarePlanes: Synthetic Data Takes Flight","URL":"https://arxiv.org/abs/2006.02963","AuthorName":"Jacob Shermeyer, Thomas Hossler, Adam Van Etten, Daniel Hogan, Ryan Lewis, Daeil Kim"}]},"RegistryEntryAdded":"2020-06-09","RegistryEntryLastModified":"2025-02-04","Slug":"rareplanes","Sources":["awslabs-open-data-registry"]}
{"Name":"RCM CEOS Analysis Ready Data | Données prêtes à l'analyse du CEOS pour le MCR","Description":"The [RADARSAT Constellation Mission (RCM)](https://www.asc-csa.gc.ca/eng/satellites/radarsat/) is Canada's third generation of Earth observation satellites. Launched on June 12, 2019, the three identical satellites work together to bring solutions to key challenges for Canadians. As part of ongoing [Open Government](https://open.canada.ca/en/about-open-government) efforts, NRCan produces a CEOS analysis ready data (ARD) of Canada landmass using a 30M Compact-Polarization standard coverage, every 12 days. RCM CEOS-ARD (POL) is the first ever polarimetric dataset [approved by the CEOS committee](https://ceos.org/ard/index.html#datasets). Previously, users were stuck ordering, downloading and processing RCM images (level 1) on their own, often with expensive software. This new dataset aims to remove these burdens with a new STAC catalog for discovery and direct download links.\n<br/> <br/>\nLa mission de la Constellation RADARSAT (MCR) est la troisième génération de satellites d'observation de la Terre du Canada. Lancés le 12 juin 2019, les trois satellites identiques travaillent ensemble pour apporter des solutions aux principaux défis des Canadiens. Dans le cadre des efforts continus pour un gouvernement ouvert, RNCan produit des données prêtes à l'analyse CEOS (ARD) de la masse terrestre du Canada en utilisant une couverture standard de 30 m en polarisation compacte, tous les 12 jours. Les CEOS-ARD (POL) du MCR constituent le premier ensemble de données polarimétriques jamais [approuvé par le comité CEOS](https://ceos.org/ard/index.html#datasets). Auparavant, les utilisateurs étaient obligés de commander, de télécharger et de traiter eux-mêmes les images RCM (niveau 1), souvent à l'aide de logiciels coûteux. Ce nouvel ensemble de données vise à supprimer ces fardeaux avec un nouveau catalogue STAC à découvrir et à télécharger directement depuis S3.","Documentation":"https://www.asc-csa.gc.ca/eng/satellites/radarsat/","Contact":"eodms-sgdot@nrcan-rncan.gc.ca","ManagedBy":"[Natural Resources Canada](https://www.nrcan.gc.ca/)","UpdateFrequency":"The initial dataset will be Canada-wide, 30M Compact-Polarization standard coverage, every 12 days, per mission revisit frequency.\n<br/> <br/>\nL'ensemble de données initial couvrira l'ensemble du Canada, une couverture standard de 30 metres de polarisation compacte, tous les 12 jours, par fréquence de revisite de mission.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","analysis ready data","aws-pds","ceos","disaster response","earth observation","geospatial","satellite imagery","stac","sustainability","synthetic aperture radar"],"License":"RCM image products are available free of charge, to the broadest extent possible, in order to promote the development of innovative products and services derived from SAR data. The Government of Canada retains ownership of all RCM data and image products. Intellectual property rights to value-added products (VAPs) created from the RCM image products will remain with the creator of the VAP. Acceptable uses of RCM image products are set out in the [RCM Public User License Agreement](https://www.asc-csa.gc.ca/eng/satellites/radarsat/access-to-data/public-user-license-agreement.asp), which is provided with each RCM image product. \n<br/> <br/>\nLes produits d'image de la MCR sont disponibles sans frais, le plus possible accessibles, pour encourager la mise au point de produits et services novateurs dérivés des données RSO. Le gouvernement du Canada conserve la propriété de toutes les données et de tous les produits d'image de la MCR. Les droits de propriété intellectuelle sur les produits à valeur ajoutée (PVA) créés à partir des produits d'image de la MCR demeureront la propriété du créateur du PVA. Les utilisations acceptables des produits d'image de la MCR sont énoncées dans le [contrat de licence d'utilisation](https://www.asc-csa.gc.ca/fra/satellites/radarsat/acces-aux-donnees/contrat-licence-utilisation.asp) fourni avec chaque produit d'image de la MCR","Resources":[{"Description":"RCM CEOS Analysis Ready Data. Données prêtes à l'analyse (DPA) du CEOS pour le MCR","ARN":"arn:aws:s3:::rcm-ceos-ard","Region":"ca-central-1","Type":"S3 Bucket","Explore":["[STAC for RCM CEOS ARD products](https://radiantearth.github.io/stac-browser/#/external/eodms-sgdot.nrcan-rncan.gc.ca/search/collections/rcm-ard)"]}],"DataAtWork":{"Tutorials":[{"Title":"Workflows for accessing and manipulating RCM ARD SpatioTemporal Asset Catalog (STAC) in JupyterLab Python Notebooks - Flux de travail pour accéder et manipuler le catalogue d'actifs spatio-temporels (STAC) RCM ARD dans les notebooks Python JupyterLab","URL":"https://github.com/eodms-sgdot/radarsat-notebooks","AuthorName":"Canada Centre for Remote Sensing | Centre canadien de télédétection","AuthorURL":"https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-remote-sensing"}],"Publications":[{"Title":"CEOS Analysis Ready Data - Données prêtes à l'analyse du CEOS","URL":"https://ceos.org/ard/","AuthorName":"Committee on Earth Observation Satellites (CEOS)","AuthorURL":"https://ceos.org/"},{"Title":"Copernicus Global Digital Elevation Model","URL":"https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM","AuthorName":"European Space Agency (ESA)","AuthorURL":"https://www.esa.int/"},{"Title":"RCM CEOS ARD Dataset on GEO.ca | Ensemble de données RCM CEOS ARD sur GEO.ca","URL":"https://app.geo.ca/en-ca/map-browser/record/eodms-rcm-ard","AuthorName":"Canada Centre for Remote Sensing | Centre canadien de télédétection","AuthorURL":"https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-remote-sensing"},{"Title":"Sentinel-1 Global Backscatter Model (S1GBM)","URL":"https://researchdata.tuwien.ac.at/records/n2d1v-gqb91","AuthorName":"TU Wien Research Data","AuthorURL":"https://researchdata.tuwien.ac.at/"},{"Title":"Synthetic Aperture Radar (CEOS-ARD SAR)","URL":"https://ceos.org/ard/files/PFS/SAR/v1.1/CEOS-ARD_PFS_Synthetic_Aperture_Radar_v1.1.pdf","AuthorName":"Committee on Earth Observation Satellites (CEOS) for developing the CEOS ARD Standards. Specific acknowledgement to François Charbonneau (NRCan) for contributions to the standard development through CEOS committee membership as well as application to Canadian RADARSAT data. - Comité sur les satellites d'observation de la Terre (CEOS) pour l'élaboration des normes CEOS ARD. Remerciements particuliers à François Charbonneau (RNCan) pour ses contributions au développement des normes par le biais de son appartenance au comité CEOS ainsi que pour l'application aux données canadiennes RADARSAT.","AuthorURL":"https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-remote-sensing"}]},"RegistryEntryAdded":"2024-01-26","RegistryEntryLastModified":"2026-05-15","Slug":"rcm-ceos-ard","Sources":["awslabs-open-data-registry"]}
{"Name":"A Cardiovascular Simulator for Research","Description":"We introduce here the Research CardioVascular SIMulator (RCVSIM) software in order to complement research with the experimental data sets provided by PhysioBank. The human cardiovascular model upon which RCVSIM is based includes three major components. The first component is a lumped parameter model of the pulsatile heart and circulation. The second component is a short-term regulatory system model which includes an arterial baroreflex system, a cardiopulmonary baroreflex system, and a direct neural coupling mechanism between respiration and heart rate.","Documentation":"https://physionet.org/content/rcvsim/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/rcvsim/","ARN":"arn:aws:s3:::physionet-open/rcvsim/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"rcvsim","Sources":["awslabs-open-data-registry"]}
{"Name":"real-changesets","Description":"The real-changesets is an augmented representation of OpenStreetMap changesets in JSON format. It contains the current and the previous version of each feature in a changeset.\nIt's primary used by OSMCha, the main OpenStreetMap validation tool, to have a visualization of the changeset and provide to the user the understanding of what was changed on the map.\nThe real-changesets are created by combining the changeset metadata and the augmented diff generated by overpass.\n","Documentation":"https://github.com/osmus/osmcha-charter-project/blob/main/real-changesets-docs.md","Contact":"team@openstreetmap.us","ManagedBy":"OpenStreetMap US","UpdateFrequency":"Minutely","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aws-pds","disaster response","geospatial","mapping","osm"],"License":"[Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/)\n","Resources":[{"Description":"real-changesets","ARN":"arn:aws:s3:::real-changesets","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"New File Notification","ARN":"arn:aws:sns:us-west-2:877446169145:real-changesets-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"changeset-map","URL":"https://osmlab.github.io/changeset-map/","AuthorName":"OSM Lab","AuthorURL":"https://github.com/osmlab/"},{"Title":"OSMCha","URL":"https://osmcha.org","AuthorName":"Wille Marcel, Mapbox et al.","AuthorURL":"https://github.com/willemarcel/"},{"Title":"real-changesets-parser","URL":"https://www.npmjs.com/package/real-changesets-parser","AuthorName":"Mapbox","AuthorURL":"https://www.mapbox.com/"}],"Publications":[{"Title":"Preparing accurate history and caching changesets","URL":"https://www.openstreetmap.org/user/geohacker/diary/40846","AuthorName":"Sajjad Anwar","AuthorURL":"https://github.com/geohacker/"}]},"RegistryEntryAdded":"2023-11-15","RegistryEntryLastModified":"2025-07-09","Slug":"real-changesets","Sources":["awslabs-open-data-registry"]}
{"Name":"recount3","Description":"recount3 is an online resource consisting of RNA-seq gene, exon, and exon-exon junction counts as well as coverage bigWig files for 8,679 and 10,088 different studies for human and mouse respectively. It is the third generation of the ReCount project and part of recount.bio. recount2 is also included for historical purposes. The pipeline used to generate the data in recount3 (but not recount2) is available [here](https://github.com/langmead-lab/monorail-external).\n","Documentation":"https://rna.recount.bio/","Contact":"https://rna.recount.bio/docs/how-to-ask-for-help.html","ManagedBy":"Johns Hopkins University","UpdateFrequency":"Not currently updated","Tags":["aws-pds","bioinformatics","biology","cancer","csv","gene expression","genetic","genomic","Homo sapiens","life sciences","Mus musculus","neuroscience","transcriptomics"],"License":"[recount3 is Public Domain, recount2 is CC0](https://creativecommons.org/share-your-work/public-domain/cc0/)","Citation":"Additionally, please cite Wilks, C., Zheng, S.C., Chen, F.Y. et al. recount3: summaries and queries for large-scale RNA-seq expression and splicing. Genome Biol 22, 323 (2021). https://doi.org/10.1186/s13059-021-02533-6 if referencing the recount3 data or the Monorail software in a publication. If referencing recount2 data, please use this citation: Collado-Torres, L., Nellore, A., Kammers, K. et al. Reproducible RNA-seq analysis using recount2. Nat Biotechnol 35, 319–321 (2017). https://doi.org/10.1038/nbt.3838.","Resources":[{"Description":"https://rna.recount.bio/docs/","ARN":"arn:aws:s3:::recount-opendata","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"recount3 quick start guide","URL":"https://www.bioconductor.org/packages/devel/bioc/vignettes/recount3/inst/doc/recount3-quickstart.html","AuthorName":"Leonardo Collado-Torres","AuthorURL":"https://lcolladotor.github.io/"}]},"RegistryEntryAdded":"2023-01-10","RegistryEntryLastModified":"2023-01-10","Slug":"recount","Sources":["awslabs-open-data-registry"]}
{"Name":"REDASA COVID-19 Open Data","Description":"The REaltime DAta Synthesis and Analysis (REDASA) COVID-19 snapshot contains the output of the curation protocol produced by our curator community. A detailed description can be found in [our paper](https://www.jmir.org/2021/5/e25714). The first S3 bucket listed in Resources contains a large collection of medical documents in text format extracted from the [CORD-19 dataset](https://registry.opendata.aws/cord-19/), plus other sources deemed relevant by the REDASA consortium.  The second S3 bucket contains a series of documents surfaced by [Amazon Kendra](https://aws.amazon.com/kendra/) that were considered relevant for each medical question asked. The final S3 bucket contains the GroundTruth annotations created by our curator community.\n","Documentation":"https://github.com/PanSurg/redasa-sample-data/blob/master/open-data.md","Contact":"redasa-open-data@imperial.ac.uk","ManagedBy":"REDASA Consortium, Imperial College London, UK","UpdateFrequency":"Yearly updates","Tags":["aws-pds","coronavirus","COVID-19","information retrieval","life sciences","natural language processing","text analysis"],"License":"CC-BY-4.0","Resources":[{"Description":"This is the raw data repository containing a common crawl of CORD-19 papers and other sources identified by the REDASA Project.","ARN":"arn:aws:s3:::pansurg-curation-raw-open-data","Region":"eu-west-2","Type":"S3 Bucket"},{"Description":"For all the questions curated during the REDASA project, we created a Kendra index. The documents available in this S3 bucket were surfaced by the Kendra index as being relevant to the research medical question.","ARN":"arn:aws:s3:::pansurg-curation-workflo-kendraqueryresults50d0eb-open-data","Region":"eu-west-2","Type":"S3 Bucket"},{"Description":"An S3 bucket that contains the final curation data in GroundTruth format","ARN":"arn:aws:s3:::pansurg-curation-final-curations-open-data","Region":"eu-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Curadr - Curation Platform","URL":"https://curadr.com/","AuthorName":"REDASA Consortium, Imperial College London","AuthorURL":"https://www.pansurg.org/redasa/"}],"Publications":[{"Title":"Using a Secure, Continually Updating, Web Source Processing Pipeline to Support the Real-Time Data Synthesis and Analysis of Scientific Literature: Development and Validation Study","URL":"https://www.jmir.org/2021/5/e25714","AuthorName":"Uddhav Vaghela, Simon Rabinowicz, Paris Bratsos, Guy Martin, Epameinondas Fritzilas, et al."}]},"RegistryEntryAdded":"2021-06-25","RegistryEntryLastModified":"2021-06-25","Slug":"redasa-covid-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Refgenie reference genome assets","Description":"Pre-built refgenie reference genome data assets used for aligning and analyzing DNA sequence data.","Documentation":"http://refgenie.databio.org","Contact":"https://github.com/databio/refgenie/issues","ManagedBy":"Sheffield lab at the University of Virginia","UpdateFrequency":"As new data becomes available (roughly quarterly)","Tags":["aws-pds","bioinformatics","biology","genetic","genomic","infrastructure","life sciences","single-cell transcriptomics","transcriptomics","whole genome sequencing"],"License":"Public domain","Resources":[{"Description":"Refgenie S3 Bucket","ARN":"arn:aws:s3:::awspds.refgenie.databio.org","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Basic Refgenie tutorial","URL":"http://refgenie.databio.org/en/latest/tutorial/","AuthorName":"Nathan Sheffield","AuthorURL":"http://www.databio.org","Services":null}],"Tools & Applications":[{"Title":"Pipeline for ATAC-seq data analysis","URL":"http://pepatac.databio.org","AuthorName":"Jason Smith and Nathan Sheffield","AuthorURL":"http://databio.org"},{"Title":"Pipeline for PRO-seq data analysis","URL":"http://peppro.databio.org","AuthorName":"Jason Smith and Nathan Sheffield","AuthorURL":"http://databio.org"}],"Publications":[{"Title":"Refgenie: a reference genome resource manager","URL":"https://doi.org/10.1093/gigascience/giz149","AuthorName":"Michał Stolarczyk, Vincent P Reuter, Jason P Smith, Neal E Magee, Nathan C Sheffield","AuthorURL":"http://www.databio.org"}]},"RegistryEntryAdded":"2020-07-06","RegistryEntryLastModified":"2022-11-08","Slug":"refgenie","Sources":["awslabs-open-data-registry"]}
{"Name":"Pathologist Reasoning-Guided Report Generation (REG2026) Dataset","Description":"This dataset contains the data for the Pathologist Reasoning-Guided Report Generation challenge (REG2026), a computational pathology benchmark for pathology report generation from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) with paired Chain-of-Thought (CoT) data. Participants are tasked with generating pathology reports based on WSIs, and their models are evaluated not only on report generation but also on the reasoning process underlying report generation. WSIs have been preprocessed to anonymize patient information and retain only 20x magnification images. The dataset is sourced from multiple institutions across Korea, Türkiye, Japan, India, and Germany. Pathology reports are structured texts derived from actual pathological diagnostic records and include fields such as organ, procedure, histologic type, and histologic grade. Reports are standardized according to the College of American Pathologists (CAP) protocol, and diagnoses follow the histologic type and subtype nomenclature defined by the WHO Classification of Tumours. CoT data consists of a series of question-answer pairs constructed based on the actual pathology report writing process, where each pair is linked to a logically subsequent question and the sequence concludes with a final report-producing question. The dataset comprises about 12,000 cases spanning 7 organs and a broad range of diagnostic categories, including malignant, pre-malignant, benign, and non-neoplastic entities. Under the current challenge setup, the dataset is divided into Training, Test Phase 1, and Test Phase 2 splits, and the exact number of cases may change during the challenge.","Documentation":"https://reg2026.grand-challenge.org/","Contact":"mailto:vanitasahn@gmail.com","ManagedBy":"Korea University","UpdateFrequency":"No regular updates are planned after the initial release.","Tags":["aws-pds","benchmark","computational pathology","computer vision","deep learning","digital pathology","histopathology","imaging","life sciences","machine learning","medical imaging","natural language processing"],"License":"CC BY-NC-SA","Resources":[{"Description":"De-identified H&E-stained pathology whole-slide images (WSIs) paired with Chain-of-Thought (CoT) data for reasoning-guided pathology report generation. WSIs are provided as TIFF files and have been preprocessed to anonymize patient information while retaining only 20x magnification images. The pathology reports and CoT data are provided as JSON objects. The dataset spans 7 organs and includes a diverse range of diagnostic categories, including malignant, pre-malignant, benign, and non-neoplastic entities.","ARN":"arn:aws:s3:::reg2026-challenge-dataset","Region":"ap-northeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Pathologist Reasoning-Guided Report Generation Challenge","URL":"https://reg2026.grand-challenge.org/","AuthorName":"PathfinderLab","AuthorURL":"https://github.com/PathfinderLab"},{"Title":"REG2026 dataset description","URL":"https://reg2026.grand-challenge.org/data-description/","AuthorName":"PathfinderLab","AuthorURL":"https://github.com/PathfinderLab"},{"Title":"REG2026 tutorial notebook","URL":"https://github.com/hanjoo7/REG2026-challenge-tutorial","AuthorName":"PathfinderLab","AuthorURL":"https://github.com/PathfinderLab"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-04-16","RegistryEntryLastModified":"2026-09-22","Slug":"reg-2026","Sources":["awslabs-open-data-registry"]}
{"Name":"Registry of Open Data on AWS","Description":"The Registry of Open Data on AWS contains publicly available datasets that are available for access from AWS resources. Note that datasets in this registry are available via AWS resources, but they are not provided by AWS; these datasets are owned and maintained by a variety of government organizations, researchers, businesses, and individuals. This dataset contains derived forms of the data in [https://github.com/awslabs/open-data-registry](https://github.com/awslabs/open-data-registry) that have been transformed for ease of use with machine interfaces. Currently, only the [ndjson](https://github.com/ndjson/ndjson-spec) form of the registry is populated here.","Documentation":"https://github.com/awslabs/open-data-registry#how-are-datasets-added-to-the-registry","Contact":"opendata@amazon.com","ManagedBy":"[Amazon Web Services](https://aws.amazon.com/)","UpdateFrequency":"Automatically when new datasets are added","Tags":["amazon.science","json","metadata"],"License":"[Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)","Resources":[{"Description":"Registry of Open Data on AWS","ARN":"arn:aws:s3:::registry.opendata.aws/roda/ndjson/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"SNS topic for object create events","ARN":"arn:aws:sns:us-east-1:652627389412:roda-object_created","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2021-08-13","RegistryEntryLastModified":"2025-06-10","Slug":"registry-open-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Respiratory dataset from PEEP study with expiratory occlusion","Description":"A trial was conducted to collect gauge pressure and flow from a venturi-based flow-meter, dynamic abdominal and thoracic circumference from rotary-encoder based tape measures, and aeration data from electrical impedance tomography (EIT). Data was collected from 80 adults, breathing with continuous positive airway pressure (CPAP) ventilation, under ethical consent from the University of Canterbury Human Research Ethics Committee (HREC 2023/04/LR-PS). In each subject’s recording the positive end expiratory pressure (PEEP) was increased from 4 to 12 cmH2O in 0.5 cmH2O increments, with data recorded for 30 seconds at each level. Time was built in for researchers to change PEEP settings, in which data continued to be collected. The recording also begins and ends with a 60 second period of breathing without CPAP. During the trial subjects breathed through a full-face mask and filter collected to the data collection device and, when PEEP was applied, CPAP circuitry. A camera-shutter based device was used to rapidly occlude the expiratory pathway to enable identification of passive lung mechanics. Subjects were instructed to breathe normally throughout the trial. Both raw and processed data is included in this publication to maximise its utility. Subject demographic data was self-reported using a questionnaire completed prior to each trial and is collated in a spreadsheet as part of this dataset. The demographic data collected was as follows: sex; height; weight; age; any history of asthma; smoking, or vaping; and resting chest width and depth. Ultimately, this dataset was collected to enable the development and validation of model-based respiratory function assessment methods. These methods aim to increase the capacity of automated testing, removing clinical burden of respiratory monitoring and improving patient-specific care.","Documentation":"https://doi.org/10.13026/d767-e709","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/d767-e709","ARN":"arn:aws:s3:::physionet-open/respiratory-dataset/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"respiratory-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"Respiratory and heart rate monitoring dataset from aeration study","Description":"A study was conducted to collect respiratory pressure and flow data for model-based assessment, alongside electrical impedance tomography (EIT) aeration, electrocardiogram (ECG), and heart-rate belt (HRB) data. A 20 subjects set was selected with no known significant respiratory abnormalities, under University of Canterbury Human Research Ethics Committee (HREC) consent (HREC 2023/30/LR-PS). Subject demographic data was collected using a combination of measurements and a self-reported questionnaire. Subject demographic information to ascertain: sex; height; weight; age; any history of asthma, smoking, or vaping; and approximate thoracic cage and breast tissue volumes. The trial was setup with subjects breathing through the pressure and flowmeter using a full-face mask and filter. Inspiratory and expiratory flow were separated using one-way valves for measurement purposes and both ports were rapidly occluded every 200ms to generate passive mechanics assessment intervals. A continuous positive airway pressure (CPAP) machine was used to provide positive airway pressure (PEEP) in series with the inspiratory port. Three trials were conducted per subject. Two of which, involved increasing PEEP (0, 4, and 8 cmH2O), and in one of which subjects were asked to perform two 10s breath holds at each level. The third was a recording of three forced expiratory manoeuvres (FEM). These tests were conducted to provide an initial comparison of current respiratory techniques to model-based methods, which aims to inform the design of future clinical testing on subjects with known respiratory abnormalities.","Documentation":"https://doi.org/10.13026/e4dt-f689","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/e4dt-f689","ARN":"arn:aws:s3:::physionet-open/respiratory-heartrate-dataset/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"respiratory-heartrate-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"Respiratory and Pulse Oximetry Waveforms from Healthy Adults During Simulated Apnoea Events","Description":"This dataset contains airway gauge pressure, inspiratory differential pressure, and pulse oximetry waveforms collected from 20 healthy adult participants (10 females, 10 males) during simulated obstructive sleep apnoea (OSA) trials. Recordings include photoplethysmography (PPG) signals from a custom neck-worn reflectance oximeter, which records both arterial and venous blood waveforms from the carotid artery and internal jugular vein, respectively. Each subject completed a a series of 8 × 60-second breathing trials with varying positive airway pressures (0, 4, and 8 cmH₂O) set with a continuous positive airway pressure (CPAP) device. Normal breathing was recorded initially before subsequent trials of three breaths followed by 10- and 20-second breath holds, before normal breathing for the rest of the 60 seconds. The dataset is structured to support algorithm development and validation for OSA detection, modelling of airway resistance and oxygen desaturation responses, and non-invasive estimation of arterial and venous oxygen saturation. Supplementary code and figures are included to facilitate data analysis.","Documentation":"https://doi.org/10.13026/s45r-k263","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/s45r-k263","ARN":"arn:aws:s3:::physionet-open/respiratory-oximetry-apnoea/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"respiratory-oximetry-apnoea","Sources":["awslabs-open-data-registry"]}
{"Name":"Rain over Africa","Description":"The Rain over Africa (RoA) dataset consists of spaceborn estimates of precipitation of Rain over Africa using only geostationary imagery and obtained through a convolutional and quantile regression neural network. The dataset also contains some uncertainty estimates.","Documentation":"https://github.com/SEE-GEO/roa","Contact":"https://github.com/SEE-GEO/roa","ManagedBy":"[Geoscience and Remote Sensing at Chalmers University of Technology](https://www.chalmers.se/en/departments/see/research/geo)","UpdateFrequency":"At most, yearly","Tags":["agriculture","analysis ready data","atmosphere","aws-pds","aws-pds","climate","deep learning","earth observation","geophysics","geoscience","hydrology","machine learning","precipitation","satellite imagery","weather","zarr"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Citation":"Please refer to https://github.com/SEE-GEO/roa#5-how-to-cite for instructions on how to cite the RoA data.","Resources":[{"Description":"RoA expected rain rate and quantiles at levels 5%, 16%, 25%, 50%, 75%, 84%, and 95% in Zarr format","ARN":"arn:aws:s3:::rainoverafrica","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for new Rain over Africa data","ARN":"arn:aws:sns:us-west-2:261854712492:rainoverafrica-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"How to use the data","URL":"https://github.com/SEE-GEO/roa?tab=readme-ov-file#3-how-to-use-the-data","AuthorName":"Adrià Amell","Services":["Amazon S3"]},{"Title":"Reading RoA data","URL":"https://github.com/SEE-GEO/roa?tab=readme-ov-file#22-reading-roa-data","AuthorName":"Adrià Amell","Services":["Amazon S3"]}],"Publications":[{"Title":"Probabilistic near real-time retrievals of Rain over Africa using deep learning","URL":"https://doi.org/10.1029/2025JD044595","AuthorName":"Adrià Amell, Lilian Hee, Simon Pfreundschuh, and Patrick Eriksson"}]},"DeprecatedNotice":null,"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-10-15","RegistryEntryLastModified":"2025-10-27","Slug":"roa","Sources":["awslabs-open-data-registry"]}
{"Name":"NIH Roadmap Epigenomics","Description":"The NIH Roadmap Epigenomics Mapping Consortium was launched with the goal of producing a public resource of human epigenomic data to catalyze basic biology and disease-oriented research. The project has generated high-quality, genome-wide maps of several key histone modifications, chromatin accessibility, DNA methylation and mRNA expression across 100s of human cell types and tissues. To see what data is available, please check the directory listing: https://roadmapepigenomics.s3.us-west-2.amazonaws.com/index.html.\n","Contact":"dli23@wustl.edu","ManagedBy":"NIH Roadmap Epigenomics Mapping Consortium, Ting Wang Lab at WashU (https://wang.wustl.edu/)","Documentation":"https://egg2.wustl.edu/roadmap/web_portal/","UpdateFrequency":"NIH Roadmap Epigenomics Mapping Consortium ended, no updates would be provided.","Tags":["aws-pds","bioinformatics","biology","epigenomics","genetic","genomic","life sciences"],"License":"External data users may freely download, analyze, and publish results based on any NIH Roadmp Epigenomics data provided here without restrictions.","Resources":[{"Description":"Released and archived NIH Roadmp Epigenomics data","ARN":"arn:aws:s3:::roadmapepigenomics","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Navigation of Roadmap data using Roadmap web portal","URL":"https://egg2.wustl.edu/roadmap/web_portal/","AuthorName":"Anshul Kundaje Lab","AuthorURL":"https://kundajelab.github.io/"},{"Title":"Visualize Roadmp data with WashU Epigenome Browser","URL":"https://epigenomegateway.wustl.edu/browser/?genome=hg19&hub=https://vizhub.wustl.edu/public/hg19/new/roadmap9_methylC.md","AuthorName":"WashU Epigenome Browser","AuthorURL":"https://epigenomegateway.wustl.edu/browser/"}],"Publications":[{"Title":"Chromatin architecture reorganization during stem cell differentiation","URL":"https://www.nature.com/articles/nature14222","AuthorName":"GJesse R. Dixon, Inkyung Jung, Siddarth Selvaraj, Yin Shen, Jessica E. Antosiewicz-Bourget, Ah Young Lee, Zhen Ye, Audrey Kim, Nisha Rajagopal, Wei Xie, Yarui Diao, Jing Liang, Huimin Zhao, Victor V. Lobanenkov, Joseph R. Ecker, James A. Thomson & Bing Ren"},{"Title":"Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease.","URL":"https://www.nature.com/articles/nature14252","AuthorName":"Elizabeta Gjoneska, Andreas R. Pfenning, Hansruedi Mathys, Gerald Quon, Anshul Kundaje, Li-Huei Tsai & Manolis Kellis."},{"Title":"Human body epigenome maps reveal noncanonical DNA methylation variation","URL":"https://www.nature.com/articles/nature14465","AuthorName":"Matthew D. Schultz, Yupeng He, John W. Whitaker, Manoj Hariharan, Eran A. Mukamel, Danny Leung, Nisha Rajagopal, Joseph R. Nery, Mark A. Urich, Huaming Chen, Shin Lin, Yiing Lin, Inkyung Jung, Anthony D. Schmitt, Siddarth Selvaraj, Bing Ren, Terrence J. Sejnowski, Wei Wang & Joseph R. Ecker"},{"Title":"Integrative analysis of 111 reference human epigenomes","URL":"https://www.nature.com/articles/nature14248","AuthorName":"Roadmap Epigenomics Consortium, Anshul Kundaje, Wouter Meuleman, Jason Ernst, Misha Bilenky, Angela Yen, Alireza Heravi-Moussavi, Pouya Kheradpour etc.al, Ting Wang, Manolis Kellis"},{"Title":"The epigenomic landscape of transposable elements across normal human development and anatomy","URL":"https://www.nature.com/articles/s41467-019-13555-x","AuthorName":"Erica C. Pehrsson, Mayank N. K. Choudhary, Vasavi Sundaram & Ting Wang"},{"Title":"WashU Epigenome Browser update 2019","URL":"https://doi.org/10.1093/nar/gkz348","AuthorName":"Daofeng Li, Silas Hsu, Deepak Purushotham, Renee L Sears and Ting Wang"}]},"RegistryEntryAdded":"2025-06-19","RegistryEntryLastModified":"2025-06-25","Slug":"roadmapepigenomics","Sources":["awslabs-open-data-registry"]}
{"Name":"The Religious Orders Study and Memory and Aging Project (ROSMAP) Study","Description":"The Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP) are\nongoing longitudinal clinical-pathologic cohort studies of aging and\nAlzheimer's disease. Participants enroll without known dementia, undergo\nannual clinical and cognitive evaluations, and agree to organ donation at\ndeath. The ROSMAP study provides genomic, transcriptomic, epigenomic,\nproteomic, metabolomic, imaging, and related metadata generated from these\ncohorts to support research into cognitive decline, neuropathology, brain\naging, and the molecular mechanisms of Alzheimer's disease.\n","Documentation":"https://doi.org/10.7303/9618239","Contact":"https://www.synapse.org/Synapse:syn2580853/discussion/default","ManagedBy":"Sage Bionetworks","UpdateFrequency":"AD Knowledge Portal Data Releases are published monthly (https://news.adknowledgeportal.org/). Updates to specific studies are dependent on data contributor and ingress timelines.","Tags":["aws-pds","bioinformatics","biology","epigenomics","gene expression","genomic","genotyping","Homo sapiens","imaging","life sciences","medicine","neurobiology","neuroscience","protein","RNA-seq","transcriptomics","whole genome sequencing"],"License":"https://adknowledgeportal.synapse.org/Explore/Studies/DetailsPage/StudyDetails?Study=syn3219045#Acknowledgement","Citation":"The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (NIH-NIA, U24AG061340).","Resources":[{"Description":"This study provides controlled-access genomic variants, gene expression, epigenetic, proteomic, metabolomic, imaging, analysis, and related metadata generated from participants in the Religious Orders Study and Rush Memory and Aging Project.","ARN":"arn:aws:s3:::rosmap.opendata.sagebase.org","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://adknowledgeportal.synapse.org/Data%20Access"}],"DataAtWork":{"Publications":[{"Title":"A cerebrospinal fluid synaptic protein biomarker for prediction of cognitive resilience versus decline in Alzheimer's disease","URL":"https://www.nature.com/articles/s41591-025-03565-2","AuthorName":"Oh HS, Urey DY, Karlsson L, Zhu Z, Shen Y, Farinas A, et al."},{"Title":"A multi-omic atlas of the human frontal cortex for aging and Alzheimer's disease research","URL":"https://pmc.ncbi.nlm.nih.gov/articles/PMC6080491/","AuthorName":"De Jager PL, Ma Y, McCabe C, Xu J, Vardarajan BN, Felsky D, et al."},{"Title":"Religious Orders Study and Rush Memory and Aging Project","URL":"https://pmc.ncbi.nlm.nih.gov/articles/PMC6380522/","AuthorName":"Bennett DA, Buchman AS, Boyle PA, Barnes LL, Wilson RS, Schneider JA"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-09-23","RegistryEntryLastModified":"2026-10-05","Slug":"rosmap","Sources":["awslabs-open-data-registry"]}
{"Name":"RR interval time series from healthy subjects","Description":"Heart rate variability (HRV) is mainly controlled by the autonomic nervous system (ANS) interacting with receptors on the sinoatrial node (SAN). HRV is influenced by many several factors such as chemical, hormonal and neural modulations, circadian changes, exercise, emotions, posture, and preload. The heart rate adaptation to changing factors is carried out by the activity of a variety of regulatory subsystems and results in a complex linear and non-linear time behaviour, which changes with age, gender and pathological conditions. Many studies explore an age range of 1-99 years. This study focused on the development of a database of long-term time series of healthy volunteers aged between 1 month and 55 years.","Documentation":"https://doi.org/10.13026/51yd-d219","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/51yd-d219","ARN":"arn:aws:s3:::physionet-open/rr-interval-healthy-subjects/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"rr-interval-healthy-subjects","Sources":["awslabs-open-data-registry"]}
{"Name":"RSNA Abdominal Trauma Detection (RSNA-ABT)","Description":"Blunt force abdominal trauma is among the most common types of traumatic injury, with the most frequent cause being motor vehicle accidents. Abdominal trauma may result in damage and internal bleeding of the internal organs, including the liver, spleen, kidneys, and bowel. Detection and classification of injuries are key to effective treatment and favorable outcomes. A large proportion of patients with abdominal trauma require urgent surgery. Abdominal trauma often cannot be diagnosed clinically by physical exam, patient symptoms, or laboratory tests. Prompt diagnosis of abdominal trauma using medical imaging is thus critical to patient care. AI tools that assist and expedite diagnosis of abdominal trauma have the potential to substantially improve patient care and health outcomes in the emergency setting. To create the ground truth dataset, RSNA collected imaging data sourced from 23 sites in 14 countries on six continents, including more than 4,000 CT exams with various abdominal injuries and a roughly equal number of cases without injury.","Documentation":"https://github.com/RSNA/AI-Challenge-Data/wiki/RSNA-Abdominal-Trauma-Detection","Contact":"informatics@rsna.org","ManagedBy":"Radiological Society of North America (https://www.rsna.org/)","UpdateFrequency":"The dataset may be updated with additional or corrected data on a need-to-update basis.","Tags":["aws-pds","computed tomography","computer vision","csv","labeled","life sciences","machine learning","medical image computing","medical imaging","radiology","x-ray tomography"],"License":"You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. You may not share or redistribute a copy of any portion or all of the data in any form. If you reference the data in published material, include a citation to the “Brain CT Hemorrhage Dataset, Copyright RSNA, 2019” as follows: Flanders AF, et al. The RSNA Brain CT Hemorrhage Dataset [10.1148/ryai.2020190211]. Radiology: Artificial Intelligence 2020;2:3.","Resources":[{"Description":"Zip archive containing DCM and CSV files","ARN":"arn:aws:s3:::abdominal-trauma-detection","Region":"us-west-2","Type":"S3 Bucket","ControlledAccess":"https://mira.rsna.org/dataset/5"}],"DataAtWork":{"Publications":[{"Title":"The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset","AuthorName":"Rudie, Jeffrey D.","URL":"https://doi.org/10.48550/arXiv.2405.19595"}]},"RegistryEntryAdded":"2024-07-26","RegistryEntryLastModified":"2026-04-27","Slug":"rsna-abdominal-trauma-detection","Sources":["awslabs-open-data-registry"]}
{"Name":"RSNA Cervical Spine Fracture Detection (RSNA-CSF) Dataset","Description":"Over 1.5 million spine fractures occur annually in the United States alone resulting in over 17,730 spinal cord injuries annually. The most common site of spine fracture is the cervical spine. There has been a rise in the incidence of spinal fractures in the elderly and in this population, fractures can be more difficult to detect on imaging due to degenerative disease and osteoporosis. Imaging diagnosis of adult spine fractures is now almost exclusively performed with computed tomography (CT). Quickly detecting and determining the location of any vertebral fractures is essential to prevent neurologic deterioration and paralysis after trauma. RSNA has teamed with the American Society of Neuroradiology (ASNR) and the American Society of Spine Radiology (ASSR) to create this ground truth dataset, collecting imaging data from twelve sites on six continents, including approximately 2,000 CT studies. Spine radiology specialists from the ASNR and ASSR provided expert image level annotations these studies to indicate the presence, vertebral level and location of any cervical spine fractures.","Documentation":"https://github.com/RSNA/AI-Challenge-Data/wiki/RSNA-Cervical-Spine-Fracture-Detection","Contact":"informatics@rsna.org","ManagedBy":"[Radiological Society of North America](https://www.rsna.org/)","UpdateFrequency":"The dataset may be updated with additional or corrected data on a need-to-update basis.","Tags":["aws-pds","computed tomography","computer vision","csv","labeled","life sciences","machine learning","medical image computing","medical imaging","radiology","x-ray tomography"],"License":"You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. You may not share or redistribute a copy of any portion or all of the data in any form. If you reference the data in published material, include a citation to the “Brain CT Hemorrhage Dataset, Copyright RSNA, 2019” as follows: Flanders AF, et al. The RSNA Brain CT Hemorrhage Dataset [10.1148/ryai.2020190211]. Radiology: Artificial Intelligence 2020;2:3.","Resources":[{"Description":"Zip archive containing DCM and CSV files","ARN":"arn:aws:s3:::cervical-spine-fracture","Region":"us-west-2","Type":"S3 Bucket","ControlledAccess":"https://mira.rsna.org/dataset/4"}],"DataAtWork":{"Publications":[{"Title":"The RSNA Cervical Spine Fracture CT Dataset","URL":"https://pubs.rsna.org/doi/full/10.1148/ryai.230034","AuthorName":"Ming, Hui Lin"}]},"RegistryEntryAdded":"2024-07-26","RegistryEntryLastModified":"2026-04-27","Slug":"rsna-cervical-spine-fracture-detection","Sources":["awslabs-open-data-registry"]}
{"Name":"RSNA Intracranial Aneurysm Detection Dataset (RSNA-ICA)","Description":"The Radiological Society of North America Intracranial Aneurysm Detection (RSNA-ICA) dataset is a collection of over 4,000 CT brain scans annotated by a cohort of over 40 volunteer radiologists from RSNA and the American Society of Neuroradiology to show the presence and location of intracranial aneurysms. It also includes a set of about 200 imaging studies that are annotated with AI-generated segmentations highlighting abnormalities. The imaging data was provided by 18 institutions. Initially compiled in 2025 for the RSNA Intracranial Aneurysm Detection AI Challenge hosted on Kaggle competition platform (https://www.kaggle.com/competitions/rsna-intracranial-aneurysm-detection), it represents the largest publicly available collection of its kind. Additional information on the dataset and how to make use of it is provided in a forthcoming Data Resource Publication listed below, as well as on the Kaggle competition website, which also provides access to models developed during the competition.","Documentation":"https://github.com/RSNA/AI-Challenge-Data/wiki/RSNA-Intracranial-Aneurysm-Detection-Dataset","Contact":"informatics@rsna.org","ManagedBy":"Radiological Society of North America (https://www.rsna.org/)","UpdateFrequency":"The dataset may be updated with additional or corrected data on a need-to-update basis.","Tags":["aws-pds","computer vision","csv","labeled","life sciences","machine learning","medical image computing","medical imaging","radiology"],"License":"You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. You may not share or redistribute a copy of any portion or all of the data in any form. If you reference the data in published material, include a citation to the “Radiological Society of North America Intracranial Aneurysm Detection (RSNA-ICA) Dataset, July 2025” [https://doi.org/10.1148/dataset.ica.2025].","Resources":[{"Description":"Zip archive containing DCM and CSV files","ARN":"arn:aws:s3:::intracranial-aneurysm-detection","Region":"us-west-2","Type":"S3 Bucket","ControlledAccess":"https://mira.rsna.org/dataset/7"}],"DataAtWork":{"Publications":[{"Title":"The RSNA Intercranial Aneurysm Detection Dataset","URL":"https://pubs.rsna.org/doi/full/10.1148/ryai.2021200254","AuthorName":"Authors, Various"}]},"RegistryEntryAdded":"2025-11-14","RegistryEntryLastModified":"2026-04-24","Slug":"rsna-intracranial-aneurysm-detection-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"RSNA Intracranial Hemorrhage Detection","Description":"RSNA assembled this dataset in 2019 for the RSNA Intracranial Hemorrhage Detection AI Challenge (https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/). De-identified head CT studies were provided by four research institutions. A group of over 60 volunteer expert radiologists recruited by RSNA and the American Society of Neuroradiology labeled over 25,000 exams for the presence and subtype classification of acute intracranial hemorrhage.","Documentation":"https://github.com/RSNA/AI-Challenge-Data","Contact":"informatics@rsna.org","ManagedBy":"Radiological Society of North America (https://www.rsna.org/)","UpdateFrequency":"The dataset may be updated with additional or corrected data on a need-to-update basis.","Tags":["aws-pds","computed tomography","computer vision","csv","labeled","life sciences","machine learning","medical image computing","medical imaging","radiology","x-ray tomography"],"License":"You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. You may not share or redistribute a copy of any portion or all of the data in any form. If you reference the data in published material, include a citation to the “Brain CT Hemorrhage Dataset, Copyright RSNA, 2019” as follows: Flanders AF, et al. The RSNA Brain CT Hemorrhage Dataset [10.1148/ryai.2020190211]. Radiology: Artificial Intelligence 2020;2:3.","Resources":[{"Description":"Zip archive containing DCM and CSV files","ARN":"arn:aws:s3:::intracranial-hemorrhage","Region":"us-west-2","Type":"S3 Bucket","ControlledAccess":"https://mira.rsna.org/dataset/1"}],"DataAtWork":{"Publications":[{"Title":"Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge","URL":"https://pubs.rsna.org/doi/10.1148/ryai.2020190211","AuthorName":"Rudie, Jeffrey D."}]},"RegistryEntryAdded":"2024-07-26","RegistryEntryLastModified":"2026-04-27","Slug":"rsna-intracranial-hemorrhage-detection","Sources":["awslabs-open-data-registry"]}
{"Name":"RSNA Lumbar Spine Degenerative Classification Dataset (RSNA-LSDD)","Description":"The Radiological Society of North America Lumbar Spine Degenerative Classification dataset (RSNA-LSDD) is a collection of over 2,600 magnetic resonance imaging (MR) scans of the lumbar spine annotated by a cohort of about 60 volunteer radiologists recruited by the RSNA, the American Society for Spine Radiology and the American Society of Neuroradiology to identify the location and severity of five degenerative conditions across the five intervertebral disc levels (L1/L2, L2/L3, L3/L4, L4/L5, and L5/S1). The imaging data, comprising over 8,500 image series (Sagittal “T2”, Axial T2 and Sagittal T1), was provided by twelve institutions from across the globe. Initially compiled in 2024 for the RSNA Lumbar Spine Degenerative Classification AI Challenge hosted on Kaggle competition platform (https://www.kaggle.com/competitions/rsna-2024-lumbar-spine-degenerative-classification), it represents the largest publicly available collection of its kind. Additional information on the dataset and how to make use of it is provided in the Data Resource Publication listed below, as well as on the Kaggle competition website, which also provides access to models developed during the competition.","Documentation":"https://github.com/RSNA/AI-Challenge-Data/wiki/RSNA-Lumbar-Spine-Degenerative-Classification-Dataset","Contact":"informatics@rsna.org","ManagedBy":"Radiological Society of North America (https://www.rsna.org/)","UpdateFrequency":"The dataset may be updated with additional or corrected data on a need-to-update basis.","Tags":["aws-pds","computer vision","csv","labeled","life sciences","machine learning","medical image computing","medical imaging","radiology"],"License":"You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. You may not share or redistribute a copy of any portion or all of the data in any form. If you reference the data in published material, include a citation to the “Radiological Society of North America Screening Mammography Breast Cancer Detection (RSNA-SMBC) Dataset, November 2022” [https://doi.org/10.1148/dataset.smbc.2024].","Resources":[{"Description":"Zip archive containing DCM and CSV files","ARN":"arn:aws:s3:::lumbar-spine-degenerative-classification","Region":"us-west-2","Type":"S3 Bucket","ControlledAccess":"https://mira.rsna.org/dataset/6"}],"DataAtWork":{"Publications":[{"Title":"The RSNA Lumbar Spine Degenerative Classification Dataset","URL":"https://pubs.rsna.org/doi/full/10.1148/ryai.2021200254","AuthorName":"Authors, Various"}]},"RegistryEntryAdded":"2025-05-29","RegistryEntryLastModified":"2026-04-27","Slug":"rsna-lumbar-spine-degenerative-classification-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"RSNA Pulmonary Embolism Detection","Description":"RSNA assembled this dataset in 2020 for the RSNA STR Pulmonary Embolism Detection AI Challenge (https://www.kaggle.com/c/rsna-str-pulmonary-embolism-detection/). With more than 12,000 CT pulmonary angiography (CTPA) studies contributed by five international research centers, it is the largest publicly available annotated PE dataset. RSNA collaborated with the Society of Thoracic Radiology to recruit more than 80 expert thoracic radiologists who labeled the dataset with detailed clinical annotations.","Documentation":"https://github.com/RSNA/AI-Challenge-Data","Contact":"informatics@rsna.org","ManagedBy":"[Radiological Society of North America (https://www.rsna.org/)","UpdateFrequency":"The dataset may be updated with additional or corrected data on a need-to-update basis.","Tags":["aws-pds","computed tomography","computer vision","csv","labeled","life sciences","machine learning","medical image computing","medical imaging","radiology","x-ray tomography"],"License":"You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. You may not share or redistribute a copy of any portion or all of the data in any form. If you reference the data in published material, include a citation to the “RSNA-STR Pulmonary Embolism CT (RSPECT) Dataset, Copyright RSNA, 2020” as follows: E Colak, FC Kitamura, SB Hobbs, et al. The RSNA Pulmonary Embolism CT Dataset [10.1148/ryai.2021200254]. Radiology: Artificial Intelligence 2021;3:2.","Resources":[{"Description":"Zip archive containing DCM and CSV files","ARN":"arn:aws:s3:::pulmonary-embolism-detection","Region":"us-west-2","Type":"S3 Bucket","ControlledAccess":"https://mira.rsna.org/dataset/2"}],"DataAtWork":{"Publications":[{"Title":"The RSNA Pulmonary Embolism CT Dataset","URL":"https://pubs.rsna.org/doi/full/10.1148/ryai.2021200254","AuthorName":"Colak, Errol"}]},"RegistryEntryAdded":"2024-07-26","RegistryEntryLastModified":"2026-04-27","Slug":"rsna-pulmonary-embolism-detection","Sources":["awslabs-open-data-registry"]}
{"Name":"RSNA Abdominal Traumatic Injury CT (RATIC)","Description":"Blunt force abdominal trauma is among the most common types of traumatic injury, with the most frequent cause being motor vehicle accidents. Abdominal trauma may result in damage and internal bleeding of the internal organs, including the liver, spleen, kidneys, and bowel. Detection and classification of injuries are key to effective treatment and favorable outcomes. A large proportion of patients with abdominal trauma require urgent surgery. Abdominal trauma often cannot be diagnosed clinically by physical exam, patient symptoms, or laboratory tests. Prompt diagnosis of abdominal trauma using medical imaging is thus critical to patient care. AI tools that assist and expedite diagnosis of abdominal trauma have the potential to substantially improve patient care and health outcomes in the emergency setting. To create the ground truth dataset, RSNA collected imaging data sourced from 23 sites in 14 countries on six continents, including more than 4,000 CT exams with various abdominal injuries and a roughly equal number of cases without injury.","Documentation":"https://github.com/RSNA/AI-Challenge-Data/wiki/RSNA-Abdominal-Traumatic-Injury-CT","Contact":"informatics@rsna.org","ManagedBy":"Radiological Society of North America (https://www.rsna.org/)","UpdateFrequency":"The dataset may be updated with additional or corrected data on a need-to-update basis.","Tags":["aws-pds","computed tomography","computer vision","csv","labeled","life sciences","machine learning","medical image computing","medical imaging","radiology","x-ray tomography"],"License":"You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. You may not share or redistribute a copy of any portion or all of the data in any form. If you reference the data in published material, include a citation to the “Brain CT Hemorrhage Dataset, Copyright RSNA, 2019” as follows: Flanders AF, et al. The RSNA Brain CT Hemorrhage Dataset [10.1148/ryai.2020190211]. Radiology: Artificial Intelligence 2020;2:3.","Resources":[{"Description":"Zip archive containing DCM and CSV files","ARN":"arn:aws:s3:::abdominal-trauma-detection","Region":"us-west-2","Type":"S3 Bucket","ControlledAccess":"https://mira.rsna.org/dataset/5"}],"DataAtWork":{"Publications":[{"Title":"The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset","AuthorName":"Rudie, Jeffrey D.","URL":"https://doi.org/10.48550/arXiv.2405.19595"}]},"RegistryEntryAdded":"2024-10-29","RegistryEntryLastModified":"2026-04-27","Slug":"rsna-ratic","Sources":["awslabs-open-data-registry"]}
{"Name":"RSNA Screening Mammography Breast Cancer Detection (RSNA-SMBC) Dataset","Description":"According to the WHO, breast cancer is the most commonly occurring cancer worldwide. In 2020 alone, there were 2.3 million new breast cancer diagnoses and 685,000 deaths. Yet breast cancer mortality in high-income countries has dropped by 40% since the 1980s when health authorities implemented regular mammography screening in age groups considered at risk. Early detection and treatment are critical to reducing cancer fatalities, and your machine learning skills could help streamline the process radiologists use to evaluate screening mammograms. Currently, early detection of breast cancer requires the expertise of highly-trained human observers, making screening mammography programs expensive to conduct. RSNA collected screening mammograms and supporting information from two sites, totaling just under 20,000 imaging studies.","Documentation":"https://github.com/RSNA/AI-Challenge-Data/wiki/RSNA-Screening-Mammography-Breast-Cancer-Detection","Contact":"informatics@rsna.org","ManagedBy":"Radiological Society of North America (https://www.rsna.org/)","UpdateFrequency":"The dataset may be updated with additional or corrected data on a need-to-update basis.","Tags":["aws-pds","breast cancer","cancer","computer vision","csv","labeled","life sciences","machine learning","mammography","medical image computing","medical imaging","radiology"],"License":"You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. You may not share or redistribute a copy of any portion or all of the data in any form. If you reference the data in published material, include a citation to the “Radiological Society of North America Screening Mammography Breast Cancer Detection (RSNA-SMBC) Dataset, November 2022” [https://doi.org/10.1148/dataset.smbc.2024].","Resources":[{"Description":"Zip archive containing DCM and CSV files","ARN":"arn:aws:s3:::screening-mammography-breast","Region":"us-west-2","Type":"S3 Bucket","ControlledAccess":"https://mira.rsna.org/dataset/3"}],"RegistryEntryAdded":"2024-07-26","RegistryEntryLastModified":"2026-04-27","Slug":"rsna-screening-mammography-breast-cancer-detection","Sources":["awslabs-open-data-registry"]}
{"Name":"run_dbcan CAZyme and CGC annotation database on AWS","Description":"Database for use with run_dbcan (CAZyme and CGC annotation), including CAZyme, Transporter, Transcription factor, Signaling Transduction Protein, Sulfatase, Peptidase, and Polysaccharide utilization Loci.","Documentation":"https://run-dbcan.readthedocs.io/en/latest/","Contact":"xzhang55@huskers.unl.edu; hyi@stjude.org; yyin@unl.edu","ManagedBy":"Xinpeng Zhang; Haidong Yi; Yanbin Yin","UpdateFrequency":"This database will update annually and usually in July/August.","Tags":["aws-pds","benchmark","bioinformatics","life sciences","metagenomics","microbiome"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"run_dbcan CAZyme and CGC annotation database","ARN":"arn:aws:s3:::dbcan","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://dbcan.s3.us-west-2.amazonaws.com/)"]},{"Description":"Notifications for new dbcan data","ARN":"arn:aws:sns:us-west-2:770494479040:dbcan-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"run_dbcan Documentation","URL":"https://run-dbcan.readthedocs.io/en/latest/","AuthorName":"Xinpeng Zhang; Haidong Yi; Yanbin Yin","AuthorURL":"https://github.com/bcb-unl/run_dbcan"}],"Tools & Applications":[{"Title":"run_dbcan","URL":"https://run-dbcan.readthedocs.io/en/latest/","AuthorName":"Xinpeng Zhang; Haidong Yi; Jinfang Zheng; Le Huang; Qiwei Ge; Yanbin Yin","AuthorURL":"https://github.com/bcb-unl/run_dbcan"}],"Publications":[{"Title":"dbCAN3: automated carbohydrate-active enzyme and substrate annotation","URL":"https://doi.org/10.1093/nar/gkad328","AuthorName":"Jinfang Zheng, Qiwei Ge, Yuchen Yan, Xinpeng Zhang, Le Huang, Yanbin Yin"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-03-02","RegistryEntryLastModified":"2026-03-02","Slug":"run_dbcan","Sources":["awslabs-open-data-registry"]}
{"Name":"Response to Valsalva Maneuver in Humans","Description":"Functional metrics of autonomic control of heart rate, including baroreflex sensitivity, have been shown to be strongly associated with cardiovascular risk. A decrease in baroreflex sensitivity with aging is hypothesized to represent a contributing causal factor in the etiology of primary hypertension. To assess baroreflex function in human subjects, two complementary methods to simulate the response in heart rate elicited by the Valsalva maneuver were developed and applied to data obtained from a cohort of healthy normal volunteers.","Documentation":"https://doi.org/10.13026/C24M3P","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C24M3P","ARN":"arn:aws:s3:::physionet-open/rvmh1/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"rvmh1","Sources":["awslabs-open-data-registry"]}
{"Name":"Sentinel-1 Precise Orbit Determination (POD) Products","Description":"Sentinel-1 Precise Orbit Determination (POD) products contain auxiliary data on satellite position and velocity for the European Space Agency's (ESA) Sentinel-1 mission. Sentinel-1 is a C-band Synthetic Aperture Radar (SAR) satellite constellation first launched in 2014 as part of the European Union's Copernicus Earth Observation programme. POD products are a necessary auxiliary input for nearly all Sentinel-1 data processing workflows.\n\nThis dataset is a mirror of the [Sentinel-1 Orbits](https://documentation.dataspace.copernicus.eu/Data/ComplementaryData/Additional.html#sentinel-1-orbits) dataset hosted in the Copernicus Data Space Ecosystem (CDSE). New files are added within 20 minutes of their publication to CDSE. This dataset includes two types of POD files: RESORB and POEORB.\n\n- RESORB POD files are restituted orbit files generated within 180 minutes from sensing time and published multiple times per day, with accuracy requirement of 10 cm in 2D RMS, but typically below 5 cm. RESORB products from the last 90 days are included in this dataset.\n- POEORB POD files are precise orbit files generated with a timeliness of 20 days from sensing time and published once per day, with accuracy requirement of 5 cm in 3D RMS, but typically below 1 cm. Once available, these files supersede the RESORB products, as they contain the same data with better accuracy. POEORB products are available from the entire length of the Sentinel-1 mission.\n\nFor more information on the orbit products, see the [POD Sentinel-1 Products Specification](https://sentiwiki.copernicus.eu/web/s1-processing#S1Processing-PODSentinel-1ProductsSpecificationS1-Processing-POD-Sentinel-1-Products-Specification).\n","Documentation":"https://s1-orbits.s3.us-west-2.amazonaws.com/README.html","Contact":"https://asf.alaska.edu/asf/contact-us/","ManagedBy":"[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)","UpdateFrequency":"Updated as new data becomes available on the [Copernicus Data Space Ecosystem](https://documentation.dataspace.copernicus.eu/Data/ComplementaryData/Additional.html#sentinel-1-orbits). Typically AUX_POEORB files are published daily and AUX_RESORB files are published every other hour.\n","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["auxiliary data","disaster response","earth observation","earthquakes","floods","geophysics","sentinel-1","synthetic aperture radar"],"License":"Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://dataspace.copernicus.eu/terms-and-conditions).\n","Resources":[{"Description":"Sentinel-1 Orbits bucket","ARN":"arn:aws:s3:::s1-orbits","Region":"us-west-2","Type":"S3 Bucket","Explore":["[AWS S3 Explorer](https://s1-orbits.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new data","ARN":"arn:aws:sns:us-west-2:211125554030:s1-orbits-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"REST API for downloading Sentinel-1 Orbit files from the Registry of Open Data on AWS","URL":"https://s1-orbits.asf.alaska.edu/ui/","AuthorName":"Alaska Satellite Facility","AuthorURL":"https://asf.alaska.edu/"},{"Title":"s1-orbits, A Python utility for downloading Sentinel-1 Orbit files from the Registry of Open Data on AWS","URL":"https://pypi.org/project/s1-orbits/","AuthorName":"Alaska Satellite Facility","AuthorURL":"https://asf.alaska.edu/"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2024-08-06","RegistryEntryLastModified":"2026-07-09","Slug":"s1-orbits","Sources":["awslabs-open-data-registry"]}
{"Name":"Multidecadal Convection-permitting Simulations of Current and Future Climate over South America using WRF","Description":"A high-resolution convection-permitting climate change simulation was performed at 4 km grid spacing covering the entire continent of South America. The simulation was performed using Weather Research Forecasting (WRF) model. This dataset is from a 22-year simulation representing the current climate between January 2001 and January 2022. ERA5 data was used to initialize the model and force boundary every 3 hours. This high-resolution simulation allowed for much improved representation of topography, convection, and orographic precipitation and snowpack, resulting in a one-of-a-kind dataset that can be used to study the hydroclimate processes in South America. A second simulation was then performed using the ERA5 weather with a climate perturbation from the CESM2-Large Ensemble (LENS2) for near the end of the 21st century (2060-2080). Monthly mean change signals were computed from the LENS2 and applied to the sub-daily ERA5 data to create the future simulation data used to drive the WRF model. This dataset provides a warmer and wetter 2060-2080 future scenario for South America using the same 2001-2022 day-to-day weather. The combined datasets will enable myriad research studies, spanning fundamental and applied research into regional hydroclimatic change over South America and corresponding impacts across sectors. \n","Documentation":"https://rda.ucar.edu/datasets/d616000 This website describes the current climate simulation files and variables.  The future climate files are exactly the same, excpet for file names indicating the future simulation.\n","Contact":"rdahelp@ucar.edu, Erin Dougherty, doughert@ucar.edu; Andy Newman, anewman@ucar.edu","ManagedBy":"[NSF National Center for Atmospheric Research](https://ncar.ucar.edu/)","UpdateFrequency":"Rare. This dataset is unlikely to be updated unless there are file problems discovered in the AWS copy.","Tags":["air temperature","atmosphere","aws-pds","climate","climate model","geospatial","hydrology","land","model","precipitation"],"License":"https://www.ucar.edu/terms-of-use/data","Citation":"C. Liu, K. Ikeda, E. Dougherty, A. J. Newman, M. L. Bettolli, F. Dominguez, K. R. Rasmussen, and R. Rasmussen. 2025. Multi-decadal Convection-permitting Simulation of Current and Future Climate over South America using WRF. Developed by the National Science Foundataion National Center for Atmospheric Research. Amazon Web Services Open Data Sponsorship Program. Accessed† dd mmm yyyy.","Resources":[{"Description":"SAAG WRF output files","ARN":"arn:aws:s3:::saag-wrf","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Documentation](https://rda.ucar.edu/datasets/d616000)","[Browse Bucket](https://saag-wrf.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new SAAG data","ARN":"arn:aws:sns:us-west-2:284614374041:ncar-saag-southamerica-wrf-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"SAAG_readin.ipnyb","URL":"https://github.com/NCAR/SAAG/blob/main/SAAG_readin.ipynb","AuthorName":"Erin Dougherty","AuthorURL":"https://scholar.google.com/citations?user=peK6SY4AAAAJ&hl=en&oi=ao"}],"Publications":[{"Title":"Advancing South American Water and Climate Science through Multidecadal Convection-Permitting Modeling","URL":"https://doi.org/10.1175/BAMS-D-22-0226.1","AuthorName":"Dominguez et al. (2024), Bull. Amer. Meteor. Soc., 105, E32–E44","AuthorURL":"https://doi.org/10.1175/BAMS-D-22-0226.1"}]},"RegistryEntryAdded":"2026-07-14","RegistryEntryLastModified":"2026-07-16","Slug":"saag-wrf","Sources":["awslabs-open-data-registry"]}
{"Name":"Safecast","Description":"An ongoing collection of radiation and air quality measurements taken by devices involved in the Safecast project.","Documentation":"https://github.com/Safecast/safecastapi/wiki/Data-Sets","Contact":"https://groups.google.com/forum/#!forum/safecast-devices","ManagedBy":"[Safecast](https://safecast.org/)","UpdateFrequency":"Continuous","Collabs":{"ASDI":{"Tags":["air quality"]}},"Tags":["air quality","aws-pds","climate","environmental","geospatial","radiation"],"License":"Safecast data is published under a [CC0 designation](https://creativecommons.org/publicdomain/zero/1.0/). This is a public domain designation and means the data is free and open for anyone to use under any circumstance.\n","Resources":[{"Description":"Bulk exports of air and radiation measurements","ARN":"arn:aws:s3:::safecast-opendata-public-us-east-1","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://safecast-opendata-public-us-east-1.s3.amazonaws.com/index.html)"]},{"Description":"New air and radiation measurement payloads","ARN":"arn:aws:sns:us-west-2:985752656544:ingest-measurements-prd","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"Safecast Map","URL":"http://safecast.org/tilemap/","AuthorName":"Nick Dolezal","AuthorURL":"https://github.com/frangible"}]},"RegistryEntryAdded":"2018-12-14","RegistryEntryLastModified":"2023-02-28","Slug":"safecast","Sources":["awslabs-open-data-registry"]}
{"Name":"Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) Raw Data","Description":"The Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) study provides\ntranscriptomic and chromatin accessibility data from single nuclei isolated\nfrom postmortem brain tissue from individuals spanning the Alzheimer's disease\nspectrum. The study supports investigation of cellular and molecular changes\nassociated with Alzheimer's disease progression and includes data generated as\npart of the broader SEA-AD effort to characterize brain cell types,\ndisease-associated cellular states, and related neuropathology.\n","Documentation":"https://doi.org/10.7303/9618137","Contact":"https://www.synapse.org/Synapse:syn2580853/discussion/default","ManagedBy":"Sage Bionetworks","UpdateFrequency":"AD Knowledge Portal Data Releases are published monthly (https://news.adknowledgeportal.org/). Updates to specific studies are dependent on data contributor and ingress timelines.","Tags":["aws-pds","bioinformatics","biology","cell biology","epigenomics","gene expression","genomic","genotyping","Homo sapiens","life sciences","medicine","neurobiology","neuroscience","single-cell transcriptomics","transcriptomics"],"License":"https://adknowledgeportal.synapse.org/Explore/Studies/DetailsPage/StudyDetails?Study=syn26223298#Acknowledgement","Citation":"The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (NIH-NIA, U24AG061340).","Resources":[{"Description":"This study provides transcriptomic and chromatin accessibility data from single nuclei isolated from brain tissue of 84 aged individuals spanning the full spectrum of Alzheimer's disease severity and 5 neurotypical adult reference individuals, as well as genotype data for those individuals.","ARN":"arn:aws:s3:::sea-ad.opendata.sagebase.org","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://adknowledgeportal.synapse.org/Explore/Studies/DetailsPage/StudyDetails?Study=syn26223298"}],"DataAtWork":{"Publications":[{"Title":"SEA-AD is a multimodal cellular atlas and resource for Alzheimer's disease","URL":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11577961/","AuthorName":"Hawrylycz M, Kaplan ES, Travaglini KJ, Gabitto MI, Miller JA, Ng L, et al."}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-06-29","RegistryEntryLastModified":"2026-10-05","Slug":"sage-sea-ad-atlas","Sources":["awslabs-open-data-registry"]}
{"Name":"Aging Mouse Brain Epigenetic","Description":"Aging is a major risk factor for neurodegenerative diseases, yet underlying epigenetic mechanisms remain unclear. Here, we generated a comprehensive single-nucleus cell atlas of brain aging across multiple brain regions, comprising 132,551 single-cell methylomes and 72,666 joint chromatin conformation-methylome nuclei. Integration with companion transcriptomic and chromatin accessibility data yielded a cross-modality taxonomy of 36 major cell types. The bucket holds two parts of this atlas, from mice at 2, 9 and 18 months: (1) `female-amb-sc-allc/`, single-cell DNA methylation (ALLC) files for 77,001 female nuclei in 29 pools (`pool_amb*/`), one tabix-indexed `.allc.tsv.gz` file per nucleus (8.4 TB); (2) raw MERFISH spatial transcriptomics runs of brain slices, one `.tar.gz` archive per run (10 runs, 22.3 TB).","Contact":"ecker@salk.edu","Documentation":"https://doi.org/10.1101/2025.04.21.648266","ManagedBy":"[Salk Institute](http://www.salk.edu)","UpdateFrequency":"Never","Tags":["aws-pds","bam","cram","epigenomics","fastq","genetic","genomic","life sciences","neuroscience","spatial transcriptomics","transcriptomics","whole exome sequencing","whole genome sequencing"],"License":"[NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomic Data Sharing Policy ](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)","Resources":[{"Description":"Aging mouse brain single-cell DNA methylation (ALLC) files and MERFISH spatial transcriptomics runs","ARN":"arn:aws:s3:::salk-aging-mouse-brain-epigenetics","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Dataset update notifications","ARN":"arn:aws:sns:us-west-2:855738613743:salk-aging-mouse-brain-epigenetics-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"Cell-type-specific transposable element demethylation and TAD remodeling in the aging mouse brain","URL":"https://doi.org/10.1101/2025.04.21.648266","AuthorName":"Zeng, Q., Wei, T., Klein, A., Bartlett, A., Liu, H., Nery, J.R., Castanon, R., Osteen, J., Johnson, N.D., Wang, W., Ding, W., Chen, H., Altshul, J., Kenworthy, M., Valadon, C., Owens, W., Wu, Z., Amaral, M.L., Song, Báez-Becerra, T.a.t.i.a.n.a., Cho, S., Chen, C., Willier, J., Cao, S., Rink, J., Lee, J., Barcoma, A., Arzavala, J., Emerson, N., Lu, Y.R., Ren, B., Behrens, M.a.r.g.a.r.i.t.a., Ecker, J.R.","AuthorURL":"https://www.salk.edu/scientist/joseph-ecker/"}]},"RegistryEntryAdded":"2025-11-21","RegistryEntryLastModified":"2026-11-12","Slug":"salk-aging-mouse-brain-epigeneti","Sources":["awslabs-open-data-registry"]}
{"Name":"Human Brain snm3C-seq","Description":"The human brain comprises hundreds of epigenomically distinct cell types whose identity is governed by DNA methylation and the three-dimensional folding of the genome, yet these two regulatory layers have rarely been measured jointly across its full anatomical breadth. While transcriptomic censuses have resolved the cellular diversity of the human brain and single-cell methylation and 3D-genome atlases have been generated for the whole mouse brain, a comprehensive joint methylome and 3D-genome atlas of the entire human brain, including subcortical, cerebellar, and brainstem structures that remain underrepresented in existing resources, has been lacking. This gap limits the functional interpretation of non-coding risk variants for neurological and psychiatric disorders, which requires maps that assign regulatory elements to specific cell types, resolve the enhancer-promoter contacts through which distal variants act, and situate these features within disease-relevant anatomical regions. Here we present a multimodal single-cell epigenomic and 3D-genome atlas of the whole human brain, using single-nucleus methyl-3C sequencing (snm3C-seq) to simultaneously profile DNA methylation and chromatin conformation across 12 adult donors. Harmonized to the [HMBA/BICAN] consensus taxonomy and integrated with matched RNA-seq, ATAC-seq, histone-modification, and MERFISH spatial data, this atlas provides a foundational framework for interpreting non-coding genetic variation and for dissecting the cell-type-specific regulatory basis of human brain function and disease.","Documentation":"https://github.com/DingWB/human_brain_snm3C-seq","Contact":"ecker@salk.edu","ManagedBy":"[Salk Institute](http://www.salk.edu)","UpdateFrequency":"New data is added as soon as it is available.","Tags":["aws-pds","bam","cram","epigenomics","fastq","genetic","genomic","life sciences","transcriptomics","whole genome sequencing"],"License":"[NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomic Data Sharing Policy ](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)","Resources":[{"Description":"Human basal ganglia snm3c-seq dataset","ARN":"arn:aws:s3:::salk-human-brain-snm3c-seq","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Dataset update notifications","ARN":"arn:aws:sns:us-west-2:855738613743:salk-human-brain-snm3c-seq-updates","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get to Know a Dataset - human_brain_snm3C-seq","URL":"https://github.com/DingWB/human_brain_snm3C-seq/blob/main/notebooks/about_datasets.ipynb","NotebookURL":"https://github.com/DingWB/human_brain_snm3C-seq/blob/main/notebooks/about_datasets.ipynb","AuthorName":"Wubin Ding","AuthorURL":"https://github.com/DingWB"}],"Tools & Applications":[{"Title":"Basal Ganglia Epigenome","URL":"https://basalganglia.epigenomes.net","AuthorName":"Wenjin Zhang, Wubin Ding, Kai Li,..Bing Ren, Joseph R Ecker, Ting Wang, Daofeng Li"},{"Title":"Single Cell Multi-Omics Data Analysis Platform (SCMDAP)","URL":"https://neomorph.salk.edu/SCMDAP/BasalGanglia","AuthorName":"Wubin Ding and Joseph R Ecker","AuthorURL":"https://github.com/DingWB"}],"Publications":[{"Title":"Multimodal single-cell epigenomic and 3D genome atlas of the human basal ganglia","URL":"https://doi.org/10.1016/j.cell.2026.10.019","AuthorName":"Wubin Ding, Amit Klein, Cindy Tatiana Baez-Becerra, Jonathan A. Rink, Anna Bartlett, Ruoxuan Wang, Qiurui Zeng, Rosa Gomez Castanon, Joseph R. Nery, Emma Osgood, William Owens, Alaina Petrella, Chumo Chen, Andrea Saldana Acerbo, Ariana S. Barcoma, Jiayi Liu, Kaitlyn G. Russo, Kyle W. Knutson, Carissa K. Young, Jackson K. Willier, Cesar Barragan, Jessica Arzavala, Silvia Cho, Jordan Altshul, Derek Chan, Eshaan Soma, Jammy Luo, Manya Jain, Sarah Velazquez, Guha V. Sundaram, Austin C. Manning, Yareli Sanchez, Aleksandra Bikkina, Yuanyuan Fu, Kaushik Komandur, Ethan Doan, Shuai Fu, Carolyn O’Connor, Michelle Liem, Mikayla V. Marrin, Cynthia Rose, Shane N. Alt, Jillian Berry, Joseph Colin Kern, Eric Boone, Wei Tian, Huamin Chen, Yang Xie, Yue Wu, Manoj Hariharan, Kai Li, Lei Chang, Nelson Johansen, Wenjin Zhang, Zoey Zhao, Jesus Flores, Chu-Yi Tai, Jacqueline Olness, Natalie Schenker-Ahmed, Xiao-Ping Liu, Quan Zhu, Brian E. Kalmbach, Rebecca D. Hodge, Trygve E. Bakken, Ed S. Lein, Daofeng Li, Ting Wang, Xiangmin Xu, Bing Ren, Maria Margarita Behrens, Joseph R. Ecker"}]},"RegistryEntryAdded":"2026-11-10","RegistryEntryLastModified":"2026-11-13","Slug":"salk-human-brain-snm3c-seq","Sources":["awslabs-open-data-registry"]}
{"Name":"Sample Entropy Estimation","Description":"Sample Entropy is a useful tool for investigating the dynamics of heart rate and other time series. Sample Entropy is the negative natural logarithm of an estimate of the conditional probability that subseries (epochs) of length m that match pointwise within a tolerance r also match at the next point.","Documentation":"https://physionet.org/content/sampen/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/sampen/","ARN":"arn:aws:s3:::physionet-open/sampen/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"sampen","Sources":["awslabs-open-data-registry"]}
{"Name":"Santa Fe Time Series Competition Data Set B","Description":"This is a multivariate data set recorded from a patient in the sleep laboratory of the Beth Israel Hospital (now the Beth Israel Deaconess Medical Center) in Boston, Massachusetts. This data set was extracted from record slp60 of the MIT-BIH Polysomnographic Database, and it was submitted to the Santa Fe Time Series Competition in 1991 by our group. The data are presented in text form and have been split into two sequential parts. Each line contains simultaneous samples of three parameters; the interval between samples in successive lines is 0.5 seconds. The first column is the heart rate, the second is the chest volume (respiration force), and the third is the blood oxygen concentration (measured by ear oximetry). The sampling frequency for each measurement is 2 Hz (i.e., the time interval between measurements in successive rows is 0.5 seconds).","Documentation":"https://doi.org/10.13026/C20W2T","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C20W2T","ARN":"arn:aws:s3:::physionet-open/santa-fe/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"santa-fe","Sources":["awslabs-open-data-registry"]}
{"Name":"Satellogic EarthView dataset","Description":"Satellogic EarthView dataset includes high-resolution satellite images captured over all continents. The dataset is organized in Hive partition format and hosted by AWS. The dataset can be accessed via STAC browser or aws cli. Each item of the dataset corresponds to a specific region and date, with some of the regions revisited for additional data. The dataset provides Top-of-Atmosphere (TOA) reflectance values across four spectral bands (Red, Green, Blue, Near-Infrared) at a Ground Sample Distance (GSD) of 1 meter, accompanied by comprehensive metadata such as off-nadir angles, sun elevation, and other pertinent details. Users should note that due to an artifact in region delineation, a small number of regions present overlaps.","Documentation":"https://satellogic-earthview.s3.us-west-2.amazonaws.com/index.html","Contact":"https://www.satellogic.com/","ManagedBy":"[Satellogic](https://www.satellogic.com)","UpdateFrequency":"New data will be made available periodically, with annual updates expected in the future covering the same or other new regions.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","cog","computer vision","earth observation","geospatial","image processing","satellite imagery","stac"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/deed.en)","Resources":[{"Description":"Satellogic data includes TOA RGBN COG, VISUAL RGB COG files data and metadata","ARN":"arn:aws:s3:::satellogic-earthview","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC Catalog](https://satellogic-earthview.s3.us-west-2.amazonaws.com/stac/catalog.json)","[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/satellogic-earthview.s3.us-west-2.amazonaws.com/stac/catalog.json)"]}],"DataAtWork":{"Tutorials":[{"Title":"Explore Satellogic EarthView in SageMaker Studio Lab (SMSL)","URL":"https://github.com/satellogic/satellogic-earthview/","NotebookURL":"https://github.com/satellogic/satellogic-earthview/blob/main/satellogic_earthview_exploration.ipynb","AuthorName":"Javier Marin","Services":["Amazon SageMaker Studio Lab"]}],"Publications":[{"Title":"EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision","URL":"https://satellogic-earthview.s3.us-west-2.amazonaws.com/index.html","AuthorName":"Velázquez, Diego and Rodríguez, Pau and Alonso, Sergio and Gonfaus, Josep M. and González, Jordi and, Richarte, Gerardo and Marín, Javier and Bengio, Yoshua and Lacoste, Alexandre"}]},"RegistryEntryAdded":"2025-01-08","RegistryEntryLastModified":"2025-07-09","Slug":"satellogic-earthview","Sources":["awslabs-open-data-registry"]}
{"Name":"SatPM2.5","Description":"Fine particulate matter (PM2.5) concentrations are estimated using information from satellite-, simulation- and monitor-based sources. Aerosol optical depth from multiple satellites (MODIS, VIIRS, MISR, SeaWiFS, and VIIRS) and their respective retrievals (Dark Target, Deep Blue, MAIAC) is combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations to produce geophysical estimates that explain most of the variance in ground-based PM2.5 measurements. A subsequent statistical fusion incorporates additional information from ground-based PM2.5 measurements.","Documentation":"https://www.satpm.org/","Contact":"support@satpm.org","ManagedBy":"https://satpm.org","UpdateFrequency":"Yearly","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["air quality","atmosphere","aws-pds","environmental","health","netcdf"],"License":"Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Satellite-Derived Fine Particulate Matter (PM2.5) concentrations from the Atmospheric Composition Analysis Group and Washington University in St. Louis","ARN":"arn:aws:s3:::satpmdata/","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](http://satpmdata.s3-website-us-east-1.amazonaws.com/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Importing and Plotting the V6.GL.03 dataset into Matlab","URL":"https://sites.wustl.edu/acag/importing-and-plotting-the-v6-gl-02-02-dataset-into-matlab/","AuthorName":"Aaron van Donkelaar","AuthorURL":"https://sites.wustl.edu/acag/people/aaron-van-donkelaar/"}],"Publications":[{"Title":"Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning","URL":"https://pubs.acs.org/doi/full/10.1021/acsestair.3c00054","AuthorName":"Siyuan Shen, Chi Li, Aaron van Donkelaar, Nathan Jacobs, Chenguang Wang, and Randall V. Martin","AuthorURL":"https://sites.wustl.edu/acag/"}]},"RegistryEntryAdded":"2026-08-28","RegistryEntryLastModified":"2026-08-28","Slug":"satpmdata","Sources":["awslabs-open-data-registry"]}
{"Name":"Sea Around Us Global Fisheries Catch Data","Description":"The project presents Sea Around Us Global Fisheries Catch Data aggregated at EEZ level. The data are computed from reconstructed catches from various official fisheries statistics, scientific, technical and policy reports about the fisheries, and includes estimation of discards, unreported and illegal catch data from all maritime countries and major territories of the world.\n\nThis project was the result of a work between Sea Around Us and the CIC programme, a collaborative programme between the University of British Columbia (UBC) and AWS.\n","Documentation":"https://www.seaaroundus.org/ubc-cic-sea-around-us-project-collaboration/","Contact":"https://www.seaaroundus.org/feedback/","ManagedBy":"[Sea Around Us](https://www.seaaroundus.org/)","UpdateFrequency":"The full dataset is computed only once or twice a year or when there is a significant change in data. Updates will occur as soon as newly recomputed datasets are available.\n","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["aws-pds","biodiversity","ecosystems","fisheries","marine"],"License":"This data is available for anyone to use under the [Sea Around Us Terms of Use](https://www.seaaroundus.org/terms-and-conditions/)","Resources":[{"Description":"Global Fisheries Catch Data","ARN":"arn:aws:s3:::fisheries-catch-data","Region":"us-west-2","Type":"S3 Bucket"}],"Citation":"https://www.seaaroundus.org/citation-policy/","DataAtWork":{"Tutorials":[{"Title":"Tutorial for analyzing Sea Around Us Global Fisheries Catch Data using Jupyter Notebook","URL":"https://docs.google.com/document/d/1PNcTBnQHE9imF0JjZCs3zWNv1kOXbUHl/edit?usp=sharing&ouid=105083864237876642152&rtpof=true&sd=true","AuthorName":"Shelumiel Ortiz","AuthorURL":"http://www.seaaroundus.org/shelumiel-ortiz-website-manager/"}],"Tools & Applications":[{"Title":"Sea Around Us Global Fisheries Catch Data","URL":"https://github.com/SeaAroundUs/sau-ubc-cic-catchdata","AuthorName":"Sea Around Us, CIC"},{"Title":"Sea Around Us Tools & Data","URL":"https://www.seaaroundus.org/data/","AuthorName":"Sea Around Us","AuthorURL":"https://www.seaaroundus.org/people-2/"}],"Publications":[{"Title":"Global Atlas of Marine Fisheries: A critical appraisal of catches and ecosystem impacts. Island Press, Washington D.C. (2016)","URL":"https://islandpress.org/books/global-atlas-marine-fisheries","AuthorName":"Pauly, D.;  Zeller, D. (eds)","AuthorURL":"https://www.seaaroundus.org/people-2/"},{"Title":"List of the Sea Around Us publications","URL":"https://www.seaaroundus.org/articles/","AuthorName":"Sea Around Us","AuthorURL":"https://www.seaaroundus.org/people-2/"}]},"RegistryEntryAdded":"2021-12-07","RegistryEntryLastModified":"2022-12-13","Slug":"sau-global-fisheries-catch-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Catalina Sky Survey (CSS) subset data on AWS","Description":"Raw data that discovers Near Earth Objects (NEOs) which potentially could impact Earth","Documentation":"https://sbn.psi.edu/pds/resource/css.html","Contact":"pds-operator@jpl.nasa.gov","ManagedBy":"[Planetary Data Systems Small Bodies Node (SBN)](https://sbn.psi.edu/pds/support/)","UpdateFrequency":"CSS data are delivered nightly into the PDS archive starting with the night of Jan. 20, 2022. Earlier data will also be delivered into the archive on an irregular schedule.","Tags":["astronomy","aws-pds","object detection","planetary","survey"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"Catalina Sky Survey Raw Data","ARN":"arn:aws:s3:::pds-css-archive","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse archive](https://sbnarchive.psi.edu/pds4/surveys/gbo.ast.catalina.survey/data_raw/V06/2023/23Jul15/)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Astropy","URL":"https://docs.astropy.org/en/stable/install.html","AuthorName":"The Astropy Developers"},{"Title":"CATCH (Comet-Asteroid Telescopic Catalog Hub) service: A moving-target search tool for large astronomical data sets, such as time-domain or near-Earth object surveys.","URL":"https://catch.astro.umd.edu/about","AuthorName":"CATCH service is built and operated by the Small Bodies Node of the NASA Planetary Data System group at the University of Maryland."},{"Title":"FITS image compression programs (fpack & funpack)","URL":"https://heasarc.gsfc.nasa.gov/fitsio/fpack/","AuthorName":"Rob Seaman (NOAO), William Pence (NASA/GSFC), Rick White (STScI)."},{"Title":"PDS4 Viewer","URL":"https://sbnwiki.astro.umd.edu/wiki/PDS4_Viewer","AuthorName":"Small Bodies Node of the NASA Planetary Data System group at the University of Maryland."},{"Title":"Transform Tool","URL":"https://nasa-pds.github.io/transform/","AuthorName":"Planetary Data System (PDS) Engineering Node"}],"Publications":[{"Title":"Catalina Sky Survey Operations and Processing","URL":"https://sbnarchive.psi.edu/pds4/surveys/gbo.ast.catalina.survey/document/CSS_operations_v8.pdf","AuthorName":"R. Seaman, E. Christensen, A. Gibbs, S. Larson, F. Shelly"},{"Title":"Catalina Sky Survey Telescopes and Instrumentation","URL":"https://sbnarchive.psi.edu/pds4/surveys/gbo.ast.catalina.survey/document/CSS_telescopes_v6.pdf","AuthorName":"R. Seaman, E. Christensen, A. Gibbs, S. Larson, F. Shelly"},{"Title":"History of the Catalina Sky Survey","URL":"https://sbnarchive.psi.edu/pds4/surveys/gbo.ast.catalina.survey/document/CSS_history_v6.pdf","AuthorName":"S. Larson, edited by R. Seaman"},{"Title":"Overview of Catalina Sky Survey PDS Archive","URL":"https://sbnarchive.psi.edu/pds4/surveys/gbo.ast.catalina.survey/document/CSS_bundle_overview_v11.pdf","AuthorName":"R. Seaman, C. Neese, J. Stone, E. Christensen"}]},"RegistryEntryAdded":"2024-02-22","RegistryEntryLastModified":"2024-03-27","Slug":"sbn-css","Sources":["awslabs-open-data-registry"]}
{"Name":"Swiss Public Transport Stops","Description":"The basic geo-data set for public transport stops comprises public transport stops in Switzerland and additional selected geo-referenced public transport locations that are of operational or structural importance (operating points).\n","Documentation":"https://www.bav.admin.ch/bav/de/home/allgemeine-themen/fachthemen/geoinformation/geobasisdaten/haltestellen-des-oeffentlichen-verkehrs.html","Contact":"fredi.daellenbach@bav.admin.ch","UpdateFrequency":"annually","ManagedBy":"Swiss Geoportal","Collabs":{"ASDI":{"Tags":["infrastructure"]}},"Tags":["aws-pds","cities","geospatial","infrastructure","mapping","traffic","transportation"],"License":"You may use this dataset for non-commercial purposes. You may use this dataset for commercial purposes. You must cite the source (title of the dataset, Federal Office of Transport FOT).","Resources":[{"Description":"data files ESRI FGDB, CSV , MapInfo, Interlis","ARN":"arn:aws:s3:::data.geo.admin.ch/ch.bav.haltestellen-oev/data.zip","Region":"eu-west-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://data.geo.admin.ch/index.html)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"Map Viewer","URL":"https://map.geo.admin.ch/?layers=ch.bav.haltestellen-oev","AuthorName":"Swiss Geoportal","AuthorURL":"https://twitter.com/swiss_geoportal"}]},"RegistryEntryAdded":"2018-06-26","RegistryEntryLastModified":"2024-03-27","Slug":"schweizer-haltestellen-oev","Sources":["awslabs-open-data-registry"]}
{"Name":"ScientISST MOVE: Annotated Wearable Multimodal Biosignals recorded during Everyday Life Activities in Naturalistic Environments","Description":"Existing datasets containing physiological data are mostly acquired at rest or in controlled scenarios. As a result, algorithms developed using such data may not perform as well as with biosignals acquired in dynamic and uncontrolled environments. ScientiSST MOVE is a multimodal dataset recording natural everyday activities, including lifting a chair, greeting, gesticulating, walking and running. Data was collected using three wearable devices, namely: a chestband, an armband, and the Empatica E4 wristband. This setup enabled recording of multi-channel Electrodermal Activity (EDA), Photoplethysmography (PPG) and Electrocardiography (ECG). Additionally, recordings were also made for bicep Electromyography (EMG), wrist temperature and chest and wrist actigraphy. A total of 17 healthy volunteers participated in the experimental data acquisition sessions, resulting in an average of 37 useful minutes of synchronised data from all sensors. ScientISST MOVE has been primarily designed to study the effect of daily activities on physiological data acquisition. Having been acquired with multiple wearable devices, some of which measuring the same modalities, it can also be useful in signal quality comparison studies.","Documentation":"https://doi.org/10.13026/hyxq-r919","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/hyxq-r919","ARN":"arn:aws:s3:::physionet-open/scientisst-move-biosignals/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"scientisst-move-biosignals","Sources":["awslabs-open-data-registry"]}
{"Name":"Scottish Public Sector LiDAR Dataset","Description":"This dataset is Lidar data that has been collected by the Scottish public sector and made available under the Open Government Licence. The data are available as point cloud (LAS format or in LAZ compressed format), along with the derived Digital Terrain Model (DTM) and Digital Surface Model (DSM) products as Cloud optimized GeoTIFFs (COG) or standard GeoTIFF. The dataset contains multiple subsets of data which were each commissioned and flown in response to different organisational requirements. The details of each can be found at https://remotesensingdata.gov.scot/data#/list","Documentation":"https://remotesensingdata.gov.scot/data#/list","Contact":"https://remotesensingdata.gov.scot/feedback or email Scottish Government on gi-sat@gov.scot","ManagedBy":"[Joint Nature Conservation Committee](https://jncc.gov.uk/)","UpdateFrequency":"New datasets have historically been added every 2-3 years but there is no guarantee that this will continue.","Collabs":{"ASDI":{"Tags":["elevation"]}},"Tags":["aws-pds","cities","coastal","cog","elevation","environmental","lidar","urban"],"License":"All data is made available under the [Open Government Licence v3](http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/) unless otherwise stated. Currently the exception is only the phase 2 - LAZ files which are under [non-commercial government license](https://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/).","Resources":[{"Description":"LiDAR data (DSM, DTM and Laz)","ARN":"arn:aws:s3:::srsp-open-data","Region":"eu-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"LiDAR Tutorial using QGIS","URL":"https://datasciencescotland.github.io/LiDAR-Tutorial-QGIS/","AuthorName":"Michal Michalski"},{"Title":"LiDAR Tutorial using R","URL":"https://datasciencescotland.github.io/LiDAR-Tutorial-R/","AuthorName":"Michal Michalski"}],"Tools & Applications":[{"Title":"Scottish Remote Sensing Portal","URL":"https://remotesensingdata.gov.scot/","AuthorName":"Scottish Government and Joint Nature Conservation Committee (JNCC)","AuthorURL":"https://remotesensingdata.gov.scot/about"}],"Publications":[{"Title":"Large area archaeological survey in the age of big data and machine learning","URL":"https://github.com/DataScienceScotland/LiDAR-Tutorial-QGIS/blob/main/resources/expedite_and_upscale_large_area_archaeol.pdf","AuthorName":"Dave Cowley, George Geddes, Łukasz Banaszek, Kirsty Millican and Iris Kramer"},{"Title":"Making LiGHT Work of Large Area Survey? Developing Approaches to Rapid Archaeological Mapping and the Creation of Systematic National-scaled Heritage Data","URL":"https://journal.caa-international.org/articles/10.5334/jcaa.49/","AuthorName":"Dave Cowley, Łukasz Banaszek, George Geddes, Angela Gannon, Mike Middleton and Kirsty Millican"},{"Title":"New light on medieval settlement in lowland Scotland","URL":"https://github.com/DataScienceScotland/LiDAR-Tutorial-QGIS/blob/main/resources/new_light_on_medieval_settlement_in_lowl.pdf","AuthorName":"Dave Cowley and Piers Dixon"},{"Title":"Towards National Archaeological Mapping. Assessing Source Data and Methodology - A Case Study from Scotland","URL":"https://www.mdpi.com/2076-3263/8/8/272","AuthorName":"Łukasz Banaszek, Dave Cowley and Mike Middleton"}]},"RegistryEntryAdded":"2021-09-29","RegistryEntryLastModified":"2022-06-06","Slug":"scottish-lidar","Sources":["awslabs-open-data-registry"]}
{"Name":"Sudden Cardiac Death Holter Database","Description":"PhysioNet has inaugurated a Sudden Cardiac Death Database to support research and to stimulate progress in this important area of electrophysiology. We initiate this database with 23 complete Holter recordings (originally collected by Scott Greenwald while he was at MIT), from which half-hour excerpts have been available to researchers since 1989 as the MIT-BIH Malignant Ventricular Arrhythmia Database. The database currently includes 18 patients with underlying sinus rhythm (4 with intermittent pacing), 1 who was continuously paced, and 4 with atrial fibrillation. All patients had a sustained ventricular tachyarrhythmia, and most had an actual cardiac arrest.","Documentation":"https://doi.org/10.13026/C2W306","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2W306","ARN":"arn:aws:s3:::physionet-open/sddb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"sddb","Sources":["awslabs-open-data-registry"]}
{"Name":"My School Today","Description":"This database provides estimates of walking travel time of school-aged populations to schools recorded in OpenStreetMap. Population counts of male and female students are sorted into 3 groups of travel time - under 30 minutes, 30-60 minutes, and over 60 minutes. It covers the African continent and is aggregated by first-level administrative divisions.","Documentation":"https://sdgstoday.org/dataset/my-school-today","Contact":"sdgstoday@unsdsn.org","ManagedBy":"SDSN SDGs Today","UpdateFrequency":"Monthly (weekly updates in progress)","Tags":["aws-pds","education","geospatial","infrastructure","schools"],"License":"This work is licensed under a Creative Commons by Attribution (CC BY 4.0) license.","Citation":"SDSN SDGs Today. Mapping School Locations. UN Sustainable Development Solutions Network. Accessed `<DAY> <MONTH> <YEAR>`.","Resources":[{"Description":"Using open-source georeferenced data and satellite data products, we construct travel-time isochrones from school locations and overlay subnational population counts to construct a dataset of age-specific population counts within travel-time catchment areas in Africa. The resulting walk-time data can help support gaps in existing education data and highlight open-source methods. Our School location data is derived from OpenStreetMap, a powerful open-source data repository of georeferenced buildings, roads, amenities, and other physical features. While OpenStreetMap is vast, data quality varies by region and many schools remain missing across the globe. Please review the methodological note (bit.ly/MST-methodology) where we discuss the implications of missing data. Finally, population data is derived from WorldPop constrained Sex/Age demographic population images. The outputs are available as shapefiles.","ARN":"arn:aws:s3:::my-school-today","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Map a School","URL":"https://sdgstoday.org/myschooltoday","AuthorName":"SDGs Today","AuthorURL":"https://sdgstoday.org/"}]},"ADXCategories":["Retail, Location & Marketing Data","Resources Data"],"RegistryEntryAdded":"2024-03-08","RegistryEntryLastModified":"2026-07-16","Slug":"sdgstoday-mst","Sources":["awslabs-open-data-registry"]}
{"Name":"Solar Dynamics Observatory (SDO) Machine Learning Dataset","Description":"The v1 dataset includes AIA/HMI observations 2010-2018 and v2 includes AIA/HMI observations 2010-2020 in all 10 wavebands (94A, 131A, 171A, 193A, 211A, 304A, 335A, 1600A, 1700A, 4500A), with 512x512 resolution and 6 minutes cadence; HMI vector magnetic field observations in Bx, By, and Bz components, with 512x512 resolution and 12 minutes cadence; The EVE observations in 39 wavelengths from 2010-05-01 to 2014-05-26, with 10 seconds cadence.","Documentation":"https://github.com/SDOML/sdoml.github.io","Contact":"Meng Jin (jinmeng@lmsal.com) and Paul Wright (paul@pauljwright.co.uk)","ManagedBy":"[NASA](http://www.nasa.gov/)","UpdateFrequency":"N/A (The IDL/Python scripts for generating the datasets are published online, which can be used by users to generate the v1 dataset after 2018 and v2 dataset after 2020)","Tags":["aws-pds","machine learning","NASA SMD AI"],"License":"There are no restrictions on the use of this data.","Citation":null,"Resources":[{"Description":"The v1 dataset includes AIA observations 2010-2018 and v2 includes AIA observations 2010-2020 in all 10 wavebands (94A, 131A, 171A, 193A, 211A, 304A, 335A, 1600A, 1700A, 4500A), with 512x512 resolution and 6 minutes cadence; HMI vector magnetic field observations in Bx, By, and Bz components, with 512x512 resolution and 12 minutes cadence; The EVE observations in 39 wavelengths from 2010-05-01 to 2014-05-26, with 10 seconds cadence.","ARN":"arn:aws:s3:::gov-nasa-hdrl-data1/contrib/fdl-sdoml/","Type":"S3 Bucket","Region":"us-west-2"}],"DataAtWork":{"Tutorials":[{"Title":"SDOMLv2 Github","URL":"https://github.com/spaceml-org/helionb-sdoml/tree/main/notebooks/01_sdoml_dataset_2018","AuthorName":"Wright, Paul J.; Jin, Meng; Cheung, Mark C. M."}],"Tools & Applications":[{"Title":"ML applications based on the SDOMLv1 dataset","URL":"https://gitlab.com/frontierdevelopmentlab/living-with-our-star/expanding-sdo-capabilities","AuthorName":"Salvatelli, Valentina; dos Santos, Luiz F. G.; Bose, Souvik; Neuberg, Brad; Cheung, Mark C. M.; Janvier, Miho; Jin, Meng; Gal, Yarin; Boerner, Paul; Baydin, Atılım Güneş"},{"Title":"ML applications based on the SDOMLv2 dataset","URL":"https://github.com/spaceml-org/helionb-sdoml","AuthorName":"Wright, Paul J."},{"Title":"Scripts for generating the SDOMLv1 dataset","URL":"https://github.com/SDOML/SDOML","AuthorName":"Fouhey, David F.; Jin, Meng"},{"Title":"Scripts for generating the SDOMLv2 dataset","URL":"https://github.com/SDOML/SDOMLv2","AuthorName":"Jin, Meng"}],"Publications":[{"Title":"A Machine-learning Data Set Prepared from the NASA Solar Dynamics Observatory Mission","URL":"http://adsabs.harvard.edu/abs/2019ApJS..242....7G","AuthorName":"Galvez, Richard; Fouhey, David F.; Jin, Meng; Szenicer, Alexandre; et al"}]},"DeprecatedNotice":null,"ADXCategories":null,"RegistryEntryAdded":"2023-05-04","RegistryEntryLastModified":"2023-06-02","Slug":"sdoml-fdl","Sources":["awslabs-open-data-registry"]}
{"Name":"The SEA-AD Integrative Analysis Center Study (SEA-AD IAC)","Description":"The Seattle Alzheimer's Disease Brain Cell Atlas Integrative Analysis Center\n(SEA-AD IAC) reprocesses datasets from SEA-AD and other large-scale\nAlzheimer's disease and related dementia studies to support harmonized\ncross-study analyses. The study provides uniformly processed single-cell\ngenomics and multiomic data, harmonized metadata, and representations of\ndisease pathology mapped to highly annotated BRAIN Initiative Cell Atlas\nNetwork (BICAN) and SEA-AD cell classifications. These resources support\ninvestigation of cell-type-specific molecular changes across Alzheimer's\ndisease cohorts and stages of disease progression.\n","Documentation":"https://doi.org/10.7303/9618107","Contact":"https://www.synapse.org/Synapse:syn2580853/discussion/default","ManagedBy":"Sage Bionetworks","UpdateFrequency":"AD Knowledge Portal Data Releases are published monthly (https://news.adknowledgeportal.org/). Updates to specific studies are dependent on data contributor and ingress timelines.","Tags":["aws-pds","bioinformatics","biology","epigenomics","gene expression","genomic","Homo sapiens","life sciences","medicine","neurobiology","neuroscience","RNA-seq","single-cell transcriptomics","transcriptomics"],"License":"https://adknowledgeportal.synapse.org/Explore/Studies/DetailsPage/StudyDetails?Study=syn64410371#Acknowledgement","Citation":"The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (NIH-NIA, U24AG061340).","Resources":[{"Description":"This study provides controlled-access, uniformly reprocessed single-nucleus RNA sequencing and multiomic data, harmonized metadata, cell-type classifications, and associated representations of Alzheimer's disease pathology from SEA-AD and other community studies.","ARN":"arn:aws:s3:::sea-ad-iac.opendata.sagebase.org","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://adknowledgeportal.synapse.org/Data%20Access"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-09-23","RegistryEntryLastModified":"2026-10-05","Slug":"sea-ad-iac","Sources":["awslabs-open-data-registry"]}
{"Name":"SeeFar V0","Description":"A collection of multi-resolution satellite images from both public and commercial satellites. The dataset is specifically curated for training geospatial foundation models.","Documentation":"https://coastalcarbon.ai/seefar","Contact":"James Lowman","ManagedBy":"Coastal Carbon","UpdateFrequency":"Yearly","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","biodiversity","climate","coastal","earth observation","environmental","geospatial","global","machine learning","mapping","natural resource","satellite imagery","sustainability"],"License":"The SeeFar dataset includes multiple licensing terms, specific to each satellite data source:\n  - **Satellogic (Earthview)**: \"Creative Commons Attribution 4.0 International (CC BY 4.0) — allows for free use, sharing, and adaptation with attribution and indication of changes.\"\n  - **WorldStrat (Airbus)**: \"Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) — permits non-commercial use with attribution and indication of changes.\"\n  - **Sentinel**: \"Creative Commons Attribution-Share Alike 3.0 IGO (CC BY-SA 3.0 IGO) — requires attribution, share-alike terms, and indication of any changes.\"\n  - **USGS Landsat**: \"USGS Landsat License — unrestricted use with requested data source acknowledgment.\"\n","Citation":"Lowman, J., Zheng, K. L., Fraser, R., The, J. V. G., & Valipour, M. (2024). SeeFar: Satellite Agnostic Multi-Resolution Dataset for Geospatial Foundation Models. arXiv preprint arXiv:2406.06776.","Resources":[{"Description":"Primary SeeFar dataset containing multi-resolution satellite imagery in cloud-optimized GeoTIFF format across four spectral bands.","ARN":"arn:aws:s3:::seefar-dataset","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Getting Started with SeeFar for Multi-Resolution Geospatial Analysis","URL":"https://coastalcarbon.ai/seefar","AuthorName":"James Lowman - Coastal Carbon","AuthorURL":"https://www.coastalcarbon.ai/team","Services":["Amazon SageMaker","Amazon S3"]}],"Publications":[{"Title":"SeeFar: Satellite Agnostic Multi-Resolution Dataset for Geospatial Foundation Models","URL":"https://arxiv.org/abs/2406.06776","AuthorName":"Lowman, J., Zheng, K. L., Fraser, R., The, J. V. G., & Valipour, M."}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2024-11-14","RegistryEntryLastModified":"2025-07-09","Slug":"seefar","Sources":["awslabs-open-data-registry"]}
{"Name":"Surface electromyographic signals collected during long-lasting ground walking of young able-bodied subjects","Description":"The present dataset is composed of long-lasting (around 5 minutes) surface electromyographic (sEMG) signals recorded from 2011 and 2018 during ground walking of 31 young (20 years < age < 30 years) able-bodied subjects in the Movement Analysis Lab, Università Politecnica delle Marche, Ancona, Italy. Underweight, overweight, and obese people (body mass index, BMI < 18.5 Kg/m2 and BMI > 25 Kg/m2) and subjects affected by any pathological condition, joint pain, or undergone orthopedic surgery are not included in the present dataset. sEMG signals are acquired from the following ten different leg muscles (five per leg): gastrocnemius lateralis (GL), tibialis anterior (TA), rectus femoris (RF), hamstrings (Ham), and vastus lateralis (VL). Synchronized footswitch and electrogoniometric data are provided in order to allow users to achieve a spatial/temporal characterization of the sEMG signals. Data have been acquired in subjects walking barefoot on level ground for around 5 min at their natural speed and pace, following an eight-shaped path which includes rectilinear segments and curves. The considerable length of the signals makes this dataset very suitable for those studies where the numerosity of the data is essential, such as machine/deep learning approaches, studies for analyzing and quantifying the variability of muscle recruitment during physiological walking, and creation of reference dataset in the characterization of pathological conditions.","Documentation":"https://doi.org/10.13026/grdj-qx06","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/grdj-qx06","ARN":"arn:aws:s3:::physionet-open/semg/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"semg","Sources":["awslabs-open-data-registry"]}
{"Name":"SensSmartTech database of cardiovascular signals synchronously recorded by ECG, PCG, PPG and accelerometer","Description":"The SensSmartTech database comprises cardiovascular signals synchronously recorded by electrocardiograph (ECG), phonocardiograph (PCG), photoplethysmograph (PPG) and accelerometer (ACC) at heart rates at rest and after activity (HRs). It is composed of 338 30-s recordings taken from 32 healthy volunteers. Recordings contain information from 10 channels: 4 ECG (limb, V3 and V4 leads), 1 PCG (measured at heart apex), 4 PPG (measured at carotid and brachial arteries at two light wavelengths each) and 1 ACC (measured between V3 and V4 ECG electrodes), along with the recording time of each signal. The original recordings are stored in CSV format. Resampled recordings are stored in WFDB format. The large HR range with the average minimum on the set of 83 ± 11 bpm and the average maximum on the set of 143 ± 14 bpm, along with several recordings per heart relaxation period, enable studying of the cardiovascular dynamics and gaining insights into the corresponding cardio-respiratory and electro-mechanical couplings. Of particular clinical significance are the opportunities to derive corrections of multiparameter biomarkers for HR.","Documentation":"https://doi.org/10.13026/fy9p-n277","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/fy9p-n277","ARN":"arn:aws:s3:::physionet-open/senssmarttech/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"senssmarttech","Sources":["awslabs-open-data-registry"]}
{"Name":"Analysis Ready Sentinel-1 Backscatter Imagery","Description":"The [Sentinel-1 mission](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) is a constellation of\nC-band Synthetic Aperature Radar (SAR) satellites from the European Space Agency launched since 2014.\nThese satellites collect observations of radar backscatter intensity day or night, regardless of the\nweather conditions, making them enormously valuable for environmental monitoring.\nThese radar data have been processed from original Ground Range Detected (GRD) scenes into a Radiometrically\nTerrain Corrected, tiled product suitable for analysis. This product is available over the Contiguous United States (CONUS)\nsince 2017 when Sentinel-1 data became globally available.\n","Documentation":"https://sentinel-s1-rtc-indigo-docs.s3-us-west-2.amazonaws.com/index.html","Contact":"For questions regarding data methodology or delivery, contact sentinel1@indigoag.com.","ManagedBy":"[Indigo Ag, Inc.](https://www.indigoag.com/)","UpdateFrequency":"Data updates are paused while we repair the processing pipeline, but the target is to update on a daily cadence for the dataset's spatial domain (CONUS).","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","disaster response","earth observation","environmental","geospatial","satellite imagery","stac","synthetic aperture radar"],"License":"The use of these data fall under the terms and conditions of the [Indigo Atlas Sentinel License](https://www.indigoag.com/forms/atlas-sentinel-license). \n","Resources":[{"Description":"Sentinel-1 RTC tiled data and metadata in a S3 bucket","ARN":"arn:aws:s3:::sentinel-s1-rtc-indigo","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://scottyhq.github.io/sentinel1-rtc-stac/#/)"]},{"Description":"Simple Notification Service (SNS) topic for notification of new tile uploads","ARN":"arn:aws:sns:us-west-2:410373799403:sentinel-s1-rtc-indigo-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Compare Cloud-Optimized Geotiffs from Amazon Sustainability Data Initiative (ASDI) hosted on S3 using SageMaker Studio Lab (SMSL)","URL":"https://github.com/aws-samples/asdi-smsl-demo-delta","NotebookURL":"https://github.com/aws-samples/asdi-smsl-demo-delta/blob/main/Compare-GeoTiffs-S3.ipynb","AuthorName":"Gianfranco Rapino","Services":["Amazon SageMaker Studio Lab"]}]},"RegistryEntryAdded":"2020-09-28","RegistryEntryLastModified":"2025-02-04","Slug":"sentinel-1-rtc-indigo","Sources":["awslabs-open-data-registry"]}
{"Name":"Sentinel-1","Description":"[Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) is a pair of European radar imaging (SAR) satellites launched in 2014 and 2016. Its 6 days revisit cycle and ability to observe through clouds makes it perfect for sea and land monitoring, emergency response due to environmental disasters, and economic applications. This dataset represents the global Sentinel-1 GRD archive, from beginning to the present, converted to [cloud-optimized GeoTIFF format](https://www.cogeo.org/).","Documentation":"https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html","Contact":"https://forum.sentinel-hub.com/c/aws-sentinel","ManagedBy":"[Element 84](http://www.element84.com/)","UpdateFrequency":"New Sentinel data are added regularly, usually within few hours after they are available on Copernicus OpenHub.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","disaster response","earth observation","geospatial","satellite imagery","synthetic aperture radar"],"License":"Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice).\n","Resources":[{"Description":"Sentinel-1 GRD S3 bucket","ARN":"arn:aws:s3:::sentinel-s1-l1c","Region":"eu-central-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://sentinel-s1-l1c-stac.s3.amazonaws.com/)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents) files for L1C (ORC and CSV)","ARN":"arn:aws:s3:::sentinel-inventory/","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"SNS topic for notification of new scenes, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/).","ARN":"arn:aws:sns:eu-central-1:214830741341:SentinelS1L1C","Region":"eu-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"EOS Land Viewer","URL":"https://eos.com/landviewer/","AuthorName":"Earth Observing System","AuthorURL":"https://eos.com/"},{"Title":"Planet Insights Platform","URL":"https://insights.planet.com/","AuthorName":"Planet","AuthorURL":"http://www.planet.com/"},{"Title":"Sentinel Hub WMS/WMTS/WCS Service by Sinergise","URL":"http://www.sentinel-hub.com/apps/wms","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2026-07-16","Slug":"sentinel-1","Sources":["awslabs-open-data-registry"]}
{"Name":"Sentinel-2 Cloud-Optimized GeoTIFFs","Description":"The [Sentinel-2 mission](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) is\na land monitoring constellation of two satellites that provide high resolution\noptical imagery and provide continuity for the current SPOT and Landsat missions.\nThe mission provides a global coverage of the Earth's land surface every 5 days,\nmaking the data of great use in ongoing studies.\nThis dataset is the same as the [Sentinel-2](https://registry.opendata.aws/sentinel-2/)\ndataset, except the JP2K files were converted into Cloud-Optimized GeoTIFFs (COGs).\nAdditionally, SpatioTemporal Asset Catalog metadata has were in a JSON file\nalongside the data, and a STAC API called [Earth-search](https://earth-search.aws.element84.com/v1)\nis freely available to search the archive. This dataset contains all of the scenes in the\noriginal Sentinel-2 Public Dataset and will grow as that does.\nL2A data are available from April 2017 over wider Europe region and globally since December 2018.\n","Documentation":"https://github.com/Element84/earth-search","Contact":"opendata@element84.com","ManagedBy":"[Element 84](https://www.element84.com/)","UpdateFrequency":"New Sentinel data are added regularly, usually within few hours after they are available on Copernicus OpenHub.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac"],"License":"Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice).\n","Resources":[{"Description":"Collection 1 Level 2A scenes and metadata","ARN":"arn:aws:s3:::e84-earth-search-sentinel-data","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Earth Search STAC C1 L2A Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-c1-l2a)","[STAC Browser C1 L2A Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-c1-l2a)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents) files for L2A (parquet)","ARN":"arn:aws:s3:::e84-earth-search-sentinel-data-inventory","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false},{"Description":"New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item.","ARN":"arn:aws:sns:us-west-2:608149789419:cirrus-es-prod-publish","Region":"us-west-2","Type":"SNS Topic"},{"Description":"Level 2A scenes and metadata","ARN":"arn:aws:s3:::sentinel-cogs","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Earth Search STAC L2A Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)","[STAC Browser L2A Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents) files for L1C (ORC and CSV)","ARN":"arn:aws:s3:::sentinel-cogs-inventory","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item.","ARN":"arn:aws:sns:us-west-2:608149789419:cirrus-v0-publish","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Access satellite imagery using Python","URL":"https://carpentries-incubator.github.io/geospatial-python/05-access-data.html","AuthorName":"The Carpentries"},{"Title":"Create a Dynamic Tiler with TiTiler","URL":"https://github.com/developmentseed/titiler/blob/master/docs/examples/notebooks/Create_CustomSentinel2Tiler.ipynb","AuthorName":"Vincent Sarago"},{"Title":"Field-level NDVI time series for a smallholder farm field","URL":"https://github.com/tanmaysahare/sentinel2-field-ndvi","NotebookURL":"https://github.com/tanmaysahare/sentinel2-field-ndvi/blob/main/field_ndvi_tutorial.ipynb","AuthorName":"Tanmay Sahare","AuthorURL":"https://www.varshakrishi.com"},{"Title":"How to process Sentinel-2 data in a serverless Lambda on AWS?","URL":"https://www.linkedin.com/pulse/how-sunny-my-city-better-process-stac-sentinel2-lambda-alvaro-huarte","AuthorName":"Alvaro Huarte","Services":["AWS Lambda","Amazon SNS"]},{"Title":"How to Work with Landsat and Sentinel-2 on AWS with Python","URL":"https://www.matecdev.com/posts/landsat-sentinel-aws-s3-python.html","AuthorName":"Martin D. Maas"},{"Title":"Intake-STAC with sat-search","URL":"https://github.com/intake/intake-stac/blob/master/examples/aws-earth-search.ipynb","AuthorName":"Scott Henderson"},{"Title":"Monitoring Lake Mead drought using the new Amazon SageMaker geospatial capabilities","URL":"https://aws.amazon.com/blogs/machine-learning/monitoring-lake-mead-drought-using-the-new-amazon-sagemaker-geospatial-capabilities/","AuthorName":"Xiong Zhou, Anirudh Viswanathan, Erran Li, Trenton Lipscomb, and Xingjian Shi","Services":["Amazon SageMaker"]},{"Title":"Monitoring of methane (CH4) emission point sources on AWS","URL":"https://github.com/aws-samples/aws-opendata-samples/blob/main/notebooks/aws-methane-emissions-monitor/monitor_methane_ch4_emission_point_sources.ipynb","AuthorName":"Janosch Woschitz, Karsten Schroer","Services":["Amazon SageMaker","Amazon S3"]},{"Title":"STAC, COG, Python and QGIS","URL":"http://www.acgeospatial.co.uk/stac-cog-python-and-qgis/","AuthorName":"Andrew Cutts"},{"Title":"Transforming Geospatial Data to Cloud-Native Frameworks with Element 84 on AWS","URL":"https://aws.amazon.com/blogs/apn/transforming-geospatial-data-to-cloud-native-frameworks-with-element-84-on-aws/","AuthorName":"Matt Hanson, Imtranur Rahman, Mary-Elizabeth Morin, and Ariel Walcutt","Services":["Amazon EKS","Amazon SageMaker","Amazon S3"]},{"Title":"Use Sentinel-2 data in the Open Data Cube","URL":"https://github.com/opendatacube/cube-in-a-box","AuthorName":"Alex Leith"},{"Title":"Using Sentinel-2 Data from the AWS Open Data Project to measure water hyacinth growth","URL":"https://github.com/aws-samples/monitor-water-hyacinth-invasion","NotebookURL":"https://github.com/aws-samples/monitor-water-hyacinth-invasion/blob/main/Harties.ipynb","AuthorName":"Louise Liddell","Services":["Amazon SageMaker Studio Lab"]}],"Publications":[{"Title":"OpenSentinelMap: A Large-Scale Land Use Dataset using OpenStreetMap and Sentinel-2 Imagery","URL":"https://openaccess.thecvf.com/content/CVPR2022W/EarthVision/papers/Johnson_OpenSentinelMap_A_Large-Scale_Land_Use_Dataset_Using_OpenStreetMap_and_Sentinel-2_CVPRW_2022_paper.pdf","AuthorName":"Noah Johnson, Wayne Treible, Daniel Crispell"},{"Title":"STAC and Sentinel-2 COGs (ESIP Summer Meeting 2020)","URL":"https://docs.google.com/presentation/d/14NsKFZ3UF2Swwx_9L7sPMX9ccFUK1ruQyZXWK9Cz4L4/edit?usp=sharing","AuthorName":"Matthew Hanson"}],"Tools & Applications":[{"Title":"FilmDrop Demo","URL":"https://console.demo.filmdrop.io/","AuthorName":"Element 84"},{"Title":"Fire detection Twitter bot using satellite imagery","URL":"https://twitter.com/FirewatchBot","AuthorName":"Firewatch"},{"Title":"rio-tiler-pds","URL":"https://github.com/cogeotiff/rio-tiler-pds","AuthorName":"Vincent Sarago, et al."},{"Title":"Sat-search","URL":"https://github.com/sat-utils/sat-search","AuthorName":"Matthew Hanson"},{"Title":"SITS - Satellite Image Time Series Analysis for Earth Observation Data Cubes","URL":"https://github.com/e-sensing/sits","AuthorName":"e-Sensing","AuthorURL":"http://www.esensing.org/"},{"Title":"SpatioTemporal Asset Catalogs","URL":"https://stacindex.org/","AuthorName":"STAC Contriubutors"}]},"RegistryEntryAdded":"2020-10-05","RegistryEntryLastModified":"2026-10-16","Slug":"sentinel-2-l2a-cogs","Sources":["awslabs-open-data-registry"]}
{"Name":"Sentinel-2","Description":"The [Sentinel-2 mission](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) is\na land monitoring constellation of two satellites that provide high resolution\noptical imagery and provide continuity for the current SPOT and Landsat missions.\nThe mission provides a global coverage of the Earth's land surface every 5 days,\nmaking the data of great use in on-going studies. L1C data are available from\nJune 2015 globally. L2A data are available from November 2016 over Europe\nregion and globally since January 2017.\n","Documentation":"Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/sentinel-s2-l1c/readme.html) and [Sentinel-2 L2A](https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html).","Contact":"https://forum.sentinel-hub.com/c/aws-sentinel","ManagedBy":"[Element 84](https://www.element84.com/)","UpdateFrequency":"New Sentinel data are added regularly, usually within few hours after they are available on Copernicus OpenHub.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac"],"License":"Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice).\n","Resources":[{"Description":"Level 1C scenes and metadata, in [Requester Pays](https://docs.aws.amazon.com/AmazonS3/latest/dev/RequesterPaysBuckets.html) S3 bucket","ARN":"arn:aws:s3:::sentinel-s2-l1c","Region":"eu-central-1","Type":"S3 Bucket","RequesterPays":true,"Explore":["[Earth Search STAC L1C Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)","[Earth Search STAC Browser L1C Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)","[STAC V1.0.0 endpoint](https://sentinel-s2-l1c-stac.s3.amazonaws.com/)","[Earth Viewer by Element 84](https://viewer.aws.element84.com/)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents) files for L1C (ORC and CSV)","ARN":"arn:aws:s3:::sentinel-inventory/sentinel-s2-l1c","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"Level 2A scenes and metadata, in S3 bucket","ARN":"arn:aws:s3:::sentinel-s2-l2a","Region":"eu-central-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC V1.0.0 endpoint](https://sentinel-s2-l2a-stac.s3.amazonaws.com/)"]},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents) files for L2A (ORC and CSV)","ARN":"arn:aws:s3:::sentinel-inventory/sentinel-s2-l2a","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"Zipped archives for each L1C product with 3 day retention period, in Requester Pays bucket","ARN":"arn:aws:s3:::sentinel-s2-l1c-zips","Region":"eu-central-1","Type":"S3 Bucket","RequesterPays":true},{"Description":"Zipped archives for each L2A product with 3 day retention period, in Requester Pays bucket","ARN":"arn:aws:s3:::sentinel-s2-l2a-zips","Region":"eu-central-1","Type":"S3 Bucket","RequesterPays":true},{"Description":"New scene notifications for L1C, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/).","ARN":"arn:aws:sns:eu-west-1:214830741341:NewSentinel2Product","Region":"eu-west-1","Type":"SNS Topic"},{"Description":"New scene notifications for L2A, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/).","ARN":"arn:aws:sns:eu-central-1:214830741341:SentinelS2L2A","Region":"eu-central-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Detecting deforestation using unsupervised K-means clustering on Sentinel-2 satellite imagery and SageMaker Studio Lab (SMSL)","URL":"https://github.com/aws-samples/aws-smsl-geospatial-analysis-deforestation/","NotebookURL":"https://github.com/aws-samples/aws-smsl-geospatial-analysis-deforestation/blob/main/geospatial_analysis_deforestation.ipynb","AuthorName":"Parth Patel","Services":["Amazon SageMaker Studio Lab"]},{"Title":"Exploring the Chile wildfires with Landsat and Sentinel-2 imagery","URL":"https://www.gearthblog.com/blog/archives/2017/02/exploring-chile-wildfires-landsat-sentinel-2-imagery.html","AuthorName":"Timothy Whitehead"},{"Title":"Getting Started With Geospatial Data Analysis","URL":"https://github.com/samx18/geospatial_analysis/","NotebookURL":"https://github.com/samx18/geospatial_analysis/blob/main/geospatial_analysis.ipynb","AuthorName":"Sam Palani","AuthorURL":"https://samx18.medium.com","Services":["Amazon SageMaker Studio Lab"]},{"Title":"How to Work with Landsat and Sentinel-2 on AWS with Python","URL":"https://www.matecdev.com/posts/landsat-sentinel-aws-s3-python.html","AuthorName":"Martin D. Maas","AuthorURL":"https://www.matecdev.com"},{"Title":"Integrate imagery from the Sentinel-2 archive into your own apps, maps, and analysis with the Sentinel-2 image service","URL":"https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/imagery/sentinel-2-more-than-meets-the-eye/","AuthorName":"Esri","AuthorURL":"https://www.esri.com/en-us/home"},{"Title":"Learning Custom Scripts to Make Useful and Beautiful Satellite Images","URL":"https://medium.com/sentinel-hub/create-useful-and-beautiful-satellite-images-with-custom-scripts-8ef0e6a474c6","AuthorName":"Monja Šebela"},{"Title":"TCS Digital Farming Generates Near Real-Time Insights on Crop Conditions for Actionable Decision Making","URL":"https://aws.amazon.com/blogs/apn/tcs-digital-farming-generates-near-real-time-insights-on-crop-conditions-for-actionable-decision-making/","AuthorName":"Dr. Suryakant Sawant, Mahesh Sarode, Rajoy Jose, and Kiran Killedar","Services":["Amazon SageMaker","AWS Lambda"]},{"Title":"Tutorial for using Sentinel-2 data","URL":"https://medium.com/@anttilip/seeing-earth-from-space-from-raw-satellite-data-to-beautiful-high-resolution-images-feb522adfa3f","AuthorName":"Antti Lipponen","AuthorURL":"https://medium.com/@anttilip"},{"Title":"Vegetation Analysis Pre/Post Lava Fire Using SentinelHub and Sentinel-2 Imagery on AWS","URL":"https://github.com/aws-samples/aws-sentinel2-smsl-notebook","NotebookURL":"https://github.com/aws-samples/aws-sentinel2-smsl-notebook/blob/main/geospatial_imagery_analysis.ipynb","AuthorName":"Mike Jeffe","Services":["Amazon SageMaker Studio Lab"]}],"Tools & Applications":[{"Title":"EO Browser","URL":"http://apps.sentinel-hub.com/eo-browser/","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"},{"Title":"EOS Land Viewer","URL":"https://eos.com/landviewer/","AuthorName":"Earth Observing System","AuthorURL":"https://eos.com/"},{"Title":"FME Landsat-8/Sentinel-2 File Selector","URL":"http://demos.fmeserver.com/landsat-sentinel-selector/","AuthorName":"Safe Software","AuthorURL":"https://www.safe.com/"},{"Title":"Interactive map with AI detected fields and crops based on Sentinel-2 imagery","URL":"https://map.onesoil.ai/","AuthorName":"OneSoil","AuthorURL":"https://onesoil.ai/"},{"Title":"Pera Portal","URL":"https://portal.geopera.com/","AuthorName":"Geopera","AuthorURL":"https://geopera.com/"},{"Title":"Planet Insights Platform","URL":"https://insights.planet.com/","AuthorName":"Planet","AuthorURL":"http://www.planet.com/"},{"Title":"Python package for working with Sentinel-2 AWS data","URL":"https://github.com/sentinel-hub/sentinelhub-py","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"},{"Title":"QGIS plugin for Sentinel-2 data","URL":"https://medium.com/sentinel-hub/control-sentinel-hub-from-within-qgis-2a83eb7f13db","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"},{"Title":"Sentinel Hub WMS/WMTS/WCS Service","URL":"http://www.sentinel-hub.com/apps/wms","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"},{"Title":"Sentinel-2 Cloudless Atlas","URL":"https://s2maps.eu/","AuthorName":"EOX","AuthorURL":"https://eox.at"},{"Title":"Spectator - tracking Sentinel 2, accessing the data and quick preview","URL":"https://spectator.earth/","AuthorName":"Spectator","AuthorURL":"https://spectator.earth/"},{"Title":"Sterling Geo Using Sentinel-2 on Amazon Web Services to Create NDVI","URL":"https://www.youtube.com/watch?v=3tIIUr-keps","AuthorName":"Sterling Geo"},{"Title":"Use the Sentinel Explorer app to explore, visualize, and analyze the entire Sentinel-2 archive","URL":"https://sentinel2explorer.esri.com/","AuthorName":"Esri","AuthorURL":"https://www.esri.com/en-us/home"}],"Publications":[{"Title":"Coral-spawn slicks: Reflectance spectra and detection using optical satellite data","URL":"https://www.sciencedirect.com/science/article/pii/S0034425720304284","AuthorName":"Hiroya Yamano, Asahi Sakuma, Saki Harii"},{"Title":"Using Remote Sensing Images and Cloud Services on AWS to Improve Land Use and Cover Monitoring","URL":"https://ieeexplore.ieee.org/abstract/document/9165649","AuthorName":"K. R. Ferreira, et al."}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2026-07-16","Slug":"sentinel-2","Sources":["awslabs-open-data-registry"]}
{"Name":"Sentinel-3","Description":"This data set consists of observations from the Sentinel-3 satellite of the European Commission’s Copernicus Earth Observation Programme. Sentinel-3 is a polar orbiting satellite that completes 14 orbits of the Earth a day. It carries the Ocean and Land Colour Instrument (OLCI) for medium resolution marine and terrestrial optical measurements, the Sea and Land Surface Temperature Radiometer (SLSTR), the SAR Radar Altimeter (SRAL), the MicroWave Radiometer (MWR) and the Precise Orbit Determination (POD) instruments. The satellite was launched in 2016 and entered routine operational phase in 2017. Data is available from July 2017 onwards.","Documentation":"https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Description.md","Contact":"sentinel3@meeo.it","ManagedBy":"[Meteorological Environmental Earth Observation](http://www.meeo.it/)","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","cog","earth observation","environmental","geospatial","land","oceans","satellite imagery","stac"],"License":"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice","Resources":[{"Description":"Sentinel-3 Near Real Time Data (NRT) format","ARN":"arn:aws:s3:::meeo-s3/NRT/","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"Sentinel-3 Not Time Critical (NTC) format","ARN":"arn:aws:s3:::meeo-s3/NTC/","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"Sentinel-3 Short Time Critical (STC) format","ARN":"arn:aws:s3:::meeo-s3/STC/","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"Sentinel-3 Cloud Optimized GeoTIFF (COG) format","ARN":"arn:aws:s3:::meeo-s3-cog/","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://meeo-s3.s3.amazonaws.com/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Accessing Sentinel-3 Data on S3 by MEEO","URL":"https://github.com/Sentinel-5P/data-on-s3/blob/master/notebooks/Sentinel3_Tutorial.ipynb","AuthorName":"Meteorological Environmental Earth Observation","AuthorURL":"http://www.meeo.it/"}],"Tools & Applications":[{"Title":"Catalogue of data set","URL":"https://meeo-s3.s3.amazonaws.com/index.html#/?t=catalogs","AuthorName":"Meteorological Environmental Earth Observation","AuthorURL":"https://www.meeo.it/"},{"Title":"Sentinel-3 Toolbox","URL":"https://step.esa.int/main/toolboxes/sentinel-3-toolbox/","AuthorName":"European Space Agency","AuthorURL":"https://www.esa.int/"}],"Publications":[{"Title":"Sentinel-3 Document Library","URL":"https://sentinel.esa.int/web/sentinel/user-guides","AuthorName":"European Space Agency","AuthorURL":"https://www.esa.int/"}]},"RegistryEntryAdded":"2020-07-08","RegistryEntryLastModified":"2023-02-28","Slug":"sentinel-3","Sources":["awslabs-open-data-registry"]}
{"Name":"Sentinel Near Real-time Canada Mirror | Miroir Sentinel temps quasi réel du Canada","Description":"The official Government of Canada (GC) 🍁 Near Real-time (NRT) Sentinel Mirror connected to the [EU Copernicus programme](https://www.copernicus.eu), focused on Canadian coverage. In 2015, [Canada joined the Sentinel collaborative ground segment](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Canada_joins_Sentinel_collaborative_ground_segment) which introduced an NRT Sentinel mirror site for users and programs inside the Government of Canada (GC). In 2022, the [Commission signed a Copernicus Arrangement with the Canadian Space Agency](https://defence-industry-space.ec.europa.eu/signature-copernicus-arrangement-between-canadian-space-agency-and-european-commission-2022-05-16_en) with the aim to share each other’s satellite Earth Observation data on the basis of reciprocity. Further to this arrangement as well as ongoing [Open Government](https://open.canada.ca/en/about-open-government) efforts, the private mirror was made open to the public, here on the AWS Open Dataset Registry.\n<br/> <br/>\nLe Sentinel Mirror officiel du gouvernement du Canada (GC) 🍁 en temps quasi réel (NRT) connecté au [programme Copernicus de l'UE] (https://www.copernicus.eu), axé sur la couverture canadienne. En 2015, [le Canada a rejoint le segment terrestre collaboratif Sentinel](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Canada_joins_Sentinel_collaborative_ground_segment) qui a introduit un site miroir NRT Sentinel pour les utilisateurs et les programmes au sein du gouvernement du Canada (GC). . En 2022, la [Commission a signé un accord Copernicus avec l'Agence spatiale canadienne](https://defence-industry-space.ec.europa.eu/signature-copernicus-arrangement-between-canadian-space-agency-and-european-commission-2022-05-16_fr) dans le but de partager mutuellement les données satellitaires d'observation de la Terre sur la base de la réciprocité. Suite à cet arrangement ainsi qu'aux efforts continus de [gouvernement ouvert](https://open.canada.ca/en/about-open-government), le miroir privé a été rendu ouvert au public, ici sur le registre des ensembles de données ouvertes AWS.","Documentation":"https://sentinel.esa.int/web/sentinel/home","Contact":"eodms-sgdot@nrcan-rncan.gc.ca","ManagedBy":"[Natural Resources Canada](https://www.nrcan.gc.ca/)","UpdateFrequency":"Sentinel-1 is an NRT dataset retrieved from ESA within 90 minutes of satellite downlink. Non-NRT Sentinel-2 and Sentinel-3 are also updated as quickly as possible based on Canada coverage and availability at the source.\n<br/> <br/>\nSentinel-1 est un ensemble de données NRT récupéré de l'ESA dans les 90 minutes suivant la liaison descendante du satellite. Sentinel-2 et Sentinel-3 non NRT sont également récupérés le plus rapidement possible en fonction de la couverture du Canada et de la disponibilité à la source.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","disaster response","earth observation","geospatial","satellite imagery","stac","sustainability","synthetic aperture radar"],"License":"The access and use of Copernicus Sentinel data is available on a free, full and open basis through the Copernicus Data Space Ecosystem and shall be governed by the Legal Notice on the use of Copernicus Sentinel Data and Service published here: https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice.","Resources":[{"Description":"Sentinel data over Canada | Données sentinelles au Canada.","ARN":"arn:aws:s3:::sentinel-products-ca-mirror","Region":"ca-central-1","Type":"S3 Bucket","Explore":["[STAC for Sentinel products](https://radiantearth.github.io/stac-browser/#/external/eodms-sgdot.nrcan-rncan.gc.ca/search/collections/Sentinel-1)"]}],"DataAtWork":{"Tutorials":[{"Title":"Sentinel notebooks: reusable workflows for data discovery, preprocessing, spectral indices, visualization, and fire mapping","URL":"https://github.com/eodms-sgdot/sentinel-notebooks","AuthorName":"Natural Resources Canada","AuthorURL":"https://github.com/eodms-sgdot"}]},"RegistryEntryAdded":"2023-01-26","RegistryEntryLastModified":"2026-11-04","Slug":"sentinel-products-ca-mirror","Sources":["awslabs-open-data-registry"]}
{"Name":"Sentinel-2 L2A 120m Mosaic","Description":"Sentinel-2 L2A 120m mosaic is a derived product, which contains best pixel values for 10-daily periods, modelled by removing the cloudy pixels and then performing interpolation among remaining values. As there are some parts of the world, which have lengthy cloudy periods, clouds might be remaining in some parts. The actual modelling script is available [here](https://sentinel-hub.github.io/custom-scripts/sentinel-2/interpolated_time_series/).","Documentation":"Documentation is available [here](https://sentinel-s2-l2a-mosaic-120.s3.amazonaws.com/readme.html).","Contact":"https://forum.sentinel-hub.com/c/aws-sentinel","ManagedBy":"[Sinergise](https://www.sinergise.com/)","UpdateFrequency":"New data will be added annually.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","earth observation","geospatial","machine learning","natural resource","satellite imagery"],"License":"CC-BY 4.0, Credit: Contains modified Copernicus data [year] processed by Sentinel Hub\n","Resources":[{"Description":"Sentinel-2 L2A 120m mosaics data in a S3 bucket","ARN":"arn:aws:s3:::sentinel-s2-l2a-mosaic-120","Region":"eu-central-1","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Tutorials":[{"Title":"How to Make the Perfect Time-Lapse of the Earth","URL":"https://medium.com/sentinel-hub/how-to-make-the-perfect-time-lapse-of-the-earth-351f214527f6","AuthorName":"Matic Lubej","AuthorURL":"https://medium.com/@lubej.matic"}],"Tools & Applications":[{"Title":"Digital Twin Sandbox","URL":"https://apps.sentinel-hub.com/digital_twin_sandbox/","AuthorName":"Sentinel Hub","AuthorURL":"https://www.sentinel-hub.com/"},{"Title":"Sentinel Hub WMS/WMTS/WCS Service and Process API","URL":"https://github.com/sentinel-hub/public-collections/blob/main/collections/sentinel-s2-l2a-mosaic-120.md","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"}],"Publications":[{"Title":"Digital Twin Sandbox Sentinel-2 collection available to everyone","URL":"https://medium.com/sentinel-hub/digital-twin-sandbox-sentinel-2-collection-available-to-everyone-20f3b5de846e","AuthorName":"Grega Milcinski & Matic Lubej","AuthorURL":"http://www.sinergise.com/"}]},"RegistryEntryAdded":"2021-01-26","RegistryEntryLastModified":"2023-02-28","Slug":"sentinel-s2-l2a-mosaic-120","Sources":["awslabs-open-data-registry"]}
{"Name":"Sentinel-1 SLC dataset for South and Southeast Asia, Taiwan, Korea and Japan","Description":"The S1 Single Look Complex (SLC) dataset contains Synthetic Aperture Radar (SAR) data in the C-Band wavelength. The SAR sensors are installed on a two-satellite (Sentinel-1A and Sentinel-1B) constellation orbiting the Earth with a combined revisit time of six days, operated by the European Space Agency. The S1 SLC data are a Level-1 product that collects radar amplitude and phase information in all-weather, day or night conditions, which is ideal for studying natural hazards and emergency response, land applications, oil spill monitoring, sea-ice conditions, and associated climate change effects.\n","Documentation":"https://github.com/earthobservatory/sentinel1-opds/","Contact":"For any enquires regarding data delivery, please email ehill@ntu.edu.sg and stchin@ntu.edu.sg","ManagedBy":"[Earth Observatory of Singapore, Nanyang Technological University](https://earthobservatory.sg)","UpdateFrequency":"S1 SLC data for the region of interest will be updated regularly, as it becomes available on the Alaska Satellite Facility endpoint.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","disaster response","earth observation","environmental","geospatial","satellite imagery","synthetic aperture radar"],"License":"The data usage will inherit and fully comply with the free and open data policy from ESA’s [Sentinel Data Policy](https://scihub.copernicus.eu/twiki/pub/SciHubWebPortal/TermsConditions/TC_Sentinel_Data_31072014.pdf).\n","Resources":[{"Description":"Public access to Sentinel-1 SLC IW scenes over South and Southeast Asia, Taiwan and Japan","ARN":"arn:aws:s3:::sentinel1-slc-seasia-pds","Region":"ap-southeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Sentinel-1 Opendataset Wiki and Tutorials","URL":"https://github.com/earthobservatory/sentinel1-opds/wiki","AuthorName":"Earth Observatory of Singapore","AuthorURL":"https://earthobservatory.sg/research-group/geodesy-group-emma-hill/people"}],"Publications":[{"Title":"Rapid flood and damage mapping using synthetic aperture radar in response to Typhoon Hagibis, Japan","URL":"https://www.nature.com/articles/s41597-020-0443-5","AuthorName":"Cheryl W. J. Tay, Sang-Ho Yun, Shi Tong Chin, Alok Bhardwaj, Jungkyo Jung & Emma M. Hill"}]},"RegistryEntryAdded":"2019-04-10","RegistryEntryLastModified":"2023-02-28","Slug":"sentinel1-slc-seasia-pds","Sources":["awslabs-open-data-registry"]}
{"Name":"Sentinel-1 SLC dataset for Germany","Description":"The Sentinel1 Single Look Complex (SLC) unzipped dataset contains Synthetic Aperture Radar (SAR) data from the European Space Agency’s Sentinel-1 mission. Different from the zipped data provided by ESA, this dataset allows direct access to individual swaths required for a given study area, thus drastically minimizing the storage and downloading time requirements of a project. Since the data is stored on S3, users can utilize the boto3 library and s3 get_object method to read the entire content of the object into the memory for processing, without actually having to download it. The Sentinel-1 constellation consists of two satellites equipped with SAR sensors and a combined revisit time of six days. SAR imagery gets recorded regardless of weather conditions and daylight, which makes it ideally suited for monitoring land-use changes, surface deformations, land applications, oil spills, sea-ice, natural hazards, and for emergency response. In its current first stage, the dataset covers the entirety of Germany and is being updated continuously. As a next stage, the dataset will provide up-to-date coverage of the sentinel-1 SLC data over Europe.\nThis dataset is retrieved from Alaska Satellite Facility (ASF) and consists of all Sentinel1-SLC imagery from the beginning (2014) to present.\n","Documentation":"https://github.com/live-eo/sentinel1-slc/","Contact":"For any enquires regarding the dataset, please email OpenData at Live-EO opendata@live-eo.com","ManagedBy":"[LiveEO](https://live-eo.com/)","UpdateFrequency":"New Sentinel1-SLC IW data are updated regularly in an interval of 6 days, after they are available on the Alaska Satellite Facility endpoint.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","disaster response","earth observation","environmental","geospatial","satellite imagery","sustainability","synthetic aperture radar"],"License":"The data usage will inherit and fully comply with the free and open data policy from ESA. View [Terms and Conditions](https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice).\n","Resources":[{"Description":"Public access to Sentinel-1 SLC IW scenes over Germany","ARN":"arn:aws:s3:::sentinel1-slc","Region":"eu-west-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Interferometric Synthetic Aperture Radar Tutorial","URL":"https://github.com/live-eo/sentinel1-slc/blob/main/docs/tutorial_InSAR.md","AuthorName":"LiveEO","AuthorURL":"https://live-eo.com/"}]},"RegistryEntryAdded":"2022-07-07","RegistryEntryLastModified":"2026-07-16","Slug":"sentinel1-slc","Sources":["awslabs-open-data-registry"]}
{"Name":"Sentinel-5P Level 2","Description":"This data set consists of observations from the Sentinel-5 Precursor (Sentinel-5P) satellite of the European Commission’s Copernicus Earth Observation Programme. Sentinel-5P is a polar orbiting satellite that completes 14 orbits of the Earth a day. It carries the TROPOspheric Monitoring Instrument (TROPOMI) which is a spectrometer that senses ultraviolet (UV), visible (VIS), near (NIR) and short wave infrared (SWIR) to monitor ozone, methane, formaldehyde, aerosol, carbon monoxide, nitrogen dioxide and sulphur dioxide in the atmosphere. The satellite was launched in October 2017 and entered routine operational phase in March 2019. Data is available from July 2018 onwards.","Documentation":"https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Description.md","Contact":"sentinel5p@meeo.it","ManagedBy":"[Meteorological Environmental Earth Observation](http://www.meeo.it/)","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["air quality","atmosphere","aws-pds","cog","earth observation","environmental","geospatial","satellite imagery","stac"],"License":"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice","Resources":[{"Description":"Sentinel-5p Near Real Time Data (NRTI). NetCDF format.","ARN":"arn:aws:s3:::meeo-s5p/NRTI/","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"Sentinel-5p Off Line Data (OFFL) NetCDF format.","ARN":"arn:aws:s3:::meeo-s5p/OFFL/","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"Sentinel-5p Reprocessed Data (RPRO) NetCDF format.","ARN":"arn:aws:s3:::meeo-s5p/RPRO/","Region":"eu-central-1","Type":"S3 Bucket"},{"Description":"Sentinel-5p Cloud Optimised GeoTIFF (COGT). TIFF format.","ARN":"arn:aws:s3:::meeo-s5p/COGT/","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[STAC V1.0.0 endpoint](https://meeo-s5p.s3.amazonaws.com/index.html?t=catalogs)"]}],"DataAtWork":{"Tutorials":[{"Title":"Accessing Sentinel-5P Data on S3 by MEEO","URL":"https://github.com/Sentinel-5P/data-on-s3/blob/master/notebooks/Sentinel5P_Tutorial.ipynb","AuthorName":"Meteorological Environmental Earth Observation","AuthorURL":"http://www.meeo.it/"}],"Tools & Applications":[{"Title":"Catalogue of data set","URL":"https://meeo-s5p.s3.amazonaws.com/index.html#/?t=catalogs","AuthorName":"Meteorological Environmental Earth Observation","AuthorURL":"https://www.meeo.it/"},{"Title":"The Atmospheric Toolbox","URL":"https://atmospherictoolbox.org/","AuthorName":"European Space Agency","AuthorURL":"https://www.esa.int/"}],"Publications":[{"Title":"Sentinel-5P TROPOMI Document Library","URL":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi/document-library","AuthorName":"European Space Agency","AuthorURL":"https://www.esa.int/"}]},"RegistryEntryAdded":"2020-01-24","RegistryEntryLastModified":"2023-11-09","Slug":"sentinel5p","Sources":["awslabs-open-data-registry"]}
{"Name":"Serratus: Ultra-deep Search for Novel Viruses - Versioned Data Release","Description":"Serratus is a collaborative open science project for ultra-rapid discovery of known and unknown coronaviruses in response to the COVID-19 pandemic through re-analysis of publicly available genomic data. Our resulting vertebrate viral alignment data is explorable via the [Serratus Explorer](https://serratus.io/explorer) and directly accessible on Amazon S3.","Documentation":"https://github.com/ababaian/serratus/wiki/Access-Data-Release","Contact":"https://github.com/ababaian/serratus/issues","ManagedBy":"Serratus / UBC Cloud Innovation Centre","UpdateFrequency":"Quarterly","Tags":["aws-pds","bam","COVID-19","genetic","genomic","life sciences","MERS","SARS","SARS-CoV-2","virus"],"License":"[CC-0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)","Resources":[{"Description":"Versioned and structured data releases from the Serratus project. Current version is v210225.","ARN":"arn:aws:s3:::lovelywater2","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Serratus Explorer","URL":"https://serratus.io/explorer","AuthorName":"Serratus Team"},{"Title":"Tantalus: An R Package for exploration of Serratus data","URL":"https://github.com/serratus-bio/tantalus","AuthorName":"Serratus Team"}],"Publications":[{"Title":"coronaSPAdes. From biosynthetic gene clusters to RNA viral assemblies","URL":"https://www.biorxiv.org/content/10.1101/2020.07.28.224584v2","AuthorName":"Meleshko D., Hajirasouliha I., and Korobeynikov A. (2021)"},{"Title":"Diversification of mammalian deltaviruses by host shifting","URL":"https://www.pnas.org/content/118/3/e2019907118","AuthorName":"Bergner L.M., Orton R.J., et al (2021)"},{"Title":"Petabase-scale sequence alignment catalyses viral discovery","URL":"https://www.biorxiv.org/content/10.1101/2020.08.07.241729v2","AuthorName":"Edgar R., Taylor J., Lin V., et al (2021)"},{"Title":"Ribovirus classification by a polymerase barcode sequence","URL":"https://www.biorxiv.org/content/10.1101/2021.03.02.433648v1","AuthorName":"Babaian A., and Edgar R. (2021)"}]},"RegistryEntryAdded":"2021-01-21","RegistryEntryLastModified":"2024-03-26","Slug":"serratus-lovelywater","Sources":["awslabs-open-data-registry"]}
{"Name":"Storm EVent ImageRy (SEVIR)","Description":"Collection of spatially and temporally aligned GOES-16 ABI satellite imagery, NEXRAD radar mosaics, and GOES-16 GLM lightning detections.","Documentation":"https://nbviewer.jupyter.org/github/MIT-AI-Accelerator/eie-sevir/blob/master/examples/SEVIR_Tutorial.ipynb","Contact":"mark.veillette@mit.edu","ManagedBy":"Mark S. Veillette","UpdateFrequency":"New events will be added to SEVIR yearly","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["aws-pds","meteorological","satellite imagery","weather"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"Dataset of storm imagery","ARN":"arn:aws:s3:::sevir","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Introduction to SEVIR","URL":"https://nbviewer.jupyter.org/github/MIT-AI-Accelerator/eie-sevir/blob/master/examples/SEVIR_Tutorial.ipynb","AuthorName":"Mark Veillette","AuthorURL":"https://github.com/markveillette"},{"Title":"Using Generators for SEVIR data","URL":"https://nbviewer.jupyter.org/github/MIT-AI-Accelerator/eie-sevir/blob/master/examples/Generators.ipynb","AuthorName":"Mark Veillette","AuthorURL":"https://github.com/markveillette"}],"Tools & Applications":[{"Title":"sevir -- python utilities for working with SEVIR dataset","URL":"https://github.com/MIT-AI-Accelerator/eie-sevir","AuthorName":"Mark Veillette","AuthorURL":"https://github.com/markveillette"}],"Publications":[{"Title":"Towards a More Realistic and Detailed Deep-Learning-Based Radar Echo Extrapolation Method","URL":"https://doi.org/10.3390/rs14010024","AuthorName":"Yuan Hu, Lei Chen, Zhibin Wang, Xiang Pan and Hao Li"}]},"RegistryEntryAdded":"2020-04-23","RegistryEntryLastModified":"2022-01-25","Slug":"sevir","Sources":["awslabs-open-data-registry"]}
{"Name":"Squid Giant Axon Membrane Potential","Description":"The SGAMP database contains single-unit neuronal recordings of North Atlantic squid (Loligo pealei) giant axons in response to stimulus currents. The membrane potential and stimulus current are given for a total of 170 trials across 8 different axons.","Documentation":"https://doi.org/10.13026/C25C73","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C25C73","ARN":"arn:aws:s3:::physionet-open/sgamp/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"sgamp","Sources":["awslabs-open-data-registry"]}
{"Name":"The Singapore Nanopore Expression Data Set","Description":"The Singapore Nanopore Expression (SG-NEx) project is an international collaboration to generate reference transcriptomes and a comprehensive benchmark data set for long read RNA-Seq. Transcriptome profiling is done using Nanopore PCR-cDNA sequencing (PCR-cDNA), Nanopore amplification-free cDNA sequencing (direct cDNA), Nanopore direct sequencing of native RNA (direct RNA), PacBio IsoSeq, and short read RNA-Seq. The SG-NEx core data includes 5 of the most commonly used cell lines and it is extended with additional cell lines and samples that cover a broad range of human tissues. All core samples are sequenced with at least 3 high quality replicates. For a subset of samples spike-in RNAs are used and matched m6A profiling data is available.","Documentation":"https://github.com/GoekeLab/sg-nex-data","Contact":"[SG-NEx team](https://github.com/GoekeLab/sg-nex-data)","ManagedBy":"The Genome Institute of Singapore (https://www.a-star.edu.sg/gis)","UpdateFrequency":"Datasets will be updated periodically as additional data are generated.","Tags":["aws-pds","bam","bioinformatics","fast5","fasta","fastq","genomic","life sciences","long read sequencing","short read sequencing","transcriptomics"],"License":"[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)","Citation":"In addition, please cite Chen et al. A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines. Nature Methods (2025). doi: https://doi.org/10.1038/s41592-025-02623-4 when referencing the SG-NEx data in publications.","Resources":[{"Description":"Nanopore and PacBio long read RNA Seq data and matched short read RNA-Seq from the Singapore Nanopore Expression Project (SG-NEx). The data includes raw signal data (fast5), basecalled reads (fastq), aligned reads (bam), processed data for RNA modification detection (json), reference genome annotation files (gtf and fa) and sample metadata (txt).","ARN":"arn:aws:s3:::sg-nex-data","Region":"ap-southeast-1","Type":"S3 Bucket","Explore":["[Browse Bucket](http://sg-nex-data.s3-website-ap-southeast-1.amazonaws.com/)"]},{"Description":"Nanopore long read RNA Seq data from the Singapore Nanopore Expression Project (SG-NEx). The data includes raw signal data (blow5), converted from raw signal data (fast5).","ARN":"arn:aws:s3:::sg-nex-data-blow5","Region":"ap-southeast-1","Type":"S3 Bucket","Explore":["[Browse Bucket](http://sg-nex-data-blow5.s3-website-ap-southeast-1.amazonaws.com/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Accessing the SG-NEx dataset on AWS","URL":"https://github.com/GoekeLab/sg-nex-data/blob/master/docs/AWS_data_access_tutorial.md","AuthorName":"Ying Chen"},{"Title":"Basecalling and analysing SG-NEx samples in S/BLOW5 format","URL":"https://github.com/GoekeLab/sg-nex-data/blob/master/docs/SG-NEx_blow5_tutorial.md","AuthorName":"Hasindu Gamaarachchi"},{"Title":"Differential RNA modification analysis tutorial with xPore","URL":"https://github.com/GoekeLab/sg-nex-data/blob/master/docs/SG-NEx_xPore_tutorial.md","AuthorName":"Yu Song Chuah"},{"Title":"Performing transcript discovery and quantification with Bambu","URL":"https://github.com/GoekeLab/sg-nex-data/blob/master/docs/SG-NEx_Bambu_tutorial.md","AuthorName":"Min Hao Ling"},{"Title":"RNA modification analysis tutorial with m6Anet","URL":"https://github.com/GoekeLab/sg-nex-data/blob/master/docs/SG-NEx_m6Anet_tutorial.md","AuthorName":"Yu Song Chuah"}],"Tools & Applications":[{"Title":"Bambu-pipe: Context-aware transcript discovery and quantification from long read single-cell and spatial transcriptomics data","URL":"https://github.com/GoekeLab/bambu-pipe","AuthorName":"Andre Sim et al."},{"Title":"Bambu: Transcript discovery and quantification using long read RNA-Seq data","URL":"https://github.com/GoekeLab/bambu","AuthorName":"Ying Chen, Andre Sim et al."},{"Title":"JAFFAL: Detection of fusion genes from long read RNA-Seq data","URL":"https://github.com/Oshlack/JAFFA/wiki","AuthorName":"Nadia M Davidson et al."},{"Title":"m6Anet: Identification of m6A from a single direct RNA-Seq sample","URL":"https://github.com/GoekeLab/m6anet","AuthorName":"Christopher Hendra et al."},{"Title":"nf-core/nanoseq: A nanopore DNA and RNA-Seq demultiplexing, QC, alignment and analysis pipeline","URL":"https://nf-co.re/nanoseq","AuthorName":"Chelsea Sawyer et al."},{"Title":"xPore: Identification of differential RNA modification from direct RNA-Seq data","URL":"https://github.com/GoekeLab/xpore","AuthorName":"Ploy Pratanwanich et al."}],"Publications":[{"Title":"A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines.","URL":"https://doi.org/10.1038/s41592-025-02623-4","AuthorName":"Ying Chen et al."},{"Title":"Beyond sequencing: machine learning algorithms extract biology hidden in Nanopore signal data.","URL":"https://doi.org/10.1016/j.tig.2021.09.001","AuthorName":"Yuk Kei Wan et al."},{"Title":"Detection of m6A from direct RNA sequencing using a Multiple Instance Learning framework.","URL":"https://doi.org/10.1101/2021.09.20.461055","AuthorName":"Christopher Hendra et al."},{"Title":"Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore.","URL":"https://doi.org/10.1038/s41587-021-00949-w","AuthorName":"Ploy N Pratanwanich et al."},{"Title":"Isoform-level discovery, quantification and fusion analysis from single-cell and spatial long-read RNA-seq data with Bambu-Clump.","URL":"https://doi.org/10.1101/2024.12.30.630828","AuthorName":"Andre Sim et al."},{"Title":"JAFFAL: Detecting fusion genes with long read transcriptome sequencing.","URL":"https://doi.org/10.1186/s13059-021-02588-5","AuthorName":"Nadia M Davidson et al."}]},"RegistryEntryAdded":"2022-08-12","RegistryEntryLastModified":"2026-08-04","Slug":"sgnex","Sources":["awslabs-open-data-registry"]}
{"Name":"Smart Health for Assessing the Risk of Events via ECG Database","Description":"The SHAREE database was developed in order to investigate the possibility to identify hypertensive subjects at higher risk to develop vascular events based on Heart Rate Variability analysis. The research material in the SHAREE database included nominal 24-h electrocardiographic (ECG) Holter recordings of 139 hypertensive patients recruited at the Centre of Hypertension of the University Hospital of Naples Federico II, Naples, Italy.","Documentation":"https://doi.org/10.13026/C2RC7C","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2RC7C","ARN":"arn:aws:s3:::physionet-open/shareedb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"shareedb","Sources":["awslabs-open-data-registry"]}
{"Name":"SHDB-AF: a Japanese Holter ECG database of atrial fibrillation","Description":"Saitama Heart Database Atrial Fibrillation (SHDB-AF) is a novel open-sourced Holter ECG database from Japan, containing data from 122 unique subjects with paroxysmal atrial fibrillation. Among the 128 recordings, 98 contain raw ECG data with rhythm annotations at the beat level, manually performed by a cardiology fellow. The remaining recordings consist only of ECG traces without annotations. The dataset was collected as part of a study evaluating the generalization performance of a deep learning atrial fibrillation event detection model across different distribution shifts.","Documentation":"https://doi.org/10.13026/n6yq-fq90","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/n6yq-fq90","ARN":"arn:aws:s3:::physionet-open/shdb-af/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"shdb-af","Sources":["awslabs-open-data-registry"]}
{"Name":"Sleep Heart Health Study PSG Database","Description":"The Sleep Heart Health Study (SHHS) is a prospective cohort study designed to investigate the relationship between sleep disordered breathing and cardiovascular disease.","Documentation":"https://doi.org/10.13026/C2MK5F","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2MK5F","ARN":"arn:aws:s3:::physionet-open/shhpsgdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"shhpsgdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Shopping Humor Generation","Description":"This dataset provides a set of non-shoppable items, which are items that can't be purchased via a virtual assistant (love, vampires, etc). \nIn addition, for each non-shoppable item, the dataset contains humorous responses generated by two different large language models, and a template-based generation solution that uses a commonsense knowledge graph called ConceptNet.\nFinally, each row contains a score provided by human annotators that judge how funny each response is.\nThe columns provided for each datapoint are as follows:\nquestion- purchase request of the non-shoppable item\nanswer- a generated response\nmodel- the name of the model or solution that generated the response\nnon_shoppable_item- the name of the non shoppable item\nrelation- commonsense relation that was used to generate the response in the template-based approach\nprompt_or_template- the prompt that was used to generate the responses\nscore- the average response score of the human evaluation\n","Documentation":"https://shopping-humor-generation.s3.amazonaws.com/README.txt","Contact":"orenk@amazon.com, alibov@amazon.com, sofiato@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not currently being updated","Tags":["amazon.science","commerce","natural language processing"],"License":"Dataset is published under [CDLA-Permissive](https://cdla.io/).\n","Resources":[{"Description":"Shopping humor generation dataset","ARN":"arn:aws:s3:::shopping-humor-generation","Region":"us-east-1","Type":"S3 Bucket","Explore":["[shopping_humor_gen.zip](https://shopping-humor-generation.s3.amazonaws.com/ShoppingHumorGenDataset.zip)"]}],"DataAtWork":{"Publications":[{"Title":"Evaluating Humorous Response Generation to Playful Shopping Requests","AuthorName":"Natalie Shapira, Oren Kalinsky, Alex Libov, Chen Shani, Sofia Tolmach","URL":"https://ecir2023.org/"}]},"RegistryEntryAdded":"2023-02-12","RegistryEntryLastModified":"2023-02-12","Slug":"shopping-humor-generation","Sources":["awslabs-open-data-registry"]}
{"Name":"Conformational Space of Short Peptides","Description":"Co-managed by [Toyoko](toyoko.io) and the [Structural Biology Group at the Universidad Nacional de Quilmes](http://ufq.unq.edu.ar/sbg/), this dataset allows us to explore the conformational space of all possible peptides using the 20 common amino acids. It consists of a collection of exhaustive molecular dynamics simulations of tripeptides and pentapeptides.","Documentation":"https://github.com/ToyokoLabs/SBG/blob/master/README.md","Contact":"awsopendata@toyoko.io","ManagedBy":"[Toyoko](https://www.toyoko.io/)","UpdateFrequency":"Not updated","Tags":["amino acid","aws-pds","bioinformatics","biomolecular modeling","life sciences","molecular dynamics","protein","structural biology"],"License":"CC-BY 4.0 See https://github.com/ToyokoLabs/SBG/blob/master/LICENSE","Resources":[{"Description":"Tri-peptides. All possible 8000 tri-peptides, 2 replicas. File types: Amber .out, .traj and .crd. To retrieve files use this format: N{AA}{AA}C{AA}-R{replic_number}.dmd.1.{ext}, for example, for ALA-SER-ASN traj file for replica 2, the name is NALASERCASN-R2.dmd.1.traj","ARN":"arn:aws:s3:::toyokounqpeptides/tripep/","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Penta-peptides. All possible 3200000 penta-peptides, no replicas. File types: .tar.bz2. To retrieve files use this format: N{AA}{AA}{AA}{AA}C{AA}-R1.tar.bz2","ARN":"arn:aws:s3:::toyokounqpeptides/5pep/","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Intro to Conformational Space of Short Peptides","URL":"https://www.toyoko.io/pepdataset/","AuthorName":"Sebastian Bassi and Virginia Gonzalez","AuthorURL":"https://about.me/bassi"}]},"RegistryEntryAdded":"2021-06-24","RegistryEntryLastModified":"2021-07-02","Slug":"short_peptides","Sources":["awslabs-open-data-registry"]}
{"Name":"Sid Sijbrandij's osteosarcoma dataset","Description":"Sid was diagnosed with osteosarcoma in November 2022 and after running out of standard of care treatment options he began: maximum diagnostics, created new treatments, started doing treatments in parallel, and scaling this for others. This dataset includes the clinical and molecular data generated by and for Sid throughout this journey: longitudinal single cell and bulk RNA and DNA sequencing, spatial transcriptomics, residual disease testing (MRD), flow cytometry, imaging, and clinical lab results. This data is being freely shared with the community to advance cancer research.","Documentation":"https://osteosarc.com/data/","Contact":"cancer@sytse.com","ManagedBy":"[Rare Cancer Research Foundation](https://rarecancer.org/)","UpdateFrequency":"Periodically as new data is generated","License":"CC0 1.0; https://creativecommons.org/publicdomain/zero/1.0/","Tags":["aws-pds","bioinformatics","cancer","genomic","life sciences","spatial omics"],"Resources":[{"Description":"Sid Sijbrandij's osteosarcoma dataset","ARN":"arn:aws:s3:::sid-sijbrandij-osteosarc-dataset","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for updates to the osteosarcoma dataset","ARN":"arn:aws:sns:us-west-2:330217004422:sid-sijbrandij-osteosarc-dataset-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: Sid Sijbrandij's osteosarcoma dataset","URL":"https://github.com/RCRF/open-data-examples/blob/main/sid-osteosarc/get-to-know-osteosarc.ipynb","AuthorName":"Marshall Thompson"},{"Title":"Interacting with Sid Sijbrandij's osteosarcoma dataset in a Browser-Based Notebook on Scigantic","URL":"https://scigantic.com/archive/62af7e52-038c-56b3-abe6-cda74cdb46cc/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"}],"Tools & Applications":[{"Title":"Osteosarc.com","URL":"https://osteosarc.com/","AuthorName":"Sid's Osteosarcoma Team"}]},"RegistryEntryAdded":"2026-06-12","RegistryEntryLastModified":"2026-06-22","Slug":"sid-osteosarc","Sources":["awslabs-open-data-registry"]}
{"Name":"Siena Scalp EEG Database","Description":"The database consists of EEG recordings of 14 patients acquired at the Unit of Neurology and Neurophysiology of the University of Siena. Subjects include 9 males (ages 25-71) and 5 females (ages 20-58). Subjects were monitored with a Video-EEG with a sampling rate of 512 Hz, with electrodes arranged on the basis of the international 10-20 System. Most of the recordings also contain 1 or 2 EKG signals. The diagnosis of epilepsy and the classification of seizures according to the criteria of the International League Against Epilepsy were performed by an expert clinician after a careful review of the clinical and electrophysiological data of each patient.","Documentation":"https://doi.org/10.13026/5d4a-j060","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/5d4a-j060","ARN":"arn:aws:s3:::physionet-open/siena-scalp-eeg/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"siena-scalp-eeg","Sources":["awslabs-open-data-registry"]}
{"Name":"SILAM Air Quality","Description":"Air Quality is a global SILAM atmospheric composition and air quality forecast performed on a daily basis for > 100 species and covering the troposphere and the stratosphere. The output produces 3D concentration fields and aerosol optical thickness. The data are unique: 20km resolution for global AQ models is unseen worldwide.","Documentation":"http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3","ManagedBy":"[Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/)","Contact":"avoin-data@fmi.fi","UpdateFrequency":"1 time a day","Collabs":{"ASDI":{"Tags":["air quality"]}},"Tags":["air quality","aws-pds","climate","earth observation","meteorological","weather"],"License":"Creative Commons Attribution 4.0 International (CC BY 4.0)","Resources":[{"Description":"Surface NetCDF files","ARN":"arn:aws:s3:::fmi-opendata-silam-surface-netcdf","Region":"eu-west-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://fmi-opendata-silam-surface-netcdf.s3.amazonaws.com/index.html)"]},{"Description":"Surface Zarr files","ARN":"arn:aws:s3:::fmi-opendata-silam-surface-zarr","Region":"eu-west-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://fmi-opendata-silam-surface-zarr.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for new netcdf surface data","ARN":"arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-netcdf","Region":"eu-west-1","Type":"SNS Topic"},{"Description":"Notifications for new zarr surface data","ARN":"arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-zarr","Region":"eu-west-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Simple examples","URL":"https://github.com/fmidev/opendata-resources/tree/master/examples/python","AuthorName":"Roope Tervo"}]},"RegistryEntryAdded":"2019-09-20","RegistryEntryLastModified":"2023-02-28","Slug":"silam","Sources":["awslabs-open-data-registry"]}
{"Name":"SILO climate data on AWS","Description":"[SILO](https://www.longpaddock.qld.gov.au/silo) is a database of Australian [climate data](https://www.longpaddock.qld.gov.au/silo/about/climate-variables) from 1889 to the present. It provides continuous, daily time-step [data products](https://www.longpaddock.qld.gov.au/silo/about/data-products) in ready-to-use [formats](https://www.longpaddock.qld.gov.au/silo/about/file-formats-and-samples) for research and operational applications.\nSILO's gridded datasets (in NetCDF and GeoTiff formats) are hosted on AWS Public Data. Point data (at both station and grid cell locations) are available from the [SILO website](https://www.longpaddock.qld.gov.au/silo/). Incremental update files for mirroring point datasets at station locations are also available on AWS Public Data.\n","Documentation":"https://www.longpaddock.qld.gov.au/silo/gridded-data","Contact":"https://www.longpaddock.qld.gov.au/silo/contact-us","ManagedBy":"Queensland Government","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","climate","earth observation","environmental","meteorological","model","sustainability","water","weather"],"License":"SILO datasets are constructed by the [Queensland Government](http://www.qld.gov.au/) using observational data provided by the [Australian Bureau of Meteorology](http://www.bom.gov.au/) and other providers as noted in the metadata. All SILO datasets are provided under the [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.\n","Resources":[{"Description":"SILO open data","ARN":"arn:aws:s3:::silo-open-data","Region":"ap-southeast-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Using relative humidity grids with xarray from s3","URL":"https://nbviewer.jupyter.org/github/RichardScottOZ/Adelaide-Temperatue/blob/main/Humidity-grids.ipynb","AuthorName":"Richard Scott"}],"Tools & Applications":[{"Title":"Convert NetCDF to ESRI ArcASCII or GeoTIFF","URL":"https://www.longpaddock.qld.gov.au/silo/gridded-data/conversion","AuthorName":"SILO"},{"Title":"NetCDF Operators to calculate seasonal means","URL":"https://www.longpaddock.qld.gov.au/silo/gridded-data/nco","AuthorName":"SILO"},{"Title":"Python script to calculate a regional mean","URL":"https://www.longpaddock.qld.gov.au/silo/gridded-data/python","AuthorName":"SILO"}]},"RegistryEntryAdded":"2018-11-26","RegistryEntryLastModified":"2022-06-16","Slug":"silo","Sources":["awslabs-open-data-registry"]}
{"Name":"Simulated Fetal Phonocardiograms","Description":"This data set is a series of synthetic fetal phonocardiographic signals (PCGs) relative to different fetal states and recording conditions.","Documentation":"https://doi.org/10.13026/C2GW25","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2GW25","ARN":"arn:aws:s3:::physionet-open/simfpcgdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"simfpcgdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Simultaneous physiological measurements with five devices at different cognitive and physical loads","Description":"This open database contains physiological measurements from 13 adult and healthy subjects during a standardized experimental setup. The physiology was examined with five different devices (NeXus-10 MKII, eMotion Faros 360°, SOMNOtouch NIBP, Hexoskin Hx1, Polar RS800 Multi), which, in contrast to other studies, were recorded simultaneously. The experiment included a five minute baseline measurement, five minute walking on the treadmill, a five minute cognitive audio test, and a five minute uphill walking on the treadmill. Measurements included electrocardiography (ECG), photoplethysmography, accelerometry, oxygen saturation, respiration, heart rate, heart rate variability, and RR intervals sampled between 1 Hz and 8000 Hz. The dataset was originally generated during an investigation of functionality, accuracy, and usability of several ECG-measurement devices. This resource included the complete raw data files (EDF and HRM) and merged data with a manual expert annotation of heart beats and annotation files with information about the beginning of each experimental phase.","Documentation":"https://doi.org/10.13026/wce5-fj54","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/wce5-fj54","ARN":"arn:aws:s3:::physionet-open/simultaneous-measurements/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"simultaneous-measurements","Sources":["awslabs-open-data-registry"]}
{"Name":"Single-Cell Atlas of Human Blood During Healthy Aging","Description":"Comprehensive, large-scale single-cell profiling of healthy human blood at different ages is one of the critical pending tasks required to establish a framework for systematic understanding of human aging. Here, using single-cell RNA/TCR/BCR-seq with protein feature barcoding (20 antibodies), we profiled 317 samples from 166 healthy individuals aged 25 to 85 years old drawn over 3-year period. Dataset spanning ~2 million cells describes 50 subpopulations of blood immune cells, with 14 subpopulations changing with age, including a novel NKG2C+ CD8 Tcm population that decreases with age. We describe age-associated accumulation of Th2 and HLA-DR+ memory CD4 T cells, CCR4+ CD8 Tcm cells and GZMK+ CD8 Tem cells. We validate key findings using 30-plex spectral cytometry panel. We characterize patterns of antigen receptor clonality across subpopulations of T and B cells and describe their age-dependence. Our work provides novel insights into healthy human aging and unique annotated resource of unprecedented depth.","Documentation":"https://www.synapse.org/#!Synapse:syn49637038/","Contact":"martyomov@wustl.edu","ManagedBy":"Sage Bionetworks","UpdateFrequency":"Never","Tags":["aws-pds","life sciences","protein","single-cell transcriptomics"],"License":"[CC BY]","Citation":"Terekhova M, Swain A, Bohacova P, et al. Single-cell atlas of healthy human blood unveils age-related loss of NKG2C+GZMB-CD8+ memory T cells and accumulation of type 2 memory T cells. Immunity. 2023;56(12):2836-2854.e9. doi:10.1016/j.immuni.2023.10.013","Resources":[{"Description":"Raw sequencing data (fastq.gz), TCR/BCR clonotype tables (.csv), normalized counts matrix (.rds, .h5ad), UMAP coordinates (.csv), metadata (.csv), cytometry data (.fcs)","ARN":"arn:aws:s3:::single.cell.human.blood.atlas.opendata.sagebase.org","Region":"us-east-1","Type":"S3 Bucket","AccountRequired":false,"ControlledAccess":"https://www.synapse.org/#!Synapse:syn49637038/"}],"DataAtWork":{"Publications":[{"Title":"Single-cell atlas of healthy human blood unveils age-related loss of NKG2C+GZMB−CD8+ memory T cells and accumulation of type 2 memory T cells","URL":"https://doi.org/10.1016/j.immuni.2023.10.013","AuthorName":"Terekhova M, Swain A, Bohacova P, Aladyeva E, Arthur L, et al"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2024-01-12","RegistryEntryLastModified":"2025-06-19","Slug":"singlecellhumanbloodatlas","Sources":["awslabs-open-data-registry"]}
{"Name":"SiPeCaM (Sitios Permanentes de la Calibración y Monitoreo de la Biodiversidad)","Description":"The SiPeCaM goal is to create a data source that allows to evaluate changes in the biodiversity state, considering key aspect of how does the ecosystem behaves.","Documentation":"https://github.com/CONABIO/sipecam-open-data/blob/main/docs/SiPeCaM.md","Contact":"[mschmidt@conabio.gob.mx](mschmidt@conabio.gob.mx)","ManagedBy":"[Conabio](https://monitoreo.conabio.gob.mx/)","UpdateFrequency":"SiPeCaM offers data releases once the biologist recollect the data from the protected natural areas (every 3 months)","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["aws-pds","biodiversity","biology","ecosystems","image processing","multimedia","wildlife"],"License":"Attribution 4.0 International (CC BY 4.0)","Resources":[{"Description":"Audio, video and image representation of mexican fauna","ARN":"arn:aws:s3:::sipecam-open-data","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Sample search query on all images files for cumulus 92, using Alfresco.","URL":"https://github.com/CONABIO/sipecam-open-data/blob/main/tutorials/notebooks/images/01_alfresco_consultation.ipynb","AuthorName":"Carolina Acosta","Services":["Amazon S3"]},{"Title":"Sample search query on november audio files for cumulus 92, using Alfresco.","URL":"https://github.com/CONABIO/sipecam-open-data/blob/main/tutorials/notebooks/audios/01_alfresco_consultation.ipynb","AuthorName":"Carolina Acosta","Services":["Amazon S3"]},{"Title":"Sample search query on november video files for cumulus 92, using Alfresco.","URL":"https://github.com/CONABIO/sipecam-open-data/blob/main/tutorials/notebooks/videos/01_alfresco_consultation.ipynb","AuthorName":"Carolina Acosta","Services":["Amazon S3"]}],"Publications":[{"Title":"Sitios Permanente de la Calibración y Monitoreo de la Biodiversidad","URL":"https://monitoreo.conabio.gob.mx/sipecam_files/Proyecto_SiPeCaM_2020_Fase_I.pdf","AuthorName":"Michael Schmidt et. al."}]},"RegistryEntryAdded":"2022-02-28","RegistryEntryLastModified":"2025-02-04","Slug":"sipecam","Sources":["awslabs-open-data-registry"]}
{"Name":"CRC-SAS/SISSA historical seasonal and subseasonal forecast database","Description":"En el marco del Sistema de Información de Sequías del Sur de Sudamérica (SISSA) se ha desarrollado una base de predicciones en escala subestacional y estacional con datos corregidos y sin corregir, con el propósito que permita estudiar predictibilidad en distintas escalas y también que sirva para alimentar modelos de sectores como agricultura e hidrología. \n<br/>\n<br/>\nLa base contiene datos en escala diaria entre 2000-2019 (sin corregir) y 2010-2019 (corregidos) para diversas variables incluyendo: temperatura media, máxima y mínima, así como también lluvia, viento medio y otras variables pensadas para alimentar modelos hidrológicos y de cultivo.\n<br/>\n<br/>\nLa base de datos abarca toda el área del Centro Regional del Clima para el sur de sudamérica (CRC-SAS), abarcando desde Bolivia y centro-sur de Brasil hasta la Patagonia incluyendo los países miembros como Chile, Argentina, Brasil, Paraguay, Uruguay y Bolivia.\n<br/>\n<br/>\nLa base fue generada a partir de datos de GEFSv12 para escala subestacional ([GEFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast)) y CFS2 para escala estacional ([CFS2](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00878)). Para la generación de los datos corregidos se utilizaron los datos del reanálisis de ERA5 ([ERA5](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview)).\n<br/>\n<br/>\n<br/>\nWithin the framework of the Southern South American Drought Information System (SISSA), a base of sub-seasonal and seasonal scale predictions has been developed with corrected and uncorrected data, with the purpose of studying predictability at different scales and also to be used to feed models for sectors such as agriculture and hydrology.\n<br/>\n<br/>\nThe database contains daily scale data between 2000-2019 (uncorrected) and 2010-2019 (corrected) for several variables including: mean, maximum and minimum temperature, as well as rainfall, mean wind and other variables intended to feed hydrological and crop models.\n<br/>\n<br/>\nThe database covers the entire area of the Regional Climate Center for Southern South America (CRC-SAS), from Bolivia and south-central Brazil to Patagonia, including member countries such as Chile, Argentina, Brazil, Paraguay, Uruguay and Bolivia.\n<br/>\n<br/>\nThe base was generated from GEFSv12 data for subseasonal scale ([GEFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast)) and CFS2 for seasonal scale ([CFS2](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00878)). Data from the ERA5 reanalysis ([ERA5](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview)) were used to generate the corrected data.\n","Documentation":"General information, tutorials and examples, contact us atl sissa-aws@smn.gob.ar\n<br/>\n","Contact":"For any questions regarding the data set or any general questions, you can contact us atl sissa-aws@smn.gob.ar. We also would like to know how this data is being used.","ManagedBy":"[SISSA](https://sissa.crc-sas.org/)","UpdateFrequency":"Static database from 2000-2019 without correction and 2010-2019 with correction.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["agriculture","aws-pds","earth observation","forecast","hydrology","meteorological","natural resource","weather"],"License":"[Creative Commons Attribution 2.5 Argentina License](https://creativecommons.org/licenses/by/2.5/ar)","Resources":[{"Description":"CRC-SAS/SISSA Retrospective Daily forecast database","ARN":"arn:aws:s3:::sissa-forecast-database","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://s3-us-west-2.amazonaws.com/sissa-forecast-database/index.html)"]}],"RegistryEntryAdded":"2024-01-05","RegistryEntryLastModified":"2024-09-05","Slug":"sissa-forecast-database-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"Metagenomic reference libraries for Slacken","Description":"Metagenomic indexes for use with the Slacken taxonomic classification tool","Documentation":"https://github.com/JNP-Solutions/Slacken/wiki/Pre%E2%80%90built-Slacken-indexes-on-Amazon-S3","Contact":"johan@jnpsolutions.io","ManagedBy":"Johan Nyström-Persson","UpdateFrequency":"These indexes are currently what was used in our 2025 publication introducing the concept of 2-step classification and comparing Kraken2 with Slacken. We aim to update the data at least once per year, resources permitting.","Tags":["bioinformatics","biology","genomic","life sciences","metagenomics","microbiome"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"Metagenomic indexes for Slacken, a metagenomic classifier, based on NCBI RefSeq genomes.","ARN":"arn:aws:s3:::slacken","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://slacken.s3.amazonaws.com/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Classifying metagenomic samples on AWS ElasticMapReduce","URL":"https://github.com/JNP-Solutions/Slacken/wiki/Classifying-metagenomic-samples-on-AWS-Elastic-MapReduce","Services":["Amazon EC2","Amazon EMR","Amazon S3"],"AuthorName":"Johan Nyström-Persson","AuthorURL":"https://github.com/jtnystrom"}],"Tools & Applications":[{"Title":"Slacken","URL":"https://github.com/JNP-Solutions/Slacken","AuthorName":"Johan Nyström-Persson, Nishad Bapatdhar","AuthorURL":"https://github.com/jtnystrom"}],"Publications":[{"Title":"Precise and scalable metagenomic profiling with sample-tailored minimizer libraries","URL":"https://www.biorxiv.org/content/10.1101/2024.12.22.629657","AuthorName":"Johan Nyström-Persson, Nishad Bapatdhar and Samik Ghosh"}]},"RegistryEntryAdded":"2025-05-11","RegistryEntryLastModified":"2025-05-19","Slug":"slacken","Sources":["awslabs-open-data-registry"]}
{"Name":"Motion and heart rate from a wrist-worn wearable and labeled sleep from polysomnography","Description":"This project contains acceleration (in units of g) and heart rate (bpm, measured from photoplethysmography) recorded from the Apple Watch, as well as labeled sleep scored from gold-standard polysomnography. Data were collected at the University of Michigan from June 2017 to March 2019, and there are 31 subjects in total. Code to read and process these files is available on GitHub. The paper corresponding to the work is Walch et al., \"Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device\", SLEEP (2019).","Documentation":"https://doi.org/10.13026/hmhs-py35","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/hmhs-py35","ARN":"arn:aws:s3:::physionet-open/sleep-accel/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"sleep-accel","Sources":["awslabs-open-data-registry"]}
{"Name":"Effect of 24-hour sleep deprivation on cerebral hemodynamics and cognitive performance","Description":"Sleep deprivation (SD) is associated with decreased cognitive performance, but the underlying mechanisms are poorly understood. To assess the impact of SD on cerebral hemodynamics in the frontal lobe, functional near-infrared spectroscopy (fNIRS) measurements were carried out on young subjects. Ten young healthy adults (males, age of 27.6±3.7 years, one left-handed participant) were recruited for this study conducted in 2018 at University of Oklahoma, Translational Geroscience Laboratory. The examination protocol began with assessment of cognitive performance with the aid of Cambridge Neuropsychological Test Automated Battery (CANTAB). The following tests were used: motor screening task, rapid visual processing, reaction time, spatial working memory, paired associated learning, and delayed matching to visual sample. Subsequently fNIRS measurements were taken from participants while carrying out a finger-tapping exercise. Each subject was presented with an auditory command to perform a finger-tapping task over three sets of 10 seconds using the left index finger and three sets of 10 seconds using the right index finger, with 10 seconds inter-stimulus periods between each task. Functional NIRS recordings were acquired using a NIRScout platform (NIRx Medical Technologies LLC, NY, USA). Relative oxy- and deoxyhemoglobin time series can be obtained from measured intensities by applying differential modified Beer Lamber Law. All measurements were performed before and after 24 hours of SD. The dataset forms the basis for two studies that explore: (1) the relationship between sleep deprivation, cognitive performance, and neurovascular coupling response in young healthy adults; and (2) sleep deprivation and functional connectivity of the frontal cortex.","Documentation":"https://doi.org/10.13026/669h-cx11","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"PhysioNet Contributor Review Health Data License 1.5.0","Resources":[{"Description":"https://doi.org/10.13026/669h-cx11","ARN":"arn:aws:s3:::physionet-open/sleep-dep-hemo-cog/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"sleep-dep-hemo-cog","Sources":["awslabs-open-data-registry"]}
{"Name":"Sleep-EDF Database","Description":"The sleep-edf database contains whole-night PolySomnoGraphic sleep recordings, containing EEG, EOG, chin EMG, and event markers.","Documentation":"https://doi.org/10.13026/C2C30J","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2C30J","ARN":"arn:aws:s3:::physionet-open/sleep-edf/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"sleep-edf","Sources":["awslabs-open-data-registry"]}
{"Name":"Sleep-EDF Database Expanded","Description":"The sleep-edf database contains 197 whole-night PolySomnoGraphic sleep recordings, containing EEG, EOG, chin EMG, and event markers. Some records also contain respiration and body temperature. Corresponding hypnograms (sleep patterns) were manually scored by well-trained technicians according to the Rechtschaffen and Kales manual, and are also available.","Documentation":"https://doi.org/10.13026/C2X676","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2X676","ARN":"arn:aws:s3:::physionet-open/sleep-edfx/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"sleep-edfx","Sources":["awslabs-open-data-registry"]}
{"Name":"Sleep Bioradiolocation Database","Description":"The database contains 32 records of non-contact sleep monitoring by a bioradar. The records are accompanied by results of sleep scoring, based on polysomnography according to the rules of the American Academy of Sleep Medicine.","Documentation":"https://doi.org/10.13026/C29H4K","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C29H4K","ARN":"arn:aws:s3:::physionet-open/sleepbrl/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"sleepbrl","Sources":["awslabs-open-data-registry"]}
{"Name":"MIT-BIH Polysomnographic Database","Description":"The MIT-BIH Polysomnographic Database is a collection of recordings of multiple physiologic signals during sleep. Subjects were monitored in Boston's Beth Israel Hospital Sleep Laboratory for evaluation of chronic obstructive sleep apnea syndrome, and to test the effects of constant positive airway pressure (CPAP), a standard therapeutic intervention that usually prevents or substantially reduces airway obstruction in these subjects.","Documentation":"https://doi.org/10.13026/C23K5S","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C23K5S","ARN":"arn:aws:s3:::physionet-open/slpdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"slpdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Somatic Mosaicism across Human Tissues (SMaHT)","Description":"The Somatic Mosaicism across Human Tissues (SMaHT) project is an NIH Common\nFund consortium (2023-) aimed to comprehensively characterize somatic variation\n(\"mosaicism\") in normal human tissues. While most genetic studies have relied\non blood-derived DNA, SMaHT captures the full spectrum of DNA variation across\ncell types, tissues, and organs from phenotypically normal individuals to\nbetter understand the role of somatic mosaicism in human development, aging,\nand disease progression.\n\nResearchers in the consortium develop and apply experimental and computational\nmethods, paired with the state-of-the-art sequencing technologies, to accurately\ndetect even rare mutations (frequency < 1%) in subpopulations of cells. In\naddition to generating the production data across ~20 tissue types from 150\npost-mortem donors, SMaHT also produces datasets from cell line and tissue\nhomogenate samples, to benchmark and develop new technologies and computational\ntools for mosaic variant detection.\n\nThe resulting data include high-coverage whole-genome and transcriptome data\nusing both short-read and long-read sequencing technologies from multiple platforms\n(e.g., Illumina, PacBio, Oxford Nanopore Technologies, Ultima Genomics). SMaHT will\nalso generate comprehensive genome-wide catalogs of somatic variants. We anticipate\nthat this resource will be valuable not only for researchers studying somatic\nmosaicism, but also for the broader scientific community interested in large-scale\nWGS data from normal human tissues. More about the SMaHT project:\nprogram announcement, https://commonfund.nih.gov/smaht, and https://smaht.org/.\nMore about the data portal: https://data.smaht.org/ and types of data generated:\nhttps://data.smaht.org/about/consortium/data\n","Documentation":"https://data.smaht.org/docs","Contact":"smhelp@hms-dbmi.atlassian.net","ManagedBy":"SMaHT Data Analysis Center (DAC)","UpdateFrequency":"Bi-annually","Tags":["aws-pds","bam","bioinformatics","biology","genetic","genomic","imaging","life sciences","whole genome sequencing"],"License":"NIH Genomic Data Sharing Policy - https://gdc.cancer.gov/access-data/data-access-policies","Citation":"The SMaHT datasets were generated as part of the NIH Common Fund consortium initiative, Somatic Mosaicism across Human Tissues (SMaHT). The SMaHT datasets are submitted under dbGaP studies (http://www.ncbi.nlm.nih.gov/gap), with the study accession numbers, phs004193 for the SMaHT Benchmarking data and phs004194 for the SMaHT Production data. The datasets were provided by the SMaHT Data Analysis Center (DAC) [1UM1DA058230] on behalf of the SMaHT network. More information about the SMaHT Network is available online at https://smaht.org/, about the SMaHT Data Portal at https://data.smaht.org/ , and types of data generated by the Network at https://data.smaht.org/about/consortium/data","Resources":[{"Description":"SMaHT Open-Access Data - Publicly available data files without restriction, including\naligned reads from WGS and RNA-Seq, as well as variants identified from cell line\nsamples that are commercially available without restriction. Somatic (non-inherited)\nvariants from donor tissue samples are also open-access data.\n","ARN":"arn:aws:s3:::smaht-open-data-public","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"SMaHT Controlled Access Data - Controlled-access data files, including aligned reads\nfrom WGS and RNA-Seq, as well as  germline (inherited) from donor tissue samples.\nAccess to these data is managed through dbGaP.\n","ARN":"arn:aws:s3:::smaht-open-data-protected","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs004194"},{"Description":"Amazon SNS topic that publishes notifications when public access data is added for this dataset.\n","ARN":"arn:aws:sns:us-east-1:874962955096:smaht-open-data-public-object_created","Region":"us-east-1","Type":"SNS Topic"},{"Description":"Amazon SNS topic that publishes notifications when new controlled access data is added for this dataset.\n","ARN":"arn:aws:sns:us-east-1:874962955096:smaht-open-data-protected-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tools & Applications":[{"Title":"Somatic Mosaicism across Human Tissues Data Portal","URL":"https://data.smaht.org/","AuthorName":"SMaHT Data Analysis Center (DAC)"}],"Publications":[{"Title":"The Somatic Mosaicism across Human Tissues Network","URL":"https://www.nature.com/articles/s41586-025-09096-7","AuthorName":"Coorens T, Oh J, Choi Y, Lim N, Zhao B, Voshall A et al."}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-01-09","RegistryEntryLastModified":"2026-01-09","Slug":"smaht","Sources":["awslabs-open-data-registry"]}
{"Name":"Smithsonian Open Access","Description":"The Smithsonian’s mission is the \"increase and diffusion of knowledge\" and has been collecting since 1846. The Smithsonian, through its efforts to digitize its multidisciplinary collections, has created millions of digital assets and related metadata describing the collection objects. On February 25th, 2020, the Smithsonian released over 2.8 million CC0 interdisciplinary 2-D and 3-D images, related metadata, and additionally, research data from researches across the Smithsonian. The 2.8 million \"open access\" collections are a subset of the Smithsonian’s 155 million objects, 2.1 million library volumes and 156,000 cubic feet of archival collections held in 19 museums, 9 research centers, libraries, archives and the National Zoo. Digitization of collections is ongoing.\n","Documentation":"http://edan.si.edu/openaccess/docs/","Contact":"openaccess@si.edu","ManagedBy":"[SI](http://www.si.edu/)","UpdateFrequency":"New / updated metadata and image files will be pushed weekly.","Tags":["art","aws-pds","culture","encyclopedic","history","museum"],"License":"CC0","Resources":[{"Description":"Smithsonian Open Access Media and Metadata","ARN":"arn:aws:s3:::smithsonian-open-access","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2020-02-27","RegistryEntryLastModified":"2020-02-28","Slug":"smithsonian-open-access","Sources":["awslabs-open-data-registry"]}
{"Name":"SMN Hi-Res Weather Forecast over Argentina","Description":"The Servicio Meteorológico Nacional de Argentina (SMN-Arg), the National Meteorological Service of Argentina, shares its deterministic forecasts generated with WRF 4.0 (Weather and Research Forecasting) initialized at 00 and 12 UTC every day.\n<br/>\n<br/>\nThis forecast includes some key hourly surface variables –2 m temperature, 2 m relative humidity, 10 m wind magnitude and direction, and precipitation–, along with other daily variables, minimum and maximum temperature.\n<br/>\n<br/>\nThe forecast covers Argentina, Chile, Uruguay, Paraguay and parts of Bolivia and Brazil  in a Lambert conformal projection, with 4 km horizontal resolution. The maximum lead time is 72 hours. \n<br/>\n<br/>\nForecasts are initialized with the NCEP-NOAA Global Forecast Systems analysis and forecasts ([GFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast)).\n","Documentation":"General information, tutorials and examples:\n<br/>\n[https://odp-aws-smn.github.io/documentation_wrf_det](https://odp-aws-smn.github.io/documentation_wrf_det)\n","Contact":"For any questions regarding the data set or any general questions, you can contact us atl odp-aws@smn.gov.ar. We also would like to know how this data is being used in order to improve our available products.","ManagedBy":"[SMN](http://www.smn.gov.ar/)","UpdateFrequency":"New data is added as soon as it's available. Two forecast cycles a day initialized at 00 UTC and 12 UTC.","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["aws-pds","earth observation","meteorological","natural resource","weather"],"License":"[Creative Commons Attribution 2.5 Argentina License](https://creativecommons.org/licenses/by/2.5/ar)","Resources":[{"Description":"WRF SMN data","ARN":"arn:aws:s3:::smn-ar-wrf","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://smn-ar-wrf.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2022-04-01","RegistryEntryLastModified":"2023-02-28","Slug":"smn-ar-wrf-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"SnpEff & SnpSift Genomic Variant Annotation Databases","Description":"SnpEff is a variant annotation and effect prediction tool that annotates and predicts the effects of genetic variants on genes and proteins (such as amino acid changes). It supports over 38,000 genomes and provides comprehensive genomic databases for variant annotation. The databases include reference genomes, gene annotations, protein sequences, and regulatory elements from trusted sources like ENSEMBL, RefSeq, and UCSC. SnpSift complements SnpEff by providing tools to annotate genomic variants using databases, filter large genomic datasets, and manipulate annotated variants. Together, these tools provide a complete solution for genomic variant analysis, supporting research in human genetics, cancer genomics, pharmacogenomics, and model organism studies.","Contact":"Pablo Cingolani <http://www.linkedin.com/in/pablocingolani>","Documentation":"https://pcingola.github.io/SnpEff/","ManagedBy":"[Pablo Cingolani](http://www.linkedin.com/in/pablocingolani)","Resources":[{"Description":"SnpEff databases for genomic variant annotation","ARN":"arn:aws:s3:::snpeff-public","Region":"us-east-2","Type":"S3 Bucket"}],"UpdateFrequency":"Monthly","Tags":["aws-pds","bioinformatics","cancer","genetic","genome","genomic","life sciences","protein","structural variation","transcriptomics","variant annotation","vcf","whole exome sequencing","whole genome sequencing"],"License":"[MIT License](https://opensource.org/licenses/MIT)","DataAtWork":{"Tutorials":[{"Title":"Building Custom SnpEff Databases","URL":"https://pcingola.github.io/SnpEff/snpeff/build_db/","AuthorName":"SnpEff Project","AuthorURL":"https://pcingola.github.io/SnpEff/"},{"Title":"SnpEff Documentation","URL":"https://pcingola.github.io/SnpEff/","AuthorName":"Pablo Cingolani","AuthorURL":"http://www.linkedin.com/in/pablocingolani"},{"Title":"SnpEff Introduction and Quick Start","URL":"https://pcingola.github.io/SnpEff/snpeff/introduction/","AuthorName":"SnpEff Project","AuthorURL":"https://pcingola.github.io/SnpEff/"}],"Tools & Applications":[{"Title":"SnpEff","URL":"https://github.com/pcingola/SnpEff","AuthorName":"Pablo Cingolani","AuthorURL":"http://www.linkedin.com/in/pablocingolani"},{"Title":"SnpSift","URL":"https://github.com/pcingola/SnpSift","AuthorName":"Pablo Cingolani","AuthorURL":"http://www.linkedin.com/in/pablocingolani"}],"Publications":[{"Title":"A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3","URL":"https://www.ncbi.nlm.nih.gov/pubmed/22728672","AuthorName":"Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM","AuthorURL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=Cingolani%20P%5BAuthor%5D&cauthor=true&cauthor_uid=22728672"},{"Title":"Using Drosophila melanogaster as a model for genotoxic chemical mutational studies with a new program, SnpSift","URL":"https://www.frontiersin.org/articles/10.3389/fgene.2012.00035/full","AuthorName":"Cingolani P, Patel VM, Coon M, Nguyen T, Land SJ, Ruden DM, Lu X","AuthorURL":"https://www.frontiersin.org/people/u/4691"}]},"RegistryEntryAdded":"2025-09-06","RegistryEntryLastModified":"2025-11-21","Slug":"snpeff","Sources":["awslabs-open-data-registry"]}
{"Name":"SocialGene RefSeq Databases","Description":"Precomputed SocialGene Neo4j graph databases of various sizes built from RefSeq genomes and MIBiG BGCs.","Documentation":"https://socialgene.github.io/precomputed_databases/2023_v0.4.1/aws/aws","Contact":"https://github.com/socialgene/socialgene.github.io/issues","ManagedBy":"University of Wisconsin-Madison","UpdateFrequency":"This database is currently what was published in our 2024 paper introducing SocialGene, but an updated database may be added in the future.","Tags":["amino acid","bioinformatics","chemical biology","genomic","graph","life sciences","metagenomics","microbiome","pharmaceutical","protein"],"License":"Where applicable, SocialGene data is released under CC0 (https://creativecommons.org/public-domain/cc0/), some individual components of the database may have their own license (see https://socialgene.github.io/precomputed_databases/2023_v0.4.1/general)","Citation":"C. M. Clark, J. C. Kwan, Creating and leveraging bespoke large-scale knowledge graphs for comparative genomics and multi-omics drug discovery with SocialGene. bioRxivorg, www.biorxiv.org/content/10.1101/2024.08.16.608329v1 (2024).","Resources":[{"Description":"SocialGene 2023_v0.4.1 Data and Database Dumps","ARN":"arn:aws:s3:::socialgene-open-data","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://socialgene-open-data.s3.amazonaws.com/)"]}],"DataAtWork":{"Tutorials":[{"Title":"How to use SocialGene on AWS","URL":"http://socialgene.github.io/precomputed_databases/2023_v0.4.1/aws/aws_instructions/","AuthorName":"Chase M. Clark","AuthorURL":"chasemc.github.io","Services":["Amazon EC2"]}],"Publications":[{"Title":"Creating and leveraging bespoke large-scale knowledge graphs for comparative genomics and multi-omics drug discovery with SocialGene.","URL":"https://www.biorxiv.org/content/10.1101/2024.08.16.608329v1","AuthorName":"C. M. Clark, J. C. Kwan"}],"Tools & Applications":[{"Title":"socialgene on pypi.python.org - python module for creating and interacting with SocialGene Neo4j databases","URL":"https://pypi.org/project/socialgene/","AuthorName":"Chase M. Clark","AuthorURL":"https://github.com/chasemc"}]},"RegistryEntryAdded":"2024-10-30","RegistryEntryLastModified":"2026-06-05","Slug":"socialgene","Sources":["awslabs-open-data-registry"]}
{"Name":"Sofar Spotter Archive","Description":"This dataset includes archival hourly data from the [Sofar Spotter buoy global network] (https://weather.sofarocean.com/) from 2019 to March 2022. <br />","Documentation":"[Spotter Technical Reference Manual](https://content.sofarocean.com/hubfs/Spotter%20product%20documentation%20page/Sofar%20-%20Technical_Reference_Manual.pdf)","Contact":"opendata@sofarocean.com","ManagedBy":"[Sofar Ocean](https://www.sofarocean.com/company/contact-us)","UpdateFrequency":"As available","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","climate","environmental","meteorological","oceans","oceans","sustainability","weather"],"License":"[Sofar Data Access Agreement](https://sofarocean.notion.site/sofarocean/Sofar-Data-Access-Agreement-fe4e091fa0b5453d9a8bd97c4b6940d5)","Citation":null,"Resources":[{"Description":"Hourly position, wave spectra and bulk wave parameters from global free drifting Spotter buoys.","ARN":"arn:aws:s3:::sofar-spotter-archive","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://sofar-spotter-archive.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Analyzing Spotter data with CloudDrift","URL":"https://github.com/sofarocean/spotter-data-archive/blob/main/examples/spotter_data_with_clouddrift.ipynb","AuthorName":"Milan Curcic"},{"Title":"Exploring Bulk Variables from the Spotter Archive","URL":"https://github.com/sofarocean/spotter-data-archive/blob/main/examples/plot_spotter_bulk_data.ipynb","AuthorName":"Isabel A. Houghton"},{"Title":"Exploring Wave Spectra Variables from the Spotter Archive","URL":"https://github.com/sofarocean/spotter-data-archive/blob/main/examples/plot_spotter_spectra_data.ipynb","AuthorName":"Isabel A. Houghton"}],"Publications":[{"Title":"Performance Characteristics of “Spotter,” a Newly Developed Real-Time Wave Measurement Buoy (2019)","URL":"https://journals.ametsoc.org/view/journals/atot/36/6/jtech-d-18-0151.1.xml","AuthorName":"K. Raghukumar, G. Chang, F. Spada, C. Jones, T. Janssen, A. Gans"},{"Title":"Performance Statistics of a Real-Time Pacific Ocean Weather Sensor Network (2021)","URL":"https://journals.ametsoc.org/view/journals/atot/38/5/JTECH-D-20-0187.1.xml","AuthorName":"I. Houghton, P. Smit, D. Clark, C. Dunning, A. Fisher, N. Nidzieko, P. Chamberlain, T. Janssen"}]},"RegistryEntryAdded":"2024-02-01","RegistryEntryLastModified":"2025-07-09","Slug":"sofar-spotter-archive","Sources":["awslabs-open-data-registry"]}
{"Name":"Software Heritage Graph Dataset","Description":"[Software Heritage](https://www.softwareheritage.org/) is the largest\nexisting public archive of software source code and accompanying\ndevelopment history. The Software Heritage Graph Dataset is a fully\ndeduplicated Merkle DAG representation of the Software Heritage archive.\n\nThe dataset links together file content identifiers, source code\ndirectories, Version Control System (VCS) commits tracking evolution over\ntime, up to the full states of VCS repositories as observed by Software\nHeritage during periodic crawls. The dataset’s contents come from major\ndevelopment forges (including GitHub and GitLab), FOSS distributions (e.g.,\nDebian), and language-specific package managers (e.g., PyPI). Crawling\ninformation is also included, providing timestamps about when and where all\narchived source code artifacts have been observed in the wild.\nAuthor and committer information is anonymized.\n","Documentation":"https://docs.softwareheritage.org/devel/swh-export/graph/athena.html","Contact":"aws@softwareheritage.org","ManagedBy":"Software Heritage","UpdateFrequency":"Data is updated yearly","Tags":["aws-pds","digital preservation","free software","open source software","source code"],"License":"The term \"Software Heritage Graph Dataset\" designates the internal structure of the Software Heritage archive, and explicitly excludes the file contents.\nThe \"Software Heritage Graph Dataset\" is distributed under the Creative Commons Attribution 4.0 International license.\nFor terms of use of all other contents found in the S3 buckets, contact datasets@softwareheritage.org\n\nBy accessing the dataset, you agree with the Software Heritage [Ethical\nCharter for using the archive\ndata](https://www.softwareheritage.org/legal/users-ethical-charter/),\nthe [terms of use for bulk\naccess](https://www.softwareheritage.org/legal/bulk-access-terms-of-use/),\nand the [Software Heritage principles for large language\nmodels](https://www.softwareheritage.org/2023/10/19/swh-statement-on-llm-for-code/).\n","Resources":[{"Description":"Software Heritage Graph Dataset","ARN":"arn:aws:s3:::softwareheritage","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents) files","ARN":"arn:aws:s3:::softwareheritage-inventory","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Using the Software Heritage Graph Dataset","URL":"https://docs.softwareheritage.org/devel/swh-export/graph/","AuthorName":"The Software Heritage team"}],"Tools & Applications":[{"Title":"The SWH-Graph module","URL":"https://docs.softwareheritage.org/devel/swh-graph/","AuthorName":"The Software Heritage team"}],"Publications":[{"Title":"The Software Heritage Graph Dataset","URL":"https://dx.doi.org/10.1145/3379597.3387510","AuthorName":"Antoine Pietri, Diomidis Spinellis, Stefano Zacchiroli"}]},"RegistryEntryAdded":"2019-03-12","RegistryEntryLastModified":"2025-07-24","Slug":"software-heritage","Sources":["awslabs-open-data-registry"]}
{"Name":"SoilMicrobeDB genome database on AWS","Description":"SoilMicrobeDB is a Kraken2 genome database with extensive representation of high-quality genomes of soil organisms, including uncultured and fungal species.","Documentation":"https://github.com/zoey-rw/SoilMicrobeDB/blob/master/AWS_conda_environment_example/tutorial.md","Contact":"zoeywerbin@gmail.com","ManagedBy":"Zoey Werbin","UpdateFrequency":"Database is associated with a 2025 publication, and may be expanded with additional genomes in the future.","Tags":["aws-pds","bioinformatics","life sciences","metagenomics","microbiome"],"License":"There are no restrictions on the use of this data.","Citation":"The SoilMicrobeDB includes data from multiple other publications, which we recommend citing. See Table 1 in our open-access manuscript (Werbin et al. 2025) for details.","Resources":[{"Description":"SoilMicrobeDB, a genome reference database for soil shotgun metagenomics","ARN":"arn:aws:s3:::kraken2-soil-microbe-database","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Using SoilMicrobeDB to classify metagenomic soil samples","URL":"https://github.com/zoey-rw/SoilMicrobeDB/blob/master/AWS_conda_environment_example/tutorial.md","AuthorName":"Zoey Werbin","AuthorURL":"https://github.com/zoey-rw"}],"Tools & Applications":[{"Title":"SoilMicrobeDB Explorer","URL":"https://zoeywerbin.shinyapps.io/soil_microbe_db/","AuthorName":"Zoey Werbin","AuthorURL":"https://github.com/zoey-rw"}],"Publications":[{"Title":"Improved detection of fungi and uncultivated microorganisms in soil metagenomes using a comprehensive genome database","URL":"https://www.biorxiv.org/content/biorxiv/early/2025/03/25/2025.03.21.644662.full.pdf","AuthorName":"Zoey Werbin, Winston E Anthony, Ilija Dukovski, Dylan Mankel, Daniel Segrè, Jennifer M. Bhatnagar, Marianna Felici","AuthorURL":"https://github.com/zoey-rw"}]},"RegistryEntryAdded":"2025-03-08","RegistryEntryLastModified":"2026-06-23","Slug":"soil_microbe_db","Sources":["awslabs-open-data-registry"]}
{"Name":"NASA Lunar FM Bench","Description":"Benchmark Dataset for Advancing Machine Learning for Lunar Surface understanding formerly known as SomBench","Documentation":"https://huggingface.co/datasets/nasa-ibm-ai4science/Sombench-pretraining-data","Contact":"sujit.roy@nasa.gov","ManagedBy":"NASA ODSI","UpdateFrequency":"This is the final version of the Dataset.","Tags":["aws-pds","machine learning","planetary"],"License":"Creative Commons Attribution 4.0 International.\n","Citation":null,"Resources":[{"Description":"NASA Lunar FM Bench","ARN":"arn:aws:s3:::nasa-lunar-fm-bench","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://nasa-lunar-fm-bench.s3.us-west-2.amazonaws.com)"]},{"Description":"Notifications for Lunar FM bench data","ARN":"arn:aws:sns:us-west-2:614929158106:nasa-lunar-fm-bench-object_created","Region":"us-west-2","Type":"SNS Topic"}],"RegistryEntryAdded":"2026-09-01","RegistryEntryLastModified":"2026-09-08","Slug":"som-bench","Sources":["awslabs-open-data-registry"]}
{"Name":"SondeHub Radiosonde Telemetry","Description":"SondeHub Radiosonde telemetry contains global radiosonde (weather balloon) data captured by SondeHub from our participating radiosonde_auto_rx receiving stations. radiosonde_auto_rx is a open source project aimed at receiving and decoding telemetry from airborne radiosondes using software-defined-radio techniques, enabling study of the telemetry and sometimes recovery of the radiosonde itself.\nCurrently 313 receiver stations are providing data for an average of 384 radiosondes a day.  The data within this repository contains received telemetry frames, including radiosonde type, gps position, and for some radiosondes atmospheric sensor data (temperature, humidity, pressure). As the downlinked telemetry does not always contain calibration information, any atmospheric sensor data should be considered to be uncalibrated. Note that radiosonde_auto_rx does not have sensor data support for all radiosonde types.\n","Documentation":"https://github.com/projecthorus/sondehub-infra/wiki/Amazon-Open-Data","Contact":"Michaela Wheeler <vk3fur@sondehub.org>","ManagedBy":"[SondeHub](https://sondehub.org/)","UpdateFrequency":"Data is updated as we receive it","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["aws-pds","climate","environmental","GPS","weather"],"License":"Creative Commons BY-SA 2.0","Resources":[{"Description":"Radiosonde Telemetry as JSON blobs of Universal Telemetry format","ARN":"arn:aws:s3:::sondehub-history","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket by serial number](http://sondehub-history.s3-website-us-east-1.amazonaws.com/#serial/)","[Browse Bucket by date/time](http://sondehub-history.s3-website-us-east-1.amazonaws.com/#date/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Loading example notebooks into SageMaker","URL":"https://github.com/projecthorus/pysondehub/blob/main/examples/README.md","AuthorName":"Michaela Wheeler","Services":["Amazon SageMaker"]},{"Title":"Using Athena to read radiosonde data","URL":"https://github.com/projecthorus/pysondehub/blob/main/examples/sondehub-athena.ipynb","AuthorName":"Michaela Wheeler","Services":["Amazon Athena"]},{"Title":"Using pysondehub to read radiosonde data","URL":"https://github.com/projecthorus/pysondehub/blob/main/examples/sondehub.ipynb","AuthorName":"Michaela Wheeler","Services":["Amazon S3"]}],"Tools & Applications":[{"Title":"pysondehub","URL":"https://pypi.org/project/sondehub/","AuthorName":"Sondehub","AuthorURL":"https://github.com/projecthorus/pysondehub"}],"Publications":[{"Title":"STM32 Development Boards (literally) Falling From The Sky (How to submit data)","URL":"https://www.youtube.com/watch?v=YBy-bXEWZeM","AuthorName":"Mark Jessop & Michaela Wheeler"}]},"RegistryEntryAdded":"2021-02-13","RegistryEntryLastModified":"2025-02-04","Slug":"sondehub-telemetry","Sources":["awslabs-open-data-registry"]}
{"Name":"Sophos/ReversingLabs 20 Million malware detection dataset","Description":"A dataset intended to support research on machine learning\ntechniques for detecting malware.  It includes metadata and EMBER-v2\nfeatures for approximately 10 million benign and 10 million malicious\nPortable Executable files, with disarmed but otherwise complete\nfiles for all malware samples.  All samples are labeled using Sophos\nin-house labeling methods, have features extracted using the\nEMBER-v2 feature set, well as metadata obtained via the pefile\npython library, detection counts obtained via ReversingLabs\ntelemetry, and additional behavioral tags that indicate the rough\nbehavior of the samples.\n","Documentation":"https://github.com/sophos-ai/SOREL-20M/blob/master/README.md","Contact":"sorel-dataset@sophos.com","ManagedBy":"Sophos AI","UpdateFrequency":"At most annually","Tags":["aws-pds","cyber security","deep learning","labeled","machine learning"],"License":"See the [Terms of Use](https://github.com/sophos-ai/SOREL-20M/blob/master/Terms%20and%20Conditions%20of%20Use.pdf)","Resources":[{"Description":"Sophos/ReversingLabs 20 million sample dataset","ARN":"arn:aws:s3:::sorel-20m/","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"SOREL-20M quickstart","URL":"https://github.com/sophos-ai/SOREL-20M#quickstart","AuthorName":"Richard Harang","Services":["Amazon S3","Amazon EC2"]}],"Tools & Applications":[{"Title":"SOREL-20M dataset interface code","URL":"https://github.com/sophos-ai/SOREL-20M","AuthorName":"Richard Harang and Ethan M Rudd"}],"Publications":[{"Title":"SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection\n","URL":"https://arxiv.org/abs/2012.07634","AuthorName":"Richard Harang and Ethan M Rudd"}]},"RegistryEntryAdded":"2020-12-17","RegistryEntryLastModified":"2025-02-04","Slug":"sorel-20m","Sources":["awslabs-open-data-registry"]}
{"Name":"Southern California Earthquake Data","Description":"This dataset contains ground motion velocity and acceleration seismic waveforms recorded by the Southern California Seismic Network (SCSN) and archived at the Southern California Earthquake Data Center (SCEDC). A Distributed Acousting Sensing (DAS) dataset is included.","Documentation":"https://scedc.caltech.edu/data/cloud.html","Contact":"scedc@gps.caltech.edu","ManagedBy":"[Southern California Earthquake Data Center](https://scedc.caltech.edu)","UpdateFrequency":"Daily","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aws-pds","earth observation","earthquakes","seismology"],"License":"SCEDC herby grants the non-exclusive, royalty free, non-transferable, worldwide right and license to use, reproduce and publicly display in all media public data from the Southern California Seismic Network. Please cite the SCEDC (doi:10.7909/C3WD3xH1) and SCSN (doi:10.7914/SN/CI) for any research publications using this data.","Resources":[{"Description":"Seismic waveform data (miniSEED format) and earthquake catalog (ascii)","ARN":"arn:aws:s3:::scedc-pds","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Cactus to Clouds: Processing The SCEDC Open Data Set on AWS","URL":"https://nextjournal.com/thclements/cactus-to-clouds/#accessing-the-scedc-dataset-on-aws","AuthorName":"Tim Clements","AuthorURL":"https://tclements.github.io/"},{"Title":"Getting Started with SCEDC AWS Public Dataset","URL":"https://scedc.caltech.edu/data/getstarted-pds.html","AuthorName":"Ellen Yu","AuthorURL":"https://scedc.caltech.edu"},{"Title":"Jupyter Notebook tutorial on Ridgecrest DAS dataset","URL":"https://github.com/SCEDC/cloud/blob/master/pds_ridgecrest_das.ipynb","AuthorName":"Jiuxun Yin"},{"Title":"Script to Download Seismic Waveforms from the SCEDC AWS Public Dataset","URL":"https://github.com/SCEDC/cloud/tree/master/pds-fetch-continuous-data","AuthorName":"Aparna Bhaskaran","AuthorURL":"https://scedc.caltech.edu"},{"Title":"SeisNoise.jl GPU Computing Tutorial - Another example of accessing data s3://scedc-pds for ambient noise cross-correlation","URL":"https://nextjournal.com/thclements/seisnoisejl-gpu-computing-tutorial","AuthorName":"Tim Clements"},{"Title":"Using Amazon API Gateway and Lambda to window and decimate waveforms","URL":"https://github.com/SCEDC/cloud/tree/master/pds-lambda-apis","AuthorName":"Shang-Lin Chen","AuthorURL":"https://scedc.caltech.edu","Services":["AWS Lambda"]},{"Title":"Using Lambda to Process Seismograms","URL":"https://github.com/SCEDC/cloud/tree/master/pds-lambda-example","AuthorName":"Shang-Lin Chen","AuthorURL":"https://scedc.caltech.edu","Services":["AWS Lambda"]}],"Publications":[{"Title":"Southern California Earthquake Data Now Available in the AWS Cloud","URL":"https://pubs.geoscienceworld.org/ssa/srl/article-abstract/doi/10.1785/0220210039/600883/Southern-California-Earthquake-Data-Now-Available","AuthorName":"Ellen Yu; Aparna Bhaskaran; Shang‐Lin Chen; Zachary E. Ross; Egill Hauksson; Robert W. Clayton"},{"Title":"The Seismic Signature of California's Earthquakes, Droughts, and Floods.","URL":"https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022JB025553","AuthorName":"Tim Clements; Marine Denolle"}]},"RegistryEntryAdded":"2019-09-23","RegistryEntryLastModified":"2025-02-04","Slug":"southern-california-earthquakes","Sources":["awslabs-open-data-registry"]}
{"Name":"SpaceNet","Description":"SpaceNet, launched in August 2016 as an open innovation project offering a repository of freely available\nimagery with co-registered map features. Before SpaceNet, computer vision researchers had minimal options\nto obtain free, precision-labeled, and high-resolution satellite imagery. Today, SpaceNet hosts datasets\ndeveloped by its own team, along with data sets from projects like IARPA’s Functional Map of the World (fMoW).\n","Documentation":"https://spacenet.ai/","Contact":"https://spacenet.ai/contact-us/","ManagedBy":"[SpaceNet](https://spacenet.ai/)","UpdateFrequency":"New imagery and features are added quarterly","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","computer vision","disaster response","earth observation","geospatial","machine learning","satellite imagery"],"License":"Various (See [here](https://spacenet.ai/datasets/) for more details)","Resources":[{"Description":"Imagery and metadata in a S3 bucket","ARN":"arn:aws:s3:::spacenet-dataset","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Extracting buildings and roads from AWS Open Data using Amazon SageMaker","URL":"https://aws.amazon.com/blogs/machine-learning/extracting-buildings-and-roads-from-aws-open-data-using-amazon-sagemaker/","AuthorName":"Yunzhi Shi, Tianyu Zhang, and Xin Chen","Services":["Amazon SageMaker"]},{"Title":"Getting Started with SpaceNet Data","URL":"https://medium.com/the-downlinq/getting-started-with-spacenet-data-827fd2ec9f53","AuthorName":"Adam Van Etten","AuthorURL":"https://medium.com/@avanetten"}],"Tools & Applications":[{"Title":"Solaris: an open source Python library for analyzing overhead imagery with machine learning","URL":"https://medium.com/the-downlinq/announcing-solaris-an-open-source-python-library-for-analyzing-overhead-imagery-with-machine-48c1489c29f7","AuthorName":"Nick Weir"}],"Publications":[{"Title":"Accelerating Ukraine Intelligence Analysis with Computer Vision on Synthetic Aperture Radar Imagery","URL":"https://bair.berkeley.edu/blog/2022/03/21/ukraine-sar-maers/","AuthorName":"Ritwik Gupta, Colorado Reed, Anja Rohrbach, and Trevor Darrell"},{"Title":"Creating Training Datasets for the SpaceNet Road Detection and Routing Challenge","URL":"https://medium.com/the-downlinq/creating-training-datasets-for-the-spacenet-road-detection-and-routing-challenge-6f970d413e2f","AuthorName":"Adam Van Etten and Jake Shermeyer","AuthorURL":"https://medium.com/@avanetten"},{"Title":"Deploying the SpaceNet 6 Baseline on AWS","URL":"https://medium.com/the-downlinq/deploying-the-spacenet-6-baseline-on-aws-c811ad82da1","AuthorName":"Adam Van Etten and Nick Weir","AuthorURL":"https://medium.com/@avanetten"},{"Title":"FCAU-Net for the Semantic Segmentation of Fine-Resolution Remotely Sensed Images","URL":"https://doi.org/10.3390/rs14010215","AuthorName":"Xuerui Niu, Qiaolin Zeng, Xiaobo Luo and Liangfu Chen"},{"Title":"Introducing the SpaceNet Off-Nadir Imagery Dataset","URL":"https://medium.com/the-downlinq/introducing-the-spacenet-off-nadir-imagery-and-buildings-dataset-e4a3c1cb4ce3","AuthorName":"David Lindenbaum","AuthorURL":"https://medium.com/@david.lindenbaum"},{"Title":"Introducing the SpaceNet Road Detection and Routing Challenge and Dataset","URL":"https://medium.com/the-downlinq/introducing-the-spacenet-road-detection-and-routing-challenge-and-dataset-7604de39b779","AuthorName":"David Lindenbaum","AuthorURL":"https://medium.com/@david.lindenbaum"},{"Title":"SpaceNet 5 Dataset Release","URL":"https://medium.com/the-downlinq/spacenet-5-dataset-release-80bd82d7c528","AuthorName":"Adam Van Etten and Ryan Lewis","AuthorURL":"https://medium.com/@avanetten"},{"Title":"SpaceNet 6: Dataset Release","URL":"https://medium.com/the-downlinq/spacenet-6-dataset-release-66076c8fb79b","AuthorName":"Jake Shermeyer","AuthorURL":"https://medium.com/@jshermeyer"},{"Title":"SpaceNet 8 - The Detection of Flooded Roads and Buildings","URL":"https://openaccess.thecvf.com/content/CVPR2022W/EarthVision/papers/Hansch_SpaceNet_8_-_The_Detection_of_Flooded_Roads_and_Buildings_CVPRW_2022_paper.pdf","AuthorName":"Ronny Hansch, Jacob Arndt, Dalton Lunga, Matthew Gibb, Tyler Pedelose, Arnold Boedihardjo, Desiree Petrie, Todd M. Bacastow"},{"Title":"SpaceNet: Winning Implementations and New Imagery Release","URL":"https://medium.com/the-downlinq/spacenet-winning-implementations-and-new-imagery-release-55f738e14253","AuthorName":"Todd Stavish","AuthorURL":"https://medium.com/@toddstavish"},{"Title":"The SpaceNet 7 Multi-Temporal Urban Development Challenge: Dataset Release","URL":"https://medium.com/the-downlinq/the-spacenet-7-multi-temporal-urban-development-challenge-dataset-release-9e6e5f65c8d5","AuthorName":"Adam Van Etten","AuthorURL":"https://medium.com/@avanetten"},{"Title":"The SpaceNet 8 Flood Detection Challenge: Dataset and Algorithmic Baseline Release","URL":"https://medium.com/@SpaceNet_Project/the-spacenet-8-flood-detection-challenge-dataset-and-algorithmic-baseline-release-e0c9f5a44154","AuthorName":"SpaceNet","AuthorURL":"https://medium.com/@SpaceNet_Project"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2025-02-04","Slug":"spacenet","Sources":["awslabs-open-data-registry"]}
{"Name":"SPARC: Datasets bridging the body and the brain","Description":"The SPARC Datasets comprise a collection of scientific data that is focused\non bridging the body and the brain. The datasets focus on neural connectivity,\norgan innervation and detailed anatomical mapping of the peripheral nervous system.\nSPARC datasets distinguish themselves from other data resources through its multi-modal\napproach to scientific data and integrates molecular, imaging, timeseries and other\ndatatypes associated with the interaction between the peripheral nervous system and \norgans. SPARC data provides a unique integrated effort to develop next generation mapping\nof anatomical and functional connectivity of the nervous system.\n","Documentation":"https://docs.sparc.science","Contact":"joostw@seas.upenn.edu, support@sparc.science","ManagedBy":"[The SPARC Data and Resource Center](https://sparc.science/about)","UpdateFrequency":"Continually adding new datasets and releasing versions of datasets","Tags":["bioinformatics","electrophysiology","life sciences","microscopy","neurophysiology","neuroscience"],"License":"CC-BY-4.0","Resources":[{"Description":"SPARC Public Datasets S3 Bucket","ARN":"arn:aws:s3:::sparc-prod-aod-discover-publish50-use1","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[SPARC Datasets](https://sparc.science/data?type=dataset)"]}],"DataAtWork":{"Tutorials":[{"Title":"Download public data, scaffolds and run computations","URL":"https://docs.sparc.science/docs/getting-started-with-the-sparc-python-client","AuthorName":"The SPARC Data and Resource Center"},{"Title":"Downloading large scale SPARC datasets","URL":"https://docs.sparc.science/recipes","AuthorName":"The SPARC Data and Resource Center"},{"Title":"Explore SPARC Data in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/420af15a-399d-5041-8bde-85ff7b7eea60/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"},{"Title":"Using sparc.client for data movement in SPARC","URL":"https://docs.sparc.science/docs/tutorial-using-sparcclient-for-data-movement-in-sparc","AuthorName":"The SPARC Data and Resource Center"}],"Tools & Applications":[{"Title":"OSPARC","URL":"https://osparc.io/","AuthorName":"Esra Neufeld","AuthorURL":"https://osparc.io/"},{"Title":"SODA Tool","URL":"https://docs.sodaforsparc.io/","AuthorName":"Bhavesh Patel","AuthorURL":"https://fairdataihub.org/"},{"Title":"The Pennsieve Data Management Platform","URL":"https://app.pennsieve.io","AuthorName":"Joost Wagenaar","AuthorURL":"https://discover.pennsieve.io/about/team"},{"Title":"The SPARC Portal","URL":"https://sparc.science","AuthorName":"Peter Hunter, Maryann Martone, Esra Neufeld, Joost Wagenaar","AuthorURL":"https://sparc.science"}],"Publications":[{"Title":"The SPARC DRC: Building a Resource for the Autonomic Nervous System Community","URL":"https://pubmed.ncbi.nlm.nih.gov/34248680/","AuthorName":"Osanlouy M, Bandrowski A, de Bono B, Brooks D, Cassara A, Christie R, Ebrahimi N, Gillespie T, Grethe J, Guercio L, Heal M, Lin M, Kuster N, Martone M, Neufeld E, Nickerson D, Soltani E, Tappan S, Wagenaar J, Zhuang K, Hunter P"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2024-10-16","RegistryEntryLastModified":"2026-11-03","Slug":"sparc","Sources":["awslabs-open-data-registry"]}
{"Name":"SPARTAN Data","Description":"SPARTAN (Surface PARTiculate mAtter Network) measures and provides surface ambient particulate matter (PM2.5 and PM10) concentration and the chemical composition around the world, with the purpose of connecting ground-based PM2.5 and satellite remote sensing.","Documentation":"https://www.spartan-network.org/data","Contact":"SPARTAN.PM25@gmail.com","UpdateFrequency":"New measurement or estimation products will be added when available, usually multiple times a month.","ManagedBy":"The [Atmospheric Composition Analysis Group](https://sites.wustl.edu/acag/)","Collabs":{"ASDI":{"Tags":["air quality"]}},"Tags":["air quality","aws-pds","environmental"],"License":"SPARTAN data is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/?ref=chooser-v1)","Resources":[{"Description":"All data products (PM2.5, aerosol chemical components, scattering) provided by SPARTAN.","ARN":"arn:aws:s3:::spartan-cloud","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Tutorial on using SPARTAN data on AWS","URL":"https://spartan-cloud.readthedocs.io/en/latest/index.html","AuthorName":"Haihui Zhu","AuthorURL":"https://github.com/haihui-zhu"}],"Publications":[{"Title":"A Global-Scale Mineral Dust Equation","URL":"https://doi.org/10.1029/2022JD036937","AuthorName":"Xuan Liu, et al."},{"Title":"Elemental Characterization of Ambient Particulate Matter for a Globally Distributed Monitoring Network: Methodology and Implications","URL":"https://pubs.acs.org/doi/10.1021/acsestair.3c00069","AuthorName":"Xuan Liu, et al."},{"Title":"SPARTAN: A global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications","URL":"https://doi.org/10.5194/amt-8-505-2015","AuthorName":"Graydon Snider, et al."},{"Title":"Variation in global chemical composition of PM2.5: emerging results from SPARTAN","URL":"https://doi.org/10.5194/acp-16-9629-2016","AuthorName":"Graydon Snider, et al."}]},"RegistryEntryAdded":"2024-07-21","RegistryEntryLastModified":"2024-10-15","Slug":"spartan-cloud","Sources":["awslabs-open-data-registry"]}
{"Name":"Geosnap Data, Center for Geospatial Sciences","Description":"This bucket contains multiple datasets (as Quilt packages) created by the\nCenter for Geospatial Sciences (CGS) at the University of California-Riverside.\nThe data in this bucket contains the following:\n\n1) Tabular and geographic data from the US Census\n2) Land Cover imagery collected from [Multi-Resolution Land Characteristics Consortium](https://www.mrlc.gov/)\n3) Road network data processed from OpenStreetMap\n","Contact":"Eli Knaap <knaap@ucr.edu>","Documentation":"https://spatialucr.github.io/geosnap-guide/content/home","ManagedBy":"[UCR Center for Geospatial Sciences](https://spatial.ucr.edu)","UpdateFrequency":"Annually","Collabs":{"ASDI":{"Tags":["socioeconomic"]}},"Tags":["aws-pds","demographics","geospatial","urban"],"License":"BSD","Resources":[{"Description":"Data files stored as Apache parquet and GeoTiff in a public bucket","ARN":"arn:aws:s3:::spatial-ucr","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Geosnap User Guide","URL":"https://spatialucr.github.io/geosnap-guide/content/home","AuthorName":"Eli Knaap","AuthorURL":"https://knaaptime.com"}]},"RegistryEntryAdded":"2020-03-11","RegistryEntryLastModified":"2021-12-28","Slug":"spatial-ucr","Sources":["awslabs-open-data-registry"]}
{"Name":"Spatiam Corporation National Lab Research Announcement International Space Station  Technology Demonstration","Description":"Experiment data for the Spatiam DTN Network Platform Technology demonstration carried to test a Delay and Disruption Tolerant Network between the ISS and Earth.","Documentation":"https://spatiam-nlra-iss-experiment-open-data.s3.amazonaws.com/README.md","Contact":"info@spatiam.com","ManagedBy":"[Spatiam Corporation](https://www.spatiam.com/)","UpdateFrequency":"No updates envisioned after the conclusion of the experiment.","Tags":["network traffic","telecommunications"],"License":"There are no restrictions on the use of this data.","Citation":null,"Resources":[{"Description":"Spatiam Corporation NLRA ISS Technology Demonstration archive data","ARN":"arn:aws:s3:::spatiam-nlra-iss-experiment-open-data","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://spatiam-nlra-iss-experiment-open-data.s3.amazonaws.com/index.html)"]}],"RegistryEntryAdded":"2024-08-09","RegistryEntryLastModified":"2024-08-09","Slug":"spatiam-nlra-iss-experiments","Sources":["awslabs-open-data-registry"]}
{"Name":"Speedtest by Ookla Global Fixed and Mobile Network Performance Maps","Description":"Global fixed broadband and mobile (cellular) network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is provided in both Shapefile format as well as Apache Parquet with geometries represented in Well Known Text (WKT) projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy.\n","Documentation":"[Performance Maps Overview](https://github.com/teamookla/ookla-open-data)","Contact":"opendata@ookla.com","ManagedBy":"[Ookla](https://www.ookla.com/ookla-for-good)","UpdateFrequency":"Quarterly","Collabs":{"ASDI":{"Tags":["infrastructure"]}},"Tags":["analytics","aws-pds","broadband","cities","civic","disaster response","geospatial","global","government spending","infrastructure","internet","mapping","network traffic","parquet","regulatory","telecommunications","tiles"],"License":"\"[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)\"\n","Citation":"Speedtest® by Ookla® Global Fixed and Mobile Network Performance Maps.\nBased on analysis by Ookla of Speedtest Intelligence® data for [DATA TIME PERIOD].\nProvided by Ookla and accessed [DAY MONTH YEAR].\nOokla trademarks used under license and reprinted with permission.\n","Resources":[{"Description":"Parquet and Shapefiles","ARN":"arn:aws:s3:::ookla-open-data","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Bootstrapping Dask on 1000 cores with AWS Fargate","URL":"https://towardsdatascience.com/bootstrapping-dask-on-1000-cores-with-aws-fargate-1e1b7fd1a32c","AuthorName":"Imri Paran","Services":["AWS Fargate","Amazon EKS"]},{"Title":"How to deliver performant GIS desktop applications with Amazon AppStream 2.0","URL":"https://aws.amazon.com/blogs/publicsector/how-to-deliver-performant-gis-desktop-applications-amazon-appstream-2-0/","AuthorName":"Ethan Fahy and Spencer DeBrosse","Services":["Amazon AppStream 2.0","Amazon RDS"]},{"Title":"Launching Lonboard - A Python library for extremely fast geospatial vector data visualization in Jupyter","URL":"https://developmentseed.org/blog/2023-10-23-lonboard","AuthorName":"Kyle Barron","AuthorURL":"https://developmentseed.org/team/kyle-barron"},{"Title":"New Year, Great Data: The Best Ookla Open Data Projects We’ve Seen So Far","URL":"https://www.speedtest.net/insights/blog/best-ookla-open-data-projects-2021/","AuthorName":"Katie Jolly"}]},"RegistryEntryAdded":"2020-09-30","RegistryEntryLastModified":"2025-07-09","Slug":"speedtest-global-performance","Sources":["awslabs-open-data-registry"]}
{"Name":"SPHEREx Quick Release (QR): An All-Sky Spectral Survey","Description":"The Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) is a NASA Astrophysics Medium-class Explorer (MIDEX) mission launched in March 2025. During its planned two-year mission, SPHEREx will perform the first ever all-sky spectral survey in the optical to near-infrared (0.75-5 microns). SPHEREx data will be used to probe inflation and the early universe, trace the history of galactic light production, and investigate the origin of planetary systems and biogenic ices, in addition to contributing to many other astrophysics research topics. IRSA began releasing SPHEREx QR2 data on a weekly basis in October 2025. QR2 features substantially improved calibrations and supersedes QR1.","Documentation":"https://irsa.ipac.caltech.edu/Missions/spherex.html","Contact":"https://irsa.ipac.caltech.edu/docs/help_desk.html","ManagedBy":"NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech","UpdateFrequency":"SPHEREx QR is updated weekly. The data may also be presented in new ways as the products become available.","Tags":["astronomy","aws-pds","imaging","object detection","satellite imagery","survey"],"License":"https://irsa.ipac.caltech.edu/data_use_terms.html","Citation":"If you use SPHEREx data from the IRSA archive, please cite the appropriate Digital Object Identifier: [10.26131/IRSA629](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA629) for QR1 or [10.26131/IRSA652](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA652) for QR2, include the following acknowledgement: \"This publication makes use of data products from the Spectro-Photometer for the History of the Universe, Epoch of Reionization and Ices Explorer (SPHEREx), which is a joint project of the Jet Propulsion Laboratory and the California Institute of Technology, and is funded by the National Aeronautics and Space Administration.\", and follow the [IRSA acknowledgement guidelines](https://irsa.ipac.caltech.edu/ack.html).","Resources":[{"Description":"QR2 Spectral Images: Calibrated Spectral Images plus per-pixel status and processing flags, variance map, zodiacal model, exposure-averaged PSF, and wavelength WCS. Multi-extension FITS format.","ARN":"arn:aws:s3:::nasa-irsa-spherex/qr2/level2","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"QR2 Absolute Gain Matrix: Pixel-to-pixel gain variations within a single spectral channel and relative gain differences across channels. FITS format.","ARN":"arn:aws:s3:::nasa-irsa-spherex/qr2/abs_gain_matrix","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"QR2 Exposure-Averaged PSF: Wavelength-dependent point spread function (PSF) estimates on a fine positional grid across each detector. FITS format.","ARN":"arn:aws:s3:::nasa-irsa-spherex/qr2/average_psf","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"QR2 Dark Current: Per pixel dark current. FITS format.","ARN":"arn:aws:s3:::nasa-irsa-spherex/qr2/dark","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"QR2 Dichroic: Map of pixels affected by flux attenuation due to the dichroic filter. FITS format.","ARN":"arn:aws:s3:::nasa-irsa-spherex/qr2/dichroic","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"QR2 Gain Factors: Gain factors for each detector. YAML format.","ARN":"arn:aws:s3:::nasa-irsa-spherex/qr2/gain_factors","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"QR2 Non-functional Pixel Map: Map of permanently non-functioning pixels. FITS format.","ARN":"arn:aws:s3:::nasa-irsa-spherex/qr2/nonfunc","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"QR2 Non-Linearity Correction: Corrections applied to compensate for detector non-linearity due to gain degradation. FITS format.","ARN":"arn:aws:s3:::nasa-irsa-spherex/qr2/nonlinear_pars","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"QR2 Readout Noise: Per-detector read noise maps. FITS format.","ARN":"arn:aws:s3:::nasa-irsa-spherex/qr2/readnoise_pars","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"QR2 Solid Angle Pixel Map: Per-detector measure of the solid angle per pixel in units of squared arcsec. FITS format.","ARN":"arn:aws:s3:::nasa-irsa-spherex/qr2/solid_angle_pixel_map","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"QR2 Spectral WCS Map: Detailed World Coordinate System (WCS) map. FITS format.","ARN":"arn:aws:s3:::nasa-irsa-spherex/qr2/spectral_wcs","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"SPHEREx Quick Release 1 (QR1): IRSA began releasing SPHEREx QR1 data on a weekly basis in July 2025. QR1 is superseded by QR2 and only available through January 2026. The data products and their organization in the bucket are described in the [SPHEREx Archive at IRSA User Guide](https://caltech-ipac.github.io/spherex-archive-documentation/).","ARN":"arn:aws:s3:::nasa-irsa-spherex/qr","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false}],"DataAtWork":{"Tutorials":[{"Title":"Notebook Tutorials","URL":"https://irsa.ipac.caltech.edu/docs/notebooks/#accessing-spherex-data","AuthorName":"Caltech/IPAC-IRSA","AuthorURL":"https://irsa.ipac.caltech.edu"}]},"RegistryEntryAdded":"2025-07-01","RegistryEntryLastModified":"2025-10-23","Slug":"spherex-qr","Sources":["awslabs-open-data-registry"]}
{"Name":"Spitzer Enhanced Imaging Products (SEIP) Super Mosaics","Description":"Spitzer was an infrared astronomy space telescope with imaging from 3 to 160 microns and spectroscopy from 5 to 37 microns, launched into an Earth-trailing solar orbit as the last of NASA's Great Observatories. The SEIP Super Mosaics include data from the four channels of IRAC (3.6, 4.5, 5.8, 8 microns) and the 24 micron channel of MIPS. Data from multiple programs are combined where appropriate. Cryogenic Release v3.0 includes Spitzer data taken during commissioning and cryogenic operations, including calibration data.","Documentation":"https://irsa.ipac.caltech.edu/data/SPITZER/Enhanced/SEIP/overview.html","Contact":"https://irsa.ipac.caltech.edu/docs/help_desk.html","ManagedBy":"NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech","UpdateFrequency":"This data set may be updated once or twice in the future.","Tags":["astronomy","aws-pds","imaging","satellite imagery","survey"],"License":"https://irsa.ipac.caltech.edu/data_use_terms.html","Citation":"If you use the SEIP Super Mosaics please cite the Digital Object Identifier (DOI): [10.26131/IRSA433](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA433). In addition, follow the [Spitzer acknowledgement guidelines](https://irsa.ipac.caltech.edu/data/SPITZER/docs/spitzermission/publications/ackn/) and the [IRSA acknowledgement guidelines](https://irsa.ipac.caltech.edu/ack.html).","Resources":[{"Description":"SEIP Super Mosaics: 3.6, 4.5, 5.8, 8, and 24 micron mean and median mosaics with corresponding uncertainty, coverage, and mask maps in Flexible Image Transport System (FITS) format.","ARN":"arn:aws:s3:::nasa-irsa-spitzer/spitzer/seip","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false}],"DataAtWork":{"Tutorials":[{"Title":"Notebook Tutorials","URL":"https://irsa.ipac.caltech.edu/docs/notebooks/","AuthorName":"Caltech/IPAC-IRSA","AuthorURL":"https://irsa.ipac.caltech.edu"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2023-08-17","RegistryEntryLastModified":"2024-03-26","Slug":"spitzer-seip","Sources":["awslabs-open-data-registry"]}
{"Name":"SSL4EO S12 Landsat Multi Product Dataset","Description":"This dataset combines SSL4EO-S12 and SSL4EO-L to create a multi-view dataset for multi-modal fusion using self-supervised learning for earth observation.","Documentation":"https://github.com/sunny1401/ssl4eo_multi_satellite_products","Contact":"https://github.com/sunny1401/ssl4eo_multi_satellite_products","ManagedBy":"Sankranti Joshi","UpdateFrequency":"Not updated","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["satellite imagery"],"License":"https://creativecommons.org/licenses/by-nc-sa/4.0/","Resources":[{"Description":"Satellite imagery and context from Sentinel-12 and Landsat 4-5, 7, 8-9.","ARN":"arn:aws:s3:::ssl4eo-s12-landsat-combined","Region":"us-west-2","Type":"S3 Bucket"}],"ADXCategories":["Public Sector Data"],"DataAtWork":{"Publications":[{"Title":"SSL4EO-L - Datasets and Foundation Models for Landsat Imagery","URL":"https://proceedings.neurips.cc/paper_files/paper/2023/file/bbf7ee04e2aefec136ecf60e346c2e61-Paper-Datasets_and_Benchmarks.pdf","AuthorName":"Adam J. Stewart, Nils Lehmann, Isaac A. Corley, Yi Wang, Yi-Chia Chang, Nassim Ait Ali Braham, Shradha Sehgal, Caleb Robinson, Arindam Banerjee","AuthorURL":"https://github.com/adamjstewart"},{"Title":"SSL4EO-S12 - A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation","URL":"https://ieeexplore.ieee.org/abstract/document/7989594","AuthorName":"Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong, Chenying Liu, Conrad M Albrecht, Xiao Xiang Zhu","AuthorURL":"https://scholar.google.de/citations?user=rEADuMEAAAAJ&hl=en"}]},"RegistryEntryAdded":"2024-09-23","RegistryEntryLastModified":"2025-07-09","Slug":"ssl4eo-multi-product-data","Sources":["awslabs-open-data-registry"]}
{"Name":"SpaceEye-T VVHR EO Open Data","Description":"SpaceEye-T satellite collects the highest resolution optical imagery among the commercial satellites, 25 cm resolution. The Open Data features various satellite images around the world for end users to experience the power of VVHR optical data.\n","Documentation":"https://www.si-imaging.com/page/72?sca=SpaceEye-T","Contact":"https://www.si-imaging.com","UpdateFrequency":"The dataset is frequently updated. The frequent updates include the time-series data for regular monitoring, and the data for disaster management. SI Imaging wants to provide the user expierence on what is possible with VVHR optical satellite data. If you have a suggestion for a new location, feedback on the dataset, or any questions, contact us.","Tags":["aws-pds","disaster response","earth observation","geospatial","image processing","satellite imagery"],"License":"Creative Commons Attribution 4.0 International (CC BY 4.0).\nFor more information, See the document \"ST-1 Product Terms of Use\" at [our Terms of Use webpage](https://si-imaging.com/page/73)\n","Citation":"When publicly post of ST products Open Data unedited, \"SpaceEye-T © [Year] Satrec Initiative (Licensed under CC BY 4.0)\".\nWhen publicly post derivative data created by using ST products Open Data, \"SpaceEye-T-derived data © [Year] Satrec Initiative (Originally licensed under CC BY 4.0)\".\n","ManagedBy":"[SI Imaging Services](https://www.si-imaging.com/)","Resources":[{"Description":"SpaceEye-T Imagery Collection","ARN":"arn:aws:s3:::st-vvhr-opendata","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](http://st-vvhr-opendata.s3-website.us-west-2.amazonaws.com/)"]},{"Description":"Notifications for new SpaceEye-T VVHR EO Open data","ARN":"arn:aws:sns:us-west-2:348881531141:st-vvhr-opendata-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Publications":[{"Title":"SpaceEye-T Data Manual","URL":"https://www.si-imaging.com/page/72?sca=SpaceEye-T","AuthorName":"SI-Imaging Services"}]},"ADXCategories":["Resources Data"],"RegistryEntryAdded":"2025-09-23","RegistryEntryLastModified":"2025-10-10","Slug":"st-open-data","Sources":["awslabs-open-data-registry"]}
{"Name":"MIT-BIH ST Change Database","Description":"This database includes 28 ECG recordings of varying lengths, most of which were recorded during exercise stress tests and which exhibit transient ST depression. The last five records (323 through 327) are excerpts of long-term ECG recordings and exhibit ST elevation.","Documentation":"https://doi.org/10.13026/C2ZW2H","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2ZW2H","ARN":"arn:aws:s3:::physionet-open/stdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"stdb","Sources":["awslabs-open-data-registry"]}
{"Name":"stdpopsim species resources","Description":"Contains all resources (genome specifications, recombination maps, etc.) required for species specific simulation with the stdpopsim package. These resources are originally from a variety of other consortium and published work but are consolidated here for ease of access and use. If you are interested in adding a new species to the stdpopsim resource please raise an issue on the stdpopsim GitHub page to have the necessary files added here.","Documentation":"https://stdpopsim.readthedocs.io/en/latest/catalog.html","Contact":"https://github.com/popsim-consortium/stdpopsim/issues","ManagedBy":"Andrew Kern & Jerome Kelleher","UpdateFrequency":"Data will be added as new species, genome assemblies, and genetic map data for already included species become available.","Collabs":{"ASDI":{"Tags":["biodiversity"]}},"Tags":["aws-pds","genetic maps","life sciences","population genetics","recombination maps","simulations"],"License":"Please see the individual datasets compiled here for licensing details and make sure to cite the original sources of any elements of this data that you use.","Resources":[{"Description":"https://stdpopsim.readthedocs.io/en/latest/","ARN":"arn:aws:s3:::stdpopsim","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2020-03-28","RegistryEntryLastModified":"2025-07-09","Slug":"stdpopsim_kern","Sources":["awslabs-open-data-registry"]}
{"Name":"Steinegger Lab Datasets","Description":"The Steinegger Lab Dataset comprises biological databases and resources critical for protein sequence and structure analysis, developed to support ColabFold, MMseqs2, and Foldseek/Foldcomp—three high-performance computational tools widely used in bioinformatics.\n\nThe MMseqs2 dataset serves as the backbone for our fast structure prediction tool, ColabFold, and includes UniRef30, BFD, and the ColabFold environmental databases.\nThese datasets are specifically designed for the rapid generation of multiple sequence alignments (MSAs), which are essential for high-accuracy structure prediction.\nBeyond MSA generation, these resources allow for fast taxonomy annotations and functional annotation, supporting a wide range of bioinformatics applications.\n\nThe Foldseek dataset includes preprocessed databases such as the AlphaFold Database (AFDB), PDB, SwissProt, and CATH, specifically designed for protein structure similarity searches.\nThese datasets encompass the majority of both experimental and predicted structural resources, supporting analyses for monomers and multimers alike.\n","Documentation":"For the MMseqs2/ColabFold dataset, please see https://colabfold.mmseqs.com\nFor the Foldseek dataset, please see https://search.foldseek.com\n","Contact":"martin.steinegger@snu.ac.kr","ManagedBy":"[Steinegger Lab, Seoul National University](https://steineggerlab.com)","UpdateFrequency":"Occasionally, where new data is available","Tags":["aws-pds","bioinformatics","life sciences","metagenomics","open source software","protein","protein folding"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","Citation":"If you’re using MMseqs2, please cite:\n“Steinegger M and Söding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology (2017), doi: [10.1038/nbt.3988](https://www.nature.com/articles/nbt.3988)\nIf you're using Foldseek, please cite:\n\"van Kempen M, Kim S, Tumescheit C, Mirdita M, Lee J, Gilchrist CLM, Söding J, and Steinegger M. Fast and accurate protein structure search with Foldseek. Nature Biotechnology (2023), doi:[10.1038/s41587-023-01773-0](https://www.nature.com/articles/s41587-023-01773-0)\"\nIf you're using ColabFold, please cite:\n\"Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S and Steinegger M. ColabFold: Making protein folding accessible to all. Nature Methods (2022) doi: [10.1038/s41592-022-01488-1](https://www.nature.com/articles/s41592-022-01488-1)\"\n","Resources":[{"Description":"Steinegger Lab Datasets","ARN":"arn:aws:s3:::steineggerlab","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"ColabFold: Making protein folding accessible to all","URL":"https://www.nature.com/articles/s41592-022-01488-1","AuthorName":"Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S and Steinegger M"},{"Title":"Fast and accurate protein structure search with Foldseek","URL":"https://www.nature.com/articles/s41587-023-01773-0","AuthorName":"van Kempen M, Kim S, Tumescheit C, Mirdita M, Lee J, Gilchrist CLM, et al."},{"Title":"MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets","URL":"https://www.nature.com/articles/nbt.3988","AuthorName":"Steinegger M and Söding J"}],"Tools & Applications":[{"Title":"ColabFold Google Colab Notebook","URL":"https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb","AuthorName":"Ovchinnikov S, Mirdita M and Steinegger M"},{"Title":"Foldseek Search Server","URL":"https://search.foldseek.com/search","AuthorName":"van Kempen M, Kim S, Tumescheit C, Mirdita M, Lee J, Gilchrist CLM, et al."},{"Title":"Run ColabFold on your local computer","URL":"https://github.com/YoshitakaMo/localcolabfold","AuthorName":"Moriwaki Y"}],"Tutorials":[{"Title":"ColabFold Tutorial","URL":"https://docs.google.com/presentation/d/1mnffk23ev2QMDzGZ5w1skXEadTe54l8-Uei6ACce8eI","AuthorName":"Ovchinnikov S, Mirdita M and Steinegger M"},{"Title":"ColabFold User Guide","URL":"https://github.com/sokrypton/ColabFold/wiki","AuthorName":"Mirdita M and Ovchinnikov S"},{"Title":"Foldseek User Guide","URL":"https://github.com/steineggerlab/foldseek/wiki","AuthorName":"Mirdita M and Steinegger M"}]},"RegistryEntryAdded":"2025-02-25","RegistryEntryLastModified":"2025-05-01","Slug":"steineggerlab","Sources":["awslabs-open-data-registry"]}
{"Name":"STOIC2021 Training","Description":"The STOIC project collected Computed Tomography (CT) images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence, based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This S3 bucket contains the training sample of the STOIC dataset as used in the STOIC2021 challenge on grand-challenge.org.","Documentation":"https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384","Contact":"support@grand-challenge.org","ManagedBy":"Radboud University Medical Center","UpdateFrequency":"The full training set was published at the release.","Tags":["aws-pds","computed tomography","computer vision","coronavirus","COVID-19","grand-challenge.org","imaging","life sciences","SARS-CoV-2"],"License":"CC-BY-NC 4.0","Citation":"STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.","Resources":[{"Description":"The data set contains 2000 CT scans stored as compressed .mha files. Each file corresponds to a unique patient. the reference.csv file contains the reference labels for COVID-19 presence and severity, indexed by patient ID.","ARN":"arn:aws:s3:::stoic2021-training","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"STOIC2021 Challenge","URL":"https://stoic2021.grand-challenge.org/","AuthorName":"Diagnostic Image Analysis Group, Radboudumc, Nijmegen","AuthorURL":"https://www.diagnijmegen.nl/"}],"Publications":[{"Title":"How Well Do Self-Supervised Models Transfer to Medical Imaging?","URL":"https://www.mdpi.com/2313-433X/8/12/320","AuthorName":"Anton J, Castelli L, Chan MF, Outthers M, Tang WH, Cheung V, et al."},{"Title":"Study of Thoracic CT in COVID-19: The STOIC Project","URL":"https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384","AuthorName":"Revel MP, Boussouar S, de Margerie-Mellon C, Saab I, Lapotre T, Mompoint D, et al.","AuthorURL":"https://www.aphp.fr/"}]},"RegistryEntryAdded":"2021-11-30","RegistryEntryLastModified":"2022-12-30","Slug":"stoic2021-training","Sources":["awslabs-open-data-registry"]}
{"Name":"SUCHO Ukrainian Cultural Heritage Web Archives","Description":"The dataset contains web archives of Open Access collections of digitised cultural heritage from more than 3,000+ websites of Ukrainian cultural institutions, such as museums, libraries or archives. The web archives have been produced by SUCHO, which is a volunteer group of more than 1,300 international cultural heritage professionals – librarians, archivists, researchers, programmers - who have joined forces to save as much digitised cultural heritage during the 2022 invasion of Ukraine before the servers hosting them get destroyed, damaged or go offline for any other reason. The web archives were created using the tools of the Webrecorder Open Source project in the open WACZ format: https://webrecorder.github.io/wacz-spec/1.1.1/. WACZ files are zipped containers of WARC (Web Archive Format) files enriched with metadata, which can contain several crawls in a single file. The file sizes can range from a few MBs to several TBs.\n","Documentation":"https://www.sucho.org/tutorials","Contact":"info@sucho.org","ManagedBy":"Saving Ukrainian Cultural Heritage Online (SUCHO)","UpdateFrequency":"Periodically","Tags":["aws-pds","cultural preservation","internet","ukraine"],"License":"Public Domain (CC0)","Resources":[{"Description":"WACZ archives","ARN":"arn:aws:s3:::sucho-opendata","Region":"eu-central-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2022-04-07","RegistryEntryLastModified":"2022-08-16","Slug":"sucho","Sources":["awslabs-open-data-registry"]}
{"Name":"Sudachi Language Resources","Description":"Japanese dictionaries and pre-trained models (word embeddings and language models) for natural language processing.\n[SudachiDict](https://github.com/WorksApplications/SudachiDict) is the dictionary for a Japanese tokenizer (morphological analyzer) [Sudachi](https://github.com/WorksApplications/Sudachi).\n[chiVe](https://github.com/WorksApplications/chiVe) is Japanese pretrained word embeddings (word vectors), trained using the ultra-large-scale web corpus NWJC by National Institute for Japanese Language and Linguistics, analyzed by Sudachi.\n[chiTra](https://github.com/WorksApplications/SudachiTra) is a library for using large-scale pre-trained language models with the Japanese tokenizer SudachiPy.\n","Documentation":"https://worksapplications.github.io/Sudachi/","Contact":"sudachi@worksap.co.jp","ManagedBy":"[Works Applications](https://www.worksap.co.jp/about/csr/nlp/)","UpdateFrequency":"The dictionaries are updated every few months to include neologism and fixes for the existing words.","Tags":["aws-pds","natural language processing"],"License":"Apache-2.0","Resources":[{"Description":"SudachiDict: Binary format of the mophological analysis dictionaries\nchiVe: Pretrained word embedding in various formats\n","ARN":"arn:aws:s3:::sudachi","Region":"ap-northeast-1","Type":"S3 Bucket"},{"Description":"Cloudfront CDN mirror","Host":"d2ej7fkh96fzlu.cloudfront.net","Region":"ap-northeast-1","Type":"CloudFront Distribution"}],"DataAtWork":{"Tutorials":[{"Title":"analysis-sudachi Tutorial","URL":"https://github.com/WorksApplications/elasticsearch-sudachi/blob/develop/docs/tutorial.md","AuthorName":"Works Applications"},{"Title":"chiTra Tutorial","URL":"https://github.com/WorksApplications/SudachiTra/blob/main/README.md","AuthorName":"Works Applications"},{"Title":"chiVe Tutorial","URL":"https://github.com/WorksApplications/chiVe/blob/master/docs/tutorial.md","AuthorName":"Works Applications"},{"Title":"Sudachi Tutorial","URL":"https://github.com/WorksApplications/Sudachi/blob/develop/docs/tutorial.md","AuthorName":"Works Applications"},{"Title":"SudachiPy Tutorial","URL":"https://github.com/WorksApplications/SudachiPy/blob/develop/docs/tutorial.md","AuthorName":"Works Applications"}],"Tools & Applications":[{"Title":"analysis-sudachi: Sudachi pluglin for Elasticsearch","URL":"https://github.com/WorksApplications/elasticsearch-sudachi","AuthorName":"Works Applications"},{"Title":"chiTra: SudachiPy for hugging face Transformers","URL":"https://github.com/WorksApplications/SudachiTra","AuthorName":"Works Applications"},{"Title":"jdartsclone: TRIE Data Structure using Double-Array","URL":"https://github.com/WorksApplications/jdartsclone","AuthorName":"Works Applications"},{"Title":"Kintoki: Dependency Parser","URL":"https://github.com/WorksApplications/kintoki","AuthorName":"Works Applications"},{"Title":"Sudachi: Japanese Tokenizer for Business","URL":"https://github.com/WorksApplications/Sudachi","AuthorName":"Works Applications"},{"Title":"sudachidict_core on pypi.python.org - a Python module to download and install SudachiDict for the python tokenizer","URL":"https://pypi.org/project/SudachiDict-core/","AuthorName":"Works Applications"},{"Title":"sudachidict_full on pypi.python.org - a Python module to download and install SudachiDict for the python tokenizer","URL":"https://pypi.org/project/SudachiDict-full/","AuthorName":"Works Applications"},{"Title":"sudachidict_small on pypi.python.org - a Python module to download and install SudachiDict for the python tokenizer","URL":"https://pypi.org/project/SudachiDict-small/","AuthorName":"Works Applications"},{"Title":"SudachiPy: Python version of Sudachi","URL":"https://github.com/WorksApplications/Sudachi","AuthorName":"Works Applications"}],"Publications":[{"Title":"chiVe 2.0: SudachiとNWJCを用いた実用的な日本語単語ベクトルの実現に向けて","URL":"https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P6-16.pdf","AuthorName":"河村宗一郎, 久本空海, 真鍋陽俊, 髙岡一馬, 内田佳孝, 岡照晃, 浅原正幸"},{"Title":"chiVe: 製品利用可能な日本語単語ベクトル資源の実現へ向けて ～形態素解析器Sudachiと超大規模ウェブコーパスNWJCによる分散表現の獲得と改良～","URL":"https://www.ieice.org/ken/paper/20200910U1zQ/","AuthorName":"久本空海, 山村崇, 勝田哲弘, 竹林佑斗, 髙岡一馬, 内田佳孝, 岡照晃, 浅原正幸"},{"Title":"Sudachi: a Japanese Tokenizer for Business","URL":"http://www.lrec-conf.org/proceedings/lrec2018/summaries/8884.html","AuthorName":"Kazuma Takaoka, Sorami Hisamoto, Noriko Kawahara, Miho Sakamoto, Yoshitaka Uchida, Yuji Matsumoto"},{"Title":"形態素解析器『Sudachi』のための大規模辞書開発","URL":"https://pj.ninjal.ac.jp/corpus_center/lrw/lrw2018/P-1-08.pdf","AuthorName":"坂本美保, 川原典子, 久本空海, 髙岡一馬, 内田佳孝"},{"Title":"複数粒度の分割結果に基づく日本語単語分散表現","URL":"https://www.anlp.jp/proceedings/annual_meeting/2019/pdf_dir/P8-5.pdf","AuthorName":"真鍋陽俊, 岡照晃, 海川祥毅, 髙岡一馬, 内田佳孝, 浅原正幸"},{"Title":"詳細化した同義関係をもつ同義語辞書の作成","URL":"https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P4-14.pdf","AuthorName":"高岡一馬, 岡部裕子, 川原典子, 坂本美保, 内田佳孝"}]},"RegistryEntryAdded":"2020-10-06","RegistryEntryLastModified":"2023-08-15","Slug":"sudachi","Sources":["awslabs-open-data-registry"]}
{"Name":"Shiraz University Fetal Heart Sounds Database","Description":"The Shiraz University (SU) fetal heart sounds database (SUFHSDB) contains fetal and maternal phonocardiogram (PCG) recordings from 109 pregnant women in single and twin pregnancies. The recordings were made at Hafez Hospital of Shiraz University of Medical Sciences, Shiraz, Iran, from mothers aged from 16 to 47 years with a digital stethoscope placed on the lower maternal abdomen. In total, 99 subjects had one signal recorded, three subjects had two and seven cases of twins were recorded individually, resulting in 119 recordings. The average duration of each record is about 90 seconds. The sampling rate was generally 16,000 Hz with 16-bit quantization and a few recordings at 44,100 Hz. The data was recorded in wide-band mode of the digital stethoscope, with a frequency response of 20 Hz to 1 kHz.","Documentation":"https://doi.org/10.13026/42eg-8e59","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/42eg-8e59","ARN":"arn:aws:s3:::physionet-open/sufhsdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"sufhsdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Sea Surface Temperature Daily Analysis: European Space Agency Climate Change Initiative product version 2.1","Description":"Global daily-mean sea surface temperatures, presented on a 0.05° latitude-longitude grid, with gaps between available daily observations filled by statistical means, spanning late 1981 to recent time.  Suitable for large-scale oceanographic meteorological and climatological applications, such as evaluating or constraining environmental models or case-studies of marine heat wave events. Includes temperature uncertainty information and auxiliary information about land-sea fraction and sea-ice coverage. For reference and citation see: www.nature.com/articles/s41597-019-0236-x.","Documentation":"https://surftemp.github.io/sst-data-tutorials/","Contact":"https://www.reading.ac.uk/met/","ManagedBy":"[University of Reading, Department of Meteorology](https://www.reading.ac.uk/met/)","UpdateFrequency":"yearly","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","climate","earth observation","environmental","geospatial","global","oceans"],"License":"Creative Commons Licence by attribution (https://creativecommons.org/licenses/by/4.0/).  For attribution cite www.nature.com/articles/s41597-019-0236-x.","Resources":[{"Description":"Global daily-mean sea surface temperatures from 1981 onwards, in Zarr format. The data/sst.zarr directory contains a zarr store chunked (50, 180, 360) along the dimensions (time, lat, lon).","ARN":"arn:aws:s3:::surftemp-sst","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Working with surftemp-sst data - Tutorial 1 - Getting started","URL":"https://surftemp.github.io/sst-data-tutorials#tutorial1","AuthorName":"Niall McCarroll"},{"Title":"Working with surftemp-sst data - Tutorial 2 - Analysing Marine Heatwaves","URL":"https://surftemp.github.io/sst-data-tutorials#tutorial2","AuthorName":"Niall McCarroll"}],"Publications":[{"Title":"Adjusting for desert-dust-related biases in a climate data record of sea surface temperature (2020).","URL":"https://www.mdpi.com/2072-4292/12/16/2554","AuthorName":"Merchant, C.J. and Embury, O."},{"Title":"Satellite-based time-series of sea-surface temperature since 1981 for climate applications (2019).","URL":"https://www.nature.com/articles/s41597-019-0236-x","AuthorName":"Merchant, C.J., Embury, O., Bulgin, C.E., Block, T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R. and Donlon, C."}]},"RegistryEntryAdded":"2021-10-12","RegistryEntryLastModified":"2022-06-06","Slug":"surftemp-sst","Sources":["awslabs-open-data-registry"]}
{"Name":"Surya Bench","Description":"This dataset provides machine learning (ML)-ready solar data curated from NASA’s Solar Dynamics Observatory (SDO), covering observations from May 13, 2010, to July 31, 2024. It includes Level-1.5 processed data from:\n- Atmospheric Imaging Assembly (AIA):\n- Helioseismic and Magnetic Imager (HMI):\nThe dataset is designed to facilitate large-scale ML applications in heliophysics, such as solar activity forecasting, unsupervised representation learning, and scientific foundation model development.\n","Documentation":"https://huggingface.co/datasets/nasa-impact/Surya-bench","Contact":"sujit.roy@nasa.gov","ManagedBy":"NASA IMPACT","UpdateFrequency":"This is the final version of the Dataset.","Tags":["aws-pds","heliophysics","machine learning","solar"],"License":"Creative Commons Attribution 4.0 International.\n","Citation":"Roy, S., Singh, T., Freitag, M., Schmude, J., Lal, R., Hegde, D., Ranjan, S., Lin, A., Gaur, V., Vos, E.E. and Ghosal, R., 2024. AI Foundation Model for Heliophysics: Applications, Design, and Implementation. arXiv preprint arXiv:2410.10841.\n","Resources":[{"Description":"Surya Bench","ARN":"arn:aws:s3:::nasa-surya-bench","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](http://nasa-surya-bench.s3.amazonaws.com/index.html)"]},{"Description":"Notifications for Surya bench data","ARN":"arn:aws:sns:us-west-2:614929158106:nasa-surya-bench-object_created","Region":"us-west-2","Type":"SNS Topic"}],"RegistryEntryAdded":"2025-05-21","RegistryEntryLastModified":"2025-12-08","Slug":"surya-bench","Sources":["awslabs-open-data-registry"]}
{"Name":"MIT-BIH Supraventricular Arrhythmia Database","Description":"This database includes 78 half-hour ECG recordings chosen to supplement the examples of supraventricular arrhythmias in the MIT-BIH Arrhythmia Database.","Documentation":"https://doi.org/10.13026/C2V30W","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2V30W","ARN":"arn:aws:s3:::physionet-open/svdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"svdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Synthea Coherent Data Set","Description":"This is a synthetic data set that includes FHIR resources, DICOM images, genomic data, physiological data (i.e., ECGs), and simple clinical notes. FHIR links all the data types together.","Documentation":"https://doi.org/10.3390/electronics11081199","Contact":"synthea-list@groups.mitre.org","ManagedBy":"[The MITRE Corporation](https://www.mitre.org)","UpdateFrequency":"Rarely","Tags":["aws-pds","bioinformatics","csv","dicom","genomic","health","imaging","life sciences","medicine"],"License":"[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)","Citation":"Walonoski J, Hall D, Bates KM, Farris MH, Dagher J, Downs ME, Sivek RT, Wellner B, Gregorowicz A, Hadley M, Campion FX, Levine L, Wacome K, Emmer G, Kemmer A, Malik M, Hughes J, Granger E, Russell S. The “Coherent Data Set”: Combining Patient Data and Imaging in a Comprehensive Synthetic Health Record. Electronics. 2022; 11(8):1199. https://doi.org/10.3390/electronics11081199","Resources":[{"Description":"Synthetic data set that includes FHIR resources, DICOM images, genomic data, physiological data (i.e., ECGs), and simple clinical notes. FHIR links all the data types together. Data is available as single zip archive or as individual files within an `unzipped` folder.","ARN":"arn:aws:s3:::synthea-open-data/coherent/","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"The “Coherent Data Set”: Combining Patient Data and Imaging in a Comprehensive Synthetic Health Record.","URL":"https://doi.org/10.3390/electronics11081199","AuthorName":"Walonoski J, Hall D, Bates KM, Farris MH, Dagher J, Downs ME, Sivek RT, Wellner B, Gregorowicz A, Hadley M, Campion FX, Levine L, Wacome K, Emmer G, Kemmer A, Malik M, Hughes J, Granger E, Russell S."}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2023-02-01","RegistryEntryLastModified":"2024-03-26","Slug":"synthea-coherent-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Synthea synthetic patient generator data in OMOP Common Data Model","Description":"The Synthea generated data is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,800,000 persom (2.8m) data sets in the [OMOP Common Data Model](https://www.ohdsi.org/data-standardization/) format.  SyntheaTM is a synthetic patient generator that models the medical history of synthetic patients. Our mission is to output high-quality synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from cost, privacy, and security restrictions. It can be used without restriction for a variety of secondary uses in academia, research, industry, and government (although a citation would be appreciated).\nYou can read our first academic paper here: [https://doi.org/10.1093/jamia/ocx079](https://doi.org/10.1093/jamia/ocx079)\n","Documentation":"https://github.com/synthetichealth/synthea/wiki","Contact":"Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6riTWmh-cGw/aws-open-data) and use the `AWS Open Data` tag.","ManagedBy":"[Amazon Web Sevices](https://aws.amazon.com/)","UpdateFrequency":"Not updated","Tags":["aws-pds","bioinformatics","health","life sciences","natural language processing","us"],"License":"https://github.com/synthetichealth/synthea/blob/master/LICENSE","Resources":[{"Description":"Project data files","ARN":"arn:aws:s3:::synthea-omop","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Create data science environments on AWS for health analysis using OHDSI","URL":"https://aws.amazon.com/blogs/big-data/creating-data-science-environments-on-aws-for-health-analysis-using-ohdsi/","AuthorName":"James Wiggins","Services":["AWS CloudFormation","Amazon EC2","Amazon RDS"]},{"Title":"Map clinical notes to the OMOP Common Data Model and healthcare ontologies using Amazon Comprehend Medical","URL":"https://aws.amazon.com/blogs/machine-learning/map-clinical-notes-to-the-omop-common-data-model-and-healthcare-ontologies-using-amazon-comprehend-medical/","AuthorName":"James Wiggins","Services":["Amazon Comprehend","AWS CloudFormation","Amazon EC2","Amazon RDS"]},{"Title":"Predict patient health outcomes using OHDSI and machine learning on AWS","URL":"https://aws.amazon.com/blogs/industries/predict-patient-health-outcomes-using-ohdsi-and-machine-learning-on-aws/","AuthorName":"James Wiggins","Services":["AWS CloudFormation","Amazon EC2","Amazon RDS"]}],"Tools & Applications":[{"Title":"OHDSIonAWS","URL":"https://github.com/OHDSI/OHDSIonAWS","AuthorName":"James Wiggins"}]},"RegistryEntryAdded":"2023-01-03","RegistryEntryLastModified":"2025-02-04","Slug":"synthea-omop","Sources":["awslabs-open-data-registry"]}
{"Name":"Post-Ictal Heart Rate Oscillations in Partial Epilepsy","Description":"This database contains “post-ictal heart rate oscillations in a heterogeneous group of patients with partial epilepsy.","Documentation":"https://doi.org/10.13026/C2QC72","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2QC72","ARN":"arn:aws:s3:::physionet-open/szdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"szdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Tabula Muris Senis","Description":"Tabula Muris Senis is a comprehensive compendium of single cell transcriptomic data from the model organism *Mus musculus* comprising more than 500,000 cells from 18 organs and tissues across the mouse lifespan. We discovered cell-specific changes occurring across multiple cell types and organs, as well as age related changes in the cellular composition of different organs. Using single-cell transcriptomic data we were able to assess cell type specific manifestations of different hallmarks of aging, such as senescence, changes in the activity of metabolic pathways, depletion of stem-cell populations, genomic instability and the role of inflammation as well as other changes in the organism’s immune system. Tabula Muris Senis provides a wealth of new molecular information about how the most significant hallmarks of aging are reflected in a broad range of tissues and cell types.See: https://www.biorxiv.org/content/10.1101/661728v1\n","Documentation":"https://github.com/czbiohub/tabula-muris-senis/blob/master/tabula-muris-senis-on-aws.md","Contact":"If you have questions about the data, you can create an Issue at https://github.com/czbiohub/tabula-muris-senis.","ManagedBy":"[Biohub](https://www.biohub.org)","UpdateFrequency":"This is the first version of the dataset and it will be updated after the manuscript has been peer reviewed.","Tags":["aws-pds","Biohub","biology","encyclopedic","genomic","health","life sciences","medicine","single-cell transcriptomics"],"License":"https://github.com/czbiohub/tabula-muris-senis/blob/master/LICENSE","Resources":[{"Description":"https://github.com/czbiohub/tabula-muris-senis","ARN":"arn:aws:s3:::czb-tabula-muris-senis","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Fast queries of scRNAseq datasets with Amazon Athena","URL":"https://aws.amazon.com/blogs/industries/fast-queries-of-scrnaseq-datasets-with-amazon-athena/","AuthorName":"Andrew Ang, James Golden, Lisa McFerrin, and Lee Pang","Services":["Amazon Athena","AWS Glue"]}]},"RegistryEntryAdded":"2019-08-06","RegistryEntryLastModified":"2026-05-13","Slug":"tabula-muris-senis","Sources":["awslabs-open-data-registry"]}
{"Name":"Tabula Muris","Description":"Tabula Muris is a compendium of single cell transcriptomic data from the model organism *Mus musculus* comprising more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations, and allow for direct and controlled comparison of gene expression in cell types shared between tissues, such as T-lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3’-end counting, enabled the survey of thousands of cells at relatively low coverage, while the other, FACS-based full length transcript analysis, enabled characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology. See: https://www.nature.com/articles/s41586-018-0590-4\n","Documentation":"https://github.com/czbiohub/tabula-muris/blob/master/tabula-muris-on-aws.md","Contact":"If you have questions about the data, you can create an Issue at https://github.com/czbiohub/tabula-muris.","ManagedBy":"[Biohub](https://www.biohub.org)","UpdateFrequency":"This is the final version of the dataset, it will not be updated.","Tags":["aws-pds","Biohub","biology","encyclopedic","genomic","health","life sciences","medicine"],"License":"https://github.com/czbiohub/tabula-muris/blob/master/LICENSE","Resources":[{"Description":"https://github.com/czbiohub/tabula-muris","ARN":"arn:aws:s3:::czb-tabula-muris","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris.","URL":"https://www.ncbi.nlm.nih.gov/pubmed/30283141","AuthorName":"Tabula Muris Consortium (2019)","AuthorURL":"https://tabula-muris.ds.czbiohub.org/"}]},"RegistryEntryAdded":"2018-09-27","RegistryEntryLastModified":"2026-05-13","Slug":"tabula-muris","Sources":["awslabs-open-data-registry"]}
{"Name":"Tabula Sapiens","Description":"Tabula Sapiens is a benchmark, first-draft human cell atlas of over 1.1M cells from 28 organs of 24 normal human subjects. \nThis work is the product of the Tabula Sapiens Consortium. \nTaking the organs from the same individual controls for genetic background, age, environment, and epigenetic effects, \nand allows detailed analysis and comparison of cell types that are shared between tissues. \nOur work creates a detailed portrait of cell types as well as their distribution and variation in gene expression \nacross tissues and within the endothelial, epithelial, stromal and immune compartments.\nWe have built directly on our unique skills, experience, and data infrastructure from Tabula Muris and Tabula Muris Senis \nto create a high-quality human reference dataset and portal at a 10-fold larger scale from these prior efforts.\n\nA critical factor in the Tabula projects is our large collaborative network of PIs with deep expertise at preparation of diverse organs, \nenabling all organs from a subject to be successfully processed within a single day. \nWe have built the logistics and infrastructure capable of tracking hundreds of samples and thousands of 384-well plates \nfrom tissue through sample prep, library construction and on to sequencing and ultimately computational \nand expert cell annotation with tight quality control.\n\nTabula Sapiens leverages our network of human tissue experts and a close collaboration with a Donor Network West, \na not-for-profit organ procurement organization. We use their experience to balance and assign cell types \nfrom each tissue compartment and optimally mix high-quality plate-seq data and high-volume droplet-based data to \nprovide a broad and deep benchmark atlas.\n\nThe first version of Tabula Sapiens contained nearly 500,000 cells from 24 organs of 15 normal human subjects.\nWith the second version, Tabula Sapiens 2.0, we have built an integrated map of 28 tissues collected across 24 donors. \nNine new donors and four new tissues were collected and analyzed together with the original Tabula Sapiens 1.0 dataset. \nAll tissues and organs, with the exception of the respective reproductive organs, were profiled from both male (n=11) and female (n=13) donors. \nThe donor's age ranges from 22 to 74 years old, offering one of the most comprehensive molecular profiles of human tissues across the \nadult lifespan (7 donors under the age of 40, 11 donors between 40 and 60, and 6 donors over 60 years of age).\n\nOur goal is to make sequence data rapidly and broadly available to the scientific community as a community resource and \nwe welcome collaborative interaction on the project and analyses. By accessing these data, you agree to cite our work as \nThe Tabula Sapiens Consortium, Science 376, eabl4896 (2022) if using the original v1 dataset, or to cite our \nwork as The Tabula Sapiens Consortium, biorxiv (2024) if you are using the Tabula Sapiens v2 data release. \nAll processed data is available (see below) and the raw data is browsable from AWS. Redistribution of these data should \ninclude the full text of the data use policy.\n\nIf you wish to gain access to the raw fastq files, please submit a request using the controlled access form to sign the data use agreement, \nupon which we may grant you access to the files. Raw fastq files should not be transferred to any third party.\n","Documentation":"https://tabula-sapiens.sf.czbiohub.org/","Contact":"tabula-sapiens-data@czbiohub.org, angela.oliveira.pisco@gmail.com","ManagedBy":"[Biohub](https://www.biohub.org)","UpdateFrequency":"This is the second version of the dataset. We expect to update every 1-2 years until project completion.","Tags":["aws-pds","Biohub","biology","encyclopedic","genetic","genomic","health","life sciences","medicine","single-cell transcriptomics"],"License":"https://tabula-sapiens.sf.czbiohub.org/whereisthedata","Resources":[{"Description":"https://tabula-sapiens.sf.czbiohub.org/about","ARN":"arn:aws:s3:::czb-tabula-sapiens","Region":"us-west-2","Type":"S3 Bucket","ControlledAccess":"https://us.services.docusign.net/webforms-ux/v1.0/forms/929255c461f4bb15efc65d776f103035"}],"DataAtWork":{"Publications":[{"Title":"Tabula Sapiens reveals transcription factor expression, senescence effects, and sex-specific features in cell types from 28 human organs and tissues","URL":"https://www.biorxiv.org/content/10.1101/2024.12.03.626516v1","AuthorName":"The Tabula Sapiens Consortium, Stephen R Quake"},{"Title":"The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans","URL":"https://www.science.org/doi/10.1126/science.abl4896","AuthorName":"The Tabula Sapiens Consortium"}]},"RegistryEntryAdded":"2021-03-31","RegistryEntryLastModified":"2026-05-13","Slug":"tabula-sapiens","Sources":["awslabs-open-data-registry"]}
{"Name":"COVID-19 Harmonized Data","Description":"A harmonized collection of the core data pertaining to COVID-19 reported cases by geography, in a format prepared for analysis","Documentation":"https://www.stitchdata.com/docs/integrations/saas/covid-19","Contact":"covid19.dataset@talend.com","ManagedBy":"[Talend / Stitch](http://www.stitchdata.com/)","UpdateFrequency":"New COVID-19 data is added twice daily","Tags":["aws-pds","coronavirus","COVID-19","life sciences"],"License":"There are no restrictions on the use of this data.","Resources":[{"Description":"COVID-19 Harmonized Dataset","ARN":"arn:aws:s3:::covid19-harmonized-dataset","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://covid19-harmonized-dataset.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"How Talend is joining the fight against COVID-19","URL":"https://www.talend.com/blog/2020/04/05/talend-joins-fight-against-covid-19-unlocking-data-for-health-researchers/","AuthorName":"Thomas Bennett","AuthorURL":"https://www.linkedin.com/in/thomasbennettw/"}],"Tools & Applications":[{"Title":"Stitch COVID-19 Integration","URL":"https://www.stitchdata.com/integrations/covid-19/","AuthorName":"Jeff Huth","AuthorURL":"https://github.com/jeffhuth-bytecode"},{"Title":"Tap COVID-19 Python","URL":"https://github.com/singer-io/tap-covid-19","AuthorName":"Jeff Huth","AuthorURL":"https://github.com/jeffhuth-bytecode"}]},"RegistryEntryAdded":"2020-05-08","RegistryEntryLastModified":"2020-11-20","Slug":"talend-covid19","Sources":["awslabs-open-data-registry"]}
{"Name":"Tappy Keystroke Data","Description":"This is the keystroke dataset for the study titled 'High-accuracy detection of early Parkinson's Disease using multiple characteristics of finger movement while typing'. This research report is currently under review for publication by PLOS ONE. The dataset contains keystroke logs collected from over 200 subjects, with and without Parkinson's Disease (PD), as they typed normally on their own computer (without any supervision) over a period of weeks or months (having initially installed a custom keystroke recording app, Tappy). This dataset has been collected and analyzed in order to indicate that the routine interaction with computer keyboards can be used to detect changes in the characteristics of finger movement in the early stages of PD.","Documentation":"https://doi.org/10.13026/C2K08D","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2K08D","ARN":"arn:aws:s3:::physionet-open/tappy/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"tappy","Sources":["awslabs-open-data-registry"]}
{"Name":"Therapeutically Applicable Research to Generate Effective Treatments (TARGET)","Description":"Therapeutically Applicable Research to Generate Effective Treatments (TARGET) is the collaborative effort of a large, diverse consortium of extramural and NCI investigators. The goal of the effort is to accelerate molecular discoveries that drive the initiation and progression of hard-to-treat childhood cancers and facilitate rapid translation of those findings into the clinic.\nTARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers.The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive.\n","Documentation":"https://ocg.cancer.gov/programs/target/","Contact":"dcf-support@datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genomic","life sciences","STRIDES","whole genome sequencing"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq\nIsoform Expression Quantification, miRNA-Seq miRNA Expression Quantification, GDC Legacy      \nArchive\n","ARN":"arn:aws:s3:::gdc-target-phs000218-2-open","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"Broad Institute FireCloud","URL":"https://firecloud.terra.bio/","AuthorName":"The Broad Institute of MIT & Harvard","AuthorURL":"https://www.broadinstitute.org/"},{"Title":"Cancer Genomics Cloud","URL":"https://cgc-accounts.sbgenomics.com/","AuthorName":"Seven Bridges","AuthorURL":"https://docs.sevenbridges.com/docs/target-data"},{"Title":"GDC Legacy Archive","URL":"https://portal.gdc.cancer.gov/legacy-archive/","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"},{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"},{"Title":"ISB Cancer Genomics Cloud","URL":"https://isb-cgc.appspot.com/","AuthorName":"Institute for Systems Biology","AuthorURL":"https://isb-cancer-genomics-cloud.readthedocs.io/en/latest/sections/data/TARGET_top.html\n"},{"Title":"TARGET data matrix","URL":"https://ocg.cancer.gov/programs/target/data-matrix","AuthorName":"National Cancer Institute","AuthorURL":"https://ocg.cancer.gov/"}],"Publications":[{"Title":"A Children's Oncology Group and TARGET initiative exploring the genetic landscape of Wilms\ntumor\n","URL":"https://doi.org/10.1038/ng.3940","AuthorName":"Gadd S, Huff V, Walz AL, et al."},{"Title":"Ancestry and pharmacogenomics of relapse in acute lymphoblastic leukemia","URL":"https://www.ncbi.nlm.nih.gov/pubmed/21297632","AuthorName":"Yang JJ, Cheng C, Devidas M, et al."},{"Title":"Biomarker significance of plasma and tumor miR-21, miR-221, and miR-106a in osteosarcoma\n","URL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=29228567","AuthorName":"Nakka M, Allen-Rhoades W, Li Y, et al."},{"Title":"CSF3R mutations have a high degree of overlap with CEBPA mutations in pediatric AM","URL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=27143256","AuthorName":"Maxson JE, Ries RE, Wang YC, et al."},{"Title":"Genetic predisposition to neuroblastoma mediated by a LMO1 super-enhancer polymorphism","URL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=26560027","AuthorName":"Oldridge DA, Wood AC, Weichert-Leahey N, Crimmins I, Sussman R, Winter C, McDaniel LD,\nDiamond M, Hart LS, Zhu S, Durbin AD, Abraham BJ, et al.\n"},{"Title":"Genome-Wide Profiles of Extra-cranial Malignant Rhabdoid Tumors Reveal Heterogeneity and\nDysregulated Developmental Pathways\n","URL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=26977886","AuthorName":"Chun HJ, Lim EL, Heravi-Moussavi A, et al."},{"Title":"Genomic classification and identification of the cell of origin of pediatric mixed phenotype\nacute leukemia\n","URL":"https://doi.org/10.1038/s41586-018-0436-0","AuthorName":"Thomas B. Alexander, Zhaohui Gu, Ilaria Iacobucci, et al."},{"Title":"Genomics in childhood acute myeloid leukemia comes of age","URL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=29315296","AuthorName":"Brunner AM, Graubert TA."},{"Title":"Identification and analyses of extra-cranial and cranial rhabdoid tumor molecular subgroups\nreveal tumors with cytotoxic T cell infiltration\n","URL":"https://doi.org/10.1016/j.celrep.2019.10.013","AuthorName":"Hye-Jung E. Chun, Pascal D. Johann, Katy Milne et al."},{"Title":"MicroRNA Expression-Based Model Indicates Event-Free Survival in Pediatric Acute Myeloid\nLeukemia\n","URL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=29068783","AuthorName":"Lim EL, Trinh DL, Ries RE, et al."},{"Title":"MLLT1 YEATS domain mutations in clinically distinctive Favourable Histology Wilms tumours\n","URL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=26635203","AuthorName":"Perlman EJ, Gadd S, Arold ST, et al."},{"Title":"Recurrent DGCR8, DROSHA, and SIX homeodomain mutations in favorable histology Wilms tumors\n","URL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=25670082","AuthorName":"Walz AL, Ooms A, Gadd S, et al."},{"Title":"Relapsed neuroblastomas show frequent RAS-MAPK pathway mutations","URL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=26121087","AuthorName":"Eleveld TF, Oldridge DA, Bernard V, Koster J, et al."},{"Title":"Significance of TP53 Mutation in Wilms Tumors with Diffuse Anaplasia: A Report from the\nChildren's Oncology Group\n","URL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=27702824","AuthorName":"Ooms AH, Gadd S, Gerhard DS, et al."},{"Title":"TCF21 hypermethylation in genetically quiescent clear cell sarcoma of the kidney","URL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=26158413","AuthorName":"Gooskens SL, Gadd S, Guidry Auvil JM, et al."},{"Title":"The genetic landscape of high-risk neuroblastoma","URL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=23334666","AuthorName":"Pugh TJ, Morozova O, Attiyeh EF, Asgharzadeh S, Wei JS, Auclair D, Carter SL, Cibulskis K,\nHanna M, Kiezun A, Kim J, Lawrence MS, Lichenstein L, et al.\n"},{"Title":"The genomic landscape of pediatric and young adult T-lineage acute lymphoblastic leukemia\n","URL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=28671688","AuthorName":"Yu Liu, John Easton, Ying Shao, et al."},{"Title":"The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural\nalterations and age-specific mutational interactions\n","URL":"https://www.ncbi.nlm.nih.gov/pubmed/?term=29227476","AuthorName":"Bolouri H, Farrar JE, Triche T Jr, et al."}]},"RegistryEntryAdded":"2020-04-07","RegistryEntryLastModified":"2020-10-05","Slug":"target","Sources":["awslabs-open-data-registry"]}
{"Name":"Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription (TaRGET)","Description":"The TaRGET (Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription) Program is a research consortium funded by the National Institute of Environmental Health Sciences (NIEHS). The goal of the collaboration is to address the role of environmental exposures in disease pathogenesis as a function of epigenome perturbation, including understanding the environmental control of epigenetic mechanisms and assessing the utility of surrogate tissue analysis in mouse models of disease-relevant environmental exposures.\n","Contact":"targetdcc16@gmail.com","ManagedBy":"TaRGET II Data Coordination Center (TaRGET-DCC)","Documentation":"https://data.targetepigenomics.org/","UpdateFrequency":"TaRGET-DCC offers monthly data releases, although this dataset may not be updated at every release.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","bioinformatics","biology","environmental","epigenomics","genetic","genomic","life sciences"],"License":"External data users may freely download, analyze, and publish results based on any TaRGET II data provided here without restrictions.","Resources":[{"Description":"Released and archived TaRGET II data","ARN":"arn:aws:s3:::targetepigenomics","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Finding and Downloading TaRGET II Data files","URL":"https://data.targetepigenomics.org/help/user-guide/download-files","AuthorName":"TaRGET-DCC","AuthorURL":"https://data.targetepigenomics.org"}],"Tools & Applications":[{"Title":"Visualize TaRGET II data with WashU Epigenome Browser","URL":"https://epigenomegateway.wustl.edu/browser/","AuthorName":"WashU Epigenome Browser","AuthorURL":"https://epigenomegateway.wustl.edu"}],"Publications":[{"Title":"Comparison of differential accessibility analysis strategies for ATAC-seq data","URL":"https://pubmed.ncbi.nlm.nih.gov/32576878/","AuthorName":"Gontarz P, Fu S, Xing X, Liu S, Miao B et.al."},{"Title":"Environmental Determinants of cardiovasular disease: lessons learned from air pollution","URL":"https://pubmed.ncbi.nlm.nih.gov/32382149/","AuthorName":"Al-Kindi SG, Brook RD, Biswal S, Rajagopalan S."},{"Title":"Epigenetic biomarkers and preterm birth","URL":"https://pubmed.ncbi.nlm.nih.gov/32551139/","AuthorName":"Park B, Khanam R, Vinayachandran V, et.al."},{"Title":"Metabolic effects of air pollution exposure and reversibility","URL":"https://www.jci.org/articles/view/137315","AuthorName":"Rajagopalan S, Park B, Palanivel R, et al."},{"Title":"The NIEHS TaRGET II Consortium and environmental epigenomics","URL":"https://doi.org/10.1038/nbt.4099","AuthorName":"Wang, T., Pehrsson, E., Purushotham, D. et al."},{"Title":"The role of environmental exposures and the epigenome in health and disease.","URL":"https://pubmed.ncbi.nlm.nih.gov/31177562","AuthorName":"Perera BPU, Faulk C, Svoboda LK, Goodrich JM, Dolinoy DC."}]},"RegistryEntryAdded":"2021-10-11","RegistryEntryLastModified":"2025-07-09","Slug":"targetepigenomics","Sources":["awslabs-open-data-registry"]}
{"Name":"The Cancer Genome Atlas","Description":"The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer. TCGA has analyzed matched tumor and normal tissues from 11,000 patients, allowing for the comprehensive characterization of 33 cancer types and subtypes, including 10 rare cancers.\nThe dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA Expression Quantification, Genotyping Array Copy Number Segment, Genotyping Array Masked Copy Number Segment, Genotyping Array Gene Level Copy Number Scores, and WXS Masked Somatic Mutation data from Genomic Data Commons (GDC).\nThis dataset also contains controlled Whole Exome Sequencing (WXS), RNA-Seq, miRNA-Seq, ATAC-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and WXS Aggregated Somatic Mutation data from GDC.\nTCGA is made available on AWS via the [NIH STRIDES Initiative](https://aws.amazon.com/blogs/publicsector/aws-and-national-institutes-of-health-collaborate-to-accelerate-discoveries-with-strides-initiative/).\n","Documentation":"https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga","Contact":"dcf-support@datacommons.io","ManagedBy":"[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)","UpdateFrequency":"Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n","Tags":["aws-pds","cancer","genomic","life sciences","STRIDES","whole genome sequencing"],"License":"NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies","Resources":[{"Description":"Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq\nIsoform Expression Quantification, miRNA Expression Quantification, Genotyping Array Copy\nNumber Segment, Genotyping Array Masked Copy Number Segment, Genotyping Array Gene Level Copy\nNumber Scores, WXS Masked Somatic Mutation\n","ARN":"arn:aws:s3:::tcga-2-open","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"WXS/RNA-Seq/miRNA-Seq/ATAC-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic\nMutation, WXS Aggregated Somatic Mutation\n","ARN":"arn:aws:s3:::tcga-2-controlled","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000178.v1.p1"}],"DataAtWork":{"Tutorials":[{"Title":"Explore The Cancer Genome Atlas in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/3da8edb0-74d6-569e-a809-1bb6cf704a6f/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"}],"Tools & Applications":[{"Title":"Broad Institute FireCloud","URL":"https://firecloud.terra.bio/","AuthorName":"The Broad Institute of MIT & Harvard","AuthorURL":"https://www.broadinstitute.org/"},{"Title":"Cancer Genomics Cloud","URL":"https://cgc-accounts.sbgenomics.com/","AuthorName":"Seven Bridges","AuthorURL":"https://docs.sevenbridges.com/docs/tcga-data"},{"Title":"GDC Legacy Archive","URL":"https://portal.gdc.cancer.gov/legacy-archive/","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"},{"Title":"Genomic Data Commons","URL":"https://portal.gdc.cancer.gov/projects/","AuthorName":"National Cancer Institute","AuthorURL":"https://gdc.cancer.gov/"},{"Title":"ISB Cancer Genomics Cloud","URL":"https://isb-cgc.appspot.com/","AuthorName":"Institute for Systems Biology","AuthorURL":"https://isb-cancer-genomics-cloud.readthedocs.io/en/latest/sections/data/TCGA_top.html\n"},{"Title":"TCGA Cancers Selected for Study","URL":"https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/studied-cancers\n","AuthorName":"National Cancer Institute","AuthorURL":"https://ocg.cancer.gov/"},{"Title":"Using TCGA Data, Resources, and Materials","URL":"https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/using-tcga\n","AuthorName":"National Cancer Institute","AuthorURL":"https://ocg.cancer.gov/"}],"Publications":[{"Title":"\"Before and After: A Comparison of Legacy and Harmonized TCGA Data at the Genomic Data\nCommons\"\n","URL":"https://doi.org/10.1016/j.cels.2019.06.006","AuthorName":"Galen F. Gao, Joel S. Parker, et al."},{"Title":"A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples","URL":"https://doi.org/10.1016/j.cell.2018.03.027","AuthorName":"Han Chen, Chunyan Li, et al."},{"Title":"An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome\nAnalytics\n","URL":"https://doi.org/10.1016/j.cell.2018.02.052","AuthorName":"Jianfang Liu, Tara Lichtenberg, et al."},{"Title":"Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types\nof Cancer\n","URL":"https://doi.org/10.1016/j.cell.2018.03.022","AuthorName":"Katherine A. Hoadley, Christina Yau, et al."},{"Title":"Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas","URL":"https://doi.org/10.1016/j.ccell.2018.03.010","AuthorName":"Yang Liu, Nilay S. Sethi, et al."},{"Title":"Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients","URL":"https://doi.org/10.1016/j.ccell.2018.07.001","AuthorName":"André Kahles, Kjong-Van Lehmann, et al."},{"Title":"Comprehensive Characterization of Cancer Driver Genes and Mutations","URL":"https://doi.org/10.1016/j.cell.2018.02.060","AuthorName":"Matthew H. Bailey, Collin Tokheim, et al."},{"Title":"Genomic and Functional Approaches to Understanding Cancer Aneuploidy","URL":"https://doi.org/10.1016/j.ccell.2018.03.007","AuthorName":"Alison M. Taylor, Juliann Shih, et al."},{"Title":"Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome\nAtlas\n","URL":"https://doi.org/10.1016/j.celrep.2018.03.076","AuthorName":"Theo A. Knijnenburg, Linghua Wang, et al."},{"Title":"Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas","URL":"https://doi.org/10.1016/j.celrep.2018.03.063","AuthorName":"Joshua D. Campbell, Christina Yau, et al."},{"Title":"Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types","URL":"https://doi.org/10.1016/j.celrep.2018.03.047","AuthorName":"Zhongqi Ge, Jake S. Leighton, et al."},{"Title":"Machine Learning Detects Pan-Cancer Ras Pathway Activation in The Cancer Genome Atlas","URL":"https://doi.org/10.1016/j.celrep.2018.03.046","AuthorName":"Gregory P. Way, Francisco Sanchez-Vega, et al."},{"Title":"Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation\n","URL":"https://doi.org/10.1016/j.cell.2018.03.034","AuthorName":"Tathiane M. Malta, Artem Sokolov, et al."},{"Title":"Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human\nCancers\n","URL":"https://doi.org/10.1016/j.celrep.2018.03.077","AuthorName":"Xinxin Peng, Zhongyuan Chen, et al."},{"Title":"Oncogenic Signaling Pathways in The Cancer Genome Atlas","URL":"https://doi.org/10.1016/j.cell.2018.03.035","AuthorName":"Francisco Sanchez-Vega, Marco Mina, et al."},{"Title":"Pan-Cancer Alterations of the MYC Oncogene and its Proximal Network Across The Cancer Genome\nAtlas\n","URL":"https://doi.org/10.1016/j.cels.2018.03.003","AuthorName":"Franz X. Schaub, Varsha Dhankani, et al."},{"Title":"Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each\nTumor Context\n","URL":"https://doi.org/10.1016/j.celrep.2018.03.064","AuthorName":"Hua-Sheng Chiu, Sonal Somvanshi, et al."},{"Title":"Pathogenic Germline Variants in 10,389 Adult Cancers","URL":"https://doi.org/10.1016/j.cell.2018.03.039","AuthorName":"Kuan-lin Huang, R. Jay Mashl, et al."},{"Title":"Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic\nPipelines\n","URL":"https://doi.org/10.1016/j.cels.2018.03.002","AuthorName":"Kyle Ellrott, Matthew H. Bailey, et al."},{"Title":"Spatial Organization And Molecular Correlation Of Tumor-Infiltrating Lymphocytes Using Deep\nLearning On Pathology Images\n","URL":"https://doi.org/10.1016/j.celrep.2018.03.086","AuthorName":"Joel Saltz, Rajarsi Gupta, et al."},{"Title":"The chromatin accessibility landscape of primary human cancers","URL":"https://doi.org/10.1126/science.aav1898","AuthorName":"M. Ryan Corces, Jeffrey M. Granja, et al."},{"Title":"The Immune Landscape of Cancer","URL":"https://doi.org/10.1016/j.immuni.2018.03.023","AuthorName":"Vésteinn Thorsson, David L. Gibbs, et al."}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2026-11-03","Slug":"tcga","Sources":["awslabs-open-data-registry"]}
{"Name":"Terra Fusion Data Sampler","Description":"The Terra Basic Fusion dataset is a fused dataset of the original Level 1 radiances\nfrom the five Terra instruments. They have been fully validate to contain the original\nTerra instrument Level 1 data. Each Level 1 Terra Basic Fusion file contains one full\nTerra orbit of data and is typically 15 – 40 GB in size, depending on how much data was\ncollected for that orbit. It contains instrument radiance in physical units; radiance\nquality indicator; geolocation for each IFOV at its native resolution; sun-view geometry;\nbservation time; and other attributes/metadata. It is stored in HDF5, conformed to CF\nconventions, and accessible by netCDF-4 enhanced models. It’s naming convention\nfollows: TERRA_BF_L1B_OXXXX_YYYYMMDDHHMMSS_F000_V000.h5. A concise description of the\ndataset, along with links to complete documentation and available software tools, can\nbe found on the Terra Fusion project page: https://terrafusion.web.illinois.edu.</br></br>\n\nTerra is the flagship satellite of NASA’s Earth Observing System (EOS). It was launched\ninto orbit on December 18, 1999 and carries five instruments. These are the\nModerate-resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging\nSpectroRadiometer (MISR), the Advanced Spaceborne Thermal Emission and Reflection\nRadiometer (ASTER), the Clouds and Earth’s Radiant Energy System (CERES), and the\nMeasurements of Pollution in the Troposphere (MOPITT).</br></br>\n\nThe Terra Basic Fusion dataset is an easy-to-access record of the Level 1 radiances\nfor instruments on the Terra mission for selected WRS-2 paths covering the years\n2000-2015. These paths are Paths 20-26 (e.g., US corn belt), 108 (e.g., Japan),\n125 (e.g., China), 143 (e.g., India), 150 (e.g., Showa Station, Antarctica),\n169 (e.g., Europe and Africa), 188 (e.g., Nigeria calibration site), and 233\n(e.g., Greenland).\n","Documentation":"https://go.illinois.edu/terra-fusion-doc","Contact":"gdi@illinois.edu","ManagedBy":"University of Illinois","UpdateFrequency":"Static, with a planned update for years 2016-2020 in the future.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","geospatial","satellite imagery"],"License":"Creative Commons Level 0","Resources":[{"Description":"AWS S3 Public Bucket Containing Terra Basic Fusion Hierarchical Data Format 5 (HDF5) files","ARN":"arn:aws:s3:::terrafusiondatasampler","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Basic Terra fusion product algorithm theoretical basis and data specifications","URL":"https://go.illinois.edu/terra-fusion-doc","AuthorName":"Zhao, Guangu; Yang, Muqun; Clipp, Landon; Gao, Yizhao; Lee, Joe H.","AuthorURL":"http://hdl.handle.net/2142/105967"}],"Tools & Applications":[{"Title":"TerraFusion GitHub","URL":"https://github.com/TerraFusion","AuthorName":"University of Illinois"}]},"RegistryEntryAdded":"2019-12-07","RegistryEntryLastModified":"2023-02-28","Slug":"terrafusion","Sources":["awslabs-open-data-registry"]}
{"Name":"Terrain Tiles","Description":"A global dataset providing bare-earth terrain heights, tiled for easy usage and provided on S3.","Documentation":"https://github.com/tilezen/joerd/tree/master/docs","Contact":"https://github.com/tilezen/joerd/issues","UpdateFrequency":"New data is added based on community feedback","ManagedBy":"Mapzen, a Linux Foundation project","Collabs":{"ASDI":{"Tags":["elevation"]}},"Tags":["agriculture","aws-pds","disaster response","earth observation","elevation","geospatial"],"License":"https://github.com/tilezen/joerd/blob/master/docs/attribution.md","Resources":[{"Description":"Gridded elevation tiles","ARN":"arn:aws:s3:::elevation-tiles-prod","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Map](https://elevation-tiles-prod.s3.amazonaws.com/index.html)"]},{"Description":"Gridded elevation tiles - replication in EU region","ARN":"arn:aws:s3:::elevation-tiles-prod-eu","Region":"eu-central-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Using GDAL to Produce a Geotiff for an Area","URL":"https://gis.stackexchange.com/questions/201831/how-to-efficiently-access-files-with-gdal-from-an-s3-bucket-using-vsis3/202608#202608","AuthorName":"Chris Henrick"}],"Tools & Applications":[{"Title":"Arnis: Generate any location from the real world in Minecraft with a high level of detail","URL":"https://github.com/louis-e/arnis","AuthorName":"Louis Erbkamm","AuthorURL":"https://louisdev.de/"},{"Title":"CASAS.ES: free data sheet for every building in Spain, from official open data","URL":"https://casas.es/en/","AuthorName":"CASAS.ES (Propalytics Intelligence Ltd)","AuthorURL":"https://casas.es/en/about"},{"Title":"climate.you: an interactive map showing climate change at the local level","URL":"https://climate.you","AuthorName":"Benoit Leveau & Fanny Chaleon","AuthorURL":"https://github.com/climate-you"},{"Title":"Customizable Terrain Tiles","URL":"https://clockworkmicro.com/","AuthorName":"Clockwork Micro","AuthorURL":"https://clockworkmicro.com/"},{"Title":"Download topographical map contour lines as SVGs","URL":"https://buntinglabs.com/tools/download-topo-map-contour-lines","AuthorName":"Bunting Labs","AuthorURL":"https://buntinglabs.com/"},{"Title":"EOS Land Viewer","URL":"https://eos.com/landviewer/","AuthorName":"Earth Observing System","AuthorURL":"https://eos.com/"},{"Title":"Heliora: free Sun and Moon planner that ray-casts the real terrain horizon","URL":"https://heliora.app/","AuthorName":"Simon Carroll","AuthorURL":"https://heliora.app/about"},{"Title":"MapToNap: site analysis maps and 3D terrain context models for any address","URL":"https://maptonap.com/","AuthorName":"MapToNap","AuthorURL":"https://maptonap.com/about/"},{"Title":"Mapzen Terrain Tiles on AWS Reader","URL":"https://docs.safe.com/fme/html/FME_Desktop_Documentation/FME_ReadersWriters/terraintilesaws/terraintilesaws.htm","AuthorName":"Safe Software","AuthorURL":"https://www.safe.com/"},{"Title":"On The Go Map","URL":"https://onthegomap.com/","AuthorName":"On The Go Map","AuthorURL":"https://onthegomap.com/"},{"Title":"Open-Source Elevation Service","URL":"https://elevation.racemap.com","AuthorName":"Racemap","AuthorURL":"https://racemap.com/"},{"Title":"PlanetUtils (GitHub): Scripts and a Docker container to download, merge, and resample terrain tiles","URL":"https://github.com/interline-io/planetutils","AuthorName":"Interline Technologies","AuthorURL":"https://www.interline.io/"},{"Title":"PODPAC: Python Library supporting TerrainTiles for analysis","URL":"https://podpac.org/datasets.html#terraintiles","AuthorName":"Creare","AuthorURL":"http://www.creare.com/"},{"Title":"R package for accessing Terrain Tiles","URL":"https://cran.r-project.org/web/packages/elevatr/vignettes/introduction_to_elevatr.html","AuthorName":"Jeffrey W. Hollister","AuthorURL":"https://github.com/jhollist"},{"Title":"Rua de Cima: house prices and travel times in mainland Portugal","URL":"https://ruadecima.pt","AuthorName":"Miguel Silva","AuthorURL":"https://ruadecima.pt"},{"Title":"Sentinel Hub WMS/WMTS/WCS Service for DEM","URL":"http://www.sentinel-hub.com/apps/wms","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"},{"Title":"Sentinel Playground for DEM","URL":"https://apps.sentinel-hub.com/sentinel-playground/?source=DEM&lat=41.902277040963696&lng=-102.83203125&zoom=4","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"},{"Title":"ShadeMap: Simulate sun shadows for any time and place on Earth","URL":"https://shademap.app/","AuthorName":"Ted Piotrowski","AuthorURL":"https://github.com/ted-piotrowski"},{"Title":"Shadowmap | Sun-Powered Insights. Anywhere on Earth.","URL":"https://app.shadowmap.org/","AuthorName":"Shadowmap Technologies GmbH","AuthorURL":"https://shadowmap.org"},{"Title":"Terradactile: Generate DEM and hillshade Cloud Optimized GeoTIFFs for visualization and download based on terrain tiles","URL":"https://terradactile.sparkgeo.com/","AuthorName":"Sparkgeo","AuthorURL":"https://sparkgeo.com/"}],"Publications":[{"Title":"Interactive Visualization of 3D Terrain Data Stored in the Cloud","URL":"https://ieeexplore.ieee.org/abstract/document/9298063","AuthorName":"Gregory Larrick, Yun Tian, Uri Rogers, Halim Acosta, and Fangyang Shen"},{"Title":"Landscape transformations produce favorable roosting conditions for turkey vultures and black vultures","URL":"https://www.nature.com/articles/s41598-021-94045-3","AuthorName":"Jacob E. Hill, Kenneth F. Kellner, Bryan M. Kluever, Michael L. Avery, John S. Humphrey, Eric A. Tillman, Travis L. DeVault & Jerrold L. Belant"}]},"RegistryEntryAdded":"2018-04-17","RegistryEntryLastModified":"2026-10-30","Slug":"terrain-tiles","Sources":["awslabs-open-data-registry"]}
{"Name":"TESSERA (Temporal Embeddings of Surface Spectra for Earth Representation and Analysis)","Description":"TESSERA is a global geospatial foundation model that turns the European Space Agency's raw Sentinel‑1 (SAR) + Sentinel‑2 (optical) time series into compact, gap-free, 128‑dimensional, 10‑meter embeddings, producing an annual “representation map” for every 10m land pixel worldwide from 2017–2025 (rolling forward each new year) enabling detailed analysis of land cover dynamics, vegetation health, and environmental monitoring over time. Each 10m pixel encodes a full year’s spectral‑temporal behavior across the two major sensor modalities (optical + radar), enabling downstream tasks (carbon quantification, land cover, canopy height, change detection, hazards, habitat mapping, biodiversity) to start from a compact annual descriptor instead of petabytes of raw scenes. This collapses costs and latency for analysts and builders while enhancing accuracy and enabling indexing.\n","Documentation":"[TESSERA Documentation](https://github.com/ucam-eo/tessera)\nHow to use the embeddings https://github.com/dClimate/tessera-embeddings/blob/main/docs/global-store.md\nHow to use the TESSERA dataset, model details, and example applications. https://geotessera.readthedocs.io/en/latest/index.html\nHow to generate the embeddings using AWS data https://github.com/dClimate/tessera-embeddings\n","Contact":"Questions about the data can be raised as an issue at https://github.com/ucam-eo/geotessera/issues","ManagedBy":"\"[Energy and Environment Group, Department of Computer Science and Technology, University of Cambridge](https://www.cst.cam.ac.uk/research/eeg)\"\n\"[dClimate](https://dclimate.net/)\"\n","UpdateFrequency":"Annually (as new Sentinel data becomes available)","Tags":["agriculture","aws-pds","climate","climate model","earth observation","environmental","geospatial","land cover","land use","satellite imagery"],"License":"CC‑BY‑SA","Citation":"https://arxiv.org/abs/2506.20380","Resources":[{"Description":"TESSERA v1.1 Embeddings","ARN":"arn:aws:s3:::tessera-embeddings/v1.1/dclimate.icechunk","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Icechunk Zarr](https://tessera-embeddings.s3.us-west-2.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Basic Data Analysis, Point Sampling Workflow, GIS Integration Workflow, Coverage Analysis with Boundary Visualization, Custom Analysis Workflows, Cloud-Native Analysis with Zarr","URL":"https://geotessera.readthedocs.io/en/latest/tutorials.html","AuthorName":"Cambridge Centre for Earth Observation","AuthorURL":"https://github.com/ucam-eo"},{"Title":"Get to Know a Dataset: Tessera","URL":"https://github.com/ucam-eo/geotessera-aws-open-data/tree/main","NotebookURL":"https://github.com/ucam-eo/geotessera-aws-open-data/blob/main/get-to-know-geotessera.ipynb","AuthorName":"Frank Feng, Anil Madhavapeddy","AuthorURL":"https://github.com/ucam-eo"}],"Tools & Applications":[{"Title":"GeoTessera Python Package","URL":"https://github.com/ucam-eo/geotessera","AuthorName":"Cambridge Centre for Earth Observation","AuthorURL":"https://github.com/ucam-eo"},{"Title":"Interactive Tessera Embedding Classifier","URL":"https://github.com/ucam-eo/tessera-interactive-map","AuthorName":"Cambridge Centre for Earth Observation","AuthorURL":"https://github.com/ucam-eo"},{"Title":"Solar Panel Detection with GeoTessera","URL":"https://github.com/ucam-eo/geotessera-examples","AuthorName":"Cambridge Centre for Earth Observation","AuthorURL":"https://github.com/ucam-eo"}],"Publications":[{"Title":"TESSERA Temporal Embeddings of Surface Spectra for Earth Representation and Analysis","URL":"https://arxiv.org/abs/2506.20380","AuthorName":"Zhengpeng F.(Cambridge), Atzberger C.(dClimate), Jaffer S.(Cambridge), Knezevic J.(Cambridge), Sormunen S.(Aalto Uni), Young R.(Cambridge), et al"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-12-18","RegistryEntryLastModified":"2026-09-16","Slug":"tessera","Sources":["awslabs-open-data-registry"]}
{"Name":"Transfer Entropy With Partitioning","Description":"This is a repository of MATLAB functions that can estimate transfer entropy (information flow) from one time series to another using a non-parametric partitioning algorithm.","Documentation":"https://physionet.org/content/tewp/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/tewp/","ARN":"arn:aws:s3:::physionet-open/tewp/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"tewp","Sources":["awslabs-open-data-registry"]}
{"Name":"Poseidon 3D Seismic, Australia","Description":"Near, mid, far, full stack (with AGC) imaged 3D seismic data. We also include the decimated\nstacking velocity field. The dataset is used in oil and gas exploration. Survey size is\napproximately 2,900 km2.\n\nCoordinate system used is: GDA94 / MGA Zone 51 Petrel, 700004\n\nDatasets are converted to open-source MDIO format (v1 specification).\n\nOriginal SEG-Y files are licensed as CC BY 3.0 AU and are downloaded from\n[Google Drive](https://drive.google.com/drive/folders/0B7brcf-eGK8Cbk9ueHA0QUU4Zjg?resourcekey=0-njGYrj9ZlE751Oi1-89sOg)\naccessed via [SEG Open Data Wiki](https://wiki.seg.org/wiki/Open_data). The datasets are available\ncourtesy of ConocoPhillips and Geoscience Australia. Raw data can be requested from Geoscience\nAustralia's [NOPIMS](https://nopims.dmp.wa.gov.au/Nopims/) systems.\n","Documentation":"TBD","Contact":"For any questions regarding the datasets and MDIO, email the TGS Open Data Team at opendata@tgs.com.\n","ManagedBy":"[TGS](https://www.tgs.com)","UpdateFrequency":"Dataset is static.","Tags":["exploration","geophysics","seismology"],"License":"CC BY 4.0","Resources":[{"Description":"Poseidon 3D Seismic MDIO volumes and Reports","ARN":"arn:aws:s3:::tgs-opendata-poseidon","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://tgs-opendata-poseidon.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"MDIO","URL":"https://mdio.dev","AuthorName":"TGS","AuthorURL":"https://www.tgs.com"}]},"RegistryEntryAdded":"2024-08-22","RegistryEntryLastModified":"2024-09-09","Slug":"tgs-opendata-poseidon","Sources":["awslabs-open-data-registry"]}
{"Name":"Thal-Kak Multiple Sequence Alignment Databases","Description":"A collection of prebuilt protein sequence databases for multiple sequence alignment (MSA) search. MSAs are the main input of protein structure prediction and are also used to study conservation, coevolution and protein function. The nine databases, in the native formats of MMseqs2 and HH-suite, derive from UniProt UniRef100 (release 2026_01), the reduced BFD, MGnify (release 2024_04), EnVhogDB and the Logan protein catalogue from the NCBI Sequence Read Archive. The sequences of UniRef100, MGnify and the reduced BFD were re-clustered, EnVhogDB was re-clustered at its upstream standard level, and Logan was built into an MMseqs2 expandable-profile database. Each database is published as one .tar.zst archive. They are set up for Thal-Kak, a pipeline that unifies several structure prediction models, so that its local MSA search writes MSAs in the input format of those models.","Documentation":"https://github.com/CSSB-SNU/Thal-Kak/tree/main/aws_open_data","Contact":"https://github.com/CSSB-SNU/Thal-Kak/issues","ManagedBy":"[CSSB, Seoul National University](https://biosci.snu.ac.kr/cssb)","UpdateFrequency":"Irregularly. New releases are added as new archives.","Tags":["aws-pds","bioinformatics","biomolecular modeling","life sciences","metagenomics","msa","protein","protein folding","structural biology"],"License":"Each database carries the license of the source it derives from; no database mixes sources.\n[LICENSE.txt](https://thal-kak-msa.s3.amazonaws.com/LICENSE.txt) at the bucket root is the full\nrecord of upstream licenses, attribution, citation and changes.\n\n| Archives | License | Source |\n| --- | --- | --- |\n| hhblits/uniref100_2026_01 | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | UniProt UniRef100, release 2026_01 |\n| mmseqs/bfd_reduced | [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) | BFD reduced subset, via the AlphaFold databases |\n| mmseqs/mgnify_clusters, hhblits/mgnify_clusters | [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) | MGnify protein database, release 2024_04 |\n| mmseqs/envhog_std, hhblits/envhog | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | EnVhogDB |\n| mmseqs/logan_human, hhblits/logan_human, hhblits/logan_nonhuman | No license is granted. [NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomic Data Sharing Policy](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/) | Logan v1 (SRA cutoff 2023-12-10) |\n","Citation":"For the pipeline that searches these databases, cite Bae J. et al. Thal-Kak: unifying biomolecular structure predictors reveals a sampling-selection gap. bioRxiv 2026.08.19.745680 (2026). doi:10.64898/2026.08.19.745680. Also cite the upstream database of each archive you use, as given in LICENSE.txt; for the CC BY and CC BY-SA databases this attribution is a license condition.","Resources":[{"Description":"MMseqs2 expandable profile databases, one .tar.zst archive per database (mgnify_clusters, envhog_std, bfd_reduced, logan_human). Each archive holds four MMseqs2 databases; build the .idx search index after extraction.","ARN":"arn:aws:s3:::thal-kak-msa/mmseqs/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"HH-suite (FFindex) databases, one .tar.zst archive per database (uniref100_2026_01, mgnify_clusters, logan_nonhuman, envhog, logan_human). Each archive holds the a3m, hhm and cs219 databases.","ARN":"arn:aws:s3:::thal-kak-msa/hhblits/","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Notifications for new Thal-Kak Multiple Sequence Alignment Databases data","ARN":"arn:aws:sns:us-east-1:029678959616:thal-kak-msa-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: Thal-Kak Multiple Sequence Alignment Databases","URL":"https://github.com/CSSB-SNU/Thal-Kak/blob/main/aws_open_data/get-to-know-a-dataset.ipynb","NotebookURL":"https://github.com/CSSB-SNU/Thal-Kak/blob/main/aws_open_data/get-to-know-a-dataset.ipynb","AuthorName":"Kwanwook Kim (CSSB, Seoul National University)","AuthorURL":"https://github.com/Alphaketo1103"},{"Title":"Read the Thal-Kak MSA Databases in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/e113956b-74d2-50dc-95c3-8d59d0fb6705/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"}],"Tools & Applications":[{"Title":"Thal-Kak","URL":"https://github.com/CSSB-SNU/Thal-Kak","AuthorName":"CSSB, Seoul National University","AuthorURL":"https://biosci.snu.ac.kr/cssb"}],"Publications":[{"Title":"Thal-Kak: unifying biomolecular structure predictors reveals a sampling-selection gap","URL":"https://doi.org/10.64898/2026.08.19.745680","AuthorName":"Bae J., Jo S., Kim Y., Kim D., Kim K., Park S., et al."}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-11","Slug":"thal-kak-msa","Sources":["awslabs-open-data-registry"]}
{"Name":"TIGER Training","Description":"\"This dataset contains the training data for the [Tumor InfiltratinG lymphocytes in breast cancER or TIGER](https://tiger.grand-challenge.org) challenge. TIGER is the first challenge on fully automated assessment of tumor-infiltrating lymphocytes (TILs) in breast cancer histopathology slides. TILs are proving to be an important biomarker in cancer patients as they can play a part in killing tumor cells, particularly in some types of breast cancer. Identifying and measuring TILs can help to better target treatments, particularly immunotherapy, and may result in lower levels of other more aggressive treatments, including chemotherapy.\"\n","Documentation":"https://tiger.grand-challenge.org/Data/","Contact":"support@grand-challenge.org","ManagedBy":"Radboud University Medical Center","UpdateFrequency":"As required","Tags":["aws-pds","cancer","computational pathology","computer vision","deep learning","grand-challenge.org","histopathology","life sciences"],"License":"CC BY-NC 4.0","Resources":[{"Description":"Whole slide images with corresponding annotations including tumor, stroma and tumor infiltrating lymphocytes","ARN":"arn:aws:s3:::tiger-training","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"TIGER Challenge","URL":"https://tiger.grand-challenge.org/","AuthorName":"Diagnostic Image Analysis Group, Radboudumc, Nijmegen","AuthorURL":"https://www.diagnijmegen.nl/"},{"Title":"Whole slide data - python package for working with wholeslide images and annotations files","URL":"https://github.com/DIAGNijmegen/pathology-whole-slide-data","AuthorName":"Diagnostic Image Analysis Group, Radboudumc, Nijmegen","AuthorURL":"https://www.diagnijmegen.nl/"}]},"RegistryEntryAdded":"2022-01-18","RegistryEntryLastModified":"2024-08-09","Slug":"tiger","Sources":["awslabs-open-data-registry"]}
{"Name":"Surrogate Data with Correlations, Trends, and Nonstationarities","Description":"The data in this collection include: (1) 6 surrogate stationary signals with different correlations; (2) 7 surrogate correlated signals with linear, sinusoidal and power-law trends; and (3) 15 surrogate correlated signals with different types of nonstationarities. Each data file contains one column of data in ASCII format.","Documentation":"https://doi.org/10.13026/C2KK54","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2KK54","ARN":"arn:aws:s3:::physionet-open/tns/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"tns","Sources":["awslabs-open-data-registry"]}
{"Name":"tOLIet: Single-lead Thigh-based Electrocardiography Using Polimeric Dry Electrodes","Description":"Our team previously introduced an innovative concept for an \"invisible\" Electrocardiography (ECG) system, incorporating electrodes and sensors into a toilet seat design to enable signal acquisition from the thighs. Building upon that work, we now present a novel dataset featuring real-world, single-lead ECG signals captured at the thighs, offering a valuable resource for advancing research on thigh-based ECG for cardiovascular disease assessment. To our knowledge, this is the first dataset of its kind. The tOLIet dataset comprises 149 ECG recordings collected from 86 individuals (50 females, 36 males) with an average age of 31.73 ± 13.11 years, a mean weight of 66.89 ± 10.70 kg, and an average height of 166.82 ± 6.07 cm. Participants were recruited through direct contact with the Principal Investigator at Centro Hospitalar Universitário de Lisboa Central (CHULC) and via clinical consultations conducted at the same institution. Each recording includes four differential signals acquired from electrode pairs embedded in the toilet seat, with reference signals obtained from a standard 12-lead hospital ECG system.","Documentation":"https://doi.org/10.13026/411k-1476","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/411k-1476","ARN":"arn:aws:s3:::physionet-open/tollet/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"tollet","Sources":["awslabs-open-data-registry"]}
{"Name":"Enriched Topical-Chat Dataset for Knowledge-Grounded Dialogue Systems","Description":"This dataset provides extra annotations on top of the publicly released\nTopical-Chat dataset(https://github.com/alexa/Topical-Chat) which will help in reproducing the results in our paper\n\"Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems\" (https://arxiv.org/abs/2005.12529?context=cs.CL). \nThe dataset contains 5 files: train.json, valid_freq.json, valid_rare.json, test_freq.json and test_rare.json. \nEach of these files will have additional annotations on top of the original Topical-Chat dataset.\nThese specific annotations are: dialogue act annotations and knowledge sentence annotations.\nThe annotations were computed automatically using off the shelf models which are mentioned in the README.txt\n","Documentation":"https://github.com/alexa/Topical-Chat/blob/master/TopicalChatEnriched/README.md","Contact":"behnam@amazon.com, karthgop@amazon.com, seokhwk@amazon.com, yangliud@amazon.com, mihaeric@amazon.com, hakkanit@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not currently being updated","Tags":["amazon.science","conversation data","machine learning","natural language processing"],"License":"[CDLA-Sharing](https://cdla.io/sharing-1-0/)","Resources":[{"Description":"Enriched Topical-Chat Dataset for Knowledge-Grounded Dialogue Systems","ARN":"arn:aws:s3:::enriched-topical-chat","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems","URL":"https://arxiv.org/abs/2005.12529?context=cs.CL","AuthorName":"Behnam Hedayatnia, Karthik Gopalakrishnan, Seokhwan Kim, Yang Liu, Mihail Eric & Dilek Hakkani-Tur"}]},"RegistryEntryAdded":"2020-08-24","RegistryEntryLastModified":"2022-06-06","Slug":"topical-chat-enriched","Sources":["awslabs-open-data-registry"]}
{"Name":"Term-Preterm EHG Database","Description":"The Electrohysterogram records (uterine EMG records) included in the Term-Preterm ElectroHysteroGram DataBase (TPEHG DB) were obtained from 1997 to 2005 at the University Medical Centre Ljubljana, Department of Obstetrics and Gynecology. The records were obtained during regular check-ups either around the 22nd week of gestation or around the 32nd week of gestation.","Documentation":"https://doi.org/10.13026/C2FW2V","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2FW2V","ARN":"arn:aws:s3:::physionet-open/tpehgdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"tpehgdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Term-Preterm EHG DataSet with Tocogram","Description":"The Term-Preterm ElectroHysteroGram DataSet with Tocogram (TPEHGT DS) contains 26 four-signal 30-min uterine EHG records, i.e., three EHG signals accompanied by a simultaneously recorded external tocogram measuring mechanical uterine activity (TOCO signal) of pregnant women, and another five 30-min uterine records (EHG signals and TOCO signal) of non-pregnant women.","Documentation":"https://doi.org/10.13026/C2166R","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2166R","ARN":"arn:aws:s3:::physionet-open/tpehgt/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"tpehgt","Sources":["awslabs-open-data-registry"]}
{"Name":"Transformer-DeID: Deidentification of free-text clinical notes with transformers","Description":"Sharing patient data such as clinical notes among clinicians and researchers is fundamental to advancements in clinical practice and biomedical research. Prior to distribution, it is often necessary and legally mandated to remove identifiers such as names, contact details, identification numbers, and dates to safeguard patient privacy. This process is known as deidentification. Here we provide a neural network model for the removal of patient identifiers from clinical text based upon Bidirectional Encoder Representations from Transformers (BERT) and two variations of BERT, namely Robustly Optimized BERT Approach (RoBERTa) and Distilled BERT (DistilBERT). The models demonstrated excellent performance on a publicly available benchmark and are made freely available to maximize reuse.","Documentation":"https://doi.org/10.13026/7dj5-7x85","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"MIT License","Resources":[{"Description":"https://doi.org/10.13026/7dj5-7x85","ARN":"arn:aws:s3:::physionet-open/transformer-deid/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"transformer-deid","Sources":["awslabs-open-data-registry"]}
{"Name":"Treadmill Maximal Exercise Tests from the Exercise Physiology and Human Performance Lab of the University of Malaga","Description":"The present database is an ensemble of the cardiorespiratory measurements acquired during 992 treadmill maximal graded exercise tests (GET) performed in the Exercise Physiology and Human Performance Lab of the University of Malaga. Heart rate, oxygen consumption, carbon dioxide generation, and pulmonary ventilation are measured on a breath-to-breath basis along with the treadmill speed during maximal effort tests. Participants are amateur and professional athletes are of ages ranging from 10 to 63 years old. The age, height, and weight of the participants are provided, as well as the temperature and humidity during the test.","Documentation":"https://doi.org/10.13026/7ezk-j442","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"PhysioNet Contributor Review Health Data License 1.5.0","Resources":[{"Description":"https://doi.org/10.13026/7ezk-j442","ARN":"arn:aws:s3:::physionet-open/treadmill-exercise-cardioresp/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"treadmill-exercise-cardioresp","Sources":["awslabs-open-data-registry"]}
{"Name":"Effect of Deep Brain Stimulation on Parkinsonian Tremor","Description":"The recordings of this database are of rest tremor velocity in the index finger of 16 subjects with Parkinson's disease (PD) who receive chronic high frequency electrical deep brain stimulation (DBS).","Documentation":"https://doi.org/10.13026/C2D097","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2D097","ARN":"arn:aws:s3:::physionet-open/tremordb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"tremordb","Sources":["awslabs-open-data-registry"]}
{"Name":"TSBench","Description":"TSBench comprises thousands of benchmark evaluations for time series forecasting methods. It provides various metrics (i.e. measures of accuracy, latency, number of model parameters, ...) of 13 time series forecasting methods across 44 heterogeneous datasets. Time series forecasting methods include both classical and deep learning methods while several hyperparameters settings are evaluated for the deep learning methods.\n\nIn addition to the tabular data providing the metrics, TSBench includes the probabilistic forecasts of all evaluated methods for all 44 datasets. While the tabular data is small (about 10 MiB), the forecasts amount to almost 600 GiB of data.\n","Documentation":"https://github.com/awslabs/gluon-ts/tree/master/src/gluonts/nursery/tsbench/README.md","Contact":"borchero@in.tum.de, dsalina@amazon.com","ManagedBy":"[Amazon Web Services](https://aws.amazon.com)","UpdateFrequency":"Not expected to be updated","Tags":["benchmark","deep learning","machine learning","meta learning","time series forecasting"],"License":"[CC BY](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"TSBench Evaluation Metrics and Probabilistic Forecasts","ARN":"arn:aws:s3:::odp-tsbench","Region":"us-east-1","Type":"S3 Bucket"}],"RegistryEntryAdded":"2022-02-09","RegistryEntryLastModified":"2022-02-09","Slug":"tsbench","Sources":["awslabs-open-data-registry"]}
{"Name":"T-Wave Alternans Challenge Database","Description":"This database has been assembled for the PhysioNet/Computers in Cardiology Challenge 2008. It contains 100 multichannel ECG records sampled at 500 Hz with 16 bit resolution over a ± 32 mV range. The subjects include patients with myocardial infarctions, transient ischemia, ventricular tachyarrhythmias, and other risk factors for sudden cardiac death, as well as healthy controls and synthetic cases with calibrated amounts of T-wave alternans.","Documentation":"https://doi.org/10.13026/C2B59B","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2B59B","ARN":"arn:aws:s3:::physionet-open/twadb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"twadb","Sources":["awslabs-open-data-registry"]}
{"Name":"University of British Columbia Sunflower Genome Dataset","Description":"This dataset captures Sunflower's genetic diversity originating\nfrom thousands of wild, cultivated, and landrace sunflower\nindividuals distributed across North America.\n\nThe data consists of raw sequences and associated botanical metadata,\naligned sequences (to three different reference genomes), and sets of\nSNPs computed across several cohorts.\n","Documentation":"https://rieseberglab.github.io/ubc-sunflower-genome/","Contact":"UBC Botany Sunflower <sunflower@botany.ubc.ca>","ManagedBy":"The  Rieseberg Lab at the University of British Columbia","UpdateFrequency":"Twice per year.","Tags":["agriculture","aws-pds","biodiversity","bioinformatics","biology","food security","genetic","genomic","life sciences","whole genome sequencing"],"License":"Public Domain","Resources":[{"Description":"UBC Sunflower Genome Data 1","ARN":"arn:aws:s3:::ubc-sunflower-genome","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2020-04-09","RegistryEntryLastModified":"2021-02-05","Slug":"ubc-sunflower-genome","Sources":["awslabs-open-data-registry"]}
{"Name":"St. Vincent's University Hospital / University College Dublin Sleep Apnea Database","Description":"This database contains 25 full overnight polysomnograms with simultaneous three-channel Holter ECG, from adult subjects with suspected sleep-disordered breathing. A revised version of this database was posted on 1 September 2011.","Documentation":"https://doi.org/10.13026/C26C7D","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C26C7D","ARN":"arn:aws:s3:::physionet-open/ucddb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"ucddb","Sources":["awslabs-open-data-registry"]}
{"Name":"UCSC Genome Browser Sequence and Annotations","Description":"The UCSC Genome Browser is an online graphical viewer for genomes, a genome browser, hosted by the University of California, Santa Cruz (UCSC). The interactive website offers access to genome sequence data from a variety of vertebrate and invertebrate species and major model organisms, integrated with a large collection of aligned annotations. This dataset is a copy of the MySQL tables in MyISAM binary and tab-sep format and all binary files in custom formats, sometimes referred as 'gbdb'-files. Data from the UCSC Genome Browser is free and open for use by anyone. However, every genome annotation track has been created by an academic research group, or, in a few cases, by commercial companies. Please acknowledge them by citing them. The information can be found by going to https://genome.ucsc.edu, selecting the respective genome assembly and clicking on the data track. At the end of the documentation, we provide a list of references and acknowledgements.","Documentation":"https://hgdownload.soe.ucsc.edu/downloads.html","Contact":"https://genome.ucsc.edu/contacts.html","ManagedBy":"University of California Santa Cruz Genome Institute","UpdateFrequency":"Monthly","Tags":["aws-pds","bioinformatics","biology","genetic","genomic","life sciences"],"License":"[Public domain, some tracks require attribution](https://genome.ucsc.edu/license/)","Resources":[{"Description":"https://genome.ucsc.edu/FAQ/FAQformat.html","ARN":"arn:aws:s3:::genome-browser","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Explore UCSC Genome Browser Data in a Browser-Based Notebook with Scigantic","URL":"https://scigantic.com/archive/4cdd817c-bd1f-5972-9a47-9ece7a8273c9/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"}]},"RegistryEntryAdded":"2021-12-14","RegistryEntryLastModified":"2026-11-03","Slug":"ucsc-genome-browser","Sources":["awslabs-open-data-registry"]}
{"Name":"The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset","Description":"The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) dataset is a public, clinical, multimodal brain MRI dataset consisting of 560 brain MRIs from 412 patients with expert annotations of 5136 brain metastases. Data consists of registered and skull stripped T1 post-contrast, T1 pre-contrast, FLAIR and subtraction (T1 pre-contrast - T1 post-contrast) images and voxelwise segmentations of enhancing brain metastases in NifTI format. ","Documentation":"https://imagingdatasets.ucsf.edu/dataset/1","Contact":"dmi-support@ucsf.edu","ManagedBy":"[UCSF Center for Intelligent Imaging](https://intelligentimaging.ucsf.edu/)","UpdateFrequency":"ad hoc","Tags":["aws-pds","cancer","life sciences","magnetic resonance imaging","medical imaging","medicine","radiology"],"License":"Custom, non-commerical, attribution, no redistribution, no re-identification.  Full license is available [here](http://d1atcui2oic5ew.cloudfront.net/terms/2a109330-9769-44af-a18b-c4be64ee26b5).","Citation":"https://arxiv.org/abs/2304.07248","Resources":[{"Description":"Zip archive containing NifTI files","ARN":"arn:aws:s3:::ucsf-dmi/UCSF_BrainMetastases_v1.zip","Region":"us-west-1","Type":"S3 Bucket","ControlledAccess":"https://imagingdatasets.ucsf.edu/dataset/1"}],"RegistryEntryAdded":"2023-05-31","RegistryEntryLastModified":"2023-05-31","Slug":"ucsf-bmsr","Sources":["awslabs-open-data-registry"]}
{"Name":"UCSF Primary Central Nervous System Lymphoma MRI Dataset","Description":"This BIDS-formatted dataset provides multimodal brain MRI data from 150 patients with primary central nervous system lymphoma (PCNSL), including T1-weighted, contrast-enhanced T1-weighted, FLAIR, and ADC sequences. The dataset includes expert-annotated lesion segmentations with radiomic features, along with anonymized clinical data including demographics, diagnosis history, and medications.\n","Documentation":"https://github.com/rauschecker-sugrue-labs/UCSF-PCNSL","Contact":"[Andreas Rauschecker](andreas.rauschecker@ucsf.edu)","ManagedBy":"[BRAIN Lab](https://brainlab.ucsf.edu/)","UpdateFrequency":"ad hoc","Tags":["aws-pds","brain images","cancer","life sciences","magnetic resonance imaging","medical imaging","medicine","neuroimaging","radiology"],"License":"https://creativecommons.org/licenses/by/4.0/","Citation":null,"Resources":[{"Description":"UCSF PCNSL MRI Data on S3","ARN":"arn:aws:s3:::ucsf-dmi/pcnsl-dataset_v1.0.zip","Region":"us-west-1","Type":"S3 Bucket","ControlledAccess":"https://imagingdatasets.ucsf.edu/dataset/4"},{"Description":"Notifications for new PCNSL MRI data","ARN":"arn:aws:sns:us-west-1:905542596225:ucsf-dmi-object_created","Region":"us-west-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"PCNSL Data Access Tutorial","URL":"https://github.com/rauschecker-sugrue-labs/UCSF-PCNSL","AuthorName":"Michael Francis Romano","AuthorURL":"https://scholar.google.com/citations?user=WeaMpL0AAAAJ&hl=en","NotebookURL":"https://github.com/rauschecker-sugrue-labs/UCSF-PCNSL/blob/main/get-to-know-a-dataset-pcnsl.ipynb"}]},"DeprecatedNotice":null,"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-01-21","RegistryEntryLastModified":"2026-04-14","Slug":"ucsf-pcnsl","Sources":["awslabs-open-data-registry"]}
{"Name":"UCSF Prostate MRI Dataset","Description":"This dataset provides 973 prostate MRI exams performed between January 2016 and March 2019 for patients who underwent MRI/ultrasound fusion biopsy within one year at UCSF.","Documentation":"https://github.com/LarsonLab/UCSF-ProstateMR","Contact":"peder.larson@ucsf.edu","ManagedBy":"[UCSF Larson Advanced Imaging Lab](https://larsonlab.github.io/)","UpdateFrequency":"ad hoc","Tags":["aws-pds","cancer","life sciences","magnetic resonance imaging","medical imaging","medicine","radiology","segmentation"],"License":"https://creativecommons.org/licenses/by/4.0/","Citation":null,"Resources":[{"Description":"Prostate MRI Data","ARN":"arn:aws:s3:::ucsf-dmi/UCSF-ProstateMR.zip","Region":"us-west-1","Type":"S3 Bucket","ControlledAccess":"https://imagingdatasets.ucsf.edu/dataset/5"},{"Description":"Notifications for new Prostate MRI Data","ARN":"arn:aws:sns:us-west-1:905542596225:ucsf-dmi-object_created","Region":"us-west-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get to Know a Dataset - UCSF-ProstateMR","URL":"https://github.com/LarsonLab/UCSF-ProstateMR/blob/main/tutorials/get-to-know-a-dataset_UCSF-ProstateMR.ipynb","NotebookURL":"https://github.com/LarsonLab/UCSF-ProstateMR/blob/main/tutorials/get-to-know-a-dataset_UCSF-ProstateMR.ipynb","AuthorName":"UCSF Larson Advanced Imaging Lab","AuthorURL":"https://larsonlab.github.io/"}],"Tools & Applications":[{"Title":"UCSF-ProstateMR Repository","URL":"https://github.com/LarsonLab/UCSF-ProstateMR","AuthorName":"UCSF Larson Advanced Imaging Lab","AuthorURL":"https://larsonlab.github.io/"}],"Publications":[{"Title":"Federated learning with research prototypes - application to multi-center MRI-based detection of prostate cancer with diverse histopathology","URL":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10869141/","AuthorName":"Rajagopal A et al.","AuthorURL":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10869141/"},{"Title":"Mixed supervision of histopathology improves prostate cancer classification from MRI","URL":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11361281/","AuthorName":"Rajagopal A et al.","AuthorURL":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11361281/"},{"Title":"US lesion visibility predicts clinically significant upgrade of prostate cancer by systematic biopsy","URL":"https://pmc.ncbi.nlm.nih.gov/articles/PMC8863714/","AuthorName":"Velarde N et al.","AuthorURL":"https://pmc.ncbi.nlm.nih.gov/articles/PMC8863714/"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-05-11","RegistryEntryLastModified":"2026-09-17","Slug":"ucsf-prostatemr","Sources":["awslabs-open-data-registry"]}
{"Name":"UCSF Renal Mass CT Dataset","Description":"This dataset provides a set of 831 3D Multiphase CT exams of renal masses, registered across phases with annotations identifying the masses","Documentation":"https://github.com/LarsonLab/UCSF-RMaC","Contact":"[Peder Larson](peder.larson@ucsf.edu)]","ManagedBy":"[UCSF Larson Advanced Imaging Lab](https://larsonlab.github.io/)","UpdateFrequency":"ad hoc","Tags":["aws-pds","cancer","computed tomography","life sciences","medical imaging","medicine","radiology"],"License":"https://creativecommons.org/licenses/by/4.0/","Citation":null,"Resources":[{"Description":"Renal Mass CT Data on S3","ARN":"arn:aws:s3:::ucsf-rmac-dataset","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Notifications for new Renal Mass CT data","ARN":"arn:aws:sns:us-west-1:905542596225:ucsf-dmi-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Label Exploration Tutorial","URL":"https://github.com/LarsonLab/UCSF-RMaC/blob/main/tutorials/labelexploration.ipynb","NotebookURL":null,"AuthorName":"Sule Sahin","AuthorURL":"https://github.com/sule-sahin"},{"Title":"Mask Overlays","URL":"https://github.com/LarsonLab/UCSF-RMaC/blob/main/tutorials/maskoverlays.ipynb","NotebookURL":null,"AuthorName":"Sule Sahin","AuthorURL":"https://github.com/sule-sahin"}],"Tools & Applications":[{"Title":"UCSF Renal Mass CT Dataset","URL":"https://github.com/LarsonLab/UCSF-RMaC","AuthorName":"Peder Larson","AuthorURL":"https://scholar.google.com/citations?user=LrQ7YekAAAAJ&hl=en"}]},"DeprecatedNotice":null,"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2025-09-02","RegistryEntryLastModified":"2025-09-24","Slug":"ucsf-rmac","Sources":["awslabs-open-data-registry"]}
{"Deprecated":true,"Name":"Retired – UK Met Office Atmospheric Deterministic and Probabilistic Forecasts","Description":"As of the 13th July, no further Met Office forecasts or data will be available via this service.\n\nMeteorological data reusers now have an exciting opportunity to sample, experiment and evaluate\nMet Office atmospheric model data, whilst also experiencing a transformative method of requesting\ndata via Restful APIs on AWS.\n\nFor information about the data see the [Met Office website](https://www.metoffice.gov.uk/services/data/met-office-data-for-reuse/discovery).\nFor examples of using the data check out the [examples repository](https://github.com/MetOffice/aws-earth-examples).\nIf you need help and support using the data please raise an issue on the examples repository.\n\n**Please note:** Met Office continuously improves and updates its operational forecast models.\nOur last update became effective 04/12/2019. Please find the details [here](https://www.metoffice.gov.uk/services/data/met-office-data-for-reuse/ps43_aws).\n","Documentation":"https://github.com/MetOffice/aws-earth-examples","Contact":"We want your feedback.\n<br><br>\nThe Met Office are always looking to improve our services. If you wish to be kept informed of changes to our data offerings, please add your contact details to this [short form](https://www.metoffice.gov.uk/forms/met-office-data-product-feedback) along with any feedback you wish to give.\n<br><br>\nhttps://www.metoffice.gov.uk/about-us/contact\n","UpdateFrequency":"Daily (with 24 hour delay)","ManagedBy":"[UK Met Office](https://www.metoffice.gov.uk/)","Collabs":{"ASDI":{"Tags":["weather"]}},"Tags":["aws-pds","agriculture","earth observation","climate","weather","meteorological"],"License":"This data is made available under a [Creative Commons licence](https://creativecommons.org/licenses/by-nc-nd/4.0/).\nThis data is free to use for non-commercial purposes under the terms of the licence. You must\nensure that you acknowledge the source of this data as prescribed by this licence. Data or any\nderivatives made using the data are for internal use only and may not be distributed or otherwise\nshared.\n","Resources":[{"Description":"[Met Office UK Atmospheric Hi-Res Model (UKV) - 2km Deterministic](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/data/ukv-aws-parameters-nov-2019.pdf)","ARN":"arn:aws:s3:::aws-earth-mo-atmospheric-ukv-prd","Region":"eu-west-2","Type":"S3 Bucket"},{"Description":"[Met Office Global Atmospheric Hi-Res Model - 10km Deterministic](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/data/global-model-aws-parameters-nov-2019.pdf)","ARN":"arn:aws:s3:::aws-earth-mo-atmospheric-global-prd","Region":"eu-west-2","Type":"S3 Bucket"},{"Description":"[Met Office Global and Regional Ensemble Prediction System (UK) MOGREPS-UK](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/data/mogreps-uk-aws-parameters-november-2019.pdf)","ARN":"arn:aws:s3:::aws-earth-mo-atmospheric-mogreps-uk-prd","Region":"eu-west-2","Type":"S3 Bucket"},{"Description":"[Met Office Global and Regional Ensemble Prediction System (Global) - MOGREPS-G](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/data/mogreps-g-aws-parameters-nov-2019.pdf)","ARN":"arn:aws:s3:::aws-earth-mo-atmospheric-mogreps-g-prd","Region":"eu-west-2","Type":"S3 Bucket"},{"Description":"[Met Office UK Atmospheric Hi-Res Model (UKV) - 2km Deterministic](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/data/ukv-aws-parameters-nov-2019.pdf)","ARN":"arn:aws:sns:eu-west-2:021908831235:aws-earth-mo-atmospheric-ukv-prd","Region":"eu-west-2","Type":"SNS Topic"},{"Description":"[Met Office Global Atmospheric Hi-Res Model - 10km Deterministic](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/data/global-model-aws-parameters-nov-2019.pdf)","ARN":"arn:aws:sns:eu-west-2:021908831235:aws-earth-mo-atmospheric-global-prd","Region":"eu-west-2","Type":"SNS Topic"},{"Description":"[Met Office Global and Regional Ensemble Prediction System (UK) MOGREPS-UK](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/data/mogreps-uk-aws-parameters-november-2019.pdf)","ARN":"arn:aws:sns:eu-west-2:021908831235:aws-earth-mo-atmospheric-mogreps-uk-prd","Region":"eu-west-2","Type":"SNS Topic"},{"Description":"[Met Office Global and Regional Ensemble Prediction System (Global) - MOGREPS-G](https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/data/mogreps-g-aws-parameters-nov-2019.pdf)","ARN":"arn:aws:sns:eu-west-2:021908831235:aws-earth-mo-atmospheric-mogreps-g-prd","Region":"eu-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Met Office AWS Earth data - Getting Started","URL":"https://github.com/MetOffice/aws-earth-examples/blob/master/examples/1.%20Getting%20Started.ipynb","AuthorName":"Jacob Tomlinson"},{"Title":"Met Office AWS Earth data - Subscribing to data","URL":"https://github.com/MetOffice/aws-earth-examples/blob/master/examples/2.%20Subscribing%20to%20data.ipynb","AuthorName":"Jacob Tomlinson"}],"Tools & Applications":null,"Publications":null},"DeprecatedNotice":"#### The provider of this dataset will no longer maintain this dataset.  We are open to talking with anyone else who might be willing to provide this dataset to the community.  Contact [opendata@amazon.com](mailto:opendata@amazon.com)\n","RegistryEntryAdded":"2018-12-13","RegistryEntryLastModified":"2026-07-20","Slug":"uk-met-office","Sources":["awslabs-open-data-registry"]}
{"Name":"UK Biobank Linkage Disequilibrium Matrices","Description":"Linkage disequilibrium (LD) matrices of UK Biobank participants of a British ancestry, based on imputed genotypes.","Documentation":"https://github.com/omerwe/polyfun","Contact":"https://github.com/omerwe/polyfun/issues","ManagedBy":"[Omer Weissbrod](https://github.com/omerwe)","UpdateFrequency":"None","Tags":["aws-pds","genetic","genome wide association study","genomic","life sciences","population genetics"],"License":"[CC BY-4.0](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Linkage disequilibrium (LD) matrices of UK Biobank participants of a British ancestry, based on imputed genotypes.","ARN":"arn:aws:s3:::broad-alkesgroup-ukbb-ld","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"PolyFun Wiki","URL":"https://github.com/omerwe/polyfun/wiki","AuthorName":"Omer Weissbrod"}],"Tools & Applications":[{"Title":"PolyFun and PolyPred software","URL":"https://github.com/omerwe/polyfun","AuthorName":"Omer Weissbrod"}],"Publications":[{"Title":"Functionally informed fine-mapping and polygenic localization of complex trait heritability","URL":"https://www.nature.com/articles/s41588-020-00735-5","AuthorName":"Weissbrod et al."},{"Title":"Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores","URL":"https://www.nature.com/articles/s41588-022-01036-9","AuthorName":"Weissbrod et al."}]},"RegistryEntryAdded":"2023-04-25","RegistryEntryLastModified":"2023-04-25","Slug":"ukbb-ld","Sources":["awslabs-open-data-registry"]}
{"Name":"UK Biobank Pharma Proteomics Project (UKB-PPP)","Description":"The UKB-PPP is a collaboration between the UK Biobank (UKB) and thirteen biopharmaceutical companies characterising the plasma proteomic profiles of 54,219 UKB participants. As part of a collaborative analysis across the thirteen UKB-PPP partners, we conducted comprehensive protein quantitative trait loci (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 85% are newly discovered, in addition to ancestry-specific pQTL mapping in non-Europeans. We identify independent secondary associations in 87% of cis and 30% of trans loci, expanding the catalogue of genetic instruments for downstream analyses.","Documentation":"https://www.synapse.org/#!Synapse:syn51364943/","Contact":"matthias.arnold@helmholtz-munich.de","ManagedBy":"Sage Bionetworks","UpdateFrequency":"Never","Tags":["aws-pds","genome wide association study","life sciences","population genetics"],"License":"[CC BY]","Citation":"Sun, B.B., Chiou, J., Traylor, M. et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature 622, 329–338 (2023). https://doi.org/10.1038/s41586-023-06592-6","Resources":[{"Description":"Population-specific GWAS summary statistics per protein measurement, as well as meta-data for annotating protein measurements and genetic variants.","ARN":"arn:aws:s3:::ukbiobank.opendata.sagebase.org","Region":"us-east-1","Type":"S3 Bucket","AccountRequired":false,"ControlledAccess":"https://doi.org/10.7303/syn51364943"}],"DataAtWork":{"Publications":[{"Title":"Plasma proteomic associations with genetics and health in the UK Biobank","URL":"https://doi.org/10.1038/s41586-023-06592-6","AuthorName":"Sun B, Chiou J, Traylor M, Benner C, Hsu Y, Richardson T, et al"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2023-12-06","RegistryEntryLastModified":"2024-12-20","Slug":"ukbppp","Sources":["awslabs-open-data-registry"]}
{"Name":"Umbra Synthetic Aperture Radar (SAR) Open Data","Description":"Umbra satellites generate the highest resolution Synthetic Aperture Radar (SAR) imagery ever offered from space, up to 16-cm resolution. SAR can capture images at night, through cloud cover, smoke and rain. SAR is unique in its abilities to monitor changes. The Open Data Program (ODP) features over twenty diverse time-series locations that are updated frequently, allowing users to experiment with SAR's capabilities. We offer single-looked spotlight mode in either 16cm, 25cm, 35cm, 50cm, or 1m resolution, and multi-looked spotlight mode. The ODP also features an assorted collection of over 250+ images and counting of various locations around the world, ranging from emergency response, to gee-whiz sites. If you have a suggestion for a new location, feedback on the dataset, or any questions, contact us at umbra.space/open-data.","Documentation":"https://help.umbra.space/product-guide","Contact":"help@umbra.space","ManagedBy":"[Umbra](http://umbra.space/)","UpdateFrequency":"New data is added frequently. The frequent updates enable users to analyze the time-series data to detect changes in each location. Umbra's goal is to provide the user with a deep understanding on what's possible with our data.","Tags":["aws-pds","earth observation","geospatial","image processing","satellite imagery","stac","synthetic aperture radar"],"License":"All data is provided with a Creative Commons License ([CC by 4.0](https://umbra.space/open-license)), which gives you the right to do just about anything you want with it.\n","Resources":[{"Description":"Umbra Spotlight collects including GEC, SICD, SIDD, CPHD data and metadata","ARN":"arn:aws:s3:::umbra-open-data-catalog","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"Explore":["[Browse Bucket](http://umbra-open-data-catalog.s3-website.us-west-2.amazonaws.com/)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"umbra-py documentation and interactive catalog showcase","URL":"https://umbra-py.space/","AuthorName":"umbra-py contributors","AuthorURL":"https://github.com/reesehammer/umbra-py"},{"Title":"umbra-py — Python toolkit to search, preview, load, and convert Umbra open SAR data","URL":"https://pypi.org/project/umbra-py/","AuthorName":"umbra-py contributors","AuthorURL":"https://github.com/reesehammer/umbra-py"}],"Tutorials":[{"Title":"umbra-py example notebooks (search, load, change detection, chips, SICD)","URL":"https://github.com/reesehammer/umbra-py/tree/main/examples","NotebookURL":"https://github.com/reesehammer/umbra-py/blob/main/examples/01_hello_umbra.ipynb","AuthorName":"umbra-py contributors","AuthorURL":"https://github.com/reesehammer/umbra-py"},{"Title":"umbra-py quickstart (search, preview, and load a scene)","URL":"https://umbra-py.space/quickstart/","AuthorName":"umbra-py contributors","AuthorURL":"https://github.com/reesehammer/umbra-py"}]},"RegistryEntryAdded":"2023-03-14","RegistryEntryLastModified":"2026-08-21","Slug":"umbra-open-data","Sources":["awslabs-open-data-registry"]}
{"Name":"Long-term Recordings of Gait Dynamics","Description":"Stride interval fluctuations were studied in ten young, healthy men. Participants had no history of any neuromuscular, respiratory or cardiovascular disorders, and were taking no medications. Mean age was 21.7 years (range: 18-29 years). Height was 1.77 ± 0.08 meters (mean ± S.D.) and weight was 71.8 ± 10.7 kg. All subjects provided informed written consent. Subjects walked continuously on level ground around an obstacle free, long (either 225 or 400 meters), approximately oval path and the stride interval was measured using ultra-thin, force sensitive switches taped inside one shoe.","Documentation":"https://doi.org/10.13026/C28679","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C28679","ARN":"arn:aws:s3:::physionet-open/umwdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"umwdb","Sources":["awslabs-open-data-registry"]}
{"Name":"UniCA ElectroTastegram Database (PROP)","Description":"The UniCA ElectroTastegram Database (PROP) contains 39 differential biopotential measurements recorded from the tongues of as many healthy voluntary human subjects (16 males, 23 females, equally divided into the three PROP taster status classes), during a stimulation with 30uL, 3.2 mmol/L solution of 6-n-propylthiouracil (PROP).","Documentation":"https://doi.org/10.13026/C2BH3H","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2BH3H","ARN":"arn:aws:s3:::physionet-open/unicaprop/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"unicaprop","Sources":["awslabs-open-data-registry"]}
{"Name":"UniProt","Description":"The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. The UniProt databases are the UniProt Knowledgebase (UniProtKB), the UniProt Reference Clusters (UniRef), and the UniProt Archive (UniParc). The UniProt consortium and host institutions [EMBL-EBI](https://www.ebi.ac.uk), [SIB Swiss Institute of Bioinformatics](https://www.sib.swiss) and [PIR](https://proteininformationresource.org/) are committed to the long-term preservation of the [UniProt](https://www.uniprot.org) databases.","Contact":"https://www.uniprot.org/contact","Documentation":"https://www.uniprot.org/help/about","ManagedBy":"[SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the UniProt Consortium","UpdateFrequency":"Under 1 months after a new UniProt release.","Tags":["aws-pds","bioinformatics","biology","chemistry","enzyme","graph","life sciences","molecule","protein","RDF","SPARQL"],"License":"http://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"UniProt 2026_02","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2026-02/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2026_01","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2026-01/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2025_04","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2025-04/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2025_03","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2025-03/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2025_02","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2025-02/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2025_01","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2025-01/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2024_06","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2024-06/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2024_05","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2024-05/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2024_04","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2024-04/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2024_03","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2024-03/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2024_02","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2024-02/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2024_01","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2024-01/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2023_05","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2023-05/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2023_04","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2023-04/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2023_03","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2023-03/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2023_02","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2023-02/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2023_01","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2023-01/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2022_05","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2022-05/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2022_04","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2022-04/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2022_03","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2022-03/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2022_02","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2022-02/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2022_01","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2022-01/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2021_04","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2021-04/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2021_03","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2021-03/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2021_02","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2021-02/","Region":"eu-west-3","Type":"S3 Bucket"},{"Description":"UniProt 2021_01","ARN":"arn:aws:s3:::aws-open-data-uniprot-rdf/2021-01/","Region":"eu-west-3","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Exploring the UniProt protein knowledgebase with AWS Open Data and Amazon Neptune","URL":"https://aws.amazon.com/blogs/industries/exploring-the-uniprot-protein-knowledgebase-with-aws-open-data-and-amazon-neptune/","AuthorName":"Eric Greene, Rafa Xu, Yuan Shi (AWS)","Services":["Amazon Neptune"]},{"Title":"UniProt SPARQL","URL":"https://github.com/sib-swiss/sparql-training/tree/master/uniprot","AuthorName":"Swiss-Prot Group at SIB Swiss Institute of Bioinformatics"}]},"RegistryEntryAdded":"2021-04-06","RegistryEntryLastModified":"2026-08-04","Slug":"uniprot","Sources":["awslabs-open-data-registry"]}
{"Name":"Covid Job Impacts - US Hiring Data Since March 1 2020","Description":"This dataset provides daily updates on the volume of US job listings filtered by geography industry job family and role; normalized to pre-covid levels.\n\nThese data files feed the business intelligence visuals at covidjobimpacts.greenwich.hr, a public-facing site hosted by Greenwich.HR and OneModel Inc.\nData is derived from online job listings tracked continuously, calculated daily and published nightly.  On average data from 70% of all new US jobs are captured,\nand the dataset currently contains data from 3.3 million hiring organizations.\n\nData for each filter segment is represented as the 7-day average of new job listings for a specific date, expressed as a percentage of the corresponding value \non March 1, 2020.\n","Documentation":"https://greenwichhr-covidjobimpacts.s3.us-east-2.amazonaws.com/ghr_data_specs_covid_public.pdf","Contact":"info@Greenwich.HR","ManagedBy":"[Greenwich.HR](http://www.greenwich.hr/)","UpdateFrequency":"Daily","Tags":["aws-pds","COVID-19","economics","financial markets","hiring","market data"],"License":"This data is made available under a [Creative Commons license](https://creativecommons.org/licenses/by-nc-nd/4.0/).\nThis data is free to use for non-commercial purposes under the terms of the license. You must\nensure that you acknowledge the source of this data as prescribed by this license. Data or any\nderivatives made using the data are for internal use only and may not be distributed or otherwise\nshared.\n","Resources":[{"Description":"Greenwich.HR daily US hiring data (normalized to March 1 2020)","ARN":"arn:aws:s3:::greenwichhr-covidjobimpacts","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Documentation of dataset schemas","URL":"https://greenwichhr-covidjobimpacts.s3.us-east-2.amazonaws.com/ghr_data_specs_covid_public.pdf","AuthorName":"Greenwich.HR","AuthorURL":"https://www.greenwich.hr"}],"Tools & Applications":[{"Title":"CovidJobImpats.Greenwich.HR - online visualization of daily hiring data and weekly unemployment data including links to recorded discussions using the data","URL":"https://covidjobimpacts.greenwich.hr","AuthorName":"Greenwich.HR and OneModel Inc."}]},"RegistryEntryAdded":"2020-08-06","RegistryEntryLastModified":"2024-03-26","Slug":"us-hiring-rates-pandemic","Sources":["awslabs-open-data-registry"]}
{"Name":"USearch Molecules","Description":"Collection of 7 billion small molecules in SMILES notation with 28 billion fingerprints, including MACCS, ECFP4, FCFP4, and PubChem, with pre-constructed USearch indexes over them.","Documentation":"https://github.com/ashvardanian/usearch-molecules","Contact":"ash.vardanian@unum.cloud","ManagedBy":"[Ash Vardanian](https://ashvardanian.com)","UpdateFrequency":"Not updated","Tags":["aws-pds","biology","chemical biology","life sciences","pharmaceutical"],"License":"[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)","Resources":[{"Description":"Project data files in a public bucket","ARN":"arn:aws:s3:::usearch-molecules","Region":"us-west-2","Type":"S3 Bucket"}],"RegistryEntryAdded":"2023-11-17","RegistryEntryLastModified":"2023-11-17","Slug":"usearch-molecules","Sources":["awslabs-open-data-registry"]}
{"Name":"USGS Landsat","Description":"This joint NASA/USGS program provides the longest continuous space-based record of \nEarth’s land in existence. Every day, Landsat satellites provide essential information \nto help land managers and policy makers make wise decisions about our resources and our environment.\nData is provided for Landsats 1, 2, 3, 4, 5, 7, 8, and 9 (excludes Landsat 6).\n\nAs of June 28, 2023 ([announcement](https://www.usgs.gov/landsat-missions/news/changes-coming-landsats-simple-notification-service-amazon-web-service)),\nthe previous single SNS topic `arn:aws:sns:us-west-2:673253540267:public-c2-notify` was replaced with\nthree new SNS topics for different types of scenes.\n","Documentation":"https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-data-access","Contact":"https://answers.usgs.gov/","ManagedBy":"[United States Geological Survey](https://www.usgs.gov)","UpdateFrequency":"New scenes are added daily.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["agriculture","aws-pds","cog","disaster response","earth observation","geospatial","natural resource","satellite imagery","stac"],"License":"There are no restrictions on Landsat data downloaded from the USGS; it can be used or \nredistributed as desired. We do request that you include a [statement of the data source](https://www.usgs.gov/centers/eros/data-citation?qt-science_support_page_related_con=0#qt-science_support_page_related_con) \nwhen citing, copying, or reprinting USGS Landsat data or images.\n","Resources":[{"Description":"Scenes and metadata","ARN":"arn:aws:s3:::usgs-landsat/collection02/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":true,"Explore":["[STAC Catalog](https://landsatlook.usgs.gov/stac-server/collections)"]},{"Description":"New scene notifications, Level-1 and Level-2 Scenes","ARN":"arn:aws:sns:us-west-2:673253540267:public-c2-notify-v2","Region":"us-west-2","Type":"SNS Topic"},{"Description":"New scene notifications, Level 3 Science Products","ARN":"arn:aws:sns:us-west-2:673253540267:public-c2-level-3-tile-notify-v2","Region":"us-west-2","Type":"SNS Topic"},{"Description":"New scene notifications, U.S. ARD Tiles","ARN":"arn:aws:sns:us-west-2:673253540267:public-c2-ard-tile-notify-v2","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"A Gentle Introduction to GDAL Part 4: Working with Satellite Data","URL":"https://medium.com/planet-stories/a-gentle-introduction-to-gdal-part-4-working-with-satellite-data-d3835b5e2971","AuthorName":"Planet","AuthorURL":"https://www.planet.com/"},{"Title":"Cloud Segmentation in Landsat-8 Images","URL":"https://medium.com/sentinel-hub/clouds-segmentation-in-landsat-8-images-da370815235","AuthorName":"Ingrid Grenet"},{"Title":"Creating a STAC of Landsat data","URL":"https://pystac.readthedocs.io/en/stable/tutorials/creating-a-landsat-stac.html","AuthorName":"PySTAC Contributors"},{"Title":"Exploring the Chile wildfires with Landsat and Sentinel-2 imagery","URL":"https://www.gearthblog.com/blog/archives/2017/02/exploring-chile-wildfires-landsat-sentinel-2-imagery.html","AuthorName":"Timothy Whitehead"},{"Title":"How to Work with Landsat and Sentinel-2 on AWS with Python","URL":"https://www.matecdev.com/posts/landsat-sentinel-aws-s3-python.html","AuthorName":"Martin D. Maas","AuthorURL":"https://www.matecdev.com/"},{"Title":"Integrate imagery from the full Landsat archive into your own apps, maps, and analysis with Landsat image services","URL":"http://www.arcgis.com/home/group.html?id=a74dff13f1be4b2ba7264c3315c57077#overview","AuthorName":"Esri","AuthorURL":"https://www.esri.com/en-us/home"},{"Title":"Landsat Cloud Direct Access Requester Pays Tutorial","URL":"https://www.usgs.gov/media/files/landsat-cloud-direct-access-requester-pays-tutorial","AuthorName":"USGS","AuthorURL":"https://www.usgs.gov"},{"Title":"Landsat Data is Moving to the Cloud","URL":"https://www.usgs.gov/media/videos/landsat-data-cloud","AuthorName":"USGS","AuthorURL":"https://www.usgs.gov"},{"Title":"Landsat in the Cloud – Supporting Current Workflows","URL":"https://www.usgs.gov/media/videos/landsat-cloud-will-my-workflow-be-supported","AuthorName":"USGS","AuthorURL":"https://www.usgs.gov"},{"Title":"Using Cloud-Optimized GeoTIFF","URL":"http://www.acgeospatial.co.uk/cog-part-2-python/","AuthorName":"acgeospatial","AuthorURL":"http://www.acgeospatial.co.uk/"}],"Tools & Applications":[{"Title":"Aggi: the fire detecting Twitter bot","URL":"https://twitter.com/aggibot","AuthorName":"Aggi","AuthorURL":"https://twitter.com/aggibot"},{"Title":"Apps for exploring and analyzing Landsat imagery on the fly","URL":"http://www.esri.com/landing-pages/software/landsat/unlock-earths-secrets","AuthorName":"Esri","AuthorURL":"https://www.esri.com/en-us/home"},{"Title":"COG-Explorer - View Cloud Optimized GeoTIFF images in the browser directly from object storage","URL":"https://geotiffjs.github.io/cog-explorer/","AuthorName":"EOX","AuthorURL":"https://eox.at/"},{"Title":"Development Seed Geolambda","URL":"https://developmentseed.org/blog/2017/08/17/geolambdas/","AuthorName":"Matthew Hanson","AuthorURL":"https://developmentseed.org/team/matt-hanson/"},{"Title":"Dynamic map tiling with Cloud-Optimized GeoTIFFs","URL":"https://kylebarron.dev/blog/cog-mosaic/overview","AuthorName":"Kyle Barron"},{"Title":"EO Browser","URL":"http://apps.sentinel-hub.com/eo-browser/","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"},{"Title":"EOS Land Viewer","URL":"https://eos.com/landviewer/","AuthorName":"Earth Observing System","AuthorURL":"https://eos.com/"},{"Title":"FME Landsat-8 on AWS Reader","URL":"https://docs.safe.com/fme/html/FME_Desktop_Documentation/FME_ReadersWriters/landsat8aws/landsat8aws.htm","AuthorName":"Safe Software","AuthorURL":"https://www.safe.com/"},{"Title":"FME Landsat-8/Sentinel-2 File Selector","URL":"http://demos.fmeserver.com/landsat-sentinel-selector/","AuthorName":"Safe Software","AuthorURL":"https://www.safe.com/"},{"Title":"Sentinel Hub WMS/WMTS/WCS Service for Landsat","URL":"http://www.sentinel-hub.com/apps/wms","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"},{"Title":"Sentinel Playground for Landsat","URL":"https://apps.sentinel-hub.com/sentinel-playground/?source=L8","AuthorName":"Sinergise","AuthorURL":"http://www.sinergise.com/"},{"Title":"Spectator - tracking Landsat 8, accessing the data and quick preview","URL":"https://spectator.earth/","AuthorName":"Spectator","AuthorURL":"https://spectator.earth/"}],"Publications":[{"Title":"Using Remote Sensing Images and Cloud Services on AWS to Improve Land Use and Cover Monitoring","URL":"https://ieeexplore.ieee.org/abstract/document/9165649","AuthorName":"K. R. Ferreira, et al."}]},"RegistryEntryAdded":"2021-03-25","RegistryEntryLastModified":"2024-03-27","Slug":"usgs-landsat","Sources":["awslabs-open-data-registry"]}
{"Name":"USGS 3DEP LiDAR Point Clouds","Description":"The goal of the [USGS 3D Elevation Program ](https://www.usgs.gov/core-science-systems/ngp/3dep) (3DEP) is to collect elevation data in the form of light detection and ranging (LiDAR) data over the conterminous United States, Hawaii, and the U.S. territories, with data acquired over an 8-year period. This dataset provides two realizations of the 3DEP point cloud data. The first resource is a public access organization provided in [Entwine Point Tiles](https://entwine.io/entwine-point-tile.html) format, which a lossless, full-density, streamable octree based on [LASzip](https://laszip.org) (LAZ) encoding. The second resource is a [Requester Pays](https://docs.aws.amazon.com/AmazonS3/latest/dev/RequesterPaysBuckets.html) of the original, Raw LAZ (Compressed LAS) 1.4 3DEP format, and more complete in coverage, as sources with incomplete or missing CRS, will not have an ETP tile generated.  Resource names in both buckets correspond to the USGS project names.","Documentation":"https://github.com/hobu/usgs-lidar/","Contact":"https://github.com/hobu/usgs-lidar","ManagedBy":"[Hobu, Inc.](https://hobu.co)","UpdateFrequency":"Periodically","Collabs":{"ASDI":{"Tags":["elevation"]}},"Tags":["agriculture","aws-pds","disaster response","elevation","geospatial","lidar","stac"],"License":"US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map","Resources":[{"Description":"Public access Entwine Point Tiles of most resources from the ``arn:aws:s3:::usgs-lidar`` bucket.","ARN":"arn:aws:s3:::usgs-lidar-public","Region":"us-west-2","Type":"S3 Bucket","Explore":["[STAC Catalog](https://usgs-lidar-stac.s3-us-west-2.amazonaws.com/ept/catalog.json)"]},{"Description":"A [Requester Pays](https://docs.aws.amazon.com/AmazonS3/latest/dev/RequesterPaysBuckets.html) Bucket of Raw LAZ 1.4 3DEP data. Data in this bucket is more complete in coverage than the EPT bucket, but it is not a complete 3DEP mirror. Some resources in this bucket also have incomplete and missing coordinate system information, which is why they might not be mirrored into the EPT bucket.","ARN":"arn:aws:s3:::usgs-lidar","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":true}],"DataAtWork":{"Tutorials":[{"Title":"Extracting buildings and roads from AWS Open Data using Amazon SageMaker","URL":"https://aws.amazon.com/blogs/machine-learning/extracting-buildings-and-roads-from-aws-open-data-using-amazon-sagemaker/","AuthorName":"Yunzhi Shi, Tianyu Zhang, and Xin Chen","Services":["Amazon SageMaker"]},{"Title":"Using Lambda Layers with USGS 3DEP LiDAR Point Clouds","URL":"https://github.com/hobu/usgs-lidar/tree/master/lambda","AuthorName":"Howard Butler","AuthorURL":"https://twitter.com/howardbutler","Services":["AWS Lambda"]},{"Title":"WebGL Visualization of USGS 3DEP Lidar Point Clouds with Potree and Plasio.js","URL":"https://usgs.entwine.io/","AuthorName":"Connor Manning","AuthorURL":"https://twitter.com/csmannin"}],"Tools & Applications":[{"Title":"Equator - View, Process, and Download USGS 3DEP LiDAR data in-browser","URL":"https://equatorstudios.com/lidar-viewer/","AuthorName":"Equator Studios","AuthorURL":"https://equatorstudios.com"},{"Title":"Facebook Line of Sight Check","URL":"https://www.facebook.com/isptoolbox/line-of-sight-check/","AuthorName":"Facebook","AuthorURL":"https://www.facebook.com/isptoolbox/"},{"Title":"Jupyter Notebooks to enable programmatic access to cloud-hosted USGS 3D Elevation Program (3DEP) lidar data","URL":"https://github.com/OpenTopography/OT_3DEP_Workflows","AuthorName":"OpenTopography","AuthorURL":"https://opentopography.org/"},{"Title":"OpenTopography access to 3DEP lidar point cloud data","URL":"https://portal.opentopography.org/datasets","AuthorName":"OpenTopography","AuthorURL":"https://opentopography.org/"}],"Publications":[{"Title":"Statewide USGS 3DEP Lidar Topographic Differencing Applied to Indiana, USA","URL":"https://www.mdpi.com/2072-4292/14/4/847/htm","AuthorName":"Chelsea Phipps Scott, Matthew Beckley, Minh Phan, Emily Zawacki, Christopher Crosby, Viswanath Nandigam, and Ramon Arrowsmith"},{"Title":"USGS 3DEP Lidar Point Cloud Now Available as Amazon Public Dataset","URL":"https://www.usgs.gov/news/usgs-3dep-lidar-point-cloud-now-available-amazon-public-dataset","AuthorName":"Department of the Interior, U.S. Geological Survey","AuthorURL":"https://www.usgs.gov"}]},"RegistryEntryAdded":"2019-01-17","RegistryEntryLastModified":"2025-02-04","Slug":"usgs-lidar","Sources":["awslabs-open-data-registry"]}
{"Name":"Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States","Description":"Aquatic reflectance produced with the dark spectrum fitting (DSF) algorithm as implemented in the Atmospheric Correction for OLI “lite” (ACOLITE) software (version 20221114.0). Aquatic reflectance is defined here as unitless water-leaving radiance reflectance and represents the ratio of water-leaving radiance (units of watts per square meter per steradian per nanometer) to downwelling irradiance (units of watts per square meter per nanometer) multiplied by pi.","Documentation":"https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed","Contact":"tvking@usgs.gov","ManagedBy":"[United States Geological Survey](https://www.usgs.gov)","UpdateFrequency":"New scenes are added daily.","Collabs":{"ASDI":{"Tags":["oceans"]}},"Tags":["aws-pds","cog","earth observation","geospatial","natural resource","satellite imagery","water"],"License":"Contains modified Copernicus Sentinel data, which is available under the Creative Commons CC BY-SA 3.0 IGO license. Please reference King et al., 2024 (doi 10.5066/P904243C) when referring to the aquatic reflectance, and include the statement 'Contains modified Copernicus Sentinel data [Year]' to acknowledge the data originator.","Citation":"King, T.V., Meyer, M.F., Hundt, S.A., Ball, G.P., Hafen, K.C., Avouris, D.M., Ducar, S.D., Wakefield, B.F., Stengel, V.S., and Vanhellemont, Q., 2024, Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States; U.S. Geological Survey Data Release, doi 10.5066/P904243C.","Resources":[{"Description":"Scenes and metadata","ARN":"arn:aws:s3:::usgs-wma-sentinel-2-aqr-acolite-dsf/version_01","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"New scene notification","ARN":"arn:aws:sns:us-west-2:242201296900:usgs-wma-sentinel-2-aqr-acolite-dsf-object_created","Region":"us-west-2","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"tutorial.zip","URL":"https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed","AuthorName":"S.D. Ducar"}],"Tools & Applications":[{"Title":"GLOBUS Access Point","URL":"https://app.globus.org/file-manager?origin_id=8fd8727f-c464-4e86-a5ed-c6db72848c02&origin_path=%2F","AuthorName":"T.V. King, et al."}],"Publications":[{"Title":"Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States","URL":"https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed","AuthorName":"T.V. King, et al."}]},"RegistryEntryAdded":"2025-01-07","RegistryEntryLastModified":"2025-07-09","Slug":"usgs_aqr","Sources":["awslabs-open-data-registry"]}
{"Name":"DCR Office of Resilience Planning – Public File Repository","Description":"The Virginia Department of Conservation and Recreation’s Office of Resilience Planning maintains this public file repository to provide access to flood resilience open data products. The repository is designed to house public data produced for the Virginia Coastal Resilience Master Plan (CRMP), Virginia Flood Protection Master Plan (VFPMP), and other purposes. At present, the repository hosts only data products produced for the CRMP Phase II (2025) and Phase I (2021). \n<br/>\n","Documentation":"https://www.dcr.virginia.gov/crmp/ \nhttps://www.dcr.virginia.gov/vfpmp/\n","Contact":"flood.resilience@dcr.virginia.gov","ManagedBy":"[Virginia Department of Conservation and Recreation](https://www.dcr.virginia.gov)","UpdateFrequency":"Every 5 years or as data becomes available","Collabs":{"ASDI":{"Tags":["infrastructure"]}},"Tags":["aws-pds","coastal","floods"],"License":"Conditions of Release - Data is available by permission of the Virginia Department of Conservation and Recreation. Use of data in publications, either digital or hardcopy, must be cited as follows:`<dataset name>`. `<date acquired>`. Virginia Department of Conservation and Recreation.\n<br/>\nDisclaimer - All data products are provided herein with the expressed understanding that the Virginia Department of Conservation and Recreation (DCR) and the Commonwealth of Virginia are releasing these products at the user's own risk. The data presented is based on the best information, science and techniques available; however, no warranty, expressed or implied, is made regarding the accuracy, adequacy, completeness or reliability of the data provided. Projections of future conditions and the data they are based on reflect possible scenarios. As with all models and forward-looking data, uncertainty and constraints exist. The inclusion of local resilience projects in the open data does not constitute an endorsement of the project or validation of its projected outcomes. Users should review the Limitations information in the Coastal Resilience Web Explorer (https://dsfmportal.dcr.virginia.gov/portal/apps/storymaps/stories/a85ead86e2ab4f5da20ef90147d99c32#n-QvmIyI) and refer to the additional resources referenced therein prior to using the open data.  \n<br/>\n The data is provided on an \"as is\" basis. Neither DCR nor the contributors of said data to DCR shall be held liable for any use or application of the data provided whatsoever, whether or not that use is improper or incorrect, and assume no responsibility for the use or application of the data or information derived from interpretation of the data. In no event shall DCR or its collaborators be liable for any direct, indirect or incidental damages arising from the use or application of these data.   \n<br/> \nThis disclaimer of liability applies to any damages or injury, including but not limited to those caused by any failure of performance including failures resulting in personal injury or property damage, error, omission, defect, delay in operation or transmission, computer virus, alteration, use, application, analysis or interpretation of data. It is the owner's engineer and/or other end users who are expected to take all steps necessary to ensure proper use and understanding of these products and that all information is being properly interpreted and applied.  \n<br/>\nUsers should check back routinely for updates as data will be added and changed periodically resulting in some data becoming out-of-date. It is recommended that the user not let a significant period of time elapse between obtaining and using the data. Disclaimer - All data products for the Coastal Resilience Master Plan are being provided herein with the expressed understanding that the Virginia Department of Conservation and Recreation (DCR) and the Commonwealth of Virginia are releasing these products at the users own risk. This study is based on the best information, science and techniques available at the time of publication, however no warranty, expressed or implied, is made regarding the accuracy, adequacy, completeness or reliability of the data provided. As additional data becomes available, periodic updates will be published to this site.\n<br/>\n","Resources":[{"Description":"Public CRMP data available to browse through this repository is nested under various folders, grouped by the type of flood hazard: coastal, pluvial (rainfall), fluvial (riverine) or combined hazards.  See readme.txt file for more information on the folder structure and contents.","ARN":"arn:aws:s3:::vadcr-frp","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Data](https://vadcr-frp.s3.us-east-1.amazonaws.com/index.html)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"ArcGIS REST Services Directory","URL":"https://vacrmp.dewberryanalytics.com/arcgis/rest/services","AuthorName":"Virginia Department of Conservation and Recreation","AuthorURL":"https://www.dcr.virginia.gov/"},{"Title":"Virginia Coastal Resilience Web Explorer","URL":"https://www.dcr.virginia.gov/crmp/ResilienceExplorer","AuthorName":"Virginia Department of Conservation and Recreation","AuthorURL":"https://www.dcr.virginia.gov/"}],"Publications":[{"Title":"Appendix F Data Product List","URL":"https://www.dcr.virginia.gov/crmp/document/Appendix-F-Data-Product-List.pdf","AuthorName":"Virginia Department of Conservation and Recreation","AuthorURL":"https://www.dcr.virginia.gov/"},{"Title":"Pluvial Model Use Case","URL":"https://vadcr-frp.s3.amazonaws.com/Pluvial_CRMP/CRMP2_Pluvial_Model_Use_Case_Guide.pdf","AuthorName":"Virginia Department of Conservation and Recreation","AuthorURL":"https://www.dcr.virginia.gov/"},{"Title":"Pluvial Modeling Final Report","URL":"https://vadcr-frp.s3.amazonaws.com/Pluvial_CRMP/VACRMP_PluvialModelingReport_Final_20240614.pdf","AuthorName":"Virginia Department of Conservation and Recreation","AuthorURL":"https://www.dcr.virginia.gov/"},{"Title":"Virginia Coastal Resilience Master Plan, Phase One December 2021","URL":"https://www.dcr.virginia.gov/crmp/plan","AuthorName":"Virginia Department of Conservation and Recreation","AuthorURL":"https://www.dcr.virginia.gov/"}]},"RegistryEntryAdded":"2022-07-06","RegistryEntryLastModified":"2026-07-16","Slug":"vadcr-crmp-aws","Sources":["awslabs-open-data-registry"]}
{"Name":"Vantor Open Data Program","Description":"Pre and post event high-resolution satellite imagery in support of emergency planning, risk assessment, \nmonitoring of staging areas and emergency response, damage assessment, and recovery. These images are generated\nas full-strip COGs instead of the tiling system used by the previous Maxar Open Data images.\n","Documentation":"https://www.vantor.com","Contact":"https://vantor.com","UpdateFrequency":"New data is released in response to activations. Older data may be migrated to match this format as needed.","Collabs":{"ASDI":{"Tags":["disaster response"]}},"Tags":["aws-pds","cog","disaster response","earth observation","geospatial","satellite imagery","stac","sustainability"],"License":"Creative Commons Attribution Non Commercial 4.0","ManagedBy":"[Vantor](https://www.vantor.com/)","Resources":[{"Description":"Imagery and metadata","ARN":"arn:aws:s3:::vantor-opendata","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Radiant Earth STAC Catalog](https://radiantearth.github.io/stac-browser/#/external/vantor-opendata.s3.amazonaws.com/events/catalog.json)","[Development Seed STAC Map](https://developmentseed.org/stac-map/?href=https%3A%2F%2Fvantor-opendata.s3.amazonaws.com%2Fevents%2Fcatalog.json)"]}],"DataAtWork":{"Tutorials":[{"Title":"Exploring Vantor Open Data in a Browser-Based Notebook on Scigantic","URL":"https://scigantic.com/archive/46735d71-1dcd-5707-b4eb-f676688c8640/sample","AuthorName":"Scigantic","AuthorURL":"https://scigantic.com"}],"Tools & Applications":[{"Title":"Vantor Open Data Geolibre Plugin","URL":"https://geolibre.app","AuthorName":"Qiusheng Wu","AuthorURL":"https://gishub.org/"},{"Title":"Vantor Open Data QGIS Plugin","URL":"https://plugins.qgis.org/plugins/vantor/","AuthorName":"Qiusheng Wu","AuthorURL":"https://gishub.org/"}],"Publications":[{"Title":"Disaster, Infrastructure and Participatory Knowledge The Planetary Response Network","URL":"https://eprints.lancs.ac.uk/id/eprint/167032/1/Simmons_et_al_2022_CSTP_PRN_Paper_Special_Issue_Accepted_Version.pdf","AuthorName":"Brooke Simmons, Chris Lintott, Steven Reece, et al."},{"Title":"Seeing a Better World from Space","URL":"https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1124&context=purduegisday","AuthorName":"Carly Sakumura"},{"Title":"Using Data from Earth Observation to Support Sustainable Development Indicators: An Analysis of the Literature and Challenges for the Future","URL":"https://doi.org/10.3390/su14031191","AuthorName":"Ana Andries, Stephen Morse, Richard J. Murphy, Jim Lynch, and Emma R. Woolliams"}]},"RegistryEntryAdded":"2026-06-25","RegistryEntryLastModified":"2026-07-17","Slug":"vantor-open-data","Sources":["awslabs-open-data-registry"]}
{"Name":"VENUS L2A Cloud-Optimized GeoTIFFs","Description":"The [Venµs science mission](https://www.theia-land.fr/en/product/venus/) is a joint research mission undertaken by CNES and ISA, \nthe Israel Space Agency. It aims to demonstrate the effectiveness of high-resolution multi-temporal observation optimised through \nCopernicus, the global environmental and security monitoring programme. Venµs was launched from the Centre Spatial Guyanais by a \nVEGA rocket, during the night from 2017, August 1st to 2nd. Thanks to its multispectral camera (12 spectral bands in the visible \nand near-infrared ranges, with spectral characteristics provided [here](https://labo.obs-mip.fr/multitemp/?page_id=14229)), it \nacquires imagery every 1-2 days over 100+ areas at a spatial resolution of 4 to 5m. This dataset has been converted into Cloud \nOptimized GeoTIFFs (COGs). Additionally, SpatioTemporal Asset Catalog metadata are generated in a JSON file alongside the data. \nThis dataset contains all of the Venus L2A datasets and will continue to grow as the Venus mission acquires new data over the \npreselected sites.\n","Documentation":"https://github.com/earthdaily/venus-on-aws/","Contact":"Klaus Bachhuber - klaus.bachhuber@earthdaily.com","ManagedBy":"[EarthDaily Analytics](https://earthdaily.com/)","UpdateFrequency":"New Venus data are added regularly","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["activity detection","agriculture","aws-pds","cog","disaster response","earth observation","environmental","geospatial","image processing","land cover","natural resource","satellite imagery","stac"],"License":"https://creativecommons.org/licenses/by-nc/4.0/","Resources":[{"Description":"Venus L2A dataset (COG) and metadata (STAC)","ARN":"arn:aws:s3:::venus-l2a-cogs","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"Explore":["[STAC Browser Venus L2A (COG) Catalog](https://radiantearth.github.io/stac-browser/#/external/venus-l2a-cogs.s3.us-east-1.amazonaws.com/catalog.json)"]},{"Description":"New Venus L2A dataset notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains link to STAC record for each new dataset made available.","ARN":"arn:aws:sns:us-east-1:794383284256:venus-l2a-cogs-object_created","Region":"us-east-1","Type":"SNS Topic"}],"RegistryEntryAdded":"2023-03-29","RegistryEntryLastModified":"2025-07-09","Slug":"venus-l2a-cogs","Sources":["awslabs-open-data-registry"]}
{"Name":"Vesuvius Challenge - CT Scans of Herculaneum Papyri","Description":"This dataset contains reconstructed micro-CT volumes of carbonized Herculaneum papyri\nproduced as part of the Vesuvius Challenge. The scanned scroll library survived the\neruption of Mount Vesuvius in AD 79. It is the only intact library known to have\nsurvived from antiquity.\nThe volumetric reconstructions are distributed as OME-Zarr multiscale datasets to support\nresearch in virtual unwrapping, segmentation, and text recovery of ancient scrolls.\nDeciphering these scrolls could forever change our understanding of Roman history.\n","Documentation":"https://github.com/ScrollPrize/open-data","Contact":"https://scrollprize.org/","ManagedBy":"[Vesuvius Challenge](https://scrollprize.org/)","UpdateFrequency":"As additional papyri are scanned and reconstructed.","Tags":["aws-pds","computed tomography","cultural preservation","digital preservation","history","image processing","imaging","volumetric imaging","x-ray","x-ray microtomography","x-ray tomography","zarr"],"License":"[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)","Citation":"Please refer to https://github.com/ScrollPrize/open-data?tab=readme-ov-file#citation for instructions on how to cite the data.","Resources":[{"Description":"OME-Zarr multiscale micro-CT reconstructions of Herculaneum papyri with associated metadata.","ARN":"arn:aws:s3:::vesuvius-challenge-open-data","Region":"us-east-1","Type":"S3 Bucket"},{"Description":"Notifications for new CT scans of Herculaneum papyri data","ARN":"arn:aws:sns:us-east-1:597423010625:vesuvius-challenge-open-data-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know a Dataset: Vesuvius Challenge - CT Scans of Herculaneum Papyri","URL":"https://github.com/ScrollPrize/open-data/blob/main/examples/get-to-know-a-dataset.ipynb","AuthorName":"Vesuvius Challenge Team","AuthorURL":"https://scrollprize.org/"},{"Title":"Vesuvius Challenge - Tutorials","URL":"https://scrollprize.org/tutorial","AuthorName":"Vesuvius Challenge Team","AuthorURL":"https://scrollprize.org/"}],"Tools & Applications":[{"Title":"Vesuvius Challenge Monorepository (villa)","URL":"https://github.com/ScrollPrize/villa","AuthorName":"Vesuvius Challenge Team","AuthorURL":"https://github.com/ScrollPrize"}]},"RegistryEntryAdded":"2026-01-15","RegistryEntryLastModified":"2026-03-26","Slug":"vesuvius-challenge-herculaneum-scrolls","Sources":["awslabs-open-data-registry"]}
{"Name":"AdaptiveFlow Ligand Libraries","Description":"AdaptiveFlow Versions of Ligand Libraries in Ready-To-Dock Format","Documentation":"https://adaptive-flow.ai/real-space-2022q12","Contact":"christoph.gorgulla@stjude.org","ManagedBy":"[AdaptiveFlow Project](https://adaptive-flow.ai/)","UpdateFrequency":"Every ~3 years","Tags":["aws-pds","bioinformatics","life sciences","medicine","pharmaceutical","structural biology"],"License":"There are no restrictions on the use of this data. Redistribution is not allowed without permission from Christoph Gorgulla. Keeping access credentials have to be kept confidential (each user gets their own access credentials).","Resources":[{"Description":"AdaptiveFlow Version of the Enamine REAL Space Molecule Library (version 2022q12) Containing 69 Billion Molecules in Ready-To-Dock Format.","ARN":"arn:aws:s3:::vf-libraries","Region":"us-east-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://vf-libraries.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"AdaptiveFlow tutorial","URL":"https://adaptive-flow.ai/tutorials","AuthorName":"Christoph Gorgulla","AuthorURL":"https://www.stjude.org/directory/g/christoph-gorgulla.html"}],"Tools & Applications":[{"Title":"AdaptiveFlow for Virtual Screening (AFVS) Module on GitHub","URL":"https://github.com/LigandUniverse/AFVS","AuthorName":"Christoph Gorgulla","AuthorURL":"https://www.stjude.org/directory/g/christoph-gorgulla.html"}],"Publications":[{"Title":"VirtualFlow 2.0 - The Next Generation Drug Discovery Platform Enabling Adaptive Screens of 69 Billion Molecules","URL":"https://www.biorxiv.org/content/10.1101/2023.04.25.537981v1.abstract","AuthorName":"Christoph Gorgulla, AkshatKumar Nigam, Matt Koop, Süleyman Selim Çınaroğlu, Christopher Secker, Mohammad Haddadnia, Abhishek Kumar, Yehor Malets, Alexander Hasson, Roni Levin-Konigsberg, Dmitry Radchenko, Aditya Kumar, Minko Gehev, Pierre-Yves Aquilanti, Henry Gabb, Amr Alhossary, Gerhard Wagner, Al, Yurii S. Moroz, Konstantin Fackeldey, Haribabu Arthanari","AuthorURL":"https://www.stjude.org/directory/g/christoph-gorgulla.html"}]},"RegistryEntryAdded":"2023-04-29","RegistryEntryLastModified":"2025-05-26","Slug":"vf-libraries","Sources":["awslabs-open-data-registry"]}
{"Name":"MIT-BIH Malignant Ventricular Ectopy Database","Description":"This database includes 22 half-hour ECG recordings of subjects who experienced episodes of sustained ventricular tachycardia, ventricular flutter, and ventricular fibrillation.","Documentation":"https://doi.org/10.13026/C22P44","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C22P44","ARN":"arn:aws:s3:::physionet-open/vfdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"vfdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Video Pulse Signals in Stationary and Motion Conditions","Description":"The data set contains 40 pulse signal recordings obtained from 15 healthy volunteers (12 men and 3 women between 23 and 35 years). For each subject, there is a simultaneous face video recording, and a finger pulse recording reference system, in a sitting position.","Documentation":"https://doi.org/10.13026/C28D5M","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C28D5M","ARN":"arn:aws:s3:::physionet-open/videopulse/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"videopulse","Sources":["awslabs-open-data-registry"]}
{"Name":"Virtual Shizuoka, 3D Point Cloud Data","Description":"This dataset comprises high-precision 3D point cloud data that encompasses the entire Shizuoka prefecture in Japan, covering 7,200 out of its 7,777 square kilometers. The data is produced through aerial laser survey, airborne laser bathymetry and mobile mapping systems, the culmination of many years of dedicated effort.This data will be visualized and analyzed for use in infrastructure maintenance, disaster prevention measures and autonomous vehicle driving.","Documentation":"https://github.com/aigidjp/opendata_virtualshizuoka/README.md","Contact":"virtualshizuoka@aigid.jp","ManagedBy":"[AIGID](https://aigid.jp/)","UpdateFrequency":"Currently not scheduled","Tags":["aws-pds","bathymetry","disaster response","elevation","geospatial","japanese","land","lidar","mapping"],"License":"Creative Commons Attribution 4.0 International (CC-BY 4.0) and Open Data Commons Open Database License (ODbL)","Resources":[{"Description":"Point Cloud Data of Shizuoka Prefecture, Japan","ARN":"arn:aws:s3:::virtual-shizuoka","Region":"ap-northeast-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Tutorial of handling LAS format point cloud data","URL":"https://github.com/aigid.jp/opendata_virutalshizuoka/tutorials.md","AuthorName":"AIGID"}]},"RegistryEntryAdded":"2024-10-23","RegistryEntryLastModified":"2024-10-24","Slug":"virtual_shizuoka","Sources":["awslabs-open-data-registry"]}
{"Name":"Visual Anomaly (VisA)","Description":"Largest Visual Anomaly detection dataset containing objects from 12 classes in 3 domains across 10,821(9,621 normal and 1,200 anomaly) images. Both image and pixel level annotations are provided.","Documentation":"https://github.com/amazon-research/spot-diff","Contact":"Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6riTWmh-cGw/aws-open-data) and use the `AWS Open Data` tag.","ManagedBy":"[Amazon Web Services](https://aws.amazon.com/)","UpdateFrequency":"Not updated","Tags":["amazon.science","anomaly detection","classification","fewshot","industrial","segmentation"],"License":"https://creativecommons.org/licenses/by/4.0/","Citation":"Zou, Yang, Jongheon Jeong, Latha Pemula, Dongqing Zhang, and Onkar Dabeer. \"SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation.\" arXiv preprint arXiv:2207.14315 (2022).","Resources":[{"Description":"The .tar file for the Visual Anomaly (VisA) dataset","ARN":"arn:aws:s3:::amazon-visual-anomaly/VisA_20220922.tar","Region":"us-west-2","Type":"S3 Bucket","Explore":null}],"DataAtWork":{"Publications":[{"Title":"SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation","URL":"https://arxiv.org/abs/2207.14315","AuthorName":"Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, and Onkar Dabeer"}]},"RegistryEntryAdded":"2022-11-08","RegistryEntryLastModified":"2024-03-27","Slug":"visa","Sources":["awslabs-open-data-registry"]}
{"Name":"Consented Activities of People","Description":"The Consented Activities of People (CAP) dataset is a fine grained activity dataset for visual AI research curated using the [Visym Collector](https://www.visym.com/collector/) platform.","Documentation":"https://visym.github.io/cap","Contact":"info@visym.com","ManagedBy":"[Visym Labs](https://visym.com)","UpdateFrequency":"Not updated","Tags":["activity detection","activity recognition","aws-pds","computer vision","labeled","machine learning","privacy","video"],"License":"Creative Commons Attribution 4.0 International (CC BY 4.0). Every subject in this dataset has consented to their personally identifable information to be shared publicly for the purpose of advancing computer vision research. Non-consented subjects have their faces blurred out.","Resources":[{"Description":"The Consented Activities of People (CAP) dataset is a fine grained activity dataset for visual AI research curated using the [Visym Collector](https://www.visym.com/collector/) platform. The CAP dataset contains annotated videos of [fine-grained activity classes](https://github.com/visym/cap/blob/main/classification/cap_labels.csv) of consented people. Videos are annotated with bounding box tracks around the primary actor along with temporal start/end frames for each activity instance, and distributed in [vipy](https://visym.github.io/vipy/vipy/) json format.","ARN":"arn:aws:s3:::consented-activities-of-people/train/","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tools & Applications":[{"Title":"OpenFAD - Open Fine Grained Activity Detection Challenge","URL":"https://openfad.nist.gov","AuthorName":"Visym Labs & NIST"},{"Title":"Visym Collector","URL":"https://github.com/visym/collector","AuthorName":"Visym Labs & Systems & Technology Research"}]},"RegistryEntryAdded":"2022-03-23","RegistryEntryLastModified":"2022-03-29","Slug":"visym-cap","Sources":["awslabs-open-data-registry"]}
{"Name":"Vital File Web Viewer: A Browser-Based Tool for Viewing High-Resolution Vital Signs Data","Description":"Vital File Web Viewer is an open-source, browser-based tool for visualizing high-resolution vital signs data stored in the .vital format, commonly used during surgery or intensive care monitoring. The viewer enables seamless access to time-series waveform data across operating systems without requiring installation or server-side processing. This tool facilitates rapid data exploration and quality assessment in clinical research environments, particularly when centralized software infrastructure is unavailable.","Documentation":"https://doi.org/10.13026/3642-e205","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/3642-e205","ARN":"arn:aws:s3:::physionet-open/vital-file-web-viewer/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"vital-file-web-viewer","Sources":["awslabs-open-data-registry"]}
{"Name":"VitalDB Arrhythmia Database: Anesthesiologist-Validated Intraoperative Arrhythmia Dataset with Beat and Rhythm Labels","Description":"Intraoperative cardiac arrhythmias present distinct characteristics and clinical challenges compared to non-surgical environments, yet publicly available electrocardiogram (ECG) databases have primarily focused on ambulatory and intensive care environments. To address this gap, we present the VitalDB Arrhythmia Database, a comprehensive collection of annotated intraoperative ECG recordings specifically designed for developing and validating arrhythmia detection algorithms in the surgical context. The database comprises 734,528 seconds of continuous ECG data from 482 surgical patients, with over 660,000 individually annotated heartbeats classified across four beat types and 10 distinct rhythm categories. To efficiently process the extensive source data, we developed a custom deep learning beat classifier that served as an automated screening tool for arrhythmia candidate segments. All annotations underwent rigorous validation by five anesthesiologists, with each segment independently reviewed by at least two anesthesiologists. Inter-rater reliability analysis demonstrated excellent agreement with an overall Cohen's kappa of 0.930 ± 0.130. This publicly accessible resource provides the research community with clinically validated intraoperative arrhythmia data, facilitating the development of robust detection algorithms suited to the unique physiological and technical challenges of the perioperative environment.","Documentation":"https://doi.org/10.13026/axd6-wm13","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/axd6-wm13","ARN":"arn:aws:s3:::physionet-open/vitaldb-arrhythmia/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"vitaldb-arrhythmia","Sources":["awslabs-open-data-registry"]}
{"Name":"VitalDB","Description":"VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients.","Documentation":"https://vitaldb.net/dataset","Contact":"Post any questions to [re:Post](https://vitaldb.net/boards/?id=bugreport).","ManagedBy":"[VitalLab](https://vitallab.ai/)","UpdateFrequency":"Not updated","Tags":["aws-pds","biology","health","life sciences","medicine","signal processing"],"License":"Creative Commons Attribution 4.0 International Public License https://creativecommons.org/licenses/by/4.0/","Citation":"Hyung-Chul Lee, Yoonsang Park, Soo Bin Yoon, Seong Mi Yang, Dongnyeok Park, and Chul-Woo Jung. \"VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients.\" Scientific Data. 279 (2022).","Resources":[{"Description":"The .vital and .csv files for VitalDB dataset","ARN":"arn:aws:s3:::vitaldb-open","Region":"ap-northeast-2","Type":"S3 Bucket","ControlledAccess":"https://vitaldb.net/registration-agreement","Explore":null}],"DataAtWork":{"Publications":[{"Title":"VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients","URL":"https://www.nature.com/articles/s41597-022-01411-5","AuthorName":"Hyung-Chul Lee, Yoonsang Park, Soo Bin Yoon, Seong Mi Yang, Dongnyeok Park, and Chul-Woo Jung"}]},"RegistryEntryAdded":"2023-01-23","RegistryEntryLastModified":"2024-03-26","Slug":"vitaldb","Sources":["awslabs-open-data-registry"]}
{"Name":"VOICED Database","Description":"This database includes 208 voice samples, from 150 pathological, and 58 healthy voices.","Documentation":"https://doi.org/10.13026/C25Q2N","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C25Q2N","ARN":"arn:aws:s3:::physionet-open/voiced/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"voiced","Sources":["awslabs-open-data-registry"]}
{"Name":"Vermont Open Geospatial on AWS","Description":"The State of Vermont has partnered with Amazon's Open Data Initative to make a wide range of geospatial data available in the public domain. Vermont acquires aerial imagery and LiDAR during leaf-off conditions. The imagery typically ranges from 30-centimeter to 15-centimeter in resolution and is available from Vermont's Amazon S3 bucket in a Cloud Optimized GeoTiff (COG) format. LiDAR data has been acquired and is available as USGS Quality Level-1 (QL1) and Level-2 (QL2) compliant datasets in COG format. Geospatial datasets derived from imagery and/or lidar are also available as COGs, including high resolution landcover.","Documentation":"https://vcgi.vermont.gov/data-and-programs/","Contact":"If you have specific questions please contact - vcgi@vermont.gov","ManagedBy":"[Vermont Center for Geographic Information](https://vcgi.vermont.gov)","UpdateFrequency":"Vermont acquires statewide imagery approximately once every other year. Lidar is acquired approximately once every 5-8 years. High-resolution landcover is generated once every other year.","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aerial imagery","earth observation","elevation","geospatial","land cover","lidar"],"License":"Public Domain with Attribution\n","Resources":[{"Description":"Elevation datsets (primarily lidar based) are organized in this bucket as statewide file mosaics and by acquisition year (often a portion of the state in any given year). These data are available in Cloud Optimized GeoTIFF (COG) format and use the following naming convention; 1) Statewide COGs - `STATEWIDE_<AQYEAR-AQYEAR>_<RESOLUTION>cm_<DERIVATIVE>`, 2) By Acquisition Year - `<AQYEAR>_<RESOLUTION>cm_<DERIVATIVE>`. Individual tiles are also available as lossless COGs under the ./_Tiles subfolder.","ARN":"arn:aws:s3:::vtopendata-prd/Elevation","Region":"us-east-2","Type":"S3 Bucket","RequesterPays":false},{"Description":"Imagery datsets are organized in this bucket as statewide file mosaics and by acquisition year (often a portion of the state in any given year). These data are available in Cloud Optimized GeoTIFF (COG) format and use the following naming convention; 1) Statewide - `STATEWIDE_<AQYEAR-AQYEAR>_<RESOLUTION>cm_<LEAFSTATUS>_<#BANDS>Band`, 2) By Acquisition Year - `<AQYEAR>_<RESOLUTION>cm_<LEAFSTATUS>_<#BANDS>Band`. Individual tiles are also available as lossless COGs under the ./_Tiles subfolder.","ARN":"arn:aws:s3:::vtopendata-prd/Imagery","Region":"us-east-2","Type":"S3 Bucket","RequesterPays":false},{"Description":"Landcover datsets are organized in this bucket as statewide file mosaics. These data are available in Cloud Optimized GeoTIFF (COG) format and use the following naming convention `STATEWIDE_<AQYEAR>_<RESOLUTION>cm_LANDCOVER_<DerivativeType>`.","ARN":"arn:aws:s3:::vtopendata-prd/Landcover","Region":"us-east-2","Type":"S3 Bucket","RequesterPays":false}],"DataAtWork":{"Tutorials":[{"Title":"Imagery Program FAQs","URL":"https://vcgi.vermont.gov/resources/frequently-asked-questions/imagery-program-faqs","AuthorName":"Vermont Center for Geographic Information"},{"Title":"Lidar Program FAQs","URL":"https://vcgi.vermont.gov/resources/frequently-asked-questions/lidar-program-faqs","AuthorName":"Vermont Center for Geographic Information"}],"Tools & Applications":[{"Title":"Elevation Page - Vermont Open Geodata Portal","URL":"https://geodata.vermont.gov/pages/elevation","AuthorName":"Vermont Center for Geographic Information"},{"Title":"Imagery Page - Vermont Open Geodata Portal","URL":"https://geodata.vermont.gov/pages/imagery","AuthorName":"Vermont Center for Geographic Information"},{"Title":"Landcover Page - Vermont Open Geodata Portal","URL":"https://geodata.vermont.gov/pages/land-cover","AuthorName":"Vermont Center for Geographic Information"},{"Title":"Vermont AWS S3 Open Data Bucket Browser - Elevation","URL":"https://vtopendata-prd.s3-us-east-2.amazonaws.com/index.html#Elevation/","AuthorName":"Vermont Center for Geographic Information"},{"Title":"Vermont AWS S3 Open Data Bucket Browser - Imagery","URL":"https://vtopendata-prd.s3-us-east-2.amazonaws.com/index.html#Imagery/","AuthorName":"Vermont Center for Geographic Information"},{"Title":"Vermont AWS S3 Open Data Bucket Browser - Landcover","URL":"https://vtopendata-prd.s3-us-east-2.amazonaws.com/index.html#Landcover/","AuthorName":"Vermont Center for Geographic Information"}]},"RegistryEntryAdded":"2024-06-24","RegistryEntryLastModified":"2026-07-16","Slug":"vt-opendata","Sources":["awslabs-open-data-registry"]}
{"Name":"WashU Repeat Browser genomic and epigenomic profiles","Description":"The WashU Repeat Browser dataset provides processed genomic and epigenomic profiles for studying repetitive elements in human (hg38) and mouse (mm10). It contains 8,774 indexed entries spanning CAGE, epigenomic, conservation, transcription-factor and repeat-overlap resources in CSV, BigWig and Zarr formats. The data support genome-wide, repeat-family and consensus-level analysis in Repeat Browser and standard bioinformatics tools.","Documentation":"https://github.com/twlab/Repeat-Browser/blob/main/WashU_Repeat_Browser_AWS/README.md","Contact":"Daofeng Li (dli23@wustl.edu); Jiawei Shen (jiaweishen@wustl.edu)","ManagedBy":"[Ting Wang Laboratory, Department of Genetics, Washington University in St. Louis](https://wang.wustl.edu/)","UpdateFrequency":"The collection is updated irregularly as new supported public datasets and processed profiles become available.","Tags":["aws-pds","bioinformatics","csv","epigenomics","genomic","Homo sapiens","life sciences","Mus musculus","zarr"],"License":"Creative Commons Attribution 4.0 International (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/","Citation":"Please cite Shen J, Cheng S, Purushotham D, Zhuo X, Du AY, Zhang W, Li D, Wang T. Exploring the epigenome profiles of repetitive elements with the WashU Repeat Browser. Genome Research. 2025. https://doi.org/10.1101/gr.279764.124","Resources":[{"Description":"Public S3 bucket containing the WashU Repeat Browser dataset.","ARN":"arn:aws:s3:::washu-repeat-browser","Region":"us-east-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Get to Know a Dataset: WashU Repeat Browser","URL":"https://github.com/twlab/Repeat-Browser/blob/main/WashU_Repeat_Browser_AWS/get-to-know-washu-repeat-browser.ipynb","NotebookURL":"https://github.com/twlab/Repeat-Browser/blob/main/WashU_Repeat_Browser_AWS/get-to-know-washu-repeat-browser.ipynb","AuthorName":"Ting Wang Laboratory","AuthorURL":"https://wang.wustl.edu/","Services":["Amazon S3"]}],"Tools & Applications":[{"Title":"WashU Repeat Browser","URL":"https://repeatbrowser.org/","AuthorName":"Ting Wang Laboratory","AuthorURL":"https://wang.wustl.edu/"},{"Title":"WashU Repeat Browser data-processing workflows","URL":"https://github.com/twlab/Repeat-Browser_data_processing","AuthorName":"Ting Wang Laboratory","AuthorURL":"https://wang.wustl.edu/"}],"Publications":[{"Title":"Exploring the epigenome profiles of repetitive elements with the WashU Repeat Browser","URL":"https://doi.org/10.1101/gr.279764.124","AuthorName":"Shen J, Cheng S, Purushotham D, Zhuo X, Du AY, Zhang W, et al.","AuthorURL":"https://pubmed.ncbi.nlm.nih.gov/39984199/"}]},"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-07-23","RegistryEntryLastModified":"2026-09-08","Slug":"washu-repeat-browser","Sources":["awslabs-open-data-registry"]}
{"Name":"World Bank - Light Every Night","Description":"Light Every Night - World Bank Nighttime Light Data – provides open access to all nightly imagery and data from the Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS DNB) from 2012-2020 and the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) from 1992-2013. The underlying data are sourced from the NOAA National Centers for Environmental Information (NCEI) archive. Additional processing by the University of Michigan enables access in Cloud Optimized GeoTIFF format (COG) and search using the Spatial Temporal Asset Catalog (STAC) standard. The data is published and openly available under the terms of the World Bank’s open data license.","Documentation":"https://worldbank.github.io/OpenNightLights/wb-light-every-night-readme.html","Contact":"Trevor Monroe tmonroe@worldbank.org; Benjamin P. Stewart bstewart@worldbankgroup.org; Brian Min brianmin@umich.edu; Kim Baugh kim.baugh@noaa.gov","ManagedBy":"[World Bank Group](https://www.worldbank.org/en/home)","UpdateFrequency":"Quarterly","Collabs":{"ASDI":{"Tags":["satellite imagery"]}},"Tags":["aws-pds","cog","disaster response","earth observation","satellite imagery","stac"],"License":"[World Bank Open Database License (ODbL)](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"Light Every Night dataset of all VIIRS DNB and DMSP-OLS nighttime satellite data","ARN":"arn:aws:s3:::globalnightlight","Region":"us-east-1","Type":"S3 Bucket","Explore":["[STAC 1.0.0-beta.2 endpoint](https://stacindex.org/catalogs/world-bank-light-every-night#/)"]}],"DataAtWork":{"Tutorials":[{"Title":"Open Nighttime Lights","URL":"https://worldbank.github.io/OpenNightLights/welcome.html","AuthorName":"Daynan Crull, Trevor Monroe","AuthorURL":"https://worldbank.github.io/OpenNightLights/welcome.html"}],"Tools & Applications":[{"Title":"Global Scale Nightlight Time Series Dataset","URL":"https://nightlight.eoatlas.org","AuthorName":"Alameen Najjar","AuthorURL":"https://github.com/eoameen"},{"Title":"High Resolution Electricity Access Indicators (HREA) - Settlement-level measures of electricity access, reliability, and usage.","URL":"http://www-personal.umich.edu/~brianmin/HREA/","AuthorName":"Brian Min, Zachary O'Keeffe","AuthorURL":"http://www-personal.umich.edu/~brianmin/"},{"Title":"Twenty Years of India Lights","URL":"http://nightlights.io","AuthorName":"Kwawu Mensan Gaba, Brian Min, Anand Thakker, Christopher Elvidge","AuthorURL":"http://nightlights.io"}],"Publications":[{"Title":"Detection of Rural Electrification in Africa using DMSP-OLS Night Lights Imagery. International Journal of Remote Sensing","URL":"https://openknowledge.worldbank.org/handle/10986/16192","AuthorName":"Brian Min, Kwawu Mensan Gaba, Ousmane Fall Sarr, Alassane Agalassou."},{"Title":"Mainstreaming Disruptive Technologies in Energy. World Bank Report. 2019","URL":"http://documents.worldbank.org/curated/en/305771562750007469/Mainstreaming-Disruptive-Technologies-in-Energy","AuthorName":"Kwawu Mensan Gaba, Brian Min, Olaf Veerman, Kimberly Baugh"},{"Title":"Mapping city lights with nighttime data from the DMSP Operational Linescan System. Photogrammetric Engineering and Remote Sensing, 63(6)727-734.","URL":"https://www.asprs.org/wp-content/uploads/pers/97journal/june/1997_jun_727-734.pdf","AuthorName":"Elvidge, C.D., Baugh, K.E., Kihn, E.A., Kroehl, H.W. and Davis, E.R."},{"Title":"Nighttime lights compositing using the VIIRS day-night band - Preliminary results. Proceedings of the Asia-Pacific Advanced Network 35 (2013)70-86.","URL":"https://journals.sfu.ca/apan/index.php/apan/article/view/8/0","AuthorName":"Kimberly Baugh, Feng-Chi Hsu, Christopher D. Elvidge, and Mikhail Zhizhin."},{"Title":"Power and the Vote - Elections and Electricity in the Developing World. Cambridge. 2015.","URL":"https://www.cambridge.org/core/books/power-and-the-vote/8091820171410B4FA557B6EBC3A5DD06","AuthorName":"Brian Min"}]},"RegistryEntryAdded":"2021-01-21","RegistryEntryLastModified":"2024-06-30","Slug":"wb-light-every-night","Sources":["awslabs-open-data-registry"]}
{"Name":"World Bank Climate Change Knowledge Portal (CCKP)","Description":"CCKP provides open access to a comprehensive suite of climate and climate change resources derived from the latest generation of climate data archives. Products are based on a consistent and transparent approach with a systematic way of pre-processing the raw observed and model-based projection data to enable inter-comparable use across a broad range of applications. Climate products consist of basic climate variables as well as a large collection (70+) of more specialized, application-orientated variables and indices across different scenarios. Precomputed data can be extracted per specified variables, select timeframes, climate projection scenarios, across ensembles or individual models, etc. CCKP adheres to data distributions standards defined under the Coupled Model Intercomparison Project (CMIP) and its contributions to the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports and latest scientific methodologies identified by the World Meteorological Organization and climate science community. Climate products are available for the following collections. Downscaled CMIP6 global 0.25-degree – 1950-2100; ERA5 global 0.25-degree – 1950-2022; CRU global 0.50-degree – 1901-2022; Population global 0.25-degree – 1995-2100 (GPW v4).","Documentation":"https://worldbank.github.io/climateknowledgeportal","Contact":"C. MacKenzie Dove cdove@worldbank.org; askclimate@worldbank.org","ManagedBy":"[World Bank Group](https://www.worldbank.org/en/home)","UpdateFrequency":"Semi-annually","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["aws-pds","climate","climate model","climate projections","CMIP6","earth observation","netcdf"],"License":"[World Bank Open Database License (ODbL)](https://creativecommons.org/licenses/by/4.0/)","Resources":[{"Description":"World Bank Climate Change Knowledge Portal observed and projected climate datasets","ARN":"arn:aws:s3:::wbg-cckp","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"World Bank Climate Change Knowledge Portal observed and projected climate datasets","URL":"https://worldbank.github.io/climateknowledgeportal/","AuthorName":"World Bank","AuthorURL":"https://climateknowledgeportal.worldbank.org"}]},"RegistryEntryAdded":"2024-01-18","RegistryEntryLastModified":"2025-07-09","Slug":"wbg-cckp","Sources":["awslabs-open-data-registry"]}
{"Name":"Wilson Central Terminal ECG Database","Description":"The Wilson Central Terminal ECG Database (WCTECGdb) contains the Wilson Central Terminal (WCT), the voltage of right arm (RA), left arm (LA) and left leg (LL), six unipolar chest leads associated with three limb leads and six precordial leads. With our ECG device, the potentials of RA, LA, LL and six true unipolar leads are measured using the right leg as the reference point. Having this configuration, we have been able to overcome the difficulty of recording the WCT in a clinical setting. Our data confirmed once again that the WCT does not have a small value, and could be as high as 241% of lead II and has standard ECG characteristics such as the p-wave and the t-wave. The dataset comprises of 540 ten-second segments recorded from 92 patients.","Documentation":"https://doi.org/10.13026/f73z-an96","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/f73z-an96","ARN":"arn:aws:s3:::physionet-open/wctecgdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"wctecgdb","Sources":["awslabs-open-data-registry"]}
{"Name":"Wearable Device Dataset from Induced Stress and Structured Exercise Sessions","Description":"This original dataset contains physiological signals collected during structured acute stress induction and aerobic and anaerobic exercise sessions using a wearable device. Blood volume pulse, motion-based activity, skin temperature, and electrodermal activity were recorded with the Empatica E4, a research-grade wearable. The stress induction protocol involved math and emotional tasks designed to provoke stress responses, interleaved with rest periods. Self-reported stress levels were also recorded during this procedure. For the exercise sessions, distinct routines on a stationary bike were created for aerobic and anaerobic activities. The dataset includes records from 36 healthy volunteers for stress sessions, 30 for aerobic exercise, and 31 for anaerobic exercise. By examining the variations in physiological signals, the effects of these activities can be analyzed. This dataset is a valuable resource for research on stress and exercise detection and classification.","Documentation":"https://doi.org/10.13026/he0v-tf17","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/he0v-tf17","ARN":"arn:aws:s3:::physionet-open/wearable-device-dataset/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"wearable-device-dataset","Sources":["awslabs-open-data-registry"]}
{"Name":"A Wearable Exam Stress Dataset for Predicting Cognitive Performance in Real-World Settings","Description":"Preliminary datasets containing skin conductance recordings where experiments involve artificial stimuli are available online. However, recordings of a longer duration acquired during real-world cognitive stressors are lacking significantly. This dataset seeks to bridge the gap between a body of knowledge concerning the human stress response primarily built up on laboratory acquired data, and how people’s skin conductance fluctuates in reality when they undergo a stressful exam experience. The data contains electrodermal activity, heart rate, blood volume pulse, skin surface temperature, inter beat interval and accelerometer data recorded during three exam sessions (midterm 1, midterm 2 and finals) as well as their corresponding grades. The purpose of this dataset is to gauge the influence of stress on exam performance.","Documentation":"https://doi.org/10.13026/kvkb-aj90","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/kvkb-aj90","ARN":"arn:aws:s3:::physionet-open/wearable-exam-stress/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"wearable-exam-stress","Sources":["awslabs-open-data-registry"]}
{"Name":"Wearable-based signals during physical exercises from patients with frailty after open-heart surgery","Description":"This data collection contains a single-lead electrocardiogram and triaxial acceleration signals of 80 elderly frailty patients, who entered a cardiac rehabilitation program after open-heart surgery. The signals were collected using a chest-based heart rate monitor worn by the patients performing a series of exercise tests for health status assessment. The tests include veloergometry, 6-minute walk test, stair climbing test, timed up and go test, and gait analysis on a treadmill. The signals are supplemented with the demographic and clinical characteristics of the patients including the Edmonton frail scale score and quantitative results of the exercise tests. This data collection could be particularly useful in developing wearable technology-based methods for (1) assessment of gait and balance in frailty patients and (2) monitoring of heart response to daily physical stressors.","Documentation":"https://doi.org/10.13026/mp8k-7p27","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License","Resources":[{"Description":"https://doi.org/10.13026/mp8k-7p27","ARN":"arn:aws:s3:::physionet-open/wearable-exercise-frailty/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"wearable-exercise-frailty","Sources":["awslabs-open-data-registry"]}
{"Name":"Waveform Database Software Package (WFDB) for MATLAB and Octave","Description":"Physiological waveforms - such as electrocardiograms (ECG), electroencephalograms (EEG), electromyograms (EMG) - are generated during the course of routine care. These signals contain information that can be used to understand underlying conditions of health. Effective processing and analysis of physiological data requires specialized software. The WaveForm DataBase (WFDB) Toolbox for MATLAB and Octave is a collection of over 30 functions and utilities that integrate PhysioNet's open-source applications and databases with the high-precision numerical computational and graphics environment of MATLAB and Octave.","Documentation":"https://doi.org/10.13026/6zcz-e163","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"GNU General Public License version 3","Resources":[{"Description":"https://doi.org/10.13026/6zcz-e163","ARN":"arn:aws:s3:::physionet-open/wfdb-matlab/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"wfdb-matlab","Sources":["awslabs-open-data-registry"]}
{"Name":"Waveform Database Software Package (WFDB) for Python","Description":"Physiological waveforms - such as electrocardiograms (ECG), electroencephalograms (EEG), electromyograms (EMG) - are generated during the course of routine care. These signals contain information that can be used to understand underlying conditions of health. Effective processing and analysis of physiological data requires specialized software. The waveform-database (WFDB) package for Python is a library of tools for reading, writing, and processing physiological signals and annotations, written in the Python programming language. Core components of the Python package are loosely based on the specifications of the original C-language WFDB software. We aim to implement as many of the core WFDB features as possible, with user-friendly APIs. Additional features are added over time.","Documentation":"https://doi.org/10.13026/9njx-6322","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"MIT License","Resources":[{"Description":"https://doi.org/10.13026/9njx-6322","ARN":"arn:aws:s3:::physionet-open/wfdb-python/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"wfdb-python","Sources":["awslabs-open-data-registry"]}
{"Name":"WFDB SWIG Toolbox for MATLAB","Description":"The WFDB SWIG Toolbox for MATLAB is a collection of applications for reading, writing, manipulating, and plotting PhysioBank data, implemented as functions in MATLAB.","Documentation":"https://doi.org/10.13026/C28378","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C28378","ARN":"arn:aws:s3:::physionet-open/wfdb-swig-matlab/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"wfdb-swig-matlab","Sources":["awslabs-open-data-registry"]}
{"Name":"WFDB SWIG","Description":"The wfdb-swig package provides interfaces between the WFDB library and software written in a variety of other languages, so that such software can have access to the full range of capabilities supported by current and future versions of the WFDB library. The package provides interfaces for software written using Perl, Python, C# (and other .NET languages), and Java. The wfdb-swig package can be readily extended for use with other target languages supported by SWIG, such as PHP, Ruby, TCL, and several versions of Lisp.","Documentation":"https://physionet.org/content/wfdb-swig/","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://physionet.org/content/wfdb-swig/","ARN":"arn:aws:s3:::physionet-open/wfdb-swig/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"wfdb-swig","Sources":["awslabs-open-data-registry"]}
{"Name":"WFDB Software Package","Description":"Effective processing and analysis of physiological data requires specialized software. We have developed a large collection of such software over the past thirty years, and much of it is contained within the WFDB (Waveform Database) Software Package. The WFDB Software Package comprises over 70 applications for signal processing and automated analysis. A comprehensive collection of documentation, including tutorials and reference manuals, is also included in the package. The package is written in highly portable C and can be used on all popular platforms, including GNU/Linux, MacOS, MS-Windows, and all versions of Unix. The package has also been ported to other popular languages, including Matlab and Python.","Documentation":"https://doi.org/10.13026/gjvw-1m31","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"GNU General Public License version 3","Resources":[{"Description":"https://doi.org/10.13026/gjvw-1m31","ARN":"arn:aws:s3:::physionet-open/wfdb/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"wfdb","Sources":["awslabs-open-data-registry"]}
{"Name":"The Whole Genome Sequence Harmonization Study (WGS_Harmonization)","Description":"The Whole Genome Sequence Harmonization Study provides uniformly processed\nwhole-genome sequencing data generated from the three primary AMP-AD cohort\nstudies: the Religious Orders Study and Memory and Aging Project (ROSMAP),\nthe Mount Sinai Brain Bank (MSBB), and the Mayo RNAseq Study. The project\nharmonizes whole-genome sequence data across these cohorts to support\ncross-study analyses of genomic variation associated with aging,\nAlzheimer's disease, and related dementias.\n","Documentation":"https://doi.org/10.7303/9618322","Contact":"https://www.synapse.org/Synapse:syn2580853/discussion/default","ManagedBy":"Sage Bionetworks","UpdateFrequency":"AD Knowledge Portal Data Releases are published monthly (https://news.adknowledgeportal.org/). Updates to specific studies are dependent on data contributor and ingress timelines.","Tags":["aws-pds","bioinformatics","biology","genomic","genotyping","Homo sapiens","life sciences","medicine","neurobiology","neuroscience","whole genome sequencing"],"License":"https://adknowledgeportal.synapse.org/Explore/Studies/DetailsPage/StudyDetails?Study=syn22264775#Acknowledgement","Citation":"The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (NIH-NIA, U24AG061340).","Resources":[{"Description":"This study provides controlled-access harmonized whole-genome sequencing alignments, joint genomic variant calls, and related clinical and biospecimen metadata from the ROSMAP, MSBB, and MayoRNAseq cohorts.","ARN":"arn:aws:s3:::wgs-harmonization.opendata.sagebase.org","Region":"us-east-1","Type":"S3 Bucket","ControlledAccess":"https://adknowledgeportal.synapse.org/Data%20Access"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-09-23","RegistryEntryLastModified":"2026-10-05","Slug":"wgs-harmonization","Sources":["awslabs-open-data-registry"]}
{"Name":"Whiffle WINS50 Open Data on AWS","Description":"Large Eddy Simulation (LES) data of the Winds of the North Sea in 2050 (WINS50) project.","Documentation":"https://gitlab.com/whiffle-public/whiffle-open-data","Contact":"support@whiffle.nl","ManagedBy":"[Whiffle](http://www.whiffle.nl/)","UpdateFrequency":"No updates planned.","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["atmosphere","aws-pds","electricity","meteorological","model","sustainability","turbulence","weather","zarr"],"License":"CC BY-SA 4.0","Resources":[{"Description":"Whiffle WINS50 LES Data","ARN":"arn:aws:s3:::whiffle-wins50-data","Region":"eu-central-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://whiffle-wins50-data.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Whiffle WINS50 Tutorials","URL":"https://gitlab.com/whiffle-public/whiffle-open-data","AuthorName":"Whiffle","AuthorURL":"https://whiffle.nl"}],"Tools & Applications":[],"Publications":[{"Title":"Investigating energy production and wake losses of multi-gigawatt offshore wind farms with atmospheric large-eddy simulation","URL":"https://wes.copernicus.org/articles/8/787/2023","AuthorName":"Baas, P., Verzijlbergh, R., van Dorp, P., and Jonker, H"}]},"RegistryEntryAdded":"2024-02-05","RegistryEntryLastModified":"2025-07-09","Slug":"whiffle-wins50","Sources":["awslabs-open-data-registry"]}
{"Name":"WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation","Description":"This dataset provides how-to articles from [wikihow.com](https://www.wikihow.com) and their summaries,\nwritten as a coherent paragraph.\nThe dataset itself is available at [wikisum.zip](https://wikisum.s3.amazonaws.com/WikiSumDataset.zip),\nand contains the article, the summary, the wikihow url, and an official fold (train, val, or test).\nIn addition, human evaluation results are available at\n[wikisum-human-eval.zip](https://wikisum.s3.amazonaws.com/HumanEvaluation.zip).\nIt consists of human evaluation of the summary of the Pegasus system, annotators response regarding the difficulty\nof the task, and words they marked as unknown.\n","Documentation":"https://wikisum.s3.amazonaws.com/README.txt","Contact":"nachshon@amazon.com, orenk@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not currently being updated","Tags":["amazon.science","machine learning","natural language processing"],"License":"Dataset is published under [CC-NC-SA-3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/).\nHuman evaluation is published under [CC-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/).\n","Resources":[{"Description":"WikiSum Dataset","ARN":"arn:aws:s3:::wikisum","Region":"us-east-1","Type":"S3 Bucket","Explore":["[wikisum.zip](https://wikisum.s3.amazonaws.com/WikiSumDataset.zip)","[wikisum-human-eval.zip](https://wikisum.s3.amazonaws.com/HumanEvaluation.zip)"]}],"DataAtWork":{"Publications":[{"Title":"WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation","URL":"https://2021.aclweb.org/","AuthorName":"Nachshon Cohen, Oren Kalinsky, Yftah Ziser & Alessandro Moschitti"}]},"RegistryEntryAdded":"2021-05-20","RegistryEntryLastModified":"2022-06-06","Slug":"wikisum","Sources":["awslabs-open-data-registry"]}
{"Name":"Global Cache of Japan","Description":"The Global Cache of Japan is a WIS2 Global Cache service operated by the Japan Meteorological Agency (JMA). [WMO Information System 2.0 (WIS2)](https://community.wmo.int/site/knowledge-hub/programmes-and-initiatives/wmo-information-system-wis/wis2-overview) is the WMO framework for sharing Earth system data using open standards, Web technologies, and publish-subscribe notifications. The standard WIS2 workflow is to discover datasets, subscribe to notifications from a Global Broker, and download data using links in WIS2 notification messages. See [WIS 2.0 Global Services](https://community.wmo.int/site/knowledge-hub/programmes-and-initiatives/wmo-information-system-wis/wis-20-global-services) and [Operational WIS2 Nodes](https://community.wmo.int/site/knowledge-hub/programmes-and-initiatives/wmo-information-system-wis/operational-wis2-nodes). The AWS S3 access shown below is provided through AWS Open Data / ASDI for users who want to access public objects directly on AWS. It should be used alongside the standard WIS2 notification-based workflow. Data is retained for 24 hours. There are no restrictions on use; attribution of the original source is requested. ","Documentation":"https://www.wis-jma.go.jp/cms/index.html","Contact":"wis-jma@met.kishou.go.jp","ManagedBy":"[Japan Meteorological Agency](https://www.wis-jma.go.jp/cms/index.html)","UpdateFrequency":"Data is updated with new information every 24 hours or less.","Tags":["atmosphere","aws-pds","climate","climate model","climate projections","climate risk","earth observation","forecast","hydrology","meteorological","oceans","radar","satellite imagery","space weather","weather"],"License":"There are no restrictions on the use of this data. Attribution of original source is requested.","Resources":[{"Description":"Core data as defined in the [WMO Unified Data Policy (Resolution 1 (Cg-19))](https://library.wmo.int/idurl/4/58009). Data covers a global extent. Data is provided in WMO approved formats - GRIB, BUFR and NetCDF. In WIS2, users normally subscribe to notification messages from a Global Broker and download newly available data using the links provided in those messages.","ARN":"arn:aws:s3:::global-cache-of-japan","Region":"ap-northeast-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://global-cache-of-japan.s3.ap-northeast-1.amazonaws.com/index.html)"]},{"Description":"Notifications for new wis2globalcache data","ARN":"arn:aws:sns:ap-northeast-1:477591777891:wis2globalcachejma-object_created","Region":"ap-northeast-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"WIS2 Downloader Quick Start","URL":"https://world-meteorological-organization.github.io/wis2downloader/en/quick-start.html","AuthorName":"World Meteorological Organization","AuthorURL":"https://wmo.int/"},{"Title":"WIS2 Downloader User Guide","URL":"https://world-meteorological-organization.github.io/wis2downloader/en/user-guide.html","AuthorName":"World Meteorological Organization","AuthorURL":"https://wmo.int/"}],"Publications":[{"Title":"About the Guide to the WMO Information System (WMO-No. 1061), Volume II - WMO Information System 2.0","URL":"https://community.wmo.int/site/knowledge-hub/programmes-and-initiatives/wmo-information-system-wis/about-guide-wis2-volume-ii","AuthorName":"World Meteorological Organization","AuthorURL":"https://wmo.int/"},{"Title":"About the Manual on the WMO Information System (WMO-No. 1060), Volume II - WMO Information System 2.0","URL":"https://community.wmo.int/site/knowledge-hub/programmes-and-initiatives/wmo-information-system-wis/about-manual-wis2-volume-ii","AuthorName":"World Meteorological Organization","AuthorURL":"https://wmo.int/"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2025-04-22","RegistryEntryLastModified":"2026-07-21","Slug":"wis2-global-cache-jma","Sources":["awslabs-open-data-registry"]}
{"Name":"WIS2 Global Cache on AWS","Description":"Global real-time Earth system data deemed by the World Meteorological Organisation (WMO) as essential for provision of services for the protection of life and property and for the well-being of all nations. Data is sourced from all WMO Member countries / territories and retained for 24-hours. Met Office and NOAA operate this Global Cache service curating and publishing the dataset on behalf of WMO.","Documentation":"https://www.metoffice.gov.uk/services/data/external-data-channels","Contact":"gisc-exeter@metoffice.gov.uk","ManagedBy":"[Met Office](https://www.metoffice.gov.uk/)","UpdateFrequency":"New data added as soon as available from origin WIS2 Nodes.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["atmosphere","aws-pds","climate","earth observation","forecast","geoscience","hydrology","meteorological","model","oceans","weather"],"License":"There are no restrictions on the use of this data. Attribution of original source is requested.","Resources":[{"Description":"Core data as defined in the [WMO Unified Data Policy (Resolution 1 (Cg-19))](https://library.wmo.int/idurl/4/58009) and the [initial Catalogue of Core Data](http://library.wmo.int/doc_num.php?explnum_id=11001#page=139). Data covers a global extent. Data is provided in WMO approved formats - GRIB, BUFR and NetCDF. Users should subscribe to receive notification messages about newly available data. Please refer to the documentation.","ARN":"arn:aws:s3:::wis2globalcache","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://wis2globalcache.s3.us-east-1.amazonaws.com/index.html)"]},{"Description":"Notifications for new wis2globalcache data","ARN":"arn:aws:sns:us-east-1:211648629506:wis2globalcache-object_created","Region":"us-east-1","Type":"SNS Topic"}],"DataAtWork":{"Tutorials":[{"Title":"WIS 2.0 video for 19th World Meterological Congress","URL":"https://vimeo.com/761382308","AuthorName":"WMO Secretariat"}],"Publications":[{"Title":"Guide to the WMO Information System (WMO-No. 1061), Volume II, WMO Information System 2.0","URL":"https://wmo-im.github.io/wis2-guide/guide/wis2-guide-DRAFT.html","AuthorName":"World Meteorological Organisation"},{"Title":"Manual on the WMO Information System (WMO-No. 1060), Volume II, WMO Information System 2.0","URL":"https://library.wmo.int/idurl/4/68731","AuthorName":"World Meteorological Organisation"}]},"ADXCategories":["Environmental Data"],"RegistryEntryAdded":"2024-10-11","RegistryEntryLastModified":"2025-10-23","Slug":"wis2-global-cache","Sources":["awslabs-open-data-registry"]}
{"Name":"All-Sky Data | Wide-field Infrared Survey Explorer (WISE)","Description":"The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The All-Sky Release includes all data taken during the WISE full cryogenic mission phase, 7 January 2010 to 6 August 2010, in the 3.4, 4.6, 12, and 22 micron bands (i.e., W1, W2, W3, W4) that were processed with improved calibrations and reduction algorithms.","Documentation":"https://irsa.ipac.caltech.edu/Missions/wise.html","Contact":"https://irsa.ipac.caltech.edu/docs/help_desk.html","ManagedBy":"NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech","UpdateFrequency":"The All-Sky Data Release has been finalized and will not be updated.","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["astronomy","aws-pds","imaging","satellite imagery","survey"],"License":"https://irsa.ipac.caltech.edu/data_use_terms.html","Citation":"If you use the All-Sky Single-exposure Image Sets, please cite the Digital Object Identifier (DOI): [10.26131/IRSA152](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA152). In addition, follow the [All-Sky acknowledgement guidelines](https://wise2.ipac.caltech.edu/docs/release/allsky/expsup/sec1_6b.html) and the [IRSA acknowledgement guidelines](https://irsa.ipac.caltech.edu/ack.html).","Resources":[{"Description":"All-Sky Single-exposure Image Sets: 1,491,686 calibrated 1024x1024 pix @2.75\"/pix FITS image sets for the individual 7.7 sec (W1 and W2) and 8.8 sec (W3 and W4) WISE survey exposures. Each image set consists of four intensity images, noise maps, and bit-masks indicating pixel use status, one for each WISE band.","ARN":"arn:aws:s3:::nasa-irsa-wise/wise/allsky","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false}],"DataAtWork":{"Tutorials":[{"Title":"Notebook Tutorials","URL":"https://irsa.ipac.caltech.edu/docs/notebooks/","AuthorName":"Caltech/IPAC-IRSA","AuthorURL":"https://irsa.ipac.caltech.edu"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2023-09-18","RegistryEntryLastModified":"2025-07-09","Slug":"wise-allsky","Sources":["awslabs-open-data-registry"]}
{"Name":"AllWISE Data | Wide-field Infrared Survey Explorer (WISE)","Description":"The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The AllWISE Data Release combines data from all cryogenic and post-cryogenic survey phases and provides a comprehensive view of the mid-infrared sky. The Images Atlas includes 18,240 FITS image sets at 3.4, 4.6, 12 and 22 microns. The Source Catalog contains position, apparent motion, and flux information for over 747 million objects detected on the Atlas Images.","Documentation":"https://irsa.ipac.caltech.edu/Missions/wise.html","Contact":"https://irsa.ipac.caltech.edu/docs/help_desk.html","ManagedBy":"NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech","UpdateFrequency":"The AllWISE Data Release has been finalized and will not be updated. However, the data may be presented in new ways as the products become available.","Tags":["astronomy","aws-pds","imaging","object detection","parquet","satellite imagery","survey"],"License":"https://irsa.ipac.caltech.edu/data_use_terms.html","Citation":"If you use the AllWISE Images Atlas, please cite the Digital Object Identifier (DOI): [10.26131/IRSA153](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA153). If you use the AllWISE Source Catalog, please cite the Digital Object Identifier (DOI): [10.26131/IRSA1](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA1). In addition, follow the [AllWISE acknowledgement guidelines](https://wise2.ipac.caltech.edu/docs/release/allwise/expsup/sec1_6b.html) and the [IRSA acknowledgement guidelines](https://irsa.ipac.caltech.edu/ack.html).","Resources":[{"Description":"The AllWISE Images Atlas includes 18,240 4-band (3.4, 4.6, 12, 22 microns) calibrated 1.56 deg x1.56 deg FITS images, depth-of-coverage and noise maps, and image metadata produced by coadding nearly 7.9 million single-exposure images from all survey phases. The AllWISE Source Catalog contains accurate positions, apparent motion measurements, four-band fluxes and flux variability statistics for over 747 million objects detected on the deep, coadded Atlas Images. It is available in Apache Parquet, partitioned by HEALPix order 5. Additional presentations of this data may be added when available.","ARN":"arn:aws:s3:::nasa-irsa-wise/wise/allwise","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false}],"DataAtWork":{"Tutorials":[{"Title":"Notebook Tutorials","URL":"https://irsa.ipac.caltech.edu/docs/notebooks/","AuthorName":"Caltech/IPAC-IRSA","AuthorURL":"https://irsa.ipac.caltech.edu"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2023-07-27","RegistryEntryLastModified":"2024-03-26","Slug":"wise-allwise","Sources":["awslabs-open-data-registry"]}
{"Name":"3-Band Cryo Data | Wide-field Infrared Survey Explorer (WISE)","Description":"The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The 3-Band Cryo Data Release contains 3.4, 4.6 and 12 micron (W1, W2, W3) imaging data that were acquired between 6 Aug and 29 Sept 2010 while the detectors were cooled by the inner cryogen tank following the exhaustion of the outer tank.","Documentation":"https://irsa.ipac.caltech.edu/Missions/wise.html","Contact":"https://irsa.ipac.caltech.edu/docs/help_desk.html","ManagedBy":"NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech","UpdateFrequency":"The WISE 3-Band Cryo Data Release has been finalized and will not be updated.","Tags":["astronomy","aws-pds","imaging","satellite imagery","survey"],"License":"https://irsa.ipac.caltech.edu/data_use_terms.html","Citation":"If you use the 3-Band Cryo Single-exposure Image Sets, please cite the Digital Object Identifier (DOI): [10.26131/IRSA149](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA149). In addition, follow the [WISE acknowledgement guidelines](https://wise2.ipac.caltech.edu/docs/release/allsky/expsup/sec1_6b.html) and the [IRSA acknowledgement guidelines](https://irsa.ipac.caltech.edu/ack.html).","Resources":[{"Description":"3-Band Cryo Single-exposure Image Sets: 392,879 calibrated 1024x1024 pix @2.75\"/pix FITS image sets for the individual 7.7 sec (W1 and W2) and 8.8/4.4/2.2/1.1 sec (W3) WISE survey exposures. Each image set consists of three intensity images, noise maps, and bit-masks indicating pixel use status, one each for the W1, W2 and W3 WISE bands.","ARN":"arn:aws:s3:::nasa-irsa-wise/wise/cryo-3band","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false}],"DataAtWork":{"Tutorials":[{"Title":"Notebook Tutorials","URL":"https://irsa.ipac.caltech.edu/docs/notebooks/","AuthorName":"Caltech/IPAC-IRSA","AuthorURL":"https://irsa.ipac.caltech.edu"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2023-09-18","RegistryEntryLastModified":"2024-03-26","Slug":"wise-cryo-3band","Sources":["awslabs-open-data-registry"]}
{"Name":"NEOWISE Reactivation Data | Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE)","Description":"The Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) is a NASA Medium-class Explorer satellite in low-Earth orbit conducting an all-sky astronomical imaging survey over two infrared bands. The NEOWISE Reactivation mission began in 2013 when the original WISE satellite was brought out of hibernation to learn more about the population of near-Earth objects and comets that could pose an impact hazard to the Earth. The data is also used to study a wide range of astrophysical phenomena in the time domain including brown dwarfs, supernovae and active galactic nuclei.","Documentation":"https://irsa.ipac.caltech.edu/Missions/wise.html","Contact":"https://irsa.ipac.caltech.edu/docs/help_desk.html","ManagedBy":"NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech","UpdateFrequency":"The NEOWISE-R dataset is updated annually. The data may also be presented in new ways as the products become available.","Tags":["astronomy","aws-pds","imaging","object detection","parquet","satellite imagery","survey"],"License":"https://irsa.ipac.caltech.edu/data_use_terms.html","Citation":"If you use the NEOWISE-R Single-exposure Image Sets, please cite the Digital Object Identifier (DOI): [10.26131/IRSA147](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA147). If you use the NEOWISE-R Single-exposure Source Table, please cite the Digital Object Identifier (DOI): [10.26131/IRSA144](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA144). In addition, follow the [NEOWISE acknowledgement guidelines](https://wise2.ipac.caltech.edu/docs/release/neowise/expsup/sec1_6b.html) and the [IRSA acknowledgement guidelines](https://irsa.ipac.caltech.edu/ack.html).","Resources":[{"Description":"The Single-exposure Image Sets consist of more than 20 million calibrated 1016x1016 pix @2.75\"/pix FITS image sets for the individual 7.7 sec NEOWISE survey exposures in the W1 and W2 bands (centered at 3.4 and 4.6 microns). Each image set consists of two intensity images, noise maps, and bit-masks indicating pixel use status, one for each band. The Single-exposure Source Table contains time-tagged fluxes, positions and measurement information for more than 150 billion source detections extracted from the Single-exposure images. It is available in Apache Parquet format, partitioned by HEALPix order 5. Additional presentations of this data may be added when available.","ARN":"arn:aws:s3:::nasa-irsa-wise/wise/neowiser","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false}],"DataAtWork":{"Tutorials":[{"Title":"Notebook Tutorials","URL":"https://irsa.ipac.caltech.edu/docs/notebooks/","AuthorName":"Caltech/IPAC-IRSA","AuthorURL":"https://irsa.ipac.caltech.edu"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2023-09-18","RegistryEntryLastModified":"2024-03-26","Slug":"wise-neowiser","Sources":["awslabs-open-data-registry"]}
{"Name":"NEOWISE Post-Cryo Data | Wide-field Infrared Survey Explorer (WISE)","Description":"The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The NEOWISE Post-Cryo Data Release contains 3.4 and 4.6 micron (W1 and W2) imaging data that were acquired between 29 September 2010 and 1 February 2011 following the exhaustion of the inner and outer cryogen tanks.","Documentation":"https://irsa.ipac.caltech.edu/Missions/wise.html","Contact":"https://irsa.ipac.caltech.edu/docs/help_desk.html","ManagedBy":"NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech","UpdateFrequency":"The NEOWISE Post-Cryo Data Release has been finalized and will not be updated.","Tags":["astronomy","aws-pds","imaging","satellite imagery","survey"],"License":"https://irsa.ipac.caltech.edu/data_use_terms.html","Citation":"If you use the NEOWISE Post-Cryo Single-exposure Image Sets, please cite the Digital Object Identifier (DOI): [10.26131/IRSA148](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA148). In addition, follow the [WISE and NEOWISE acknowledgement guidelines](https://wise2.ipac.caltech.edu/docs/release/postcryo/#acknowl) and the [IRSA acknowledgement guidelines](https://irsa.ipac.caltech.edu/ack.html).","Resources":[{"Description":"NEOWISE Post-Cryo Single-exposure Image Sets: 901,271 calibrated 1016x1016 pix @2.75\"/pix FITS image sets for the individual 7.7 sec W1 and W2 WISE survey exposures. Each image set consists of two intensity images, noise maps, and bit-masks indicating pixel use status, one each for the W1 and W2 bands.","ARN":"arn:aws:s3:::nasa-irsa-wise/wise/postcryo","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false}],"DataAtWork":{"Tutorials":[{"Title":"Notebook Tutorials","URL":"https://irsa.ipac.caltech.edu/docs/notebooks/","AuthorName":"Caltech/IPAC-IRSA","AuthorURL":"https://irsa.ipac.caltech.edu"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2023-09-18","RegistryEntryLastModified":"2024-03-26","Slug":"wise-postcryo","Sources":["awslabs-open-data-registry"]}
{"Name":"Unblurred Coadds of the Wide-field Infrared Survey Explorer (unWISE)","Description":"unWISE is a reprocessing of Wide-field Infrared Survey Explorer (WISE) data which preserves the native angular resolution and is optimized for forced photometry. WISE was a NASA satellite producing all-sky imaging in four infrared bands centered at 3.4, 4.6, 12 and 22 microns (W1, W2, W3, and W4) starting in 2010 until the coolant was exhausted in 2011. It was reactivated in 2013 as NEOWISE and continued imaging in W1 and W2 until 2024.","Documentation":"https://irsa.ipac.caltech.edu/data/WISE/unWISE/overview.html","Contact":"https://irsa.ipac.caltech.edu/docs/help_desk.html","ManagedBy":"NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech","UpdateFrequency":"The unWISE dataset is updated periodically to include new data released by NEOWISE. The data may also be presented in new ways as the products become available.","Tags":["astronomy","aws-pds","object detection","parquet","survey"],"License":"https://irsa.ipac.caltech.edu/data_use_terms.html","Citation":"If you use the unWISE Time-Domain Catalog, please cite the dataset Digital Object Identifier (DOI): [10.26131/IRSA580](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA580). In addition, follow the [unWISE acknowledgement guidelines](https://irsa.ipac.caltech.edu/data/WISE/unWISE/overview.html) and the [IRSA acknowledgement guidelines](https://irsa.ipac.caltech.edu/ack.html).","Resources":[{"Description":"The unWISE Time-Domain Catalog is based on 'time-resolved' coadds, each of which stacks together the WISE/NEOWISE exposures from one (biannual) sky pass. It includes 43 billion total detections in W1 and W2. It is available in Apache Parquet, partitioned by HEALPix order 5.","ARN":"arn:aws:s3:::nasa-irsa-wise/unwise/","Region":"us-west-2","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false}],"DataAtWork":{"Tutorials":[{"Title":"Notebook Tutorials","URL":"https://irsa.ipac.caltech.edu/docs/notebooks/","AuthorName":"Caltech/IPAC-IRSA","AuthorURL":"https://irsa.ipac.caltech.edu"}]},"ADXCategories":["Public Sector Data"],"RegistryEntryAdded":"2024-09-13","RegistryEntryLastModified":"2024-09-13","Slug":"wise-unwise","Sources":["awslabs-open-data-registry"]}
{"Name":"Wizard of Tasks","Description":"Wizard of Tasks (WoT) is a dataset containing conversations for Conversational Task Assistants (CTAs). A CTA is a conversational agent whose goal is to help humans to perform real-world tasks. A CTA can help in exploring available tasks, answering task-specific questions and guiding users through step-by-step instructions. WoT contains about 550 conversations with ~18,000 utterances in two domains, i.e., Cooking and Home Improvement.","Documentation":"https://wizard-of-tasks.s3.us-west-2.amazonaws.com/README.md","Contact":"giusecas@amazon.com","ManagedBy":"[Amazon](https://www.amazon.com/)","UpdateFrequency":"Not currently being updated","Tags":["amazon.science","conversation data","dialog","machine learning","natural language processing"],"License":"[cc-by-sa 4.0](https://creativecommons.org/licenses/by-sa/4.0/)","Resources":[{"Description":"Wizard of Tasks Dataset","ARN":"arn:aws:s3:::wizard-of-tasks","Region":"us-west-2","Type":"S3 Bucket","Explore":["[wizard_of_tasks_cooking_v1.0.json](https://wizard-of-tasks.s3.us-west-2.amazonaws.com/wizard_of_tasks_cooking_v1.0.json)","[wizard_of_tasks_diy_v1.0.json](https://wizard-of-tasks.s3.us-west-2.amazonaws.com/wizard_of_tasks_diy_v1.0.json)"]}],"DataAtWork":{"Publications":[{"Title":"Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings","URL":"https://aclanthology.org/events/coling-2022/","AuthorName":"Jason Ingyu Choi, Saar Kuzi, Nikhita Vedula, Jie Zhao, Giuseppe Castellucci, Marcus Collins, Shervin Malmasi, Oleg Rokhlenko and Eugene Agichtein"}]},"RegistryEntryAdded":"2022-09-15","RegistryEntryLastModified":"2022-09-15","Slug":"wizard-of-tasks","Sources":["awslabs-open-data-registry"]}
{"Name":"DOE's Water Power Technology Office's (WPTO) US Wave dataset","Description":"Released to the public as part of the Department of Energy's Open Energy Data Initiative,\nthis is the highest resolution publicly available long-term wave hindcast\ndataset that – when complete – will cover the entire U.S. Exclusive Economic\nZone (EEZ).\n","Documentation":"https://github.com/openEDI/documentation/blob/master/US_Wave.md","Contact":"Levi.Kilcher@nrel.gov","ManagedBy":"[National Renewable Energy Laboratory](https://www.nrel.gov/)","UpdateFrequency":"As needed","Collabs":{"ASDI":{"Tags":["energy"]}},"Tags":["aws-pds","earth observation","energy","geospatial","meteorological","water"],"License":"https://github.com/openEDI/documentation/blob/master/US_Wave.md","Resources":[{"Description":"DOE's Water Power Technology Office's Wave Hindcast datasets","ARN":"arn:aws:s3:::wpto-pds-us-wave/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave)"]},{"Description":"32 Year Wave Hindcast (1979-2010) for the West Coast of the United States at 3-hour temporal resolution and down to 200m spatial resolution in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::wpto-pds-us-wave/v1.0.0/West_Coast/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FWest_Coast%2F)"]},{"Description":"32 Year Wave Hindcast (1979-2010) for the Pacific Ocean around US state of Hawaii at 3-hour temporal resolution and down to 200m spatial resolution in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Hawaii/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FHawaii%2F)"]},{"Description":"32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of the United States at 3-hour temporal resolution and down to 200m spatial resolution in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Atlantic/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FAtlantic%2F)"]},{"Description":"32 Year Wave Hindcast (1979-2010) from select virtual buoys along the West Coast of the United States with 1-hour temporal resolution and direction wave spectrum data in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::wpto-pds-us-wave/v1.0.0/virtual_buoy/West_Coast/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FWest_Coast%2F)"]},{"Description":"32 Year Wave Hindcast (1979-2010) from select virtual buoys along the Atlantic Coast of the United States with 1-hour temporal resolution and direction wave spectrum data in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::wpto-pds-us-wave/v1.0.0/virtual_buoy/Atlantic/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FAtlantic%2F)"]},{"Description":"[HSDS](https://github.com/NREL/hsds-examples) US Wave domains","ARN":"arn:aws:s3:::nrel-pds-hsds/nrel/US_wave/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2FUS_wave%2F)"]},{"Description":"[HSDS](https://github.com/NREL/hsds-examples) US Virtual Buoy domains","ARN":"arn:aws:s3:::nrel-pds-hsds/nrel/US_wave/virtual_buoy/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2FUS_wave%2Fvirtual_buoy%2F)"]},{"Description":"Updated version of 32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of the United States at 3-hour temporal resolution and down to 200m spatial resolution in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format. Updates resolve issues with NaNs.","ARN":"arn:aws:s3:::wpto-pds-us-wave/v1.0.1/Atlantic/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.1%2FAtlantic%2F)"]},{"Description":"42 Year Wave Hindcast (1979-2020) from select virtual buoys along the Gulf of Mexico and Puerto Rico, with 1-hour temporal resolution and direction wave spectrum data in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format","ARN":"arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Gulf_of_Mexico_and_Puerto_Rico/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FGulf_of_Mexico_and_Puerto_Rico%2F)"]},{"Description":"Updated version of 42 Year Wave Hindcast (1979-2020) for Alaska at 3-hour temporal resolution and down to 200m spatial resolution in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format. Updates resolve issues with NaNs.","ARN":"arn:aws:s3:::wpto-pds-us-wave/v1.0.1/Alaska/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.1%2FAlaska%2F)"]},{"Description":"Updated version of 42 Year Wave Hindcast (1979-2020) for the West Coast of the United States at 3-hour temporal resolution and down to 200m spatial resolution in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format. Updates resolve issues with NaNs.","ARN":"arn:aws:s3:::wpto-pds-us-wave/v1.0.1/West_Coast/","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.1%2FWest_Coast%2F)"]}],"DataAtWork":{"Tools & Applications":[{"Title":"HSDS Examples","URL":"https://github.com/NREL/hsds-examples","AuthorName":"Caleb Phillips, Caroline Draxl, John Readey, Jordan Perr-Sauer, Michael Rossol"}],"Publications":[{"Title":"Development and validation of a high-resolution regional wave hindcast model for U.S. West Coast wave resource characterization","URL":"https://www.osti.gov/biblio/1599105","AuthorName":"Wu, Wei-Cheng; Wang, Taiping; Yang, Zhaoqing; Garcia Medina, Gabriel"},{"Title":"Development and validation of a regional-scale high-resolution unstructured model for wave energy resource characterization along the US East Coast","URL":"https://doi.org/10.1016/j.renene.2019.01.020","AuthorName":"Allahdadi, M.N., Gunawan, J. Lai, R. He, V.S. Neary"},{"Title":"High-resolution hindcasts for U.S. wave energy resource characterization","URL":"https://doi.org/10.36688/imej.3.65-71","AuthorName":"Yang, Z. and V.S. Neary"},{"Title":"High-Resolution Regional Wave Hindcast for the U.S. West Coast","URL":"https://www.osti.gov/biblio/1573061/","AuthorName":"Yang, Zhaoqing; Wu, Wei-Cheng; Wang, Taiping; Castrucci, Luca"},{"Title":"Nearshore wave energy resource characterization along the East Coast of the United States","URL":"https://doi.org/10.1016/j.renene.2021.03.037","AuthorName":"Ahn, S. V.S. Neary, Allahdadi, N. and R. He"},{"Title":"Predicting ocean waves along the US East Coast during energetic winter storms: sensitivity to whitecapping parameterizations","URL":"https://doi.org/10.5194/os-15-691-2019","AuthorName":"Allahdadi, M.N., He, R., and Neary, V.S"},{"Title":"SWAN Cycle III version 41.31A","URL":"http://swanmodel.sourceforge.net/download/zip/swanuse.pdf","AuthorName":"The SWAN team"}]},"RegistryEntryAdded":"2020-06-17","RegistryEntryLastModified":"2026-01-08","Slug":"wpto-pds-us-wave","Sources":["awslabs-open-data-registry"]}
{"Name":"Downscaled Climate Data for Alaska (v1.1, August 2023)","Description":"This dataset contains historical and projected dynamically downscaled climate data for the State of Alaska and surrounding regions at 20km spatial resolution and hourly temporal resolution. Select variables are also summarized into daily resolutions. This data was produced using the Weather Research and Forecasting (WRF) model (Version 3.5). We downscaled both ERA-Interim historical reanalysis data (1979-2015) and both historical and projected runs from 2 GCM’s from the Coupled Model Inter-comparison Project 5 (CMIP5): GFDL-CM3 and NCAR-CCSM4 (historical run: 1970-2005 and RCP 8.5: 2006-2100). Note - this dataset was updated in August, 2023 to retain the latitude and longitude grids as variables in the files.","Documentation":"https://catalog.snap.uaf.edu/geonetwork/srv/eng/catalog.search#/metadata/7825535c-edff-4a82-89f3-9183e6cb2b42","Contact":"http://directory.iarc.uaf.edu/peter-bieniek","ManagedBy":"Scenarios Network for Alaska + Arctic Planning at the International Arctic Research Center, University of Alaska, Fairbanks","UpdateFrequency":"as needed","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","aws-pds","climate","coastal","earth observation","environmental","sustainability","weather"],"License":"https://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"Dynamically downscaled climate data for Alaska and surrounding regions.","ARN":"arn:aws:s3:::wrf-ak-ar5","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](http://wrf-ak-ar5.s3-website-us-east-1.amazonaws.com/)"]}],"DataAtWork":{"Publications":[{"Title":"Atmospheric Circulation Drivers of Extreme High Water Level Events at Foggy Island Bay, Alaska","URL":"https://www.mdpi.com/2073-4433/13/11/1791/pdf","AuthorName":"Peter A. Bieniek, Li Erikson, and Jeremy Kasper"},{"Title":"Dynamical Downscaling of ERA-Interim Temperature and Precipitation for Alaska","URL":"https://doi.org/10.1175/JAMC-D-15-0153.1","AuthorName":"Peter A. Bieniek, Uma S. Bhatt, John E. Walsh, T. Scott Rupp, Jing Zhang, Jeremy R. Krieger, and Rick Lader"}]},"RegistryEntryAdded":"2018-10-08","RegistryEntryLastModified":"2024-01-04","Slug":"wrf-alaska-snap","Sources":["awslabs-open-data-registry"]}
{"Name":"CMIP6 GCMs downscaled using WRF","Description":"High-resolution historical and future climate simulations from 1980-2100","Documentation":"https://atmos.ucla.edu/alexhall/cmip6-downscaling-using-wrf/","Contact":"srahimi@uwyo.edu, leih@ucla.edu","ManagedBy":"[UCLA Center for Climate Science](https://dept.atmos.ucla.edu/)","UpdateFrequency":"New downscaled results are uploaded as soon as they become available","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","atmosphere","aws-pds","climate","earth observation","environmental","model","oceans","simulations","weather"],"License":"Creative Commons Attribution 4.0 International License","Resources":[{"Description":"WRF output files","ARN":"arn:aws:s3:::wrf-cmip6-noversioning","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](https://wrf-cmip6-noversioning.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Tutorials":[{"Title":"Downscaling file descriptions, directory structure, and data access","URL":"https://www.researchgate.net/publication/374504614_Data_tier_descriptions_directory_structure_and_data_access_of_the_Western_US_Dynamically_Downscaled_Dataset_WUS-D3_version_1","AuthorName":"Stefan Rahimi & Lei Huang"},{"Title":"Jupyter Notebook Example","URL":"https://nbviewer.jupyter.org/urls/wrf-cmip6-noversioning.s3.amazonaws.com/Example_notebook/Notebook_example.ipynb","AuthorName":"Stefan Rahimi"}],"Publications":[{"Title":"An Overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)","URL":"https://gmd.copernicus.org/articles/17/2265/2024/gmd-17-2265-2024.html","AuthorName":"Rahimi, S., Huang, L., Norris, J., Hall, A., Goldenson, N., Krantz, W., Bass, B., Thackeray, C., Lin, H., Chen, D., Dennis, E., Collins, E., Lebo, Z. J., Slinskey, E., Graves, S., Biyani, S., Wang, B., Cropper, S., and the UCLA Center for Climate Science Team"},{"Title":"Is Bias Correction in Dynamical Downscaling Defensible?","URL":"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GL105979","AuthorName":"Risser, M. D., Rahimi, S., Goldenson, N., Hall, A., Lebo, Z. J., and Feldman, D. R."},{"Title":"Memo on the Development and Availability of Dynamically Downscaled Projections Using WRF","URL":"https://www.energy.ca.gov/sites/default/files/2022-09/20220907_CDAWG_MemoDynamicalDownscaling_EPC-20-006_May2022-ADA.pdf","AuthorName":"Stefan Rahimi"},{"Title":"Memo on the Evaluation of Downscaled GCMs Using WRF","URL":"https://dev.cal-adapt.org/files/01_Memo_Evaluation_of_Downscaled_GCMs_Using_WRF_CEC_final.pdf","AuthorName":"Stefan Rahimi"},{"Title":"Memorandum on Evaluating Global Climate Models for Studying Regional Climate Change in California","URL":"https://www.energy.ca.gov/sites/default/files/2022-09/20220907_CDAWG_MemoEvaluating_GCMs_EPC-20-006_Nov2021-ADA.pdf","AuthorName":"Will Krantz, David Pierce, Naomi Goldenson, Daniel Cayan"},{"Title":"Understanding the Cascade: Removing GCM Biases Improves Dynamically Downscaled Climate Projections","URL":"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GL106264","AuthorName":"Rahimi, S., Huang, L., Norris, J., Hall, A., Goldenson, N., Risser, M., Feldman, D. R., Lebo, Z. J., Dennis, E., and Thackeray, C."}]},"RegistryEntryAdded":"2021-01-06","RegistryEntryLastModified":"2026-05-05","Slug":"wrf-cmip6","Sources":["awslabs-open-data-registry"]}
{"Name":"High Resolution Downscaled Climate Data for Southeast Alaska","Description":"This dataset contains historical and projected dynamically downscaled climate data for the Southeast region of the State of Alaska at 1 and 4km spatial resolution and hourly temporal resolution. Select variables are also summarized into daily resolutions. This data was produced using the Weather Research and Forecasting (WRF) model (Version 4.0). We downscaled both Climate Forecast System Reanalysis (CFSR) historical reanalysis data (1980-2019) and both historical and projected runs from two GCM’s from the Coupled Model Inter-comparison Project 5 (CMIP5): GFDL-CM3 and NCAR-CCSM4 (historical run: 1980-2010 and RCP 8.5: 2030-2060).","Documentation":"https://www.sciencebase.gov/catalog/item/5f93658882ce720ee2d598a7","Contact":"http://directory.iarc.uaf.edu/richard-lader","ManagedBy":"Scenarios Network for Alaska + Arctic Planning at the International Arctic Research Center, University of Alaska, Fairbanks","UpdateFrequency":"as needed","Collabs":{"ASDI":{"Tags":["climate"]}},"Tags":["agriculture","aws-pds","aws-pds","climate","coastal","earth observation","environmental","sustainability","weather"],"License":"https://creativecommons.org/licenses/by/4.0/","Resources":[{"Description":"High-res dynamically downscaled climate data for Southeast Alaska.","ARN":"arn:aws:s3:::wrf-se-ak-ar5","Region":"us-west-2","Type":"S3 Bucket","Explore":["[Browse Bucket](http://wrf-se-ak-ar5.s3-website-us-west-2.amazonaws.com/)"]}],"RegistryEntryAdded":"2020-12-01","RegistryEntryLastModified":"2020-12-01","Slug":"wrf-se-alaska-snap","Sources":["awslabs-open-data-registry"]}
{"Name":"Wrist PPG During Exercise","Description":"This database contains wrist PPGs recorded during walking, running and bike riding. Simultaneous motion estimates are collected using both accelerometers and gyroscopes to give multiple options for the removal of motion interference from the PPG traces. A reference chest ECG is included to allow a gold-standard comparison of heart rate during exercise.","Documentation":"https://doi.org/10.13026/C2PQ1X","Contact":"https://physionet.org/about/#contact_us","ManagedBy":"[PhysioNet](https://physionet.org/)","UpdateFrequency":"Not updated","Tags":["aws-pds","health","life sciences","medicine"],"License":"Open Data Commons Attribution License v1.0","Resources":[{"Description":"https://doi.org/10.13026/C2PQ1X","ARN":"arn:aws:s3:::physionet-open/wrist/","Region":"us-east-1","Type":"S3 Bucket"}],"ADXCategories":["Healthcare & Life Sciences Data"],"RegistryEntryAdded":"2026-10-16","RegistryEntryLastModified":"2026-11-05","Slug":"wrist","Sources":["awslabs-open-data-registry"]}
{"Name":"Xiph.Org Test Media","Description":"Uncompressed video used for video compression and video processing research.","Documentation":"https://media.xiph.org/aws.html","Contact":"Thomas Daede tdaede@xiph.org","UpdateFrequency":"New videos are added when contributors submit them.","ManagedBy":"[Xiph.org](https://xiph.org/)","Tags":["aws-pds","computer vision","image processing","imaging","media","movies","multimedia","video"],"License":"Various. Most are under the CC-BY license. License text accompanies each sequence.","Resources":[{"Description":"Video and imagery data","ARN":"arn:aws:s3:::xiph-media","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Encoding video with AV1 on EC2","URL":"https://media.xiph.org/aws.html","AuthorName":"Thomas Daede","AuthorURL":"https://www.xiph.org/"}]},"RegistryEntryAdded":"2018-09-12","RegistryEntryLastModified":"2022-06-14","Slug":"xiph-media","Sources":["awslabs-open-data-registry"]}
{"Name":"Yale-CMU-Berkeley (YCB) Object and Model Set","Description":"This project primarily aims to facilitate performance benchmarking in robotics research. The dataset provides mesh models, RGB, RGB-D and point cloud images of over 80 objects. The physical objects are also available via the [YCB benchmarking project](http://www.ycbbenchmarks.com/). The data are collected by two state of the art systems: UC Berkley's scanning rig and the Google scanner. The UC Berkley's scanning rig data provide meshes generated with Poisson reconstruction, meshes generated with volumetric range image integration, textured versions of both meshes, Kinbody files for using the meshes with OpenRAVE, 600 High-resolution RGB images, 600 RGB-D images, and 600 point cloud images for each object. The Google scanner data provides 3 meshes with different resolutions (16k, 64k, and 512k polygons), textured versions of each mesh, Kinbody files for using the meshes with OpenRAVE.","Documentation":"http://www.ycbbenchmarks.com/","Contact":"bcalli@wpi.edu","UpdateFrequency":"Yearly","ManagedBy":"Yale University and Berkeley","Tags":["aws-pds","robotics"],"License":"Creative Commons Attribution 4.0 International (CC BY 4.0)","Resources":[{"Description":"Project data files","ARN":"arn:aws:s3:::ycb-benchmarks","Region":"us-east-1","Type":"S3 Bucket","Explore":["[Browse Bucket](https://ycb-benchmarks.s3.amazonaws.com/index.html)"]}],"DataAtWork":{"Publications":[{"Title":"Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set","URL":"https://ieeexplore.ieee.org/document/7254318","AuthorName":"Berk Calli, Aaron Walsman, Arjun Singh, Siddhartha Srinivasa, Pieter Abbeel, Aaron M Dollar","AuthorURL":"https://www.wpi.edu/people/faculty/bcalli"},{"Title":"Label Fusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes","URL":"https://ieeexplore.ieee.org/abstract/document/8460950","AuthorName":"Pat Marion, Peter R. Florence, Lucas Manuelli, Russ Tedrake","AuthorURL":"https://groups.csail.mit.edu/locomotion/russt.html"},{"Title":"Pre-touch sensing for sequential manipulation","URL":"https://ieeexplore.ieee.org/abstract/document/7989594","AuthorName":"Boling Yang, Patrick Lancaster, Joshua R. Smith","AuthorURL":"https://sensor.cs.washington.edu/jrs.html"},{"Title":"The Closure Signature: A Functional Approach to Model Underactuated Compliant Robotic Hands","URL":"https://ieeexplore.ieee.org/abstract/document/8304743","AuthorName":"Maria Pozzi, Gionata Salvietti, João Bimbo, Monica Malvezzi, Domenico Prattichizzo","AuthorURL":"http://www.dii.unisi.it/~domenico/"}]},"RegistryEntryAdded":"2019-04-17","RegistryEntryLastModified":"2022-06-15","Slug":"ycb-benchmarks","Sources":["awslabs-open-data-registry"]}
{"Name":"YouTube 8 Million - Data Lakehouse Ready","Description":"This both the original .tfrecords and a Parquet representation of the [YouTube 8 Million dataset](https://research.google.com/youtube8m/). YouTube-8M is a large-scale labeled video dataset that consists of millions of YouTube video IDs, with high-quality machine-generated annotations from a diverse vocabulary of 3,800+ visual entities. It comes with precomputed audio-visual features from billions of frames and audio segments, designed to fit on a single hard disk. This dataset also includes the YouTube-8M Segments data from June 2019. \nThis dataset is 'Lakehouse Ready'. Meaning, you can query this data in-place straight out of the Registry of Open Data S3 bucket. [Deploy this dataset's corresponding CloudFormation template](https://us-west-2.console.aws.amazon.com/cloudformation/home?region=us-west-2#/stacks/quickcreate?templateUrl=https://aws-roda-ml-datalake.s3.us-west-2.amazonaws.com/YT8MRodaTemplate.RodaTemplate.json&stackName=YT8M-RODA) to create the  AWS Glue Catalog entries into your account in about 30 seconds. That one step will enable you to interact with the data with AWS Athena, AWS SageMaker, AWS EMR, or join into your AWS Redshift clusters. More detail in (the documentation)[https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/README.md.\n","Documentation":"https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_install.md","Contact":"https://github.com/aws-samples/data-lake-as-code/issues","ManagedBy":"[Amazon Web Services](https://aws.amazon.com/)","UpdateFrequency":"Google Research has not updated the dataset since 2019.","Tags":["amazon.science","computer vision","labeled","machine learning","parquet","video"],"License":"https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_attributions.txt","Resources":[{"Description":"Original YT8M *.tfrecords. [File structure info can be found here](https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_install.md).","ARN":"arn:aws:s3:::aws-roda-ml-datalake/yt8m/","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Lakehouse ready YT8M as Glue Parquet files. [Install instructions here](https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_install.md).","ARN":"arn:aws:s3:::aws-roda-ml-datalake/yt8m_ods/","Region":"us-west-2","Type":"S3 Bucket"},{"Description":"Replica of the two locations above in us-east-1.","ARN":"arn:aws:s3:::aws-roda-ml-datalake-us-east-1/","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Data Lake as Code Deployment Guide","URL":"https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_install.md","AuthorName":"AWS Industry Blueprints Team","Services":["Amazon Athena","AWS Glue","AWS Lake Formation"]}],"Publications":[{"Title":"YouTube 8 Million","URL":"https://research.google.com/youtube8m/","AuthorName":"Google Research"}]},"RegistryEntryAdded":"2022-02-17","RegistryEntryLastModified":"2025-02-04","Slug":"yt8m","Sources":["awslabs-open-data-registry"]}
{"Name":"ZINC Database","Description":"3D models for molecular docking screens.","Contact":"[John Irwin](chemistry4biology@gmail.com)","ManagedBy":"[John Irwin](chemistry4biology@gmail.com)","UpdateFrequency":"Monthly","Tags":["aws-pds","biology","chemical biology","life sciences","molecular docking","pharmaceutical","protein"],"License":"ZINC is free as in beer. You may not redistribute without the written permission of [John Irwin](chemistry4biology@gmail.com)","Documentation":"http://wiki.docking.org/index.php/ZINC15:Resources","Resources":[{"Description":"3D molecular docking structure files in db2.gz, sdf and mol2 formats.","ARN":"arn:aws:s3:::zinc3d","Region":"us-east-1","Type":"S3 Bucket"}],"DataAtWork":{"Publications":[{"Title":"ZINC Database","URL":"http://zinc15.docking.org","AuthorName":"John Irwin","AuthorURL":"http://irwinlab.compbio.ucsf.edu"}]},"RegistryEntryAdded":"2019-01-24","RegistryEntryLastModified":"2020-09-01","Slug":"zinc15","Sources":["awslabs-open-data-registry"]}
{"Name":"US Home Environmental Risk Compilation (ZipCheckup Open Data)","Description":"A normalized, address- and ZIP-level compilation of US public environmental\nhealth records: machine-parsed Consumer Confidence Reports (drinking water\ncontaminant tables, including PFAS, for 9,700+ water systems, 2023-2024),\nlead service line records aggregated from 94 state and utility inventories\n(18.4M address-level records), a ZIP-level summary layer covering 41,000+\nZIP codes across 17 environmental risk categories, and companion layers\nbuilt from EPA SDWIS, UCMR5, FEMA NRI, USGS, CDC, and Census sources.\nAll records are machine-readable, carry per-record source provenance\n(source document URL, legal authority, parse/refresh date), and are\nreproducible from cited public documents. CSV, JSON, and Parquet formats;\nParquet mirrors of the large collections are provided for Amazon Athena\nand columnar workflows.\n","Documentation":"https://github.com/artakulov/open-data-examples/blob/main/us-home-environmental-risk-compilation/DATA-DICTIONARY.md","Contact":"contact@zipcheckup.com","ManagedBy":"[ZipCheckup](https://zipcheckup.com)","UpdateFrequency":"Core layers are updated daily via an automated pipeline; the ZIP summary layer is published as a versioned snapshot rather than on a fixed interval; the parsed CCR corpus follows the annual Consumer Confidence Report cycle; the lead service line compilation is refreshed as source inventories update. Per-layer cadence is recorded in metadata/sources-manifest.json. Versioned archival snapshots are published to Zenodo.","Citation":"Akulov, A. (2026). ZipCheckup U.S. Environmental Safety Data by ZIP Code [Data set]. Zenodo. https://doi.org/10.5281/zenodo.19427201","Tags":["aws-pds","census","csv","disaster response","environmental","geospatial","json","parquet","sustainability","water"],"License":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)\n","Resources":[{"Description":"ZIP summary layer (41,000+ ZIP codes, 17 risk categories), parsed CCR corpus (9,811 water-system JSON documents + consolidated Parquet), lead service line address compilation (18,465,827 records across 5,795 ZIP codes, JSON per ZIP + consolidated Parquet), and companion layers.","ARN":"arn:aws:s3:::zipcheckup-open-data","Region":"us-west-2","Type":"S3 Bucket"}],"DataAtWork":{"Tutorials":[{"Title":"Get To Know A Dataset: US Home Environmental Risk Compilation","URL":"https://github.com/artakulov/open-data-examples/blob/main/us-home-environmental-risk-compilation/get-to-know-a-dataset.ipynb","AuthorName":"Artem Akulov","AuthorURL":"https://zipcheckup.com"}],"Tools & Applications":[{"Title":"ZipCheckup - Home Safety Profile by ZIP code","URL":"https://zipcheckup.com","AuthorName":"ZipCheckup"}],"Publications":[{"Title":"Public comment on EPA docket EPA-HQ-OW-2025-0654: PFAS occurrence documented in utilities' own Consumer Confidence Reports (511 systems with quantified detections)","URL":"https://www.regulations.gov/docket/EPA-HQ-OW-2025-0654","AuthorName":"Artem Akulov"}]},"RegistryEntryAdded":"2026-08-18","RegistryEntryLastModified":"2026-08-18","Slug":"zipcheckup-us-home-environmental-risk","Sources":["awslabs-open-data-registry"]}
{"Name":"Zwicky Transient Facility (ZTF)","Description":"The Zwicky Transient Facility (ZTF) is a time-domain astronomy survey that uses the Palomar 48 inch Schmidt telescope and a custom-built wide-field camera to image the night sky in three photometric filters (g, r, and i). It is a fully-automated survey aimed at a systematic exploration of optical transient phenomena. It completes a scan of the observable northern sky approximately every three nights.","Documentation":"https://irsa.ipac.caltech.edu/Missions/ztf.html","Contact":"https://irsa.ipac.caltech.edu/docs/help_desk.html","ManagedBy":"NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech","UpdateFrequency":"ZTF datasets may be updated approximately twice per year. The data may also be presented in new ways as the products become available.","Tags":["astronomy","aws-pds","object detection","parquet","survey"],"License":"https://irsa.ipac.caltech.edu/data_use_terms.html","Citation":"If you use the Objects Table, please cite the Digital Object Identifier (DOI): [10.26131/IRSA597](https://www.ipac.caltech.edu/doi/10.26131/IRSA597). If you use the Lightcurves, please cite the Digital Object Identifier (DOI): [10.26131/IRSA598](https://www.ipac.caltech.edu/doi/10.26131/IRSA598). In addition, please follow the [ZTF acknowledgement guidelines](https://irsa.ipac.caltech.edu/data/ZTF/docs/releases/ztf_release_notes_latest) and the [IRSA acknowledgement guidelines](https://irsa.ipac.caltech.edu/ack.html).","Resources":[{"Description":"Objects Table is a catalog of PSF-fit photometry detections extracted from ZTF reference images. The reference images were generated by stacking single exposures acquired from all science programs in the survey, resulting in photometry up to 2.5 magnitudes deeper than single-exposure detections. Objects Table contains both point-like and extended objects. The survey covers ~25,000 square degrees of the northern hemisphere. This version of Objects Table is an enhanced data product created by IRSA. It is structured as a [HATS Collection](https://irsa.ipac.caltech.edu/docs/parquet_catalogs/#hats) and includes three Apache Parquet datasets: HATS Catalog (main product), Margin Cache, and Index Table.","ARN":"arn:aws:s3:::ipac-irsa-ztf/ztf/enhanced/dr24/objects/hats","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false},{"Description":"Lightcurves is a catalog of PSF-fit photometry detections extracted from single-exposure images at the locations of Objects Table detections. An object ID identifies related data in both catalogs. Photometry is in the native ZTF photometric system and the epoch-dependent zeropoints have already been applied. Note that Lightcurves detections may be missing, for example, in cases where the Objects Table detection is fainter or approximately equal to the single-exposure sensitivity limits. This version of Lightcurves is an enhanced data product created by IRSA. It is structured as a [HATS Collection](https://irsa.ipac.caltech.edu/docs/parquet_catalogs/#hats) and includes three Apache Parquet datasets: HATS Catalog (main product), Margin Cache, and Index Table.","ARN":"arn:aws:s3:::ipac-irsa-ztf/ztf/enhanced/dr24/lc/hats","Region":"us-east-1","Type":"S3 Bucket","RequesterPays":false,"AccountRequired":false}],"DataAtWork":{"Tutorials":[{"Title":"Cross-Match ZTF and Pan-STARRS","URL":"https://nasa-fornax.github.io/fornax-demo-notebooks/ztf-ps1-crossmatch/","AuthorName":"Fornax Initiative","AuthorURL":"https://pcos.gsfc.nasa.gov/Fornax/"},{"Title":"IRSA Notebook Tutorials","URL":"https://irsa.ipac.caltech.edu/docs/notebooks/","AuthorName":"Caltech/IPAC-IRSA","AuthorURL":"https://irsa.ipac.caltech.edu"},{"Title":"Multi-Wavelength Light Curves","URL":"https://nasa-fornax.github.io/fornax-demo-notebooks/light-curve-collector/","AuthorName":"Fornax Initiative","AuthorURL":"https://pcos.gsfc.nasa.gov/Fornax/"},{"Title":"Multi-Wavelength Light Curves with Large Samples","URL":"https://nasa-fornax.github.io/fornax-demo-notebooks/scale-up/","AuthorName":"Fornax Initiative","AuthorURL":"https://pcos.gsfc.nasa.gov/Fornax/"}]},"RegistryEntryAdded":"2025-02-25","RegistryEntryLastModified":"2026-06-01","Slug":"ztf","Sources":["awslabs-open-data-registry"]}